Evidence map›Paper›PMID 31159875›Full record

ArticleMicrobiome2019

Understanding microbial community dynamics to improve optimal microbiome selection.

Robyn J Wright, Matthew I Gibson, Joseph A Christie-Oleza

Abstract read
In one paragraph

Article in Microbiome, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

37 citing papers in PubMed.

  1. Article
  2. Microbial biotechnology meets ecological theory: a unified framework to design industrial microbial consortia.International microbiology : the official journal of the Spanish Society for Microbiology · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Robyn J WrightSchool of Life Sciences, University of Warwick, Coventry, UK. R.Wright.1@warwick.ac.uk.
Matthew I GibsonDepartment of Chemistry, University of Warwick, Coventry, UK.
Joseph A Christie-OlezaSchool of Life Sciences, University of Warwick, Coventry, UK. J.Christie-Oleza@warwick.ac.uk.ORCID 0000-0002-0992-2226

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial selection of microbial communities that perform better at a desired process has seduced scientists for over a decade, but the method has not been systematically optimised nor the mechanisms behind its success, or failure, determined. Microbial communities are highly dynamic and, hence, go through distinct and rapid stages of community succession, but the consequent effect this may have on artificially selected communities is unknown.

resultsUsing 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 transfers 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 transfers were optimal, the system was dominated by Gammaproteobacteria (i.e. main bearers of chitinase enzymes and drivers of chitin degradation), before being succeeded by cheating, cross-feeding and grazing organisms.

conclusionsThe selection of microbiomes to enhance a desired process is widely used, though the success of artificially selecting microbial communities appears to require optimal incubation times in order to avoid the loss of the desired trait as a consequence of an inevitable community succession. A comprehensive understanding of microbial community dynamics will improve the success of future community selection studies.

Indexed as

Bacterial Physiological PhenomenaMicrobiotaBacteriaChitinChitinasesChitinChitinasesArtificial microbiome selectionChitin degradationEcological successionMicrobial communitiesMicrobial community dynamicsMicrobial ecologyPolymer degradation

Identifiers

PMID31159875
PMCPMC6547603

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.