Studies of the human microbiome have revealed that even healthy individuals differ remarkably in the microbes that occupy habitats such as the gut, skin, and vagina. Much of this diversity remains unexplained, although diet, environment, host genetics, and early microbial exposure have all been implicated. Accordingly, to characterize the ecology of human-associated microbial communities, the Human Microbiome Project has analyzed the largest cohort and set of distinct, clinically relevant body habitats to date. We found the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals. The project encountered an estimated 81–99% of the genera, enzyme families, and community configurations occupied by the healthy Western microbiome. Metagenomic carriage of metabolic pathways was stable among individuals despite variation in community structure, and ethnic/racial background proved to be one of the strongest associations of both pathways and microbes with clinical metadata. These results thus delineate the range of structural and functional configurations normal in the microbial communities of a healthy population, enabling future characterization of the epidemiology, ecology, and translational applications of the human microbiome.

A variety of microbial communities and their genes (microbiome) exist throughout the human body, playing fundamental roles in human health and disease. The NIH funded Human Microbiome Project (HMP) Consortium has established a population-scale framework which catalyzed significant development of metagenomic protocols resulting in a broad range of quality-controlled resources and data including standardized methods for creating, processing and interpreting distinct types of high-throughput metagenomic data available to the scientific community. Here we present resources from a population of 242 healthy adults sampled at 15 to 18 body sites up to three times, which to date, have generated 5,177 microbial taxonomic profiles from 16S rRNA genes and over 3.5 Tb of metagenomic sequence. In parallel, approximately 800 human-associated reference genomes have been sequenced. Collectively, these data represent the largest resource to date describing the abundance and variety of the human microbiome, while providing a platform for current and future studies.

BackgroundCharacterizing the biogeography of the microbiome of healthy humans is essential for understanding microbial associated diseases. Previous studies mainly focused on a single body habitat from a limited set of subjects. Here, we analyzed one of the largest microbiome datasets to date and generated a biogeographical map that annotates the biodiversity, spatial relationships, and temporal stability of 22 habitats from 279 healthy humans.ResultsWe identified 929 genera from more than 24 million 16S rRNA gene sequences of 22 habitats, and we provide a baseline of inter-subject variation for healthy adults. The oral habitat has the most stable microbiota with the highest alpha diversity, while the skin and vaginal microbiota are less stable and show lower alpha diversity. The level of biodiversity in one habitat is independent of the biodiversity of other habitats in the same individual. The abundances of a given genus at a body site in which it dominates do not correlate with the abundances at body sites where it is not dominant. Additionally, we observed the human microbiota exhibit both cosmopolitan and endemic features. Finally, comparing datasets of different projects revealed a project-based clustering pattern, emphasizing the significance of standardization of metagenomic studies.ConclusionsThe data presented here extend the definition of the human microbiome by providing a more complete and accurate picture of human microbiome biogeography, addressing questions best answered by a large dataset of subjects and body sites that are deeply sampled by sequencing.

The emergence of viruses such as severe acute respiratory syndrome coronavirus and Nipah virus has underscored the role of animal reservoirs in human disease and the need for reservoir surveillance. Here, we used a panviral DNA microarray to investigate the death of a captive beluga whale in an aquatic park. A highly divergent coronavirus, tentatively named coronavirus SW1, was identified in liver tissue from the deceased whale. Subsequently, the entire genome of SW1 was sequenced, yielding a genome of 31,686 nucleotides. Phylogenetic analysis revealed SW1 to be a novel virus distantly related to but most similar to group III coronaviruses.

Multiple sclerosis (MS) is an inflammatory, demyelinating, and neurodegenerative disease of the central nervous system (CNS) triggered by autoimmune mechanisms. Microglia are critical for the clearance of myelin debris in areas of demyelination, a key step to allow remyelination. TREM2 is expressed by microglia and promotes microglial survival, proliferation, and phagocytic activity. Herein we demonstrate that TREM2 was highly expressed on myelin-laden phagocytes in active demyelinating lesions in the CNS of subjects with MS. In gene expression studies, macrophages from subjects with TREM2 genetic deficiency displayed a defect in phagocytic pathways. Treatment with a new TREM2 agonistic antibody promoted the clearance of myelin debris in the cuprizone model of CNS demyelination. Effects included enhancement of myelin uptake and degradation, resulting in accelerated myelin debris removal by microglia. Most importantly, antibody-dependent TREM2 activation on microglia increased density of oligodendrocyte precursors in areas of demyelination, as well as the formation of mature oligodendrocytes thus enhancing remyelination and axonal integrity. These results are relevant as they propose TREM2 on microglia as a potential new target to promote remyelination.

No abstract

Unexplained fever (UF) is a common problem in children under 3 years old. Although virus infection is suspected to be the cause of most of these fevers, a comprehensive analysis of viruses in samples from children with fever and healthy controls is important for establishing a relationship between viruses and UF. We used unbiased, deep sequencing to analyze 176 nasopharyngeal swabs (NP) and plasma samples from children with UF and afebrile controls, generating an average of 4.6 million sequences per sample. An analysis pipeline was developed to detect viral sequences, which resulted in the identification of sequences from 25 viral genera. These genera included expected pathogens, such as adenoviruses, enteroviruses, and roseoloviruses, plus viruses with unknown pathogenicity. Viruses that were unexpected in NP and plasma samples, such as the astrovirus MLB-2, were also detected. Sequencing allowed identification of virus subtype for some viruses, including roseoloviruses. Highly sensitive PCR assays detected low levels of viruses that were not detected in approximately 5 million sequences, but greater sequencing depth improved sensitivity. On average NP and plasma samples from febrile children contained 1.5- to 5-fold more viral sequences, respectively, than samples from afebrile children. Samples from febrile children contained a broader range of viral genera and contained multiple viral genera more frequently than samples from children without fever. Differences between febrile and afebrile groups were most striking in the plasma samples, where detection of viral sequence may be associated with a disseminated infection. These data indicate that virus infection is associated with UF. Further studies are important in order to establish the range of viral pathogens associated with fever and to understand of the role of viral infection in fever. Ultimately these studies may improve the medical treatment of children with UF by helping avoid antibiotic therapy for children with viral infections.

The Human Microbiome Project will establish a reference data set for analysis of the microbiome of healthy adults by surveying multiple body sites from 300 people and generating data from over 12,000 samples. To characterize these samples, the participating sequencing centers evaluated and adopted 16S rDNA community profiling protocols for ABI 3730 and 454 FLX Titanium sequencing. In the course of establishing protocols, we examined the performance and error characteristics of each technology, and the relationship of sequence error to the utility of 16S rDNA regions for classification- and OTU-based analysis of community structure. The data production protocols used for this work are those used by the participating centers to produce 16S rDNA sequence for the Human Microbiome Project. Thus, these results can be informative for interpreting the large body of clinical 16S rDNA data produced for this project.