TB database 2010: overview and update

A comprehensive map of genome-wide gene regulation in Mycobacterium tuberculosis

Mycobacterium tuberculosis (MTB) is a pathogenic bacterium responsible for 12 million active cases of tuberculosis (TB) worldwide. The complexity and critical regulatory components of MTB pathogenicity are still poorly understood despite extensive research efforts. In this study, we constructed the first systems-scale map of transcription factor (TF) binding sites and their regulatory target proteins in MTB. We constructed FLAG-tagged overexpression constructs for 206 TFs in MTB, used ChIP-seq to identify genome-wide binding events and surveyed global transcriptomic changes for each overexpressed TF. Here we present data for the most comprehensive map of MTB gene regulation to date. We also define elaborate quality control measures, extensive filtering steps, and the gene-level overlap between ChIP-seq and microarray datasets. Further, we describe the use of TF overexpression datasets to validate a global gene regulatory network model of MTB and describe an online source to explore the datasets.

Identification of novel Mycobacterium tuberculosis CD4 T-cell antigens via high throughput proteome screening

Elicitation of CD4 IFN-gamma T cell responses to Mycobacterium tuberculosis (MTB) is a rational vaccine strategy to prevent clinical tuberculosis. Diagnosis of MTB infection is based on T-cell immune memory to MTB antigens. The MTB proteome contains over four thousand open reading frames (ORFs). We conducted a pilot antigen identification study using 164 MTB proteins and MTB-specific T-cells expanded in vitro from 12 persons with latent MTB infection. Enrichment of MTB-reactive T-cells from PBMC used cell sorting or an alternate system compatible with limited resources. MTB proteins were used as single antigens or combinatorial matrices in proliferation and cytokine secretion readouts. Overall, our study found that 44 MTB proteins were antigenic, including 27 not previously characterized as CD4 T-cell antigens. Antigen truncation, peptide, NTM homology, and HLA class II tetramer studies confirmed malate synthase G (encoded by gene Rv1837) as a CD4 T-cell antigen. This simple, scalable system has potential utility for the identification of candidate MTB vaccine and biomarker antigens.

Clonality and micro-diversity of a nationwide spreading genotype of Mycobacterium tuberculosis in Japan

Mycobacterium tuberculosis transmission routes can be estimated from genotypic analysis of clinical isolates from patients. In Japan, still a middle-incidence country of TB, a unique genotype strain designated as ‘M-strain’ has been isolated nationwide recently. To ascertain the history of the wide spread of the strain, 10 clinical isolates from different areas were subjected to genome-wide analysis based on deep sequencers. Results show that all isolates possessed common mutations to those of referential strains. The greatest number of accumulated single nucleotide variants (SNVs) from the oldest coalescence was 13 nucleotides, indicating high clonality of these isolates. When an SNV common to the isolates was used as a surrogate marker of the clone, authentic clonal isolates with variation in a reliable subset of variable number of tandem repeat (VNTR) genotyping method can be selected successfully from clinical isolates populations of M. tuberculosis. When the authentic clones can also be assigned to sub-clonal groups by SNVs derived from the genomic comparison, they are classifiable into three sub-clonal groups with a bias of geographical origins. Feedback from genomic analysis of clinical isolates of M. tuberculosis to genotypic markers will be an efficient strategy for the big data in various settings for public health actions against TB.

Network representations of immune system complexity

The mammalian immune system is a dynamic multiscale system composed of a hierarchically organized set of molecular, cellular, and organismal networks that act in concert to promote effective host defense. These networks range from those involving gene regulatory and protein–protein interactions underlying intracellular signaling pathways and single‐cell responses to increasingly complex networks of in vivo cellular interaction, positioning, and migration that determine the overall immune response of an organism. Immunity is thus not the product of simple signaling events but rather nonlinear behaviors arising from dynamic, feedback‐regulated interactions among many components. One of the major goals of systems immunology is to quantitatively measure these complex multiscale spatial and temporal interactions, permitting development of computational models that can be used to predict responses to perturbation. Recent technological advances permit collection of comprehensive datasets at multiple molecular and cellular levels, while advances in network biology support representation of the relationships of components at each level as physical or functional interaction networks. The latter facilitate effective visualization of patterns and recognition of emergent properties arising from the many interactions of genes, molecules, and cells of the immune system. We illustrate the power of integrating ‘omics’ and network modeling approaches for unbiased reconstruction of signaling and transcriptional networks with a focus on applications involving the innate immune system. We further discuss future possibilities for reconstruction of increasingly complex cellular‐ and organism‐level networks and development of sophisticated computational tools for prediction of emergent immune behavior arising from the concerted action of these networks. WIREs Syst Biol Med 2015, 7:13–38. doi: 10.1002/wsbm.1288

