System-level analysis of Salmonella metabolism during infection

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.

Salmonella Proteomic Profiling during Infection Distinguishes the Intracellular Environment of Host Cells

Salmonella Typhimurium is one of the leading causes of foodborne bacterial infection. Nevertheless, how Salmonella adapts to distinct types of host cells during infection remains poorly understood. By contrasting intracellular Salmonella proteomes from both infected macrophages and epithelial cells, we found striking proteomic signatures specific to particular types of host cells. Notably, Salmonella proteomic remodeling exhibited quicker kinetics in macrophages than in epithelial cells with respect to bacterial virulence and flagellar and chemotaxis systems. Furthermore, we unveiled high levels of induction of bacterial histidine biosynthesis in macrophages but not in epithelial cells, which is attributable to differing intracellular levels of this amino acid. Intriguingly, we found that a defective hisG gene renders a Salmonella strain hypersensitive to histidine shortage in macrophages. Overall, our work reveals specific Salmonella adaptation mechanisms in distinct host cells, which should aid in the development of novel anti-infection strategies.

Essential to bacterial pathogenesis, Salmonella enterica serovar Typhimurium (S. Typhimurium) has evolved the capacity to quickly sense and adapt to specific intracellular environment within distinct host cells. Here we examined S. Typhimurium proteomic remodeling within macrophages, allowing direct comparison with our previous studies in epithelial cells. In addition to many shared features, our data revealed proteomic signatures highly specific to one type of host cells. Notably, intracellular S. Typhimurium differentially regulates the two type III secretion systems (T3SSs) far more quickly in macrophages than in epithelial cells; bacterial flagellar and chemotaxis systems degenerate more quickly in macrophages than in HeLa cells as well. Importantly, our comparative analysis uncovered high levels of induction of bacterial histidine biosynthesis in macrophages but not in epithelial cells. Targeted metabolomic measurements revealed markedly lower histidine levels within macrophages. Intriguingly, further functional studies established that histidine biosynthesis that is defective (due to a hisG mutation) renders the bacterium (strain SL1344) hypersensitive to intracellular shortage of this amino acid. Indeed, another S. Typhimurium strain, namely, strain 14028s, with a fully functional biosynthetic pathway exhibited only minor induction of the his operon within infected macrophages. Our work thus provided novel insights into S. Typhimurium adaptation mechanisms within distinct host cells and also provided an elegant paradigm where proteomic profiling of intracellular pathogens is utilized to discriminate specific host environments (e.g., on the basis of nutrient availability).

IMPORTANCESalmonella Typhimurium is one of the leading causes of foodborne bacterial infection. Nevertheless, how Salmonella adapts to distinct types of host cells during infection remains poorly understood. By contrasting intracellular Salmonella proteomes from both infected macrophages and epithelial cells, we found striking proteomic signatures specific to particular types of host cells. Notably, Salmonella proteomic remodeling exhibited quicker kinetics in macrophages than in epithelial cells with respect to bacterial virulence and flagellar and chemotaxis systems. Furthermore, we unveiled high levels of induction of bacterial histidine biosynthesis in macrophages but not in epithelial cells, which is attributable to differing intracellular levels of this amino acid. Intriguingly, we found that a defective hisG gene renders a Salmonella strain hypersensitive to histidine shortage in macrophages. Overall, our work reveals specific Salmonella adaptation mechanisms in distinct host cells, which should aid in the development of novel anti-infection strategies.

Targeted inactivation of Salmonella Agona metabolic genes by group II introns and in vivo assessment of pathogenicity and anti-tumour activity in mouse model

The fight against cancer has been a never-ending battle. Limitations of conventional therapies include lack of selectivity, poor penetration and highly toxic to the host. Using genetically modified bacteria as a tumour therapy agent has gained the interest of scientist from the past few decades. Low virulence and highly tolerability of Salmonella spp. in animals and humans make it as the most studied pathogen with regards to anti-tumour therapy. The present study aims to construct a genetically modified S. Agona auxotroph as an anti-tumour agent. LeuB and ArgD metabolic genes in ΔSopBΔSopD double knockout S. Agona were successfully knocked out using a Targetron gene knockout system. The knockout was confirmed by colony PCR and the strains were characterized in vitro and in vivo. The knockout of metabolic genes causes significant growth defect in M9 minimal media. Quadruple knockout ΔSopBΔSopDΔLeuBΔArgD (BDLA) exhibited lowest virulence among all of the strains in all parameters including bacterial load, immunity profile and histopathology studies. In vivo anti-tumour study on colorectal tumour bearing-BALB/c mice revealed that all strains of S. Agona were able to suppress the growth of the large solid tumour as compared with negative control and ΔLeuBΔArgD (LA) and BDLA auxotroph showed better efficacy. Interestingly, higher level of tumour growth suppression was noticed in large tumour. However, multiple administration of bacteria dosage did not increase the tumour suppression efficacy. In this study, the virulence of BDLA knockout strain was slightly reduced and tumour growth suppression efficacy was successfully enhanced, which provide a valuable starting point for the development of S. Agona as anti-tumour agent.

