Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Dutta, Esha, DeJesus, Michael A., Ruecker, Nadine, Zaveri, Anisha, Koh, Eun-Ik, Sassetti, Christopher M., Schnappinger, Dirk, Ioerger, Thomas R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
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
_version_ 1784577672574140416
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
work_keys_str_mv AT duttaesha animprovedstatisticalmethodtoidentifychemicalgeneticinteractionsbyexploitingconcentrationdependence
AT dejesusmichaela animprovedstatisticalmethodtoidentifychemicalgeneticinteractionsbyexploitingconcentrationdependence
AT rueckernadine animprovedstatisticalmethodtoidentifychemicalgeneticinteractionsbyexploitingconcentrationdependence
AT zaverianisha animprovedstatisticalmethodtoidentifychemicalgeneticinteractionsbyexploitingconcentrationdependence
AT koheunik animprovedstatisticalmethodtoidentifychemicalgeneticinteractionsbyexploitingconcentrationdependence
AT sassettichristopherm animprovedstatisticalmethodtoidentifychemicalgeneticinteractionsbyexploitingconcentrationdependence
AT schnappingerdirk animprovedstatisticalmethodtoidentifychemicalgeneticinteractionsbyexploitingconcentrationdependence
AT ioergerthomasr animprovedstatisticalmethodtoidentifychemicalgeneticinteractionsbyexploitingconcentrationdependence
AT duttaesha improvedstatisticalmethodtoidentifychemicalgeneticinteractionsbyexploitingconcentrationdependence
AT dejesusmichaela improvedstatisticalmethodtoidentifychemicalgeneticinteractionsbyexploitingconcentrationdependence
AT rueckernadine improvedstatisticalmethodtoidentifychemicalgeneticinteractionsbyexploitingconcentrationdependence
AT zaverianisha improvedstatisticalmethodtoidentifychemicalgeneticinteractionsbyexploitingconcentrationdependence
AT koheunik improvedstatisticalmethodtoidentifychemicalgeneticinteractionsbyexploitingconcentrationdependence
AT sassettichristopherm improvedstatisticalmethodtoidentifychemicalgeneticinteractionsbyexploitingconcentrationdependence
AT schnappingerdirk improvedstatisticalmethodtoidentifychemicalgeneticinteractionsbyexploitingconcentrationdependence
AT ioergerthomasr improvedstatisticalmethodtoidentifychemicalgeneticinteractionsbyexploitingconcentrationdependence