This article is categorized under: 1

Analytical and Computational Methods > Computational Methods

2

Laboratory Methods and Technologies > Macromolecular Interactions, Methods

The DNA-binding network of Mycobacterium tuberculosis

Mycobacterium tuberculosis (MTB) infects 30% of all humans and kills someone every 20-30 s. Here we report genome-wide binding for ~80% of all predicted MTB transcription factors (TFs), and assayed global expression following induction of each TF. The MTB DNA-binding network consists of ~16,000 binding events from 154 TFs. We identify >50 TF-DNA consensus motifs and >1,150 promoter-binding events directly associated with proximal gene regulation. An additional ~4,200 binding events are in promoter windows and represent strong candidates for direct transcriptional regulation under appropriate environmental conditions. However, we also identify >10,000 'dormant' DNA-binding events that cannot be linked directly with proximal transcriptional control, suggesting that widespread DNA binding may be a common feature that should be considered when developing global models of coordinated gene expression.

Consequences of genomic diversity in Mycobacterium tuberculosis

The causative agent of human tuberculosis, Mycobacterium tuberculosis complex (MTBC), comprises seven phylogenetically distinct lineages associated with different geographical regions. Here we review the latest findings on the nature and amount of genomic diversity within and between MTBC lineages. We then review recent evidence for the effect of this genomic diversity on mycobacterial phenotypes measured experimentally and in clinical settings. We conclude that overall, the most geographically widespread Lineage 2 (includes Beijing) and Lineage 4 (also known as Euro-American) are more virulent than other lineages that are more geographically restricted. This increased virulence is associated with delayed or reduced pro-inflammatory host immune responses, greater severity of disease, and enhanced transmission. Future work should focus on the interaction between MTBC and human genetic diversity, as well as on the environmental factors that modulate these interactions.

A high-resolution network model for global gene regulation in Mycobacterium tuberculosis

The resilience of Mycobacterium tuberculosis (MTB) is largely due to its ability to effectively counteract and even take advantage of the hostile environments of a host. In order to accelerate the discovery and characterization of these adaptive mechanisms, we have mined a compendium of 2325 publicly available transcriptome profiles of MTB to decipher a predictive, systems-scale gene regulatory network model. The resulting modular organization of 98% of all MTB genes within this regulatory network was rigorously tested using two independently generated datasets: a genome-wide map of 7248 DNA-binding locations for 143 transcription factors (TFs) and global transcriptional consequences of overexpressing 206 TFs. This analysis has discovered specific TFs that mediate conditional co-regulation of genes within 240 modules across 14 distinct environmental contexts. In addition to recapitulating previously characterized regulons, we discovered 454 novel mechanisms for gene regulation during stress, cholesterol utilization and dormancy. Significantly, 183 of these mechanisms act uniquely under conditions experienced during the infection cycle to regulate diverse functions including 23 genes that are essential to host-pathogen interactions. These and other insights underscore the power of a rational, model-driven approach to unearth novel MTB biology that operates under some but not all phases of infection.

New target prediction and visualization tools incorporating open source molecular fingerprints for TB Mobile 2.0

BACKGROUND

We recently developed a freely available mobile app (TB Mobile) for both iOS and Android platforms that displays Mycobacterium tuberculosis (Mtb) active molecule structures and their targets with links to associated data. The app was developed to make target information available to as large an audience as possible.

RESULTS

We now report a major update of the iOS version of the app. This includes enhancements that use an implementation of ECFP_6 fingerprints that we have made open source. Using these fingerprints, the user can propose compounds with possible anti-TB activity, and view the compounds within a cluster landscape. Proposed compounds can also be compared to existing target data, using a näive Bayesian scoring system to rank probable targets. We have curated an additional 60 new compounds and their targets for Mtb and added these to the original set of 745 compounds. We have also curated 20 further compounds (many without targets in TB Mobile) to evaluate this version of the app with 805 compounds and associated targets.

