The human oral cavity is home to a large and diverse community of viruses that have yet to be characterized in patients with periodontal disease. We recruited and sampled saliva and oral biofilm from a cohort of humans either periodontally healthy or with mild or significant periodontal disease to discern whether there are differences in viral communities that reflect their oral health status. We found communities of viruses inhabiting saliva and the subgingival and supragingival biofilms of each subject that were composed largely of bacteriophage. While there were homologous viruses common to different subjects and biogeographic sites, for most of the subjects, virome compositions were significantly associated with the oral sites from which they were derived. The largest distinctions between virome compositions were found when comparing the subgingival and supragingival biofilms to those of planktonic saliva. Differences in virome composition were significantly associated with oral health status for both subgingival and supragingival biofilm viruses but not for salivary viruses. Among the differences identified in virome compositions was a significant expansion of myoviruses in subgingival biofilm, suggesting that periodontal disease favors lytic phage. We also characterized the bacterial communities in each subject at each biogeographic site by using the V3 hypervariable segment of the 16S rRNA and did not identify distinctions between oral health and disease similar to those found in viral communities. The significantly altered ecology of viruses of oral biofilm in subjects with periodontal disease compared to that of relatively periodontally healthy ones suggests that viruses may serve as useful indicators of oral health status.
Little is known about the role or the constituents of viruses as members of the human microbiome. We investigated the composition of human oral viral communities in a group of relatively periodontally healthy subjects or significant periodontitis to determine whether health status may be associated with differences in viruses. We found that most of the viruses present were predators of bacteria. The viruses inhabiting dental plaque were significantly different on the basis of oral health status, while those present in saliva were not. Dental plaque viruses in periodontitis were predicted to be significantly more likely to kill their bacterial hosts than those found in healthy mouths. Because oral diseases such as periodontitis have been shown to have altered bacterial communities, we believe that viruses and their role as drivers of ecosystem diversity are important contributors to the human oral microbiome in health and disease states.
The healthy microbiota show remarkable variability within and among individuals. In addition to external exposures, ecological relationships (both oppositional and symbiotic) between microbial inhabitants are important contributors to this variation. It is thus of interest to assess what relationships might exist among microbes and determine their underlying reasons. The initial Human Microbiome Project (HMP) cohort, comprising 239 individuals and 18 different microbial habitats, provides an unprecedented resource to detect, catalog, and analyze such relationships. Here, we applied an ensemble method based on multiple similarity measures in combination with generalized boosted linear models (GBLMs) to taxonomic marker (16S rRNA gene) profiles of this cohort, resulting in a global network of 3,005 significant co-occurrence and co-exclusion relationships between 197 clades occurring throughout the human microbiome. This network revealed strong niche specialization, with most microbial associations occurring within body sites and a number of accompanying inter-body site relationships. Microbial communities within the oropharynx grouped into three distinct habitats, which themselves showed no direct influence on the composition of the gut microbiota. Conversely, niches such as the vagina demonstrated little to no decomposition into region-specific interactions. Diverse mechanisms underlay individual interactions, with some such as the co-exclusion of Porphyromonaceae family members and Streptococcus in the subgingival plaque supported by known biochemical dependencies. These differences varied among broad phylogenetic groups as well, with the Bacilli and Fusobacteria, for example, both enriched for exclusion of taxa from other clades. Comparing phylogenetic versus functional similarities among bacteria, we show that dominant commensal taxa (such as Prevotellaceae and Bacteroides in the gut) often compete, while potential pathogens (e.g. Treponema and Prevotella in the dental plaque) are more likely to co-occur in complementary niches. This approach thus serves to open new opportunities for future targeted mechanistic studies of the microbial ecology of the human microbiome.
The human body is a complex ecosystem where microbes compete, and cooperate. These interactions can support health or promote disease, e.g. in dental plaque formation. The Human Microbiome Project collected and sequenced ca. 5,000 samples from 18 different body sites, including the airways, gut, skin, oral cavity and vagina. These data allowed the first assessment of significant patterns of co-presence and exclusion among human-associated bacteria. We combined sparse regression with an ensemble of similarity measures to predict microbial relationships within and between body sites. This captured known relationships in the dental plaque, vagina, and gut, and also predicted novel interactions involving members of under-characterized phyla such as TM7. We detected relationships necessary for plaque formation and differences in community composition among dominant members of the gut and vaginal microbiomes. Most relationships were strongly niche-specific, with only a few hub microorganisms forming links across multiple body areas. We also found that phylogenetic distance had a strong impact on the interaction type: closely related microorganisms co-occurred within the same niche, whereas most exclusive relationships occurred between more distantly related microorganisms. This establishes both the specific organisms and general principles by which microbial communities associated with healthy humans are assembled and maintained.
