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Modeling microbial community metabolic interactions in hypersaline ecosystems

EMSL Project ID


The proposed research will test the hypothesis that hypersaline microbial communities interact to maintain stability despite changes in salinity and nutrient availability. Hypersaline-adapted Archaea, or halophiles, provide a unique model for investigating the metabolic interactions in microbial communities. Halophiles exhibit extensive diversity in how they generate energy but share a common extreme habitat in the Great Salt Lake, where salinity can reach saturation. Nutrients are intermittently available in hypersaline lakes during seasonal variation, resulting in severe energy stress. In response, halophiles have evolved a wide array of possible metabolic solutions to survive on the same pool of scarce resources. Hypersaline microbial communities have great potential to teach us about general principles of community resilience to environmental perturbation. However, knowledge regarding the mechanisms of metabolic interactions within these microbial communities remain largely uncharacterized. To address these challenges, the microbial communities in the Great Salt Lake have been sampled across the seasons to determine community response to seasonal variation. Community composition and activity have been profiled using metagenomics and transcriptomics, respectively (data collected in a private communication). Predictive genome-scale metabolic model of halophilic species have been built. To test the hypothesis, metabolite data are needed to refine the model and increase predictive accuracy. However, metabolite quantitation from samples saturated with salt poses substantial challenges to mass spectrometric workflows. Here the following objectives will be pursued: (a) use the model to identify key metabolites, then isolate these metabolites from high salinity samples using mass spectrometry metabolomics; and (b) conduct metabolic flux analysis to quantify the rate of nutrient exchange between microbes. Expertise from EMSL collaborators is required for the success of this project. They will use specialized chemistry capture techniques to isolate metabolites from high salt samples (MetFish). Success of this project requires the expertise in cutting edge mass spectrometry capabilities available only at PNNL. The PI will use resultant data to constrain the metabolic model to improve predictive accuracy for how metabolites flow through microbial communities. Model predictions will be tested in iterative rounds of metabolomics experiments. This iterative systems approach is expected to yield novel insights into nutrient use in hypersaline microbial communities, which are understudied relative to other environmentally important ecological sites.

Project Details

Project type
Exploratory Research
Start Date
End Date


Principal Investigator

Amy Schmid
Duke University

Team Members

Alex Phillips
Duke University

Mary Lipton
Environmental Molecular Sciences Laboratory