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. Author manuscript; available in PMC: 2026 Jul 14.
Published in final edited form as: Arch Oral Biol. 2026 May 25;189:106635. doi: 10.1016/j.archoralbio.2026.106635

Polymicrobial periodontal infection selectively alters the fecal microbiome in mice: Implications for the oral-gut microbial axis

Syam Jeepipalli 1, Parvathi Gurusamy 1, Ana Rafaela Luz Martins 1, Saravanan Sampoornam Pape Reddy 2, Bikash Sahay 3, Edward K L Chan 4, L Kesavalu 1,4
PMCID: PMC13359653  NIHMSID: NIHMS2191171  PMID: 42242180

Abstract

Objective(s)

This study aimed to evaluate the impact of polybacterial oral infection with Streptococcus gordonii, Fusobacterium nucleatum, Porphyromonas gingivalis, Treponema denticola, and Tannerella forsythia on the gut microbiota in a murine model.

Design

We investigated alterations in fecal bacterial microbiome in infected mice. Fecal samples were collected after the 15th week of infection. Libraries of 16S rRNA amplicons generated from fecal DNA were sequenced using the Illumina sequencing platform.

Results

Feces from infected mice showed upregulation in the classes Verrucomicrobiae, Bacteroidia, Bacilli, and downregulation of Actinomycetia, Coriobacterota, and Clostridia. The genera Lactobacillus (p-value < 0.001) were significantly upregulated, and Acutalibacter (p-value < 0.05), Limosilactobacillus (p-value < 0.05) were downregulated in the infection compared to sham-infected mice. The infection process upregulated 15 strains of bacteria most prominently Akkermansia muciniphila and Dubosiella and downregulated 19 strains. Feces from sham infection had 21 strains of bacteria that completely disappeared in the infected mice, and the feces from infected mice detected 16 strains (Bacteroides, Bifidobacterium globosum) absent in sham-infected mice. Fecal microbiome data indicate that bacterial infection disrupts gut microbiome composition (selective dysbiosis) at class, genus, and strain levels. Periodontal infection appeared to eliminate certain bacterial strains and to promote the emergence of new strains.

Conclusion

Using the Illumina sequencing platform, the present study found that polymicrobial infection changed the gut microbiome at the class, genus, and strain levels of fecal bacteria. The genomic DNA for S. gordonii, F. nucleatum, and P. gingivalis was detected in the infected mice's feces.

Keywords: Fecal microbiome, Illumina sequencing, Oral bacteria, Polymicrobial infection

INTRODUCTION

Periodontal disease (PD) is a chronic, multifactorial, immuno-inflammatory disease that progresses with the accumulation of dysbiotic biofilm on subgingival surfaces, followed by periodontal tissue destruction and tooth loss (Socransky et al., 1998; Tonetti et al., 2018). Humans produce and swallow approximately 1500 mL of saliva daily and ingest an estimated 1.5 × 1012 oral commensal bacteria (Iorgulescu et al., 2009; Schmidt et al., 2019; Bardow et al., 2004). Supra- and subgingival microbes can be ingested and naturally translocated to the upper and lower digestive tract, including the oesophagus, stomach, small intestine, and colon (Kitamoto et al., 2020; Kitamoto & Kamada., 2022) A previous study confirmed that oral Fusobacterium nucleatum (F.n) reached the intestine by resisting the acidic gastric environment due to the presence of erucic acid in its cell membrane (Li et al., 2025). Interestingly, certain oral bacteria, such as Porphyromonas gingivalis (P.g), can tolerate the harsh acidic environment in the stomach and evade the gastric barrier (Walker et al., 2018). Sharma et al., (1984) cultured gastric secretions from healthy men and identified several bacterial genera, including Streptococcus species. The interplay between Helicobacter pylori and the oral–gut microbiota can lead to dysbiosis and gastric disease (Chen et al., 2022).

Hence, PD has a direct potential link with gut microbial dysbiosis (Yamazaki., 2023), defined as an imbalance in the composition or function of microbial taxa. Dysbiotic microbes in the subgingival region lead to chronic inflammation and PD (Johansson et al., 2016; Hajishengallis., 2015; Curtis et al., 2020). Oral biofilms associated with PD include P. gingivalis, Tannerella forsythia (T.f), Aggregatibacter actinomycetemcomitans (A.a), Prevotella intermedia (P.i), F.n, Capnocytophaga gingivalis, Treponema denticola (T.d), Eubacterium nodatum, and Campylobacter rectus (Teles et al., 2013). Transmission of oral microbes into the intestine and colonization of the large intestine have been observed in healthy individuals (Schmidt et al., 2019), and oral dysbiosis has been shown to dysregulate gut microbiota and shed into feces in animal and human studies (Ozdemir & Buyuktuncer., 2025; Liu et al., 2025; Song et al., 2025; Li et al., 2024; Zhang et al., 2023; Liao et al., 2024) The cecum functions as a fermentation vessel rich in commensal microbes (Lagkouvardos et al., 2016) and may serve as a habitat where gastric acid-resistant periodontal bacteria interact with resident gut microbiota (Bao et al., 2022).

