Probing periodontal microbial dark matter using metataxonomics and metagenomics

Microbial diagnostics in periodontal diseases

Microbial analytical methods have been instrumental in elucidating the complex microbial etiology of periodontal diseases, by shaping our understanding of subgingival community dynamics. Certain pathobionts can orchestrate the establishment of dysbiotic communities that can subvert the host immune system, triggering inflammation and tissue destruction. Yet, diagnosis and management of periodontal conditions still rely on clinical and radiographic examinations, overlooking the well-established microbial etiology. This review summarizes the chronological emergence of periodontal etiological models and the co-evolution with technological advances in microbial detection. We additionally review the microbial analytical approaches currently accessible to clinicians, highlighting their value in broadening the periodontal assessment. The epidemiological importance of obtaining culture-based antimicrobial susceptibility profiles of periodontal taxa for antibiotic resistance surveillance is also underscored, together with clinically relevant analytical approaches to guide antibiotherapy choices, when necessary. Furthermore, the importance of 16S-based community and shotgun metagenomic profiling is discussed in outlining dysbiotic microbial signatures. Because dysbiosis precedes periodontal damage, biomarker identification offers early diagnostic possibilities to forestall disease relapses during maintenance. Altogether, this review highlights the underutilized potential of clinical microbiology in periodontology, spotlighting the clinical areas most conductive to its diagnostic implementation for enhancing prevention, treatment predictability, and addressing global antibiotic resistance.

Effect of Probiotic Supplements on the Oral Microbiota-A Narrative Review

Data from systematic reviews and meta-analyses show that probiotics positively impact clinical parameters of oral diseases such as gingivitis, dental caries, and periodontitis. However, the working mechanism of probiotics is not fully understood, but is hypothesized to be mediated by direct and indirect interactions with the oral microbiota and the human host. In the present narrative review, we focused on the microbiological effect of probiotic supplements based on data retrieved from randomized clinical trials (RCTs). In addition, we assessed to what extent contemporary molecular methods have been employed in clinical trials in the field of oral probiotics. Multiple RCTs have been performed studying the potential effect of probiotics on gingivitis, dental caries, and periodontitis, as evaluated by microbial endpoints. In general, results are conflicting, with some studies reporting a positive effect, whereas others are not able to record any effect. Major differences in terms of study designs and sample size, as well as delivery route, frequency, and duration of probiotic consumption, hamper comparison across studies. In addition, most RCTs have been performed with a limited sample size using relatively simple methods for microbial identification, such as culturing, qPCR, and DNA-DNA checkerboard, while high-throughput methods such as 16S sequencing have only been employed in a few studies. Currently, state-of-the-art molecular methods such as metagenomics, metatranscriptomics, and metaproteomics have not yet been used in RCTs in the field of probiotics. The present narrative review revealed that the effect of probiotic supplements on the oral microbiota remains largely uncovered. One important reason is that most RCTs are performed without studying the microbiological effect. To facilitate future systematic reviews and meta-analyses, an internationally agreed core outcome set for the reporting of microbial endpoints in clinical trials would be desirable. Such a standardized collection of outcomes would most likely improve the quality of probiotic research in the oral context.

Transcriptomic features of programmed and inflammatory cell death in gingival tissues

The local gingival tissue environment with homeostasis and tissue-destructive events of periodontitis demonstrates major changes in histological features and biology of the oral/sulcular epithelium, fibroblasts, vascular cells, inflammatory cell infiltration, and alveolar bone.

OBJECTIVE

This study used an experimental periodontitis model to detail the gingival transcriptome related to cell death processes of pyroptosis, necroptosis, ferroptosis, and cuproptosis.

MATERIALS AND METHODS

Healthy Macaca mulatta primates stratified by age, ≤3 years (young), 7-12 years (adolescent), 12-15 years (adult), and 17-23 years (aged), provided gingival tissue biopsies for microarray analysis focused on 257 genes representative of the four cell death processes and bacterial plaque samples for 16S rRNA gene analysis.

RESULTS

Age differences in the profiles of gene expression in healthy tissues were noted for cuproptosis, ferroptosis, necroptosis, and pyroptosis. Major differences were then observed with disease initiation, progression, and resolution also related to the age of the animals. Distinct bacterial families/consortia of species were significantly related to the gene expression differences for the cell death pathways.

CONCLUSIONS

These results emphasized age-associated differences in the gingival tissue molecular response to changes in the quality and quantity of bacteria accumulating with the disease process reflected in regulated cell death pathways that are both physiological and pathophysiological.

