Abstract

BackgroundMicrobial 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.ResultsWe 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.ConclusionThe 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.

Highlights

  • Microbial communities are ubiquitous throughout ecosystems and are commensal with hosts across taxonomic boundaries

  • Genomics analyses are frequently applied to respond to a diversity of hypotheses in environmental science, because DNA is more stable than RNA and proteins, and genomics, unlike proteomics, does not depend upon a pre-existing database to get results

  • The parallel analysis of metagenomics and metaproteomics data definitively demonstrates that potential genetic function does not accurately predict which proteins are translated for probable metabolic activity

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Summary

Introduction

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. Aquatic systems are home to dynamic interactions among microbial species, macrofauna, and the physical environment, the outcomes of which determine ecosystem health. Understanding which taxa are present in a given system is a first step towards uncovering these dynamics, which can lead to more accurate predictions and modeling of ecosystems. Especially for microbes, detected presence does not accurately predict metabolic contribution to the ecosystem [3,4,5]. Microbiome taxonomy is poorly correlated with environmental variables, whereas metabolic potential of functional groups of microbes is well predicted by environment [6]. By combining DNA (metagenomics) with protein abundance (metaproteomics) we can achieve a substantially more accurate understanding of microbial metabolic contributions in a given environment

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