More recently, the oral-gut microbial axis has gained attention, as inflammation in the oral cavity can affect distant microbial ecosystems, including the gut (Kitamoto et al., 2020). Inflammatory mediators, microbial translocation, and immune modulation are increasingly recognized as bidirectional mechanisms linking these mucosal sites. Oral bacteria such as P.g have been isolated from intestinal tissues, suggesting a microbial continuum between oral and gut compartments (Zhao et al., 2025). However, only a limited number of experimental studies have investigated oral microbiome-induced modulation of gut microbial composition using controlled in vivo models. Importantly, gut dysbiosis does not necessarily imply a complete restructuring of microbial community architecture. Increasing evidence suggests that disease-associated dysbiosis may manifest as selective shifts in the relative abundance of specific microbial taxa, without marked alterations in overall alpha or beta diversity. Such compositional dysbiosis has been reported in multiple inflammatory and microbiome-associated disease models, where taxon-specific alterations occur despite preservation of global ecological diversity indices (Lloyd-Price et al., 2016; Hooks & O'Malley., 2017; Petersen & Round., 2014; Duvallet et al., 2017). Accordingly, taxon-level compositional changes may represent a biologically relevant form of dysbiosis even in the absence of global ecological separation.

The present study aimed to address this knowledge gap by evaluating downstream changes in fecal microflora in a murine model of polymicrobial infection-induced periodontitis. Specifically, fecal microbiota from polymicrobial infection-induced periodontitis mice were compared with sham-infected controls using next-generation sequencing to identify changes at the class, genus, and strain levels (Gangula et al., 2015; Nayar et al., 2016; Bakthavatchalu et al., 2011; Chukkapalli et al., 2018). While the present study focuses on characterizing downstream alterations in gut microbial composition following polymicrobial periodontal infection, it does not directly assess functional or host-level outcomes. Accordingly, the findings are intended to define compositional signatures associated with periodontal infection and to generate mechanistic hypotheses regarding oral-gut microbial interactions, rather than to establish causal functional effects.

MATERIALS AND METHODS

Experimental mice and ethical approval

Eight-week-old C57BL/6J male and female mice were obtained from Jackson Laboratory (Bar Harbor, ME, USA) and allowed to acclimatize for one week prior to initiation of ecological time-sequential polybacterial periodontal infection (ETSPPI) or sham infection (Aravindraja et al., 2022). Mice were housed under controlled environmental conditions with a 12 h light/dark cycle and provided mouse chow and water ad libitum. All animal procedures were approved by the University of Florida Institutional Animal Care and Use Committee (IACUC - Institutional Animal Care and Use Committee Protocol number 202200000223) and conducted in accordance with ARRIVE 2.0 guidelines and NIH (National Research Council) Guide for the Care and Use of Laboratory Animals (Percie du Sert et al., 2020). Animals were randomly assigned to polymicrobial infection and sham infection groups. Fecal samples used in this study were collected as part of a larger microRNA study approved by IACUC.

Sample size and randomization

Sample size determination was based on previous studies evaluating alterations in gut microbial diversity following experimental periodontitis (Aravindraja et al., 2022). With an anticipated moderate-to-large effect size (Cohen’s d = 1.2), a power of 80%, and a two-tailed significance level of 0.05, a minimum of four animals per group was considered sufficient. To account for potential attrition during infection, DNA extraction, or sequencing, five mice per group were included. Random assignment was used to minimize selection bias.

Polymicrobial infection protocol

Partial human mouth microbes (PAHMM), including S. gordonii DL1, F. nucleatum ATCC 49256, P. gingivalis ATCC 53977, T. denticola ATCC 35404, and T. forsythia ATCC 43037, were cultured and prepared as previously described. Prior to infection, mice received kanamycin (500 mg/2 L) in drinking water for three days, followed by oral rinsing with 0.12% chlorhexidine gluconate to suppress resident oral microflora. Mice in the polymicrobial infection group underwent sequential intraoral infection cycles consisting of S. gordonii (two cycles), F. nucleatum (two cycles), followed by P. gingivalis, T. denticola, and T. forsythia (five cycles). Each infection cycle consisted of four days per week of intraoral inoculation with 2.5 × 108 bacterial cells suspended in reduced transport fluid containing 6% carboxymethyl cellulose, generating the ETSPPI model (Aravindraja et al., 2022). Sham-infected mice received vehicle alone. The antibiotic and chlorhexidine pre-treatment regimen was implemented to suppress resident oral microbiota and facilitate reproducible polymicrobial colonization, as established in prior ETSPPI studies. This intervention was applied identically to polymicrobial-infected and sham-infected mice to control for its potential ecological effects.

