Project description:Intestinal microbial dysbiosis, intestinal inflammation, and Th17 immunity are all linked to the pathophysiology of spondyloarthritis (SpA); however, the mechanisms linking them remain unknown. One potential hypothesis suggests that the dysbiotic gut microbiome as a whole produces metabolites that influence human immune cells. To identify potential disease-relevant, microbiome-produced metabolites, we performed metabolomics screening and shotgun metagenomics on paired colon biopsies and fecal samples, respectively, from subjects with axial SpA (axSpA, N=21), Crohn's disease (CD, N=27), and Crohn's-axSpA overlap (CD-axSpA, N=12), as well as controls (HC, N=24). Using LC-MS based metabolomics of 4 non-inflamed pinch biopsies of the distal colon from subjects, we identified significant alterations in tryptophan pathway metabolites, including an expansion of indole-3-acetate (IAA) in axSpA and CD-axSpA compared to HC and CD and indole-3-acetaldehyde (I3Ald) in axSpA and CD-axSpA but not CD compared to HC, suggesting possible specificity to the development of axSpA. We then performed shotgun metagenomics of fecal samples to characterize gut microbial dysbiosis across these disease states. In spite of no significant differences in alpha-diversity among the 4 groups, our results confirmed differences in gene abundances of numerous enzymes involved in tryptophan metabolism. Specifically, gene abundance of indolepyruvate decarboxylase, which generates IAA and I3Ald, was significantly elevated in individuals with axSpA while gene abundances in HC demonstrated a propensity towards tryptophan synthesis. Such genetic changes were not observed in CD, again suggesting disease specificity for axSpA. Given the emerging role of tryptophan and its metabolites in immune function, altogether these data indicate that tryptophan metabolism into I3Ald and then IAA is one mechanism by which the gut microbiome potentially influences the development of axSpA.
Project description:BackgroundPhosphorylation of proteins is a key step in the regulation of many cellular processes including activation of enzymes and signaling cascades. The abundance of a phosphorylated peptide (phosphopeptide) is determined by the abundance of its parent protein and the proportion of target sites that are phosphorylated.ResultsWe quantified phosphopeptides, proteins, and transcripts in heart, liver, and kidney tissue samples of mice from 58 strains of the Collaborative Cross strain panel. We mapped ~700 phosphorylation quantitative trait loci (phQTL) across the three tissues and applied genetic mediation analysis to identify causal drivers of phosphorylation. We identified kinases, phosphatases, cytokines, and other factors, including both known and potentially novel interactions between target proteins and genes that regulate site-specific phosphorylation. Our analysis highlights multiple targets of pyruvate dehydrogenase kinase 1 (PDK1), a regulator of mitochondrial function that shows reduced activity in the NZO/HILtJ mouse, a polygenic model of obesity and type 2 diabetes.ConclusionsTogether, this integrative multi-omics analysis in genetically diverse CC strains provides a powerful tool to identify regulators of protein phosphorylation. The data generated in this study provides a resource for further exploration.
Project description:Pathway analyses are key methods to analyze 'omics experiments. Nevertheless, integrating data from different 'omics technologies and different species still requires considerable bioinformatics knowledge.Here we present the novel ReactomeGSA resource for comparative pathway analyses of multi-omics datasets. ReactomeGSA can be used through Reactome's existing web interface and the novel ReactomeGSA R Bioconductor package with explicit support for scRNA-seq data. Data from different species is automatically mapped to a common pathway space. Public data from ExpressionAtlas and Single Cell ExpressionAtlas can be directly integrated in the analysis. ReactomeGSA greatly reduces the technical barrier for multi-omics, cross-species, comparative pathway analyses.We used ReactomeGSA to characterize the role of B cells in anti-tumor immunity. We compared B cell rich and poor human cancer samples from five of the Cancer Genome Atlas (TCGA) transcriptomics and two of the Clinical Proteomic Tumor Analysis Consortium (CPTAC) proteomics studies. B cell-rich lung adenocarcinoma samples lacked the otherwise present activation through NFkappaB. This may be linked to the presence of a specific subset of tumor associated IgG+ plasma cells that lack NFkappaB activation in scRNA-seq data from human melanoma. This showcases how ReactomeGSA can derive novel biomedical insights by integrating large multi-omics datasets.
Project description:Physical exercise is beneficial to keep physical and mental health. The molecular mechanisms underlying exercise are still worth exploring.The healthy adult mice after six weeks of moderate-intensity exercise (experimental group) and sedentary mice (control group) were used to perform transcriptomic, proteomic, lactylation modification, and metabolomics analysis. In addition, gene sets related to hypoxia, glycolysis, and fatty acid metabolism were used to aid in the screening of hub genes. The mMCP-counter was employed to evaluate infiltration of immune cells in murine liver tissues.