Properties and mechanism of d-glucosaminate-6-phosphate ammonia-lyase: An aminotransferase family enzyme with d-amino acid specificity

Salmonella enterica serovar Typhimurium utilizes a wide range of growth substrates, some of which are relatively novel. One of these unusual substrates is d-glucosaminate, which is metabolized by the enzymes encoded in the dga operon. d-Glucosaminate is transported and converted to d-glucosaminate-6-phosphate (G6P) by a phosphotransferase system, composed of DgaABCD. The protein product of dgaE, d-glucosaminate-6-phosphate ammonia lyase (DGL), converts G6P to 2-keto-3-deoxygluconate-6-phosphate, which undergoes a retroaldol reaction catalyzed by the DgaF protein to give d-glyceraldehyde-3-phosphate and pyruvate. We have now developed an improved synthesis of G6P which gives a higher yield. The DGL reaction is of mechanistic interest because it is one of only a few enzymes in the pyridoxal-5'-phosphate (PLP) dependent aminotransferase superfamily known to catalyze reaction of a d-amino acid substrate. The pH dependence of DGL shows an optimum at 7.5-8.5, suggesting a requirement for a catalytic base. α-Glycerophosphate and inorganic phosphate are weak competitive inhibitors, with K_i values near 30mM, and d-serine is neither a substrate nor an inhibitor. We have found in rapid-scanning stopped-flow experiments that DGL reacts rapidly with its substrate to form a quinonoid intermediate with λ_max=480nm, within the dead time (ca. 2msec), which then rapidly decays (k=279s^-1) to an intermediate with absorption between 330 and 350nm, probably an aminoacrylate complex. We suggest a mechanism for DGL and propose that the unusual stereochemistry of the DGL reaction requires a catalytic base poised on the opposite face of the PLP-substrate complex from the other members of the aminotransferase superfamily.

Current knowledge of metabolomic approach in infectious fish disease studies

The approaches of transcriptomic and proteomic have been widely used to study host-pathogen interactions in fish diseases, and this is comparable to the recently emerging application of metabolomic in elucidating disease-resistant mechanisms in fish that gives new insight into potential therapeutic strategies to improve fish health. Metabolomic is defined as the large-scale study of all metabolites within an organism and represents the frontline in the 'omics' approaches, providing direct information on the metabolic responses and perturbations in metabolic pathways. In this review, the current research in infectious fish diseases using metabolomic approach will be summarized. The metabolomic approach in economically important fish infected with viruses, bacteria and nematodes will also be discussed. The potential of the metabolomic approach for management of these infectious diseases as well as the challenges and the limitations of metabolomic in fish disease studies will be explored. Current review highlights the impacts of metabolomic studies in infectious fish diseases, which proposed the potential of new therapeutic strategies to enhance disease resistance in fish.

Intracellular Salmonella metabolism

Growth of Salmonella inside infected host cells is a key aspect of their ability to cause local enteritis or systemic disease. This growth depends on exploitation of host nutrients through a large Salmonella metabolism network with hundreds of metabolites and enzymes. Studies in cell culture infection models are unravelling more and more of the underlying molecular and cellular mechanisms but also show striking Salmonella metabolic plasticity depending on host cell line and experimental conditions. In vivo studies have revealed a qualitatively diverse, but quantitatively poor, host-Salmonella nutritional interface, which on one side makes Salmonella fitness largely resilient against metabolic perturbations, but on the other side severely limits Salmonella biomass generation and growth rates. This review discusses goals and techniques for studying Salmonella intracellular metabolism, summarises main results and implications, and proposes key issues that could be addressed in future studies.

Use of systems biology to decipher host-pathogen interaction networks and predict biomarkers

In systems biology, researchers aim to understand complex biological systems as a whole, which is often achieved by mathematical modelling and the analyses of high-throughput data. In this review, we give an overview of medical applications of systems biology approaches with special focus on host-pathogen interactions. After introducing general ideas of systems biology, we focus on (1) the detection of putative biomarkers for improved diagnosis and support of therapeutic decisions, (2) network modelling for the identification of regulatory interactions between cellular molecules to reveal putative drug targets and (3) module discovery for the detection of phenotype-specific modules in molecular interaction networks. Biomarker detection applies supervised machine learning methods utilizing high-throughput data (e.g. single nucleotide polymorphism (SNP) detection, RNA-seq, proteomics) and clinical data. We demonstrate structural analysis of molecular networks, especially by identification of disease modules as a novel strategy, and discuss possible applications to host-pathogen interactions. Pioneering work was done to predict molecular host-pathogen interactions networks based on dual RNA-seq data. However, currently this network modelling is restricted to a small number of genes. With increasing number and quality of databases and data repositories, the prediction of large-scale networks will also be feasible that can used for multidimensional diagnosis and decision support for prevention and therapy of diseases. Finally, we outline further perspective issues such as support of personalized medicine with high-throughput data and generation of multiscale host-pathogen interaction models.

