Measuring the subgingival microbiota in periodontitis patients: Comparison of the surface layer and the underlying layers

Comparison of the performance of MiSeq and NovaSeq in oral microbiome study

Objective

Next generation sequencing is commonly used to characterize the microbiome structure. MiSeq is most commonly used to analyze the microbiome due to its relatively long read length. Illumina also introduced the 250 × 2 chip for NovaSeq. The purpose of this study was to compare the performance of MiSeq and NovaSeq in the context of oral microbiome study.

Methods

Total read count, read quality score, relative bacterial abundance, community diversity, and correlation between two platforms were analyzed. Phylogenetic trees were analyzed for Streptococcus and periodontopathogens.

Results

NovaSeq produced significantly more read counts and assigned more operational taxonomic units (OTUs) compared to MiSeq. Community diversity was similar between MiSeq and NovaSeq. NovaSeq were able to detect more unique OTUs compared to MiSeq. When phylogenetic trees were constructed for Streptococcus and periodontopathogens, both platforms detected OTUs for most of the clades.

Conclusion

Taken together, while both MiSeq and NovaSeq platforms effectively characterize the oral microbiome, NovaSeq outperformed MiSeq in terms of read counts and detection of unique OTUs, highlighting its potential as a valuable tool for large scale oral microbiome studies.

Design and validation of a quantitative polymerase chain reaction test for the identification and quantification of uncultivable bacteria associated with periodontitis

OBJECTIVE

This study aimed to standardize a quantitative polymerase chain reaction (qPCR)-based test to identify and quantify the uncultivable bacteria associated with periodontitis.

METHODS

The standardization of qPCR, the curves for the quantification of Eubacterium saphenum, Eubacterium brachy, Desulfobulbus oralis, and Filifactor alocis were developed by cloning the 16 S rRNA target gene fragment, using the GEMTEasy vector. The qPCRs were validated in 55 subgingival biofilm clinical samples, from different stages of periodontitis and from periodontally healthy/gingivitis individuals, which were previously evaluated by next-generation sequencing (NGS). The results obtained by the two methods were compared by the concordance of Cohen's Kappa index, and sensitivity, specificity, receiver operating characteristic (ROC) curve, and predictive values were established.

RESULTS

obtained by the two methods were compared using the concordance of Cohen's Kappa index, and sensitivity, specificity, predictive values, and ROC curves were generated. The qPCR test was standardized with efficiencies between 90% and 100% and R2: 0.997-0.999. Concordance between the qPCR and NSG was moderate to F. alocis (agreement 78.2%; kappa 0.56, p < 0.05) and fair to the other microorganisms (agreement 67.27%-72.73; kappa 0.37-0.38, p < 0.05). qPCR exhibited a high sensitivity (82.2-100%) and specificity (100%) for E. brachy, E. saphenum, and F. alocis. Sensitivity was lower to D. oralis. Conversely, qPCR demonstrated higher sensitivity to E. saphenum than NSG (100 vs. 68.1).

CONCLUSIONS

The uncultivable microorganisms associated with periodontitis, D. oralis, E. brachy, E. saphenum, and F. alocis can be detected and quantified with the newly developed and validates qPCR test.

Comparative Analysis of Primers Used for 16S rRNA Gene Sequencing in Oral Microbiome Studies

Recent advances in genomic technologies have enabled more in-depth study of the oral microbiome. In this study, we compared the amplicons generated by primers targeting different sites of the 16S rRNA gene found in the Human Oral Microbiome Database (HOMD). Six sets of primer targeting V1-V2, V1-V3, V3-V4, V4-V5, V5-V7 and V6-V8 regions of 16S rRNA were tested via in silico simulation. Primers targeting the V1-V2, V3-V4, and V4-V5 regions generated more than 90% of the original input sequences. Primers targeting the V1-V2 and V1-V3 regions exhibited a low number of mismatches and unclassified sequences at the taxonomic level, but there were notable discrepancies at the species level. Phylogenetic tree comparisons showed primers targeting the V1-V2 and V3-V4 regions showed performances similar to primers targeting the whole 16s RNA region in terms of separating total oral microbiomes and periodontopathogens. In an analysis of clinical oral samples, V1-V2 primers showed superior performance for identifying more taxa and had better resolution sensitivity for Streptococcus than V3-V4 primers. In conclusion, primers targeting the V1-V2 region of 16S rRNA showed the best performance for oral microbiome studies. In addition, the study demonstrates the need for careful PCR primer selections.

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.

