5,018 Matching Annotations
  1. Oct 2022
    1. Author Response

      Reviewer #1 (Public Review):

      Previous studies have linked several lifestyle-related factors, such as body mass index and smoking, alcohol use with accelerated biological aging measured using epigenetic clocks, however, most of them focused on single lifestyle factors based on cross-sectional data from older adults. The current study has a couple of major strengths: it has a decent sample size, lifestyle was measured longitudinally during puberty and adolescence, it looked at the effect of multiple lifestyle measures collectively, it looked at multiple epigenetic clocks, and due to the data from twins, it could examine the contribution of genetic and environmental influences to the outcomes. I have a couple of comments that are mainly aimed at improving the clarity of the methods (e.g. how was multiple testing correction done, how did the association model account for the clustering of twin data, how many samples were measured on 450k vs EPIC and were raw or pre-QC'd data supplied to the online epigenetic age calculator), and interpretation of findings (why were 2 measures of Dunedin PACE of aging used, how much are results driven by BMI versus the other lifestyle factors, and the discussion on shared genetic influences should be more nuanced; it includes both pleiotropic effects and causal effects among lifestyle and biological ageing).

      Thank you for the encouraging comments and important suggestions.

      Reviewer #2 (Public Review):

      Kankaanpää and colleagues studied how lifestyle factors in adolescence (e.g., smoking, BMI, alcohol and exercise) associate with advanced epigenetic age in early adulthood.

      Strengths:

      The manuscript is very well written. Although the analyses and results are complex, the authors manage very well to convey the key messages.

      The twin dataset is large and longitudinal, making this an excellent resource to assess the research questions.

      The analyses are advanced including LCA capitalizing on the strength of these data.

      The authors also include a wider range of epigenetic age measures (n=6) as well as a broader range of lifestyle habits. This provides a more comprehensive view that also acknowledges that associations were not uniform across all epigenetic age measures.

      Weaknesses:

      The accuracy of the epigenetic age predictions was moderate with quite large mean absolute errors (e.g., +7 years for Horvath and -9 years for PhenoAge). Also, no correlations with chronological age are presented. With these large errors it is difficult to tease apart meaningful deviations (between chronological and biological age) from prediction error.

      The authors claim that 'the unhealthiest lifestyle class, in which smoking and alcohol use co-occurred, exhibited accelerated biological aging...'. However, this is only partially true. For example, PhenoAge was not accelerated in lifestyle class C5. Similarly, all classes showed some degree of deceleration (not acceleration) with respect to DunedinPACE (Figure 3D). The large degree of heterogeneity across different epigenetic age measures needs to be acknowledged.

      The authors claim that 'Practically all variance of AAPheno and DunedinPACE common with adolescent lifestyle was explained by shared genetic factors'. However, Figure 4 suggest that most of the variation (up to 96%) remained unexplained and genetics only explained around 10-15% of total variation. The large amount of unexplained variation should be acknowledged.

      Thank you for the encouraging comments and important notes.

      We have now acknowledged that the standard deviations of epigenetic age estimates were high (lines 409-418). Due to the narrow age range of this study, the correlations between chronological age and epigenetic age estimates were weak. We aimed to overcome these weaknesses and calculated the epigenetic age estimates using recently developed principal component (PC)-based clocks, which are shown to improve the reliability and validity of epigenetic clocks (Higgins-Chen et al., 2022). In our data, the standard deviations of epigenetic age estimates were similar or even higher compared with those obtained with the original clocks, but the correlations between epigenetic age acceleration measures assessed with different clocks were consistently higher when PC-based epigenetic clocks were used. Importantly, the observed associations with the adolescent lifestyle behavior patterns did not substantially change.

      Moreover, we have now more carefully reported and interpreted the results obtained using different epigenetic aging measures and acknowledged their heterogeneity (lines 459-467).

      Figure 4 presents the genetic and environmental influences on biological aging shared with adolescent lifestyle and biological aging. There are also unique genetic and environmental influences on biological aging not shown in the figure. Therefore, the unexplained variation in biological aging was not that large. Most of the total variation in biological aging was explained by the genetic factors unique to biological aging. We have now clarified the description of the estimation of genetic and environmental influences (lines 283-300) and the presentation of the results (lines 437-449).

      References:

      Higgins-Chen, A. T., Thrush, K. L., Wang, Y., Minteer, C. J., Kuo, P.-L., Wang, M., Niimi, P., Sturm, G., Lin, J., Moore, A. Z., Bandinelli, S., Vinkers, C. H., Vermetten, E., Rutten, B. P. F., Geuze, E., Okhuijsen-Pfeifer, C., van der Horst, M. Z., Schreiter, S., Gutwinski, S., … Levine, M. E. (2022). A computational solution for bolstering reliability of epigenetic clocks: implications for clinical trials and longitudinal tracking. Nature Aging, 2(7), 644–661. https://doi.org/10.1038/s43587-022-00248-2

  2. Sep 2022
    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

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      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity (Required)): ____ *A significant criticism of the paper is an assumption that readers will be familiar with all of the findings in the author's previous 2016 paper and the PGL-1 papers by Aoki et al. Minimal context is given for each approach. *

      To address this concern, we have added a paragraph in the Introduction section of the revised manuscript.

      *Some conclusions are not well supported and require further analysis, proper controls, and more extensive descriptions of the experiments performed. *

      We have addressed the reviewer’s concerns as detailed below.

      Most importantly, the central conclusion and title of the paper is that composition can buffer the dynamics of individual proteins within liquid-like condensates. In other words, in vitro condensation assays often do not recapitulate LLPS behavior in vivo. That said, the findings in this study would be significantly strengthened and complemented by observing endogenously tagged PGL-3 and PGL-3 mutants in living worms, considering the efficiency of using CRISPR in C. elegans to insert tags and make precise mutations.

      The original manuscript already contained data where we microinjected wild-type PGL-3 and mutant PGL-3 proteins (recombinantly purified) into adult C. elegans gonads to assay how the P granule phase supports diffusion of these proteins.

      In the revised version, we now include additional data which shows “dynamics buffering” in transgenic worms generated using CRISPR/Cas9 technology. Briefly, we used CRISPR/Cas9 to generate transgenic C. elegans which expresses PGL-3-mEGFP or PGL-3(D425-452)-mEGFP from the native pgl-3 locus. In vitro, wild-type PGL-3-mEGFP protein generates liquid-like condensates. On the other hand, the recombinantly purified PGL-3(D425-452)-mEGFP protein generates condensates that are non-dynamic. In contrast to these observations in vitro, both wild-type PGL-3-mEGFP and PGL-3(D425-452)-mEGFP show similar dynamics (half-time of FRAP recovery) within P granules in vivo.

      *To improve readability, the introduction to P granules should be expanded, and include the reasons for looking at the nematode-specific PGL-3 protein among all the other known P granule proteins. A recap of previous findings on PGL-3 phase separation, in vivo and in vitro, is warranted, starting with the significant results of Saha et al 2016. Setting up the investigative questions in the context of recent work on PGL-1 (Aoki, et al) is also necessary. *

      To address this concern, we have added a paragraph in the Introduction section of the revised manuscript.

      The physiological concentration of PGL-3 should be more transparent, including why some experiments in this study are done at physiological concentrations while others are not. Describing why salt concentrations, crowding agents, and protein abundance are similar or different for each experiment is necessary and relevant. For example, after showing in Figure 1 that PGL-3 protein phase separates, the paragraph starting on line 161 says that it was previously shown that PGL-3 doesn't phase separate at physiological concentrations without RNA. One has to go back to Figure 1 to realize it was done differently than Figure 2 and Saha 2016.

      The concentrations of PGL-3 protein and use of crowding agents (if any) have already been specified within figures or figure legends. Salt concentrations used are specified within figure legends or materials and methods section.

      We have added the following paragraph to the materials and methods section of the revised manuscript.

      “Saha et al. 2016 showed that at physiological concentrations (approx. 1 mM), the PGL-3 protein is unable to phase separate into condensates. At these concentrations, mRNA promotes phase separation of PGL-3. To assay for mRNA-dependence of condensate assembly, it is therefore essential to use physiological concentrations of the PGL-3 protein or mutants (e.g. Figure 2). However, these condensates are generally too small to assay rate of internal rearrangement of PGL-3 molecules within condensates using fluorescence recovery after photobleaching experiments. Therefore, to generate large condensates for measuring internal rearrangement of PGL-3 or mutant molecules, we primarily used higher concentrations of these proteins where binding to RNA is not essential for phase separation. However, to mimic the in vivo P granule phase as closely as possible, we generally added constituent proteins in proportion to their in vivo abundance estimated in Saha et al. 2016.”

      The added paragraph in the Introduction section of the revised manuscript may be helpful to the readers. * *

      *Statements in the same paragraph like "in contrast to full-length PGL-3, mRNA does not support phase separation..." should be qualified by stating the concentration observed, with or without salts or other crowding agents. Similarly, line 230 "suggests that interactions involving the disordered C-terminal region of PGL-3 are not essential for the fast dynamics" and should be qualified with "at non-physiological concentrations and with XX crowding agents or salt concentration." It would be more consistent if physiological concentrations were consistent from figure to figure, as extra variables weaken some of the stated conclusions. *

      We thank the reviewer for this suggestion. However, we feel the statements (without full experimental details within main text) help convey the conceptual essence of the findings better. Of course, all these statements contain reference to figures or prior publications which provide relevant details about experimental conditions.

      *The 2010 review reference stating that there are 40 P granule enriched proteins is outdated. More recent reviews put the number much higher. This is relevant because the approach to put PGL-3 in a more physiological environment by including just PGL-1, GLH-1 and mRNA with the condensate assays, out of ~100 P granule enriched proteins, may not be sufficient to conclude "that the influence of complex composition on dynamics is modest" (line 223), or imply that the multicomponent nature of the P granule is reconstituted by adding these components (line 355). *

      We revised the text to indicate that P granules contain approx. 70 proteins and added appropriate references.

      • *

      Based on current information of constitutive P granule components (PGL-1, PGL-3, GLH-1, GLH-2, GLH-3, GLH-4, DEPS-1, MIP-1 and mRNA), (Kawasaki et al, 1998, 2004; Spike et al, 2008a, 2008b; Price et al, 2021; Cipriani et al, 2021; Phillips & Updike, 2022) we reconstituted P granule-like phase in vitro with mRNA, PGL- and GLH- proteins that likely constitute the most abundant components within P granules in vivo (based on concentration estimates in Saha et al. 2016).

      We do appreciate the reviewer’s comment that more components can be added to our in vitro reconstitution in addition to the limited set of components used in our study. However, we feel it is interesting to observe that a limited set of components can support dynamics buffering (the main message of the paper). Further, the complementary in vivo experiments show that the P granule phase can also support dynamics buffering.

      *Figure 1C needs to include PGL-3(370-693) in the analysis. Figure 1E is also incomplete without a comparison of FRAP recovery between PGL-3(1-452) and full PGL-3 as the control.

      *

      Fig. 1c already includes data with PGL-3 (370-693) [top row, central panel]. FRAP recovery data with full-length PGL-3 is already available in Supplementary Fig. 2c, g.

      *Figure 4C is missing an essential control where PGL-3 and S1 FRAP is performed without PGL-1, GLH-1, and mRNA. *

      In the revised version, we have added Supplementary Fig. 5f, where FRAP recovery of the following condensates are plotted together: 1) PGL-3 alone, 2) S1 alone, 3) PGL-3 + PGL-1, GLH-1 and mRNA, 4) S1 + PGL-1, GLH-1 and mRNA.

      *It would also help show sup Fig4A in the main figure to show concentration dependence. *

      We revised Fig. 4 to address the reviewer’s suggestion.

      Consider adding subtitles to supplementary figures.

      We considered the suggestion but felt it may not be essential.

      *M&M should include an explanation for statistical analysis *

      We added a paragraph describing statistical analysis within the Materials and Methods section.

      *CROSS-CONSULTATION COMMENTS I am also in agreement with the comments and critiques of reviewers 2 and 3.

      * Reviewer #1 (Significance (Required)): The paper by Saha and colleagues investigate the in vitro liquid-liquid phase separation propensity of a P granule protein PGL-3 and its structural domains. The findings largely replicate and support the phase-separation properties of a paralogous protein called PGL-1, as recently described by Aoki et al. 2021. Furthermore, they show that the dynamics demonstrated by recombinant PGL-3 may be maintained or buffered by the complex composition of P granules.

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

      *Jelenic et al. describe the effect of partner proteins on the FRAP dynamics of recombinant PGL-3 protein and variants in in vitro condensates and C elegans p-granules. The study shows that the N terminal a-helical dimerization domains is required for condensate formation and modulate of it alters aggregation and the FRAP dynamics of its condensates. Interestingly, a construct including the entire IDR region (370-693) by itself does not phase separate on its own at these conditions. The K126E K129E mutant (known previously to disrupt dimerization) and the deletion mutant abrogate llps. A mutant construct that shuffles the sequence in the region 423-453 called S1 here reduces the helicity and the condensate FRAP dynamics but recovered in the presence of a few P granule components. Also, the reduced dynamics of partially unfolded PGL-3 condensates are also rescued by the p-granule components to a certain degree of the unfolded PGL3 concentrations. This threshold concentration for recovering the condensate dynamics is further reduced in the helix reducing S1 mutant, which is also dependent on the number and the nature of P granule components.

      Overall, the study aims to probe how "composition can buffer protein dynamics within liquid-like condensates" - yet several underlying aspects of the study do not fully support that conclusion. The introduction does not sufficiently introduce the known structural information of the two dimerization domains in C elegans PGL proteins for which structures are known. The region is discussed as "alpha helical" but really there are two evolutionarily conserved independently folding dimerization domains (referring to the mutants as "reduced alpha helicity" is not helpful - these are mutations that destabilize a folded domain).*

      To address this concern, we have added a paragraph in the Introduction section of the revised manuscript.

      *Additionally, the abstract and introduction ignore the aspects of aggregation (touched on in discussion) - this is likely what the disruption to the helical region in residue 450 region is doing (the helix is not on the dimer interface based on homology / sequence identity to the crystal structure of PGL-1 central dimerization domain. *

      We think elucidating the molecular mechanism of apparent aggregation of PGL-3 (D425-452) could be an interesting direction for future investigation. Here, we focused our analysis predominantly on the mutant S1 since it generates liquid-like condensates with ~20- fold slower dynamics (compared to wild-type) in contrast to non-dynamic condensates/aggregates. Therefore, influence of other P granule components on the dynamics of PGL-3 in liquid-like condensates is easier to address using the mutant S1 rather than PGL-3 (D425-452). We didn’t find evidence that S1 aggregates as we did not detect aggregates of S1 molecules using fluorescence confocal microscopy and the slow dynamics in condensates of S1 does not change significantly over 24 h (Supplementary Fig. 3f).

      However, in the revised version, we now include additional in vivo data with C. elegans expressing the aggregation-prone PGL-3 (D425-452)-mEGFP. Briefly, we used CRISPR/Cas9 to generate transgenic C. elegans which expresses PGL-3-mEGFP or PGL-3(D425-452)-mEGFP from the native pgl-3 locus. In vitro, wild-type PGL-3-mEGFP protein generates liquid-like condensates. On the other hand, the recombinantly purified PGL-3(D425-452)-mEGFP protein generates condensates that are non-dynamic. In contrast to these observations in vitro, both wild-type PGL-3-mEGFP and PGL-3(D425-452)-mEGFP show similar dynamics (half-time of FRAP recovery) within P granules in vivo.

      Finally, the "dynamics buffering" is not really clearly established and could also be explained as small concentrations of aggregated proteins act like clients while increasing the concentration results in aggregation and "cross linking" in the entire droplet - and this concentration is never achieved in the in worm experiments so it is not clear. In other words, the change in FRAP dynamics not observed in worms is perhaps not surprising if small amount of recombinant proteins are incorporated into the granules. *

      *

      Data with the S1 mutant establishes that dynamics buffering can be observed in condensates with different sets of additives both in vitro (Fig. 5a, b) and in vivo (Fig. 4a, b). Further, data with condensates of S1 containing the additives PGL-3 (K126E K129E) or S1 (K126E K129E) demonstrate that dynamics (half-time of FRAP recovery) within S1 condensates, and in turn “dynamics buffering” depend on inter-molecular interactions. With respect to the hypothesis proposed by the reviewer, we did not detect aggregates within S1 condensates using confocal fluorescence microscopy.

      In contrast to S1 condensates, condensates containing partially unfolded PGL-3-mEGFP together with PGL-1, GLH-1 and mRNA showed spatial inhomogeneities in fluorescence signal throughout the condensate (Fig. 4g). We have not tested if areas with higher fluorescence signal represent aggregates. It is a possibility that the partially unfolded PGL-3-mEGFP fluorescence signal becomes more homogeneous if higher concentrations of additives (PGL-1, GLH-1 and mRNA) are used. However, the presented data demonstrate the significant effect of the P granule components (PGL-1, GLH-1 and mRNA) on the FRAP recovery rate of partially unfolded PGL-3-mEGFP in condensates (compare figures Fig. 3e and Fig. 4g).

      However, consistent with dynamics buffering, the P granule phase in vivo supports wild-type dynamics of different PGL-3 constructs over a range of concentrations - PGL-3(D425-452)-mEGFP at physiological concentration (CRISPR transgenic strain, Fig. 4e) or at higher concentrations (microinjected S1 and partially unfolded PGL-3-mEGFP, Fig. 4b).

      • *

      *It is also not clear what the mechanism of the changes is - is the protein driven to fold more properly (despite S1 disruption of its conserved sequence) inside the condensate? Does it still self interact and act as a dimerization domain? Does this change disrupt interactions? *

      We agree with the reviewer that identifying the precise structural changes of the S1 protein within the condensate vs. dilute phase could be an interesting direction for future investigation. However, we have already discussed the issues raised by the reviewer in the original manuscript.

      “Our data is consistent with the model that other regions of S1 molecules cooperate with residues 425-452 (shuffled) to generate stronger inter-molecular interactions. For instance, addition of the mutant S1 (K126E K129E) enhances dynamics of S1 within condensates in contrast to maintaining the slower dynamics observed within condensates of S1 alone. This suggests that the interactions disrupted by the mutations K126E and K129E also contribute to slow S1 dynamics. One possibility is that interactions involving the residues K126 and K129 favor S1 conformations that enhance 425-452 (shuffled)-dependent interactions. Indeed, the mutations K126E K129E have been reported to interfere with interactions among N-termini of PGL-3 molecules (Aoki et al, 2021). While two self-association domains within the α-helical N-terminus of PGL-3 have been mapped (Aoki et al, 2021, 2016), structural insights into those associations are limited. However, PGL-3 shares significant sequence similarity with another protein PGL-1. Crystal structures are available for fragments of the PGL-1 protein that show the two self-association domains at the N-terminus are predominantly α-helical and globular in nature (Aoki et al, 2016, 2021). Therefore, one possibility is that shuffling the sequence 425-452 of PGL-3 or heat-induced unfolding of PGL-3 exposes hydrophobic residues that become available to participate in inter-molecular interactions.”

      What is the real mechanism by which PGL-3 phase separates if not via the disordered domains? *

      *

      We agree with the reviewer that elucidating the detailed mechanism of phase separation of PGL-3 is an interesting direction for future investigation. However, we feel this is not required to support the main message of this manuscript.

      Throughout the manuscript, the term "dynamics" is used to indicate FRAP, but it would be better to define what is meant (diffusion of PGL-3 in condensates) instead of using dynamics a term that could mean many things. Secondly, FRAP cannot directly measure liquidity etc (see recent critiques by McSwiggen elife 2019, etc) so it is better to be cautious in the claims. Finally, discussing "dyanmics buffering" adds more terminology where it is not needed - perhaps say "changes to diffusion of PGL-3 in condensates".

      We feel it is useful to introduce a term that describes our observation. To our knowledge, our observation is novel and therefore requires a new term to describe it.

      However, we do appreciate the concern raised by the reviewer. We used a more generic term “dynamics buffering” in contrast to the more specific “diffusion buffering” since we did not directly estimate diffusion behavior at the ‘single-molecule’ level. However, we already described what we mean by “dynamics buffering” in the text as follows.

      “We used condensates of similar size for our analysis (average ± 1 SD of diameter of condensates are 6.4 ± 1.7 mm (Fig. 5a) and 5.9 ± 0.4 mm (Fig. 5b)). Therefore, dynamics buffering here is likely to represent similar diffusion rates of S1 within condensates.”

      • *

      *The "N-terminus" is not 65% of the protein. One could define this as the N-terminal domain, but again there are two clear folded domains in the first 65% of the protein and this needs to be described better. *

      We revised the text to replace the terms “N-terminus” and “N-terminal domain” to “N-terminal fragment”.

      *The description of "stickers" and the references to tau and hnRNPA1 are confusing as this is a predominantly ordered domain while those are IDRs. *

      • *

      We feel this is important as it aids discussing our work in the context of current literature describing the mechanisms of macromolecular phase separation.

      The suggestion in the discussion that "P granule components support dynamics by participating in intermolecular interactions wth PGL-3-mEGFP molecules" is not well supported because no interaction assays are performed and no mutaitons are made that disrupt these interactions to test this.

      Indeed, we have not conducted interaction assays or mutational analysis to directly test this. However, our detailed analysis with the S1 mutant supports this suggestion.

      While partially unfolded PGL-3-mEGFP molecules lose 30% of a-helicity, the a-helicity of the S1 mutant is reduced by 15% compared to wild-type PGL-3. Data with S1 and partially unfolded PGL-3-mEGFP molecules show that loss of a-helicity correlates with slower diffusion of protein molecules within condensates. Using the mutants PGL-3 (K126E K129E) and S1 (K126E K129E), we show that diffusion rate of S1 molecules within condensates depend on inter-molecular interactions, and presence of other P granule components support faster diffusion rate of S1 molecules within condensates. Therefore, we feel it is safe to speculate that intermolecular interactions with P granule components can support dynamics of a “more unfolded” (compared to S1) version of PGL-3 molecule. * *

      *More detailed analysis of some of the claims: Claim 1: An a-helical region mediates the phase separation of PGL-3, and the C-terminal disordered region by itself does not phase separate. The N-terminal dimerization is essential for LLPS. The C-terminal IDR interactions with mRNA facilitate the LLPS. Comments: The authors show sufficient experimental data using microscopy and FRAP on truncated constructs with the N-terminal and C-terminal regions - but see above regarding how these are described - a proper domain structure with the folded domains shown and the RGG motifs highlighted should be added and integrated throughout the discussion. *

      In the revised version of the manuscript, we described the predicted PGL-3 domains within a paragraph in the introduction: “The interactions that support phase separation of the PGL-3 protein remains unclear. Structural studies on the orthologous PGL-1 protein revealed two dimerization domains. This raises the possibility that PGL-3 also contains similar dimerization domains, and phase separation depends on interactions involving these domains.”

      Our Fig. 1a already includes the schematic representation of PGL-3 with predicted N-terminal and Central Dimerization domains and RGG repeats.

      *They show that the N-terminus is necessary and adequate for LLPS, and the C-terminus by itself does not phase separate. But, how does the N-terminal domains phase separate? This is not explained - what are the interactions? *

      • *

      Also, a di-mutant (K126E K129E) that is known, and also authors use SEC-MALS to show their N-terminal construct is consistent with the published results. Disrupting the n-terminal dimerization prevents phase separation, suggesting the importance of these residues in the N-terminus for self-assembly and LLPS. The Microscopy data backs the claim that the mRNA-mediated LLPS is facilitated by binding with C-terminus. However, the m-RNA binding to IDR is not sufficient for LLPS. Yet, the authors do not explain how higher salt prevents phase separation - again the mechanism of phase separation is unclear. Is it multivalent interaction of the two dimerization domains? A basic model (that is tested) would be important.

      We agree with the reviewer that elucidating the detailed mechanism of phase separation of PGL-3 is an interesting direction for future investigation. However, we feel this is not required to support the main message of this manuscript.

      However, our manuscript already provides some relevant insights as follows.

      “To investigate the underlying mechanism further, we began by testing if the N-terminal α-helical region of PGL-3 can self-associate. Our analysis using size exclusion chromatography followed by multi-angle light scattering (SEC-MALS) showed that this PGL-3 fragment 1-452 forms a dimer (Supplementary Fig. 2f). Mutation of two residues (K126E K129E) have been shown to interfere with interactions among the N-termini of PGL-3 molecules (Aoki et al, 2021). We mutated these two residues within the full-length PGL-3 protein (K126E K129E) (Fig. 1a) and found that this mutant PGL-3 (K126E K129E) protein cannot phase separate even at high protein concentrations up to ~130 µM (Fig. 1b, c). Addition of mRNA does not trigger phase separation of this protein at physiological concentrations either (Fig. 2a, b). Taken together, our data is consistent with a model where association among folded N-termini of PGL-3 molecules is essential for phase separation.”

      A likely possibility is that phase separation of PGL-3 depends on electrostatic inter-molecular interactions among the folded N-terminal fragment of PGL-3 molecules. Therefore, high salt prevents phase separation.

      Are the tags removed to ensure that phase separation is not caused by tags or remaining linker regions? Is the protein purified to be without nucleic acid contamination or other purity metrics?

      Most of the experiments were done with only 5% of total protein tagged with 6x-His-mEGFP. No additional tags were present on the constructs. For recombinant expression and purification, proteins were cloned such that it is possible to remove the 6xHis-mEGFP tag following treatment with TEV protease. Following removal of the 6xHis-mEGFP tag, the residual linker is just two amino acid residues long. We used 100% tagged-protein for our experiments only in very few cases (indicated in the figure legends).

      To demonstrate purity of recombinant proteins, SDS-PAGE gels with all protein constructs used in this study are shown in Supplementary Fig. 1.

      To minimize contamination of nucleic acids, we treated samples with Benzonase during the course of purification.

      To assess the extent of nucleic acid contamination, the ratio of absorbance at 260 nm and 280 nm (A260/A280) was monitored. In exceptional cases with high A260/A280 values, we analyzed samples further by purifying RNA from the sample using RNA purification kit (Qiagen) and found that RNA represented 1% or less of the sample mass.* *

      Claim2: The N-terminal a-helical region modulates the dynamics within condensates. The IDR region has minimal effect on the fast dynamics of PGL-3. Comments: The authors show that the full-length PGL-3 condensates have modest influence of components by comparing the FRAP half times with or without the P granule components, including mRNA. However, have the authors tried this in the presence of mRNAs for the constructs lacking the IDRs as they have several RGG domains and bind with mRNA and are likely to change the dynamics.

      We thank the reviewer for this suggestion. However, this experiment is not essential to support the claim made in the context of homotypic condensates of PGL-3 : “The N-terminal a-helical region modulates the dynamics within condensates. The IDR region has minimal effect on the fast dynamics of PGL-3.”

      *The authors report the importance of the N-terminal a-helical region by making a construct that lacks/disrupts a part of the helices lowers the thermal stability and significantly lowers the dynamics of the condensates. Also unfolding of helices is shown to reduce the dynamics. One primary concern is whether these "rescued" protein dynamics imply protein functionality. *

      An assay of “functionality” e.g. an enzymatic activity of the PGL-3 protein is not available.

      However, we compared the fecundity of C. elegans worms expressing from the native pgl-3 locus, PGL-3-mEGFP or the mutant protein PGL-3(D425-452)-mEGFP, to assay the functionality of P granules in these strains. We found that worms of both genotypes produced similar number of offspring (Fig. 4d). This suggests that deletion of residues 425-452 of PGL-3 does not result in significant loss of function of P granules.

      Are these semi denatured proteins refolded in the presence of P-granule components?

      We feel that identifying the precise structural changes of the semi-denatured PGL-3 proteins within the condensate vs. dilute phase could be an interesting direction for future investigation.

      Finally, it is not clear why the authors chose to disrupt folding of the central dimerization domain?

      The manuscript included a paragraph to describe the rationale.

      “This suggests that interactions involving the disordered C-terminal region of PGL-3 are not essential for the fast dynamics within condensates. Therefore, we addressed the role of the N-terminal α-helical region (1-452) in driving dynamics. In order to avoid engineering mutations that result in significant misfolding of PGL-3 and concomitant loss of its ability to phase separate, we focused our mutational analysis close to the junction of the folded N-terminus and the disordered C-terminus of PGL-3. Surprisingly, we found that a full-length PGL-3 construct (D425-452) that lacks only 27 residues phase separates into condensates that are non-dynamic (Fig. 3a, c). Sequence analysis of the PGL-3 protein predicts that this region 425-452 spans two α-helices (one complete helix and fraction of a second helix) (Supplementary Fig. 3d). We generated a PGL-3 construct (hereafter called ‘S1’) (Fig. 3a) in which the sequence in the region, 425-452, is shuffled while keeping the overall amino acid composition unchanged. We found that S1 phase separates into condensates that are 20- fold less dynamic than with wild-type PGL-3 (Fig. 3d, Supplementary Fig. 3c).”

      Saying that "reduced alpha-helicity of PGL-3 correlates with slower dynamics in condensates" may be factual in these assays but "correlation" should be expanded upon to include mechanism and to me it seems that the statement should read "aggregation of PGL-3 causes slower dynamics in condensates" (both the partially destabilized mutant and the fully unfolded WT show similar effects perhaps to different degrees).

      We feel that identifying the precise structural changes of the semi-denatured PGL-3 proteins within the condensate vs. dilute phase could be an interesting direction for future investigation.

      We did not use the term "aggregation" since we did not detect aggregates of S1 molecules using fluorescence confocal microscopy.

      *CROSS-CONSULTATION COMMENTS I agree with the other reviewer's comments and critiques, I have concerns about the biological relevance and also the biophysical mechanisms. Reflecting on the other reviewers' comments, the papers could provide more depth in one or both of these areas to come to firm conclusions that are either revealing about PGL biology or elucidate a (possible) general biophysical mechanism. *

      In the revised version, we now include additional data which shows “dynamics buffering” in transgenic worms generated using CRISPR/Cas9 technology. Briefly, we used CRISPR/Cas9 to generate transgenic C. elegans which expresses PGL-3-mEGFP or PGL-3(D425-452)-mEGFP from the native pgl-3 locus. In vitro, wild-type PGL-3-mEGFP protein generates liquid-like condensates. On the other hand, the recombinantly purified PGL-3(D425-452)-mEGFP protein generates condensates that are non-dynamic. In contrast to these observations in vitro, both wild-type PGL-3-mEGFP and PGL-3(D425-452)-mEGFP show similar dynamics (half-time of FRAP recovery) within P granules in vivo.

      Reviewer #2 (Significance (Required)): *Hence, although the authors shows how inclusion of other components can alter the one protein component phase separation, this is done with entirely artificial means of destabilizing the fold of one of the domains which likely leads to aggregation. So the true impact of the work is hard to understand because the mutations impact on the basic biophysical properties of the domain (stability, interaction) are not completely characterized and the reason for disrupting this folding is not clear. *

      A major impact of our work is elucidation of a novel “dynamics buffering” property within biomolecular condensates in vitro. Our in vivo data is consistent with this finding.

      • *

      We have chosen two orthogonal ways of perturbing the PGL-3 protein (i.e. mutations and temperature-dependent unfolding) to assay the effect on diffusion rate against different levels of perturbation (e.g. 30% loss of a-helicity in heat-denatured PGL-3-mEGFP vs. 15% loss of a-helicity in the S1 mutant, compared to wild-type PGL-3). Studying the phase separation behavior of these “artificially-generated” constructs provided the understanding that dynamics of PGL-3 in condensates depends on inter-molecular interactions, and slower dynamics generally correlate with stronger inter-molecular interactions. Further, interactions among two or more P granule components can buffer against large change in dynamics / aggregation within the P granule phase. These insights may lay the groundwork for addressing how more “natural” modifications (e.g., post-translational modifications, high local concentration of “sticky” molecules) may influence dynamics within biomolecular condensates in vivo.

      Based on current knowledge of P granule composition, chaperone proteins (e.g. heat-shock family proteins) do not show abundant concentration within P granules. However, it is unclear if chaperone proteins are completely excluded from the P granule phase. Therefore, we speculate that weak interactions among two or more non-chaperone proteins contribute significantly to “dynamics buffering” within the P granule phase in vivo.

      In the discussion section of the manuscript, we had speculated that “dynamics buffering” may potentially explain observations reported in the nucleolus: “Similarly, interactions among components could be a potential mechanism of storage of misfolding-prone proteins in non-aggregated state within the liquid-like nucleolus under stress in vivo (Frottin et al, 2019).”

      Our finding is also relevant in the context of synthetic biology with applications that require steady diffusion rate of macromolecules during biochemical reactions within biomolecular condensates.

      • *

      My field of expertise is protein phase separation and protein structure. * *

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

      Summary: P granules are liquid condensates found in the developing germlines and embryos of C. elegans. Prior work by the authors and others have established P granules as a tractable model to investigate the basic biophysical properties of liquid condensates. Much of the prior published work focused on specific P granule scaffold proteins, PGL-1 and PGL-3. How attributes of these PGL proteins and the effect of other P granule components affect condensate properties is not fully understood. Here, Jelenic, et al. probe the biophysical properties of PGL-3. Using recombinant protein, they show that an N-terminal, alpha-helical region of PGL-3 is sufficient for liquid condensate formation and that N-terminal assembly is required for this formation. Creation of a scrambled alpha-helical region in PGL-3 and heat treatment affects PGL-3 fluidity. This fluidity can be "rescued" in vivo and in vitro with the inclusion of other P granule factors, including wildtype PGL-3, PGL-1, GLH-1 and mRNA. The authors note an inverse correlation between fluidity and mutant PGL-3 fluorescent intensity. They propose a model that heterotypic compositions of condensates can buffer their fluidity against components with stronger multivalent interactions. *

      MAJOR: 1. PGL-3 is a fantastic model to study the biophysical properties of a liquid condensate. But as the authors address in their discussion, the S1 mutant will likely affect the central domain folding, at its minimum causing exposure of a hydrophobic surface not typically exposed in biology. These helices are found at the terminal portion of the domain determined in the crystal structure and as depicted in the authors' Figure 1A. While the cause of S1's enhanced molecular interactions does not affect the in vitro work presented in this manuscript, it does affect how the conclusions connect to the biological nature of P granules and liquid condensates more generally. *

      We have chosen two orthogonal ways of perturbing the PGL-3 protein (i.e. mutations and temperature-dependent unfolding) to assay the effect on diffusion rate against different levels of perturbation (e.g. 30% loss of a-helicity in heat-denatured PGL-3-mEGFP vs. 15% loss of a-helicity in the S1 mutant, compared to wild-type PGL-3). Studying the phase separation behavior of these “artificial” constructs provided the understanding that dynamics of PGL-3 in condensates depends on inter-molecular interactions, and slower dynamics generally correlate with stronger inter-molecular interactions. Further, interactions among two or more P granule components can buffer against large change in dynamics / aggregation within the P granule phase. These insights may lay the groundwork for addressing how more “natural” modifications (e.g., post-translational modifications, high local concentration of “sticky” molecules) may influence dynamics within biomolecular condensates in vivo.

      Based on current knowledge of P granule composition, chaperone proteins (e.g. heat-shock family proteins) do not show abundant concentration within P granules. However, it is unclear if chaperone proteins are completely excluded from the P granule phase. Therefore, we speculate that weak interactions among two or more non-chaperone proteins contribute significantly to “dynamics buffering” within the P granule phase in vivo.

      In the discussion section of the manuscript, we had speculated that “dynamics buffering” may potentially explain observations reported in the nucleolus: “Similarly, interactions among components could be a potential mechanism of storage of misfolding-prone proteins in non-aggregated state within the liquid-like nucleolus under stress in vivo (Frottin et al, 2019).”

      Our finding is also relevant in the context of synthetic biology with applications that require steady diffusion rate of macromolecules during biochemical reactions within biomolecular condensates.

      • Recombinant PGL-3 experiments added PGL-1, GLH-1 and mRNA simultaneously and measured fluidity. It will be interesting to know which components contribute to fluidity and whether fluidity enhancement of each component is dependent on one another. Addition experiments with each component should be included and/or at least discussed in the main text. *

      Our data with S1-mEGFP or PGL-3-mEGFP (pre-heated at 50°C) proteins microinjected into C. elegans gonads, and the transgenic strain expressing PGL-3(D425-452)-mEGFP from the pgl-3 locus showed that the P granule phase can support fast dynamics of these mutant PGL-3 constructs. Since P granules have a complex composition, one possibility is that fast dynamics of these constructs is supported by interactions involving many P granule components. We found that using only a limited set of P granule components (PGL-1, GLH-1 and mRNA) can buffer dynamics of S1 in condensates in vitro.

      In absence of a systematic analysis investigating the individual role of approx. 70 P granule proteins in buffering S1 dynamics in condensates in vitro, we have claimed in the text that dynamics-buffering of S1 in condensates is supported by interactions among two or more components. However, we do appreciate the reviewer’s comment and feel it would be interesting to investigate the contribution of individual P granule components towards fluidity in future studies. We have discussed this in the ‘Discussion’ section of the manuscript.

      • The biological relevance of PGL-1, GLH-1, and mRNA were not discussed in the main text. How these factors contribute to P granule assembly and function should be mentioned in the Introduction or Results. *

      To address this concern, we have added a paragraph in the Introduction section of the revised manuscript.

      *MINOR: 1. Line 20, "most non-membrane-bound compartments...have complex composition": Are there examples of condensates that do not have complex composition? *

      Not all non-membrane-bound compartments may have been characterized. To accommodate this possibility, we refrained from making a more general statement, but stated “most non-membrane-bound compartments…”.

      • Lines 40-43, RNA interactions driving LLPS: Please include citations from the Parker Lab (e.g. Van Treeck and Parker, Cell. 2018 doi: 10.1016/j.cell.2018.07.023) *

      We added the reference suggested by the reviewer.

      • *

      • Line 60, condensates contain hundreds of different proteins and RNA: Please cite at least a few examples of condensates with their components identified. *

      We added some references following suggestion by the reviewer.

      • Lines 82-84, PGL-3 drives assembly: Please cite Kawasaki, et al. Genetics 2004 for the discovery of PGL-3. *

      We added the reference suggested by the reviewer.

      • Lines 88-89, PGL-3 N-terminal fragment predominantly alpha-helical: The PGL domain structures should be cited here as supporting evidence that these regions are composed primarily of alpha helices (Aoki, et al 2016, 2021) *

      • *

      To address this concern, we have added a paragraph in the Introduction section of the revised manuscript.

      • Lines 158-159, driving forces for phase separation: This statement should be removed or expanded. The authors point regarding the protein concentrations is not clear here but clarified in the Discussion (Lines 691-693). Recommend removing due to its speculative nature. *

      We retained the speculative comment in the results section. We feel that this prepares the readers for the discussion later in the manuscript.

      • Lines 210: Add commas before and after "PGL-1 and GLH-1"*

      We addressed the reviewer’s suggestion.

      • Lines 218-219: add "and" instead of comma between PGL-1 and GLH-1 *

      We addressed the reviewer’s suggestion.

      • Lines 238-239, alpha-helices: The PGL CDD structure should also be referenced here (Aoki, et al 2016). *

      To address this concern, we have added a paragraph in the Introduction section of the revised manuscript.

      • Lines 680-682, MEG proteins: Please cite accordingly. *

      We added the reference suggested by the reviewer.

      • Lines 694-695, heterotypic interactions: Please cite Saha, et al. 2016. *

      We added the reference suggested by the reviewer.

      • Figure 1: Add space between 1 and mM DTT *

      We addressed the reviewer’s suggestion.

      • Figure 2b: Please provide statistics between condensate numbers. *

      We provide statistics between condensate numbers in Fig. 2b.

      • Figure 4A: The region of the germline imaged and analyzed should be mentioned in the caption or the main text. *

      We revised the Figure legend of Fig. 4a to address this issue.

      • Figure 4B,C: Please include statistics between the FRAP curves. *

      We have included statistics comparing FRAP curves in Supplementary Fig. 4a-c.

      • Figure 4D: It will be helpful to compare this curve to Figure S4A in the same graph. Please also include graph statistics. *

      We have revised Fig. 4 to address the reviewer’s suggestion.

      • Figure 5: The data points are difficult to resolve. Recommend use of color.*

      We considered the suggestion, but felt it works better in the original form.

      • Figure 6: This is a very general model that does not highlight the extensive experimental work performed by the authors. Recommend incorporating PGL-3, mutants and P granule factors into this model. *

      We thank the reviewer for appreciating our extensive work. However, we retained the original Fig. 6 for the sake of simplicity.

      • Methods, Line 939, C. elegans section: What worms were used? TH623? Please describe the genotype. *

      We have included a table listing the strains used in the study and their genotype. * CROSS-CONSULTATION COMMENTS While my review was arguably the more favorable of the three, I agree with the other reviewers' comments and evaluation, particularly with Reviewer #1. As written in my review, my primary concern was the biological relevance of the work.*

      Reviewer #3 (Significance (Required)):

      Overall, the in vitro work presented investigating the biophysical properties of this minimal P granule system was thorough and well-analyzed, and the manuscript was clearly written. Additional citations and statistics will improve the manuscript and the strength of the conclusions, respectively. The biological relevance of this study to P granule form and function in vivo, and to condensates in vivo, is debatable. This work will interest those who study condensate biology, the biophysics of protein-protein and protein-RNA interactions, and RNA biochemists more generally.

      A major impact of our work is elucidation of a novel “dynamics buffering” property within biomolecular condensates in vitro. Our in vivo data is consistent with this finding.

      We have chosen two orthogonal ways of perturbing the PGL-3 protein (i.e. mutations and temperature-dependent unfolding) to assay the effect on diffusion rate against different levels of perturbation (e.g. 30% loss of a-helicity in heat-denatured PGL-3-mEGFP vs. 15% loss of a-helicity in the S1 mutant, compared to wild-type PGL-3). Studying the phase separation behavior of these “artificially-generated” constructs provided the understanding that dynamics of PGL-3 in condensates depends on inter-molecular interactions, and slower dynamics generally correlate with stronger inter-molecular interactions. Further, interactions among two or more P granule components can buffer against large change in dynamics / aggregation within the P granule phase. These insights may lay the groundwork for addressing how more “natural” modifications (e.g., post-translational modifications, high local concentration of “sticky” molecules) may influence dynamics within biomolecular condensates in vivo.

      • *

      Based on current knowledge of P granule composition, chaperone proteins (e.g. heat-shock family proteins) do not show abundant concentration within P granules. However, it is unclear if chaperone proteins are completely excluded from the P granule phase. Therefore, we speculate that weak interactions among two or more non-chaperone proteins contribute significantly to “dynamics buffering” within the P granule phase in vivo.

      In the discussion section of the manuscript, we had speculated that “dynamics buffering” may potentially explain observations reported in the nucleolus: “Similarly, interactions among components could be a potential mechanism of storage of misfolding-prone proteins in non-aggregated state within the liquid-like nucleolus under stress in vivo (Frottin et al, 2019).”

      Our finding is also relevant in the context of synthetic biology with applications that require steady diffusion rate of macromolecules during biochemical reactions within biomolecular condensates.

      *I have expertise in P granules, protein/RNA biochemistry, condensate assembly, and C. elegans. *

      References

      Aoki ST, Kershner AM, Bingman CA, Wickens M & Kimble J (2016) PGL germ granule assembly protein is a base-specific, single-stranded RNase. Proceedings of the National Academy of Sciences of the United States of America

      Aoki ST, Lynch TR, Crittenden SL, Bingman CA, Wickens M & Kimble J (2021) C. elegans germ granules require both assembly and localized regulators for mRNA repression. Nat Commun 12: 996

      Cipriani PG, Bay O, Zinno J, Gutwein M, Gan HH, Mayya VK, Chung G, Chen J-X, Fahs H, Guan Y, et al (2021) Novel LOTUS-domain proteins are organizational hubs that recruit C. elegans Vasa to germ granules. Elife 10: e60833

      Frottin F, Schueder F, Tiwary S, Gupta R, Körner R, Schlichthaerle T, Cox J, Jungmann R, Hartl FU & Hipp MS (2019) The nucleolus functions as a phase-separated protein quality control compartment. Science 365: 342–347

      Kawasaki I, Amiri A, Fan Y, Meyer N, Dunkelbarger S, Motohashi T, Karashima T, Bossinger O & Strome S (2004) The PGL family proteins associate with germ granules and function redundantly in Caenorhabditis elegans germline development. Genetics 167: 645–661

      Kawasaki I, Shim YH, Kirchner J, Kaminker J, Wood WB & Strome S (1998) PGL-1, a predicted RNA-binding component of germ granules, is essential for fertility in C. elegans. Cell 94: 635–645

      Phillips CM & Updike DL (2022) Germ granules and gene regulation in the Caenorhabditis elegans germline. Genetics 220: iyab195

      Price IF, Hertz HL, Pastore B, Wagner J & Tang W (2021) Proximity labeling identifies LOTUS domain proteins that promote the formation of perinuclear germ granules in C. elegans. Elife 10: e72276

      Saha S, Weber CA, Nousch M, Adame-Arana O, Hoege C, Hein MY, Osborne Nishimura E, Mahamid J, Jahnel M, Jawerth L, et al (2016) Polar Positioning of Phase-Separated Liquid Compartments in Cells Regulated by an mRNA Competition Mechanism. Cell 166: 1572-1584.e16

      Spike C, Meyer N, Racen E, Orsborn A, Kirchner J, Kuznicki K, Yee C, Bennett K & Strome S (2008a) Genetic analysis of the Caenorhabditis elegans GLH family of P-granule proteins. Genetics 178: 1973–1987

      Spike CA, Bader J, Reinke V & Strome S (2008b) DEPS-1 promotes P-granule assembly and RNA interference in C. elegans germ cells. Development (Cambridge, England) 135: 983–993

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

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      Reply to the reviewers

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

      The authors have assembled an enormous amount of statistical data on the genomes and phylogeny of Arctic algae, including the genomes of four new species that they sequenced for this study. Their main finding is that horizontal gene transfer has led to convergent evolution in distantly related microalgae.

      **Major comments**

      Reviewer #1__: The purpose of the study is not clearly stated in the abstract or the introduction. The authors say (line 93) "Defining the genetic adaptations underpinning these small algal species is crucial as a baseline to understand their response to anthropogenic global change (Notz & Stroeve,2016)." Is this their goal? Or are they just quoting another study? The authors state (line 103) "We extend by sequencing the genomes of four distantly related microalgae...". This is not really a question or a hypothesis. I am sure the authors can provide a more compelling reason to embark on such a labor-intensive study.__

      Reply: We agree that the aim was lost in the details and the Introduction is now focused towards the original goal of the study, which was to investigate convergent evolution in a biogeographically isolated ocean. Additional references on the formation and history of the Arctic Basin have been added to the Introduction to provide context. “An ocean has been present at the pole since the beginning of the Cretaceous. Shaped by tectonic processes (Nikishin et al., 2021) the Arctic Ocean has been a relatively closed basin since the Masstrichtian at the end of the late Cretaceous epoch (ca. 70 million years before present), with episodic sea-ice cover since that time (Niezgodzki et al., 2019). This long history suggests limited gene flow from the global ocean over vast time scales and Arctic marine species including microalgae could well have unique adaptations to cold arctic conditions.” Line 78-83.

      And following this we provide a clear hypothesis “The potential for lineages of ancient Arctic origin and the episodic input of outside species led us to our hypothesis that Arctic microalgae convergently evolved traits or adaptations aiding survival in an ice-influenced ocean. Line 112-117.

      We also discuss both the adaptive and distinct physical environment of the Arctic, and its topographical separation from other ocean regions as dispersal limitation would enhance the Arctic-specific genomic signatures. We now cite the recent paper by Sommeria-Kline et al. (2020), which puts eukaryotic plankton biogeography into a global context (Line 72)

      Reveiwer #1__: The most prominent shared trait that the authors found are genes for ice-binding proteins. However, in view of their importance, little information is given about their different types and possible functions.__

      Reply: We appreciate the comment and have added information on relevant ice binding proteins found in the Arctic Algae. In addition, we discuss how the functional and secretory diversity of IBP would enhance the survivability of pelagic taxa. Lines 534 to 564.

      Although ice binding proteins from multicellular animals and plants are outside the scope of this study, there is a recent review; Bar Doley, Braslavsky and Davies 2016 Annual review of Biochemisty 85: 515-542.

      .

      Reviewer #1__: The HGT of ice-binding proteins is a major focus of this study, but little is said about what previous studies have said about this. What are the previous studies, what are their findings and how do the present findings contribute to this?__

      Reply: We agree that this aspect should have been more visible. We incorporated new data to characterize IBPs drawn from MMETSP transcriptomes, and environmental Tara Ocean metagenomes, as well as our Arctic strains. We note that as we take a PFAM-based approach, the IBPs treated are DUF3494/PF11999 domain, which are type 1 IBPs / algal IBPs (Raymond and Remia 2019). As an example of novelty, we identify the position of IBPs from dinoflagellates, within a larger Arctic Clade that included CCMP2293, CCMP2436 and CCMP2097 and Arctic TARA IBP, rendering this a pan-algal IBD clade.

      In addition, we were able to resolve the position of anomalous F. cylindrus IBP that fell between two Arctic associated clades (A and B, in our Fig 4). This finding is consistent with F. cylindrus originating in the Arctic as previously suggested and subsequently invading the Southern Ocean.

      The recurrent acquisition of multiple diverse IBP isoforms in individual species through HGT events has not been previously reported, and the extent of isoforms in the Arctic was surprising. See for example multiple different IBP forms with separate origins in Pavlovales CCMP2436 (Fig 4). The previous studies are referred to in the context of the phylogeny of the IBD within the results section: Lines 322- 413, and Lines 534-585.

      Reviewer #1: Figure 5 on HGT of ice-binding proteins is difficult to follow. It would be clearer if each panel could be described separately, clearly stating its main finding. I doubt that a reader could look at this figure and explain to a colleague what it shows.

      Reply: We have revised rearranged the figure (now Fig 4) with Arctic A, B, C and D clearly indicated as well as the two Antarctic dominated clades. The upper schematic includes the deepest phylogeny of algal IBDs to date, incorporating all of UniRef, MMETSP and TARA Oceans. The fasta files underlying the tree and the nexus file used are provided the S1 Data Folder, which is an excel folder with information on the analysis of the data. The callout and order of the clades has been revised to facilitate interpretation of the phylogenies more clearly. The entire section has been completely rewritten.

      Reviewer #1: This is also a problem with many of the other figures. For each figure, what is the question being asked and what is its take-home message?

      Reply: We agree that the message was lost and have now focused on our original question in our accepted proposal to JGI. “Is there a convergence among arctic microalgae at the genomic level?”. We found some genome properties were common among the Arctic isolates (more unknown PFAMS and several expanded PFAMs). The importance of ice binding proteins in Arctic Isolates and the widespread inter-algal HGT of this important protein among the Arctic strains. The IBP biogeography and phylogeny strongly indicate that the Arctic microalga have acquired IBP locally and that the Antarctic strains have acquired additional isoforms independently from Antarctic bacteria and fungi (Lines 565-585).

      Reviewer ____#1____: ____The paper has more data than a reader can absorb. It could be strengthened by reducing the number of figures, simplifying them if possible, and more clearly stating the value of the remaining figures.

      Reply. As suggested, we have refocused the paper, removing more speculative statistics based analysis and associated figures. The main conclusions are supported by the 5 main figures. We are now present 5 main figures and 11 supplementary figures (previously 23 downloadable supplementary figures and 40 on-line only figures supporting the support figures). We agree with the reviewer, and we feel the revised version is a more transparent synthesis. Briefly the Figures illustrate the following points. Fig. 1. The multigene tree of available algal genomes and transcriptomes provides a clear framework for judging the divergence of subsequent individual gene and PFAMs phylogenies. Fig. 2 (originally Fig. 3). Indicates the convergence of PFAM domains in the Arctic strains, in contrast to strains from elsewhere. Fig. 3 (originally Figure 4) shows Arctic specific expansions and contraction of PFAM domains, again demonstrating convergent evolution in the Arctic. The figure identifies specific PFAMs that contribute to the within-Arctic convergence. This figure is based on statistical methods independent of Fig 2. Figure 4 is the most extensive IBP phylogeny to date and has been discussed above. Figure 5, which was supplementary in our non-peer reviewed version, shows the biogeographic distribution of IBP, and can be compared to the distributions of the 18S rRNA genes from the four Arctic algae provided as supplementary (S6 Fig.)

      **Minor comments**Reviewer #1

      1. The figure citations are confusing. E.g., what does "Fig.1- Figure supplement 1" refer to? Does this refer to 1 or 2 figures? Apparently, it refers only to Fig. S1, so many readers will be confused when they look at Fig. 1.

      Reply: We apologize for the confusing format; the manuscript had been formatted for the online journal eLife. Our revision follows the more traditional style of PLoS Biology and other Review Commons journals.

      .

      Multiple citations should be in order of publication date, not alphabetical order.

      Reply ; We agree that date of publications is quite standard and recognizes priority of publication. Several on line journals no longer follow this rule and citation order will follow the specific style used by our accepting journal.

      Reviewer #1 (Significance (Required)): It is well known that useful genes tend to be shared among microorganisms. The present study strengthens previous studies in showing that gene transfer is an important process in polar regions.

      Reply: We thank the reviewer for recognizing the importance of our study.


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

      This manuscript is the result of a large international collaborative effort, including the US Department of Energy Joint Genome Institute. Its focus is comparative genomics of eukaryotic Arctic algae. The primary data described in the ms are four new genome and transcriptome sequences from diverse Arctic algae, represented by a cryptomonad, a haptophyte, a chrysophyte, and a pelagophyte.

      The authors compare these new data to previously published genomic/transcriptomic data from eukaryotic algae with the goal of understanding genome evolution in the Artic. The results of the paper are a series large-scale comparative genomic bioinformatics analyses, including the associated statistical analyses. The key findings center on statistically significant features of Arctic genomes, features that stand out as compared to the genomes of algae that are not primarily found in the Arctic. Together, these findings allow the authors to make various hypotheses and suggestions about genetic adaptations to polar environments.

      By far the most significant finding is that the genomes of Arctic algae are enriched in genes encoding proteins with an ice-binding domain, paralleling findings from Antarctic algae. These genes appear to have spread among Arctic algal genomes via horizontal gene transfer, which raises a series of interesting questions. In my opinion, the major conclusions of this paper are supported by the data. Listed below are a few comments that may improve the ms:

      Reviewer #2.

      1) In today's post-genomics era, everyone seems to be sequencing nuclear genomes. Often what distinguishes high-impact and low-impact genome papers is the number of genomes presented and the quality of the genome assembly. I may have missed it, but reading the main text, the figures/tables, and the supplementary data I was not able to get a sense of the quality of the four genome assemblies from which the main findings are based. I was eventually able to find this information from PhycoCosm (note: some of the links to this site are not working in the ms). My quick scan of the PhycoCosm summary info for the four genomes indicates that the assemblies are highly fragmented, likely because they are based on short-read Illumina sequencing rather than a combination of short and long reads. I think it is important to briefly discuss (and or present) the quality of the assemblies in the ms and to highlight the potential limitations/drawbacks of employing highly fragmented assemblies when carrying out large-scale comparative genomics.

      Reply: We agree and the data concerning the genome quality assemblies has been moved to the main text Table 1. The comparison with other paired related strains is provided in an excel folder designated S2 Data Folder.

      Reviewer #2.

      2) Horizontal gene transfer is undeniably a major driving force in evolution, and one that has shaped genomic architecture across the Tree of Life. I believe the data presented here support a role for HGT in the genome of evolution of Arctic algae, particularly with respect to genes encoding proteins with an ice-binding domain. However, we can all think of numerous instances when authors of genome papers were too quick to point to HGT. Thus, I would urge more caution and balance when presenting the HGT data, including some discussion about factors that could incorrectly lead researchers to conclude a significant role for HGT, such as contamination, gene duplication, mis-assemblies, etc. I'm not suggesting that you change the main conclusions, but just tone down the language in places (e.g., "we reveal remarkable convergence in the coding content ... ").

      Reply: We understand the reviewers concerns and now more clearly outline the pipeline we have used to identify HGTs. This included: filtering each genome to remove all possible contaminant sequences first, considering both contig co-presence of vertical- and horizontally-derived genes, and reciprocal and independent annotations of gene sequences in both genome sequences and MMETSP transcriptomes. Retained genes were subjected to simultaneous BLAST analysis and manually curated phylogenies using decontaminated reference datasets. The most parsimonious explanation for our final IBP domain microbial algal clusters (Fig 4) is HGT. On the side of caution, we removed the entire section that identified potential arctic HGT based primarily on a less targeted broad statistical analysis. The focus is now on 3 genes that have clearly identifiable utility in the Arctic, were found to be enriched in Arctic genomes via a separate analysis and had homologs in the Tara Ocean Polar circle data. In addition, we describe more clearly the role of expansion and enrichment of PFAMs and the high proportion genes without an identifiable PFAMs in the Arctic strains as evidence for Arctic convergence separate from potential HGT.

      Reviewer #2.

      3) The downside of studying protists (as compared to multicellular animals, for instance) is that most are not widely known by the scientific community and even fewer scientists can picture what they actually look like (e.g., Pavlovales sp. CCMP2436). A few more details about the four Arctic algae that make up the focus of this paper might be helpful for the casual reader. My sense is that if at the next departmental meeting I asked my colleagues what a pelagophyte was most would look at me with a blank stare. Moreover, am I right to assume that all four algae are psychrotolerant rather than psychrophilic (Supplement Fig. 1 makes me think otherwise). It might be good to point out the difference in the text.

      Reply: High resolution images of each strain are available on the JGI home page for each alga, given the multiple figures we feel photos would not add information.

      Reviewer #2

      4) I don't think Supp. Table 1 (the Pan-algal dataset) got uploaded correctly during the manuscript submission stage. The first link I click on gives me Supp. Table 2.

      Reply: We apologize for this, the format was incorrect for the file designation and there were lost links. We now more actually refer to these as Data Folders as they are excel folders containing multiple sheets, All supplementary links will be verified again on final submission.

      .

      Reviewer #2 (Significance (Required)):

      By far the most significant finding from this paper is that the genomes of Arctic algae are enriched in genes encoding proteins with an ice-binding domain, paralleling findings from Antarctic algae. These genes appear to have spread among Arctic algal genomes via horizontal gene transfer, which raises a series of interesting questions. This is not the first paper to present these types of ideas, but it is arguably the broadest analysis yet, at least with respect to eukaryotic algae. This work will be of great interest to polar scientists, phycologists, protistologists, and the genomics community. I am genome scientist studying protists, including algae.

      Reply. We thank the reviewer for their insightful comments.

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

      **Summary:**

      This manuscript is focused on Arctic microalgae, an important yet understudied community in permanently cold ecosystems. By sequencing the genomes of four phylogenetically diverse and uncharacterized polar algae, the authors seek to elucidate genomic features and protein families that are similar in polar species (and differ from their relatives from temperate environments) This work used high-throughput genomic sequencing and computational analysis to demonstrate significant horizontal gene transfer (HGT) in several gene families, including ice-binding proteins. The authors suggest that this HGT is an effector of environmental adaptation to Arctic environments.

      **Major comments and experiment suggestions:**

      The authors conclude that HGT between arctic species is a driver of polar adaptation. The authors strongly support the claim that HGT is present more frequently in the polar algae examined here. Whether this is adaptive should be further explored though. For instance, ice-binding domains were one PFAM group found at significantly higher frequencies in the polar species - but are all of these species associated with ice? What would be the benefit of IBDs in an alga that is found in the open ocean. Similar with the other domains (Lns 333-335), its not clear whether these are truly adaptive features. ____This is more speculative.

      Reply: We agree that detail was lacking and have considerably expanded our introduction on the character of the Arctic Ocean and have stated the goals and underlying hypothesis. Briefly, all surface water organisms that live in the Arctic encounter ice during the year as the ocean freezes in winter, and surface waters reman around negative 1.7 °C for much of the year. This information has been added to the introduction. We have also expanded the discussion on the multiple effects of different IBPs that would be ecologically beneficial for plankton as well as ice-algae and cite relevant experimental studies and reviews.

      Reviewer #3) ____HGT was a major conclusion of this study, putting this in a wider perspective would strengthen the conclusion, especially in the context of HGT from prokaryotes. Are there insights on whether IBDs are present in Arctic prokaryotes?

      Reply: This is a good question, and we now point out that there were 91 Arctic bacterial and archaeal IBP sequences in our comparative dataset. In contrast to the Antarctic clades, none were closely related to the Arctic strain IBPs (Fig 4). Line 336.

      Reviewer #3) ____The data obtained from the genomic works supports the conclusions stronger that ones from transcriptomes, where what genes/domains are present would depend largely on the sampling conditions. This should be emphasized.

      Reply: The main rational for using transcriptomes was that more of these are available and enabled us to detect convergences and HGT across a broader taxonomic range than would be possible with genome-only data, where we had access to a total of only 21 microalgal genomes. In general transcriptome studies are aimed at identifying responses under different conditions and rely on comparative expression data, usually 2-fold differences in up or down expression under different growth conditions, see for example Freyria et al. 2022 (Communications Biology). Unlike a transcriptome expression study, our data mining detected any (constitutive or regulated) expression in these unicellular haploid cells, we would have detected genes used under any condition that an algal happened to be growing. IBD was not detected in any of the temperate genomes, and only detected in transcriptomes of Arctic and Arctic-Boreal groups. However, we agree that there may be some limitation of transcriptomes only studies and mention this. Lines 522-528.

      Reviewer #3) ____An experiment to determine whether the species are cold extremophiles (psychrophiles) would be useful here to strongly support the data in Figure 1. The authors state that their species can not survive >6C but this is based on experiments done on older studies. Considering the cultures have been maintained as a continuous culture for decades, confirming that they still have psychrophilic characteristic would be useful. This is a straightforward and low cost experiment that requires simply measuring growth rates at several temperatures to define the optimal and confirm that the cells are not viable above 6C.

      Reply: These are interesting points, and the broad “background” statements in the original manuscript would require a separate study,and have been deleted. Temperature tolerance experiments are not so simple for cold adapted algae with slow growth rates. Such experiments require specialized incubators to maintain low temperatures. Temperature experiments have been carried out on the cultures in the context of other studies, see for example, Daugberg et al. 2018, J. Phycol. But this is not within the scope of the present study.

      We now restrict our conclusions to the specific question of convergence among Arctic strains. We apologize for the misunderstanding on the history of the cultures. They have not been in “continuous culture” but are cryopreserved. We now simply indicate that they grow below 6 °C, which is sufficient to assume that they are likely cryophiles, our experience is that they do not grow well or at all at higher temperatures, our efforts have been to maintain the cultures that are otherwise easily lost. We now make no claims about optimality or limits. Here we simply examined genomes and available transcriptomes that were generated from algae growing at 4-6 °C.

      Reviewer #3) ____**Minor comments:**

      Defining the species used here as psychrophiles would put the study in context better. The authors relate their finding to Antarctic species (HGT, ice-binding domains, large genomes) all of which are confirmed psychrophiles.

      Reply: The temperature definition of psychrophiles is surprisingly high (optimal growth below 15 °C) and this definition of psychrophiles is now given in the introduction. The point is really that there are few isolates from cold surface waters that have been well studied. We now add. “A handful of polar algal genomes have been extensively studied, with 4 of these from around Antarctica and classified as psychrophiles (not being able to grow above 15 °C (Feller & Gerday, 2003)”. Lines 103-107.

      Reviewer #3) ____A short rationale on why these species at all would be useful - are they representative of their classes? Do they have psychrophilic characteristics that might make them useful models in the future? Are they widely used now?

      Reply: We appreciate the point as the definition of utility in discovery-based science is an open dialog.

      We agree that the study requires context and have added our rational for selecting the species for genome sequencing to the introduction. “To address questions on genetic adaptations to this ice-influenced environment, we sequenced 4 phylogenetically divergent microalgae, from 4 algal classes belonging to 3 algal phyla: Cryptophyceae (Cryptophyta), Pavlovophyceae (Haptophyta), Chrysophyceae and Pelagophyceae (both in the Ochrophyta) isolated from the ca. 77 °N, where surface ice flow persists through June (Mei et al., 2002). The four isolates were selected as representatives of different water and ice conditions and phylogeny from available strains collected in April and June 1998 during the North Water Polynya study”.

      Reviewer #3) ____Starting algal cultures were maintained in a continuous culture since 1998 and under continuous light since at least 2015, have the authors confirmed that these algae retain their physiological features even after this long time? The accumulation of mutations is a possibility here.

      Reply: We apologize for the misunderstanding of the timeline; the history of the cultures was not given in the manuscript and the inferred history is not quite correct. The 2015 date was the year of publication for the MMETSP data. Our continuous light statement is a record of our standard culture conditions. We now elaborate on the material used in the current study. The cultures were deposited in the Bigelow culture collection (now NCMA) in 2002 and cryopreserved once they had been verified and given a culture designation. We obtained fresh cultures in 2005 and these were used for the MMETSP project. We obtained fresh cultures again in 2011, specifically for the JGI genome project. These algae do not grow fast and most of the DNA was sent to JGI in 2012 for most of the isolates. This history is rather long and not relevant, since one would speculate that over the years the algae would tend to lose the ice associated functionality, e.g. they were not frozen in seawater every year for 4 to 6 months or subject to sudden freshwater exposure, when ice melts. We would encourage other researchers to order the cultures and run experiments. We note that many of the 40 or so algae isolated from the same campaign have been used by others for specific studies and at least 8 are in the MMETSP data set. The presence of 18S rRNA and phylogenetic position of the IBP sequences compared to Tara Arctic circle data confirms long-term Arctic presence of each species and the IBP domains in the Arctic without marked changes over the last 20 years.

      Reviewer #3) ____Ln381 - The culture collection IDs for each sequenced species should be included here

      Reply: we have added the culture IDs throughout.

      Reviewer #3) ____Ln. 389 - Algal cells are harvested and used for nucleic acid extraction, the nucleic acids themselves are not harvested

      Reply: we agree and corrected the wording

      Reviewer #3 (Significance (Required)):

      This study is well places in the current state of research on polar alga and represents a significant and very valuable addition to the current knowledge pool. Algae in general are lagging behind other groups of photosynthetic organisms in the number of sequenced and analyzed genomes, despite algae being one of the main primary producers globally. This is even more strongly felt in polar research, where only 4 species have been sequenced, most of which are restricted to Antarctica. There is a true gap in our knowledge when it comes to Arctic species, and this study fills this gap. As the authors correctly state, we need more knowledge on polar environments and the primary producers that support these important ecosystems in light of current climate change trends.

      Reply: we appreciate the succinct summary of our study and thank the reviewer for insights and suggestions that have improved the manuscript.

      Reviewer field of expertise: Polar algae, stress responses, plant and algal energetics, cell signalling

      Reply: We appreciate the incites and perspective steming from the reviewer's expertise.

      Relevant key references cited in the reply:

      Daugbjerg N, Norlin A, Lovejoy C. Baffinella frigidus gen. et sp. nov. (Baffinellaceae fam. nov., Cryptophyceae) from Baffin Bay: Morphology, pigment profile, phylogeny, and growth rate response to three abiotic factors. Journal of Phycology. 2018;54(5):665-80

      Feller, G. and Gerday, C. (2003) Psychrophilic enzymes: Hot topics in cold adaptation. Nat Rev Microbiol, 1, 200-208.

      Freyria NJ, Kuo A, Chovatia M, Johnson J, Lipzen A, Barry KW, et al. Salinity tolerance mechanisms of an Arctic Pelagophyte using comparative transcriptomic and gene expression analysis. Communications Biology. 2022;5(1). doi: 10.1038/s42003-022-03461-2

      Mei, Z. P., Legendre, L., Gratton, Y., Tremblay, J. E., Leblanc, B., Mundy, C. J., Klein, B., Gosselin, M., Larouche, P., Papakyriakou, T. N., Lovejoy, C. and Von Quillfeldt, C. H. (2002) Physical control of spring-summer phytoplankton dynamics in the North Water, April-July 1998. Deep-Sea Research Part Ii-Topical Studies in Oceanography, 49, 4959-4982.

      Niezgodzki, I., Tyszka, J., Knorr, G. and Lohmann, G. (2019) Was the Arctic Ocean ice free during the latest Cretaceous? The role of CO2 and gateway configurations. Global and Planetary Change, 177, 201-212.

      Nikishin, A. M., Petrov, E. I., Cloetingh, S., Freiman, S. I., Malyshev, N. A., Morozov, A. F., Posamentier, H. W., Verzhbitsky, V. E., Zhukov, N. N. and Startseva, K. (2021) Arctic Ocean Mega Project: Paper 3-Mesozoic to Cenozoic geological evolution. Earth-Science Reviews, 217.

    1. Author Response

      Reviewer #1 (Public Review):

      This is a very interesting paper showing that postsynaptic bursts in the presence of dopamine produce input-specific LTP in hippocampal synapses 10 minutes after they were primed with negatively coincident pre- and postsynaptic activity. LTP requires NMDAR activation during priming and involves a cAMP-PKA cascade and protein synthesis. When this synaptic rule is incorporated into a computational model, reinforced learning is possible through selective reactivation of neurons. Experiments in behaving mice confirmed that neurons reactivated after an exploratory period display more activity than non-reactivated neurons.

      We thank the Reviewer for their positive comments on our manuscript. We have incorporated the Reviewer‘s suggestions.

      Reviewer #2 (Public Review):

      Building on their previous 2015 study with Brzosko, Fuchsberger et al. propose a potential solution for how the brain associates with memory events that are separated in time. The authors find that in the presence of dopamine, postsynaptic bursts produce input-specific LTP at hippocampal CA3-CA1 synapses ten minutes after priming with a post-before-pre spiking-pairing protocol. They explore the signalling somewhat, for example showing a need for postsynaptic NMDARs as well as for protein synthesis. Using a computer model, they find that this form of plasticity enables reinforcement learning. A few key predictions were verified using an in-vivo spatial learning model.

      This is a strong study that addresses a long-standing fundamental problem in modern neuroscience research, namely the temporal credit assignment problem of how temporally well-separated signals can be meaningfully associated and learned in the brain. The experiments are carefully executed, the rationale is clearly explained, and - excepting Fig 6-8 - the figures are for the most part easy to understand. The study ranges from in-vitro electrophysiology across computer modelling to awake-behaving in-vivo experiments to persuasively argue that their novel findings may provide a candidate solution to the temporal credit assignment problem. Taken at face value, this work is likely to be highly impactful, however, some control experiments were missing or are perhaps just not shown (e.g., stability, stability in the presence of anisomycin, the effect of anisomycin on firing, and similar), which makes the validity of the findings a bit hard to evaluate at times.

      We thank the Reviewer for their positive evaluation of our study and address all the points raised below.

      Reviewer #3 (Public Review):

      Fuchsberger et al. demonstrate that an otherwise LTD-inducing STDP protocol can produce LTP if followed by burst reactivation of post-synaptic neurons in the presence of dopamine. Using computational modeling and single-photon imaging in the CA1 in mice, they propose these findings are relevant to spatial over-representation at a reward location.

      This is a follow-up of the two previous studies from the same group (Brzosko et al., 2015 and Andrade-Talavera et al., 2016) where they showed a post-before-pre STDP protocol, which by default induces a (pre-synaptic) LTD, will induce synaptic potentiation in the presence of dopamine and continuous synaptic activity. The main conceptual difference between this manuscript and these previous studies is that continuous synaptic activity can be replaced by post-synaptic burst. This means that reactivation of post-synaptic neurons without any further pre-synaptic instruction is sufficient for successful LTP induction.

      Mechanistically, the two protocols (continuous vs burst activation) appear to be similar (but not identical). For example, both require the activation of post-synaptic NMDAr during STDP pairing, and both depend on the AC/PKA pathways. Additionally, there are two new observations here: The activity of voltage-gated calcium channels during bursting is required for potentiation; the burst-induced potentiation also requires protein synthesis.

      The evidence provided at this stage is strong.

      Major point:

      It is not clear to me how the STDP studied here relates to the next part of the study, the reward-based navigation task. My interpretation is that the authors consider the activity before reaching the reward location (approaching time) as resembling the STDP priming protocol, the activity at the reward location as equivalent to the bursting protocol, and consumption of the reward as similar to dopamine application. If so, what is the circumvential evidence that the activity during the approach induces any form of plasticity?

      The link between the two is not obvious and I see the manuscript as two interesting but not naturally linked stories.

      The Reviewer’s interpretation is correct. We considered the activity during navigation on the maze as the animal approaches the reward resembling the STDP priming protocol. Substantial evidence supports a role of NMDAR-dependent STDP in the formation of place fields during navigation (Mehta, Hippocampus 2015; Moore et al., 2021). It has been postulated that both LTP and LTD are involved in place field formation. This was based on the observation that place fields shift backwards with experience (Mehta & McNaughton PNAS 1997), and a computational model predicted that without LTD place field broadening would occur (Mehta et al. Neuron 2000). Thus LTP is required when entering the place field, and LTD when the animal exits the place field (Mehta et al. Neuron 2000). This is specific to navigation, as opposed to just walking on a linear track without task, and place field plasticity is predictive of navigational performance (Moore et al. Nature 2021).

      We have added this to the Discussion section (page 13, line 344).

      Mehta MR. 2015. From synaptic plasticity to spatial maps and sequence learning. Hippocampus 25:756-62.<br /> Mehta MR, Quirk MC, Wilson MA. 2000. Experience-dependent asymmetric shape of hippocampal receptive fields. Neuron. 25: 707-15. Moore JJ, Cushman JD, Acharya L, Popeney B, Mehta MR. 2021. Linking hippocampal multiplexed tuning, Hebbian plasticity and navigation. Nature. 599: 442-448.

    1. &Husks

      OED

      husk, n.1

      (hʌsk)

      [Late ME. huske, of uncertain origin.    A common word since c 1400, of which no earlier trace has been found. Conjectures have been offered of its relationship to Ger. hülse, Du. hulze, huls, which (notwithstanding the identity of sense) appear to be historically and phonetically untenable, and of its ultimate derivation from hús ‘house’, which is perhaps possible: cf. for the form, chink, dalk, halk, holk, polk, stalk (and see Kluge, Stammbildung. §61); for the sense, LG. hûske = Ger. häuschen, ‘little house’, in E. Fris. also ‘core (of an apple)’, ‘case’ (e.g. spectacle-case), ‘paper bag’; also MDu. huuskijn, huusken, Du. huisken, ‘little house’, core (of an apple); Ger. gehäuse, ‘case, capsule’, etc. The connexion of Norwegian husk ‘piece of leather used to enlarge a shoe-last’, is quite uncertain.]

      1. a.1.a The dry outer integument of certain fruits and seeds; esp. the hard fibrous sheath of grain, nuts, etc.; a glume or rind; spec. in U.S., the outer covering of an ear of maize or Indian corn.

      1398 Trevisa Barth. De P.R. xvii. cliv. (1495), Codde and an huske hyght Siliqua.    c 1400 Mandeville xxi. (1839) 188 As the Note of the Haselle hathe an Husk with outen.    Ibid. (Roxb.) 94 Þe macez er þe huskes of þe nutemuge.    c 1440 Promp. Parv. 254/2 Huske of frute, or oþer lyke, corticillus.    1474 Caxton Chesse 81 The huske whiche is about the grayn.    1548 Udall Erasm. Par. Luke xv. (R.), To fil his bealie‥with the verai huskes and coddes, wherwith the hogges were fedde.    1557 N. T. (Genev.) Luke xv. 16 The huskes [Wycl., Tind., Coverd. coddis, coddes] that the swyne ate.    1631 Widdowes Nat. Philos. (ed. 2) 36 The Chesnut‥is covered with a sharpe huske, and within it hath a red huske.    1665 Hooke Microgr. 156 Carret seeds are like a cleft of a Coco-Nut Husk.    1704 J. Harris Lex. Techn. s.v. Verdegrease, The Husks of pressed Grapes.    1830 M. Donovan Dom. Econ. I. 87 The malt is parched until it has acquired a slight tinge of yellowness on the husk.    1855 Longfellow Hiaw. xiii. 29 The women who in Autumn Stripped the yellow husks of harvest.

      †b.1.b The calyx or involucre of a flower. Obs.

      1450–1530 Myrr. our Ladye 210 Whyche floure yf he se yt not yet sprynge oute of the huske.    1727–41 Chambers Cycl., Husks, among botanists, the part which a flower grows out of‥Of these there are several kinds, as bulbous or round husks, bottle husks, middle husks, foot husks, hose husks.

      c.1.c Husks collectively, husky matter.

      1883 C. J. Wills Mod. Persia 233 By about the twenty-fourth day the wine was ready for clearing of the husk.    Ibid. 234 The sweet wine had already no husk in it.

      2.2 Applied to animal coverings or shells: †a.2.a The coriaceous wing-case of an insect; an elytron. Obs. b.2.b The shell or case of a chrysalis; a cocoon. ? arch. c.2.c In Georgia, U.S., an oyster shell.

      1552 Huloet, Byttel flye with a blacke huske.    1616 Surfl. & Markh. Country Farme 488 Euerie one [silkworm] shutting vp himselfe in his scale or huske, which they make and build vp in two daies.    1653 Walton Angler xii. 226 A good bait is the young brood of Wasps or Bees, baked or hardned in their husks.    1665 Hooke Microgr. 187 Several of them flew away in Gnats, leaving their husks behind them in the water floating under the surface.    Ibid. 215 They seem cover'd, upon the upper side of them, with a small husk, not unlike the scale, or shell of a Wood-louse.    1802 Paley Nat. Theol. xix. (1830) 228 This [chrysalis] also in its turn dies; its dead and brittle husk falls to pieces, and makes way for the appearance of the fly or moth.    1842 Tennyson Two Voices ii, I saw the dragon-fly Come from the wells where he did lie. An inner impulse rent the veil Of his old husk.

      3.3 techn. Applied to a frame of various kinds: see quots.

      1688 R. Holme Armoury iii. 100/2 Husk is a square Frame of Moulding‥set over the Mantle Tree of a Chimney between two Pillasters.    1873 Knight Dict. Mech., Husk, the supporting frame of a run of millstones.

      4.4 transf. and fig. a.4.a The outside or external part of anything; mostly in depreciatory sense, the mere rough or worthless exterior, as contrasted with the substantial inner part or essence.

      1547–64 Bauldwin Mor. Philos. (Palfr.) 98 That‥the bitternesse & hardnesse of his [Death's] rough huske should hinder vs from the sweet taste of such a comfortable kirnell.    1644 Hunton Vind. Treat. Monarchy iii. 10 A few huskes of reason.    1652 L. S. People's Liberty xvi. 39 Their acquiescing in God's choice should be the pith and kernel of the precept, and the setting up of a King onely the husk and shell of it.    1841–4 Emerson Ess., Friendship Wks. (Bohn) I. 85 Bashfulness and apathy are a tough husk, in which a delicate organization is protected from premature ripening.    1861–8 Lowell Emerson Pr. Wks. 1890 I. 355 He‥gave us ravishing glimpses of an ideal under the dry husk of our New England.    1887 W. H. Stone Harveian Oration 21 The mere reproduction of the dry husks of thought termed words.

      b.4.b Applied to the human body.

      a 1677 Barrow Serm. Wks. 1716 I. 62 May not our soul‥challenge a good share of our time‥or shall this mortal husk engross it all?    1818 M. G. Lewis Jrnl. W. Ind. (1834) 102 It is a matter of perfect indifference to me what becomes of this little ugly husk of mine, when once I shall have ‘shuffled off this mortal coil’.

      †c.4.c Applied to a person. Obs.

      1601 ? Marston Pasquil & Kath. i. 76 in Simpson Sch. Shaks. (1878) II. 138 You keepe too great a house‥Yon same drie throated huskes Will sucke you vp.    Ibid. iv. 39    Ibid. 183 Bra. Iu. How like you the new Poet Mellidus? Bra. Sig. A slight bubling spirit, a Corke, a Huske.

      d.4.d A figure or ornament somewhat resembling a husk.

      1934 Burlington Mag. Oct. p. xv/2 The tablet is carved with festoons, and the frieze and jambs inlaid with festoons and pendants of husks and coloured marble.    1955 R. Fastnedge Eng. Furnit. Styles 285 Husk, with ‘honeysuckle’ ‘wheat-ear’ a favourite ornament on furniture of the Adam and Hepplewhite periods.    1971 Country Life 3 June 1356/3 The ground paint was decorated with motifs such as festoons of drapery and husks, interlacing hearts, urn patterns, and so on.

      5.5 attrib. and Comb. (from 1), as husk-porridge; husk-like adj.; ‘in the husk’, as husk corn, husk nut; (from 4 d) husk design, husk festoon, husk ornament, husk pattern; husk-hackler, ‘a machine for tearing corn-husks into shreds for stuffing for mattresses, pillows, cushions, etc.’ (Knight Dict. Mech. 1875).

      1687 S. Sewall Diary 3 Oct. (1878) I. 191 *Husk Corn.

      1904 P. Macquoid Hist. Eng. Furnit. vii. 191 The sides are inlaid with the‥ *husk design so popular at this time.    1973 Country Life 31 May 1567 Chestnut wood window seats‥the‥legs‥faced by well carved husk design.

      1770 J. Wedgwood Let. 20 Aug. (1965) 94 First, his Majesty approved of the *husk festoons in particular, and I think more so than the desert pattern.

      1796 Withering Brit. Plants (ed. 3) II. 60 Flowers with valves like grasses, and *husk-like calyxes.

      1888 Pall Mall G. 24 Jan. 5/2 The *husk nuts piled on the top.

      1934 Burlington Mag. Oct. 165/1 The back shows the honeysuckle, *husk or catkin ornament.    1960 H. Hayward Antique Coll. 146/2 Husk ornament, an ornamental motif resembling the husk of a wheat ear used continually by architects and craftsmen during the Adam period.

      1876 C. Schreiber Jrnl. 14 Nov. (1911) I. 485 A good set of Wedgewood, *husk pattern.

      1851 Mrs. Browning Casa Guidi Wind. i. 1003 To see the people swallow hot *Husk-porridge which his chartered churchmen stir.

    1. Author Response

      Reviewer #1 (Public Review):

      This is a well-done analysis using the very robust Swedish national population registry.

      The study strengths include large size, prolonged follow-up, and use of two comparison populations.

      Thank you for the encouraging comments on our study.

      The main limitations which need to be addressed by the authors are accounting for reverse causality, namely if a psychiatric illness (PI) developed before or about the same time as the CVD. The much steeper risk relationships early after a CVD event are so suggestive. Some further analyses to tease out those with clearly NO PI before CVD would be in order.

      Thank you for the comment. Previous studies have consistently reported an association between psychiatric disorders and CVD [1,2], thus, we agree that reverse causality may, in principle, explain some of the observed results indicating a rise in incident psychiatric disorders after incident CVD, particularly during the immediate period. Yet, it is reasonable to assume that a diagnosis of a lifethreatening disease, such as CVD, is in many cases a traumatic experience resulting in an immediate rise in risks of psychiatric disorders. Others have reported such associations e.g. after natural disasters and we have indeed observed such a pattern in our previous work, e.g., after cancer diagnosis [3]. However, we agree that reverse causality cannot be excluded and may partly contribute to the highly increased risk of psychiatric disorder immediately after CVD diagnosis. Indeed, some of these patients may have been attended for their psychiatric disorders in primary care before the incident CVD. As the Patient Register only captures in- and outpatient hospital care, we have conducted an additional analysis, also excluding individuals with previous prescriptions of psychotropic drugs (ATC codes: N05, N06) before their incident CVD – thereby adding a detection of patients with prevalent mental health problems attended by primary care. The results show similar point estimates (Supplementary Appendix Table S5, listed also as below) thus not supporting the notion that reverse causality is a major concern. Furthermore, the association is noted up to 28 years after CVD diagnosis, which is unlikely due to reverse causality.

      We have now added our motivation for this additional analysis on the Method (Page 9), as below. “Because the Swedish Patient Register includes only information related to specialist care, we might have misclassified patients with a history of milder psychiatric disorders diagnosed before index date attended only in primary care. To account for the reverse causality of having undetected psychiatric disorders or symptoms before the incident CVD, we performed a sensitivity analysis additionally excluding study participants with prescribed use of psychotropic drugs before the index date (ascertained from the Swedish Prescribed Drug Register including information on all prescribed medication use in Sweden since July 2005), and followed the remaining participants from 2006 to 2016.”

      Second, for the robust matched cohort design, the authors age and sex matched each patient with 10 individuals from the general population and then also stratified their model by the matching variables. Could adjusting for matched factors in such cohort studies re-introduce bias into these estimates?

      Thank you for the comment. Adjusting for matching factors should provide estimates with the same validity as using a stratified model. In our study, we matched individuals diagnosed with a CVD with their unaffected full siblings as well as 10 randomly selected, unexposed individuals, on the same age and sex, without such diagnosis. As controlling for matching variables is recommended when there are additional confounders [1,2], we used a stratified Cox model commonly applied in family-based studies [3,4].

      References:

      1.Sjölander A, Greenland S. Ignoring the matching variables in cohort studies - when is it valid and why? Stat Med. 2013 Nov 30;32(27):4696-708.<br /> 2.Mansournia MA, Hernán MA, Greenland S. Matched designs and causal diagrams. Int J Epidemiol. 2013 Jun;42(3):860-9.<br /> 3.D'Onofrio BM, Lahey BB, Turkheimer E, Lichtenstein P. Critical need for family-based, quasiexperimental designs in integrating genetic and social science research. Am J Public Health. 2013 Oct;103 Suppl 1(Suppl 1):S46-55.<br /> 4.Song, H., Fang, F., Arnberg, F. K., Mataix-Cols, D., de la Cruz, L. F., Almqvist, C., ... & Valdimarsdóttir, U. A. (2019). Stress related disorders and risk of cardiovascular disease: population based, sibling controlled cohort study. bmj, 365.

      Third, the range of PIs associated with CVD is a lot broader than would be expected or unexpected (eg eating disorders!).

      Thank you for the comment. We agree with the reviewer that the strong association between CVD and incident eating disorders is somewhat surprising although the link between cardiovascular risk factors (e.g. obesity) and binge eating have indeed been reported [1,2]. We have now performed the analysis on the association between first-onset CVD and following incident eating disorder, additionally excluding individuals with a history of psychotropic medication use. We found that the associations became even stronger after this exclusion (Supplementary table 5). It is possible that individuals suffering their first CVD indeed drastically alter their lifestyle, in some cases resulting in dysfunctional eating and may therefore be vulnerable to eating disorders. Given that the evidence assessing the risk of eating disorder among CVD patients is still limited, our study adds a valuable piece of knowledge on this regard and calls for further investigations to better understand this association.

      References:

      1.Mitchell JE. Medical comorbidity and medical complications associated with binge-eating disorder. Int J Eat Disord. 2016 Mar;49(3):319-23.<br /> 2.Bulik CM, Sullivan PF, Kendler KS. Medical and psychiatric morbidity in obese women with and without binge eating. Int J Eat Disord 2002;32:72–78.

      Lastly, the authors should try to account for secular changes in smoking and alcohol consumption or BMI over the study period. In particular, while Sweden never had very high smoking rates (due to Snus) alcohol use within specific cohorts might have both affected CVD risk (particularly stroke) and PI risk. Examining trends in for example liver cirrhosis over the study time period might help or use sales/consumption data. The authors do recognize a limitation in being unable to adjust for smoking, alcohol, and adiposity.

      Some additional analyses to address these points and some more caution in the discussion are required.

      Thank you for the comment. As the reviewer points out, we do recognize the potential unmeasured influence of lifestyle factors (e.g. smoking and alcohol consumption) on the studied associations as these data are not collected in the Swedish registries. However, the associations between CVD and psychiatric disorders were quite stable across calendar time, although somewhat stronger by the end of the observation period. The evidence does not suggest a drastic change in lifestyle factors in Sweden during the latter part of the observation period except for a slight increase in alcohol consumption [1,2] and liver cirrhosis [3]. Although we find it implausible that such underlying secular trends in lifestyle are a major contributor in the reported associations, we have now conducted additional analyses, excluding individuals with alcoholic cirrhosis of liver (ICD-10 code: K70.3) or COPD (chronic obstructive pulmonary disease, ICD-10 code: J44) as a proxy for heavy drinking or smoking. The results remained virtually unchanged.

      We have now added reasons for stratified analysis by calendar years in Method (Pages 8-9), and as below:

      “We performed subgroup analyses by sex, age at index date (<50, 50-60, or >60 years), age at follow-up (<60 or ≥60 years), history of somatic diseases (no or yes), and family history of psychiatric disorder (no or yes). We also performed subgroup analysis by calendar year at index date (1987-1996, 1997-2006, or 2007-2016) to check for potentially different associations over time (i.e., due to lifestyle factors that changed over time, including smoking and alcohol use).”

      We found somewhat higher risk of psychiatric disorder observed in recent calendar years than earlier years (as in shown Supplementary Table S3).

      We found similar associations between first-onset CVD and incident psychiatric disorder with and without excluding individuals with a history of alcoholic cirrhosis of liver or COPD, used as a proxy for heavy drinking or smoking. The table has now added as Supplementary Table S8, and also shown as below).

      We have now added justifications in Method (Page 10) and in Discussion (Page 21), and as below: In method, Page 10:

      “To account for potential impact of unmeasured confounding due to lifestyle factors, we performed a sensitivity analysis excluding individuals with a history of alcoholic cirrhosis of liver (ICD-10 code K703) or chronic obstructive pulmonary disease (COPD, ICD-10 code J44), as proxies for heavy drinking or smoking.”

      In Discussion (Page 21):<br /> “although we found similar results with and without excluding individuals with a history of liver cirrhosis or COPD, as proxies for heavy drinking or smoking (Supplementary Table S8). We did not have direct access to hazardous behaviors that could potentially modify this association, and therefore cannot exclude the possibility of residual confounding not fully controlled for in the sibling comparison.”

      References:

      1.Statista. https://www.statista.com/statistics/693505/per-capita-consumption-of-alcohol-in-thenordic-countries/. Retrieved on 19 Aug.<br /> 2.Alcohol and Drug Report. Nordic Baltic Region. https://www.nordicalcohol.org/swedenconsumption-trends. Retrieved on 19 Aug. 3.Gunnarsdottir SA, Olsson R, Olafsson S, Cariglia N, Westin J, Thjódleifsson B, Björnsson E. Liver ;cirrhosis in Iceland and Sweden: incidence, aetiology and outcomes. Scandinavian journal of gastroenterology. 2009 Jan 1;44(8):984-93.

      Reviewer #2 (Public Review):

      Shen et. al investigated the associations between CVD and subsequent risk of psychiatric disorders using a prospective study design. The authors also performed subgroup analysis by sex, age at cohort entry and at follow-up, calendar year, history of somatic diseases, family history of psychiatric disease, and finally assessed the potential role of psychiatric comorbidity in cardiovascular mortality in CVD patients. The main takeaway of the analyses are the increased risk of psychiatric disorders in CVD patients compared to the different comparison groups.

      Though the study uses nationwide registers in a prospective study design setting, there are some methodological flaws with respect to study design.

      For assessing the primary aim the authors chose a rather unusual starting point by preselecting the exposure (CVD) group, rather than depicting the nationwide cohort of the general population followed up for a disease outcome with each category having exposed and unexposed individuals. Assuming that the population comparison group comes from the same study population as CVD patients, it is not clear why a similar strategy of study design as those cited in the manuscript (Zhang et. al 2015, Kivimäki et. al 2012, Godin et. al, 2012) was not followed. Similarly, one would expect sibling comparison group w.r.t outcome (psychiatric disorders) and not for exposure (CVD).

      Thank you for the comment. As correctly pointed out by the reviewer, we used a matched cohort design, both in the population- and sibling comparison. We firstly identified a nationwide cohort of general population who were born after 1932 and were residing in Sweden 1987-2016. We then identified all exposed individuals with first-ever diagnosis of CVD and matched population controls from this same nationwide population.

      A matched cohort design is applied here due to the strong confounding effects of some variables, e.g., age and sex, on the studied association between CVD and risk of psychiatric disorder. Exact matching on age and sex in our study makes the exposed and unexposed groups comparable and relief the confounding effects from matching factors in the design phase. Another practical viewpoint for why we use a matched cohort is a straightforward understanding of the comparison between exposed and unexposed groups being always at the same time, providing measures (such as risks and rates) during the follow-up period that are easily interpreted. Further, we have used this matched cohort design in many of our previous works [1,2] to maintain an identical design in both sibling and population comparison, so that the point estimates can be directly compared. The matched cohort design generates results of equal validity of the more conventional cohort design suggested by the reviewer [3] but has the additional quality of making the results from the various cohorts (here: population- and sibling comparison) more comparable. Our study therefore takes advantage of using a siblingcontrolled matched cohort, which is indeed a cohort design recommended for family-based studies [4] and provides results with similar validity as a full cohort.

      We have now added a sentence and a reference in Method to motivate the use of matched cohort design (Page 7).

      “We constructed a sibling-controlled matched cohort to control for the familial confounding according to guidelines for designing family-based studies.24”

      We have now updated the flowchart to add a box in the top reflecting the source population where both groups were identified from, shown in Supplementary Figure S1.

      References:

      1.Song H, Fang F, Arnberg FK, Mataix-Cols D, Fernández de la Cruz L, Almqvist C, Fall K, Lichtenstein P, Thorgeirsson G, Valdimarsdóttir UA. Stress related disorders and risk of cardiovascular disease: population based, sibling controlled cohort study. BMJ. 2019 Apr 10;365:l1255.<br /> 2.Song H, Fang F, Tomasson G, Arnberg FK, Mataix-Cols D, Fernández de la Cruz L, Almqvist C, Fall K, Valdimarsdóttir UA. Association of Stress-Related Disorders With Subsequent Autoimmune Disease. JAMA. 2018 Jun 19;319(23):2388-2400.<br /> 3.Sjölander A, Greenland S. Ignoring the matching variables in cohort studies–when is it valid and why?. Statistics in medicine. 2013 Nov 30;32(27):4696-708. 4.D'Onofrio BM, Lahey BB, Turkheimer E, Lichtenstein P. Critical need for family-based, quasiexperimental designs in integrating genetic and social science research. Am J Public Health. 2013 Oct;103 Suppl 1(Suppl 1):S46-55.

      Reviewer #3 (Public Review):

      Shen et al. investigated the relationship between the diagnosis of cardiovascular disease (CVD) and subsequent diagnosis of psychiatric disorders using national databases and health records over a 30year period in Sweden. They also investigated the association between the diagnosis of psychiatric disorder and subsequent CVD-related mortality. Comparisons were made between participants diagnosed with CVD and siblings without CVD, and between the CVD participants and random age- and sex-matched controls from the general population.

      They show that diagnosis of all types of CVD investigated was associated with increased risk of all types of psychiatric disorders considered, both in comparison to non-CVD siblings and general population controls. They also showed that diagnosis of psychiatric diagnosis subsequent to CVD diagnosis was associated with greater CVD-related mortality.

      A key strength of this study is the use of national databases and populations, as it has allowed for sufficiently large numbers for important subgroup analyses investigating specific types of CVD and psychiatric disorders. In addition to disease and disorder subtypes, the authors have investigated many other factors that may be important for understanding these relationships, including time of diagnosis during follow-up, year of diagnosis, age of participant, and various comorbidities. The duration of follow-up is another important strength of this study, as is the use of sibling controls to mitigate the potential confounding effect of genetic and early-life environment.

      However, while it is acknowledged as a limitation by authors, the lack of lifestyle data is a notable weakness of the study. The authors allude to causal inference in the abstract and discuss controlling for important confounding factors, but this is somewhat undermined by not being able to account for lifestyle factors, particularly since there are shared biological pathways such as inflammation linked to both CVD and many psychiatric disorders. As such, the associations reported in this study are potentially influenced substantially by unmeasured confounding related to lifestyle factors.

      Overall, this is important data, and the conclusions around these findings supporting surveillance of psychiatric disorders in individuals diagnosed with CVD due to its association with increased risk of mortality may be of interest to clinical settings.

      Thank you for the very positive comments.

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      Reply to the reviewers

      We are very grateful about the thorough reading and deep understanding of the work that these 4 reviewers have provided. Although it is evident that they still request an improved profiling of some aspects, it is very encouraging that all four think the work is very interesting, original, insightful and adds a new layer of knowledge to the regulation of DNA damage sensing and repair. It is also very rewarding that the four reviewers estimate that this work will sew connections between different fields and interest a broad readership. This is why we have designed here a very deep revision, tailored to satisfy all the raised concerns except one, and this just for technical reasons.

      Please find below the original reviewers’ comments and our answers to them preceded by the symbol “>”:

      Reviewer #1 (Evidence, reproducibility and clarity (Required)): Ovejero et al. report an increase in lipid droplet (LD) abundance after long (from 120' on) exposure of budding yeast cells to DNA damaging agents zeocin and camptothecin (CPT). Next, they analyze DNA damage signaling in yeast mutants that impair triacylglycerol (TAGs) or sterol (STEs) esterification. They observe a slight anticipation in Rad53/CHK2 phosphorylation (indicative of DDR signaling) in yeast stem mutants, as well as in yeast cells or human cells lines pre-treated with oleate upon zeocin treatment. Yeast stem mutants are sensitive to zeocin and captothecin, but only confer sensitivity to hydroxyurea upon combination with tagD mutations. Authors relate these phenotypes to a somewhat decreases DSB resection in yeh2D mutants (expected to have reduced steryl esters pools) and RPA-foci in steD yeast cells. Next, a reduction in single strand annealing recombination repair events upon zeocin treatment is reported using a genetic reporter in steD mutants and oleate-treated cells. From these data they conclude that inability to process sterols in response to DSBs leads to an exacerbated DDR and prevents DNA repair. Next, it is shown that Flag-tagged Tel1 distinctly interacts with mono-phosphate phosphoinositides, including PI(4)P. An interaction in vivo is also inferred through Proximity Ligation Assays (PLA) using anti-PI(4)P and anti-ATM antibodies in human cell lines, which was moderately downregulated upon treatment with MMS or zeocin. Over-expression of the Osh4/OSBP1 transporter, which consumes PI(4)P, increased the number of Tel1 (nuclear) foci upon zeocin treatment. Conversely Sac1 ablation, in which accumulation of PI(4)P is expected, abrogated nuclear Tel1 foci formation and reduced telomere length (a phenotype related to lack of Tel1 function). From these results authors conclude that Tel1 availability in the nucleus is influenced by PI(4)P availability. Lastly, treatment with an OSBP1 inhibitor led to a cell line and damaging agent -variable reduction of ATM phosphorylation and a mostly non-significant reduction of DNA resection, measured by native BrdU detection, in response to CPT treatment. Overall, authors conclude that i) biding of Tel1/ATM to PI(4)P modulates its functional availability in the nucleus, and that ii) DNA damage elicits the esterification and storage of sterols toward LDs, which contributes to tritate Tel1/ATM away from the nucleus dampening the DDR and affecting long-range resection.

      Major comments: While the conclusion that Tel1/ATM binds PI(4)P and this interaction modulates Tel1/ATM functional availability at the nucleus is convincing, the conclusion that DSBs elicit a change in the metabolism of this lipid to "control" Tel1/ATM function is not demonstrated. The notion that sterol processing occurs in response to DSBs is not sufficiently supported by the data presented, as the increase in LD numbers is observed much after activation of the DDR (Rad53 phosphorylation) in Zeozin-treated yeast cells.

      We are afraid that we have not been clear enough in explaining the kinetics giving rise to our model. As indicated by the reviewer, our work shows, through kinetic studies, that the storage of sterols within LD occurs at later stages than the activation of the DDR by Tel1 and Rad53 phosphorylation. Tel1 foci decline is necessary for subsequent engagement of downstream DNA long-range resection. Since we propose that sterol storage within LD is a means to attenuate Tel1 engagement at DSBs, it is thus logical (and thus compatible with the data we show) that LD number increase occurs simultaneously with Tel1 foci decrease, at late stages of the reactionWe will include this explanation and graph in the revised version of the work.

      In addition, evidence is not provided on the mechanisms by which PI(4)P metabolism would be controlled, which would be expected to be DDR-independent as they are placed upstream of this signaling pathway in the author's model.

      The key mechanism through which, in the end, PI(4)P metabolism will be controlled, is the esterification of sterols within LD. Given that, as clarified above, LD formation in response to DSBs occurs “late” (i.e., after 120 min), it is not excluded that the DDR itself can instruct, through phosphorylation of some effector(s), LD formation. In other words, by ordering LD formation, the DDR would be launching a self-limiting mechanism. In support, we now know, although we do not show in this work, that eliminating key DDR proteins prevents the formation of LD in response to DNA damage. Because of this, we have undertaken an educated-guess approach and chosen critical or rate-limiting enzymes in LD biology either possessing an S/T-Q cluster domain (predicted to be a phosphorylation substrate for the DNA Damage Response kinases (1), and/or retrieved in phospho-proteomic screens as specific DDR targets (2,3). This adds up to 28 proteins in S. cerevisiae and 45 proteins in Homo sapiens. Importantly, the emergent candidates fall into two identical categories in both organisms. To provide initial support for their pertinence, we have generated a point mutant in the putative S/T-Q cluster of one of the yeast candidates. Of high relevance, we find that the concerned mutant is impaired in correctly triggering LD formation in response to DNA damage, and we have now obtained a specific funding to pursue this characterization that, as such, constitutes a different work from the one presented in this manuscript. We hope that the reviewer is now convinced yet that she/he agrees in keeping this information for subsequent manuscript(s).

      The damaging agents used have been suggested to alter the redox metabolism and even lipid peroxidation (Kitanovic 2009, Mizumoto 1993, Krol 2015, Todorova 2015, Ren 2019, Singh 2014). Hence it is possible that PI(4)P changes are not due to DSBs, but an indirect though relevant effect. In absence of direct evidence supporting an active regulation of PI(4)P dynamics in response to DNA breaks, this conclusion remains speculative and this should be noted in the manuscript.

      We fully agree with the reviewer that the used genotoxins are triggering a myriad of effects which could elicit the same phenomenon by indirect means. Yet, we want to stress that the use of camptothecin, which elicits a very robust LD formation phenotype (Figure 1C), is very likely specific, as it is proven as a potent and direct trapper of Top1 onto DNA after having cleaved it. Nevertheless, we propose in the next paragraph two specific experiments to dismiss this problem, please see immediately below.

      Authors conclude that LD is specific to DSB induction. This seems an overstatement as they just reported LD increases in response to two agents that also induce other kinds of DNA damage. To also strengthen the link between DSBs and PI(4)P modulation of Tel1 function, authors should analyze LD numbers, Rad53 phosphorylation and Tel1 nuclear re-localization in response to HO-induced DNA breaks (e.g., using the system employed in Figure 3C).

      We humbly think that enzymatically-induced DNA breaks will both activate Rad53 phosphorylation and Tel1 nuclear concentration, as this has already been established, thus requiring no further exploration. Yet, it is very important to assess the reviewer’s suggestion concerning whether enzymatically-induced DNA breaks also trigger the formation of LD. To this end, we will perform two complementary studies in which, instead of using HO, which cuts only a few times in the genome, we will:

      1. a) exploit the naturally DSB-accumulating mutant rad3-102, which we previously characterized in the past (4), and which we already exploit in this work for recombination analyses (Figure S4A), to evaluate whether it endogenously harbors more LD in comparison with the WT.
      2. b) we have recently created a tool in which gRNAs targeted to different subsets of transposons in the genome can drive Cas9 to create DSB in a dose-dependent manner ((9), under revision in Genetics). We will use this system to monitor the LD formation in response to Cas9-triggered cuts. In addition, on figure 5A, significant differences in GFP-Tel1 foci abundance between WT and steD or yeh2D cells are only observed after 210', way after the slight effect on Rad53 phosphorylation is observed. This is at odds with the conclusion that Tel1 association to STEs modulates DDR signaling.

      We are afraid that we have not been clear enough in explaining the kinetics giving rise to our model. As indicated by the reviewer, our work shows, through kinetic studies, that the storage of sterols within LD occurs at later stages than the activation of the DDR by Tel1 and Rad53 phosphorylation. Tel1 foci decline is necessary for subsequent engagement of downstream DNA long-range resection. Since we propose that sterol storage within LD is a means to attenuate Tel1 engagement at DSBs, it is thus logical (and thus compatible with the data we show) that LD number increase occurs simultaneously with Tel1 foci decrease, at late stages of the reactionWe will include this explanation and graph in the revised version of the work.

      Minor comments:

      Figure S1D and E, experiments should be carried out to include time points in which LD accumulation and cell cycle arrest are observed upon zeocin treatment (i.e., up to 210' as in Figure 1A)

      We will provide cytometry profiles of cells at 210 min. These data exist already in our laboratory.

      How do authors explain increased single strand annealing recombination frequencies in steD and oleate-treated wild type cells (Figure 4A). Should it not be expected that increased STEs also impair recombination induced by endogenous damage?

      Only ste∆ (and not +oleate) indeed manifests an increase in basal recombination frequencies, likely arising from endogenous damage. Although the increase is observed, it is not significant. We agree anyway with the reviewer that, was the experiment to be repeated more times, the increase may be found significantly different. We do not have any honest proposal to explain this.

      Data presented in figure 4B and 4C are not fully convincing. Performing time course experiments might help concluding if the differences observed represent a relevant defect in DSB processing.

      We will perform a Pulsed Field Gel Electrophoresis (PFGE) kinetcis in response to zeocin with or without oleate pre-loading to reinforce the conclusion.

      Is Figure 5B referring to Flag-tagged Tel1 or GFP-tagged Tel1 as stated in the figure legend?

      There is a misunderstanding here, as the mentioned Figure 5B corresponds to P-ATM immunofluorescences in human cells, not to any tagged Tel1 experiment.

      Treatment with the ATM inhibitor AZD0156 increased PI(4)P-ATM PLA signals. From these authors conclude that "association of ATM and PI(4)P inversely correlated with the need for ATM within the nucleus. Do they imply that treatment with ATM-inhibitors reduces the requirement for ATM function in the nucleus? The interpretation of this result should be further elaborated to sustain this conclusion.

      We may have conveyed a wrong notion at this point. We do not imply at all that ATM inhibitors reduce the need for ATM in the nucleus. Instead, we imply that, by reinforcing ATM attachment to Golgi-resident PI(4)P, ATM inhibitors end up titrating ATM away from the nucleus. We will clarify our explanation to avoid misunderstandings.

      An increase of GFP-Tel1 foci upon OSH4 overexpression is described on Figure 7B. These are described as nuclear in the results, but no reference is made in the figure or legend as to how nucleus positions are addressed in these experiments. This should be clarified.

      We systematically combine the tagging of a nucleoplasmic protein (mCherry-Pus1) with the detection of GFP-Tel1 foci, as to unambiguously assess the nuclear position of Tel1 foci. We will include this explanation and the corresponding mCherry-Pus1 channel to clarify this.

      Also, WT controls and quantifications should be included in the experiments shown on Figure 7C.

      These experiments are quantified from the moment we did them, although we did not include such quantifications in the present version for the sake of space. We will do so in the revised version.

      Reviewer #1 (Significance (Required)):

      While the conclusion of lipid metabolism responding to DSBs is not convincing, the observation that Tel1/ATM function is modulated by PI(4)P biding is significant and advances the understanding on the function and regulation of this key kinase in promoting genome integrity maintenance. This is an unanticipated result which is highly novel and has implications for the modulation of Tel1/ATM function through pharmacological manipulation of lipid metabolism. This finding would be of broad interest for scientists working on the response to DNA damage and the maintenance of genome integrity. This reviewer belongs to that group and has limited expertise to evaluate the lipid metabolism genetic manipulation in the manuscript.

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

      The authors show that cytoplasmic PI4P have a regulatory role on ATM response to DNA double strand breaks. The process involves a balance between exchange of PI4P between Golgi and ER in exchange of esterified sterols. The study is of interest, however provides indirect evidences to support their conclusions.

      Major comments : 1). Since the major conclusion relates to PI4P association with ATM in basal conditions to keep ATM outside nucleus and known presence of PI4P, ATM in other organelles of a cell, further experiments such as cell fractionation experimental that show golgi specific interaction would support the main conclusion.

      In continuation of 1st comment, since PI4P in substrate of PI4 phosphoinositol kinases, is there a competition between PI4kinases and ATM for PI4P binding should be addressed through immunoprecipitation studies.

      First of all, we need to specify here that PI4kinases will phosphorylated PI4 to create PI(4)P. Thus, PI(4)P is the product, and not the substrate, of PI4kinases. We therefore do not expect any competition between such kinases and ATM.

      Second, we take good note of the reviewer’s concern that the pool of PI(4)P at the Golgi may be shared, and also that it would be important to reinforce the notion of the relative subcellular localization of ATM under different treatments. To this end, we will perform the following integrative experiment:

      Immunoprecipitation of PI(4)P could theoretically be done using our specific antibody, yet the IP efficiency of a lipid cannot be verified by western blot. Further, there are PI(4)P pools elsewhere in the cell that would mess up with interpretations. We therefore dismiss the use of anti-PI(4)P as a tool to perform immunoprecipitations.

      Instead, to explore the impact of PI(4)P levels on ATM both at the Golgi and within the nucleus, we will split our cultures in two to either immunoprecipitate specific cytoplasmic Trans-Golgi Network-associated proteins (we will test separately TGN46 and GOLPH3); or the nuclear ATM-interacting factor MRE11 from nuclei, then blot for co-immunoprecipitated ATM. The relative co-immunoprecipitated ATM is expected to vary under different treatments to which the cells will be exposed, namely:

      • untreated
      • zeocin, to trigger ATM need in the nucleus
      • OSBP inhibition (+/- zeocin), to stabilize PI(4)P at the Golgi
      • PIK93, an inhibitor of PI4 kinases that prevents PI(4)P synthesis

      2). The authors claim that the ATM retention is the main function of PI4P in Golgi. The authors should rule out the possibility that the phenotype observed on DNA damage response is not due to non availability of PI4P substrate for PI4P kinases, that have recently been shown to participate in genome integrity maintenance.

      We want to explain that we do not intend to say that PI(4)P main function at the Golgi is ATM retention, as PI(4)P is a molecule binding and modulating multiple proteins, as for example the aforementioned GOLPH3. We will first revise our text to correct it, in case we have conveyed this incorrect notion, as it stems from the reviewer’s comment.

      Second, the reviewer evokes the notion that PI(4)P can be the substrate of a second phosphorylation, which could give rise to PI(3,4)P or to PI(4,5)P, which could still undergo remodeling into PI(3)P, for example. Recent work by Dr Michael Sheetz’s lab demonstrated that this set of phosphoinositides serves to drive the nucleation and activation of the ATR-Chk1 branch of the DNA Damage Response upon genotoxic stress, yet was completely inert with respect to the ATM-Chk2 branch (5). To rule out the possibility, as evoked by the reviewer, that the oleate-induced DDR phenomena we describe relate to these other events, we have now explored the response of the ATR-Chk1 branch when comparing the response of zeocin-treated cells that have been pre-loaded or not with oleate. We observe that the ATR-Chk1 branch is unaltered by oleate loading. Thus, we can now propose that the PI(4)P branch exclusively modulates the ATM-Chk2 axis.

      3). Does Oleate treatment influences Rad53 protein levels in addition to its phosphorylation that affect DNA damage response may be addressed.

      Exponential cultures from three different WT, three different ste∆ and three different yeh2∆ strains have now been taken and pre-loaded for 2 hours with 0.05% oleate, then total levels of Rad53 (without induction of DNA damage) assessed. We can now formally say that basal levels of Rad53 protein are not altered by this incubation. We will include this control in the revised manuscript.

      4). Does Yeh2 deletion reduces LDS should be checked.

      We frequently use yeh2∆ cells in our studies. In particular, we have recently published work characterizing the phenotype of this strain with respect to the formation of lipid droplets in the nucleus (6). We are currently exploiting those same sets of data to quantify the total number of LD in order to satisfy the reviewer’s concern.

      5). Figure 4D representation should show % of phospho reduction of initial activation and a better western blot image should be shown that show equal loading of samples.

      We are currently repeating these gels and blots for the sake of clarity, as requested.

      6). In immunoprecipitation experiments, kindly include isotypee IgG controls as well to rule out non-specificity.

      Of course, this important control will be included every time.

      Minor points: 1). Figure S1F do not show oleate treatment as presented in results section.

      We will revise the accurate naming.

      2). A better gel for S4B should be presented with ponceau of the same gel.

      We are currently repeating this gel and associated blot for the sake of clarity, as requested.

      3). Nuclear PI4Ps has also been previously reported, an explanation to the specific interaction of ATM and PI4P in the Golgi should be addressed/discussed.

      We take it that the reviewer is referring here to the recent work by Fáberová et al (7) in which PI(4)P and PI(4,5)P were described as very dynamic in the nucleus, and mostly related then to mRNA transcription, splicing and export. We will reinforce the connection of our phenomenon to the Golgi-associated pool of PI(4)P thanks to the co-immunoprecipitation experiments proposed above, and will timely contextualize these in light of the paper by Fáberová and co-workers in the revised version. Thank you for reminding us of this work.

      Reviewer #2 (Significance (Required)):

      The current work definitely adds a layer in our understanding to ATM regulation and cross-talk between different PIKK family of kinases. ATM localisation in extra nuclear regions of a cell has been described earlier with significant impact on cell physiology such as mitochondria etc., ATM retention at golgi and limiting nuclear ATM levels is significant advance at ATM activity regulation, while signifying non canonical function of PI4P.

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

      Summary:

      In this manuscript, the authors propose that ATM/Tel1 signaling is regulated in a spatiotemporal manner during genotoxic stress both in yeast and mammalian cells. They show that Lipid droplets accumulate in response to genotoxic stress. As a consequence, there is a decrease of exchange of PI4P from the Golgi to ER, thus dampening ATM/Tel1 signaling by sequestering this kinase into the Golgi. The authors combined findings in yeast and mammals showing that this mechanism is conserved throughout eukaryotes. For this purpose, they use a vast number of techniques that support their proposed model.

      Major comments:

      The conclusions were made based on evidence combining yeast genetics, immunofluorescence, DNA end resection analysis and pharmacological interventions. The hypothesis that ATM is kept away from the nucleus by physically interacting with PI4P at the Golgi, thus allowing processive repair is bold and contributes for a better understanding of the choreography of the DDR kinases during DSB repair. However, many of the experiments in yeast and mammals show only mild phenotypes and there is no evidence that this mode of ATM dampening impact cell viability in mammals.

      We agree with the reviewer that the effects associated to the reported phenomenon are indeed mild. This is a fact. We would like to remind that the metabolism of sterols is finely controlled, and at many different levels, in a very complex manner. For example, sterol increases in the cell will immediately be compensated by reduced synthesis, while synthesis inhibition will immediately promote uptake from the medium, and/or release from stores (for example, see (8)). As a natural consequence, the window of manipulation and, more importantly, the strength of the phenotypes we can uncover are small.

      Therefore, I have some comments and suggestions of experiments that I think could improve the quality of the manuscript. I believe that most of these new experiments does not require much time and resources.

      • Does oleate treatment in RPE-1/Huh-7 cells induce loss of viability? An experiment showing loss of viability like MT-assay or decreased cell proliferation would reinforce the importance of the mechanism proposed.

      This experiment was already included in the previous version, yet it may have escaped the attention of the reviewer. We show in Figure S2E that oleate treatment restricts viability in Huh-7 cells alone, and also worsens their tolerance to zeocin. Perhaps we should reconsider moving this result to the main figures so that it does not go unnoticed.

      • In yeast there is evidence that a ste delta strain show sensitivity to zeocin/CPT, but there is no experiment showing the same effect on cells lacking Yeh2. Since both strains share similar phenotypes, it would be interesting to show that increased kinetics of Rad53 signaling leads to sensitivity to genotoxins.

      We have now performed this experiment, we will include the matching information for yeh2∆ cells, which agrees with the predictions.

      • The conclusion that ste delta cells exposed to zeocin leads to unproductive events due to defects in DNA-end resection could be reinforced by a decrease in Rad52 foci. It has been previously shown by the group of Dr. Marcus Smolka, that inhibition of DNA-end resection decreases Rad52 foci (https://doi.org/10.1083/jcb.201607031). Since the authors were able to monitor Rad52-YFP (Figure S1A), it shouldn't consume time and resources.

      The reviewer is right that this experiment should not be time- or resources-consuming. We will evaluate the accumulation of Rad52 foci in response to the concerned genotoxin in ste∆ cells.

      • Since the authors propose that there is a DNA repair defect due to inhibition of long-range DNA-end resection, it would be important to monitor gamma-H2A(X) signal either in yeast or mammals.

      Taking into consideration the reviewer’s suggestion, we have now performed anti-yH2AX immunofluorescence of all the implied conditions (genotoxins +/- oleate pre-load) and will quantify them to answer the concern.

      • How do the authors exclude the possibility that yeast mutants or oleate treatment in yeast/mammalian cells change membrane permeability allowing an increase in genotoxin concentration?

      Although this is a very reasonable criticism, we want to remind the data we present in Figure S4A in which we use the naturally DSB-bearing rad3-102 cells for recombination analyses, showing that, in the absence of any genotoxin, the same phenotype also applies. Yet, we want to reinforce the notion that LD formation in response to DSB can also occur when the breaks are not chemically, but physically, induced. To this end, and also to match a related request by Reviewer 1, we will:

      1. a) exploit the naturally DSB-accumulating mutant rad3-102 (4) to evaluate whether it endogenously harbors more LD in comparison with the WT.
      2. b) we have recently created a tool in which gRNAs targeted to different subsets of transposons in the genome can drive Cas9 to create DSB in a dose-dependent manner ((9), under revision in Genetics). We will use this system to monitor the LD formation in response to Cas9-triggered cuts. In addition, on figure 5A, significant differences in GFP-Tel1 foci abundance between WT and steD or yeh2D cells are only observed after 210', way after the slight effect on Rad53 phosphorylation is observed. This is at odds with the conclusion that Tel1 association to STEs modulates DDR signaling.

      We are afraid that we have not been clear enough in explaining the kinetics giving rise to our model. As indicated by the reviewer, our work shows, through kinetic studies, that the storage of sterols within LD occurs at later stages than the activation of the DDR by Tel1 and Rad53 phosphorylation. Tel1 foci decline is necessary for subsequent engagement of downstream DNA long-range resection. Since we propose that sterol storage within LD is a means to attenuate Tel1 engagement at DSBs, it is thus logical (and thus compatible with the data we show) that LD number increase occurs simultaneously with Tel1 foci decrease, at late stages of the reactionWe will include this explanation and graph in the revised version of the work.

      • It would be interesting to investigate genetic interactions between ste delta (or yeh2delta) and yeast mutants with DNA-end resection problems (exo1delta; sae2delta). For instance, it has been shown that Sae2 antagonizes checkpoint signaling by competing with Rad9 to DSB sites (https://doi.org/10.1073/pnas.1816539115). Also, cells lacking Sae2 show an increase in Rad53 signaling due to increased Tel1 Signaling. Therefore, an epistatic effect between these two pathways would reinforce the hypothesis of the manuscript.

      we will build the double mutant sae2∆ yeh2∆ and assess the potential epistatic behavior they may display with respect to some key phenotypes (Tel1 foci formation, Rad53 phosphorylation…).

      • The authors showed that Tel1-GFP does not accumulate in the nucleus in cells lacking Sac1 (Figure 7C). Tel1 is important to cope with increased DSBs in the absence of Mec1, thus avoiding genomic instability. Cells lacking both Mec1 and Tel1 show a sick phenotype with an exponential increase in gross chromosomal rearrangements and sensitivity to genotoxins. Therefore, does deletion of Mec1 (and Sml1) in sac1 delta phenocopies a mec1tel1 delta? Alternatively, does pharmacological inhibition of ATR in the presence of the OSBP1 inhibitor causes loss of viability or chromosomal aberrations?

      We will delete SAC1 in mec1∆ sml1∆ and compare the fitness, through growth drop assays, with respect to the mutant tel1∆ mec1∆ sml1∆.

      We will expose cells either to OSBP1 inhibitor, ATR inhibitor, or both, and assess the phosphorylation of their downstream common effector H2AX. Additionally, we will assess the effect on cell growth of the combination of ATRi and OSBP1i using synergy matrices. We will determine if the combination of both drugs synergizes or not to impair cell proliferation and reduce cell viability.

      • Finally, it seems strange to me that ATR/Mec1 signaling is not mentioned throughout the entire manuscript. Does PI4P pathway affect only ATM/Tel1? In Figure 2D, an antibody against phospho-CHK1 could be used to monitor ATR signaling. In line with that, I would like to see in the discussion how these new findings are in line with evidence from a 2019 paper showing that phophoinositides PIP2 and PIP3, but not PI4P are important for ATR signaling (DOI: 10.1038/s41467-017-01805-9). They showed that a nuclear pool of PIP2 increases upon DNA damage induction and rapidly accumulates at DNA lesions. This event is important for the recruitment of ATR. Since PI4P is substrate for PIP2 synthesis and there is a nuclear pool of PI4P and PIP2, I think it is important to discuss if the results presented here are in line with these previous findings.

      The reviewer evokes recent work by Dr Michael Sheetz’s lab demonstrating that a different set of phosphoinositides serves to drive the nucleation and activation of the ATR-Chk1 branch of the DNA Damage Response upon genotoxic stress, yet was completely inert with respect to the ATM-Chk2 branch (5). We have now explored, also to satisfy a similar concerned raised by Reviewer 2, the response of the ATR-Chk1 branch when comparing the response of zeocin-treated cells that have been pre-loaded or not with oleate. We observe that the ATR-Chk1 branch is unaltered by oleate loading. Thus, we can now propose that the PI(4)P branch exclusively modulates the ATM-Chk2 axis.

      We will of course give the needed credit to this work and contextualize our findings accordingly.

      Minor comments:

      • Line 124: The correct is Figure S1E, lower panel and not Figure S1F -Lines 127-128: Figure S2A does not show zeocin treatment

      Both minor mistakes will be corrected.

      Reviewer #3 (Significance (Required)):

      Together, these new findings, if corroborated by others, might be important to open new lines of investigation in basic and translational research regarding human diseases as explored in the discussion section. I believe this paper will attract attention not only from the DDR field but also from other areas of research such as nutrient and lipid signaling both in yeast and mammals. I hope I was able to collaborate in this review, since my main expertise is in the area of DNA damage signaling using budding yeast as an organism model.

      Reviewer #4 (Evidence, reproducibility and clarity (Required)):

      This is a very interesting study where Sara et al. demonstrated a link between lipid metabolism with DNA repair response (DDR). In this study, they have proposed ATM as a novel PI4P-effector. The sterol deposition into lipid droplets impacts the Golgi PI4P level due to lipid exchange machinery facilitated by OSBP1, therefore regulating the cytosolic retention of ATM due to PI4P binding. Although how lipid droplets in the cytosol sense the DNA damage and control the initiation of DDR by regulating ATM is still unclear, this study linked lipid biology/PI signaling to DNA damage repair and showed the evolutionary conservation of PI signaling and DNA repair machinery from yeast to humans. The experiments are well designed, nicely controlled, with a high quality of data presentation. With some improvements, this work could be a very interesting study attracting a broad readership.

      In their model, ATM is PI4P-bound and sequestered inside the cytosol under basal conditions. Upon genotoxic stress, activation of OSBP1 removes PI4P and free PI4P-bound ATM for nuclear translocation of DNA repair. This could also be interpreted as genotoxic stress-induced PIP-kinase activity, where PI4P is processed into PIP2 or PIP3, somehow redirecting ATM into the nucleus to initiate its activation for DDR. Those aspects should be discussed and improved.

      Both Reviewers 2 and 3 have somehow evoked a similar concern. More precisely, the work by Dr Michael Sheetz’s lab demonstrating that a different set of phosphoinositides serves to drive the nucleation and activation of the ATR-Chk1 branch of the DNA Damage Response upon genotoxic stress, yet was completely inert with respect to the ATM-Chk2 branch (5). We have now explored, to satisfy all reviewers’ concerns, the response of the ATR-Chk1 branch when comparing the response of zeocin-treated cells that have been pre-loaded or not with oleate. We observe that the ATR-Chk1 branch is unaltered by oleate loading. Thus, we can now propose that the PI(4)P branch exclusively modulates the ATM-Chk2 axis.

      Additionally, we will of course give the needed credit to this work and contextualize our findings accordingly.

      Upon stress, there is nuclear activation of p53-phosphoinositide (PI) signalosomes and PIP-kinases. Also, there is a significant PIP2 pool inside the nucleus with an involvement in DNA damage repair. Those papers and their relevance to the current study need to be discussed. If ATM is a novel PI4P-effector, there is also nuclear PI4P formation or nuclear PI4P accumulation upon stresses based on recent studies; how the ATM interacts with PIPn in the nucleus upon translocation? A know ATM substrate p53 is PIP2/PIP3 bound in the nucleus based on recent studies. Will ATM prefer to interact with other PIPn-bound proteins in the nucleus or PIPn regulate their interaction needs to be discussed.

      These additional notions are in line with the previous paragraph presented by the reviewer, and our answers too. We will provide a constructive overview of all these ideas in the revised version of the manuscript.

      Major points: 1. The PI4P-ATM complex is supported only by PLA and PIP strips. Need more robust biochemical characterization of the interaction: co-IP, lipid binding, and/or in vitro constitution.

      We agree with the need to perform assays in which PI(4)P is embedded in a bilayer, as to confidently assess whether Tel1 can bind it in that context. We have now performed a pilot experiment in which we have confronted purified FLAG-Tel1 to liposomes harboring PI(4)P. Western blot analysis using anti-FLAG antibody shows the encouraging result that FLAG-Tel1 can be found there. As a control, we have performed the same process but in the absence of any liposomes. We observe that a residual fraction of FLAG-Tel1 can nevertheless be found in this control, most probably because the buffer used during the liposome assay makes part of FLAG-Tel1 precipitate.To avoid this type of background and to increase our trust in the results, we propose to perform the liposome assay but on a discontinuous density gradient, so that liposomes will be retrieved in the top layer (and bound FLAG-Tel1 with them, if that is the case), while any precipitated FLAG-Tel1 will be in the bottom fraction (liposome floatation assay). As a further control, we will include the same liposomes but lacking PI(4)P. We expect to be successful in the floatation assays. If we are not, we will repeat the experiment presented above to be confident that the observed increase is reproducible.

      1. The use of drug inhibitors only in the final figure is problematic. KD or KO experiments should be performed to confirm that ATM and the exchanger are the relevant targets.

      We have now used siRNAs against the exchanger protein, OSBP1, with a very high silencing rate success. We have next monitored the activation status of the chromatin-associated ATM target KAP1, in order to monitor the predicted decrease of ATM activity specifically inside the nucleus. Our results confirm the role of OSBP1, by KD experiments as requested by the reviewer, in attenuating ATM nuclear participation.

      1. Poor quality of some WBs (e.g Fig. S1F).

      We have now repeated the Western Blot to detect Rad53-P in response to 20 mM HU in WT versus ste∆cells.

      1. Lack of statistical analyses for some data (e.g. Fig. 1B-E)

      We had already included, in the previous version, the complete statistical analyses corresponding to Figures 1B to E and evoked here by the reviewer. They were indeed included in Figure S1C, and our brief reference to them in the text may have escaped her/his attention. We will make a clear reference to this in the revised version.

      Additional clarification points:

      Figure 1: No representative images were shown for quantifications in Figure 1C, D, E.

      If the reviewer / editor estimates it pertinent, we can of course include them. Yet, they will be very redundant with the images displayed in Figure 1A.

      Line 121: Should be Figure S1E, upper panel. Line 124: Should be Figure S1E, lower panel. Figure 2D-E, please show the quantification of the ratio of pCHK2/CHK2 with an N=3

      We will correct / include the requested changes.

      Figure S2B: needs quantification of NileRed staining to conclude induction in LD formation

      We will quantify the LD as requested.

      Figure 3C, to show the selectivity of ATM-binding toward PI4P, PLA of ATM with other PIPn species should be assessed, such as PI3P, PI4,5P2, and PI3,4,5P3.

      We have provided an overview of the binding preferences of ATM with respect to the full battery of phosphoinositides in the strip-binding assay shown in Figures S5C and 6B. Other than that, we are afraid that PLA studies as the ones we develop in the current manuscript for PI(4)P are not feasible, since no reliable antibodies exist for most of the phosphoinositide species evoked by the reviewer.

      Figure S6A, PI4P level could be assessed by IF staining using PI4P antibody besides using PI4P sensor.

      We will use our PI(4)P antibody to monitor by immunofluorescence the behavior of this molecule in response to either MMS or zeocin, as suggested.

      References

      1. Cheung HC, San Lucas FA, Hicks S, Chang K, Bertuch AA, Ribes-Zamora A. An S/T-Q cluster domain census unveils new putative targets under Tel1/Mec1 control. BMC Genomics. 2012;
      2. Bensimon A, Schmidt A, Ziv Y, Elkon R, Wang SY, Chen DJ, et al. ATM-dependent and -independent dynamics of the nuclear phosphoproteome after DNA damage. Sci Signal. 2010;
      3. BastosdeOliveira FM, Kim D, Cussiol JR, Das J, Jeong MC, Doerfler L, et al. Phosphoproteomics Reveals Distinct Modes of Mec1/ATR Signaling during DNA Replication. Mol Cell. 2015;
      4. Moriel-Carretero M, Aguilera A. A Postincision-Deficient TFIIH Causes Replication Fork Breakage and Uncovers Alternative Rad51- or Pol32-Mediated Restart Mechanisms. Mol Cell. 2010;37(5):690–701.
      5. Wang YH, Hariharan A, Bastianello G, Toyama Y, Shivashankar G V., Foiani M, et al. DNA damage causes rapid accumulation of phosphoinositides for ATR signaling. Nat Commun. 2017;
      6. Kumanski S, Forey R, Cazevieille C, Moriel-Carretero M. Nuclear Lipid Droplet Birth during Replicative Stress. Cells. 2022;11(1390).
      7. Fáberová V, Kalasová I, Krausová A, Hozák P. Super-Resolution Localisation of Nuclear PI(4)P and Identification of Its Interacting Proteome. Cells. 2020;9(5):1–17.
      8. Luo J, Yang H, Song BL. Mechanisms and regulation of cholesterol homeostasis. Nat Rev Mol Cell Biol [Internet]. 2020;21(4):225–45. Available from: http://dx.doi.org/10.1038/s41580-019-0190-7
      9. Coiffard J, Santt O, Kumanski S, Pardo B, Moriel-Carretero M. A CRISPR-Cas9-based system for the dose-dependent study of 4 DNA double strand breaks sensing and repair 5 6. bioRxiv [Internet]. 2021;1–37. Available from: https://doi.org/10.1101/2021.10.21.465387.
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      Comparaciones

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    1. 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

      1. General Statements

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

      1. Description of the planned revisions

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

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

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

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

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

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

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

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

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

      Authors’ responses to the reviewers' comments

      Reviewer #1

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

      Reviewer #1 (Significance (Required)):

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

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

      Reviewer #2

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

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

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

      Major comments

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

      Reviewer #2 (Significance (Required)):

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

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

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

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

      Reviewer #3

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

      Reviewer #3 (Significance (Required)):

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

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

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

    1. ② ③ ④ ⑤ ⑥ ⑦ ⑧ ⑨ ⑩ ⑪ ⑫ ⑬ ⑭ ⑮ ⑯ ⑰ ⑱ ⑲ ⑳ ⑴ ⑵ ⑶ ⑷ ⑸ ⑹ ⑺ ⑻ ⑼ ⑽ ⑾ ⑿ ⒀ ⒁ ⒂ ⒃ ⒄ ⒅ ⒆ ⒇ ⒈ ⒉ ⒊ ⒋ ⒌ ⒍ ⒎ ⒏ ⒐ ⒑ ⒒ ⒓ ⒔ ⒕ ⒖ ⒗ ⒘ ⒙ ⒚ ⒛ ⒜ ⒝ ⒞ ⒟ ⒠ ⒡ ⒢ ⒣ ⒤ ⒥ ⒦ ⒧ ⒨ ⒩ ⒪ ⒫ ⒬ ⒭ ⒮ ⒯ ⒰ ⒱ ⒲ ⒳ ⒴ ⒵ Ⓐ Ⓑ Ⓒ Ⓓ Ⓔ Ⓕ Ⓖ Ⓗ Ⓘ Ⓙ Ⓚ Ⓛ Ⓜ Ⓝ Ⓞ Ⓟ Ⓠ Ⓡ Ⓢ Ⓣ Ⓤ Ⓥ Ⓦ Ⓧ Ⓨ Ⓩ ⓐ ⓑ ⓒ ⓓ ⓔ ⓕ ⓖ ⓗ ⓘ ⓙ ⓚ ⓛ ⓜ ⓝ ⓞ ⓟ ⓠ ⓡ ⓢ ⓣ ⓤ ⓥ ⓦ ⓧ ⓨ ⓩ ⓪ ⓫ ⓬ ⓭ ⓮ ⓯ ⓰ ⓱ ⓲ ⓳ ⓴ ⓵ ⓶ ⓷

      ② ③ ④ ⑤ ⑥ ⑦ ⑧ ⑨ ⑩ ⑪ ⑫ ⑬ ⑭ ⑮ ⑯ ⑰ ⑱ ⑲ ⑳ ⑴ ⑵ ⑶ ⑷ ⑸ ⑹ ⑺ ⑻ ⑼ ⑽ ⑾ ⑿ ⒀ ⒁ ⒂ ⒃ ⒄ ⒅ ⒆ ⒇ ⒈ ⒉ ⒊ ⒋ ⒌ ⒍ ⒎ ⒏ ⒐ ⒑ ⒒ ⒓ ⒔ ⒕ ⒖ ⒗ ⒘ ⒙ ⒚ ⒛ ⒜ ⒝ ⒞ ⒟ ⒠ ⒡ ⒢ ⒣ ⒤ ⒥ ⒦ ⒧ ⒨ ⒩ ⒪ ⒫ ⒬ ⒭ ⒮ ⒯ ⒰ ⒱ ⒲ ⒳ ⒴ ⒵ Ⓐ Ⓑ Ⓒ Ⓓ Ⓔ Ⓕ Ⓖ Ⓗ Ⓘ Ⓙ Ⓚ Ⓛ Ⓜ Ⓝ Ⓞ Ⓟ Ⓠ Ⓡ Ⓢ Ⓣ Ⓤ Ⓥ Ⓦ Ⓧ Ⓨ Ⓩ ⓐ ⓑ ⓒ ⓓ ⓔ ⓕ ⓖ ⓗ ⓘ ⓙ ⓚ ⓛ ⓜ ⓝ ⓞ ⓟ ⓠ ⓡ ⓢ ⓣ ⓤ ⓥ ⓦ ⓧ ⓨ ⓩ ⓪ ⓫ ⓬ ⓭ ⓮ ⓯ ⓰ ⓱ ⓲ ⓳ ⓴ ⓵ ⓶ ⓷

    1. PMFs of DISTRIBUTIONS

      x is the random variable in question.

      • Bernoulli(p): Indicator variable, which is 1 if the outcome occurs (e.g. heads) and 0 if not.

      • Geometric(x, p) = probability that you need to repeat the Bernoulli trial x times to get a success

      • Binomial(x, n, p) = probability that in n Bernoulli trials you get x successes.

      • Pascal(x, m, p) = probability that you need to repeat a Bernoulli trial x times to get m successes. This is a generalisation of the Geometric dist., where Pascal(x,1,p) = Geometric(x,p)

      • Hypergeometric(x,b,r) = the probability that you get x blue balls in a sample of k red and blue balls.

      • Poisson(x,L) = the probability that you get x events when you would normally get L.

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

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      Reply to the reviewers


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

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

      Major comments:

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

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

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

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

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

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

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

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

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

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

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

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

      • Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation. As noted above, further tests of RapZ mutants for RNA binding would be useful; if this has been done previously, needs to be presented.

      Our reply.

      This has been addressed in the response above.

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

      Our reply.

      Please see response to this point above.

      • Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments. Yes, they are. They are generally extrapolations from what is already in the paper or in previous studies by these groups.
      • Are the data and the methods presented in such a way that they can be reproduced? Yes.
      • Are the experiments adequately replicated and statistical analysis adequate? Yes.

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

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

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

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

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

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

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

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

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

      Our reply. We have simplified the text.

      Our reply:

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

      Reviewer #1 (Significance (Required)):

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

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

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

      • Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. Our expertise is in RNA-based regulation and microbial genetics; we are not able to critically evaluate the cryoEM analysis itself.

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

      Summary:

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

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

      Major comments:

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

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

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

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

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

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

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

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

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

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

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

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

      We will provide lane numbers, accordingly.

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

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

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

      We have changed the figure to make it clearer.

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

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

      Reviewer #2 (Significance (Required)):

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

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

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

      **Referees cross-commenting**

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

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

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

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

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

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

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

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

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

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

      Affinities were determined previously using complimentary techniques:

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

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

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

      We have changed the spelling to “stabilized”.

      The legends for Figure S2 c and d are reversed.

      This has now been corrected.

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

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

      Reviewer #3 (Significance (Required)):

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

    1. Author Response

      Reviewer #2 (Public Review):

      The authors argue that xgO secretes Spi and Col4a1 to induce MAPKdependent L5 differentiation. However, no loss-of-function condition for these putative ligands was tested. Since they speculated that expression of Spi and Col4a1 alone may not lead to a sufficient level of MAPK activity, the results of their loss of function conditions have to be included in the paper.

      We agree with Reviewer #2 completely. Our manuscript now includes spi and Col4a1 loss-of-function data specifically in xgO, which has strengthened our manuscript considerably and allows us to draw stronger conclusions as to the roles of Spi and Col4a1.

      The authors found ectopic L5 neurons when apoptosis was repressed (Fig. 1). It is likely that cells that fail to differentiate to L5 are removed by apoptosis, but this link was not clearly demonstrated in the paper. As a result, there is a gap between the data in Fig. 1 (section 1 in the text) and the other part of the paper. The relationship between Fig. 1 and the other data should be carefully discussed. In my opinion, the first section of Results should be moved after the last section so that the results of Fig. 1 are explained as a potential mechanism to remove cells that failed to differentiate to L5.

      We have restructured the manuscript as suggested.

      Reviewer #3 (Public Review):

      There is considerable overlap with Fernandes et al 2017 Science paper: (1) That EGFR signalling is required for L5 neuron survival had been shown in their Fernandes et al 2017 Science paper, as over-expression of p35 rescued apoptosis caused by EGFRDN. Now, using Dronc mutants in the current manuscript is an equivalent experiment. (2) In Fernandes et al 2017 Science, they over-express activated MAPK in lamina neurons (Fig.1G), and in the current, they over-express its target Pnt-P1 (Fig.1I) - equivalent experiment. (3) Figure S1 reports Lamina>MAPKACT rescues Bsh and Spl2 positive neurons. These data are similar to those reported in Fernandes et al 2017 Science, where they showed the rescue of lamina neurons with this same genotype. (4) rho3 mutants cannot secrete Spi and L1-4 cannot differentiate and only a few L5 do (Fernandes et al 2017 Science), they then rescued this phenotype including L5s by over-expressing EGFRACT or Ras in wrapping glia (Figure 2F-I). With the submitted manuscript, they rescue with rho3 overexpression in photoreceptors - genetically different, but rather similar, as together they demonstrate that rescue of L5 requires rho or spi. These close similarities reduce the appeal and novelty of the current manuscript.

      We agree with the reviewer that our previous work established that MAPK signalling was necessary and sufficient to drive premature neuronal differentiation in the lamina. Therefore, we have removed the data related to this point, which were previously contained in Figure S3A-C of our prior submission; namely laminats>AopACT and DroncI24; UAS-AopACT MARCM clones.

      However, this manuscript makes substantially different points from the previous paper regarding the roles of EGFR activity and survival. Although Fernandes et al., (2017) did show that lamina neurons differentiated prematurely in lamina>MAPKACT, here we evaluate apoptosis and lamina neuron sub-type identities and show that the ‘extra’ LPCs do not die but differentiate into L5s under these conditions. This is a key message of our manuscript and was not evaluated nor reported before. Additionally, the Dronc mutants used here reveal that preventing apoptosis is not sufficient to drive differentiation of the additional LPC in each column, addressing a different point and not simply reproducing prior data showing the EGFR promotes LPC survival.

      Similarly, we previously established that photoreceptor-derived Spi was received by wrapping glia, the involvement of photoreceptor-Spi and L5 differentiation had not been thoroughly explored and the involvement of xgO is novel.

      Establishing the cells expressing spi, argos, Col41a and Ddr is key to supporting the hypothesis. The authors claim that they confirmed the best screen candidates by testing their expression using enhancer trap lines. What is the evidence that these enhancer trap reporters reproduce the endogenous expression patterns of these genes? A description of their location in the loci and potential drawbacks should be provided and discussed.

      We now clarify whether enhancer traps used in our study were validated previously and provide in situ hybridization chain reaction data where enhancer traps were not previously validated.

      Fig.4A and Fig.S3K do not demonstrate that aos-lacZ and Ddr-lacZ are in L5 neurons, and showing this with Bsh and Spl2 as they do for other data would support the claim that L5 neurons receive Col4a1 and distal L5 neurons can receive aos.

      We use L5 specific markers with aoslacZ. For Ddr-Gal4>UAS-lacZ the entire lamina was labelled, and we provide new data showing Ddr expression by in situ hybridization chain reaction to show that it is expressed throughout the lamina.

      Fig.S3M uses HCR in situ to show that spi mRNA is found in xg{degree sign} glia. However, the given images are not convincing. Since in situs detect mRNA, wouldn't the nuclear signal correspond to two sites of transcription, whereas a more abundant signal would be expected in the cytoplasm? Instead, the nucleus contains as many spots as the surrounding background and there is no clear signal in the cytoplasm. The authors must provide separate channels and convincing evidence that spi mRNA is present in xg{degree sign} glia or remove/weaken the claim (ie use only the GAL4 evidence).

      We have understood that the main concern around the spi HCR included in our manuscript relates to the fact that the signal detected in the nucleus was more abundant than just two puncta as would be expected from two sites of transcription.

      The reviewers are correct that only two puncta corresponding to active sites of transcription would be expected in the nucleus when detected by single molecule FISH (smFISH). However, here we are not using smFISH but HCR with maximal amplification. This results in signal proportional to the relative abundance of transcripts (Choi et al., 2018; Trivedi et al., 2018) and as such all transcripts, including those moving away from the transcription site in the nucleus, are also detected by this method. Other groups who have used this method also report the same (Andrews et al., 2020; Duckhorn et al., 2022; Schwarzkopf et al., 2020; Zhuang et al., 2020). We used this form of HCR over single molecule HCR (smHCR or digital-HCR), which uses limited amplification (Trivedi et al., 2018), as these other methods require diffraction-limited spot detection, which would be very challenging in our system.

      We apologise for not explaining the HCR protocol sufficiently and have included more details in the Materials and Methods.

      In addition to using HCR to detect spi expression in xgO in controls and when EGFR signalling is blocked in xgO, we now also provide new data to show Col4a1 and Ddr expression using HCR, to lend support to enhancer traps that were not validated previously. We found that both spi and Col4a1 expression in xgO decreased when EGFR signalling was blocked in xgO and provide single channel images in Figure 3 – figure supplement 1.

      With this clarification, we hope the reviewers will reconsider the inclusion of these data as we feel it is important to show that xgO express these ligands in an EGFR signalling-dependent manner, especially in light of the spi and Col4a1 loss-of-function data detailed above. Nonetheless, if the reviewers still feel that these data should be removed from the manuscript, we will be happy to do so.

      Involvement of Spi does not seem to have been entirely unresolved. They show that over-expression of rho3 in photoreceptors in rho 3 mutants rescued L5 neurons, suggesting that Spi from photoreceptors can rescue L5 neurons. As this is slightly different from what they saw before, what is the penetrance of these phenotypes? These phenotypes have not been quantified (other than providing sample size) and the incomplete penetrance of phenotypes could explain both observations.

      Spi secreted from photoreceptor axons is insufficient to induce L5 neuronal differentiation directly as it is unable to do so when EGFR signalling is blocked in xgO (Figure 1F,H, Figure 1 – figure supplement 1N). Therefore our results argue that xgO are a critical mediator of photoreceptor signals. Since restoring rho3 expression in photoreceptors in rho3 background rescues neuronal differentiation of all lamina neurons, these results imply that the signalling relays through both wrapping glia and xgO have been reactivated.

      We have quantified of the number of L5s per column in rho3 heterozygotes, rho3 homozygotes and in rho3 homozygotes when rho3 expression was restored in photoreceptors only (Figure 3C). Importantly, compared to rho3 heterozygotes, the number of L5s per column in rho3 homozygotes was significantly reduced (Figure 3C; one-way ANOVA with Dunn’s multiple comparisons test with rho3/-; GMR as control; P****<0.0001), whereas they were fully rescued in rho3; GMR>rho3 (Figure 3C; one-way ANOVA with Dunn’s multiple comparisons test with rho3/-; GMR as control; P>0.05).

      They claim that whereas L5 neurons are lost in xg{degree sign}>EGFRDN over-expressing glia, concomitant over-expression of Spi rescues L5 neurons. Also, over-expression of spi with xg{degree sign}>spi clearly results in ectopic L5 neurons. However, in Fig.3P they show rescue with membrane-tethered m.spi and not secreted s.spi. Why was secreted s.spi not used instead? How does membrane-tethered spi from glia reach to rescue distal L5 neurons?

      Spi is initially produced as an inactive transmembrane precursor (mSpi) that needs to be cleaved into its active secreted form (sSpi) (Tsruya et al., 2002). This requires the intracellular trafficking protein Star and Rhomboid proteases (Tsruya et al., 2002; Urban et al., 2002; Yogev et al., 2008). mSpi thus represents wild-type (unprocessed) Spi. Whereas misexpression of sSpi results in secretion of active Spi from any cell type, misexpression of mSpi results in secretion of active Spi only from cells capable of processing mSpi to sSpi.

      Thus, mis-expressing mSpi to rescue L5 neurons in the xgO>EGFRDN background also demonstrates that xgO are capable of processing mSpi into sSpi, which is a more stringent experimental condition and gives us more confidence in our results. We also performed these experiments with sSpi and observed an equivalent and statistically significant rescue (included in the quantifications in Figure 3 – figure supplement 1C). We have also clarified the use of these reagents in the text as follows:

      Page 6, lines 166-168:

      “Spi is initially produced as an inactive transmembrane precursor (mSpi) that needs to be cleaved into its active secreted form (sSpi) (Tsruya et al., 2002). This requires the intracellular trafficking protein Star and Rhomboid proteases (Tsruya et al., 2002; Urban et al., 2002; Yogev et al., 2008).”

      And Page 8, lines 221-223:

      “Note that expressing either sSpi or wild-type (unprocessed) mSpi (referred to as Spiwt) in xgO rescued L5 numbers (Figure 3 – Figure supplement 1C), indicating that xgO are capable of processing mSpi into the active form (sSpi).”

      To support the involvement of spi in promoting survival of proximal L5 in wildtype, a loss of function experiment would be required e.g. xg{degree sign}>spi-RNAi, and visualise apoptosis with Dcp1 and remaining L5 neurons.

      We knocked down spi and Col4a1 simultaneously in xgO and observed a statistically significant decrease in the number of L5 neurons relative to controls (Figure 3T-W and Figure 3 – figure supplement 2A-B). Under these conditions we also observed Dcp1 positive cells in the most proximal row of the lamina, which were never observed in controls. Thus, suggesting that Spi and Col4a1 promote L5 neuronal differentiation and survival.

      Quantifications are incomplete in places and statistical analysis is incorrect in places. For genotypes that are not quantified in graphs (ie cell number), sample sizes have been provided, but phenotypic penetrance has not (Fig.1F dronc-/-; Fig.2K, L rho3 and rescue) and this is required to report variability.

      We apologise for these omissions. We have quantified the rho3 mutant and rescue phenotypes. The Dronc mutant phenotype was fully penetrant and we have stated this explicitly in the text.

      Fig.2I, J: A quantification is provided within the text for apoptosis caused by xg{degree sign}>EGFRDN, with 5.93{plus minus}0.18 Dcp1 cells per column (N=19). However, this number alone does not mean much unless it is compared to Dcp1 in wild-type. Apoptosis in wild-type is shown but not quantified in Fig.2I. A comparison of Dcp1 counts in control and xg{degree sign}>EGFRDN is required and validated with statistical analysis.

      We thank the reviewer for pointing out this mistake. We have now added the graph to the figure (Figure 2D) and have stated this explicitly in the text as follows:

      Page 5-6, line 151-156 (Figure 2D):

      “We used an antibody against the cleaved form of Death Caspase-1 (Dcp-1), an effector caspase, to detect apoptotic cells (Akagawa et al., 2015) and, indeed, observed a significant increase in the number of Dcp-1 positive cells in the lamina when EGFR signalling was blocked in the xgO (132.8 cells/unit volume ± 19.48 standard error of the mean) compared to controls (49.14 cells/unit volume ± 4.53) (Figure 2A-B, 2D, P<0.0005, Mann-Whitney U Test).”

      Fig.S3L, P: authors claim that over-expression of spi in xg{degree sign}>EGFRDN does not rescue nuclear dpMAPK in xg{degree sign}, but it does in L5 neurons. However, the quantification of these data in Fig.S3L shows that nuclear:cytopl dpMAPK levels are not statistically significantly different from xg{degree sign}>EGFRDN. No evidence has been provided of how this single piece of data supports both contradictory claims. The authors must either quantify accurately and separately dpMAPK in xg{degree sign} glia and L5 neurons - it is unclear how this could be done from the data provided - or remove or modify the claim to adjust accurately to the data.

      We have now quantified dpMAPK levels in both xgO and L5s in these conditions.

      Statistical analysis needs revising. It is unclear why they use non-parametric tests throughout, are data always not normally distributed? The use of bar charts, means, and s.e.m. combined with non-parametric tests does not faithfully represent the data, and box plots or other displays (eg volcano or dot plots, etc) that show the distribution would be more appropriate. And multiple comparison corrections are required. For example, if Fig.S3F is a Kurskal Wallis ANOVA (should be, but it is not stated explicitly), then this requires multiple comparison tests to a fixed control (post hoc Dunn test), and the figure legend should provide the p-value for the ANOVA. Fig.3K, P use Mann Whitney test, whereas these graphs have both more than 2 sample types and therefore should be Kruskal Wallis ANOVA (if distributions are not normal, if they are normal they should be One Way ANOVA), and Dunn post hoc comparison to fixed control, box plots, and no s.e.m as above.

      Thank you for flagging that we had not reported our statistical analyses appropriately. We apologise for this and have made sure to explicitly state the statistical test performed for multiple and pairwise comparisons with the Pvalues as detailed by Reviewer 3. These are highlighted throughout the text with track-changes. As well, we have changed all our graphs to box and whisker plots showing the entire distribution of the data as well as the interquartile range, as recommended.

      Much of the data in our manuscript are proportions generated from cell counts and, by definition, are limited to numerical values between 0 and 1 (inclusive). As such, as with count data (i.e. discrete numbers such as from cell counts), parametric statistics are generally inappropriate for proportion data because the data violate assumptions about normality (Douma and Weedon, 2019). Therefore, we used non-parametric tests throughout the manuscript except for Figure 1- Figure Supplement 1R where appropriate assumptions were met..

    1. he widespread adu1t use of ADH Ddrugs is often driven b y the hope o f enhancing one's performance and competitiveness in the workplace - and, moreharshly, methamphetamine addiction is often l inked todestructive delusions about performance and self-aggrandizement

      Dangerous coping mechanisms

  3. drive.google.com drive.google.com
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      When thinking of rhetoric I would often just think of the word rhetorical and not think that persuasion has anything to do with it and the reason as to why I think this is gold nugget information is because It would not have crossed my mind that there are other activities like Business and Education that factor Rhetoric into it but as it continues to state that in education a professor will often have you read material followed by an assignment and that allows for rhetoric to be present.

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      I really felt this part opened my eyes to what the definition of "rhetoric" meant. Persuasion exists in almost every situation whether we know it or not. I find id funny, that it states "...we find it difficult not to persuade." I did not really think that was necessarily true, but when I looked at my relationships and how I communicate within them. I practice this almost everyday within my work and personal life.

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      this was easily my golden nugget for the article since i was amazed by the concept of nonverbal communication since i was a child. of course, i take issue with thinking too deeply into things like this, but i figured that for the most part rhetoric is planned to this level of detail

    4. , e/.alc~i·al i ' a u,t/1.·.~ di· loJJlnent,0 • ded b, tl1'- 1 .ar ligl11o.f th -tmd -- tanding~ o, .tli,- a ·ti ti -n ea, · rlzich a;-- . l, a l (o·L,1dir I nP 1a,

      This quote sums up the meaning of, rhetorical. Therefore, changing the narrative of rhetorical as we once knew to be , ' a question that doesn't require an answer'. To then, become an understanding of the 'unconscious language'.

    1. Reviewer #2 (Public Review):

      This manuscript analyzes if bumblebees choose feeding options based on their absolute or relative remembered subjective value. The experiments relate to previous work done in starlings where comparable questions were raised (1). The design used in the four experiments presented is elegant and provides support for the conclusion that bees guide their choices by remembered ranking of feeders instead of focusing on their absolute rewards. Bees preferred the options that were ranked higher within each experimental context experienced, irrespective of the absolute reward they provided. As a consequence, they even preferred a sucrose solution of low concentration (15%) to one that was more profitable (30%), simply because the former was experienced together with a poorer alternative (10%) while the latter was experienced together with a more attractive alternative (45%) (Exp. 2). All four experiments provide results that are consistent with the hypothesis that contextual ranking is essential to determine the bees' choices.

      Three main points require consideration to render this manuscript even more attractive than what it is already.

      1) The experiments involved in all cases four different colors and different sucrose concentrations (range: 5 - 45 % w/w). An essential requisite of these experiment is that bees should be able to discriminate the options provided, both in terms of color and in terms of reward quality. Asking about ranking or absolute value makes no sense if bees cannot distinguish, say, 15% from 10%, or yellow from orange, and so on. The authors are obviously aware of this point as they mention it explicitly (lines 267-269). Yet, although they mentioned that they verified this point, the only experimental proof available is provided in Fig. Suppl. 4, where a single comparison (from the many possible) was tested; the discrimination test provided involved blue and yellow, which were associated in a balanced way with the two highest sucrose concentrations used, 45% and 30%. In terms of color information, the choice involved the colors that were easy to distinguish (see their loci in the color hexagon). Yet, what about the other colors? Could they be equally well discriminated? Probably not, because some occupied very close loci in the hexagon. Admittedly, the tests B vs. C involved similar colors (yellow vs. orange) and bees showed significant preferences supporting the presence of color discrimination. Yet, no information is available for yellow and green and other color combinations assayed. Even more important would be to show that bees rank the different sucrose solutions differently, which is not clear in all cases. Concentrations were chosen following theoretical considerations based on Weber's law (2), but do bees really respond differently to them? Providing an experimental assessment of this question would be important.

      2) Figures B, D, and F are of fundamental importance to draw conclusions about the strategy used by the bees in the three adjacent experiments. Yet, the kind of representation chosen by the authors does not help to follow their conclusions. Firstly, it is not clear what the data points represent. If, for instance, in Fig. 1B, 40 bees were tested (line 247), how many bees per combination were tested (only one combination is mentioned in line 249)? Moreover, given that bees were tested with B vs. C, and if I guess correctly, there are ca. 10 data points per combination, what do these 10 proportions represent? How were these values computed? I could not find this information in the Methods section. No description of the test methodology is provided for Experiments 1 to 3. Moreover, data points in Figs B, D, F are barely visible and appear clustered around 50% in several cases, thus casting doubt on the reported significance of the comparisons. This needs to be improved by means of visible and clear graphic displays. The same kind of consideration can be applied to Fig. 2B, even if the results are clearer.

      3) A final point relates to results obtained in a different experimental framework but which asks whether animals can rank and order in transitive terms experienced alternatives. A considerable amount of work in the field of experimental psychology has addressed the question of transitive inferences in many species (3-12), and even in bees and wasps (13, 14). In these studies, animals are trained with premise pairs presenting different reinforcement outcomes (e.g. A+ B-/B+ C-/C+ D-/D+ E-) to determine if they establish relative rankings (A ˃ B ˃ C ˃ D ˃ E), or on the contrary use associative learning of absolute reinforcement outcomes (in which case, A ˃ B = C = D ˃ E). To determine the strategy followed by animals, they are tested with a non-overlapping pair never experienced during the training (B vs. D). In the first case, animals prefer B to D while in the second case they choose equally between both options. There might be, therefore, some parallels or contact points between these experiments and the experiments reported in this manuscript. Could the authors discuss these parallels and provide a broader view of absolute vs. relative remembered subjective value?

      References<br /> 1. L. Pompilio, A. Kacelnik, Context-dependent utility overrides absolute memory as a determinant of choice. Proc Natl Acad Sci U S A 107, 508-512 (2010).<br /> 2. K. L. Akre, S. Johnsen, Psychophysics and the evolution of behavior. Trends Ecol Evol 29, 291-300 (2014).<br /> 3. E. L. Maclean, D. J. Merritt, E. M. Brannon, Social complexity predicts transitive reasoning in prosimian primates. Animal Behaviour 76, 479-486 (2008).<br /> 4. L. Grosenick, T. S. Clement, R. D. Fernald, Fish can infer social rank by observation alone. Nature 445, 429-432 (2007).<br /> 5. Y. M. C. G. Paz, A. B. Bond, A. C. Kamil, R. P. Balda, Pinyon jays use transitive inference to predict social dominance. Nature 430, 778-781 (2004).<br /> 6. H. Markovits, C. Dumas, Can pigeons really make transitive inferences? Journal of Experimental Psychology: Animal Behavior Processes 18, 311-312 (1992).<br /> 7. G. Jensen, Y. Alkan, F. Munoz, V. P. Ferrera, H. S. Terrace, Transitive inference in humans (Homo sapiens) and rhesus macaques (Macaca mulatta) after massed training of the last two list items. J Comp Psychol 131, 231-245 (2017).<br /> 8. O. F. Lazareva, E. A. Wasserman, Transitive inference in pigeons: measuring the associative values of Stimuli B and D. Behav Processes 89, 244-255 (2012).<br /> 9. A. B. Bond, C. A. Wei, A. C. Kamil, Cognitive representation in transitive inference: a comparison of four corvid species. Behav Processes 85, 283-292 (2010).<br /> 10. M. Vasconcelos, Transitive inference in non-human animals: an empirical and theoretical analysis. Behav Processes 78, 313-334 (2008).<br /> 11. J. J. Bryson, J. C. Leong, Primate errors in transitive 'inference': a two-tier learning model. Anim Cogn 10, 1-15 (2007).<br /> 12. A. B. Bond, A. C. Kamil, R. P. Balda, Social complexity and transitive inference in corvids. Animal Behaviour 65, 479-487 (2003).<br /> 13. E. A. Tibbetts, J. Agudelo, S. Pandit, J. Riojas, Transitive inference in Polistes paper wasps. Biol. Lett. 15, 20190015 (2019).<br /> 14. J. Benard, M. Giurfa, A test of transitive inferences in free-flying honeybees: unsuccessful performance due to memory constraints. Learn Mem 11, 328-336 (2004).

    1. Sr-p38 binds to sorafenib, is activated by environmental stressors, and regulates S. rosetta cell proliferation.(A) Sorafenib binds to Sr-p38. The ActivX ATP probe was used to pull down kinases from S. rosetta lysates that were pretreated with either DMSO or the ATP-competitive inhibitor sorafenib. We found that pretreatment with sorafenib reduced the level of Sr-p38 recovered using the ActivX ATP probe, indicating that sorafenib and Sr-p38 interact and outcompete ActivX ATP probe binding. Kinases plotted are only those that were identified in both vehicle and sorafenib pre-treatments. For full kinase enrichment list, see Table S2, and for alignment of Sr-p38 with those from animals and fungi, see Fig. S7.(B-C) Sr-p38 kinase is activated by heat shock and oxidative stress. S. rosetta cells, normally cultured at 22°C were incubated at 37°C or treated with hydrogen peroxide for 10 min. or 30 min. Lysates from the treated cultures were analyzed by western blot with a p38 antibody specific for phosphorylated p38 kinase (phospho-p38) to identify if any changes in p38 phosphorylation occurred. (B) 30 minutes of heat shock was sufficient to induce p38 phosphorylation as was (C) 10 min of treatment with 0.5M H2O2. A 12% Bis-Tris SDS-PAGE gel was used to resolve the western bands observed. An anisomycin-treated human cell lysate was used as a positive control to validate the phospho-p38 antibody in Figure S7C. Raw blot images and details on western blot cropping are available at: https://doi.org/10.6084/m9.figshare.20669730.v1(D) Sr-p38 phosphorylation is inhibited by sorafenib, but not by the sorafenib analog APS6-46. S. rosetta cultures pretreated with 10 µM or 1 µM sorafenib for 30 minutes followed by 30 minutes of heat shock at 37°C had decreased p38 phosphorylation. APS6-46 treated cultures were not different from vehicle (DMSO) control. Data from all sorafenib analogs tested are shown in Figure S8A-B. Treatment growth curves, dose response, and tyrosine phosphorylation analysis with APS6-46 treated cultures are in Figure S8C-E. Raw blot image and details on western blot cropping are available at: https://doi.org/10.6084/m9.figshare.20669730.v1(E-F) Selective inhibition of Sr-p38 disrupts S. rosetta cell proliferation. S. rosetta cultures were treated with sorafenib or one of two p38-specific inhibitors, skepinone-L or BIRB 796, in 24-well plates over an 80-hour growth course. (E) At 40 hours, cells treated with 10 µM skepinone-L, BIRB 796 or sorafenib showed little evidence of cell proliferation in comparison to vehicle (DMSO) control (p-value <0.01). (F) Cells treated with 1 µM skepinone-L or BIRB 796 had reduced cell density in comparison to vehicle (DMSO) control (p-value <0.01) at 60 hours. Three biological replicates were conducted per experiment and significance was determined by determined by a two-way ANOVA multiple comparisons test. Movie S5 shows a timelapse of S. rosetta cells treated with skepinone-L.(G) Sr-p38 phosphorylation is not inhibited by the p38-specific inhibitors skepinone-L or BIRB 796. S. rosetta cultures pretreated with 10 µM of skepinone-L and BIRB 796 for 30 minutes followed by 30 minutes of heat shock at 37°C were not different from vehicle (DMSO) control. Raw blot image and details on western blot cropping are available at: https://doi.org/10.6084/m9.figshare.20669730.v1(H) Proposed mechanism for regulation of Sr-p38 by tyrosine kinases and the essentiality of Sr-p38 for S. rosetta cell proliferation. Sr-p38 kinase is phosphorylated by upstream tyrosine kinases and is necessary for cell proliferation. Sorafenib inhibits Sr-p38 phosphorylation by blocking the activity of upstream tyrosine kinases. p38 inhibitors that do not inhibit these upstream tyrosine kinases also do not reduce Sr-p38 phosphorylation but do block Sr-p38 kinase activity and thereby block S. rosetta cell proliferation.

      It's awesome to identify the specific target of a kinase inhibitor! Such a clever experiment!

    1. Author Response

      Reviewer #2 (Public Review):

      McCoy et al. has developed a new urban tree species database from existing city tree inventories. They designed procedures to collect and clean a large amount of data, i.e., more than five million trees from 63 US cities. They found that urban trees were significantly clustered by species in 93% of cities using the compiled data. They also showed that climate significantly shaped both nativity and tree diversity. Also, they identified the homogenization effect of the non-native species. The interest in patterns of urban biodiversity and its driving mechanism has been rising recently. This paper provides an important data source for addressing research questions on this topic. The finding presented by the authors exemplified its potential. Strengths Compared to the existing urban tree database, such as the one developed by Ossola et al.(Global Ecology and Biogeography 2020), the new database added information on spatial location, nativity statuses, and tree health conditions besides occurrences. The new information expands data usability and saves valuable time for researchers. The authors also make the tools available so others can use them to process their own data sets. Because of the added information, various analyses of the diversity pattern of urban trees and the potential driving mechanism could be conducted. The authors found that individual species nonrandomly clustered urban trees. This finding corroborates the existing knowledge that some common species dominate urban trees. Nevertheless, the authors showed that the dominance was apparent in the spatial dimension. The preliminary finding that the native status of a tree had no apparent impact on tree health is interesting. It can potentially contribute to the debate on native vs. exotic in urban tree species selection, which the author mentioned in the paper.

      Thank you for the feedback!

      Weakness

      While the new database and the analysis based on it has strengths, some aspects of the concepts and data analysis need to be clarified and extended.

      We appreciate these helpful comments and have made many changes in response, detailed below.

      First, the authors need to define several critical concepts used in the paper, including city trees, urban forests, biodiversity, and species diversity. The authors used city trees and urban forests interchangeably throughout the paper. Nevertheless, a widely accepted definition of the urban forest is:"All woody and associated vegetation in and around dense human settlements." Konijnendijk et al. had a good discussion on the terminology used in urban forestry (Urban Forestry & Urban Greening, 2006). Similarly, biodiversity is different from species diversity. Effective species number is a diversity indicator. Therefore, it is challenging to accept conclusions being drawn on biodiversity in urban forests without clear definitions.

      We appreciate these clarifications– we have clarified our terminology throughout and added these important definitions.

      • “...urban forests, which are the woody and associated vegetation in and around dense human settlements (Konijnendijk et al., 2006).”

      • “City tree communities, an essential component of urban forests, provide many services.”

      We replaced the term “biodiversity” throughout the text where really we meant to say “tree species diversity” or just “diversity.”

      Second, the tree inventories varied significantly regarding the number of records (214~720,140). The variation can be due to the actual variation of tree abundance in studied cities or incomplete inventories. Biases can be introduced into the findings when comparing these inventories without adjusting the unequal sample sizes. The authors did not detail how they dealt with this issue when conducting the analysis.

      We redid all of our relevant analyses and applied Chao’s rarefaction and extrapolation techniques throughout the manuscript. The (substantial) changes are fully described above in the “Essential Revisions” section. We also copy them here.

      First, we redid all of our diversity calculations applying Chao’s rarefaction and extrapolation techniques through the R package iNext. Therefore, our summary datasheet now has many new columns to include the following values for each city:

      ○ Effective species number:

      ■ Raw effective species number

      ■ Asymptotic estimate of effective species number with confidence interval

      ■ Estimate of effective species number for a given population size (37,000 trees– the median population size rounded to the nearest 1,000) with confidence interval

      ○ Species richness:

      ■ Raw species richness (number of species)

      ■ Asymptotic estimate of number of species with confidence interval

      ■ Estimate of number of species for a given population size (37,000 trees– the median population size rounded to the nearest 1,000) with confidence interval

      ○ The same for the native-only population of trees in each city (e.g., not just raw number of effective number of native species but also the iNext estimates and confidence intervals)

      ○ Whether or not each of the values above was calculated using extrapolation or interpolation

      ○ Sample coverage estimates

      Second, we re-ran our models testing for significant correlations between species diversity in a city and other factors (including climate), where we used the extrapolated / interpolated effective species numbers from iNext. Specifically, we found the best fit model, which included the following predictors: environmental PCA1, environmental PCA1:environmental PCA2, and whether or not a city was designated as a Tree City USA. Then, we ran this model under six sensitivity conditions, varying the independent variable and/or which cities we included based on completeness of their sample. Climate was still a significant correlate of diversity.

      ○ first, with independent variable = effective species as calculated for a given population of 37,000 trees ("effective species for a standardized population size");

      ○ second, independent variable = the asymptotic estimate of the effective species number for that city as calculated using iNext;

      ○ third, the raw effective species number;

      ○ fourth, excluding cities with fewer than 10,000 trees;

      ○ fifth, excluding cities with <50% spatial coverage;

      ○ sixth, excluding cities with <0.995 sample coverage as calculated by iNext.

      ○ For the fourth, fifth, and sixth models, the independent variable was effective species for a standardized population size of 37,000 trees.

