Fernández-Penny, F. E., Jolkovsky, E. L., Shofer, F. S., Hemmert, K. C., Valiuddin, H., Uspal, J. E., Sands, N. A., & Abella, B. S. (2021). COVID-19 vaccine hesitancy among patients in two urban emergency departments. Academic Emergency Medicine, 28(10), 1100–1107. https://doi.org/10.1111/acem.14376

SciScore for 10.1101/2022.03.26.485922: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from 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.

About SciScore

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

SciScore for 10.1101/2022.03.25.485875: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

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.

About SciScore

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

SciScore for 10.1101/2022.03.28.486075: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from 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">NCT04335136</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">Recombinant Human Angiotensin-converting Enzyme 2 (rhACE2) a…</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.

About SciScore

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

SciScore for 10.1101/2022.03.25.485832: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from 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">NCT04784767</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">SARS-COV-2-Spike-Ferritin-Nanoparticle (SpFN) Vaccine With A…</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.

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.

I don't think I totally understand what he is saying here...I kind of got lost in this long sentence and had to reread it several times...

So not true...I often am frustrated working with "classically trained" musicians in my church jobs who struggle working off of lead sheets, chord charts, improvising, understanding "groove" and the performance practices of more contemporary and popular musical styles.

SciScore for 10.1101/2022.03.24.22272871: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

This study has several limitations. First, the follow-up duration for the assessment of BA.1 /BA.2 co-infection is short, and as BA.2 detection is still increasing in France, the prevalence of BA.1 /BA.2 co-infection may be underestimated. In addition, ICU admission and vaccination status information was lacking for some Flash cases, limiting the clinical characterization of co-infected patients. Finally, additional sequencing methods were not tested, such as metagenomics or hybrid capture which are less prone to amplification-bias toward specific lineages 7. However, these techniques have lower sensitivity and lower throughput and were therefore not suitable as first-line sequencing methods in our laboratory 20. In conclusion, our findings emphasize the importance of using appropriate experimental and bioinformatic methods for the comprehensive identification of SARS-CoV-2 co-infections. Although these events are rare, SARS-CoV-2 co-infections need to be properly identified as they can lead to the emergence of new variants after a recombination event.

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.

About SciScore

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

SciScore for 10.1101/2022.03.23.485570: (What is this?)

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

Table 1: Rigor

Table 2: Resources

Results from OddPub: Thank you for sharing your data.

Results from 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.

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.

Aguilar et. al define "robophysics" as the pursuit of principles of self-generated motion.

Reviewer #1 (Public Review):

Overview

This is a well-conducted study and speaks to an interesting finding in an important topic, whether ethological validity causes co-variation in gamma above and beyond the already present ethological differences present in systemic stimulus sensitivity.

I like the fact that while this finding (seeing red = ethnologically valid = more gamma) seems to favor views the PI has argued for, the paper comes to a much simpler and more mechanistic conclusion. In short, it's good science.

I think they missed a key logical point of analysis, in failing to dive into ERF <----> gamma relationships. In contrast to the modeled assumption that they have succeeded in color matching to create matched LGN output, the ERF and its distinct features are metrics of afferent drive in their own data. And, their data seem to suggest these two variables are not tightly correlated, so at very least it is a topic that needs treatment and clarity as discussed below.

Minor concerns

In generally, very well motived and described, a few terms need more precision (speedily and staircased are too inaccurate given their precise psychophysical goals)

I got confused some about the across-group gamma analysis:

"The induced change spectra were fit per participant and stimulus with the sum of a linear slope and up to two Gaussians." What is the linear slope?

To me, a few other analyses approaches would have been intuitive. First, before averaging peak-aligned data, might consider transforming into log, and might consider making average data with measures that don't confound peak height and frequency spread (e.g., using the FWHM/peak power as your shape for each, then averaging).

Moderate

I. I would like to see a more precise treatment of ERF and gamma power. The initial slope of the ERF should, by typical convention, correlate strongly with input strength, and the peak should similarly be a predictor of such drive, albeit a weaker one. Figure 4C looks good, but I'm totally confused about what this is showing. If drive = gamma in color space, then these ERF features and gamma power should (by Occham's sledgehammer...) be correlated. I invoke the sledgehammer not the razor because I could easily be wrong, but if you could unpack this relationship convincingly, this would be a far stronger foundation for the 'equalized for drive, gamma doesn't change across colors' argument...(see also IIB below)...

...and, in my own squinting, there is a difference (~25%) in the evoked dipole amplitudes for the vertically aligned opponent pairs of red- and green (along the L-M axis Fig 2C) on which much hinges in this paper, but no difference in gamma power for these pairs. How is that possible? This logic doesn't support the main prediction that drive matched differences = matched gamma...Again, I'm happy to be wrong, but I would to see this analyzed and explained intuitively.

II. As indicated above, the paper rests on accurate modeling of human LGN recruitment, based in fact on human cone recruitment. However, the exact details of how such matching was obtained were rapidly discussed-this technical detail is much more than just a detail in a study on color matching: I am not against the logic nor do I know of a flaw, but it's the hinge of the paper and is dealt with glancingly.

A. Some discussion of model limitations

B. Why it's valid to assume LGN matching has been achieved using data from the periphery: To buy knowledge, nobody has ever recorded single units in human LGN with these color stimuli...in contrast, the ERF is 'in their hands' and could be directly related (or not) to gamma and to the color matching predictions of their model.

SciScore for 10.1101/2022.03.21.485247: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from 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">NCT04871737</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Active, not recruiting</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Study of a Live rNDV Based Vaccine Against COVID-19</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT05181709</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 Live Recombinant Newcastle Disease Virus-vectored COVID-19…</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04830800</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 Safety and Immunogenicity Trial of COVID-19 Vacc…</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04764422</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">Assess the Safety and Immunogenicity of NDV-HXP-S Vaccine in…</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04993209</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">Clinical Trial of the COVID-19 Vaccine (Recombinant, Inactiv…</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.

About SciScore

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

SciScore for 10.1101/2022.03.22.485425: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from 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.

About SciScore

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

SciScore for 10.1101/2022.03.22.22272773: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

Our study encompasses several limitations. Patients were encouraged to forcefully cough twice to contaminate surfaces. However, we cannot exclude that potentially repeated coughing over a prolonged time results in a more effective virus transfer compared to our controlled conditions. Moreover, sneezing can produce significantly more infectious droplets potentially containing infectious particles, therefore, we cannot exclude potential transmissions via other this route. Furthermore, a selection bias cannot be excluded, and the included patients are not demographically representative. Strengths of the present study include the high viral load of the patients included, a standardized protocol for sample acquisition, laboratory procedures and the inclusion of VoC.

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.

About SciScore

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

SciScore for 10.1101/2022.03.22.22271707: (What is this?)

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

Table 1: Rigor

Table 2: Resources

No key resources detected.

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

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

Our study also has limitations. Provision of supplements to participants randomized to intervention was contingent on demonstrating inadequate vitamin D status: thus, a subset (13.7%) of participants randomized to intervention did not receive study supplements. On the other hand, another subset (49.9%) of participants randomized to no offer took a vitamin D supplement on one or more occasions during follow-up. This may have led to increases in 25(OH)D concentrations in the no offer arm over the course of the study, although seasonal effects (sampling in June vs. December) will also have contributed. Together, these factors could have diluted any effect of vitamin D in the primary intention-to-treat analysis. We sought to overcome this by conducting a sensitivity analysis, which included only those randomized to offer vs. no offer who did vs. did not take supplemental vitamin D, respectively. The fact that this analysis showed no effect of vitamin D supplementation on all outcomes investigated, despite the larger differences in end-study 25(OH)D concentrations between intervention vs. no-offer arms seen for this analysis vs. the intention-to-treat analysis (Fig. 2), provides some reassurance that the null result yielded by the intention-to-treat analysis is valid. Ultimately, however, this trial was designed to investigate the effectiveness of a pragmatic ‘test-and-treat’ approach to boosting population vitamin D status, rather than biologic efficacy of vitamin D to prevent AR...

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">NCT04579640</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Active, not recruiting</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Trial of Vitamin D to Reduce Risk and Severity of COVID-19 a…</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.

About SciScore

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

SciScore for 10.1101/2022.03.22.485230: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from 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.

About SciScore

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

SciScore for 10.1101/2022.03.21.22272672: (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

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

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

This study has a number of limitations. Firstly, it aggregates studies using different therapeutics and with different enrolment and outcome criteria. Secondly, it tries to equate administered doses of convalescent plasma and monoclonal antibodies based on a single study comparing in vitro neutralisation of pseudovirus32. In the case of convalescent plasma, we consider mean titre of donor plasma and mean plasma volume for dilution, which does not reflect the considerable variability that exists between individual donor plasma neutralisation titres and individual recipients’ plasma volumes. In addition, we consider only the ‘administered dose’, as the studies did not directly measure plasma neutralisation titres in recipients after administration. Gordon et al33 studied the pharmacokinetics of convalescent plasma after administration of a volume of 5 ml/kg to infants (leading to an estimated dilution of approximately 10-fold34). Direct measurement of recipient titres 30 minutes after infusion found a mean of 6.2% of donor titres, suggesting a decrease in titre of around 40% compared to what would be predicted by dilution alone. Thus, there may be a tendency for the ‘administered dose’ to be higher than a serum neutralisation level that might be directly measured in vivo after treatment. Despite these limitations, a major strength of our approach is to apply a rigorous quantitative analysis to the available data on passive antibody treatment for SARS-CoV-2. A major factor that ...

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.

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.

2c) The Pedagogical Science in Science Communication – Applications of a BIO202 Undergraduate Scientific Journal

When it comes to scientific communication, I am most passionate about pedagogy and how teaching can be altered to better reach students. In other words, the way in which our studies are taught can be better tailored towards our learning capabilities, allowing for clearer understanding of course content. More effectively comprehending material is imperative to the success of students alike, and I am interested in devising a fresh pedagogical approach to allow for this success to be sparked at the University of Toronto.

In classes, such as BI0202, which are tailored towards the potential training of novice scientists and professionals, it is imperative that the study design is optimized so that the future remains bright and innovative. Research is therefore aided by the facilitation of proper teaching facilitated by post-secondary programs. Optimizing learning will therefore optimize research, contributing to the development of science and other related fields.

Junior scientists often learn to communicate their research during coursework. They become socialized into their disciplines as they adjudicate the teachings from their courses into future applications (Lea, 2004; Street et al., 2015). The problem is that, in university, there is little opportunity to fully expand your learning through active learning and external assignments that help to apply your knowledge. Completing specialized assignments, alike this one, is important to aid in the facilitation of knowledge acquisition among undergraduates, but it leaves students to collaborate within their own disciplinary bubble. Classroom projects rarely get adapted for a context beyond that space, which means that they do not learn how to translate their work for wider audiences, a key component in research.

A combination of specialization and lack of formal and external training and projects (Brownell et al., 2013) has contributed to the problem. Bankston and McDowell (2018) highlighted that this lack is at the core of challenges when students bring their knowledge into the working world, where knowledge is not effectively applied to real-word contexts. The solution, for many sources (e.g., Brownell et al., 2013; Norris et al., 2019), is increased communication training in scientific programs to resolve the “deficit” (Bubela et al., 2009) in scientific communication pedagogy. The idea is that emphasizing communication skills in the scientific classroom will better prepare junior scientists to engage with wider audiences.

For this reason, I find great importance in allowing students the ability to branch out of their disciplinary bubble and seek opportunities to interact with different avenues with their newfound knowledge.

An assignment to devise an essay on a related BIO202 topic could mean greater understanding of the topic for later students. Posting essays and reviews of BIO202 topics done by previous students in the class will allow for a greater transmission of thought and knowledge. Acting as a BIO202 scientific journal, active learning done by previous students, and continued by current students, will allow for active learning to be instilled within BIO202’s teaching paradigms. I find great value in undergraduate scientific journals as they help to highlight the learning by other students, while increasing your comprehension of the course content. In essence, the pedagogical initiative enables students to become what Caprio (2014) called “change agents” through their scholarship and dissemination.

Essay topics can be derived from any chapter, giving BIO202 students an expanse of research avenues to pursue and continue their learning and comprehension with. Narrowing specifically to “6.2 – Types of Locomotion”, a potential research topic could be regarding swimming locomotion in extinct animals, such as the Hesperornithiformes (as mentioned in the video at the end of the chapter). This furthers understanding since students are able to learn more about the limitations of locomotion by focusing their research on a specific subsection of locomotion that will benefit current and future students in BIO202. Active learning through innovative thoughts and analyses has been a proven method to facilitate comprehension, and I wholeheartedly believe that the addition of this assignment will assist students looking to apply their learning through an undergraduate journal, a method of disseminating their knowledge in an academic environment. Furthermore, publishing through undergraduate journals enhances their prospects as future graduate students, meaning the benefits surrounding the addition of a journal dedicated to BIO202 is twofold. This assignment will follow the teaching paradigm of Learning, Studying, and then Application of content, as demonstrated in my figure below.

Visual Aid Below

https://docs.google.com/document/d/1IOxnWPzPtVEtp0vwED-wlFuCr4Np4aF54Oi8i06Z4P4/edit?usp=sharing

Reforming scientific curricula to emphasize communication is useful, but it is important to also consider what happens beyond formal classroom curricula. Asking how scientists learn communication skills is a good start, but it is equally important to ask how they apply these skills to an authentic, non-expert audience while they complete their education.

References

Bankston, A., & McDowell, G.S. (2018). Changing the culture of science communicatin training of junior scientists. Journal of Microbiology & Biology Education, 19(1), 1-6. https://doi.org/10.1128/jmbe.v19i1.1413

Brownell, S.E., Price, J.V., & Steinman, L. (2013). Science communication to the general public: Why we need to teach undergraduate and graduate students this skill as part of their formal scientific training. The Journal of Undergraduate Neuroscience Education, 12(1), E6-E10.

Bubela, T., Nisbet, M., Borchelt, R., Brunger, F., Critchley, C., Einsiedel, E., Geller, G., Gupta, A., Hampel, J., Hyde-Lay, R., Jandciu, E., Jones, S.A., Kolopack, P., Lane, S., Lougheed, T., Nerlich, B., Ogbogu, U., O’Riordan, K., Oulette, C…Caulfield, T. (2009). Science communication reconsidered. Nature Biotechnology, 27, 514-518.

Caprio, M.J. (2014). Student publishing: Future scholars as change agents. OCLC Systems & Services, 30(3), 144-157. http://dx.doi.org/10.1108/OCLC-01-2014-0003

Lea, M. (2004). Academic literacies: A pedagogy for course design. Studies in Higher<br /> Education, 29(6), 739–756.

Norris, S.L., Murphrey, T.P., & Legette, H.R. (2019). Do they believe they can communicate? Assessing college students’ perceived ability to communicate about agricultural sciences. Journal of Agricultural Education, 60(4), 53-70. https://doi.org/10.5032/jae.2019.04053

Street, B., Lea, M. R., & Lillis, T. (2015). Revisiting the question of transformation in academic<br /> literacies: The ethnographic imperative. In T. Lillis, K. Harrington, M. R. Lea, & S. Mitchell (Eds.), Working with academic literacies: Case studies towards transformative practice (pp. 383-391). Fort Collins, CO: WAC Clearinghouse.

