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Microbial communities evolve faster in extreme environments.

ABSTRACT: Evolutionary analysis of microbes at the community level represents a new research avenue linking ecological patterns to evolutionary processes, but remains insufficiently studied. Here we report a relative evolutionary rates (rERs) analysis of microbial communities from six diverse natural environments based on 40 metagenomic samples. We show that the rERs of microbial communities are mainly shaped by environmental conditions, and the microbes inhabiting extreme habitats (acid mine drainage, saline lake and hot spring) evolve faster than those populating benign environments (surface ocean, fresh water and soil). These findings were supported by the observation of more relaxed purifying selection and potentially frequent horizontal gene transfers in communities from extreme habitats. The mechanism of high rERs was proposed as high mutation rates imposed by stressful conditions during the evolutionary processes. This study brings us one stage closer to an understanding of the evolutionary mechanisms underlying the adaptation of microbes to extreme environments.

SUBMITTER: Li SJ

PROVIDER: S-EPMC4145313 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature

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Microbial communities evolve faster in extreme environments.

Li Sheng-Jin SJ Hua Zheng-Shuang ZS Huang Li-Nan LN Li Jie J Shi Su-Hua SH Chen Lin-Xing LX Kuang Jia-Liang JL Liu Jun J Hu Min M Shu Wen-Sheng WS

Scientific reports 20140827

Evolutionary analysis of microbes at the community level represents a new research avenue linking ecological patterns to evolutionary processes, but remains insufficiently studied. Here we report a relative evolutionary rates (rERs) analysis of microbial communities from six diverse natural environments based on 40 metagenomic samples. We show that the rERs of microbial communities are mainly shaped by environmental conditions, and the microbes inhabiting extreme habitats (acid mine drainage, sa ...[more]

PMID: 25158668

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Project description:Metabolites have essential roles in microbial communities, including as mediators of nutrient and energy exchange, cell-to-cell communication, and antibiosis. However, detecting and quantifying metabolites and other chemicals in samples having extremes in salt or mineral content using liquid chromatography-mass spectrometry (LC-MS)-based methods remains a significant challenge. Here, we report a facile method based on in situ chemical derivatization followed by extraction for analysis of metabolites and other chemicals in hypersaline samples, enabling for the first time direct LC-MS-based exometabolomics analysis in sample matrices containing up to 2 M total dissolved salts. The method, MetFish, is applicable to molecules containing amine, carboxylic acid, carbonyl, or hydroxyl functional groups, and it can be integrated into either targeted or untargeted analysis pipelines. In targeted analyses, MetFish provided limits of quantification as low as 1 nM, broad linear dynamic ranges (up to 5 to 6 orders of magnitude) with excellent linearity, and low median interday reproducibility (e.g., 2.6%). MetFish was successfully applied in targeted and untargeted exometabolomics analyses of microbial consortia, quantifying amino acid dynamics in the exometabolome during community succession; in situ in a native prairie soil, whose exometabolome was isolated using a hypersaline extraction; and in input and produced fluids from a hydraulically fractured well, identifying dramatic changes in the exometabolome over time in the well. IMPORTANCE The identification and accurate quantification of metabolites using electrospray ionization-mass spectrometry (ESI-MS) in hypersaline samples is a challenge due to matrix effects. Clean-up and desalting strategies that typically work well for samples with lower salt concentrations are often ineffective in hypersaline samples. To address this gap, we developed and demonstrated a simple yet sensitive and accurate method-MetFish-using chemical derivatization to enable mass spectrometry-based metabolomics in a variety of hypersaline samples from varied ecosystems and containing up to 2 M dissolved salts.

Dataset Information

Microbial communities evolve faster in extreme environments.

Publications

Microbial communities evolve faster in extreme environments.

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