Compositional Data Analysis of Periodontal Disease Microbial Communities

Microbial Markers for Diagnosis and Risk Assessment for Periodontal Diseases: A Systematic Literature Search and Narrative Synthesis

AIM

To examine the accuracy of microbiological biomarkers in diagnosing periodontal diseases, specifically addressing three focus questions: (FQ1) distinguishing health from disease; (FQ2) predicting disease progression; and (FQ3) assessing treatment outcomes.

MATERIALS AND METHODS

A PRISMA-guided search in MEDLINE, EMBAS and WEB OF SCIENCE included cross-sectional and longitudinal studies (e.g., randomised controlled trials, cohort studies) with ≥ 20 participants per group. Eligible studies involved individuals diagnosed with periodontal health, gingivitis or periodontitis, based on well-defined clinical criteria, and utilised microbiological analyses of oral fluids and/or dental plaque. Diagnostic accuracy had to be evaluated using sensitivity, specificity or area under the receiver operating characteristic (ROC) curve (AUC), or alternatively, data for their computation had to be provided.

RESULTS

Thirty-one studies were included, mostly cross-sectional or case-control, with significant variability in sampling sites, microbial analyses and diagnostic definitions, complicating direct comparisons. Frequently investigated biomarkers included Aggregatibacter actinomycetemcomitans (JP2 genotype), Porphyromonas gingivalis, Tannerella forsythia and Treponema denticola. The highest diagnostic accuracy (AUC > 0.95) was achieved through composite microbiome-based metrics such as the subgingival microbial dysbiosis index. However, methodological heterogeneity and inconsistent criteria limited reliability.

CONCLUSION

Although microbiological biomarkers hold promise for periodontal disease diagnostics and monitoring, current evidence is insufficient for clinical implementation. Future research should standardise methodologies, sampling protocols and diagnostic criteria to ensure robust validation and facilitate integration into precision dentistry.

Microbiological and molecular aspects of periodontitis pathogenesis: an infection-induced inflammatory condition

Periodontitis (PD) is the most common oral infectious disease. The primary etiologic cause of the onset and development of PD is dental plaque, which consists of bacterial biofilm domiciled within a complex extracellular mass. In PD patients, there is a progressive breakdown of the periodontal ligament and the alveolar bone. In more advanced stages, tooth loss occurs. The progression of this chronic inflammatory disease involves interactions among numerous microbial pathogens particularly, bacteria, the host's immune factors, and various environmental factors. Due to persistent infection by periodonto-pathogenic bacteria, there is an impairment of both innate and acquired immunity, leading to tissue destruction. Chronic inflammation in PD may be associated with several systemic diseases, including cardiovascular conditions, respiratory issues, diabetes, neurological diseases, cancer, and adverse pregnancy outcomes. Antibiotic treatment is one of the effective strategies for treating PD cases, although the emergence of some resistant strains may limit the effectiveness some antibiotics. In this review study, we discussed the main bacteria in PD, the interaction with the immune response, the pathogenesis of bacteria in PD and antibiotic treatment. We also outlined the emergence of resistance to antibiotics among these pathogens.

Effects of 0.01 mM strontium on human periodontal ligament stem cell osteogenic differentiation via the Wnt/-catenin signaling pathway

OBJECTIVES

Strontium (Sr2+) is a crucial trace element in humans, mainly present in the bones. We investigated the effects of Sr2+ on human periodontal ligament stem cell (hPDLSC) proliferation and osteogenesis and the relevant pathways.

METHODS

hPDLSCs were harvested from extracted premolars and characterized by flow cytometry, then cultured and treated with various Sr2+ concentrations. Cell-counting kit-8 (CCK-8) assays were used to assess hPDLSC proliferation, with alkaline phosphatase (ALP) staining, Alizarin red S staining, and ALP activity assays used to analyze their osteogenic capacity. Quantitative reverse transcription polymerase chain reaction and western blots were used to examine the expression levels of relevant factors, such as collagen I (COL-1), ALP, and Runx family transcription factor 2 (RUNX2). Moreover, tankyrase inhibitor XAV939 treatment was used to investigate the role of Sr2+ in the canonical Wnt/β-catenin signaling pathway.

