The abundance of different SSU rRNA ("16S") gene sequences in environmental samples is widely used in studies of microbial ecology as a measure of microbial community structure and diversity. However, the genomic copy number of the 16S gene varies greatly - from one in many species to up to 15 in some bacteria and to hundreds in some microbial eukaryotes. As a result of this variation the relative abundance of 16S genes in environmental samples can be attributed both to variation in the relative abundance of different organisms, and to variation in genomic 16S copy number among those organisms. Despite this fact, many studies assume that the abundance of 16S gene sequences is a surrogate measure of the relative abundance of the organisms containing those sequences. Here we present a method that uses data on sequences and genomic copy number of 16S genes along with phylogenetic placement and ancestral state estimation to estimate organismal abundances from environmental DNA sequence data. We use theory and simulations to demonstrate that 16S genomic copy number can be accurately estimated from the short reads typically obtained from high-throughput environmental sequencing of the 16S gene, and that organismal abundances in microbial communities are more strongly correlated with estimated abundances obtained from our method than with gene abundances. We re-analyze several published empirical data sets and demonstrate that the use of gene abundance versus estimated organismal abundance can lead to different inferences about community diversity and structure and the identity of the dominant taxa in microbial communities. Our approach will allow microbial ecologists to make more accurate inferences about microbial diversity and abundance based on 16S sequence data.

Phylogenetic diversity--patterns of phylogenetic relatedness among organisms in ecological communities--provides important insights into the mechanisms underlying community assembly. Studies that measure phylogenetic diversity in microbial communities have primarily been limited to a single marker gene approach, using the small subunit of the rRNA gene (SSU-rRNA) to quantify phylogenetic relationships among microbial taxa. In this study, we present an approach for inferring phylogenetic relationships among microorganisms based on the random metagenomic sequencing of DNA fragments. To overcome challenges caused by the fragmentary nature of metagenomic data, we leveraged fully sequenced bacterial genomes as a scaffold to enable inference of phylogenetic relationships among metagenomic sequences from multiple phylogenetic marker gene families. The resulting metagenomic phylogeny can be used to quantify the phylogenetic diversity of microbial communities based on metagenomic data sets. We applied this method to understand patterns of microbial phylogenetic diversity and community assembly along an oceanic depth gradient, and compared our findings to previous studies of this gradient using SSU-rRNA gene and metagenomic analyses. Bacterial phylogenetic diversity was highest at intermediate depths beneath the ocean surface, whereas taxonomic diversity (diversity measured by binning sequences into taxonomically similar groups) showed no relationship with depth. Phylogenetic diversity estimates based on the SSU-rRNA gene and the multi-gene metagenomic phylogeny were broadly concordant, suggesting that our approach will be applicable to other metagenomic data sets for which corresponding SSU-rRNA gene sequences are unavailable. Our approach opens up the possibility of using metagenomic data to study microbial diversity in a phylogenetic context.

Urban green space provides health benefits for city dwellers, and new evidence suggests that microorganisms associated with soil and vegetation could play a role. While airborne microorganisms are ubiquitous in urban areas, the influence of nearby vegetation on airborne microbial communities remains poorly understood. We examined airborne microbial communities in parks and parking lots in Eugene, Oregon, using high-throughput sequencing of the bacterial 16S rRNA gene on the Illumina MiSeq platform to identify bacterial taxa, and GIS to measure vegetation cover in buffer zones of different diameters. Our goal was to explore variation among highly vegetated (parks) versus non-vegetated (parking lots) urban environments. A secondary objective was to evaluate passive versus active collection methods for outdoor airborne microbial sampling. Airborne bacterial communities from five parks were different from those of five parking lots (p=0.023), although alpha diversity was similar. Direct gradient analysis showed that the proportion of vegetated area within a 50m radius of the sampling station explained 15% of the variation in bacterial community composition. A number of key taxa, including several Acidobacteriaceae were substantially more abundant in parks, while parking lots had higher relative abundance of Acetobacteraceae. Parks had greater beta diversity than parking lots, i.e. individual parks were characterized by unique bacterial signatures, whereas parking lot communities tended to be similar to each other. Although parks and parking lots were selected to form pairs of nearby sites, spatial proximity did not appear to affect compositional similarity. Our results also showed that passive and active collection methods gave comparable results, indicating the "settling dish" method is effective for outdoor airborne sampling. This work sets a foundation for understanding how urban vegetation may impact microbial communities, with potential implications for designing neighborhoods and open space systems that foster better human health.

