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.

Microbial communities are at the heart of all ecosystems, and yet microbial community behavior in disturbed environments remains difficult to measure and predict. Understanding the drivers of microbial community stability, including resistance (insensitivity to disturbance) and resilience (the rate of recovery after disturbance) is important for predicting community response to disturbance. Here, we provide an overview of the concepts of stability that are relevant for microbial communities. First, we highlight insights from ecology that are useful for defining and measuring stability. To determine whether general disturbance responses exist for microbial communities, we next examine representative studies from the literature that investigated community responses to press (long-term) and pulse (short-term) disturbances in a variety of habitats. Then we discuss the biological features of individual microorganisms, of microbial populations, and of microbial communities that may govern overall community stability. We conclude with thoughts about the unique insights that systems perspectives – informed by meta-omics data – may provide about microbial community stability.

Although natural selection appears to favor the elimination of gene redundancy in prokaryotes, multiple copies of each rRNA-encoding gene are common on bacterial chromosomes. Despite this conspicuous deviation from single-copy genes, no phenotype has been consistently associated with rRNA gene copy number. We found that the number of rRNA genes correlates with the rate at which phylogenetically diverse bacteria respond to resource availability. Soil bacteria that formed colonies rapidly upon exposure to a nutritionally complex medium contained an average of 5.5 copies of the small subunit rRNA gene, whereas bacteria that responded slowly contained an average of 1.4 copies. In soil microcosms pulsed with the herbicide 2,4-dichlorophenoxyacetic acid (2,4-D), indigenous populations of 2,4-D-degrading bacteria with multiple rRNA genes (x ‫؍‬ 5.4) became dominant, whereas populations with fewer rRNA genes (x ‫؍‬ 2.7) were favored in unamended controls. These findings demonstrate phenotypic effects associated with rRNA gene copy number that are indicative of ecological strategies influencing the structure of natural microbial communities.Genes encoding the 5S, 16S, and 23S rRNAs are typically organized into an operon in members of the domain Bacteria. The copy number of rRNA operons per bacterial genome varies from 1 to as many as 15 (28). For example, the pathogenic bacteria Rickettsia prowazekii (2) and Mycoplasma pneumoniae (4) have one rRNA operon, while the enteric bacteria Escherichia coli (12) and Salmonella enterica serovar Typhimurium (1) each possess seven copies per genome. The greatest number of rRNA operons per genome known can be found among spore-forming bacteria isolated from soil; Bacillus subtilis (23) and Clostridium paradoxum (28) possess 10 and 15 copies, respectively. Several hypotheses have been proposed to explain the wide variation observed in rRNA operon copy number.It is generally assumed that multiple copies of rRNA operons in prokaryotic organisms are required to achieve high growth rates. However, the short doubling time observed for certain bacteria with a single rRNA operon (37) and the marginal impact of rRNA operon inactivation on maximal growth rate (8,27) suggest that the capacity for rapid growth is not the sole determinant of rRNA operon copy number. The number of transcripts that can be initiated at an rRNA operon promoter and the transcriptional rate of RNA polymerase set a maximum rate on the number of ribosomes that can be produced from a single rRNA operon. Calculations including promoter initiation efficiency and transcription rates indicate that one copy of the rRNA operon is insufficient to supply the number of ribosomes required to achieve maximal growth rates observed in E. coli (5).Given the high demand for rRNA transcription and the central role of rRNAs in the regulation of ribosome synthesis, it is conceivable that the number of rRNA operons may dictate the rapidity with which microbes can synthesize ribosomes and respond to favorable changes in growth conditions (8,...

Rationale: Results from 16S rDNA-encoding gene sequence-based, culture-independent techniques have led to conflicting conclusions about the composition of the lower respiratory tract microbiome. Objectives: To compare the microbiome of the upper and lower respiratory tract in healthy HIV-uninfected nonsmokers and smokers in a multicenter cohort. Methods: Participants were nonsmokers and smokers without significant comorbidities. Oral washes and bronchoscopic alveolar lavages were collected in a standardized manner. Sequence analysis of bacterial 16S rRNA-encoding genes was performed, and the neutral model in community ecology was used to identify bacteria that were the most plausible members of a lung microbiome. Measurements and Main Results: Sixty-four participants were enrolled. Most bacteria identified in the lung were also in the mouth, but specific bacteria such as Enterobacteriaceae, Haemophilus, Methylobacterium, and Ralstonia species were disproportionally represented in the lungs compared with values predicted by the neutral model. Tropheryma was also in the lung, but not the mouth. Mouth communities differed between nonsmokers and smokers in species such as Porphyromonas, Neisseria, and Gemella, but lung bacterial populations did not. Conclusions: This study is the largest to examine composition of the lower respiratory tract microbiome in healthy individuals and the first to use the neutral model to compare the lung to the mouth. Specific bacteria appear in significantly higher abundance in the lungs than would be expected if they originated from the mouth, demonstrating that the lung microbiome does not derive entirely from the mouth. The mouth microbiome differs in nonsmokers and smokers, but lung communities were not significantly altered by smoking.