The transcriptional regulatory network of Mycobacterium tuberculosis

Predicting bacterial fitness in Mycobacterium tuberculosis with transcriptional regulatory network-informed interpretable machine learning

Mycobacterium tuberculosis (Mtb) is the causative agent of tuberculosis disease, the greatest source of global mortality by a bacterial pathogen. Mtb adapts and responds to diverse stresses such as antibiotics by inducing transcriptional stress-response regulatory programs. Understanding how and when these mycobacterial regulatory programs are activated could enable novel treatment strategies for potentiating the efficacy of new and existing drugs. Here we sought to define and analyze Mtb regulatory programs that modulate bacterial fitness. We assembled a large Mtb RNA expression compendium and applied these to infer a comprehensive Mtb transcriptional regulatory network and compute condition-specific transcription factor activity profiles. We utilized transcriptomic and functional genomics data to train an interpretable machine learning model that can predict Mtb fitness from transcription factor activity profiles. We demonstrated that this transcription factor activity-based model can successfully predict Mtb growth arrest and growth resumption under hypoxia and reaeration using only RNA-seq expression data as a starting point. These integrative network modeling and machine learning analyses thus enable the prediction of mycobacterial fitness under different environmental and genetic contexts. We envision these models can potentially inform the future design of prognostic assays and therapeutic intervention that can cripple Mtb growth and survival to cure tuberculosis disease.

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.

The gene regulatory network of Staphylococcus aureus ST239-SCCmecIII strain Bmb9393 and assessment of genes associated with the biofilm in diverse backgrounds

Introduction

Staphylococcus aureus is one of the most prevalent and relevant pathogens responsible for a wide spectrum of hospital-associated or community-acquired infections. In addition, methicillin-resistant Staphylococcus aureus may display multidrug resistance profiles that complicate treatment and increase the mortality rate. The ability to produce biofilm, particularly in device-associated infections, promotes chronic and potentially more severe infections originating from the primary site. Understanding the complex mechanisms involved in planktonic and biofilm growth is critical to identifying regulatory connections and ways to overcome the global health problem of multidrug-resistant bacteria.

Methods

In this work, we apply literature-based and comparative genomics approaches to reconstruct the gene regulatory network of the high biofilm-producing strain Bmb9393, belonging to one of the highly disseminating successful clones, the Brazilian epidemic clone. To the best of our knowledge, we describe for the first time the topological properties and network motifs for the Staphylococcus aureus pathogen. We performed this analysis using the ST239-SCCmecIII Bmb9393 strain. In addition, we analyzed transcriptomes available in the literature to construct a set of genes differentially expressed in the biofilm, covering different stages of the biofilms and genetic backgrounds of the strains.

Results and discussion

The Bmb9393 gene regulatory network comprises 1,803 regulatory interactions between 64 transcription factors and the non-redundant set of 1,151 target genes with the inclusion of 19 new regulons compared to the N315 transcriptional regulatory network published in 2011. In the Bmb9393 network, we found 54 feed-forward loop motifs, where the most prevalent were coherent type 2 and incoherent type 2. The non-redundant set of differentially expressed genes in the biofilm consisted of 1,794 genes with functional categories relevant for adaptation to the variable microenvironments established throughout the biofilm formation process. Finally, we mapped the set of genes with altered expression in the biofilm in the Bmb9393 gene regulatory network to depict how different growth modes can alter the regulatory systems. The data revealed 45 transcription factors and 876 shared target genes. Thus, the gene regulatory network model provided represents the most up-to-date model for Staphylococcus aureus, and the set of genes altered in the biofilm provides a global view of their influence on biofilm formation from distinct experimental perspectives and different strain backgrounds.

Homology-based reconstruction of regulatory networks for bacterial and archaeal genomes

Gene regulation is a key process for all microorganisms, as it allows them to adapt to different environmental stimuli. However, despite the relevance of gene expression control, for only a handful of organisms is there related information about genome regulation. In this work, we inferred the gene regulatory networks (GRNs) of bacterial and archaeal genomes by comparisons with six organisms with well-known regulatory interactions. The references we used are: Escherichia coli K-12 MG1655, Bacillus subtilis 168, Mycobacterium tuberculosis, Pseudomonas aeruginosa PAO1, Salmonella enterica subsp. enterica serovar typhimurium LT2, and Staphylococcus aureus N315. To this end, the inferences were achieved in two steps. First, the six model organisms were contrasted in an all-vs-all comparison of known interactions based on Transcription Factor (TF)-Target Gene (TG) orthology relationships and Transcription Unit (TU) assignments. In the second step, we used a guilt-by-association approach to infer the GRNs for 12,230 bacterial and 649 archaeal genomes based on TF-TG orthology relationships of the six bacterial models determined in the first step. Finally, we discuss examples to show the most relevant results obtained from these inferences. A web server with all the predicted GRNs is available at https://regulatorynetworks.unam.mx/ or http://132.247.46.6/.

