Dynamic partitioning of branched-chain amino acids-derived nitrogen supports renal cancer progression

Marco Sciacovelli, Aurelien Dugourd, Lorea Valcarcel Jimenez, Ming Yang, Efterpi Nikitopoulou, Ana S. H. Costa, Laura Tronci, Veronica Caraffini, Paulo Rodrigues, Christina Schmidt, Dylan Gerard Ryan, Timothy Young, Vincent R. Zecchini, Sabrina H. Rossi, Charlie Massie, Caroline Lohoff, Maria Masid, Vassily Hatzimanikatis, Christoph Kuppe, Alex Kriegsheim, Rafael Kramann, Vincent Gnanapragasam, Anne Y. Warren, Grant D. Stewart, Ayelet Erez, Sakari Vanharanta, Julio Saez-Rodriguez () and Christian Frezza ()
Additional contact information
Marco Sciacovelli: University of Cambridge, Hutchison/MRC Research Centre
Aurelien Dugourd: Faculty of Medicine and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg University
Lorea Valcarcel Jimenez: University of Cambridge, Hutchison/MRC Research Centre
Ming Yang: University of Cambridge, Hutchison/MRC Research Centre
Efterpi Nikitopoulou: University of Cambridge, Hutchison/MRC Research Centre
Ana S. H. Costa: University of Cambridge, Hutchison/MRC Research Centre
Laura Tronci: University of Cambridge, Hutchison/MRC Research Centre
Veronica Caraffini: University of Cambridge, Hutchison/MRC Research Centre
Paulo Rodrigues: University of Cambridge, Hutchison/MRC Research Centre
Christina Schmidt: University of Cambridge, Hutchison/MRC Research Centre
Dylan Gerard Ryan: University of Cambridge, Hutchison/MRC Research Centre
Timothy Young: University of Cambridge, Hutchison/MRC Research Centre
Vincent R. Zecchini: University of Cambridge, Hutchison/MRC Research Centre
Sabrina H. Rossi: University of Cambridge
Charlie Massie: University of Cambridge
Caroline Lohoff: Faculty of Medicine and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg University
Maria Masid: Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL)
Vassily Hatzimanikatis: Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL)
Christoph Kuppe: RWTH Aachen University
Alex Kriegsheim: Edinburgh Cancer Research UK Centre, Institute of Genetics and Molecular Medicine
Rafael Kramann: RWTH Aachen University
Vincent Gnanapragasam: University of Cambridge and Cambridge University Hospitals NHS Cambridge Biomedical Campus
Anne Y. Warren: Department of Histopathology-Cambridge University Hospitals NHS
Grant D. Stewart: University of Cambridge and Cambridge University Hospitals NHS Cambridge Biomedical Campus
Ayelet Erez: Weizmann Institute of Science
Sakari Vanharanta: University of Cambridge, Hutchison/MRC Research Centre
Julio Saez-Rodriguez: Faculty of Medicine and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg University
Christian Frezza: University of Cambridge, Hutchison/MRC Research Centre

Nature Communications, 2022, vol. 13, issue 1, 1-20

Abstract: Abstract Metabolic reprogramming is critical for tumor initiation and progression. However, the exact impact of specific metabolic changes on cancer progression is poorly understood. Here, we integrate multimodal analyses of primary and metastatic clonally-related clear cell renal cancer cells (ccRCC) grown in physiological media to identify key stage-specific metabolic vulnerabilities. We show that a VHL loss-dependent reprogramming of branched-chain amino acid catabolism sustains the de novo biosynthesis of aspartate and arginine enabling tumor cells with the flexibility of partitioning the nitrogen of the amino acids depending on their needs. Importantly, we identify the epigenetic reactivation of argininosuccinate synthase (ASS1), a urea cycle enzyme suppressed in primary ccRCC, as a crucial event for metastatic renal cancer cells to acquire the capability to generate arginine, invade in vitro and metastasize in vivo. Overall, our study uncovers a mechanism of metabolic flexibility occurring during ccRCC progression, paving the way for the development of novel stage-specific therapies.

Date: 2022
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DOI: 10.1038/s41467-022-35036-4

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