Modeling Host-Pathogen Interaction to Elucidate the Metabolic Drug Response of Intracellular Mycobacterium tuberculosis

Multi-omics-based characterization of the influences of Mycobacterium tuberculosis virulence factors EsxB and PPE68 on host cells

Mycobacterium tuberculosis, the ancient master of causing tuberculosis, is one of the most successful pathogens capable of persistently colonizing host lungs. The EsxB (CFP-10) of ESX-1 system and PPE68 of the PPE family contribute to the virulence of M. tuberculosis. However, the virulence potential and pathogenetic characteristics of these two proteins during M. tuberculosis infection remain unclear. In this study, two prokaryotic expression plasmids for EsxB or PPE68 of M. tuberculosis were constructed and the recombinant proteins His-EsxB or His-PPE68 were purified. The proteome and transcriptome of MH-S cells treated with His-EsxB or His-PPE68 were explored, followed by validating the expression of the identified differentially expressed genes (DEGs) using quantitative PCR. A total of 159/439 specific proteins or 633/1117 DEGs were obtained between control and His-EsxB or His-PPE68 treated groups in the MH-S proteomes and transcriptomes. Additionally, 37/60 signal pathways were predicted in the His-EsxB or His-PPE68 treated groups and "Cytokine-cytokine receptor interaction" was the most represented pathway. Furthermore, the expression of the DEGs (IL-1β, IL-6, and TNF-α) was significantly upregulated, suggesting that these DEGs contributed to the host response during EsxB or PPE68 treatment. These findings provide detailed information on developing an effective intervention strategy to control M. tuberculosis infection.

Ovarian transcriptional response to Wolbachia infection in D. melanogaster in the context of between-genotype variation in gene expression

Wolbachia is a maternally transmitted endosymbiotic bacteria that infects a wide variety of arthropod and nematode hosts. The effects of Wolbachia on host biology are far-reaching and include changes in host gene expression. However, previous work on the host transcriptional response has generally been investigated in the context of a single host genotype. Thus, the relative effect of Wolbachia infection versus vs. host genotype on gene expression is unknown. Here, we explicitly test the relative roles of Wolbachia infection and host genotype on host gene expression by comparing the ovarian transcriptomes of 4 strains of Drosophila melanogaster (D. melanogaster) infected and uninfected with Wolbachia. Our data suggest that infection explains a small amount of transcriptional variation, particularly in comparison to variation in gene expression among strains. However, infection specifically affects genes related to cell cycle, translation, and metabolism. We also find enrichment of cell division and recombination processes among genes with infection-associated differential expression. Broadly, the transcriptomic changes identified in this study provide novel understanding of the relative magnitude of the effect of Wolbachia infection on gene expression in the context of host genetic variation and also point to genes that are consistently differentially expressed in response to infection among multiple genotypes.

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

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

Mycobacterium tuberculosis/Mycobacterium bovis triggered different variations in lipid composition of Bovine Alveolar Macrophages

The lipid composition performs important functions in interaction between macropha-ge and Mycobacterium tuberculosis (MTB)/Mycobacterium bovis (MB). Current understanding regarding the lipid responses of bovine alveolar macrophage (BAM) to MTB/MB is quite limited. The present study conducted lipidomics and transcriptome to assess alterations in BAM lipid compositions upon MB and MTB infection. We found that both MTB and MB induced glycerophospholipids accumulation in BAM, and MTB induced more alterations in lipid composition. MTB could affect the contents of various lipids, especially ceramide phosphocholines, polystyrene (PS) (17:0/0:0), testolic acid and testosterone acetate. Meanwhile, MB particularly induced accumulation of 1-alkyl,2-acylglycerophosphoinositols. Both MB and MTB suppressed the contents of palmitoleamide, N-ethyl arachidonoyl amine, N-(1,1-dimethyl-2-hydroxy-ethyl) arachidonoyll amine, eicosanoyl-EA, and PS (O-18:0/17:0) in BAM. Additionally, transcriptome analysis revealed that only MTB triggered genes involved in immune signaling and lipid related pathways in BAM. And MTB mainly activated genes CXCL2 and CXCL3 relevant to NOD-like receptor, IL-17 and TNF to further induce lipid accumulation in BAM, which in turn promoted the formation of foam cells. Meanwhile, time course RT-qPCR results showed that MTB was recognized by BAM to triggered dramatic immune responses, whereas MB could effectively escape the recognition system of BAM, leading rearrangement of lipid metabolisms in BAM at early infection stage. Altogether, the results of the present study provided evidence for changes in lipid metabolism of MTB/MB attacked BAM and contributed to the detection and treatment of zoonotic tuberculosis.

