Gene Essentiality Analyzed by In Vivo Transposon Mutagenesis and Machine Learning in a Stable Haploid Isolate of Candida albicans

Ella Shtifman Segal, Vladimir Gritsenko, Anton Levitan, Bhawna Yadav, Naama Dror, Jacob L. Steenwyk, Yael Silberberg, Kevin Mielich, Antonis Rokas, Neil A. R. Gow, Reinhard Kunze, Roded Sharan, Judith Berman (Corresponding Author), Di Pietro (Editor)

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Abstract

Knowing the full set of essential genes for a given organism provides important information about ways to promote, and to limit, its growth and survival. For many non-model organisms, the lack of a stable haploid state and low transformation efficiencies impede the use of conventional approaches to generate a genome-wide comprehensive set of mutant strains and the identification of the genes essential for growth. Here we report on the isolation and utilization of a highly stable haploid derivative of the human pathogenic fungus Candida albicans, together with a modified heterologous transposon and machine learning (ML) analysis method, to predict the degree to which all of the open reading frames are required for growth under standard laboratory conditions. We identified 1,610 C. albicans essential genes, including 1,195 with high “essentiality confidence” scores, thereby increasing the number of essential genes (currently 66 in the Candida Genome Database) by >20-fold and providing an unbiased approach to determine the degree of confidence in the determination of essentiality. Among the genes essential in C. albicans were 602 genes also essential in the model budding and fission yeasts analyzed by both deletion and transposon mutagenesis. We also identified essential genes conserved among the four major human pathogens C. albicans, Aspergillus fumigatus, Cryptococcus neoformans, and Histoplasma capsulatum and highlight those that lack homologs in humans and that thus could serve as potential targets for the design of antifungal therapies.IMPORTANCE 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.
Original languageEnglish
Article number02048-18
JournalmBio
Volume9
Issue number5
DOIs
Publication statusPublished - 30 Oct 2018

Fingerprint

Haploidy
Essential Genes
Mutagenesis
Candida albicans
Genes
Genome
Growth
Histoplasma
Machine Learning
High-Throughput Nucleotide Sequencing
Cryptococcus neoformans
Saccharomycetales
Aspergillus fumigatus
Survival
Schizosaccharomyces
Gene Deletion
Human Genome
Candida
Pharmaceutical Preparations
Open Reading Frames

Keywords

  • candida albicans
  • genome analysis
  • genomics
  • machine learning
  • phenotypic identification
  • transposons

Cite this

Segal, E. S., Gritsenko, V., Levitan, A., Yadav, B., Dror, N., Steenwyk, J. L., ... Pietro, D. (Ed.) (2018). Gene Essentiality Analyzed by In Vivo Transposon Mutagenesis and Machine Learning in a Stable Haploid Isolate of Candida albicans. mBio, 9(5), [02048-18]. https://doi.org/10.1128/mBio.02048-18

Gene Essentiality Analyzed by In Vivo Transposon Mutagenesis and Machine Learning in a Stable Haploid Isolate of Candida albicans. / Segal, Ella Shtifman; Gritsenko, Vladimir; Levitan, Anton; Yadav, Bhawna; Dror, Naama; Steenwyk, Jacob L.; Silberberg, Yael; Mielich, Kevin; Rokas, Antonis; Gow, Neil A. R.; Kunze, Reinhard; Sharan, Roded; Berman, Judith (Corresponding Author); Pietro, Di (Editor).

In: mBio, Vol. 9, No. 5, 02048-18, 30.10.2018.

Research output: Contribution to journalArticle

Segal, ES, Gritsenko, V, Levitan, A, Yadav, B, Dror, N, Steenwyk, JL, Silberberg, Y, Mielich, K, Rokas, A, Gow, NAR, Kunze, R, Sharan, R, Berman, J & Pietro, D (ed.) 2018, 'Gene Essentiality Analyzed by In Vivo Transposon Mutagenesis and Machine Learning in a Stable Haploid Isolate of Candida albicans', mBio, vol. 9, no. 5, 02048-18. https://doi.org/10.1128/mBio.02048-18
Segal, Ella Shtifman ; Gritsenko, Vladimir ; Levitan, Anton ; Yadav, Bhawna ; Dror, Naama ; Steenwyk, Jacob L. ; Silberberg, Yael ; Mielich, Kevin ; Rokas, Antonis ; Gow, Neil A. R. ; Kunze, Reinhard ; Sharan, Roded ; Berman, Judith ; Pietro, Di (Editor). / Gene Essentiality Analyzed by In Vivo Transposon Mutagenesis and Machine Learning in a Stable Haploid Isolate of Candida albicans. In: mBio. 2018 ; Vol. 9, No. 5.
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abstract = "Knowing the full set of essential genes for a given organism provides important information about ways to promote, and to limit, its growth and survival. For many non-model organisms, the lack of a stable haploid state and low transformation efficiencies impede the use of conventional approaches to generate a genome-wide comprehensive set of mutant strains and the identification of the genes essential for growth. Here we report on the isolation and utilization of a highly stable haploid derivative of the human pathogenic fungus Candida albicans, together with a modified heterologous transposon and machine learning (ML) analysis method, to predict the degree to which all of the open reading frames are required for growth under standard laboratory conditions. We identified 1,610 C. albicans essential genes, including 1,195 with high “essentiality confidence” scores, thereby increasing the number of essential genes (currently 66 in the Candida Genome Database) by >20-fold and providing an unbiased approach to determine the degree of confidence in the determination of essentiality. Among the genes essential in C. albicans were 602 genes also essential in the model budding and fission yeasts analyzed by both deletion and transposon mutagenesis. We also identified essential genes conserved among the four major human pathogens C. albicans, Aspergillus fumigatus, Cryptococcus neoformans, and Histoplasma capsulatum and highlight those that lack homologs in humans and that thus could serve as potential targets for the design of antifungal therapies.IMPORTANCE 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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note = "This work was supported by European Research Council Advanced Award 340087 (RAPLODAPT) to J.B., the Dahlem Centre of Plant Sciences (DCPS) of the Freie Universit{\"a}t Berlin (R.K.), Israel Science Foundation grant no. 715/18 (R.S.), the Wellcome Trust (grants 086827, 075470, 101873, and 200208) and the MRC Centre for Medical Mycology (N006364/1) (N.A.R.G.). Data availability.All of the code and required dependencies for analysis of the TnSeq data are available at https://github.com/berman-lab/transposon-pipeline. Library insertion sequences are available at NCBI under project PRJNA490565 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA490565). Datasets S1 through S9 are available at https://doi.org/10.6084/m9.figshare.c.4251182.",
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T1 - Gene Essentiality Analyzed by In Vivo Transposon Mutagenesis and Machine Learning in a Stable Haploid Isolate of Candida albicans

