Longitudinal gene expression analysis in COVID-19 sepsis highlights dynamic immune, cellular, and metabolic dysfunction in high severity patients

COVID-19 patients experience dynamic changes in immune and cellular function over time, similar to that in sepsis. However, there is insufficient research investigating, at the gene expression level, the mechanisms that become activated or suppressed over time as patients deteriorate or recover. This has potential prognostic and therapeutic implications. In this longitudinal study, 300 whole blood samples were analyzed from 128 adult patients throughout their COVID-19 hospitalization. Transcriptome sequencing (RNA-Seq), differential gene expression analysis, pathway enrichment, and drug-gene set enrichment analysis were performed to elucidate key mechanisms for therapeutic targeting during six distinct disease phases through the COVID-19 trajectory. Adaptive immune dysfunction, inflammation, and metabolic dysregulation were most pronounced during phases with higher disease severity. Hemostatic dysregulation was present early and persisted throughout the disease course, in contrast to an early antiviral response and late heme metabolism activity. Drug-gene set enrichment analysis predicted repurposed medications for potential use, including platelet inhibitors, antidiabetic medications, and dasatinib. Disease phases had distinct transcriptional signatures and were highly correlated to previously developed sepsis endotypes, indicating that severity and disease timing were significant contributors to heterogeneity observed in COVID-19 sepsis. These findings provide an opportunity for better prognostication of patients and potential time-dependent personalized treatments.

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Publication Details

Journal
Scientific Reports
Published
2025-12-19
DOI
https://doi.org/10.1038/s41598-025-32557-y
Primary Topic
COVID-19 Clinical Research Studies
Type
article
Field-Weighted Citation Impact
0.00

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article

Longitudinal gene expression analysis in COVID-19 sepsis highlights dynamic immune, cellular, and metabolic dysfunction in high severity patients

Peter Zhang, Arjun Baghela, Travis M. Blimkie, Jeff Gauthier et al.
Scientific Reports
COVID-19 Clinical Research Studies
article

Longitudinal gene expression analysis in COVID-19 sepsis highlights dynamic immune, cellular, and metabolic dysfunction in high severity patients

Peter Zhang, Arjun Baghela, Travis M. Blimkie, Jeff Gauthier, Amy Huei‐Yi Lee, Daniel E. Kaufmann, Erica Acton, Roger C. Lévesque, Andy Y. An, Robert E. W. Hancock
article en

Abstract

COVID-19 patients experience dynamic changes in immune and cellular function over time, similar to that in sepsis. However, there is insufficient research investigating, at the gene expression level, the mechanisms that become activated or suppressed over time as patients deteriorate or recover. This has potential prognostic and therapeutic implications. In this longitudinal study, 300 whole blood samples were analyzed from 128 adult patients throughout their COVID-19 hospitalization. Transcriptome sequencing (RNA-Seq), differential gene expression analysis, pathway enrichment, and drug-gene set enrichment analysis were performed to elucidate key mechanisms for therapeutic targeting during six distinct disease phases through the COVID-19 trajectory. Adaptive immune dysfunction, inflammation, and metabolic dysregulation were most pronounced during phases with higher disease severity. Hemostatic dysregulation was present early and persisted throughout the disease course, in contrast to an early antiviral response and late heme metabolism activity. Drug-gene set enrichment analysis predicted repurposed medications for potential use, including platelet inhibitors, antidiabetic medications, and dasatinib. Disease phases had distinct transcriptional signatures and were highly correlated to previously developed sepsis endotypes, indicating that severity and disease timing were significant contributors to heterogeneity observed in COVID-19 sepsis. These findings provide an opportunity for better prognostication of patients and potential time-dependent personalized treatments.

Scientific ReportsVol. 16(1)
University of British Columbia (CA), Simon Fraser University (CA), Université Laval (CA), Université de Montréal (CA), University of Lausanne (CH)
Public Health Agency of Canada, Génome Québec, Public Health Agency, Killam Trusts, Canadian Institutes of Health Research
Openalex Percentile: Top 52%
COVID-19 Clinical Research Studies
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