Shifts of subgingival bacterial population after nonsurgical and pharmacological therapy of localized aggressive periodontitis, followed for 1 year by Ion Torrent PGM platform

Critical review of 16S rRNA gene sequencing workflow in microbiome studies: From primer selection to advanced data analysis

The multi-batch reanalysis approach of jointly reevaluating gene/genome sequences from different works has gained particular relevance in the literature in recent years. The large amount of 16S ribosomal ribonucleic acid (rRNA) gene sequence data stored in public repositories and information in taxonomic databases of the same gene far exceeds that related to complete genomes. This review is intended to guide researchers new to studying microbiota, particularly the oral microbiota, using 16S rRNA gene sequencing and those who want to expand and update their knowledge to optimise their decision-making and improve their research results. First, we describe the advantages and disadvantages of using the 16S rRNA gene as a phylogenetic marker and the latest findings on the impact of primer pair selection on diversity and taxonomic assignment outcomes in oral microbiome studies. Strategies for primer selection based on these results are introduced. Second, we identified the key factors to consider in selecting the sequencing technology and platform. The process and particularities of the main steps for processing 16S rRNA gene-derived data are described in detail to enable researchers to choose the most appropriate bioinformatics pipeline and analysis methods based on the available evidence. We then produce an overview of the different types of advanced analyses, both the most widely used in the literature and the most recent approaches. Several indices, metrics and software for studying microbial communities are included, highlighting their advantages and disadvantages. Considering the principles of clinical metagenomics, we conclude that future research should focus on rigorous analytical approaches, such as developing predictive models to identify microbiome-based biomarkers to classify health and disease states. Finally, we address the batch effect concept and the microbiome-specific methods for accounting for or correcting them.

Screening for prevalence and abundance of Capnocytophaga spp by analyzing NGS data: A scoping review

BACKGROUND

Capnocytophaga spp. are commensal bacteria of the oral cavity and constitute a genus of the core microbiome.

OBJECTIVE

This genus is responsible for many local and systemic conditions in both the immunocompetent and immunocompromised patients, but its beneficial or deleterious role in the microbiota has been little explored.

DESIGN

Online databases were used to identify papers published from 1999 to 2019 based on next-generation sequencing (NGS) data to study comparative trials. Work using other identification methods, case reports, reviews, and non-comparative clinical trials was excluded.

RESULTS AND CONCLUSION

We selected 42 papers from among 668 publications. They showed a link between the abundance of Capnocytophaga spp. in the oral microbiota and various local pathologies (higher for gingivitis and halitosis; lower in active smokers, etc.) or systemic diseases (higher for cancer and carcinomas, IgA nephropathy, etc.). After discussing the limits inherent to the NGS techniques, we present several technical and biological hypotheses to explain the diversity of results observed between studies, as well as the links between the higher or lower abundance of Capnocytophaga spp and the appearance of local or systemic conditions and diseases.

Profiling of Oral Microbiota and Cytokines in COVID-19 Patients

The presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been recently demonstrated in the sputum or saliva, suggesting how the shedding of viral RNA outlasts the end of symptoms. Recent data from transcriptome analysis show that the oral cavity mucosa harbors high levels of angiotensin-converting enzyme 2 (ACE2) and transmembrane protease, serine 2 (TMPRSS2), highlighting its role as a double-edged sword for SARS-CoV-2 body entrance or interpersonal transmission. Here, we studied the oral microbiota structure and inflammatory profile of 26 naive severe coronavirus disease 2019 (COVID-19) patients and 15 controls by 16S rRNA V2 automated targeted sequencing and magnetic bead-based multiplex immunoassays, respectively. A significant diminution in species richness was observed in COVID-19 patients, along with a marked difference in beta-diversity. Species such as Prevotella salivae and Veillonella infantium were distinctive for COVID-19 patients, while Neisseria perflava and Rothia mucilaginosa were predominant in controls. Interestingly, these two groups of oral species oppositely clustered within the bacterial network, defining two distinct Species Interacting Groups (SIGs). COVID-19-related pro-inflammatory cytokines were found in both oral and serum samples, along with a specific bacterial consortium able to counteract them. We introduced a new parameter, named CytoCOV, able to predict COVID-19 susceptibility for an unknown subject at 71% of power with an Area Under Curve (AUC) equal to 0.995. This pilot study evidenced a distinctive oral microbiota composition in COVID-19 subjects, with a definite structural network in relation to secreted cytokines. Our results would be usable in clinics against COVID-19, using bacterial consortia as biomarkers or to reduce local inflammation.

