The Earth Microbiome Project: The Meeting Report for the 1st International Earth Microbiome Project Conference, Shenzhen, China, June 13th-15th 2011

A practical guide to sample preservation and pre-PCR processing of aquatic environmental DNA

Environmental DNA (eDNA) is rapidly growing in popularity as a tool for community assessments and species detection. While eDNA approaches are now widely applied, there is not yet agreement on best practices for sample collection and processing. Investigators looking to integrate eDNA approaches into their research programme are required to examine a growing collection of disparate studies to make an often uncertain decision about which protocols best fit their needs. To promote the application of eDNA approaches and to encourage the generation of high-quality data, here we review the most common techniques for the collection, preservation and extraction of metazoan eDNA from water samples. Specifically, we focus on experimental studies that compare various methods and outline the numerous challenges associated with eDNA. While the diverse applications of eDNA do not lend themselves to a one-size-fits-all recommendation, in most cases, capture/concentration of eDNA on cellulose nitrate filters (with pore size determined by water turbidity), followed by storage of filters in Longmire's buffer and extraction with a DNeasy Blood & Tissue Kit (or similar) has been shown to provide sufficient, high-quality DNA. However, we also emphasize the importance of testing and optimizing protocols for the system of interest.

Comparison of two DNA extraction methods widely used in aquatic microbial ecology

In recent years, the rapid advances of culture-independent methods and new molecular tools have revolutionized our understanding of microbial biodiversity and ecological functions. DNA extraction from microbial communities is a critical step in this process and several methods have been proposed and used, but the influence of the extraction method on the outcome and ultimately on ecological inferences from the results is not yet precisely determined. Here, we compared two of the most commonly used extraction methods in aquatic microbial ecology, and investigated whether the two methods yielded comparable results for community ecology analyses. We extracted DNA from 15 different shallow lakes with phenol:chloroform, a classical and widely used extraction method, and with the PowerSoil DNA isolation Kit, often suggested as the standard DNA extraction method, with some adaptations for aquatic environments. We found that although only 5% of all OTUs showed significant differences in pairwise comparisons (using the 15 lakes as replicates), these OTUs accounted for >35% (on average) of the relative abundance. Diversity and richness did not differ significantly between the two extraction methods, but the beta-dispersion of the communities indicated that the organic extraction yielded more homogeneous communities, while the kit extraction generated variability. Consequently, we conclude that despite the small number of OTUs with significant differences, their impact on the community composition obtained was not negligible, and therefore the results from these two extraction methods were not comparable.

Distinct Oceanic Microbiomes From Viruses to Protists Located Near the Antarctic Circumpolar Current

Microbes occupy diverse ecological niches and only through recent advances in next generation sequencing technologies have the true microbial diversity been revealed. Furthermore, lack of perceivable marine barriers to genetic dispersal (i.e., mountains or islands) has allowed the speculation that organisms that can be easily transported by currents and therefore proliferate everywhere. That said, ocean currents are now commonly being recognized as barriers for microbial dispersal. Here we analyzed samples collected from a total of six stations, four located in the Indian Ocean, and two in the Southern Ocean. Amplicon sequencing was used to characterize both prokaryotic and eukaryotic plankton communities, while shotgun sequencing was used for the combined environmental DNA (eDNA), microbial eDNA (meDNA), and viral fractions. We found that Cyanobacteria dominated the prokaryotic component in the South-West Indian Ocean, while γ-Proteobacteria dominated the South-East Indian Ocean. A combination of γ- and α-Proteobacteria dominated the Southern Ocean. Alveolates dominated almost exclusively the eukaryotic component, with variation in the ratio of Protoalveolata and Dinoflagellata depending on station. However, an increase in haptophyte relative abundance was observed in the Southern Ocean. Similarly, the viral fraction was dominated by members of the order Caudovirales across all stations; however, a higher presence of nucleocytoplasmic large DNA viruses (mainly chloroviruses and mimiviruses) was observed in the Southern Ocean. To our knowledge, this is the first that a statistical difference in the microbiome (from viruses to protists) between the subtropical Indian and Southern Oceans. We also show that not all phylotypes can be found everywhere, and that meDNA is not a suitable resource for monitoring aquatic microbial diversity.

A microbial survey of the International Space Station (ISS)

BACKGROUND

Modern advances in sequencing technology have enabled the census of microbial members of many natural ecosystems. Recently, attention is increasingly being paid to the microbial residents of human-made, built ecosystems, both private (homes) and public (subways, office buildings, and hospitals). Here, we report results of the characterization of the microbial ecology of a singular built environment, the International Space Station (ISS). This ISS sampling involved the collection and microbial analysis (via 16S rDNA PCR) of 15 surfaces sampled by swabs onboard the ISS. This sampling was a component of Project MERCCURI (Microbial Ecology Research Combining Citizen and University Researchers on ISS). Learning more about the microbial inhabitants of the "buildings" in which we travel through space will take on increasing importance, as plans for human exploration continue, with the possibility of colonization of other planets and moons.

