New HLA alleles
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB29858 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Characterisation and Confirmation of new HLA-Alleles found by DKMS Life Science Lab
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Project description:Several HLA allelic variants have been associated with protection from, or susceptibility to infectious and autoimmune diseases. Here, we examined whether specific HLA alleles would be associated with different Mtb infection outcomes. We found that DQA1*03:01, DPB1*04:02, and DRB4*01:01 were signficantly more frequent in inividuals with active TB (susceptibility alleles). Furthermore, individuals who express any of the three susceptibility alleles were associated with lower magnitude of responses against Mtb antigens. We investigated the gene expression changes induced in PBMCs by Mtb lysate and a peptide pool (MTB300) in individuals with or without expression of the susceptibility alleles.
2023-03-29 | GSE207214 | GEO
Project description:HLA-DRB1 alleles have been associated with several autoimmune diseases. In anti-citrullinated protein antibody positive rheumatoid arthritis (ACPA-positive RA), HLA-DRB1 shared epitope (SE) alleles are the major genetic risk factors. In order to investigate whether expression of different alleles of major histocompatibility complex (MHC) Class II genes influence functions of immune cells, we investigated transcriptomic profiles of a variety of immune cells from healthy individuals carrying different HLA-DRB1 alleles. Sequencing libraries from peripheral blood mononuclear cells, CD4+ T cells, CD8+ T cells, and CD14+ monocytes of 32 genetically pre-selected healthy female individuals were generated, sequenced and reads were aligned to the standard reference. For the MHC region, reads were mapped to available MHC reference haplotypes and AltHapAlignR was used to estimate gene expression. Using this method, HLA-DRB and HLA-DQ were found to be differentially expressed in different immune cells of healthy individuals as well as in whole blood samples of RA patients carrying HLA-DRB1 SE-positive versus SE-negative alleles. In contrast, no genes outside the MHC region were differentially expressed between individuals carrying HLA-DRB1 SE-positive and SE-negative alleles. Existing methods for HLA-DR allele-specific protein expression were evaluated but were not mature enough to provide appropriate complementary information at the protein level. Altogether, our findings suggest that immune effects associated with different allelic forms of HLA-DR and HLA-DQ may be associated not only with differences in the structure of these proteins, but also with differences in their expression levels.
2020-12-22 | GSE163605 | GEO
Project description:The precise identification of Human Leukocyte Antigen class I (HLA-I) binding motifs plays a central role in our ability to understand and predict (neo-)antigen presentation in infectious diseases and cancer. Here, by exploiting co-occurrence of HLA-I alleles across publicly available as well as ten newly generated high quality HLA peptidomics datasets, we show that we can rapidly and accurately identify HLA-I binding motifs and map them to their corresponding alleles without any a priori knowledge of HLA-I binding specificity. This fully unsupervised approach uncovers new motifs for several alleles without known ligands and significantly improves neo-epitope predictions in three melanoma patients.
2017-08-18 | PXD005231 | Pride
Project description:Sample od DVMO Brasil, with new allele HLA-DRB1*03.
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Project description:Modern antigen vaccine designs and studies of human leukocyte antigen (HLA)-mediated immune responses rely heavily on the knowledge of HLA allele-specific binding motifs and computational prediction of antigen-HLA binding affinity. Breakthroughs in HLA peptidomics have considerably expanded the databases of natural HLA antigens and enabled detailed characterizations of antigen-HLA binding specificity. However, cautions must be made when analyzing HLA peptidomics data because identified peptides may be contaminants or may weakly bind to the HLA molecules. Here, a hybrid de novo peptide sequencing approach was applied to large-scale mono-allelic HLA peptidomics datasets to uncover new antigens and refine current knowledge of HLA binding motifs. Up to 12-40% contaminations in the form of tryptic peptides were identified in the peptidomics data of HLA alleles whose binding motifs do not involve an arginine or a lysine at the C-terminus. Thousands of these peptides were reported in a community database as positive antigens and might be erroneously used to train prediction models. Furthermore, unsupervised clustering of identified antigens not only revealed additional binding motifs for several HLA class I alleles but also effectively isolated outliers which were confirmed to be false positives in a binding experiment. Overall, our findings expanded the knowledge of HLA binding specificity and indicated that a more careful HLA peptidomics data interpretation protocol is needed to ensure the high quality of community antigen databases.
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