Proteogenomics-based identification of the repertoire of human MHC class I-associated peptides
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE67174 | GEO | 2016/12/22
SECONDARY ACCESSION(S): PRJNA279172
REPOSITORIES: GEO
Similar Datasets
Project description:We developed a novel approach combining next generation sequencing, bioinformatics and mass spectrometry to assess the impact of non-MHC polymorphisms on the repertoire of MHC I-associated peptides (MIPs). We compared the genomic landscape of MIPs eluted from B lymphoblasts of two MHC-identical siblings and determined that MIPs mirror the genomic frequency of non-synonymous polymorphisms but they behave as recessive traits at the surface level. Moreover, we showed that 11.7% of the MIP coding exome is polymorphic at the population level. Our method provides fundamental insights into the relation between the genomic self and the immune self and accelerates the discovery of polymorphic MIPs (also known as minor histocompatibility antigens), which play a major role in allo-immune responses. RNA-seq of human B lymphoblasts derived from peripheral blood mononuclear cells from 2 HLA-identical female siblings.
2013-12-08 | E-GEOD-48918 | biostudies-arrayexpress
Project description:We developed a novel approach combining next generation sequencing, bioinformatics and mass spectrometry to assess the impact of non-MHC polymorphisms on the repertoire of MHC I-associated peptides (MIPs). We compared the genomic landscape of MIPs eluted from B lymphoblasts of two MHC-identical siblings and determined that MIPs mirror the genomic frequency of non-synonymous polymorphisms but they behave as recessive traits at the surface level. Moreover, we showed that 11.7% of the MIP coding exome is polymorphic at the population level. Our method provides fundamental insights into the relation between the genomic self and the immune self and accelerates the discovery of polymorphic MIPs (also known as minor histocompatibility antigens), which play a major role in allo-immune responses.
2013-12-08 | GSE48918 | GEO
Project description:We employed a proteogenomics workflow to identify microproteins encoded by small Open Reading Frames (ORFs) in the genome of Mycobacterium smegmatis strain mc²155.
2022-04-21 | PXD025604 | JPOST Repository
Project description:Proteogenomics methods have identified many non-annotated protein-coding genes in the human genome. Many of the newly discovered protein-coding genes encode peptides and small proteins, referred to collectively as microproteins. Microproteins are produced through ribosome translation of small open reading frames (smORFs). The discovery of many smORFs reveals a blind spot in traditional gene-finding algorithms for these genes. Biological studies have found roles for microproteins in cell biology and physiology, and the potential that there exists additional bioactive microproteins drives the interest in detection and discovery of these molecules. A key step in any proteogenomics workflow is the assembly of RNA-Seq data into likely mRNA transcrips that are then used to create a searchable protein databases. Here we demonstrate that specific features of the assembled transcriptome impact microprotein detection by shotgun proteomics. By tailoring transcript assembly for downstream mass spectrometry searching, we show that we can detect more than double the number of high-quality microprotein candidates and introduce a novel open-source mRNA assembler for proteogenomics (MAPS) that incorporates all of these features. By integrating our specialized assembler, MAPS, and a popular generalized assembler into our proteogenomics pipeline, we detect 45 novel human microproteins from a high quality proteogenomics dataset of a human cell line. We then characterize the features of the novel microproteins, identifying two classes of microproteins. Our work highlights the importance of specialized transcriptome assembly upstream of proteomics validation when searching for short and potentially rare and poorly conserved proteins.
2017-12-20 | GSE92659 | GEO
Project description:For decades, the global impact of genomic polymorphisms on the repertoire of peptides presented by major histocompatibility complex (MHC) has remained a matter of speculation. Here we present a novel approach that enables high-throughput discovery of polymorphic MHC class I-associated peptides (MIPs), which play a major role in allorecognition. On the basis of comprehensive analyses of the genomic landscape of MIPs eluted from B lymphoblasts of two MHC-identical siblings, we show that 0.5% of non-synonymous single nucleotide variations are represented in the MIP repertoire. The 34 polymorphic MIPs found in our subjects are encoded by bi-allelic loci with dominant and recessive alleles. Our analyses show that, at the population level, 12% of the MIP-coding exome is polymorphic. Our method provides fundamental insights into the relationship between the genomic self and the immune self and accelerates the discovery of polymorphic MIPs (also known as minor histocompatibility antigens).
| S-EPMC3996541 | biostudies-literature
Project description:High-grade serous ovarian cancer (HGSC), the principal cause of death from gynecological malignancies in the world, has not significantly benefited from advances in cancer immunotherapy. Although HGSC infiltration by lymphocytes correlates with superior survival, the nature of antigens that can elicit anti-HGSC immune responses is unknown. The goal of this study was to establish the global landscape of HGSC tumor-specific antigens (TSAs) using a mass spectrometry pipeline that interrogated all reading frames of all genomic regions. In 23 HGSC tumors, we identified 103 TSAs. Classic TSA discovery approaches focusing only on mutated exonic sequences would have uncovered only three of these TSAs. Other mutated TSAs resulted from out-of-frame exonic translation (n=2) or from noncoding sequences (n=7). One group of TSAs (n=91) derived from aberrantly expressed unmutated genomic sequences, which were not expressed in normal tissues. These aberrantly expressed TSAs (aeTSAs) originated primarily from non-exonic sequences, in particular intronic (29%) and intergenic (22%) sequences. Their expression was regulated at the transcriptional level by variations in gene copy number and DNA methylation. Although mutated TSAs were unique to individual tumors, aeTSAs were shared by a large proportion of HGSCs. Taking into account the frequency of aeTSA expression and HLA allele frequencies, we calculated that, in Caucasians, the median number of aeTSAs per tumor would be five. We conclude that, in view of their number and the fact that they are shared by many tumors, aeTSAs may be the most attractive targets for HGSC immunotherapy.
2020-02-08 | GSE131880 | GEO
Project description:Proteogenomics is an emerging approach to improve gene annotation and interpretation of proteomics data. Here we present JUMPg, an integrative proteogenomics pipeline including customized database construction, tag-based database search, peptide-spectrum match filtering, and data visualization. JUMPg creates multiple databases of DNA polymorphisms, mutations, splice junctions, partially trypticity, as well as protein fragments translated from the whole transcriptome in all six frames after RNA-seq de novo assembly. We use a multistage strategy to search these databases sequentially, in which the performance is optimized by re-searching only unmatched high quality spectra, and re-using amino acid tags generated by the JUMP search engine. The identified peptides/proteins are displayed with gene loci using the UCSC genome browser. The JUMPg is applied to process a label-free mass spectrometry dataset of Alzheimer’s disease postmortem brain, uncovering 496 new peptides of amino acid substitutions, alternative splicing, frame shift, and “non-coding gene” translation. The novel protein PNMA6BL specifically expressed in the brain is highlighted. We also tested JUMPg to analyze a stable-isotope labeled dataset of multiple myeloma cells, revealing 991 sample-specific peptides that include protein sequences in the immunoglobulin light chain variable region. Thus, the JUMPg program is an effective proteogenomics tool for multi-omics data integration.
2017-02-20 | PXD004010 | Pride
Project description:International Cancer Proteogenomics Consortium (ICPC): Proteogenomics of East-Asian Breast Cancer
| PRJNA909918 | ENA
Project description:International Cancer Proteogenomics Consortium (ICPC): Proteogenomics of East-Asian Breast Cancer
| PRJNA909917 | ENA