Sample collection

Fecal samples were collected after 15 weeks from the final infection cycle, immediately placed in sterile cryovials, and stored at −20 °C. Samples were shipped on dry ice to the CosmosID laboratory (CosmosID Inc., Rockville, MD, USA) for metagenomic analysis.

Detection of oral bacterial DNA in fecal samples

Genomic DNA from fecal samples was screened for S. gordonii, F. nucleatum, P. gingivalis, T. denticola, and T. forsythia using species-specific 16S Ribosomal Ribonucleic Acid (rRNA) gene primers. PCR amplification was performed using 1–2 μL of DNA template in a 25 μL reaction volume. Amplified products were resolved on 1.5% agarose gels and visualized under UV illumination. Genomic Deoxyribonucleic acid (DNA) from reference strains served as positive controls, and nuclease-free water was used as a negative control. Primer sequences are listed in Supplementary Table 1. Detection of species-specific 16S rRNA gene sequences reflects the presence of bacterial genomic DNA and does not distinguish between viable organisms, transient passage, or non-replicative bacterial remnants.

DNA isolation, sequencing, and bioinformatics analysis

DNA was isolated from fecal samples using the QIAGEN DNeasy PowerSoil Pro Kit according to the manufacturer’s instructions and quantified using a Qubit 4 fluorometer (Thermo Fisher Scientific, MA, USA). Libraries were prepared using the Nextera XT DNA Library Preparation Kit and sequenced on the Illumina NovaSeq platform (2 × 150 bp) (Plassais et al., 2021). Taxonomic profiling was performed using the CosmosID k-mer–based bioinformatics pipeline, which identifies microorganisms by mapping phylogenetically informative k-mers to curated microbial genome databases. Alpha diversity was assessed using the Shannon diversity index. Statistical analyses were performed using Prism 9.4.1 (GraphPad Software, San Diego, CA, USA). Two-way ANOVA with Sidak’s multiple-comparisons test and Mann–Whitney U tests were applied as appropriate. Statistical significance was set at p ≤ 0.05. To reduce the influence of rare taxa and potential false-positive signals, taxa with very low relative abundance or inconsistent detection across samples were interpreted descriptively. Fold-change estimates were considered biologically meaningful only when supported by consistent detection across animals and corresponding statistical testing. Non-significant taxa were not interpreted as definitive treatment-associated effects.

RESULTS

ETSPPI-induced colonization and sequencing output

Mice in the polymicrobial infection and sham infection groups were nine weeks old at the start of the experiment. The polymicrobial infection group received sequential inoculation with S. gordonii (Sg), F. nucleatum (Fn), and the late colonizers P. gingivalis (Pg), T. denticola (Td), and T. forsythia (Tf). Previous studies with ETSPPI have previously shown successful intraoral colonization by this polymicrobial infection protocol, as supported through gingival swab PCR, bacterial detection assays and host immune response profiling. In this study colonization was not further evaluated on its own, and these previously validated data are referenced to put into perspective the downstream changes in fecal microbiome composition (Assarsson et al., 2018). In the present study, colonization analysis was not repeated, and previously published evidence is cited to contextualize fecal microbiome findings. Fecal DNA from sham-infected mice was analyzed in parallel using the same species-specific PCR assays targeting S. gordonii, F. nucleatum, P. gingivalis, T. denticola, and T. forsythia. None of the sham-infected mice demonstrated detectable amplification for any of the five periodontal bacteria. 16S rRNA amplicon sequencing yielded approximately 25–35 million read pairs per sample using the Illumina NovaSeq platform.

Fecal microbiota richness and alpha diversity

Sixteen-S rRNA sequencing was performed on fecal samples from polymicrobial-infected and sham-infected mice (n = 5 per group). Alpha diversity indices, including Chao1, Shannon, and Simpson indices, were calculated to evaluate microbial richness and evenness. No statistically significant differences were observed between polymicrobial-infected and sham-infected groups for any of the alpha diversity indices (Figure 1A–1D).

