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

Pangenome and genomic signatures linked to the dominance of the lineage-4 of Mycobacterium tuberculosis isolated from extrapulmonary tuberculosis patients in western Ethiopia

BACKGROUND

The lineage 4 (L4) of Mycobacterium tuberculosis (MTB) is not only globally prevalent but also locally dominant, surpassing other lineages, with lineage 2 (L2) following in prevalence. Despite its widespread occurrence, factors influencing the expansion of L4 and its sub-lineages remain poorly understood both at local and global levels. Therefore, this study aimed to conduct a pan-genome and identify genomic signatures linked to the elevated prevalence of L4 sublineages among extrapulmonary TB (EPTB) patients in western Ethiopia.

METHODS

A cross-sectional study was conducted at an institutional level involving confirmed cases of extrapulmonary tuberculosis (EPTB) patients from August 5, 2018, to December 30, 2019. A total of 75 MTB genomes, classified under lineage 4 (L4), were used for conducting pan-genome and genome-wide association study (GWAS) analyses. After a quality check, variants were identified using MTBseq, and genomes were de novo assembled using SPAdes. Gene prediction and annotation were performed using Prokka. The pan-genome was constructed using GET_HOMOLOGUES, and its functional analysis was carried out with the Bacterial Pan-Genome Analysis tool (BPGA). For GWAS analysis, Scoary was employed with Benjamini-Hochberg correction, with a significance threshold set at p-value ≤ 0.05.

RESULTS

The analysis revealed a total of 3,270 core genes, predominantly associated with orthologous groups (COG) functions, notably in the categories of '[R] General function prediction only' and '[I] Lipid transport and metabolism'. Conversely, functions related to '[N] Cell motility' and '[Q] Secondary metabolites biosynthesis, transport, and catabolism' were primarily linked to unique and accessory genes. The pan-genome of MTB L4 was found to be open. Furthermore, the GWAS study identified genomic signatures linked to the prevalence of sublineages L4.6.3 and L4.2.2.2.

CONCLUSIONS

Apart from host and environmental factors, the sublineage of L4 employs distinct virulence factors for successful dissemination in western Ethiopia. Given that the functions of these newly identified genes are not well understood, it is advisable to experimentally validate their roles, particularly in the successful transmission of specific L4 sublineages over others.

Bacterial Transcriptional Regulators: A Road Map for Functional, Structural, and Biophysical Characterization

The different niches through which bacteria move during their life cycle require a fast response to the many environmental queues they encounter. The sensing of these stimuli and their correct response is driven primarily by transcriptional regulators. This kind of protein is involved in sensing a wide array of chemical species, a process that ultimately leads to the regulation of gene transcription. The allosteric-coupling mechanism of sensing and regulation is a central aspect of biological systems and has become an important field of research during the last decades. In this review, we summarize the state-of-the-art techniques applied to unravel these complex mechanisms. We introduce a roadmap that may serve for experimental design, depending on the answers we seek and the initial information we have about the system of study. We also provide information on databases containing available structural information on each family of transcriptional regulators. Finally, we discuss the recent results of research about the allosteric mechanisms of sensing and regulation involving many transcriptional regulators of interest, highlighting multipronged strategies and novel experimental techniques. The aim of the experiments discussed here was to provide a better understanding at a molecular level of how bacteria adapt to the different environmental threats they face.

Microbiome in Eosinophilic Esophagitis-Metagenomic, Metatranscriptomic, and Metabolomic Changes: A Systematic Review