Metabolic Adaptations of Intracellullar Bacterial Pathogens and their Mammalian Host Cells during Infection ("Pathometabolism")

Several bacterial pathogens that cause severe infections in warm-blooded animals, including humans, have the potential to actively invade host cells and to efficiently replicate either in the cytosol or in specialized vacuoles of the mammalian cells. The interaction between these intracellular bacterial pathogens and the host cells always leads to multiple physiological changes in both interacting partners, including complex metabolic adaptation reactions aimed to promote proliferation of the pathogen within different compartments of the host cells. In this chapter, we discuss the necessary nutrients and metabolic pathways used by some selected cytosolic and vacuolar intracellular pathogens and--when available--the links between the intracellular bacterial metabolism and the expression of the virulence genes required for the intracellular bacterial replication cycle. Furthermore, we address the growing evidence that pathogen-specific factors may also trigger metabolic responses of the infected mammalian cells affecting the carbon and nitrogen metabolism as well as defense reactions. We also point out that many studies on the metabolic host cell responses induced by the pathogens have to be scrutinized due to the use of established cell lines as model host cells, as these cells are (in the majority) cancer cells that exhibit a dysregulated primary carbon metabolism. As the exact knowledge of the metabolic host cell responses may also provide new concepts for antibacterial therapies, there is undoubtedly an urgent need for host cell models that more closely reflect the in vivo infection conditions.

Proteomic Analyses of Intracellular Salmonella enterica Serovar Typhimurium Reveal Extensive Bacterial Adaptations to Infected Host Epithelial Cells

Salmonella species can gain access into nonphagocytic cells, where the bacterium proliferates in a unique membrane-bounded compartment. In order to reveal bacterial adaptations to their intracellular niche, here we conducted the first comprehensive proteomic survey of Salmonella isolated from infected epithelial cells. Among ∼ 3,300 identified bacterial proteins, we found that about 100 proteins were significantly altered at the onset of Salmonella intracellular replication. In addition to substantially increased iron-uptake capacities, bacterial high-affinity manganese and zinc transporters were also upregulated, suggesting an overall limitation of metal ions in host epithelial cells. We also found that Salmonella induced multiple phosphate utilization pathways. Furthermore, our data suggested upregulation of the two-component PhoPQ system as well as of many downstream virulence factors under its regulation. Our survey also revealed that intracellular Salmonella has increased needs for certain amino acids and biotin. In contrast, Salmonella downregulated glycerol and maltose utilization as well as chemotaxis pathways.

A review on computational systems biology of pathogen-host interactions

Pathogens manipulate the cellular mechanisms of host organisms via pathogen-host interactions (PHIs) in order to take advantage of the capabilities of host cells, leading to infections. The crucial role of these interspecies molecular interactions in initiating and sustaining infections necessitates a thorough understanding of the corresponding mechanisms. Unlike the traditional approach of considering the host or pathogen separately, a systems-level approach, considering the PHI system as a whole is indispensable to elucidate the mechanisms of infection. Following the technological advances in the post-genomic era, PHI data have been produced in large-scale within the last decade. Systems biology-based methods for the inference and analysis of PHI regulatory, metabolic, and protein-protein networks to shed light on infection mechanisms are gaining increasing demand thanks to the availability of omics data. The knowledge derived from the PHIs may largely contribute to the identification of new and more efficient therapeutics to prevent or cure infections. There are recent efforts for the detailed documentation of these experimentally verified PHI data through Web-based databases. Despite these advances in data archiving, there are still large amounts of PHI data in the biomedical literature yet to be discovered, and novel text mining methods are in development to unearth such hidden data. Here, we review a collection of recent studies on computational systems biology of PHIs with a special focus on the methods for the inference and analysis of PHI networks, covering also the Web-based databases and text-mining efforts to unravel the data hidden in the literature.

Mass spectrometry-based proteomic approaches to study pathogenic bacteria-host interactions

Elucidation of molecular mechanisms underlying host-pathogen interactions is important for control and treatment of infectious diseases worldwide. Within the last decade, mass spectrometry (MS)-based proteomics has become a powerful and effective approach to better understand complex and dynamic host-pathogen interactions at the protein level. Herein we will review the recent progress in proteomic analyses towards bacterial infection of their mammalian host with a particular focus on enteric pathogens. Large-scale studies of dynamic proteomic alterations during infection will be discussed from the perspective of both pathogenic bacteria and host cells.

