Impact of Periodontitis on the Leakage of Oral Bacteria to the Gut

Abstract

Colorectal cancer (CRC) and periodontitis have recently been related due to the higher incidence of CRC in periodontal patients and the involvement of periodontal pathogens in carcinogenesis, suggesting that leakage from the oral cavity to the gut occurs. However, the magnitude of this pass-through in healthy individuals is controversial, and the effect that periodontitis could play in it is understudied. To evaluate the rate of bacterial leakage from the oral cavity to the gut, we analyzed the microbial composition of saliva, subgingival plaque, and fecal samples in healthy individuals without gastrointestinal disorders, including 20 periodontitis patients and 20 oral healthy controls, using PacBio full-length 16S rRNA gene sequencing. As expected, we observed a higher abundance of periodontal pathogens in the subgingival plaque and saliva of periodontal patients. In contrast, no significant differences were found between the fecal samples of both groups, implying that gut samples from periodontal patients were not enriched in periodontal pathogens. Fusobacterium nucleatum, a biomarker of CRC, was not found in the fecal samples of any participant. Our study does show a small leakage of some oral bacteria (mainly streptococci) to the gut, regardless of periodontal health status. Future studies should test whether other host factors and/or the preexistence of a gut disorder must be present in addition to periodontitis to promote the colonization of the gut by oral pathogens. The absence of periodontal pathogens in feces supports the idea that these bacteria could be used as biomarkers of intestinal disorders, including CRC.

Keywords

microbiota periodontal diseases bacterial translocation oral health colorectal neoplasms biomarkers

Background

A general feature of the human microbiome under healthy conditions is that every niche in the body appears to keep a distinctive microbial composition, which is specially adapted to maintain local homeostasis (Lloyd-Price et al. 2016). Most oral bacteria are considered commensals, but an imbalance in the microbiota community (dysbiosis) can have local effects and lead to oral diseases such as caries and periodontitis (Mira et al. 2017) and could also be involved in the development of several nonoral diseases, such as cardiovascular diseases, endocarditis, systemic inflammation, or cancer (Park et al. 2021; Giordano-Kelhoffer et al. 2022; Khaledi et al. 2022). Although under healthy conditions, individuals swallow about 1 L of saliva per day, the stomach barrier and several immune mechanisms prevent the colonization of the gut by oral bacteria. As a consequence, fecal samples normally display low levels of oral bacteria (Cheung et al. 2023), which increase in proportion in immune-compromised patients (Lozupone et al. 2013) or in patients with rheumatoid arthritis or cancer (Schmidt et al. 2019).

In the case of colorectal cancer (CRC), which is the third most common cancer type worldwide (Sung et al. 2021), it was observed that patients with periodontal disease have a 44% higher risk of developing this pathology (Li et al. 2021). Furthermore, several oral-associated species, specially periodontal pathogens such as Fusobacterium nucleatum and Parvimonas micra, have been detected in tumors and associated with the development of carcinogenesis and metastasis (Zhao et al. 2022; Wang and Fang 2023). In fact, tests based on the detection of these microorganisms in fecal samples have been proposed as early diagnosis tools for CRC (Zhang et al. 2022; Wang and Fang 2023).

Several lines of evidence suggest that periodontal bacteria can translocate from the oral cavity to the tumors (Abed et al. 2020; Conde-Pérez et al. 2022). However, the exact pathway for the ectopic colonization of these oral bacteria to the tumor is still uncertain, although hypotheses include a hematogenous route or a saliva-mediated enteral transmission (Koliarakis et al. 2019).

