SARS-CoV-2 infection severity and mortality is modulated by repeat-mediated regulation of alternative splicing

ABSTRACT Like single-stranded RNA viruses, SARS-CoV-2 hijacks the host transcriptional machinery for its own replication. Numerous traditional differential gene expression-based investigations have examined the diverse clinical symptoms caused by SARS-CoV-2 infection. The virus, on the other hand, also affects the host splicing machinery, causing host transcriptional dysregulation, which can lead to diverse clinical outcomes. Hence, in this study, we performed host transcriptome sequencing of 125 hospital-admitted COVID-19 patients to understand the transcriptomic differences between the severity sub-phenotypes of mild, moderate, severe, and mortality. We performed transcript-level differential expression analysis, investigated differential isoform usage, looked at the splicing patterns within the differentially expressed transcripts (DET), and elucidated the possible genome regulatory features. Our DTE analysis showed evidence of diminished transcript length and diversity as well as altered promoter site usage in the differentially expressed protein-coding transcripts in the COVID-19 mortality patients. We also investigated the potential mechanisms driving the alternate splicing and discovered a compelling differential enrichment of repeats in the promoter region and a specific enrichment of SINE (Alu) near the splicing sites of differentially expressed transcripts. These findings suggested a repeat-mediated plausible regulation of alternative splicing as a potential modulator of COVID-19 disease severity. In this work, we emphasize the role of scarcely elucidated functional role of alternative splicing in influencing COVID-19 disease severity sub-phenotypes, clinical outcomes, and its putative mechanism. IMPORTANCE The wide range of clinical symptoms reported during the COVID-19 pandemic inherently highlights the numerous factors that influence the progression and prognosis of SARS-CoV-2 infection. While several studies have investigated the host response and discovered immunological dysregulation during severe infection, most of them have the common theme of focusing only up to the gene level. Viruses, especially RNA viruses, are renowned for hijacking the host splicing machinery for their own proliferation, which inadvertently puts pressure on the host transcriptome, exposing another side of the host response to the pathogen challenge. Therefore, in this study, we examine host response at the transcript-level to discover a transcriptional difference that culminates in differential gene-level expression. Importantly, this study highlights diminished transcript diversity and possible regulation of transcription by differentially abundant repeat elements near the promoter region and splicing sites in COVID-19 mortality patients, which together with differentially expressed isoforms hold the potential to elaborate disease severity and outcome.

If the manuscript is accepted the transcriptome data from sequencing of 125 hospital admitted COVID-19 patients needs to be submitted on GEO datasets.
Reviewer #5 (Comments for the Author): The current study reveal a novel mechanism of alternative splicing in COVID-19 differential studies.The importance of transcriptomic analysis and possible mechanism of action is very well highlighted by this study.However, I have a few minor questions that you need to address.1-Did you validate using another pathways tool whether you observe consistent finding?2-What was the concentration of AMPure beads used to purify the library?3-Figure 2 labelling and text is not matching.For eg. fig. 2 (D-H

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Dear Authors, This is a well written manuscript, I enjoyed reading it.
I would like to comment about two points: 1) About the virus, did you make RT-PCR for the virus subtypes?Which SARS-CoV-2 variant was present in the patients?Did all the patents have the same virus variant?I think this is another criteria about the clinical outcome, some subtypes are more virulent.Could you please mention and discuss about it in the discussion or in the material methods section.2) We know that COVID19 is more fatal in older age group as you mentioned in the text.Did you also compare the age groups for the differences in transcriptome.Are the changes related to mortality more abundant in the old patients?If possible you can compare the age groups and discuss about the effect of aging on the transcriptome, if there is no difference between the age and clinical severity groups.
Dear Editor and the Reviewers, We would like to take this opportunity to thank you for your valuable time and effort towards providing a thorough assessment of our research article, as well as helpful ideas for enhancing the inferences presented in the manuscript.
During the revised manuscript submission, we have addressed all the suggestions as applicable with additional analysis, figures, and supplemental material.
Best wishes,

