The human microbiome influences and reflects the health or disease state of the host. Periodontitis, a disease affecting about half of American adults, is associated with alterations in the subgingival microbiome of individual tooth sites. Although it can be treated, the disease can reoccur and may progress without symptoms. Without prognostic markers, follow-up examinations are required to assess reoccurrence and disease progression and to determine the need for additional treatments. To better identify and predict the disease progression, we aim to determine whether the subgingival microbiome can serve as a diagnosis and prognosis indicator. Using metagenomic shotgun sequencing, we characterized the dynamic changes in the subgingival microbiome in periodontitis patients before and after treatment at the same tooth sites. At the taxonomic composition level, the periodontitis-associated microorganisms were significantly shifted from highly correlated in the diseased state to poorly correlated after treatment, suggesting that coordinated interactions among the pathogenic microorganisms are essential to disease pathogenesis. At the functional level, we identified disease-associated pathways that were significantly altered in relative abundance in the two states. Furthermore, using the subgingival microbiome profile, we were able to classify the samples to their clinical states with an accuracy of 81.1%. Follow-up clinical examination of the sampled sites supported the predictive power of the microbiome profile on disease progression. Our study revealed the dynamic changes in the subgingival microbiome contributing to periodontitis and suggested potential clinical applications of monitoring the subgingival microbiome as an indicator in disease diagnosis and prognosis.
Periodontitis is a common oral disease. Although it can be treated, the disease may reoccur without obvious symptoms. Current clinical examination parameters are useful in disease diagnosis but cannot adequately predict the outcome of individual tooth sites after treatment. A link between the subgingival microbiota and periodontitis was identified previously; however, it remains to be investigated whether the microbiome can serve as a diagnostic and prognostic indicator. In this study, for the first time, we characterized the subgingival microbiome of individual tooth sites before and after treatment using a large-scale metagenomic analysis. Our longitudinal study revealed changes in the microbiota in taxonomic composition, cooccurrence of subgingival microorganisms, and functional composition. Using the microbiome profiles, we were able to classify the clinical states of subgingival plaque samples with a high accuracy. Follow-up clinical examination of sampled sites indicates that the subgingival microbiome profile shows promise for the development of diagnostic and prognostic tools.
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.
Periodontitis is a polymicrobial biofilm-induced inflammatory disease that affects 743 million people worldwide. The current model to explain periodontitis progression proposes that changes in the relative abundance of members of the oral microbiome lead to dysbiosis in the host-microbiome crosstalk and then to inflammation and bone loss. Using combined metagenome/metatranscriptome analysis of the subgingival microbiome in progressing and non-progressing sites, we have characterized the distinct molecular signatures of periodontitis progression.
Metatranscriptome analysis was conducted on samples from subgingival biofilms from progressing and stable sites from periodontitis patients. Community-wide expression profiles were obtained using Next Generation Sequencing (Illumina). Sequences were aligned using ‘bowtie2’ against a constructed oral microbiome database. Differential expression analysis was performed using the non-parametric algorithm implemented on the R package ‘NOISeqBio’. We summarized global functional activities of the oral microbial community by set enrichment analysis based on the Gene Ontology (GO) orthology.
Gene ontology enrichment analysis showed an over-representation in the baseline of active sites of terms related to cell motility, lipid A and peptidoglycan biosynthesis, and transport of iron, potassium, and amino acids. Periodontal pathogens (Tannerella forsythia and Porphyromonas gingivalis) upregulated different TonB-dependent receptors, peptidases, proteases, aerotolerance genes, iron transport genes, hemolysins, and CRISPR-associated genes. Surprisingly, organisms that have not been usually associated with the disease (Streptococcus oralis, Streptococcus mutans, Streptococcus intermedius, Streptococcus mitis, Veillonella parvula, and Pseudomonas fluorenscens) were highly active transcribing putative virulence factors. We detected patterns of activities associated with progression of clinical traits. Among those we found that the profiles of expression of cobalamin biosynthesis, proteolysis, and potassium transport were associated with the evolution towards disease.
We identified metabolic changes in the microbial community associated with the initial stages of dysbiosis. Regardless of the overall composition of the community, certain metabolic signatures are consistent with disease progression. Our results suggest that the whole community, and not just a handful of oral pathogens, is responsible for an increase in virulence that leads to progression.