SciScore for 10.1101/2022.03.21.485224: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from 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">NCT04742738</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">Safety and Immunogenicity Study of SARS-CoV-2 Nanoparticle V…</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04750343</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">Safety and Immunogenicity Study of SARS-CoV-2 Nanoparticle V…</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT05175950</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Not yet recruiting</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Safety, Reactogenicity, and Immunogenicity Study of Heterolo…</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04523571</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">Safety and Immunogenicity of SARS-CoV-2 mRNA Vaccine (BNT162…</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.

About SciScore

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

SciScore for 10.1101/2022.03.22.485248: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from 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.

About SciScore

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

SciScore for 10.1101/2022.03.22.485299: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from 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">NCT04619290</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">Outpatient Treatment With CoVid-19 With Prexablu</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04635605</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">Methylene Blue Treatment of COVID-19</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04370288</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">Clinical Application of Methylene Blue for Treatment of Covi…</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.

About SciScore

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

SciScore for 10.1101/2022.03.20.485044: (What is this?)

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

Table 1: Rigor

Table 2: Resources

Results from OddPub: Thank you for sharing your code.

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

To address this limitation, we added a second siRNA (COV-siRNA2) as a two-siRNA combination and found this could prevent the emergence of escape mutants. This is consistent with prior results demonstrating that the use of multiple siRNAs to target other CoVs was more effective than a single siRNA (19). Using deep sequencing and a luciferase reporter assay, we confirmed that viral mutations observed with the single siRNA approach accumulated vRNA mismatches at key positions causing a reduction of siRNA efficacy. This finding highlighted the need to employ a multi-siRNA targeting distinct sites or combinations of orthogonal interventions to minimize the risk of viral resistance in any real-world therapeutic applications. Since late 2020, new SARS-CoV-2 variants have steadily replaced the spike D614G variant highly prevalent in the first wave of the pandemic. We performed an exhaustive in silico evaluation of current WHO VOI and VOC including those encoding the receptor binding motif N501Y spike mutation which emerged simultaneously in the variants Alpha, Beta, Gamma, Delta, and Omicron (20–22). While these variants encode substitutions across the viral genome, they possess similar mutation patterns associated with improved affinity for the ACE2, the known entry receptor for SARS-CoV-2. Favorably, the sequences targeted by COV-siRNA1+2 are located far away from the spike encoding region. Our in-silico analysis of public databases suggests no anticipated loss of activity (target ...

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.

About SciScore

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

SciScore for 10.1101/2022.03.18.484814: (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

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:

Limitation of our study consists of selection bias of donors enrolled in our study with inclusion criteria that considered most donors that had SARS-CoV-2 RT-qPCR-positive results and not healthy donors. The control population came from a pre-COVID era, and did not include an RT-qPCR, but attempted to match most of the demographics. Since our analysis was based on convalescent samples passively collected from persons with COVID-19 in the recovery phase, our study result does not directly reflect responses during the active disease phase of the viral infection. Some parameters, such as RBD binding IgG responses, seem to be already waned and did not display significant differences from healthy. While we could detect unresolved inflammatory status lagged from the acute phase of COVID-19, our study setting was not optimal to elucidate innate immunopathogenesis. As our data set was missing critical information, such as viral load, we cannot fully exclude the confounding factors that could possibly impact the antibody and proteomic alterations. Also, the samples were collected at only a one-time point, and antibody levels or proteomic responses were not adjusted by the different baseline of each individual and it was not possible to draw predictive conclusions on our findings but instead correlations. Most importantly, this is our cross-sectional study investigating one time point comparing health vesus disease and saliva versus plasma.Future longitudinal studies should investigate...

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.

About SciScore

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

SciScore for 10.1101/2022.03.20.485050: (What is this?)

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

Table 1: Rigor

Table 2: Resources

Results from OddPub: Thank you for sharing your data.

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

Limitations of the study: Since RBD of the SPs were used as a model of SARS-CoV-2, uptake of the actual virus might be different. However, the SP of SARS-CoV-2 is essential for viral entry into host cells. Thus, the attachment part of the viral life cycle can be explored with the SP. Also other cell types of the neurovascular unit, such as astrocytes, microglia and neurons need to be explored for SP uptake mechanisms. However, it is likely that there will be similar mechanisms since these cell types also express ACE2 and have glycans, including GM1.

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.

About SciScore

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

SciScore for 10.1101/2022.03.18.484178: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

Our findings suggest that the benefit demonstrated with baricitinib clinically likely does not result from its predicted anti-NAK effect and point out a limitation of the artificial intelligence approach for drug discovery in predicting antiviral activity. Lastly, we show that combining compounds with anti-AAK1/BIKE and anti-GAK activities may provide a synergistic antiviral effect against SARS-CoV-2 in vitro. This finding is in agreement with our prior data with sunitinib/erlotinib combinations in HCV, DENV, and EBOV in vitro (Neveu et al., 2015; Bekerman et al., 2017) and the finding that their combination protected 85% and 50% of mice from DENV and EBOV challenges, respectively (Bekerman et al., 2017; Pu et al., 2018). AAK1/BIKE and GAK have partially overlapping functions (Zhang et al., 2005; Neveu et al., 2015; Pu et al., 2020), which may explain moderate or no antiviral effect with drug alone, yet synergistic activity upon treatment with sunitinib/erlotinib combinations. The observed synergistic effect may also result from inhibition of additional targets by these drugs. In summary, these findings validate NAKs as candidate druggable targets for antiviral therapy against SARS-CoV-2 infection and provide a proof of concept that anti-NAKs approaches may have a utility for treating SARS-CoV-2 and possibly other coronavirus infections.

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.

About SciScore

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

SciScore for 10.1101/2022.03.20.485024: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from 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.

About SciScore

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

SciScore for 10.1101/2022.03.18.484956: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

Our study has limitations that must be considered. Firstly, we focused exclusively on the airway epithelium, and it is now established that SARS-CoV-2 is capable of infecting other cell lineages including monocytes, monocyte-derived macrophages, and microglia (Boumaza et al., 2021; Jeong et al.; Liu et al., 2021). It is possible that Gal-9 does not exert similar effects on viral replication or immune signaling in other target cell types. Secondly, our studies were all performed in vitro or in transplant tissue-derived primary epithelial cells ex vivo. As it is well-established that Gal-9 exerts conditional, pleiotropic immunomodulatory effects, the net effect of Gal-9 signaling on SARS-CoV-2 pathogenesis cannot be fully appreciated outside of an animal model with a functional immune system. Validation and extension of our observations in murine, hamster or nonhuman primate models of SARS-CoV-2 infection will help in evaluating the clinical relevance of our reported findings. In relevance to the implementation of animal models, our study did not elucidate the principal cell or tissue sources responsible for secreting Gal-9 in the setting of SARS-CoV-2 infection. Leveraging an animal model to identify these source compartments will be critical in developing interventions to manipulate Gal-9 signaling as a therapeutic approach. To our knowledge, the data presented here are the first to show that Gal-9 is directly involved in enhancing SARS-CoV-2 infection and virus-induced pro-i...

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.

About SciScore

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

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

Learn more at Review Commons

Reply to the reviewers

Reviewer #1: General comments:

Fujimoto and collaborators use Nanopore-based cDNA sequencing for genome-wide transcriptome analysis of a collection of hepatocellular carcinomas (HCCs) and matched normal liver tissues. To improve detection of alternatively spliced isoforms and hybrid transcripts potentially deriving from genomic rearrangements, they develop a dedicated pipeline SPLICE, which they benchmark against available software used for the same analysis. Besides having dual functionality (calls both alternative transcripts and fused transcripts), SPLICE seems to outperform previous software in calling alternative/fused transcripts and accuracy. They use the SPLICE pipeline to call isoforms and gene fusions in normal liver cells and HCCs and perform basic functional validations on novel fusions identified. The manuscript is well written, and the analyses are well performed. Perhaps the benchmarking of the SPLICE pipeline could have been more extensive (i.e., performed on additional independent datasets).

Major points: 1. Line 149-150: "We compared the results of mapping to the reference genome and the reference transcriptome sequences, and removed candidates if both were inconsistent (removal of mapping errors). " Please specify what "both were inconsistent" means.

Our reply; Thank you for this comment. The accuracy of fusion gene detection is influenced by mapping errors. To remove possible mapping errors, SPLICE aligned reads to the reference genome and the reference transcriptome sequences and compared the results. If the results are inconsistent (for example, GeneA-GeneB in the reference genome and GeneA-GeneB in the transcriptome genome, or GeneA-GeneB in the reference genome and GeneA in the transcriptome genome), SPLICE considers the candidates as false positive and removes them from the analysis.

          We changed the sentence “We compared the results of mapping to the reference genome and the reference transcriptome sequences, and removed candidates if both were inconsistent (removal of mapping errors).” to “we compared the results of mapping to the reference genome and the reference transcriptome sequences, and removed candidates if both results did not detect same fusion genes (removal of mapping errors).”  (line 150-152).

• Concerning TE-derived novel exons, in principle, this may lead to altered expression of the TE-transcript (as the Authors report for L1-MET) or to altered splicing of the transcript (i.e., other exon/introns could be retained or excluded). Can the Authors assess whether the inclusion of the TE in a transcript enhances its expression or affects the splicing of the "parental" transcript? If so, can they verify if the position of the insertion of the TE has any effect on expression and splicing?*

Our reply; Thank you very much for this important comment. As the reviewer mentioned, exonization of TE may affect the splicing patterns and gene expression levels of transcripts. To determine the effect of TE on expression levels, we compared the expression levels of transcripts with TE-derived novel exons with those of known transcripts of the gene. We found that the expression levels of transcripts with TE-derived novel exon were lower than those of known transcripts (Figure 1 in the reply). Since the same results were observed in all novel transcripts (Fig. 1E,F), most TE exonization would not affect the expression level of transcripts.

          We then analyzed the effects of TE in the splicing change, we compared the numbers of novel splicing junctions between transcripts with TE-derived novel exons and other transcripts in each gene. The proportions of genes with novel splicing junctions were not significantly different between the transcripts with TE-derived novel exons and others (transcripts with TE-derived novel exons; 9.1% and others; 11.9%)  (Figure 2 in the reply). As observed in L1-*MET* and L2-*RHR1*, transposons can affect expression levels and structures of transcripts, however, their effect would be limited to a part of genes.

Figure 1

Comparison of expression levels of transcripts with TE-derived novel exon and known transcripts. Only transcripts derived from genes with TE-derived novel exons were compared. The total number of transcripts is shown below the plot. Transcript abundance was measured in reads per million reads (RPM), and log10 converted values for RPM were shown in the violinplot. P-values were calculated by Wilcoxon rank-sum test.

Figure 2

Comparison of the percentage of novel splicing junction in transcripts with novel TE-derived exon and other transcripts. The total number of genes are shown below the plot. Transcripts with TE-derived novel exons and other transcripts were compared. P-value was calculated by Fisher’s exact test.

• Can the Authors explain why the NBEAL1-RPL12 was not detected by SPLICE?*

Our reply; Thank you for this comment. Although NBEAL1-RPL12 fusion was detected by SPLICE, mapping results to the reference genome and the reference transcriptome were inconsistent and removed from the final result. AsNBEAL1-RPL12 was not validated by PCR (Supplemental Fig. S4B) (line 183-184), we consider that this fusion-gene is a false positive, and filtering of SPLICE successfully removed false-positive fusions.

• Line 332: Can the Authors explain how the total amount of HVB mRNA was determined in each sample? Is it a relative amount calculated from the sequencing data? If so, it should be made clear in the text that this is a fractional measure.*

Our reply; Thank you very much for this comment. Expression levels were calculated by log10 converted reads per million reads (log10(RPM)) for each sample. We added the following sentences to the "Expression from HBV" subsection in the Results (line 337-338); “Expression levels were estimated by log10 converted support reads per million reads (log10(RPM)) for each sample.”.

• Fig4a: please specify if the y-axis "number of support reads" reports library normalized values.*

Our reply; Thank you for this comment. The values of the y-axis are row read counts. We added the following sentences to the Figure legend (line 348); “Y-axis shows the total number of support reads (raw counts).”.

• HCCs have more HBV-human genome fusion transcripts than normal liver. Could the authors clarify if these HCC transcripts are selectively found in tumors? or whether they are also expressed in normal liver samples? The paragraph starting from line 356 is confusing, and it is difficult to retrieve the above information for both HBs and HBx fusions.*

Our reply; We apologize for the confusing description. All HBV-human genome fusion transcripts were selectively expressed in tumor or normal liver. We added the following sentence to the "Expression from HBV" subsection in the Results (line 365-366); “All of these HBV-human genome fusion transcripts were selectively expressed in the HCCs and the livers.”.

• Figure 4C: what was the control used to calculate the relative viability in these analyses?*

Our reply; Thank you for this comment. Fig. 4C shows the number of HBV-human fusion transcripts in the six categories. If this comment refers to Fig. 4H, cell lines transfected with the empty vector (pIRES2-AcGFP1-Nuc) was used as controls. This has been described in the "Gene overexpression" subsection of Methods (line 716-717).

• MYT1L: the Authors report the identification of a novel MYT1L transcript downregulated in HCC, and argue it may have a potential tumor-suppressive function. For the sake of clarity, it will be advisable to show also the differential expression (HCC vs. Liver) of the other transcripts expressed from the same locus.*

Our reply; Thank you for this important comment. In HCCs and normal livers, only the novel MYT1L transcript was expressed from this locus, and no known transcript of MYT1L was expressed. We changed the sentence “In the MYT1Lgene, a highly-conserved novel exon was detected (Fig. 2E), and this transcript was significantly down-regulated in the HCCs” to “In the MYT1L gene, a highly-conserved novel exon was detected (Fig. 2E), and only a transcript with the novel exon was expressed.” (line 471-472).

• *

Minor points: 1. Table S4: there is a typo, correct “secific” in “specific”

Our reply; Thank you very much for this comment. We corrected the typo of Table S4.

• *

• *

*Reviewer #2: General comments:

Summary: This is both a presentation of a pipeline for analysis of Nanopore RNA-seq data, as well as an analysis of a cohort of 44 hepatocellular carcinomas against matched-normal liver tissue. It presents a number of quite intriguing results from the long-read RNA analysis, and suggests potential new targets for study in HCC. It is also worth noting that the current version of guppy (6) has functionality to detect primer sequences in the middle of reads and split those reads, which may obviate one of the steps in SPLICE.*

*Major comments:

1) The work done in this study used data that was basecalled using guppy 3.0.3. Since that version, I am aware of at least two major upgrades to the base caller accuracy, which would likely also improve the accuracy of isoform resolution. Given that the data is relatively low-coverage and that you have an automated workflow for the analysis, I would recommend re-basecalling using an updated basecaller and re-running your analysis using that. This is especially important given your comments in the paper about splice site misalignment.*

Our reply; Thank you very much for this important comment. We performed basecalling of a sequence data of MCF7 using the latest guppy v6.0.6 and compared the result with that by guppy v3.0.3. We randomly extracted 1M reads from MCF-7 reads that passed qscore filtering in guppy basecaller. The same reads were extracted and basecalled by guppy v3.0.3. These two data were analyzed by SPLICE.