CONCLUSIONS

TB Mobile can now manage a small collection of compounds that can be imported from external sources, or exported by various means such as email or app-to-app inter-process communication. This means that TB Mobile can be used as a node within a growing ecosystem of mobile apps for cheminformatics. It can also cluster compounds and use internal algorithms to help identify potential targets based on molecular similarity. TB Mobile represents a valuable dataset, data-visualization aid and target prediction tool.

Looking back to the future: predicting in vivo efficacy of small molecules versus Mycobacterium tuberculosis

Selecting and translating in vitro leads for a disease into molecules with in vivo activity in an animal model of the disease is a challenge that takes considerable time and money. As an example, recent years have seen whole-cell phenotypic screens of millions of compounds yielding over 1500 inhibitors of Mycobacterium tuberculosis (Mtb). These must be prioritized for testing in the mouse in vivo assay for Mtb infection, a validated model utilized to select compounds for further testing. We demonstrate learning from in vivo active and inactive compounds using machine learning classification models (Bayesian, support vector machines, and recursive partitioning) consisting of 773 compounds. The Bayesian model predicted 8 out of 11 additional in vivo actives not included in the model as an external test set. Curation of 70 years of Mtb data can therefore provide statistically robust computational models to focus resources on in vivo active small molecule antituberculars. This highlights a cost-effective predictor for in vivo testing elsewhere in other diseases.

Systems biology-based identification of Mycobacterium tuberculosis persistence genes in mouse lungs

Identifying Mycobacterium tuberculosis persistence genes is important for developing novel drugs to shorten the duration of tuberculosis (TB) treatment. We developed computational algorithms that predict M. tuberculosis genes required for long-term survival in mouse lungs. As the input, we used high-throughput M. tuberculosis mutant library screen data, mycobacterial global transcriptional profiles in mice and macrophages, and functional interaction networks. We selected 57 unique, genetically defined mutants (18 previously tested and 39 untested) to assess the predictive power of this approach in the murine model of TB infection. We observed a 6-fold enrichment in the predicted set of M. tuberculosis genes required for persistence in mouse lungs relative to randomly selected mutant pools. Our results also allowed us to reclassify several genes as required for M. tuberculosis persistence in vivo. Finally, the new results implicated additional high-priority candidate genes for testing. Experimental validation of computational predictions demonstrates the power of this systems biology approach for elucidating M. tuberculosis persistence genes.

Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), has a genetic repertoire that permits it to persist in the face of host immune responses. Identification of such persistence genes could reveal novel drug targets and elucidate mechanisms by which the organism eludes the immune system and resists drugs. Genetic screens have identified a total of 31 persistence genes, but to date only 15% of the ~4,000 M. tuberculosis genes have been tested experimentally. In this paper, as an alternative to brute force experimental screens, we describe computational methods that predict new persistence genes by combining known examples with growing databases of biological networks. Experimental testing demonstrated that these predictions are highly accurate, validating the computational approach and providing new information about M. tuberculosis persistence in host tissues. Using the new experimental results as additional input highlights additional genes for testing. Our approach can be extended to other data types and target organisms to characterize host-pathogen interactions relevant to this and other infectious diseases.

Drug resistance to Mycobacterium tuberculosis: from the traditional Chinese view to modern systems biology

The pathogen, Mycobacterium tuberculosis (M. tuberculosis) is a well-evolved, organized pathogen that has developed drug resistance, specifically multidrug resistance (MDR) and extensive drug resistance (XDR). This review primarily summarizes the mechanisms of drug resistance by M. tuberculosis according to the traditional Chinese view. The traditional Chinese view of drug resistance includes: the physical barrier of the cell wall; mutations relating to current anti-TB agents; drug efflux pumps; and drug stress, including the SOS response systems, the mismatch repair systems and the toxin-antitoxin systems. In addition, this review addresses the integrated systems biology of genomics, transcriptomics, proteomics, metabolomics and interactomics. Development of the various levels of systems biology has enabled determination of the anatomy of bacteria. Finally, the current review proposes that further investigation regarding the population of individuals with a high drug metabolic speed is vital to further understand drug resistance in M. tuberculosis.

MazF6 toxin of Mycobacterium tuberculosis demonstrates antitoxin specificity and is coupled to regulation of cell growth by a Soj-like protein

Background

Molecular programs employed by Mycobacterium tuberculosis (Mtb) for the establishment of non-replicating persistence (NRP) are poorly understood. In order to investigate mechanisms regulating entry into NRP, we asked how cell cycle regulation is linked to downstream adaptations that ultimately result in NRP. Based on previous reports and our recent studies, we reason that, in order to establish NRP, cells are halted in the cell cycle at the point of septum formation by coupled regulatory mechanisms.