Background: Our understanding of human gut microbiota has expanded in recent years with the introduction of high-throughput sequencing methods. These technologies allow for the study of metagenomic, metatranscriptomic, and metabolomic bacterial alterations as they relate to human disease. Work in this area has described the human gut microbiome in both healthy individuals and those with chronic gastrointestinal diseases, such as eosinophilic esophagitis (EoE). Objectives: A systematic review of the current available literature on metagenomic, metatranscriptomic, and metabolomic changes in EoE was performed. Methods: This review was performed following the PRISMA guidelines for reporting systematic reviews and meta-analyses. All relevant publications up to March 2021 were retrieved using the search engines PubMed, Google Scholar, and Web of Science. They were then extracted, assessed, and reviewed. Only original studies published in English were included. Results: A total of 46 potential manuscripts were identified for review. Twelve met criteria for further review based on relevance screening and 9 met criteria for inclusion, including 6 studies describing the microbiome in EoE and 3 detailing metabolomic/tissue biochemistry alterations in EoE. No published studies examined metatranscriptomic changes. Samples for microbiome analysis were obtained via esophageal biopsy (n = 3), esophageal string test (n = 1), salivary sampling (n = 1), or stool specimen (n = 1). Samples analyzing tissue biochemistry were obtained via esophageal biopsy (n = 2) and blood plasma (n = 1). There were notable differences in how samples were collected and analyzed. Metabolomic and tissue biochemical alterations were described using Raman spectroscopy, which demonstrated distinct differences in the spectral intensities of glycogen, lipid, and protein content compared to controls. Finally, research in proteomics identified an increase in the pro-fibrotic protein thrombospondin-1 in patients with EoE compared with controls. Conclusions: While there are notable changes in the microbiome, these differ with the collection technique and method of analysis utilized. Techniques characterizing metabolomics and tissue biochemistry are now being utilized to further study patients with EoE. The lack of published data related to the human microbiome, metagenome, metatranscriptome, and metabolome in patients with EoE highlights the need for further research in these areas.

SyNDI: synchronous network data integration framework

BACKGROUND

Systems biology takes a holistic approach by handling biomolecules and their interactions as big systems. Network based approach has emerged as a natural way to model these systems with the idea of representing biomolecules as nodes and their interactions as edges. Very often the input data come from various sorts of omics analyses. Those resulting networks sometimes describe a wide range of aspects, for example different experiment conditions, species, tissue types, stimulating factors, mutants, or simply distinct interaction features of the same network produced by different algorithms. For these scenarios, synchronous visualization of more than one distinct network is an excellent mean to explore all the relevant networks efficiently. In addition, complementary analysis methods are needed and they should work in a workflow manner in order to gain maximal biological insights.

RESULTS

In order to address the aforementioned needs, we have developed a Synchronous Network Data Integration (SyNDI) framework. This framework contains SyncVis, a Cytoscape application for user-friendly synchronous and simultaneous visualization of multiple biological networks, and it is seamlessly integrated with other bioinformatics tools via the Galaxy platform. We demonstrated the functionality and usability of the framework with three biological examples - we analyzed the distinct connectivity of plasma metabolites in networks associated with high or low latent cardiovascular disease risk; deeper insights were obtained from a few similar inflammatory response pathways in Staphylococcus aureus infection common to human and mouse; and regulatory motifs which have not been reported associated with transcriptional adaptations of Mycobacterium tuberculosis were identified.

CONCLUSIONS

Our SyNDI framework couples synchronous network visualization seamlessly with additional bioinformatics tools. The user can easily tailor the framework for his/her needs by adding new tools and datasets to the Galaxy platform.

Genome-scale biological models for industrial microbial systems

The primary aims and challenges associated with microbial fermentation include achieving faster cell growth, higher productivity, and more robust production processes. Genome-scale biological models, predicting the formation of an interaction among genetic materials, enzymes, and metabolites, constitute a systematic and comprehensive platform to analyze and optimize the microbial growth and production of biological products. Genome-scale biological models can help optimize microbial growth-associated traits by simulating biomass formation, predicting growth rates, and identifying the requirements for cell growth. With regard to microbial product biosynthesis, genome-scale biological models can be used to design product biosynthetic pathways, accelerate production efficiency, and reduce metabolic side effects, leading to improved production performance. The present review discusses the development of microbial genome-scale biological models since their emergence and emphasizes their pertinent application in improving industrial microbial fermentation of biological products.