RESULTS

Sterile swabs were used to sample 15 surfaces onboard the ISS. The sites sampled were designed to be analogous to samples collected for (1) the Wildlife of Our Homes project and (2) a study of cell phones and shoes that were concurrently being collected for another component of Project MERCCURI. Sequencing of the 16S rDNA genes amplified from DNA extracted from each swab was used to produce a census of the microbes present on each surface sampled. We compared the microbes found on the ISS swabs to those from both homes on Earth and data from the Human Microbiome Project.

CONCLUSIONS

While significantly different from homes on Earth and the Human Microbiome Project samples analyzed here, the microbial community composition on the ISS was more similar to home surfaces than to the human microbiome samples. The ISS surfaces are species-rich with 1,036-4,294 operational taxonomic units (OTUs per sample). There was no discernible biogeography of microbes on the 15 ISS surfaces, although this may be a reflection of the small sample size we were able to obtain.

Comprehensive Meta-analysis of Ontology Annotated 16S rRNA Profiles Identifies Beta Diversity Clusters of Environmental Bacterial Communities

Comprehensive mapping of environmental microbiomes in terms of their compositional features remains a great challenge in understanding the microbial biosphere of the Earth. It bears promise to identify the driving forces behind the observed community patterns and whether community assembly happens deterministically. Advances in Next Generation Sequencing allow large community profiling studies, exceeding sequencing data output of conventional methods in scale by orders of magnitude. However, appropriate collection systems are still in a nascent state. We here present a database of 20,427 diverse environmental 16S rRNA profiles from 2,426 independent studies, which forms the foundation of our meta-analysis. We conducted a sample size adaptive all-against-all beta diversity comparison while also respecting phylogenetic relationships of Operational Taxonomic Units(OTUs). After conventional hierarchical clustering we systematically test for enrichment of Environmental Ontology terms and their abstractions in all possible clusters. This post-hoc algorithm provides a novel formalism that quantifies to what extend compositional and semantic similarity of microbial community samples coincide. We automatically visualize significantly enriched subclusters on a comprehensive dendrogram of microbial communities. As a result we obtain the hitherto most differentiated and comprehensive view on global patterns of microbial community diversity. We observe strong clusterability of microbial communities in ecosystems such as human/mammal-associated, geothermal, fresh water, plant-associated, soils and rhizosphere microbiomes, whereas hypersaline and anthropogenic samples are less homogeneous. Moreover, saline samples appear less cohesive in terms of compositional properties than previously reported.

Tentacle: distributed quantification of genes in metagenomes

Background

In metagenomics, microbial communities are sequenced at increasingly high resolution, generating datasets with billions of DNA fragments. Novel methods that can efficiently process the growing volumes of sequence data are necessary for the accurate analysis and interpretation of existing and upcoming metagenomes.

Findings

Here we present Tentacle, which is a novel framework that uses distributed computational resources for gene quantification in metagenomes. Tentacle is implemented using a dynamic master-worker approach in which DNA fragments are streamed via a network and processed in parallel on worker nodes. Tentacle is modular, extensible, and comes with support for six commonly used sequence aligners. It is easy to adapt Tentacle to different applications in metagenomics and easy to integrate into existing workflows.

Conclusions

Evaluations show that Tentacle scales very well with increasing computing resources. We illustrate the versatility of Tentacle on three different use cases. Tentacle is written for Linux in Python 2.7 and is published as open source under the GNU General Public License (v3). Documentation, tutorials, installation instructions, and the source code are freely available online at: http://bioinformatics.math.chalmers.se/tentacle.

Electronic supplementary material

The online version of this article (doi:10.1186/s13742-015-0078-1) contains supplementary material, which is available to authorized users.

A systematic evaluation of high-dimensional, ensemble-based regression for exploring large model spaces in microbiome analyses

BACKGROUND

Microbiome studies incorporate next-generation sequencing to obtain profiles of microbial communities. Data generated from these experiments are high-dimensional with a rich correlation structure but modest sample sizes. A statistical model that utilizes these microbiome profiles to explain a clinical or biological endpoint needs to tackle high-dimensionality resulting from the very large space of variable configurations. Ensemble models are a class of approaches that can address high-dimensionality by aggregating information across large model spaces. Although such models are popular in fields as diverse as economics and genetics, their performance on microbiome data has been largely unexplored.