      Third, we redid our comparisons of tree populations in parks versus those in urban areas. Parks were still more diverse than urban areas.

      ○ Specifically, we used iNext to calculate diversity metrics based on the smaller of the two population sizes (park vs urban) to enable fair comparison for each city.

      ○ We reported comparison results for (i) raw effective species number, (ii) asymptotic estimate, and (iii) estimate for a given population.

      ○ In doing so, we eliminated Milwaukee from the comparison (it had only 28 trees recorded as being in an urban setting).

      Fourth, we redid our pairwise comparisons of tree community composition between cities in order to account for different population sizes and sampling efforts. To do so, we randomly subsampled the larger city to make its population equal to the smaller city, calculated comparison metrics, and repeated this process 50 times. We report the average comparison metrics.

      Our new Methods text is copied here for your convenience:

      ○ “Throughout our analyses, it was necessary to control for different sample sizes (and different, but unknown, sampling efforts across cities). To do so, we relied on the rarefaction / extrapolation methods developed by Chao and colleagues (Chao et al., 2015, 2014; Chao & Jost, 2012) and implemented through the R software package iNext (Hsieh et al., 2016). In short, these methods use statistical rarefaction and/or extrapolation to generate comparable estimates of diversity across populations with different sampling efforts or population sizes, alongside confidence intervals for these diversity estimates. iNext performs these tasks for Hill numbers of orders q = 0, 1, and 2. We used two techniques in iNext to allow for comparisons across cities (and between parks and urban areas within cities). First, we generated asymptotic diversity estimates for each; second, we generated diversity estimates for a given standardized population size. For our diversity analyses, the standardized population size we used was 37,000 trees (the rounded median of all cities). For analyses of the diversity of native trees, we used a standardized population size of 10,000 trees. For comparisons of the diversity between park and urban areas in a city, we used the smaller of the two population sizes (park or urban). In all cases we also recorded confidence estimates, and plotted rarefaction/extrapolation curves.

      ○ To control for variation in how uniformly trees were sampled across a city’s geographic range, we developed a procedure to score each city’s spatial coverage (see section Spatial Structure below).

      ○ We identified the best-fitting model, and then repeated our analysis under six sensitivity conditions to control for differences in population size, sampling effort, spatial coverage, and sample coverage. Our sensitivity analyses were as follows: first, with independent variable = effective species as calculated for a given population of 37,000 trees ("effective species for a standardized population size"); second, independent variable = the asymptotic estimate of the effective species number for that city as calculated using iNext; third, the raw effective species number; fourth, excluding cities with fewer than 10,000 trees; fifth, excluding cities with <50% spatial coverage; sixth, excluding cities with <0.995 sample coverage as calculated by iNext. For the fourth, fifth, and sixth models, the independent variable was effective species for a standardized population size of 37,000 trees.”

      Reviewer #3 (Public Review):

      This paper's strength is in the utility of the assembled datasets and some interesting and creative proof of concept analyses. This is an amazing resource for comparative analysis. However the paper felt a little sparse in the conceptual and methodological underpinnings of the questions asked to demonstrate the utility of the analysis. Specifically, I suggest:

      A) More substance in the introduction (currently only two short paragraphs) and a clear statement of research questions.

      We have added text to frame our goals and hypotheses:

      ○ “In particular, we wanted to know whether local climatic conditions are associated with the species diversity of city tree communities, how species diversity was distributed in space within cities, and whether introduced tree species contribute to biotic homogenization among urban ecosystems.”

      B) Add data on the extent to which each dataset represents a complete sample of each city's trees. I know are complete inventories, but some consist of 720 trees and cannot be a complete sample. A column in the meta data indicating effort and if there were any bias in where sampling occurred if the dataset is not complete are needed for others to use this data appropriately. For example, we know tree cover/diversity increases with wealth (which the author rightly cites). Let's say in City X, trees were only inventoried in one wealthy neighborhood. They would not be a representative sample of the city and dataset users need to be aware of this before they draw incorrect conclusions about City X where the sample was biased compared to city Y where the inventory was complete, including a sampling of all affluent and poor areas. This is also needed to support the research questions throughout the paper.

      We completely agree, and have made two major changes in response.

      First, we redid all of our diversity analyses after applying Chao’s rarefaction and extrapolation methods to permit comparison between populations of different sizes and sampling efforts. We added new columns to our datasheet with sample coverage estimates, asymptotic estimates of diversity, and diversity estimates for a given population size.

      Second, we also examined spatial coverage in a city because of the valid concern you raised that trees may only be sampled from particular neighborhoods or areas. In short, we divided each city into grid cells, counted trees per grid cell, and calculated metrics of coverage (adjusted number of trees per grid cell, and proportion grid cells that were empty) and bias (skew, kurtosis of number trees in occupied grid cells). These factors are presented in Spatial_Coverage_Supplement.zip. AS you can see even just from a glance at the spatial coverage plots, some cities are indeed extremely biased! Therefore, we ran a sensitivity analysis where we excluded cities with <50% spatial coverage.

      C) The authors chose to use effective species counts as their alpha diversity metric of choice. They explain why: "effective species counts (a measure that allows comparison between cities of different sizes)" (Ln 109). While effective species number is an excellent metric with much better behavior and attributes in linear modeling, I believe it is still strongly dependent on both city area and the number of individual trees sampled and so the above statement and all of the comparisons that flow out of it in the manuscript are currently unsupported. Just as species richness needs to be rarified or extrapolated to be compared at an equivalent # of individuals or area to be accurate so too does EFN (effective species count). Fortunately there is an R package (iNext) based on Chao's method (citation below) that makes it very easy to create effective species accumulation curves for each city by tree individuals sampled.

      a. Chao, Anne, Nicholas J. Gotelli, T. C. Hsieh, Elizabeth L. Sander, K. H. Ma, Robert K. Colwell, and Aaron M. Ellison. 2014. "Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies." Ecological Monographs 84 (1): 45-67. https://doi.org/https://doi.org/10.1890/13-0133.1.

      b. The standardization (rarefaction/extrapolation) of EFN or richness for # individual trees sampled needs to be made for all analyses that make claims to compare diversity metrics across cities or between groups like urban and park areas (i.e. Fig 2a,b,c; Fig 3b; Fig 5a,b, S1a, S2a, S5, Table S2)

      c. If the authors have an argument for why diversity/area or diversity/sampling effort relationships do not apply for a particular question, then they should make that case instead.

      We very much appreciate this suggestion. Indeed, as described above, we applied Chao’s method to all of our analyses.

      D) The question posed by the Beta diversity analysis is fascinating (i.e. is it non-native species that are driving biotic homogenization across species. However, while frequency (which I assume is relative abundance but maybe it is incidence data- please define) is used to deal with different sample sizes consider whether it makes sense to include incomplete, or very small city datasets in the analysis even with frequency data. For example one city only has ~720 trees listed. If this is an incomplete dataset which seems likely, it will probably be much more differentiated (overlap less) from another city with small numbers simply due to incomplete sampling. Diversity analysis in cities always requires tradeoffs and cannot be identical to methods used in "natural" forested ecosystems, but I encourage the authors to explore this a bit. Perhaps a sensitivity analysis could help where incomplete or small sample sizes are dropped or datasets are resampled via random draw to equalize sizes? The latter would handle incomplete samples but would not deal with bias in which neighborhoods were sampled (see point B above).

      Great suggestion. We redid this analysis using a random drawn approach, as you suggested, to equalize sizes. The new analysis found the same results as our old analysis, with slightly different values. The new method is described here:

      ○ “How similar are species compositions across cities? For N = 1953 city-city comparisons of street tree communities, we could calculate weighted measures of similarity because we had frequency data. We calculated similarity scores for the entire tree population, the naturally-occurring trees only, and the introduced trees only. We used chi-square distance metrics on species frequency data, and we controlled for different population sizes (and potentially, sampling efforts) between cities by sub-sampling the larger city 50 times to match the smaller city’s tree population size and calculating average metrics. In this manner we controlled for differences in sample size.”

      E) Additional context/conceptual underpinning the clustering analysis would be great.

      a. The authors state in Line 390-395:"For city trees, which are often organized along grids or the underlying street layout of a city, this method can more meaningfully cluster trees than merely calculating the meters between trees and identifying nearest neighbors (which may be close as the crow flies but separated from each other by tall buildings)."- I very much agree with this sentiment and it is biologically meaningful for animal and plant dispersal, but as written it is unclear to me how the method described in the text "knows" that a tall building or elevation or some sort of feature exists to separate clusters rather than empty space or a ball field. Please clarify.

      We appreciate these comments, and we have added text and references for the interested reader. Here is the new description in full:

      ○ “We wanted to quantify the degree to which trees were spatially clustered by species within a city (rather than randomly arranged). To do so, we first clustered all trees within each city using hierarchical density based spatial clustering through the hdbscan library in Python (McInnes et al., 2017). HDBSCAN, unlike typical methods such as “k nearest neighbors”, takes into account the underlying spatial structure of the dataset and allows the user to modify parameters in order to find biologically meaningful clusters. For city trees, which are often organized along grids or the underlying street layout of a city, this method can more meaningfully cluster trees than merely calculating the meters between trees and identifying nearest neighbors (which may be close as the crow flies but separated from each other by tall buildings). In particular, using the Manhattan metric rather than Euclidean metrics improves clustering analysis in cities (which tend to be organized along city blocks). For further discussion of why hbdscan is preferable to other clustering metrics, see (Berba, 2020; Leland McInnes et al., 2016; McInnes et al., 2017).”

      b. Would you ever expect composition to be truly random either in a city or a natural forest given environmental conditions etc.? In some sense, the ones closest to random are the most surprising. Can you dive into one to give an example of what is going on in that city?

      c. It seems like there are two metrics here- the size of the cluster and then the observed/expected EFN per cluster. The latter is analyzed in this paper but is there any important information in the former? It seems like an interesting structural measurement of the city and possibly useful in its own right.

      d. Are there any target levels of randomness? Could the authors suggest how this might be determined moving forward with their datasets to illustrate this for foresters?

      Great points. We have given a lot of thought to your comments– these are large and interesting questions!! In the end, I think these questions fall mostly beyond the scope of this study, but we added a substantial amount of text to address your comments:

      ○ “Clustering by species is not necessarily a negative, nor indeed should we necessarily expect trees to be randomly arranged (see suggestions for further research in “Future Analyses” section below). Here, we take a first step toward making spatial clustering a metric of interest in city tree planning.”

      ○ “Researchers could also use this dataset to perform more refined analysis of clustering. For example, what is the biological significance of variation in cluster size (as determined by the hdbscan clustering algorithms)? The size and arrangement of the clusters themselves may be useful metrics. How clustered should we expect trees to be in both wild and urban settings? That is, what our are null expectations? Further, researchers could apply network theory to predict how pest species would proliferate through each of these cities (depending on the spatial arrangement of pest-sensitive trees).”

      F) The statement that this dataset enables "the design of rich heterogenous ecosystems built around urban forests" (Ln 72) seems strange. To my mind this tool will enable a more nuanced evaluation of the urban forests that already exist and suggest ways to target future plantings for increased resilience to climate, pest resistance, biodiversity support etc. I don't understand what ecosystem you would build around and not in the urban forest. If this is what is meant please elaborate. For example, do you mean non-tree installations?

      We agree with you and have changed the text as follows:

      ○ “With these tools, we may evaluate existing city tree communities with more nuance and design future plantings to maximize resistance to pests and climate change. We depend on city trees.”

    1. Author Response

      Reviewer #2 (Public Review):

      According to the authors, the goal is to identify a method to study changes in hospital presentation and outcomes of new COVID-19 variants using publicly available population-level data on variant relative frequency to infer SARS-CoV variants likely responsible for clinical cases. This would assist in answering questions asked by public health authorities as to differences in disease severity and risk factors and vaccine protection.

      Authors use patients' data collected prospectively in 30 countries in their pre-Omicron period (Omicron variant is less than 10% of SARS-CoV2 variants) to the Omicron period (Omicron variant prevalence is >90% of circulating variants). The following factors are analyzed and adjusted for: age/gender, symptoms, comorbidities, vaccination, and outcomes during pre and Omicron periods.

      Their model shows that overall, patients were younger, had less symptoms and that the mortality rate was lower in the Omicron period (even if it doesn't reflect in some country reports). No conclusion can be made on vaccination status.

      Major weaknesses and strengths:

      1) The study is presented as a multi-center international study that includes more than 100,000 patients from 30 countries, however, 96.6% of the study patients originated from 2 countries, South Africa (54%) and the United Kingdom (42.6%) (and the relative contribution of South Africa to the study data was hugely different in the 2 study periods, pre-Omicron and Omicron period).

      The huge imbalance in the number of patients recruited by center could create many bias in data interpretation. For example, some countries do not report any increase in patients aged less than 12 years old in the omicron period. Country specific medians suggest that the younger age of patients after the Omicron variant experience in the combined dataset is at least partially explained by an increase of data contributed by South Africa, relative to the proportion of data contributed by other countries. In total only 11 countries contributed data on more than 100 hospitalized cases.

      The differences in study data contribution between countries, with more than 90% of all records being from the United Kingdom and South Africa, required both an adapted analytical approach, that transparently presented country-level data rather than only aggregated estimates, and careful discussion of our findings. Indeed, we agree with the reviewer that this imbalance in country-level data contribution and the varying contribution of some countries to the two study periods could lead to erroneous inferences if ignored (i.e. if only aggregated results were reported); for this reason, we presented country-specific data in the Results section. In our descriptive analyses, to achieve this goal without jeopardising intelligibility, we present findings for a subset of countries, those with at least 50 observations per study period; note that this criterion was modified based on another comment from this reviewer. This approach also addresses the reviewer’s concern, which we share, that the varying relative contribution of different countries to study periods could lead to spurious aggregated patterns. In fact, we highlight this problem in the following paragraph of the Results section:

      “The median (IQR) ages of patients during the pre-Omicron and Omicron periods were 62 (43 – 76) and 50 (30 – 72) years, respectively; however, country-specific medians suggest that the younger age of patients after Omicron variant emergence in the combined dataset is at least partially explained by an increase in the proportion of data contributed by South Africa, relative to the proportion of data contributed by other countries (Table S6).”

      Recruitment of patients is unclear. We don't really know which patients are selected to be part of the study. The authors mention the use of the ISARIC (International Severe Acute Respiratory and Emerging Infections Consortium) COVID-19 database (l. 173). This would imply that patients with severe respiratory symptomatic COVID-19 are recruited in the study. It could explain why patients recruited from Brazil or the Netherlands have the same proportion of patients presenting with shortness of breath in the pre- and Omicron period.

      Due to the time-sensitivity and scale of this work, involving hundreds of investigators in 30 countries, although the study only included hospitalised patients with SARS-CoV-2 infection, the approach used for patient recruitment in each institution was defined by local investigators. Whilst the sampling strategy was not uniform across sites, one should keep in mind that: (i) recommendations on sampling strategy were shared with local investigators; and (ii) most of the partner institutions involved in this work had previously contributed data to the ISARIC platform and are experienced in patient recruitment and clinical and epidemiological research.

      More generally, recruitment approaches could influence the interpretation of our findings in two ways: by reducing the representativeness of the study population in each country; and by inducing bias that could affect the association of interest (the association between study period and fatality risk). Regarding the former, it is possible that in some countries hospitals contributing to this effort admitted patients with more severe disease compared to the local population of COVID-19 hospitalised patients, the target population. Regarding the second potential problem, bias, hospital-based studies might suffer from collider bias, where both the exposure of interest and the outcome directly influence recruitment (selection) to the study or are associated with selection or recruitment through confounders; this is a well-described problem in hospital-based studies that assess COVID-19 outcomes (see Griffith et al. Collider bias undermines our understanding of COVID-19 disease risk and severity. Nature Communications 2020. for a discussion on how different COVID-19 clinical factors can induce bias when different sampling frames are used). Note that collider bias is not the only mechanism of selection bias affecting effect measures; as explained by Miguel Hernán (in Invited Commentary: Selection Bias Without Colliders. American Journal of Epidemiology 2017) between-exposure stratum heterogeneity in the association between the outcome and selection could bias the association between the exposure and the outcome (relative to the effect measure in the target population). However, recruitment approaches used by partner institutions are unlikely to have systematically changed during the study period, and we are unaware of evidence suggesting any association that might have existed between recruitment procedure and outcome differed in the two study periods for most, or indeed some, partner institutions.

      We have now modified the Discussion section to highlight this potential weakness of our study:

      “Another weakness of our study is that recruitment procedure was not standardised and was defined locally. Whilst this likely affected the generalisability of our descriptive estimates (fatality risk and frequencies of symptoms and comorbidities) to local populations of hospitalised COVID-19 cases (Lash and Rothman, Selection Bias and Generalizability. in Modern Epidemiology 4th Edition 2021; Rothman et al. Why representativeness should be avoided. International Journal of Epidemiology 2013), it might not have affected the association between study period and fatality risk, at least not beyond the well-described potential for collider bias in hospital-based studies on COVID-19 outcomes (Griffith et al. Collider bias undermines our understanding of COVID-19 disease risk and severity. Nature Communications 2020).”

      In Nepal, patients were more often recruited from critical care setting (l.572).

      However, the authors mention elsewhere that patients recruited for the study were:

      • Omicron variant infections in hospitalised patients (I. 161),

      • Patients with confirmed or suspected COVID-19 (l.183),

      • "some patients were admitted for a medical condition other than covid19 but tested incidentally during hospitalization (l.243)"

      • In some countries, information on whether covid-19 was the main reason for hospitalization was also collected. 69.0% of patients admitted during the omicron periods were admitted due to covid-19, patients for whom this information was available were primarily from South Africa (94.9%), (L.310)

      • For 35.5% of patients admitted to hospital date of symptoms onset was missing and it was assumed that these were not hospital acquired infections (l.233)

      • Information on whether covid-19 was the main reason for hospitalization was collected during the study period and suggest that for a non-negligible proportion of patients, others clinical conditions might have prompted hospitalization.

      • In their discussion the authors state that "Finally it is also possible that the question on the primary reason for hospitalization might have been interpreted differently in different countries and even in different hospitals in the same country." In the few clinical studies from United Kingdom and South Africa 40% to 70% of admissions were qualified as "incidental" COVID-19.

      This comment relates to the previous comment and to the sampling strategy used in the study. Please, see our response to the previous comment.

      Regarding incidental infections, we have now included information on recent studies (Klann et al. Distinguishing Admissions Specifically for COVID-19 From Incidental SARS-CoV-2 Admissions: National Retrospective Electronic Health Record Study. J Med Internet Res; Voor in ’t holt et al. Admissions to a large tertiary care hospital and Omicron BA.1 and BA.2 SARS-CoV-2 polymerase chain reaction positivity: primary, contributing, or incidental COVID-19. International Journal of Infectious Diseases 2022).

      “One possible explanation for this finding would be if incidental SARS-CoV-2 infections, i.e. infections that were not the primary reason for hospitalisation, were more frequent during the Omicron period; the high transmissibility of this variant, and the consequent peaks in numbers of infections, together with its reported association with lower severity, provides support for this hypothesis. However, in the subset of patients with data on the reason for hospitalisation there was no increase in the proportion of admissions thought to be incidental infections and indeed proportions in both study periods were consistent with frequencies of incidental infections in recent studies in the United States (Klann et al. Distinguishing Admissions Specifically for COVID-19 From Incidental SARS-CoV-2 Admissions: National Retrospective Electronic Health Record Study. J Med Internet Res) and the Netherlands (Voor in ’t holt et al. Admissions to a large tertiary care hospital and Omicron BA.1 and BA.2 SARS-CoV-2 polymerase chain reaction positivity: primary, contributing, or incidental COVID-19. International Journal of Infectious Diseases 2022), although in the latter, non-incidental infections included patients for whom COVID-19 was a contributing but not the main cause of hospitalisation.”

      Absence of data standardization.

      There doesn't seem to be standardized questionnaires across all countries. Some countries do not report on symptoms, others do not report on vaccination status. In total, it seems that less than a third of patients have full data (symptoms, co-morbidities, vaccination, and outcome), and such patients are reported by few countries.

      South Africa (that represents 54% of patients) didn't systematically report on symptoms. Hence data showed for symptoms might reflect in volume mainly the United Kingdom patients. In the United Kingdom vaccination rates during the omicron period was 70.3% as compared to 27.9% for South Africa. The authors find that patients with Omicron variant display less symptoms, (which confirms previous findings) however it could have been as plausible that patients from South Africa being less vaccinated exhibit more symptoms.

      Analysis for each group of data is based on different patients' group according to the data available for such group.

      Data from South Africa used in this analysis are part of the DATCOV national hospital surveillance database. The case report form (CRF) used by the National Institute for Communicable Diseases in South Africa was adapted from the ISARIC CRF; although most sections of that CRF were used for the data collection in the country, information on symptoms was not systematically collected. However, as mentioned above, in our analysis, we also report country-level frequencies of symptoms, rather than only presenting aggregated estimates. We agree with the reviewer that we cannot exclude the possibility that in South Africa a different pattern occurred. Based on this comment, we have now included the following statement in the Discussion section:

      “Finally, missing information on symptoms for patients from South Africa prevented our descriptive analysis of changes in clinical presentation in an African setting.”

      Vaccination data.

      Vaccination data are available for less than 50% of the patients and there is considerable inter-country variation in vaccination rates, as we know but also in the recruitment of patients for the study.

      As an example, Table 1 shows the vaccination status by country and study period for 24 countries: Brazil has a vaccination rate of 84.6% and India of 34.8% but on respectively 13 and 23 observations. There are less than 30 observations in 19 countries for pre omicron and less than 30 observations in 15 countries for the omicron period. No conclusion can be made.

      Our study was not designed to assess vaccine effectiveness against the Omicron and non-Omicron variants as controls (e.g. patients hospitalised with respiratory infection caused by pathogens other than SARS-CoV-2) were not recruited. Whilst we descriptively report the frequency of previous vaccination by country and age groups (see Figure S3 in the Supplementary Appendix, with numbers of records in each category presented for transparency), the primary objective in using vaccination data was to control confounding by this factor. The point made by the reviewer, that missing data on vaccination reduced sample size for this comparison, is valid and we have included the following statement in the Discussion section:

      “We also observed that history of COVID-19 vaccination was more frequent during the Omicron period, although for most countries the number of patients with vaccination information was limited, especially after stratification by age. Whilst this pattern would be expected if current vaccines were less effective against the Omicron variant compared to previously circulating variants, as suggested by a recent study in England analysing symptomatic disease, there were changes in vaccination coverage in many settings during the second half of 2021 and early 2022, including in response to the reports of Omicron variant cases. Since non-COVID-19 patients (e.g., patients with respiratory infections caused by other pathogens) were not systematically recruited for this multi-country study, it is not possible to estimate vaccine effectiveness during the two study periods and assess its change.”

      Major findings of the study:

      Major findings of the study match previous individual-based reports: 1-in many settings patients hospitalized with Omicron less often presented with commonly reported symptoms compared to patients infected with pre-omicron variants.

      2) In a mixed-effects logistic model on 14-day fatality risk that adjusted for sex, age categories and vaccination status hospitalization during the Omicron period were associated with lower risk of death. Similar results were obtained when using 28-days fatality risk and when excluding patients who reported being admitted to hospital due to a medical condition other than covid-19.

      3) History of COVID-19 vaccination was more frequent during the Omicron period, but the authors cannot make any conclusion on vaccine effectiveness

      How to interpret these data? The impact in terms of disease severity of new variants has been shown to be context specific due to regional differences in terms of variability of previous exposure, vaccinations rates and population comorbidity level frequency. As a result of recruitment bias and small recruitment in some countries, several countries have different findings described that do not fit with the conclusions.

      As mentioned by the authors, the strength of the project is to have succeeded in engaging so many countries to work together which could definitely assist in the future in understanding new variants characteristics shared globally and identify country specific impact on these variants according to the history of previous variant exposure, vaccine coverage, population morbidity and access to health.

      Reviewer #3 (Public Review):

      The authors combine outcomes data from patients hospitalised with COVID-19 across 30 countries to investigate differences in likelihood of death from the Omicron variant vs pre-Omicron variants. Data are from the ISARC COVID-19 database; variant status is inferred from country-specific GISAID data. The principal finding is a 36% reduced risk of 14-day death in the Omicron period (OR 0.64 (0.59 - 0.69)) compared with the pre-Omicron period, after multiple adjustment.

      The strengths of this paper are the large N and large number of participating countries from different regions, and also the careful and thorough analytical approaches. The main findings are stress-tested through a range of sensitivity analyses using different variant-dominance thresholds and statistical approaches and found to be robust. The figures are clear, well-chosen and easily interpretable.

      The principal weaknesses, as acknowledged in the discussion, are the imbalance in the data sources (96.6% of the observations came from GBR or SA), and the lack of fidelity of data on vaccination (vaccination status is limited to a binary 'one or more vaccinations received Y/N' variable). This latter means that conclusions about the innate severity of Omicron vs pre-Omicron variants cannot be drawn.

      Nonetheless the findings represent a useful contribution to the literature on the severity of COVID-19 variants, and the approach establishes a template for rapid international collaboration, using GISAID data to infer variant status, that will be useful for formulating policy in response to new variants in the future.

      The limited data on timing of vaccination and number of previous doses imply that residual confounding could partially explain the observed association; we mention this limitation in the Discussion section. Although our data alone cannot provide sufficient evidence for differences in innate severity between variants, mechanistic studies (see Shuai et al. Attenuated replication and pathogenicity of SARS-CoV-2 B.1.1.529 Omicron. Nature 2022, and Halfmann et al. SARS-CoV-2 Omicron virus causes attenuated disease in mice and hamsters. Nature 2022) suggest the Omicron variant might be less virulent. We modified the following paragraph in the Discussion section:

      “All these factors might have contributed to the observed association, possibly to different degrees in different countries, reason for which this result should not be assumed to necessarily relate to the differences in variant virulence previously suggested by mechanistic studies (Shuai et al. Attenuated replication and pathogenicity of SARS-CoV-2 B.1.1.529 Omicron. Nature 2022; Halfmann et al. SARS-CoV-2 Omicron virus causes attenuated disease in mice and hamsters. Nature 2022).”

  4. Aug 2022
    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

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      Reply to the reviewers

      Reply to the Reviewers

      We thank the reviewers for dedicating time to review our manuscript and providing highly valuable feedback. Please find below a point-by-point answer.

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

      Summary:

      In the present manuscript, van der Plas et al. compellingly illustrated a novel technique for engendering a whole-brain functional connectivity map from single-unit activities sampled through a large-scale neuroimaging technique. With some clever tweaks to the restricted Boltzmann Machine, the cRBM network is able to learn a low-dimensional representation of population activities, without relying on constrained priors found in some traditional methods. Notably, using some 200 hidden layer neurons, the employed model was able to capture the dynamics of over 40,000 simultaneously imaged neurons with a high degree of accuracy. The extracted features both illustrate the anatomical topography/connectivities and capture the temporal dynamics in the evolution of brain states. The illustrated technique has the potential for wide-spread applications spanning diverse recording techniques and animal species. Furthermore, the prospectives of modeling whole-brain network dynamics in 'neural trajectory' space and of generating artificial data in silico make for very enticing reasons to adopt cRBM.

      Major comments:

      1. Line 164. The authors claim that conventional methods "such as k-means, PCA and non-negative matrix factorization" cannot be quantitatively assessed for quality on the basis that they are unable to generate new artificial data. Though partly true, in most neuroscience applications, this is hardly cause for concern. Most dimensionality reduction methods (with few exceptions such as t-sne) allow new data points to be embedded into the reduced space. As such, quality of encoding can be assessed by cross-validation much in the same way as the authors described, and quantified using traditional metrics such as percentage explained variance. The authors should directly compare the performance of their proposed model against that of NNMF and variational auto-encoders. Doing so would offer a more compelling argument for the advantage of their proposed method over more widely-used methods in neuroscience applications. Furthermore, a direct comparison with rastermap, developed by Stringer lab at Janelia (https://github.com/MouseLand/rastermap), would be a nice addition. This method presents itself as a direct competitor to cRBM. Additionally, the use of GLM doesn't do complete justice to the comparison point used, since a smaller fraction of data were used for calculating performance using GLM, understandably due to its computationally intensive nature.

      PLANNED REVISION #2

      We thank the reviewer for the comment, and certainly agree that there are multiple methods for unsupervised feature extraction from data and that they can be validated for encoding quality by cross-validation. However, we stress that reconstructing through a low-dimensional, continuous bottleneck is a different (and arguably, easier) task, than generating whole distributions. Reconstruction delineates the manifold of possible configurations, whereas generative modeling must weigh such configurations adequately. Moreover, none of the methodologies mentioned can perform the same tasks as cRBMs. For instance, NNMF learns localized assemblies, but cannot faithfully model inhibitory connections since, by definition, only non-negative weights are learnt. Also, the connection between the learnt assemblies and the underlying connectivity is unclear. Similarly, rastermap is an algorithm for robustly i) sorting neurons along a set number of dimensions (typically 1 or 2) such that neighboring neurons are highly correlated, and ii) performing dimensionality reduction by clustering along these dimensions. Because Rastermap uses k-means as the basis for grouping together neurons, it does not quantify connections between neurons and assemblies, nor assign neurons to multiple assemblies. Moreover, it is not a generative model, and thus cannot predict perturbation experiments, infer connectivities or assign probabilities to configurations. Therefore, we do not believe that NNMF or Rastermap would be a suitable alternative for cRBM in our study. We nonetheless appreciate the reviewer’s suggestions and agree that we should motivate more clearly why these methods are not applicable for our purposes. Therefore, to emphasize the relative merit of cRBM with respect to other unsupervised algorithms, we now provide a table (Supplementary Table 2) that lists their specific characteristics. We stress that we do not claim that cRBM are consistently better than these classical tools for dimensionality reduction, but focus only on the properties relevant to our study.

      Further, we agree that VAEs, which also jointly learn a representation and distribution of data, are close competitors of cRBMs. In Tubiana et al. Neural Computation 2019, we previously compared sparse VAEs with cRBMs for protein sequence modeling, and found that RBMs consistently outperformed VAEs in terms of the interpretability-performance trade-off. In the revised manuscript, we propose to repeat the comparison with VAE for zebrafish neural recordings, and expect similar conclusions.

      As for GLM, it is true that the comparison involved subsampling of the neurons (due to the very high computational cost of GLM, where we could estimate the connectivity of 1000 neurons per day). This was already denoted in the relevant figure caption, as the reviewer has seen, but we have now also clarified this point in Methods 7.10.3. Still, we performed our GLM analysis on 5000 neurons (using all neurons as regressors), which is 10% of all neurons, and we believe this is a sufficient number for comparison. This emphasizes the ability of our optimized cRBMs to handle very large datasets, such as the presently used zebrafish whole brain recordings.

      Line 26. The authors describe their model architecture as a formalization of cell assemblies. Cell assemblies, as originally formulated by Hebb, pertains to a set of neurons whose connectivity matrix is neither necessarily complete nor symmetric. Critically, in the physiological brain, the interactions between the individual neurons that are part of an assembly would occur over multiple orders of dependencies. In a restricted Boltzmann machine, neurons are not connected within the same layer. Instead, visible layer neurons are grouped into "assemblies" indirectly via a shared connection with a hidden layer neuron. Furthermore, a symmetrical weight matrix connects the bipartite graph, where no recurrent connectivities are made. As such, the proposed model still only elaborates symmetric connections pertaining to first-order interactions (as illustrated in Figure 4C). Such a network may not be likened with the concept of cell assemblies. The authors should refrain from detailing this analogy (of which there are multiple instances of throughout the text). It is true that many authors today refer to cell assemblies as any set of temporally-correlated neurons. However, saying "something could be a cell assembly" is not the same as saying "something is a cell assembly". How about sticking with cRBM-based cell assemblies (as used in section 2.3) and defining it beforehand?

      We thank the reviewer for this excellent question. We agree that there is, in general, a discrepancy between computationally-defined assemblies and conceptual/neurophysiological definition of cell assemblies. We have added a clarification in Results 2.1 to clarify the use of this term when it first occurs in Results. However, we still believe that our work contributes to narrowing the gap. Indeed, our RBM-defined assemblies are i) localized, ii) overlapping, iii) rooted in connectivity patterns (both excitatory and inhibitory), and iv) cannot be reduced to a simple partitioning of the brain with full & uniform connectivity within and between partitions. This is unlike previous work based on clustering (no overlaps or heterogeneous weights), NNMF (no inhibition) or correlation network analysis (no low-dimensional representation).

      Regarding the specific comments pointed here, we stress that:

      • Effective interactions between neurons are not purely pairwise (“First order”), due to the usage of the non-quadratic potential. (see Eqn 12-13). If the reviewer means by “First-order” interactions the lack of hierarchical organization, we agree, to some extent: in the current formulation, correlations between assemblies are mediated by overlaps between their weights. Fully-hierarchical organization, e.g. by using Deep Boltzmann Machines or pairwise connections within the hidden layer is an interesting future direction, but on the other hand may make it hard to clearly identify assemblies as they might be spread out over multiple layers
      • Neurons that participate in a given assembly (as defined by a specific hidden unit) are not all connected with one another with equal strength. Indeed, these neurons may participate in other assemblies, resulting in heterogeneity of connections (see Eqn. 15-17) and interactions between assemblies.
      • We acknowledge that the constraint of symmetrical connections is a core limitation of our method. Arguably, asymmetric connections are critical for predicting temporal evolution but less important for inferring a steady-state distribution from data, as we do here. In the revised submission, we added a new paragraph in the discussion section (lines 351-357) in which these limitations are discussed, including the imposed symmetry of the connections and the lack of hierarchical structures, copied below. We trust that this addresses the reviewer’s criticism:

      In sum, cRBM-inferred cell assemblies display many properties that one expects from physiological cell assemblies: they are anatomically localized, can overlap, encompass functionally identified neuronal circuits and underpin the collective neural dynamics (Harris, 2005, 2012; Eichenbaum, 2018). Yet, the cRBM bipartite architecture lacks many of the traits of neurophysiological circuits. In particular, cRBMs lack direct neuron-to-neuron connections, asymmetry in the connectivity weights and a hierarchical organization of functional dependencies beyond one hidden layer. Therefore, to what extent cRBM-inferred assemblies identify to neurophysiological cell assemblies, as postulated by Hebb (1949) and others, remains an open question.


      I would strongly recommend adding a paragraph discussing the limitation of using the cRBM, things future researchers need to keep in mind before using this method. One such recommendation is moving the runtime-related discussion for cRBM, i.e. 8-12 hrs using 16 CPU from Methods to Discussion, since it's relevant for an algorithm like this. Additionally, a statement mentioning how this runtime will increase with the length of recordings and/or with the number of neurons might be helpful. What if the recordings were an hour-long rather than 25mins. This would help readers decide if they can easily use a method like this.

      We thank the reviewer for the suggestion, and agree that it is important to cover the computational cost in the main text. Regarding the runtime for longer recordings, the general rule of thumb is that the model requires a fixed number of gradient updates to converge (20-80k depending on the data dimensionality) rather than a fixed number of epochs. Thus, runtime should not depend on recording length, as the number of epochs can be reduced for longer recordings. While we did not verify this rule for neural recordings, this is what we previously observed when modeling protein/DNA sequence data sets, whose size range from few hundreds to hundreds of thousands of samples (Tubiana et al., 2019, eLife; Tubiana et al. 2019, Neural Computation; Bravi et al. Cell Systems 2021; Bravi et al. PLOS CB 2021; Fernandez de Cossio Diaz et al. Arxiv 2022 Di Gioacchino et al. BiorXiv 2022). We have now added a summary of these points in Methods 7.7.2, also refer to this with explicit mention of the runtime in the Discussion, end of 2nd paragraph:


      By implementing various algorithmic optimizations (Methods 7.7), cRBM models converged in approximately 8-12 hours on high-end desktop computers (also see Methods 7.7.2).


      Line 515. A core feature of the proposed compositional RBM is the addition of a soft sparsity penalty over the weight matrix in the likelihood function. The authors claim that "directed graphical models" are limited by the a priori constraints that they impose on the data structure. Meanwhile, a more accurate statistical solution can be obtained using a RBM-based model, as outlined by the maximum entropy principle. The problem with this argument is that the maximum entropy principle no longer applies to the proposed model with the addition of the penalty term. In fact, the lambda regularization term, which was estimated from a set of data statistics motivated by the experimenter's research goals (Figure S1), serves to constrict the prior probability. Moreover, in Figure S1F, we clearly see that reconstruction quality suffers with a higher penalty, suggesting that the principle had indeed been violated. That being said, RBMs are notoriously hard to train, possibly due to the unconstrained nature of the optimization. I believe that cRBM can help bring RBM into wider practical applications. The authors could test their model on a few values of the free parameter and report this as a supplementary. I believe that different parameters of lambda could elaborate on different anatomical clusters and temporal dynamics. Readers who would like to implement this method for their own analysis would also benefit tremendously from an understanding of the effects of lambda on the interpretation of their data. Item (1) on line 35 (and other instances throughout the text) should be corrected to reflect that cRBM replaces the hard constraints found in many popular methods with a soft penalty term, which allows for more accurate statistical models to be obtained.

      We thank the reviewer for their analysis and suggestion. Indeed, adding the regularization term - not present in the classical formulation of the RBM (Hinton & Salakhutdinov, 2006, Science) - was critical for significantly enhancing its performance, which allowed us to implement this model on our large scale datasets (~50K visible units). We agree that providing more information on the effect of the regularization term will benefit readers who would like to use this method, and we propose to add this in the revised manuscript, which would implement the reviewer’s suggestion. See “PLANNED REVISION #1”.

      The reviewer’s comment on the Maximum Entropy issue calls for some clarification. The maximum entropy principle is a recipe for finding the least constrained model that reproduces specified data-dependent moments. However, it cannot determine which moments are statistically meaningful in a finite-sized data set. A general practice is to only include low-order moments (1st and 2nd), but this is sometimes already too much for biological data. Regularization provides a practical means to select stable moments to be fitted and others to be ignored. This can be seen from the optimality condition, which writes, e.g., for the weights wi,mu:

      | i h,mu>data - i h,mu>model | i,mu = 0.

      i h,mu>data - i h,mu>model | = lambda sign(wi,mu) if |wi,mu| > 0.

      Essentially, this lets the training decide which subset of the constraints should actually be used. Thus, regularized models are closer to the uniform distribution (g=w=0), and actually have higher entropy than unregularized one (see, e.g., Fanthomme et al. Journal of Statistical Mechanics, 2022). Therefore, we believe that a regularized maximum entropy model can still be considered a bona fide MaxEnt model. This formulation should not be confused with another formulation (that perhaps the reviewer has in mind) where a weighted sum of the entropy and the regularization term is maximized under the same moment-matching constraints. In this case, we agree that maximum entropy principle (MaxEnt) would be violated.

      The choice of regularization value should be dictated by bias-variance trade-off considerations. Ideally, we would use the same criterion as for training, i.e., maximization of log-likelihood for the held-out test set, but it is intractable. Thus, we used a consensus between several tractable performance metrics as a surrogate; we believe this consensus to be principally independent of the research goal. While the reconstruction error indeed increases for large regularization values, this is simply because too few constraints are retained at high regularizations.

      Essentially, the parameters selected by likelihood maximization find the finest assembly scale that can be accommodated by the data presented. Thus, the number and size of the assemblies are not specified by the complexity of the data set alone. Rather, the temporal resolution and length of the recordings play a key role; higher resolution recordings will allow the inference of a larger number of smaller assemblies, and enable the study of their hierarchical organization.

      That being said, we fully agree that the regularization strength and number of hidden units have a strong impact on the nature of the representation learnt. In the revised manuscript, we will follow the reviewer’s suggestion and provide additional insights on the effect of these parameters on the representation learnt (please see revision plan).

      Minor comments:

      From a neuroscience point of view, it might be interesting to show what results are achieved using different values of M (say 100 or 300), rather than M=200, while still maintaining the compositional phase. Is there any similarity between the cRBM-based cell assemblies generated at different values of M? Is there a higher chance of capturing certain dynamics either functional or structural using cRBM? For example, did certain cRBM-based cell assemblies pop up more frequently than others at all values of M (100,200,300)?

      This point will be addressed in the future, as detailed in our response to reviewer 2 (see PLANNED REVISION #1).

      The authors have mentioned that this approach can be readily applied to data obtained in other animal models and using different recording techniques. It might be nice to see a demonstration of that.

      We agree that showing additional data analysis would be interesting, but we feel that it would overburden the supplementary section of the manuscript, which is already lengthy. In previous works, we and collaborators have used cRBMs for analyzing MNIST data (Tubiana & Monasson, 2017, PRL; Roussel et al. 2022 PRE), protein sequence data (Tubiana et al., 2019, eLife; Tubiana et al. 2019, Neural Computation; Bravi et al. Cell Systems 2021; Bravi et al. PLOS CB 2021; Fernandez de Cossio Diaz et al. Arxiv 2022), DNA sequences (Di Gioacchino et al. BiorXiv 2022), spin systems (Harsh et al. J. Phys. A 2020), etc. Many are included as example notebooks - next to the zebrafish data - in the linked code repository. For neural data, we have recently shared our code with another research group working on mice auditory cortex (2-photon, few thousands of neurons, Léger & Bourdieu). Preliminary results are encouraging, but not ready for publication yet.

      Line 237. The justification for employing a dReLU transfer function as opposed to ReLU is unclear, at least within the context of neurobiology. Given that this gives rise to a bimodal distribution for the activity of HUs, the rationale should be clearly outlined to facilitate interpretability.

      We thank the reviewer for the question. As we detail in the manuscript (Methods), the dReLU potential is one of the sufficient requirements for the RBM to achieve the compositional phase. The compositional phase is characterized by localized assemblies that co-activate to generate the whole-brain neural dynamics. This property reflects neurobiological systems (Harris, 2005, Neuron), which is one of the reasons why we employed compositional RBMs for this study.

      As the reviewer points out, the HUs that we infer exhibit bimodal activity (Figure 4). Importantly, the HU activity is not constrained by the model to take this shape, as dReLU potentials allow for several activity distributions (see Methods 7.5.4; “Choice of HU potential”). In fact, ReLU potentials are a special case of dReLU (by $(\gamma_{\mu, -} \to \infty)$), so our model allows HU potentials to behave like ReLUs, but in practice they converge to a double-well potential for almost all HUs, leading to bimodal activity distributions.

      Following the suggestion of the reviewer, we have now added this detail for clarity in Methods 7.5.4 and referenced this Methods section at line 237.

      Reviewer #1 (Significance (Required)):

      van der Plas et al. highlighted a novel dimensionality reduction technique that can be used with success for discerning functional connectivities in large-scale single-unit recordings. The proposed model belongs to a large collection of dimensionality reduction techniques (for review, Cunningham, J., Yu, B. Dimensionality reduction for large-scale neural recordings. Nat Neurosci 17, 1500-1509 (2014). https://doi.org/10.1038/nn.3776; Paninski, L., & Cunningham, J. P. (2018). Neural data science: accelerating the experiment-analysis-theory cycle in large-scale neuroscience. Current opinion in neurobiology, 50, 232-241.). The authors themselves highlighted some of the key methods, such as PCA, ICA, NNMF, variational auto-encoders, etc. The proposed cRBM model has also been published a few times by the same authors in previous works, although specifically pertaining to protein sequences. The use of RBM-like methods in uncovering functional connectivities is not novel either (see Hjelm RD, Calhoun VD, Salakhutdinov R, Allen EA, Adali T, Plis SM. Restricted Boltzmann machines for neuroimaging: an application in identifying intrinsic networks. Neuroimage. 2014 Aug 1;96:245-60. doi: 10.1016/j.neuroimage.2014.03.048.). However, given that the authors make a substantial improvement on the RBM network and have demonstrated the value of their model using physiological data, I believe that this paper would present itself as an attractive alternative to all readers who are seeking better solutions to interpret their data. However, as I mentioned in my comments, I would like to see more definitive evidence that the proposed solution has a serious advantage over other equivalent methods.

      Reviewer's expertise:

      This review was conducted jointly by three researchers whose combined expertise includes single-unit electrophysiology and two-photon calcium imaging, using which our lab studies the neurobiology of learning and memory and spatial navigation. We also have extensive experience in computational neuroscience, artificial neural network models, and machine learning methods for the analysis of neurobiological data. We are however limited in our knowledge of mathematics and engineering principles. Therefore, our combined expertise is insufficient to evaluate the correctness of the mathematical developments.

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

      In their manuscript, van der Plas et al. present a generative model of neuron-assembly interaction. The model is a restricted Boltzmann machine with its visible units corresponding to neurons and hidden units to neural assemblies. After fitting their model to whole-brain neural activity data from larval zebrafish, the authors demonstrate that their model is able to replicate several data statistics. In particular, it was able to replicate the pairwise correlations between neurons as well as assemblies that it was not trained on. Moreover, the model allows the authors to extract neural assemblies that govern the population activity and compose functional circuits and can be assigned to anatomical structures. Finally, the authors construct functional connectivity maps from their model that are then shown to correlate with established structural connectivity maps.

      Overall, the authors present convincing evidence for their claims. Furthermore, the authors state that their code to train their restricted Boltzmann machine models is already available on GitHub and that the data underlying the results presented in this manuscript will be made publicly available upon publication, which will allow people to reproduce the results and apply the methods to their data.

      One thing the authors could maybe discuss a bit more is the "right" parameter value M, especially since they used the optimal value of 200 found for one sample also for all the others. More specifically, how sensitive are the results to this value?

      PLANNED REVISION #1

      In the following we jointly address three of the reviewers’ questions (2 from reviewer 1, and 1 from reviewer 2).

      Shortly summarized, the cRBM model has 2 free parameters; the number of hidden units M and the regularization parameter lambda. In figures 2 and S1 we optimize their values through cross-validation, and then perform our further analyses on models with these optimal values. The reviewers ask us to examine the outcome of the model for slightly different values of both parameters, in particular in relation to the sensitivity of the cRBM results to selecting the optimal parameters and the change in inferred assemblies and their dynamics.

      We thank the reviewers for these questions and appreciate their curiosity to understand the effects of changing either of these two free parameters.

      We inspected these models when we performed the model selection (of Figures 2 and S1), but did not formalize our findings into figures for the manuscript. We found that small changes in the parameter setting did not abruptly change the inferred assemblies (e.g., M=100) apart from slightly changing in size, so we expect that the statistics that we intend to include in the proposed supplementary figure would reflect that, and it would definitely benefit the manuscript to include this analysis. Very-low-M settings are interesting to include, because assemblies are much larger - essentially merging smaller assemblies of higher-M models - at the cost of model performance.

      We propose to create additional supplementary figures that address these questions. As suggested, we will pick a few example cRBMs with different parameter settings (below-optimal M, above-optimal M, and same for lambda), as well as very low M settings (M~20 or 50). We will then show example assemblies and assembly dynamics, as well as the relevant statistics (assembly size, dynamics time scale etc) that describe them.

      And, what happens if one would successively increase that number, would the number of assemblies (in the sense of hidden units that strongly couple to some of the visible units) eventually saturate?

      This point will be addressed by inspecting models at different M values (see Revision Plan #1). We would like to further answer this question by referring to past work. In Tubiana et al., 2019, elife (Appendix 1) we have done this analysis, and the result is consistent with the reviewer’s intuition. Because of the sparsity regularization, if M becomes larger than its optimum, the assemblies further sparsify without benefiting model performance, and eventually new assemblies duplicate previous assemblies or become totally sparse (i.e., all weights = 0) to not further induce a sparsity penalty in the loss function. So the ‘effective’ number of assemblies indeed saturates for high M.

      Moreover, regarding the presentation, I have a few minor suggestions and comments that the authors also might want to consider:

      * In Figure 6C, instead of logarithmic axes, it might be better to put the logarithmic connectivity on a linear axis. This way the axes can be directly related to the colour bars in Figures 6A and B.

      We agree and thank you for the suggestion. We have changed this accordingly (and also in the equivalent plots in figure S6).

      * In Equation (8), instead of $\Gamma_{\mu}(I)$ it should be $\Gamma_{\mu}(I_{\mu}(v))$.

      Done, thank you.

      * In Section 7.0.5, it might make sense to have the subsection about the marginal distributions before the ones about the conditional distributions. The reason would be that if one wants to confirm Equation (8) one necessarily has to compute the marginal distribution in Equation (12) first.

      We thank the reviewer for the suggestion, but respectfully propose to leave the section ordering as is. We understand what the reviewer means, but Equation (8) can also be obtained by factorizing P(v,h) Equation (7) and removing the v_i dependency. In Equation (8), \Gamma can then be obtained by normalization. We believe this flow aligns better with the main text (where conditionals come first, when used for sampling, followed by the marginal of P(v) used for the functional connectivity inference).

      * In Line 647f, the operation the authors are referring to is strictly speaking not an L1-norm of the matrix block. It might be better to refer to that e.g. as a normalised L1-norm of the matrix block elements.

      Done, thank you.

      * In Line 22, when mentioning dimensionality reduction methods to identify assemblies, it might make sense to also reference the work by Lopes-dos-Santos et al. (2013, J. Neurosci. Methods 220).

      Done, thank you for the suggestion.

      Reviewer #2 (Significance (Required)):

      The work presented in this manuscript is very interesting for two reasons. First, it has long been suggested that assemblies are a fundamental part of neural activity and this work seems to support that by showing that one can generate realistic whole-brain population activity imposing underlying assembly dynamics. Second, in recent years much work has been devoted to developing methods to find and extract neural assemblies from data and this work and the modelling approach can also be seen as a new method to achieve that. As such, I believe this work is relevant for anyone interested in neural population activity and specifically neural assemblies and certainly merits publication.

      Regarding my field of expertise, I used to work on data analysis of neural population activity and in particular on the question of how one can extract neural assemblies from data. I have to say that I have not much experience with fitting statistical models to data, so I can't provide any in-depth comments on that part of the work, although what has been done seems plausible.

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

      Summary: Understanding the organization of neural population activities in the brain is one of the most important questions in neuroscience. Recent technique advance has enabled researchers to record a large number of neurons and some times the whole brain. Interpreting and extracting meaningful insights from such data sets are challenging. van der Pals \textit{et al} applied a generative model called compositional Restricted Boltzmann Machine (cRBM) to discover neuron assemblies from spontaneous activities of zebra fish brain. They found that neurons can be grouped into around 200 assemblies. Many of them have clear neurophysiological meaning, for example, they are anatomically localized and overlapped with known neural circuits. The authors also inferred a coarse-grained functional connectivity which is similar to known structural connectivity.

      The structure of the paper is well organized, the conclusion seems well supported by their numerical results. While this study provides a compelling demonstration that cRBM can be used to uncover meaningful structures from large neural recordings, the following issues limit my enthusiasm.

      Major:

      1) The overall implication is not clear to me. Although the authors mentioned this briefly in the discussion. It is not clear what else do we learn from discovered assemblies beyond stating that they are consist with previous study. For example, the author could have more analysis of the assembly dynamics, such as whether there are low dimensional structure etc.

      First, we will comment on our analysis of the assemblies, before we continue to discuss the main implications of our work, which we believe are the inferred generative model of the zebrafish brain and the perturbation-based connectivity matrix that we discovered. Further, we have implemented the reviewer’s suggestion of analyzing the low-dimensional structure of the hidden unit activity, as further detailed in Question 7.

      Indeed, the example assemblies that we show in Figure 3 have been thoroughly characterized in previous studies, which is why we chose to showcase these examples. Previous studies (including our own) typically focused on particular behaviors or sensory modalities, and aimed at identifying the involved neural circuit. Here, we demonstrate that by using cRBM on spontaneous activity recordings, one can simultaneously identify many of those circuits. In other words, these functional circuits/assemblies activate spontaneously, but in many different combinations and perhaps infrequently, so that it is very difficult to infer them from the full neural dynamics that they generate. cRBM has been able to do so, and Figure 3 (and supplementary video 1) serve to illustrate the variety of (known) circuits and assemblies that it inferred, some of which may represent true but not yet characterized circuits, which thus provide hypotheses for subsequent studies.

      Further, we believe that the implication of our study goes beyond the properties of the assemblies we’ve identified, in several ways.

      We demonstrate the power of cRBM’s generative capacity for inferring low-dimensional representations in neuroscience. Unlike standard dimensional reductionality methods, generative models can be assessed by comparing the statistics of experimental vs in-silico generated data. This is a powerful approach to validate a model, rarely used in neuroscience because of the scarcity of generative models compatible with large-scale data, and we hope that our study will inspire the use of this method in the field. We have made our cRBM code available, including notebook tutorials, to facilitate this.

      The generative aspect of our model allowed us to predict the effect of single-neuron perturbations between all ~ 10^9 pairs of neurons per fish, resulting in a functional connectivity matrix.

      We believe that the functional connectivity matrix is a major result for the field, similar to the structural connectivity matrix from Kunst et al., 2019, Neuron. The relation between functional and structural connectivity is unknown and of strong interest to the community (e.g., Das & Fiete, 2020, Nature Neuroscience). Our results allowed for a direct comparison of whole-brain region-by-region structural and functional connectivity. We were thus able to quantify the similarity between these two maps, and to identify specific region-pair matches and non-matches of functional and structural connectivity - which will be of particular interest to the zebrafish neuroscience community for developing future research questions.

      Further, using these trained models - that will be made public upon publication - anyone can perform any type of in silico perturbation experiments, or generate endless artificial data with matching data statistics to the in vivo neural recordings.

      We hope that this may convince the reviewer of the multiple directions of impact of our study. We will further address their comment on analysis of assembly dynamics below (question 7).

      2) The learning algorithm of cRBM can be interpreted as matching certain statistics between the model and the experiment. For a general audience, it is not easy to understand $\langle h_{\mu} \rangle_{data}, \langle v_ih_{\mu}\rangle_{data}$. Since these are not directly calculated from experimental observed activities $v_i$, but rather the average is conditioned on the empirical distribution of $p(v_i)$. For example, the meaning of $\langle v_ih_{\mu}\rangle_{data}$ means

      \begin{equation}

      \langle v_ih_{\mu}\rangle_{data} = \frac{1}{l}\sum_{\mathbf{v}\in S} \mathbb{E}{p(\mathbf{h|v})}(v_ih{\mu}),

      \end{equation}

      where $S$ is the set of all observed neural activities: $S = {\mathbf{v}^1, \cdots, \mathbf{v}^l}$. The authors should explain this in the main text or method, since they are heavily loaded in figure 2.

      We thank the reviewer for their suggestion, and have now implemented this. Their mathematics are correct; and we agree that it is not easy to understand without going through the full derivation of the (c)RBM. At the same time, we have tried not to alienate readers who might be more interested in the neuroscience findings than in understanding the computational method used. Therefore, we have kept mathematical details in the main text to a minimum (and have used schematics to indicate the statistics in Figures 2C-G), while explaining it in detail in Methods.

      Accordingly, we have now extended section 7.10.2 (“Assessment of data statistics”) that explains how the data statistics were computed in Methods (and have referenced this in Results and in Methods 7.5.5), using the fact that we already explain the process of conditioning on __v __in Methods 7.5.1. The following sentences were added:


      [..] However, because (c)RBM learn to match data statistics to model statistics (see Methods

      7.5.5), we can directly compare these to assess model performance. [..]

      [..]

      For each statistic 〈 fk〉 we computed its value based on empirical data 〈 fk〉_data and on the model 〈 fk〉_model, which we then quantitatively compared to assess model performance. Data statistics 〈 fk〉_data were calculated on withheld test data (30% of recording). Naturally, the neural recordings consisted only of neural data v and not of HU data h. We therefore computed the expected value of __ht at each time point t conditioned on the empirical data _v_t, as further detailed in Methods 7.5.1.

      [..]


      3) As a modeling paper, it would be great to have some testable predictions.

      We thank the reviewer for the enthusiasm and suggestion. We agree, and that is why we have included this in the form of functional connectivity matrices in Figures 5 and 6. To achieve this, we leveraged the generative aspect of the cRBM to perform in silico single-neuron perturbation experiments, which we aggregated to connectivity matrices. In other words, we have used our model to predict the functional connectivity between brain regions using the influence of single-neuron perturbations.

      Obtaining a measure of functional connectivity/influence using single-neuron perturbations is also possible using state-of-the-art neuro-imaging experiments (e.g., Chettih & Harvey, 2019, Nature), though not at the scale of our in silico experiments. We therefore verify our predictions using structural data from Kunst et al., 2019, which we have extended substantially. We provide our functional connectivity result in full, and hope that this can inspire future zebrafish research by predicting which regions are functionally connected, which includes many pairs of regions that have not yet directly been studied in vivo.

      Minor:

      1) The assembly is defined by the neurons that are strongly connected with a given hidden unit. Thus, some neurons may enter different assemblies. A statistics of such overlap would be helpful. For example, a ven diagram in figure 1 that shows how many of them assigned to 1, 2, etc assemblies.

      We thank the reviewer for this excellent suggestion. Indeed, neurons can be embedded in multiple assemblies. This is an important property of cRBMs, which deserves to be quantified in the manuscript. We have now added this analysis as a new supplementary figure 4. Neurons are embedded in an assembly if their connecting weight w_{i, \mu} is ‘significantly’ non-zero, depending on what threshold one uses. We have therefore shown this statistic for 3 values of the threshold (0.001, 0.01 and 0.1) - demonstrating that most neurons are strongly embedded in at least 1 assembly and that many neurons connect to more than 1 assembly.

      Updated text in Results:


      Further, we quantified the number of assemblies that each neuron was embedded in, which showed that increasing the embedding threshold did not notably affect the fraction of neurons embedded in at least 1 assembly (93% to 94%, see Figure S4).


      2) What does the link between hidden units in Figure 1B right panel mean?

      Thank you for the question, and we apologize for the confusion: if we understand the question right, the reviewer asks why the colored circles under the title ‘Neuronal assemblies of Hidden Units’ are linked. This schematic shows the same network of neurons as shown in gray at the left side of Fig 1B, but now colored by the assembly ‘membership’ of each neuron. Hence, the circles shown are still neurons (and not HUs), and their links still represent synaptic connections between neurons. We apologize for the confusion, and have updated the caption of Fig 1B to explain this better:


      “[..] The neurons that connect to a given HU (and thus belong to the associated assembly), are depicted by the corresponding color labeling (right panel).[..]”.


      3) A side-by-side comparison of neural activity predicted by model and the experimentally recorded activities would help the readers to appreciate the performance of the model. Such comparison can be done at both single neuron level or assembly level.

      We thank the reviewer for this suggestion. The cRBM model is a statistical model, meaning that it fits the statistics of the data, and not the dynamics. The data that it generates therefore (should) adhere to the statistics of the training data, but does not reflect their dynamics. We believe that showing generated activity side-by-side of empirical activity is therefore not a meaningful example of generated data, as this would exemplify the dynamics, which this model is not designed to capture. Instead, in Figure 2, we show the statistics of the generated data versus the statistics of the empirical data (e.g., Fig 2C for the mean activity of all neurons). We believe that this is a better example representation of the generative performance of the model.

      4) Definition of reconstruction quality in line 130.

      We thank the reviewer for the suggestion, and have added the following sentence after line 130:


      The reconstruction quality is defined as the log-likelihood of reconstructed neural data v___{recon} (i.e., __v that is first transformed to the low-dimensional h, and then back again to the high-dimensional __v___{recon}, see Methods 7.10.2).


      Further, please note that Methods describes the definition in detail (Eq 18 of the submitted manuscript), although we agree with the reviewer that more detail was required in the Results section at line 130.

      5) Line 165. If PCA is compared with cRBM, why other dimensionality reduction methods, such as k-means and non-negative matrix factorization, can not be compared in terms of the sparsity?

      Please see answer to question 1 from R1 and Revision Plan #2.

      6) Line 260, please provide minimum information about how the functional connectivity is defined based on assemblies discovered by cRBM.

      We apologize if this was not clear. The first paragraph of this section (lines 248-259) of the submitted manuscript, provides the detail that the reviewer asks for, and we realize that the sentence of line 260 is better placed in the first paragraph, as it has come across as a very minimal explanation of how functional connectivity is defined.

      We have now moved this sentence to the preceding paragraph, as well as specified the Method references (as suggested by this reviewer below), for additional clarity. We thank the reviewer for pointing out this sentence.

      7) Some analysis of the hidden units population activities. Such as whether or not there are interesting low dimensional structure from figure 4A.

      We thank the reviewer for their suggestion. In our manuscript we have used the cRBM model to create a low-dimensional (M=200) representation of zebrafish neural recordings (N=50,000). The richness of this model owes to possible overlaps between HUs/assemblies that can result in significant correlation in their activities. The latter is illustrated in Figure 4A-C: the activity of some HUs can be strongly correlated.

      The reviewer’s suggestion is similar; to perform some form of dimensionality reduction on the low-dimensional HU activity shown in Fig 4. We have now added a PCA analysis to Figure 4 to quantify the degree of low-dimensional structure in the HU dynamics, and show the results in a new panel Figure 4D.

      The following text has been added to the Results section:


      These clusters of HUs with strongly correlated activity suggest that much of the HU variance could be captured using only a small number of variables. We quantified this by performing PCA on the HU dynamics, finding that indeed 52% of the variance was captured by the first 3 PCs, and 85% by the first 20 PCs (Figure 4D).


      We believe that further visualization of these results, such as plotting the PC trajectories, would not further benefit the manuscript. The manuscript focuses on cRBM, and the assemblies/HUs it infers. Unlike PCA, these are not ranked/quantified by how much variance they explain individually, but rather they together ‘compose’ the entire system and explain its (co)variance (Figure 2). Breaking up a dominant activity mode (as found by PCA), such as the ARTR dynamics, into multiple HUs/assemblies, allows for some variation in activity of individual parts of the ARTR circuit (such as tail movement and eye movement generation), even though at most times the activity of these HUs is coordinated. We hope the reviewer agrees with our motivation to keep the manuscript focused on the nature of cRBM-inferred HUs.

      8) Figure 4B right panel, how did the authors annotate the cluster manually? As certain assembly may overlap with several different brain regions, for example, figure 4D.

      We thank the reviewer for this question, and we presume they meant to reference figure 3D as an example? For figure 4, as well as Figure 3, we used the ZBrain Atlas (Randlett et al., 2015) for definition of brain regions. This atlas presents a hierarchy of brain regions: for example, many brain regions are part of the rhombencephalon/hindbrain. This is what we used for midbrain/hindbrain/diencephalon. Further, many assemblies are solely confined to Optic Tectum (see Fig 3L), which we therefore used (split by hemisphere). Then, many brain regions are (partly) connected to the ARTR circuit, such as the example assembly of Figure 3D that the reviewer mentions. These we have all labeled as ARTR (left or right), though technically only part of their assembly is the ARTR. These two clusters therefore rather mean ‘ARTR-related’, in particular because their activity is locked to the rhythm of the ARTR (see Fig 4A). The final category is ‘miscellaneous’ (like Figure 3G).

      However we agree that this wasn’t clear from the manuscript text, so we have changed the figure 4C caption to mention that ‘ARTR’ stands for ARTR-related assemblies, which we hope clarifies that ARTR-clustered assemblies can exist of multiple, disjoint groups of neurons, which relate to the ARTR circuit.

      9) Better reference of the methods cited in the main text. The method part is quite long, it would be helpful to cite the section number when referring it in the main text.

      We thank the reviewer for this helpful suggestion, we agree that it would benefit the manuscript to reference specific sections of the Methods. We have now changed all references to Methods to incorporate this.

      10) Some discussion about the limitation of cRBM would be great.

      We thank the reviewer for this suggestion, and have now included this. As Reviewer 1 had the same suggestion, we refer our answer to questions 2 and 3 from R1 for more detail.

      Reviewer #3 (Significance (Required)):

      This work provides a timely new technique to extract meaningful neural assemblies from large scale recordings. This study should be interested to both researchers doing either experiments and computation/theory. I am a computational neuroscientist.

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

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      In the present manuscript, van der Plas et al. compellingly illustrated a novel technique for engendering a whole-brain functional connectivity map from single-unit activities sampled through a large-scale neuroimaging technique. With some clever tweaks to the restricted Boltzmann Machine, the cRBM network is able to learn a low-dimensional representation of population activities, without relying on constrained priors found in some traditional methods. Notably, using some 200 hidden layer neurons, the employed model was able to capture the dynamics of over 40,000 simultaneously imaged neurons with a high degree of accuracy. The extracted features both illustrate the anatomical topography/connectivities and capture the temporal dynamics in the evolution of brain states. The illustrated technique has the potential for wide-spread applications spanning diverse recording techniques and animal species. Furthermore, the prospectives of modeling whole-brain network dynamics in 'neural trajectory' space and of generating artificial data in silico make for very enticing reasons to adopt cRBM.

      Major comments:

      Line 164. The authors claim that conventional methods "such as k-means, PCA and non-negative matrix factorization" cannot be quantitatively assessed for quality on the basis that they are unable to generate new artificial data. Though partly true, in most neuroscience applications, this is hardly cause for concern. Most dimensionality reduction methods (with few exceptions such as t-sne) allow new data points to be embedded into the reduced space. As such, quality of encoding can be assessed by cross-validation much in the same way as the authors described, and quantified using traditional metrics such as percentage explained variance. The authors should directly compare the performance of their proposed model against that of NNMF and variational auto-encoders. Doing so would offer a more compelling argument for the advantage of their proposed method over more widely-used methods in neuroscience applications. Furthermore, a direct comparison with rastermap, developed by Stringer lab at Janelia (https://github.com/MouseLand/rastermap), would be a nice addition. This method presents itself as a direct competitor to cRBM. Additionally, the use of GLM doesn't do complete justice to the comparison point used, since a smaller fraction of data were used for calculating performance using GLM, understandably due to its computationally intensive nature.

      Line 26. The authors describe their model architecture as a formalization of cell assemblies. Cell assemblies, as originally formulated by Hebb, pertains to a set of neurons whose connectivity matrix is neither necessarily complete nor symmetric. Critically, in the physiological brain, the interactions between the individual neurons that are part of an assembly would occur over multiple orders of dependencies. In a restricted Boltzmann machine, neurons are not connected within the same layer. Instead, visible layer neurons are grouped into "assemblies" indirectly via a shared connection with a hidden layer neuron. Furthermore, a symmetrical weight matrix connects the bipartite graph, where no recurrent connectivities are made. As such, the proposed model still only elaborates symmetric connections pertaining to first-order interactions (as illustrated in Figure 4C). Such a network may not be likened with the concept of cell assemblies. The authors should refrain from detailing this analogy (of which there are multiple instances of throughout the text). It is true that many authors today refer to cell assemblies as any set of temporally-correlated neurons. However, saying "something could be a cell assembly" is not the same as saying "something is a cell assembly". How about sticking with cRBM-based cell assemblies (as used in section 2.3) and defining it beforehand?

      I would strongly recommend adding a paragraph discussing the limitation of using the cRBM, things future researchers need to keep in mind before using this method. One such recommendation is moving the runtime-related discussion for cRBM, i.e. 8-12 hrs using 16 CPU from Methods to Discussion, since it's relevant for an algorithm like this. Additionally, a statement mentioning how this runtime will increase with the length of recordings and/or with the number of neurons might be helpful. What if the recordings were an hour-long rather than 25mins. This would help readers decide if they can easily use a method like this.

      Line 515. A core feature of the proposed compositional RBM is the addition of a soft sparsity penalty over the weight matrix in the likelihood function. The authors claim that "directed graphical models" are limited by the a priori constraints that they impose on the data structure. Meanwhile, a more accurate statistical solution can be obtained using a RBM-based model, as outlined by the maximum entropy principle. The problem with this argument is that the maximum entropy principle no longer applies to the proposed model with the addition of the penalty term. In fact, the lambda regularization term, which was estimated from a set of data statistics motivated by the experimenter's research goals (Figure S1), serves to constrict the prior probability. Moreover, in Figure S1F, we clearly see that reconstruction quality suffers with a higher penalty, suggesting that the principle had indeed been violated. That being said, RBMs are notoriously hard to train, possibly due to the unconstrained nature of the optimization. I believe that cRBM can help bring RBM into wider practical applications. The authors could test their model on a few values of the free parameter and report this as a supplementary. I believe that different parameters of lambda could elaborate on different anatomical clusters and temporal dynamics. Readers who would like to implement this method for their own analysis would also benefit tremendously from an understanding of the effects of lambda on the interpretation of their data. Item (1) on line 35 (and other instances throughout the text) should be corrected to reflect that cRBM replaces the hard constraints found in many popular methods with a soft penalty term, which allows for more accurate statistical models to be obtained.

      Minor comments:

      From a neuroscience point of view, it might be interesting to show what results are achieved using different values of M (say 100 or 300), rather than M=200, while still maintaining the compositional phase. Is there any similarity between the cRBM-based cell assemblies generated at different values of M? Is there a higher chance of capturing certain dynamics either functional or structural using cRBM? For example, did certain cRBM-based cell assemblies pop up more frequently than others at all values of M (100,200,300)?

      The authors have mentioned that this approach can be readily applied to data obtained in other animal models and using different recording techniques. It might be nice to see a demonstration of that.

      Line 237. The justification for employing a dReLU transfer function as opposed to ReLU is unclear, at least within the context of neurobiology. Given that this gives rise to a bimodal distribution for the activity of HUs, the rationale should be clearly outlined to facilitate interpretability.

      Significance

      van der Plas et al. highlighted a novel dimensionality reduction technique that can be used with success for discerning functional connectivities in large-scale single-unit recordings. The proposed model belongs to a large collection of dimensionality reduction techniques (for review, Cunningham, J., Yu, B. Dimensionality reduction for large-scale neural recordings. Nat Neurosci 17, 1500-1509 (2014). https://doi.org/10.1038/nn.3776; Paninski, L., & Cunningham, J. P. (2018). Neural data science: accelerating the experiment-analysis-theory cycle in large-scale neuroscience. Current opinion in neurobiology, 50, 232-241.). The authors themselves highlighted some of the key methods, such as PCA, ICA, NNMF, variational auto-encoders, etc. The proposed cRBM model has also been published a few times by the same authors in previous works, although specifically pertaining to protein sequences. The use of RBM-like methods in uncovering functional connectivities is not novel either (see Hjelm RD, Calhoun VD, Salakhutdinov R, Allen EA, Adali T, Plis SM. Restricted Boltzmann machines for neuroimaging: an application in identifying intrinsic networks. Neuroimage. 2014 Aug 1;96:245-60. doi: 10.1016/j.neuroimage.2014.03.048.). However, given that the authors make a substantial improvement on the RBM network and have demonstrated the value of their model using physiological data, I believe that this paper would present itself as an attractive alternative to all readers who are seeking better solutions to interpret their data. However, as I mentioned in my comments, I would like to see more definitive evidence that the proposed solution has a serious advantage over other equivalent methods.

      Reviewer's expertise:

      This review was conducted jointly by three researchers whose combined expertise includes single-unit electrophysiology and two-photon calcium imaging, using which our lab studies the neurobiology of learning and memory and spatial navigation. We also have extensive experience in computational neuroscience, artificial neural network models, and machine learning methods for the analysis of neurobiological data. We are however limited in our knowledge of mathematics and engineering principles. Therefore, our combined expertise is insufficient to evaluate the correctness of the mathematical developments.

  5. www.researchgate.net www.researchgate.net
    1. Cross-group friendship led to decreases incortisol reactivity (a hormonal correlate of stress; W. R. Lovallo & T. L. Thomas, 2000) over 3 friendshipmeetings among participants high in race-based rejection sensitivity (R. Mendoza-Denton, G. Downey,V. J. Purdie, A. Davis, & J. Pietrzak, 2002) and participants high in implicit prejudice (A. G. Greenwald,B. A. Nosek, & M. R. Banaji, 2003). Cross-group partners’ prior intergroup contact moderated therelationship between race-based rejection sensitivity and cortisol reactivity. Following the manipulation,participants kept daily diaries of their experiences in an ethnically diverse setting. Implicitly prejudicedparticipants initiated more intergroup interactions during the diary period after making a cross-groupfriend. Participants who had made a cross-group friend reported lower anxious mood during the diaryperiod, which compensated for greater anxious mood among participants high in race-based rejectionsensitivity.
    1. Author Response

      Reviewer #1 (Public Review):

      This study is a follow-up to the previous work by the authors in establishing a surprising role for the presynaptic adhesion molecules, neurexin (Nrxn) variants containing the SS4+ splice site, in differentially controlling postsynaptic NMDA and AMPA receptors by forming links through a shared system of extracellular cerebellins (Cbln) and postsynaptic GluD1. Here the authors show at CA1 to subiculum synapses, that the role for Clbn2 in mediating the effects of Nrxn1-SS4+ and Nrxn3-SS4+ in enhancing NMDAR and suppressing AMPAR, respectively, is redundant with that of Clbn1. Moreover, Clbns do not appear to play a role in synapse formation. Dai and colleagues extend their previous work also by highlighting the common function for Nrxn-Clbn signaling system across different synapses albeit with subtle differences and point to a lack of a role for Nrxn-Clbn signaling in morphological synapse development. Overall the data are solid, while the key findings are mostly incremental, and the basis for the selectivity in the observed differential regulation of AMPARs and NMDARs via the same trans-synaptic link through Clbns at various types of synapses remain to be clarified. Importantly, the authors make a definitive conclusion concerning the lack of a role for Nrxn-Cbln signaling complexes in synapse formation during development. Nevertheless, this is a contentious issue, and as such, the conclusions could be more compellingly supported with further experiments.

      We appreciate the reviewer’s positive assessment of our study.

      Reviewer #2 (Public Review):

      In this manuscript Dai et al. investigated the role of Nrxn-Cbln complexes in regulating AMPA- and NMDA- receptor function in different brain regions. Using a combination of genetic manipulations, together with electrophysiological and biochemical assays, the authors showed that, at CA1-subiculum synapses, Cbln2 regulates NMDA- and AMPA- receptors via Nrxn1SS4+ -Cbln2 and Nrxn3SS4+-Cbln2 signaling complexes, respectively. In the prefrontal cortex, only Nrxn1SS4+-Cbln2 signaling-dependent regulation of NMDA receptors occurs, while in the cerebellum, only Nrxn3SS4+-Cbln1 signaling-dependent regulation of AMPA receptor occurs. This systematic investigation of the function of different Neurexin-Cerebellin signaling complexes contributes to our understanding of how different members of the same family, in combination pairs, regulate synaptic transmission with circuit specificity. This work adds to the authors' systemic investigation of molecular mechanisms regulating synaptogenesis, synaptic transmission and synaptic plasticity.

      We thank the reviewer for the positive and astute comments.

      Some suggestions for clarifications:

      1) Regarding expression of Cbln1 in the subiculum, in lines 271-273, the authors stated that "in these and earlier experiments we only studied Cbln2, but quantifications show that Cbln1 is also expressed in the subiculum, albeit at lower levels Figure S3)." However, Figure S3 does not include any quantifications, and the example image does not show visible Cbln1 expression. Thus, the above-mentioned statement is inconsistent with the data presented. Please revise. If the authors would like to keep the statement about quantifications of Cbln1, then quantification should be provided for all panels of this Figure, in order to give the readers some ideas about relative expression levels.

      We agree, and have addressed this issue as described above (introductory point 4).

      2) Does Cbln4, which is also broadly expressed in the brain, play a role in regulating AMPA- and NMDA-receptors at the synapses investigated? Does Cbln3 contribute to regulation of synaptic transmission in the cerebellum? Please discussion.

      Cbln4 is not expressed in the subiculum, but is expressed in the PFC. Specifically, Cbln1, Cbln2, and Cbln4 are broadly expressed in brain, whereas Cbln3 is restricted to cerebellar granule cells and requires Cbln1 or Cbln2 for secretion (Bao et al., 2006; Miura et al., 2006). Remarkably, Cbln1, Cbln2, and Cbln4 are not uniformly expressed in all neurons, but synthesized in restricted subsets of neurons (Seigneur and Südhof, 2017). For example, cerebellar granule cells express high levels of Cbln1 but only modest levels of Cbln2, excitatory entorhinal cortex (EC) neurons express predominantly Cbln4, and neurons in the medial habenula (mHb) express Cbln2 or Cbln4 (Seigneur and Südhof, 2017).

      Cbln4 is poorly studied, and Cbln3 has not been functionally studied at all. To the best of our knowledge, there are only four studies on Cbln4 function, three of which are from our lab. The Seigneur & Sudhof (2018) paper showed that the deletion of Cbln4 in a large number of brain regions caused no change in excitatory or inhibitory synapse numbers. Subsequently, the Seigneur et al. (2018) paper demonstrated that genetic deletion of Cbln4 in the mHb had no major effect on synapse numbers, but because of the limits of this preparation (synaptic transmission is hard to monitor in the mHB), no detailed synaptic studies were done. The Fossati et al. (2019) paper in Neuron shows that Cbln4 regulates inhibitory synapse numbers in the cortex by binding to GluD1, but this study depended on RNAi, not genetic manipulations. Its results are puzzling because structural biology studies have shown that Cbln4 does not bind to GluD2, which is highly homologous to GluD1 and has the same function as GluD1. Instead of binding to GluD’s, Cbln4 binds to another class of receptors, Neogenin-1 and DCC, making the Fossati et al. (2019) paper difficult to interpret. The Liakath-Ali et al. (2022) paper, finally, demonstrated that deletion of Cbln4 in the EC or deletion of Neo1 in the dentate gyrus (DG) blocks long-term potentiation at EC→DG synapses but does not change basal synaptic transmission or synapse numbers, again consistent with the notion that Cbln4 regulates synapse properties similar to Cbln1 and Cbln2.

      We have now described these studies in the introduction to the paper. Many synaptic proteins are associated with contentious studies in the literature, and we completely concur that it is essential to evenly discuss the issues in detail, even if this expands the size of a paper.

      Reviewer #3 (Public Review):

      In this study, Dai and colleagues used genetic models combined to electrophysiological recordings and behavior as well as immunostaining and immunoblotting to investigate the role of trans-synaptic complexes involving presynaptic neurexins and cerebellins in shaping the function of central synapses. The study extends previous findings from the same authors as well as other groups showing an important role of these complexes in regulating the function of central synapses. Here, the authors sought to achieve two main objectives: (1) investigating whether their previous findings obtained at mature CA1-> subiculum synapses (Aoto et al., 2013; Dai et al., Neuron 2019; Dai et al., Nature 2021) extend to different synapse subtypes in the subiculum as well as to other central synapses including cortical and cerebellar synapses and (2) investigating whether Nrx-Cbln-GluD trans-synaptic complexes play a role in synapse formation as previously proposed by other groups.

      Overall, the study provides interesting and solid electrophysiological data showing that different Nrxns and Cblns assemble trans-synaptic complexes that differently regulate AMPAR and NMDAmediated synaptic transmission across distinct synaptic circuits (most likely through binding to postsynaptic GluD receptors).

      We appreciate the reviewer’s accurate and positive assessment of our study.

      However, the study has several important weaknesses:

      1) The novelty of the findings appears limited. Indeed, previous studies from the same authors with similar experimental paradigms and readouts already demonstrated the role of Nrxn-CblnGluD complexes in regulating AMPARs versus NMDARs in mature neurons (Aoto et al., Cell 2013; Dai et al., Neuron 2019; Dai et al., Nature 2021). Moreover, the absence of role of Cblns and GluD receptors in synapse formation was already suggested in previous studies from the same authors (Seigneur and Sudhof, J Neurosci 2018; Seigneur et al., PNAS 2018; Dai et al., Nature 2021).

      Not surprisingly, we disagree with this comment. We do concur that our data are consistent with previous studies, but believe that this reproducibility is a strength since so many data in the literature are irreproducible.

      We do not agree, however, that our findings lack novelty. The novelty is admittedly limited, after all we like to be consistent, but our paper is the first to demonstrate that the Nrxn1/Cbln/GluD and Nrxn3/Cbln/GluD complexes are differentially active in different synapses, with the subiculum synapses having both, the mPFC synapses only the former, and the cerebellum only the latter. This is a very important innovation that illustrates the power of the Nrxn/Cbln/GluD signaling complex in shaping synapses. In addition, our paper is the first to analyze a possible developmental function of Cbln2 in depth, to analyze its differential role at the two dominant types of pyramidal neurons in the subiculum, regular- and burst-spiking neurons, to analyze conditional deletions of Cbln1 in the adult cerebellum, and to directly measure the effect of Cbln2 deletions in the PFC. Especially in view of the recent Nature paper that concluded that Cbln2 regulates spine numbers in the PFC, these findings are highly relevant.

      2) The conclusion made by the authors that the Nrxn-Cbln-GluD trans-synaptic complexes do not play a role in synapse formation/development is not sufficiently supported by their data, while previous studies suggest the opposite. Actually, this conclusion is essentially based on the two following measurements taken as a 'proxy' for synapse density: (1) 'the average vGluT1 intensity calculated from the entire area of subiculum' and (2) the 'synaptic proteins levels' assessed by immunoblotting. None of these measurements (only performed in the subiculum) allow to precisely assess synapse density on the neurons of interest. While the average vGluT1 intensity over large fields of view does not directly reflect the density of synapses and does not take into account the postsynaptic compartment, the immunoblotting data only reflects the overall expression of synaptic proteins without discriminating between intracellular, surface and synaptic pools and between cell types. In the subiculum from Cbln1+2 KO mice, the authors performed mEPSCs recordings and found an increase in frequency. However, this increase may reflect the unsilencing and/or potentiation of AMPAR-EPSCs above the detection threshold, irrespectively of the actual synapse number. Finally, the decrease in NMDAR-EPSCs is not discussed by the authors while it could actually reflect a decrease in synapse number.

      We agree that additional data on synapse numbers are helpful for our paper. We have now performed these studies as described in detail in our response to introductory point 1 above. However, we would also like to refer to the already existing body of evidence on the role of neurexin-based complexes in synapse numbers. We have shown in papers published over the last two decades that deletions of individual neurexins or of multiple neurexins, as well as blocking cerebellin binding to neurexins by ablating splicing site #4 (SS4) in neurexins, have NO effect on synapse numbers. The most important of these papers are:

      1. Missler, M., Zhang, W., Rohlmann, A., Kattenstroth, G., Hammer, R.E., Gottmann, K., and Südhof, T.C. (2003) α-Neurexins Couple Ca2+-Channels to Synaptic Vesicle Exocytosis. Nature 423, 939948.
      2. Kattenstroth, G., Tantalaki, E., Südhof, T.C., Gottmann, K., and Missler, M. (2004) Postsynaptic Nmethyl-D-aspartate receptor function requires α-neurexins. Proc. Natl. Acad. Sci. U.S.A. 101, 2607-2612.
      3. Dudanova, I., Tabuchi, K., Rohlmann, A., Südhof, T.C., and Missler, M. (2007) Deletion of α-Neurexins Does Not Cause a Major Impairment of Axonal Pathfinding or Synapse Formation. J. Comp. Neurol. 502, 261-274.
      4. Etherton, M.R., Blaiss, C., Powell, C.M., and Südhof, T.C. (2009) Mouse neurexin-1α deletion causes correlated electrophysiological and behavioral changes consistent with cognitive impairments. Proc. Natl. Acad. Sci. U.S.A. 106, 17998-18003.
      5. Soler-Llavina, G.J., Fuccillo, M.V., Ko, J., Südhof, T.C., and Malenka, R.C. (2011) The neurexin ligands, neuroligins and LRRTMs, perform convergent and divergent synaptic functions in vivo. Proc. Natl. Acad. Sci. U.S.A. 108, 16502-16509.
      6. Aoto, J., Martinelli, D.C., Malenka, R.C., Tabuchi, K., and Südhof, T.C. (2013) Presynaptic Neurexin-3 Alternative Splicing Trans-Synaptically Controls Postsynaptic AMPA-Receptor Trafficking. Cell 154, 75-88. PMCID: PMC3756801.
      7. Aoto, J., Földy, C., Ilcus, S.M., Tabuchi, K., and Südhof, T.C. (2015) Distinct circuit-dependent functions of presynaptic neurexin-3 at GABAergic and glutamatergic synapses. Nat Neurosci. 18, 997-1007.
      8. Anderson, G.R., Aoto, J., Tabuchi, K., Földy, F., Covy, J., Yee, A.X., Wu, D., Lee, S.-J., Chen, L., Malenka, R.C., Südhof, T.C. (2015) α-Neurexins Control Neural Circuit Dynamics by Regulating Endocannabinoid Signaling at Excitatory Synapses. Cell 162, 593-606. PMCID: PMC4709013
      9. Chen, L.Y., Jiang, M., Zhang, B., Gokce, O., and Südhof, T.C. (2017) Conditional Deletion of All Neurexins Defines Diversity of Essential Synaptic Organizer Functions for Neurexins. Neuron 94, 611-625. PMCID: PMC5501922
      10. Dai, J., Aoto, J., and Südhof, T.C. (2019) Alternative Splicing of Presynaptic Neurexins Differentially Controls Postsynaptic NMDA- and AMPA-Receptor Responses. Neuron 102, 993-1008. PMCID: PMC6554035
      11. Luo, F., Sclip, A., Jiang, M., and Südhof, T.C. (2020) Neurexins Cluster Ca2+ Channels within presynaptic Active Zone. EMBO J. 39, e103208. PMCID: PMC7110102
      12. Khajal, A.J., Sterky, F.H., Sclip, A., Schwenk, J., Brunger, A.T., Fakler, B., and Südhof, T.C. (2020) Deorphanizing FAM19A Proteins as Pan-Neurexin Ligands with an Unusual Biosynthetic Binding Mechanism. J. Cell Biol. 219, e202004164
      13. Luo, F., Sclip, A., and Südhof, T.C. (2021) Universal role of neurexins in regulating presynaptic GABAB-receptors. Nature Comm. 12, 2380. PMCID: PMC8062527
      14. Wang, C.Y., Trotter, J.H., Liakath-Ali, K., Lee, S.J., Liu, X., and Südhof, T.C. (2021) Molecular SelfAvoidance in Synaptic Neurexin Complexes. Science Advances 7, eabk1924. PMCID: PMC8682996
      15. Dai, J., Patzke, C., Liakath-Ali, K., Seigneur, E., and Südhof, T.C. (2021) GluD1, A signal transduction machine disguised as an ionotropic receptor. Nature 595, 261-265. PMCID: PMC8776294

      Individual papers may not convince the reviewer, but the cumulative evidence seems to us to be hopefully persuasive. We have published less evidence on the lack of a role of cerebellins and GluD’s in synapse numbers than on neurexins, but the only in-depth studies of these molecules by others are in the cerebellum. Here, deletions of Cbln1 and GluD2 indeed cause a significant, albeit partial, loss of synapses. However, this loss may not be due a lack of synapse formation, but to an elimination of synapses that have been formed, as demonstrated by many beautiful papers from leading investigators. It is regrettable that reviews and textbooks continue to state that cerebellins mediate synapse formation as an established fact because as far as we can see, there is limited evidence for that conclusion, but it keeps coming back again and again.

      3) The authors do not provide sufficient data in order to interpret the increase in AMPAR-EPSCs and decrease in NMDAR-EPSCs amplitudes. Are the changes in AMPARs and NMDARs occurring at pre-existing synapses or do they result from alterations in the number of physical synapses and/or active synapses (see point#2)? In particular, the increase in AMPAR/NMDAR ratio accompanied by the increase in mEPSCs frequency might be well explained by the unsilencing of some synapses and/or by the fact that the available pool of AMPARs is distributed over a smaller number of synapses, resulting in higher quantal size. These effects could explain the blockade of LTP, i.e., through an occlusion mechanism.

      We addressed these points in previous studies (Aoto et al., 2013; Dai et al., 2019 and 2021), as discussed and cited in the present paper, and expanded on these points in the present paper.

      In a nutshell, we showed by surface AMPAR staining that presynaptic Nrxn3-SS4+ decreases postsynaptic AMPAR levels, and by direct application of AMPA that it decreases the functional surface levels of AMPARs, whereas presynaptic Nrxn1-SS4+ increases the functional surface levels of NMDARs. We also demonstrated the opposite effects for the GluD1 KO, and furthermore showed by minimal stimulation experiments that the Cbln2 deletion does not alter the number of silent synapses. In the present manuscript, we performed a detailed analysis of the miniature quantal size for AMPAR- and NMDAREPSCs.

      Finally, we have demonstrated in a large number of papers, including this one, that genetic manipulations of neurexins, cerebellins, and GluD’s do not alter synapse numbers with a few exceptions in which synapses are secondarily eliminated, like in the cerebellum. Together, these data show that the observed changes are mediated by a regulation of postsynaptic functional AMPARs and NMDARs, not by alterations in synapse numbers or by synapse silencing/unsilencing.

      4) The authors did not demonstrate (or did not cite relevant studies) that the deletion of Cbln1 and/or Cbln2 does not affect the expression of the remaining Cblns isoforms (Cbln2 and/or Cbln4) or Nrxns1/3 and GluD1/2. This verification is important to preclude the emergence of any compensatory effect.

      To address this point, we have now measured the mRNA expression levels of Nrxns, Cblns, and GluDs in both the subiculum and the prefrontal cortex in littermate P35-42 Cbln2 WT and KO mice. The result show that the constitutive Cbln2 deletion causes no compensatory expression effects (new suppl Fig. S5). Please note that compensatory expression effects are often raised as a possibility for explaining genetically induced changes (or the lack thereof), but such effects are virtually never found.

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      Reply to the reviewers

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

      Summary: Klein and colleagues generate an ES cell model system with inducible FACT depletion to understand how loss of FACT affects gene regulation in ES cells. They find that FACT is critical for ES cell maintenance through multiple mechanisms including direct regulation of key pluripotency transcription factors (Sox2, Oct4, and Nanog), maintaining open chromatin at enhancers, and regulated enhancer RNA transcription. The paper is well-written, the experiments are generally well-controlled and appropriately interpreted and placed within the context of the field.

      We appreciate the Reviewer’s support of this manuscript.

      Major comments: 1. In general, the ChIP-seq and CUT&RUN data are not that similar. Although correlation seems reasonable (S2A), looking at the heatmaps in S2B/C these seem pretty different. It's not very clear if this is a case where CUT&RUN has higher specificity (and signal-to-noise, which is very clear from example tracks) or if these two methods are picking up biologically different sites. Could the authors include some overlap analysis of peaks and comment on these discrepancies. Looking at the example tracks in Figure 2B, it seems likely that prior SPT16 and SSRP1 ChIP-seq were relatively high-noise.

      We have identified overlapping peaks between the two techniques, and while CUT&RUN identified substantially more peaks overall, percentage of peaks shared between datasets were relatively consistent (1-6% of total) between the individual ChIP-seq datasets and the CUT&RUN dataset (Response Figure 1). We note that the biological classes identified through all datasets were remarkably consistent (Fig. 2D), and therefore attribute the discrepancies to the greater number of reproducible peaks called from CUT&RUN data. As discussed in the paper, peak calling algorithms designed for the specific data types were used, and therefore peak calling could also contribute to differences.

      Response Figure 1. ChIP-seq and CUT&RUN peak overlap. Pie chart depicting the unique and overlapping peaks called from V5-SPT16 CUT&RUN data and FACT ChIP-seq data. These data are included in the revised manuscript (as a new Figure panel 2E). Peaks must have been identified in at least two technical or biological replicates.

      Are motifs described in Figure 2E CUT&RUN only, and do prior ChIP-seq experiments also identify these motifs?

      The motifs shown in Figure 2E (now 2F) are indeed CUT&RUN peaks only. We were unable to confidently assign enriched motifs to the ChIP-seq datasets (the most enriched motifs were approximately p = 10-18). By analyzing all SPT16 ChIP-seq peaks, rather than only intersected SPT16 ChIP-seq peaks, we were able to identify motifs recognized by two of the top three CUT&RUN motif hits (SOX2 and OCT4/SOX2/TCF/NANOG); however, enrichment was quite poor (p = 10-3). By limiting the analysis to intergenic regions, we were able to identify strong enrichment for motifs recognized by CTCF and BORIS (p = 10-58 and 10-51, respectively). As validation, we also called motifs from peak files published as supplementary material to the original Tessarz lab manuscript but were still unable to confidently call motifs (all p > 10-7 for SPT16 peaks, p > 10-15 for SSRP1 peaks). Related to major comment 1, we suspect that the weak motif enrichment is due to high background in ChIP-seq datasets compared to CUT&RUN datasets.

      The authors state that FACT depletion affects eRNA transcription and measured this using TT-seq. The analysis in Figure 3B seems to be all the different types of sites looked at together (genes, PROMPTs, etc). Is there evidence that eRNAs specifically are regulated by FACT loss.

      We apologize for the confusion and have clarified that Figure 3B (now 3A) is referring to mRNAs only in the text and figure. Our analysis of eRNA regulation by FACT is predominantly contained within Fig. 4B (TT-seq from DHSs, but no histone mark overlap assessment), Supp Fig. S4 (as in Fig 4B, but at DHSs overlapping H3K27ac or H3K4me1), Fig. 5E (FACT localization to putative enhancers, defined as in S4), and Fig. 6D (ATAC-seq demonstrating loss of accessibility at putative enhancers upon FACT depletion). Based on these results, we believe there are many eRNAs specifically misregulated by FACT loss and that potential direct targets (based on change in depletion and containing FACT binding) are in Fig 5E.

      Could these be compared to DHS sites that lack FACT binding to support a direct role for FACT at these sites?

      We appreciate the suggestion and have performed this analysis (see Response Figure 2). Relatedly, we analyzed putative silencers, defined as DHSs marked by H3K27me3, for FACT binding and expression changes (measured by TT-seq) following FACT depletion (Supp Fig. S7). As expected, FACT does not bind these putative silencer DHSs and transcription does not markedly increase or decrease from these regions after FACT depletion. Complicating the matter, FACT binds at many DHSs, even those that did not to meet our stringent peak-calling criteria (see Response Figure 2, middle cluster).

      __Response Figure 2. Overlap between FACT binding sites and gene-distal DHSs. __Individual clusters are sorted by V5-SPT16 binding. Clusters were assigned based on direct overlap between called V5-SPT16 peaks and assigned gene-distal DHSs. Overall, 17.6% of DHSs overlapped a FACT peak identified in at least one CUT&RUN replicate (8.5% of DHSs overlapped a peak present in multiple replicates).

      One mechanism proposed for how FACT regulates enhancers is that it is required for maintaining a nucleosome free area, and when FACT is depleted nucleosomes invade the site (Figure 7). It wasn't clear if they compared distal DHS sites were FACT normal bound to those without FACT binding in the MNase experiments, which could help support the direct role or specificity of FACT in regulating those enhancers (or a subset of them).

      We have subset the V5-SPT16 CUT&RUN peaks and distal DHSs into groups and have identified increased nucleosome occupancy after depletion at both FACT-bound and FACT-unbound DHSs suggesting both direct and indirect regulation (Fig. 6A, D). There is disruption to nucleosome arrays at non-FACT-bound DHSs (although more modest relative to the FACT bound locations), and therefore we speculate that a nucleosome remodeler is involved downstream of FACT (possibly CHD1, per recent work out of Patrick Cramer and François Robert’s labs, among others).

      1. Data quality for nucleosome occupancy was a little strange (Figure 7F), where the two clones had very different MNase patterns at TSS sites. Could the authors comment on why there is such a strong difference between clones here.

      We agree that the trends identified by visualizing differential MNase-seq signal near TSSs do not fully replicate; however, in examining the nondifferential MNase-seq heatmaps, we see a more expected distribution (see new Figure 7A). Per our newly-added Supp Fig. S9B, all MNase-seq replicates had a pairwise Pearson correlation value of at least 0.73 (SPT16-depleted clone 1/rep 1 vs untagged rep 3), and the vast majority of samples had pairwise correlations of above 0.85, suggesting that these discrepancies are not due to strong differences in sequencing depth or MNase-protected regions. We therefore suspect that the clonal distinctions are a result of different background occupancy of nucleosomes near the TSS, resulting in an array with increased occupancy in one clone and more generalized increased occupancy in the other clone. We also added the MNase-seq data over TSSs in a non-differential form in Fig 7A, and believe the difference between the clones is due to the differential analysis, and have commented accordingly in the revised manuscript.

      More details on some of the analysis steps would be really helpful in evaluating the experiments. Specifically, was any normalization done other than depth normalization? I ask this because the baseline levels for many samples in metaplots look quite different. For example, see Figure 7B where either clone 1 has a globally elevated (at least out 2kb) ratio of nucleosome in the IAA samples relative to the EtOH, or there is some technical difference in MNase. One suggestion is to look at methods in the CSAW R package to allow TMM based normalization strategies which may help.

      We appreciate the suggestion – we have expanded our explanation of normalization methodology in the paper. We initially used quartile and RPGC normalizations to attempt to mitigate technical differences in MNase-seq data. Size distribution plots did not suggest differences in MNase digestion between samples, and neither quartile/RPGC nor TMM-based normalization fully resolved this issue. Because our ATAC-seq datasets agree with the general trends identified by MNase-seq (which are consistent, despite technical differences between clones), we do not believe that the differences constitute true biological difference, but rather experimental noise.

      1. I appreciated the speculation section, and the possible relationship between FACT and paused RNAPII is interesting. While further experiments may be outside the scope of this work and I am not suggesting they do them, I am wondering if others have information on locations of paused RNAPII in ESC that would allow them to test if genes with paused RNAPII have a special requirement for FACT that they could use their current data to assess.

      We agree that experiments to test the relationship between paused RNAPII and FACT are an intriguing next step, and plan to dissect those in the near future.

      Minor comments: 1. When describing the peaks found in the text related to Figure 2 they refer to 'nonunique' peaks. Does this mean the intersection of the independent peak calls? Could they clarify this.

      We apologize for the confusion and have clarified in the text that nonunique peaks does indeed refer to the intersection of independent peak calls (now specified on manuscript page 8, line 15).

      In the text they refer to H3K56ac data in S2D and I don't see that panel. The color scheme for the 1D heatmaps (Figure 5A) is tough to appreciate the differences. I'd suggest something more linear rather than this spectral one might be easier to see.

      We apologize for the confusion and removed the remaining H3K56ac-related data and references in the text. We appreciate the suggestion regarding the 1D heatmap color scheme and have adjusted the colors to a linear (white à red) scheme.

      For the 2D heatmaps of binding, could they include the number of elements they are looking at for each group?

      We appreciate the suggestion and have included numbers of elements visualized wherever applicable in the figure panels and legends.

      1. Also for 2D heatmaps, I think the scale is Log2 (IAA/EtoH), but could they confirm that and include it in the figure?

      We apologize for the confusion; the only heatmaps displaying log2(IAA:EtOH) are those in Fig. 6; for those panels, we have clarified the scale in the figure and legend.

      Reviewer #1 (Significance (Required)):

      • The use of degrader based approaches to depleting a protein allows refined kinetic and temporal assays which I think are important. Several papers showed a rapid invasion of nucleosomes after SWI/SNF loss using these kinds of approaches and revealed surprisingly fast replacement of SWI/SNF. This paper is consistent with those models, showing that another remodeler behaves the same, suggesting there may be general requirements for active chromatin remodeling to maintain the expression of these genes. It also highlights a key gap in how specificity works to target these enzymes remains somewhat unknown.

      • This work will be of interest to those studying detailed mechanisms of gene regulation. Compared to some other chromatin regulators, FACT is understudied and so this work will allow comparison between different chromatin remodeling complexes.

      • My experience: chromatin, gene regulation, cancer, genomics

      We appreciate the thorough review and hope that we have sufficiently addressed your concerns.

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

      The authors propose that the FACT complex can regulate pluripotency factors along with their regulatory targets through non-genic locations. They find that acute depletion of FACT leads to a "reduction" in pluripotency in mouse embryonic stem cell by disrupting transcription of master regulators of pluripotency. They also show FACT depletion affected the transcription of gene distal regulatory sites, but not silencers. They also stated that SPT16 depletion resulted in both, a reduction of chromatin accessibility and increase of nucleosome occupancy over FACT bound sites.

      Overall the study appears technically well executed. The use of an Auxin induced depletion system is a good model to study the acute effects of FACT depletion. However, I have a number of concerns relating to specificity and interpretation of the results that need to be addressed. We appreciate the careful review and have addressed your comments below:

      Major points o Authors claimed that depletion of the FACT complex "triggers a reduction in pluripotency". As evidence supporting this statement they present images of alkaline phosphatase assays of a time course performed upon depletion of FACT. These experiments indeed show that ESCs are destabilized in the absence of SPT16. However, some key questions regarding the phenotype remain unresolved: o What is are the kinetics of expression of selected naïve pluripotency and early differentiation markers? Are differentiation markers upregulated, consistent with normal differentiation upon FACT depletion?

      We appreciate the suggestion and have emphasized the decrease in pluripotency factor expression, accompanied by an increase in differentiation marker expression across all three germ layers. We graphed 7 pluripotency factors and 7 differentiation markers for each germ layer; generally speaking, pluripotency factors are decreased while differentiation markers are increased (Response Figure 4; pluripotency factors are included in the new Fig. 3B, while differentiation markers are included in the new Supp Fig. S3 F-H).

      We have also performed an immunocytochemistry (ICC) timecourse, per Reviewer 3’s suggestion. This ICC timecourse allows us to orthogonally assess decreased pluripotency factor expression, to pair with the OCT4 Western blot shown in Supp Fig. S1B. These new ICC data are shown in the new Fig. 1D and included here for convenience (Response Figure 5). In addition, we have added alkaline phosphatase staining at 12 hours of depletion to Fig. 1C.

      __Response Figure 4. Plots of DESeq2 analysis across experimental timecourse. __Shown are lineage markers denoting: A. Pluripotency B. Endoderm C. Mesoderm and D. Ectoderm. Generally, expression of pluripotency factors decrease over time, while differentiation markers of each lineage increase over time. These data are shown in Figure 3B and Supplemental Figure S3F-H.

      __Response Figure 5. Immunocytochemistry timecourse depicting DAPI staining (left panels, blue) and OCT4 immunofluorescence (right panels, green). __Images are representative of plate-wide immunofluorescence changes.

      O Is only ESC identity affected or does loss of FACT impair viability also of cells that have exited pluripotency? To address this, growth curves and/or cell cycle analysis upon FACT depletion could be performed. Alternatively, the authors could utilize surface markers to distinguish naïve pluripotent form differentiated cells in the cell cycle analysis experiments to identify a potential differential response of pluripotent and differentiated cells to FACT depletion.

      We have performed a growth curve with FACT depletion as suggested; as the two points are related, we will explain further below:

      o Another key question is whether it is only the metastable pluripotent state of ESCs in heterogeneous FCS/LIF conditions which is affected by FACT loss, and whether cells cultured in the more homogeneous and more robust 2i-LIF conditions can tolerate FACT removal. If that is indeed the case it would enable the authors to address one main concern I have with this manuscript, which is that it is nearly impossible to distinguish the direct effect of FACT loss from differences induced by differentiation (and maybe cell death, see comment above). This is a critical concern that needs to be addressed and discussed appropriately.

      We apologize for the confusion – all original experiments for this project were performed in the presence of LIF as well as GSK and MEK inhibitors CHIR99021 and PD0325091, respectively (2i+LIF conditions). To address the reviewers question, we have now performed a timecourse growth assay under both LIF-only and 2i+LIF conditions (Response Figure 6 and new Supp Fig S1F), and as suggested by the reviewer, observe a stronger effect of FACT depletion on cell viability in LIF-alone (FACT-depletion results in ~90% death within ~24 hours, with differences in growth observed by 12 hours) than in 2i+LIF (FACT-depletion results ~80% death within 48 hours, with differences in growth observed starting around 18 hours). Overall, ES cells in LIF alone are indeed more sensitive to FACT loss, supporting our decision to perform the experiments throughout the manuscript in 2i+LIF conditions.

      LIF alone LIF + 2i

      Response Figure 6. __Growth assays in LIF (left) and 2i+LIF (right) conditions. __Cells were treated with either EtOH or 3-IAA and counted at the indicated times. Viability was assessed using trypan blue exclusion. Error bars indicate standard deviation for biological triplicate experiments.

      o A further major concern is about the specificity of the effect of FACT depletion. The authors claim that FACT is required to maintain pluripotency. From the data presented this is unclear. FACT appears to be part of the general transcription machinery in ESCs. It appears generally associated with active promoters and active genes, according to the data in this manuscript. Whether there is any specific link to pluripotency remains to be shown. It is unclear how enrichment analyses have been performed. If they haven't been performed using a background list of genes actively transcribed in ES cells, they will obviously show enrichment of ESC specific GO categories, because ESCs express ESC specific genes robustly expressed in ESCs?

      We apologize for the confusion and have updated our methods section to include more comprehensive details on our pathway enrichment analyses. We have confirmed that pluripotency-related categories are still highly enriched in FACT-regulated DEGs, even when using a background dataset of all transcribed genes, per our TT-seq datasets (baseMean ≥ 1 in DESeq2 output).

      In line with this: the authors show that FACT bound loci well overlap with Oct4 bound regions. But which proportion of FACT targets loci are actually Oct4 bound too?Is FACT binding exclusive to Oct4 regulated enhancers and promoters? In other words, will FACT be recruited to all actively transcribed genes in ES cells? In that case, a specific effect on pluripotency network regulation cannot be claimed.

      We appreciate the suggestion, and have added the number of OCT4/SOX2/NANOG-bound FACT peaks and vice versa in the text and legend of Fig 3E-F. We have also summarized this information in Response Table 1, below (and included these data as Table 2 in the revised manuscript).

      OCT4 peaks

      Sox2 Peaks

      Nanog Peaks

      Any of OSN

      V5 Peaks

      8,544

      5,948

      5,307

      9,682

      OSN Peaks

      45,476

      19,211

      16,817

      52,899

      % of OSN peaks bound by FACT

      18.33%

      30.72%

      31.40%

      17.91%

      % of V5 peaks bound by pluripotency factor(s)

      52.41%

      36.85%

      32.94%

      59.63%

      V5-bound promoters

      4,261

      2,719

      2,327

      4,452

      OSN-bound promoters

      6,550

      1,542

      666

      6,948

      V5- and OSN-bound promoters

      2,040

      801

      343

      2,202

      OSN-bound gene-distal peaks

      38,926

      17,669

      16,151

      45,938

      V5-bound gene-distal OSN peaks

      6,504

      5,147

      4,964

      7,480

      __Response Table 1. Overlapping CUT&RUN and ChIP-seq peaks shared between OCT4, SOX2, NANOG, and V5-SPT16 under various stratifications. __Shown are numbers or percentages of peaks overlapping between V5 and OSN. The last column are peaks containing any of OCT4, SOX2, and/or NANOG. The first four rows include all peaks, regardless of location, and the last five rows are broken down by promoter (as defined by an annotated mRNA) or gene-distal location (defined by a minimum of +/- 1kb from a gene).

      Of the 45,865 OCT4 peaks, 3,688 are located at promoters, and 1,209 of these peaks are bound by V5-SPT16 (32.8%). Inversely, 13,228 of 42,177 gene-distal OCT4 peaks are called as SPT16-V5 peaks in at least one CUT&RUN replicate (31.36%), suggesting a relationship between OCT4 binding and FACT binding, which has long been identified with genic transcription, but has roles extending beyond gene-proximal regulation. We observe similar trends with NANOG and SOX2.

      o It is disappointing that neither raw data (GEO submission set to private) nor any Supplemental Tables containing differentially expressed transcripts and ChIP or Cut and Run peaks and associated genes were made available. This strongly reduces the depth of review that can be performed.

      We apologize if the reviewer token in the cover letter was not accessible. The GEO datasets (including differentially expressed transcripts, raw fastq files, and analyzed datasets) will be made public upon publication; in the meantime, the GEO entry (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE181624) can still be accessed using the previously provided reviewer token: wvkvwmwynjeffux.

      o To what extent do FACT bound loci overlap with genes differentially expressed 24h after FACT depletion? This analysis would help determine the direct targets of FACS regulation.

      We appreciate the suggestion. This analysis can be found in the original Figure S6, broken down by FACT-repressed (expression increased upon FACT depletion), unchanged, and FACT-stimulated (expression decreased upon FACT depletion) DESeq2 results (ordered left-to-right, respectively). Figure S6A-C shows that V5-SPT16 binding is enriched, but not exclusive to, genes with FACT-regulated expression, while Fig. S6D-F shows TT-seq data for each group, sorted by log2-fold change assigned by DESeq2.

      o The paper mainly relies on NGS analysis. Therefore, it is crucial that authors show as Supplemental Material some basic QC of these data. PCA analyses to show congruency of replicates are the minimum requirement.

      We appreciate the suggestion and have included a new Supp. Fig S9, with pairwise comparative Pearson correlation scatterplots and heatmaps for replicates in each dataset, in addition to the scatterplots shown for CUT&RUN and ChIP-seq data in the original Supp Fig. S2A.

      o Did the authors perform any filtering for gene expression levels before analysis? Are genes in the analysis robustly expressed in at least one of the conditions?

      We apologize for the confusion. Due to the sensitive nature of TT-seq and the germ layer-inconsistent pattern of cell differentiation following FACT depletion, we did not perform filtering for gene expression prior to any analyses. For the vast majority of genes analyzed, however, we are able to identify transcription via TT-seq, even in those that do not significantly change expression upon FACT depletion (see Supp Fig S6E). As discussed above, we did include a cutoff for expressed genes in our revised pathway analysis.

      o Wherever p values were reported for enrichment analyses, adjusted p values should be used

      We apologize for the oversight; the p values were in fact adjusted p values and have updated the text and figures to make it explicit that the adjusted p values were used wherever applicable.

      o I cannot follow the logic used by the authors to explain discrepant results from Chen et al about the role of FACT in ESCs. Chen et al showed that FACT disruption by SSRP1 depletion is compatible with ESC survival and leads to ERV deregulation. The authors of the present study attribute these differences to potential FACT independent roles of SSRP1. However, I would assume that if there are indeed FACT independent roles of SSRP1, then the phenotype of SSRP1 KOs in which FACT and other processes should be dysfunctional should be even stronger than a plain FACT KO. This needs a proper and careful explanation.

      We apologize that our discussion of FACT-independent roles of SSRP1 was not clear and have clarified our wording in the text (page 4, line 49 – page 5, line 4)in the revised manuscript); we intended to reconcile the results of Chen et al. 2020 with Goswami et al. 2022 and Cao et al. 2003; despite SSRP1 knockout viability in embryonic stem cells, SSRP1 knockout is lethal in mice between 5-40 weeks and general SSRP1 knockout is lethal 3.5 days post-conception (per Goswami et al. 2022). We therefore posit that the general requirement for SSRP1 may be due to distinct roles from those carried out by the FACT complex in ES cells, as discussed by Spencer et al. 1999, Zeng et al. 2002, Li et al. 2007, and Marciano et al. 2018.

      We note that our findings are in agreement with papers from the Gurova lab and others in that depletion of mRNA or protein of SPT16 leads to concomitant loss of SSRP1; we therefore do not expect total SSRP1 loss to have a stronger effect than SPT16 depletion. We therefore expect, and confirmed via Western blotting (Figure 1B, Supplemental Figure 1), that depletion of SPT16 leads to loss of both FACT subunits, and therefore all FACT subunit activity, complex-dependent or -independent.

      Also, did the authors observe any evidence for ERV deregulation upon acute SPT16 depletion?

      We did indeed observe ERV deregulation upon SPT16 depletion. When reviewing our TT-seq datasets, 7.1% of ERVs were derepressed, while 2.4% decreased in expression upon 24h FACT depletion (mm10 ERVs sourced from gEVE, Nakagawa and Takahashi, 2016). Further, we identified increased chromatin accessibility after FACT depletion at annotated LTR elements, as shown in the table below (Response Table 2). Here we are displaying the calculated enrichment score for accessibility detected at these locations. A negative value indicates lower accessibility than expected by region size, while a positive score indicates that reads are more enriched than expected at the indicated region.

      ATAC-seq enrichment score for locations losing accessibility with FACT depletion

      3h

      6h

      12h

      24h

      LTR Enrichment

      -1.445

      -1.299

      -0.917

      -0.559

      Intergenic Enrichment

      -6.046

      -4.765

      -3.926

      -2.972

      Promoter Enrichment

      3.335

      2.789

      2.726

      2.233

      ATAC-seq enrichment score for locations gaining accessibility with FACT depletion

      3h

      6h

      12h

      24h

      LTR Enrichment

      -1

      -0.436

      1.103

      1.13

      Intergenic Enrichment

      -1

      0.134

      0.435

      0.236

      Promoter Enrichment

      -1

      -3.585

      1.171

      1.39

      __Response Table 2. Changes in ATAC-seq peak enrichment for selected regions, annotated via HOMER. __At regions differentially accessible between SPT16-depleted and SPT16-undepleted samples, regions were assigned to an annotated genomic feature using HOMER annotatePeaks.pl and assigned an enrichment score based on the ratio of ATAC-seq signal to region size. Over time, LTR elements become more enriched among the ATAC-seq peaks both gaining and losing accessibility, indicating a role for FACT in maintaining LTR accessibility.

      We do wish to note, however, that Lopez et al. 2016 identified SPT16-independent regulation of LEDGF/HIV-1 replication by SSRP1, and therefore cannot rule out effects on ERV dysregulation due to SSRP1 loss that accompanies SPT16 depletion.

      Minor points o Figure S2A is very small and resolution is low. Page 10: "...while all four Yamanaka factors (Pou5f1, Sox2, Klf4, and Myc) and Nanog were significantly 24 reduced after 24 hours (Fig. 3A, S3A-B)". No data for myc is being shown.

      We apologize for the figure resolution and have included a larger image. Because pairwise comparative scatterplots are not space-efficient, we opted to display the Pearson correlations for the datasets including more samples (TT-seq and ATAC-seq timecourses) as heatmaps in the new Supp Fig S9. We have added Myc labeling to the volcano plot (now in Fig. 3A) and included a trace of Myc expression over time to the new pluripotency factor graph in Fig. 3B.

      o Are the two bands in the middle in figure 1B is supposed to be a ladder? This should be clarified.

      We thank the reviewer for noticing this and apologize for the oversight.

      o Figure 3C- This Figure is complicated to read. Also, information appears redundant with the Table 1, I recommend to remove this panel.

      We have moved the panel to the supplement (now Supp Fig. S3A). While the information is somewhat redundant with Table 1, we chose to include the former panel 3C as a visual representation of the consistent deregulation over depletion time across transcript categories.

      o Figure 6 and figure 7 could be presented in one single figure since both aspects are complementary and target related aspects.

      While we thank the reviewer for this suggestion, we do not believe that the information contained in Figs. 6 and 7 can effectively be conveyed in a single figure. While both figures focus on chromatin accessibility and nucleosome occupancy, Fig. 6 is designed to address the changes in chromatin accessibility over time, while Fig.­­­ 7 is more relevant to the biological mechanism through which FACT co-regulates targets of the core pluripotency network (OCT4/SOX2/NANOG) after 24 hours of depletion.

      o Are the authors certain that the effects observed are directly linked to the FACT complex in contrast to FACT independent roles of SPT16, if any exist? The experiment to address this would be to deplete SSRP1 and investigate whether the effects are identical, which would be the hypothesis to be tested.

      We thank the reviewer for this suggestion. We did attempt to create additional SSRP1-AID-tagged lines; however, generating these lines proved to be technically challenging, and comparison of the FACT-dependent and -independent roles of the individual subunits is beyond the scope of this work. Further complicating the matter, SSRP1 is effectively depleted within 6 hours of 3-IAA addition in SPT16-AID lines due to the interdependence of FACT subunits. We again thank the reviewer for their suggestion and will consider this work for a future study.

      Reviewer #2 (Significance (Required)):

      My expertise is pluripotency and GRNs.

      I would judge the significance of the study as presented as low, mainly because at this moment it remains unclear what FACT indeed does concerning regulation of pluripotency.

      We respect the reviewer’s opinion and hope that our revisions have made more clear how the FACT complex prevents nonspecific differentiation from occurring, thereby maintaining pluripotency and self-renewal in embryonic stem cells. Importantly, neither untagged cells treated with 3-IAA nor tagged cells treated with vehicle display the growth defects, loss of pluripotency factor expression, increased differentiation marker expression, phenotypic evidence of differentiation, and reduced alkaline phosphatase staining that the FACT-depleted cells do, highlighting a key requirement for FACT in pluripotent cells. Beyond this, we believe the novel gene distal regulatory role we have identified for FACT presents an exciting new role for this complex in gene regulation.

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

      In this manuscript, Klein, et al. addressed function of FACT complex in mouse ESCs, using cut&run, TT-seq, ATAC-seq, MNase-seq, together with Auxin-mediated FACT degradation system. The authors first reported that efficient and acute depletion of SPT16 with the Auxin-mediated degradation system resulted in over 5,000 up- and 5,000 down-regulated genes within 24 hours, including down-regulation of pluripotent gens. Then, they demonstrated that many of FACT binding sites overlap with Oct4, Sox2, Nanog binding sites by Cut&Run, and those loci increase nucleosome occupancy 24 hour after removal of FACT.

      The Auxin-mediated degradation system seems to be working very well (while I would like to see an over exposed version of Western blotting), and efficient degradation might explain the different phenotypes from the previous reported phenotypes by shRNA and the chemical inhibitor, which might not deplete FACT function completely and/or might have off-target effects. The Cut&Run data also have much sharper peaks than previously reported SSRP1, SPT16 ChIP-seq data. Doing ATAC-seq, MNase-seq upon removal of FACT is excellent. WIth the excellnet degradation system, depletion of FACT resulted in loss and gain of gene expression and differentiation. However, unfortunately it was not very clear to me what was the direct consequences of FACT removal and its mechanisms, waht was consequence of differentiation.

      We appreciate the kind words regarding our choice and execution of techniques and the reviewer’s time spent on this manuscript. We have made a number of changes to the manuscript in order to clarify the direct role of FACT and the consequences of FACT loss on embryonic stem cells.

      Although we did not develop the blots for a longer period when we performed the Westerns, we have artificially overexposed our V5-SPT16 Western blot from Figure S1 (in Adobe Illustrator) to highlight the more subtle bands at later depletion timepoints; we hope that this helps to clarify the effectiveness of the degron system.

      Response Figure 7. V5-SPT16 Western blot with adjusted exposure. We manually adjusted the entire blots’ exposures using Adobe Illustrator. L indicates ladders, and the timecourse depletion is shown above the blot.

      In my opinion, doing many of the analysis 24 hours after FACT depletion, where differential expressed (coding) genes (DEGs) are >10,000 (Table 1)), is too late to understand what the direct consequences are. Seeing 214 up- and 174 down-regulated DEGs 6 hours after FACT depletion, I do agree that FCAT seems to do both suppression and activation of target genes. It could have been really interesting to investigate what % of FACT bindign sites change chromatin accesibility and nucleosome occupancy at that time point, if those loci are close to any of the up- or down-regualted DEGs.

      We appreciate the suggestion and have included more information regarding the percentage of FACT binding sites with altered chromatin accessibility, as well as included some analyses to address the directness of FACT’s contribution to DEGs at all timepoints (see Supp Figs S4, S6). We would like to note that, we performed the TT-seq and ATAC-seq experiments at 0, 3, 6, 12, and 24 hours post 3-IAA treatment in order for us to explore the progressive change in both the transcriptome and chromatin accessibility, with only the MNase-seq limited to 24 hours. As originally shown in our Sankey plot in Supp Fig 4, we see a progressive change in expression for a small subset of genes over our timecourse running from 0-24 hours, with the largest effect observed at 24 hours, once the FACT protein levels are almost entirely depleted. Similarly, we see a progressive change in ATAC-seq signal over the same regions, with the strongest effects over the same regions visible at 24 hours post-depletion. Due to our observation that SPT16 is not depleted at 3 or 6 hours, with significant depletion seen at 24 hours (see Response Figure 7) and because we intended to study the FACT complex’s role in preventing differentiation, we were most interested in the effects at 24 hours of depletion, which allow us to analyze both the disruption of pluripotency factor expression and the facilitation of differentiation marker expression across all three germ layers (see Response Figure 4).

      Followings are reasons of above my judgement and suggestions to improve the manuscript.

      Major points 1. Figure 1. ALP staining is not very sensitive way to evaluate ESC differentiation. I recommend Immunofluorescence for pluripotency genes (NANOG and/or SOX2) and quantification. Or present changes of pluripotency genes in graphs over time course from RNA-seq data.

      We appreciate the suggestions and have taken both into account. We have included a new panel in Figure 3 (new 3B) to display the changes of pluripotency factor expression over our timecourse. We have also included some data showing differentiation factors as part of a response to Reviewer 1, which can be found above (Response Figure 4). In addition, we performed immunocytochemistry to examine OCT4 abundance over a depletion timecourse and have added a 12-hour to our alkaline phosphatase assay to address the sensitivity of differentiation over time (Figure 1C, D and Response Figure 5).

      1. Fig 2A, 3E, 3F. How many transcription start sites are shown here? (Throughout the manuscript, it is hard to know how many loci are shown in the heatmaps. It should be described within the figures)

      We apologize for the omission and have added numbers of loci shown to relevant Figure panels throughout the paper.

      It is nice to see nascent transcription high sites have high FACT binding, but can you also show actual nascent transcription of these loci as a heatmap, before and after FACT depletion? These heatmaps should be shown in a descending order of FACT Cut&Run signalling, as FACT binding is important in this manuscript.

      We appreciate the suggestion and have plotted those data below (see Response Figure 8).

      Response Figure 8. Nascent transcription from sites with high FACT binding. Top: TPM-normalized TT-seq signal after 12-hour treatment, oriented to mRNA strand and plotted as entire mRNA length, ± 500 bp. Data are sorted by SPT16 CUT&RUN signal over 1kb upstream of annotated TSSs. n = 1 over 22,597 rows (RefSeq Select mRNAs). Bottom: TPM-normalized TT-seq signal after 24-hour treatment, oriented to mRNA strand and plotted as entire mRNA length, ± 500 bp. Data are sorted by SPT16 CUT&RUN signal over 1kb upstream of annotated TSSs. n = 3 (mean) over 22,597 rows (RefSeq Select mRNAs).

      Strong FACT binding sites have strong transcription. Is FACT really supressing transcription?

      We agree that it is very difficult to disentangle FACT function due to its binding correlation with transcription; however, we see a clear trend of FACT binding at promoters that are sensitive to FACT depletion (Supp Fig. S6A/D and C/F). Intriguingly, the genes that see the greatest derepression by DESeq2 analysis are those that are directly bound by FACT (per ChIP-seq and CUT&RUN; Supplemental Figure S6A/D), while the greatest decrease in expression occurs at genes that are less bound by FACT (Supp Fig S6C/F). In our opinion, this trend lends credence to both direct repression by FACT and distal gene regulation. We note that others (e.g., Kolundzic et al. 2018) have shown direct repression of gene expression by FACT, in line with that aspect of our data.

      1. Fig 3ABD. It is more important to show 3h, 6h 12 h time points. The same apply to Fig 4. What %, how many of DEGs (coding and non-coding) at each time point had FACT binding nearby in ESCs?

      We agree that the early timepoints are important and have added volcano plots to the supplemental material for earlier timepoints, with genes of interest specifically annotated. We have also examined pluripotency and differentiation markers at earlier timepoints, per other reviewers’ suggestions, and have included the percentage of DEGs with nearby FACT binding in the manuscript. Specifically, 2013 replicated V5 peaks (out of 16,054; 12.54%) occurred within 1000 bp of a RefSeq Select TSS.

      Timepoint

      Total DEGs (up)

      V5-bound DEGs (up)

      Total DEGs (down)

      V5-bound DEGs (down)

      3h

      58

      16 (27.59%)

      5

      1 (20%)

      6h

      214

      38 (17.76%)

      174

      31 (17.82%)

      12h

      1366

      123 (9.00%)

      1932

      281 (14.54%)

      24h

      5398

      431 (7.98%)

      5000

      663 (13.26%)

      __Response Table 3. Table of DESeq2-assigned DEGs that are bound by SPT16-V5. __To be defined as V5-SPT16-bound, a DEG must have SPT16-V5 binding within 1000 bp upstream of its RefSeq-select annotated TSS.

      We believe that these earliest depletion timepoints are in line with FACT-mediated gene regulation occurring distal to the regulated genes’ promoters.

      Fig 3EF. Interesting data and the overlap between SPT16 binding sites and pluripotency binding sites look very strong. But it is difficult to know what % is overlapping from these figures.

      We appreciate the difficulty in quantifying the overlap between pluripotency factor binding sites and FACT binding sites; we have added those data to the manuscript below Figure 3E for OCT4; for other pluripotency factors, these data can be found in Response Figure 9 and Response Table 1. Briefly, 18.33% of OCT4 ChIP-seq peaks are bound by V5-SPT16 and 52.41% of V5-SPT16 peaks are bound by OCT4. Interestingly, 34.6% of gene-distal OCT4 ChIP-seq peaks are bound by V5-SPT16, implying greater convergence between FACT and pluripotency factors at gene-distal sites, in line with known trends for OCT4 binding. Overall, 59.63% of V5-SPT16 peaks are co-bound by at least one of OCT4, SOX2, or NANOG.

      Can you show 1 heatmap split into 3 groups, a. SPT16-V5 unique, common between SPT16-V5 and Oct4 ChIP-seq, Oct4 ChIP-seq unique, with indication of numbers each group has? Also make the same figures for Sox2 and Nanog. (E is less important. If the authors want, they can use the published FACT ChIP-seq data in the same loci.)

      We appreciate the suggestion and have plotted V5-SPT16 CUT&RUN data and pluripotency factor ChIP-seq over unique and shared regions for OCT4 (top) SOX2 (middle) and NANOG (bottom). Interestingly, although some peaks in the non-overlapping cluster were not called as peaks by the algorithms’ threshold, one can observe that a subset do seem to have overlapping binding. We again appreciate the suggestion and think that this was an excellent way to display the data and have included these data as a new panel (Fig. 3E) but also show below in Response Figure 9.

      Fig. 5. Basic information what % (how many) of SPT16-V5 CUT&RUN peaks belong to this 'enhancer' category is missing.

      We apologize for the oversight and have added numbers to the figure and legend.

      I am not sure the meaning of separating enhancers and TSS of coding genes in the analyses, though. If majority of SPT16-V5 CUT&RUN peaks overlap with Oct4 binding sites, it is not surprising that SPT16-V5 CUT&RUN peaks overlaps with ATAC-seq signal and enhancer marks.

      We agree that it is unsurprising that V5-SPT16 overlaps with accessible chromatin and enhancers, given the extensive overlap with OCT4 ChIP-seq peaks. We wanted to emphasize our novel finding of gene-distal FACT binding, given the more established trend of binding at promoters.

      1. Fig 6A. I could not figure out what % of DHSs overlaps with FACT binding sites.

      We have added this percentage to Fig 5C and included an analysis of altered chromatin accessibility in a new Table 3 (page 20). Briefly, 11,234 replicated V5-SPT16 peaks (out of 16,043; 70%) directly overlap a gene distal DHS. Orthogonally, 11,234 DHSs (out of 132,555; 8.5%) directly overlap a V5-SPT16 peak.

      I do not see the point of showing DHSs which do not overlap with FACT binding sites.

      In agreement with Reviewer 1, we believe that it is important to include FACT-unbound DHSs for a clearer understanding of the direct vs indirect effects of FACT depletion. We have condensed some of these data into a single heatmap, clustered between FACT-bound DHSs, non-FACT-bound DHSs, and FACT-bound non-DHS sites to streamline the information (now shown in Fig 3E).

      Response Figure 9. Heatmaps of clustered SPT16 and OSN binding data. Shown are clustered heatmaps depicting V5-SPT16 CUT&RUN binding overlapping ChIP-seq peaks for OCT4 (top) SOX2 (middle) and NANOG (bottom). In each set of heatmaps the top cluster is pluripotency factor-unique, the middle cluster is shared, and the V5-unique cluster is on the bottom. Each cluster is sorted by descending strength of V5-SPT16 binding (CUT&RUN). Clusters were assigned by directly overlapping peaks.

      How ATAC-seq signal changes upon depletion of FACT at FACT binding sites (Fig 6B) is important. Can you explain why ATAC-seq signals increase at the FACT binding site flanking regions (across +/- 2kb) where FACT binding is strong (without changing the chromatin accessibility at the FACT binding sites)? Perhaps authors need to show actual ATAC-seq track with EtOH or 3-IAA treatment over ~10kb regions flanking FACT binding sites. It is difficult to understand what is happening seeing only the changes (ratio) of ATAC-seq read counts, how big the differences are.

      We agree that the local window and ratio of ATAC-seq signal somewhat muddles the true biological trends. We have plotted non-differential ATAC-seq signal for each SPT16-AID clone over V5 binding sites, ±10 kb, to more accurately depict the local chromatin status (shown below in Response Figure 10). There is an apparent trend at V5-SPT16 CUT&RUN peaks of accessible chromatin, and this high local accessibility very likely contributes to the high ATAC-seq signal immediately flanking V5 binding sites; over the binding sites themselves, however, FACT depletion consistently triggers decreased accessibility (see Fig. 6).

      Can you identify differentially open loci based on 3-IAA- and Et-OH treated ATAC-seq data at each time point, and then how many of them overlap with FACT binding sites? There are a few tools to identify differential open regions with ATAC-seq data. That could help to understand the direct roles of FACT binding.

      We appreciate the suggestion and have performed this analysis using a combination of PEPATAC and HOMER (see Response Tables 4-6 below). FACT depletion leads to the following accessibility changes:

      3-hour

      6-hour

      12-hour

      24-hour

      Decreased accessibility

      220 (0.35%)

      3,713 (5.99%)

      6,885 (11.11%)

      8,441 (13.62%)

      Increased accessibility

      2 (0.00%)

      12 (0.02%)

      276 (0.45%)

      6,031 (9.73%)

      Response Table 4. Accessibility changes over consensus ATAC-seq peaks. Consensus ATAC-seq peaks were defined per PEPATAC standards (peaks called by MACS2 in (n/2)+1 samples, irrespective of condition.

      3-hour

      6-hour

      12-hour

      24-hour

      Decreased accessibility

      848 (1.64%)

      1870 (3.51%)

      2525 (4.83%)

      4,092 (7.90%)

      Increased accessibility

      107 (0.21%)

      283 (0.55%)

      534 (1.03%)

      2,449 (4.73%)

      Response Table 5. Accessibility changes over regions bound by V5-SPT16.

      Response Figure 10. ATAC-seq data shown over a 20kb window. Heatmaps depicting non-differential ATAC-seq data over FACT binding sites for SPT16-AID clones 1 (top) and 2 (bottom). Data are sorted by V5-SPT16 binding strength.

      All

      3-hour

      6-hour

      12-hour

      24-hour

      Decreased accessibility

      3,294 (2.46%)

      3,175 (2.37%)

      3,636 (2.71%)

      7,018 (5.23%)

      Increased accessibility

      102 (0.08%)

      313 (0.23%)

      1,797 (1.34%)

      5,975 (4.45%)

      V5-bound DHSs (11,234 total)

      3-hour

      6-hour

      12-hour

      24-hour

      Decreased accessibility

      1 (0.01%)

      9 (0.08%)

      96 (0.85%)

      2006 (17.86%)

      Increased accessibility

      5 (0.04%)

      28 (0.25%)

      71 (0.63%)

      87 (0.77%)

      Response Table 6. Accessibility changes over gene-distal DHSs and over only FACT-bound gene-distal DHSs.

      Together with Fig 1A and Fig 6C, do they mean the more FACT binding, the more transcription (Fig 1A). Also the higher transcription rate, the more increased chromatin accessibility upon depletion of FACT (Fig 6C)?

      While we do see that FACT binding correlates with transcription and with FACT-dependent chromatin accessibility, we do not wish to make the argument that FACT binding alone is indicative of high transcription, nor that transcription is necessarily the deciding factor in FACT-depleted chromatin accessibility changes. We do want to note that transcriptional disruption is a likely contributor to increased chromatin accessibility in the absence of FACT as it pertains to paused RNAPII, as speculated in our discussion, but that experiments to truly test this hypothesis are beyond the scope of this work. That being said, in response to Reviewer 1, we did assess the potential correlation of FACT binding to locations with greater paused RNAPII (Response Figure 3) and see a connection. We are excited to explore this more in future work.

      Perhaps plotting nascent transcripts at 12hr, 24 hr of FACT depletion next to these heatmaps might show if it colleates with transcription changes as well?

      We appreciate the suggestion, and have included this plot in Response Figure 8, sorted by FACT binding to gene promoters; however, we find it difficult to visualize differences in transcription with non-differential heatmaps.

      Sites losing chromatin accessibility (bottom half of Fig 6C) seem not to have FACT binding (bottom half of Fig 1A), thus it is likely to be indirect effects. It is better to make figures focussing on 'direct effects'.

      We agree that there are sites with reduced chromatin accessibility upon FACT depletion that are not bound by FACT; however, given the extensive binding of FACT at gene-distal regulatory regions (F2D, F4A, F5, F6A/D), we would suggest that these “indirect” effects are possibly the result of FACT-dependent gene-distal regulation.

      Fig 1A and Fig 6C indicated that FACT binding sites (i.e. TSS) decrease chromatin accessibility. I thought it does not fit with the idea of increasing nucleosome occupancy. But actually the data (Fig 7F) shows that TSS does not show increased nucleosome occupancy unlike Fig 7A-E. In fact, Fig 6B showed that about bottom 50% of weaker V5 binding sites decreased chromatin accessibility at 24 hr, which fits with increased nucleosome occupancy in Fig 7A. But then if you looked at only top 50% of stronger V5 binding sites, which did not decrease chromatin accessibility, nucleosome occupancy did not change as well? Why don't you make heatmap of MNase-seq next to Fig 6B?

      We have added heatmaps of non-differential MNase-seq data to Fig. 7A to address both concerns. Regarding Figure 6B, we note that the V5-SPT16 peaks themselves invariantly show decreased chromatin accessibility, and that it is the surrounding chromatin, not the V5-SPT16 peak itself, that shifts from increased to decreased chromatin accessibility at 12-24 hours of depletion. We would also like to clarify that the original heatmaps in Fig 6B were sorted by change in chromatin accessibility at 24h, rather than V5 binding.

      We disagree that the TSSs do not show increased nucleosome occupancy in Fig. 7F, as there is an increase in signal above background directly over the TSS in both replicates, per the differential metaplot shown in Fig. 7B, that is specific to the AID-tagged lines. However, the two clones did show variable results. To address this, we have plotted the non-differential MNase-seq plots (Fig. 7A), which show more consistent trends; it appears that the transformation of the data into differential at this location was the cause of the slightly variable plots over TSSs.

      1. I could not follow based on which data the model in Fig 8 is made. Again it is better to focus in the direct effects.

      Thank you for the suggestion; we have updated our model to focus more on the direct effects.

      Minor points. 10. Line 1 page 5, Kolundzic paper did not have MEF reprograming data. They reported human fibroblast reprogramming was enhanced by FACT KD.

      We appreciate the correction and have clarified the language to specify that the work of Kolundzic et al. included human fibroblast reprogramming and Shen et al. performed MEF reprogramming.

      1. Line 3, I disagree with "these data establish FACT as essential in pluripotent cells". One paper said FACT KD increased proliferation of mESCs, the other paper said chemical inhibition of FACT was necessary for passaging ESCs, but not proliferation. Importance of FACT in pluripotent cells was very unclear to me.

      We have clarified our language to specify that pluripotent cells have a FACT dependency that differentiated cells do not. We note that we were unable to recapitulate a relationship between FACT and trypsinization/passaging of ES cells, suggesting a more nuanced role for FACT in pluripotent cells, in line with work from the Tessarz and Gurova labs.

      Line 7 Page 7, reference the paper with the ChIP-seq data.

      We apologize for the oversight and have added the reference.

      Line 16, Page 7. It doesn't seem the the Cut&run and previously published ChIP-seq data agree well.. >50% look different. It is nothing the authors can do, but can you show venn diagram of peak overlap?

      In response to Reviewer 1, we have generated Response Figure 1 where we display a pie chart of the overlap. In addition to displaying this again to the right in Response Figure 11 this, we have included another analysis below in Response Figure 11, to address this comment. Specifically, we have plotted peak overlaps as a Venn diagram to compare peaks identified in at least two experimental replicates from either the CUT&RUN or ChIP-seq data (left). We have also overlapped replicated peaks between the individual targets and displayed them as a pie chart (right; same as Response Figure 1). While the CUT&RUN data do display a greater signal:noise ratio and call far more peaks, we note that more peak conservation between experiments is relatively consistent (1-6%) between all datasets, including the ChIP-seq experiments profiling opposite factors.

      Overall, we see strongly reproducible trends (albeit with less sharp definition in the ChIP-seq), complemented by highly similar biological feature assignment in Fig. 2D and Pearson correlation values of between 0.76 and 0.78 between SPT16 ChIP-seq and V5-SPT16 CUT&RUN (Supp Fig. S2A).

      __Response Figure 11. Overlaps between SPT16-V5 CUT&RUN, SPT16 ChIP-seq, and SSRP1 ChIP-seq. __Called peaks were compared between V5-SPT16 CUT&RUN, SPT16 ChIP-seq, and SSRP1 ChIP-seq, using both our own analysis pipeline (left) and the peaks published with the original manuscript by Tessarz et al. (2018; right). While our ChIP-seq peak-calling appears to have applied more stringent thresholds, trends are generally agreeable.

      Line 12, 22 page 10. Fig.3AB is 24 hrs. Do not match with the text.

      We apologize for the error and have changed the references in the text to the new panel 3C.

      1. Line 23, 24, page 10, Highlight Klf4 and Myc in the volcano plot.

      We have added KLF4 and MYC annotation to the volcano plot in Fig. 3A, as well as plotted their log2FC over time in the new panel 3B.

      1. Line 18, 19, page 16. This is not accurate statement. Sample 2 increased the accessibility at 6 hours. Sample 1 decreased, but even the control did so.

      We apologize for the unclear wording; we intended to suggest that all timepoints after 6 hours (i.e., 12 and 24 hours) display decreased accessibility directly over the DHS. We have corrected the text.

      1. Line 48-50, page 16. Two replicates show very different patterns. Difficult to agree with the statement based on the figure.

      We agree that the differential replicate patterns are not ideal; however, both replicates display an increase in nucleosome-sized reads over the promoter region, consistent with our ATAC-seq results presented in Fig 6C. Size distribution plots did not suggest differences in MNase digestion between samples, and neither quartile/RPGC nor TMM-based normalization fully solved this issue. Because our ATAC-seq datasets agree with the general trends identified by MNase-seq (which are consistent, despite technical differences between clones), we do not believe that the differences constitute biological difference, but rather experimental noise. We have included a heatmap of non-differential MNase-seq signal around TSSs in Fig 7A to highlight the experimental reproducibility between replicates. Based on this analysis it appears that the transformation of the data into differential at this location was the cause of the slightly variable plots over TSSs.

      1. Line 15, page 19. Where does "1.5 times" come from? which is 1.5 times more, and is that different from the proportion of those?

      We apologize for the unclear reference to the altered transcripts in Table 1 and have changed our wording to be more precise.

      1. Line 32, page 19. Is Fig S2B correct figure?

      We appreciate the correction; the text should have referred to Fig. 4 and has been fixed.

      Line 35-39, page 21. I understand FACT does not bind to silenced loci. If FACT does not bind, it is not surprising that expression from those loci does not change upon FACT deletion. I do not understand what the authors said.

      We agree that a lack of binding and unchanged expression after FACT depletion at putative silencers are unsurprising; given FACT’s extensive genic and gene-distal binding, we wished to show a class of transcribed regions unbound by FACT as a control, to show that non-FACT-regulated transcription was not affected by FACT transcription. We have clarified our wording in the text to emphasize that a lack of change was expected at silencers.

      Reviewer #3 (Significance (Required)):

      Previously it has been shown that Oct4 physically interacts with the FAcilitates Chromatin Transactions (FACT) complex. Seemingly contradicting phenotypes have been reporting upon suppression of FACT function in the maintenance and induction of pluripotent cells. Mylonas has reported that knockdown of SSRP1, a component of FACT complex, increased ESC proliferation (2018). Shen has described that chemical inhibition of FACT complex affected passaging of ESCs, but proliferation was not affected without passaging. Kolundzic has found that both SSRP1 and SUPT16H, another component of FACT complex, enhance human fibroblast reprogramming into iPSCs (2018), while Shen has reported that chemical inhibition of FACT blocks mouse iPSC generation form MEFs.

      My expertise lies on pluripotent stem cells and transcriptional regulations. I did like the Auxin-mediated FACT degradation system these authors used and acute depletion of FACT is an excellent way of evaluating FACT function in ESC, compared to previously published shRNA based knockdown or use of a chemical inhibitor. However, as I described above, it was not very clear what could the direct effects and I feel looking at 24 hours after depletion might be to late to address this question.

      We appreciate the review and agree that acute depletion of FACT has great potential to understand the complex’s function in ES cells. We understand that the nature of gene-distal regulation does make it difficult to cleanly elucidate direct regulation, and hope that our revisions have clarified that our goal was to examine direct, gene-distal regulation, rather than indirect effects. We would like to note that we examined transcription and chromatin accessibility after 3, 6, 12, and 24 hours of 3-IAA treatment, with all these data included in the original manuscript, and saw minimal change (likely because FACT was not fully depleted until later timepoints); to capture the true biological effects of FACT depletion, we explored most thoroughly the 24 hour 3-IAA treatment to understand the downstream effects between FACT loss and cellular differentiation. However, we have expanded discussion and analyses of the earlier timepoints in this revised manuscript.

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      Referee #2

      Evidence, reproducibility and clarity

      This paper revisits aggregate formation by ORF1p, a nucleic acid (NA) binding protein encoded by the L1 retrotransposon. This topic dates to 1996 (Hohjoh and Singer - 1996 Embo J 15: 630) and was extensively examined again in 2012 using highly purified ORF1p by Callahan et al (Callahan et al - 2012 Nucleic Acids Res., 40, 813), to determine the effect of salt and nucleic acid on this process. The earlier studies employed chemical cross linking and gel electrophoresis to examine ORF1p aggregates in the presence and absence of NA and neither were cited in the present study. As ORF1p contains several intrinsically disordered regions (IDRs) ORF1p aggregates can form phase separated condensates (droplets) which were characterized microscopically in the present study, and the authors assume that condensate formation is intrinsic to the function of ORF1p in retrotransposition, or as they state on page 2: "...we hypothesized that ORF1p undergoes condensation to carry out its roles in L1 RNP formation...". The authors attempt to correlate the ability of L1 encoded ORF1p complexed or not with RNA to form phase separated condensates in parallel with retrotransposition assays. They couple these observations with in vitro studies on condensate formation by the purified protein.

      I have the following major comments:

      • (A) The functional relevance of condensate formation by IDR-containing proteins has been questioned (Martin, E. W. and A. S. Holehouse - 2020; Emerging Topics in Life Sciences 4: 307). These authors conclude their review as follows: "In summary, IDRs are ubiquitous and play a wide range of functional roles across the full spectrum of biology, and in a large number (likely the majority) of cases their biological function has nothing to do with the ability to form large macroscopic liquid droplets. The notion that the presence of an IDR means a protein has evolved to phase separate is an inaccurate inference that has unfortunately been used to justify questionable lines of inquiry and questionable experimental design." And in terms of ORF1p this admonition is exemplified by the findings of Newton et al (2021, Biophys J 120;2181) cited by the present authors. This study showed that phase separated condensates readily form by just the N-terminal 152 amino acids (NTD + coiled coil). As this region of ORF1p cannot bind NA, condensate formation is indifferent to RNA binding, an obviously critical function of ORF1p.
      • (B) Earlier studies (Ostertag et al - 2000; NAR 28:1418) showed that sufficient retrotransposition events have occurred by 48 hours after introduction of an L1 retrotransposition reporter to be readily detectable by whole cell staining for the retrotransposition-generated reporter gene product. The 48-hour lag presumably reflects the time to accumulate sufficient L1RNPs or their retrotransposed products to be detectable. Does this mean that the puncta (Fig 1F) accumulating during the first 24 hours after introduction of their full-length L1 retrotransposition reporter (Fig 1C) are the L1RNPs generated by the reporter? If not, what are they? If they are L1RNPs, are they thought to be or expected to exhibit the properties of phase separated condensates or are such properties just a feature of disembodied ORF1p that the authors posit could form an active L1RNP? The Ostertag paper should be cited here given its relevance to this issue.
      • (C) Four of the IDRs in ORF1p harbor or are juxtaposed to phosphorylation sites essential for retrotransposition (their citation - Cook et al, 2015). As the authors expressed their purified proteins in E. coli, it is not phosphorylated and would not only be inactive for retrotransposition and given the structural effects of phosphorylation (e.g., Bah, A., et al.;2015; Nature, 510, 106) it would differ significantly from the structure of the active protein. As variables they introduce into ORF1p several not too subtle mutations particularly regarding the ORF1 coiled coil. They thereby aim to assess the role or particulars of ORF1p condensate formation for L1 retrotransposition. In their Abstract they state (p.1, l. 11) "...we propose that ORF1p oligomerization on L1 RNA drives the formation of a dynamic L1 condensate that is essential for retrotransposition."
      • (D) Although the authors provide no direct experimental evidence for the above statement and whatever the authors mean by "dynamic L1 condensate" how does this conclusion materially differ from the conclusions published by Naufer et al, in 2016 (NAR; 44,281), which also was not cited by the authors. Naufer et al used single molecule studies and highly purified ORF1p that had been expressed in insect cells (and thus was fully phosphorylated, Cook et al, 2015). They showed that oligomerization of nucleic acid (NA)-ORF1p complexes to a compacted stably bound polymer was positively correlated with retrotransposition. Both properties could be eliminated by coiled coil mutations that had no effect on biochemical assays of ORF1p activity - high affinity NA binding and NA chaperone activity. As both properties map to the carboxy terminal-half of ORF1p, the inactivating coiled coil mutations are an example of the numerous instances of strong epistasis exerted by amino acid substitutions in the coiled coil on the retrotransposition activity of ORF1p. In some cases epistasis is exerted at the single residue level (e.g., Martin,et al - 2008, Nucleic Acids Res., 36, 5845; Furano, et al. - 2020, PLOS Genetics 16 e1008991.)

      While the authors are apparently also not mindful of the PLOS Genetics paper examining the effect of a single inactivating coiled coil substitution at the level of microscopically observed condensates could have provided compelling evidence linking their formation and retrotransposition. On the other hand, lack of a condensate-based readout for single amino acid inactivating coiled coil mutations would question the validity of equating ORF1p condensates with retrotransposition competence. - (E) The afore mentioned Callahan et al study (2012, NAR, 40, 813) in addition to producing results partly recapitulated in Fig. 2 of the present paper, showed that ORF1p polymerization was mediated by interactions between the highly conserved RRM-containing region of ORF1p. This observation is consistent with previous studies showing RRM-mediated protein interactions of other proteins (Clery, et al 2008, Curr. Opin. Struct. Biol., 18, 290; Kielkopf, et al Genes Dev., 18, 1513)

      As well as including the missing citations of the L1 literature, implications of the above considerations need to be addressed before publication.

      I have the following additional comments and issues:

      1. p.2. l. 8, the citation to TPRT should include Luan,et al.- 1993, Cell 72: 595
      2. p. 5, middle of 2nd para - what does "different diffusivity" mean? - what are "stereotyped puncta"?

      Any invocation of cis preference should cite the foundational study by Kaplan, N., et al. (1985). "Evolution and extinction of transposable elements in Mendelian populations." Genetics 109 459. 3. p.10 middle paragraph, the authors state: "The decreased phase separation of the R261A mutant was unexpected, as we predicted that mutating a core RNA-binding residue would only affect condensation in the presence of RNA. We also noted that the protein partition coefficients of the R261A condensed phases were higher than their counterparts for WT and K3A/K4A. Taken together, these experiments showed that K3/K4 and R261 are not essential for protein condensation in vitro."

      these findings would have been predicted by the afore mentioned findings of Newton et al, which should be cited here. 4. p. 14, first paragraph "we predicted that stammer-deleted ORF1p would maintain an elongated coiled coil conformation that might disfavor trimer- trimer interactions that are mediated by the N terminal half of the protein (Figure 4A, left two cartoons)."

      It seems that the authors are stating that different fully formed trimers can form larger complexes mediated by interactions between their coiled coils, an idea apparently based on results published by Khazina and Weichenreider (2018). This paper states that "Additional biophysical characterizations suggest that L1ORF1p trimers form a semi-stable structure that can partially open up, indicating how trimers could form larger assemblies of L1ORF1p on LINE-1 RNA." However, the cited Khazina structural data ((PDB) entry 6FIA)) were derived from coiled coils that had been solubilized to monomers in guanidinium HCl from inclusion bodies (insoluble aggregates) that had accumulated during their synthesis in E. coli...a common condition for highly expressed proteins. Fully denatured ORF1p coiled coils such as these, which also lack the entire NTD are an in vitro artifact and never exist in "nature". It is almost certain that ORF1p monomers trimerize while being synthesized on adjacent ribosomes (e.g., Bertolini et al.- 2021; Science 371: 57). I am not aware of any biochemical evidence from the Martin laboratory on mouse ORF1p or the Weichenrieder or Furano laboratories on human ORF1p indicating that the coiled coils of fully formed trimers synthesized in vivo can unravel to mediate interactions between different trimers. In fact, the authors' results in Fig 1F supports this contention. 5. p.10, Legend to Figure 1G The cells were stained simultaneously with two Halo ligand dyes (Halo-JF549 and Halo- JF646), giving a positive control for colocalization.

      Why is staining the same ligand (Halo) with two different dyes a colocalization control? 6. The authors conclude their paper with the statement "The L1 system characterized in this work employs a uniquely powerful combination of biochemical reconstitution, live-cell imaging, and functional phenotyping in cells. In vitro reconstitution allows us to study the biophysical properties of condensates in a minimal and controllable system."

      However, there are several instances where the in vitro biochemical properties of ORF1p variants are somewhat discordant with their in vivo results. In the case of their coiled coil mutants. replacement of the coiled coil stammer, MEL (uniquely invariant for more than 50 Myr of primate coiled coil evolution) with AAA or AEA exhibited reduced retrotransposition that was not accompanied by a corresponding reduction in condensate formation (Fig 4). In another instance, while mutation of the highly conserved residue (R261) necessary for RNA binding eliminated retrotransposition it did not have a corresponding effect on condensate formation even in the presence of RNA (Fig 3).

      General comments on the Figures - Although I rather liked the cartoon version of ORF1p (Fig 1B) and when used to show the location of mutated site, versions that purport to show the effect of mutations on structure (Fig 4A) are misleading and should be eliminated.

      Closing Comment:

      Overall, I enjoyed reading this paper, and feel that when the issues I raised are appropriately addressed and the relevant missing citations are included it would make a useful contribution. However, it seems that the authors could make a more compelling case that dissociates condensate formation of ORF1p and its activity in retrotransposition, consistent with the Martin and Holehouse review cited above. So, I urge them to reconsider their conclusions. I did not find the highly speculative discussion about the relevance of phase separation / condensate formation to cis preference at all convincing as it is just as it is just as likely (maybe more so) to be enforced at the level of selection - evolutionary failures, by definition, are not propagated.

      Significance

      Although this paper addresses a long-studied topic in L1 biology, namely how the L1 encoded proteins assemble into an L1 RNP (the retrotransposition intermediate), the authors posit that the formation of phase/separated protein condensates (visible as microscopic droplets) are involved. Such droplets are a currently popular biochemical feature exhibited by some proteins, but their functional relevance is a currently a contentious topic in protein biochemistry. I do not think that the authors make a convincing case that condensate formation is involved, rather I think that their evidence provides reasonable evidence that condensation has no role. I urge the authors to consider this possibility, but whatever which conclusion proves to be correct, their study would make a useful contribution to the field.

    Annotators

    1. Review coordinated via ASAPbio’s crowd preprint review

      This review reflects comments and contributions by Ruchika Bajaj and Gary McDowell. Review synthesized by Bianca Melo Trovò.


      This study demonstrates the utility of an L-Methionine analog - ProSeMet - to tag and enrich proteins which have residues that are methylated in vivo, ex vivo and in vitro. Furthermore, the study demonstrates that this can be used in combination with mass spectrometry to identify these sites. Overall this is a useful, well-verified and well-described approach that will be helpful for future identification and investigation of methylation sites.

      Major comments

      It would be helpful if the manuscript could additionally discuss the reversibility of methylation generally, and the reversibility of the modification of protein residues by the alkyne group specifically, in the discussion, and whether that has any implications for their results. It may be that the dynamics of methylation and demethylation vary between the two; or it may be that they are the same - either way, that may affect how they suggest others use this method and interpret its results.

      Perhaps related to the question of reversibility, it would be helpful if the manuscript would comment on whether these are “true” methylation sites or not; i.e. whether they consider all these methylation sites to be functional. Trying to determine this would be an interesting direction for future work, but for this study a reflection on whether these novel functional methylation sites are simply capable of being methylated, or are likely to be methylation sites that are meaningful biologically, would be helpful.

      Results, ProSeMet competes with L-Met to pseudo methylate protein in the cytoplasm and nucleus: the manuscript claims that ProSeMet is not incorporated into newly synthesized proteins but rather converted to ProSeAM and used by native methyltransferases. There does appear to be some reduction in the labeling with ProSeMet on cycloheximide treatment in Figure 2D - could this suggest that it is incorporated into newly synthesized proteins as well as being converted to ProSeAM? If not, could the manuscript explain why not? This experiment clearly shows that in contrast to AHA labeling, there is still use of ProSeMet as a substrate when translation is inhibited; however, it is not clear how this demonstrates that it is not incorporated at all into newly synthesized proteins. If methyl has been incorporated in previously present proteins, perhaps this can be clarified in the text.

      Results, ProSeMet competes with L-Met to pseudomethylate protein in the cytoplasm and nucleus: the conclusion that “Cell fractionation of the cytosolic and nuclear compartments followed by SDS-PAGE fluorescent analysis revealed no fluorescent labeling of the L-Met control” is correct but may be overstated as there appears to be some background in the cytosolic fraction.

      Minor comments

      • Introduction: Recommend including a mention to ProSeMet's permeability.
      • Introduction, Figure 1: the last step with CuAAC and N3 labeling in the description of the Chemoenzymatic approach for metabolic MTase labeling is not clear. Please, add the description in the legend.
      • Results, Figure 2D: the image suggests an overloaded gel, consider using an alternative gel image.
      • Supplementary Material, Fig. S1: the data with L-met is only shown with T47D stacks.
      • Supplementary Material, Fig. S3: please add the control for the no treatment condition.
      • Results, Fig. 2A ‘ incubating for 30 m in L-Met free media’: Please confirm that the length of incubation was 30 minutes.
      • Results, Enrichment of pseudo methylated proteins used to determine breadth of methyl proteome: Please provide some description for the SMARB1-deficient G401 cell line. Why smarb1 deficient?
      • Results, Figure 3: Please define BP, MF, HP, NES, and label the x and y axes in panel D.
      • Results, ProSeMet-directed pseudo methylation is detectable in vivo: Please, clarify if the administration was oral.

      Comments on reporting

      • Results, ProSeMet competes with L-Met to pseudo methylate protein in the cytoplasm and nucleus: Please verify the quantity reported: 5µg on SDS-PAGE gel seems low.
      • Results, ProSeMet-directed pseudo methylation is detectable in vivo: the manuscript reports that “mice starved prior to ProSeMet injection had increased ProSeMet labeling in the heart, whereas mice fed prior to ProSeMet administration had increased labeling in the brain and lungs”. The error bars are large, it would be helpful to show the individual real data points for the graphs in Figure 4.
      • Results, Figure 4C: please report the mathematical expression used to calculate the relative fluorescence.
      • Supplementary Material, Fig. S7: please provide more details on the antibody employed.

      Suggestions for future studies

      Future studies could investigate the biological functionality of the novel methylation sites - but this is a great proof of principle.

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      Reply to the reviewers

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

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

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

      Reviewer #1

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

      Reviewer #2

      *Major comments: *

      *Early CRC *

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

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

      *Model system *

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

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

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

      *Gene Expression analysis *

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

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

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

      *Western Blot controls *

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

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

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

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

      *Minor comments: *

      *Statistical analysis of proliferation assays *

      The statistical significance for proliferation assays are missing.

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

      Reviewer #3

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

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

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

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

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

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

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

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

    1. Author Response

      We thank the reviewers for their thoughtful and constructive comments which have helped us improve our manuscript. In our revised manuscript, we will respond to three main weaknesses:

      1. We will address the inconsistency in the experimental design across the behavior and the transcription experiments by repeating the behavior with an experimental timeline that more exactly matches that of the animals used in transcriptional studies;

      2. We will further validate and justify our use of TRAP and our focus on the NAc as the sole brain region of investigation;

      3. We will revise the language throughout the manuscript, especially in the discussion, to reduce anthropomorphizing of our results and interpretations. Below we have provided responses to specific concerns articulated by each reviewer.

      Reviewer #1 (Public Review):

      The monogamous vole provides unique opportunities to study the neural basis of pair bonding and this study exploits that opportunity in a novel way. Focusing on the nucleus accumbens, the authors conduct RNA-Seq to characterize the transcriptome in same-sex and opposite-sex pairs when bonded, when separated for a short time and when separated for a long time at which point the literature has in the past demonstrated the willingness to form a new bond. They determine that the transcriptome of pair bonding includes a preponderance of glial-associated gene changes and that it degrades with long-term separation. To the latter point, they then conduct a neuron enriching trap schema to find those genes subject to change specifically in neurons.

      The strength of the report is the clever experimental design, the unusual animal model, and the comparisons of same-sex and opposite-sex pairs and long-term and short-term separations.

      The weakness is that the behavioral changes observed are not what was expected based on prior work and are relatively modest, providing a disconnect between the outcome and the more dramatic transcriptional changes. A second weakness is the focus on the nucleus accumbens which is a brain region most closely associated with reward. While pair bonding may be rewarding, that component may be independent of the memory of a partner or the willingness to partner anew. Lastly, there is no clear connection between the identified transcriptome and either the formation or degradation of the pair bond.

      We thank the reviewer for noting the unique strengths of using prairie voles to investigate this specific question and for praising our experimental design, which compares opposite-sex and same-sex paired males at each time point to disentangle the effects of pair bonding from general social affiliation and isolation.

      Reviewers #1 and #3 noted the mismatch between the behavioral and transcriptional responses. Specifically, we found little evidence of bond dissolution following long term separation despite substantial erosion of the pair bond transcriptional signature. They further note that the experimental design employed to assess behavior and transcription differed, which may have contributed to the apparent mismatch. Importantly, our initial behavioral assessment as presented in Figure 1 of the manuscript had two strengths. It measured intra-animal changes in behavior over time and minimized the number of animals required. However, we agree with the reviewers, and we are currently repeating the behavior experiments to match the transcription experiments. Specifically, separated partners will be kept in separate colony rooms to ensure no possible access to partner-associated sensory cues (visual, auditory, olfactory), and we will use separate cohorts of animals for short- and long-term separation. This design avoids partner re-introduction during the short-term partner preference test. The results of this work will be informative regardless of outcome. If we observe a dissolution of pair bond behaviors, it indicates that re-exposure to a partner after a short, 48-hour separation has a powerful effect on bond duration following separation. If we do not observe any change in pair bond behaviors following separation, it would confirm that pair bond behaviors are more resistant to erosion than are transcriptional signatures of pair bonding.

      We have focused on the NAc because it is a critical hub that is engaged upon attachment formation and is implicated in loss processing. Specifically, studies have shown that blockade of neuromodulatory signaling (i.e. oxytocin and dopamine) in this region impairs bond formation and can lead to bond dissolution. Our group and others have demonstrated that plasticity within this region - in patterns of neuronal activity and in synaptic response to oxytocin - are associated with bond formation and maturation (1, 2). And literature on drugs of abuse has demonstrated an important role for the NAc in encoding of reward associations (3), which ultimately underlies partner preference. Additionally, in human neuroimaging studies, Prolonged Grief Disorder is associated with an enhanced signal in the NAc when viewing images of the lost loved one, suggesting that normal resolution of grief corresponds with a decrease in NAc activity elicited by reminders of the lost loved one (4). Thus, our focus on this region is well supported. Nonetheless, we recognize that the NAc does not act in a vacuum, and the efferent and afferent connectivity of different NAc cell types is well delineated, paving the way for future work (5, 6).

      Additionally, we agree with the reviewer that pair bonding behavior is multifaceted and comprised of several discrete behaviors that are not dissociable in the partner preference test. Partner-associated reward and partner memory may be independently encoded, and disruption of either process would manifest as a decrease or lack of partner preference. In our complete response to reviewers and revision of the manuscript, we will address this point more thoroughly. Finally, we interpret the reviewer’s last comment to be a request for functional manipulations to validate that the predicted transcriptional changes have a behavioral effect. This is beyond the scope of this manuscript but an active area of future research.

      Reviewer #2 (Public Review):

      The goal of this study is to understand the molecular mechanisms by which pair bonded animals recover following the loss of a partner.

      Strengths of this work include: (1) The organism - a novel model for studying pair bonding and loss; (2) The integrative nature of the study; it integrates behavior and brain gene expression RNASeq data and vTRAP; (3) The important and understudied question about how pair bonded animals recover from loss; (4) The thorough and careful analysis of highly multidimensional and complex datasets

      Weaknesses include: (1) the major comparison is between same vs opposite sex housed pairs. This design controls for social effects somewhat, but the two treatment groups differ not just with respect to whether or not they are pair bonded, but also in whether or not they had associated with a male or female. Differences between the treatments could reflect pair bonding, or perhaps something about the sex of the partner. It would be useful to have an additional control group, or data on the behavior of individuals within both types of pairs while they are co-housed. Were transcriptomic effects more detectable in pairs that were more bonded together behaviorally? That would suggest that the gene expression signatures really reflect something about the bond rather than other confounds, for example; (2) The vTRAP method is fancy but what is it really adding? (3) The authors interpret the transcriptomic differences as promoting the ability to form a new bond but there are probably other processes that are contributing to the differences in gene expression. Some of the differentially expressed genes could be involved in promoting a new pair bond, but there could also be a signature of the memory of the identity of the partner, the signature of the bond itself, etc. (4) Some of the interpretations go a little too far, especially in terms of anthropomorphism. The impact of the work includes further development of voles as an important model for studying social behavior and insights into the molecular processes important for recovering from the loss of a partner.

      We thank the reviewer for recognizing the strength of our study organism and experimental techniques as well as rigorous analyses to answer an important question about adapting to partner loss.

      Regarding the noted weaknesses:

      (1) We chose to compare opposite sex pair bonds to same sex affiliative relationships as this is the standard within the field, and we note that reviewers 1 and 3 found this to be a strength of our study design (7–11). Peer relationships in prairie voles are difficult to distinguish behaviorally from those of opposite-sex pairs (Fig 1) because both same and opposite-sex paired voles show selective preference for their pairmate and selective agression towards other voles (7). As such, the critical feature that makes pair bonding different is mating, which requires an opposite sex partner in voles, and our experiments are optimally designed to identify the longitudinal transcriptional changes that result from mating and cohabitating with an opposite-sex partner. In order to best match our two groups, only animals with a preference score >50% were included in the transcriptional experiment, ensuring that we were comparing animals with an affiliative preference for their partner - whether that individual was the same or opposite sex.

      We interpret the reviewers comment to be that they want us to compare opposite-sex-paired animals with and without bonds. This can be achieved two ways. First, we can compare to a promiscuous species, such as meadow voles, which will mate and cohabitate without forming bonds, but this is confounded by species differences in transcription that may exist independent of bonding. Second, we can compare bonded voles to the small subset that do not form bonds. While intriguing, this is experimentally challenging as only ~10-20% of males fail to form a bond when paired with a sexually receptive female (in the current study, 16% had a preference < 50% after two weeks of pairing, which is consistent with prior reports - (9–11)). Put simply, we would need to pair hundreds of voles to opportunistically collect a sufficient number of non-bonders for transcriptional assessment across our experimental conditions. While we hope to eventually be able to do such an experiment, litter sizes, consideration of animal welfare, and other constraints make this largely untenable at present.

      Data on the behavior of individuals within both types of pairs while they are co-housed is already provided via results of a partner preference test performed after 2 weeks of co-housing and prior to re-housing or separation (Fig 2B and 3B). We find the reviewer’s suggestion of finding a relationship between the transcriptional signature and the pair bonding strength an interesting question, and we undertook a preliminary analysis examining whether animals with different pair bond strength aggregate on a PCA analysis of gene expression. There was no apparent relationship, although we are performing additional analyses such as exploratory factor analysis. The fact that we have not found a relationship between the baseline partner preference and the transcription in these initial analyses is perhaps unsurprising. First, bonding may require some threshold change in gene expression, with bond strength reflected in non-genomic information, such as synapse formation or strengthening, or axonal ensheathment. Second, we only performed transcriptional analyses on animals with a baseline partner preference >50%; we would not necessarily expect a dissociation given the uniformly strong bonds across these animals.

      (2) We feel that inclusion of TRAP adds substantially to this manuscript and to our understanding of the neuromolecular underpinnings of bonding and loss in the NAc. The value of this experiment is twofold. As noted by Reviewer 3, “the TRAP approach in prairie voles is novel and will provide a great resource to the research community.” The prairie vole community has just developed its first transgenic Cre lines, which could be paired with vTRAP to query bond-associated gene expression changes exclusively in Cre-expressing neurons (15). Second, we noticed a puzzle in our tissue-level data. The majority of cells in the NAc are neurons (16, 17), and the vast majority of pair bonding studies of this region have focused on neuronal phenotypes, but our transcriptional signatures were linked to changes in glial populations. Ultimately, changes in glia are likely to act via their interactions with neurons, and vTRAP enables us to query the neuronal transcriptional changes within our data. Supporting that this provides novel insights into our datasets, when we cluster transcripts based on their expression profiles following short and long-term separation, we predict different GO terms from the tissue level and neuronally-enriched gene sets. For instance, the GO terms resulting from cluster 2 for neuronal genes (Fig 4) includes “response to amphetamine” within the top 10 results, but the same cluster of genes from tissue level sequencing predicts this GO term as the 34th result.

      (3) We agree with the reviewer that adapting to partner loss is a multifaceted process that likely engages numerous biological and emotional systems in voles. The explanation we offer for the transcriptional changes during loss is based on previous work in the field and is one possible interpretation. We will expand on this point during revision of the manuscript.

      (4) We thank the reviewer for encouraging us to be objective with our interpretations. We will address this comment during revision of the manuscript.

      Finally, we thank the reviewer for recognizing the value of our study for not only the field of voles but the bereavement field more broadly.

      Reviewer #3 (Public Review):

      In this manuscript, the authors investigate the behavioral and brain transcriptional alterations associated with short- and long-term partner separation in the monogamous male prairie vole. Male prairie voles continue to show affiliative behavior after short- (2 days) and long-term (4-weeks) partner separation, with similar effects for same and opposite-sex pairs. However, the transcriptional signature in the nucleus accumbens exhibits marked alterations after long-term separation.

      Strengths:

      1) A key strength of this manuscript is its use of the monogamous prairie vole to investigate transcriptional alterations associated with pair bonding and subsequent pair separation. This sort of behavior cannot be investigated in typical rodent model systems (e.g., mice, rats), and the choice of using prairie voles allows for dissection of potential mechanisms of social bonding with relevance to partner loss in humans.

      2) Investigation of behavioral measures and transcriptional alterations at both short- and long-term time points after pairing and separation is a strength of the manuscript. These time points were selected based on previous studies in laboratory and wild prairie voles related to the time it takes to form a pair bond and for the male prairie vole to leave the nest after the loss of the female pair. The datasets generated will be of great use to the scientific community.

      3) The authors investigate the behavior and transcriptional profiles after same-sex as well as opposite-sex pairing. This is considered a thoughtful decision on the authors' part which allows them to tease apart the effects of same vs. opposite sex.

      4) The use of numerous behavioral measures to assess both affiliative and aggressive behaviors is a strength of the approach.

      5) The authors use many biostatistical approaches (e.g., RRHO, WGCNA, Enrichr) to probe the transcriptomics data. These approaches allow the authors to move beyond simply assessing transcriptional profiles separately, but to look for patterns that are similar or different across datasets.