Placental TLR recognition of salivary and subgingival microbiota is associated with pregnancy complications

BACKGROUND

Pre-term birth, the leading cause of neonatal mortality, has been associated with maternal periodontal disease and the presence of oral pathogens in the placenta. However, the mechanisms that underpin this link are not known. This investigation aimed to identify the origins of placental microbiota and to interrogate the association between parturition complications and immune recognition of placental microbial motifs. Video Abstract METHODS: Saliva, plaque, serum, and placenta were collected during 130 full-term (FT), pre-term (PT), or pre-term complicated by pre-eclampsia (PTPE) deliveries and subjected to whole-genome shotgun sequencing. Real-time quantitative PCR was used to measure toll-like receptors (TLR) 1-10 expression in placental samples. Source tracking was employed to trace the origins of the placental microbiota.

RESULTS

We discovered 10,007 functionally annotated genes representing 420 taxa in the placenta that could not be attributed to contamination. Placental microbial composition was the biggest discriminator of pregnancy complications, outweighing hypertension, BMI, smoking, and maternal age. A machine-learning algorithm trained on this microbial dataset predicted PTPE and PT with error rates of 4.05% and 8.6% (taxonomy) and 6.21% and 7.38% (function). Logistic regression revealed 32% higher odds of parturition complication (95% CI 2.8%, 81%) for every IQR increase in the Shannon diversity index after adjusting for maternal smoking status, maternal age, and gravida. We also discovered distinct expression patterns of TLRs that detect RNA- and DNA-containing antigens in the three groups, with significant upregulation of TLR9, and concomitant downregulation of TLR7 in PTPE and PT groups, and dense correlation networks between microbial genes and these TLRs. 70-82% of placental microbiota were traced to serum and thence to the salivary and subgingival microbiomes. The oral and serum microbiomes of PTPE and PT groups displayed significant enrichment of genes encoding iron transport, exosome, adhesion, quorum sensing, lipopolysaccharide, biofilm, and steroid degradation.

CONCLUSIONS

Within the limits of cross-sectional analysis, we find evidence to suggest that oral bacteria might translocate to the placenta via serum and trigger immune signaling pathways capable of inducing placental vascular pathology. This might explain, in part, the higher incidence of obstetric syndromes in women with periodontal disease.

Impact of surface characteristics on the peri-implant microbiome in health and disease

BACKGROUND

Because little is known about the impact of implant surface modifications on the peri-implant microbiome, we aimed to examine peri-implant communities in various surface types in order to better understand the impact of these surfaces on the development of peri-implantitis (PI).

METHODS

One hundred and six systemically healthy individuals with anodized (AN), hydroxyapatite-coated (HA), or sandblasted acid-etched (SLA) implants that were >6 months in function were recruited and categorized into health (H) or PI. Peri-implant biofilm was analyzed using 16S rRNA gene sequencing and compared between health/disease and HA/SLA/AN using community-level and taxa-level metrics.

RESULTS

Healthy implants did not demonstrate significant differences in clustering, alpha- or beta-diversity based on surface modification. AN and HA surfaces displayed significant differences between health and PI (p < 0.05); however, such a clustering was not evident with SLA (p > 0.05). AN and HA surfaces also differed in the magnitude and diversity of differences between health and PI. Six species belonging to the genera Shuttleworthia, Scardovia, and Prevotella demonstrated lower abundances in AN implants with PI, and 18 species belonging to the genera Fretibacterium, Tannerella, Treponema, and Fusobacterium were elevated, while in HA implants with PI, 20 species belonging to the genera Streptococcus, Lactobacillus, Veillonella, Rothia, and family Ruminococcaceae were depleted and Peptostreptococcaceae, Atopobiaceae, Veillonellaceae, Porphyromonadaceae, Desulfobulbaceae, and order Synergistales were enriched.

CONCLUSIONS

Within the limitations of this study, we demonstrate that implant surface can differentially modify the disease-associated microbiome, suggesting that surface topography must be considered in the multi-factorial etiology of peri-implant diseases.

Mouthwash Effects on the Oral Microbiome: Are They Good, Bad, or Balanced?

