annotation meta: may need new tag: demonstrating....

Reminds me of that really long series of things on a contamination theory of obesity.

SciScore for 10.1101/2022.01.14.22268980: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from TrialIdentifier: No clinical trial numbers were referenced.

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

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

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

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.

JD

SciScore for 10.1101/2022.01.11.475922: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from TrialIdentifier: No clinical trial numbers were referenced.

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

Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on pages 32, 26, 28 and 30. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

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

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 10.1101/2022.01.11.475901: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from TrialIdentifier: No clinical trial numbers were referenced.

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

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

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

牛了牛了终于实现pdf带tag批注自由

SciScore for 10.1101/2022.01.10.475532: (What is this?)

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

Table 1: Rigor

Table 2: Resources

Results from OddPub: Thank you for sharing your data.

Results from TrialIdentifier: No clinical trial numbers were referenced.

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

Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on pages 33, 34 and 31. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

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

Author Response

Reviewer #1 (Public Review):

This manuscript addresses a major issue facing consumers of structure-organism pair data: the landscape of databases is very difficult to navigate due to the way data is made available (many resources do not have structured data dumps) and the way data is standardized (many resources' structured data dumps do not standardize their nomenclature or use stable entity identifiers). The solution presented is a carefully constructed pipeline (see Figure 1) for importing data, harmonizing/cleaning it, automating decisions about exclusions, and reducing redundancy. The results are disseminated through Wikidata to enable downstream consumption via SPARQL and other standard access methods as well as through a bespoke website constructed to address the needs of the natural products community. The supplemental section of the manuscript provides a library of excellent example queries for potential users. The authors suggest that users may be motivated to make improvements through manual curations on Wikidata, through semi-automated and automated interaction with Wikidata mediated by bots, or by addition of importer modules to the LOTUS codebase itself.

Despite the potential impact of the paper and excellent summary of the current landscape of related tools, it suffers from a few omissions and tangents:

1. It does not cite specific examples of downstream usages of structure-organism pairs, such as an illustration on how this information in both higher quantity and quality is useful for drug discovery, agriculture, artificial intelligence, etc. These would provide a much more satisfying bookend to both the introduction and conclusion.

Thank you for this remark. We deliberately decided not to insist too heavily on the application examples of the LOTUS outputs. Indeed we are somehow biased by our main investigation field, natural products chemistry, and expect that the dissemination of specialized metabolites occurrences will benefit a wide range of scientific disciplines (ecology, drug discovery, chemical ecology, ethnopharmacology, etc.)

However, Figure 5 was established to illustrate how the information available through LOTUS is quantitatively (size) and qualitatively (color classes) superior to what is available through single natural products resources.

As added in the introduction, one of the downstream usages of those pairs is for example to perform taxonomically informed scoring as described in https://doi.org/10.3389/fpls.2019.01329. Obtaining an open database of natural products’ occurrences to fuel such taxonomically informed metabolite annotation tools was the initial impulse for us to build LOTUS. These metabolite annotation strategies, tailored for specialized metabolites, have been shown to offer appreciable performance improvements for current state-of-the-art computational metabolite annotation tools. Since metabolite annotation is still regularly cited as “the major bottleneck” in metabolomics in the scientific literature over the last 15 years (https://europepmc.org/article/med/15663322, https://doi.org/10.1021/acs.analchem.1c00238), any tangible improvement in this field is welcome. With LOTUS we offer a reliable and reusable structures-organisms data source that can be exploited by the community to tackle such issues of importance.

Other possible usages are suggested in the conclusion, but benchmarking or even exemplifying such uses is clearly out of the scope of this paper, each one of them being an article per se.

The additional queries are written in our first answer (see “essential revisions”) and demonstrate the impact of LOTUS on accelerating the initial bibliographic survey of chemical structures occurrences over the tree of life.

This query (https://w.wiki/4VGC) can be compared to a literature review work, such as https://doi.org/10.1016/j.micres.2021.126708. In seconds, it allows retrieving a table listing compounds reported in given taxa and limits the search by years.

1. The mentions of recently popular buzzwords FAIR and TRUST should be better qualified and be positioned as a motivation for the work, rather than a box to be checked in the modern publishing climate.

It is true that the modern publishing system certainly suffers from some drawbacks (also critically mentioned within the paper). However, after consultation of all authors, we believe that because LOTUS checks both boxes of FAIR and TRUST, we would rather stick to these two terms. In our view, rules 1 (Don’t reinvent the wheel) and 5 (put yourself in your user’s shoes) of https://doi.org/10.1371/journal.pcbi.1005128 apply here. Both terms are indeed commonly (mis-)used but we felt that redefining other complicated terms would not help the reader/user.

1. The current database landscape really is bad; and the authors should feel emboldened to emphasize this in order to accentuate the value of the work, with more specific examples on some of the unmaintained databases

We perfectly agree with this statement and it is the central motivation of the LOTUS initiative to improve this landscape. It was a deliberate choice not to emphasize how bad the actual landscape is, but rather to focus on better habits for the future. We do not want to start devaluing other resources and elevate our initiative at the cost of others. We also believe that an attentive look at the complexity of the LOTUS gathering, harmonization, and curation speaks for itself and describes the huge efforts required to access properly formatted natural products occurrence data.

If the reviewer and editors insist, although not in our scope, we are happy to list a series of specific (but anonymized) examples of badly formatted entries, of wrong structures-organisms associations, or poorly accessible resources.

1. While the introduction and supplemental tables provide a thorough review of the existing databases, it eschews an important more general discussion about data stewardship and maintenance. Many databases in this list have been abandoned immediately following publication, have been discontinued after a single or limited number of updates, or have been decommissioned/taken down. This happens for a variety of reasons, from the maintainer leaving the original institution, from funding ending, from original plans to just publish then move on, etc. The authors should reflect on this and give more context for why this domain is in this situation, and if it is different from others.

We do agree with the reviewer and added a “status” column in the table https://github.com/lotusnprod/lotus-processor/blob/main/docs/dataset.csv We chose 4 possible statuses:

• Maintained (self-explanatory)
• Unmaintained: the database did not see any update in the last year.
• Retired: the authors stated they will not maintain the database anymore.
• Defunct: the database is not accessible anymore

As for question 3 above, we decided not to focus too heavily on the negative points and resume the current situation in the previous table. Reasons for the databases publishing being in this situation are multiple, and we think they are well summarized in https://doi.org/10.1371/journal.pcbi.1005128 (Rule 10: Maintain, update, or retire), already cited in the manuscript introduction.

1. Related to data stewardship: the LOTUS Initiative has ingested several databases that are no longer maintained as well as several databases with either no license or a more restrictive license than the CC0 under which LOTUS and Wikidata are distributed. These facts are misrepresented in Supplementary Table 1 (Data Sources List), which links to notes in one of the version controlled LOTUS repositories that actually describes the license. For example, https://gitlab.com/lotus7/lotus-processor/-/blob/8b60015210ea476350b36a6e734ad6b66f2948bc/docs/licenses/biofacquim.md states that the dataset has no license information. First, the links should be written with exactly what the licenses are, if available, and explicitly state if no license is available. There should be a meaningful and transparent reflection in the manuscript on whether this is legally and/or scientifically okay to do - especially given the light that many of these resources are obviously abandoned.

This point is a very important one. We did our best to be as transparent as possible in our initial table. Following the reviewer’s suggestion, we updated it to better reflect the licensing status of each resource (https://github.com/lotusnprod/lotus-processor/blob/main/docs/dataset.csv). Therefore, we removed the generic “license” header, which could indeed be misleading, and replaced it with ”licensing status”, filled with the attributed license type and hyperlink to its content). It remains challenging since some resources changed their copyright in the meantime. We remain at the editor and reviewers’ disposal for any further improvement.

Moreover, as stated in the manuscript, we took care of collecting all licenses and contacted authors of resources whose license was not perfectly explicit to us, therefore accomplishing our due diligence. Additionally, we contacted legal offices in our University and explained our situation. We did everything that we had been advised.

1) To the best of our knowledge, the dissemination of the LOTUS initiative data falls under the Right to quote for scientific articles, as we do not share the whole information, but only a very small part.

2) We do not redistribute original content. What comes out of LOTUS has undergone several curation and validation steps, adding value to the original data. The 500 random test entries, provided in their original form for the sake of reproducibility and testing, are the only exception.

Many scientific authors forget about the importance of proper licensing. While it might be deliberate to restrict the use, inappropriate license choice (or omission) is too often due to a lack of information on its implication.

All authors of the utilized resources can freely benefit from our curation. We are sharing with the community the results of our work, while always citing the original reference.

Concerning the possible evolution of licensing, it remains a real challenge. While we tried to “freeze” the license status when we accessed the data, some resources updated their licensing since then. This can be tracked in the git history of the table (https://github.com/lotusnprod/lotus-processor/blob/main/docs/dataset.csv). Discrepancies between our frozen licensing (at the time of gathering) and actual license can therefore occur. Initiatives such as https://archive.org/web could help solving this issue, coming with other legal challenges.

1. The order of sections of the manuscript results in several duplicated, but not further substantiated explanations. Most importantly, the methods should be much more specific throughout and the results/discussion should more heavily cross-link to it, as a reader who examines the paper from top to bottom will be left with large holes of misunderstanding throughout.

As our paper focuses a lot on the methods, the barrier between results & methods becomes thinner. We took into account the reviewers’ suggestions and added some additional cross-links for the reader to be able to quickly access related methods.

1. The work presented was done in a variety of programming languages across a variety of repositories (and even version control systems), making it difficult to give a proper code review. It could be argued that the most popular language in computational science at the moment is Python, with languages like R, Bash, and in some domains, still, Java maintaining relevance. The usage of more esoteric languages (again, with respect to the domain) such as Kotlin hampers the ability for others to deeply understand the work presented. Further, as the authors suggest additional importers may implemented in the future, this restricts what external authors may be able to contribute.

Scientific software has indeed always been written in multiple languages. To this day, scientists have used all kinds of languages adapted both to their needs and their knowledge. Numpy uses Fortran libraries and many projects published in biology and chemistry recently are in Java, R, Python, C#, PHP, Groovy, Scala… We understand that some authors are more comfortable with one language or another. But R syntax is for example much more distant from Python's syntax than Kotlin can be. We needed a highly performant language for some parts of the pipeline and R, Bash, or Python were not sufficient. We decided to use Kotlin as it provides an easier syntax than Java while staying 100% compatible with it.

The advantage of the way LOTUS is designed is that importers are language-agnostic. As long as the program can produce a file or write to the DB in the accepted format, it can be integrated into the pipeline. This was our goal from the beginning, to have a pipeline that can have its various parts replaced without breaking any of the processes.

