Despite rapid advances in characterizing the human microbiome, the ecological pressures shaping its transitions from healthy to diseased states remain poorly resolved. This is particularly true for periodontitis, a slow-progressing chronic inflammatory disease associated with well-defined shifts in the subgingival microbiome. Here, we report the development of a complex synthetic community model of the subgingival microbiome, designed for systematic interrogation of ecological factors that drive community restructuring. The model includes 22 prevalent and abundant subgingival species maintained in mucin-rich medium under microaerophilic, continuous culture conditions, in a chemostat. Using this system, we interrogated the impact of serum, as a surrogate for the inflammatory exudate produced by the host in response to biofilm accumulation, on community structure and function. Through integrated 16S rRNA gene sequencing, metatranscriptomics, and metabolomics, we found that serum was not required for a community with a periodontitis-like configuration to establish, but its presence intensified features of dysbiosis. Serum increased total biomass, promoted polymicrobial aggregate formation, promoted nitrogen and protein metabolism thereby modifying the environmental pH towards alkalinity, and introduced nitrosative stress. Serum also modified the community metatranscriptome in ways that paralleled microbiome activities in human periodontitis. Serum, however, decreased community diversity by disproportionally conferring a competitive advantage to the pathogen Porphyromonas gingivalis. This synthetic community model has revealed serum as a key nutritional pressure that modulates subgingival microbiome ecology and may perpetuate dysbiosis.

Myocardial infarction (MI) is a major global health concern influenced by diverse risk factors. Despite growing evidence of oral– systemic connections, current MI models largely exclude oral health indicators, reflecting the longstanding separation between dental and medical paradigms. This study introduces a multidomain, interpretable machine learning framework that integrates detailed periodontal and oral hygiene variables, marking one of the first efforts to quantitatively incorporate these features into MI incidence identification. A population-based case-control dataset comprising 1,355 individuals and heterogeneous variables was used to train and evaluate seven supervised classifiers via nested cross-validation. Among them, XGBoost achieved the best performance (AUC = 0.88±0.01; F1 score = 0.74±0.03) and was further probability-calibrated using isotonic regression, yielding a mean Brier score of 0.14±0.01 and demonstrating well-aligned predicted probabilities. SHAP values confirmed the importance of conventional cardiovascular predictors, while several periodontal indicators such as mean clinical attachment loss, plaque index, and gingival bleeding emerged among the most influential features. Sex-stratified SHAP analysis revealed sex-specific patterns in the relative impact of oral features. Additionally, individual-level waterfall plots illustrated how oral inflammation may contribute independently or in combination with conventional factors to MI incidence identification. These findings support a systems-level view of periodontitis as a modifiable, biologically relevant factor in cardiovascular health and underscore the value of considering oral-health markers within screening and management frameworks.

Tristetraprolin (TTP) is an RNA-binding protein essential for controlling cytokine production, and its deficiency leads to profound skeletal deterioration. Although TTP deficiency is associated with systemic inflammation and microbial dysbiosis, the contribution of the gut microbiota to bone pathology remains poorly defined. We investigated whether the microbiome causally modulates osteoimmune mechanisms and bone microarchitecture in TTP-deficient mice. To isolate the effects of the microbiome, we utilized specific pathogen-free (SPF) and germ-free (GF) co-housing mouse models. Microbial transfer bidirectionally regulated systemic inflammation and the expansion of monocytic myeloid-derived suppressor cells (M-MDSCs), a population with potent osteoclastogenic capacity. Importantly, microbiota transfer was sufficient to induce osteoclast activation and a selective deterioration of trabecular bone microarchitecture in otherwise healthy mice, without affecting overall bone mass. Crucially, the transmission of these osteoimmune and skeletal phenotypes was microbiota-dependent; while a baseline genetic bone deficit persisted in GF TTP-deficient mice, the co-housing-induced M-MDSC expansion and trabecular bone alterations were not observed under GF conditions. Our findings identify a microbiota-dependent osteoimmune axis that amplifies inflammatory bone loss in TTP deficiency. This work establishes the gut microbiome as a mechanistic modifier of bone quality in genetically driven inflammatory disease and highlights microbial targeting as a potential therapeutic strategy for inflammatory bone loss.

