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Extreme environments offer an unprecedented opportunity to understand microbial eukaryotic ecology, evolution, and genome biology.

ABSTRACT: Research in extreme environments has substantially expanded our understanding of the ecology and evolution of life on Earth, but a major group of organisms has been largely overlooked: microbial eukaryotes (i.e., protists). In this Perspective, we summarize data from over 80 studies of protists in extreme environments and identify focal lineages that are of significant interest for further study, including clades within Echinamoebida, Heterolobosea, Radiolaria, Haptophyta, Oomycota, and Cryptophyta. We argue that extreme environments are prime sampling targets to fill gaps in the eukaryotic tree of life and to increase our understanding of the ecology, metabolism, genome architecture, and evolution of eukaryotic life.

SUBMITTER: Rappaport HB

PROVIDER: S-EPMC10432404 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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Extreme environments offer an unprecedented opportunity to understand microbial eukaryotic ecology, evolution, and genome biology.

Rappaport Hannah B HB Oliverio Angela M AM

Nature communications 20230816 1

Research in extreme environments has substantially expanded our understanding of the ecology and evolution of life on Earth, but a major group of organisms has been largely overlooked: microbial eukaryotes (i.e., protists). In this Perspective, we summarize data from over 80 studies of protists in extreme environments and identify focal lineages that are of significant interest for further study, including clades within Echinamoebida, Heterolobosea, Radiolaria, Haptophyta, Oomycota, and Cryptoph ...[more]

PMID: 37587119

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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

Extreme environments offer an unprecedented opportunity to understand microbial eukaryotic ecology, evolution, and genome biology.

Publications

Extreme environments offer an unprecedented opportunity to understand microbial eukaryotic ecology, evolution, and genome biology.

Similar Datasets

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