This narrative review describes the oral microbiome, and its role in oral health and disease, before considering the impact of commonly used over-the-counter (OTC) mouthwashes on oral bacteria, viruses, bacteriophages, and fungi that make up these microbial communities in different niches of the mouth. Whilst certain mouthwashes have proven antimicrobial actions and clinical effectiveness supported by robust evidence, this review reports more recent metagenomics evidence, suggesting that mouthwashes such as chlorhexidine may cause "dysbiosis," whereby certain species of bacteria are killed, leaving others, sometimes unwanted, to predominate. There is little known about the effects of mouthwashes on fungi and viruses in the context of the oral microbiome (virome) in vivo, despite evidence that they "kill" certain viral pathogens ex vivo. Evidence for mouthwashes, much like antibiotics, is also emerging with regards to antimicrobial resistance, and this should further be considered in the context of their widespread use by clinicians and patients. Therefore, considering the potential of currently available OTC mouthwashes to alter the oral microbiome, this article finally proposes that the ideal mouthwash, whilst combatting oral disease, should "balance" antimicrobial communities, especially those associated with health. Which antimicrobial mouthwash best fits this ideal remains uncertain.

Illuminating the oral microbiome and its host interactions: recent advancements in omics and bioinformatics technologies in the context of oral microbiome research

The oral microbiota has an enormous impact on human health, with oral dysbiosis now linked to many oral and systemic diseases. Recent advancements in sequencing, mass spectrometry, bioinformatics, computational biology, and machine learning are revolutionizing oral microbiome research, enabling analysis at an unprecedented scale and level of resolution using omics approaches. This review contains a comprehensive perspective of the current state-of-the-art tools available to perform genomics, metagenomics, phylogenomics, pangenomics, transcriptomics, proteomics, metabolomics, lipidomics, and multi-omics analysis on (all) microbiomes, and then provides examples of how the techniques have been applied to research of the oral microbiome, specifically. Key findings of these studies and remaining challenges for the field are highlighted. Although the methods discussed here are placed in the context of their contributions to oral microbiome research specifically, they are pertinent to the study of any microbiome, and the intended audience of this includes researchers would simply like to get an introduction to microbial omics and/or an update on the latest omics methods. Continued research of the oral microbiota using omics approaches is crucial and will lead to dramatic improvements in human health, longevity, and quality of life.

Subgingival microbiome in periodontal health, gingivitis and different stages of periodontitis

AIM

To characterize the subgingival microbiome in subjects with different periodontal health statuses.

MATERIALS AND METHODS

In this cross-sectional observational study, subgingival samples were harvested from Spanish subjects with different periodontal health statuses, based on the 2018 Classification of Periodontal and Peri-Implant Diseases and Conditions. Samples were processed using high-throughput sequencing technologies (Illumina MiSeq). Taxa differentially abundant were identified using Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC). α- and β-diversity metrics were calculated using q2-diversity in QIIME2. The analyses were adjusted for age, gender and smoking status.

RESULTS

The identified subgingival microbiome showed statistically significant differences among subjects, categorized into periodontal health, gingivitis and stages I-II and III-IV periodontitis (p < .05). In patients with severe (stages III-IV) periodontitis, the genera Filifactor and Fretibacterium were detected 24 times more frequently than in periodontally healthy subjects. Similarly, the genera Porphyromonas, Prevotella and Tannerella were detected four times more frequently (p < .05). The genera Granulicatella, Streptococcus, Paracoccus, Pseudomonas, Haemophilus, Actinobacteria, Bergeyella and Capnocytophaga were significantly associated with healthier periodontal status (p < .05).

CONCLUSIONS

Significant differences were detected in the subgingival microbiome among periodontal health, gingivitis and stages I-II or III-IV periodontitis, suggesting overlapping, yet distinguishable microbial profiles.

Precision periodontal care: from omics discoveries to chairside diagnostics

The interface of molecular science and technology is guiding the transformation of personalized to precision healthcare. The application of proteomics, genomics, transcriptomics, and metabolomics is shaping the suitability of biomarkers for disease. Prior validation of such biomarkers in large and diverse patient cohorts helps verify their clinical usability. Incorporation of molecular discoveries into routine clinical practice relies on the development of customized assays and devices that enable the rapid delivery of analytical data to the clinician, while the patient is still in session. The present perspective review addresses this topic under the prism of precision periodontal care. Selected promising research attempts to innovate technological platforms for oral diagnostics are brought forward. Focus is placed on (a) the suitability of saliva as a conveniently sampled biological specimen for assessing periodontal health, (b) proteomics as a high-throughput approach for periodontal disease biomarker identification, and (c) chairside molecular diagnostic assays as a technological funnel for transitioning from the laboratory benchtop to the clinical point-of-care.