Chemotherapy-induced oral mucositis is a common and debilitating complication, yet the mechanisms underlying oral mucosa injury and repair remain poorly defined. Using a mouse model of 5-fluorouracil (5-FU)-induced mucositis, we define gene networks and oral mucosal cellular landscape dynamics in response to chemotoxic stress. We show that 5-FU-induced epithelial atrophy is driven primarily by cell cycle arrest rather than apoptosis, despite concurrent activation of p53-dependent transcriptional programs linked to both cell fates within individual cells. Relative to intestine, the recovering oral mucosa exhibits a more effective cell cycle checkpoint response and uniquely undergoes metabolic reprogramming toward lipid oxidation. Single-cell RNA sequencing revealed putative epithelial progenitor populations with distinct responses to chemotherapy, including chemoresistant cells with a p53- and AP-1 complex gene signature, reminiscent of lung transitional cell states. These findings define diverse progenitor dynamics and p53-driven responses as key determinants of oral mucosal injury and repair following chemotherapy.

Despite rapid advances in characterizing the human microbiome, the ecological pressures shaping its transitions from healthy to diseased states remain poorly resolved. This is particularly true for periodontitis, a slow-progressing chronic inflammatory disease associated with well-defined shifts in the subgingival microbiome. Here, we report the development of a complex synthetic community model of the subgingival microbiome, designed for systematic interrogation of ecological factors that drive community restructuring. The model includes 22 prevalent and abundant subgingival species maintained in mucin-rich medium under microaerophilic, continuous culture conditions, in a chemostat. Using this system, we interrogated the impact of serum, as a surrogate for the inflammatory exudate, on community structure and function. Through integrated 16S rRNA gene sequencing, metatranscriptomics, and metabolomics, we found that serum was not required for a community with a periodontitis-like configuration to establish, but its presence intensified features of dysbiosis. Serum increased total biomass, promoted polymicrobial aggregate formation, promoted nitrogen and protein metabolism thereby modifying the environmental pH towards alkalinity, and introduced nitrosative stress. Serum also modified the community metatranscriptome in ways that paralleled microbiome activities in human periodontitis. Serum, however, decreased community diversity by disproportionally conferring a competitive advantage to the pathogen Porphyromonas gingivalis . This synthetic community model has revealed serum as a key nutritional pressure that modulates subgingival microbiome ecology and may perpetuate dysbiosis.

Rationale: Chronic kidney disease (CKD) is a progressively debilitating condition leading to kidney dysfunction and severe complications. While dysbiosis of the gut bacteriome has been linked to CKD, the alteration in the gut viral community and its role in CKD remain poorly understood. Methods: Here, we characterize the gut virome in CKD using metagenome-wide analyses of faecal samples from 425 patients and 290 healthy individuals. Results: CKD is associated with a remarkable shift in the gut viral profile that occurs regardless of host properties, disease stage, and underlying diseases. We identify 4,649 differentially abundant viral operational taxonomic units (vOTUs) and reveal that some CKD-enriched viruses are closely related to gut bacterial taxa such as Bacteroides, [Ruminococcus], Erysipelatoclostridium, and Enterocloster spp. In contrast, CKD-depleted viruses include more crAss-like viruses and often target Faecalibacterium, Ruminococcus, and Prevotella species. Functional annotation of the vOTUs reveals numerous viral functional signatures associated with CKD, notably a marked reduction in nicotinamide adenine dinucleotide (NAD+) synthesis capacity within the CKD-associated virome. Furthermore, most CKD viral signatures are reproducible in the gut viromes of diabetic kidney disease and several other common diseases, highlighting the considerable universality of disease-associated viromes. Conclusions: This research provides comprehensive resources and novel insights into the CKD-associated gut virome, offering valuable guidance for future mechanistic and therapeutic investigations.

