Biology subjects

Mayer-Blackwell, K.

Publications and source records attributed to Mayer-Blackwell, K..

6 recordsLinked to original sources

bioRxiv2026

HIV-1 prime-boost vaccination shapes distinct clonal trajectories and memory precursor states of Env- and Gag-specific T cells

Despite decades of HIV-1 vaccine development, the clonal and cellular determinants of durable vaccine-induced T cell memory remain incompletely understood. Here, we combined antigen-specific T cell receptor (TCR) identification, longitudinal TCR sequencing, and single-cell multi-omics to characterize Env- and Gag-specific memory precursor T cells elicited by the HIV Vaccine Trials Network (HVTN) 505 DNA prime-recombinant adenovirus serotype 5 (rAd5) boost (DNA/rAd5) vaccine regimen. We developed a generalizable high-throughput approach to identify HIV-1 Env- and Gag-specific TCRs and found distinct patterns of clonal expansion, persistence, and contribution to memory between Env- and Gag-specific CD8 T cell responses. The DNA prime and rAd5 boost differentially shaped these repertoires, with rAd5-induced clones contributing proportionally more to the Gag-specific than to the Env-specific memory precursor compartment. Single-cell immune profiling further revealed distinct memory precursor states, with Env-specific responses enriched for GZMBPRF1 cytotoxic effector-memory (EM) CD8 T cells and Gag-specific responses containing a larger cycling/proliferative population. Together, these findings demonstrate that heterologous DNA/rAd5 vaccination generates antigen-specific CD8 T cell memory with distinct clonal trajectories and cellular programs, providing new insights into how vaccine platform and antigen-specificity shape the durability and functional properties of HIV-1 specific cellular immunity.

immunology↗

bioRxiv2026

Deciphering antigen-driven T cell responses through vectorized TCRdist sequence neighborhood quantification

T cells provide precise mechanisms to defend the body against infection and malignancies, mediated through the expression of their hypervariable T cell receptors (TCRs). Interpreting similarity between TCRs, however, remains a significant challenge. While performant clustering methods exist, these often fail to distinguish between antigen-driven convergent selection and patterns arising stochastically from biases in the V(D)J recombination mechanism. Moreover, defining enrichment in sequence similarity among large repertoires is computationally taxing. To address these limitations, we present an efficient computational framework for rapid approximation of TCRdist distances using fixed-length vector embeddings and highly optimized nearest neighbor search, allowing sequence similarity enrichment testing at a multi-repertoire-wide scale. This framework leverages a novel shuffling-based background model that preserves important repertoire characteristics such as V gene frequency, CDR3 sequence length and generation probability more accurately than synthetic models. Together, these tools enable the efficient and robust identification of significantly neighbor enriched (SNE) TCR sequences at scale. We validate this approach by showing a significant enrichment of SNE clones in memory T cell fractions and further demonstrate its utility in identifying convergent T cell signatures of response to vaccination and viral infections, providing a scalable approach for antigen-agnostic T cell response profiling.

immunology↗

bioRxiv2025

TCR2HLA: calibrated inference of HLA genotypes from TCR repertoires enables identification of immunologically relevant metaclonotypes

T cell receptors (TCRs) recognize peptides presented by polymorphic human leukocyte antigen (HLA) molecules, but HLA genotype data are often missing from TCR repertoire sequencing studies. To address this, we developed TCR2HLA, an open-source tool that infers HLA genotypes from TCR{beta} repertoires. Expanding on work linking public TRBV-CDR3 sequences to HLA genotypes, we incorporated "quasi-public" metaclonotypes - composed of rarer TCR{beta} sequences with shared amino acid features - enriched by HLA genotypes. Using four TCR{beta}seq datasets from 3,150 individuals, we applied TRBV gene partitioning and locality-sensitive hashing to identify [~]96,000 TCR{beta} features strongly associated with specific HLA alleles from 71M input TCRs. Binary HLA classifiers built with these features achieved high balanced accuracy (>0.9) across common HLA-A (9/12), B (9/12), C (6/13), DRB1 (11/11) alleles and prevalent DPA1/DPB1 (6/10), DQA1/DQB1 (8/17) heterodimers. We also introduced a high-sensitivity calibration to support predictions in samples with as few as 5,000 unique clonotypes. Calibrated predictions with confidence filtering improved reliability. Beyond genotype imputation, TCR2HLA enables the discovery of novel HLA- and exposure-associated TCRs, as shown by the identification of SARS-CoV-2 related TCRs in a large COVID-19 dataset lacking HLA data. TCR2HLA provides a scalable framework for bridging the gap between TCRseq data and HLA genotype for biomarker discovery.

