Mycobacterium tuberculosis Response Regulators, DevR and NarL, Interact in Vivo and Co-regulate Gene Expression during Aerobic Nitrate Metabolism

Mycobacterium tuberculosis genes Rv0844c/Rv0845 encoding the NarL response regulator and NarS histidine kinase are hypothesized to constitute a two-component system involved in the regulation of nitrate metabolism. However, there is no experimental evidence to support this. In this study, we established M. tuberculosis NarL/NarS as a functional two-component system and identified His(241) and Asp(61) as conserved phosphorylation sites in NarS and NarL, respectively. Transcriptional profiling between M. tuberculosis H37Rv and Delta narL mutant strain during exponential growth in broth cultures with or without nitrate defined an similar to 30-gene NarL regulon that exhibited significant overlap with DevR-regulated genes, thereby implicating a role for the DevR response regulator in the regulation of nitrate metabolism. Notably, expression analysis of a subset of genes common to NarL and DevR regulons in M. tuberculosis Delta devR, Delta devS Delta dosT, and Delta narL mutant strains revealed that in response to nitrite produced during aerobic nitrate metabolism, the DevRS/DosT regulatory system plays a primary role that is augmented by NarL. Specifically, NarL itself was unable to bind to the narK2, acg, and Rv3130c promoters in phosphorylated or unphosphorylated form; however, its interaction with DevR similar to P resulted in cooperative binding, thereby enabling co-regulation of these genes. These findings support the role of physiologically derived nitrite as a metabolic signal in mycobacteria. We propose NarL-DevR binding, possibly as a heterodimer, as a novel mechanism for co-regulation of gene expression by the DevRS/DosT and NarL/NarS regulatory systems.

The influence of reduced oxygen availability on gene expression in laboratory (H37Rv) and clinical strains (S7 and S10) of Mycobacterium tuberculosis

Mycobacterium tuberculosis has the ability to persist within the host in a dormant stage. One important condition believed to contribute to dormancy is reduced access to oxygen known as hypoxia. However, the response of M. tuberculosis to such hypoxia condition is not fully characterized. Virtually all dormant models against tuberculosis tested in animals used laboratory strain H37Rv or Erdman strain. But major outbreaks of tuberculosis (TB) occur with the strains that have widely different genotypes and phenotypes compared to H37Rv. In this study, we used a custom oligonucleotide microarray to determine the overall transcriptional response of laboratory strain (H37Rv) and most prevalent clinical strains (S7 and S10) of M. tuberculosis from South India to hypoxia. Analysis of microarray results revealed that a total of 1161 genes were differentially regulated (>= 1.5 fold change) in H37Rv, among them 659 genes upregulated and 502 genes down regulated. Microarray data of clinical isolates showed that a total of 790 genes were differentially regulated in S7 among which 453 genes were upregulated and 337 down regulated. Interestingly, numerous genes were also differentially regulated in S10 (total 2805 genes) of which 1463 genes upregulated and 1342 genes down regulated during reduced oxygen condition (Wayne's model). One hundred and thirty-four genes were found common and upregulated among all three strains (H37Rv, S7, and S10) and can be targeted for drug/vaccine development against TB. (C) 2015 Published by Elsevier B.V.

Regulation of Growth, Cell Shape, Cell Division, and Gene Expression by Second Messengers (p)ppGpp and Cyclic Di-GMP in Mycobacterium smegmatis