BackgroundIt has been well documented that periodontal treatment decreases the levels of certain disease-associated species in subgingival plaque. Few studies, however, investigate to which extent periodontal therapy restores a health-like subgingival community. Here, we conducted a secondary analysis to evaluate microbiome outcomes of nonsurgical periodontal therapy alone or followed by an intensive antiplaque regimen, analyzing microbiome trajectories at the community level with respect to health.MethodsEighty-six subjects with periodontitis stages II/III were evaluated at baseline and 6 months after receiving scaling and root planing alone (SRP, n = 41) or followed by an antiplaque regimen consisting of use of 0.12% chlorhexidine for 3 months and interdental cleaners for 6 months (SRP + P + S, n = 45). Thirty periodontally healthy subjects served as reference. The subgingival microbiome was characterized by 16S rRNA gene sequencing, and longitudinal within-subject changes were quantified with respect to a healthy plane (HPL) modeled from the reference group.ResultsEvaluation of individual microbiome trajectories showed that only the SRP + P + S group had a statistically significant reduction in distance to the HPL. However, responses were variable in both groups, with only a fraction of individuals changing in the direction of health. Random forest analysis revealed baseline microbiome composition as a greater predictor of microbiome response than type of treatment rendered.ConclusionAn adjunct antiplaque regimen resulted in a greater approximation of the microbiome to the healthy state. However, responses varied greatly among subjects highlighting the need for robust and personalized approaches to restore eubiosis.Plain Language SummaryThis study looked at how different treatments for gum disease change the bacteria in the gums of people with moderate to severe gum problems. Eighty-six people received standard gum treatments, and some also used a mouthwash and special tools to clean between their teeth at home. After 6 months, more people in the group that added the extra cleaning steps had healthier bacteria in their gums than people who received the standard treatment. However, not everyone responded the same way. The study found that the types of bacteria someone had at the start were better at predicting how well the treatment would work, more than the type of treatment itself. This means gum disease treatments may need to be personalized for better results.

It has been well documented that periodontal treatment decreases the levels of certain disease-associated species in subgingival plaque. Few studies, however, investigate to which extent periodontal therapy restores a health-like subgingival community. Here, we conducted a secondary analysis to evaluate microbiome outcomes of nonsurgical periodontal therapy alone or followed by an intensive antiplaque regimen, analyzing microbiome trajectories at the community level with respect to health. Eighty-six subjects with periodontitis stages II/III were evaluated at baseline and 6 months after receiving scaling and root planing alone (SRP, n  = 41) or followed by an antiplaque regimen consisting of use of 0.12% chlorhexidine for 3 months and interdental cleaners for 6 months (SRP + P + S, n  = 45). Thirty periodontally healthy subjects served as reference. The subgingival microbiome was characterized by 16S rRNA gene sequencing, and longitudinal within-subject changes were quantified with respect to a healthy plane (HPL) modeled from the reference group. Evaluation of individual microbiome trajectories showed that only the SRP + P + S group had a statistically significant reduction in distance to the HPL. However, responses were variable in both groups, with only a fraction of individuals changing in the direction of health. Random forest analysis revealed baseline microbiome composition as a greater predictor of microbiome response than type of treatment rendered. An adjunct antiplaque regimen resulted in a greater approximation of the microbiome to the healthy state. However, responses varied greatly among subjects highlighting the need for robust and personalized approaches to restore eubiosis. This study looked at how different treatments for gum disease change the bacteria in the gums of people with moderate to severe gum problems. Eighty-six people received standard gum treatments, and some also used a mouthwash and special tools to clean between their teeth at home. After 6 months, more people in the group that added the extra cleaning steps had healthier bacteria in their gums than people who received the standard treatment. However, not everyone responded the same way. The study found that the types of bacteria someone had at the start were better at predicting how well the treatment would work, more than the type of treatment itself. This means gum disease treatments may need to be personalized for better results.

