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To gain a thorough appreciation of microbiome dynamics, researchers characterize the functional relevance of expressed microbial genes or proteins. This can be accomplished through metaproteomics, which characterizes the protein expression of microbiomes. Several software tools exist for analyzing microbiomes at the functional level by measuring their combined proteome-level response to environmental perturbations. In this survey, we explore the performance of six available tools, to enable researchers to make informed decisions regarding software choice based on their research goals. Tandem mass spectrometry-based proteomic data obtained from dental caries plaque samples grown with and without sucrose in paired biofilm reactors were used as representative data for this evaluation. Microbial peptides from one sample pair were identified by the X! tandem search algorithm via SearchGUI and subjected to functional analysis using software tools including eggNOG-mapper, MEGAN5, MetaGOmics, MetaProteomeAnalyzer (MPA), ProPHAnE, and Unipept to generate functional annotation through Gene Ontology (GO) terms. Among these software tools, notable differences in functional annotation were detected after comparing differentially expressed protein functional groups. Based on the generated GO terms of these tools we performed a peptide-level comparison to evaluate the quality of their functional annotations. A BLAST analysis against the NCBI non-redundant database revealed that the sensitivity and specificity of functional annotation varied between tools. For example, eggNOG-mapper mapped to the most number of GO terms, while Unipept generated more accurate GO terms. Based on our evaluation, metaproteomics researchers can choose the software according to their analytical needs and developers can use the resulting feedback to further optimize their algorithms. To make more of these tools accessible via scalable metaproteomics workflows, eggNOG-mapper and Unipept 4.0 were incorporated into the Galaxy platform.

Harmful algal blooms (HABs) have become a worldwide environmental and human health problem, stressing the urgent need for a reliable forecasting tool. Dynamic interactions between algae, including harmful algae, and bacteria play a large role regulating water chemistry. Free-living bacteria quickly respond to small physical and/or chemical environmental changes by adjusting their proteome. We hypothesize that this response is detectable at the peptide level and occurs before rapid phytoplankton growth characteristic of harmful bloom events. To characterize the microbiome’s physiological changes preceding bloom onset, we collected and analyzed a high-resolution metaproteomic time series of a free-living microbiome in a coastal ecosystem. We confirm that twelve candidate HAB biomarkers are detectable, quantifiable, and correlated across two pre-bloom periods. This study identifies proteomic shifts in bacterial peptides which may be used as predictive biomarkers for forecasting harmful algal bloom initiation, potentially mitigating detrimental algal bloom outcomes in the future. Harmful algal blooms (HABs) pose negative worldwide impacts that could be minimized through the development of a forecasting tool. Quantitative analysis of peptides produced by a coastal microbiome prior to a HAB reveals predictive biomarkers that can forecast bloom events over 24 hours in advance.

Data-independent acquisition (DIA)-based mass spectrometry is becoming an increasingly popular mass spectrometry acquisition strategy for carrying out quantitative proteomics experiments. Most of the popular DIA search engines make use of in silico generated spectral libraries. However, the generation of high-quality spectral libraries for DIA data analysis remains a challenge, particularly because most such libraries are generated directly from data-dependent acquisition (DDA) data or are from in silico prediction using models trained on DDA data. In this study, we introduce Carafe, a tool that generates high-quality experiment-specific in silico spectral libraries by training deep learning models directly on DIA data. We demonstrate the performance of Carafe on a wide range of DIA datasets, where we observe improved fragment ion intensity prediction and peptide detection relative to existing pretrained DDA models. To make Carafe more accessible to the community, we integrate Carafe into the widely used Skyline tool. Accurate spectral libraries are essential for analyzing data-independent acquisition (DIA) proteomics data. Here, the authors present Carafe, which trains on DIA data to build experiment-specific spectral libraries, boosting peptide detection across diverse datasets.