Figure 1 –

Figure 1 –

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Alpha diversity analysis of fecal microbiota in polymicrobial-infected and sham-infected mice. Alpha diversity metrics calculated from 16S rRNA sequencing data comparing polymicrobial periodontal infection and sham-infected control mice (n = 5 per group). Panels show diversity indices including Chao1 richness estimator (A), Shannon diversity index (B), Simpson diversity index (C), and observed species richness (D). No statistically significant differences were observed between groups, indicating that polymicrobial periodontal infection did not significantly alter overall fecal microbial diversity. Data are presented as mean ± standard error of the mean (SEM). Statistical comparisons were performed using the Mann–Whitney U test with significance defined as p ≤ 0.05.

Beta diversity and community relationships

Beta diversity was assessed to evaluate differences in microbial community composition between groups (Williams et al., 2024). Principal coordinate analysis based on Jaccard and Bray-Curtis distance matrices did not demonstrate significant separation between polymicrobial-infected and sham-infected fecal samples (Figure 2A,B), indicating broadly similar overall community structure. These findings indicate that polymicrobial periodontal infection did not induce a wholesale restructuring of fecal microbial communities at the global ecological level, but rather was associated with more subtle compositional changes detectable at lower taxonomic resolution.

Figure 2 –

Figure 2 –

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Beta diversity analysis of fecal microbial communities. Principal coordinate analysis (PCoA) plots generated using Jaccard (A) and Bray–Curtis (B) distance matrices comparing fecal microbiota composition between polymicrobial periodontal infection and sham-infected mice. Each point represents an individual mouse fecal sample (n = 5 per group). No clear clustering or separation between groups was observed, indicating broadly similar global microbial community structures despite infection status.

Phylum-level fecal microbiota composition

At the phylum level, polymicrobial-infected mice showed relative enrichment of Verrucomicrobiota, Bacteroidota, and Bacillota compared with sham-infected mice, while Bacillota_A and Actinomycetota were reduced. Median relative abundance values and fold-change estimates are presented in Supplementary Table 2. None of the phylum-level differences reached statistical significance (p > 0.05). Heatmap analysis demonstrated clustering patterns across samples (Figure 3A), while statistical comparisons confirmed the absence of significant phylum-level differences between groups (Figure 3A,B).

Figure 3 –

Figure 3 –

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Taxonomic composition of fecal microbiota at phylum and class levels. (A–B) Relative abundance of dominant bacterial phyla detected in fecal samples from polymicrobial-infected and sham-infected mice. (C) Heatmap illustrating differential abundance patterns of bacterial classes across samples. Polymicrobial periodontal infection was associated with relative enrichment of Verrucomicrobiae, Bacteroidia, and Bacilli, and reduction of Actinomycetia, Coriobacteriia, and Clostridia. Relative abundance values represent normalized sequencing counts.

Class-level relative abundance

At the class level, Verrucomicrobiae, Bacteroidia, and Bacilli were relatively upregulated in polymicrobial-infected mice, whereas Actinomycetia, Coriobacteriia, and Clostridia were downregulated (Supplementary Table 3). Class-level heatmap analysis illustrated compositional variation in microbial taxa across samples from polymicrobial-infected and sham-infected mice (Figure 3C), although no clear group-level separation was observed.

Genus-level differences

Genus-level relative abundance analysis identified selective differences between polymicrobial-infected and sham-infected mice. Lactobacillus (p < 0.001) and Limosilactobacillus (p < 0.05) were significantly increased in polymicrobial-infected mice, while Acutalibacter showed differential abundance compared with sham-infected controls (Figure 4A–C). Other genera did not demonstrate statistically significant differences and are presented descriptively in tables and heatmaps for completeness (Figure 4D,E & Figure 5A). Statistically significant differences were observed for selected genera, while several other taxa showed descriptive fold-change trends without statistical significance. Given the small sample size and inter-individual variability in fecal microbiota, non-significant taxa were interpreted cautiously and not considered definitive treatment-associated changes.

Figure 4 –

Figure 4 –

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Genus-level alterations in fecal microbiota following polymicrobial periodontal infection. (A–C) Differential abundance analysis showing significant changes in selected bacterial genera including Lactobacillus, Limosilactobacillus, and Acutalibacter between polymicrobial-infected and sham-infected mice. (D–F) Heatmap visualization of genus-level abundance patterns across fecal samples. Bars represent relative abundance values and statistical significance was determined using Mann–Whitney U tests (p < 0.05).