Identification of robust genes in transcriptional regulatory network of Mycobacterium tuberculosis

About 30% of the world population is infected with Mycobacterium tuberculosis (MTB). It is well known that the gene expression in MTB is highly variable, thus screening of traditional single-gene in MTB has been incapable to meet the desires of clinical diagnosis. In this report, the authors systemically analysed the transcription regulatory network (TRN) in MTB H37Rv. The complex interplay of these gene interactions has been revealed using exhaustive topological and global analysis of TRN using parameters including indegree, outdegree, degree, directed and undirected average path length (APL), and randomly performed. Results from indegree analysis reveal a set of important genes, including papA5 and Rv0177 which are associated with high indegree values. Gene ontology analysis suggested their importance in the virulence of MTB. In addition, APL and analysis of highly significant genes further identified some critical genes with different APL values. Among the list of genes identified, the csoR gene has the shortest directed APL score and high outdegree value, thus suggesting their importance in maintaining network topology. This study provides a comprehensive analysis of TRN and offers a good basis of understanding for developing experimental study in search of new therapeutic targets against MTB H37Rv pathogen.

Computational Identification of the Proteins Associated With Quorum Sensing and Biofilm Formation in Mycobacterium tuberculosis

With prolonged therapy and increased instances of drug resistance, tuberculosis is viewed as a serious infectious disease causing high mortality. Emerging concepts in Mycobacterium tuberculosis pathogenicity include biofilm formation, which endows bacterial survival in the host for a long time. To tackle chronic tuberculosis infection, a detailed understanding of the bacterial survival mechanisms is crucial. Using comparative genomics and literature mining, 115 M. tuberculosis proteins were shortlisted for their likely association with biofilm formation or quorum sensing. These include essential genes such as secA2, lpqY-sugABC, Rv1176c, and Rv0195, many of which are also known virulence factors. Furthermore, the functional relationship among these proteins was established by considering known protein-protein interactions, regulatory interactions, and gene expression correlation data/information. Graph centrality and motif analyses predicted the importance of proteins, such as Rv0081, DevR, RegX3, Rv0097, and Rv1996 in M. tuberculosis biofilm formation. Analysis of conservation across other biofilm-forming bacteria suggests that most of these genes are conserved in mycobacteria. As the processes, such as quorum sensing, leading to biofilm formation involve diverse pathways and interactions between proteins, these system-wide studies provide a novel perspective toward understanding mycobacterial persistence.

Multiple transcription factors co-regulate the Mycobacterium tuberculosis adaptation response to vitamin C

Background

Latent tuberculosis infection is attributed in part to the existence of Mycobacterium tuberculosis in a persistent non-replicating dormant state that is associated with tolerance to host defence mechanisms and antibiotics. We have recently reported that vitamin C treatment of M. tuberculosis triggers the rapid development of bacterial dormancy. Temporal genome-wide transcriptome analysis has revealed that vitamin C-induced dormancy is associated with a large-scale modulation of gene expression in M. tuberculosis.

Results

An updated transcriptional regulatory network of M.tuberculosis (Mtb-TRN) consisting of 178 regulators and 3432 target genes was constructed. The temporal transcriptome data generated in response to vitamin C was overlaid on the Mtb-TRN (vitamin C Mtb-TRN) to derive insights into the transcriptional regulatory features in vitamin C-adapted bacteria. Statistical analysis using Fisher’s exact test predicted that 56 regulators play a central role in modulating genes which are involved in growth, respiration, metabolism and repair functions. Rv0348, DevR, MprA and RegX3 participate in a core temporal regulatory response during 0.25 h to 8 h of vitamin C treatment. Temporal network analysis further revealed Rv0348 to be the most prominent hub regulator with maximum interactions in the vitamin C Mtb-TRN. Experimental analysis revealed that Rv0348 and DevR proteins interact with each other, and this interaction results in an enhanced binding of DevR to its target promoter. These findings, together with the enhanced expression of devR and Rv0348 transcriptional regulators, indicate a second-level regulation of target genes through transcription factor- transcription factor interactions.

Conclusions

Temporal regulatory analysis of the vitamin C Mtb-TRN revealed that there is involvement of multiple regulators during bacterial adaptation to dormancy. Our findings suggest that Rv0348 is a prominent hub regulator in the vitamin C model and large-scale modulation of gene expression is achieved through interactions of Rv0348 with other transcriptional regulators.

System-wide coordinates of higher order functions in host-pathogen environment upon Mycobacterium tuberculosis infection

Molecular signatures and their interactions behind the successful establishment of infection of Mycobacterium tuberculosis (Mtb) inside macrophage are largely unknown. In this work, we present an inter-system scale atlas of the gene expression signatures, their interactions and higher order gene functions of macrophage-Mtb environment at the time of infection. We have carried out large-scale meta-analysis of previously published gene expression microarray studies andhave identified a ranked list of differentially expressed genes and their higher order functions in intracellular Mtb as well as the infected macrophage. Comparative analysis of gene expression signatures of intracellular Mtb with the in vitro dormant Mtb at different hypoxic and oxidative stress conditions led to the identification of the large number of Mtb functional groups, namely operons, regulons and pathways that were common and unique to the intracellular environment and dormancy state. Some of the functions that are specific to intracellular Mtb are cholesterol degradation and biosynthesis of immunomodulatory phenolic compounds. The molecular signatures we have identified to be involved in adaptation to different stress conditions in macrophage environment may be critical for designing therapeutic interventions against tuberculosis. And, our approach may be broadly applicable for investigating other host-pathogen interactions.

