Biology subjects

Byron, R.

Publications and source records attributed to Byron, R..

3 recordsLinked to original sources

bioRxiv2024

Identifying immune signatures of common exposures through co-occurrence of T-cell receptors in tens of thousands of donors

BackgroundMemory T cells are records of clonal expansion from prior immune exposures such as infections, vaccines and chronic diseases. Some of the receptors of these expanded T cell clones in a typical immune repertoire are highly public (present in many individuals) because they respond to the same peptide from a prevalent immune exposure, presented by the same Human Leukocyte Antigen (HLA) allele. Only a tiny fraction of public T-cell receptor {beta} sequences (TCRs) have known associations with exposures or specific peptides. MethodsWe mined the TCR repertoires of tens of thousands of donors to define "ECOclusters": clusters of public TCRs that tend to occur in the same donors. First, we built models to infer donor HLA type from the TCR repertoire, then associated public TCRs with HLA alleles. Next, we derived co-occurrence clusters of TCRs responding to antigens presented by the same HLA allele, then combined those clusters by co-occurrence across HLA alleles. Each such cross-HLA ECOcluster putatively represents a public TCR signature of a single exposure. ResultsWe constructed sensitive, specific models to predict the presence of 220 HLA alleles from TCR repertoires and clustered 8,618,285 HLA allele-associated TCRs to define 11,058 ECOclusters. Using serologically labeled repertoires, we identified ECOclusters associated with HSV-1, HSV-2, EBV, Parvovirus, Toxoplasma gondii, Cytomegalovirus and SARS-CoV-2, and constructed sensitive, specific classifiers of exposure. ECOclusters represent a step toward deciphering the ledger of immune exposure history encoded by the T-cell repertoire.

immunology↗

bioRxiv2024

Seq2MAIT: A Novel Deep Learning Framework for Identifying Mucosal Associated Invariant T (MAIT) Cells

Mucosal-associated invariant T (MAIT) cells are a group of unconventional T cells that mainly recognize bacterial vitamin B metabolites presented on MHC-related protein 1 (MR1). MAIT cells have been shown to play an important role in controlling bacterial infection and in responding to viral infections. Furthermore, MAIT cells have been implicated in different chronic inflammatory diseases such as inflammatory bowel disease and multiple sclerosis. Despite their involvement in different physiological and pathological processes, a deeper understanding of MAIT cells is still lacking. Arguably, this can be attributed to the difficulty of quantifying and measuring MAIT cells in different biological samples which is commonly done using flow cytometry-based methods and single-cell-based RNA sequencing techniques. These methods mostly require fresh samples which are difficult to obtain, especially from tissues, have low to medium throughput, and are costly and labor-intensive. To address these limitations, we developed sequence-to-MAIT (Seq2MAIT) which is a transformer-based deep neural network capable of identifying MAIT cells in bulk TCR-sequencing datasets, enabling the quantification of MAIT cells from any biological materials where human DNA is available. Benchmarking Seq2MAIT across different test datasets showed an average area-under-the-receiver-operator-curve (AU[ROC]) >0.80. In conclusion, Seq2MAIT is a novel, economical, and scalable method for identifying and quantifying MAIT cells in virtually any biological sample.

bioinformatics↗

bioRxiv2020

Identification of cellular context sensitive regulatory variation in mouse genomes

Assessment of the functional consequences of disease-associated sequence variation at non-coding regulatory elements is complicated by their high degree of context sensitivity to both the local chromatin and nuclear environments. Allelic profiling of DNA accessibility across individuals has shown that only a select minority of sequence variation affects transcription factor (TF) occupancy, yet the low sequence diversity in human populations means that no experimental assessment is available for the majority of disease-associated variants. Here we describe high-resolution in vivo maps of allelic DNA accessibility in liver, kidney, lung and B cells from 5 increasingly diverged strains of F1 hybrid mice. The high density of heterozygous sites in these hybrids enables precise quantification of the effect size and cell-type specificity of hundreds of thousands of variants throughout the mouse genome. We show that chromatin-altering variants delineate characteristic sensitivity profiles for hundreds of TF motifs. We develop a compendium of TF-specific sensitivity profiles accounting for genomic context effects. Finally, we link these maps of allelic accessibility to allelic transcript levels in the same samples. This work provides a foundation for quantitative prediction of cell-type specific effects of non-coding variation on TF activity, which will dramatically facilitate both fine-mapping and systems-level analyses of common disease-associated variation in human genomes.

genomics↗