Drug resistance to Mycobacterium tuberculosis: from the traditional Chinese view to modern systems biology

The pathogen, Mycobacterium tuberculosis (M. tuberculosis) is a well-evolved, organized pathogen that has developed drug resistance, specifically multidrug resistance (MDR) and extensive drug resistance (XDR). This review primarily summarizes the mechanisms of drug resistance by M. tuberculosis according to the traditional Chinese view. The traditional Chinese view of drug resistance includes: the physical barrier of the cell wall; mutations relating to current anti-TB agents; drug efflux pumps; and drug stress, including the SOS response systems, the mismatch repair systems and the toxin-antitoxin systems. In addition, this review addresses the integrated systems biology of genomics, transcriptomics, proteomics, metabolomics and interactomics. Development of the various levels of systems biology has enabled determination of the anatomy of bacteria. Finally, the current review proposes that further investigation regarding the population of individuals with a high drug metabolic speed is vital to further understand drug resistance in M. tuberculosis.

A link between gut community metabolism and pathogenesis: molecular hydrogen-stimulated glucarate catabolism aids Salmonella virulence

Glucarate, an oxidized product of glucose, is a major serum organic acid in humans. Still, its role as a carbon source for a pathogen colonizing hosts has not been studied. We detected high-level expression of a potential glucarate permease encoding gene gudT when Salmonella enterica serovar Typhimurium are exposed to hydrogen gas (H₂), a gaseous by-product of gut commensal metabolism. A gudT strain of Salmonella is deficient in glucarate-dependent growth, however, it can still use other monosaccharides, such as glucose or galactose. Complementation of the gudT mutant with a plasmid harbouring gudT restored glucarate-dependent growth to wild-type (WT) levels. The gudT mutant exhibits attenuated virulence: the mean time of death for mice inoculated with WT strain was 2 days earlier than for mice inoculated with the gudT strain. At 4 days postinoculation, liver and spleen homogenates from mice inoculated with a gudT strain contained significantly fewer viable Salmonella than homogenates from animals inoculated with the parent. The parent strain grew well H₂-dependently in a minimal medium with amino acids and glucarate provided as the sole carbon sources, whereas the gudT strain achieved approximately 30% of the parent strain's yield. Glucarate-mediated growth of a mutant strain unable to produce H₂ was stimulated by H₂ addition, presumably owing to the positive transcriptional response to H₂. Gut microbiota-produced molecular hydrogen apparently signals Salmonella to catabolize an alternative carbon source available in the host. Our results link a gut microbiome-produced diffusible metabolite to augmenting bacterial pathogenesis.

Factors that affect proliferation of Salmonella in tomatoes post-harvest: the roles of seasonal effects, irrigation regime, crop and pathogen genotype

Main Objectives

Fresh fruits and vegetables become increasingly recognized as vehicles of human salmonellosis. Physiological, ecological, and environmental factors are all thought to contribute to the ability of Salmonella to colonize fruits and vegetables pre- and post-harvest. The goal of this study was to test how irrigation levels, fruit water congestion, crop and pathogen genotypes affect the ability of Salmonella to multiply in tomatoes post-harvest.

Experimental Design

Fruits from three tomato varieties, grown over three production seasons in two Florida locations, were infected with seven strains of Salmonella and their ability to multiply post-harvest in field-grown tomatoes was tested. The field experiments were set up as a two-factor factorial split plot experiment, with the whole-plot treatments arranged in a randomized complete-block design. The irrigation treatment (at three levels) was the whole-plot factor, and the split-plot factor was tomato variety, with three levels. The significance of the main, two-way, and three-way interaction effects was tested using the (type III) F-tests for fixed effects. Mean separation for each significant fixed effect in the model was performed using Tukey’s multiple comparison testing procedure.

Most Important Discoveries and Significance

The irrigation regime per se did not affect susceptibility of the crop to post-harvest proliferation of Salmonella. However, Salmonella grew significantly better in water-congested tissues of green tomatoes. Tomato maturity and genotype, Salmonella genotype, and inter-seasonal differences were the strongest factors affecting proliferation. Red ripe tomatoes were significantly and consistently more conducive to proliferation of Salmonella. Tomatoes harvested in the driest, sunniest season were the most conducive to post-harvest proliferation of the pathogen. Statistically significant interactions between production conditions affected post-harvest susceptibility of the crop to the pathogen. UV irradiation of tomatoes post-harvest promoted Salmonella growth.

