Integrating genetic and protein–protein interaction networks maps a functional wiring diagram of a cell

VanderSluis, Benjamin; Costanzo, Michael; Billmann, Maximilian; Ward, Henry N.; Myers, Chad L.; Andrews, Brenda; Boone, Charles

doi:10.1016/j.mib.2018.06.004

Cited by 40 publications

(24 citation statements)

References 60 publications

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“…S3 ). In addition, the Sc Tn-Ess genes had a higher degree of genetic interaction profile density (GIPD)—representing the number of synthetic genetic interactions for a given gene divided by the number of interactions tested ( 51 , 52 ) ( Fig. 6A ).…”

Section: Resultsmentioning

confidence: 99%

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

Segal

Gritsenko

Levitan

et al. 2018

mBio

117

147

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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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Section: Resultsmentioning

confidence: 99%

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

Segal

Gritsenko

Levitan

et al. 2018

mBio

117

147

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“…Whereas a negative GI denotes a reduced fitness of the double mutant compared to both single mutants, a positive GI refers to a higher fitness than expected. Genes encoding for proteins of different pathways often show a negative GI and those encoding for proteins of the same pathway mostly have a positive GI [ 16 , 48 , 49 ]. As control, we used the double mutant Δpro45Δpro11 and both single mutants since both are known STRIPAK core subunits and show direct physical interaction [ 35 ].…”

Section: Resultsmentioning

confidence: 99%

The STRIPAK signaling complex regulates dephosphorylation of GUL1, an RNA-binding protein that shuttles on endosomes

et al. 2020

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The striatin-interacting phosphatase and kinase (STRIPAK) multi-subunit signaling complex is highly conserved within eukaryotes. In fungi, STRIPAK controls multicellular development, morphogenesis, pathogenicity, and cell-cell recognition, while in humans, certain diseases are related to this signaling complex. To date, phosphorylation and dephosphorylation targets of STRIPAK are still widely unknown in microbial as well as animal systems. Here, we provide an extended global proteome and phosphoproteome study using the wild type as well as STRIPAK single and double deletion mutants (Δpro11, Δpro11Δpro22,

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“…Additionally, having interaction does not necessarily mean that it would have a modified effect on the phenotype [96]. Functional interaction between two genes also depends on the underlying pathways and their reciprocal dependence [97,98].…”

Section: Gwas and Genetic Interactionsmentioning

confidence: 99%

Genetic Modifiers and Rare Mendelian Disease

Rahit

Tarailo‐Graovac

2020

Genes

113

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Despite advances in high-throughput sequencing that have revolutionized the discovery of gene defects in rare Mendelian diseases, there are still gaps in translating individual genome variation to observed phenotypic outcomes. While we continue to improve genomics approaches to identify primary disease-causing variants, it is evident that no genetic variant acts alone. In other words, some other variants in the genome (genetic modifiers) may alleviate (suppress) or exacerbate (enhance) the severity of the disease, resulting in the variability of phenotypic outcomes. Thus, to truly understand the disease, we need to consider how the disease-causing variants interact with the rest of the genome in an individual. Here, we review the current state-of-the-field in the identification of genetic modifiers in rare Mendelian diseases and discuss the potential for future approaches that could bridge the existing gap.

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Integrating genetic and protein–protein interaction networks maps a functional wiring diagram of a cell

Cited by 40 publications

References 60 publications

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

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

The STRIPAK signaling complex regulates dephosphorylation of GUL1, an RNA-binding protein that shuttles on endosomes

Genetic Modifiers and Rare Mendelian Disease

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