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The aim of this study is to obtain a systems-level understanding of the interactions between Dehalococcoides and corrinoid-supplying microorganisms by analyzing community structures and functional compositions, activities, and dynamics in trichloroethene (TCE)-dechlorinating enrichments. Metagenomes and metatranscriptomes of the dechlorinating enrichments with and without exogenous cobalamin were compared. Seven putative draft genomes were binned from the metagenomes. At an early stage (2 days), more transcripts of genes in the Veillonellaceae bin-genome were detected in the metatranscriptome of the enrichment without exogenous cobalamin than in the one with the addition of cobalamin. Among these genes, sporulation-related genes exhibited the highest differential expression when cobalamin was not added, suggesting a possible release route of corrinoids from corrinoid producers. Other differentially expressed genes include those involved in energy conservation and nutrient transport (including cobalt transport). The most highly expressed corrinoid de novo biosynthesis pathway was also assigned to the Veillonellaceae bin-genome. Targeted quantitative PCR (qPCR) analyses confirmed higher transcript abundances of those corrinoid biosynthesis genes in the enrichment without exogenous cobalamin than in the enrichment with cobalamin. Furthermore, the corrinoid salvaging and modification pathway of Dehalococcoides was upregulated in response to the cobalamin stress. This study provides important insights into the microbial interactions and roles played by members of dechlorinating communities under cobalamin-limited conditions. IMPORTANCE The key chloroethene-dechlorinating bacterium Dehalococcoides mccartyi is a cobalamin auxotroph, thus acquiring corrinoids from other community members. Therefore, it is important to investigate the microbe-microbe interactions between Dehalococcoides and the corrinoid-providing microorganisms in a community. This study provides systems-level information, i.e., taxonomic and functional compositions and dynamics of the supportive microorganisms in dechlorinating communities under different cobalamin conditions. The findings shed light on the important roles of Veillonellaceae species in the communities compared to other coexisting community members in producing and providing corrinoids for Dehalococcoides species under cobalamin-limited conditions., (Copyright © 2017 American Society for Microbiology.)

A new functional gene database, FOAM (Functional Ontology Assignments for Metagenomes), was developed to screen environmental metagenomic sequence datasets. FOAM provides a new functional ontology dedicated to classify gene functions relevant to environmental microorganisms based on Hidden Markov Models (HMMs). Sets of aligned protein sequences (i.e. 'profiles') were tailored to a large group of target KEGG Orthologs (KOs) from which HMMs were trained. The alignments were checked and curated to make them specific to the targeted KO. Within this process, sequence profiles were enriched with the most abundant sequences available to maximize the yield of accurate classifier models. An associated functional ontology was built to describe the functional groups and hierarchy. FOAM allows the user to select the target search space before HMM-based comparison steps and to easily organize the results into different functional categories and subcategories. FOAM is publicly available at http://portal.nersc.gov/project/m1317/FOAM/., (© The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The Deepwater Horizon (DWH) oil spill in the spring of 2010 resulted in an input of ∼4.1 million barrels of oil to the Gulf of Mexico; >22% of this oil is unaccounted for, with unknown environmental consequences. Here we investigated the impact of oil deposition on microbial communities in surface sediments collected at 64 sites by targeted sequencing of 16S rRNA genes, shotgun metagenomic sequencing of 14 of these samples and mineralization experiments using (14)C-labeled model substrates. The 16S rRNA gene data indicated that the most heavily oil-impacted sediments were enriched in an uncultured Gammaproteobacterium and a Colwellia species, both of which were highly similar to sequences in the DWH deep-sea hydrocarbon plume. The primary drivers in structuring the microbial community were nitrogen and hydrocarbons. Annotation of unassembled metagenomic data revealed the most abundant hydrocarbon degradation pathway encoded genes involved in degrading aliphatic and simple aromatics via butane monooxygenase. The activity of key hydrocarbon degradation pathways by sediment microbes was confirmed by determining the mineralization of (14)C-labeled model substrates in the following order: propylene glycol, dodecane, toluene and phenanthrene. Further, analysis of metagenomic sequence data revealed an increase in abundance of genes involved in denitrification pathways in samples that exceeded the Environmental Protection Agency (EPA)'s benchmarks for polycyclic aromatic hydrocarbons (PAHs) compared with those that did not. Importantly, these data demonstrate that the indigenous sediment microbiota contributed an important ecosystem service for remediation of oil in the Gulf. However, PAHs were more recalcitrant to degradation, and their persistence could have deleterious impacts on the sediment ecosystem.

Microbial ecologists can now start digging into the accumulating mountains of metagenomic data to uncover the occurrence of functional genes and their correlations to microbial community members. Limitations and biases in DNA extraction and sequencing technologies impact sequence distributions, and therefore, have to be considered. However, when comparing metagenomes from widely differing environments, these fluctuations have a relatively minor role in microbial community discrimination. As a consequence, any functional gene or species distribution pattern can be compared among metagenomes originating from various environments and projects. In particular, global comparisons would help to define ecosystem specificities, such as involvement and response to climate change (for example, carbon and nitrogen cycle), human health risks (eg, presence of pathogen species, toxin genes and viruses) and biodegradation capacities. Although not all scientists have easy access to high-throughput sequencing technologies, they do have access to the sequences that have been deposited in databases, and therefore, can begin to intensively mine these metagenomic data to generate hypotheses that can be validated experimentally. Information about metabolic functions and microbial species compositions can already be compared among metagenomes from different ecosystems. These comparisons add to our understanding about microbial adaptation and the role of specific microbes in different ecosystems. Concurrent with the rapid growth of sequencing technologies, we have entered a new age of microbial ecology, which will enable researchers to experimentally confirm putative relationships between microbial functions and community structures.

Metagenomics approaches represent an important way to acquire information on the microbial communities present in complex environments like soil. However, to what extent do these approaches provide us with a true picture of soil microbial diversity? Soil is a challenging environment to work with. Its physicochemical properties affect microbial distributions inside the soil matrix, metagenome extraction and its subsequent analyses. To better understand the bias inherent to soil metagenome 'processing', we focus on soil physicochemical properties and their effects on the perceived bacterial distribution. In the light of this information, each step of soil metagenome processing is then discussed, with an emphasis on strategies for optimal soil sampling. Then, the interaction of cells and DNA with the soil matrix and the consequences for microbial DNA extraction are examined. Soil DNA extraction methods are compared and the veracity of the microbial profiles obtained is discussed. Finally, soil metagenomic sequence analysis and exploitation methods are reviewed., (© 2011 Federation of European Microbiological Societies. Published by Blackwell Publishing Ltd. All rights reserved.)