Semi-automated curation of metabolic models via flux balance analysis: a case study with Mycoplasma gallisepticum

Primarily used for metabolic engineering and synthetic biology, genome-scale metabolic modeling shows tremendous potential as a tool for fundamental research and curation of metabolism. Through a novel integration of flux balance analysis and genetic algorithms, a strategy to curate metabolic networks and facilitate identification of metabolic pathways that may not be directly inferable solely from genome annotation was developed. Specifically, metabolites involved in unknown reactions can be determined, and potentially erroneous pathways can be identified. The procedure developed allows for new fundamental insight into metabolism, as well as acting as a semi-automated curation methodology for genome-scale metabolic modeling. To validate the methodology, a genome-scale metabolic model for the bacterium Mycoplasma gallisepticum was created. Several reactions not predicted by the genome annotation were postulated and validated via the literature. The model predicted an average growth rate of 0.358±0.12, closely matching the experimentally determined growth rate of M. gallisepticum of 0.244±0.03. This work presents a powerful algorithm for facilitating the identification and curation of previously known and new metabolic pathways, as well as presenting the first genome-scale reconstruction of M. gallisepticum.

Flux balance analysis (FBA) is a powerful approach for genome-scale metabolic modeling. It provides metabolic engineers with a tool for manipulating, predicting, and optimizing metabolism for biotechnological and biomedical purposes. However, we posit that it can also be used as tool for fundamental research in understanding and curating metabolic networks. Specifically, by using a genetic algorithm integrated with FBA, we developed a curation approach to identify missing reactions, incomplete reactions, and erroneous reactions. Additionally, it was possible to take advantage of the ensemble information from the genetic algorithm to identify the most critical reactions for curation. We tested our strategy using Mycoplasma gallisepticum as our model organism. Using the genome annotation as the basis, the preliminary genome-scale metabolic model consisted of 446 metabolites involved in 380 reactions. Carrying out our analysis, we found over 80 incorrect reactions and 16 missing reactions. Based upon the guidance of the algorithm, we were able to curate and resolve all discrepancies. The model predicted an average bacterial growth rate of 0.358±0.12 h⁻¹ compared to the experimentally observed 0.244±0.03 h⁻¹. Thus, our approach facilitated the curation of a genome-scale metabolic network and generated a high quality metabolic model.

Bugs in the system

Immunity to respiratory virus infection is governed by complex biological networks that influence disease progression and pathogenesis. Systems biology provides an opportunity to explore and understand these multifaceted interactions based on integration and modeling of multiple biological parameters. In this review, we describe new and refined systems-based approaches used to model, identify, and validate novel targets within complex networks following influenza and coronavirus infection. In addition, we propose avenues for extension and expansion that can revolutionize our understanding of infectious disease processes. Together, we hope to provide a window into the unique and expansive opportunity presented by systems biology to understand complex disease processes within the context of infectious diseases.

Salmonella utilizes D-glucosaminate via a mannose family phosphotransferase system permease and associated enzymes

Salmonella enterica is a globally significant bacterial food-borne pathogen that utilizes a variety of carbon sources. We report here that Salmonella enterica subsp. enterica serovar Typhimurium (S. Typhimurium) uses d-glucosaminate (2-amino-2-deoxy-d-gluconic acid) as a carbon and nitrogen source via a previously uncharacterized mannose family phosphotransferase system (PTS) permease, and we designate the genes encoding the permease dgaABCD (d-glucosaminate PTS permease components EIIA, EIIB, EIIC, and EIID). Two other genes in the dga operon (dgaE and dgaF) were required for wild-type growth of S. Typhimurium with d-glucosaminate. Transcription of dgaABCDEF was dependent on RpoN (σ(54)) and an RpoN-dependent activator gene we designate dgaR. Introduction of a plasmid bearing dgaABCDEF under the control of the lac promoter into Escherichia coli strains DH5α, BL21, and JM101 allowed these strains to grow on minimal medium containing d-glucosaminate as the sole carbon and nitrogen source. Biochemical and genetic data support a catabolic pathway in which d-glucosaminate, as it is transported across the cell membrane, is phosphorylated at the C-6 position by DgaABCD. DgaE converts the resulting d-glucosaminate-6-phosphate to 2-keto-3-deoxygluconate 6-phosphate (KDGP), which is subsequently cleaved by the aldolase DgaF to form glyceraldehyde-3-phosphate and pyruvate. DgaF catalyzes the same reaction as that catalyzed by Eda, a KDGP aldolase in the Entner-Doudoroff pathway, and the two enzymes can substitute for each other in their respective pathways. Examination of the Integrated Microbial Genomes database revealed that orthologs of the dga genes are largely restricted to certain enteric bacteria and a few species in the phylum Firmicutes.