The alarmone (p)ppGpp regulates transcription, translation, replication, virulence, lipid synthesis, antibiotic sensitivity, biofilm formation, and other functions in bacteria. Signaling nucleotide cyclic di-GMP (c-di-GMP) regulates biofilm formation, motility, virulence, the cell cycle, and other functions. In Mycobacterium smegmatis, both (p) ppGpp and c-di-GMP are synthesized and degraded by bifunctional proteins Rel(Msm) and DcpA, encoded by rel(Msm) and dcpA genes, respectively. We have previously shown that the Delta rel(Msm) and Delta dcpA knockout strains are antibiotic resistant and defective in biofilm formation, show altered cell surface properties, and have reduced levels of glycopeptidolipids and polar lipids in their cell wall (K. R. Gupta, S. Kasetty, and D. Chatterji, Appl Environ Microbiol 81:2571-2578, 2015, http://dx.doi.org/10.1128/AEM.03999-14). In this work, we have explored the phenotypes that are affected by both (p) ppGpp and c-di-GMP in mycobacteria. We have shown that both (p) ppGpp and c-di-GMP are needed to maintain the proper growth rate under stress conditions such as carbon deprivation and cold shock. Scanning electron microscopy showed that low levels of these second messengers result in elongated cells, while high levels reduce the cell length and embed the cells in a biofilm-like matrix. Fluorescence microscopy revealed that the elongated Delta rel(Msm) and Delta dcpA cells are multinucleate, while transmission electron microscopy showed that the elongated cells are multiseptate. Gene expression analysis also showed that genes belonging to functional categories such as virulence, detoxification, lipid metabolism, and cell-wall-related processes were differentially expressed. Our results suggests that both (p) ppGpp and c-di-GMP affect some common phenotypes in M. smegmatis, thus raising a possibility of cross talk between these two second messengers in mycobacteria. IMPORTANCE Our work has expanded the horizon of (p) ppGpp and c-di-GMP signaling in Gram-positive bacteria. We have come across a novel observation that M. smegmatis needs (p) ppGpp and c-di-GMP for cold tolerance. We had previously shown that the Delta rel(Msm) and Delta dcpA strains are defective in biofilm formation. In this work, the overproduction of (p) ppGpp and c-di-GMP encased M. smegmatis in a biofilm-like matrix, which shows that both (p) ppGpp and c-di-GMP are needed for biofilm formation. The regulation of cell length and cell division by (p) ppGpp was known in mycobacteria, but our work shows that c-di-GMP also affects the cell size and cell division in mycobacteria. This is perhaps the first report of c-di-GMP regulating cell division in mycobacteria.

Geometric optimization methods for the analysis of gene expression data

DNA microarrays provide such a huge amount of data that unsupervised methods are required to reduce the dimension of the data set and to extract meaningful biological information. This work shows that Independent Component Analysis (ICA) is a promising approach for the analysis of genome-wide transcriptomic data. The paper first presents an overview of the most popular algorithms to perform ICA. These algorithms are then applied on a microarray breast-cancer data set. Some issues about the application of ICA and the evaluation of biological relevance of the results are discussed. This study indicates that ICA significantly outperforms Principal Component Analysis (PCA).

An in vitro transposon system for highly regulated gene expression: construction of Escherichia coli strains with arabinose-dependent growth at low temperatures.

Placing a gene of interest under the control of an inducible promoter greatly aids the purification, localization and functional analysis of proteins but usually requires the sub-cloning of the gene of interest into an appropriate expression vector. Here, we describe an alternative approach employing in vitro transposition of Tn Omega P(BAD) to place the highly regulable, arabinose inducible P(BAD) promoter upstream of the gene to be expressed. The method is rapid, simple and facilitates the optimization of expression by producing constructs with variable distances between the P(BAD) promoter and the gene. To illustrate the use of this approach, we describe the construction of a strain of Escherichia coli in which growth at low temperatures on solid media is dependent on threshold levels of arabinose. Other uses of the transposable promoter are also discussed.

ICGE: an R package for detecting relevant clusters and atypical units in gene expression

Background: Gene expression technologies have opened up new ways to diagnose and treat cancer and other diseases. Clustering algorithms are a useful approach with which to analyze genome expression data. They attempt to partition the genes into groups exhibiting similar patterns of variation in expression level. An important problem associated with gene classification is to discern whether the clustering process can find a relevant partition as well as the identification of new genes classes. There are two key aspects to classification: the estimation of the number of clusters, and the decision as to whether a new unit (gene, tumor sample ... ) belongs to one of these previously identified clusters or to a new group. Results: ICGE is a user-friendly R package which provides many functions related to this problem: identify the number of clusters using mixed variables, usually found by applied biomedical researchers; detect whether the data have a cluster structure; identify whether a new unit belongs to one of the pre-identified clusters or to a novel group, and classify new units into the corresponding cluster. The functions in the ICGE package are accompanied by help files and easy examples to facilitate its use. Conclusions: We demonstrate the utility of ICGE by analyzing simulated and real data sets. The results show that ICGE could be very useful to a broad research community.

Control of gene expression by modulated self-assembly

Numerous transcription factors self-assemble into different order oligomeric species in a way that is actively regulated by the cell. Until now, no general functional role has been identified for this widespread process. Here, we capture the effects of modulated self-assembly in gene expression with a novel quantitative framework. We show that this mechanism provides precision and flexibility, two seemingly antagonistic properties, to the sensing of diverse cellular signals by systems that share common elements present in transcription factors like p53, NF-kappa B, STATs, Oct and RXR. Applied to the nuclear hormone receptor RXR, this framework accurately reproduces a broad range of classical, previously unexplained, sets of gene expression data and corroborates the existence of a precise functional regime with flexible properties that can be controlled both at a genome-wide scale and at the individual promoter level.