Nature Communications (Nov 2021)
Leveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets
- 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 VanderSluis,
- Jing Hou,
- Yoko Yashiroda,
- Anne-Claude Gingras,
- Charles Boone,
- Teresa R. O’Meara,
- Matthew J. O’Meara,
- Suzanne Noble,
- Nicole Robbins,
- Chad L. Myers,
- Leah E. Cowen
Affiliations
- Ci Fu
- Department of Molecular Genetics, University of Toronto
- Xiang Zhang
- Department of Computer Science and Engineering, University of Minnesota
- Amanda O. Veri
- Department of Molecular Genetics, University of Toronto
- Kali R. Iyer
- Department of Molecular Genetics, University of Toronto
- Emma Lash
- Department of Molecular Genetics, University of Toronto
- Alice Xue
- Department of Molecular Genetics, University of Toronto
- Huijuan Yan
- Department of Microbiology and Immunology, UCSF School of Medicine
- Nicole M. Revie
- Department of Molecular Genetics, 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
- Department of Molecular Genetics, University of Toronto
- Sean D. Liston
- Department of Molecular Genetics, University of Toronto
- Benjamin VanderSluis
- Department of Computer Science and Engineering, University of Minnesota
- Jing Hou
- Department of Molecular Genetics, University of Toronto
- Yoko Yashiroda
- RIKEN Center for Sustainable Resource Science
- Anne-Claude Gingras
- Department of Molecular Genetics, University of Toronto
- Charles Boone
- Department of Molecular Genetics, University of Toronto
- Teresa R. O’Meara
- Department of Microbiology and Immunology, University of Michigan Medical School
- Matthew J. O’Meara
- Department of Computational Medicine and Bioinformatics, University of Michigan
- Suzanne Noble
- Department of Microbiology and Immunology, UCSF School of Medicine
- Nicole Robbins
- Department of Molecular Genetics, University of Toronto
- Chad L. Myers
- Department of Computer Science and Engineering, University of Minnesota
- Leah E. Cowen
- Department of Molecular Genetics, University of Toronto
- DOI
- https://doi.org/10.1038/s41467-021-26850-3
- Journal volume & issue
-
Vol. 12,
no. 1
pp. 1 – 18
Abstract
The analysis of essential genes in pathogens can be used to discover potential antimicrobial targets. Here, the authors use a machine learning model and chemogenomic analyses to generate genome-wide gene essentiality predictions for the fungal pathogen Candida albicans, define the function of three uncharacterized essential genes, and identify the target of a new antifungal compound.