NCT01489839, 6 December 2011.
Electronic supplementary material
The online version of this article (doi:10.1186/s13073-015-0153-3) contains supplementary material, which is available to authorized users.
Conventional periodontal therapy aims at controlling supra- and subgingival biofilms. Although periodontal therapy was shown to improve periodontal health, it does not completely arrest the disease. Almost all subjects compliant with periodontal maintenance continue to experience progressive clinical attachment loss and a fraction of them loses teeth. An oral microbial transplant may be a new alternative for treating periodontitis (inspired by fecal transplant). First, it must be established that microbiomes of oral health and periodontitis are distinct. In that case, the health-associated microbiome could be introduced into the oral cavity of periodontitis patients. This relates to the goals of our study: (i) to assess if microbial communities of the entire oral cavity of subjects with periodontitis were different from or oral health contrasted by microbiotas of caries and edentulism patients; (ii) to test in vitro if safe concentration of sodium hypochlorite could be used for initial eradication of the original oral microbiota followed by a safe neutralization of the hypochlorite prior transplantation.
Sixteen systemically healthy white adults with clinical signs of one of the following oral conditions were enrolled: periodontitis, established caries, edentulism, and oral health. Oral biofilm samples were collected from sub- and supra-gingival sites, and oral mucosae. DNA was extracted and 16S rRNA genes were amplified. Amplicons from the same patient were pooled, sequenced and quantified. Volunteer’s oral plaque was treated with saline, 16 mM NaOCl and NaOCl neutralized by ascorbate buffer followed by plating on blood agar.
Ordination plots of rRNA gene abundances revealed distinct groupings for the oral microbiomes of subjects with periodontitis, edentulism, or oral health. The oral microbiome in subjects with periodontitis showed the greatest diversity harboring 29 bacterial species at significantly higher abundance compared to subjects with the other assessed conditions. Healthy subjects had significantly higher abundance in 10 microbial species compared to the other conditions. NaOCl showed strong antimicrobial properties; nontoxic ascorbate was capable of neutralizing the hypochlorite.
Distinct oral microbial signatures were found in subjects with periodontitis, edentulism, or oral health. This finding opens up a potential for a new therapy, whereby a health-related entire oral microbial community would be transplanted to the diseased patient.
Electronic supplementary material
The online version of this article (doi:10.1186/s12903-015-0109-4) contains supplementary material, which is available to authorized users.
Bacteriotherapy; Microbial transplant; Caries; Edentulism; Periodontitis; Red complex
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
The human oral microbiome is known to play a significant role in human health and disease. While less well studied, the feline oral microbiome is thought to play a similarly important role. To determine roles oral bacteria play in health and disease, one first has to be able to accurately identify bacterial species present. 16S rRNA gene sequence information is widely used for molecular identification of bacteria and is also useful for establishing the taxonomy of novel species.
The objective of this research was to obtain full 16S rRNA gene references sequences for feline oral bacteria, place the sequences in species-level phylotypes, and create a curated 16S RNA based taxonomy for common feline oral bacteria.
Clone libraries were produced using “universal” and phylum-selective PCR primers and DNA from pooled subgingival plaque from healthy and periodontally diseased cats. Bacteria in subgingival samples were also cultivated to obtain isolates. Full-length 16S rDNA sequences were determined for clones and isolates that represent 171 feline oral taxa. A provisional curated taxonomy was developed based on the position of each taxon in 16S rRNA phylogenetic trees.
The feline oral microbiome curated taxonomy and 16S rRNA gene reference set will allow investigators to refer to precisely defined bacterial taxa. A provisional name such as “Propionibacterium sp. feline oral taxon FOT-327” is an anchor to which clone, strain or GenBank names or accession numbers can point. Future next-generation-sequencing studies of feline oral bacteria will be able to map reads to taxonomically curated full-length 16S rRNA gene sequences.
