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: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
Project description:Metabolic studies at single cell level could directly define the cellular phenotype closest to physiological or disease states. However, the current single cell metabolome (SCM) study using mass spectroscopy has the difficulty to give a complete view of the metabolic activity in the cell, while the prediction of metabolism-phenotype relationship is limited by the potential inconsistency between transcriptomic and metabolic levels. Here, single-cell simultaneous metabolome and transcriptome profiling method (scMeT-seq) is developed, based on sub-picoliter sampling from the cell for the initial metabolome profiling followed by single cell transcriptome sequencing. This design does not only provide sufficient cytoplasm for SCM, but also nicely keeps the cellular viability for the accurate transcriptomic analysis in the same cell. Diverse relationships between the two omics are revealed in macrophages with the stimulation of lactate and lactate transporter (MCT1) antagonist. Moreover, we mapped metabolite states to the single-cell differentiation trajectory and gene correlation network of macrophages, which allows the unsupervised functional interpretation of metabolome. Thus, the established scMeT-seq should lead to a new perspective in metabolic research by transforming metabolomics from metabolite snapshot to functional approach.
2025-09-21 | GSE270852 | GEO