Results

Using bioinformatic consensus modeling, we identified an alternative cell cycle regulatory element, Soj_Mtb encoded by rv1708. Soj_Mtb coordinates a regulatory mechanism involving cell cycle control at the point of septum formation and elicits the induction of the MazF6 toxin. MazF6 functions as an mRNA interferase leading to bacteriostasis that can be prevented by interaction with its cognate antitoxin, MazE6. Further, MazEF6 acts independently of other Maz family toxin:antitoxin pairs. Notably, soj_Mtb and mazEF6 transcripts where identified at 20, 40 and 100 days post-infection in increasing abundance indicating a role in adaption during chronic infection.

Conclusions

Here we present the first evidence of a coupled regulatory system in which cell cycle regulation via Soj_Mtb is linked to downstream adaptations that are facilitated through the activity of the MazEF6 TA pair.

Computational models for neglected diseases: gaps and opportunities

Neglected diseases, such as Chagas disease, African sleeping sickness, and intestinal worms, affect millions of the world's poor. They disproportionately affect marginalized populations, lack effective treatments or vaccines, or existing products are not accessible to the populations affected. Computational approaches have been used across many of these diseases for various aspects of research or development, and yet data produced by computational approaches are not integrated and widely accessible to others. Here, we identify gaps in which computational approaches have been used for some neglected diseases and not others. We also make recommendations for the broad-spectrum integration of these techniques into a neglected disease drug discovery and development workflow.

TB Mobile: a mobile app for anti-tuberculosis molecules with known targets

BACKGROUND

An increasing number of researchers are focused on strategies for developing inhibitors of Mycobacterium tuberculosis (Mtb) as tuberculosis (TB) drugs.

RESULTS

In order to learn from prior work we have collated information on molecules screened versus Mtb and their targets which has been made available in the Collaborative Drug Discovery (CDD) database. This dataset contains published data on target, essentiality, links to PubMed, TBDB, TBCyc (which provides a pathway-based visualization of the entire cellular biochemical network) and human homolog information. The development of mobile cheminformatics apps could lower the barrier to drug discovery and promote collaboration. Therefore we have used this set of over 700 molecules screened versus Mtb and their targets to create a free mobile app (TB Mobile) that displays molecule structures and links to the bioinformatics data. By input of a molecular structures and performing a similarity search within the app we can infer potential targets or search by targets to retrieve compounds known to be active.

CONCLUSIONS

TB Mobile may assist researchers as part of their workflow in identifying potential targets for hits generated from phenotypic screening and in prioritizing them for further follow-up. The app is designed to lower the barriers to accessing this information, so that all researchers with an interest in combatting this deadly disease can use it freely to the benefit of their own efforts.

A vitamin B₁₂ transporter in Mycobacterium tuberculosis

Vitamin B₁₂-dependent enzymes function in core biochemical pathways in Mycobacterium tuberculosis, an obligate pathogen whose metabolism in vivo is poorly understood. Although M. tuberculosis can access vitamin B₁₂in vitro, it is uncertain whether the organism is able to scavenge B₁₂ during host infection. This question is crucial to predictions of metabolic function, but its resolution is complicated by the absence in the M. tuberculosis genome of a direct homologue of BtuFCD, the only bacterial B₁₂ transport system described to date. We applied genome-wide transposon mutagenesis to identify M. tuberculosis mutants defective in their ability to use exogenous B₁₂. A small proportion of these mapped to Rv1314c, identifying the putative PduO-type ATP : co(I)rrinoid adenosyltransferase as essential for B₁₂ assimilation. Most notably, however, insertions in Rv1819c dominated the mutant pool, revealing an unexpected function in B₁₂ acquisition for an ATP-binding cassette (ABC)-type protein previously investigated as the mycobacterial BacA homologue. Moreover, targeted deletion of Rv1819c eliminated the ability of M. tuberculosis to transport B₁₂ and related corrinoids in vitro. Our results establish an alternative to the canonical BtuCD-type system for B₁₂ uptake in M. tuberculosis, and elucidate a role in B₁₂ metabolism for an ABC protein implicated in chronic mycobacterial infection.