Resistance and tolerance of Mycobacterium tuberculosis to antimicrobial agents-How M. tuberculosis can escape antibiotics

Tuberculosis (TB) poses a serious threat to public health worldwide since it was discovered. Until now, TB has been one of the top 10 causes of death from a single infectious disease globally. The treatment of active TB cases majorly relies on various anti-tuberculosis drugs. However, under the selection pressure by drugs, the continuous evolution of Mycobacterium tuberculosis (Mtb) facilitates the emergence of drug-resistant strains, further resulting in the accumulation of tubercle bacilli with multiple drug resistance, especially deadly multidrug-resistant TB and extensively drug-resistant TB. Researches on the mechanism of drug action and drug resistance of Mtb provide a new scheme for clinical management of TB patients, and prevention of drug resistance. In this review, we summarized the molecular mechanisms of drug resistance of existing anti-TB drugs to better understand the evolution of drug resistance of Mtb, which will provide more effective strategies against drug-resistant TB, and accelerate the achievement of the EndTB Strategy by 2035. This article is categorized under: Infectious Diseases > Molecular and Cellular Physiology.

Dual transcriptome based reconstruction of Salmonella-human integrated metabolic network to screen potential drug targets

Salmonella enterica serovar Typhimurium (S. Typhimurium) is a highly adaptive pathogenic bacteria with a serious public health concern due to its increasing resistance to antibiotics. Therefore, identification of novel drug targets for S. Typhimurium is crucial. Here, we first created a pathogen-host integrated genome-scale metabolic network by combining the metabolic models of human and S. Typhimurium, which we further tailored to the pathogenic state by the integration of dual transcriptome data. The integrated metabolic model enabled simultaneous investigation of metabolic alterations in human cells and S. Typhimurium during infection. Then, we used the tailored pathogen-host integrated genome-scale metabolic network to predict essential genes in the pathogen, which are candidate novel drug targets to inhibit infection. Drug target prioritization procedure was applied to these targets, and pabB was chosen as a putative drug target. It has an essential role in 4-aminobenzoic acid (PABA) synthesis, which is an essential biomolecule for many pathogens. A structure based virtual screening was applied through docking simulations to predict candidate compounds that eliminate S. Typhimurium infection by inhibiting pabB. To our knowledge, this is the first comprehensive study for predicting drug targets and drug like molecules by using pathogen-host integrated genome-scale models, dual RNA-seq data and structure-based virtual screening protocols. This framework will be useful in proposing novel drug targets and drugs for antibiotic-resistant pathogens.

In Silico Exploration of Mycobacterium tuberculosis Metabolic Networks Shows Host-Associated Convergent Fluxomic Phenotypes

Mycobacterium tuberculosis, the causative agent of tuberculosis, is composed of several lineages characterized by a genome identity higher than 99%. Although the majority of the lineages are associated with humans, at least four lineages are adapted to other mammals, including different M. tuberculosis ecotypes. Host specificity is associated with higher virulence in its preferred host in ecotypes such as M. bovis. Deciphering what determines the preference of the host can reveal host-specific virulence patterns. However, it is not clear which genomic determinants might be influencing host specificity. In this study, we apply a combination of unsupervised and supervised classification methods on genomic data of ~27,000 M. tuberculosis clinical isolates to decipher host-specific genomic determinants. Host-specific genomic signatures are scarce beyond known lineage-specific mutations. Therefore, we integrated lineage-specific mutations into the iEK1011 2.0 genome-scale metabolic model to obtain lineage-specific versions of it. Flux distributions sampled from the solution spaces of these models can be accurately separated according to host association. This separation correlated with differences in cell wall processes, lipid, amino acid and carbon metabolic subsystems. These differences were observable when more than 95% of the samples had a specific growth rate significantly lower than the maximum achievable by the models. This suggests that these differences might manifest at low growth rate settings, such as the restrictive conditions M. tuberculosis suffers during macrophage infection.

