Regulation of the<i>Mycobacterium tuberculosis</i>hypoxic response gene encoding α-crystallin

Sherman, David R.; Voskuil, Martin I.; Schnappinger, Dirk; Liao, Reiling; Harrell, Maria I.; Schoolnik, Gary K.

doi:10.1073/pnas.121172498

Cited by 682 publications

(772 citation statements)

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“…2): (1) divalent gas: when stimulated by hypoxia [Park et al, 2003;Sherman et al, 2001], nitric oxide [Sherman et al, 2001], and carbon monoxide [Kumar et al, 2008;Shiloh et al, 2008], the expression level of DosR regulon is upregulated, followed by the oxygen consumption shifting, ATP levels maintenance and redox state (NAD/NADH ratio) homeostasis. Its expression level is also vital to the optimal transition of Mtb from the anaerobic nonreplicating state back to aerobic growth state .…”

Section: The Expression and Regulation Of Dosr Regulonmentioning

confidence: 99%

The Mycobacterium DosR regulon structure and diversity revealed by comparative genomic analysis

Chen

Deng

et al. 2012

J of Cellular Biochemistry

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Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), which claims approximately two million people annually, remains a global health concern. The non-replicating or dormancy like state of this pathogen which is impervious to anti-tuberculosis drugs is widely recognized as the culprit for this scenario. The dormancy survival regulator (DosR) regulon, composed of 48 co-regulated genes, is held as essential for Mtb persistence. The DosR regulon is regulated by a two-component regulatory system consisting of two sensor kinases-DosS (Rv3132c) and DosT (Rv2027c), and a response regulator DosR (Rv3133c). The underlying regulatory mechanism of DosR regulon expression is very complex. Many factors are involved, particularly the oxygen tension. The DosR regulon enables the pathogen to persist during lengthy hypoxia. Comparative genomic analysis demonstrated that the DosR regulon is widely distributed among the mycobacterial genomes, ranging from the pathogenic strains to the environmental strains. In-depth studies on the DosR response should provide insights into its role in TB latency in vivo and shape new measures to combat this exceeding recalcitrant pathogen.

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Section: The Expression and Regulation Of Dosr Regulonmentioning

confidence: 99%

The Mycobacterium DosR regulon structure and diversity revealed by comparative genomic analysis

Chen

Deng

et al. 2012

J of Cellular Biochemistry

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“…Since DosR positively regulates Rv0081 [23], the repression of dosR by Rv0081 provides a negative feedback mechanism. In contrast, the expressions of dosR and dosS were not significantly changed in Δ Rv3334 , which is consistent with the notion that Rv3334 mainly functions in the later stage of hypoxia.…”

Section: Resultsmentioning

confidence: 99%

“…Based on our results and previous studies, we propose a model for the regulatory network of M. tb in response to hypoxia (Figure 7(a)). In this model, the early hypoxic response of M. tb is mediated by the DosR-DosS/T system, [23,39] which induces the expression of the DosR regulon including Rv0081 . DosR positively regulates its own expression, which forms a positive loop that continuously increases the expression of Rv0081 and other DosR-regulated genes.…”

Section: Discussionmentioning

confidence: 99%

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Transcription factors Rv0081 and Rv3334 connect the early and the enduring hypoxic response of Mycobacterium tuberculosis

et al. 2018

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The ability of Mycobacterium tuberculosis (M. tb) to survive and persist in the host for decades in an asymptomatic state is an important aspect of tuberculosis pathogenesis. Although adaptation to hypoxia is thought to play a prominent role underlying M. tb persistence, how the bacteria achieve this goal is largely unknown. Rv0081, a member of the DosR regulon, is induced at the early stage of hypoxia while Rv3334 is one of the enduring hypoxic response genes. In this study, we uncovered genetic interactions between these two transcription factors. RNA-seq analysis of ΔRv0081 and ΔRv3334 revealed that the gene expression profiles of these two mutants were highly similar. We also found that under hypoxia, Rv0081 positively regulated the expression of Rv3334 while Rv3334 repressed transcription of Rv0081. In addition, we demonstrated that Rv0081 formed dimer and bound to the promoter region of Rv3334. Taken together, these data suggest that Rv0081 and Rv3334 work in the same regulatory pathway and that Rv3334 functions immediately downstream of Rv0081. We also found that Rv3334 is a bona fide regulator of the enduring hypoxic response genes. Our study has uncovered a regulatory pathway that connects the early and the enduring hypoxic response, revealing a transcriptional cascade that coordinates the temporal response of M. tb to hypoxia.

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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%

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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Regulation of theMycobacterium tuberculosishypoxic response gene encoding α-crystallin

Cited by 682 publications

References 40 publications

The Mycobacterium DosR regulon structure and diversity revealed by comparative genomic analysis

The Mycobacterium DosR regulon structure and diversity revealed by comparative genomic analysis

Transcription factors Rv0081 and Rv3334 connect the early and the enduring hypoxic response of Mycobacterium tuberculosis

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

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