The association between the number of HLA risk alleles and drug allergy and its implications for HLA screening – a case-control study
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HLA I immunopeptidome of synthetic long peptide pulsed human dendritic cells for therapeutic vaccine design
Synthetic long peptides (SLPs) are a promising vaccine modality that exploit dendritic cells (DC) to treat chronic infections or cancer. Currently, the design of SLPs relies on in silico prediction and multifactorial T cells assays to determine which SLPs are best cross-presented on DC human leukocyte antigen class I (HLA-I). Furthermore, it is unknown how TLR ligand-based adjuvants affect DC cross-presentation. Here, we generated a unique, high-quality immunopeptidome dataset of human DCs pulsed with 12 hepatitis B virus (HBV)-based SLPs combined with either a TLR1/2 (Amplivant®) or TLR3 (PolyI:C) ligand. The obtained immunopeptidome reflected adjuvant-induced differences, but no differences in cross-presentation of SLPs. We uncovered dominant (cross-)presentation on B-alleles, and identified 33 unique SLP-derived HLA-I peptides, several of which were not in silico predicted and some were consistently found across donors. Our work puts forward DC immunopeptidomics as a valuable tool for therapeutic vaccine design.
Deep learning enhances the prediction of HLA class I-presented CD8+ T cell epitopes in foreign pathogens
Accurate in silico determination of CD8+ T cell epitopes would greatly enhance T cell-based vaccine development, but current prediction models are not reliably successful. Here, motivated by recent successes applying machine learning to complex biology, we curated a dataset of 651,237 unique human leukocyte antigen class I (HLA-I) ligands and developed MUNIS, a deep learning model that identifies peptides presented by HLA-I alleles. MUNIS shows improved performance compared with existing models in predicting peptide presentation and CD8+ T cell epitope immunodominance hierarchies. Moreover, application of MUNIS to proteins from Epstein–Barr virus led to successful identification of both established and novel HLA-I epitopes which were experimentally validated by in vitro HLA-I-peptide stability and T cell immunogenicity assays. MUNIS performs comparably to an experimental stability assay in terms of immunogenicity prediction, suggesting that deep learning can reduce experimental burden and accelerate identification of CD8+ T cell epitopes for rapid T cell vaccine development.
Too big to purge: persistence of deleterious Mutations in Island populations of the European Barn Owl (Tyto alba)
A key aspect of assessing the risk of extinction/extirpation for a particular wild species or population is the status of inbreeding, but the origin of inbreeding and the current mutational load are also two crucial factors to consider when determining survival probability of a population. In this study, we used samples from 502 barn owls from continental and island populations across Europe, with the aim of quantifying and comparing the level of inbreeding between populations with differing demographic histories. In addition to comparing inbreeding status, we determined whether inbreeding is due to non-random mating or high co-ancestry within the population. We show that islands have higher levels of inbreeding than continental populations, and that this is mainly due to small effective population sizes rather than recent consanguineous mating. We assess the probability that a region is autozygous along the genome and show that this probability decreased as the number of genes present in that region increased. Finally, we looked for evidence of reduced selection efficiency and purging in island populations. Among island populations, we found an increase in numbers of both neutral and deleterious minor alleles, possibly as a result of drift and decreased selection efficiency but we found no evidence of purging.
Modeling critical dosing strategies for stromal-induced resistance to cancer therapy
Complex interactions between stromal cells, tumor cells and therapies can influence environmental factors that in turn impact anticancer treatment efficacy. Disentangling these phenomena is critical for understanding treatment response and designing effective dosing strategies. We propose a mathematical model for a common tumor-stromal interaction motif where stromal cells secrete factors that promote drug resistance. We demonstrate that the presence of this interaction modulates the therapeutic dose window of efficacy and can lead to nonmonotonic treatment response. We consider combination strategies that target stromal cells and their secretome, and identify strategies that constrain drug concentrations within the efficacious window for long-term response. We explore an experimental dataset from colorectal cancer cells treated with anti-EGFR targeting therapy, cetuximab, where cancer-associated fibroblasts increase epidermal growth factor secretion under treatment. We apply our general approach to identify a critical drug concentration threshold and study effective dosing regimens for single-drug and combination therapies.
Immunopeptidomics for autoimmunity: unlocking the chamber of immune secrets
T cells mediate pathogenesis of several autoimmune disorders by recognizing self-epitopes presented on Major Histocompatibility Complex (MHC) or Human Leukocyte Antigen (HLA) complex. The majority of autoantigens presented to T cells in various autoimmune disorders are not known, which has impeded autoantigen identification. Recent advances in immunopeptidomics have started to unravel the repertoire of antigenic epitopes presented on MHC. In several autoimmune diseases, immunopeptidomics has led to the identification of novel autoantigens and has enhanced our understanding of the mechanisms behind autoimmunity. Especially, immunopeptidomics has provided key evidence to explain the genetic risk posed by HLA alleles. In this review, we shed light on how immunopeptidomics can be leveraged to discover potential autoantigens. We highlight the application of immunopeptidomics in Type 1 Diabetes (T1D), Systemic Lupus Erythematosus (SLE), and Rheumatoid Arthritis (RA). Finally, we highlight the practical considerations of implementing immunopeptidomics successfully and the technical challenges that need to be addressed. Overall, this review will provide an important context for using immunopeptidomics for understanding autoimmunity.
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