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Leveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets

Author

Listed:
  • Ci Fu

    (University of Toronto)

  • Xiang Zhang

    (University of Minnesota)

  • Amanda O. Veri

    (University of Toronto)

  • Kali R. Iyer

    (University of Toronto)

  • Emma Lash

    (University of Toronto)

  • Alice Xue

    (University of Toronto)

  • Huijuan Yan

    (UCSF School of Medicine)

  • Nicole M. Revie

    (University of Toronto)

  • Cassandra Wong

    (Lunenfeld-Tanenbaum Research Institute, Sinai Health System)

  • Zhen-Yuan Lin

    (Lunenfeld-Tanenbaum Research Institute, Sinai Health System)

  • Elizabeth J. Polvi

    (University of Toronto)

  • Sean D. Liston

    (University of Toronto)

  • Benjamin VanderSluis

    (University of Minnesota)

  • Jing Hou

    (University of Toronto
    University of Toronto)

  • Yoko Yashiroda

    (RIKEN Center for Sustainable Resource Science)

  • Anne-Claude Gingras

    (University of Toronto
    Lunenfeld-Tanenbaum Research Institute, Sinai Health System)

  • Charles Boone

    (University of Toronto
    University of Toronto
    RIKEN Center for Sustainable Resource Science)

  • Teresa R. O’Meara

    (University of Michigan Medical School)

  • Matthew J. O’Meara

    (University of Michigan)

  • Suzanne Noble

    (UCSF School of Medicine)

  • Nicole Robbins

    (University of Toronto)

  • Chad L. Myers

    (University of Minnesota)

  • Leah E. Cowen

    (University of Toronto)

Abstract

Fungal pathogens pose a global threat to human health, with Candida albicans among the leading killers. Systematic analysis of essential genes provides a powerful strategy to discover potential antifungal targets. Here, we build a machine learning model to generate genome-wide gene essentiality predictions for C. albicans and expand the largest functional genomics resource in this pathogen (the GRACE collection) by 866 genes. Using this model and chemogenomic analyses, we define the function of three uncharacterized essential genes with roles in kinetochore function, mitochondrial integrity, and translation, and identify the glutaminyl-tRNA synthetase Gln4 as the target of N-pyrimidinyl-β-thiophenylacrylamide (NP-BTA), an antifungal compound.

Suggested Citation

  • Ci Fu & Xiang Zhang & Amanda O. Veri & Kali R. Iyer & Emma Lash & Alice Xue & Huijuan Yan & Nicole M. Revie & Cassandra Wong & Zhen-Yuan Lin & Elizabeth J. Polvi & Sean D. Liston & Benjamin VanderSlui, 2021. "Leveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets," Nature Communications, Nature, vol. 12(1), pages 1-18, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26850-3
    DOI: 10.1038/s41467-021-26850-3
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    References listed on IDEAS

    as
    1. Ilan Wapinski & Avi Pfeffer & Nir Friedman & Aviv Regev, 2007. "Natural history and evolutionary principles of gene duplication in fungi," Nature, Nature, vol. 449(7158), pages 54-61, September.
    2. Junyue Cao & Malte Spielmann & Xiaojie Qiu & Xingfan Huang & Daniel M. Ibrahim & Andrew J. Hill & Fan Zhang & Stefan Mundlos & Lena Christiansen & Frank J. Steemers & Cole Trapnell & Jay Shendure, 2019. "The single-cell transcriptional landscape of mammalian organogenesis," Nature, Nature, vol. 566(7745), pages 496-502, February.
    3. Joshua M. Dempster & Clare Pacini & Sasha Pantel & Fiona M. Behan & Thomas Green & John Krill-Burger & Charlotte M. Beaver & Scott T. Younger & Victor Zhivich & Hanna Najgebauer & Felicity Allen & Ema, 2019. "Agreement between two large pan-cancer CRISPR-Cas9 gene dependency data sets," Nature Communications, Nature, vol. 10(1), pages 1-14, December.
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