A Review of Using Mathematical Modeling to Improve Our Understanding of Bacteriophage, Bacteria, and Eukaryotic Interactions

Phage therapy, the therapeutic usage of viruses to treat bacterial infections, has many theoretical benefits in the ‘post antibiotic era.’ Nevertheless, there are currently no approved mainstream phage therapies. One reason for this is a lack of understanding of the complex interactions between bacteriophage, bacteria and eukaryotic hosts. These three-component interactions are complex, with non-linear or synergistic relationships, anatomical barriers and genetic or phenotypic heterogeneity all leading to disparity between performance and efficacy in in vivo versus in vitro environments. Realistic computer or mathematical models of these complex environments are a potential route to improve the predictive power of in vitro studies for the in vivo environment, and to streamline lab work. Here, we introduce and review the current status of mathematical modeling and highlight that data on genetic heterogeneity and mutational stochasticity, time delays and population densities could be critical in the development of realistic phage therapy models in the future. With this in mind, we aim to inform and encourage the collaboration and sharing of knowledge and expertise between microbiologists and theoretical modelers, synergising skills and smoothing the road to regulatory approval and widespread use of phage therapy.

Dual Transcriptomic Analyses Unveil Host-Pathogen Interactions Between Salmonella enterica Serovar Enteritidis and Laying Ducks (Anas platyrhynchos)

Salmonella enteritidis (SE) is a pathogen that can readily infect ovarian tissues and colonize the granulosa cell layer such that it can be transmitted via eggs from infected poultry to humans in whom it can cause food poisoning. Ducks are an important egg-laying species that are susceptible to SE infection, yet the host–pathogen interactions between SE and ducks have not been thoroughly studied to date. Herein, we performed dual RNA-sequencing analyses of these two organisms in a time-resolved infection model of duck granulosa cells (dGCs) by SE. In total, 10,510 genes were significantly differentially expressed in host dGCs, and 265 genes were differentially expressed in SE over the course of infection. These differentially expressed genes (DEGs) of dGCs were enriched in the cytokine–cytokine receptor interaction pathway via KEGG analyses, and the DEGs in SE were enriched in the two-component system, bacterial secretion system, and metabolism of pathogen factors pathways as determined. A subsequent weighted gene co-expression network analysis revealed that the cytokine–cytokine receptor interaction pathway is mostly enriched at 6 h post-infection (hpi). Moreover, a number of pathogenic factors identified in the pathogen–host interaction database (PHI-base) are upregulated in SE, including genes encoding the pathogenicity island/component, type III secretion, and regulators of systemic infection. Furthermore, an intracellular network associated with the regulation of SE infection in ducks was constructed, and 16 cytokine response-related dGCs DEGs (including IL15, CD40, and CCR7) and 17 pathogenesis-related factors (including sseL, ompR, and fliC) were identified, respectively. Overall, these results not only offer new insights into the mechanisms underlying host–pathogen interactions between SE and ducks, but they may also aid in the selection of potential targets for antimicrobial drug development.

Modeling the energy metabolism in immune cells

In this review, we summarize and briefly discuss various approaches to modeling the metabolism in human immune cells, with a focus on energy metabolism. These approaches include metabolic reconstruction, elementary modes, and flux balance analysis, which are often subsumed under constraint-based modeling. Further approaches are evolutionary game theory and kinetic modeling. Many immune cells such as macrophages show the Warburg effect, meaning that glycolysis is upregulated upon activation. We outline a minimal model for explaining that effect using optimization. The effect of a confrontation with pathogen cells on immunometabolism is highlighted. Models describing the differences between M1 and M2 macrophages, ROS production in neutrophils, and tryptophan metabolism are discussed. Obstacles and future prospects are outlined.

Bacterial nucleomodulins: A coevolutionary adaptation to the eukaryotic command center

Through long-term interactions with their hosts, bacterial pathogens have evolved unique arsenals of effector proteins that interact with specific host targets and reprogram the host cell into a permissive niche for pathogen proliferation. The targeting of effector proteins into the host cell nucleus for modulation of nuclear processes is an emerging theme among bacterial pathogens. These unique pathogen effector proteins have been termed in recent years as "nucleomodulins." The first nucleomodulins were discovered in the phytopathogens Agrobacterium and Xanthomonas, where their nucleomodulins functioned as eukaryotic transcription factors or integrated themselves into host cell DNA to promote tumor induction, respectively. Numerous nucleomodulins were recently identified in mammalian pathogens. Bacterial nucleomodulins are an emerging family of pathogen effector proteins that evolved to target specific components of the host cell command center through various mechanisms. These mechanisms include: chromatin dynamics, histone modification, DNA methylation, RNA splicing, DNA replication, cell cycle, and cell signaling pathways. Nucleomodulins may induce short- or long-term epigenetic modifications of the host cell. In this extensive review, we discuss the current knowledge of nucleomodulins from plant and mammalian pathogens. While many nucleomodulins are already identified, continued research is instrumental in understanding their mechanisms of action and the role they play during the progression of pathogenesis. The continued study of nucleomodulins will enhance our knowledge of their effects on nuclear chromatin dynamics, protein homeostasis, transcriptional landscapes, and the overall host cell epigenome.