New Approaches and Therapeutic Options for Mycobacterium tuberculosis in a Dormant State

We are far away from the days when tuberculosis (TB) accounted for 1 in 4 deaths during the 19th century. However, Mycobacterium tuberculosis complex (MTBC) strains are still the leading cause of morbidity and mortality by a single infectious disease, with 9.6 million cases and 1.5 million deaths reported. One-third of the world's population is estimated by the WHO to be infected with latent TB. During the last decade, several studies have aimed to define the characteristics of dormant bacteria in these latent infections. General features of the shift to a dormant state encompass several phenotypic changes that reduce metabolic activity. This low metabolic state is thought to increase the resistance of MTBC strains to host/environmental stresses, including antibiotic action. Once the stress ceases (e.g., interruption of treatment), dormant cells can reactivate and cause symptomatic disease again. Therefore, a proper understanding of dormancy could guide the rational development of new treatment regimens that target dormant cells, reducing later relapse. Here, we briefly summarize the latest data on the genetics involved in the regulation of dormancy and discuss new approaches to TB treatment.

GeNET: a web application to explore and share Gene Co-expression Network Analysis data

Gene Co-expression Network Analysis (GCNA) is a popular approach to analyze a collection of gene expression profiles. GCNA yields an assignment of genes to gene co-expression modules, a list of gene sets statistically over-represented in these modules, and a gene-to-gene network. There are several computer programs for gene-to-gene network visualization, but these programs have limitations in terms of integrating all the data generated by a GCNA and making these data available online. To facilitate sharing and study of GCNA data, we developed GeNET. For researchers interested in sharing their GCNA data, GeNET provides a convenient interface to upload their data and automatically make it accessible to the public through an online server. For researchers interested in exploring GCNA data published by others, GeNET provides an intuitive online tool to interactively explore GCNA data by genes, gene sets or modules. In addition, GeNET allows users to download all or part of the published data for further computational analysis. To demonstrate the applicability of GeNET, we imported three published GCNA datasets, the largest of which consists of roughly 17,000 genes and 200 conditions. GeNET is available at bengi.cs.mun.ca/genet.

Universal attenuators and their interactions with feedback loops in gene regulatory networks

Using a combination of mathematical modelling, statistical simulation and large-scale data analysis we study the properties of linear regulatory chains (LRCs) within gene regulatory networks (GRNs). Our modelling indicates that downstream genes embedded within LRCs are highly insulated from the variation in expression of upstream genes, and thus LRCs act as attenuators. This observation implies a progressively weaker functionality of LRCs as their length increases. When analyzing the preponderance of LRCs in the GRNs of Escherichia coli K12 and several other organisms, we find that very long LRCs are essentially absent. In both E. coli and M. tuberculosis we find that four-gene LRCs are intimately linked to identical feedback loops that are involved in potentially chaotic stress response, indicating that the dynamics of these potentially destabilising motifs are strongly restrained under homeostatic conditions. The same relationship is observed in a human cancer cell line (K562), and we postulate that four-gene LRCs act as ‘universal attenuators’. These findings suggest a role for long LRCs in dampening variation in gene expression, thereby protecting cell identity, and in controlling dramatic shifts in cell-wide gene expression through inhibiting chaos-generating motifs.

Polymorphisms of 20 regulatory proteins between Mycobacterium tuberculosis and Mycobacterium bovis

Mycobacterium tuberculosis and Mycobacterium bovis are responsible for tuberculosis in humans and animals, respectively. Both species are closely related and belong to the Mycobacterium tuberculosis complex (MTC). M. tuberculosis is the most ancient species from which M. bovis and other members of the MTC evolved. The genome of M. bovis is over >99.95% identical to that of M. tuberculosis but with seven deletions ranging in size from 1 to 12.7 kb. In addition, 1200 single nucleotide mutations in coding regions distinguish M. bovis from M. tuberculosis. In the present study, we assessed 75 M. tuberculosis genomes and 23 M. bovis genomes to identify non-synonymous mutations in 202 coding sequences of regulatory genes between both species. We identified species-specific variants in 20 regulatory proteins and confirmed differential expression of hypoxia-related genes between M. bovis and M. tuberculosis.

Intrinsic noise and deviations from criticality in Boolean gene-regulatory networks

Gene regulatory networks can be successfully modeled as Boolean networks. A much discussed hypothesis says that such model networks reproduce empirical findings the best if they are tuned to operate at criticality, i.e. at the borderline between their ordered and disordered phases. Critical networks have been argued to lead to a number of functional advantages such as maximal dynamical range, maximal sensitivity to environmental changes, as well as to an excellent tradeoff between stability and flexibility. Here, we study the effect of noise within the context of Boolean networks trained to learn complex tasks under supervision. We verify that quasi-critical networks are the ones learning in the fastest possible way -even for asynchronous updating rules- and that the larger the task complexity the smaller the distance to criticality. On the other hand, when additional sources of intrinsic noise in the network states and/or in its wiring pattern are introduced, the optimally performing networks become clearly subcritical. These results suggest that in order to compensate for inherent stochasticity, regulatory and other type of biological networks might become subcritical rather than being critical, all the most if the task to be performed has limited complexity.