High-throughput sequencing identifies salivary microbiota in Chinese caries-free preschool children with primary dentition

OBJECTIVES

The study aimed at identifying salivary microbiota in caries-free Chinese preschool children using high-throughput sequencing.

METHODS

Saliva samples were obtained from 35 caries-free preschool children (18 boys and 17 girls) with primary dentition, and 16S ribosomal DNA (rDNA) V3-V4 hypervariable regions of the microorganisms were analyzed using Illumina MiSeq.

RESULTS

At 97% similarity level, all of these reads were clustered into 334 operational taxonomic units (OTUs). Among these, five phyla (Firmicutes, Proteobacteria, Actinobacteria, Bacteroidetes, and Candidate division TM7) and 13 genera (Streptococcus, Rothia, Granulicatella, Prevotella, Enterobacter, Veillonella, Neisseria, Staphylococcus, Janthinobacterium, Pseudomonas, Brevundimonas, Devosia, and Gemella) were the most dominant, constituting 99.4% and 89.9% of the salivary microbiota, respectively. The core salivary microbiome comprised nine genera (Actinomyces, Capnocytophaga, Gemella, Granulicatella, Lachnoanaerobaculum, Neisseria, Porphyromonas, Rothia,and Streptococcus). Analysis of microbial diversity and community structure revealed a similar pattern between male and female subjects. The difference in microbial community composition between them was mainly attributed to Neisseria (P=0.023). Furthermore, functional prediction revealed that the most abundant genes were related to amino acid transport and metabolism.

CONCLUSIONS

Our results revealed the diversity and composition of salivary microbiota in caries-free preschool children, with little difference between male and female subjects. Identity of the core microbiome, coupled with prediction of gene function, deepens our understanding of oral microbiota in caries-free populations and provides basic information for associating salivary microecology and oral health.

Label-free analysis of gingival crevicular fluid (GCF) by surface enhanced Raman scattering (SERS)

Gingival crevicular fluid (GCF) is an interesting biofluid reflecting the physiological and pathological states of a single dental element. Due to this unique feature, in recent years, metabolomic analysis of GCF has gained attention as a biometric tool for the diagnosis and therapy of periodontal disease. Traditional methods are, however, too slow, cumbersome and expensive for a health-care routine. Surface enhanced Raman scattering (SERS) can offer rapid and label-free detailed molecular fingerprints that can be used for biofluid analysis. Here we report the first SERS characterization of GCF using an easy and quick sample preparation. The dominant features in the SERS spectrum of GCF are ascribed to very few metabolites, in particular to uric acid, hypoxanthine, glutathione and ergothioneine. Additionally, we succeeded in differentiating between the SERS signal of GCF collected from healthy volunteers and the one collected from patients with periodontal disease.

Alterations and Correlations in Microbial Community and Metabolome Characteristics in Generalized Aggressive Periodontitis

