Dataset Information

D2Ome - rate constant estimation from heavy water metabolic labeling and LC-MS

ABSTRACT: We developed a software for extraction of protein decay rate constants from heavy water labeling followed by LC-MS experiments. We illustrate the program on application to normal diet and western diet fed LDLR-/- mice. We demonstrate that proteins subunits of 40S ribosomal complex have reduced stability in western diet fet mice.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Mus Musculus (mouse)

TISSUE(S): Liver

SUBMITTER: Ahmad Borzoo

LAB HEAD: Rovshan G. Sadygov

PROVIDER: PXD009493 | Pride | 2019-04-10

REPOSITORIES: Pride

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Publications

Using Heavy Mass Isotopomers for Protein Turnover in Heavy Water Metabolic Labeling.

Sadygov Rovshan G RG

Journal of proteome research 20210304 4

Metabolic labeling followed by LC-MS-based proteomics is a powerful tool to study proteome dynamics in high-throughput experiments both <i>in vivo</i> and <i>in vitro.</i> High mass resolution and accuracy allow differentiation in isotope profiles and the quantification of partially labeled peptide species. Metabolic labeling duration introduces a time domain in which the gradual incorporation of labeled isotopes is recorded. Different stable isotopes are used for labeling. Labeling with heavy w ...[more]

PMID: 33661639

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Project description:Adipose tissue inflammation and atherosclerosis are the main mechanisms behind type 2 diabetes and cardiovascular disease respectively, the major risks associated with the metabolic syndrome. Studies considering more than single factors behind the complexity of the metabolic syndrome are valuable to achieve a better and wider understanding of the metabolic syndrome. In this study common dysregulated pathways between adipose tissue inflammation and atherosclerosis were identified using two different bioinformatic tools to perform pathway analysis. First, we run a gene set enrichment analysis utilizing with data from two microarray experiments done with gonadal white adipose tissue and atherosclerotic aorta. Once the common dysregulated pathways between both tissues were identify, the inflammatory response and the oxidative phosphorylation pathways from the Hallmark geneset were selected to conduct a deeper checkup at the single gene level of these pathways. Second, we carried out a pathway analysis validation with the Pantherâ¢ software combining the microarray data with a published type 2 diabetes mellitus metanalysis and cardiovascular disease metanalysis which included human data. In conclusion, this study provides worthwhile data pointing out and describing several dysregulated and common pathways in adipose tissue inflammation and atherosclerotic aorta with a potential implication in the pathogenesis of type 2 diabetes and atherosclerosis. LDLR-/- on a C57BL/6J background, purchased from Charles River Laboratories (Sulzfeld, Germany) were used. At 9 weeks of age the LDLR-/- were placed for up to 20 weeks on sucrose-enriched high-fat diet (HFSC) (with 17.5 kcal% from sucrose; (D09071704, Research Diets Inc.). Their respective controls were kept on normal chow diet (NC) for up to 20 weeks. After sacrificing the gonadal white adipose tissue (GWAT) from LDLR-/- animals on NC (Samples 22-27) or HFSC (Samples 28-33) was collected as well as the whole aortae from LDLR-/- animals on NC (Samples 13-15) or HFSC (Samples16-18). The collected tissues were immediately snap frozen in liquid nitrogen. RNA was isolated for gene expression microarray analyses at the exon level (GeneChip Mouse Exon 2.0 ST Array, Affymetrix, Santa Clara, CA, USA). To isolate RNA, the frozen tissue samples were homogenized in TRIzolÂ® reagent (Invitrogen/Life Technologies, Carlsbad, CA, USA) and processed based on manufacturerâs instructions. Total RNA (1Î¼g) was then used for GeneChip analysis, individual samples were used in GWAT preparation and three samples were pooled and used for aorta preparations. Terminal-labeled cDNA, hybridization to genome-wide Mouse Gene 2.0 ST Gene Chips and scanning of the arrays were carried out according to the manufactureâs indications (Affymetrix).Output primary row data was analyzed with Expression Console software (Affymetrix).

Dataset Information

D2Ome - rate constant estimation from heavy water metabolic labeling and LC-MS

Publications

Using Heavy Mass Isotopomers for Protein Turnover in Heavy Water Metabolic Labeling.

Similar Datasets

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