The average error rate was 4.6 % for v6.0.6 and 6.8 % for v3.0.3. The number of transcripts was 9,674 for v6.0.6 and 9,329 for v3.0.3. Of these, the number of novel transcripts was 446 and 410, respectively. The number of fusion genes was 2 (BCAS3-BCAS4, and BCAS3-ATXN7) by v6.0.6 and one (BCAS3-BCAS4) by v3.0.3. As the reviewer mentioned, we found that using the latest version of guppy improved the accuracy and detected a larger number of transcripts.

We added the results to Supplemental Table S12. We also changed the sentences from “Second, our analysis removed the change of splicing sites within 5 bp to remove alignment errors (Fig. 1B). We consider that this cutoff value is necessary due to currently available high-error reads (S____upplemental Data S____2). However, sequencing technologies and basecallers are improving, and in the near future, we should be able to use a smaller cutoff value and identify larger numbers of splicing changes.” to “Second, the accuracy of the analysis depends on the sequencing error rate. Although several filters are used for currently available high-error reads (Fig. 1B and ____Supplemental____ Fig. S1), sequencing errors would affect the accuracy of the result. Sequencing technologies and basecallers are improving, and in the near future, we should be able to identify larger numbers of splicing changes with high accuracy (Supplemental Table S10).” (line 538-542).

2) You have compared your software to another tool for isoform analysis on Nanopore sequencing data, TALON. But a number of other tools exist for this purpose, including stringtie2, flair and bambu. My own testing has shown that stringtie2 outperforms TALON in terms of concordance with Illumina RNA-seq. It is quite important that you perform a complete comparison of your software to the state of the art for this purpose.

Our reply; Thank you very much for this important comment. We compared our tool with four tools (TALON, FLAIR, StringTie, and bambu). For this comparison, we used sequence data of MCF-7 and HCC (RK107C). We randomly extracted 1 M reads from MCF-7 and HCC (RK107C) sequence data using Seqtk (v1.3) (params: sample -s1 1000000). Reads were mapped to the reference genome sequence (hg38) with minimap2 (v2.17) (params: -ax splice --MD), and the output SAM files were converted to BAM files and sorted with samtools (v1.7) (Li et al. 2009).

For benchmarking of TALON (v5.0), we corrected aligned reads with TranscriptClean (v2.0.3) (Wyman and Mortazavi 2018). Next, we ran the talon_label_reads module to flagging reads for internal priming (params: --ar 20). TALON database was initialized by running the talon_initialize_database module (params: --l o --5p 500 --3p 300). Then, we ran the talon module to annotate the reads (params: --cov 0.8 --identity 0.8). To output transcript abundance, we first obtained a whitelist using the talon_filter_transcripts module (params: --maxFracA 0.5 --minCount 5), and then quantified transcripts using the talon_abundance module based on the whitelist. For FLAIR (v1.5), the sorted BAM file was converted to BED12 using bin/bam2Bed12.py. We then corrected misaligned splice sites with the flair-correct module. High-confidence isoforms were defined from the corrected reads using the flair-collapse module (params: -s 3 --generate_map). For benchmarking of StringTie (v2.2.1), Stringtie was performed with input files consisting of long-read alignment and reference annotation (params: -L -c 3). For benchmarking of bambu (v2.0.0), Bambu was performed with input files consisting of long-read alignment, reference annotation and reference genome (hg38) (params: min.readCount = 3). Candidates with low expression levels (support reads As a result, SPLICE identified the third-highest number of transcripts followed by FLAIR and StringTie (Supplemental Fig. S3A). In MCF-7 the concordance rate with IsoSeq MCF-7 transcriptome data was the highest in SPLICE for known transcripts and the second highest in SPLICE for novel transcripts (Supplemental Fig. S3B). These results indicate that SPLICE has sufficient accuracy for analyzing transcript aberrations.

We added the text to the "Comparison of SPLICE method with other tools" subsection of the Results (line 165-177) and the "Benchmarking" subsection of the Methods (line 640-679). We added the results to Supplemental Fig. S3.

3) Likewise, for fusion detection, you compare to LongGF. You should also compare to (and cite) JAFFAL.

Our reply; Thank you very much for this important comment. We compared our tool with the two tools (LongGF and JAFFAL). We used 1 M reads randomly extracted from MCF-7 and HCC (RK107C) sequence data as described above.

          For benchmarking of LongGF (v0.1.2), reads were mapped to the reference genome sequence (hg38) with minimap2 (v2.17) (params: -ax splice --MD), and the output SAM files were converted to BAM files and sorted with samtools (v1.7). We then ran the *longgf* module and obtained the list of fusion genes (params: min-overlap-len 100 bin_size 50 min-map-len 200 pseudogene 0 secondary_alignment 0 min_sup_read 3). For benchmarking of JAFFAL (v2.2), we ran the *JAFFAL.groovy* module with zipped fastq files.

          In this comparison, close gene pairs (We added the text to the "Comparison of SPLICE method with other tools" subsection in the Results (line 178-186) and the "Benchmarking" subsection in the Methods (line 667-679). We showed the results in Supplemental Fig. 4.

4) In terms of the source code, I have questions. Why did you use BASH to run the Python code, instead of making this into a Python package? Why did you not use the functionality already available in BioPython for a number of basic sequence data handling tasks? Why is there not even a single function defined anywhere, let alone classes?

At some level, if it works, it works. But I have serious concerns about the long-term maintainability of the code in its current state.

Our reply; Thank you very much for this critical comment. As the reviewer mentioned, we think it is better to make a python package and use BioPython for maintenance and long-term maintainability of the code. We have been building our analysis pipeline by trial and error, and at this stage, the current scripts are convenient for us (our group may need to learn software development). We provided a Docker package (see the reply to comment 5)), and this would promote usability.

5) Also related to the code, it is generally the standard now to create a BioConda package or Docker container for a bioinformatics package. BioConda has the advantage that the BioContainers project automatically generate Docker and Singularity containers from it. Please provide one of these.

Our reply; Thank you very much for this critical comment. We made a Docker file and provided it from our github page. It is available from the "Installation and usage via Docker" section.

6) There is some quite nice functional validation work done on some of the DE transcripts that would have been hidden in a gene-level analysis. There is also some nice work on detecting HBV fusion genes. These both contain important results which are not mentioned at all in the abstract. I feel like the abstract as it stands is selling the paper short.

Our reply; Thank you very much for this important comment. We added the following sentences to the abstract; “Comparison of expression levels identified 9,933 differentially expressed transcripts (DETs) in 4,744 genes. Interestingly, 746 genes with DETs, including LINE1-MET transcript, were not found by the gene-level analysis. We also found that fusion transcripts of transposable elements and hepatitis B virus (HBV) were overexpressed in HCCs. In vitro experiments on DETs showed that LINE1-MET and HBV-human transposable elements promoted cell growth.”.

7) Fig 5C shows a Venn diagram of fusions detected by short-read vs long-read sequencing, in which there is quite low overlap between these. You make the statement in the paper that "a combination of short- and long-reads can detect more fusion genes". I find it more likely that the short-read ICGC data had much greater depth of coverage than the MinION data you produced, which allowed for the detection of fusions that were expressed at much lower levels. This could be easily tested by downsampling the ICGC data to the same amount of sequence data as was generated on the MinION, and re-creating the Venn diagram with the fusions detected that way.

Our reply; Thank you very much for this very important comment. We compared the amount of data between our long-reads and the previous short-reads. However, the amounts of data were not quite different (Supplemental Fig. S14A). Therefore, differences in depth are not likely to be the cause of the low overlap. We considered that two possibilities could explain the low overlap. First, most of the fusion genes missed by short-read were very low expression levels, less than 1 reads per million reads (RPM) (Supplemental Fig. S14B), therefore, there are many fusion-genes with low expression levels, and they are difficult to be detected. Second, 28.9 % of transcripts in long-reads lacked 5' region (Supplemental Fig. S5 and Supplemental Fig. S14C,D). Therefore fusion-genes whose breakpoints are located in the 5' region were difficult to detect by long-read.

We added the following sentences to the "Fusion genes" subsection in the Results (line 400-405); “We considered that two possibilities could explain the low overlap. Since the most of the fusion genes missed by short-reads had very low expression levels (Supplemental Fig. S14B), many fusion-genes with low expression levels would be missed by a single approach. In addition, 28.9 % of transcripts in long-reads lacked 5' region (Supplemental Fig. S5 and Supplemental Fig. S14C, D). Therefore fusion-genes whose breakpoints are located in the 5' region would be difficult to detect by long-read.”. We also added a figure on the amount of data to Supplemental Information (Supplemental Fig. S14A).

8) Figure 5D is very interesting. What do you conclude from that result? Please comment in the manuscript.

Our reply; Thank you very much for this important comment. We used samples that used for whole-genome sequencing in our previous study. Therefore, a list of SVs is available. We classified fusion-gene to these supported by SVs (SV detected fusion-genes) and others (no SV detected fusion-genes), and compared the expression levels of them (Figure 5D).

Whole-genome sequencing can accurately identify clonal (high frequency) SVs, however, would miss sub-clonal (low frequency) SVs. Therefore, we considered that no SV detected fusion-genes were generated by sub-clonal SVs. This result suggests that there are a lot of sub-clonal fusion genes, and their expression levels are lower than clonal fusion genes. Although the functional importance of sub-clonal fusion genes is currently unknown, deeper RNA sequencing would detect a larger number of fusion genes.

          We added the following sentences to the “Fusion genes” subsection in the Results (line 410-412); “This result suggests that there are a lot of sub-clonal fusion genes, and their expression levels are lower than clonal fusion genes. Although the functional importance of sub-clonal fusion genes is currently unknown, deeper RNA sequencing would detect a larger number of fusion genes.”.

*Minor comments:

1) The manuscript has many small errors in English grammar, spelling and style. I would strongly recommend sending it for copy editing before submitting it to a journal.*

Our reply; Thank you very much for this comment. Due to the limitation of time, the current version has not been proofread by a native-English speaker. We are planning to review English grammar by a native-English speaker.

2) Neither the results section nor the methods section describing the sequencing that was performed specify whether it was done on a MinION or PromethION (or flongle). While this is implied elsewhere in the paper, it should definitely be specified in the methods at a minimum.

Our reply; Thank you for this comment. We used a MinION for sequencing. We added the following sentences to the Method section (line 579-580); “Libraries were sequenced on a SpotON FlowCell MKⅠ(R9.4) (Oxford Nanopore), using the MinION sequencer (Oxford Nanopore)”.

3) You also write in the introduction that your method, SPLICE, was developed for the MinION specifically. Please comment on its applicability to data generated on the PromethION and flongle Nanopore sequencers.

Our reply; Thank you very much for this comment. We consider that our method is applicable to data from MinION, PromethION, and flongle. We added the following sentence to the Methods section (line 592-593); “In the present study, we analyzed sequence data from MinION. We consider that our method is applicable to data from MinION, PromethION, and flongle.”.

4) The volcano plot in Fig 3A is missing its dots.

Our reply; Thank you very much for this comment. We modified the Fig. 3A.

*Reviewer #3: General comments:

Summary: In this manuscript, Kiyose et al have developed and tested a novel methodology for identifying splicing alterations, and fusions, from full-length transcript or long read sequencing data. They apply this approach to liver cancer and paired, non-cancerous liver tissue from a prior publication, and use wet-lab/experimental methods to validate their in silico findings. They conclude that their new methodology, SPLICE, outperforms one existing method, and is uniquely suitable to identifying fusion genes.*

Major Comments: 1) Figure 1B shows a schematic of common error patterns from MinION cDNA sequencing, and the text of the manuscript describes how the authors' new approach (SPLICE), overcomes several of these, e.g. sequencing errors, artificial chimeras, and mapping errors of highly homologous genes. However, there is a fundamental disconnect between the text and the graphic in Figure 1B. This should either be revised for clarity, or an additional graphic or flowchart placed in the supplementary materials to clearly show *how* SPLICE overcomes each of these limitations.

Our reply; We apologize for the insufficient explanation in Figure 1. We showed a detailed explanation of the data analysis procedure in Supplemental Fig. S1.

2) Why was TALON the only alternative approach chosen for validation of SPLICE performance? There are a number of other, more advanced pipelines such as SUPPA2, and IsoformSwitchAnalyzeR. It would strengthen the manuscript, and its conclusions, to incorporate at least one of these methods as a second comparator. This is particularly true for IsoformSwitchAnalyzeR, since Kiyose et al identify a number of differentially expressed transcripts (DETs) for genes that are not differentially expressed.

Our reply; Thank you very much for this important comment. Another reviewer also requested additional benchmarking, therefore we performed an additional performance comparison for the revised manuscript. As SUPPA2 and IsoformSwichAnalyzeR are used to analyze the annotated output GTF files, and direct comparison with SPLICE is difficult. Since IsoformSwichAnalyzeR recommends StringTie as an annotation soft, we compared using StringTie instead.

We compared the performance of SPLICE with that of four other methods (TALON, FLAIR, StringTie and Bambu) for splicing variant detection. SPLICE identified the third-highest number of transcripts followed by FLAIR and StringTie (Supplemental Fig. S3A). In MCF-7 the concordance rate with IsoSeq MCF-7 transcriptome data was the highest in SPLICE for known transcripts and the second highest in SPLICE for novel transcripts (Supplemental Fig. S3B).

We added the text to the "Comparison of SPLICE method with other tools" subsection of the Results (line 165-177) and the "Benchmarking" subsection of the Methods (line 640-665). We added the results to Supplemental Fig. 3.

3) The Venn diagram in Figure 5C appears to show that conventional short read sequencing identifies 46 fusion genes that are not also detected by long read sequencing. However, this result, and its implications are never addressed in the text.

Our reply; Thank you very much for this important comment. We apologize for the insufficient explanation. We considered that two possibilities could explain the low overlap. First, most of the fusion genes missed by short-read were very low expression levels, less than 1 reads per million reads (RPM) (Supplemental Fig. S14B), therefore these are many fusion-gene with low expression level and they are difficult to be detected. Second, 28.9 % of transcripts in long-reads lacked 5' region (Supplemental Fig. S5 and Supplemental Fig. S14C,D). Therefore fusion-genes whose breakpoints are located in the 5' region were difficult to detect by long-read.

          We added the following sentences to the "Fusion genes" subsection in the Results (line 400-405); “We considered that two possibilities could explain the low overlap. The most of the fusion genes missed by short-reads had very low expression levels (Supplemental Fig. S14B). This result suggests that there are many missed fusion-genes with low expression levels. In addition, 28.9 % of transcripts in long-reads lacked 5' region (Supplemental Fig. S5 and Supplemental Fig. S14C, D). Therefore fusion-genes whose breakpoints are located in the 5' region would be difficult to detect by long-read.”. We also added a figure on the amount of data to Supplemental Information (Supplemental Fig. S14A).

Minor Comments: 1) On pages 20-21, the language used to describe the HBV and/or HCV postive vs negative materials is very confusing. Please clarify that by "HBV- and HCV-related tissues" you in fact mean "HBV-and HCV-infected samples."

Our reply; We apologize for the confusing wording. We converted "HBV and HCV-related tissues" to " HBV and HCV-infected samples" in the manuscript.

SciScore for 10.1101/2022.03.18.484873: (What is this?)

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

Table 1: Rigor

Table 2: Resources

Results from OddPub: Thank you for sharing your data.

Results from 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.

About SciScore

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

SciScore for 10.1101/2022.03.17.484786: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from 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.

