The Mycobacterium tuberculosis dosRS two-component system is induced by multiple stresses

Kendall, Sharon L.; Movahedzadeh, Farahnaz; Rison, Stuart C.G.; Wernisch, Lorenz; Parish, Tanya; Duncan, Ken; Betts, Joanna; Stoker, Neil G.

doi:10.1016/j.tube.2003.12.007

Cited by 127 publications

(128 citation statements)

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“…Altered genes include sigma factors [39,40] and PGRS-encoding genes [41], as well as activation of alternative pathways in the citrate cycle to allow metabolism of the abundant lipids in the necrotic centre of granulomas [42,43] and general adaptation to low oxygen tension [44]. More recently, in vitro studies identified a regulon consisting of two sensor kinases, DosS and DosT, under the control of the transcription factor DosR, which sense and control the host response to hypoxia and nitric oxide stress [45][46][47], thus enabling M. tuberculosis to adapt to local conditions during latency [48] (Figure 5). The DosR system contributes significantly to transition into mycobacterial dormancy in an in vivo model of artificial granulomas in mice [49].…”

Section: Transition Of M Tuberculosis Into a Dormant State Within Grmentioning

confidence: 99%

New insights into the function of granulomas in human tuberculosis

Ulrichs

Kaufmann

2005

The Journal of Pathology

353

337

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The human tuberculous granuloma provides the morphological framework for local immune processes central to the outcome of tuberculosis. This review article describes investigations on human lung granulomas aimed at better understanding the regional host response and counter-measures to Mycobacterium tuberculosis. These findings lead to a revised view of the regional immune response in human tuberculosis. Novel insights into this dynamic cross-talk form the basis of novel intervention strategies.

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Section: Transition Of M Tuberculosis Into a Dormant State Within Grmentioning

confidence: 99%

New insights into the function of granulomas in human tuberculosis

Ulrichs

Kaufmann

2005

The Journal of Pathology

353

337

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“…This is an advantage over previous DosR regulon studies [13-15] using batch cultures, where uncontrolled local heterogeneity in the conditions of the cultures is a major problem. Using the estimated TFA of DosR, we proceeded to detect DosR-regulated genes in a low oxygen environment.…”

Section: Discussionmentioning

confidence: 99%

“…As one of the most extensively studied regulons in M. tuberculosis , DosR plays a major role in mediating hypoxia response in the organism [13-15]. Several studies attempt to identify DosR-regulated genes through comparison of the changes in the gene expression levels in wild type and dosR mutant strains [14,15]. …”

Section: Introductionmentioning

confidence: 99%

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An integrated machine learning approach for predicting DosR-regulated genes in Mycobacterium tuberculosis

Zhang

Hatch²,

Bacon³

et al. 2010

BMC Syst Biol

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BackgroundDosR is an important regulator of the response to stress such as limited oxygen availability in Mycobacterium tuberculosis. Time course gene expression data enable us to dissect this response on the gene regulatory level. The mRNA expression profile of a regulator, however, is not necessarily a direct reflection of its activity. Knowing the transcription factor activity (TFA) can be exploited to predict novel target genes regulated by the same transcription factor. Various approaches have been proposed to reconstruct TFAs from gene expression data. Most of them capture only a first-order approximation to the complex transcriptional processes by assuming linear gene responses and linear dynamics in TFA, or ignore the temporal information in data from such systems.ResultsIn this paper, we approach the problem of inferring dynamic hidden TFAs using Gaussian processes (GP). We are able to model dynamic TFAs and to account for both linear and nonlinear gene responses. To test the validity of the proposed approach, we reconstruct the hidden TFA of p53, a tumour suppressor activated by DNA damage, using published time course gene expression data. Our reconstructed TFA is closer to the experimentally determined profile of p53 concentration than that from the original study. We then apply the model to time course gene expression data obtained from chemostat cultures of M. tuberculosis under reduced oxygen availability. After estimation of the TFA of DosR based on a number of known target genes using the GP model, we predict novel DosR-regulated genes: the parameters of the model are interpreted as relevance parameters indicating an existing functional relationship between TFA and gene expression. We further improve the prediction by integrating promoter sequence information in a logistic regression model. Apart from the documented DosR-regulated genes, our prediction yields ten novel genes under direct control of DosR.ConclusionsChemostat cultures are an ideal experimental system for controlling noise and variability when monitoring the response of bacterial organisms such as M. tuberculosis to finely controlled changes in culture conditions and available metabolites. Nonlinear hidden TFA dynamics of regulators can be reconstructed remarkably well with Gaussian processes from such data. Moreover, estimated parameters of the GP can be used to assess whether a gene is controlled by the reconstructed TFA or not. It is straightforward to combine these parameters with further information, such as the presence of binding motifs, to increase prediction accuracy.

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“…A set of 48 genes of dos regulon is up-regulated in stress conditions and presumably play important roles in survival of mycobacteria within the granuloma, under latency. The expression of these genes are controlled by a regulated gene cluster consisting of three genes (Rv3134c/3133c/3132c), among them, DosR (Rv3133c) functions as a cognate response regulator [1][2][3][4][5][6][7][8]. It has been shown that the cellular composition of granuloma is in a dynamic state with respect to oxygen tension [9].…”

Section: Introductionmentioning

confidence: 99%