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We examined metaproteome profiles from two Arctic microbiomes during 10-day shipboard incubations to directly track early functional and taxonomic responses to a simulated algal bloom and an oligotrophic control. Using a novel peptide-based enrichment analysis, significant changes (p-value

In principle, tandem mass spectrometry can be used to detect and quantify the peptides present in a microbiome sample, enabling functional and taxonomic insight into microbiome metabolic activity. However, the phylogenetic diversity constituting a particular microbiome is often unknown, and many of the organisms present may not have assembled genomes. In ocean microbiome samples, with particularly diverse and uncultured bacterial communities, it is difficult to construct protein databases that contain the bulk of the peptides in the sample without losing detection sensitivity due to the overwhelming number of candidate peptides for each tandem mass spectrum. We describe a method for deriving "metapeptides" (short amino acid sequences that may be represented in multiple organisms) from shotgun metagenomic sequencing of microbiome samples. In two ocean microbiome samples, we constructed site-specific metapeptide databases to detect more than one and a half times as many peptides as by searching against predicted genes from an assembled metagenome and roughly three times as many peptides as by searching against the NCBI environmental proteome database. The increased peptide yield has the potential to enrich the taxonomic and functional characterization of sample metaproteomes.

Assigning links between microbial activity and biogeochemical cycles in the ocean is a primary objective for ecologists and oceanographers. Bacteria represent a small ecosystem component by mass, but act as the nexus for both nutrient transformation and organic matter recycling. There are limited methods to explore the full suite of active bacterial proteins largely responsible for degradation. Mass spectrometry (MS)-based proteomics now has the potential to document bacterial physiology within these complex systems. Global proteome profiling using MS, known as data dependent acquisition (DDA), is limited by the stochastic nature of ion selection, decreasing the detection of low abundance peptides. The suitability of MS-based proteomics methods in revealing bacterial signatures outnumbered by phytoplankton proteins was explored using a dilution series of pure bacteria (Ruegeria pomeroyi) and diatoms (Thalassiosira pseudonana). Two common acquisition strategies were utilized: DDA and selected reaction monitoring (SRM). SRM improved detection of bacterial peptides at low bacterial cellular abundance that were undetectable with DDA from a wide range of physiological processes (e.g. amino acid synthesis, lipid metabolism, and transport). We demonstrate the benefits and drawbacks of two different proteomic approaches for investigating species-specific physiological processes across relative abundances of bacteria that vary by orders of magnitude.

Fluorescence was used to examine the quality of dissolved and particulate organic matter (DOM and POM) exchanging between a tidal creek in a created salt marsh and its adjacent estuary in eastern North Carolina, USA. Samples from the creek were collected hourly over four tidal cycles in May, July, August, and October 2011. Absorbance and fluorescence of chromophoric DOM (CDOM) and of base-extracted POM (BEPOM) served as the tracers for organic matter quality while dissolved organic carbon (DOC) and base-extracted particulate organic carbon (BEPOC) were used to compute fluxes. Fluorescence was modeled using parallel factor analysis (PARAFAC) and principle components analysis (PCA) of the PARAFAC results. Of nine PARAFAC components (C) modeled, C3 represented recalcitrant DOM and C4 represented fresher soil-derived source DOM. Component 1 represented detrital POM, and C6 represented planktonic POM. Based on mass balance, recalcitrant DOC export was 86 g C m−2 yr−1 and labile DOC export was 49 g C m−2 yr−1; no planktonic DOC was exported. The marsh also exported 41 g C m−2 yr−1 of detrital terrestrial POC, which likely originated from lands adjacent to the North River estuary. Planktonic POC export from the marsh was 6 g C m−2 yr−1. Assuming the exported organic matter was oxidized to CO2 and scaled up to global salt marsh area, respiration of salt marsh DOC and POC transported to estuaries could amount to a global CO2 flux of 11 Tg C yr−1, roughly 4% of the recently estimated CO2 release for marshes and estuaries globally.

Metaproteomics is the characterization of all proteins being expressed by a community of organisms in a complex biological sample at a single point in time. Applications of metaproteomics range from the comparative analysis of environmental samples (such as ocean water and soil) to microbiome data from multicellular organisms (such as the human gut). Metaproteomics research is often focused on the quantitative functional makeup of the metaproteome and which organisms are making those proteins. That is: What are the functions of the currently expressed proteins? How much of the metaproteome is associated with those functions? And, which microorganisms are expressing the proteins that perform those functions? However, traditional protein-centric functional analysis is greatly complicated by the large size, redundancy, and lack of biological annotations for the protein sequences in the database used to search the data. To help address these issues, we have developed an algorithm and web application (dubbed "MetaGOmics") that automates the quantitative functional (using Gene Ontology) and taxonomic analysis of metaproteomics data and subsequent visualization of the results. MetaGOmics is designed to overcome the shortcomings of traditional proteomics analysis when used with metaproteomics data. It is easy to use, requires minimal input, and fully automates most steps of the analysis-including comparing the functional makeup between samples. MetaGOmics is freely available at https://www.yeastrc.org/metagomics/.

Critical decisions in metaproteomics: achieving high confidence protein annotations in a sea of unknowns

Excitation-emission matrix (EEM) fluorescence was combined with parallel factor analysis (PARAFAC) to model base-extracted particulate (POM) and dissolved (DOM) organic matter quality in the Neuse River Estuary (NRE), North Carolina, before and after passage of Hurricane Irene in August 2011. Principle components analysis was used to determine that four of the PARAFAC components (C1-C3 and C6) were terrestrial sources to the NRE. One component (C4), prevalent in DOM of nutrient-impacted streams and estuaries and produced in phytoplankton cultures, was enriched in the POM and in surface sediment pore water DOM. One component (C5) was related to recent autochthonous production. Photoexposure of unfiltered Neuse River water caused an increase in slope ratio values (S(R)) which corresponded to an increase in the ratio C2:C3 for DOM, and the production of C4 fluorescence in both POM and DOM. Changes to the relative abundance of C4 in POM and DOM indicated that advection of pore water DOM from surface sediments into overlying waters could increase the autochthonous quality of DOM in shallow microtidal estuaries. Modeling POM and DOM simultaneously with PARAFAC is an informative technique that is applicable to assessments of estuarine water quality.