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A computational exploration of bacterial metabolic diversity identifying metabolic interactions and growth-efficient strain communities. 2011

Eleftheria Tzamali, and Panayiota Poirazi, and Ioannis G Tollis, and Martin Reczko
Computer Science Department, University of Crete, P.O. Box 2208, Heraklion, 71409, Greece. etzamali@gmail.com

BACKGROUND Metabolic interactions involve the exchange of metabolic products among microbial species. Most microbes live in communities and usually rely on metabolic interactions to increase their supply for nutrients and better exploit a given environment. Constraint-based models have successfully analyzed cellular metabolism and described genotype-phenotype relations. However, there are only a few studies of genome-scale multi-species interactions. Based on genome-scale approaches, we present a graph-theoretic approach together with a metabolic model in order to explore the metabolic variability among bacterial strains and identify and describe metabolically interacting strain communities in a batch culture consisting of two or more strains. We demonstrate the applicability of our approach to the bacterium E. coli across different single-carbon-source conditions. RESULTS A different diversity graph is constructed for each growth condition. The graph-theoretic properties of the constructed graphs reflect the inherent high metabolic redundancy of the cell to single-gene knockouts, reveal mutant-hubs of unique metabolic capabilities regarding by-production, demonstrate consistent metabolic behaviors across conditions and show an evolutionary difficulty towards the establishment of polymorphism, while suggesting that communities consisting of strains specifically adapted to a given condition are more likely to evolve. We reveal several strain communities of improved growth relative to corresponding monocultures, even though strain communities are not modeled to operate towards a collective goal, such as the community growth and we identify the range of metabolites that are exchanged in these batch co-cultures. CONCLUSIONS This study provides a genome-scale description of the metabolic variability regarding by-production among E. coli strains under different conditions and shows how metabolic differences can be used to identify metabolically interacting strain communities. This work also extends the existing stoichiometric models in order to describe batch co-cultures and provides the extent of metabolic interactions in a strain community revealing their importance for growth.

UI MeSH Term Description Entries
D004926 Escherichia coli A species of gram-negative, facultatively anaerobic, rod-shaped bacteria (GRAM-NEGATIVE FACULTATIVELY ANAEROBIC RODS) commonly found in the lower part of the intestine of warm-blooded animals. It is usually nonpathogenic, but some strains are known to produce DIARRHEA and pyogenic infections. Pathogenic strains (virotypes) are classified by their specific pathogenic mechanisms such as toxins (ENTEROTOXIGENIC ESCHERICHIA COLI), etc. Alkalescens-Dispar Group,Bacillus coli,Bacterium coli,Bacterium coli commune,Diffusely Adherent Escherichia coli,E coli,EAggEC,Enteroaggregative Escherichia coli,Enterococcus coli,Diffusely Adherent E. coli,Enteroaggregative E. coli,Enteroinvasive E. coli,Enteroinvasive Escherichia coli
D016680 Genome, Bacterial The genetic complement of a BACTERIA as represented in its DNA. Bacterial Genome,Bacterial Genomes,Genomes, Bacterial
D053858 Metabolic Networks and Pathways Complex sets of enzymatic reactions connected to each other via their product and substrate metabolites. Metabolic Networks,Metabolic Pathways,Metabolic Network,Metabolic Pathway,Network, Metabolic,Networks, Metabolic,Pathway, Metabolic,Pathways, Metabolic
D055786 Gene Knockout Techniques Techniques to alter a gene sequence that result in an inactivated gene, or one in which the expression can be inactivated at a chosen time during development to study the loss of function of a gene. Gene Knock-Out Techniques,Gene Knock Out,Gene Knock Out Techniques,Gene Knockout,Gene Knock Outs,Gene Knock-Out Technique,Gene Knockout Technique,Gene Knockouts,Knock Out, Gene,Knock Outs, Gene,Knock-Out Technique, Gene,Knock-Out Techniques, Gene,Knockout Technique, Gene,Knockout Techniques, Gene,Knockout, Gene,Knockouts, Gene,Out, Gene Knock,Outs, Gene Knock,Technique, Gene Knock-Out,Technique, Gene Knockout,Techniques, Gene Knock-Out,Techniques, Gene Knockout
D056265 Microbial Interactions The inter- and intra-relationships between various microorganisms. This can include both positive (like SYMBIOSIS) and negative (like ANTIBIOSIS) interactions. Examples include virus - bacteria and bacteria - bacteria. Competitive Behavior, Microbial,Cooperative Behavior, Microbial,Behavior, Microbial Competitive,Behavior, Microbial Cooperative,Behaviors, Microbial Competitive,Behaviors, Microbial Cooperative,Competitive Behaviors, Microbial,Cooperative Behaviors, Microbial,Interaction, Microbial,Interactions, Microbial,Microbial Competitive Behavior,Microbial Competitive Behaviors,Microbial Cooperative Behavior,Microbial Cooperative Behaviors,Microbial Interaction

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