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

MtrA modulates Mycobacterium tuberculosis cell division in host microenvironments to mediate intrinsic resistance and drug tolerance

The success of Mycobacterium tuberculosis (Mtb) is largely attributed to its ability to physiologically adapt and withstand diverse localized stresses within host microenvironments. Here, we present a data-driven model (EGRIN 2.0) that captures the dynamic interplay of environmental cues and genome-encoded regulatory programs in Mtb. Analysis of EGRIN 2.0 shows how modulation of the MtrAB two-component signaling system tunes Mtb growth in response to related host microenvironmental cues. Disruption of MtrAB by tunable CRISPR interference confirms that the signaling system regulates multiple peptidoglycan hydrolases, among other targets, that are important for cell division. Further, MtrA decreases the effectiveness of antibiotics by mechanisms of both intrinsic resistance and drug tolerance. Together, the model-enabled dissection of complex MtrA regulation highlights its importance as a drug target and illustrates how EGRIN 2.0 facilitates discovery and mechanistic characterization of Mtb adaptation to specific host microenvironments within the host.

Using a whole genome co-expression network to inform the functional characterisation of predicted genomic elements from Mycobacterium tuberculosis transcriptomic data

A whole genome co-expression network was created using Mycobacterium tuberculosis transcriptomic data from publicly available RNA-sequencing experiments covering a wide variety of experimental conditions. The network includes expressed regions with no formal annotation, including putative short RNAs and untranslated regions of expressed transcripts, along with the protein-coding genes. These unannotated expressed transcripts were among the best-connected members of the module sub-networks, making up more than half of the 'hub' elements in modules that include protein-coding genes known to be part of regulatory systems involved in stress response and host adaptation. This data set provides a valuable resource for investigating the role of non-coding RNA, and conserved hypothetical proteins, in transcriptomic remodelling. Based on their connections to genes with known functional groupings and correlations with replicated host conditions, predicted expressed transcripts can be screened as suitable candidates for further experimental validation.

Mycobacterium tuberculosis transcriptional regulator Rv1019 is upregulated in hypoxia, and negatively regulates Rv3230c-Rv3229c operon encoding enzymes in the oleic acid biosynthetic pathway

The main obstacle in eradicating tuberculosis is the ability of Mycobacterium tuberculosis to remain dormant in the host, and then to get reactivated even years later under immunocompromised conditions. Transcriptional regulation in intracellular pathogens plays an important role in their adapting to the challenging environment inside the host cells. Previously, we demonstrated that Rv1019, a putative transcriptional regulator of M. tuberculosis H37Rv, is an autorepressor. We showed that Rv1019 is cotranscribed with Rv1020 (mfd) and Rv1021 (mazG) which encode DNA repair proteins and negatively regulates the expression of these genes. In the present study, we show that Rv1019 regulates the expression of the genes Rv3230c and Rv3229c (desA3) also which form a two-gene operon in M. tuberculosis. Overexpression of Rv1019 in M. tuberculosis significantly downregulated the expression of these genes. Employing Wayne's hypoxia-induced dormancy model of M. tuberculosis, we show that Rv1019 is upregulated three-fold under hypoxia. Finally, by reporter assay, using Mycobacterium smegmatis as a model, we validate that Rv1019 is recruited to the promoter of Rv3230c-Rv3229c during hypoxia, and negatively regulates this operon which is involved in the biosynthesis of oleic acid.

A systematic computational analysis of Mycobacterium tuberculosis H37Rv and human CD34+ genomic expression reveals crucial molecular entities involved in infection progression

The co-evolution of Mycobacterium tuberculosis H37Rv along with its host systems enables the pathogenic bacterium to emerge as a multi-drug resistant form. This creates challenges for a more efficacious treatment strategy that can mitigate the infection. Working towards the same, our study followed a mathematical and statistical approach proposing that mycobacterial transcription factors regulating virulence and adaptation, host cell cytoplasmic component metabolism, oxidoreductase activity and respiratory ETC would be targets for antibiotics against Mycobacterium tuberculosis. Simultaneously, extending the statistical study on Mycobacterium-infected human cord blood CD34+ cells revealed that the human CD34+ genes, S100A8 and FGR (tyrosine-protein kinase, Src2), might be affected in the infection pathogenesis by Mycobacterium. Further, the deduced Mycobacterium-human gene interaction network proposed that mycobacterial coregulators Rv0452 (MarR family regulator) and Rv3862c (WhiB6) triggered genes controlling bacterial metabolism, which influences human immunological pathways involving TLR2 and CXCL8/MAPK8.Communicated by Ramaswamy H. Sarma.

A Feedback Regulatory Loop Containing McdR and WhiB2 Controls Cell Division and DNA Repair in Mycobacteria

