Transcriptomic clustering of critically ill COVID-19 patients.

BACKGROUND: Infections caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may cause a severe disease, termed coronavirus disease 2019 (COVID-19), with significant mortality. Host responses to this infection, mainly in terms of systemic inflammation, have emerged as key pathogenetic mechanisms and their modulation has shown a mortality benefit. METHODS: In a cohort of 56 critically ill COVID-19 patients, peripheral blood transcriptomes were obtained at admission to an intensive care unit (ICU) and clustered using an unsupervised algorithm. Differences in gene expression, circulating microRNAs (c-miRNAs) and clinical data between clusters were assessed, and circulating cell populations estimated from sequencing data. A transcriptomic signature was defined and applied to an external cohort to validate the findings. RESULTS: We identified two transcriptomic clusters characterised by expression of either interferon-related or immune checkpoint genes, respectively. Steroids have cluster-specific effects, decreasing lymphocyte activation in the former but promoting B-cell activation in the latter. These profiles have different ICU outcomes, despite no major clinical differences at ICU admission. A transcriptomic signature was used to identify these clusters in two external validation cohorts (with 50 and 60 patients), yielding similar results. CONCLUSIONS: These results reveal different underlying pathogenetic mechanisms and illustrate the potential of transcriptomics to identify patient endotypes in severe COVID-19 with the aim to ultimately personalise their therapies.

Identification of host endotypes using peripheral blood transcriptomics in a prospective cohort of patients with endocarditis.

BACKGROUND: Host responses to infection are a major determinant of outcome. However, the existence of different response profiles in patients with endocarditis has not been addressed. Our objective was to apply transcriptomics to identify endotypes in patients with infective endocarditis. METHODS: Thirty-two patients with infective endocarditis were studied. Clinical data and a blood sample were collected at diagnosis, and RNA sequenced. Gene expression was used to identify two clusters (endocarditis endotypes EE1 and EE2). RNA sequencing was repeated after surgery. Transcriptionally active cell populations were identified by deconvolution. Differences between endotypes in clinical data, survival, gene expression and molecular pathways involved were assessed. Identified endotypes were recapitulated in a cohort of COVID19 patients. RESULTS: 18 and 14 patients were assigned to EE1 and EE2 respectively, with no differences in clinical data. Patients assigned to EE2 showed an enrichment in genes related to T-cell maturation and a decrease in the activation of the STAT pathway, with higher counts of active T-cells and lower counts of neutrophils. Fourteen patients (9 in EE1 and 5 in EE2) were submitted to surgery. Surgery in EE2 patients shifted gene expression towards a EE1-like profile. In-hospital mortality was higher in EE1 (56% vs 14%, p=0.027) with adjusted hazard ratio of 12.987 (95% confidence interval 3.356 - 50]. Translation of these endotypes to COVID19 and non-COVID septic patients yielded similar results in cell populations and outcome. CONCLUSIONS: Gene expression reveals two endotypes in patients with acute endocarditis, with different underlying pathogenetic mechanisms, response to surgery and outcome.