bioRxiv Science⌕ Search

Biology subjects

Clancy, T.

Publications and source records attributed to Clancy, T..

8 recordsLinked to original sources

Machine Learning-Driven Antigen Selection Reveals Conserved T-Cell Targets for Broad Coronavirus Vaccination

BackgroundCoronavirus outbreaks remain a persistent threat to global health, and vaccines based primarily on spike-specific immune responses are susceptible to antigenic variation. T-cell immunity directed against conserved internal viral proteins may provide a complementary and more variant-tolerant strategy for next-generation coronavirus vaccines. MethodsWe combined machine learning-guided antigen prioritization with ex vivo functional immunological validation to identify conserved non-spike T-cell targets across betacoronaviruses. Candidate sequences were screened for immunogenicity using primary human peripheral blood mononuclear cells from healthy donors using intracellular cytokine staining and activation-induced marker assays. Top-ranked conserved regions were incorporated into multiepitope mRNA constructs, and their intracellular expression and HLA class I presentation were confirmed by immunopeptidomics. Immunogenicity was further evaluated ex vivo and in vivo using mRNA immunization of mice and T-cell FluoroSpot assays. FindingsAcross a panel of 97 peptides derived from 19 viral proteins, evolutionary conservation across distinct betacoronavirus taxa was strongly associated with functional T-cell immunogenicity in human donors. Highly conserved peptides elicited significantly stronger and more frequent CD4 and CD8 T-cell responses than taxon-restricted peptides. Multiepitope mRNA constructs encoding conserved regions were efficiently expressed and presented on HLA class I molecules and induced T-cell responses in human PBMCs. In mice, mRNA immunization with conserved multiepitope constructs generated robust interferon-{gamma}- and interleukin-2-producing T-cell responses that exceeded those induced by unconserved control constructs. InterpretationThese results link evolutionary conservation to functional cellular immunogenicity and demonstrate the feasibility of multiepitope mRNA delivery for inducing conserved coronavirus-directed T-cell responses. Although protective efficacy remains to be established, conservation-guided antigen selection represents a scalable strategy for developing T-cell-focused vaccines with broad lineage coverage, supporting pandemic preparedness beyond spike-centered immunity. FundingThe research was supported by CEPI, NEC, University of Oslo and Oslo university hospital. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSPrior coronavirus vaccine development has focused predominantly on spike protein-directed neutralizing antibodies. While highly effective against matched strains, spike-centered immunity is vulnerable to antigenic drift and lineage-specific escape. Multiple observational and experimental studies have shown that T-cell responses, particularly against internal viral proteins, are more conserved and correlate with reduced disease severity and cross-variant recognition. Epitope prediction algorithms and immunoinformatics approaches have been widely used to nominate candidate T-cell targets; however, systematic functional validation of conserved non-spike antigens across betacoronaviruses in primary human immune systems, combined with antigen presentation data and in vivo vaccine testing, has remained limited. Searches of PubMed and bioRxiv up to December 2025 using terms including "coronavirus T-cell vaccine," "conserved coronavirus epitopes," "betacoronavirus cross-reactive T cells," and "mRNA T-cell vaccine" identified studies demonstrating cross-reactive T-cell immunity and computational epitope selection, but few integrated machine-learning-guided antigen prioritization with ex vivo human functional screening, immunopeptidomics, and in vivo mRNA immunization in a unified workflow. Added value of this studyThis study provides an integrated experimental and computational framework for identifying and validating conserved non-spike T-cell antigens across betacoronaviruses. We functionally screened a panel of candidate peptides derived from multiple viral proteins and demonstrated that evolutionary conservation across species is strongly associated with T-cell immunogenicity. We further demonstrate that multiepitope mRNA constructs encoding these top-ranked conserved regions can be intracellularly expressed, presented on HLA class I molecules to induce polyfunctional T-cell responses in primary human PBMCs. Finally, in vivo mRNA immunization in mice induces robust interferon-{gamma} and interleukin-2 T-cell responses exceeding those induced by unconserved control constructs. Together, these findings link evolutionary conservation to functional cellular immunogenicity and extend beyond in silico prediction by demonstrating antigen processing, presentation, and immunogenicity across human and murine systems. Implications of all the available evidenceCollectively, the available evidence indicates that T-cell immunity directed toward conserved internal coronavirus proteins represents a complementary and potentially more variant-tolerant axis of vaccine design than spike-only strategies. Our findings suggest that evolutionary conservation can serve as a practical selection principle for prioritizing T-cell antigens with broad lineage coverage and that multiepitope mRNA delivery is a feasible platform for inducing such responses. While direct protection and heterologous challenge studies will be required to establish clinical efficacy, the integration of computational prioritization with functional validation supports a scalable approach to pandemic preparedness that may be applicable to other rapidly evolving viral families.