About SciScore

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

SciScore for 10.1101/2022.03.16.22272513: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from 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">NCT04456595</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">Clinical Trial of Efficacy and Safety of Sinovac's Adsorbed …</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.

About SciScore

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

SciScore for 10.1101/2022.03.17.22272516: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

Sample size and study period are also limitations, especially in our ability to assess the impact of vaccination status or dissociate vaccination or other demographics from age. Ct values also cannot be converted to a quantitative representation of viral load, and cannot be used to directly infer differences in infectiousness. However, despite these limitations, clinical interpretation of Ct values (or their trajectories) may be “tempting “ (IDSA and AMP joint statement on the use of SARS-CoV-2 PCR cycle threshold (Ct) values for clinical decision-making, page 3) [14]. The present data are directly relevant to these interpretations. Specifically, specimens that were collected within 4 days of symptom onset may represent periods when Ct values are still declining. These findings contribute to our understanding of RT-PCR Ct values during SARS-CoV-2 infections over a broad range of ages, in a community setting with mostly mildly symptomatic illness, and among individuals with a known date of first shedding. While these data were collected prior to Delta and Omicron circulation, and prior to widespread vaccination, they may provide context for interpreting trajectories in Ct values in similar populations during later SARS-CoV-2 outbreaks.

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.

About SciScore

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

SciScore for 10.1101/2022.03.17.484640: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

It is important to note that the current investigation has limitations. A critical factor to consider is the pre-selection of patients for this cohort, with most of the patients included already having moderate to severe COVID-19. It is also noteworthy to consider that health systems vary across geographical locations so patients in other counties might be more diverse due to different thresholds of illness required for hospital admission. Additionally, due to severe disease presentations, there were challenges with obtaining clinical samples from some patients and, in some cases, the cell numbers were low and/or of poor quality. During the study some COVID-19 patients were vaccinated against SARS-CoV-2 and therefore were excluded from our data set as this interfered with some results. Despite the immensely challenging conditions during the pandemic, we do believe that the cohort presented in this investigation is well characterized and of high quality and value. Altogether, this study highlights the alterations in the immune response incurred during hospital treated SARS-CoV-2 infection and convalescence. These novel longitudinal data illustrate the substantial changes to the T cell landscape, with a persistent increase in markers of activation, exhaustion and senescence lasting for more than 6 months. We further showed an accompanying decay in SARS-CoV-2-specific antibody responses at the same time. Our findings, in combination with others, are valuable in providing insight...

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.

About SciScore

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

SciScore for 10.1101/2022.03.16.484099: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

One of the limitations of this study is that we relied on accurate reporting of demographic information, comorbidities, previous SARS-CoV-2 testing and symptoms, and vaccine information by the participants. While the sample size for each of the four infected/vaccinated groups compared is robust, not all timepoints were available for all participants. Only participants who were convalescent prior to vaccination were reported in the infected vaccinated group, participants who had breakthrough infections after vaccination will be included in a future study. The sample size for the in vitro differentiation experiment was low due to the uncommon nature of losing seroprotective status amongst our participants. All participants who lost their confirmed seroprotected status were included in the analysis; future studies are necessary to strengthen our findings with additional participants. In conclusion, vaccinated groups seroconverted to higher antibody levels and experienced significant antibody waning regardless of pre-immunity. In contrast, infected unvaccinated participants had no significant waning during the first 14 months after infection. The rate of antibody waning was not significantly different between pre-immune and immunologically naïve participants. Antibody levels are greatly increased by the administration of a booster dose, to a level 3-fold higher than their previous peak antibody level 2-4 weeks after the second dose. Memory B cell recall responses were absent in a...

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.

About SciScore

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

SciScore for 10.1101/2022.03.14.484208: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

However, a major limitation of these measurements is that viral mRNA replication and transcription is massive, and occurs within a unique endomembrane system (Wolff et al., 2020) that would thus render much of the viral mRNA inaccessible to ribosomes. This inaccessibility of potentially large portions of viral RNA will skew our measurements and it is likely the true translation efficiency of viral transcripts is significantly higher. Comparing the translation efficiencies of viral and cellular transcripts in cells infected with CoV2-wt or with CoV2-mut allowed us to overcome this limitation and to demonstrate that in infected cells nsp1 indeed specifically promotes the translation of viral transcripts compared to cellular transcripts, as we observed that in cells infected with CoV2-mut, viral genes are translated even less efficiently compared to their cellular counterparts. However, at the same time our measurements of the overall translation capacity in infected cells, using incorporation of OPP into nascent chains, show that nsp1-WT leads to a major reduction in the absolute levels of translation in infected cells. Since the vast majority of mRNA in infected cells is viral mRNA this means that viral mRNAs translation is likely also inhibited in the presence of nsp1. Together these results support a model in which nsp1 acts as a strong inhibitor of translation that tightly binds to ribosomes and thus inhibits the translation of both viral and cellular mRNAs. Viral transcrip...

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.

About SciScore

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

SciScore for 10.1101/2022.03.15.484542: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from 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.

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.

Exercises

2.1.b

Counterexample: \(\to := {(a, c), (b, c)}\)

2.3

\(a \to b\) iff \(a\) encodes Turing machine \(M_a\) and \(b\) encodes a valid terminating computation (sequence of states) of \(M_a\).

2.9

Let \(|w|_a := \varphi_a(w)\).

\(\varphi(w) := 3^{|w|_a} 2^{|w|_b}\)

Proof

1. Let \(u \to_1 v\). Then \(\varphi(v) = 3^{|v|_a} 2^{|v|_b} = 3^{|u|_a+1} 2^{|u|_b-2} = 3^{|u|_a} 2^{|u|_b} \frac{3}{4} = \varphi(u) \frac{3}{4} < \varphi(u)\).
2. Let \(u \to_2 v\). Then \(\varphi(v) = 3^{|v|_a} 2^{|v|_b} = 3^{|u|_a-1} 2^{|u|_b+1} = 3^{|u|_a} 2^{|u|_b} \frac{2}{3} = \varphi(u) \frac{2}{3} < \varphi(u)\).

2.17

No.

Let \(a > b\). Then \([b^n a | n \in [0, 1, \ldots]]\) is an infinite chain according to \(>_{Lex}\).

Note: This exercise completes the discussion of Lemma 2.4.3.

4.2

Let \(s, t\) be terms. Run BFS from \(s\) using \(\leftrightarrow^E\). If \(t\) is encountered, conclude that \(s \approx_E t\). If the BFS finishes enumerating the equivalence class without encountering \(t\), conclude that \(\lnot s \approx_E t\).

4.4

Let \(x \in Var(r) \setminus Var(l)\). Let \(p\) be a position of \(x\) in \(r\).

Infinite chain:

• \(t_0 = x\)
• \(t_{i+1} = r[t_i]_p\)

4.18

1. a
   • Unifier: \({x \to h(a), y \to h(a)}\)
   • Matcher: \({x \to h(a), y \to x}\)
2. b
   • Unifier: Unsolvable
   • Matcher: \({x \to h(x), y \to x}\)
3. c
   • Unifier: \({x \to h(y), z \to b}\)
   • Matcher: Unsolvable
4. d
   • Unifier: Unsolvable
   • Matcher: Unsolvable

5.2

Counterexample TRS \(R\):

1. \(a \to b\)
2. \(b \to b\)

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

In their manuscript, Hattori et al., put forward evidence that the knock-out of CD38 expression in astrocytes at approximately post-natal day 10 (referred to as CD38 AS-cKO P10) leads to a specific deficit in social memory in adult mice, while other types of memory remain unaltered. Using immunohistochemistry (IHC), the authors found a reduced number of excitatory synapses in the medial prefrontal cortex (mPFC) of CD38 AS-cKO P10 mice. Switching to in vitro primary cell culture models, the authors identify the astrocyte secreted protein SPARCL1 as a relevant synaptogenic factor. Using pharmacological dissection of relevant signaling pathways, Hattori et al., propose that cADPR formation and calcium released from intracellular stores, is essential for SPARCL1 secretion from astrocytes. Finally, the authors analyzed the transcriptome of primary CD38 KO astrocytes using bulk mRNA sequencing, and found that genes related to calcium signaling were downregulated in these cells.

Major commments:

• Are the key conclusions convincing?
  1. From a global perspective, the multiple lines of evidence provided by the authors strongly suggest that expression of CD38 in astrocytes is important for synaptogenesis in the mPFC of P10 mice, with ablation of CD38 and reduced synapse formation leading to social memory deficits at P70. However, the data concerning the role of astrocyte-secreted SPARCL1 is not particularly strong: further experiments are needed to support this claim (see below).
• Are the claims preliminary or speculative?
  1. As it stands, there is no proof that the claimed astrocyte-specific deletion of CD38 is actually astrocyte specific. This evidence is crucial: without it the reported effects could be due to non-specific CD38 knock-out in other CNS cells. In this respect, the Western Blot in Supplementary Figure 1A does not provide information on astrocyte-specific deletion, merely that CD38 was globally reduced in the mPFC. Interestingly, the authors have previously published data (Hattori et al., 2017, 10.1002/glia.23139) showing that CD38 expression is mostly astrocyte-specific, peaking at p14, which coincides with the peak period of synaptogenesis. The degree of CD38 heterogeneity is also an issue that I think the authors need to consider. Do they information on this? Is CD38 expressed in every astrocyte of the CNS, or are there some astrocytes that are CD38 negative at P14? Is the mPFC a region specifically enriched in CD38 positive astrocytes and does this explain the observed behavioral deficit? I think if this is known, the authors should mention it in the "Introduction" or "Discussion". If this is not known, maybe the authors could provide data addressing the issue.
  2. I think the authors should take more caution in claiming that SPARCL1 is the main factor secreted through the CD38 signaling pathway and responsible for increased synaptogenesis. This is for several reasons, all centered on data displayed in Figure 4 and Supplementary Figure 6:
     • a) Western Blot (WB) data: The "Materials and Methods" section for WB does not indicate how protein loading and transfer efficiency were controlled for. Normalizing to β-Actin levels is an acceptable way to control for loading and transfer efficiency when using cell lysates. However, in the absence of such an abundant structural protein in conditioned media it is unclear how loading and transfer was controlled for under these conditions. Do the authors normalized the CD 38 KO AS ACM data by expressing protein levels relative to those from WT AS ACM? Is BDNF being used as a control, based on proteomics data? If so, why is proteomics data not given in the manuscript and why is this control not shown for all ACM blots? I realize that (quantitative) blotting using ACM is difficult, but I am also not convinced that the methodology used is sufficiently rigorous. Simple steps to give confidence would be Coomassie staining of gels both before and after membrane transfer, to show that i) the total protein amount loaded was the same in each lane of the gel and ii) the transfer to the nitrocellulose membrane was complete. In addition, Ponceau S staining of the nitrocellulose membrane should also have been performed and displayed, to show (roughly) equal amounts of protein were transferred for each lane. In summary, the WB data quantification needs to be better controlled. The values of the Y axis in these graphs (and throughout the manuscript) are simply too small to be read properly. Finally, I want to highlight the general lack of precision regarding the nature of the replication unit (the "n"). For example, the legend of Figure4C-D states "n = 6", but we have no idea if these are 6 independent primary cultures originating from 6 mice, 6 independent cultures from the same mouse, 6 repeats of the Western Blot using the same sample etc. This issue is valid for the whole manuscript: in my opinion, the authors should be more much careful when it comes to these crucial elements of scientific reporting.
     • b) While the data hint at an important role of SPARCL1 in synapse formation, when the authors tested if ACM from CD38 KO astrocytes supplemented with exogenous SPARCL1 could rescue synapse formation, the effect was incomplete, with only a trend to an increase in synapse number (Figure 4J-K). Perhaps the authors simply forgot to indicate the statistical significance of differences between the experimental groups (Figure 4K)? However, if there really were no statistically significant differences observed, the authors should reduce the strength of their conclusions regarding SPARCL1. This protein may well be pro-synaptogenic but, as it stands, other factors could well be in play. Perhaps the authors should have tried higher concentrations of SPARCL1 to further boost synaptogenesis? In this respect, the SPARCL1 knockdown (KD) experiment in Supplementary Figure 6B-D is an important addition, but should be supplemented by rescue with an siRNA-resistant recombinant SPARCL1? If SPARCL1 is a major player in synaptogenesis, the prediction is that synapse numbers would be close to wild type levels with this approach.
     • c) In my opinion, there are also issues with the data displayed in Figure 4H-I. The authors want to convince the reader that SPARCL1 is mostly an astrocytic protein using immunohistochemistry on mouse mPFC sections, co-labelled with antibodies against neuronal and astrocytic markers. In these panels, we are presented with images showing a few cells, in which it seems SPARCL1 is absent from NeuN positive cells, present in WT astrocytes and reduced in CD38 AS-cKO P10 astrocytes. However, the numbers of cell counted and lack of quantification severely impact on the strength of this conclusion. In my opinion, the authors should have quantified their IHC data by counting cells and establishing the ratios of SPARCL1 positive over NeuN or S100β positive cells, in both control and CD38 AS-cKO P10 animals. This experiment would provide critical information that the conditional gene targeting strategy is robust. The authors should also consider quantifying the intensity of the SPARCL1 signal in astrocytes. This is recommended as the image displayed in Figure 4I for the CD38 AS-cKO is problematic: are the authors really claiming that the reduction in SPARCL1 expression following cKO of CD38 in astrocytes is at best only partial? Is 11 days between the first tamoxifen injection and tissue fixation actually sufficient to allow for CD38 turnover? With low levels of protein turnover, the possibility exists that residual levels of CD38 are still sufficient to impact SPARCL1 levels. What would happen if there is a greater interval between tamoxifen administration and tissue recovery? Would levels of synaptogenesis be further reduced? Is this an issue of production versus secretion or a combination of factors?
  3. The heatmap (Figure 5E-F) is simply too small to interpret. The color choice is also not accessible for colorblind readers. The authors might consider displaying this heatmap in a separate figure. The authors should also provide a supplementary table where all the genes detected are listed along with their respective counts. Furthermore, it is surprising that the authors only found genes being downregulated in CD 38 KO astrocytes. Were they really no genes up-regulated? The authors might also want to indicate the genes belong to each of the ontological categories listed in Figure 5F. On p. 11, Figure 5E: The authors should indicate in the main text they performed bulk RNA-sequencing and not another type of RNA sequencing (like single cell RNA sequencing for instance). The authors indicate n = 2 but we have no indications of the nature of the replicate (also see earlier comments). Please amend.
• Are additional experiments necessary? I think supplementary experiments are essential to support the claims of the paper. Most are described in the section above, but to summarize:
  1. Show data to prove that the CD38 AS-cKOP10 model is astrocyte-specific and leads to a total loss of CD38 in these cells.
  2. WB data: The issue of protein loading and transfer efficiency should be dealt with. Quantifications should be revisited.
  3. The authors should quantitatively analyze the different IHC performed in Figure 4H-I.
  4. The authors should provide more information on their RNA sequencing data: list of genes detected with their FPKM values etc. The authors should display the RNA sequencing data in a separate figure, allowing the heatmap to be enlarged.
  5. LC-MS/MS data: the authors should provide the list of all the proteins they identified in their LC-MS/MS experiment. As a supplementary table for instance? The majority of these experiments should be able to be performed with pre-existing samples/tissue slices. If not, the experimental pipeline necessary exits and these supporting experiments should not be too burdensome.
• Data and methods presentation Methods: The authors need to work on this aspect of the manuscript. Most of the important details are already described, but some crucial ones are missing, while the phrasing used to describe methods is sometimes misleading. I will give some examples here, but this is not an exhaustive list. The fact that the manuscript is riddled with small mistakes, inconsistencies and/or oversights makes it difficult to read and creates a negative impression. The whole manuscript would benefit from a thorough proof-reading, preferably by a native speaker.
  1. in the "Immunohistochemistry and Synaptic Puncta Analysis" section on p. 21-22, we have no indication of which antibodies against "GFAP, NDRG2, VGlut1, PSD95, S100β, NenN(?) and SPARCL1" were used. It is standard practice to indicate the company, product number and lot number. The authors must also indicate the dilution at which they use these antibodies. On p.22, the authors write the cells were incubated with "Alexa- or Cy3-conjugated secondary antibodies". The excitation wavelengths of the Alexa dyes used need to be given.
  2. The authors need to provide more details on the microscope they used. Merely writing "using a 63× lens on a fluorescence microscope" (p.23) is insufficient.
  3. In the "LC-MS/MS" method the authors wrote: "Briefly, these proteins were reduced, alkylated, and digested by trypsin". I think that in the reduction and alkylation steps, chemicals other than trypsin were actually used. This sentence should be modified to reflect this.
  4. p.19: "uM" is written when the authors very likely mean "µM". Please check the whole manuscript for repeat examples. I know this is often lab "short-hand", but it should be avoided in scientific publications.
  5. The authors should be careful when describing their data to always indicate whether they referring to experiments performed using cultured astrocytes or not. As it stands, the text is confusing: for instance, when describing RNA-sequencing data in Figure 5, the main text appears to indicate that these astrocytes were acutely isolated from adult mice, when in fact they were obtained from primary cultures. Given concerns in the literature about potential differences between acutely isolated and cultured astrocytes (Foo et al., Neuron, 2011), this is essential. Data presentation: The figures appear to have been produced in a rush - and almost have a "screenshot" feel to them. This is not a scientific issue per se, but does impact on the overall impression given by the manuscript. The following is a non-exhaustive list of issues with the figures. I list the major ones that the authors should correct.
  6. Almost all Y axis labels are too small. The authors should comply to the basic journal requirements in terms of font sizes. Some axes do not end on a tick (e.g. Figure 3R). This is not dramatic, but should be corrected. Globally, the authors need to display bigger bar plots - most of them are extremely hard to read. Labeling should also be checked: Figure 4K, the Y axis label indicates values displayed are in %, when I think the axis graduation displays ratio values. Some of the IHC pictures are also too small to be easily interpreted.
  7. The heatmap in Figure 5E is impossible to read and, as such, has little or no value for the manuscript.
  8. Scale bars: where is the scale bar in Figure 2A? Figure 3A-H: Is the scale bar really representing 10 millimeters? Supplementary Figure 3A: scale bar is missing. Please check for similar issues throughout the manuscript.
  9. Figure Legends are problematic, and often contain incorrect or incomplete information. Examples include: Supplementary Figure 1: The description of panels J, L and N appears to be missing. Please also use the Greek letter beta and not 'b' for S100β. Supplementary Figure 5: I think the term "KO" is missing after CD 38 in the legend title. Figure 3: why state that nuclei were counterstained with DAPI in Figure 3P,Q, when this precision is not given for panels Figure 3A-H? Figure 3A-H: If the authors choose to explicitly state PSD95 is a post-synaptic marker, why not indicate that VGlut1 is a pre-synaptic marker? Same issue in Supplementary Figure 4.
  10. There are multiple instances of panels being wrongly referred to in the main text. On p.10, Figure 4H is referenced, when I think the authors mean Figure 4I; on p.10, Figure 4I-J are referred to when the authors clearly describe data found in Figure 4J-K. These types of mistakes are problematic and recur throughout the manuscript.
• Statistical analysis As mentioned above, the exact nature of the replicates is often not stated, when the "n" number is indicated. The authors must correct this issue and give the information either at the appropriate point in the main text or in the figure legend.