Association of nine pathobionts with periodontitis in four South American and European countries

Aim

Our aim was to compare the prevalence and load of nine pathobionts in subgingival samples of healthy individuals and periodontitis patients from four different countries.

Methods

Five hundred and seven subgingival biofilm samples were collected from healthy subjects and periodontitis patients in Belgium, Chile, Peru and Spain. The prevalence and load of Eubacterium brachy, Filifactor alocis, Fretibacterium fastidiosum, Porphyromonas endodontalis, Porphyromonas gingivalis, Selenomonas sputigena, Treponema denticola, Tannerella forsythia and Treponema socranskii were measured by quantitative PCR.

Results

The association with periodontitis of all species, except for T. socranskii, was confirmed in all countries but Peru, where only P. endodontalis, P. gingivalis and T. denticola were found to be significantly associated. Moreover, most species showed higher loads at greater CAL and PPD, but not where there was BOP. Through Principal Component Analysis, samples showed clearly different distributions by diagnosis, despite observing a smaller separation in Peruvian samples.

Conclusions

Unlike prevalence, relative load was found to be a reliable variable to discriminate the association of the species with periodontitis. Based on this, F. alocis, P. endodontalis, P. gingivalis, T. denticola and T. forsythia may be biomarkers of disease in Belgium, Chile and Spain, due to their significantly higher abundance in periodontitis patients.

Next-Generation Examination, Diagnosis, and Personalized Medicine in Periodontal Disease

Periodontal disease, a major cause of tooth loss, is an infectious disease caused by bacteria with the additional aspect of being a noncommunicable disease closely related to lifestyle. Tissue destruction based on chronic inflammation is influenced by host and environmental factors. The treatment of periodontal disease varies according to the condition of each individual patient. Although guidelines provide standardized treatment, optimization is difficult because of the wide range of treatment options and variations in the ideas and skills of the treating practitioner. The new medical concepts of “precision medicine” and “personalized medicine” can provide more predictive treatment than conventional methods by stratifying patients in detail and prescribing treatment methods accordingly. This requires a new diagnostic system that integrates information on individual patient backgrounds (biomarkers, genetics, environment, and lifestyle) with conventional medical examination information. Currently, various biomarkers and other new examination indices are being investigated, and studies on periodontal disease-related genes and the complexity of oral bacteria are underway. This review discusses the possibilities and future challenges of precision periodontics and describes the new generation of laboratory methods and advanced periodontal disease treatment approaches as the basis for this new field.

Toward Personalized Oral Diagnosis: Distinct Microbiome Clusters in Periodontitis Biofilms

Periodontitis is caused by pathogenic subgingival microbial biofilm development and dysbiotic interactions between host and hosted microbes. A thorough characterization of the subgingival biofilms by deep amplicon sequencing of 121 individual periodontitis pockets of nine patients and whole metagenomic analysis of the saliva microbial community of the same subjects were carried out. Two biofilm sampling methods yielded similar microbial compositions. Taxonomic mapping of all biofilms revealed three distinct microbial clusters. Two clinical diagnostic parameters, probing pocket depth (PPD) and clinical attachment level (CAL), correlated with the cluster mapping. The dysbiotic microbiomes were less diverse than the apparently healthy ones of the same subjects. The most abundant periodontal pathogens were also present in the saliva, although in different representations. The single abundant species Tannerella forsythia was found in the diseased pockets in about 16–17-fold in excess relative to the clinically healthy sulcus, making it suitable as an indicator of periodontitis biofilms. The discrete microbial communities indicate strong selection by the host immune system and allow the design of targeted antibiotic treatment selective against the main periodontal pathogen(s) in the individual patients.

Discrimination of Bacterial Community Structures among Healthy, Gingivitis, and Periodontitis Statuses through Integrated Metatranscriptomic and Network Analyses

Periodontal disease is an inflammatory condition caused by polymicrobial infection. The inflammation is initiated at the gingiva (gingivitis) and then extends to the alveolar bone, leading to tooth loss (periodontitis). Previous studies have shown differences in bacterial composition between periodontal healthy and diseased sites. However, bacterial metabolic activities during the health-to-periodontitis microbiome shift are still inadequately understood. This study was performed to investigate the bacterial characteristics of healthy, gingivitis, and periodontitis statuses through metatranscriptomic analysis. Subgingival plaque samples of healthy, gingivitis, and periodontitis sites in the same oral cavity were collected from 21 patients. Bacterial compositions were then determined based on 16S rRNA reads; taxonomic and functional profiles derived from genes based on mRNA reads were estimated. The results showed clear differences in bacterial compositions and functional profiles between healthy and periodontitis sites. Co-occurrence networks were constructed for each group by connecting two bacterial species if their mRNA abundances were positively correlated. The clustering coefficient values were 0.536 for healthy, 0.600 for gingivitis, and 0.371 for periodontitis sites; thus, network complexity increased during gingivitis development, whereas it decreased during progression to periodontitis. Taxa, including Eubacterium nodatum, Eubacterium saphenum, Filifactor alocis, and Fretibacterium fastidiosum, showed greater transcriptional activities than those of red complex bacteria, in conjunction with disease progression. These taxa were associated with periodontal disease progression, and the health-to-periodontitis microbiome shift was accompanied by alterations in bacterial network structure and complexity.