Rajesh
Reviewer #4 (Comments for the Author): This is a well written manuscript, I enjoyed reading it.
Thank you for your appreciation of our effort towards putting together the findings together in this manuscript covering an unexplored aspect of host response during COVID-19.
I would like to comment about two points: 1) About the virus, did you make RT-PCR for the virus subtypes?Which SARS-CoV-2 variant was present in the patients?Did all the patents have the same virus variant?I think this is another criteria about the clinical outcome, some subtypes are more virulent.Could you please mention and discuss about it in the discussion or in the material methods section.
We thank the reviewer for this insightful perspective.
We would like to share that these patients were first tested for COVID-19 positivity using RT-PCR.
Subsequently, these RT-PCR patients are undertaken for SARS-CoV-2 whole genome sequencing.
Thereafter, the viral phylogeny and identification of the variant have been determined using the genome sequences obtained from the Oxford Nanopore Platform.
As these COVID-19 patients are from pre-VOC (variants of concern) time point, majority of them have similar variant which lead us to hypothesize that despite having a similar variant, why the patients experienced differential disease severity levels (mild, moderate, severe, mortality).
We have included the clade information in the Supplementary table 1 as well as added a phylogenetic tree of the clades in the Figure 1.We have also added a paragraph in both the discussion and methods section as well about the patient SARS-CoV-2 clades in this study.It is also important to share that these SARS-CoV-2 sequences are uploaded to GISAID (IDs included in the (Supplementary table 1).We have added the following section to the result.
"We also sequenced the whole genome of the SARS-CoV-2 virus isolated from nasopharyngeal swabs of the patients to determine whether patients with varying severity levels are infected with different strains of the virus.Despite the differences in clinical severity and outcome, we discovered that the virus strain (19A, 20A and 20B) was similar between mild, moderate, severe and mortality patients (Figure 1F).Overall, these clinical, sequencing, and demographic data represent the diversity of  2) We know that COVID19 is more fatal in older age group as you mentioned in the text.Did you also compare the age groups for the differences in transcriptome.Are the changes related to mortality more abundant in the old patients?If possible you can compare the age groups and discuss about the effect of aging on the transcriptome, if there is no difference between the age and clinical severity groups.
We thank the reviewer for this important observation.
While the patient grouping was done with respect to the severity parameter as per ICMR (Indian Council of Medical Research) guidelines wherein SpO2 levels and Ct values are important parameters, we observed that there was a significant age difference ONLY between the Mild and Moderate/Severe/Mortality groups as shown in the Figure 2 below.However, there was NO significant difference between other severity groups, viz.moderate vs severe, severe vs mortality, moderate vs mortality.
However, the 10 differentially expressed transcripts (including one pseudogene, AL731559.1)have an average abundance greater in the Mortality group patients falling above the median age of 61 compared to the ones below that age group Figure 3.To check for its possible modulation due to age, we also performed the simple linear regression analysis between the age and expressions of the 10 differentially expressed transcripts between Mortality and Mild patients.
Importantly, we do not see a significant association between the transcripts and the age of the patients.
This suggests that despite age being an important component, it is not a major confounder alone.But in conjunction with comorbidities, treatment regimen, host immune response and the viral strain, it may affect the expression of these transcripts.We have added a paragraph of the same in the discussion of the revised manuscript as below: "As age can modulate disease severity, we compared it between the patient sub-groups.While the median age varied between mild and moderate/severe/mortality patients, there was no significant differences between other severity groups, viz.moderate vs severe, severe vs mortality, and moderate vs mortality.Thus, we checked the effect of age on transcriptome between mild and mortality, however, found no significant association between age and the significantly expressed transcripts.This suggests that despite age being an important component, it is not a major confounder alone.Reviewer #5 (Public repository details (Required)): If the manuscript is accepted the transcriptome data from sequencing of 125 hospital admitted COVID-19 patients needs to be submitted on GEO datasets.
We thank the reviewer for this suggestion.
Reviewer #5 (Comments for the Author): The current study reveal a novel mechanism of alternative splicing in COVID-19 differential studies.
The importance of transcriptomic analysis and possible mechanism of action is very well highlighted by this study.However, I have a few minor questions that you need to address.
We thank the reviewer for appreciating the findings presented within the manuscript elucidating an unexplored aspect of COVID-19 for granular understanding of the COVID-19 differential disease severity types.
1-Did you validate using another pathways tool whether you observe consistent finding?
We thank the reviewer for this suggestion.
Based on your suggestion, we have used reactome pathways and performed gene set enrichment analysis using fgsea package in R. Taking a cutoff of p value<0.1, we find similar pathways such as integrin cell surface interactions pathway, extracellular matrix organisation pathways to be negatively enriched in the mortality patients which is consistent with the pathways obtained by the KEGG database, where we find suppression of cell-cell adhesion regulation as well as integrin-mediated signalling pathways as shown in Figure 4 below .2-What was the concentration of AMPure beads used to purify the library?
We thank the reviewer for this query.The library was purified using AMPure XP beads, with bead to sample ratio of 1:1.4-What are the limitation of your study?
We acknowledge the reviewer suggestion.In the updated submission, we have added a section discussing the study's limitations.The section is as follows: "One of the major limitations of the study is the lack of transcript-specific pathway information for understanding transcript-specific functions.Because different transcript isoforms exhibit different expression patterns, understanding the specific function of the variably spliced transcripts is crucial.
Furthermore, the study is based on samples gathered from patients on the day they were admitted to the 1.Some of the answers to the reviewers' comments were only provided in the rebuttal letter (e.g., Reviewer #5's comments 1 and 2).Please consider incorporating them in the main text accordingly.2. Please check typographical, grammatical and formatting errors throughout the manuscript before the acceptance.
Thank you for submitting your manuscript to Microbiology Spectrum.As you will see your paper is very close to acceptance.Please modify the manuscript along the lines I have recommended.As these revisions are quite minor, I expect that you should be able to turn in the revised paper in less than 30 days, if not sooner.If your manuscript was reviewed, you will find the reviewers' comments below.
When submitting the revised version of your paper, please provide (1) point-by-point responses to the issues raised by the reviewers as file type "Response to Reviewers," not in your cover letter, and (2) a PDF file that indicates the changes from the original submission (by highlighting or underlining the changes) as file type "Marked Up Manuscript -For Review Only".Please use this link to submit your revised manuscript.Detailed instructions on submitting your revised paper are below.