RESULTS

The hPDLSCs were successfully isolated and cultured in vitro. A 0.01 mM Sr2+ concentration significantly enhanced hPDLSC proliferation and osteogenic differentiation. However, XAV939-mediated inhibition of the canonical Wnt/β-catenin pathway could reverse the Sr2+-induced osteogenic effects.

CONCLUSIONS

Sr2+ can enhance hPDLSC proliferation and osteogenesis by stimulating canonical Wnt/β-catenin signaling, suggesting it may play a critical role in periodontal regeneration and has clinical application potential.

In Vitro Infection of Human Macrophages with Porphyromonas gingivalis W83

This study aimed to investigate the innate immune response of human macrophages to Porphyromonas gingivalis W83 using a novel in vitro infection model. The growth kinetics of P. gingivalis W83 were analyzed, revealing an exponential growth phase at 8 h (optical density = 0.70). To establish a reliable macrophage model, the differentiation of THP-1 monocytes into macrophages was optimized using low concentrations of phorbol 12-myristate 13-acetate (PMA). This approach induced enhanced adherence and morphological changes, with full differentiation achieved after 48 h of PMA treatment followed by 24 h of rest. Polarization towards the pro-inflammatory M1 phenotype was successfully induced with interferon-γ (IFN-γ) and lipopolysaccharide (LPS), as confirmed using cytokine profiling. Cytokine analysis using Luminex® technology demonstrated significant increases in interleukin (IL)-1β, tumor necrosis factor-α (TNF-α), and IL-6, indicating the effective activation of macrophages towards a pro-inflammatory phenotype. Building upon this macrophage model, this study investigated the interactions between macrophages and P. gingivalis W83 during its exponential growth phase. After a one-hour infection period, bacterial DNA quantification in supernatants and lysed macrophages revealed minimal levels of internalized or adherent bacteria, supporting the hypothesis that P. gingivalis effectively evades immune detection. These findings emphasize the utility of this model in uncovering the sophisticated immune evasion strategies employed by P. gingivalis, with significant implications for the development of targeted therapeutic interventions.

Oral Microbiome and Cognition Among Black Cancer Caregivers

BACKGROUND

Despite known links between oral health and dementia and the growing understanding of the role of the human microbiome in health, few studies have explored the relationship between the oral microbiome and cognition. Additionally, there is a notable absence of research on how the oral microbiome is associated with cognitive function in Black adult caregivers of cancer patients despite their elevated risk for both oral disease and cognitive impairment.

OBJECTIVES

This study aimed to characterize the oral microbiome of Black caregivers of people living with cancer and explore the association of the oral microbiome with cognitive performance.

METHODS

Thirty-one self-identified Black or African American caregivers of cancer patients in the greater metropolitan Atlanta area participated in the study. They provided oral microbiome samples. Cognitive performance was assessed using the Montreal Cognitive Assessment (MoCA), depressive symptoms with the Center for Epidemiological Studies-Depression Scale, and individual race-related stress with the Index of Race-Related Stress-Brief. Salivary microbiome diversity was analyzed using alpha and beta diversity metrics, and taxa associated with cognition were identified through differential abundance testing, adjusting for potential confounders.

RESULTS

The mean age of participants was 54.8 years. MoCA scores ranged from 18 to 30, with a mean of 25. Participants were categorized into normal cognition (MoCA ≥ 26, n = 12) and low cognition (MoCA < 26, n = 16) groups. Education level and individual race-related stress were associated with cognition group and were controlled for in the oral microbiome analysis. Alpha and beta diversity analyses showed no significant overall differences between cognition groups. Differential abundance testing suggested 48 taxa were associated with cognition status, many of which are known to be associated with periodontal disease and cognition.

