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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8486102/ https://www.ncbi.nlm.nih.gov/pubmed/34597304 http://dx.doi.org/10.1371/journal.pone.0257911 |
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author | Dutta, Esha DeJesus, Michael A. Ruecker, Nadine Zaveri, Anisha Koh, Eun-Ik Sassetti, Christopher M. Schnappinger, Dirk Ioerger, Thomas R. |
author_facet | Dutta, Esha DeJesus, Michael A. Ruecker, Nadine Zaveri, Anisha Koh, Eun-Ik Sassetti, Christopher M. Schnappinger, Dirk Ioerger, Thomas R. |
author_sort | Dutta, Esha |
collection | PubMed |
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 | Online Article Text |
id | pubmed-8486102 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-84861022021-10-02 An improved statistical method to identify chemical-genetic interactions by exploiting concentration-dependence Dutta, Esha DeJesus, Michael A. Ruecker, Nadine Zaveri, Anisha Koh, Eun-Ik Sassetti, Christopher M. Schnappinger, Dirk Ioerger, Thomas R. PLoS One Research Article 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. Public Library of Science 2021-10-01 /pmc/articles/PMC8486102/ /pubmed/34597304 http://dx.doi.org/10.1371/journal.pone.0257911 Text en © 2021 Dutta et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Dutta, Esha DeJesus, Michael A. Ruecker, Nadine Zaveri, Anisha Koh, Eun-Ik Sassetti, Christopher M. Schnappinger, Dirk Ioerger, Thomas R. An improved statistical method to identify chemical-genetic interactions by exploiting concentration-dependence |
title | 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_short | 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 |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8486102/ https://www.ncbi.nlm.nih.gov/pubmed/34597304 http://dx.doi.org/10.1371/journal.pone.0257911 |
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