Identification of Metabolic Pathways Essential for Fitness of <i>Salmonella</i> Typhimurium <i>In Vivo</i>

Bacterial infections remain a threat to human and animal health worldwide, and there is an urgent need to find novel targets for intervention. In the current study we used a computer model of the metabolic network of Salmonella enterica serovar Typhimurium and identified pairs of reactions (cut sets) predicted to be required for growth in vivo. We termed such cut sets synthetic auxotrophic pairs. We tested whether these would reveal possible combined targets for new antibiotics by analyzing the performance of selected single and double mutants in systemic mouse infections. One hundred and two cut sets were identified. Sixty-three of these included only pathways encoded by fully annotated genes, and from this sub-set we selected five cut sets involved in amino acid or polyamine biosynthesis. One cut set (asnA/asnB) demonstrated redundancy in vitro and in vivo and showed that asparagine is essential for S. Typhimurium during infection. trpB/trpA as well as single mutants were attenuated for growth in vitro, while only the double mutant was a cut set in vivo, underlining previous observations that tryptophan is essential for successful outcome of infection. speB/speF,speC was not affected in vitro but was attenuated during infection showing that polyamines are essential for virulence apparently in a growth independent manner. The serA/glyA cut-set was found to be growth attenuated as predicted by the model. However, not only the double mutant, but also the glyA mutant, were found to be attenuated for virulence. This adds glycine production or conversion of glycine to THF to the list of essential reactions during infection. One pair (thrC/kbl) showed true redundancy in vitro but not in vivo demonstrating that threonine is available to the bacterium during infection. These data add to the existing knowledge of available nutrients in the intra-host environment, and have identified possible new targets for antibiotics

A community effort towards a knowledge-base and mathematical model of the human pathogen Salmonella Typhimurium LT2

<p>Abstract</p> <p>Background</p> <p>Metabolic reconstructions (MRs) are common denominators in systems biology and represent biochemical, genetic, and genomic (BiGG) knowledge-bases for target organisms by capturing currently available information in a consistent, structured manner. <it>Salmonella enterica </it>subspecies I serovar Typhimurium is a human pathogen, causes various diseases and its increasing antibiotic resistance poses a public health problem.</p> <p>Results</p> <p>Here, we describe a community-driven effort, in which more than 20 experts in <it>S</it>. Typhimurium biology and systems biology collaborated to reconcile and expand the <it>S</it>. Typhimurium BiGG knowledge-base. The consensus MR was obtained starting from two independently developed MRs for <it>S</it>. Typhimurium. Key results of this reconstruction jamboree include i) development and implementation of a community-based workflow for MR annotation and reconciliation; ii) incorporation of thermodynamic information; and iii) use of the consensus MR to identify potential multi-target drug therapy approaches.</p> <p>Conclusion</p> <p>Taken together, with the growing number of parallel MRs a structured, community-driven approach will be necessary to maximize quality while increasing adoption of MRs in experimental design and interpretation.</p

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

Genome-scale metabolic modelling of Salmonella and Lactobacillus species

Salmonella Typhimurium is a major cause of morbidity and mortality in humans. It is also a commonly used model organism for intracellular Gram negative pathogens, a group of bacteria that is becoming increasingly resistant to available antibiotics. Systemic Salmonella infection involves proliferation in the small intestine followed by infection of epithelial and later macrophage host cells. In order to advance the understanding of the r^ole of metabolism in virulence, a genome-scale metabolic model of S. Typhimurium was constructed, based on genomic and biochemical data obtained from public databases. A method for modelling metabolic interactions between cells was developed and applied to models of S. Typhimurium and the probiotic Lactobacillus plan-tarum, in order to simulate the intestinal stage of infection. The analysis indicated that interactions, involving the transfer of glycolate from L. plantarum to S. Typhimurium, that favour growth of S. Typhimurium, are possible, by unlikely to occur in vivo. Data from Phenotype Microarray (PM), as well as DNA microarray data obtained during infection of cultured macrophage cells, was integrated with the S. Typhimurium model. The PM data was largely in agreement with model results for growth on carbon and nitrogen sources, and indicated moderate agreement for sulphur and phosphorus sources. A model-based method for analysis of nutrient availability during growth inside host cells, based on PM and DNA microarray data, was developed. This environment is poorly characterised and direct experimental methods for obtaining this information are not available. The analysis indicated a nutritionally complex host environment, dominated by glycerol 3-phosphate and certain nucleosides and amino acids. Owing to the complexity of the host environment, a method for identication of a sub-network of the model, required for viability on all growth supporting carbon sources was developed. The impact of sequentially removing combinations of reactions in the sub-network from the genome-scale model was evaluated. This analysis suggested approximately 60 reactions that in various combinations could be of relevance for designing antimicrobial intervention strategies, including antimicrobial agents and live attenuated vaccines

Inactivation of pathogens on food and contact surfaces using ozone as a biocidal agent

This study focuses on the inactivation of a range of food borne pathogens using ozone as a biocidal agent. Experiments were carried out using Campylobacter jejuni, E. coli and Salmonella enteritidis in which population size effects and different treatment temperatures were investigate

Análise de fluxos metabólicos com substrato isotopicamente marcado (13C-MFA) em S. typhimurium