DISCUSSION

This study revealed associations between cognition status and specific oral bacteria, many of which are known to be associated with periodontal disease and cognitive impairment. These findings underscore the complex relationship between oral health and cognitive function, suggesting a need for further research to develop oral microbiome profiles capable of identifying individuals at risk for cognitive decline and guiding targeted interventions for promoting overall well-being and cognitive health.

Integrating host and microbiome biology using holo-omics

Holo-omics is the use of omics data to study a host and its inherent microbiomes - a biological system known as a "holobiont". A microbiome that exists in such a space often encounters habitat stability and in return provides metabolic capacities that can benefit their host. Here we present an overview of beneficial host-microbiome systems and propose and discuss several methodological frameworks that can be used to investigate the intricacies of the many as yet undefined host-microbiome interactions that influence holobiont homeostasis. While this is an emerging field, we anticipate that ongoing methodological advancements will enhance the biological resolution that is necessary to improve our understanding of host-microbiome interplay to make meaningful interpretations and biotechnological applications.

Effect of Dietary Composite Probiotic Supplementation on the Microbiota of Different Oral Sites in Cats

Probiotics demonstrated effectiveness in modulating oral microbiota and improving oral health in humans and rodents. However, its effects and applications on the oral microbiota of cats remain underexplored. Twelve healthy cats were randomly assigned to a control group (CON) and a composite probiotic group (CPG) for a 42-day trial. The CPG diet included additional supplementation of Bifidobacterium animalis subsp. lactis HN019, Lactobacillus acidophilus NCFM, and Lactobacillus casei LC-11, each at approximately 1 × 1010 CFU/kg. On days 0 and 42, microbial samples were collected from the gingiva, tooth surfaces, and tongue of all cats for 16S rRNA gene sequencing. Bacteroidetes, Firmicutes, and Proteobacteria were the dominant phyla across all oral sites. The CPG treatment enriched seven genera, such as Moraxella, Actinomyces, and Frederiksenia in the gingiva. Meanwhile, Bergeyella and Streptococcus were enriched on the tooth surfaces, while Bergeyella, Flavobacterium, and Luteimonas were enriched on the tongue. Furthermore, the composite probiotic effectively suppressed eight genera, such as Bacteroides, Desulfovibrio, and Filifactor in the gingiva of CPG cats, as well as Helcococcus, Lentimicrobium, and Campylobacter on tooth surfaces, and Porphyromonas, Treponema, and Fusibacter on the tongue. These findings suggest that the composite probiotic used in this study modulates the feline oral microbiota by supporting beneficial or commensal bacteria and inhibiting oral pathogens, demonstrating potential to improve oral health in cats.

From wolves to humans: oral microbiome resistance to transfer across mammalian hosts

The mammalian mouth is colonized by complex microbial communities, adapted to specific niches, and in homeostasis with the host. Individual microbes interact metabolically and rely primarily on nutrients provided by the host, with which they have potentially co-evolved along the mammalian lineages. The oral environment is similar across mammals, but the diversity, specificity, and evolution of community structure in related or interacting mammals are little understood. Here, we compared the oral microbiomes of dogs with those of wild wolves and humans. In dogs, we found an increased microbial diversity relative to wolves, possibly related to the transition to omnivorous nutrition following domestication. This includes a larger diversity of Patescibacteria than previously reported in any other oral microbiota. The oral microbes are most distinct at bacterial species or strain levels, with few if any shared between humans and canids, while the close evolutionary relationship between wolves and dogs is reflected by numerous shared taxa. More taxa are shared at higher taxonomic levels including with humans, supporting their more ancestral common mammalian colonization followed by diversification. Phylogenies of selected oral bacterial lineages do not support stable human-dog microbial transfers but suggest diversification along mammalian lineages (apes and canids). Therefore, despite millennia of cohabitation and close interaction, the host and its native community controls and limits the assimilation of new microbes, even if closely related. Higher resolution metagenomic and microbial physiological studies, covering a larger mammalian diversity, should help understand how oral communities assemble, adapt, and interact with their hosts.IMPORTANCENumerous types of microbes colonize the mouth after birth and play important roles in maintaining oral health. When the microbiota-host homeostasis is perturbed, proliferation of some bacteria leads to diseases such as caries and periodontitis. Unlike the gut microbiome, the diversity of oral microbes across the mammalian evolutionary space is not understood. Our study compared the oral microbiomes of wild wolves, dogs, and apes (humans, chimpanzees, and bonobos), with the aim of identifying if microbes have been potentially exchanged between humans and dogs as a result of domestication and cohabitation. We found little if any evidence for such exchanges. The significance of our research is in finding that the oral microbiota and/or the host limit the acquisition of exogenous microbes, which is important in the context of natural exclusion of potential novel pathogens. We provide a framework for expanded higher-resolution studies across domestic and wild animals to understand resistance/resilience.