Regulation of Three Virulence Strategies of Mycobacterium tuberculosis: A Success Story

Tuberculosis remains one of the deadliest diseases. Emergence of drug-resistant and multidrug-resistant M. tuberculosis strains makes treating tuberculosis increasingly challenging. In order to develop novel intervention strategies, detailed understanding of the molecular mechanisms behind the success of this pathogen is required. Here, we review recent literature to provide a systems level overview of the molecular and cellular components involved in divalent metal homeostasis and their role in regulating the three main virulence strategies of M. tuberculosis: immune modulation, dormancy and phagosomal rupture. We provide a visual and modular overview of these components and their regulation. Our analysis identified a single regulatory cascade for these three virulence strategies that respond to limited availability of divalent metals in the phagosome.

Use of Microarray Datasets to generate Caco-2-dedicated Networks and to identify Reporter Genes of Specific Pathway Activity

Intestinal epithelial cells, like Caco-2, are commonly used to study the interaction between food, other luminal factors and the host, often supported by microarray analysis to study the changes in gene expression as a result of the exposure. However, no compiled dataset for Caco-2 has ever been initiated and Caco-2-dedicated gene expression networks are barely available. Here, 341 Caco-2-specific microarray samples were collected from public databases and from in-house experiments pertaining to Caco-2 cells exposed to pathogens, probiotics and several food compounds. Using these datasets, a gene functional association network specific for Caco-2 was generated containing 8937 nodes 129711 edges. Two in silico methods, a modified version of biclustering and the new Differential Expression Correlation Analysis, were developed to identify Caco-2-specific gene targets within a pathway of interest. These methods were subsequently applied to the AhR and Nrf2 signalling pathways and altered expression of the predicted target genes was validated by qPCR in Caco-2 cells exposed to coffee extracts, known to activate both AhR and Nrf2 pathways. The datasets and in silico method(s) to identify and predict responsive target genes can be used to more efficiently design experiments to study Caco-2/intestinal epithelial-relevant biological processes.

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.

Metagenomics, Metatranscriptomics, and Metabolomics Approaches for Microbiome Analysis

Microbiomes are ubiquitous and are found in the ocean, the soil, and in/on other living organisms. Changes in the microbiome can impact the health of the environmental niche in which they reside. In order to learn more about these communities, different approaches based on data from multiple omics have been pursued. Metagenomics produces a taxonomical profile of the sample, metatranscriptomics helps us to obtain a functional profile, and metabolomics completes the picture by determining which byproducts are being released into the environment. Although each approach provides valuable information separately, we show that, when combined, they paint a more comprehensive picture. We conclude with a review of network-based approaches as applied to integrative studies, which we believe holds the key to in-depth understanding of microbiomes.

Separable roles for Mycobacterium tuberculosis ESX-3 effectors in iron acquisition and virulence

Mycobacterium tuberculosis (Mtb) encodes five type VII secretion systems (T7SS), designated ESX-1-ESX-5, that are critical for growth and pathogenesis. The best characterized is ESX-1, which profoundly impacts host cell interactions. In contrast, the ESX-3 T7SS is implicated in metal homeostasis, but efforts to define its function have been limited by an inability to recover deletion mutants. We overcame this impediment using medium supplemented with various iron complexes to recover mutants with deletions encompassing select genes within esx-3 or the entire operon. The esx-3 mutants were defective in uptake of siderophore-bound iron and dramatically accumulated cell-associated mycobactin siderophores. Proteomic analyses of culture filtrate revealed that secretion of EsxG and EsxH was codependent and that EsxG-EsxH also facilitated secretion of several members of the proline-glutamic acid (PE) and proline-proline-glutamic acid (PPE) protein families (named for conserved PE and PPE N-terminal motifs). Substrates that depended on EsxG-EsxH for secretion included PE5, encoded within the esx-3 locus, and the evolutionarily related PE15-PPE20 encoded outside the esx-3 locus. In vivo characterization of the mutants unexpectedly showed that the ESX-3 secretion system plays both iron-dependent and -independent roles in Mtb pathogenesis. PE5-PPE4 was found to be critical for the siderophore-mediated iron-acquisition functions of ESX-3. The importance of this iron-acquisition function was dependent upon host genotype, suggesting a role for ESX-3 secretion in counteracting host defense mechanisms that restrict iron availability. Further, we demonstrate that the ESX-3 T7SS secretes certain effectors that are important for iron uptake while additional secreted effectors modulate virulence in an iron-independent fashion.

