Biology subjects

Boyd, S. D.

Publications and source records attributed to Boyd, S. D..

13 recordsLinked to original sources

bioRxiv2026

H5N1 influenza binding and cell entry via human class II MHC, and blocking by cross-reactive antibodies

Summary paragraphHighly pathogenic avian influenza (HPAI) H5N1 clade 2.3.4.4b viruses are currently responsible for a multi-species outbreak affecting wild birds, poultry, numerous mammalian species, and humans. Influenza A viruses typically initiate infection through binding to sialic acid, although select bat and human influenza viruses can also exploit class II major histocompatibility complex (MHC-II) molecules for cell entry. Here we show that emerging H5N1 clade 2.3.4.4b viruses, but not historical H5 lineages, bind human MHC-II HLA-DR and mediate sialic acid-independent cell entry. Hemagglutinin binding to primary human immune cells varies with MHC-II expression and is further shaped by HLA-DR allelic variation, identifying host genetic determinants that may influence susceptibility to infection. Mammalian-adaptive substitutions within the hemagglutinin sialic acid receptor-binding domain reduce MHC-II binding, suggesting this interaction is remodeled during clade 2.3.4.4b H5 adaptation to a human host. Lastly, cross-reactive monoclonal antibodies isolated from clade 2.3.4.4b H5-naive humans can block the hemagglutinin-MHC-II interaction. These findings identify a previously unrecognized receptor pathway in contemporary H5N1 viruses and reveal that both human genetic variation and pre-existing humoral immunity can modulate this interaction, with implications for host range, cellular tropism, spillover risk, and therapeutic intervention.

immunology↗

bioRxiv2026

BenchRep-T: A Systematic Evaluation of T-Cell Repertoire-Based Disease Diagnostics

Adaptive immune receptor repertoire sequencing data has emerged as a promising potential modality for disease diagnosis, relying on computational methods to analyze T-cell receptor (TCR) sequences from an individuals blood sample. Published methods rely on different cohorts, data preprocessing pipelines, and evaluation metrics, making direct comparison across methods challenging. We present BenchRep-T, a unified benchmark that standardizes multiple publicly available TCR repertoire datasets and evaluates nine computational approaches, spanning statistical enrichment of shared sequences, feature-engineered ensembles, deep learning, and sequence clustering. BenchRep-T evaluates methods on four tasks: disease classification across conditions, performance scaling under restricted sequence-sampling depth, recovery of known antigen-specific driver sequences, and evaluation of sensitivity to demographic confounding. Under controlled evaluation, simple baselines prove competitive, with tree-based models trained on V- and J-gene usage and short sequence motifs approaching the classification performance of more complex methods. Our findings underscore the complexity of modeling TCR repertoire data, and show that no single method dominates across all tasks. BenchRep-T provides a framework for rigorous and reproducible evaluation of TCR repertoire classification methods to accelerate the development of immune repertoire-based diagnostics.

immunology↗

bioRxiv2024

Identification of Covalent Cyclic Peptide Inhibitors Targeting Protein-Protein Interactions Using Phage Display

Peptide macrocycles are promising therapeutics for a variety of disease indications due to their overall metabolic stability and potential to make highly selective binding interactions with targets. Recent advances in covalent macrocycle peptide discovery, driven by phage and mRNA display methods, have enabled the rapid identification of highly potent and selective molecules from large libraires of diverse macrocycles. However, there are currently limited examples of macrocycles that can be used to disrupt protein-protein interactions and even fewer examples that function by formation of a covalent bond to a target protein. In this work, we describe a directed counter-selection method that enables identification of covalent macrocyclic ligands targeting a protein-protein interaction using a phage display screening platform. This method utilizes binary and ternary screenings of a chemically modified phage display library, employing the stable and weakly reactive aryl fluorosulfate electrophile. We demonstrate the utility of this approach using the SARS-CoV-2 Spike-ACE2 protein-protein interaction and identify multiple covalent macrocyclic inhibitors that disrupt this interaction. The resulting compounds displayed antiviral activity against live virus that was irreversible after washout due to the covalent binding mechanism. These results highlight the potential of this screening platform for developing covalent macrocyclic drugs that disrupt protein-protein interactions with long lasting effects.

microbiology↗

bioRxiv2024

Sequence-based TCR-Peptide Representations Using Cross-Epitope Contrastive Fine-tuning of Protein Language Models