EuPathDB: the eukaryotic pathogen database

EuPathDB (http://eupathdb.org) resources include 11 databases supporting eukaryotic pathogen genomic and functional genomic data, isolate data and phylogenomics. EuPathDB resources are built using the same infrastructure and provide a sophisticated search strategy system enabling complex interrogations of underlying data. Recent advances in EuPathDB resources include the design and implementation of a new data loading workflow, a new database supporting Piroplasmida (i.e. Babesia and Theileria), the addition of large amounts of new data and data types and the incorporation of new analysis tools. New data include genome sequences and annotation, strand-specific RNA-seq data, splice junction predictions (based on RNA-seq), phosphoproteomic data, high-throughput phenotyping data, single nucleotide polymorphism data based on high-throughput sequencing (HTS) and expression quantitative trait loci data. New analysis tools enable users to search for DNA motifs and define genes based on their genomic colocation, view results from searches graphically (i.e. genes mapped to chromosomes or isolates displayed on a map) and analyze data from columns in result tables (word cloud and histogram summaries of column content). The manuscript herein describes updates to EuPathDB since the previous report published in NAR in 2010.

Adenylating enzymes in Mycobacterium tuberculosis as drug targets

Adenylation or adenylate-forming enzymes (AEs) are widely found in nature and are responsible for the activation of carboxylic acids to intermediate acyladenylates, which are mixed anhydrides of AMP. In a second reaction, AEs catalyze the transfer of the acyl group of the acyladenylate onto a nucleophilic amino, alcohol, or thiol group of an acceptor molecule leading to amide, ester, and thioester products, respectively. Mycobacterium tuberculosis encodes for more than 60 adenylating enzymes, many of which represent potential drug targets due to their confirmed essentiality or requirement for virulence. Several strategies have been used to develop potent and selective AE inhibitors including highthroughput screening, fragment-based screening, and the rationale design of bisubstrate inhibitors that mimic the acyladenylate. In this review, a comprehensive analysis of the mycobacterial adenylating enzymes will be presented with a focus on the identification of small molecule inhibitors. Specifically, this review will cover the aminoacyl tRNAsynthetases (aaRSs), MenE required for menaquinone synthesis, the FadD family of enzymes including the fatty acyl- AMP ligases (FAAL) and the fatty acyl-CoA ligases (FACLs) involved in lipid metabolism, and the nonribosomal peptide synthetase adenylation enzyme MbtA that is necessary for mycobactin synthesis. Additionally, the enzymes NadE, GuaA, PanC, and MshC involved in the respective synthesis of NAD, guanine, pantothenate, and mycothiol will be discussed as well as BirA that is responsible for biotinylation of the acyl CoA-carboxylases.

Importance of the genetic diversity within the Mycobacterium tuberculosis complex for the development of novel antibiotics and diagnostic tests of drug resistance

Despite being genetically monomorphic, the limited genetic diversity within the Mycobacterium tuberculosis complex (MTBC) has practical consequences for molecular methods for drug susceptibility testing and for the use of current antibiotics and those in clinical trials. It renders some representatives of MTBC intrinsically resistant against one or multiple antibiotics and affects the spectrum and consequences of resistance mutations selected for during treatment. Moreover, neutral or silent changes within genes responsible for drug resistance can cause false-positive results with hybridization-based assays, which have been recently introduced to replace slower phenotypic methods. We discuss the consequences of these findings and propose concrete steps to rigorously assess the genetic diversity of MTBC to support ongoing clinical trials.

Rv2190c, an NlpC/P60 family protein, is required for full virulence of Mycobacterium tuberculosis

Mycobacterium tuberculosis, the etiologic agent of tuberculosis (TB) possesses at least five genes predicted to encode proteins with NlpC/P60 hydrolase domains, including the relatively uncharacterized Rv2190c. As NlpC/P60 domain-containing proteins are associated with diverse roles in bacterial physiology, our objective was to characterize Rv2190c in M. tuberculosis growth and virulence. Our data indicate that lack of Rv2190c is associated with impaired growth, both in vitro and during an in vivo mouse model of TB. These growth defects are associated with altered colony morphology and phthiocerol dimycocerosate levels, indicating that Rv2190c is involved in cell wall maintenance and composition. In addition, we have demonstrated that Rv2190c is expressed during active growth phase and that its protein product is immunogenic during infection. Our findings have significant implications, both for better understanding the role of Rv2190c in M. tuberculosis biology and also for translational developments.