The goals of this study were to better understand the ecology of oral subgingival communities in health and periodontitis and elucidate the relationship between inflammation and the subgingival microbiome. Accordingly, we used 454-pyrosequencing of 16S rRNA gene libraries and quantitative PCR to characterize the subgingival microbiome of 22 subjects with chronic periodontitis. Each subject was sampled at two sites with similar periodontal destruction but differing in the presence of bleeding, a clinical indicator of increased inflammation. Communities in periodontitis were also compared with those from 10 healthy individuals. In periodontitis, presence of bleeding was not associated with different α-diversity or with a distinct microbiome, however, bleeding sites showed higher total bacterial load. In contrast, communities in health and periodontitis largely differed, with higher diversity and biomass in periodontitis. Shifts in community structure from health to periodontitis resembled ecological succession, with emergence of newly dominant taxa in periodontitis without replacement of primary health-associated species. That is, periodontitis communities had higher proportions of Spirochetes, Synergistetes, Firmicutes and Chloroflexi, among other taxa, while the proportions of Actinobacteria, particularly Actinomyces, were higher in health. Total Actinomyces load, however, remained constant from health to periodontitis. Moreover, an association existed between biomass and community structure in periodontitis, with the proportion of specific taxa correlating with bacterial load. Our study provides a global-scale framework for the ecological events in subgingival communities that underline the development of periodontitis. The association, in periodontitis, between inflammation, community biomass and community structure and their role in disease progression warrant further investigation.
bacterial load; community structure; inflammation; periodontitis; subgingival microbiome
Microbial communities inhabiting human mouth are associated with oral health and disease. Previous studies have indicated the general prevalence of adult gingivitis in China to be high. The aim of this study was to characterize in depth the oral microbiota of Chinese adults with or without gingivitis, by defining the microbial phylogenetic diversity and community-structure using highly paralleled pyrosequencing.
Six non-smoking Chinese, three with and three without gingivitis (age range 21-39 years, 4 females and 2 males) were enrolled in the present cross-sectional study. Gingival parameters of inflammation and bleeding on probing were characterized by a clinician using the Mazza Gingival Index (MGI). Plaque (sampled separately from four different oral sites) and salivary samples were obtained from each subject. Sequences and relative abundance of the bacterial 16 S rDNA PCR-amplicons were determined via pyrosequencing that produced 400 bp-long reads. The sequence data were analyzed via a computational pipeline customized for human oral microbiome analyses. Furthermore, the relative abundances of selected microbial groups were validated using quantitative PCR.
The oral microbiomes from gingivitis and healthy subjects could be distinguished based on the distinct community structures of plaque microbiomes, but not the salivary microbiomes. Contributions of community members to community structure divergence were statistically accessed at the phylum, genus and species-like levels. Eight predominant taxa were found associated with gingivitis: TM7, Leptotrichia, Selenomonas, Streptococcus, Veillonella, Prevotella, Lautropia, and Haemophilus. Furthermore, 98 species-level OTUs were identified to be gingivitis-associated, which provided microbial features of gingivitis at a species resolution. Finally, for the two selected genera Streptococcus and Fusobacterium, Real-Time PCR based quantification of relative bacterial abundance validated the pyrosequencing-based results.
This methods study suggests that oral samples from this patient population of gingivitis can be characterized via plaque microbiome by pyrosequencing the 16 S rDNA genes. Further studies that characterize serial samples from subjects (longitudinal study design) with a larger population size may provide insight into the temporal and ecological features of oral microbial communities in clinically-defined states of gingivitis.
oral microbiota; gingivitis; saliva; plaque; pyrosequencing
DNA from phylogenetically diverse microbes is routinely recovered from healthy human lungs and used to define the lung microbiome. The proportion of this DNA originating from microbes adapted to the lungs, as opposed to microbes dispersing to the lungs from other body sites and the atmosphere, is not known. We use a neutral model of community ecology to distinguish members of the lung microbiome whose presence is consistent with dispersal from other body sites and those that deviate from the model, suggesting a competitive advantage to these microbes in the lungs. We find that the composition of the healthy lung microbiome is consistent with predictions of the neutral model, reflecting the overriding role of dispersal of microbes from the oral cavity in shaping the microbial community in healthy lungs. In contrast, the microbiome of diseased lungs was readily distinguished as being under active selection. We also assessed the viability of microbes from lung samples by cultivation with a variety of media and incubation conditions. Bacteria recovered by cultivation from healthy lungs represented species that comprised 61% of the 16S rRNA-encoding gene sequences derived from bronchoalveolar lavage samples.
Importance Neutral distribution of microbes is a distinguishing feature of the microbiome in healthy lungs, wherein constant dispersal of bacteria from the oral cavity overrides differential growth of bacteria. No bacterial species consistently deviated from the model predictions in healthy lungs, although representatives of many of the dispersed species were readily cultivated. In contrast, bacterial populations in diseased lungs were identified as being under active selection. Quantification of the relative importance of selection and neutral processes such as dispersal in shaping the healthy lung microbiome is a first step toward understanding its impacts on host health.
Neutral distribution of microbes is a distinguishing feature of the microbiome in healthy lungs, wherein constant dispersal of bacteria from the oral cavity overrides differential growth of bacteria. No bacterial species consistently deviated from the model predictions in healthy lungs, although representatives of many of the dispersed species were readily cultivated. In contrast, bacterial populations in diseased lungs were identified as being under active selection. Quantification of the relative importance of selection and neutral processes such as dispersal in shaping the healthy lung microbiome is a first step toward understanding its impacts on host health.
CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) loci, together with cas (CRISPR–associated) genes, form the CRISPR/Cas adaptive immune system, a primary defense strategy that eubacteria and archaea mobilize against foreign nucleic acids, including phages and conjugative plasmids. Short spacer sequences separated by the repeats are derived from foreign DNA and direct interference to future infections. The availability of hundreds of shotgun metagenomic datasets from the Human Microbiome Project (HMP) enables us to explore the distribution and diversity of known CRISPRs in human-associated microbial communities and to discover new CRISPRs. We propose a targeted assembly strategy to reconstruct CRISPR arrays, which whole-metagenome assemblies fail to identify. For each known CRISPR type (identified from reference genomes), we use its direct repeat consensus sequence to recruit reads from each HMP dataset and then assemble the recruited reads into CRISPR loci; the unique spacer sequences can then be extracted for analysis. We also identified novel CRISPRs or new CRISPR variants in contigs from whole-metagenome assemblies and used targeted assembly to more comprehensively identify these CRISPRs across samples. We observed that the distributions of CRISPRs (including 64 known and 86 novel ones) are largely body-site specific. We provide detailed analysis of several CRISPR loci, including novel CRISPRs. For example, known streptococcal CRISPRs were identified in most oral microbiomes, totaling ∼8,000 unique spacers: samples resampled from the same individual and oral site shared the most spacers; different oral sites from the same individual shared significantly fewer, while different individuals had almost no common spacers, indicating the impact of subtle niche differences on the evolution of CRISPR defenses. We further demonstrate potential applications of CRISPRs to the tracing of rare species and the virus exposure of individuals. This work indicates the importance of effective identification and characterization of CRISPR loci to the study of the dynamic ecology of microbiomes.
Human bodies are complex ecological systems in which various microbial organisms and viruses interact with each other and with the human host. The Human Microbiome Project (HMP) has resulted in >700 datasets of shotgun metagenomic sequences, from which we can learn about the compositions and functions of human-associated microbial communities. CRISPR/Cas systems are a widespread class of adaptive immune systems in bacteria and archaea, providing acquired immunity against foreign nucleic acids: CRISPR/Cas defense pathways involve integration of viral- or plasmid-derived DNA segments into CRISPR arrays (forming spacers between repeated structural sequences), and expression of short crRNAs from these single repeat-spacer units, to generate interference to future invading foreign genomes. Powered by an effective computational approach (the targeted assembly approach for CRISPR), our analysis of CRISPR arrays in the HMP datasets provides the very first global view of bacterial immunity systems in human-associated microbial communities. The great diversity of CRISPR spacers we observed among different body sites, in different individuals, and in single individuals over time, indicates the impact of subtle niche differences on the evolution of CRISPR defenses and indicates the key role of bacteriophage (and plasmids) in shaping human microbial communities.
The human gut harbors thousands of bacterial taxa. A profusion of metagenomic sequence data has been generated from human stool samples in the last few years, raising the question of whether more taxa remain to be identified. We assessed metagenomic data generated by the Human Microbiome Project Consortium to determine if novel taxa remain to be discovered in stool samples from healthy individuals. To do this, we established a rigorous bioinformatics pipeline that uses sequence data from multiple platforms (Illumina GAIIX and Roche 454 FLX Titanium) and approaches (whole-genome shotgun and 16S rDNA amplicons) to validate novel taxa. We applied this approach to stool samples from 11 healthy subjects collected as part of the Human Microbiome Project. We discovered several low-abundance, novel bacterial taxa, which span three major phyla in the bacterial tree of life. We determined that these taxa are present in a larger set of Human Microbiome Project subjects and are found in two sampling sites (Houston and St. Louis). We show that the number of false-positive novel sequences (primarily chimeric sequences) would have been two orders of magnitude higher than the true number of novel taxa without validation using multiple datasets, highlighting the importance of establishing rigorous standards for the identification of novel taxa in metagenomic data. The majority of novel sequences are related to the recently discovered genus Barnesiella, further encouraging efforts to characterize the members of this genus and to study their roles in the microbial communities of the gut. A better understanding of the effects of less-abundant bacteria is important as we seek to understand the complex gut microbiome in healthy individuals and link changes in the microbiome to disease.
The complexity of the human microbiome makes it difficult to reveal organizational principles of the community and even more challenging to generate testable hypotheses. It has been suggested that in the gut microbiome species such as Bacteroides thetaiotaomicron are keystone in maintaining the stability and functional adaptability of the microbial community. In this study, we investigate the interspecies associations in a complex microbial biofilm applying systems biology principles. Using correlation network analysis we identified bacterial modules that represent important microbial associations within the oral community. We used dental plaque as a model community because of its high diversity and the well known species-species interactions that are common in the oral biofilm. We analyzed samples from healthy individuals as well as from patients with periodontitis, a polymicrobial disease. Using results obtained by checkerboard hybridization on cultivable bacteria we identified modules that correlated well with microbial complexes previously described. Furthermore, we extended our analysis using the Human Oral Microbe Identification Microarray (HOMIM), which includes a large number of bacterial species, among them uncultivated organisms present in the mouth. Two distinct microbial communities appeared in healthy individuals while there was one major type in disease. Bacterial modules in all communities did not overlap, indicating that bacteria were able to effectively re-associate with new partners depending on the environmental conditions. We then identified hubs that could act as keystone species in the bacterial modules. Based on those results we then cultured a not-yet-cultivated microorganism, Tannerella sp. OT286 (clone BU063). After two rounds of enrichment by a selected helper (Prevotella oris OT311) we obtained colonies of Tannerella sp. OT286 growing on blood agar plates. This system-level approach would open the possibility of manipulating microbial communities in a targeted fashion as well as associating certain bacterial modules to clinical traits (e.g.: obesity, Crohn's disease, periodontal disease, etc).
