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Res Microbiol. 2013 Dec;164(10):1045-54. doi: 10.1016/j.resmic.2013.08.006. Epub 2013 Sep 4.

Exploration and prediction of interactions between methanotrophs and heterotrophs.

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KERMIT, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure links 653, 9000 Gent, Belgium. Electronic address:


Methanotrophs can form the basis of a methane-driven food web on which heterotrophic microorganisms can feed. In return, these heterotrophs can stimulate growth of methanotrophs in co-culture by providing growth additives. However, only a few specific interactions are currently known. We incubated nine methanotrophs with 25 heterotrophic strains in a pairwise miniaturized co-cultivation setup. Through principal component analysis and k-means clustering, methanotrophs and heterotrophs could be grouped according to their interaction behaviour, suggesting strain-dependent methanotroph-heterotroph complementarity. Co-cultivation significantly enhanced the growth parameters of three methanotrophs. This was most pronounced for Methylomonas sp. M5, with a threefold increase in maximum density and a fourfold increase in maximum increase in density in co-culture with Cupriavidus taiwanensis LMG 19424. In contrast, co-cultivation with Methylobacterium radiotolerans LMG 2269 and Pseudomonas aeruginosa LMG 12228 inhibited growth of most methanotrophs. Functional genomic analysis suggested the importance of vitamin metabolism for co-cultivation success. The generated data set was then successfully exploited as a proof-of-principle for predictive modelling of co-culture responses based on other interactions of the same heterotrophs and methanotrophs, yielding values of the area under the receiver operating characteristic curve of 0.73 upon 50% missing values for the maximum increase in density parameter. As such, these modelling-based tools were shown to hold great promise in reducing the amount of data that needs to be generated when conducting large co-cultivation studies.


Methanotroph; Microbial interaction; Predictive modelling

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