Effects of Sample Size and Dimensionality on the Performance of Four Algorithms for Inference of Association Networks in Metabonomics

We investigated the effect of sample size and dimensionality on the performance of four algorithms (ARACNE, CLR, CORR, and PCLRC) when they are used for the inference of metabolite association networks. We report that as many as 100-400 samples may be necessary to obtain stable network estimations, depending on the algorithm and the number of measured metabolites. The CLR and PCLRC methods produce similar results, whereas network inference based on correlations provides sparse networks; we found ARACNE to be unsuitable for this application, being unable to recover the underlying metabolite association network. We recommend the PCLRC algorithm for the inference on metabolite association networks.

Comprehensive insights into transcriptional adaptation of intracellular mycobacteria by microbe-enriched dual RNA sequencing

Background

The human pathogen Mycobacterium tuberculosis has the capacity to escape eradication by professional phagocytes. During infection, M. tuberculosis resists the harsh environment of phagosomes and actively manipulates macrophages and dendritic cells to ensure prolonged intracellular survival. In contrast to other intracellular pathogens, it has remained difficult to capture the transcriptome of mycobacteria during infection due to an unfavorable host-to-pathogen ratio.

Results

We infected the human macrophage-like cell line THP-1 with the attenuated M. tuberculosis surrogate M. bovis Bacillus Calmette–Guérin (M. bovis BCG). Mycobacterial RNA was up to 1000-fold underrepresented in total RNA preparations of infected host cells. We employed microbial enrichment combined with specific ribosomal RNA depletion to simultaneously analyze the transcriptional responses of host and pathogen during infection by dual RNA sequencing. Our results confirm that mycobacterial pathways for cholesterol degradation and iron acquisition are upregulated during infection. In addition, genes involved in the methylcitrate cycle, aspartate metabolism and recycling of mycolic acids were induced. In response to M. bovis BCG infection, host cells upregulated de novo cholesterol biosynthesis presumably to compensate for the loss of this metabolite by bacterial catabolism.

Conclusions

Dual RNA sequencing allows simultaneous capture of the global transcriptome of host and pathogen, during infection. However, mycobacteria remained problematic due to their relatively low number per host cell resulting in an unfavorable bacterium-to-host RNA ratio. Here, we use a strategy that combines enrichment for bacterial transcripts and dual RNA sequencing to provide the most comprehensive transcriptome of intracellular mycobacteria to date. The knowledge acquired into the pathogen and host pathways regulated during infection may contribute to a solid basis for the deployment of novel intervention strategies to tackle infection.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-014-1197-2) contains supplementary material, which is available to authorized users.

Systems-level modeling of mycobacterial metabolism for the identification of new (multi-)drug targets

Systems-level metabolic network reconstructions and the derived constraint-based (CB) mathematical models are efficient tools to explore bacterial metabolism. Approximately one-fourth of the Mycobacterium tuberculosis (Mtb) genome contains genes that encode proteins directly involved in its metabolism. These represent potential drug targets that can be systematically probed with CB models through the prediction of genes essential (or the combination thereof) for the pathogen to grow. However, gene essentiality depends on the growth conditions and, so far, no in vitro model precisely mimics the host at the different stages of mycobacterial infection, limiting model predictions. These limitations can be circumvented by combining expression data from in vivo samples with a validated CB model, creating an accurate description of pathogen metabolism in the host. To this end, we present here a thoroughly curated and extended genome-scale CB metabolic model of Mtb quantitatively validated using 13C measurements. We describe some of the efforts made in integrating CB models and high-throughput data to generate condition specific models, and we will discuss challenges ahead. This knowledge and the framework herein presented will enable to identify potential new drug targets, and will foster the development of optimal therapeutic strategies.