Dynamic substrate preferences predict metabolic properties of a simple microbial consortium

Journal Article · 23 January 2017 · BMC Bioinformatics

DOI: https://doi.org/10.1186/s12859-017-1478-2 · OSTI ID:1618529

Erbilgin, Onur; Bowen, Benjamin P.; Kosina, Suzanne M.; Jenkins, Stefan; Lau, Rebecca K.; Northen, Trent R.

Mixed cultures of different microbial species are increasingly being used to carry out a specific biochemical function in lieu of engineering a single microbe to do the same t ask. However, knowing how different species' metabolisms will integrate to reach a desired outcome is a difficult problem that has been studied in great detail using steady-state models. However, many biotechnological processes, as well as natural habitats, represent a more dynamic system. Examining how individual species use resources in their growth medium or environment (exometabolomics) over time in batch culture conditions can provide rich phenotypic data that encompasses regulation and transporters, creating an opportunity to integrate the data into a predictive model of resource use by a mixed community. Here we use exometabolomic profiling to examine the time-varying substrate depletion from a mixture of 19 amino acids and glucose by two Pseudomonas and one Bacillus species isolated from ground water. Contrary to studies in model organisms, we found surprisingly few correlations between resource preferences and maximal growth rate or biomass composition. We then modeled patterns of substrate depletion, and used these models to examine if substrate usage preferences and substrate depletion kinetics of individual isolates can be used to predict the metabolism of a co-culture of the isolates. We found that most of the substrates fit the model predictions, except for glucose and histidine, which were depleted more slowly than predicted, and proline, glycine, glutamate, lysine and arginine, which were all consumed significantly faster. Our results indicate that a significant portion of a model community's overall metabolism can be predicted based on the metabolism of the individuals. Based on the nature of our model, the resources that significantly deviate from the prediction highlight potential metabolic pathways affected by species-species interactions, which when further studied can potentially be used to modulate microbial community structure and/or function.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Biological and Environmental Research (BER)
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 1618529
• Alternate ID(s): OSTI ID: 1379688
• Journal Information: BMC Bioinformatics, Journal Name: BMC Bioinformatics Vol. 18 Journal Issue: 1; ISSN 1471-2105
• Publisher: Springer Science + Business MediaCopyright Statement
• Country of Publication: United Kingdom
• Language: English