1. As a follow up to the woes of point 4., 5., and 7., the manuscript fails to reflect on the longevity of the LOTUS Initiative. Like many, will the project effectively end upon publication? If not, what institutions will be maintaining it for how long, how actively, and with what funding source? If these things are not clear, it only seems fair to inform the reader and potential user.

LOTUS is an initiative that aims to improve knowledge management and sharing in natural products research. Our first project, which is the object of the current manuscript, is to provide a free and open resource of natural products occurrences for the scientific community. Its purpose is not to be a database by itself, but instead to provide through Wikidata and associated tools a way to access natural products knowledge. The objective was not to create yet another database (https://doi.org/10.1371/journal.pcbi.1005128), but instead to remove this need and give our community the tools and the power to act on its knowledge. This way, as everything is on Wikidata, the initiative is not “like many”. This also means that this project should not be considered and evaluated exactly like a classical DB. Once the initial curation, harmonization, and dissemination jobs have been done, they should ideally not be run again. The community should switch to Wikidata as a point of access, curation, and addition of data. If viewed with such arguments in mind, yes, LOTUS can live long!

Wikimedia is a public not-for-profit organization, whose financial development appears to indicate solid health https://en.wikipedia.org/wiki/Wikimedia_Foundation#Finances.

In terms of funding sources, we would like to refer to https://elifesciences.org/articles/52614#sa2 , which stated the following in response to a similar question: "Wikidata is sustained by funding streams that are different from the vast majority of biomedical resources (which are mostly funded by the NIH). Insulation from the 4-5 year funding cycles that are typical of NIH-funded biomedical resources does make Wikidata quite unique." The core of the Wikidata funding streams are donations to the Wikipedia ecosystem. These donations - with a contributor base of millions of donors from almost any country in the world, chipping in at an average order of magnitude of around 10 dollars - are likely to continue as long as that ecosystem is useful to the community of its users. See <https://wikimediafoundation.org/about/financial-reports for details>.

1. Overall, there were many opportunities for introspection on the shortcomings of the work (e.g., the stringent validation pipeline could use improvement). Because this work is already quite impactful, I don't think the authors will be opening themselves to unfair criticism by including more thoughtful introspection, at minimum, in the conclusions section.

We agree with the reviewer and therefore, list again the major limitations of our processing pipeline:

First, our processing pipeline is heavy. It includes many dependencies and requires a lot of time for understanding. We are aware of this issue and tried to simplify it as much as possible while keeping what we considered necessary to ensure high data quality. Second, it can sometimes induce errors. Those errors, ranging from unnecessary discarded correct entries to more problematic ones can be attributed to various parameters, reflecting the variety of our input. We will therefore try listing them, keeping in mind that the list won’t be exhaustive. For each detected issue, we tried fixing it at best, knowing it will not lead to an ideal result, but hopefully increase data quality gradually.

● Compounds

○ Sanitization (the three steps below are performed automatically since we observed a higher ratio of incorrect salts, charged or dimerized compounds. However, this also means that true salts, charged or dimeric compounds were erroneously “sanitized”.)

■ Salt removals

■ Charged molecules

■ Dimers

○ Translation (both processes below are pretty error-prone)

■ Name to structure

■ Structure to name

● Biological organisms

○ Synonymy

■ Lotus (https://www.wikidata.org/wiki/Q3645698, https://www.wikidata.org/wiki/Q16528).

This is also one of the reasons why we decided to call the resource Lotus, as it illustrates part of the problem.

■ Iris (https://www.wikidata.org/wiki/Q156901, https://www.wikidata.org/wiki/Q2260419)

■ Ficus variegata (https://www.wikidata.org/wiki/Q502030, https://www.wikidata.org/wiki/Q5446649)

○ External and internal dictionaries are not exhaustive, impacting translation

○ Some botanical names we use might not be the accepted ones anymore because of the tools we use and the pace taxonomy is renaming taxa.

● References

○ The tool we favored, Crossref, returns a hit whatever the input. This generates noise and incorrect translations, which is why our filtering rules focus on reference types.

● Filtering rules:

○ Limited validation set, requires manual validation

○ Validates some incorrect entries (False positives)

○ Does not validate some correct entries (False negatives)

Again, our processing pipeline removes entries we do not yet know how to process properly.

Our restrictive filters but substantial contribution to Wikidata in terms of structure-organisms pairs data upload should hopefully incentivize the community to contribute by further adding its human validated data.

We updated the conclusion part of the manuscript accordingly. See https://github.com/lotusnprod/lotus-manuscript/commit/a866a01bad10dfd8b3af90e2f30bb3ae51dd7b9e.

Reviewer #2 (Public Review):

Rutz et al. introduce a new open-source database that links natural products structures with the organisms they are present in (structure-organism pairs). LOTUS contains over 700,000 referenced structure-organism pairs, and their web portal (https://lotus.naturalproducts.net/) provides a powerful platform for mining literature for published data on structure-organism pairs. Lotus is built within the computer-readable Wikidata framework, which allows researchers to easily contribute, edit and reuse data within a clear and open CC0 license. In addition to depositing the database into Wikidata, the authors provide many domain-specific resources, including structure-based database searches and taxon-oriented searches.

Strengths:

The Lotus database presented in this study represents a cutting-edge resource that has a lot of potentials to benefit the scientific community. Lotus contains more data than previous databases, combines multiple resources into a single resource.

Moreover, they provide many useful tools for mining the data and visualizing it. The authors were thoughtful in thinking about the ways that researchers could/would use this resource and generating tools to make it ways to use. For example, their inclusion of structure-based searches and multiple taxonomy classification schemes is very useful.

Overall the authors seem conscientious in designing a resource that is updatable and that can grow as more data become available.

Weaknesses/Questions:

1) Overall, I would like to know to what degree LOTUS represents a comprehensive database. LOTUS is clearly, the best database to date, but has it reached a point where it is truly comprehensive, and can thus be used for a metanalysis or as a data source for research questions. Can it truly replace doing a manual literature search/review?

As highlighted by the reviewer, even if LOTUS might be the most comprehensive natural products occurrences ressources at the moment, TRUE or FULL comprehensive quality of such resource will always be limited to the available data in the litterature. And the community is far from fully describing the metabolome of living beings. We however hope that the LOTUS infrastructure will offer a good place to start this ambitious and systematic description process.

1) Yes it can serve as data source for research questions, as exemplified in the query table

2) No, it cannot and must not replace manual literature search. Manual literature search is the best but at an enormous cost. If the outcome of such search can be made available to the whole community (eg. via Wikidata), the value of such would be even bigger. However, LOTUS can expedite a decent part of a manual litterature search and liberate time to complement this search. See our comment to the editors “To further showcase the possibilities opened by LOTUS, and also answer the remark on the comprehensiveness of our resource, we established an additional query (https://w.wiki/4VGC).This query is comparable to a literature review work, such as: https://doi.org/10.1016/j.micres.2021.126708. In seconds, it allows retrieving a table listing compounds reported in given taxa and limits the search by years.”

We added these examples in the manuscript (see https://github.com/lotusnprod/lotus-manuscript/commit/a6ee135b83e56e8e2041d09d7ce2d5b913c1029d)

2) Data Cleaning & Validation. The manuscript could be improved by adding more details about how and why data were excluding or included in the final upload. Why did only 30% of the initial 2.5 million get uploaded? Was it mostly due to redundant data or does the data mining approach result in lots of missed data?

The reason for this “low” yield is that we highly favored quality over quantity (as in the F-score equation, ß being equal to 0.5, so more importance is given to the precision than the recall). Of course there is redundancy, but the rejected entries are mostly because of too low confidence level according to our developed rules. It is not fully discarded data as we keep it for further curation (ideally including the community) before uploading to Wikidata. We adapted the text accordingly.

3) Similarly, more information about the accuracy of the data mining is needed. The authors report that the test dataset (420 referenced structure-organisms pairs) resulted in 97% true positives, what about false negatives? Also, how do we know that 420 references are sufficiently large to build a model for 2.5M datapoints? Is the training data set is sufficiently large to accurately capture the complexities of such a large dataset?

False negatives are 3%, which is, in our opinion, a fair amount of “loss” given the quality of the data. We actually manually checked 500+ documented pairs, which is more or less the equivalent of a literature review. We were careful in sampling the entries in the right proportions, but we cannot (and did not) state they are enough. We cannot model it either, since the 2.5M+ points have absolutely different distributions, in terms of databases, quality, etc. Only “hint” is the similar behaviour among all subsets. (the 420 + 100 entries) were divided between 3 authors, which obtained similar results.

4) Data Addition and Evolution: The authors have outlined several mechanisms for how the LOTUS database will evolve in the future. I would like to know if/how their scripts for data mining will be maintained if they will continue to acquire new data for the database. To what extent does the future of LOTUS depend on the larger natural products community being aware of the resource and voluntarily uploading to it? Are there mechanisms in place such as those associated with sequencing data and NCBI?

Programs have been not only maintained but also updated with new possibilities (as, for example: the addition of a “manual mode” allowing user to run the LOTUS processing pipeline on a set of their own entries and make them Wikidata-ready (https://github.com/lotusnprod/lotus-processor/commit/f49e4e2b3814766d5497f9380bfe141692f13f23). We will of course do our best to keep on maintaining it, but as no one in academia can state he/she will maintain programs forever. However the LOTUS initiative hopefully embraces a new way of considering database dynamics. If the repository and website of the LOTUS initiative shut down tomorrow, all the work done will still be available to anyone on Wikidata. Of course, future data addition strongly relies on community involvement. We have already started to advocate for the community to start taking part of it, in the form of direct upload to Wikidata, ideally. At the time, there are no mechanisms in place to push publishing of the pairs on Wikidata (as for sequencing, mass spec data), but we will be engaged in pushing forward this direction. The initiative needs stronger involvement of the publishing sector (also reviewers) to help change those habits.

5) Quality of chemical structure accuracy in the database. I would imagine that one of the largest sources of error in the LOTUS database would be due to variation in the quality of chemical structures available. Are all structure-organism pairs based on fully resolved NMR-based structures are they based on mass spectral data with no confirmational information? At what point is a structural annotation accurate enough to be included in the database. More and more metabolomics studies are coming out and many of these contain compound annotations that could be included in the database, but what level (in silico, exact mass database search, or relative to a known standard) are required.

This is a very interesting point and some databases have this “tag” (NMR, cristal, etc.). We basically rely on original published articles, included in specialized databases. If poorly reported structures have been accepted for publication, labelled as “identified” (and not “annotated”) and the authors publishing the specialized databases overlooked it, we might end up with such structures.

