PCSY-D-24-00047

System and transcript dynamics of cells infected with severe acute respiratory syndrome virus 2 (SARS-CoV-2)

PLOS Complex Systems

Dear Dr. Elena,

Thank you for submitting your manuscript to PLOS Complex Systems. After careful consideration, we feel that it has merit but does not fully meet PLOS Complex Systems's publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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We look forward to receiving your revised manuscript.

Kind regards,

Chris Gaiteri

Academic Editor

PLOS Complex Systems

Nataša Przulj

Section Editor

PLOS Complex Systems

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Additional Editor Comments (if provided):

Dear Santiago,

I'm happy to share that everyone was interested in your paper and concur that a minor revision will likely have this ready to go. I surveyed the reviewers' comments and they seem generally reasonable to me. So I'd appreciate a point-by-point response to them with related changes in the revised main text.

For my part, I'm interested in 1) if any signature of cell death (https://www.nature.com/articles/s41598-024-59117-0 or similar) might be used to further validate or help understand the dynamics of the system. 2) How genes with particular statistical features that you've defined relate to baseline network properties such as connectivity or betweenness in gene or protein networks. My motivation for asking this question is that when you have a relatively novel construct like yours, it can be helpful and advantageous to you to link it back or contrast with prior systems biology characterizations. 3) You currently have some discussion of congruent results with other covid studies, but are there genes your approach highlights that have not been found by more standard methodologies?

Another minor point at the beginning of results when you start talking about the various single cell data sets data sets, I felt like they weren't introduced with enough context. For instance, I'd like to know how many cells were in each without having to go to the references and when you start talking about cell types, it's not clear if these are cell types that you derived as a subset of all cells or if they were established in the prior references, etc.

Regards,

Chris

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Does this manuscript meet PLOS Complex Systems’s publication criteria? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available (please refer to the Data Availability Statement at the start of the manuscript PDF file)?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception. The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS Complex Systems does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: In the paper entitled “System and transcript dynamics of cells infected with severe acute respiratory syndrome virus 2 (SARS-CoV-2)” the authors apply several statistical analyses to gene expression data obtained from three types of human cells infected with SARS-CoV-2 virus. They observe that the abundance data follow a Taylor’s power law whose parameters change during the infection and depend on the cell type, and they also see that genes with punctual rank stability or long-range dependence behavior are associated with certain functions of critical importance during the interaction between viruses and the immune system of the host cells. This way, the authors develop both a systemic and individual analysis of the system.

The paper is scientifically sound and the subject is of the maximum importance in the context of analyzing virus-host interaction during viral infections. While some of the results are not conclusive (as authors say in line 288), the main results are interesting and the limitations of the work are well described. For all of these reasons, I find this work of potential interest for the broad audience of PLOS Complex Systems, although in my opinion authors should still do some work to increase the clarity and quality of the work, which I believe will be very positive for the future impact of the manuscript. If the authors address the requirements I describe below, I will be happy to recommend this paper for publication in PLOS Complex Systems.

My main issue is that the paper will be published in a journal devoted to complexity theory, but it is strongly focused on the biological and bioinformatics perspective of the system under study. In my opinion, some important parts of it are not clear enough and are hard to follow for a non-expert reader in virus bioinformatics. A very detailed reading of the methods and even the captions of the Supplementary Figures (e.g. Fig S3) is needed to find out and understand very basic information (e.g. how the main calculations were done, at least in a descriptive way). I believe that the main manuscript should be much more self-contained, at least in a qualitative manner. For this reason, I recommend a detailed rewriting of the main manuscript and exhort the authors to:

- Describe RSI and PSI coefficients and their applicability for non experts when they are first used. For example, punctual rank stability was cited in the introduction but not defined.

- Include short descriptions of the main virology terms used in the paper (for example UMI). Just an example: In line 114, please include (days post infection) and (hours post infection) after dpi and hpi respectively for clarity.

- Explain how the simulated datasets were calculated and their usefulness in a qualitative way when they are used, not only in the Methods section.

