Dataset Information

Insulin response in mouse liver

ABSTRACT: An effective combination of multi-omic datasets can enhance our understanding of complex biological phenomena. To build a context-dependent network with multiple omic layers, i.e., a trans-omic network, we performed phosphoproteomics, transcriptomics, proteomics, and metabolomics of murine liver for 4 h after insulin administration and integrated the time series. Structural characteristics and dynamic nature of the network were analyzed to elucidate the impact of insulin. Early and prominent changes in protein phosphorylation and persistent and asynchronous changes in mRNA and protein levels through non-transcriptional mechanisms indicate enhanced crosstalk between phosphorylation-mediated signaling and protein expression regulation. Metabolic response shows different temporal regulation with transient increases at early time points across categories and enhanced response in the amino acid and nucleotide categories at later time points due to process convergence. This extensive and dynamic view of the trans-omic network elucidates prominent regulatory mechanisms that drive insulin responses through intricate interlayer coordination.

ORGANISM(S): Mus musculus

PROVIDER: GSE166336 | GEO | 2021/08/24

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Background: In diabetes chronic hyperinsulinemia is responsible for the instability of the atherosclerotic plaque and stimulates cellular proliferation through the activation of the MAP kinases, which, in turn regulate cellular proliferation. So far, it is not known, however, whether insulin itself could increase the transcription of specific genes for cellular proliferation in the endothelium. Hence, the characterization of the global pattern of transcriptional modifications in endothelium is an important step for a better understanding of the mechanism of action of insulin and of its defects in situations of insulin resistance. Methodology and principal findings: The transcriptional response of endothelial cells in the eight hours following insulin stimulation was monitored using microarrays and compared to a control condition. About 1700 genes were selected as differentially expressed based on their treated minus control profile, thus allowing the detection of even small but systematic changes in gene expression. Genes were clustered in 7 groups according to their time expression profile and classified into 15 functional categories that can support the biological effects of insulin, based on Gene Ontology enrichment analysis. In terms of endothelial function, the most prominent processes affected were NADH dehydrogenase activity, N-terminal myristoylation domain binding, nitric-oxide synthase regulator activity and growth factor binding. Pathway based enrichment analysis revealed âElectron Transport Chainâ and âMatrix Metalloproteinasesâ significantly enriched. Results were validated on genes belonging to âElectron Transport Chainâ pathway, using quantitative RT-PCR. Conclusions: As far as we know, this is the first systematic study in the literature monitoring transcriptional response to insulin in endothelial cells, in a time series microarray experiment. Since chronic hyperinsulinemia is responsible for the instability of the atherosclerotic plaque and stimulates cellular proliferation, some of the genes identified in the present work are potential novel candidates in diabetes complications related to endothelial dysfunction. Experiments were carried on human umbilical venous endothelial cells (HUVEC) to elucidate the regulatory aspects of insulin from dynamic gene expression data. HUVEC incubated with or without insulin were collected at times 0', 40', 100', 200', 340' and 440'. RNA was extracted and quantified for high-density oligonucleotide microarrays Human Genome U133 Plus 2.0 GeneChip (Affymetrix, Santa Clara, CA).

Dataset Information

Insulin response in mouse liver

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets