Predicting severity in COVID-19 disease using sepsis blood gene expression signatures

• Published in: Scientific reports Vol. 13; no. 1; p. 1247
• Main Authors: 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.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 23.01.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114; 631/114/2413; 692/420/254; 692/420/256; 692/53; Biomarkers; Blood; Coronaviruses; COVID-19; COVID-19 - complications; Gene expression; Humanities and Social Sciences; Humans; Lethality; Mechanical ventilation; Mortality; multidisciplinary; Multiple Organ Failure; Patients; Respiratory distress syndrome; Science; Science (multidisciplinary); Sepsis; Transcriptome; Transcriptomes
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-28259-y

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.