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. 2017 Jan 23;18(1):57.
doi: 10.1186/s12859-017-1478-2.

Dynamic Substrate Preferences Predict Metabolic Properties of a Simple Microbial Consortium

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Free PMC article

Dynamic Substrate Preferences Predict Metabolic Properties of a Simple Microbial Consortium

Onur Erbilgin et al. BMC Bioinformatics. .
Free PMC article

Abstract

Background: 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 task. 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.

Results: 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.

Conclusions: 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.

Keywords: Microbiology; Predicting community function; Quantitative metabolomics; Substrate preferences.

Figures

Fig. 1
Fig. 1
Modeling usage parameters. Example curve fitting to Behrends model (cyan). Blue square indicates the modeled T50 parameter of the Behrends model, or inflection point of the curve, and the width parameter of the model is depicted by the green bar centered at T50. The orange square represents the calculated Th value, or when half of the total amount of compound has been depleted, and the red bar depicts the calculated usage window, or time when the compound is depleted from 90 to 10% of the total amount used by the species
Fig. 2
Fig. 2
Th and width values for the strains. ac Th and width for each compound mapped onto the growth curve of each strain. Colored circles represent average Th and colored horizontal lines represent the average usage window (time of depletion from 90 to 10% of total resource used by the strain). Solid black line is the average OD600 of each strain measured over time (n = 3), with shading representing standard deviation. d Comparison of Th values between strains, of all compounds, with error bars representing standard error. Dashed boxes in a and d indicate the grouping of compounds utilized by Bc, and dashed brackets in c and d indicate the different growth phases observed for Pb
Fig. 3
Fig. 3
Physiological Correlations. Correlations between specific growth rate on a compound as a sole carbon source, and Th (a, c, e) or maximum compound depletion rate relative to biomass (grams cell dry weight (gCDW)) (b, d, f) in complete defined medium for species Bc (a, b), Pl (c, d) and Pb (e, f). Compounds that did not support growth as a sole carbon source (specific growth rate of zero) are shaded lighter at the bottom of each plot. Pearson correlation coefficients (r) and p-values (p) for the set of compounds for which the specific growth rate was nonzero are depicted in the upper-right of each plot. Correlations that had a p-value less than 0.05 were colored red. Error bars depict standard error
Fig. 4
Fig. 4
Co-culture observations compared to predictions, normalized to t0 concentration of each metabolite. Blue, green and red dashed lines represent the observed depletion of each compound by Bc, Pl and Pb, respectively, when grown in isolation. The solid black line is the predicted depletion of a co-culture of all three strains. The golden circles represent the measured compound concentration in the co-culture medium. Error bars and/or shading represent standard error (n = 3). Glycine at time point 4 could not be calculated because the measurement was outside the dynamic range of the calibration curve, and the r 2 was not determined (n.d.) for glycine. Non-normalized figure is shown as Additional file 1: Figure S5

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References

    1. Kovarova-Kovar K, Egli T. Growth kinetics of suspended microbial cells: from single-substrate-controlled growth to mixed-substrate kinetics. Microbiol Mol Biol Rev. 1998;62(3):646–666. - PMC - PubMed
    1. Shong J, Diaz MRJ, Collins CH. Towards synthetic microbial consortia for bioprocessing. Curr Opin Biotechnol. 2012;23(5):798–802. doi: 10.1016/j.copbio.2012.02.001. - DOI - PubMed
    1. Brenner K, You LC, Arnold FH. Engineering microbial consortia: a new frontier in synthetic biology. Trends Biotechnol. 2008;26(9):483–489. doi: 10.1016/j.tibtech.2008.05.004. - DOI - PubMed
    1. Du R, Yan JB, Li SZ, Zhang L, Zhang SR, Li JH, Zhao G, Qi PL. Cellulosic ethanol production by natural bacterial consortia is enhanced by Pseudoxanthomonas taiwanensis. Biotechnol Biofuels. 2015;8:10. doi: 10.1186/s13068-014-0186-7. - DOI - PMC - PubMed
    1. Silva LP, Northen TR. Exometabolomics and MSI: deconstructing how cells interact to transform their small molecule environment. Curr Opin Biotechnol. 2015;34:209–216. doi: 10.1016/j.copbio.2015.03.015. - DOI - PubMed
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