MassIVE MSV000088835

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GNPS - Multi-omics approach reveals dysregulation of protein phos-phorylation correlated with lipid metabolism in mouse fatty liver

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Description

Obesity caused by overnutrition is a major risk factor for non-alcoholic fatty liver disease (NAFLD). Several lipid intermediates such as fatty acids, glycerophospholipids and sphingolipids are implicated in NAFLD, but detailed characterization of lipids and their functional links to proteome and phosphoproteome remain to be elucidated. To characterize this complex molecular relationship, we used multi-omics approach by conducting comparative proteomic, phoshopro-teomic and lipidomic analyses of high fat (HFD) and low fat (LFD) diet fed mice livers. We quantified 2447 proteins and 1339 phosphoproteins containing 1650 class I phosphosites (with localization probability > 0.75), of which 669 phosphosites were significantly different between HFD and LFD mice livers. We detected alterations of proteins associated with cellular metabolic processes such as small molecule catabolic process, monocarboxylic acid, long- and medium-chain fatty acid, and ketone body metabolic processes, and peroxisome organization. We observed significant downregulation of protein phosphorylation in HFD fed mice liver in general. Untargeted lipidomics identified upregulation of triacylglycerols, glycerolipids and ether glycerophosphocholines and downregulation of glycerophospholipids such as lysoglycerophospholipids, as well as ceramides and acylcarnitines. Analysis of differentially regulated phosphosites revealed phosphorylation dependent deregulation of insulin signaling as well as lipogenic and lipolytic pathways during HFD induced obesity. Thus, this study reveals a molecular connection between decreased protein phosphorylation and lipolysis, as well as lipid-mediated signaling in diet-induced obesity. [doi:10.25345/C5728D] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: NAFLD ; Fatty liver ; High Fat Diet ; Mass spectrometry ; Proteomics ; Lipidomics

Contact

Principal Investigators:
(in alphabetical order) 		Uma Aryal, Purdue University, United States
Submitting User: uma_aryal
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GNPS content goes here (MSV000088835 [task=f6b992d9df474e74a72efccd122223a0])
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Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

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