This study aimed to characterize the microbial community and metabolic profiles in generalized aggressive periodontitis (AgP) using 16S ribosomal RNA (rRNA) gene high-throughput sequencing and gas chromatography-mass spectrometry (GC-MS). A total of 146 subgingival plaque samples and 50 gingival crevicular fluid (GCF) samples were collected from 24 patients with AgP and 10 periodontally healthy subjects (PH). Striking differences were observed in subgingival microbiome and GCF metabolomics between patients with AgP and PH, but not between samples with different probing depths (PDs). Metabolomics analysis combined with enrichment analysis showed that periodontitis significantly altered the concentration of compounds associated with biosynthesis of amino acids (e.g., alanine, leucine, isoleucine, and valine), galactose metabolism (e.g., myo-inositol, galactose, glucose, and hexitol), and pyrimidine metabolism (e.g., uracil, uridine, beta alanine, and thymine). Correlation analysis showed that the genera with significant difference between AgP and PH were usually significantly correlated with more metabolites, such as Aggregatibacter, Rothia, Peptostreptococcaceae_[XI][G-5], and Bacteroidaceae_[G-1]. While glucose and oxoproline had the most significant correlations with microorganisms. Our results revealed distinct microbial communities and metabolic profiles between AgP and PH. The significant correlation between microbial taxa and metabolites suggested the possible mechanisms for periodontitis. Our results also provided effective approaches for detecting periodontal disease and managing periodontitis.

Did Omics change periodontal therapy?

The starting point for defining effective treatment protocols is a clear understanding of the etiology and pathogenesis of a condition. In periodontal diseases, this understanding has been hindered by a number of factors, such as the difficulty in differentiating primary pathogens from nonpathogens in complex biofilm structures. The introduction of DNA sequencing technologies, including taxonomic and functional analyses, has allowed the oral microbiome to be investigated in much greater breadth and depth. This article aims to compile the results of studies, using next-generation sequencing techniques to evaluate the periodontal microbiome, in an attempt to determine how far the knowledge provided by these studies has brought us in terms of influencing the way we treat periodontitis. The taxonomic data provided, to date, by published association and elimination studies using next-generation sequencing confirm previous knowledge on the role of classic periodontal pathogens in the pathobiology of disease and include new species/genera. Conversely, species and genera already considered as host-compatible and others less explored were associated with periodontal health as their levels were elevated in healthy individuals and increased after therapy. Functional and transcriptomic analyses also demonstrated that periodontal biofilms are taxonomically diverse, functionally congruent, and highly cooperative. Very few interventional studies to date have examined the effects of treatment on the periodontal microbiome, and such studies are heterogeneous in terms of design, sample size, sampling method, treatment provided, and duration of follow-up. Hence, it is still difficult to draw meaningful conclusions from them. Thus, although OMICS knowledge has not yet changed the way we treat patients in daily practice, the information provided by these studies opens new avenues for future research in this field. As new pathogens and beneficial species become identified, future randomized clinical trials could monitor these species/genera more comprehensively. In addition, the metatranscriptomic data, although still embryonic, suggest that the interplay between the host and the oral microbiome may be our best opportunity to implement personalized periodontal treatments. Therapeutic schemes targeting particular bacterial protein products in subjects with specific genetic profiles, for example, may be the futuristic view of enhanced periodontal therapy.

Grade C molar-incisor pattern periodontitis subgingival microbial profile before and after treatment

Aim: This study evaluated the influence of periodontal therapy on the microbiological profile of individuals with Grade C Molar-Incisor Pattern Periodontitis (C/MIP).

Methods: Fifty-three African-American participants between the ages of 5–25, diagnosed with C/MIP were included. Patients underwent full mouth mechanical debridement with systemic antibiotics (metronidazole 250 mg + amoxicillin 500 mg, tid, 7 days). Subgingival samples were collected from a diseased and a healthy site from each individual prior to treatment and at 3, 6, 12, 18 and 24 months after therapy from the same sites. Samples were subjected to a 16S rRNA gene based-microarray.

Results: Treatment was effective in reducing the main clinical parameters of disease. Aggregatibacter actinomycetemcomitans (A.a.) was the strongest species associated with diseased sites. Other species associated with diseased sites were Treponema lecithinolyticum and Tannerella forsythia. Species associated with healthy sites were Rothia dentocariosa/mucilaginosa, Eubacterium yurii, Parvimonas micra, Veillonella spp., Selenomonas spp., and Streptococcus spp. Overall, treatment was effective in strongly reducing A.a. and other key pathogens, as well as increasing health-associated species. These changes were maintained for at least 6 months.