Many chronic inflammatory diseases are attributed to disturbances in host–microbe interactions, which drive immune-mediated tissue damage. Depending on the anatomic setting, a chronic inflammatory disease can exert unique local and systemic influences, which provide an exceptional opportunity for understanding disease mechanism and testing therapeutic interventions. The oral cavity is an easily accessible environment that allows for protective interventions aiming at modulating the immune response to control disease processes driven by a breakdown of host–microbe homeostasis. Periodontal disease (PD) is a prevalent condition in which quantitative and qualitative changes of the oral microbiota (dysbiosis) trigger nonresolving chronic inflammation, progressive bone loss, and ultimately tooth loss. Here, we demonstrate the therapeutic benefit of local sustained delivery of the myeloid-recruiting chemokine (C-C motif) ligand 2 (CCL2) in murine ligature-induced PD using clinically relevant models as a preventive, interventional, or reparative therapy. Local delivery of CCL2 into the periodontium inhibited bone loss and accelerated bone gain that could be ascribed to reduced osteoclasts numbers. CCL2 treatment up-regulated M2-macrophage and downregulated proinflammatory and pro-osteoclastic markers. Furthermore, single-cell ribonucleic acid (RNA) sequencing indicated that CCL2 therapy reversed disease-associated transcriptomic profiles of murine gingival macrophages via inhibiting the triggering receptor expressed on myeloid cells-1 (TREM-1) signaling in classically activated macrophages and inducing protein kinase A (PKA) signaling in infiltrating macrophages. Finally, 16S ribosomal ribonucleic acid (rRNA) sequencing showed mitigation of microbial dysbiosis in the periodontium that correlated with a reduction in microbial load in CCL2-treated mice. This study reveals a novel protective effect of CCL2 local delivery in PD as a model for chronic inflammatory diseases caused by a disturbance in host–microbe homeostasis.

Abstract Periodontal Disease (PD) is a dysbiotic inflammatory condition of the periodontium (tooth-supporting tissues) that exerts an adverse impact on systemic health. IL-22 mediates unidirectional communication from immune cells to tissue stromal cells and plays a key role in promoting homeostatic immunity with some implications in certain inflammatory disorders. In this study, we investigated whether IL-22 is required for periodontal tissue homeostasis at steady state using IL-22-deficient (IL-22–/–) mice and IL-22+/+ littermate controls. The mice were analyzed in terms of disease phenotype, gingival transcriptomic profile (bulk RNA sequencing and quantitative real-time PCR) and microbiota composition (16S rRNA sequencing). Analysis of the bone levels in 10-week-old IL-22–/– mice revealed significantly increased naturally occurring bone loss vs controls. The IL-22 deficiency-induced bone loss was associated with significantly increased gingival tissue expression of the pro-inflammatorycytokine IL-17 and several molecules with pro-inflammatory and antimicrobial properties (e.g., S100 and Reg3 proteins). Moreover, IL-22 deficiency was associated with a significant increase in the periodontal microbial burden and alterations to the microbiota composition consistent with dysbiosis. Therefore, IL-22 appears to be required in setting the homeostatic tone of the periodontal tissue, suggesting that loss-of-function genetic polymorphisms in IL-22 may affect periodontal health.

At mucosal surfaces, epithelial cells provide a structural barrier and an immune defense system. However, dysregulated epithelial responses can contribute to disease states. Here, we demonstrated that epithelial cell-intrinsic production of interleukin-23 (IL-23) triggers an inflammatory loop in the prevalent oral disease periodontitis. Epithelial IL-23 expression localized to areas proximal to the disease-associated microbiome and was evident in experimental models and patients with common and genetic forms of disease. Mechanistically, flagellated microbial species of the periodontitis microbiome triggered epithelial IL-23 induction in a TLR5 receptor-dependent manner. Therefore, unlike other Th17-driven diseases, non-hematopoietic-cell-derived IL-23 served as an initiator of pathogenic inflammation in periodontitis. Beyond periodontitis, analysis of publicly available datasets revealed the expression of epithelial IL-23 in settings of infection, malignancy, and autoimmunity, suggesting a broader role for epithelial-intrinsic IL-23 in human disease. Collectively, this work highlights an important role for the barrier epithelium in the induction of IL-23-mediated inflammation.

BACKGROUND:The aim of the present study was to evaluate the subgingival microbiome in patients with grade C molar-incisor pattern periodontitis (C-MIP) affecting the primary or permanent dentitions. METHODS:DNA was isolated from subgingival biofilm samples from diseased and healthy sites from 45 C-MIP patients and subjected to phylogenetic microarray analysis. C-MIP sites were compared between children affected in the primary to those affected in the permanent dentitions. Within-subject differences between C-MIP-affected sites and dentition-matched healthy sites were also evaluated. RESULTS:C-MIP sites of subjects affected in the primary dentition showed partially overlapping but distinct microbial communities from C-MIP permanent dentition sites (p < 0.05). Differences were due to increased levels in primary C-MIP sites of certain species of the genera Capnocytophaga and Leptotrichia, while C-MIP permanent dentition sites showed higher prevalence of Filifactor alocis. Aggregatibacter actinomycetemcomitans (Aa) was among species seen in high prevalence and levels in both primary and permanent C-MIP sites. Moreover, both permanent and primary C-MIP sites showed distinct microbial communities when compared to dentition-matched healthy sites in the same subject (p < 0.01). CONCLUSIONS:Primary and permanent teeth with C-MIP showed a dysbiotic microbiome, with children affected in the primary dentition showing a distinct profile from those affected in the permanent dentition. However, Aa was enriched in both primary and permanent diseased sites, confirming that this microorganism is implicated in C-MIP in both dentitions.

