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

OBJECTIVES: 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: A total of 32 patients with infective endocarditis were studied. Clinical data and blood samples were collected at diagnosis and RNA sequenced. Gene expression was used to identify two clusters (endocarditis endotype 1 [EE1] and endocarditis endotype 2 [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. The identified endotypes were recapitulated in a cohort of COVID-19 patients. RESULTS: A total of 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 signal transducer and activator of transcription protein family pathway, with higher counts of active T cells and lower counts of neutrophils. A total of 14 patients (nine in EE1 and five in EE2) were submitted to surgery. Surgery in EE2 patients shifted gene expression toward a EE1-like profile. In-hospital mortality was higher in EE1 (56% vs 14%, P = 0.027), with an adjusted hazard ratio of 12.987 (95% confidence interval 3.356-50). Translation of these endotypes to COVID-19 and non-COVID-19 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, responses to surgery, and outcomes.

Mechanical Stretch Induces Senescence of Lung Epithelial Cells and Drives Fibroblast Activation by Paracrine Mechanisms.

Severe lung injury requiring mechanical ventilation may lead to secondary fibrosis. Senescence, a cell response characterized by cell cycle arrest and a shift towards a proinflammatory/profibrotic phenotype, is one of the involved mechanisms. Here, we explore the contribution of mechanical stretch as trigger of senescence of the respiratory epithelium and its link with fibrosis. Human lung epithelial cells and fibroblasts were exposed in vitro to mechanical stretch, and senescence assessed. In addition, fibroblasts were exposed to culture media preconditioned by senescent epithelial cells and their activation was studied. Transcriptomic profiles from stretched, senescent epithelial cells and activated fibroblasts were combined to identify potential activated pathways. Finally, the senolytic effects of digoxin were tested in these models. Mechanical stretch induced senescence in lung epithelial cells, but not in fibroblasts. This stretch-induced senescence has specific features compared to senescence induced by doxorubicin. Fibroblasts were activated after exposure to supernatants conditioned by epithelial senescent cells. Transcriptomic analyses revealed notch signaling as a potential responsible for the epithelial-mesenchymal crosstalk, as blockade of this pathway inhibits fibroblast activation. Treatment with digoxin reduced the percentage of senescent cells after stretch and ameliorated the fibroblast response to preconditioned media. These results suggest that lung fibrosis in response to mechanical stretch may be caused by the paracrine effects of senescent cells. This pathogenetic mechanism can be pharmacologically manipulated to improve lung repair.

Exploring the translational landscape of the long noncoding RNA transcriptome in acute respiratory distress syndrome: it is a long way to the top.

Acute respiratory distress syndrome (ARDS) poses a significant and widespread public health challenge. Extensive research conducted in recent decades has considerably improved our understanding of the disease pathophysiology. Nevertheless, ARDS continues to rank among the leading causes of mortality in intensive care units and its management remains a formidable task, primarily due to its remarkable heterogeneity. As a consequence, the syndrome is underdiagnosed, prognostication has important gaps and selection of the appropriate therapeutic approach is laborious. In recent years, the noncoding transcriptome has emerged as a new area of attention for researchers interested in biomarker development. Numerous studies have confirmed the potential of long noncoding RNAs (lncRNAs), transcripts with little or no coding information, as noninvasive tools for diagnosis, prognosis and prediction of the therapeutic response across a broad spectrum of ailments, including respiratory conditions. This article aims to provide a comprehensive overview of lncRNAs with specific emphasis on their role as biomarkers. We review current knowledge on the circulating lncRNAs as potential markers that can be used to enhance decision making in ARDS management. Additionally, we address the primary limitations and outline the steps that will be essential for integration of the use of lncRNAs in clinical laboratories. Our ultimate objective is to provide a framework for the implementation of lncRNAs in the management of ARDS.