Proportion-based normalizations outperform compositional data transformations in machine learning applications

BACKGROUND

Normalization, as a pre-processing step, can significantly affect the resolution of machine learning analysis for microbiome studies. There are countless options for normalization scheme selection. In this study, we examined compositionally aware algorithms including the additive log ratio (alr), the centered log ratio (clr), and a recent evolution of the isometric log ratio (ilr) in the form of balance trees made with the PhILR R package. We also looked at compositionally naïve transformations such as raw counts tables and several transformations that are based on relative abundance, such as proportions, the Hellinger transformation, and a transformation based on the logarithm of proportions (which we call "lognorm").

RESULTS

In our evaluation, we used 65 metadata variables culled from four publicly available datasets at the amplicon sequence variant (ASV) level with a random forest machine learning algorithm. We found that different common pre-processing steps in the creation of the balance trees made very little difference in overall performance. Overall, we found that the compositionally aware data transformations such as alr, clr, and ilr (PhILR) performed generally slightly worse or only as well as compositionally naïve transformations. However, relative abundance-based transformations outperformed most other transformations by a small but reliably statistically significant margin.

CONCLUSIONS

Our results suggest that minimizing the complexity of transformations while correcting for read depth may be a generally preferable strategy in preparing data for machine learning compared to more sophisticated, but more complex, transformations that attempt to better correct for compositionality. Video Abstract.

Data-driven mathematical and visualization approaches for removing rare features for Compositional Data Analysis (CoDA)

Sparse feature tables, in which many features are present in very few samples, are common in big biological data (e.g. metagenomics). Ignoring issues of zero-laden datasets can result in biased statistical estimates and decreased power in downstream analyses. Zeros are also a particular issue for compositional data analysis using log-ratios since the log of zero is undefined. Researchers typically deal with this issue by removing low frequency features, but the thresholds for removal differ markedly between studies with little or no justification. Here, we present CurvCut, an unsupervised data-driven approach with human confirmation for rare-feature removal. CurvCut implements two distinct approaches for determining natural breaks in the feature distributions: a method based on curvature analysis borrowed from thermodynamics and the Fisher-Jenks statistical method. Our results show that CurvCut rapidly identifies data-specific breaks in these distributions that can be used as cutoff points for low-frequency feature removal that maximizes feature retention. We show that CurvCut works across different biological data types and rapidly generates clear visual results that allow researchers to confirm and apply feature removal cutoffs to individual datasets.

