Understanding communication signals during mycobacterial latency through predicted genome-wide protein interactions and boolean modeling

Septum site placement in Mycobacteria - identification and characterisation of mycobacterial homologues of Escherichia coli MinD

A major virulence trait of Mycobacterium tuberculosis (M. tb) is its ability to enter a dormant state within its human host. Since cell division is intimately linked to metabolic shut down, understanding the mechanism of septum formation and its integration with other events in the division pathway is likely to offer clues to the molecular basis of dormancy. The M. tb genome lacks obvious homologues of several conserved cell division proteins, and this study was aimed at identifying and functionally characterising mycobacterial homologues of the E. coli septum site specification protein MinD (Ec MinD). Sequence homology based analyses suggested that the genomes of both M. tb and the saprophyte Mycobacterium smegmatis (M. smegmatis) encode two putative Ec MinD homologues - Rv1708/MSMEG_3743 and Rv3660c/ MSMEG_6171. Of these, Rv1708/MSMEG_3743 were found to be the true homologues, through complementation of the E. coli ∆minDE mutant HL1, overexpression studies, and structural comparisons. Rv1708 and MSMEG_3743 fully complemented the mini-cell phenotype of HL1, and over-expression of MSMEG_3743 in M. smegmatis led to cell elongation and a drastic decrease in c.f.u. counts, indicating its essentiality in cell-division. MSMEG_3743 displayed ATPase activity, consistent with its containing a conserved Walker A motif. Interaction of Rv1708 with the chromosome associated proteins ScpA and ParB, implied a link between its septum formation role, and chromosome segregation. Comparative structural analyses showed Rv1708 to be closer in similarity to Ec MinD than Rv3660c. In summary we identify Rv1708 and MSMEG_3743 to be homologues of Ec MinD, adding a critical missing piece to the mycobacterial cell division puzzle.

SNPPar: identifying convergent evolution and other homoplasies from microbial whole-genome alignments

Homoplasic SNPs are considered important signatures of strong (positive) selective pressure, and hence of adaptive evolution for clinically relevant traits such as antibiotic resistance and virulence. Here we present a new tool, SNPPar, for efficient detection and analysis of homoplasic SNPs from large whole genome sequencing datasets (>1000 isolates and/or >100 000 SNPs). SNPPar takes as input an SNP alignment, tree and annotated reference genome, and uses a combination of simple monophyly tests and ancestral state reconstruction (ASR, via TreeTime) to assign mutation events to branches and identify homoplasies. Mutations are annotated at the level of codon and gene, to facilitate analysis of convergent evolution. Testing on simulated data (120 Mycobacterium tuberculosis alignments representing local and global samples) showed SNPPar can detect homoplasic SNPs with very high specificity (zero false-positives in all tests) and high sensitivity (zero false-negatives in 89 % of tests). SNPPar analysis of three empirically sampled datasets (Elizabethkingia anophelis, Burkholderia dolosa and M. tuberculosis) produced results that were in concordance with previous studies, in terms of both individual homoplasies and evidence of convergence at the codon and gene levels. SNPPar analysis of a simulated alignment of ~64 000 genome-wide SNPs from 2000 M. tuberculosis genomes took ~23 min and ~2.6 GB of RAM to generate complete annotated results on a laptop. This analysis required ASR be conducted for only 1.25 % of SNPs, and the ASR step took ~23 s and 0.4 GB of RAM. SNPPar automates the detection and annotation of homoplasic SNPs efficiently and accurately from large SNP alignments. As demonstrated by the examples included here, this information can be readily used to explore the role of homoplasy in parallel and/or convergent evolution at the level of nucleotide, codon and/or gene.