Plant-associated microorganisms are essential for their hosts' survival and performance. Yet, most plant microbiome studies to date have focused on terrestrial species sampled across relatively small spatial scales. Here, we report the results of a global-scale analysis of microbial communities associated with leaf and root surfaces of the marine eelgrass Zostera marina throughout its range in the Northern Hemisphere. By contrasting host microbiomes with those of surrounding seawater and sediment, we uncovered the structure, composition, and variability of microbial communities associated with eelgrass. We also investigated hypotheses about the assembly of the eelgrass microbiome using a metabolic modeling approach. Our results reveal leaf communities displaying high variability and spatial turnover that mirror their adjacent coastal seawater microbiomes. By contrast, roots showed relatively low compositional turnover and were distinct from surrounding sediment communities, a result driven by the enrichment of predicted sulfur-oxidizing bacterial taxa on root surfaces. Predictions from metabolic modeling of enriched taxa were consistent with a habitat-filtering community assembly mechanism whereby similarity in resource use drives taxonomic cooccurrence patterns on belowground, but not aboveground, host tissues. Our work provides evidence for a core eelgrass root microbiome with putative functional roles and highlights potentially disparate processes influencing microbial community assembly on different plant compartments.IMPORTANCE Plants depend critically on their associated microbiome, yet the structure of microbial communities found on marine plants remains poorly understood in comparison to that for terrestrial species. Seagrasses are the only flowering plants that live entirely in marine environments. The return of terrestrial seagrass ancestors to oceans is among the most extreme habitat shifts documented in plants, making them an ideal testbed for the study of microbial symbioses with plants that experience relatively harsh abiotic conditions. In this study, we report the results of a global sampling effort to extensively characterize the structure of microbial communities associated with the widespread seagrass species Zostera marina, or eelgrass, across its geographic range. Our results reveal major differences in the structure and composition of above- versus belowground microbial communities on eelgrass surfaces, as well as their relationships with the environment and host.

Creative approaches at the interface of ecology, statistics, mathematics, informatics, and computational science are essential for improving our understanding of complex ecological systems. For example, new information technologies, including powerful computers, spatially embedded sensor networks, and Semantic Web tools, are emerging as potentially revolutionary tools for studying ecological phenomena. These technologies can play an important role in developing and testing detailed models that describe real-world systems at multiple scales. Key challenges include choosing the appropriate level of model complexity necessary for understanding biological patterns across space and time, and applying this understanding to solve problems in conservation biology and resource management. Meeting these challenges requires novel statistical and mathematical techniques for distinguishing among alternative ecological theories and hypotheses. Examples from a wide array of research areas in population biology and community ecology highlight the importance of fostering synergistic ties across disciplines for current and future research and application. © 2005 American Institute of Biological Sciences.

Genomic approaches to characterizing bacterial communities are revealing significant differences in diversity and composition between environments. But bacterial distributions have not been mapped at a global scale. Although current community surveys are way too sparse to map global diversity patterns directly, there is now sufficient data to fit accurate models of how bacterial distributions vary across different environments and to make global scale maps from these models. We apply this approach to map the global distributions of bacteria in marine surface waters. Our spatially and temporally explicit predictions suggest that bacterial diversity peaks in temperate latitudes across the world's oceans. These global peaks are seasonal, occurring 6 months apart in the two hemispheres, in the boreal and austral winters. This pattern is quite different from the tropical, seasonally consistent diversity patterns observed for most macroorganisms. However, like other marine organisms, surface water bacteria are particularly diverse in regions of high human environmental impacts on the oceans. Our maps provide the first picture of bacterial distributions at a global scale and suggest important differences between the diversity patterns of bacteria compared with other organisms.