Cell division must be coordinated with DNA repair, which is strictly regulated in response to different drugs and environmental stresses in bacteria. However, the mechanisms by which mycobacteria orchestrate these two processes remain largely uncharacterized. Here, we report a regulatory loop between two essential mycobacterial regulators, McdR (Rv1830) and WhiB2, in coordinating the processes of cell division and DNA repair. McdR inhibits cell division-associated whiB2 expression by binding to the AATnACAnnnnTGTnATT motif in the promoter region. Furthermore, McdR overexpression simultaneously activates imuAB and dnaE2 expression to promote error-prone DNA repair, which facilitates genetic adaptation to stress conditions. Through a feedback mechanism, WhiB2 activates mcdR expression by binding to the cGACACGc motif in the promoter region. Importantly, analyses of mutations in clinical Mycobacterium tuberculosis strains indicate that disruption of this McdR-WhiB2 feedback regulatory loop influences expression of both cell growth- and DNA repair-associated genes, which further supports the contribution of McdR-WhiB2 regulatory loop in regulating mycobacterial cell growth and drug resistance. This highly conserved feedback regulatory loop provides fresh insight into the link between mycobacterial cell growth control and stress responses. IMPORTANCE Drug-resistant M. tuberculosis poses a threat to the control and prevention of tuberculosis (TB) worldwide. Thus, there is a need to identify the mechanisms enabling M. tuberculosis to adapt and grow under drug-induced stress. Rv1830 has been shown to be associated with drug resistance in M. tuberculosis, but its mechanisms have not yet been elucidated. Here, we reveal a regulatory role of Rv1830, which coordinates cell division and DNA repair in mycobacteria, and rename it McdR (mycobacterial cell division regulator). An increase in McdR levels represses the expression of cell division-associated whiB2 but activates the DNA repair-associated, error-prone enzymes ImuA/B and DnaE2, which in turn facilitates adaptation to stress responses and drug resistance. Furthermore, WhiB2 activates the transcription of mcdR to form a conserved regulatory loop. These data provide new insights into the mechanisms controlling mycobacterial cell growth and stress responses.

Machine Learning of All Mycobacterium tuberculosis H37Rv RNA-seq Data Reveals a Structured Interplay between Metabolism, Stress Response, and Infection

Mycobacterium tuberculosis is one of the most consequential human bacterial pathogens, posing a serious challenge to 21st century medicine. A key feature of its pathogenicity is its ability to adapt its transcriptional response to environmental stresses through its transcriptional regulatory network (TRN). While many studies have sought to characterize specific portions of the M. tuberculosis TRN, and some studies have performed system-level analysis, few have been able to provide a network-based model of the TRN that also provides the relative shifts in transcriptional regulator activity triggered by changing environments. Here, we compiled a compendium of nearly 650 publicly available, high quality M. tuberculosis RNA-sequencing data sets and applied an unsupervised machine learning method to obtain a quantitative, top-down TRN. It consists of 80 independently modulated gene sets known as “iModulons,” 41 of which correspond to known regulons. These iModulons explain 61% of the variance in the organism’s transcriptional response. We show that iModulons (i) reveal the function of poorly characterized regulons, (ii) describe the transcriptional shifts that occur during environmental changes such as shifting carbon sources, oxidative stress, and infection events, and (iii) identify intrinsic clusters of regulons that link several important metabolic systems, including lipid, cholesterol, and sulfur metabolism. This transcriptome-wide analysis of the M. tuberculosis TRN informs future research on effective ways to study and manipulate its transcriptional regulation and presents a knowledge-enhanced database of all published high-quality RNA-seq data for this organism to date.

IMPORTANCEMycobacterium tuberculosis H37Rv is one of the world's most impactful pathogens, and a large part of the success of the organism relies on the differential expression of its genes to adapt to its environment. The expression of the organism's genes is driven primarily by its transcriptional regulatory network, and most research on the TRN focuses on identifying and quantifying clusters of coregulated genes known as regulons. While previous studies have relied on molecular measurements, in the manuscript we utilized an alternative technique that performs machine learning to a large data set of transcriptomic data. This approach is less reliant on hypotheses about the role of specific regulatory systems and allows for the discovery of new biological findings for already collected data. A better understanding of the structure of the M. tuberculosis TRN will have important implications in the design of improved therapeutic approaches.

Pre-Clinical Tools for Predicting Drug Efficacy in Treatment of Tuberculosis

Combination therapy has, to some extent, been successful in limiting the emergence of drug-resistant tuberculosis. Drug combinations achieve this advantage by simultaneously acting on different targets and metabolic pathways. Additionally, drug combination therapies are shown to shorten the duration of therapy for tuberculosis. As new drugs are being developed, to overcome the challenge of finding new and effective drug combinations, systems biology commonly uses approaches that analyse mycobacterial cellular processes. These approaches identify the regulatory networks, metabolic pathways, and signaling programs associated with M. tuberculosis infection and survival. Different preclinical models that assess anti-tuberculosis drug activity are available, but the combination of models that is most predictive of clinical treatment efficacy remains unclear. In this structured literature review, we appraise the options to accelerate the TB drug development pipeline through the evaluation of preclinical testing assays of drug combinations.

Transcriptome signature of cell viability predicts drug response and drug interaction in Mycobacterium tuberculosis

There is an urgent need for new drug regimens to rapidly cure tuberculosis. Here, we report the development of drug response assayer (DRonA) and "MLSynergy," algorithms to perform rapid drug response assays and predict response of Mycobacterium tuberculosis (Mtb) to drug combinations. Using a transcriptome signature for cell viability, DRonA detects Mtb killing by diverse mechanisms in broth culture, macrophage infection, and patient sputum, providing an efficient and more sensitive alternative to time- and resource-intensive bacteriologic assays. Further, MLSynergy builds on DRonA to predict synergistic and antagonistic multidrug combinations using transcriptomes of Mtb treated with single drugs. Together, DRonA and MLSynergy represent a generalizable framework for rapid monitoring of drug effects in host-relevant contexts and accelerate the discovery of efficacious high-order drug combinations.

Quantitative prediction of conditional vulnerabilities in regulatory and metabolic networks using PRIME

The ability of Mycobacterium tuberculosis (Mtb) to adopt heterogeneous physiological states underlies its success in evading the immune system and tolerating antibiotic killing. Drug tolerant phenotypes are a major reason why the tuberculosis (TB) mortality rate is so high, with over 1.8 million deaths annually. To develop new TB therapeutics that better treat the infection (faster and more completely), a systems-level approach is needed to reveal the complexity of network-based adaptations of Mtb. Here, we report a new predictive model called PRIME (Phenotype of Regulatory influences Integrated with Metabolism and Environment) to uncover environment-specific vulnerabilities within the regulatory and metabolic networks of Mtb. Through extensive performance evaluations using genome-wide fitness screens, we demonstrate that PRIME makes mechanistically accurate predictions of context-specific vulnerabilities within the integrated regulatory and metabolic networks of Mtb, accurately rank-ordering targets for potentiating treatment with frontline drugs.