Understanding T-Cell receptor (TCR) and epitope interactions is critical for advancing our knowledge of the human immune system. Traditional approaches that use sequence similarity or structure data often struggle to scale and generalize across diverse TCR/epitope interactions. To address these limitations, we introduce ImmuneCLIP, a contrastive fine-tuning method that leverages pre-trained protein language models to align TCR and epitope embeddings in a shared latent space. ImmuneCLIP is evaluated on epitope ranking and binding prediction tasks, where it consistently outperforms sequence-similarity based methods and existing deep learning models. Furthermore, ImmuneCLIP shows strong generalization capabilities even with limited training data, highlighting its potential for studying diverse immune interactions and uncovering patterns that improve our understanding of human immune recognition systems.

immunology↗

bioRxiv2024

Genetically and Functionally Distinct Immunoglobulin Heavy Chain Locus Duplication in Bats

The genetic locus encoding immunoglobulin heavy chains (IgH) is critical for vertebrate humoral immune responses and diverse antibody repertoires. Immunoglobulin and T cell receptor loci of most bat species have not been annotated, despite the recurrent role of bats as viral reservoirs and sources of zoonotic pathogens. We investigated the genetic structure and function of IgH loci across the largest bat family, Vespertilionidae, focusing on big brown bats (Eptesicus fuscus). We discovered that E. fuscus and ten other species within Vespertilionidae have two complete, functional, and distinct immunoglobulin heavy chain loci on separate chromosomes. This locus organization is previously unknown in mammals, but is reminiscent of more limited duplicated loci in teleost fish. Single cell transcriptomic data validate functional rearrangement and expression of immunoglobulin heavy chains of both loci in the expressed repertoire of Eptesicus fuscus, with maintenance of allelic exclusion, bias of usage toward the smaller and more compact IgH locus, and evidence of differential selection of antigen-experienced B cells and plasma cells varying by IgH locus use. This represents a unique mechanism for mammalian humoral immunity and may contribute to bat resistance to viral pathogenesis.

immunology↗

bioRxiv2023

Ultrasensitive allele inference from immune repertoire sequencing data with MiXCR

Allelic variability in the adaptive immune receptor loci, which harbor the gene segments that encode B cell and T cell receptors (BCR/TCR), has been shown to be of critical importance for immune responses to pathogens and vaccines. In recent years, B cell and T cell receptor repertoire sequencing (Rep-Seq) has become widespread in immunology research making it the most readily available source of information about allelic diversity in immunoglobulin (IG) and T cell receptor (TR) loci in different populations. Here we present a novel algorithm for extra-sensitive and specific variable (V) and joining (J) gene allele inference and genotyping allowing reconstruction of individual high-quality gene segment libraries. The approach can be applied for inferring allelic variants from peripheral blood lymphocyte BCR and TCR repertoire sequencing data, including hypermutated isotype-switched BCR sequences, thus allowing high-throughput genotyping and novel allele discovery from a wide variety of existing datasets. The developed algorithm is a part of the MiXCR software (https://mixcr.com) and can be incorporated into any pipeline utilizing upstream processing with MiXCR. We demonstrate the accuracy of this approach using Rep-Seq paired with long-read genomic sequencing data, comparing it to a widely used algorithm, TIgGER. We applied the algorithm to a large set of IG heavy chain (IGH) Rep-Seq data from 450 donors of ancestrally diverse population groups, and to the largest reported full-length TCR alpha and beta chain (TRA; TRB) Rep-Seq dataset, representing 134 individuals. This allowed us to assess the genetic diversity of genes within the IGH, TRA and TRB loci in different populations and demonstrate the connection between antibody repertoire gene usage and the number of allelic variants present in the population. Finally we established a database of allelic variants of V and J genes inferred from Rep-Seq data and their population frequencies with free public access at https://vdj.online.

immunology↗

bioRxiv2022

Chance and contingency in B cell evolution limit the similarity of antibody responses to infection across individuals

