Ocean acidification as a result of increased anthropogenic CO2 emissions is occurring in marine and estuarine environments worldwide. The coastal ocean experiences additional daily and seasonal fluctuations in pH that can be lower than projected end-of-century open ocean pH reductions. In order to assess the impact of ocean acidification on marine invertebrates, Pacific oysters (Crassostrea gigas) were exposed to one of four different pCO2 levels for four weeks: 400 μatm (pH 8.0), 800 μatm (pH 7.7), 1000 μatm (pH 7.6), or 2800 μatm (pH 7.3). At the end of the four week exposure period, oysters in all four pCO2 environments deposited new shell, but growth rate was not different among the treatments. However, micromechanical properties of the new shell were compromised by elevated pCO2. Elevated pCO2 affected neither whole body fatty acid composition, nor glycogen content, nor mortality rate associated with acute heat shock. Shotgun proteomics revealed that several physiological pathways were significantly affected by ocean acidification, including antioxidant response, carbohydrate metabolism, and transcription and translation. Additionally, the proteomic response to a second stress differed with pCO2, with numerous processes significantly affected by mechanical stimulation at high versus low pCO2 (all proteomics data are available in the ProteomeXchange under the identifier PXD000835). Oyster physiology is significantly altered by exposure to elevated pCO2, indicating changes in energy resource use. This is especially apparent in the assessment of the effects of pCO2 on the proteomic response to a second stress. The altered stress response illustrates that ocean acidification may impact how oysters respond to other changes in their environment. These data contribute to an integrative view of the effects of ocean acidification on oysters as well as physiological trade-offs during environmental stress.

Environmental meta-omics is rapidly expanding as sequencing capabilities improve, computing technologies become more accessible, and associated costs are reduced. The in situ snapshots of marine microbial life afforded by these data provide a growing knowledge of the functional roles of communities in ecosystem processes. Metaproteomics allows for the characterization of the dynamic proteome of a complex microbial community. It has the potential to reveal impacts of microbial metabolism on biogeochemical transport, storage and cycling (for example, Hawley et al., 2014), while additionally clarifying which taxonomic groups perform these roles. Previous work illuminated many of the important functions and interactions within marine microbial communities (for example, Morris et al., 2010), but a review of ocean metaproteomics literature revealed little standardization in bioinformatics pipelines for detecting peptides and inferring and annotating proteins. As prevalence of these data sets grows, there is a critical need to develop standardized approaches for mass spectrometry (MS) proteomic spectrum identification and annotation to maximize the scientific value of the data obtained. Here, we demonstrate that bioinformatics decisions made throughout the peptide identification process are as important for data interpretation as choices of sampling protocol and bacterial community manipulation experimental design. Our analysis offers a best practices guide for environmental metaproteomics. MS-based metaproteomics is now practical due to advances in duty cycle and increased mass accuracy for both precursor and fragment masses. These improvements allow for the detection of over 104 tandem mass spectra from a single data-dependent acquisition MS analysis of a mixed microbial sample. These spectra must then be associated with peptides from thousands of proteins from diverse taxonomic groups. The most common approach is database searching: scoring observed tandem mass spectra against theoretical peptide spectra generated in silico from a protein or peptide database (Eng et al., 1994). However, the approach to database selection, or construction, can vary dramatically. In an ocean metaproteomics experiment, the two main approaches for creating a protein identification database are to (1) leverage vast quantities of public sequence data or (2) sequence and assemble a metagenome. Further, when exploring and assembling possible public databases, a wide range of databases and sequence selection methods are used. As the field of environmental proteomics grows, the integrity of metaproteomics data sets and our ability to directly compare them across time and space depends on the adoption of a standardized procedure for peptide identification and annotation. Here, we reveal how highly influential the protein database selection is to the biological interpretations of a metaproteomics experiment. We applied four database selection techniques in order to perform peptide detection, protein inference, and taxonomic and functional assignments from MS-based, oceanic, microbial community metaproteomics (Figure 1). The metaproteome in question represents a diverse and relatively under-sequenced area of the ocean, the Pacific Arctic. Our results from this study offer a path forward as well as a caution for investigators that the biological conclusions drawn from metaproteomics data are highly database specific. Our study followed traditional procedures currently employed in ocean metaproteomics (details in Supplementary Information 1). Water samples were collected and selectively filtered from the Bering Strait as described in May et al. (2016) and incubated shipboard over 10 days (T0=day 0, T10=day 10). Bacterial community proteomes from the incubations were analyzed on a Q-Exactive-HF (Thermo Fisher Scientific, Waltham, MA, USA) and resulting data were searched against four different peptide identification databases (Supplementary Information 2): (1) site/time-specific metagenome collected concurrently with the incubated water; (2) NCBI’s env_NR database; (3) Arctic-bacterial database of NCBI protein sequences from known polar taxonomic groups (Supplementary Information 3) North Pacific database derived from a subset of the Ocean Microbiome sequencing project (Sunagawa et al., 2015; Supplementary Information 4). Peptides were identified and proteins were inferred using Comet v. 2015.01 rev. 2 (Eng et al., 2012, 2015), followed by peptide and protein match scoring (Pedrioli, 2010; Deutsch et al., 2015) at a false discovery rate threshold of 0.01 (Supplementary Information 5). Proteins from all databases were annotated using BLASTp (Altschul et al., 1990; Camacho et al., 2009) against the UniProtKB TrEMBL database (downloaded April 28, 2015) with an e-value cutoff of 1E-10 (Supplementary Information 6). Shifts in community biological functions over the 10-day incubation were quantified using a Gene Ontology (GO) analysis where peptide spectrum matches were associated with GO terms. Additionally, database-driven peptide score sensitivity as a function of database size was investigated by searching the site/time-specific metagenome database with increasing numbers of decoy peptides. The number of peptide experimental spectra that yielded spectrum matches was very different among databases. The highest number of confidently scored unique peptide matches and protein inferences resulted from the search against the site/time-specific metagenome database. This number of peptide matches was augmented 1.5 times by searching the same data against unassembled reads. This ‘metapeptide’ approach (May et al., 2016) avoids sequence loss and potential noise introduced by read assembly (for example Cantarel et al., 2011). The peptides identified by the four assembled databases overlapped relatively little, suggesting that the different databases cover different parts of the acquired metaproteome (May et al.,...

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/.

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 < 0.01) in biological and molecular functions associated with carbon and nitrogen recycling were observed. Within the first day under both organic matter conditions, Bering Strait surface microbiomes increased protein synthesis, carbohydrate degradation, and cellular redox processes while decreasing C1 metabolism. Taxonomic assignments revealed that the core microbiome collectively responded to algal substrates by assimilating carbon before select taxa utilize and metabolize nitrogen intracellularly. Incubations of Chukchi Sea bottom water microbiomes showed similar, but delayed functional responses to identical treatments. Although 24 functional terms were shared between experimental treatments, the timing, and degree of the remaining responses were highly variable, showing that organic matter perturbation directs community functionality prior to alterations to the taxonomic distribution at the microbiome class level. The dynamic responses of these two oceanic microbial communities have important implications for timing and magnitude of responses to organic perturbations within the Arctic Ocean and how community-level functions may forecast biogeochemical gradients in oceans.