High-resolution metagenomics targets specific functional types in complex microbial communities

Most microbes in the biosphere remain unculturable. Whole genome shotgun (WGS) sequencing of environmental DNA (metagenomics) can be used to study the genetic and metabolic properties of natural microbial communities. However, in communities of high complexity, metagenomics fails to link specific microbes to specific ecological functions. To overcome this limitation, we developed a method to target microbial subpopulations by labeling DNA through stable isotope probing (SIP), followed by WGS sequencing. Metagenome analysis of microbes from Lake Washington in Seattle that oxidize single-carbon (C1) compounds shows specific sequence enrichments in response to different C1 substrates, revealing the ecological roles of individual phylotypes. We also demonstrate the utility of our approach by extracting a nearly complete genome of a novel methylotroph, Methylotenera mobilis, reconstructing its metabolism and conducting genome-wide analyses. This high-resolution, targeted metagenomics approach may be applicable to a wide variety of ecosystems.     

HAM-ART: An optimised culture-free Hi-C metagenomics pipeline for tracking antimicrobial resistance genes in complex microbial communities

Shotgun metagenomics is a powerful tool to identify antimicrobial resistance (AMR) genes in microbiomes but has the limitation that extrachromosomal DNA, such as plasmids, cannot be linked with the host bacterial chromosome. Here we present a comprehensive laboratory and bioinformatics pipeline HAM-ART (Hi-C Assisted Metagenomics for Antimicrobial Resistance Tracking) optimised for the generation of metagenome-assembled genomes including both chromosomal and extrachromosomal AMR genes. We demonstrate the performance of the pipeline in a study comparing 100 pig faecal microbiomes from low- and high-antimicrobial use pig farms (organic and conventional farms). We found significant differences in the distribution of AMR genes between low- and high-antimicrobial use farms including a plasmid-borne lincosamide resistance gene exclusive to high-antimicrobial use farms in three species of Lactobacilli. The bioinformatics pipeline code is available at https://github.com/lkalmar/HAM-ART.     

Targeted access to the genomes of low-abundance organisms in complex microbial communities

Current metagenomic approaches to the study of complex microbial consortia provide a glimpse into the community metabolism and occasionally allow genomic assemblies for the most abundant organisms. However, little information is gained for the members of the community present at low frequencies, especially those representing yet-uncultured taxa, which include the bulk of the diversity present in most environments. Here we used phylogenetically directed cell separation by fluorescence in situ hybridization and flow cytometry, followed by amplification and sequencing of a fraction of the genomic DNA of several bacterial cells that belong to the TM7 phylum. Partial genomic assembly allowed, for the first time, a look into the evolution and potential metabolism of a soil representative from this group of organisms for which there are no species in stable laboratory cultures. Genomic reconstruction from targeted cells of uncultured organisms isolated directly from the environment represents a powerful approach to access any specific members of a community and an alternative way to assess the community's metabolic potential.     

High definition for systems biology of microbial communities: metagenomics gets genome-centric and strain-resolved

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Soil-specific limitations for access and analysis of soil microbial communities by metagenomics

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.     

Dirichlet multinomial mixtures: generative models for microbial metagenomics

We introduce Dirichlet multinomial mixtures (DMM) for the probabilistic modelling of microbial metagenomics data. This data can be represented as a frequency matrix giving the number of times each taxa is observed in each sample. The samples have different size, and the matrix is sparse, as communities are diverse and skewed to rare taxa. Most methods used previously to classify or cluster samples have ignored these features. We describe each community by a vector of taxa probabilities. These vectors are generated from one of a finite number of Dirichlet mixture components each with different hyperparameters. Observed samples are generated through multinomial sampling. The mixture components cluster communities into distinct 'metacommunities', and, hence, determine envirotypes or enterotypes, groups of communities with a similar composition. The model can also deduce the impact of a treatment and be used for classification. We wrote software for the fitting of DMM models using the 'evidence framework' (http://code.google.com/p/microbedmm/). This includes the Laplace approximation of the model evidence. We applied the DMM model to human gut microbe genera frequencies from Obese and Lean twins. From the model evidence four clusters fit this data best. Two clusters were dominated by Bacteroides and were homogenous; two had a more variable community composition. We could not find a significant impact of body mass on community structure. However, Obese twins were more likely to derive from the high variance clusters. We propose that obesity is not associated with a distinct microbiota but increases the chance that an individual derives from a disturbed enterotype. This is an example of the 'Anna Karenina principle (AKP)' applied to microbial communities: disturbed states having many more configurations than undisturbed. We verify this by showing that in a study of inflammatory bowel disease (IBD) phenotypes, ileal Crohn's disease (ICD) is associated with a more variable community.     

Advanced microbial analysis for wastewater quality monitoring: metagenomics trend

Applied Microbiology and Biotechnology - Urban Wastewater treatment plants (UWWTPs) have played an important and fundamental role in society for water purification of contaminated human wastewaters...     