Predictive regulatory and metabolic network models for systems analysis of Clostridioides difficile

We present predictive models for comprehensive systems analysis of Clostridioides difficile, the etiology of pseudomembranous colitis. By leveraging 151 published transcriptomes, we generated an EGRIN model that organizes 90% of C. difficile genes into a transcriptional regulatory network of 297 co-regulated modules, implicating genes in sporulation, carbohydrate transport, and metabolism. By advancing a metabolic model through addition and curation of metabolic reactions including nutrient uptake, we discovered 14 amino acids, diverse carbohydrates, and 10 metabolic genes as essential for C. difficile growth in the intestinal environment. Finally, we developed a PRIME model to uncover how EGRIN-inferred combinatorial gene regulation by transcription factors, such as CcpA and CodY, modulates essential metabolic processes to enable C. difficile growth relative to commensal colonization. The C. difficile interactive web portal provides access to these model resources to support collaborative systems-level studies of context-specific virulence mechanisms in C. difficile.

Expression Dysregulation as a Mediator of Fitness Costs in Antibiotic Resistance

Antimicrobial resistance (AMR) poses a threat to global health and the economy. Rifampicin-resistant Mycobacterium tuberculosis accounts for a third of the global AMR burden. Gaining the upper hand on AMR requires a deeper understanding of the physiology of resistance. AMR often results in a fitness cost in the absence of drug. Identifying the molecular mechanisms underpinning this cost could help strengthen future treatment regimens. Here, we used a collection of M. tuberculosis strains that provide an evolutionary and phylogenetic snapshot of rifampicin resistance and subjected them to genome-wide transcriptomic and proteomic profiling to identify key perturbations of normal physiology. We found that the clinically most common rifampicin resistance-conferring mutation, RpoB Ser450Leu, imparts considerable gene expression changes, many of which are mitigated by the compensatory mutation in RpoC Leu516Pro. However, our data also provide evidence for pervasive epistasis—the same resistance mutation imposed a different fitness cost and functionally distinct changes to gene expression in genetically unrelated clinical strains. Finally, we report a likely posttranscriptional modulation of gene expression that is shared in most of the tested strains carrying RpoB Ser450Leu, resulting in an increased abundance of proteins involved in central carbon metabolism. These changes contribute to a more general trend in which the disruption of the composition of the proteome correlates with the fitness cost of the RpoB Ser450Leu mutation in different strains.

History and Future Perspectives on the Discipline of Quantitative Systems Pharmacology Modeling and Its Applications

Mathematical biology and pharmacology models have a long and rich history in the fields of medicine and physiology, impacting our understanding of disease mechanisms and the development of novel therapeutics. With an increased focus on the pharmacology application of system models and the advances in data science spanning mechanistic and empirical approaches, there is a significant opportunity and promise to leverage these advancements to enhance the development and application of the systems pharmacology field. In this paper, we will review milestones in the evolution of mathematical biology and pharmacology models, highlight some of the gaps and challenges in developing and applying systems pharmacology models, and provide a vision for an integrated strategy that leverages advances in adjacent fields to overcome these challenges.

A systems-level gene regulatory network model for Plasmodium falciparum

Many of the gene regulatory processes of Plasmodium falciparum, the deadliest malaria parasite, remain poorly understood. To develop a comprehensive guide for exploring this organism's gene regulatory network, we generated a systems-level model of P. falciparum gene regulation using a well-validated, machine-learning approach for predicting interactions between transcription regulators and their targets. The resulting network accurately predicts expression levels of transcriptionally coherent gene regulatory programs in independent transcriptomic data sets from parasites collected by different research groups in diverse laboratory and field settings. Thus, our results indicate that our gene regulatory model has predictive power and utility as a hypothesis-generating tool for illuminating clinically relevant gene regulatory mechanisms within P. falciparum. Using the set of regulatory programs we identified, we also investigated correlates of artemisinin resistance based on gene expression coherence. We report that resistance is associated with incoherent expression across many regulatory programs, including those controlling genes associated with erythrocyte-host engagement. These results suggest that parasite populations with reduced artemisinin sensitivity are more transcriptionally heterogenous. This pattern is consistent with a model where the parasite utilizes bet-hedging strategies to diversify the population, rendering a subpopulation more able to navigate drug treatment.

Drivers and sites of diversity in the DNA adenine methylomes of 93 Mycobacterium tuberculosis complex clinical isolates

This study assembles DNA adenine methylomes for 93 Mycobacterium tuberculosis complex (MTBC) isolates from seven lineages paired with fully-annotated, finished, de novo assembled genomes. Integrative analysis yielded four key results. First, methyltransferase allele-methylome mapping corrected methyltransferase variant effects previously obscured by reference-based variant calling. Second, heterogeneity analysis of partially active methyltransferase alleles revealed that intracellular stochastic methylation generates a mosaic of methylomes within isogenic cultures, which we formalize as ‘intercellular mosaic methylation’ (IMM). Mutation-driven IMM was nearly ubiquitous in the globally prominent Beijing sublineage. Third, promoter methylation is widespread and associated with differential expression in the ΔhsdM transcriptome, suggesting promoter HsdM-methylation directly influences transcription. Finally, comparative and functional analyses identified 351 sites hypervariable across isolates and numerous putative regulatory interactions. This multi-omic integration revealed features of methylomic variability in clinical isolates and provides a rational basis for hypothesizing the functions of DNA adenine methylation in MTBC physiology and adaptive evolution.

