Dataset Information

T Cell Senescence by Extensive Phenotyping: An Emerging Feature of COVID-19 Severity.

ABSTRACT:

Objective

To identify the potential prognostic value of lymphocyte subsets in COVID-19 patients, where lymphopenia is a common finding.

Methods

In 353 COVID-19 inpatients and 40 controls T cell subsets with markers of senescence and exhaustion were studied by flow cytometry.

Results

In severe illness, total lymphocytes B, NK, and all T subsets were dampened. Senescent CD4+, but mainly CD8+ T cells, increased in patients with respect to controls. The most significant index predicting fatal outcome was neutrophils/CD3+ T ratio.

Conclusion

In conclusion, an altered T cell pattern underlies COVID-19 severity and is involved in predicting the outcome.

SUBMITTER: Zuin J

PROVIDER: S-EPMC9214174 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Publications

T Cell Senescence by Extensive Phenotyping: An Emerging Feature of COVID-19 Severity.

Zuin Jenny J Fogar Paola P Musso Giulia G Padoan Andrea A Piva Elisa E Pelloso Michela M Tosato Francesca F Cattelan Annamaria A Basso Daniela D Plebani Mario M

Laboratory medicine 20221101 6

<h4>Objective</h4>To identify the potential prognostic value of lymphocyte subsets in COVID-19 patients, where lymphopenia is a common finding.<h4>Methods</h4>In 353 COVID-19 inpatients and 40 controls T cell subsets with markers of senescence and exhaustion were studied by flow cytometry.<h4>Results</h4>In severe illness, total lymphocytes B, NK, and all T subsets were dampened. Senescent CD4+, but mainly CD8+ T cells, increased in patients with respect to controls. The most significant index p ...[more]

PMID: 35716073

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Project description:Using RNA-seq and high-resolution mass spectrometry we performed a comprehensive systems analysis on 128 plasma and leukocyte samples from hospitalized patients with or without COVID-19 (n=102 and 26 respectively) and with differing degrees of disease severity. We generated abundance measurements for over 17,000 transcripts, proteins, metabolites, and lipids and compiled them with clinical data into a curated relational database. This resource offers the unique opportunity to perform systems analysis and cross-ome correlations to both molecules and patient outcomes. In total 219 molecular features were mapped with high significance to COVID-19 status and severity, including those involved in processes such as complement system activation, dysregulated lipid transport, and B cell activation. In one example, we detected a trio of covarying molecules – citrate, plasmenyl-phosphatidylcholines, and gelsolin (GSN) – that offer both pathophysiological insight and potential novel therapeutic targets. Further, our data revealed in some cases, and supported in others, that several biological processes were dysregulated in COVID-19 patients including vessel damage, platelet activation and degranulation, blood coagulation, and acute phase response. For example, we observed that the coagulation-related protein, cellular fibronectin (cFN), was highly increased within COVID-19 patients and provide new evidence that the upregulated proteoform stems from endothelial cells, consistent with endothelial injury as a major activator of the coagulation cascade. The abundance of prothrombin, which is cleaved to form thrombin during clotting, was significantly reduced and correlated with severity and might help to explain the hyper coagulative environment of SARS-CoV-2 infection. From transcriptomic analysis of leukocytes, we concluded that COVID-19 patients with acute respiratory distress syndrome (ARDS) demonstrated a phenotype that overlapped with, but was distinct from, that found in patients with non-COVID-19-ARDS. To aid in the global efforts toward elucidation of disease pathophysiology and therapeutic development, we created a web-based tool with interactive visualizations allowing for easy navigation of this systems-level compendium of biomolecule abundance in relation to COVID-19 status and severity. Finally, we leveraged these multi-omic data to predict COVID-19 patient outcomes with machine learning, which highlighted the predictive power of these expansive molecular measurements beyond the standardized clinical estimate of 10-year survival Charlson score.

Dataset Information

T Cell Senescence by Extensive Phenotyping: An Emerging Feature of COVID-19 Severity.

Objective

Methods

Results

Conclusion

Publications

T Cell Senescence by Extensive Phenotyping: An Emerging Feature of COVID-19 Severity.

Similar Datasets

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