Biology subjects

Minervina, A. A.

Publications and source records attributed to Minervina, A. A..

11 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↗

bioRxiv2025

T-cell receptor specificity landscape revealed through de novo peptide design

T-cells play a key role in adaptive immunity by mounting specific responses against diverse pathogens. An effective binding between T-cell receptors (TCRs) and pathogen-derived peptides presented on Major Histocompatibility Complexes (MHCs) mediate an immune response. However, predicting these interactions remains challenging due to limited functional data on T-cell reactivities. Here, we introduce a computational approach to predict TCR interactions with peptides presented on MHC class I alleles, and to design novel immunogenic peptides for specified TCR-MHC complexes. Our method leverages HERMES, a structure-based, physics-guided machine learning model trained on the protein universe to predict amino acid preferences based on local structural environments. Despite no direct training on TCR-pMHC data, the implicit physical reasoning in HERMES enables us to make accurate predictions of both TCR-pMHC binding affinities and T-cell activities across diverse viral epitopes and cancer neoantigens, achieving up to 0.72 correlation with experimental data. Leveraging our TCR recognition model, we develop a computational protocol for de novo design of immunogenic peptides. Through experimental validation in three TCR-MHC systems targeting viral and cancer peptides, we demonstrate that our designs--with up to five substitutions from the native sequence--activate T-cells at success rates of up to 50%. Lastly, we use our generative framework to quantify the diversity of the peptide recognition landscape for various TCR-MHC complexes, offering key insights into T-cell specificity in both humans and mice. Our approach provides a platform for immunogenic peptide and neoantigen design, as well as for evaluating TCR specificity, offering a computational framework to inform design of engineered T-cell therapies and vaccines.

biophysics↗

bioRxiv2024

TIRTL-seq: Deep, quantitative, and affordable paired TCR repertoire sequencing

[a]/{beta} T cells are key players in adaptive immunity. The specificity of T cells is determined by the sequences of the hypervariable T cell receptor (TCR) [a] and {beta} chains. Although bulk TCR sequencing offers a cost-effective approach for in-depth TCR repertoire profiling, it does not provide chain pairings, which are essential for determining T cell specificity. In contrast, single-cell TCR sequencing technologies produce paired chain data, but are limited in throughput to thousands of cells and are cost-prohibitive for cohort-scale studies. Here, we present TIRTL-seq (Throughput-Intensive Rapid TCR Library sequencing), a novel approach that generates ready-to-sequence TCR libraries from live cells in less than 7 hours. The protocol is optimized for use with non-contact liquid handlers in an automation-friendly 384-well plate format. Reaction volume miniaturization reduces library preparation costs to <$0.50 per well. The core principle of TIRTL-seq is the parallel generation of hundreds of libraries providing multiple biological replicates from a single sample that allows precise inference of both frequencies of individual clones and TCR chain pairings from well-occurrence patterns. We demonstrate scalability of our approach up to 1 million unique paired [a]{beta}TCR clonotypes corresponding to over 30 million T cells per sample at a cost of less than $2000. For a sample of 10 million cells the cost is [~]$200. We benchmarked TIRTL-seq against state-of-the-art 5RACE bulk TCR-seq and 10x Genomics Chromium technologies on longitudinal samples. We show that TIRTL-seq is able to quantitatively identify expanding and contracting clonotypes between timepoints while providing accurate TCR chain pairings, including distinct temporal dynamics of SARS-CoV-2-specific and EBV-specific CD8+ T cell responses after infection. While clonal expansion was followed by sharp contraction for SARS-CoV-2 specific TCRs, EBV-specific TCRs remained stable once established. The sequences of both [a] and {beta} TCR chains are essential for determining T cell specificity. As the field moves towards greater applications in diagnostics and immunotherapy that rely on TCR specificity, we anticipate that our scalable paired TCR sequencing methodology will be instrumental for collecting large paired-chain datasets and ultimately extracting therapeutically relevant information from the TCR repertoire.

immunology↗

bioRxiv2021

Characterization of SARS-CoV-2 public CD4+ αβ T cell clonotypes through reverse epitope discovery