Competition is a major force structuring marine planktonic communities. The release of compounds that inhibit competitors, a process known as allelopathy, may play a role in the maintenance of large blooms of the red-tide dinoflagellate Karenia brevis , which produces potent neurotoxins that negatively impact coastal marine ecosystems. K. brevis is variably allelopathic to multiple competitors, typically causing sublethal suppression of growth. We used metabolomic and proteomic analyses to investigate the role of chemically mediated ecological interactions between K. brevis and two diatom competitors, Asterionellopsis glacialis and Thalassiosira pseudonana . The impact of K. brevis allelopathy on competitor physiology was reflected in the metabolomes and expressed proteomes of both diatoms, although the diatom that co-occurs with K. brevis blooms (A. glacialis) exhibited more robust metabolism in response to K. brevis . The observed partial resistance of A. glacialis to allelopathy may be a result of its frequent exposure to K. brevis blooms in the Gulf of Mexico. For the more sensitive diatom, T. pseudonana , which may not have had opportunity to evolve resistance to K. brevis , allelopathy disrupted energy metabolism and impeded cellular protection mechanisms including altered cell membrane components, inhibited osmoregulation, and increased oxidative stress. Allelopathic compounds appear to target multiple physiological pathways in sensitive competitors, demonstrating that chemical cues in the plankton have the potential to alter large-scale ecosystem processes including primary production and nutrient cycling.

Corals in nearshore marine environments are increasingly exposed to reduced water quality, which is the primary local threat to Hawaiian coral reefs. It is unclear if corals surviving in such conditions have adapted to withstand sedimentation, pollutants, and other environmental stressors. Lobe coral populations from Maunalua Bay, Hawaii showed clear genetic differentiation between the 'polluted, high-stress' nearshore site and the 'less polluted, lower-stress' offshore site. To understand the driving force of the observed genetic partitioning, reciprocal transplant and common-garden experiments were conducted to assess phenotypic differences between these two populations. Physiological responses differed significantly between the populations, revealing more stress-resilient traits in the nearshore corals. Changes in protein profiles highlighted the inherent differences in the cellular metabolic processes and activities between the two; nearshore corals did not significantly alter their proteome between the sites, while offshore corals responded to nearshore transplantation with increased abundances of proteins associated with detoxification, antioxidant defense, and regulation of cellular metabolic processes. The response differences across multiple phenotypes between the populations suggest local adaptation of nearshore corals to reduced water quality. Our results provide insight into coral’s adaptive potential and its underlying processes, and reveal potential protein biomarkers that could be used to predict resiliency.

Phytoplankton growth rates are limited by the supply of iron (Fe) in approximately one third of the open ocean, with major implications for carbon dioxide sequestration and carbon (C) biogeochemistry. To date, understanding how alteration of Fe supply changes phytoplankton physiology has focused on traditional metrics such as growth rate, elemental composition, and biophysical measurements such as photosynthetic competence (Fv/Fm). Researchers have subsequently employed transcriptomics to probe relationships between changes in Fe supply and phytoplankton physiology. Recently, studies have investigated longer-term (i.e. following acclimation) responses of phytoplankton to various Fe conditions. In the present study, the coastal diatom, Thalassiosira pseudonana, was acclimated (10 generations) to either low or high Fe conditions, i.e. Fe-limiting and Fe-replete. Quantitative proteomics and a newly developed proteomic profiling technique that identifies low abundance proteins were employed to examine the full complement of expressed proteins and consequently the metabolic pathways utilized by the diatom under the two Fe conditions. A total of 1850 proteins were confidently identified, nearly tripling previous identifications made from differential expression in diatoms. Given sufficient time to acclimate to Fe limitation, T. pseudonana up-regulates proteins involved in pathways associated with intracellular protein recycling, thereby decreasing dependence on extracellular nitrogen (N), C and Fe. The relative increase in the abundance of photorespiration and pentose phosphate pathway proteins reveal novel metabolic shifts, which create substrates that could support other well-established physiological responses, such as heavily silicified frustules observed for Fe-limited diatoms. Here, we discovered that proteins and hence pathways observed to be down-regulated in short-term Fe starvation studies are constitutively expressed when T. pseudonana is acclimated (i.e., nitrate and nitrite transporters, Photosystem II and Photosystem I complexes). Acclimation of the diatom to the desired Fe conditions and the comprehensive proteomic approach provides a more robust interpretation of this dynamic proteome than previous studies.