Figure 5 –

Figure 5 –

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Strain-level alterations in fecal microbiota following polymicrobial periodontal infection. (A) Relative abundance distribution of bacterial strains detected in polymicrobial-infected and sham-infected mice. (B) Venn diagram illustrating shared and unique bacterial strains between groups. Approximately 47.9% of strains were shared, while 22.5% were unique to polymicrobial-infected mice and 29.6% were unique to sham controls. (C) Heatmap demonstrating strain-level abundance patterns across fecal samples, highlighting enrichment of strains including Akkermansia muciniphila and Dubosiella spp. in polymicrobial-infected mice.

Gain and loss of genera following polymicrobial infection

Several genera were uniquely detected in fecal samples from polymicrobial-infected mice and were absent in sham-infected controls, including Bacteroides, Prevotella, Muribaculum, and Duncaniella (Supplementary Table 4). Conversely, genera detected exclusively in sham-infected mice were absent following polymicrobial infection. Akkermansia and Dubosiella emerged as dominant genera in polymicrobial-infected mice.

Strain-level alterations

Strain-level analysis revealed both gain and loss of specific bacterial strains following polymicrobial infection. Approximately 47.9% of strains were shared between groups, while 22.5% were unique to polymicrobial-infected mice and 29.6% were unique to sham-infected mice (Figure 5B). Polymicrobial infection was associated with enrichment of 15 strains, including Akkermansia muciniphila (A. muciniphila) and Dubosiella species, and reduction of 18 strains relative to sham-infected mice (Supplementary Tables 5–8). Heatmap visualization illustrated strain-level abundance patterns across samples (Figure 5C).

Detection of periodontal bacterial DNA in feces

PCR analysis of fecal DNA demonstrated the presence of 16S rRNA gene amplicons corresponding to S. gordonii, F. nucleatum, and P. gingivalis in fecal samples from polymicrobial-infected mice (Figure 6A,B). T. denticola and T. forsythia were not detected in fecal samples. Thus, fecal detection was limited to three of the five inoculated periodontal bacterial species. None of the sham-infected mice tested positive for any of the five periodontal bacteria. These findings indicate the presence of periodontal bacterial DNA in fecal samples following polymicrobial infection but do not provide evidence of active colonization, persistence, or functional integration of these organisms within the gut microbiota. Targeted PCR/qPCR validation of sequencing-derived differential resident gut taxa was not performed in the present study.

Figure 6 –

Figure 6 –

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Detection of periodontal bacterial DNA in fecal samples. (A) Agarose gel electrophoresis showing PCR amplification of species-specific 16S rRNA gene fragments corresponding to periodontal bacteria detected in fecal DNA. (B) Representative gel image demonstrating detection of Streptococcus gordonii, Fusobacterium nucleatum, and Porphyromonas gingivalis in fecal samples from polymicrobial-infected mice. No amplification was detected in sham-infected controls. These findings indicate the presence of periodontal bacterial genomic DNA in feces following polymicrobial oral infection.

DISCUSSION

The present study investigated the biological impact of polymicrobial periodontal bacterial infection on the gut microbiota in a murine model, providing experimental evidence supporting the concept of an oral–gut microbial axis. Using next-generation sequencing, we demonstrated that polymicrobial periodontal infection induced distinct compositional alterations in the fecal microbiome at the class, genus, and strain levels, despite the absence of significant changes in overall microbial diversity. Alpha diversity indices, including Chao1, Shannon, and Simpson indices, did not differ significantly between polymicrobial-infected and sham-infected mice. Similar findings have been reported in other disease models, indicating that alpha diversity alone is not a reliable indicator of microbial dysbiosis or disease severity (Ren et al., 2020). Instead, disease-associated dysbiosis may be better reflected by changes in the relative abundance of specific microbial taxa rather than overall richness or evenness.