Collateral Sensitivity to β-Lactam Drugs in Drug-Resistant Tuberculosis Is Driven by the Transcriptional Wiring of BlaI Operon Genes

The evolution of resistance to one antimicrobial can result in enhanced sensitivity to another, known as "collateral sensitivity." This underexplored phenomenon opens new therapeutic possibilities for patients infected with pathogens unresponsive to classical treatments. Intrinsic resistance to β-lactams in Mycobacterium tuberculosis (the causative agent of tuberculosis) has traditionally curtailed the use of these low-cost and easy-to-administer drugs for tuberculosis treatment. Recently, β-lactam sensitivity has been reported in strains resistant to classical tuberculosis therapy, resurging the interest in β-lactams for tuberculosis. However, a lack of understanding of the molecular underpinnings of this sensitivity has delayed exploration in the clinic. We performed gene expression and network analyses and in silico knockout simulations of genes associated with β-lactam sensitivity and genes associated with resistance to classical tuberculosis drugs to investigate regulatory interactions and identify key gene mediators. We found activation of the key inhibitor of β-lactam resistance, blaI, following classical drug treatment as well as transcriptional links between genes associated with β-lactam sensitivity and those associated with resistance to classical treatment, suggesting that regulatory links might explain collateral sensitivity to β-lactams. Our results support M. tuberculosis β-lactam sensitivity as a collateral consequence of the evolution of resistance to classical tuberculosis drugs, mediated through changes to transcriptional regulation. These findings support continued exploration of β-lactams for the treatment of patients infected with tuberculosis strains resistant to classical therapies. IMPORTANCE Tuberculosis remains a significant cause of global mortality, with strains resistant to classical drug treatment considered a major health concern by the World Health Organization. Challenging treatment regimens and difficulty accessing drugs in low-income communities have led to a high prevalence of strains resistant to multiple drugs, making the development of alternative therapies a priority. Although Mycobacterium tuberculosis is naturally resistant to β-lactam drugs, previous studies have shown sensitivity in strains resistant to classical drug treatment, but we currently lack understanding of the molecular underpinnings behind this phenomenon. We found that genes involved in β-lactam susceptibility are activated after classical drug treatment resulting from tight regulatory links with genes involved in drug resistance. Our study supports the hypothesis that β-lactam susceptibility observed in drug-resistant strains results from the underlying regulatory network of M. tuberculosis, supporting further exploration of the use of β-lactams for tuberculosis treatment.

Towards next-generation diagnostics for tuberculosis: identification of novel molecular targets by large-scale comparative genomics

MOTIVATION

Tuberculosis (TB) remains one of the main causes of death worldwide. The long and cumbersome process of culturing Mycobacterium tuberculosis complex (MTBC) bacteria has encouraged the development of specific molecular tools for detecting the pathogen. Most of these tools aim to become novel TB diagnostics, and big efforts and resources are invested in their development, looking for the endorsement of the main public health agencies. Surprisingly, no study has been conducted where the vast amount of genomic data available is used to identify the best MTBC diagnostic markers.

RESULTS

In this work, we used large-scale comparative genomics to identify 40 MTBC-specific loci. We assessed their genetic diversity and physiological features to select 30 that are good targets for diagnostic purposes. Some of these markers could be used to assess the physiological status of the bacilli. Remarkably, none of the most used MTBC markers is in our catalog. Illustrating the translational potential of our work, we develop a specific qPCR assay for quantification and identification of MTBC DNA. Our rational design of targeted molecular assays for TB could be used in many other fields of clinical and basic research.

AVAILABILITY AND IMPLEMENTATION

The database of non-tuberculous mycobacteria assemblies can be accessed at: 10.5281/zenodo.3374377.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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.

