An improved statistical method to identify chemical-genetic interactions by exploiting concentration-dependence.

Chemical-genetics (C-G) experiments can be used to identify interactions between inhibitory compounds and bacterial genes, potentially revealing the targets of drugs, or other functionally interacting genes and pathways. C-G experiments involve constructing a library of hypomorphic strains with esse...

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Autores principales:	Esha Dutta, Michael A DeJesus, Nadine Ruecker, Anisha Zaveri, Eun-Ik Koh, Christopher M Sassetti, Dirk Schnappinger, Thomas R Ioerger
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/19a1ca45325a42529d142e9df2c1aa29
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spelling	oai:doaj.org-article:19a1ca45325a42529d142e9df2c1aa292021-12-02T20:17:26ZAn improved statistical method to identify chemical-genetic interactions by exploiting concentration-dependence.1932-620310.1371/journal.pone.0257911https://doaj.org/article/19a1ca45325a42529d142e9df2c1aa292021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0257911https://doaj.org/toc/1932-6203Chemical-genetics (C-G) experiments can be used to identify interactions between inhibitory compounds and bacterial genes, potentially revealing the targets of drugs, or other functionally interacting genes and pathways. C-G experiments involve constructing a library of hypomorphic strains with essential genes that can be knocked-down, treating it with an inhibitory compound, and using high-throughput sequencing to quantify changes in relative abundance of individual mutants. The hypothesis is that, if the target of a drug or other genes in the same pathway are present in the library, such genes will display an excessive fitness defect due to the synergy between the dual stresses of protein depletion and antibiotic exposure. While assays at a single drug concentration are susceptible to noise and can yield false-positive interactions, improved detection can be achieved by requiring that the synergy between gene and drug be concentration-dependent. We present a novel statistical method based on Linear Mixed Models, called CGA-LMM, for analyzing C-G data. The approach is designed to capture the dependence of the abundance of each gene in the hypomorph library on increasing concentrations of drug through slope coefficients. To determine which genes represent candidate interactions, CGA-LMM uses a conservative population-based approach in which genes with negative slopes are considered significant only if they are outliers with respect to the rest of the population (assuming that most genes in the library do not interact with a given inhibitor). We applied the method to analyze 3 independent hypomorph libraries of M. tuberculosis for interactions with antibiotics with anti-tubercular activity, and we identify known target genes or expected interactions for 7 out of 9 drugs where relevant interacting genes are known.Esha DuttaMichael A DeJesusNadine RueckerAnisha ZaveriEun-Ik KohChristopher M SassettiDirk SchnappingerThomas R IoergerPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0257911 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Esha Dutta Michael A DeJesus Nadine Ruecker Anisha Zaveri Eun-Ik Koh Christopher M Sassetti Dirk Schnappinger Thomas R Ioerger An improved statistical method to identify chemical-genetic interactions by exploiting concentration-dependence.
description	Chemical-genetics (C-G) experiments can be used to identify interactions between inhibitory compounds and bacterial genes, potentially revealing the targets of drugs, or other functionally interacting genes and pathways. C-G experiments involve constructing a library of hypomorphic strains with essential genes that can be knocked-down, treating it with an inhibitory compound, and using high-throughput sequencing to quantify changes in relative abundance of individual mutants. The hypothesis is that, if the target of a drug or other genes in the same pathway are present in the library, such genes will display an excessive fitness defect due to the synergy between the dual stresses of protein depletion and antibiotic exposure. While assays at a single drug concentration are susceptible to noise and can yield false-positive interactions, improved detection can be achieved by requiring that the synergy between gene and drug be concentration-dependent. We present a novel statistical method based on Linear Mixed Models, called CGA-LMM, for analyzing C-G data. The approach is designed to capture the dependence of the abundance of each gene in the hypomorph library on increasing concentrations of drug through slope coefficients. To determine which genes represent candidate interactions, CGA-LMM uses a conservative population-based approach in which genes with negative slopes are considered significant only if they are outliers with respect to the rest of the population (assuming that most genes in the library do not interact with a given inhibitor). We applied the method to analyze 3 independent hypomorph libraries of M. tuberculosis for interactions with antibiotics with anti-tubercular activity, and we identify known target genes or expected interactions for 7 out of 9 drugs where relevant interacting genes are known.
format	article
author	Esha Dutta Michael A DeJesus Nadine Ruecker Anisha Zaveri Eun-Ik Koh Christopher M Sassetti Dirk Schnappinger Thomas R Ioerger
author_facet	Esha Dutta Michael A DeJesus Nadine Ruecker Anisha Zaveri Eun-Ik Koh Christopher M Sassetti Dirk Schnappinger Thomas R Ioerger
author_sort	Esha Dutta
title	An improved statistical method to identify chemical-genetic interactions by exploiting concentration-dependence.
title_short	An improved statistical method to identify chemical-genetic interactions by exploiting concentration-dependence.
title_full	An improved statistical method to identify chemical-genetic interactions by exploiting concentration-dependence.
title_fullStr	An improved statistical method to identify chemical-genetic interactions by exploiting concentration-dependence.
title_full_unstemmed	An improved statistical method to identify chemical-genetic interactions by exploiting concentration-dependence.
title_sort	improved statistical method to identify chemical-genetic interactions by exploiting concentration-dependence.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/19a1ca45325a42529d142e9df2c1aa29
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An improved statistical method to identify chemical-genetic interactions by exploiting concentration-dependence.

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