Linhagens atenuadas de Salmonella têm sido estudadas para produção e veiculação de substâncias com fins terapêuticos. Modelos metabólicos à escala genômica são ferramentas importantes no desenvolvimento de estratégias de engenharia metabólica. Este trabalho teve por objetivo obter dados experimentais para aprimorar o modelo metabólico e aprofundar o conhecimento do metabolismo de S. typhimurium (St). Foram realizados cultivos contínuos à taxa de diluição (D) de 0,24 e 0,48 h-1, utilizando U-13C-glicose como substrato. Aminoácidos da biomassa foram analisados por GC-MS. A análise de fluxos metabólicos permitiu determinar a distribuição de fluxos nas principais vias metabólicas de St. A glicólise foi a via majoritamente utilizada para catabolisar a glicose. As maiores diferenças nos fluxos estimados, para as duas D, verificaram-se nas vias anapleróticas. Sobrepondo os dados de fluxos intracelulares ao modelo metabólico será gerado um modelo mais preciso do metabolismo de S. typhimurium

Environments that Induce Synthetic Microbial Ecosystems

Interactions between microbial species are sometimes mediated by the exchange of small molecules, secreted by one species and metabolized by another. Both one-way (commensal) and two-way (mutualistic) interactions may contribute to complex networks of interdependencies. Understanding these interactions constitutes an open challenge in microbial ecology, with applications ranging from the human microbiome to environmental sustainability. In parallel to natural communities, it is possible to explore interactions in artificial microbial ecosystems, e.g. pairs of genetically engineered mutualistic strains. Here we computationally generate artificial microbial ecosystems without re-engineering the microbes themselves, but rather by predicting their growth on appropriately designed media. We use genome-scale stoichiometric models of metabolism to identify media that can sustain growth for a pair of species, but fail to do so for one or both individual species, thereby inducing putative symbiotic interactions. We first tested our approach on two previously studied mutualistic pairs, and on a pair of highly curated model organisms, showing that our algorithms successfully recapitulate known interactions, robustly predict new ones, and provide novel insight on exchanged molecules. We then applied our method to all possible pairs of seven microbial species, and found that it is always possible to identify putative media that induce commensalism or mutualism. Our analysis also suggests that symbiotic interactions may arise more readily through environmental fluctuations than genetic modifications. We envision that our approach will help generate microbe-microbe interaction maps useful for understanding microbial consortia dynamics and evolution, and for exploring the full potential of natural metabolic pathways for metabolic engineering applications

Reconstruction of the temporal signaling network in Salmonella-infected human cells

Salmonella enterica is a bacterial pathogen that usually infects its host through food sources. Translocation of the pathogen proteins into the host cells leads to changes in the signaling mechanism either by activating or inhibiting the host proteins. Using high-throughput ‘omic’ technologies, changes in the signaling components can be quantified at different levels; however, experimental hits are usually incomplete to represent the whole signaling system as some driver proteins stay hidden within the experimental data. Given that the bacterial infection modifies the response network of the host, more coherent view of the underlying biological processes and the signaling networks can be obtained by using a network modeling approach based on the reverse engineering principles in which a confident region from the protein interactome is found by inferring hits from the omic experiments. In this work, we have used a published temporal phosphoproteomic dataset of Salmonella-infected human cells and reconstructed the temporal signaling network of the human host by integrating the interactome and the phosphoproteomic datasets. We have combined two well-established network modeling frameworks, the Prize-collecting Steiner Forest (PCSF) approach and the Integer Linear Programming (ILP) based edge inference approach. The resulting network conserves the information on temporality, direction of interactions, while revealing hidden entities in the signaling, such as the SNARE binding, mTOR signaling, immune response, cytoskeleton organization, and apoptosis pathways. Targets of the Salmonella effectors in the host cells such as CDC42, RHOA, 14-3-3δ, Syntaxin family, Oxysterol-binding proteins were included in the reconstructed signaling network although they were not present in the initial phosphoproteomic data. We believe that integrated approaches have a high potential for the identification of clinical targets in infectious diseases, especially in the Salmonella infections

Salmonella typhimurium and Escherichia coli dissimilarity: closely related bacteria with distinct metabolic profiles

Live attenuated strains of Salmonella typhimurium have been extensively investigated as vaccines for a number of infectious diseases. However, there is still little information available concerning aspects of their metabolism. S. typhimurium and Escherichia coli show a high degree of similarity in terms of their genome contents and metabolic networks. However, this work presents experimental evidence showing that significant differences exist in their abilities to direct carbon fluxes to biomass and energy production. It is important to study the metabolism of Salmonella in order to elucidate the formation of acetate and other metabolites involved in optimizing the production of biomass, essential for the development of recombinant vaccines. The metabolism of Salmonella under aerobic conditions was assessed using continuous cultures performed at dilution rates ranging from 0.1 to 0.67 h1, with glucose as main substrate. Acetate assimilation and glucose metabolism under anaerobic conditions were also investigated using batch cultures. Chemostat cultivations showed deviation of carbon towards acetate formation, starting at dilution rates above 0.1 h1. This differed from previous findings for E. coli, where acetate accumulation was only detected at dilution rates exceeding 0.4 h1, and was due to the lower rate of acetate assimilation by S. typhimurium under aerobic conditions. Under anaerobic conditions, both microorganisms mainly produced ethanol, acetate, and formate. A genome-scale metabolic model, reconstructed for Salmonella based on an E. coli model, provided a poor description of the mixed fermentation pattern observed during Salmonella cultures, reinforcing the different patterns of carbon utilization exhibited by these closely related bacteria. This article is protected by copyright. All rights reserved.Special thanks to Amadeus Azevedo for the HPLC analyses and technical assistance. The authors acknowledge the national funding received from CNPq (Conselho Nacional de Desenvolvimento Cientifico e Tecnologico, Brazil), the international cooperation project CAPES-FCT (Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior/Brazil-Fundacao para a Ciencia e a Tecnologia/Portugal-Process 315/11), CAPES (Atracao de Jovens Talentos-Process 064922/2014-01) and to Fundacao para a Ciencia e Tecnologia the strategic funding of UID/BIO/04469/2013 unit