feline; oral; bacteria; microbiome; 16S rRNA; taxonomy; phylogeny
Given the advent of massively parallel DNA sequencing, human microbiome is analyzed comprehensively by metagenomic approaches. However, the inter- and intra-individual variability and stability of the human microbiome remain poorly characterized, particularly at the intra-day level. This issue is of crucial importance for studies examining the effects of microbiome on human health. Here, we focused on bacteriome of oral plaques, for which repeated, time-controlled sampling is feasible. Eighty-one supragingival plaque subjects were collected from healthy individuals, examining multiple sites within the mouth at three time points (forenoon, evening, and night) over the course of 3 days. Bacterial composition was estimated by 16S rRNA sequencing and species-level profiling, resulting in identification of a total of 162 known bacterial species. We found that species compositions and their relative abundances were similar within individuals, and not between sampling time or tooth type. This suggests that species-level oral bacterial composition differs significantly between individuals, although the number of subjects is limited and the intra-individual variation also occurs. The majority of detected bacterial species (98.2%; 159/162), however, did not fluctuate over the course of the day, implying a largely stable oral microbiome on an intra-day time scale. In fact, the stability of this data set enabled us to estimate potential interactions between rare bacteria, with 40 co-occurrences supported by the existing literature. In summary, the present study provides a valuable basis for studies of the human microbiome, with significant implications in terms of biological and clinical outcomes.
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.
Leukocyte Adhesion Deficiency I (LAD-I) is a primary immunodeficiency caused by single gene mutations in the CD18 subunit of β2 integrins which result in defective transmigration of neutrophils into the tissues. Affected patients suffer from recurrent life threatening infections and severe oral disease (periodontitis). Microbial communities in the local environment (subgingival plaque) are thought to be the triggers for inflammatory periodontitis, yet little is known regarding the microbial communities associated with LAD-I periodontitis. Here we present the first comprehensive characterization of the subgingival communities in LAD-I, using a 16S rRNA gene-based microarray, and investigate the relationship of this tooth adherent microbiome to the local immunopathology of periodontitis. We show that the LAD subgingival microbiome is distinct from that of health and Localized Aggressive Periodontitits. Select periodontitis-associated species in the LAD microbiome included Parvimonas micra, Porphyromonas endodontalis, Eubacterium brachy and Treponema species. Pseudomonas aeruginosa, a bacterium not typically found in subgingival plaque is detected in LAD-I. We suggest that microbial products from LAD-associated communities may have a role in stimulating the local inflammatory response. We demonstrate that bacterial LPS translocates into the lesions of LAD-periodontitis potentially triggering immunopathology. We also show in in vitro assays with human macrophages and in vivo in animal models that microbial products from LAD-associated subgingival plaque trigger IL-23-related immune responses, which have been shown to dominate in patient lesions. In conclusion, our current study characterizes the subgingival microbial communities in LAD-periodontitis and supports their role as triggers of disease pathogenesis.
Leukocyte adhesion deficiency (LAD) is a primary immunodeficiency resulting from gene mutations in the CD18 subunit of β2 integrins that lead to defective neutrophil adhesion and transmigration into tissues. Affected patients suffer from recurrent life threatening infections and from a severe form of the oral disease periodontitis. The setting of this rare monogenic immune disorder provides a unique opportunity to explore consequences of defective neutrophil tissue transmigration on immunity and microbial colonization in barrier sites such as the oral mucosa. Furthermore, characterization of the oral- subgingival microbiome in LAD expands our understanding of LAD periodontitis, an aggressive disease which is recalcitrant to treatment and often leads to loss of the entire dentition in adolescence. Our current studies in a cohort of LAD patients show that the subgingival microbiome in LAD- periodontitis is unique in its composition and differs from that of health and aggressive periodontitis. Notably our studies reveal that the subgingival communities of LAD can serve as initial triggers for local immunopathology through translocation of bacterial products into tissues and stimulation of local IL-23-related destructive inflammatory responses.
