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Metabolic Proximity in the Order of Colonization of a Microbial Community

Overview of attention for article published in PLOS ONE, October 2013
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Title
Metabolic Proximity in the Order of Colonization of a Microbial Community
Published in
PLOS ONE, October 2013
DOI 10.1371/journal.pone.0077617
Pubmed ID
Authors

Varun Mazumdar, Salomon Amar, Daniel Segrè

Abstract

Microbial biofilms are often composed of multiple bacterial species that accumulate by adhering to a surface and to each other. Biofilms can be resistant to antibiotics and physical stresses, posing unresolved challenges in the fight against infectious diseases. It has been suggested that early colonizers of certain biofilms could cause local environmental changes, favoring the aggregation of subsequent organisms. Here we ask whether the enzyme content of different microbes in a well-characterized dental biofilm can be used to predict their order of colonization. We define a metabolic distance between different species, based on the overlap in their enzyme content. We next use this metric to quantify the average metabolic distance between neighboring organisms in the biofilm. We find that this distance is significantly smaller than the one observed for a random choice of prokaryotes, probably reflecting the environmental constraints on metabolic function of the community. More surprisingly, this metabolic metric is able to discriminate between observed and randomized orders of colonization of the biofilm, with the observed orders displaying smaller metabolic distance than randomized ones. By complementing these results with the analysis of individual vs. joint metabolic networks, we find that the tendency towards minimal metabolic distance may be counter-balanced by a propensity to pair organisms with maximal joint potential for synergistic interactions. The trade-off between these two tendencies may create a "sweet spot" of optimal inter-organism distance, with possible broad implications for our understanding of microbial community organization.

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The data shown below were compiled from readership statistics for 77 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 5%
Iran, Islamic Republic of 1 1%
China 1 1%
Belgium 1 1%
Unknown 70 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 25 32%
Researcher 17 22%
Other 5 6%
Student > Doctoral Student 4 5%
Student > Bachelor 4 5%
Other 12 16%
Unknown 10 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 31 40%
Biochemistry, Genetics and Molecular Biology 8 10%
Medicine and Dentistry 6 8%
Engineering 5 6%
Computer Science 2 3%
Other 13 17%
Unknown 12 16%