ABSTRACT: Metaproteomics analyses of mixed-layer water from Hawaii Ocean Time-series Station ALOHA, incubated with 15N2 to track biosynthetic incorporation by diazotrophic microbes
Project description:These metaproteomic datasets are from active layer soil samples collected from the area of Toolik Field Station, Arctic Alaska, USA. These datasets are described and analyzed in the forthcoming paper, "Functional partitioning and vegetational variation among Arctic soil bacteria revealed by metaproteomics."
Project description:Dataset from a shipboard incubation experiment of an ocean surface-water microbial community sampled at 25m depth at Station ALOHA in the North Pacific Subtropical Gyre. Incubations were amended with ammonium, glutamate, leucine, nitrate and urea, in two isotopic variants: 15N (to track incorporation by various community members) and 14N (for quantitation of abundance changes by diDO-IPTL).
Project description:This project presents field metaproteomics data from Trichodesmium colonies collected from the surface ocean. Most were collected from the tropical and subtropical Atlantic ocean, but there is also data from the long term Bermuda Atlantic Time Series and Hawaii Ocean Time Series. Trichodesmium is a globally important marine microbe and its growth and nitrogen fixation activity is limited by nutrient availability in the surface ocean. This dataset was generated to answer questions about limitations on Trichodesmium's growth and activity in the nature.
Project description:In this study we characterize microbial community features on the surface of Indian Ocean. 11 samples were collected from Indian Ocean and subjected for quantitative metaproteomics analysis for taxonomic and functional analysis. Our results suggested that metabolic tuning at metaproteomics levels enabled microbial community to sustain stable when subjected to environmental perturbations in the oligotrophic ocean.
Project description:Microarrays are useful tools for detecting and quantifying specific functional and phylogenetic genes in natural microbial communities. In order to track uncultivated microbial genotypes and their close relatives in an environmental context, we designed and implemented a “genome proxy” microarray that targets microbial genome fragments recovered directly from the environment. Fragments consisted of sequenced clones from large-insert genomic libraries from microbial communities in Monterey Bay, the Hawaii Ocean Time-series station ALOHA, and Antarctic coastal waters. In a prototype array, we designed probe sets to thirteen of the sequenced genome fragments and to genomic regions of the cultivated cyanobacterium Prochlorococcus MED4. Each probe set consisted of multiple 70-mers, each targeting an individual ORF, and distributed along each ~40-160kbp contiguous genomic region. The targeted organisms or clones, and close relatives, were hybridized to the array both as pure DNA mixtures and as additions of cells to a background of coastal seawater. This prototype array correctly identified the presence or absence of the target organisms and their relatives in laboratory mixes, with negligible cross-hybridization to organisms having ≤~75% genomic identity. In addition, the array correctly identified target cells added to a background of environmental DNA, with a limit of detection of ~0.1% of the community, corresponding to ~10^3 cells/ml in these samples. Signal correlated to cell concentration with an R2 of 1.0 across six orders of magnitude. In addition the array could track a related strain (at 86% genomic identity to that targeted) with a linearity of R2=0.9999 and a limit of detection of ~1% of the community. Closely related genotypes were distinguishable by differing hybridization patterns across each probe set. This array’s multiple-probe, “genome-proxy” approach and consequent ability to track both target genotypes and their close relatives is important for the array’s environmental application given the recent discoveries of considerable intra-population diversity within marine microbial communities. Keywords: target addition experiment, proof-of-concept for GPL6012
Project description:Quantitative metaproteomics is a relatively new research field by applying proteomics technique to study microbial proteins of microbiome, and holds the great potential to truly quantify the functional proteins actually expressed by microbes in the biological environment such as gastrointestinal tract. The significant association between arsenic exposure and gut microbiome perturbations has been reported; however, metaproteomics has not yet been applied to study arsenic induced proteome changes of microbiome. Most importantly, to our knowledge, isobaric-labeling based large-scale metaproteomics has not been reported using the advanced database search approaches such as MetaPro-IQ and matched metagenome database search strategies to provide high quantification accuracy and less missing quantification values. In the present study, a new experimental workflow coupled with isobaric labeling and MetaPro-IQ was demonstrated for metaproteomics study of arsenic induced gut microbiome perturbations. The advantages of this workflow were also discussed. For all 18 fecal samples analyzed, 7,611 protein groups were quantified without any missing values. The consistent results of expression profiles were observed between 16S rRNA gene sequencing and metaproteomics. This isobaric labeling based workflow demonstrated the significant improvement of quantitative metaproteomics for gut microbiome study.
Project description: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.
Project description:Untargeted proteomics from a 5,000 km+ transect across the central Pacific Ocean from Hawaii to Tahiti. The expedition crossed multiple biogeochemical provinces, inlcuding the oligotrophic North Pacific Subtropical Gyre, the extremety of the Eastern Tropical North Pacific Oxygen Deficient Zone, and the relatively productive equatorial region associated with upwelling. This dataset focuses on the microbial fraction (0.2-3.0 micrometer filter size) and the microbial community dynamics across these biogeochemical provinces, from the surface oceance to the mesopelagic (1,250 m depth maximum).