immunology↗

Deep learning of antibody epitopes using molecular permutation vectors

BackgroundThe accurate computational prediction of B cell epitopes can vastly reduce the cost and time required for identifying potential epitope candidates for the design of vaccines and immunodiagnostics. However, current computational tools for B cell epitope prediction perform poorly and are not fit-for-purpose, and there remains enormous room for improvement and the need for superior prediction strategies. ResultsHere we propose a novel approach that improves B cell epitope prediction by encoding epitopes as binary molecular permutation vectors that represent the position and structural properties of the amino acids within a protein antigen sequence that interact with an antibody, rather than the traditional approach of defining epitopes as scores per amino acid on a protein sequence that pertain to their probability of partaking in a B cell epitope antibody interaction. In addition to defining epitopes as binary molecular permutation vectors, the approach also uses the 3D macrostructure features of the unbound 3D protein structures, and in turn uses these features to train another deep learning model on the corresponding antibody-bound protein 3D structures. We demonstrate that the strategy predicts B cell epitopes with improved accuracy compared to the existing tools. Additionally, we demonstrate that this approach reliably identifies the majority of experimentally verified epitopes on the spike protein of SARS-CoV-2 not seen by the model in training and generalizes in very robust manner on dissimilar data not seen by the model in training. ConclusionsWith the approach described herein, a primary protein sequence with the query molecular permutation vector alone is required to predict B cell epitopes in a reliable manner, potentially advancing the use of computational prediction of B cell epitopes in biomedical research applications.

bioinformatics↗

Pan-cancer profiling of tumor-infiltrating natural killer cells through transcriptional reference mapping

The functional diversity of natural killer (NK) cell repertoires stems from differentiation, homeostatic receptor-ligand interactions, and adaptive-like responses to viral infections. Here, we generated a single-cell transcriptional reference map of healthy human blood and tissue-derived NK cells, with temporal resolution and fate-specific expression of gene regulator networks defining NK cell differentiation. Using transfer learning, transcriptomes of tumor-infiltrating NK cells from seven solid tumor types (427 patients), combined from 39 datasets, were incorporated into the reference map and interrogated for tumor microenvironment (TME)-induced perturbations. We identified six functionally distinct NK cellular states in healthy and malignant tissues, two of which were commonly enriched for across tumor types: a dysfunctional stressed CD56bright state susceptible to TME-induced immunosuppression and a cytotoxic TME-resistant effector CD56dim state. The ratio of stressed CD56bright and effector CD56dim was predictive of patient outcome in malignant melanoma and osteosarcoma. This resource may inform the design of novel NK cell therapies and can be extended endlessly through transfer learning to interrogate new datasets from experimental perturbations or disease conditions.

