Biology subjects

Chernyshev, M.

Publications and source records attributed to Chernyshev, M..

5 recordsLinked to original sources

bioRxiv2025

Human IGH germline gene diversity and allele frequencies in 2486 individuals from 25 global populations delineated by ultra-high throughput genotyping

The extraordinary diversity of human immunoglobulin (IG) genes underpins effective antibody responses, yet the full scope and functional impact of germline-encoded IG variation across populations is largely unknown. Here, we present ImmuneDiscover, an ultra-high-throughput sequencing platform enabling individualized IG genotyping from nanogram-scale DNA and simultaneous analysis of over 1,000 individuals. Using ImmuneDiscover, we generated high-resolution IG genotypes for 2,486 individuals from the 1000 Genomes Project, spanning diverse ancestries, and built KIARVA, an open-access atlas of IG gene variation. Cross-validation with SNP data from over one million genomes confirmed the accuracy and novelty of discovered alleles. Our analysis reveals striking population-specific patterns, including a multigene IGHD segment deletion found in up to 30% of East Asian individuals, and haplotypic differences linked to disease-associated loci. These findings illuminate the evolutionary forces shaping IG diversity and provide a foundational resource for investigating the immunogenetic basis of pathogen susceptibility and vaccine responsiveness worldwide.

immunology↗

bioRxiv2025

Isolation of genetically diverse influenza antibodies highlights the role of IG germline gene variation and informs the design of population-comprehensive vaccine strategies

The regular emergence of influenza strains with pandemic potential creates a strong incentive to develop vaccines that stimulate protective responses across all human populations. A critical consideration is how variation in the human immunoglobulin (IG) loci influences B cell recognition of viral epitopes and elicitation of neutralizing antibodies. Here, we applied personalized IG germline genotyping and high-throughput sequencing of paired antibody chains from influenza A virus hemagglutinin (HA)-binding B cells to demonstrate that the response to HA is highly individual. We show that a germline-encoded polymorphism in IGHV2-70 alters the functionality of the LPAF-a class of neutralizing antibodies, and we describe HA stem-targeting broadly neutralizing antibodies (bNAbs) that use germline IGHV genes other than the population-restricted IGHV genes used by many previously known stem bNAbs. Our results demonstrate that the approach used here can be used to discover and avert population vulnerabilities arising from IG gene variation when designing HA-based influenza vaccines aimed for the global human population.

immunology↗

bioRxiv2025

Detection of PCR chimeras in adaptive immune receptor repertoire sequencing using hidden Markov models

Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) has emerged as a central approach for studying T cell and B cell receptor populations, and is now an important component of studies of autoimmunity, immune responses to pathogens, vaccines, allergens, and cancers, and for antibody discovery. When amplifying the rearranged V(D)J genes encoding antigen receptors, each cycle of the Polymerase Chain Reaction (PCR) can produce spurious "chimeric" hybrids of two or more different template sequences. While the generation of chimeras is well understood in bacterial and viral sequencing, and there are dedicated tools to detect such sequences in bacterial and viral datasets, this is not the case for AIRR-seq. Further, the process that results in immune receptor sequences has domain-specific challenges, such as somatic hypermutation (SHM), and domain-specific opportunities, such as relatively well-known germline gene "reference" sequences. Here we describe CHMMAIRRa, a hidden Markov model for detecting chimeric sequences in AIRR-seq data, that specifically models SHM and incorporates germline reference sequences. We use simulations to characterize the performance of CHMMAIRRa and compare it to existing methods from other domains, we test the effect of PCR conditions on chimerism using IgM libraries generated in this study, and we apply CHMMAIRRa to four published AIRR-seq datasets to show the extent and impact of artifactual chimerism.

bioinformatics↗

bioRxiv2022

Archaic humans have contributed to large-scale variation in modern human T cell receptor genes

The human T cell receptor (TCR) genes are critical for mediating immune responses to pathogens, tumors and regulating self-antigen recognition. A detailed analysis and validation of expressed TCR alpha, beta, gamma, and delta genes in 45 donors from 4 human populations: African, East Asian, South Asian, and European, revealed a total of 175 novel TCR variable and junctional alleles. The majority of novel alleles contained coding changes and were present at widely differing frequencies in the populations, a finding confirmed using DNA samples and sequences from the 1000 Genomes Project. Importantly, we identified three Neanderthal-derived, introgressed TCR regions, including a highly divergent novel TRGV4 variant, present in all archaic assemblies, that was frequent in all modern Eurasian population groups. Our results demonstrate significant variation in TCR genes at both individual and population levels, providing a strong incentive for including allelic variation in studies of TCR function in human biology.

immunology↗

bioRxiv2018

Long-read amplicon denoising

Long-read next generation amplicon sequencing shows promise for studying complete genes or genomes from complex and diverse populations. Current long-read sequencing technologies have challenging error profiles, hindering data processing and incorporation into downstream analyses. Here we consider the problem of how to reconstruct, free of sequencing error, the true sequence variants and their associated frequencies. Called \"amplicon denoising\", this problem has been extensively studied for short-read sequencing technologies, but current solutions do not appear to generalize well to long reads with high indel error rates. We introduce two methods: one that runs nearly instantly and is very accurate for medium length reads (here ~2.6kb) and high template coverage, and another, slower method that is more robust when reads are very long or coverage is lower.\n\nOn one real dataset with ground truth, and on a number of simulated datasets, we compare our two approaches to each other and to existing algorithms. We outperform all tested methods in accuracy, with competitive run times even for our slower method.\n\nFast Amplicon Denoising (FAD) and Robust Amplicon Denoising (RAD) are implemented purely in the Julia scientific computing language, and are hereby released along with a complete toolkit of functions that allow long-read amplicon sequence analysis pipelines to be constructed in pure Julia. Further, we make available a webserver to dramatically simplify the processing of long-read PacBio sequences.

bioinformatics↗