Here, the Evidence Ontology (http://obofoundry.org/ontology/eco.html) might be a good direction to look at and further characterize the occurrences links in the LOTUS dataset.

Reviewer #3 (Public Review):

Due to missing or incomplete documentation of the LOTUS processes and software, a full review could not be completed.

Some parts of LOTUS were indeed not sufficiently described and we improved both our documentation and accessibility to external users a lot. We thank the reviewer for insisting on this point as it will surely improve the adoption of our tool by the community.

• My tagnote structure for my tag doesnt work on repeated trying 1248hrs

This seems inconsistent (since we do specify a folder for Drafts), but I confirmed that it still gets stored in Sent even after unchecking this. I guess because Gmail adds the Sent tag on the server, whereas with a draft, it is initiated client-side so the client has to be responsible for adding that tag.

.imp inline tag和双链在我的认知里面 第一次完成了联合

SciScore for 10.1101/2022.01.05.475037: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Limitations of the study: ZCB11 probably represents the broadest breadth among bNAbs reported thus far with comparable potency against all current SARS-CoV-2 VOCs including Omicron and OmicronR346K. We are still in the process in determining its mode of action by solving structures of the RBD-ZCB11 Fab complex. Such information will be useful to guide vaccine design as mentioned because the frequency of elite vaccine remains low (2/34 in this study). To understand the frequency of ZCB11-like bNAb among BNT162b2-vaccinees, we need to investigate other elite responders who show equally potent bNAb responses. More ZCB11-like bNAbs should be also discovered to improve current antibody-based cocktail immunotherapy. For animal challenge experiments, we have done a single dose efficacy experiment. Different doses and routes of administration or antibody combination will be tested in future experiments to provide useful information to support clinical development of ZCB11 and ZCB11-like bNAb.

Results from TrialIdentifier: No clinical trial numbers were referenced.

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

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

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

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 10.1101/2022.01.04.474979: (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 TrialIdentifier: No clinical trial numbers were referenced.

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

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

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

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.

Kaffee

SciScore for 10.1101/2022.01.03.22268599: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from TrialIdentifier: No clinical trial numbers were referenced.

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

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

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

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 10.1101/2021.12.30.474561: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from TrialIdentifier: No clinical trial numbers were referenced.

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

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

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

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 10.1101/2021.12.30.21268554: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from TrialIdentifier: No clinical trial numbers were referenced.

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

Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on page 29. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

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

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.

But this seems to conflict with https://hyp.is/NO4vMmzVEeylBfOiPbtB2w/kit.svelte.dev/docs

The load function is reactive, and will re-run when its parameters change, but only if they are used in the function.

which seems to imply that load is not just run once for the component statically, but rather, since it can be reactive to:

url, params, fetch, session and stuff

may be sufficiently like a per-instance callback, that it could be used instead of onMount?

SciScore for 10.1101/2021.12.25.21268392: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from TrialIdentifier: No clinical trial numbers were referenced.

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

Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on pages 17 and 20. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

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

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 10.1101/2021.12.26.474192: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from TrialIdentifier: No clinical trial numbers were referenced.

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

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

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

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 10.1101/2021.12.25.474155: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from TrialIdentifier: No clinical trial numbers were referenced.

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

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

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

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 10.1101/2021.12.28.474244: (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.

Our study had several limitations. These included the absence of characterization of cross-neutralizing antibodies in vaccinated mice against the B.1.1.529 Omicron variant, which emerged during the writing of this manuscript. However, we could expect that our vaccine would elicit T-cell responses against N epitopes because of the high homology (100%) between vaccine sequences and this VOC. Due to the limited availability of hCD40/K18-hACE2 mice, we did not evaluate various VOC challenges in mice. Finally we favored the analysis of T cell responses using samples from recovered individuals instead of in vivo preclinical models. Although these responses may be dependent on the “clinical history” of patients and their HLA haplotypes, they are less biased than those that would be observed in an animal model. Our results show that the in-vitro vaccine responses are directed against all vaccine proteins, which confirmed the broad HLA coverage of the vaccine sequences. In conclusion, it is becoming urgent to develop a “pan-sarbecovirus vaccine”. The development of a new protein-based vaccine with expected improved tolerability suitable for people with specific vulnerabilities and children would extend the portfolio of current vaccines and be instrumental in controlling the circulation of the virus and the emergence of new variants. By selecting a narrow range of immunodominant epitopes, presented by a wide variety of HLA alleles and less prone to genetic variations across sarbecoviru...

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">NCT04842682</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">Dose Escalation Trial of CD40.HIVRI.Env Vaccine Combined or …</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04262921</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">French COVID Cohort</td></tr></table>

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

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

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

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.

"Export" annotations

The costs here tend to be overstated (and stated by people without a strong background in either browsers or languages, I've found).

I remember someone mentioning to Boris Zbarsky (and expecting him to agree) that IE compatibility was a source of bloat in Gecko. Of course, he corrected them.

SciScore for 10.1101/2021.12.23.473975: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from TrialIdentifier: No clinical trial numbers were referenced.

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

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

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

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 10.1101/2021.12.21.473733: (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 TrialIdentifier: No clinical trial numbers were referenced.

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

Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on page 54. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

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

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 10.1101/2021.12.23.474009: (What is this?)

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

Table 1: Rigor

Table 2: Resources

Results from OddPub: Thank you for sharing your data.

Results from TrialIdentifier: No clinical trial numbers were referenced.

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

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

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

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 10.1101/2021.12.24.474091: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

A primary limitation of our study is the lack of longitudinal analysis after the third dose of vaccines in our cohort. Therefore, we cannot determine the extents to which the numbers of cross-neutralizing Bmem subset correlate with the magnitudes of cross-neutralizing antibody responses following the third vaccine dose. Owing to the paucity of Bmem subsets, detailed phenotypic and transcriptomic characterization of the cross-neutralizing Bmem subset is hampered in this study. Finally, we focused on the neutralizing antibodies but non-neutralizing antibodies also confer the protection in vivo at least in animal models. The analysis on non-neutralizing and protective antibodies against the variants may generate the distinct outcome owing to the epitope difference.

Results from TrialIdentifier: No clinical trial numbers were referenced.

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

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

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

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.

WARC Format

The WARC format is the raw data from the crawl, providing a direct mapping to the crawl process. Not only does the format store the HTTP response from the websites it contacts (WARC-Type: response), it also stores information about how that information was requested (WARC-Type: request) and metadata on the crawl process itself (WARC-Type: metadata).

For the HTTP responses themselves, the raw response is stored. This not only includes the response itself, what you would get if you downloaded the file, but also the HTTP header information, which can be used to glean a number of interesting insights.

In the example below, we can see the crawler contacted http://102jamzorlando.cbslocal.com/tag/nba/page/2/ and received a HTML page in response. We can also see the page was served from the nginx web server and that a special header has been added, X-hacker, purely for the purposes of advertising to a very specific audience of programmers who might look at the HTTP headers!

WARC/1.0
WARC-Type: response
WARC-Date: 2013-12-04T16:47:32Z
WARC-Record-ID: 
Content-Length: 73873
Content-Type: application/http; msgtype=response
WARC-Warcinfo-ID: 
WARC-Concurrent-To: 
WARC-IP-Address: 23.0.160.82
WARC-Target-URI: http://102jamzorlando.cbslocal.com/tag/nba/page/2/
WARC-Payload-Digest: sha1:FXV2BZKHT6SQ4RZWNMIMP7KMFUNZMZFB
WARC-Block-Digest: sha1:GMYFZYSACNBEGHVP3YFQNOSTV5LPXNAU

HTTP/1.0 200 OK
Server: nginx
Content-Type: text/html; charset=UTF-8
Vary: Accept-Encoding
Vary: Cookie
X-hacker: If you're reading this, you should visit automattic.com/jobs and apply to join the fun, mention this header.
Content-Encoding: gzip
Date: Wed, 04 Dec 2013 16:47:32 GMT
Content-Length: 18953
Connection: close


...HTML Content...

.readwise .tagging How to tag chapters and sections

SciScore for 10.1101/2021.12.21.473668: (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.

Another limitation is our focus on intravenous delivery, whereas intratracheal or inhalation delivery may be more readily applied in patients, especially in the outpatient setting. It would be useful in future studies to use varying doses and delivery route combinations for defined disease stages to identify the optimal combination prior to initiating human efficacy trials. Finally, we chose to fuse the ACE2 peptide to an unmodified Fc from IgG1 (isoallotype nG1m1), whereas others have considered fusions to Fc mutants or IgG4 to dampen interactions with FcγR subtypes that might contribute to inflammation24, 68. We instead reasoned that IgG1 effector functions will be necessary for optimum in vivo protection. In summary, we show for the first time that an engineered decoy ACE2 peptide demonstrating much higher affinity for the SARS-CoV-2 Spike protein is efficacious in vivo against multiple SARS-CoV-2 variants. This peptide prevented viral entry into cells and the peptide’s efficacy against a variant of concern prevented lung endothelial injury and ARDS and significantly reduced mortality. The results show the potential of this engineered peptide to treat COVID-19 patients and others with inadequate antibody titer to protect against emerging, more virulent SARS-CoV-2 variants.

Results from TrialIdentifier: No clinical trial numbers were referenced.

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

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

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

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 10.1101/2021.12.21.473774: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from TrialIdentifier: No clinical trial numbers were referenced.

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

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

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

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 10.1101/2021.12.19.473391: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from TrialIdentifier: No clinical trial numbers were referenced.

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

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

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

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 10.1101/2021.12.20.473401: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from TrialIdentifier: No clinical trial numbers were referenced.

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

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

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

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 10.1101/2021.12.18.473303: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from TrialIdentifier: We found the following clinical trial numbers in your paper:<br><table><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Identifier</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Status</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Title</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04960202</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">EPIC-HR: Study of Oral PF-07321332/Ritonavir Compared With P…</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT05011513</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">Evaluation of Protease Inhibition for COVID-19 in Standard-R…</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT05047783</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">Masitinib in Patients With Symptomatic Mild to Moderate 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.

• 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 10.1101/2021.12.16.472155: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from TrialIdentifier: We found the following clinical trial numbers in your paper:<br><table><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Identifier</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Status</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Title</td></tr><tr style="background-color:#FF0000"><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT0427541464</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Trial number did not resolve on clinicaltrials.gov. Is the number correct?</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NA</td></tr></table>

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

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

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
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SciScore for 10.1101/2021.12.15.472864: (What is this?)

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Table 1: Rigor

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

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• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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Results from scite Reference Check: We found no unreliable references.

About SciScore

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SciScore for 10.1101/2021.12.14.472668: (What is this?)