The oral-gut microbial axis has gained increasing attention, as oral inflammation and dysbiosis have been shown to influence distant mucosal sites, including the gastrointestinal tract (Sharma et al., 1984). Inflammatory mediators, immune modulation, and direct microbial translocation are proposed mechanisms underlying this bidirectional interaction. Oral bacteria such as P. gingivalis have been isolated from intestinal tissues, suggesting a potential microbial continuum between the oral cavity and the gut (Duvllet et al.,2017). In the present study, PCR detection of S. gordonii, F. nucleatum, and P. gingivalis genomic DNA in fecal samples from polymicrobial-infected mice supports the hypothesis that periodontal pathogens can reach the gut and be shed through feces. Detection of S. gordonii, F. nucleatum, and P. gingivalis genomic DNA in fecal samples from polymicrobial-infected mice supports the concept of oral-to-gut microbial passage associated with periodontal infection. A pertinent finding in the current work was that only 3 out of the five initial periodontal bacteria inoculated, S. gordonii, F. nucleatum and P. gingivalis were identified from fecal specimens. This result suggests that these three organisms may be the main detectable oral bacterial taxa related to fecal microbial restructuring in this model. However, lack of detectable T. denticola and T. forsythia DNA in feces may not rule out a role during oral establishment that includes some combination of supporting polymicrobial biofilm maturation, promoting periodontal inflammation, or transient effects which were no longer evidence at the time of fecal sampling. Thus, changes in the gut microbiome seen may represent both direct effects of passage of detectable oral bacterial DNA but also bacteria acting indirectly through the entire polymicrobial periodontal-infection process. However, it is essential to distinguish DNA detection from evidence of viable colonization or functional integration within the gut ecosystem. The present findings are therefore best interpreted as indicating translocation or shedding of oral bacterial DNA through the gastrointestinal tract rather than stable engraftment. Whether orally derived bacteria can persist, replicate, or exert functional effects within the gut requires dedicated viability assays, culture-based approaches, or transcriptomic analyses, which were beyond the scope of this study. Although beta diversity analyses did not demonstrate significant separation between polymicrobial-infected and sham-infected groups, this does not preclude the presence of biologically meaningful dysbiosis. Dysbiosis is increasingly recognized as a context-dependent phenomenon that may involve selective expansion or depletion of specific taxa rather than uniform community-wide disruption. In this study, polymicrobial periodontal infection was associated with reproducible and directional changes at the class, genus, and strain levels, supporting the presence of a selective, compositional form of gut dysbiosis despite preserved global community structure. Although beta diversity analyses did not demonstrate clear separation between polymicrobial-infected and sham-infected groups, notable compositional shifts were observed at lower taxonomic levels. The absence of pronounced beta diversity separation further suggests that antibiotic pre-treatment did not induce divergent community instability between groups, supporting the internal validity of infection-associated comparisons. At the class level, Verrucomicrobiae, Bacteroidia, and Bacilli were relatively enriched in polymicrobial-infected mice, while Actinomycetia and Clostridia were reduced. These findings indicate selective ecological restructuring of the gut microbiome rather than a global disruption of microbial communities.

The most interesting compositional observation in this study was the increased abundance of A. muciniphila in polymicrobial-infected mice. Intestinal bacterium A.muciniphila is known as a bacterium capable of degrading the mucin and has been frequently reported to be involved in intestinal barrier function, immuno-regulation well as metabolic homeostasis of hosts from various experimental models. It should be noted that the current study did not measure gut barrier integrity, mucin turnover, host immune responses or metabolic results. Therefore, the enhanced prevalence of A.muciniphila in this study is a compositional signature related to polymicrobial periodontal infection, and not likely reflecting functional or immunologic implications. This enrichment is believed to be justified as it is consistent with taxa previously associated with host-microbe interactions, which will require future mechanistic studies (Lakshmanan et al., 2022; Ghaffari et al., 2023; Rodrigues et al., 2022; Zhang et al 2024). Altered abundance of A. muciniphila has been reported in inflammatory bowel disease, metabolic disorders, and other systemic conditions. The increased abundance observed in the present study suggests that periodontal infection-associated ‘selective’ oral dysbiosis may influence mucin dynamics and gut barrier interactions, although functional consequences were not directly assessed. The genus Dubosiella was also enriched among polymicrobial-infected mice. Dubosiella, a representative of family Erysipelotrichaceae, has been reported as an immune modulating gut symbiont and implicated in inflammatory and metabolic disorder in experimental models (Wang et al., 2021). Similarly, enrichment of the genus Dubosiella was observed in polymicrobial-infected mice. Members of this genus have been linked in prior studies to immune modulation and metabolic phenotypes in murine models. In the absence of host-response measurements in the current study, these associations should be viewed as contextual rather than mechanistic. The presence of Dubosiella therefore represents a reproducible compositional change that may inform hypothesis generation regarding periodontal infection-associated gut microbial remodeling. The consistent enrichment of Akkermansia and Dubosiella underscores the selective expansion of host-microbe interaction-immune modulating-related taxa in response to periodontal infection. Collectively, these taxon-specific shifts define compositional patterns associated with periodontal infection but do not, in isolation, support conclusions regarding systemic inflammation, epithelial barrier function, or metabolic consequences.