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To submit your modified manuscript, log onto the eJP submission site at https://spectrum.msubmit.net/cgi-bin/main.plex.Go to Author Tasks and click the appropriate manuscript title to begin the revision process.The information that you entered when you first submitted the paper will be displayed.Please update the information as necessary.Here are a few examples of required updates that authors must address: • Point-by-point responses to the issues raised by the reviewers in a file named "Response to Reviewers," NOT IN YOUR COVER LETTER.
• Upload a compare copy of the manuscript (without figures) as a "Marked-Up Manuscript" file.
• Each figure must be uploaded as a separate file, and any multipanel figures must be assembled into one file.For complete guidelines on revision requirements, please see the journal Submission and Review Process requirements at https://journals.asm.org/journal/Spectrum/submission-review-process.Submissions of a paper that does not conform to Microbiology Spectrum guidelines will delay acceptance of your manuscript." Please return the manuscript within 60 days; if you cannot complete the modification within this time period, please contact me.If you do not wish to modify the manuscript and prefer to submit it to another journal, please notify me of your decision immediately so that the manuscript may be formally withdrawn from consideration by Microbiology Spectrum.
If your manuscript is accepted for publication, you will be contacted separately about payment when the proofs are issued; please follow the instructions in that e-mail.Arrangements for payment must be made before your article is published.For a complete list of Publication Fees, including supplemental material costs, please visit our website.
Corresponding authors may join or renew ASM membership to obtain discounts on publication fees.Need to upgrade your membership level?Please contact Customer Service at Service@asmusa.org.
Thank you for submitting your paper to Microbiology Spectrum.
Dear Editor and the Reviewers, We would like to take this opportunity to thank you for your valuable time and effort towards providing a thorough assessment of our research article, as well as helpful ideas for enhancing the inferences presented in the manuscript.
During the revised manuscript submission, we have addressed all the suggestions as applicable with additional analysis, figures, and supplemental material.
Best wishes,

Rajesh
Reviewer #4 (Comments for the Author): This is a well written manuscript, I enjoyed reading it.
Thank you for your appreciation of our effort towards putting together the findings together in this manuscript covering an unexplored aspect of host response during COVID-19.
I would like to comment about two points: 1) About the virus, did you make RT-PCR for the virus subtypes?Which SARS-CoV-2 variant was present in the patients?Did all the patents have the same virus variant?I think this is another criteria about the clinical outcome, some subtypes are more virulent.Could you please mention and discuss about it in the discussion or in the material methods section.
We thank the reviewer for this insightful perspective.
We would like to share that these patients were first tested for COVID-19 positivity using RT-PCR.
Subsequently, these RT-PCR patients are undertaken for SARS-CoV-2 whole genome sequencing.
Thereafter, the viral phylogeny and identification of the variant have been determined using the genome sequences obtained from the Oxford Nanopore Platform.
As these COVID-19 patients are from pre-VOC (variants of concern) time point, majority of them have similar variant which lead us to hypothesize that despite having a similar variant, why the patients experienced differential disease severity levels (mild, moderate, severe, mortality).
We have included the clade information in the Supplementary table 1 as well as added a phylogenetic tree of the clades in the Figure 1.We have also added a paragraph in both the discussion and methods section as well about the patient SARS-CoV-2 clades in this study.It is also important to share that these SARS-CoV-2 sequences are uploaded to GISAID (IDs included in the (Supplementary table 1).We have added the following section to the result.
"We also sequenced the whole genome of the SARS-CoV-2 virus isolated from nasopharyngeal swabs of the patients to determine whether patients with varying severity levels are infected with different strains of the virus.Despite the differences in clinical severity and outcome, we discovered that the virus strain (19A, 20A and 20B) was similar between mild, moderate, severe and mortality patients (Figure 1F).2) We know that COVID19 is more fatal in older age group as you mentioned in the text.Did you also compare the age groups for the differences in transcriptome.Are the changes related to mortality more abundant in the old patients?If possible you can compare the age groups and discuss about the effect of aging on the transcriptome, if there is no difference between the age and clinical severity groups.
We thank the reviewer for this important observation.
While the patient grouping was done with respect to the severity parameter as per ICMR (Indian  S2A).To check for its possible modulation due to age, we also performed the simple linear regression analysis between the age and expressions of the 10 differentially expressed transcripts between Mortality and Mild patients.
Importantly, we do not see a significant association between the transcripts and the age of the patients.
This suggests that despite age being an important component, it is not a major confounder alone.But in conjunction with comorbidities, treatment regimen, host immune response and the viral strain, it may affect the expression of these transcripts.We have added a paragraph of the same in the result and discussion of the revised manuscript as below: "Within the mortality group we compared the expression of these 10 transcripts to check if there is any association between age and the outcome, while the average expression of these transcripts (excluding pseudogene AL731559.1)were more in mortality patients above the median age of 61 compared to below (Supplementary Figure S2A), there was no significant association between age and expression of these transcripts."-Pageno.9,lines 202-207.
"As age can modulate disease severity, we compared it between the patient sub-groups.While the median age varied between mild and moderate/severe/mortality patients, there was no significant differences between other severity groups, viz.moderate vs severe, severe vs mortality, and moderate vs mortality.Thus, we checked the effect of age on transcriptome between mild and mortality, however, found no significant association between age and the significantly expressed transcripts.
This suggests that despite age being an important component, it is not a major confounder alone.But ) are not correctly labelled according to the text, please correct it.Fig 3 (GH) graph boundaries thickness is inconsistent to Fig (IJ), keep it consistent.4-What are the limitation of your study?Staff Comments: symptoms within the COVID-19 sub-phenotypes despite similarity in the underlying viral infection and emphasize the need of understanding the transcriptional dynamics within the COVID-19 severity subphenotypes."-Pageno 6, line no.137-142.

