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:Neurons and glia are distinct in their morphology, development, and function. They have unique transcriptomes and proteomes, but little is known about their metabolomes. The challenge of brain cell metabolic profiling is to obtain a large number of pure cells for reliable analysis. Here, we purify microglia, astrocytes, and neurons from the genetically labeled-brain. We identified >70 metabolites in them with targeted metabolomics and 9,854 metabolite features with untargeted metabolomics. We systematically characterized cell type–enriched metabolites and metabolic pathways. The enrichment of glutathione (GSH) metabolism in microglia was further validated in vivo. A significant decrease in GSH levels and GSH metabolism observed in microglia in aging and Alzheimer's disease (AD) models. Disrupting GSH metabolism in microglia results in aberrant morphogenesis, upregulation of mitophagy-related genes, and the deposition of β-Amyloid. Our results provide a valuable resource for metabolic studies related to aging, AD and other neurological diseases.
2026-02-04 | GSE316928 | GEO
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:This dataset contains processed aptamer-based serum proteomics data from ME/CFS patients and healthy controls, analyzed using the 7k SomaScan assay (v4.1) platform. It includes log2-transformed intensities for 7326 aptamers (6494 protein targets), cohort metadata (age range, sex, BMI category, fasting status, SF-36 physical function score, metabotype), and aptamer annotations. The dataset supports the manuscript: Charting the Circulating Proteome in ME/CFS Using Cross System Profiling to Uncover Mechanistic Insights.
2026-01-21 | PAD000026 | Pride
Project description:Embryonic genome activation (EGA), a pivotal transcriptional event during preimplantation development, is accompanied by post-transcriptional regulation of maternal mRNAs. Disentangling the transcriptional output of the newly activated embryonic genome from concomitant post-transcriptional processing is important for decoding EGA dynamics.Here, using optimized low-input SLAM-seq (thiol(SH)-linked alkylation for the metabolic sequencing) in mouse embryos, we delineates the temporal hierarchy of EGA nascent transcription during mouse preimplantation embryogenesis and uncovers a mechanistic link between EGA and the first lineage specification, providing new insights into the regulatory architecture of early mammalian development.
2026-01-01 | GSE297541 | GEO
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