Conclusions:Treatment reduced putative disease-associated species, particularly A.a., and shifted the microbial profile to more closely resemble a healthy-site profile. (Clinicaltrials.gov registration #NCT01330719).

Evaluation of Effect of Surgical and Nonsurgical Periodontal Therapy on Serum C-Reactive Protein, Triglyceride, Cholesterol, Serum Lipoproteins and Fasting Blood Sugar in Patients with Severe Chronic Periodontitis

Background:

Cardiovascular disease has been associated with multiple risk factors such as dyslipidemia. However, the focus has recently shifted towards some novel risk factorsi.e. infection from periodontitis.

Given this background, we aimed to assess the effect of periodontal therapy on some CVD risk factors including Total Cholesterol (TC), Low-Density Lipoprotein(LDL), High-Density Lipoprotein(HDL), Triglycerides(TG) and C-Reactive Protein (CRP). Fasting Blood Sugar (FBS) level has also been measured.

Methods:

Thirty patients (12 male and 18 female) who had severe periodontitis were tested for different blood parameters; namely Total Cholesterol (TC), Low-Density Lipoprotein (LDL), High-Density Lipoprotein(HDL), Triglycerides (TGs), C-Reactive Protein (CRP) and Fasting Blood Sugar (FBS). Enzymatic colorimetric methods were applied to measure all the parameters’ values except for CRP. The first stage of periodontal treatment comprised oral hygiene instruction as well as scaling and root planing. After 1 month, at the next stage, open flap debridement surgery was performed on all 4 quadrants of the mouth. The blood parameters were reassessed and compared with the baseline values after 3 months. Two patients (female) failed to participate in the follow-ups. The Pearson's and Spearman's correlation coefficients were calculated to determine whether changes in laboratory variables are associated with age and average probing depth or not.

Results:

All the assessed parameters related to 28 patients showed mean reduction which proved to be significant for CRP (p=0.011) and cholesterol (p=0.035). Among all parameters, only CRP level was found to have a significant positive correlation with pocket depth. Other blood parameters' relationship with age and probing depth proved to be insignificant.

Conclusion:

Considering the results, periodontal treatment may significantly lower lipid profile serum levels and some inflammatory factors.

Comparison of the oral microbiome of patients with generalized aggressive periodontitis and periodontitis-free subjects

OBJECTIVE

The primary objectives of the study were to assess differences in complex subgingival bacterial composition between periodontitis-free persons and patients with generalized aggressive periodontitis (gAgP).

BACKGROUND

The composition of the oral microbiota plays an important role for both oral and systemic diseases. However, the complex nature of the oral microbiome and its homeostasis is still poorly understood.

MATERIAL AND METHODS

We compared the microbiome of 13 periodontitis-free persons to 13 patients with gAgP. The 16S rRNA genes were amplified, targeting the V3/V4 region using the MiSeq platform.

RESULTS

In total, 1713 different bacterial species were mapped according to the Greengenes database. Using the Shannon index, no significant differences in alpha diversity were found between the two study groups. In principal component and linear discriminant analyses, disease-specific differences in beta diversity of the microbiome composition were evaluated. Bacteroidetes, Spirochaetes, and Synergistetes were more abundant in gAgP whereas Proteobacteria, Firmicutes, and Actinobacteria were associated with a healthy periodontium. At the bacterial species level, we showed that Porphyromonas gingivalis is the strongest indicator of gAgP. Treponema denticola and Tanerella forsythia of the "red complex" as well as Filifactor alocis were among the ten best biomarkers for gAgP.

CONCLUSIONS

These results broaden our knowledge of disease-specific differences in the microbial community associated with generalized AgP. A more complex view of the composition of the oral microbiome describes the etiology of generalized AgP in more detail. These results could help to individually adapt periodontal therapy in these patients.