A metagenomic analysis of the dynamic changes of the composition of the
intestinal microbiome of five participants of the MARS-500 experiment was
performed. DNA samples were isolated from the feces of the participants taken
just before the experiment, upon 14, 30, 210, 363 and 510 days of isolation in
the experimental module, and two weeks upon completion of the experiment. The
taxonomic composition of the microbiome was analyzed by pyrosequencing of 16S
rRNA gene fragments. Both the taxonomic and functional gene content of the
microbiome of one participant were analyzed by whole metagenome sequencing
using the SOLiD technique. Each participant had a specific microbiome that
could be assigned to one of three recognized enterotypes. Two participants had
enterotype I microbiomes characterized by the prevalence of
Bacteroides, while the microbiomes of two others, assigned to
type II, were dominated by Prevotella. One participant had a
microbiome of mixed type. It was found that (1) changes in the taxonimic
composition of the microbiomes occurred in the course of the experiment, but
the enterotypes remained the same; (2) significant changes in the compositions
of the microbiomes occurred just 14-30 days after the beginning of the
experiment, presumably indicating the influence of stress factors in the first
stage of the experiment; (3) a tendency toward a reversion of the microbiomes
to their initial composition was observed two weeks after the end of the
experiment, but complete recovery was not achieved. The metagenomic analysis of
the microbiome of one of the participants showed that in spite of variations in
the taxonomic compositions of microbiomes, the “functional” genetic composition
was much more stable for most of the functional gene categories. Probably in
the course of the experiment the taxonomic composition of the gut microbiome
was adaptively changed to reflect the individual response to the experimental
conditions. A new, balanced taxonomic composition of the microbiome was formed
to ensure a stable gene content of the community as a whole without negative
consequences for the health of the participants.
metagenomics; intestinal microbiota; stressful influences; enterotypes
Microbial communities carry out the majority of the biochemical activity on the planet, and they play integral roles in processes including metabolism and immune homeostasis in the human microbiome. Shotgun sequencing of such communities' metagenomes provides information complementary to organismal abundances from taxonomic markers, but the resulting data typically comprise short reads from hundreds of different organisms and are at best challenging to assemble comparably to single-organism genomes. Here, we describe an alternative approach to infer the functional and metabolic potential of a microbial community metagenome. We determined the gene families and pathways present or absent within a community, as well as their relative abundances, directly from short sequence reads. We validated this methodology using a collection of synthetic metagenomes, recovering the presence and abundance both of large pathways and of small functional modules with high accuracy. We subsequently applied this method, HUMAnN, to the microbial communities of 649 metagenomes drawn from seven primary body sites on 102 individuals as part of the Human Microbiome Project (HMP). This provided a means to compare functional diversity and organismal ecology in the human microbiome, and we determined a core of 24 ubiquitously present modules. Core pathways were often implemented by different enzyme families within different body sites, and 168 functional modules and 196 metabolic pathways varied in metagenomic abundance specifically to one or more niches within the microbiome. These included glycosaminoglycan degradation in the gut, as well as phosphate and amino acid transport linked to host phenotype (vaginal pH) in the posterior fornix. An implementation of our methodology is available at http://huttenhower.sph.harvard.edu/humann. This provides a means to accurately and efficiently characterize microbial metabolic pathways and functional modules directly from high-throughput sequencing reads, enabling the determination of community roles in the HMP cohort and in future metagenomic studies.
The human body is inhabited by trillions of bacteria and other microbes, which have recently been studied in many different habitats (including gut, mouth, skin, and urogenital) by the Human Microbiome Project (HMP). These microbial communities were assayed using high-throughput DNA sequencing, but it can be challenging to determine their biological functions based solely on the resulting short sequences. To reconstruct the metabolic activities of such communities, we have developed HUMAnN, a method to accurately infer community function directly from short DNA reads. The method's accuracy was validated using a collection of synthetic microbial communities. Applying HUMAnN to data from the HMP, we showed that, unlike individual microbial species, many metabolic processes were present among all body habitats. However, the frequencies of these processes varied dramatically, and some were highly enriched within individual habitats to provide niche specialization (e.g. in the gut, which is abundant in food matter but low in oxygen). Other community functions were linked specifically to properties of the human host, such as biochemical processes only present in vaginal habitats with particularly high or low pH. Studying additional environmental or disease-associated communities using HUMAnN will further improve our understanding of how the microbial organisms in a community are linked to the biological processes they carry out.
We examined the subgingival bacterial biodiversity in untreated chronic periodontitis patients by sequencing 16S rRNA genes. The primary purpose of the study was to compare the oral microbiome in deep (diseased) and shallow (healthy) sites. A secondary purpose was to evaluate the influences of smoking, race and dental caries on this relationship. A total of 88 subjects from two clinics were recruited. Paired subgingival plaque samples were taken from each subject, one from a probing site depth >5 mm (deep site) and the other from a probing site depth ≤3mm (shallow site). A universal primer set was designed to amplify the V4–V6 region for oral microbial 16S rRNA sequences. Differences in genera and species attributable to deep and shallow sites were determined by statistical analysis using a two-part model and false discovery rate. Fifty-one of 170 genera and 200 of 746 species were found significantly different in abundances between shallow and deep sites. Besides previously identified periodontal disease-associated bacterial species, additional species were found markedly changed in diseased sites. Cluster analysis revealed that the microbiome difference between deep and shallow sites was influenced by patient-level effects such as clinic location, race and smoking. The differences between clinic locations may be influenced by racial distribution, in that all of the African Americans subjects were seen at the same clinic. Our results suggested that there were influences from the microbiome for caries and periodontal disease and these influences are independent.