Intestinal colonization of the oral bacterium Haemophilus parainfluenzae has been associated with Crohn's disease (CD) severity and progression. This study examines the role of periodontal disease (PD) as a modifier for colonization of H. parainfluenzae in patients with CD and explores the mechanisms behind H. parainfluenzae-mediated intestinal inflammation. Fifty subjects with and without CD were evaluated for the presence of PD, and their oral and fecal microbiomes were characterized. PD is associated with increased levels of H. parainfluenzae strains in subjects with CD. Oral inoculation of H. parainfluenzae elicits strain-dependent intestinal inflammation in murine models of inflammatory bowel disease, which is associated with increased intestinal interferon-γ (IFN-γ)+ CD4+ T cells and disruption of the host hypusination pathway. In summary, this study establishes a strain-specific pathogenic role of H. parainfluenzae in intestinal inflammation and highlights the potential effect of PD on intestinal colonization by pathogenic H. parainfluenzae strains in patients with CD.

Limited research exists on carbohydrate intake and oral microbiome diversity and composition assessed with next-generation sequencing. We aimed to better understand the association between habitual carbohydrate intake and the oral microbiome, as the oral microbiome has been associated with caries, periodontal disease, and systemic diseases. We investigated if total carbohydrates, starch, monosaccharides, disaccharides, fiber, or glycemic load (GL) were associated with the diversity and composition of oral bacteria in subgingival plaque samples of 1204 post-menopausal women. Carbohydrate intake and GL were assessed from a food frequency questionnaire, and adjusted for energy intake. The V3–V4 region of the 16S rRNA gene from subgingival plaque samples were sequenced to identify the relative abundance of microbiome compositional data expressed as operational taxonomic units (OTUs). The abundance of OTUs were centered log(2)-ratio transformed to account for the compositional data structure. Associations between carbohydrate/GL intake and microbiome alpha-diversity measures were examined using linear regression. PERMANOVA analyses were conducted to examine microbiome beta-diversity measures across quartiles of carbohydrate/GL intake. Associations between intake of carbohydrates and GL and the abundance of the 245 identified OTUs were examined by using linear regression. Total carbohydrates, GL, starch, lactose, and sucrose intake were inversely associated with alpha-diversity measures. Beta-diversity across quartiles of total carbohydrates, fiber, GL, sucrose, and galactose, were all statistically significant (p for PERMANOVA p < 0.05). Positive associations were observed between total carbohydrates, GL, sucrose and Streptococcus mutans; GL and both Sphingomonas HOT 006 and Scardovia wiggsiae; and sucrose and Streptococcus lactarius. A negative association was observed between lactose and Aggregatibacter segnis, and between sucrose and both TM7_[G-1] HOT 346 and Leptotrichia HOT 223 . Intake of total carbohydrate, GL, and sucrose were inversely associated with subgingival bacteria alpha-diversity, the microbial beta-diversity varied by their intake, and they were associated with the relative abundance of specific OTUs. Higher intake of sucrose, or high GL foods, may influence poor oral health outcomes (and perhaps systemic health outcomes) in older women via their influence on the oral microbiome.

A microbial community is a dynamic system undergoing constant change in response to internal and external stimuli. These changes can have significant implications for human health. However, due to the difficulty in obtaining longitudinal samples, the study of the dynamic relationship between the microbiome and human health remains a challenge. Here, we introduce a novel computational strategy that uses massive cross-sectional sample data to model microbiome landscapes associated with chronic disease development. The strategy is based on the rationale that each static sample provides a snapshot of the disease process, and if the number of samples is sufficiently large, the footprints of individual samples populate progression trajectories, which enables us to recover disease progression paths along a microbiome landscape by using computational approaches. To demonstrate the validity of the proposed strategy, we developed a bioinformatics pipeline and applied it to a gut microbiome dataset available from a Crohn’s disease study. Our analysis resulted in one of the first working models of microbial progression for Crohn’s disease. We performed a series of interrogations to validate the constructed model. Our analysis suggested that the model recapitulated the longitudinal progression of microbial dysbiosis during the known clinical trajectory of Crohn’s disease. By overcoming restrictions associated with complex longitudinal sampling, the proposed strategy can provide valuable insights into the role of the microbiome in the pathogenesis of chronic disease and facilitate the shift of the field from descriptive research to mechanistic studies.