It is time to apply biclustering: a comprehensive review of biclustering applications in biological and biomedical data

Biclustering is a powerful data mining technique that allows clustering of rows and columns, simultaneously, in a matrix-format data set. It was first applied to gene expression data in 2000, aiming to identify co-expressed genes under a subset of all the conditions/samples. During the past 17 years, tens of biclustering algorithms and tools have been developed to enhance the ability to make sense out of large data sets generated in the wake of high-throughput omics technologies. These algorithms and tools have been applied to a wide variety of data types, including but not limited to, genomes, transcriptomes, exomes, epigenomes, phenomes and pharmacogenomes. However, there is still a considerable gap between biclustering methodology development and comprehensive data interpretation, mainly because of the lack of knowledge for the selection of appropriate biclustering tools and further supporting computational techniques in specific studies. Here, we first deliver a brief introduction to the existing biclustering algorithms and tools in public domain, and then systematically summarize the basic applications of biclustering for biological data and more advanced applications of biclustering for biomedical data. This review will assist researchers to effectively analyze their big data and generate valuable biological knowledge and novel insights with higher efficiency.

System OMICs analysis of Mycobacterium tuberculosis Beijing B0/W148 cluster

Mycobacterium tuberculosis Beijing B0/W148 is one of the most widely distributed clusters in the Russian Federation and in some countries of the former Soviet Union. Recent studies have improved our understanding of the reasons for the "success" of the cluster but this area remains incompletely studied. Here, we focused on the system omics analysis of the RUS_B0 strain belonging to the Beijing B0/W148 cluster. Completed genome sequence of RUS_B0 (CP020093.1) and a collection of WGS for 394 cluster strains were used to describe the main genetic features of the population. In turn, proteome and transcriptome studies allowed to confirm the genomic data and to identify a number of finds that have not previously been described. Our results demonstrated that expression of the whiB6 which contains cluster-specific polymorphism (a151c) increased almost 40 times in RUS_B0. Additionally, the level of ethA transcripts in RUS_B0 was increased by more than 7 times compared to the H37Rv. Start sites for 10 genes were corrected based on the combination of proteomic and transcriptomic data. Additionally, based on the omics approach, we identified 5 new genes. In summary, our analysis allowed us to summarize the available results and also to obtain fundamentally new data.

Translational regulation in mycobacteria and its implications for pathogenicity

Protein synthesis is a fundamental requirement of all cells for survival and replication. To date, vast numbers of genetic and biochemical studies have been performed to address the mechanisms of translation and its regulation in Escherichia coli, but only a limited number of studies have investigated these processes in other bacteria, particularly in slow growing bacteria like Mycobacterium tuberculosis, the causative agent of human tuberculosis. In this Review, we highlight important differences in the translational machinery of M. tuberculosis compared with E. coli, specifically the presence of two additional proteins and subunit stabilizing elements such as the B9 bridge. We also consider the role of leaderless translation in the ability of M. tuberculosis to establish latent infection and look at the experimental evidence that translational regulatory mechanisms operate in mycobacteria during stress adaptation, particularly focussing on differences in toxin-antitoxin systems between E. coli and M. tuberculosis and on the role of tuneable translational fidelity in conferring phenotypic antibiotic resistance. Finally, we consider the implications of these differences in the context of the biological adaptation of M. tuberculosis and discuss how these regulatory mechanisms could aid in the development of novel therapeutics for tuberculosis.

Comparative label-free lipidomic analysis of Mycobacterium tuberculosis during dormancy and reactivation

Mycobacterium tuberculosis employs several strategies to combat and adapt to adverse conditions encountered inside the host. The non-replicative dormant state of the bacterium is linked to drug resistance and slower response to anti-tubercular therapy. It is known that alterations in lipid content allow dormant bacteria to acclimatize to cellular stress. Employing comparative lipidomic analysis we profiled the changes in lipid metabolism in M. tuberculosis using a modified Wayne’s model of hypoxia-induced dormancy. Further we subjected the dormant bacteria to resuscitation, and analyzed their lipidomes until the lipid profile was similar to that of normoxially grown bacteria. An enhanced degradation of cell wall-associated and cytoplasmic lipids during dormancy, and their gradual restoration during reactivation, were clearly evident. This study throws light on distinct lipid metabolic patterns that M. tuberculosis undergoes to maintain its cellular energetics during dormancy and reactivation.

Path-seq identifies an essential mycolate remodeling program for mycobacterial host adaptation

The success of Mycobacterium tuberculosis (MTB) stems from its ability to remain hidden from the immune system within macrophages. Here, we report a new technology (Path-seq) to sequence miniscule amounts of MTB transcripts within up to million-fold excess host RNA Using Path-seq and regulatory network analyses, we have discovered a novel transcriptional program for in vivo mycobacterial cell wall remodeling when the pathogen infects alveolar macrophages in mice. We have discovered that MadR transcriptionally modulates two mycolic acid desaturases desA1/desA2 to initially promote cell wall remodeling upon in vitro macrophage infection and, subsequently, reduces mycolate biosynthesis upon entering dormancy. We demonstrate that disrupting MadR program is lethal to diverse mycobacteria making this evolutionarily conserved regulator a prime antitubercular target for both early and late stages of infection.