The amount of scientific data and level of public sharing produced as a consequence of the COVID-19 pandemic, as well as the speed at which these data were produced, far exceeds any previous effort against a specific disease condition. This unprecedented situation allows for development and application of new research approaches. One of the major technical hurdles in immunology is the characterization of HLA-antigen-T cell receptor (TCR) specificities. Most approaches aim to identify reactive T cells starting from known antigens using functional assays. However, the need for a reverse approach identifying the antigen specificity of orphan TCRs is increasing. Utilizing large public single-cell gene expression and TCR datasets, we identified highly public CD4+ T cell responses to SARS-CoV-2, covering >75% of the analysed population. We performed an integrative meta-analysis to deeply characterize these clonotypes by TCR sequence, gene expression, HLA-restriction, and antigen-specificity, identifying strong and public CD4+ immunodominant responses with confirmed specificity. CD4+ COVID-enriched clonotypes show T follicular helper functional features, while clonotypes depleted in SARS-CoV-2 individuals preferentially had a central memory phenotype. In total we identify more than 1200 highly public CD4+ T cell clonotypes reactive to SARS-CoV-2. TCR similarity analysis showed six prominent TCR clusters, for which we predicted both HLA-restriction and cognate SARS-CoV-2 immunodominant epitopes. To validate our predictions we used an independent cohort of TCR repertoires before and after vaccination with ChAdOx1, a replication-deficient simian adenovirus-vectored vaccine, encoding the SARS-CoV-2 spike protein. We find statistically significant enrichment of the predicted spike-reactive TCRs after vaccination with ChAdOx1, while the frequency of TCRs specific to other SARS-CoV-2 proteins remains stable. Thus, the CD4-associated TCR repertoire differentiates vaccination from natural infection. In conclusion, our study presents a novel reverse epitope discovery approach that can be used to infer HLA- and antigen-specificity of orphan TCRs in any context, such as viral infections, antitumor immune responses, or autoimmune disease. HighlightsO_LIIdentification of highly public CD4+ T cell responses to SARS-CoV-2 C_LIO_LISystematic prediction of exact immunogenic HLA class II epitopes for CD4+ T cell response C_LIO_LIMethodological framework for reverse epitope discovery, which can be applied to other disease contexts and may provide essential insights for future studies and clinical applications C_LI O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

immunology↗

bioRxiv2021

SARS-CoV-2 mRNA vaccination elicits robust and persistent T follicular helper cell response in humans

SARS-CoV-2 mRNA vaccines induce robust anti-spike (S) antibody and CD4+ T cell responses. It is not yet clear whether vaccine-induced follicular helper CD4+ T (TFH) cell responses contribute to this outstanding immunogenicity. Using fine needle aspiration of draining axillary lymph nodes from individuals who received the BNT162b2 mRNA vaccine, we show that frequency of TFH correlates with that of S-binding germinal center B cells. Mining of the responding TFH T cell receptor repertoire revealed a strikingly immunodominant HLADPB1* 04-restricted response to S167-180 in individuals with this allele, which is among the most common HLA alleles in humans. Paired blood and lymph node specimens show that while circulating S-specific TFH cells peak one week after the second immunization, S-specific TFH persist at nearly constant frequencies for at least six months. Collectively, our results underscore the key role that robust TFH cell responses play in establishing long-term immunity by this efficacious human vaccine.

immunology↗

bioRxiv2020

Longitudinal high-throughput TCR repertoire profiling reveals the dynamics of T cell memory formation after mild COVID-19 infection

COVID-19 is a global pandemic caused by the SARS-CoV-2 coronavirus. T cells play a key role in the adaptive antiviral immune response by killing infected cells and facilitating the selection of virus-specific antibodies. However neither the dynamics and cross-reactivity of the SARS-CoV-2-specific T cell response nor the diversity of resulting immune memory are well understood. In this study we use longitudinal high-throughput T cell receptor (TCR) sequencing to track changes in the T cell repertoire following two mild cases of COVID-19. In both donors we identified CD4+ and CD8+ T cell clones with transient clonal expansion after infection. The antigen specificity of CD8+ TCR sequences to SARS-CoV-2 epitopes was confirmed by both MHC tetramer binding and presence in large database of SARS-CoV-2 epitope-specific TCRs. We describe characteristic motifs in TCR sequences of COVID-19-reactive clones and show preferential occurence of these motifs in publicly available large dataset of repertoires from COVID-19 patients. We show that in both donors the majority of infection-reactive clonotypes acquire memory phenotypes. Certain T cell clones were detected in the memory fraction at the pre-infection timepoint, suggesting participation of pre-existing cross-reactive memory T cells in the immune response to SARS-CoV-2.

immunology↗

bioRxiv2019

Comprehensive analysis of antiviral adaptive immunity formation and reactivation down to single-cell level

