Emma Timmins-Schiffman1, Samuel J White2, Rhonda Elliott Thompson3,4, Brent Vadopalas5, Benoit Eudeline3, Brook L Nunn1, Steven B Roberts6. 1. Department of Genome Sciences, University of Washington, 3720 15th Ave NE, Seattle, WA, 98195, USA. 2. School of Aquatic and Fishery Sciences, University of Washington, 1122 Boat St., Seattle, WA, 98195, USA. 3. Taylor Shellfish Hatchery, 701 Broadspit Rd., Quilcene, WA, 98376, USA. 4. Mason County Public Health, 415 N 6th St., Shelton, WA, 98584, USA. 5. Washington Sea Grant, University of Washington, 3716 Brooklyn Ave NE, Seattle, WA, 98105, USA. 6. School of Aquatic and Fishery Sciences, University of Washington, 1122 Boat St., Seattle, WA, 98195, USA. Sr320@uw.edu.
Abstract
BACKGROUND: Microbial communities are ubiquitous throughout ecosystems and are commensal with hosts across taxonomic boundaries. Environmental and species-specific microbiomes are instrumental in maintaining ecosystem and host health, respectively. The introduction of pathogenic microbes that shift microbiome community structure can lead to illness and death. Understanding the dynamics of microbiomes across a diversity of environments and hosts will help us to better understand which taxa forecast survival and which forecast mortality events. RESULTS: We characterized the bacterial community microbiome in the water of a commercial shellfish hatchery in Washington state, USA, where the hatchery has been plagued by recurring and unexplained larval mortality events. By applying the complementary methods of metagenomics and metaproteomics we were able to more fully characterize the bacterial taxa in the hatchery at high (pH 8.2) and low (pH 7.1) pH that were metabolically active versus present but not contributing metabolically. There were shifts in the taxonomy and functional profile of the microbiome between pH and over time. Based on detected metagenomic reads and metaproteomic peptide spectral matches, some taxa were more metabolically active than expected based on presence alone (Deltaproteobacteria, Alphaproteobacteria) and some were less metabolically active than expected (e.g., Betaproteobacteria, Cytophagia). There was little correlation between potential and realized metabolic function based on Gene Ontology analysis of detected genes and peptides. CONCLUSION: The complementary methods of metagenomics and metaproteomics contribute to a more full characterization of bacterial taxa that are potentially active versus truly metabolically active and thus impact water quality and inter-trophic relationships.
BACKGROUND: Microbial communities are ubiquitous throughout ecosystems and are commensal with hosts across taxonomic boundaries. Environmental and species-specific microbiomes are instrumental in maintaining ecosystem and host health, respectively. The introduction of pathogenic microbes that shift microbiome community structure can lead to illness and death. Understanding the dynamics of microbiomes across a diversity of environments and hosts will help us to better understand which taxa forecast survival and which forecast mortality events. RESULTS: We characterized the bacterial community microbiome in the water of a commercial shellfish hatchery in Washington state, USA, where the hatchery has been plagued by recurring and unexplained larval mortality events. By applying the complementary methods of metagenomics and metaproteomics we were able to more fully characterize the bacterial taxa in the hatchery at high (pH 8.2) and low (pH 7.1) pH that were metabolically active versus present but not contributing metabolically. There were shifts in the taxonomy and functional profile of the microbiome between pH and over time. Based on detected metagenomic reads and metaproteomic peptide spectral matches, some taxa were more metabolically active than expected based on presence alone (Deltaproteobacteria, Alphaproteobacteria) and some were less metabolically active than expected (e.g., Betaproteobacteria, Cytophagia). There was little correlation between potential and realized metabolic function based on Gene Ontology analysis of detected genes and peptides. CONCLUSION: The complementary methods of metagenomics and metaproteomics contribute to a more full characterization of bacterial taxa that are potentially active versus truly metabolically active and thus impact water quality and inter-trophic relationships.
Authors: Emma Timmins-Schiffman; José M Guzmán; Rhonda Elliott Thompson; Brent Vadopalas; Benoit Eudeline; Steven B Roberts Journal: Ecol Evol Date: 2019-12-06 Impact factor: 2.912