Location: Plant Genetics ResearchTitle: A community effort towards a knowledge-base and mathematical model of the human pathogen Salmonella Typhimurium LT2) Author
|Allen, Douglas - Doug|
Submitted to: BMC Systems Biology
Publication Type: Peer reviewed journal
Publication Acceptance Date: 1/18/2011
Publication Date: 1/18/2011
Citation: Thiele, I., Huduke, D.R., Steeb, B., Fankam, G., Allen, D.K., Bazzani, S., Charusanti, P., Chen, F., Fleming, R.M., Hsiung, C.A., Dekeersmaecker, S.C., Liao, Y., Marchal, K., Mo, M.L., Ozdemir, E., Raghunathan, A., Reed, J.L., Shin, S., Sigurbjornsdottir, S., Steinmann, J., Sudarsan, S., Swainston, N., Thijs, I.M., Zengler, K., Palsson, B.O., Adkins, J.N., Dirk, B. 2011. A community effort towards a knowledge-base and mathematical model of the human pathogen Salmonella typhimurium LT2. BMC Systems Biology. 5:8. Interpretive Summary: The development of biotechnology and associated methods such as genetic engineering, offer the potential to rationally alter living systems to produce valuable compounds that can benefit mankind. But metabolism operates at a "Systems" level and therefore requires a systems level description for understanding and subsequent modification. These descriptions are made up of metabolic networks called genome-scale network reconstructions that can be generated from biochemical, genetic, and genomic knowledge and databases. As more genomes are being sequenced and annotated this allows for the development of expansive networks and subsequently allows for their computational analysis mathematically. Here, an extensive metabolic network reconstruction is detailed. The reconstruction specifically identified metabolic processes such as iron chelation that are important for interactions of the Salmonella with other organisms and also to predict biomass production during growth and non-growth phases of the Salmonella and host life cycles to compare with experimental data. Metabolic reconstructions provide a way to approach pathogen resistance, by possibly identifying new protein targets for metabolic engineering of microbial systems. Additionally they provide insight to metabolic engineering for value-added product generation in fermentation of microbes or growth of plants. Ultimately, a better understanding of cellular metabolism in living systems will enable rational genetic engineering that benefits society.
Technical Abstract: 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. Salmonella enterica subspecies I serovar Typhimurium is a human pathogen, causes various diseases and its increasing antibiotic resistance poses a public health problem. Here, we describe a community-driven effort, in which more than 20 experts in S. Typhimurium biology and systems biology collaborated to reconcile and expand the S. Typhimurium BiGG knowledge-base. The consensus MR was obtained starting from two independently developed MRs for S. 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. The consensus model was mathematically used to assess the predictive ability to generate biomass and compared with experimental data for these instances and found to be in good qualitative agreement with the notable exception of sulfur metabolism that represents a target for further experimental study. Additionally, single and double gene deletion studies were performed in silico to identify 56 synthetic gene knockouts under different growth/media environments that disrupt growth. These synthetic deletions represent canditates for manipulation of metabolism and, as is shown, could be subgrouped based upon shared pathways or protein complexes. Moreover the analysis indicates that gene pairs are often required, implying that some metabolic redundancy may serve to make organisms more robust and confer protection to genetic changes. Compared to previous reconstructions such as AJRrecon (an E. coli MR) the current model was able to make use of an additional 20 carbon sources and 15 nitrogen sources, due in part to the description of an additional 200 gene products in the current model. With the growing number of parallel MRs, a structured community-driven approach will continue to be necessary to maximize quality while increasing adoption of MRs in experimental design and interpretation.