Seasonal shifts in Fe-acquisition strategies in Southern Ocean microbial communities revealed by metagenomics and autonomous sampling

Iron (Fe) governs the cycling of organic carbon in large parts of the Southern Ocean. The strategies of diverse microbes to acquire the different chemical forms of Fe under seasonally changing organic carbon regimes remain, however, poorly understood. Here, we report high-resolution seasonal metagenomic observations from the region off Kerguelen Island (Indian Sector of the Southern Ocean) where natural Fe-fertilization induces consecutive spring and summer phytoplankton blooms. Our data illustrate pronounced, but distinct seasonal patterns in the abundance of genes implicated in the transport of different forms of Fe and organic substrates, of siderophore biosynthesis and carbohydrate-active enzymes. The seasonal dynamics suggest a temporal decoupling in the prokaryotic requirements of Fe and organic carbon during the spring phytoplankton bloom and a concerted access to these resources after the summer bloom. Taxonomic assignments revealed differences in the prokaryotic groups harbouring genes of a given Fe-related category and pronounced seasonal successions were observed. Using MAGs we could decipher the respective Fe- and organic substrate-related genes of individual taxa assigned to abundant groups. The ecological strategies related to Fe-acquisition provide insights on how this element could shape microbial community composition with potential implications on organic matter transformations in the Southern Ocean.     

Applications of functional gene microarrays for profiling microbial communities

Functional gene arrays (FGAs) have been considered as a specific, sensitive, quantitative, and high throughput metagenomic tool to detect, monitor and characterize microbial communities. Especially GeoChips, the most comprehensive FGAs have been applied to analyze the functional diversity, composition, structure, and metabolic potential or activity of a variety of microbial communities from different habitats, such as aquatic ecosystems, soils, contaminated sites, extreme environments, and bioreactors. FGAs are able to address fundamental questions related to global change, bioremediation, land use, human health, and ecological theories, and link the microbial community structure to environmental properties and ecosystem functioning. This review focuses on applications of FGA technology for profiling microbial communities, including target preparation, hybridization and data processing, and data analysis. We also discuss challenges and future directions of FGA applications.     

Phylogenetic detection of conserved gene clusters in microbial genomes

Background  

Microbial genomes contain an abundance of genes with conserved proximity forming clusters on the chromosome. However, the conservation can be a result of many factors such as vertical inheritance, or functional selection. Thus, identification of conserved gene clusters that are under functional selection provides an effective channel for gene annotation, microarray screening, and pathway reconstruction. The problem of devising a robust method to identify these conserved gene clusters and to evaluate the significance of the conservation in multiple genomes has a number of implications for comparative, evolutionary and functional genomics as well as synthetic biology.     

A network approach to elucidate and prioritize microbial dark matter in microbial communities

Microbes compose most of the biomass on the planet, yet the majority of taxa remain uncharacterized. These unknown microbes, often referred to as “microbial dark matter,” represent a major challenge for biology. To understand the ecological contributions of these Unknown taxa, it is essential to first understand the relationship between unknown species, neighboring microbes, and their respective environment. Here, we establish a method to study the ecological significance of “microbial dark matter” by building microbial co-occurrence networks from publicly available 16S rRNA gene sequencing data of four extreme aquatic habitats. For each environment, we constructed networks including and excluding unknown organisms at multiple taxonomic levels and used network centrality measures to quantitatively compare networks. When the Unknown taxa were excluded from the networks, a significant reduction in degree and betweenness was observed for all environments. Strikingly, Unknown taxa occurred as top hubs in all environments, suggesting that “microbial dark matter” play necessary ecological roles within their respective communities. In addition, novel adaptation-related genes were detected after using 16S rRNA gene sequences from top-scoring hub taxa as probes to blast metagenome databases. This work demonstrates the broad applicability of network metrics to identify and prioritize key Unknown taxa and improve understanding of ecosystem structure across diverse habitats.Subject terms: Microbial ecology, Metagenomics     

Nested PCR-denaturing gradient gel electrophoresis approach to determine the diversity of sulfate-reducing bacteria in complex microbial communities

Here, we describe a three-step nested-PCR-denaturing gradient gel electrophoresis (DGGE) strategy to detect sulfate-reducing bacteria (SRB) in complex microbial communities from industrial bioreactors. In the first step, the nearly complete 16S rRNA gene was amplified using bacterial primers. Subsequently, this product was used as a template in a second PCR with group-specific SRB primers. A third round of amplification was conducted to obtain fragments suitable for DGGE. The largest number of bands was observed in DGGE patterns of products obtained with primers specific for the Desulfovibrio-Desulfomicrobium group, indicating a large diversity of these SRBs. In addition, members of other phylogenetic SRB groups, i.e., Desulfotomaculum, Desulfobulbus, and Desulfococcus-Desulfonema-Desulfosarcina, were detected. Bands corresponding to Desulfobacterium and Desulfobacter were not detected in the bioreactor samples. Comparative sequence analysis of excised DGGE bands revealed the identity of the community members. The developed three-step PCR-DGGE strategy is a welcome tool for studying the diversity of sulfate-reducing bacteria.     