Compartmentalized gene regulatory network of the pathogenic fungus Fusarium graminearum

Head blight caused by Fusarium graminearum threatens world-wide wheat production, resulting in both yield loss and mycotoxin contamination. We reconstructed the global F. graminearum gene regulatory network (GRN) from a large collection of transcriptomic data using Bayesian network inference, a machine-learning algorithm. This GRN reveals connectivity between key regulators and their target genes. Focusing on key regulators, this network contains eight distinct but interwoven modules. Enriched for unique functions, such as cell cycle, DNA replication, transcription, translation and stress responses, each module exhibits distinct expression profiles. Evolutionarily, the F. graminearum genome can be divided into core regions shared with closely related species and variable regions harboring genes that are unique to F. graminearum and perform species-specific functions. Interestingly, the inferred top regulators regulate genes that are significantly enriched from the same genomic regions (P < 0.05), revealing a compartmentalized network structure that may reflect network rewiring related to specific adaptation of this plant pathogen. This first-ever reconstructed filamentous fungal GRN primes our understanding of pathogenicity at the systems biology level and provides enticing prospects for novel disease control strategies involving the targeting of master regulators in pathogens. The program can be used to construct GRNs of other plant pathogens.

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.

Intervality and coherence in complex networks

Food webs-networks of predators and prey-have long been known to exhibit "intervality": species can generally be ordered along a single axis in such a way that the prey of any given predator tend to lie on unbroken compact intervals. Although the meaning of this axis-usually identified with a "niche" dimension-has remained a mystery, it is assumed to lie at the basis of the highly non-trivial structure of food webs. With this in mind, most trophic network modelling has for decades been based on assigning species a niche value by hand. However, we argue here that intervality should not be considered the cause but rather a consequence of food-web structure. First, analysing a set of 46 empirical food webs, we find that they also exhibit predator intervality: the predators of any given species are as likely to be contiguous as the prey are, but in a different ordering. Furthermore, this property is not exclusive of trophic networks: several networks of genes, neurons, metabolites, cellular machines, airports, and words are found to be approximately as interval as food webs. We go on to show that a simple model of food-web assembly which does not make use of a niche axis can nevertheless generate significant intervality. Therefore, the niche dimension (in the sense used for food-web modelling) could in fact be the consequence of other, more fundamental structural traits. We conclude that a new approach to food-web modelling is required for a deeper understanding of ecosystem assembly, structure, and function, and propose that certain topological features thought to be specific of food webs are in fact common to many complex networks.

Abasy Atlas: a comprehensive inventory of systems, global network properties and systems-level elements across bacteria

The availability of databases electronically encoding curated regulatory networks and of high-throughput technologies and methods to discover regulatory interactions provides an invaluable source of data to understand the principles underpinning the organization and evolution of these networks responsible for cellular regulation. Nevertheless, data on these sources never goes beyond the regulon level despite the fact that regulatory networks are complex hierarchical-modular structures still challenging our understanding. This brings the necessity for an inventory of systems across a large range of organisms, a key step to rendering feasible comparative systems biology approaches. In this work, we take the first step towards a global understanding of the regulatory networks organization by making a cartography of the functional architectures of diverse bacteria. Abasy (Across-bacteria systems) Atlas provides a comprehensive inventory of annotated functional systems, global network properties and systems-level elements (global regulators, modular genes shaping functional systems, basal machinery genes and intermodular genes) predicted by the natural decomposition approach for reconstructed and meta-curated regulatory networks across a large range of bacteria, including pathogenically and biotechnologically relevant organisms. The meta-curation of regulatory datasets provides the most complete and reliable set of regulatory interactions currently available, which can even be projected into subsets by considering the force or weight of evidence supporting them or the systems that they belong to. Besides, Abasy Atlas provides data enabling large-scale comparative systems biology studies aimed at understanding the common principles and particular lifestyle adaptions of systems across bacteria. Abasy Atlas contains systems and system-level elements for 50 regulatory networks comprising 78 649 regulatory interactions covering 42 bacteria in nine taxa, containing 3708 regulons and 1776 systems. All this brings together a large corpus of data that will surely inspire studies to generate hypothesis regarding the principles governing the evolution and organization of systems and the functional architectures controlling them.

Database URL:http://abasy.ccg.unam.mx

Integration of Metabolic Modeling with Gene Co-expression Reveals Transcriptionally Programmed Reactions Explaining Robustness in Mycobacterium tuberculosis

Robustness of metabolic networks is accomplished by gene regulation, modularity, re-routing of metabolites and plasticity. Here, we probed robustness against perturbations of biochemical reactions of M. tuberculosis in the form of predicting compensatory trends. In order to investigate the transcriptional programming of genes associated with correlated fluxes, we integrated with gene co-expression network. Knock down of the reactions NADH2r and ATPS responsible for producing the hub metabolites, and Central carbon metabolism had the highest proportion of their associated genes under transcriptional co-expression with genes of their flux correlated reactions. Reciprocal gene expression correlations were observed among compensatory routes, fresh activation of alternative routes and in the multi-copy genes of Cysteine synthase and of Phosphate transporter. Knock down of 46 reactions caused the activation of Isocitrate lyase or Malate synthase or both reactions, which are central to the persistent state of M. tuberculosis. A total of 30 new freshly activated routes including Cytochrome c oxidase, Lactate dehydrogenase, and Glycine cleavage system were predicted, which could be responsible for switching into dormant or persistent state. Thus, our integrated approach of exploring transcriptional programming of flux correlated reactions has the potential to unravel features of system architecture conferring robustness.