Considering the growing evidence of the oral origin of periodontal pathogens found in CRC tumor tissues (Abed et al. 2020; Conde-Pérez et al. 2022) and the increased proportion of these periodontal pathogens in the subgingival sulcus and saliva of periodontal patients (Lundmark et al. 2019), it has become crucial to determine if a higher concentration of periodontal pathogens reaches the gut in these individuals and the dissemination route. We hypothesized that periodontal patients have higher translocation rates of periodontal pathogens to the gut due to the higher abundance of these in the oral cavity and the gum bleeding facilitating hematogenous translocation (Tomás et al. 2012; Damgaard et al. 2021). To address this, we conducted a study analyzing the microbial composition of paired saliva, subgingival sulcus, and fecal samples of periodontal patients and oral healthy controls. To allow the identification of oral bacteria in the gut, we used full-length 16S (FL-16S) rRNA gene sequencing, which allows for species- and subspecies-level resolution (Callahan et al. 2019; Buetas et al. 2023). Through this approach, we aim to identify the co-occurrence of the same species in all 3 niches, thereby enabling us to estimate the degree of ectopic colonization of oral bacteria in the gut.

Methods

Participants

Forty volunteers were recruited during 2020 to 2021 and examined by the same odontologist in the Odontology Clinic of Valencia University, Spain. Cancer and inflammatory bowel diseases were exclusion criteria. Half of the donors were diagnosed with periodontitis stage III and grade B (periodontal patients; P-group), and the other 20 individuals were periodontally healthy or control (C-group; no attachment loss, <10% of bleeding on probing [BOP] and plaque). Sample size calculation was based on a previous report of the topic (Bao et al. 2022), in which up to 5.88% of salivary bacteria were found in the feces of the P-group whereas only 0.6% was detected in the C-group. Assuming a standard deviation of 3% to 5%, a high size effect of 1.7 was expected, and to achieve a power of 0.99, 14 to 20 individuals per group would be needed. To evaluate the staging and grading of periodontitis, the criteria from Tonetti et al. (2018) were followed. Smoking more than 10 cigarettes per day, receiving antibiotic treatment in the past month, or using mouth antiseptics in the last 2 wk or having received periodontal treatment in the last 12 mo were exclusion criteria. In addition, volunteers filled out a questionnaire about health status, diet, and oral hygiene habits. All patients agreed with the enrollment and signed the informed consent. The study was approved by the ethical committee from the University of Valencia (reference 1601392).

Sample Collection and Processing

Fecal samples were collected by patients in the 24 h prior to the dental visit using conical tubes (provided by the researchers) with RNAlater (Invitrogen) 1:1 and taken to the clinic. The volunteers did not have oral care 4.5 h before the visit. At the clinic, up to 2 mL of unstimulated saliva was collected and stored at 4 °C. Before collecting subgingival samples, supragingival plaque was removed with curettes. Then, 4 sterile paper points (No. 40) were placed into the periodontal pockets for 1 min and then kept in 1 mL of RNAlater (Simon-Soro et al. 2013). On the same day of collection, all sample types arrived at the laboratory and were stored at −80 °C until processing.

DNA isolation was performed using the MagNA Pure LC DNA Isolation Kit III for Bacteria and Fungi (Roche Diagnostics) with additional enzymatic lysis by MagNA Pure LC 2.0 Instrument (Roche Diagnostics) (Buetas et al. 2023). After DNA quantification with QubitTM 1X dsDNA HS Assay Kit, the library was prepared and sequenced using the Sequel II Sequencing Kit 2.0 (PacBio) on the Sequel II PacBio system (Callahan et al. 2019). Circular consensus sequences were quality checked using PacBio error’s model with DADA2 and annotated using the naive Bayesian classifier against the species train set of Silva v.138.1 database (McLaren and Callahan 2021). Further details can be found in Appendix M&M.