In the same line of thought of the former paragraph, please include more information in the captions, to make them more descriptive of what the figures show. An example: Titling “Taylor’s law plots” to figure 1 is not very descriptive. Also, please remark in caption of figure 1 that the distributions refer to gene abundances data.

Some other comments:

Line 144: The authors affirm that “We found that 30 bins were a good compromise between number of bins and number of cells in each bin.” How sensitive to this number of bins the results are?

Figure 2: Why do all V and beta curves for the infected case grow so fast for pseudotime around 30? (line 162) That seems an interesting behavior to me.

Line 180: The authors show that as infection progresses, the breakpoint increases for all three cell types (info plotted in Fig S2). Can the authors explain this behavior? Also, as requested above, for the sake of clarity please describe FigS2’s caption in more detail.

Figure 3: Are gray circles eclipsing red circles or vice-versa? If this is so, please change the circle properties to be able to see all data in the plot.

Figure 4: It seems that genes close to the breakpoint show larger H values than the rest. If this is so, why?

Figure 5: Why are there so large differences in H value between the 3 different cell types?

Line 270: The authors affirm that “2]. A biphasic fit to Taylor’s law was observed, where the most expressed genes followed an exponential distribution, and the remaining genes followed a Poisson distribution.” This information agrees with [4]. However, I couldn’t see in this paper any of these abundance distributions. Did the authors check that there is indeed a transition from Poisson distributions to exponential ones? Printing some of these curves (in the Supp. Info, for example), would be clarifying for the reader.

Line 287: “Whether there is a direct or indirect relationship between infection progression and Taylor’s parameters is inconclusive.” What do the authors recommend in order to cast light on this critical question for the paper?

Line 298: “These genes followed the exponential distribution, which can be interpreted as aggregation behavior (Fig 1)”. Please include some information on what the viral aggregation behavior is, as this journal has a very broad audience. The same for line 76 regarding Taylor’s law.

Line 377: Is this sentence correct?: “To analyze the progression of infection through infection,...”

The results are limited to SARS-CoV-2 virus. Do the authors have an intuition of how the results would be modified when other viruses or other host cells are studied?

In [4], besides Taylor’s law, other emergent statistical laws in single-sell transcriptomic data were studied, such as the Zipf Law, Heaps Law, etc. Could any of these other emergent laws be applied to virus-host transcriptomic data here or in future work?

TYPOS:

Line 272: A biphasic behavior in scRNA-seq has been previously identified and were mainly attributed to the sampling process. >> ...and WAS mainly attributed...

Line 288: (i.e., all transcripts in a cell is sequenced) >> (i.e., all transcripts in a cell ARE sequenced)

Reviewer #2: Review for “System and transcript dynamics of cells infected with severe acute respiratory

syndrome virus 2 (SARS-CoV-2)”

The study by Silva et al. provides an interesting and rigorous exploration of transcription dynamics under infection by SARS-CoV-2. Their findings establish a solid basis and a guideline for further research into crucial genes for SARS-CoV-2 infection and thus therapeutic strategies, which of course is still relevant and valuable to the complex systems community at large. Their methodology is clear, well explained and although not completely novel, the article reinforces a growing corpus of findings on the statistical patterns of scRNA-seq data. Additionally, scripts and data appear to be fully accessible (data coming from a previous publication from the authors “Single-cell RNA-sequencing data analysis reveals a highly correlated triphasic transcriptional response to SARS-CoV-2 infection”) to the community allowing full reproducibility. My recommendation is acceptance with minor revisions, which I have listed below:

Line 48-50: Transcriptomics analyses commonly rely on linear models…

Does this sentence refer exclusively to lack of pseudotime or is this a critique of linear models in transcriptomics? If the second, please provide an example citation or an article where linearity is discussed as a potential issue, ignore otherwise.

Line 51: biology should not be capitalized.