Towards understanding the biological function of the unusual chaperonin Cpn60.1 (GroEL1) of Mycobacterium tuberculosis

The 60 kDa heat shock proteins, also known as Cpn60s (GroELs) are components of the essential protein folding machinery of the cell, but are also dominant antigens in many infectious diseases. Although generally essential for cellular survival, in some organisms such as Mycobacterium tuberculosis, one or more paralogous Cpn60s are known to be dispensable. In M. tuberculosis, Cpn60.2 (GroEL2) is essential for cell survival, but the biological role of the non-essential Cpn60.1 (GroEL1) is still elusive. To understand the relevance of Cpn60.1 (GroEL1) in M. tuberculosis physiology, detailed transcriptomic analyses for the wild type H37Rv and cpn60.1 knockout (groEL1-KO) were performed under in vitro stress conditions: stationary phase, cold shock, low aeration, mild cold shock and low pH. Additionally, the survival of the groEL1-KO was assessed in macrophages at multiplicity of infection (MOI) of 1:1 and 1:5. We observed that survival under low aeration was significantly compromised in the groEL1-KO. Further, the gene expression analyses under low aeration showed change in expression of several key virulence factors like two component system PhoP/R and MprA/B, sigma factors SigM and C and adversely affected known hypoxia response regulators Rv0081, Rv0023 and DosR. Our work is therefore suggestive of an important role of Cpn60.1 (GroEL1) for survival under low aeration by affecting the expression of genes known for hypoxia response.

From System-Wide Differential Gene Expression to Perturbed Regulatory Factors: A Combinatorial Approach

High-throughput experiments such as microarrays and deep sequencing provide large scale information on the pattern of gene expression, which undergoes extensive remodeling as the cell dynamically responds to varying environmental cues or has its function disrupted under pathological conditions. An important initial step in the systematic analysis and interpretation of genome-scale expression alteration involves identification of a set of perturbed transcriptional regulators whose differential activity can provide a proximate hypothesis to account for these transcriptomic changes. In the present work, we propose an unbiased and logically natural approach to transcription factor enrichment. It involves overlaying a list of experimentally determined differentially expressed genes on a background regulatory network coming from e.g. literature curation or computational motif scanning, and identifying that subset of regulators whose aggregated target set best discriminates between the altered and the unaffected genes. In other words, our methodology entails testing of all possible regulatory subnetworks, rather than just the target sets of individual regulators as is followed in most standard approaches. We have proposed an iterative search method to efficiently find such a combination, and benchmarked it on E. coli microarray and regulatory network data available in the public domain. Comparative analysis carried out on artificially generated differential expression profiles, as well as empirical factor overexpression data for M. tuberculosis, shows that our methodology provides marked improvement in accuracy of regulatory inference relative to the standard method that involves evaluating factor enrichment in an individual manner.

Flux Balance Analysis with Objective Function Defined by Proteomics Data-Metabolism of Mycobacterium tuberculosis Exposed to Mefloquine

We present a study of the metabolism of the Mycobacterium tuberculosis after exposure to antibiotics using proteomics data and flux balance analysis (FBA). The use of FBA to study prokaryotic organisms is well-established and allows insights into the metabolic pathways chosen by the organisms under different environmental conditions. To apply FBA a specific objective function must be selected that represents the metabolic goal of the organism. FBA estimates the metabolism of the cell by linear programming constrained by the stoichiometry of the reactions in an in silico metabolic model of the organism. It is assumed that the metabolism of the organism works towards the specified objective function. A common objective is the maximization of biomass. However, this goal is not suitable for situations when the bacterium is exposed to antibiotics, as the goal of organisms in these cases is survival and not necessarily optimal growth. In this paper we propose a new approach for defining the FBA objective function in studies when the bacterium is under stress. The function is defined based on protein expression data. The proposed methodology is applied to the case when the bacterium is exposed to the drug mefloquine, but can be easily extended to other organisms, conditions or drugs. We compare our method with an alternative method that uses experimental data for adjusting flux constraints. We perform comparisons in terms of essential enzymes and agreement using enzyme abundances. Results indicate that using proteomics data to define FBA objective functions yields less essential reactions with zero flux and lower error rates in prediction accuracy. With flux variability analysis we observe that overall variability due to alternate optima is reduced with the incorporation of proteomics data. We believe that incorporating proteomics data in the objective function used in FBA may help obtain metabolic flux representations that better support experimentally observed features.