The authors should also be more consistent in the way they indicate which statistical tests were performed. This should also be indicated either at the appropriate point in the main text or in the figure legend. Furthermore, care should be taken to ensure statistics are presented in an appropriate manner: at the end of legend for Figure 4, it is indicated #p < 0.05 vs. CD38 KO ACM. This hashtag symbol is completely absent from the figure. In Figure 4F-G, the lack of statistical symbols seems to indicate no statistical tests were performed on these data, when the legend covering these panels states "*p < 0.05 versus P70", indicating some tests were done. We cannot interpret this panel without knowing which comparisons were done exactly and which were significant.

In the "Materials and Methods", the authors give no indication that the assumptions of the statistical test they used were met (normality of data distribution for t-tests, homogeneity of variances for ANOVA...). This needs to be checked, and if not met, appropriate non-parametric tests should be used instead.

Minor commments:

• Specific experimental issues that are easily addressable. Most of the experimental issues that need to be addressed are given in previous sections and should be easily addressable.
• Citation of previous studies? Adequate
• Clarity and accuracy of text and figures There are issues with the clarity and accuracy of text and figures - which are described above. The text is also often problematic in its phrasing and other, more fundamental aspects. For instance, the authors spent a considerable amount of time speaking about the role of oxytocin, when they only performed one measurement of oxytocin levels in mice.
• Suggestions to improve the presentation of data and conclusions? All my suggestions to improve the presentation of data can found in previous sections. As for improving the authors presentation of their conclusions, the authors should make a considerable re-drafting effort, particularly for the "Discussion", which lacks clarity in how supporting arguments are built and presented. For example, on p.13, I am confused with the argument made by the authors. Their data are focused on synapses onto pyramidal neurons of the mPFC, but here the discussion states that the behavioral phenotype they observed in CD38 AS-cKOP10 might be explained by a lack of mPFC neurons synapsing onto neurons in the Nucleus Accumbens (assuming that "NAc" really refers to this brain region, as the definition is missing from the text). I think the authors should make it clear if this is their interpretation of their own result, which essentially renders their focus on mPFC pointless, or a speculation on possible other mechanisms that could also explain their behavioral results. Personally, given the data shown, I believe the authors should focus on explaining how their data in mPFC might explain the behavioral output observed. The authors could also provide perspectives on how the hypothesis laid down in this paragraph would be tested. When the authors write on p.14 "We identified SPARCL1 as a potential molecule for synapse formation in cortical neurons" why use the word "potential"? Does this mean the authors consider their data on SPARCL1 (one of the key messages of the paper) invalid? If the authors themselves think the role of SPARCKL1 is ambiguous based on their own data, they should perform further experiments. P. 13, the authors write: "Moreover, many studies have shown that astrocyte-specific molecules, including extracellular molecules such as IL-6, are involved in memory function"; Interleukin 6 (Il-6, abbreviation not defined in the manuscript) is definitely not an astrocyte-specific molecule (see, for example, Erta et al., 2021 10.7150/ijbs.4679).

Significance

NATURE AND SIGNIFICANCE OF THE ADVANCE: I think that despite the issues described above, this manuscript, once revised, could have a strong impact in the field. It would fuel the current paradigm shift which puts astrocytes at the forefront of neuronal circuit wiring during development with links to adult behavior. By identifying clear molecular targets involved in astrocyte-driven synaptogenesis, this article could help the clinical field to find new druggable targets, which may help reverse aging-related cognitive decline.

COMPARISON TO EXISTING PUBLISHED KNOWLEGDE: This work adds new data in the specific and growing line of research that study how astrocytes control synaptogenesis. Recent reviews have summarized advances in this field (Shan et al., 2021, 10.3389/fcell.2021.680301; Baldwin et al., 2021, 10.1016/j.conb.2017.05.006).

AUDIENCE: Neuroscientists in general, clinicians interested in cellular and molecular causes of neurodevelopmental disorders leading to social dysfunctions.

REVIEWER EXPERTISE: Astrocyte biology; Astrocyte-neuron interactions and synapse assembly; Neuronal circuit formation and plasticity

Referees cross-commenting

After careful reading of the other comments, I feel that there is considerable agreement/overlap between the reviewers on the main issues with this manuscript. Perhaps the major difference relates to the amount of further work necessary for the manuscript to be publication ready.

As Reviewer 3 rightly points out, this is always a moot point: how much is it reasonable for reviewers to ask authors to do? While I agree with all of Reviewer 1's comments regarding the rigour of the mass-spec/western blot analysis, it seems to me that from a molecular/cell biological point of view, the key issue is whether Sparcl1 is a synaptogenic factor released from astrocytes following CD38/cADPR/calcium signaling (irrespective of whether other factors may be in play); and whether raising Sparcl1 levels is sufficient to recover spine morphology and synapse numbers. Of course, if these experiments were performed in vivo using AAV-mediated overexpression of Sparcl1, it is also reasonable to think that the deficit in social memory may be reversed on testing.

The issues of whether there is a difference in observable behavioral phenotypes between the astrocyte-specific and constitutive CD38 knock-outs is an interesting one, as is why there is only a deficit in social memory seen following astrocyte-specific CD38 ablation. These issues should at least be discussed.

SciScore for 10.1101/2022.03.16.22272465: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

The positive sample was collected downstream from the largest CSO on the Danube River (Pančevo Bridge), where there is a high dilution potential due to an average discharge of 5,600 m3/s in this section of river (13), highlighting the limitations of point samples from surface water for environmental monitoring. Research suggests that high percentages of enveloped viruses (26%) can adsorb to the solid fraction of wastewater (43), that suspended solids may protect viruses from inactivation (44), and that sediments can provide a source of pathogens to the water column (45). Studying the presence of viruses in sediments, rather than water, may provide greater insight into sites that are susceptible to accumulating and harbouring potential pathogens, rather than taking water column point samples, that can suffer from high variability (46). While most studies have focused on the presence of SARS-CoV-2 RNA in wastewater (reviewed by (5), and river water (13,14), very little is known about the potential survival of SARS-CoV-2 in aquatic environments (47,48). Here we tested whether a validated TFUF protocol for the concentration of human non-enveloped enteric viruses from river water (34) could also be used to concentrate an enveloped betacoronavirus, Murine hepatitis virus (MHV). Considering the rigorous biosafety requirements necessary for working with infectious SARS-CoV-2, surrogate viruses, such as MHV, are useful for assessing method performance during monitoring campaigns (49)....

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.

About SciScore

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

SciScore for 10.1101/2022.03.15.484379: (What is this?)

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

Table 1: Rigor

Table 2: Resources

Results from OddPub: Thank you for sharing your data.

Results from 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.

About SciScore

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

SciScore for 10.1101/2022.03.15.484018: (What is this?)

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

Table 1: Rigor

Table 2: Resources

Results from OddPub: Thank you for sharing your data.

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

Despite being a well-defined animal model of COVID-19 with human-like pathology, one key limitation of using golden Syrian hamsters is the limited immunological resources such as well-characterized and validated antibodies (anti-CD8 Abs for example) and the relative paucity of immunologic data on hamster immune responses for correlation with our scRNAseq data. Along these lines, it was surprising that no CD8+ T cells were observed across the hamster groups even though they have been well-described in human scRNAseq studies of BAL samples in SCV2 (57, 58). While BCG vaccination is routinely used in most countries as a TB prevention, the vaccine is generally given intradermally, and recent human studies showing protection by BCG against COVID-19 disease also used intradermal BCG (13). Like other animal studies using BCG in animal models of SCV2 (28) we used the intravenous route based on literature that IV BCG is far more potent against tuberculosis in non-human primates, and evidence that IV BCG reprograms bone marrow hematopoietic stem cells towards a more protective state against bacterial challenge (22). Nevertheless, SCV2 animal studies using percutaneous BCG have shown significant immunologic benefits at the level of flow cytometry (29), and we anticipate that many of the scRNAseq shifts which we observed in IV BCG vaccinated hamsters would be present following intradermal BCG albeit potentially at a lower level. In summary, our study reveals that BCG vaccination reduces ...

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.

About SciScore

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

SciScore for 10.1101/2022.03.11.484006: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from 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.

About SciScore

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

SciScore for 10.1101/2022.03.10.22272213: (What is this?)

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

Table 1: Rigor

Table 2: Resources

Results from OddPub: Thank you for sharing your code.

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

One limitation of our study is that we do not have exposure, contact or location information to explore this application. Sequences belonging to a P.1 sublineage (P.1.14) form a single, large cluster (illustrated as the red cluster in the delta wave dataset in Figure 2), coinciding with a high number of low-diversity P.1 cases present in BC from April 2021 onwards 22, where almost all P.1 samples were within 0-1 SNPs of another P.1 sequence. This phenomenon is also expected with the recent Omicron variant, where rapid spread has led to high numbers of low diversity cases 23. Increasing the probability threshold to 0.9 (or conducting phylogenetic clustering with a smaller maximum clade divergence threshold) breaks up the cluster into smaller groups of identical or near-identical sequences, but this does not reflect genuine underlying clustering (Supplementary figure S2). In such circumstances, we recommend including additional metadata to refine clusters into genetically related groups with shared demography and epidemiology. Alternatively, our approach could be used as a surveillance tool focusing on a particular individuals or settings of interest, identifying sequences that are linked to the focal individuals or exposure sites, moving outwards to a desired number of “rings”. While COVID-19 remains at pandemic levels with high case numbers in many regions globally, it is anticipated that there will be a shift to endemicity characterized by persistent, lower levels of the dis...

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.

About SciScore

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

SciScore for 10.1101/2022.03.11.483867: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

A potential caveat is that the cross-linking rate can be negatively impacted when the binding affinity between protein drug and target decreases drastically due to target mutation, such as mNb6(108FFY) and Beta RBD. In rare cases, mutation can even occur at the cross-linking residue, which would abolish cross-linking at that site. However, as we demonstrated here and previously,19 44 PERx is a general strategy that can be applied on diverse nanobodies and each nanobody can have FFY incorporated at multiple sites to target different natural residues on Spike RBD to achieve crosslink (Figure 3). Therefore, other nanobodies able to bind mutated Spike RBD could be similarly engineered, and diverse covalent nanobodies could be used in combination to ensure the cross-linking and inhibition of future potential variants. When in use, the irreversible cross-linking ability together with multi-targeting of these covalent nanobodies should achieve complete viral inhibition and mechanistically prevent drug resistance. Nonetheless, animal tests and clinical trial are warranted to confirm these potential benefits of covalent protein drugs. Fast reaction kinetics would be critical for effective inhibition of viral infection, as it enables covalent crosslinks to promptly form between the protein drug and the target within a shorter contact time, and to reach higher extent for more effective neutralization. By introducing electron withdrawing fluorine substitution we designed and genetically ...

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.