IMPORTANCE The characteristics of the periodontal microbiome influence clinical periodontal status. Gingivitis involves reversible gingival inflammation without alveolar bone resorption. In contrast, periodontitis is an irreversible disease characterized by inflammatory destruction in both soft and hard tissues. An imbalance of the microbiome is present in both gingivitis and periodontitis. However, differences in microbiomes and their functional activities in the healthy, gingivitis, and periodontitis statuses are still inadequately understood. Furthermore, some inflamed gingival statuses do not consistently cause attachment loss. In this study, metatranscriptomic analyses were used to investigate the specific bacterial composition and gene expression patterns of the microbiomes of the healthy, gingivitis, and periodontitis statuses. In addition, co-occurrence network analysis revealed that the gingivitis site included features of networks observed in both the healthy and periodontitis sites. These results provide transcriptomic evidence to support gingivitis as an intermediate state between the healthy and periodontitis statuses.

The oral microbiome in alcohol use disorder: a longitudinal analysis during inpatient treatment

Background

Alcohol use disorder (AUD)-induced disruption of oral microbiota can lead to poor oral health; there have been no studies published examining the longitudinal effects of alcohol use cessation on the oral microbiome.

Aim

To investigate the oral microbiome during alcohol cessation during inpatient treatment for AUD.

Methods

Up to 10 oral tongue brushings were collected from 22 AUD patients during inpatient treatment at the National Institutes of Health. Alcohol use history, smoking, and periodontal disease status were measured. Oral microbiome samples were sequenced using 16S rRNA gene sequencing.

Results

Alpha diversity decreased linearly during treatment across the entire cohort (P = 0.002). Alcohol preference was associated with changes in both alpha and beta diversity measures. Characteristic tongue dorsum genera from the Human Microbiome Project such as Streptococcus, Prevotella, Veillonella and Haemophilus were highly correlated in AUD. Oral health-associated genera that changed longitudinally during abstinence included Actinomyces, Capnocytophaga, Fusobacterium, Neisseria and Prevotella.

Conclusion

The oral microbiome in AUD is affected by alcohol preference. Patients with AUD often have poor oral health but abstinence and attention to oral care improve dysbiosis, decreasing microbiome diversity and periodontal disease-associated genera while improving acute oral health.

Oral Microbiome in Dogs and Cats: Dysbiosis and the Utility of Antimicrobial Therapy in the Treatment of Periodontal Disease

Advances in gene sequence technology and data analysis have enabled the detection and taxonomic identification of microorganisms in vivo based on their unique RNA or DNA sequences. Standard culture techniques can only detect those organisms that readily grow on artificial media in vitro. Culture-independent technology has been used to provide a more accurate assessment of the richness (total number of species) and diversity (relative abundance of each species) of microorganisms present in a prescribed location. The microbiome has been defined as the genes and genomes of all microbial inhabitants within a defined environment. Microorganisms within a microbiome interact with each other as well as with the host. A microbiome is dynamic and may change over time as conditions within the defined environment become altered. In oral health, neither gingivitis nor periodontitis is present, and the host and microbiome coexist symbiotically without evoking an inflammatory response. The circumstances that cause a shift from immune tolerance to a proinflammatory response remain unknown, and a unified, all-encompassing hypothesis to explain how and why periodontal disease develops has yet to be described. The purpose of this review is to clarify the current understanding of the role played by the oral microbiome in dogs and cats, describe how the microbiome changes in periodontal disease, and offer guidance on the utility of systemic antimicrobial agents in the treatment of periodontitis in companion animals.