Astragalus-cultivated soil was a suitable bed soil for nurturing Angelica sinensis seedlings from the rhizosphere microbiome perspective

Angelica sinensis (Oliv.) Diels is an important Chinese medicinal plant. A. sinensis seedlings are grown on an undisturbed alpine meadow soil to ensure the high-quality seedlings, but these soils are disappearing year after year. Thus, selecting a suitable bed soil for A. sinensis seedlings could ensure their long-term sustainability. Using HiSeq sequencing of 16S and 18S marker genes, we investigated the rhizosphere bacterial and fungal microbiotas of the seedlings grown in wheat, astragalus, potato, and angelica-cultivated soils at a geo-authentic habitat. Co-occurrence network analysis, canonical correspondence analysis, Mantel test, and Envfit test were used to examine the relationship between the microbiotas and the surrounding factors. Astragalus-cultivated soils exhibited the following properties: the highest plant weight, the highest neighborhood connectivity in the bacterial network, the highest ratio of positive/negative relationship in both bacterial and fungal networks, the highest relative abundance of the arbuscular mycorrhizal fungi and the ectomycorrhizal fungi, the lowest relative abundance of Rhizoctonia solani, the suitable soil pH, and the close relationship between the rhizosphere microbiotas and the ecological factors. Moreover, each growth stage has its own major drivers in all crop-cultivated soils. Climate temperature and soil pH at 56 days after planting, precipitation at 98 days, and plant weight as well as microbial biomass C and N at 129 days were the major drivers of the bacterial and fungal microbiotas. Overall, the astragalus-cultivated soil was a suitable bed soil for nurturing A. sinensis seedlings to replace the undisturbed alpine meadow soils.

Ecological shifts of salivary microbiota associated with metabolic-associated fatty liver disease

Introduction

Metabolic-associated fatty liver disease (MAFLD) is the most common chronic liver disease related to metabolic syndrome. However, ecological shifts in the saliva microbiome in patients with MAFLD remain unknown. This study aimed to investigate the changes to the salivary microbial community in patients with MAFLD and explore the potential function of microbiota.

Methods

Salivary microbiomes from ten MAFLD patients and ten healthy participants were analyzed by 16S rRNA amplicon sequencing and bioinformatics analysis. Body composition, plasma enzymes, hormones, and blood lipid profiles were assessed with physical examinations and laboratory tests.

Results

The salivary microbiome of MAFLD patients was characterized by increased α-diversity and distinct β-diversity clustering compared with control subjects. Linear discriminant analysis effect size analysis showed a total of 44 taxa significantly differed between the two groups. Genera Neisseria, Filifactor, and Capnocytophaga were identified as differentially enriched genera for comparison of the two groups. Co-occurrence networks suggested that the salivary microbiota from MAFLD patients exhibited more intricate and robust interrelationships. The diagnostic model based on the salivary microbiome achieved a good diagnostic power with an area under the curve of 0.82(95% CI: 0.61-1). Redundancy analysis and spearman correlation analysis revealed that clinical variables related to insulin resistance and obesity were strongly associated with the microbial community. Metagenomic predictions based on Phylogenetic Investigation of Communities by Reconstruction of Unobserved States revealed that pathways related to metabolism were more prevalent in the two groups.

Conclusions

Patients with MAFLD manifested ecological shifts in the salivary microbiome, and the saliva microbiome-based diagnostic model provides a promising approach for auxiliary MAFLD diagnosis.

Features of the oral microbiome in Japanese elderly people with 20 or more teeth and a non-severe periodontal condition during periodontal maintenance treatment: A cross-sectional study

Introduction

The aim of the present study was to characterize the profile and diversity of the oral microbiome of a periodontally non-severe group with ≥20 teeth in comparison with a severe periodontitis group of elderly Japanese people.

Methods

A total of 50 patients who had ≥20 teeth and aged ≥60 years were recruited, and 34 participants (13 non-severe participants) were analyzed. After oral rinse (saliva after rinsing) sample collection, the V3–V4 regions of the 16S rRNA gene were sequenced to investigate microbiome composition, alpha diversity (Shannon index, Simpson index, richness, and evenness), and beta diversity using principal coordinate analysis (PCoA) based on weighted and unweighted UniFrac distances. A linear discriminant analysis effect size was calculated to identify bacterial species in the periodontally non-severe group.