Computational genomics-proteomics and Phylogeny analysis of twenty one mycobacterial genomes (Tuberculosis & non Tuberculosis strains)

Background

The genus Mycobacterium comprises different species, among them the most contagious and infectious bacteria. The members of the complex Mycobacterium tuberculosis are the most virulent microorganisms that have killed human and other mammals since millennia. Additionally, with the many different mycobacterial sequences available, there is a crucial need for the visualization and the simplification of their data. In this present study, we aim to highlight a comparative genome, proteome and phylogeny analysis between twenty-one mycobacterial (Tuberculosis and non tuberculosis) strains using a set of computational and bioinformatics tools (Pan and Core genome plotting, BLAST matrix and phylogeny analysis).

Results

Considerably the result of pan and core genome Plotting demonstrated that less than 1250 Mycobacterium gene families are conserved across all species, and a total set of about 20,000 gene families within the Mycobacterium pan-genome of twenty one mycobacterial genomes.

Viewing the BLAST matrix a high similarity was found among the species of the complex Mycobacterium tuberculosis and less conservation is found with other slow growing pathogenic mycobacteria.

Phylogeny analysis based on both protein conservation, as well as rRNA clearly resolve known relationships between slow growing mycobacteria.

Conclusion

Mycobacteria include important pathogenic species for human and animals and the Mycobacterium tuberculosis complex is the most cause of death of the humankind. The comparative genome analysis could provide a new insight for better controlling and preventing these diseases.

A genome-wide regulator-DNA interaction network in the human pathogen Mycobacterium tuberculosis H37Rv

Transcription regulation translates static genome information to dynamic cell behaviors, making it central to understand how cells interact with and adapt to their environment. However, only a limited number of transcription regulators and their target genes have been identified in the pathogen Mycobacterium tuberculosis , which has greatly impeded our understanding of its pathogenesis and virulence. In this study, we constructed a genome-wide transcription regulatory network of M. tuberculosis H37Rv using a high-throughput bacterial one-hybrid technique. A transcription factor skeleton network was derived on the basis of the identification of more than 5400 protein-DNA interactions. Our findings further highlight the regulatory mechanism of the mammalian cell entry 1 (mce1) module, which includes mce1R and the mce1 operon. Mce1R was linked to global negative regulation of cell growth, but was found to be positively regulated by the dormancy response regulator DevR. Expression of the mce1 operon was shown to be negatively regulated by the virulence regulator PhoP. These findings provide important new insights into the molecular mechanisms of several mce1 module-related hypervirulence phenotypes of the pathogen. Furthermore, a model of mce1 module-centered signal circuit for dormancy regulation in M. tuberculosis is proposed and discussed.

Characterization of a novel ArsR-like regulator encoded by Rv2034 in Mycobacterium tuberculosis

The genome of Mycobacterium tuberculosis, the causative agent of tuberculosis, encodes a large number of putative transcriptional regulators. However, the identity and target genes of only a few of them have been clearly identified to date. In a recent study, the ArsR family regulator Rv2034 was characterized as a novel positive regulator of phoP. In the current study, we characterized the auto-repressive capabilities of Rv2034 and identified several residues in the protein critical for its DNA binding activities. We also provide evidence that Rv2034 forms dimers in vitro. Furthermore, by using DNaseI footprinting assays, a palindromic sequence was identified as its binding site. Notably, we found that the dosR promoter region contains the binding motif for Rv2034, and that Rv2034 positively regulates the expression of the dosR gene. The potential roles of Rv2034 in the regulation of lipid metabolism and hypoxic adaptation are discussed.

Combining cheminformatics methods and pathway analysis to identify molecules with whole-cell activity against Mycobacterium tuberculosis

PURPOSE

New strategies for developing inhibitors of Mycobacterium tuberculosis (Mtb) are required in order to identify the next generation of tuberculosis (TB) drugs. Our approach leverages the integration of intensive data mining and curation and computational approaches, including cheminformatics combined with bioinformatics, to suggest biological targets and their small molecule modulators.

METHODS

We now describe an approach that uses the TBCyc pathway and genome database, the Collaborative Drug Discovery database of molecules with activity against Mtb and their associated targets, a 3D pharmacophore approach and Bayesian models of TB activity in order to select pathways and metabolites and ultimately prioritize molecules that may be acting as substrate mimics and exhibit activity against TB.

