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Britanova, O.

Publications and source records attributed to Britanova, O..

2 recordsLinked to original sources

Towards understanding of NK cell antigenic specificity

NK cells can form clonal populations demonstrating features of adaptive immunity, including long-term memory and at least partial antigenic specificity. Given the limited individual diversity of activating receptors, the nature of NK cell antigenic specificity remains elusive. To explore this riddle, we combined scRNA-Seq of ex vivo FACS-sorted NK cell subsets expressing specific KIR receptors, single-cell cloning and bulk RNA-Seq of in vitro cultured KIR2DS4 NK cell clones, transcriptomic profiling of antigen-stimulated NK cells, and in silico modeling of glycosylated KIR2DS4-peptide-HLA complexes. scRNA-Seq resolved 12-15 clusters per KIR subset with highly heterogeneous KIR, KLRC and NCR expression patterns, consistent with clonal lineages. Notably, those clusters demonstrated over 30 differentially expressed glycosyltransferase genes, potentially involved in post-translational modification of NK cell receptors. Single-cell-derived KIR2DS4 cultures exhibited clone-specific cytotoxic, chemokine and KIR receptor genes, and transcriptional differences in > 40 glycosyltransferases. In peptide culturing autologous assays, SARS-CoV-2 (KTFPPTEPK) and EBV (CRAKFKHLL) peptides elicited NK cell proliferation and distinct transcriptional programs linking cytotoxicity genes, KIR2DS4 and glycosyltransferases. Structural modeling revealed that N-linked glycosyl residues in specific regions of KIR2DS4 may alter its contacts and interaction with MHCI and the presented peptide. We conclude that KIR human NK cells comprise clonally imprinted populations with distinct glycosyltransferase expression profiles, and site-specific KIR2DS4 glycosylation may modulate interaction with peptide-MHCI complexes, suggesting a post-translational layer of clonal NK cell diversification as a clue to their antigenic specificity.

immunology↗

Robust detection of SARS-CoV-2 exposure in population using T-cell repertoire profiling

The COVID-19 pandemic offers a powerful opportunity to develop methods for monitoring the spread of infectious diseases based on their signatures in population immunity. Adaptive immune receptor repertoire sequencing (AIRR-seq) has become the method of choice for identifying T cell receptor (TCR) biomarkers encoding pathogen specificity and immunological memory. AIRR-seq can detect imprints of past and ongoing infections and facilitate the study of individual responses to SARS-CoV-2, as shown in many recent studies. Here, we have applied a machine learning approach to two large AIRR-seq datasets with more than 1,200 high-quality repertoires from healthy and COVID-19-convalescent donors to infer TCR repertoire features that were induced by SARS-CoV-2 exposure. The new batch effect correction method allowed us to use data from different batches together, as well as combine the analysis for data obtained using different protocols. Proper standardization of AIRR-seq batches, access to human leukocyte antigen (HLA) typing, and the use of both - and {beta}-chain sequences of TCRs resulted in a high-quality biomarker database and a robust and highly accurate classifier for COVID-19 exposure. This classifier is applicable to individual TCR repertoires obtained using different protocols, paving the way to AIRR-seq-based immune status assessment in large cohorts of donors.

immunology↗