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Predicting severity in COVID-19 disease using sepsis blood gene expression signatures.
Baghela, Arjun; An, Andy; Zhang, Peter; Acton, Erica; Gauthier, Jeff; Brunet-Ratnasingham, Elsa; Blimkie, Travis; Freue, Gabriela Cohen; Kaufmann, Daniel; Lee, Amy H Y; Levesque, Roger C; Hancock, Robert E W.
Afiliación
  • Baghela A; Department of Microbiology and Immunology, University of British Columbia (UBC), Vancouver, Canada.
  • An A; Department of Microbiology and Immunology, University of British Columbia (UBC), Vancouver, Canada.
  • Zhang P; Asep Medical, Vancouver, Canada.
  • Acton E; Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, Canada.
  • Gauthier J; Institut de Biologie Intégrative et des Systèmes (IBIS), Département de Microbiologie-Infectiologie et d'immunologie, Université Laval, Quebec, QC, Canada.
  • Brunet-Ratnasingham E; Département de Microbiologie, Infectiologie Et Immunologie, Université de Montréal, Montreal, Canada.
  • Blimkie T; Centre de Recherche du CHUM, Montreal, QC, Canada.
  • Freue GC; Department of Microbiology and Immunology, University of British Columbia (UBC), Vancouver, Canada.
  • Kaufmann D; Department of Statistics, UBC, Vancouver, Canada.
  • Lee AHY; Centre de Recherche du CHUM, Montreal, QC, Canada.
  • Levesque RC; Département de Médecine, Université de Montréal, Montreal, Canada.
  • Hancock REW; Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, Canada.
Sci Rep ; 13(1): 1247, 2023 01 23.
Article en En | MEDLINE | ID: mdl-36690713
Severely-afflicted COVID-19 patients can exhibit disease manifestations representative of sepsis, including acute respiratory distress syndrome and multiple organ failure. We hypothesized that diagnostic tools used in managing all-cause sepsis, such as clinical criteria, biomarkers, and gene expression signatures, should extend to COVID-19 patients. Here we analyzed the whole blood transcriptome of 124 early (1-5 days post-hospital admission) and late (6-20 days post-admission) sampled patients with confirmed COVID-19 infections from hospitals in Quebec, Canada. Mechanisms associated with COVID-19 severity were identified between severity groups (ranging from mild disease to the requirement for mechanical ventilation and mortality), and established sepsis signatures were assessed for dysregulation. Specifically, gene expression signatures representing pathophysiological events, namely cellular reprogramming, organ dysfunction, and mortality, were significantly enriched and predictive of severity and lethality in COVID-19 patients. Mechanistic endotypes reflective of distinct sepsis aetiologies and therapeutic opportunities were also identified in subsets of patients, enabling prediction of potentially-effective repurposed drugs. The expression of sepsis gene expression signatures in severely-afflicted COVID-19 patients indicates that these patients should be classified as having severe sepsis. Accordingly, in severe COVID-19 patients, these signatures should be strongly considered for the mechanistic characterization, diagnosis, and guidance of treatment using repurposed drugs.
Asunto(s)

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Sepsis / COVID-19 Tipo de estudio: Guideline / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Sci Rep Año: 2023 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Sepsis / COVID-19 Tipo de estudio: Guideline / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Sci Rep Año: 2023 Tipo del documento: Article