Molecular MetabolismCancer BiologyPeer-reviewed journal article
Acetate drives ovarian cancer quiescence via ACSS2-mediated acetyl-CoA production
Allison C. SharrowiD, Emily MegilliD, Amanda J Chen, Afifa Farooqi, Naveen Kumar TanguduiD, Apoorva UbovejaiD, Stacy C. McGonigaliD, Nadine HempeliD, Nathaniel W. SnyderiD, Ronald J. BuckanovichiD, Katherine M. AirdiD
Abstract
Quiescence is a reversible cell cycle exit traditionally thought to be associated with a metabolically inactive state. Recent work in muscle cells indicates that metabolic reprogramming is associated with quiescence. Whether metabolic changes occur in cancer to drive quiescence is unclear. Using a multi-omics approach, we found that the metabolic enzyme ACSS2, which converts acetate into acetyl-CoA, is both highly upregulated in quiescent ovarian cancer cells and required for their survival. Indeed, quiescent ovarian cancer cells have increased levels of acetate-derived acetyl-CoA, confirming increased ACSS2 activity in these cells. Furthermore, either inducing ACSS2 expression or supplementing cells with acetate was sufficient to induce a reversible quiescent cell cycle exit. RNA-Seq of acetate treated cells confirmed negative enrichment in multiple cell cycle pathways as well as enrichment of genes in a published G0 gene signature. Finally, analysis of patient data showed that ACSS2 expression is upregulated in tumor cells from ascites, which are thought to be more quiescent, compared to matched primary tumors. Additionally, high ACSS2 expression is associated with platinum resistance and worse outcomes. Together, this study points to a previously unrecognized ACSS2-mediated metabolic reprogramming that drives quiescence in ovarian cancer. As chemotherapies to treat ovarian cancer, such as platinum, have increased efficacy in highly proliferative cells, our data give rise to the intriguing question that metabolically-driven quiescence may affect therapeutic response.
- organism: human
- assay: bulk RNA-seq
- research focus: cancer & tumor microenvironment
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- approach: experimental assay
- Citations
- 11
- FWCI
- 1.61
- Field percentile
- top 16%
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