      6) The authors use rigorous statistical methods to assess behavioral measures.

      7) The TRAP approach in prairie voles is novel and will provide a great resource to the research community.

      Weaknesses:

      1) The methods state that prairie voles were treated differently in the behavioral and transcriptomics studies. Specifically, for the separation in the behavioral studies, prairie voles were separated by sight, but not necessarily by the smell from partners (i.e., partners were kept ~1 foot apart). However, prairie voles in the transcriptomics studies were separated by both sight and smell (i.e., partners were sacrificed after separation). Thus, it is possible that the lack of degradation of pair bond-related behavior after long-term separation might be due to these prairie voles being able to smell their partners after separation. This is considered a moderate flaw in the design of the studies which limits the integration of results between behavior and transcriptomics. This might be why the authors do not see a strong behavioral degradation of pair bond-related behavior after long-term separation but do see a strong transcriptional signature.

      2) While RRHO is helpful to assess overall patterns of transcriptional signatures across datasets, its utility for determining the exact transcripts is limited. This is because of how RRHO determines the overlapping transcripts for its Venn diagram feature (by taking the point where the p-value is most significant and taking the list to the outside corner of that quadrant).

      3) TRAP expression was verified in only one animal. Thus, the approach has not been appropriately confirmed.

      We thank the reviewer for their thoughtful comments on the innovative strengths and advantages of our manuscript.

      Regarding the noted weaknesses:

      (1) Please see our response to Reviewer #1, who shares your concerns.

      (2) We agree that RRHO is particularly useful for assessment of overall patterns. We interpret the Reviewer’s comment to mean that when extracting the overlapping gene lists from an RRHO quadrant for downstream analyses, we should filter that list for genes whose differential expression passes a nominal p-value cutoff to reduce the amount of biologically insignificant conclusions we are drawing from the RRHO data. Our initial analyses used just such a threshold-based approach by identifying GO terms via differentially expressed genes of the combined pair bond (Figure 2) using both p-value and log2Fold cutoffs. This analysis revealed a number of terms associated with glial cell proliferation, differentiation, and function (Fig 2H). Such processes occur over a time frame of days to weeks, with different phases of differentiation characterized by different gene expression profiles. To explore this further, we used the genes in the UU and DD RRHO quadrants without implementing a p-value cutoff to see if additional genes associated with these GO-identified pathways may be showing subtle but consistent directional changes (Fig 3). Importantly, we only use the overlapping RRHO gene lists to determine how previously defined biological processes via DEG-predicted GO terms change across conditions; we are not using the RRHO gene lists to generate new GO terms. This allowed us to look for patterns within the identified pathways that may give insight into how transcription might be affecting gliogenesis. This analysis was similarly suggested to us from other experienced users of RRHO plots (see Acknowledgements). There are also several published studies that use RRHO UU and DD quadrant overlap (18–22).

      (3) Most labs rarely confirm Cre-dependence of vectors in more than one or two animals as the results, including those shown in Fig S9A, are typically definitive (i.e. no expression in the absence of Cre, expression in the presence of Cre). In addition to the images shown in figure S9A, we used fluorescent guided dissection to harvest tissue/mRNA, serving as an additional visual confirmation of RPL10-GFP expression in the animals used to generate Figure 4. Since submission, we have also confirmed that this vector also expresses in rats when Cre-recombinase is present. However, prior to resubmission, we will perform additional surgeries to confirm that TRAP is only expressed in the presence of Cre-recombinase.

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    1. The special considerations which enter into the determina-tlon of the credibility of historical statements are discussed,with considerable fullness, in Hereford B. George, Histori-aal evidence, Oxford, 1909; Allen Johnson, Hhtorian andhistorical evidence, New York, 1926; and Charles G. Crump,Hwtory and historical reeearch, London, 1928. The studentwill receive some aid in handltng the roblems of conflictingauthorities by using Frederic Doncalf and August C. Krey,Parallel source p r o b l e m in medieval history, New York andLondon, 1912 [Harper‘s parallel aource problems], or one ofthe other volumes in the same series.

      These look fascinating, but alas for another day.

    1. 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:

      The manuscript presented by Joshi et al presents a body of results describing the aggregation of the peptidoglycan receptor PGRP-LE, which is an intracellular protein, in response to intestinal infection by oral ingestion in Drosophila. This study is based on the generation of two CRISPR/Cas9 mutant lines in which the PGRP-LE sequence has been fused to the V5 epitope (inserted into the PGRP domain) or the fluorescent protein eGFP (added at the C-terminal position). In each case, the "sensor protein" is expressed under the control of the endogenous promoter that ensures a physiological expression of the sensor.

      As expected from the literature, the authors show that the expression of each of the two PGRP-LE sensors is strongly induced in the digestive tract by the ingestion of the bacterium Erwinia carotorova carotorova (E.cc), which is known to produce a strong activation of the NF-kB signaling cascade under these infection conditions. In this study, the authors show that PGRP-LE-V5 sensors form clusters in the immunocompetent domains of the gut, particularly in the R4 domain where NF-kB activation is known to be primarily dependent on PGRP-LE. This clustering is not observed in clones with little or no expression of PGRP-LE due to RNAi-mediated knockdown of gene expression. The transcription of endogenous PGRP-LE or that of the PGRP-LE-V5 and eGFP sensors is not increased by the infection, allowing the authors to propose that the PGRP-LE protein pre-existing in the intestinal cells relocalizes into clusters or aggregates. These aggregates are also marked by the Rab5 protein, a marker of early endosomes, but not by the Rab7 marker, a marker of late endosomes. The expression of the antimicrobial peptide AttD is similar in the presence of the sensors as in control flies, which indicates that the immune response is not drastically affected by these sensors. Moreover, the kinetics of receptor aggregation parallels that of NF-kB pathway activation followed by AttD expression.

      Ingestion of E. coli or commensal bacteria or PGN, which do not induce a significant immune response according to the literature and data reproduced here by the authors, do not induce receptor aggregation either. Surprinsingly, heat-killed E.cc bacteria, which induce no or a very slight expression of AttD cause more but smaller aggregates of PGRP-LE. Moreover, these aggregates are not labeled by the Rab5 protein. The authors show that this aggregation of PGRP-LE is not affected by the down-regulation of the HH pathway, and is correctly induced by a uracil auxotrophic Ecc mutant. The expression of RNAi directed against the dFADD protein, an adaptor of the PGRP-LC membrane receptor contributing to the activation of the Imd/NF-kB pathway, does not alter this aggregation either. Finally, the authors observed that a set of genes whose expression in response to E.cc is dependent on PGRP-LE shows a differential dependence on Rab5 expression: while PGRP-SC1 expression is affected by Rab5 silencing, this is not the case for PGRP-LB or PGRP-SC2 expression. Furthermore, directed Rab5 knock-down in the adult gut induces an exacerbated immune response in the fat body. The combined action of PGRP-LE and Rab5 would therefore be necessary for the activation of PGRP-SC1 but not of PGRP-LB or PGRP-SC2. From these results the authors propose the existence of two pathways of activation of NF-kB target genes downstream of PGRP-LE, depending or not on an endosomal Rab5 signaling platform. The authors also propose that the amount of PGN may control the choice of Rab5-dependent or Rab5-independent pathway activation.

      Major comments:

      The authors have constructed beautiful genetic tools (PGRP-LE sensors). They present a set of convincing results concerning the formation of PGRP-LE protein aggregates in response to E.cc infection under different infection conditions or genetic backgrounds. Nevertheless, the study remains essentially descriptive and based on immunofluorescence and expression studies of a small set of genes responsive to the NF-kB pathway. To better support the hypotheses and conclusions, deep sequencing studies would be very powerful to reveal whether the differential expression observed for the target genes PGRP-SC1 versus PGRP-SC2 and PGRP-LB is also true for a large set of genes of the immune response, which would make the results more accurate. It would also be interesting to study more genetic conditions, e.g. affecting the endocytic pathway, proteasomal degradation or autophagy in order to determine the fate of aggregates and the mechanisms of their removal/resolution. Furthermore, biochemical studies, such as immunoblots, would allow following the fate of PGRP-LE at the protein level. The authors indeed show that the expression of PGRP-LE gene is not induced by E.cc but one can wonder if the protein is stabilized. They propose that PGRP-LE is not recycled because it does not colocalize with Rab7, but it might be also degraded by the lysosomal pathway rather than recycled. It would be interesting to test if aggregates are removed by the lysosomal pathway or by autophagy. Moreover, a recycling via Rab7 is maybe not expected for a protein that is not localized on the plasma membrane. A kinetic study including co-staining with Rab7 would better support the conclusion that there is no colocalization with Rab7. Otherwise, they may miss the right timing to observe this colocalization. Similarly, the absence of colocalization with Lamp1 at a given time does not allow concluding with certainty that PGRP-LE is not degraded by the lysosomal pathway. The 24h staining (Fig2A) sounds similar to a Lamp1 profile. One should therefore be more cautious in drawing conclusions about these co-staining experiments. Moreover, Rab7 and Lamp-1 staining are faint and miss RNAi controls to show the specificity of the staining.

      In conclusion, a corpus of additional experiments would be necessary to significantly advance the field and demonstrates the existence of a Rab5 signalization platform causing differential expression of target genes of the immune response. The expression of a large set of genes could be tested, some of the RNAi lines used needs to be better characterized, complementary genetic and biochemistry experiments would help to understand the fate of PGRP-LE, the effect of the Imd pathway could be more documented with other RNAi than FADD... The role of other components of the endocytic pathway tan Rab5 could be assayed with other RNAi (Rab7, ESCRT, ... ) to block the endocytic pathway and observe if it interferes with the aggregates. The authors could also possibly test the proposed hypothesis on the amount of PGN/bacteria that would be at the origin of a differential response.

      In the figure and figures legends and methods, the authors describe the aggregates as oligomers, but no experiment support this assumption. In the text, the authors stick with the nomenclature as clusters or aggregates which is more appropriate.

      Minor comments:

      • The abstract would benefit from being rewritten: the first half provides general information that is not strictly necessary, which prevents a more thorough description of the results. I disagree or misunderstand the statement "little is known about the subcellular events required to translate these early steps into downstream target gene transcription" because extensive studies of the fly immune response have been done.
      • Two spellings in the intro: PeptidoGlycaN or PeptidoGlycan. I suggest peptidoglycan
      • "the innate immune response that might otherwise be obscured by the action of the adaptive immune response": this is a rather archaic way of thinking because it is clear that the two responses are complex and intimately intertwined.
      • "to visualize PGN detection by PGRP": correct "by PGRP-LE". -avoid "to our surprise". -"locus-directed": I suggest "tissue directed" or "in a localized manner in the digestive tract".
      • Describe the purpose and procedure of smurf methodology.
      • As noted above, do not describe clusters as oligomers in the methods and figures and figure legends. -"PGRP-LE recruits Rab5 protein": do the authors suggest a direct interaction between the two products? If so, it would be interesting to test this with co-IP experiments. However, it is possible that the aggregates are internalized in the endosomal compartment, independently of any Rab5/PGRP-LC interaction. Therefore, the term "recruits" is confusing here. -To make the results accessible to a broader audience, the authors may clarify the drosophila-specific genetic tools used in this study (Flpout clones, Gal80ts conditional expression...)
      • In some cases, statistical analysis of RT-qPCR data are performed using a one-way ANOVA (fig 1H, 5A) whereas in others (fig 2H and L, 5B) a non-parametric Kruskal-Wallis test is used. The rationale for these discrepancies should be explained. Moreover, in all these experiments the data are compared to a control that is set to 100% and has no standard deviation. This violates some of the ANOVA assumptions (normality of the data points). To be correct, an outside control should be used to normalize the data (including the control to which the other genotypes are compared)
      • Could the authors better explain the rationale for using PGRP-LE::V5 in some experiments and PGRP-LE::GFP in others? -Fig 1H: in this experiment, according to the legend, all the genotypes are infected. So it's not clear how the authors conclude that infection does not activate PGRP-LE expression in the absence of a non-infected control. We may have missed some points. Furthermore, as stated above, the authors could also perform a Western blot to ensure that PGRP-LE translation is not activated, or the protein stabilized, following infection.
      • Fig 2A: The PGRP-LE aggregates a 24 hpi look different from the previous time points. It would be interesting to make a double staining with a Lamp1 antibody to check for colocalization at this late time points.
      • Fig 2H: attD induction by hk E.cc is indicated as not significantly different from uninfected control and presumably not from E. coli and PGN. So the statement "hk E.cc which induced a weak AttD transcription" in the text is not correct.
      • Fig 3: The RNAi lines used in this figure have no effect on PGRP-LE aggregation. To safely conclude that the corresponding proteins do not play a role in this process, the efficiency of the RNAi lines against their respective targets should be shown.
      • Fig 3A,B : why no quantification of the aggregates are presented in this particular figure?
      • Fig 4D: the pictures are too small, use the same magnification as in A and C

      Significance

      The studies presented in this manuscript are interesting and well done but remain mainly descriptive without sufficient data to support what could be a conceptual advance. Further work is needed to demonstrate that PGRP-LE would signal via two different pathways, dependent or not on Rab5 and the endocytic machinery. Further genetic and biochemical studies would allow to better describe these two putative signaling pathways leading to differential immune response genes expression, and/or the nature (oligomeric or not) and fate of PGRP-LE aggregates (endocytic-, lysosomal-, autophagic-patways,...). Such endosomal signaling platform has been described for the activation of the Toll pathway. Exacerbated immune response in the fat body following inactivation of Rab5, Fab1, and ESCRT components has been described earlier suggesting that accurate termination of IMD signaling also requires the endocytic machinery.

      This study concerns fly scientists interested in the fine understanding of the signaling mechanisms of the innate immune response and may have a wider audience in the community of scientists interested in the molecular mechanisms of cell signaling in eukaryotic cells in response to external stimuli, and the role of endocytic trafficking in this response. Our expertise (reviewer and co-reviewer) covers the NF-kB-dependent immune response and some aspects of intracellular trafficking.

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      Referee #1

      Evidence, reproducibility and clarity

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

      Macroscopic conductance (G) depends on channel number (N), microscopic or unitary conductance (), and open probability (PO) by G=NPO. The authors assume that the level of WT and D174A mutant protein expression on plasma membrane, which determines N, are equal; however, this critical assumption does not appear to have been tested. The fact that conductance density (nS/pF) is plotted in Fig. 2E does not alter this caveat because this procedure normalizes the data only for cell surface area (i.e., size).

      The authors' conclude that "The conductance density relationship (Fig. 2E) compares the maximal conduction of both constructs; this is the fully open channel (open probability ≈ 1)"(lines 87-88). However, neither raw currents nor G-V data are shown. Typically, currents measured at large, near-saturating PO are used to compare the relative conductances of WT and mutant ion channels. The currents shown in Fig. 2A and 2B exhibit prominent 'droop' at even modest depolarizing potentials (+10 mV for D174A and +30 mV for WT), indicating that the proton gradient has been substantially perturbed by the flow of ge depolarizing voltages needed to drive channels to near-maximal PO. Furthermore, there is no evidence that maximal PO itself is also not different in WT and D174A channels. Indeed, maximal PO for native Hv1 channels measured using variance analysis is reported by significantly smaller than 1.0, and assuming that PO = 1.0 for either WT or D174A is therefore not well supported. Maximal could be altered by the D174A mutation, which has a clear and strong effect on channel gating evidenced by the large (-70 mV) negative shift in threshold potential reported both here and previously in the literature. Effects of mutations on maximal PO due to altered gating behavior could be separate and distinct from any change in plasma membrane channel number (N). Lastly, because D174A channels have a much higher PO than WT at 0 mV, the mutant will necessarily conduct inward proton currents at the physiological resting membrane potential (RMP) in tsa-201 cells (perhaps -30 mV?). Inwardly directed proton currents will therefore cause intracellular acidification under resting conditions. The constitutive acid load in cells expressing D174A, but not WT, is likely to have a variety of physiological consequences, including decreased protein expression or plasma membrane targeting of D174A. There is evidence that another-constitutively open Hv1 mutant (R205H) also generates smaller currents macroscopic conductance than WT, and this phenomenon is likely to result from decreased cell surface expression. To conclude that the microscopic conductances of WT and D174A are unequal, the authors must demonstrate that N is not different The authors' conclusion that D174A "conducts protons at a lower rate" (line 89) is therefore not well supported by the experimental data. 2. The authors indirectly measure apparent proton flux rates (D) in LUVs containing WT and D174A mutant Hv1 channels using a fluorescence-based approach, and conclude that D is 2.4 times smaller for D174A than WT. However, the method for estimating D is not performed under voltage clamp, and the driving force for proton current is neither known nor measured. The authors state that "Transmembrane voltage constituted the driving force for proton uptake into LUVs (Figure M). It resulted from facilitated K+ efflux out of the vesicles (30)", (lines 261-262), but this voltage is unknown and not likely to equal the Nernst equilibrium potential for K+ once Hv1 channels begin to open.

      Once Hv1 channels begin to open, intra-lumenal pH (pHi) will necessarily occur during the experiment. Such changes are likely exacerbated by a) the low proton buffering capacity of the system (5 mM HEPES) and b) the absence of any counter-charge pathway to balance the effect of proton charge movement on the membrane potential. Given the small volume of LUVs, even a relatively modest difference in either membrane potential or pHi could substantially alter the driving force for proton movement. Together, these factors are highly likely to result in a rapid and potentially large change in the driving force for proton flux.

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

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

      In contrast to the authors' statement that "Our experiments with the purified and reconstituted channels corroborated the conclusion (Fig. 3A)", (lines 92-93) it is not clear that unitary proton flux rates/unitary conductances are actually different in WT and D174A. 3. The presumed differences in unitary conductances (i.e., 'transport rate') between WT and D174A are used to estimate Arrhenius activation energies (Ea): ("The difference in measures transport rates allows a rough estimation of the Arrhenius 128 activation energy Ea for HV1-mediated proton flow. It amounts to 40 kJ/mol for the wild type and 23 kJ for the mutant. Thus, Ea exceeds the corresponding 15 kJ/mol barrier measured for gramicidin A (32, 33)", (lines 128-130).

      The method for determining Ea in the current work is not well-described. In Ref. 32, the authors estimate Arrhenius activation energy (Ea = 20 kJ/mol) for gramicidin D (not gramicidin A) from the slope of a line fit to measurements of currents at various temperatures. Here, the authors measure AMCA fluorescence decay rates at 4{degree sign}C and 23{degree sign}C and observe a similar temperature-dependent difference in WT and D174A (Fig. S2). Given that the data indicate that WT and D174A are similarly temperature-dependent, it is unclear how the authors arrive at different Ea values. The authors' conclusion that "The increment in Ea suggests that the transport mechanism may be different from a pure Grotthuss type, where the proton uses an uninterrupted water wire to cross the membrane", (lines 131-133) therefore does not appear to be well-supported. 4. The authors report no difference in water permeability in WT vs. D174A (Fig. 5 and S1) and interpret the results to mean that proton currents are not associated with measurable bulk water flow. A similar conclusion was reached for native Hv1 channels using deuterium substitution (DeCoursey & Cherny, 1997). However, the absence of bulk water flow does not itself rule out the possibility that 'trapped' waters within the Hv1 pore do not themselves carry the measured proton current. If intra-pore water molecules are tethered by hydrogen bonds with protein atoms, they may not move when Hv1 channels open. Proton transfer through a hydrogen-bonded network of waters requires only that the electronic structure of the network be rearranged during proton transfer; water is not required. As in the previous study (DeCoursey & Cherny, 1997), the lack of water flux reported here demonstrates seems to reinforce the notion that H+ moves separately from its waters of hydration (i.e., hydronium, H3O+, is not the permeant species) and does not necessarily imply information about the mechanism of proton transfer (i.e., side chain ionization vs. Grotthuss-type transfer in a water-wire).

      The authors state that: 1) "every H-bond donating or receiving pore-lining residue would have contributed an increment ΔΔ𝐺‡ of 0.1 kcal/mol to the Gibbs free energy of activation Δ𝐺‡ (25)" (lines 145-147), and 2) calculating NH from this Δ𝐺‡ allows estimation of the channel's unitary water permeability (Eqn. 2). Although hydrogen bonding patterns will undoubtedly alter the free energy for channel activation, this is not the same free energy change as that for proton transfer. Hv1 gating involves conformational changes that are both voltage and pH-dependent, and the D174A mutation is known to alter the voltage dependence of gating (Fig. 2 and previous studies). The effect of D174A on Hv1 unitary conductance, however, is speculated but not unambiguous (see above). In the absence of definitive experimental data showing differences in the unitary conductance of WT vs. D174A, the authors' assumption that water permeability would be strongly temperature-dependent (lines 154-160) seems premature and their ensuing conclusion tenuous: "pore residues interrupt the HV1 spanning water wire, trapping the water molecules inside the HV1 channel. In contrast to water, protons cross the pore by hopping from one acidic residue to another through one or more bridging water molecules (Fig. 6)" (lines 161-164).

      Furthermore, the authors calculate the number of hydrogen bonds (NH) that pore waters could form with pore-lining residues based on an X-ray structure of a chimeric proton channel protein (pdb: 3WKV) that is: a) manifests discontinuous transmembrane water density and is known to represent a non-conductive conformation, b) contains residues from Ci-VSP in the critical S2-S3 linker that form part of the proton transfer pathway, and c) exhibits structural features (i.e., highly conserved ionizable residues such as D185 and R205, which like D174 are reported to dramatically alter Hv1 gating, are packed into a solvent-free crevice) that are inconsistent with physiological function. Given that all Hv1 ionizable mutant combinations tested so far (the sole exception of D112V - other non-ionizable substitutions at D112 are tolerated) remain functional (Musset, Smith et al., 2011, Ramsey, Mokrab et al., 2010), the identities of water-interacting residues speculative. Interpreting differences in the calculated NH based on pdb: 3WKV therefore seems unlikely to reveal fundamentally important insights into Hv1 function. The author's conclusion that "The observation rules out the formation of an uninterrupted water chain spanning the open channel from the aqueous solution at one side of the membrane to the other. NH would have governed water mobility if such a water wire had formed (24)", (lines 143-145) therefore does not appear to be strongly supported.

      References

      Bennett AL, Ramsey IS (2017a) CrossTalk opposing view: proton transfer in Hv1 utilizes a water wire, and does not require transient protonation of a conserved aspartate in the S1 transmembrane helix. J Physiol

      Bennett AL, Ramsey IS (2017b) Rebuttal from Ashley L. Bennett and Ian Scott Ramsey. J Physiol

      De La Rosa V, Bennett AL, Ramsey IS (2018) Coupling between an electrostatic network and the Zn(2+) binding site modulates Hv1 activation. J Gen Physiol

      De La Rosa V, Ramsey IS (2018) Gating Currents in the Hv1 Proton Channel. Biophys J 114: 2844-2854

      DeCoursey TE (2017) CrossTalk proposal: Proton permeation through HV 1 requires transient protonation of a conserved aspartate in the S1 transmembrane helix. J Physiol 595: 6793-6795

      DeCoursey TE, Cherny VV (1997) Deuterium isotope effects on permeation and gating of proton channels in rat alveolar epithelium. J Gen Physiol 109: 415-34

      Musset B, Smith SM, Rajan S, Morgan D, Cherny VV, Decoursey TE (2011) Aspartate 112 is the selectivity filter of the human voltage-gated proton channel. Nature 480: 273-7

      Ramsey IS, Mokrab Y, Carvacho I, Sands ZA, Sansom MS, Clapham DE (2010) An aqueous H+ permeation pathway in the voltage-gated proton channel Hv1. Nat Struct Mol Biol 17: 869-75

      Ramsey IS, Moran MM, Chong JA, Clapham DE (2006) A voltage-gated proton-selective channel lacking the pore domain. Nature 440: 1213-6

      Randolph AL, Mokrab Y, Bennett AL, Sansom MS, Ramsey IS (2016) Proton currents constrain structural models of voltage sensor activation. Elife 5: e18017

      Significance

      Here the authors attempt to ascertain whether water molecules may mediate proton transfer in the voltage-gated proton channel Hv1 using a combination of whole-cell voltage clamp electrophysiology, protein purification, reconstitution, and pH-dependent AMCA fluorescence measurement and estimates of water permeability, and hydrogen bond calculations based on an X-ray structure of a chimeric Hv1 proton channel model protein. The authors address an important question that is fundamental to the exquisitely proton-selective Hv1 channel and which may be applicable to other proton transporting proteins.

      Although there is high potential for significance to a wide range of experimenters studying biologically fundamental mechanisms of proton transport, the experimental data fail to strongly support most of the authors main conclusions, and it is unclear whether the work represents a technial advance for the field. Previous work in the literature has described two main hypotheses for the proton transport mechanism in Hv1:

      • A) an intra-protein transmembrane water wire that allows permeating H+ to move along a chain of hydrogen-bonded water molecules and does not require explicit ionization of any particular amino acid side chain (Bennett & Ramsey, 2017a, Bennett & Ramsey, 2017b, Ramsey et al., 2010), and
      • B) Explicit ionization of a conserved side chain in the S1 helix (D112 in human Hv1) is required for proton transfer in Hv1 channels (DeCoursey, 2017, Musset et al., 2011). The Reviewer is an expert in the field, having originally identified and functionally characterized Hv1 channels in 2006 (Ramsey, Moran et al., 2006), contributed to the identification of key side chains and structural determinants of Hv1 function (De La Rosa, Bennett et al., 2018, Ramsey et al., 2010, Randolph, Mokrab et al., 2016), measured gating currents in Hv1 (De La Rosa & Ramsey, 2018), and authored the hypothesis that Hv1 utilizes a water-wire type mechanism for proton transfer (Ramsey et al., 2010).
    1. I add mass to each of these… mental clusters? planetary bodies in the Mindscape? by hyperlinking the phrase as I type.

      Nothing particular to what's described here, but this gives me an idea for a design of an efficient IME that doesn't require manually adding the brackets or even starting with an a priori intention of linking when you begin writing the to-be-linked phrase. The idea is that you start typing something, realize you want to link it, and then do so—from the keyboard, without having to go back and insert the open brackets—at least not with ordinary text editing commands. Here's how it goes:

      Suppose you begin typing "I want to go to Mars someday", but after you type "Mars", you realize you want to link "go to Mars", as this example shows. The idea is that, with your cursor positioned just after "Mars", you invoke a key sequence, use Vim-inspired keys b and w (or h and l for finer movements) to select the appropriate number of words back from your current position, and then complete the action by pressing Enter.

      This should work really well here and reasonably well in the freeform editor originally envisioned for w2g/graph.global.

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      Reply to the reviewers

      Manuscript number: RC-2022-01481R

      Corresponding author(s): Sebastian Voigt. Mirko Trilling, David Schwefel

      1. General Statements [optional]

      -

      2. Description of the planned revisions

      Reviewer #1: Evidence, reproducibility and clarity

      Using proteome profiling of rat CMV infected cells, the authors of this study identify the E27 protein of rat cytomegalovirus as being crucial for proteasomal degradation of STAT2. Since E27 shares 56% sequence identity to the previously characterized STAT2 antagonist M27 of murine CMV the authors investigated association of E27 with the Cullin4-RING UbL CRL4. Using gel filtration chromatography they provide evidence that E27 forms a stable ternary complex with DDB1 and STAT2 suggesting that E27 bridges STAT2 to DDB1 which is further corroborated by data from cross-linking mass spectrometry. A cross-linked DDB1/DDA1/E27/STAT2 complex was then used for cryo-EM imaging experiments. The subsequent single particle analysis yielded a density map at 3.8 A resolution that was further used to generate an E27 molecular model. At this point it should be noted that resolution was not very high and data form AlphaFold2 prediction and CLMS experiments were necessary to build a model which was described as having "sufficient quality", however, no quality parameters are included for this model. In this model, a cryptic zinc-binding motif was identified that turned out to be well conserved in M27. At this point the study switches to a mutational analysis of M27: MCMV mutants either lacking M27 or bearing an AxAxxAA triple mutation were investigated both in cell culture and in animal models. Surprisingly, the M27-AxAxxA mutant while exhibiting attenuated IFN inhibition was still more active than an M27 deletion mutant. Later during the study it is postulated that this may be due to the fact that E27 binding to STAT2 abrogates the interaction with IRF9, however, this is only predicted from modeling and no experimental data are provided for this hypothesis. Furthermore, modeling approaches were used to predict how E27 replaces endogenous CRL4 substrate receptors and how E27 recruits STAT2 to mediate CRL4-catalysed ubiquitin transfer.

      Reviewer #1: Significance

      __Reviewer #1: __This is an interesting and well written paper describing for the first time in molecular detail how a cytomegalovirus-encoded interferon antagonist degrades STAT2 by mimicking the molecular surface properties of cellular CRL4 substrate receptors.

      This study should be of broad interest for both virologists and structural biologists.

      Authors Response: We thank the reviewer for the insightful and constructive evaluation. We are very grateful for highlighting the significance of our work.

      Reviewer #1: Major points

      __Reviewer #1: __To my opinion the authors should perform mutational analysis in the context of E27 and RCMV. I accept that switching to M27 may be easier due to established procedures for MCMV mutagenesis and analysis, however, since all structural work is primarily done on E27 it would be consequent to confirm these structural predictions in the context of E27 before switching to a related protein.

      Authors Response: As the Reviewer appreciated, there were multiple reasons for the switch from RCMV-E E27 to MCMV M27. Most importantly, the MCMV in vivo infection model in mice is very well-established. Please also note that MCMV is applied far more often by virologists and immunologist as a standard model. Thus, the extension of our findings from RCMV to MCMV increases the relevance and outreach of the study. By performing the experiments in the MCMV context, we also aimed to emphasise that the function of the zinc-binding motif, which structurally organises the DDB1-binding domain, is functionally conserved among E27/M27-like proteins. Obviously, Reviewer #1 could ask why we do not solve the structure of M27 parallel to E27. With the sole exception of E27, none of the rodent M27 homologues could be produced recombinantly in a soluble form, preventing the purification and structure analysis of M27.

      Since we agree with Reviewer #1 that the extension from E27 to M27 may read “a bit rough” without a mutational analysis in the E27 context, we will construct RCMV-E E27 mutants leading to Cys=>Ala exchanges in the Zn-binding motif. An analysis of the interaction between DDB1 and these E27 mutants will be included in the revised manuscript.

      __Reviewer #1: __Moreover, data on the replication of the generated E27 deletion RCMV should be included in the manuscript (i.e. growth curves).

      Authors Response: RCMV mutants lacking the E27 gene exhibit an impaired replication. According to the suggestion, the growth curves will be part of the revised manuscript.

      Reviewer #1: The hypothesis that STAT2/E27 interaction is sterically incompatible with IRF9 binding is only based on structural prediction. It would help if the authors could present experimental evidence for such a mechanism.

      Authors Response: The hypothesis is based on three lines of argumentation: (i) structural data regarding the binding interface between STAT2 and E27 covering the known STAT2-IRF9 interface (Fig. 7F) (Rengachari et al., 2018). (ii) The finding that M27 mutants incapable to bind DDB1 and induce STAT2 degradation along the ubiquitin proteasome pathway retain a residual capacity to inhibit ISRE signaling, suggesting that the binding of M27 to STAT2 suffices to elicit some signaling inhibitory functions (Fig. 7G). (iii) To elicit their function, CRL4 substrate receptors such as E27 interact with two partners. As we discussed elsewhere (Le-Trilling and Trilling, 2020), a simultaneous development of two independent traits violates evolutionary and probability theories. Thus, these receptors must acquire their binding interfaces sequentially, and the first interaction must provide an evolutionary advantage allowing the fixation of the allele in the population. Afterwards, the second binding interface evolves. Thus, a hypothesis in which E27/M27 precursors evolved the capacity to bind STAT2, preventing its association with IRF9 thereby establishing relevant but incomplete IFN inhibition (before the DDB1 interface was invented leading to STAT2 degradation by the proteasome), provides a parsimonious explanation for all these findings without violating evolutionary constraints. To corroborate our argumentation, we will analyse if E27 indeed displaces IRF9 from STAT2 by analytical gel filtration and/or co-immunoprecipitation experiments.

      Reviewer #2: Evidence, reproducibility and clarity

      __Reviewer #2: __The manuscript entitled "Structure and mechanism of a novel cytomegaloviral DCAF mediating interferon antagonism" by Dr. Schwefel and colleagues cleverly combines biochemistry, mass-spectrometry, Cryo-EM and cell biology to dissect how RCMV-E hijacks its hosts ubiquitylation machinery to mediate proteasomal degradation of STAT2, a key player driving the antiviral IFN response. They identify E27 as DDB1-binding element, which is able promote CRL4-dependent ubiquitylation of STAT2, and demonstrate its effect on STAT2 levels by knockout RCMV-E strains. These findings are supported by in vitro reconstitution of the DDB1/E27/STAT2 complex and analyses via XL-MS and Cryo-EM. The obtained data are then powerfully validated and analysed in mutational strains via infection of homologue in vivo models. The results collectively explain how E27 mimics endogenous CRL4 substrate receptors, thereby recruiting STAT2 to be targeted by CLR4 for ubiquitylation in a NEDD8-dependent manner.

      Overall this is an important study that provides convincing insights on how rodent CMVs antagonize their host interferon response by exploiting its ubiquitin-proteasome system.

      The manuscript is well written and its introduction is extraordinarily comprehensive. There are a few minor points for the authors to consider below.

      Authors Response: We thank the reviewer for this very positive assessment.

      Reviewer #2: Significance

      Reviewer #2: The work of Schwefel and colleagues combines several powerful state-of-the art techniques to dissect the mechanism of the viral protein E27 and, for the first time, provides a rational for its ability to act as STAT2 antagonist. They performed outstanding structure-function analyses of the ubiquitin system, including the first global proteomic profiling of RCMV-infected cells, setting the standard for its human counterpart as rodent CMVs are commonly used as infection models. The manuscript is highly suitable for publication in any of the journals associated with the review commons platform.

      Authors Response: Again, we thank the reviewer for these kind words and the appreciation of our work.

      Reviewer #2: CROSS-CONSULTATION COMMENTS

      Reviewer #2: This reviewer agrees that at least testing mutants in the E27 in some assays would be appropriate.

      Authors Response: As detailed in the response to Reviewer #1, we will generate RCMV-E E27 mutants targeting the Zn-binding motif by site-directed mutagenesis. An analysis of the interaction between DDB1 and these E27 mutants will be included in the revised manuscript.

      Reviewer #3: Evidence, reproducibility and clarity

      __Reviewer #3: __Le-Trilling et al. present the first proteomic analysis of RCMV-infected cells, where they identified STAT2 as one of the most heavily downregulated (and degraded) proteins. This analysis showed that RCMV mediated degradation of STAT2 is conserved in closely related species used as animal models (rat and mouse) and human, despite the intra-host adaptation of each CMV. They also identify E27 as the RCMV factor that targets STAT2 for degradation, that exhibits ~50% homology with MCMV pM27. This study also identifies a Zinc binding motif in E27 using Cryo-EM which is conserved in other CMV species and is potentially involved in antagonising Type I and III responses.

      Reviewer #3: Significance

      __Reviewer #3: __The present work provides the first proteomics analysis of RCMV infection in rat cells, comparing infected vs non-infected rat fibroblasts to access potential RCMV targets. Then, it focuses on the characterisation of RCMV E27 and its role targeting and interacting with STAT2 (plus recruiting the Cul4 complex for STAT2 degradation). Finally, it provides the Cryo-EM structure of E27 and its CMV homologues, and the structure of the complex of E27 with elements of the CUL4 complex and STAT2. This is the first time that E27 function and structure are characterised. These are all novel findings - although the mouse homologue M27 has previously been found to interact with and degrade STAT2 (published by some of the same authors in Plos pathogens in 2011, (https://doi.org/10.1371/journal.ppat.1002069). Therefore the chief novel information is the structural studies.

      The manuscript will be of interest to researchers working with human and animal herpesviruses.

      My field of expertise is in Virology, Innate Immunity and host-virus interactions from an evolutionary perspective. I do not have expertise in Cryo-EM, so I could not evaluate the methods used in the section.

      __Authors Response: __We thank the reviewer for the positive evaluation of our work and its significance.

      Reviewer #3: Major points

      __Reviewer #3: __1. The authors claim the identification of a Zinc-binding motif in the protein E27 (RCMV) using Cryo-EM, then validation of the phenotype with MCMV WT, delM27 and M27 AxAxxA. To justify the change to MCMV to perform the functional validation, they stated "MCMV M27, the closest E27 homologue, exhibits 56% and 76% amino acid sequence identity and similarity, respectively (Fig. S4B). E27 and M27 AlphaFold2 structure predictions are almost indistinguishable (RMSD of 1.195 Å, 6652 aligned atoms) (Figs. 3B, S4A), and structural alignment of these predictions demonstrated conservation of side chain positions involved in zinc-binding (Fig. 3C). Thus, M27 represents a valid model to study functional consequences of interference with the zinc coordination motif through site-directed mutagenesis, and to test the predictive power of our E27/M27 model". Although they rationalise the change to MCMV to validate the functional outcomes of the newly identified zinc binding motif with alignments and Cryo-EM data, it falls within the DDB1 binding region that is less conserved (Fig S4B). The addition of a mouse model here provides a solid result but given the aim of the paper is to provide a proper characterisation of RCMV and elucidate some inter-species adaptations, I strongly recommend the validation with E27 here given the potential impact of this motif. Rather than having to repeat this in a rat model (which would clearly be a large amount of work), this could simply be achieved by constructing the relevant deletion / mutant viruses and assessing in vitro in a relevant cell line (readout - either virus titre or luciferase assay as shown in Figure 3G/H).

      __Authors Response: __Please also see our responses to the other reviewers. Briefly, we will apply side-directed mutagenesis to alter the CxCxxC motif in E27 that binds the zinc ion, and analyse the interaction of these E27 mutants with DDB1. In this context, we would like to add that almost two thirds of E27 residues in direct contact with DDB1 are at least type-conserved in M27, and the zinc-coordinating side chains are totally conserved (Fig. 3C). Together with a predicted similar structural organization of the respective binding regions (Fig. S11), and in light of our MCMV mutagenesis results (Fig. 7), it is highly likely that the DDB1-binding mode is conserved between E27 and M27. As mentioned above, we will put this assumption to the test in the revision process.

      __Reviewer #3: __Furthermore, in Figure 2, the GF assay was performed using full-length DDB1, however CLMS was performed using DDB1 delBPB (interchange between these two proteins continues in the remainder of the paper). This should be at least justified, and preferably one or other of wt DDB1 and DDB1 delBPB used in the GF or CLMS assay where this has not yet been performed. Later on in the results section (Fig 5E), the authors use wt DDB1 while in fig 4 they used the delBPB to describe the interaction with E27 - would be relevant to have consistency across the paper and some supplementary data that could support using one or the other in each assay.

      __Authors Response: __Protein complex preparations including full length DDB1 did not yield cryo-EM reconstructions at appropriate resolution for model building, almost certainly due to the known flexibility of the DDB1 BPB, impeding proper alignment of the cryo-EM particle images. This is why we switched to DDB1ΔBPB. Importantly, the structure model including full length DDB1 (Fig. S12B) clearly demonstrates that the BPB is located on the opposite side of the E27 binding interface on DDB1 (where it is situated to flexibly connect to the CUL4 scaffold to create the ubiquitination zone around immobilised substrates [Fig. 6]). This rules out an involvement of DDB1 BPB in E27- and/or STAT2-binding processes. Several previous studies have employed DDB1ΔBPB to facilitate structure determination, and have successfully applied the resulting structural models for functional follow-up experiments in the context of complete CRL4 assemblies (Bussiere et al., 2020; Petzold et al., 2016; Slabicki et al., 2020). Nevertheless, we will repeat GF experiments with DDB1ΔBPB for consistency and include these data in the revised manuscript.

      Reviewer #3: Minor points

      __Reviewer #3: __2. Although they present sufficient detail in the methods, further details in the text should be given as to the number of repeats performed in each case, and whether the data shown is representative or based on an average of repeats (preferably the latter; if representative, the data for other repeats should be shown in supplementary information).

      Authors Response: We will add this information in the revised version of the manuscript.

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

      Reviewer #1: Major points

      __Reviewer #1: __Resolution of the cryoEM structure is rather low and many predictions of the manuscript are based on modeling using AlphaFold2 prediction. The authors describe their model as of "sufficient quality", however, no quality measures are included in the manuscript. At least the discussion should address limitations of the used approach.

      Authors Response: While we apologize for not sufficiently describing our quality measures, we respectfully disagree regarding the conclusion. Our resolution (3.8 Å, map 1) lies well within the 3–4 Å resolution range of the vast majority of structures deposited to the Electron Microscopy Data Bank during the last five years (https://www.emdataresource.org/statistics.html). Nevertheless, de novo modelling in this resolution regime is challenging. This is why we sought additional guidance through cross-linking mass spectrometry (XL-MS) restraints and AlphaFold2. Please also note that modelling of E27 was not based solely on the AlphaFold2 prediction. Instead, a partial model corresponding to the α-domain was manually built in map 1, guided by XL-MS information (see Methods - “Model building and refinement” and Fig. S5B, grey cartoon). This partial model proved to be in very good agreement with AlphaFold2 predictions (RMSD of 1.489 Å, 2764 aligned atoms). Only after this initial sanity check, the computational prediction was used for model completion, adjustment, and refinement.

      We now added graphical overviews of model fits in Figs. S5 and S10. Furthermore, we included detailed views of the fit of relevant side chains involved in intermolecular interaction to the experimental density (Fig. S7, S9). We also calculated and listed quality indicators of the model-to-map fit in Table S1 (correlation coefficients and model resolution based upon model-map FSC). To ensure the validity of our atomic model using an alternative method besides cryo-EM and XL-MS, we have performed site-directed mutagenesis of critical binding regions in E27, followed by in vitro reconstitution and analytical GF (Fig. S7B, C, S9B, C). The text was revised accordingly (see p10 [ll22] and p14 [ll26]).


      __Reviewer #1: __The authors identify a cryptic zinc-binding motif in E27 that is conserved in homologous proteins. For this reviewer it is not clear: is there experimental evidence for zinc binding of E27 or can the presence of zinc reliably be detected in their structural data? If not, it would be worth to confirm zinc binding.

      Authors Response: Our structural data show a tetragonal metal coordination geometry, involving three cysteine side chains and one histidine side chain, with coordination bond lengths of 2.2 Å between the histidine nitrogen and the metal ion, and of 2.4 Å between the cysteine sulfurs and the metal ion. The density feature cannot be explained by another type of side chain interaction, e.g. a disulfide bond, because this would lead to a steric clash with the remaining adjacent side chains. Based on the knowledge on metal-binding sites in proteins and metal-coordination chemistry, these characteristics indicate the presence of a structural zinc-binding site for the following reasons: (i) after magnesium, zinc is the second most prevalent metal in the Protein Data Bank (https://metalpdb.cerm.unifi.it/getSummary), however, magnesium is coordinated octahedrally by oxygen ligands (Tang and Yang, 2013); (ii) the most abundant zinc ligands are cysteine and histidine; (iii) the most abundant zinc coordination number is four ligands; (iv) the average coordination bond lengths are 2.12±0.19 Å and 2.33±0.12Å for nitrogen-zinc and sulfur-zinc interactions, respectively (Ireland and Martin, 2019; Laitaoja et al., 2013), which is in very good agreement with our structural observations. We included this argumentation in the revised manuscript (see p9 [ll21]), and added Fig. S5C for visualization.


      Reviewer #2: Minor points


      Reviewer #2: Page 2, line 3. "Here," should be inserted before "Global proteome profiling..." to highlight the work of this manuscript.

      Authors Response: We changed the text accordingly.

      Reviewer #2: Page 3, line 21. "IFNs" instead of "IFN"

      Authors Response: We changed the text accordingly.

      Reviewer #2: Page 4, lines 9,15,27. "Ubiquitin Ligases (UbL)" is not a common abbreviation and could be mistaken for Ubl (Ubiquitin-like proteins). Possible abbreviation is "E3s" for Ubiquitin E3 ligases

      Authors Response: We have amended the respective abbreviations accordingly.

      Reviewer #2: Page 4 line 25. "RBX1" is the more common term for "ROC1"

      Authors Response: This has been corrected throughout the manuscript.

      Reviewer #2: Page 5 lines 1-9. Citing of the first structure of DDB1 in complex with a viral protein is recommended. (Ti Li et al. Cell 2006)

      Authors Response: We thank the reviewer for this important suggestions and cited this landmark publication.

      Reviewer #2: Figure 1 a) STAT2 dot is cut off in second panel. I recommend highlighting STAT2 in both panels.

      We amended the figure accordingly. We furthermore additionally highlighted the “STAT2” text in both panels by increasing the font size and putting it in bold type.

      Reviewer #2: Page 7 line 17. "Cross-linking MS (CLMS)" is commonly abbreviated as (XL-MS)

      Authors Response: We changed the text accordingly.

      Reviewer #2: Figure 2 a-c) These panels could benefit from thinner lines in order to increase visibility of chromatograms and cross-links.

      Authors Response: The panels were changed accordingly.

      Reviewer #2: Figure 2 a-b) Could the authors elaborate on why STAT2 is stoichiometrically

      underrepresented in the SDS-PAGE of the E27/DDB1/STAT2 complex?

      Authors Response: We applaud Reviewer #2 for their in-depth examination. Honestly, we were also puzzled by this. Based on the cryo-EM single particle analysis, we found an explanation: We separated a major contamination in silico during 2D classification (~12% of all particles). Out of curiosity, we reconstructed a density map from these particles (now shown in Fig. S3). The map was identical to a previous cryo-EM structure of the E. coli protein ArnA (Yang et al., 2019), a notorious contaminant in E. coli Ni-NTA protein purifications (Andersen et al., 2013). ArnA migrates similar to E27 on the SDS-PAGE, the band runs just a little bit faster (compare fraction 6 [ArnA] and fractions 8/9 [E27] from the SDS-PAGE of the analytical GF run of E27 in isolation, Fig. 2A, green trace). However, in analytical GF, ArnA elutes at higher molecular weight fractions, since it forms a hexamers (Ve~10.2 ml). Incidentally, this elution volume of the ArnA hexamer almost equals the one of DDB1 or DDB1ΔBPB/DDA1/E27/STAT2 complexes. This leads to a superposition of ArnA and E27 bands in the respective SDS-PAGE lanes corresponding to GF fraction 6. Accordingly, we conclude that it is actually not STAT2 that is underrepresented, but rather E27 seems overrepresented due to SDS-PAGE band overlap with the ArnA contaminant. We have now indicated the contaminant in Fig. 2A, amended the legend, and extended Fig. S3 to indicate at which point of the cryo-EM analysis the contaminating ArnA particles were separated, and to show the ArnA model to map fit.

      In addition to this, it might be that potential STAT2 degradation products (marked by ** in Fig. 2), which seem to co-migrate with STAT2/E27 complexes, occupy FL STAT2 binding sites on E27.

      Reviewer #2: Paragraph "The E27 structure.." page 9. Placing this paragraph after the overall

      structure is recommended.

      Authors Response: Accordingly, we have now moved this section to the end of the results section.

      Reviewer #2: Figure 3 a) The grey mesh being laid over the ribbon structures is not contributing to the overall visibility. Adding a panel of the cryo-EM structure alone in cost of alphafold models is recommended.

      Figure 4a) same issue with grey mesh

      Authors Response: Thank you very much for the very good suggestions. We have removed the mesh representation, and included panels just showing the segmented cryo-EM map in the new Fig. 3A.

      Reviewer #2: c) panels could benefit from fewer amino acids being labeled/shown

      Authors Response: We understand the motives of the Reviewer. However, we would prefer to depict all relevant side chain interactions in these panels. The rearrangement of the figure, i.e. showing the overview of the interacting regions before the detailed panels, should make them more accessible (new Fig. 3B).

      __Reviewer #2: __d) may want to avoid red-green coloring to improve for colorblindness

      Authors Response: We are deeply sorry for our ignorance in this regard. We changed the colors accordingly (see new Fig. 3B, C).

      __Reviewer #2: __Figure 6a) s.a grey mesh

      Authors Response: We removed the mesh representations and included panels just showing the segmented cryo-EM density in the new Fig. 5C.


      Reviewer #2: CROSS-CONSULTATION COMMENTS

      __Reviewer #2: __A 3.8 A overall resolution map and the approach to fitting may be suitable, but it is unclear from the authors' figures whether the side-chains shown in the figures are clearly visible in the map or if they are modeled by some other approach. Side chains should ideally be visible in the maps if shown in figures, and if not, close-ups of the corresponding regions of the maps should be shown with sufficient depthcue to allow the reader to gauge how the map corresponds to the model.

      Authors Response: This is a crucial point. As mentioned in the response to Reviewer #1, major point 2, we have now included very detailed views of the fit of relevant side chains involved in intermolecular interaction to the experimental density (Fig. S7, S9).

      __Reviewer #2: __Along these lines, the figures with the mesh maps do not clearly show how well the model fits the map. This needs to be clearly visible in figures, and ideally maps and models provided to reviewers in order for the reviewers to gauge the level of accuracy of the fit.

      Authors Response: Please see our response to Reviewer #1, major point 2. Briefly, we have now included graphical overviews of model fits in Figs. S5 and S10. We also calculated and listed quality indicators of the model-to-map fit in Table S1 (correlation coefficients and model resolution based upon model-map FSC). To ensure the validity of our atomic model using an alternative method besides cryo-EM and XL-MS, we have performed site-directed mutagenesis of critical binding regions in E27, followed by in vitro reconstitution and analytical GF (Fig. S7B, C, S9B, C). The text was extended accordingly (see p10 [ll22] and p14 [ll26]).

      __Reviewer #2: __At minimum, the authors have nicely assembled proteomics and cell biological data indicating that E27 hijacks CRL4 to turn over Stat2 in rat cells in a manner paralagous to M27 hijacking in mouse cells, biophysical/structural data for a model of a CUL4-DDB1-E27-Stat2 complex, and mutagenesis of a putative zinc binding site in M27.

      I feel most of the issues raised by all 3 reviewers could be addressed in the text, with more clarity about the structural models, and better explanation for why the construct with proteins from various organisms were used for structural studies (the authors had made human DDB1 before, and it expressed well, and perhaps didn't consider to make from rat? Or this mixture expressed, purified best? Gave best quality EM data?).

      Authors Response: We thank Reviewer #2 for her/his overall assessment. As mentioned in the two cross-consultation comments before, and in the response to Reviewer #1, major point 2, we strived to provide adequate measures allowing to judge the quality of our structural models in the present updated version of the manuscript. In addition, as indicated in the response to reviewer #3, major point 2, we have now added Fig. S12 and extended the Discussion to explain and justify the use of different protein constructs.

      __Reviewer #2: __Also, the presentation of the zinc binding site should come after the overall structure. As for the use of MCMV to assess the role of the zinc binding site, placing this last in the text might allow this to flow better.

      Authors Response: Thank you very much for this suggestion. The manuscript has been restructured as recommended: details of the zinc-binding motif and the MCMV assays are now shown in Fig. 7 and described in the text just before the Discussion.



      Reviewer #3: Major points

      __Reviewer #3: __2. Given that previous data in mice showed that the E27 homologue pM27 binds a component of host Cullin4-RING UbLs (CRL4), to induce the poly-ubiquitination of STAT2, the current study also addressed if this mechanism was preserved in RCMV. Yet, they seemed to do this with E27, rnSTAT2 and hsDDB1 - Page 7 lines 1 to 3: "These results prompted us to explore the association of E27 with Rattus norvegicus (rn) STAT2 and Homo sapiens (hs) DDB1 in vitro. Importantly, 1128 of 1140 amino acids are identical between hsDDB1 and rnDDB1 (...)". They identify the residues and regions where the DDB1 is different between both species, but should provide a structure/alignment with this highlighted. In addition, DDB1 is a DNA damage protein that is annotated in the Rattus norvegicus genome. The authors should justify the assays between rnSTAT2-hsDDB1 instead of using the both proteins from rn, and present the equivalent data for rnDDB1 in the paper.

      Authors Response: Among the 12 alterations between human and rat DDB1, 4 are type-conserved (Fig. S12A). Thus, >99% of amino acids are identical or similar. We mapped all exchanges on a model of full length human DDB1 bound to E27 and the rat STAT2 CCD. None are involved in intermolecular interactions (Fig. S12B, C). Please note that due to the high conservation of DDB1 across eukaryotes, this inter-species approach has been used by us and others to study DDB1-containing complexes (e.g., the SV5V, WHX, SIV Vpx and Vpr, zebrafish DDB2, and chicken CRBN proteins have been in vitro reconstituted with human DDB1 for structural characterisation) and valid biological conclusions have been drawn from these studies (Angers et al., 2006; Banchenko et al., 2021; Fischer et al., 2014; Fischer et al., 2011; Li et al., 2006; Li et al., 2010; Schwefel et al., 2015; Schwefel et al., 2014; Wu et al., 2015).


      Reviewer #3: Minor points

      __Reviewer #3: __1. In fig 5D, the authors present the H-box alignment, where it is clear that this motif is not conserved. The lack of H-box conservation should be discussed in the results and discussion, to provide an explanation for the competition/binding observed.

      Authors Response: We respectfully disagree. There is conservation of amino acid side chains, regarding their physicochemical properties, observable in the H-box motif. Furthermore, the secondary structure is conserved. Please note, that the H-box is not our invention but rather represented a well-accepted motif known in the field, see e.g., (Li et al., 2010). We extended the discussion to cover this point (p21 [ll15]).


      __Reviewer #3: __3. The authors commence their abstract justifying the study on the grounds of the usefulness of rodent HCMV counterparts as common infection models for HCMV. They should return to this theme in the discussion - what is the usefulness of their findings with regards to HCMV (particularly given the relatively low homology between E27 and HCMV pUL27, and the alternative mechanism for STAT2 antagonism encoded by HCMV UL145)?

      Authors Response: We extended the discussion in this regard. Briefly, our data, to our knowledge for the first time, reveal that RCMV (like MCMV) exploits CRL4 to induce proteasomal degradation of STAT2. With pUL145, HCMV relies on an analogous protein. In clear contrast to HCMV, RMCV and MCMV are both amenable to in vivo experiments in small animal models. Over 40 years ago, HCMV has been called the troll of transplantation due to its grim impact on immunosuppressed individuals after transplantation surgery (Balfour, 1979). Despite tremendous efforts, HCMV still harms and kills graft recipients. While MCMV allows various experiments regarding general principles of cytomegaloviral pathogenesis and antiviral immunity, one shortcoming is that the mouse obviously is a rather small animal, preventing various chirurgical and solid organ transplantation (SOT) procedures. In clear contrast, SOT procedures that are indispensable for human medicine can be recapitulated in rat models. Thus, according to our opinion, our work lays the molecular foundation for future studies addressing the relevance of STAT2 and CMV-induced STAT2 degradation in rat SOT models.

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

      -

      • *

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    1. Author Response

      Reviewer #1 (Public Review):

      The authors asked to what extent early visual and visuomotor experience is essential for developing the ability to recalibrate the visuo-motor system flexibly. This kind of recalibration crucially underpins everyday actions, allowing the brain to issue effective feed-forward motor control commands that correctly account for temporary changes in sensory-motor mappings (e.g. when using tools, carrying objects, wearing new glasses). To address the role of experience in developing these recalibration abilities, they used the unusual clinical population of late-operated cataract patients: children and adolescents who initially had many years of sensory experience that is atypical in that it lacked effective pattern vision. They used a standard sensory-motor task in which participants point to targets with and without displacement of the visual image via a prism lens: after the prism displacement, the visuo-motor mapping needs to be recalibrated to enable effective pointing. They compared late-operated cataract patients with controls matched in age, controls matched in both age and visual acuity (via added visual blur), as well as an extensive broader comparison group of typically developing 6- to 17-year-olds. Their key findings were that recalibration was less effective - both in the initial effect and in the subsequent after-effect - in the patient group than in control groups; this was not related to chronological age but was related to time post-operation, such that performance came to match controls after around 2 years of improved visual experience. The authors conclude that flexible sensory recalibration abilities normally rely on extensive sensory-motor experience in childhood, and suggest that the underlying computational problem is establishing the correct correspondences between sensory and motor coordinate frames. This may be achieved through extended exposure to the sensory consequences of self-generated movements.

      Strengths of the approach include use of the established (although rare and difficult to access) model population of late-operated cataract patients and a well-established experimental task (pointing after displacement of the visual image by viewing through prism lenses). The task has a known typical time-course of behaviour - supplemented here by an extensive additional study on typical development using the exact same main task, which even alone would be a meaningful contribution to literature on sensory-motor development. The procedure, measures, analysis, and the approach to control groups are careful and rigorous. The findings are rich in showing not only an initial deficit in patient vs control groups but also an approximate time course for further learning and development after which point (by ~2 years) the patients come to match controls. A challenge is the heterogenous group, in terms of age at operation and ages at testing and follow-up. However, this is very usual and almost inevitable in the literature with this kind of population, and is dealt with well in the analyses. The approach is also well supplemented by repeated follow-up of a portion (actually more than half) of the group.

      One potential issue is the role of baseline pointing precision differences across the groups. It would be useful to better understand the potential role of the reduced pointing precision that was found in the cataract group (Supplemental Figure 1B). It is not surprising that, following visual deprivation, this group's predictive feedforward visuo-motor control was less precise than that of controls, even in the baseline measures before any prism manipulation, and even when the controls' vision is comparably blurred. It seems likely (although is not shown) that during the adaptation phase and the post-adaptation phase, the variability of individuals around their (gradually shifting) mean pointing location would also be higher than in controls. I wonder how large an explanatory role there could be simply for this noisier initial visuo-motor mapping in the patient group. It might be said that, on each trial, they intend to carry out a feedforward plan with a certain endpoint, but because of noise, they are on average substantially further from that endpoint than comparable controls are. So, during recalibration, while controls are dealing mainly with cancelling out one kind of error - the constant error due to the prism adaptation - the cataract patients are also dealing with more variable errors due to their own noisier visuo-motor system. In theory, could this alone - higher initial noise in the system - explain the difference? This seems like a simpler explanation than that the system has developed differently in substantial ways to do with its abilities to learn and adapt. One starting point for checking in to this would be asking if initial pointing variability predicts recalibration (perhaps controlling for visual acuity), both at first test and in the repeated participants. Another would be looking into ways to perturb controls' baseline pointing performance further (perhaps with something like an unexpected added weight rather than more visual blurring) so that their variable pointing errors were matched to the cataract group.

      We thank Reviewer 1 for drawing our attention to this important point. The Reviewer is right in suggesting that precision at baseline (measured as the variance of the pointing errors in the pre-prism phase) might predict recalibration abilities (as measured by the recalibration index irecal ). Indeed, we found that the variance of the errors in pre-prism phase correlates with irecal in cataract-treated participants. Thus, the higher sensorimotor noise in cataract-treated participants (indicating more uncertainty) slows down their rate of recalibration. This finding is in accordance with Burge and colleagues (2008) who found that higher uncertainty (in their case in the form of visual blur leading to more motor variability) slows down the adaptation rate. We have now reported this analysis in the Results section and discussed the contribution of sensorimotor noise to recalibration in the Discussion. However, higher sensorimotor noise cannot explain alone the performance of the cataract-treated individuals. Indeed, the subset of participants tested a second time after surgery (4-to-16 months after the first post-surgery test) presented better recalibration ability (i.e., higher irecal ), although their precision at baseline did not increase accordingly, but stayed basically unchanged. Moreover, in their second test, their precision at baseline did not correlate with the successive irecal.

      In the Discussion, we added the greater sensorimotor noise as a factor contributing to recalibration. However, as it does not explain alone the improvement of recalibration performance over time, we still discuss the contribution of their lack of experience with the sensorimotor mapping to their recalibration performance.

      Another question is how well the contrast sensitivity function (CSF) as a whole (not just the maximum acuity point) was matched - this is dealt with only briefly. I am not sure to what extent the blurring manipulation would be expected to change the shape of the CSF as a whole to be in line with that of patients, and to what extent other aspects of the CSF besides the maximum acuity point determine the precision and accuracy of ballistic pointing movements under the experimental and lighting conditions used in the study. Depending on the answers to these questions, the concern could be that visual differences relevant to control of pointing remained across the patient and blurred control groups.

      We have now provided more information on this point in the Methods section and in the Supplementary Information. In a pilot study, we determined the range of distances between the blurring screen and the visual target that would be needed to reproduce–in controls–the range of visual acuity values of the cataract-treated participants. Nonetheless, to ensure the procedure would lead to the desired contrast sensitivity function (CSF) for each participant, we tested the visual acuity also of the sighted controls. We visually inspected the CSF of each sighted participant (tested with visual blur) and we included in the study only those whose CSF matched the desired CSFs in terms of both cut off frequency and shape. In other words, when the CSF of a sighted control did not match the one of the to-be-matched cataract-treated participant (in the cut off frequency and/or in the shape of the function), that sighted control was not included in the study. This led to excluding 8 sighted controls, before reaching the final sample of 20 controls, individually matched to the cataract-treated participants. We have now reported these further details in the paragraph entitled ‘Procedure to blur vision in sighted controls’ (Materials and Methods). Moreover, we have provided a Figure in the Supplemental Material, showing the mean CSF in the group of cataract-treated participants and in the group of sighted controls tested with visual blur (Figure supplement 1). In that figure, it is possible to appreciate that we ensured matching the two groups not only for the cutoff frequency, but also for the shape of the whole function. However, we have now also mentioned in the Discussion that we cannot exclude that other possible visual differences, besides spatial visual acuity, that we did not consider, between the group of cataract-treated and that of controls tested with visual blur might have influenced the recalibration performance.

      Another more minor or technical issue is some lack of detail in how the calibration index, which feeds into most of the key analyses, is calculated. It is likely that many different ways of doing this would lead to similar conclusions, but it should be clear, including for the sake of replicability.

      While the index is briefly mentioned in the Results section, we have now explained it in detail in the Material and Methods section. This recalibration index combined the amount of recalibration in the prism phase and at the beginning of the post-prism phases (Adaptation and Initial Aftereffect, respectively). Adaptation was calculated as the error reduction in the prism phase (the induced prism distortion–11.31°–minus the average of the last three pointing errors of the prism phase, cf. Fortis et al. (2010)). Initial Aftereffect was calculated as the magnitude of the aftereffect exhibited right after prism removal (i.e., average of the first three pointing errors of the post-prism phase). The Initial Aftereffect was correlated with the amount of Adaptation in the prism phase (see Material and Methods) and thus provides converging information which in order to increase power can be summarised in the recalibration index. That is, the recalibration index irecal was calculated as the average between Adaptation and the (negative) Initial Aftereffect. Such index is normalized on the induced prism distortion (i.e., the index is divided by 11.31°), so that it ranges between 0 and 1. Further details are provided in the Material and Methods section.

      Reviewer #2 (Public Review):

      It is very interesting that recalibration effects in the cataract-reversal group increase over time. However, it seems as if the conclusion that it takes about two years to reach recalibration effects comparable to those of typically sighted controls is based on repeated measurements of two participants tested 2 and 3 years after their surgery as well as on singular measurements of two participants tested 10 years after their surgery. Close inspection of Figure 1F suggests that four participants reached comparable levels in their second testing session already about 6 months after surgery. Consistently, the confidence interval of the time constant b is rather large (it also seems to differ between the main text and the figure caption). Given this high degree of uncertainty around the time estimate it would be advisable to not report and discuss a fixed duration of two years but rather focus on the increase of recalibration effects and report an interval during which recalibration effects might reach asymptotic levels.

      We thank Reviewer 2 for drawing our attention to this important point. Following this advice, we have now discussed the high inter-subject variability in the recalibration performance over time, and we have discussed the uncertainty inherent in the estimate of the rate of improvement leading to a performance comparable to healthy controls within about 2 years - this estimate for sure is very uncertain (see Results and Discussion).

      It is important to note that the exponential fit on all measurements (Figure 1F, dark green curve) is not driven by the 2 participants tested more than 10 years after surgery: when excluding them from the exponential fit, the time constant b (b=1.5, 95% CI=[0.39, 2.67]) is comparable to the one obtained in the whole sample.

      We have also reported the linear correlation between time since surgery and recalibration index in the first testing session without the 2 participants tested more than 10 years after surgery, as they would drive the correlation. Note that the effect of time since surgery is evident even when removing them from this analysis (main text, red line in Figure 1 F, and Material and Methods). Importantly, also the linear fit on the first test session alone (excluding the participants tested more than 10 y after surgery) provides converging evidence of the fact that the performance level of controls (tested with visual blur) is reached at roughly 2 years from surgery, as visible in Figure 1F (red regression line crossing dashed line of controls).

      Regarding the time costant b previously reported in the figure caption, this was related to the inlaid reported in Figure 1 F in the last submission (i.e., the exponential fit on the difference between each pair of cataract participants and controls). We have now removed this inlaid from the figure and its relative fit (in the figure and figure caption) to avoid confusion.

      Having longitudinal data from several participants is great and can provide interesting insights. However, to get an idea about the role of visuo-motor experience it would be helpful to not collapse across the different time points for the second evaluation in the depiction of the data and their analysis. Moreover, it would be helpful to have an idea of the degree of variability across repeated measurements in control participants.

      We decided to report these data in two ways: 1) In agreement with Reviewer 2, we showed these longitudinal data in their different time points (Figure 1F), so that the progression of the recalibration ability over time after surgery would be more transparent and easier to appreciate; 2) We still present these data also collapsed in Figure 1 E, because we believe this representation helps clarity and completeness: given that we also included the pre-surgical assessment in that figure, it is easier to visually appreciate the differences between pre- vs. first post-surgical assessment and second post-surgical assessment in the re-tested participants. We also rearranged the text accordingly. However, if the Reviewer still believes that this way of reporting the results is unclear or redundant, we will remove Figure 1E.<br /> Unfortunately, we were unable to collect comparable repeated measures from the control children with the same temporal gap between the first and second test.

      Visuo-motor adaptation and aftereffects are related but clearly separate phenomena not least because visual feedback about the position of the finger was only present during the adaptation phase. Combining both effects into one index potentially obscures differential effects of developmental vision on the processes underlying either phenomenon. This concern is supported by the result that the manipulation of visual precision in typically developed controls affected visuo-motor adaptation and aftereffects differentially. Thus, it would be preferable to drop the combined index and analyze adaptation and aftereffects separately throughout. This will have the additional advantage of allowing for direct comparisons of both effects to those reported in the extensive literature on the topic.

      We are grateful to Reviewer 2 for bringing this important point to our attention. We have now run all the correlational analyses separately for adaptation (i.e., error reduction in the prism phase) and aftereffect (mean systematic error in the post-prism phase). We have described these analyses in the Results section and in the Material and Methods section. However, as these separate analyses led to comparable results for adaptation and aftereffect, we did not report them in detail in the main text, as they would be very redundant. While it is possible to appreciate each of them in detail in the Supplemental Materials (Figure 1– figure supplement 4), in the main text we avoided this redundancy by combining them into a unified measure, the recalibration index (irecal). Reviewer 2 is right in highlighting the difference between adaptation and aftereffect. Note, however, that the recalibration index does not include the entire aftereffect (which may have a different time constant as it may well be distinct from the adaptation), but only the amplitude of the initial three trials of the aftereffect after removing the prism (i.e., the mean of the first three pointing errors of the post-prism phase). This initial amplitude of the aftereffect (that we have now called “Initial Aftereffect”) is highly correlated with the amount of recalibration in the prism phase. We have now discussed this point in the Results section. In other word, the recalibration index did not include the aftereffect in the entire post-prism phase (i.e., the systematic error across all trials of the post-prism phase). In fact, we agree with the Reviewer that including the development of the after effect across all trials of the post-prism phase would have potentially shown a different phenomenon, namely the effect of proprioception while reinstating the usual sensorimotor mapping. Indeed, at odds with the prism phase, the pointing task in the post-prism phase was performed in the absence of any optical distortion and in the absence of visual feedback. The development of the aftereffect across all trials of the post-prism phase is analysed in the main text and in Figure supplement 3, while the correlations between each factor (age, visual acuity, etc.) and the mean aftereffect across all trials of the post-prism phase is reported in Figure supplement 4. We have now also clarified all these points in the main text and in the Materials and Methods.

      The absence of a significant statistical effect does not provide evidence for the absence of the effect. This problem arises in several instances throughout the paper. For example, a non-significant Kruskal-Wallis-Test does not indicate a similar distribution of baseline pointing errors. A figure showing the distribution of pointing errors from this phase provides far more convincing evidence (l. 134). A non-significant t-test does not provide for the absence of a relation between the change in recalibration effects and visual acuity (l. 225). Here, it would be correct to state that there was no statistically significant difference between visual acuity at the two different post-tests.

      The problem that the absence of statistical effects does not allow for any conclusions is even more evident for the correlational analyses, which are severely underpowered. The non-significant correlations should be reported in the supplement rather than in a prominent position in the manuscript and all conclusions based on non-significant correlations must be dropped.

      We have now modified the text and Figure 1 accordingly, by rephasing the text and removing the non-significant correlations from the figure.

      Figures 1C and 1F suggest that the significant correlation between the time since surgery and recalibration effects might be driven by outliers. The analysis should be repeated without outlier data to make sure that the effect is present in the data.

      As reported in the first response to Reviewer 2, we have now re-run the analyses also without the participants tested more than 10 years after surgery. The effect of time since surgery is present even when removing the outliers (See main text and Figure 1F).

      The abstract makes rather general claims about the influence of developmental vision on recalibration and plasticity which are not supported by the data. All conclusions should be restricted to the visuo-motor domain, which in my view will not impact their importance.

      We thank the Reviewer for the comments, and we have adapted the abstract accordingly.

      Given that most participants had residual light perception, it would be more accurate to consistently speak of absent pattern vision rather than visual deprivation.

      We have rephrased the text accordingly.

    2. Reviewer #2 (Public Review):

      It is very interesting that recalibration effects in the cataract-reversal group increase over time. However, it seems as if the conclusion that it takes about two years to reach recalibration effects comparable to those of typically sighted controls is based on repeated measurements of two participants tested 2 and 3 years after their surgery as well as on singular measurements of two participants tested 10 years after their surgery. Close inspection of Figure 1F suggests that four participants reached comparable levels in their second testing session already about 6 months after surgery. Consistently, the confidence interval of the time constant b is rather large (it also seems to differ between the main text and the figure caption). Given this high degree of uncertainty around the time estimate it would be advisable to not report and discuss a fixed duration of two years but rather focus on the increase of recalibration effects and report an interval during which recalibration effects might reach asymptotic levels.

      Having longitudinal data from several participants is great and can provide interesting insights. However, to get an idea about the role of visuo-motor experience it would be helpful to not collapse across the different time points for the second evaluation in the depiction of the data and their analysis. Moreover, it would be helpful to have an idea of the degree of variability across repeated measurements in control participants.

      Visuo-motor adaptation and aftereffects are related but clearly separate phenomena not least because visual feedback about the position of the finger was only present during the adaptation phase. Combining both effects into one index potentially obscures differential effects of developmental vision on the processes underlying either phenomenon. This concern is supported by the result that the manipulation of visual precision in typically developed controls affected visuo-motor adaptation and aftereffects differentially. Thus, it would be preferable to drop the combined index and analyze adaptation and aftereffects separately throughout. This will have the additional advantage of allowing for direct comparisons of both effects to those reported in the extensive literature on the topic.

      The absence of a significant statistical effect does not provide evidence for the absence of the effect. This problem arises in several instances throughout the paper.<br /> - For example, a non-significant Kruskal-Wallis-Test does not indicate a similar distribution of baseline pointing errors. A figure showing the distribution of pointing errors from this phase provides far more convincing evidence (l. 134). A non-significant t-test does not provide for the absence of a relation between the change in recalibration effects and visual acuity (l. 225). Here, it would be correct to state that there was no statistically significant difference between visual acuity at the two different post-tests.<br /> - The problem that the absence of statistical effects does not allow for any conclusions is even more evident for the correlational analyses, which are severely underpowered. The non-significant correlations should be reported in the supplement rather than in a prominent position in the manuscript and all conclusions based on non-significant correlations must be dropped.

      Figures 1C and 1F suggest that the significant correlation between the time since surgery and recalibration effects might be driven by outliers. The analysis should be repeated without outlier data to make sure that the effect is present in the data.

      The abstract makes rather general claims about the influence of developmental vision on recalibration and plasticity which are not supported by the data. All conclusions should be restricted to the visuo-motor domain, which in my view will not impact their importance.

      Given that most participants had residual light perception, it would be more accurate to consistently speak of absent pattern vision rather than visual deprivation.

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

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      Reply to the reviewers

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

      In recent years, the field has investigated crosstalk between cGMP and cAMP signaling (PMID: 29030485), lipid and cGMP signaling (PMID: 30742070), and calcium and cGMP signaling (PMID: 26933036, 26933037). In contrast to the Plasmodium field, which has benefited from proteomic experiments (ex: PMID 24594931, 26149123, 31075098, 30794532), second messenger crosstalk in T. gondii has been probed predominantly through genetic and pharmacological perturbations. The present manuscript compares the features of A23187- and BIPPO-stimulated phosphoproteomes at a snapshot in time. This is similar to a dataset generated by two of the authors in 2014 (PMID: 24945436), except that it now includes one BIPPO timepoint. The sub-min​​ute phosphoproteomic timecourse following A23187 treatment in WT and ∆cdpk3 parasites is novel and would seem like a useful resource.

      CDPK3-dependent sites were detected on adenylate cyclase, PI-PLC, guanylate cyclase, PDE1, and DGK1. This motivated study of lipid and cNMP levels following A23187 treatment. The four PDEs determined to have A23187-dependent phosphosites were characterized, including the two PDEs with CDPK3-dependent phosphorylation, which were found to be cGMP-specific. However, cGMP levels do not seem to differ in a CDPK3- or A23187-dependent manner. Instead, cAMP levels are elevated in ∆cdpk3 parasites. This would seem to implicate a feedback loop between CDPK3, the adenylyl cyclase, and PKA/PKG: CDPK3 activity reduces adenylyl cyclase activity, which reduces PKA activity, which increases PKG activity. The authors don't pursue this direction, and instead characterize PDE2, which does not have CDPK3-dependent phosphosites, and seems out of place in the study

      Response:

      We agree with reviewer 1 that a feedback loop between CDPK3, the adenylyl cyclase and PKA/PKG is certainly one of several possibilities (and we acknowledge this in the manuscript).

      We felt, however, that given the observation that A23187 and BIPPO treatment leads to phosphorylation of numerous PDEs (hinting at the presence of an Ca2+-regulated feedback loop), it was entirely relevant to study these in greater detail. Coupled with the A23187 egress assay on ΔPDE2 parasites - our findings suggest that PDE2 plays an important role in this signalling loop (an entirely novel finding). While PDE2 appears to exert its effects in a CDPK3-independent manner (indeed suggesting that CDPK3 might exert its effects on cAMP levels in a different fashion), this does not detract from the important finding that PDE2 is one of the (likely numerous) components that is regulated in a Ca2+-dependent feedback loop to regulate egress.

      We have modified our writing to better reflect the fact that our decision to pursue study of the PDEs was not solely CDPK3-centric.

      While we feel that our reasoning for studying the PDEs is solid, we appreciate that further clarification on the putative CDPK3-Adenylate cyclase link would make it easier for the reader to follow the rationale.

      We have not studied the direct link between CDPK3 and the Adenylate Cyclase β in more detail, as ACβ alone was shown to not play a major role in regulating lytic growth (Jia et al., 2017).

      **MAJOR COMMENTS**

      1.Some of the key conclusions are not convincing.

      The data presented in Figure 6E, F, and G and discussed in lines 647-679 are incongruent. In Figure 6E, the plaques in the PDE2+RAP image are hardly visible; how can it be that the plaques were accurately counted and determined not to differ from vehicle-treated parasites?

      Are the images in 6E truly representative? Was the order of PDE1 and PDE2 switched? The cited publication by Moss et al. 2021 (preprint) is not in agreement with this study, as stated. That preprint determined that parasites depleted of PDE2 had significantly reduced plaque number and plaque size (>95% reduction); and parasites depleted of PDE1 had a substantially reduced plaque size but a less substantial reduction in plaque number.

      Response:

      The plaques for PDE2+RAP were counted using a microscope since they are difficult to see by eye. We thank the reviewer for detecting our incorrect reference to Moss et al. (2021). This has been corrected in the text. We confirm, however, that the images in 6E are representative of what we observed and do indeed differ from what was seen by Moss et al.. We have acknowledged this clearly in the text.

      The differences cannot easily be explained other than by the different genetic systems used. Further studies of the individual PDEs will likely illuminate their role in invasion/ growth, but we feel this would be beyond the scope of this study.

      Unfortunately, the length of time required for PDE depletion (72h) is incompatible with most T. gondii cellular assays (typically performed within one lytic cycle, 40-48h). Although the authors performed the assays 3 days after initial RAP treatment, is there evidence that non-excised parasites don't grow out of the population. This should be straightforward to test: treat, wait 3 days, infect onto monolayers, wait 24-48h fix, and stain with anti-YFP and an anti-Toxoplasma counterstain. The proportion of the parasite population that had excised the PDE at the time of the cellular assays will then be known, and the reader will have a sense of how complete the observed phenotypes are. As a reader, I will regard the phenotypes with some level of skepticism due to the long depletion time, especially since a panel of PDE rapid knockdown strains (depletion in __Response:

      1. Cellular assays using KO parasites are commonly performed at the point at which protein depletion is detected. Both our western blots and plaque assay results demonstrate that, at the point of assay, there is no substantial outgrowth of non-excised parasites. The original manuscript also includes PCRs performed at the 72 hr time point (See Fig. 6B) to support this.
      2. We appreciate the reviewer’s comment re the panel of PDE KD strains. The reviewer notes that there are substantial limitations to conditional KO systems, which similarly applies to KD systems - there are notable pros and cons to each approach. When designing our strategy (pre-publication of the Moss et al., 2022), we made a deliberate decision to use conditional KO strains in light of the fact that residual protein levels in KD systems can cause significant problems, particularly for membrane proteins (all of the investigated PDEs have a transmembrane domain). Tagging of proteins with the degradation domain can have further issues, leading to protein mis-localisation, which we have experienced with several unrelated proteins in the lab.

        The authors should qualify some of their claims as preliminary or speculative, or remove them altogether.

      The claims in lines 240-260 are confusing. It seems likely that the two drug treatments have at least topological distinctions in the signaling modules, given that cGMP-triggered calcium release is thought to occur at internal stores, whereas A23187-mediated calcium influx likely occurs first at the parasite plasma membrane.The authors' proposed alternative, that treatment-specific phosphosite behavior arises from experimental limitations and "mis-alignment", is unsatisfying for the following reasons: (1) From the outset, the authors chose different time frames to compare the two treatments (15s for BIPPO vs. 50s for A23187); (2) the experiment comprises a single time point, so it does not seem appropriate to compare the kinetics of phosphoregulation. There is still value in pointing out which phosphosites appear treatment-specific under the chosen thresholds, but further claims on the basis of this single-timepoint experiment are too speculative. Lines 264-267 and 281-284 should also be tempered.

      Relatedly, graphing of the data in Figure 1G (accompanying the main text mentioned above) was confusing. Why is one axis a ratio, and the other log10 intensity? What does log10 intensity tell you without reference to the DMSO intensity? Wouldn't you want the L2FC(A23187) vs. L2FC(BIPPO) comparisons? Could you use different point colors to highlight these cases on plot 1E? Additionally, could you use a pseudocount to include peptides only identified in one treatment condition on the plot in 1E? (Especially since these sites are mentioned in lines 272-278 but are not on the plot)

      Response:

      1. The kinetics of the responses to A23187 and BIPPO are very different. This is why treatment timings are purposely different as they were selected to align pathways to a point where calcium levels peak just prior to calcium re-uptake. We make no mention of kinetic comparisons, and merely demonstrate that at the chosen timepoints, overall signalling correlation is very high. The observation that most of the sites that behave differently between conditions sit remarkably close to the threshold for differential regulation (in the treatment condition where they are not DR - see Fig. 1G) led us to speculate that many of these sites are likely on the cusp of differential regulation. While it is entirely possible that some of these differences are, in fact, treatment specific (and we clearly acknowledge this in the text), we simply state that we cannot confidently discern clear signalling features that allow us to distinguish between the two treatments. We feel that this is an entirely relevant observation given the observed preponderance of both A23187 and BIPPO-dependent DR phosphosites on proteins in the PKG signalling pathway (as current models place this upstream of Ca2+release).
      2. Log10 intensity only serves to spread the data for easier visualisation. The only comparison being made relates to the LFCs. Fig. 1Gi shows the LFC scores (x axis) for all sites regulated following A23187 treatment (for which peptides were also identified in BIPPO treatment). On this plot we have highlighted the sites that are differentially regulated following BIPPO but not A23187 treatment (with red showing the DRup and blue showing the DRdown sites). This demonstrates that many of the sites that are regulated following BIPPO but not A23187 treatment cluster close to the threshold for differential regulation in the A23187 dataset - suggesting that many of these sites are likely on the cusp of differential regulation. Fig. 1Gii shows the reverse. While we could highlight the above-mentioned sites on the plot in Fig. 1E, we do not feel that it would demonstrate our point as clearly.

      We feel that including a pseudocount on Fig. 1E for peptides lacking quantification in one treatment condition would be visually misleading as the direct correlation being made in Fig. 1E is BIPPO vs A23187 treatment. The sites mentioned in lines 272-278 in the original manuscript (now lines 268-276) are available in the supplement tables.

      3.Additional experiments would be essential to support the main claims of the paper.

      Genetic validation is necessary for the experiments performed with the PKA inhibitor H89. H89 is nonspecific even in mammalian systems (PMID: 18523239) and in this manuscript it was used at a high concentration (50 µM) The heterodimeric architecture of PKA in apicomplexans dramatically differs from the heterotetrameric enzymes characterized in metazoans (PMID: 29263246), so we don't know what the IC50 of the inhibitor is, or whether it inhibits competitively. Two inducible knockdown strains exist for PKA C1 (PMID: 29030485, 30208022). The authors could request one of these strains and construct a ∆cdpk3 in that genetic background, as was done for the PDE2 cKO strain. Estimated time: 3-4 weeks to generate strain, 2 weeks to repeat assays.

      Response:

      1. While we appreciate that H89 is not 100% specific for PKA, this is not our only line of evidence that cAMP levels are altered. We demonstrate that cAMP levels are elevated in CDPK3 KO parasites – further substantiating our finding.

      The H89 concentration used in our experiment is in keeping with/lower than the concentrations used in other Toxoplasma publications (Jia et al., 2017), and both the Toxoplasma and Plasmodium fields have shown convincingly that H89 treatment phenocopies cKD/cKO of PKA (see Jia et al., 2017; Flueck et al., 2019).

      While we agree that the genetic validation suggested by reviewer 1 would serve to further support our findings (though it would not provide further novel insights), the suggested time frame for experimental execution was not realistic. Line shipment, strain generation, subcloning and genetic validation would take substantially longer than 3-4 weeks.

      cGMP levels are found to not increase with A23187 treatment, which is at odds with a previous study (lines 524-560). The text proposes that the differences could arise from the choice of buffer: this study used an intracellular-like Endo buffer (no added calcium, high potassium), whereas Stewart et al. 2017 used an extracellular-like buffer (DMEM, which also contains mM calcium and low potassium). An alternative explanation is that 60 s of A23187 treatment does not achieve a comparable amount of calcium flux as 15 s of BIPPO treatment, and a calcium-dependent effect on cGMP levels, were it to exist, could not be observed at the final timepoint in the assay. The experiments used to determine the kinetics of calcium flux following BIPPO and A23187 treatments (Fig. 1B, C) were calibrated using Ringer's buffer, which is more similar to an extracellular buffer (mM calcium, low potassium). In this buffer, A23187 treatment would likely stimulate calcium entry from across the parasite plasma membrane, as well as across the membranes of parasite intracellular calcium stores. By contrast, A23187 treatment in Endo buffer (low calcium) would likely only stimulate calcium release from intracellular stores, not calcium entry, since the calcium concentration outside of the parasite is low. Because calcium entry no longer contributes to calcium flux arising from A23187 treatment, it is possible that the calcium fluxes of A23187-treated parasites at 60 s are "behind" BIPPO-treated parasites at 15 s. The researchers could control these experiments by *either* (i) performing the cNMP measurements on parasites resuspended in the same buffer used in Figure 1B, C (Ringer's) or (ii) measuring calcium flux of extracellular parasites in Endo buffer with BIPPO and A23187 to determine the "alignment" of calcium levels, as was done with intracellular parasites in Figure 1C. No new strains would have to be generated and the assays have already been established in the manuscript. Estimated time to perform control experiments with replicates: 2 weeks. This seems like an important control, because the interpretation of this experiment shifts the focus of the paper from feedback between calcium and cGMP signaling, which had motivated the initial phosphoproteomics comparisons, to calcium and cAMP signaling. Further, the lipidomics experiments were performed in an extracellular-like buffer, DMEM, so it's unclear why dramatically different buffers were used for the lipidomics and cNMP measurements.

      Response:

      While the initial calibration experiments to measure calcium flux were indeed performed in Ringer’s buffer, the parasites were intracellular. We therefore chose to measure cNMP concentrations of extracellular parasites syringe lysed in Endo buffer, which is better at mimicking intracellular conditions than any other described buffer.

      As the reviewer suggested, we measured the calcium flux of extracellular parasites in Endo buffer upon stimulation with either A23187 or BIPPO.

      We found that peak calcium response to BIPPO in Endo buffer was similar to that of intracellular parasites (~15 seconds post treatment) (See Supp Fig. 6A). Upon treatment with A23187, extracellular parasites in Endo buffer had a much faster response compared to their intracellular counterparts, with peak flux measured at ~25 seconds post treatment (see Supp Fig. 6B). This indeed does suggest that extracellular parasites in Endo buffer behave differently to A23187 compared to their intracellular counterparts. However, peak calcium response is still occuring within the experimental time course and is not being missed, as the reviewer worries. Moreover, since we are able to detect increased cAMP levels in A23187 treated parasites, Ca2+ flux appears sufficient to alter cNMP signalling.

      We did notice however that the intensity of the calcium flux was much weaker in Endo buffer compared to intracellular parasites (see Supp Fig. 6B). We found that this was due to the lack of host-derived Ca2+, since supplementation of Endo buffer with 1 uM CaCl2 restored the intensity of the calcium response to match that of intracellular parasites (see Supp Fig. 6C). We therefore decided to repeat our cGMP measurements, this time using extracellular parasites in Endo buffer supplemented with 1 uM CaCl2. However, we found no differences in cGMP levels in the response to ionophore under these conditions (now Supp Fig. 6D) compared to the previous experiments, so the conclusions from the previous data do not change.

      As for the lipidomics experiments, we chose to use DMEM so that our dataset could be compared with other published lipidomic datasets (Katris et al., 2020; Dass et al., 2021) where DMEM was also used as a buffer when measuring global lipid profiles of parasites.

      We now acknowledge in the paper that Endo buffer has its shortcomings, and that this could be the reason why we do not detect changes in cGMP concentrations. We do, however, believe that Endo buffer is the best alternative to intracellular parasites and is supported by its consistent use in numerous publications studying Toxoplasma signalling (McCoy et al., 2012; Stewart et al., 2017).

      Additional information is required to support the claim that PDE2 has a moderate egress defect (lines 681-687). T. gondii egress is MOI-dependent (PMID: 29030485). Although the parasite strains were used at the same MOI, there is no guarantee that the parasites successfully invaded and replicated. If parasites lacking PDE2 are defective in invasion or replication, the MOI is effectively decreased, which could explain the egress delay. Could the authors compare the MOIs (number of vacuoles per host cell nuclei) of the vehicle and RAP-treated parasites at t = 0 treatment duration to give the reader a sense of whether the MOIs are comparable?

      Response:

      Since PDE2 KO parasites have a substantial growth defect, we did notice that starting MOIs were consistently lower for the RAP-treated samples compared to the DMSO-treated samples. However, this was also the case for PDE1 KO parasites where we did not see an egress delay. We also found that the egress delay was still evident for ∆CDPK3 parasites, despite having higher starting MOIs than WT parasites in our experiments. Therefore there does not appear to be a link between starting MOIs and the egress delay.

      To be sure of our results, we also performed egress assays where we co-infected HFFs with mCherry-expressing WT parasites (WT ∆UPRT) and GFP-expressing PDE2 cKO parasites that were treated with either DMSO or RAP or ∆CDPK3 parasites. This recapitulated our previous findings, confirming the deletion of PDE2 leads to delay in A23187-mediated egress.

      4.A few references are missing to ensure reproducibility.

      The manuscript states that the kinetic lipidomics experiments were performed with established methods, but the cited publication (line 497) is a preprint. These are therefore not peer reviewed and should be described in greater detail in this manuscript, including any relevant validation.

      Response:

      We thank the reviewer for pointing this out. We have included a greater description of the methods used in the materials and methods section such that the experiment is reproducible, as per the reviewer’s suggestion. We decided to still make mention of the BioRxiv preprint since we thought it was appropriate for the reader to be informed of ongoing developments in the field.

      Please cite the release of the T. gondii proteomes used for spectrum matching (lines 972-973).

      Response:

      We have included this as per the reviewer’s suggestion.

      Please include the TMT labeling scheme so the analysis may be reproduced from the raw files.

      Response:

      We have included this as per the reviewer’s suggestion in Supp Fig. 3A.

      5.Statistical analyses should be reviewed as follows:

      Have the authors examined the possibility that some changes in phosphopeptide abundance reflect changes in protein abundance? This may be particularly relevant for comparisons involving the ∆cdpk3 strain. Did the authors collect paired unenriched proteomes from the experiments performed? Alternatively, there may be enriched peptides that did not change in abundance for many of the proteins that appear dynamically phosphorylated.

      Response:

      We did not collect unenriched proteomes from the experiments performed (although we did perform unenriched mixing checks to ensure equal loading between samples), and believe that this wasn’t a necessity for the following reasons:

      1. For within-line treatment analyses, treatment timings are so short (a maximum of 15-50s in the single timepoint experiment) that it would be unlikely to detect substantial changes in protein abundance. Moreover, these unlikely events would affect all phosphosites across a protein, and therefore be detectable.

      In our CDPK3 dependency timecourse experiments, we normalise both the WT and ∆CDPK3 strain to 0s, and measure signalling progression over time. Therefore, any difference at timepoints that are not “0” are not originating from basal differences. We also see a consistent increase/decrease in phosphosite detection across the sub-minute timecourse, further confirming that the observed changes are truly down to dynamic changes in phosphorylation and not protein levels.

      In the single timepoint CDPK3 dependency analyses (44 regulated sites identified, Data S2), we acknowledge that there could be some risk of altered starting protein abundance between lines. However, if protein abundance were responsible for the changes in phosphosite detection, we would expect all phosphosites across the protein to shift, and we do not observe this. Moreover, when we look at these CDPK3 dependent proteins and compare their phosphosite abundance in untreated WT and ∆CDPK3 lines, we find that for each protein, either all or the majority of phosphosites detected are unchanged (highlighting that there is no substantial difference in this protein’s abundance between lines). Where there are phosphosite differences between lines, these are only ever on single sites on a protein while most other sites are unchanged - implying that these are changes to basal phosphorylation states and not protein levels.

      It seems like for Figs. 3B and S5 the maximum number of clusters modeled was selected. Could the authors provide a rationale for the number of clusters selected, since it appears many of the clusters have similar profiles.

      The number of clusters is chosen automatically by the Mclust algorithm as the value that maximizes the Bayes Information Criterion (BIC). BIC in effect balances gains in model fit (increasing log-likelihood) against increasing the number of parameters (i.e. number of clusters).

      Please include figure panel(s) relating to gene ontology. Relevant information for readers to make conclusions includes p-value, fold-enrichment or gene ratio, and some sort of metric of the frequency of the GO term in the surveyed data set. See PMID: 33053376 Fig. 7 and PMID: 29724925 Fig. 6 for examples or enrichment summaries. Additionally, in the methods, specify (i) the background set, (ii) the method used for multiple test correction, (iii) the criteria constituting "enrichment", (iv) how the T. gondii genome was integrated into the analysis, (v) the class of GO terms (molecular function, biological process, or cellular component), (vi) any additional information required to reproduce the results (for example, settings modified from default).

      Response:

      We have included the additional information requested in the materials and methods.

      We purposely did not include GO figure panels as our analyses are being done across many clusters, making it very difficult to display this information cohesively. We have included all data in Tables S2-S5. These tables included all the relevant information on p-value, enrichment status, ratio in study/ratio in population, class of GO terms etc.

      The presentation of the lipidomics experiments in Figure 4A-C is confusing. First, the ∆cdpk3/WT ratio removes information about the process in WT parasites, and it's unclear why the scale centers on 100 and not 1. Second, the data in Figure S6 suggests a more modest effect than that represented in Fig. 4; is this due to day to day variability? How do the authors justify pairing WT and mutant samples as they did to generate the ratios?

      Response:

      This is a common strategy used by many metabolomics experts (Bailey et al., 2015; Dass et al., 2021; Lunghi et al., 2022). We had originally chosen to represent the data as a ratio since this form of representation helps get rid of the variability that arises between experiments and allows us to see very clear patterns which would otherwise go unnoticed. This variability arises from the amount of lipids in each sample which varies between parasites in a dish, the batch of FBS and DMEM used, and the solutions and even room temperature used to extract lipids on a given day.

      However, we agree with the reviewer that depicting the data in Figure 4A-C as a ratio of ∆CDPK3/WT parasites can be confusing, so we have now changed the graphs, plotting WT and ∆CDPK3 levels instead, and have moved the ratio of ∆CDPK3/WT to the Supplementary Figure 5.

      The significance test seems to be performed on the difference between the WT and ∆cdpk3 strains, but not relative to the DMSO treatment? Wouldn't you want to perform a repeated measures ANOVA to determine (i) if lipid levels change over time and (ii) if this trend differs in WT vs. mutant strain?

      Response:

      The reviewer correctly points out that ANOVA is often used for time courses, but we must point out that it is not always strictly appropriate since it can overlook the purpose of the individual experiment design, which in this case is, 1) to investigate the role of CDPK3 compared to the WT parental strain, and 2) specifically to find the exact point at which the DAG begins to change after stimulus to match the proteomics time course.

      Our data is clearly biassed towards earlier time points where we have 0, 5, 10, 30, 45 seconds where DAG levels are mostly unchanged compared to the single timepoint 60 seconds which shows a significant difference in DAG using our method of statistical comparison by paired two tailed t-test. Therefore, it would be unwise to use ANOVA when we really want to see when the A23187 stimulus takes effect, which appears to be after the 45 second mark. Therefore, analysing the data by ANOVA would likely provide a false negative result, where the result is non-significant but there is clearly more DAG in WT than CDPK3 after 60 seconds. T-tests are commonly used when comparing the same cell lines grown in the same conditions with a test/treatment, and in this case the test/treatment is CPDK3 present or absent (Lentini et al., 2020).

      In the main text, it would be preferable to see the data presented as the proteomics experiments were in Figure 4B and 4C, with fold changes relative to the DMSO (t = 0) treatment, separately for WT and ∆cdpk3 parasites.

      Response:

      We have now changed the way that we represent the data, plotting %mol instead of the ratio.

      Signaling lipids constitute small percentages of the overall pool (e.g. PMID: 26962945), so one might not necessarily expect to observe large changes in lipid abundance when signaling pathways are modulated. Is there any positive control that the authors could include to give readers a sense of the dynamic range? Maybe the DGK1 mutant (PMID: 26962945)?

      Response:

      DGK1 is maybe not a good example because the DGK1 KO parasites effectively “melt” from a lack of plasma membrane integrity ((Bullen et al., 2016), so this would likely be technically challenging. We don’t see the added value in including an additional mutant control since we can already see the dynamic change over time from no difference (0 seconds) to significant difference (60 seconds) between WT and CDPK3 for DAG and most other lipids. We already see a significant difference between WT and CDPK3 after 60 seconds for DAG, and we can clearly see in sub-minute timecourses the changes or not at the specific points where the A23187 is added (0-5 seconds), the parasites acclimatise, for the A23187 to take effect (10-30 seconds) and for the parasite lipid response to be visible by lipidomics (45-60 +seconds).

      Figure 4E: are the differences in [cAMP] with DMSO treatment and A23187 treatment different at any of the timepoints in the WT strain? The comparison seems to be WT/∆cdpk3 at each timepoint. Does the text (lines 562-568) need to be modified accordingly?

      Response:

      In WT (and ∆CDPK3) parasites, [cAMP] is significantly changed at 5s of A23187 treatment (relative to DMSO). We have modified our figures to include this analysis. The existing text accurately reflects this.

      Figure 6I: is the difference between PDE2 cKO/∆cdpk3 + DMSO or RAP significant?

      Response

      In our original manuscript, there was no statistical difference in [cAMP] between PDE2cKO/∆CDPK3+DMSO and PDE2cKO/∆CDPK3+DMSO+RAP, likely due to the variation between biological replicates. To overcome the issues in variability between replicates, we have now included more biological replicates (n=7). This has led to a significant difference in [cAMP] between PDE2cKO/∆CDPK3 DMSO- and RAP-treated parasites and between PDE2cKO DMSO- and RAP-treated parasites (now Fig. 6I).

      **MINOR COMMENTS**

      1.The following references should be added or amended:

      Lines 83-85: in the cited publication, relative phosphopeptide abundances of an overexpressed dominant-negative, constitutively inactive PKA mutant were compared to an overexpressed wild-type mutant. In this experimental setup, one would hypothesize that targets of PKA should be down-regulated (inactive/WT ratios). However, the mentioned phosphopeptide of PDE2 was found to be up-regulated, suggesting that it is not a direct target of PKA.

      Response:

      We thank the reviewer for spotting this error, we have now modified our wording.

      Cite TGGT1_305050, referenced as calmodulin in line 458, as TgELC2 (PMID: 26374117).

      Response:

      We have included this as per the reviewer’s suggestion.

      Cite TGGT1_295850 as apical annuli protein 2 (AAP2, PMID: 31470470).

      Response:

      We have included this as per the reviewer’s suggestion.

      Cite TGGT1_270865 (adenylyl cyclase beta, Acβ) as PMID: 29030485, 30449726.

      Response:

      We have included this as per the reviewer’s suggestion.

      Cite TGGT1_254370 (guanylyl cyclase, GC) as PMID: 30449726, 30742070.

      Response:

      We have included this as per the reviewer’s suggestion.

      Note that Lourido, Tang and David Sibley, 2012 observed that treatment with zaprinast (a PDE inhibitor) could overcome CDPK3 inhibition. The target(s) of zaprinast have not been determined and may differ from those of BIPPO (in identity and IC50). The cited study also used modified CDPK3 and CDPK1 alleles, rather than ∆cdpk3 and intact cdpk1 as used in this manuscript. That is to say, the signaling backgrounds of the parasite strains deviate in ways that are not controlled.

      Response:

      While it is true that zaprinast targets have not been unequivocally identified, zaprinast-induced egress is widely thought to be the result of PKG activation, a conclusion that is further supported by the finding that Compound 1 completely blocks zaprinast-induced egress (Lourido, Tang and David Sibley, 2012). Similarly, BIPPO-induced egress is inhibited by chemical inhibition of PKG by Compound 1 and Compound 2 (Jia et al., 2017). Moreover, like zaprinast, BIPPO has been clearly shown to partially overcome the ∆CDPK3 egress delay (Stewart et al., 2017).

      2.The following comments refer to the figures and legends:

      Part of the legend text for 1G is included under 1H.

      Response:

      This has been corrected

      Figure 1H: The legend mentions that some dots are blue, but they appear green. Please ensure that color choices conform to journal accessibility guidelines. See the following article about visualization for colorblind readers: https://www.ascb.org/science-news/how-to-make-scientific-figures-accessible-to-readers-with-color-blindness____/ . Avoid using red and green false-colored images; replace red with a magenta lookup table. Multi-colored images are only helpful for the merged image; otherwise, we discern grayscale better. Applies to Figures 1B, 5C, 6D. (Aside: anti-CAP seems an odd choice of counterstain; the variation in the staining, esp. at the apical cap, is distracting.)

      Response:

      We thank reviewer #1 for bringing this to our attention, and have modified our colour usage for all IFAs and Figures 1H and 3E.

      We chose CAP staining as the antibody is available in the laboratory and stains both the apical end (which has been shown to contain several proteins important for signalling as well as PDE9) and the parasite periphery, the location of CDPK3.

      Figure 1B: When showing a single fluorophore, please use grayscale and include an intensity scale bar, since relative values are being compared.

      Response:

      We have modified this as per the reviewer’s suggestion

      Figure 1C: it is difficult to compare the kinetics of the calcium response when the curves are plotted separately. Since the scales are the same, could the two treatments be plotted on the same axes, with different colors? Additionally, according to the legend, a red line seems to be missing in this panel.

      Response:

      Fig1C is not intended to compare kinetics, merely to show peak calcium release in each separate treatment condition. We have removed mention of a red line in the figure legend.

      Figure 2A: Either Figure S4 can be moved to accompany Figure 2A, or Figure 2A could be moved to the supplemental.

      Figure S4 has now been incorporated into Figure 2.

      Reviewer #1 (Significance (Required)):

      This manuscript would interest researchers studying signaling pathways in protozoan parasites, especially apicomplexans, as CDPK3 and PKG orthologs exist across the phylum. To my knowledge, it is the first study that has proposed a mechanism by which a calcium effector regulates cAMP levels in T. gondii. Unfortunately, the experiments fall short of testing this mechanism.

      Response:

      We thank reviewer #1 for their comments, but disagree with their assessment that the key points of the manuscript “fall short of experimental testing”.

      1. We demonstrate that, following both BIPPO and A23187 treatment, there is differential phosphorylation of numerous components traditionally believed to sit upstream of PKG activation (as well as several components within the PKG signalling pathway itself).
      2. We show that some of these sites are CDPK3 dependent, and that deletion of CDPK3 leads to changes in lipid signalling and an elevation in levels of cAMP (dysregulation of which is known to alter PKG signalling).
      3. We show that pre-treatment with a PKA inhibitor is able to largely rescue this phenotype.
      4. We demonstrate that a cAMP-specific PDE is phosphorylated following A23187 treatment (i.e. Ca2+ flux)
      5. We show that this cAMP specific PDE plays a role in A23187-mediated egress.
      6. While the latter PDE may not be directly regulated by CDPK3, these findings suggest that there are likely several Ca2+-dependent kinases that contribute to this feedback loop.

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

      **Summary:**

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

      In this manuscript, Dominicus et al investigate the elusive role of calcium-dependent kinase 3 during the egress of Toxoplasma gondii. Multiple functions have already been proposed for this kinase by this group including the regulation of basal calcium levels (24945436) or of a tyrosine transporter (30402958). However, one of the most puzzling phenotypes of CDPK3 deficient tachyzoites is a marked delay in egress when parasites are stimulated with a calcium ionophore that is rescued with phosphodiesterase (PDE) inhibitors. Crosstalk between, cAMP, cGMP, lipid and calcium signalling has been previously described to be important in regulating egress (26933036, 23149386, 29030485) but the role of CDPK3 in Toxoplasma is still poorly understood.

      Here the authors first take an elegant phosphoproteomic approach to identify pathways differentially regulated upon treatment with either a PDE inhibitor (BIPPO) and a calcium ionophore (A23187) in WT and CDPK3-KO parasites. Not much difference is observed between BIPPO or A23187 stimulation which is interpreted by the authors as a regulation through a feed-back loop.

      The authors then investigate the effect of CDPK3 deletion on lipid, cGMP and cAMP levels. The identify major changes in DAG, phospholipid, FFAs, and TAG levels as well as differences in cAMP levels but not for cGMP. Chemical inhibition of PKA leads to a similar egress timing in CDPK3-KO and WT parasites upon A23187 stimulation.

      As four PDEs appeared differentially regulated in the CDPK3-KO line upon A23187, the authors investigate the requirement of the 4 PDEs in cAMP levels. They show diverse localisation of the PDEs with specificities of PDE1, 7 and 9 for cGMP and of PDE2 for cAMP. They further show that PDE1, 7 and 9 are sensitive to BIPPO. Finally, using a conditional deletion system, they show that PDE1 and 2 are important for the lytic cycle of Toxoplasma and that PDE2 shows a slightly delayed egress following A23187 stimulation.

      **Major comments:**

      -Are the key conclusions convincing?

      The title is supported by the findings presented in this study. However I am not sure to understand why the authors imply a positive feed back loop. This should be clarified in the discussion of the results.

      Response:

      We believe in a positive feedback loop as, upon A23187 treatment (resulting in a calcium flux), ΔCDPK3 parasites are able to egress, albeit in a delayed manner. This egress delay is substantially, but not completely, alleviated upon treatment with BIPPO (a PDE inhibitor known to activate the PKG signalling pathway). In conjunction with our phosphoproteomic data (where we see phosphorylation of numerous pathway components upstream of PKG upon BIPPO and A23187 treatment - both in a CDPK3 dependent and independent manner), these observations suggest that calcium-regulated proteins (CDPK3 among them) feed into the PKG pathway. As deletion of CDPK3 delays egress, it is reasonable to postulate that this feedback is one that amplifies egress signalling (i.e. is positive).

      The phosphoproteome analysis seems very strong and will be of interest for many groups working on egress. However, the key conclusion, i.e. that a substrate overlaps between PKG and CDPK3 is unlikely to explain the CDPK3 phenotype, seems premature to me in the absence of robustly identified substrates for both kinases.

      Response:

      We certainly do not fully exclude the possibility of a substrate overlap but do lean more heavily towards a feedback loop given (a) the inability to clearly detect treatment-specific signalling profiles and (b) the phospho targets observed in the A23187 and BIPPO phosphoproteomes. We have further clarified our reasoning, and overall tempered our language in the manuscript as per the reviewer’s suggestion.

      I am not sure there is a clear key conclusion from the lipidomic analysis and how it is used by the authors to build their model up. Major changes are observed but how could this be linked with CDPK3, particularly if cGMP levels are not affected?

      Response:

      Our phosphoproteomic analyses identify several CDPK3-dependent phospho sites on phospholipid signalling components (DGK1 & PI-PLC), suggesting that there is indeed altered signalling downstream of PKG. To test whether these lead to a measurable phenotype, we performed the lipidomics analysis. We did not pursue this arm of the signalling pathway any further as we postulated that the changes in the lipid signalling pathway were less likely to play a role in the feedback loop. Nevertheless, we felt that it was worthwhile to include these findings in our manuscript as they support the conclusions drawn from the phosphoproteomics - namely that lipid signalling is perturbed in CDPK3 mutants. We, or others, may follow up on this in future.

      We agree with the reviewer that it is surprising that cGMP levels remain unchanged in our experiments when we treat with A23187. Given the measurable difference in cAMP levels between WT and ΔCDPK3 parasites, we postulate that CDPK3 directly or indirectly downregulates levels of cAMP. This would, in turn, alter activity of the cAMP-dependent protein kinase PKAc. Jia et al. (2017) have shown a clear dependency on PKG for parasites to egress upon PKAc depletion, but were also unable to reliably demonstrate cGMP accumulation in intracellular parasites. Similarly, their hypothesis that dysregulated cGMP-specific PDE activity results in altered cGMP levels has not been proven (the PDE hypothesised to be involved has since been shown to be cAMP-specific).

      While it is possible that our collective inability to observe elevated cGMP levels is explained by the sensitivity limits of the assay, it is similarly possible that cAMP-mediated signalling is exerting its effects on the PKG signalling pathway in a cGMP-independent manner.

      The evidence that CDPK3 is involved in cAMP homeostasis seems strong. However, the analysis of PKA inhibition is a bit less clear. The way the data is presented makes it difficult to see whether the treatment is accelerating egress of CDPK3-KO parasites or affecting both WT and CDPK3-KO lines, including both the speed and extent of egress. This is important for the interpretation of the experiment.

      Response:

      Fig. 4F shows that there is a significant amount of premature egress in both WT and ∆CDPK3 parasites following 2 hrs of H89 pre-treatment (consistent with previous reports that downregulation of cAMP signalling stimulates premature egress). When we subsequently investigated A23187-induced egress rates of the remaining intracellular H89 pre-treated parasites (Fig. 4Gi-ii) we found that the ∆CDPK3 egress delay was largely rescued. We have moved Fig. 4F to the supplement (now Supp Fig. 5E) in order to avoid confusion between the distinct analyses shown in 4F (pre-treatment analyses) and 4G (egress experiment). These experiments provided a hint that cAMP signalling is affected, which we then validate by measuring elevated cAMP levels in CDPK3 mutant parasites.

      The biochemical characterisation of the four PDE is interesting and seems well performed. However, PDE1 was previously shown to hydrolyse both cAMP and cGMP (____https://doi.org/10.1101/2021.09.21.461320____) which raises some questions about the experimental set up. Could the authors possibly discuss why they do not observe similar selectivity? Could other PDEs in the immunoprecipitate mask PDE activity? In line with this question, it is not clear what % of "hydrolytic activity (%)" means and how it was calculated.

      The experiments describing the selectivity of BIPPO for PDE1, 7 and 9 as well as the biological requirement of the four tested PDEs are convincing.

      Response:

      We believe that the disagreement between our findings and those published by Moss and colleagues are due to the differences in experimental conditions. We performed our assays at room temperature for 1 hour with higher starting cAMP concentrations (1 uM) compared to them. They performed their assays at 37ºC for 2 hours with 10-fold lower starting cAMP concentrations (0.1 uM). We have now repeated this set of experiments using the Moss et al. conditions, and find that PDEs 1, 7 and 9 can be dual specific, while PDE2 is cAMP-specific, thereby recapitulating their findings (Now included in the revised manuscript under Supp Fig. 7B). However, we also now performed a timecourse PDE assay using our original conditions and show that the cAMP hydrolytic activity for PDE1 can only be detected following 4 hours of incubation, compared to cGMP activity that can be detected as early as 30 minutes, suggesting that it possesses predominantly cGMP activity (See Supp Fig. 7C). We therefore believe that our experimental setup is more stringent, because if one starts with a lower level of substrate and incubates for longer and at a higher temperature, even minor dual activity could make a substantial difference in cAMP levels. Our data suggests that the cAMP hydrolytic activity of PDEs 1, 7 and 9 is substantially lower than the cGMP hydrolytic activity that they display.

      We have also included a clear description of how % hydrolytic activity was calculated in the methods section.

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

      The claim that CDPK3 affects cAMP levels seems strong however the exact links between CDPK3 activity, lipid, cGMP and cAMP signalling remain unclear and it may be important to clearly state this.

      Response:

      We have modified our wording in the text to more clearly describe our current hypothesis and reasoning.

      -Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

      I think that the manuscript contains a significant amount of experiments that are of interest to scientists working on Toxoplasma egress. Requesting experiments to identify the functional link between above-mentioned pathways would be out of the scope for this work although it would considerably increase the impact of this manuscript. For example, would it be possible to test whether the CDPK3-KO line is more or less sensitive to PKG specific inhibition upon A23187 induced?

      -Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.

      The above-mentioned experiment is not trivial as no specific inhibitors of PKG are available. Ensuring for specificity of the investigated phenotype would require the generation of a resistant line which would require significant work.

      __Response: __We agree that this would be an interesting experiment to further substantiate our findings. As indicated by the reviewer, however, the lack of specific inhibitors of PKG means a resistant line would likely be required to ensure specificity.

      -Are the data and the methods presented in such a way that they can be reproduced?

      It is not clear how the % of hydrolytic activity of the PDE has been calculated.

      Response: We have included a clearer description of how % hydrolytic activity was calculated in the methods section.

      -Are the experiments adequately replicated and statistical analysis adequate?

      This seems to be performed to high standards.

      **Minor comments:**

      -Specific experimental issues that are easily addressable.

      I do not have any comments related to minor experimental issues.

      -Are prior studies referenced appropriately?

      Most of the studies relevant for this work are cited. It is however not clear to me why some important players of the "PKG pathway" are not indicated in Fig 1H and Fig 3E, including for example UGO or SPARK.

      Response:

      We have modified Fig 1H and 3E to include all key players involved in the PKG pathway.

      -Are the text and figures clear and accurate?

      While all the data shown here is impressive and well analysed, I find it difficult to read the manuscript and establish links between sections of the papers. The phosphoproteome analysis is interesting and is used to orientate the reader towards a feedback mechanism rather than a substrate overlap. But why do the authors later focus on PDEs and not on AC or CNBD, as in the end, if I understand well, there is no evidence showing a link between CDPK3-dependent phosphorylation and PDE activity upon A23187 stimulation?

      Response:

      We thank reviewer#2 and appreciate their constructive feedback re the flow of the manuscript.

      Our key findings from the phosphoproteomics study were that 1) BIPPO and A23187 treatment trigger near identical signalling pathways, 2) that both A23187 and BIPPO treatment leads to phosphorylation of numerous components both upstream and downstream of PKG signalling (hinting at the presence of an Ca2+-regulated feedback loop) and 3) several of the abovementioned components are phosphorylated in a CDPK3 dependent manner.

      While several avenues of study could have been pursued from this point onwards, we chose to focus on the feedback loop in a broader sense as its existence has important implications for our general understanding of the signalling pathways that govern egress.

      We reasoned that, given the differential phosphorylation of 4 PDEs following A23187 and BIPPO treatment (none of which had been studied in detail previously), it was relevant to study these in greater detail.

      Coupled with the A23187 egress assay on PDE2 knockout parasites - our findings suggest that PDE2 plays a role in the abovementioned Ca2+ signalling loop. While PDE2 may not exert its effects in a CDPK3-dependent manner (and CDPK3 may, therefore, alter cAMP levels in a different fashion), this does not detract from the important finding that PDE2 is one of the (likely numerous) components that is regulated in a Ca2+-dependent feedback loop to facilitate rapid egress.

      We have modified our wording to better reflect our rationale for studying the PDEs irrespective of their CDPK3 phosphorylation status.

      While we feel that our reasoning for studying the PDEs is solid, we do appreciate that further clarification on the putative CDPK3-Adenylate cyclase link would elevate the manuscript substantially. However, given the data that the ACb is not playing a sole role in the control of egress, this is likely a non-trivial task and requires substantial work.

      It is also unclear how the authors link CDPK3-dependent elevated cAMP levels with the elevated basal calcium levels they previously described. This is particularly difficult to reconcile particularly in a PKG independent manner.

      Response:

      We previously postulated that elevated Ca2+ levels allowed ΔCDPK3 mutants to overcome a complete egress defect, potentially by activating other CDPKs (e.g. CDPK1). It is similarly plausible that elevated Ca2+ levels in ΔCDPK3 parasites may lead to elevated cAMP levels in order to prevent premature egress.

      As noted in our previous responses, we acknowledge that our inability to detect cGMP is surprising. However, given the clarity of our cAMP findings, and the phosphoproteomic evidence to suggest that various components in the PKG signalling pathway are affected, we postulate that we are either unable to reliably detect cGMP due to sensitivity issues, or that cAMP is exerting its regulation on the PKG pathway in a cGMP-independent manner. As noted previously, while the link between cAMP and PKG signalling has been demonstrated by Jia et al., it is not entirely clear how this is mediated.

      The presentation of the lipidomic analysis is also not really clear to me. Why do the authors show the global changes in phospholipids and not a more detailed analysis?

      Response:

      We performed a detailed phospholipid profile of WT and ∆CDPK3 parasites under normal culture conditions. However, due to the sheer quantity of parasites required for this detailed analysis, we were unable to measure individual phospholipid species in our A23187 timecourse. We therefore opted to measure global changes following A23187 stimulation.

      As the authors focus on the PI-PLC pathway, could they detail the dynamics of phosphoinositides? I understand that lipid levels are affected in the mutant but I am not sure to understand how the authors interpret these massive changes in relationship with the function of CDPK3 and the observed phenotypes.

      Response:

      Our phosphoproteomic analyses identified several CDPK3-dependent phospho sites on phospholipid signalling components (DGK1 & PI-PLC), suggesting that (in keeping with all of our other data), there is altered signalling downstream of PKG. To test whether these changes lead to a measurable phenotype, we performed the lipidomics analysis. Following stimulation with A23187, we found a delayed production of DAG in ∆CDPK3 parasites compared to WT parasites. Since DAG is required for the production of PA, which in turn is required for microneme secretion, our finding can explain why microneme secretion is delayed in ∆CDPK3 parasites, as previously reported (Lourido, Tang and David Sibley, 2012; McCoy et al., 2012).

      We did not follow this arm of the signalling pathway any further as we postulated that the changes in the lipid signalling pathway were less likely to play a role in the feedback loop. Nevertheless, we felt that it was worthwhile to include these findings in our manuscript as they support the conclusions drawn from the phosphoproteomics - namely that lipid signalling is perturbed in CDPK3 mutants. We, or others, may follow up on this in future.

      Finally, the characterisation of the PDEs is an impressive piece of work but the functional link with CDPK3 is relatively unclear. It would also be important to clearly discuss the differences with previous results presented in this this preprint: https://doi.org/10.1101/2021.09.21.461320____.

      My understanding is while the authors aim at investigating the role of CDPK3 in A23187 induced egress, the main finding related to CDPK3 is a defect in cAMP homeostasis that is not linked to A23187. Similarly, the requirements of PDE2 in cAMP homeostasis and egress is indirectly linked to CDPK3. Altogether I think that important results are presented here but divided into three main and distinct sections: the phosphoproteomic survey, the lipidomic and cAMP level investigation, and the characterisation of the four PDEs. However, the link between each section is relatively weak and the way the results are presented is somehow misleading or confusing.

      Response:

      As mentioned in a previous response, we chose to study PDEs in greater detail because of our observation that both A23187 and BIPPO treatments lead to their phosphorylation (hinting at the presence of a Ca2+regulated feedback loop). We were particularly intrigued to study the cAMP specific PDE, as CDPK3 KO parasites suggested that cAMP may play a role in the Ca2+ feedback mechanism. As PDE2 may not be directly regulated by CDPK3, Ca2+ appears to exert its feedback effects in numerous ways. We have modified our wording to better reflect our rationale for studying the PDEs irrespective of their CDPK3 phosphorylation status.

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

      This is a very long manuscript written for specialists of this signalling pathway and I would suggest the authors to emphasise more the important results and also clearly state where links are still missing. This is obviously a complex pathway and one cannot elucidate it easily in a single manuscript.

      Response:

      We have included an additional summary in our conclusions to better illustrate our findings and clarify any missing links.

      Reviewer #2 (Significance (Required)):

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

      This is a technically remarkable paper using a broad range of analyses performed to a high standard.

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

      The cross-talk between cAMP, cGMP and calcium signalling is well described in Toxoplasma and related parasites. Here the authors show that, in Toxoplasma, CDPK3 is part of this complex signalling network. One of the most important finding within this context is the role of CDPK3 in cAMP homeostasis. With this in mind, I would change the last sentence of the abstract to "In summary we uncover a feedback loop that enhances signalling during egress and links CDPK3 with several signalling pathways together."

      Response:

      In light of feedback received from several reviewers, we have made our wording less CDPK3 centric - as our findings relate in part to CDPK3 and, in a broader sense, to a Ca2+ driven feedback loop.

      The genetic and biochemical analyses of the four PDEs are remarkable and highlight consistencies and inconsistencies with recently published work that would be important to discuss and will be of interest for the field.

      __Response: __We thank reviewer#2 and agree that the PDE findings are of significant importance to the field.

      While I understand the studied signalling pathway is complex, I think it would be important to better describe the current model of the authors. In the discussion, the authors indicate that "the published data is not currently supported by a model that fits most experimental results." I would suggest to clarify this statement and discuss whether their work helps to reunite, correct or improve previous models.

      __Response: __We have expanded on the abovementioned statement to clarify that the presence of a feedback loop is a major pillar of knowledge required for the complete interpretation of existing signalling data.

      Could the authors also speculate about a potential role of PDE/CDPK3 in host cell invasion as cAMP signalling has be shown to be important for this process (30208022 and 29030485)?

      __Response: __Existing literature (Jia et al., 2017) suggests that perturbations to cAMP signalling play a very minor role in invasion since parasites where either ACα or ACβ are deleted show no impairment in invasion levels. We currently do not have substantial data on invasion, and are not sure that pursuing this is valuable given the minor phenotypes observed in other studies.

      -State what audience might be interested in and influenced by the reported findings.

      This paper is of great interest to groups working on the regulation of egress in Toxoplasma gondii and other related apicomplexan pathogens.

      -Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

      I am working on the cell biology of apicomplexan parasites.

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

      **Summary:**

      Dominicus et al aimed to identify the intersecting components of calcium, cyclic nucleotides (cAMP, cGMP) and lipid signaling through phosphoproteomic, knockout and biochemical assays in an intracellular parasite, Toxoplasma gondii, particularly when its acutely-infectious tachyzoite stage exits the host cells. A series of experimental strategies were applied to identify potential substrates of calcium-dependent protein kinase 3 (CDPK3), which has previously been reported to control the tachyzoite egress. According to earlier studies (PMID: 23226109, 24945436, 5418062, 26544049, 30402958), CDPK3 regulated the parasite exit through multiple phosphorylation events. Here, authors identified differentially-regulated (DR) phosphorylation sites by comparing the parasite samples after treatment with a calcium ionophore (A23178) and a PDE inhibitor (BIPPO), both of which are known to induce artificial egress (induced egress as opposed to natural egress). When the DCDPK3 mutant was treated with A23187, its delayed egress phenotype did not change, whereas BIPPO restored the egress to the level of the parental (termed as WT) strain, probably by activating PKG.

      The gene ontology enrichment of the up-regulated clusters revealed many probable CDPK3-dependent DR sites involved in cyclic nucleotide signaling (PDE1, PDE2, PDE7, PDE9, guanylate and adenylate cyclases, cyclic nucleotide-binding protein or CNBP) as well as lipid signaling (PI-PLC, DGK1). Authors suggest lipid signaling as one of the factors altered in the CDPK3 mutant, albeit lipidomics (PC, PI, PS, PT, PA, PE, SM) showed no significant change in phospholipids. To reveal how the four PDEs indicated above contribute to the cAMP and cGMP-mediated egress, they examined their biological significance by knockout/knockdown and enzyme activity assays. Authors claim that PDE1,7,9 proteins are cGMP-specific while PDE2 is cAMP-specific, and BIPPO treatment can inhibit PDE1-cGMP and PDE7-cGMP, but not PDE9-cGMP. Given the complexity, the manuscript is well structured, and most experiments were carefully designed. Undoubtedly, there is a significant amount of work that underlies this manuscript; however, from a conceptual viewpoint, the manuscript does not offer significant advancement over the current knowledge without functional validation of phosphoproteomics data (see below). A large body of work preceding this manuscript has indicated the crosstalk of cAMP, cGMP, calcium and lipid signaling cascades. This work provides a further refinement of the existing model In a methodical sense, the work uses established assays, some of which require revisiting to reach robust conclusions and avoid misinterpretation. The article is quite interesting from a throughput screening point of view, but it clearly lacks the appropriate endorsement of the hits.The authors accept that identifying the phosphorylation of a protein does not imply a functional role, which is a major drawback as there is no experimental support for any phosphorylation site of the protein identified through phosphoproteomics. In terms of the mechanism, it is not clear whether and how lipid turnover and cAMP-PKA signaling control the egress phenotype (lack of a validated model at the end of this study).

      Response:

      We thank reviewer #3 for their comments, but respectfully disagree with their assessment that the work presented does not advance current knowledge.

      1. We demonstrate that, following both BIPPO and A23187 treatment, there is differential phosphorylation of numerous components traditionally believed to sit upstream of PKG activation (as well as numerous components within the PKG signalling pathway itself). While it may have been inferred from previous studies that A23187 and BIPPO signalling intersect, this has never been unequivocally demonstrated - nor has a feedback loop ever been shown.

      We provide a novel A23187-driven phosphoproteome timecourse that further bolsters the model of a Ca2+-driven feedback loop.

      We show that deletion of CDPK3 leads to a delay in DAG production upon stimulation with A23187.

      We show that some of the abovementioned sites are CDPK3 dependent, and that deletion of CDPK3 leads to elevated levels of cAMP (dysregulation of which is known to alter PKG signalling).

      We show that pre-treatment with a PKA inhibitor is able to largely rescue this phenotype.

      We demonstrate that a cAMP-specific PDE is phosphorylated following A23187 treatment (i.e. Ca2+ flux)

      We show that this cAMP specific PDE plays a role in egress.

      While the latter PDE may not be directly regulated by CDPK3, these findings suggest that there are likely several Ca2+-dependent kinases that contribute to this feedback loop.

      We also firmly disagree with the reviewer’s assertion that without phosphosite characterisation, we have no support for our model. Following treatment with A23187 (and BIPPO), we clearly show broad, systemic changes (both CDPK3 dependent and independent) across signalling pathways previously deemed to sit upstream of calcium flux. Given the vast number of proteins involved in these signalling pathways, and the multitude of differentially regulated phosphosites identified on each of them, it is highly likely that the signalling effects we observe are combinatorial. Accordingly, we believe that mutating individual sites on individual proteins would be a very costly endeavour which is unlikely to substantially advance our understanding of signalling during egress. Moreover, introducing multiple point mutations in a given protein to ablate phosphorylation may lead to protein misfolding and would therefore not be informative. One of the key aims of this study was to assess how egress signalling pathways are interconnected, and we believe we have been able to show strong support for a Ca2+-driven feedback mechanism in which both CDPK3 and PDE2 play a role through the regulation of cAMP.

      While we agree with the reviewer’s statement that a large body of work preceding this manuscript has indicated the crosstalk of cAMP, cGMP, calcium and lipid signalling cascades, a feedback loop has not previously been shown. We believe that this finding is absolutely central to facilitate the complete interpretation of existing signalling data. Furthermore, no previous studies have gone to this level of detail in either proteomics or lipidomics to analyse the calcium signal pathway in any apicomplexan parasite. We argue that the novelty in our manuscript is that it is a carefully orchestrated study that advances our understanding of the signalling network over time with subcellular precision. The kinetics of signalling is not well understood and we believe that our study is likely the first to include both proteomic and lipidomic analyses over a timecourse during the acute lytic cycle stage of the disease. In doing so, we found evidence for a feedback loop that controls the signalling network spatiotemporally, and we characterise elements of this feedback in the same study.

      **Major Comments:**

      Based on the findings reported here there is little doubt that BIPPO and A23187-induced signaling intersect with each other, as very much expected from previous studies. The authors selected the 50s and 15s post-treatment timing of A23187 and BIPPO, respectively for collecting phosphoproteomics samples. At these time points, which were shown to peak cytosolic Ca2+, parasites were still intracellular (Line #171). How did authors make sure to stimulate the entire signaling cascade adequately, particularly when parasites do not egress within the selected time window? There is significant variability between phosphosite intensities of replicates (Line #186), which may also be attributed to insufficient triggers for the egress across independent experiments. This work must be supported by in vitro egress assays with the chosen incubation periods of BIPPO and ionophore treatment (show the induced % egress of tachyzoites in the 50s and 15s).

      Response:

      1. We appreciate that the reviewer acknowledges that our data clearly shows that BIPPO and A23187-induced signalling intersect. While this may have been expected from previous studies, this has not previously been shown - and is therefore valuable to the field. Specifically, the fact that A23187-treatment leads to phosphorylation of targets normally deemed to sit upstream of calcium release is entirely novel and adds a substantial layer of information to our understanding of how these signalling pathways work together.

      Treatments were purposely selected to align pathways to a point where calcium levels peak just prior to calcium reuptake. At these chosen timepoints, we clearly show that overall signalling correlation is very high. We know from our egress assays using identical treatment concentrations (Fig. 2C), that the stimulations used are sufficient to result in complete egress. We are simply comparing signalling pathways at points prior to egress.

      As mentioned in point 2, we show convincingly that the treatments used are sufficient to trigger complete egress. As detailed clearly in the text, we believe that these variations in intensities between replicates are due to slight differences in timing between experiments (this is inevitable given the very rapid progression of signalling, and the difficulty of replicating exact sub-minute treatment timings). We demonstrate that the reporter intensities associated with DR sites correlate well across replicates (Supp Fig. 3C), suggesting that despite some replicate variability, the overall trends across replicates is very much consistent. This allows us to confidently average scores to provide values that are representative of a site’s phosphorylation state at the timepoint of interest.

      The reviewer’s suggestion that we should demonstrate % egress at the 50s and 15s treatment timepoints is obsolete - we state clearly in the text that parasites have not egressed at these timepoints. Our egress assays (Fig. 2C) further support this.

      The authors discuss that CDPK3 controls the cAMP level and PKA through activation of one or more yet-to-be-identified PDEs(s). cAMP could probably also be regulated by an adenylate cyclase, ACbeta that was found to have CDPK3-dependent phosphorylation sites. If CDPK3 is indeed a regulator of cAMP through the activation of PDEs or ACbeta, it would be expected that the deletion of CDPK3 would perturb the cAMP level, resulting in dysregulation of PKAc1 subunit, which in turn would dysregulate cGMP-specific PDEs (PMID: 29030485) and thereby PKG. All these connections need to explain in a more clear manner with experimental support (what is positive and what is negatively regulated by C____DPK3).

      Response:

      1. We do not firmly state that CDPK3 regulates cAMP by phosphorylation of a PDE - this is one of the possibilities addressed. We acknowledge the possibility that this could also be via the adenylate cyclase (see line 792).

      PMID: 29030485 demonstrates clearly a link between cAMP signalling and PKG signalling, but does not demonstrate how this is mediated. The authors postulate that a cGMP-specific PDE is dysregulated given their observation that PDE2 is differentially phosphorylated in a constitutively inactive PKA mutant, however this was not validated experimentally. We and others (Moss et al., 2022), however, demonstrate that PDE2 is cAMP-specific. This suggests that the model built by PMID: 29030485 requires revisiting. We acknowledge clearly in the text that Jia et al. have shown a link between cAMP and PKG signalling, and hypothesise that CDPK3’s modulation of cAMP levels may affect this (this is in keeping with our phosphoproteomic data).

      Moreover, the egress defect is not due to a low influx of calcium in the cytosol because when the ionophore A23187 was added to the CDPK3 mutant, its phenotype was not recovered. Rather, the defect may be due to the low or null activity of PKG that would activate PI4K to generate IP3 and DAG. The latter would be used as a substrate by DGK to generate PA that is involved in the secretion of micronemes and Toxoplasma egress. In this context, authors should evaluate the role of CDPK3 in the secretion of micronemes that is directly related to the egress of the parasite.

      1. We agree with the reviewer on their point about calcium influx, and have already acknowledged in the text that the feedback loop does not control release of Ca2+ from internal stores as disruption of CDPK3 does not lead to a delay in Ca2+

      We agree, and clearly address in the text, that the egress defect could be due to altered PKG/phospholipid pathway signalling.

      (Lourido, Tang and David Sibley, 2012; McCoy et al., 2012) have both previously shown that microneme secretion is regulated by CDPK3. We therefore do not deem it necessary to repeat this experiment, but have made clearer mention of their findings in our writing.

      When the Dcdpk3 mutant with BIPPO treatment was evaluated, it was observed that the parasite recovered the egress phenotype. It is concluded that CDPK3 could probably regulate the activity of cGMP-specific PDEs. CDPK3 could (in)activate them, or it could act on other proteins indirectly regulating the activity of these PDEs. Upon inactivation of PDEs, an increase in the cGMP level would activate PKG, which will, in turn, promote egress. From the data, it is not clear whether any phosphorylation by CDPK3 would activate or inactivate PDEs, and if so, then how (directly or indirectly). To reach unambiguous interpretation, authors should perform additional assays.

      Response:

      As mentioned previously, given the abundance of differentially regulated phosphosites, we do not believe that mutating individual sites on individual proteins is a worthwhile or realistic pursuit.

      We clearly show systematic A23187-mediated phosphorylation of key signalling components in the PKA/PKG/PI-PLC/phospholipid signalling cascade, and demonstrate that several of these are CDPK3-dependent. We demonstrate that CDPK3 alters cAMP levels (and that the ∆CDPK3 egress delay in A23187 treated parasites is largely rescued following pre-treatment with a PKA inhibitor). We similarly demonstrate that A23187 treatment leads to phosphorylation of numerous PDEs, including the cAMP specific PDE2, and show that PDE2 knockout parasites show an egress delay following A23187 treatment. While PDE2 may not be directly regulated by CDPK3 (suggesting other Ca2+ kinases are also involved), these findings collectively demonstrate the existence of a calcium-regulated feedback loop, in which CDPK3 and PDE2 play a role (by regulating cAMP).

      We acknowledge that we have not untangled every element of this feedback loop, and do not believe that it would be realistic to do so in a single study given the number of sites phosphorylated and pathways involved. We do believe, however, that we have shown clearly that the feedback loop exists - this in itself is entirely novel, and of significant importance to the field.

      On a similar note, a possible experiment that can be done to improve the work would be to treat the CDPK3 mutant with BIPPO in conjunction with a calcium chelator (BAPTA-AM) to reveal, which proteins are phosphorylated prior to activation of the calcium-mediated cascades?

      Response:

      We agree that this would be an interesting experiment to carry out but would involve significant work. This could be pursued in another paper or project but is beyond the scope of this work.

      The manuscript claims that PDE1, PDE7, PDE9 are cGMP specific, and BIPPO inhibits only cGMP-specific PDEs. All assays are performed with 1-10 micromolar cAMP and cGMP for 1h. There is no data showing the time, protein and substrate dependence. Given the suboptimal enzyme assays, authors should re-do them as suggested here. (1) Repeat the pulldown assay with a higher number of parasites (50-100 million) and measure the protein concentration. (2) Set up the PDE assay with saturating amount of cAMP and cGMP, which is critical if the PDE1,7,9 have a higher Km Value for cAMP (means lower affinity) compared to cGMP. An adequate amount of substrate and protein allows the reaction to reach the Vmax. Once you have re-determined the substrate specificity (revise Fig 5D), you should retest BIPPO (Fig 5E) in the presence of cAMP and cGMP. It is very likely that you would find the same result as PDE9 and PfPDEβ (BIPPO can inhibit both cAMP and cGMP-specific PDE), as described previously

      We have repeated our assay using the exact same conditions outlined by Moss et al. This involved using a similar number of parasites, a longer incubation time of 2 hours at a higher temperature (37ºC) and with a lower starting concentration of cAMP (0.1 uM). We demonstrate that we are able to recapitulate both the Moss et al. and Vo et al. (see Supp Fig. 7B). However, we noticed that these reactions were not carried out with saturating cAMP/cGMP concentrations, since all reactions had reached 100% completion at the end of the assay whereby all substrate was hydrolysed. We therefore believe that based on our original assay, as well as the new PDE1 timecourse that we have performed (Supp Fig. 7C), that PDEs 1, 7 and 9 display predominantly cGMP hydrolysing activity, with moderate cAMP hydrolysing activity.

      We also repeated the BIPPO inhibition assay using the Moss et al. conditions, and still observe that the cGMP activity of PDE1 is the most potently inhibited of all 4 PDEs. We also see moderate inhibition of the cAMP activities of PDE1 and PDE9, suggesting that cAMP hydrolytic activity can also be inhibited. Interestingly, the cGMP hydrolytic activities of PDEs 7 & 9, which were previously inhibited using our original assay conditions, no longer appear to be inhibited. This is likely due to the longer incubation time, which masks the reduced activities of these two PDEs following treatment with BIPPO.

      The authors did not identify any PKG substrate, which is quite surprising as cAMP signaling itself could impact cGMP. Authors should show if they were able to observe enhanced cGMP levels in BIPPO-treated sample (which is expected to stimulate cGMP-specific PDEs). The author mention their inability to measure cGMP level but have they analyzed cGMP in the positive control (BIPPO-treated parasite line)? Why have they focused only on CDPK3 mutant, whereas in their phosphoproteomic data they could see other CDPKs too? It could be that other CDPK-mediated signaling differs and need PKA/PKG for activation.

      In the title, the authors have mentioned that there is a positive feedback loop between calcium release, cyclic nucleotide and lipid signaling, which is quite an extrapolation as there is no clear experimental data supporting such a positive feedback loop so the author should change the title of the paper.

      Response:

      1. As addressed in our previous response to the reviewer, PMID: 29030485 demonstrates clearly a link between cAMP signalling and PKG signalling, but does not confirm how this is mediated. The authors surmise that a cGMP-specific PDE is dysregulated (although the PDE hypothesised to be involved has since been shown to be cAMP-specific), but are similarly unable to detect changes in cGMP levels. This suggests that their model may be incomplete.

      The BIPPO treatment experiment suggested by the reviewer was already included in the original manuscript (see Fig. 4D in original manuscript, now Fig. 4E). With BIPPO treatment we are able to detect changes in cGMP levels.

      We did not deem it to be within the scope of this study to study every single other CDPK. We chose to study CDPK3, as its egress phenotype was of particular interest given its partial rescue following BIPPO treatment. We reasoned that its study may lead us to identify the signalling pathway that links BIPPO and A23187 induced signalling.

      As addressed in greater detail in our response to reviewer #2, the fact that the feedback loop appears to stimulate egress implies that it is positive.

      **Minor Comments:**

      Materials & Methods

      Explanation of parameters is not clear (Line #360-367). Phosphoproteomics with A23187 (8 micromolar) treatment in CDPK3-KO and WT, for 15, 30 and 60s at 37{degree sign}C incubation with DMSO control. Simultaneously passing the DR and CDPK3 dependency thresholds: CDPK3-dependent phosphorylation

      __Response: __We have modified the wording to make this clearer as per the reviewer’s suggestion.

      Line #368: At which WT-A23187 timepoint did the authors identify 2408 DR-up phosphosites (15s, 30s or 60s)? Or consistently in all? It should be clarified?

      __Response: __As already stated in the manuscript (see line 366 in original manuscript, now line 1047), phosphorylation sites were considered differentially regulated if at any given timepoint their log2FC surpassed the DR threshold.

      A23187 treatment of the CDPK3-KO mutant significantly increased the cAMP levels at 5 sec post-treatment, but BIPPO did not show any change. The authors concluded that BIPPO presumably does not inhibit cAMP-specific PDEs. However, the dual-specific PDEs are known to be inhibited by BIPPO, as shown recently (____https://www.biorxiv.org/content/10.1101/2021.09.21.461320v1____). Authors do confirm that BIPPO-treatment can inhibit hydrolytic activity of PfPDEbeta for cAMP as well as cGMP (Line #612). Besides, it was shown in Fig 5E that BIPPO can partially though not significantly block cAMP-specific PDE2. The statements and data conflict each other under different subtitles and need to be reconciled. Elevation of basal cAMP level in the CDPK3 mutant indicates the perturbation of cAMP signaling, however BIPPO data requires additional supportive experiments to conclude its relation with cAMP or dual-specific PDE.

      Response:

      1. The manuscript to which the reviewer refers does not use BIPPO in any of their experiments. They show that continuous treatment with zaprinast blocks parasite growth in a plaque assay, but do not test whether zaprinast specifically blocks the activity of any of the PDEs.

      Having repeated the PDE assay using the Moss et al. conditions (as outlined above), we are now able to recapitulate their findings, showing that PDEs 1, 7 and 9 can display dual hydrolytic activity while PDE2 is cAMP specific. As explained further above, we believe that our original set of experiments are more stringent than the Moss *et al. * To confirm this, we also performed an additional experiment, incubating PDE1 for varying amounts of time using our original conditions (1 uM cAMP or 10 uM cGMP, at room temperature). This revealed that PDE1 is much more efficient at hydrolysing cGMP, and only begins to display cAMP hydrolysing capacity after 4 hours of incubation.

      We also measured the inhibitory capacity of BIPPO on the PDEs using the Moss *et al. * During the longer incubation time, it seems that BIPPO is unable to inhibit PDEs 7 and 9, while with the more stringent conditions it was able to inhibit both PDEs. We reasoned that since BIPPO is unable to inhibit these PDEs fully, the residual activity over the longer incubation period would compensate for the inhibition, eventually leading to 100% hydrolysis of the cNMPs. We also see that while the cGMP hydrolysing capacity of PDE1 is completely inhibited, its cAMP hydrolysing capacity is only partially inhibited. These findings and the fact that PDE2 is not inhibited by BIPPO are in line with our experiments where we measured [cAMP] and showed that treatment with BIPPO did not lead to alterations in [cAMP].

      The method used to determine the substrate specificity of PDE 1,2,7 and 9 resulted in the hydrolytic activity of PDE2 towards cAMP, while the remaining 3 were determined as cGMP-specific. However, PDE1 and PDE9 have been reported as being dual-specific (Moss et al, 2021; Vo et al, 2020), which questions the reliability of the preferred method to characterize substrate specificity by the authors. It is also suggested to use another ELISA-based kit to double check the results.

      Response:

      As outlined above, we have repeated the assay using the conditions described by Moss et al. (lower starting concentrations of cAMP, 2 hour incubation period at 37ºC) and find that we are able to recapitulate the results of both Moss et al. and Vo et al.. However, using the Moss et al. conditions, the PDEs have hydrolysed 100% of the cyclic nucleotide, suggesting that these conditions are less stringent than the ones we used originally using higher starting concentrations of cAMP and incubating for 1 hour only at room temperature. With enzymatic assays it is always important to perform them at saturating conditions (as already suggested by the reviewer) and therefore we believe that our original conditions are more stringent than the results using the Moss et al. conditions.

      Line #607-608: Authors found PDE9 less sensitive to BIPPO-treatment and concluded PDE2 as refractory to BIPPO inhibition; however, the reduction level of activity seems similar as seen in PDE9-BIPPO treated sample? This strong statement should be replaced with a mild explanation.

      __Response: __We have tempered our wording as per the reviewer’s suggestion

      Figures and legends:

      The introductory model in Fig S1 is difficult to understand and ambiguous despite having it discussed in the text. For example, CDPK1 is placed, but only mentioned at the beginning, and the role of other CDPKs is not clear. In addition, the arrows in IP3 and PKG are confusing. The location of guanylate and adenylate cyclase is wrong, and so on... The figure should include only the egress-related signaling components to curate it. The illustration of host cell in orange color must be at the right side of the figure in connection with the apical pole of the parasite (not on the top). Figure legend should also be rearranged accordingly and citations of the underlying components should be included (see below).

      __Response: __We have modified Supp Fig. 1 as per the suggestions of reviewer#2 and #3. We have now modified the localisations of the proteins and have also removed the lines showing the cross talk between pathways. We have also highlighted to the reader that this is only a model and may not represent the true localisations of the proteins, despite our best efforts.

      In Figure 5D, would you please provide the western blot analysis of samples before and after pulling down to demonstrate the success of your immunoprecipitation assay. Mention the protein concentration in your PDE enzyme assay. Please refer to the M&M comments above to re-do the enzyme assays.

      Response:

      We have now included western blots for the pull downs of PDEs 1, 2, 7 and 9 (Supp Fig. 7A). We chose not to measure protein concentrations of samples since all experiments were performed using the same starting parasite numbers, and we do not see large differences in activities between biological replicates of the PDEs.

      Figure legend 1C: Line #194: There is no red-dotted line shown in graph! Correct it!

      __Response: __We have modified this.

      Figure 4Gi-ii: Shouldn't it be labelled i: H89-treatment and ii: A23178, respectively instead of DMSO and H89? (based on the text Line #579).

      __Response: __Our labelling of Fig. 4Gi-ii is correct as panel i parasites were pre-treated with DMSO, while panel ii parasites were pre-treated with H89. Subsequent egress assays on both parasites were then performed using A23187.

      We have modified the figures to include mention of A23187 on the X axis, and modified the figure legend to clarify pre-treatment was performed with DMSO and H89 respectively.

      Bibliography:

      Line #57 and 58: Citations must be selected properly! Carruthers and Sibley 1999 revealed the impact of Ca2+ on the microneme secretion within the context of host cell attachment and invasion, not egress as indicated in the manuscript! Similar case is also valid for the reference Wiersma et al 2004; since the roles of cyclic nucleotides were suggested for motility and invasion. Also notable in the fact that several citations describing the localization, regulation and physiological importance of cAMP and cGMP signaling mediators (PMID: 30449726 , 31235476 , 30992368 , 32191852 , 25555060 , 29030485 ) are either completely omitted or not appropriately cited in the introduction and discussion sections.

      Response:

      We have modified the citations as per the reviewer’s suggestions. We now cite Endo et al., 1987 for the first use of A23187 as an egress trigger, and Lourido, Tang and David Sibley, 2012 for the role of cGMP signalling in egress. We also cite all the GC papers when we make first mention of the GC. We have also removed the Howard et al., 2015 citation (PMID: 25555060) when referring to the fact that BIPPO/zaprinast can rescue the egress delay of ∆CDPK3 parasites.

      Grammar/Language

      Line #31: After "cAMP levels" use comma

      Response:

      We have modified this.

      36: Sentence is not clear. Does conditional deletion of all four PDEs support their important roles? If so, the role in egress of the parasite?

      Response:

      We have clarified our wording as per the reviewer’s suggestion. We state that PDEs 1 and 2 display an important role in growth since deletion of either these PDEs leads to reduced plaque growth. We have not investigated exactly what stage of the lytic cycle this is.

      40: "is a group involving" instead of "are"

      Response:

      We found no mention of “a group involving” in our original manuscript at line 40 or anywhere else in the manuscript, so we are unsure what the reviewer is referring to.

      108: isn't it "discharge of Ca++ from organelle stores to cytosol"?

      __Response: __We thank the reviewer for spotting this error. We have now modified this sentence.

      120: "was" instead of "were"

      __Response: __Since the situation we are referencing is hypothetical, then ‘were’ is the correct tense.

      Reviewer #3 (Significance (Required)):

      There is a significant amount of work that underlies this manuscript; however, from a conceptual viewpoint, the manuscript does not offer significant advancement over the current knowledge without functional validation of phosphoproteomics data. In terms of the mechanism, it is not clear whether and how lipid turnover and cAMP-PKA signaling control the egress phenotype (lack of a validated model at the end of this study).In a methodical sense, the work uses established assays, some of which require revisiting to reach robust conclusions and avoid misinterpretation.

      Compare to existing published knowledge

      A large body of work preceding this manuscript has indicated the crosstalk of cAMP, cGMP, calcium and lipid signaling cascades. This work provides a further refinement of the existing model. The article is quite interesting from a throughput screening point of view, but it clearly lacks the appropriate endorsement of the hits.

      Response:

      Please refer to our first response to reviewer #3 for our full rebuttal to these points. We respectfully disagree with the assessment that the work presented does not advance current knowledge.

      Audience

      Field specific (Apicomplexan Parasitology)

      Expertise

      Molecular Parasitology

      References

      Bailey, A. P. et al. (2015) ‘Antioxidant Role for Lipid Droplets in a Stem Cell Niche of Drosophila’, Cell. The Authors, 163(2), pp. 340–353. doi: 10.1016/j.cell.2015.09.020.

      Bullen, H. E. et al. (2016) ‘Phosphatidic Acid-Mediated Signaling Regulates Microneme Secretion in Toxoplasma Article Phosphatidic Acid-Mediated Signaling Regulates Microneme Secretion in Toxoplasma’, Cell Host & Microbe, pp. 349–360. doi: 10.1016/j.chom.2016.02.006.

      Dass, S. et al. (2021) ‘Toxoplasma LIPIN is essential in channeling host lipid fluxes through membrane biogenesis and lipid storage’, Nature Communications. Springer US, 12(1). doi: 10.1038/s41467-021-22956-w.

      Endo, T. et al. (1987) ‘Effects of Extracellular Potassium on Acid Release and Motility Initiation in Toxoplasma gondii’, The Journal of Protozoology, 34(3), pp. 291–295. doi: 10.1111/j.1550-7408.1987.tb03177.x.

      Flueck, C. et al. (2019) Phosphodiesterase beta is the master regulator of camp signalling during malaria parasite invasion, PLoS Biology. doi: 10.1371/journal.pbio.3000154.

      Howard, B. L. et al. (2015) ‘Identification of potent phosphodiesterase inhibitors that demonstrate cyclic nucleotide-dependent functions in apicomplexan parasites’, ACS Chemical Biology, 10(4), pp. 1145–1154. doi: 10.1021/cb501004q.

      Jia, Y. et al. (2017) ‘ Crosstalk between PKA and PKG controls pH ‐dependent host cell egress of Toxoplasma gondii ’, The EMBO Journal, 36(21), pp. 3250–3267. doi: 10.15252/embj.201796794.

      Katris, N. J. et al. (2020) ‘Rapid kinetics of lipid second messengers controlled by a cGMP signalling network coordinates apical complex functions in Toxoplasma tachyzoites’, bioRxiv. doi: 10.1101/2020.06.19.160341.

      Lentini, J. M. et al. (2020) ‘DALRD3 encodes a protein mutated in epileptic encephalopathy that targets arginine tRNAs for 3-methylcytosine modification’, Nature Communications. Springer US, 11(1). doi: 10.1038/s41467-020-16321-6.

      Lourido, S., Tang, K. and David Sibley, L. (2012) ‘Distinct signalling pathways control Toxoplasma egress and host-cell invasion’, EMBO Journal. Nature Publishing Group, 31(24), pp. 4524–4534. doi: 10.1038/emboj.2012.299.

      Lunghi, M. et al. (2022) ‘Pantothenate biosynthesis is critical for chronic infection by the neurotropic parasite Toxoplasma gondii’, Nature Communications. Springer US, 13(1). doi: 10.1038/s41467-022-27996-4.

      McCoy, J. M. et al. (2012) ‘TgCDPK3 Regulates Calcium-Dependent Egress of Toxoplasma gondii from Host Cells’, PLoS Pathogens, 8(12). doi: 10.1371/journal.ppat.1003066.

      Moss, W. J. et al. (2022) ‘Functional Analysis of the Expanded Phosphodiesterase Gene Family in Toxoplasma gondii Tachyzoites’, mSphere. American Society for Microbiology, 7(1). doi: 10.1128/msphere.00793-21.

      Stewart, R. J. et al. (2017) ‘Analysis of Ca2+ mediated signaling regulating Toxoplasma infectivity reveals complex relationships between key molecules’, Cellular Microbiology, 19(4). doi: 10.1111/cmi.12685.

      Vo, K. C. et al. (2020) ‘The protozoan parasite Toxoplasma gondii encodes a gamut of phosphodiesterases during its lytic cycle in human cells’, Computational and Structural Biotechnology Journal. The Author(s), 18, pp. 3861–3876. doi: 10.1016/j.csbj.2020.11.024.

    1. Author Response

      Reviewer #1 (Public Review):

      The authors used single cell RNA-seq to assess the heterogeneity of megakaryocytes, thereby identifying a distinct CXCR4 high subpopulation that was also enriched in inflammatory genes and other chemokines or cytokines. They sort CXCR4 high cells and are able to investigate specific functional properties of this megakaryocyte population. This work complements prior studies which have suggested immune modulatory roles for certain megakaryocyte subsets such as the work of Pariser and colleagues (JCI 2021) on the antigen presentation capacity of lung megakaryocytes or the work by Liu et al (Advanced Science 2021) on immune surveillance gene expression in megakaryocytes (MKs).

      The strengths of the paper are:

      1) Analysis of scRNA-seq to identify MK subsets with validation

      2) The use of sorted CXCR4 cells to interrogate the specific in vitro functions of this immune modulatory subset (using CXCR4 low MKs as a comparison) such as phagocytosis assays

      3) Elegant use of the PF4-Cre DTR model to ablate MKs while replenishing CXCR4 high cells as a means to assess functional effects of this subset in vivo which is a reasonable approach in the absence of a Cre that would specifically delete this subset.

      We appreciate the positive feedback from this reviewer.

      Potential weaknesses are:

      1) The unclear distinction between previously identified immune modulatory MK subsets such as the lung MKs which have antigen-processing capacity (Pariser et al) and the currently identified MK5 subset. The authors indicate that the MK5 subset has transcriptomic similarities to the previously described antigen-processing MK subset but this does not explain whether MK5 and/or CXCR4 high subset is indeed the primary. This is an important question because it would help address whether the immune modulatory roles are all concentrated in one MK subset or whether different MK subsets may play distinct roles in innate and adaptive immunity. For example, in Fig 3, there is a broad claim that MKs can modulate innate and adaptive immunity but it is not clear whether this claim is valid only for the specific MK5/CXCR4 subset.

      We totally agree with this argument. Our revised data showed that CXCR4high MKs, but not CXCR4low MKs, were able to phagocytose bacteria (Revised Fig 3F), process and present ovalbumin (OVA) antigens on their cell surface (Revised Fig 3G) to activate CD8+ OT-I T cells (Revised Fig 3H) and B3Z T cells (Revised Fig 3-S2), a T cell hybridoma which expresses TCR that specifically recognizes OVA. These revised data showed that CXCR4high MKs are an antigen processing and presentation subset in MKs.

      2) It would be helpful to understand whether the CXCR4 status of MKs can change over time. Are the CXCR4 high cells generated in infection (Fig 5) generated by the conversion of CXCR4 low cells (or non MK5 cells)? Or do CXCR4 high / MK5 cells differentiate from MK progenitors directly?

      Thanks for the suggested experiment. Our revised data showed that inflammatory treatment, including interferon γ, LPS, and L. monocytogenes could not increase CXCR4 expression in CXCR4low MKs (Revised Fig 4H and Fig 4-S4D). This experiment suggested that CXCR4high MKs might not be reprogramed from CXCR4low MKs. Furthermore, our HSPC tracing experiment showed that CXCR4high MKs were generated from HSPCs as efficiently as CXCR4low MKs during the acute inflammation-induced emergency megakaryopoiesis (Revised Fig 5E-G).

      Reviewer #2 (Public Review):

      Wang J. et al. examines bone marrow megakaryocyte (MK) heterogeneity, and the role that a specific subpopulation plays in the mouse immune response to Listeria monocytogenes infection. Using single cell RNA-sequencing (scRNAseq) the authors identified a bone marrow MK subpopulation, characterized by high CXCR4 expression. This subset referred to as MK-derived immune-stimulating cell (MDIC) population has immune-stimulatory properties and supports the migration and activation of innate immune cells potentially via TNFα and IL-6 secretion.

      In agreement with recent studies mapping in situ myelopoiesis which occurs near bone marrow sinusoidal vessels upon acute inflammatory stress with L. monocytogenes (Zhang J. et al Nature 2021), the authors observed a significant association of myeloid cells with perivascular CXCR4high MK but not with the more abundant CXCR4low MK subset. This study also revealed that MK in vivo deletion leads to a significant increase in the bacterial load in extramedullary hematopoietic organs accompanied by a reduction in the number of myeloid cells, although it is unclear if a similar MDIC population exists outside the bone marrow. Accordingly, it is unclear the effect of MK depletion in the context of L. monocytogenes infection in bone marrow myelopoiesis.

      Notably, in a rescue experiment, MDIC infusion was able to partially rescue the bacterial clearance defect in MK depleted and infected mice, further confirming the important role of MDICs in regulating bacterial immune responses.

      Using Pf4-cre reporter mice the authors further evaluated the capacity of bone marrow MDIC to enter circulation and migrate into organs upon bacterial infection potentially in response to an increase in CXCL12 expression in extramedullary organs. Finally, in agreement with recent studies (Haas S. et al Cell Stem Cell 2015), Wang et al. discovered that upon inflammatory stress, emergency hematopoietic stem cell-derived megakaryopoiesis is activated to restore platelets lost upon inflammation-induced thrombocytopenia but also to regulate immune response to bacterial infection.

      Overall, this study builds on recently published work regarding MK heterogeneity which technically is very challenging to investigate. Although it's suggested that MDIC greatly overlap with the recently described CD53+LSP1+ MK immune population (Sun S. et al Blood 2021), it is still unclear the extent to which these subsets overlap, accordingly, it's still unclear the relationship between bone marrow MDIC and previously described lung MK subsets, though to be enriched in immune function. Nevertheless, the authors performed a detailed characterization of bone marrow MDIC in homeostasis and in acute inflammatory stress, providing new evidence and mechanistic clues on the mechanisms by which MK subsets regulate immune function to bacterial infection.

      While this manuscript has many strengths, some of the author's conclusions and claims require further technical support and discussion. In particular:

      1) The potential mechanism via TNFα and IL-6 secretion is very interesting, however further data is necessary to support the author's claim. First, it's unclear if steady-state MDIC MK express TNFα and IL-6. If so, does this expression change upon infection?

      MDIC MKs (now referred to as CXCR4high MKs) expressed TNFα and IL-6 during the steady state, and maintained their expression levels upon L. monocytogenes infection (Revised Fig 2J).

      Second, mechanistically it would be important to evaluate or at least discuss how MDIC sense bacterial infection and respond by secreting TNFα and IL-6.

      Thanks for the suggestion. In this revision, we have included a brief discussion about previous studies that reported that MKs express multiple inflammation signals, which enable MKs to sense inflammation signals and express cytokines, as “MKs were reported to express multiple inflammation receptors, such as Fcγ receptors (Markovic et al., Br J Haematol 1995), Toll-like receptors (Beaulieu et al., Blood 2011; Ward et al., Thromb Haemost 2005), interleukin receptors (Navarro et al., J Thromb Haemost 1991; Yang et al., Br J Haematol 2000), and IFN receptors (Negrotto et al., J Thromb Haemost 2011), which might enable MKs to receive inflammation signals and express cytokines.” (Line 15-19, Page 13).

      Third, in Fig 2L and 2M it's missing a control for the effect of anti-TNFα and anti-IL-6 on phagocytes activity in the absence of MKs.

      Thanks for the suggested control. In this revision, we have confirmed the phagocytosis activity of immune cells by flow cytometry assays as suggested by this reviewer, in which we included the anti-TNFα and anti-IL-6 controls in the absence of MKs (Revised Fig 2M, N). Our revised data consistently showed that CXCR4high MKs enhanced the phagocytosis activity of neutrophils and macrophages through a TNFα and IL-6 dependent manner.

      Fourth, in Fig 2J and 2K it's unusual to evaluate TNFα and IL-6 levels by imaging.

      We agree with the argument. In this revision, we have further evaluated the expression of TNFα and IL-6 by flow cytometry, which consistently showed that CXCR4high MKs had higher expression levels of TNFα and IL-6 than CXCR4low MKs (Revised Fig 2J).

      2) The authors further explored the potential role of MKs in regulating adaptive immune function against bacterial infection, however these studies were very superficial and further studies are needed to substantiate this claim.

      We totally agree with this argument. In this revision, we have deleted the claim that MKs regulate adaptive immune function. Furthermore, Our revised data showed that CXCR4high MKs were able to phagocytose bacteria (Revised Fig 3F), and process and present ovalbumin (OVA) antigens on their cell surface (Revised Fig 3G) to activate CD8+ OT-I T cells (Revised Fig 3H) and B3Z T cells (Revised Fig 3-S2), a T cell hybridoma which expresses TCR that specifically recognizes OVA. These revised data suggested that CXCR4high MKs had antigen processing and antigen presentation capacity, which suggested that CXCR4high MKs might contribute to the regulation of adaptive immune function. We have included a brief discussion (Line 2-5, Page 14).

      3) Overall, the study relies heavily on subjective imaging quantification. The identification of CXCR4high and low MK subsets does not seem entirely objective and it is prone to inaccuracies due to the technical difficulty of bone imaging. The usage of other surface marker(s) for the MDIC subset would significantly improve the study. Accordingly, many of the experiments should be accompanied and/or replaced by flow cytometry analyses such as the phagocytosis experiments in Fig 2; quantification of MKs in Fig 4 H, I and N.

      We totally agree with this argument, and we have discussed that additional markers are warranted to further enrich CXCR4high MKs (Line 5-9 Page 14). Furthermore, we have further confirmed our imaging quantifications by flow cytometry, such as the bacterial phagocytosis ability of immune cells and CXCR4high MKs (Revised Fig 2M, N, Fig 2-S2A, B and Fig 3F) and the number of Tomato+ CXCR4high MKs in the liver, spleen, and lung (Revised Fig 4I, O and Fig 4-S4I).

      4) Regarding MK-deletion experiments, studies from the Passegue lab have shown that this will cause persistent bone marrow myeloid granulocyte/macrophage progenitor (GMP) formation during 5FU stress, most likely due to the reduction in the levels of PF4 and TGFb1 and the effect on hematopoietic stem cells. What happens to bone marrow myelopoiesis upon MK-deletion and bacterial infection? The authors describe a significant reduction in the liver and spleen but it's unclear the effect on the bone marrow. It would be helpful to discuss this point.

      Our revised results showed MK ablation increased the number of hematopoietic stem and progenitor cells and myelopoiesis in the bone marrow upon infection (Revised Fig 3-S1A-D). However, myeloid cells were reduced in the liver and spleen after MK ablation and bacterial infection (Revised Fig 3D-E). This further suggested the important role of CXCR4high MKs in promoting the migration and function of myeloid cells. We have included a brief discussion on this point (Line 10-14, Page 14).

      Reviewer #3 (Public Review):

      Overall this is an interesting study that adds significant knowledge to our understanding and characterization of Mks as immune cells. The identification of CXCR4hi Mks as immune regulatory cells is potentially important, particularly in the bacteria model used in this study.

      We appreciate the positive feedback of this reviewer.

      At this stage, the authors have however made a number of conclusions not yet supported by the data. In particularly differentiating the role of Mks versus the platelets they produce is not clear, so many conclusions about MDIC in immune responses need to be better supported and differentiated from platelet functions.

      We agree with this argument. We cannot exclude the role of platelets in immune responses. Our revised data showed that CXCR4high MKs produced fewer platelets (Revised Fig 1-S6D) but had more robust abilities in phagocytosis and antigen processing and presentation (Revised Fig 3F-H and Fig 3-S2), and stimulating innate immune cells by secreting cytokines (Revised Fig 2E-N and Fig 2-S2) than CXCR4low MKs. Furthermore, infusion with CXCR4high MKs, but not CXCR4low MKs, partially rescued the host-defense responses in MK ablated mice, which further supported the role of CXCR4high MKs in immune responses. However, the infusion rescue experiment with CXCR4high MKs did not fully rescue the host-defense responses in MK ablated mice (Revised Fig 3K-L). This is partially due to the reduced platelets in MK ablated mice as platelets are known for immune responses. We have discussed this possibility in the current version (Line 16-17, Page 9).

    1. Author Response

      Reviewer #1 (Public Review):

      a) A "hidden gem" in the work is an exploration of whether lamotrigine directly enhances HCN function and finding it did not. While an important negative result, this was not demonstrated in native tissue, leaving the question open regarding direct effects on the native channel in neurons.

      The point is well taken, and we have added this caveat in the relevant section (page 17).

      b) One weakness of the study is the data from the set of experiments exploring impact of overexpression of the variants in neurons. This technique can be highly variable and the data interpretation in this case would benefit from more rigor.

      It is indeed very difficult to rigorously compare expression patterns obtained using different viruses. To address the reviewer’s concerns, we carried out the following additional experiments and analyses:

      i. We repeated the viral injection experiments using two different AAV serotypes for each series (HA-WT, HA-GD, and HA-MI in AAV2/8; HA-WT, HA-GD, and HA-MI in AAV2/9) to ensure that our results are reproducible and independent of virus preparation.

      ii. We evaluated multiple independent injection sites in each series, ensuring that an adequate number of repetitions was executed under the same conditions (equal virus titer, injection volume, time before animal perfusion, tissue processing, and imaging).

      iii. We presented our results in a series of new figures (Figure 7 and Figure 7 – figure supplements 1 and 2) with added panels showing equivalent vs. boosted laser intensities and gain conditions, where necessary, and parvalbumin protein counter-labeling for reference.

      c) There are minor questions about statistical methods for comparing and concluding about the significance of differences between some experimental groups.

      We have now added statistical analysis supporting all our comparisons and conclusions regarding differences between groups (please see the detailed response to Reviewer #1, Recommendations for the Authors, points g,j,l, and q).

      d) An important conceptual gap remains unanswered by the study. Given the phenotypic similarities between patients with sequence variation in Na+ channel and HCN genes, as well as evidence of reduction of other channels or pumps in this case and the strong co-localization of Na+ channels and HCN channels in the PV+ neurons thought critical in the epilepsy of the HCN sequence variants, is it possible that Na+ channels are impacted as a secondary effect of HCN channel dysfunction here?

      This is certainly a possibility, and indeed one that we very much favor. We have added a new analysis of AP morphology (Figure 5 – figure supplement 1) and performed a microarray-based experiment to screen for changes in Na+ channel expression (Source Data 1). While these experiments yielded negative results, they do not definitively rule out potential cell-type specific alterations in the function of Na+ channels or other conductances. A more thorough experimental examination of this important question will have to await future studies. We have added text to underscore how changes in other conductances may indeed impact neurons’ intrinsic properties in our mice (pages 10-11).

      Reviewer #2 (Public Review):

      a) It is not clear whether the mouse equivalent of the severe developmental disability seen in humans was present in mice.

      We have added new behavioral experiments, which show impairment in some cognitive abilities in Hcn1GD/+ mice but not in Hcn1MI/+ mice, consistent with the more severe development disability observed in patients carrying the p.G391D variant compared to patients carrying the p.M153I variant (new Figure 3 and text on page 6 and 7).

      b) (…) there is no demonstration of hyperexcitability at a cellular or network level, so we do not know how HCN1 mutation predisposes to seizures. In fact, hippocampal pyramidal neurons were shown to be hypoexcitable, at least to one method of action potential generation. There is a suggestion that parvalbumin-positive interneurons may be affected, but there is no evaluation of their excitability. It is possible that HCN1 mutation is directly causing neuronal hyperexcitability, but this would only be uncovered by studying HCN1 channel effects on pyramidal neuron dendrite excitability (where they are mostly localized); synaptic function; or on interneuron excitability. There is also no direct demonstration of the effects of channel mutation on HCN1-mediated current (Ih) in native neurons, so we cannot assess how channel biophysics is altered.

      We agree with the Reviewer that there are indeed limitations to the interpretation of our study. Each of these important questions will need additional experimentation before they can be answered definitively. We have added text to underscore such limitations in the Results (pages 10 and 17) and Discussion (pages 20-21) sections. In future studies, we plan to evaluate both the excitability of interneurons through genetic labeling of PV+ cells and patch-clamp recordings, as well as evaluate their synaptic function. Voltage-clamp recordings in pyramidal neurons and possibly dendritic recordings may also be attempted. However, each of these lines of experimentation will require considerable time to complete, particularly because of the difficulty in obtaining patch-clamp recordings from hippocampal slices from the mouse mutants. So we ask that we be allowed to leave them to a future study.

      Reviewer #3 (Public Review):

      a) The authors characterize cerebellum-dependent functional deficits in the mutant mice, basing their studies on the high expression levels of HCN1 in cerebellum, citing Notomi & Shigemoto, They do not present phenotypic deficits in function ascribed to hippocampus or cortex. (…) Therefore, it should be excellent if the authors presented functional tests of hippocampus or cortex dependent behaviors, regardless of the outcome in Fig.2. At a minimum, they should modify the text and downplay the cerebellar emphasis.

      Following the Reviewer’s helpful recommendations, we have added new behavioral experiments testing short-term and long-term memory (see new Figure 3) and modified the panels in Fig 2. The manuscript text has been revised accordingly (pages 6 and 7).

      b) The authors base their proposed mechanism for the pro-epileptic effects of the mutation on the notion that HCN1 Channels are localized to axons only of PV interneurons. Whereas this fact may be true for the adult, during development, axonal targeting is not unique to basket-type interneurons. It is observed in the developing hippocampal circuit, in medial entorhinal cortex neurons innervating dentate gyrus granule cells, i.e., the perforant path. Have the authors looked at axonal targeting in this region in the mutant mice during appropriate developmental stages? Its absence might modulate the firing of GCs, specifically during development (Bender et al., J Neurosci 2007). At a minimum this point merits discussion, particularly in view of the developmental nature of the epilepsies described.