Most studies examining the commensal human oral microbiome are focused on disease or are limited in methodology. In order to diagnose and treat diseases at an early and reversible stage an in-depth definition of health is indispensible. The aim of this study therefore was to define the healthy oral microbiome using recent advances in sequencing technology (454 pyrosequencing).
We sampled and sequenced microbiomes from several intraoral niches (dental surfaces, cheek, hard palate, tongue and saliva) in three healthy individuals. Within an individual oral cavity, we found over 3600 unique sequences, over 500 different OTUs or "species-level" phylotypes (sequences that clustered at 3% genetic difference) and 88 - 104 higher taxa (genus or more inclusive taxon). The predominant taxa belonged to Firmicutes (genus Streptococcus, family Veillonellaceae, genus Granulicatella), Proteobacteria (genus Neisseria, Haemophilus), Actinobacteria (genus Corynebacterium, Rothia, Actinomyces), Bacteroidetes (genus Prevotella, Capnocytophaga, Porphyromonas) and Fusobacteria (genus Fusobacterium).
Each individual sample harboured on average 266 "species-level" phylotypes (SD 67; range 123 - 326) with cheek samples being the least diverse and the dental samples from approximal surfaces showing the highest diversity. Principal component analysis discriminated the profiles of the samples originating from shedding surfaces (mucosa of tongue, cheek and palate) from the samples that were obtained from solid surfaces (teeth).
There was a large overlap in the higher taxa, "species-level" phylotypes and unique sequences among the three microbiomes: 84% of the higher taxa, 75% of the OTUs and 65% of the unique sequences were present in at least two of the three microbiomes. The three individuals shared 1660 of 6315 unique sequences. These 1660 sequences (the "core microbiome") contributed 66% of the reads. The overlapping OTUs contributed to 94% of the reads, while nearly all reads (99.8%) belonged to the shared higher taxa.
We obtained the first insight into the diversity and uniqueness of individual oral microbiomes at a resolution of next-generation sequencing. We showed that a major proportion of bacterial sequences of unrelated healthy individuals is identical, supporting the concept of a core microbiome at health.
The influence of resident gut microbes on xenobiotic metabolism has been investigated at different levels throughout the past five decades. However, with the advance in sequencing and pyrotagging technologies, addressing the influence of microbes on xenobiotics had to evolve from assessing direct metabolic effects on toxins and botanicals by conventional culture-based techniques to elucidating the role of community composition on drugs metabolic profiles through DNA sequence-based phylogeny and metagenomics. Following the completion of the Human Genome Project, the rapid, substantial growth of the Human Microbiome Project (HMP) opens new horizons for studying how microbiome compositional and functional variations affect drug action, fate, and toxicity (pharmacomicrobiomics), notably in the human gut. The HMP continues to characterize the microbial communities associated with the human gut, determine whether there is a common gut microbiome profile shared among healthy humans, and investigate the effect of its alterations on health. Here, we offer a glimpse into the known effects of the gut microbiota on xenobiotic metabolism, with emphasis on cases where microbiome variations lead to different therapeutic outcomes. We discuss a few examples representing how the microbiome interacts with human metabolic enzymes in the liver and intestine. In addition, we attempt to envisage a roadmap for the future implications of the HMP on therapeutics and personalized medicine.
Human microbiome project; Xenobitoics; Liver enzymes; Metagenome; Microbiota; Metabolomics; Metabonomics; Pharmacokinetics; Pharmacodynamics; Pharmacomicrobiomics
The human oral microbiome is potentially related to diverse health conditions and high-throughput technology provides the possibility of surveying microbial community structure at high resolution. We compared two oral microbiome survey methods: broad-based microbiome identification by 16S rRNA gene sequencing and targeted characterization of microbes by custom DNA microarray.
Oral wash samples were collected from 20 individuals at Memorial Sloan-Kettering Cancer Center. 16S rRNA gene survey was performed by 454 pyrosequencing of the V3–V5 region (450 bp). Targeted identification by DNA microarray was carried out with the Human Oral Microbe Identification Microarray (HOMIM). Correlations and relative abundance were compared at phylum and genus level, between 16S rRNA sequence read ratio and HOMIM hybridization intensity.
The major phyla, Firmicutes, Proteobacteria, Bacteroidetes, Actinobacteria, and Fusobacteria were identified with high correlation by the two methods (r = 0.70∼0.86). 16S rRNA gene pyrosequencing identified 77 genera and HOMIM identified 49, with 37 genera detected by both methods; more than 98% of classified bacteria were assigned in these 37 genera. Concordance by the two assays (presence/absence) and correlations were high for common genera (Streptococcus, Veillonella, Leptotrichia, Prevotella, and Haemophilus; Correlation = 0.70–0.84).
Microbiome community profiles assessed by 16S rRNA pyrosequencing and HOMIM were highly correlated at the phylum level and, when comparing the more commonly detected taxa, also at the genus level. Both methods are currently suitable for high-throughput epidemiologic investigations relating identified and more common oral microbial taxa to disease risk; yet, pyrosequencing may provide a broader spectrum of taxa identification, a distinct sequence-read record, and greater detection sensitivity.