Decomposition of flux distributions into metabolic pathways

Genome-scale reconstructions are often used for studying relationships between fundamental components of a metabolic system. In this study, we develop a novel computational method for analyzing predicted flux distributions for metabolic reconstructions. Because chemical reactions may have multiple reactants and products, a directed hypergraph where hyperarcs may have multiple tail vertices and head vertices is a more appropriate representation of the metabolic network than a conventional network. We use this view to represent predicted flux distributions by maximum generalized flows on hypergraphs. We then demonstrate that the generalized hyperflow problem may be transformed to an equivalent network flow problem with side constraints. This transformation allows a flux to be decomposed into chains of reactions. Subsequent analysis of these chains helps to characterize active pathways in a flux distribution. Such characterizations facilitate comparisons of flux distributions for different environmental conditions. The proposed method is applied to compare predicted flux distributions for Salmonella typhimurium to study changes in metabolism that cause enhanced virulence during a space flight. The differences between flux distributions corresponding to normal and enhanced virulence states confirm previous observations concerning infection mechanisms and suggest new pathways for exploration.

Salmonella modulates metabolism during growth under conditions that induce expression of virulence genes

Salmonella enterica serovar Typhimurium (S. Typhimurium) is a facultative pathogen that uses complex mechanisms to invade and proliferate within mammalian host cells. To investigate possible contributions of metabolic processes to virulence in S. Typhimurium grown under conditions known to induce expression of virulence genes, we used a metabolomics-driven systems biology approach coupled with genome-scale modeling. First, we identified distinct metabolite profiles associated with bacteria grown in either rich or virulence-inducing media and report the most comprehensive coverage of the S. Typhimurium metabolome to date. Second, we applied an omics-informed genome-scale modeling analysis of the functional consequences of adaptive alterations in S. Typhimurium metabolism during growth under our conditions. Modeling efforts highlighted a decreased cellular capability to both produce and utilize intracellular amino acids during stationary phase culture in virulence conditions, despite significant abundance increases for these molecules as observed by our metabolomics measurements. Furthermore, analyses of omics data in the context of the metabolic model indicated rewiring of the metabolic network to support pathways associated with virulence. For example, cellular concentrations of polyamines were perturbed, as well as the predicted capacity for secretion and uptake.

Systems immunogenetics of vaccines

Vaccines are the most cost effective public health measure for preventing viral infection and limiting epidemic spread within susceptible populations. However, the efficacy of current protective vaccines is highly variable, particularly in aging populations. In addition, there have been a number of challenges in the development of new vaccines due to a lack of detailed understanding of the immune correlates of protection. To identify the mechanisms underlying the variability of the immune response to vaccines, system-level tools need to be developed that will further our understanding of virus-host interactions and correlates of vaccine efficacy. This will provide critical information for rational vaccine design and allow the development of an analog to the "precision medicine" framework (already acknowledged as a powerful approach in medicine and therapeutics) to be applied to vaccinology.

Salmonella enterica: a surprisingly well-adapted intracellular lifestyle

The infectious intracellular lifestyle of Salmonella enterica relies on the adaptation to nutritional conditions within the Salmonella-containing vacuole (SCV) in host cells. We summarize latest results on metabolic requirements for Salmonella during infection. This includes intracellular phenotypes of mutant strains based on metabolic modeling and experimental tests, isotopolog profiling using ¹³C-compounds in intracellular Salmonella, and complementation of metabolic defects for attenuated mutant strains towards a comprehensive understanding of the metabolic requirements of the intracellular lifestyle of Salmonella. Helpful for this are also genomic comparisons. We outline further recent studies and which analyses of intracellular phenotypes and improved metabolic simulations were done and comment on technical required steps as well as progress involved in the iterative refinement of metabolic flux models, analyses of mutant phenotypes, and isotopolog analyses. Salmonella lifestyle is well-adapted to the SCV and its specific metabolic requirements. Salmonella metabolism adapts rapidly to SCV conditions, the metabolic generalist Salmonella is quite successful in host infection.

Technologies and approaches to elucidate and model the virulence program of salmonella

Salmonella is a primary cause of enteric diseases in a variety of animals. During its evolution into a pathogenic bacterium, Salmonella acquired an elaborate regulatory network that responds to multiple environmental stimuli within host animals and integrates them resulting in fine regulation of the virulence program. The coordinated action by this regulatory network involves numerous virulence regulators, necessitating genome-wide profiling analysis to assess and combine efforts from multiple regulons. In this review we discuss recent high-throughput analytic approaches used to understand the regulatory network of Salmonella that controls virulence processes. Application of high-throughput analyses have generated large amounts of data and necessitated the development of computational approaches for data integration. Therefore, we also cover computer-aided network analyses to infer regulatory networks, and demonstrate how genome-scale data can be used to construct regulatory and metabolic systems models of Salmonella pathogenesis. Genes that are coordinately controlled by multiple virulence regulators under infectious conditions are more likely to be important for pathogenesis. Thus, reconstructing the global regulatory network during infection or, at the very least, under conditions that mimic the host cellular environment not only provides a bird's eye view of Salmonella survival strategy in response to hostile host environments but also serves as an efficient means to identify novel virulence factors that are essential for Salmonella to accomplish systemic infection in the host.