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Table 1: Rigor

Table 2: Resources

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

Results from TrialIdentifier: No clinical trial numbers were referenced.

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

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

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

Results from scite Reference Check: We found no unreliable references.

About SciScore

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lista multiple

color==concept; tag==value from a dominion

ok, good answer

SciScore for 10.1101/2021.12.14.472513: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from TrialIdentifier: No clinical trial numbers were referenced.

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

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

Results from scite Reference Check: We found no unreliable references.

About SciScore

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If you want to define a theme color for light and dark mode, your best bet is to define both colors and use the first meta tag as a fallback for browsers that don’t support the media feature.

<meta name="theme-color" content="#319197" media="(prefers-color-scheme: light)">
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let url = document.location.href;
const canonicalElement = document.querySelector('link[rel=canonical]');
if (canonicalElement !== null) {
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navigator.share({url});

you can (and should)...

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Reply to the reviewers

Description of the planned revisions

From Review Commons: Please find below our point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees

**************

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

Comment #1: In this manuscript, the authors follow up on an interesting finding that varA null mutants of V. cholerae form spherical cells in stationary phase. The authors determine that this cell rounding is due to weakening of the cell wall via less production of tetrapeptide cross links. Mutation of the regulator csrA and the enzyme aspA lead to a model in which a varA mutant cell lacks aspartate leading to low cross-linked cell wall that is unable to hold the typical curved V. cholerae shape. The data are robust, and the manuscript is clearly written.

Authors’ reply #1: We very much appreciate the reviewer’s accurate summary and the appraisal of the robust data and the clearly written manuscript.

• *

Comment #2: I think the finding is quite interesting, even though it is not clear to me if this observed cell morphology has a biological function or if it is an artifact of completly removing VarA. However, this manuscript builds the foundation to further test this question.

Authors’ reply #2: We agree with the reviewer. However, it is worth mentioning that this two-component system (TCS) has been first described in 1998 yet the input signal (or repression of the signal under certain conditions) still remains elusive. Maybe, this isn’t too surprising, given that studies on V. cholerae are strongly biased towards its pathogenic lifestyle, while the varA/varS system is highly conserved among Gram-negative bacteria including non-pathogenic environmental V. cholerae strains. These strains can live under very diverse conditions of slow or fast growth, including long starvation periods. Unfortunately, we still lack significant insight into this part of the V. choleraebiology. We therefore believe that the current study is very important, as the elucidation of the molecular mechanism of the observed shape transition within the varA mutant will foster fresh hypotheses on the role that the system plays in V. cholerae and what signals might be sensed.

We would also like to remind the editor and reviewer(s) that a plethora of studies have been published based on varA and varS (and csrA) deletion mutants of V. cholerae with various readouts ranging from transcriptomics to quorum sensing defects, impairment of virulence, etc. Thus, the argument that the complete removal of varA might cause an artifact seems equally valid for previous work by others and, maybe, even the vast majority of studies in which TCS are investigated for which the sensed signal has yet to be identified.

In conclusion, we propose to address this valid point of critique in the revised manuscript by clearly stating the caveat of the gene deletion(s). However, as the reviewer correctly stated, “this manuscript builds the foundation to further test this question.”

Comment #3: The data all support the conclusions, but I do think the authors could have really confirmed their model by connecting mutations in csrA and aspA to restoration of high cross-linked cell well similar to the WT strain as done in Fig. 2. As it stands, this is still somewhat hypothetical and has not been directly demonstrated, although I do think their model is correct and these experiments will be conformation of it.

Authors’ reply #3: We thank the reviewer for their comment and the assumption that our model might be correct. It is very unlikely that the csrA suppressor mutant(s) or the ∆varA∆aspA mutant maintain the low level of cross-links and the high level of dipeptides that we observed for the ∆varA mutant. Indeed, it would be unclear how the cells could restore the Vibrio shape that we visualized in the phase contrast image under such conditions. However, as this point seems very important to the reviewer (see also comment #7 below), we will perform the suggested cell wall analysis of these mutants and include the new data in the revised manuscript.

Comment #4: I also have a few other suggestions to improve the manuscript, but in sum I think it is a well-done research study that will be interesting to research in V. cholerae and other gamma proteobacteria.

Authors’ reply #4: Once again, we thank the reviewer for their kind words.

Major comments:

Comment #5: 1. The enrichment for suppressors is very creative and connected the varA impact on cell morphology to misregulation of csrA as 10/10 mutants were ultimately linked to this gene. However, insertion in aspA should also suppress this phenotype, and I am curious why this gene was not identified in the transposon suppressor screen.

Authors’ reply #5: This is a very relevant and important comment. The reason why we did not isolate ∆varA-aspA::Tn mutants is most likely due to a growth defect that we observed for the double ∆varA∆aspA mutant compared to the ∆varA-csrA suppressor mutant(s). In the figure on the right, respective growth curves are shown [∆varA∆aspA in orange and the ∆varA-csrA suppressor mutant ∆varA-Tn A in gray]. Any ∆varA-aspA::Tn mutant is therefore expected to be outcompeted by the ∆varA-csrA suppressor mutants during the enrichment process. We will include this information and the corresponding data (e.g., final growth curves after 3 biologically independent experiments) in the revised manuscript.

[figure not shown in online form]

Comment #6: 2. The authors should complement at least one of their varA/csrA mutants with csrA.

Authors’ reply #6: Agreed. We are in the process of performing the suggested experiment and will include the results in the revised manuscript.

Comment #7: 3. The changes in cell wall structure are not directly connected to the genetic identification of csrA and aspA. Yes, I agree their model makes sense, but to really nail it down they should analyze the cell wall composition in the varA/csrA and varA/aspA double mutants and show it returns to WT levels of crosslinking.

Authors’ reply #7: As mentioned above, we will perform those cell wall analyses (see also authors’ reply #3 above), as requested.

Comment #8: 4. Does deletion of aspA in the WT or varA mutant impact the growth rate?

Authors’ reply #8: This is indeed the case in the ∆varA background but not in the WT background (as shown under authors reply #5). These data will be included in the revised manuscript.

• *

Minor comments

Comment #9: 5. Are the round cells able to divide? The data in Fig. S2 would suggest they can based on the increase in CFUs from hour 6 to hour 8, but the authors never comment on this point and it might be worth addressing in the discussion.

Authors’ reply #9: We never observed diving round cells. Indeed, the increase of the optical density and CFUs from 6h to 8h is most likely based on those bacteria that have not yet changed their cellular morphology and therefore keep dividing (see below the imaging/quantification for this timeframe taken from Fig. 2). What we observed though is that upon dilution into fresh medium, the round cells start to elongate and then divide resulting in newborn Vibrio shaped cells. We will include these new data in the revised manuscript.

[figure not shown in online form]

• *

Comment #10: 6. Fig. S2-Why does the OD600 increase from 8 to 24 hours but the CFUs decrease in the varA mutant?

Authors’ reply #10: This is an interesting observation that might reflect the presence of dead but not yet lysed cells in these cultures. Indeed, while it looks as if the OD600 values are still increasing for the ∆varA mutant at 24h, we cannot exclude at this point that the OD600 values increased during the 16h-time interval and went again down at 24h (e.g., like shifting the WT peak to later time points/the right of the X-axis). Notably, the purpose of this figure was mostly to i) indicate the slower growth of the ∆varA mutant while ii) emphasizing that late during growth (e.g., 24h here) the strain can still reach similar OD600 values as well as CFUs/ml as the WT strain. We will change Fig. S2 to better emphasize these two points in the revised manuscript.

Comment #11: 7. Lines 309-A little bit more detail here would help the reader. Are the authors examining whole cell lysates or lysates from specific cellular components? I am actually very surprised this worked as there are so many proteins in crude cell lysates.

Authors’ reply #11: Indeed, these are whole cell lysates, which were prepared as described in the methods section (lines 482 onwards; “SDS-PAGE, Western blotting and Coomassie blue staining”). We fully agree that there are many proteins in the crude cell lysates and realized that we might not have explained well enough that only the gel region containing the overproduced band in the ∆varA strain and the same location in the WT sample were analyzed by mass spectrometry (even though we were referring to the “gel pieces” in line 498 onwards). Please accept our sincerest apologies for this neglect. During the revision, we will ensure that this information is explicitly stated and that these details are included in the main text and the methods section.

Comment #12: 8. Lines 320-321-I don't think there is evidence that CsrA enhances aspA RNA translation, merely that CsrA enhances AspA protein production. It is likely through increasing translation, but this cannot be concluded without direct evidence.

Authors’ reply #12: We fully agree and thank the reviewer for this important comment. Indeed, we meanwhile know that the aspA mRNA levels also increase in the ∆varA mutant strain (which might or might not be linked to enhanced translation). We will add these transcript level data to the revised manuscript and discuss all possibility that could explain the AspA overproduction.

Comment #13: 9. Line 348-350-I do not understand the logic of this sentence stating that the "..until now, the signal that abrogates VarA phosphorylation..." as this manuscript does not contribute to our understanding of the VarS signal.

• *Authors’ reply #13: We apologize that this sentence or the logic behind it wasn’t clear. As this is a combined result and discussion section, the aim of the sentence was to put the observed shape transition of the bacteria into a broader context, which required us to mentioned that the input signal is still unknown. We will make sure that this becomes more obvious in the revised manuscript by rephrasing this sentence.

Comment #14: 10. I am curious if the total volume of the round versus curved cells is constant at 20 hours. This should be easy to determine using ImageJ and worth reporting.

Authors’ reply #14: We are not entirely sure how this question is relevant for the study (e.g., for this report on the observed shape transition phenotype it doesn’t matter if the cells maintain the same volume or not). However, given the importance for the reviewer, we will perform these volume measurements on our images and add a sentence to the revised manuscript on the analysis’ outcome (plus include the data as a supplementary panel).

• *

Reviewer #1 (Significance (Required)):

Comment #15: Understanding changes to cell morphology and their biological implications is a growing area of microbiology. This study makes a new contribution to this area by demonstrating a round, spherical form of V. cholerae that is driven by alterations to the cell that decrease cell-wall cross linking.

Authors’ reply #15: Once again, we thank the reviewer for this summary and for placing our study into context. We agree that cell morphological changes and the underlying molecular mechanism(s) are an exciting and growing area of microbiology.

