Gene Essentiality Analyzed by In Vivo Transposon Mutagenesis and Machine Learning in a Stable Haploid Isolate of Candida albicans
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Comprehensive understanding of an organism requires that we understand the contributions of most, if not all, of its genes. Classical genetic approaches to this issue have involved systematic deletion of each gene in the genome, with comprehensive sets of mutants available only for very-well-studied model organisms. We took a different approach, harnessing the power of in vivo transposition coupled with deep sequencing to identify >500,000 different mutations, one per cell, in the prevalent human fungal pathogen Candida albicans and to map their positions across the genome. The transposition approach is efficient and less labor-intensive than classic approaches. Here, we describe the production and analysis (aided by machine learning) of a large collection of mutants and the comprehensive identification of 1,610 C. albicans genes that are essential for growth under standard laboratory conditions. Among these C. albicans essential genes, we identify those that are also essential in two distantly related model yeasts as well as those that are conserved in all four major human fungal pathogens and that are not conserved in the human genome. This list of genes with functions important for the survival of the pathogen provides a good starting point for the development of new antifungal drugs, which are greatly needed because of the emergence of fungal pathogens with elevated resistance and/or tolerance of the currently limited set of available antifungal drugs.
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In several fungal species, including S. cerevisiae and C. albicans, it is estimated that ∼20% of open reading frames are essential.
“In several fungal species, including the model yeast Saccharomyces cerevisiae and the distantly related fungal pathogen C. albicans, it is estimated that ∼20% of the open reading frames in their genomes are essential (Winzeler et al. 1999; Segal et al. 2018).”
From Genetic analysis of Hsp90 function in <i>Cryptococcus neoformans</i> highlights key roles in stress tolerance and virulence (2022) · cites this paper as Segal et al. 2018
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Candida albicans Potassium Transporters
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From patterns to prediction: machine learning and antifungal resistance biomarker discovery
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PRACTICAL APPLICATIONS OF MACHINE LEARNING FOR ANTI-INFECTIVE DRUG DISCOVERY
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- Authors
- Segal, E. S., Gritsenko, V., Levitan, A., Yadav, B., Dror, N., Steenwyk, J. L., Silberberg, Y., Mielich, K., Rokas, A., Gow, N. A. R., Kunze, R., Sharan, R., & Berman, J.
- Affiliations
- Tel Aviv University (IL)
- University of Aberdeen (GB)
- Vanderbilt University (US)
- Freie Universität Berlin (DE)
- Corresponding author
- Berman, Judith
- ORCID
- Venue
- mBio
- Publisher
- American Society for Microbiology
- Published
- 2018-11-07
- Volume
- 9
- Issue
- 5
- ISSN
- 2150-7511 (ISSN-L)
- 2161-2129
- PMC
- PMC6212825
- OpenAlex
- W2898801056
- Cited by
- 150
- References
- 105
- Citations by year
- 2026: 11 · 2025: 20 · 2024: 18 · 2023: 9 · 2022: 21 · 2021: 20 · 2020: 26 · 2019: 24 · 2018: 1
- FWCI
- 6.31
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- mBio (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Aberdeen University Research Archive (Aberdeen University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Refubium (Universitätsbibliothek der Freien Universität Berlin) (submittedVersion, cc-by)
- Universitätsbibliothek der FU Berlin Hochschulschriftenstelle u. Dokumentenserver (cc-by)
- Funders
- Wellcome Trust (086827 075470 101873 200208)
- MRC Center for Medical Mycology (N006364/1)
- European Research Council (340087 RAPLODAPT)
- Dahlem Center of Plant Sciences (DCPS)
- Israel Science Foundation (715/18)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fungal and yeast genetics research
- Health Sciences › Medicine › Infectious Diseases › Antifungal resistance and susceptibility
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Bacterial Genetics and Biotechnology
- Keywords
- gene essentiality, Candida albicans, transposon mutagenesis, essential genes, fungal pathogens, antifungal drug targets, machine learning, deep sequencing
- MeSH terms
- Machine Learning, Aspergillus fumigatus, Candida albicans, Cryptococcus neoformans, DNA Transposable Elements, Genes, Fungal, Genetics, Microbial, Haploidy, Histoplasma, Mutagenesis, Insertional, Genes, Essential
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