The challenge of relating gene expression to the virulence of Salmonella enterica serovar Typhimurium

The first decade of transcriptomic studies of Salmonella enterica serovar Typhimurium focused upon gene expression in vitro, and during the infection of mammalian cells. The published regulons and stimulons show that the three Type Three Secretion Systems of S. Typhimurium respond to a diverse range of environmental conditions, and are controlled by a hierarchy of regulatory proteins. The integration of in vitro generated transcriptomic data with global gene expression of S. Typhimurium during infection is beginning to yield valuable information. The coordinated regulation of Salmonella gene expression is a key process for survival, adaptation and virulence capacities of the pathogen.

A systems biology approach to infectious disease research: innovating the pathogen-host research paradigm

The twentieth century was marked by extraordinary advances in our understanding of microbes and infectious disease, but pandemics remain, food and waterborne illnesses are frequent, multidrug-resistant microbes are on the rise, and the needed drugs and vaccines have not been developed. The scientific approaches of the past-including the intense focus on individual genes and proteins typical of molecular biology-have not been sufficient to address these challenges. The first decade of the twenty-first century has seen remarkable innovations in technology and computational methods. These new tools provide nearly comprehensive views of complex biological systems and can provide a correspondingly deeper understanding of pathogen-host interactions. To take full advantage of these innovations, the National Institute of Allergy and Infectious Diseases recently initiated the Systems Biology Program for Infectious Disease Research. As participants of the Systems Biology Program, we think that the time is at hand to redefine the pathogen-host research paradigm.

Analysis of phagosomal proteomes: from latex-bead to bacterial phagosomes

Phagosomal proteome characterization has contributed significantly to the understanding of host-pathogen interaction and the mechanism of infectious diseases caused by intracellular bacteria. The latex bead-containing phagosome has been widely used as a model system to study phagosomal proteomes at a global level. In contrast, the study of bacteria-containing phagosomes at a similar level has just begun. A number of intracellular microbial species are studied for their proteomes during the invasion of a host, providing insight into their metabolic adaptation in host cells and interaction with host-cell antimicrobial environments. In this review, we attempt to summarize the most recent advancements in the proteomic study of microbial phagosomes, especially those originating from mouse or human cells. We also briefly describe the proteomics of latex bead-containing phagosomes because they are often used as model phagosomes for study. We provide descriptions on major biological and technological components in phagosomal proteome studies. We also discuss the role of phagosomal proteome study in the broader horizon of systems biology and the technological challenges in phagosomal proteome characterization.

System-level strategies for studying the metabolism of Mycobacterium tuberculosis

Despite decades of research many aspects of the biology of Mycobacterium tuberculosis remain unclear and this is reflected in the antiquated tools available to treat and prevent tuberculosis and consequently this disease remains a serious public health problem. Important discoveries linking M. tuberculosis's metabolism and pathogenesis have renewed interest in this area of research. Previous experimental studies were limited to the analysis of individual genes or enzymes whereas recent advances in computational systems biology and high throughput experimental technologies now allow metabolism to be studied on a genome scale. Here we discuss the progress being made in applying system level approaches to studying the metabolism of this important pathogen. The information from these studies will fundamentally change our approach to tuberculosis research and lead to new targets for therapeutic drugs and vaccines.

An incomplete TCA cycle increases survival of Salmonella Typhimurium during infection of resting and activated murine macrophages

BACKGROUND

In comparison to the comprehensive analyses performed on virulence gene expression, regulation and action, the intracellular metabolism of Salmonella during infection is a relatively under-studied area. We investigated the role of the tricarboxylic acid (TCA) cycle in the intracellular replication of Salmonella Typhimurium in resting and activated macrophages, epithelial cells, and during infection of mice.

METHODOLOGY/PRINCIPAL FINDINGS

We constructed deletion mutations of 5 TCA cycle genes in S. Typhimurium including gltA, mdh, sdhCDAB, sucAB, and sucCD. We found that the mutants exhibited increased net intracellular replication in resting and activated murine macrophages compared to the wild-type. In contrast, an epithelial cell infection model showed that the S. Typhimurium ΔsucCD and ΔgltA strains had reduced net intracellular replication compared to the wild-type. The glyoxylate shunt was not responsible for the net increased replication of the TCA cycle mutants within resting macrophages. We also confirmed that, in a murine infection model, the S. Typhimurium ΔsucAB and ΔsucCD strains are attenuated for virulence.

