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Deconvoluting the T Cell Response to SARS-CoV-2: Specificity Versus Chance and Cognate Cross-Reactivity.

ABSTRACT: SARS-CoV-2 infection takes a mild or clinically inapparent course in the majority of humans who contract this virus. After such individuals have cleared the virus, only the detection of SARS-CoV-2-specific immunological memory can reveal the exposure, and hopefully the establishment of immune protection. With most viral infections, the presence of specific serum antibodies has provided a reliable biomarker for the exposure to the virus of interest. SARS-CoV-2 infection, however, does not reliably induce a durable antibody response, especially in sub-clinically infected individuals. Consequently, it is plausible for a recently infected individual to yield a false negative result within only a few months after exposure. Immunodiagnostic attention has therefore shifted to studies of specific T cell memory to SARS-CoV-2. Most reports published so far agree that a T cell response is engaged during SARS-CoV-2 infection, but they also state that in 20-81% of SARS-CoV-2-unexposed individuals, T cells respond to SARS-CoV-2 antigens (mega peptide pools), allegedly due to T cell cross-reactivity with Common Cold coronaviruses (CCC), or other antigens. Here we show that, by introducing irrelevant mega peptide pools as negative controls to account for chance cross-reactivity, and by establishing the antigen dose-response characteristic of the T cells, one can clearly discern between cognate T cell memory induced by SARS-CoV-2 infection vs. cross-reactive T cell responses in individuals who have not been infected with SARS-CoV-2.

SUBMITTER: Lehmann AA

PROVIDER: S-EPMC8196231 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundComprehensive characterization of exposures and immune responses to viral infections is critical to a basic understanding of human health and disease. We previously developed the VirScan system, a programmable phage-display technology for profiling antibody binding to a library of peptides designed to span the human virome. Previous VirScan analytical approaches did not carefully account for antibody cross-reactivity among sequences shared by related viruses or for the disproportionate representation of individual viruses in the library.MethodsHere we present the AntiViral Antibody Response Deconvolution Algorithm (AVARDA), a multi-module software package for analyzing VirScan datasets. AVARDA provides a probabilistic assessment of infection with species-level resolution by considering sequence alignment of all library peptides to each other and to all human viruses. We employed AVARDA to analyze VirScan data from a cohort of encephalitis patients with either known viral infections or undiagnosed etiologies. We further assessed AVARDA's utility in associating viral infection with type 1 diabetes and lupus.FindingsBy comparing acute and convalescent sera, AVARDA successfully confirmed or detected encephalitis-associated responses to human herpesviruses 1, 3, 4, 5, and 6, improving the rate of diagnosing viral encephalitis in this cohort by 44%. AVARDA analyses of VirScan data from the type 1 diabetes and lupus cohorts implicated enterovirus and herpesvirus infections, respectively.InterpretationAVARDA, in combination with VirScan and other pan-pathogen serological techniques, is likely to find broad utility in the epidemiology and diagnosis of infectious diseases.FundingThis work was made possible by support from the National Institutes of Health (NIH), the US Army Research Office, the Singapore Infectious Diseases Initiative (SIDI), the Singapore Ministry of Health's National Medical Research Council (NMRC) and the Singapore National Research Foundation (NRF).

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Deconvoluting the T Cell Response to SARS-CoV-2: Specificity Versus Chance and Cognate Cross-Reactivity.

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