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

Journal: Nature communications
Published Date: Nov 11, 2021

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.

Authors

  • Ci Fu
    Department of Molecular Genetics, University of Toronto, Toronto, ON, M5G 1M1, Canada.
  • Xiang Zhang
    Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • Amanda O Veri
    Department of Molecular Genetics, University of Toronto, Toronto, ON, M5G 1M1, Canada.
  • Kali R Iyer
    Department of Molecular Genetics, University of Toronto, Toronto, ON, M5G 1M1, Canada.
  • Emma Lash
    Department of Molecular Genetics, University of Toronto, Toronto, ON, M5G 1M1, Canada.
  • Alice Xue
    Department of Molecular Genetics, University of Toronto, Toronto, ON, M5G 1M1, Canada.
  • Huijuan Yan
    Department of Microbiology and Immunology, UCSF School of Medicine, San Francisco, CA, 94143, USA.
  • Nicole M Revie
    Department of Molecular Genetics, University of Toronto, Toronto, ON, M5G 1M1, Canada.
  • Cassandra Wong
    Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, M5G 1X5, Canada.
  • Zhen-Yuan Lin
    Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, M5G 1X5, Canada.
  • Elizabeth J Polvi
    Department of Molecular Genetics, University of Toronto, Toronto, ON, M5G 1M1, Canada.
  • Sean D Liston
    Department of Molecular Genetics, University of Toronto, Toronto, ON, M5G 1M1, Canada.
  • Benjamin VanderSluis
    Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, 55455, USA.
  • Jing Hou
    Wuhan Institute for Food and Cosmetic Control, Wuhan 430014, China.
  • Yoko Yashiroda
    RIKEN Center for Sustainable Resource Science (CSRS), Wako, Saitama 351-0198, Japan.
  • Anne-Claude Gingras
    Department of Molecular Genetics, University of Toronto, Toronto, ON, M5G 1M1, Canada.
  • Charles Boone
    The Donnelly Centre, University of Toronto, Toronto, ON M5S3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S3E1, Canada.
  • Teresa R O'Meara
    Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
  • Matthew J O'Meara
    Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA.
  • Suzanne Noble
    Department of Microbiology and Immunology, UCSF School of Medicine, San Francisco, CA, 94143, USA.
  • Nicole Robbins
    Department of Molecular Genetics, University of Toronto, Toronto, ON, M5G 1M1, Canada.
  • Chad L Myers
    Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, Minnesota 55455, United States.
  • Leah E Cowen
    Department of Molecular Genetics, University of Toronto, Toronto, ON, M5G 1M1, Canada. leah.cowen@utoronto.ca.

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