The oral microbiome plays a key role for caries, periodontitis, and systemic diseases. A method for rapid, high-resolution, robust taxonomic profiling of subgingival bacterial communities for early detection of periodontitis biomarkers would therefore be a useful tool for individualized medicine. Here, we used Illumina sequencing of the V1-V2 and V5-V6 hypervariable regions of the 16S rRNA gene. A sample stratification pipeline was developed in a pilot study of 19 individuals, 9 of whom had been diagnosed with chronic periodontitis. Five hundred twenty-three operational taxonomic units (OTUs) were obtained from the V1-V2 region and 432 from the V5-V6 region. Key periodontal pathogens like Porphyromonas gingivalis, Treponema denticola, and Tannerella forsythia could be identified at the species level with both primer sets. Principal coordinate analysis identified two outliers that were consistently independent of the hypervariable region and method of DNA extraction used. The linear discriminant analysis (LDA) effect size algorithm (LEfSe) identified 80 OTU-level biomarkers of periodontitis and 17 of health. Health- and periodontitis-related clusters of OTUs were identified using a connectivity analysis, and the results confirmed previous studies with several thousands of samples. A machine learning algorithm was developed which was trained on all but one sample and then predicted the diagnosis of the left-out sample (jackknife method). Using a combination of the 10 best biomarkers, 15 of 17 samples were correctly diagnosed. Training the algorithm on time-resolved community profiles might provide a highly sensitive tool to detect the onset of periodontitis.
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).
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.
Social relationships have profound effects on health in humans and other primates, but the mechanisms that explain this relationship are not well understood. Using shotgun metagenomic data from wild baboons, we found that social group membership and social network relationships predicted both the taxonomic structure of the gut microbiome and the structure of genes encoded by gut microbial species. Rates of interaction directly explained variation in the gut microbiome, even after controlling for diet, kinship, and shared environments. They therefore strongly implicate direct physical contact among social partners in the transmission of gut microbial species. We identified 51 socially structured taxa, which were significantly enriched for anaerobic and non-spore-forming lifestyles. Our results argue that social interactions are an important determinant of gut microbiome composition in natural animal populations—a relationship with important ramifications for understanding how social relationships influence health, as well as the evolution of group living.
The digestive system is home to a complex community of microbes—known as the gut microbiome—that contributes to our health and wellbeing by digesting food, producing essential vitamins, and preventing the growth of harmful bacteria. The recent development of rapid genome sequencing techniques has made it much easier to identify the species of microbes found in the gut microbiome, and how this microbiome's composition varies between individuals.
Studies in humans and other primates suggest that direct contact during social interactions may alter the composition of the gut microbiome in an individual. This could explain why there is a strong association between social interactions and health in humans and other social animals. However, similarities in the gut microbiomes of individuals within a social group could also be due to a shared diet or a common environment. The information collected during long-term studies of wild primates offers an opportunity to analyze and assess the influence of diet, environment and social interaction on the gut microbiome.
Here, Tung et al. studied the gut microbiomes of 48 wild baboons belonging to two different social groups in Amboseli, Kenya. Using a technique called shotgun metagenomic sequencing, they sequenced DNA extracted from samples of feces collected from individual baboons. The sequence data revealed that an individual's social group and social network can predict the species found in its gut microbiome. This remained the case even when other factors—such as diet, kinship, and shared environments—were taken into account.
Tung et al.'s findings suggest that direct physical contact during social interactions may be important in transmitting gut microbiomes between members of the same social group. However, scientists still don't know whether this exchange is good or bad for the health of the baboons. Future work will try to understand whether baboons benefit from acquiring gut microbes from their group members, and if the gut microbes of some social groups are better than others.
Papio cynocephalus; social behavior; gut microbiome; metagenomics; transmission; social network; other
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.
Determining the composition and function of subgingival dental plaque is crucial to understanding human periodontal health and disease, but it is challenging because of the complexity of the interactions between human microbiomes and human body. Here, we examined the phylogenetic and functional gene differences between periodontal and healthy individuals using MiSeq sequencing of 16S rRNA gene amplicons and a specific functional gene array (a combination of GeoChip 4.0 for biogeochemical processes and HuMiChip 1.0 for human microbiomes). Our analyses indicated that the phylogenetic and functional gene structure of the oral microbiomes were distinctly different between periodontal and healthy groups. Also, 16S rRNA gene sequencing analysis indicated that 39 genera were significantly different between healthy and periodontitis groups, and Fusobacterium, Porphyromonas, Treponema, Filifactor, Eubacterium, Tannerella, Hallella, Parvimonas, Peptostreptococcus and Catonella showed higher relative abundances in the periodontitis group. In addition, functional gene array data showed that a lower gene number but higher signal intensity of major genes existed in periodontitis, and a variety of genes involved in virulence factors, amino acid metabolism and glycosaminoglycan and pyrimidine degradation were enriched in periodontitis, suggesting their potential importance in periodontal pathogenesis. However, the genes involved in amino acid synthesis and pyrimidine synthesis exhibited a significantly lower relative abundance compared with healthy group. Overall, this study provides new insights into our understanding of phylogenetic and functional gene structure of subgingival microbial communities of periodontal patients and their importance in pathogenesis of periodontitis.
functional gene array; Illumina sequencing; periodontitis; subgingival dental plaque
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
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.