Adipocyte Model of Mycobacterium tuberculosis Infection Reveals Differential Availability of Iron to Bacilli in the Lipid-Rich Caseous Environment

Mycobacterium tuberculosis, a successful human pathogen, utilizes multiple carbon sources from the host but adapts to a fatty-acid-rich environment in vivo. We sought to delineate the physiologic response of M. tuberculosis to a lipid-rich environment by using differentiated adipocytes as a model system. Global transcriptome profiling based on RNA sequencing was performed for bacilli from infected adipocytes and preadipocytes. Genes involved in de novo fatty acid synthesis were downregulated, while those predicted to be involved in triglyceride biosynthesis were upregulated, in bacilli isolated from adipocytes, indicating reliance on host-derived fatty acids. Transcription factor network analysis indicated suppression of IdeR-regulated genes, suggesting decreased iron uptake by M. tuberculosis in the adipocyte model. This suppression of iron uptake coincided with higher ferritin and iron levels in adipocytes than in preadipocytes. In accord with the role of iron in mediating oxidative stress, we observed upregulation of genes involved in mitigating oxidative stress in M. tuberculosis isolated from adipocytes. We provide evidence that oleic acid, a major host-derived fatty acid, helps reduce the bacterial cytoplasm, thereby providing a safe haven for an M. tuberculosis mutant that is sensitive to iron-mediated oxidative stress. Via an independent mechanism, host ferritin is also able to rescue the growth of this mutant. Our work highlights the inherent synergy between macronutrients and micronutrients of the host environment that converge to provide resilience to the pathogen. This complex synergy afforded by the adipocyte model of infection will aid in the identification of genes required by M. tuberculosis in a caseous host environment.

Antigen Availability Shapes T Cell Differentiation and Function during Tuberculosis

CD4 T cells are critical for protective immunity against Mycobacterium tuberculosis (Mtb), the cause of tuberculosis (TB). Yet to date, TB vaccine candidates that boost antigen-specific CD4 T cells have conferred little or no protection. Here we examined CD4 T cell responses to two leading TB vaccine antigens, ESAT-6 and Ag85B, in Mtb-infected mice and in vaccinated humans with and without underlying Mtb infection. In both species, Mtb infection drove ESAT-6-specific T cells to be more differentiated than Ag85B-specific T cells. The ability of each T cell population to control Mtb in the lungs of mice was restricted for opposite reasons: Ag85B-specific T cells were limited by reduced antigen expression during persistent infection, whereas ESAT-6-specific T cells became functionally exhausted due to chronic antigenic stimulation. Our findings suggest that different vaccination strategies will be required to optimize protection mediated by T cells recognizing antigens expressed at distinct stages of Mtb infection.

Delayed effects of transcriptional responses in Mycobacterium tuberculosis exposed to nitric oxide suggest other mechanisms involved in survival

Mycobacterium tuberculosis has succeeded as a human pathogen for tens of thousands of years thanks to its ability to resist and adapt to the adverse conditions it encounters upon infection. Bacterial adaptation to stress is commonly viewed in the context of transcriptional regulation, with the implicit expectation that an initial transcriptomic response is tightly coupled to an ensuing proteomic response. However, after challenging M. tuberculosis with nitric oxide we found that the rapid transcriptional responses, detectable within minutes of nitric oxide exposure, typically took several hours to manifest on the protein level. Furthermore, early proteomic responses were dominated by the degradation of a set of proteins, specifically those containing damaged iron-sulphur clusters. Overall, our findings are consistent with transcriptional responses participating mostly in late-stage recovery rather than in generating an immediate resistance to nitric oxide stress, suggesting that survival of M. tuberculosis under acute stress is contingent on mechanisms other than transcriptional regulation. These findings provide a revised molecular understanding of an important human pathogen.

Proteomics for the Investigation of Mycobacteria

The physiology of Mycobacterium tuberculosis, the causative agent of tuberculosis, is being studied with intensity. However, despite the genomic and transcriptomic data available today, the pathogenic potential of these bacteria remains poorly understood. Therefore, proteomic approaches seem relevant in studying mycobacteria. This review covers the main stages in the proteomic analysis methods used to study mycobacteria. The main achievements in the area of M. tuberculosis proteomics are described in general. Special attention is paid to the proteomic features of the Beijing family, which is widespread in Russia. Considering that the proteome is a set of all the proteins in the cell, post-translational modifications of mycobacterium proteins are also described.

Transcriptional and Physiological Changes during Mycobacterium tuberculosis Reactivation from Non-replicating Persistence

Mycobacterium tuberculosis can persist for years in the hostile environment of the host in a non-replicating or slowly replicating state. While active disease predominantly results from reactivation of a latent infection, the molecular mechanisms of M. tuberculosis reactivation are still poorly understood. We characterized the physiology and global transcriptomic profiles of M. tuberculosis during reactivation from hypoxia-induced non-replicating persistence. We found that M. tuberculosis reactivation upon reaeration was associated with a lag phase, in which the recovery of cellular physiological and metabolic functions preceded the resumption of cell replication. Enrichment analysis of the transcriptomic dynamics revealed changes to many metabolic pathways and transcription regulons/subnetworks that orchestrated the metabolic and physiological transformation in preparation for cell division. In particular, we found that M. tuberculosis reaeration lag phase is associated with down-regulation of persistence-associated regulons/subnetworks, including DosR, MprA, SigH, SigE, and ClgR, as well as metabolic pathways including those involved in the uptake of lipids and their catabolism. More importantly, we identified a number of up-regulated transcription regulons and metabolic pathways, including those involved in metal transport and remobilization, second messenger-mediated responses, DNA repair and recombination, and synthesis of major cell wall components. We also found that inactivation of the major alternative sigma factors SigE or SigH disrupted exit from persistence, underscoring the importance of the global transcriptional reprogramming during M. tuberculosis reactivation. Our observations suggest that M. tuberculosis lag phase is associated with a global gene expression reprogramming that defines the initiation of a reactivation process.