immunology↗

bioRxiv2023

The Regulation of Nucleic Acid Vaccine Responses by the Microbiome

SummaryNucleic acid vaccines, including both RNA and DNA platforms, are key technologies that have considerable promise in combating both infectious disease and cancer. However, little is known about the extrinsic factors that regulate nucleic acid vaccine responses and which may determine their effectiveness. The microbiome is recognized as a significant regulator of immune development and response, whose role in regulating some traditional vaccine platforms has recently been discovered. Using germ-free and specific-pathogen-free mouse models in combination with different protein, DNA, and mRNA vaccine regimens, we demonstrate that the microbiome is a significant regulator of nucleic acid vaccine immunogenicity. While the presence of the microbiome enhances CD8+ T cell responses to mRNA lipid nanoparticle (LNP) immunization, the microbiome suppresses immunoglobulin and CD4+ T cell responses to DNA-prime, DNA-protein-boost immunization, indicating contrasting roles for the microbiome in the regulation of these different nucleic acid vaccine platforms. In the case of mRNA-LNP vaccination, germ-free mice display reduced dendritic cell/macrophage activation that may underlie the deficient vaccine response. Our study identifies the microbiome as a relevant determinant of nucleic acid vaccine response with implications for their continued therapeutic development and deployment.

immunology↗

bioRxiv2020

TCR meta-clonotypes for biomarker discovery with tcrdist3: quantification of public, HLA-restricted TCR biomarkers of SARS-CoV-2 infection

As the mechanistic basis of adaptive cellular antigen recognition, T cell receptors (TCRs) encode clinically valuable information that reflects prior antigen exposure and potential future response. However, despite advances in deep repertoire sequencing, enormous TCR diversity complicates the use of TCR clonotypes as clinical biomarkers. We propose a new framework that leverages antigen-enriched repertoires to form meta-clonotypes - groups of biochemically similar TCRs - that can be used to robustly identify and quantify functionally similar TCRs in bulk repertoires. We apply the framework to TCR data from COVID-19 patients, generating 1831 public TCR meta-clonotypes from the 17 SARS-CoV-2 antigen-enriched repertoires with the strongest evidence of HLA-restriction. Applied to independent cohorts, meta-clonotypes targeting these specific epitopes were more frequently detected in bulk repertoires compared to exact amino acid matches, and 59.7% (1093/1831) were more abundant among COVID-19 patients that expressed the putative restricting HLA allele (FDR < 0.01), demonstrating the potential utility of meta-clonotypes as antigen-specific features for biomarker development. To enable further applications, we developed an open-source software package, tcrdist3, that implements this framework and facilitates flexible workflows for distance-based TCR repertoire analysis.

immunology↗

bioRxiv2019

High-throughput multi-parallel enteropathogen quantification via nano-liter qPCR

Quantitative molecular diagnostic methods, such as qPCR, can effectively detect pathogen-specific nucleic acid sequences. However, costs associated with multi-pathogen quantitative molecular diagnostics hinder their widespread use. Nano-liter qPCR (nL-qPCR) is a miniaturized tool for quantification of multiple targets in large numbers of samples based on assay parallelization on a single chip, with potentially significant cost-savings due to rapid throughput and reduced reagent volumes. We evaluated a suite of novel and published assays to detect 17 enteric pathogens using a commercially available nL-qPCR technology. Assay efficiencies ranged from 88-98% (mean 91%) and were reproducible across four operators at two separate facilities. When applied to complex fecal material, assays were sensitive and selective (99.8% of DNA amplified were genes from the target organism). Detection limits were 1-2 orders of magnitude higher for nL-qPCR than an existing enteric TaqMan Array Card (TAC), due to nanofluidic volumes. Compared to the TAC, nL-qPCR displayed 97% (95% CI 0.96, 0.98) negative percent agreement and 63% (95% CI 0.60, 0.66) overall positive percent agreement. Positive percent agreement was 90% for target concentrations above the nL-qPCR detection limits. nL-qPCR assays showed an underestimation bias of 0.34 log10 copies/gram of stool [IQR -0.41, -0.28] compared with the enteric TAC. Higher detection limits, inherent to nL-qPCR, do not hinder detection of clinically relevant pathogen concentrations. With 12 times higher throughput for a sixth of the per-sample cost of the enteric TAC, the nL-qPCR chip described here is a viable alternative for enteropathogen quantification for studies where other technologies are cost-prohibitive.

microbiology↗