      The Reviewer correctly points out that HCN1 channels are present not only in the axons of PV+ interneurons but also in the axons of certain subclasses of excitatory neurons (see Huang et al., 2011, 2012, and 2019). Regarding axons from medial entorhinal cortex neurons innervating dentate gyrus granule cells, i.e., the perforant path, there is an interesting difference between mice and rats. While HCN1 channel subunits at this site are downregulated in adult rats, they persist in adult mice. This can be seen in the immunostainings shown in Figure 5A (formerly 4A) of the manuscript. Similar to hippocampal PV+ axons in CA3 (Figure 7A, formerly 6A), it can be noted that HCN1 expression in the perforant path is considerably decreased in Hcn1GD/+ mice compared to wildtype and Hcn1MI/+ mice.

      c) In this context, there are distinct developmental profiles for the 4 HCN subunits, including HCN1, and these profiles might contribute to age-specific defects leading to seizures. This point merits discussion.

      We thank the reviewer for raising this important point and have added text underscoring the potential contribution of altered HCN1 channel function to brain development (page 19) to address this issue, and in accord with the comments raised by Reviewer #1 above (see point p).

      d) Whereas the focus of this paper is on the role of genetic mutations in HCN1 in epilepsy, the paper may be enriched by being placed in the context of the overall contributions of HCN1 channels to human epilepsy, including "acquired epilepsy"" via potential epigenetic changes in the expression of normal HCN channels (Bender et al., 2003 and others).

      We agree with the Reviewer and now refer to these datasets in the Introduction, citing the excellent review by Brennan et al., 2016 (page 4).

    1. 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

      This is already a full revision, not a revision plan. All points were carefully addressed. TMF

      July 28, 2022

      RE: Review Commons Refereed Preprint #RC-2022-01555

      Dear Dr. Fuchs,

      Thank you for sending your manuscript entitled "Dissecting the invasion of Galleria mellonella by Yersinia enterocolitica reveals metabolic adaptations and a role of a phage lysis cassette in insect killing" to Review Commons. We have now completed the peer review of the manuscript. Please find the full set of reports below.

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

      In this manuscript Saenger et al. concentrate on the pathophysiological details of insect larvae infection by Yersinia enterocolitica. The authors studied the colonisation, proliferation, tissue invasion, and killing activity of the bacteria in Galleria mellonella larvae. Their study provides valuable evidence for the biological relevance of Tc toxins and a neighboring holin-endolysin cassette during establishment of Y. enterocolitica infection in Galleria mellonella larvae through the oral route. The findings of the authors provide important novel insights, that can be used for the development of Tc toxins as biopesticides.

      In general, this is a nice study. The data and the methods are presented well so that they can be reproduced and the key conclusions convincing.

      Unfortunately, the manuscript is sloppily written in some places, including grammatical and formatting errors. Citations regarding the structure and mechanism of action of Tc toxins are arbitrarily chosen, often taking the wrong ones and important aspects are left out. I highly recommend that the authors read the review of Roderer and Raunser 2019 that nicely describes and summarizes the molecular mechanism of Tc toxins.

      Answer: We have now improved the writing of the manuscript and corrected several errors and typos. In particular, the review by Roderer and Raunser, as well as other literature in the field, is now considered and cited in the text.

      The abstract ends with a speculation: "Suggesting that this dual lysis cassette is an example for a phage-related function that has been adapted for the release of a bacterial toxin" - this is likely true, but not proven in this work. What if it is used for the release of something else like extracellular DNA needed for biofilm formation (see https://doi.org/10.1038/ncomms11220)?

      Answer: This sentence was carefully written as a hypothesis strengthened by the data obtained in our study. Experimental evidence for this assumption is the strong correlation of toxin and HE cassette phenotypes of mutants (see abstract), the highly conserved localisation of the cassette within Tc loci of distinct bacterial genera (see discussion for literature), and the synchronic regulation of both the toxin and the lysis genes (manuscript in preparation). Moreover, strain W22703 is unable to form biofilms in contact with invertebrates (Spanier et al., AEM 2010). There, also in accordance with other reviewers, we would like to keep this statement in the text. However, to address this interesting point, we now mention the finding of Turnbull et al. in the discussion (see last paragraph).

      In addition to that, several outstanding issues must be addressed:

      1. Line 45 3-D structural analysis of the tripartite Tc suggests a 4:1:1 stoichiometry of the A, B and C subunits, with the A subunit forming a cage-like pentamer that associates with a tightly bound 1:1 sub-complex of B and C. This is wrong. The stoichiometry is 5:1:1 and the structure is not a cage. The statement was taken from citation 3. However, citation 3 should not be used, since the stoichiometry as well as the structure that was determined there is wrong. Use Landsberg et al. 2012 PNAS, Gatsogiannis et al. 2013 Nature instead.

      Answer: We apologize for misunderstanding the literature. Reference Lee et al. was removed here, and the two papers plus Meusch et al. (Nature, 2014) are now cited. The stoichiometry was corrected, “cage” was removed.

      "Few bacteria are known to successfully colonize and infect invertebrates" - needs a reference.

      Answer: This was modified to “Several bacteria…”, and we cite the recent paper by Weber and Fuchs (in press) that in Table 7g lists more than 40 bacterial species pathogenic towards insects.

      "Their oral insecticidal activity is comparable to that of the Bacillus thuringiensis- (Bt)- toxin" - reference missing.

      Answer: The reference is now cited (Bowen et al., Science 1998). Please see the last paragraph of the paper.

      "Type a, type b and type c" subunits is not usual for the literature. Please use TcA, TcB, TcC. A-, B-, and C-components should be abbreviated as TcA, TcB and TcC respectively in order to be in line with recent literature on the topic.

      Answer: This was corrected accordingly.

      Is TccC an ADP-ribosyltransferase or does it have a different biochemical activity?

      Answer: This is unknown with respect to the Tc of Y. enterocolitica. In the introduction, we now refer on P. luminescens and do not further attribute such a function to the TcC of Y. enterocolitica. In the abstract, we replaced “ADP-ribosylating” with “toxic”.

      "The toxic and highly variable carboxyl-terminus of TccC that has recently been demonstrated to ADP-ribosylate actin and Rho-GTPases" - this is only certain for TccC3 and TccC5 from P. luminescens. There are many such C-termini, called HVRs which have not had their activities determined yet, see here: https://doi.org/10.1371/journal.ppat.1009102

      Answer: We agree and cite this article. See also the response to comment 5 above.

      "is probably followed by receptor-mediated endocytosis" - more recent references exist for the receptor binding of Tc toxins.

      Answer: We added two references pointing to glycans as receptors of the Tc (line 52).

      "A pH decrease then triggers the injection of a translocation channel formed by the pentameric TcaA subunits into the endosomal vacuole, followed by the subsequent release of the BC subcomplex into the cytosol of the target cell" - this again is incorrect. Please read the above mentioned review and correct this passage accordingly.

      Answer: We agree. This phrase was rewritten to “The attachment of the Tc to the host cell membrane is either followed by receptor-mediated endocytosis or release of the ADP-ribosyltransferase into the target cell {Landsberg, 2011 #738;Sheets, 2011 #742}{Meusch, 2014 #788}. In a pH-dependent manner, the TcA translocation channel injected into the membrane of the host cell. Conformational changes then allow the toxic component to be released into the translocation channel of TcA and from there into the cytosol {Meusch, 2014 #788}{Roderer, 2019 #871}.” (Lines 51-56)

      What is meant by "environmental Yersinia species"?

      Answer: This was corrected to “…and in Y. mollaretii.”

      In the relevant W22703 pathogenicity island sequence (https://www.ncbi.nlm.nih.gov/nuccore/AJ920332) previously submitted by the same group, something odd is going on with the TcA component: it appears to be split into three polypeptides (tcaA, tcaB1, tcaB2). In the manuscript you state TcA is made up from only tcaA and tcaB. Could you please address this?

      Answer: Shotgun sequencing was performed 15 years ago, and mapping revealed a frameshift within tcaB that resulted in the split annotation of tcaB. Even if this frameshift is not the result of a sequencing error, it obviously does not result in Tc inactivation. As this frameshift was not identified in most other Tc-PAI of yersiniae, we assume our statement to be correct.

      "And their products were recently shown to act as a holin and an endolysin, respectively" - missing reference.

      Answer: The reference is now cited (Springer et al., JB 2018).

      "Its Tc proteins are produced at environmental temperatures, but silenced at 37{degree sign}C." versus "Remarkably, HolY and ElyY lyse Y. enterocolitica at body temperature, but not at 15{degree sign}C". Please address the issue that HolY/ElyY lyse the bacteria at temperatures where Tc proteins are not produced.

      Answer: In the absence of in vitro conditions activating the HE gene cassette, we used the pBAD system to artificially overexpress the two genes and showed cell lysis at 37°C, but not at 15°C (Springer et al., JB, 2018). This finding points to a lack of cell lysis as prerequisite for TC release and strengthens the hypothesis of a new secretion system as now corroborated in the last paragraph of the discussion. To avoid confusion of readers, the sentence was removed from the manuscript.

      "Nematodes, which are easily maintained in the laboratory without raising ethical issues, have successfully been used to identify virulence-related genes in a broad set of bacterial pathogens" - what is the relevance of this for the current manuscript?

      Answer: Invertebrates are introduced here as infection models. Nematodes are mentioned here for two reasons: yersiniae are nematocidal due to the Tc, and their immune system is less elaborated than that of G. mellonella, thus explaining its preferred use as insect model. We shortened the sentence by deleting the phrase in commas.

      Fig. 1C - no description is given for the labels 1-8.

      Answer: This is given below figures 1E-H. The labels are valid for all figure panels to ease reading.

      "The hemolymph of these cadavers was found full of Y. enterocolitica cells" - injected CFUs are provided here, but not final CFUs in the cadavers (although referred to in a later section). Please address this.

      Answer: These were preliminary experiments to identify the optimal infection dose. Hemolymph content was plated, but cell numbers in the hemolymph were not enumerated. This sentence therefore now reads: “…and the hemolymph of these cadavers contained Y. enterocolitica cells.” (lines 113-114).

      What is the inducing agent used for pACYC-tcaA and pACYC-HE? Why would "slight leakiness of the pBAD-promoter" make pBAD-tccC non-inducible? Were colonies taken from the cadavers to verify that the bacteria still contained these plasmids?

      Answer: Within pACYC, the genes tcaA and hlyY/elyY (HE) are under control of their own promoters as indicated in Table S2. In general, pACYC vectors are often and successfully used for complementation due to middle copy number.

      This now reads “Due to the slight leakiness of the pBAD-promoter, arabinose was not added to further induce tccC transcription.” (lines 133-134).

      The presence of the plasmids in vivo was confirmed by periodic plating on selective and non-selective plates, not revealing differences in cell numbers.

      Can the authors please address the TD50 of 1.83 days for W22703 ΔHE/pACYC-HE versus 3.67 days for WT bacteria? This would mean that the former kill larvae twice as fast as usual. I would not call this "did not significantly differ in their insecticidal activity".

      Answer: This statement is indeed not very intuitive given the variations of the TD50-values. However, the significance here (and elsewhere in the text) is based on a statistical calculation. For the Kaplan-Meier-plot, we used an application (K.T.Bogen, Advances in Molecular Toxicology, 2016; Exponent Health Sciences, Oakland, CA, United States; Johann Kummermehr, Klaus-Rüdiger Trott, Stem Cells, 1997; Academic Press, London, San Diego) based on all data of a graph. However, to consider this point and to not confuse the readers, the phrase was modified to “…did not significantly differ in their insecticidal activity from that of the parental strain W22703 after one week, demonstrating…” (lines 135-138).

      Fig. 2 is missing survival data for larvae infected with tcaA, HE, and tccC KO bacteria.

      Answer: These data are shown and are equal to the LB-control, e. g. the survival rate of larvae infected with strains W22703 lacking HE, tcaA, or tccC were 100%.

      "And a slight colouring of some of the larvae from one h p.i. on (data not shown)" - best show the data or remove this statement.

      Answer: Although we observed this phenomenon regularly, monitoring and documentation cannot be provided and would not substantially strengthen the manuscript. We therefore deleted this phrase.

      The infection of larvae by W22703 ΔtccC/pBAD-tccC is missing, the other bacterial variants are present. Please address this.

      Answer: Infections with W22703 DtccC are not shown to not overload the figure, please see the panel below. W22703 DtccC/pBAD-tccC infections have not been documented by photos. Figure legend 3 now reads “Infections with W22703 DtccC and DtccC/pBAD-tccC are not shown.”

      "initially proliferated from an application dose of 4.0 × 105 CFU and 4.0 × 105 CFU, respectively, to 2.2 × 106 CFU and 2.8 × 106 CFU, but could not be detected from day three on. This finding strongly suggests that TcaA is involved in adherence to epithelial cells and thus in midgut colonization". Please address the "initially proliferated" (which day post-infection?), their elimination from the larvae (how, why?), why the tccC KO bacteria were more virulent than tcaA KO bacteria, and where the suggestion about TcaA involvement specifically in adherence comes from.

      Answer: “initially proliferated” was rewritten to “proliferated within the first day p.i.”. (line 163)

      Elimination: This now reads “…was completely absent six days p.i., probably due to passage through the gut followed by excretion”. (lines 161-162)

      In our view, the tccC knockout mutant is not more virulent than W22703 DtcaA (se Fig. 2), but replicates during the first day post infection, whereas the cell numbers of the tcaA KO mutant strongly decrease already within the first 24 h p.i.. This prompted us to speculate that Tc is involved in two infection steps, e.g. adherence and hemocyte inactivation. For clarity, this sentence was modified to: “This discrepancy suggests that TcaA is involved in adherence to epithelial cells and thus in midgut colonization, without requiring TccC.” (lines 165-166)

      In Fig. 4, the CFUs for W22703 ΔtccC/pBAD-tccC are essentially the same as for the other rescued KOs and WT, while in the text a point about weaker growth is made. Is this justified? Also, even though the CFU data is present here, data on infection of larvae by W22703 ΔtccC/pBAD-tccC is missing unlike the other bacterial variants. Please explain.

      Answer: We agree that this part of the results is misleading. We want to stress that the complementation very well restores the phenotype of the wildtype. The weaker growth of DtccC may be due to the distinct vector system used here. This part was there shortened and rephrased to: “When larvae were infected with 4.0 × 105 CFU of the DtcaA and DHE mutants, and with 1.4 × 106 CFU of strain W22703 DtccC/pBAD-tccC, all of which carrying the deleted genes on recombinant plasmids, the bacterial burden at days one to six p.i. increased approximately to that of the parental strain W22703 applied with 9.0 × 105 CFU, indicating a successful complementation of the gene deletions.”

      ” (lines 166-170).

      Missing data on W22703 ΔtccC/pBAD-tccC infection in Fig. 3, please the answer to point 20 above.

      Fig. 6b - The presence of an anti-RFP signal is not obvious in any of the bottom row images. The top row images are missing the same kind of annotation provided for Fig. 6a, without which non-histologists will find understanding the figure difficult.

      Answer: The anti-RFP signal is visible only on the left photo of the bottom panel, and not in the other three photos as explained in the text. We understand that the signals are not very strong, but they are visible on the screen.

      "In the absence of the lysis cassette, however, TcaA::Rfp was not detected despite the presence of W22703 ΔHE tcaA::rfp cells." + "To test whether or not the promoter of the lysis cassette is active in vivo, we infected G. mellonella larvae with strain W22703 PHE::rfp. Although Y. enterocolitica cells densely proliferated within the hemolymph (FIG. 6B), no staining signal that would point to the presence of TcaA was obtained, possibly due to no or weak PHE activity." Does this mean that without HE, tcaA does not express?

      Answer: No, we performed Western Blots showing that TcaA is detected in cells lacking HE. Therefore, a negative feedback regulation (e. g. increasing intracellular amounts of TcaA repress its own transcription) can be excluded. This is also in line with the low transcriptional activity of the lysis cassette in vivo (new Fig. S1B).

      "These data suggest that the HE cassette is responsible for the extracellular activity of the insecticidal Tc." Please explain how the preceding paragraph leads to this conclusion.

      Answer: This was poorly written and now reads “…for the transport…” (line 224).

      "As expected, bacterial cells, e.g. Y. enterocolitica, are visible in the hemolymph obtained from W22703-infected animals, but not in all other preparations." - which figure are the authors referring to?

      Answer: We have indeed identified, but not immunostained, bacterial cells in those preparations, but they are not visible in Fig. 7. This sentence was removed. However, the presence of W22703, but not its tc-PAIYe-mutants, in the hemolymph is demonstrated in Fig. 6A.

      "To delineate the transcriptional profile of Y. enterocolitica during infection of G. mellonella, we applied immunomagnetic separation to isolate Y. enterocolitica from the larvae 12 h and 24 h after infection" - do the authors store the bacteria for up to 24 h at 4 {degree sign}C, as indicated in the methods section?

      Answer: Yes, the probes were stabilized with RNAlater and then stored up to 24 h to synchronize all samples of one experiment.

      "The endolysin located within Tc-PAIYe was significantly up-regulated after 24 h, but not after 12 h, pointing to its possible role in the release of the Tc" - I could not find the endolysin in Table S1. Could the authors mark it clearly? Also, why is the holin also not upregulated?

      Answer: The endolysin gene is lacking in Table S1 due to its FC=1.02. We now added a table to Fig. S1 that shows the FC values of all genes from Tc-PAIYe. The FC-value of holin gene is 0.87, thus pointing to a very slight transcription of this lysis gene as discussed, thus preventing cell death.

      "This is in line with the fact that a T3SS is lacking in strain W22703" - Is a complete genomic sequence available for this strain, so readers could validate this statement?

      Answer: The genome sequence is available, and the reference is now cited (line 358). The common virulence plasmid of yersiniae, pYV that encodes the T3SS, is missing in this strain. We do not mention here the presence of a second, but probably incomplete, chromosomally encoded T3SS in strain W22703 do not overload the manuscript.

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

      This is a very, very nice study as it actually describes the role of different Tc toxin components in a model infection system using an important bacterium- really for the first time in a properly controlled manner. The mutants lacking either the syringe (AB) or the bullet (C) make 'sense' for a loss of function perspective. The description of the phage cassette in loss of function is also interesting and could do with some more speculation? For example, some groups of Photorhabdus bacteria release their oral toxicity (Tc's) into their bacterial supernatants- whereas in others it remains cell associated. The likely role of this phage cassette in this process should be discussed (is cell suicide required for release?).

      Answer: We now discuss the possibly role of the lysis cassette in more detail, including the possibility that a subpopulation commits cell suicide (see lines 375-396).

      Reviewer #2 (Significance (Required)):

      This is highly significant finding as despite all of the very elegant structural studies done on these important toxins there is still very little work in vivo. These studies clearly show the role of the different components of these ABC toxins in vivo. It should be published with priority.

      Congratulations to the authors.

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

      Summary: The authors analyze the phases of infection of Galleria mellonella by Yersinia enterocolitica following forced oral feeding. They study different phases of infection, including survival within the gut and invasion of the hemolymph. By analyzing differences in the genes up- and down regulated, they show that for example transporters for food sources from the hemocoel are regulated for making those sources available for the bacteria.

      Major comments: This is an interesting paper demonstrating genes of Y. enterocolitica dependent for colonization, growth and crossing of the epithelial gut barrier in G. mellonella.

      Major points which have to be addressed:

      Introduction: line 54: the BC subcomplex is not released into the cytosol! It is only the hypervariable region (enzymatic part) which enters the cytosol. This has to be corrected.

      Answer: This has been corrected accordingly.

      Fig.2/3: Why have different CFU been used for the distinct bacterial strains? This does not allow a direct comparison of their toxicity. For me the dead larvae shown in Fig. 3 are not represented in Fig 2 (data are not concordant), because of the loss before day one depicted in Fig. 2: The curves should be normalized to the same starting point (should be 100 %)?

      Answer: We would like to stress here that infection doses are hard to reproduce if frozen and diluted stocks are used. We decided for overnight culture to better mimic natural conditions and controlled each culture for its viable cell numbers by plating. Moreover, we choose the infection doses in a conservative manner, e.g. the number of mutants was higher than that of the parental strain.

      The data of Fig. 3 are concordant with Fig. 2 for two reasons: First, this experiments was performed in replicates with a total of 36 larvae per strain (see Fig. 2 legend), so that representative photos are shown. Second, larvae were considered dead if they failed to respond to touch, and many larvae without strong sign of melanisation were already killed.

      We analysed the algorithmus of the Kaplan-Meier-plot. All graphs start at 100%, this is now mentioned in the legend. There are no data between day 0 and day 1, and a stepwise graph is essential for this plot.

      Fig. 3: Why is the strain W22703 delta tccC/pBAD - tccC missing in the data set?

      Infections with W22703 DtccC are not shown to not overload the figure, please see the panel below. Answer: W22703 DtccC/pBAD-tccC infections have not been documented by photos. Figure legend 4 now reads “Infections with W22703 DtccC and DtccC/pBAD-tccC are not shown.”

      Minor: line 221: "the" is doubled

      Answer: This has been corrected accordingly.

      Reviewer #3 (Significance (Required)):

      The manuscript shows the use of G. mellonella as a straight foreward method to study gene functions of pathogenic bacteria, a significant knowledge for scientists of the field.

      Reviewer #4 (Evidence, reproducibility and clarity (Required)):

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

      Answer: There are already three sections that summarize the results and the methods applied, namely the abstract, the last paragraph of the introduction, and the conclusion following the discussion. In our view, a further summary would overload the manuscript. Nevertheless, depending on the journal the manuscript will be published in, an additional authors´ summary would be provided.

      Outlines proposed role of lysis cassette in oral infection of Galleria as a model insect for host pathogen interaction, data which is fortified through use of histology and RNAseq.

      Introduction could extend to additional background eg Aleniz et al and other entomopathogen transcriptome data, more so other studies using Yersinia and Galleria as a model (refer references provided in the below comments)

      Answer: We again carefully screened PubMed for studies in the field and added few papers. However, in vivo transcriptome analyses are still rare, as indicated by a lack of a respective investigations with the highly relevant entomopathogen Photorhabdus luminescens. The literature suggested by the reviewer is now cited in the introduction and the discussion (see below for details).

      The strength of the paper lies in understanding the progression of the disease in the insect host as mentioned L316-317 and clearance of the bacteria via in TcaA mutant

      Major comments: - Are the key conclusions convincing? Yes for mode of action Fig 5 could have additional panels -this is a strength of the paper

      Answer: We agree that this time course is a strength of the paper, and we carefully selected representative photos. There are several to be shown, but to our view, they are rather illustrative than providing a substantial additional value.

      Fig 6 legend could better describe the observed insect components

      Answer: The insect components are now indicated in Fig. 6B and in Fig. 5.

      Figure 7 may be lost in PDF conversion -the figure appears un resolved? are there more high resolution photos

      Answer: Fig. 7 was present in the merged PDF provided by the publisher. We used the photos with the best resolution.

      • Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? the data provided is in places rudimentary (i.e. validation of the role of the lysis cassette in virulence) and could be bolstered with the construction and use of a lysis translational reporter etc I was left unsure how the HE::rfp and TcA::rfp constructs were made. I had assumed red florescent protein however it appears an antibody is used. This needs to be clarified as I then found it hard to interpret the results.

      Answer: The transcriptional PHE::rfp fusion is mentioned in the results section, but immunostaining failed probably due to a very low promoter activity (line 223). This is well in line with the transcriptome data. Please see a detailed answer how the HE::rfp and tcaA::rfp were constructed below. We applied the RFP-antibody for two reasons: first, fluorescence microscopy did not reveal clear red fluorescence in the tissue sections, and second, a TcaA antibody failed to match quality criteria for this purpose.

      It appear l114-125 that their may be enough data to derive a LD50 values and or LT value at a fixed dose - if so reporting this data of interest. It may also allude as to why a 10e5 dose was selected for subsequent expts

      Answer: This is an interesting point. The LD50 (dose of cells that kills 50% of all larvae) is usually not calculated in publications in this field of research, because its calculation requires a very huge separate data set that cannot be used to answer the questions addressed here. Such a dat set is not available. We published the dose-dependent toxicity of Y.enterocolitica W22703 upon subcutaneous injection, and from these data, we determined a LD50 for this strain of approximately 2 x 104 cells. The paper is cited in our manuscript. The 10E05 dose was selected due to our preliminary work and the reproducibility of the experimental phenotypes.

      • Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation. Use of lysis the reporter - discuss commonalties of the in host transcriptome with other Yersinia Galleria systems eg Paulson etc al (refer below). Are there any thoughts on the host range of this Yersinia and can this be placed in a pathogen host evolutionary context?

      Answer: Paulson et al. are now cited twice in the text. The host range of Yersinia enterocolitica has not been investigated to our knowledge. However, its nematocidal activity has been described by Spanier et al., and Manduca sexta larvae, the tobacco hornworm, is also killed by W22703 (see references). Moreover, there are two copies of tccC in the genome of strain W22703 encoding the cytotoxic Tc subunit with its hypervariable C-terminus that is assumed to contribute to host specificity. This is discussed in very detail by Song et al. (see references).

      Evolution: Yes, this has been addressed by Waterfield et al. 2004 (see references) where insects are hypothesized as a source of emerging pathogens. We placed our findings in the context of this article in lines 91-94 and 305-310.

      • Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments. Yes

      • Are the data and the methods presented in such a way that they can be reproduced? yes but I think some vector construction methodology is missing e.g. ::rfp (refer above)

      Answer: The plasmids used to construct the two strains W22703 tcaA::rfp and W22703 PHE::rfp are listed in Table S2. References for details are given (Starke et. al., 2013, Starke and Fuchs, 2014). Briefly, we used a suicide vector (pUTs) carrying the gene encoding the red fluorescent protein (RFP). This vector replicates in E. coli helper strains such as SM10, but not in Y. enterocolitica. Strain SM10 is now listed in Table 2. Following conjugation, the construct is chromosomally inserted upon recombination via the fragments cloned into the plasmid. In case of tcaA, we cloned the 3´-end of the gene to generate a translational fusion, and in case of HE its promoter, resulting in a transcriptional fusion with the reporter RFP.

      Fig 2 I am a little lost mortality seems quick on day 0 is this a result of aberrant injection damage mortality or are the authors observing a different effect across mutants through the initial 24 hours? If data available could this time plot be extended out 0-24 hours. The dash used for W222703 tcaA /TccC look similar can a different symbol be used.

      Answer: The reviewer is right that the mortality is high on the first day. However, larvae monitoring for up to nine days is a standard in the literature. No data are available for a better resolution of the first 24 h that, however, were investigated in more detail in the time course of Fig. 5. Moreover, we observed changes in motility and colouring of some of the larvae from one h p.i. on (data not shown). Aberrant injection damage was avoided, and damaged larvae or larvae that not completely took up the infection solution were not further considered in the experiment. This is mentioned in lines 107-109.

      A different symbol is now used for W222703 DtccC /pBAD-tccC.

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

      Minor comments: - Specific experimental issues that are easily addressable. - Are prior studies referenced appropriately? Other entomopathogenic transcriptome studies could be compared to and or cross referenced (I have provided references in the response

      Answer: Repetition of our answer above: We again carefully screened PubMed for studies in the field and added few papers. However, in vivo transcriptome analyses are still rare, as indicated by a lack of a respective investigations with the highly relevant entomopathogen Photorhabdus luminescens. The literature suggested by the reviewer is now cited in the introduction and the discussion (see below for details).

      I am unsure on the use of immuno pulldown and efficiency of recovering the Yersinia using this method as opposed to direct sequencing total RNA has this method been used in other systems,

      Answer: Isolating RNA from in vivo probes of infected insects encounters two challenges: first, a possible contamination with commensal bacteria, and a too high amount of host RNA that reduces the number of sequence reads. This might be the reason for the relatively low sequence depth found in related papers in the field of in vivo transcriptomics. We overcame these problems by immunomagnetic separation that is easily applicable and enriches the samples with respect to Yersinia cells, this is now mentioned in the results. We also cite a study (Prax et al., in which we established the protocol of IMS.

      • Are the text and figures clear and accurate? Yes though in places better naming of insect components could be listed

      Answer: This was done, see above.

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

      As listed above potential use of reporters and or comparison and transcriptome analysis to other systems and an evolutionary pathogen host context (refer comments above) would strengthen the manuscript

      Answer: Please see answer to comments above. We explained the use of the reporter fusions, and put the transcriptome analysis into the context of related studies.

      Minor comments as per below When first mentioned good to state the larval instar used

      Answer: We used larvae of instar 5-6 according to Jorjao et al. (2018), this is now mentioned and cited in the M&M section, line 434.

      l 78 lon protease? what type? this is an important SOS protease affecting many regulatory systems please clarify

      Answer: This is a Lon A endopeptidase, and its function for the temperature-dependent activity of the lysis cassette has ben described (Springer et al. 2021, see references). Its relevance for the thermodependent regulation of Yersinia virulence has been documented by Herbst et al. (PMID: 19468295) and Jackson et al. (https://doi.org/10.1111/j.1365-2958.2004.04353.x).

      l103-113 an description of the elemental tract which is depicted, perhaps this could be placed in the Fig. 1 figure legend

      Answer: We agree and substantially shortened the first paragraph of the results. Relevant aspects are now mentioned in Figure legend 2, redundancies with the figure legend were removed.

      l 133 use of the word larvae in place of the word animals might be more appropriate

      Answer: This was corrected accordingly.

      l 133 clarify delta HE mutant description when first mentioned

      Answer: The abbreviation HE is now introduced in the introduction in line 74.

      Lines 220-234 hard to follow mainly as I am unsure how then strains are constructed, perhaps clarify what rfp is how was it made :: demotes and insertion but yet then they seek to detect TcaA? I could not find the methodology on its or HE::rfp construction

      Answer: The plasmids used to construct the two strains W22703 tcaA::rfp and W22703 PHE::rfp are listed in Table S2. References for details is given (Starke et. Al., 2013, Starke et al. 2014). Briefly, we used a suicide vector (pUTs) carrying the gene encoding the red fluorescent protein (RFP). Following conjugation, the construct is chromosomally inserted upon recombination via the fragments cloned into the plasmid. In case of tcaA, we cloned the 3´-end of the gene to generate a translational fusion, and in case of HE its promoter, resulting in a transcriptional fusion with the reporter RFP.

      Please see above why we used RFP-antibodies to detect TcaA.

      l247 immuno-magnetic separation to isolate Yersinia - is there an efficiency behind this method, might be good to mention (I am unfamiliar with this technique)

      Answer: We here repeat our answer to the point above: Isolating RNA from in vivo probes of infected insects encounters two challenges: first, a possible contamination with commensal bacteria, and a too high amount of host RNA that reduces the number of sequence reads. This might be the reason for the relatively low sequence depth found in related papers in the field of in vivo transcriptomics. We overcame these problems by immunomagnetic separation that is easily applicable and enriches the samples with respect to Yersinia cells, this is now mentioned in the results. We also cite a study (Prax et al., in which we established the protocol of IMS.

      l313 alludes to role of Tca in hemoceol which contradicts an earlier statements in l 130 please clarify

      Answer: The reviewer is right. The sentence in former line 130 (now lines 123-124) was corrected to “…suggesting that the Tc plays a main role in the initial phases of infection”. This statement does not exclude its activity towards hemocytes. Moreover, subcutaneous infection is very artificial and was therefore replaced by oral application in our study to mimic natural routes of infection. This is now elaborated in more detail in the discussion (Lines 305-310).

      For clarity table 1 could colour highlight (different colours) tc and lysis genes

      Answer: We now added a table to Fig. S1 that shows the FC values of all genes from Tc-PAIYe.

      CROSS-CONSULTATION COMMENTS I am in agreement with all points of reviewer 1 who has a clear understanding on Tc toxin composition TcA pentamer etc. Being familiar to the field I regret I did not pick up on these errors

      Answer: This has been corrected according to R1.

      Point 13 agree and should possibly bring in other researchers who have used Galleria as a model. It also needs to be kept in mind that the target host for many Tcs has yet to be determined hence the importance of oral activity of this isolate

      Answer: This has been corrected according to R1.

      I am similarly in agreement with comments of reviewer 3

      Reviewer 4 I over looked the LT50 data -- apologies but agree with reviewer 1 where WT should be the more potent strain --I still think if possible LD50 for WT would be of value more so to define its oral activity

      Answer: We repeat our answer from above. This is an interesting point. The LD50 (dose of cells that kills 50% of all larvae) is usually not calculated in publications in this field of research, because its calculation requires a very huge separate data set that cannot be used to answer the questions addressed here. Such a dat set is not available. We published the dose-dependent toxicity of Y.enterocolitica W22703 upon subcutaneous injection, and from these data, we determined a LD50 for this strain of approximately 2 x 104 cells. The paper is cited in our manuscript. The 10E05 dose was selected due to our preliminary work and the reproducibility of the experimental phenotypes.

      Reviewer #4 (Significance (Required)):

      SECTION B - Significance ========================

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

      Extends from work of Fuchs - research group Extends from work of Palmer et al on lysis cassettes as potential T10SS Extends from work off Vesga Pseudomonas and Paulson Yersinia(refs provided below) on insect transcriptomics

      Of interest and possibly understated is the oral activity of enterocolitica in the insect host as mentioned L316-317 and how this might relate to the lifestyle/evolution of this microbe further elaboration here would be of interest

      Answer: We agree that this is an important aspect. Therefore, we added the following sentences here: “In contrast to subcutaneous injection in the use of insect larvae as model for bacterial virulence properties towards mammals, oral application mimics natural routes of infection that in particular take place during the bioconversion of animal cadavers by bacteria, fungi, and larvae {Carter, 2007 #879}. Together with the broad cytocidal host spectrum of bacterial toxins {Mendoza-Almanza, 2020 #880}, investigation of yet neglected natural infections of invertebrates will contribute to a better understanding of microbial pathogenicity {Waterfield, 2004 #480}.” (lines 305-310)

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

      Relevant Transcriptome papers which could be referred to in the discussion i.e. are similar genes in play or is their a point of difference? https://doi.org/10.1093/g3journal/jkaa024;https://doi.org/10.1038/s41396-020-0729-9; https://doi.org/10.1099/mic.0.000311

      Answer: Paulson et al. mainly address virulence factors, whereas metabolism is not uncovered. We now cite similarities with respect to hemolysis and iron scavenging. The focus of Vesga et al. is on the interaction of a plant pathogen with wheat and two insect hosts, including their transcriptome. Although metabolic details are missing, there is an interesting overlap with the paper by Vesga et al. (hemocoel as permissive environment for proliferation) and a difference (upregulation of chitinases was not observed) that are now cited in the discussion. The Alenzi paper mainly investigated the general virulence of Y. enterocolitica strain. We cite its finding on the importance of motility, thus confirming our transcriptome analysis.

      • State what audience might be interested in and influenced by the reported findings. The oral activity of enterocolitica towards Galleria of interest and an evolutionary context insect vs mammalian activity in the discussion could be provided. Potential role of TcaA in gut association For the targeted journal I feel additional technical data is required and a broader context to other global systems (bacterial species) provided

      Answer: All points were addressed carefully and in detail. We refer to our answers to points detailed above.

      • Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. Reviewers expertise entomopathogens, their toxins and pathogen ecology
    2. 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 #4

      Evidence, reproducibility and clarity

      Summary:

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

      Outlines proposed role of lysis cassette in oral infection of Galleria as a model insect for host pathogen interaction, data which is fortified through use of histology and RNAseq. Introduction could extend to additional background eg Aleniz et al and other entomopathogen transcriptome data, more so other studies using Yersinia and Galleria as a model (refer references provided in the below comments) The strength of the paper lies in understanding the progression of the disease in the insect host as mentioned L316-317 and clearance of the bacteria via in TcaA mutant

      Major comments:

      • Are the key conclusions convincing?

      Yes for mode of action

      Fig 5 could have additional panels -this is a strength of the paper

      Fig 6 legend could better describe the observed insect components

      Figure 7 may be lost in PDF conversion -the figure appears un resolved? are there more high resolution photos - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

      the data provided is in places rudimentary (i.e. validation of the role of the lysis cassette in virulence) and could be bolstered with the construction and use of a lysis translational reporter etc I was left unsure how the HE::rfp and TcA::rfp constructs were made. I had assumed red florescent protein however it appears an antibody is used. This needs to be clarified as I then found it hard to interpret the results. It appear l114-125 that their may be enough data to derive a LD50 values and or LT value at a fixed dose - if so reporting this data of interest. It may also allude as to why a 10e5 dose was selected for subsequent expts - Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

      Use of lysis the reporter - discuss commonalties of the in host transcriptome with other Yersinia Galleria systems eg Paulson etc al (refer below). Are there any thoughts on the host range of this Yersinia and can this be placed in a pathogen host evolutionary context? - Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.

      Yes

      • Are the data and the methods presented in such a way that they can be reproduced? yes but I think some vector construction methodology is missing e.g. ::rfp (refer above)

      Fig 2 I am a little lost mortality seems quick on day 0 is this a result of aberrant injection damage mortality or are the authors observing a different effect across mutants through the initial 24 hours? If data available could this time plot be extended out 0-24 hours. The dash used for W222703 tcaA /TccC look similar can a different symbol be used. - Are the experiments adequately replicated and statistical analysis adequate?

      Yes

      Minor comments:

      • Specific experimental issues that are easily addressable.
      • Are prior studies referenced appropriately?

      Other entomopathogenic transcriptome studies could be compared to and or cross referenced (I have provided references in the response

      I am unsure on the use of immuno pulldown and efficiency of recovering the Yersinia using this method as opposed to direct sequencing total RNA has this method been used in other systems,<br /> - Are the text and figures clear and accurate?

      Yes though in places better naming of insect components could be listed - Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

      as listed above potential use of reporters and or comparison and transcriptome analysis to other systems and an evolutionary pathogen host context (refer comments above) would strengthen the manuscript

      Minor comments as per below

      When first mentioned good to state the larval instar used l 78 lon protease? what type? this is an important SOS protease affecting many regulatory systems please clarify

      l103-113 an description of the elemental tract which is depicted, perhaps this could be placed in the Fig. 1 figure legend

      l 133 use of the word larvae in place of the word animals might be more appropriate

      l 133 clarify delta HE mutant description when first mentioned

      Lines 220-234 hard to follow mainly as I am unsure how then strains are constructed, perhaps clarify what rfp is how was it made :: demotes and insertion but yet then they seek to detect TcaA? I could not find the methodology on its or HE::rfp construction

      l247 immuno-magnetic separation to isolate Yersinia - is there an efficiency behind this method, might be good to mention (I am unfamiliar with this technique)

      l313 alludes to role of Tca in hemoceol which contradicts an earlier statements in l 130 please clarify

      For clarity table 1 could colour highlight (different colours) tc and lysis genes

      Referees cross-commenting

      I am in agreement with all points of reviewer 1 who has a clear understanding on Tc toxin composition TcA pentamer etc. Being familiar to the field I regret I did not pick up on these errors

      Point 13 agree and should possibly bring in other researchers who have used Galleria as a model. It also needs to be kept in mind that the target host for many Tcs has yet to be determined hence the importance of oral activity of this isolate

      I am similarly in agreement with comments of reviewer 3

      Reviewer 4 I over looked the LT50 data -- apologies but agree with reviewer 1 where WT should be the more potent strain --I still think if possible LD50 for WT would be of value more so to define its oral activity

      Significance

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

      Extends from work of Fuchs - research group

      Extends from work of Palmer et al on lysis cassettes as potential T10SS

      Extends from work off Vesga Pseudomonas and Paulson Yersinia(refs provided below) on insect transcriptomics

      Of interest and possibly understated is the oral activity of enterocolitica in the insect host as mentioned L316-317 and how this might relate to the lifestyle/evolution of this microbe further elaboration here would be of interest - Place the work in the context of the existing literature (provide references, where appropriate).

      Relevant Transcriptome papers which could be referred to in the discussion i.e. are similar genes in play or is their a point of difference?

      Amber R Paulson, Maureen O'Callaghan, Xue-Xian Zhang, Paul B Rainey, Mark R H Hurst, In vivo transcriptome analysis provides insights into host-dependent expression of virulence factors by Yersinia entomophaga MH96, during infection of Galleria mellonella, G3 Genes|Genomes|Genetics, Volume 11, Issue 1, January 2021, jkaa024, https://doi.org/10.1093/g3journal/jkaa024

      Vesga, P., Flury, P., Vacheron, J. et al. Transcriptome plasticity underlying plant root colonization and insect invasion by Pseudomonas protegens. ISME J 14, 2766-2782 (2020). https://doi.org/10.1038/s41396-020-0729-9

      Dhahi Alenizi, Tamara Ringwood, Alya Redhwan, Bouchra Bouraha, Brendan W. Wren, Michael Prentice, Alan McNally (2016) All Yersinia enterocolitica are pathogenic: virulence of phylogroup 1 Y. enterocolitica in a Galleria mellonella infection model https://doi.org/10.1099/mic.0.000311 - State what audience might be interested in and influenced by the reported findings.

      The oral activity of enterocolitica towards Galleria of interest and an evolutionary context insect vs mammalian activity in the discussion could be provided. Potential role of TcaA in gut association

      For the targeted journal I feel additional technical data is required and a broader context to other global systems (bacterial species) provided - Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

      Reviewers expertise entomopathogens, their toxins and pathogen ecology

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    1. Author Response

      Reviewer 2

      The authors use the model of polyamine attraction and build on their previous observation that mated Drosophila females show increased attraction to polyamines that is outlasting a short term modulation of olfactory sensory neurons. Females do not require exposure to seminal fluid or sperm and do not need to start to ovulate for this change in preference. This is remarkable since the vast majority of female postmating changes in behavior have been shown to rely on sex peptide in the male seminal fluid. It also sets an exciting starting point for the present work, suggesting new mechanisms of how a female can adjust their behavior to mating state.

      We thank the reviewer very much for their encouraging comments.

      The authors find that females have to be able to smell odors detected by the Or system (but not polyamines) during mating in order to change their preference for polyamine.

      Mushroom body Kenyon cells are required during mating and during choice behavior for the polyamine preference of mated females. Activation cVA responsive PAM-b1 neurons of the mushroom body is sufficient to replace the mating experience and change polyamine preference in virgin flies. Activation of the same neurons during mating abolishes the preference development. Other specific mushroom body neurons are required during the choice behavior to promote attraction in mated females or repress it in virgins. Calcium imaging of different mushroom body neurons does not uncover a clear difference in polyamine response between mated and virgin flies. Connectome mining and genetic silencing further indicates that circuit motifs in the lateral horn are also involved in the response to polyamines and might interact with mushroom body circuits.

      While the exact circuits and mechanisms of plasticity that explain the change in postmating preference of polyamines remain to be discovered, this work makes substantial progress in identifying neurons that have a strong impact on development or expression of the preference. It is an exciting paradigm that invites further research.

      This work explores a very interesting example of state-dependent behavioral change in Drosophila. Previously, state dependent changes in sensory neurons have been demonstrated- here, the authors tackle the experimentally much more challenging task of identifying changes in higher order processing areas. The data suggests that polyamine attraction is encoded by a recurrent network of mushroom body neurons. Although the authors do not demonstrate an exact mechanism by which mating/male exposure reconfigures this polyamine attraction network, they have made a substantial advance for our understanding how odor valence is encoded in a flexible and experience dependent way by identifying and characterizing the neuronal players and their roles in induction and expression of preference behavior.

      Their experimental paradigm is special in that it is not a case of classical odor reward learning (mating could be the rewarding experience, but polyamine odor does not have to be present during mating to induce preference). It is also special in that it is a case of long lasting mating induced behavior change that is not dependent on sex peptide or other male seminal fluid proteins. The paradigm has thus great potential to uncover novel mechanisms of encoding experience and adaptively changing behavior.

      Reviewer 3

      Mating changes behavior of female fruit flies. Authors previously reported that putrescine-rich foods increase number of progenies per mated female and mated females detect putrescine with IR76b and IR41a and are attracted to putrescine odor (Hussain, Zhang et al., 2016). In another paper, authors reported that this change of putrescine preference is mediated by sex peptide receptor (SPR) and its ligand, myoinhibiotry peptides (MIPs; Hussain, Ucpunar et al., 2016). In yet another paper, authors reported that two types of dopaminergic neurons (DANs) which innervate alpha prime 3 (a'3) or beta prime 1 (b'1) compartment of the mushroom body (MB) show enhanced response to cVA, the male sex pheromone 11-cis-Vaccenyl acetate (Siju et al., 2020). The present study investigated neural circuits that potentially link these observations.

      The authors first showed that putrescine-attraction in mated females is sustained over 7-days, which cannot be explained by SPR-MIP dependent mechanism that disappears in one week. Then they explored a factor that is transferred from males during copulation and required for putrescineattraction in mated females. They found that blocking synaptic transmission of cVA-sensitive OR67d olfactory receptor neurons during 24 hour period of pairing with males reduces putrescineattraction 3-5 days later (Figure 1). On the other hand, experiments with mutant flies lacking ability to generate eggs or sperms indicated that fertilization is not essential for the change in odor preference. In a proposed scenario, cVA transferred to the female during copulation activates DANs projecting to the b'1 and that in turn induces a shift in how the MB regulates the expression of polyamine odor preference, possibly by alternating activity of MB output neurons (MBONs) in the beta prime 2 (b'2) compartment.

      Some data are in line with this scenario. Blocking synaptic transmissions of Kenyon cells during mating or odor preference test reduced attraction to putrescine (Figure 2). Activation of dopaminergic neurons projecting to the beta prime 1, gamma 3 and gamma 4 in virgin females promoted attraction to putrescine when tested 3-5 days later (Figure 3). Flies expressing shibire ts1 in the MBONs in the b'1 compartment showed reduced putrescine preference when females were mated at restrictive temperature (Figure 4). Using calcium imaging and EM connectome, authors also found candidate lateral horn output neurons that may mediate putrescine signals from olfactory projection neurons to the b'1 DANs.

      This study utilized molecular genetic tools, behavioral experiments and calcium imaging to comprehensively investigate neural circuits from sensory neurons for cVA or putrescine to the learning circuits of the MB. Addressing points detailed below will strengthen a causal link between enhanced cVA response in beta prime 1 DANs and enhanced putrescine preference in mated females.

      1) The MB is the center for olfactory associative learning. It is not so surprising that 24-hour long activation of any MB cell types have long-term consequence on fly's odor preference. As authors showed in Hussain et al., 2016 and Figure S1, mated females change preference to polyamines but not ammonium. Therefore, it is important to show odor specificity of the circuit manipulations to claim that phenomenon in mated females are recapitulated by each manipulation. Wang et al., 2003 (DOI:https://doi.org/10.1016/j.cub.2003.10.003) reported that blocking a broad set of Kenyon cells impairs innate odor attraction to fruit odors and diluted odors but not repulsion.

      We very much appreciate the thorough comments of this reviewer. We have carried out the experiments suggested in the editor’s summary. Due to time and people limitations encountered by the lab’s move during the week of July 11, we were forced to prioritize the number and type of experiments we carried out for this revision.

      We also agree that the change in odor preference due to manipulation of KCs during test is not a very surprising result. We do, however, strongly believe, that the result we received with the inhibition of KCs during mating is not expected. Previous studies using associative learning paradigms suggested that KCs are not essential during learning but only during test:

      • McGuire, S. E., Le, P. T. & Davis, R. L. The Role of Drosophila Mushroom Body Signaling in Olfactory Memory. Science 293, 1330–1333 (2001).

      • Schwaerzel, M., Heisenberg, M. & Zars, T. Extinction Antagonizes Olfactory Olfactory Memory at the Subcellular Level. Neuron 35, 951–960 (2002).

      • Dubnau, J., Grady, L., Kitamoto, T. & Tully, T. Disruption of neurotransmission in Drosophila mushroom body blocks retrieval but not acquisition of memory. Nature 411, 476–480 (2001).).

      Only a very recent study (currently only on BioRXiv, Pribbenow et al. 2022 (https://www.biorxiv.org/content/10.1101/2021.07.01.450776v2) showed that KC output is required during appetitive training suggesting that postsynaptic plasticity in KCs is needed to establish appetitive memories.

      These new findings are in line with our results given that the KCs are likely providing the odor input to DANs and MBONs. We have included a paragraph in the discussion section.

      2) Requirement of PAM-b'1 DANs for putrescine-attraction in mated females should be demonstrated. The authors suggested existence of alternative mechanisms that may mask requirement of PAM-b'1 (Figure 3B). In a previous study, the authors reported SPR-dependent mechanism. I suggest testing the requirement of PAM-b'1 DANs in SPR mutant background or oneweek after mating when SPR-dependent effect on sensory neurons disappear.

      Please see response above to point 4 of the editorial summary. SPR mutants do not undergo the switch in polyamine odor preference. Therefore, SPR signaling likely presents this compensatory mechanism. Nevertheless, MBON-β’1 is required during mating for the transition from virgin to mated female behavior. In the future, we plan to analyze the relationship between SPR and this MBON in detail.

      3) Activation phenotype of MB188B-split-GAL4/UAS-dTrpA1 cannot be ascribed to activation of PMA-b'1 alone because of additional expression in DANs projecting to gamam3 and gamma4 compartments. Run the same experiment with more PMA-b'1 specific driver line.

      Please see response to point 3 of the editorial summary. We do observe a very high preference in virgins with the genetic background MB025B>TrpA1 even in the absence of temperature-mediated activation. Therefore, the experiment, unfortunately, provided no meaningful result. We have instead adjusted the text to include a possible role of γ3 and γ4.

      4) Some of EM connections are too low to be considered (e.g. two in Figure S3 and five in Figure 5). Although these connections could be functional, previous EM connectome analysis typically set much higher threshold (e.g. 10 in Hulse et al., 2021 DOI: 10.7554/eLife.66039) to avoid considering artifacts.

      We thank the reviewer for pointing this out. We have included this reference and the customary threshold of 10 in the methods section.

      5) Data for Kenyon cells (Figure 2) and LHON (Figure 6) are interesting, but not directly related to other data regarding PAM-b'1 and MBON-b'1. Due to lack of long-term changes in MBON's odor responses in mated females (Figure 5), it is unclear what information needs to be read out from Kenyon cells and how does it affect processing of putrescine signals potentially carried by LHAD1b2.

      We agree. In the revised version of the manuscript, we now show that LHAD1b2 neurons appear to undergo a change upon mating. Please see response to the editor’s summary, point 6.

      Kenyon cell output during mating could be required for odor input and odor (i.e. cVA)-mediated activation of MBONs and DANs involved. This would be in line with our data in Fig. 1D,E where we show that ORCO and OR67d OSNs are required during mating to induce the change in behavior.

  6. Jul 2022
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Your email has been sent by Franklin Okeke in Developer on July 7, 2022, 7:48 AM PDT The 12 best IDEs for programming IDEs are essential tools for software development. Here is a list of the top IDEs for programming in 2022. Image: Chaosamran_Studio/Adobe Stock Software developers have battled with text editors and command-line tools that offered little or nothing in the automation, debugging and speedy execution of codes. However, the software development landscape is rapidly changing, and this includes programming tools. To accommodate the evolution in software development, software engineers came up with more sophisticated tools known as integrated development environments. To keep up with the fast pace of emerging technologies, there has been an increasing demand for the best IDEs among software development companies. We will explore the 12 best IDEs that offer valuable solutions to programmers in 2022. Jump to: What is an IDE? The importance of IDEs in software programming Standard features of an IDE Classifications of IDEs Best IDEs for programmers Factors to consider when picking an IDE What is an IDE? IDEs are software development tools developers use to simplify their programming and design experience. IDEs come with an integrated user interface that combines everything a developer needs to write codes conveniently. The best IDEs are built with features that allow developers to write and edit code with a code editor, debug code with a debugger, compile code with a code compiler and automate some software development tasks. SEE: Hiring kit: Back-end Developer (TechRepublic Premium) The best IDEs come with class browsers to examine and reference properties, object browsers to investigate objects and class hierarchy diagrams to see object-oriented programming code. IDEs are designed to increase software developer productivity by incorporating close-knit components that create a perfect playground where they can write, test and do whatever they want with their code. Why are IDEs important in software programming? IDEs provide a lot of support to software developers, which was not available in the old text editors. The best IDEs around do not need to be manually configured and integrated as part of the setup process. Instead, they enable developers to begin developing new apps on the go. Must-read developer coverage The 12 best IDEs for programming Best DevOps Tools & Solutions 2022 CI/CD platforms: How to choose the right system for your business Hiring kit: Python developer Additionally, since every feature a programmer needs is available in the same development environment, developers don’t have to spend hours learning how to use each separately. This can be extremely helpful when bringing on new developers, who may rely on an IDE to familiarize themselves with a team’s standard tools and procedures. In reality, most IDE capabilities, such as intelligent code completion and automatic code creation, are designed to save time by eliminating the need to write out entire character sequences. Other standard IDE features are designed to facilitate workflow organization and problem-solving for developers. IDEs parse code as it is written, allowing for real-time detection of human-related errors. As such, developers can carry out operations without switching between programs because the needed utilities are represented by a single graphical user interface. Most IDEs also have a syntax highlighting feature, which uses visual clues to distinguish between grammar in the text editor. Class and object browsers, as well as class hierarchy diagrams for certain languages, are additional features that some IDEs offer. All these features help the modern programmer to turn out software development projects fast. For a programming project requiring software-specific features, it’s possible to manually integrate these features or utilities with Vim or Emacs. The benefit here is that software developers can easily have their custom-made IDEs. However, for enterprise uses, the above process might take time and impact standardization negatively. Most enterprises encourage their development teams to go for pre-configured IDEs that suit their job demands. Other benefits of IDEs An IDE serves as a centralized environment for the needs of most software developers, such as version control systems, Platform-as-a-Service and debugging tools. An IDE improves workflow due to its fast code completion capabilities. An IDE automates error-checking on the fly to ensure top-quality code. An IDE has refactoring capabilities that allow programmers to make comprehensive and renaming changes. An IDE ensure a seamless development cycle. An IDE facilitates developer efficiency and satisfaction. Standard features of an IDE Text editor Almost all IDEs will offer a text editor made specifically for writing and modifying source code. While some tools may allow users to drag and drop front-end elements visually, the majority offers a straightforward user interface that emphasizes language-specific syntax. Debugger Debugging tools help developers identify and correct source code mistakes. Before the application is published, programmers and software engineers can test the various code parts and find issues. Compiler The compiler feature in IDE assists programmers in translating programming languages into machine-readable languages such as binary code. The compiler also helps to ensure the accuracy of these machine languages by analyzing and optimizing them. Code completion This feature helps developers to intelligently and automatically complete common code components. This process helps developers to save time and reduces bugs that come from typos. Programming language support Although some IDEs are pre-configured to support one programming language, others offer multi-programming language support. Most times, in choosing an IDE, users have to figure out which programming languages they will be coding in and pick an IDE accordingly. Integrations and plugins Integration capability is one feature that makes an IDE stand out. IDEs support the integration of other development tools through plugins to enhance productivity. Classifications of IDEs IDEs come in different types and according to the programming languages they support. While some support one language, others can support more than one. Multi-language IDE Multi-language IDEs are IDE types that support multiple programming languages. This IDE type is best suited for beginner programmers still at the exploration stage. An example of this type of IDE is the Visual Studio IDE. It’s popular for its incredible supporting features. For example, users can easily code in a new programming language by adding the language plugin. Mobile development IDE As the market for mobile app development grows, numerous programming tools are becoming available to help software developers build efficient mobile apps. Mobile development IDEs for the Android and iOS platforms include Android Studio and Xcode. Web/cloud-based IDE If an enterprise supports a cloud-based development environment, it may need to adopt a cloud-based IDE. One of the advantages of using this type of IDE is that it can run heavy projects without occupying any computational resources in a local system. Again, this type of IDE is always platform-independent, making it easy to connect to many cloud development providers. Specific-language IDE This IDE type is a typical opposite of the multiple-language IDE. They are specifically built to support developers who work on only one programming language. Some of these IDEs include Jcreator for Java, Idle for Python and CodeLite for C++. Best IDEs for programmers in 2022 Visual Studio Microsoft Visual Studios The Visual Studio IDE is a Microsoft-powered integrated development interface developed to help software developers with web developments. The IDE uses artificial intelligence features to learn from the edit programmer’s make to their codes, making it easy for it to complete lines of code automatically. One of the top features many developers have come to like about Visual Studio is that it aids collaborative development between teams in live development. This feature is very crucial, especially during the debugging process. The IDE also allows users to share servers, comments and terminals. Visual Studio has the capability to support mobile app, web and game development. It also supports Python language, Node.js, ASP.NET and Azure. With Visual Studio, developers can easily create a development environment in the cloud. SEE: Hiring kit: Python developer (TechRepublic Premium) With its multi-language support, Visual Studio has features that integrate flawlessly with Django and Flask frameworks. It can be used as an IDE for Python on the Mac, Windows and Linux operating systems. IntelliJ IDEA IntelliJ IDEA IntelliJ Idea has been around for years and has served as one of the best IDEs for Java programming. The IntelliJ Idea UI is designed in a sleek way that makes coding appealing to many Java developers. With this IDE, code can get indexed, providing relevant suggestions to help complete code lines. It also takes this suggestive coding further by automating several tasks that may be repetitive. Apart from supporting web, enterprise, and mobile Java programming, it is also a good option for JavaScript, SQL and JPQL programming Xcode Xcode Xcode might be the best IDE tool for Apple product developers. The tool supports iOS app development with its numerous iOS tools. The IDE supports programming languages such as Swift, C++ and Object-C. With XCode, developers can easily manage their software development workflow with quality code suggestions from the interface. Android Studio Android Studio The Android Studio is one of the best IDEs for Android app development. This IDE supports Kotlin and Java programming languages. Some important features users can get from the Android Studio are push alerts, camera integrations and other mobile technology features. Developers can also create variants and different APKs with the help of this flexible IDE, which also offers extended template support for Google Services. AWS Cloud9 IDE AWS Cloud9 The AWS Cloud9 IDE is packed with a terminal, a debugger and a code editor, and it supports popular programming languages such as Python and PHP. With Cloud9 IDE, software developers can work on their projects from almost anywhere in the globe as long as they have a computer that is connected to the internet, because it is cloud-based. Developers may create serverless applications using Cloud9 and easily collaborate with different teams in different development environments. Eclipse Eclipse Eclipse is one of the most popular IDEs. It’s a cross-platform tool with a powerful user interface that supports drag and drop. The IDE is also packed with some important features such as static analysis tools, debugging and profiling capabilities. Eclipse is enterprise development-friendly and it allows developers to work on scalable and open-source software development easily. Although Eclipse is best associated with Java, it also supports multiple programming languages. In addition, users can add their preferred plugins to the IDE to support software development projects. Zend Studio Zend Studio Zend Studio is a leading PHP IDE designed to support PHP developers in both web and mobile development. The tool features advanced debugging capabilities and a code editor with a large community to support its users. There is every possibility that PHP developers will cling to the Zend IDE for a long time as it has consistently proven to be a reliable option for server-side programming. Furthermore, programmers can take advantage of Zend Studio’s plugin integrations to maximize PHP applications’ deployment on any server. PhpStorm PhpStorm PhpStorm is another choice to consider if users use PHP for web development. Although it focuses on the PHP programming language, front-end languages like HTML 5, CSS, Sass, JavaScript and others are also supported. It also supports popular website-building tools, including WordPress, Drupal and Laravek. It offers simple navigation, code completion, testing, debugging and refactoring capabilities. PhpStorm comes with built-in developer tools that help users perform routine tasks directly from the IDE. Some of these built-in tools serve as a version control system, remote deployment, composer and Docker. Arduino IDE Arduino Arduino is another top open source, cross-platform IDE that helps developers to write clean code with an option to share with other developers. This IDE offers both online and local code editing environments. Developers who want to carry out sophisticated tasks without putting a strain on computer resources love it for how simple it is to utilize. The Arduino IDE includes current support for the newest Arduino boards. Additionally, it offers a more contemporary editor and a dynamic UI with autocompletion, code navigation and even live debugger features. NetBeans NetBeans You can’t have a list of the best IDE for web development without including NetBeans. It’s among one of the most popular options for the best IDE because it’s a no-nonsense software for Java, JavaScript, PHP, HTML 5, CSS and more. It also helps users create bug-free codes by highlighting code syntactically and semantically. It also has a lot of powerful refactoring tools while being open source. RubyMine RubyMine Although RubyMine primarily supports the Ruby, it also works well with JavaScript, CSS, Less, Sass and other programming languages. The IDE has some crucial automation features such as code completion, syntax and error-highlighting, an advanced search option for any class and symbol. WebStorm WebStorm The WebStorm IDE is excellent for programming in JavaScript. The IDE features live error detection, code autocompletion, a debugger and unit testing. It also comes with some great integrations to aid web development. Some of these integrations are GitHub, Git and Mercurial. Factors to consider when picking an IDE Programming language support An IDE should be able to support the programming language used in users’ software development projects. Customizable text editors Some IDEs offer the ability to edit the graphical user interface. Check if the preferred IDE has this feature, because it can increase productivity. Unit testing Check if the IDE can add mock objects to some sections of the code. This feature helps test code straight away without completing all the sections. Source code library Users may also wish to consider if the IDE has resources such as scripts and source code. Error diagnostics and reports For new programmers, sometimes it’s good to have an IDE that can automatically detect errors in code. Have this factor in mind if users will need this feature. Code completion Some IDEs are designed to intelligently complete lines of code, especially when it comes to tag closing. If developers want to save some coding time from tag closing, check for IDEs that offer this option. Integrations and plugins Do not forget to check the integration features before making a choice. Code search Some IDEs offer the code search option to help search for elements quickly in code. Look for IDEs that support this productivity feature. Hierarchy diagrams If users often work on larger projects with numerous files and scripts that all interact in a certain way, look for IDEs that can organize and present these scripts in a hierarchy. This feature can help programmers observe the order of file execution and the relationships between different files and scripts by displaying a hierarchy diagram. Model-driven development Some IDEs help turn models into code. If users love creating models for the IDE, consider this factor before choosing an IDE. Programming language courses No matter what language you write or want to learn, TechRepublic Academy has a number of different programming courses to help you level-up your skills. Start with these: Python JavaScript Ruby on Rails All programming languages courses Developer Essentials Newsletter From the hottest programming languages to the jobs with the highest salaries, get the developer news and tips you need to know. 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Your email has been sent Share: The 12 best IDEs for programming By Franklin Okeke Franklin Okeke is a freelance content writer with a strong focus on cybersecurity, search engine optimization, and software development content. 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Brenna Miles Published:  July 19, 2022, 7:48 AM PDT Modified:  July 21, 2022, 7:48 AM PDT Read More See more Developer Image: Nuthawut/Adobe Stock Software Best project management software and tools 2022 With so many project management software options to choose from, it can seem daunting to find the right one for your projects or company. We’ve narrowed them down to these nine. Sam Ingalls Published:  July 19, 2022, 12:25 PM PDT Modified:  July 29, 2022, 1:58 PM PDT Read More See more Software Apple announced iOS 16 on June 6, 2022 during the WWDC keynote. Image: Apple Mobility iOS 16 cheat sheet: Complete guide for 2022 Learn about the new features available with iOS 16, and how to download and install the latest version of Apple’s mobile operating system. Cory Bohon Published:  July 14, 2022, 7:00 AM PDT Modified:  July 29, 2022, 7:37 AM PDT Read More See more Mobility Image: Chaosamran_Studio/Adobe Stock Developer The 12 best IDEs for programming IDEs are essential tools for software development. Here is a list of the top IDEs for programming in 2022. Franklin Okeke Published:  July 7, 2022, 7:48 AM PDT Modified:  July 29, 2022, 10:40 PM PDT Read More See more Developer window.googletag = window.googletag || { cmd: [] }; window.googletag.cmd.push(function() { googletag.display("leader-bottom"); }); TechRepublic Premium TechRepublic Premium Industrial Internet of Things: Software comparison tool IIoT software assists manufacturers and other industrial operations with configuring, managing and monitoring connected devices. A good IoT solution requires capabilities ranging from designing and delivering connected products to collecting and analyzing system data once in the field. 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Downloads Published:  May 19, 2022, 5:00 PM PDT Modified:  May 21, 2022, 12:00 PM PDT Read More See more TechRepublic Premium TechRepublic Premium Quick glossary: Industrial Internet of Things The digital transformation required by implementing the industrial Internet of Things (IIoT) is a radical change from business as usual. This quick glossary of 30 terms and concepts relating to IIoT will help you get a handle on what IIoT is and what it can do for your business.. From the glossary’s introduction: While the ... Downloads Published:  May 19, 2022, 5:00 PM PDT Modified:  May 21, 2022, 12:00 PM PDT Read More See more TechRepublic Premium TechRepublic Premium Software Procurement Policy Procuring software packages for an organization is a complicated process that involves more than just technological knowledge. There are financial and support aspects to consider, proof of concepts to evaluate and vendor negotiations to handle. 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      and seriously you don't mention visual code???

    1. 1944. VIII. 22. B–24 Kápolnásnyék Carl F. Sutter 12208670 T-43-44 Warren Klein 32800139 T-43-44 Ernst C. Philips T-62301 T-42-543 Archie L. Hyslop 16123835 T-43S44 Hugo O. Pertolla 16147933 T 44-44 Russel M. Sarples 0-74769 T 42-43a L. D. Whitaker 0-695281 T 43-440 Howard K. Withe 0-1168133 T-42-43a Willis L. Engelhardt 0-701581 T 43-43a

      A B-24J bombázó (42-99799) legénysége

      A különbség : az itteni utolsó 2 ember nincs a legénységi listán (a linken mások szerepelnek.)<br /> A lezuhanás dátuma sem stimmel (1944.07.02.)

      A gép Budapesten a Shell olajfinomítót bombázta. Pesttől nyugatra 50km-re zuhant le, egy BF 109G lőtte le. 7 katona meghalt, 3 kiugrott (ők fogságba kerültek és '45-ben szabadultak).

    1. The evolution of virtual potential temperature θv BLθv BL<math xmlns="http://www.w3.org/1998/Math/MathML" id="i22" overflow="scroll" alttext="No alternative text available"><mrow><msub><mi>θ</mi><mrow><mi>v</mi><mo> </mo><mi>B</mi><mi>L</mi></mrow></msub></mrow></math> and humidity qBLqBL<math xmlns="http://www.w3.org/1998/Math/MathML" id="i23" overflow="scroll" alttext="No alternative text available"><mrow><msub><mi>q</mi><mrow><mi>B</mi><mi>L</mi></mrow></msub></mrow></math> in the boundary layer is governed by

      Having calculations and formulas such as this make the article appear more credible for most people, since the math goes over their heads and makes the writer seem significantly well-taught and knowledgeable about the topic.

    1. nineteen women out of twenty would have accommodated themselves to it without a pang

      This ‘statistic’ Isabel puts forward signifies how most women in her position would have accepted Lord Warburton’s proposal without a second thought. This is reflective of the Victorian conception of marriage as a means of gaining wealth for women. In the Victorian era, patrilineal property laws overwhelmingly excluded women from inheriting property or wealth. The common law doctrine of coverture extinguished the independent legal existence of a woman and merged her legal identity completely with that of her husband. Marriage essentially became the only arena for women to secure their financial security and gain control over property. It must be noted, however, that whatever control over property women gained through marriage was limited, as their influence was restricted mostly to day-to-day upkeep while the actual title remained in the name of either their husband or male children.

      Wardley, L. (2010) ‘Courtship, marriage, family’ in D. McWhirter (ed) Henry James in Context, Cambridge University Press, 150-160.

      Crosswhite, A. B. (2002) ‘Women and Land: Aristrocratic Ownership of Property in Early Modern England’, New York University Law Review 77(4): 1119-1156.

    1. 3] P. R. Holland, The Quantum Theory of Motion, Cambridge University Press, Cambridge,1993. See also of course: D. Bohm, Part I, Phys. Rev. 85,166 (1952); Part II, 85 180 (1952).The fundamental work by de Broglie is discussed in L. de Broglie, J. Phys. Radium 8, 225(1927) and in the report of the 5th Solvay’s conference (in french) reproduced recently in:G. Bacciagaluppi and A. Valentini (ed.s): Quantum Theory at the Crossroads - Reconsider-ing the 1927 Solvay Conference, Cambridge University Press (2007). The best book on thesubject is still probably: D. Bohm and B. J. Hiley, The Undivided Universe - An OntologicalInterpretation of Quantum Theory, Routledge, London (1993).
      • BASIC BIBLIOGRAPHY
    1. h e f o r m a l notation became known as B N F -standing for "Backus N o r m a l F o r m , " or "Backus N a u r F o r m " torecognize the further contributions by Peter N a u r of Denmark.

      Backus's work alongside John Allen's Anatomy of Lisp book discussing Syntax Directed Translation, and Abelson and Sussman's Structure and Interpretation of Computer Programs inspired my PhD on "Language-Oriented Programming in Meta-Lisp" at Leeds University.

      Revisiting this paper allows me to articulate on the margin the ideas relating to a revival of Simomnyi;s intentional software, based on the recognition that Software is a conversation about intents and a matter of Mutual Learning Symmathesy.

      The original motivation for managing complexity by maintaining referential transparency and intellectual manageability over the past two decades, inspired by the works of Douglas Engelbart, Ted Nelson, Alan Kay and Ward Cunningham, evolved into the recognition of the need to developing means that augment the intellectual effectiveness of the individual human being and bootstrap collaboration co-creation of co-evolution of man-computer symbiotic systems.

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      Reply to the reviewers

      Answers to reviewers’ comments

      (Reviewers comments are in italics. Text modifications in the manuscript file are in blue.)

      Overall, we acknowledge referee’s careful reading of the paper and comments that we think have helped further improvement of the manuscript.

      On the attached pages are our detailed point by point responses to the referees’ comments along with a description of how the manuscript was modified in accordance.

      New data included:

      In response to the comments and suggestions of both reviewers 1 and 3, we conducted new experiments to test genetic interactions between different actors of the BMP and activin pathways. These new results confirm and complement the analyses described in the original manuscript. Furthermore, as suggested by reviewer 2, we have further studied the phenotypes of hiPSC-CM, by analyzing gene expression profiles and by analyzing the morphological changes induced as a result of PAX9 knockdown.

      NB: The title has been slightly modified, to highlight the conserved features of the genetic architecture of cardiac performance revealed in the study

      __Former title: __Genetic architecture of natural variation of cardiac performance in flies.

      __Novel title: __Genetic architecture of natural variation of cardiac performance: From flies to humans.

      Reviewer 1

      1. 1. The authors utilized the RNAi-mediated knockdown approach in their functional validation studies. It is not clear how each genetic variation (SNP) affects its associated genes. Could some of the SNPs activate the candidate gene expression? For the 4 candidate genes that failed to show cardiac defects, could the overexpression of these 4 genes alter cardiac performance? Answer 1- Of course, we cannot predict direction of the effect of the variants on the function of the genes. In this context, loss-of-function experiments are subjected to a risk of false negatives. It is indeed possible that in the case of a lack of effect of the loss of function, a gain of function could reveal an effect. But gain-of-function experiments are difficult to control, and often subjected to non-specific effects because it is complicated to control the level of over-expression compared to endogenous expression. This did not seem suitable for an extensive analysis of a large number of genes. We therefore chose to test only for loss of function.

      In addition, our approach to testing heart-specific RNAi aims to assess the quality of the association results by comparing RNAi for genes identified by GWAS to randomly selected genes. It is not intended to describe precisely the involvement of each gene individually.

      (See also answer to reviewer 2 comment n°2 and the modifications to the manuscript that have been made and which address these criticism)

      * 2. babo is the type I activin receptor, not type 2. *

      Answer 2- Thank you, we have corrected this error.

      • The authors show BMP and activin pathway genetically interacts to affect cardiac performance. But it is interesting to find that these interactions are in a trait-dependent manner. For example, it seems that babo and dpp epistatically interact to regulate FS, while they additively regulate HP and DI. The authors need to discuss the complex genetic interaction further. *

      Answer 3- See reply to reviewer 3, comment N°2 below.

      4*. Both snoo and sog are identified from GWAS. How about babo and dpp? Are there any identified SNPs associated with babo and dpp? *

      Answer 4- Considering GWAS for mean phenotypes, there is no variant in dpp that are within the 100 best ranked SNPs nor within the variants identified using fast epistasis. But given the size of the DGRP population we are far from being exhaustive, as we do not reach saturation. It is therefore difficult to comment on these ‘negative’ results. However, we do identify one variant in babo using fast epistasis (see figure 2B and Table S3).

      5. It is unclear why the mad KD behaves oppositely to dpp mutant, although both proteins are involved in BMP pathway. In Figure S5, the mad KD shows reduced FS and HP, but dpp LOF mutant shows increased FS and HP (Figure S4). Can the authors perform RNAi to knockdown dpp specific in the heart to reexamine the role of dpp in the regulation of cardiac function. The whole body LOF mutant dpp-d14 might not target cardiac tissue directly to control heart performance like mad KD.

      Answer 5- (see also answer to reviewer 3 comment n°2) We did perform heart specific dpp RNAi experiments together with other tests for interactions using new allelic combinations of activin and BMP pathways and therefore can compare heart specific knock down to heterozygotes for amorphic mutations for both dpp and mad.

      Regarding dpp, congruent effects on HP, DI, SI, ESD and EDD were observed between mutant and RNAi, while RNAi had opposite effects on FS compared to heterozygotes dppd14 mutants (decreased and increased FS compared to control, respectively). In the case of mad, heterozygous mutants had no effect on FS, EDD and ESD, but similarly to dpp mutants it increased SI, DI and HP. mad RNAi uniquely decreased HP, DI and SI and increased AI. However, similarly to dpp RNAi, it induced a decrease of FS.

      Thus, systemic versus heart specific knockdown of genes induce specific effects, suggesting cardiac non-autonomous interactions. This complex picture of TGFb involvement is now discussed in the result section (see below, Reviewer 3, major comment 2).

      6*. The authors selected two novel genes to study the conversed regulation in both flies and human iPSC cells. Besides testing these novel genes, the authors should also verify whether the conserved pathways, like TGF-beta, regulate heart performance in human iPSC cells similar to the flies. *

      Answer 6- We focused on poxm/Pax9 and sr/Egr2 because none of these TFs were known to have cardiac function in fly nor in mammals. Our paralleled analyses in fly and hiPS-CM illustrates how the description of the genetic architecture of cardiac traits in flies can accelerate discovery in mammals.

      There is extensive literature describing the involvement of TGF B /BMP and Activin pathways in heart development and diseases in humans, hence the choice not to focus on these pathways in iPS-CM.

      Reviewer 2:

        • It will be interesting to compare this fly GWAS to human heart disease GWAS data (for example, cardiomyopathy, arrhythmia, heart failure) from patients. Such cross comparison could make the data set more valuable. * Answer 1- We actually did make this comparison (Table 2, Table S11) and we agree it significantly validates our approach. This identified a set of orthologous genes associated with cardiac traits both in Drosophila and humans, supporting the conservation of the genetic architecture of cardiac performance traits, from arthropods to mammals.
      1. RNAi is the only experimental approach in this manuscript to validate the functional significance from data analyses. Authors may consider using genetic mutations such as deficiency lines or P-element lines to offer an alternative approach. This is simply a suggestion to improve the rigor and reproducibility, not absolutely required. *

      Answer 2- In an attempt to provide a consistent analysis of loss of gene function, our strategy was to concentrate our analysis on the effects of heart specific knock down. This allows us to compare -in a global way- the effects of the knock down of genes identified by GWAS to those of randomly selected genes.

      Our objective was to provide a global view of the heart specific effects of the identified genes, and not to characterize precisely the involvement of each of them, using a combination of mutant alleles, RNAi and gain of function. Given the experimental burden of analyzing cardiac function, such a strategy would have indeed required us to concentrate only a very small number of genes.

      We however recognize that this strategy has limitations:

      • Some variants may lead to gain-of-function effects of genes, and our strategy is not able to test for these effects.

      • Some variants may come from non-cell-autonomous effects, which would not be replicated by our targeted RNAi strategy in the heart.

      Therefore, the false negative rate of our experiments is difficult to estimate.

      We have tried to put this into perspective and to highlight the limitations of our analysis in the results section describing RNAi validation of GWAS results.

      “To assess in an extensive way whether mutations in genes harboring SNPs associated with variation in cardiac traits contributed to these phenotypes ….. (…)

      …… These results therefore supported our association results. It is important to emphasize that our approach is limited to testing the effect of tissue-specific gene knock down. Since some of the variants may lead to increased gene function and/or expression, this can lead to a false negative rate that is difficult to estimate. In addition, some of the associated variants may influence heart function by non cell-autonomous mechanisms, which would not be replicated by cardiac specific RNAi knock down.”

      *In order to validate the roles of predicted TF binding sites, the best approach would be introducing point mutations using CRISPR/Cas9 within the binding motif then testing out molecular and physiological outcomes. Rather authors chose to test indirectly to knock down those TFs. If so, authors need to at least acknowledge the potential caveats of such approach and the limitation in related data interpretation. *

      Answer 3- The reviewer is right, the definitive proof of the involvement of a potential TF binding site on the regulation of a gene located in cis requires to mutate the binding site and to analyze the effect on the expression of the corresponding gene. But this may not be sufficient to definitely demonstrate that the potential TF is indeed a regulator of that gene (the binding motif may be target of yet another TF): definitive proof may require motifs/TF DNA binding domain swaps. This would have been out of the scope of the present study. In addition, the effects on heart performance of mutating one TFBS at a time (among several dozens) may be too weak to allow their characterization with available tools and approaches.

      We acknowledge however that our approach provides an indirect validation of transcription factors binding sites predictions. This was, in our opinion, the most efficient way to evaluate the potential effect of predicted transcription factors.

      We clarify this in the result section:

      “We did not test individually the effects on cardiac performance of mutations in predicted TFBSs located near the SNPs because any individual effect would probably be too small to be detectable by the available methods. Rather, we tested the potential involvement of their cognate TFs by cardiac specific RNAi mediated KD”

      • hiPSC-CM data is somewhat limited by only showing the HR and AP duration data. It is recommended to include some immunocytochemistry data to show the morphology, sarcomere structure of these hiPSC-CMs. Gene expression data generated by qPCR or RNA-seq in particular focusing CM structure and function genes would be helpful too.*

      Answer 4- As suggested by referee 2, we have now performed gene expression analysis and immunostaining of PAX9 KD which gave the strongest phenotype in iPSC-CM (Figure 4 J-M). This unraveled increased expression of Na+ and K+ channels, which is in line with APD shortening phenotype, as well as down regulation of CASQ2, consistent with calcium transient shortening. Expression analysis also revealed increased sarcomeric genes and NPPA/B expression, which was consistent with increased CM size as quantified by the area of TNNT2 staining per nuclei.

      These new data are described at the end of the result section:

      “APD shortening for PAX9 KD was coincident with increased expression of Na+ and K+ ion channels (SCN5A, KCNH2 and KNCQ1) (Figure 4J), supporting the APD shortening phenotype. In this context, the AP kinetics also correlated with shorter calcium transient duration (Figure S8A-D and H-K), including faster upstroke and downstroke calcium kinetics and increased beat rate (peak frequency) (Figure S8E-G and L, M), consistent with decreased expression of Calsequestrin 2 isoform (CASQ2) associated with PAX9 KD (Figure 4J). Finally, assessment of the PAX9 KD effect on sarcomeric content revealed an increase in sarcomeric gene expression (Figure 4K), and an upregulation of genes associated with an hypertrophic response (NPPA, NPPB and NPR1 (Battistoni Et al Circulating biomarkers with preventive, diagnostic and prognostic implications in cardiovascular diseases, Int J Cardiol, 2012, vol. 157) which was coincident with increased CM size as quantified by the area of TNNT2 staining per cardiac nuclei (Figure 4 L, M).

      Collectively, these data illustrate conserved functions for poxm/PAX9 and sr/EGR2 in setting the cardiac rhythm and identify PAX9 as a novel and key regulator of cardiac performance at the cellular level, via the integrated regulation of expression of genes controlling electrophysiology, calcium handling and sarcomeric functions in hiPSC-CMs.”

      Reviewer 3

      Major Comments:

      1- There is an assumption in the use of RNAi knockdown to validate the genes identified in the quantitative analysis, and that is that natural variants are themselves hypomorphic. It is possible that among the variants identified some are hypermorphic, or among the transcription factor binding sites that variants lead to increased factor binding. While RNAi knockdown is an excellent choice to begin validation, I do not think the authors can rule out that a gene not functionally validated by their RNAi tests does not have a role in cardiac function.

      Answer 1. Please see our answers to reviewer 1 comment n°1 and reviewer 2 comment n°2.

      * 2- After performing RNAi knockdown to validate genes identified by GWAS the authors focus on the TGFbeta signaling pathway for downstream analysis. To do so they examine heterozygotes for sog, a repressor of BMP signaling, and snoo, an activator of Activin pathway. The data from the snoo/sog heterozygote is compelling in its disruption of heart phenotypes, and the authors conclude a "coordinated action of activin and BMP." snoo, however, also works as a transcriptional repressor in the BMP pathway, so it's possible that the effects the authors are seeing here could be confined to an increase in BMP signaling. Unlike snoo and sog, mutations in babo and dpp are both expected to have negative effects on Activin and BMP signaling, respectively. The babo/dpp interaction is not as quantitatively convincing as the snoo/sog data, despite the integral roles both babo and dpp play in their respective pathways. If both pathways are connected, why do snoo/sog heterozygotes affect SI phenotypes, while babo/dpp heterozygotes affect fractional shortening? I think the authors data suggest an interesting potential interaction between these pathways, which could be confirmed by examining further mutant combinations, knockdowns or increased expression transgenes, but falls short of a "confirmed synergystic genetic interaction." It does, however, underscore the value of the data in the paper for opening up new avenues for future study. *

      Answer 2 (and reviewer 1 comments 3 and 5).

      These comments led us to reconsider the analysis of the phenotypes associated with loss of function of the TGFb pathway, and to analyze other pathway components combinations.

      We acknowledge reviewer 3 criticisms on snoo/sog experiments, which are difficult to interpret given the broad action snoo may have on both BMP and activin pathways. We have addressed this in the result section.

      We have also analyzed other allelic combinations of BMP and activin pathways components, which strengthen the analysis performed on dpp/babo. Indeed, we tested babo/tkv heterozygotes (respectively specific activin and BMP receptors) and found significant genetic interactions for ESD and EDD. Albeit non-significant, babo/tkv double heterozygotes display a tendency to non-additive effects on FS (p= 0,054). mad/smox heterozygotes (respectively specific downstream TFs of BMP and activin pathways) display interactions (non-additive effects) on HP, SI, DI, ESD and EDD. These new results (Supplemental Figure 4) are thus supporting the hypothesis of genetic interactions between the pathways, but also reveal, as suggested by reviewer 3, a complex relationship between both pathways since interactions are revealed for specific traits in each of the mutant combinations analyzed.

      The phenotypes related to the individual loss of function of each of the actors of these pathways (dpp, tkv and mad for BMP; babo and smox for activin) are however very similar. When they have an effect, heterozygous amorphic alleles of these genes display increased phenotypes related to rhythmicity (HP, DI, SI, AI) and FS, but decreased cardiac diameters (ESD and EDD).

      Finally, as pointed out by reviewer 1, the picture is certainly even more complex since the phenotypes of RNAi mediated heart specific loss of function are not always similar to those of systemic loss of function. Indeed, mad RNAi causes a reduction of HP, DI, SI and FS (Figure S5) whereas heterozygotes for mad12 have either no or opposite effect on these phenotypes, and mad RNAi causes a significative increase in AI whereas mad12 has no effect (Figure S4). The discrepancy between tissue specific RNAi and heterozygous background was also found in the case of dpp, but specifically for the FS. Indeed, as suggested by reviewer 1 we have analyzed the loss of function of dpp by heart-specific RNAi. dpp RNAi results in a reduction of the FS (like mad RNAi) whereas the loss of function in the whole-body results in an increase of the FS.

      We therefore re-wrote the whole corresponding section of the results and modified Figure S4 to include babo/tkv; smox/mad and dppRNAi data.

      “We further focused on the TGFb pathway, since members of both BMP and activin pathways were identified in our analyses. We tested different members of the TGFb pathway for cardiac phenotypes using cardiac specific RNAi knockdown (Figure 2C), and confirmed the involvement of the activin agonist snoo (Ski orthologue) and the BMP antagonist sog (chordin orthologue). Notably, Activin and BMP pathways are usually antagonistic (Figure 2D). Their joint identification in our GWAS suggest that they act in a coordinated fashion to regulate heart function. Alternatively, it may simply reflect their involvement in different aspects of cardiac development and/or functional maturation. In order to discriminate between these two hypotheses, we tested if different components of these pathways interacted genetically. Single heterozygotes for loss of function alleles show dosage-dependent effects of snoo and sog on several phenotypes, providing an independent confirmation of their involvement in several cardiac traits (Figure S4). Importantly, compared to each single heterozygotes, snooBSC234/ sogU2 double heterozygotes flies showed non additive SI phenotypes (two-way ANOVA p val: 2,1 10-7) suggesting a genetic interaction (Figure 2E and Figure S4A). It is worth noting however that snoo is also a transcriptional repressor of the BMP pathway (PMID: 16951053). The effect observed in snooBSC234/ sogU2 double heterozygotes can therefore alternatively arise as a consequence of an increased BMP signaling without affecting the activin pathway. We thus tested other allelic combinations for loss of function alleles of BMP and activin pathways. babo/tkv heterozygotes (respectively activin and BMP type 1 receptors) displayed non additive ESD and EDD phenotypes (Figure S4C). Synergistic interaction of BMP and activin pathways was also suggested by the analysis of fractional shortening in loss of function mutants for babo and dpp, the BMP ligand (Figure S4B). Of note, babo/tkv double heterozygotes also displayed a tendency to non-additive effects on FS albeit non-significant (two-way anova p= 0,054). In addition, mad/smox heterozygotes (specifc downstream TFs of BMP and activin pathways) displayed non-additive effects on several traits, including phenotypes related to rhythmicity (HP, SI, DI) and contractility (ESD and EDD) (Figure S4D). Altogether, cardiac performance in response to allelic combinations of activin and BMP supported a coordinated action of both pathways in the establishment and/or maintenance of cardiac activity. This was further supported by the observation that simple heterozygotes for the tested loss of function alleles displayed similar trends with respect to cardiac performance, irrespective of the pathway considered (dpp, tkv and mad for BMP; babo and smox for activin). Indeed, they displayed either no effect or increased fractional shortening and rhythmicity phenotypes (HP, DI, SI, AI), and decreased cardiac diameters (ESD and EDD). This suggests coordinated activity of both pathways. Importantly, the genetic interactions were tested using amorphic alleles that lead to systemic loss of function. The observed phenotypes may thus not unravel cardiac specific effects of the pathways. In support of this, mad cardiac specific RNAi knock down was tested (see below, Figure S5) and lead to a decreased HP, DI, SI and FS whereas heterozygotes for mad12 have either no (FS) or opposite (HP, DI, SI) effect on these phenotypes (Figure S4D). Inversely, mad RNAi caused a significant increase in AI whereas mad12 had no effect. However, heart specific dpp RNAi knock down (Figure S4E) lead to similar phenotypic trends compared to dppd14 (increased HP, DI, SI, decreased EDD and ESD) with the notable exception of FS which was reduced following cardiac specific KD (Figure S4E), but increased in dppd14heterozygotes (Figure S4B). Taken together, these data point to a complex picture of TGFb pathway activity in regulating cardiac performance, involving both the activin and the BMP pathways as well as gene specific effects with both systemic and tissue-specific contributions.”

      *Minor Comments: *

      * There is an enormous amount of data in this paper, but there are places where things are summarized a little too briefly. For example, there are no definitions given at the beginning of the Results section for traits like "Heart Period" or "Systolic Interval," which would make this work significantly more accessible for other Drosophila researchers. (They do touch on this when they explain later in the paper that certain variants are "associated with quantitative traits linked to heart size and contractility" but more background earlier would be helpful.) When we consider heart performance traits, what is the baseline from known mutants? In other words, where is the line between variation and defect? *

      Answers:

      • We have detailed the description of the traits analyzed at the beginning of the result section. We hope this improves the ease of reading in the direction suggested by the reviewer. “7 cardiac traits were analyzed across the whole population (Dataset S1 and Table 1). As illustrated in Figure 1A, we analyzed phenotypes related to the rhythmicity of cardiac function: the systolic interval (SI) is the time elapsed between the beginning and the end of one contraction, the diastolic interval (DI) is the time elapsed between two contractions and the heart period (HP) is the duration of a total cycle (contraction + relaxation (DI+SI)). The arrhythmia index (AI, std-dev(HP)/mean (HP)) is used to evaluate the variability of the cardiac rhythm. In addition, 3 traits related to contractility were measured. The diameters of the heart in diastole (End Diastolic Diameter, EDD), in systole (End Systolic Diameter, ESD), and the Fractional Shortening (FS), which measures the contraction efficacy (EDD-ESD/EDD).“

      • With respect to the baseline of cardiac performance, there is no simple answer. The baseline is influenced by the genetic background and the experimental conditions. This is the reason why any analysis of mutants or RNAi is conducted in comparison with its own control, analyzed at the same time. Concerning the DGRP lines, no baseline can be defined, since the objective is to measure the diversity of cardiac performance traits within a natural population.

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      Please find bellow the preliminary revision plan for our manuscript entitled “Recognition of copyback defective interfering rabies virus genomes by RIG-I triggers the antiviral response against vaccine strains” by Wahiba Aouadi et al. (RC-2022-01386). Reviewer’s’ comments/questions and suggestions are represented in blue in the text.

      **Description of the planned revisions**

      We thank the Reviewer#1 for underlining that identification of rabies virus 5’ copy-back DI genomes as “presumably bound to RIG-I is a useful advancement”, her/his interest in “observed difference between the responses to the two strains of virus” (THA strain and the vaccine SAD strain), and for emphasizing that “identification of the rabies viral RNAs that activate RIG-I is a significant finding for the rabies specialists”.

      Reviewer#1: In more details for the Evidence, reproducibility and clarity (Required) Much of the studies relied on weak methodologies. For example, in Fig 1, reporter assays were used, instead of measuring IFN mRNA levels; it is also not clear what is the nature of the promoter driving the reporter. Is it ISRE, which responds to IFN or is it the IFNb promoter, which responds to transcription factors activated by RIG-I? It is also not clear what is the nature of the RNA that was transfected. Is it total RNA from infected cells or is it purified viral RNA? No matter what, these results are quite predictable from the literature.

      Regarding the Reviewer #1 comment on type-I IFN cell report results “relying “on weak methodologies”, we would like to recall that to provide pieces of evidence that RIG-I-specific RNA ligands are produced during the infection with rabies virus we used several previously validated technics: - i) Fig.1A: transfected into ISRE-reporter cell line (ISRE, which responds to IFN) that is a classical validated tool to efficiently detect ISRE-activation even upon transfection of low quantities of immunoactive RNA ligands (PMID: 28768856, PMID: 27011352, PMID: 24098125, PMID: 29996094, PMID: 31761719, PMID: 23595062). - ii) Fig1B: Cell overexpressing LGP2 approach that has been previously developed and validated (Sanchez et al., 2019). LGP2 overexpressing cells provide a possibility to functionally distinguish between RIG-I and MDA5-driven activation of type-I IFN signaling. As noted in the corresponding figure legend in this experiment, the IFN-b promoter-reporter assay was used (“which responds to transcription factors activated by RIG-I”). - iii) Fig.1C -similar to Fig1A experiments performed in ISRE-reported cell lines partially depleted in either RIG-I or MDA5 (siRNA-based approach) to complement the Fig. 1B with additional functional validation using siRNAs.

      We apologize that we haven’t provided a detailed explanation for the origin of transfected RNA used all through Fig. 1. In the revised Fig1 we will correct the figure legend to explain the origin of total RNA used in experiments: Total RNA purified from SK.N.SH cells infected with THA or SAD for all experimental approaches presented in the figure. Moreover, as suggested by Reviewer#1 for Fig.1 we will add experiments measuring IFN-b mRNA by RT-qPCR.

      Referee #1

      Evidence, reproducibility and clarity

      A lot of effort was devoted to distinguish between RIG-I and MDA5 as the receptor of rabies viral RNA producing conflicting results from the binding assays and the reporter assays.

      This comment of Reviewer#1 is not clear to us. We have the feeling that our results do not show any conflict when analyzing the results represented in Fig. 2-3. They demonstrate that RIG-I and not MDA5 works as the key cytosolic sensor upon infection with rabies virus. Further, the apparent conflict observed by the Reviewer#1 about the fact that we failed to detect any specific RABV RNA ligands upon infection with THA strain (Fig.3A) while significant enrichment of immunoactive RNA ligands on RIG-I (Fig.2C) were observed can be easily commented and explained. We proposed in the revised version of our manuscript to discuss the possibility and to provide the results showing that enrichment in 5’PPP endogenous RNA ligands on RIG-I upon infection with THA RABV could explain the results observed on Fig.2C /Fig.3A. Indeed, in our recently accepted for publication study, we observed that a large spectrum of RNA virus infections leads to the mobilization of endogenous RNA ligands (transcripts of RNA Polymerase III) on RIG-I (https://www.cell.com/iscience/fulltext/S2589-0042(22)00871-9). Furthermore, we observed that upon infection Polymerase III transcripts can activate RIG-I signaling pathways even in the absence of RIG-I-specific viral RNA ligands. To address this possibility in the revised manuscript, we propose to perform additional analysis of our RNAseq results to demonstrate enrichment of endogenous RNA ligands on RIG-I in rabies virus-infected cells.

      Significance

      Conceptually, the paper does not add much to the literature. As pointed out by the authors, RIG-I-specific partners had been identified before for many RNA viruses including other rhabdoviruses.

      We additionally underline that although there is a slowly growing number of studies characterizing RLR-specific RNA ligands directly from infected cells with a slowly growing number of characterized viruses, to our knowledge our study provides the first characterization of RLRspecific RNA ligands in Rabies virus-infected cells and that the amount of these ligands differs between wild type viruses and vaccine strains. Furthermore, none of the previously published studies on Rabies virus used similar experimental approaches. We believe that only stepwise characterization of RLR-specific RNA ligands for different RNA virus families is fully original regarding rabies virus and will further provide a wider and more fundamental vision on the distribution of RIG-I and MDA5 specificities for sensing RNA viruses.

      Referee #3

      Evidence, reproducibility and clarity

      We thank Reviewer#3 for stressing that our “study is highly significant for understanding virus sensing mechanisms and to inform understanding of vaccine actions.” For the Reviewer#3 specific comments:

      The signaling analyses is focused on ISRE/promoter induction, which is several steps downstream from RIG-I. An more comprehensive signaling analysis is required to define the RLR pathway engagement, including examination of RIG-I binding to MAVS, IRF3 activation induced by viral RNA and recovered RIG-I or MDA5 ligands, and induction of IRF3-target gene expression (such as RSAD, IFI44, IFIT1, IFIT2) and interferon-stimulated gene (ISG) expression such as Mx1, Mx1, OAS, etc.

      We thank Reviewer#3 for his comments and also appreciate that additional characterization of type-I IFN signaling pathway activation by RABV RNA will deeper our research results. We will add additional experimental results to answer the comments suggested by the Reviewer#3 for each Figure, as presented below:

      Figure 1. RLR activation readout here relies exclusively on promoter/reporter assay. Assessment of endogenous IRF3, IRF3-target gene expression, and ISG expression needs to be included. Also, what are the dynamics of RLR signaling activation during infection over a time course? This is important to know and to associate with the accumulation of the cb RNAs.

      We will perform additional transfection of total RNA purified from SK.N.SH cells infected with THA or SAD to HEK293T (or other relevant cells) to detect by WB analysis the phosphorylation of IRF3. As suggested by the Reviewer#3 we will also perform gene expression analysis targeting RSAD, IFI44, IFIT1, IFIT2. Additionally, kinetics of the SK.N.SH cells infection with THA or SAD strains of RABV will be studied to detect the accumulation of 5’cbDI genomes during the infection as suggested at the second part of the comment by the Reviewer#3.

      Figure 2. The RLR-bound RNA signaling analysis is incomplete. The authors need to include analysis of IRF3 and gene expression as noted above. Also, the authors should assess RLRbound RNAs collected over a time course of infection, thus enabling an understanding of the temporal dynamics of RLR ligand and biological activity of this virus-host interaction.

      In order to reply to this comment we will provide additional characterization of type-I IFN signaling in ST-RLR cells infected with THA and SAD, comparing to the mock-infected cells. For this, we will perform western blot analysis of IRF3P in total protein lysates and carry additional analysis of our NGS data to visualize ISG expression profiles in the same conditions (THA, SAD, and mock). Unfortunately, it will be experimentally difficult to assess RLR-bound RNAs collected over a time course of infection. However, as our NGS analysis demonstrated accumulation of 5’cb DI RNA as specific RNA ligands of RIG-I, we can follow the kinetics of accumulation of these 5’cb DI RNAs in SK.N.SH and ST-RLR cells as described above in response to the Fig.1 comment of the Reviewer#3.

      Figure 3. These are strong data sets and are convincing. For panel C, one can see several RIGI-bound peaks. The authors should provide more information on the length of these peaks, please include in Table 1. Also for MDA5 there also are peaks but the histogram is saturated. The peaks and valleys need to be delineated, ideally in a large table. The needs to be confirmation of these motifs or RNAs as actually binding to RIG-I and MDA5. This binding activity needs to be shown in gel-shift assay or other suitable approach of direct RIG-I binding of specific RNAs produced in vitro corresponding to mapped regions shown in the figure 3. Also, a more careful analysis of MDA5-assocaited RNA needs to be conducted to ascertain if it has immune stimulatory/signaling activity. By assess IRF3 activation this activity might be identified.

      Based on the Reviewer#3 suggestions for the Fig.3C we will additionally summarize in Supplementary Table 4 RNA reads that are represented as enriched on RIG-I for the 5’ part of the RABV genome. Indeed, the full-length genome binding to MDA5 was observed for RNA- reads importantly in SAD-infected cells. However, we believe that how encapsidated full-length viral genome can still be detected by MDA5 in virus-infected cells needs to be addressed in a separate study. Additional experiments for detecting the IRF3 activation in ST-RLR cells will be performed as described above.

      Figure 4: VERY important: Do these RNAs bind to RIG-I in vitro, and do they activate IRF3 when transfected into cells, what is the role of 5'ppp in this activity?? These data are needed to make the strong conclusions stated by the authors.

      We are grateful to Reviewer#3 suggestions for Fig.4. We will address whether the detected RABV 5’cb DI RNAs are specific RIG-I ligands. We will synthetize and transfect these RNA molecules and study how efficiently they activate type-I IFN signaling (by IFN-b and ISRE reporter approaches as well as by gene expression assay analysis as suggested in Fig.1 by Reviewer#3). We will also address IRF3P efficiency upon cell transfection with DI-2170 and DI-1668. As controls, we will use previously described RIG-I/MDA5-specific RNA ligands and treat RNA transcripts with calf intestine alkaline phosphatase (CIP) to remove 5’ppp groups.

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

      No revisions have already been incorporated in the transferred manuscript.

      **Description of analyses that authors prefer not to carry out**

      As described above to answer to the Reviewer#3 suggestion, how encapsidated full-length viral genome can still be detected by MDA5 in virus-infected cells needs to be addressed in a separate study.

      Referee #2

      Evidence, reproducibility and clarity

      We thank the Reviewer#2 for underlining that our study “shed light on the RLR recognition of RABV RNAs upon infection” and that our study “clarify the mechanism of cellular immunity differences between RABV pathogenic strain and vaccine attenuated strain. Reviewer#2 suggested to “verify whether the difference in this mechanism is caused by the difference in the viral genome, whether the N gene or L gene of the two can be exchanged by reverse genetics, and then infect the cells to verify whether the 5'cb DI genomes can be generated just as this paper.”

      We agree with Reviewer#2 that applying reverse genetics for RABV genome by exchanging N and L genes could provide a more in-depth characterization of 5’cb DI generation and pathogenicity of RABV. However, these additional experiments cannot be provided within the scope of this paper and will take time for the revision process. We believe, that this question needs to be addressed in a separate study by exchanging either N and L genes using reverse genetics.

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      Reply to the reviewers

      Response to the reviewers

      Manuscript number: RC-2022-01407

      Corresponding author(s): Ivana, Nikić-Spiegel

      1. General Statements

      We would like to thank the reviewers for careful reading of our manuscript and for their insightful and useful comments. We are happy to see that the reviewers find these results to be of interest and significance. The way we understand reviewers’ reports, their main concerns can be roughly divided in following categories: 1) providing more quantitative data 2) interpretation of the Annexin V/PI assay 3) additional evidence for calpain involvement. We intend to address these experimentally or by modifying the text, as outlined below.

      2. Description of the planned revisions

      Reviewer #1

      Fig1A/B o SYTO 16 staining suggests slight reshaping of nucleus upon spermine NONOate, showing less blurry punctae. From the SYTO 16 profile, this should be quantifiable.

      By looking at the shown examples and the entire dataset, it appears to us as if neuronal nuclei are shrinking upon spermine NONOate treatment resulting in their less blurry appearance. We are not sure if this is what the reviewer is referring to, but this can also be quantified by measuring changes in neuronal nuclear size. We already have this data from the measurements shown in Fig4 and we intend to show it in the revised version of the manuscript. Line profile measurements are also possible, but the nuclear size quantification might be more suitable for this purpose.

      o There is a subset of neuron nuclei that are SYTO 16 positive. Please quantify the ratio

      We will use our existing dataset to quantify the ratio of NFL positive and SYTO16 positive nuclei.

      FigS1A o Show NeuN with Anti-NFL merged figures

      We will show merged NeuN and anti-NFL images, which might require rearrangement of the existing figures and figure panels. We will do this in the revised manuscript.

      FigS1C o Show quantification and timeline. I want to know whether there is also a plateau reached here.

      As the data shown in the FigS1C do not include NeuN staining, we will do additional experiments and perform proposed quantifications.

      FigS2A-F o Though the statements might be true, selecting one nucleus for a line profile as a statement for the whole dataset seems problematic. Average a larger number of unbiased selected nuclei profiles across multiple cultures to make a stronger statement, or a percentage of positive nuclei as in FigS1b.

      Corresponding images and line profiles are representative of the entire dataset. However, we agree with the reviewer that this is not obvious from the current manuscript version. Thus, to strengthen our findings, we intend to quantify the percentage of positive nuclei as in FigS1b. The only difference will be that instead of NeuN, we will use SYTO16 as a nuclear marker. The reason being that the existing datasets contain images of NFL and SYTO16 and not NeuN.

      FigS3 • There are no fluorescence profiles, no quantification

      As the reviewer suggests, we will quantify the ratio of NFL positive and SYTO16 positive nuclei, and include the quantifications in the revised manuscript.

      General statement: There do seem to be punctated patterns of non-nucleus accumulating NFL fragments. Can they be localized to any specific structure?

      We assume that the reviewer is referring to neuronal/axonal debris. They are present after injury but they do not colocalize with nuclear stains. We will address this in the revised manuscript.

      Fig1C-F • I find it too simplistic to categorize c+f and d+e together. There is a huge difference in the examples of nuclear localization between d and e. To not comment on their distinction (if that is consistent) is problematic. Also, since we don't see a merge with either NeuN or SYTO 16, reader quantification is difficult.

      We thank the reviewer for bringing this up. We will carefully check our entire dataset and we will update the figures and the text accordingly. We will also show the corresponding SYTO16 images, as the reviewer suggested.

      Would the microfluidic device construction allow for time to transport any axonally damaged fragments to the soma?

      Yes, the construction of the microfluidic devices allows the transport of axonal proteins back to the soma. Based on our experiments, it seems that damaged NFL from the axonal compartment could be contributing to the accumulation of NFL fragments in the nuclei. However, this contribution seems to be minimal as we cannot detect nuclear NFL upon the injury of axons alone. Alternatively, it could be that the processing of axonal NFL fragments proceeds differently if neuronal bodies are not injured and that this is the reason we don’t detect the NFL nuclear accumulation upon injury of axons alone. We will discuss this in the revised manuscript.

      Fig2C+D • The statement ".... no annexin V was detected on the cell membrane" needs to be shown more clearly

      We will modify figures to address this comment.

      • Please provide merged AnnexinV/PI images

      We will modify figures to address this comment.

      • The conclusion about 2D, that nuclear accumulated NFL overlaps with PI is not supported by the example image shown. There are plenty of PI positive spots that are not NFL positive and even several NFL positive ones that do not have a clear PI staining. Please quantify and then show a very clear result in order to be able to suggest necrosis as the underlying process.

      We are not sure if we understand the reviewer’s concern correctly. We will try to clarify it here and in the revised text. If necessary, we will tone down our conclusion, but the reason why not all of PI positive spots are NFL positive is most likely due to the fact that not all injured nuclei are NFL positive. We quantified in FigS1 that up to 60% of nuclei under injury conditions show NFL accumulations. That is why we are not surprised to see some PI positive/NFL negative nuclei. And the fact that there are some NFL positive nuclei which appear to be PI negative is most likely related to the fact that the PI binding is affected. In addition, upon closer inspection of NFL and PI panels in Fig2d it can be observed that NFL positive nuclei are also PI positive, albeit with a lower PI fluorescence intensity. We will modify the figure to show this clearly in the revised manuscript.

      FigS5 C+D • If the case is made that nitric oxide damage induces necrosis, then why is it that the AnnexinV example of Staurosporine exposure (which induces apoptosis) looks similar to that of nitric oxide damage in Fig2d and necrosis induction with Saponin looks very different?

      We thank the reviewer for bringing this up. We will try to clarify this in the revised manuscript. Regarding the specific questions, the most likely explanation why staurosporine treated neurons look similar to the ones treated with spermine NONOate is that in the late stages of apoptosis cell membrane ruptures and allows for the PI to label nuclei. This is probably the case here as illustrated by the nucleus in the middle of the image (FigS5c) that shows the fragmentation characteristic for the apoptosis. This is not happening in early apoptotic cells due to the presence of an intact plasma membrane. On the other hand, the reason why saponin treated cultures look different compared to spermine NONOate is that membranes are destroyed by saponin so that the PI can enter the cell. For that reason, there could have not been any AnnexinV binding to the membrane which would correspond to the AnnexinV signal of spermine NONOate treated neurons. As we will discuss below, we did not try to mimic spermine NONOate-induced injury with saponin treatment. Instead this was a control condition for PI labeling and imaging. We also used a rather high concentration of saponin which probably destroyed all the membranes which was not the case with spermine NONOate treatment. We intend to do additional control experiments to address this.

      • Additionally, does necrosis induction with Saponin also cause NFL fragment accumulation in the nucleus? Please show a co-staining of them. Also, the authors want to make a claim about reduce PI binding in NFL accumulated necrotic cells. In these examples, the intensity of the nuclear stain of PI with Saponin looks dimmer than with Staurosporine. Are the color scalings similar? It might be that the necrotic process itself causes reducing binding of PI and is not related to the presence of NFL.

      With regards to this question, it is important to note that Annexin V and PI imaging was done in living cells. To obtain the corresponding anti-NFL signal as shown in Fig 2c,d we had to fix the neurons, perform immunocytochemistry and identify the same field of view. We tried to do the same procedure after saponin treatment (Supplementary Figure 5d) but the correlative imaging was very difficult due to the detachment of neurons from the coverslip after the saponin treatment. For this reason, we could not identify the same field of view co-stained with NFL. However, other fields of view did not show NFL fragment accumulation. This could also be the consequence of the high saponin concentration that we used as we discuss above. We have also noticed the reduced intensity of PI binding in the nuclei of saponin-treated neurons. However, if the necrotic process itself reduces the binding of PI to the DNA, then all of the neurons treated with spermine NONOate would have an equally low PI signal. In our experiments, only the nuclei which contained NFL accumulations had a low PI signal, while the signal of NFL-negative nuclei was higher (as shown in Fig2d). We would also like to point out again that the saponin treatment was our control of the PI’s ability to penetrate cells and bind the DNA, as well as our imaging conditions, and not the control of the necrotic process itself. This is the reason why we didn’t go into details about neuronal morphology and NFL localization upon saponin treatment. We thank the reviewer for pointing this out since it prompted us to reevaluate what we wrote in the corresponding paragraph of the manuscript. We realized that the confusion might stem from our explanation of the AnnexinV/PI assay controls in the lines 196-198 (“Additional control experiments in which neurons were treated with 10 μM staurosporine (a positive control for induction of apoptosis) or with 0.1% saponin (a positive control for induction of necrosis) confirmed the efficiency of the annexin V/PI assay (Supplementary Fig. 5c,d).”). We will modify this portion of the text to clearly state that staurosporine and saponin treatments were controls of the AnnexinV and PI binding to their respective targets and not of the apoptosis/necrosis process. When it comes to the saponin treatment, our intention was only to permeabilize the membranes in order to allow PI penetration and DNA binding and not to induce necrosis or to mimic the effect of the spermine NONOate. We also intend to perform experiments with lower concentration of saponin to try to address this experimentally in addition to the text modifications.

      Fig3d • Please show similarly scaled images from controls for proper comparison

      We will show similarly scaled images of the control neurons so that they can be properly compared. They were initially not scaled the same for visualization purposes, but we will modify this in the revised manuscript.

      • How do the authors scale the degree and kinetics of induced damage between application of hydrogen peroxide/CCCP and glutamate toxicity? Does glutamate toxicity take longer to affect the cell, not allowing enough time to accumulate NFL fragments in the nucleus?

      It is challenging to scale the degree and kinetics of induced damage with different stressors. That is why we did not intend to do this. Instead we set different injury conditions based on the published literature. That is why can only speculate when it comes to this. In this regard, it can be that the glutamate toxicity takes “longer” to affect the cells even though it is very difficult to compare them on a timescale, especially when considering different mechanisms of action. We will discuss this limitation in the revised manuscript.

      Fig4B • Some groups (like NO and NO + emricasan) have much larger numbers of close to 0 intensity, compared to the control group. Why?

      We were wondering the same when we analyzed the data. The fact that our nuclear fluorescence intensity analysis picked up NFL signal in control neurons which had no nuclear NFL accumulation made us realize that the intensity measured in the nuclei of control group comes entirely from the out of focus fluorescence – from neurofilaments in cell bodies, dendrites and axons (an example can be seen in the FigS6). That is why we presented the corresponding data with a cut-off value based on the control signal (as mentioned in lines 238-240). Since the oxidative injury causes NFL degradation (not only in neuronal soma, but also neuronal processes), the overall fluorescence intensity of the NFL immunocytochemical staining is reduced in injured neurons. We can see that in all of our images. Consequently, there is no contribution of out of focus fluorescent signal to the measured fluorescence intensity in the majority of nuclei. Due to that, the nuclei without NFL accumulation (at least 40% of injured nuclei) will appear to have a close to 0 intensity of the fluorescent signal. We will discuss and clarify this additionally in the revised manuscript.

      • Please add the ratio of above/below threshold (50/50 obviously in controls)

      We will update the figure in the revised manuscript.

      • The description of the CTCF value calculation seems a little... muddled? Several parameters are described whereas "integrated density" is not even used. Why not simply mean intensity of nuclear ROI-mean intensity of background ROI?

      We included the integrated density in the description since it is measured together with the raw integrated density and can also be used for the CTCF value calculation. However, since we didn’t use it for the CTCF calculation, we will remove it from the corresponding section of the manuscript. We calculated the CTCF value instead of calculating mean intensity of the nuclear ROI - mean intensity of the background ROI, since the CTCF value also takes into account the area of the ROI and not just the mean intensity.

      • Also, please tell me if the areas for nuclear ROIs change, as I noted for Fig1A/B

      We will include this information in the revised manuscript.

      • To make sure that one of the 3 experimental repeats didn't skew the results, please show the median fluorescence intensity for each individual experiment to clarify that the supposed effect is repeated across experiments.

      We have already noticed that in the earliest of the three experiments overall fluorescence intensity was higher, but this was consistent across all the experimental groups and did not skew the results or affect the overall conclusion. However, we will double-check this and revise the figure.

      • From the text "...and due to the NFL degradation during injury...": this seems to contradict the process? Either the NFL fragment accumulates in the nucleus or it is degraded during injury. And isn't the degradation through calpain what supposedly allows this fragment of NFL to go to the nucleus in the first place? I reckon that the authors are possibly trying to reconcile why there are many close-to-0 intensity nuclei in the NO and NO + emricasan groups, but I don't feel the explanation given here fits.

      As we tried to explain in our response above, we think that the overall degradation of neurofilaments in neurons affects the fluorescence intensity originating from the out of focus neurofilaments. Therefore, the nuclei without NFL accumulation in injured conditions have a close to 0 fluorescence intensity. Additionally, we think that this is not an either/or situation, but that both degradation and nuclear accumulation of NFL happen simultaneously. We also think that degradation of axonal NFL and the transport of its tail domain to the soma will at least partially contribute to the accumulation in the nucleus. In any case, degradation and nuclear accumulation seem to be differentially regulated in individual neurons, as some of them show nuclear NFL accumulation and some not. Furthermore, calpain and other mechanisms could also cause NFL degradation up to the point at which these fragments can no longer be recognized by the anti-NFL antibody leading to the loss of signal. We will try to clarify this in the revised version of the manuscript.

      Fig5 • Does the distribution of this GFP in B match any of the various antibody stainings of different NFL fragments? Perhaps this is still a valid fragment of NFL, just not picked up by any AB?

      The GFP signal in B appears rather homogenous and it does not match any of the various antibody stainings of different NFL fragments. As the reviewer points out, this could also be a valid fragment of NFL fused to GFP that none of our antibodies is recognizing. We will clarify this in the revised manuscript.

      • "... and was indistinguishable from the full277 length NFL-GFP." Based on what parameters?

      We will clarify this in the revised text, but we meant in terms of overall neurofilament network and cell appearance, which is commonly used to test the effect of NFL mutations.

      • The authors claim that b is different from d, but I am not convinced. I would like to see a time dependent curve from multiple cells showing a differential change in nuclear and cytosolic GFP signal.

      As we also wrote in the manuscript, in the majority of neurons that were monitored during injury we were not able to detect an increase in the GFP fluorescence intensity in the nucleus. This is what prompted further experiments with NFL(ΔA461–D543)-FLAG. We will clarify this additionally in the revised manuscript and perform line profile intensity measurements to show the difference in nuclear and cytosolic GFP signal.

      • Secondly, the somatic GFP intensity for NFL increases for full length NFL-GFP. How is this explained, if it is only a separation of NFL and GFP? If anything, GFP should float away. And if the answer is that NFL is recruited to the nucleus, you showed that inhibition of calpain activity partially prevents that. So, if calpain activity is necessary for the transport of NFL to the nucleus, then wouldn't it also cut the GFP from NFL before it reaches the nucleus?

      We thank the reviewer for bringing this up and we apologize for the confusion. This can be explained by the fact that the images were scaled in a way that the GFP signal over time could still be seen easily (i.e. differently across different time points which we unfortunately forgot to mention in the figure legend). In the revised manuscript, we will either scale the images the same or we will alternatively show the displayed grey values in individual panels.

      Fig6 • It is recommended to overlap the transfected cells with a stain for endogenous NFL to show that despite the absence of the FLAG-tag, there is still NFL.

      We did not overlap the anti-NFL with anti-FLAG and SYTO16 staining, due to the space constraint and the intent to clearly show the overlap of FLAG and SYTO16 signals in the merged images above the graphs. However, the line profile intensity measurements were done in all three channels and show that despite the absence of FLAG, there is still NFL in the nucleus (Fig6b), or that both FLAG and NFL are present in the nucleus (Fig6d, NFL signal shown in gray). However, as this is not obvious and can easily be overlooked, we will show the endogenous NFL staining overlap in the revised version of the manuscript.

      Fig7 • „ ...all disrupted neurofilament assembly...": this sounds like the staining for native NFL supposedly shows a distortion due to a dominant negative effect of the expression of these constructs? Please clarify.

      Yes, we were referring to the disruption of neurofilament assembly due to a dominant negative effect of the expression of NFL domains. We will clarify this in the revised version of the manuscript.

      Discussion: • The authors show that after overepression of the head domain only, it possibly passively diffuses into the nucleus even in the absence of oxidative injury. However, it seems to be suggested as well that the head domain would not be freely floating around if it wouldn't be for increased calpain activity as a result of oxidative injury in the first place. Therefore, a head domain fragment localized in the nucleus would still more prominently happen upon oxidative injury and interact with DNA through prior identified putative DNA interaction sites from Wang et al. Please comment.

      That is correct. Upon injury and calpain cleavage, it is conceivable that a fragment containing the NFL head domain would also be present in the cell and could potentially diffuse to the nucleus and interact with the DNA. However, by staining injured neurons with an antibody that recognizes amino acids 6-25 of the NFL head domain, we were not able to detect an NFL signal in the nucleus (FigS2a,b). It could be that either the NFL head domain does not localize in the nuclei upon injury, or that the fragment localizing in the nucleus does not contain amino acids 6-25 of the NFL head domain. As the putative DNA-binding sites described by Wang et al involve 7 amino acids located in the first 25 residues of the NFL head domain, we would expect to detect it with the aforementioned antibody. However, as that was not the case we speculated that the interaction of NFL and DNA occurs differently in living cells, as opposed to the test tube conditions utilized by Wang et al. We will comment and clarify this in the revised version of the manuscript.

      • Reviewer #2*

      • Major Comments:

      • The initial data presented in the paper is good, does response of oxidative damage with proper controls, testing the antibodies to NF-L and etc. (Fig. 1-Fig. 4). *

      We thank the reviewer for their positive feedback.

      1. The evidence for calpain involvement in NF-L cleavage during oxidative damage is missing. Provide the evidence for full length NF-L construct and deletion mutants transfected into cells by immunoblot for cleavage of NF-L, perform nuclear and cytoplasmic extract preparations and show that enrichment of the tagged cleaved NF-L fragment in nuclear fraction.

      We thank the reviewer for their comments and suggestions. Since we saw in our microscopy experiments that calpain inhibition reduced the accumulation of NFL in the nucleus, and since it is known that NFL is a calpain substrate (Schlaepfer et al., 1985; Kunz et al., 2004 and others), we did not perform additional experiments to confirm the involvement of calpain in NFL degradation during injury. However, to strengthen our findings, we intend to perform the suggested experiments and include the results in the revised manuscript.

      1. Show calpain activation during oxidative damage by performing alpha-Spectrin immunoblots identify calpain specific 150-kda Spectrin and caspase specific 120-kDa fragment generation in these cells. Also, calpain activation can be measured by MAP2 level alteration and p35 to p25 conversion. Without this evidence it's very hard to believe if the calpain activity is increased or decreased during oxidative damage and these markers are altered by using calpain inhibitors.

      To confirm the calpain activation, we intend to perform anti-alpha spectrin and/or anti-MAP2 blots in lysates of control and injured neurons and include the results in the revised manuscript.

      1. The premise that NF proteins are absent in cell bodies and present only in axons is not correct. It has been demonstrated by multiple investigators that NFs are present in the perikaryon and dendrites of many types of neurons (Dahl, 1983, Experimental Cell Research)., Dr. Ron Liem's group showed NF protein expression in cell bodies of dorsal root ganglion cells (Adebola et ., 2015, Human Mol Genetics) and also showed N-terminal antibodies for NF-L, NF-M and NF-H stain rat cerebellar neuronal cell bodies and dendrites (Kaplan et al., 1991, Journal of Neuroscience Research) when NFs are less phosphorylated. (Schlaepfer et al., 1981, Brain Research) show staining of cell bodies of cortex and dorsal root ganglion cell bodies with NF antibody Ab150, and Yuan et al., 2009 in mouse cortical neurons with GFP tagged NF-L.

      We are not sure what the reviewer is referring to since we cannot find a corresponding section in which we claim that NF proteins are absent in cell bodies. We wrote the following “Anti-NFL antibody staining of neurons treated with the control compound showed the expected neurofilament morphology, that is, a strong fluorescence intensity in axons and lower intensity in cell bodies and dendrites (Fig. 1a)” in our results section (lines 119-121), but the claim we were trying to make there was that NF proteins are particularly abundant in axons. We will clarify this in the revised manuscript.

      1. Quantifying NF-L signal or tagged NF-L fragment signals in the cell body by ICC has many problems and making conclusions. It's extremely difficult to have control over levels of proteins in transfected overexpression models and comparing two or three different constructs with each other by ICC. Not every cell expresses same levels of protein in transfected cells and quantifying it by ICC again has a major problem. This can be addressed if there are stable lines that express equal levels of protein in all cells that comparisons can be made. Under thesese circumstances validation of the hypothesis presented in the study has no strong direct evidence to demonstrate that calpain is activated and NF-L fragment translocate to the nucleus.

      We agree that the results from overexpression-based experiments should be interpreted with caution as levels of expression vary between the cells. We intend to discuss this in the revised manuscript. However, we find it difficult to experimentally address this comment since we are not sure which specific experiments the reviewer is referring to. With regards to this, we would like to emphasize that most of the initial experiments in which we observed NFL accumulation in the nuclei of injured neurons were based on the ICC labeling of endogenous NFL and didn’t involve its overexpression. This includes labeling of endogenous NFL in various types of neurons, comparing the effects of different types of oxidative injury, as well as testing the effects of calpain inhibition on the observed nuclear accumulation (Figures 1-4; Supplementary Figures 1-6). We later resorted to the overexpression experiments in primary neurons (Figures 5-7; Supplementary Figure 7, 10) to gain more information about the identity of NFL fragment which was detected in the nucleus. Due to the low transfection efficiency of primary neurons, we performed an additional set of overexpression experiments in neuroblastoma ND7/23 cells (Figure 8; Supplementary Figures 8,9) and obtained similar results in a higher number of cells. We agree that having stable cell lines which e.g. express same levels of NFL domains would be a more elegant approach and we intend to make them for our follow-up studies, however the generation of said stable cell lines might be beyond the scope of this revision. Furthermore, looking at our data with overexpression of NFL domains in ND7/23 cells (Supplementary Figure 8,9), it appears to us as if different domains are rather homogenously expressed in different cells. While the expression levels might vary, it seems that they all show the same trend when it comes to their localization (which was the main point of those experiments).

      1. The interpretation that NF-L preventing DNA labeling cells is misinterpretation. NFs have very long half-life compared to other proteins. Due to oxidative damage, DNA is degraded in the cells but NFs that have very long half-life you see as NFs rings in the dead cells. So, NFs do not prevent DNA labeling, but DNA or chromatin is degraded in dead cells.

      We thank the reviewer for their useful insight. DNA degradation could certainly be the reason why we observe a lower fluorescence intensity of the propidium iodide fluorescence in the nuclei of injured neurons. We intend to discuss this in the revised manuscript. However, if the DNA degradation is the only reason for the lower PI fluorescence intensity, then the PI fluorescence intensity would be the same in all injured nuclei. In our experiments, we saw the reduced PI fluorescence intensity in nuclei that contained NFL accumulations and not in other nuclei. Additionally, we observed a reduction of SYTO16 fluorescent labeling of nuclei which contained accumulations of the NFL tail domain, even in the absence of oxidative injury. Due to these reasons we speculated that NFL accumulation in the nucleus might hinder nuclear dyes from interacting with the DNA. But this is only a speculation and we will try to clarify this further in the revised manuscript including alternative explanations.

      Minor comments: 1. In the introduction on page 4 reference is missing for NF transport, aggregation and perikaryal accumulation (on line 93).

      We will add a reference to the revised manuscript.

      1. The statement in discussion on page 14 line 454 for Zhu et al., 1997 study is not accurate. It should be modified to sciatic nerve crush not spinal cord injury.

      We will correct this mistake in the revised manuscript.

      1. What is the size of the calpain cleaved NF-L tail domain? If you perform immunoblots on cell extracts treated with oxidative agents one would know it.

      We will perform immunoblots on cell lysates and incorporate the corresponding results in the revised manuscript.

      1. Authors could make their conclusions clear. This is particularly true for the experiments in Figure 4 panels c and d. It is very difficult to understand the conclusions of the experiments. First state the expectation and then described whether the expectation is true or different.

      We will do as the reviewer suggested in the revised manuscript.

      1. The ICC images are at extremely low magnification. They should be shown at 100x or 120x so that details of the cell body and the nucleus can be seen.

      Our intention was to show larger fields of view and wherever appropriate insets, but we will try to improve this in the revised manuscript by either zooming in, cropping or adding additional insets with individual cell bodies and nuclei. In general, images were taken with an optimal resolution/pixel size in mind for any of the used objectives (60x/1.4 NA or 100x/1.49 NA) and we can easily modify our figure panels to show more details.

      1. Oxidative damage leads to beaded accumulation of NF-L in neurites and axons. Authors should address this issue.

      We will discuss this in the revised manuscript.

      1. The combination treatment of the inhibitors (last 3 sets of the Fig. 4 b) has no statistical significance should be removed.

      Actually, these differences were statistically significant (Supplementary Table 1). For clarity and as described in the figure legend (line 516: “The most relevant significant differences are indicated with an asterisk”) we showed only a subset of them on the graph, but we will change this in the revised manuscript.

      1. Why only two antibodies recognize cleaved NF-L? If the antibodies at directed at tail region, they should recognize it unless the phosphorylated tail at Ser473 may inibit the antibody binding. In that case NF-L Ser473 specific antibody (EMD Millipore: MABN2431) may be used to test this idea.

      This is a very good point that we also wonder about. Even if all antibodies are directed at tail region, exact epitopes are not described for all of them. That makes it also difficult for us to understand and speculate on this. However, we have already ordered the new antibody as suggested by the reviewer and we will experimentally test it.

      **Referees cross-commenting**

      I agree with the reviewer#1 about presenting the quantification data for the indicated figures to make conclusions strong and see how much of variation is there among sampled cells.

      As discussed in our response to reviewer #1, we will provide additional quantifications.

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

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

      Reviewer #2, major comment 7. Authors could do chromatin immunoprecipitation (chip) analysis to identify NF-L binding sites on chromatin and perform gel shift assays to show NF-L tail domain binding to specific consensus DNA sequences.

      We thank the reviewer for their suggestion. We are very interested in performing additional experiments and identifying the NFL binding sites on the DNA (either by chromatin immunoprecipitation or DamID-seq) and we intend to perform these experiments as soon as possible. Unfortunately, at the moment we do not have the expertise to perform such experiments in our lab. Instead, this type of follow-up project requires establishing a collaboration which is beyond the scope of this revision.

    2. 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:

      The authors hypothesize that Neurofilament-L subunit of NFs participates in oxidative medicated damage by calpain activation and cleavage of its C-terminal region, translocation to nucleus and activation of toxicity driven gene expression. Authors used cell culture system, NF-L gene transfections and immunocytochemistry to make their conclusions.

      Major Comments:

      1. The initial data presented in the paper is good, does response of oxidative damage with proper controls, testing the antibodies to NF-L and etc. (Fig. 1-Fig. 4).
      2. The evidence for calpain involvement in NF-L cleavage during oxidative damage is missing. Provide the evidence for full length NF-L construct and deletion mutants transfected into cells by immunoblot for cleavage of NF-L, perform nuclear and cytoplasmic extract preparations and show that enrichment of the tagged cleaved NF-L fragment in nuclear fraction.
      3. Show calpain activation during oxidative damage by performing alpha-Spectrin immunoblots identify calpain specific 150-kda Spectrin and caspase specific 120-kDa fragment generation in these cells. Also, calpain activation can be measured by MAP2 level alteration and p35 to p25 conversion. Without this evidence it's very hard to believe if the calpain activity is increased or decreased during oxidative damage and these markers are altered by using calpain inhibitors.
      4. The premise that NF proteins are absent in cell bodies and present only in axons is not correct. It has been demonstrated by multiple investigators that NFs are present in the perikaryon and dendrites of many types of neurons (Dahl, 1983, Experimental Cell Research)., Dr. Ron Liem's group showed NF protein expression in cell bodies of dorsal root ganglion cells (Adebola et ., 2015, Human Mol Genetics) and also showed N-terminal antibodies for NF-L, NF-M and NF-H stain rat cerebellar neuronal cell bodies and dendrites (Kaplan et al., 1991, Journal of Neuroscience Research) when NFs are less phosphorylated. (Schlaepfer et al., 1981, Brain Research) show staining of cell bodies of cortex and dorsal root ganglion cell bodies with NF antibody Ab150, and Yuan et al., 2009 in mouse cortical neurons with GFP tagged NF-L.
      5. Quantifying NF-L signal or tagged NF-L fragment signals in the cell body by ICC has many problems and making conclusions. It's extremely difficult to have control over levels of proteins in transfected overexpression models and comparing two or three different constructs with each other by ICC. Not every cell expresses same levels of protein in transfected cells and quantifying it by ICC again has a major problem. This can be addressed if there are stable lines that express equal levels of protein in all cells that comparisons can be made. Under thesese circumstances validation of the hypothesis presented in the study has no strong direct evidence to demonstrate that calpain is activated and NF-L fragment translocate to the nucleus.
      6. The interpretation that NF-L preventing DNA labeling cells is misinterpretation. NFs have very long half-life compared to other proteins. Due to oxidative damage, DNA is degraded in the cells but NFs that have very long half-life you see as NFs rings in the dead cells. So, NFs do not prevent DNA labeling, but DNA or chromatin is degraded in dead cells.
      7. Authors could do chromatin immunoprecipitation (chip) analysis to identify NF-L binding sites on chromatin and perform gel shift assays to show NF-L tail domain binding to specific consensus DNA sequences.

      Minor comments:

      1. In the introduction on page 4 reference is missing for NF transport, aggregation and perikaryal accumulation (on line 93).
      2. The statement in discussion on page 14 line 454 for Zhu et al., 1997 study is not accurate. It should be modified to sciatic nerve crush not spinal cord injury.
      3. What is the size of the calpain cleaved NF-L tail domain? If you perform immunoblots on cell extracts treated with oxidative agents one would know it.
      4. Authors could make their conclusions clear. This is particularly true for the experiments in Figure 4 panels c and d. It is very difficult to understand the conclusions of the experiments. First state the expectation and then described whether the expectation is true or different.
      5. The ICC images are at extremely low magnification. They should be shown at 100x or 120x so that details of the cell body and the nucleus can be seen.
      6. Oxidative damage leads to beaded accumulation of NF-L in neurites and axons. Authors should address this issue.
      7. The combination treatment of the inhibitors (last 3 sets of the Fig. 4 b) has no statistical significance should be removed.
      8. Why only two antibodies recognize cleaved NF-L? If the antibodies at directed at tail region, they should recognize it unless the phosphorylated tail at Ser473 may inibit the antibody binding. In that case NF-L Ser473 specific antibody (EMD Millipore: MABN2431) may be used to test this idea.

      Significance

      The study has very high significance. The results obtained with proper experimentation great implications in understanding how NF proteins are degraded in the cells and how these degraded fragments would alter neurodegeneration during oxidative stress and other conditions.

      Referees cross-commenting

      I agree with the reviewer#1 about presenting the quantification data for the indicated figures to make conclusions strong and see how much of variation is there among sampled cells.

    1. Author Response

      Reviewer #1 (Public Review):

      This paper examines EEG responses time-locked to (or "entrained" by) musical features and how these depend on tempo and feature identity. Results revealed stronger entrainment to "spectral flux" than to other, more commonly tested features such as amplitude envelope. Entrainment was also strongest for lowest rates tested (1-2 Hz).

      The paper is well written, its structure is easy to follow and the research topic is explained in a way that makes it accessible to readers outside of the field. Results will advance the scientific field and give us further insights into neural processes underlying auditory and music perception. Nevertheless, there are a few points that I believe need to be clarified or discussed to rule out alternative explanations or to better understand the acquired data.

      We thank the Reviewer for taking the time to evaluate our manuscript and for the positive response. We have now conducted further analyses to strengthen our conclusion that neural synchronization was strongest at slower musical tempi and to rule out an alternative explanation that neural synchronization was strongest for music presented near its own original or “natural” tempo. We also added some points to the Discussion in response to your comments; revised text is reproduced as part of our point-by-point responses below for your convenience. The page and line numbers correspond to the manuscript file without track changes.

      1) Results reveal spectral flux as the musical feature producing strongest entrainment. However, entrainment can only be compared across features in an unbiased way if these features are all equally present in the stimulus. I wonder whether entrainment to spectral flux is only most pronounced because the latter is the most prominent feature in music. Can the authors rule out such an explanation?

      Respectfully, it is not fully clear to us based on the literature that entrainment can only be compared across features fairly when those features are equally presented in the stimulus. Previous work in the speech domain has compared entrainment to amplitude envelope vs. spectrogram, vs. a symbolic representation of the time of occurrence of different phonemes (Di Liberto et al., 2015). Work in the music domain has compared entrainment to amplitude envelope (and its derivative) vs. features quantifying melodic expectation (surprise and entropy, quantified using a hidden Markov-model trained on a corpus of Western music; Di Liberto et al., 2020). In these papers, there was no quantification of the degree to which each feature was present in the stimulus material, and when comparing such qualitatively different features, it is not clear to us how one would do so. Nonetheless, these studies used the resulting TRF-based dependent measures to evaluate which feature best predicted the neural response. Here, although we do not know what acoustic feature might be most present / strongest in music, we believe that we can investigate the degree to which each feature predicts the neural response. In fact, we might argue the sort of reverse of the logic in your comment – that the TRF results actually tell us which feature is perceptually or psychologically the most important in terms of driving brain responses, which may not be fully predictable from the acoustics of those features.

      From a data analysis perspective, we have independently normalized (z-scored) each feature as well as the neural data, as prescribed in Crosse et al., 2021, to try to level the playing field for the musical features we are comparing. Moreover, we made changes in the discussion to acknowledge your concern. The text is reproduced here for your convenience.

      p. 26, l. 489-497: “One hurdle to performing any analysis of the coupling between neural activity and a stimulus time course is knowing ahead of time the feature or set of features that will well characterize the stimulus on a particular time scale given the nature of the research question. Indeed, there is no necessity that the feature that best drives neural synchronization will be the most obvious or prominent stimulus feature. Here, we treated feature comparison as an empirical question (Di Liberto et al., 2015), and found that spectral flux is a better predictor of neural activity than the amplitude envelope of music. Beyond this comparison though, the issue of feature selection also has important implications for comparisons of neural synchronization across, for example, different modalities.”

      2) Spectral analyses of neural data often yield the strongest power at lowest frequencies. Measures of entrainment can be biased by the amount of power present, where entrainment increases with power. Can the authors rule out that the advantage for lower frequencies is a reflection of such an effect?

      Thank you for this insightful comment. In response to your comment and the comments of Reviewer 3, we normalized the TRF correlations, stimulus–response correlations, and stimulus–response coherences by surrogate distributions that were calculated separately for each musical feature and – importantly – for every tempo condition. Following Zuk et al., 2021, we formed surrogate distributions by shifting the relevant neural data time course relative to the stimulus-feature time courses by a random amount. We did this 50 times, and for each shift re-calculated all dependent measures. We then normalized our dependent measures calculated from the intact time series relative to these surrogate distributions by subtracting the mean and dividing by the standard deviation of the surrogate distribution (“z-scoring”). Since the approach of shifting the neural data leaves the neural time series intact, the power spectrum of the data is preserved, but only its relationship to the stimulus is destroyed. After normalization, the plots obviously look a little different, but the main results – a higher level of neural synchronization to slower stimulation tempi and in response to the spectral flux – remain.

      The changes can be found throughout the manuscript, but especially on p. 11, l. 210-218, Figures 2-3 and a more detailed explanation in the Methods section.

      p. 39, l. 821-829: “In order to control for any frequency-specific differences in the overall power of the neural data that could have led to artificially inflated observed neural synchronization at lower frequencies, the SRCorr and SRCoh values were z-scored based on a surrogate distribution (Zuk et al., 2021). Each surrogate distribution was generated by shifting the neural time course by a random amount relative to the musical feature time courses, keeping the time courses of the neural data and musical features intact. For each of 50 iterations, a surrogate distribution was created for each stimulation subgroup and tempo condition. The z-scoring was calculated by subtracting the mean and dividing by the standard deviation of the surrogate distribution.”

      A related point, what was the dominant rate of spectral flux in the original set of stimuli, before tempo was manipulated? Could it be that the slow tempo was preferred because in this case participants listened to a most "natural" stimulus?

      This is a good point, thank you. We did two things to attempt to address this (see also comment Reviewer 3). First, the original tempo for each song can be found in Supplementary Table 1. To make the table more readable and more comparable with the main manuscript, we have updated the table and now state the original tempi in BPM and Hz. Second, we added histograms of the original tempi across all songs as well as the maximum amount by which all songs were tempo-shifted (i.e., the maximum tempo difference between the slowest (or fastest) version of each song segment compared to the original tempo). These histograms have been added to Figure 1 – figure supplement 2, and are paraphrased here for your convenience (p. 13 l. 265-273): The original tempo of the set of musical stimuli ranges between 1-2.75 Hz. This indeed overlaps with the tempo range that revealed strongest neural synchronization. When songs were tempo-shifted to be played at a slower tempo than the original, they were shifted by ~0.25-1.25 Hz. In contrast, shifting a song to have a faster tempo typically involved a larger shift of ~1-2.25 Hz. Thus, it is definitely possible that tempo, degree of tempo shift, and proximity to “natural” tempo were not completely independent values.

      For that reason, to investigate the effects of the amount of tempo manipulation on neural synchronization, we conducted an additional analysis. We compared TRF correlations for a) songs that were shifted very little relative to their original tempi to b) songs that were shifted a lot relative to their original tempi. We did not have enough song stimuli to do this for every stimulation tempo, but we were able to do the TRF correlation comparison for two illustrative stimulation tempo conditions (at 2.25 Hz and 1.5 Hz). In those tempo conditions, we took the TRF correlations for up to three trials per participant when the original tempo was around the manipulated tempo (1.25-1.6 Hz for 1.5 Hz or 2.01-2.35 Hz for 2.25 Hz) and compared it to those trials where the original tempo was around 0.75¬–1 Hz faster or slower than the manipulated tempo at which the participants heard the songs (Figure 3 – figure supplement 2). This analysis revealed that there was no significant effect of the original music tempi on the neural response (please see Material and Methods, p. 40, l. 855-861 and Results p. 13, l. 265-273). In response to your and Reviewer’s 3 comments, we also added this additional point to the discussion.

      p. 23-24 l. 427-436: “The tempo range within which we observed strongest synchronization partially coincides with the original tempo range of the music stimuli (Figure 1 – figure supplement 2). A control analysis revealed that the amount of tempo manipulation (difference between original music tempo and tempo the music segment was presented to the participant) did not affect TRF correlations. Thus, we interpret our data as reflecting a neural preference for specific musical tempi rather than an effect of naturalness or the amount that we had to tempo shift the stimuli. However, since our experiment was not designed to answer this question, we were only able to conduct this analysis for two tempi, 2.25 Hz and 1.5 Hz (Figure 3 – figure supplement 3), and thus are not able to rule out the influence of the magnitude of tempo manipulation on other tempo conditions.”

      3) The authors have a clear hypothesis about the frequency of the entrained EEG response: The one that corresponds to the musical tempo (or harmonics). It seemed to me that analyses do not sufficiently take that hypothesis into account and often include all possible frequencies. Restricting the analysis pipeline to frequencies that are expected to be involved might reduce the number of comparisons needed and therefore increase statistical power.

      Although we manipulated tempo, and so had an a priori hypothesis about the frequency at which the beat would be felt, natural music is a complex stimulus composed of different instruments playing different lines at different time scales, many or most of which are nonisochronous. Thus, we analyzed the data in two different ways – 1) based on TRFs and 2) based on stimulus–response correlation and coherence. Stimulus–response coherence is a frequency-domain measure, and so it was possible to do exactly as you suggest here and consider coherence only at the stimulation tempo and first harmonic, which we did (Figure 2E-J). However, for the TRF analyses, we followed previous literature (e.g., Ding et al., 2014; Di Liberto et al., 2020; Teng et al., 2021), and considered broader-band EEG activity (bandpass filtered at 0.5-30 Hz). Previous work has shown that the beat in music evokes a neural response at harmonics up to at least 4 times the beat rate (Kaneshiro et al., 2020), so we wanted to leave a broad frequency range intact in the neural data. Despite being based on differently filtered data, we found that the dependent measures from the two analysis approaches were correlated, which suggests to us that neural tracking at the stimulation tempo itself was probably the largest contributor to the results we observed here.

      Related to your comment, we added two points to our discussion, which we reproduce here for your convenience.

      p. 24-25, l. 453-461: “Regardless of the reason, since frequency-domain analyses separate the neural response into individual frequency-specific peaks, it is easy to interpret neural synchronization (SRCoh) or stimulus spectral amplitude at the beat rate and the note rate – or at the beat rate and its harmonics – as independent (Keitel et al., 2021). However, music is characterized by a nested, hierarchical rhythmic structure, and it is unlikely that neural synchronization at different metrical levels goes on independently and in parallel. One potential advantage of TRF-based analyses is that they operate on relatively wide-band data compared to Fourier-based approaches, and as such are more likely to preserve nested neural activity and perhaps less likely to lead to over- or misinterpretation of frequency-specific effects.”

      p. 29 l. 564-577: “Despite their differences, we found strong correspondence between the dependent variables from the two types of analyses. Specifically, TRF correlations were strongly correlated with stimulation-tempo SRCoh, and this correlation was higher than for SRCoh at the first harmonic of the stimulation tempo for the amplitude envelope, derivative and beat onsets (Figure 4 - figure supplement 1). Thus, despite being computed on a relatively broad range of frequencies, the TRF seems to be correlated with frequency-specific measures at the stimulation tempo. The strong correspondence between the two analysis approaches has implications for how users interpret their results. Although certainly not universally true, we have noticed a tendency for TRF users to interpret their results in terms of a convolution of an impulse response with a stimulus, whereas users of stimulus–response correlation or coherence tend to speak of entrainment of ongoing neural oscillations. The current results demonstrate that the two approaches produce similar results, even though the logic behind the techniques differs. Thus, whatever the underlying neural mechanism, using one or the other does not necessarily allow us privileged access to a specific mechanism.”

      Reviewer #2 (Public Review):

      Kristin Weineck and coauthors investigated the neural entertainment to different features of music, specifically the amplitude envelope, its derivative, the beats and the spectral flux (which describes how fast are spectral changes) and its dependence on the tempo of the music and self-reports of enjoyment, familiarity and ease of beat perception.

      They use and compare analysis approaches typically used when working with naturalistic stimuli: temporal response functions (TRFs) or reliable components analysis (RCA) to correlate the stimulus with its neural response (in this case, the EEG). The spectral flux seems the best music descriptor among the tested ones with both analyses. They find a stronger neural response to stimuli with slower beat rates and predictable stimuli, namely familiar music with an easy-to-perceive beat. Interestingly, the analysis does not show a statistically significant difference between musicians and non-musicians.

      The authors provide an extensive analysis of the data, but some aspects need to be clarified and extended.

      We thank the Reviewer for taking the time to evaluate and summarize our manuscript and for the great comments. We addressed the concerns and made changes throughout the manuscript, but especially in the introduction and discussion sections about the terminology (neural entrainment and neural measures), musical features of the stimuli, and musical experience of the participants. Below you can find the alterations described in more detail. The page and line numbers correspond to the manuscript file without track changes.

      1) It would be helpful to clarify better the concepts of neural entertainment, synchronization and neural tracking and their meaning in this specific context. Those terms are often used interchangeably, and it can be hard for the reader to follow the rest of the paper if they are not explicitly defined and related to each other in the introduction. Note that this is fundamental to understanding the primary goal of the paper. The authors clarify this point only at the end of the discussion (lines 570-576). I suggest moving this part in the introduction. Still, it is unclear why the authors use the TRF model and then say they want to be agnostic about the physiological mechanisms underlying entertainment. The choice of the TRF (as well as the stimulus representation) automatically implies a hypothesis about a physiological mechanism, i.e., the EEG reflects convolution of the stimulus properties with an impulse response. Please could you clarify this point? I might have missed it.

      Thank you for this valuable comment. We agree that it is fundamental to define and uniformly use terminology, and have made changes throughout the manuscript along these lines. First of all, we have changed all instances of “neural entrainment” or “neural tracking” to “neural synchronization”, as we think this term avoids evoking a specific theoretical background or strong mechanistic assumptions. Second, we have moved the Discussion paragraph you mention to the Introduction and expanded it. Specifically, we take the opportunity to address the association between specific analysis approaches (TRFs vs. stimulus–response correlation or coherence) and specific mechanistic assumptions (convolution of stimulus properties with an impulse response vs. entrainment of an ongoing oscillation, respectively). This allowed us to clarify what we mean when we say we prefer to stay agnostic to specific mechanistic interpretations. We are happy to have had the chance to strengthen this discussion, and think it benefits the manuscript a lot.

      We reproduce the new Introduction paragraph here for your convenience.

      p. 5-6, l. 101-123: “The current study investigated neural synchronization to natural music by using two different analysis approaches: Reliable Components Analysis (RCA) (Kaneshiro et al., 2020) and temporal response functions (TRFs) (Di Liberto et al., 2020). A theoretically important distinction here is whether neural synchronization observed using these techniques reflects phase-locked, unidirectional coupling between a stimulus rhythm and activity generated by a neural oscillator (Lakatos et al., 2019) versus the convolution of a stimulus with the neural activity evoked by that stimulus (Zuk et al., 2021). TRF analyses involve modeling neural activity as a linear convolution between a stimulus and relatively broad-band neural activity (e.g., 1–15 Hz or 1–30 Hz; (Crosse et al., 2016, Crosse et al., 2021); as such, there is a natural tendency for papers applying TRFs to interpret neural synchronization through the lens of convolution (though there are plenty of exceptions to this e.g., (Crosse et al., 2015, Di Liberto et al., 2015)). RCA-based analyses usually calculate correlation or coherence between a stimulus and relatively narrow-band activity, and in turn interpret neural synchronization as reflecting entrainment of a narrow-band neural oscillation to a stimulus rhythm (Doelling and Poeppel, 2015, Assaneo et al., 2019). Ultimately, understanding under what circumstances and using what techniques the neural synchronization we observe arises from either of these physiological mechanisms is an important scientific question (Doelling et al., 2019, Doelling and Assaneo, 2021, van Bree et al., 2022). However, doing so is not within the scope of the present study, and we prefer to remain agnostic to the potential generator of synchronized neural activity. Here, we refer to and discuss “entrainment in the broad sense” (Obleser and Kayser, 2019) without making assumptions about how neural synchronization arises, and we will moreover show that these two classes of analyses techniques strongly agree with each other.”

      2) Interestingly, the neural response to music seems stronger for familiar music. Can the authors clarify how this is not in contrast with previous works that show that violated expectations evoke stronger neural responses ([Di Liberto et al., 2020] using TRFs and [Kaneshiro et al., 2020] using RCA])? [Di Liberto et al., 2020] showed that the neural response of musicians is stronger than non-musicians as they have a stronger expectation (see point 2). However, in the present manuscript, the analysis does not show a statistically significant difference between musicians and non-musicians. The authors state that they had different degrees of musical training in their dataset, and therefore it is hard to see a clear difference. Still, in the "Materials and Methods" section, they divided the participants into these two groups, confusing the reader.

      Our findings are consistent with previous studies showing stronger inter-subject correlation in response music in a familiar style vs. music in an unfamiliar style (Madsen et al., 2019) and stronger phase coherence in response to familiar relative to unfamiliar sung utterances (Vanden Bosch der Nederlanden et al., 2022). We actually don’t think our results (stronger neural synchronization for familiar music) or these previous results are incompatible with work showing that violations of expectations evoke stronger neural responses. This work either manipulated music so it violated expectations (Kaneshiro et al., 2020) or explicitly modeled “surprisal” as a feature (Di Liberto et al., 2020). Thus, we could think of those stronger neural responses to expectancy violations as reflecting something like “prediction error”. Our music stimuli did not contain any violations, and we were unable to model responses to surprisal given the nature of our music stimuli, as we better explain below (p. 27 l. 514-529). Thus, neural synchronization was stronger to familiar music, and we would argue that listeners were able to form stronger expectations about music they already knew. We would predict that expectancy violations in familiar music would evoke stronger neural responses to those in unfamiliar music, though we did not test that here. We now include a paragraph in the Discussion reconciling our findings with the papers you have cited.

      p. 27 l. 514-529: “We found that the strength of neural synchronization depended on the familiarity of music and the ease with which a beat could be perceived (Figure 5). This is in line with previous studies showing stronger neural synchronization to familiar music (Madsen et al., 2019) and familiar sung utterances (Vanden Bosch der Nederlanden et al., 2022). Moreover, stronger synchronization for musicians than for nonmusicians has been interpreted as reflecting musicians’ stronger expectations about musical structure. On the surface, these findings might appear to contradict work showing stronger responses to music that violated expectations in some way (Kaneshiro et al., 2020, Di Liberto et al., 2020). However, we believe these findings are compatible: familiar music would give rise to stronger expectations and stronger neural synchronization, and stronger expectations would give rise to stronger “prediction error” when violated. In the current study, the musical stimuli never contained violations of any expectations, and so we observed stronger neural synchronization to familiar compared to unfamiliar music. There was also higher neural synchronization to music with subjectively “easy-to-tap-to” beats. Overall, we interpret our results as indicating that stronger neural synchronization is evoked in response to music that is more predictable: familiar music and with easy-to-track beat structure.”

      Your other question was why we did not see effects of musical training / sophistication on neural synchronization to music, when other studies have. There are a few possible reasons for this. One is that previous studies aiming to explicitly test the effects of musical training recruited either professional musicians or individuals with a high degree of musical training for their “musician” sample. In contrast, we did not target individuals with any degree of musical training, but attempted this analysis in a post-hoc way. For this reason, our musicians and nonmusicians were not as different from each other in terms of musical training as in previous work. Given this, we have opted to remove the artificial split into musician and nonmusician groups, and now only include a correlation with musical sophistication (as you suggest in your next comment), which was also nonsignificant (Figure 5 – figure supplement 2).

      3) Musical expertise was also assessed using the Goldsmith Music Sophistication Index, which could be an alternative to the two-group comparison between musicians and non-musicians. Does this mean that in Figure 5, we should see a regression line (the higher the Gold-MSI, the higher should be the TRF correlation)? Since we do not see any significant effect, might this be due to the choice of the audio descriptor? The spectral flux is not a high-level descriptor; maybe it is worth testing some high-level descriptors such as entropy and surprise. The choice of the stimulus features defines linear models such as the TRF as they determine the hierarchical level of auditory processing, and for testing the musical expertise, we might need more than acoustic features. The authors should elaborate more on this point.

      It is true that the Goldsmith Music Sophistication Index serves as an alternative way of investigating the effects of musical expertise on neural synchronization to natural music, and we now include this approach exclusively instead of dividing our sample (see response to the previous comment). Indeed, if musical sophistication would have an effect on the TRF correlations in this study, we would see a regression line in Figure 5 – figure supplement 2. Based on our experiment it is difficult to assess whether the lack of a correlation between neural measures and musical expertise is based on our choice of stimulus features. That is because our experiment was designed to investigate the effects of fundamental acoustic features of music, and it was not possible to calculate high-level descriptors, such as the entropy or surprisal, for the music stimuli we chose to work with – the stimuli were polyphonic, and moreover were purchased in a .wav format, so we do not have access to the individual MIDI versions or sheet music of each song that would have been necessary to apply, for example, the IDyOM (Information Dynamics of Music) model. As we cannot rule out that the (lack of) effects of varying levels of musical expertise on TRF correlations is due to our choice of stimulus features, we added this to the discussion.

      p. 28 l. 541-546: “Another potential reason for the lack of difference between musicians and non-musicians in the current study could originate from the choice of utilizing pure acoustic audio-descriptors as opposed to “higher order” musical features. However, “higher order” features such as surprise or entropy that have been shown to be influenced by musical expertise (Di Liberto et al., 2020), are difficult to compute for natural, polyphonic music.”

      4) Regarding the stimulus representation, I have a few points. The authors say that the amplitude envelope is a too limited representation for music stimuli. However, before testing the spectral flux, why not test the spectrogram as in previous studies? Moreover, the authors tested the TRF on combining all features, but it was not clear how they combined the features.

      One of the main reasons that we did not use the spectrogram as a feature was that it wouldn’t be possible to use a two-dimensional representation for the RCA-based measures, SRCorr and SRCoh, so we would not have been able to compare across analysis approaches. However, spectral flux is calculated directly from the spectrogram, and so is a useful one-dimensional measure that captures the spectro-temporal fluctuations present in the spectrogram (https://musicinformationretrieval.com/novelty_functions.html). Thank you for making this important point, we added this explanation to the Materials and Methods section (p. 35 l. 726-727).

      Sorry for not explaining the multivariate TRF approach better. Instead of using only one stimulus feature, e. g. the amplitude envelope, several stimulus features can be concatenated into a matrix (with the dimensions: time T x 4 musical features M at different time lags), which is then used as an input for the mTRFcrossval, mTRFtrain and mTRFpredict of the mTRF Matlab Toolbox (Crosse et al., 2016) – actually this is exactly how using a 2D feature like the spectrogram would work. The multivariate TRF is calculated by extending the stimulus lag matrix (time course of one musical feature at different time lags, T × τwindow) by an additional dimension (time course of several musical features at different time lags, T × M x τwindow). We added an explanation to the Methods section of the manuscript and hope that it is this way better understandable:

      p. 39 l. 840-842: “For the multivariate TRF approach, the stimulus features were combined by replacing the single time-lag vector by several time-lag vectors for every musical feature (Time x 4 musical features at different time lags).”

      Reviewer #3 (Public Review):

      Subjects listened to various excerpts from music recordings that were designed to cover musical tempi ranging from 1-4 Hz, and EEG was recorded as subjects listened to these excerpts. The main and novel findings of the study were: 1) spectral flux, measuring sudden changes in frequency, were tracked better in the EEG than other measures of fluctuations in amplitude, 2) neural tracking seemed to be best for the slowest tempi, 3) measures of neural tracking were higher when subject's rated an excerpt as high for ease-of-tapping and familiarity, and 4) their measure of the mapping between stimulus feature and response could predict whether a subject tapped at the expected tempo or at 2x the expected tempo after listening to the musical excerpt.

      One of the key strengths of this study is the use of novel methodologies. The authors in this study used natural and digitally manipulated music covering a wide range of tempi, which is unique to studies of musical beat tracking. They also included both measures of stimulus-response correlation and phase coherence along with a method of linear modeling (the temporal response function, or TRF) in order to quantify the strength of tracking, showing that they produce correlated results. Lastly, and perhaps most importantly, they also had subjects tap along with the music after listening to the full excerpt. While having a measure of tapping rate itself is not new, combined with their other measures they were able to demonstrate that neural data predicted the hierarchical level of tapping rate, opening up opportunities to study the relationship between neural tracking, musical features, and a subject's inferred metrical level of the musical beat.

      Additionally, the finding that spectral flux produced the best correlations with the EEG data is an important one. Many studies have focused primarily on the envelope (amplitude fluctuations) when quantifying neural tracking of continuous sounds, but this study shows that, for music at least, spectral flux may add information that is tracked by the EEG. However, given that it is also highly correlated with the envelope, what additional features spectral flux contributes to measuring EEG tracking is not clear from the current results and worth further study.

      All four of their main findings are important for research into the neural coding of musical rhythm. I have some concerns, however, that two of these findings could be a consequence of the methods used, and one could be explained by related correlations to acoustic features:

      We thank the Reviewer for the very helpful review, the summary, and the great suggestions. We addressed the comments and performed additional analysis. We made changes throughout the manuscript, but especially 1) concerning the potential advantage of the neural response to slower music, 2) the effects of the amount of tempo manipulation on neural synchronization, 3) the SVM-related analysis and 4) the relation between stimulus features and behavioral ratings. The implemented modifications can be found below in more detail. The page and line numbers correspond to the manuscript file without track changes.

      The authors found that their measures of neural tracking were highest for the lowest musical tempos. This is interesting, but it is also possible that this is a consequence of lower frequencies producing a large spread of correlations. Imagine two signals that are fluctuating in time with a similar pattern of fluctuation. When they are correctly-aligned they are correlated with each other, but if you shift one of the signals in time those fluctuations are mismatched and you can end up with zero or negative correlations. Now imagine making those fluctuations much slower. If you use the same time shifts as before, the signals will still be fairly correlated, because the rates of signal change are much longer. As a result, the span of null correlations also increases. This can be corrected by normalizing the true correlations and prediction accuracies with a null distribution at each tempo. But with this in mind, it is hard to conclude if the greater correlations found for lower musical tempos in their current form are a true effect.

      Thank you for this great suggestion. We followed your lead (Zuk et al., 2021), and normalized all measures of neural synchronization (TRF correlation, SRCorr, SRCoh) relative to a surrogate distribution. The surrogate distribution was calculated by randomly and circularly shifting the neural data relative to the musical features for each of 50 iterations. This was done separately for every musical feature and stimulation tempo condition (Figures 2 and 3). After normalization, the results look qualitatively similar and the main results – spectral flux and slow stimulation tempi resulting in highest levels of neural synchronization – persist.

      The changes in the manuscript based on your comment (and the comment of Reviewer 1) can be found throughout the manuscript, but especially on p. 11, l. 210-218, Figures 2-3 and a more detailed explanation in the Methods section:

      p. 39, l. 821-829: “In order to control for any frequency-specific differences in the overall power of the neural data that could have led to artificially inflated observed neural synchronization at lower frequencies, the SRCorr and SRCoh values were z-scored based on a surrogate distribution (Zuk et al., 2021). Each surrogate distribution was generated by shifting the neural time course by a random amount relative to the musical feature time courses, keeping the time courses of the neural data and musical features intact. For each of 50 iterations, a surrogate distribution was created for each stimulation subgroup and tempo condition. The z-scoring was calculated by subtracting the mean and dividing by the standard deviation of the surrogate distribution.”

      If the strength of neural tracking at low tempos is a true effect, it is worth noting that the original tempi for the music clips span 1 - 2.5 Hz (Supplementary Table 1), roughly the range of tempi exhibiting the largest prediction accuracies and correlations. All tempos above this range are produced by digitally manipulating the music. It is possible that the neural tracking measures are higher for music without any digital manipulations rather than reflecting the strength of tracking at various tempi. This could also be related to the author's finding that neural tracking was better for more familiar excerpts. This alternative interpretation should be acknowledged and mentioned in the discussion.

      Thank you for these important suggestions (see also comment #2 (part 2) from Reviewer 1). First up, it is important to say that all music stimuli were tempo manipulated: even if the tempo of an original music segment was e. g. 2 Hz and the same song was presented at 2 Hz, it was still converted via the MAX patch to 2 Hz again (to make it comparable to the other musical stimuli). Second, it is true that we cannot fully exclude the possibility that the amount of tempo manipulation could have an effect on neural synchronization to music – meaning that less tempo manipulated music segments (so a stimulation tempo close to the original tempo) could result in higher neural synchronization. However, we have now conducted an additional analysis to address this as best we could.

      We compared TRF correlations for a) songs that were shifted very little relative to their original tempi to b) songs that were shifted a lot relative to their original tempi. We did not have enough song stimuli to do this for every stimulation tempo, but we were able to do the TRF correlation comparison for two illustrative stimulation tempo conditions (at 2.25 Hz and 1.5 Hz). In those tempo conditions, we took the TRF correlations for up to three trials per participant when the original tempo was around the manipulation tempo (1.25-1.6 Hz for 1.5 Hz or 2.01-2.35 Hz for 2.25 Hz) and compared it to those trials where the original tempo was around 0.75¬–1 Hz faster or slower than the manipulated tempo at which the participants heard the songs (Figure 3 – figure supplement 2). This analysis revealed that there was no significant effect of the original music tempi on the neural response (please see Material and Methods, p. 40, l. 855-861 and Results p. 13, l. 265-273). In response to your and Reviewer’s 1 comments, we also added it to the discussion.

      p. 23-24 l. 427-436: “The tempo range within which we observed strongest synchronization partially coincides with the original tempi of the music stimuli (Figure 1 – figure supplement 2). A control analysis revealed that the amount of tempo manipulation (difference between original music tempo and tempo the music segment was presented to the participant) did not affect TRF correlations. Thus, we interpret our data as reflecting a neural preference for specific musical tempi rather than an effect of naturalness or the amount that we had to tempo shift the stimuli. However, since our experiment was not designed to answer this question, we were only able to conduct this analysis for two tempi, 2.25 Hz and 1.5 Hz (Figure 3 – figure supplement 3), and thus are not able to rule out the influence of tempo manipulation on other tempo conditions.”

      We also provide more information to the reader about the amount of tempo shift that each stimulus underwent. We added two plots to the manuscript that show 1) the distribution of original tempi of the music stimuli and 2) the distribution of the amount of tempo manipulation across all stimuli (Figure 1 – figure supplement 2).

      Their last finding regarding predicting tapping rates is novel and important, and the model they use to make those predictions does well. But I am concerned by how well it performs (Figure 6), since it is not clear what features of the TRF are being used to produce this discrimination. Are the effects producing discriminable tapping rates and stimulation tempi apparent in the TRF? I noticed, though, that these results came from two stages of modeling: TRFs were first fit to groups of excerpts with different tapping rates or stimulation tempo separately, then a support vector machine (SVM) was used to discriminate between the two groups. So, another way to think about this pipeline is that two response models (TRFs) were generated for the separate groups, and the SVM finds a way of differentiating between them. There is no indication about what features of the TRFs the SVM is using, and it is possible this is overfitting. Firstly, I think it needs to be clearer how the TRFs are being computed from individual trials. Secondly, the authors construct surrogate data by shuffling labels (before training) but it is not clear at which training stage this is performed. They can correct for possible issues of overfitting by comparing to surrogate data where shuffling happens before the TRF computation, if this wasn't done already.

      Thank you for noticing this important point. You are absolutely right – when re-analyzing that part of the results based on your comment, we noticed that we had an error in our understanding of the analysis pipeline. Indeed, we first calculated two TRF models for the separate groups (e. g. stimulation tempo = tapping tempo vs. stimulation tempo = 2* tapping tempo) based on all trials of each group apart from the left-out-trial. Next, the resulting TRFs were fed into the SVM which was used to predict the group. The shuffling of the surrogate data occurred at the SVM training step.

      Based on your comment, we tried several approaches to solve this problem. First, we calculated TRFs on a single-trial basis (instead of using the two-group TRFs as before, only one trial was used to calculate the TRFs) and submitted the resulting TRFs to the SVM. The resulting SVM accuracy was compared to a “surrogate SVM accuracy” which was calculated based on shuffling the labels when training the SVM classifier. Second, we shuffled, as you suggest, the labels not at the SVM training step, but instead prior to the TRF calculation. This way we could compare our “original” SVM accuracies (based on the two-group TRFs) to a fairer surrogate dataset. However, in both cases the resulting SVM accuracies did not perform better than the surrogate data. Therefore, we felt that it is the fairest to remove this part from the manuscript. We are aware that this was one of the main results of the paper and we are sorry that we had to remove it. However, we feel that our paper is still strong and offers a variety of different results that are important for the auditory neuroscience community.

      Lastly, they show that their measures of neural tracking are larger for music with high familiarity and high ease-of-tapping. I expect these qualitative ratings could be a consequence of acoustic features that produce better EEG correlations and prediction accuracies, especially ease-of-tapping. For example, music with acoustically-salient events are probably easier to tap to and would produce better EEG correlations and prediction accuracies, hence why ease-of-tapping is correlated with the measures of neural tracking. To understand this better, it would be useful to see how the stimulus features correlate with each of these behavioral ratings.

      We agree that our rating-based results could be influenced by acoustic stimulus features (at least for ease of tapping, it’s actually not clear to us why familiarity would be related to acoustics). As it is difficult to correlate stimulus features (time-domain, and one time course per song) with behavioral ratings (one single value per song per participant), we conducted frequency-domain analysis on the musical features to arrive at a single value quantifying the strength of spectral flux at the stimulation frequency and its first harmonic. We calculated single-trial FFTs on the spectral flux (which was used for the main Figure 5) for the 15 highest- and 15 lowest-rated trials per behavioral category (enjoyment, familiarity, ease to tap the beat) and participant. We compared the z-scored FFT peaks at the stimulation tempo and first harmonic for the top- and bottom-rated stimuli. We did observe significant acoustic differences between top- and bottom-rated stimuli in each category, but the differences were not in the direction that would be expected based on acoustically more salient events leading to better TRF correlations, with the exception of ease of tapping. Easy-to-tap music did indeed have stronger spectral flux than difficult-to-tap music, which is intuitive. However, spectral flux was stronger for more enjoyed music (we did not see any significant differences between TRF correlations of more vs. less enjoyed music; Figure 5C) and for less familiar music (this is the opposite of what we saw for the TRF measures). Overall, given the inconsistent relationship between acoustics, behavioral ratings, and TRF measures, we would argue that acoustic features alone cannot solely explain our results (Figure 5 – figure supplement 1, p. 21 l. 381 – 387).

  7. Jun 2022
    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

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      Reply to the reviewers

      To reviewer #1

      Reviewer #1 (Evidence, reproducibility and clarity (Required)): Please see combined review below in the next section, Reviewer #1 (Significance (Required)):

      This is a descriptive manuscript providing a few new insights into a well-recognized and biologically important phenomenon - the lymphatic endothelial cells have heterogeneous origins in different organs. Overall, the idea of Islet1 lineage contributes to regional lymphatic vessel formation during a particular developmental stage is exciting and proven with detailed and careful lineage tracing. The first observation that Islet1 lineage gives rise to cardiac lymphatic vessels was published by the same group in Dev Bio in 2019 so the novelty here is dampened, although the pharyngeal lymphatics and the exact time of these non-venous origin lymphatic vessels arise were not previously characterized - so the current manuscript does provide new important insights. Both the data quality and manuscript layout need improvements, especially when it comes to defining where Islet1 is expressed at all the stages and statistics. The following suggestions will deepen the scope of the manuscript:

      First of all, we would like to express our appreciation to the reviewer for all the constructive comments. We carefully read the reviewer’s comments and discussed it. We agree with the reviewer that our manuscript needs improvements with changes in layout several additional experiments. We have also included several description and new immunostaining data (e.g., Isl1,VEGFR3 and LYVE1 co-staining), to confirm our new findings and highlight the importance of the current manuscript beyond our previous one in Dev Biol in 2019. We also have included detailed quantification methods, single-channel images with improved data resolution, and improved clarity of the manuscript.

      Specific points were addressed as follows:

      COMMENTS BY THE REVIEWER

      1. Whether Isl1 lineage is independent of venous-derived endothelial cells.

      2. This point is very important: the manuscript does not actually show Isl1 expression through the stages they are inducing. I would want to be sure that lymphatic endothelial cells at this stage don't express Isl1. Another way to get at this is to maybe use other second-heart fields or even broader mesoderm drivers that are known to be never expressed in endothelial cells to confirm the findings.

      Response:

      In our previous work (Maruyama et al, Dev Biol 452:134–143, 2019, Figure 2C), we demonstrated that Isl1+ lineages using Isl1-Cre mice did not contribute to endothelial cells in the cardinal vein and its branches (intersomitic vessels: ISVs), which had been thought to the primary and biggest sources of lymphatic endothelial cells (LECs). In this paper, we confirmed this finding using Isl1-MerCreMer mice with tamoxifen treatment at E8.5 (Figure 4J). We have scanned whole embryos and detected no eYFP+ cells in the cardinal vein or ISVs (the detailed quantification methods have been added in Methods section). Consistently, another group (Lioux et al., Dev Cell 52:350-363, 2020) re-evaluated this point using Isl1-Cre mice that the Isl1+ lineage contribute to endothelial cells of the cardinal vein only by less than 2%, which neither explains the abundant contribution of the Isl1+ lineage to coronary lymphatics (>50%) nor its restriction to the ventral heart. Based on these reports, we supposed that the Isl1+ lineage was independent of LECs derived from the cardinal vein and ISVs.