The diverse repertoire of T-cell receptors (TCR) plays a key role in the adaptive immune response to infections. Previous studies show that secondary responses to the yellow fever vaccine -- the model for acute infection in humans -- are weaker than primary ones, but only quantitative measurements can describe the concentration changes and lineage fates for distinct T-cell clones in vivo over time. Using TCR alpha and beta repertoire sequencing for T-cell subsets, as well as single-cell RNAseq and TCRseq, we track the concentrations and phenotypes of individual T-cell clones in response to primary and secondary yellow fever immunization showing their large diversity. We confirm the secondary response is an order of magnitude weaker, albeit 10 days faster than the primary one. Estimating the fraction of the T-cell response directed against the single immunodominant epitope, we identify the sequence features of TCRs that define the high precursor frequency of the two major TCR motifs specific for this particular epitope. We also show the consistency of clonal expansion dynamics between bulk alpha and beta repertoires, using a new methodology to reconstruct alpha-beta pairings from clonal trajectories.

immunology↗

bioRxiv2018

Detecting T-cell receptors involved in immune responses from single repertoire snapshots

Hypervariable T-cell receptors (TCR) play a key role in adaptive immunity, recognising a vast diversity of pathogen-derived antigens. High throughput sequencing of TCR repertoires (RepSeq) produces huge datasets of T-cell receptor sequences from blood and tissue samples [1, 2]. However, our ability to extract clinically relevant information from RepSeq data is limited, mainly because little is known about TCR-disease associations. Here we present a statistical approach called ALICE (Antigen-specific Lymphocyte Identification by Clustering of Expanded sequences) that identifies TCR sequences that are actively involved in the current immune response from a single RepSeq sample, and apply it to repertoires of patients with a variety of disorders -- autoimmune disease (ankylosing spondylitis [3]), patients under cancer immunotherapy [4, 5], or subject to an acute infection (live yellow fever vaccine [6]). The methods robustness is demonstrated by the agreement of its predictions with independent assays, and is supported by its ability to selectively detect responding TCR in the memory but not in the naive subset. ALICE requires no longitudinal data collection [6, 7] nor large cohorts [8-10], and is thus directly applicable to most RepSeq datasets. Its results facilitate the identification of TCR variants associated with a wide variety of diseases and conditions, which can be used for diagnostics, rational vaccine design and evaluation of the adaptive immune system state.

immunology↗

bioRxiv2018

Precise tracking of vaccine-responding T-cell clones reveals convergent and personalized response in identical twins

T-cell receptor (TCR) repertoire data contain information about infections that could be used in disease diagnostics and vaccine development, but extracting that information remains a major challenge. Here we developed a statistical framework to detect TCR clone proliferation and contraction from longitudinal repertoire data. We applied this framework to data from three pairs of identical twins immunized with the yellow fever vaccine. We identified 500-1500 responding TCRs in each donor and validated them using three independent assays. While the responding TCRs were mostly private, albeit with higher overlap between twins, they could be well predicted using a classifier based on sequence similarity. Our method can also be applied to samples obtained post-infection, making it suitable for systematic discovery of new infection-specific TCRs in the clinic.

immunology↗

bioRxiv2018

Genomic normalization for sequencing libraries enrichment for rare somatic retroelement insertions

BackgroundThere is increasing evidence that the transpositional activity of retroelements (REs) is not limited to germ line cells, but often occurs in tumor and normal somatic cells. Somatic transpositions were found in several human tissues and are especially typical for the brain. Several computational and experimental approaches for detection of somatic retroelement insertions was developed in the past few years. These approaches were successfully applied to detect somatic insertions in clonally expanded tumor cells. At the same time, identification of somatic insertions presented in small proportion of cells, such as neurons, remains a considerable challenge.\n\nResultsIn this study, we developed a normalization procedure for library enrichment by DNA sequences corresponding to rare somatic RE insertions. Two rounds of normalization increased the number of fragments adjacent to somatic REs in the sequenced sample by more than 26-fold, and the number of identified somatic REs was increased by 7.9-fold.\n\nConclusionsThe developed technique can be used in combination with vast majority of modern RE identification approaches and can dramatically increase their capacity to detect rare somatic RE insertions in different types of cells.

genomics↗

bioRxiv2017

Method for identification of condition-associated public antigen receptor sequences

Diverse repertoires of hypervariable immunoglobulin receptors (TCR and BCR) recognize antigens in the adaptive immune system. The development of immunoglobulin receptor repertoire sequencing methods makes it possible to perform repertoire-wide disease association studies of antigen receptor sequences. We developed a statistical framework for associating receptors to disease from only a small cohort of patients, with no need for a control cohort. Our method successfully identifies previously validated Cytomegalovirus and type 1 diabetes responsive receptors.

immunology↗