AU - Segal, Ella Shtifman

AU - Gritsenko, Vladimir

AU - Levitan, Anton

AU - Yadav, Bhawna

AU - Dror, Naama

AU - Steenwyk, Jacob L.

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AU - Mielich, Kevin

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AU - Sharan, Roded

AU - Berman, Judith

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N1 - This work was supported by European Research Council Advanced Award 340087 (RAPLODAPT) to J.B., the Dahlem Centre of Plant Sciences (DCPS) of the Freie Universität Berlin (R.K.), Israel Science Foundation grant no. 715/18 (R.S.), the Wellcome Trust (grants 086827, 075470, 101873, and 200208) and the MRC Centre for Medical Mycology (N006364/1) (N.A.R.G.). Data availability.All of the code and required dependencies for analysis of the TnSeq data are available at https://github.com/berman-lab/transposon-pipeline. Library insertion sequences are available at NCBI under project PRJNA490565 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA490565). Datasets S1 through S9 are available at https://doi.org/10.6084/m9.figshare.c.4251182.

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N2 - Knowing the full set of essential genes for a given organism provides important information about ways to promote, and to limit, its growth and survival. For many non-model organisms, the lack of a stable haploid state and low transformation efficiencies impede the use of conventional approaches to generate a genome-wide comprehensive set of mutant strains and the identification of the genes essential for growth. Here we report on the isolation and utilization of a highly stable haploid derivative of the human pathogenic fungus Candida albicans, together with a modified heterologous transposon and machine learning (ML) analysis method, to predict the degree to which all of the open reading frames are required for growth under standard laboratory conditions. We identified 1,610 C. albicans essential genes, including 1,195 with high “essentiality confidence” scores, thereby increasing the number of essential genes (currently 66 in the Candida Genome Database) by >20-fold and providing an unbiased approach to determine the degree of confidence in the determination of essentiality. Among the genes essential in C. albicans were 602 genes also essential in the model budding and fission yeasts analyzed by both deletion and transposon mutagenesis. We also identified essential genes conserved among the four major human pathogens C. albicans, Aspergillus fumigatus, Cryptococcus neoformans, and Histoplasma capsulatum and highlight those that lack homologs in humans and that thus could serve as potential targets for the design of antifungal therapies.IMPORTANCE 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.

AB - Knowing the full set of essential genes for a given organism provides important information about ways to promote, and to limit, its growth and survival. For many non-model organisms, the lack of a stable haploid state and low transformation efficiencies impede the use of conventional approaches to generate a genome-wide comprehensive set of mutant strains and the identification of the genes essential for growth. Here we report on the isolation and utilization of a highly stable haploid derivative of the human pathogenic fungus Candida albicans, together with a modified heterologous transposon and machine learning (ML) analysis method, to predict the degree to which all of the open reading frames are required for growth under standard laboratory conditions. We identified 1,610 C. albicans essential genes, including 1,195 with high “essentiality confidence” scores, thereby increasing the number of essential genes (currently 66 in the Candida Genome Database) by >20-fold and providing an unbiased approach to determine the degree of confidence in the determination of essentiality. Among the genes essential in C. albicans were 602 genes also essential in the model budding and fission yeasts analyzed by both deletion and transposon mutagenesis. We also identified essential genes conserved among the four major human pathogens C. albicans, Aspergillus fumigatus, Cryptococcus neoformans, and Histoplasma capsulatum and highlight those that lack homologs in humans and that thus could serve as potential targets for the design of antifungal therapies.IMPORTANCE 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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KW - genome analysis

KW - genomics

KW - machine learning

KW - phenotypic identification

KW - transposons

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