Characterisation of the blood RNA host response underpinning severity in COVID-19 patients
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Abstract
Infection with SARS-CoV-2 has highly variable clinical manifestations, ranging from asymptomatic infection through to life-threatening disease. Host whole blood transcriptomics can offer unique insights into the biological processes underpinning infection and disease, as well as severity. We performed whole blood RNA Sequencing of individuals with varying degrees of COVID-19 severity. We used differential expression analysis and pathway enrichment analysis to explore how the blood transcriptome differs between individuals with mild, moderate, and severe COVID-19, performing pairwise comparisons between groups. Increasing COVID-19 severity was characterised by an abundance of inflammatory immune response genes and pathways, including many related to neutrophils and macrophages, in addition to an upregulation of immunoglobulin genes. In this study, for the first time, we show how immunomodulatory treatments commonly administered to COVID-19 patients greatly alter the transcriptome. Our insights into COVID-19 severity reveal the role of immune dysregulation in the progression to severe disease and highlight the need for further research exploring the interplay between SARS-CoV-2 and the inflammatory immune response.
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SciScore for 10.1101/2021.09.16.21263170: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics Consent: Subjects granted informed consent for their participation in the study.
IRB: GEN-COVID Study was approved by the Ethics Committee of Galicia by fast-track procedure on 18th March 2020 (CEIC Galicia, reg 2020/178).Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Table 2: Resources
Software and Algorithms Sentences Resources The RNA-Seq analysis pipeline consisted of quality control using FastQC 20, MultiQC 21 and annotations modified with BEDTools 22, alignment and read counting using STAR 23, SAMtools 24, FeatureCounts 25 and version 89 ensembl GCh38 genome and annotation 26. 2.3. FastQCsuggested: (FastQC, RRID:SCR_014583)SciScore for 10.1101/2021.09.16.21263170: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics Consent: Subjects granted informed consent for their participation in the study.
IRB: GEN-COVID Study was approved by the Ethics Committee of Galicia by fast-track procedure on 18th March 2020 (CEIC Galicia, reg 2020/178).Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Table 2: Resources
Software and Algorithms Sentences Resources The RNA-Seq analysis pipeline consisted of quality control using FastQC 20, MultiQC 21 and annotations modified with BEDTools 22, alignment and read counting using STAR 23, SAMtools 24, FeatureCounts 25 and version 89 ensembl GCh38 genome and annotation 26. 2.3. FastQCsuggested: (FastQC, RRID:SCR_014583)BEDToolssuggested: (BEDTools, RRID:SCR_006646)STARsuggested: (STAR, RRID:SCR_004463)SAMtoolssuggested: (SAMTOOLS, RRID:SCR_002105)FeatureCountssuggested: (featureCounts, RRID:SCR_012919)DESeq2 28 was used for differential expression analysis of COVID-19 severity groups. DESeq2suggested: (DESeq, RRID:SCR_000154)The lists of SDE genes were subjected to pathway analysis using Ingenuity Pathway Analysis (IPA; QIAGEN Inc., https://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis). Ingenuity Pathway Analysissuggested: (Ingenuity Pathway Analysis, RRID:SCR_008653)Results from OddPub: Thank you for sharing your data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Limitations: COVID-19 severity is highly influenced by age which leads to major confounding between these two variables. Although we controlled for this confounder (by including age and the interaction between age and severity) when exploring transcriptomic changes with severity, it is possible that we may have a) failed to identify key drivers of severity as they are confounded with age, b) inadvertently included spurious genes that are really driven by age rather than severity. The sample sizes in our analyses are modest for some severity groups. For example, in the severe COVID-19 group, only 10 samples could be included due to concomitant bacterial infection, because coinfections were likely to have had profound transcriptional impacts and may have masked the genuine SARS-CoV-2 signal. 5.2. Conclusion: We have explored the transcriptomic impact of SARS-CoV-2 infection through evaluating the transcriptomic differences between individuals with varying levels of COVID-19 severity. We have observed considerable transcriptomic perturbation which offer insights into the host factors that influence development of severe COVID-19. Upregulation of inflammatory immune pathways was observed with increasing severity, with multiple neutrophil, macrophage and immunoglobulin-associated genes and pathways identified, suggesting that increased COVID-19 severity may be mediated in part by neutrophil activation, which may be related to production of immunoglobulin as acquired immunity devel...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a protocol registration statement.
Results from scite Reference Check: We found no unreliable references.
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