Project description:Genomic analysis of lung adenocarcinomas has revealed that the MGA gene, which encodes a heterodimeric partner of the MYC-interacting protein MAX, is significantly mutated or deleted in lung adenocarcinomas. Most of the mutations are loss of function for MGA, suggesting that MGA may act as a tumor suppressor. Here, we characterize both the molecular and cellular role of MGA in lung adenocarcinomas and illustrate its functional relevance in the MYC pathway. Although MGA and MYC interact with the same binding partner, MAX, and recognize the same E-box DNA motif, we show that the molecular function of MGA appears to be antagonistic to that of MYC. Using mass spectrometry-based affinity proteomics, we demonstrate that MGA interacts with a noncanonical PCGF6-PRC1 complex containing MAX and E2F6 that is involved in gene repression, while MYC is not part of this MGA complex, in agreement with previous studies describing the interactomes of E2F6 and PCGF6. Chromatin immunoprecipitation-sequencing and RNA sequencing assays show that MGA binds to and represses genes that are bound and activated by MYC. In addition, we show that, as opposed to the MYC oncoprotein, MGA acts as a negative regulator for cancer cell proliferation. Our study defines a novel MYC/MAX/MGA pathway, in which MYC and MGA play opposite roles in protein interaction, transcriptional regulation, and cellular proliferation. IMPLICATIONS: This study expands the range of key cancer-associated genes whose dysregulation is functionally equivalent to MYC activation and places MYC within a linear pathway analogous to cell-cycle or receptor tyrosine kinase/RAS/RAF pathways in lung adenocarcinomas.
Project description:Combining omics data from different layers using integrative methods provides a better understanding of the biology of a complex disease such as cancer. The discovery of biomarkers related to cancer development or prognosis helps to find more effective treatment options. This study integrates multi-omics data of different cancer types with a network-based approach to explore common gene modules among different tumors by running community detection methods on the integrated network. The common modules were evaluated by several biological metrics adapted to cancer. Then, a new prognostic scoring method was developed by weighting mRNA expression, methylation, and mutation status of genes. The survival analysis pointed out statistically significant results for GNG11, CBX2, CDKN3, ARHGEF10, CLN8, SEC61G and PTDSS1 genes. The literature search reveals that the identified biomarkers are associated with the same or different types of cancers. Our method does not only identify known cancer-specific biomarker genes, but also proposes new potential biomarkers. Thus, this study provides a rationale for identifying new gene targets and expanding treatment options across cancer types.
Project description:Histone H3 lysine 9 dimethylation and trimethylation (H3K9me2/3) are prevalent in human genomes, especially in heterochromatin and specific euchromatic genes. Methylation of H3K9 is modulated by enzymes such as SUV39H1, SUV39H2, SETDB1, SETDB2, and EHMT1/2, which influence cancer progression. This study reveals differential expression of these six H3K9 methyltransferases in tumors, with SUV39H1, SUV39H2, and SETDB1 showing significant links to cancer phenotypes. We developed the "H3K9me3 MtSig" (H3K9me3 methyltransferases signature) based on these findings. H3K9me3 MtSig is unique to various tumors, with prognostic significance and associations with key signaling pathways, especially in glioblastoma (GBM). Elevated H3K9me3 MtSig was observed in GBM samples, correlating with the G2/M cell cycle and reduced immune responses. H3K9me3-mediated repetitive sequence silencing by H3K9me3 MtSig contributed to these phenotypes, and inhibiting H3K9me3 MtSig in patient-derived GBM cells suppressed proliferation and increased immune responses. H3K9me3 MtSig serves as an independent prognostic factor and potential therapeutic target.
Project description:Gliomas have highly variable clinical outcomes that are not adequately predicted on the basis of histologic class nor genetic markers. We performed a comprehensive analysis that integrated multi-omics datasets to elucidate factors influencing glioma prognosis. We detected 17 grade-related genes different in expression between LGG and GBM cohorts. By combining multi-layer information including genetic markers, DNA methylation and gene expression, we constructed two gene networks of relations from the grade-related genes. From the networks, we identified 178 prognostic genes whose activity states, in terms of DNA methylation and gene expression level, are relevant to prognostic outcomes of gliomas, with low activity associated with better outcomes. The efficacy of these 178 genes' activity states as prognostic factors beyond genetic markers, i.e. IDH1 or PTEN gene mutations, was validated externally. Among the 178 prognostic genes, we validated roles in gliomas for 121 genes from previous literature, and more importantly, we identified 67 novel candidate genes for glioma research. Expression of these 178 genes was particularly pronounced during fetal development in various brain regions. Our findings provide clues for understanding glioma prognosis and highlight some novel glioma-associated genes that warrant further investigation.
Project description:Pathways are composed of proteins forming a network to represent specific biological mechanisms and are often used to measure enrichment scores based on a list of genes in means to measure their biological activity. The pathway analysis is a de facto standard downstream analysis procedure in most genomic and transcriptomic studies. Here, we present MOPA (Multi-Omics Pathway Analysis), which is a multi-omics integrative method that scores individual pathways in a sample wise manner in terms of enriched multi-omics regulatory activity, which we refer to mES (multi-omics Enrichment Score). The mES score reflects the strength of regulatory relations between multi-omics in units of pathways. In addition, MOPA is able to measure how much each omics contribute to mES that may be used to observe what kind of omics are active in a pathway within a sample group (e.g., subtype, gender), which we refer to OCR (Omics Contribution Rate). Using nine different cancer types, 93 clinical features and three types of omics (i.e., gene expression, miRNA and methylation), MOPA was used to search for clinical features that were explainable in context of multi-omics. By evaluating the performance of MOPA, we showed that it yielded higher or at least equal performance compared to previous single and multi-omics pathway analysis tools. We find that the advantage of MOPA is the ability to explain pathways in terms of omics relation using mES and OCR. As one of the results, the TGF-beta signaling pathway was captured as an important pathway that showed distinct mES and OCR values specific to the CMS4 subtype in colon adenocarcinoma. The mES and OCR metrics suggested that the mRNA and miRNA expressions were significantly different from the other subtypes, which was concordant with previous studies. The MOPA software is available at https://github.com/jaeminjj/MOPA.