RESULTS

We developed a simulation framework that accurately captures the constraints of experimental microbiome data. Using this setup, we systematically evaluated a selection of both frequentist and Bayesian regression modeling ensembles. These are represented by variants of stability selection in conjunction with elastic net and spike-and-slab Bayesian model averaging (BMA), respectively. BMA ensembles that explore a larger space of models relative to stability selection variants performed better and had lower variability across simulations. However, stability selection ensembles were able to match the performance of BMA in scenarios of low sparsity where several variables had large regression coefficients.

CONCLUSIONS

Given a microbiome dataset of interest, we present a methodology to generate simulated data that closely mimics its characteristics in a manner that enables meaningful evaluation of analytical strategies. Our evaluation demonstrates that the largest ensembles yield the strongest performance on microbiome data with modest sample sizes and high-dimensional measurements. We also demonstrate the ability of these ensembles to identify microbiome signatures that are associated with opportunistic Candida albicans colonization during antibiotic exposure. As the focus of microbiome research evolves from pilot to translational studies, we anticipate that our strategy will aid investigators in making evaluation-based decisions for selecting appropriate analytical methods.

The environment ontology: contextualising biological and biomedical entities

As biological and biomedical research increasingly reference the environmental context of the biological entities under study, the need for formalisation and standardisation of environment descriptors is growing. The Environment Ontology (ENVO; http://www.environmentontology.org) is a community-led, open project which seeks to provide an ontology for specifying a wide range of environments relevant to multiple life science disciplines and, through an open participation model, to accommodate the terminological requirements of all those needing to annotate data using ontology classes. This paper summarises ENVO’s motivation, content, structure, adoption, and governance approach. The ontology is available from http://purl.obolibrary.org/obo/envo.owl - an OBO format version is also available by switching the file suffix to “obo”.

Genomics in marine monitoring: new opportunities for assessing marine health status

This viewpoint paper explores the potential of genomics technology to provide accurate, rapid, and cost efficient observations of the marine environment. The use of such approaches in next generation marine monitoring programs will help achieve the goals of marine legislation implemented world-wide. Genomic methods can yield faster results from monitoring, easier and more reliable taxonomic identification, as well as quicker and better assessment of the environmental status of marine waters. A summary of genomic methods that are ready or show high potential for integration into existing monitoring programs is provided (e.g. qPCR, SNP based methods, DNA barcoding, microarrays, metagenetics, metagenomics, transcriptomics). These approaches are mapped to existing indicators and descriptors and a series of case studies is presented to assess the cost and added value of these molecular techniques in comparison with traditional monitoring systems. Finally, guidelines and recommendations are suggested for how such methods can enter marine monitoring programs in a standardized manner.

A novel method to discover fluoroquinolone antibiotic resistance (qnr) genes in fragmented nucleotide sequences

BACKGROUND

Broad-spectrum fluoroquinolone antibiotics are central in modern health care and are used to treat and prevent a wide range of bacterial infections. The recently discovered qnr genes provide a mechanism of resistance with the potential to rapidly spread between bacteria using horizontal gene transfer. As for many antibiotic resistance genes present in pathogens today, qnr genes are hypothesized to originate from environmental bacteria. The vast amount of data generated by shotgun metagenomics can therefore be used to explore the diversity of qnr genes in more detail.

RESULTS

In this paper we describe a new method to identify qnr genes in nucleotide sequence data. We show, using cross-validation, that the method has a high statistical power of correctly classifying sequences from novel classes of qnr genes, even for fragments as short as 100 nucleotides. Based on sequences from public repositories, the method was able to identify all previously reported plasmid-mediated qnr genes. In addition, several fragments from novel putative qnr genes were identified in metagenomes. The method was also able to annotate 39 chromosomal variants of which 11 have previously not been reported in literature.

CONCLUSIONS

The method described in this paper significantly improves the sensitivity and specificity of identification and annotation of qnr genes in nucleotide sequence data. The predicted novel putative qnr genes in the metagenomic data support the hypothesis of a large and uncharacterized diversity within this family of resistance genes in environmental bacterial communities. An implementation of the method is freely available at http://bioinformatics.math.chalmers.se/qnr/.

Report of the 13(th) Genomic Standards Consortium Meeting, Shenzhen, China, March 4-7, 2012

This report details the outcome of the 13(th) Meeting of the Genomic Standards Consortium. The three-day conference was held at the Kingkey Palace Hotel, Shenzhen, China, on March 5-7, 2012, and was hosted by the Beijing Genomics Institute. The meeting, titled From Genomes to Interactions to Communities to Models, highlighted the role of data standards associated with genomic, metagenomic, and amplicon sequence data and the contextual information associated with the sample. To this end the meeting focused on genomic projects for animals, plants, fungi, and viruses; metagenomic studies in host-microbe interactions; and the dynamics of microbial communities. In addition, the meeting hosted a Genomic Observatories Network session, a Genomic Standards Consortium biodiversity working group session, and a Microbiology of the Built Environment session sponsored by the Alfred P. Sloan Foundation.