Proteome analysis of the Mycobacterium tuberculosis Beijing B0/W148 cluster

Beijing B0/W148, a "successful" clone of Mycobacterium tuberculosis, is widespread in the Russian Federation and some countries of the former Soviet Union. Here, we used label-free gel-LC-MS/MS shotgun proteomics to discover features of Beijing B0/W148 strains that could explain their success. Qualitative and quantitative proteome analyses of Beijing B0/W148 strains allowed us to identify 1,868 proteins, including 266 that were differentially abundant compared with the control strain H37Rv. To predict the biological effects of the observed differences in protein abundances, we performed Gene Ontology analysis together with analysis of protein-DNA interactions using a gene regulatory network. Our results demonstrate that Beijing B0/W148 strains have increased levels of enzymes responsible for long-chain fatty acid biosynthesis, along with a coincident decrease in the abundance of proteins responsible for their degradation. Together with high levels of HsaA (Rv3570c) protein, involved in steroid degradation, these findings provide a possible explanation for the increased transmissibility of Beijing B0/W148 strains and their survival in host macrophages. Among other, we confirmed a very low level of the SseA (Rv3283) protein in Beijing B0/W148 characteristic for all «modern» Beijing strains, which could lead to increased DNA oxidative damage, accumulation of mutations, and potentially facilitate the development of drug resistance.

Construction and application of a co-expression network in Mycobacterium tuberculosis

Because of its high pathogenicity and infectivity, tuberculosis is a serious threat to human health. Some information about the functions of the genes in Mycobacterium tuberculosis genome was currently available, but it was not enough to explore transcriptional regulatory mechanisms. Here, we applied the WGCNA (Weighted Gene Correlation Network Analysis) algorithm to mine pooled microarray datasets for the M. tuberculosis H37Rv strain. We constructed a co-expression network that was subdivided into 78 co-expression gene modules. The different response to two kinds of vitro models (a constant 0.2% oxygen hypoxia model and a Wayne model) were explained based on these modules. We identified potential transcription factors based on high Pearson’s correlation coefficients between the modules and genes. Three modules that may be associated with hypoxic stimulation were identified, and their potential transcription factors were predicted. In the validation experiment, we determined the expression levels of genes in the modules under hypoxic condition and under overexpression of potential transcription factors (Rv0081, furA (Rv1909c), Rv0324, Rv3334, and Rv3833). The experimental results showed that the three identified modules related to hypoxia and that the overexpression of transcription factors could significantly change the expression levels of genes in the corresponding modules.

Network analysis identifies Rv0324 and Rv0880 as regulators of bedaquiline tolerance in Mycobacterium tuberculosis

Resilience of Mycobacterium tuberculosis (MTB) emerges from its ability to effectively counteract immunological, environmental, and antitubercular challenges. Here, we demonstrate that MTB can tolerate drug treatment by adopting a tolerant state that can be deciphered through systems analysis of its transcriptional responses. Specifically, we demonstrate how treatment with the antitubercular drug bedaquiline activates a regulatory network that coordinates multiple resistance mechanisms to push MTB into a tolerant state. Disruption of this network, by knocking out its predicted transcription factors, Rv0324 and Rv0880, significantly increased bedaquiline killing and enabled the discovery of a second drug pretomanid that potentiated killing by bedaquiline. We demonstrate that the synergistic effect of this combination emerges, in part, through disruption of the tolerance network. We discuss how this network strategy also predicts drug combinations with antagonistic interactions, potentially accelerating the discovery of new effective combination drug regimens for TB.

Reconstruction and topological characterization of the sigma factor regulatory network of Mycobacterium tuberculosis

Accessory sigma factors, which reprogram RNA polymerase to transcribe specific gene sets, activate bacterial adaptive responses to noxious environments. Here we reconstruct the complete sigma factor regulatory network of the human pathogen Mycobacterium tuberculosis by an integrated approach. The approach combines identification of direct regulatory interactions between M. tuberculosis sigma factors in an E. coli model system, validation of selected links in M. tuberculosis, and extensive literature review. The resulting network comprises 41 direct interactions among all 13 sigma factors. Analysis of network topology reveals (i) a three-tiered hierarchy initiating at master regulators, (ii) high connectivity and (iii) distinct communities containing multiple sigma factors. These topological features are likely associated with multi-layer signal processing and specialized stress responses involving multiple sigma factors. Moreover, the identification of overrepresented network motifs, such as autoregulation and coregulation of sigma and anti-sigma factor pairs, provides structural information that is relevant for studies of network dynamics.

Sigma factors are regulatory proteins that reprogram the bacterial RNA polymerase in response to stress conditions to transcribe certain genes, including those for other sigma factors. Here, Chauhan et al. describe the complete sigma factor regulatory network of the pathogen Mycobacterium tuberculosis.

BioTapestry now provides a web application and improved drawing and layout tools

Gene regulatory networks (GRNs) control embryonic development, and to understand this process in depth, researchers need to have a detailed understanding of both the network architecture and its dynamic evolution over time and space. Interactive visualization tools better enable researchers to conceptualize, understand, and share GRN models. BioTapestry is an established application designed to fill this role, and recent enhancements released in Versions 6 and 7 have targeted two major facets of the program. First, we introduced significant improvements for network drawing and automatic layout that have now made it much easier for the user to create larger, more organized network drawings. Second, we revised the program architecture so it could continue to support the current Java desktop Editor program, while introducing a new BioTapestry GRN Viewer that runs as a JavaScript web application in a browser. We have deployed a number of GRN models using this new web application. These improvements will ensure that BioTapestry remains viable as a research tool in the face of the continuing evolution of web technologies, and as our understanding of GRN models grows.