Identification of specific corrinoids reveals corrinoid modification in dechlorinating microbial communities

Cobalamin and other corrinoids are essential cofactors for many organisms. The majority of microbes with corrinoid‐dependent enzymes do not produce corrinoids de novo, and instead must acquire corrinoids produced by other organisms in their environment. However, the profile of corrinoids produced in corrinoid‐dependent microbial communities, as well as the exchange and modification of corrinoids among community members have not been well studied. In this study, we applied a newly developed liquid chromatography tandem mass spectrometry‐based corrinoid detection method to examine relationships among corrinoids, their lower ligand bases and specific microbial groups in microbial communities containing Dehalococcoides mccartyi that has an obligate requirement for benzimidazole‐containing corrinoids for trichloroethene respiration. We found that p‐cresolylcobamide ([p‐Cre]Cba) and cobalamin were the most abundant corrinoids in the communities. It suggests that members of the family Veillonellaceae are associated with the production of [p‐Cre]Cba. The decrease of supernatant‐associated [p‐Cre]Cba and the increase of biomass‐associated cobalamin were correlated with the growth of D. mccartyi by dechlorination. This supports the hypothesis that D. mccartyi is capable of fulfilling its corrinoid requirements in a community through corrinoid remodelling, in this case, by importing extracellular [p‐Cre]Cba and 5,6‐dimethylbenzimidazole (DMB) (the lower ligand of cobalamin), to produce cobalamin as a cofactor for dechlorination. This study also highlights the role of DMB, the lower ligand produced in all of the studied communities, in corrinoid remodelling. These findings provide novel insights on roles played by different phylogenetic groups in corrinoid production and corrinoid exchange within microbial communities. This study may also have implications for optimizing chlorinated solvent bioremediation.     

Functional differences between Arctic seawater and sedimentary microbial communities: contrasts in microbial hydrolysis of complex substrates

The activities and structural specificities of extracellular enzymes that initiate microbial remineralization of high-molecular-weight (MW) organic matter were investigated in surface waters and sediments of an Arctic fjord of Svalbard. Hydrolysis rates of a suite of fluorescently labeled macromolecular substrates, including seven commercially available polysaccharides and three high-carbohydrate-content plankton extracts ranged from rapid to not detectable, and differed markedly between seawater and sediments. Order (fastest to slowest) of hydrolysis in surface water was laminarin, Spirulina extract, xylan>chondroitin, alginic acid, Wakame extract>arabinogalactan, fucoidan>Isochrysis extract>pullulan, while in sediments the order was pullulan, laminarin, alginic acid, Wakame extract>chondroitin, xylan>arabinogalactan, Isochrysis extract>Spirulina extract>fucoidan. These differences cannot be explained by simple scaling factors such as differences in microbial cell numbers between seawater and sediments. Other investigations have shown that microbial community composition of Svalbard sediments and of polar bacterioplankton samples differ markedly. These results demonstrate that sedimentary and seawater microbial communities also differ fundamentally in their abilities to access specific high-MW substrates. Substrate bioavailability depends on the capabilities of a microbial community, as well as the chemical and structural features of the substrate itself.     

UniFrac: a new phylogenetic method for comparing microbial communities

We introduce here a new method for computing differences between microbial communities based on phylogenetic information. This method, UniFrac, measures the phylogenetic distance between sets of taxa in a phylogenetic tree as the fraction of the branch length of the tree that leads to descendants from either one environment or the other, but not both. UniFrac can be used to determine whether communities are significantly different, to compare many communities simultaneously using clustering and ordination techniques, and to measure the relative contributions of different factors, such as chemistry and geography, to similarities between samples. We demonstrate the utility of UniFrac by applying it to published 16S rRNA gene libraries from cultured isolates and environmental clones of bacteria in marine sediment, water, and ice. Our results reveal that (i) cultured isolates from ice, water, and sediment resemble each other and environmental clone sequences from sea ice, but not environmental clone sequences from sediment and water; (ii) the geographical location does not correlate strongly with bacterial community differences in ice and sediment from the Arctic and Antarctic; and (iii) bacterial communities differ between terrestrially impacted seawater (whether polar or temperate) and warm oligotrophic seawater, whereas those in individual seawater samples are not more similar to each other than to those in sediment or ice samples. These results illustrate that UniFrac provides a new way of characterizing microbial communities, using the wealth of environmental rRNA sequences, and allows quantitative insight into the factors that underlie the distribution of lineages among environments.