Simultaneous identification of DNA variation and methylation at HLA class II locus and immune gene promoters using targeted SureSelect Methyl-Sequencing
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ABSTRACT:
PROVIDER: PRJNA997991 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR25403306_1.fastq.gz | Fastqsanger.gz | |||
| SRR25403306_2.fastq.gz | Fastqsanger.gz | |||
| SRR25403307_1.fastq.gz | Fastqsanger.gz | |||
| SRR25403307_2.fastq.gz | Fastqsanger.gz | |||
| SRR25403308_1.fastq.gz | Fastqsanger.gz |
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Project description:The Human Leukocyte Antigen (HLA) locus associates with a variety of complex diseases, particularly autoimmune and inflammatory conditions. The HLA-DR15 haplotype, for example, confers the major risk for developing Multiple Sclerosis in Caucasians, pinpointing important role in the etiology of this chronic inflammatory disease of the central nervous system. In addition to the protein-coding variants that shape the functional HLA-antigen-T cell interaction, recent studies suggest that the levels of HLA molecule expression, that are epigenetically controlled, also play a role in disease development. However, deciphering the exact molecular mechanisms of the HLA association has been hampered by the tremendous genetic complexity of the locus and a lack of robust approaches to investigate it. Here we developed a method to specifically enrich the genomic DNA from the HLA class II locus (chr6:32,426,802-34,167,129) and proximal promoters of 2,157 immune-relevant genes, utilizing the Agilent RNA-based SureSelect Methyl-Seq Capture method, followed by sequencing to detect genetic and epigenetic variation. We demonstrated successful simultaneous detection of the genetic variation and quantification of DNA methylation levels in the HLA locus. Moreover, by utilizing differentially methylated positions in promoters of immune-related genes, we detected relevant pathways following stimulation of cells. Taken together, we present a method that can be utilized to study the impact of the interplay of genetic and epigenetic regulation in HLA class II region on cellular states in a wide context of HLA haplotypes and diseases.
2023-08-15 | GSE238121 | GEO
Project description:The Human Leukocyte Antigen (HLA) locus associates with a variety of complex diseases, particularly autoimmune and inflammatory conditions. The HLA-DR15 haplotype, for example, confers the major risk for developing Multiple Sclerosis in Caucasians, pinpointing an important role in the etiology of this chronic inflammatory disease of the central nervous system. In addition to the protein-coding variants that shape the functional HLA-antigen-T cell interaction, recent studies suggest that the levels of HLA molecule expression, that are epigenetically controlled, also play a role in disease development. However, deciphering the exact molecular mechanisms of the HLA association has been hampered by the tremendous genetic complexity of the locus and a lack of robust approaches to investigate it. Here, we developed a method to specifically enrich the genomic DNA from the HLA class II locus (chr6:32,426,802-34,167,129) and proximal promoters of 2,157 immune-relevant genes, utilizing the Agilent RNA-based SureSelect Methyl-Seq Capture related method, followed by sequencing to detect genetic and epigenetic variation. We demonstrated successful simultaneous detection of the genetic variation and quantification of DNA methylation levels in HLA locus. Moreover, by the detection of differentially methylated positions in promoters of immune-related genes, we identified relevant pathways following stimulation of cells. Taken together, we present a method that can be utilized to study the interplay between genetic variance and epigenetic regulation in the HLA class II region, potentially, in a wide disease context.
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Project description:Sequencing of bisulfite-converted mouse genomic DNA captured on SureSelect Methyl-Seq
2018-12-26 | GSE114512 | GEO
Project description:SureSelect targeted hybridisation of RetNet coding exons.
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Project description:HLA class II full-length submissions 30/08/2022
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Project description:The sensitivity of malignant tissues to T cell-based cancer immunotherapies is dependent on the presence of targetable HLA class I ligands on the tumor cell surface. Peptide intrinsic factors, such as HLA class I affinity, likelihood of proteasomal processing, and transport into the ER lumen have all been established as determinants of HLA ligand presentation. However, the role of sequence features at the gene and protein level as determinants of epitope presentation has not been systematically evaluated. To address this, we performed HLA ligandome mass spectrometry on patient-derived melanoma lines and used this data-set to evaluate the contribution of 7,124 gene and protein sequence features to HLA sampling. This analysis reveals that a number of predicted modifiers of mRNA and protein abundance and turn-over, including predicted mRNA methylation and protein ubiquitination sites, inform on the presence of HLA ligands. Importantly, integration of gene and protein sequence features into a machine learning approach augments HLA ligand predictions to a comparable degree as predictive models that include experimental measures of gene expression. Our study highlights the value of gene and protein features to HLA ligand predictions.
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Project description:The sensitivity of malignant tissues to T cell-based cancer immunotherapies is dependent on the presence of targetable HLA class I ligands on the tumor cell surface. Peptide intrinsic factors, such as HLA class I affinity, likelihood of proteasomal processing, and transport into the ER lumen have all been established as determinants of HLA ligand presentation. However, the role of sequence features at the gene and protein level as determinants of epitope presentation has not been systematically evaluated. To address this, we performed HLA ligandome mass spectrometry on patient-derived melanoma lines and used this data-set to evaluate the contribution of 7,124 gene and protein sequence features to HLA sampling. This analysis reveals that a number of predicted modifiers of mRNA and protein abundance and turn-over, including predicted mRNA methylation and protein ubiquitination sites, inform on the presence of HLA ligands. Importantly, integration of gene and protein sequence features into a machine learning approach augments HLA ligand predictions to a comparable degree as predictive models that include experimental measures of gene expression. Our study highlights the value of gene and protein features to HLA ligand predictions.
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Project description:Treatment of cancer cells with anti-cancer drugs often fails to achieve complete remission. Yet, such drug treatments may induce alteration in the tumorâs gene expression patterns, including those of Cancer/Testis Antigens (CTA). The degradation products of such antigens can be presented as HLA peptides on the surface of the tumor cells and be developed into anti-cancer immunotherapeutics. For example, the DNA methyl transferase inhibitor, 5-aza-2'-deoxycytidine (Decitabine) has limited anti-tumor efficacy, yet it induces the expression of many genes, including CTAs that are normally silenced in the healthy adult tissues. In this study, the presentation of many new HLA peptides derived from CTAs and induced by Decitabine was demonstrated in three human Glioblastoma cell lines. Such presentation of CTA-derived HLA peptides can be exploited for development of new treatment modalities, combining drug treatment with anti-CTA targeted immunotherapy. The Decitabine-induced HLA peptidomes include many CTAs that are not normally detected in healthy tissues or in cancer cells, unless treated with the drug. In addition, the study included large-scale analyses of the simultaneous effects of Decitabine on the transcriptomes, proteomes and HLA peptidomes of the human Glioblastoma cells. It demonstrates the poor correlations between these three levels of gene expression, both in their total levels and in their response to the drug. The transcriptomes, proteomes and HLA peptidomes of the U-87, T98G and LNT-229 GBM human cell lines were analyzed before and after treatment with Decitabine. Overall, the RNA-Seq transcriptome analyses resulted in the identification of above 26000 transcripts, the proteome analyses identified about 7500 proteins and the HLA class I peptidome analyses resulted in above 25000 identified HLA peptides. Two biological repetitions of the transcriptome, three of the proteome and three of the HLA peptidome were performed with each of the cell lines and treatment, resulting in highly reproducible datasets.
2016-07-19 | E-GEOD-80137 | biostudies-arrayexpress
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.
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