BM
Ontology highlight
ABSTRACT:
PROVIDER: EGAD00010002150 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:metabolite levels measured by general metabolomics (Boston, USA) (the data is raw abundance. Mapping was applied on log10 transformed data)
| EGAD00010002148 | EGA
Project description:Full clinical data for a cohort of 199 individuals with acute coronary syndrome.
Untargeted serum metabolomics using the Metabolon platform for individuals with ACS (n=156).
Serum metabolomics using the Nightingale Health (NMR) platform for individuals with ACS and controls (ACS, n=191; controls, n=961).
| EGAD00001007724 | EGA
Project description:levels of 92 circulating proteins measured by Olink platform, CVDIII panel
| EGAD00001009268 | EGA
Project description:metabolite levels provided by UM platform (Creative Dynamics Inc, NY, USA) (the data is raw abundance. Mapping was applied on log10 transformed data)
| EGAD00010002146 | EGA
Project description:Changes in cellular metabolism contribute to the development and progression of tumors, and can render tumors vulnerable to interventions. However, studies of human cancer metabolism remain limited due to technical challenges of detecting and quantifying small molecules, the highly interconnected nature of metabolic pathways, and the lack of designated tools to analyze and integrate metabolomics with other âomics data. Our study generates the largest comprehensive metabolomics dataset on a single cancer type, and provides a significant advance in integration of metabolomics with sequencing data. Our results highlight the massive re-organization of cellular metabolism as tumors progress and acquire more aggressive features. The results of our work are made available through an interactive public data portal for cancer research community. 10 RNA samples from human ccRCC tumors analyzed from the high glutathione cluster
2016-03-08 | E-GEOD-74734 | biostudies-arrayexpress
Project description:Obesity-induced ectopic fat disposition in the liver is a major risk factor in the pathogenesis of type 2 diabetes as it impairs hepatic insulin sensitivity, a crucial component of whole body glucose homeostasis; however molecular mechanisms remain largely elusive. Understanding the pathogenesis of fatty liver, including the identification of novel genetic regulators that are involved in the regulation of liver metabolism under excess energy supply, is crucial for the development and implementation of efficient prevention and treatment strategies. We here developed a new method to integrate metabolomics and transcriptomics data based on pairwise correlation analysis of metabolites coupled to partial correlation combining the metabolite correlations with the transcript expression profiles followed by the construction of undirected, weighted graphs.This Correlation based Network Integration (CoNI) approach was applied to liver metabolome and transcriptome datasets of lean and HFD-fed obese mice to unravel previously hidden local regulator genes (LRG). The selected candidate genes were validated by transcriptome-proteome correlation analysis, by association studies with liver lipid metabolism in humans and by analysis of cellular metabolite levels after siRNA knockdown. Overall, the new bioinformatic CoNI approach for Omics datasets allowed us to identify genes regulating metabolic networks in livers of obese mice that if solely analyzing the transcriptome dataset would have remained hidden.
2021-07-09 | GSE137923 | GEO
Project description:Heritable epigenetic factors can contribute to complex disease etiology. In this study we examine, on a global scale, the contribution of DNA methylation to complex traits that are precursors to heart disease, diabetes and osteoporosis. We profiled DNA methylation patterns in the liver using bisulfite sequencing in 90 mouse inbred strains, genome-wide expression levels, proteomics, metabolomics and sixty-eight clinical traits, and performed epigenome-wide association studies (EWAS). We found associations with numerous clinical traits including bone mineral density, plasma cholesterol, insulin resistance, gene expression, protein and metabolite levels. A large proportion of associations were unique to EWAS and were not identified using GWAS. Methylation levels were regulated by genetics largely in cis, but we also found evidence of trans regulation, and we demonstrate that genetic variation in the methionine synthase reductase gene Mtrr affects methylation of hundreds of CpGs throughout the genome. Our results indicate that natural variation in methylation levels contributes to the etiology of complex clinical traits. Reduced representation bisulfite sequencing in mouse strains using liver genomic DNA
2015-04-02 | E-GEOD-67507 | biostudies-arrayexpress
Project description:The project aims to create dynamic maps of protein-protein-metabolite complexes in S. cerevisiae across growth phases using PROMIS (PROtein–Metabolite Interactions using Size separation). It is a biochemical, untargeted, proteome- and metabolome-wide method to study protein-protein and protein–metabolite complexes close to in vivo conditions. Approach involves using size exclusion chromatography (SEC) to separate complexes, followed by LC-MS-based proteomics and metabolomics analysis. This dataset was used for mashie learning approach: SLIMP, supervised learning of metabolite-protein interactions from multiple co-fractionation mass spectrometry datasets, to compute a global map of metabolite-protein interactions.
2022-10-25 | PXD026639 | Pride
Project description:This dataset was generated with the goal of comparative study of gene expression in three brain regions and two non-neural tissues of humans, chimpanzees, macaque monkeys and mice. Using this dataset, we performed studies of gene expression and gene splicing evolution across species and search of tissue-specific gene expression and splicing patterns. We also used the gene expression information of genes encoding metabolic enzymes in this dataset to support a larger comparative study of metabolome evolution in the same set of tissues and species. 120 tissue samples of prefrontal cortex (PFC), primary visual cortex (VC), cerebellar cortex (CBC), kidney and skeletal muscle of humans, chimpanzees, macaques and mice. The data accompanies a large set of metabolite measurements of the same tissue samples. Enzyme expression was used to validate metabolite measurement variation among species.
2014-05-30 | E-GEOD-49379 | biostudies-arrayexpress