The sensor kinase MtrB of Mycobacterium tuberculosis regulates hypoxic survival and establishment of infection

Paired two-component systems (TCSs), having a sensor kinase (SK) and a cognate response regulator (RR), enable the human pathogen Mycobacterium tuberculosis to respond to the external environment and to persist within its host. Here, we inactivated the SK gene of the TCS MtrAB, mtrB, generating the strain ΔmtrB We show that mtrB loss reduces the bacterium's ability to survive in macrophages and increases its association with autophagosomes and autolysosomes. Notably, the ΔmtrB strain was markedly defective in establishing lung infection in mice, with no detectable lung pathology following aerosol challenge. ΔmtrB was less able to withstand hypoxic and acid stresses and to form biofilms and had decreased viability under hypoxia. Transcriptional profiling of ΔmtrB by gene microarray analysis, validated by quantitative RT-PCR, indicated down-regulation of the hypoxia-associated dosR regulon, as well as genes associated with other pathways linked to adaptation of M. tuberculosis to the host environment. Using in vitro biochemical assays, we demonstrate that MtrB interacts with DosR (a noncognate RR) in a phosphorylation-independent manner. Electrophoretic mobility shift assays revealed that MtrB enhances the binding of DosR to the hspX promoter, suggesting an unexpected role of MtrB in DosR-regulated gene expression in M. tuberculosis Taken together, these findings indicate that MtrB functions as a regulator of DosR-dependent gene expression and in the adaptation of M. tuberculosis to hypoxia and the host environment. We propose that MtrB may be exploited as a chemotherapeutic target against tuberculosis.

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.

Understanding the role of interactions between host and Mycobacterium tuberculosis under hypoxic condition: an in silico approach

Background

Mycobacterium tuberculosis infection in humans is often associated with extended period of latency. To adapt to the hostile hypoxic environment inside a macrophage, M. tuberculosis cells undergo several physiological and metabolic changes. Previous studies have mostly focused on inspecting individual facets of this complex process. In order to gain deeper insights into the infection process and to understand the coordination among different regulatory/ metabolic pathways in the pathogen, the current in silico study investigates three aspects, namely, (i) host-pathogen interactions (HPIs) between human and M. tuberculosis proteins, (ii) gene regulatory network pertaining to adaptation of M. tuberculosis to hypoxia and (iii) alterations in M. tuberculosis metabolism under hypoxic condition. Subsequently, cross-talks between these components have been probed to evaluate possible gene-regulatory events as well as HPIs which are likely to drive metabolic changes during pathogen’s adaptation to the intra-host hypoxic environment.

Results

The newly identified HPIs suggest the pathogen’s ability to subvert host mediated reactive oxygen intermediates/ reactive nitrogen intermediates (ROI/ RNI) stress as well as their potential role in modulating host cell cycle and cytoskeleton structure. The results also indicate a significantly pronounced effect of HPIs on hypoxic metabolism of M. tuberculosis. Findings from the current study underscore the necessity of investigating the infection process from a systems-level perspective incorporating different facets of intra-cellular survival of the pathogen.

Conclusions

The comprehensive host-pathogen interaction network, a Boolean model of M. tuberculosis H37Rv (Mtb) hypoxic gene-regulation, as well as a genome scale metabolic model of Mtb, built for this study are expected to be useful resources for future studies on tuberculosis infection.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4947-8) contains supplementary material, which is available to authorized users.

Prediction of host - pathogen protein interactions between Mycobacterium tuberculosis and Homo sapiens using sequence motifs

Background

Emergence of multiple drug resistant strains of M. tuberculosis (MDR-TB) threatens to derail global efforts aimed at reigning in the pathogen. Co-infections of M. tuberculosis with HIV are difficult to treat. To counter these new challenges, it is essential to study the interactions between M. tuberculosis and the host to learn how these bacteria cause disease.

Results

We report a systematic flow to predict the host pathogen interactions (HPIs) between M. tuberculosis and Homo sapiens based on sequence motifs. First, protein sequences were used as initial input for identifying the HPIs by ‘interolog’ method. HPIs were further filtered by prediction of domain-domain interactions (DDIs). Functional annotations of protein and publicly available experimental results were applied to filter the remaining HPIs. Using such a strategy, 118 pairs of HPIs were identified, which involve 43 proteins from M. tuberculosis and 48 proteins from Homo sapiens. A biological interaction network between M. tuberculosis and Homo sapiens was then constructed using the predicted inter- and intra-species interactions based on the 118 pairs of HPIs. Finally, a web accessible database named PATH (Protein interactions of M. tuberculosis and Human) was constructed to store these predicted interactions and proteins.