immunology↗

The T cell epitope landscape of SARS-CoV-2 variants of concern

During the COVID-19 pandemic, several SARS-CoV-2 variants of concern (VOC) emerged, bringing with them varying degrees of health and socioeconomic burdens. In particular, the Omicron VOC displayed distinct features of increased transmissibility accompanied by anti-genic drift in the spike protein that partially circumvented the ability of pre-existing anti-body responses in the global population to neutralize the virus. However, T cell immunity has remained robust throughout all the different VOC transmission waves and has emerged as a critically important correlate of protection against SARS-CoV-2 and its VOCs, in both vaccinated and infected individuals. Therefore, as SARS-CoV-2 VOCs continue to evolve, it is crucial that we characterize the correlates of protection and the potential for immune escape for both B cell and T cell human immunity in the population. Generating the insights necessary to understand T cell immunity, experimentally, for the global human population is at present critical but a time consuming, expensive, and laborious process. Further, it is not feasible to generate global or universal insights into T cell immunity in an actionable time frame for potential future emerging VOCs. However, using computational means we can expedite and provide early insights into the correlates of T cell protection. In this study, we generated and reveal insights on the T cell epitope landscape for the five main SARS-CoV-2 VOCs observed to date. We demonstrated here using a unique AI prediction platform, a strong concordance in global T cell protection across all mutated peptides for each VOC. This was modeled using the most frequent HLA alleles in the human population and covers the most common HLA haplotypes in the human population. The AI resource generated through this computational study and associated insights may guide the development of T cell vaccines and diagnostics that are even more robust against current and future VOCs, and their emerging subvariants.

immunology↗

Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2: toward universal blueprints for vaccine designs

The global population is at present suffering from a pandemic of Coronavirus disease 2019 (COVID-19), caused by the novel coronavirus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The goals of this study were to use artificial intelligence (AI) to predict blueprints for designing universal vaccines against SARS-CoV-2, that contain a sufficiently broad repertoire of T-cell epitopes capable of providing coverage and protection across the global population. To help achieve these aims, we profiled the entire SARS-CoV-2 proteome across the most frequent 100 HLA-A, HLA-B and HLA-DR alleles in the human population, using host-infected cell surface antigen presentation and immunogenicity predictors from the NEC Immune Profiler suite of tools, and generated comprehensive epitope maps. We then used these epitope maps as input for a Monte Carlo simulation designed to identify statistically significant "epitope hotspot" regions in the virus that are most likely to be immunogenic across a broad spectrum of HLA types. We then removed epitope hotspots that shared significant homology with proteins in the human proteome to reduce the chance of inducing off-target autoimmune responses. We also analyzed the antigen presentation and immunogenic landscape of all the nonsynonymous mutations across 3400 different sequences of the virus, to identify a trend whereby SARS-COV-2 mutations are predicted to have reduced potential to be presented by host-infected cells, and consequently detected by the host immune system. A sequence conservation analysis then removed epitope hotspots that occurred in less-conserved regions of the viral proteome. Finally, we used a database of the HLA genotypes of approximately 22 000 individuals to develop a "digital twin" type simulation to model how effective different combinations of hotspots would work in a diverse human population, and used the approach to identify an optimal constellation of epitopes hotspots that could provide maximum coverage in the global population. By combining the antigen presentation to the infected-host cell surface and immunogenicity predictions of the NEC Immune Profiler with a robust Monte Carlo and digital twin simulation, we have managed to profile the entire SARS-CoV-2 proteome and identify a subset of epitope hotspots that could be harnessed in a vaccine formulation to provide a broad coverage across the global population.