About SciScore

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

SciScore for 10.1101/2022.03.12.484088: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

Our study has several limitations. First, each cohort in this study consists of a relatively small sample size. Due to patterns of SARS-CoV-2 viral circulation and competition among variants (e.g., early 2020, Beta, and Delta viruses circulated during different time periods and in different geographic regions), as well as the introduction of SARS-CoV-2 vaccination in 2021, the cohorts differ with regards to geographic location, age, and time of sample collection. Second, the antibody-escape mapping experiments were performed in a yeast-displayed deep mutational scanning system which only measures antibody binding to the RBD. There is not always a direct relationship between antibody binding and neutralization, as some epitopes are more immunodominant with regards to neutralization than others, and the relative contributions to neutralization of antibodies targeting different spike epitopes may depend on cell type and ACE2 expression level used in the experimental assays (3,34,35). Despite these caveats, our results have important implications for future viral evolution and vaccine design. Due to the rapid antigenic evolution of SARS-CoV-2, public health experts must decide if and when to update SARS-CoV-2 vaccines (51,52). But because each variant elicits an antibody response with its own susceptibilities to erosion by mutation, the impacts of mutations in future variants may depend on prior exposure history. This knowledge will help to understand which vulnerabilities may ex...

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.

About SciScore

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

SciScore for 10.1101/2022.03.11.22271912: (What is this?)

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

Table 1: Rigor

Table 2: Resources

No key resources detected.

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

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

(as this was not possible) Limitations: It has been suggested that the minimum sample size for external validation should be at least 100 events and 100 non-events. [35] The Leiden cohort had 41 deaths, falling below the number suggested by this rule of thumb. The sample size of the Lombardy cohort was acceptable for external validation. However, the Lombardy cohort consisted of patients that were enrolled in the first months of 2020. After this period, the incidence COVID-19 related mortality has changed a lot due to many treatment changes. This limits the applicability of the recalibrated 4C risk score as the population in which the scores were recalibrated may not be representative of the current patient population in 2022. Furthermore, some patients who were already very ill before being hospitalized for COVID-19 would have chosen to receive end-of-life care at home. Despite not dying in the hospital (in-hospital mortality was the study outcome), the 4C mortality score would have assigned these patients a very high predicted in-hospital mortality risk. This will have reduced the model performance (especially model discrimination), depending on the proportion of patients that received end-of-life care at home. Lastly, missing data may have influenced model performance. For example, the oxygen saturation on room air (a predictor in the 4C mortality score) was missing in more than half of all patients in both the Lombardy cohort and the Leiden cohort. This is most likely bec...

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.

About SciScore

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

SciScore for 10.1101/2022.03.09.22272155: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

Due to the limitations of the instrument and limited availability of chemical references standards, fourteen compounds out of thirty were detected and quantified (Table 4) and (Table 5). Table 4 summarizes the molecular ions, product ions, retention times, and ionization modes for targeted LC-MSMS analysis of these compounds. The results showed that most of the bioactive compounds had higher concentrations in the wastewater of facilities exhibiting SARS-CoV-2 signal suppression than the control facilities. Four compounds had much higher concentrations in the suppression facilities than the control facilities. In particular, 4-nonylphenol, palmitelaidic acid, sodium oleate, and polyethyleneglycol dioleate exhibited concentrations that were 73.3%, 35.3%, 54%, and 58.8% higher in the suppression facilities than the control facilities, respectively (Figure 6). These compounds are mainly used in the production of surfactants and detergents in various industries [53, 54] The concentrations of 4-nonylphenol in the urban wastewaters were determined in Japan, China, and USA. The concentrations were about 190 µg/L [55], 2 µg/L [56], and 400 µg/L [57], respectively. In this study, average concentrations of 4-nonylphenol were 1169 ± 13.3 µg/L and 2025.7 ± 247 µg/L in the control and suppression facilities, respectively. No information was found regarding the concentrations of the other three compounds in wastewater. Palmitelaidic acid was reported to be used to produce cosmetics, soaps, ...

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.

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.

hide

SciScore for 10.1101/2022.03.01.22271507: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

This study has several limitations. First, the participants included in this study were all white, although conversely, this also provided the first data in a non-Chinese, European population. Second, this trial did not include children or pregnant women, and there were only a small number of older adults (35 were ≥60 years in the Ad5-nCoV group). An ideal candidate vaccine for COVID-19 should cover vulnerable populations of all ages. Anti-S protein-specific antibodies have been reported to decline rapidly in individuals who have recovered from COVID-19, especially those who were asymptomatic or had mild symptoms [23]. Third, the sample size was relatively small and some of the calculated 95% CIs were wide. Finally, virus mutation, an emerging problem, may reduce the effectiveness of current vaccines [24]. It is not known whether participants of this study were exposed to any COVID-19 variants. Further study is underway to determine neutralising antibodies to the widely circulating variants following vaccination with Ad5-nCoV, which include but are not limited to the Alpha (B.1.1.7), Beta (B.1.351) and Gamma (P.1 ) variants. Conclusions: Analysis of data from this phase 3 trial demonstrated the immunogenicity and safety of this Ad5-vector based COVID-19 vaccine. More data are required to determine whether this vaccine reduces infections and transmission. Overall, this stable, single-dose vaccine could contribute to the global fight against the evolving SARS-CoV-2 virus.

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">NCT04540419</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">Clinical Trial of Recombinant Novel Coronavirus Vaccine (Ade…</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.

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.

Shen, X.-R., Geng, R., Li, Q., Chen, Y., Li, S.-F., Wang, Q., Min, J., Yang, Y., Li, B., Jiang, R.-D., Wang, X., Zheng, X.-S., Zhu, Y., Jia, J.-K., Yang, X.-L., Liu, M.-Q., Gong, Q.-C., Zhang, Y.-L., Guan, Z.-Q., … Zhou, P. (2022). ACE2-independent infection of T lymphocytes by SARS-CoV-2. Signal Transduction and Targeted Therapy, 7(1), 1–11. https://doi.org/10.1038/s41392-022-00919-x

L’autisme est un trouble neuro-développemental caractérisé par des anomalies ==>problème de définition du champ dans l’interaction sociale, dans la communication et dans le comportement (activités répétitives et stéréotypées). Ces anomalies causent, pour la personne atteinte d’autisme, de grandes difficultés cognitives ==>elles ne sont pas conséquence des interactions sociales ni des stéréotypies mais liées au développent du cerveau

Qu’est-ce que le trouble du spectre de l’autisme : Jadhav, M., & Schaepper, M. A. (2021, août). What is Autism Spectrum Disorder? American Psychiatric Association. Consulté le 11 mars 2022, à l’adresse https://www.psychiatry.org/patients-families/autism/what-is-autism-spectrum-disorderDiagnosis of Autism Spectrum Disorders : signs and symptoms on Social communication & Restricted interests and repetitive behaviors

Of Course Philosophy will loop back on itself

SciScore for 10.1101/2022.03.04.479488: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from 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.

About SciScore

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

SciScore for 10.1101/2022.03.05.483092: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from 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.

About SciScore

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

SciScore for 10.1101/2022.03.03.481940: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from 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.

About SciScore

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

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

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Reply to the reviewers

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

In this manuscript by Wang and colleagues, the authors analyse single-cell RNA-seq (scRNAseq) data by applying transition path theory to infer gene regulatory network (GRN) changes along the transition (reaction coordinate, trajectory) between free energy stable states (i.e. cell types). The work aims to understand how stable cell types, and their regulatory programs (combination of active and repressed genes) switches during differentiation/reprogramming/response (i.e. cell phenotypic transition/CPT). The premise of the work is to assess whether genes within GRNs undergo step-wise repression, state-change and activation (& vice-versa; analogous to SN1) or concurrently regulate gene expression (analogous to SN2). The GRNs are inferred based on highly variable genes and their expression dynamics from RNA velocity over CPT, across 3 scRNA-seq datasets.

The authors first analyse public scRNA-seq dataset of 3003 human A549 adenocarcinomic basal epithelial cells treated with TGF-b for 0hrs, 8hrs, 1 day and 3 days (4 timepoints). The authors select two stable states (Day0-untreated; Epithelial and Day 3-treatment; Mesenchymal) using local kernel densities and set transition paths using Dijkstra shortest path, dividing state space into Voronoi cells (i.e. reaction coordinate value), and constructed single-cell GRNs based on RNA velocity differences (n=500 genes) and a linear model (from Qiu et al). This GRN is based on expression and velocity estimates, and does not distinguish direct from indirect regulation. Calculating interaction frequency (edges) across two stable states over 4 louvain clusters, the authors find global increase in effective edges that correlates with increased active genes; but with variable trend within inter-cluster edges. To quantify the concerted GRN changes between clusters, the authors utilise a "frustration" score (from Tripathi et al 2020). The average frustration score increases and peaks at day 1 treatment, followed by a decline over terminal stable state (day 3-treatment); similar to interaction frequency trends. The author also separately measure network heterogeneity and repeat analysis using alternative transition matrix. The authors conclude that EMT proceeds through concerted regulation of multiple genes first with an increase in inter-cluster edges, frustration and heterogeneity followed by a decrease into final stable state. The authors apply the analysis to scRNA-seq data from (i) pancreatic endocrine differentiation where Ngn3-low progenitors give rise to Ngn3-high, then Fev-high and into glucagon producing a-endocrine cells; (ii) dendate gyrus; radial glial cell differentiation into nIPCs, neuroblast, immature granule and mature granule cells. In both cases, the authors observe concerted regulation with initial increase in inter-community edges, heterogeneity during differentiation followed by decrease towards final stable state. **

The study and ideas in the manuscript are interesting and the methods would be potentially be useful. However, there are a few specific and general comments stated below, which the authors should try to address.

1 • P4: "RC increases first and reaches a peak when cells were treated with TGF-β for about one day, then decreases (Fig. 1G)". It would be better to label the figure with the treatment information. *

Reply: Thanks for your advice. In the revised manuscript, we analyzed two additional datasets, and moved the EMT result in the supplemental Fig. EV8. In the new Fig. 1d, we marked the cell types along the reaction coordinate.

*2 • Fig. 1G and EV1D: Why are the trends different? *

Reply: In the original figures, ____Fig____.1g is the frustration score and EV1D shows the variation of pseudo-Hamiltonian along the reaction coordinate. The frustration score is the focus of this work. We also calculated the pseudo-Hamiltonian since it has been used in the literature. However, we realized that showing both of the results might lead to confusion, so we deleted all pseudo-Hamiltonian results in the revised manuscript.

* 3 • How is the appropriate community/cluster/Louvain resolution selected? This can have a major impact on number of cell states, types and transition path from initial to final state. *

Reply: The number of cell states, types and transition path from initial to final state____ are not determined from the community/cluster/Louvain analyses. For the EMT data, we assume most cells in the initial treatment time are epithelial cells, and those in the final time point are mesenchymal cells. For other datasets, we followed the original publications to assign cell types based on known marker expression.

The Louvain method was applied to coarse grain the gene regulation network, and it does not affect the number of cell states, types and transition path, which were determined separately. To address the reviewer’s question, we also use the Leiden method to adjust the resolution ____(1)____. The resolution does not affect the result. The results are added to Fig. EV12. We tried three different resolution values 0.8,1.0 and 1.2. The number of inter-community edges consistently shows the trend that it increases first then decreases.

Figure EV12 Cell-specific variation of the number of effective inter-community edges between communities calculated with different resolution parameter values for dentate gyrus neurogenesis (a), pancreatic endocrinogenesis (b), and bone marrow marrow hematopoiesis (c). Each dot represents a cell and the color represents the number of inter-community edges____.

• * What effect does the Louvain resolution have on e.g. frustration scores? * Reply: The resolution of community division algorithm doesn’t affect the frustration scores, since the frustration score is based on the gene-gene interactions instead of community assignment.

• * The authors match resolution to samples/timepoints/known prior cell types i.e. 3-4 communities. However it is unclear whether this is enough to describe entire differentiation/transition process. * Reply: This is a good question. In one above reply we have explained how the cell types were determined____. We also agree with the reviewer that these coarse-grained communities cannot reflect the overall heterogeneity and dynamics of the whole process. Notice in most of our analyses (e.g., reaction coordinate and transition paths), we treated the transition as continuous and the distribution of single cell data points in all datasets cover the whole space that involved in cell phenotype transition. The coarse-grained analyses are for further mechanistic insights on how gene regulatory networks are reorganized during the transition process.

• * Gene selection: The selection based on minimum 20 counts as highly expressed genes is arbitrary and dependent on sequencing depth. Perhaps the authors could show distribution of gene counts for the datasets and have a data-driven filtering criteria * Reply: Thanks for the advice. The number 20 is a default value suggested in the package (scVelo) we use, and in another package dynamo the default number is 30. Following the reviewer’s suggestion (together with the next question on the influence of all highly variable genes), we looked for a data-drive filtering criterion. The method has been described in different tools ____(2-4)____. We first grouped the genes into 20 bins by their mean expression values, and____ scaled their dispersions by subtracting the mean of dispersions and dividing standard deviation of dispersions____. Figure EV9 shows the distribution of the minimum shared counts. ____As one can see, most genes counts are larger than 10, and using a smaller value causes error in the following velocity analysis. Therefore we set the minimum shared counts as 10 in the new results.

Figure EV9 Shared counts distribution of the datasets. (a) Dentate gyrus neurogenesis; (b) Pancreatic endocrinogenesis; (c) Bone marrow hematopoiesis.

• * The choice of 500 variable genes (for human A549 cells) is also quite arbitrary. Perhaps, the authors could compare how additional genes (all highly variable genes) affects their analysis and interpretation. * Reply: ____Thanks. Following previous question on shared counts and ____data-driven filtering criteria____,____ we take all the highly variable genes into consideration. The details of gene selection and binarization are given in the Materialss and Methods (Materials and Methods 2) section.

• * How are other factors (sequencing depth, genes detected, #of cell types, multiple branches) affects the connectivity between communities at different phases of transition/development? * Reply: This is a good question. The A549 EMT dataset has a sequence depth of 40000-50000. The ____dentate gyrus neurogenesis dataset____ has a sequence depth of 56,700 reads. A saturation depth would be close to 1,000,000, but there is a compromise between cell number and depth. There are genes that are not detected even under the saturation reads setting. That is why the preprocessing is needed. On the other hand, the network we inferred include both direct and indirect interaction, so the influence of sequence depth and gene number detected can be reduced to a certain extent. We used a random subset of the selected gene and performed the same analyses. The results are consistent with what we obtained using all the genes (Fig. EV11b). With the new gene selection criteria (Materials and Method 2), our analyses are not related with the number of cell types.

We did analysis on another beta branch of pancreatic endocrinogenesis data. The other branches show the same results (Fig. EV4). There are two additional branches in the pancreatic endocrinogenesis dataset. It has been reported that the RNA velocity estimation for the epsilon branch is incorrect ____(3)____. There are too few cells in the delta branch for reliable analyses. Therefore we didn’t present results for these two branches.

Figure EV4 Analyses on the branch of glucagon producing β-cells in pancreatic endocrinogenesis.

(a) Transition graph based on RNA velocity.

(b) The RCs and corresponding Voronoi cells. The large colored dots represent the RC points (start from blue and ends in red). The small dots represent cells with color as cell type.

(c) Frustration score along the RCs.

(d) Cell-specific variation of effective intercommunity regulation. Each dot represents a cell. Color represents the number of effective intercommunity edges within each cell in the GRN.