Conclusions

This interaction network will facilitate the research on host-pathogen protein-protein interactions, and may throw light on how M. tuberculosis interacts with its host.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0535-y) contains supplementary material, which is available to authorized users.

Understanding the sequential activation of Type III and Type VI Secretion Systems in Salmonella typhimurium using Boolean modeling

Background

Three pathogenicity islands, viz. SPI-1 (Salmonella pathogenicity island 1), SPI-2 (Salmonella pathogenicity island 2) and T6SS (Type VI Secretion System), present in the genome of Salmonella typhimurium have been implicated in the virulence of the pathogen. While the regulation of SPI-1 and SPI-2 (both encoding components of the Type III Secretion System - T3SS) are well understood, T6SS regulation is comparatively less studied. Interestingly, inter-connections among the regulatory elements of these three virulence determinants have also been suggested to be essential for successful infection. However, till date, an integrated view of gene regulation involving the regulators of these three secretion systems and their cross-talk is not available.

Results

In the current study, relevant regulatory information available from literature have been integrated into a single Boolean network, which portrays the dynamics of T3SS (SPI-1 and SPI-2) and T6SS mediated virulence. Some additional regulatory interactions involving a two-component system response regulator YfhA have also been predicted and included in the Boolean network. These predictions are aimed at deciphering the effects of osmolarity on T6SS regulation, an aspect that has been suggested in earlier studies, but the mechanism of which was hitherto unknown. Simulation of the regulatory network was able to recreate in silico the experimentally observed sequential activation of SPI-1, SPI-2 and T6SS.

Conclusions

The present study integrates relevant gene regulatory data (from literature and our prediction) into a single network, representing the cross-communication between T3SS (SPI-1 and SPI-2) and T6SS. This holistic view of regulatory interactions is expected to improve the current understanding of pathogenesis of S. typhimurium.

Latent Tuberculosis: Models, Computational Efforts and the Pathogen's Regulatory Mechanisms during Dormancy

Latent tuberculosis is a clinical syndrome that occurs after an individual has been exposed to the Mycobacterium tuberculosis (Mtb) Bacillus, the infection has been established and an immune response has been generated to control the pathogen and force it into a quiescent state. Mtb can exit this quiescent state where it is unresponsive to treatment and elusive to the immune response, and enter a rapid replicating state, hence causing infection reactivation. It remains a gray area to understand how the pathogen causes a persistent infection and it is unclear whether the organism will be in a slow replicating state or a dormant non-replicating state. The ability of the pathogen to adapt to changing host immune response mechanisms, in which it is exposed to hypoxia, low pH, nitric oxide (NO), nutrient starvation, and several other anti-microbial effectors, is associated with a high metabolic plasticity that enables it to metabolize under these different conditions. Adaptive gene regulatory mechanisms are thought to coordinate how the pathogen changes their metabolic pathways through mechanisms that sense changes in oxygen tension and other stress factors, hence stimulating the pathogen to make necessary adjustments to ensure survival. Here, we review studies that give insights into latency/dormancy regulatory mechanisms that enable infection persistence and pathogen adaptation to different stress conditions. We highlight what mathematical and computational models can do and what they should do to enhance our current understanding of TB latency.