      In the revised manuscript, we added new data showing thorough expression patterns of Isl1, Prox1, Flk1, and PECAM in the E9.0 to E11.5 pharyngeal arches and cardinal veins by immunostaining and presented them as Supplemental Figure 4. In these sections, we detected Isl1 and Prox1 expression with partial overlapping within the pharyngeal mesodermal core, whereas Isl1 was co-expressed with Flk1, or PECAM neither in vessel-like structures around the mesodermal core nor in the cardinal vein and their surrounding Prox1+/PECAM+ LECs (Supplemental Figure 4H’ and J’) confirming the independency. These findings have been described in the manuscript as follows:

      To identify possible Isl1+ LEC progenitors, we investigated the expression patterns of Isl1, Prox1, and vascular endothelial markers (Flk1 and PECAM) by immunostaining sections of E9.0 to E11.5 pharyngeal arches and cardinal veins. Consistent with the previous report (Cai et al., 2003), Isl1 was abundantly expressed in the core mesoderm of the first and second pharyngeal arches corresponding to the CPM from E9.0 to E11.5 (Nathan et al., 2008), where Prox1+ cells also aggregated and partially overlapped with Isl1 signals (Supplemental Figure 4A, A’ C, C’ E, E’ G, G’ I, I’). By contrast, Flk1+ or PECAM+ cells were distributed mainly around the CPM and not expressed Isl1 (Supplemental Figure 4A, A’ C, C’ E, E’ G, G’ I, I’). Furthermore, Isl1 was expressed neither in the endothelial layer of the cardinal vein nor in surrounding Prox1+/PECAM+ LECs (Supplemental Figure 4B, B’ D, D’, F, F’, H, H’, J, and J’). Taken together with the result from Myf5-CreERT2 mice, these results indicate that Isl1+ non-myogenic CPM cells may serve as LEC progenitors independent of venous-derived LECs and the commitment to LEC differentiation occurs before E9.5 in the pharyngeal arch region. (Page 6-7, lines 187-200)

      Regarding the use of other second-heart field drivers as the reviewer recommended,

      Lioux et al., have already shown the contribution of Mef2c-AHF+ , which marked CPM-derived cells including second heart field, cranial musculatures and connective tissues(Adachi et al., 2020), to ventral cardiac lymphatics. We are also trying to introduce Mef2c-AHF-Cre mice, but it is unfortunately delayed due to the pandemic of COVID-19.

      The author stated that Islet1 lineage gives rise to lymphatic endothelial cells via the Tie2 mechanism but did not elaborate on this part. What is the potential relationship between Islet1 and Tie2? Or Tie2 just serves as a pan-endothelial lineage marker here?

      Response:

      To clearly demonstrate the relationship between Isl1+ and Tie2+ lineages in facial lymphatics, we added schematic representation in Figure 6G, which showed the differential Tie2 expression in lymphatic vessels in the tongue and facial skin.

      Related to point 1 and 2, it has been thought that almost all LECs are formed from cardinal vein-derived Tie2+ endothelial cells. However, we identified the presence of Isl1+/Tie2+ LECs in the tongue, which are apparently not originated from the cardinal vein. In previous reports using Tie2-GFP mice or in situ hybridization of Tie2, Tie2 was not detected in the developing LECs at E9.5, 11.5, 13.5, and E15.5(Motoike et al., 2000; Srinivasan et al., 2007). In adult mice, Tie2 expression in lymphatics was only observed in restricted regions (Morisada et al., 2005; Tammela et al., 2005). Taken together with our present data that the differentiation fate of Isl1+ CPM-derived LECs was determined between E6.5 and E9.5 (Figure 2-4, Supplemental Figure 3), Tie2 is supposed to be transiently expressed during LEC differentiation in the tongue from early Isl1+ CPM cells, although it remains difficult to identify the Tie2-expressing stage during non-venous LEC differentiation.

      It will be an important future subject to identify the stage and implication of transient Tie2 expression in the lineage and, in this paper, we want to just note that the Tie2+ lineage does not always mean the derivation from cardinal vein endothelial cells.

      This point has already been included in the manuscript as follows:

      The present study further indicates that the LECs in the tongue are derived from Tie2-expressing cells among the Isl1+ lineage. Although it is unclear whether Isl1+-derived cells at the Tie2-expressing stage represent a venous endothelial identity, this result means that Tie2+ LECs are not equivalent to cardinal vein-derived LECs. (Page 10, 298-301)

      The effect of lineage-specific Prox1 knockout is very descriptive, without any discussion of the potential biological function of such cellular origin heterogeneity. This part may be worth a few follow-up experiments in later embryonic stages or even in postnatal stages. The authors demonstrated that loss of Prox1 in Islet1 lineage decreases the number of lymphatic vessels and leads to lymphangiectasia, but whether this phenotype can be later compensated or shows any clinical impact was not proven. Therefore, the statement made in line 206 is questionable, and whether Islet1 lineage-derived lymphatic endothelial cells are dispensable/indispensable remains unclear.

      Response:

      We agree with the reviewer in that additional follow up experiments using later embryonic or postnatal stages will give an insight into the potential biological function of cellular origin heterogeneity. We are generating lineage-specific Prox1 knockout mice by treating Isl1-MerCreMer; Prox1fl/fl mice with tamoxifen at E8.5 to analyze phenotypes in the facial lymphatic vessels.

      The layout of the manuscript needs to be reorganized: 1. Details in statistical methods and quantification logic were completely missing from the manuscript. For example, definitions of "a sample" (how many sections are taken from one biological sample and how many fields take from one section, etc.), "number of vessels per field", "diameters", and of what parameters the numbers were normalized to, etc. need to be described in the materials and methods section. For instance, it is not clear how "tomato+ lymphatic vessels per field/Vegfr3+ lymphatic vessels" was defined. First, what proportion of tomato+ cells need to colocalize with Vegfr3 expression cells in a specific vessel to make this vessel being determined as a "tomato+ lymphatic vessel"? Most data provided here are section immunostaining where "multiple vessels" are very likely coming from different cross-sections of one same vessel in the same field. Second, Vegfr3 can stain venous endothelial cells in earlier stages so the specificity of this marker can be controversial. These are some important technical aspects to include in the revised version. Figures needing more description in quantification methods include but are not limited to Fig 1H, 2H, 2P, 5K-R.

      We have revised the statistical methods from the ratio of the count of the number to ratio of the area of lymphatic vessels in Figure 1H, 2H, P, and Supplemental Figure 3I to represent more precisely the contribution of Tomato+ cells to lymphatic vessels. We also added more detailed description of the quantification methods in ‘Materials and Methods’ section, as follows:

      Quantification of the section and whole mount images

      For the quantification of section immunostaining at E16.5 embryos, the average of two 16-μm-thick sections taken every 50 μm and 10x power field of views (0.42 mm2/field) for each anatomical part (the larynx, the skin of the lower jaw, the tongue, and the cardiac outflow tracts) were subjected to the analyses. In the facial skin, lymphatic vessels in superficial layers of dermis were subjected to the analyses. The middle sagittal sections, including the aorta, larynx, and tongue, which were selected as hall marks of midline, was chosen from created sections. The coronal sections, including both eyes, tongue, and olfactory lobes with left and right symmetrical features, was selected. For E12.5 embryos (Figure 4O), two 16-μm-thick sagittal sections taken every 50 μm, including the 1st and 2nd pharyngeal arches and outflow tracts, were subjected to analyses. The area and the number of cells were measured manually using ImageJ software. For the whole mount immunostaining of embryos and the heart, the whole samples were scanned every 20 μm and confirmed eYFP contribution to LECs (Figure 3) and cardinal veins (Figure 4J, and Supplemental Figure 4B, D, F, H, J). (Page 13, lines 402-416)

      We also have tested expression patterns of VEGFR3 with Prox1 or LYVE1 as Supplemental Figure 1. At E14.5, VEGFR3 was widely co-expressed with Prox1 in the tongue, facial skin, and around the pulmonary artery (Supplemental Figure 1A-C’). At E16.5, VEGFR3 was co-expressed with LYVE1 in the tongue, facial skin, and around the pulmonary artery. Thus, we thought that VEGFR3 could be used as a marker of LECs in these cardiopharyngeal region.

      This point has been included in the manuscript as follows:

      Co-immunostaining of platelet endothelial cell adhesion molecule (PECAM) and vascular endothelial growth factor receptor 3 (VEGFR3), which we confirmed its co-localization with lymphatic vessel endothelial hyaluronan receptor 1 (LYVE1) at E14.5 and E16.5 (Supplemental Figure 1), revealed tdTomato+ LECs in and around the larynx, the skin of the lower jaw, the tongue, and the cardiac outflow tracts, at various frequencies, whereas no such cells were found on the dorsal side of the ventricles, which agrees with our previous study (Maruyama et al., 2019). (Page 4, lines 112-119)

      Data resolution needs to be improved. The magnification of the figures in Fig 1-4 is not sufficient to demonstrate the marker colocalization as described in the texts. Single-channel images (such as the ones shown in Fig 5-6 but in higher magnifications) are also necessary to show the co-expression of markers.

      Response:

      There was a limit on the data capacity when submitting the manuscript. We were therefore obliged to reduce the quality of images and file size. We have revised the figures to add several higher magnification and single-channel images with improved data resolution throughout Figure 1-4.

      The experimental design is not well-elaborated in the context. For example, the scientific logic of choosing a particular time point/stage for lineage-knockout induction or sample collection needs to be justified. Also, it seems that the authors are using fl/+ as control littermates in most of the experiments. Any specific reason favors using fl/+ heterozygous instead of fl/fl littermates without cre exposure, which is the more commonly used control sample in this kind of comparison, should be addressed.

      Response:

      Knockdown of Prox1 in the Tie2+ lineage has shown to cause an initial failure in specification of LECs at E14.5 with no appearance of lymphatics even at E17.5(Klotz et al., 2015; Lioux et al., 2020; Maruyama et al., 2019), indicating that the effect on lymphatic vessels would not be compensated even at E16.5. In addition, the systemic lymphatic network formation is almost completed at E16.5(Srinivasan et al., 2007), and the lineage trace was also evaluated at this stage. Thus, it was reasonable to compare the phenotype at E16.5.

      This point has been addressed in the text as follows:

      When Prox1 is knocked down in the Tie2+ lineage, an initial failure in specification of LECs was confirmed at E14.5 with a lack of LECs even at E17.5(Klotz et al., 2015; Lioux et al., 2020; Maruyama et al., 2019). Therefore, we compared lymphatic vessel phenotypes at E16.5, by which systemic lymphatics formation is normally completed(Srinivasan et al., 2007). (Page 7, lines 208-212)

      In Prox1-flox(Prox1fl/+) mice, recombinant cells were labeled with EGFP(Iwano et al., 2012), as already described in the manuscript (Page 7, lines206-208). Therefore, the recombined cells can be visualized by EGFP expression in both heterozygous (fl/+) and homozygous (fl/fl) mice, which enables phenotype analysis referring to the recombined (knocked-out in fl/fl) cells. Importantly, these mice showed no specific phenotypes(Klotz et al., 2015; Maruyama et al., 2019). It is therefore reasonable to use heterozygous mice as controls to compare the phenotype appropriately. Although fl/fl littermates without cre exposure could usually serve as controls, they do not express EGFP in the Prox1 lineage, detracting from their utility(Klotz et al., 2015; Maruyama et al., 2019).

      Some of the phrases are not clear in the text- either because of the writing style or because the corresponding figures failed to support the statements. These include but are not limited to lines 104-106, 122, 206, 226, and 228-233.

      104-106: we crossed Isl1-Cre mice, which express Cre recombinase under the control of the Isl1 promoter and in which second heart field-derivatives are effectively labeled, with the transgenic reporter line R26R-tdTomato at E16.5.

      Response:

      We have re-phrased this sentence as follows:

      we crossed Isl1-Cre mice, which express Cre recombinase under the control of the Isl1 promoter and in which second heart field-derivatives are effectively labeled, with the transgenic reporter line R26R-tdTomato and analyzed at E16.5, when lymphatic networks are distributed throughout the whole body. (Pages 4, lines 109-112)

      122: After tamoxifen was administered at E8.5, tdTomato+ cells were broadly detected in the muscle in the head and neck regions at E16.5, indicating effective Cre-mediated recombination of the target gene.

      Response;

      We have re-phrased this sentence as follows:

      After tamoxifen was administered at E8.5, tdTomato+ cells were broadly detected in the skeletal muscle in the head and neck regions at E16.5, indicating effective Cre recombination in CPM-derived musculatures. (Page 4, lines 130-132)

      We have also included red arrowheads, indicating CPM-derived musculatures in Supplemental Figure 1.

      206: These results suggested that defects in LEC differentiation and/or maintenance due to Prox1 deletion in the Isl1+ lineage were compensated for by other cell sources, probably of venous origin, in facial skin, but not in the tongue, resulting in impaired lymphatic vessel formation in the tongue.

      Response:

      We have re-phrased this sentence as follows:

      These results suggested that defects in LEC differentiation and/or maintenance due to Prox1 deletion in the Isl1+ lineage were compensated for by LECs from other cell sources, probably of venous origin, in facial skin, but not in the tongue. (Page 7-8, lines 231-233)

      226:  Almost all of the LYVE1+/PECAM+ lymphatic vessels in the tongue were positive for eGFP in the Tie2-Cre;Prox1fl/+ heterozygous mice (Figure 6A and Supplemental Figure 3D), indicating that the majority of LECs derived from Isl1+ CPM cells developed through Tie2 expression in the tongue.

      Response:

      We have added new cartoon in Figure 6G to more clearly show the relation of Tie2 expression in Isl1+ lineages. Previous reports have used Tie2-Cre mice to show the vein-derived LECs (Klotz et al., 2015; Srinivasan et al., 2007) , because most of cardinal vein endothelium were composed of Tie2+ lineages. In our present study, in the tongue, most of the LECs were derived from Isl1+/Tie2+ lineages (Figure 1D, H, Figure 2D, Figure 4G, Figure 5B, N, Figure 6A and Supplemental Figure 3F, I). These data suggested that there was a group of Tie2+ lineages even though they are derived from non-venous Isl1+ lineages.

      Reference needed for Myf5-Cre as a driver for Myogenic CPM in the results section. Response:

      We have included several reference, as shown below:

      (Harel et al., 2012, 2009; Heude et al., 2018)

      1. Harel et al., Dev cell, 2009
      2. Harel et al., PNAS, 2012
      3. Heude et al., eLife, 2018
      4. In discussion the reference to Pitx2-driven mesenteric lymphatic heterogeneity (Mahadevan et al 2014) is missing yet Islet1 has been shown downstream of Pitx2 (Davis et al 2008). The authors should discuss their findings of gut lymphatic heterogeneity in this context, considering that mediastinum is mesentery-derived.

      Response:

      Isl1+ CPM-derived LECs have been distributed to the anterior mediastinum and their relationship to mesenteric lymphatic vessels, which continuous with the thoracic duct in the posterior mediastinum, is currently unclear. However, since this paper is valuable for understanding the heterogeneity of the origins of LECs, we have included the indicated paper (Mahadevan et al., 2014) in ‘Introduction’ section to show gut lymphatic heterogeneity. (Page 3, line 67)

      To reviewer #2

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

      The manuscript entitled "The cardiopharyngeal mesoderm contributes to lymphatic vessel development" identified a novel non-venous origin of craniofacial and cardiac LECs using genetic lineage tracing. Their results also revealed the spatiotemporal difference between CPM- and venous-derived LECs. Overall, the paper is well-organized and has certain implications for understanding lymphatic development. However, some issues still need to be improved:

      First of all, we would like to express our appreciation to the reviewer for all the constructive comments. We carefully read the reviewer’s comments and discussed it. We agree with the reviewer’s comments to make the text easier to understand and emphasize what we really want to say.

      Specific points were addressed as follows:

      (1). Clearly, the introduction needs to be more concise and focused on the main questions you propose to answer and why these questions are important.

      Response:

      We have revised introduction section to be more concise and focus on the developmental process of lymphatic vessels and its relation to CPM. (Page 2-4, lines 41-103)

      (2). In the discussion section, you should focus on how the questions have been answered and what they mean. And it would be rash to infer the role of LECs in lymphatic malformation. It would be helpful to validate the changes of CPM-derived LECs in LM patient samples.

      Response:

      We have revised the discussion section to be more concise. To demonstrate our findings more clearly, we have also revised and added some cartoons in Figure 6G and Figure 7.

      (3). For the statistical analysis, all the quantitative data should be tested for statistical significance. There are several bar charts lacking P values.

      Response:

      We have included P values in the Figure legends.

      Reviewer #2 (Significance (Required)):

      This study enriched the contribution of CPMs to broader regions of the facial, cardiac and laryngeal lymphatic network and revealed the spatiotemporally difference between CPM- and venous-derived LECs, which provided some basic reference for understanding lymphatic vessel development.

      To reviewer #3

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

      Short summary of the findings and key conclusions:

      The work from Murayama and colleagues traces the ontogenetic origin of the endothelial cells of the lymphatic vessels in the head and neck region. Using the Cre-lox-based mouse genetics approach, they conclude that the lymphatic endothelial cells (LECs) in this region have mixed origin, with contributions from both the cardiopharyngeal mesoderm (CPM) as well as from cardinal vein. The lineage tracing study is buttressed by assaying LEC formation following selective deletion of the key LEC regulator Prox1 in CPM lineage.

      First of all, we would like to express our appreciation to the reviewer for all the constructive comments. We carefully read the reviewer’s comments and discussed it.

      Specific points were addressed as follows:

      Major comments: 1) The key conclusions: LECs in the head and neck region derive from CPM. LECs in this region have mixed developmental origins. Both these conclusions are convincingly supported by the study. However, the work would greatly be strengthened by Pax3-Cre lineage tracing. This would complement the Isl1-Cre lineage tracing. As the authors observe, the LEC descendants of Isl1+ cells also appear to go through Tie2+ state. Therefore, Tie2-Cre study has not helped to delineate the LECs of CPM and cardinal vein origins. In this context, tracing with Pax3-Cre is likely to give a very clear picture of LEC origins.

      Response:

      We agree with the reviewer in that the data using Pax3-Cre mice will strengthen our manuscripts. Unfortunately, we could not find out researchers who had this line in our society in Japan. For using this line, we need to get cryo-recovered mice from Jaxon laboratory. It will take at least several months. Therefore it is not realistic for us to use Pax3-Cre mice in this work because of time limitation. Instead, we addressed this issue by rewriting the discussion on the possible complementation with the Pax3-Cre lineage by citing (Lupu et al., 2022; Stone and Stainier, 2019).

      This point has been addressed in the text as follows:

      A recent study has suggested that Pax3+ paraxial mesoderm-derived cells contribute to the cardinal vein and therefore venous-derived LECs originate from the Pax3+ lineage (Stone and Stainier, 2019). The same group has further argued that the Pax3+ lineage gives rise to lymphatic vessels on the trunk side through lymphangiogenesis(Lupu et al., 2022). Therefore, the Isl1+ and Pax3+ lineages may complement each other to form systemic lymphatic vessels. (Page 10, lines 314-319)

      2) In addition, the article should be revised to include the number of sections and the number of cells counted per embryo in the Figure legend in each case. This will help assess how robust and reliable are the measurements.

      Response:

      We have revised the statistical methods from the ratio of the count of the number to the area in Figure 1H, 2H, P, and Supplemental Figure 3I to demonstrate more precisely the contribution of Tomato+ cells in lymphatic vessels. We also added more detailed description of the quantification methods in ‘Materials and Methods’ section, as follows:

      Quantification of the section and whole mount images

      For the quantification of section immunostaining at E16.5 embryos, the average of two 16-μm-thick sections taken every 50 μm and 10x power field of views (0.42 mm2) for each anatomical part (the larynx, the skin of the lower jaw, the tongue, and the cardiac* outflow tracts) were subjected to the analyses. In the facial skin, lymphatic vessels in superficial layers of dermis were subjected to the analyses. The middle sagittal sections, including the aorta, larynx, and tongue, which were hall marks of midline, was selected from created sections. The coronal sections, including both eyes, tongue, and olfactory lobes with left and right symmetrical features, was selected. For E12.5 embryos (Figure 4O), two 16-μm-thick sagittal sections taken every 50 μm, including the 1st and 2nd pharyngeal arches and outflow tracts, were subjected to analyses. The area and the number of Prox1+ cells were measured manually using ImageJ software. For the whole mount immunostaining of embryos and the heart, the whole samples were scanned every 20 μm and confirmed eYFP contribution to LECs (Figure 3) and cardinal veins (Figure 4J, and Supplemental Figure 4B, D, F, H, J). * (Pages 13, lines 402-416)

      We have also included the number of eYFP+/Prox1+ cells among Prox1+ cells in the first and second pharyngeal in the Figure 4O legends as follows;

      • (the number of eYFP+/Prox1+ cells (10.83 (mean) ± 1.249 (SEM)): Prox1+ cells (30.83 ± 4.549)) or E9.5 (the number of eYFP+/Prox1+ cells (2.833 ± 1.108): Prox1+ cells (35.50 ± 5.847)). (Page 23, lines 684-686)*

      Minor comments: 1) Several groups have contributed to the CPM literature. The citation of seminal works from Tzahor and Kelly groups is good, however, work from other groups has not been cited. For example, reports such as Heude et al and Grimaldi et al from Tajbakhsh group are very relevant to this work.

      Response:

      According to reviewer’s suggestion, we have included following references in the introduction section for the explanation of CPM derivatives. (P3, line 70)

      1. (Heude et al., 2018)
      2. (Grimaldi et al., 2022)

        2) It would help the reader if the authors explain the reasons for selecting specific regions, such as the tongue, and the skin of the lower jaw, for the study.

      Response:

      This is because many lymphatic vessels are distributed in these cardiopharyngeal area and these area is well known as anatomical parts where lymphatic malformation most often occurs. This has been mentioned in the manuscript as follows:

      From a clinical viewpoint, head and neck regions contributed by the CPM are the most common sites of lymphatic malformations (LMs) (Page 3-4, lines 99-100)

      3) The authors should consider presenting the wholemount images, such as those in Figures 3A and 3E for Figures 5 and 6. This would help assess the lymphatic vessel development in a holistic manner.

      Response:

      Although we tried to do the whole mount images of facial and tongue lymphatics, we could not succeed. Antibodies did not penetrate well on the tongue and, as for lymphatics of facial skin, their complicated morphology prevented clear visualization. Whole-mount imaging of the entire head was difficult for the same reason. In our experience, the antibody was useful for immunostaining of the early-stage embryos (up to E11.5) and the surface area of the heart, where lymphatic vessels were distributed on the epicardium. Even in the whole-mount heart, we have not succeeded in clear and estimable imaging of the vascular structure in the myocardium. Instead, we improved the quality of images and statistical comparisons in the revised manuscript, which we believe makes it more convincing.

      Reviewer #3 (Significance (Required)):

      The nature and significance of the advance for the field & the work in the context of the existing literature: Groups working in the domain of cardiopharyngeal mesoderm (CPM) have focussed on skeletal muscle and heart development. This pool is also known to give rise to skeletal tissues as well as blood vessel endothelium. A recent work Nomaru et al. (Morrow group, Nat Commun 2021) has identified a multi-lineage primed population in the cardiopharyngeal field. In this context, the work from Maruyama and colleagues highlights the versatility of CPM by providing evidence for the emergence of LEC from this multipotent pool. This complex developmental potential of CPM has implications to understand the evolutionary origin of CPM itself.

      The connective tissues in the head/neck have mixed origins (Heude et al, 2018 and Grimaldi et al 2022 from Tajbakhsh group)- from CPM as well as neural crest. This work shows mixed origin for LECs. These works begin to put together the pieces of the puzzle of vertebrate head evolution. Jacob proposed evolution is tinkering. This appears to be true both at the molecular level as well as the cellular level. Head tissues appear to have been put together by exploiting varied sources.

      The study is of broad interest to developmental biologists.

      Reviewer: A developmental biologist with an interest in understanding the axial patterning of mesoderm early during mammalian development. Not an expert in lymphatic vasculature development.

      References for the revision

      Adachi N, Bilio M, Baldini A, Kelly RG. 2020. Cardiopharyngeal mesoderm origins of musculoskeletal and connective tissues in the mammalian pharynx. Development 147:dev185256. doi:10.1242/dev.185256

      Cai C-L, Liang X, Shi Y, Chu P-H, Pfaff SL, Chen J, Evans S. 2003. Isl1 Identifies a Cardiac Progenitor Population that Proliferates Prior to Differentiation and Contributes a Majority of Cells to the Heart. Dev Cell 5:877–889. doi:10.1016/s1534-5807(03)00363-0

      Grimaldi A, Comai G, Mella S, Tajbakhsh S. 2022. Identification of bipotent progenitors that give rise to myogenic and connective tissues in mouse. Elife 11:e70235. doi:10.7554/elife.70235

      Harel I, Maezawa Y, Avraham R, Rinon A, Ma H-Y, Cross JW, Leviatan N, Hegesh J, Roy A, Jacob-Hirsch J, Rechavi G, Carvajal J, Tole S, Kioussi C, Quaggin S, Tzahor E. 2012. Pharyngeal mesoderm regulatory network controls cardiac and head muscle morphogenesis. Proc National Acad Sci 109:18839–18844. doi:10.1073/pnas.1208690109

      Harel I, Nathan E, Tirosh-Finkel L, Zigdon H, Guimarães-Camboa N, Evans SM, Tzahor E. 2009. Distinct Origins and Genetic Programs of Head Muscle Satellite Cells. Dev Cell 16:822–832. doi:10.1016/j.devcel.2009.05.007

      Heude E, Tesarova M, Sefton EM, Jullian E, Adachi N, Grimaldi A, Zikmund T, Kaiser J, Kardon G, Kelly RG, Tajbakhsh S. 2018. Unique morphogenetic signatures define mammalian neck muscles and associated connective tissues. Elife 7:e40179. doi:10.7554/elife.40179

      Klotz L, Norman S, Vieira JM, Masters M, Rohling M, Dubé KN, Bollini S, Matsuzaki F, Carr CA, Riley PR. 2015. Cardiac lymphatics are heterogeneous in origin and respond to injury. Nature 522:62–67. doi:10.1038/nature14483

      Lioux G, Liu X, Temiño S, Oxendine M, Ayala E, Ortega S, Kelly RG, Oliver G, Torres M. 2020. A Second Heart Field-Derived Vasculogenic Niche Contributes to Cardiac Lymphatics. Dev Cell 52:350–363. doi:10.1016/j.devcel.2019.12.006

      Lupu I-E, Kirschnick N, Weischer S, Martinez-Corral I, Forrow A, Lahmann I, Riley PR, Zobel T, Makinen T, Kiefer F, Stone OA. 2022. Direct specification of lymphatic endothelium from non-venous angioblasts. Biorxiv 2022.05.11.491403. doi:10.1101/2022.05.11.491403

      Mahadevan A, Welsh IC, Sivakumar A, Gludish DW, Shilvock AR, Noden DM, Huss D, Lansford R, Kurpios NA. 2014. The Left-Right Pitx2 Pathway Drives Organ-Specific Arterial and Lymphatic Development in the Intestine. Dev Cell 31:690–706. doi:10.1016/j.devcel.2014.11.002

      Maruyama K, Miyagawa-Tomita S, Mizukami K, Matsuzaki F, Kurihara H. 2019. Isl1-expressing non-venous cell lineage contributes to cardiac lymphatic vessel development. Dev Biol 452:134–143. doi:10.1016/j.ydbio.2019.05.002

      Morisada T, Oike Y, Yamada Y, Urano T, Akao M, Kubota Y, Maekawa H, Kimura Y, Ohmura M, Miyamoto T, Nozawa S, Koh GY, Alitalo K, Suda T. 2005. Angiopoietin-1 promotes LYVE-1-positive lymphatic vessel formation. Blood 105:4649–4656. doi:10.1182/blood-2004-08-3382

      Motoike T, Loughna S, Perens E, Roman BL, Liao W, Chau TC, Richardson CD, Kawate T, Kuno J, Weinstein BM, Stainier DYR, Sato TN. 2000. Universal GFP reporter for the study of vascular development. Genesis 28:75–81. doi:10.1002/1526-968x(200010)28:23.0.co;2-s

      Nathan E, Monovich A, Tirosh-Finkel L, Harrelson Z, Rousso T, Rinon A, Harel I, Evans SM, Tzahor E. 2008. The contribution of Islet1-expressing splanchnic mesoderm cells to distinct branchiomeric muscles reveals significant heterogeneity in head muscle development. Development 135:647–57. doi:10.1242/dev.007989

      Srinivasan RS, Dillard ME, Lagutin OV, Lin F-J, Tsai S, Tsai M-J, Samokhvalov IM, Oliver G. 2007. Lineage tracing demonstrates the venous origin of the mammalian lymphatic vasculature. Gene Dev 21:2422–2432. doi:10.1101/gad.1588407

      Stone OA, Stainier DYR. 2019. Paraxial Mesoderm Is the Major Source of Lymphatic Endothelium. Dev Cell 50:247-255.e3. doi:10.1016/j.devcel.2019.04.034

      Tammela T, Saaristo A, Lohela M, Morisada T, Tornberg J, Norrmén C, Oike Y, Pajusola K, Thurston G, Suda T, Yla-Herttuala S, Alitalo K. 2005. Angiopoietin-1 promotes lymphatic sprouting and hyperplasia. Blood 105:4642–4648. doi:10.1182/blood-2004-08-3327

    1. Reviewer #3 (Public Review):

      In this manuscript the authors unify a public datasets of capture HiC data within a common framework and use these data to examine the relationships between topological chromatin organization, enhancer function, gene expression, and the evolutionary conservation thereof. The introduction correctly states several pending and exciting questions in the field and the authors performed a large body of work in multiple directions to address some of them. The results are well presented in clear and pleasant figures, even though the text of the manuscript sometimes lacks similar clarity (see some examples below). Overall, I feel that the manuscript can be substantially improved in several key areas.

      1. First, it critically lacks focus, accumulating analyses in many directions, most of which lead to either unsurprising, or sometimes unconvincing conclusions (see examples below). This huge amount of results (6 Figures and 36 supplementary figures!) hampers the reader's interest and dilute the few novel and exciting results in a crowd of less significant observations. In their current form, the results remain too descriptive, with lots of scattered observations.

      2. Most experiments presented in the manuscript use a 'control' dataset, constructed by a sort of 'shuffling' of the actual data. While this sounds like a good idea in principle, I remained unable to grasp exactly how this procedure was performed, which unfortunately prevented me from fully appreciating the significance of the results.

      Broadly, I understand that the simulated dataset is made by attributing to each promoter the same number of enhancers as in the real data, picked among all enhancers in its vicinity with a probability depending only on their distance to the promoter of interest but irrespective of their 'real' HiC target. If this is correct, some results seem to raise unaddressed questions about its relevance and possible biases.<br /> - l 222-225: the authors note that restriction fragments are more conserved than control data in gene-rich, but not in in gene-poor regions. Couldn't this happen simply because in gene-poor regions, the simulated data are in fact closer to the real data : if there are no other genes in the vicinity of the promoter of interest there will be no fragments targeting other promoters, hence no shuffling of the enhancer-promoter links can occur.<br /> - In the simulated data, one expects that some fragments will contact 0 baits. Why are they not shown in figures 1, 1S1c,f, 1S2b, 1S3b ?<br /> - Fig 1S1c,f show that in the simulated dataset, each fragment contacts less baits than in the actual dataset. Why can we see the opposite in Fig 1S2b and 1S3b ?<br /> - While Fig1S1b,e show that each bait contacts the same number of fragments in the simulated and actual dataset, which is expected by construction, why can we see a marked difference in Fig1S2a and 1S3a? Even if there is a small difference due to a posteriori filtering of simulated data, it should go in the opposite direction of what is seen (it should lower the number of fragments per bait, not increase it).

      3. I appreciate that the authors do not attempt to overestimate the importance of their results, but my impression is that almost none of the conclusions are really novel with respect to the existing literature. Roughly, figures 1 to 4 do not say much more beyond the fact that the dataset is enriched in enhancer-promoter interactions. This is not uninteresting, but not really a surprise in itself either, given that it represents topological contacts of promoters.<br /> Being enriched in enhancer-promoter interactions, it ensues that the dataset also tends to be more conserved, both sequence-wise (Figure 3) and synteny-wise (Figure 4).<br /> Not only is this expected, but the observed size effect seems very small, both for the enrichment itself (measured overlap with known enhancers in Fig.2) and for the consequences on conservation. This is exemplified in lines 195-197 of the manuscript results section: "For the comparison between human and mouse, the median aligned length fraction of contacted fragments is 27% in PCHi-C data, which is significantly higher than the 23% observed in the simulated dataset". It seems to me that even a small enrichment could generate such small effects, with clear statistical significance but limited biological significance.

      4. More exciting observations come only with Figures 5 and 6. They however still need more solid support.

      a) For example, data in Figures 5c and Fig5-Supp 4 and Fig5-Supp 5 would be a lot more interesting if restricted to interactions within synteny blocks, thus measuring solely interactions that are lost/kept between human and mouse independent on synteny conservation. This would be very interesting, as it has not been measured before. Would the conservation be dependent on the distance? This cannot be seen in the present data.

      b) The question of the link between the evolution of gene expression and that of enhancer landscapes, asked in Figure 6 is of major interest and has not been much explored so far. The result is however disappointing in that it only confirms the findings of a previous study (Berthelot et al., 2018), with a weaker signal than in the original study. The correlation between conservation of expression and number of chromatin contacts (Fig6c), which is supposed to be the key result, seems extremely modest, to say the least. The correlation with expression specificity or with expression levels is more convincing, but also of lesser interest.

      5. GO enrichment analyses of the conserved contacts are only briefly mentioned and relegated to supplementary data. The only conclusion of the manuscript is that it is "consistent with the presence of strong functional constraints on the cis-regulatory landscapes of developmental genes". This is already very well known. I am sure more can be drawn from these analyses, even though they should be carefully controlled for important confounding factors (eg gene density). For example, if the conservation of contacts were studied independent on the synteny, would contacts of specific GO categories be more or less conserved than others? In other terms, do rules of chromatin contact vary depending on gene function? This would be new.

    1. Reviewer #1 (Public Review):

      Bera et al. study the response of vegetation in water-limited ecosystems to changes in the precipitation regime. Previous studies have shown that spatial processes, in particular the redistribution of (soil and surface) water, may play an important role in mediating the ecosystem response. An important consequence of this redistribution is the spatial self-organization of vegetation into regular spatial patterns, consisting of vegetation patches that act as sinks for (surface) water, and surrounding areas of bare soil that act as water sources. At the ecosystem level, the additional water input in vegetation patches may enable vegetation to persist at precipitation levels that are too low to sustain a spatially uniform cover.

      While most model studies of spatial self-organization and pattern formation describe vegetation dynamics through 1-2 biomass variables, the current study extends this previous work by considering a trait diversity gradient, considering a large number (N=128) of discrete trait classes that range from stress-tolerant to fast-growing characteristics. The results show that in the absence of spatial pattern formation, a decrease in precipitation leads to a shift in the biomass distribution toward the more stress-tolerant trait classes. At the onset of pattern formation, however, soil water availability increases at the locations where vegetation patches form, enabling the more fast-growing trait classes to increase in biomass, and this shift is accompanied by an increase in functional diversity of trait classes as well. It is also shown that once these patterned ecosystem states are formed, the main adaptation to further decreases in precipitation occurs either through shrinking the size of existing patches, or by reducing the number of patches; in contrast, biomass and community composition of the patches remains relatively stable. Finally, it is shown that for certain precipitation conditions, functional diversity is maximized when the ecosystem is in a hybrid state, where part of the landscape has a spatially uniform vegetation cover, and part of the landscape is in a patterned state.

      A potential strength of this paper is that the community assembly and biodiversity perspective on spatial self-organization may highlight the relevance of pattern formation in ecosystems more clearly to a broad audience. The formulation of a trait/strategy gradient of discrete classes is certainly an interesting suggestion to connect the typical single/few biomass variable(s) approach to a community-level approach. The community assembly process is modelled in a very specific way, and the manuscript would benefit from an expanded ecological motivation of the processes that are being mimicked, and thereby explain more clearly what taxonomic level of organization is being considered. In addition, it would be useful if the authors could provide further clarification as to what extent the community diversity dynamics can be separated from total biomass dynamics of patterned water-limited ecosystems given the current approach. These points are explained in further detail below.

      • First, it was not entirely clear to this reviewer how the reaction parts of the model equations determine the optimal trait value χ, and how this value varies as a function of precipitation. Assuming a single trait class, and plotting the relevant equilibrium values of the three state variables shed some light on this issue. [Unfortunately, there does not seem to be a possibility to attach the figure with these plots to this review report]. Assuming the non-spatial equilibrium solution was derived correctly , the optimum biomass (B) value shifts across the trait spectrum with changing precipitation (in the non-spatial model version, solving the surface water equation for equilibrium will always yield that all precipitation infiltrates, i.e. regardless of the values of surface water, H, and χ). The equilibrium of soil water availability (W), which is the growth limiting resource of the vegetation, shows an inverse pattern with biomass. This result is in line with a classical results (e.g. Tilman 1982), in that the most successful strategy is the one that is able to reduce the limiting resource to the lowest equilibrium value. With all trait classes competing for the soil water resource, however, it is then not immediately clear why the most successful trait class is not outcompeting the other classes. This leads to a second point, about the way in which community trait adaptation is modelled.

      • The authors model trait adaptation through a diffusion approximation between trait classes. That is, every timestep, a small amount of biomass flows from the class with higher biomass to the neighboring trait class with lower biomass. From an ecological point of view, it seems that this process is describing adaptation of vegetation that is already present, so this process seems to be limited to intraspecific phenotypic plasticity. From the text, however, it seems that the trait classes correspond to higher taxonomic levels of organization, when describing shifts from fast growing to stress-tolerant species, for example. It is not entirely clear, however, how biomass flows as assumed in the model could occur at these higher levels of organization.

      • Combining the observations from the previous two points, there is a concern that for a given level of precipitation, there is a single trait class with optimal biomass/lowest soil water level that is dominant, with the neighboring trait classes being sustained by the diffusion of biomass from the optimal class to neighboring inferior classes. This would seem a bit problematic, as it would mean that most classes are not a true fit for the environment, and only persist due to the continuous inflow of biomass. Taking a clue from the previous papers of the authors, it seems this may not be the case, though. Specifically, in the paper by Nathan et al. (2016) it seems that all trait classes are started at low initial biomass density, and the resulting steady state (in the absence of biomass flows between classes) seems to show similar biomass profiles as shown in Figs. 4,5 and 7 of the current paper. While the current model formulation seems slightly different, similar results may apply here. Indeed, keeping all trait classes at non-zero (but low) density, and when the (abiotic and biotic) environment permits, let each class increase in biomass seems like the most straightforward approach to model community assembly dynamics. Given the above discussion about these trait classes competing for a single resource (soil water), and one trait class being able to drive this resource availability to the lowest level, it would then be useful to readers to explain why multiple trait classes can coexist here, and how (for spatial uniform solutions) the equilibrium soil water level with multiple trait classes present compares to the equilibrium soil water level when only the optimal trait class is present. Furthermore, if results as presented in Nathan et al. (2016) indeed hold in the current case, perhaps it means that the biomass profile responses as shown in e.g. Fig. 5 would also occur if there was no biomass flow between trait classes included, but that the time needed to adjust the profile would take much longer as compared to when the drift term/second trait derivative is included. In summary, further clarification of what the biomass flows between classes represent, and the role it plays in driving the presented results would be useful for readers.

      • In addition, it would be useful for readers to understand to what extent the shifts in average trait values and functional diversity can be decoupled from the biomass and soil water responses to changes in precipitation that would occur in a model with only a single biomass variable. For example, early studies on self-organization in semi-arid ecosystems already showed that the shift toward a patterned state involved the formation of patches with higher biomass, and higher soil water availability, as compared to the preceding spatially uniform state, and that the biomass in these patches remains relatively stable under decreasing rainfall, while their geometry changes (e.g. Rietkerk et al. 2002). It has also been observed that for a given environmental condition, biomass in vegetation patches tends to increase with pattern wavelength (e.g. Bastiaansen and Doelman 2018; Bastiaansen et al. 2018). Given the model formulation, one wonders whether higher biomass in the single variable model is not automatically corresponding to higher abundance of faster growing species and a higher functional diversity (as the diffusion of biomass can cover a broader range when starting from higher mass in the optimal trait class). There are some indications in the current work that the linkage is more complicated, for example, the biomass peak in Fig. 7c is lower, but also broader as compared to the distribution of Fig. 7b, but it is currently not entirely clear how this result can be explained (for example, it might be the case that in the spatially patterned states, the biomass profiles also vary in space).

      • The possibility of hybrid states, where part of the landscape is in a spatially uniform state, while the other part of the landscape is in a patterned state, is quite interesting. To better understand how such states could be leveraged in management strategies, it would be useful if a bit more information could be provided on how these hybrid states emerge, and whether one can anticipate whether a perturbation will grow until a fully patterned state, or whether the expansion will halt at some point, yielding the hybrid state. It seems that being able to distinguish these case would be necessary in the design of planning and management strategies. Also, in Fig. 3a, the region of parameter space in which hybrid states occur is not very large; it is not entirely clear whether the full range of hybrid states is left out here for visual considerations, or whether these states only occur within this narrow range in the vicinity of the Turing instability point.

      References:

      Bastiaansen R, Doelman A. 2018. The dynamics of disappearing pulses in a singularly perturbed reaction-diffusion system with parameters that vary in time and space. Physica D 388: 45-72.

      Bastiaansen R, Jaïbi O, Deblauwe V, Eppinga MB, Siteur K, Siero E, Mermoz S, Bouvet A, Doelman A, Rietkerk M. 2018. Multistability of model and real dryland ecosystems through spatial self-organization. Proceedings of the National Academy of Sciences USA 115:11256-11261.

      Nathan J, Osem Y, Shachak M, Meron E. 2016. Linking functional diversity to resource availability and disturbance: a mechanistic approach for water limited plant communities. Journal of Ecology 104: 419-429.

      Rietkerk M, Boerlijst MC, van Langevelde F, HilleRisLambers R, van de Koppel J, Kumar L, Prins HHT, De Roos AM. 2002. Self-organization of vegetation in arid ecosystems. American Naturalist 160: 524-530.

      Tilman D. 1982. Resource competition and community structure. Princeton University Press, Princeton, NJ, USA.

    1. Author Response

      Reviewer #1 (Public Review):

      In their paper, titled ‘Group II truncated haemoglobin YjbI prevents reactive oxygene species-induced protein aggregation in Bacillus subtilis’, Imai et al., suggest that the protein YjbI acts as a hydroperoxide peroxidase and therefore it may protect cell-surface cells from oxidation. Using AFM and contact angle measurements they show that yjbI mutants lead to changes in cell surface properties as well as to the formation of more hydrophilic biofilms, relative to the wild-type (WT) strain. Since both tasA and yjbI mutants experienced a similar phenotypic behaviour, the authors linked between the two proteins, TasA and YjbI, and in a series of biophysical and biochemical tests they tried to establish this link. This study touches upon an important question, how do biofilms protect themselves from reactive oxygene species (ROIs), that is nicely described in the introduction; The link between the above proteins in very interesting and relevant to the main question proposed in the study. However, the experiments presented does not always directly support the conclusions made.

      The points that I find necessary to clarify/extend:

      1) A major claim in the paper is that biofilms that do not harbour the tasA gene (tasA-) are flat, and therefore their contact angle is low, indicating that they are less hydrophobic than WT strains. However, the phenotype of biofilms of tasA mutants are normally not that flat (see for example Romero et al., PNAS 2010; Vlamakis et al., Genes and Development, 2008; Erskine et al., Molecular Microbiology 2018). As a matter of fact, even the WT biofilms that are used as a control in this study are much more flat than the biofilms that serve as standards in the papers referenced above.

      We appreciate the reviewer’s comment. As we explained above (answer to Essential Revisions, point 4), there are differences in the morphology of colonies between the 168 and NCBI3610 strains of B. subtilis, as previously pointed out in the literature (Arnaouteli et al. Nat. Rev. Microbiol., 19:600-614, 2021; Mielich-Süss and Lopez, Environ. Microbiol., 17:555-565, 2014). We employed B. subtilis strain 168 because this strain is a close representative of B. subtilis, as described by Zeigler et al. (J. Bacteriol., 190:6983-6995, 2008), and serves as a model organism for wider aspects of basic research, including oxidative damage responses.

      To clarify this point, we have added the following text to the revised manuscript in lines 269–277: “Most studies on biofilm formation in B. subtilis use the B. subtilis NCBI3610 strain as a model bacterium because of its ability to form well-structured three-dimensional biofilms (Arnaouteli et al., 2021, Mielich-Süss et al., 2014). The biofilms of the wild-type and tasA mutant strains of the B. subtilis 168 strain are known to be morphologically different from those of the B. subtilis NCBI3610 strain (Romero et al., 2010, Vlamakis et al., 2008, Erskine et al., 2018). In this study, the B. subtilis 168 strain was used because it is the most representative of B. subtilis and serves as a model organism for a wider range of research aspects (Zeigler et al., 2008) as we were not only interested in evaluating biofilm formation but also in more general aspects of oxidative damage responses in bacteria.”

      2) Figure 1. The authors use AFM phase imaging to probe differences in cellular stiffness. This AFM mode is not quantitative and the differences presented could also result from differences in adhesion between the tip and the sample. A more quantitative means to evaluate stiffness is a direct measurement of moduli in Force mode, a standard AFM module.

      Thank you for your comment. As mentioned above (answer to Essential Revisions, point 3), the AFM data have been removed.

      3) Line 147. The authors link between the lack of monomeric TasA in YjbI mutants and the formation of covalent cross linking in TasA aggregates. This is a strong statement that unfortunately is not supported by any of the experiments described in the manuscript.

      As mentioned above (answer to Essential Revisions, point 5), we have removed the statement regarding the lack of monomeric TasA in the mutant. The following has been included to highlight the potential involvement of covalent bonds in the TasA aggregate formation in lines 126–129 in the revised manuscript: “No monomeric TasA was detected in the insoluble fraction of the yjbI-deficient mutant strain. An aggregate of TasA was observed under strong reducing and heat-denaturing conditions in SDS sample buffer, suggesting that covalent bonds may be involved in aggregate formation.”

      4) The authors seek to make a connection between YjbI and TasA. However, this link is either not well established or only hinted indirectly in this manuscript, through precipitation assays, contact angle measurements and growth curves. To establish such a link, a more molecular approach is advised. Experiments that would provide a direct link between the two proteins and mark specific molecular changes of the proteins include for example titration NMR studies of labelled proteins (at least one of the proteins). In cases where the authors need to show protein localization to the cell surface, it would be of help to use TEM or high-end fluorescence microscopy.

      We thank the reviewer for this valuable advice. In response to this comment, we carried out additional experiments, as described above (answer to Essential Revisions, point 1). We will consider the suggested studies, mainly with a molecular approach including titration NMR and TEM, for future studies, as facilities for these specific studies are currently not available.

      5) This paper suggests that the protein YjbI acts as an electron donor. Given that there are other proteins with a similar role (in other organisms), it would be nice to show whether there is any homology (by sequence and/or structure) to these proteins.

      Thank you for your comments. We have added a description of animal peroxiredoxins and selenomethionine (with GSH or a thioredoxin system) that have been shown to scavenge protein hydroperoxides to the revised manuscript. We also added a description of how YjbI differs from peroxiredoxin and selenomethionine.

      The corresponding sentences have been added to the revised manuscript in lines 254–268: “Peroxiredoxins have been reported to repair intracellular protein peroxidation in mammals (Peskin et al., 2010). However, YjbI is distinct from peroxiredoxins in that it is a haem protein with no significant sequence homology (<15%). The second-order rate constants (M-1·s -1) for the reactions of mammalian peroxiredoxins 2 and 3 with BSA-OOH are 160 and 360, respectively, and have been shown to reduce protein peroxides more efficiently than GSH under physiological conditions (Peskin et al., 2010). Although direct comparison is difficult due to different experimental conditions, YjbI and peroxiredoxins are likely to have a similar catalytic rate, as both proteins can reduce BSA-OOH in the order of several mM in roughly 5 min at similar protein concentrations (Fig. 3e) (Peskin et al., 2010). Interestingly, selenomethionine can catalyze the removal of hydroperoxides from proteins in the presence of GSH or a thioredoxin system (Rahmanto & Davies, 2011). However, this system, as well as peroxiredoxins, localises in the cytoplasm of cells, which is a significant difference between YjbI and these proteins. Moreover, whether bacteria utilize peroxiredoxins and the selenomethionine system to remove hydroperoxides from proteins remains unclear.”

      6) (Minor point). The use of Pymol to demonstrate that the YjbI's pocket could serve as a binding site for haem molecule is nice, but using Molecular Dynamics (or any other calculation) would be more quantitative and convincing of the specificity of the interaction.

      We appreciate your comment regarding this point. However, we believe that analysis using molecular dynamics (or other calculations) is largely difficult because the structure of the hydroperoxidised protein substrates is not available. Further, the degree of similarity between the structure of TasA or BSA and the hydroperoxidised form is unclear. A calculation analysis with a small model substrate can be adopted in future work. Therefore, we only showed the surface opening of the YjbI structure, which is potentially relevant for binding to a hydroperoxidised protein substrate.

      Reviewer #2 (Public Review):

      In this study, Imai et al. uncover a role for the truncated haemoglobin protein YjbI in biofilm formation by the model bacterium B. subtilis. They show that yjbI gene disruption results in altered biofilms, with increased wettability and different matrix stiffness relative to cells. The absence of YjbI activity results in aggregation of the amyloid-like TasA matrix protein, and the biofilm wettability defect of the yjbI mutant can be recapitulated by anti-YjbI immune serum, suggesting that YjbI is located on the cell surface. Absence of YjbI also modestly increases the sensitivity of cells growing on agar plates to the oxidant AAPH. Using the model protein substrate BSA, purified YjbI can at least partially reverse oxidant-induced BSA aggregation in vitro, convincingly showing the YjbI has protein hydroperoxide peroxidase activity, which is evidently an unusual enzymatic activity. Finally, the authors examine lipid peroxidation and conclude that YjbI is not involved. The results are interesting in that they connect YjbI to a biofilm phenotype and convincingly show protein hydroperoxide peroxidase activity by a truncated haemoglobin protein, an activity not previously attributed to this class of proteins.

      The experiments are largely well done, but some of the corresponding conclusions are overinterpreted, connecting ideas without experimental support. Moreover, the yjbI mutant has a narrow and relatively mild phenotype.

      1) The paper identifies two separate properties of YjbI: its mutant phenotype with respect to biofilm formation, and its peroxidase activity against oxidant-induced aggregation of TasA and BSA. The authors conclude that these properties are connected, but this is not formally tested. While purified YjbI can reverse hydrogen peroxide-induced aggregation of purified TasA in vitro, and the yjbI mutant shows more TasA in the insoluble fraction of B. subtilis pellicle lysates, these experiments do not show that the TasA aggregates in pellicle lysates are caused by peroxidation, nor do they show that TasA aggregation is normally kept at bay by YjbI peroxidase activity (it is possible that YjbI has a separate role in biofilm integrity). Some experiments that might lend support to this connection include examining the biofilm phenotype of a catalytically dead point mutant of YjbI (perhaps Y25 or Y63, l. 298, or other residues informed by the crystal structure of Giangiacomo et al.) to establish whether peroxidase activity is important for biofilm formation. Such a mutant would be particularly valuable, as it could also be used to test whether inactivation of enzyme activity affects other phenotypes (cell stiffness, for example). Another approach would be to use a soluble antioxidant molecule, purified YjbI, or another peroxidase to see if the yjbI biofilm can be rescued.

      We greatly appreciate this comment, which is critical for improving our manuscript. To address this issue, we performed additional experiments using the Y25F, Y63F, and W69F variants of YjbI. The introduction of the Y63F variant gene into the yjbI-deficient strain failed to complement the defective phenotype of the yjbI-deficient strain in biofilm repellency (revised Fig. 1b). We found that the purified Y63F lost its hydroperoxide peroxidase activity (revised Fig. 3g). These results show a connection between the protein hydroperoxide peroxidase activity of YjbI and the abnormal biofilm phenotype of the yjbI-deficient strain. Accordingly, Figs. 1b and 3g have been added to the revised manuscript and figure descriptions have been included in lines 220–226, 322–324, and 327–328 (as explained above in the answer to Essential Revision, Point 2).

      2) The authors conclude on the basis of the AFM data in Fig. 1 that yjbI mutant cells are less stiff than WT cells, but the data only show relative stiffness. It is also unclear why a change in cell envelope stiffness would relate to biofilm wettability (ll. 130-131). If there truly is a change in cell envelope stiffness, a high-resolution, head-to-head AFM comparison of planktonically grown cells would be informative.

      We appreciate the reviewer’s comment on this point. As mentioned above (answer to Essential Revision, points 3 and 6), we realized that our interpretations of the AFM data were not appropriate and not relevant to biofilm repellency. Accordingly, the AFM data were removed.

      3) The data in Fig. 2F showing hypersensitivity of yjbI mutant cells to AAPH were generated in an unusual way: stationary-phase liquid culture was spotted on an LB plate, and the colonies were "fractionated" at the noted intervals and resuspended in saline for OD measurement. Measuring sensitivity to AAPH just in shaking liquid planktonic culture would make this phenotype more convincing. Under non-biofilm forming conditions, is a surface-associated peroxidase important for cell growth or survival under oxidant challenge?

      We appreciate your comment regarding this point and apologize for the error in the description “'Planktonically grown B. subtilis strains under AAPH-induced oxidative stress'” in the Methods section. No solid medium was used in the experiments. The description in the Figure legend of Fig. 2f is correct. The sentence in the text has been rewritten in the revised manuscript in lines 813.

    1. musical scores

      For example, it is a common practice in choirs (I sing in the Grinnell Oratorio) to mark up musical scores in rehearsal, as when the conductor gives specific instructions about how to sing a passage (pronunciation, stress, where to breath, dynamics, etc.). It is common practice for choir members to bring pencils to rehearsal for just such purposes. And when scores get reused, the markings are there for the next singer.

      https://youtu.be/Vl2kOqy_Uc4

    1. Author Response

      Joint Public Review:

      Strengths: The study represents a step forward in relating immune responses to infection outcomes that of urgent interest to public health, especially the timing of shedding and frequency of supershedding events. Nguyen et al.'s model provides a useful framework for understanding the links between immune effectors and infection outcomes, and it can be expanded to encompass further biological complexity. The study system is a good choice, given the ubiquity of both helminth and bacterial infections, and experimental infections of rabbits provide a useful point of comparison for past work in mice.

      We appreciated these general comments.

      Limitations: The present study does not explicitly account for differences in helminth infection dynamics across the two species represented in the data nor does it include feedbacks between the bacterial and helminth infections. Nguyen et a. therefore show the limits of what can be learned from focusing on the bacterial and immune dynamics alone, and this study should serve to motivate further work that can build on this modeling approach to produce a more comprehensive view of the interactions among species infecting the same host. Future studies examining the impact of helminth infection intensity would be tremendously useful for assessing the potential of anthelminthics to reduce the prevalence of bacterial respiratory diseases. Finally, subsequent studies may need to look beyond the factors examined here to understand why shedding varies so much through time for individual hosts.

      We agree that focusing only on the bacterial infection is a limitation in this study. We followed a parsimonious approach and decided to concentrate on B. bronchiseptica shedding in the four types of infection. While we do have data on the dynamics of infection of the two helminth species, adding these data would have been an enormous amount of work and too much to present in a single paper. Yet, we have already investigated some of these bi-directional effects using the BT group (Thakar et al. 2012 Plos Comp. Biol.) and plan to keep working on these rich datasets in the future.

      We also agree that it is important to understand the rapid variation in Bordetella shedding observed, which appears to be a common feature in many other host-pathogen systems. This requires a completely new set of experiments on infection and shedding at the local tissue level.

      Specific comments

      Definition of supershedding: A major stated goal of the MS is to investigate the effect of coinfection by helminths on supershedding. In order to compare animals with different coinfections, it is therefore necessary to have a common definition of supershedding. At present, the authors use a definition that depends on which arm of the experiment the animals belong to. This complicates the analysis and clouds its interpretation.

      We value this comment and see the implication of using different datasets to quantify supershedding. To overcome this problem, we now propose a slightly different approach where we pull the four infections together and calculate a common 99th or 95th percentile threshold. This common threshold is then used to calculate the number of hosts with at least one supershedding event above this cut-off, for every type of infection. Therefore, while the threshold is the same the percentage of hosts with supershedding events varies among infection groups.

      Inconsistent approach: Within each experimental treatment, the data display variability on at least three levels: (i) within animals, day-to-day shedding displays variability on a fast timescale; (ii) within animals, infection status varies more slowly over the course of infection; (iii) between animals, there is variation in both (i) and (ii). The authors' model seems well-designed to handle this variability, but the authors are strangely inconsistent in their use of it. To be specific, to account for level (i), the authors very sensibly adopt a zero-inflated model for the shedding data, whereby the rate of shedding (colony-forming units per second, CFU/s) is assumed to arise from a mixture of a quantitative process (which we might think of as intensity of potential shedding) and an all-or-nothing process (which might arise, for example, if some discrete behavior of the animal is necessary for shedding to occur at all). The inclusion of the all-or-nothing process necessitates an additional parameter, but it allows the non-zero shedding data to inform the model. To account for level (ii), the authors use a four-dimensional deterministic dynamical system. Three of the four variables are related to the measured components of the immune response. The fourth is related to the aforementioned potential shedding. Level (iii) is accounted for using a hierarchical Bayesian approach, whereby the individual animals have parameters drawn from a common prior distribution. This approach seems very well designed to address the authors' questions using the data at hand. However, they fail to exploit this, in at least three ways. First, even though the model appears designed specifically to allow for non-shedding animals, the authors exclude animals on an ad hoc basis. Second, rather than display the shedding data in the form recommended by the model, they display log(1+CFU/sec), which is arbitrary and problematic. Its arbitrariness stems from the fact that this quantity is sensitive to the units used for shedding rate. Third, despite the fact that the model appears specifically designed to account for variability at each of the three levels, they do not give enough information to allow the reader to judge whether the model does in fact do a good job of partitioning this variability.

      Please see comments to each specific matter below.

      Exclusion of animals: In view of the fact that the model the authors describe can account for variability on all three levels, it is strange that they exclude animals that shed too little or not at all. It would be preferable were the authors to base their conclusions on all the data they collected rather than on a subset chosen a posteriori. It is true that the non-shedders will have no information about the time-course of shedding; on the other hand, including them does not complicate the analysis, and it does allow for estimation of the all-or-nothing probability in a coherent fashion. In particular, the fact that coinfection appears to have an impact on whether animals shed at all is itself directly related to the authors' central questions. More generally, ad hoc exclusion of data raises concerns about the repeatability of the experiments that, in this case, appear entirely avoidable.

      Rabbits that were infected but never shed were excluded from all our original analysis and continue to be excluded in our updated version. Our focus is on the dynamics of shedding and including animals that do not shed is not informative to our objective. Moreover, these animals do not provide meaningful information on rabbits that are infected but do not shed, since this is a very small number (n=7) to draw meaningful conclusions across four types of infection. Rabbits with three or less shedding events larger than zero (i.e. CFU/s>0) were originally excluded from the modeling and continue to be excluded. This decision was motivated by technical reasons of model convergence and our commitment to generate meaningful results; in other words, it is difficult to fit a model, and provide robust results, on a time series with only three points larger than zero, irrespective of the number of zero points in the time series.<br /> In summary our subset of animals was not chosen a posteriori but based on clear objectives (i.e. pattern of shedding between and within types of infections), a rigorous approach and reliable results. We have further clarified our approach in the Results and Material and Methods.

      Incomplete description of the analysis: The description of the statistical analysis will not be complete until sufficient information is provided to allow the interested reader to decide for him- or herself whether the conclusions are warranted and for the motivated reader to reproduce the analysis. In particular, it is necessary to specify all priors fully. At present, these are not described at all, except in vague, and even incoherent, ways. Also, it is necessary to provide details of the MCMC performed. Specifically, the authors should describe the MCMC sampler and show their MCMC convergence diagnostics. Finally, it is good practice to display both the priors and the posteriors: it is impossible to assess the posteriors without an understanding of the priors.

      We have carefully revised our approach and results and now provide a complete description of our analysis with additional/new details on Parameter calibration, Model fitting, Model validation and Model selection in Material and Methods, and Appendix (Appendix-3 and 4). Specifically, we have included all priors, along with all posteriors, for the four types of infection in Table 2. We have also explained how the MCMC simulations were performed and how model convergence diagnosis was assessed (section ‘Parameter calibration and Model fitting’). In Appendix-3 we also show the parameter MCMC trace plots for the four types of infection.

      Second, rather than display the shedding data in the form recommended by the model, they display log(1+CFU/sec), which is arbitrary and problematic. Its arbitrariness stems from the fact that this quantity is sensitive to the units used for shedding rate.

      A clear feature of our shedding data is that there is large variation in the level of shedding both within and between hosts. Because of this, data were presented as log(1+CFU/s) to reduce the skewness of the datasets, and thus the variance, and facilitate the visualization of the experimental and simulated results. The use of data in the form of CFU/s would have made the visualization much harder, especially at low shedding where a large fraction of the data come from.

      The practice of displaying the data on a log-scale is appropriate when the underlying process is exponential or when the amount of relative variation is large, including when representing rates. This practice is widely used when modeling infectious diseases and describing biomedical results. A typical example is the overdispersion of macroparasite infections in host populations, or the large variation in the size of outbreaks by microparasite infections, these data are often described on a log-scale. An example closer to our case is the study on influenza-bacteria coinfection by Smith et al. 2013 Plos Pathogens. Given the nature of our data we found that plotting the level of shedding on a log-scale was the most effective way to represent our results.

      Model adequacy: The authors' argument rests on the model's ability to adequately account for the data. The authors need to provide some evidence of this, in one form or another. Ultimately, the question is whether the data are a plausible realization of the model. The authors should show simulations from the model (including the measurement error and not merely the deterministic trajectories) and compare these simulations to the data. In particular, it seems worryingly possible that the fitted model is capable of capturing certain averages in the data while, at the same time, failing to describe the infection progression for any of the actual infected animals.

      As previously reported, we have now provided full details on model fitting and model convergence in the section ’Parameter calibration and Model fitting’ and ‘Model validation’ in Material and Methods, and ‘Model validation’ and ‘Model convergence’ in Appendix (Appendix3 and 4).

      Regarding the evidence that the data are a plausible realization of the model, we have moved the original figure S1 in the main text (now figure 5). This figure shows the good fit of the model to neutrophil, IgA and IgG, both using individual and group data from every infection. We have also revised the quality of the plot to highlight individual simulations. To avoid too much crowding the 95% CIs for every individual are not reported, however, in Appendix-1 we provide the posterior parameter estimations and their 95% CIs, for every individual and as a group average, for the three co-infections (simulations for B rabbits were performed at the group level only).

      In the new figure 6 (original figure 5), we have now included the individual trajectories (without 95% CIs to avoid overcrowding), alongside the group trends, for the neutralization rates of neutrophils, IgA and IgG which are the important parameter regulating infection and where the CIs are large enough to show the individual data. The other rates have too narrow CIs to single out individual trajectories and, thus, we only reported the group trends.

      In the revised figure 7 (original figure 6) we have revised the quality of the plots to highlight individual trajectories, in addition to the median trend, but have not included the individual 95% CIs, again to avoid overcrowding.

      Finally, the main text associated to these figures has been updated accordingly.

      Confusion of correlation and causation: At various points, the authors succumb to the temptation to interpret their model literally and to interpret the correlations they observe as evidence for a causal linkage between the three immune components they measure, bacterial shedding, and coinfection. They should be more careful and circumspect in the description of their results.

      We have thoroughly revised the presentation and discussion of the results to avoid the overinterpretation of the findings.

      Additional Issues:

      Eqs 1-4. These equations are not mechanistic in any meaningful sense. Essentially, they posit the existence of exponential time-lags between the three immunity variables, and a simple linear killing relationship between each of the variables and pathogen load. To interpret the equations literally risks making unwarranted conclusions. For example, any physiological variable correlated with any of the three variables in the model might equally well be credited with the influence on shedding attributed to IgA, IgG, or neutrophils.

      This work tests the hypothesis that neutrophils, IgA and IgG affect the dynamics of B. bronchispetica infection and, in turn, bacterial shedding. Of course, there are many other immunological mechanisms that could contribute to the pattern observed and that can be tested, as there are many other variables correlated with these dynamics that do not play any role in these patterns, as noted by the reviewer. We follow a parsimonious approach by focusing on three immune variables previously identified as important in regulating Bordetella infection. To avoid excessive complexity and allow model tractability, our informed decision was to simplify the relationship between immunity and infection, without losing the important role of the immune variables selected. Finally, by referring to previous work by others and us we do note that the immune mechanisms described can be much more complex.

      l 456. Do the authors account for the variability in time spent with plates? Implicitly, the assumption is made that the amount of time a rabbit spends with a plate, i.e., the decision as to whether to engage in a behavior that will terminate the plate interaction, is independent of everything else. This raises the question: Does the time spent per plate correlate with anything?

      We always recorded the amount of time spent with the plate, and every rabbit had a maximum interaction time of 10 minutes. Rabbits are very inquisitive and rarely we had animals that did not interact or had to remove the plate because they were chewing the media; usually animals used the entire 10 minutes. Analyses do account for the interaction time and are presented as Colony Forming Unit/second (CFU/s). As noted in the Material and Methods section ‘Observation model’: ‘The probability of having a shedding event is independent of time since inoculation, in that shedding can occur anytime during the experiment and anytime during the interaction with the petri dish”. This assumption is based on our observations of rabbit behavior during the trials.

    1. Author Response

      Reviewer #1 (Public Review):

      Kang et al. studied the role of cystathionine beta-synthase (CBS), an enzyme involved in homocysteine catabolism, in the senescent state stimulated by Akt. They report that Akt induces expression of CBS and other enzymes necessary to convert homocysteine into cysteine, and that blocking CBS enhances cell proliferation and reduces beta-galactosidase expression. Mechanistic studies reveal that Akt activates several markers of mitochondrial metabolism, including respiration, and that CBS silencing mitigates this change and reduces reactive oxygen species. Analysis of human gastric tumors reveals methylation of the CBS locus and reduced CBS expression relative to nonmalignant gastric mucosa. Finally, reexpressing CBS in gastric cancer cells reduces growth and Ki67 staining in xenografts. The authors conclude that CBS is a required component of the Akt-induced senescence pathway, and that reducing CBS expression is a mechanism by which some cancers suppress senescence and promote growth. Overall, the paper describes an interesting metabolic process of oncogene-induced senescence that appears selective for Akt. Few such mechanisms have been described, so a thorough exploration of CBS's role in senescence could be impactful. The authors succeed in showing that manipulating CBS expression in a limited number of models has substantial effects on senescence and growth. However, not all of the conclusions are supported by the data in the current version of the paper, the metabolic analysis of CBS's function in Akt-expressing cells is incompletely characterized, and some central aspects of the overall mechanism (particularly the relevance of CBS to mitochondrial respiration) are unexplained.

      Specific comments:

      1) CBS expression is induced upon Akt activation, but there needs to be better evidence that activity of the pathway has changed. The metabolomics results are not very convincing, as siCBS has no or minimal effects on some metabolite pools that should respond. An isotope tracing study would help here.

      We thank the reviewer for the suggestions. We performed a [3-13C] L-serine tracing analysis by LC/MS in proliferating cells, AKT-induced senescent (AIS) cells, and AIS cells with CBS knockdown in cysteine-replete and depleted conditions. The results shown in Figure 3A-3E of the revised manuscript.

      The tracer [3-13C] L-serine has been reported to incorporate into the cellular GSH pool via transsulfuration-derived cysteine (Zhu et al., 2019) (Figure A). We replaced all the serine in the culture medium with [3-13C] L-serine. After six hours of labelling, a substantial fraction of [3-13C] L-serine was detected intracellularly and in the cystathionine pool in proliferating (pBabe-siOTP), AIS (myrAKT1-siOTP) and AIS escaped (myrAKT1-siCBS) cells (Figure B and C). We did not detect [3-13C] L-serine incorporation into cysteine and GSH in the proliferating cells, possibly due to the short time period of metabolic labelling (Figure D and E). However, AIS cells displayed a small but significant fraction of 13C labelled cysteine and GSH along with a significant increase of total levels of serine, cysteine and GSH (Figure BE), supporting the upregulation of transsulfuration pathway activity in AIS. Consistent with the role of CBS in catalyzing de novo cystathionine synthesis, a significant decrease of cystathionine abundance was observed in CBS-depleted AIS escaped cells. Notably, the abundance of cysteine and GSH (Figure D and E) was not affected by CBS depletion. We hypothesized that CBS-depleted cells maintained the cysteine and GSH pools via increase of cysteine uptake from the culture medium. Indeed, deprivation of cysteine from the medium markedly diminished the intracellular cysteine and GSH abundance in AIS cells (Figure D and E). On the other hand, increase of cystathionine was observed under the cysteinedepleted conditions (Figure C), possibly attributed to a marked upregulation of CBS expression observed in AIS cells after cysteine deprivation (Figure 1B in the revised manuscript). This result thus suggests that cells enhance CBS-mediated transsulfuration pathway activity in response to cysteine deficiency.

      Collectively, our results indicate that cells rely on exogenous cysteine for GSH synthesis and AKT overexpression increases cysteine import and the subsequent GSH abundance which is not affected by loss of CBS.

      2) Furthermore, the AOAA experiments are hard to interpret. This drug is a promiscuous transaminase inhibitor, so its effects on cell confluency are not surprising, and it is unclear which particular aspect of metabolism is responsible for the effect. A genetic experiment silencing the relevant transaminase would be more informative.

      We agree that the pharmacological action of AOAA is not limited to suppression of the CBS/ H2S axis. It binds irreversibly to the cofactor PLP, and therefore in addition to CBS, it also inhibits other PLP-dependent enzymes such as CTH, 3-MST, and GOT1. We therefore have modified our statement in the manuscript to be “this result suggested that H2S, the major metabolite downstream of the transsulfuration pathway, has a protective effect on AIS cells although the actions of AOAA on other PLP-dependent enzymes cannot be excluded.

      We further analysed the data from the AIS-escape siRNA screen and presented these data in Figure 3-figure supplement 1A.

      We found that except CBS, siRNA knockdown of other genes involved in the transsulfuration pathway did not significantly affect AIS cell numbers (robust Z score < 2). Therefore, it is likely that AIS escape in cysteine-replete conditions by loss of CBS is through a transsulfuration/transmethylation pathway-independent mechanism.

      3)The GC/MS data in Fig. 3L are misleading, as the range on the color scale goes from FDR of 0.0504 to 0.0498. Also, the authors claim that CBS regulates the malate-aspartate shuttle, but no mechanism is proposed and this is not intuitive.

      The altered activity of malate-aspartate shuttle is only based on the changes in glutamate and aspartate levels, as measured by GC-MS metabolomics analysis. We agree that these metabolite changes are not sufficient to support the specificity of malate-aspartate shuttle being involved in CBS-mediated metabolic alterations. Therefore, for clarity we have decided to remove this figure and the relevant text from the manuscript.

      4) CBS's role in modulating mitochondrial function is complicated, but its ability to sustain OxPhos and ROS seem to underlie its effects on AIS. The key unanswered question is how CBS promotes OxPhos in these models.

      To determine the mechanisms underlying the increased oxidative phosphorylation and ROS in CBS deficient cells, we investigated the mitochondrial localization of CBS. In addition to the immunofluorescent data showing localization of CBS in the mitochondria (Figure 4A), in the revised manuscript, we generated lentiviral expression vectors encoding wild type CBS and N-terminal and C-terminally truncated mutants. We showed that, consistent with a previous study (Teng et al., PNAS 2013), the C-terminal CBSD2 motif is required for CBS mitochondrial localization (Figure 4C-4F). We then reconstituted CBS-depleted AIS escaped cells with wild type CBS or a C-terminally truncated mutant (Δ468-551). Expression of wild type CBS prevented AIS escape while cells expressing the truncation mutant still escaped from AIS (Figure 4G and 4H), demonstrating that mitochondrial localization of CBS is required to maintain AIS. Consistent with these findings, the Seahorse analysis showed that reconstitution with wild type CBS rescued basal OCR and ATP production levels in CBSdepleted AIS cells. In contrast, AIS cells expressing C-terminally truncated CBS protein failed to restore basal OCR and ATP production. Collectively, our results support the concept that AKT overexpression promotes CBS translocation to mitochondria, increases oxidative phosphorylation and ROS production to sustain the senescence state.

      Reviewer #3 (Public Review):

      In the manuscript by Zhu, Haoran et al., titled "Cystathionine-β-synthase is essential for AKT-induced senescence and suppresses the development of gastric cancers with PI3K/AKT activation", the authors investigated the contributions of cystathionine-β-synthase (CBS) to AKT-induced senescence (AIS) and the potential mechanisms which drove these phenotypes. The authors showed that AKT hyperactivation (using myristoylated AKT) promoted H2S production and treatment with a compound (AOAA) that blocked H2S production, reduced proliferation, and promoted senescence in cells with hyperactivated AKT, compared to normally proliferating cells or cells that have expressed other oncogenes (i.e., HRAS). Next, they used genetic approaches (both knockdown of CBS and rescue experiments with reexpression of CBS in CBS-knockdown cells) to clearly demonstrate that CBS was required for AIS and loss of CBS promoted AIS-escape. The authors then extended these findings to patient tumors and in vivo systems. They found reduced CBS expression in gastric cancer samples compared to matched normal samples and that the reduced expression was due to hypermethylation of DNA encoding CBS. Finally, they found that CBS functions as a tumor suppressor in gastric cancer cells by showing that depletion of CBS promoted colony formation, and overexpression of CBS blocked tumor growth in vivo. This is a very strong study with relevance to numerous research fields. However, a major weakness of the study is the proposed mechanism by which CBS functions in AIS-escape, as the data are largely not supported by the mechanistic conclusions.

      1) In Figure 1, the authors show that AIS cells are unaffected by cysteine depletion and conclude, "Furthermore, cysteine deprivation potently increased the expression levels of CBS and CTH in AIS cells (Figure 1B) and did not affect the survival of AIS cells, consistent with increased cysteine synthesis due to elevated CBS expression being critical for cell viability (Figure 1E)". Although the authors show in Fig. 3F that cysteine levels are elevated in AIS cells compared to control cells in cystine-replete media, they do not measure cysteine synthesis via the transsulfuration pathway in AIS and control cells in cystine-replete and cystine-depleted media.

      Please see our response to the question #1 from the Reviewer 1.

      2) The metabolic changes presented in Figure 3 are unclear. The authors state, "Depletion of CBS in AIS cells increases GSH metabolism in cysteine-replete condition", but it is not clear what "GSH metabolism" means, especially for the AIS-related phenotypes. Further, the authors appear to use "GSH metabolism" interchangeable with GSH synthesis; in the Discussion, they state, "In this study we uncovered another mechanism of AKT-mediated ROS detoxification by upregulation of transsulfuration pathway activity and enhancing glutathione and H2S synthesis (Fig.4H)." These conclusions are not supported by the findings presented in Figure 3 that show GSH levels are unchanged between control, AIS, and CBSdepleted AIS cells. While the authors show an increased abundance of the GSH precursor gamma-glutamylcysteine and the GSH catabolic product cysteinylglycine, how CBS would alter these metabolites are unclear. Additionally, they show that H2S levels are unaffected by CBS depletion, which further confounds the conclusions.

      To determine the transsulfuration pathway activity in AIS cells and the effect of CBS loss, we performed a stable isotope tracing assay followed by LC-MS assay using [3-13C] L-serine. Please see our response to the question #1 from the Reviewer 1.

    1. SciScore for 10.1101/2022.06.01.494385: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">Consent: Sera were collected at the U.S. Food and Drug Administration with written consent under an approved Institutional Review Board (IRB) protocol (FDA IRB Study # 2021-CBER-045).<br>IRB: Sera were collected at the U.S. Food and Drug Administration with written consent under an approved Institutional Review Board (IRB) protocol (FDA IRB Study # 2021-CBER-045).</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Membranes were probed for the V5-tag and γ-actin using V5 epitope tag antibody (Novus Biologicals, Centennial, CO), and mouse gamma actin polyclonal antibody (Thermofisher), respectively.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>V5-tag</div><div>suggested: (Novus Cat# NB100-62264, RRID:AB_965837)</div></div><div style="margin-bottom:8px"><div>V5 epitope tag antibody (Novus Biologicals, Centennial, CO)</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>mouse gamma actin</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">ACE2 genes of various species (African green monkey (AGM), Chinese rufous horseshoe bat (Rhinolophus sinicus), ferret, mouse, Chinese hamster, Syrian golden hamster, white-tailed deer, swine, bovine, and pangolin) with a C-terminal V5 tag were synthesized by GenScript as described previously 42. 293T (ATCC, Manassas, VA, USA; Cat no: CRL-11268), 293T.ACE2 (BEI Resources, Manassas, VA, USA; Cat no: NR-52511) 64 and 293T.ACE2.TMPRSS2 cells stably expressing human angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2) (BEI Resources, Manassas, VA, USA; Cat no: NR-55293) 34 were maintained at 37°C in Dulbecco’s modified eagle medium (DMEM) supplemented with high glucose, L-glutamine, minimal essential media (MEM) non-essential amino acids, penicillin/streptomycin, HEPES, and 10% fetal bovine serum (FBS).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>293T.ACE2.TMPRSS2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Pseudoviruses comprising the spike glycoprotein and a firefly luciferase (FLuc) reporter gene packaged within HIV capsid were produced in 293T cells by co-transfection of 5 μg of pCMVΔR8.2, 5 μg of pHR’CMVLuc and 0.5 μg of pVRC8400 or 4 μg of pcDNA3.1(+) encoding a codon-optimized spike gene.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>293T</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Soluble ACE2 Protein Production: His-tagged soluble human ACE2 was produced in FreeStyle™ 293-F cells by transfecting soluble human ACE2 (1-741 aa) expression vector plasmid DNA using 293fectin (Thermo Fisher) and purified using HiTrap Chelating column charged with nickel (GE healthcare) according to the manufacturer’s instructions.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>293-F</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Plasmids and Cell Lines: Codon-optimized, full-length open reading frames of the spike genes of B.1 (D614G) and Omicron variants in the study were synthesized into pVRC8400 (B.1, BA.1, BA.2, and BA.3) or pcDNA3.1(+) (BA.1.1) were obtained from the Vaccine Research Center (National Institutes of Health, Bethesda, MD) and GenScript (Piscataway, NJ, USA).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pVRC8400</div><div>suggested: RRID:Addgene_63163)</div></div><div style="margin-bottom:8px"><div>pcDNA3.1</div><div>suggested: RRID:Addgene_79663)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The HIV gag/pol packaging (pCMVΔR8.2) and firefly luciferase encoding transfer vector (pHR’CMV-Luc) plasmids 62,63 were obtained from the Vaccine Research Center (National Institutes of Health, Bethesda, MD, USA).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pHR’CMV-Luc</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Pseudoviruses comprising the spike glycoprotein and a firefly luciferase (FLuc) reporter gene packaged within HIV capsid were produced in 293T cells by co-transfection of 5 μg of pCMVΔR8.2, 5 μg of pHR’CMVLuc and 0.5 μg of pVRC8400 or 4 μg of pcDNA3.1(+) encoding a codon-optimized spike gene.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCMVΔR8.2</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Titers were calculated using a nonlinear regression curve fit (GraphPad Prism Software Inc., La Jolla, CA, USA).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The ACE2 concentration causing a 50% reduction of luciferase activity compared to untreated control was reported as the IC50 using a nonlinear regression curve fit (GraphPad Prism software Inc., La Jolla, CA).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad Prism</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr></table>

      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 LimitationRecognizer: We detected the following sentences addressing limitations in the study:


      Our study has several caveats, including the use of pseudoviruses instead of authentic SARS-CoV-2 for conducting experiments. However, our findings using pseudoviruses agree with those reported using authentic SARS-CoV-2. For instance, authentic BA.1 /BA.1.1 VOCs were shown to undergo attenuated replication in TMPRSS2-expressing cells compared to ancestral Wuhan-Hu-1, and Alpha, Beta, and Delta VOCs 6,36. These reports also showed greater sensitivity of BA.1 pseudovirus entry to endosomal inhibitor E64d. While we used pseudovirus entry assays to determine Omicron variant usage of ACE2 receptors of various animal species, it remains unknown whether there may be intrinsic and/or innate host-specific factors that might act to inhibit live Omicron VOCs at an entry or post entry step. Furthermore, although we identified RBM substitutions in Omicron spike that conferred the ability to use mouse or horseshoe bat ACE2, we didn’t confirm ACE2 substitutions that permit or prevent Omicron spike binding. For instance, introducing K35E substitution in horseshoe bat ACE2 should permit Omicron variants’ usage. Finally, analysis of a limited number of serum samples and short follow up after the receipt of three doses of the Pfizer/BNT162b2 mRNA vaccine do not give us insights into the durability of the antibody response. While studies of antibody durability are ongoing, our findings indicate that three dose immunization with the Pfizer/BNT162b2 will likely contribute to protection from sever...


      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


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      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.

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    1. SciScore for 10.1101/2022.06.01.494461: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">Field Sample Permit: All virological studies were conducted under BSL3 conditions, and personnel wore appropriate personal protective gear.<br>IACUC: Mouse studies and in vivo infections: Mouse studies were performed at the University of North Carolina (Animal Welfare Assurance #A3410-01) using protocols approved by the UNC Institutional Animal Care and Use Committee (IACUC).</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">First, F1 mice between CC011 and CC074 were generated by cross males and females in both directions, and then the F2 mice were bred in all 4 possible F1 x F1 combinations, to ensure appropriately balanced sex Chromosome and parent-of-origin effects.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">F2 mice (226 males, 177 females) were weaned such that littermates were randomized to different experimental cages to reduce litter- or batch-effects on the study, and mice were transferred at 5-6 weeks of age to the laboratory for infection between 9-12 weeks of age.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">For Matute-Bello scoring samples were blinded and three random fields of lung tissue were chosen and scored for the following: (A) neutrophils in alveolar space (none = 0, 1–5 cells = 1, > 5 cells = 2), (B) neutrophils in interstitial space (none = 0, 1–5 cells = 1, > 5 cells = 2), (C) hyaline membranes (none = 0, one membrane = 1, > 1 membrane = 2), (D) Proteinaceous debris in air spaces (none = 0, one instance = 1, > 1 instance = 2), (E) alveolar septal thickening (< 2Å∼ mock thickness = 0, 2–4Å∼ mock thickness = 1, > 4Å∼ mock thickness = 2).</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cells were incubated with antibodies against the following markers: efluor506 Viability Dye (Thermo Fisher, 65-0866-14), BUV395 anti-CD45 (Clone 30-F11, BD Biosciences), BV711</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-CD45</div><div>suggested: (BD Biosciences Cat# 740725, RRID:AB_2740403)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For virus titration, the caudal lobe of the right lung was homogenized in PBS, resulting homogenate was serial-diluted and inoculated onto confluent monolayers of Vero E6 cells (ATCC CCL-81), followed by agarose overlay.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero E6</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Organisms/Strains</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For studies in genetically defined knockout mice, 15-week old CCR9-/- mice (strain 027041), 15-week old CXCR6-/- mice (strain 005693) 15-week old female C57BL/6NJ mice (strain 005304), and 15-week old C57BL/6J (strain 000664) were purchased from Jackson Laboratory, and the genotype of these mutant mice were confirmed via genotyping on the MiniMUGA array (Neogen, Inc.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>CCR9-/-</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>CXCR6-/-</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>C57BL/6NJ</div><div>suggested: RRID:IMSR_JAX:005304)</div></div><div style="margin-bottom:8px"><div>C57BL/6J</div><div>suggested: RRID:IMSR_JAX:000664)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">CXCR6-/-, CCR9-/-, and appropriate C57BL/6 control mice were inoculated intranasally with 1×105 PFU of either SARS-CoV MA15, SARS-CoV-2 MA10, or HKU3-SRBD MA in 50 μl of PBS. Body weight, mortality, and pulmonary function by whole body plethysmography (56) were monitored daily as indicated.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>C57BL/6</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cells were incubated with antibodies against the following markers: efluor506 Viability Dye (Thermo Fisher, 65-0866-14), BUV395 anti-CD45 (Clone 30-F11, BD Biosciences), BV711</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>BD Biosciences</div><div>suggested: (BD Biosciences, RRID:SCR_013311)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Data were acquired on a flow cytometer (BD-X20; BD Biosciences) and analyzed using FlowJo software (Tree Star) (Figure S5).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>FlowJo</div><div>suggested: (FlowJo, RRID:SCR_008520)</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

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    1. SciScore for 10.1101/2022.05.31.494211: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">IACUC: Infection of BALB/c and hACE2/k18 Mice: All animals were cared for according to the standards set forth by the Institutional Animal Care and Use Committee at the University of Maryland-Baltimore.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">H/E Staining of Lungs and Pathological Scoring: Lungs were scored in a blinded fashion with a 0-5 score given, 0 being no inflammation and 5 being the highest degree of inflammation.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Virus Reconstitution: 24 hours prior to transfection, 5e4 VeroE6 cells (ATCC,Manassas, VA) were plated per well in 1mL of VeroE6 media (DMEM (Quality Biological, Gaithersburg, MD), 10% FBS (Gibco, Waltham, MA), 1% Penicillin-Streptomycin (Gemini Bio Products, Sacramento, CA), 1% L-Glutamine (Gibco, Waltham, MA)).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>VeroE6</div><div>suggested: JCRB Cat# JCRB1819, RRID:CVCL_YQ49)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Organisms/Strains</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Infection of BALB/c and hACE2/k18 Mice: All animals were cared for according to the standards set forth by the Institutional Animal Care and Use Committee at the University of Maryland-Baltimore.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>BALB/c</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The K18-hACE2 mice were inoculated with 1e3 PFU of each virus in 50μL PBS.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>K18-hACE2</div><div>suggested: RRID:IMSR_GPT:T037657)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">To assemble DNA fragment clones, the TAR vectors were PCR amplified from pCC1BAC-his3 with KOD Xtreme Hot Start DNA polymerase (Millipore, Burlington, MA) using the construction primers (labeled “Con”, Table S1).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCC1BAC-his3</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">50 fmol of each amplicon and 15 fmol of YCpBAC vector were assembled using a standard Gibson assembly reaction (New England Biolabs, Ipswich, MA), transformed into E. coli DH10B competent cells (Thermo Fisher, Waltham, MA), and plated on LB medium with 12.5 mg/ml chloramphenicol.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>YCpBAC</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Full-length genome assembly: The TAR vector for assembly of the full-length genome was amplified from pCC1BAC-ura3 using primers ConCMVpR and ConBGHtermF with KOD Xtreme Hot Start DNA polymerase (Millipore, Burlington, MA).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCC1BAC-ura3</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Virus Reconstitution: 24 hours prior to transfection, 5e4 VeroE6 cells (ATCC,Manassas, VA) were plated per well in 1mL of VeroE6 media (DMEM (Quality Biological, Gaithersburg, MD), 10% FBS (Gibco, Waltham, MA), 1% Penicillin-Streptomycin (Gemini Bio Products, Sacramento, CA), 1% L-Glutamine (Gibco, Waltham, MA)).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Quality Biological</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The cells were rocked every 15 minutes for 1 hour at 37°C prior to overlay with 2mL of a solid agarose overlay (EMEM (Quality Biological, Gaithersburg, MD), 10% FBS, 1% Penicillin-Streptomycin, 1% L-Glutamine, 0.4% w/v SeaKem agarose (Lonza Biosciences,Morrisville, NC).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Lonza Biosciences</div><div>suggested: (Science Exchange, RRID:SCR_010620)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Statistical Analysis: All statistical analyses were carried out using the GraphPad Prism software.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad Prism</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. SciScore for 10.1101/2022.05.31.493843: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">IRB: The same experiment was also done using RNA isolated from nasopharyngeal swabs of COVID-19 infected individuals following approvals by the Institutional Human Ethics Committee (IGIB) Cell culture: All inhibition experiments were carried out on African green monkey kidney cells (Vero CCL-81).<br>Euthanasia Agents: All the animals, except unchallenged control, were challenged with 105 PFU of SARS-CoV2 administered intranasally using a catheter while under anesthesia by using ketamine (150mg/kg) and xylazine (10mg/kg) intraperitoneal injection inside ABSL3 facility (Chan et al., 2020; Rizvi et al., 2021; Sia et al., 2020).<br>IACUC: All the experimental protocols involving the handling of virus culture and animal infection were approved by RCGM, institutional biosafety and IAEC animal ethics committee.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">Animals: 6-8 weeks old male golden Syrian hamsters were procured from CDRI and transported to small animal facility (SAF), THSTI and quarantined for 7 days.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">SARS-CoV2 infection in golden Syrian hamster and dosing: Golden Syrian hamsters were randomly allotted to different drug groups (n=4), challenge control (n=2), remdesivir control (n=2) and unchallenged control (n=2) were housed in separate cages.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Briefly Vero cells were seeded in 12-well plate at 90% confluency.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Virus generation for animal experiments: SARS-Related Coronavirus 2, Isolate USA-WA1/2020 virus was grown and titrated in Vero E6 cell line cultured in Dulbecco’s Modified Eagle Medium (DMEM) complete media containing 4.5 g/L D-glucose, 100,000 U/L Penicillin-Streptomycin, 100 mg/L sodium pyruvate, 25mM HEPES and 2% FBS.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero E6</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Reverse transcription was done using reverse primers such that ORF1 pG4-1 or a non G4 forming control region gets reverse transcribed (primer sequences below).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pG4-1</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The efficiency of the reverse transcription reactions was measured by quantifying the generated cDNA by qPCR using primers overlapping the ORF1 pG4 or the non G4 forming control region (primer sequences below).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pG4</div><div>suggested: RRID:Addgene_162605)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The pre-treatment group viz pCPZ & pPCZ started receiving 8mg/kg and 5mg/kg (respectively) of the drug through intraperitoneal administration each day starting from 3 days prior to the challenge and continued till end point (day 4 post infection).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCPZ</div><div>suggested: None</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. SciScore for 10.1101/2022.05.26.22275611: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">IRB: Ethics statement: The present study was performed in accordance with regulations guidelines and approved by institutional ethical review boards from Clementino Fraga Filho University Hospital and Marcílio Dias Naval Hospital Ethics Committees (protocol numbers 4.551.702, protocol ID 361-20 and 32382820.3.0000.5256 respectively), with written informed consents obtained from all participants or their legal representatives<br>Consent: Ethics statement: The present study was performed in accordance with regulations guidelines and approved by institutional ethical review boards from Clementino Fraga Filho University Hospital and Marcílio Dias Naval Hospital Ethics Committees (protocol numbers 4.551.702, protocol ID 361-20 and 32382820.3.0000.5256 respectively), with written informed consents obtained from all participants or their legal representatives</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">We received samples from 73 patients randomly chosen from the HMMD repository of COVID-19 severe cases (322 total cases), with at least two samples collected in different hospitalization days.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Briefly, the wells of a 96-well microtiter plate (Greiner Bio-One, Austria) were coated overnight at 4 °C with anti-HMGB1 mouse monoclonal antibody (No H9537, Sigma-Aldrich, San Luis, MO) in PBS buffer (8.06 mM sodium phosphate, 1.94 mM potassium phosphate, 2.7 mM KCl, and 137 mM NaCl) at pH 7.4.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-HMGB1</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Subsequently, the wells were incubated with rabbit-produced anti-rHMGB1 polyclonal antibody diluted in PBS buffer for 1 h at 37 °C, and then incubated for 1 h at the same temperature with anti-IgG rabbit antibody conjugated to horseradish peroxidase (No W4011, Promega, Madison, WI).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-rHMGB1</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-IgG</div><div>suggested: (Promega Cat# W4011, RRID:AB_430833)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Measurement of serum anti-SARS-CoV-2 antibodies: For quantitative analysis of anti-SARS-CoV-2 spike protein IgM and IgG antibodies, we performed the S-UFRJ test, as described previously (47).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>quantitative analysis of anti-SARS-CoV-2 spike protein IgM</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>IgG antibodies</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Then, the plate was washed with 150 μL of PBS (5x) and 50 μL of 1:10000 goat anti-human IgM and IgG (Fc)-horseradish peroxidase antibody (Sourthen Biotech, Birmingham, AL) were added, and the plate was incubated for 1.5 h at RT.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>1:10000 goat anti-human IgM</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>IgG ( Fc)-horseradish peroxidase antibody ( Sourthen Biotech , Birmingham , AL )</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The bands corresponding to both proteins were quantified using Image J software (NIH, Bethesda, MD) and the ratio between tissue factor and transferrin was calculated.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Image J</div><div>suggested: (ImageJ, RRID:SCR_003070)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The optical density (OD) was read at 450 nm with 655 nm background compensation in a microplate reader (Bio-Rad Laboratories, Inc, CA).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Bio-Rad Laboratories</div><div>suggested: (Bio-Rad Laboratories, RRID:SCR_008426)</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. SciScore for 10.1101/2022.05.30.22275753: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">IRB: Ethics statement: The trial was reviewed and approved by the Research Ethics Committee of the Center for Disease Control and Prevention of Yunnan province.<br>Consent: Written informed consents were obtained from each participant before the screening.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">Participants with a previous clinical or virologic COVID-19 diagnosis or SARS-CoV-2 infection or women with positive urine pregnancy test results were excluded from this study.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">Study design: We conducted a randomized clinical trial involving 300 adults (≥18 years of age) who were tested negative by RT-PCR screening for COVID-19 at the time of participation to elucidate the immunogenicity and safety of an mRNA-based vaccine (AWcorna) as a booster compared to that of homologous booster using an inactivated viral vaccine (CoronaVac).</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">Since the different appearances of the two kinds of vaccines, inoculators could not keep in blind when vaccines had been used.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">Sample size: The sample size was determined based on the hypothesis that the booster vaccination of mRNA vaccine following the two-dose inactivated vaccine regimen be non-inferior to that of the booster of inactivated vaccine in neutralizing antibody.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">Authentication: Randomization: Each participant was assigned a unique subject ID by authorized assigners successively according to a Prespecified allocation kit, which was generated by an independent randomization statistician from Beijing Key Tech Statistical Consulting Co., Ltd. via SAS software (SAS® Institute, Cary, North Carolina, USA) with the ratio of 2:1 to the AWcorna and CoronaVac groups.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Laboratory assays: The neutralizing antibodies in sera against the wild-type strain (GenBank: MT123291), Delta variant (IQTC-IM2175251), and Omicron variant (IQTC-Y216017) (Guangzhou Customs Technology Center, Guangzhou, China) were determined by using a cytopathic effect (CPE)-based microneutralization assay.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>IQTC-Y216017) (Guangzhou Customs Technology Center, Guangzhou, China)</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The WHO reference (NIBSC code: 20/136) is equivalent to a live viral neutralizing antibody titer of 1:139 against wild-type SARS-CoV-2 and a titer of 1:213 against the Delta variant B.1.617.2, while the WHO reference (1,000 BAU/ml in serum) is equivalent to an RBD-specific IgG ELISA antibody titer of 1:5,490.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>RBD-specific IgG</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Vero E6 cells were trypsinized and resuspended in Dulbecco’s Modified Eagle Medium (DMEM) containing 4% of fetal bovine serum and 1% of pen/strep at a concentration of 1.2×105 cells/ml and 100 μl of cells suspension were then added into the 96-well plates, followed by incubation at 37 °C, 5% CO2 for 4 days.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero E6</div><div>suggested: RRID:CVCL_XD71)</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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">NCT04847102</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">A Phase III Clinical Study of a SARS-CoV-2 Messenger Ribonuc…</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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

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    1. SciScore for 10.1101/2022.05.29.493850: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The antibodies used were: PE-conjugated W6/32 (Serotec MCA81PE, 1:10) [11], anti-CD9-FITC (BD Pharmingen 555371, 1:40), anti-CD46-FITC (BD Pharmingen 555949, 1:20), anti-CD49b-PE (BD Pharmingen 555669, 1:20), anti-CD58-PE (BD Pharmingen 555921, 1:30), anti-CDw119-PE (BD Pharmingen 558934, 1:20).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-CD9-FITC (BD Pharmingen 555371</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The primary antibody used for Spike staining was human anti-SARS-CoV-2 Spike (1:682, REGN #10987), which was kindly provided by Wentao Li and Berend Jan Bosch (Utrecht University, Utrecht, The Netherlands).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-SARS-CoV-2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The goat anti-human IgM+ IgG (H+L) (1:160, Jackson #109-116-127) antibody was used as secondary antibody Inhibition of proteasome and p97: For proteasome inhibition, we employed 20 μM MG132 (Sigma-Aldrich, Zwijndrecht, NL, C2211-5MG) and for p97 inhibition 4 μM CB-5083 (HY-12861; MCE) for 4h each.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>The goat anti-human IgM+ IgG</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-human IgM+ IgG</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The primary antibodies used for immunoblotting were: HC10, monoclonal mouse anti-SARS-CoV-2 ORF7a (Genetex, 632602, 1:1000), polyclonal rabbit anti-SARS-CoV-2 ORF8a (Genetex, 135591, 1:1000), monoclonal transferrin receptor antibody (H68.4, Invitrogen, 1:1000) and monoclonal StrepII (C23.21, purified in our lab).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HC10</div><div>suggested: (Hidde L. Ploegh Cat# HC10, RRID:AB_2728622)</div></div><div style="margin-bottom:8px"><div>anti-SARS-CoV-2 ORF7a (Genetex, 632602</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-SARS-CoV-2 ORF8a (Genetex, 135591</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Secondary antibodies used were goat anti-mouse IgG-HRP (115-035-174, Jackson ImmunoResearch Europe Ltd, 1:10000) and mouse anti-rabbit IgG-HRP (211-032-171, Jackson ImmunoResearch Europe Ltd, 1:10000).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-mouse IgG-HRP</div><div>suggested: (Jackson ImmunoResearch Labs Cat# 115-035-174, RRID:AB_2338512)</div></div><div style="margin-bottom:8px"><div>anti-rabbit IgG-HRP</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For StrepII IP 25 μl Streptactin Sepharose® High Performance beads (GE Healthcare, GE28-9355-99) and for HLA-I IP 25 μl Protein G Sepharose® 4 Fast Flow (GE Healthcare, GE17-0618-01) were employed with the HC10 antibody for HLA-IP overnight at 4°C.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GE28-9355-99</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The following antibodies were used: mouse anti-SARS-CoV-2 ORF7a (Genetex, 632602, 1:1000) and mouse anti-W6/32 (own production from hybridoma, 1:1000).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-W6/32</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Secondary antibodies used were goat anti-mouse IgG2a cross-adsorbed secondary antibody, Alexa Fluor 594 (Thermo Fisher, A-21135, 1:600) and goat anti-mouse IgG1 cross-adsorbed secondary antibody, Alexa Fluor 647 (Thermo Fisher, A-21240, 1:600), together with DAPI (Sigma-Aldrich, 1:1000).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-mouse IgG2a</div><div>suggested: (Thermo Fisher Scientific Cat# A-21135, RRID:AB_2535774)</div></div><div style="margin-bottom:8px"><div>anti-mouse IgG1</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cell lines and viruses: HEK-293T and HK-1 cells were maintained in Roswell Park Memorial Institute medium (RPMI 1640; Life Technologies) supplemented with 5% FCS (Sigma), 2 mM L-glutamine, 100 U/mL penicillin, and 100 mg/mL streptomycin.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK-293T</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>HK-1</div><div>suggested: RRID:CVCL_7047)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The MelJuSo, Huh7 and A549-ACE2-TMPRSS2 cells were maintained in Dulbecco’s modified Eagle medium (DMEM; Life Technologies) supplemented with 5% FCS (Sigma), 2 mM L-glutamine, 100 U/mL penicillin, and 100 mg/mL streptomycin.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Huh7</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">All viruses were propagated and titrated on Vero E6 cells using the tissue culture infective dose 50 (TCID50) endpoint dilution method.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero E6</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">SARS-CoV-2 infections: SARS-CoV-2 viruses (see section ‘cell lines and viruses’) propagated in Vero E6 cells were used to infect Vero E6 or A549-ACE2-TMPRSS2 cells.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>A549-ACE2-TMPRSS2</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Plasmids: The plasmids of the SARS-CoV-2 cDNA library as cloned in the pLVX-EF1alpha-IRES-Puro (Takara/Clontech) vector were a kind gift from Prof. Nevan Krogan (University of California San Francisco, USA) [10].</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pLVX-EF1alpha-IRES-Puro</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cell lines expressing NSP1 and NSP14 did not survive transduction and subsequent antibiotic selection and were, therefore, excluded from the analysis For follow-up studies, we cloned a T2A-mAmetrine cassette in frame downstream of the PuroR gene in the pLV-CMV-IRES-PuroR vector.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pLV-CMV-IRES-PuroR</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For the pLV-CMV-IRES-PuroR-T2A-mAmetrine vectors, lentiviruses were produced using standard lentiviral production protocols with third-generation packaging vectors.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pLV-CMV-IRES-PuroR-T2A-mAmetrine</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cells were subjected to flow cytometry (BD FACS Canto II) and the data was analyzed with FlowJo (BD Biosciences) software.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>FlowJo</div><div>suggested: (FlowJo, RRID:SCR_008520)</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. Author Response

      Reviewer #1 (Public Review):

      Using Tet-off system, Kir2.1 was expressed (or not) during the key time of callosal development from E15 to P15. Restoring activity either by adding Dox during a critical period from P6 to P15 or using DREADDs from P10-14 could rescue the callosal projection to the cortex, whereas later restoration of activity (with Dox) was not successful. Did this successful rescue lead to normal activity? Calcium imaging in animals with Kir2.1 had low levels of any kind of activity, both highly correlated and low correlation, but P6-13 dox treatment partially restored only low-correlation activity and not high correlation activity at P13. The effects of DREADDs on activity was not similarly measured though it was effective for at least partially restoring the callosal projection.

      Overall this study builds on earlier findings regarding the importance of neuronal activity in the formation of a normal callosal projection, using in utero electroporation which is particularly well suited for this subject. It makes the case very compellingly that near-normal callosal connectivity can be produced if activity is permitted during a critical period window from P6 or P10 to P15, though the exact timing of this window is imprecise because the elimination of Kir expression was not systematically quantified. For transmembrane proteins like channels it can often take many days for protein expression to completely abate.

      We thank the reviewer for their positive evaluation and the constructive comments. Based on the comment on Kir expression, we conducted new experiments using pTRE-Tight2Kir2.1EGFP, with which EGFP signals reflect localization of over-expressed Kir2.1, and examined when the expression of Kir2.1EGFP went down after Dox treatment at P6. At P6 (before Dox treatment), the signals of Kir2.1EGFP (stained with anti-GFP antibody) were observed in the periphery of the soma and along dendrites, implying that Kir2.1EGFP was transported to the cellular membrane. At P10 and P15 (4 days and 9 days after Dox treatment), Kir2.1EGFP signals were not observed in the periphery of the soma and along dendrites. We noted that low-level green signals were observed in the central part of the cell body. These may stem from low-level expression of Kir2.1EGFP in nuclei or cytosol even after Dox treatment. Alternatively, and more likely, these may reflect bleed-through of RFP signals into GFP channel. Overall, we confirmed that Kir2.1 proteins that were localized to the cellular membrane were largely down-regulated. We described these observations in detail in the figure legend of Figure 1-figure supplement 3, and added the result as Figure 1-figure supplement 3.

      I found the quantification of the callosal projection to be rather minimal and the normalization approach not entirely transparent. For example does activity from P10-15 restore the full normal PATTERN of callosal connectivity or merely the density of input overall?

      We thank the reviewer for this comment. Based on the comment, we added analyses of the pattern of callosal projections; the width of callosal axon innervation zone in layers 2/3 and 5, and densitometric line scans across all cortical layers. Our original quantification showed that the density of callosal axons reaching their target layer (i.e. cortical layer 2/3) is almost recovered in P6-P15 DOX condition (Fig1B-D), but new analyses suggest some aspects of callosal axon projections (the width of the innervation zone in layer 2/3 and 5 (Figure 1-figure supplement 4A,B), and lamina specific innervation pattern (Figure 1-figure supplement 4C)) might be only partially recovered. We have added these new results as Figure 1-figure supplement 4. In future study, we would like to assess the effect of the manipulations at finer resolution by 3D morphological reconstruction of axons of individual neurons.

      Also in the discussion it would be nice to more clearly establish whether activity is thought to be maintaining a projection already formed by P10 or permitting the emergence of such a pattern.

      Thank you for the suggestion. We have added thorough discussions about this point as follows. Page 7, lines 198-208:

      “In the previous study, we showed that callosal axons could reach the innervation area almost normally under activity-reduction, and that the effects of activity-reduction became apparent afterwards (Mizuno et al., 2007). Callosal axons elaborate their branches extensively in P10P15 (Mizuno et al., 2010), and axon branching is regulated by neuronal activity (Matsumoto and Yamamoto, 2016). It is likely that activity is required for the processes of formation, rather than the maintenance of the connections already formed by P10, but the current study employed massive labeling of callosal axons which is not suited to clarify this. In addition, the restoration of activity in the Tet-off (Figure 1) or DREADD (Figure 2) experiment may not completely rescue the ramification pattern of individual axons. Single axon tracing experiments (Mizuno et al., 2010; Dhande et al., 2011) would be required to clarify this. Nonetheless, our findings suggest that callosal axons retain the ability, or are permitted, to grow and make region- and lamina-specific projections in the cortex during a limited period of postnatal cortical development under an activity-dependent mechanism.”

      The calcium imaging is a valuable validation of the Kir expression approach, but it the study here appears to overinterpret what may simply be an intermediate level of activity restoration rather than a specific restoration of L events, as it seems that L events would be the most likely to occur under conditions of reduced overall activity. One possibility is that the absence of H events at P13 in the calcium is due to residual Kir expression creating a drag on high level network activation rather than any more complicated change in patterned spontaneous activity/connectivity. The conclusions from this study regarding the permissive role of activity during a critical window and the lack of a requirement for highly correlated activity are valuable, even if somewhat imprecise on both counts. The authors should probably refrain from use of the term patterned activity given that this was measured but not systematically compared to unpatterned spontaneous activity.

      We thank the reviewer for this constructive comment. Based on this comment, we removed the term “patterned activity” throughout the manuscript and revised the title, abstract, introduction, results, and discussion extensively. For example, in the Discussion, we revised as follows.

      “We have shown that the projections could be established even without fully restoring highly synchronous activity (Figure 4). L events, but not H events, were present in P13 cortex after Dox treatment at P6. L events may be sufficient for the formation of callosal projections. Alternatively, any form of activity with certain level(s) (i.e., “sufficiently” high activity with no specific pattern) could be permissive for the formation of callosal connections.”

      Reviewer #2 (Public Review):

      Tezuka et al. use in vivo manipulations of spontaneous activity to identify the activitydependent mechanisms of callosal projection development. Previous research of the authors' and other labs had shown that overexpressing the potassium channel Kir2.1, which reduces activity levels in the developing cortical network, blocks the formation of callosal connections almost entirely.

      The current manuscript corroborates and extends these previous discoveries by:

      1) Demonstrating that the effect of Kir overexpression can be rescued by pharmacogenetic network activation using DREADDs.<br /> 2) Revealing the requirement of network activity for the development of callosal projections during a particular developmental time window and by<br /> 3) Directly relating perturbed callosal development to the actual changes in activity patterns caused by the experimental manipulations.

      Thus, this paper is important for our understanding of the role of neuronal activity in the development of long-range connections in the brain. In addition it provides strong evidence for a role of specific activity patterns in this process.

      In general, the approach is very straightforward and the results clearly interpreted. Nevertheless, there are a few points to consider.

      We thank the reviewer for these positive and supportive comments.

      1) It is not clear in which cortical area(s) the in vivo 2-photon recordings were performed and in how far cortical areas that actually receive/send callosal projections were included or not in the analysis.

      In response to this comment, we revised the text in the method section as follows.

      “We aimed to record spontaneous neuronal activity in putative binocular zones in V1 (2.5 mm lateral of midline and 1 mm anterior of the posterior suture). Since the boundaries between V1 and higher visual areas, AL/LM are not as obvious as those in adult, our recordings likely contained juxtaposed lateral monocular V1 and AL/LM as well.”

      Based on our colleaguesʼ unpublished observations, V1 and AL/LM can be distinguished solely by spontaneous activity patterns even before eye-opening. They also found frequencies of spontaneous activity are similar across mono/binocular regions of V1 and AL/LM (Murakami, Ohki, et al. unpublished). Thus, our results should hold even with the variability in recording sites.

      2) It is not discussed what the duration of the CNO effect is. Do daily injections rescue activity patterns for 24 hours or a significant proportion of this period?

      In response to this critical comment, we revised the text in the method section as follows.

      “A previous study showed that an intraperitoneally injected CNO was effective (in terms of increasing activity) for about 9hrs (Alexander et al., 2009). The “partial rescue” effect we observed (Figure 2) may suggest that activity was not fully restored during 24hrs by our daily CNO injections.”

      Reviewer #3 (Public Review):

      The manuscript by Tezuka adds to an emerging story about the role of activity in the formation of callosal connections across the brain. Here, the authors show that they can use a TET system to switch off the activity of an exogenous potassium channel, in order to probe when activity might be necessary or sufficient for the formation of callosal connections. The authors find that artificial restoration of activity with DREADS is sufficient to rescue the formation of callosal connections, and that there is a critical period (somewhere between P5-P15) where activity must occur in order for the connections to form within the cortex. Finally, the authors show that when the potassium channel is removed during the critical period, the cortex exhibits activity, but few highly synchronous events. These results indicate that it is activity in general and not specifically highly synchronous activity that is necessary for the final innervation of the callosal cortex.

      In general, the study is well done, and the writeup is polished, well summarized. The figures are solid. There are only a few criticisms/suggestions.

      We thank the reviewer for the positive evaluation.

      Major issue: Have the authors demonstrated a requirement for "patterned spontaneous activity"?

      The authors claim variously in the abstract ("a distinct pattern of spontaneous activity") and in the results (pg 6, "our observations indicate that patterned spontaneous activity") and discussion (pg 6, "we demonstrated that patterned spontaneous activity") that it is "patterned" spontaneous activity that is key for the formation of callosal connections. However, when I was reading the paper, I came to the opposite conclusion: that any sufficiently high spontaneous activity is sufficient for the formation of these connections.

      The authors showed that relieving the KIR expression from P5-15 allows the connections to form; however, in Figure 4, the authors show that the nature of the activity produced in the cortex (in terms of mixtures of H and L events) is very different. Nevertheless, the connections can form. Further, the authors showed that increasing activity when KIR is expressed using DREADS restores the connections. The pattern of activity produced by this DREADS + KIR expression is likely to be very different from the pattern of activity of a typically-developing animal. In total, I thought that the authors demonstrated, quite nicely, that it is just the presence of sufficient activity that is key to the innervation of the contralateral cortex. (It's not cell autonomous, as the authors showed before; there seems to be a "sufficient activity" requirement).

      Therefore, I think the authors should remove references to the requirement of patterned activity and instead say something about sufficiently high activity (or some characterization that the authors choose). I think they've shown quite nicely that a specific pattern of the spontaneous activity is not important.

      We thank the reviewer for this very important insight and interpretation. After considering all the currently presented data again, we have come to agree with the interpretation stated by the reviewer. We removed the term “patterned activity” throughout the manuscript and revised the title, abstract, introduction, results, and discussion extensively. Nevertheless, we would not completely discard the possibility that specific patterns of spontaneous activity, such as L-events, could potentially have some active contribution to the development of projection circuits, and would like to further address this in future study.

      For example, in the Discussion, we revised the text as follows.

      “We have shown that the projections could be established even without fully restoring highly synchronous activity (Figure 4). L events, but not H events, were present in P13 cortex after Dox treatment at P6. L events may be sufficient for the formation of callosal projections. Alternatively, any form of activity with certain level(s) (i.e., “sufficiently” high activity with no specific pattern) could be permissive for the formation of callosal connections.”

    1. SciScore for 10.1101/2022.05.29.493923: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">Mouse immunizations: Female BALB/c mice aged 6–8 weeks (Central Lab Animal) were intramuscularly immunized with 0.4 μg/animal VP vaccine (total volume of 50 μL, adjusted with PBS) at week 0.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Surrogate virus-neutralization assay: The sVNT was used to analyze the binding ability of RBD to ACE2 after neutralizing RBD with antibodies in the serum.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>ACE2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The reciprocal of the dilution that resulted in a binding inhibition rate of 20% or more (PI20) was defined as the neutralizing antibody titer.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>PI20</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The ELISPOT plates were coated with purified anti-mouse IFN-γ capture antibody and incubated overnight at 4 °C.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-mouse IFN-γ</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The inactivated SARS-CoV-2 vaccine produced from Vero cells contained 4 μg of viral antigens and 0.225 mg of aluminum hydroxide adjuvant in a 0.5-mL dose.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero</div><div>suggested: RRID:CVCL_A5BG)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Organisms/Strains</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Mouse immunizations: Female BALB/c mice aged 6–8 weeks (Central Lab Animal) were intramuscularly immunized with 0.4 μg/animal VP vaccine (total volume of 50 μL, adjusted with PBS) at week 0.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>BALB/c</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Vaccines: The COVID-19 GX-19N DNA vaccine, consisting of GX-19 and GX-21 at a ratio of 1:2, was constructed by inserting the antigen genes of SARS-CoV-2 into a pGX27 vector.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pGX27</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">GX-19 (pGX27-SΔTM/IC) contains the SARS-CoV-2 spike (S) gene lacking the transmembrane (TM)/intracellular (IC) domain, and GX-21 (pGX27-SRBD-F/NP) is designed to express the fusion protein of the receptor-binding domain (RBD) of the spike protein, the T4 fibritin C-terminal foldon (SRBD-Foldon), and the nucleocapsid protein (N).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pGX27-SΔTM/IC</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pGX27-SRBD-F/NP</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Statistical analysis: Data analyses were performed using GraphPad Prism 7 (GraphPad Software).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

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    1. SciScore for 10.1101/2022.05.29.493866: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">IACUC: All animal studies were approved by the Laboratory Animal Welfare and Ethics Committee of Third Military Medical University and were performed in accordance with the institutional and national policies and guidelines for the use of laboratory animals.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">Bone marrow derived dendritic cells (BMDC) maturation study: Bone marrow cells were isolated from the femurs of female BALB/c mice and cultured in RPMI 1640 complete medium (Gibco, USA) supplemented with 10% FBS, 1% penicillin/streptomycin, 10 ng/mL of Interleukin-4 (IL-4) and Granulocyte-Macrophage Colony Stimulating Factor (GM-CSF).</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">Animals were randomly divided into groups and conceded an adaption time of at least 7 days before the beginning of the experiments.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">Authentication: All cell lines used in current study were obtained from original providers who authenticated the cell lines using morphology, karyotyping and PCR-based approaches.<br>Contamination: All cell lines tested negative for mycoplasma contamination.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">In order to detect the TEM/TCM and TRM cells, the cell samples were stained with the following indicated antibodies in FACS buffer: anti-CD62L (161204, BioLegend), anti-CD44 (25-0441-82, BioLegend), anti-CD69 (104506, BioLegend) and anti-CD103 (121416, BioLegend).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-CD62L ( 161204</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-CD44</div><div>suggested: (Thermo Fisher Scientific Cat# 25-0441-82, RRID:AB_469623)</div></div><div style="margin-bottom:8px"><div>anti-CD69</div><div>suggested: (BioLegend Cat# 104506, RRID:AB_313109)</div></div><div style="margin-bottom:8px"><div>anti-CD103</div><div>suggested: (BioLegend Cat# 121416, RRID:AB_2128621)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After incubation at 37 °C, 5% CO2 for 24 h, the plates were washed with PBS and incubation with biotinylated anti-mouse IFN-γ or IL-4 antibody for 2 h at RT.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-mouse IFN-γ</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>IL-4</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cell lines: MH-S cell line (Mice alveolar macrophages cells), DC2.4 cell line (Mouse bone marrow-derived dendritic cells), BEAS-2B (human bronchial epithelial cells) cell line and Calu-3 (human lung cancer cells) cell line were obtained from ATCC (Manassas, VA,</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>MH-S</div><div>suggested: ATCC Cat# CRL-2019, RRID:CVCL_3855)</div></div><div style="margin-bottom:8px"><div>DC2.4</div><div>suggested: Millipore Cat# SCC142, RRID:CVCL_J409)</div></div><div style="margin-bottom:8px"><div>BEAS-2B</div><div>suggested: NCBI_Iran Cat# C561, RRID:CVCL_0168)</div></div><div style="margin-bottom:8px"><div>Calu-3</div><div>suggested: KCLB Cat# 30055, RRID:CVCL_0609)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">ACE2-293T cells (ACE2-expressing cell line, constructed by hygromycin B screening) were purchased from PackGene (LV-2058, Guangzhou, China).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>ACE2-293T</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Another part was serially diluted in DMEM and added into Vero E6 cells in 96-well plates.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero E6</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Serum or BALF were incubated with 10 μl of Luc-SARS-Cov-2 pseudotyped virus (LV-2058, PackGene, China) for 60 min, then added to the HEK293T cells stably expressing ACE2 to incubate in a standard incubator (37°C, 5% CO2) for 72 h.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293T</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Organisms/Strains</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Bone marrow derived dendritic cells (BMDC) maturation study: Bone marrow cells were isolated from the femurs of female BALB/c mice and cultured in RPMI 1640 complete medium (Gibco, USA) supplemented with 10% FBS, 1% penicillin/streptomycin, 10 ng/mL of Interleukin-4 (IL-4) and Granulocyte-Macrophage Colony Stimulating Factor (GM-CSF).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>BALB/c</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The SARS-CoV-2 challenge model was based on a novel mouse-adapted SARS-CoV-2 strain, C57MA14 (NCBI GenBank number: OL913104.1, details can be found in: https://www.ncbi.nlm.nih.gov/nuccore/2167992552), that causes severe respiratory symptoms, and mortality to BALB/c mice.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>C57MA14</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Reporter Vectors (pFLuc, E1320) was purchased from Promega (Madison, USA)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pFLuc</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Plasmids encoding SARS-CoV-2 S protein (pSpike) and pVax were kindly provided by Advaccine Biopharmaceuticals Co., Ltd (Suzhou, China)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pVax</div><div>suggested: RRID:Addgene_141350)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">To evaluate the in vivo transfection of PP-sNp, pDNA/PP-sNp complexes encoding firefly luciferase (i.e., pFLuc/PP-sNp) were prepared. pFLuc/PP-sNp and naked-pFLuc were incubated with cells for 4 h in Opti-MEM I Reduced Serum Medium, then were replaced by fresh complete medium.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pDNA/PP-sNp</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pFLuc/PP-sNp</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">To examine the maturation of BMDCs in vitro, BMDCs (1 × 106 mL-1) were co-cultured with pSpike/PP-sNp and naked-pSpike only for 24 h, respectively. Subsequently, FITC anti-mouse CD11c (117305, Biolegend)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pSpike/PP-sNp</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Each anesthetized mouse intratracheally received 50 μL of pSpike/PP-sNp formulation containing 15 μg pSpike. pVax/PP-sNp and phosphate buffered saline (PBS) was adopted as a mock control and a negative control, respectively.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pVax/PP-sNp</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The trajectories of the nanoparticles were precisely quantified from the videos by software (TrackMate plugin in FIJI (ImageJ)), then the trajectory data was used to calculate the MSD and the corresponding diffusion coefficients (De) in MATLAB through the following equations, as implemented in MSD Analyzer.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>MATLAB</div><div>suggested: (MATLAB, RRID:SCR_001622)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Data are analyzed with FlowJo software V10.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>FlowJo</div><div>suggested: (FlowJo, RRID:SCR_008520)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Statistics and analysis: Statistical analyses were performed using the GraphPad Prism 8 (GraphPad Software, USA).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr></table>

      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 LimitationRecognizer: We detected the following sentences addressing limitations in the study:


      There are several limitations that we did not address in this study and will be useful topics for future studies, including the absence of data on the neutralization and protection efficiency elicited by pSpike/PP-sNp against emerging SARS-CoV-2 variants of concern. Similar to those cases of authorized COVID-19 vaccines62, the neutralizing activity of NAb induced by the pSpike/PP-sNp vaccine may suffer a significant decrease within several months/years after vaccination, more boost doses may be necessary. Besides, immunization and challenge studies with larger animals such as non-human primates should be carried out to confirm the extent of protective mucosal immunity conferred by pSpike/PP-sNp. Another limitation relates to the intratracheal dosing which is not appropriate to be applied in humans when considering its poor compliance. Most of the relevant studies chose the intranasal inoculation because of its noninvasive and convenient features, but there are still huge concerns and uncertainties regarding intranasal route of vaccination. For example, negative perception for nasal vaccines was generated from reported cases of Bell’s palsy after intranasal dosing of influenza vaccines63, 64. Alternatively, the noninvasive nebulized formulations seem to be one of the most appropriate approaches in delivering mucosal vaccines to the human airway. However, the nebulized DNA formulations still face many challenges as indicated by a previous study showing that as little as 10% of ...


      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

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  8. May 2022
    1. May 1427df","id":"ci02319dddf0002719","primarySiteId":"cs022b4380a000251f"},"primaryImageId":"ci02319dddf0002719","promoTitle":"Exploration","slug":"exploration","teaser":"In the 15th century, Europeans began to sail west across the Atlantic Ocean in search of new routes to China and the East, but in the process they discovered an entirely New World: North and South America, plus many other lands."},{"id":"ci0230e527000126df","objectType":"ContentRichTerm","originalPublicationTimestamp":"2018-08-21T03:39:11Z","path":null,"publicationTimestamp":"2018-08-21T03:38:55Z","title":"May 14","commentsEnabled":true,"disqusId":"41230483-a4fb-11e8-b22c-02d4c801a0a4","slug":"05-14"}]}THIS DAY IN HISTORYMay 1412345678910111213141516171819202122232425262728293031Year1804 Month DayMay 14 Lewis and Clark depart to explore the NorthwestMay 14, 1804: One year after the United States doubled its territory with the Louisiana Purchase, the Lewis and Clark expedition leaves St. Louis, Missouri, on a mission to explore the Northwest from the Mississippi River to the Pacific Ocean.Even before the U.S. government concluded purchase negotiations with France, President Thomas Jefferson commissioned his private secretary Meriwether Lewis and William Clark, an army captain, to lead an expedition into what is now the U.S. Northwest. On May 14, the “Corps of Discovery”—featuring approximately 45 men (although only an approximate 33 men would make the full journey)—left St. Louis for the American interior.READ MORE: Lewis and Clark: A Timeline of the Extraordinary ExpeditionThe expedition traveled up the Missouri River in a 55-foot long keelboat and two smaller boats. In November, Toussaint Charbonneau, a French-Canadian fur trader accompanied by his young Native American wife Sacagawea, joined the expedition as an interpreter. The group wintered in present-day North Dakota before crossing into present-day Montana, where they first saw the Rocky Mountains. On the other side of the Continental Divide, they were met by Sacagawea’s tribe, the Shoshone Indians, who sold them horses for their journey down through the Bitterroot Mountains. After passing through the dangerous rapids of the Clearwater and Snake rivers in canoes, the explorers reached the calm of the Columbia River, which led them to the sea. On November 8, 1805, the expedition arrived at the Pacific Ocean. After pausing there for the winter, the explorers began their long journey back to St. Louis. 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But if you see something that doesn't look right, click here to contact us! HISTORY reviews and updates its content regularly to ensure it is complete and accurate.ALSO ON THIS DAYMiddle East1948State of Israel proclaimedOn May 14, 1948, in Tel Aviv, Jewish Agency Chairman David Ben-Gurion proclaims the State of Israel, establishing the first Jewish state in 2,000 years. Ben-Gurion became Israel’s first premier. In the distance, the rumble of guns could be heard from fighting that broke out ...read moreSpace Exploration1973America’s first space station, Skylab, is launchedSkylab, America’s first space station, is successfully launched into an orbit around the earth. Eleven days later, U.S. astronauts Charles Conrad, Joseph Kerwin, and Paul Weitz made a rendezvous with Skylab, repairing a jammed solar panel and conducting scientific experiments ...read moreInventions & Science1796Early smallpox vaccine is testedEdward Jenner, an English country doctor from Gloucestershire, administers the world’s first vaccination as a preventive treatment for smallpox, a disease that had killed millions of people over the centuries. While still a medical student, Jenner noticed that milkmaids who had ...read moreSports1904First American Olympiad opens in St. Louis, MissouriThe Third Olympiad of the modern era, and the first Olympic Games to be held in the United States, opens in St. Louis, Missouri. 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Clinton promised an official investigation into the ...read moreArt, Literature, and Film History1998Frank Sinatra diesOn May 14, 1998, the legendary singer, actor and show-business icon Frank Sinatra dies of a heart attack in Los Angeles, at the age of 82. Sinatra emerged from an Italian-American family in Hoboken, New Jersey, to become the first modern superstar of popular music, with an ...read moreSign up now to learn about This Day in History straight from your inbox.Sign Up1990s<{"id":"ci0230e54cb00326df","isDetailPage":true,"objectType":"ContentArticle","originalPublicationTimestamp":"2009-11-24T18:04:09Z","path":"/this-day-in-history/lewis-and-clark-depart","publicationTimestamp":"2009-11-24T18:04:09Z","title":"Lewis and Clark depart to explore the Northwest","viewProperties":{"sidebar":{"component":{"disableScrolling":false,"components":[]},"disableAboveTheFoldAd":false},"viewMeta":{"showLimitedSiteFooter":false,"title":"","suppressDisplayAds":false,"disableSiteFooter":false,"suppressContentRecommendations":false,"disableSiteHeader":false},"rss":{"collection":{"filter":{"term":"ci0230e54cb00326df"}}},"analyticsModel":{"title":"Lewis and Clark depart to explore the Northwest","exclusiveContentType":"free","sanitizedTitle":"Lewis and Clark depart to explore the Northwest","pageType":"Article Page","mavenPageType":"article","sectionPath":"Topics > Exploration","parentSectionName":"Topics","authorName":"History.com Editors","author":"tm-ci0230e4e5e0112549","videoAuthorName":"History.com Editors","videoTitle":"This Day in History: 05/14/1804 - Lewis and Clark Depart","publicationQuarter":"2009Q4","trackedRichTermNames":"","originalPublishDate":"2009-11-24","trackedExtendedAttributes":""}},"commentsEnabled":true,"disqusId":"a976460e-a4fc-11e8-aee8-02fa89c9d81c","isCanvas":true,"metaDescription":"The Lewis and Clark expedition leaves St. Louis, Missouri, on a mission to explore the Northwest from the Mississippi River to the Pacific Ocean.","primarySectionIds":["ci0230e4e5e0042549","ci0230e4e5f0022549"],"promoTitle":"Lewis and Clark depart to explore the Northwest","sectionIds":[["ci0230e4e5e0042549","ci0230e4e5f0022549"]],"slug":"lewis-and-clark-depart","hasGallery":false,"associatedRichTerms":[{"id":"ci0230e52230002549","objectType":"ContentRichTerm","originalPublicationTimestamp":"2018-08-21T03:39:27Z","path":null,"publicationTimestamp":"2018-08-21T03:39:13Z","title":"1804","commentsEnabled":true,"disqusId":"13c30a30-a4fb-11e8-aeb7-02fa89c9d81c","slug":"1804"},{"id":"ci0230e4e5e0042549","objectType":"ContentRichTerm","originalPublicationTimestamp":"2018-08-21T04:04:03Z","path":"/topics","publicationTimestamp":"2018-08-22T20:20:16Z","title":"Topics","commentsEnabled":true,"disqusId":"d49fb684-a4f8-11e8-aea1-02fa89c9d81c","slug":"topics"},{"id":"ci0230e4e5f0022549","objectType":"ContentRichTerm","originalPublicationTimestamp":"2018-08-21T04:04:03Z","path":"/topics/exploration","publicationTimestamp":"2018-08-22T20:20:20Z","title":"Exploration","commentsEnabled":true,"disqusId":"d49fb68c-a4f8-11e8-aea1-02fa89c9d81c","metaDescription":"Discover a world of information on explorers and conquistadors like Christopher Columbus, Francis Drake, Henry Hudson, Ferdinand Magellan, Hernan Cortes, and Amelia Earhart.","primaryImage":{"isReadonlyContent":false,"title":"exploration","format":"jpg","bytes":144200,"primaryPhotoId":"ci02319dddf0002719","createdTimestamp":"2018-08-29T22:42:40Z","primaryContentSiteId":"cs022b4380a000251f","height":512,"cloudinaryVersionId":1535582558,"publicId":"MTU4MDgxMDM3OTM3NjgxNjEy","objectType":"ContentPhoto","width":768,"hasFaces":false,"createdPrincipalId":"up01f489d3800027df","id":"ci02319dddf0002719","primarySiteId":"cs022b4380a000251f"},"primaryImageId":"ci02319dddf0002719","promoTitle":"Exploration","slug":"exploration","teaser":"In the 15th century, Europeans began to sail west across the Atlantic Ocean in search of new routes to China and the East, but in the process they discovered an entirely New World: North and South America, plus many other lands."},{"id":"ci0230e527000126df","objectType":"ContentRichTerm","originalPublicationTimestamp":"2018-08-21T03:39:11Z","path":null,"publicationTimestamp":"2018-08-21T03:38:55Z","title":"May 14","commentsEnabled":true,"disqusId":"41230483-a4fb-11e8-b22c-02d4c801a0a4","slug":"05-14"}]}THIS DAY IN HISTORYMay 14/div>1991Two trains crash in Japan, killing more than 40On May 14, 1991, two diesel trains carrying commuters crash head-on, killing 42 people and injuring over 400 more near Shigaraki, Japan. This was the worst rail disaster in Japan since a November 1963 Yokohama crash killed 160 people. Shigaraki, a town near Kyoto, is famous for ...read moreCrime1948A three-year-old's brutal murder begins an unusual investigationThree-year-old June Devaney, recovering from pneumonia at Queen’s Park Hospital in Blackburn, England, is kidnapped from her bed. Nurses discovered her missing at 1:20 a.m. the next day, and police were immediately summoned to investigate. Two hours later, her body was found with ...read moreCold War1955The Warsaw Pact is formedThe Soviet Union and seven of its European satellites sign a treaty establishing the Warsaw Pact, a mutual defense organization that put the Soviets in command of the armed forces of the member states. The Warsaw Pact, so named because the treaty was signed in Warsaw, included ...read moreAmerican Revolution1787Constitutional Convention delegates begin to assembleOn May 14, 1787, delegates to the Constitutional Convention begin to assemble in Philadelphia to confront a daunting task: the peaceful overthrow of the new American government as defined by the Article of Confederation. Although the convention was originally supposed to begin on ...read moreAd ChoicesAdvertiseClosed CaptioningCopyright PolicyCorporate InformationEmployment OpportunitiesFAQ/Contact UsPrivacy NoticeTerms of UseTV Parental GuidelinesRSS FeedsAccessibility Support© 2022 A&E Television Networks, LLC. 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      I chose to annotate this day because it is my birthday. also, because this was a big deal after the Louisiana purchase to explore the western parts of our country.

    1. SciScore for 10.1101/2022.05.27.22274752: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">Field Sample Permit: VeroE6/TMPRSS2 cells were purchased from the Japanese Collection of Research Bioresources (JCRB) Cell Bank.<br>IRB: Specimen collection: This study was approved by the ethics committee of Keio University (approval number: 20210081) and was conducted in accordance with the Declaration of Helsinki and Title 45, U.S. Code of Federal Regulations, Part 46, Protection of Human Subjects, effective December 13, 2001.<br>Consent: All patients provided written informed consent.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">Quantification of SAES-CoV-2 infection: First, multiple fields of view (FOVs) containing observed cells were randomly selected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Visualizing squamous epithelial cells using an antibody against pan-cytokeratin: After reaction with a fluorescent Nb, cells were incubated in 0.7 mL PBS containing 0.1% (wt/vol) Triton X-100 and 1.25 μg/mL anti–pan-cytokeratin mouse mAb (AE1/AE3) (BioLegend, 914204) at RT for 60 min.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti–pan-cytokeratin</div><div>suggested: (BioLegend Cat# 914204, RRID:AB_2616960)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Delineating individual cells by immunostaining the plasma membrane: After reaction with a fluorescent Nb, cells were incubated in 0.7 mL PBS containing 0.1% (wt/vol) Triton X-100 and 3.3 μg/mL anti–pan-cadherin rabbit polyclonal antibody (abcam, ab16505) at RT for 60 min.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti–pan-cadherin</div><div>suggested: (Abcam Cat# ab16505, RRID:AB_443397)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After washing with PBS twice, cells were incubated in a 0.7 mL PBS containing 0.1% (wt/vol) Triton X-100 and 2.0 μg/mL Alexa Fluor 546–labeled donkey anti–rabbit IgG (H+L) antibody (Thermo Fisher/Invitrogen, A10040) at RT for 60 min.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti–rabbit IgG</div><div>suggested: (Thermo Fisher Scientific Cat# A10040, RRID:AB_2534016)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">VeroE6/TMPRSS2 cells were purchased from the Japanese Collection of Research Bioresources (JCRB) Cell Bank.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>VeroE6/TMPRSS2</div><div>suggested: JCRB Cat# JCRB1819, RRID:CVCL_YQ49)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Gene construction (Nb-FP fusion): The K-874A, E9, or N10 gene was amplified using primers containing the 5’-BamHI and 3’-EcoRI sites, and the restricted products were cloned into the BamHI/EcoRI sites of pBS Coupler 4 (Shimozono and Miyawaki, 2008) to generate pBS/K-874A=, pBS/E9=, or pBS/N10=, respectively. ‘=’ denotes the “coupler linker,” a triple repeat of the amino acid linker Gly–Gly–Gly–Gly–Ser [(GGGGS)3].</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pBS/E9=</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pBS/N10=</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The KikG gene was amplified using primers containing the 5’-HindIII and 3’-SalI sites, and the restricted product was cloned in-frame into the HindIII/SalI sites of pBS/K-874A=, pBS/E9=, and pBS/N10= to generate pBS/K-874A=KikG, pBS/E9=KikG, and pBS/N10=KikG, respectively.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pBS/K-874A=KikG</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pBS/E9=KikG</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pBS/N10=KikG</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The Azalea gene was amplified using primers containing the 5’-HindIII and 3’-SalI sites, and the restricted product was cloned in-frame into the HindIII/SalI sites of pBS/K-874A=, pBS/E9=, and pBS/N10= to generate pBS/K-874A=Azalea, pBS/E9=Azalea, and pBS/N10=Azalea, respectively.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pBS/K-874A=Azalea</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pBS/E9=Azalea</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pBS/N10=Azalea</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The EGFP or Achilles gene was amplified using primers containing the 5’-HindIII and 3’-SalI sites, and the restricted product was cloned in-frame into the HindIII/SalI sites of pBS/K-874A= to generate pBS/K-874A=EGFP or pBS/K-874A=Achilles, respectively.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pBS/K-874A=</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pBS/K-874A=EGFP</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pBS/K-874A=Achilles</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">In parallel, pRSETB was engineered to have a SalI site instead of a HindIII site.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pRSETB</div><div>suggested: RRID:Addgene_89510)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The resultant plasmid was named pRSETB(S).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pRSETB(S</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The DNA fragments encoding K-874A=KikG, E9=KikG, N10=KikG, K-874A=Azalea, E9=Azalea, N10=Azalea, K-874A=EGFP, and K-874A=Achilles were cloned into the BamHI/SalI sites of pRSETB(S) to generate pRSETB(S)/K-874A=KikG, pRSETB(S)/E9=KikG, pRSETB(S)/N10=KikG, pRSETB(S)/K-874A=Azalea, pRSETB(S)/E9=Azalea, pRSETB(S)/N10=Azalea, pRSETB(S)/K-874A=EGFP, and pRSETB(S)/K-874A=Achilles, respectively.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pRSETB(S)/K-874A=KikG</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pRSETB(S)/E9=KikG</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pRSETB(S)/N10=KikG</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pRSETB(S)/K-874A=Azalea</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pRSETB(S)/E9=Azalea</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pRSETB(S)/N10=Azalea</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pRSETB(S)/K-874A=EGFP</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pRSETB(S)/K-874A=Achilles</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Second, individual cells were manually delineated using the “Freehand selections” tool (ImageJ) in each FOV.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>ImageJ</div><div>suggested: (ImageJ, RRID:SCR_003070)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Third, the average intensity and the texture of the E9=KikG fluorescence were extracted using a customized program written using C++ and OpenCV 3.4.1 (https://opencv.org).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>https://opencv.org</div><div>suggested: (OpenCV, RRID:SCR_015526)</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


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      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.

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    1. SciScore for 10.1101/2022.05.27.493682: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">Euthanasia Agents: At the end point of the experiment, all remaining animals in the monoclonal antibody-administered group received an overdose of isoflurane and were humanely euthanized.<br>IACUC: Ethics statement: This study was approved by the Experimental Animal Welfare and Ethical Review Board of Wuhan Institute of Biological Products Co.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">Male K18-hACE2 mice (6–8 weeks old, purchased from GemPharmatech Co., Ltd. Company.) were randomly distributed into groups (n = 3–6 mice per group).</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">Male K18-hACE2 mice (6–8 weeks old, purchased from GemPharmatech Co., Ltd. Company.) were randomly distributed into groups (n = 3–6 mice per group).</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Then cells were stained with anti-mouse IgG Taxes red conjugated antibody and anti-human IgG FITC-conjugated antibody (Sigma, USA) for another 30 min then analyzed by FACS Aria II (BD, USA).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-mouse IgG</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-human IgG</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Antibody Binding Kinetics Measured by SPR: The binding kinetics of mAbs to SARS-CoV-2 Delta-RBD or Omicron-RBD monomer were analyzed using SPR (Biacore 8K; GE Healthcare).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>SARS-CoV-2</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>Delta-RBD</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cells, Viruses and Proteins: Cell lines (HEK293T and Vero E6 cells) were initially acquired from the American Type Culture Collection (ATCC; USA).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero E6</div><div>suggested: RRID:CVCL_XD71)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">HEK293T-hACE2-cells were generated via the overexpression of the human ACE2 receptor in HEK293T cells and were used in the neutralization assays of pseudoviruses.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293T-hACE2-cells</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Then mixtures were added to 2.5 × 105 HEK293T cells expressing ACE2 and incubated at 4 °C for another hour.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293T</div><div>suggested: RRID:CVCL_HA71)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">HEK293-hACE2 cells (2.5 × 104 cells/100μL per well) were then added into the mixture and incubated at 37 °C in a humidified atmosphere with 5% CO2 for 23 h to 25 h.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293-hACE2</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Organisms/Strains</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Male K18-hACE2 mice (6–8 weeks old, purchased from GemPharmatech Co., Ltd. Company.) were randomly distributed into groups (n = 3–6 mice per group).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>K18-hACE2</div><div>suggested: RRID:IMSR_GPT:T037657)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The SARS-CoV-2 Spike ectodomain (1-1208) with a C-terminal Strep tag for purification and a foldon tag for trimerization was inserted into the pFastBac-Dual vector (Invitrogen) and was expressed using Bac-to-Bac baculovirus system (Invitrogen).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pFastBac-Dual</div><div>suggested: RRID:Addgene_137166)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The cDNA encoding SARS-CoV-2 Omicron Spike was synthesized (GenBank ID: ULC25168.1) and cloned into the pCAG vector.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCAG</div><div>suggested: RRID:Addgene_74288)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">All of these data were analyzed using Flow Jo.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Flow Jo</div><div>suggested: (FlowJo, RRID:SCR_008520)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">All statistical analysis was performed using GraphPad Prism 8.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Coot v.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Coot</div><div>suggested: (Coot, RRID:SCR_014222)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Figures were generated using PyMOL 2.0.779</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>PyMOL</div><div>suggested: (PyMOL, RRID:SCR_000305)</div></div></td></tr></table>

      Results from OddPub: Thank you for sharing your data.

      Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


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      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.

      </footer>
    1. SciScore for 10.1101/2022.05.27.493569: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">Euthanasia Agents: Each serial dilution was then mixed 1:1 with 2,000 TCID50/mL SARS-CoV-2 variant virus (Delta variant: strain hCoV-19/USA/MD-HP05647/2021; Omicron variant: strain hCoV-19/USA/MD-HP20874/2021) and incubated for 1 h at + 37 °C ± 2 °C and 5% ± 0.5% of CO2.<br>IACUC: All experimental animal procedures were approved by the Institutional Animal Committee of San Raffaele Scientific Institute.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">Female transgenic K18-hACE2 mice, aged 8-10 weeks, were infected via the intranasal route with 1×105 TCID50/mouse of SARS-Cov-2 variant Delta B.1.617.2 virus [hCoV-19/Italy/LOM-Milan-UNIMI9615/2021 (GISAID Accession ID: EPI_ISL_3073880)], obtained from the Laboratory of Microbiology and Virology of San Raffaele Scientific Institute.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">Images shown in all figures are representative of at least five random fields (scale bars are indicated).</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">One hour after infection, the virus solution was discarded and replaced by a volume of growth medium containing scFv76 or not-neutralizing scFv5 antibody, in a concentration ranging from 214 to 2.6 nM, in triplicate.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>scFv5</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The modelling was performed with antigen-binding fragment (Fv) as antibody format.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>antigen-binding fragment (Fv)</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">We analyzed 130 CoV-AbDab structures of antibodies (Abs) bound to RBDs (23) and found that 39 out of 42 entries with IGHV3-53/IGHV3-66 HCs, usually coupled with IGKV1-9 (16 Abs) or IGKV3-20 (10 Abs) LCs, share a common binding mode to the RBD.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>IGKV3-20</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Viral Neutralization in Calu-3 cells: To measure the SARS-CoV-2-neutralizing capability of scFv76, a live SARS-CoV-2 assay was performed by measuring the viral load in human lung adenocarcinoma Calu-3 cells, by real-time reverse transcription-quantitative PCR (RT-qPCR), 72 h after virus infection.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Calu-3</div><div>suggested: KCLB Cat# 30055, RRID:CVCL_0609)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Thirty-five μL of each diluted sample/virus mix were then applied in octuplicate to Vero E6 cells seeded at a density of 104 cells/well in a 96-well plate at day -1.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero E6</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cell-cell fusion assay: Human alveolar type II-like epithelial A549 cells and embryonic kidney 293T cells were obtained from ATCC (Manassas, VA).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>293T</div><div>suggested: KCB Cat# KCB 200744YJ, RRID:CVCL_0063)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cells were grown at 37 °C and 5% CO2, in RPMI-1640 (A549 cells) or DMEM (293T cells) medium (Euroclone) supplemented with 10% fetal calf serum (FCS), 2 mM glutamine, and antibiotics.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>A549</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Generation of A549 cells stably expressing the human ACE2 receptor (A549-hACE2 cells) has been described previously (20).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>A549-hACE2</div><div>suggested: RRID:CVCL_A5KB)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Kinetic constants were obtained by BIAevaluation 3.2 software (</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>BIAevaluation</div><div>suggested: (BIAevaluation Software, RRID:SCR_015936)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Data were processed using GraphPad Prism software (V8.0) and the IC50 values calculated using a four-parameter logistic curve fitting approach.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad Prism</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Statistical analysis was performed using one-way ANOVA (Prism 6.0 software; GraphPad).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Both models were independently refined with COOT (25) and PHENIX (26) using the full reconstruction and the local refined map, at 3.4 Å and 4.0 Å resolution, respectively.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>COOT</div><div>suggested: (Coot, RRID:SCR_014222)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The images were prepared using ChimeraX (27) and Pymol (http://www.pymol.org/pymol).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Pymol</div><div>suggested: (PyMOL, RRID:SCR_000305)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cryo-EM movies were deposited in the Electron Microscopy Public Image Archive under the accession code EMPIAR-10990.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Image Archive</div><div>suggested: None</div></div></td></tr></table>

      Results from OddPub: Thank you for sharing your data.

      Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:


      In the search of easily deployable therapeutic measures against COVID-19, we recently described 76clAbs, a cluster of human single chain antibody fragments that, in principle, could bypass all limitations of traditional monoclonal antibodies. Indeed, the use of monoclonal antibodies for the therapy of COVID-19 is being challenged by several issues: 1) difficulties in the deployment of therapy, being monoclonal antibodies parenteral drugs to be administered in hospital environments; 2) the risk of antibody-dependent enhancement (ADE) that can be ignited by different routes involving the immunoglobulin Fc interaction with Fc receptor (16) or with the ACE2, recently found to possibly act as a secondary receptor (17), or with Fcγ-expressing cells including monocytes and macrophages that, by triggering the inflammatory cell death, needed to abort the production of infectious virus, cause systemic inflammation that contributes to the severity of COVID-19 pathogenesis (18); 3) evasion properties of SARS-CoV-2 variants, particularly recently emerged Omicron lineages for which most of approved and investigational antibodies lost their neutralization activity (4-10). The single chain antibody format, because of its high stability, can be easily used for friendly self-administrable aerosol treatments. Furthermore, single-chain antibodies are, in principle, devoid of ADE risk because of lack of Fc sequence. 76clAbs, which were selected on the original SARS-CoV-2 Wuhan strain, were found ...


      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. SciScore for 10.1101/2022.05.27.493693: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">IACUC: This study was performed in accordance with the guidelines for the care and use of laboratory animals published and approved by the Committee for Ethics on Animal Experiments and the Committee for Animal Biosafety Level 3 Research of the Egyptian Military Scientific Commission.<br>Euthanasia Agents: Doses were mixed with Fc of IgG mouse anti-Human IgM added as an adjuvant immunogen dissolved in human albumin, phosphate buffer, and sodium chloride without any stabilizers or preservatives.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">For the production of different IC antibodies, twenty-four male rabbits were divided into six groups (numbered 1 through 6), each comprising four rabbits.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">Mice were randomly divided into twelve groups, five mice per group, and classified into two categories.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">nucleocapsid antibody, and membrane antibody (by AMSBIO) were procured, diluted with 1 ml of PBS; i.e. (1/400, 1/200, 1/100, 1/50, 1/25) and incubated at room temperature for at least 2 hours. 1.6. Preparation of Immune Complexes: Two different modes of immune complexes formulation were used in which mixing of a specific concentration of an antigen with its relevant concentration of an antibody took place and named A1, A2, A3, B1, and B2, where A1, A2, and A3 constitute the CRCx3 series and B1 and B2 constitute the CRCx2 series.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>A2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The first mode (a non-specific mixture composed of coronavirus antigens and their non-specific antibodies) included SARS-CoV-2 spike protein (S1 subunit) with anti-nucleocapsid antibodies (A1), nucleocapsid antigen (N) with anti-membrane antibodies (A2) and membrane antigen (M) with anti-spike antibodies (A3).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>S1 subunit ) with anti-nucleocapsid antibodies ( A1) , nucleocapsid antigen ( N ) with anti-membrane antibodies ( A2 ) and membrane antigen ( M</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The second mode (a specific mix composed of coronavirus antigens and their specific antibodies) included spike antigen (S1) with anti-spike antibodies (B1), and nucleocapsid antigen (N) with anti-nucleocapsid antibodies (B2).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>spike antigen ( S1 ) with anti-spike antibodies ( B1)</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>nucleocapsid antigen ( N ) with anti-nucleocapsid antibodies ( B2)</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">To finalize the complex preparations (A1, A2, A3, B1, and B2) for inducing anti-complex antibodies production in immunized rabbits, 3 ml of the adjuvant goat IgG anti-human IgM Fc (5 ng/ml) (by ABCAM, Cambridge, UK) was dissolved in human albumin and phosphate-buffered normal saline added to the remainder of the sediments and packaged in 1 ml vials of injectable solution with labels (See Supplement: Fig. 4). 1.7.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-complex</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-human IgM</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Six weeks later, all vaccinated animals were bled and, from their sera, anti-complex precipitated globulin antibodies were collected.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-complex precipitated globulin</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Each group of rabbits produced 15 ml of immune serum (200 mg) containing antibodies to the immune complexes A1, A2, A3, B1, or B2.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>B2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The amounts of anti-complex antibodies precipitated from each rabbit’s sample are then estimated according to the different concentrations of injected complexes mixtures by ELISA. 1.9. ELISA Assay of Produced Immune Complexes Antibodies: For quantitative titration of anti-immune-complexes produced from experimental rabbits’ sera to the different immune complexes formulae, an ELISA assay was performed by coating each one of five 96-well plates with a specific immune complex of A1, A2, A3, B1, or B2.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-immune-complexes</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Component B is an anti-complex-antibodies standard calibrator: 5, 10, 20, 30, 40, and 50 μg/ml (previously prepared from rabbits’ sera) for each of the immune complexes (A1, A2, A3, B1, or B2).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>A1</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Also, from day zero, blood samples were taken on days 14, 28, 42 & 56 to measure serum immunological markers and anti-complexes antibodies.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-complexes</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Sera optical density at 450 nm was measured and the levels of anti-CRCx3 and anti-CRCx2 neutralizing antibodies (NAbs) were evaluated.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-CRCx3</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-CRCx2 neutralizing antibodies ( NAbs )</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Increasing serum anti-ICs antibody titers in vaccinated mice was monitored from the beginning and after days 14, 28, 42 & 56 (ELISA).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-ICs</div><div>suggested: (Kadrmas JL; J Cell Biol. 2004 Cat# ics, RRID:AB_2568207)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The highest produced NAb was Anti-B1 produced against the immune complex of the spike protein and its specific anti-spike antibody.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Anti-B1</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-spike</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Then, the infected T-helper cell will send signals to excite the B-cells to produce a number of immune antibodies to attach to the viral antigens that are configured by the respiratory cells, thus forming a circulating immune complex that comprises coronavirus antigen (M, N, and S and its specific neutralizing antibody as IC1, IC2, and IC3).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>IC2</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>IC3</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">CRCx was formulated with twenty-five micrograms (25 μg) of different antigens including spike protein (S1 subunit), nucleocapsid (N), or membrane antigen (M) as well as forty micrograms (40 μg) of different antibodies including anti-nucleocapsid, anti-membrane or anti-spike (S1 subunit) antibodies.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-nucleocapsid, anti-membrane or anti-spike (S1 subunit)</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The main aim of this novel immune peptide CRCx is to stimulate the CD8+ T-cells against the foreign antigenic non-complexes as well as any other hidden circulating immune complex form with any antigenic similarity (antigen/specific-antibodies) and to put the whole immunity system on alert and restoring its normal functions.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>antigenic similarity</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The results also did not find the vaccine to cause antibody-dependent enhancement (ADE) [66] as all the data obtained in this study support the safety and immunogenicity of this candidate vaccine series.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>antibody-dependent enhancement (ADE) [66</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The antigen/nonspecific-antibody-IC vaccine series needs more experiments to validate our primary hypothesis that they may prompt more potent and more long-lasting protection against mutating versions of SARS-Cov-2 as results showed that within the time limit of this trial, the antigen/specific-antibody-IC vaccine series produced higher NAbs against the vaccine ICs. 1.4. Conclusion: The new immune complex (IC) anti-SARS-Cov-2 candidate vaccine CRCx is composed of 5 different combinations: a series of three antigen/nonspecific-antibody termed (CRCx3) and two antigen/specific-antibody termed (CRCx2).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>antigen/nonspecific-antibody-IC</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>antigen/specific-antibody-IC</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-SARS-Cov-2</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>CRCx2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">However, again, the highest titer in these groups was seen with the high dose CRCx3 subtype composed of spike and its nonspecific antibody of anti-nucleocapsid.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-nucleocapsid</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Conclusively, according to the results, the spike antigen/anti-spike specific-antibody combination of CRCx2 gives the highest immunogenicity against Covid-19 virus infection both as a prophylaxis and as a therapy.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>antigen/anti-spike</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">In addition, even though both vaccines were found to significantly reduce or abolish viral load and broncho-alveolar effects in animal models challenged with SARS-CoV-2 within 14 days after receiving the booster dose of the vaccines with no signs of pneumonia in histopathological sections of the virus-challenged animals after vaccination, a higher preference was found to the double-dose antigen-specific-antibody (CRCx2) series and/or those from both CRCx series with the spike protein antigen.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>antigen-specific-antibody (CRCx2</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Vero E6 cells with an average population of 104 cells were cultured for 18–24 h in each well in the growth medium [Eagle’s Minimum Essential Medium (EMEM)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero E6</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The genomic content of Vero cells of each well with the minimal number of plaques was extracted for further molecular characterization.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero</div><div>suggested: CLS Cat# 605372/p622_VERO, RRID:CVCL_0059)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Organisms/Strains</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Trail 2: For 14 days, starting on the day 15 from primary immunization in trail 1, the BALB/c mice of groups A, B, C, D, E, and F were intramuscularly challenged in the upper leg with a daily dose/mouse of 0.25 ml (total of 108 IU) of live SARS-Cov-2 virus to which 5 IU of DNA polymerase was added.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>BALB/c</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">(Lonza Bioscience) +L</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Lonza Bioscience</div><div>suggested: (Science Exchange, RRID:SCR_010620)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Statistical Analysis: The SPSS software Version 25 was used to analyze the level of significance, using one-way ANOVA, paired t-test (2-tailed), and Pearson’s correlation methods.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>SPSS</div><div>suggested: (SPSS, RRID:SCR_002865)</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


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      About SciScore

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    1. SciScore for 10.1101/2022.05.23.22275460: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">Field Sample Permit: Ethics: Collection of samples from the Orleans cohort had been approved by the Comité de Protection des Personnes Ile de France IV (NCT04750720).<br>IRB: Collection of samples from the Strasbourg cohort was approved by Institutional Review Board of Strasbourg University Hospital (NCT04441684).<br>Consent: Informed consent was obtained from all participants, and parents provided informed consent for any children under the age of 18 years.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">The antigen associated with the lowest sum of residual sum of squares among the four different variant-specific random forest regression models was kept in the model.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">Contamination: Cells tested negative for mycoplasma.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Samples: Viral neutralization studies: To correlate antibody measurements with neutralization titers, we collected 304 serum samples from individuals with either vaccine-induced or infection-acquired immunity to SARS-CoV-2.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>SARS-CoV-2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">R-PE) conjugated goat or donkey anti-human IgG antibody was used as detector antibody at 1/120 dilution and goat anti-human IgA at 1/200.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-human IgG</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-human IgA</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">This assay allowed simultaneous detection of antibodies to 30 antigens, including stabilized trimeric Spike ectodomain (16), RBD, Membrane protein (M), Membrane Envelope protein (E), Nucleocapsid protein (NP), and a Membrane-Envelope fusion protein (ME).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>RBD , Membrane protein ( M) , Membrane Envelope protein ( E) , Nucleocapsid protein ( NP) ,</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">In addition to the measurement of the presence of antibodies to antigens, we also measured the strength of antibody (Ab) binding with an avidity assay (Garcia et al, submitted to Viruses, 2022).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>antigens</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After these 5 minutes and washing, 100μL of secondary antibodies conjugated to R-phycoerythrin (Jackson Immunoresearch) for detection of specific IgG, diluted at 1/100 was added for 15 minutes.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>R-phycoerythrin</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Briefly, Nucleocapsid-specific IgG antibodies were assessed using an ELISA-based assays on sera incubated in antigen-coated wells.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Nucleocapsid-specific IgG</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The Anti-Fc IgG VHH (Fc1) was derived from an antibody from immunized alpaca and expressed as a tandem with an optimized catalytic domain nanoKAZ from Oplophorous gracilirostris luciferase.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Anti-Fc IgG</div><div>suggested: None</div></div></td></tr></table>

      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 LimitationRecognizer: We detected the following sentences addressing limitations in the study:


      A limitation of our study was that we assessed levels of immunity from serum only. There is clearly also a role for mucosal immunity in protecting against SARS-CoV-2 infection, especially in the case of infection-acquired immunity. For the last three session in our study we collected nasopharyngeal samples, which we plan to incorporate in future research. Our analysis is dependent on the suitability of neutralizing titers as a correlate of protection against symptomatic COVID-19, based on meta-analyses of vaccine studies (7, 8). This assumption is supported by an analysis of data from phase 3 trials of Moderna’s mRNA-1273 vaccine, which indicated that 68% of vaccine efficacy can be explained by neutralizing titers (31). This leaves up to 32% variation that may be explained by other effects such as cellular immunity or host factors. An additional limitation is that the evidence base for neutralizing titers as a correlate of protection is built on studies of infection with the Ancestral variant. However, antibody levels have been observed to be associated with reduced infection with other variants, most notably Delta (32). Although neutralizing titers have frequently been shown to be associated with protection against severe COVID-19 (7-9), there is a weaker evidence base for their use as a correlate of protection. A final, important limitation is that there is uncertainty in the statistical relationships utilized in this analysis. When considering the inferred protection from ...


      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">NCT04750720</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">Study of the Kinetics of COVID-19 Antibodies for 24 Months i…</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04441684</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">Seroprevalence of SARS-CoV-2 in Strasbourg University Hospit…</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04644159</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">Longitudinal Follow-up of a Population Cohort in a French Ci…</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04325646</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">Sero-epidemiological Study of the SARS-CoV-2 Virus Responsib…</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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


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      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.

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

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      In this manuscript, the authors address the important topic of post-transcriptional gene regulation using the larval nervous system in Drosophila. They utilize a novel approach taking advantage of existing protein trap library, which permits use of the same smFISH probe to detect an array of 200 RNAs and visualize their corresponding protein expression. Furthermore, the authors developed a computational pipeline to visualize and analyze the resulting data, which should enhance the application of this method by other researchers. A major strength of the data comes from the analysis of multiple cell types in distinct compartments of the nervous system, cell types (neuron, glia, neuroblast), and subcellular domains. From the cumulative data, the authors are able to describe several interesting observations relating to cell-specific post-transcriptional regulation, regulation within a central-neuroblast lineage and glial post-transcriptional regulation, among others.

      However, in spite of these strengths, there are several concerns related to the organization and interpretation of the manuscript that the authors should address in order to improve the manuscript:

      General concerns:

      1. The approach relies on gene traps that often fail to be made homozygous, presumably due to deleterious function of the YFP insert. This is an obvious limitation of the study, which the authors address, but do so insufficiently by only analyzing a single case Dlg1. The authors should report how many of the 200 YFP-traps can produce viable homozygous animals, whether phenotypes can be observed, and any other relevant information to assess the functional properties of the tagged genes.
      2. The term "discordant" is used for non-congruous RNA/Protein levels in soma and distal processes, and sometimes the two are analyzed in the same figure (e.g Fig 3A). When it is stated that 98% of genes are discordant, this is an over-simplification as what the authors describe as "discordant" is expected to occur frequently in the distal process, but less often in the soma (which is what the authors find when presenting the data for individual compartments - Fig 3B-C). This is confusing because the observation means completely different things in the two compartments, though both are interesting to describe. These analyses, and their interpretation, should be kept separate.
      3. There is not enough emphasis placed on the cell-type specific regulation of RNAs. There are very few studies that have investigated how localization of individual RNAs changes in different cell types or regions of the nervous system, and the authors find that this is quite prevalent. Therefore, the rather superficial analysis of these data fails to take advantage of a major strength of the data. For example, for the discordant genes that differ in neuropil localization between different regions of the CNS, what types of molecules do they encode, what is their function in neurons (if known), and why might they be required locally in one region of the CNS but not the other?
      4. The authors conclude that mRNA and protein co-localization in glia processes shows that mRNA localization makes a major contribution of the proteome in processes. However, there is not enough evidence for such conclusion since neither translation of these mRNAs nor lack of protein trafficking from the somas was shown.
      5. An important caveat of this technique that should be discussed is the lack of knowledge about the translation of these mRNAs, if the mRNA that is being detected is the same as the one that is translated. While the authors emphasize the discordance between mRNA and protein localization, it is not possible to know whether these mRNAs are being translated where they are found, e.g. soma vs neuropil. Moreover, there are many examples (e.g. BDNF) where the isoform influences the subcellular localization of the mRNA. There is no way of studying the isoforms here, and we could be looking for a different mRNA isoform localized to a specific compartment compared to the protein. These points must be discussed.

      Minor suggestions:

      • The authors should identify GO terms to understand what types of molecules are subjected to RNA regulation. They provide a supplementary table for all genes, but it would be useful to have a chart showing the proportion of different GO terms represented in the overall gene set, genes that show cell-specific regulation, genes that show neuron vs glia specific regulation, etc.
      • "However, post-transcriptional regulation can also manifest itself within a cell, so that a protein is localised to a distinct site from the mRNA that encodes it". While subcellular RNA localization may represent a regulatory layer, I do not agree that proteins that function in the cell at a different location than their translation site represents regulation per se. Many such cases exist for proteins that are trafficked!
      • "The majority of individual puncta appearing in the dlg1::YFP line (51% in the brain, 64% in larval muscles". Why is the agreement between YFP and endogenous FISH so low? Do many individual RNAs fail to hybridize? This should be discussed.
      • "However, one gene, indy, is highly transcribed in neuroblasts and a single ganglion mother cell before it is rapidly shut off (Figure S1A)". This figure does not exist. Where are the data?
      • The authors should be consistent about calling perineurial or perineural glia (both correct) in their images and text.
      • "We only observe a minority of localised axonal mRNAs that lack the protein they encode at the axon extremities, in contrast to our findings in the mushroom body, optic lobe, and ventral nerve cord neuropils" These results are not contrasted, as in all neuropils the minority of localized mRNAs are those lacking their corresponding proteins. For example, 9% in NMJ vs 7.5% in OL neuropil according to Fig. 1B. What is conflicting with the conclusion?
      • "These results suggest that motor axons are more selective than the other neuronal extensions in the mRNAs that are transported over their very long distances from the soma to the neuromuscular synapse" The current literature says that the same mechanism (cis-elements) is used to transport mRNAs to subcellular compartments, which would be inconsistent with the idea of motor axons being "more selective" than other neurons for the same mRNA, but just a result of fewer mRNAs being found in motor neurons: 34.% of the mRNAs are found in motor neurons soma vs 83% in OL soma, 86.5% in VNC soma, and 70.5% in MB soma. To get to this conclusion, the authors should show that mRNAs previously found in the neuronal extensions of other neurons are not found in the axons of motor neurons but are still expressed in thesir somas. They might want to suggest different RBPs involved in the transport or discussing the very long distance they need to travel which can influence their detection in the tips. Figures
      • Figure 1. Experimental approach summary
        • Some colors do not show well and should be changed, e.g: grey in Fig. 1A, and Fig. 1B probe sites indicated in light blue and pink within the introns of dlg1.
        • Fig. 1E': There appears to be a large discrepancy in co-detection % for CNS and muscle in the graph judging by the size of circles, yet in the text, it is stated that there is average of 51% and 64% in the two, respectively. I don't see any green circles with over 25% agreement in the graph. Are the colors correct here?
        • Fig. 1D-I: It's difficult to identify where the zoomed panels come from. E has its own square (indicating zoom in E'). Please make this square dashed or a different color in E so it is clear F and G do not come from there.
        • Comparing Fig. 1F vs K: Why does there appear to be so much more dlg1 mRNA in the YFP-tag condition? If this is due to selection of imaging area, please choose a more similar region to image so the RNA levels are comparable. Otherwise it indicates the YFP-tag line has more RNA expression, which is likely not the case.
      • Figure 2. Analysis pipeline overview
        • The lines for the first two zoomed panels are switched: The optic lobe is going to VNC and vice-versa.
      • Figure 3. Overall summary of results
        • Figure 3A: Soma/Neuropil/muscle should be separate or at least ordered such that they are next to each other to facilitate direct comparison of genes in the same region of the cell in neurons from different CNS areas. Why are glia not included in this summary? A third color should be used to indicate when there is neither mRNA nor protein expression.
        • "Compiling all the information together shows that there are that 196/200 or 98% of the genes show discordance between RNA and protein expression" However, 5 genes shown in Fig. 3A do not show "discordance": CG9650, cup, Lasb, rg, and vsg!!
      • Figure 4. Neuroblast lineage analysis
        • Is clustering around the NB sufficient to determine lineage relationship? There seems to be other neurons around the NB.
        • More examples should be shown for the post-transcriptional category, as it is the most interesting category, and there are many different possible outcomes. Are there cases of transcriptional control and post-transcriptional regulation? Are there cases where the youngest neurons (closer to the NB) in the progeny are expressing the protein while the oldest are not? If not, could this be an artifact from a slow translation and the protein being detected only after building up in the cell? Top1 protein (Fig. 4D) seems to be less expressed in the youngest neurons.
        • "The transcription rate of these genes, as indicated by the relative intensity of smFISH nuclear transcription foci, is similar across the neuroblast lineage, however protein signal is only detectable in a minority of the progeny cells (Figure 4E)". Many nuclei lack clear large spots, but have small spots indicative of RNA; how is this interpreted? Do they lack transcription, or is this due failure of the smFISH to capture all transcription sites? Were transcripts actually counted to assess cell-specific differences? This should be possible with smFISH
      • Figure 5. RNA synaptic localization
        • A have global analysis comparison of all neuropil areas would be welcome in this figure.
        • "Surprisingly, another 59 transcripts are present at synapses without detectable levels of protein (Figure 5E-H)" This text does not correspond to Fig 5E-H but 5I-L. Where is the text about 5E-H?
        • For Fig. 5J and 5N RNA appears scattered regularly throughout the entire panel area. How sure are the authors that this is not due to poor signal/noise? For example, perhaps too much probe being used for these targets.
        • Fig. 5R is not cited in the text.
      • Figure 6. RNA localization in glia
        • For Fig. 6B-G it is hard to tell if there is any overlap of the RNA and Glia. Maybe show multiple zoomed-in merged images and/or highlight the structures with lines that are present in all panels.
        • For Fig. 6L-O: How reproducible is this small amount of RNA puncta in the NMJ glia? Is this possibly biologically important?
        • Why do cartoons labelling subnuclear/perinuclear glia in Fig.6 and Fig.S6 show different localization?
        • The cartoons seem to extrapolate from the data: While in Fig 6B-D, we see neither the big bright spot of transcription in the glial nucleus nor as many transcripts in the neuropil, they are both present in the cartoon. In Fig. 6E-G there is no indication of cortical glia soma nor the transcription spot only in glia nuclei.
        • "To assess glial localisation for the 200 genes of interest, we used a pan-glial gal4 driving a membrane mCherry marker (repo-GAL4>UAS-mcd8-mCherry) to learn the expression pattern of all glial cells, and then classified the pattern in the YFP lines (without the marker) based on knowledge of that expression pattern. We validated this approach by combining the RFP marker" Did the authors use mCherry or RFP for these experiments? Also, the previous sentence is redundant.
      • Figure 7. RNA localization at neuromuscular synapse
        • RNA for these genes seems far too spread throughout the muscle to draw any conclusions
        • Also with so many RNAs distributed in the muscle, specific localization of RNA molecule to the precise PSD would have no conceivable benefit
        • I suggest drawing lines around the protein expression to facilitate visualization of the mRNA localization for panels B, F and J. It is especially hard to conclude anything from panels B and F.
        • Light grey with white dots is hard to see in the cartoons
      • Figure 8. Role of khc and activity in sgg localization
        • Presumably there is a huge number of developmental problems associated with this mutant that could cause decrease in sgg localization
        • If the authors include this, then they should characterize the mutant NMJs: what is the change in size, synapse number, etc..
        • Is there more sgg accumulated in soma as a result of less transport? Is sgg being expressed at the same level?
        • Fig. 8F-H: Why is Dlg1 accumulated in the entire axon, not just the presume synapse?
        • Fig. 8J: Why is sgg signal occurring in circles disconnected from the main axon? The authors should show a different image

      Significance

      This is a significant and complex paper that contributes with novel tools to an important issue

    1. SciScore for 10.1101/2022.05.22.492693: (What is this?)

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

      Table 1: Rigor

      NIH rigor criteria are not applicable to paper type.

