Tom O. Delmont

Accessing the soil metagenome for studies of microbial diversity.

ABSTRACT Soil microbial communities contain the highest level of prokaryotic diversity of any environment, and metagenomic approaches involving the extraction of DNA from soil can improve our access to these communities. Most analyses of soil biodiversity and function assume that the DNA extracted represents the microbial community in the soil, but subsequent interpretations are limited by the DNA recovered from the soil. Unfortunately, extraction methods do not provide a uniform and unbiased subsample of metagenomic DNA, and as a consequence, accurate species distributions cannot be determined. Moreover, any bias will propagate errors in estimations of overall microbial diversity and may exclude some microbial classes from study and exploitation. To improve metagenomic approaches, investigate DNA extraction biases, and provide tools for assessing the relative abundances of different groups, we explored the biodiversity of the accessible community DNA by fractioning the metagenomic DNA as a function of (i) vertical soil sampling, (ii) density gradients (cell separation), (iii) cell lysis stringency, and (iv) DNA fragment size distribution. Each fraction had a unique genetic diversity, with different predominant and rare species (based on ribosomal intergenic spacer analysis [RISA] fingerprinting and phylochips). All fractions contributed to the number of bacterial groups uncovered in the metagenome, thus increasing the DNA pool for further applications. Indeed, we were able to access a more genetically diverse proportion of the metagenome (a gain of more than 80% compared to the best single extraction method), limit the predominance of a few genomes, and increase the species richness per sequencing effort. This work stresses the difference between extracted DNA pools and the currently inaccessible complete soil metagenome.

Anvi'o: an advanced analysis and visualization platform for 'omics data.

Advances in high-throughput sequencing and ‘omics technologies are revolutionizing studies of naturally occurring microbial communities. Comprehensive investigations of microbial lifestyles require the ability to interactively organize and visualize genetic information and to incorporate subtle differences that enable greater resolution of complex data. Here we introduce anvi’o, an advanced analysis and visualization platform that offers automated and human-guided characterization of microbial genomes in metagenomic assemblies, with interactive interfaces that can link ‘omics data from multiple sources into a single, intuitive display. Its extensible visualization approach distills multiple dimensions of information about each contig, offering a dynamic and unified work environment for data exploration, manipulation, and reporting. Using anvi’o, we re-analyzed publicly available datasets and explored temporal genomic changes within naturally occurring microbial populations through de novo characterization of single nucleotide variations, and linked cultivar and single-cell genomes with metagenomic and metatranscriptomic data. Anvi’o is an open-source platform that empowers researchers without extensive bioinformatics skills to perform and communicate in-depth analyses on large ‘omics datasets.

Structure, fluctuation and magnitude of a natural grassland soil metagenome.

The soil ecosystem is critical for human health, affecting aspects of the environment from key agricultural and edaphic parameters to critical influence on climate change. Soil has more unknown biodiversity than any other ecosystem. We have applied diverse DNA extraction methods coupled with high throughput pyrosequencing to explore 4.88 × 109u2009bp of metagenomic sequence data from the longest continually studied soil environment (Park Grass experiment at Rothamsted Research in the UK). Results emphasize important DNA extraction biases and unexpectedly low seasonal and vertical soil metagenomic functional class variations. Clustering-based subsystems and carbohydrate metabolism had the largest quantity of annotated reads assigned although <50% of reads were assigned at an E value cutoff of 10−5. In addition, with the more detailed subsystems, cAMP signaling in bacteria (3.24±0.27% of the annotated reads) and the Ton and Tol transport systems (1.69±0.11%) were relatively highly represented. The most highly represented genome from the database was that for a Bradyrhizobium species. The metagenomic variance created by integrating natural and methodological fluctuations represents a global picture of the Rothamsted soil metagenome that can be used for specific questions and future inter-environmental metagenomic comparisons. However, only 1% of annotated sequences correspond to already sequenced genomes at 96% similarity and E values of <10−5, thus, considerable genomic reconstructions efforts still have to be performed.

Large-Scale Metagenomic-Based Study of Antibiotic Resistance in the Environment

Antibiotic resistance, including multiresistance acquisition and dissemination by pathogens, is a critical healthcare issue threatening our management of infectious diseases [1-3]. Rapid accumulation of resistance phenotypes implies a reservoir of transferable antibiotic resistance gene determinants (ARGDs) selected in response to inhibition of antibiotic concentrations, as found in hospitals [1, 3-5]. Antibiotic resistance genes were found in environmental isolates, soil DNA [4-6], secluded caves [6, 7], and permafrost DNA [7, 8]. Antibiotics target essential and ubiquitous cell functions, and resistance is a common characteristic of environmental bacteria [8-11]. Environmental ARGDs are an abundant reservoir of potentially transferable resistance for pathogens [9-12]. Using metagenomic sequences, we show that ARGDs can be detected in all (n=71) environments analyzed. Soil metagenomes had the most diverse pool of ARGDs. The most common types of resistances found in environmental metagenomes were efflux pumps and genes conferring resistance to vancomycin, tetracycline, or β-lactam antibiotics used in veterinary and human healthcare. Our study describes the diverse and abundant antibiotic resistance genes in nonclinical environments and shows that these genes are not randomly distributed among different environments (e.g., soil, oceans or human feces).