On the genus level, Lactobacillus and Limosilactobacillus were significantly more abundant in polymicrobial-infected mice. Protective roles in gut homeostasis, epithelial integrity and immune response have been attributed to the members of these genera (Xin et al., 2014; Liu et al 2024). The altered abundances may also reflect compensatory or adaptive responses of the gut microbiome when challenged by oral microbes. Furthermore, strain-level analysis indicated the gain and loss of individual bacterial strains after polymicrobial infection. About half the identified strains were common between infected and sham-infected mice, whereas subsets of AIRs were unique to each group. The dynamic appearance and extinction of particular strains in the gut bacteria after periodontal infection highlight the malleability of gut microbial ecosystems and their responsiveness to remote inflammatory influences.

The results of the present study are consistent with previous reports that periodontal pathogens or their constituents might affect gut microbial composition and systemic inflammation (Kitamoto & Kamada., 2022; Liu et al., 2024). In particular, experimental studies have shown that oral dysbiosis may be involved in shaping the gastrointestinal diseases, metabolic disorder and inflammation because of modulation of gut microbiota or host immune response (Li et al., 2021; Huang et al., 2024; Tatullo et al., 2024; Le Bars et al; 2017; Knop-Chodyła et al., 2024). Conversely, aberrant intestinal flora may exacerbate periodontal inflammation by raising of possibility of priming systemic inflammatory pathways.

A key limitation of the present study is the reliance on microbiota compositional profiling without accompanying functional or host-response assessments. Although the controlled murine model provides an ideal platform for mechanistic interrogation, the current investigation was not designed to evaluate intestinal permeability, gut histopathology, inflammatory cytokine profiles, metabolomic alterations, or systemic immune responses. Consequently, the biological implications of the observed compositional shifts particularly enrichment of taxa such as A.muciniphila and Dubosiella should be interpreted as biologically plausible associations rather than direct mechanistic outcomes. These compositional signatures nonetheless provide an essential foundation for hypothesis-driven studies incorporating multi-omics approaches and host phenotyping to delineate causal pathways linking periodontal infection to gut and systemic physiology. An additional consideration in interpreting the present findings is the use of antibiotic pre-treatment and chlorhexidine rinsing prior to polymicrobial infection. Although this protocol is integral to the ETSPPI model and necessary to achieve consistent oral colonization, such interventions represent an ecological perturbation that may influence baseline microbial stability, including potential downstream effects on the gut microbiome. Importantly, because both polymicrobial-infected and sham-infected mice were subjected to the same pre-treatment, the observed differences in fecal microbial composition are attributable to infection status rather than the pre-treatment itself. Nevertheless, the absolute microbial configuration observed here reflects a post-perturbation ecosystem, and conclusions are therefore restricted to relative, infection-associated compositional shifts within this controlled experimental context. There are few other limitations of this study that we should acknowledge. First, murine model potentially could not reflect the complexity of human oral-gut interplay. Second, although compositional alterations of the gut microbiota were described, other functional analyses, for example metabolomic profiling of inflammatory markers or measurements of intestinal permeability were not carried out. Hence, inferences about functional implications of the observed ‘selective’ dysbiosis cannot be made. Another limitation of the present study is that the sequencing-derived differential fecal taxa were not independently validated by targeted PCR or quantitative PCR. Although species-specific PCR was used to detect the inoculated periodontal bacteria in fecal samples, confirmatory qPCR was not performed for differentially abundant resident gut taxa such as Akkermansia, Dubosiella, Lactobacillus, Limosilactobacillus, and Acutalibacter. Therefore, these taxon-level differences should be interpreted as sequencing-based compositional signatures. Future studies should validate the most biologically relevant differential taxa using targeted qPCR, digital PCR, strain-specific assays, or culture/viability-based methods to confirm abundance changes and clarify their functional relevance. Future studies should include mono-species and reduced-consortium infection arms using S. gordonii, F. nucleatum, and P. gingivalis alone, compared with the complete five-species ETSPPI consortium, to determine whether these three detectable organisms are sufficient to reproduce the fecal microbiome alterations observed here. A key limitation of the present study is the small animal sample size, which may reduce statistical power and increase susceptibility to inter-individual microbiome variation. This is particularly relevant for low-abundance taxa, where fold-change estimates may be unstable and may reflect individual variability rather than treatment-associated effects. Therefore, non-significant taxa should be interpreted as exploratory compositional trends rather than confirmed infection-induced changes. Future studies should include larger animal cohorts, predefined relative-abundance filtering thresholds, correction for multiple comparisons, longitudinal fecal sampling, and targeted qPCR validation of key taxa to reduce false-positive inference and confirm robust microbial signatures. Longitudinal studies combined with multi-omics are necessary for further mechanistic pathways in the connection between periodontal infection and modulation of gut microbiome. Notwithstanding these limitations, the current study brings substantial experimental evidence of an oral-gut microbial axis in the disease of periodontitis. The findings emphasize the systemic effects of periodontal inflammation and suggest that periodontal health is required to maintain gut microbial homeostasis.