A systematic evaluation of Mycobacterium tuberculosis Genome-Scale Metabolic Networks

Metabolism underpins the pathogenic strategy of the causative agent of TB, Mycobacterium tuberculosis (Mtb), and therefore metabolic pathways have recently re-emerged as attractive drug targets. A powerful approach to study Mtb metabolism as a whole, rather than just individual enzymatic components, is to use a systems biology framework, such as a Genome-Scale Metabolic Network (GSMN) that allows the dynamic interactions of all the components of metabolism to be interrogated together. Several GSMNs networks have been constructed for Mtb and used to study the complex relationship between the Mtb genotype and its phenotype. However, the utility of this approach is hampered by the existence of multiple models, each with varying properties and performances. Here we systematically evaluate eight recently published metabolic models of Mtb-H37Rv to facilitate model choice. The best performing models, sMtb2018 and iEK1011, were refined and improved for use in future studies by the TB research community.

The tuberculosis bacillus, Mycobacterium tuberculosis (Mtb), is a global killer causing millions of deaths every year and is therefore a major burden to human health. Treatment of tuberculosis requires a cocktail of antibiotics for a minimum of 6 months. Treatment failure is common and is a major driver in the upward trend of antibiotic resistance, recognized by the World Health Organization as one of top ten threats to global health. A key to the success of Mtb as a human pathogen is ascribed to its extraordinary metabolic flexibility. Understanding the metabolism of Mtb is therefore an important goal of TB researchers as metabolic pathways present attractive drug targets. A powerful approach to study metabolism is through the use of genome-scale metabolic networks which enable metabolism to be studied at the whole system level rather than one enzyme at a time. Here, we comprehensively compare available genome scale metabolic networks. Our results identify the best performing networks for a variety of modelling approaches. This work allowed us to refine these models for the TB community to use in future studies to probe the metabolism of this formidable human pathogen.

Novel Approaches for Systems Biology of Metabolism-Oriented Pathogen-Human Interactions: A Mini-Review

Pathogenic microorganisms exploit host metabolism for sustained survival by rewiring its metabolic interactions. Therefore, several metabolic changes are induced in both pathogen and host cells in the course of infection. A systems-based approach to elucidate those changes includes the integrative use of genome-scale metabolic networks and molecular omics data, with the overall goal of better characterizing infection mechanisms for novel treatment strategies. This review focuses on novel aspects of metabolism-oriented systems-based investigation of pathogen-human interactions. The reviewed approaches are the generation of dual-omics data for the characterization of metabolic signatures of pathogen-host interactions, the reconstruction of pathogen-host integrated genome-scale metabolic networks, which has a high potential to be applied to pathogen-gut microbiota interactions, and the structure-based analysis of enzymes playing role in those interactions. The integrative use of those approaches will pave the way for the identification of novel biomarkers and drug targets for the prediction and prevention of infectious diseases.

L-Alanine specifically potentiates fluoroquinolone efficacy against Mycobacterium persisters via increased intracellular reactive oxygen species

Tuberculosis caused by Mycobacterium tuberculosis remains a major global health concern; M. tuberculosis drug resistance and persistence further fueled the situation. Nutrient supportive therapy was intensively pursued to complement the conventional treatment, as well as their synergy with current antibiotics. To explore whether L-alanine can synergize with fluoroquinolones against M. tuberculosis, M. smegmatis was used as a surrogate in this study. We found that L-alanine can boost the bactericidal efficacy of fluoroquinolones, increasing the production of intracellular reactive oxygen species. This effect is very significant for persisters. Accelerated tricarboxylic acid cycle and/or nucleotide metabolism were observed after the addition of L-alanine. M. smegmatis MSMEG2660 is a homolog of the alanine dehydrogenase (Rv2780, MSMEG2659) negative regulator Rv2779c and involved in the L-alanine potentiation of fluoroquinolone via funneling more alanine into tricarboxylic acid. Deletion mutant of the MSMEG2660 (∆Ms2660) became more susceptible, and more readily revived from persistence. We firstly found that L-alanine can synergize with fluoroquinolones against Mycobacterium, especially the persisters via promoting metabolism. This will inspire new avenue to eliminate Mycobacterium persisters.