Systematic mapping of genetic interactions for de novo fatty acid synthesis identifies C12orf49 as a regulator of lipid metabolism

Michael Aregger, Keith A. Lawson, Maximillian Billmann, Michael Costanzo, Amy H.Y. Tong, Katherine Chan, Mahfuzur Rahman, Kevin R. Brown, Catherine Ross, Matej Usaj, Lucy Nedyalkova, Olga Sizova, Andrea Habsid, Judy Pawling, Zhen Yuan Lin, Hala Abdouni, Cassandra J. Wong, Alexander Weiss, Patricia Mero, James W. DennisAnne Claude Gingras, Chad L. Myers, Brenda J. Andrews, Charles Boone, Jason Moffat

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

The de novo synthesis of fatty acids has emerged as a therapeutic target for various diseases, including cancer. Because cancer cells are intrinsically buffered to combat metabolic stress, it is important to understand how cells may adapt to the loss of de novo fatty acid biosynthesis. Here, we use pooled genome-wide CRISPR screens to systematically map genetic interactions (GIs) in human HAP1 cells carrying a loss-of-function mutation in fatty acid synthase (FASN), whose product catalyses the formation of long-chain fatty acids. FASN-mutant cells show a strong dependence on lipid uptake that is reflected in negative GIs with genes involved in the LDL receptor pathway, vesicle trafficking and protein glycosylation. Further support for these functional relationships is derived from additional GI screens in query cell lines deficient in other genes involved in lipid metabolism, including LDLR, SREBF1, SREBF2 and ACACA. Our GI profiles also identify a potential role for the previously uncharacterized gene C12orf49 (which we call LUR1) in regulation of exogenous lipid uptake through modulation of SREBF2 signalling in response to lipid starvation. Overall, our data highlight the genetic determinants underlying the cellular adaptation associated with loss of de novo fatty acid synthesis and demonstrate the power of systematic GI mapping for uncovering metabolic buffering mechanisms in human cells.

Original languageEnglish (US)
Pages (from-to)499-513
Number of pages15
JournalNature Metabolism
Volume2
Issue number6
DOIs
StatePublished - Jun 1 2020

Bibliographical note

Funding Information:
We thank members of the Moffat lab for helpful discussions. Q. Huang, M. Olivieri, C. Sheene, R. Akthar and S. Sidhu are gratefully acknowledged for assistance with molecular biology experiments. Next-generation sequencing was performed at the Donnelly Sequencing Centre at the University of Toronto. Proteomics work was performed at the Network Biology Collaborative Centre at the Lunenfeld-Tanenbaum Research Institute, a facility supported by Canada Foundation for Innovation funding, by the Ontarian Government and by Genome Canada and Ontario Genomics (OGI-097, OGI-139). M.A. was supported by a Swiss National Science Foundation Postdoctoral Fellowship; K.A.L. was supported by a Vanier Canada Graduate Scholarship and Studentship award from the Kidney Cancer Research Network of Canada. M.B. was supported by a DFG Fellowship (Bi 2086/1-1). This research was funded by grants from the Canadian Institutes for Health Research (J.M., C.B, B.J.A. and A.-C.G.), Ontario Research Fund (B.J.A., C.B. and J.M) and Canada Research Chairs Program (J.M., C.B. and A.-C.G.). C.L.M., M.B. and M.R. are partially supported by grants from the National Science Foundation (MCB 1818293) and the National Institutes of Health (R01HG005084, R01HG005853).

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

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