Chronic periodontitis is an inflammatory disease of the periodontium affecting nearly 65 million adults in the United States. Changes in subgingival microbiota have long been associated with chronic periodontitis. Recent culture-independent molecular studies have revealed the immense richness and complexity of oral microbial communities. However, data sets across studies have not been directly compared, and whether the observed microbial variations are consistent across different studies is not known. Here, we used 16S rRNA sequencing to survey the subgingival microbiota in 25 subjects with chronic periodontal disease and 25 healthy controls and compared our data sets with those of three previously reported microbiome studies. Consistent with data from previous studies, our results demonstrate a significantly altered microbial community structure with decreased heterogeneity in periodontal disease. Comparison with data from three previously reported studies revealed that subgingival microbiota clustered by study. However, differences between periodontal health and disease were larger than the technical variations across studies. Using a prediction score and applying five different distance metrics, we observed two predominant clusters. One cluster was driven by Fusobacterium and Porphyromonas and was associated with clinically apparent periodontitis, and the second cluster was dominated by Rothia and Streptococcus in the majority of healthy sites. The predicted functional capabilities of the periodontitis microbiome were significantly altered. Genes involved in bacterial motility, energy metabolism, and lipopolysaccharide biosynthesis were overrepresented in periodontal disease, whereas genes associated with transporters, the phosphotransferase system, transcription factors, amino acid biosynthesis, and glycolysis/gluconeogenesis were enriched in healthy controls. These results demonstrate significant alterations in microbial composition and function in periodontitis and suggest genes and metabolic pathways associated with periodontal disease.
Viruses make up a major component of the human microbiota but are poorly understood in the skin, our primary barrier to the external environment. Viral communities have the potential to modulate states of cutaneous health and disease. Bacteriophages are known to influence the structure and function of microbial communities through predation and genetic exchange. Human viruses are associated with skin cancers and a multitude of cutaneous manifestations. Despite these important roles, little is known regarding the human skin virome and its interactions with the host microbiome. Here we evaluated the human cutaneous double-stranded DNA virome by metagenomic sequencing of DNA from purified virus-like particles (VLPs). In parallel, we employed metagenomic sequencing of the total skin microbiome to assess covariation and infer interactions with the virome. Samples were collected from 16 subjects at eight body sites over 1 month. In addition to the microenviroment, which is known to partition the bacterial and fungal microbiota, natural skin occlusion was strongly associated with skin virome community composition. Viral contigs were enriched for genes indicative of a temperate phage replication style and also maintained genes encoding potential antibiotic resistance and virulence factors. CRISPR spacers identified in the bacterial DNA sequences provided a record of phage predation and suggest a mechanism to explain spatial partitioning of skin phage communities. Finally, we modeled the structure of bacterial and phage communities together to reveal a complex microbial environment with a Corynebacterium hub. These results reveal the previously underappreciated diversity, encoded functions, and viral-microbial dynamic unique to the human skin virome.
To date, most cutaneous microbiome studies have focused on bacterial and fungal communities. Skin viral communities and their relationships with their hosts remain poorly understood despite their potential to modulate states of cutaneous health and disease. Previous studies employing whole-metagenome sequencing without purification for virus-like particles (VLPs) have provided some insight into the viral component of the skin microbiome but have not completely characterized these communities or analyzed interactions with the host microbiome. Here we present an optimized virus purification technique and corresponding analysis tools for gaining novel insights into the skin virome, including viral “dark matter,” and its potential interactions with the host microbiome. The work presented here establishes a baseline of the healthy human skin virome and is a necessary foundation for future studies examining viral perturbations in skin health and disease.
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.
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
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.