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Title: A synthetic ecology perspective: How well does behavior of model organisms in the laboratory predict microbial activities in natural habitats?

Abstract

In this perspective article, we question how well model organisms, the ones that are easy to cultivate in the laboratory and that show robust growth and biomass accumulation, reflect the dynamics and interactions of microbial communities observed in nature. Today's -omics toolbox allows assessing the genomic potential of microbes in natural environments in a high-throughput fashion and at a strain-level resolution. However, understanding of the details of microbial activities and of the mechanistic bases of community function still requires experimental validation in simplified and fully controlled systems such as synthetic communities. We have studied methane utilization in Lake Washington sediment for a few decades and have identified a number of species genetically equipped for this activity. We have also identified cooccurring satellite species that appear to form functional communities together with the methanotrophs. Here, we compare experimental findings from manipulation of natural communities involved in metabolism of methane in this niche with findings from manipulation of synthetic communities assembled in the laboratory of species originating from the same study site, from very simple (two-species) to rather complex (50-species) synthetic communities. We observe some common trends in community dynamics between the two types of communities, toward representation of specific functional guilds.more » However, we also identify strong discrepancies between the dominant methane oxidizers in synthetic communities compared to natural communities, under similar incubation conditions. Furthermore, these findings highlight the challenges that exist in using the synthetic community approach to modeling dynamics and species interactions in natural communities.« less

Authors:
 [1];  [2];  [3];  [1]
  1. Univ. of Washington, Seattle, WA (United States). Dept. of Chemical Engineering
  2. Univ. of Washington, Seattle, WA (United States). Dept. of Microbiology
  3. Univ. of Washington, Seattle, WA (United States). Dept. of Chemical Engineering; eScience Inst.
Publication Date:
Research Org.:
Univ. of Washington, Seattle, WA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1287131
Grant/Contract Number:  
SC0010556
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Frontiers in Microbiology
Additional Journal Information:
Journal Volume: 7; Journal ID: ISSN 1664-302X
Publisher:
Frontiers Research Foundation
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; synthetic community; methylotrophy; methanotroph; Methylobacter; Methylosarcina; Methylomonas; Lake Washington; fresh-water lake; electron-transfer; soil bacteria; methane; communities; washington; microorganisms; methylotrophs; metagenomics; populations

Citation Formats

Yu, Zheng, Krause, Sascha M. B., Beck, David A. C., and Chistoserdova, Ludmila. A synthetic ecology perspective: How well does behavior of model organisms in the laboratory predict microbial activities in natural habitats?. United States: N. p., 2016. Web. doi:10.3389/fmicb.2016.00946.
Yu, Zheng, Krause, Sascha M. B., Beck, David A. C., & Chistoserdova, Ludmila. A synthetic ecology perspective: How well does behavior of model organisms in the laboratory predict microbial activities in natural habitats?. United States. doi:10.3389/fmicb.2016.00946.
Yu, Zheng, Krause, Sascha M. B., Beck, David A. C., and Chistoserdova, Ludmila. Wed . "A synthetic ecology perspective: How well does behavior of model organisms in the laboratory predict microbial activities in natural habitats?". United States. doi:10.3389/fmicb.2016.00946. https://www.osti.gov/servlets/purl/1287131.
@article{osti_1287131,
title = {A synthetic ecology perspective: How well does behavior of model organisms in the laboratory predict microbial activities in natural habitats?},
author = {Yu, Zheng and Krause, Sascha M. B. and Beck, David A. C. and Chistoserdova, Ludmila},
abstractNote = {In this perspective article, we question how well model organisms, the ones that are easy to cultivate in the laboratory and that show robust growth and biomass accumulation, reflect the dynamics and interactions of microbial communities observed in nature. Today's -omics toolbox allows assessing the genomic potential of microbes in natural environments in a high-throughput fashion and at a strain-level resolution. However, understanding of the details of microbial activities and of the mechanistic bases of community function still requires experimental validation in simplified and fully controlled systems such as synthetic communities. We have studied methane utilization in Lake Washington sediment for a few decades and have identified a number of species genetically equipped for this activity. We have also identified cooccurring satellite species that appear to form functional communities together with the methanotrophs. Here, we compare experimental findings from manipulation of natural communities involved in metabolism of methane in this niche with findings from manipulation of synthetic communities assembled in the laboratory of species originating from the same study site, from very simple (two-species) to rather complex (50-species) synthetic communities. We observe some common trends in community dynamics between the two types of communities, toward representation of specific functional guilds. However, we also identify strong discrepancies between the dominant methane oxidizers in synthetic communities compared to natural communities, under similar incubation conditions. Furthermore, these findings highlight the challenges that exist in using the synthetic community approach to modeling dynamics and species interactions in natural communities.},
doi = {10.3389/fmicb.2016.00946},
journal = {Frontiers in Microbiology},
number = ,
volume = 7,
place = {United States},
year = {Wed Jun 15 00:00:00 EDT 2016},
month = {Wed Jun 15 00:00:00 EDT 2016}
}

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