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

Comment #16: In this manuscript, Rocha et al studied the effect of the VarA response regulator on cell shape of Vibrio cholerae. VarA is part of a two-component system that also includes the histidine kinase VarS. It has previously been shown that VarA activates the expression of three redundantly acting regulatory RNAs called CsrB, CsrC, and CsrD. All three Csr RNAs share the same regulatory principle, which is to sequester the activity of the RNA-binding protein CsrA. CsrA in turn can bind hundreds of mRNA species in the cell, which in the majority of cases results in reduced translation of these mRNAs (in addition to various other modes of action that have been reported). Here, the authors discovered that deletion of the varA gene results in an abnormal, spherical cell shape in stationary phase grown V. cholerae cells. Biochemical analysis revealed an unusual peptidoglycan composition in varA-deficient cells, showing increased levels of dipeptides, a reduction of tetrapeptides, and an overall decrease in peptide-cross linkage. Interestingly, the varA phenotype was complemented by the addition of conditioned medium from wild-type cells, which are likely to provide peptidoglycan building blocks in trans. The authors further discover that varA-deficiency results in AspA over-production, which could be linked to the activity of CsrA. The authors speculate that high AspA levels deplete the cell of aspartate, which is required to produce peptidoglycan precursors. The manuscript is interesting, well-written, and the rationale of the experiments is easy to follow.

Authors’ reply #16: We thank the reviewer for this excellent summary and the kind words on the quality of the manuscript.

Comment #17: However, I have two major points of criticism, which reduce my enthusiasm for this work. First, the molecular pathways that links varA-deficiency to increased AspA levels is incomplete: please clarify how CsrA activates AspA levels and if this phenotype is linked to direct binding of CsrA to the aspA mRNA and if so how is activation is achieved at the molecular level.

Authors’ reply #17: We thank the reviewer for the comment. However, we never had the intention to decipher the entire pathway and it is indeed possible that intermediate regulators might be involved. Notably, the first part of the signaling pathway (VarA -> CsrB,C,D -> CsrA) seemed well established in the literature and we truly believe that our work supports this part of the pathway (given the numerous csrA suppressor mutants that we obtained in the varA-minus background). For the link between CsrA and AspA, we indeed do not provide direct evidence. Nonetheless, we discuss recent work in Salmonella by mentioning “Interestingly, previous studies identified the aspA mRNA amongst hundreds of direct CsrA targets in Salmonella using the CLIP-seq technique to identify protein-RNA interactions (32)”. Notably, this finding by Holmqvist et al. (2016, EMBO J.) has been reproduced for E. coli by Potts et al. (2017, Nat. Commun.; see Supplementary Data file 1, CsrA CLIP-seq data; with three highly significant peaks corresponding to aspA mRNA binding), an information that we will add to the revised manuscript. Of course, neither Salmonella nor E. coli belongs to the same genus as V. cholerae (though, of course, all are gamma-Proteobacteria). Thus, to accommodate the reviewer’s comment, we will revise the manuscript to include the caveat that direct CsrA binding of the aspA mRNA has been shown in both Salmonella and E. coli but that it is still feasible that intermediate regulatory proteins might be involved in the case of V. cholerae. We will also revise the model to show such potential intermediate steps between CsrA and AspA.

Comment #18: Second, I am not convinced about the biological relevance of the findings. The authors speculate in the discussion section that the VarA-pathway could modulate cell shape under physiological conditions, however, I am not sure such conditions exist given that VarA activity is not only controlled by VarS, but rather integrates information from multiple histidine kinases. I have a several additional comments, which I listed below.

Authors’ reply #18: We regret the referee’s personal opinion that our findings might not be of biological relevance.

However, we respectfully disagree with the notion that such physiological conditions would never occur just because several histidine kinases can feed into VarA signaling. Indeed, as discussed above under authors’ reply #2, the (VarS/)VarA-CsrA pathway is highly conserved in Vibrio species and other proteobacteria. Yet, for V. choleraemost studies have focused on virulence-inducing conditions, while the species’ environmental lifestyle has been vastly neglected in the past. Indeed, even our own work on several V. cholerae’s phenotypes (natural competence for transformation [Meibom*, Blokesch* et al., 2005, Science]; T6SS production in pandemic strains [Borgeaud et al., 2015, Science]; pilus-mediated aggregation [Adams et al., 2019, Nat. Microbiol]; etc.) has remained unknown for decades, given the chitin dependency for their induction – a substrate not commonly studied in lab settings. Interestingly, several of these findings have also initially been considered biologically irrelevant, “artifacts”, or even non-reproducible by reviewers in the past, while nowadays all these phenotypes have been extensively reproduced by many different research groups and are well accepted in the field as biologically highly relevant. Thus, we truly believe that one should be open to new phenotypes and, as reviewer #1 rightfully acknowledged, consider that “this manuscript builds the foundation to further test this question.”

It should also be noted that the (VarS/)VarA-CsrA system has been studied for >15 years based on deletion strains, as we did in this study, and the readouts of these studies have been well accepted in the field without provision of the physiological conditions that would mimic the situation of these knock-out strains.

Collectively, we truly believe that there are still many understudied physiological conditions for V. cholerae; however, finding the right conditions could take years and is therefore beyond the scope of the current study.

Major points:

Comment #19: - Figs. 1 and S1: I think it is interesting that the varS mutant strain does not share the cell shape phenotype with the varA mutant. As pointed out by the authors, this result indicates that varA activity is controlled by another histidine kinase. While I believe it might be beyond the scope of this manuscript to determine which other histidine kinases signal towards VarA, I think it would be useful to measure and compare CsrB/C/D levels in WT, DvarA, and DvarS cells.

Authors’ reply #19: Thanks for this comment. We fully agree that finding the secondary histidine kinase is beyond the scope of this study. In the revised manuscript, we will, however, include the CsrB/C/D levels of the WT, ∆varA, and ∆varS strains, as suggested by the reviewer.

Comment #20: - Figs. 1, S1, and 4C: The regulatory logic implied by these results suggest that deletion of varA results in reduced CsrB/C/D levels, which in turn leads to higher activity of CsrA in the cell. Thus, it would be useful to test if A) over-production of CsrB, CsrC, or CsrC can rescue the phenotype of an varA mutant and if B) combined deletion of csrB/C/D will phenocopy the mutation of varA.

Authors’ reply #20: These are also very good suggestions. Notably, this has been done in the past for the V. cholerae system (Lenz et al., 2005, Mol. Microbiol.). Indeed, after receiving the reviewer’s comment, we immediately asked these authors to kindly share their csrB,C,D overproduction plasmids as well as the triple knock-out strain with us (as all of these constructs have been extensively verified in their published work). Unfortunately, we are not entirely sure whether it will be possible to receive these constructs any time soon, as we were told that such shipment might take >1 year (though, upon further discussion, this timeframe was lowered to ~3 months). If we manage to receive these published constructs in a reasonable timeframe, we will certainly perform the suggested experiments.

Comment #21: - Figs. 4B & 5C: I was somewhat surprised by these results. Given that AspA overproduction is suggested to cause cell shape abnormalities in the varA mutant, I would have expected additional transposon insertion in aspA. The fact that mutations only occurred in csrA could indicate that additional (CsrA-controlled) could be involved in the phenotype.

Authors’ reply #21: See authors’ reply #5 above, where we explain that the ∆varA∆aspA strain has a slight growth disadvantage. For this reason, any ∆varA-aspA::Tn transposon mutant would likely be outcompeted by the csrAsuppressor mutants in our genetic screen.

Minor points:

Comment #22: - Throughout study: italicize gene names

Authors’ reply #22: Gene names have been italicized in the initial manuscript; however, strain names - such as strain ∆varA – haven’t been italicized, in accordance with several of our previous publications. However, for the revision, we will italicize all strain names to accommodate the reviewer’s request.

Comment #23: - Figs. 1C and S3C: please quantify the results of these western blots and indicate how many replicates were performed.

Authors’ reply #23: We apologize for this oversight – indeed, all Western Blot were performed three independent times, as is good scientific practice. We will add this information into the methods section of the revised manuscript.

Concerning the quantification: the primary claim of these figures is that the HapR protein is still produced in the ∆varA mutant in the different pandemic strain backgrounds, while the luxO-mutated strains have a significant defect in HapR production (as we have previously reported; Stutzmann and Blokesch, 2016, mSphere). These data are qualitatively very clear in the Western Blots and can be considered as “black or white” results.

However, for the revision we will quantify the bands’ intensities of the performed Western blots and provide these quantitative data, as requested by the reviewer.

Comment #24: - Fig. 5A and B: in order to properly quantify the levels of AspA in the cell (and link them to CsrA activity in the transposon mutants), I think it would be better to add a tag to the chromosomal aspA gene and perform quantitative Western blot analysis.

Authors’ reply #24: We respectfully disagree. Firstly, this is not a subtle difference that we observe in these cell lysates/the corresponding stained gel bands but a rather strong difference when WT is compared to the mutants (see, for instance, a copy of panel 5B below as a kind reminder). Together with the genetic experiments that follow afterwards, the link seems very solid to us. Secondly, adding a tag could change the proteins abundance (change of the protein’s production/degradation dynamics) and/or activity, which could cause more confusion than needed (and a loss of the spherical cell shape if the enzyme loses its activity through the tagging).

However, as mentioned above under authors’ reply#12, we meanwhile observed that the aspA mRNA levels also increase in the ∆varA mutant. Thus, we will provide qRT-PCR data in the revised manuscript (and discuss all options on how the increase of the transcript and subsequently the protein might be caused, as mentioned above under authors’ reply #12), which we truly believe will fulfill the reviewer’s request for quantification.

[figure not shown in online form]

Reviewer #2 (Significance (Required)):

Comment #25: I think this manuscript starts with an interesting observation, which is that varA mutant cells of V. cholerae display an aberrant cell shape. The manuscript also provides several important findings explaining the molecular basis of this phenotype.

Authors’ reply #25: Once again, we thank the reviewer for the kind words.

Comment #26: However, as pointed out in my report, I think the manuscript is yet incomplete in connecting this information to identify the underlying regulatory mechanism.

Authors’ reply #26: As mentioned above, the focus of this study was never on the elucidation of the entire regulatory pathway. Instead, we aimed at deciphering the molecular mechanism behind an observed phenotype - that is, the cell wall modification in the varA-deficient strain that leads to the bacterium’s spherical shape, which can be restored to the WT Vibrio shape by peptidoglycan precursor cross-feeding from neighboring cells – followed by the identification of several regulators and enzymes that trigger these phenotypes. Overall, we consider this a very complete study. However, as mentioned above, we will certainly discuss in the revised manuscript that the step between CsrA and AspA could be indirect in V. cholerae, in contrast to what was experimentally shown for Salmonella and E. coli.

**Referee Cross-commenting**

Comment #27: As pointed out in my review, I think this manuscript is well written and easy to follow. However, I agree with reviewer #1 that the underlying phenotype is most likely an artifact, which limits the biological relevance of this study. In addition, I am missing the molecular mechanism that connects CsrA with AspA production in V. cholerae.