Transcriptional Activity of Predominant Streptococcus Species at Multiple Oral Sites Associate With Periodontal Status

Background

Streptococcus species are predominant members of the oral microbiota in both health and diseased conditions. The purpose of the present study was to explore if different ecological characteristics, such as oxygen availability and presence of periodontitis, associates with transcriptional activity of predominant members of genus Streptococcus. We tested the hypothesis that genetically closely related Streptococcus species express different transcriptional activities in samples collected from environments with critically different ecological conditions determined by site and inflammatory status.

Methods

Metagenomic and metatranscriptomic data was retrieved from 66 oral samples, subgingival plaque (n=22), tongue scrapings (n=22) and stimulated saliva (n=22) collected from patients with periodontitis (n=11) and orally healthy individuals (n=11). Species-specific transcriptional activity was computed as Log2(RNA/DNA), and transcriptional activity of predominant Streptococcus species was compared between multiple samples collected from different sites in the same individual, and between individuals with different oral health status.

Results

The predominant Streptococcus species were identified with a site-specific colonization pattern of the tongue and the subgingival plaque. A total of 11, 4 and 2 pathways expressed by S. parasanguinis, S. infantis and S. salivarius, respectively, were recorded with significantly higher transcriptional activity in saliva than in tongue biofilm in healthy individuals. In addition, 18 pathways, including pathways involved in synthesis of peptidoglycan, amino acid biosynthesis, glycolysis and purine nucleotide biosynthesis expressed by S. parasanguinis and 3 pathways expressed by S. salivarius were identified with significantly less transcriptional activity in patients with periodontitis.

Conclusion

Data from the present study significantly demonstrates the association of site-specific ecological conditions and presence of periodontitis with transcriptional activity of the predominant Streptococcus species of the oral microbiota. In particular, pathways expressed by S. parasanguinis being involved in peptidoglycan, amino acid biosynthesis, glycolysis, and purine nucleotide biosynthesis were identified to be significantly associated with oral site and/or inflammation status.

Periodontitis associates with species-specific gene expression of the oral microbiota

The purpose of the present investigation was to characterize species-specific bacterial activity of the oral microbiota in periodontitis. We tested the hypotheses that chronic inflammation, i.e., periodontitis, associates with bacterial gene expression of the oral microbiota. Oral microbial samples were collected from three oral sites—subgingival plaque, tongue, and saliva from patients with periodontitis and healthy controls. Paired metagenomics and metatranscriptomics were used to perform concomitant characterization of taxonomic composition and to determine species-specific bacterial activity as expressed by the ratio of specific messenger RNA reads to their corresponding genomic DNA reads. Here, we show the association of periodontitis with bacterial gene expression of the oral microbiota. While oral site was the main determinant of taxonomic composition as well as bacterial gene expression, periodontitis was significantly associated with a reduction of carbohydrate metabolism of the oral microbiota at three oral sites (subgingival plaque, tongue, and saliva). Data from the present study revealed the association of periodontitis with bacterial gene expression of the oral microbiota. Conditions of periodontitis was associated with bacterial activity of local subgingival plaque, but also on tongue and the salivary microbiota. Collectively, data suggest that periodontitis associates with impaired carbohydrate metabolism of the oral microbiota. Future longitudinal and interventional studies are warranted to evaluate the potential pathogenic role of impaired bacterial carbohydrate metabolism not only in periodontitis but also in other diseases with low-grade inflammation, such as type 2 diabetes mellitus.

Microbiomics: Were we all wrong before?

Periodontal microbiology has historically been based on an "us against them" paradigm, one that focuses mainly on identifying microbes and viruses that cause disease. However, such a bottom-up approach limits our appreciation of the incredible diversity of this ecosystem and the essential ways in which microbial interactions contribute to health and homeostasis of the subgingival niche. Microbiomics-the science of collectively characterizing and quantifying molecules responsible for the structure, function, and dynamics of a microbial community-has enabled us to study these communities in their natural habitat, thereby revolutionizing our knowledge of host-associated microbes and reconceptualizing our definition of "human." When this systems-biology approach is combined with ecologic principles, it explicates the complex relationship that exist between microbiota and between them and us, the human. In this volume of Periodontology 2000, a group of 12 female scientists take the lead in investigating how metagenomics, genomics, metatranscriptomics, proteomics, metaproteomics, and metabolomics have achieved the following: (a) widened our view of the periodontal microbiome; (b) expanded our understanding of the evolution of the human oral microbiome; (c) shone a light on not just bacteria, but also other prokaryotic and eukaryotic members of the community; (d) elucidated the effects of anthropogenic behavior and systemic diseases on shaping these communities; and (e) influenced traditional patterns of periodontal therapeutics.