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After blocking with 3% albumin (Sigma-Aldrich) and primary antibody incubation RAGE (ab3611, Abcam), ACE2 (XXX), ADAM17 (ab2051, Abcam)), TMPRSS2 (ab109131, Abcam), the membranes were incubated with an anti-rabbit peroxidase-conjugated secondary antibody (GE healthcare).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>ACE2</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>ADAM17</div><div>suggested: (Abcam Cat# ab2051, RRID:AB_302796)</div></div><div style="margin-bottom:8px"><div>TMPRSS2</div><div>suggested: (Abcam Cat# ab109131, RRID:AB_10863728)</div></div><div style="margin-bottom:8px"><div>anti-rabbit peroxidase-conjugated secondary</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">500 μg of protein lysate was incubated with Anti-6X His tag® antibody [HIS.H8] (ab18184, Abcam) overnight at 4°C, anti-Mouse IgG (Invitrogen) was used as isotype control.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Anti-6X</div><div>suggested: (Abcam Cat# ab18184, RRID:AB_444306)</div></div><div style="margin-bottom:8px"><div>anti-Mouse IgG</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">P4417-100TAB-Sigma-Aldrich) plus 1% Bovine Serum Albumin (BSA) (Cat.A9647-500G-Sigma-Aldrich) and 0,02% NP-40 alternative (Cat.492016-100ML) for 1h at room temperature prior to overnight incubation at 4°C with primary antibody 1:100 (6xHisTag clone#HIS.H8 Cat.ab18184-Abcam or SARS-CoV-2 spike polyclonal antibody, GeneTex).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>6xHisTag</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cells were collected and stained using primary RAGE antibody 1:100 (PA5-24787, Thermo Scientific) for FACS analysis.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>RAGE</div><div>suggested: (Thermo Fisher Scientific Cat# PA5-24787, RRID:AB_2542287)</div></div><div style="margin-bottom:8px"><div>PA5-24787</div><div>suggested: (Thermo Fisher Scientific Cat# PA5-24787, RRID:AB_2542287)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">1 × 105 THP-1 cells were seeded on a 24-well plate in their culture medium.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>THP-1</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">THP1 and Monocytes infection with SARS-CoV-2: THP1 cells were plated at 5×105 cell/ml in 48-well plates in 200 μl of RPMI-1640 supplemented with 1% fetal bovine serum (FBS) (Euroclone).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>THP1</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cell culture supernatants were collected 24, 48, 72 and 144 h post-infection and stored at – 80°C until the determination of the viral titers by a plaque-forming assay in Vero cells.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero</div><div>suggested: CLS Cat# 605372/p622_VERO, RRID:CVCL_0059)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The following day, cells were pretreated or not with 2μM Azeliragon (Cat.S6415-Selleckchem) for 30 minutes before adding 100 ng/mL of Sars-CoV-2 spike protein (RBD, HisTag) (Cat. ZO3483-1-GenScript) or infected using Heat-inactivated SARS-CoV-2 (VR-1986HK, ATCC) at 4 TCID50/mL for 2h at 37°C 5%CO2.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>VR-1986HK</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Sequencing: Different library types were pooled at different ratios based on their targeted reads per cell and the nanomolarity of the library pools was confirmed using the Agilent Bioanalyzer 2100.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Agilent Bioanalyzer</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Separately for the two selected categories of disease severity (mild vs severe/critical), the pseudo-bulk counts were then fitted with a generalised linear model using the EdgeR package, to identify those genes characterised by a well-defined decreasing or increasing trend of the expression over the sample time-points.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>EdgeR</div><div>suggested: (edgeR, RRID:SCR_012802)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Among these sets, the GO:0050786 genelist was then expanded using the Cytoscape ‘stringApp’ (81) in order to identify among the nearest neighbours with confidence score > 0.7 the ones showing the highest absolute FC values in Myeloid cells.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Cytoscape</div><div>suggested: (Cytoscape, RRID:SCR_003032)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The GSEA has been done with the clusterProfiler library (82, 83), using gene lists ranked by the FDR of the differential analysis and the sign of the logFC.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GSEA</div><div>suggested: (SeqGSEA, RRID:SCR_005724)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The gating strategy and the relative analysis were performed with FlowJo software.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>FlowJo</div><div>suggested: (FlowJo, RRID:SCR_008520)</div></div></td></tr></table>

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

      Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


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      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.

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    1. SciScore for 10.1101/2022.05.21.492554: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">Consent: A blood sample was taken following consent at least 14 days after symptom onset.<br>IRB: Sera from Beta, Gamma and Delta and BA.1 infected cases: Beta and Delta samples from UK infected cases were collected under the “Innate and adaptive immunity against SARS-CoV-2 in healthcare worker family and household members” protocol affiliated to the Gastro-intestinal illness in Oxford: COVID sub study discussed above and approved by the University of Oxford Central University Research Ethics Committee.<br>Field Sample Permit: The study was approved by the Human Research Ethics Committee of the University of the Witwatersrand (reference number 200313) and conducted in accordance with Good Clinical Practice guidelines.<br>IACUC: Gamma samples were provided by the International Reference Laboratory for Coronavirus at FIOCRUZ (WHO) as part of the national surveillance for coronavirus and had the approval of the FIOCRUZ ethical committee (CEP 4.128.241) to continuously receive and analyse samples of COVID-19 suspected cases for virological surveillance.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">The mean age of vaccinees was 37 years (range 22-66), 21 male and 35 female.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">AstraZeneca-Oxford vaccine study procedures and sample processing: Full details of the randomized controlled trial of ChAdOx1 nCoV-19 (AZD1222), were previously published (PMID: 33220855/PMID: 32702298).</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">EXPERIMENTAL MODEL AND SUBJECT DETAILS: Bacterial Strains and Cell Culture: Vero (ATCC CCL-81) and VeroE6/TMPRSS2 cells were cultured at 37 °C in Dulbecco’s Modified Eagle medium (DMEM) high glucose (Sigma-Aldrich) supplemented with 10% fetal bovine serum (FBS), 2 mM GlutaMAX (Gibco, 35050061) and 100rnU/ml of penicillin– streptomycin.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>VeroE6/TMPRSS2</div><div>suggested: JCRB Cat# JCRB1819, RRID:CVCL_YQ49)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">HEK293T (ATCC CRL- 11268) cells were cultured in DMEM high glucose (Sigma-Aldrich) supplemented with 10% FBS, 1% 100X Mem Neaa (Gibco) and 1% 100X L-Glutamine (Gibco) at 37 °C with 5% CO2.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293T</div><div>suggested: ATCC Cat# CRL-11268, RRID:CVCL_1926)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The resulting S gene-carrying pcDNA3.1 was used for generating pseudoviral particles together with the lentiviral packaging vector and transfer vector encoding luciferase reporter.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pcDNA3.1</div><div>suggested: RRID:Addgene_79663)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The gene fragment was amplified with pNeoRBD333Omi_F (5’- GGTTGCGTAGCTGAAACCGGTCATCACCATCACCATCACACCAATCTGTGCCCTTTCGAC-3’) and pNeoRBD333_R (5’-GTGATGGTGGTGCTTGGTACCTTATTACTTCTTGCCGCACACGGTAGC-3’), and cloned into the pNeo vector (Supasa et al., 2021).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pNeo</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">To generate the BA.4/5 RBD construct containing a BAP- His tag, the gene fragment was amplified with RBD333_F (5’- GCGTAGCTGAAACCGGCACCAATCTGTGCCCTTTCGAC-3’) and RBD333_BAP_R (5’-GTCATTCAGCAAGCTCTTCTTGCCGCACACGGTAGC-3’), and cloned into the pOPINTTGneo-BAP vector (Huo et al., 2020a).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pOPINTTGneo-BAP</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">To express biotinylated RBDs, the RBD-BAP plasmid was co-transfected with pDisplay-BirA-ER (Addgene plasmid 20856; coding for an ER-localized biotin ligase), in the presence of 0.8 mM D-biotin (Sigma-Aldrich).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>RBD-BAP</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pDisplay-BirA-ER</div><div>suggested: RRID:Addgene_20856)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The sensorgrams were plotted using Prism9 (GraphPad).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The percentage reduction was calculated and IC50 determined using the probit program from the SPSS package.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>SPSS</div><div>suggested: (SPSS, RRID:SCR_002865)</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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">NCT04324606</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">A Study of a Candidate COVID-19 Vaccine (COV001)</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04400838</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">Investigating a 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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. SciScore for 10.1101/2022.05.23.493121: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">IRB: The study was performed in compliance with all relevant ethical regulations and the study protocols were approved by the Ethical Committee of the Canton Ticino (ECCT): CE-3428 and CE-3960.<br>Consent: Written informed consent was obtained from all participants, and all samples were coded to remove identifiers at the time of blood withdrawal.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">All assays were done in triplicate, and for each well the migrated cells were counted at 100-fold magnification in 5 randomly selected high-power fields (5HPF).</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Chemotaxis was performed with preB 300.19 expressing CCR2, at a final IgG concentration of 200 µg/ml, in the presence of the chemokine concentration resulting in peak migration when no antibodies were added (CCL2 [10nM], CCL7 [100nM], CCL8 [100nM]).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>CCR2</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>CCL2</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>CCL7</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>CCL8</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Plates were subsequently washed 4 times with washing buffer and incubated with anti-human IgG secondary antibody conjugated to horseradish peroxidase (HRP) (GE Healthcare, NA933) at a 1:5000 dilution in PBS + 0.05% Tween-20.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-human IgG</div><div>suggested: (GE Healthcare Cat# NA933-1ml, RRID:AB_772208)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Kinetic of signature anti-chemokine IgG antibodies (fig.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-chemokine IgG</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Single cell sorting by flow cytometry: B cells were enriched from PBMCs of uninfected controls or of COVID-19 convalescent individuals 6 months after COVID-19 (participant CLM9 for anti-CCL8 antibodies; CLM64 for anti-CCL20 antibodies; CLM5, CLM7 and CLM33 for anti-CXCL13 antibodies; and CLM8 and CLM30 for anti-CXCL16 antibodies), using the pan-B-cell isolation kit according to manufacturer’s instructions (Miltenyi Biotec, 130-101-638).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-CCL20</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-CXCL13</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-CXCL16</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The enriched B cells were subsequently stained in FACS buffer (PBS + 2% FCS + 1mM EDTA) with the following antibodies/reagents (all 1:200 diluted) for 30 min on ice: anti-CD20-PE-Cy7 (BD Biosciences, 335828), anti-CD14-APC-eFluor 780 (Thermo Fischer Scientific, 47-0149-42), anti-CD16-APC-eFluor 780 (Thermo Fischer Scientific, 47-0168-41), anti-CD3-APC-eFluor 780 (Thermo Fischer Scientific, 47-0037-41), anti-CD8-APC-eFluor 780 (Invitrogen, 47-0086-42)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-CD20-PE-Cy7</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-CD14-APC-eFluor 780</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-CD16-APC-eFluor 780</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-CD3-APC-eFluor 780</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-CD8-APC-eFluor 780</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Amplicons from this first PCR reaction served as templates for sequence and ligation independent cloning (SLIC) into human IgG1 antibody expression vectors.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>human IgG1</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Where indicated, the anti-Zika virus monoclonal antibody Z021 (75) was used as an isotype control.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-Zika</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">SARS-CoV-2 pseudotyped reporter virus and neutralization assay: To generate (HIV-1/NanoLuc2AEGFP)-SARS-CoV-2 particles, HEK293T cells were co- transfected with the three plasmids pHIVNLGagPol, pCCNanoLuc2AEGFP, and SARS- CoV- 2 S as described elsewhere (47, 80)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293T</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Briefly, threefold serially diluted plasma samples (from 1:50 to 1:328’050) were incubated with SARS-CoV- 2 pseudotyped virus for 1h at 37 °C, and the virus-plasma mixture was subsequently incubated with 293TACE2 cells for 48 h.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>293TACE2</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">SARS-CoV-2 pseudotyped reporter virus and neutralization assay: To generate (HIV-1/NanoLuc2AEGFP)-SARS-CoV-2 particles, HEK293T cells were co- transfected with the three plasmids pHIVNLGagPol, pCCNanoLuc2AEGFP, and SARS- CoV- 2 S as described elsewhere (47, 80)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pHIVNLGagPol</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pCCNanoLuc2AEGFP</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">S5E): Experiments were performed with plasma samples assayed at a 1:50 dilution side-by-side on the same plate, and the average optical density at 450 nm obtained from two independent experiments was plotted with GraphPad Prism.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Live single Zombie-NIR−CD14−CD16−CD3−CD8−CD20+Ova−N-loop-PE+N-loop- AF647+ B cells were single-cell sorted into 96-well plates containing 4 μl of lysis buffer (0.5× PBS, 10 mM DTT, 3,000 units/ml RNasin Ribonuclease Inhibitors [Promega, N2615]) per well using a FACS Aria III, and the analysis was performed with FlowJo software.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>FlowJo</div><div>suggested: (FlowJo, RRID:SCR_008520)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">1 was generated from the structure of inactive CCR2 (PDB code: 5T1A) (81), together with the electron microscopy structures of CCR5 and CCR6 (PDB codes: 6MEO and 6WWZ, respectively (82, 83) by using SWISS-MODEL (84) server and the molecular graphics program PyMOL 2.5.0 for modeling the N- and C-terminus of the receptor.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>PyMOL</div><div>suggested: (PyMOL, RRID:SCR_000305)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">t-SNE: t-SNE analysis was performed using the Rtsne R package v 0.15 (https://CRAN.R-project.org/package=Rtsne) using the AUC values for all chemokines.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Rtsne</div><div>suggested: (Rtsne, RRID:SCR_016342)</div></div></td></tr></table>

      Results from OddPub: Thank you for sharing your code.

      Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. SciScore for 10.1101/2022.05.21.492923: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">IACUC: Immunization and challenge of rhesus macaques: All animal studies were approved by the NIAID Animal Care and Use Committee.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">Eight juvenile to young adult male Indian-origin rhesus macaques (Macaca mulatta), confirmed to be seronegative for HPIV3 and SARS-CoV-2, were immunized intranasally (0.5 ml per nostril) and intratracheally (1 ml) with a total does of 6.3 log10 plaque-forming units (PFU) of B/HPIV3/S-6P or the empty vector control B/HPIV3.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Infected monolayers were overlaid with culture medium containing 0.8% methylcellulose, and incubated at 32°C for 6 days, fixed with 80% methanol, and immunostained with a rabbit hyperimmune serum raised against purified HPIV3 virions to detect B/HPIV3 antigens, and a goat hyperimmune serum to the secreted SARS-CoV-2 S to detect co-expression of the S protein, followed by infrared-dye conjugated donkey anti-rabbit IRDye680 IgG and donkey anti-goat IRDye800 IgG secondary antibodies (LiCor).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-rabbit IRDye680 IgG</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-goat</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Dissociation-enhanced lanthanide fluorescent (DELFIA) time resolved fluorescence (TRF) immunoassay, ELISA and live HPIV3 and SARS-CoV-2 neutralization assay: Levels of anti-SARS-CoV-2 S antibodies elicited by B/HPIV3/S-6P were determined by DELFIA-TRF (Perkin Elmer) from NW or BAL following the supplier’s protocol and from serum samples by ELISA (Liu et al., 2021) using the recombinantly-expressed secreted version of S-2P (Wrapp et al., 2020), or a fragment (aa 328-531) containing the receptor binding domain (RBD) of the SARS-CoV-2 S protein (Walls et al., 2020).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-SARS-CoV-2 S</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The secondary antibodies used in both assays were goat anti-monkey IgG(H+L) horseradish peroxidase (HRP) (Thermo Fisher, Cat #PA1-84631)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-monkey IgG(H+L</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The antibodies used for extracellular and intracellular staining were: CD69 (FITC, clone FN50, Biolegend)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>CD69</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">African green monkey kidney Vero (ATCC CCL-81) and Vero E6 (ATCC CRL-1586) cells were cultured in Dulbecco’s MEM with GlutaMAX (Thermo Fisher Scientific) with 5% FBS and 1% L-glutamine.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero E6</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The B/HPIV3/S-6P cDNA was used to transfect BHK21 cells (clone BSR T7/5, stably expressing T7 RNA polymerase (Buchholz et al., 1999)), together with helper plasmids encoding the N, P and L proteins (Buchholz et al., 2004; Liu et al., 2021), to produce the B/HPIV3/S-6P recombinant virus.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>BHK21</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Briefly, Vero cell monolayers in 24-well plates were infected in duplicate with 10-fold serially diluted samples.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">, luciferase reporter (pHR’ CMV Luc), lentivirus backbone (pCMV ΔR8.2), and human transmembrane protease serine 2 (TMPRSS2) at a ratio of 1:20:20:0.3 into HEK293T/17 cells (ATCC) with transfection reagent LiFect293™.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCMV ΔR8.2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Standard curves were generated using serially diluted pcDNA3.1 plasmids encoding gN, gE, or sgE sequences.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pcDNA3.1</div><div>suggested: RRID:Addgene_79663)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">IC50 titers were determined using a log (agonist) vs. normalized response (variable slope) nonlinear function in Prism v8 (GraphPad).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Data were analyzed using FlowJo version 10.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>FlowJo</div><div>suggested: (FlowJo, RRID:SCR_008520)</div></div></td></tr></table>

      Results from OddPub: Thank you for sharing your data.

      Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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">NCT04816643</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">A Phase 1/2/3 Study to Evaluate the Safety, Tolerability, an…</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT00686075</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">A Study to Evaluate the Safety, Tolerability, Immunogenicity…</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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. SciScore for 10.1101/2022.05.21.492920: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">IACUC: , Radiation Safety, and Animal Care and Use Committees.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">Male golden Syrian hamsters (7 to 8 weeks of age) were purchased from Envigo (Haslett, MI).</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The wells were washed and then incubated with rabbit anti-ERα (1:2000, 1 h, RT) and horseradish-conjugated secondary antibody (1:2000, 1 h, RT) that were provided with the kit.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-ERα</div><div>suggested: (Santa Cruz Biotechnology Cat# sc-542, RRID:AB_631470)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The cells were then incubated at 4°C overnight with 2 μg/ml each of anti-ERα(H222) rat IgG1 monoclonal antibody (mAb) (Santa Cruz Biotech, sc-5349, 1:100) and HA-probe (</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-ERα(H222</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>rat IgG1</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Afterwards, the cells were washed 4 times with PBS + 0.1% Tween-20 (PBS-T) for 5 minutes and incubated at room temperature for 1 hour in the dark with a fluorescent secondary antibody mixture contaning mouse IgGk BP-CFL594 (Santa Cruz Biotech, sc-516178, 1:100) and anti-rat IgG AF488 (ThermoFisher Scientific, cat no. A-11006, 1:500).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-rat IgG</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Then, cells were washed and were incubated with detector anti-BrdU antibody for 1 hour at RT.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-BrdU</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After the incubation cells were washed and incubated with the horseradish peroxidase conjugated goat anti-mouse antibody for 30 minutes at RT.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-mouse</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Briefly, 50 μl of standard were added to standard wells and 40 μl of sample-to-sample wells and then added 10 μl of anti-TRAP antibody to sample wells and 50 μl of streptavidin-HRP to sample wells and standard wells.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-TRAP</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After 72 hours, cells were washed, fixed with 4% formaldehyde, permeabilized with 0.1% Triton X-100 in PBS and stained overnight at 4°C with ACE2 protein-specific antibody (Abcam Ab15348).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>ACE2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cells were then incubated with anti-rabbit secondary antibody (Alexa Fluor 536 anti-rabbit, Invitrogen Life Technologies) for 1 hour at 37°C.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-rabbit</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Sections were then incubated with cocktails of primary antibodies: rabbit anti-SARS-CoV-2 Spike Protein (1:100, Invitrogen, #MA5-36087) + rat anti-ERα H222 (1:100, Santa Cruz Biotechnology, #sc53492) overnight at 4°C.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-SARS-CoV-2 Spike Protein ( 1:100 , Invitrogen , #MA5-36087 )</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Sections were then incubated with the primary antibodies rat anti-ERα H222(1:100, Santa Cruz Biotechnology, #sc53492), diluted in 1% normal goat serum (NGS), 4% BSA, 0.02% saponin in PB at 4°C overnight.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-ERα H222</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Sections were rinsed and incubated overnight at 4°C in the secondary antibody Nanogold-Fab’ goat anti-rat-IgG (1:100</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-rat-IgG</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">On day one, slides were blocked with a peroxidase blocker (Bio SB Catalog No. BSB 0054), washed with an immunoDNA washer buffer (Bio SB, Catalog No. BSB 0150); then, incubated with 0.2 μg/mL of anti-SARS-CoV-2 spike glycoprotein antibody (abcam, Catalog No. ab272504) for 1 hour.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-SARS-CoV-2 spike glycoprotein</div><div>suggested: (Abcam Cat# ab272504, RRID:AB_2847845)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Briefly, MCF-7 nuclear extracts (5 μg; ab14860, Abcam) were treated with either S (0.01-300 nM; Acro Biosystems)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>MCF-7</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Proliferation assays: MCF-7 and MDA-MB-23 cells were obtained from ATCC and growth in DMEM without phenol red, supplemented with 10% fetal bovine serum (FBS), penicillin/streptomycin at 37 °C in a 5% CO2 and 95% humidified atmosphere.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>MDA-MB-23</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">TRAP activity by ELISA assay in RAW-OCs: RAW264.7 (murine macrophages ATCC, USA) were cultured as manufacturer’s protocol.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>RAW264.7</div><div>suggested: CLS Cat# 400319/p462_RAW-2647, RRID:CVCL_0493)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">ACE2 expression in Calu-3 cells: Calu-3 cell line was obtained from ATCC and maintained in Eagle’s Minimum Essential Medium(EMEM; Lonza) supplemented with 10% fetal bovine serum (FBS), 1% L-glutamine and 1% penicillin/streptomycin solution at 37°C in a humidified atmosphere of 5% CO2.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Calu-3</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The next day, cells in each well were transfected with 1.5 μl of ViaFect reagent (Promega, cat no. E498A) and 0.5 μg of empty pcDNA3.1 vector, or an expression vector for the wild-type (WT) SARS-CoV2 S with a C-terminal hemagglutinin (HA) epitope tag (pBOB-CAG-SARS-CoV2-S-HA) or the double mutant (R682S,R685S) SARS-CoV2 S with a C-terminal flag epitope tag (pCAGGS-SARS2-S-FKO).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pcDNA3.1</div><div>suggested: RRID:Addgene_79663)</div></div><div style="margin-bottom:8px"><div>pBOB-CAG-SARS-CoV2-S-HA</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">pBOB-CAG-SARS-CoV2-S-HA was a gift from Gerald Pao (Addgene plasmid # 141347; http://n2t.net/addgene:141347; RRID:Addgene_141347).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div></div><div>detected: RRID:Addgene_141347)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">pCAGGS-SARS2-S-FKO (C-flag) was a gift from Hyeryun Choe & Michael Farzan (Addgene plasmid # 159364; http://n2t.net/addgene:159364; RRID:Addgene_159364).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div></div><div>detected: RRID:Addgene_159364)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Data were fitted using the non-linear curve fitting routines in Prism® (Graphpad Software Inc).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Graphpad</div><div>suggested: (GraphPad, RRID:SCR_000306)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The digitized images were also analyzed using ProtoArray Prospector v5.2 and potential hits were identified using the software’s algorithm.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>ProtoArray Prospector</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Protein binding responses were analyzed using BiaEval software.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>BiaEval</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Interactome analysis: The STRING database52, that integrates all known and predicted associations between proteins, including both physical interactions as well as functional associations has been used to analyses functional associations between biomolecules.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>STRING</div><div>suggested: (STRING, RRID:SCR_005223)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Images were prepared for presentation using ImageJ v.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>ImageJ</div><div>suggested: (ImageJ, RRID:SCR_003070)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After three washes, the Mouse/Rabbit PolyDetector Plus link &HRP label (Bio SB, Catalog No. BSB 0270) were applied.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Mouse/Rabbit PolyDetector Plus</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>PolyDetector</div><div>suggested: None</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. SciScore for 10.1101/2022.05.23.492800: (What is this?)

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

      Table 1: Rigor

      NIH rigor criteria are not applicable to paper type.

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For the other three donors (Leu163, Leu158. Leu184), naive and effector/memory CD8+ T cells were isolated by Fluorescence-activated Cell Sorting (FACS) upon staining with anti-CD8 antibody (344710 BioLegend), anti-CCR7 antibody (353227 BioLegend) and anti-CD45RA antibody (304108 BioLegend) for 30 min at 4°C.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Leu184</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-CD8</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-CCR7</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-CD45RA</div><div>suggested: (BioLegend Cat# 304108, RRID:AB_314412)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">) fluorophore-conjugated anti-human antibodies.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-human antibodies.</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For TCR transfection, 1×106 Jurkat cells were co-electroporated with 3 μg of each TCR chain using a Neon Transfection System 100μl kit (Thermo Fisher Scientific) with the following parameters: 1325V, 10ms, 3 pulses.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Jurkat</div><div>suggested: None</div></div></td></tr></table>

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

      Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. SciScore for 10.1101/2022.05.18.22275283: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">Consent: All individuals or their legal guardian gave informed consent prior to enrollment.<br>IRB: This study was overseen and approved by the MassGeneralBrigham Institutional Review Board (</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Primary natural killer cells for the antibody-dependent natural killer activation (ADNKA) assays were isolated from buffy coats from health donors using the RosetteSep NK cell enrichment kit (STEMCELL Technologies).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>antibody-dependent natural killer activation ( ADNKA</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Antibody Isotype and Fc Receptor Binding: Antigen-specific antibody isotype, subclass, and Fc receptor binding profiles were analyzed using a custom multiplex Luminex assay as previously described 35.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Antigen-specific antibody isotype , subclass ,</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Following overnight incubation, non-specific antibodies were washed off and the immune complexes were incubated with Ig isotypes or subclasses with a 1:100 diluted PE-conjugated secondary antibody for IgG1 (clone: HP6001), IgG2 (clone: 31-7-4), IgG3 (clone: HP6050), IgG4 (clone: HP6025), IgM (clone: SA-DA4), IgA1 (clone: B3506B4), or IgA2 (clone: A9604D2) (all Southern Biotech).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>IgG1</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>IgG2</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>IgG3</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>IgG4</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>IgA1</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>IgA2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Following incubation, excessive antibodies were washed off and relative antigen-specific antibody levels were determined on an iQue analyzer (Intellicyt)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>antigen-specific</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">C3 deposited on beads were stained with anti-guinea pig C3-FITC antibody (MP Biomedicals, 1:100</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-guinea pig C3-FITC</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Antibody-Dependent Neutrophil Phagocytosis (ADNP) Assays: ADNP assays were performed as previously described 37.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Antibody-Dependent Neutrophil Phagocytosis ( ADNP</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cell Lines and Primary Cells: THP-1 cells, a human leukemia monocyte cell line, were maintained in RPMI-1640 Medium (Sigma-Aldrich) with 10% fetal bovine serum (FBS, Sigma-Aldrich), 1% L-glutamine (Corning), 2% of 5000 IU/mL of Penicillin and 5,000 ug/ml of Streptomycin Solution (Pen-Strep, Corning), 1% of 1M HEPES (Corning), and 5mM 2-Mercaptoethanol (Gibco).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>THP-1</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Human embryonic kidney (HEK) 293T cells expressing ACE2 for neutralization assays were maintained in Dulbecco’s Modified Eagle’s Medium (DMEM, Corning) with 10% FBS (VWR) and 1% Pen-Strep (Corning) and incubated in 37°C with 5% CO2.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>293T</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Three-fold serial dilutions of plasma samples starting at 1:12 or 1:30 were performed before adding pseudovirus expressing either SARS-CoV-2 wild-type spike or omicron variant spike to HEK293T expressing ACE-2 cells for 1 hour.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293T</div><div>suggested: RRID:CVCL_HA71)</div></div><div style="margin-bottom:8px"><div>ACE-2</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Primary neutrophils for antibody-dependent neutrophil phagocytosis (ADNP) assays were isolated from whole blood from healthy donors using Ammonium-Chloride-Potassium (ACK) Lysing Buffer (Quality Biological) followed by centrifugation and multiple wash steps prior to assay usage.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Quality Biological</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Data Analysis and Statistics: Data analysis was performed on GraphPad Prism (v.9.3) and RStudio (v.1.3).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. SciScore for 10.1101/2022.05.20.492779: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">Field Sample Permit: Animal research was carried out under a United Kingdom Home Office License, P48DAD9B4.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Stably transduced ACE2-expressing 293T cells were produced as previously described (32), and maintained with the addition of 1 μg/ml puromycin to growth medium.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>293T</div><div>suggested: KCB Cat# KCB 200744YJ, RRID:CVCL_0063)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">All viral stocks used in this study were grown in the VeroE6-ACE2-TMPRSS2 cells.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>VeroE6-ACE2-TMPRSS2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Viral particles were produced in a 10 cm dish seeded the day prior with 5×106 HEK293T/17 cells in 10 ml of complete Dulbecco’s Modified Eagle’s Medium (DMEM-C, 10% FBS and 1% Pen/Strep) containing 10% (vol/vol)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293T/17</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Next, Hela cells stably expressing the ACE2 receptor were added (10,000 cells/25μL per well) and the plates were left for 72 hours.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Hela</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Statistical analysis: Statistical analysis was performed using Graphpad Prism.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Graphpad</div><div>suggested: (GraphPad, RRID:SCR_000306)</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. SciScore for 10.1101/2022.05.20.492832: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">Contamination: All the cell lines were cultured at 37°C under a 5% CO2 atmosphere, and were routinely screened for mycoplasma.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The membrane was blocked with 5% dried milk in PBS Tween 0.1 %, before incubation with three primary antibodies (S1, S2, and p24 Gag or actin) for 1h at room temperature (RT), followed by 3 washes in PBS Tween 0.1 %, and incubation with secondary antibodies for 30 min at RT.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>S1</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>S2</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>Gag or actin</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Primary antibodies consisted in two anti-spike antibodies (rabbit anti-S1 Genetex #GTX135356, 1:1000 and mouse anti-S2 Genetex #GTX632604, 1:1000), and one normalization antibody: mouse anti-p24 Gag (R&D Systems # MAB7360; 1:1000) or mouse anti-actin (Cell Signaling #8H10D10, 1:2000).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-spike</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-p24</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-actin (Cell Signaling #8H10D10</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Anti-rabbit and anti-mouse IgG secondary antibodies, conjugated to DyLight-800 (Bethyl Laboratories, #A80-304D8) and DyLight-680 (ThermoFisher, #SA5-35521), respectively, were used at a 1:5000 dilution each.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Anti-rabbit</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-mouse IgG</div><div>suggested: (Thermo Fisher Scientific Cat# SA5-35521, RRID:AB_2556774)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After two PBS washes, cells were stained with a Goat anti-human AF647 antibody (ThermoFisher, #A21445) at 1:500 for 30 min at 4°C.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-human AF647</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">To evaluate ACE2 and TMPRSS2 expression in the target cell lines used, cells were surface-labelled with the goat anti-ACE2 antibody (R&D #AF933) at 5 mg/mL for 30 min at 4°C.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-ACE2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After washing, cells were incubated with the secondary antibody donkey anti-goat-AF647 (ThermoFisher #A21447) at a 1:500 dilution.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-goat-AF647</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cells were intracellularly stained with a rabbit anti-TMPRSS2 antibody (Atlas antibodies, HPA035787) at 0.3 mg/mL.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-TMPRSS2</div><div>suggested: (Sigma-Aldrich Cat# HPA035787, RRID:AB_2674782)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After washing, the secondary staining was done with a donkey anti-rabbit-AF555 antibody (Fisher #16229260) at a 1:500 dilution.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-rabbit-AF555</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cell lines: HEK 293Tn (purchased from SBI Biosciences) and Calu-3 (ATCC</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Calu-3</div><div>suggested: KCLB Cat# 30055, RRID:CVCL_0609)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">HEK 293T-hACE2-TMPRSS2 cells (called herein HEK-ACE2-TMPRSS2) were induced for TPMRSS2 expression by addition of doxycycline (0.5 μg/mL, Sigma) and were maintained in DMEMc with blasticidin (10 μg/mL, InvivoGen) and puromycin (1 μg/mL, Alfa Aesar).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK 293T-hACE2-TMPRSS2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">U2OS cells expressing hACE2 and GFP1-10 or hACE2, TMPRSS2, and GFP1-10 were maintained in DMEMc supplemented with blasticidin (10 μg/mL, InvivoGen), puromycin (1 μg/mL</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>U2OS</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">HEK 293T and Vero-E6 cells expressing GFP1-10 and GFP11 were maintained in DMEMc supplemented with 1 μg/mL and 4 μg/mL of puromycin, respectively.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK 293T</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>Vero-E6</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Production of spike-pseudotyped lentivectors: GFP lentiviral particles pseudotyped with the SARS-CoV-2 spike were prepared by transfection of HEK 293Tn cells using the CaCl2 method.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK 293Tn</div><div>suggested: RRID:CVCL_UL49)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Infection with spike-pseudotyped lentivectors: The day before infection with spike-pseudotyped GFP-lentivectors, 100,000 HEK-ACE2-TMPRSS2 cells were plated in 96-well plates, and TMPRSS2 was induced when needed by the addition of doxycycline.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK-ACE2-TMPRSS2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">HEK and U2OS were infected with 0.125 μg of p24 Gag equivalent, while Calu-3 were infected with 1 μg of p24 Gag equivalent, all in a final volume of 100 μL.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For infection, 50 μL of medium containing 0.065 μg of p24 Gag equivalent was added onto HEK ACE2 +/− TMPRSS2 and U2OS ACE2 +/− TMPRSS2, while 0.5 μg of p24 Gag equivalent in 50 μl added onto Calu-3 cells, resulting in a 2x dilution of the protease inhibitors.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK ACE2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After 30 min, transfected cells were washed in DMEMna and spun at 500 g for 5 min before being mixed at a 1:1 ratio with Vero GFP11 cells in DMEMna.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero GFP11</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Plasmids: All the spike mutations were inserted into a codon-optimized version of the Wuhan-Hu-1 SARS-CoV-2 spike (GenBank: QHD43416.1) cloned into a phCMV backbone ( GenBank: AJ318514).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>phCMV</div><div>suggested: RRID:Addgene_15802)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Plasmids used to produce GFP-lentiviruses were the lentivector backbone pCDH-EF1α-GFP (System Biosciences), the packaging plasmid psPAXII (Addgene), and the pRev plasmid (a gift from P. Charneau).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCDH-EF1α-GFP</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>psPAXII</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pRev</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Transfection of HEK 293Tn cells with spike vectors: Transfection was performed with Lipofectamine 2000 (ThermoFisher, #11668019), using 125 ng of phCMV-Spike plasmid diluted in OptiMem medium in a final volume of 25 μL.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>phCMV-Spike</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The lentiviral vector pCDH-EF1a-GFP, the packaging plasmid psPAXII, the spike expression vector phCMV-Spike and the pRev plasmid were mixed at a 2:2:1:1 ratio, with a total DNA amount of 252 μg used per 175 cm2 flask.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCDH-EF1a-GFP</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The pQCXIP-empty plasmid was used to generate a spike-negative control.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pQCXIP-empty</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Luciferase lentiviral particles were produced according to the same protocol, but with different plasmid ratios: the lentiviral backbone pHAGE-CMV-Luc2-IRES-ZsGreen, the packaging plasmid pHDM-Hgpm2, the Tat and Rev plasmids pHDM-tat1b and pRC-CMV-rev1b, and the spike plasmid phCMV-Spike were used at ratio 4.4:1:1:1:1.5 to transfect a 175 cm2 flask.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pHDM-Hgpm2</div><div>suggested: RRID:Addgene_164441)</div></div><div style="margin-bottom:8px"><div>pHDM-tat1b</div><div>suggested: RRID:Addgene_164442)</div></div><div style="margin-bottom:8px"><div>pRC-CMV-rev1b</div><div>suggested: RRID:Addgene_164443)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Data were analyzed with the FlowJo software v10.7.1 (Becton Dickinson).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>FlowJo</div><div>suggested: (FlowJo, RRID:SCR_008520)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Statistical analysis: All statistical analyses were carried out with the GraphPad Prism software (v9).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad Prism</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">All the graphs were generated in GraphPad Prism, except for the spider plots, which were made using Microsoft Excel (v16.16.27).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div><div style="margin-bottom:8px"><div>Microsoft Excel</div><div>suggested: (Microsoft Excel, RRID:SCR_016137)</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

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    1. SciScore for 10.1101/2022.05.20.492764: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Primary commercial antibodies used in this work were from SantaCruz (CD3ζ, clone 6B10.2, #sc-1239), Cell Signaling (P-ZAP-70 Y319/ Syk Y352, #2701S; PTyr, #8954S), Abcam (Pericentrin, #4448) and BD Biosciences (Granzyme B, #560211).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>CD3ζ</div><div>suggested: (Miltenyi Biotec Cat# 130-127-939, RRID:AB_2904864)</div></div><div style="margin-bottom:8px"><div>P-ZAP-70 Y319/ Syk Y352, #2701S; PTyr, #8954S), Abcam (Pericentrin, #4448)</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Alexa Fluor 488-and 555-labeled secondary antibodies were from ThermoFisher Scientific (anti-mouse 488, #A11001; anti-rabbit 555, #A21428).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-mouse</div><div>suggested: (Thermo Fisher Scientific Cat# A-11001, RRID:AB_2534069)</div></div><div style="margin-bottom:8px"><div>anti-rabbit</div><div>suggested: (Molecular Probes Cat# A-21428, RRID:AB_141784)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Alternatively, CD8+ T cells (0.5×106) were incubated for 30 min at 20°C in 50 μl RPMI-HEPES in the absence of BCS with 2 μg/ml anti-ACE2 antibody (R&D Systems, #AF933) (Hoffmann et al., 2020).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-ACE2</div><div>suggested: (Thermo Fisher Scientific Cat# PA5-75453, RRID:AB_2719181)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cells were stained with primary antibodies ([1:30] CD3ζ; [1:50] P-ZAP70; [1:100] PTyr; [1:200] PCNT; [1:50] GzmB; see above catalogue #) overnight at 4°C.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>P-ZAP70</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>PCNT</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The Burkitt Lymphoma derived B cell line Raji was grown at 37°C, 5% CO2, in RPMI-1640 Medium (Merck, #R8758) supplemented with 7.5% BCS.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Raji</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Images were processed with Zen 2009 image software (Carl Zeiss, Jena, Germany).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Zen</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Relative distances (μm) of the centrosome (marked by PCNT) from the center of the contact site with the APC, and of the lytic granules (marked by GzmB) from the centrosome, were measured using ImageJ (FigS2A).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>ImageJ</div><div>suggested: (ImageJ, RRID:SCR_003070)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">3D reconstructions were obtained using Fiji (version 2.1.0).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Fiji</div><div>suggested: (Fiji, RRID:SCR_002285)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Statistical analyses were performed with Prism software (GraphPad Software)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. p r o m o te g o o d w ill a n d c o - o p e r a tio n a m o n g th e n a t io n s o f A s ia a n dA fr ic a , t o e x p lo r e a n d a d v a n c e th e ir m u tu a l a s w ell a s c o m m o n in te r e s tsa n d to e s ta b lis h a n d p r o m o te frie n d lin e s s a n d n e ig h b o u r ly r e la tio n s

      is mutual vs common a relevant distinction

    Annotators

    1. Author Response

      Reviewer #3 (Public Review):

      The import of soluble precursor proteins into the mitochondrial matrix is a complex process that involves two membranes, multiple protein interactions with the translocating substrate, and distinct forms of energetic input. The traditional approaches for in vitro measurement of protein translocation across membranes typically involve radiography or immunodetection-based assays. These end-point approaches, however, often lack optimal resolution to analyze the sequential processes of protein transport. Therefore, the development of techniques to dissect the kinetic steps of this process will be of great interest to the field of protein trafficking.

      This study by Ford et al. employs a novel bioluminescence-based technique to analyze the import of presequence-containing precursors (PCPs) into the mitochondrial matrix in real time. As a follow-up study to previous work from the Collinson group (Pereira et al. 2019), this approach makes use of the split NanoLuc luciferase enzyme strategy, whereby mitochondria are isolated from yeast expressing matrix localized 'LgBiT' (encoded by the mt-S11 gene) and used for import experiments with purified PCPs containing 'SmBiT' (the 11-residue pep86 sequence). The light intensity that results from the high-affinity interaction of pep86 with mt-S11 is convincingly shown in this study to be a reliable reporter of protein import into the matrix space. Therefore, from a technical stance, this appears to be a very promising approach for making high-resolution measurements of the different kinetic steps of protein translocation.

      The authors leverage this technology to seek insights into several features of mitochondrial protein import, with some observations challenging key longstanding paradigms in the field. Using series of PCP constructs differing in length and placement of the pep86 peptide, the authors perform luminescence-based import tests with varying protein concentration, energetic input, and presequence charge distribution. Fits to the time course data suggest two main kinetic steps that govern matrix-directed import: transit of the PCP across the TOM complex into the IMS and association of the PCP with the TIM23 motor complex. The results support some very interesting insights into TIM23-mediated protein import, including: that precursor accumulation is strongly dependent on length; that the kinetically limiting step of IM transport is engagement with the TIM23 complex, not transmembrane transport itself; and that presequence charge distribution differently affects import rate and matrix accumulation. The results of this study appear repeatable among samples and the mathematical fits to time courses are well explained. However, there remain some questions about the nature of the experimental approach and the interpretation of the kinetics data in terms of the underlying biological processes. These questions are as follows:

      Major points

      Overall system characterization and mathematical analysis

      1) The Western-based characterization of the amount of matrix-localized 11S (shown in Figure 1 - figure supplement 1) shows that the concentration of 11S varies significantly (> twofold concentration difference, quantified as a ratio to Tom40) among yeast/mitochondria preps. Is there a particular reason for this large variability? Perhaps more significantly, the import efficiency (judged by luminescence amplitude) shows high batch variability as well (> twofold efficiency difference). While this series of experiments makes the case that the luminescence readout of import is not limited by matrix-localized 11S, it does raise a potential concern of batch-to-batch variation in import competence. Could this have any implications for the reproducibility of results by this assay, particularly regarding the kinetic parameters reported?

      It is very difficult to know what causes this variability as it can be seen even between triplicate preparations carried out on the same day. It could be due to slight differences in the flasks used to grow cells (such as the size of the baffles). However, we have determined that the variability in 11S concentration does not correlate with import competence (Figure 1 – figure supplement 1C), and that the kinetics of import are not affected (Figure 1 – figure supplement 2C).

      2) My understanding from the Pereira 2019 JMB paper is that the yeast expressing the matrix-targeted 11S were engineered so that the 11S construct contained a 35 residue presequence from ATP1. In Figure 1 - figure supplement 1, panel A, it looks like the mitochondria-derived 11S constructs are significantly larger than the purified 11S constructs used to calibrate concentration. If the added residues on the mitochondrial 11S constitute a presequence, then they should be cleaved up on import to yield the mature sized protein. Why are the mitochondrial 11S constructs so much larger than the purified ones? Explicit labeling of MW markers would be useful here.

      We noted that it seemed likely that the presequence was not getting cleaved off. There may also be some kind of SDS-PAGE mobility issues for 11S (common for beta-barrels), such that the purified version has a different mobility to the matrix localised version. Therefore, the possibility remains that the MTS is cleaved off, but the mature product migrates anomalously on gels. For this reason we carried out experiments to show that 11S is matrix localised, which turned out to be the case (Figure 1 – figure supplement 1D). So irrespective non-MTS cleavage, or unexpected gel mobility of correctly processed 11S, the reporter is where it should be – in the matrix. These points are elaborated in the text.

      Labels have been added to molecular weight markers, as requested.

      3) From Figure 1D, given that the amplitude linearly increases with added Acp1pep86 up to ~45 nM, this suggests that matrix-localized 11S is in stoichiometric excess of imported peptide within this range of added substrate. Given a matrix [11S] of 2.8 uM, a stoichiometrically equivalent amount of Acp1-pep86 would be equivalent to an import of <0.5% of added substrate, and it is suggested that import efficiency is actually much lower than that. How can this very low import efficiency be explained?

      Import is single turnover under our assay conditions and is therefore limited by the number of import sites rather than matrix [11S]. Under standard conditions, we intentionally add substrate in vast excess and only anticipate that a very small proportion will be imported.

      4) Apropos of point #3 above: Given the low efficiency of import observed for the purified PCP substrates in this study, one wonders if this due to the formation of off-pathway (translocation incompetent) precursors established during the import reaction, before substrates have a chance to engage OM receptors (e.g., due to aggregation, etc.) In this case, the interpretation of single-turnover conditions may instead be caused by a vast majority of PCP losing translocation competence, rather than the requirement for energetic resetting that is suggested. Might this be a possibility?

      We anticipate that some PCP will aggregate and add substrate in excess to allow for that. Our interpretation of the reaction as single turnover was drawn from a comparison of PCP-pep86-DHFR import amplitude in the presence versus absence of MTX, rather than amplitudes from absolute amounts of PCP. We cannot think of a reason why MTX would affect protein solubility.

      5) Import time courses in many cases show a progressive drop in luminescence at later time points after a maximum value has been reached. This reduction in signal cannot be accounted for by the two rate constants in the equation used in two-step kinetic model. How were such luminescence deviations accounted for when fitting data to obtain these kinetics parameters? What might be the reason for this downward drift in signal once maximum amplitude has been reached?

      We almost always see this gradual drop in luminescence in both the mitochondrial and bacterial systems. The data points acquired after the amplitude are excluded for the fitting. The assay is based on an enzymatic reaction and we think that the downward drift is due to a combination of substrate depletion and accumulation of reaction products.

      Import kinetics: dependence on total protein size

      6) In Figure 3 - figure supplement 1, some of the kinetic parameters from the PCP concentration-dependent responses are quite noisy. For instance, responses for the shortest constructs (L and DL) show a lot of variability in the k1 and k2 parameters. Is this (partly) due to difficulty in resolving these two parameters during the nonlinear least-squares fitting protocol for these particular constructs?

      It is difficult to resolve k1 and k2 perfectly, so the numbers are only estimates.

      7) The data in Figure 3, panels E and F (derived from Figure 3 - figure supplement 1) in some cases show non-linear dependence of kinetic parameters on the 'N to pep86 distance' for the length (panel E) and position (panel F) variants. For instance, from the length series, the k1 mean goes from 132 to 385 to 237 nM for the DL, DDL, and DDDL constructs, respectively. The variances suggest that these differences are real. Is there a reason that kinetic parameters would have such non-monotonic dependence on length?

      We don’t know the reason for this variance, but it could be investigated in future studies.

      Import kinetics: dependence on energetic input

      8) The data of Figure 4A show the results of partial dissipation of the membrane potential by 10 nM valinomycin. Most studies designed to cause a gradual dissipation of membrane potential do so by protonophore (e.g., CCCP) titration. Given that matrix-directed import is completely blocked by low micromolar amounts of this potent ionophore, it would be useful to have an independent readout (e.g., TMRM measurements) of the residual membrane potential that exists upon treatment with the lower concentrations of valinomycin used here.

      We have now included data that shows the partial effect of 10 nM valinomycin on membrane potential (TMRM measurements) and protein import (Figure 4 – figure supplement 1A-B).

      9) The step associated with k1, designated as transport across the TOM complex, is suggested to go to completion before starting the step associated with k2, engagement of the TIM23 complex. The k1 step shows a strong dependence on membrane potential (Fig. 4A, middle), particularly for the length series. Why would this be, given that no part of translocation across the OM should be associated with a valinomycin-sensitive electric potential?

      This effect is relatively small and mainly affects shorter PCPs. Our interpretation is that passage of the PCP through TOM is reversible, and committing PCP to import across the IMM (which requires ∆ψ) prevents this reversibility. However, it is also possible that transport through TOM and TIM23 are partially coupled. Both these possibilities are discussed in the discussion.

      Working model

      10) One of the most surprising outcomes of this study is that passive transport of substrates across the TOM complex and energy-coupled transport via the TIM23 complex are kinetically separable and independent events. As the authors note in the Discussion, the current paradigm of the field is that matrix-targeted substrates concurrently traverse the OM and IM via the TIM-TIM23 supercomplex, and this model is supported by quite a bit of experimental evidence. Even in this study, the fact that the PCP-pep86-DHFR construct exposes the pep86 sequence to the matrix in the presence of MTX (Figure 2) is evidence of a two membrane-spanning intermediate. Key mechanistic questions arise regarding the model proposed in this study. For example, if PCPs traverse the TOM complex as a stand-alone step, what is the driving force (e.g., a simple pathway of protein interactions with increasing affinity)? And would soluble, matrix-directed substrates be expected to accumulate in the very restricted space of the IMS? If so, how would TIM23directed membrane proteins keep from aggregating in the aqueous IMS? These questions would be worth addressing in the discussion of the model.

      We have included a discussion of the experimental evidence for TOM-TIM23 supercomplexes. The acid chain hypothesis has been proposed as the driving force for PCP transport though TOM ‒ an interaction between positive charges of the presequence and negatively charged residues within the TOM40 channel. Proteins that are targeted to the IMS are imported through TOM without the participation of TIM23 and we think that matrix-targeted proteins can do the same. This could explain why TOM is in excess over TIM23. We also think that some matrix-targeted PCPs can accumulate in the IMS, although this may not be true of membrane proteins.

      Import kinetics: dependence on MTS charge distribution

      11) The fact that import rates are increased with a more electropositive presequence makes sense in terms of the electrophoretic pull exerted on the PCP (matrix, negative). However, the greater accumulation of precursors containing more electronegative presequences remains puzzling. In the manuscript, this is explained based on the concept that accumulation of positive charges will cause partial collapse the membrane potential. However, I am still uncertain about this explanation for a few reasons. First, for each PCP, the presequence will constitute just a small fraction of the total length of the precursor, and therefore contribute a small fraction of the total charge density of imported protein. Would such a small change in total PCP charge be expected to have the dramatic effect observed among samples?

      The majority of the total PCP charge is from the mature region, and whilst the positive charges in the presequence undoubtedly deplete ∆ψ, the differences in extent of ∆ψ depletion that we see between PCPs that vary in charge, is due to the difference in charge of the mature regions (as their presequences are identical).

      Second, given the small amount of protein imported under these conditions, would the total charge of imported PCPs be expected to affect transmembrane ion distribution so significantly? For instance, as I recall, it takes up to micromolar amounts of mitochondria-targeted lipophilic cations (e.g., TPP+) to cause a major change in the TMRM-detected membrane potential.

      The effect was indeed unexpected. Despite the seemingly small number of PCPs that are imported, the total number of charged residues will be much greater.

      Finally, I would expect isolated mitochondria to be capable of respiratory control. It is well known, for example, that isolated mitochondria can respond to temporary draw-down of the membrane potential (e.g., by ADP/Pi addition) by going into state 3 respiration and restoring membrane gradients. Why would that not be the case here (Figure 5D)?

      The isolated mitochondria that we used for the import assays demonstrate increased O2 consumption in response to ADP addition, as expected (Figure 5 – figure supplement 1A-B). In addition to this new figure, we have now included TMRM data (Figure 6 – figure supplement 2B) that shows a depletion of ∆ψ in response to ADP addition, that is temporary and dependent on the amount of ADP added. We are therefore confident that our isolated mitochondria are capable of respiratory control as expected. We think that the lack of restoration of ∆ψ, following import-induced dissipation, is a consequence of the import process in vitro. Perhaps the import process compromises the channel resulting in concomitant ion/ charge dissipation during the active process. Moreover, this is likely to be exacerbated in vitro upon acute exposure to PCP, causing a sudden saturation of the import sites – thereby compromising the ∆ψ and the mitochondria’s ability to rapidly recover (this possibility has been noted in the MS).

      General

      12) Although the spectral approach in this study is developed as an alternative to the more traditional import assays, it would be useful to have some control import tests (done with Westerns or autoradiography) as complements to the luminescence-based imports. For example, control tests to accompany Figure 1 that show import efficiency or tests that accompany Figure 3 to show import of the different length and position series constructs. Perhaps this could be done with immunodetection of Acp1 or the pep86 epitope, showing protease-protected, processed import substrates that appear in a membrane potential/ATP-dependent manner. Even if the results from the more traditional techniques ran contrary to the results using the NanoLuc system, this would still allow the authors to compare which effects are consistent and which are dissimilar between different approaches.

      We have now included a Western blot import assay for the PCP-pep86-DHFR substrate and show that import is ∆ψ-dependent (Figure 2 ‒ figure supplement 1).

      13) The authors might also consider conducting imports with mitoplasts as a way to test the kinetic model that includes the TIM23-mediated step alone.

      We conducted import assays with mitoplasts and have now included this as a main Figure 5.

      14) It is difficult to follow the logic in the Discussion regarding the number of TIM23 sites limiting the number of 11S imported into mitochondria in live cells (page 15, lines 23-27). Are the authors suggesting that in vivo, one TIM23 complex serves to transport a single protein? This needs to be clarified.

      This has been removed, and this section of the discussion has been clarified.

    1. SciScore for 10.1101/2022.05.19.492649: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">IACUC: Animals: All animal studies were carried out under an institute-approved Institutional Animal Care and Use Committee (IACUC) protocol following federal, state, and local guidelines for the care and use of animals.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">For mouse studies, female 6- to 8-week-old C57BL/6J mice were purchased from the Jackson Laboratory (Bar Harbor, ME).</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">For NHP studies, 8 outbred, Indian-origin, 4-5 year old female rhesus macaques (Macaca mulatta) were randomly allocated into 3 groups of 2 or 3 animals.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">106 PBMCs/well were resuspended in R10 media supplemented with anti-CD49d monoclonal antibody (clone: 9F10, BD)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-CD49d</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">anti-CD28 monoclonal antibody (clone: CD28.2, BD), and Golgi inhibitors monensin (Fisher Scientific, cat# NC0176671) and brefeldin A (Fisher Scientific, cat# 50-112-9757) and incubated at 37°C for 8 hours, then maintained at 4°C overnight.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-CD28</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The next day, cells were surface-stained with antibodies against CD4 (PE-Cy5.5, clone: S3.5, Invitrogen), CD8 (AF647, clone: RPA-T8, BioLegend), CD45RA (FITC, clone: 5H9, BD), CCR7 (BV650, clone: G043H7, BioLegend), and aqua live/dead dye (Invitrogen, L34957), and subsequently fixed with BD CytoFix/CytoPerm (BD, 554714).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>CD4</div><div>suggested: (SouthernBiotech Cat# 9522-31, RRID:AB_2796861)</div></div><div style="margin-bottom:8px"><div>CD8</div><div>suggested: (SouthernBiotech Cat# 9536-31, RRID:AB_2796896)</div></div><div style="margin-bottom:8px"><div>CD45RA</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>CCR7</div><div>suggested: (BD Biosciences Cat# 563407, RRID:AB_2738187)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cells were further stained with antibodies against CD3 (APC-Cy7, clone: SP34-2, BD), CD69 (ECD, clone: TP1.55.3, Beckman Coulter), IFNγ (AF700, clone: B27, BioLegend), IL-2 (BV421, clone: MQ1-17H12, BioLegend), IL-4 (PE, clone: 8D4-8, BioLegend)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>CD69</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>IFNγ</div><div>suggested: (Bio X Cell Cat# BE0245, RRID:AB_2687726)</div></div><div style="margin-bottom:8px"><div>IL-2</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>BV421</div><div>suggested: (BD Biosciences Cat# 562986, RRID:AB_2737933)</div></div><div style="margin-bottom:8px"><div>IL-4</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Mouse antigen-specific tetramer staining of peripheral blood cells: MHC-tetramer staining on mouse samples was performed using an RBD-PE tetramer specific for sequence VNFNFNGL (NIH Tetramer Core Facility at Emory University, cat# 54971), and antibodies against CD8a (APC, clone: 53-6.7, eBioscience), CD3 (APC-Cy7, clone: 17A2, BD), CD44 (PE-Cy7,</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>antibodies against CD8a</div><div>suggested: (GeneTex Cat# GTX111860, RRID:AB_10623584)</div></div><div style="margin-bottom:8px"><div>CD3</div><div>suggested: (Abcam Cat# ab52305, RRID:AB_955118)</div></div><div style="margin-bottom:8px"><div>CD44</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>PE-Cy7</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The detection antibody used was horseradish peroxidase (HRP)–conjugated goat anti-human IgG (H+L) (ThermoFisher, cat# SA5-10283) at a 1:2000 dilution.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-human IgG</div><div>suggested: (Thermo Fisher Scientific Cat# SA5-10283, RRID:AB_2868331)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Briefly, a HEK293T cell line overexpressing ACE2 and TMPRSS2 was seeded at a density of 1.2×104 cells/well overnight. 3-fold serial dilutions of heat inactivated serum samples were prepared and mixed with 50 µL of pseudovirus.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293T</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Organisms/Strains</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Gene transcript analysis by Nanostring: For mouse studies, inguinal lymph nodes were harvested from immunized C57BL/6J mice at the indicated time points, processed into single cell suspensions, and lysed with RLT buffer (Qiagen, cat# 79216)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>C57BL/6J</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Sample acquisition was performed on BD FACS Symphony and data were analyzed with BD FlowJo V10 software.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>BD FlowJo</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The next day, cells were surface-stained with antibodies against CD4 (PE-Cy5.5, clone: S3.5, Invitrogen), CD8 (AF647, clone: RPA-T8, BioLegend), CD45RA (FITC, clone: 5H9, BD), CCR7 (BV650, clone: G043H7, BioLegend), and aqua live/dead dye (Invitrogen, L34957), and subsequently fixed with BD CytoFix/CytoPerm (BD, 554714).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>BD CytoFix/CytoPerm</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cells fixed in 1.5% formaldehyde were acquired on a BD FACS Symphony and data were analyzed with BD FlowJo V10</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>FlowJo</div><div>suggested: (FlowJo, RRID:SCR_008520)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Red blood cells were lysed in ACK lysis buffer (Quality Biological Inc., cat# no. 118156101).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Quality Biological</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Transcriptional responses were assessed with nSolver software v4.0 (NanoString Technologies) and differential gene expression was carried out using ROSALIND software.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>nSolver</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">NanoString statistical analysis was performed using Rosalind software.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Rosalind</div><div>suggested: (Rosalind, RRID:SCR_006233)</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


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      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.

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    1. SciScore for 10.1101/2022.05.16.492112: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">Euthanasia Agents: Intranasal infection of live SARS-CoV2 (SARS-Related Coronavirus 2, Isolate USA-WA1/2020)105PFU/ 100μl or with DMEM mock control was established with the help of catheter under mild anesthetized by using ketamine (150mg/kg) and xylazine (10mg/kg) intraperitoneal injection inside ABSL3 facility.<br>IACUC: All the experimental protocols involving the handling of virus culture and animal infection were approved by RCGM, institutional biosafety and IAEC (IAEC/THSTI/105) animal ethics committee.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">Isolation of murine BMDNs and human peripheral neutrophils: Murine bone marrow-derived neutrophils were isolated from femur and tibia bones of C57BL/6 wild-type male mice (20–25 g, 12–16 weeks) using the method described previously (71).</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">Assessment for the histological score was carried out through blind scoring for each sample by a professional histologist on a scale of 0-5 (where 0 indicated absence of histological feature while 5 indicated highest score).</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After centrifugation at 1700 x g for 30 min with acceleration 5 m/s2 and deceleration 4 m/s2, band between 81% and 62% were harvested and assessed for their viability by Trypan blue and purity by anti-Ly6G and anti-CD11b antibodies.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-Ly6G</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-CD11b</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">In a parallel experiment, immunofluorescence staining of BMDNs and PMNs was carried out using mouse anti-MPO and rabbit anti-H4Cit3 antibodies.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-MPO</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-H4Cit3</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After fixation and blocking, samples were incubated overnight with 1:100 dilution of primary antibodies and were visualized after incubation with the secondary antibodies (1:200, anti-mice AF488 and anti-rabbit AF594) using the confocal microscope (Olympus FV3000) at 100X resolution.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-mice</div><div>suggested: (AgriSera Cat# AS04 040, RRID:AB_2226396)</div></div><div style="margin-bottom:8px"><div>anti-rabbit</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">titration: SARS-Related Coronavirus 2, Isolate USA-WA1/2020 virus was grown and titrated in Vero E6 cell line cultured in Dulbecco’s Modified Eagle Medium (DMEM) complete media containing 4.5 g/L D-glucose, 100,000 U/L Penicillin-Streptomycin, 100 mg/L sodium pyruvate, 25mM HEPES and 2% FBS.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero E6</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Organisms/Strains</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Isolation of murine BMDNs and human peripheral neutrophils: Murine bone marrow-derived neutrophils were isolated from femur and tibia bones of C57BL/6 wild-type male mice (20–25 g, 12–16 weeks) using the method described previously (71).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>C57BL/6</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The cells were then taken for flow cytometry using BD FACSCantoII and data was analysed with FlowJo software.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>FlowJo</div><div>suggested: (FlowJo, RRID:SCR_008520)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Multiple group comparisons have been performed using one-way ANOVA followed by the Bonferroni test using GraphPad Prism 8.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad Prism</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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">NCT04553705</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">Omega-3, Nigella Sativa, Indian Costus, Quinine, Anise Seed,…</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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. SciScore for 10.1101/2022.05.16.22274439: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">Consent: Twenty-three self-referred patients were evaluated between January to September 2021 for new onset of potential symptoms of polyneuropathy (sensory, motor, or autonomic) within 1 month of SARS-CoV-2 vaccination were enrolled after consent to an IRB approved study at the National Institutes of Health (protocol # 15-N-00125).</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">12 Multiplex fluorescence immunohistochemistry was performed by incubating sections with 5% normal donkey serum (Jackson ImmunoResearch, West Grove, PA) for 1 hour, then overnight at room temperature using 0.5-5 μg/ml mixtures of immunocompatible antibodies (anti-human IgG, anti-human IgM and anti-CD31 (Leica Biosystems; NCL-L-IgG; NCL-L-IgM and PA0414); anti-C1q (Dako; A0136); anti-C4d (Biomedica; BIRC4D and anti-NFH (Aves Labs), followed by a 1 µg/ml mixture of appropriately cross-adsorbed secondary antibodies raised in donkey (Thermo Fisher, Waltham, MA; Jackson ImmunoResearch) and conjugated to one of the following spectrally compatible fluorophores: Alexa Fluor 488, Alexa Fluor 546, and Alexa Fluor 647.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-CD31</div><div>suggested: (Bioss Cat# bs-0468R-A488, RRID:AB_2714016)</div></div><div style="margin-bottom:8px"><div>PA0414)</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-C1q</div><div>suggested: (Agilent Cat# A0136, RRID:AB_2335698)</div></div><div style="margin-bottom:8px"><div>anti-C4d</div><div>suggested: (Biomedica Cat# BI-RC4D, RRID:AB_1944063)</div></div><div style="margin-bottom:8px"><div>anti-NFH</div><div>suggested: (Antibodies Incorporated Cat# NFH, RRID:AB_2313552)</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


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      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.

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    1. SciScore for 10.1101/2022.05.16.492138: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">Field Sample Permit: All animal experiments were performed in compliance with relevant institutional, national, and international guidelines for care and humane use of animal and approved by the Landesamt für Gesundheit und Soziales in Berlin, Germany (permit number 0086/20).</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">Hamsters were randomly assigned into groups, with 50 – 60 % of the animals in each group being female.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">Hamsters were randomly assigned into groups, with 50 – 60 % of the animals in each group being female.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Antigen retrieval was performed using microwave heating (600 W) in 10 mM citric acid (pH 6.0) for 12 min for SARS-CoV-1 nucleoprotein antibody (Sino Biological Inc.; Beijing, China) and using recombinant protease from Streptomyces griseus (PanReac Applichem, Darmstadt, Germany) for 13 min at 37°C for IgA antibody.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>SARS-CoV-1 nucleoprotein</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>IgA</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Anti-SARS-CoV-1 NP mouse monoclonal antibody (Sino Biological Inc.; Beijing, China, dilution: 1:500) and rabbit anti hamster IgA antibody (Brookwood Biomedical, Jemison, AL, dilution: 1:250) were incubated at 4°C overnight followed by washing and incubation with a secondary biotinylated goat anti-mouse IgG antibody (dilution: 1:200, Vector Laboratories, Burlingame, California, USA).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Anti-SARS-CoV-1 NP</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti hamster IgA</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-mouse IgG</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The plates were covered and incubated for 2 h at room temperature before the washing step was repeated and 50 μL of secondary antibody (Brookwood biomedical, Rabbit Anti-Hamster IgA, HRP-conjugated) diluted 1:1000 was applied per well.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Anti-Hamster IgA</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Day 0 samples of the prime-boost trial could not be tested for neutralizing antibodies against B.1.351 (Beta) due to lack of material.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>B.1.351</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">In addition, the cell culture medium for Vero-TMPRSS2 cells contained 1000 μg/mL geneticin (G418) to ensure selection for cells expressing the genes for neomycin resistance and TMPRSS2.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero-TMPRSS2</div><div>suggested: JCRB Cat# JCRB1818, RRID:CVCL_YQ48)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">, Beta – B.1.351 (hCoV-19/Netherlands/NoordHolland_20159/2021), and Delta – B.1.617.2 (SARS-CoV-2, Human, 2021, Germany ex India, 20A/452R (B.1.617) were propagated on VeroE6-TMPRSS2 cells.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>VeroE6-TMPRSS2</div><div>suggested: JCRB Cat# JCRB1819, RRID:CVCL_YQ49)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Omicron BA.1 - B.1.1.529.1 (hCoV-19/Germany/BE-ChVir26335/2021, EPI_ISL_7019047) was propagated on CaLu-3 cells.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>CaLu-3</div><div>suggested: KCLB Cat# 30055, RRID:CVCL_0609)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Mock-vaccinated hamsters were vaccinated by intranasal instillation with sterile cell culture supernatant obtained from uninfected VeroE6-TMPRSS cells.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>VeroE6-TMPRSS</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Vaccine preparations: sCPD9 was grown on Vero-TMRSS cells and titrated on Vero E6 cells as described previously, final titers were adjusted to 2 × 106 FFU/mL in MEM.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero-TMRSS</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>Vero E6</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Organisms/Strains</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cell isolation from blood and lungs: White blood cells were isolated from EDTA-blood as previously described, steps included erylysis and cell filtration prior counting.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>lungs: White</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Mesocricetus auratus genome annotation: For quantification of gene expression, we used the MesAur 2.0 genome assembly and annotation available via the NCBI genome database (https://www.ncbi.nlm.nih.gov/genome/11998?genome_assembly_id=1585474).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>MesAur</div><div>suggested: None</div></div></td></tr></table>

      Results from OddPub: Thank you for sharing your code.

      Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:


      Our single-cell RNA-sequencing analysis has several limitations. This technique, as employed here, cannot fully capture processes such as reactivation of memory cells due to lack of surface markers and cell type-specific enrichment. Due to incomplete annotation of the Syrian hamster genome, we were not able to identify IgA-positive cells. Data quality of nasal mucosa cells was comparatively low due to the difficult dissociation of the tissue, which limits our observations at the site of initial infection. An important and frequently discussed issue with live attenuated vaccines is their potential susceptibility to previously established immunity (69), which would restrict vaccine virus replication and potentially limit their use as booster vaccines after initial immunization by vaccination or natural infection. Our results indicate however, that sCPD9 does effectively boost immune responses and greatly improves protection when applied three weeks after initial mRNA vaccination. Importantly, sCPD9 enhances humoral immune responses, especially against known immune escape variants such as Beta and Omicron BA.1, while also improving the virological outcome of a heterologous challenge infection when applied as a booster three weeks after initial vaccination. This indicates a wide scope for the use of live attenuated vaccines in populations that exhibit an already high degree of baseline immunity induced by previous vaccination or infection; a situation clearly present in many part...


      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


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      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.

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    1. a formación de películas de poliuretanos a base de agua es extremadamente compleja debido a la composición heterogénea junto con la progresión simultánea de varios procesos fisicoquímicos, incluida la evaporación del agua, la interdifusión molecular, la separación de fases y la coalescencia de las gotas [ 31 S. V. Gade, C. Hancock, and M. R. Van de Mark, “Synthesis of amine functional colloidal unimolecular polymer (CUP) particles and their use as cross-linker for epoxy coatings,” in Abstracts of Papers of the American Chemical Society (Vol. 246), AMER CHEMICAL SOC, Washington, DC, USA. View at: Google Scholar See in References - 33 D. B. Otts, L. A. Cueva-Parra, R. B. Pandey, and M. W. Urban, “Film formation from aqueous polyurethane dispersions of reactive hydrophobic and hydrophilic components; spectroscopic studies and Monte Carlo simulations,” Langmuir, vol. 21, no. 9, pp. 4034–4042, 2005. View at: Publisher Site | Google Scholar See in References ]. La interdifusión de cadenas poliméricas de una partícula a otra para crear fuerza cohesiva depende en gran medida de la movilidad de la cadena y juega un papel importante en las propiedades finales de las películas obtenidas. A partir de la literatura, las películas de poliuretano a base de agua sintetizadas a partir de estructuras monoméricas asimétricas proporcionaron una naturaleza amorfa que mejoró la interdifusión molecular entre partículas, lo que resultó en mejores propiedades mecánicas que una naturaleza cristalina

      Peliculas de PU

    1. SciScore for 10.1101/2022.05.13.491916: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">Euthanasia Agents: At different time-points post-infection (pi), 10 animals were euthanized by intraperitoneal (IP) injection of 100 μL Dolethal (200 mg/mL sodium pentobarbital, Vétoquinol SA) for collection of lung tissues.<br>IRB: Ethics: Housing conditions and experimental procedures were approved by the ethics committee of animal experimentation of KU Leuven (license P001/2021).</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">SARS-CoV-2 infection of SCID mice: In brief, 7-9 weeks old male severe combined immune deficient (SCID) mice were purchased from Janvier Laboratories.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">Tissue sections (5 μm) were analyzed after staining with hematoxylin and eosin and scored blindly for lung damage by an expert pathologist.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">A passage two virus on Vero E6 cells was used for the study described here.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero E6</div><div>suggested: RRID:CVCL_XD71)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Organisms/Strains</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Treatment Regimen: Male SCID mice were treated by oral gavage with either the vehicle (n=20) or Molnupiravir (EIDD-2801, n=14) at 200 mg/kg or Nirmatrelvir (PF-332, n=14) at 300 mg/kg, twice daily starting from D0, just before the infection with the Beta variant as described in the previous section.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>SCID</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Statistics: GraphPad Prism (GraphPad Software, Inc.) was used to perform statistical analysis.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr></table>

      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 LimitationRecognizer: We detected the following sentences addressing limitations in the study:


      On the other hand, the limitation of this model, is that unlike for hamsters, mice are only susceptible to the beta variant. Since small molecule inhibitors should have equipotent activity against all variants this is not of concern for studies with such drugs. However, for testing of therapeutic antibodies, infection models (in hamsters) with the different VoC will still be needed. Likewise, for vaccine studies, fully immunocompetent animals are needed, hence SCID mice are not useful for this purpose. Therefore, this SCID mice/beta variant infection model will be mainly advantageous for the evaluation of small molecule inhibitors of SARS-CoV-2 replication.


      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


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      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.

      </footer>
    1. 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

      From the start, the authors would like to thank all the reviewers for their careful and constructive consideration of our manuscript. We have now made several changes to the paper and believe it to be better for the feedback.

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

      In this study, Rees et al. perform an RNA-seq circadian time course experiment in the recently formed allopolyploid wheat. Through comparisons with other circadian transcriptomic datasets in other species it appears that the period of rhythmic genes is much more variable in wheat with a shift to longer periods compared to the other species examined. Interestingly, by analyzing circadian parameters among expressed genes, they find evidence that this newly formed allopolyploid already shows signs of divergence in circadian traits among homoeologs. A thorough comparison with circadian regulated genes in Arabidopsis reveals overlap in phasing of genes involved in certain biological processes such as photosynthesis and light signaling whereas genes involved in starch metabolism were found to have different levels of rhythmicity and phasing. This dataset will be a great resource for the community and enable new predictions about the influence of polyploidy on the circadian control of important crop improvement traits and the circadian regulation of gene expression.

      Major Comments

      1. The results section starts with very little explanation of the experiment. It would help to provide a little more detail at the start of the results to explain the context for the experiment and what was done, when samples were collected and for how long. For the methods section, it isn't until line 650 that it is clearly stated that the sampling started at ZT0. It would be better to put this in the plant materials and growth condition section.

      Thank you for highlighting the need for this context, we agree that the manuscript is improved by an introduction to the experiments. We have now included an “Experimental context” section in the results and have taken the opportunity to explain how the full 0-68h and 24-68h datasets are used within our analysis. Ln 74-82. We have also edited the Methods as suggested Ln 610-615.

      The low proportion of circadian regulated genes is likely due to the very low cutoff for calling a gene expressed, especially when there are three days of repeated timepoints. If a gene is expressed across the time course it should have values above TPM 0 for at least 3 time points in order for it to be expressed each day. I'd also be suspicious of a gene with a TPM value less than 0.5. Comparing these types of numbers is always challenging due to the various cutoffs used. Along those lines, why was a different filtering scheme used for Arabidopsis (line 657)?

      We completely agree that the proportion of genes described as rhythmic changes a great deal with the threshold at which you exclude low expression transcripts as well as the window over which measurements are taken and the q-value cut-off for rhythmicity. We performed an analysis to test the effects of applying a pre-filtering step to exclude low-expression genes and discuss our findings in Supplementary Note 1. Briefly, we removed genes with expression less than 0.1 TPM in six or more timepoints and again ran Metacycle to define numbers of rhythmic genes. Our results are discussed in Supplementary Note 1 and are presented in Supplementary Table 1. Regardless of the cut-offs applied, Arabidopsis and wheat data was treated identically, and our findings reported in the main results were consistent with those reported in the Supplementary analysis. Thank you for raising this point, as we have now improved our description of this analysis in the main text (Ln 92-95).

      Regarding the different filtering schemes, the filtering mentioned by Reviewer 1 was applied to both Arabidopsis and wheat data for a stricter retention of rhythmic genes, as part of the pre-WGCNA clustering analysis. Filtering to retain genes with >0.5TPM across 3 timepoints was applied to reduce lowly expressed genes, that act as background 'noise' when defining clusters. We applied this across 3 timepoints rather than the WGCNA suggestion of 90% of samples - because the patterns of expression in our rhythmically filtered datasets were cyclical in nature.

      In reference to the shortening of the period every day, this should be interpreted with caution. Period estimate of a single cycle are not very reliable and the SD for each day is around 3h so it is difficult to draw any conclusions about changes in period each day. One option would be to only include genes with an SD less than 1h or alternatively to remove the discussion surrounding the comparison of period across the three days and focus on the period results for the full 24h-68h window shown in 1b. While 2 days is better it is still not ideal for calling period; however, your first day will still have a strong diurnal driven pattern that will likely skew your circadian period.

      Thank you for your comments. Our question here was to determine whether the mean period lengths of rhythmic transcripts in wheat were always immediately longer upon transfer to constant light, or whether they got progressively longer over time. Upon reading the reviewer’s comment, we realize that the explanation provided of how we conducted this analysis was misleading. Our approach was to take a 44h sliding window (almost 2 days) and measure period at 0-44h, 12-56h and 24-68h. We have now added the previously missing statistics that support our findings in the main text, and which hopefully show the significance of the period changes over time (supplementary note 2). One of the most surprising findings from this analysis was that the periods in the first window were the longest 28.61h (SD=3.421), suggesting that the diel (driven) oscillation had little impact upon immediate transfer to free run. Our interpretation is that the mean period initially lengthens trying to follow the missing dusk signal, before the free-running endogenous period asserts itself in later cycles (Ln 129-128).

      Line 87-93: If the dusk cue is important for clock expression you would think this would be biased towards genes that peak later in the day or near dusk. This argument should be connected better to the period results discussed on lines 98-101.

      Following on from our statement above, we have now combined our hypothesis for why wheat transcripts expressed at dusk have longer periods with the discussion about longer periods upon transfer to constant light. We agree that the two processes are likely to be connected and have now placed them together in Ln 129-128.

      1. Lines 650-652 of the Methods mentions that one of the main interests was the response to transfer to L:L, but this isn't mentioned in the introduction and doesn't come up much in the Results section. Most of the expression comparisons are focused on the 24-68h window. It also isn't clearly explained why the first day in LL is still a diurnal cycle. This would be helpful for non-circadian readers who may wonder why the first day is not included in all the analyses.

      We believe this point is now also addressed by the addition of an Experimental Context section in the results (Ln 74-82), in response to the reviewer’s previous comment.

      1. The phase comparisons shown in Figure suppl 4 are confusing. Suppl. Note 3 states that the period from the 24-68h data window was used to establish the bins but then the phase is shown for 3 different windows for each column? When calculating the phase for each of those 3 windows which period was used as the denominator in the phase calculation? Was it the period that matches the window used to calculate phase? What does the plot look like if phase is called on the same window used to calculate period (24-68)? What method was used to call phase in Suppl. Fig 4? As shown in Suppl Fig. 3 the method can influence the phase distributions. The methods suggest that the phase was determined with Metacycle but then FFT and MESA were used to verify. What does this mean verify, were they adjusted if FFT/MESA didn't agree?

      We agree that this Figure was unnecessarily complicated. We have now simplified Supplementary Figure 4 so that only the phases from 24-68h are presented. We have also clarified the legend to explain why we used FFT-NLLS to improve accuracy of Metacycle predictions.

      It is difficult to interpret the value of the period and phase comparisons shown in Fig. 1b, c, e and f after the preceding section about how variable the period and phase is across days. It is also surprising that the full 3 days were used to calculate the circadian statistics considering the first day is still under diurnal control. Do the ratios remain the same if the statistics are performed only on the 24h-68h window? For consistency with the rest of the paper and avoid confusion it would be best to have all circadian parameters measured using the same time window (24h-68h).

      Thank you for your comments, we can see how our logic in using the different data windows was not clear enough. As mentioned above, we have now explained the use of the full and shortened data windows in Experimental context section (Ln 74-82). Fig 1c is a comparison between different circadian datasets and as such we have only compared periods across 24-68h window. Similarly, Fig 1b is a global analysis of periods in rhythmic genes in comparison with Arabidopsis and so is again measured from 24-68h. We have now clarified this in the Figure legend for 1b.

      For comparisons of homoeologs within wheat triads, our question was in identifying homoeologs which behaved differently when placed under free-running conditions. We therefore still feel justified in using the full 0-68h dataset to identify homoeolog periods and phases which indicate differential circadian regulation, but we have now clarified that we are using the full dataset for the triad analysis in the results (Ln 140).

      Fig 1h-m. How were those genes chosen? It would help to see the SD of the replicates shown, since this is just showing one triad. It would be helpful to see a plot that represents the full set of triads rather than just one that looks best. If normalized to a standard phase they could be put on the same plot. For example, panel j is meant to show the 8h lag of subgenome D. If the data is normalized so that A and B are set to the same phase all the triads could be displayed with shaded SD bars to show the variation. Something like this would be a better representation of the data rather than showing just one example.

      Fig. 1h-m are case-studies illustrating the different forms of circadian imbalance between homoeologs. We agree that it is helpful to see the standard deviation as error bars on these triad plots and have added it as suggested. In line with another Reviewer 2’s suggestion we have removed Fig 1k and have replaced this with a comparison of mean normalised data for Triad 408 and Triad 2454, highlighting the difference between imbalanced rhythmicity and imbalanced amplitudes between homoeologs. Fig 1 I and m do not have error bars as adding standard deviations to mean normalised data wasn’t appropriate.

      Thank you for your suggestion on how to display the different phases between homoeologs. We feel that if we were to plot all of the triads displaying imbalanced phases, the differences in period length and accompanying noise differences would make the plot so busy as to be unreadable. We hope that the pie charts Fig 1 d-g give a global overview of the proportions of triads with circadian imbalance, but agree with the point that it is useful to allow readers to view triads of their own preference. Therefore, we have now provided the replicate level TPM data with the triad IDs annotated (Supplementary File 12) and Supplementary file 11 provides the classification of each triad alongside Metacycle statistics, ortholog identification and cluster information discussed elsewhere in the paper. Readers can now look up a triad or gene of interest and see how it was classified and what the expression looks like over the full dataset.

      It is surprising that there aren't more comparisons with the B. rapa dataset, especially when discussing the clock genes that show balanced or imbalanced expression. Are they similar in B. rapa and does it support your hypothesis that unbalance for certain genes are selected against?

      While we agree that a thorough, multiple species, comparative transcriptomic analysis is undoubtably of interest for the future, we feel it is beyond the scope of the questions being addressed in this paper. We do compare paralogs defined as “similar” in the Greenham dataset with homoeologs described as “balanced” in our dataset and find that genes involved with “photosynthesis” and “generation of precursor metabolites and energy” tend to be common between the two groups, potentially suggesting conservation of balance for certain types of genes (Ln 206-217).

      Figure 2 networks. Why were these specific modules selected? Is it actually appropriate to directly compare these modules? I do see that some of the comparisons have high correlations from panel a, but not all. For example, in panel b the W9 and A9 modules have a correlation value of 0.92, which seems appropriate. However, panel c (modules W3 and A2) have a correlation of 0.42, which seems far too low to make any sort of comparison meaningful.

      The modules were selected to simplify the comparison of genes expressed in the dawn, midday, dusk, and night. We were interested in identifying common GO-enrichment in genes peaking throughout the day, although as you have identified, the differences in period length between Arabidopsis and wheat made this difficult. Our reasons for comparing module W3 with module A2, were that, even though their eigengenes are not highly correlated per se, when period length is taken into account, both modules peak during the subjective day (CT 6.34h and 6.19h) and they share commonly enriched GO terms which make sense for day peaking genes.

      Further, as described in methods comments, using a cutHeight as low as 0.15 will likely lead to some number of genes in any given module that do not necessarily "share" a similar expression pattern. These genes could have a pattern that has very low correlation to their module eigengene and were only placed in that module because the pattern was "less similar" to other module eigengenes. The current expression plots in this figure follow a clear pattern, but I suspect this would be even more apparent if the genes within these modules had a higher correlation to the module eigengene. Perhaps the current genes in these modules could just be filtered to have a higher correlation score?

      Thank you for your comments, we have now made changes to the Results and Methods to clarify our approach (Ln 237-239 and Ln738-765). Merging modules with highly correlated module eigengenes (ME) is the final step in constructing our co-expression networks. To do this, as the reviewer describes - we used the WGCNA default parameter of a mergeCutHeight() of 0.15. This results in the merging of modules with highly correlated ME as the 0.15 mergeCutHeight() refers to the dissimilarity metric of 1 minus the eigengene correlation. So for WGCNA, a mergeCutHeight() of 0.15 corresponded to a correlation of 0.85. For the wheat modules, we took the additional step of merging closely related modules (mergeCloseModules()) using a cutHeight of 0.25, again a dissimilarity metric of 1 minus the eigengene correlation (corresponding to a correlation of 0.75). Reducing the stringency of the cutHeight to merge highly correlated wheat modules enabled us to more easily compare significantly correlated wheat and Arabidopsis co-expression modules to identify groups of genes in wheat and Arabidopsis expressed at similar times in the day, and enable the comparison of whether similar phased transcripts in wheat and Arabidopsis had similar biological roles.

      Lines 327-334: I am not following the connection between 'response to abiotic stimulus' and the photoreceptor and light signaling proteins. At the start of this section (line 308) the authors say that the GO analysis was only done on rhythmically expressed genes but the reference to only one PHYA being rhythmic and yet multiple genes are shown in the plot in fig. S16. Does this mean that all the genes were shown and not just the rhythmic ones? This would explain why many of the PHY and CRY genes don't seem to have rhythms. This should be clarified better in the text or indicated in the plot which ones were called rhythmic. Since the first day following transfer is still the diel pattern from the entrainment condition, what does the PHY and CRY expression look like? Does it appear rhythmic under diel but lose rhythmicity in LL? It should be noted in the text that arrhythmicity in circadian conditions doesn't mean there isn't rhythmicity under diel conditions. This could be an additional explanation apart from the current one in the text that the regulation is at the level of protein stability/localization. Overall, this entire section is very long and entirely based on data shown in the supplemental material. I do appreciate having the individual gene plots that supplement Figure 4 and would suggest either providing a main figure to highlight a small subset of genes or pathways in this section or shorten it and focus on the results shown in the main figures.

      Upon reading the reviewer’s comment, we realize that we should have made our motivations and processes clearer within this section. We used the data filtered for rhythmicity to conduct the GO-enrichment analysis and then used that to identify processes which should be of interest for further investigation. We have now added an additional sentence (Ln 352-354) to explain this more clearly. We then considered the orthologs of well-known Arabidopsis gene networks and extracted their expression from our circadian dataset, whether rhythmic or not. Supplementary Table 10 contains all of the genes we investigated, their expression and their MetaCycle statistics. We have also indicated here which genes are plotted in which Supplementary Figure 18-20. The reasons for plotting non-rhythmic genes in some cases was that it illustrates the differences between circadian control in Arabidopsis versus wheat (as is the case for the PHY and CRY genes). We understand that it is useful to see at a glance which genes are classified as rhythmic or arrhythmic, so have now highlighted each row in Supplementary Table 10 to make this more intuitive, and added a read me tab.

      Regarding your point about oscillation under diel cycles, we agree that some transcripts will show rhythmic behaviour under entraining environments but not under constant conditions, and may perform time-of-day specific functions. However, these transcripts are likely to not be regulated by the circadian clock (at the transcriptional level) and so are not discussed in the context of a circadian transcriptome.

      For your interest, here is the full expression of PHY and CRY transcripts starting at ZT0:

      [Image]

      It is difficult to say for definite, but it seems likely that some of these photoreceptors will have rhythmic patterns of expression under diel cycles, but these rhythms do not endogenously persist under constant conditions.

      We appreciate your feedback that this section would benefit from cutting down of text and addition of a Figure to illustrate the text. We have now cut some of this section down and created a new main figure based on some of the oscillation plots from Supplementary Figure 18 and 19. We chose examples that reflect a conservation of relationships between transcripts of different peak phases, as we find it interesting that both species have similar patterns. (Main Figure 4, Ln 361--363, 382).

      1. Primary metabolism section: in terms of the supplemental figure, similar to the previous one I think it would declutter the plots if the genes that are not rhythmic were left out and simply indicate below the plot that they didn't meet the rhythmicity cutoff. This is another area where there is more discussion surrounding the supplemental figures than the main figure 4.

      One of the overall findings of this section was that many of the genes involved in Starch and T6P metabolism which are rhythmically expressed in Arabidopsis are not rhythmically expressed in wheat. We feel removing these genes from the results would detract from the importance of this finding. We have now edited Supplementary Table 10 to highlight which genes are classified as rhythmic. We have also added in a sentence to the start of this section which lays out our motivations for this analysis, summarises our findings and better connects the text with an explanation of Fig. 5 (Ln 408-430).

      For all gene expression figures there should be SD or SE shown either as bars or ribbons to represent the variation in replicates.

      Although we agree that error bars are informative for showing variation between replicates (and have added them to Fig. 1 to show differences within wheat triads) we feel that adding error bars to the gene expression plots in Fig. 3, Fig 4 and Supplementary Fig 19-20 would make these plots difficult to read, particularly where the wheat homeologs are very similar. The purpose of these gene expression plots is to compare circadian profiles in Arabidopsis and wheat orthologs rather than to claim significant differences in expression at any particular timepoint. This is fairly common in other circadian biology studies:

      https://www.pnas.org/doi/10.1073/pnas.1408886111 ,

      https://www.jbc.org/article/S0021-9258(17)49454-3/fulltext#seccestitle20 , https://journals.plos.org/plosone/article/comments?id=10.1371/journal.pone.0169923 , https://www.science.org/doi/10.1126/science.290.5499.2110?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed,

      https://www.frontiersin.org/articles/10.3389/fgene.2021.664334/full,

      https://www.science.org/doi/full/10.1126/science.1161403

      The replication level information for each gene has now been made available in Supplementary file 12.

      1. It would be very helpful to include the code used to generate the networks and perform the cross-correlation of eigengenes across networks should be included in the Methods. This will also save you from responding to email requests!

      Thank you for your comment, Code for the cross-correlation analysis, Loom plots and WGCNA network construction is now available from our groups GitHub repository: https://github.com/AHallLab/circadian_transcriptome_regulation_paper_2022/tree/main

      Minor Comments

      1. Figure 1, panel d: - The "unbalanced" triads that are depicted by the lighter shading; do these in fact have a different cutoff than the original rhythmic homoeologs? In the figure it says qThank you for bringing this to our attention, this has now been corrected.

      Hard to directly compare the GO term overlap in Figure 2f. Might be better to only show the results for the 4 pairs shown in b-e and put them side by side in the bubble plot.

      Thank you for this feedback, We have tried to make this plot easier to understand without losing any of the available information. Hopefully it is now more intuitive to understand which columns are being compared. We have changed the coloured lines to make them slightly wider, put the modules in corresponding coloured boxes and highlighted GO-slim terms shared by modules being compared.

      1. Line 314 -316 don't see supp tables 10, 11

      Our apologies, these files were missed previously from the upload are now available.

      1. For the selection of B. rapa circadian paralogs with similar and differential expression patterns (starting line 714), the authors choose a hard cut off of 0.001 (differentially patterned) OR 0.1 (similarly patterned). What happens to the genes that are between these two cut offs or is this a typo. Since all the other cutoffs for rhythmicity was set at 0.01 it seems likely that this is a typo.

      We have now clarified this in the methods, (Ln 807-822). This is not a typo, but it is a different method to the Metacycle approach we have used for our wheat data. We defined similar/different paralogs as characterized in Greenham et al, (2020) using DiPALM p-values. We chose these DiPALM p-value cut-offs as they gave us approximately equal numbers of paralogs in each category, which represent tails of similarly expressed or differently expressed circadian genes. We checked these cut-offs by calculating average Pearson’s correlation statistics between paralogs and found that differential Brassica paralogs had a mean Pearson correlation coefficient of 0.31 (SD = 0.43) and similar Brassica paralogs had a mean Pearson correlation of 0.75 (SD= 0.23) which confirms that the DiPALM method of defining expression patterns makes sense in the context of this analysis.

      Line 681. Should be supplemental Figure 6 not 9.

      1. References to most supplemental figures are not the correct number.

      2. Labels above the plots in Supp Fig5 do not match the legend.

      We apologise for these mistakes. We realize that we had mistakenly submitted an earlier draft of the Supplementary materials file, which was missing Supplementary Figure 5, 6 and 9 which therefore shifted the order of the remaining figures. This is now updated.

      1. Suppl table 7 should be as a separate .csv file or similar to be able to see the full table.

      This is a good suggestion, and we have added this.

      1. Line 723 should be B. rapa not B. napus.

      Thank you for catching this! Corrected.

      1. Figure 4. There is no explanation for what the black boxes represent in the figure legend.

      Thank you for your comment. Figure 4 (new Figure 5) has now been updated.

      Reviewer #1 (Significance (Required)):

      This study provides new insight into the circadian regulation of the transcriptome in a new allopolyploid. It adds a valuable resource to a growing collection of circadian studies in important crops and will greatly improve our efforts to learn more about the circadian control of important crop improvement traits. The dataset will be of interest to other plant circadian biologists as well as the general plant biology community who focus on monocot crops. My expertise is more on the transcriptomic side and I do not have the expertise to evaluate the phylogenetic work presented in this study.

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

      Summary Rees et al. present an RNAseq time course of bread wheat. Its recent polyploidisation is one motivation for this study as gene expression dosage is known to be important for clock function in other plants. The time course covers 3 days at sampling intervals of 4h of 2-week old wheat plants (all aerial tissues), in triplicates. The subsequent analysis of the RNAseq data includes analysis of the generated data by itself (e.g. GO analysis, rhythmicity, period and phase analysis, rhythmicity of transcription factor families as well as TF binding sites) as well as thorough comparison with published datasets of other species (Arabidopsis, Brassica rapa, Brachypodium dystachion). One of the key findings is that the mean period length and the period spread are larger in wheat than in these other species). Circadian clock genes largely have similar dynamics in wheat compared to Arabidopsis. In addition, one focus is the analysis of the dynamics of three genes of one triad and imbalance / balance of such triads. To the surprise of the authors, circadian regulated and clock genes were not necessarily balanced. Silencing is one of their explanation for imbalance of circadian genes as arrhythmic genes of one triad are typically those with the lowest expression level. Finally, the authors point out more examples of rhythmic processes and genes (photoreceptors and signalling, auxin, carbon metabolism) and their commonalities and differences with Arabidopsis.

      Major comments - The key conclusions and the data are convincing

      We thank the reviewer for their supportive comments.

      • line 120 and figure 1: In my opinion, q > 0.05 is not a good definition of arrhythmicity as non-significant q-values can result from either noise in spite of rhythmicity or from arrhythmicity. A more statistically sound way to detect arrhythmicity could for example be two-one-side tests (for example in the R package 'equivalence', e.g. see usage for time courses by Noordally et al. 2018, https://www.biorxiv.org/content/10.1101/287862v1).

      Thank you for pointing us in the direction of this package, we agree that choosing methods for circadian quantification and q-value cut-offs is always tricky and different approaches will perform better for noisier or non-sinusoidal waveforms. For future work, we will investigate the application of the suggested method in circadian rhythmicity analysis. However, we believe that the criteria used in this paper for rhythmicity quantification is suitable for addressing our questions, and overall, we are satisfied that rhythms with a q-value of >0.05 would also be classified by eye as being arrhythmic, and rhythms with a q-value Many other studies have used meta2d B.H q-values as a metric of rhythmicity: e.g. (https://bmcplantbiol.biomedcentral.com/articles/10.1186/s12870-022-03565-1 , https://link.springer.com/content/pdf/10.1186%2Fs12915-022-01258-7 , https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8782462/pdf/pcbi.1009762.pdf )

      • lines 480-484 and intro: In the introduction, the authors write that expression levels of clock components are important for the function of the clock, and that this is one motivation for the current study where polyploidisation is expected to affect the expression levels of clock genes and their outputs. I wonder what answers or speculations this study provides in the end, or whether such answers / speculations should be made clearer. For example, do the authors think that the higher variability of periods in wheat could be a consequence of lower robustness (in addition to possible spatial differences that are mentioned) due to polyploidisation? Is anything known about the period of rhythms of close wheat relatives that did not undergo polyploidisation? Did you look at dampening over the time course in wheat vs. Arabidopsis?

      The point above is an interesting one, and we thank the reviewer for raising it. We agree that the high variability of periods in wheat may be a product of polyploidisation, as functional redundancy between homoeologs may allow a tolerance for less tightly regulated, non-dominantly expressed circadian transcripts. We have now added this hypothesis to our discussion: Ln536-550.

      In our comparative analysis of period distributions, we looked at periods of transcripts from a diploid relative of hexaploid wheat, Brachypodium distachyon. In Brachypodium, period lengths have around the same SD as in Arabidopsis but the mean period length is slightly longer (Supplementary table 2). We have now edited our results to make the relationship between wheat and Brachypodium clearer (ln 109-110).

      Minor comments:

      Introduction - lines 49: it is unclear what is meant by ppd-1 at this position of the sentence

      We agree this was unclear and have revised it to “notably the ppd-1 locus within TaPRR3/7” Ln 52

      • line 54/55: clarify that this refers to Arabidopsis thaliana

      Corrected.

      Results - line 69 and 76: cite references for these tools here (not only in the methods section)

      Corrected.

      • line 90-93: Why wouldn't the same thing happen on subsequent subjective evenings?

      Thank you for your comments. We have now combined our hypothesis for why wheat transcripts expressed at dusk have longer periods with the discussion about longer periods upon transfer to constant light. We think that the two processes are likely to be connected and have now placed them together in Ln 126-131.

      The behaviour of mean period lengths of wheat transcripts upon transfer to constant light was unexpected and we believe is quite interesting. One explanation is that the influence of the ongoing light zeitgeber when dusk was expected causes a delay in the expression of evening peaking genes which are delayed by the continuous light signal. Then, on subsequent evenings the influence of the diel dusk signal is ‘forgotten’ as the governance of the endogenous clock takes over. The very long period observed at 0-24h (28.61h) may be due to a phase shift rather than an intrinsic lengthening of period per se. Whether this trait is unique to wheat or can also be seen in other plant species is, to our knowledge, unknown.

      • line 118: what is your defined cutoff for significance of the Chi square test (p=0.03 not regarded significant?)

      The reviewer is completely right, we have now clarified this. Ln 145-149

      • figure 1h,i: In order for the reader to see whether A and D (Figure 1h) or A (figure 1i) are indeed arrhythmic, one would need to see plots with a normalisation as done in figure 1m for 1l.

      We have now removed the triad showing one rhythmic gene and two arhythmic genes (as Fig. 1h already illustrates this type of circadian imbalance) and replaced this with a side by side comparison of how imbalance in rhythmicity differs from imbalance in relative amplitude as suggested.

      • figure 1h-m (and others with circadian time course traces): could a measure of variation (e.g. SD, SEM, confidence interval) be plotted as a shaded region around the curves (unless they're so small that they are there but not visible)?

      We have now added error bars to these plots to show standard deviation between replicates, in Fig. 1 h, j, k and l. We could not think of an accurate way to display this information for the mean normalised data (Fig 1. i and m) so have not put error bars on these plots.

      • line 139 (also in 737 and 450): give reference to Ramirez-Gonzalez et al in the same style as the rest of the manuscript (number)

      Thank you for raising this, we believe we have corrected all in-text citations (both narrative and fully parenthetical form) for consistency with the APA format used by the majority of Review Commons affiliate journals.

      • Clustering (modules): What is the reason for choosing 9 clusters? Was this number optimised or chosen for other reasons?

      WGCNA uses an unsupervised clustering algorithm that works within the supplied parameters to determine the optimum number of clusters to explain the dataset, without prior specification of the number of clusters. We have amended the manuscript text to clarify this Ln237-239.

      • lines 280 - 284: The TaELF3-1D phenotype could be explained a bit better to the non-wheat specialist, for example by mentioning in the beginning of this set of sentences.

      Done (Ln 314-318).

      • The authors present an analysis of TF binding sites. Can they say something about binding sites in a less sophisticated manner, such as on some very well-known motifs in promoters like the evening element?

      We agree that this is a very interesting question, and one that we may investigate in more detail with our data in the future. In this paper, we performed a global analysis of wheat TFBS predicted from orthologous Arabidopsis TF targets. These targets have been experimentally validated in Arabidopsis using DAP-seq, but we have not validated that these binding sites exist in wheat promoters. We therefore took a tentative approach, and presented only enrichments at the superfamily level rather than talking about specific regulatory motifs.

      The evening element would fit most likely fit within the MYB or MYB-related TFBS superfamily, however the diversity of transcription factors in this family means that there is significant enrichment of these TFBS in multiple modules throughout the day (Supplementary Figure 11). In summary, a more in depth TFBS analysis of known circadian motifs is of great interest, but we feel would be a substantial work in its own right.

      • Figure 1h-l: If known or meaningful, it would be interesting to know the gene identities behind the triads shown, as in supplementary figure 5.

      These triads were selected as case studies to exemplify the ways in which we were defining imbalanced circadian triads. They have no particular relevance to the figure, but out of curiosity, these are the closest Arabidopsis orthologs for the triads displayed in Fig. 1:

      Triad 408 has highest identity to a hypothetical protein (AT4G26415).

      Triad 2454 is similar to AT3G07600, a heavy metal transport/detoxification superfamily protein

      Triad 13405 is similar to AT3G22360, encoding an ALTERNATIVE OXIDASE 1B, AOX1B

      Triad 10854 is similar to NSE4A, a δ-kleisin component of the SMC5/6 complex, possibly involved in synaptonemal complex formation (AT1G51130).

      Information about wheat gene names in each triad and their Arabidopsis orthologs can be viewed in Supplementary Table 11, so that readers can search for genes of particular interest to them.

      • Figure 4 and text: The illustration of starch metabolism is very helpful. However, I think the paper would benefit from giving a better reason for the selection of this specific set of processes, for example by relating these findings to functional differences in starch metabolism in the two species (in contrast to Arabidopsis, wheat stores little starch in leaves but uses fructans as main reserve carbohydrate)? Are there known differences in the dynamics of starch degradation during the night?

      The reviewer raises an interesting point, and we have now clarified in our results that the stated differences between starch regulation in Arabidopsis and wheat was part of the motivation behind studying this pathway. Starch is at the centre of plant primary metabolism as a carbon storage source and is arguably one of the most important features that breeders look for in regard to grain filling and yields. Additionally, it is of interest to circadian biologists as starch (as well as sucrose) have been shown to transiently cycle and to be regulated by the circadian clock. However, in wheat, carbon storage primarily uses sucrose rather than starch, and we have now added sucrose to Figure 5 to place it in this context. We think your suggestion has now improved our explanation for why we focused on starch in the manuscript, and we are grateful for your input (Ln 408-421).

      We also agree that the differences in the ways that Arbaidopsis and wheat utilise starch versus sucrose, and perhaps the role that fructans have in as a reserve carbohydrate and in protection against freezing in wheat may be one of the reasons we are seeing differences in circadian regulation of starch. We have now added this to our discussion (Ln 584-592).

      • Figure 4: triose-phosphates can be transported in and out of the chloroplast, as is illustrated in the figure. However, the illustration looks as though they are converted to hexose phosphates during the transport process. In order to be consistent with other transport processes of the figure (maltose and glucose), triose-phosphate should be repeated on the cytosolic side.

      We have now amended this (new Fig. 5). Thank you for your feedback.

      Methods - line 543: if I understand correctly that triplicates were collected and analysed for each time point, '18 samples' is mis-leading (18 time points would be more accurate).

      We agree this was badly worded. Changed Ln 615.

      Supplementary - Supplementary figure 3: x axis label very small and contains typo

      Now corrected. Also enlarged axis for Supplementary Figure 2.

      • Supplementary table 1: Romanowski et al 2020 (add year), or use ref. number citation style as in the rest of the manuscript

      Thank you for raising this, we have now hopefully corrected all in text citations (both narrative and fully parenthetical form) to be consistent with APA format used by the majority of Review commons affiliate journals.

      • Supplementary table 9, primary metabolism: does bold highlighting of Arabidopsis accession numbers have a meaning or is it accidental?

      We apologise that this was unclear. We have corrected this. Supplementary Table 10 now also has a “Read me” tab which explains that table.

      Reviewer #2 (Significance (Required)):

      I believe this is a precious, carefully generated and analysed dataset which many biologists will benefit from, beyond wheat or circadian specialists. The dataset expands the knowledge of circadian transcriptome regulation to an important crop and contributes a resource of which only a handful of others exist in other species. Many high impact papers on RNAseq include some follow-up on candidates, for example in Romanowski et al 2020, which is admittedly easier to do in Arabidopsis than wheat due to the availability of genetic resources.

      My expertise: Plant circadian clock (Arabidopsis), dataset analysis (but not specifically for RNAseq)

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

      This manuscript is based on the analysis of a single experiment consisting in transcriptomic profiling of one (hexaploid) wheat genotype along 3 days (samples taken every 4 hours). The experiment is performed in constant light conditions, allowing detection of transcripts controlled by the circadian clock. The bioinformatic analysis studies the dynamics of the different homoeologous transcript in the polyploid genome and compares cycling transcripts in wheat with what is known from Arabidopsis.

      The manuscript is well written, the methods are correct, the analysis performed is sufficiently extensive and the figures are clear. The manuscript finds interesting expression patterns among homeologous genes, and goes into detail on important differences in circadian regulation of relevant gene families between Arabidopsis and wheat. The work is purely descriptive and does not aim at associations with physiological phenotypes, but the bioinformatic analysis is very thorough and uncovers interesting examples.

      Only one caveat: For what I gather, there is no replication in the RNA-seq experiment, although the exact method does not appear in the text. From the Methods section: "tissue was sampled every 4h for 3 days (18 samples in total)" and "At each timepoint, we sampled the entire aerial tissue from 3 replicate plants". Whether these samples were pooled or not is not described. The "Data Availability" section links to 18 RNA-seq paired end libraries, which suggest that the replicates were pooled, although some type of barcoding might have been used. The text should mention if the replicates were pooled or not, and, if so, what was the method used for poling (tissue, RNA or libraries). Even in the case of no biological replication the manuscript brings interesting insights into wheat transcriptomics and circadian biology. The editor (or the rules of the journal) should decide if they accept articles with no "real" biological replication (I am sure we all understand by now the benefits and limitations of pooling biological replicates into a single RNA-seq library).

      There was replication within the RNA sequencing experiment, and we apologise that this was unclear from our manuscript. Each timepoint consisted of three independent biological replicates. We have now created a new “Experimental context” section in the results to explain this (Ln 74-82) and have clarified in the methods how our data was processed (Ln 609-615 and 636-638).

      We have now included an additional matrix with TPMs at the replicate level to assist readers in looking at specific genes of interest (Supplementary Table 12).

      Minor comments:

      The description of the experimental setup in the first sentence of the Results section is too brief. Could you please talk about for how long the experiment was running? At what intervals the samples were taken? What conditions were used?

      We apologise that this was unclear. We hope that the new Experimental Context section, added in response to comments from several reviewers, makes this much clearer, alongside the clarification in the methods (Ln 609-615 and 636-638).

      Line 280: "...due *to* an introgression..."

      Corrected. Ln 315

      The legend of Figure 3l says elf4 instead of elf3

      We thank the reviewer for noticing this mistake that we have now corrected.

      Line 306 "says Supplementary Note 7 instead of Supplementary Note 7

      We are not sure what is to be corrected here!

      Reviewer #3 (Significance (Required)):

      This works advances our knowledge on how genome wide expression levels are controlled by the circadian clock in polyploids. Although previous works had performed similar analyses in other polyploid plants, this is the first time this is done in an hexaploid. This work is a starting step to understand gene regulation in this important crop, and have interest for researchers working in fundamental and applied plant biology.

      Thank you for your positive comments and your feedback in improving this manuscript. We would like to clarify that to our knowledge, this work presents the first analysis of a circadian transcriptome in a polyploid crop. The work by Greenham et al, although undoubtably providing insight into circadian regulation of ancient paralogs, was performed in the diploid Brassica rapa.

    1. Author Response

      Reviewer #1 (Public Review):

      The authors have developed cochlear implant prototypes with microcoils that allow magnetic stimulation of spiral ganglion neurons instead of conventional electrical stimulation. The neuronal response at the cortical level was evaluated in a mouse model. Magnetic stimulation was compared to acoustic stimulation and conventional electrical stimulation. The results obtained by the authors demonstrated a better spatial selectivity with a better dynamic.

      The article is well written with an introduction and a problematic allowing to understand the goal of the work by readers not expert of the domain. The scientific approach is logical and progressive allowing to explain the work in a very educational way. The figures are clear and illustrate the quality of the work.

      Here are my comments:

      Concerning the methodology and in particular the electrical stimulation, it would be necessary that the authors specify that the stimulation was monopolar. this choice of stimulation involves a more important diffusion of the current. This makes the comparison with magnetic stimulation more flattering.

      In the discussion, several points should be addressed to better explain to the reader the interest and the limits of the chosen technology. I think that you should start by reminding the reader that there are other modes of electrical stimulation than monopolar stimulation. bipolar or tripolar stimulation can reduce the diffusion of the current to improve selectivity. this stimulation strategy is already used by some manufacturers in the clinic.

      We agree with the comment and have added language to the Discussion section to remind the reader that the electric stimulation was delivered in a monopolar configuration and that bipolar, tripolar and focused multipolar stimulation strategies would all provide narrower spreads of activation. Comparisons to micro-coil stimulation will be conducted in a future project.

      [Line 336] “In this study, electric stimulation was delivered in a monopolar configuration. Other configurations, e.g., bipolar, tripolar, and focused multipolar result in improved spatial selectivity in both animal models (Snyder et al., 2008, Bierer et al., 2010, George et al., 2015) and human trials (van den Honert and Kelsall, 2007) although at the expense of increased thresholds (Bierer and Faulkner, 2010, Zhu et al., 2012, George et al., 2015). Due to the small size of the mouse cochlea, it was not feasible to test configurations that required the insertion of two or more electrodes into the cochlea. In addition, the advantage of multipolar stimulation is less obvious in species with smaller cochleae, e.g., even in the gerbil cochlea difference in spatial spread between monopolar and bipolar stimulation was not significant (Dieter et al., 2019). Nevertheless, it will be still interesting to compare spreads from micro-coils to the diverse configurations of electric stimulation in future studies.”

      In the animal model used, it is likely that even in spite of recent hearing loss, the trophicity of the spiral ganglion is preserved. This does not reflect the pathological conditions of the implanted patients. Thus, it is not at all certain that the better selectivity is the better dynamics observed with magnetic stimulation can be observed in case of damaged spiral ganglion.

      This is a good point – it is a limitation of our work and we have modified the text to remind the reader of this possibility. Because one of the main goals of our study was to compare the spread of activation across different stimulation modalities, all SGNs needed to be viable so as to not introduce any bias e.g., tonotopic sections without SGN innervation might obscure the measurement of spectral spread. In future studies, it will be essential to test magnetic stimulation in a model of neonatally deafened animals to further evaluate the translational potential of magnetic stimulation to human subjects.

      [Line 388] “In the present study, it is likely that most SGNs were intact since our deafening procedure mainly targeted hair cells. Maintaining uniform survival of SGNs was essential to ensure accurate comparison of the spread of activation across modalities, however, this situation does not uniformly reflect the pathological conditions of all implanted patients. Patients typically receive CIs months or years after the onset of deafness and often have considerable SGN loss (Khan et al., 2005; Nadol and Eddington, 2006). Thus, in future studies, it will be necessary to test coil effectiveness in neonatally deafened animals so as to more closely mimic pathological conditions of implanted patients.”

      If the passage of current in the microcoils generates a magnetic field, it is possible that an inverse effect, or even a heating effect, could be observed if this type of implant is subjected to an external magnetic field, as in an MRI. Have the authors considered this potential disadvantage in view of a clinical transfer of this technology?

      This is an important concern. Bonmassar and Serano (2020) conducted a study addressing this question in micro-coils for deep brain stem stimulation and compared micro-coils to a typical wire implant in a 1.5T MRI. Their results showed warming of the implant in both groups, however, the degree was far less in the microcoils (<1° C), than in the wire (~10° C). Conventional electrode-based cochlear implants have been also evaluated in 1.5T MRI, where a slight degree of warming was observed, however, less than seen in lead wires (Bonmassar and Serano 2020, Zeng et al., 2018). Nevertheless, we agree that it is important to point out this potential limitation and have added the following paragraph.

      [Line 381] “Another potential concern would be the compatibility of implanted micro-coils with strong exogenous magnetic fields. A previous study has tested the effect of 1.5T exogenous magnetic field on micro-coils and electric wire-based implants designed for deep brain stem stimulation (Bonmassar and Serano, 2020). Their results showed warming of the implants in both groups, however, the degree was far less in the micro-coils (<1° C) than in the electric wires (~10° C). Nevertheless, testing the effect of exogenous magnetic fields on coil-based CIs will be crucial for the translation of this technique to humans.”

      Reviewer #2 (Public Review):

      Lee, Seist et al. investigated whether magnetic stimulation of the cochlear would lead to less spread of activity - a major limitation of classical cochlear implants used nowadays - than electrical stimulation. To do so, they measured neuronal responses in the inferior colliculus of mice to acoustic, electric, and magnetic stimulation of the cochlea. The acoustic stimulation consisted of 5 ms long pure frequency tones covering the range from 8 to 48 kHz, whereas the magnetic and electrical stimulations were pulses of 25 um duration presented at a rate of 25 pulses/s delivered at 2 locations along the cochlear (one basal, one apical). The neuronal responses were measured along a 16-channel recording array inserted along the tonotopic axis of the inferior colliculus. The results demonstrate that magnetic stimulation elicited responses that were more spatially constrained and had a larger dynamic range than electrical stimulation. As one of the main limitations of the cochlear implants used nowadays is the large spatial spread of stimulation, these data bring a lot of hope for improving this neuroprosthetic technology and put magnetic stimulation as one of the most promising approaches to improve cochlear implant technologies.

      The conclusions of the paper are mostly well supported by data, but some aspects of the experimental procedure, the neuronal response acquisition, and the data analysis need to be clarified and extended.

      1) From the current description, it is not clear whether the recording electrode stays at the same location for the acoustic, magnetic, and electrical stimulation, or whether it is removed and reinserted. If it is removed and reinserted, it might be that slightly different regions of the IC are recorded from, or that the brain gets slightly damaged on every new insertion. A more detailed quantification of the brain state or neuronal responses would then be a welcome addition. This could be done in several ways. For example, the spontaneous activity or general excitability of IC neurons could be compared across the three different stimulation paradigms in the few experiments they were performed in the same mice (l. 407). Another possibility would be to compare electrical stimulation responses when performed before vs. after the magnetic stimulation (l. 403). More generally, any possible paired-statistical analysis (i.e., when the same recording sites were used to compare the different stimulating methods) would be welcome. Related to my previous comment, it is written that “experiments were terminated when responses to magnetic stimulation were no longer robust” (l. 406). Why would responses lose robustness? If this is due to damage of the recorded neurons or to cochlea damage, it will most probably also affect the results overall and hence the conclusions of the manuscript.

      The positioning of the recording electrode remained at the same location; we have added the following statement to clarify our methods:

      [Line 421] “After the original insertion into the inferior colliculus, the position of the multielectrode recording array was not repositioned while switching from one stimulus modality to the next (acoustic, electric, magnetic). Due to the fragility of the recording electrode array, we took extra care to avoid disturbing the skull, as dislodgement of the array would have altered tonotopicity and thus weakened the ability to accurately compare spectral spread between trials.”

      To minimize the potential for pain or discomfort to the animal, experiments were terminated when vital signs, such as heart rate and respiration rate, declined; this typically occurred at about 5 – 7 hours after onset of the experiment. Such declines were typically preceded by a decline in inferior colliculus responses. We have modified the language in the manuscript to make this clearer.

      [Line 474] “Experiments were terminated whenever the animal’s vital parameters, as measured by heart and respiratory rate, declined. The decline was typically observed at around 5 – 7 hours and preceded by a decline in inferior colliculus responses.”

      2) In a number of figures, only example data are presented (Figures 2, 3, 6). To give the reader the possibility to judge the variability of the results across different experiments (and hence the robustness of the results), it would be important to show also average values, or - in cases this is not relevant - at least 3 example mice.

      We agree with this concern and have added more data for ABR and IC responses in the supplement figure section (Figure 3 – figure supplement 1; Figure 4 – figure supplements 1 and 2). We also present data points for individual samples in each plot. All source data used to make figures have been uploaded to the repository following the guideline of the eLife journal. We believe this will help interested readers assess our results quantitatively.

      The advantages and limitations of magnetic stimulations are well described in the introduction and discussion sections and leave the reader with the information that is needed to evaluate the potential strengths and weaknesses of the technique. These sections also nicely emphasize that future experiments have to be performed to further characterize this stimulation strategy.

      Reviewer #3 (Public Review):

      This article describes a new way to activate auditory nerve fibers (ANFs) by magnetic stimuli (generated by micro-coils) instead of electrical currents (generated by conventional electrodes). The activation of ANFs triggered by the micro-coils seems clear but several physiological quantifications are inappropriate and the major claims are based on a very small number of experiments. I sincerely encourage the authors to continue their experiments and use more straightforward ways to quantify their results (closer to the raw data) to progress toward clarifying their effects.

      In the case of severe and profound deafness, cochlear implant is the solution to recover partial hearing and speech understanding. Cochlear implant is probably the most successful neuroprothesis but it still has limitations, especially as it is difficult to focus the currents inside the cochlea, the electrodes being in contact with a conductive liquid named perilymphe.

      In this study, the authors aim at describing a new way to activate the auditory nerve fibers (ANFs) by the use of small coils (micro-coils) which are supposed to confine ANF activation more narrowly than can be achieved with conventional electrodes used in cochlear implants. The authors recorded neuronal activity from the inferior colliculus (a subcortical auditory structure) and claim that the spread of activation is narrower with magnetic stimulation compared to electric stimulation. They also point out that the dynamic range is wider with the magnetic stimulation than with electric stimulation. Finally, they show that the evoked responses in the inferior colliculus also occurred in mice chronically deafened indicating that the micro-coils directly activate the ANFs. Activation of the ANFs triggered by the micro-coils seems clear, however, to what extent this activation differs between electric and magnetic stimulation; and differs with acoustic stimulation is unclear. Several basic quantifications are missing and the quantifications performed here are not appropriate. In addition, all the claims are based on very small samples.

      For most of the study, our conclusions are based on a sample size (N = 6-12) that is in line with similar types of studies. Our statistical calculations provided enough power for significant results. We agree with the reviewer that it would be desirable to have performed more than N=2 experiments with chronically deafened animals. However, constraints arising from the COVID-19 pandemic as well as the relocation of Dr. Stankovic’s laboratory, made it impossible to perform these additional experiments. We acknowledge that only limited conclusions can be drawn from the experiment with 2 animals (i.e., the result from chronically deafened animals; Figure 7), but nevertheless, feel the result is worth presenting.

      Quantification of the frequency response area FRA using the d’ index is very puzzling. If the authors want to quantify the breadth of the tuning curves they can use the Q10dB, the Q40dB or the Octave distance which are classically used in auditory neuroscience. Comparing the different levels of stimulus intensity to determine the breadth of tuning to sounds and to electric/magnetic stimuli does not make any sense.

      In general, we tried to use conventional methods so that readers can readily understand and interpret our results. We acknowledge that measuring the spread of activation at certain dB levels above threshold is commonly used to evaluate responses to acoustic stimulation. However, we felt that the use of the classic Q10dB or Q40dB measures to compare the spread of activation across different modalities was less suitable since each modality has a different dynamic range. For example, the dynamic range of electric stimulation was only ~ 3 dB, while that of acoustic stimulation was more than 20 dB. Therefore, we adopted an approach based on fixed significance of response strengths, i.e., measuring at an identical discrimination index. In this way, the estimation of the spread of excitation becomes independent of the stimulus’s nature and makes neural activation by different modalities more comparable. This approach is similar to that used in many previous studies in which new stimulation paradigms were evaluated (Middlebrooks et al., 2007; Bierer et al., 2010; Moreno et al., 2011; Richter et al., 2011; George et al., 2015; Xu et al., 2019; Dieter et al., 2019; Keppeler et al., 2020) and thus allows the performance of micro-coils to be more easily compared. Nevertheless, we agree that providing more detailed explanations would be helpful to many readers and have added additional language in the Method section of the revised manuscript.

      The italicized text indicates passages from the revised manuscript: [Line 528] “The value of d’ represents the distance between the means in units of a standard deviation – the larger the d’ value, the more separated the distributions are.”

      [Line 532] “To estimate the spread of activation from acoustic stimulation, previous studies measured the width of IC activation at a sound pressure level of 10 - 40 dB above threshold. However, given that dynamic ranges are significantly different across modalities (e.g., the dynamic ranges of acoustic and electric stimulations are 25.96 ± 9.17 dB SPL and 3.24 ± 0.99 dB mA, respectively.), comparing spatial spreads at a fixed dB level above threshold was not feasible. Alternatively, some studies measured spatial spreads at different dB levels above threshold for different modalities., e.g., 20 dB and 6 dB above threshold for acoustic and electric stimulation, respectively (Snyder et al., 2004). More recent studies that have evaluated novel stimulation modalities and compared them to acoustic and/or electric responses compared spatial spreads at a given response strength, typically at cumulative d’ values of 2-4 (Middlebrooks and Snyder, 2007, Bierer et al., 2010, Moreno et al., 2011, Richter et al., 2011, George et al., 2015, Xu et al., 2019, Dieter et al., 2019, Keppeler et al., 2020). Thus, to remain consistent with these previous studies, we also compared spectral spreads from acoustic, magnetic, and electric stimulation at cumulative discrimination indexes of 2 and 4.”

      We have also plotted the cumulative d’ index with respect to dB levels above threshold for each modality (Figure 2 – figure supplement 2) and added relevant descriptions in the Materials and Methods section. We believe these will facilitate understanding of our results by readers, especially those who are accustomed to the analysis based on fixed dB levels.

      [Line 550] “On average, the cumulative d′ levels of 2 and 4 correspond to 7.23 ± 5.34 and 18.53 ± 9.94 dB SPL above threshold for acoustic stimulation, 0.47 ± 0.30 and 1.41 ± 0.53 dB 1 mA above threshold for electric stimulation, and 2.57 ± 1.33 and 7.98 ± 5.41 dB 1 V above threshold for magnetic stimulation (Figure 2 – figure supplement 2).”

      Quantification of the spectral spread of activation used in figure 4A-B is not correct. Based on the 11 animals tested with ipsilateral tones (and not contralateral tones), the authors estimated that each electrode corresponds to a particular frequency, then the between-electrode distance is converted in an octave distance. First, what is the purpose of converting distances into octave? In fact, there is no possibility to calibrate the acoustic stimuli and the electric/magnetic stimuli the same way: we cannot know if a particular sound intensity (e.g. 80dB) corresponds a particular voltage (for magnetic stimulation) or intensity (for electric stimulation). By using ipsilateral sounds instead of contralateral sounds, the authors largely underestimated the acoustic inputs reaching the recording sites (because the main ascending pathways cross the midline between the cochlear nucleus and the superior olivary complex). Therefore, the comparisons between acoustic and electric/magnetic activation cannot be properly assessed, which is the crucial part of this paper.

      As the reviewer mentioned, we converted the distance of activated electrodes to octave distance based on the characteristic frequency of each electrode derived from Figure 2D. This translation provides an estimate of the activated frequency band across the tonotopic organization of the cochlea by stimulation and previous studies evaluating novel methods of artificial stimulation presented the spread of activation by artificial stimulation in a similar way (Dieter et al., 2019; Keppeler et al., 2020). Therefore, we felt the use of this approach would provide the most direct comparison to previous work. Nevertheless, we agree that quantifying the activation spread by electrode distance would be more intuitive to some readers and have added the corresponding plots in Figure 5.

      We thank the reviewers for highlighting the anatomy of the auditory pathway and, specifically, its crossing over the midline. The wording in the original manuscript was confusing as all modes of stimulation (acoustic, electric, magnetic) were delivered to the left cochlea and responses measured from the right inferior colliculus (IC); the side to which stimulation was delivered was referred to as ipsilateral and the opposite side was referred to as contralateral. We revised the wording as shown below and believe it will greatly reduce the potential for confusion.

      [Line 100] “We stimulated the left cochlea with acoustic, electric, and magnetic stimuli and measured responses from a 16-channel recording array implanted along the tonotopic axis of the right (contralateral) inferior colliculus (IC) in anesthetized mice (Figure 1C; MATERIALS AND METHODS).”

    1. SciScore for 10.1101/2022.05.13.491770: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">4 colonies from each transformed plate were randomly picked and the insert was checked by performing colony PCR using nested PCR primers.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cells were incubated with an anti-SARS-CoV spike primary antibody directly conjugated with alexaflour-647 (CR3022-AF647) for up to 4 hours at room temperature.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-SARS-CoV spike</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Ten million PBMCs of select COVID-19 recovered donors were stained with RBD-Alexa Fluor 488 for 1 hour at 4°C, followed by washing with PBS containing 2% FBS (FACS buffer) and incubation with efluor780 Fixable Viability (Live Dead) dye (Life Technologies, #65-0865-14) and anti-human CD3, CD19, CD20, CD27, CD38 and IgD antibodies (BD Biosciences) for 30 minutes.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-human CD3</div><div>suggested: (RayBiotech Cat# CS-11-0105, RRID:AB_1227994)</div></div><div style="margin-bottom:8px"><div>CD19</div><div>suggested: (Agilent Cat# TC67401, RRID:AB_579635)</div></div><div style="margin-bottom:8px"><div>CD20</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>CD27 , CD38</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>IgD</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">mAb antibody binding was then detected with 50 μl/well of MSD SULFO-TAG anti-human IgG antibody (diluted 1:200) incubated for one hour at room temperature with shaking at 700rpm.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-human IgG</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Briefly, 100 pfu of SARS-CoV-2 (2019-nCoV/USA_WA1/2020), Alpha, Beta, Gamma, Delta and Omicron (BA.1 and BA.2) were used on Vero TMPRSS2 cells.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero TMPRSS2</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">IC50 titers were calculated by non-linear regression analysis using the 4PL sigmoidal dose curve equation on Prism 9 (Graphpad Software).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Graphpad</div><div>suggested: (GraphPad, RRID:SCR_000306)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Data were analyzed using FlowJo software 10</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>FlowJo</div><div>suggested: (FlowJo, RRID:SCR_008520)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">CryoEM data analysis and model building: CryoEM movies were motion-corrected either in Motioncorr2 in Relion3.0 (30) or using Patch motion correction implemented in Cryosparc v3.3.1 (31).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Cryosparc</div><div>suggested: (cryoSPARC, RRID:SCR_016501)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The combined Focused Map tool in Phenix was used to integrate high resolution locally refined maps into an overall map.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Phenix</div><div>suggested: (Phenix, RRID:SCR_014224)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Glycans with visible density were modelled in Coot (36).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Coot</div><div>suggested: (Coot, RRID:SCR_014222)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Model validation was performed using Molprobity (37).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Molprobity</div><div>suggested: (MolProbity, RRID:SCR_014226)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Figures were prepared in ChimeraX(34) and PyMOL (39).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>PyMOL</div><div>suggested: (PyMOL, RRID:SCR_000305)</div></div></td></tr></table>

      Results from OddPub: Thank you for sharing your data.

      Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


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    1. SciScore for 10.1101/2022.05.10.491301: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">Field Sample Permit: All experiments with mice, hamsters, and macaques were carried out in accordance with the Regulations in the Guide for the Care and Use of Laboratory Animals of the Ministry of Science and Technology of the People’s Republic of China.<br>IACUC: All animal procedures were approved by the Institutional Animal Care and Use Committee of the Institute of Medical Biology, Chinese Academy of Medical Science.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">The 6- to 8-year-old male or female rhesus macaque experiments were performed in the animal biosafety level 4 (ABSL-4) facility at Wuhan Institute of Virology (WIV), Hubei, China.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The following primary antibodies were used in this study: anti-SARS-CoV-2 (2019-nCoV) Spike Antibody (40589-T62, Sino Biological), and anti-GAPDH Antibody (60004, Proteintech).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-SARS-CoV-2</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-GAPDH</div><div>suggested: (Proteintech Cat# 60004-1-Ig, RRID:AB_2107436)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The secondary antibodies used were Peroxidase AffiniPure Goat Anti-Rabbit IgG (H+L) (111-035-003, Jackson ImmunoResearch), Peroxidase</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Anti-Rabbit IgG</div><div>suggested: (Jackson ImmunoResearch Labs Cat# 111-035-003, RRID:AB_2313567)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For determination of S-specific antibody response, plates were incubated with goat anti-mouse IgG HRP (for mouse sera, Proteintech Cat: SA00001-1) or goat anti-Syrian hamster IgG HRP (for hamster sera, abcam Cat: ab6892) or goat anti-monkey IgG HRP (for NHP sera, Invitrogen Cat: PA1-84631) at 37°C for 1 hour and then substrate tetramethylbenzidine (TMB) solution (Invitrogen) was used to develop.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-mouse IgG</div><div>suggested: (Proteintech Cat# SA00001-1, RRID:AB_2722565)</div></div><div style="margin-bottom:8px"><div>anti-Syrian hamster IgG</div><div>suggested: (Abcam Cat# ab6892, RRID:AB_955427)</div></div><div style="margin-bottom:8px"><div>anti-monkey IgG</div><div>suggested: (Thermo Fisher Scientific Cat# PA1-84631, RRID:AB_933605)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cell lines and antibodies: HEK 293F cells were grown in FreeStyle Media (Gibco-Thermo Fisher Scientific) and transiently transfected using polyethylenimine (PEI) (Polysciences, Inc.) in an 8% CO2 environment at 37°C.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK 293F</div><div>suggested: RRID:CVCL_6642)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">HEK 293A and Vero E6 cells were maintained in high glucose DMEM(GIBCO) supplemented with 10% FBS(GIBCO) and 1% penicillin/streptomycin (P/S) (GIBCO) in a 5% CO2 environment at 37°C.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK 293A</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Virus titration: Virus titrations were performed by endpoint titration in Vero E6 cells.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero E6</div><div>suggested: RRID:CVCL_XD71)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After 1 hour of incubation, 100 μL mixtures were inoculated onto monolayer Vero cells in a 24- well plate for 1 hour with shaking every 15 minutes.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Organisms/Strains</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Animal vaccination and serum collection: Mice: For mouse vaccination, groups of 6- to 8-week-old female BALB/c mice or female K18-hACE2 Transgenic Mice were intramuscularly immunized with LNP vaccine candidates or a placebo in 50 μL, and 3 weeks later, a second dose was administered to boost the immune responses.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>BALB/c</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cloning, expression, and preparation of the RQ3013 encoded Spike proteins: The gene encoding the RQ3013 was fused with a C-terminal twin Strep-tag (LEVLFQGPSGS WSHPQFEK GGGSGGGSGGSA WSHPQFEK) and cloned into a mammalian cell expression vector pcDNA3.1.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pcDNA3.1</div><div>suggested: RRID:Addgene_79663)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The resulting plasmid, pcDNA3.1-RQ3013-Twinstrep, was transformed into HEK 293F cells using polyethylenimine (PEI) in FreeStyle Media (Gibco-Thermo Fisher Scientific).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pcDNA3.1-RQ3013-Twinstrep</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Other procedures of cryo-EM data processing were performed within RELION v3.1 or CryoSPARC v3 using the dose-weighted micrographs23, 24.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>RELION</div><div>suggested: (RELION, RRID:SCR_016274)</div></div><div style="margin-bottom:8px"><div>CryoSPARC</div><div>suggested: (cryoSPARC, RRID:SCR_016501)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Briefly, the initial templates were fit into the map using Chimera and Coot28, followed by a ten-cycle rigid body refinement using Phenix.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Phenix</div><div>suggested: (Phenix, RRID:SCR_014224)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Then, a combined manual refinement and real-space refinement were carried out for both prefusion state and postfusion state S structures in Coot and Phenix29.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Coot</div><div>suggested: (Coot, RRID:SCR_014222)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The dose-response curves were plotted from viral RNA copies versus the drug concentrations using GraphPad Prism 8.0 software.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad Prism</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Statistical Analysis: All statistics data were performed and graphed using GraphPad Prism8.0.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


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      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.

      </footer>
    1. SciScore for 10.1101/2022.05.08.491108: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">IRB: HAARVI) study and was approved by the University of Washington Human Subjects Division Institutional Review Board (STUDY00000959).<br>Consent: All donors provided written informed consent for the use of blood and blood derivatives (such as peripheral blood mononuclear cells, sera or plasma) for research.<br>Euthanasia Agents: At day 4 post-inoculation, the animals were euthanized with an excess of anesthetics (ketamine and xylazine) and exsanguination.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">For the results shown in Fig.4 (A-C), 64 male golden Syrian hamsters (Mesocricetus auratus; RjHan:AURA) of 5-6 weeks of age (average weight 60-80 grams) were purchased from Janvier Laboratories (Le Genest-Saint-Isle, France) and handled under specific pathogen-free conditions.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">Contamination: Cell lines were routinely tested for mycoplasma contamination.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">goat anti-human IgG secondary antibody (Southern Biotech, 2040-04) was added and incubated for 1 h at room temperature.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>goat anti-human IgG secondary antibody</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-human IgG</div><div>suggested: (SouthernBiotech Cat# 2040-04, RRID:AB_2795643)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Plates were washed 4 × in TBST, then anti-human (Invitrogen) horseradish peroxidase-conjugated antibodies were diluted 1:5,000 and 50 μL added to each well and incubated at 37°C for 1 h.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-human</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Twenty-four hours before infection, the hamsters received an intraperitoneal injection of different concentrations of the hamsterized monoclonal antibodies (mAb) S309 (0.6, 1.7, 5 and 15 mg/kg), S2X324 (0.2, 0.6, 1.7 and 5 mg/kg) or the control isotype MGH2 (15 mg/kg).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>S309</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cell lines: Cell lines used in this study were obtained from ATCC (HEK293T and Vero E6), Thermo Fisher Scientific (Expi-CHO-S cells, FreeStyle 293-F cells and Expi293F cells) or Takara (Lenti-X 293T cells)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293T</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>293T</div><div>suggested: KCB Cat# KCB 200744YJ, RRID:CVCL_0063)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For pseudoviruses expressing spike substitutions that resulted in decreased infectivity using Vero E6 cells, Vero E6-TMPRSS2 cells were substituted as target cells for neutralization assays.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero E6-TMPRSS2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cell-surface mAb-mediated S1 shedding: CHO cells stably expressing the prototypic SARS-CoV-2 S were harvested, washed in wash buffer (PBS 1% BSA 2 mM EDTA) and resuspended in PBS.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>CHO</div><div>suggested: CLS Cat# 603479/p746_CHO, RRID:CVCL_0213)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Vero E6 cells were seeded into 12 well plates overnight.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero E6</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After a 25-minute incubation, Jurkat cells stably expressing FcγRIIIa receptor (V158 variant) or FcγRIIa receptor (H131 variant) and NFAT-driven luciferase gene (effector cells) were added at an effector to target ratio of 6:1 for FcγRIIIa and 5:1 for FcγRIIa.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Jurkat</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">ADCC assays were performed using SARS-CoV2 CHO-K1 cells (genetically engineered to stably express a HaloTag-HiBit-tagged) as target cells and PBMC as effector cells at a E:T ratio of 30:1.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>CHO-K1</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Organisms/Strains</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The coordinates of the Wuhan-Hu-1 RBD were obtained from PDB 6M0J (42) for which ACE2 was removed and as previously described (25) the glycan at position 343 added in the RBD using ISOLDE (99) to visually place, link and minimize each monosaccharide beyond the N-acetylglucosamine for which there was density.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Wuhan-Hu-1 RBD</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After 30 min incubation, absorbance at 405 nm was measured by a plate reader (Biotek) and data were plotted using Prism GraphPad 9.1.0.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Prism GraphPad</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Plates were immediately read at 450 nm on a VarioSkanLux plate reader (ThermoFisher) and data plotted and fit in Prism (GraphPad) using nonlinear regression sigmoidal, 4PL, X is log(concentration) to determine EC50 values from curve fits.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Prism</div><div>suggested: (PRISM, RRID:SCR_005375)</div></div><div style="margin-bottom:8px"><div>GraphPad</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Binding at each time point (MFI) was determined normalizing to the MFI at 5 minutes time point and data plotted using GraphPad Prism v.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad Prism</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Two rounds of reference-free 2D classification were performed using CryoSPARC (80) to select well-defined particle images.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>CryoSPARC</div><div>suggested: (cryoSPARC, RRID:SCR_016501)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Model building and refinement: UCSF Chimera (87) and Coot (88) were used to fit atomic models into the cryoEM maps.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Coot</div><div>suggested: (Coot, RRID:SCR_014222)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Data were analyzed using FlowJo software.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>FlowJo</div><div>suggested: (FlowJo, RRID:SCR_008520)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The 3 RBD structures were each parameterized using tleap with the Amber ff14SB force field (100) for the protein, GLYCAM_06j-1 for the glycan (101), TIP3P for water (truncated octahedral cell with 18 Å buffer around solute) (102); the Joung & Cheatham parameters (103) were used for the ions neutralizing the charged solute (2 Cl- ions for Wuhan-Hu-1, 6 Cl- ions for BA.1 and BA.2) and the additional 0.15 M excess Na+ and Cl- ions (80 Na+ and 80 Cl- for Wuhan- Hu-1, 76 of each for BA.1 and BA.2).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Amber</div><div>suggested: (AMBER, RRID:SCR_016151)</div></div></td></tr></table>

      Results from OddPub: Thank you for sharing your data.

      Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. SciScore for 10.1101/2022.05.10.491266: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">Consent: Tissue samples obtained from surgical resections were obtained under a protocol approved by the Stanford University Human Subjects Research Compliance Office (IRB 15166) and informed consent was obtained from each patient before surgery.<br>IRB: The research protocol for donor samples was approved by the DNW’s internal ethics committee (Research project STAN-19-104) and the medical advisory board, as well as by the Institutional Review Board at Stanford University which determined that this project does not meet the definition of human subject research as defined in federal regulations 45 CFR 46.102 or 21 CFR 50.3.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">Case 1 was a male organ donor aged 62.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">The experiments were not randomized and investigators were not blinded to allocation during experiments and outcome assessment.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">The experiments were not randomized and investigators were not blinded to allocation during experiments and outcome assessment.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">No statistical methods were used to predetermine sample size.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">Contamination: Cell lines: VeroE6 cells were obtained from ATCC as mycoplasma-free stocks and maintained in supplemented DMEM (DMEM (Dulbecco’s Modified Eagle Medium) (Thermo 11885-092) with 1X L-glut (Thermo SH30034),</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">AlexaFluor plus secondary antibodies (488 plus, anti-mouse, Invitrogen A32723; 750, anti-rabbit, Invitrogen A21039) were used at 1:1,000.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-mouse</div><div>suggested: (Thermo Fisher Scientific Cat# A32723, RRID:AB_2633275)</div></div><div style="margin-bottom:8px"><div>anti-rabbit</div><div>suggested: (Innovative Research Cat# A21039, RRID:AB_1500687)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The following primary antibodies were used at 1:100: CD68 (mouse, Abcam ab955), RAGE (rabbit, Abcam ab216329).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>CD68</div><div>suggested: (Abcam Cat# ab955, RRID:AB_307338)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The “EPCAM-CD31-” flowthrough was collected and stained with CD206 antibodies conjugated to biotin (Miltenyi 130-095-214), washed twice with MACS buffer, then stained with Anti-Biotin MicroBeads (Miltenyi 130-090-485) and passed through an LS MACS column on a MidiMACS Separator magnet, designated “EPCAM-CD31-CD206+”.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>CD206</div><div>suggested: (Miltenyi Biotec Cat# 130-095-214, RRID:AB_10827698)</div></div><div style="margin-bottom:8px"><div>Anti-Biotin</div><div>suggested: (Miltenyi Biotec Cat# 130-090-485, RRID:AB_244365)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cell lines: VeroE6 cells were obtained from ATCC as mycoplasma-free stocks and maintained in supplemented DMEM (DMEM (Dulbecco’s Modified Eagle Medium) (Thermo 11885-092) with 1X L-glut (Thermo SH30034),</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>VeroE6</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">HeLa/ACE2/TMPRSS2 cells were a generous gift from Dr. Jesse Bloom at the Fred Hutchinson Cancer Research Center.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HeLa/ACE2/TMPRSS2</div><div>suggested: JCRB Cat# JCRB1835, RRID:CVCL_B3LV)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Plaque assay: VeroE6 or VeroE6/TMPRSS2 cells were plated at 4.5-5 x 105 cells/well in a standard 12-well tissue culture plate (Falcon) one day prior to infection.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>VeroE6/TMPRSS2</div><div>suggested: JCRB Cat# JCRB1819, RRID:CVCL_YQ49)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">, Spike pseudotyped lentiviruses encoding a nanoluciferase-tdTomato reporter were produced in HEK-293T cells (5 × 106 cells per 10-cm culture dish) by co-transfection of a 5-plasmid system as described previously58.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK-293T</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">On day 1, mAbs were serially diluted in D10 medium and then mixed with lentivirus (diluted in D10 medium, supplemented with polybrene (Sigma-Aldrich, TR1003), 1:1000, v/v) for 1 hr before being transferred to HeLa-ACE2/TMPRSS2 cells.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HeLa-ACE2/TMPRSS2</div><div>suggested: JCRB Cat# JCRB1835, RRID:CVCL_B3LV)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Based on the original lentiviral backbone plasmid (pHAGE-Luc2-IRES-ZsGreen, Addgene 164432), we replaced the Luc2-IRES-ZsGreen reporter with a cassette encoding H2B fused to Nanoluciferase (Promega) to minimize background luminescence, followed by a T2A self-cleaving peptide, and tdTomato fluorescent protein using gBlock synthesis (Integrated DNA Technologies).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pHAGE-Luc2-IRES-ZsGreen</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The 5-plasmid system includes a packaging vector (pHAGE-H2B-NanoLuc-T2A-tdTomato), a plasmid encoding full-length Spike with a 21-residue deletion on the C-terminus (pHDM SARS-CoV-2-SpikeΔ21), and three helper plasmids (pHDM-Hgpm2, pHDM-Tat1b, and pRC-CMV_Rev1b).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pHAGE-H2B-NanoLuc-T2A-tdTomato</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pHDM-Tat1b</div><div>suggested: RRID:Addgene_164442)</div></div><div style="margin-bottom:8px"><div>pRC-CMV_Rev1b</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Viral pseudotime analysis: For viral pseudotime analysis, computations were performed in R using the Seurat package (v3).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Seurat</div><div>suggested: (SEURAT, RRID:SCR_007322)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">We used STAR v.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>STAR</div><div>suggested: (STAR, RRID:SCR_004463)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">2.7.5a as the aligner and aligned fastq files from all infections to our custom built Human GRCh38 (GENCODE v29) and SARS-CoV-2 WA1 (GenBank: MN985325.1) reference.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GENCODE</div><div>suggested: (GENCODE, RRID:SCR_014966)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Micrographs were acquired with laser scanning confocal fluorescence microscopy (Leica Stellaris 8) and processed with ImageJ and Imaris (version 9.2.0, Oxford Instruments).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>ImageJ</div><div>suggested: (ImageJ, RRID:SCR_003070)</div></div><div style="margin-bottom:8px"><div>Imaris</div><div>suggested: (Imaris, RRID:SCR_007370)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Arcplots depicting number of subgenomic junctions was plotted using a custom Python function (available on Github).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Python</div><div>suggested: (IPython, RRID:SCR_001658)</div></div></td></tr></table>

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

      Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


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

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      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity):

      The manuscript by Tran et al. describes the mechanism by which IFNa treatment prevents the development of liver CRC metastasis in several mouse models. They show how continuous administration of IFNa strength liver vascular barrier by a direct effect on endothelial cells and avoids the trans-sinusoidal migration of tumour cells.

      Major points:

      1. Authors use an elegant orthotopic model of liver metastasis to confirm the effect of continuous IFNa on hepatic colonization (Fig.3). Although they extensively characterize the metastatic lesions, they do not show data on the potential impact of IFNa treatment in the primary caecum tumour. Authors should clarify if the described effects are taken place in the liver or/and in the caecum. It would be interesting to show if IFNa affects the primary tumour size, the extravasation of cancer cells and the immune infiltration since all these factors could have an impact in the number of liver lesions.

      We thank the reviewer for acknowledging the importance of our results particularly in the context of the orthotopic mouse model we developed. We agree that displaying the results of continuous IFNα therapy on primary intracecal tumors, as well as the results pertaining to the few mice that develop microscopic or macroscopic liver metastasis, is important for the interpretation of our work. Thus, we evaluated the dimension of primary intracecal CRC lesions (Fig 3D,E) and we performed additional IHC characterization of the primary tumors (Fig S4A,B). The analysis showed that the dimension of the primary lesions and the markers we analyzed were non significantly modified by continuous IFNα therapy (Fig 3D,E and Fig S4A,B). These results favor the hypothesis that IFNα therapy does not modify the number of cells that spread from the primary tumors and seed into the liver, but it rather impinges on the intravascular containment of CRC cells circulating within the liver (Fig 3F). As said earlier, the data also highlight the possibility that CRC tumors may become refractory to IFNα or that the dose and schedule we adopted does not significantly affect the growth of established liver CRCs at late time points. The data are also consistent with results obtained with MC38Ifnar1_KO CRC cells indicating that continuous IFNα therapy does not require Ifnar1 expression by tumor cells to exert its antimetastatic function (Fig 4A,C-D). This is also in line with the high IFNα concentrations required to activate the "tunable" direct antiproliferative functions of this cytokine that exceed those achieved in our system (Catarinella et al, 2016; Schreiber, 2017). Text has been added in the revised manuscript at lines 175-197 and in the discussion lines 425-431.

      1. Figure 3f right shows liver images without any obvious metastatic lesion. Since authors are analysing the effect of IFNa treatment in proliferation, vascularization and immune composition in liver tumours, they may show and quantify images with metastatic lesions and restrict the analysis to the tumour area.

      Since the main finding of our manuscript regards the prevention of hepatic colonization by continuous IFNα therapy, we think that the original data presented in Fig 3G,H are representative of the overall efficacy of our strategy that confers protection in up to 60% of the mice carrying intramesenteric tumors of increasing dimensions (Fig 3H). We have thus maintained our original results, adding the quantification of all IHC data on groups of Sham control livers (n=6), as suggested. In any case, we also included the same IHC characterization of the few and small intrahepatic lesions that have bypassed the intravascular antimetastatic barrier (Fig S4C,D). Indeed, in agreement with the results observed in primary intracecal lesions, these metastatic lesions that developed in IFNαtreated mice showed similar markers of cell proliferation, neoangiogenesis, F4/80 macrophages and CD3+ T cells, as control lesions detected in NaCl-treated mice. Once again, the results highlight the possibility that CRC tumors, once established as micro/macroscopic metastases, may become refractory and resistant to IFNα therapy by downregulating the Ifnar1 in various components of the tumor microenvironment (Boukhaled et al., 2021; Katlinski et al., 2017). Text has been added in the revised manuscript at lines 175-197 and in the discussion lines 496-515.

      1. Authors analyse the recombination efficiency of different mouse CRE lines by non-quantitative methods (PCR of hepatic genomic DNA and GFP expression by immunofluorescence in healthy liver). Since PDGFRβ-Cre/ERT2 and CD11c-Cre lines are used to exclude a role of IFNa on the targeted cells, authors should provide stronger evidences to support this. They may consider studding the ablation of Ifnar1 in FACS sorted fibroblasts and myeloid cells. Moreover, it would be important showing the proportion of GFP+ cells in the sorted populations to understand how broadly these stromal populations are targeted.

      We thank the referee for raising this important issue, which is related to the relative efficiency of Ifnar1 recombination in each of the Cre-expressing mouse models we have used in the study. To this regard, we newly performed an extensive colocalization analysis quantifying the percentage of GFP+ cells that colocalize with cell specific markers (i.e., PDGFRβ, CD11c, F4/80 and CD31) of the various mouse models (PDGFRβCreERT2, CD11cCre and VeCadCreERT2, respectively) crossed with RosaZsGreen reporter mice. Colocalization analysis of GFP in the different systems was performed using the ImageJ “colocalization” algorithm developed by Pierre Bourdoncle (Institut Jacques Monod, Service Imagerie, Paris; 2003–2004). The method allows the generation of unsupervised profiles of co-localized pixels between two channels. This methodology has been included in the section Methods and Protocols, line 806-809. Of note, we observed an almost complete recombination in liver fibroblast (GFP+/PDGFRβ+), with about 98.2 ± 0.72% hepatic stellate cells that co-expressed GFP+ and PDGFRβ+ signals (see the new Fig S5E). Similarly, hepatic DCs (GFP+/CD11c+) had 94.17 ± 2.16% colocalization, while F4/80+ KCs or LCMs (GFP+/F4/80+) colocalized in 78.14 ± 5.03% (see the new Fig S5E). Finally, HECs, including LSECs, (GFP+/CD31+) showed 85.3 ± 5.03% colocalization (see the new Fig S5E,F), with no expression of GFP signals in cells other than CD31+. Note that these values indicate an almost complete colocalization of the Cre recombinase in the target cell types analyzed (see representative IF shown in Fig S5E). Text has been added in the revised manuscript at lines 225-233. Moreover, DEGs analysis between NaCl-treated VeCadIfnar1_KO and Ifnar1fl/fl HECs showed a significant downregulation of Ifnar1 expression in CD31+ VeCadIfnar1_KO cells, with a log2 fold-change of -0.387 and an adjusted p-value of 0.033, further confirming Cre recombination in HECs isolated from VeCadIfnar1_KO mice (as depicted in the heatmap of Fig 6B; the 12th gene of the Type I IFN response is Ifnar1). We have prepared all source images at higher dimension to better appreciate the colocalization within liver microvasculature. In addition, we performed several flow cytometry analyses to identify liver cell populations of Cre-recombinant mice that express Ifnar1. Unfortunately, the predicted low cellular surface expression of this molecule coupled with the experimental conditions needed to extract viable non-parenchymal cells from the liver have prevented us from obtaining informative results.

      1. Ifnar1 ablation in VeCad+ cells prevents the effect of IFNa on tumour growth (Fig. 4d), suggesting the existence of anti-tumour mechanisms beyond the effects on hepatic colonization. Authors may consider checking proliferation, vascularization and immune infiltration in these tumours to enhance their conclusion.

      We fully agree with the referee’s concern and as above mentioned, we have followed his/her suggestion and examined the existence of antitumor mechanisms beyond the effects on hepatic colonization in VeCadIfnar1_KO mice treated with NaCl or IFNα. To this end, 4 NaCl-Ifnar1fl/fl, 7 IFNα-Ifnar1fl/fl, 4 NaCl-VeCadIfnar1_KO and 4 IFNα-VeCadIfnar1_KO mice were intrasplenically injected with MC38 CRC cells (Fig S7A,B). Twenty-one days after injection, mice were euthanized and their livers analyzed for tumor size, proliferation, signs of angiogenesis (as denoted by CD34 staining) and immune infiltration (F4/80+ macrophages and CD3+ T cells). Consistent with data presented in Fig 4D, histological analysis showed that Ifnar1fl/fl mice did not develop liver metastases in IFNα-treated mice. Furthermore, metastatic lesions detected in VeCadIfnar1_KO mice treated or not with IFNα did not show significant differences in Ki67 positivity, CD34 staining or the amount of F4/80+ resident macrophages and CD3+ T cells. This further supports that the antimetastatic potential of IFNα therapy may be primarily depend on the inhibition of hepatic trans-sinusoidal migration, a limiting step in the metastatic cascade that could secondarily influence colonization and outgrowth (Chambers et al, 2002). Corresponding text has been added at lines 248-252.

      1. Immune properties of LSECs are analysed in vivo by using a mouse CRE line that targets all endothelial cells, including those ones located in lymphoid organs, and evaluating T cell composition in the spleen. I found difficult to conclude that these properties are exerted directly by LSECs and not by other endothelial cells in vivo. To clarify the local effect of LSECs in modulating anti-tumour immunity, T cell composition and activation should be checked in tumours shortly after tamoxifen administration.

      We thank the reviewer for pointing out this issue, which cannot not be tested directly because - as also mentioned by reviewer 2 - LSEC-specific Cre-recombinant driver mice do not exist . As also indicated in the cited literature, central memory T cells accumulate after peripheral priming in secondary lymphoid organs such as the spleen (Sallusto et al, 2004; Stone et al, 2009; Yu et al, 2019). To this end, the generation and regulation of antitumor immunity is a highly orchestrated multistep process involving the uptake of tumor-associated antigens by professional APCs, their time-consuming migration to draining lymph nodes and the generation of protective T cells. Unlike other APCs, HECs/LSECs do not need to migrate to draining lymph nodes to activate effector T cells, leading to a rapid intrahepatic CD8+ T cell activation. In this context, LSECs must not only efficiently uptake, process and present CRC-derived antigens coming from intravascularly contained tumor cells, but they also require the attraction and retention within the liver micro-vasculature of T cell populations necessary for the generation of effective antitumor immune responses, where chemokines play an important role (Lalor et al, 2002). As shown in Fig 6A-C, two prominent chemokines (Cxcl10 and Cxcl9) required for T cell recruitment to the liver are specifically upregulated only in HECs/LSECs from IFNα-treated Ifnar1fl/fl mice, whereas HECs from VeCadIfnar1_KO mice maintained low expression of these chemoattractants in both NaCl- and IFNα-treated mice. These data are also consistent with the in vitro cross-priming results (see Fig 7A,B) showing that in the absence of IFNα, HECs have a low capacity to prime naïve T cells (Katz et al, 2004), indicating that LSEC-primed by tumor-derived antigens coming from apoptotic intravascular CRC metastatic cells play an important role in inducing tolerance (Berg et al, 2006; Katz et al., 2004), especially when CRC cells quickly extravasate and position within the space of Disse, likely becoming less accessible to intravascular patrolling by naïve and effector T cells (Benechet et al, 2019; Guidotti et al, 2015). On the contrary, in IFNα-treated Ifnar1fl/fl mice, CRC cells are rapidly contained in the liver microvasculature (Fig 5A,B) with CRC-derived antigens that could be immediately taken up by LSECs due to their anatomical proximity and efficient endocytosis capacity, which is among the highest of all cell types in the body (Sorensen, 2020). Here, the continuous sensing of IFNα by LSECs upregulates several genes related to antigen processing and presentation pathways (Fig. 6B,D), leading to efficient cross-priming of tumor-specific CD8+ T cells to the same extent as professional APCs, such as splenic DCs (Fig 7B). Text has been added in the revised manuscript at lines 496-515. Finally, regarding the suggestion to analyze the role of HECs/LSECs in inducing antitumor T cell immunity shortly after tamoxifen administration, while we agree that it would be interesting to analyze HEC/LSEC-mediated T cell activation by treating NaCl- and IFNαtreated Ifnar1fl/fl and VeCadIfnar1_KO mice with tamoxifen after CRC cell injection, we would like to point out that tamoxifen treatment will not only induce Cre recombination and Ifnar1 loss on endothelial cells but it may also induce several “off-target” effects complicating the interpretation of the results. Indeed, tamoxifen is known to i) inhibit the in vitro proliferation of several CRC cell lines (Ziv et al, 1994), ii) impair the growth of CRC liver metastases in vivo (Kuruppu et al, 1998) and iii) modify matrix stiffness to reduce tumor cell survival (Cortes et al, 2019). Further, as IFNα modifies the hepatic vascular barrier and the accessibility of antigens by LSECs, the specific timing of tamoxifen treatment could also affect the immunological consequences of Ifnar1 deletion making these experiment impractical. For these reasons, we’d like not to perform the suggested experiment with tamoxifen.

      Reviewer #1 (Significance):

      The conclusions of this study are consistent with previously published literature and the biological insights are potentially useful to the cancer biology community.

      Reviewer #2 (Evidence, reproducibility and clarity):

      In this study Dr. Sitia's group investigated the effect of IFNα1 as perioperative agent preventing liver metastasis formation of colorectal carcinoma (CRC). To this end, various mouse models were used such as liver colonization models, i.e. intrasplenic and mesenterial injections of MC38 and CT26 CRC cell lines. Besides, spontaneous metastasis of CRC was analyzed by orthotopic injection of MC38 into the cecum. To study the influence of IFNα1 in these settings mini-osmotic pumps releasing IFNα1 were used. Moreover, conditional mouse models with a cell-type specific deficiency of Ifnar1 were compared. Altogether, the application of IFNα1 led to a reduction in liver colonization of CRC in all models studied. This was ascribed to decreased trans-sinusoidal migration of CRC and increased cross-priming by LSEC entailing in T cell activation.

      Major comments:

      Overall the study is well performed and the major conclusions seem to be drawn well. However, there are certain points I like to address:

      • First, the authors started their experiments with MC38 and CT26 CRC cell lines. At the end they just applied MC38. The rational behind this should be clearly stated. Second, as in their previous publication (Catarinella et al, 2016) F1 hybrids of C57BL/6 x BALB/c mice were used for the experiments. However, I believe that the genetic heterogeneity might be strongly increased by this approach which might lead to difficult reproducibility of the results.

      We thank the referee for raising this important issue; additional text describing the reason of our choice has been introduced at lines: 203-205. We respectfully disagree with the comment that CB6F1 hybrids may increase genetic heterogeneity and impair reproducibility of our results. Each CB6F1 hybrid individual is genetically identical to its littermates, sharing 50% of genes of each parental mouse line and being tolerant to reciprocal MHC-I genes (thus permitting the correct engraftment of both cell lines). We agree that the use of mismatched backcrosses after the F1 generation would increase genetic heterogeneity and thus may affect outcome. This is also the reason why we could not perform experiments with CT26 in the Ifnar1fl/fl conditional lines that are in C57BL/6 background and would have needed at least 10 generations of backcrossing in the BALB/c background before being suitable to such experiments. Finally, all experiments described in Fig 4, 5, 6 and 7 were performed in C57BL/6 mice using MC38 CRC cells with results that reproduced those obtained in CB6F1 hybrids, and very similarly to what we have previously reported with MC38 in C57BL/6 mice (see Fig 5 (Catarinella et al., 2016)).

      • At page 16 the authors conclude that "patients suffering from chronic liver fibrotic disease... display lower incidence of hepatic metastases". In the community there is contradictory data (see Kondo et al, BJC, 2016, https://www.nature.com/articles/bjc2016155). This should be precisely discussed, otherwise this claim should be removed.

      We thank the referee for raising this issue and modified the discussion accordingly. Text has been added in the revised manuscript at lines 455-457.

      We agree with the reviewer's suggestion and added new text to recognized the interplay between different cell types such as dendritic cells within the hepatic niche (see new text at lines 505-515).

      • Last, multiple times the authors write about data that is "not shown". Please either include these data in the manuscript or delete corresponding phrases because it is not possible for the reader to scrutinize it.

      We fully agree with the referee’s concern and displayed all “not shown results” in Fig S1E and Fig S9C-I.

      • Besides, I suggest additional experiments further substantiating the study:
      • To see if this effect of IFNα1 is cell type-specific liver metastasis of other solid tumors such as breast cancer or melanoma should be investigated.

      We agree with the reviewer's suggestion, as also indicated in our original discussion. We believe that additional experiments with other solid tumor cell lines would be important to generalize the potential of perioperative IFNα therapy. In particular, we believe that pancreatic ductal adenocarcinoma (PDAC), a highly lethal disease that most commonly metastasizes to the liver (Lambert et al, 2017), may benefit from our approach. It should be noted, however, that the pleotropic nature of IFNα allows this cytokine to inhibit tumor growth by several mechanisms. Above all, the ability of IFNα therapy to directly reduce tumor growth depends on the relative surface expression of Ifnar1 on each tumor cell and the ability to maintain such expression in the harsh tumor microenvironment during IFNα therapy. As the degradation of Ifnar1 by CRC tumors has been well described (Katlinski et al., 2017), it is possible that CRC tumors thus escaping the antitumor properties of endogenous type I interferons may respond less efficiently to therapeutic IFNα regimens such as those herein described. This notion is consistent with our data on primary orthotopic tumors (Fig. 3D,E), which are no longer responsive to continuous IFNα therapy as early as 7 days after implantation of CT26LM3 cells. In addition, the definition of the HEC/LSEC antimetastatic barrier has been possible only because CRC cells are not directly susceptible to the IFNα antiproliferative activity, which we observed in vitro at extremely high IFNα dosages (Catarinella et al., 2016) but not in vivo (as formally demonstrated by using MC38Ifnar_ko cells, Fig 4A). At any rate, we followed the reviewer’s suggestion and performed an additional experiment in which we intramesenterically injected the PDAC cell line Panc02 (H-2b, C57BL/6-derived) (Soares et al, 2014) into C57BL/6 mice 7 days after of NaCl or IFNα therapy initiation. As shown below, MRI analysis at day 21 showed that none of the IFNα-treated Panc02 challenged mice developed metastatic lesions, while NaCl controls displayed a high metastatic burden that required euthanization for ethical reasons of about 67% of these mice shortly after MRI analysis. These data indicate that perioperative IFNα therapy completely curbs metastatic development in IFNα-treated PDAC animals. The notion that these cells may be more IFNα-susceptible than CRCs may well depend on the relative capacity of the former cells to maintain Ifnar1 expression, as suggested by others (Zhu et al, 2014). Properly addressing the reviewer’s comment would thus require extensive investigations involving the establishment of new mouse models of metastases from other solid tumors, starting from the in vitro and in vivo regulation of surface Ifnar1 expression in each tumor cell. We strongly believe that this work has merit but we think that it should be reported separately.

      • The authors applied a broad range of cell type-specific mice. However, a thorough characterization of the deletion of Ifnar1 in the corresponding cell types is missing. This is crucial for the manuscript.

      We fully agree with the referee’s concern and as previously mentioned, we have improved the characterization of Ifnar1 deletion (see response to the same critique received from reviewer 1, comment 3).

      • The capillarization of the hepatic vascular niche is a crucial point in this story. I believe that the hepatic endothelium should be further characterized by additional vascular markers.

      In response to the reviewer’s suggestion, we have included in our analysis the characterization of Lyve-1, a marker of hepatic capillarization (Pandey et al, 2020; Wohlfeil et al, 2019). Indeed, IFNα treatment of Ifnar1fl/fl mice significantly increased the expression of Lyve-1, whereas IFNα treatment of VeCadIfnar1_KO mice showed no effect (Fig S9A,B), further corroborating our findings. Text has been added in the revised manuscript at lines 291-294. To better aid readers, we have prepared high-resolution images for each IF channel and have provided these data as source date for Fig S9A.

      • Last, the data and methods appear adequately presented and experiments seem to be reproducible. Just in Figure 4 the exact number of mice and replicates are not clearly presented. Otherwise, everything is fine.

      We thank the reviewer for raising this issue, which apparently was not properly described in our original submission. We have now included the exact number of mice in each experimental group in the figure legend to Fig 4.

      Minor comments:

      Overall the text and figures are accurately presented. However, I would like to add further minor comments:

      • In Fig. 1 you present the IFNα dosing regimen. How do you explain the decrease in serum IFNα after day 2? Besides, the data points at day 0 should be excluded since measuring startet from day 2! Why did you decide to treat for seven days until the start of the experiment? One could think 2 days might already be enough.

      We thank the reviewer for raising these important points. Regarding the pharmacokineticpharmacodynamic (PK-PD) behavior of our approach, we do not believe that MOP reduced its pumping efficacy after day 2 (Theeuwes & Yum, 1976), nor that counterregulatory mechanisms, such as the induction of anti-IFNα blocking antibodies, occurred in such a short time frame (Wang et al, 2001). It is neither feasible that IFNα treatment significantly downregulated Ifnar1 in the liver (as demonstrated by pSTAT1 activation after MOP treatment in Fig S1E). Rather, our results reflect the PK-PD behavior of other long-lasting formulations of IFNα, which depend on intrinsic pharmacological properties of IFNα already described in (Jeon et al, 2013). Text has been added in the revised manuscript at lines 110-112. We also corrected the figures in which we quantified serum IFNα. Indeed, blood was drawn one day before MOP implantation rather than on the same day of surgery to avoid additional blood loss, which could be a source of unnecessary stress for the animals. Therefore, we corrected the results section and Fig S1A-C and Fig 1A,B. The decision to start treatment 7 days rather than 2 days before seeding was made for several reasons: i) this study follows our previous gene/cell therapy approach, in which the time interval between reconstitution of the transduced bone marrow with Tie2-IFNα and tumor challenge was at least 7-8 weeks. We therefore thought that 7 days might be a sufficient/necessary time period to induce similar phenotypes in the liver after continuous IFNα administration; ii) 7 days is a time frame compatible with the perioperative period in humans (Horowitz et al, 2015). Furthermore, the side effects that patients may experience after IFNα therapy are generally limited to the first few days after administration, allowing patients to benefit from IFNα-induced vascular antimetastatic barriers at the time of surgery without potential side effects of IFNα. Because oncologic guidelines recommend starting adjuvant chemotherapy at least 4 weeks after surgery in stage 2-3 CRC patients at risk of later developing liver metastases (Engstrand et al, 2019; van Gestel et al, 2014), our proposed perioperative time frame does not even conflict with these indications (Van Cutsem et al, 2016). We have included additional text in the lines 131-132 to motivate the timing of our regimens.

      • Fig. 2: Did you check for metastases in other organs than the liver at the timepoint of euthanization, e.g. lungs. In the discussion section you talk about a potential influence of IFNα1 on other organs. Therefore, I think that the mice should be thoroughly analyzed and the data presented. The manuscript will benefit from it.

      We thank the reviewer for this valuable comment. Indeed, we always check for dissemination of CRC metastases on MRI analysis and necroscopy. As stated at lines 146-147 and 158 CRC tumors seeded in the liver vasculature after colonizing the liver do not spread to other organs such as the lungs. Indeed, CRC cells intravascularly seeded in the portal circulation, are trapped at the beginning of hepatic sinusoids because their diameter is bigger than that of liver sinusoids (Fig S8A,B). These micro-anatomic peculiarities are also thought to impede the spreading of tumor cells from periportal to centrilobular areas and to the general circulation (Catarinella et al., 2016; Vidal-Vanaclocha, 2008), and this is consistent with studies showing that in CRC patients undergoing surgery the majority of CRC-derived circulating tumor cells are found in the portal vein (Deneve et al, 2013).

      • Overall, MRI pictures and pictures of IHC or IF are sometimes too small to see. Please provide pictures with larger magnification or enlarge the images.

      We thank you for this suggestion and we have indeed increased the size of all MRI, IHC, and IF images to the maximum that will fit within the figure. In addition, we presented the images at the highest magnification available, without making digital enlargements that would significantly reduce resolution.

      • Fig. 3 F, G: immune cell infiltration in the liver was analyzed. Please compare it to untreated, tumor-free wildtype liver tissue.

      We appreciated the reviewer's suggestion and included the results of six Sham mice per each marker in our analysis. The text was added on the figure legends to Fig 3H and Fig S4B,D.

      • Fig. 6: the graphs are too small to be read, especially the volcano plot and the gene names of the heatmap.

      We increased the font size of genes in the volcano plots and heatmap in Fig 6A,B, as suggested.

      • Fig. S6: Pictures of co-immunofluorescences are presented. For the reader it is really hard to distinguish the stainings and to identify colocalized areas. Please provide pictures with one channel to better compare the marker expression.

      We thank the reviewer for pointing this out and we have tried to make each panel as large as possible to fit into a two-column figure. We have also prepared high magnification images of each channel for all immunofluorescence images, which we provide as source data. We hope that this is sufficient to help readers to interpret our results without increasing the number of main or supplementary figures.

      • From page 8 onwards (section about transgenic mice) LSEC was used as kind of synonym for hepatic endothelial cells. Since there is still no LSEC-specific driver mouse, it should be stated "hepatic endothelial cells" instead.

      We agree with this suggestion and thus have indicated that the results refer to HECs but include a large majority of LSECs. Indeed, LSECs make up the majority (~89%) of the total HEC population (Su et al, 2021). In addition, some SEM and TEM analyses were performed only on LSECs, as well as the IF analyses. Therefore, we believe that LSECs play an important role in this process. Although not specifically suggested, we have also changed the title of our manuscript to reflect the reviewer's suggestion. Thus, we propose "Continuous sensing of IFNα by hepatic endothelial cells shapes a vascular antimetastatic barrier" as new title.

      • P. 11: there is a typo: Fig. Fig. S6G,H

      We corrected this typo.

      • P. 13: the authors describe Gata4 as inhibitor of subendothelial matrix deposition. This should be precisely written, since Gata4 originally is described as master-regulator of liver sinusoidal differentiation which leads to liver fibrosis development upon loss of Gata4.<br /> Besides, I came across a study of the same group that investigated the role of Notch signaling in hepatic CRC and melanoma metastasis (Wohlfeil et al, Cancer Res, 2019, https://aacrjournals.org/cancerres/article/79/3/598/638600/Hepatic-Endothelial-Notch-Activation-Protects). Similar to your study they tie the reduction in hepatic metastasis to capillarization of the hepatic microvasculature.

      We agree with this suggestion and modified text accordingly. We are also glad that our results agree with previous reported literature that has now been correctly cited at lines 351-356 and in the discussion lines 474-476.

      • The discussion reads like paraphrasing the results section. The manuscript would clearly benefit if the discussion section had been rewritten short and concisely.

      We agree with this suggestion, and we have modified discussion accordingly. We are also willing to shorten the discussion by removing the schematic model that could possibly be used as a graphical abstract.

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      Reviewer #2 (Significance):

      • Since liver metastases of various tumor are tremendously hard to treat and mediates therapy resistance, the authors focus on a very important field of research - prevention of liver metastasis formation.
      • This study adds insights into the mechanisms of action of IFNα1 in the hepatic microenvironment. It extends previous findings of Toyoshima who described anti-tumoral effects of IFNα1 released by dendritic cells in the liver.
      • The study is well designed and will be of great interest for the scientific community. Besides, it will be appreciated by physicians, However, as mentioned in the discussion, further clinical studies by physicians are needed to translate its findings into the clinic.
      • The author of this review works as physician and often deals with liver metastasis. It is one field of focus of her/his research.
    1. Author response


      • A comment on the overall organization of the paper. Figure 2 has a major location in the paper, but it seems that its main takeaway is that these MAPs aren't really involved in the main process this paper is probing. While these are important findings, it might be more satisfying to move some of the central results earlier.