Line 98-99: The robustness of these methods was further assessed by the use of control datasets. I think the reader would benefit from a brief extension of this sentence presenting the core idea of the synthetic controls, namely that they are produced from empirical data simulating dropout / sampling noise.

Line 112: a threshold for calling infected cells. Do you mean annotating here?

Line 137: [...] high expressed genes. [...] highly expressed genes.

Figure 1 caption: here the number of genes in each case should also be mentioned.

Figure 2a: here the dropout / sampling noise should be shown by plotting the sparsity in each pseudotime bin.

Line 237-238: we found that low expressed genes tended to exhibit slightly higher H exponents, most noticeably for ileum cells (Fig 4B). I will leave this to the discretion of the authors since this is not a main claim of the article but this would be better visualized by plotting Hurst exponent vs. mean expression.

Figure 4: Again for the sake of clarity towards the reader, and given the emphasis the authors put on specific Hurst exponent thresholds (either 0.5 for persistence / anti-persistence or 0.7 for the Hurst phenomenon) I would highly encourage the authors to use a diverging colormap centered at the chosen threshold. https://matplotlib.org/stable/users/explain/colors/colormaps.html#diverging

Line 254-256: An enrichment of genes related to IRES-dependent viral translation is unexpected since SARS-CoV-2 is not known to contain an IRES. This strikes me as a really interesting finding, could you expand on why you think there is an enrichment in these genes? What could it mean for our understanding of SARS-CoV-2 and its treatment? If this is too speculative or the evidence right now does not allow for the formulation of hypothesis then please drop this suggestion.

Lines 376-390: Fit to Taylor’s law. Could you provide some rationale (statistical or references using the same criteria) behind the statements: Genes exhibiting more than 95% of zeros were filtered out (line 379) and Additionally, for binned data only, genes with more than 70% of zeros were filtered out when fitting the data to a biphasic model with one breakpoint (line 387).

Line 401: there are as much simulated cells as there are infected cells. There are as many simulated cells as there are infected cells.

Reviewer #3: This paper investigate the use of a combination of Taylor's law and the Hurst exponent to relates changes in gene expression at single cell level with systems level information and regulation of the biological pathways that might change in time. The authors use SARS-CoV-2 infection as a example.

The authors find that SARS-CoV-2 transcripts consistently fit a biphasic Taylor's law, likely suggesting the higher sensitity of some genes to sampling noise. When extended in time, the technique reveals a complex cell-type dependent pattern, which cannot be consistently replicated by simulations. From the plots shown, I am not convinced that one would not be able to fit a triphasic distribution, but the estimation of the exponents is likely be noisy. Can the authors potentially comment on this?

Gene ontology analysis shows interesting biological patterns, which are nonetheless expected for viral infections (e.g., protein translation and ribosomal involvment, much likely an activation and then suppression of one or more stress response pathways). Hurst exponent analysis shows strong persistence for genes from infected cells, which is also likely correlated with low transcript abundance.

This is a good paper, with lots of interesting insight. My main concern is how much of that insight is really just a realization that sample noise dominates transcript acquisition and hence analysis. Although significant, it seems from the analysis that the signal is heavily reduced with the course of infection, likely revealing an imperfect picture of the overall regulation. The authors do acknowledge this effect, but I would recommend that, would they agree with my assessment, it came out more strongly in the discussion/conclusions.

I would recommend this paper for publication with the minor changes regarding the overall insight suggested above.

P.s. No AI was used in aid of this review beyond standard literature search.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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System and transcript dynamics of cells infected with severe acute respiratory syndrome virus 2 (SARS-CoV-2)

PCSY-D-24-00047R1

Dear Prof. Elena,

We are pleased to inform you that your manuscript 'System and transcript dynamics of cells infected with severe acute respiratory syndrome virus 2 (SARS-CoV-2)' has been provisionally accepted for publication in PLOS Complex Systems.

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Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Complex Systems.

Best regards,

Nataša Przulj, Ph.D.

Section Editor

PLOS Complex Systems

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Reviewer Comments (if any, and for reference):