Recent epidemiological studies have shown that inflammatory bowel disease is associated with periodontal disease. The oral-gut microbiota axis is a potential mechanism intersecting the two diseases. Porphyromonas gingivalis is currently considered a keystone oral pathogen involved in periodontal disease pathogenesis and disease progression. Recent studies have shown that oral ingestion of P. gingivalis leads to intestinal inflammation. However, the molecular underpinnings of P. gingivalis-mediated gut inflammation have remained elusive. In this study, we show that the oral administration of P. gingivalis indeed leads to ileal inflammation and alteration in gut microbiota with significant reduction in bacterial alpha diversity despite the absence of P. gingivalis in the lower gastrointestinal tract. Utilizing an antibiotic-conditioned mouse model, cecal microbiota transfer experiments were performed to demonstrate that P. gingivalis-induced dysbiotic gut microbiota is sufficient to reproduce gut pathology. Furthermore, we observed a significant expansion in small intestinal lamina propria IL9+ CD4+ T cells, which was negatively correlated with both bacterial and fungal alpha diversity, signifying that P. gingivalis-mediated intestinal inflammation may be due to the subsequent loss of gut microbial diversity. Finally, we detected changes in gene expression related to gut epithelial barrier function, showing the potential downstream effect of intestinal IL9+ CD4+ T-cell induction. This study for the first time showed the mechanism behind P. gingivalis-mediated intestinal inflammation where P. gingivalis indirectly induces intestinal IL9+ CD4+ T cells and inflammation by altering the gut microbiota. Understanding the mechanism of P. gingivalis-mediated intestinal inflammation may lead to the development of novel therapeutic approaches to alleviate the morbidity from inflammatory bowel disease patients with periodontal disease.

Summary:Quantifying pairwise sequence similarities is a key step in metagenomics studies. Alignment-free methods provide a computationally efficient alternative to alignment-based methods for large-scale sequence analysis. Several neural network-based methods have recently been developed for this purpose. However, existing methods do not perform well on sequences of varying lengths and are sensitive to the presence of insertions and deletions. In this article, we describe the development of a new method, referred to as AsMac that addresses the aforementioned issues. We proposed a novel neural network structure for approximate string matching for the extraction of pertinent information from biological sequences and developed an efficient gradient computation algorithm for training the constructed neural network. We performed a large-scale benchmark study using real-world data that demonstrated the effectiveness and potential utility of the proposed method.Availability and implementation:The open-source software for the proposed method and trained neural-network models for some commonly used metagenomics marker genes were developed and are freely available at www.acsu.buffalo.edu/~yijunsun/lab/AsMac.html.Supplementary information:Supplementary data are available at Bioinformatics online.

Motivation: Sequence comparison is a fundamental problem in bioinformatics and plays a key role in a wide range of applications. Alignment-free methods provide a computationally efficient alternative to alignment-based methods for large-scale sequence analysis. Several neural network-based methods have recently been developed for this purpose. However, due to the fact that the neural networks employed are not designed specifically for biological sequence analysis, existing methods do not perform well on sequences of varying lengths and are sensitive to the presence of insertions and deletions. Results: In this paper, we describe the development of a new method, referred to as AsMac, that addresses the aforementioned issues. We proposed a novel neural network structure for approximate string matching for the extraction of pertinent information from biological sequences and an efficient gradient computation algorithm for training the constructed neural network. We performed a large-scale benchmark study using real-world data that demonstrated the effectiveness and potential utility of the proposed method. Availability: Open-source software for the proposed method is developed and freely available at www.acsu.buffalo.edu/~yijunsun/lab/AsMac.html . Supplementary information: Supplementary data are available at Bioinformatics online.