Analysis of subgingival micro-organisms based on multi-omics and Treg/Th17 balance in type 2 diabetes with/without periodontitis

Type 2 diabetes mellitus (T2DM) and periodontitis are common and interrelated diseases, resulting in altered host response microbiota. The subgingival micro-organisms play a key role in periodontitis pathogenesis. To assess the shift of subgingival microbiome and metabolome in T2DM, we performed an analysis of the subgingival microbiome in patients with T2DM (n = 20) compared with non-diabetes (ND) subjects (n = 21). Furthermore, patients were subdivided into 10 T2DM with periodontitis (DP), 10 T2DM without periodontitis (DNP), 10 periodontitis (P), and 11 healthy control (H) groups. 16SrRNA gene sequencing combined with ultra high-performance liquid chromatography-mass spectrometry (UHPLC-MS) based metabolomics was performed in all participants. T lymphocyte immunity was analyzed by flow cytometry. Furthermore, the network relationship among subgingival micro-organisms, metabolites, blood glucose level, and T lymphocyte immunity were analyzed. The results showed that the difference of the subgingival microbiome from healthy to periodontitis status was less prominent in T2DM compared with ND, though the clinical signs of disease were similar. The bacteria Eubacterium nodatum group, Filifactor, Fretibacterium, Peptostreptococcus, and Desulfovibrio, amongst others, may be important in the pathopoiesia of periodontitis in the T2DM state. In addition, some dominant bacteria showed network relationships. The Treg/Th17 ratio was lower in the DP and DNP groups than in the P and H groups-though that of P was lower than for H. The percentage of CD4+/CD8+ PD1 and CD8+ PDL1 was higher in the DP and DNP groups than in the H group; the percentage of CD8+ PDL1 was higher in the DP than P groups. Subgingival micro-organisms in periodontitis had a significant metabolic shift in terms of their signature metabolites. Butyrate metabolism and phenylalanine metabolism may play a role in the pathogenesis of periodontitis with/without T2DM. Specifically, biphenyl degradation, tryptophan metabolism, and the two-component system may play important roles in periodontitis with T2DM. Lastly, the network relationship among subgingival micro-organisms, metabolites, blood glucose level, and T lymphocyte immunity were unbalanced. This study identified the changes in the subgingival microbiome associated with periodontitis in T2DM, as well as the associated network between bacterial flora, metabolism dysbiosis, and immune regulation.

Differences in the subgingival microbiome according to stage of periodontitis: A comparison of two geographic regions

No microbiological criteria were included in the 2018 EFP-AAP classification of periodontal diseases that could be used to differentiate between stages and grades. Furthermore, differences in the subgingival microbiome depending on stage and grade have not been established. Sixty subgingival biofilm samples were collected in Spain (n = 30) and Colombia (n = 30) from three distinct patient categories: those with periodontal health/gingivitis (n = 20), those with stage I-II periodontitis (n = 20), and those with stage III-IV periodontitis (n = 20). Patients were evaluated by 16S rRNA gene amplification sequencing. Amplicon sequence variants were used to assign taxonomic categories compared to the Human Oral Microbiome Database (threshold ≥97% identity). Alpha diversity was established by Shannon and Simpson indices, and principal coordinate analysis, ANOSIM, and PERMANOVA of the UNIFRAC distances were performed using QIIME2. Although differences in the alpha diversity were observed between samples according to country, Filifactor alocis, Peptostreptococcaceae [XI][G-4] bacterium HMT 369, Fretibacterium fastidiosum, Lachnospiraceae [G-8] bacterium HMT 500, Peptostreptococcaceae [XI][G-5] [Eubacterium] saphenum, Peptostreptococcus stomatis, and Tannerella forsythia were associated with periodontitis sites in all stages. However, only F. alocis, Peptostreptococcaceae [XI][G-4] bacterium HMT 369, Peptostreptococcaceae [XI][G-9] [Eubacterium] brachy, Peptostreptococcaceae [XI][G-5] [Eubacterium] saphenum, and Desulfobulbus sp. HMT 041 were consistent in stage III-IV periodontitis in both countries. Porphyromonas gingivalis and Tannerella forsythia were differentially expressed in severe lesions in the countries studied. Although some non-cultivable microorganisms showed differential patterns between the different stages of periodontitis, they were not the same in the two countries evaluated. Further studies using larger samples with advanced next-generation techniques for high-throughput sequencing of phyla and non-cultivable bacteria within the subgingival microbiome could provide more insight into the differences between stages of periodontitis.

Structure and Function of Oral Microbial Community in Periodontitis Based on Integrated Data

Objective

Microorganisms play a key role in the initiation and progression of periodontal disease. Research studies have focused on seeking specific microorganisms for diagnosing and monitoring the outcome of periodontitis treatment. Large samples may help to discover novel potential biomarkers and capture the common characteristics among different periodontitis patients. This study examines how to screen and merge high-quality periodontitis-related sequence datasets from several similar projects to analyze and mine the potential information comprehensively.

Methods

In all, 943 subgingival samples from nine publications were included based on predetermined screening criteria. A uniform pipeline (QIIME2) was applied to clean the raw sequence datasets and merge them together. Microbial structure, biomarkers, and correlation network were explored between periodontitis and healthy individuals. The microbiota patterns at different periodontal pocket depths were described. Additionally, potential microbial functions and metabolic pathways were predicted using PICRUSt to assess the differences between health and periodontitis.