Background Ocean acidification as a result of increased anthropogenic CO 2 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 p CO 2 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). Results At the end of the four week exposure period, oysters in all four p CO 2 environments deposited new shell, but growth rate was not different among the treatments. However, micromechanical properties of the new shell were compromised by elevated p CO 2 . Elevated p CO 2 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 p CO 2 , with numerous processes significantly affected by mechanical stimulation at high versus low p CO 2 (all proteomics data are available in the ProteomeXchange under the identifier PXD000835). Conclusions Oyster physiology is significantly altered by exposure to elevated p CO 2 , indicating changes in energy resource use. This is especially apparent in the assessment of the effects of p CO 2 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.

Bacteria and Archaea play critical roles in marine energy fluxes and nutrient cycles by incorporating and redistributing dissolved organic matter and inorganic nutrients in the oceans. How these microorganisms do this work at the level of the expressed protein is known only from a few studies of targeted lineages. We used comparative membrane metaproteomics to identify functional responses of communities to different nutrient concentrations on an oceanic scale. Comparative analyses of microbial membrane fractions revealed shifts in nutrient utilization and energy transduction along an environmental gradient in South Atlantic surface waters, from a low-nutrient gyre to a highly productive coastal upwelling region. The dominant membrane proteins identified (19%) were TonB-dependent transporters (TBDTs), which are known to utilize a proton motive force to transport nutrients across the outer membrane of Gram-negative bacteria. The ocean-wide importance of TonB-dependent nutrient acquisition in marine bacteria was unsuspected. Diverse light-harvesting rhodopsins were detected in membrane proteomes from every sample. Proteomic evidence of both TBDTs and rhodopsins in the same lineages suggest that phototrophic bacterioplankton have the potential to use energy from light to fuel transport activities. We also identified viral proteins in every sample and archaeal ammonia monooxygenase proteins in the upwelling region, suggesting that Archaea are important nitrifiers in nutrient-rich surface waters.

Aqueous-soluble hydrocarbons dissolve into the ocean’s interior and structure deep-sea microbial populations influenced by natural oil seeps and spills. Among these hydrocarbons, n-pentane, is a seawater-soluble, volatile compound abundant in petroleum products and reservoirs, which partially partitions to the deep-water column following release from the seafloor. In this study, we explore the ecology and niche partitioning of two free-living Cycloclasticus strains recovered from seawater incubations with n-pentane and distinguish them as an open ocean variant and a seep-proximal variant, each with distinct capabilities for hydrocarbon catabolism. Comparative metagenomic analysis indicates the variant more frequently observed further from natural seeps encodes more general pathways for hydrocarbon consumption, including short-chain alkanes, aromatics, and long-chain alkanes, and also possesses redox versatility in the form of respiratory nitrate reduction and thiosulfate oxidation; in contrast, the seep variant specializes in short-chain alkanes and relies strictly on oxygen as the terminal electron acceptor. Both variants observed in our work were dominant ecotypes of Cycloclasticus observed during the Deepwater Horizon disaster, a conclusion supported by 16S rRNA gene analysis and read-recruitment of sequences collected from the submerged oil plume during active flow. A comparative genomic analysis of Cycloclasticus across various ecosystems suggests distinct strategies for hydrocarbon transformations among each clade. Our findings suggest Cycloclasticus is a versatile and opportunistic consumer of hydrocarbons and may have a greater role in the cycling of sulfur and nitrogen, thus contributing broad ecological impact to various ecosystems globally.