Dysbiotic oral bacterial communities have a critical role in the etiology and progression of periodontal diseases. The goal of this study was to investigate the extent to which smoking increases risk for disease by influencing the composition of the subgingival microbiome in states of clinical health. Subgingival plaque samples were collected from 200 systemically and periodontally healthy smokers and nonsmokers. 16S pyrotag sequencing was preformed generating 1 623 713 classifiable sequences, which were compared with a curated version of the Greengenes database using the quantitative insights into microbial ecology pipeline. The subgingival microbial profiles of smokers and never-smokers were different at all taxonomic levels, and principal coordinate analysis revealed distinct clustering of the microbial communities based on smoking status. Smokers demonstrated a highly diverse, pathogen-rich, commensal-poor, anaerobic microbiome that is more closely aligned with a disease-associated community in clinically healthy individuals, suggesting that it creates an at-risk-for-harm environment that is primed for a future ecological catastrophe.
Current practice in the normalization of microbiome count data is inefficient in the statistical sense. For apparently historical reasons, the common approach is either to use simple proportions (which does not address heteroscedasticity) or to use rarefying of counts, even though both of these approaches are inappropriate for detection of differentially abundant species. Well-established statistical theory is available that simultaneously accounts for library size differences and biological variability using an appropriate mixture model. Moreover, specific implementations for DNA sequencing read count data (based on a Negative Binomial model for instance) are already available in RNA-Seq focused R packages such as edgeR and DESeq. Here we summarize the supporting statistical theory and use simulations and empirical data to demonstrate substantial improvements provided by a relevant mixture model framework over simple proportions or rarefying. We show how both proportions and rarefied counts result in a high rate of false positives in tests for species that are differentially abundant across sample classes. Regarding microbiome sample-wise clustering, we also show that the rarefying procedure often discards samples that can be accurately clustered by alternative methods. We further compare different Negative Binomial methods with a recently-described zero-inflated Gaussian mixture, implemented in a package called metagenomeSeq. We find that metagenomeSeq performs well when there is an adequate number of biological replicates, but it nevertheless tends toward a higher false positive rate. Based on these results and well-established statistical theory, we advocate that investigators avoid rarefying altogether. We have provided microbiome-specific extensions to these tools in the R package, phyloseq.
The term microbiome refers to the ecosystem of microbes that live in a defined environment. The decreasing cost and increasing speed of DNA sequencing technology has recently provided scientists with affordable and timely access to the genes and genomes of microbiomes that inhabit our planet and even our own bodies. In these investigations many microbiome samples are sequenced at the same time on the same DNA sequencing machine, but often result in total numbers of sequences per sample that are vastly different. The common procedure for addressing this difference in sequencing effort across samples – different library sizes – is to either (1) base analyses on the proportional abundance of each species in a library, or (2) rarefy, throw away sequences from the larger libraries so that all have the same, smallest size. We show that both of these normalization methods can work when comparing obviously-different whole microbiomes, but that neither method works well when comparing the relative proportions of each bacterial species across microbiome samples. We show that alternative methods based on a statistical mixture model perform much better and can be easily adapted from a separate biological sub-discipline, called RNA-Seq analysis.
The complex microbiome of the ceca of chickens plays an important role in nutrient utilization, growth and well-being of these animals. Since we have a very limited understanding of the capabilities of most species present in the cecum, we investigated the role of the microbiome by comparative analyses of both the microbial community structure and functional gene content using random sample pyrosequencing. The overall goal of this study was to characterize the chicken cecal microbiome using a pathogen-free chicken and one that had been challenged with Campylobacter jejuni.
Comparative metagenomic pyrosequencing was used to generate 55,364,266 bases of random sampled pyrosequence data from two chicken cecal samples. SSU rDNA gene tags and environmental gene tags (EGTs) were identified using SEED subsystems-based annotations. The distribution of phylotypes and EGTs detected within each cecal sample were primarily from the Firmicutes, Bacteroidetes and Proteobacteria, consistent with previous SSU rDNA libraries of the chicken cecum. Carbohydrate metabolism and virulence genes are major components of the EGT content of both of these microbiomes. A comparison of the twelve major pathways in the SEED Virulence Subsystem (metavirulome) represented in the chicken cecum, mouse cecum and human fecal microbiomes showed that the metavirulomes differed between these microbiomes and the metavirulomes clustered by host environment. The chicken cecum microbiomes had the broadest range of EGTs within the SEED Conjugative Transposon Subsystem, however the mouse cecum microbiomes showed a greater abundance of EGTs in this subsystem. Gene assemblies (32 contigs) from one microbiome sample were predominately from the Bacteroidetes, and seven of these showed sequence similarity to transposases, whereas the remaining sequences were most similar to those from catabolic gene families.
This analysis has demonstrated that mobile DNA elements are a major functional component of cecal microbiomes, thus contributing to horizontal gene transfer and functional microbiome evolution. Moreover, the metavirulomes of these microbiomes appear to associate by host environment. These data have implications for defining core and variable microbiome content in a host species. Furthermore, this suggests that the evolution of host specific metavirulomes is a contributing factor in disease resistance to zoonotic pathogens.