      We agree that this figure displays mostly negative results. However, most work on anaphase B microtubule dynamics from our group and others has focused on the effect that motors and MAPs may have on microtubule dynamics (EB1 and kinesin-8 in budding yeast, klp9 in fission yeast). Therefore, we consider it is important to clearly show that previously proposed candidates are not required for the observed decrease in microtubule growth speed, prior to introducing the unexpected effect of the membrane.

      *A model schematic might drive home the main finding of the paper, and be particularly useful for readers who are not experts in microtubule or spindle dynamics. That said, the Discussion does an excellent job of summarizing the findings and explaining the takeaway message(s), even for the non-expert.

      We have added a model schematic and we have referred to it in the main text.

      Specific comments

      • ‘In higher eukaryotes’ - Suggest avoiding the terms higher and lower when describing organisms, and instead, directly defining which organisms, for instance in animals/metazoans that would be a better description.

      We have removed this terminology.

      • Figure 1 E-F - It is hard to see the difference in the distribution, maybe a different color could be used instead of stars.

      We have used a different color.

      • Figure 1 Data shown in pink in G comes from 832 midzone length measurements during anaphase, from 60 cells in 10 independent experiments - The pink here does not correspond to the pink coding in D, consider colour choice for clarity across panels.

      We have changed this.

      • Finally, yeasts undergo closed mitosis - How does this relate to the findings in the Dey paper (cited here) which shows it was somewhat semi-closed or semi-open. According to the Dey paper, the membrane disassembles locally twice, at the SPB and the bridge.

      Membrane disassembly at the nuclear membrane bridge occurs at late anaphase, and leads to the disassembly of the spindle, presumably by the action of cytoplasmic factors (Dey et al. 2020). We do not believe the membrane disassembly itself has a role in spindle elongation or microtubule dynamics, as when it happens the spindle is then disassembled. However, the fact that les1D reduces the decrease in microtubule growth speed associated with internalisation of microtubules in the nuclear membrane bridge suggest that the organisation of the nuclear membrane bridge required for its local disassembly at late anaphase might affect microtubule growth (see section “Formation of Les1 stalks […]”).

      • ‘vertical comets in kymographs (Fig. 1C) do not correspond to non-growing microtubules, but rather microtubules that grow at a speed matching the sliding speed’- For clarity, it might be nice to add: "(as the SPB moves away from the plus end in the kymograph)".

      We have included this useful clarification.

      • ‘significantly shorter than in interphase, where growth events last more than 120 seconds on average [42, 43]. Microtubule shrinking speed did not change during anaphase either (Fig. 1-Supplement 1D), and was on average 3.56±1.75 μm/min, also lower than in interphase (~8 min/μm)’ - This comment concerns the comparison of growth and shrinking rate as well as growth duration. The authors did not measure microtubule dynamics in interphase in this manuscript but compared their numbers to literature values. The comparison raises some questions for three reasons: 1) the microscopy method used is different in this paper and the two references provided, 2) the sample is mounted differently compared to the two references provided - 1) and 2) combined could lead to different levels of stress on the cells which could affect MT dynamics-, 3) (probably the most important caveat) the experiments are done at different temperatures: 27C in this paper versus 25C in the references provided. Microtubule dynamics are sensitive to temperature so this could explain part of the differences observed. Also, there are multiple values published for MT dynamics in interphase depending on the strain used and the microscopy method used. Suggest that the authors measure microtubule dynamics in interphase cells at 27C in SIM to ensure that the differences are not due to the technical parameters employed. Small item - should ‘8 min/μm’ read “8 μm/min"?

      We have measured microtubule growth speed and growth event duration using GFP-Mal3 during interphase and anaphase B in the same conditions as proposed (see Figure 1 – Supplement 2). Unfortunately, shrinkage speed cannot be measured using GFP-Mal3, so we cannot confirm that the difference between our measurements and the literature values would be observed.

      • ‘we observed two populations of microtubules (fast and slow growing)’ - Does this statement about thistle fast and slow growing populations refer to the data in Fig. 1C and 2A?

      Yes, we have added reference to this figures in the next sentence (mentioned below).

      • ‘In some cells, all microtubules seemed to switch to the slow growing phase simultaneously (Fig. 1C), while in others fast and slow growing microtubules co-existed (Fig. 2A)’ - This is a very interesting observation, could we know how many cells (%) were detected in each case? Is it that in 90% of the cells the switch is simultaneous, and hence the microtubule growth is somehow synchronized? Or is it more random, e.g. around 50%?

      This was just to point the reader to two kymographs and show that a clear point where all microtubules change speed is not present in all kymographs, as one may think from Fig. 1C. Later in the paper, we show that the change in growth depends on whether the microtubule rescue occurs inside or outside the nuclear membrane bridge, so it is a matter of where microtubules are rescued once the dumbbell transition occurs, which is a stochastic process. We have added another sentence pointing the reader to examples in the kymograph (see line 152, This representation captures…).

      • On such a plot, the data points visibly cluster in two separate clouds and the variation of growth speeds can be fitted by an error function (Fig. 1F)’ - It is unclear that there are two distinct clusters, maybe the assertion should be toned down, or some sort of cluster analysis provided.

      We acknowledge that the data is widely spread across the y axis, and given that the magnitude “distance to the closest pole at rescue” is continuous the transition is not a clear cut. However, we consider the fact that the averaged curve closely matches the error function fit to be sufficient evidence for the existence of two populations of microtubule growth. Additionally, R2 of the fit is ~0.5 indicating that half of the variance is explained by this model. In any case, we show later that these two populations do exist (Fig. 3D), and why plotting microtubule growth against distance to the closest pole at rescue is a good way to segregate them (Fig. 3E).

      • ‘speed of interphase microtubules (~2.3 μm/min)’ - It would be interesting to see the dynamics in a les1 mutant (Dey Nature 2020) paper. Just as a control for presence/absence of the bridge?

      We thank the reviewers for kindly suggesting this interesting experiment. We have included it after the ase1 section. Les1 forms stalks at the edges of the nuclear membrane bridge that restrict nuclear membrane disassembly to the center of the bridge at the end of mitosis (Dey at al. 2020). While les1 deletion does not prevent the formation of the nuclear membrane bridge, it has been proposed that Les1 stalks may constitute sites of close interaction between the nuclear membrane and the spindle. Therefore, these sites may influence microtuble growth. Indeed, we have found that removing these Les1 stalks by either deleting les1 or nem1 leads to a smaller decrease in microtubule growth speed when plus ends enter the nuclear membrane bridge (see section “Formation of Les1 stalks […]”)

      *‘Figure 2, Transition from fast to slow microtubule growth occurs in the absence of known anaphase MAPs’ - It looks like the overlap zone is larger on the mal3 kymograph. Is the size of the midzone changed in some of the mutants? It could be important to report. Related to it, is the spindle length changed in some of the mutants? (It does not look like it from the kymographs displayed).

      The midzone is indeed longer in mal3D strains, now this can be seen in Fig. 2 – Supp. 2 and it is mentioned in the main text in line 272. As for the spindle length, diverse kinds of alterations in spindle length have been previously reported for the mutants that we used in this study. For instance, ase1D /cls1off cells have shorter spindles at anaphase onset (Loiodice et al. 2005 and data not shown), and klp5Dklp6D have longer spindles at anaphase onset (Syrivatkina et al. 2013). klp9D / clp1D / dis1D cells have lower spindle elongation velocity and may not reach the wild-type spindle length by the end of anaphase (Kruger et al. 2019). Despite these differences, the decrease in microtubule growth as a function of distance to the closest pole has a similar tendency across conditions, suggesting that the mentioned differences in spindle length are unlikely to have an important effect.

      • Additionally, adding the data about rescue localization in the mutant (equivalent of Fig 1 G) would be interesting to better describe the role of these different proteins. Figure 2, Panel G to L - Could the authors indicate the value for the average +/- error in each bin for the WT and the mutants? Also, it is hard to say from the plots, but it looks like the WT average speed in the first bin is different in every panel, that would be good to know to have an idea of the reproducibility/variability.

      We have added a figure with the rescue distribution (see Fig. 2 – Supp. 2). This apparent difference in the wt speed in different experiments might have come from looking at normalised data. The new way of representing the data in fig. 2H and J shows that the microtubule growth velocity in the wild-type is very consistent across experiments. We have added a table with microtubule growth velocity values (Table 1), and the source data is available.

      • The dots making up the "thick lines" are centered on 1.5/2.5/etc.. in some panels (G and K) and centered on 1/2/3/etc.. the others (I,J,L). Could the authors provide some clarification?

      We have fixed this inconsistency across the paper.

      • Figure 3 - Can the authors indicate the average values +/- error for each of the distributions in Fig. 3D? Maybe on the plot itself, in the legend or as a table. This would make them easily available without having to infer them from the Y axis. This comment is also valid for Fig 4I and 4J.

      We have added tables with average values and confidence intervals in the appendix.

      • Figure 3E ‘Distance from the plus-end to the nuclear membrane bridge edge at rescue as a function of distance from the plus-end to the closest pole at rescue’ - The Y axis reads as "distance to the bridge edge" but it shows negative values, could this be "position to the bridge edge" instead? (same item throughout the text).

      We have fixed this.

      • Figure 3 ‘Number of events: 442 (30 cells) wt, 260 (27 cells) klp9OE, 401 (35 cells) cdc25-22, from 3 independent experiments’ - P values this small raise a concern. Presumably the number of degrees of freedom in the regression analysis should not exceed the number of independent experiments. Instead, the DoF listed under "error" in the analysis output is hundreds or thousands instead of 3. To address this, the regression analysis should use either the "Error" function in R or a linear mixed-effects model to account for the nesting of the repeated measurements within each independent experiment. Alternatively, it is also possible to just calculate summary means for each independent experiment, and calculate p values based on that N=3. See: Lazic. Experimental Design for Laboratory Biologists. p. 157. and the supplemental file of: https://doi.org/10.1371/journal.pbio.2005282 and the additional file 1 of: https://doi.org/10.1186/s12868-015-0228-5 and this for an alternative plotting approach: https://doi.org/10.1083/jcb.202001064 Recommend either recalculating the p values by one of the methods above or removing the reported p values from the paper. The large effects observed in many cases are self-evident without a significance metric, so eliminating the p values would be acceptable here. (This comment applies to other figures through the paper that report p values based on number of cells or number of measurements instead of number of independent samples/experiments.)

      We thank the reviewers for suggesting the improvements to the statistical analysis, as well as for pointing us to useful resources that described the statistical methods and their implementation in detail. We have followed Aarts et al. 2015 and used a linear mixed effects model (see Methods>Statistical Analysis)

      Due to the change in statistical analysis method, to show that some of the differences we had reported previously were significant, we included more cells in the analysis from our existing data. We did this for klp5Dklp6D kymographs (Fig. 2I and Fig.2 – Supp. 1). Spindle dynamics in ase1D (Fig. 5D and Fig. 5 – Supp. 1) and klp9D (Fig. 2 – Supp. 3 A, C). Cell length (Fig. 3 – Supp. 1A).

      For the same reason, we measured anaphase spindle elongation velocity (Fig. 3 – Supp. 1C) from kymographs instead of measuring them from the 1 minute interval movies that we had used previously (from Fig. 3 – Supp 1B). We have reflected this in the methods (see added text in line 800 and deleted text in line 809 in the document with changes highlighted).

      None of these changes has altered our conclusions.

      • Figure 4 - Nice experiment. It brings the question of how cell-shape affects all these dynamics (probably out of the scope of this work). But a for3 mutant for example?

      This is an interesting suggestion, to be tested in the future. Furthermore, we believe that nuclear shape should also have an important effect, since the spindle is confined inside the nuclear membrane. We would expect that mutants that perturb nuclear shape might have effects on microtubule growth. We have observed that the decrease in growth speed associated with internalisation of microtubules in the nuclear membrane bridge is reduced upon nem1 deletion, which increases nuclear membrane surface, and produces membrane ruffling (Fig. 4-Supplement 2). However, nem1 deletion also removes les1 stalks from the nuclear bridge (Dey et al. 2020). It would be interesting to find a perturbation of the nuclear membrane that does not remove the les1 stalks.

      • ‘Ase1 is required for microtubule growth speed to decrease during anaphase B, this is unlikely to be a direct effect’ - If it is unlikely to be a direct Ase1 effect is the title of the section accurate? "Ase1 is required for normal rescue distribution and for microtubule growth speed to decrease in anaphase B"

      Ase1 recruits multiple proteins to the spindle midzone, so the fact that ase1 deletion produces a given phenotype does not necessarily mean that this phenotype results from the absence of Ase1 protein activity. For instance, deleting ase1 perturbs rescue distribution, but it does not mean that Ase1 acts as a rescue factor itself, or at least to a relevant extent, given that deletion of cls1 completely prevents rescue, but ase1 deletion does not. In the discussion we propose some indirect effects of ase1 deletion that may produce this effect. In any case, upon more careful analysis we have found that ase1 deletion does not prevent the decrease in microtubule growth speed during anaphase B, but rather makes it smaller (see section “The decrease in growth speed associated with internalisation of microtubules in the nuclear membrane bridge is reduced upon ase1 deletion”).

      • Figure 5 - What about an ase1 lem1 double mutant?

      We suppose that the intended gene is les1. We have studied the effects of les1 deletion in the new version of the manuscript. However, we do not see the information we would obtain from a double deletion ase1D les1D.

      • ‘In summary, Ase1 is required for rescue organisation and for microtubule growth speed to decrease during anaphase B ‘- In this context it could make sense to discuss the observations from this paper (doi:10.1371/journal.pone.0056808) about the role of Ase1 ortholog's MAP65-1 in coordinating MT dynamics within bundles.

      In the mentioned paper, the authors showed that the presence of PRC1 (ase1 orthologue) in bundles increases microtubule rescue rate, and that it slightly reduces microtubule growth speed.

      We observe a small increase in microtubule growth speed throughout anaphase upon ase1 deletion (Fig. 5), which is consistent with the in vitro observation that PRC1 decreases microtubule growth. However, once more this might not be a direct effect of Ase1, since less Cls1 is recruited if ase1 is deleted, and Cls1 reduces microtubule growth speed (Fig. 2). In addition, this can also be a result of higher concentration of tubulin / MAPs resulting from less polymerised tubulin in ase1 deleted cells, which have less spindle microtubules on average.

      Regarding the increase in rescue rate produced by PRC1 in vitro, it is possible that Ase1 contributes to microtubule rescue in the spindle. However, given that no rescues occur upon inactivation of cls1 (Bratman et al. 2007), we believe Cls1 is the dominant factor, and Ase1 contribution is likely negligible.

      • ‘We initially set the microtubule growth velocity to 1.6 μm/min (early anaphase speed, Fig. 1F), and aimed to reproduce the experimental distribution of positions of rescue and catastrophe at early anaphase (spindle length < 6 μm’ - Kudos to the authors for detailing the model and its parameters in a way that even non-modelling experts can understand.

      Discussion - ‘Our data suggests that microtubule growth speed is mainly governed by spatial cues’ - Is it right to assume that in the cases where fast and slow growing microtubules were simultaneously observed, the fast microtubules were not/had not yet reached the midzone?

      Our data suggests that it’s not about being inside the midzone, but rather inside the nuclear membrane bridge formed after the dumbbell transition. We have elaborated more on this in the main text, pointing the reader to examples in the kymograph, and giving a quantitative argument for distance to the closest pole being a better predictor than anaphase progression or position with respect to the center (which is equivalent to distance to the midzone), see line 152.

      • Methods - ‘PIFOC module (perfect image focus), and sCMOS camera’ - Is this Nikon's "Perfect Focus" autofocus, or some other manufacturer's system? And back-thinned sCMOS.

      We have clarified this in the Methods section.

    1. 14. A. Lacis, W. Wang, J. Hansen, NASA Weather and Climate Science Review (NASA Goddard Space Flight Center, Greenbelt, Md., 1979). 15. R. A. McClatchey et al., U.S. Air Force Cambridge Res. Lab. Tech. Rep. TR-73-0096 (1973). 16. R. E. Roberts, J. E. A. Selby, L. M. Biberman, Appl. Opt.15, 2085 (1976). 17. O. B. Toon and J. B. Pollack, J. Appl. Meteorol. 12, 225 (1976). 18. R. D. Cess, J. Quant. Spectrosc. Radiat. Transfer 14, 861 (1974). 19. W. C. Wang and P. H. Stone, J. Atmos. Sci. 37, 545 (1980). 20. R. D. Cess, ibid. 35, 1765 (1978).

      Experimental studies are vital to the construction of accurate climate models. These studies include measurements of the absorption of radiation by gases, aerosols, and the Earth's surface to supply parameters for programs that predict energy flows through the atmosphere.

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      Reply to the reviewers

      1. General Statements

      We thank the reviewers for their careful and constructive analysis of our work. Our manuscript aims to exemplify the use of cryo-soft-X-ray tomography (cryoSXT) as a technique to study the dynamic changes to host-cell morphology that accompanies virus infection. This emerging method has several strengths when compared to other ultrastructural analysis techniques. Specifically, cryoSXT does not require the addition of contrast agents and therefore samples can be prepared via plunge cryopreservation alone, allowing us to capture them in a near-native state. Furthermore, the penetrating power of soft X rays and large field of view in cryoSXT allow rapid data acquisition, facilitating quantitative analysis of 10s to 100s of individual cells. We combined high-throughput cryoSXT data collection with semi-automated tomogram segmentation and fluorescence cryo-microscopy to study a recombinant herpes simplex virus (HSV)-1 that produces a pattern of fluorescence indicative of the stage of the infection in a single cell (‘timestamp’ HSV-1) and quantitatively monitored changes in lipid droplet, vesicle and mitochondrial morphology as HSV-1 infection progresses. In response to the reviewers’ comments, we have expanded our analysis of lipid droplet morphology, identifying a transient increase in the size of lipid droplets at early stages of HSV-1 infection, and completed additional fluorescence microscopy analysis to support our statements about the changes to microtubule, mitochondrial and Golgi morphology that accompany infection. Furthermore, we have included additional discussion on the relative merits of cryoSXT versus other ultrastructural analysis techniques like transmission electron microscopy, electron cryo-microscopy and electron cryotomography. We believe that our study serves as a powerful example of how cryoSXT can be used for quantitative cell biology and will be of broad interest to an audience of cell biologists and colleagues who study infection processes.

      1. Point-by-point description of the revisions

      Reviewer #1 (Evidence, reproducibility and clarity):

      Summary

      The authors have performed an explorative study, investigating morphological changes that occur in cells upon infection with Herpes Simplex Virus 1 (HSV-1) by the use of cryo soft X-ray tomography (cryoSXT). cryoSXT is an emerging technique for imaging of biological material, that allows for 3D imaging of significant volumes of cells under near-native conditions, without the need for sectioning or sample preparation other than rapid freezing. Reference (Groen et al. 2019) provides a nice list of examples from various biological samples. By the use of cryoSXT, the authors confirm findings that they have previously published by use of light and expansion microscopy (ref 16 from manuscript), namely an enrichment of small vesicles close to the nucleus and elongation and branching of mitochondria into interconnected networks in infected cells.

      Infection experiments were done in two different cell types in this study (HFF and U2OS), and a timestamp reporter virus that allows to distinguish between early and late stages of infection was used to provide more context to the observed morphological changes in the cells.

      Major comments

      It is a bit difficult to follow the main message throughout the manuscript, as the topics brought up in the introduction, results and discussion sections are not very coherent. The introduction gives some background on the virus and the timestamp reporter system, and further focuses on cryoSXT as a method and how this can overcome sample preparation artefacts that might be introduced by chemical fixation and sample processing. The results do not contain any direct comparisons between cryoSXT and other methods or sample preparations (light microscopy or EM-based), and the discussion only to a small extent comes back to the advantages brought by cryoSXT compared to other methods. Rather the discussion largely revolves around the possible involvement of microtubules in generating the observed morphological changes, and the possible meaning of elongated mitochondria in infected cells. Both of these topics are barely introduced, and not at all experimentally interrogated in the case of microtubules. There is also some discussion about Golgi fragmentation, although this is also not directly interrogated by cryoSXT in the current manuscript.

      We thank the reviewer for these comments. We have: - Updated the introduction to enunciate more clearly the aims of our study - Included a substantial comparison of the relative merits of cryoSXT versus other ultrastructural analysis techniques (TEM, cryoEM and cryoET) in the discussion - Updated the introduction to introduce the concepts of microtubule and mitochondrial morphology changes during infection that are covered in depth in the discussion - Included additional microscopy experiments, including super-resolution structured illumination microscopy (SIM), to demonstrate the changes in Golgi (Figures 6 and 7), microtubule (Figure 8) and mitochondrial (Suppl. Figure 4) morphology that accompany HSV-1 infection. These additional experiments support the hypotheses presented in the submitted manuscript, namely that microtubule organising centres are disrupted, Golgi membranes dispersed, and mitochondria redistributed as HSV-1 infection progresses.

      The authors perform imaging with a 40nm or a 25nm zone plate, where the 25nm zone plate provides improved resolution of a smaller volume compared to the 40nm zone plate. The authors do not really make use of the improved resolution offered by the 25nm zone plate in the results, so the motivation for turning to this (and therefor also changing cell line) is a bit unclear. The reason for the U2OS cell line to better preserved during X ray imaging is also not discussed, maybe it has to do with the thickness of the cells (as the U2OS cells are very flat). Furthermore, images from the 25 nm zone plate are not compared side by side to neither the 40nm zone plate nor standard TEM, which makes it hard to judge what the increased resolution really brings.

      Only one zone plate can be installed at any one time in the microscope and altering the zone plates requires extensive hardware changes that are outside the control of beamline users. We agree that this was not clearly discussed in the text. We have included additional text in the results (lines 207–208) and methods (lines 633–638) explaining this operational limitation and clarifying which zone plate was used for which experiment. In this study we observed that tomograms acquired with the 25 nm zone plate did not provide significantly more biological information than with the 40 nm zone plate, and thus both are suitable for characterisation of overarching cellular ultrastructural changes that accompany infection. We have added a sentence to this effect to the discussion (lines 410–412). Like U2OS cells, HFF-hTERT cells are also very flat. They appear more robust compared to HFFs when used for protracted exposures to soft X-rays and less likely to suffer from heat deposition after an extensive data collection round. We can speculate at this point that this could conceivably be due to the particular chemical composition of the intracellular environment in different cell lineages but it is impossible to offer anything other than speculation and therefore we have refrained from commenting further on this in the manuscript.

      The switch from a 40 to a 25nm zone plate required a switch in the model system, as mentioned above. The chosen cell types are not linked to biological relevance however (neurons and epithelial cells are mentioned as relevant cell types in the introduction), and it is therefor a bit unclear what the relevance is of keeping results from both cell types and comparing the two, rather than sticking to the one that works with cryoSXT. The results from the U2OS cells could still be compared by LM to the HFF cells if this contributes to the aim of the study.

      U2OS cells were chosen because they have been used previously for studies of HSV-1 infection (references 55–56) and are known to be well suited to cryoSXT analysis (references 32–33). We have added a sentence to this effect to the results (lines 208–211).

      The distribution of the viral proteins of the timestamp reporter virus is used to categorize infected HFF cells into 4 infection stages. In the U2OS cells the protein distribution is a bit different, which only allows them to be categorized into early (stage 1+2) and late (stage 3+4) stage of infection. Although this is what the authors state in the text, all 4 stages are included in Fig.2 for the U2OS cells, so it is not clear how this subdivision is performed and it does not seem like an accurate representation of the data. Furthermore, the uninfected population is not included in the timecourse, and there is not really a gradual change in infection states over the different timepoints as one could have expected. Therefor it is a bit hard to see the relevance of the timecourse. In the paper where the reporter virus is published (ref 16), shorter infection times were used, which leads to a more gradual change in infection stages.

      We thank the reviewer for pointing out these omissions. We have updated Figure 2A to only show the categories early (stage 1+2) and late (stage 3+4) for the U2OS cells. Furthermore, we have repeated the infection time course experiment, quantitating uninfected cells in addition to infected cells and including additional time points (2-, 4- and 6-hours post-infection). This new data (Figure 2B) demonstrates that the temporal profiles of infection progression are similar in HFF-hTERT and U2OS cells. Furthermore, it supports our choice of 9 hours post-infection as a suitable time point for plunge freezing of samples in order to obtain a mixture of cells at early and late stages of infection.

      There is a lot of importance given to the morphological changes of mitochondrial networks in infected cells. However, the quantification represented in Fig.5B is a bit unclear. The mitochondria are classified into different groups, but there is no specific description of the definition and cutoff values of each group. The name of some groups is also confusing, such as "short and long" mitochondria. Furthermore, there are large differences between replicates (suppl. fig. 2). The authors state that some mitochondria are swollen, which they interpret as a sign of apoptosis. They find these swollen mitochondria in 75% of the tomograms of uninfected cells in replicate number 3. If this is indeed cell death this replicate is not healthy.

      We apologise that the categorisation of mitochondria was not sufficiently clear in the submitted manuscript. The categories were percentage of tomograms that had the different mitochondrial morphologies present, not percentages of mitochondria. Thus, tomograms with both short and long mitochondria were classified as “short and long”. We have re-generated Figure 5C and Suppl. Figure 2C as a Venn diagram to illustrate this point more clearly. We have also updated the legend of Figure 5C (lines 845–850) to state clearly that the diagram shows percentage of tomograms with the relevant mitochondrial morphologies. The categorisation was performed manually and we have included examples of each category in Figure 5A. Manual classification can be subjective but, given the large number of tomograms analysed and the clear distinction between morphology in uninfected vs early- and late-stage infected cells, we are confident that our results are robust. We note that we have deposited all of the source tomograms in the Apollo repository at the University of Cambridge (https://doi.org/10.17863/CAM.78593); the data we used for this analysis are thus freely available for inspection and re-analysis by interested colleagues. We note that the swollen mitochondria were observed in multiple samples of uninfected and infected cells. This suggests that, regardless of infection, this is a common phenotype of U2OS cells. Others have observed this morphology by EM in the context of apoptosis and suggest it may represent porous mitochondria (reference 61). Although the proportion of tomograms containing these swollen mitochondria were higher in the uninfected sample of replicate 3, the other 25% contained typical mitochondrial morphologies that we could include in our analysis. The presence of inter-cell morphological variability such as this highlights the importance of imaging multiple cells within a population and performing several distinct biological replicates, as we have done in this study, to ensure project-relevant information is captured and delineated from the background structural variability inherent within a cell population. Previous cryoSXT studies had observed (but did not specifically comment on) a similar swollen mitochondrial morphology (reference 59). However, out of an abundance of caution we excluded all tomograms with swollen mitochondria from our analysis of mitochondrial branching (Figure 5C). Moreover, Tukey tests were performed per replicate for each pair of conditions in Figure 5C and statistical significance was reported only if it was observed independently in all three replicates. We are thus confident that any sampling error in replicate 3 that may arise from excluding tomograms will not have meaningfully altered our conclusions.

      Minor comments

      Results section 1, line 115-117: Where the authors state that it is unclear whether "naked" HSV-1 capsids would be visible by cryoSXT, it would be useful to refer to literature where these are observed by TEM, or to compare to TEM in their own experiments.

      We have included references to previous TEM studies in the results (lines 128–129), as requested. However, we note that TEM and cryoSXT are fundamentally different as TEM uses contrast agents whereas contrast in cryoSXT arises from differential elemental densities (in particular the density of oxygen versus carbon or phosphorous). We have updated the results (lines 129–131) to clarify this point.

      Results line 143: The authors state that it's hard to observe the perinuclear viruses with TEM, but there are several examples of this in the literature that could be referenced, e.g. (Skepper et al. 2001; Leuzinger et al. 2005; Baines et al. 2007; Johnson and Baines 2011), although this does not mean that they are not hard to find or that 3D is not advantegous.

      We thank the reviewer for these references and we have added them to the manuscript.

      Fig.4: It is unclear why all the vesicles are open-ended

      This is due to the differential path-length of carbon rich (and thus high contrast) membrane traversed by the X-rays for the membranes normal or parallel to the incident X-ray beam. We have clarified this point in the results (lines 290–301).

      Some places in the manuscript PFU per cell is used, other places MOI

      Thank you for pointing this out. For consistency, we have changed all instances of PFU per cell to MOI.

      If some specific adjustments to the methods had to be implemented for bio safely reasons (virus work), this should be stated in the methods.

      We have added a section on biosafety measures to the methods (lines 562–568).

      Access to the synchrotron should also be described

      We have expanded the synchrotron access attribution the Acknowledgments section (lines 737– 738).

      Discussion line 320: "consistent with previous research" - there is a reference missing.

      Thank you for spotting this. We have now added the reference.

      The quantifications are based on a limited number of tomograms, but there is no statement as to how the specific tomograms were selected. With a variability between replicates and tomograms, a random selection is important.

      We included all tomograms collected for the relevant experimental condition in all our analyses unless otherwise stated. For the vesicle segmentation we chose four reconstructed tomograms from each condition at random (lines 690–691). For lipid droplet volume analysis and mitochondrial branching analysis we included all tomograms that matched our quality-control criteria. We have added a few sentences to the Segmentation and Graphs and Statistics sections of the methods (lines 691–694 and 724–733) describing our selection criteria for the lipid droplet, vesicle and mitochondrial branching analysis, respectively.

      If gold fiducials are visible in the tomograms it could be useful to indicate, as they can look similar to lipid droplets to a non-expert reader.

      We have indicated gold fiducials Figure 1 H, the only figure in which they are visible, with a gold star as requested.

      Suppl. Fig.2: For clarity it would be good not to use the same color arrows to indicate different things in A and B.

      Suppl. Figure 2B has been removed in response to another reviewer request.

      Reviewer #1 (Significance):

      The authors of this study demonstrate that cells infected by HSV-1 virus can be investigated by the use of cryoSXT, and use this to show that infected cells have more elongated and interconnected mitochondria, and an enrichment of small vesicles close to the nucleus. They thereby also show that cryoSXT offers a nice resolution for characterizing morphological changes in significant volumes of near native-state cells, and that the method offers a promising throughput for screening of large amounts of cells. However, the study does not really present new biological or technical advances compared to previously published literature, see e.g. Müller et.al. 2012, Duke et.al 2014, Perez Berna et.al. 2016, Groen et.al. 2019, Weinhardt et.al. 2020, Loconte et.al. 2021 (not cryo but demonstrates the advantage of capillaries), Kounatidis et.al. 2020, Scherer 2021 (ref 16 from paper), some of which are also referenced in the current study. The study could thus have profited from a more defined focus and possibly further experiments (live-cell imaging, CLEM, TEM, microtubules or more mechanistically focused) depending on the main interest of the authors. The advantage with the current broad focus (assuming that the main concerns are addressed) is that the study could interest a larger audience, ranging from virology, cell biology and immunology to microscopy and methods development.

      We thank the reviewer for recognising the broad audience that will be interested in our manuscript. We believe that our analysis highlights the broad applicability of cryoSXT for analysing cell ultrastructure and changes that occur in response to infection. Furthermore, we think that our use of robust numerical analysis to quantitate the phenotypes we observe highlights the strength of cryoSXT as a high throughput technique for ultrastructural analysis. Our study is the first to investigate HSV-1 infection using cryoSXT and, in addition to confirming previous ultrastructural changes observed using other methods, we present new biological insight in organelle architecture and distribution such as that lipid droplets undergo a transient size increase during early stages of infection. We believe that we have demonstrated the robust utility of cryoSXT as a tool to study ultrastructural changes in response to insults, such as infection by intracellular pathogens, and hope that our manuscript will act as inspiration for others seeking to use cryoSXT to image cellular ultrastructure.

      Reviewer #2 (Evidence, reproducibility and clarity):

      The authors use soft X-ray tomography to examine cell structure following infection by herpes simplex virus-1 (HSV-1). This imaging method can provide 3D images of cryo-preserved intact cells without chemical fixation or staining. The authors find several morphological differences between uninfected and infected cells, including changes in the number and size of vesicles and in the size and shape of mitochondria.

      This is a well-done study with careful and extensive analysis that in general produces convincing images to support the authors' conclusions. The procedures are clearly described and reproducible, and the authors have examined an impressive number of images and have performed appropriate statistical analyses.

      We thank the reviewer for their positive comments.

      I had two comments / suggestions regarding the findings about changes in morphology after infection. First, in the Discussion, the authors consider the possibility of Golgi fragmentation. Can the authors test this by counting Golgi before and after fragmentation?

      We did not frequently observe well-defined Golgi apparatuses in our tomograms, consistent with previous cryoSXT studies (reference 61). We therefore performed new experiments using SIM microscopy to demonstrate the disruption of Golgi apparatus and trans-Golgi network in fixed U2OS cells stained with the markers GM130 and TGN46, respectively. These new results are presented in Figures 6 and 7 and in the results (lines 342–355).

      Second, in the Results the authors report that they did not observe a change in lipid droplets after infection. However, the late-stage image in Fig. 5A seems to show such a change, with the lipid droplets becoming larger and darker relative to the early stage or uninfected cells. Maybe this is just the particular image that was selected, but perhaps it is worth looking at more images by eye just in case the segmentation procedure somehow missed this change.

      We thank the reviewer for suggesting we re-visit the properties of lipid droplets. Based on this suggestion we segmented the lipid droplets from 94 tomograms and found a robust change in the median volume of lipid droplets at early stages of infection. We have included this new data in Figure 4C, Suppl Figure 2 and the text of the results (lines 302–312). The observation that lipid droplet volumes change is particularly interesting as another group recently observed similar changes in lipid droplets in response to HSV-1 infection of astrocytes and they postulate that this may modulate the cellular immune response (reference 85). Our data support and extend their conclusions, as described in the discussion (lines 476–494).

      Minor comments:

      Line 127 - As I understand it, the alignment by fiducial markers corrects primarily for small inaccuracies in tilting of the stage. Hopefully there are not significant vibrations in the microscope because this would also lead to loss of resolution during the exposure of each tilt angle.

      Thank you, we have corrected “vibrations” to “small inaccuracies in tilting of the microscope stage”.

      Line 145 - "electron light" Is this common usage? To me it seems more accurate to just say electrons because light to me means photons.

      Thank you, we have corrected “electron light” to “electrons”.

      Line 390 - detection OF ("of" is missing)

      Thank you, we have made the correction.

      Line 564 - Fig. 2 legend. "partial retention in the nucleus of U2OS cells". I am not sure where the nucleus is in the images. To me, it looks like there is almost no stain for ICP0 in hTERT at stage 1 and stage 3, and then cytoplasmic stain at stage 2 and stage 4. In contrast, for U2OS, the stain looks mostly nuclear until stage 4 when it is partially cytoplasmic. This all needs to be better explained, and perhaps arrows added to the images such that the reader does not have to guess.

      We agree and have added a silhouette around each nuclei in Figure 2 to make this clearer. We have also added arrows to indicate the gC-mCherry enriched juxtanuclear compartment in cells at stage 3 (HFF-hTERT) or a late stage (U2OS) of infection.

      Line 585 - The authors could consider rotating the images by 180{degree sign} in panel A (late) in order to maintain the same orientation of nucleus and cytoplasm. This would make it easier for readers to see the point.

      Done as requested.

      Line 614 - I could not find the length of the scale bar in the legend.

      We apologise for omitting this – is has now been added.

      Reviewer #2 (Significance):

      The significance of the study is two-fold. First, it is a nice technical demonstration of what can be accomplished using soft X-ray tomography. I am qualified to evaluate this, since my expertise is in biological applications of this technique. The second significant aspect of the study is the demonstration of morphological changes in mitochondria and vesicles. I am not a virologist, so I do not know the literature on this point with regard to virus infection, but I find it interesting that the authors were able to detect such changes.

      We thank the reviewer for their positive assessment of our work.

      I believe the authors should cite a couple of papers:

      10.1016/j.cell.2015.11.029 which looks at HSV infection and reports viral particles between the inner and outer nuclear membrane.

      We have included a citation to this work as requested (lines 162–165).

      10.1016/j.jsb.2011.11.025 which also reports nuclear membrane separations or bulges by soft X-ray tomography.

      We have elaborated on this section and incorporated the reference as requested (lines 265– 276).

      Regarding these nuclear membrane bulges, there are a number of papers that show they can also arise from mutations in nuclear-lamin associated proteins like nesprin and SUN (see for example https://doi.org/10.1093/hmg/ddm338). This is perhaps something interesting for the authors to think about, but not necessary for the current manuscript.

      Thank you for this comment. We did consider studying the breakdown of the nuclear lamina during HSV-1 infection, as this has been shown in previous studies [e.g. 10.1101/2021.06.02.446771]. However, we could not robustly resolve the nuclear lamina from the nuclear envelope in uninfected cells. The nuclear lamina is quite thin (30–100 nm in width) and this may have confounded its identification.

      Reviewer #3 (Evidence, reproducibility and clarity):

      Summary:

      The manuscript by Nahas et al. describes the structural studies performed in U2OS cells infected with a recombinant HSV-1 virus that enables tracing the stage of the infection using fluorescent markers. This system was used to determine major structural changes in HSV-1 infected cells using cryo-soft X ray tomography (cryo-SXT) on near native-state samples. The data presented complement previous studies (particularly ref.16) using similar reagents but different microscopy techniques. While the data are generally well presented and discussed, they do not provide any substantially novel information on the structural changes in HSV-1. Nevetheless, they constitute an interesting technical achievement.

      We thank the reviewer for supporting the technical quality of the analysis. In response to the comments of another reviewer we have extended our analysis and documented new biological information for this system relating to lipid droplet re-shaping and distribution in response to HSV-1 infection; all our new findings are included in the updated manuscript.

      Major comments:

      There are no major concerns on the data, although some of the statements could be revised for a more realistic interpretation of the results.

      • In Figure 1F and lines 152-156 it is stated that a bulging of the nuclear envelope occurs around some of the putative particles, while in lines 243-244 and lines 625-628, it is stated that bulging occurs both in mock and infected cells. This should be clarified to avoid confusion. It is possible that authors differentiate both situations and this should be more clearly stated.

      Many thanks for identifying a possible area of confusion. We have updated the results to clearly distinguish the expansion of the perinuclear space that accompanies virus nuclear egress (lines 160–175) from the bulges of the nuclear envelope that are observed in uninfected and infected cells (lines 265–276).

      • The statistical tests are different for different hypothesis testing throughout the manuscript. The authors should justify in the methods section the use of one or another test. This will contribute to clarity in the hypothesis that is being test and will clarify the reason for the selected test.

      We have significantly expanded the Graphs and Statistics section of the methods (lines 703– 734) to further justify the statistical tests used throughout our study.

      • Sentence: "Our observation..." in lines 349-352. Even though the sentence is in the Discussion it is wildly speculative. The authors could use different approaches to tackle experimentally the question of whether active fusion or faulty fission is involved, but this is not the main subject the manuscript. Please revise the sentence or address experimentally, this would provide new insight into the impact of HSV-1 infection on mitochondrial network morphology. This sentence could be qualified as "speculative".

      We agree that this section of the discussion strayed into speculative territory and have removed it from the updated manuscript.

      • Although ref.16 provides evidence supporting Golgi fragmentation and mitochondrial elongation after HSV-1_timestamp virus infection in HFF cells, it would be important to show confocal microscopy data in U2OS cells, which were used for cryo-SXT, particularly since the authors refer differential virus kinetics and subcellular distribution of viral antigens in these cells. These would greatly contribute to support the statements regarding these two phenomena. It is very likely that the authors already have the data and could easily show them.

      We have included new microscopy experiments to demonstrate changes in mitochondrial (Suppl. Figure 4) and Golgi (Figures 6 and 7) morphology that accompany HSV-1 infection, and these new experiments are now included in the results (lines 335–310 and 342–355).

      -Line 269: Apposition of lipid droplets and mitochondria is not thoroughly described. This statement requires quantitation. Optimally, confocal imaging using Mitotracker and bodipy493/503 or superresolution imaging using specific antibodies may also contribute to strengthen the statement.

      We agree with the reviewer that we do not at this stage have adequate data to support this assertion and have therefore removed it from the manuscript.

      • It would be of great interest to document the budding events observed by cryo-SXT using higher resolution techniques and the kinetic resolution provided by the fluorescent infection fiducials. This would confirm the nature of the particles (using immunogold) and would demonstrate the the usefulness of the cryo-SXT data. This by itself would justify the use of cryo-SXT to temporally locate events that are difficult to visualize otherwise (as stated by the authors).

      We agree with the reviewer that a correlative imaging strategy involving cryoSXT and fluorescence microscopy could aid in identifying features of infection, and have highlighted this interesting future direction in the discussion (line 406–409). However, performing such analysis will be a substantial experimental commitment in its own and is outside the scope of our current manuscript.

      Minor comments:

      • Given that the software used for segmentation (Contour) is not published, a minimal comparative description between manual and semi-automated segmentation may be shown in the supplementary, to illustrate the robustness of the new method and the reliability of the measurements.

      We have now published a preprint (recently accepted in the journal Biological Imaging) that describes Contour in detail, which we have referenced in the updated manuscript: Nahas, K. L., Ferreira Fernandes, J., Crump, C., Graham, S. C. & Harkiolaki, M. (2021) Contour, a semi-automated segmentation and quantitation tool for cryo-soft-X-ray tomography. http://biorxiv.org/lookup/doi/10.1101/2021.12.03.470962

      • Lines 278-280: statistical test and p value are not shown.

      We have updated the text to include details of the statistical test and p value as requested (lines 326–330 of the updated manuscript).

      • After line 376: It would be interesting to mention that transient elongation of mitochondria is observed during dengue virus infection (https://doi.org/10.1016/j.chom.2016.07.008) and that this has also consequences for innate immunity against viruses.

      We thank the reviewer for this suggestion, which we have incorporated into the discussion (lines 522–523).

      • Given that HSV-1 is a BSL-2 level virus and that a recombinant version (GMO) has been used in the study, the authors should describe the biosafety measures taken to image non-inactivated infectious samples by cryo-SXT. The authors should state that a biosafety committee has reviewed these activities.

      We have included a Biosafety Measures section to the methods (lines 562–568) that details the biosafety measures used and their approval by the relevant committees.

      Reviewer #3 (Significance):

      This study constitutes an incremental technical advance in the study of HSV-1 infection. The broad context and the quasi-native structure of the cells enables documenting events that are difficult to observe thin sections for TEM.

      This study is one of the few examples of the use of cryo-SXT for infected cell imaging. Other examples of the literature are cited as well as previous structural studies performed with higher resolution techniques.

      The manuscript may be suitable for HSV-1 specialists and cell biologists interested in using near-native samples for gross cellular imaging and documentation of low-resolution maps revealing alterations in large subcellular structures.

      We thank the reviewer for highlighting that ours is one of only a few comprehensive studies using cryoSXT, illustrating how it can be used to image cellular processes that are hard to ‘catch’ using techniques that require ultra-thin sectioning, and as such that it will be of interest to cell biologists studying infection processes in cellulo.

    1. SciScore for 10.1101/2022.05.07.491038: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">Field Sample Permit: Single cell RNA-seq: PBMCs were collected from mRNA-LNP vaccinated and control mice were collected as described above for mouse immunization and sample collection, and normalized to 1000 cells/μL.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">Mouse immunization: 6-8 weeks old female C57BL/6Ncr (B6) mice were purchased from Charles River and used for vaccine immunogenicity study.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">All experiments utilize randomized littermate controls.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">Replication, randomization, blinding and reagent validations: Replicate experiments have been performed for all key data shown in this study.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">Contamination: All cell lines tested negative for mycoplasma. Figs.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Thereafter, cells were washed twice and incubated with PE–anti-human FC antibody (Biolegend, 410708) in MACS buffer for 30 min on ice.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>PE–anti-human FC</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Anti-mouse secondary antibody (Fisher, Cat# A-10677) was diluted to 1:2500 in blocking buffer and incubated at room temperature for one hour.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Anti-mouse</div><div>suggested: (Thermo Fisher Scientific Cat# A-10677, RRID:AB_2534060)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cell culture: HEK293T (ThermoFisher), Huh-7 and 293T-hACE2 (Dr Bieniasz’ lab) cell lines were cultured in complete growth medium, Dulbecco’s modified Eagle’s medium (DMEM; ThermoFisher) supplemented with 10% Fetal bovine serum (FBS, Hyclone), 1% penicillin-streptomycin (Gibco) (D10 media for short).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293T</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Briefly, 293T cells were seeded in 150 mm plates, and transfected with 21 µg pHIVNLGagPol, 21 µg pCCNanoLuc2AEGFP, and 7.5 µg of corresponding spike plasmids, in the presence of 198 µl PEI.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>293T</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The SARS-CoV and SARS-CoV-2 pseudovirus neutralization assays were performed on 293T-hACE2 cell, while the MERS-CoV neutralization assay was performed on Huh-7 cells.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Huh-7</div><div>suggested: CLS Cat# 300156/p7178_HuH7, RRID:CVCL_0336)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">One day before infection, 293T-hACE2 cells were plated in a 96 well plate with 0.01 x106 cells per well.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>293T-hACE2</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Organisms/Strains</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Mouse immunization: 6-8 weeks old female C57BL/6Ncr (B6) mice were purchased from Charles River and used for vaccine immunogenicity study.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>C57BL/6Ncr</div><div>suggested: RRID:MGI:2160593)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The spike sequences were cloned by Gibson Assembly (NEB) into pcDNA3.1 plasmid for the mRNA transcription and pseudovirus assay.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pcDNA3.1</div><div>suggested: RRID:Addgene_79663)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">1.617.2 variant S protein (Delta variant-Δ19), SARS-CoV S protein (SARS-CoV-Δ19) and MERS S protein (MERS-CoV-Δ16) were generated based on the pSARS-CoV-2-Δ19.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pSARS-CoV-2-Δ19</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Briefly, 293T cells were seeded in 150 mm plates, and transfected with 21 µg pHIVNLGagPol, 21 µg pCCNanoLuc2AEGFP, and 7.5 µg of corresponding spike plasmids, in the presence of 198 µl PEI.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCCNanoLuc2AEGFP</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Coronavirus spike sequence alignment: The spike sequence used to produce the LNP-mRNA vaccines were aligned using Clustal Omega 43 and visualized in Jalview 44.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Clustal Omega</div><div>suggested: (Clustal Omega, RRID:SCR_001591)</div></div><div style="margin-bottom:8px"><div>Jalview</div><div>suggested: (Jalview, RRID:SCR_006459)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cell culture: HEK293T (ThermoFisher), Huh-7 and 293T-hACE2 (Dr Bieniasz’ lab) cell lines were cultured in complete growth medium, Dulbecco’s modified Eagle’s medium (DMEM; ThermoFisher) supplemented with 10% Fetal bovine serum (FBS, Hyclone), 1% penicillin-streptomycin (Gibco) (D10 media for short).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>ThermoFisher</div><div>suggested: (ThermoFisher; SL 8; Centrifuge, RRID:SCR_020809)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Analysis was performed using FlowJo software.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>FlowJo</div><div>suggested: (FlowJo, RRID:SCR_008520)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The 50% inhibitory concentration (IC50) was calculated with a four-parameter logistic regression using GraphPad Prism (GraphPad Software Inc.).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad Prism</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div><div style="margin-bottom:8px"><div>GraphPad</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Differential expression was performed using the edgeR analysis pipeline and quasi-likelihood (QL) F tests 53, 54.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>edgeR</div><div>suggested: (edgeR, RRID:SCR_012802)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Schematic illustrations: Schematic illustrations were created with Affinity Designer or BioRender.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>BioRender</div><div>suggested: (Biorender, RRID:SCR_018361)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Genomic sequencing raw data are deposited to Gene Expression Omnibus (GEO) with a pending accession code.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Gene Expression Omnibus</div><div>suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)</div></div></td></tr></table>

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

      Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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">NCT04796896</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">A Study to Evaluate Safety and Effectiveness of mRNA-1273 CO…</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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


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      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.

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    1. SciScore for 10.1101/2022.05.07.491022: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">Contamination: Reagents: Cell lines: All cells were maintained in Dulbecco’s modified Eagle medium (DMEM) supplemented with 10% fetal calf serum (FCS), 100 U ml−1 penicillin and 100 mg ml−1 streptomycin and regularly tested and found to be mycoplasma free.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Anti-rabbit IgG, HRP-linked Antibody (7074); Cyclin D3 Mouse mAb (DCS22, 2936); from Cell Signaling.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Anti-rabbit IgG</div><div>suggested: (Cell Signaling Technology Cat# 7074, RRID:AB_2099233)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Goat anti-Mouse IgG (H+L) Cross-Adsorbed Secondary Antibody: Alexa 488 (A-11001), Alexa 594 (A-11032), Alexa 647 (A-21236); Goat anti-Rabbit IgG (H+L) Cross-Adsorbed Secondary Antibody: Alexa 488 (A-11034), Alexa 405 (A-48254); Rabbit polyclonal SARS-CoV-2 Spike (PA1-41165); Rabbit monoclonal SARS-CoV-2 Nucleocapsid (MA5-29982) from Thermo Fisher Scientific.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-Mouse IgG</div><div>suggested: (Thermo Fisher Scientific Cat# A-11001, RRID:AB_2534069)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Rabbit Polyclonal Cyclin A2 antibody (GTX103042); Rabbit Polyclonal Cyclin D1 antibody (N1C3, GTX108824); Rabbit Polyclonal Cyclin E1 antibody (GTX103045); Rabbit Polyclonal Cyclin B1 antibody (GTX100911); monoclonal SARS-CoV-2 Spike (GTX632604) from GeneTex.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Cyclin A2</div><div>suggested: (GeneTex Cat# GTX103042, RRID:AB_1949884)</div></div><div style="margin-bottom:8px"><div>Cyclin D1</div><div>suggested: (GeneTex Cat# GTX108824, RRID:AB_10618686)</div></div><div style="margin-bottom:8px"><div>Cyclin E1</div><div>suggested: (GeneTex Cat# GTX103045, RRID:AB_10731259)</div></div><div style="margin-bottom:8px"><div>Cyclin B1</div><div>suggested: (GeneTex Cat# GTX100911, RRID:AB_1949886)</div></div><div style="margin-bottom:8px"><div>GTX632604</div><div>suggested: (GeneTex Cat# GTX632604, RRID:AB_2864418)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">pre-cleared cell lysates were incubated with a-HA magnetic beads, MagStrep beads (IBA-Lifescience, Gottingen, Germany) or anti-cyclin D3 monoclonal antibody (sc-xx) bound Protein G Dynabeads for 1h at 4°C.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-cyclin D3</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Following cells were a gift from: A549 ACE2/TMPRSS2 40 Massimo Palmerini, Vero E6 ACE2/TMPRSS2 from Emma Thomson, HeLa-ACE2 from James Voss, 293T (a human embryonic kidney cell line, ATCC CRL-3216).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>A549</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>Vero E6</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">293T GFP11 cells and Vero-GFP10 cells for Split GFP assay were a gift from Leo James41.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>293T GFP11</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>Vero-GFP10</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">293Tv cells were transfected with pEXN-MNCX-Fucci, CMVi and pMD2.G.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>293Tv</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Plasmids: pBOB-EF1-FastFUCCI-Puro was a gift from Kevin Brindle & Duncan Jodrell (Addgene plasmid # 86849 ; http://n2t.net/addgene:86849 ; RRID:Addgene_86849) 29. pCMV5 cyclin D3 HA was obtained from MRC-PPU Reagents and Services. Rc/CMV cyclin D1 HA was a gift from Philip Hinds (Addgene plasmid # 8948 ; http://n2t.net/addgene:8948 ; RRID:Addgene_8948) 44. pLVX-EF1alpha-SARS-CoV-2-E-2xStrep-IRES-Puro (Addgene plasmid # 141385 ; http://n2t.net/addgene:141385 ; RRID:Addgene_141385); pLVX-EF1alpha-SARS-CoV-2-M-2xStrep-IRES-Puro (Addgene plasmid # 141386 ; http://n2t.net/addgene:141386 ; RRID:Addgene_141386).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div></div><div>detected: RRID:Addgene_86849)</div></div><div style="margin-bottom:8px"><div>pCMV5</div><div>suggested: RRID:Addgene_15002)</div></div><div style="margin-bottom:8px"><div></div><div>detected: RRID:Addgene_8948)</div></div><div style="margin-bottom:8px"><div></div><div>detected: RRID:Addgene_141385)</div></div><div style="margin-bottom:8px"><div></div><div>detected: RRID:Addgene_141386)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">pLVX-EF1alpha-SARS-CoV-2-nsp9-2xStrep-IRES-Puro (Addgene plasmid # 141375 ; http://n2t.net/addgene:141375 ; RRID:Addgene_141375); pLVX-EF1alpha-SARS-CoV-2-N-2xStrep-IRES-Puro (Addgene plasmid # 141391 ; http://n2t.net/addgene:141391 ; RRID:Addgene_141391) were a gift from Nevan Krogan 34. pEXN-MNCX, MLV vector encoding N-terminal double HA tag 45.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div></div><div>detected: RRID:Addgene_141375)</div></div><div style="margin-bottom:8px"><div></div><div>detected: RRID:Addgene_141391)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">pCAGGS_SARS-CoV-2_Spike was obtained from NIBS.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCAGGS_SARS-CoV-2_Spike</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cell cycle analysis using fluorescence ubiquitination cell cycle indicator (Fucci): Fucci cassete was cloned from pBOB-EF1-FastFucci-Puro vector to pEXN-MNCX using BamHI/NotI restriction sites.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pEXN-MNCX</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">293Tv cells were transfected with pEXN-MNCX-Fucci, CMVi and pMD2.G.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pEXN-MNCX-Fucci</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pMD2.G</div><div>suggested: RRID:Addgene_12259)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cell to cell fusion assay: 293T GFP11 cells were transfected with WT full length Spike, and/or with WT Envelope, Membrane, cyclin D3, and empty vector (pCDNA, to ensure equal amount of transfected DNA).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCDNA</div><div>suggested: RRID:Addgene_66792)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Harmony (PerkinElmer, Waltham, MA, USA) and ImageJ software were used to measure MFI for each protein in each region.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>ImageJ</div><div>suggested: (ImageJ, RRID:SCR_003070)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cell populations positive or negative for SARS-CoV-2 nucleocapsid staining were gated and Cdt1-RFP positive (G1 phase), Geminin-GFP positive (S/G2/M phase), and Cdt1-RFP/ Geminin-GFP positive (early S phase) populations were identified using flow cytometry using LSRFortessa X-20 (BD Biosciences, UK) and FlowJo software (Tree Star, OR, USA).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>FlowJo</div><div>suggested: (FlowJo, RRID:SCR_008520)</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


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      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.

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    1. 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

      Centriole growth is not limited by a finite pool of components, but is limited by the Cdk1/Cyclin-dependent phosphorylation of Ana2/STIL

      Authors: Thomas L. Steinacker, Siu-Shing Wong, Zsofia A. Novak, Saroj Saurya, Lisa Gartenmann, Eline J.H. van Houtum, Judith R. Sayers, B. Christoffer Lagerholm, Jordan W. Raff

      Centriole biogenesis is a tightly regulated process that occurs once per cell cycle. Defects in this process can lead to the acquisition of abnormal centriole numbers which has been linked to several human diseases. Centriole duplication starts with the assembly of a procentriole on the mother centriole in early S-phase followed by procentriole growth during G2 phase. A big question in the centrosome field is how new procentrioles assemble at the right time and acquire the correct final size.

      In this manuscript, Wong et al. analyse whether the cytoplasmic concentration of several proteins changes during centriole assembly (Asl, Plk4, Ana2, Sas-6, and Sas-4). The authors show that the cytoplasmic concentration of these proteins remains constant during centriole duplication, indicating they are not limiting components for procentriole assembly. Nevertheless, the authors found that Ana2/STIL's cytoplasmic diffusion rate increases before the onset of mitosis, concurrent with an increase in Cdk1/Cyclin activity. Mutation of 10 putative phosphorylation sites in Ana2 prevented the diffusion rate change and enabled centrioles to grow for a longer period. This suggests that phosphorylation of Ana2/STIL by Cdk1/Cyclin could control the period of centriole growth.

      Minor points:

      In the introduction, the authors describe how PLK4 is required to recruit STIL and Sas-6 to promote the formation of the cartwheel during centriole duplication. However, there is also literature describing a role for STIL in regulating PLK4 abundance and localization pattern (i.e ring or dot) at the centriole.

      The authors note that the levels of the Ana2(12A) mutant keep increasing until the onset of mitosis. The authors claim that this phenotype is consistent with the timing of increased Cdk1 activity. It would be interesting to show the increase in Cdk1 kinase activity over the same time-course and test whether dampening Cdk1 has the same effect on Ana2 recruitment.

      While I appreciate detecting in vivo phosphorylation sites can be very challenging, It would be valuable to show the 10 Ana2 phosphorylation sites can be phosphorylated by Cdk1, at least in vitro.

      Other points:

      Figure 3: Amino acids numbers for CC domain are not the same in the figure and in the figure legend.

      Figure 5Aii, the x-axis should be changed to minutes for easier comparison with other figures.

      There are some typos in the figure legends.

      Significance

      This study attempts to address a central question in the centrosome field: how centriole growth is controlled. Although the paper does not provide a detailed mechanistic advance, the authors do provide some evidence against a limited pool of centriole components controlling centriole length, and they are careful not to overstate conclusions. The manuscript is well written and easy to follow. While it is not clear at present how phosphorylation of Ana2 alters its diffusion rate or limits centriole growth, I feel the study will be of interest to members of the centriole community and will stimulate new lines of investigation. Given that the cartwheel stops elongating in S phase in mammalian systems, it is not clear if the mechanism proposed would be conserved. That notwithstanding, I found this to be a rigorous study that advances our understanding of the regulation of the centriole duplication.

    1. SciScore for 10.1101/2022.05.05.490850: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Secondary antibody incubation was performed at room temperature for 2 hours using Goat Anti-Mouse IgG H&L (31430, Thermo) or Goat Anti-Rabbit IgG H&L (31460, Thermo).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Anti-Mouse IgG</div><div>suggested: (Thermo Fisher Scientific Cat# 31430, RRID:AB_228307)</div></div><div style="margin-bottom:8px"><div>Anti-Rabbit IgG</div><div>suggested: (Thermo Fisher Scientific Cat# 31460, RRID:AB_228341)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">ORF6 variant mutants were constructed by subcloning the construct in a TA backbone, followed by substituting the residues as described before (Edelheit et al, 2009) and cloning in the pCAGGS backbone using EcoRI and XhoI. Cell Lines and cell culture: Human embryonic kidney 293T (HEK293T), A549 lung adenocarcinoma, and HeLa cells were purchased from National Centre for Cell Science (NCCS), and HEK-ACE2, Vero cells were procured from the America Type Culture Collection (ATCC, Bethesda, MD).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293T</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>A549</div><div>suggested: NCI-DTP Cat# A549, RRID:CVCL_0023)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Viruses and infection: Infection: SARS-CoV2 (Isolate Hong Kong/VM20001061/2020, NR-52282, BEI Resources, NIAID, NIH) was propagated and titered by plaque assay in Vero E6 cells as described before (Case et al, 2020).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero E6</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Plasmid transfection: HEK-293T cells (0.1 X 106 cells/well) were seeded in a 24 well plate pre-coated with 0.1 mg/mL poly-L-lysine (P9155-5MG, Sigma-Aldrich) and 24 hours later used for transfection.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK-293T</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Similarly, Vero cells were seeded on coverslips in 24 well plates (0.1 X 106 cells/well) for overnight incubation.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Similarly, HeLa cells were transfected with 250 ng of RIG I Flag or MAVS Flag and 250 ng of ORF6 strep for 24 hours.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HeLa</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For constructing the deletion mutants, the desired sequence was PCR amplified from pLVX-EF1alpha-SARS-CoV-2-ORF6-2xStrep-IRES-Puro plasmid, followed by cloning in the pCAGGS backbone using EcoRI (ER0271, Thermo scientific) and XhoI (ER0691, Thermo scientific) restriction enzyme including the full-length ORF6.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pLVX-EF1alpha-SARS-CoV-2-ORF6-2xStrep-IRES-Puro</div><div>suggested: RRID:Addgene_141387)</div></div><div style="margin-bottom:8px"><div>pCAGGS</div><div>suggested: RRID:Addgene_127347)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Luciferase Reporter Assay: For the IFN induction assay, HEK-293T cells were co-transfected, in duplicates, with 50 ng of IFNβ-luc firefly luciferase reporter plasmid, and 20 ng of pRL-TK Renilla luciferase reporter plasmid along with 500 ng of SARS-CoV-2 protein expression plasmid or empty vector.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pRL-TK Renilla luciferase reporter</div><div>suggested: RRID:Addgene_12179)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">They were then co-transfected with 500 ng of IRF3-GFP and 500 ng of SARS-CoV-2 protein-expressing plasmid using Lipofectamine 2000 reagent (11668019, Invitrogen).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>SARS-CoV-2 protein-expressing</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Graphical representations and statistical analysis: All numerical data of luciferase assays and qRt-PCR were analysed and plotted using GraphPad Prism v8.0.2.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad Prism</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The model diagram of ORF6 action (Fig 7) and 3-D structure (Sup Fig 3C) were made by using Biorender</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Biorender</div><div>suggested: (Biorender, RRID:SCR_018361)</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. SciScore for 10.1101/2022.05.06.490867: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">IRB: Ethics statement: Ethical approval was provided by NHSGGC Biorepository (application 550).</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cells: HEK293T and 293-ACE2 cells were maintained at 37°C, 5% CO2, in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% foetal bovine serum, 2mM L-glutamine, 100μg/ml streptomycin and 100 IU/ml penicillin.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293T</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>293-ACE2</div><div>suggested: RRID:CVCL_DR94)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">HEK293 cells were used to produce HEK293-ACE2 target cells by stable transduction with pSCRPSY-hACE2 and were maintained in complete DMEM supplemented with 2μg/ml puromycin.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">HEK293 cells were used to produce HEK293-ACE2 target cells by stable transduction with pSCRPSY-hACE2 and were maintained in complete DMEM supplemented with 2μg/ml puromycin.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pSCRPSY-hACE2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">HEK293T cells were transfected with the appropriate SARS-CoV-2 Spike gene expression vector (Wuhan, Alpha, Delta, or Omicron) together with p8.9171 and pCSFLW72 using polyethylenimine (PEI, Polysciences, Warrington, USA).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCSFLW72</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">All analyses and data visualisations were executed using the stats[25] and ggplot2[26] packages respectively, from R version 4.0.5.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>ggplot2</div><div>suggested: (ggplot2, RRID:SCR_014601)</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


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      About SciScore

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    1. Reviewer #1 (Public Review):

      To address this question, the authors combine fully resolved fluid mechanics numerical simulations of an odor plume with the framework of partially observed Markov decision process (POMDP) - a framework for devising the optimal decision-making policy that an autonomous agent should use in order to achieve its goal given that it only has partial access to information to guide its decisions. The main result is that while stopping to sniff in the air bears the cost of halting progression towards the source - animals tend to stop moving to sniff in the air - this is offset by the benefit of being able to detect odor packets at a larger distance from the source (odors travel a shorter distance near the ground). Interestingly, sniffing in the air takes place more often when the agent loses the plume and tends to coincide with periods when the agent casts crosswind (a known strategy used by animals to regain contact with the plume) while sniffing near the ground is preferred when the agents are within the plume.

      In a second part of the paper, the authors concentrate on the search dynamics far from the source where ground cues tend to be absent. Combining analytical calculations and a simplified POMDP (for this part they ignore ground sniffing) they ask: 1) how wide should the agent cast? 2) how long should the agent spend casting before surging upwind? 3) where should the agent sniff during the casting phase? Here the main results are that surge length and cast width should equal the detection range x_thr and the prior width of the plume L_y respectively. They also find that the optimal time to spent casting obeys the marginal value theory, i.e. it is the time at which the marginal value of staying in a cast equals that of surging and exploring a new yet unexplored patch in the agent's belief of its own position relative to the source. These results provide a rationale for the observed alternation between ground and air sniffing, and for casting and how the timing between these events should be selected.

      I find the question relevant, the quantitative analysis carefully reasoned, and the results compelling and of broad interest. The authors should address the following comments, which mostly center around clarifying the assumptions made regarding the agents' prior knowledge, and the need for better placing this study within the context of previous research, especially regarding memory requirements of the strategy and comparison with more reactive (memory-less) strategies. Finally, a broader discussion of the limitations of the current study (e.g. what happens if x_thr and y_thr change over time?) and of the next steps would strengthen the paper.

      An assumption behind the entire study is that agents can hold in memory their belief, which in this case is their location relative to the expected location of the source. Over time this memory enables agents that start with a wide prior to refining their belief. This strong assumption makes the strategy discussed here quite different from other more reactive strategies proposed in the literature that do not require agents to build an internal map of the expected location of the source. While it is easy for a robot to maintain such a memory, how and to what extent animals do so using known mechanisms such as path integration and/or systems such as grid and place cells is less clear. A more explicit description of the key memory requirements of the strategy discussed here (once learned) and a discussion of how it might be implemented by animals, as well as a discussion of the differences in that aspect with other strategies proposed in the literature, including reactive strategies, would strengthen the paper and significantly broaden is significance.

      Along the same lines, the study assumes that the agent stores an internal model of the statistics of the plume, e.g. x_thr and y_thr, L_y etc. The predictions made in 6e/f, for example, are likely only valid if the agent already knows the constraints of the plume it is searching for (i.e. x_thr and y_thr), which seems unlikely in most natural scenarios. Perhaps the authors could discuss some ways in which these might be inferred. The authors nicely show that an agent trained with the Poisson model navigates well even in the full time-dependent simulation. But what is missing is a discussion of how animals would get trained in the first place and what information they would need access to in order to do so. Perhaps examine how an agent trained in environment A performs in environment B as a function of how strong the statistical difference between environment A and B are. One could for example change the Poisson statistics between A and B.

      Related to the previous point: the simulated plume is straight, i.e. there is no variation in the mean flow and therefore no random meandering of the plume. This means that once the walker hits the center of the plume, if it orients upwind, it is likely to reach the source because there is a continuous stream of odor on the ground it can follow, with just a few castings whenever it drifts slightly off the centerline. Is there a way for the authors to explore what would happen in the case of meandering plumes without having to run another massive simulation? Perhaps a simplified model of odor plume could be used or one could even just use the same simulated plume Poisson statistics but translate this solution perpendicular to the main flow at a slow oscillatory rate. Will the navigator now stop and sniff in the air more often? Will these sniffing events coincide with moments when the navigator loses the plume? Will agents be able to still use a constant x_thr and y_thr or would they have to learn their statics? Or will agents revert to a more memoryless or hybrid strategy?

      How does the benefit of sniffing the ground vs the air change if odor molecules adsorb and de-adsorb on the surface, thus increasing the distance from the source where ground odor can be detected?

      There is a difference in clarity between the first part of the paper and the second part that starts at line 232 with the section "Searching for airborne cues". I recommend the authors work on that second section to improve clarity. For example, the goal of that section is not immediately clear. The first paragraph talks about expanding on the intuition gained from the first part and "to address the search dynamics" but does not spell out what key question about search dynamics is to be addressed. This only becomes clear at line 260. Knowing where this is going would help readers understand the motivation behind the simplified model. Maybe lines 258-263 or something similar could be moved into the first paragraph of that section. Also related to the previous comments it would be helpful to clearly state what is assumed known by the agent and what is not. Is the agent assumed to have learned the values of x_thr, v and N in equation (2) before starting the search?

      As we progress through that section, important details start to be omitted and making it more difficult to follow. For example, what is the definition of t_sniff (I am guessing it is given in line 313?)? What is meant by optimization depth (line 316)? What is meant by episode index, is this referring to N (line 322)? Can the authors provide intuition about why the optimized casting strategy expands over time rather than starting wide right away (line 315)?

    1. Reviewer #3 (Public Review):

      This paper's strength is in the utility of the assembled datasets and some interesting and creative proof of concept analyses. This is an amazing resource for comparative analysis. However the paper felt a little sparse in the conceptual and methodological underpinnings of the questions asked to demonstrate the utility of the analysis.

      Specifically, I suggest:

      A) More substance in the introduction (currently only two short paragraphs) and a clear statement of research questions.

      B) Add data on the extent to which each dataset represents a complete sample of each city's trees. I know are complete inventories, but some consist of 720 trees and cannot be a complete sample. A column in the meta data indicating effort and if there were any bias in where sampling occurred if the dataset is not complete are needed for others to use this data appropriately. For example, we know tree cover/diversity increases with wealth (which the author rightly cites). Let's say in City X, trees were only inventoried in one wealthy neighborhood. They would not be a representative sample of the city and dataset users need to be aware of this before they draw incorrect conclusions about City X where the sample was biased compared to city Y where the inventory was complete, including a sampling of all affluent and poor areas. This is also needed to support the research questions throughout the paper.

      C) The authors chose to use effective species counts as their alpha diversity metric of choice. They explain why: "effective species counts (a measure that allows comparison between cities of different sizes)" (Ln 109). While effective species number is an excellent metric with much better behavior and attributes in linear modeling, I believe it is still strongly dependent on both city area and the number of individual trees sampled and so the above statement and all of the comparisons that flow out of it in the manuscript are currently unsupported. Just as species richness needs to be rarified or extrapolated to be compared at an equivalent # of individuals or area to be accurate so too does EFN (effective species count). Fortunately there is an R package (iNext) based on Chao's method (citation below) that makes it very easy to create effective species accumulation curves for each city by tree individuals sampled.<br /> a. Chao, Anne, Nicholas J. Gotelli, T. C. Hsieh, Elizabeth L. Sander, K. H. Ma, Robert K. Colwell, and Aaron M. Ellison. 2014. "Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies." Ecological Monographs 84 (1): 45-67. https://doi.org/https://doi.org/10.1890/13-0133.1.<br /> b. The standardization (rarefaction/extrapolation) of EFN or richness for # individual trees sampled needs to be made for all analyses that make claims to compare diversity metrics across cities or between groups like urban and park areas (i.e. Fig 2a,b,c; Fig 3b; Fig 5a,b, S1a, S2a, S5, Table S2)<br /> c. If the authors have an argument for why diversity/area or diversity/sampling effort relationships do not apply for a particular question, then they should make that case instead.

      D) The question posed by the Beta diversity analysis is fascinating (i.e. is it non-native species that are driving biotic homogenization across species. However, while frequency (which I assume is relative abundance but maybe it is incidence data- please define) is used to deal with different sample sizes consider whether it makes sense to include incomplete, or very small city datasets in the analysis even with frequency data. For example one city only has ~720 trees listed. If this is an incomplete dataset which seems likely, it will probably be much more differentiated (overlap less) from another city with small numbers simply due to incomplete sampling. Diversity analysis in cities always requires tradeoffs and cannot be identical to methods used in "natural" forested ecosystems, but I encourage the authors to explore this a bit. Perhaps a sensitivity analysis could help where incomplete or small sample sizes are dropped or datasets are resampled via random draw to equalize sizes? The latter would handle incomplete samples but would not deal with bias in which neighborhoods were sampled (see point B above).

      E) Additional context/conceptual underpinning the clustering analysis would be great.<br /> a. The authors state in Line 390-395:"For city trees, which are often organized along grids or the underlying street layout of a city, this method can more meaningfully cluster trees than merely calculating the meters between trees and identifying nearest neighbors (which may be close as the crow flies but separated from each other by tall buildings)."- I very much agree with this sentiment and it is biologically meaningful for animal and plant dispersal, but as written it is unclear to me how the method described in the text "knows" that a tall building or elevation or some sort of feature exists to separate clusters rather than empty space or a ball field. Please clarify.<br /> b. Would you ever expect composition to be truly random either in a city or a natural forest given environmental conditions etc.? In some sense, the ones closest to random are the most surprising. Can you dive into one to give an example of what is going on in that city?<br /> c. It seems like there are two metrics here- the size of the cluster and then the observed/expected EFN per cluster. The latter is analyzed in this paper but is there any important information in the former? It seems like an interesting structural measurement of the city and possibly useful in its own right.<br /> d. Are there any target levels of randomness? Could the authors suggest how this might be determined moving forward with their datasets to illustrate this for foresters?

      F) The statement that this dataset enables "the design of rich heterogenous ecosystems built around urban forests" (Ln 72) seems strange. To my mind this tool will enable a more nuanced evaluation of the urban forests that already exist and suggest ways to target future plantings for increased resilience to climate, pest resistance, biodiversity support etc. I don't understand what ecosystem you would build around and not in the urban forest. If this is what is meant please elaborate. For example, do you mean non-tree installations?

    1. SciScore for 10.1101/2022.05.02.490272: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">Quantification of SG-positive cells was performed by counting the number of cells with at least two discrete cytoplasmic foci from at least 3 randomly selected fields of view, analysing >100 cells per treatment in each replicate.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After 1-h blocking with 5% bovine serum albumin (BSA, BioShop, Burlington, ON, Canada) in PBS, staining was performed overnight at +4°C with antibodies to the following targets: CoV2 N (1:400; rabbit, Novus Biologicals, NBP3-05730); eIF3B (1:400; rabbit, Bethyl Labs, A301761A); eIF4G (1:200; rabbit, Cell Signaling, #2498); G3BP1 (1:400; mouse, BD Transduction, 611126); G3BP2 (1:1000; rabbit, Millipore Sigma,</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>NBP3-05730); eIF3B</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Alexa Fluor (AF)-conjugated secondary antibodies used were: donkey anti-mouse IgG AF488 (Invitrogen, A21202)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-mouse IgG</div><div>suggested: (Molecular Probes Cat# A-21202, RRID:AB_141607)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Aliquots of lysates thawed on ice were incubated at 95°C for 3 min, cooled on ice, separated using denaturing PAGE, transferred onto PVDF membranes using Trans Blot Turbo Transfer System with RTA Transfer Packs (Bio-Rad Laboratories, Hercules, CA, USA) according to manufacturer’s protocol and analysed by immunoblotting using antibody-specific protocols.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>antibody-specific protocols .</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Antibodies to the following targets were used: β-actin (1:2000; HRP-conjugated, mouse, Santa Cruz Biotechnology, sc- 47778); CoV2 N (1:1,000; rabbit, Novus Biologicals,</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>β-actin</div><div>suggested: (Santa Cruz Biotechnology Cat# sc-47778 HRP, RRID:AB_2714189)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Puromycin incorporation into nascent polypeptides was visualised using anti-puromycin antibody (1:6,000; mouse, MilliporeSigma, MABE343).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-puromycin</div><div>suggested: (Millipore Cat# MABE343, RRID:AB_2566826)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cells: Human Embryonic Kidney (HEK) 293A cells and human colon adenocarcinoma (HCT-8) cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with heat-inactivated 10% fetal bovine serum (FBS), and 2 mM L-glutamine (all purchased from Thermo Fisher Scientific (</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>HCT-8</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">BEAS-2B cells were cultured in Bronchial Epithelial Cell Growth Medium (BEGM, Lonza, Kingston, ON, Canada) on plates prepared with coating media (0.01 mg/ml fibronectin, 0.03 mg/mL bovine collagen type I, and 0.01 mg/ml bovine serum albumin (all from Millipore Sigma, Oakville, ON, Canada) dissolved in Basal Epithelial Cell Growth Medium (BEBM, Lonza)). 293A and BEAS-2B cells were purchased from American Type Culture Collection (ATCC, Manassas, VA, USA)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>BEAS-2B</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For SARS-CoV-2 stocks, Vero E6 cells in a confluent T-175 cm2 flask were infected at a MOI of 0.01 for 1 h at 37°C in 2.5 mL of serum-free DMEM with intermittent shaking every 10 min.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero E6</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">To generate lentivirus stocks, HEK 293T cells (ATCC) were reverse- transfected with polyethylenimine (PEI, Polysciences, Warrington, PA, USA) and the following plasmids for lentiviral generation: pLJM1-B*</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK 293T</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Transfection: 293A cells were seeded into 20-mm wells of 12-well cluster dishes with or without glass coverslips and the next day transfected with 500 ng DNA mixes/well containing expression vectors (250 ng) and pUC19 filler DNA (250 ng) using Fugene HD (Promega, Madison, WI, USA) according to manufacturer’s protocol.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>293A</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Then, coding sequences were inserted between EcoRI and XhoI sites into pCR3.1-EGFP vector (75) to generate pCR3.1-EGFP-OC43-N, pCR3.1-EGFP-CoV2-N, pCR3.1-EGFP-OC43-Nsp1, pCR3.1-EGFP-CoV2-Nsp1, and pCR3.1-EGFP-OC43-Nsp15 plasmids.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCR3.1-EGFP</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pCR3.1-EGFP-OC43-N</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pCR3.1-EGFP-CoV2-N</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pCR3.1-EGFP-OC43-Nsp1</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pCR3.1-EGFP-CoV2-Nsp1</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pCR3.1-EGFP-OC43-Nsp15</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">To generate N-terminally HA-tagged Nsp1 constructs, coding sequences were inserted between KpnI and XhoI sites into 3xHA-miniTurbo-NLS_pCDNA3 vector (a gift from Alice Ting, Addgene plasmid # 107172) to generate pCDNA3-HA-OC43-Nsp1 and pCDNA3-HA-CoV2-Nsp1 vectors (miniTurbo-NLS coding sequence was replaced by Nsp1 sequences)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>3xHA-miniTurbo-NLS_pCDNA3</div><div>suggested: RRID:Addgene_107172)</div></div><div style="margin-bottom:8px"><div>pCDNA3-HA-OC43-Nsp1</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pCDNA3-HA-CoV2-Nsp1</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Amino acid substitutions in pCDNA3-HA-CoV2-Nsp1 vector were introduced using Phusion PCR mutagenesis (New England Biolabs) to generate pCDNA-HA-CoV2-Nsp1(R99A) and pCDNA-HA-CoV2-Nsp1(R124A,K125A) vectors.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCDNA-HA-CoV2-Nsp1</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">To generate lentivirus vectors pLJM1-ACE2-BSD, pLJM1-EGFP-BSD, and pLJM1-EGFP- G3BP1-BSD, the PCR-amplified ACE2, EGFP, and G3BP1 coding sequences were inserted into the multicloning site of pLJM1-B* vector (76).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pLJM1-EGFP-BSD</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pLJM1-EGFP- G3BP1-BSD</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pLJM1-B*</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">backbone-based constructs, pMD2.G, and psPAX2. pMD2.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pMD2 . G</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>psPAX2</div><div>suggested: RRID:Addgene_12260)</div></div><div style="margin-bottom:8px"><div>pMD2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Generation of stably transduced cell lines: To generate 293A-ACE2 cells, 293A cells were stably transduced with a lentivirus vector encoding ACE2 (pLJM1-ACE2-BSD) and selected and maintained in 10 μg/mL Blasticidin S HCl (Thermo Fisher).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pLJM1-ACE2-BSD</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Transfection: 293A cells were seeded into 20-mm wells of 12-well cluster dishes with or without glass coverslips and the next day transfected with 500 ng DNA mixes/well containing expression vectors (250 ng) and pUC19 filler DNA (250 ng) using Fugene HD (Promega, Madison, WI, USA) according to manufacturer’s protocol.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pUC19</div><div>suggested: RRID:Addgene_50005)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Where indicated, the amount of filler DNA was reduced to 150 ng and 100 ng of the pCR3.1- EGFP plasmid was co-transfected with expression vectors for Nsp1 proteins.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCR3.1-</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Analysis of SG number and size was performed on cropped images of individual cells using ImageJ software Analyze Particles function after automatic background substraction and thresholding.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>ImageJ</div><div>suggested: (ImageJ, RRID:SCR_003070)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Aliquots of lysates thawed on ice were incubated at 95°C for 3 min, cooled on ice, separated using denaturing PAGE, transferred onto PVDF membranes using Trans Blot Turbo Transfer System with RTA Transfer Packs (Bio-Rad Laboratories, Hercules, CA, USA) according to manufacturer’s protocol and analysed by immunoblotting using antibody-specific protocols.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Bio-Rad Laboratories</div><div>suggested: (Bio-Rad Laboratories, RRID:SCR_008426)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For analyses of protein band intensities, western blot signals were quantified using Bio-Rad Image Lab 5.2.1 software and values normalized to the Stain-free signal for each lane.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Bio-Rad Image</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Statistical analyses for each data set are described in figure legends and were performed using GraphPad Prism 8 software.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad Prism</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


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      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.

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    1. SciScore for 10.1101/2022.04.30.486882: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">Field Sample Permit: SARS-CoV-2 reverse genetics system: All work with potentially infectious SARS-CoV-2 particles was conducted under enhanced biosafety level 3 (BSL-3) conditions and approved by the institutional biosafety committee of Washington University in St. Louis.<br>IACUC: The protocols were approved by the Institutional Animal Care and Use Committee at the Washington University School of Medicine (assurance number A3381– 01).</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">Five to six-week-old male hamsters were obtained from Charles River Laboratories and housed at Washington University.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">Propagation of a clinical isolate of SARS-CoV-2 containing a deletion of the s2m: As part of ongoing SARS-CoV-2 variant surveillance, a random set of RT-PCR positive respiratory secretions from the Barnes Jewish Hospital Clinical microbiology laboratory were subjected to whole genome sequencing using the ARTIC primer amplicon strategy 42.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cell culture conditions: BHK-21, HEK293T and Caco-2 cells were maintained in Dulbecco’s Modified Eagle Medium (DMEM) with L-glutamine (Gibco) supplemented with 10% fetal bovine serum (FBS) and 100 units/mL penicillin/streptomycin and incubated at 37°C and 5% CO2.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293T</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">A total of 1.5 µg/well of VA1 IVT RNA was transfected into BHK-21 cells in 12-well plates using 3 µL/well of Lipofectamine MessengerMax (Invitrogen) and following the manufacturer’s protocol.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>BHK-21</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After 3 freeze-thaw cycles, viral stocks were titrated using Caco-2 cells on 96-well plates in the absence of trypsin, enabling single-round infection 16.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Caco-2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Huh7.5.1 and HEK293T cells were transfected in triplicate with HAstV1 replicon IVT RNA using Lipofectamine 2000 (Invitrogen), following a previously described protocol in which suspended cells are added directly to the RNA complexes in 96-well plates 7.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Huh7.5.1</div><div>suggested: RRID:CVCL_E049)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">SARS CoV-2 growth curves and titration assays: Vero-hTMPRSS2 and Calu-3 cells were grown to confluency.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Calu-3</div><div>suggested: KCLB Cat# 30055, RRID:CVCL_0609)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">This virus was expanded twice on Vero-hACE2-hTMPRSS2 cells and the virus titer was determined by plaque assay.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero-hACE2-hTMPRSS2</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Organisms/Strains</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Vero cells expressing human TMPRSS2 (Vero-hTMPRSS2) 35 or human ACE2 and human TMPRSS2 (Vero-hACE2-hTMPRSS2, gift from Drs.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero-hACE2-hTMPRSS2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Vero-hTMPRSS2 and Vero-hACE2-hTMPRSS2 cells were maintained by selection with 5 µg/mL Blasticidin or 10 µg/mL puromycin respectively.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero-hTMPRSS2</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Plasmid p1629 contains a BamHI restriction site, T7 promoter (TAATACGACTCACTATAG) followed by the first 1629 nucleotides of the VA1 genome inserted into the pSMART plasmid (Lucigen).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>p1629</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pSMART</div><div>suggested: RRID:Addgene_102283)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">A second plasmid, p5023 contains a BamHI and BlpI restriction site, nucleotides 1610-6586 followed by a poly-A sequence of 18 adenosines, followed by EciI and SbfI restriction digest sites inserted into pUC19.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pUC19</div><div>suggested: RRID:Addgene_50005)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Wild-type and mutant p5023 plasmids were linearized using BamHI and BlpI.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>p5023</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Human astrovirus 1 reverse genetics system: Mutant s2m sequences were introduced using site-directed mutagenesis of a previously published plasmid encoding HAstV1 (pAVIC1) 15 and confirmed by sequencing.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pAVIC1</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The pAVIC1- derived HAstV1 RNA was electroporated into BSR cells as previously described 16.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pAVIC1-</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Fragments A and C-G were cloned into plasmid pUC57 vector and amplified in E.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pUC57</div><div>suggested: RRID:Addgene_40306)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The bacteria toxic fragment B was cloned into low copy inducible BAC vector pCCI and amplified through plasmid induction in EPI300.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCCI</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The SARS-CoV-2 N gene was PCR amplified from plasmids pUC57-SARS-CoV-2-N (GenScript) using forward primers with T7 promoter and reverse primers with poly(T)34 sequences.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pUC57-SARS-CoV-2-N</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Astrovirus VA1 reverse genetics system: The reference VA1 genome (NC_013060.1) was synthesized using gBlocks (Integrated DNA Technologies)36.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>gBlocks</div><div>suggested: (Gblocks, RRID:SCR_015945)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The immunofluorescence-based detection with 0.26 µg/mL of 8E7 astrovirus antibody (Santa Cruz Biotechnolgy, sc-53559) was combined with infrared detection readout and automated LI-COR software-based quantification.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Santa Cruz Biotechnolgy</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">SARS-CoV-2 RNA levels were measured by one-step quantitative reverse transcriptase PCR (RT-qPCR) TaqMan assay as described previously using a SARS-CoV-2 nucleocapsid (N) specific primers/probe set from the Centers for Disease Control and Prevention (F primer: GACCCCAAAATCAGCGAAAT; R primer: TCTGGTTACTGCCAGTTGAATCTG; probe: 5′-FAM/ACCCCGCATTACGTTTGGTGGACC/3′-ZEN/IBFQ)40.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GACCCCAAAATCAGCGAAAT</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The P2 of WUSTL_000226_A131/2021 was sequenced by NGS to confirm the presence of the s2m deletion and rule out any tissue culture adaptations in the rest of the genome.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>NGS</div><div>suggested: (ANGSD, RRID:SCR_021865)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">In brief, Illumina sequencing data were filtered using fastp (https://github.com/OpenGene/fastp) trimming bases with a Phred quality score ≥Q30.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>https://github.com/OpenGene/fastp</div><div>suggested: (fastp, RRID:SCR_016962)</div></div><div style="margin-bottom:8px"><div>Phred</div><div>suggested: (Phred, RRID:SCR_001017)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">High-quality reads were then aligned to the SARS-CoV-2 reference genome sequence (NC_045512.2) using BWA 45.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>BWA</div><div>suggested: (BWA, RRID:SCR_010910)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">SAMtools was used to sort, index and remove duplicates from bam files, and local realignment and variant calling were achieved by LoFreq to generate the mutant report file 46.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>SAMtools</div><div>suggested: (SAMTOOLS, RRID:SCR_002105)</div></div><div style="margin-bottom:8px"><div>LoFreq</div><div>suggested: (LoFreq, RRID:SCR_013054)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Sequences were visualized using Jalview 2 48.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Jalview</div><div>suggested: (Jalview, RRID:SCR_006459)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Statistical analysis: Data was graphed using Prism 9.3.1 (GraphPad).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Prism</div><div>suggested: (PRISM, RRID:SCR_005375)</div></div><div style="margin-bottom:8px"><div>GraphPad</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

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    1. SciScore for 10.1101/2022.04.28.489942: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">IRB: , Zuckerberg San Francisco General Hospital) under research protocol 20-30497 approved by the University of California San Francisco Institutional Review Board.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For selected experiments, BAL cells were treated for 2h before infection with an ACE2 blocking antibody at 10 µg/ml (AF933, R&D systems).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>ACE2</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">SARS-CoV-2 infections: Vero E6 and Vero-TMPRSS2 cells (gift from Dr. Melanie Ott) were cultured in DMEM supplemented with 10% FBS, penicillin/streptomycin, and L-glutamine (Corning) in a humidified incubator at 37°C and 5% CO2.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero E6</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>Vero-TMPRSS2</div><div>suggested: JCRB Cat# JCRB1818, RRID:CVCL_YQ48)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Briefly, 10-fold dilutions of the virus stock were added to Vero cells in a 12-well plate for 1 hour, after which an overlay of 1.2% Avicel RC-581 in DMEM was added.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">This solution was used as inoculum for Vero E6 cells (SARS-CoV-2) or MDCK cells (IAV-Venus).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>MDCK</div><div>suggested: CLS Cat# 602280/p823_MDCK_(NBL-2, RRID:CVCL_0422)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Organisms/Strains</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">SARS-CoV-2 B.1.617.2 (delta) variant was acquired from the California Department of Public Health, cultured in Vero-TMPRSS2 cells.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>SARS-CoV-2 B.1.617.2</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Confocal imaging was performed using a Nikon A1R laser scanning confocal microscope with NIS-Elements software and a 16X LWD water dipping objective.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>NIS-Elements</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">50 – 100 µm-thick images with a z-step of 1.5 µm were taken and analyzed using Imaris (Bitplane).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Imaris</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Data were collected using the BD LSRII Cytometer and analyzed using FlowJo version 10 (BD Biosciences).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>FlowJo</div><div>suggested: (FlowJo, RRID:SCR_008520)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Briefly, raw gene-expression fastqs were aligned to the GRCh38 reference genome annotated with Ensembl v85, and Lipid Hashtag fastqs were processed to count the incidences of each expected index per cell.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Ensembl</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Data quality control and normalization: The gene expression count matrices were normalized, and variance stabilized using negative binomial regression using the scTransform algorithm21 in the Seurat package.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Seurat</div><div>suggested: (SEURAT, RRID:SCR_007322)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Statistical analyses: Statistical analysis was performed using GraphPad Prism v7.0e.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad Prism</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. SciScore for 10.1101/2022.04.26.489580: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">IACUC: Animal, Ethics, Biosafety statement: All research performed was approved by West Virginia University IACUC protocol number 2004034204.<br>Field Sample Permit: All SARS-CoV-2 viral propagation or challenge studies were conducted in the West Virginia University Biosafety Laboratory Level 3 facility under the IBC protocol number 20-04-01.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">High fat diet induced obesity and Type 2 Diabetes model and intraperitoneal glucose tolerance test (IPGTT): Diet induction of obesity was achieved through feeding a high fat diet (Bio-serv Mouse diet high fat 60% kCAL from fat #S3282) for 8 weeks to cohorts of 6-week-old female and male K18-hACE2-mice.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Viral doses were prepared from the first passage collections from infected Vero E6 cells.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero E6</div><div>suggested: RRID:CVCL_XD71)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Organisms/Strains</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">K18-hACE2-mice (B6.Cg-Tg(K18-ACE2)2Prlmn/J; JAX strain number #034860).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>B6.Cg-Tg(K18-ACE2)2Prlmn/J; JAX</div><div>suggested: RRID:IMSR_JAX:034860)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Concurrently, control age-matched K18-hACE2-mice remained on a standard chow diet.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>K18-hACE2-mice</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">K18-hACE2 mice were challenged with virus by intranasal administration of 25μL dose per nare (50μL total).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>K18-hACE2</div><div>suggested: RRID:IMSR_GPT:T037657)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Ingenuity Pathway Analysis: RNAseq fold change gene expression data was submitted to Ingenuity Pathway analysis using a cut off of P= 0.05.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Ingenuity Pathway analysis</div><div>suggested: (Ingenuity Pathway Analysis, RRID:SCR_008653)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Statistical analyses: Tests to determine statistical significance were performed using GraphPad Prism version 9.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad Prism</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr></table>

      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 LimitationRecognizer: We detected the following sentences addressing limitations in the study:


      Our study does have some caveats that warrant discussion. In our experiment, we only evaluated one SARS-CoV-2 challenge strain, Alpha, and there have now been three VOC strain surges (Beta, Delta, Omicron) since Alpha was dominantly circulating. In additional studies since then, we have observed enhanced airway inflammation due to challenge with the Delta variant (68). We anticipate that different strains would result in variable host responses to what was identified using Alpha; however, additional studies will need to be performed. Another caveat is that only one challenge dose was evaluated (1,000 PFU). If lower or higher challenge doses were to be studied, we would expect to have either shorter or longer time to morbidity during which host response profiles may further develop or remain hidden due to the disease timeline. Finally, our study focused on defining transcriptomic responses to characterize the altered host responses to SARS-CoV-2 challenge. We did not analyze specific cell populations through cell isolation and flow cytometry, nor did we evaluate potential mechanisms responsible for this co-morbidity. SARS-CoV-2 infection in humans is generally heterogenous in symptomology, however, increased susceptibility to severe infection requiring hospitalization are common across patients with T2DM metabolic disease and increased adiposity (obesity) (89). The role of elevated glucose and fat accumulation downstream of metabolic dysfunction has been shown in other setting...


      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. SciScore for 10.1101/2022.04.28.489772: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">Euthanasia Agents: 2 variant at 100 TCID50/mouse under isoflurane anesthesia.<br>IACUC: All procedures were performed according to the animal study protocols approved by the FDA White Oak Animal Program Animal Care and Use Committee.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">In the ABSL-3 lab, K18-hACE2 mice were randomly grouped and were inoculated intranasally with NY (G614), Delta, Omicron BA.1 or Omicron BA.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Western blot: Western blot was performed using an anti-SARS-COV-2 S antibody following a protocol described previously (58).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-SARS-COV-2 S</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Alkaline phosphatase conjugated anti-Rabbit IgG (1:5000) (Sigma-Aldrich, St. Louis, MO) was used as a secondary antibody.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-Rabbit IgG</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Control sensors with no ACE2 or antibody were also dipped in the S protein solutions and the running buffer as references.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>ACE2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For antibody staining, an Alexa Fluor 647 conjugated donkey anti-human IgG Fc F(ab’)2 fragment (Jackson ImmunoResearch, West Grove, PA) was used as secondary antibody at 5 μg/ml concentration.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-human IgG</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After washing, plates were probed with 1 μg/ml of inhouse developed rabbit polyclonal antibody specific for SARS-CoV-2 membrane/nucleocapsid (33) at 4°C overnight followed by peroxidase-conjugated goat anti-rabbit secondary antibody (SeraCare #5220-0336, 1:2000) for 2h at room temperature.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-rabbit</div><div>suggested: (SeraCare KPL Cat# 5220-0336, RRID:AB_2857917)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Briefly, Expi293F cells transfected with monomeric ACE2 or dimeric ACE2 expression construct and the supernatant of the cell culture was collected.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Expi293F</div><div>suggested: RRID:CVCL_D615)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Murine Leukemia Virus (MLV) particles (plasmids of the MLV components kindly provided by Dr. Gary Whittaker at Cornell University and Drs. Catherine Chen and Wei Zheng at National Center for Advancing Translational Sciences, National Institutes of Health), pseudotyped with various SARS-CoV-2 S protein constructs, were generated in HEK 293T cells, following a protocol described previously for SARS-CoV (59, 60).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK 293T</div><div>suggested: KCB Cat# KCB 200744YJ, RRID:CVCL_0063)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">To prepare for infection, 7.5×103 of HEK 293 cells, stably transfected with a full-length human ACE2 expression construct, in 15 μl culture medium were plated into a 384-well white-clear plate coated with poly-D-Lysine to enhance the cell attachment.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK 293</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Pseudotyped virus particles were produced in 293T/17 cells (ATCC) by co-transfection of plasmids encoding codon-optimized SARS-CoV-2 full-length S constructs, packaging plasmid pCMV DR8.2, and luciferase reporter plasmid pHR’ CMV-Luc.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>293T/17</div><div>suggested: ATCC Cat# CRL-11268, RRID:CVCL_1926)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The 293T cell line stably overexpressing the human ACE2 cell surface receptor protein was kindly provided by Drs.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>293T</div><div>suggested: KCB Cat# KCB 200744YJ, RRID:CVCL_0063)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Seed viruses were amplified in Vero E6 (ATCC CRL-1586) or Vero E6 with TMPRSS2 overexpression (BPS Bioscience #78081)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero E6</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">In vitro virus replication and focus-forming assay: Vero-E6 cells were pre-seeded in 12-well tissue culture plates overnight and were infected with authentic viruses (G614, Delta, Omicron BA.1 or BA.2) at MOI of 0.01 in Gibco™ high glucose DMEM containing 3% FBS.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero-E6</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Briefly, 10-fold serially diluted postinfection were added at 100 μl/well to Vero E6-TMPRSS2 cells pre-seeded in 96-well tissue culture plates.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero E6-TMPRSS2</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Organisms/Strains</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Mouse study: Hemizygous B6.Cg-Tg(K18-ACE2)2Prlmn/J (K18-hACE2</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>B6.Cg-Tg(K18-ACE2)2Prlmn/J</div><div>suggested: RRID:IMSR_JAX:034860)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">In the ABSL-3 lab, K18-hACE2 mice were randomly grouped and were inoculated intranasally with NY (G614), Delta, Omicron BA.1 or Omicron BA.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>K18-hACE2</div><div>suggested: RRID:IMSR_GPT:T037657)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The S gene was fused with a C-terminal twin Strep tag (SGGGSAWSHPQFEKGGGSGGGSGGSSAWSHPQFEK) and cloned into a mammalian cell expression vector pCMV-IRES-puro (Codex BioSolutions, Inc, Gaithersburg,</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCMV-IRES-puro</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Pseudotyped virus particles were produced in 293T/17 cells (ATCC) by co-transfection of plasmids encoding codon-optimized SARS-CoV-2 full-length S constructs, packaging plasmid pCMV DR8.2, and luciferase reporter plasmid pHR’ CMV-Luc.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCMV DR8.2 , and luciferase reporter</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pHR’</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Serially diluted pCMV6-AC-ACE2-GFP plasmid or pCC1-CoV2-F7 plasmid expressing SARS-CoV-2 N (62) was used to construct a standard curve.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCMV6-AC-ACE2-GFP</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pCC1-CoV2-F7</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The KD was obtained by fitting Req value and its corresponding concentration to the model: “one site-specific” using GraphPad Prism 8.0.2 according to H.J. Motulsky, Prism 5 Statistics Guide, 2007, GraphPad Software Inc.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad Prism</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div><div style="margin-bottom:8px"><div>GraphPad</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Automated data collection was carried out using SerialEM version 3.8.6 (63) at a nominal magnification of 105,000× and the K3 detector in counting mode (calibrated pixel size, 0.83 Å) at an exposure rate of 13.761 electrons per pixel per second.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>SerialEM</div><div>suggested: (SerialEM, RRID:SCR_017293)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Local resolution was also determined using cryoSPARC.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>cryoSPARC</div><div>suggested: (cryoSPARC, RRID:SCR_016501)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Several rounds of manual building were performed in Coot.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Coot</div><div>suggested: (Coot, RRID:SCR_014222)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Iteratively, refinement was performed in both Phenix (real space refinement) and ISOLDE (66), and the Phenix refinement strategy included minimization_global, local_grid_search, and adp, with rotamer, Ramachandran, and reference-model restraints, using 7KRQ and 7KRR as the reference models.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Phenix</div><div>suggested: (Phenix, RRID:SCR_014224)</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. SciScore for 10.1101/2022.04.28.489850: (What is this?)

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

      Table 1: Rigor

      NIH rigor criteria are not applicable to paper type.

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Blocking Experiments: DC were incubated at 37°C with 50 ng/ml anti–human CD40 antibody, or 50ng/ml anti-human ICOS-L or 50ng/ml of the corresponding isotype control (Biolegend).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>CD40 antibody</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-human ICOS-L</div><div>suggested: (Thermo Fisher Scientific Cat# 14-5889-80, RRID:AB_467683)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Library quality and quantity were analyzed by Agilent Bioanalyzer 2100 and Life Technologies Qubit 3.0 Fluorometer.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Agilent Bioanalyzer</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The reads were first mapped to the hg19 UCSC transcript set using Bowtie2 version 2.1.0 and the gene expression level was estimated using RSEM v1.2.15.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Bowtie2</div><div>suggested: (Bowtie 2, RRID:SCR_016368)</div></div><div style="margin-bottom:8px"><div>RSEM</div><div>suggested: (RSEM, RRID:SCR_013027)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Downstream analyses were performed using R (v3.6.0) and DESeq2 package (v1.26.0)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>DESeq2</div><div>suggested: (DESeq, RRID:SCR_000154)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Differentially expressed genes were determined with an absolute log-fold change threshold at 2 and an adjusted p-value below 0.01 Microarray Data analysis: We loaded the normalized dataset using GEOquery R package, and GSE19904 as studyID.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GEOquery</div><div>suggested: (GEOquery, RRID:SCR_000146)</div></div></td></tr></table>

      Results from OddPub: Thank you for sharing your code.

      Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
  9. Apr 2022
    1. SciScore for 10.1101/2022.04.28.489834: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">Immunization of mice with RBD mRNA-LNPs: Male C57BL/6J mice (aged 4–5 weeks) were purchased from the Jackson Laboratory and housed according to the regulatory standards of the University of California, San Diego.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">Mice were randomly allocated to experimental groups.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">Contamination: The cell lines were tested and confirmed to be negative for mycoplasma.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cell lines: HEK293FT and Vero E6 cells were maintained in Dulbecco’s Modified Eagle’s Medium containing 10% fetal bovine serum (GIBCO).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293FT</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>Vero E6</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">SARS-CoV-2 pseudovirus production: Plasmids encoding the Spike proteins (lacking the C-terminal 19-amino acids) of SARS-CoV-1 (CUHK-W1), SARS-CoV-2 (Wuhan-Hu-1), variant B.1.351, variant B.1.617.2, Wuhan-N501Y, and Wuhan-E484K were transfected into 293T cells with Lipofectamine 3000 (ThermoFisher).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>293T</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Verification of protein-coding capability of vaccine mRNAs: To confirm that the synthesized mRNAs could be translated into GFP or RBD proteins, 293FT cells were seeded at 3 × 105 cells/mL in 6-well plates, grown for 24 h, and then transfected with 1 mg mRNA per well using Lipofectamine 3000 (Invitrogen) according to the manufacturer’s instructions.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>293FT</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Vero cells were seeded at 2 × 105 cells/mL of 100 μL/well in 96-well plates and cultured overnight at 37°C.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Organisms/Strains</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Immunization of mice with RBD mRNA-LNPs: Male C57BL/6J mice (aged 4–5 weeks) were purchased from the Jackson Laboratory and housed according to the regulatory standards of the University of California, San Diego.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>C57BL/6J</div><div>suggested: RRID:IMSR_JAX:000664)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Statistical analyses were conducted using GraphPad Prism 8.0.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad Prism</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. SciScore for 10.1101/2022.04.28.489537: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">IRB: Ethics statements for human subjects and animal experimentation: All ferret and hamster experiments were evaluated by the responsible ethics committee of the State Office of Agriculture, Food Safety, and Fishery in Mecklenburg–Western Pomerania (LALLF M-V) and gained governmental approval under registration number LVL MV TSD/7221.3-1-004/21.<br>Field Sample Permit: Mouse studies were approved by the Commission for Animal Experimentation of the Cantonal Veterinary Office of Bern and conducted in compliance with the Swiss Animal Welfare legislation and under license BE43/20.<br>IACUC: Mouse studies were approved by the Commission for Animal Experimentation of the Cantonal Veterinary Office of Bern and conducted in compliance with the Swiss Animal Welfare legislation and under license BE43/20.<br>Consent: Written informed consent was obtained for all the patients and the study protocols were approved by the respective local Ethics Commissions (KEK-BE_2018-01801, EKSG 11/044, and EKSG 11/103).<br>Euthanasia Agents: Nasal washing samples were obtained under a short-term isoflurane inhalation anesthesia from individual hamsters by administering 200 µl PBS to each nostril and collecting the reflux.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">For single-infection experiments, 7- to 17-week-old male mice were inoculated with a dose of 2×104 TCD50/mouse of either Delta (EPI_ISL_2535433) or Omicron-BA.1 (EPI_ISL_7062525) isolates.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">A 1:3000 dilution of a rabbit polyclonal anti-SARS-CoV nucleocapsid antibody (Rockland, 200-401-A50) was used for the immunohistochemical (IHC) analysis of the lung and the brain.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-SARS-CoV nucleocapsid antibody</div><div>suggested: (Rockland Cat# 200-401-A50, RRID:AB_828403)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cells and culture conditions: At IVI, IBSCVeroE6 (Vero C1008, ATCC) and VeroE6/TMPRSS2 cells (NIBSC) were cultured in Dulbecco‟s modified Eagle‟s medium (DMEM).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>C1008</div><div>suggested: ECACC Cat# 940606176, RRID:CVCL_K755)</div></div><div style="margin-bottom:8px"><div>VeroE6/TMPRSS2</div><div>suggested: JCRB Cat# JCRB1819, RRID:CVCL_YQ49)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Viruses were cultivated on VeroE6, Vero-TMPRSS2, or Calu-3 cells and sequence verified by performing whole-genome NGS sequencing (see below).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Calu-3</div><div>suggested: KCLB Cat# 30055, RRID:CVCL_0609)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For the hamster and ferret infection studies SARS-CoV-2 Alpha (hCoV-19/Germany/NW-RKI-I-0026/2020, L4549, SARS-CoV-2 B.1.1.7 NW-RKI-I-0026/2020 passage 3 of EPI_ISL_751799), one silent mutation in the ORF1a (sequence position 11741), SARS-CoV-2 Delta AY.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>L4549</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">1 viruses were propagated (three passages for Alpha, two passages for Omicron-BA.1, one passage for Delta) on Vero E6 cells (Collection of Cell Lines in Veterinary Medicine CCLV-RIE 0929) using a mixture of equal volumes of Eagle MEM (Hanks‟ balanced salts solution) and Eagle MEM (Earle‟s balanced salts solution) supplemented with 2 mM L-Glutamine, nonessential amino acids adjusted to 850 mg/L, NaHCO3, 120 mg/L sodium pyruvate, 10% fetal bovine serum (FBS), pH 7.2.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero E6</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Transcribed capped mRNA was electroporated into baby hamster kidney (BHK-21) cells expressing SARS-CoV N protein.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>BHK-21</div><div>suggested: ATCC Cat# CRL-6281, RRID:CVCL_1914)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Electroporated cells were co-cultured with susceptible Vero E6TMPRSS cells to produce passage 0 (P.0) of the recombinant viruses.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero E6TMPRSS</div><div>suggested: JCRB Cat# JCRB1819, RRID:CVCL_YQ49)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Virus titers were assessed by standard TCID50 assays on Vero-TMPRSS2 cells.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero-TMPRSS2</div><div>suggested: JCRB Cat# JCRB1818, RRID:CVCL_YQ48)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Organisms/Strains</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Mouse studies: hACE2-KI mice (B6.Cg-Ace2tm1(ACE2)Dwnt) and hACE2-K18Tg mice (Tg(K18-hACE2)2Prlmn) were described previously 9,21.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>hACE2-KI</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>B6.Cg-Ace2tm1(ACE2)Dwnt</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>hACE2-K18Tg</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>Tg(K18-hACE2)2Prlmn)</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">K18-hACE2 mice (all female, 7 to 15 weeks old) were immunized intramuscularly with a single dose of 1 μg of mRNA-Vaccine Spikevax (Moderna).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>K18-hACE2</div><div>suggested: RRID:IMSR_GPT:T037657)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Nanopore sequencing workflow: Virus stocks, inoculum mixtures, and samples from competition assays in NECs, BECs, and lung explants were sequenced using the MinION sequencer (Oxford Nanopore Technologies) following the ARTIC nCoV-2019 sequencing protocol V3 (LoCost) (https://protocols.io/view/ncov-2019-sequencing-protocol-v3-locost-bh42j8ye) with the following modifications: the Midnight primer scheme (1200 bp amplicons) was used to perform the multiplex PCR (https://www.protocols.io/view/sars-cov2-genome-sequencing-protocol-1200bp-amplic-rm7vz8q64vx1/v6) instead of the ARTIC V3 primer scheme.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>MinION</div><div>suggested: (MinION, RRID:SCR_017985)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Libraries were then loaded onto a R9.4.1 flow cell on a MinION sequencer (Oxford Nanopore Technologies) and monitored using the MinKNOW software (Version 21.11.9).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>MinKNOW</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">When cells reached confluence, as assessed by measuring the trans-epithelial electrical resistance (TEER) using a Volt/Ohm Meter (EVOM2/STX2, World Precision Instruments) and microscopical evaluation, the apical medium was removed, cells were washed with pre-warmed Hank‟s balanced salt solution (HBSS, ThermoFisher), and then exposed to the air.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>ThermoFisher</div><div>suggested: (ThermoFisher; SL 8; Centrifuge, RRID:SCR_020809)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Statistical analysis: Statistical analysis was performed using GraphPad Prism 8.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad Prism</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


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      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.

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    1. SciScore for 10.1101/2022.04.27.489676: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">IRB: The manipulation of these animals was performed in Biosafety Levels 3 (BSL3) facility and the study was approved by Ethics Committee on the Use of Animals of the Ribeirão Preto Medical School, University of São Paulo (#066/2020).</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">DNase I treatment in SARS-CoV-2 experimental infection: Male K18-hACE2 mice, aged 8 weeks, were infected with 2×104 PFU of SARS-CoV-2 (in 40 μL) by intranasal route.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">A total of 10 photomicrographs in 40X magnification per animal were randomly obtained using a microscope ScanScope (Olympus) and Leica.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After blocking with IHC Select Blocking Reagent (Millipore, cat. 20773-M) for 2 hours at room temperature (RT), the following primary antibodies were incubated overnight at 4°C: rabbit polyclonal anti-myeloperoxidase (anti-MPO; Thermo Fisher Scientific; cat.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-myeloperoxidase</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-MPO</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The slides were then washed with TBS-T (Tris-Buffered Saline with Tween 20) and incubated with secondary antibodies alpaca anti-rabbit IgG AlexaFluor 488 (Jackson ImmunoReseacher; Cat. 615-545-215; 1:1000) and alpaca anti-rabbit IgG AlexaFluor 594 (Jackson ImmunoReseacher; Cat. 611-585-215; 1:1000).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-rabbit IgG</div><div>suggested: (Biorbyt Cat# orb14385, RRID:AB_10735740)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cells were then stained with Fixable Viability Dye eFluor 780 (eBioscience; cat. 65–0865-14; 1:3,000) and monoclonal antibodies specific for CD45 (BioLegend; clone 30-F11; cat. 103138; 1:200), CD11b (BD Biosciences; clone M1/70; cat. 553311) and Ly6G (Biolegend; clone 1A8; cat. 127606) for 30 min at 4°C.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>CD45</div><div>suggested: (BioLegend Cat# 103138, RRID:AB_2563061)</div></div><div style="margin-bottom:8px"><div>CD11b</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>Ly6G</div><div>suggested: (BioLegend Cat# 127606, RRID:AB_1236494)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Human alveolar basal epithelial A549 cells (5×104) were maintained in DMEM and cultured with purified NETs (10 ng/ml) pretreated, or not, with DNase I (0.5 mg/ml; Pulmozyme, Roche).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>A549</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Organisms/Strains</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">K18-hACE2 mice: K18-hACE2 humanized mice (B6.Cg-Tg(K18-ACE2)2Prlmn/J) were obtained from The Jackson Laboratory and were bred in the Centro de Criação de Animais Especiais (Ribeirão Preto Medical School/University of São Paulo).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>B6.Cg-Tg(K18-ACE2)2Prlmn/J</div><div>suggested: RRID:IMSR_JAX:034860)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">DNase I treatment in SARS-CoV-2 experimental infection: Male K18-hACE2 mice, aged 8 weeks, were infected with 2×104 PFU of SARS-CoV-2 (in 40 μL) by intranasal route.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>K18-hACE2</div><div>suggested: RRID:IMSR_GPT:T037657)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Images were analyzed with Fiji by Image J.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Fiji</div><div>suggested: (Fiji, RRID:SCR_002285)</div></div><div style="margin-bottom:8px"><div>Image J</div><div>suggested: (ImageJ, RRID:SCR_003070)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Neutrophils isolation and NETs purification: Peripheral blood samples were collected from healthy controls by venipuncture and the neutrophil population was isolated by Percoll density gradient (GE Healthcare; cat. 17-5445-01).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GE Healthcare</div><div>suggested: (GE Healthcare, RRID:SCR_000004)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Apoptosis assay: Lung tissue were harvested for detection of apoptotic cells in situ with Click-iT Plus TUNEL Assay Alexa Fluor 488, according to the manufacturer’s instructions (Thermo Fisher Scientific; cat. C10617).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Thermo Fisher Scientific</div><div>suggested: (Thermo Fisher Scientific, RRID:SCR_008452)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Data were collected on a FACSVerse (BD Biosciences) and analyzed with FlowJo (TreeStar).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>FlowJo</div><div>suggested: (FlowJo, RRID:SCR_008520)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Statistical analyses and graph plots were performed and built with GraphPad Prism 9.3.1 software.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad Prism</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. Mathew, D., Giles, J. R., Baxter, A. E., Oldridge, D. A., Greenplate, A. R., Wu, J. E., Alanio, C., Kuri-Cervantes, L., Pampena, M. B., D’Andrea, K., Manne, S., Chen, Z., Huang, Y. J., Reilly, J. P., Weisman, A. R., Ittner, C. A. G., Kuthuru, O., Dougherty, J., Nzingha, K., … Wherry, E. J. (2020). Deep immune profiling of COVID-19 patients reveals distinct immunotypes with therapeutic implications. Science, 369(6508), eabc8511. https://doi.org/10.1126/science.abc8511

    1. 24. S.-J. Park, M. Gazzola, K. S. Park, S. Park, V. Di Santo, E. L. Blevins, J. U. Lind, P. H. Campbell, S. Dauth, A. K. Capulli, F. S. Pasqualini, S. Ahn, A. Cho, H. Yuan, B. M. Maoz, R. Vijaykumar, J.-W. Choi, K. Deisseroth, G. V. Lauder, L. Mahadevan, K. K. Parker, Phototactic guidance of a tissue-engineered soft-robotic ray. Science 353, 158–162 (2016).

      Park et al. created a biohybrid system that enables an artificial animal to swim with light stimulation. The device was inspired by batoids (like sting rays), where the researchers reverse-engineered the animal’s musculoskeletal structure and used optical signals to enable steering and turning maneuvers.

    1. Author Response

      Reviewer #1 (Public Review):

      Morck et al. report the effect of five HCM causing lever arm mutations - two in the light chain binding region and three in the pliant region - on beta-cardiac myosin motor function and autoinhibition. Overall, this is a strong and very interesting work, especially since the functional consequences of mutations in the lever arm are understudied. The authors carefully compared light chain binding stoichiometries to the myosin heavy chain, steady-state ATPases, in vitro gliding velocities, and single ATP turnover kinetics of lever arm mutants in the context of short-tailed and long-tailed double-headed myosin constructs to investigate their effect on motor function and autoinhibition. They additionally used harmonic force spectroscopy to measure load-dependent detachment rates and step sizes of single-headed pliant region mutants and then calculate parameters including ensemble force, power output, and duty ratio. Finally, the authors discuss their findings with a structural model of the autoinhibited state of beta-cardiac myosin and conclude that mutations in the light chain binding region lead to changes in myosin motor activity and the formation of the autoinhibited state whereas mutations in the pliant region impact the ability of myosin to form the autoinhibited state. In summary, this work makes a significant contribution to the mechanisms of disease-causing mutations in beta-cardiac myosin and SRX myosin biology and will be of wide general interest.

      We thank the reviewer for their kind comments and wish to point out a minor typo in the above public review–the reviewer states, “the authors…conclude that mutations in the light chain binding region lead to changes in myosin motor activity and the formation of the autoinhibited state whereas mutations in the pliant region impact the ability of myosin to form the autoinhibited state.” The statement should be reversed to read, “the authors…conclude that mutations in the light chain binding pliant region lead to changes in myosin motor activity and the formation of the autoinhibited state whereas mutations in the pliant light chain binding region impact the ability of myosin to form the autoinhibited state.”

      The strengths of this work are the rigorous and well controlled experimental design and data analysis in addition to the use of human proteins to study human disease-causing mutations.

      A weakness of this work is that the interpretation/discussion of the experimental results heavily relies on previous homology models of beta-cardiac myosin (e.g. Fig. S3) rather than the relevant parts of the recent high-resolution structures of smooth muscle myosin in the autoinhibited state (PMIDs: 34936462, 33268893, 33268888). For example, one of these studies showed a previously unknown conformation of the RLC bound to the lever arms of autoinhibited myosin. The same study also showed that the C-terminus of the RLC interacts with the hook to stabilize the autoinhibited state and that the RLC interacts with the ELC. It would be insightful to analyze or comment if the studied lever arm mutations may change these interactions and possibly alter an allosteric pathway that operates between the light chain bound lever arm and the motor domain.

      While we have cited and discussed the structures from PMIDs 33268893 and 33268888 (references 42 and 43), we are grateful to the reviewer for bringing to our attention our omission of the structure from PMID 34936462 and apologize for this oversight. We have now included this citation whenever we refer to smooth muscle myosin structures and have added a comment on the interaction of the RLC with the hook (pg. 25 line 7 - 9). We thank the reviewer for this comment.

      We have also added these three experimentally solved structures to figure S7 (previously fig. S3) and added commentary on how these structures differ from the homology-modeled structures. We thank the reviewer for this comment.

      We wish to point out that the reason homology-modeled structures are also included in figure S7 (previously S3) is because the sequence of the smooth muscle myosin differs from the sequence of human β-cardiac myosin; thus, assessing the impact of an individual point mutation in the background of many baseline mutations becomes difficult. Ideally, a new modeled structure of human β-cardiac myosin should be created based on the newly available structures of the smooth muscle myosin in the autoinhibited state or an atomic structure of human β-cardiac myosin in the off state should be determined experimentally, but we believe that this is outside the scope of the present work. Additionally, it is possible that the autoinhibited structure of human β-cardiac myosin will differ from the autoinhibited structure of smooth muscle myosin in meaningful ways because smooth muscle myosin forms the autoinhibited state outside of sarcomeres, whereas human β-cardiac myosin would experience autoinhibition within the context of sarcomeres. The goal of including the modeled structures is, in essence, to show that they are all likely incorrect with regards to the real lever arm conformation and to highlight how they differ from the lever arm structures in the aforementioned smooth muscle myosin structures. This point has been clarified in the figure legend and text (pg. 25 line 19 - 22).

      Reviewer #3 (Public Review):

      The paper by Morck et al. explores the functional consequences of a group of single mutations in the lever arm of myh7 that are associated with hypertrophic cardiomyopathy (HCM). The underlying hypothesis is that these mutations affect the population of the super-relaxed state of myosin. The investigators use range of biochemical and biophysical techniques to explore the activities of these myosins. They conclude that the mutations have a range of effects on the motors, and there is not a single mechanism that can account for hypercontractility that leads to HCM. Although there is not a straightforward connection between the mutations and HCM, the study is important in that it reveals the range of functional effects of the mutations.

      A strength of the paper is the range of techniques used to examine the functional consequences of a range of myh7 mutations. Using single-molecule and ensemble techniques, they conclude the lever-arm mutations affect SRX to various extents, in addition to affecting force-dependent actin detachments, actin-activated ATPase activities, and power output. The effort required to express, purify, and characterize the six constructs (WT + 5 mutants) is considerable. A unified mechanism is not proposed as to how these mutants drive HCM, but the work remains significant in showing the range of functional effects that should be considered when modeling SRX, thin-filament activation, and interaction with other sarcomeric proteins.

      A weakness in the paper is the variability in the reported ATPase activities as outlined below. This variability leads one to question the validity of the conclusions about actin-activation of the ATPase activity. Additionally, the paper does not show primary single-molecule data, and it does not adequately discuss limitations of the harmonic force spectroscopy method. To be clear, this method is appropriate for this study, but its model-dependent limitations need to be stated.

      Specific Points:

      The authors ability to conclude that there are differences in the ATPase activities among the isoforms is not convincing. The authors are to be commended for providing the detailed data summary in Table S1, but it is these data that raise concerns. For example, the ranges in the values of kcat's (3.8 - 6.1 s-1) and Km's (1.5 - 13.6 uM) in Table S1 obtained from the different WT-control experiments are very large. In a well-controlled ATPase assay, these numbers should be very similar. It makes one question the health of the proteins and the ability to know the active site concentrations. Normalizing each mutant to the paired WT protein provides a control for assay variability, but it does not control for variability in the health of the proteins. The reader is left to wonder if the percent differences reported for the mutants are meaningful.

      We hope that the analysis and adjustments described in the “essential revisions” section help to alleviate these concerns.

      Readers need to see primary optical trapping data. Only the results of analyses are shown. It would be helpful to see single interactions, and it would be useful to see displacement distributions. Given the mutations are in the myosin lever, one might expect changes in average displacements or changes in the width of the displacement. These data are not provided.

      We thank the reviewer for this comment. We have now added a figure (Figure S3) that includes representative raw optical trapping data (S3 A, B, C, and D) and the process of identification of the respective stroking events from the changes in phase and amplitudes of oscillation of the trapped beads. We have also added displacement distribution for one representative single molecule (Figure S3 Q, R, S, and T).

      It is surprising that the authors do not show lifetimes of attachment durations from the optical trapping in the absence of force. Figure 3B is from the model-dependent fitting of the harmonic force spectroscopy experiment.

      We have now added representative raw data as obtained from HFS experiments (Figure S3 A, B, C, D) and the analysis of the HFS data for one representative molecule for each myosin type (Figure S3 E, F, G, H, I, J, K, L). In the HFS experiments, due to oscillation of the sample stage, an actin interacting myosin molecule experiences a sinusoidally oscillating load with a definite mean force for each stroking event. In this manner, some of the stroking events occur against zero or near zero mean external force (3rd force bin from the left in Figure S3 I, J, K, L). We thank the reviewer for pointing this out. We hope with the inclusion of the raw data, this concern is now addressed.

    1. Reviewer #2 (Public Review):

      The report employs an oligodendrocyte botulinum B toxin transgenic mouse line by which they are able to perturb vesicular release in a cell-specific manner. Using this mouse line the authors contend that disrupting VAMP in this ibot mouse line is evidence for vesicular release from oligodendrocytes and extracellular release of signaling factors that contribute to the maturation of oligodendrocytes.

      Using a transcriptomic approach the authors determined that expression of Prostaglandin-Endoperoxide Synthase 1 was elevated in OPCs and a pharmacological inhibitor results in a concentration-dependent reduction in OL maturation. Lastly, the authors determined that a global L-PGDS knockout mouse phenocopies the ibot mouse with respect to reduced developmental myelination.

      These data build upon prior work identifying elevated expression of L-PGDS in OPCs. These data also provide preliminary evidence for the extracellular release of signals from OPCs which may then, in an autocrine or local paracrine manner, impact the potential for the maturation of this cell type.

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      Reply to the reviewers

      We are grateful for the referees' rigorous review of our manuscript and for their overall positive reception of our work. We have pasted below the entirety of the reviewers’ comments, interleaved with our responses.

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

      In this manuscript, Gama et al. use a biophysical assay DAmFRET, structural analysis, and optogenetic tools to uncover the nucleation mechanism of CBM signalosome. They performed experiments first in yeast cells that lack death folds or related signaling networks, then confirmed their discoveries in human cells. The results presented here are clear and convincing. The paper is very well presented and clearly written.

      They found it is the CARD domain of BCL10 that acts as a molecular switch that drives all-or-none activation of NF-kB. Monomeric BCL10 possesses an unfavorable conformation and serves as a nucleation barrier, keeping BCL10 in a supersaturated inactive state that allows for binary activation upon stimulation.

      They also characterized CARD9 CARD domain and a coiled-coil region. They reasoned that CARD9CARD functions as a polymer seed to nucleate BCL10, and that the coiled-coil region has multimerization ability to facilitate nucleation. Furthermore, they characterized that MALT1 activation doesn't depend on BCL10 polymers but its own proximity. And MALT1 induces graded NF-kB activation, thus further demonstrating the binary activation is conferred by BCL10.

      Major comments:

      1. Fig S1D and E, the authors used TNF-a to activate NF-kB independent of CBM signalosome and found the activation in each cell increased with dose. In contrast, CBM activation led to bimodal cell activation. The authors claim that this is evidence that positive feedback upstream of NF-kB. We do not believe this claim can be made from this comparative experiment alone. We agree that positive feedback is important for activating an NF-kB response, but the comparison between CBM and TNFa is inaccurate and glosses over published data. Specifically, there is published data that TNF-a does activate a 'switch-like' or digital response, as defined by the translocation of p65 (see (Tay et al. 2010) among other studies that have examined p65 translocation at the single-cell level). The difference in T-sapphire expression between CBM and TNF activation is most likely due to TNFa induced oscillations of p65 translocation (although this is speculation on our part). Therefore we suggest to the authors that the TNF-a data (Fig S1D and E) should be omitted, as the claim of switch or not-switch as pertains to TNF signaling is more complex and nuanced than presented here. We believe omitting this data will strengthen the manuscript and avoid confusion in the field. The bimodal expression of the T-sapphire NF-kB reporter driven by the CBM signalosome activation is sufficient to claim an all-or-none response.

      We thank the reviewer for this suggestion. We acknowledge that the activation of NF-κB by TNF-ɑ is more complex than we had presented, and agree that the differences in T-Sapphire reporter output could be attributed to p65 oscillations. We had not previously considered this interesting possibility -- which is not addressed by the present data -- believe it is worth future investigation. As suggested by the reviewer, we have now omitted the TNF-a data, and agree that this change does not impact the overall claims of the paper.

      Fig 3B, the authors introduced CARD9CARD-µNS as a stable condensed seed for BLC10. However, considering CARD9CARD can form polymers at high concentration (Fig 3B and S3D), are these high expression levels of CARD9CARD able to induce BCL10-mEos3.1 assembly (as measured by DamFRET in yeast cells)? Can the authors examine BCL10 FRET at these high expression level of CARD9CARD? We assume that BCL10 will be assembled in these cells. This would provide a valuable control experiment and support the author's conclusions.

      Indeed, this question is amenable to DAmFRET. Accordingly, we have now performed DAmFRET of yeast cells expressing Bc10-mEos3.1 in the presence of either CARD9CARD-mCardinal or mCardinal itself (see new Fig S6A and B, and associated results section). We confirmed that cells with high CARD9CARD-mCardinal expression had higher FRET on average than cells with low expression. Importantly, cells expressing high or low levels of mCardinal itself had the same FRET level (Fig S6).

      Fig 3C, the text said "Whereas WT CARD9CARD assembled into polymers at high concentration, the pathogenic mutants R18W, R35Q, R57H, and G72S failed to do so (Fig 3C and S7B,C), explaining why they cannot nucleate BCL10". This claim that these mutants can not nucleate BCL10 does not have a figure call out or a reference. The authors then show the results in Fig 3E which supports this claim. Even though they were done in the context of full-length CARD, all proteins contain the I107E mutation that releases autoinhibition. For clarity, the authors should consider rearranging the text to avoid explaining a phenomenon and making conclusions before showing the results.

      We have now rearranged this section to match the figures and claims.

      Fig 4D, E and Video 1, the authors showed the nucleation of BCL10 into puncta within live cells is followed by p65 translocation to the nucleus. The authors claim that 'this result suggests that BCL10 is indeed supersaturated prior to stimulation' (paragraph 2 section titled BCL10 is endogenously supersaturated'). We fail to understand how this live-cell experiment leads to the conclusion BCL10 is supersaturated before stimulation. We think this text should be deleted from the text, or put into context with the DAmFRET data that lead the authors to make this claim. It would be interesting for the authors to define in discussion what are the golden criteria to claim a protein exists in a supersaturated state with live cells (by microscopy or other methods)? Adaptor protein assembly into puncta and the subsequent nuclear translocation of transcription factors is a common phenomenon across signalling pathways. Not all these pathways rely on signaling adaptors existing in a supersaturated state. The field of cell signaling (and cell biology in general) would benefit from a detailed definition of how these physical-chemical definitions of proteins are supported by experimental data. We believe that this paper will become a seminal paper in the field, and future work will benefit from a clear definition of how a claim of supersaturation is derived from the data.

      We appreciate that the concept of supersaturation will be foreign to many biologists, and welcome this opportunity to elaborate. We have now rephrased the corresponding results section for figure 4D, E, and have added new evidence to support our claim that BCL10 is supersaturated, as had been requested by reviewer 2 (see below in response to point 1). Supersaturation, as we (correctly) use the term, occurs when the concentration of a protein in solution exceeds its equilibrium solubility for the given conditions. The term is also sometimes used to describe __global __protein “concentrations” in excess of the solubility limit, even if a dense phase has already formed and potentially depleted the effective concentration (in solution) to the solubility limit. This is a key distinction, as only the former implies a high-energy out-of-equilibrium scenario that predetermines a future change -- release of the excess energy via phase separation.

      How does one experimentally determine if a protein is supersaturated? In theory, one may conclude that a protein is supersaturated if its assembly causes a net loss of energy from the system (i.e. exothermic). Unfortunately, it is likely not yet possible to perform such measurements with sufficient sensitivity inside a living cell. However, it is possible to infer that a protein is supersaturated if assembly can be shown to occur without a net input of energy to the system, i.e. without any change in thermodynamic control parameters such as temperature, pH, post-translational modifications, concentration of the protein, or concentration of any interacting factor. To do this, one introduces a substoichiometric amount of pre-assembled protein to the system. This manipulation will trigger assembly if the protein is supersaturated. If the protein is instead subsaturated, assembly will not occur and the exogenously added assemblies will simply dissolve. This phenomenon, known as “seeding” in the prion field, is considered a golden criterion sufficient to conclude that a protein has prion behavior. However, because bona fide prions additionally require a means for dissemination between cells, seeding analyzed at the cellular rather than population level is more appropriately considered a sufficient criterion for supersaturation (which is a prerequisite for classical prion behavior (Khan et al. 2018)). Our CARD9CARD-Cry2 experiment was designed to test this criterion. Specifically, it allowed us to introduce a seed independently of receptor activation, thereby precluding any orthogonal cellular response that might lower Bcl10 solubility through e.g. a post-translational change. That the seeds were substoichiometric is evidenced by the fact that Bcl10 polymerized homotypically following stimulation (i.e. it didn’t just bind to the CARD9CARD puncta, but went on to deposit onto itself).

      How does assembly under this scenario differ in principle from the many examples of puncta formed by other signaling proteins that occur upon stimulation of their respective pathways? Puncta formation that is induced by a thermodynamic change in the cell cannot be said to have resulted from pre-existing supersaturation. Rather, the stimulus may have caused some change that either increases the effective concentration of the protein (e.g. upregulates its expression, induces a post-translational change that activates it, or releases an inhibitory factor) or reduces solvent activity (e.g. change in pH).

      An additional requirement (necessary but not sufficient) is that the assembly must be regular with respect to some order parameter. That is to say, it must be a bona fide “phase”. At a minimum, this implies a uniform density. Additionally, for supersaturation to persist over biological timescales under physiological conditions and confinement volumes, the assembly (once formed) must also have structural repetition in at least two dimensions, i.e. crystallinity (Rodríguez Gama et al. 2021; Zhang and Schmit 2016). We know this to be true for Bcl10.

      Rodríguez Gama A, Miller T, Halfmann R. 2021. Mechanics of a molecular mousetrap-nucleation-limited innate immune signaling. Biophys J 120:1150–1160. doi:10.1016/j.bpj.2021.01.007

      Khan, T., Kandola, T.S., Wu, J., Venkatesan, S., Ketter, E., Lange, J.J., Rodríguez Gama, A., Box, A., Unruh, J.R., Cook, M., et al. (2018). Quantifying nucleation in vivo reveals the physical basis of prion-like phase behavior. Mol. Cell 71, 155-168.e7.

      Zhang L, Schmit JD. 2016. Pseudo-one-dimensional nucleation in dilute polymer solutions. Phys Rev E 93:060401. doi:10.1103/PhysRevE.93.060401

      Regarding the supersaturated state of BCL10, the authors convincingly use optogenetics to show how transient assemblies of CARD-Cry2 can template BCL10 assembly. This is a convincing experiment that shows templated nucleation of BCL10. To strengthen the claim that BCL10 is supersaturated endogenously we suggest the author quantify the expression of BCL10-mScarlet and CARD-Cry2 and ideally show that this phenomenon can be observed at expression levels equivalent to endogenous.

      As stated above, that BCL10-mScarlet formed polymers that we observed to elongate homotypically off of the CARD9CARD seeds indicates that the protein was supersaturated under the conditions of the experiment. The concentration of CARD9 is not a relevant parameter in this case. We had already compared the expression of BCL10-mScarlet to endogenous BCL10 in 293T, THP-1, and human fibroblast cells by quantitative immunodetection (Fig. S10D), revealing that the expression level of our BCL10-mScarlet constructs matched that of endogenous BCL10, which was approximately the same in all cell lines. We also compared the distribution of expression levels of BCL10-mScarlet versus that of endogenous BCL10 using antibody staining followed by flow cytometry, which confirmed that the range of expression levels of BCL10-mScarlet falls within that of endogenous BCL10 in 293T cells (Fig. S10F). Hence, we believe our data suffice to conclude that Bcl10 is supersaturated at endogenous levels of expression.

      Minor comments:

      1. Special character "delta" is not displayed in the text (instead only a space).

      This error occurred upon exporting the manuscript from our text editor to a PDF. We now have made sure all special characters are present in the PDF version.

      Several cell lines including mouse, human, and yeast lines were used across this manuscript. It would be clearer and more helpful if the exact cell type of the line could be indicated. Such as, "BCL10-mEos3.1 yeast cells" instead of "BCL10-mEos3.1 cells", "BCL10-mScarlet HEK293T cells" instead of "BCL10-mScarlet cells".

      We have now modified all instances to indicate the origin of the cell lines tested.

      Fig 5B, the authors indicated that BCL10 colocalized with CARD9CARD, then please show the merged image as well.

      We have now included the merged image to indicate colocalization in the inset images.

      Fig 6E, authors claimed that cells were stimulated with blue light for the indicated durations. The longest duration is 12 hours. Please specify if it was continuous exposure or several rounds of exposure in the indicated durations.

      We have now specified in the figure legends, text, and methods section, that this specific experiment used a continuous exposure of blue light.

      Reviewer #1 (Significance (Required)):

      This work used a combination of FRET and optogenetic tools to engineer CBM signaling and visualize the effects. They incorporated knowledge from structure biology, together with their results from mutations and truncations, dissected the significance of each protein in CBM signalosome, and demonstrated in detail how higher-order assemblies make all-or-none cellular decisions. We believe this paper will be a seminal paper in the field of cell signalling and cytoplasmic organization. It defines a new paradigm of macromolecules assembly of signalling complexes as being dependent on protein existing in a supersaturated state. Importantly this paper opens up new questions regarding macromolecular signaling complexes (found in many innate immune signaling pathways): How is protein supersaturation maintained and used throughout evolution to construct biochemical signalling switches?

      This paper will be of particular interest to scientists working on immunity and cell signalling, especially in the field of higher-order assemblies. However, we feel the impact of this paper goes beyond these fields, and we believe this manuscript will be of broad interest to the cell biology and biophysics communities. For reference, our expertise is in innate immunity and cell biology.

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

      In their manuscript entitled "A nucleation barrier springloads..." Rodriguez-Gama et al. dissect the assembly mechanism of the signalosome, composed of the proteins CARD9, BCL10 and MALT1, using a novel in-cell biophysical approach (DAmFRET). They first overexpressed fluorescently tagged versions of the proteins to promote their assembly in yeast and mammalian cells, finding that CARD9 forms higher order assemblies across a wide range of concentrations with no discontinuity in the DAmFRET profile. In contrast, the DAmFRET profile of BCL10 showed a clear separation between monomers and higher order assemblies, which started to form spontaneously only at higher BCL10 concentrations. Furthermore, at the two states of the proteins co-exist at all concentrations. These observations imply that there is a nucleation barrier to forming BCL10 assemblies. MALT1 showed no change in FRET regardless of its expression level. These observations, alongside fluorescence microscopy of the assemblies, and previous structural studies, suggest that BCL10 forms self-templating polymers that act as a switch for an all-or-nothing immune response, assayed in this case by monitoring the nuclear translocation of the NF-kB subunit p65. The authors also assessed the effects of known disease-causing mutations on the nucleation barrier, showing that changes in the strength of the nucleation barrier can have major effects on signalosome function. Finally, they used optogenetic methods to trigger assembly of individual signalosome components, providing insight into the minimal components/conditions required for signalosomes to work.

      Major comments

      Overall, the experiments by Rodriguez-Gama et al. offer convincing evidence that there is a nucleation barrier to BCL10 polymerisation, and that a CARD9 template is sufficient to overcome the barrier. Although the existence of a nucleation barrier had already been postulated, based on structural and other studies (referenced by the authors), it had lacked a rigorous demonstration. This work provides that demonstration, which is important for the signalosome field and more broadly applicable to researchers studying cellular decision making. The study further demonstrates that DaMFRET is an excellent to study protein assembly processes in their native environment, allowing the authors to tackle a question that would have been technically very difficult to address otherwise. The optogenetic experiments are a nice sufficiency test for their ideas.

      We feel there are a few key points to address before publication.

      1) One of the main conclusions is that spring-loading the nucleation barrier with high super-saturating BCL10 concentrations allows a decisive response. Although much of the data strongly imply this conclusion, the dependence of the immune response on BCL10 concentration was not tested directly. A key prediction of the nucleation barrier is that at concentrations below saturation, BCL10 should not be able to induce an all-or-nothing response when stimulated. At saturated/super-saturated concentrations BCL10 should be able to induce a response. At deeply super-saturated concentrations the response should start to be activated spontaneously in the absence of an external stimulus. These predictions could be tested using the doxycycline-inducible BCL10 system (Figure S2D), without establishing major new experimental avenues. We feel that such an experiment would strengthen the main conclusion. It might also help to shed light on whether being highly supersaturated enables a more decisive response than being just saturated.

      This is a great idea. As the reviewer suggested, our Doxycycline-inducible BCL10 system enables us to induce and track the state of BCL10 over time. We have now performed the requested experiments (Fig. S9D, E) and incorporated the results into the relevant section of the text. In short, our new analyses show that BCL10 indeed has a concentration threshold for activation by stimulation, and that it can also nucleate spontaneously when overexpressed. Note that our original analyses in Fig. 4B and C also demonstrate spontaneous BCL10 activation at high concentrations. With this new evidence and the orthogonal approaches used in Fig. 5, we believe our data definitively support our conclusion that BCL10 is supersaturated.

      2) Intuitively, readers might expect that if BCL10 is supersaturated then, once nucleated, it would rapidly assemble at the nucleation sites. In Figure 5B, CARD9CARD-miRFP670nano-Cry2 assemblies are optically induced throughout the cell. However, BCL10 appears to nucleate at just a few sites with a few minutes delay. More widespread nucleation and growth of BCL10 polymers seems to take longer (20-40 minutes, Figures 5B and 5C), after CARD9CARD-miRFP670nano-Cry2 has disassembled. Furthermore, in Figures 4D and 4E, very few BCL10 assemblies are visible/quantifiable after 70 minutes PMA exposure, but p65 has clearly entered the nucleus. It looks like BCL10 assembly slightly lags behind p65 nuclear entry. Can the authors provide a more detailed explanation of these kinetics?

      We do note that the number of CARD9CARD clusters formed upon opto-stimulation exceeds the apparent number of BCL10 nucleation sites. We believe this is consistent with nucleation-limited kinetics, where the clustering of CARD9-CARD increases the local probability of nucleation. As nuclei form and grow, they lower the probability of subsequent nucleation elsewhere in the cell. Additionally, it is possible that our artificial seeds do not perfectly mimic the native CARD9 seeds that form upon natural stimulation (e.g. due to potential steric interference from the fluorophore and Cry2). We also acknowledge that there is a slight delay in the visible appearance of BCL10 polymers relative to p65 nuclear translocation. We expect that MALT1 activates already when the polymers are still too small to see (sub-resolution), whereas the polymers only become microscopically visible once they’ve grown quite a bit more.

      3) Related to point 2 above, in Figure 5D, the leftmost cell in the field of view clearly contains CARD9CARD assemblies but there are no BCL10 assemblies and p65 is not imported into the nucleus (in contrast to the central cell in the field of view). How often does CARD9CARD optogenetic assembly lead to BCL10 assembly? In other words, can the authors quantify the cell-to-cell variability in this experiment?

      Throughout our experiments, whether analyzing BCL10 puncta formation, NF-kB transcriptional activity, or p65 translocation, we observed a persistent nonresponsive fraction of cells even at saturating levels of stimulation. Specifically, approximately 30% of THP-1 cells failed to acquire T-Sapphire fluorescence or form BCL10-mEos3.2 puncta when stimulated with high levels of β-glucan (Fig 1B and E, respectively), and approximately 25% of 293T cells failed to acquire T-Sapphire fluorescence or exhibit p65 nuclear translocation when stimulated with high levels of PMA (Fig 1C and Fig 4E, respectively). Because these numbers did not depend on whether BCL10 was endogenously or exogenously expressed, we know that the underlying cell-to-cell heterogeneity involves factors upstream of BCL10. Indeed, the fraction of recalcitrant cells drops to 10% in our optogenetic experiments that bypass upstream factors (Fig S11E). Possible sources of heterogeneity include different physiological states of the cells or fluctuations in the expression levels of any upstream factor in the signaling pathway. We believe that this phenomenon is not unique to the CBM signalosome, as we (unpublished) and others (Fernandes-Alnemri T et al, 2009, Dick M et al, 2016) have similarly observed a fraction of non-responding cells upon activation of the inflammasome, which involves nucleation-limited polymerization of the adaptor protein ASC. While this phenomenon is interesting and may be important to our understanding of the full complexity of signalosomes in vivo, we believe that identifying the source of heterogeneity would be outside the scope of the present manuscript. We now describe this phenomenon in the final paragraph of the “Endogenous BCL10 is constitutively supersaturated” section.

      Fernandes-Alnemri, T., Yu, JW., Datta, P. et al. AIM2 activates the inflammasome and cell death in response to cytoplasmic DNA. Nature 458, 509–513 (2009). https://doi.org/10.1038/nature07710

      Dick, M., Sborgi, L., Rühl, S. et al. ASC filament formation serves as a signal amplification mechanism for inflammasomes. Nat Commun 7, 11929 (2016). https://doi.org/10.1038/ncomms11929

      Minor comments

      While the work is scientifically well done, the text reads as though it is meant for experts rather than a broad audience. This is a pity because it risks alienating readers. We suggest that some adjustments to the text (mainly additional explanations and not ruling out alternative interpretations of the data) would widen the audience and increase the impact of this important study. Below are some suggestions that might help.

      1. In the first results section, the authors write: 'This suggests that Bcl10 but not CARD9 assembly occurs in a highly cooperative fashion that could, in principle (Koch, 2020), underlie the feed forward mechanism.' It isn't obvious how Figure 1 leads to this statement. Could the authors give a more detailed explanation?

      We have now revised the text to elaborate on this interpretation.

      One limitation of DAmFRET is that it can only detect a nucleation barrier where there is a difference in FRET between the monomer and the assembled form of the protein. However, it can't necessarily detect when there is not a nucleation barrier i.e. if there's no difference in FRET. The text seems to suggest that CARD9 and MALT1 don't have nucleation barriers to their assembly. While this might not be intentional, it would be helpful to explicitly state that CARD9 and MALT1 could also possess such barriers that are not detectable by this method. This wouldn't detract from the finding that BCL10 has a barrier that plays an important function.

      The reviewer is correct that DAmFRET would not be able to detect a nucleation barrier if the assembled phase does not condense the fluorophore to a sufficiently high density for FRET to occur. In our experience, this is only a concern for very large proteins whose bulk “dilutes” the fluorophores within the assembly. Death domains, on the other hand, are only ~ 3 nm in diameter, and FRET occurs within a range of ~10 nm; hence we think it very unlikely that the death domains could be forming cryptic polymers that escape our detection. In any case, when assembly does produce a change in FRET, we can with confidence determine how strongly that form of assembly is governed by concentration. Hence, for CARD9, which does produce a FRET signal upon assembly, we can say that assembly has a smaller intrinsic nucleation barrier than that of BCL10. We further eliminated the possibility of multi-step nucleation (which would reduce the apparent nucleation barrier relative to the one-step ideal case) for CARD9 by showing that artificial condensates of the protein expressed in trans do not influence the concentration-dependence of FRET (Fig. 4 B). Finally, under all conditions where CARD9 lacked FRET, it also lacked signaling activity, suggesting there is not a cryptic functional assembly that evades our assay. Likewise MALT1, which lacked FRET at all concentrations, was entirely unable to activate NF-kB upon overexpression (Fig. S8 A and B), suggesting that it too is not forming a cryptic functional assembly that evades our assay. We therefore feel confident in our conclusion that CARD9 and MALT1 lack nucleation barriers of a magnitude comparable to that of BCL10. Note that our claim is not that they entirely lack a nucleation barrier (CARD9 after all does form a multi-dimensionally ordered polymer), but rather that we fail to observe a nucleation barrier and hence any barrier that may exist is insufficient to manifest at the cellular level.

      In the final results section, the idea that MALT1 activation doesn't depend on BCL10 polymer structure doesn't necessarily follow from the data. An alternative interpretation is that optogenetic clustering of MALT1 causes it to recruit BCL10 and form BCL10-MALT1 filaments (structure solved by Schlauderer et al., 2018). Also, the optogenetic clustering of MALT1 may mimic some structure found in the BCL10 cluster. Therefore, we are neither convinced that the data unambiguously show that MALT1 activation strictly depends on multi-valency rather than an ordered structure of BCL10 polymers nor that this conclusion is truly necessary for the paper.

      We agree that the reviewer’s alternative interpretation of this experiment is possible. However, we consider it unlikely because we performed the experiment with MALT1 lacking its Death Domain (residues 126-824), which mediates its interaction with BCL10 (Schlauderer et al., 2018). Our experiments then suggest that MALT1 clustering is sufficient for activation independent of any structuring mediated by BCL10. Nevertheless, we have now performed an additional control in which we treated these cells with PMA to induce BCL10 polymerization. As expected, the NF-kB transcriptional reporter utterly failed to activate in this condition, indicating that MALT1 does not interact with BCL10 polymers when it lacks its death domain. This aspect has been further elaborated in our response to reviewer 3 point 5.

      What optical density do the yeast cells reach during the 16h induction in galactose? If they are in stationary phase, this could affect the assembly status of the proteins being expressed, as the cytoplasm becomes glassy when cells are starved, and this coincides with widespread protein aggregation/assembly (Joyner et al., 2016; Munder et al., 2016).

      In our DAmFRET strategy, we first dilute an overnight culture and regrow the cells to log phase prior to resuspending them in galactose media. Our strain is engineered to undergo cell cycle arrest upon protein induction, hence exponential growth is prevented and the cells do not deplete galactose during the 16 hr induction. We have also performed many time courses of DAmFRET following induction and generally find no qualitative difference between early and late times (unpublished). Early time points simply have lower expression and correspondingly fewer cells in the high FRET state. Importantly, all comparisons between proteins are made with the same 16 hr induction.

      Although these experiments show that thermodynamically lowering the BCL10 nucleation barrier (e.g. by post-translational modifications or protein expression levels) isn't required for a response, they don't rule it out. It would be good to state this in the discussion, as cells may have multiple mechanisms of switching on the signalosome.

      We thank the reviewer for this suggestion and have now explicitly stated in the discussion that our experiments do not argue against possible thermodynamic tuning of the nucleation barrier.

      The discussion compares signalosomes with condensates formed by liquid-liquid phase separation. This is an interesting comparison but it suggests that disordered assemblies would not be capable of performing signalosome-like functions. This needs to be explained more clearly. For example, non-amyloid prions seem to form gel-like assemblies with a high nucleation barrier that are capable of driving heritable traits, likely through self-templating (Chakravarty et al., 2020). Such examples could represent disordered assemblies with signalosome switch-like behaviour. Furthermore, there are examples of condensates that are induced by environmental changes e.g. Pab1 and Ded1 condensates (Riback et al., 2017; Iserman et al., 2020). This potentially allows the proteins to reach high concentrations and remain un-condensed until a change in heat or pH overcomes a nucleation barrier required for condensate formation. Although the condensates aren't self-templating, they seem to require energy for their disassembly. Combined, this also allows switch-like behaviour, where the switch is flipped back to the uncondensed off state once conditions return to normal. In general, crossing a phase boundary can represent a switch-like response. Finally, recent electron-tomography experiments show that ASC puncta comprise clusters of filaments (Liu et al., 2021, biorxiv). CARD9/BCL10 assemblies may have similar ultrastructures and liquid-liquid phase separation may well play a role in their assembly.

      Indeed, we explicitly maintain that liquid phases cannot themselves perform signalosome-like functions. Chakravarty et al. 2020 did not observe amyloids associated with their phenomena, but the relevant experiments were not designed to exhaustively exclude an underlying ordered phase. To the extent that gelation is involved, their observations are fully consistent with ours. IUPAC defines a “gel” as a colloidal network involving a solid phase and a dispersed phase. The existence of a solid phase necessarily implies an underlying disorder-to-order transition, even if limited to small length scales. In the case of gelation associated with liquid-liquid phase separation, nucleation of the ordered phase simply occurs in two steps (first condensation, then ordering). Note also that a liquid phase could in principle give rise to a heritable phenotype if it activates a positive feedback in a molecular biological process involving the protein of interest (e.g. upregulation of its expression or a change in interacting factors). Chakravarty et al. did not exclude such phenomena (it would be very difficult to do so); hence it cannot be concluded that phase separation is responsible for the sustained phenotypic changes.

      We do not fully follow the reviewer’s logic concerning the relevance of Pab1 and Ded1 condensates. These proteins only condense when their respective phase boundaries fall below the endogenous protein concentration, as upon thermal stress. The proteins are not supersaturated in the absence of such conditions (for example, they cannot be seeded), and it is incorrect to characterize the change in heat or pH as overcoming a pre-existing nucleation barrier. The concept of a nucleation barrier only applies under conditions where a phase is thermodynamically favored. It is also misleading to state that the Ded1 and Pab1 condensates require energy for disassembly. Rather, they require energy to disassemble rapidly. Unless the assemblies have accessed a more ordered phase as described above (two step nucleation), involving a lower phase boundary, they will inevitably dissolve after the conditions return to normal.

      We have much prior experience with ASC. Although it has not been explicitly shown, that it forms ordered polymers and can behave as a prionoid in vivo suggests that it very likely operates the same way as BCL10 (i.e. is physiologically supersaturated). That full-length ASC forms clusters of filaments is not relevant (in our view) to the mechanism shown here, which only requires that filaments are indeed formed. Formally, the size of the relevant nucleus determines the minimum length scale at which ordering must manifest in our mechanism. Based on the structure of death domain filaments, this could be as small as tetramers or hexamers (a minimal but structurally complete “polymer”).

      As stated above, and now elaborated in the discussion, our data do not exclude a role of thermodynamic regulation, as could lead to liquid-liquid phase separation, in tuning the nucleation barrier of Bcl10. What they do exclude is that such changes are required for Bcl10 to activate in the first place.

      Can the authors comment on the loss of BCL10 in Echinodermata, Anthropoda, Nematoda? Is there another protein that plays a similar role? Could a CARD or PCASP protein possess self-templating properties? Could other methods of control be at play e.g. protein expression?

      This is a very interesting question! We think the reviewer’s suggested explanations for the loss of BCL10 in those lineages are valid and worthy of future exploration. Nematodes such as C. elegans have lost multiple components of innate immunity. They have very few pathogen recognition receptors and also lack NF-kB! They do, however, have other adaptor proteins that the literature and our unpublished data suggest may have self-templating ability, such as TIR-1. Drosophila also encodes multiple TIR-containing proteins that are essential for innate immunity. In short, it is possible that other proteins have acquired the hypothetically essential role of supersaturation and nucleation-limited signaling in these organisms.

      Figures 1B/1C: Can the authors comment on why the active cells plateau at about 70-75%? This is a striking feature of the plots, but the explanation may not be obvious to readers.

      See our response to major point 3, above.

      Figures 1D/1E: What was the concentration of B-glucan used in this experiment? This could be included in the figure legend. If greater than 1ug/ml this means that the % of active cells in Figure 1B matches the % of cells with BCL10 assemblies in Figures 1D/1E, which is potentially an important point.

      We thank the reviewer for bringing this point to our attention. We have now indicated in the figure legend the concentration of B-glucan used in this experiment (10 μg/ml). That the percentage of active cells in Fig. 1B matches that of cells containing BCL10 polymers in Fig. 1D and E indeed strengthens the stated relationship between BCL10 assembly and NF-kB activation in THP-1 cells subjected to a relatively physiological stimulus. Additionally, we have performed experiments to measure the levels of p65 translocation in THP-1 cells treated with B-glucan that express BCL10-mEos3.2. This data is shown in Figs. S1D and E in response to reviewer 3.

      Use of both 'BCL10' and 'Bcl10' when referring to the protein.

      We have now replaced all instances where Bcl10 was used to follow guidelines for gene and protein name conventions.

      Bruford EA, Braschi B, Denny P, Jones TEM, Seal RL, Tweedie S. Guidelines for human gene nomenclature. Nat Genet. 2020;52(8):754-758. doi:10.1038/s41588-020-0669-3

      In the supplementary figures there are some formatting problems/missing words in the figure legends. In Figure S11 there is a black box covering the lower part of the figure.

      We have now fixed these instances.

      References used in this review

      Chakravarty, A.K. et al. (2020) "A Non-amyloid Prion Particle that Activates a Heritable Gene Expression Program," Molecular Cell, 77(2), pp. 251-265.e9. doi:10.1016/j.molcel.2019.10.028.

      Iserman, C. et al. (2020) "Condensation of Ded1p Promotes a Translational Switch from Housekeeping to Stress Protein Production," Cell, 181, pp. 818-831.e19. doi:10.1016/j.cell.2020.04.009.

      Joyner, R.P. et al. (2016) "A glucose-starvation response regulates the diffusion of macromolecules," eLife, 5. doi:10.7554/eLife.09376.

      Munder, M.C. et al. (2016) "A pH-driven transition of the cytoplasm from a fluid- to a solid-like state promotes entry into dormancy," eLife, 5(MARCH2016). doi:10.7554/ELIFE.09347.

      Riback, J.A. et al. (2017) "Stress-Triggered Phase Separation Is an Adaptive, Evolutionarily Tuned Response," Cell, 168(6), pp. 1028-1040.e19. doi:10.1016/j.cell.2017.02.027.

      Schlauderer, F. et al. (2018) "Molecular architecture and regulation of BCL10-MALT1 filaments," Nature Communications 2018 9:1, 9(1), pp. 1-12. doi:10.1038/s41467-018-06573-8.

      Reviewer #2 (Significance (Required)):

      The existence of a nucleation barrier had already been postulated, based on structural and other studies (referenced by the authors), it had lacked a rigorous demonstration. This work provides that demonstration, which is important for the signalosome field and more broadly applicable to researchers studying cellular decision making. The study further demonstrates that DaMFRET is an excellent to study protein assembly processes in their native environment, allowing the authors to tackle a question that would have been technically very difficult to address otherwise.

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

      The study by Rodriguez Gama et al. addresses the molecular function of CBM complex-forming proteins CARD9, BCL10 and MALT1 in the activation of myeloid cells, using optogenetic tools, transcriptional reporters and biochemical approaches. It is known from previous studies that Bcl10 oligomerizes into filamentous oligomeric structures incorporating Malt1, and that these structures are nucleated by receptor-induced activation of CARD proteins such as CARD11 (in lymphocytes) or CARD9 (in myeloid cells), but the mechanism underlying the assembly of the resulting CBM complexes remain incompletely understood.

      The authors develop beautiful optogenetic tools to address this question, and convincingly demonstrate that CARD9-mediated nucleation of BCL10 triggers a binary cellular NF-kB response in a spring-load-like fashion, and identify mutants of BCL10 and CARD9 that impact this capacity. Unfortunately, however, the authors do not do a good job to simplify this complex problem so it can be easily understood. In particular, the choices of mutants, models and experiments are not consistent between figures, and some data seem to be arbitrarily added or omitted. Complex hybrid constructs are also used, without assessing whether these are indeed functional in the corresponding ko cells. The paper would therefore benefit from a major overhaul. We also noticed that the literature is often not cited adequately and have included a (non-exhaustive) list of examples of wrong, incomplete, or erroneous citations below.

      1. The initial observations of binary signaling are derived from a reporter system. Although there are controls to show that the reporter used does not function intrinsically cooperatively, it would be nice to see additional data to show that cooperativity occurs also at the level of endogenous response systems, for instance by qPCR-based assessment of a natural NF-kB target gene (induced for example by TNFa versus B-glucan in THP-1 cells, and by TNFa versus PMA in 293T cells).

      As detailed in the introduction, NF-kB has been shown by multiple labs to activate in a binary fashion. Our manuscript shows that NF-kB activation occurs in a binary fashion both at the level of transcription and at the level of nuclear translocation (upstream of any transcriptional output). While we do agree that additional data could further illustrate the biological significance of our findings, we do not feel it is necessary for our conclusions. Note also that because NF-kB activation occurs in a binary fashion per cell, a simple qPCR experiment would not suffice to extend our findings to the broader Nf-kB regulon. Instead, one would have to use e.g. RNA-FISH or single cell RNA-seq, nontrivial experiments that would take months to complete.

      The cell lines in Figures 1D-E (and also some of the BCL10 mutants used later on) would have been better run in the assays in the early parts of Figure 1. The final conclusion prior to the section The adaptor protein BCL10 is a nucleation-mediated switch is otherwise not justified. This is a central tenet of the paper, that is referred to again, with some other ancillary data to support it. These mutants reappear later in the paper, but it would have been better, and easier to make rescue lines of BCL10 KO in Figure 1, otherwise the logic is lost, and the models seem chosen arbitrarily.

      The choice of experiments in different panels of Fig. 1 resulted from a chronological progression of reagent construction as the project evolved. We do appreciate that switching between the assays may lead readers to doubt one or the other. Therefore, we have now immunostained for endogenous p65 in the same experiment as for Fig. 1D and confirmed that p65 translocated to the nucleus only in THP-1 BCL10-KO cells that have been reconstituted with WT BCL10-mEos3.2, but not E53R. We think this additional evidence along with our orthogonal measurements in other reporter systems confirms our findings that BCL10 nucleation determines NF-kB activity.

      Expression with microNS is not well controlled and gives little real evidence for what is occurring. It is unclear what the concentration of the protein expressed was, but certainly the relative expression of the CARD9(CARD) and the microNS version should be assessed.

      We believe these concerns result from a misunderstanding. We assume the reviewer is referring to the experiment in Fig. 3B. Expression of muNS on its own has no effect on the DAmFRET of other proteins, and we have previously used it in exactly the same way as here (Holliday M et al. 2019 and Kandola T et al. 2021). Please note that muNS fusion proteins in our experiment have an orthogonal fluorescent protein whose spectra do not significantly overlap with those of mEos3.1. The experiment evaluates a protein’s ability, when condensed via its fusion to muNS, to nucleate an mEos3.1-fused protein that is expressed in trans. Fusion of proteins to muNS does not affect their expression levels, as we now show for CARD9CARD-muNS-mCardinal versus CARD9CARD-mCardinal (Fig. S6D).

      Also, the AmFRET profile of CARD9CARD looks very weird, it cannot be compared to BCL10.

      We are unsure in what way the AmFRET profile of CARD9CARD is “weird”. It is fully consistent with expectations and has been thoroughly explained in the text. We suspect the reviewer was bothered by the sharp acquisition of FRET at approximately 100 uM. As explained in the text, this represents the phase boundary, also known as the solubility line, for CARD9CARD polymers, which we previously showed in vitro (Holliday M et al. 2019). Above this concentration, the protein self-assembles without a nucleation barrier, hence the sharp but continuous change in FRET. BCL10 plots, in contrast, show a discontinuous acquisition of FRET, which indicates a nucleation barrier. In order to highlight that the CARD9CARD transition is understood and expected, we have also now added a line to the plot to demarcate the phase boundary.

      We are not convinced of the usefulness of the introduction of a slew of disease-causing CARD9 mutations that may or may not be relevant to the authors' point. The fact that they do or do not function in a specific sub portion of an assay that may or may not be relevant to biological activity seems to be of interest but without biochemical understanding, little is clear.

      While several reports have shown the clinical importance of these CARD9 mutations on susceptibility to fungal infections, little was known about the molecular mechanism underlying their effects. The inclusion of the disease-causing mutants to this paper is justified for the following reasons. First, they demonstrate the relevance of our work to disease. Second, they build off our findings to provide an otherwise unknown molecular mechanism of these mutants. We showed using independent methods that CARD9CARD mutations disrupt the ability to nucleate BCL10, via two different mechanisms. Finally, validating the disease-causing mutations allowed us to use them as controls for subsequent experiments demonstrating that BCL10 is supersaturated.

      The Optogenetic experiments are interesting, but difficult to interpret without evidence that these MALT1 constructs are indeed still functional when expressed in MALT1-deficient THP-1 cells. We do not therefore think that this experiment shows a necessity for clustering to signal, just a sufficiency, and in a highly artificial construct.

      We welcome the opportunity to elaborate on the optogenetic experiments. Since BCL10 and MALT1 are expressed ubiquitously across cell types, the validity of our findings should not depend on the cell type used. Indeed, much of what we already know about innate immunity signalosomes comes from work in HEK293T cells. Our optogenetic experiments using MALT1 were performed in 293T MALT1-KO cells in Figures 6E and F, and employed two distinct functional assays (p65 nuclear translocation and a transcriptional reporter). While our approach employs light to control clustering, similar approaches using (no less-artificial) chemically induced dimerization domains have been used to study caspase activation (Oberst A et al, 2010, Boucher D et al, 2018). Our use of light affords higher specificity, reversibility, and spatial and temporal control over MALT1 assembly than does chemically induced dimerization.

      To demonstrate the necessity of clustering, we have now performed an experiment with MALT1(126-824)-miRFP670-Cry2 expressed in 293T MALT1 KO cells that contain a transcriptional reporter of NF-kB ,as in figures 6E and F. We added PMA to the cells and found that it failed to activate NF-kB (Fig. 6), confirming that the interaction of MALT1 (via its death domain) with polymerized BCL10 is required for activation. Note that MALT1 and BCL10 exist as a soluble heterodimer prior to BCL10 polymerization; hence it is polymerization, rather than the interaction itself, that activates MALT1. That artificial clustering rescues this defect strongly suggests that the effect of polymerization can be attributed to increased proximity rather than some allosteric effect communicated from BCL10 polymers through the MALT1 DD to its caspase-like domain.

      Oberst, A., Pop, C., Tremblay, A.G., Blais, V., Denault, J.-B., Salvesen, G.S., and Green, D.R. (2010). Inducible dimerization and inducible cleavage reveal a requirement for both processes in caspase-8 activation. J. Biol. Chem. 285, 16632–16642.

      Boucher, D., Monteleone, M., Coll, R.C., Chen, K.W., Ross, C.M., Teo, J.L., Gomez, G.A., Holley, C.L., Bierschenk, D., Stacey, K.J., et al. (2018). Caspase-1 self-cleavage is an intrinsic mechanism to terminate inflammasome activity. J. Exp. Med. 215, 827–840.

      In the introduction and other parts of the paper, there are numerous instances where the previous literature in the field is not adequately cited. Examples include:

      • In the introduction, it is weird to cite one original paper (a MALT1 ko study by Ruland et al., 2001; there are several other studies of ko papers for CBM components that would merit being citated along with this study) together with two reviews on that topic (Ruland and Hartjes 2019 and Gehring et al. 2018)

      • In the introduction, the original study by Wang et al., 2002 should be cited together with Rebeaud et al., 2002; the two studies on the same topic were published back-to-back

      • In the introduction, the statement "CARD10 and CARD14 are expressed in nonhematopoietic cells including intestinal and skin epithelia, respectively" should be supported by citations.

      • Still in the introduction, the 2 references for the statement "... CARD14 gain of function mutations cause psoriasis (Howes et al., 2016; Jordan et al., 2012)" are not appropriate. There are several reports of patients with CARD14 mutations (the study by Jordan et al is only one of them) and several CARD14 mouse models that provoke a psoriasis-like phenotype, which would merit being cited.

      • In the following sentence: "Point mutations and translocations involving BCL10 and MALT1 cause immunodeficiencies (Ruland and Hartjes, 2019), testicular cancer (Kuper-Hommel et al., 2013), and lymphomas (Zhang et al., 1999).", the citation style also seems completely random, combining the citation of a single original paper for lymphomas (Zhang et al. 1999) (there are several other important original studies on that topic or recent reviews that could be cited instead), together with a review on immunodeficiencies (Ruland and Hartjes, 2019) and then another single example for a role of BCL10 and MALT1 in carcinoma (the study by Kuper-Hommel et al. is one, but several other original publications exist on the latter topic, showing for example a role in breast carcinoma or glioblastoma).

      • In the first section of the results, the reference cited for endogenous CARD10 expression in 293T cells (Ruland et al., 2001) is wrong, no endogenous CARD10 expression was assessed in that study

      We have now revised the citations mentioned above and other instances to ensure adequate citations in each case.

      Reviewer #3 (Significance (Required)):

      The paper deals with a complex question, namely how the CBM signalosome assembles and functions to stimulate NF-kB signaling. This question is important to the understanding of pro-inflammatory immune responses and basic life sciences in general. As the focal point of the paper is complex, and tools to study such phenomena are at the limit of technical capabilities, this further increases the potential impact of the work.

      Reviewer #4 (Evidence, reproducibility and clarity (Required)):

      The characterization of open-ended signalosomes in a number of innate-immunity and cell-death pathways, in particular formed by domains from the death-fold family, has led to the suggestions that these complexes allow a switch-like signalling response suitable for these pathways. It appears that this has been widely accepted. However, these suggestions are based largely on indirect observations and speculation.

      Rodriguez-Gama and coworkers have decided to test these suggestions more directly. Their results confirm the suggestions. Based on my own experience, papers that validate widely adopted suggestions are often not considered seriously by top journals, who are looking for hot topics/paradigm-changing/surprising type results. I would urge the editors to consider seriously work such as in this paper, which directly tests important suggestions and does so at a technically high standard. The authors use a range of ingenious approaches, both with recombinant proteins and in cells, and including proteins from organisms from different parts of the evolutionary tree, to support their interpretations, so it is an extensive and high-quality study. I am impressed that so many different fusion proteins with fluorescent tags continued to function as expected, but I guess the authors controlled for this as much as they could.

      Having said all this, I do get the feeling the authors are "over-selling" the nucleation barrier aspect of these signalling mechanisms. It is clearly an important and critical aspect of signalling in many systems, but then it is not the only important aspect; a number of other regulatory inputs play a role in different systems. So the statement "Our findings introduce a novel structure-function paradigm" in my view is overstretching things somewhat. Further in the Discussion section, the authors state "Existing explanations for the preponderance of ordered polymers in immune cell signalosomes have centered on the functions of multivalency at steady state, such as scaffolding and sensitivity enhancement resulting from the cooperativity of homo-oligomerization". They cite a small (and non-exhaustive) number of papers discussing this topic; all these include "seeding" or "nucleation" as an important part of the proposed mechanism. So I suggest the authors provide a more balanced discussion of this aspect. Different pathways appear to display a different level of switch-like behaviour, and one thing that the current version of the manuscript is missing is more discussion of other death fold-based systems and how the results on the CBM signalosome apply to these, and also other systems such as TIR domain-based ones, which currently get no mention whatsoever. In the CBM system, there seems to be one main nucleation barrier; can there be more than one in others?

      We appreciate the reviewer’s perspective and have now acknowledged in the introduction and discussion additional prior literature that has paved the way for our study. Nevertheless, we maintain -- as now stated in the abstract -- that “our results defy the usual protein structure/function paradigm, and demonstrate that protein structure can evolve via selection for energetic maxima in addition to minima”. We have elaborated in the introduction and discussion how immune signaling provides the functional context in which such a paradigm can evolve, and how our findings uniquely support the paradigm.