CONCLUSION

This study shows that polymicrobial infection of the periodontium results in unique changes in gut microbiota composition in murine model. Polymicrobial oral infection biologically impacted the fecal microbiota at the class, genus, and strain levels including significant enrichment of A. muciniphila and Dubosiella as well as appearance or loss of strains from multiple bacterial taxa. These results lend support to an oral-gut microbial axis, whereby periodontal inflammation is associated with selective alterations in gut microbial composition rather than global ecological restructuring. Even though functional implications were not evaluated, the observed compositional ‘taxon specific’ dysbiosis suggests possible systemic consequences of periodontal disease. Although functional consequences were not directly evaluated, the compositional alterations observed here define reproducible microbial signatures associated with polymicrobial periodontal infection and provide a rational framework for future mechanistic investigations integrating host immune, metabolic, and barrier-function assessments. Overall, our work constitutes experimental evidence that connects infection of periodontal tissues with changes in the gut microbiome, and emphasizes the significance of maintaining periodontal health in order to maintain systemic microbial homeostasis.

Supplementary Material

1

Highlights.

  • Polymicrobial periodontal infection alters murine fecal microbiome composition.

  • Oral bacterial DNA detected in feces following periodontal infection.

  • Infection induces taxon-specific microbiome shifts without global diversity change.

  • Selective enrichment of Akkermansia and Dubosiella in infected mice.

  • Findings support a compositional oral–gut microbial axis.

Acknowledgments

The authors acknowledge CosmosID sample processing and software support for analysing the results.

STATEMENT OF FUNDING SOURCE

This study was supported by the NIH National Institute of Dental and Craniofacial Research (NIDCR) (R01 DE028536) to L. Kesavalu and Edward K. L. Chan. The funders had no role in study design, data collection, analysis, decision to publish, or preparation of the manuscript.

ABBREVIATIONS

A muciniphila

Akkermansia muciniphila

A.a

Aggregatibacter actinomycetemcomitans

DNA

Deoxyribonucleic acid

ETSPPI

Ecological time-sequential polybacterial periodontal infection

Fn

Fusobacterium nucleatum

IACUC

Institutional Animal Care and Use Committee

PAHMM

Partial human mouth microbes

PD

Periodontal disease

PCR

Polymerase Chain Reaction

Pg

Porphyromonas gingivalis

Pi

Prevotella intermedia

qPCR

Quantitative polymerase chain reaction

rRNA

Ribosomal Ribonucleic Acid

S.gordonii

Streptococcus gordonii

Td

Treponema denticola

Tf

Tannerella forsythia

Footnotes

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CONFLICT OF INTEREST STATEMENT

Conflict of interest disclosure - The authors declare that they have no potential conflicts of interest.

Syam Jeepipalli declares that there are no actual or potential conflicts of interest.

Parvathi Gurusamy declares that there are no actual or potential conflicts of interest.

Ana Rafaela Luz Martins that there are no actual or potential conflicts of interest.

Saravanan Sampoornam Pape Reddy declares that there are no actual or potential conflicts of interest.

Edward KL Chan declares that there are no actual or potential conflicts of interest.

Kesavalu L declares that there are no actual or potential conflicts of interest.

ETHICAL APPROVAL STATEMENT

All animal experiments were conducted in accordance with ARRIVE 2.0 guidelines and were approved by the University of Florida Institutional Animal Care and Use Committee (IACUC Protocol No. 202200000223).

Declaration of generative AI use

During the preparation of this manuscript, the authors declare that they did not use generative AI tools. The authors take full responsibility for the accuracy and integrity of the manuscript.

PERMISSION TO REPRODUCE MATERIAL FROM OTHER SOURCES – Not applicable as none of the material was reproduced from any sources

Declaration of interests

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

KESAVALAU LAKSHMYYA reports financial support was provided by National Institutes of Health. KESAVALU LAKSHMYYA reports a relationship with National Institutes of Health that includes: funding grants. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data Availability Statement

The bioproject described in this paper belongs to the BioProject ID PRJNA1299394.

The corresponding BioSample accession number is SUB15504515. https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1299394

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1
NIHMS2191171-supplement-1.docx (37.6KB, docx)

Data Availability Statement

The bioproject described in this paper belongs to the BioProject ID PRJNA1299394.

The corresponding BioSample accession number is SUB15504515. https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1299394

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