Metagenomic comparison of direct and indirect soil DNA extraction approaches

Full pyrosequencing runs of both direct-extracted (high yield, low DNA size) and indirect-extracted DNA (low yield, high DNA size) from the same prairie soil show that the sequence distribution of the majority of the metabolic functions and species detected were statistically similar. Although some microbial functions differed at the 95% confidence interval in bootstrap analyses, the overall functional diversity was the same.

Metagenomic mining for microbiologists

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.

Metagenomic exploration of antibiotic resistance in soil

The ongoing development of metagenomic approaches is providing the means to explore antibiotic resistance in nature and address questions that could not be answered previously with conventional culture-based strategies. The number of available environmental metagenomic sequence datasets is rapidly expanding and henceforth offer the ability to gain a more comprehensive understanding of antibiotic resistance at the global scale. Although there is now evidence that the environment constitutes a vast reservoir of antibiotic resistance gene determinants (ARGDs) and that the majority of ARGDs acquired by human pathogens may have an environmental origin, a better understanding of their diversity, prevalence and ecological significance may help predict the emergence and spreading of newly acquired resistances. Recent applications of metagenomic approaches to the study of ARGDs in natural environments such as soil should help overcome challenges concerning expanding antibiotic resistances.

Microbial community development and unseen diversity recovery in inoculated sterile soil

Soil is considered as one of the most biodiverse environments on Earth; yet, the taxonomy, occurrence, and role of its different microbial populations are largely unknown. Here, two sterilized soils (from England and Italy) were inoculated with a subsample of their initial microbial communities and/or those from the other soil to study their microbial community evolution. This approach compared two driving factors (original community and soil physico-chemical characteristics) for microbial community definition. After 2xa0months of incubation and based on metagenomic datasets, the two inoculated communities (from an English grassland and an Italian forest) possessed similar functional and taxonomical structures when inoculated in the same sterile soil. For example, the newly colonized Italian soil was dominated by Actinobacteria related organisms (>66xa0% of the detected community) with a functional distribution independent of the inoculated soil origin. In addition, some of the organisms that dominated the different inoculated communities after 2xa0months were similar for a given sterile soil whether they came from the English grassland or the Italian forest, and they had not been detected in the original microbial community from either soil. Thus, similar microorganisms with low representation from the two distinct communities emerged in each sterilized soil, thus increasing the microbial diversity recovered from the microbial community of the donor soil. So far, these observations support the idea that different temperate soil microbial communities have different evenness due to environmental physico-chemical variations, yet have similar community composition (richness), and thus develop similarly when colonizing the same habitat.

Life on Human Surfaces: Skin Metagenomics

The human skin microbiome could provide another example, after the gut, of the strong positive or negative impact that human colonizing bacteria can have on health. Deciphering functional diversity and dynamics within human skin microbial communities is critical for understanding their involvement and for developing the appropriate substances for improving or correcting their action. We present a direct PCR-free high throughput sequencing approach to unravel the human skin microbiota specificities through metagenomic dataset analysis and inter-environmental comparison. The approach provided access to the functions carried out by dominant skin colonizing taxa, including Corynebacterium, Staphylococcus and Propionibacterium, revealing their specific capabilities to interact with and exploit compounds from the human skin. These functions, which clearly illustrate the unique life style of the skin microbial communities, stand as invaluable investigation targets for understanding and potentially modifying bacterial interactions with the human host with the objective of increasing health and well being.

Phaeocystis antarctica blooms strongly influence bacterial community structures in the Amundsen Sea polynya

Rising temperatures and changing winds drive the expansion of the highly productive polynyas (open water areas surrounded by sea ice) abutting the Antarctic continent. Phytoplankton blooms in polynyas are often dominated by the haptophyte Phaeocystis antarctica, and they generate the organic carbon that enters the resident microbial food web. Yet, little is known about how Phaeocystis blooms shape bacterial community structures and carbon fluxes in these systems. We identified the bacterial communities that accompanied a Phaeocystis bloom in the Amundsen Sea polynya during the austral summers of 2007–2008 and 2010–2011. These communities are distinct from those determined for the Antarctic Circumpolar Current (ACC) and off the Palmer Peninsula. Diversity patterns for most microbial taxa in the Amundsen Sea depended on location (e.g., waters abutting the pack ice near the shelf break and at the edge of the Dotson glacier) and depth, reflecting different niche adaptations within the confines of this isolated ecosystem. Inside the polynya, P. antarctica coexisted with the bacterial taxa Polaribacter sensu lato, a cryptic Oceanospirillum, SAR92 and Pelagibacter. These taxa were dominated by a single oligotype (genotypes partitioned by Shannon entropy analysis) and together contributed up to 73% of the bacterial community. Size fractionation of the bacterial community [<3 μm (free-living bacteria) vs. >3 μm (particle-associated bacteria)] identified several taxa (especially SAR92) that were preferentially associated with Phaeocystis colonies, indicative of a distinct role in Phaeocystis bloom ecology. In contrast, particle-associated bacteria at 250 m depth were enriched in Colwellia and members of the Cryomorphaceae suggesting that they play important roles in the decay of Phaeocystis blooms.