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A Bayesian Change point model for differential gene expression patterns of the DosR regulon of Mycobacterium tuberculosis

BACKGROUND: Low oxygen availability has been shown previously to stimulate M. tuberculosis to establish non-replicative persistence in vitro. The two component sensor/regulator dosRS is a major mediator in the transcriptional response of M. tuberculosis to hypoxia and controls a regulon of approxima...

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Autores principales: Zhang, Yi, Hatch, Kim A, Wernisch, Lorenz, Bacon, Joanna
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2275270/
https://www.ncbi.nlm.nih.gov/pubmed/18294384
http://dx.doi.org/10.1186/1471-2164-9-87
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author Zhang, Yi
Hatch, Kim A
Wernisch, Lorenz
Bacon, Joanna
author_facet Zhang, Yi
Hatch, Kim A
Wernisch, Lorenz
Bacon, Joanna
author_sort Zhang, Yi
collection PubMed
description BACKGROUND: Low oxygen availability has been shown previously to stimulate M. tuberculosis to establish non-replicative persistence in vitro. The two component sensor/regulator dosRS is a major mediator in the transcriptional response of M. tuberculosis to hypoxia and controls a regulon of approximately 50 genes that are induced under this condition. The aim of this study was to determine whether the induction of the entire DosR regulon is triggered as a synchronous event or if induction can unfold as a cascade of events as the differential expression of subsets of genes is stimulated by different oxygen availabilities. RESULTS: A novel aspect of our work is the use of chemostat cultures of M. tuberculosis which allowed us to control environmental conditions very tightly. We exposed M. tuberculosis to a sudden drop in oxygen availability in chemostat culture and studied the transcriptional response of the organism during the transition from a high oxygen level (10% dissolved oxygen tension or DOT) to a low oxygen level (0.2% DOT) using DNA microarrays. We developed a Bayesian change point analysis method that enabled us to detect subtle shifts in the timing of gene induction. It results in probabilities of a change in gene expression at certain time points. A computational analysis of potential binding sites upstream of the DosR-controlled genes shows how the transcriptional responses of these genes are influenced by the affinity of these binding sites to DosR. Our study also indicates that a subgroup of DosR-controlled genes is regulated indirectly. CONCLUSION: The majority of the dosR-dependent genes were up-regulated at 0.2% DOT, which confirms previous findings that these genes are triggered by hypoxic environments. However, our change point analysis also highlights genes which were up-regulated earlier at levels of about 8% DOT indicating that they respond to small fluctuations in oxygen availability. Our analysis shows that there are pairs of divergent genes where one gene in the pair is up-regulated before the other, presumably for a flexible response to a constantly changing environment in the host.
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spelling pubmed-22752702008-03-26 A Bayesian Change point model for differential gene expression patterns of the DosR regulon of Mycobacterium tuberculosis Zhang, Yi Hatch, Kim A Wernisch, Lorenz Bacon, Joanna BMC Genomics Research Article BACKGROUND: Low oxygen availability has been shown previously to stimulate M. tuberculosis to establish non-replicative persistence in vitro. The two component sensor/regulator dosRS is a major mediator in the transcriptional response of M. tuberculosis to hypoxia and controls a regulon of approximately 50 genes that are induced under this condition. The aim of this study was to determine whether the induction of the entire DosR regulon is triggered as a synchronous event or if induction can unfold as a cascade of events as the differential expression of subsets of genes is stimulated by different oxygen availabilities. RESULTS: A novel aspect of our work is the use of chemostat cultures of M. tuberculosis which allowed us to control environmental conditions very tightly. We exposed M. tuberculosis to a sudden drop in oxygen availability in chemostat culture and studied the transcriptional response of the organism during the transition from a high oxygen level (10% dissolved oxygen tension or DOT) to a low oxygen level (0.2% DOT) using DNA microarrays. We developed a Bayesian change point analysis method that enabled us to detect subtle shifts in the timing of gene induction. It results in probabilities of a change in gene expression at certain time points. A computational analysis of potential binding sites upstream of the DosR-controlled genes shows how the transcriptional responses of these genes are influenced by the affinity of these binding sites to DosR. Our study also indicates that a subgroup of DosR-controlled genes is regulated indirectly. CONCLUSION: The majority of the dosR-dependent genes were up-regulated at 0.2% DOT, which confirms previous findings that these genes are triggered by hypoxic environments. However, our change point analysis also highlights genes which were up-regulated earlier at levels of about 8% DOT indicating that they respond to small fluctuations in oxygen availability. Our analysis shows that there are pairs of divergent genes where one gene in the pair is up-regulated before the other, presumably for a flexible response to a constantly changing environment in the host. BioMed Central 2008-02-22 /pmc/articles/PMC2275270/ /pubmed/18294384 http://dx.doi.org/10.1186/1471-2164-9-87 Text en Copyright © 2008 Zhang et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Zhang, Yi
Hatch, Kim A
Wernisch, Lorenz
Bacon, Joanna
A Bayesian Change point model for differential gene expression patterns of the DosR regulon of Mycobacterium tuberculosis
title A Bayesian Change point model for differential gene expression patterns of the DosR regulon of Mycobacterium tuberculosis
title_full A Bayesian Change point model for differential gene expression patterns of the DosR regulon of Mycobacterium tuberculosis
title_fullStr A Bayesian Change point model for differential gene expression patterns of the DosR regulon of Mycobacterium tuberculosis
title_full_unstemmed A Bayesian Change point model for differential gene expression patterns of the DosR regulon of Mycobacterium tuberculosis
title_short A Bayesian Change point model for differential gene expression patterns of the DosR regulon of Mycobacterium tuberculosis
title_sort bayesian change point model for differential gene expression patterns of the dosr regulon of mycobacterium tuberculosis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2275270/
https://www.ncbi.nlm.nih.gov/pubmed/18294384
http://dx.doi.org/10.1186/1471-2164-9-87
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