Improving gene regulatory network inference using network topology information

Inferring the gene regulatory network (GRN) structure from data is an important problem in computational biology. However, it is a computationally complex problem and approximate methods such as heuristic search techniques, restriction of the maximum-number-of-parents (maxP) for a gene, or an optimal search under special conditions are required. The limitations of a heuristic search are well known but literature on the detailed analysis of the widely used maxP technique is lacking. The optimal search methods require large computational time. We report the theoretical analysis and experimental results of the strengths and limitations of the maxP technique. Further, using an optimal search method, we combine the strengths of the maxP technique and the known GRN topology to propose two novel algorithms. These algorithms are implemented in a Bayesian network framework and tested on biological, realistic, and in silico networks of different sizes and topologies. They overcome the limitations of the maxP technique and show superior computational speed when compared to the current optimal search algorithms.

Boolean modeling: a logic-based dynamic approach for understanding signaling and regulatory networks and for making useful predictions

The biomolecules inside or near cells form a complex interacting system. Cellular phenotypes and behaviors arise from the totality of interactions among the components of this system. A fruitful way of modeling interacting biomolecular systems is by network-based dynamic models that characterize each component by a state variable, and describe the change in the state variables due to the interactions in the system. Dynamic models can capture the stable state patterns of this interacting system and can connect them to different cell fates or behaviors. A Boolean or logic model characterizes each biomolecule by a binary state variable that relates the abundance of that molecule to a threshold abundance necessary for downstream processes. The regulation of this state variable is described in a parameter free manner, making Boolean modeling a practical choice for systems whose kinetic parameters have not been determined. Boolean models integrate the body of knowledge regarding the components and interactions of biomolecular systems, and capture the system's dynamic repertoire, for example the existence of multiple cell fates. These models were used for a variety of systems and led to important insights and predictions. Boolean models serve as an efficient exploratory model, a guide for follow-up experiments, and as a foundation for more quantitative models.

Gene expression analysis of two extensively drug-resistant tuberculosis isolates show that two-component response systems enhance drug resistance

Global analysis of expression profiles using DNA microarrays was performed between a reference strain H37Rv and two clinical extensively drug-resistant isolates in response to three anti-tuberculosis drug exposures (isoniazid, capreomycin, and rifampicin). A deep analysis was then conducted using a combination of genome sequences of the resistant isolates, resistance information, and related public microarray data. Certain known resistance-associated gene sets were significantly overrepresented in upregulated genes in the resistant isolates relative to that observed in H37Rv, which suggested a link between resistance and expression levels of particular genes. In addition, isoniazid and capreomycin response genes, but not rifampicin, either obtained from published works or our data, were highly consistent with the differentially expressed genes of resistant isolates compared to those of H37Rv, indicating a strong association between drug resistance of the isolates and genes differentially regulated by isoniazid and capreomycin exposures. Based on these results, 92 genes of the studied isolates were identified as candidate resistance genes, 10 of which are known resistance-related genes. Regulatory network analysis of candidate resistance genes using published networks and literature mining showed that three two-component regulatory systems and regulator CRP play significant roles in the resistance of the isolates by mediating the production of essential envelope components. Finally, drug sensitivity testing indicated strong correlations between expression levels of these regulatory genes and sensitivity to multiple anti-tuberculosis drugs in Mycobacterium tuberculosis. These findings may provide novel insights into the mechanism underlying the emergence and development of drug resistance in resistant tuberculosis isolates and useful clues for further studies on this issue.

Evolution of regulatory networks towards adaptability and stability in a changing environment

Diverse biological networks exhibit universal features distinguished from those of random networks, calling much attention to their origins and implications. Here we propose a minimal evolution model of Boolean regulatory networks, which evolve by selectively rewiring links towards enhancing adaptability to a changing environment and stability against dynamical perturbations. We find that sparse and heterogeneous connectivity patterns emerge, which show qualitative agreement with real transcriptional regulatory networks and metabolic networks. The characteristic scaling behavior of stability reflects the balance between robustness and flexibility. The scaling of fluctuation in the perturbation spread shows a dynamic crossover, which is analyzed by investigating separately the stochasticity of internal dynamics and the network structure differences depending on the evolution pathways. Our study delineates how the ambivalent pressure of evolution shapes biological networks, which can be helpful for studying general complex systems interacting with environments.

Integration of heterogeneous molecular networks to unravel gene-regulation in Mycobacterium tuberculosis

BACKGROUND

Different methods have been developed to infer regulatory networks from heterogeneous omics datasets and to construct co-expression networks. Each algorithm produces different networks and efforts have been devoted to automatically integrate them into consensus sets. However each separate set has an intrinsic value that is diluted and partly lost when building a consensus network. Here we present a methodology to generate co-expression networks and, instead of a consensus network, we propose an integration framework where the different networks are kept and analysed with additional tools to efficiently combine the information extracted from each network.

RESULTS

We developed a workflow to efficiently analyse information generated by different inference and prediction methods. Our methodology relies on providing the user the means to simultaneously visualise and analyse the coexisting networks generated by different algorithms, heterogeneous datasets, and a suite of analysis tools. As a show case, we have analysed the gene co-expression networks of Mycobacterium tuberculosis generated using over 600 expression experiments. Regarding DNA damage repair, we identified SigC as a key control element, 12 new targets for LexA, an updated LexA binding motif, and a potential mismatch repair system. We expanded the DevR regulon with 27 genes while identifying 9 targets wrongly assigned to this regulon. We discovered 10 new genes linked to zinc uptake and a new regulatory mechanism for ZuR. The use of co-expression networks to perform system level analysis allows the development of custom made methodologies. As show cases we implemented a pipeline to integrate ChIP-seq data and another method to uncover multiple regulatory layers.