bioinformatics↗

A Temporal Transcriptional Map of Human Natural Killer Cell Differentiation

Natural killer cell repertoires are functionally diversified as a result of differentiation, homeostatic receptor-ligand interactions and adaptive responses to viral infections. However, the regulatory gene-circuits that define the manifold cell states and drive NK cell differentiation have not been clearly resolved. Here, we performed single-cell RNA sequencing of 26,506 cells derived from sorted phenotypically-defined human NK cell subsets to delineate a tightly coordinated differentiation process from a small population of CD56bright precursors to adaptive NKG2C+ CD56dim NK cells. RNA velocity analysis identified a clear directionality in the transition from CD56bright to CD56dim NK cells, which was dominated by genes involved in transcription and translation as well as acquisition of NK cell effector function. Gene expression trends mapped to pseudotime, defined by increasing entropy, identified three distinct transcriptional checkpoints, reflecting important changes in regulatory gene-circuits. The CD56bright NK cell population dominated pseudotime with two distinct checkpoints separating precursors from intermediate states that gradually took on transcriptional signatures similar to CD56dim NK cells. The final checkpoint occurred during late terminal differentiation of CD56dim NK cells and was associated with unique divergent gene-expression trends. Furthermore, we utilized this single-cell RNA sequencing resource to decipher the regulation of genes involved in lysosomal biogenesis and found a coordinated gradual increase in the RAB4 and BLOC1S gene families with differentiation into CD56dim NK cells. These results identify important gene programs driving functional diversification and specialization during NK cell differentiation and hold potential to guide new strategies for NK cell-based cancer immunotherapy.

immunology↗

Intra-lineage Plasticity and Functional Reprogramming Maintain Natural Killer Cell Repertoire Diversity

Natural killer (NK) cell repertoires are made up of a vast number of phenotypically distinct subsets with different functional properties. The molecular programs involved in maintaining NK cell repertoire diversity under homeostatic conditions remains elusive. Here we show that subset-specific NK cell proliferation kinetics correlate with mTOR activation, and that global repertoire diversity is maintained through a high degree of intra-lineage subset plasticity during IL-15-driven homeostatic proliferation in vitro. High-resolution flow cytometry and single cell RNA sequencing revealed that slowly cycling sorted KIR+CD56dim NK cells with an induced CD57 phenotype display increased functional potential associated with inhibitory MHC interactions and activating DAP12 signaling. In contrast, rapidly cycling cells upregulate NKG2A and display a general loss of functionality associated with a transcriptional increase in RNA-binding metabolic enzymes and cytokine signaling pathways. These results shed new light on the role of intra-lineage plasticity during NK cell homeostasis and suggest that the functional fate of the cell is tightly linked to the acquired phenotype and determined by transcriptional reprogramming.\n\nOne Sentence SummaryHigh-resolution flow cytometry combined with single-cell RNA sequencing reveal a role for intra-lineage plasticity and functional reprogramming in maintaining phenotypically and functionally diverse NK cell repertoires during IL-15-driven homeostatic proliferation.

immunology↗

TRPML1-mediated Modulation of Dense-core Granules Tunes Functional Potential in NK Cells

Inhibitory signaling during natural killer (NK) cell education translates into increased responsiveness to activation; however the intracellular mechanism for functional tuning by inhibitory receptors remains unclear. We found that educated NK cells expressing self-MHC specific inhibitory killer cell immunoglobulin-like receptors (KIR) show accumulation of granzyme B, localized in dense-core secretory lysosomes, converged close to the centrosome. This discrete morphological phenotype persists in self-KIR+ NK cells independently of transcriptional programs that regulate effector function, metabolism and lysosomal biogenesis. The granzymeB dense, large secretory lysosomes in self-KIR+ NK cells were efficiently released upon target cell recognition, contributing to their enhanced cytotoxic capacity. Secretory lysosomes are part of the acidic lysosomal compartment, which has been shown to channel calcium and mediate intracellular signalling in several cell types. Interference of signaling from acidic Ca2+ stores in primary NK cells reduced both target-specific Ca2+-flux, degranulation and cytokine production. Furthermore, inhibition of PI(3,5)P2 synthesis or genetic silencing of the PI(3,5)P2-regulated lysosomal Ca2+-channel TRPML1 led to increased levels of granzyme B and enhanced functional potential. These results indicate an intrinsic role for lysosomal homeostasis in NK cell education.

immunology↗