Figure 1 :
Figure 1: Phylogenetic tree of the SARS-CoV-2 clades from positive patients.Sample labelled with pink colour belong to 20B.Disease severity types and SARS-CoV-2 lineages are distributed across the phylogeny as represented by the color of nodes (green for mild, yellow for moderate, blue for severe, red for mortality).
But in conjunction with comorbidities, treatment regimen, host immune response and the viral strain, it may affect the expression of transcripts.Next, we compared the viral clade between different severity/outcomes and observed that despite different clinical severity, the underlying viral clade was similar (19A, 20A, 20B) (Figure 1F)." -Page no.16, line no.358-368.

Figure 2 :
Figure 2: Age distribution between mild, moderate, severe and mortality patients.

Figure 3 :
Figure 3: Average abundance distribution of mortality patients divided based on their median age.Patients above 61 years of age and patients below 61 years of age.It does not include the pseudogene, AL731559.1.

3-
Figure 2 labelling and text is not matching.For eg. fig. 2 (D-H) are not correctly labelled according to the text, please correct it.Fig 3 (GH) graph boundaries thickness is inconsistent to Fig (IJ), keep it consistent.We thank the reviewer for bringing it to our notice.During the revised submission, we have now corrected the Figure legend 2 as well as made the graph thickness uniform in Figure 3 (GH).
hospital.Although the samples are optimal for examining the early host response to COVID-19, a longitudinal data can help elucidate the dynamics of alternative splicing during infection."-23R1 (SARS-CoV-2 infection Severity and Mortality is modulated by Repeat-mediated regulation of Alternative Splicing) Dear Dr. Rajesh Pandey: Overall, these clinical, sequencing, and demographic data represent the diversity of symptoms within the COVID-19 sub-phenotypes despite similarity in the underlying viral infection and emphasize the need of understanding the transcriptional dynamics within the COVID-19 severity sub-phenotypes."-Pageno 6, line no.136-141.

Figure 1 :
Figure 1: Phylogenetic tree of the SARS-CoV-2 clades from positive patients.Sample labelled with pink colour belong to 20B.Disease severity types and SARS-CoV-2 lineages are distributed across the phylogeny as represented by the color of nodes (green for mild, yellow for moderate, blue for severe, red for mortality).
in conjunction with comorbidities, treatment regimen, host immune response and the viral strain, it may affect the expression of transcripts.Next, we compared the viral clade between different severity/outcomes and observed that despite different clinical severity, the underlying viral clade was similar (19A, 20A, 20B) (Figure 1F)." -Page no.16, line no.358-368.

Figure 2 :
Figure 2: Age distribution between mild, moderate, severe and mortality patients.

Figure 3 :
Figure 3: Average abundance distribution of mortality patients divided based on their median age.Patients above 61 years of age and patients below 61 years of age.It does not include the pseudogene, AL731559.1.
• Manuscript: A .DOC version of the revised manuscript • Figures: Editable, high-resolution, individual figure files are required at revision, TIFF or EPS files are preferred