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

Author

Listed:
  • Marco Sciacovelli

    (University of Cambridge, Hutchison/MRC Research Centre
    University of Liverpool)

  • Aurelien Dugourd

    (Faculty of Medicine and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg University
    RWTH Aachen University)

  • Lorea Valcarcel Jimenez

    (University of Cambridge, Hutchison/MRC Research Centre
    Faculty of Medicine-University Hospital Cologne)

  • Ming Yang

    (University of Cambridge, Hutchison/MRC Research Centre
    Faculty of Medicine-University Hospital Cologne)

  • Efterpi Nikitopoulou

    (University of Cambridge, Hutchison/MRC Research Centre)

  • Ana S. H. Costa

    (University of Cambridge, Hutchison/MRC Research Centre
    Matterworks)

  • 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
    Faculty of Medicine-University Hospital Cologne)

  • 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)
    University of Lausanne)

  • Vassily Hatzimanikatis

    (Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL))

  • Christoph Kuppe

    (RWTH Aachen University
    RWTH Aachen University)

  • Alex Kriegsheim

    (Edinburgh Cancer Research UK Centre, Institute of Genetics and Molecular Medicine)

  • Rafael Kramann

    (RWTH Aachen University
    RWTH Aachen University
    Nephrology and Transplantation, Erasmus Medical Center)

  • 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
    University of Helsinki
    University of Helsinki)

  • 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
    Faculty of Medicine-University Hospital Cologne)

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.

Suggested Citation

  • Marco Sciacovelli & Aurelien Dugourd & Lorea Valcarcel Jimenez & Ming Yang & Efterpi Nikitopoulou & Ana S. H. Costa & Laura Tronci & Veronica Caraffini & Paulo Rodrigues & Christina Schmidt & Dylan Ge, 2022. "Dynamic partitioning of branched-chain amino acids-derived nitrogen supports renal cancer progression," Nature Communications, Nature, vol. 13(1), pages 1-20, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-35036-4
    DOI: 10.1038/s41467-022-35036-4
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    References listed on IDEAS

    as
    1. Maria Masid & Meric Ataman & Vassily Hatzimanikatis, 2020. "Author Correction: Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN," Nature Communications, Nature, vol. 11(1), pages 1-1, December.
    2. Edoardo Gaude & Christian Frezza, 2016. "Tissue-specific and convergent metabolic transformation of cancer correlates with metastatic potential and patient survival," Nature Communications, Nature, vol. 7(1), pages 1-9, December.
    3. Bo Li & Bo Qiu & David S. M. Lee & Zandra E. Walton & Joshua D. Ochocki & Lijoy K. Mathew & Anthony Mancuso & Terence P. F. Gade & Brian Keith & Itzhak Nissim & M. Celeste Simon, 2014. "Fructose-1,6-bisphosphatase opposes renal carcinoma progression," Nature, Nature, vol. 513(7517), pages 251-255, September.
    4. Melusine Bleu & Swann Gaulis & Rui Lopes & Kathleen Sprouffske & Verena Apfel & Sjoerd Holwerda & Marco Pregnolato & Umut Yildiz & Valentina Cordoʹ & Antonella F. M. Dost & Judith Knehr & Walter Carbo, 2019. "PAX8 activates metabolic genes via enhancer elements in Renal Cell Carcinoma," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    5. Maria Masid & Meric Ataman & Vassily Hatzimanikatis, 2020. "Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN," Nature Communications, Nature, vol. 11(1), pages 1-12, December.
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