CONCLUSIONS/SIGNIFICANCE

Our results suggest that disruption of the TCA cycle increases the ability of S. Typhimurium to survive within resting and activated murine macrophages. In contrast, epithelial cells are non-phagocytic cells and unlike macrophages cannot mount an oxidative and nitrosative defence response against pathogens; our results show that in HeLa cells the S. Typhimurium TCA cycle mutant strains show reduced or no change in intracellular levels compared to the wild-type. The attenuation of the S. Typhimurium ΔsucAB and ΔsucCD mutants in mice, compared to their increased net intracellular replication in resting and activated macrophages suggest that Salmonella may encounter environments within the host where a complete TCA cycle is advantageous.

Primary transcriptomes of Mycobacterium avium subsp. paratuberculosis reveal proprietary pathways in tissue and macrophages

Background

Mycobacterium avium subsp. paratuberculosis (MAP) persistently infects intestines and mesenteric lymph nodes leading to a prolonged subclinical disease. The MAP genome sequence was published in 2005, yet its transcriptional organization in natural infection is unknown. While prior research analyzed regulated gene sets utilizing defined, in vitro stress related or advanced surgical methods with various animal species, we investigated the intracellular lifestyle of MAP in the intestines and lymph nodes to understand the MAP pathways that function to govern this persistence.

Results

Our transcriptional analysis shows that 21%, 8% and 3% of the entire MAP genome was represented either inside tissues, macrophages or both, respectively. Transcripts belonging to latency and cell envelope biogenesis were upregulated in the intestinal tissues whereas those belonging to intracellular trafficking and secretion were upregulated inside the macrophages. Transcriptomes of natural infection and in vitro macrophage infection shared genes involved in transcription and inorganic ion transport and metabolism. MAP specific genes within large sequence polymorphisms of ancestral M. avium complex were downregulated exclusively in natural infection.

Conclusions

We have unveiled common and unique MAP pathways associated with persistence, cell wall biogenesis and virulence in naturally infected cow intestines, lymph nodes and in vitro infected macrophages. This dichotomy also suggests that in vitro macrophage models may be insufficient in providing accurate information on the events that transpire during natural infection. This is the first report to examine the primary transcriptome of MAP at the local infection site (i.e. intestinal tissue). Regulatory pathways that govern the lifecycle of MAP appear to be specified by tissue and cell type. While tissues show a "shut-down" of major MAP metabolic genes, infected macrophages upregulate several MAP specific genes along with a putative pathogenicity island responsible for iron acquisition. Many of these regulatory pathways rely on the advanced interplay of host and pathogen and in order to decipher their message, an interactome must be established using a systems biology approach. Identified MAP pathways place current research into direct alignment in meeting the future challenge of creating a MAP-host interactome.

Systems biology of the metabolism of Mycobacterium tuberculosis

Despite decades of research, many aspects of the biology of Mycobacterium tuberculosis remain unclear, and this is reflected in the antiquated tools available to treat and prevent tuberculosis and consequently this disease remains a serious public health problem. Important discoveries linking the metabolism of M. tuberculosis and pathogenesis has renewed interest in this area of research. Previous experimental studies were limited to the analysis of individual genes or enzymes, whereas recent advances in computational systems biology and high-throughput experimental technologies now allows metabolism to be studied on a genome scale. In the present article, we discuss the progress being made in applying system-level approaches to study the metabolism of this important pathogen.

Carbon metabolism of intracellular bacterial pathogens and possible links to virulence

New technologies such as high-throughput methods and 13C-isotopologue-profiling analysis are beginning to provide us with insight into the in vivo metabolism of microorganisms, especially in the host cell compartments that are colonized by intracellular bacterial pathogens. In this Review, we discuss the recent progress made in determining the major carbon sources and metabolic pathways used by model intracellular bacterial pathogens that replicate either in the cytosol or in vacuoles of infected host cells. Furthermore, we highlight the possible links between intracellular carbon metabolism and the expression of virulence genes.

Integration of ‘omics’ data: does it lead to new insights into host–microbe interactions?

The interaction between both beneficial and pathogenic microbes and their host has been the subject of many studies. Although the field of systems biology is rapidly evolving, the use of a systems biology approach by means of high-throughput techniques to study host–microbe interactions is just beginning to be explored. In this review, we discuss some of the most recently developed high-throughput ‘omics’ techniques and their use in the context of host–microbe interaction. Moreover, we highlight studies combining several techniques that are pioneering the integration of ‘omics’ data related to host–microbe interactions. Finally, we list the major challenges ahead for successful systems biology research on host–microbe interactions.