An experimentally supported model of the Bacillus subtilis global transcriptional regulatory network

Organisms from all domains of life use gene regulation networks to control cell growth, identity, function, and responses to environmental challenges. Although accurate global regulatory models would provide critical evolutionary and functional insights, they remain incomplete, even for the best studied organisms. Efforts to build comprehensive networks are confounded by challenges including network scale, degree of connectivity, complexity of organism–environment interactions, and difficulty of estimating the activity of regulatory factors. Taking advantage of the large number of known regulatory interactions in Bacillus subtilis and two transcriptomics datasets (including one with 38 separate experiments collected specifically for this study), we use a new combination of network component analysis and model selection to simultaneously estimate transcription factor activities and learn a substantially expanded transcriptional regulatory network for this bacterium. In total, we predict 2,258 novel regulatory interactions and recall 74% of the previously known interactions. We obtained experimental support for 391 (out of 635 evaluated) novel regulatory edges (62% accuracy), thus significantly increasing our understanding of various cell processes, such as spore formation.

Cis-Antisense Transcription Gives Rise to Tunable Genetic Switch Behavior: A Mathematical Modeling Approach

Antisense transcription has been extensively recognized as a regulatory mechanism for gene expression across all kingdoms of life. Despite the broad importance and extensive experimental determination of cis-antisense transcription, relatively little is known about its role in controlling cellular switching responses. Growing evidence suggests the presence of non-coding cis-antisense RNAs that regulate gene expression via antisense interaction. Recent studies also indicate the role of transcriptional interference in regulating expression of neighboring genes due to traffic of RNA polymerases from adjacent promoter regions. Previous models investigate these mechanisms independently, however, little is understood about how cells utilize coupling of these mechanisms in advantageous ways that could also be used to design novel synthetic genetic devices. Here, we present a mathematical modeling framework for antisense transcription that combines the effects of both transcriptional interference and cis-antisense regulation. We demonstrate the tunability of transcriptional interference through various parameters, and that coupling of transcriptional interference with cis-antisense RNA interaction gives rise to hypersensitive switches in expression of both antisense genes. When implementing additional positive and negative feed-back loops from proteins encoded by these genes, the system response acquires a bistable behavior. Our model shows that combining these multiple-levels of regulation allows fine-tuning of system parameters to give rise to a highly tunable output, ranging from a simple-first order response to biologically complex higher-order response such as tunable bistable switch. We identify important parameters affecting the cellular switch response in order to provide the design principles for tunable gene expression using antisense transcription. This presents an important insight into functional role of antisense transcription and its importance towards design of synthetic biological switches.

cMonkey2: Automated, systematic, integrated detection of co-regulated gene modules for any organism

The cMonkey integrated biclustering algorithm identifies conditionally co-regulated modules of genes (biclusters). cMonkey integrates various orthogonal pieces of information which support evidence of gene co-regulation, and optimizes biclusters to be supported simultaneously by one or more of these prior constraints. The algorithm served as the cornerstone for constructing the first global, predictive Environmental Gene Regulatory Influence Network (EGRIN) model for a free-living cell, and has now been applied to many more organisms. However, due to its computational inefficiencies, long run-time and complexity of various input data types, cMonkey was not readily usable by the wider community. To address these primary concerns, we have significantly updated the cMonkey algorithm and refactored its implementation, improving its usability and extendibility. These improvements provide a fully functioning and user-friendly platform for building co-regulated gene modules and the tools necessary for their exploration and interpretation. We show, via three separate analyses of data for E. coli, M. tuberculosis and H. sapiens, that the updated algorithm and inclusion of novel scoring functions for new data types (e.g. ChIP-seq and transcription factor over-expression [TFOE]) improve discovery of biologically informative co-regulated modules. The complete cMonkey2 software package, including source code, is available at https://github.com/baliga-lab/cmonkey2.

Mapping and manipulating the Mycobacterium tuberculosis transcriptome using a transcription factor overexpression-derived regulatory network

BACKGROUND

Mycobacterium tuberculosis senses and responds to the shifting and hostile landscape of the host. To characterize the underlying intertwined gene regulatory network governed by approximately 200 transcription factors of M. tuberculosis, we have assayed the global transcriptional consequences of overexpressing each transcription factor from an inducible promoter.

RESULTS

We cloned and overexpressed 206 transcription factors in M. tuberculosis to identify the regulatory signature of each. We identified 9,335 regulatory consequences of overexpressing each of 183 transcription factors, providing evidence of regulation for 70% of the M. tuberculosis genome. These transcriptional signatures agree well with previously described M. tuberculosis regulons. The number of genes differentially regulated by transcription factor overexpression varied from hundreds of genes to none, with the majority of expression changes repressing basal transcription. Exploring the global transcriptional maps of transcription factor overexpressing (TFOE) strains, we predicted and validated the phenotype of a regulator that reduces susceptibility to a first line anti-tubercular drug, isoniazid. We also combined the TFOE data with an existing model of M. tuberculosis metabolism to predict the growth rates of individual TFOE strains with high fidelity.

CONCLUSION

This work has led to a systems-level framework describing the transcriptome of a devastating bacterial pathogen, characterized the transcriptional influence of nearly all individual transcription factors in M. tuberculosis, and demonstrated the utility of this resource. These results will stimulate additional systems-level and hypothesis-driven efforts to understand M. tuberculosis adaptations that promote disease.

