Final Report Summary - SYSBIOFILM (Systems biology of Pseudomonas aeruginosa in biofilms)
A systems biology approach is well suited for investigating environment-genotype-phenotype interactions as it permits studying complex biological systems in their parts as well as a whole. A state-of-the-art computational modeling approach in systems biology is constraint-based modeling. In this approach, genome-scale metabolic reconstructions are assembled in a bottom-up manner from genomic and biochemical information. Once assembled, the metabolic reconstructions can be converted into predictive computational models. These models can be used to derive novel hypothesis and drive experiments, which will ultimately lead to new insights about the biology of the target organism.
Antibiotic resistance is an increasing problem in the health care system. Biofilm-associated growth is thought to play a key role in antibiotic resistance. Pseudomonas aeruginosa is an opportunistic pathogen of clinical relevance with an increasing resistance against available antibiotics, which is partially attributed to its ability to form biofilms. We employed the systems biology approach to identify candidate drug targets for biofilm-associated bacteria by imitating specific microenvironments found in microbial communities associated with biofilm formation. We used a metabolic reconstruction of P. aeruginosa to study the effect of gene deletion on bacterial growth in planktonic and biofilm-like environmental conditions and identified 26 essential genes. These genes have no homology with any human gene. While none of these genes were essential in only one of the conditions, we found condition-dependent genes, which could be targeted to slow growth specifically in biofilm-associated P. aeruginosa. Furthermore, we performed an in silico double gene deletion study and obtained 17 combinations consisting of 21 different genes, which were conditionally essential. We observed a clear effect of changes in oxygen availability on the growth performance. Eight gene pairs were found to be synthetic lethal in oxygen-limited conditions and may serve as novel metabolic drug targets to combat particularly biofilm-associated P. aeruginosa. We demonstrate that metabolic modeling of human pathogens can be used to identify oxygen-sensitive drug targets and thus, that this systems biology approach represents a powerful tool to identify novel candidate antibiotic targets [1].
We then set out to investigate in silico how microbes, such as P. aeruginosa and those commonly found in the human gut, could influence the metabolism of the human host. We therefore developed a novel computational modeling framework. We first validated the framework using a genome-scale metabolic reconstruction of Bacteroides thetaiotaomicron, a prominent representative of the human gut microbiota and mouse metabolic reconstruction. The two metabolic models were linked through a joint compartment, the lumen, allowing metabolite exchange and providing a route for simulating different dietary regimes varying in fat, carbohydrate, and protein content. The integrated model captured mutually beneficial cross-feeding as well as competitive interactions. Furthermore, we identified metabolites that were exchanged between the two organisms, which were compared with published metabolomics data. This analysis resulted for the first time in a comprehensive description of the co-metabolism between a host and its commensal microbe. We also demonstrate in silico that the presence of B. thetaiotaomicron could rescue the growth phenotype of the host with an otherwise lethal enzymopathy and vice versa [2]. This work required us to refine metabolic reconstructions of gut microbes as well as the human and murine host.
Within this research project the most comprehensive human metabolic model was created, together with leading researchers from the molecular systems biology community as well as a comprehensive metabolic model of global murine metabolism. The systems approach developed within this project represents a powerful tool for modeling metabolic interactions between a gut microbe and its host in health and disease.
Taken together, the work performed under the Marie Curie reintegration grant has set new standards in reconstruction, analysis, and modeling techniques and built the first large-scale bacterial-community-based network. The gained knowledge will be directly applicable to other single or multi-species bacterial communities. The project is well placed within the recent European efforts to apply systems biological approaches towards improving early diagnosis and accelerating the discovery of novel interventions.
More information regarding this project, and related projects, can be found under http://thielelab.eu.
References:
[1] Sigurdsson, G., Fleming, R.M.T. Heinken, A., Thiele, I., "A systems biology approach to drug targets in Pseudomonas aeruginosa biofilm", PLoS One, 7(4): e34337 (2012).
[2] Heinken, A., Sahoo, S., Fleming, R. M. T., Thiele, I., ”Systems-level characterization of the host-microbe metabolic symbiosis in the mammalian gut.”, Gut Microbes. 4(1):1-13 (2013).
Antibiotic resistance is an increasing problem in the health care system. Biofilm-associated growth is thought to play a key role in antibiotic resistance. Pseudomonas aeruginosa is an opportunistic pathogen of clinical relevance with an increasing resistance against available antibiotics, which is partially attributed to its ability to form biofilms. We employed the systems biology approach to identify candidate drug targets for biofilm-associated bacteria by imitating specific microenvironments found in microbial communities associated with biofilm formation. We used a metabolic reconstruction of P. aeruginosa to study the effect of gene deletion on bacterial growth in planktonic and biofilm-like environmental conditions and identified 26 essential genes. These genes have no homology with any human gene. While none of these genes were essential in only one of the conditions, we found condition-dependent genes, which could be targeted to slow growth specifically in biofilm-associated P. aeruginosa. Furthermore, we performed an in silico double gene deletion study and obtained 17 combinations consisting of 21 different genes, which were conditionally essential. We observed a clear effect of changes in oxygen availability on the growth performance. Eight gene pairs were found to be synthetic lethal in oxygen-limited conditions and may serve as novel metabolic drug targets to combat particularly biofilm-associated P. aeruginosa. We demonstrate that metabolic modeling of human pathogens can be used to identify oxygen-sensitive drug targets and thus, that this systems biology approach represents a powerful tool to identify novel candidate antibiotic targets [1].
We then set out to investigate in silico how microbes, such as P. aeruginosa and those commonly found in the human gut, could influence the metabolism of the human host. We therefore developed a novel computational modeling framework. We first validated the framework using a genome-scale metabolic reconstruction of Bacteroides thetaiotaomicron, a prominent representative of the human gut microbiota and mouse metabolic reconstruction. The two metabolic models were linked through a joint compartment, the lumen, allowing metabolite exchange and providing a route for simulating different dietary regimes varying in fat, carbohydrate, and protein content. The integrated model captured mutually beneficial cross-feeding as well as competitive interactions. Furthermore, we identified metabolites that were exchanged between the two organisms, which were compared with published metabolomics data. This analysis resulted for the first time in a comprehensive description of the co-metabolism between a host and its commensal microbe. We also demonstrate in silico that the presence of B. thetaiotaomicron could rescue the growth phenotype of the host with an otherwise lethal enzymopathy and vice versa [2]. This work required us to refine metabolic reconstructions of gut microbes as well as the human and murine host.
Within this research project the most comprehensive human metabolic model was created, together with leading researchers from the molecular systems biology community as well as a comprehensive metabolic model of global murine metabolism. The systems approach developed within this project represents a powerful tool for modeling metabolic interactions between a gut microbe and its host in health and disease.
Taken together, the work performed under the Marie Curie reintegration grant has set new standards in reconstruction, analysis, and modeling techniques and built the first large-scale bacterial-community-based network. The gained knowledge will be directly applicable to other single or multi-species bacterial communities. The project is well placed within the recent European efforts to apply systems biological approaches towards improving early diagnosis and accelerating the discovery of novel interventions.
More information regarding this project, and related projects, can be found under http://thielelab.eu.
References:
[1] Sigurdsson, G., Fleming, R.M.T. Heinken, A., Thiele, I., "A systems biology approach to drug targets in Pseudomonas aeruginosa biofilm", PLoS One, 7(4): e34337 (2012).
[2] Heinken, A., Sahoo, S., Fleming, R. M. T., Thiele, I., ”Systems-level characterization of the host-microbe metabolic symbiosis in the mammalian gut.”, Gut Microbes. 4(1):1-13 (2013).