Microbial diversity is typically characterized by clustering ribosomal RNA (SSU-rRNA) sequences into operational taxonomic units (OTUs). Targeted sequencing of environmental SSU-rRNA markers via PCR may fail to detect OTUs due to biases in priming and amplification. Analysis of shotgun sequenced environmental DNA, known as metagenomics, avoids amplification bias but generates fragmentary, non-overlapping sequence reads that cannot be clustered by existing OTU-finding methods. To circumvent these limitations, we developed PhylOTU, a computational workflow that identifies OTUs from metagenomic SSU-rRNA sequence data through the use of phylogenetic principles and probabilistic sequence profiles. Using simulated metagenomic data, we quantified the accuracy with which PhylOTU clusters reads into OTUs. Comparisons of PCR and shotgun sequenced SSU-rRNA markers derived from the global open ocean revealed that while PCR libraries identify more OTUs per sequenced residue, metagenomic libraries recover a greater taxonomic diversity of OTUs. In addition, we discover novel species, genera and families in the metagenomic libraries, including OTUs from phyla missed by analysis of PCR sequences. Taken together, these results suggest that PhylOTU enables characterization of part of the biosphere currently hidden from PCR-based surveys of diversity?

This report details the outcome of the 1st Hospital Microbiome Project workshop held on June 7th-8th, 2012 at the University of Chicago, USA. The workshop was arranged to determine the most appropriate sampling strategy and approach to building science measurement to characterize the development of a microbial community within a new hospital pavilion being built at the University of Chicago Medical Center. The workshop made several recommendations and led to the development of a full proposal to the Alfred P. Sloan Foundation as well as to the creation of the Hospital Microbiome Consortium.

Buildings represent habitats for microorganisms that can have direct or indirect effects on the quality of our living spaces, health, and well-being. Over the last ten years, new research has employed sophisticated tools, including DNA sequencing-based approaches, to study microbes found in buildings and the overall built environment. These investigations have catalyzed new insights into and questions about the microbes that surround us in our daily lives. The emergence of the “microbiology of the built environment” field has required bridging disciplines, including microbiology, ecology, building science, architecture, and engineering. Early insights have included a fuller characterization of sources of microbes within buildings, important processes that structure the distributions and abundances of microbes, and a greater appreciation of the role that occupants can have on indoor microbiology. This ongoing work has also demonstrated that traditional culture- and microscopy-based approaches for studying microbiology vastly underestimate the types and quantity of microbes present in environmental samples. We offer ten questions that highlight important lessons learned regarding the microbiology of buildings and suggest future areas of investigation.

Observations from human microbiome studies are often conflicting or inconclusive. Many factors likely contribute to these issues including small cohort sizes, sample collection, and handling and processing differences. The field of microbiome research is moving from 16S rDNA gene sequencing to a more comprehensive genomic and functional representation through whole-genome sequencing (WGS) of complete communities. Here we performed quantitative and qualitative analyses comparing WGS metagenomic data from human stool specimens using the Illumina Nextera XT and Illumina TruSeq DNA PCR-free kits, and the KAPA Biosystems Hyper Prep PCR and PCR-free systems. Significant differences in taxonomy are observed among the four different next-generation sequencing library preparations using a DNA mock community and a cell control of known concentration. We also revealed biases in error profiles, duplication rates, and loss of reads representing organisms that have a high %G+C content that can significantly impact results. As with all methods, the use of benchmarking controls has revealed critical differences among methods that impact sequencing results and later would impact study interpretation. We recommend that the community adopt PCR-free-based approaches to reduce PCR bias that affects calculations of abundance and to improve assemblies for accurate taxonomic assignment. Furthermore, the inclusion of a known-input cell spike-in control provides accurate quantitation of organisms in clinical samples.