SIRT5 deacylates metabolism-related proteins and attenuates hepatic steatosis in ob/ob mice

SIRT5 deacylates metabolism-related proteins and attenuates hepatic steatosis in ob/ob mice

Abstract

Background

Sirtuin 5 (SIRT5) is a NAD+-dependent lysine deacylase. The SIRT5 deficiency mouse model shows that it is dispensable for metabolic homeostasis under normal conditions. However, the biological role of SIRT5 and acylation in pathological states such as obesity and type 2 diabetes (T2D) remains elusive.

Methods

The hepatic SIRT5-overexpressing ob/ob mouse model (ob/ob-SIRT5 OE) was established by CRISPR/Cas9 gene editing tool Protein malonylation and succinylation lysine sites were identified by immunoprecipitation coupled lipid chromatography – tandem mass spectrometry (LC-MS/MS) methods.

Findings

The ob/ob-SIRT5 OE mice showed decreased malonylation and succinylation, improved cellular glycolysis, suppressed gluconeogenesis, enhanced fatty acid oxidation, and attenuated hepatic steatosis. A total of 955 malonylation sites on 434 proteins and 1377 succinylation sites on 429 proteins were identified and quantitated. Bioinformatics analysis revealed that malonylation was the major SIRT5 target in the glycolysis/gluconeogenesis pathway, whereas succinylation was the preferred SIRT5 target in the oxidative phosphorylation pathway.

Interpretation

Hepatic overexpression of SIRT5 ameliorated the metabolic abnormalities of ob/ob mice, probably through demalonylating and desuccinylating proteins in the main metabolic pathways. SIRT5 and related acylation might be potential targets for metabolic disorders.

Fund

National Key R&D Program of China, the National Natural Science Foundation of China, the Strategic Priority Research Programs (Category A) of the Chinese Academy of Sciences, the Interdisciplinary Medicine Seed Fund of Peking University and the National Laboratory of Biomacromolecules.

Authors: Yipeng Dua, Hao Hua,e,1, Saisi Qub,1, Jifeng Wangc,1, Chaoju Huaa,e, Jialing Zhanga,e, Peng Weia,e, Xiaolong Hea,e, Junfeng Haof, Pingsheng Liua, Fuquan Yangc, Tingting Lib,d, Taotao Wei2

Influence Factor: 6.18

Citation: EBioMedicine 36, 347-357 (2018).

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