Predicting bioprocess targets of chemical compounds through integration of chemical-genetic and genetic interactions.
Chemical-genetic interactions-observed when the treatment of mutant cells with chemical compounds reveals unexpected phenotypes-contain rich functional information linking compounds to their cellular modes of action. To systematically identify these interactions, an array of mutants is challenged wi...
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doaj-cc4b7ae2190a401d93c6e05659c6a94d2020-11-24T21:56:05ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582018-10-011410e100653210.1371/journal.pcbi.1006532Predicting bioprocess targets of chemical compounds through integration of chemical-genetic and genetic interactions.Scott W SimpkinsJustin NelsonRaamesh DeshpandeSheena C LiJeff S PiotrowskiErin H WilsonAbraham A GebreHamid SafizadehReika OkamotoMami YoshimuraMichael CostanzoYoko YashirodaYoshikazu OhyaHiroyuki OsadaMinoru YoshidaCharles BooneChad L MyersChemical-genetic interactions-observed when the treatment of mutant cells with chemical compounds reveals unexpected phenotypes-contain rich functional information linking compounds to their cellular modes of action. To systematically identify these interactions, an array of mutants is challenged with a compound and monitored for fitness defects, generating a chemical-genetic interaction profile that provides a quantitative, unbiased description of the cellular function(s) perturbed by the compound. Genetic interactions, obtained from genome-wide double-mutant screens, provide a key for interpreting the functional information contained in chemical-genetic interaction profiles. Despite the utility of this approach, integrative analyses of genetic and chemical-genetic interaction networks have not been systematically evaluated. We developed a method, called CG-TARGET (Chemical Genetic Translation via A Reference Genetic nETwork), that integrates large-scale chemical-genetic interaction screening data with a genetic interaction network to predict the biological processes perturbed by compounds. In a recent publication, we applied CG-TARGET to a screen of nearly 14,000 chemical compounds in Saccharomyces cerevisiae, integrating this dataset with the global S. cerevisiae genetic interaction network to prioritize over 1500 compounds with high-confidence biological process predictions for further study. We present here a formal description and rigorous benchmarking of the CG-TARGET method, showing that, compared to alternative enrichment-based approaches, it achieves similar or better accuracy while substantially improving the ability to control the false discovery rate of biological process predictions. Additional investigation of the compatibility of chemical-genetic and genetic interaction profiles revealed that one-third of observed chemical-genetic interactions contributed to the highest-confidence biological process predictions and that negative chemical-genetic interactions overwhelmingly formed the basis of these predictions. We also present experimental validations of CG-TARGET-predicted tubulin polymerization and cell cycle progression inhibitors. Our approach successfully demonstrates the use of genetic interaction networks in the high-throughput functional annotation of compounds to biological processes.http://europepmc.org/articles/PMC6226211?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Scott W Simpkins Justin Nelson Raamesh Deshpande Sheena C Li Jeff S Piotrowski Erin H Wilson Abraham A Gebre Hamid Safizadeh Reika Okamoto Mami Yoshimura Michael Costanzo Yoko Yashiroda Yoshikazu Ohya Hiroyuki Osada Minoru Yoshida Charles Boone Chad L Myers |
spellingShingle |
Scott W Simpkins Justin Nelson Raamesh Deshpande Sheena C Li Jeff S Piotrowski Erin H Wilson Abraham A Gebre Hamid Safizadeh Reika Okamoto Mami Yoshimura Michael Costanzo Yoko Yashiroda Yoshikazu Ohya Hiroyuki Osada Minoru Yoshida Charles Boone Chad L Myers Predicting bioprocess targets of chemical compounds through integration of chemical-genetic and genetic interactions. PLoS Computational Biology |
author_facet |
Scott W Simpkins Justin Nelson Raamesh Deshpande Sheena C Li Jeff S Piotrowski Erin H Wilson Abraham A Gebre Hamid Safizadeh Reika Okamoto Mami Yoshimura Michael Costanzo Yoko Yashiroda Yoshikazu Ohya Hiroyuki Osada Minoru Yoshida Charles Boone Chad L Myers |
author_sort |
Scott W Simpkins |
title |
Predicting bioprocess targets of chemical compounds through integration of chemical-genetic and genetic interactions. |
title_short |
Predicting bioprocess targets of chemical compounds through integration of chemical-genetic and genetic interactions. |
title_full |
Predicting bioprocess targets of chemical compounds through integration of chemical-genetic and genetic interactions. |
title_fullStr |
Predicting bioprocess targets of chemical compounds through integration of chemical-genetic and genetic interactions. |
title_full_unstemmed |
Predicting bioprocess targets of chemical compounds through integration of chemical-genetic and genetic interactions. |
title_sort |
predicting bioprocess targets of chemical compounds through integration of chemical-genetic and genetic interactions. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS Computational Biology |
issn |
1553-734X 1553-7358 |
publishDate |
2018-10-01 |
description |
Chemical-genetic interactions-observed when the treatment of mutant cells with chemical compounds reveals unexpected phenotypes-contain rich functional information linking compounds to their cellular modes of action. To systematically identify these interactions, an array of mutants is challenged with a compound and monitored for fitness defects, generating a chemical-genetic interaction profile that provides a quantitative, unbiased description of the cellular function(s) perturbed by the compound. Genetic interactions, obtained from genome-wide double-mutant screens, provide a key for interpreting the functional information contained in chemical-genetic interaction profiles. Despite the utility of this approach, integrative analyses of genetic and chemical-genetic interaction networks have not been systematically evaluated. We developed a method, called CG-TARGET (Chemical Genetic Translation via A Reference Genetic nETwork), that integrates large-scale chemical-genetic interaction screening data with a genetic interaction network to predict the biological processes perturbed by compounds. In a recent publication, we applied CG-TARGET to a screen of nearly 14,000 chemical compounds in Saccharomyces cerevisiae, integrating this dataset with the global S. cerevisiae genetic interaction network to prioritize over 1500 compounds with high-confidence biological process predictions for further study. We present here a formal description and rigorous benchmarking of the CG-TARGET method, showing that, compared to alternative enrichment-based approaches, it achieves similar or better accuracy while substantially improving the ability to control the false discovery rate of biological process predictions. Additional investigation of the compatibility of chemical-genetic and genetic interaction profiles revealed that one-third of observed chemical-genetic interactions contributed to the highest-confidence biological process predictions and that negative chemical-genetic interactions overwhelmingly formed the basis of these predictions. We also present experimental validations of CG-TARGET-predicted tubulin polymerization and cell cycle progression inhibitors. Our approach successfully demonstrates the use of genetic interaction networks in the high-throughput functional annotation of compounds to biological processes. |
url |
http://europepmc.org/articles/PMC6226211?pdf=render |
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