Authors’ reply #27: See authors’ reply #18 above. We disagree that there is any strong indication that the observed phenotype is an artifact. Given that it is state-of-the-art to study TCS by deleting their genes, our study isn’t any more prone to being an artifact than any other study on TCSs.

We truly believe that it is also important to not take reviewer #1’s comment out of context by stating “I agree with reviewer #1 that the underlying phenotype is most likely an artifact”. Indeed, he/she provided a rather encouraging statement in which he/she mentions the possibility of an artifact but also clearly states that this study is interesting and builds the foundation to further investigate the newly observed phenotype(s): “I think the finding is quite interesting, even though it is not clear to me if this observed cell morphology has a biological function or if it is an artifact of completly removing VarA. However, this manuscript builds the foundation to further test this question.”

Moreover, whether there is a direct (as in Salmonella and E. coli) or indirect connection between CsrA and AspA production is not a key aspect of the current study, as discussed above.

• *

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Referee #2

Evidence, reproducibility and clarity

In this manuscript, Rocha et al studied the effect of the VarA response regulator on cell shape of Vibrio cholerae. VarA is part of a two-component system that also includes the histidine kinase VarS. It has previously been shown that VarA activates the expression of three redundantly acting regulatory RNAs called CsrB, CsrC, and CsrD. All three Csr RNAs share the same regulatory principle, which is to sequester the activity of the RNA-binding protein CsrA. CsrA in turn can bind hundreds of mRNA species in the cell, which in the majority of cases results in reduced translation of these mRNAs (in addition to various other modes of action that have been reported). Here, the authors discovered that deletion of the varA gene results in an abnormal, spherical cell shape in stationary phase grown V. cholerae cells. Biochemical analysis revealed an unusual peptidoglycan composition in varA-deficient cells, showing increased levels of dipeptides, a reduction of tetrapeptides, and an overall decrease in peptide-cross linkage. Interestingly, the varA phenotype was complemented by the addition of conditioned medium from wild-type cells, which are likely to provide peptidoglycan building blocks in trans. The authors further discover that varA-deficiency results in AspA over-production, which could be linked to the activity of CsrA. The authors speculate that high AspA levels deplete the cell of aspartate, which is required to produce peptidoglycan precursors. The manuscript is interesting, well-written, and the rationale of the experiments is easy to follow. However, I have two major points of criticism, which reduce my enthusiasm for this work. First, the molecular pathways that links varA-deficiency to increased AspA levels is incomplete: please clarify how CsrA activates AspA levels and if this phenotype is linked to direct binding of CsrA to the aspA mRNA and if so how is activation is achieved at the molecular level. Second, I am not convinced about the biological relevance of the findings. The authors speculate in the discussion section that the VarA-pathway could modulate cell shape under physiological conditions, however, I am not sure such conditions exist given that VarA activity is not only controlled by VarS, but rather integrates information from multiple histidine kinases. I have a several additional comments, which I listed below.

Major points:

• Figs. 1 and S1: I think it is interesting that the varS mutant strain does not share the cell shape phenotype with the varA mutant. As pointed out by the authors, this result indicates that varA activity is controlled by another histidine kinase. While I believe it might be beyond the scope of this manuscript to determine which other histidine kinases signal towards VarA, I think it would be useful to measure and compare CsrB/C/D levels in WT, DvarA, and DvarS cells.
• Figs. 1, S1, and 4C: The regulatory logic implied by these results suggest that deletion of varA results in reduced CsrB/C/D levels, which in turn leads to higher activity of CsrA in the cell. Thus, it would be useful to test if A) over-production of CsrB, CsrC, or CsrC can rescue the phenotype of an varA mutant and if B) combined deletion of csrB/C/D will phenocopy the mutation of varA.
• Figs. 4B & 5C: I was somewhat surprised by these results. Given that AspA overproduction is suggested to cause cell shape abnormalities in the varA mutant, I would have expected additional transposon insertion in aspA. The fact that mutations only occurred in csrA could indicate that additional (CsrA-controlled) could be involved in the phenotype.

Minor points:

• Throughout study: italicize gene name
• Figs. 1C and S3C: please quantify the results of these western blots and indicate how many replicates were performed.
• Fig. 5A and B: in order to properly quantify the levels of AspA in the cell (and link them to CsrA activity in the transposon mutants), I think it would be better to add a tag to the chromosomal aspA gene and perform quantitative Western blot analysis.

Significance

I think this manuscript starts with an interesting observation, which is that varA mutant cells of V. cholerae display an aberrant cell shape. The manuscript also provides several important findings explaining the molecular basis of this phenotype. However, as pointed out in my report, I think the manuscript is yet incomplete in connecting this information to identify the underlying regulatory mechanism.

Referee Cross-commenting

As pointed out in my review, I think this manuscript is well written and easy to follow. However, I agree with reviewer #1 that the underlying phenotype is most likely an artifact, which limits the biological relevance of this study. In addition, I am missing the molecular mechanism that connects CsrA with AspA production in V. cholerae.

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Table 1: Rigor

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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).

As a result, a second generation of antibodies might be required to overcome these limitations and could be obtained by reshaping the initial sequences by conferring them the necessary properties, such as affinity and selectivity, to have optimal therapeutic efficacy for treatment in humans. From this perspective, many teams have proposed a wide range of methods to generate candidates with the expected properties, mostly increased affinity.29-32 Affinity maturation aims at improving biological activity by adjusting the kinetic parameters of the binding to the target, which in turn may confer greater therapeutic efficacy.25,33 However, the magnitude of this effect depends largely on the epitope recognized by the antibody and the initial affinity along with the format of the antibody and its valence.25 In the context of the current COVID-19 pandemic, several studies have described affinity maturation of VHH or conventional antibodies to enhance their binding to SARS-CoV-2 antigens, by CDR-swapping approaches 34, saturation mutagenesis in CDRs 35,36 or light-chain shuffling36. In recent years, Deep Mutational Scanning (DMS) approaches have emerged as a powerful tool for understanding protein/protein interactions. Deep Mutational Scanning (DMS) explores in a selected protein all possible unique substitutions, i.e. all unique mutations for each position. DMS defines mutational landscape of the protein and helps to understand the interaction modalities as recently shown for the RBD...

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enlaces crean redes "fisicas"; tags crean redes "logicas"

"siguiendo la pista": referencia externa link

"fuente original (secundaria)"; now, come back to esfinge

citation needed

SciScore for 10.1101/2021.12.05.471263: (What is this?)

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Results from OddPub: Thank you for sharing your data.

Results from TrialIdentifier: No clinical trial numbers were referenced.

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

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

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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Results from scite Reference Check: We found no unreliable references.

About SciScore

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DOI: 10.1016/j.isci.2021.103484

Resource: (MBL International Cat# M180-3, RRID:AB_10951811)

Curator: @Naa003

SciCrunch record: RRID:AB_10951811

Curator comments: anti-HA-tag antibody MBL International Cat# M180-3

What is this?

• If a file contains only PHP code, it is preferable to omit the PHP closing tag at the end of the file;
• but if you are embedding php with html then enclose php code with opening and closing tag;
• The type of a variable is not usually set by the programmer; rather, it is decided at runtime by PHP depending on the context in which that variable is used;

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

Results from TrialIdentifier: No clinical trial numbers were referenced.

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

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

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About SciScore

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SciScore for 10.1101/2021.12.06.21267328: (What is this?)

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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).

The limited positive serology reported (65-70% of PCR+ mothers) demonstrates the limitations of examining isolated antibody epitopes, and specifically of RBD only epitope. SARS CoV-2 Immunity: While prior studies have focused on SARS-CoV-2 spike protein, we evaluated a range of epitopes. Our results demonstrated high sensitivity of our platform with detection of spike (full length)- IgM and IgG in ∼90% of documented PCR infection. We found that, as in non-pregnant patients, high levels of N-IgG and IgM titers in those with a history of COVID-1923. Studies in non-pregnant individuals identified IgM peaks in N and S epitopes in second week of infection24. We found that maternal nucleocapsid antibodies demonstrated the highest specificity for documented COVID-19 infection and were highly expressed in both maternal and cordblood samples. Nucleocapsid proteins of many coronaviruses are highly immunogenic and highly expressed during acute infection25. While the focus of vaccine and monoclonal antibody therapeutics have been on spike antigen, these results, consistent with other studies in non-pregnant adults, highlights the potential import of N-antibodies in SARS-CoV-2 immunity and target for therapy. Finally, similar to the reports cited above18,19,22, we found a high degree of correlation and a linear fit with a slope of 1.0, between maternal and cordblood IgG with cordblood IgG concentrations, indicating highly efficient transfer of CoV-2 IgG antibodies. Finally, in examining s...

Results from TrialIdentifier: No clinical trial numbers were referenced.

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

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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Results from scite Reference Check: We found no unreliable references.

About SciScore

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SciScore for 10.1101/2021.12.05.471310: (What is this?)

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Table 1: Rigor

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

Results from TrialIdentifier: No clinical trial numbers were referenced.

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

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

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

Results from scite Reference Check: We found no unreliable references.

About SciScore

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Linking to a New York Times tag archive would not be considered evidence by any self-respecting conspiracy theorist.

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Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on page 24. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

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About SciScore

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.ReadWise

zotero

anystyle.io

answer

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

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

*The manuscript by Wibisana et al. describes an impressive set of experiments that analyse the NFkB response at the single-cell level, using a variety of cutting-edge techniques (live cell imaging, single-cell RNA-seq, single-molecule RNA FISH, and single-cell ATAC-seq) in chicken DT40 B-cells.

In the fist half of the paper, the authors perform a detailed characterization of the cell-to-cell variation arising from a homogeneous stimulation with various doses of anti-IgM. They observe that the NFKB TF RelA forms clear nuclear 'foci' upon stimulation in DT40 cells: this was anecdotally shown in a different cell-type by the same authors in ref 7, but (to my knowledge) has never been systematically studied. This allows them to quantitatively analyse the foci formed in response to stimulation, and they show that this is dose-dependent, heterogeneous and biomodal, and exhibits properties of cooperativity. In parallel, the authors analyse the resulting stimulus-driven changes in gene expression, first using single-cell RNA-seq, and then, elegantly, using RNA FISH, which allows them to directly compare the number of RelA foci to gene expression in individual cells. Like the RelA foci, they find that cell-to-cell gene expression is heterogeneous and bimodal (this has been described before). Interestingly, though, they are able to show that individual stimulus-responsive genes exhibit distinct patterns of cell-to-cell hetereogeneity: they can categorize 4 clusters of responding genes according to different patterns of cell-to-cell variation at distinct stimulus doses, and moreover they show that while the heterogeneity of NFKBIA arises due to bimodal expression levels, that of CD83 is simply due to broad variation between cells. Although focused on NFkB, there is a lot of information here with some important (and non-intuitive) implications that could apply to many other stimulus-driven or developmental responses that exhibit heterogeneous patterns of gene expression. A more in-depth analysis of the single-cell datasets would certainly be very worthwhile and fruitful.