Results

The periodontally non-severe group showed lower alpha diversity than that of the severe periodontitis group (p <0.05); however, the beta diversities were not significantly different. A higher relative abundance of four bacterial species (Prevotella nanceiensis, Gemella sanguinis, Fusobacterium periodonticum, and Haemophilus parainfluenzae) was observed in the non-severe group than that in the severe periodontitis group.

Conclusion

The oral microbiome in elderly Japanese people with ≥20 teeth and a non-severe periodontal condition was characterized by low alpha diversity and the presence of four bacterial species.

Chemomechanical preparation influences the microbial community and the levels of LPS, LTA and cytokines in combined endodontic-periodontal lesions: A clinical study

BACKGROUND

This study was conducted to compare the microbiomes, the levels of lipopolysaccharides (LPS), lipoteichoic acid (LTA), and cytokines (interleukin [IL]-1β and tumor necrosis factor-alpha [TNF-α]), before and after chemomechanical preparation (CMP) of the root canals (RC) and their associated periodontal pockets (PP) in teeth with combined EPL.

MATERIALS

Samples were taken from 10 RC and PP, before and after CMP. The microbiomes (next-generation sequencing, V3-V4 hypervariable region of the 16S rRNA gene), microbiome diversity (bioinformatics analyses), LPS (limulus amebocyte lysate), LTA, IL-1β, and TNF-α (ELISA) were evaluated. A statistical analysis was performed with significance level set at 5%.

RESULTS

The most abundant phyla in both sites were Firmicutes and Proteobacteria. Comparative studies of bacterial genera species revealed that some increased and others decreased after CMP at both sites. A 3% reduction in Gram-negative bacteria (RC) and a 4% increase in Gram-positive bacteria (PP) were detected. LPS levels were 4.4 times higher in PP than in the RC. LTA was detected in all samples investigated. Higher levels of IL-1β and TNF-α were detected in both sites at baseline. After CMP, LPS, LTA, IL-1β and TNF-α were reduced in both sites.

CONCLUSION

The microbial community in the RC and PP in teeth with combined EPL indicated a similarity between both sites. CMP effectively reduced the microbial load and the LPS levels from teeth with EPL, and consequently diminished the cytokine levels. The reduction in LTA levels in the RC and PP proved challenging.

Macroscopic Changes in Oral Mucosa and Hygiene Indicators in Smokers

Smoking is the most important factor affecting the oral cavity by components born in the tobacco combustion process and acting directly on the oral mucous membranes, dental arch and indirectly on the teeth support. Recent studies show the tobacco action on the oral cavity, manifestations in the form of gingivitis, bacterial plaque, dental plaque, papillary bleeding at drilling, periodontitis.

PURPOSE OF THE STUDY

In this study, we have set out to assess the macroscopic modifications of oral cavity on smokers.

MATERIALS AND METHODS

The participants in the study were divided into two groups, the first group of smokers with a smoking period over 5 years and the control group of nonsmokers. The patients in the two groups underwent a physical examination and an objective clinical examination, the resulting data being compared with the control group.

RESULTS

For the bacterial plaque indicatorin the smoker group there was obtained a mean value of 35.68±12.45, compared to a mean value of 16.32±6.61 for the nonsmoker group, the dental plaque indicatorfor the smoker group had a mean value of 2.24±1.02, higher than the one in the nonsmoker group, namely 0.94±0.68, and for the drilling bleeding indicator we obtained a mean value of 19.54±7.89 in the nonsmoker group, which is lower than that in the smoker group, namely 42.86±14.93.

CONCLUSIONS

Smoking is a cause that maintains and aggravates the periodontal disease, including the risk of periodontitis, allowing the aggravation of gingivitis, considered a reversible surface inflammation of the gum mucosa which, by accumulation of dental plaque, the dental plaque accompanied by incorrect oral hygiene, favors the progression to periodontitis.