Evidence supporting a role for dormant bacteria in the pathogenesis of spondylarthritis

Spondylarthritis is still viewed as a reaction to infectious agents, as opposed to an infection by persistent bacteria, for several reasons: (a) an infection is considered proven only when the organism can be cultured; (b) no studies have identified dormant bacteria in the tissues targeted by spondylarthritis; (c) the bacterial persistence hypothesis has no therapeutic implications at the time being, since antibiotics are effective neither on dormant bacteria nor on the manifestations of spondylarthritis; and (d) the high prevalence of borderline disorders combining features of spondylarthritis and of psoriatic arthritis, or even rheumatoid arthritis (RA), would indicate a role for dormant bacteria in these last two diseases. However, recent data on dormant bacteria have rekindled interest in the bacterial persistence hypothesis. Dormant bacteria cannot be cultured, because they express only a small group of genes, known as the regulon, which includes genes for transcription factors that block the expression of the usual bacterial genes. Certain forms of cell stress, such as molecule misfolding, promote the entry of bacteria into a state of dormancy, which induces the low-level release by the host cells of cytokines such as TNF. Whether HLA-B27 misfolding facilitates the persistence of dormant bacteria within spondylarthritis tissue targets remains to be determined. If it does, then treatments that reactivate dormant bacteria might make these organisms susceptible to appropriate antibiotics and might therefore serve as useful adjuncts to nonsteroidal anti-inflammatory drugs and TNFα antagonists. TNFα antagonists rarely reactivate dormant bacteria, with the exception of Mycobacterium tuberculosis, which, together with metastatic cells, is the most extensively studied latency model to date.

A chemical proteomics approach to profiling the ATP-binding proteome of Mycobacterium tuberculosis

Tuberculosis, caused by Mycobacterium tuberculosis, remains one of the leading causes of death worldwide despite extensive research, directly observed therapy using multidrug regimens, and the widespread use of a vaccine. The majority of patients harbor the bacterium in a state of metabolic dormancy. New drugs with novel modes of action are needed to target essential metabolic pathways in M. tuberculosis; ATP-competitive enzyme inhibitors are one such class. Previous screening efforts for ATP-competitive enzyme inhibitors identified several classes of lead compounds that demonstrated potent anti-mycobacterial efficacy as well as tolerable levels of toxicity in cell culture. In this report, a probe-based chemoproteomic approach was used to selectively profile the M. tuberculosis ATP-binding proteome in normally growing and hypoxic M. tuberculosis. From these studies, 122 ATP-binding proteins were identified in either metabolic state, and roughly 60% of these are reported to be essential for survival in vitro. These data are available through ProteomeXchange with identifier PXD000141. Protein families vital to the survival of the tubercle bacillus during hypoxia emerged from our studies. Specifically, along with members of the DosR regulon, several proteins involved in energy metabolism (Icl/Rv0468 and Mdh/Rv1240) and lipid biosynthesis (UmaA/Rv0469, DesA1/Rv0824c, and DesA2/Rv1094) were found to be differentially abundant in hypoxic versus normal growing cultures. These pathways represent a subset of proteins that may be relevant therapeutic targets for development of novel ATP-competitive antibiotics.

Understanding TB latency using computational and dynamic modelling procedures

The Mycobacterium tuberculosis bacilli's potency to cause persistent latent infection that is unresponsive to the current cocktail of TB drugs is strongly associated with its ability to adapt to changing intracellular environments, and tolerating, evading and subverting host defence mechanisms. We applied a combination of bioinformatics and mathematical modelling methods to enhance the understanding of TB latency dynamics. Analysis of time course microarray gene expression data was carried out and gene profiles for bacilli adaptation and survival in latency, simulated by hypoxia were determined. Reverse network engineering techniques were used to predict gene dependencies and regulatory interactions. Biochemical systems theory was applied to mathematically model the inferred gene regulatory networks. Significant regulatory genes involved in latency were determined by a combination of systems biology procedures and mathematical modelling of the inferred regulatory networks. Analysis of gene clusters of the inferred networks in the stationary and non-replicating phases of the bacilli predicted probable functions of some of the latency genes to be associated with latency genes of known functions. The systems biology approach and mathematical computational deletion experiments predicted key genes in the TB latency/dormancy program that may be possible TB drug targets. However, these gene candidates require experimental testing and validation.