Mapping and manipulating the Mycobacterium tuberculosis transcriptome using a transcription factor overexpression-derived regulatory network

Background

Mycobacterium tuberculosis senses and responds to the shifting and hostile landscape of the host. To characterize the underlying intertwined gene regulatory network governed by approximately 200 transcription factors of M. tuberculosis, we have assayed the global transcriptional consequences of overexpressing each transcription factor from an inducible promoter.

Results

We cloned and overexpressed 206 transcription factors in M. tuberculosis to identify the regulatory signature of each. We identified 9,335 regulatory consequences of overexpressing each of 183 transcription factors, providing evidence of regulation for 70% of the M. tuberculosis genome. These transcriptional signatures agree well with previously described M. tuberculosis regulons. The number of genes differentially regulated by transcription factor overexpression varied from hundreds of genes to none, with the majority of expression changes repressing basal transcription. Exploring the global transcriptional maps of transcription factor overexpressing (TFOE) strains, we predicted and validated the phenotype of a regulator that reduces susceptibility to a first line anti-tubercular drug, isoniazid. We also combined the TFOE data with an existing model of M. tuberculosis metabolism to predict the growth rates of individual TFOE strains with high fidelity.

Conclusion

This work has led to a systems-level framework describing the transcriptome of a devastating bacterial pathogen, characterized the transcriptional influence of nearly all individual transcription factors in M. tuberculosis, and demonstrated the utility of this resource. These results will stimulate additional systems-level and hypothesis-driven efforts to understand M. tuberculosis adaptations that promote disease.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0502-3) contains supplementary material, which is available to authorized users.

Systems biology approaches to defining transcription regulatory networks in halophilic archaea

To survive complex and changing environmental conditions, microorganisms use gene regulatory networks (GRNs) composed of interacting regulatory transcription factors (TFs) to control the timing and magnitude of gene expression. Genome-wide datasets; such as transcriptomics and protein-DNA interactions; and experiments such as high throughput growth curves; facilitate the construction of GRNs and provide insight into TF interactions occurring under stress. Systems biology approaches integrate these datasets into models of GRN architecture as well as statistical and/or dynamical models to understand the function of networks occurring in cells. Previously, these types of studies have focused on traditional model organisms (e.g. Escherichia coli, yeast). However, recent advances in archaeal genetics and other tools have enabled a systems approach to understanding GRNs in these relatively less studied archaeal model organisms. In this report, we outline a systems biology workflow for generating and integrating data focusing on the TF regulator. We discuss experimental design, outline the process of data collection, and provide the tools required to produce high confidence regulons for the TFs of interest. We provide a case study as an example of this workflow, describing the construction of a GRN centered on multi-TF coordinate control of gene expression governing the oxidative stress response in the hypersaline-adapted archaeon Halobacterium salinarum.

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.

Improving the tuberculosis drug development pipeline

Mycobacterium tuberculosis is considered one of the most successful pathogens and multidrug-resistant tuberculosis, a disease that urgently requires new chemical entities to be developed for treatment. There are currently several new molecules under clinical investigation in the tuberculosis (TB) drug development pipeline. However, the complex lifestyle of M. tuberculosis within the host presents a barrier to the development of new drugs. In this review, we highlight the reasons that make TB drug discovery and development challenging as well as providing solutions, future directions and alternative approaches to new therapeutics for TB.

Architectural plan of transcriptional regulation in Mycobacterium tuberculosis

Transcriptional regulation enables adaptation in bacteria. Typically, only a few transcriptional events are well understood, leaving many others unidentified. The recent genome-wide identification of transcription factor binding sites in Mycobacterium tuberculosis has changed this by deciphering a molecular road-map of transcriptional control, indicating active events and their immediate downstream effects.

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.

Transcriptional program for nitrogen starvation-induced lipid accumulation in Chlamydomonas reinhardtii

BACKGROUND

Algae accumulate lipids to endure different kinds of environmental stresses including macronutrient starvation. Although this response has been extensively studied, an in depth understanding of the transcriptional regulatory network (TRN) that controls the transition into lipid accumulation remains elusive. In this study, we used a systems biology approach to elucidate the transcriptional program that coordinates the nitrogen starvation-induced metabolic readjustments that drive lipid accumulation in Chlamydomonas reinhardtii.

RESULTS

We demonstrate that nitrogen starvation triggered differential regulation of 2147 transcripts, which were co-regulated in 215 distinct modules and temporally ordered as 31 transcriptional waves. An early-stage response was triggered within 12 min that initiated growth arrest through activation of key signaling pathways, while simultaneously preparing the intracellular environment for later stages by modulating transport processes and ubiquitin-mediated protein degradation. Subsequently, central metabolism and carbon fixation were remodeled to trigger the accumulation of triacylglycerols. Further analysis revealed that these waves of genome-wide transcriptional events were coordinated by a regulatory program orchestrated by at least 17 transcriptional regulators, many of which had not been previously implicated in this process. We demonstrate that the TRN coordinates transcriptional downregulation of 57 metabolic enzymes across a period of nearly 4 h to drive an increase in lipid content per unit biomass. Notably, this TRN appears to also drive lipid accumulation during sulfur starvation, while phosphorus starvation induces a different regulatory program. The TRN model described here is available as a community-wide web-resource at http://networks.systemsbiology.net/chlamy-portal.

CONCLUSIONS

In this work, we have uncovered a comprehensive mechanistic model of the TRN controlling the transition from N starvation to lipid accumulation. The program coordinates sequentially ordered transcriptional waves that simultaneously arrest growth and lead to lipid accumulation. This study has generated predictive tools that will aid in devising strategies for the rational manipulation of regulatory and metabolic networks for better biofuel and biomass production.