The etiology of dental caries remains elusive because of our limited understanding of the complex oral microbiomes. The current methodologies have been limited by insufficient depth and breadth of microbial sampling, paucity of data for diseased hosts particularly at the population level, inconsistency of sampled sites and the inability to distinguish the underlying microbial factors. By cross-validating 16S rRNA gene amplicon-based and whole-genome-based deep-sequencing technologies, we report the most in-depth, comprehensive and collaborated view to date of the adult saliva microbiomes in pilot populations of 19 caries-active and 26 healthy human hosts. We found that: first, saliva microbiomes in human population were featured by a vast phylogenetic diversity yet a minimal organismal core; second, caries microbiomes were significantly more variable in community structure whereas the healthy ones were relatively conserved; third, abundance changes of certain taxa such as overabundance of Prevotella Genus distinguished caries microbiota from healthy ones, and furthermore, caries-active and normal individuals carried different arrays of Prevotella species; and finally, no ‘caries-specific' operational taxonomic units (OTUs) were detected, yet 147 OTUs were ‘caries associated', that is, differentially distributed yet present in both healthy and caries-active populations. These findings underscored the necessity of species- and strain-level resolution for caries prognosis, and were consistent with the ecological hypothesis where the shifts in community structure, instead of the presence or absence of particular groups of microbes, underlie the cariogenesis.
caries; metagenomics; oral-microbiome; Prevotella; saliva
This study tested the feasibility of a high throughput metagenomic approach to analyze the pre- and posttreatment of subgingival plaque in two subjects with aggressive periodontitis. DNA was extracted from subgingival samples and subjected to PCR amplification of the c2-c4 regions of the 16S rDNA using primers with bar codes to identify individual samples. The PCR products were pooled and sequenced for the v4 region of the 16S rDNA using the 454 FLX standard platform. The results were analyzed for species/phylotypes in the Human Oral Microbiome Database (HOMD) and Ribosomal Database Project (RDP) database. The sequencing of the amplicons resulted in 24,673 reads and identified 208 species/phylotypes. Of those, 129 species/phylotypes were identified in both patients but their proportions varied. While >120 species/phylotypes were identified in all samples, 28-42 species/phylotypes cumulatively represent 90% of all subgingival bacteria in each sample. The remaining species/phylotypes each constituted ≤0.2% of the total subgingival bacteria. In conclusion, the subgingival microbiota are characterized by high species richness dominated by a few species/ phylotypes. The microbiota changed after periodontal therapy. High throughput metagenomic analysis is applicable to assess the complexity and changes of the subgingival microbiota.
Aggressive periodontitis; metagenomics; subgingival plaque; nonsurgical treatment
Periodontally involved teeth have been implicated as ‘microbial reservoirs’ in the etiology of peri-implant diseases. Therefore, the purpose of this investigation was to use a deep-sequencing approach to identify the degree of congruence between adjacent peri-implant and periodontal microbiomes in states of health and disease. Subgingival and peri-implant biofilm samples were collected from 81 partially edentulous individuals with periodontal and peri-implant health and disease. Bacterial DNA was isolated, and the 16S rRNA gene was amplified and sequenced by pyrotag sequencing. Chimera-depleted sequences were compared against a locally hosted curated database for bacterial identification. Statistical significance was determined by paired Student’s t tests between tooth-implant pairs. The 1.9 million sequences identified represented 523 species. Sixty percent of individuals shared less than 50% of all species between their periodontal and peri-implant biofilms, and 85% of individuals shared less than 8% of abundant species between tooth and implant. Additionally, the periodontal microbiome demonstrated significantly higher diversity than the implant, and distinct bacterial lineages were associated with health and disease in each ecosystem. Analysis of our data suggests that simple geographic proximity is not a sufficient determinant of colonization of topographically distinct niches, and that the peri-implant and periodontal microbiomes represent microbiologically distinct ecosystems.
dental implants; phylogenetic biogeography; peri-implantitis; periodontitis; computational biology; biofilms
Characterizing 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.
We 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.
The 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.
Biogeography; Human microbiome; Biodiversity; Temporal stability
Although our microbial community and genomes (the human microbiome) outnumber our genome by several orders of magnitude, to what extent the human host genetic complement informs the microbiota composition is not clear. The Human Microbiome Project (HMP) Consortium established a unique population-scale framework with which to characterize the relationship of microbial community structure with their human hosts. A wide variety of taxa and metabolic pathways have been shown to be differentially distributed by virtue of race/ethnicity in the HMP. Given that mtDNA haplogroups are the maternally derived ancestral genomic markers and mitochondria’s role as the generator for cellular ATP, characterizing the relationship between human mtDNA genomic variants and microbiome profiles becomes of potential marked biologic and clinical interest.
We leveraged sequencing data from the HMP to investigate the association between microbiome community structures with its own host mtDNA variants. 15 haplogroups and 631 mtDNA nucleotide polymorphisms (mean sequencing depth of 280X on the mitochondria genome) from 89 individuals participating in the HMP were accurately identified. 16S rRNA (V3-V5 region) sequencing generated microbiome taxonomy profiles and whole genome shotgun sequencing generated metabolic profiles from various body sites were treated as traits to conduct association analysis between haplogroups and host clinical metadata through linear regression. The mtSNPs of individuals with European haplogroups were associated with microbiome profiles using PLINK quantitative trait associations with permutation and adjusted for multiple comparisons. We observe that among 139 stool and 59 vaginal posterior fornix samples, several haplogroups show significant association with specific microbiota (q-value < 0.05) as well as their aggregate community structure (Chi-square with Monte Carlo, p < 0.005), which confirmed and expanded previous research on the association of race and ethnicity with microbiome profile. Our results further indicate that mtDNA variations may render different microbiome profiles, possibly through an inflammatory response to different levels of reactive oxygen species activity.
