Allelic variation in class I HLA determines CD8+ T cell repertoire shape and cross-reactive memory responses to SARS-CoV-2.

Effective presentation of antigens by human leukocyte antigen (HLA) class I molecules to CD8+ T cells is required for viral elimination and generation of long-term immunological memory. In this study, we applied a single-cell, multiomic technology to generate a unified ex vivo characterization of the CD8+ T cell response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) across four major HLA class I alleles. We found that HLA genotype conditions key features of epitope specificity, TCRα/ß sequence diversity, and the utilization of pre-existing SARS-CoV-2-reactive memory T cell pools. Single-cell transcriptomics revealed functionally diverse T cell phenotypes of SARS-CoV-2-reactive T cells, associated with both disease stage and epitope specificity. Our results show that HLA variations notably influence the CD8+ T cell repertoire shape and utilization of immune recall upon SARS-CoV-2 infection.

Predicting Mycobacterium tuberculosis complex clades using knowledge-based Bayesian networks.

We develop a novel approach for incorporating expert rules into Bayesian networks for classification of Mycobacterium tuberculosis complex (MTBC) clades. The proposed knowledge-based Bayesian network (KBBN) treats sets of expert rules as prior distributions on the classes. Unlike prior knowledge-based support vector machine approaches which require rules expressed as polyhedral sets, KBBN directly incorporates the rules without any modification. KBBN uses data to refine rule-based classifiers when the rule set is incomplete or ambiguous. We develop a predictive KBBN model for 69 MTBC clades found in the SITVIT international collection. We validate the approach using two testbeds that model knowledge of the MTBC obtained from two different experts and large DNA fingerprint databases to predict MTBC genetic clades and sublineages. These models represent strains of MTBC using high-throughput biomarkers called spacer oligonucleotide types (spoligotypes), since these are routinely gathered from MTBC isolates of tuberculosis (TB) patients. Results show that incorporating rules into problems can drastically increase classification accuracy if data alone are insufficient. The SITVIT KBBN is publicly available for use on the World Wide Web.