T Cell-mediated Immune Response and Correlates of Inflammation and their relationship with COVID-19 clinical severity: not an intuitive guess.

Author:

Mantovani Nathalia1,Santana Luiz1,Hunter James1,Blum Vinicius1,Vergara Tania1,Gouvea Celso2,Leal Elcio3,Bellei Nancy1,Schechter Mauro1,Diaz Ricardo1

Affiliation:

1. Federal University of São Paulo

2. Centro de Hematologia e Hemoterapia do Ceará

3. Federal University of Para

Abstract

Abstract Background: Predictors of the outcome of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Cov-2) infection remain to be fully determined. We evaluated selected viral characteristics and immunological responses that might predict and/or correlate to the clinical outcome of COVID-19. Methods: The magnitude and breadth of T cell-mediated responses were measured within 36 hours of symptom onset for individuals developing divergent clinical outcomes. Peripheral Blood Mononuclear Cells (PBMCs) were subjected to in vitro stimulation with SARS-CoV-2-based peptides. In addition, SARS-CoV-2 sequences were generated by metagenome, and HLA typing was performed using Luminex technology. Findings: CD4+ T cell activation was found to be negatively correlated with SARS-CoV-2 basal viral load in patients with severe COVID-19 (p = 0·043). The overall cellular immune response, as inferred by IFN-γ signal, was higher at baseline for patients that progressed to mild disease compared to patients that progressed to severe disease (p = 0·0044). Subjects with milder disease developed higher T cell responses for MHC class I and II-restricted peptides (p = 0·033). Interpretation: Mounting specific cellular immune responses in the first days after symptom onset, as inferred by IFN-γ magnitude in the ELISPOT assay, may efficiently favor a positive outcome. In contrast, progression to severe COVID-19 was accompanied by stronger cellular immune responses, higher CD4+ T cell activation, and a higher number of in silico predicted high-affinity class I HLA alleles. Funding: Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) - Grant 2020/10396-2, and Conselho Nacional de Desenvolvimento Científico e Tecnológico - Grant 441817/2018-1.

Publisher

Research Square Platform LLC

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