These data provide initial evidence for the association between host ancestral genome with the structure of its microbiome.
HMP; Mitochondrial DNA haplogroup; Association; Microbiome; mtDNA SNP
The oral microbiota survives daily physical and chemical perturbations from the intake of food and personal hygiene measures, resulting in a long-term stable microbiome. Biological properties that confer stability in the microbiome are important for the prevention of dysbiosis—a microbial shift toward a disease, e.g., periodontitis or caries. Although processes that underlie oral diseases have been studied extensively, processes involved in maintaining of a normal, healthy microbiome are poorly understood. In this review we present our hypothesis on how a healthy oral microbiome is acquired and maintained. We introduce our view on the prenatal development of tolerance for the normal oral microbiome: we propose that development of fetal tolerance toward the microbiome of the mother during pregnancy is the major factor for a successful acquisition of a normal microbiome. We describe the processes that influence the establishment of such microbiome, followed by our perspective on the process of sustaining a healthy oral microbiome. We divide microbiome-maintenance factors into host-derived and microbe-derived, while focusing on the host. Finally, we highlight the need and directions for future research.
oral microbiome; placenta; tolerance; mucosal immunity; stability; colonization resistance
Porcine tonsils are the colonization site for many pathogenic as well as commensal microorganisms and are the primary lymphoid tissue encountered by organisms entering through the mouth or nares. The goal of this study was to provide an in-depth characterization of the composition and structure of the tonsillar microbial communities and to define the core microbiome in the tonsils of healthy pigs, using high throughput bar-coded 454-FLX pyrosequencing.
Whole tonsils were collected at necropsy from 12 16-week-old finisher pigs from two healthy herds. Tonsil brushes were also used to collect samples from four of these animals. Bacterial DNA was isolated from each sample, amplified by PCR with universal primers specific for the bacterial 16S rRNA genes, and the PCR products sequenced using pyrosequencing. An average of 13,000 sequences were generated from each sample. Microbial community members were identified by sequence comparison to known bacterial 16S rRNA gene sequences.
The microbiomes of these healthy herds showed very strong similarities in the major components as well as distinct differences in minor components. Pasteurellaceae dominated the tonsillar microbiome in all animals, comprising ~60% of the total, although the relative proportions of the genera Actinobacillus, Haemophilus, and Pasteurella varied between the herds. Also found in all animals were the genera Alkanindiges, Peptostreptococcus, Veillonella, Streptococcus and Fusobacterium, as well as Enterobacteriaceae and Neisseriaceae. Treponema and Chlamydia were unique to Herd 1, while Arcanobacterium was unique to Herd 2.
Tonsil brushes yielded similar results to tissue specimens, although Enterobacteriaceae and obligate anaerobes were more frequently found in tissue than in brush samples, and Chlamydia, an obligately intracellular organism, was not found in brush specimens.
We have extended and supported our previous studies with 16S clone libraries, using 16S rRNA gene pyrosequencing to describe the microbial communities in tonsils of healthy pigs. We have defined a core microbiome, dominated by Pasteurellaceae, in tonsil specimens, and have also demonstrated the presence of unique minor components of the tonsillar microbiome present in each herd. We have validated the use of non-invasive tonsil brushes, in comparison to tonsil tissue, which will facilitate future studies.
Infection is a leading cause of preterm birth (PTB). A focus of many studies over the past decade has been to characterize microorganisms present in the uterine cavity and document any association with negative pregnancy outcome. A range of techniques have been used to achieve this, including microbiological culture and targeted polymerase chain reaction assays, and more recently, microbiome-level analyses involving either conserved, phylogenetically informative genes such as the bacterial 16S rRNA gene or whole shotgun metagenomic sequencing. These studies have contributed vast amounts of data toward characterization of the uterine microbiome, specifically that present in the amniotic fluid, fetal membranes, and placenta. However, an overwhelming emphasis has been placed on the bacterial microbiome, with far less data produced on the viral and fungal/yeast microbiomes. With numerous studies now referring to PTB as a polymicrobial condition, there is the need to investigate the role of viruses and fungi/yeasts in more detail and in particular, look for associations between colonization with these microorganisms and bacteria in the same samples. Although the major pathway by which microorganisms are believed to colonize the uterine cavity is vertical ascension from the vagina, numerous studies are now emerging suggesting hematogenous transfer of oral microbiota to the uterine cavity. Evidence of this has been produced in mouse models and although DNA-based evidence in humans appears convincing in some aspects, use of methodologies that only detect viable cells as opposed to lysed cells and extracellular DNA are needed to clarify this. Such techniques as RNA analyses and viability polymerase chain reaction are likely to play key roles in the clinical translation of future microbiome-based data, particularly in confined environments such as the uterus, as detection of viable cells plays a key role in diagnosis and treatment of infection.
preterm birth; bacteria; virus; fungi; yeast; infection; amniotic fluid; placenta