RESULTS

In this study we combined the TB cheminformatics and pathways databases that enabled us to computationally search >80,000 vendor available molecules and ultimately test 23 compounds in vitro that resulted in two compounds (N-(2-furylmethyl)-N'-[(5-nitro-3-thienyl)carbonyl]thiourea and N-[(5-nitro-3-thienyl)carbonyl]-N'-(2-thienylmethyl)thiourea) proposed as mimics of D-fructose 1,6 bisphosphate, (MIC of 20 and 40 μg/ml, respectively).

CONCLUSION

This is a simple yet novel approach that has the potential to identify inhibitors of bacterial growth as illustrated by compounds identified in this study that have activity against Mtb.

ChIP-Seq and the complexity of bacterial transcriptional regulation

Transcription factors (TFs) play a central role in regulating gene expression in all bacteria. Yet, until recently, studies of TF binding were limited to a small number of factors at a few genomic locations. Chromatin immunoprecipitation followed by sequencing enables mapping of binding sites for TFs in a global and high-throughput fashion. The NIAID funded TB systems biology project http://www.broadinstitute.org/annotation/tbsysbio/home.html aims to map the binding sites for every transcription factor in the genome of Mycobacterium tuberculosis (MTB), the causative agent of human TB. ChIP-Seq data already released through TBDB.org have provided new insight into the mechanisms of TB pathogenesis. But in addition, data from MTB are beginning to challenge many simplifying assumptions associated with gene regulation in all bacteria. In this chapter, we review the global aspects of TF binding in MTB and discuss the implications of these data for our understanding of bacterial gene regulation. We begin by reviewing the canonical model of bacterial transcriptional regulation using the lac operon as the standard paradigm. We then review the use of ChIP-Seq to map the binding sites of DNA-binding proteins and the application of this method to mapping TF binding sites in MTB. Finally, we discuss two aspects of the binding discovered by ChIP-Seq that were unexpected given the canonical model: the substantial binding outside the proximal promoter region and the large number of weak binding sites.

MTCID: a database of genetic polymorphisms in clinical isolates of Mycobacterium tuberculosis

Tuberculosis (TB) is a major cause of morbidity and mortality throughout the world, particularly in developing countries. The response of the patients and treatment outcome depends, in addition to diagnosis, appropriate and timely treatment and host factors, on the virulence of Mycobacterium tuberculosis and genetic polymorphism prevalent in clinical isolates of the bacterium. A number of studies have been carried out to characterize clinical isolates of M. tuberculosis obtained from TB patients. However, the data is scattered in a large number of publications. Though attempts have been made to catalog the observed variations, there is no database that has been developed for cataloging, storing and dissemination of genetic polymorphism information. MTCID (M. tuberculosis clinical isolate genetic polymorphism database) is an attempt to provide a comprehensive repository to store, access and disseminate single nucleotide polymorphism (SNPs) and spoligotyping profiles of M. tuberculosis. It can be used to automatically upload the information available with a user that adds to the existing database at the backend. Besides it may also aid in maintaining clinical profiles of TB and treatment of patients. The database has 'search' features and is available at http://ccbb.jnu.ac.in/Tb.

GenomeView: a next-generation genome browser

Due to ongoing advances in sequencing technologies, billions of nucleotide sequences are now produced on a daily basis. A major challenge is to visualize these data for further downstream analysis. To this end, we present GenomeView, a stand-alone genome browser specifically designed to visualize and manipulate a multitude of genomics data. GenomeView enables users to dynamically browse high volumes of aligned short-read data, with dynamic navigation and semantic zooming, from the whole genome level to the single nucleotide. At the same time, the tool enables visualization of whole genome alignments of dozens of genomes relative to a reference sequence. GenomeView is unique in its capability to interactively handle huge data sets consisting of tens of aligned genomes, thousands of annotation features and millions of mapped short reads both as viewer and editor. GenomeView is freely available as an open source software package.

MtbSD--a comprehensive structural database for Mycobacterium tuberculosis

The Mycobacterium tuberculosis Structural Database (MtbSD) (http://bmi.icmr.org.in/mtbsd/MtbSD.php) is a relational database for the study of protein structures of M. tuberculosis. It currently holds information on description, reaction catalyzed and domains involved, active sites, structural homologues and similarities between bound and cognate ligands, for all the 857 protein structures that are available for M. tb proteins. The database will be a valuable resource for TB researchers to select the appropriate protein-ligand complex of a given protein for molecular modelling, docking, virtual screening and structure-based drug designing.