Results

The subgingival microbial communities and functions in subjects with periodontitis were significantly different from those in healthy subjects. Treponema, TG5, Desulfobulbus, Catonella, Bacteroides, Aggregatibacter, Peptostreptococcus, and Eikenella were periodontitis biomarkers, while Veillonella, Corynebacterium, Neisseria, Rothia, Paludibacter, Capnocytophaga, and Kingella were signature of healthy periodontium. With the variation of pocket depth from shallow to deep pocket, the proportion of Spirochaetes, Bacteroidetes, TM7, and Fusobacteria increased, whereas that of Proteobacteria and Actinobacteria decreased. Synergistic relationships were observed among different pathobionts and negative relationships were noted between periodontal pathobionts and healthy microbiota.

Conclusion

This study shows significant differences in the oral microbial community and potential metabolic pathways between the periodontitis and healthy groups. Our integrated analysis provides potential biomarkers and directions for in-depth research. Moreover, a new method for integrating similar sequence data is shown here that can be applied to other microbial-related areas.

Periodontal Pathogens' strategies disarm neutrophils to promote dysregulated inflammation

Periodontitis is an irreversible, chronic inflammatory disease where inflammophilic pathogenic microbial communities accumulate in the gingival crevice. Neutrophils are a major component of the innate host response against bacterial challenge, and under homeostatic conditions, their microbicidal functions typically protect the host against periodontitis. However, a number of periodontal pathogens developed survival strategies to evade neutrophil microbicidal functions while promoting inflammation, which provides a source of nutrients for bacterial growth. Research on periodontal pathogens has largely focused on a few established species: Tannerella forsythia, Treponema denticola, Fusobacterium nucleatum, Aggregatibacter actinomycetemcomitans, and Porphyromonas gingivalis. However, advances in culture-independent techniques have facilitated the identification of new bacterial species in periodontal lesions, such as the two Gram-positive anaerobes, Filifactor alocis and Peptoanaerobacter stomatis, whose characterization of pathogenic potential has not been fully described. Additionally, there is not a full understanding of the pathogenic mechanisms used against neutrophils by organisms that are abundant in periodontal lesions. This presents a substantial barrier to the development of new approaches to prevent or ameliorate the disease. In this review, we first summarize the neutrophil functions affected by the established periodontal pathogens listed above, denoting unknown areas that still merit a closer look. Then, we review the literature on neutrophil functions and the emerging periodontal pathogens, F. alocis and P. stomatis, comparing the effects of the emerging microbes to that of established pathogens, and speculate on the contribution of these putative pathogens to the progression of periodontal disease.

Marked virulence and azole resistance in Candida albicans isolated from patients with periodontal disease

Candida albicans is an opportunistic fungus frequently associated with periodontal diseases. The objective of this study was to determine the expression patterns of virulence genes associated with those of azole resistance among the strains of C. albicans isolated from patients with periodontal disease. We isolated 80 strains of C. albicans from patients with periodontal disease enrolled from two dental clinics and their antifungal susceptibilities were evaluated using the disc diffusion method. C. albicans and its virulence genes were identified using PCR. The expressions of the virulence genes of C. albicans were analyzed using real-time PCR post in vitro infection of the cell line A549. The phenotype for resistance against azoles such as ketoconazole and fluconazole was observed in all analyzed strains (n = 80), which coincided with the high frequency of occurrence of the genes CDR1 and MDR1 associated with resistance. The frequencies of detection and expression of the genes HWP1 (47/15), ALS1 (80/66), ALS3 (70/30), LIP1 (78/44), LIP4 (77/65), LIP5 (79/58), LIP6 (79/58), PLB1 (79/65), and PLB2 (80/66) were found to be higher in the strains of C. albicans isolated from patients with moderate periodontitis and different expression patterns associated with those for azole resistance were identified. It could be elucidated that the high expression of virulence markers associated with azole resistance in C. albicans might be contributing to the chronicity of periodontal infections.

The Nexus Between Periodontal Inflammation and Dysbiosis

The nexus between periodontal inflammation and the polymicrobial biofilm in the gingival sulcus is critical to understanding the pathobiology of periodontitis. Both play a major role in the etiology and pathogenesis of periodontal diseases and each reinforces the other. However, this nexus is also at the center of a significant conundrum for periodontology. For all mucosal polymicrobial biofilms, the most confounding issue is the paradoxical relationship between inflammation, infection, and disease. Despite significant advances made in both periodontal microbiology and periodontal pathobiology, the issue of which comes first, the inflammatory response or the change to a dysbiotic subgingival microbiota, is still debated. In this paper, we present a model for the pathogenesis of periodontitis based on the central role of inflammation and how this modulates the polymicrobial biofilm within the context of the continuum of health, gingivitis, and periodontitis. We propose a new model termed “Inflammation-Mediated Polymicrobial-Emergence and Dysbiotic-Exacerbation” (IMPEDE), which is designed to integrate into and complement the 2017 World Workshop Classification of Periodontitis.