CONCLUSIONS

Our workflow is based on representing the multiple types of information as network representations and presenting these networks in a synchronous framework that allows their simultaneous visualization while keeping specific associations from the different networks. By simultaneously exploring these networks and metadata, we gained insights into regulatory mechanisms in M. tuberculosis that could not be obtained through the separate analysis of each data type.

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.

Buffered Qualitative Stability explains the robustness and evolvability of transcriptional networks

The gene regulatory network (GRN) is the central decision-making module of the cell. We have developed a theory called Buffered Qualitative Stability (BQS) based on the hypothesis that GRNs are organised so that they remain robust in the face of unpredictable environmental and evolutionary changes. BQS makes strong and diverse predictions about the network features that allow stable responses under arbitrary perturbations, including the random addition of new connections. We show that the GRNs of E. coli, M. tuberculosis, P. aeruginosa, yeast, mouse, and human all verify the predictions of BQS. BQS explains many of the small- and large-scale properties of GRNs, provides conditions for evolvable robustness, and highlights general features of transcriptional response. BQS is severely compromised in a human cancer cell line, suggesting that loss of BQS might underlie the phenotypic plasticity of cancer cells, and highlighting a possible sequence of GRN alterations concomitant with cancer initiation.

Genome-wide screening of pathogenicity islands in Mycobacterium tuberculosis based on the genomic barcode visualization

Mycobacterium tuberculosis (M. tuberculosis) is one of the most widely spread human pathogenic bacteria, and it frequently exchanges pathogenesis genes among its strains or with other pathogenic microbes. The purpose of this study was to screen the pathogenicity islands (PAIs) in M. tuberculosis using the genomic barcode visualization technique and to characterize the functions of the detected PAIs. By visually screening the barcode image of the M. tuberculosis chromosomes, three candidate PAIs were detected as MPI-1, MPI-2 and MPI-3, among which MPI-2 and MPI-3 were known to harbor pathogenesis genes, and MPI-1 represents a novel candidate. Based on the functional annotations of Pfam domains and GO categories, both MPI-2 and MPI-3 carry genes encoding PE/PPE family proteins, MPI-2 encodes the type VII secretion system, and MPI-3 encodes genes for mycolic acid synthesis in the cell wall. Some of these genes were already widely used in early diagnosis or treatment of M. tuberculosis. The novel candidate PAI MPI-1 encodes CRISPR-C as family proteins, which are known to be associated with persistent infection of M. tuberculosis. Our data represents a molecular basis and protocol for comprehensive annotating the pathogenic systems of M. tuberculosis, and will also facilitate the development of diagnosis and vaccination techniques of M. tuberculosis.

Integrated gene co-expression network analysis in the growth phase of Mycobacterium tuberculosis reveals new potential drug targets

We have carried out weighted gene co-expression network analysis of Mycobacterium tuberculosis to gain insights into gene expression architecture during log phase growth. The differentially expressed genes between at least one pair of 11 different M. tuberculosis strains as source of biological variability were used for co-expression network analysis. This data included genes with highest coefficient of variation in expression. Five distinct modules were identified using topological overlap based clustering. All the modules together showed significant enrichment in biological processes: fatty acid biosynthesis, cell membrane, intracellular membrane bound organelle, DNA replication, Quinone biosynthesis, cell shape and peptidoglycan biosynthesis, ribosome and structural constituents of ribosome and transposition. We then extracted the co-expressed connections which were supported either by transcriptional regulatory network or STRING database or high edge weight of topological overlap. The genes trpC, nadC, pitA, Rv3404c, atpA, pknA, Rv0996, purB, Rv2106 and Rv0796 emerged as top hub genes. After overlaying this network on the iNJ661 metabolic network, the reactions catalyzed by 15 highly connected metabolic genes were knocked down in silico and evaluated by Flux Balance Analysis. The results showed that in 12 out of 15 cases, in 11 more than 50% of reactions catalyzed by genes connected through co-expressed connections also had altered fluxes. The modules 'Turquoise', 'Blue' and 'Red' also showed enrichment in essential genes. We could map 152 of the previously known or proposed drug targets in these modules and identified 15 new potential drug targets based on their high degree of co-expressed connections and strong correlation with module eigengenes.

Bacterial computing: a form of natural computing and its applications

The capability to establish adaptive relationships with the environment is an essential characteristic of living cells. Both bacterial computing and bacterial intelligence are two general traits manifested along adaptive behaviors that respond to surrounding environmental conditions. These two traits have generated a variety of theoretical and applied approaches. Since the different systems of bacterial signaling and the different ways of genetic change are better known and more carefully explored, the whole adaptive possibilities of bacteria may be studied under new angles. For instance, there appear instances of molecular "learning" along the mechanisms of evolution. More in concrete, and looking specifically at the time dimension, the bacterial mechanisms of learning and evolution appear as two different and related mechanisms for adaptation to the environment; in somatic time the former and in evolutionary time the latter. In the present chapter it will be reviewed the possible application of both kinds of mechanisms to prokaryotic molecular computing schemes as well as to the solution of real world problems.