      One other aspect I need to express some criticism about is attention to detail - especially with a paper focusing on the physics behind biological processes, I would expect a higher standard of getting the terminology and units correct - see specific examples below. This can obviously be fixed easily.

      Specific points are listed below. No page or line numbers are provided so I have done my best to make it clear what the comments refer to.

      1. Abstract line 6 and throughout: in "NF-kB", the "k" is supposed to be "kappa" (Greek letter) - it stands for "nuclear factor kappa-light-chain-enhancer of activated B cells", not fully defined in the manuscript as far as I can see. Occasionally, small k is also used instead of the small cap K or whatever the authors used most of the time, but I don't think any of them use the Greek letter.

      We had indeed used a version of the small “kappa” κ. We have now fixed the cases where we mistakenly used k instead of κ.

      Page 2 (Introduction) paragraph 2 line 9: period missing at the end of sentence. Same Page 4 (Results: Assembly) paragraph 4 line 3.

      This is now fixed.

      Page 2 (Introduction) paragraph 2 line 15 and throughout: in long sentences, more commas can help help readability, for example before "leading" here. Similar page 15 paragraph 2 line 3 after "Additionally", paragraph 4 line 2 before "which".

      We have now included more commas and tried to improve readability throughout.

      Page 4 (Results: Assembly) paragraph 2 line 2: is "positive feedback" different from "cooperativity"? Is it a broader term that includes cooperativity, nucleation and other mechanisms? It may be useful to introduce some of these terms to avoid confusion by the readers.

      “Positive feedback” is the broadest term as it is agnostic to mechanism. “Nucleation” refers to the initiation of a first order phase transition, which is one mechanism of positive feedback. Nucleation involves “cooperativity”, in that a higher order species is more stable than smaller species. However, cooperativity can occur for oligomers of finite size, whereas nucleation is reserved for phase transitions to species of infinite size. We appreciate that the use of so many related terms may have created more confusion than necessary. Hence, we have now revised the text to omit the more general terms -- “positive feedback” and “cooperativity” where possible.

      Page 4 (Results: Assembly) paragraph 2 line 3: please define "TNF".

      We have now fixed this and other acronyms.

      Page 4 (Results: Assembly) paragraph 3 line 2: the use of size-exclusion chromatography to follow the size of complexes would assume that they are irreversible or very stable. It appears this may be the case here, but some discussion may be warranted.

      We have now explained that SEC is appropriate for this experiment because large nucleation barriers generally imply stable assemblies.

      Page 4 (Results: Assembly) paragraph 3 line 4 and throughout: the symbol for "kilodalton" is "kDa".

      We have now fixed this mistake.

      Page 4 (Results: Assembly) paragraph 3: I am not sure how the results discussed in this paragraph demonstrate that assembly occurs in cooperative fashion - just that there is a change in oligomeric states upon stimulation.

      Cooperativity is implied by the absence of oligomer sizes between monomer and the large assembly. Nevertheless, we realized this can only be concluded in the case of homotypic assembly, which we cannot yet assume at this point in the paper. Therefore, we have revised this paragraph to say that the distribution is “consistent with” an underlying phase transition (which we then go on to prove).

      Page 4 (Results: Assembly) paragraph 4 line 2: "WT" is not defined. Wild-type what? I presume "protein"?

      We refer here to the wild-type protein. We have now fixed this mistake.

      Page 4 (Results: Assembly) paragraph 4: it may be worth pointing out here the wild-type and mutant proteins expressed at similar levels; clearly the outcomes will depend on protein concentration in the cell. I believe the supplementary figure shows this to a large extent.

      Indeed, our supplementary figure shows that the WT and mutant protein express to comparable levels. We have now pointed this out in the text.

      Page 4 (Results: The adaptor) paragraph 1 line 4: "CARD domain" would stand for "caspase activation and recruitment domain domain". Please check throughout (including Supplementary Material).

      We have fixed this mistake.

      Page 4 (Results: The adaptor) paragraph 1 line 9: "expressed over a range of concentrations in cells" - this would imply the authors controlled expression - please rephrase to explain what exactly was done.

      We have now rephrased this sentence to indicate that the range of expression results from the use of a genetic construct with cell-to-cell variation in copy number.

      Page 5 (Results: The adaptor) paragraph 2 line 3 and throughout (including Supplementary Material): please use the Greek letter rather that "u" for micro.

      We have now fixed this mistake.

      Page 5 (Results: The adaptor) paragraph 3: this analysis is rather simplistic, it is not just the RMSD value, it is the nature of conformational change that is important? Please elaborate, I would think the papers presenting structural work have already discussed this to some extent?

      The reviewer is correct; it is the nature of the conformational change that is most important. We are unsure how to accurately estimate the energy barrier separating the two conformations for each protein. However, we have now undertaken a collaboration to attempt to do so via FAST molecular simulations (Zimmerman and Bowman 2015). In lieu of the results of these ongoing studies, we have modified the text to acknowledge that RMSD does not necessarily relate to nucleation barriers.

      Maxwell I. Zimmerman and Gregory R. Bowman. Journal of Chemical Theory and Computation, 2015, 11 (12), 5747-5757 DOI: 10.1021/acs.jctc.5b00737

      Page 5 (Results: The adaptor) paragraph 4 line 5 and further in this section: some symbol(s) do not show in the pdf - before "(delta)", next page line 3-5 after "higher" and "both".

      We have fixed this issue that resulted from exporting to a PDF file from our text editor.

      Page 6 (Results: The adaptor) paragraph 4: interface IIa and IIIb are not introduced, and there is not even any reference provided here.

      We have now added a reference for these mutations and elaborated on the interfaces IIa and IIIb.

      Page 6 (Results: Pathogenic) paragraph 1 line 12: "FL" is not introduced.

      We have now fixed this mistake.

      Page 8 (Results: Pathogenic) paragraph 7: the text "absent the pathogenic mutations" is missing something.

      We have now reworded this section.

      Page 10 (Results: BCL10) paragraph 3: why does CARD9 CARD clustering peak and then disassemble (I guess "clustering" doesn't disassemble, please rewrite as well).

      We have now fixed this mistake.

      Page 11 (Results: MALT1) paragraph 1: I presume dimerization doesn't achieve the same level of proximity as higher-order multimerization?

      Our interpretation here is that for MALT1, activation requires close proximity of more than two molecules. Although our dimerization module did not activate the caspase-like domain of MALT1, we know that it achieves close enough proximity to activate the caspase domain of CASP8. Hence we believe the MALT1 mechanism has a stoichiometry requirement in addition to a proximity requirement. This is, of course, consistent with the fact that activation normally occurs in the context of polymers rather than dimers.

      Page 11 (Results: Ancient) paragraph 1 line 4: is this AlphaFold2?

      That is correct, we used AlphaFold2. We have added that detail.

      Page 12 (Discussion) paragraph 4: not sure if "molecular examples of evolutionary spandrels" will be clear to most readers.

      We have now explained what evolutionary spandrels are, and elaborated on the relationship to our findings.

      Page 14 (Materials: Plasmid) line 2 and throughout: "Golden Gate" is usually capitalized. Similar for "Gibson" further in the paragraph. The English in this paragraph is not up to standard in general; for example "Then placing..." is not a complete sentence, and a number of sentences ending with "via gibson" need to be rewritten.

      We have now rewritten this paragraph.

      Page 16 (Materials: Cell) line 4 and throughout: "2" in "CO2" should be subscripted.

      This is now fixed.

      Page 16 (Materials: Transient) line 6 and throughout (including Supplementary Material): please use a space between number and unit ("35 mm").

      This is now fixed.

      Page 16 (Materials: Generation) line 4 and throughout: to distinguish from "gram", please italicize "g" and/or use "x g".

      We have now fixed this.

      Page 17 (Materials: Yeast) line 3: please specify which table is "table X".

      We have now fixed this mistake.

      Page 17 (Materials: Mammalian) line 1: please provide full reference. Same next paragraph line 2.

      We have now fixed this.

      Page 17 (Materials: DAmFRET) line 3: "SSC" and "FSC" are not defined.

      We have now fixed this.

      Page 18 (Materials: Fluorescence) line 10: "Coefficient" does not have to be capitalized. It does not have to be defined again in the next paragraph.

      We have now fixed this.

      Page 19 (Materials: Optogenetic) line 1: "performed" rather than "made"?

      We have now fixed this.

      Page 19 (Materials: Protein) line 12: the Compass software doesn't have a reference?

      We have now added the reference to the software.

      References: please make format consistent: articles titles in sentence or title case.

      We have now formatted all references to be consistent.

      Legend to Fig. 1: I suggest "Schematic diagram"; and "h" rather than "hrs"; please check throughout (including Supplementary Material).

      We agree with this suggestion.

      Legend to Fig. S1: is "TNF-a" supposed to be "TNF-alpha"?

      We have fixed this.

      Legend to Fig. S7: please capitalize "Figure 2H".

      We have fixed this.

      Legend to Fig. S10F: please move "Dox" behind the concentration.

      We have fixed this.

      Fig. S14B: the colours in the superposition make it difficult to see the differences.

      We have used a different color now.

      Legend to Fig. S14: I suggest "structure...predicted by AlphaFold" (2?) and include the reference.

      We agree with this suggestion.

      Reviewer #4 (Significance (Required)):

      As argued above, the significance of this paper is that it tests directly important hypotheses proposed or assumed previously, and does so at a technically high standard. No published report has done so to a similar extent.

      The paper should be of interest to a broad audience from cell biologists and immunologists to biochemists, biophysicists and structural biologists.

      My expertise is in structural biology or systems similar to the one studied here.

    1. Consolidated peer review report (6 April 2022)

      GENERAL ASSESSMENT

      The sweet and umami sensor proteins, taste receptors type 1 (T1Rs) are important GPCRs underlying taste sensation. In humans, amino acids bind and activate the T1r1/3 heterodimeric receptors leading to umami taste perception, whereas sugars activate the T1r2/3 receptors leading to sweet taste perception. In this manuscript, Atsumi and colleagues combine structural, biophysical and electrophysiological methods to show that Cl- ions also bind to T1Rs, at low mM concentrations, to evoke taste sensation. The authors (1) identify a putative evolutionarily conserved Cl- binding site in the crystal structures of isolated LBDs from medaka fish T1r2a/3 receptors, (2) show that Cl- ions promote protein stability and induce conformational changes in these mfT1r2a/3 LBDs, independent of orthosteric ligands, and (3) demonstrate that mouse chorda tympani nerves are activated by Cl- ions via a T1R-specific mechanism. Based on these findings, the authors conclude that low concentrations of Cl- may bind to sweet receptors and mediate the commonly reported sweet taste sensation following ingestion of low concentrations of table salt.

      The elucidation of the molecular mechanism(s) underlying salt taste sensation is a physiologically relevant question that will appeal to a broad audience. Moreover, the authors use an impressive array of different approaches to broadly cover numerous aspects, ranging from structural biology, to biophysics and physiological recordings. Overall, the identification of the chloride ion binding site is convincing, based on the previously solved structure, as well as the bromide ion substitution and long-wavelength Cl- anomalous difference analysis performed in this work. This analysis is supported by biophysical measurements showing that Cl- substantially stabilizes the wild type complex against thermal denaturation, but does not stabilize a point mutant in the putative Cl- binding site. The single fiber recordings suggest there is physiological relevance to the biophysical and structural findings, although they could be strengthened by additional control experiments. Overall, the possibility of Cl- ions acting as a sweet receptor ligand is enticing and the work will likely motivate additional research on this subject.

      RECOMMENDATIONS

      Revisions essential for endorsement:

      1) The authors should provide refinement statistics and methodology for both the Cl-- and Br-- bound structures, and some comparison between these two structures (global structural alignment & RMSD should be sufficient).

      2) We would recommend that the authors perform nerve recordings using artificial saliva rather than water as the perfusate.  This is a key point because the chloride concentration in saliva is approximately 15 mM. Thus, according to their binding data, most T1rs should have chloride bound at baseline. Perhaps this means that chloride binding is required to allow sucrose or other ligands to cause sufficient conformational changes and receptor activation?  If this is the mechanism, it would still be quite interesting, but would change the framing/interpretation as presented in the manuscript. If additional experiments are not feasible, the authors should carefully discuss this point.

      3) Some of the conclusions would be strengthened by additional control experiments, especially for the data obtained using FSEC-TS (Fig. 2C) and single fibre recordings (Fig. 3). For instance, how specific is the T105A mutation in abolishing Cl--dependent conformational changes? Did the authors check how the T105A mutation affects the ability of the LBD to undergo conformational changes in response to (1) L-Gln only and (2) Cl- only? Have the authors tried running these experiments at lower Cl- concentrations? 304 mM Cl- (page 16, line 363) is much higher compared to the effective concentration range claimed by the authors. For the single fibre recordings, have the authors tried applying 10 mM NMDG-gluconate? Having this negative control will provide more confidence in the specificity of Cl--induced impulses. Also, we would recommend a demonstration of reversibility in the gurmarin effect shown in Fig 3A.

      Additional suggestions for the authors to consider:

      1) The introduction would benefit from greater focus and clarity to make the work more accessible to readers. Despite the overall focus on T1rs, only a quarter of the introduction revolves around these receptors. Additional information would help the reader to understand the research topic. For example, how many isoforms are there? Are these receptors obligate heterodimers? How similar are the mf T1r2a/3 compared to the human T1r2/3 receptors? If mf T1r2a/3 receptors are activated by amino acids, how useful a proxy are they in understanding sweet-sensing human T1r2/3 receptors? If T1r3 is found in both heterodimers, and amino acids bind to T1r3, how do these receptors discern between sweet and umami taste? What are the mechanisms underlying activation of these receptors? How are these receptors usually studied functionally?

      2) Given the focus on isolated LBDs of (non-human) mfT1r2a/3 receptors, the authors are encouraged to comment on the probability of Cl- binding, and the subsequent conformational rearrangement observed in the isolated LBDs, actually translating to activation of (full-length) human receptors (and ultimately taste stimulation). Since the authors have previously assessed the function of hsT1r2/3 in HEK293 cells using Ca2+ imaging (PMID: 25029362), evaluation of the activation properties of Cl- at full-length receptors and testing the effects of T1r3 mutations on these Cl- effects would help to strengthen the manuscript. Also, there are several reported polymorphisms in the gnomAD database around the Cl- ion binding site (Thr102Met, Gly143Arg, Pro144Ser/Leu), so it would be interesting and helpful to test the effects of these variants that are found in the population. We do not expect the authors to perform these experiments, but in the absence of more conclusive functional data on full-length receptors, the authors should consider discussing these potential caveats in the text.

      3) Given the availability of AlphaFold Multimer and the well-defined stoichiometry of the complex, did the authors attempt to predict a model of the full-length heterodimer? This may be informative with regards to the mechanism of signal transduction to the transmembrane domain.

      4) The nerve recording data would be more convincing if the authors could provide electrical recordings to truly sweet compounds at physiologically relevant concentrations (sucrose and artificial sweeteners). Currently, they only show data for 20 mM L-glutamine, which is not particularly sweet in Fig 3a-b, and then summary data for sucrose in Fig 3b.

      5) The authors may wish to include a comment about whether bromide has the same effect on taste perception as chloride, and point out that gurmarin is a non-selective antagonist. Ideally, the nerve recordings should be done in T1r knockout mice to formally prove the mechanism. Although this may be beyond the scope of this work, a brief mention of this caveat seems warranted.

      6) Finally, the discussion would benefit from additional mention of ligand binding in relevant heterodimeric class C GPCRs, as well as the observation that chloride appears to work via a distinct mechanism despite its binding site being spatially very close to that of Gln.

      REVIEWING TEAM

      Reviewed by:

      Alexander T. Chesler, Principal Scientist, NCCIH, NIH, USA: Ion channel function, regulation and physiology

      Han Chow Chua, Assistant Professor, University of Copenhagen, Denmark: Ion channel structure and function

      Oliver B. Clarke, Assistant Professor, Columbia University, USA: Protein structural biology

      Curated by:

      Stephan A. Pless, Professor, University of Copenhagen, Denmark

      (This consolidated report is a result of peer review conducted by Biophysics Colab on version 1 of this preprint. Minor corrections and presentational issues have been omitted for brevity.)

    1. A Zimbabwean study found that cloth and cotton wool were the most widely used materials (Averbach et al. 2009Averbach, S., N. Sahin-Hodoglugil, P. Musara, T. Chipato, and A. van der Straten. 2009 Duet™ for menstrual protection: A feasibility study in Zimbabwe. Contraception 79:463–468. [Crossref], [PubMed], [Web of Science ®], [Google Scholar]), while in Tanzania, 60 percent of women interviewed reported using cloth (Allen et al. 2010Allen C. F., N. Desmond, B. Chiduo, L. Medard, S. S. Lees and et al. 2010 Intravaginal and menstrual practices among women working in food and recreational facilities in Mwanza, Tanzania: Implications for microbicide trials. AIDS & Behavior 14 (5):1169–1181. [Crossref], [PubMed], [Web of Science ®], [Google Scholar]). In similar low-income settings in Bangladesh, Nepal, and India, women also most commonly use reusable cloth, and their priority, therefore, is for hygienic and private washing and drying facilities, rather than appropriate disposal systems (

      !!!!!! focus on helping this way

    1. SciScore for 10.1101/2022.04.22.22274032: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">IRB: Subjects or households with suspected or confirmed SARS-CoV-2 infection were recruited from the Greater New Orleans community under Tulane Biomedical Institutional Review Board (federalwide assurance number FWA00002055, under study number 2020-585).</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Determination of antigen-specific antibody reactivity by multiplexed Luminex analysis: Recombinant SARS-CoV-2 antigens (full-length spike, RBD, and N) and the recombinant spike protein from OC43, HKU1, 229E, and NL63 (Frederick National Laboratories) were coupled with MagPlex beads (Luminex) via sulfo-NHS coupling chemistry.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>antigen-specific</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>HKU1</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The Spike protein ELISA for IgG antibodies has been validated by testing a standard set of positive and negative samples provided by NCI SeroNet staff.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>IgG</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">NK92 cells in complete alphaMEM culture medium were added at 5 × 104 cells/well in the presence of 4 µg/ml brefeldin A (Biolegend Cat# 420601), 5 µg/ml GolgiStop (BD Biosciences Cat# 554724) and 0.15µg of anti-CD107a antibody (Clone H4A3 PE-Cy7, Biolegend Cat# 328618) for 5 hours at 37°C.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-CD107a</div><div>suggested: (BioLegend Cat# 328618, RRID:AB_11147955)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Antibody-dependent neutrophil phagocytosis (ADNP): Protocol was adapted from [72]</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Antibody-dependent neutrophil phagocytosis</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Beads were washed with PBS containing 15 mM EDTA and stained with an FITC-conjugated anti-guinea pig C3 antibody (MP Biomedicals).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-guinea pig C3</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Neutralization of SARS CoV-2 in Pseudovirus Assay: CHO cells were generated and stably expressed ACE2 by transfecting CHO cells with an ACE2 expression plasmid containing the blasticidin resistance gene.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>CHO</div><div>suggested: CLS Cat# 603479/p746_CHO, RRID:CVCL_0213)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">CHO-ACE2 cells were similar in SARS CoV-2 susceptibility to the 293T/ACE2 cell line developed by Dr.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>CHO-ACE2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Virus neutralization was measured in CHO/ACE2 cells.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>CHO/ACE2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Pseudoviruses were produced by co-transfection of the four plasmids into 293T cells grown in T75 flasks with Fugene 6 as transfection reagent.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>293T</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Unbound antibodies were removed by centrifugation before adding THP-1 cells at 2.5×104 cells/well.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>THP-1</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">RBD (aa321-535) was similarly expressed in the phCMV plasmid and purified on Streptactin X affinity columns.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>phCMV</div><div>suggested: RRID:Addgene_15802)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">A DNA fragment encoding SARS CoV-2 N protein, including its natural leader sequence was generated by PCR of full-length N protein gene from a lentiviral N Protein expression vector (pLVX-EF1alpha-SARS-CoV-2-N-2xStrep-IRES-Puro, which was a gift from Nevan Krogan (Addgene plasmid # 141391 ; http://n2t.net/addgene:141391; RRID:Addgene_141391, [68]).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div></div><div>detected: RRID:Addgene_141391)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">These included an expression plasmid for full-length spike protein of the Wuhan-1 strain containing the D614G amino acid chain (VRC7480.G614) [70], a pCMV ΔR8.2 lentivirus backbone plasmid (VRC5602) [71], the VRC5601 plasmid pHR’ CMV Luc containing the firefly luciferase reporter gene [71], and VRC9260 for TMPRSS2 expression.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCMV ΔR8.2 lentivirus</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>VRC5601</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pHR’</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Neutralization titers were defined as the serum dilution (ID50) at which relative luminescence units (RLU) were reduced by 50% compared to virus control wells after subtraction of background RLUs (determined by GraphPad Prism, version 9 for macOS, GraphPad Software, San Diego, California USA).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad Prism</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Spike Glycoprotein (stabilized) from SARS-Related Coronavirus 2, Wuhan-Hu-1 with C-Terminal Histidine Tag, Recombinant from Baculovirus), and SARS-CoV-2 specific mega pools at 0.2 μg/well including PepTivator SARS-CoV-2 Prot_S (Miltinyi - 130-126-700), SARS-CoV-2 Prot_M (130-126-702), SARS-CoV-2 Prot_N (130-126-699) in 96-well U bottom tissue culture plate (CytoOne CC7672-7596) in 200 μl RPMI-1640 with 10% FBS.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>PepTivator</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">) GraphPad Prism (version 9.0.0, GraphPad Software, San Diego, CA), JMP (version 16.2.0, SAS Institute, Inc.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">, Cary, NC), and SAS (version 9.4, SAS Institute, Inc.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>SAS Institute</div><div>suggested: (Statistical Analysis System, RRID:SCR_008567)</div></div></td></tr></table>

      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 LimitationRecognizer: We detected the following sentences addressing limitations in the study:


      Study limitations primarily involved using SARS-CoV-2 infection to differentiate subjects rather than pre-pandemic samples. In addition, the assays were limited to peripheral blood samples and not tissue-specific responses, which included only effector functions to spike protein and cytokine secretion instead of T-cell subset analyses. Detection of secreted cytokines allowed a greater number of cytokines to be evaluated but prevented confirmation of cells producing cytokines as would be observed intracellular stained cytokines for specific T-cell populations. However, cytokines between spike or peptide pools were highly correlated (Figure S5), indicating T-cell production. Also, high expression of IL-2 has been routinely observed from CD4+ T-cell and not CD8+ T-cells after SARS-CoV-2 infection [28, 38]. In this study, IL-17A secretion was closely correlated to IL-2 and Th1 cytokine release after stimulation with protein or peptide pools, suggesting that IL-17A may be serving as a proxy for a Th1/Th17 subset, as identified in other post-vaccination studies [61] which should be more closely examined. Finally, while the critical role for age in SARS-CoV-2 immunity was validated, it remains an ongoing question of why children exhibit less severity with infection and how differences in qualitative features of immunity depend on patient age. Our study used samples collected from subjects only shortly after the pandemic which will be difficult to perform as COVID subsides and vaccin...


      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


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      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.

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    1. SciScore for 10.1101/2022.04.14.22273819: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">Consent: Specimen collection: Blood samples have been obtained from BNT162b2 fully vaccinated healthy volunteers within several weeks after the second dose (Supplemental Table 1, for Vax146-149, which were most frequently used in this study, the average is 22.75 weeks post-boost) and after informed consent (Supplemental Table 1)</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Materials: Antibodies for flow cytometry staining (Supplemental Table 2 B), monoclonal antibodies against CD28 and CD49d costimulatory molecules, BD GolgiPlug™ protein transport inhibitor (Brefeldin A, BFA), BD Stain Buffer supplemented with BSA, and BD Cytofix/Cytoperm™ solution kit were provided by BD.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>antibodies against CD28</div><div>suggested: (Novus Cat# NB100-93558, RRID:AB_1236789)</div></div><div style="margin-bottom:8px"><div>CD49d</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Monoclonal antibodies anti-CD28 and /or anti-CD49d at 1 μg/ml were added as co-stimulus.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-CD28</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cell staining: After incubation with BD GolgiPlug™, cells were washed with 100 ul of Stain Buffer to remove culture medium.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>BD GolgiPlug™</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Samples were finally washed with Stain Buffer and re-suspended in 400 μl of PBS in 5 ml polystyrene tubes for acquisition with BD FACSCelesta™ flow cytometer equipped with Blue, Violet and Red lasers.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>BD FACSCelesta™</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Flow cytometry and data analysis: Cells were acquired on a BD FACSCelesta™ flow cytometer and analyzed using BD FACSDiva™ software (v9.0).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>BD FACSDiva™</div><div>suggested: (BD FACSDiva Software, RRID:SCR_001456)</div></div></td></tr></table>

      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 LimitationRecognizer: We detected the following sentences addressing limitations in the study:


      The main limitations of the current study are: 1) the experimental model system is rooted in the context of SARS-Cov-2 and 2) the small number of donors and variability. On the first, the study is conducted using vaccinated donors assessed several weeks after BNT62b2 vaccination (Supplemental Table 1) and stimulated with Spike peptides, as well as using control stimulation with PHA, but has not been studied for other T-cell activation conditions. On the second, the study used four vaccinated donors with samples split into several cryopreserved aliquots (106 PBMCs/sample) for comparison across multiple conditions and variability ensued for at least two reasons, i) relatively few activated T cells were detected in each sample, perhaps due to the starting cell numbers and since on average 22.75 weeks had ensued since the last boost (for Vax 146-149), such that in most cases another boost would now be recommended, and ii) the introduction of more random noise by needing to subtract background activation from stimulated activation to properly determine the percentage of AIM+ T cells. Recommended improvements for evaluating individual samples in detail are to consider running multiple replicates, multiple timepoints, evaluation closer to time of boost, and increasing the number of processed cells. Nevertheless, taken altogether the data presented here clearly show the optimal performance of intracellular CD137 and CD69 together with intracellular cytokine detection. In conclusion, ...


      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


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      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.

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    1. SciScore for 10.1101/2022.04.20.485440: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">IRB: Ethics: The animal experiments were evaluated and approved by the ethics committee of the State Office of Agriculture, Food safety, and Fishery in Mecklenburg–Western Pomerania (LALLF M-V: LVL MV/TSD/7221.3-1-055/20) and the State Office for Occupational Safety, Consumer Protection and Health in Brandenburg (LAVG: 2347-5-2021).<br>Euthanasia Agents: The animals were infected under short-term isoflurane inhalation anesthesia with 25 μl of either 104.4 TCID50 SARS-CoV-2 lineage B.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">To discriminate between parenchymal and vascular T cells, vaccinated mice received 3 μg (in PBS) of anti-mouse CD45 antibody (retro-orbital administration) for 3 minutes during lethal anesthesia.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-mouse CD45</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Unspecific antibody binding was blocked with TruStain FcX (anti-mouse CD16/32) solution (BioLegend) for 5 minutes at 4°C before adding freshly prepared antibody cocktails.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-mouse CD16/32</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The virus was harvested after 72 hours, titrated on Vero E6 cells and stored at −80 °C until further use.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero E6</div><div>suggested: RRID:CVCL_XD71)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Organisms/Strains</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Study design: K18-hACE2 transgenic mice were vaccinated on Day 0 (prime) and Day 28 (boost) and infected (challenge) on Day 56, as detailed in Fig.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>K18-hACE2</div><div>suggested: RRID:IMSR_GPT:T037657)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Samples (mouse swabs/organs) that tested positive for viral genomic RNA were evaluated using an assay specifically detecting sgRNA of the ORF7a as described in Hoffmann et al 2021.26 Wistar rats were vaccinated on Day 0 (prime) and Day 21 (booster), as detailed in Fig.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Wistar</div><div>suggested: None</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. Reviewer #3 (Public Review): 

      In this paper, Troendle et al investigate changes in alpha oscillation across childhood and adolescence. The main goal of this investigation is to examine how alpha oscillations change across these age ranges, by investigating a large open dataset and adopting new methods that should help to address methodological limitations of many previous analyses. In particular, a key goal is to examine changes in periodic alpha power, and control for potential confounds due to changes in peak frequency and/or aperiodic activity. To do so, they employ a novel spectral parametrization method, and systematically compare measures of isolated periodic alpha activity to conventional measures. Overall, they find that they can replicate the age-related decrease of total alpha power when using conventional methods. However, when explicitly measuring and controlling for aperiodic activity, they find that periodic alpha activity actually increases with age. They suggest this discrepancy can be explained by changes in aperiodic activity, as the aperiodic slope and intercept are found to systematically change across age, in a way that likely drives the finding decrease of total alpha power, while the periodic alpha power actually increases. There are also some follow up analyses, including relating alpha power to anatomical measures of the thalamus, and to performance on an attention task. 

      Strengths of this investigation include that it analyzes multiple, large datasets with well motivated methods. I think the goal of this paper addresses an important question, in terms of seeking to clarify some basic patterns of oscillation changes across development, and doing so in a rigorous way, both in terms of employing methods that are robust to estimating different features of the data, and in terms of using multiple, large datasets, including an internal replication of the main findings. I find the main goal and analysis compelling in terms of examining how alpha activity changes across this age range. 

      I also find some limitations to some aspects of this paper and analysis that could be improved, as they do not always clearly describe the context or support the claims that are made for some of the follow-up analyses, as described in the following. 

      1. Framing and prior literature 

      I find some limitations in the organizing of this paper and it's relationship to prior work that could be improved, as I find that the paper could do better situating the analyses here with prior work, in particular in relation to the methodological issues it is addressing, and prior work on aperiodic activity. 

      For example, in the abstract it is stated that "simulations in this study show that conventional measures of alpha power are confounded". Despite this statement, simulations are not a core feature of this study. There are a couple simulated examples in the supplement, which are referred to in lines 89-95, however it's worth nothing noting that while this section does not include any citations, the described issues, and related simulations, are very similar to points that have been made previously in the literature, that seem like they should be cited here: <br /> - Donoghue, T., Dominguez, J., & Voytek, B. (2020). Electrophysiological Frequency Band Ratio Measures Conflate Periodic and Aperiodic Neural Activity. ENeuro, 7(6), ENEURO.0192-20.2020. https://doi.org/10.1523/ENEURO.0192-20.2020 <br /> - Donoghue, T., Schaworonkow, N., & Voytek, B. (2021). Methodological considerations for studying neural oscillations. European Journal of Neuroscience, ejn.15361. https://doi.org/10.1111/ejn.15361 

      The paper also understates previous work on aperiodic activity, and the degree to which it is known to vary with age, in line 116-117 stating "there is insufficient evidence for the reported significant association between age and aperiodic signal components". This seems to ignore the large number of studies that have replicated this finding, including (some non-exhaustive examples): <br /> - Thuwal, K., Banerjee, A., & Roy, D. (2021). Aperiodic and Periodic Components of Ongoing Oscillatory Brain Dynamics Link Distinct Functional Aspects of Cognition across Adult Lifespan. Eneuro, 8(5), ENEURO.0224-21.2021. https://doi.org/10.1523/ENEURO.0224-21.2021 <br /> - Voytek, B., Kramer, M. A., Case, J., Lepage, K. Q., Tempesta, Z. R., Knight, R. T., & Gazzaley, A. (2015). Age-Related Changes in 1/f Neural Electrophysiological Noise. Journal of Neuroscience, 35(38), 13257-13265. https://doi.org/10.1523/JNEUROSCI.2332-14.2015 <br /> Perhaps this claim is supposed to more specifically reflect the age-range analyzed here, in which case recent studies examining this (in relatively large datasets) are also not mentioned here, including, for example: <br /> - Donoghue, T., Dominguez, J., & Voytek, B. (2020). Electrophysiological Frequency Band Ratio Measures Conflate Periodic and Aperiodic Neural Activity. ENeuro, 7(6), ENEURO.0192-20.2020. https://doi.org/10.1523/ENEURO.0192-20.2020 <br /> - Hill, A. T., Clark, G. M., Bigelow, F. J., Lum, J. A. G., & Enticott, P. G. (2022). Periodic and aperiodic neural activity displays age-dependent changes across early-to-middle childhood. Developmental Cognitive Neuroscience, 54, 101076. https://doi.org/10.1016/j.dcn.2022.101076 

      The notes above do not undermine the utility of examining alpha oscillations in detail, but I think the specific contribution of this work could be better contextualized in terms of other existing work. In the introduction, for example, the following review is an important piece of work that could be cited when introducing aperiodic activity: <br /> - He, B. J. (2014). Scale-free brain activity: Past, present, and future. Trends in Cognitive Sciences, 18(9), 480-487. https://doi.org/10.1016/j.tics.2014.04.003 

      2. Model quality control 

      A limitation to the methods employed in this study is a lack of description of if and how model fit quality was evaluated. For the method of parametrizing neural power spectra that is employed, it is important to validate that models fit the data well, otherwise the estimated parameters may be unreliable. This is especially important in developmental and clinical data, as analyzed here, as this data can be quite noisy, and differences in levels of noise across ages or between clinical groups could plausibly lead to differences in model fit quality. Useful quality checks for this kind of analysis would be to report the average r-squared (or model error) for the parametrized data, and to examine whether model fit quality is significantly related to age, or clinical status. 

      Note that there is also a detailed guide for how best to apply spectral parametrization to developmental datasets, including notes on quality control, that may be useful: <br /> - Ostlund, B., Donoghue, T., Anaya, B., Gunther, K. E., Karalunas, S. L., Voytek, B., & Pérez-Edgar, K. E. (2022). Spectral parameterization for studying neurodevelopment: How and why. Developmental Cognitive Neuroscience, 54, 101073. https://doi.org/10.1016/j.dcn.2022.101073 

      Not reporting any quality control metrics of the model fits also deviates from the analysis of the validation dataset as described in the pre-registered analysis (https://osf.io/7uwy2), which includes the note that the plan is for data to be excluded from the analysis if there is a bad model fit (R-squared < 0.9). It is unclear from the manuscript if this was done at all - and if so, why it was not described, and if not, why this deviates from the pre-registration. Note that though examining and reporting model fit quality is important, it is unclear where the value of 0.9 in the pre-registration came from, and it is unclear if this is an appropriate threshold for these specific datasets. 

      3. The analysis of the relationship between the aperiodic intercept and aperiodic exponent 

      There is an analysis in this paper that attempts to evaluate whether the change in aperiodic intercept that is observed is more than expected due to the measured change in aperiodic exponent. The approach taken for this analysis is ill-posed, and the interpretations made of this analysis are not supported. The issue is that the degree to which the intercept changes due to a change in exponent depend on the rotation frequency, which is not acknowledged or addressed in the analysis employed here. 

      For example, for spectra rotated at 0 Hz, there is no measured change in offset from a change in exponent, whereas for a rotation at 100 Hz, there is a large influence of exponent on the change in offset, with different degrees of impact in between. The results of this analysis are therefore heavily influenced by the rotation frequency that is used. The analysis by the authors uses a rotation frequency of 19 Hz, however, there is no justification provided for this value. It is noted as being the middle point of the analyzed range, however, this itself is unrelated to whether it is an appropriate rotation frequency (since which frequency the spectrum rotates at is unrelated to the experimenter's decision of which frequency range to analyze). 

      In real data, we don't a priori know what the rotation frequency point is, and in general it need not be a single, consistent point, and between subjects, is difficult to measure. To get a sense of what it might be, anecdotally, we can see in Figure 2C that in this particular subset, the rotation point is not at 19 Hz, and appears to be at a higher frequency. If the rotation point is actually higher than 19 Hz, then the analysis employed will systematically under-estimate the impact of the measured exponent change - leading to the conclusion that intercept is changing over and above the influence of the exponent. However, this conclusion is only valid if the rotation point of 19 Hz is accurate, and we would likely arrive at a different conclusion by picking a different rotation point. This analysis, by itself, is therefore invalid. Such an analysis would require a clear motivation of having measured the correct rotation frequency to be interpretable. 

      4. Flanker Analysis 

      Also relating to organization (similar to point 1) it is unclear why the analysis of the Flanker task, which is alluded to in the abstract, is only mentioned in the Discussion section. Given that this appears to be a key analysis, it is unclear why it is not presented in detail in the Results. The Flanker task and analysis is also not described in much detail in the methods. An issue with the Flanker analysis only being mentioned in the Discussion, with a link to supplemental table, is that the details of the results are somewhat obfuscated from the reader. When looking at these results, two key features seem notable - the first that though it is significant effect of aperiodic-adjusted alpha power, the beta value is very small (many times smaller than the coefficients for age and gender), and second, that although it doesn't quite pass significance, the estimated beta value for the total alpha power has the same magnitude as for the individualized alpha power. Between these two features, it is not clear if the relationship between aperiodic-adjusted alpha power and the Flanker performance is of sufficient magnitude to interpret that alpha power is related to attentional performance, and it's not clear that aperiodic-adjusted alpha power is more related to attentional performance than total alpha power (since a difference in significance does not necessarily imply a significant difference in the parameters). I think this analyses, as presented, therefore does not clearly support the claim made in the abstract that alpha power is found to relate to improved attentional performance.

    1. SciScore for 10.1101/2022.04.18.488614: (What is this?)

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

      Table 1: Rigor

      NIH rigor criteria are not applicable to paper type.

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">To each well, we added 500 μL total volume of 5 μM Alexa Fluor® 488 anti-mouse secondary (SouthernBiotech 1031-30) and 10 μM Alexa Fluor® 647 anti-human secondary (SouthernBiotech 2048-31) antibodies in PBS-BSA.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-human secondary ( SouthernBiotech 2048-31 )</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">We used the following equation to calculate the expression of spike variant (x) relative to WT (6P-D614G): To correct for changes in transfection efficiency or spike expression in antibody or ACE2 binding measurements, we also included anti-FLAG signal as an internal normalization control.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>ACE2</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-FLAG</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">We hydrated anti-mouse Fc capture (AMC) biosensors (FortéBio 18-5088) in BLI buffer for 10 min in an Octet RED96e (FortéBio) system and then immobilized mouse anti-FLAG M2 (Sigma-Aldrich F3165) antibodies to the AMC sensor tips.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-mouse</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For each assay, we performed the following steps: 1) baseline: 60 s with BLI buffer; 2) IgG immobilization: 360 s with anti-FLAG IgG; 3) spike loading: 360 s with diluted supernatants; 4) baseline: 300 s with BLI buffer; 5) association: 600 s with serially diluted analytes (antibodies or ACE2); 6) dissociation: 600 s with BLI buffer.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-FLAG IgG</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Expression and purification of neutralizing anti-spike monoclonal antibodies: We cultured Expi293 cells in Expi293 Expression Medium (Sigma-Aldrich A1435101) and used a humidified cell culture incubator to maintain cells at 37°C and 8% CO2 with continuous shaking at 125 rpm.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Expi293</div><div>suggested: RRID:CVCL_D615)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Briefly, we transfected the ACE2-Fc expression vector into Expi293T cells (Sigma-Aldrich).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Expi293T</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">We added predetermined concentrations of primary antibody or chimeric cell receptor (ACE2-Fc) diluted in PBS-BSA and 50 μL (1.5 × 105) of HEK293T cells to each well.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293T</div><div>suggested: RRID:CVCL_HA71)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">We incubated diluted serum or antibody with 100-150 fluorescent focus units (FFU) of mNG SARS-CoV-2 at 37°C for 1 h before loading the serum-virus mixtures into 96-well plates pre-seeded with Vero E6 cells.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero E6</div><div>suggested: RRID:CVCL_XD71)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">We used the following equation to calculate normalized binding measurements of spike variant (x) expression relative to WT (6P-D614G): We used FlowJo v9 for all flow cytometry data analyses.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>FlowJo</div><div>suggested: (FlowJo, RRID:SCR_008520)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">We plotted curves of relative infectivity versus serum dilution using Prism 9 (GraphPad).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">All data were visualized in GraphPad Prism 9.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad Prism</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr></table>

      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 LimitationRecognizer: We detected the following sentences addressing limitations in the study:


      Our study has several limitations. First, we used a prefusion stabilized spike protein that does not precisely mimic the dynamics of the native Omicron spike protein26. Second, our binding assays use a set of potent neutralizing mAbs which only serve as proxies for the antibodies found in patient antibody repertoires after immunization or natural infection. Third, our work only touches on antibody recognition and hACE2 binding; T-cell immunity plays a critical role in protecting against SARS-CoV-2 disease. Additional studies focused on the perturbations of spike variants on T-cell response will continue to bridge the gap in the understanding of immune escape between humoral and cell-mediated immunity. In the aggregate, the data presented here add critical new information about key features of Omicron spike protein mutations and how these mutations synergize to create spike variants that successfully evade antibodies while maintaining high affinity hACE2 binding. Our binding maps largely complement prior structure-based studies of binding escape, but now provide new insights into the role of compensatory substitutions in the NTD that impact both expression/stability and conformation. We conclude that the continuing accumulation of NTD mutations will further alter the conformational equilibrium and stability of the spike protein to allow for the accumulation of new, more virulent mutations in the RBD. Further, our study also highlights the importance of rapidly analyzing novel ...


      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. SciScore for 10.1101/2022.04.17.22273938: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">Consent: Following written informed consent, participants answered a demographic and health history questionnaire.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Plates were washed three times with 0.1% PBST followed by addition of a 1:3,000 dilution of goat anti-human IgG–horseradish peroxidase (HRP) conjugated secondary antibody (50μl) well and incubated 1h.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-human IgG–horseradish</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Genscript cPass™ surrogate neutralization antibody assay: This semi-quantitative SARS-CoV-2 surrogate neutralizing antibody assay, which measures the inhibition of RBD and ACE2 interactions, was performed in accordance with manufacturer’s instructions.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>ACE2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">This assay is calibrated to the 1st WHO International Standard Anti-SARS-CoV02 Immunoglobulin (Human), NIBSC [26] with results reported in the WHO standard units of IgG Binding Antibody Units/ml (BAU/ml) to recombinant spike S1 domain.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Anti-SARS-CoV02</div><div>suggested: None</div></div></td></tr></table>

      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 LimitationRecognizer: We detected the following sentences addressing limitations in the study:


      This work has several limitations, principally the small sample size and variable immune experience of the cohort. Further, as is the case for the majority of the available antibody assays, the Mount Sinai Laboratory assay and the cPass assay have not yet been calibrated to the WHO standard. Our study does provide evidence for a correlation between the Mount Sinai Laboratory and Ortho-Clinical VITROS assays, however. We believe that further standardization of the serological assays to an international standard will allow better correlations of immunity between independent clinical trials. Finally, this study examined only samples immediately prior to breakthrough infection so factors regarding temporal relationship of infection, clinical presentation, and sample collection may have affected our observations. Additional studies including individuals who appear to be susceptible to re-infection or who are poor immunologic responders to SARS-CoV-2 infection and/or vaccination, are needed to better understand differential immune kinetics in those populations. Of particular interest will be characterizing the T-cell immune responses to SARS-CoV-2 in individuals who have developed strong antibody responses yet experience breakthrough infection. Importantly, since we did not compare the titers of breakthrough cases with titers of non-breakthrough cases, we cannot draw conclusions regarding where these titers would fall and if they trend lower than those of individuals who may have b...


      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. SciScore for 10.1101/2022.04.17.22273949: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">IRB: The Institutional Review Board of Stanford University determined that this project does not meet the definition of human subject research as defined in federal regulations 45 CFR 46.102 or 21 CFR 50.3 and indicated that no formal IRB review is required.<br>Field Sample Permit: The solids samples were processed within 24 hours of collection exactly according to the methods described by Wolfe et al.2 and are summarized in the SI.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">Downsampling simulation: In order to estimate the SARS-CoV-2 N gene and PMMoV RNA concentration we would have obtained if we had run a smaller number of wells (X = 1 - 9), we randomly selected X wells from the 10 wells to calculate the resultant concentration:where 0.00085 μL is the volume of a single droplet14.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Data for each individual well was downloaded from QuantaSoft Analysis Pro software (BioRad, CA, version 1.0.596).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>QuantaSoft Analysis Pro</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Statistical analysis: Statistics were computed using RStudio (version 1.4.1106).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>RStudio</div><div>suggested: (RStudio, RRID:SCR_000432)</div></div></td></tr></table>

      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 LimitationRecognizer: We detected the following sentences addressing limitations in the study:


      There are a few limitations of this analysis. First, in our analysis, we assumed that the measurement obtained using 10 wells is the “true concentration” and compared all results simulated with fewer than 10 wells to the true concentration and its error from the ddRT-PCR instrument. Second, the results presented herein regarding assay sensitivity, and in particular the C0.5 values in Table 1 are specific to the methods applied in this study. The relationship between the number of wells used to the number of non-detects, and the lowest measurable concentration will be impacted by the pre-analytical and analytical processes used. While the specific values in Table 1 are not externally valid, unless others are using our exact methods (available on protocols.io18–20), the framework for examining the required sensitivity for wastewater surveillance is. That is, careful attention to how sensitivity affects the lowest measurable concentration and the number of non-detects, as well as the relationships between these values and laboratory confirmed COVID-19 incidence rates is needed to fully understand how decisions on assay implementation are made.


      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. Pérez-Then, E., Lucas, C., Monteiro, V. S., Miric, M., Brache, V., Cochon, L., Vogels, C. B. F., Cruz, E. D. la, Jorge, A., Santos, M. D. los, Leon, P., Breban, M. I., Billig, K., Yildirim, I., Pearson, C., Downing, R., Gagnon, E., Muyombwe, A., Razeq, J., … Iwasaki, A. (2021). Immunogenicity of heterologous BNT162b2 booster in fully vaccinated individuals with CoronaVac against SARS-CoV-2 variants Delta and Omicron: The Dominican Republic Experience (p. 2021.12.27.21268459). medRxiv. https://doi.org/10.1101/2021.12.27.21268459

    1. Abu-Raddad, L. J., Chemaitelly, H., Ayoub, H. H., AlMukdad, S., Tang, P., Hasan, M. R., Coyle, P., Yassine, H. M., Al-Khatib, H. A., Smatti, M. K., Al-Kanaani, Z., Al-Kuwari, E., Jeremijenko, A., Kaleeckal, A. H., Latif, A. N., Shaik, R. M., Abdul-Rahim, H. F., Nasrallah, G. K., Al-Kuwari, M. G., … Bertollini, R. (2022). Effectiveness of BNT162b2 and mRNA-1273 COVID-19 boosters against SARS-CoV-2 Omicron (B.1.1.529) infection in Qatar (p. 2022.01.18.22269452). medRxiv. https://doi.org/10.1101/2022.01.18.22269452

    1. SciScore for 10.1101/2022.04.13.488132: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">IACUC: The samples were taken using protocols approved by the Complutense University of Madrid’s Ethics Committee for Animal Experiments (Project License 14/2020).<br>Field Sample Permit: In addition, a survey of the owners was carried out in order to know the potential symptoms they were presenting to confirm Omicron variant associated signs, as well as a nasal swab sample collection in some cases to confirm the SARS-CoV-2 variant involved in the infection by RT-qPCR and sequencing.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">At 1-hour post-incubation, 200 μL of Vero E6 cell suspension were added to the virus-serum mixtures, and the plates were incubated at 37°C with 5% CO2.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero E6</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Phylogenetic analysis was performed using MEGA X software (Tamura, 1992).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>MEGA X</div><div>suggested: None</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. SciScore for 10.1101/2022.04.09.487739: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The membrane was incubated with ACE-2 Antibody (1:2,000, Novus Biologicals, CO, USA), Myc-Tag (9B11</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Myc-Tag</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The membrane was then incubated with secondary HRP-linked, Anti-rabbit IgG (1:10,000, Cell Signaling Technology, MA, USA) and Goat anti-Mouse IgG (H+L) Cross-Adsorbed Secondary Antibody, HRP (1:2,000, Thermo Fisher Scientific, MA, USA) for 1 h at room temperature.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Anti-rabbit IgG</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-Mouse IgG</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">HEK293T cells were cultured in FP medium (DMEM containing 10% FBS, 2 mM GlutaMAX™ Supplement, 0.1 mM MEM Non-Essential Amino Acids, 50 U/mL and 50 μg/mL Penicillin-Streptomycin).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293T</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">To establish ACE2-expressed cell line (ACE2-HEK293T cells), HEK293T cells were infected with ACE2-expressing lentivirus and ACE2-positive cells were selected by 2 ug/mL of puromycin.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>ACE2-HEK293T</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For protein expression, the cell membrane penetrating peptide (TAT), red fluorescence protein (DsRed) and NK-NT or NKN1 fragments were cloned into pET6xHN-N Vector (Takara, CA, USA)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pET6xHN-N</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For ACE2-expressing lentivirus packaging, pscALPSpuro-HsACE2 (human) (Addgene, MA, USA) were co-transfected with psPAX2 and pCMV-VSV-G packaging plasmids into HEK293T cells using FuGENE 6 (Promega, WI, USA)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCMV-VSV-G</div><div>suggested: RRID:Addgene_8454)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For doxycycline (Dox) inducible, Spike protein pseudotyped luciferase-expressing lentivirus preparation, HEK293T cells were transfected with FUW-RLuc-T2A-PuroR(Kanarek et al., 2018) (Addgene, MA), psPAX2 and pUNO1-SARS2-S (D614G) (InvivoGen, CA) packaging plasmids using FuGENE 6.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>psPAX2</div><div>suggested: RRID:Addgene_12260)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">In competition BiFC Assay, HEK293T cells were co-transfected with 0.5 μg of each construct expressed in pBiFC-VN155 (I152L) and 0.5 μg pBiFC-VC155 vectors, together with and 5 μg competitor constructs with stop codon in pBiFC-VN155 (I152L) vector.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pBiFC-VC155</div><div>suggested: RRID:Addgene_22011)</div></div><div style="margin-bottom:8px"><div>pBiFC-VN155</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Fluorescence images were taken at 24 h and 48 h after transfection using a Nikon fluorescence microscope and fluorescence intensity was quantified by Image J.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Image J</div><div>suggested: (ImageJ, RRID:SCR_003070)</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. SciScore for 10.1101/2022.04.11.22272784: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">Consent: Ethics statement: Collection and testing of biological specimens and protected health information were performed in concordance with the University of Wisconsin IRB # 2021-0076 following informed consent from the patient.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">Root-to-tip analysis: To examine the rate at which the patient’s virus accumulated mutations, and compare that rate with SARS-CoV-2 globally, we randomly subsampled approximately 5,000 SARS-CoV-2 sequences 33 from the GISAID global dataset using scripts made available in the Nextstrain command line interface 34.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The immunocompromised individual’s virus was isolated from the ∼10-months post-diagnosis nasopharyngeal swab sample cultured on Vero E6/TMPRSS2 cells.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero E6/TMPRSS2</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For those samples being sequenced with ARTIC on an ONT minION (see Supplemental Table 1), we mixed 12 μl of the purified DNA library, 37.5 μl of the ONT Sequencing Buffer II, and 25.5 μl of ONT Loading Solution, and loaded the mixture onto the MinION for sequencing.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>MinION</div><div>suggested: (MinION, RRID:SCR_017985)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">SARS-CoV-2 sequence analysis: Processing raw sequence data for consensus sequence analyses: The majority of analyses described here use consensus sequences, which we assembled for each sample using a workflow customized to run remotely with compute resources from the UW-Madison Center For High Throughput Computing (https://chtc.cs.wisc.edu/), and using the workload manager HTC Condor.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Condor</div><div>suggested: (Condor, RRID:SCR_017664)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">First, we mapped the reads to MN908947.3 using minimap2’s ‘map-ont’ preset, clipped reads down to MIDNIGHT amplicon pileups using SAMtools, and then used covtobed to produce BED-format tables of regions where read depth-of-coverage was below 20 28–30.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>SAMtools</div><div>suggested: (SAMTOOLS, RRID:SCR_002105)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">We then annotated protein effects onto these VCFs using snpEff 32.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>snpEff</div><div>suggested: (SnpEff, RRID:SCR_005191)</div></div></td></tr></table>

      Results from OddPub: Thank you for sharing your data.

      Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. SciScore for 10.1101/2022.04.11.487879: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">Consent: All participants in the convalescent cohort provided informed consent for their blood products to be used for research purposes by signing the standard New York Blood Center (NYBC<br>IRB: For participants who received the SARS-CoV-2 mRNA-1273 vaccine (Moderna), whole blood, plasma and serum samples were obtained at the NIH Clinical Research Center in Bethesda, MD under protocols approved by the NIH Institutional Review Board, ClinicalTrials<br>IACUC: Animal ethics statement: Animal research was conducted under an IACUC approved protocols at the Integrated Research Facility, Frederick, Maryland, in compliance with the Animal Welfare Act and other federal statutes and regulations relating to animals and experiments involving animals.<br>Field Sample Permit: Animal ethics statement: Animal research was conducted under an IACUC approved protocols at the Integrated Research Facility, Frederick, Maryland, in compliance with the Animal Welfare Act and other federal statutes and regulations relating to animals and experiments involving animals.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">Hamsterization of human monoclonal antibodies: Genomes corresponding to the mouse IgG2a heavy and light chains were aligned to the genome assembly MesAur1.0 (GCA_000349665.1) for a female Syrian golden hamster downloaded from Genbank.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">No randomization or blinding was applied to the analysis of participants’ plasma, serum or PBMC samples, but all samples were anonymized before being used in this study.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">No randomization or blinding was applied to the analysis of participants’ plasma, serum or PBMC samples, but all samples were anonymized before being used in this study.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Binding of secreted antibody to the beads was detected in the CY5 or TRED channels by capturing images at 6 min intervals over a 30 min time course.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>CY5</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Shotgun mutagenesis epitope mapping of antibodies by alanine scanning: Epitope mapping was performed essentially as previously described (43), using a SARS-CoV-2 (Wuhan Hu-1 strain) S2 subunit shotgun mutagenesis mutation library, made using a full-length expression construct for the SARS-CoV-2 spike glycoprotein.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>SARS-CoV-2 spike glycoprotein .</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Vaccinee and convalescent plasma binding to peptides: Polyclonal IgG antibodies from plasma or sera of vaccinated, convalescent, or naïve donors were purified using the Pierce Protein G Spin Plate (Thermo Scientific).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Polyclonal IgG</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Baculoviruses were produced by transfection of bacmid DNA into Sf9 cells and used to infect High Five cells (Life Technologies) at high (5 to 10) multiplicity of infection (MOI).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Sf9</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Plasma IgG reactivity to human coronaviruses and donor selection: Multiplexed beads for SARS-CoV-2, SARS-CoV-1, MERS-CoV, HCoV-OC43, HCoV-HKU1, HCoV-229E and HCoV-NL63 spike proteins, as well as CD4 as a negative control, were incubated with donor plasma diluted at 1/50, 1/250 or 1/1250 for 30 min at room temperature, then washed and stained with 2.5 μg/mL goat anti-human IgG Alexa Fluor 647 (Jackson ImmunoResearch, 109-606-170).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HCoV-229E</div><div>suggested: JCRB Cat# JCRB1838, RRID:CVCL_B3M4)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">mAbs were also expressed in-house by transient transfection of Expi293 cells (Gibco, A14527) using the ExpiFectamine 293 Transfection Kit (Gibco, A14524) according to manufacturer’s instructions.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Expi293</div><div>suggested: RRID:CVCL_D615)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">To generate green fluorescent protein (GFP)-tagged receptor cell lines, HeLa-ACE2 cells were transduced with lentivirus encoding GFP and sorted to collect the GFPhigh/ACE2high population.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HeLa-ACE2</div><div>suggested: JCRB Cat# JCRB1845, RRID:CVCL_B3LW)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">mAbs were added to the wells at a final concentration of 200 μg/mL and cultures were further incubated at 37 °C for 1h. 8,000 GFP+/ACE2+ HeLa cells were then added to each well and the co-cultures were maintained overnight to allow for syncytia development.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HeLa</div><div>suggested: CLS Cat# 300194/p772_HeLa, RRID:CVCL_0030)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">A plasmid encoding cDNA for each spike protein mutant was transfected into HEK-293T cells and allowed to express for 22 h.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK-293T</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For neutralization assays, 5 × 104 RD cells were inoculated at TCID75% OC43-GFP virus and incubated for 1h at 35°C. 4-fold serial dilutions (73 ng/mL - 300 μg/mL) of each mAb were incubated with TCID75 OC43-GFP virus for 1h at 35°C. 60 μL of mAb- virus mixture was used to inoculate each well containing 5 × 104 RD cells and cultures were incubated for 24 h at 35°C.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>RD</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">transducing plasmid pHR’ CMV-Luc, a TMPRSS2 plasmid and full-length spike plasmids from SARS-CoV-2, SARS-CoV, MERS-CoV, HCoV-NL63 and HCoV-229E into 293T cells using Lipofectamine 3000 transfection reagent (ThermoFisher Scientific, Asheville, NC, L3000-001) (49). 293 flpin-TMPRSS2-ACE2 cells (provided by Dr. Adrian Creanga, VRC/NIH) were used for SARS-CoV-2, SARS-CoV and hCoV-NL63 while HuH7.5 cells were used for MERS-CoV and hCoV-229E neutralization assay.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HCoV-NL63</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>293T</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>HuH7.5</div><div>suggested: RRID:CVCL_7927)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">SARS: Addgene #170447; SARS2 #170442; MERS #170448; NL63 #172666; alpha strain #170451; beta #170449; gamma #170450; delta #172320; omicron 180375) were co-transfected in HEK293T with Lipofectamine 2000 (ThermoFisher Scientific, 11668019) to produce single-round infection-competent pseudoviruses.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293T</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Organisms/Strains</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">SARS: Addgene #170447; SARS2 #170442; MERS #170448; NL63 #172666; alpha strain #170451; beta #170449; gamma #170450; delta #172320; omicron 180375) were co-transfected in HEK293T with Lipofectamine 2000 (ThermoFisher Scientific, 11668019) to produce single-round infection-competent pseudoviruses.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>#172666</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Pre-fusion stabilized constructs for CCoV HuPn-2018 (Accession # QVL91811.1, aa1-1384 with E1140P and E1141P mutations) and PdCoV0081-4 ( Accession # MW685622.1, aa1-1092 with E854P and V855P mutations) were synthesized and cloned into pCDNA3.1- vectors (Genscript) with the following C-terminal modifications: T4 fibritin trimerization motif, HRV3C protease cleavage site, poly-GS linker, Avi-tag, and 8× His tag.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCDNA3.1-</div><div>suggested: RRID:Addgene_52535)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Briefly, the SARS-CoV-2 NTD and RBD were cloned into an in-house pFastBac vector.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pFastBac</div><div>suggested: RRID:Addgene_1925)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The spike S2 domain (699 to 1207 with F817P, A892P, A899P, A942P, K986P, V987P) was constructed into phCMV3 vector which contained an N-terminal secreting signal peptide, and C-terminal thrombin cleavage site and His6 tag.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>phCMV3</div><div>suggested: RRID:Addgene_173431)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">SARS-CoV-2 RBD, SARS- CoV-2 NTD, SARS-CoV-1 spike and SARS-CoV-1 RBD, MERS-CoV spike, OC43-CoV spike, CCoV-HuPn-2018 spike, pPDCoV-0081-4 spike, HCoV-NL63 spike, HCoV-229E spike, HCoV- HKU1 spike, H1 HA and recombinant CD4 (gifted by Gavin Wright, (35)).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pPDCoV-0081-4</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Spike-containing lentiviral pseudovirions were produced by co-transfection of packaging plasmid pCMVdR8.2,</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCMVdR8.2</div><div>suggested: RRID:Addgene_8455)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">transducing plasmid pHR’ CMV-Luc, a TMPRSS2 plasmid and full-length spike plasmids from SARS-CoV-2, SARS-CoV, MERS-CoV, HCoV-NL63 and HCoV-229E into 293T cells using Lipofectamine 3000 transfection reagent (ThermoFisher Scientific, Asheville, NC, L3000-001) (49). 293 flpin-TMPRSS2-ACE2 cells (provided by Dr. Adrian Creanga, VRC/NIH) were used for SARS-CoV-2, SARS-CoV and hCoV-NL63 while HuH7.5 cells were used for MERS-CoV and hCoV-229E neutralization assay.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pHR’</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>TMPRSS2</div><div>suggested: RRID:Addgene_53887)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">2.5μg 2nd generation lentivirus backbone plasmid pCMV-dR8.2 dvpr (Addgene, 8455), 2μg pBOBI-FLuc (Addgene, 170674) and 1μg truncated coronavirus spike expressing plasmids (</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCMV-dR8.2</div><div>suggested: RRID:Addgene_8455)</div></div><div style="margin-bottom:8px"><div>pBOBI-FLuc</div><div>suggested: RRID:Addgene_170674)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Hamster genes with the highest homology to the mouse IgG2a heavy chain, lambda and kappa light chains genes were cloned into a pCDNA3.4 vector (Genscript) and expressed in Expi293 cells as described above.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pCDNA3.4</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">) high-throughput flow cytometer and FACS data were analysed with FlowJo (Version 10.8.1</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>FlowJo</div><div>suggested: (FlowJo, RRID:SCR_008520)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Analyses of the VH and Vλ/Vκ genes, CDR3 sequences, and percentage of somatic mutations were carried out using Geneious Prime (Version 2021.0.3, https://www.geneious.com) and the International Immunogenetics Information System database (IMGT, http://www.imgt.org/) (40).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>https://www.geneious.com</div><div>suggested: (Geneious, RRID:SCR_010519)</div></div><div style="margin-bottom:8px"><div>http://www.imgt.org/</div><div>suggested: (IMGT - the international ImMunoGeneTics information system, RRID:SCR_012780)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Recombinant IgG mAbs were purified using HiTrap Protein A columns (Cytiva/GE Healthcare Life Sciences, 17040303).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Cytiva/GE Healthcare</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Phylogenetic tree generation: Full-length amino acid sequences of SARS-CoV-2 (accession #NC_045512.2), SARS-CoV (accession # AY278741.1), MERS-CoV (accession # NC_019843) , HCoV-NL63 (accession #NC_005831.2), HCoV-229E (accession #NC_002645.1), CCoV HuPn-2018 (accession #MW591993.2) and PDCov-0081-4 (accession #MW685622) were aligned using the L-INS-i method of MAFFT version 7.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>MAFFT</div><div>suggested: (MAFFT, RRID:SCR_011811)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The sequence alignment was used to generate a sequence logo plot using the Weblogo 3.0 server and to color conserved amino acid residues on a pre-fusion stabilized spike protein (PDB 6VSB).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Weblogo</div><div>suggested: (WEBLOGO, RRID:SCR_010236)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Images were acquired in A488, A568 and DAPI channels using a BZ-X fluorescence microscope (KEYENCE) and processed using Fiji ImageJ (42)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>ImageJ</div><div>suggested: (ImageJ, RRID:SCR_003070)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Iterative model building and refinement were carried out in Coot (46) and PHENIX (47), respectively.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Coot</div><div>suggested: (Coot, RRID:SCR_014222)</div></div><div style="margin-bottom:8px"><div>PHENIX</div><div>suggested: (Phenix, RRID:SCR_014224)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Authentic OC43-CoV-GFP virus propagation and neutralization assay: Rhabdomyosarcoma cells (RD, ATCC CCL-136) were maintained at 37°C and 5% CO2 in No-glucose DMEM (Gibco, 11966-025), supplemented with 10% HI-FBS, 4500 mg/mL glucose, 1 mM sodium pyruvate (Gibco, 11360-070), 1 mM HEPES (Gibco, 15630-080) and 50 μg/mL gentamycin (Quality Biological, 120-098-661)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Quality Biological</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">50% neutralization titers (NT50) were calculated using the dose- response-inhibition model with 5-parameter Hill slope equation in GraphPad Prism 9.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad Prism</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Descriptive statistics (mean ± SEM or mean ± SD) and statistical analyses were performed using Prism version 9.3.1 (GraphPad).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Prism</div><div>suggested: (PRISM, RRID:SCR_005375)</div></div><div style="margin-bottom:8px"><div>GraphPad</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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">NCT00001281</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">Studies of Blood and Reproductive Fluids in HIV-Infected and…</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT05078905</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">Vaccine Responses to SARS-CoV-2 and Other Emerging Infectiou…</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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


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      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.

      </footer>
    1. SciScore for 10.1101/2022.04.11.487924: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Organisms/Strains</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Separate fasta files were created for viral genomes belonging to the following lineages: alpha; beta; gamma; delta; BA.1; BA.1.1; BA.2 (excluding genomes with RIR1 insertions; BA.2+ins(L) and BA.2+ins(LIII) (see Table 1 and Figure 1); sequences not belonging to any VOC from the year 2020 Particular attention was placed in an additional filtering step, which involved the fasta file including the BA.2 sequences bearing RIR1 insertions, aimed at discarding the sequences that may possibly derive from low quality assemblies.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>alpha; beta; gamma; delta; BA.1; BA.1.1; BA.2</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The Wuhan-Hu-1 strain genome (NCBI accession ID: NC_045512.2) was added as reference sequence All the sampled sequences were merged and aligned with mafft v7.490 (Katoh and Standley, 2013), configured for closely related viral genomes as suggested by the authors.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>mafft</div><div>suggested: (MAFFT, RRID:SCR_011811)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The Augur pipeline (Huddleston et al., 2021) was then used to produce the sampling date-refined phylogenetic tree, with the Wuhan-Hu-1 genome set as a reference for rooting.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Augur</div><div>suggested: None</div></div></td></tr></table>

      Results from OddPub: Thank you for sharing your code.

      Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. SciScore for 10.1101/2022.04.07.22273534: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">IRB: The public health surveillance program was approved by the Institutional Review Board (#20-258); this research protocol had a separate filing (approval #21-140).</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Standard curves for the N1 and N2 analyses were generated by quantifying a synthesized SARS-CoV-2 plasmid manufactured by IDT (Cat No. 10006625).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>SARS-CoV-2</div><div>suggested: RRID:Addgene_164583)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">2.6 Data analysis: Statistical analyses were performed using SAS 9.4 (SAS Institute, Cary, NC, USA), and visualizations were created using RStudio (ver. 1.4.1103) with ggplot2 (ver. 3.3.5).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>SAS Institute</div><div>suggested: (Statistical Analysis System, RRID:SCR_008567)</div></div><div style="margin-bottom:8px"><div>RStudio</div><div>suggested: (RStudio, RRID:SCR_000432)</div></div><div style="margin-bottom:8px"><div>ggplot2</div><div>suggested: (ggplot2, RRID:SCR_014601)</div></div></td></tr></table>

      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 LimitationRecognizer: We detected the following sentences addressing limitations in the study:


      3.5 Limitations: Several characteristics relating to this research need to be further explored, and various limitations should be considered in generalizing the findings outlined above. First, most passive samples (nearly 90%) remained in the sewer for 24 hours; however, some were left at the isolation residence for more than one day. A sensitivity analysis excluding these samples left in the sewer for an extended period shows a 44% increase in the occupancy count for each one unit increase in the log-transformed wastewater SARS-CoV-2 daily load, adjusting for BRSV recovery and the percentage of females in the building (IRR 1.44, 95% CI 1.22-1.70). Though only students with a positive COVID-19 clinical test resided in the isolation building, we cannot exclude the possibility of staff members contributing to the building-level water use, which could bias results. Also, it is necessary to note that the quantification of SARS-CoV-2 gene copies/L of wastewater, as measured using the raw influent sewage captured by our passive samplers, may not precisely represent the actual composition of sewage throughout the 24-hour sampling period. Instead, our quantification of SARS-CoV-2 gene copies in the wastewater comes from the “extracted” wastewater over the 24-hour time span, which was required to normalize the N1 and N2 signals to daily flow conveniently. Moreover, hourly flow data and bathroom-level flush counts may have provided more information on day-to-day student behavior. All s...


      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. SciScore for 10.1101/2022.04.08.22273605: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">IRB: Study design: The study was authorized by the local ethics committee of the Ruhr-University Bochum (21-7351 and 20-6953-bio).<br>Consent: The written informed consent was obtained from all the patients.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Anti-SARS-CoV-2 spike antibody titer: Plasma samples were analyzed for the spike (receptor-binding domain [RBD]; sequence derived from the original wildtype SARS-CoV-2 strain) specific immunoglobulin G antibodies by enzyme-linked immunosorbent assay (ELISA) as described previously 38.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Anti-SARS-CoV-2</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Single cycle VSV*ΔG(FLuc) pseudoviruses bearing the SARS-CoV-2 spike (D614G) protein 40 or SARS-CoV-2 B.1.617.2 (Delta) (EPI_ISL_1921353) spike in the envelope were incubated with quadruplicates of two-fold serial dilutions from 1:20 to 1:2560 of immune sera in 96-well plates prior to infection of Vero E6 cells (1×104 cells/well) in DMEM with 10% FBS (Life Technologies).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero E6</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">PBMC stimulation using SARS-CoV-2 peptide pool: PBMCs were thawed in a 37 °C water bath and diluted in 10 ml RPMI 1640 with Glutamine (Capricorn) with 5 % human AB serum (PAN-Biotech) and 5 U/ml Benzonase (Merck/Sigma) and centrifuged at 400 g for 5 min.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Merck/Sigma</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Statistics: Mann-Whitney or Kruskal–Wallis one-way ANOVA tests were performed to calculate statistical significance using Prism (GraphPad Software v9.2.0, San Diego, CA).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Prism</div><div>suggested: (PRISM, RRID:SCR_005375)</div></div><div style="margin-bottom:8px"><div>GraphPad</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr></table>

      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 LimitationRecognizer: We detected the following sentences addressing limitations in the study:


      Limitations of the study: The PLWH participants in our study received well-adjusted anti-retroviral therapy (ART) and had CD4+ T cells counts higher than 200 cells/μl. Whether immune responses upon COVID-19 vaccination correlate with T cell numbers in the peripheral blood awaits the analysis of a larger cohort with more diverse T cell counts. Moreover, we did not test directly for vaccine safety or efficacy since this would be beyond the scope of this study. Nevertheless, no gross adverse effects were reported by the participants of our PLWH cohort. Furthermore, our results suggest that vaccination of PLWH with COVID-19 mRNA vaccines might elicit partial humoral and cellular immune protection. Future studies have to show, whether or not booster vaccination will enhance immune protection, especially against upcoming variants of concern.


      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. SciScore for 10.1101/2022.04.07.22273545: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">IRB: All experiments and analyses involving samples from human donors were conducted with the approval of the ethics committee of the canton Zurich, Switzerland (KEK-ZH-Nr. 2015-0561, BASEC-Nr. 2018-01042, and BASEC-Nr. 2020-01731), in accordance with the provisions of the Declaration of Helsinki and the Good Clinical Practice guidelines of the International Conference on Harmonisation.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After the sample incubation for 2 h at RT, the wells were washed five times with wash buffer, and the presence of anti–SARS-CoV-2 antibodies was detected using horseradish peroxidase (HRP)-linked antibodies (1. anti-human IgG antibody: Peroxidase AffiniPure Goat Anti-Human IgG, Fcγ Fragment Specific; Jackson; 109-035-098 at 1:4,000 dilution. 2. anti-human IgA antibody: Goat Anti-Human IgA Heavy Chain Secondary Antibody, HRP; Thermo Fisher Scientific; 31417 at 1:750 dilution. 3. anti-human IgM antibody: anti-human IgM μ-chain–specific antibody; Sigma-Aldrich; A6907 at 1:3,000 dilution. 4. anti-human IgG1 antibody: mouse anti-human IgG1 Fc-HRP; Southern Biotech; 9054-05 at 1:3,000 dilution. 5. anti-human IgG2 antibody: mouse anti-human IgG2 Fc-HRP; Southern Biotech; 9060-05 at 1:3,000 dilution. 6. anti-human IgG3 antibody: mouse anti-human IgG3 Hinge-HRP; Southern Biotech; 9210-05 at 1:3,000 dilution. 7. anti-human IgG4 antibody: mouse anti-human IgG4 Fc-HRP; Southern Biotech; 9200-05 at 1:3,000 dilution), all of them diluted in sample buffer at 3 μL per well dispensed on Biotek Multiflo FX.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti–SARS-CoV-2</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-human IgG</div><div>suggested: (Jackson ImmunoResearch Labs Cat# 109-035-098, RRID:AB_2337586)</div></div><div style="margin-bottom:8px"><div>anti-human IgA</div><div>suggested: (Thermo Fisher Scientific Cat# 31417, RRID:AB_228253)</div></div><div style="margin-bottom:8px"><div>Anti-Human IgA Heavy Chain Secondary Antibody,</div><div>suggested: (Thermo Fisher Scientific Cat# 31417, RRID:AB_228253)</div></div><div style="margin-bottom:8px"><div>anti-human IgM</div><div>suggested: (Sigma-Aldrich Cat# A6907, RRID:AB_258318)</div></div><div style="margin-bottom:8px"><div>anti-human IgM μ-chain–specific</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-human IgG3</div><div>suggested: (SouthernBiotech Cat# 9200-05, RRID:AB_2796691)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For quality testing, the same procedure was applied as above using the same clone (HP6025) of the HRP-linked secondary antibody but from a different vendor, including a different storage buffer: mouse anti-human IgG4; Invitrogen; A-10654 at 1:500 dilution.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-human IgG4</div><div>suggested: (Thermo Fisher Scientific Cat# A-10654, RRID:AB_2534054)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Fisher’s test was conducted in Graph Pad Prism, with α < 0.01.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Graph Pad Prism</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For visualisation of individual data points in boxplots, violin plots, ridge plots (ggridges package), density plots (with geom_density_2d where a 2D kernel density estimation was performed on the X and Y coordinates of the input data and the results were displayed with contours), heatmaps (using heatmap.2, a part of the gplots 3.1.1 library), and as scatter dot plots, ggplot2 (version 3.3.5) functions were used.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>ggplot2</div><div>suggested: (ggplot2, RRID:SCR_014601)</div></div></td></tr></table>

      Results from OddPub: Thank you for sharing your data.

      Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:


      The limitations of our investigations reside in the number of patients enrolled in the study and the vast number of variables reported, which may constrain the generalizability of results and conclusions. Therefore, all variables underlying this study are available for further studies and for comparison with future cohorts. On the other hand, our findings describing the antibody response of pre-omicron convalescent or post-vaccination sera to the SARS-CoV-2 omicron variant are congruent with those found by others with other methods, including viral neutralization and clinical observations. In conclusion, we have investigated antibody affinity and concentration following infection and/or vaccination in the presence of an antigenic drift. We found that the tolerance to the omicron drift was surprisingly robust, whereas the currently approved therapeutic monoclonal antibodies lost much of their affinity. The most plausible scenario is that antibodies are selected in vivo for immunodominant spike domains that are invariant between clades of virus, whereas therapeutic monoclonals were presumably selected in vitro for highest affinity but not for cross-clade protection. Ultimately, our finding, along with others, suggests that the B-cell-mediated immunity, possibly concomitant with a T-cell response, elicited upon infection and/or vaccination might be broad enough to confer a layer of protection in the event of further waves of mutated SARS-CoV-2 variants.


      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. Fig. 8 | EV-shed NGFR favors LN metastasis and influences survival. a,b, Representative images (a) and quantification (b) of mCherry+ B16-F1 cells inpopliteal LNs. Mice were educated with B16-F1-GFP-derived and B16-F1-NGFR–GFP-derived sEVs as in Fig. 1i. Data were collected from two independentexperiments (n = 10 mice per group; GFP group, 28 LN sections and NGFR–GFP group, 26 LN sections). Scale bars, 200 μm and 40 μm. c,d, Representativeimages (c) and quantification (d) of mCherry+ B16-F1 cells in popliteal LNs. Animals were educated with control or Ngfr-KO B16-F1R2-derived sEVs asin Fig. 1i (n = 5 mice per group; control group, n = 21 LN sections and Ngfr-KO group, n = 24 LN sections). Scale bar, 20 μm. e, Metastatic area in miceeducated with control and Ngfr-KO B16-F1R2-derived sEVs as in Fig. 1k. Two independent experiments were performed (n = 9 mice per group). f, Survivalof animals educated with control and Ngfr-KO B16-F1R2-secreted sEVs as indicated in e (control sEVs, n = 12 mice and Ngfr-KO sEVs, n = 10 mice).g–i, Percentage of animals (g), number of LN metastases (h) and representative images (i) in animals bearing B16-F1R2 flank tumors untreated ortreated with THX-B as in Fig. 7j (vehicle, n = 7 mice and THX-B, n = 8 mice). Met, metastasis. j, NGFR h scores in skin and LN sections from patients withmelanoma (n = 26 skin or soft tissue samples and n = 17 LN samples). k, Percentage of NGFR+MITF+ tumor cells in skin and LN samples from patientswith melanoma (n = 21 matched samples). l, Overall survival (OS) of patients with stage II or III melanoma according to NGFR+MITF+ cell numbers in LNbiopsies (less than 75 NGFR+MITF+ cells, n = 13 patients;

      NGFR KO had prolonged survival Patients NGFR+ correlated with decreased survival

    2. racellular matrix; JAK, Janus kinase; PPAR, peroxisome proliferator-activated receptor; STAT, signal transducer and activator oftranscription. b, Correlation between RNA-seq data in hLECs and proteomic data in SK-MEL-147-derived sEVs. The color code indicates significantlyregulated gene–protein pairs (FDR < 0.05). FC, fold change; ITGB, integrin subunit β; VCAN, versican. c, Top pathways significantly enriched in the groupof positively correlated gene–proteins pairs shown in b. Pathways were obtained using PANTHER over-representation analysis by applying Fisher’s exacttest and FDR correction. d,e, Representative images (d) of PKH26-labeled SK-MEL-147 cells adhered to sEV-treated LECs in flow. LECs were previouslyexposed to PKH67-labeled sEVs from primary melanocytes (melano) or SK-MEL-147 cells for 24 h. The plot in e shows quantification of attachedtumor cells at t = 4 h. Two independent experiments were performed (all groups, n = 20 fields from one representative experiment). Scale bar, 50 μm.f, ICAM1 expression in hLECs treated with SK-MEL-147-derived sEVs for 48 h. Two independent experiments were performed (n = 6 LNs per group).g,h, Representative images (g) and quantification (h) of ICAM-1 expression in LNs treated with B16-F1R2-derived sEVs intra-footpad for 10 d. Two independentexperiments were performed (n = 7 LNs per group). Scale bars, 100 μm and 200 μm. i, LYVE-1 and ICAM-1 staining in LNs treated with B16-F1R2-derivedsEVs or PBS for 48 h (n = 3 LNs per group). Scale bar, 50 μm. j, Quantification by flow cytometry of LECs expressing high levels of ICAM-1 in LNs ofanimals injected intra-footpad with B16-F1R2-derived sEVs for the indicated times. Two independent experiments were performed (control and 48 h,n = 3 LNs per group and 7 d, n = 4 LNs per group). k,l, Representative images (k) and quantification (l)

      Hypothesis: Human cells will show similar effects as the mouse model and will have up & down regulated genes

      Conclusion: Found genes positively correlated --> nGFR in particular. Melanoma derived sEVs influence LEC phenotype and promote cell adhesion. Block ICAM prevents adhesion

    3. ntralateral LN; RFU, relative fluorescent units. d,e, Representative images (d) and quantification (e) of sEV-associated signal in mice 1, 4, 24and 48 h after intra-footpad injection with NIR815-labeled sEVs. Data correspond to two independent experiments (n = 4 mice per group). Squaresindicate popliteal LN area. f, Representative images of the distribution of melanoma DiD-labeled sEVs (DiD-sEVs) in popliteal LNs 16 h after intra-footpadinjection (n = 4 LNs per group). Scale bar, 150 μm. DAPI, 4′,6-diamidino-2-phenylindole. g,h, Representative flow cytometry plots (g) and quantification(h) of GFP+ B16-F1 cells in the CD45− population in LNs educated with sEVs for 10 d. LNs were analyzed 24 h after injection of tumor cells (PBS andB16-F1R2, n = 5 mice per group; B16-F1 and B16-F10, n = 4 mice per group). i,j, Representative images (i) and quantification (j) of mCherry+ B16-F1 cellsin sections of popliteal LNs 10 d after tumor cell injection. Melanoma-derived sEVs or PBS were injected intra-footpad for 10 d before tumor inoculation.Two independent experiments were performed (control, B16-F1 and B16-F1R2 groups, n = 5 mice per group; F10 group, n = 4 mice). Scale bar, 20 μm.k,l, Representative images (k) and quantification (l) of metastatic area in inguinal LNs of animals bearing B16-F1-GFP flank tumors and educated withB16-F1R2 sEVs injected intra-footpad for 21 d. LNs were stained against human melanoma black 45 (HMB-45) antigen. Two independent experiments wereperformed (n = 12 mice per group). Scale bars, 500 μm and 200 μm. B

      Hypothesis: Do the different mouse melanoma cell lines show different spread through the lymph nodes

      Results: F10 are more enriched that F1 cells

      Hypothesis 2: Does lymphnode education change the spread of cancer cells through lymph nodes accross cell lines.

      Results 2: Increased GFP activity in lymph nodes in F10, but GFP is eliminated in immunocompetent mice, soo...

      Hypothesis 3: Does popliteal lymph node education effect these?

      Results 3: F10 were able to establish and colonize popliteal lymph node

      Melanoma derived extracellular cicrulate in lymph nodes and enhance melanoma metastases

    Annotators

    1. SciScore for 10.1101/2022.04.08.487674: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">IRB: The protocol and consent document were reviewed and approved by ethical review boards for all sites, and all subjects provided written informed consent.<br>Consent: The protocol and consent document were reviewed and approved by ethical review boards for all sites, and all subjects provided written informed consent.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">Human Subjects: Peripheral blood mononuclear cells (PBMC) were obtained from subjects in study 2019nCoV-101, a phase I/II clinical trial of NVX-CoV2373 carried out in male and female adult subjects in Australia and the United States.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">Donors of peripheral blood mononuclear cell fractions for the studies reported here were selected randomly from among subjects who had adequate specimens at all three specified dates (baseline, 7 days after dose 1 and 7 days after dose 2) and were treated twice with 5µg SARS-CoV-2 rS antigen plus 50µg Matrix-M™ adjuvant at a 21-day interval, as this was the dose and regimen selected to go forward for further clinical development.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">PBMC from 5 recipients of placebo were included among the study samples in a blinded fashion.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Anti-S IgG ELISAs: Recombinant SARS-CoV-2 S protein was immobilised onto the surface of the 96-well microtiter plates by direct adsorption at 2°C to 8°C, followed by washing and blocking, Diluted reference standard (2-fold dilution series of 12 dilutions starting 1:1000) and human serum samples (3-fold dilution series of 12 dilutions) in assay buffer were then added in duplicate (100 µL per well) to the S protein-coated wells and specific antibodies are allowed to complex with the coated antigen for 2 hours ± 10 minutes at 24°C ± 2°C.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Anti-S IgG</div><div>suggested: (LSBio (LifeSpan Cat# LS-C132241-1000, RRID:AB_10835882)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After washing, IgG bound to the rSARS-CoV-2 S protein was detected using a horseradish peroxidase (HRP)-conjugated goat anti-human IgG antibody (Southern Biotech) incubated for 1 hour ± 10 minutes at 24°C ± 2°C.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-human IgG</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Anti-rSARS-CoV-2 S protein IgG antibody level in clinical serum samples was quantitated in ELISA unit, EU/mL, by comparison to a reference standard curve.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Anti-rSARS-CoV-2 S protein IgG</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">hACE2 Binding Inhibition Assay: SARS-CoV-2 (rSARS-CoV-2) S protein was immobilised onto the surface of the 96-well microtiter plates by direct adsorption at 2°C to 8°C, followed by washing and blocking, Serial dilutions of human serum samples, including assay quality controls (QCs), were then added to the spike-coated wells and any molecules that could bind to the S protein, presumptively primarily spike-specific antibodies, were allowed to complex with the immobilized S protein (for 1 hour at 24±2°C) After a plate wash step, a fixed concentration of human ACE2 receptor (hACE2) with a polyhistidine-Tag (His-Tag) (SinoBiological) was added to the plate for incubation (1 hour at 24±2°C) during which the hACE2 bound to the S protein residues with binding sites not obstructed by bound antibody.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>His-Tag</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After incubation of the mixtures at 37°C and 5% CO2 for 1 hour, the mixtures were transferred to 96-well plates with confluent VeroE6 cells.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>VeroE6</div><div>suggested: JCRB Cat# JCRB1819, RRID:CVCL_YQ49)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Samples were diluted inn duplicate to a base dilution of 1:5 or 1:10, followed by 11 × 1:2 serial dilutions in Dulbecco’s minimal essential medium (DMEM, Quality Biologicals) supplemented with 10% fetal bovine serum (heat inactivated, Sigma), 1% penicillin/streptomycin)(Gemini Bio-products) and 2mM L-glutamine (Gibco) resulting in 100µL per well.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Quality Biologicals</div><div>suggested: (Aldevron, RRID:SCR_011017)</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


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    1. (A) Whole-mount fluorescence images of mammary and lung of N-cad-EMTracer mouse. Inserts are bright-field images. Tamoxifen was induced at 7 weeks andtissues were collected at 18 weeks.(B) Immunostaining for ZsGreen, tdTomato, and E-Cad on mammary and lung sections of N-cad-EMTracer mouse. No tdTomato + cells were detected inmammary and lung epithelial cells.(C and H) Whole-mount fluorescence images of mammary tissue collected at early (8–12 weeks) (C) or late (18–24 weeks) (H) stages from N-cad-EM-Tracer;MMTV-PyMT mouse. Inserts are bright-field images.(legend continued on next page)llArticle

      Figure 5: N Cadherin --> Control Figure A) Control to show that model works and is not unspecific B) Control to show that model works and is not unspecific C -G) Early stage tumor, lots of green, N cadherin is not expressed in early stage H-L) Late stage tumor, some red, N Cadherin is more involved in later stage tumors

    2. Schematic figure showing experimental strategy. Mammary and lung tissues were collected for analysis at early stage (B–D) and late stage (E–M).(B) Whole-mount fluorescence image of mammary tissue and lung. T, tumor nodule.(C) Immunostaining for PyMT, ZsGreen and tdTomato on sections from mammary tissue. Arrowheads indicate tdTomato +PyMT + tumor cells.(D) Quantification of percentage of ZsGreen + or tdTomato + cells in PyMT + tumor cells. Data are mean ± SEM; n = 5.(E) Whole-mount fluorescence image of primary tumor at late stage.(F) Immunostaining for PyMT, ZsGreen and tdTomato on mammary tumor sections. Arrowheads, PyMT +tdTomato + tumor cells(G) Quantification of percentage of ZsGreen + or tdTomato + cells in PyMT + tumor cells. Data are mean ± SEM; n = 5.(H) Immunostaining for vimentin, ZsGreen, and tdTomato on mammary tumor sections. Arrowheads, Vimentin + tdTomato + tumor cells.(I) Whole-mount fluorescence image of lung at late stage. Small size lung metastasis (1, <0.1 mm, arrow) and large size lung metastasis (2, >0.5 mm).(J and L) Immunostaining for PyMT, ZsGreen, and tdTomato on lung sections. In small size nodules, PyMT+ tumor cells are ZsGreen +tdTomato – (J). In large sizenodule, most PyMT + tumor cells are ZsGreen +tdTomato –, while a minority of PyMT + cells are ZsGreen –tdTomato + (arrowhea

      A) Stratgey B) A lot of green --> Lots of primary tumors in the mammary gland --> very potent. No vimentin in the lung C) Each stain individually then merged, making sure that only looking at tumor cells. --> Green is cancer. Very little vimentin in breast. d) quantification of C e-h) Vimentin is not that important in breast cancer. Increases, but not much after expression. I) Small nodules in lungs J and K) Only green tumors, no red tumors L and M) Large nodules N) PCR control that stop codon was cleaved --> Proves stop was removed. Small vs large --> early versus late stage Stained for vimentin because the Cre model could stop fluorescing, the stain shows if vimentin was ever expressed

    Annotators

    1. We now establish a triple integral in the spherical coordinate system, as we did before in the cylindrical coordinate system. Let the function f(ρ,θ,φ)f(ρ,θ,φ)f(\rho,\theta,\varphi) be continuous in a bounded spherical box, B={(ρ,θ,φ)|a≤ρ≤b,α≤θ≤β,γ≤φ≤ψ}B={(ρ,θ,φ)|a≤ρ≤b,α≤θ≤β,γ≤φ≤ψ}B = \{(\rho,\theta,\varphi) | a \leq \rho \leq b, \, \alpha \leq \theta \leq \beta, \, \gamma \leq \varphi \leq \psi \}. We then divide each interval into l,m,nl,m,nl,m,n and nnn subdivisions such that Δρ=b−al,Δθ=β−αm.Δφ=ψ−γnΔρ=b−al,Δθ=β−αm.Δφ=ψ−γn\Delta \rho = \frac{b - a}{l}, \, \Delta \theta = \frac{\beta - \alpha}{m}. \, \Delta \varphi = \frac{\psi - \gamma}{n}. Now we can illustrate the following theorem for triple integrals in spherical coordinates with (ρ∗ijk,θ∗ijk,φ∗ijk)(ρijk∗,θijk∗,φijk∗)(\rho_{ijk}^*, \theta_{ijk}^*, \varphi_{ijk}^*) being any sample point in the spherical subbox BijkBijkB_{ijk}. For the volume element of the subbox ΔVΔV\Delta V in spherical coordinates, we have ΔV=(Δρ)(ρΔφ)(ρsinφΔθ)ΔV=(Δρ)(ρΔφ)(ρsinφΔθ)\Delta V = (\Delta \rho)\, (\rho \Delta \varphi)\, (\rho \, \sin \, \varphi \, \Delta \theta), as shown in the following figure.

      This drawing is inaccurate. The rhosin(phi)delta-theta arrows point incorrectly to the area and not the side length and phi is incorrectly labeled as delta phi.

    1. Author Response:

      Reviewer #1:

      This study reports on the inference of the evolutionary trajectory of two specialist species that evolved from one generalist species. The process of speciation is explained as an adaptive process and the changing genetic architecture of the process is analyzed in great detail. The genomic dataset is big and the inference from it solid. The authors reach the conclusion that introgression and de novo mutations, but not standing genetic variation, are the main players in this adaptive process.

      I would avoid the term adaptive radiation for the group of fish studied here. It is misleading. It is generally accepted to use the term adaptive radiation when a fairly large number of new species originate from a common ancestor (cichlids in big African lakes, gammarids in Lake Baikal, etc). Here are only 2 new lines that evolved from a common ancestor. Furthermore, I do not see much parallel between the ideas and concepts used when people study real adaptive radiations and one studied here. I actually believe that the term adaptive radiation even distracts from the beauty of the current study.

      We would like to acknowledge that the usage of the term “adaptive radiation” has a long, rich history of debate in the literature over how it should be applied to empirical systems. Some example definitions of adaptive radiation are listed below:

      1) “The evolution of ecological and phenotypic diversity within a rapidly multiplying lineage” - Schluter, 2001 (The ecology of adaptive radiation). This definition implies that abundant ecological and morphological diversity that arose in a single lineage over a short time are the hallmarks of adaptive radiation and has been frequently applied to stickleback species pairs. The pupfishes of San Salvador Island meet these criteria (two trophic specialists arose from a generalist ancestor within 10,000 years). Importantly, please note that in this foundational textbook on adaptive radiation, no statement is made about the number of species necessary to be considered an adaptive radiation.

      2) “The evolutionary divergence of members of a clade to adapt to the environment in a variety of different ways.” – Losos, 2009 (Lizards in an evolutionary tree: Ecology and adaptive radiation of Anoles). Here again, the pupfish system described meets the definition. Unlike the previous definition, no statement about the rate of diversification (species or morphological/ecological) is made.

      3) “The rise of a diversity of ecological roles and attendant adaptations in different species within a lineage” – Givnish, 1997 (Adaptive plant evolution on islands: classical patterns, molecular data, new insights. Evolution on islands). As with the previous definition, no qualification is made with respect to rates of diversification. The pupfishes again meet the definition.

      As discussed by Givnish in 2015 (“Adaptive radiation versus ‘radiation’ and ‘explosive diversification’: why conceptual distinctions are fundamental to understanding evolution” – New Phytologist), few of the early definitions of adaptive radiations contained any reference to the rapidity of speciation – Simpson (1953) perhaps being the only notable exception. However, despite this, no definition states that the application of “adaptive radiation” to a given system is contingent upon a given number of species having arisen by the present day.

      The pupfishes of Salvador island meet all definitions of adaptive radiation – exceptional rates of morphological diversification and ecological diversification, as well as truly exceptional rates of speciation – focusing just on the three species here, two species have arisen within the last 10,000 years – this roughly translates to a speciation rate of 200 species per million years. While this pace is highly unlikely to be maintained, we feel that every line of evidence points towards the pupfishes of San Salvador Island as an adaptive radiation at the earliest stages of the process. We disagree that an adaptive radiation must be ‘complete’ or nearly so, for it to be deemed as such.

      Finally, we have also discovered a fourth pupfish species on the island (Richards and Martin 2016; Richards et al. 2021), and even more undiscovered species may exist there. Thus, this is an adaptive radiation of four sympatric species, not two as suggested.

      The "Result and discussion" section has rather little discussion. There is not much about other systems or studies, neither in concepts nor in biology. The results are not linked to the bigger questions and the larger field. The same is true for the conclusion, which is very strongly centered on the here reported study. What can we learn from this study for other systems? Is there a generalizable take-home message? How do the findings relate to commonly held ideas/theory on how adaptive speciation works? Without this, it reads like a report of a case study, disconnected from the larger field. To achieve this aim, it may be good to split the main section into a result and a discussion section, but this is only a suggestion.

      We followed this helpful suggestion and have split the results and discussion section and significantly expanded and revised our discussion section. We now relate our findings to the broader fitness landscape theory literature and emphasize how our findings inform the process of speciation. We conclude by emphasizing that our findings point to a process in which adaptive introgression and de novo mutation not only provide diversity that is useful in reaching novel fitness peaks on a static landscape but alter the shape of the landscape itself.

      Reviewer #2:

      This is a really interesting and challenging question the authors are addressing here. I enjoyed reading the manuscript and a few comments below:

      One major concern I have concerns the analysis of the two treatments (low and high density, l411). I believe that the two treatments should analyzed separately as the authors are estimating two different fitness landscapes. When conducting their analysis, experiment is treated as a single factor. Yet, in Martin and Wainwrigth (2013), it was established that the fitness landscapes were quite different between the two treatments (Figure S7 of said paper), meaning that different phenotypes (and therefore genotypes) were affected differently. I do not think that the complex effect described there can be capture by a single factor as done here.

      We examined this concern further and now include new analyses of only data from the second field experiment to address these concerns (described in more detail below), resulting in qualitatively similar conclusions to those conducted using all samples.

      Please also note that only the high-density treatments from the 2013 study were included in the current study due to the low sample sizes of the original low-density treatments. In the 2020 fitness landscape study, we found no evidence of a treatment effect (frequency-manipulation) on the curvature of the fitness landscape. In all our analyses, we do include the effect of lake accounting for environmental differences between lake replicates.

      While the two high-density treatments in Martin and Wainwright 2013 were analyzed and visualized in some cases as distinct adaptive landscapes as pointed out by the reviewer, many aspects of stabilizing and disruptive selection were comparable between the lake environments and detected in similar regions of morphospace as described in Table 1 in that paper. All statistical analyses of the second field experiment (e.g. Figure 5A of Martin & Gould 2020 Evol. Letters) indicated no effect of the frequency treatment between the two field enclosures in each lake; accounting for treatment did not improve model fit to the data. In the second field experiment, the authors found that the two frequency treatments in each lake could in fact be summarized by a single fitness landscape accounting for lake-specific effects which was as the best fitting GAM model. This surface bore remarkable similarities to the high-density fitness surfaces of the 2013 in the placement of fitness peaks and valleys on the morphospace (Martin and Gould 2020). Thus, we tend to view the fitness landscape of interest to us as a single landscape connecting the fitness of different species phenotypes while treating lake-specific environmental effects on this landscape as background noise.

      Unfortunately, we do not have sufficient resequenced samples to analyze only data from the first experiment alone (Martin and Wainwright 2013); fewer than half of our samples come from the 2013 study – the remainder come from the second field experiment. Therefore, we now include a second set of analyses focused on just the subset of resequenced fish from the second field experiment (Figure 5—figure supplement 1-2, Appendix 1—table 18-19). Our primary goal was to assess whether our major findings held within a single field experiment by focusing on the latter, more data-rich experiment.

      Because we believe the most significant analyses from our paper are those pertaining to genotypic fitness landscapes and accessibility, using the subset of data from the second field experiment we performed 1) analyses of models fit between ancestry proportion and fitness (i.e. Figure 1—figure supplement 3), and 2) analyses estimating accessibility between generalists and either trophic specialist (reported in Appendix 1—table 19).

      Overall, we found qualitatively similar results between analyses conducted using either all samples or only those in the second experiment. As a result, we report results for all samples in the main text while referencing the analyses of the second field experiment alone which are presented in the supplementary material.

      A second major concern I have is in the use of the Admixture software (Figure 1 and l152.) The generalist type is assumed to be the ancestral type. Yet, a unique group was not assigned to it. This is a known problem for Admixture (Lawson et al. 2018). Groups that are under-sampled are far more likely to be consider a mixture of different ancestry groups even when this is impossible (Rasmussen et al 2010, Skolung et al 2012). While this in itself is not problematic, I am concerned about the use the authors are making of these ancestry proportions (l 156-165). The authors analyzed how ancestry of scale eater or molluscivore affect survival probability, growth, or the hybrid composite fitness. However, the ancestries values are partly generated due to an artefact, so I wonder how modelling the ancestral type as a group, and therefore acknowledging some amount of share ancestry between the three species may further affect this analysis.

      We agree that the ancestries estimated for the generalists by our unsupervised admixture analyses appear to be confounded and we briefly allude to this in the text. In our original submission, we focused exclusively on molluscivore and scale-eater ancestry, which appear less biased by this artifact. To address this concern, we ran new admixture analyses using a supervised analysis, a priori assigning generalists, molluscivores, and scale-eaters to one of three populations. Ancestry proportions of hybrids were then inferred for each of three clusters. We now include new analyses of fitness by ancestry associations using these admixture proportions and found qualitatively similar results. We report these new analyses in the results and supplemental material.

      We also conducted analyses using only samples from the second field experiment (related to the first concern raised by the reviewer). In all, we now include the following analyses of the extent to which the three fitness measures are associated with each of the three ancestry proportions using:

      1) an unsupervised admixture analysis (Appendix 1—table 2), 2) all samples using a supervised admixture analysis (i.e. model is informed a priori which samples are known to belong to either of the three assumed populations/parental species: Appendix 1—table 3), 3) only samples from the second field experiment (Martin & Gould 2020) in which lake was not found to significantly affect fitness using an unsupervised analysis (Appendix 1—table 4).

      Importantly, results are qualitatively the same; ancestry proportions do not strongly influence fitness in this system. There is one exception – generalist ancestry appears to positively predict growth when modeled using all samples and the supervised admixture analysis (Appendix 1—table 3). However, the inconsistency of this result across the three analyses leads us to cautiously interpret this exception

      I understand the need to use subsets of a network, due to impossibly large dimension size of the network in the first place. However, subsetting said network may give the wrong impression of the whole network (Fragata et al 2019). I wish this point was further discussed here.

      We have followed this suggestion. In our now-expanded and significantly revised discussion, we include discussion of this limitation, citing Fragata et al (2019) as well as related works. We also discuss how estimation of combinatorially complete fitness landscapes may be misleading, as their topography is determined in part by epistasis that occurs among loci that are not segregating in natural populations. We also suggest that the ‘realized epistasis’ that occurs among only those loci that are naturally segregating in a population may be why the shape of the fitness landscape, and thus accessibility of fitness peaks, changes upon the appearance of adaptive introgression and de novo mutations.

      L 294-295: I wonder whether the results here could be used to discuss the geometry of the different fitness peaks. The small number of steps within molluscivores suggest a rather narrow peak, while the rather large ones within the generalist suggest a rather flat fitness peak. The shape of the peak can be linked to the amount of genetic variation that can be maintained within populations, as well as the mutational load of said populations.

      This is an excellent suggestion and led us to consider the ruggedness of our fitness landscapes as an additional factor affecting evolutionary accessibility. We now interrogate the geometry of the fitness landscape further, asking for each specialist, how many local peaks exist on their respective landscapes (i.e. the ruggedness), how far specialists are from these peaks, and how accessible these peaks are to specialists. We elaborate on these findings in the discussion as recommended.

      These expanded analyses further led us to similarly investigate the influence of each source of genetic variation on the ruggedness of the fitness landscape. Consequently, we now discuss in more detail the interplay between fitness landscape ruggedness and accessibility of interspecific genotypic paths, in the context of what sources of genetic variation are available. We show that the presence of adaptive introgression and de novo mutations both increase the accessibility of interspecific genotypic paths, while decreasing fitness landscape ruggedness. We now discuss how this finding makes sense in light of epistasis; changes to the pool of segregating genetic variation alters the ‘realized epistasis’ in natural populations, thus altering the shape of the fitness landscapes and ultimately the evolutionary outcomes favored by natural selection.

      L74-75 I would suggest to more cautious in the phrasing here. While this is true within Fisher geometric model, where population are assumed monomorphic and infinite, this is not true in general. Deleterious mutations can fix within populations, especially when drift is non negligible. Crossing fitness valleys has been quite widely investigated (see Weissman et al 2010 for example). Even the authors themselves mention it later (l 108).

      We tempered these statements as recommended and expand our references to include Weissman et al. 2010 and additional references describing these caveats.

      Lastly, I would be more cautious about the conclusion. Line 373-374, the authors mentioned that "de novo mutations may enable the crossing of a large fitness valley". Given that the authors focus only on adaptive walk (fitness always has to increase between each mutational step), there is no crossing of fitness valleys. Switching from one fitness peak to another is simply a matter of walking along a (very) narrow ridge.

      We revised our language as recommended, emphasizing that our results support an interpretation in which apparent phenotypic fitness valleys are crossed along narrow fitness ridges, which are not observed in a three dimensional morphospace, to reach new fitness optima.

      Reviewer #3:

      This paper uses sophisticated regression methods and numerical experiments to produce a genotype-fitness relationship for three closely related sympatric pupfish species, forming an adaptive radiation. In addition to providing insights into the genetic targets of selection, this paper goes further in attempting to tease out what types of genetic variation were most likely to have played key roles in this radiation.

      Strengths:

      The idea behind this study is excellent, and clearly a large amount of thought and effort went into collecting the underlying data. The attention paid to linking evolutionary dynamics with the fitness results is laudable. The system is extremely exciting and I think an experiment and analysis of this sort could potentially be interesting to a broad audience within evolutionary biology.

      Weaknesses:

      The claim that this is the first genotypic fitness network in a vertebrate needs additional qualifiers: as far as I can tell, the claim to novelty is based on the inclusion of multiple species, the number of alleles, and measuring fitness in the field. I can't fully assess this claim but I would urge the authors to avoid staking a stronger claim to priority than is really needed, as it might be a lightening rod for criticism and hair-splitting that would distract from the contents of the paper.

      We tempered this claim as suggested, removing it from the title, and de-emphasizing or removing this claim elsewhere throughout the manuscript.

      One of my major questions while reading this was whether these three species were better or worse adapted to subenvironments within the lakes. This is partially answered in a few places in the manuscript, but I think that resolving this point more precisely would help interpret if positioning all three species on the same fitness landscape is fair.

      We have included more description/discussion of the ecological differences between species to the manuscript, particularly their habitats within the lake. We now point out that all three species coexist within the benthic littoral zone of each lake. No habitat segregation among these species has been observed in 13 years of field studies, suggesting that it is reasonable to position all three species within the same fitness landscape. Their foraging also occurs within the same benthic microhabitat throughout each lake; indeed, the scale-eaters target their generalist neighbors for scale attacks. This thinking also underlies much of the theory of speciation and adaptive radiation. We now include these qualifiers in the text as well.

      I find it a little hard to follow the construction of the landscapes in Fig. 2 B and C. I am not clear why the landscapes don't cover the location of the molluscivore population.

      We now include a brief statement that estimated values of fitness are only plotted for samples within the observed morphospace in the hybrids. That is, because none of the hybrid phenotypes were morphologically similar to the most divergent molluscivore phenotypes, we could not measure fitness values for this region of morphospace. However, there were hybrid phenotypes that fell within the 95% confidence ellipse of the lab-reared molluscivore population, suggesting that we have good power to detect adaptive walks to this region of the morphospace.

      I think the fitnesses predicted for the main bulk of the generalists and scale-eaters are the same across the two landscapes (as I expect they would be), but this is obscured by the differing fitness ranges of the two landscapes. I would suggest using a single color-fitness relationship for the two panels to aid cross-comparison.

      We re-plotted these landscapes using a uniform color scheme across panels as recommended.

      Also, two salient features of the landscape-the major peak at the top center and the deep pit at the bottom center-seem to be supported by few fish in each case. I would imagine that something like boot-strapping could be done for fitness landscapes, where the support for each feature of the landscape could be judged by how often it appears in subsets of the data (or in inferred models with nearly as high support as the best model), but I acknowledge that might be very hard to do. Still, I think some statement of uncertainty should be prominently included.

      We followed this suggestion and now more explicity address uncertainty in our estimation of three-dimensional fitness landscapes, with particular focus on the landscape we devote the most attention to (Fig. 2c-d – composite fitness + genotypes).

      To quantify uncertainty, we conducted a bootstrap procedure as suggested in which we resampled hybrids with replacement, re-estimated the fitness landscape, and compared the topology of the predicted fitness landscapes to that of the observed fitness landscape (Figure 2—figure supplement 7). Even across the bootstrap replicates, we still recovered the same general features – a peak localized near generalists, a fitness valley near scale-eaters, and a fitness ridge/modest peak near molluscivores.

      Furthermore, we emphasize more strongly in the revised manuscript our point that three-dimensional representations of the fitness landscape may in fact mislead interpretations of how evolution proceeds. In that respect, even though we recover the same features of the landscape when accounting for uncertainty, we articulate that these inferred peaks and valleys separating populations may be bridged in multidimensional genotype space.

      More generally, the landscapes reconstructed in Fig. 2 do not show very clear evidence that the M or S types are separated by valleys from the G type. Close inspection of the figure suggests a very shallow valley might be present between G and M, but the overall trend is declining fitness; between G and S, fitness appears to simply decline. While peaks may occur within the landscapes composed of limited sets of loci, the overall pattern seen in Fig. 2 doesn't seem conducive to analyzing how adaptive evolution in generalists crossed valleys to reach the putatively higher peaks of the two specialists. As such, I find the connection between these phenotypic-fitness landscapes and the later genotypic fitness landscapes quite confusing.

      We thank the reviewer for this comment. The apparent disconnect noted by the reviewer is in fact a point that we would like to draw more attention to. Thus, we have revised much of the discussion of these results to address this.

      As discussed in our response to the reviewer’s previous comment, the three dimensional landscape contrasts with our inferences from genotypic fitness landscapes. This incongruence demonstrates, through example, how three-dimensional fitness landscapes may in fact mislead our intuition about how evolution proceeds.

      As has been discussed extensively in the fitness landscape literature (e.g. Kaplan et al. 2008; Gavrilets 2010; Fragata et al. 2019), reduction of the fitness landscape, which is inherently highly multidimensional (as originally recognized by Wright), to only three dimensions can mask viable evolutionary trajectories, underestimate the number of peaks, and oversimplify our understanding of how populations evolve. We now attempt to better clarify and discuss this in the revised manuscript.

      I also had trouble understanding the role of fitness in the analysis of mutational distances in a subset of loci between the three species (lines 282-296). While the illustration in Fig. 3C uses directed edges to capture fitness data, this framework doesn't seem to be applied in Fig. 3d or the resulting analyses in 3e. As such, I don't see how this section is about genotypic fitness landscapes at all.

      We followed this suggestion and have rearranged our figures and their constituent panels to provide a more coherent illustration of our results and analyses. Figure 3 now serves to describe 1) the focal loci used to construct genotypic networks and 2) the general structure of genotypic networks constructed using loci sampled across all three species. What is now figure 4 is dedicated explicitly towards investigation of genotypic fitness landscapes, describing how we incorporated fitness measures into these networks to identify accessible path. This figure also serves to describe the fitness landscapes for each specialist, quantifying accessibility of interspecific genotypic trajectories, and landscape ruggedness. Our discussion of these sections similarly attempts to distinguish their respective focus, emphasizing that investigation of the general isolation of each species on genotypic networks will help provide context for our later focused investigation of fitness landscapes.

      The final part of the conclusion sketches a story in which de novo and introgressed alleles reduce the accessibility of reverse evolution, back to a generalist. I think this is conceptually confusing because we don't expect evolution to favor paths toward lower fitness, even if those paths do not pass through a valley. Again, the framing here-that generalists are less fit than either specialist-is hard to square with the facts that generalists seem to be coexisting with the specialists, and much closer to the hypothesized fitness peak than is either specialist.

      We agree and have completely rewritten this section and removed this framing. We omitted this part of the conclusion entirely, as we felt it too speculative, and as noted by the reviewer, difficult to square with some of the rest of our findings. Instead, we now devote more focus on other aspects and implications of our findings in a new discussion section as requested by reviewer 1.

      This is a complicated and ambitious paper, on an exciting system and aiming at important questions. I think the main results about genotypic-fitness networks are hard to relate back to the other major analyses in the paper due to the points raised above. Moreover, using fitness measurements of three coexisting species to infer how they evolved faces a major obstacle: if fitnesses are frequency-dependent, then the actual trajectory of an initially rare variant will be completely obscured post-invasion. This possibility, as well as the potential issue that data on reproductive success might change these findings, need to be discussed, especially in light of the puzzling fact that the specialists appear less fit than their ancestor in at least one of the paper's major analyses.

      We now emphasize the apparent disconnect between three-dimensional fitness landscapes and the highly dimensional genotypic fitness landscapes as noted by the reviewer (see above). We hope to demonstrate through example how highly dimensional genotypic fitness landscapes may harbor numerous viable evolutionary trajectories (e.g. fitness ridges) on rugged fitness landscapes that are unobservable on low-dimensional representations. Additionally, we expand our discussion of the caveats in our analyses pertaining to the use of data on contemporary species to infer historical dynamics on the fitness landscape as recommended by the reviewer.

      We also now note that no evidence for frequency-dependent selection has been found in this system (Martin and Gould 2020; Martin 2016). We previously explicitly manipulated the frequency of rare phenotypes between treatments and found no effect of treatment across lake populations. Rather, these fitness peaks and valleys appear surprisingly stable across lakes, treatments, and years.

      Regardless, we now include in the discussion that we necessarily have taken a ‘birds-eye view’ of evolution here, describing the influences of different sources of genetic variation on the fitness landscape, after these have already undergone selective sweeps. Likewise, we acknowledge that it is impossible to quantify reproductive success in this system using field enclosures due to the very small size of newly hatched fry and continuous egg-laying life history of pupfishes. This is a limitation of our system. We take this opportunity to emphasize that other experimental or simulation studies would be invaluable to quantify the changing influence of these different sources of genetic variation on the fitness landscape as a function of time, during the process of selective sweeps.

    1. SciScore for 10.1101/2022.04.05.22273450: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">Consent: Following the written informed consent process, participants answered questions detailing their demographics, lifestyle habits, past medical history (including COVID-19), and COVID-19 infection symptoms.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">The selected predictors from each of the best-fitting cross-validated LASSO models were then included as fixed effects in follow-up LMMs with by-participant random intercepts, allowing us to control for individual differences.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Plates were washed three times with 0.1% PBST followed by addition of a 1:3,000 dilution of goat anti-human IgG–horseradish peroxidase (HRP) conjugated secondary antibody (50μl) well and incubated 1h.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-human IgG–horseradish</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Plots were produced using the ggplot2 [30].</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>ggplot2</div><div>suggested: (ggplot2, RRID:SCR_014601)</div></div></td></tr></table>

      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 LimitationRecognizer: We detected the following sentences addressing limitations in the study:


      Our study has several limitations. Sample sizes for each cohort examined were small due to variability in vaccination timelines and participant scheduling. Some individuals were excluded due to a confounding effect on our predictive modeling, which is controlled by the fixed effect of time. The natural infection group was further limited by the study timeline, as the first SARS-CoV-2 vaccination became available shortly after enrollment began and therefore limited the number of individuals we were able to follow longitudinally. Additionally, our analysis only included quantitative antibody binding titers. Although recent work has demonstrated that higher binding antibodies correlate to higher neutralizing antibodies [13], expansive, multi-center longitudinal studies are needed. An ideal analysis would consist of a multivariate analysis of reactogenicity, demographics, and quantitatively characterized antibody, B-cell and T-cell responses, as immune protection seems to be contingent on all three tiers of the immune response [50]. Further, some of the predictors used in our statistical analysis were found to be significant in one test but not in post-hoc tests. Large, longitudinal studies are required to confirm a significant group difference, but the predictors utilized herein should be included in future analyses. Our bivariate analysis of symptoms experienced following the 1st and 2nd doses failed to demonstrate that individual symptoms can influence peak antibody titers fol...


      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


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      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.

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    1. SciScore for 10.1101/2022.04.05.487060: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">Aminoadamantane and aminoadamantane nitrate drugs: Aminoadamantane nitrate compounds (blindly coded NMT2, NMT3, NMT5-NMT9, and NMT5-Met (metabolite, sans nitro group) were synthesized by and obtained from EuMentis Therapeutics, Inc. (Newton, MA), and have been described previously6–9, 33.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After incubation with secondary antibodies (IR-dye 680LT-conjugated goat anti-mouse [1:20,000; Li-Cor, 926-68020] or IR-dye 800CW-conjugated goat anti-rabbit [1:15,000; Li-Cor, 926-32211]), membranes were scanned with an Odyssey infrared imaging system (Li-Cor)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-mouse</div><div>suggested: (LI-COR Biosciences Cat# 926-68020, RRID:AB_10706161)</div></div><div style="margin-bottom:8px"><div>anti-rabbit</div><div>suggested: (Santa Cruz Biotechnology Cat# sc-53804, RRID:AB_783976)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Immunoprecipitants were eluted and subjected to immunoblotting with anti-ACE2 antibody (1:1000, Cell Signaling, #15983) and anti-SARS-CoV-2 Spike protein antibody (1:2000, Abcam, ab275759)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-SARS-CoV-2 Spike protein</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Primary antibodies and dilutions were as follows: Mouse anti-TNFα (5 µg/ml, Abcam, #ab1793) and rabbit anti-macrophage inflammatory protein 1α (MIP-1α)/CCL3+CCL3L1 (1:250</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-TNFα</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-macrophage inflammatory protein 1α ( MIP-1α)/CCL3+CCL3L1</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After 30 min, all cells were collected and subjected to biotin switch-assay and immunoblotting with anti-ACE2 antibody to assess the levels of SNO-ACE2 and total input ACE2.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-ACE2</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>total input ACE2</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cell lines: HEK293T (System Biosciences, LV900A-1) and HEK293-Spike cells (SARS-CoV-2 Spike (D614)-expressing 293 cells [293-SARS2-S cells, InvivoGen]) were maintained in Dulbecco’s modified Eagle’s medium (DMEM) with GlutaMAX™ (DMEM, high glucose, GlutaMAX™ Supplement, Life Technologies, 10566016) supplemented with 10% fetal bovine serum (FBS; Sigma, F7524), 100 IU/ml, and 100 µg/ml penicillin-streptomycin (Thermo Fisher Scientific, 10378016) at 37 °C in a 5% CO2 incubator.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293T</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>HEK293-Spike</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>293</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">SARS-CoV-2 virus generation: Monkey Vero E6 cells were plated in a T225 flask with complete DMEM containing 10% FBS, 1×PenStrep, 2 mM L-glutamine and incubated for overnight at 37 °C in a humified atmosphere of 5% CO2.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero E6</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">HeLa-ACE2 cells were seeded in the assay-ready plates at 1.6×103 cells/well in assay medium, and plates were incubated for 24 h at 37 ℃ with 5% CO2.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HeLa-ACE2</div><div>suggested: JCRB Cat# JCRB1845, RRID:CVCL_B3LW)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Homogenized lungs were titrated 1:10 over 6 steps and layered over Vero cells.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero</div><div>suggested: CLS Cat# 605372/p622_VERO, RRID:CVCL_0059)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">. Monkey Vero E6 cells (ATCC CRL-1586) were maintained in complete DMEM (Corning, 15-013-CV) containing 10% FBS, 1×PenStrep (Corning 20-002-CL), 2 mM L-glutamine (Corning, 25-005-CL) at 37 °C in a 5% CO2 incubator. Plasmids: hACE2 was a gift from Hyeryun Choe (Addgene plasmid #1786; http://n2t.net/addgene:1786 ; RRID:Addgene_1786)46.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div></div><div>detected: RRID:Addgene_1786)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The C262A, C498A, C261/498A mutant ACE2 constructs were generated using the QuikChange Lightning Multi Site-Directed Mutagenesis Kit (Agilent Technologies, 210514) according to the manufacturer’s protocol. pGBW-m4252984 (SARS-CoV-2 E [envelope]) was a gift from Ginkgo Bioworks (Addgene plasmid #153898; http://n2t.net/addgene:153898; RRID:Addgene_153898).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div></div><div>detected: RRID:Addgene_153898)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">MLV-gag/pol, MLV-CMV-Luciferase, SARS-CoV-2, and VSV-G plasmids were a gift from David Nemazee</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>VSV-G</div><div>suggested: RRID:Addgene_138479)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Expression and purification of human ACE2 protein: The N-terminal peptidase domain of human ACE2 (residues 19 to 615, GenBank: BAB40370.1) was cloned into phCMV3 vector and fused with C-terminal His-tag.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>phCMV3</div><div>suggested: RRID:Addgene_173431)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">In brief, HEK293T cells were transiently co-transfected with MLV-gag/pol, MLV-CMV-Luciferase plasmid, and SARS- CoV-2 Spike (D614) or VSV-G plasmid.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>MLV-CMV-Luciferase</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For transient expression in HEK293T cells, we used a transfection reagent (Fugene® HD, Promega) to co-transfect plasmids containing cDNAs for SARS-CoV-2 E protein (pGBW-m4133502, Addgene) and green fluorescent protein (GFP) at a ratio of 1:0.1 (0.5:0.05 µg/well, respectively).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pGBW-m4133502</div><div>suggested: RRID:Addgene_153565)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Maximum intensity projection of images was generated, and fluorescence intensity was analyzed with ImageJ software (https://imagej.nih.gov/ij/download.html) as previously described50.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>ImageJ</div><div>suggested: (ImageJ, RRID:SCR_003070)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Molecular dynamics (MD) simulations were performed on the Frontera supercomputer at the Texas Advanced Supercomputing Center (TACC) using NAMD 2.1461 and CHARMM36m all-atom additive force fields62–64.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>NAMD</div><div>suggested: (NAMD, RRID:SCR_014894)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Molecular Devices) with a 10× objective, and total live cells per well quantified in the acquired images using the Live Dead Application Module (MetaXpress).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>MetaXpress</div><div>suggested: (MetaXpress, RRID:SCR_016654)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Statistical analyses were performed using GraphPad Prism software.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad Prism</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr></table>

      Results from OddPub: Thank you for sharing your data.

      Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. SciScore for 10.1101/2022.04.04.486920: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">flurochrome conjugated FACS anti mouse antibodies-CD16/32, CD4, CD8, CD19 (BD, USA), TMB substrate (BD, USA), HRP streptavidin (BioLegend, Switzerland), HRP Goat anti-mouse IgG antibody (BioLegend, Switzerland)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti mouse antibodies-CD16/32 , CD4</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>CD8</div><div>suggested: (BioLegend Cat# 391503, RRID:AB_2721611)</div></div><div style="margin-bottom:8px"><div>CD19</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-mouse IgG</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The sera was stored at 0 C and later was used for antibody titer: IgG, IgG1, IgG2a. 2.2.3 Spleen and splenocyte isolation: Mice were sacrificed with light ether anesthesia.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>IgG</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>IgG1</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>IgG2a . 2.2.3 Spleen</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The cells were then stained with 5μl of PE-antiCD4, FITC-A antiCD8 and FITC-A antiCD19 flurochrome tagged antibodies for 45 min at 4°C.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>PE-antiCD4</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>FITC-A</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>antiCD8</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>antiCD19</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Briefly, IFN-gamma, TNF-alpha and IL-6 coating antibody was diluted in bicarbonate-carbonate buffer and coated on ELISA plate for overnight at 4°C.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>IFN-gamma , TNF-alpha</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>IL-6</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Organisms/Strains</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">2.2 Methodology: 2.2.1 Immunization: 4-6 weeks female BALB/c mice purchased from RCC Laboratories India Private Limited were grouped as antigen alone (1μg/dose), MF59 either in combination with antigen alone or in combination with MPL-A and CpG ODN at varying concentration of 10, 20 and 30μg.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>BALB/c</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The pellets so collected were resuspended in sheath fluid and cell population was quantified using BD FACSsuit software [8].</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>BD FACSsuit</div><div>suggested: None</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. SciScore for 10.1101/2022.04.04.486994: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The mouse anti-Flag (M2, Sigma-Aldrich), and mouse anti-GAPDH (G8140, US Biological, Salem MA) antibodies were used in this study.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-GAPDH</div><div>suggested: (US Biological Cat# G8140-01L, RRID:AB_2278712)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After 3 weeks of selection, expanded clones were examined for the expression of Flag-tagged nsp12 (RdRp) by western blot using anti-Flag antibody.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-Flag</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Establishment of the Stable cell lines: Permissive HEK293 cells were transfected with RdRp-luciferase reporter and pcDNA3.1.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293</div><div>suggested: CLS Cat# 300192/p777_HEK293, RRID:CVCL_0045)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After 24h, cells were selected using 250ug/mL G418 (for 293/RdRp cell lines) or 250ug/mL G418 and 25ug/mL hygromycin (for 293/RdRp-nsp5,7,8,12-Flag cell lines).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>293/RdRp</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>293/RdRp-nsp5,7,8,12-Flag</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For relative viral genome quantification post infection and compound treatment, control or USA-WA1/2020 virus was added onto the Vero E6 cells plated in a 24-well plate (100,000 cells per well) for 2h (37 °C and 5% CO2).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero E6</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Establishment of the Stable cell lines: Permissive HEK293 cells were transfected with RdRp-luciferase reporter and pcDNA3.1.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pcDNA3.1</div><div>suggested: RRID:Addgene_79663)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The input samples and the IP samples were resuspended in 45μL of SDS-PAGE loading buffer and denatured at 95°C for 5 min, resolved by SDS-PAGE and western blotted onto a 0.45 μM nitrocellulose membrane using standard protocols (Bio-Rad Laboratories, Hercules, CA, United States).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Bio-Rad Laboratories</div><div>suggested: (Bio-Rad Laboratories, RRID:SCR_008426)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The absolute half-maximal inhibitory concentrations (IC50) of dasabuvir for SARS-CoV-2 USA-WA1/2020 and B.1.617.2 were calculated, based on the amounts of residual virus in the supernatant at each concentration, using the Graphpad Prism™. 2.8. Plaque Assay: Vero E6 cells (100,000 cells/well) in 1mL medium were seeded in a 24-well culture plate for 2h (37 °C and 5% CO2).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Graphpad</div><div>suggested: (GraphPad, RRID:SCR_000306)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Statistical analyses were performed using Prism 8.0 software (GraphPad Inc.).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Prism</div><div>suggested: (PRISM, RRID:SCR_005375)</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
  10. inst-fs-iad-prod.inscloudgate.net inst-fs-iad-prod.inscloudgate.net
    1. As students nwve through the Berkck:y High system, they become increasingly stratified and segregated hy race and class. The racial a~hievement gap, as measured by course-Laking trajectories anJ grades, docs not level off after the ninth grade b~1t grows wider over time. In part, this is because the largely white, middlc-clas student popul;:irinn, who entered high school at or above grade level in math, spent their ninth-grade ye:1r taking care of graduation requirements and prerequisites for advanced science and math classes, and then they rook off in tenth grade along a college-bound track. It is also du~ inI?~rt to a cycle of failure am.ong many studen of color, who often end up failing Algebra l or Math A and then repeating it in summer school and tenth grade. With each failure and repetition, the e students fall further behind.

      I also often struggle with whether to take courses that satisfy the graduation requirements in advance or to take courses that I am really interested in and need in the future, but these courses are usually difficult to ensure that my grades do not drop. Tradeoff between two option is necessary

    1. SciScore for 10.1101/2022.04.02.486853: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">IRB: Ethics: Ethics approval was obtained from the Institutional Animal Ethics Committee (IAEC) (ref. no. RCB/ IAEC/2021/093), and Institutional Biosafety Committee (<br>IACUC: Ethics: Ethics approval was obtained from the Institutional Animal Ethics Committee (IAEC) (ref. no. RCB/ IAEC/2021/093), and Institutional Biosafety Committee (</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">Male hamsters of 8 weeks old were infected with one-time SARS CoV-2 via nasal route inoculation using 1×105 plaque-forming units (PFU).</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">Contamination: The Vero E6 cell line was validated for free of mycoplasma contamination.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The lysate samples were further processed for SDS-PAGE followed by immunoblotting using primary antibodies against pAkt-Ser473, Akt, pAkt1-Thr308, Akt1, HIF1α, HIF2α, β-Actin (Cell Signalling, USA), as described in our work (15).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pAkt-Ser473 , Akt , pAkt1-Thr308</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>Akt1</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>HIF1α</div><div>suggested: (ABclonal Cat# A17906, RRID:AB_2861751)</div></div><div style="margin-bottom:8px"><div>HIF2α</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>β-Actin</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The plates were incubated at room temperature (RT) for 1 h and then washed three times with washing buffer (PBS + 0.1 % tween 20) and incubated with HRP conjugated antihamster IgG antibody at 1:10,000 dilution for another 1 h and washed four times with the washing buffer and incubated further with 100 μl of TMB substrate (Thermo Fisher Scientific), The reaction stopped with 1N H2SO4 solution.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HRP conjugated antihamster IgG</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">SARS CoV-2 infection in Vero and U937 cells: Monkey kidney epithelial cell line Vero E6 (0.1×106 cells/well) was infected with P-3 Wuhan SARS CoV-2 (world reference # USA-WA-1/2020, as mentioned in our earlier work (15) at 0.01 MOI for 1 hr in Dulbecco’s Modified Eagle Medium (DMEM) with 2% FBS, supplemented with 1% non-essential amino acids.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>Vero E6</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Confocal microscopy: Vero E6, U937 cells or U937/PHD2-KD cells from above experiment were fixed in 4% paraformaldehyde for 20 min at room temperature.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>U937</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Organisms/Strains</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Experiments using BALB/c mouse strain (RRID: IMSR_JAX_000651) and Syrian golden hamster (available form ICMR-National Institute of Nutrition, Hyderabad, India) were conducted within the guidelines of IAEC in the Biosafety level 3 (BSL3) facility of the institute.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>BALB/c</div><div>detected: (IMSR Cat# JAX_000651, RRID:IMSR_JAX:000651)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The lysate samples were further processed for SDS-PAGE followed by immunoblotting using primary antibodies against pAkt-Ser473, Akt, pAkt1-Thr308, Akt1, HIF1α, HIF2α, β-Actin (Cell Signalling, USA), as described in our work (15).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pAkt1-Thr308</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Imaging was performed using Z-stacks at 0.25 μm per slice by sequential scanning and Image J Fiji software was used to obtain maximum intensity projection images.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Image J</div><div>suggested: (ImageJ, RRID:SCR_003070)</div></div><div style="margin-bottom:8px"><div>Fiji</div><div>suggested: (Fiji, RRID:SCR_002285)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cells were then washed and acquired on BD FACS Verse and were analyzed with FlowJo software (Tree Star, USA)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>FlowJo</div><div>suggested: (FlowJo, RRID:SCR_008520)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Graph Pad Prism version 8.0 software was used for data analysis and P-values.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Graph Pad Prism</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. References Artz, B., Johnson, M., Robson, D., & Taengnoi, S. (2017). Note-taking in the digital age: Evidence from classroom random control trials. http://doi.org/10.2139/ssrn.3036455 Boyle, J. R. (2013). Strategic note-taking for inclusive middle school science classrooms. Remedial and Special Education, 34(2), 78-90. https://doi.org/10.1177%2F0741932511410862 Carter, S. P., Greenberg, K., & Walker, M. S. (2017). The impact of computer usage on academic performance: Evidence from a randomized trial at the United States Military Academy. Economics of Education Review, 56, 118-132. https://doi.org/10.1016/j.econedurev.2016.12.005 Chang, W., & Ku, Y. (2014). The effects of note-taking skills instruction on elementary students’ reading. The Journal of Educational Research, 108(4), 278–291. https://doi.org/10.1080/00220671.2014.886175 Dynarski, S. (2017). For Note Taking, Low-Tech is Often Best. Retrieved from https://www.gse.harvard.edu/news/uk/17/08/note-taking-low-tech-often-best Haydon, T., Mancil, G.R.,  Kroeger, S.D., McLeskey, J., & Lin, W.J. (2011). A review of the effectiveness of guided notes for students who struggle learning academic content. Preventing School Failure: Alternative Education for Children and Youth, 55(4), 226-231. http://doi.org/10.1080/1045988X.2010.548415 Holland, B. (2017). Note taking editorials – groundhog day all over again. Retrieved from http://brholland.com/note-taking-editorials-groundhog-day-all-over-again/ Kiewra, K.A. (1985). Providing the instructor’s notes: an effective addition to student notetaking. Educational Psychologist, 20(1), 33-39. https://doi.org/10.1207/s15326985ep2001_5 Kiewra, K.A. (2002). How classroom teachers can help students learn and teach them how to learn. Theory into Practice, 41(2), 71-80. https://doi.org/10.1207/s15430421tip4102_3 Luo, L., Kiewra, K.A. & Samuelson, L. (2016). Revising lecture notes: how revision, pauses, and partners affect note taking and achievement. Instructional Science, 44(1). 45-67. https://doi.org/10.1007/s11251-016-9370-4 Mueller, P.A., & Oppenheimer, D.M. (2014). The pen is mightier than the keyboard: Advantages of longhand over laptop note taking. Psychological Science, 25(6), 1159-1168. https://doi.org/10.1177/0956797614524581 Nye, P.A., Crooks, T.J., Powley, M., & Tripp, G. (1984). Student note-taking related to university examination performance. Higher Education, 13(1), 85-97. https://doi.org/10.1007/BF00136532 Rahmani, M., & Sadeghi, K. (2011). Effects of note-taking training on reading comprehension and recall. The Reading Matrix, 11(2). Retrieved from https://pdfs.semanticscholar.org/85a8/f016516e61de663ac9413d9bec58fa07bccd.pdf Reynolds, S.M., & Tackie, R.N. (2016). A novel approach to skeleton-note instruction in large engineering courses: Unified and concise handouts that are fun and colorful. American Society for Engineering Education Annual Conference & Exposition, New Orleans, LA, June 26-29, 2016. Retrieved from https://www.asee.org/public/conferences/64/papers/15115/view Robin, A., Foxx, R. M., Martello, J., & Archable, C. (1977). Teaching note-taking skills to underachieving college students. The Journal of Educational Research, 71(2), 81-85. https://doi.org/10.1080/00220671.1977.10885042 Wammes, J.D., Meade, M.E., & Fernandes, M.A. (2016). The drawing effect: Evidence for reliable and robust memory benefits in free recall. The Quarterly Journal of Experimental Psychology, 69(9). http://doi.org/10.1080/17470218.2015.1094494 Wu, J. Y., & Xie, C. (2018). Using time pressure and note-taking to prevent digital distraction behavior and enhance online search performance: Perspectives from the load theory of attention and cognitive control. Computers in Human Behavior, 88, 244-254. https://doi.org/10.1016/j.chb.2018.07.008

      References

      Artz, B., Johnson, M., Robson, D., & Taengnoi, S. (2017). Note-taking in the digital age: Evidence from classroom random control trials. http://doi.org/10.2139/ssrn.3036455

      Boyle, J. R. (2013). Strategic note-taking for inclusive middle school science classrooms. Remedial and Special Education, 34(2), 78-90. https://doi.org/10.1177%2F0741932511410862

      Carter, S. P., Greenberg, K., & Walker, M. S. (2017). The impact of computer usage on academic performance: Evidence from a randomized trial at the United States Military Academy. Economics of Education Review, 56, 118-132. https://doi.org/10.1016/j.econedurev.2016.12.005

      Chang, W., & Ku, Y. (2014). The effects of note-taking skills instruction on elementary students’ reading. The Journal of Educational Research, 108(4), 278–291. https://doi.org/10.1080/00220671.2014.886175

      Dynarski, S. (2017). For Note Taking, Low-Tech is Often Best. Retrieved from https://www.gse.harvard.edu/news/uk/17/08/note-taking-low-tech-often-best

      Haydon, T., Mancil, G.R.,  Kroeger, S.D., McLeskey, J., & Lin, W.J. (2011). A review of the effectiveness of guided notes for students who struggle learning academic content. Preventing School Failure: Alternative Education for Children and Youth, 55(4), 226-231. http://doi.org/10.1080/1045988X.2010.548415

      Holland, B. (2017). Note taking editorials – groundhog day all over again. Retrieved from http://brholland.com/note-taking-editorials-groundhog-day-all-over-again/

      Kiewra, K.A. (1985). Providing the instructor’s notes: an effective addition to student notetaking. Educational Psychologist, 20(1), 33-39. https://doi.org/10.1207/s15326985ep2001_5

      Kiewra, K.A. (2002). How classroom teachers can help students learn and teach them how to learn. Theory into Practice, 41(2), 71-80. https://doi.org/10.1207/s15430421tip4102_3

      Luo, L., Kiewra, K.A. & Samuelson, L. (2016). Revising lecture notes: how revision, pauses, and partners affect note taking and achievement. Instructional Science, 44(1). 45-67. https://doi.org/10.1007/s11251-016-9370-4

      Mueller, P.A., & Oppenheimer, D.M. (2014). The pen is mightier than the keyboard: Advantages of longhand over laptop note taking. Psychological Science, 25(6), 1159-1168. https://doi.org/10.1177/0956797614524581

      Nye, P.A., Crooks, T.J., Powley, M., & Tripp, G. (1984). Student note-taking related to university examination performance. Higher Education, 13(1), 85-97. https://doi.org/10.1007/BF00136532

      Rahmani, M., & Sadeghi, K. (2011). Effects of note-taking training on reading comprehension and recall. The Reading Matrix, 11(2). Retrieved from https://pdfs.semanticscholar.org/85a8/f016516e61de663ac9413d9bec58fa07bccd.pdf

      Reynolds, S.M., & Tackie, R.N. (2016). A novel approach to skeleton-note instruction in large engineering courses: Unified and concise handouts that are fun and colorful. American Society for Engineering Education Annual Conference & Exposition, New Orleans, LA, June 26-29, 2016. Retrieved from https://www.asee.org/public/conferences/64/papers/15115/view

      Robin, A., Foxx, R. M., Martello, J., & Archable, C. (1977). Teaching note-taking skills to underachieving college students. The Journal of Educational Research, 71(2), 81-85. https://doi.org/10.1080/00220671.1977.10885042

      Wammes, J.D., Meade, M.E., & Fernandes, M.A. (2016). The drawing effect: Evidence for reliable and robust memory benefits in free recall. The Quarterly Journal of Experimental Psychology, 69(9). http://doi.org/10.1080/17470218.2015.1094494

      Wu, J. Y., & Xie, C. (2018). Using time pressure and note-taking to prevent digital distraction behavior and enhance online search performance: Perspectives from the load theory of attention and cognitive control. Computers in Human Behavior, 88, 244-254. https://doi.org/10.1016/j.chb.2018.07.008

    1. SciScore for 10.1101/2022.03.31.486531: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">IRB: The study was approved by the Hadassah Medical Center (#0296-20-HMO) and the Sheba Medical Center (#2832-15-SMC) Institutional Review Boards.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The following primary antibodies were used: α-E-Cadherin (Mouse monoclonal, 1:100, Abcam, ab1416; for the detection of epithelial cells), α-SARS-CoV-2 Nucleocapsid (Rabbit monoclonal, 1:500, Abcam, ab271180).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>α-E-Cadherin</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>α-SARS-CoV-2 Nucleocapsid</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The following secondary antibodies were used: Donkey anti-Mouse IgG pre-adsorbed, Alexa Fluor® 568 (1:250, Abcam, Cat# ab175700), Goat anti-Rabbit IgG Highly Cross-Adsorbed Alexa Fluor Plus 647 (1:250, Thermo Fisher Scientific, Cat# A32733).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-Mouse IgG</div><div>suggested: (DSHB Cat# LEP100 IgG, RRID:AB_528124)</div></div><div style="margin-bottom:8px"><div>anti-Rabbit IgG</div><div>suggested: (Biorbyt Cat# orb14385, RRID:AB_10735740)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cells and viruses: Simian kidney Vero E6 (ATCC CRL-1586), Calu-3 (ATCC HTB-55), Madin-Darby Canine Kidney (MDCK, ATCC CCL-34™) cells and H1299-ACE2 overexpressed cells (kindly provided by Dr. Alex Sigal)1 were maintained in Dulbecco’s Modified Eagle Medium (DMEM; Biological Industries, Beit Haemek, Israel), supplemented with 10% fetal bovine serum, 2 mM L-Glutamine, 10 IU/ml Penicillin, and 10 μg/ml streptomycin (Biological Industries, Beit Haemek, Israel).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>E6</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>Calu-3</div><div>suggested: ATCC Cat# HTB-55, RRID:CVCL_0609)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Influenza virus A(H1N1) pdm09 (NIBRG-121xp, Cat# 09/268; obtained from NIBSC, UK) was propagated in MDCK cells.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>MDCK</div><div>suggested: CLS Cat# 602280/p823_MDCK_(NBL-2, RRID:CVCL_0422)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The virus titers of cleared infected cells- and tissue supernatants were determined by a standard plaque assay on H1299-ACE2 cells (SARS-CoV-2) or MDCK cells (influenza virus).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>H1299-ACE2</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Whole-mount tissues were visualized using a Nikon A1R confocal microscope and were analyzed using NIS Elements software (Nikon).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>NIS Elements</div><div>suggested: (NIS-Elements, RRID:SCR_014329)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Statistical analysis: All data, presented as means ± standard errors of the mean (SEM), were analyzed using paired, two-tailed t test in GraphPad Prism 9 software (GraphPad Software Inc., San Diego CA).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad Prism</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div><div style="margin-bottom:8px"><div>GraphPad</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr></table>

      Results from OddPub: Thank you for sharing your data.

      Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:


      Our study has several limitations. Native respiratory tissues in organ culture are relatively short-lived (up to 7 days in culture). Thus, our ex vivo infection models mirror early events of infection and do not address the late phase of viral transmission or the combined effects of the local and systemic immune responses. Nonetheless, our studies recapitulate SARS-CoV-2 infection and innate immune response within the authentic multicellular complexity of both the upper and the lower human respiratory tract, containing tissue-specific compositions of cell types, including immune cells, and extracellular matrix. To date, we are not aware of studies that have examined the distinctive innate tissue responses to Omicron as related to its altered replication phenotype in the lower respiratory tract. Such data are critical to better understand and address the evolution of SARS-CoV-2 into a less virulent human-tropic virus. In summary, our studies in native human nasal and lung tissues infected ex vivo reveal a significantly enhanced interferon response to Omicron, compared to precedent SARS-CoV-2 VOC. The findings imply that the early induction of antiviral ISG, which was most prominent in lung tissues, could play a part in the restricted replication and pathology of Omicron in the lungs. They provide insights for the attenuated pathogenicity of Omicron, and for further studies of pathways involved in the enhanced mucosal innate immune responses to this newly evolving variant.


      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. SciScore for 10.1101/2022.03.25.22272599: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">IRB: The trial was reviewed and approved by the Erasme Hospital Ethics Committee (P2021/251) and the Federal Agency for Medicines and Health Products (EudraCT: 2021-002088-23A).<br>Consent: At the baseline visit, participants provided informed consent before having blood drawn and being vaccinated.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">Study design: REDU-VAC is a participant-blinded, randomised, phase 4, multicentre, non-inferiority study investigating safety, reactogenicity and immunogenicity of a fractional dose of the mRNA COVID-19 vaccine BNT162b2 (Pfizer-BioNTech).</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">To ensure participant blinding to the vaccine dose, randomisation lists were kept out of sight, vaccines were prepared, and syringes were filled beforehand.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">Statistical analysis: The sample size was calculated assuming a true difference of geometric means of the primary outcome on the log10 scale being 0 between the reduced and the full dose, and a standard deviation of GMT on the log10 scale being 0.27 (17).</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">SARS-CoV-2 anti-receptor binding domain (RBD) specific IgG concentrations were measured by ELISA (reported as Binding Antibody Units [BAU]/mL) on days 0/21/49 and month 6.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>SARS-CoV-2 anti-receptor binding domain (RBD</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">SARS-CoV-2 Specific Binding Antibodies: Enzyme-linked immunosorbent assay: Binding antibodies at baseline and after vaccination were assessed using an enzyme-linked immunosorbent assay (ELISA) for the quantitative detection of IgG-class antibodies to RBD (Receptor Binding Domain, Wuhan strain) (Wantai SARS-CoV-2 IgG ELISA (Quantitative); CE-marked; WS-1396; Beijing Wantai Biological Pharmacy Enterprise Co., Ltd, China).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>IgG-class</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Next, plates were incubated (37°C, 30 min) with horseradish peroxidase (HRP)-conjugated anti-human IgG antibodies and washed five times before adding a TMB and urea peroxide solution for 15 min (37°C, dark).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-human IgG</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Multiplex Immunoassay (Luminex): Antibody responses at baseline were tested with an in house multiplex immunoassay (MIA).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>MIA</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">In this test, IgG antibodies to SARS-CoV-2 antigens RBD, S1, S2 and N (Wuhan strain) were measured simultaneously in one assay run.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>IgG</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>SARS-CoV-2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">SARS-CoV-2 Neutralizing Antibodies: Serial dilutions of heat-inactivated serum (1/50-1/25600 in EMEM supplemented with 2mM L-glutamine, 100U/ml - 100μg/ml of Penicillin-Streptomycin and 2% fetal bovine serum) were incubated during 1h (37°C, 7% CO2) with 3xTCID100 of a wild type Wuhan strain (2019-nCoV-Italy-INMI1, reference 008V-03893), the B.1.617.2 Delta variant (83DJ-1) and the BA.1 Omicron variant of SARS-CoV-2, in parallel.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>83DJ-1</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Plates were then coated with human IFN-γ antibody (15 µg/ml) overnight at 4°C, washed and blocked with 200µl of Roswell Park Memorial Institute (RPMI) containing 10% fetal bovine serum (FBS) for at least two hours.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>IFN-γ</div><div>suggested: (MABTECH Cat# 3420-2APT, RRID:AB_2877719)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After incubation, the plates were washed and incubated with the human biotinylated IFN-γ detection antibody (1µg/ml) for 2 hours, washed and the streptavidin–Horseradish Peroxidase (streptavidin-HRP) diluted at 1/750 in PBS-0,5% FBS was added for one hour.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>streptavidin-HRP</div><div>suggested: (Cell Signaling Technology Cat# 3999, RRID:AB_10830897)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Flow cytometry: Cells were stimulated in 96-well round-bottom plates with 1 × 106 PBMCs in RPMI 1640 medium (Lonza, Basel, Switzerland) supplemented with 10% heat-inactivated FBS (Sigma-Aldrich, Kawasaki, Japan), penicillin/streptomycin, amino acids and PepMix SARS-CoV-2 spike glycoprotein peptide pools (SUB1-SUB2, JPT, Berlin, Germany) in the presence of 1µg/mL purified anti-CD28 antibody (clone CD28.2, BD Biosciences, New Jersey, USA).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-CD28</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After stimulation, Live/Dead fixable red stain (ThermoFisher, Massachusetts, USA) was used to exclude dead cells and the staining of surface antigens was carried out for 20 min with the following fluorochrome-conjugated antibodies: CD3 BV711 (UCHT-1; BD), anti-CD8 PeCy7 (RPA-T8; BD), CD4 HV450 (RPA-T4; BD).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>CD3</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-CD8</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>CD4</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Sample-virus mixtures and virus/cell controls were added to Vero cells (18.000 cells/well) in a 96-well plate and incubated for five days (37°C, 7% CO2).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Net OD values were converted to arbitrary IgG units per ml by interpolation from a point-by-point plot fitted with the standard concentrations and net OD values (correlation coefficient R2≥0.9801), using GraphPad Prism version 9.0.0 for Windows (GraphPad Software, San Diego, California USA) and exported to Microsoft Excel.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad Prism</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div><div style="margin-bottom:8px"><div>GraphPad</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div><div style="margin-bottom:8px"><div>Microsoft Excel</div><div>suggested: (Microsoft Excel, RRID:SCR_016137)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Samples were acquired on a BD LSRFortessa flow cytometer and analyzed with FlowJo v9.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>FlowJo</div><div>suggested: (FlowJo, RRID:SCR_008520)</div></div></td></tr></table>

      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 LimitationRecognizer: We detected the following sentences addressing limitations in the study:


      This study has several limitations, the first being the relatively limited sample size. A larger number of participants would have resulted in smaller confidence intervals around the GMRs, which might have impacted the conclusions on non-inferiority. Although males were underrepresented in this study, we do not believe this is a major limitation as immune responses to COVID-19 mRNA vaccination in healthy, younger subjects are only minimally gender-dependent and importantly, there is no basis to assume that fractional dosing would affect immune responses differently between males and females (24–26). Secondly, the small proportion of previously infected participants in our study population (17/144, 12%) does not allow for a separate sensitivity analysis in this group. With record high COVID-19 incidences worldwide, the proportion of the population who experienced a past infection is rapidly growing, making analyses including previously infected people ever more relevant. In addition, breakthrough infections were not actively monitored by regular molecular testing. Therefore, we may have missed asymptomatic infections, which are not reported by the study participants. Thirdly, while protection from infection or disease has been convincingly correlated with titres of binding and neutralizing antibodies, as discussed previously, it is not possible to determine with certainty that the moderately lower titres observed in our study will translate to equally moderately lower efficacy...


      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">NCT04852861</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Enrolling by invitation</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">COVID-19: Safety and Immunogenicity of a Reduced Dose of the…</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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

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    1. SciScore for 10.1101/2022.03.30.486313: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Membranes were first incubated with the primary antibody (Anti-Glutathione antibody [D8], ab19534,</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Anti-Glutathione</div><div>suggested: (Abcam Cat# ab19534, RRID:AB_880243)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">; Mouse anti DDDDK-Tag (FLAG-tag) mAb, AE005, ABclonal) in 5% BSA TBST for 16 h at 4 °C, washed with TBST three times, then incubated with the secondary antibody (HRP-labeled Goat Anti-Mouse IgG(H+L), A0216, Beyotime) in 5% BSA TBST (1 h, 25 °C), and washed with TBST three times.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti DDDDK-Tag (FLAG-tag</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>Anti-Mouse IgG(H+L)</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">In the present study, HEK293T cells were seeded in 12-well plates overnight.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293T</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For bacterial expression, the cDNA encoded the SARS-CoV-2 PLpro with E. coli codon optimization was ordered from GenScript and cloned into the pET15b expression vector with an N-terminal 6 × His-SUMO2 fusion tag.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pET15b</div><div>suggested: RRID:Addgene_129689)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For transfection of mammalian cell, the cDNA encoded the SARS-CoV-2 PLpro with mammalian codon optimization was also ordered from GenScript and cloned into the pcDNA 3.1 with an C-terminal FLAG tag.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pcDNA 3.1</div><div>suggested: RRID:Addgene_20407)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The sequence of pcDNA3-PL-flipGFP-T2A-mCherry was designed based on plasmid pcDNA3-TEV-flipGFP-T2A-mCherry (Addgene catalog NO.124429) where TEV cleave site was replaced by SARS-CoV-2 PLpro cleavage site (LRGGAPTK), and ordered from GenScript.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pcDNA3-PL-flipGFP-T2A-mCherry</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pcDNA3-TEV-flipGFP-T2A-mCherry</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The resulting kobs values were then plotted versus compound concentrations ([C]), then kinact and Ki or Ka values were calculated according to the equation: kobs = kinact × ([C]/([C] + Ki or Ka)) using GraphPad Prism.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad Prism</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. SciScore for 10.1101/2022.03.30.486377: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">IRB: The study protocols were approved by the Cantonal Ethics Committee of Ticino, Switzerland (CE-TI-3428, 2018-02166; CE-TI-3687, 2020-01572).<br>Consent: All blood donors provided written informed consent for participation in the study.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">Female hamsters of 6-8 weeks old were anesthetized with ketamine/xylazine/atropine and inoculated intranasally with 50 µl containing 1×104 TCID50 SARS-CoV-2 gamma variant (day 0).</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">Tissue sections (5 μm) were analyzed after staining with hematoxylin and eosin and scored blindly for lung damage by an expert pathologist.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Plates were washed 4 × in TBST and 30 μl of anti-human (Invitrogen) horseradish peroxidase-conjugated antibodies diluted 1:5,000 was added to each well and incubated at 37°C.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-human</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After washing, secondary antibody goat anti-human IgG (H+L) DyLight680 (0.2 µg/ml) was incubated for 45 min at room temperature before reading on Innopsys InnoScan 710-IR Microarray Scanner. Generation of recombinant ACE2-mFc: Residues 18-615 of human ACE2 (UniProtKB - Q9BYF1) were synthesized by Genscript and cloned into pINFUSE-mIgG2b-Fc2 expression plasmid (InvivoGen)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-human IgG</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>ACE2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">After 2 h, infected cells were washed four times with DMEM before adding medium supplemented with anti-VSV-G antibody (I1- mouse hybridoma supernatant diluted 1 to 25, from CRL-2700, ATCC).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-VSV-G</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Recombinant protein was produced by transient transfection of Expi293 cells and purified using HiTrap Protein A column.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Expi293</div><div>suggested: RRID:CVCL_D615)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cell lines and media culture: 293T and A549 cells were cultured in high glucose DMEM (Gibco, catalog no. 61965-026) supplemented with 10% FBS, 1% (vol/vol)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>293T</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">HuH-7 cell line was obtained from JCRB Cell Bank and cultured in DMEM (Gibco, catalog no. 31885-023) supplemented with 10% FBS, 1% (vol/vol)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HuH-7</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Vero-TMPRSS2 cells were cultured in DMEM (Gibco, catalog no. 11995-040) supplemented with 10% FBS (VWR, catalog no. 97068-085) and PenStrep (Gibco, catalog no. 15140-122) (35).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero-TMPRSS2</div><div>suggested: JCRB Cat# JCRB1818, RRID:CVCL_YQ48)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The lentivirus-containing pellet was resuspended in 100 μl media and was used to transduce 293T or A549 cell lines.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>A549</div><div>suggested: NCI-DTP Cat# A549, RRID:CVCL_0023)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">293T-ACE2, 293T-S, A549-ACE2 and A549-S cell lines were selected using 10 μg/ml puromycin (InvivoGen, catalog no. ant-pr-1) 4 days post-transduction.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>A549-S</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">293T-ACE2-TMPRSS2-GFP cell line was generated from 293T-ACE2 cells by subsequent transduction of pLVX-EF1a-TMPRSS2-IRES-ZsGreen1-containing lentivral prep and sorted using BD FACSAria III.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>293T-ACE2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">A549-ACE2-TMPRSS2 and Huh7-TMPRSS2 stable cell lines were generated using commercial lentivirus (Addgene, catalog no. 154982-LV) and selected using 10 μg/ml blasticidin (InvivoGen</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Huh7-TMPRSS2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">To produce NL63 VSV virus, HEK-293 cells were transfected with a pcDNA3.1 expression vector encoding full-length S harboring a truncation of the 20 C-terminal residues to improve membrane transport.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK-293</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For NL63 S pseudotyped virus neutralization assay, Vero E6-TMPRSS2 cells maintained in DMEM supplemented with 10% FBS and 1% PenStrep, were seeded into white 96-well plates at 45,000 cells/well and cultured overnight at 37°C.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero E6-TMPRSS2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Inhibition of cell-to-cell fusion: For testing inhibition of spike-mediated cell–cell fusion, A549-S and A549-ACE2-TMPRSS2 cells were stained with CFSE (Thermo Fisher, catalog no. C1157) and CellTrace™ Far Red (Thermo Fisher, catalog no. C34572), respectively, according to manufacturer’s instruction.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>A549-ACE2-TMPRSS2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The titer of the virus stock was determined by end-point dilution on Vero-E6 cells by the Reed and Muench method (76)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero-E6</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">To quantify infectious SARS-CoV-2 particles, endpoint titrations were performed on confluent Vero E6 cells in 96- well plates.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero E6</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Organisms/Strains</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Transient expression and monoclonal antibody staining of HCoV S-expressing 293 cells: For transient expression of HCoV spike proteins, 293T cells were co-transfected, with plasmid encoding ZsGreen (Bei Resources, catalog no. NR-52516) and corresponding HCoV spike proteins: SARS-CoV-2 Wuhan-Hu-1 S (catalog no. NR-52514) from Bei Resources; MERS-CoV S (VG40069-G-N)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>SARS-CoV-2 Wuhan-Hu-1 S</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">SARS-CoV-2 S PentaPro gene, codon optimized for mammalian expression, was synthesized by GeneScript and cloned into pcDNA3.1 (-) expression vector.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pcDNA3.1 ( - )</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Generation of overexpression cell lines: pLVX-puro-ACE2 transfer plasmid was kindly provided by Manfred Kopf (ETH Zurich).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pLVX-puro-ACE2</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">pLVX-EF1a-TMPRSS2-IRES-ZsGreen1 transfer plasmid was generated from the reference pWPI-IRES-Bla-Ak-TMPRSS2 plasmid (Addgene, catalog no. 154982).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pLVX-EF1a-TMPRSS2-IRES-ZsGreen1</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>pWPI-IRES-Bla-Ak-TMPRSS2</div><div>suggested: RRID:Addgene_154982)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">pLVX-puro-spike transfer plasmid was generated from reference pHDM-SARS-CoV-2 spike (BEI resources, catalog no. NR-52514)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pLVX-puro-spike</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Briefly, 293T cells at 70-80% confluency in T75 flask were co-transfected with transfer plasmids encoding genes of interest (ACE2, TMPRSS2 or SARS-CoV-2 S) and packaging plasmid psPax2 and envelope plasmid pMD.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>psPax2</div><div>suggested: RRID:Addgene_12260)</div></div><div style="margin-bottom:8px"><div>pMD</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Briefly, 293T cells were co-transfected with a lentiviral backbone encoding luciferase reporter (pHAGE-CMV-Luc2-IRES-ZsGreen-W (Bei Resources, catalog no. NR-52516), HIV-based packaging plasmids (Tat, Gag-Pol and Rev) (Bei Resources, catalog no. NR-52518, NR-52517 and NR-52519) and various spike expression plasmids using PEI in Optimem.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pHAGE-CMV-Luc2-IRES-ZsGreen-W</div><div>suggested: RRID:Addgene_164432)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">To produce NL63 VSV virus, HEK-293 cells were transfected with a pcDNA3.1 expression vector encoding full-length S harboring a truncation of the 20 C-terminal residues to improve membrane transport.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pcDNA3.1</div><div>suggested: RRID:Addgene_79663)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">, NL63 S (VG40604-UT) from SinoBiological; SARS-CoV S (VG40150-G-N) from SinoBiological that was cloned into pHDM expression plasmid with 19 amino-acid C-terminal truncation (85), using PEI in Optimem as above.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pHDM</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Plates were immediately read at 450 nm on a BioTek plate reader and data plotted and fit in Prism 9 (GraphPad) using nonlinear regression sigmoidal, 4PL, X is the concentration to determine EC50 values from curve fits.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">First, the sequences were annotated using IgBlast version 1.16 (65) and IMGT as reference sequences (66).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>IgBlast</div><div>suggested: (IgBLAST, RRID:SCR_002873)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Data was processed using GraphPad Prism v9.0.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad Prism</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Nine fields per well were imaged and were subsequently processed with Metaxpress and Powecore softwares.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Metaxpress</div><div>suggested: (MetaXpress, RRID:SCR_016654)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The variant was subjected to sequencing on a MinION platform (Oxford Nanopore) directly from the nasopharyngeal swabs; passage 2 virus on Vero E6 cells was used for the study described here.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>MinION</div><div>suggested: (MinION, RRID:SCR_017985)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Initial phases were obtained by molecular replacement in Phaser (81) on the CCP4 suite, using crystal structures of Fabs as search models.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>CCP4</div><div>suggested: (CCP4, RRID:SCR_007255)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Several subsequent rounds of model building and refinement were performed using Coot (82), Refmac5 (83) and Buster (84) to arrive to the final model for each complex.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Coot</div><div>suggested: (Coot, RRID:SCR_014222)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Cells were then washed and analyzed by flow cytometry using BD Symphony and FlowJo.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>FlowJo</div><div>suggested: (FlowJo, RRID:SCR_008520)</div></div></td></tr></table>

      Results from OddPub: Thank you for sharing your data.

      Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


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      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.

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    1. SciScore for 10.1101/2022.03.28.22273068: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">IRB: The study protocol was reviewed and approved by the Mount Sinai Hospital Institutional Review Board (IRB-20-03374).<br>Consent: All participants provided written informed consent.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">Sex (female, male), age (<40 years, 40+ years), and baseline titer (<1:800, ≥1:800) were included as covariates in the model, along with a random intercept for participant ID to account for repeated measures.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">Sex (female, male), age (<40 years, 40+ years), and baseline titer (<1:800, ≥1:800) were included as covariates in the model, along with a random intercept for participant ID to account for repeated measures.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The data of 813 distinct study visits from these 137 seropositive participants provide the basis for the modeling SARS-CoV-2 spike-binding IgG antibody durability.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>SARS-CoV-2 spike-binding IgG</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Plates were washed three times with PBS-T and 50 μl/well of anti-human IgG (Fab-specific) horseradish peroxidase antibody (produced in goat; Sigma, A0293) diluted to 1:3,000 in PBS-T, 1% milk powder, were added to each well.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-human IgG</div><div>suggested: (Sigma-Aldrich Cat# A0293, RRID:AB_257875)</div></div><div style="margin-bottom:8px"><div>A0293</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Endpoint titers, expressed as the last dilution before the signal dropped below an OD490nm of 0.15, were calculated in excel and data was plotted using GraphPad Prism 9.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad Prism</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr></table>

      Results from OddPub: Thank you for sharing your data.

      Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:


      This analysis did, however, also have a few limitations. First, since we started enrollment during the first wave, a good portion of participants were unable to get molecular tests at the time of infection and we relied on retrospective reports of clinical signs and symptoms suggestive of COVID-19 for illness onset date. As such, recall bias in reported illness onset is a possibility. However, we anticipate that this exerted only a minor impact on our conclusions given the relatively homogenous exposures of participants who are all health care workers. Second, with healthcare worker vaccination beginning in December 2020 we were unable to effectively assess how circulating variants of concern may affect one’s risk of re-infection following natural infection. The increase in vaccinated participants (excluded from this analysis), while fortunate, also resulted in a smaller sample size at the end of the follow-up period extending into August 2021. In conclusion, our study shows that SARS-CoV-2 infection provides strong protection from reinfection and this protection may be associated with the presence of spike binding antibodies. In addition, it suggests that antibody levels induced by infection with ancestral SARS-CoV-2 variants are relatively stable over time and that the rate of seroreversion is low when measuring SARS-CoV-2 spike binding IgG antibodies.


      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


      <footer>

      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.

      </footer>
    1. SciScore for 10.1101/2022.03.29.486190: (What is this?)

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

      Table 1: Rigor

      <table><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ethics</td><td style="min-width:100px;border-bottom:1px solid lightgray">IRB: Ethics statement: This research has been determined to be exempt by the Institutional Review Board of the Boston University Medical Center since it does not meet the definition of human subjects research, since all human samples were collected in an anonymous fashion and no identifiable private information was collected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sex as a biological variable</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Randomization</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Blinding</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Power Analysis</td><td style="min-width:100px;border-bottom:1px solid lightgray">not detected.</td></tr><tr><td style="min-width:100px;margin-right:1em; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Cell Line Authentication</td><td style="min-width:100px;border-bottom:1px solid lightgray">Contamination: All cell lines are routinely tested for mycoplasma contamination and confirmed negative.</td></tr></table>

      Table 2: Resources

      <table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Antibodies</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For CD169 blocking experiments, primary MDMs from 3 different donors were pre-incubated with 20 μg/ml anti-CD169 antibody (HSn 7D2, Novus Biologicals) or IgG1k (P3.6.2.8.1, eBioscience) for 30 min at 4°C prior to infection.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-CD169</div><div>suggested: (Thermo Fisher Scientific Cat# MA1-16891, RRID:AB_568734)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">This is followed by secondary staining for 30 min at 4°C with APC-conjugated mouse anti-His antibody (BioLegend, #362605, 1:50) or isotype control.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-His</div><div>suggested: (BioLegend Cat# 362605, RRID:AB_2715818)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The cells were incubated overnight at 4°C with a rabbit antibody directed against the SARS-CoV nucleocapsid protein (Rockland; 1:1000 dilution in 5% goat serum), which cross-reacts with the SARS-CoV-2 nucleocapsid protein, as previously described (99).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>SARS-CoV nucleocapsid protein</div><div>suggested: (Creative Diagnostics Cat# DMAB8869, RRID:AB_2392503)</div></div><div style="margin-bottom:8px"><div>SARS-CoV-2 nucleocapsid protein</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The cells were washed four times in PBS and incubated with goat anti-rabbit antibody conjugated with AlexaFluor594 for 1 hour at room temperature (Invitrogen; 1:200 dilution in blocking reagent). 4’,6-diamidino-2-phenylindole (DAPI; Sigma-Aldrich) was used at 200 ng/ml for nuclei staining.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-rabbit</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For dsRNA staining (61), anti-dsRNA (Pan-Enterovirus Reagent, clone 9D5, Light Diagnostics, Millipore) antibody was used 1:2 overnight and anti-mouse-AF488 (Invitrogen) 1:200 dilution as secondary antibody with DAPI.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-dsRNA ( Pan-Enterovirus Reagent , clone 9D5 , Light Diagnostics , Millipore )</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-dsRNA ( Pan-Enterovirus Reagent ,</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-mouse-AF488</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Flow cytometry: To examine cell surface expression of CD169 or ACE2 in transduced THP1 or primary MDMs, approximately 0.5x106 cells were harvested with CellStripper (Corning), stained with Zombie-NIR (BioLegend, #423105, 1:250) followed by staining for 30 min at 4°C with the following antibodies; Alexa647-conjugated mouse anti-CD169 antibody (BioLegend, #346006, 1:50), Alexa647-conjugated</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>ACE2</div><div>suggested: (Enzo Life Sciences Cat# BML-SA445, RRID:AB_2273641)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">mouse anti-ACE2 antibody (R&D systems, 1:200), or unconjugated goat anti-ACE2 polyclonal antibody (R&D systems, #AF933, 1:200) followed by Alexa488-conjugated chicken anti-goat antibody (Invitrogen, #A-21467, 1:100).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-goat</div><div>suggested: (Molecular Probes Cat# A-21467, RRID:AB_141893)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Immunoblot Analysis: To assess expression of endogenous or transduced proteins, cell lysates containing 30- 40 µg total protein were separated by SDS-PAGE, transferred to nitrocellulose membranes and the membranes were probed with the following antibodies: mouse anti- TMPRSS2 (Santa Cruz, #515727, 1:1000), mouse anti-Cathepsin-L (Santa Cruz, #32320, 1:1000), goat anti-ACE-2 (R&D systems, #AF933, 1;1000), rabbit anti-STING (Cell Signaling, #13647, 1:1000), rabbit anti-MAVS (Thermo Fisher, #PA5-17256, 1:1000), mouse anti-RIG-I (AdipoGen, #20B-0009, 1:1000)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti- TMPRSS2 ( Santa Cruz , #515727</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-Cathepsin-L ( Santa Cruz , #32320</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-ACE-2</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-STING ( Cell Signaling , #13647</div><div>suggested: (Cell Signaling Technology Cat# 13647, RRID:AB_2732796)</div></div><div style="margin-bottom:8px"><div>anti-MAVS</div><div>suggested: (Thermo Fisher Scientific Cat# PA5-17256, RRID:AB_10979584)</div></div><div style="margin-bottom:8px"><div>anti-RIG-I ( AdipoGen , #20B-0009</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Specific staining was visualized with secondary antibodies, goat anti-mouse-IgG-DyLight 680 (Thermo Scientific, #35518, 1:20000), goat anti-rabbit-IgG-DyLight 800 (Thermo Scientific, #SA5-35571, 1:20000), or a donkey anti-goat-IgG-IR-Dye 800 (Licor, #926-32214, 1:20000).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>anti-mouse-IgG-DyLight 680</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>anti-rabbit-IgG-DyLight</div><div>suggested: None</div></div><div style="margin-bottom:8px"><div>#SA5-35571</div><div>suggested: (Thermo Fisher Scientific Cat# SA5-35571, RRID:AB_2556775)</div></div><div style="margin-bottom:8px"><div>anti-goat-IgG-IR-Dye</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Experimental Models: Cell Lines</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">THP1 cells (ATCC) were maintained in RPMI/1640 (Gibco) containing 10% FBS and 1% pen/strep (50)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>THP1</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">To generate HEK293T/ACE2+, THP1/ACE2+ and THP1/CD169+/ACE2+ cells, HEK293T, THP1 or THP1/CD169 cells were transduced with pLenti-ACE2-IRES-puro lentivector and cultured in puromycin-containing media (2 μg/ml).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>THP1/CD169</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">SARS-CoV-2 titer was determined in Vero E6 cells by tissue culture infectious dose 50 (TCID50) assay using the Spearman Kärber algorithm.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Vero E6</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Infection: For RNA analysis, 1x106 cells (THPI/PMA, MDMs, HEK293T) were seeded in 12-well plates.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293T</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">S binding: To evaluate SARS-CoV-2 S binding to various THP1 monocytes expressing different surface receptors, approximately 0.25x106 cells from parental THP1 or those expressing wt CD169, mutant CD169 (R116A), ACE2, or both wt CD169 and ACE2 were incubated for 30 min at 4 °C with 2 μg of spike glycoprotein (stabilized) from Wuhan-Hu-1 SARS- CoV-2 containing a C-terminal Histidine Tag, recombinant from HEK293F cells (BEI resources, #NR-52397).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>HEK293F</div><div>suggested: None</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Recombinant DNA</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For ACE2 cloning, the NotI-XhoI fragment from pLenti-ACE2- IRES-puro was inserted into the LV-3’LTR backbone.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pLenti-ACE2- IRES-puro</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For cloning CD169 into LV-3’LTR vector, a BglII-AgeI fragment from LNC-CD169 was inserted into LV-3’LTR vector.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>LV-3’LTR</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">HIV-1 packaging plasmid psPAX2 and VSV-G expression constructs have been previously described (39).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>psPAX2</div><div>suggested: RRID:Addgene_12260)</div></div><div style="margin-bottom:8px"><div>VSV-G</div><div>suggested: RRID:Addgene_138479)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">All lentiviral vectors (pLKO.1) expressing shRNAs used for knockdown of host proteins were purchased from Sigma</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>pLKO.1</div><div>suggested: RRID:Addgene_13425)</div></div></td></tr><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Data analysis was performed using FlowJo software (FlowJo).</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>FlowJo</div><div>suggested: (FlowJo, RRID:SCR_008520)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">63x oil immersion objective; numerical aperture 1.4) controlled by Metamorph image acquisition software (Molecular Devices, San Jose, CA)</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Metamorph</div><div>suggested: None</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Statistics: All the statistical analysis was performed using GraphPad Prism 9.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>GraphPad Prism</div><div>suggested: (GraphPad Prism, RRID:SCR_002798)</div></div></td></tr></table>

      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 LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.



      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.

      Results from rtransparent:


      • 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.

      Results from scite Reference Check: We found no unreliable references.


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