Katherine P. Lemon

BACKGROUND: To understand the relationship between our bacterial microbiome and health, it is essential to define the microbiome in the absence of disease. The digestive tract includes diverse habitats and hosts the human body's greatest bacterial density. We describe the bacterial community composition of ten digestive tract sites from more than 200 normal adults enrolled in the Human Microbiome Project, and metagenomically determined metabolic potentials of four representative sites. RESULTS: The microbiota of these diverse habitats formed four groups based on similar community compositions: buccal mucosa, keratinized gingiva, hard palate; saliva, tongue, tonsils, throat; sub- and supra-gingival plaques; and stool. Phyla initially identified from environmental samples were detected throughout this population, primarily TM7, SR1, and Synergistetes. Genera with pathogenic members were well-represented among this disease-free cohort. Tooth-associated communities were distinct, but not entirely dissimilar, from other oral surfaces. The Porphyromonadaceae, Veillonellaceae and Lachnospiraceae families were common to all sites, but the distributions of their genera varied significantly. Most metabolic processes were distributed widely throughout the digestive tract microbiota, with variations in metagenomic abundance between body habitats. These included shifts in sugar transporter types between the supragingival plaque, other oral surfaces, and stool; hydrogen and hydrogen sulfide production were also differentially distributed. CONCLUSIONS: The microbiomes of ten digestive tract sites separated into four types based on composition. A core set of metabolic pathways was present across these diverse digestive tract habitats. These data provide a critical baseline for future studies investigating local and systemic diseases affecting human health.

The eHOMD (http://www.ehomd.org) is a valuable resource for researchers, from basic to clinical, who study the microbiomes and the individual microbes in body sites in the human aerodigestive tract, which includes the nasal passages, sinuses, throat, esophagus, and mouth, and the lower respiratory tract, in health and disease. The eHOMD is an actively curated, web-based, open-access resource. eHOMD provides the following: (i) species-level taxonomy based on grouping 16S rRNA gene sequences at 98.5% identity, (ii) a systematic naming scheme for unnamed and/or uncultivated microbial taxa, (iii) reference genomes to facilitate metagenomic, metatranscriptomic, and proteomic studies and (iv) convenient cross-links to other databases (e.g., PubMed and Entrez). By facilitating the assignment of species names to sequences, the eHOMD is a vital resource for enhancing the clinical relevance of 16S rRNA gene-based microbiome studies, as well as metagenomic studies.

Community composition within the human microbiome varies across individuals, but it remains unknown if this variation is sufficient to uniquely identify individuals within large populations or stable enough to identify them over time. We investigated this by developing a hitting set-based coding algorithm and applying it to the Human Microbiome Project population. Our approach defined body site-specific metagenomic codes: sets of microbial taxa or genes prioritized to uniquely and stably identify individuals. Codes capturing strain variation in clade-specific marker genes were able to distinguish among 100s of individuals at an initial sampling time point. In comparisons with follow-up samples collected 30-300 d later, ∼30% of individuals could still be uniquely pinpointed using metagenomic codes from a typical body site; coincidental (false positive) matches were rare. Codes based on the gut microbiome were exceptionally stable and pinpointed >80% of individuals. The failure of a code to match its owner at a later time point was largely explained by the loss of specific microbial strains (at current limits of detection) and was only weakly associated with the length of the sampling interval. In addition to highlighting patterns of temporal variation in the ecology of the human microbiome, this work demonstrates the feasibility of microbiome-based identifiability-a result with important ethical implications for microbiome study design. The datasets and code used in this work are available for download from huttenhower.sph.harvard.edu/idability.

Two general models have been proposed for DNA replication. In one model, DNA polymerase moves along the DNA (like a train on a track); in the other model, the polymerase is stationary (like a factory), and DNA is pulled through. To distinguish between these models, we visualized DNA polymerase of the bacterium Bacillus subtilis in living cells by the creation of a fusion protein containing the catalytic subunit (PolC) and green fluorescent protein (GFP). PolC-GFP was localized at discrete intracellular positions, predominantly at or near midcell, rather than being distributed randomly. These results suggest that the polymerase is anchored in place and thus support the model in which the DNA template moves through the polymerase.