Polymorphisms in isoniazid and prothionamide resistance genes of the Mycobacterium tuberculosis complex

Sequence analyses of 74 strains that encompassed major phylogenetic lineages of the Mycobacterium tuberculosis complex revealed 10 polymorphisms in mshA (Rv0486) and four polymorphisms in inhA (Rv1484) that were not responsible for isoniazid or prothionamide resistance. Instead, some of these mutations were phylogenetically informative. This genetic diversity must be taken into consideration for drug development and for the design of molecular tests for drug resistance.

Overview of errors in the reference sequence and annotation of Mycobacterium tuberculosis H37Rv, and variation amongst its isolates

Since its publication in 1998, the genome sequence of the Mycobacterium tuberculosis H37Rv laboratory strain has acted as the cornerstone for the study of tuberculosis. In this review we address some of the practical aspects that have come to light relating to the use of H37Rv throughout the past decade which are of relevance for the ongoing genomic and laboratory studies of this pathogen. These include errors in the genome reference sequence and its annotation, as well as the recently detected variation amongst isolates of H37Rv from different laboratories.

A systems biology approach to infectious disease research: innovating the pathogen-host research paradigm

The twentieth century was marked by extraordinary advances in our understanding of microbes and infectious disease, but pandemics remain, food and waterborne illnesses are frequent, multidrug-resistant microbes are on the rise, and the needed drugs and vaccines have not been developed. The scientific approaches of the past-including the intense focus on individual genes and proteins typical of molecular biology-have not been sufficient to address these challenges. The first decade of the twenty-first century has seen remarkable innovations in technology and computational methods. These new tools provide nearly comprehensive views of complex biological systems and can provide a correspondingly deeper understanding of pathogen-host interactions. To take full advantage of these innovations, the National Institute of Allergy and Infectious Diseases recently initiated the Systems Biology Program for Infectious Disease Research. As participants of the Systems Biology Program, we think that the time is at hand to redefine the pathogen-host research paradigm.

The impact of transcriptomics on the fight against tuberculosis: focus on biomarkers, BCG vaccination, and immunotherapy

In 1882 Robert Koch identified Mycobacterium tuberculosis as the causative agent of tuberculosis (TB), a disease as ancient as humanity. Although there has been more than 125 years of scientific effort aimed at understanding the disease, serious problems in TB persist that contribute to the estimated 1/3 of the world population infected with this pathogen. Nonetheless, during the first decade of the 21st century, there were new advances in the fight against TB. The development of high-throughput technologies is one of the major contributors to this advance, because it allows for a global vision of the biological phenomenon. This paper analyzes how transcriptomics are supporting the translation of basic research into therapies by resolving three key issues in the fight against TB: (a) the discovery of biomarkers, (b) the explanation of the variability of protection conferred by BCG vaccination, and (c) the development of new immunotherapeutic strategies to treat TB.

Transcriptional profiling of mycobacterium tuberculosis during infection: lessons learned

Infection with Mycobacterium tuberculosis, the causative agent of tuberculosis, is considered one of the biggest infectious disease killers worldwide. A significant amount of attention has been directed toward revealing genes involved in the virulence and pathogenesis of this air-born pathogen. With the advances in technologies for transcriptional profiling, several groups, including ours, took advantage of DNA microarrays to identify transcriptional units differentially regulated by M. tuberculosis within a host. The main idea behind this approach is that pathogens tend to regulate their gene expression levels depending on the host microenvironment, and preferentially express those needed for survival. Identifying this class of genes will improve our understanding of pathogenesis. In our case, we identified an in vivo expressed genomic island that was preferentially active in murine lungs during early infection, as well as groups of genes active during chronic tuberculosis. Other studies have identified additional gene groups that are active during macrophage infection and even in human lungs. Despite all of these findings, one of the lingering questions remaining was whether in vivo expressed transcripts are relevant to the virulence, pathogenesis, and persistence of the organism. The work of our group and others addressed this question by examining the contribution of in vivo expressed genes using a strategy based on gene deletions followed by animal infections. Overall, the analysis of most of the in vivo expressed genes supported a role of these genes in M. tuberculosis pathogenesis. Further, these data suggest that in vivo transcriptional profiling is a valid approach to identify genes required for bacterial pathogenesis.