In the second half of the paper, the authors attempt to use their single-cell data, alongside ATAC-seq genomic analyses, to draw inferences about how or whether the model genes NFKBIA and CD83 are regulated by super-enhancers (SEs). Both of these genes are associated with SEs that gain accessibility upon stimulation (recapitulating the authors' findings in ref 8 in a different cell-type), and the CD83 promoter exhibits co-accessibility with two regions within an adjacent SE. The authors also show that both genes are sensitive to treatment with 1.6-HD, a compound that disrupts liquid-like condensates (a characteristic that has been reported for SEs), and CD83 is sensitive to an inhibitor of Brd4 (which has been associated to SE function). However, while these findings could be considered to be suggestive of regulation by SEs, they are clearly not definitive (nor do the authors claim so).

Finally, the authors show (figure 4a-c) that while the level of stimulus-driven gene upregulation correlates with co-accessibility with both SEs and typical enhancers (TEs), the cell-to-cell heterogeneity of gene expression correlates only with co-accessibility with SEs. This would agree with a model in which SE-regulated gene regulation may generally impart heterogeneous or switch-like gene expression. *

**Specific comments**

• The experiments are adequately presented, and the authors indicate that not only the sequencing data but also the analysis code is available. Nevertheless, the methods section is rather terse, and could benefit from more detail to understand the various analyses, particularly concerning the analyses of SEs in figures 3 and S7, where it is often difficult to understand how peaks or genes are categorized.

Response: We thank the Reviewer for pointing this out and we agree that the Methods section was not described in detail, particularly in how the SEs were analyzed and categorized. Therefore, we have added more details on how SEs were categorized in the Methods section as follows:

“ Peak calling and enhancer identification from ATAC-seq data were performed using Homer v4.10.4 (http://homer.ucsd.edu/homer/) using the bam files generated from the Cell Ranger pipeline. Tag directories were created for the bam file from each condition using the “makeTagDirectory” program with the “--sspe -single -tbp 1” option. Peak calling was performed using the “findPeaks” program with the “-style super -typical -minDist 5000 -L 0 -fdr 0.0001” option. This procedure stitches peaks within 5 kb and ranks regions by their total normalized number reads and classifies TE and SE by a slope threshold of 1. Peak annotation was subsequently performed using the “annotatePeaks.pl” program with the GRCg6a.96 annotation file. The consequent peak files were merged between each stimulation condition for the SE and TE peaks separately using the “mergeBed” program of bedtools. Peak annotation was performed for the second time for the merged peaks to create the final SE and TE peaks. ATAC fold-change was then calculated between both conditions for the merged peaks separately for SE and TE. Genes associated with both SE and TE were assigned only to the SE.”

Similarly, we have added more details for other analyses in the Method section and the main sentences.

• The imaging, scRNA-seq and RNA-FISH experiments are well-presented, although the supplementary figures 4 and 5 include key results that would merit inclusion within the main figures. *

Response: We thank the Reviewer for this comment. We have included supplementary figures 4b and 5d in the main figures (new Fig. 2g) since both of these figures represent the raw data revealing the differences between smFISH counts and RNA-seq derived gene expression.

• It is strking that although all the conclusions about SEs are drawn almost exclusively from analysis of ATAC-seq data, no raw ATAC-seq data is directly shown in any figure (even in the browser snapshots of figure 4d & e). It would be important to show the actual ATAC data from which the inferences of figures 3 and 4 are drawn, especially so that it is possible to visualize the implication of a particular 'ATAC fold-change' or of 'ATAC-gained enhancers'. Response: We have added a browser snapshot of the ATAC-seq data, presenting the super-enhancer region assigned to both CD83 and NFKBIA* (new Fig. 3c).

Reviewer #1 (Significance (Required)):

• This manuscript can be considered as a follow-up of the authors' previous paper (Michida 2020, ref 8), here focusing on cell-to-cell heterogeneity rather than on the overall magnitude of the stimulus-induced response. Overall, the experiments are well-performed and bring new data to an interesting angle of gene regulation. However, the analyses presented do not seem to fully exploit the data, and the authors do not manage to present any strong conclusions, particularly relating to the possible involvement of super enhancers.

Response: To strengthen our conclusions about the possible involvement of super-enhancers in regulating heterogeneity, we performed additional analyses on the properties of the SE including the number of transcription factors, NF-κB and PU.1 binding motifs and the length of the enhancers, according to a previous report (Michida et al., 2020, Cell Rep). This was also conducted to confirm whether the ATAC-seq-based SE identification method presents results consistent with those provided by H3K27Ac-ChIP-based methods utilized in the previous study (Michida et al., 2020, Cell Rep). SEs revealed longer genomic length (new Supplementary Fig. 8a) and this length was positively correlated with the ATAC signal (new Supplementary Fig. 8b). Furthermore, gained and lost SE revealed a correlation with enhanced gene expression upregulation and downregulation, respectively, compared to TE (new Fig. 3g). We also demonstrated that SE-regulated genes have a higher Fano factor change, which is consistent with the state of an SE whether it is gained or lost (new Fig. 5a, 5b). For binding motif analysis, we observed a slightly higher PU.1 motif density at SEs (new Supplementary Fig. 11), corresponding to the results of the previous study (Michida et al., 2020, Cell Rep). Interestingly, only the density of NF-κB and not PU.1 was correlated with ATAC signal change in SE (new Fig. 4a), suggesting that those SEs were controlled by nuclear translocation of NF-κB.

As a mechanism to produce gene expression heterogeneity in phenotypically identical cells, we observed that co-accessibility, which has been reported to be concordant with genomic contacts is correlated to Fano factor change, indicating that gene expression heterogeneity possibly stems from cis-regulatory interactions. NF-κB activation has been reported to increase the heterogeneity in some genes and is attributed to the accumulation of Ser5p RNAPII (Wong et al., 2018, Cell Rep). Additionally, Ser5p RNAPII has been reported to accumulate at enhancer regions (Koch et al., 2011, Nat Struct Mol Biol), and that the accumulation of RNAPII is suggested to assist in gene expression activation through enhancer-promoter contact (Thomas et al., 2021, Mol Cell). Our results support these conclusions since co-accessibility or putative cis-regulatory interactions correlate to Fano factor changes. SE can form phase-separated transcription hubs containing multiple enhancers and/or promoters, which may enable the higher diffusion rate of active enhancers; therefore, it may induce a higher possibility of genomic DNA interactions (Gu et al., 2018, Science). In contrast, the enrichment of TATA motif has also been proposed to generate transcriptional heterogeneity (Faure et al., 2017, Cell Syst). Therefore, we examined this possibility with our data. However, we observed a higher occurrence of TATA box in genes associated with lost SE (new Supplementary Fig. 18) which might have caused gene expression heterogeneity in unstimulated cells. This heterogeneity might be due to the differences in Pol II loading intervals (Tunnacliffe & Chubb, 2020, Trends Genet) however the noise associated with gained SE is possibly generated by the fluctuation of high-order biomolecular assembly. Therefore, we believe that the source of heterogeneity in these conditions were different.

Additionally, we performed Hill function analysis to reveal the threshold behavior of gene expression in our analysis since previously gained SEs were associated with threshold gene expression (Michida et al., 2020, Cell Rep). In this study, we presented that threshold behavior in gained SE is related to motif density of NF-κB (Fig. 4d), however, threshold behavior does not seem to be related to heterogeneous gene expression.

Following these results, we concluded that NF-κB activated SE has two closely related but distinct functions for gene control: (1) enhanced heterogeneity and fold-changes and (2) switch-like expression. These are controlled by different mechanisms stemming from chromatin status: (1) frequency of cis-regulatory genomic interactions possibly mediated by phase separation and (2) cooperative binding of NF-κB to DNA. These differences were well represented by expression profiles of CD83 (higher heterogeneity and weak bimodal expression) and NFKBIA (lower heterogeneity and strong bimodal expression).

• For instance, the existence of multiple gene clusters that exhibit distinct patterns of heterogeneity implies that switch-like gene activation occurs on a per-gene basis, rather than corresponding to an all-or-nothing activation of individual cells. This would be an exciting finding, and the authors have the data to test this. Likewise, the division of heterogeneous gene expression into bimodal (like NFKBIA) or unimodal (like CD83) distributions could be a nice paradigm if systematically applied to the other 1335 differentially-expressed genes identified by the authors. * Response: We appreciate this comment. Following your comment, we analyzed the relationship between heterogeneity and bimodality (switch-like expression or high Hill coefficient) for the remaining genes. We observed that SE having a high Hill coefficient contained a higher number of NF-κB motif in SE (new Fig. 4), indicating that cooperative binding of NF-κB to DNA shaped non-linear gene expression profiles as we indicated in a previous paper (Michida et al., 2020, Cell Rep). Additionally, as described in the earlier section, we observed that heterogeneity arises from cis-regulatory genomic interaction. We compared these gene groups and observed that these properties were not completely shared (new Supplementary Fig. 15), indicating that bimodality and heterogeneity originated from different mechanisms. We assume that those differences are mediated through a combination of chromatin accessibility and the biophysical properties of NF-κB.

• In contrast, although the authors try to use their data to investigate gene regulation by SEs, these inferences are all somewhat indirect, and the authors themselves do not manage to draw any definitive conclusions. Response: We appreciate this comment. We performed the additional computational analysis and carefully interpreted the data. Additionally, we have now concluded that SEs have two major biological functions: (1) gene expression heterogeneity, which is mediated via cis-regulatory interactions (Fig. 5) and (2) bimodal gene expression, which is mediated by NF-κB binding (new Fig. 4). The latter finding has also been reported in a mouse primary B cell, albeit the mechanism causing heterogeneity was a novel conclusion of this study.

• I feel that the authors are under-selling their data here. As-is, the data represents more of a resource than a study with a clear message, but I believe that with more in-depth analysis the authors could make a much more significant advance, particularly concerning the cell-to-cell heterogeneity of gene expression. I would be very enthusiastic to review the same data again with a more detailed analysis, which I believe would enormously improve the manuscript. Response: We appreciate this comment. As described in this report and the revised manuscript, we performed a considerably detailed computational analysis and gained several novel insights to answer the question regarding the functional roles of SE. We are grateful to learn that gene expression patterns may be estimated from ATAC-seq profiles and that they may even be controlled. We hope that this Reviewer would observe the scientific value of our study and provide us with your valuable feedback on our revised manuscript.