• • Are the velocity graph, transition matrix and further shortest path estimation derived in a reduced latent space, and if so, how much (nPCs) and what impact does it have. Presumably, the density estimation is not performed in expression space. Reply: Yes. ____The calculation of transition matrix is based on neighbor information. The calculation of neighbors was in the reduced latent space in scVelo and Dynamo. We performed the same analysis by varying number of principal components. The results are similar because the first several components account for large proportion of variance. Figure R1 shows the results of dentate gyrus neurogenesis with the number of principal components being 10, 20 and 30, respectively. In the revised manuscript, we delete the step of using density estimation constrain to simplify the procedure. __Figure R1 Frustration scorer along RCs (left) and cell specific variation of number of effective intercommunity edges (Each dot represents a cell and color represents the number of effective intercommunity edges) in the GRN within each cell (right) when using different number of PCs in analyses (dentate gyrus neurogenesis): (a) number of PCs is 10.*__

(b) number of PCs is 20. (c) number of PCs is 30

* - The figure legends and labels were hard to read. These should be improved for better readability. *

Reply: Thanks. We modified the figure legends and labels.

* - A suggestion would be move the initial results section to methods and highlight the biological interpretation. *

Reply: Thanks for your advice. We moved large part of this section to the Materials and Methods.

*The authors could highly which GRN and representative genes/edge pairs are highest ranked within inter-community and to overall final stable states. *

Reply: Thanks. We list some representative gene pairs in the Table. EV 2&EV 3 &EV 4 for different datasets. And we performed gene enrichment analysis for each community.

* - How does the GRN inference compare to current state-of-the-art GRN inference scRNA-seq methods? *

Reply: we used the method GRISLI to perform the same analysis ____(5)____. The results are similar to what obtained with our current method (Figure EV6). We want to emphasize that the focus of this work is not on another GRN inference method, but discussing some general principles of GRN reorganization during a cell phenotypic transition process.

Figure EV6 Analyses of datasets of dentate gyrus neurogenesis (a), pancreatic endocrinogenesis (b), and hematopoiesis (c) based on GRN inferred with GRISLI.

(a) Frustration score along the RCs of dentate gyrus neurogenesis (left) and cell-specific variation of the number of inter-community edges (right). Each dot represents a cell and color represents the number of inter-community edges in GRN within each cell.

(b) Same as in panel (a), except for pancreatic endocrinogenesis.

(c) Same as in panel (a), except for hematopoiesis.

* - How do extremely noisy/stochastic genes vary in metrics between final stable states? How are the metrics affected by number of cells and stochasticity of expression within a given cluster/community. *

Reply: To address this question, we selected two genes, Id2 and Cdkn1c, with high variance and compare their distributions in the initial and final states. ____The gene distributions show significant shift between the Ngn3 low EP cells and Alpha cells (Fig. R2 a &b left).____ Then we randomly selected a subset (half) of cells and compared the distributions of these high-variance genes in the sub-population (Fig. R2 a&b right). The results are similar to the full-set results.

Fig. R2 Comparison of gene distribution in the initial and final states in pancreatic endocrinogenesis. (a) Comparison of the distribution of gene Id2 at the initial and final states (left), and in the randomly selected sub-population at the initial and final states (right). (b) Comparison of the distribution of Cdkn1c at the initial and final states (left), and in the randomly selected sub-population at the initial and final states (right).

* - Given that the author's approach includes both direct and indirect genes effects, the authors could further prune genes based on existing TF databases or protein-protein validated networks. *Reply: This is a good suggestion. We will work on this idea in future work. As we mentioned, due to constrains of data quality, only tens of transcription factors can be analyzed in these dataset. We list some regulations of transcription factors inferred with current method in Table EV1.

• *It is unclear which GRNs are already known and which ones are novel and biologically relevant * Reply: We compare some regulations inferred with the method and compare these interactions w____ith some references in Table. EV1____.

* - It would be good for authors to comment when there are multiple bifurcations instead of A-B transitions. Particularly in datasets with multiple discrete stable states. *Reply: This is a good question.____ In our analysis, we focus on the transition from one stable state to another stable state. For transition process with multiple bifurcations like____ the pancreatic endocrinogenesis, the results are similar across different branches. For the transition that goes through multiple discrete stable states, for example, a transition from state A____à____B____à____C, we expect to observe two peaks in the frustration score and the number of inter-community edges. We added some discussions in the Discussion section.

• *Another suggestion would be to highlight gene expression of selected markers based on f-regression and mi over the trajectory * Reply: As we modified the criteria of gene selection, we plotted trajectories of some high-variance genes versus the reaction coordinate obtained with different datasets in Fig. EV10 based on current criteria.

Figure EV10 ____Typical trajectories of high variance genes versus RCs of dentate gyrus neurogenesis (a), pancreatic endocrinogenesis (b) and bone marrow ____hematopoiesis ____(c).

* - If possible, a proof of principle could be re-analysis of a perturbation scRNA-seq dataset (e.g. where one path/transition path is stalled) *

Reply: Thanks. This is a really a good suggestion. We will perform more systematic studies in future work.

* Reviewer #1 (Significance (Required)): Nature and significance of advance: The study and ideas in the manuscript are interesting and the methods would be potentially be useful to community. Compare to existing published knowledge: *

*Audience: Predominantly computational audience *

*Your Expertise: PI with background in experimental, computational biology and expertise in single-cell genomic tools and developmental biology *

*

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

Understanding the cellular and molecular basis of cell type or cell state transitions occurring during development or reprogramming is a fundamental challenge. scRNA-seq has provided a window into gene expression programs across thousands of cells undergoing such transitions. Wang and colleagues leverage scRNA-seq and develop an approach to reverse engineer gene regulatory network underlying cells along a path from one cell type/state to another, and characterize community-level properties of this network associated with various stages of the cell phenotype transition. The study is innovative and rigorous, and their results point to how intercommunity interactions increase and then decrease, indicating a concerted regulatory rewiring that orchestrates transitions. Application of their approach to three different datasets also shows that this trend is consistent across three different transitions and maybe a general trend. However, there are some major and minor concerns that need to be addressed.

**Major comments and questions**

1. The analogy to SN1 and SN2 mechanisms of chemical bond formation is very nice.
2. What is the basis for the two statements made in paragraph 3 of Introduction (beginning with "A question arises ...") about transitions being sequential or concurrent? Please *Reply: Thanks. We added references in this paragraph.

* 2.1. Provide references to previous experimental and computational studies that have investigated developmental and reprogramming gene expression programs. *

Reply: Thanks. We added a paragraph in the Introduction.

*

2.2. Describe specific examples of findings that support the two possible transitions highlighted here. Why couldn't transitions happen through an entirely gradual process involving changes to overlapping subsets of genes. *

Reply: Thanks. In the review paper of Naomi Moris et. al., they proposed the hypothesis that cell phenotype transition is similar to a chemical reaction ____(6)____. Thus we extrapolate this hypothesis and test it in our study. For the example of SN1 mechanism, ____Kalkan et al. showed that mouse embryonic stem cells can exit from ____naïve pluripotency____ but remain uncommitted ____(7)____.

Just like the SN1 and SN2 mechanisms are two extremes in chemical reactions and there are cases lie in between, for cell phenotypic transitions we agree with the reviewer that such gradual process may exist. Actually the result in Fig. EV4d shows that the frustration score remains flat for the Fev+ ____à____ Beta transition, suggesting a possible gradual process. With the analyses provided in this work, such as the reaction coordinate, frustration score, heterogeneity, and inter-/intra- community edges, one may perform more systematic studies on a larger number of datasets and enumerate/classify possible patterns of transitions.

• Please make plots of the number of effective intra-community edges vs. number of active genes to support the statement that these two numbers are correlated. *

Reply: We plotted the corresponding intra-community active genes and calculated its correlation coefficient with the number of effective intra-community edges in dentate gyrus neurogenesis (Fig. EV1d). ____The correlation coefficients are 0.91,0.96, 0.99 and 0.96 for community 0, 1, 2 and 3 separately.

* A bunch of notations are not clear:

4.1. What is the "r" in "strongest intercommunity interactions at r = 10 (Fig. 1F)"? Is it the same as the "r" mentioned in the Methods section? *

Reply: r____ is the index number of the discretized reaction coordinate. We added it when we define the reaction coordinate. We modified the conflict usage of r in Materials and Method 4.

4.2. What is "s_i" in "cell-specific effective matrix, Fbar_ij = (2*s_i - 1)*F_ij"? Also, that description of F_ij, f_ij, and H should be moved to the Methods section, and a more high-level, intuitive description should instead be included in this Results paragraph. Reply: represent the binarized gene expression state. is 0 for when gene is in low expression level (silence) and is 1 when gene is in high express level (active). We modified this part following your advice.

* How were the h_f and h_m thresholds chosen? *

Reply: and are based on the distribution of each dataset. Following suggestions from another reviewer, we modified this part. All the highly variable genes were selected and the genes were binarized with the Silverman’s bandwidth method and ____K____means (Materials and Methods 2).

* What is the "density of each single cell" ("⍴_t")? The formulation of the penalty of the distance between cells i and j (the expression with -logP_ij...) is unclear. What is the intuition behind it? What is r? How were the values of r (0.5 and 0.8) chosen? *

Reply: The probability density of cells in the expression space is based on the kernel density estimation. Intuitively, a region in the expression space with more cells is more likely passed by more cell trajectories. The values are based on the distribution of kernel density estimation in different datasets.

In the modified manuscript, we used trajectory simulation and deleted this assumption for simplification.

* One of the reasons the authors state to justify the choice of PLSR is "In the scRNA dataset, the number of genes is often comparable to or larger than the number of cells." This is not true most of the time. In nearly all recent studies, the number of cells is way larger than the number of genes measured. *

Reply: The PLSR method definitely can be used for the data whose number of cells is larger than the number of genes. Also the PLSR method was applied on cells that are the k nearest neighbors of each reaction coordinate, which are a subset of the whole dataset (Materials and Methods 5). While we mainly presented results with the PLSR method, in this revised manuscript we also added results with another method of GRISLI (Materials and Methods 9). The results are similar with what we obtained with PLSR.

* There is a fleeting reference to a nice previous finding that supports their observations: "several lines of evidence support that EMT proceeds through a concerted mechanism. Indeed, both in vivo and in vitro studies have identified intermediate states of EMT that have co-expressed epithelial and mesenchymal genes (Pastushenko et al, 2018; Zhang et al, 2014)". The authors should thoroughly survey the literature related to EMT transition, development of pancreatic endocrine cells, and development of the granule cell lineage in dentate gyrus, to find more previously identified molecular/cellular features relevant to cell state/type transitions, compared and contrasted with findings from this study. *

Reply: Thanks. We added references on these cell phenotype transitions and modified the corresponding part. We do want to point out that the main focus of this work is that all these processes share a common feature of transient increase of intercommunity interactions.

* What is the "dynamo" package, which is supposed to contain a Python notebook? As of now, the code and data have not been made available. Both need to be released along with thorough documentation on how to run the code to reproduce the analyses described here. *Reply: Thanks. Dynamo is a python package accompanying our recent publication ____(8)____. We uploaded the code on Github and added the link of Dynamo.

* **Minor comments and questions**

1. Replace "confliction" throughout the manuscript with "conflict" or "conflicting" as appropriate. *

Reply: Thanks. We modified them.

* Paragraph two of the Introduction (beginning with "Another example of transitions ...") is missing multiple references, esp. for the last four sentences. *

Reply: Thanks. We added references.

* There are direct quotes from previous papers like "predicts the future state of individual cells on a timescale of hours". The authors are highly encouraged to check for usage of exact phrasing using available text software such as iThenticate. *

Reply____: ____Thanks a lot for pointing out this severe mistake. We re-edited the manuscript and checked with iThenticate. *

*

• "Each community contains both E and M genes": what does this mean? *

Reply: The E (M) genes are defined as those genes that are active or have high expression levels in epithelial (mesenchymal) state or sample. As we reorganized the manuscript, we add this explanation for all datasets in the caption of Fig.1i.*

*

• Reference to Qui 2021 is missing in the "Path analysis" subsection under Methods. *

Reply: We added it in the Methods.

* Fix: "transition between the cells that their sample time points are successive" in Methods. *

Reply: Thanks. ____We modified it.

* In Methods, under "Network inference", it is "partial least square regression" (not *least* s square). *

Reply: Thanks. We modified it.

* Figure 1: The cyan, magenta, and lime in 1C are very hard to see and, perhaps, the grey of the points can be made lighter. Also, change the red and green colors for the arrows in 1I to something else. These colors are not colorblind-friendly. *

Reply: Thanks. We re-plotted the figures and changed the colormap.*

*

• Periods and commas are missing at several places. Reply: Thanks. We modify these and re-edit the manuscript.

Reviewer #2 (Significance (Required)):

The study uses RNA-velocity calculated from scRNA-seq data in an inventive way to characterize paths that reflect cell phenotype transitions. Then, a sparse gene regulatory network is reverse engineered from the data and the community structure within this network is examined at various stages along the transition to make observations about inter- and intra-community regulation and network "frustration". However, the study lacks the context of existing literature in terms of previous work studying cell transitions both experimentally and computationally. Adding this context (as suggested in the comments) will considerably improve the utility and significance of the findings. Overall, this study will be of broad interest to researchers interested in development and reprogramming as well as computational scientists developing and applying methods for scRNA-seq data analysis, trajectory inference, and network reconstruction. All the comments and questions raised here are based on my background and expertise in omics data (including scRNA-seq) analysis and network biology.

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

The authors analyze three datasets of Single cell RNA velocity measured during phenotypic transition. They infer the gene regulatory network in each case and characterize the transition between the initial and final expression states (in which different sets of genes are expressed). Their motivating question was to find whether during such transitions first genes characterizing the initial state are no longer expressed and only then the genes associated with the final state start expressing or alternatively there is gradual transition through an intermediate state in which subsets of both initial and final state genes are transiently expressed.

They define a measure of regulatory frustration representing the mismatch between regulatory signals a gene receives and its current expression state. They conclude that phenotypic transitions involve transient interactions between otherwise non-interacting gene modules and a temporary increase of gene frustration, which is relaxed once the final expression state is reached.

The study uses of advanced inference and machine learning methods.

I find the question studied in this manuscript interesting, opening avenue to further questions and studies and relevant to different scientific communities. Personally I think that the focus of the paper should be the exposition of the methods used this manuscript would benefit from a longer format, but that depends of course on the journal they are aiming at. *

*

Statistical analysis is missing. Especially since the authors mention the potential of over-fitting due to large number of genes (on the order of the number of cells) - I think the authors should provide a sensitivity analysis testing how sensitive are the conclusions to the choice of cells or genes by applying the methods to subsets of the cells / genes. *

Reply: Thanks. For the subset of cells, we randomly selected cells from the dataset and performed the analyses (Fig. EV11a). For the subset of genes, we selected a subset of genes randomly and performed the analyses (Fig. EV 11b). We found the results are not affected. We also perform another statistical analysis by varying the value of resolution in community detection algorithm. And we found that the conclusion on variation of inter-community edges is not affected (Fig. EV12).

Figure EV11 Statistical analyses of dentate gyrus neurogenesis. Each dot represents a cell and color represents the number of inter-community edges.

(a) Frustration score along the RCs (left) and cell-specific variation of the number of inter-community edges (right) of a randomly selected sub-population of 2000 cells (from a total of 3184 cells);

(b) Frustration score along the RCs (left) and cell-specific variation of the number of inter-community edges) (right) of cells on the space of 400 randomly selected genes (from a total of 678 genes).

