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Cited by 341

The DOI matches its Crossref record; 111 of the 111 references it cites were found in a registry or an index.

Understanding microbial community dynamics to improve optimal microbiome selection

Open access
Wright, Robyn J., Gibson, Matthew I., Christie-Oleza, Joseph A.·2019·Microbiome

Abstract

Abstract Recalcitrant polymers are widely distributed in the environment. This includes natural polymers, such as chitin, but synthetic polymers are becoming increasingly abundant, for which biodegradation is uncertain. Distribution of labour in microbial communities commonly evolves in nature, particularly for arduous processes, suggesting a community may be better at degrading recalcitrant compounds than individual microorganisms. Artificial selection of microbial communities with better degradation potential has seduced scientists for over a decade, but the method has not been systematically optimised nor applied to polymer degradation. Using chitin as a case study, we successfully selected for microbial communities with enhanced chitinase activities but found that continuous optimisation of incubation times between selective generations was of utmost importance. The analysis of the community composition over the entire selection process revealed fundamental aspects in microbial ecology: when incubation times between generations were optimal, the system was dominated by Gammaproteobacteria , main bearers of chitinase enzymes and drivers of chitin degradation, before being succeeded by cheating, cross-feeding and grazing organisms. Importance Artificial selection is a powerful and atractive technique that can enhance the biodegradation of a recalcitrant polymer and other pollutants by microbial communities. We show, for the first time, that the success of artificially selecting microbial communities requires an optimisation of the incubation times between generations when implementing this method. Hence, communities need to be transferred at the peak of the desired activity in order to avoid community drift and replacement of the efficient biodegrading community by cheaters, cross-feeders and grazers.

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Metadata

Authors
Wright, R. J., Gibson, M. I., & Christie-Oleza, J. A.
Venue
Microbiome
Published
2019-06-03
Volume
7
Issue
1
Pages
85-85
ISSN
2049-2618 (ISSN-L)
OpenAlex
W2948050975
Cited by
341
References
111
FWCI
23.48
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Understanding microbial community dynamics to improve optimal microbiome selection. https://app.cochat.ai/papers/10.1186/s40168-019-0702-x
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Wright, R. J., Gibson, M. I., & Christie-Oleza, J. A. (2019). Understanding microbial community dynamics to improve optimal microbiome selection. Microbiome. https://doi.org/10.1186/s40168-019-0702-x

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