Reviewer #2

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

Imaging and single cell sequencing analyses of super-enhancer activation mediated by NF-κB in B cells" by Wibisana et al. examined the relationship between super-enhancers, NF-κB nuclear aggregation, and target gene regulation. The authors have generated a large amount of data from fluorescent microscopy, scRNA-seq, scATAC-seq, smRNA FISH. While this is an impressive dataset in terms of diverse technically advanced methods employed, it is not clear what to take as a main conceptual advance. What could be the functional implications of observed cell-cell variability in B cell transcriptional responses to environmental stimuli? In addition to this general point, the following are specific comments that could improve the manuscript.

2. In Figure 2, smRNA FISH foci of CD83 and NFKBIA are quantified as # of spots per cell (Supplementary figure 5). But it is difficult to see in Figure 2 the colocalization of any mRNA spots with RelA foci. Ideally, it will be convincing to show by DNA FISH that these target loci are indeed located within NF-κB occupied super-enhancer puncta. Even with the current RNA FISH data, some colocalization analysis could have been performed. * Response: In Figure 2, we were unable to perform accurate colocalization analysis with the current smFISH data as the probes used by us map to exons. Moreover, we have also previously performed DNA-FISH; nevertheless, it was difficult to assess co-localization between the DNA and RelA proteins secondary to the degradation of RelA-GFP proteins. Therefore, we decided to perform intronic smRNA-FISH, which can be used to pinpoint the site of active transcription (Levesque and Raj, 2013, Nat Methods). The results, along with the quantification results, are presented in the new Fig. 2f.

• Supplementary Figure 5a shows lower correlations of # GFP-RelA foci to CD83 transcripts in comparison to NFKBIA. Even though the foci and smRNA FISH spots are derived from high resolution imaging data, we should remember that any snapshot measurements have limited information content for gene regulatory relationships. Live cell studies (for example, from the groups of Suzanne Gaudet, Kathryn Miller-Jensen, and Myong-Hee Sung) have shown that time-integrated measures (e.g. maximum fold change and area under the curve of RelA signaling time course in single cells) are better correlates to transcriptional output of target genes (Lee REC et al 2014 Mol Cell; Wong VC et al 2019 Biophysical J; Sung MH et al. 2014 Science Signal; Martin EW et al. 2020 Science Signal). *

Response: We thank the Reviewer for this valuable comment. One of the reasons for a lower correlation between GFP-RelA foci and CD83 transcripts compared to NFKBIA may be the difference in expression timing of CD83 and NFKBIA and the timing of nuclear localization of GFP-RelA. RelA localizes in the nucleus 10−30 mins after cell stimulation, and NFKBIA is an early responsive gene, however, CD83 is expressed later (new Supplementary Fig. 17). Therefore, this time difference possibly affects correlation accuracy. Although we agree that high-throughput time-course measurement of RelA-GFP combined with smFISH measurements, such as that reported in Wong VC et al., 2019, will be ideal, it is technically difficult since DT40 are suspension cells and the smFISH protocol requires multiple washing and centrifugation steps. Thus, with this experimental setup, we were unable to perform the time-course analysis.

Nonetheless, we measured the time-course foci formation at the same single-cells (new Supplementary Fig. 1b) and observed that it effectively represents Figure 1a, which is a snapshot of the population dynamics of RelA foci across time. Additionally, the observed dynamics, which revealed a steep initial increase and slight decrease with time, effectively recapitulates the previous reports (Lee et al., 2014, Mol Cell; Wong et al., 2019, Biophys J).

In our analysis, we performed imaging analysis to demonstrate that NF-κB foci formation is switch-like, and this formation might be involved in the formation of phase-separated condensates enhancing DNA to DNA contact. The number of foci may depend upon the intracellular concentration of NF-κB, and fold change in the RelA signal may be correlated with gene expression as previously reported (Lee et al., 2014, Mol Cell; Wong et al., 2019, Biophys J). However, there is another report presenting that promote/enhancer proximity is not related to gene expression (Alexander et al. 2019, eLife). Although we were unable to perform this analysis owing to the limitations stated above, we tried to find the relationship between RelA foci and gene expression by performing biochemical perturbations (Fig 1e-f, Fig 5h) and presented that these foci are related to gene expression.

• The analyses have been performed using DT40 cells. In the Methods section, no description was provided about what type of B cells DT40 is, even though few outside of the field may not know that the cells were immortalized from chicken. This is an important consideration, because some nuclear bodies and genome organization features are different between host species and they also depend on whether the cells are primary or transformed. Because the authors do not discuss this point, it seems possible that the findings about NF-κB aggregates and super-enhancers may not necessarily hold true for primary B cells. *

Response: We thank the Reviewer for pointing out these issues. We have added the following description on DT40 cells in the Methods section describing that DT40 cells are chicken bursal lymphoma cells.

DT40 B lymphocytes have been widely used as a B cell model for studying B cell receptor signaling (Mori et al., 2002, J. Exp. Med.; Patterson et al., 2002, Cell; Saeki et al., 2003, EMBO J.) due to its high gene targeting efficiency. We also previously confirmed that anti-IgM stimulation induces the NF-κB signaling pathway in mouse primary splenic B cells and DT40 and that the signaling molecules and dynamics in these cells are well conserved (Shinohara et al., 2014, Science; Shinohara et al., 2016, Sci. Rep.; Inoue et al., 2016, NPJ Syst. Biol. Appl.). However, we understand the Reviewer’s concerns. Therefore, we have provided the track view of primary B cell ATAC-seq data to demonstrate that the chromatin accessibility changes upon anti-IgM stimulation in CD83 and NFKBIA were similarly observed in primary B cell data (new Supplementary Fig. 9b) and that the upregulation and association with SE of CD83 and NFKBIA were also observed in primary B cell (new Supplementary Fig. 9a).

• Similarly, the GFP-RelA expressing DT40 cell generation should be described with more detail (beyond "provided by ..."). N-terminal or C-terminal fusion? Did the fusion construct contain an artificial promoter (e.g. CMV) or an upstream fragment of the genomic Rela locus (chicken or human)? Methods of transfection and cloning of stable lines? These choices affect the interpretation of the data, so they must be fully described and justified. *

Response: We thank you for pointing this out. We have added the following details on the RelA-GFP construct in the Methods section:

Mouse RelA-eGFP with eGFP on the C terminal was cloned into a pGAP vector containing Ecogpt resistance gene targeting endogenous GAPDH locus. This construct was further electroporated into wild-type cells and selected using Ecogpt to produce RelA-GFP-expressing DT40 cells.

• DT40 cells were cultured in 39 degrees. Michael White and colleagues have shown that high temperatures can alter NF-kappaB dynamics and function (https://www.pnas.org/content/115/22/E5243). Did the authors try lower temperatures to ascertain that the NF-kB aggregates and other major findings are still observed in 37 degrees? *

Response: We performed the experiments at 39 degrees to mimic the natural body temperature of chicken since DT40 cells were derived from chicken bursal lymphoma (Saribasak and Arikawa, 2006, Subcell Biochem). Previously, we cultured DT40 cells at 37 degrees and observed that the cell growth was inhibited, and thus, we believed that it was not ideal to perform experiments of DT40 cells at 37 degrees.

Reviewer #2 (Significance (Required)):

It is not clear what to take as a main conceptual advance.

Response: Considering the original manuscript, we agree with the Reviewer on the lack of strong emphasis on the conclusions of our study. Therefore, in this revised manuscript, we have focused on the comprehensive mechanism of heterogeneity and switch-like activation in gene expression control. As we described in the comments to Reviewer #1, we performed an additional in-depth computational analysis on SE and TE. Consequently, we demonstrated that enhanced heterogeneity and expression fold-changes mediated by SE are defined by the number of cis-regulatory genomic interactions in open chromatin regions (Figure 5), however, switch-like expression (bimodal patterns) is determined by the number of NF-κB binding in SE (new Figure 4). The latter finding has also been reported in a mouse primary B cell in our previous study (Michida et al. 2020, Cell Rep.). However, the mechanism causing heterogeneity is a novel conclusion obtained in this study. We also concluded that these similar, albeit quantitatively and slightly different characteristics in gene control can be achieved through a combination of chromatin accessibility of host cells and biophysical properties of NF-κB molecule, which is involved in phase separation.

What could be the functional implications of observed cell-cell variability in B cell transcriptional responses to environmental stimuli?

Response: We performed gene ontology analysis to reveal how the heterogeneously expressed genes (cluster 4) (Fig. 2d) presented enrichment for immune-related functions (Supplementary Fig. 5b). This result supports a previous study, which stated that variability in gene expression is related to function (Osorio et al., 2019, Cells).

This discussion is incorporated in the manuscript as follows:

“We observed that genes with an increased heterogeneity upon increasing stimulation dose are enriched with cell-type-specific immune regulatory genes (Supplementary Fig. 5b), supporting a previous report where heterogeneity in gene expression is tied to biological functions and may be used by cells as a bet-hedging or a response distribution mechanism (Osorio et al., 2019, Cells), where cells exhibit heterogeneity to enable response to changing environment and also allowing dose-dependent fractional activation respectively. This was observed in CD83, a B cell activation marker, demonstrating the involvement of heterogeneity in B cell development.”

truco

I am unsure of what "automatic OER Processing" might mean/ Can anyone help?

The closes I came was in the section of a International Journal of OER paper by Stephen Downes: A Look at the Future of Open Educational Resources where in the Artificial Intelligence section he illustrates an example of AI creating OER (?)

What is relevant to open education is that the services offered by these programs will be available as basic resources to help build courses, learning modules, or interactive instruction. For example, Figure 3 illustrates a simple case. It takes the URL of an image, loads it, and connects an online artificial intelligence gateway offered by Microsoft as part of its Azure cloud services using an API key generated from an Azure account.

The Azure AI service automatically generates a description of the image, which is used as an alt tag, so the image can be accessible; the alt tag can be read by a screen reader for those who aren’t able to actually see the image. In this case, the image recognition technology automatically created the text “a large waterfall over a rocky cliff,” along with a more complete set of analytical data about the image.

Yes this is interesting and is a useful tool for content creation, but to me seems a far leap to creating educational content.

SciScore for 10.1101/2021.11.25.470011: (What is this?)

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

Table 1: Rigor

Table 2: Resources

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

Results from TrialIdentifier: We found the following clinical trial numbers in your paper:<br><table><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Identifier</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Status</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Title</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT03831503</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Active, not recruiting</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">A Study of INO-A002 in Healthy Dengue Virus-naive Adults</td></tr></table>

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