Antibodies result from the competition of B cell lineages evolving under selection for improved antigen recognition, a process known as affinity maturation. High-affinity antibodies to pathogens such as HIV, influenza, and SARS-CoV-2 are frequently reported to arise from B cells whose receptors, the precursors to antibodies, are encoded by particular immunoglobulin alleles. This raises the possibility that the presence of particular germline alleles in the B cell repertoire is a major determinant of the quality of the antibody response. Alternatively, initial differences in germline alleles propensities to form high-affinity receptors might be overcome by chance events during affinity maturation. We first investigate these scenarios in simulations: when germline-encoded fitness differences are large relative to the rate and effect size variation of somatic mutations, the same germline alleles persistently dominate the response of different individuals. In contrast, if germline-encoded advantages can be easily overcome by subsequent mutations, allele usage becomes increasingly divergent over time, a pattern we then observe in mice experimentally infected with influenza virus. We investigated whether affinity maturation might nonetheless strongly select for particular amino acid motifs across diverse genetic backgrounds, but we found no evidence of convergence to similar CDR3 sequences or amino acid substitutions. These results suggest that although germline-encoded specificities can lead to similar immune responses between individuals, diverse evolutionary routes to high affinity limit the genetic predictability of responses to infection and vaccination. Author SummaryAntibodies arise as B cell receptors encoded by the stochastic recombination of immunoglobulin genes. While those genes evolve over millions of years, the receptors themselves evolve within weeks as B cells compete under selection for improved antigen recognition. This competition shapes the response to infection and vaccination; how much the outcome depends on the initial choice of immunoglobulin genes versus subsequent receptor evolution is an open question that informs the predictability of the immune response and the long-term evolution of immunoglobulins. In simulations, we show that immunoglobulin genes with hardcoded specificity for the antigen can lead to either transient or persistent similarity in the response of different individuals. When the initial advantage is large relative to the effects of mutation, B cells using the same genes consistently dominate the response across individuals. Weaker initial advantages lead to similar responses early on but are later overcome by B cell evolution playing out differently in each individual due to chance events. We observe such increasingly divergent responses in mice infected with influenza virus. While long-term selection might hardcode specificities for particular pathogens on immunoglobulin genes, our results suggest diverse paths to potent antibodies can nonetheless limit the predictability of the response.

immunology↗

bioRxiv2022

Mouse lemur transcriptomic atlas elucidates primate genes, physiology, disease, and evolution

Mouse lemurs (Microcebus spp.) are an emerging primate model organism. However, little is known about their genetics or cellular and molecular biology. In the accompanying paper, we used large-scale single cell RNA-sequencing of 27 organs and tissues to identify over 750 molecular cell types, characterize their full transcriptomic profiles, and study evolution of primate cell types. Here we use the atlas to characterize mouse lemur genes, mutations, physiology, and disease. We uncover thousands of previously unidentified lemur genes and hundreds of thousands of new splice junctions that globally define lemur gene structures and reveal over 85,000 primate splice junctions missing in mice. We systematically explore the lemur immune system, comparing the global expression profiles of key immune genes in health and disease, and molecular mapping of immune cell development, trafficking, and their local and global activation to infection. We characterize primate/lemur-specific physiology and disease including molecular features of the immune program, of lemur adipocytes that exhibit dramatic seasonal rhythms, and of metastatic endometrial cancer that resembles the human malignancy. We identify and describe the expression patterns of over 400 primate genes missing in mice, many with similar expression patterns in human and lemur and some implicated in human disease. Finally, we provide an experimental framework for reverse genetic analysis by identifying naturally-occurring nonsense (null) mutations in three primate genes missing in mice and analyzing their transcriptional phenotypes. This work establishes mouse lemur as a tractable primate model organism for genetic and molecular analysis, and it prioritizes primate genes, splice junctions, physiology, and disease for future study.

genetics↗

bioRxiv2022

Antibody repertoire gene usage is explained by common genetic variants in the immunoglobulin heavy chain locus

Variation in the antibody response has been linked to differential outcomes in disease, and suboptimal vaccine and therapeutic responsiveness, the determinants of which have not been fully elucidated. Countering models that presume antibodies are generated largely by stochastic processes, we demonstrate that polymorphisms within the immunoglobulin heavy chain locus (IGH) significantly impact the naive and antigen-experienced antibody repertoire, indicating that genetics predisposes individuals to mount qualitatively and quantitatively different antibody responses. We pair recently developed long-read genomic sequencing methods with antibody repertoire profiling to comprehensively resolve IGH genetic variation, including novel structural variants, single nucleotide variants, and genes and alleles. We show that IGH germline variants determine the presence and frequency of antibody genes in the expressed repertoire, including those enriched in functional elements linked to V(D)J recombination, and overlapping disease-associated variants. These results illuminate the power of leveraging IGH genetics to better understand the regulation, function and dynamics of the antibody response in disease.

