MSACL 2019 US Abstract

Loss of SETD2 Induces a Metabolic Switch in Renal Cell Carcinoma Cell Lines Toward Enhanced Oxidative Phosphorylation

Jingping Liu (1,2), Paul D. Hanavan (2), Katon Kras (2), Yvette W. Ruiz (2), Erik P. Castle (3), Douglas F. Lake (2), Xianfeng Chen (3) Daniel O’Brien (4), Huijun Luo (3), Keith D. Robertson (4), Haiwei Gu (2), Thai H. Ho (3)
(1) West China Hospital of Sichuan University, Chengdu, China (2) Arizona State University, Scottsdale, AZ (3) Mayo Clinic Arizona, Phoenix, AZ (4) Mayo Clinic Rochester, Rochester, MN

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Haiwei Gu (Presenter) Arizona State University

Presenter Bio: Haiwei Gu focuses on mass spectrometry (MS)-based metabolomics and its applications for biomarker discovery and systems biology research. I am skilled in the development, optimization, and applications of MS methods for both metabolite level measurements and metabolic flux analysis. I utilize a wide range of platforms, including LC- and GC-MS for global aqueous profiling and lipidomics. I have developed a number of targeted LC-MS/MS assays to detect panels of metabolites, including >400 identified metabolites from >35 metabolic pathways, as well as assays to interrogate bile acids, acyl carnitines, co-enzymes, and cardiolipins. Recent methods developed by my group include quantitative methods to measure metabolite concentrations, innovative metabolic flux analysis approaches, and ratio analysis methods for unknown identification.

Relevant Financial Disclosures (within past 24 months) No relevant financial relationship(s) to disclose. Discussing Products? NO, presenter will NOT present educational content related to products/services of the ineligible company(ies) listed above. Conflict mitigation NOT REQUIRED.

SETD2 is frequently inactivated and associated with recurrence of clear cell renal cell carcinoma (ccRCC), but the impact of SETD2 loss on metabolic alterations in ccRCC is still unclear. By using GC-MS-based targeted metabolomics, our study observed that loss of SETD2 is associated with a metabolic switch in ccRCC cell lines toward enhanced oxidative phosphorylation and lipogenesis, and its mechanism can be potentially related to PGC1α-mediated metabolic networks. Moreover, our results suggest a need for a comprehensive metabolomics analysis of cancer cells with SETD2 inactivation in vivo to specifically identify pathways involved in this metabolic switch, which provides a number of opportunities to identify novel therapeutic targets in kidney cancer.