Updating and curating metabolic pathways of TB

The sequencing of complete genomes has accelerated biomedical research by providing information about the overall coding capacity of bacterial chromosomes. The original TB annotation resulted in putative functional assignment of ∼60% of the genes to specific metabolic functions, however, the other 40% of the encoded ORFs where annotated as conserved hypothetical proteins, hypothetical proteins or encoding proteins of unknown function. The TB research community is now at the beginning of the next phases of post-genomics; namely reannotation and functional characterization by targeted experimentation. Arguably, this is the most significant time for basic microbiology in recent history. To foster basic TB research, the Tuberculosis Community Annotation Project (TBCAP) jamboree exercise began the reannotation effort by providing additional information for previous annotations, and refining and substantiating the functional assignment of ORFs and genes within metabolic pathways. The overall goal of the TBCAP 2012 exercise was to gather and compile various data types and use this information with oversight from the scientific community to provide additional information to support the functional annotations of encoding genes. Another objective of this effort was to standardize the publicly accessible Mycobacterium tuberculosis reference sequence and its annotation. The greatest benefit of functional annotation information of genome sequence is that it fuels TB research for drug discovery, diagnostics, vaccine development and epidemiology.

Boolean modeling of biological regulatory networks: a methodology tutorial

Given the complexity and interactive nature of biological systems, constructing informative and coherent network models of these systems and subsequently developing efficient approaches to analyze the assembled networks is of immense importance. The integration of network analysis and dynamic modeling enables one to investigate the behavior of the underlying system as a whole and to make experimentally testable predictions about less-understood aspects of the processes involved. In this paper, we present a tutorial on the fundamental steps of Boolean modeling of biological regulatory networks. We demonstrate how to infer a Boolean network model from the available experimental data, analyze the network using graph-theoretical measures, and convert it into a predictive dynamic model. For each step, the pitfalls one may encounter and possible ways to circumvent them are also discussed. We illustrate these steps on a toy network as well as in the context of the Drosophila melanogaster segment polarity gene network.

Virulence factors of the Mycobacterium tuberculosis complex

The Mycobacterium tuberculosis complex (MTBC) consists of closely related species that cause tuberculosis in both humans and animals. This illness, still today, remains to be one of the leading causes of morbidity and mortality throughout the world. The mycobacteria enter the host by air, and, once in the lungs, are phagocytated by macrophages. This may lead to the rapid elimination of the bacillus or to the triggering of an active tuberculosis infection. A large number of different virulence factors have evolved in MTBC members as a response to the host immune reaction. The aim of this review is to describe the bacterial genes/proteins that are essential for the virulence of MTBC species, and that have been demonstrated in an in vivo model of infection. Knowledge of MTBC virulence factors is essential for the development of new vaccines and drugs to help manage the disease toward an increasingly more tuberculosis-free world.

Topological effects of data incompleteness of gene regulatory networks

BACKGROUND

The topological analysis of biological networks has been a prolific topic in network science during the last decade. A persistent problem with this approach is the inherent uncertainty and noisy nature of the data. One of the cases in which this situation is more marked is that of transcriptional regulatory networks (TRNs) in bacteria. The datasets are incomplete because regulatory pathways associated to a relevant fraction of bacterial genes remain unknown. Furthermore, direction, strengths and signs of the links are sometimes unknown or simply overlooked. Finally, the experimental approaches to infer the regulations are highly heterogeneous, in a way that induces the appearance of systematic experimental-topological correlations. And yet, the quality of the available data increases constantly.

RESULTS

In this work we capitalize on these advances to point out the influence of data (in)completeness and quality on some classical results on topological analysis of TRNs, specially regarding modularity at different levels.

CONCLUSIONS

In doing so, we identify the most relevant factors affecting the validity of previous findings, highlighting important caveats to future prokaryotic TRNs topological analysis.

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.

Uncovering potential Drug Targets for Tuberculosis using Protein Networks

The emergence of HIV-TB co-infection and multi-drug resistant strains of Mycobacterium tuberculosis (Mtb) drive the need for new therapeutics against the infectious disease tuberculosis. Among the reported putative TB targets in the literature, the identification and characterization of the most probable therapeutic targets that influence the complex infectious disease, primarily through interactions with other influenced proteins, remains a statistical and computational challenge in proteomic epidemiology. Protein interaction network analysis provides an effective way to understand the relationships between protein products of genes by interconnecting networks of essential genes and its protein-protein interactions for 5 broad functional categories in Mtb. We also investigated the substructure of the protein interaction network and focused on highly connected nodes known as cliques by giving weight to the edges using data mining algorithms. Cliques containing Sulphate assimilation and Shikimate pathway enzymes appeared continuously inspite of increasing constraints applied by the K-Core algorithm during Network Decomposition. The potential target narrowed down through Systems approaches was Prephanate Dehydratase present in the Shikimate pathway this gives an insight to develop novel potential inhibitors through Structure Based Drug Design with natural compounds.