genomics↗

bioRxiv2022

Disease diagnostics using machine learning of immune receptors

Clinical diagnosis typically incorporates physical examination, patient history, and various laboratory tests and imaging studies, but makes limited use of the human systems own record of antigen exposures encoded by receptors on B cells and T cells. We analyzed immune receptor datasets from 593 individuals to develop MAchine Learning for Immunological Diagnosis (Mal-ID), an interpretive framework to screen for multiple illnesses simultaneously or precisely test for one condition. This approach detects specific infections, autoimmune disorders, vaccine responses, and disease severity differences. Human-interpretable features of the model recapitulate known immune responses to SARS-CoV-2, Influenza, and HIV, highlight antigen-specific receptors, and reveal distinct characteristics of Systemic Lupus Erythematosus and Type-1 Diabetes autoreactivity. This analysis framework has broad potential for scientific and clinical interpretation of human immune responses.

immunology↗

bioRxiv2021

Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19

The biological determinants of the wide spectrum of COVID-19 clinical manifestations are not fully understood. Here, over 1400 plasma proteins and 2600 single-cell immune features comprising cell phenotype, basal signaling activity, and signaling responses to inflammatory ligands were assessed in peripheral blood from patients with mild, moderate, and severe COVID-19, at the time of diagnosis. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identified and independently validated a multivariate model classifying COVID-19 severity (multi-class AUCtraining = 0.799, p-value = 4.2e-6; multi-class AUCvalidation = 0.773, p-value = 7.7e-6). Features of this high-dimensional model recapitulated recent COVID-19 related observations of immune perturbations, and revealed novel biological signatures of severity, including the mobilization of elements of the renin-angiotensin system and primary hemostasis, as well as dysregulation of JAK/STAT, MAPK/mTOR, and NF-{kappa}B immune signaling networks. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for the prevention of COVID-19 progression. SummaryFeyaerts et al. demonstrate that an integrated analysis of plasma and single-cell proteomics differentiates COVID-19 severity and reveals severity-specific biological signatures associated with the dysregulation of the JAK/STAT, MAPK/mTOR, and NF-{kappa}B immune signaling networks and the mobilization of the renin-angiotensin and hemostasis systems.

immunology↗

bioRxiv2020

Shared B cell memory to coronaviruses and other pathogens varies in human age groups and tissues

Vaccination and infection promote the formation, tissue distribution, and clonal evolution of B cells encoding humoral immune memory. We evaluated convergent antigen-specific antibody genes of similar sequences shared between individuals in pediatric and adult blood, and deceased organ donor tissues. B cell memory varied for different pathogens. Polysaccharide antigen-specific clones were not exclusive to the spleen. Adults convergent clones often express mutated IgM or IgD in blood and are class-switched in lymphoid tissues; in contrast, children have abundant class-switched convergent clones in blood. Consistent with serological reports, pre-pandemic children had class-switched convergent clones to SARS-CoV-2, enriched in cross-reactive clones for seasonal coronaviruses, while adults showed few such clones in blood or lymphoid tissues. These results extend age-related and anatomical mapping of human humoral pathogen-specific immunity. One Sentence SummaryChildren have elevated frequencies of pathogen-specific class-switched memory B cells, including SARS-CoV-2-binding clones.

immunology↗

bioRxiv2020

Exceptional diversity and selection pressure on SARS-CoV and SARS-CoV-2 host receptor in bats compared to other mammals

Pandemics originating from pathogen transmission between animals and humans highlight the broader need to understand how natural hosts have evolved in response to emerging human pathogens and which groups may be susceptible to infection. Here, we investigate angiotensin-converting enzyme 2 (ACE2), the host protein bound by SARS-CoV and SARS-CoV-2. We find that the ACE2 gene is under strong selection pressure in bats, the group in which the progenitors of SARS-CoV and SARS-CoV-2 are hypothesized to have evolved, particularly in residues that contact SARS-CoV and SARS-CoV-2. We detect positive selection in non-bat mammals in ACE2 but in a smaller proportion of branches than in bats, without enrichment of selection in residues that contact SARS-CoV or SARS-CoV-2. Additionally, we evaluate similarity between humans and other species in residues that contact SARS-CoV or SARS-CoV-2, revealing potential susceptible species but also highlighting the difficulties of predicting spillover events. This work increases our understanding of the relationship between mammals, particularly bats, and coronaviruses, and provides data that can be used in functional studies of how host proteins are bound by SARS-CoV and SARS-CoV-2 strains.

evolutionary biology↗