*What is the meaning of the distribution in the frustration plots? *

Reply: For each cell we calculated a frustration score. Therefore for cells in each Voronoi cell (which is a geometric cell, don’t be confused with the biological “cells”) along the reaction coordinate (Fig.1d, Fig. 2b &2g), we obtained a distribution of the frustration scores.*

In general, the conclusions are well-justified, but I think some statements in the discussion are inaccurate: "intercommunity interactions of a GRN are indeed minimized' - are they minimal or are they only lower at the stable states? There are two stable states - for which of them is intercommunity interaction lower? *

Reply: Thank. We agree with the reviewer and modified the writing. Comparing with the transition state, the number of intercommunity interactions is less for the stable states. ____The datasets' quality are not high enough for us to investigate whether ____"intercommunity interactions of a GRN are indeed minimized”.*

It is written in the discussion that 'for all three datasets frustration decreases with differentiation', but then Fig. 1g shows the opposite (final state is more frustrated than initial state). It is interesting to discuss the differences between the datasets analyzed in that respect and what could cause transition to a more frustrated state. I suggest that the authors also refer in the discussion to related questions and possible follow-up studies, such as: what determines the duration of the phenotypic transition? A relevant number is the switching time of a single gene. *

Reply: Good suggestion. Compared to other datasets, we found that the result of EMT shows larger variances. The relative difference of the frustration score is also affected by the GRN inference algorithm. For example, the difference between initial and final frustration scores of the pancreatic endocrinogenesis is more significant when using the GRISLI method (Figure EV6b). Given these, the trend that the frustration scores in the transition states transiently increase keep consistent.

Our conclusion is limited by the quality of the data. So we delete this part of discussion in the manuscript.

Qiu et al. have shown that splicing-based ____RNA velocities are relative, while metabolic-labeling-based RNA velocities are more quantitative and accurate____(8)____. We will re-analyze this problem if data with metabolic labeling becomes available.

* The authors mention at the end that the networks can often reach multiple final states from a common initial states. Do such transitions share some of their path (and in particular the intermediate frustrated state)? Given the intermediate connected state, it would be interesting to characterize the network stability to perturbations. *

Reply: This is a very important question. To reliably address these questions, we need higher quality data. We plan to characterize the network stability to perturbations in future studies, while in our recent paper using a full nonlinear modeling framework____(8)____, we performed in silico perturbations.

* While interesting, the manuscript itself is unfortunately hard to read and would benefit from major editing, including better exposition of the science and language editing. *

Reply: Thanks. We revised the manuscript extensively.*

Methods: Description of PCA and 'revised finite temperature string method' are missing in the Methods section. *

Reply:____ Thanks. PCA is used in RNA velocity analysis for dimension reduction. We added this in Materials and Methods 3. The revised string method is in Materials and Methods ____4.

*

Some examples:

Figure captions are very short and often non-informative. Some variables are not defined (or only defined later on) and the reader then needs to guess their meaning: it took me a while to understand what is 'r' in Fig. 1f and what 'r=10' (p. 4) means. *

Reply: Thanks. ____r____ represents the index number of reaction coordinates. We added this in the manuscript where we define reaction coordinates.*

p. 4: what are 'f' (as opposed to F) and 's_ij' and 's_j' (expression states?) Or is fs_ij one variable? What does a Hamiltonian of a cell mean (p. 4, bottom)? *

Reply: is the regulation of gene ____j on gene i, and is the expression state of gene i (0 for silence, and 1 for active expression). is the frustration value of regulation from gene j to gene i.

The pseudo Hamiltonian value is proposed in the literature as an analogy of ____the magnetic systems following the work of Boolean model in EMT ____(9)____. A high Hamiltonian value indicates that the cell is in an unstable state. In the original manuscript we included this quantity since it has been discussed in the literature. However we found it causes confusion and is not necessary for our discussions, so we removed the pseudo-Hamiltonian results in the revised manuscript. * P. 4: how are 'E and M genes' defined? *

Reply: The E (M) genes are defined as those genes that are active or have high expression levels at the epithelial (mesenchymal) state or sample. We explained our general strategy in the caption of Fig.1i . * What does 'network heterogeneity' (p. 5) mean? *

Reply: Network heterogeneity measures how homogenously the connections are distributed among the genes____(10)____. A high heterogeneity ____means that some genes have high degree of connectivity (the so-called hubs), while some have low degree of connectivity.

*

Fig. 1 is too tiny and hard to read and details are missing. *

Reply: Thanks. We modified this figure and caption.*

A glossary for all the acronyms used would be very helpful. *

Reply: Thanks. We added glossary in the manuscript.*

Language (some examples):

p. 5 bottom: Another system is on development... invitro -> in vitro

p. 6: 'measure on developmental potential' -> measure of... *

Reply: Thanks. We modified these and re-edited the whole manuscript.*

Reviewer #3 (Significance (Required)):

This study presents a methodological advance in demonstrating the application of data analysis methods to study developmental phenotypic transitions. High throughput measurements and computation power available today enable putting to test theoretical conjectures, as made by Waddington. I think this is a promising line of research, which could be used to further develop the computational methods as well as to further our understanding of developmental transitions and potentially develop associated mathematical modeling frameworks.

This study should be of interest to a diverse readership composed of developmental biologists as well as to quantitative biologists and CS researchers applying optimization techniques and data analysis methods to high-throughput biological data.

I am not an expert on the computational methods applied in this manuscript and hence cannot assess their correct use and statistical analysis.

*

1. Traag VA, Waltman L, & van Eck NJ (2019) From Louvain to Leiden: guaranteeing well-connected communities. Scientific Reports 9(1):5233.
2. Stuart T, et al. (2019) Comprehensive Integration of Single-Cell Data. Cell 177(7):1888-1902.e1821.
3. Bergen V, Lange M, Peidli S, Wolf FA, & Theis FJ (2020) Generalizing RNA velocity to transient cell states through dynamical modeling. Nature Biotechnology 38(12):1408-1414.
4. Wolf FA, Angerer P, & Theis FJ (2018) SCANPY: large-scale single-cell gene expression data analysis. Genome Biology 19(1):15.
5. Aubin-Frankowski P-C & Vert J-P (2020) Gene regulation inference from single-cell RNA-seq data with linear differential equations and velocity inference. Bioinformatics (Oxford, England) 36(18):4774-4780.
6. Moris N, Pina C, & Arias AM (2016) Transition states and cell fate decisions in epigenetic landscapes. Nature reviews. Genetics 17(11):693-703.
7. Kalkan T, et al. (2017) Tracking the embryonic stem cell transition from ground state pluripotency. Development 144(7):1221-1234.
8. Qiu X, et al. (2022) Mapping Transcriptomic Vector Fields of Single Cells. Cell 185(4):690-711.
9. Font-Clos F, Zapperi S, & La Porta CAM (2018) Topography of epithelial–mesenchymal plasticity. Proceedings of the National Academy of Sciences 115(23):5902-5907.
10. Gao J, Barzel B, & Barabási A-L (2016) Universal resilience patterns in complex networks. Nature 530(7590):307-312.

Figure 3: How does p300 acetylation effect MER/ERK signaling A) Different mutations, arginine mutation was similar to WT, but other mutants had lots of MEK/ERK phospho B) C) Kinase activity --> increased MEK/ERK phosphorylation D-E) Meh F) KQ results in higher tumor volume G) Qualitative tumor size comparison --> KQ is bigger H) Graphically I) Rat with tumors J) KSR with mutant are higher than wt K) Mutations effect on heterodimers L) CRAF heterodimers M) N) Myc-p300 increased scaffold protein binding O) Arginine mutant isn't as active Mutants result in increased protein interactions

Figure 2: Focusing on K601 of BRAF, what is the deacetylase? --> p300 A) Shows B) C) Meh D) shRNA knockdown enhances MEK and ERK signaling E) Meh F) Knockout has more MEK ERK signaling G) Sirt1 has more MEK ERK signaling H) Insulin activates p300, activates acetylation I) Small difference in pERK --> pathway turned off sooner, then later, 5 min and 40 min. J) Meh K) Knockdown effect on colony formation --> Sirt1 depends on BRAF L) Sirt1 depends on BRAF M) Xenograph into mice and graphed. Sirt1 depends on BRAF

SciScore for 10.1101/2022.03.03.22271601: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

Limitations of this study were that the participants were not blinded for the study vaccine, which may have influenced the reporting of adverse reactions. Secondly, the small sample size in each group did not allow us to identify any potential rare AEs. However, findings from this study have been used to inform the Thailand National COVID-19 vaccination program recommendation by including the CoronaVac-ChAdOx1 regimen during the time when mRNA vaccines were not available. Ongoing surveillance of any rare AEs and the clinical effectiveness of this heterologous schedule will inform the feasibility of such schedule against the SARS-CoV-2 variants. Lastly, the BNT162b2 booster in this study was only given to two groups who received CoronaVac and ChAdOx1, and there was no homologous three-dose BNT162b2 group for comparison of their immunogenicity. Despite this, it was clear that BNT162b2 as the third booster vaccination provided high neutralizing activity against Delta and Omicron, which is likely to provide protection against these two predominant variants. Future studies incorporating mRNA booster to other heterologous primary schedules will be of interest, particularly with other COVID-19 vaccines that have recently received WHO EUL. In conclusion, we found that heterologous COVID-19 primary series using either ChAdOx1 or BNT162b2 as second dose were highly immunogenic against SARS-CoV-2 ancestral strain, Beta and Delta variants, but low neutralizing antibody against Omicron. A...

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.

About SciScore

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

SciScore for 10.1101/2022.03.02.482639: (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

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.

About SciScore

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

Reviewer #1 (Public Review):

The authors investigated spindle growth dynamics during anaphase B in S. pombe, a unicellular eukaryote which undergoes closed mitosis. In order to accurately quantify spindle growth speeds, the authors tagged Alp7, a protein that localizes to plus ends of microtubules, with 3xGFP, and tracked its position in the dividing cell during mitosis, resulting in precise measurements of both the duration and velocity of microtubule growth events for the first time. By performing these experiments across a set of different genotypes (e.g. in strains with knock-outs of specific microtubule-associated proteins (MAPs)) the authors conclude that (1) microtubule rescues preferentially occur at the midzone edges, (2) microtubule growth speed decreases throughout anaphase B, independent of several known anaphase MAPs and (3) wrapping of the nuclear membrane around the spindle is responsible for this reduction in MT growth speed.

The data in this study is of very high quality and the conclusions are largely well supported by the data, but some changes could be made in the interest of simplicity and clarity, and some additional experiments should ideally be performed to strengthen the third claim.

Many changes occur in the nuclear bridge in late anaphase, including the disassembly of nuclear pore complexes and the fenestration of the nuclear envelope (Lucena et al. 2015, Exposito-Serrano et al. 2020, Dey et al. 2020). This opens up the possibility for a different interpretation of some of the authors' data - for example, that local alterations in the permeability barrier directly alter microtubule polymerisation dynamics, rather than the wrapping of the nuclear envelope in the bridge per se. This could help explain the ark1-as3 data, for example, in which (non-physiological) membrane tubes wrap around the spindle but local NEB is prevented (Dey et al. 2020).

The authors state that 'transition from fast to slow microtubule growth occurs in the absence of known anaphase MAPs' (heading paragraph 3 (239-240) and Figure 2), yet two paragraphs later, they show that the MAP Ase1 does in fact impact this transition. This distinction might prove confusing for readers, especially those unfamiliar with the S. pombe spindle.<br /> The authors state that Ase1 is required for the decrease in growth speed during anaphase. While the example kymograph in Figure 5B looks convincing, there seem to be too few points at the right side of Figure 5F to properly see the two distributions. Without any statistics to compare wt to ase1∆, it is difficult to tell if the apparent absence of decrease is real. In addition, as the authors also show in Figure 1C-E, the spindle collapses, causing the final spindle length to be shorter than wt. It could be that the spindle collapses before the characteristic drop in growth rate could be observed.<br /> One of Ase1's direct interactors, Cls1, was shown to not have an impact on the transition of fast to slow microtubule growth (Figure 2). In Ase1's case, a full deletion of the gene was necessary to reveal loss of the transition. The ase1off strain with reduced Ase1 expression showed a similar transition to wt, similar to the cls1off strain. cls1 is an essential gene and cannot be deleted, but similar to its interactor, its phenotype might be hidden even at low expression values.

Some observations, such as Figure 2G-L, lack associated statistics. In addition, since Alp7 tagged with 3xGFP is used throughout the paper, it seems important to test whether this tag influences microtubule dynamics.

SciScore for 10.1101/2022.02.28.22271643: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

Several limitations exist in the mechanistic literature we identified: i) little direct empiric evidence was available to support or refute the proposed hypotheses; where direct empiric evidence was available, it often derived from case reports or small series, ii) when assessing laboratory findings in case reports/series/retrospective studies, it is not clear whether any differences seen (e.g., increases in NK cells, autoantibodies) reflect a causal pathological immune response or reactive adaptive responses to the myocardial inflammation, iii) the emergence of new studies refuting some of the proposed mechanisms; for example, articles stating no reports of eosinophilia, are out-dated due to reports finding evidence of this, iv) there has been a lack of invasive investigations (e.g., biopsy, tissue morphology, special studies to detect injury, immune activity, virus, etc.) given the typically mild course of the clinical conditions observed, and v) it is difficult to confirm a causal link; for example, an important proportion of cases observed or reported may not be vaccine-related and this will contribute to the heterogeneity of presentations, clinical characteristics, and resulting hypotheses. Choi et al.87 described a fatal case of myocarditis after mRNA vaccination and compared the case to another fatality reported by Verma et al.69 both of which had comprehensive clinicopathological analysis. The two cases were remarkably different, suggesting “that myocarditis after COV...

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.

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.

no errors

SciScore for 10.1101/2022.02.22.481551: (What is this?)

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

Table 1: Rigor

Table 2: Resources

Results from OddPub: Thank you for sharing your data.

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

It should be noted there were some limitations in genome quality and coverage that may have resulted in failure to detect additional mutations that were present. All Ontario WTD clade samples (including the associated human case) had missing terminal domains and contained internal regions with no or low coverage when sequenced using the ARTIC v4 amplicon scheme. This is a widespread issue that may explain the rarity of the 3’ proximal ORF10:L37F in GISAID. Significantly in our samples this meant there was no or <10x coverage in all 5 WTD sequences from ∼27000-27177 (dropout of ARTICv4 amplicons 90-91) which includes regions of the M gene. However, by combining the ARTIC v4 sequencing with additional sequencing using probe-based enrichment we were able to compensate for this dropout and generate high coverage and completeness (<100 positions with no coverage in all WTD and <100 positions with <10X coverage in 3/5 WTD genomes; see Table S3). The neutral non-synonymous to synonymous mutation ratio (dN/dS ∼ 1) and evenly distributed mutations in the WTD lineage contrasts with the signatures of strong selection in the equivalently divergent Omicron VOC (dN/dS ∼6 and accumulation of spike mutations). In combination with the most recent common ancestor being phylogenetically distant and dating from 2020, we infer that the WTD lineage likely diverged in 2020 and has been maintained in wildlife under relatively neutral selection since that time. It is possible that the absence of pre-...

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.

About SciScore

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

SciScore for 10.1101/2022.02.24.481899: (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

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.

About SciScore

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

SciScore for 10.1101/2022.02.24.481778: (What is this?)

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

Table 1: Rigor

Table 2: Resources