Statistical Analysis

Differences in individual characteristics among groups were assessed using Fisher exact test (categorical variables) and Wilcoxon test (continuous variables). Rarefaction curves at the Amplicon sequence variants (ASV) level as well as alpha diversity of the samples were calculated using the minimum number of reads obtained in a sample (7,225) with Vegan R library (Oksanen et al. 2022), and indexes were compared using the Wilcoxon test. Species differential abundance between control and periodontitis patients was performed using ANCOM-BC (Lin and Peddada 2020), taking as covariates the significantly different lifestyle habits (alcohol consumption and dental floss use). The P values were corrected for multiple testing using the false discovery rate method. Members of the red and orange Socransky complexes were considered periodontitis associated; moreover, species were classified as health or periodontitis associated according to Chen et al. (2022). Fast Expectation-mAximization microbial Source Tracking (FEAST) was used to estimate the oral source contribution on fecal samples using default parameters (Shenhav et al. 2019). Subsequently, Bray Curtis (BC) distances were calculated and represented using a Principal Coordinates Analysis (PCoA). The mean BC distance between paired (oral-gut) samples of the P-group and C-group was compared using the Wilcoxon test. The potential effect of clinical variables (age, body mass index [BMI], periodontal probing depth, plaque index, BOP, water intake [drinking water consumed]) on the oral-gut BC distance was evaluated using Spearman correlations. Differences in BC distance by categorical variables (sex, diet, physical activity, alcohol consumption, antibiotic intake) were assessed using the Wilcoxon test or Kruskal test (for cases with >2 categorical variables). This report conforms to the STROBE guidelines, and a checklist can be found in the Appendix.

Results

To investigate the potential effect of periodontitis in the translocation of oral bacteria from the mouth to the gut, a comparative analysis was conducted on the oral and fecal microbiota of individuals with and without periodontitis (Fig. 1). Clinical characteristics of controls (C-group) and periodontal patients (P-group) are shown in Appendix Table 1. None of the participants had been previously diagnosed with bowel carcinogenesis, and there were no statistical differences in their physical parameters (e.g., age, sex, BMI, other pathologies) except for halitosis, which was more frequent in the P-group (P = 0.047). There were no significant differences between groups in the lifestyle habits recorded (e.g., diet, physical activity, sleep hours, dental brush frequency) with the exception of alcohol consumption (35% of the P-group consumed alcohol often v. 10% in the C-group, P = 0.03) and dental floss use (25% of the P-group v. 80% of the C-group, P = 0.001; Appendix Table 2).

Microbiome Differences between Periodontal Patients and Controls

FL-16S rRNA gene PacBio sequencing was used to obtain higher resolution at species-level annotation (Buetas et al. 2023). After quality filtering, a mean of 20,440 reads per sample were obtained, 69.4% of which were assigned to the species level (Appendix Table 3). The differences in alpha diversity between the C-group and P-group were significant for Chao index in fecal samples (P = 0.024; Fig. 2A), whereas the Shannon index was higher in saliva (P = 0.01) and subgingival plaque (P = 0.048) of the P-group (Fig. 2B). Periodontitis has a clear impact in subgingival microbiome composition, whereas less effect can be seen in saliva and fecal samples, as shown in Figure 3A and B. As expected, several species associated with periodontitis such as Tannerella forsythia (log2foldchange[control/perio] [logFC] = −3.91, P < 0.001), Porphyromonas gingivalis (logFC = −4.18, P < 0.001), or Porphyromonas endodontalis (logFC = −1.95, P < 0.001) were significantly more abundant in the subgingival pockets of the P-group as compared with controls (Fig. 2C). Similarly, species associated with the disease were enriched in the saliva of the P-group, for example Porphyromonas gingivalis (logFC = −2.28, P < 0.048), Treponema socranskii (logFC = −3.2, P < 0.01), or Fretibacterium fastidiosum (logFC = −3.18, P < 0.01) (Fig. 2D). In contrast, and contrary to the working hypothesis, no significant differences in bacterial composition were found between the fecal samples from the C-group and P-group (Fig. 3C).

Oral Bacteria Detected in Fecal Samples

Interestingly, no Fusobacterium species were detected in the fecal samples of the P-group nor in controls. The presence of the same species in oral and fecal samples was studied. To be considered as oral, a bacterial species needed to have more than 25% prevalence among the oral samples. According to this criterion, in fecal samples, 29 and 26 oral species were detected in controls and in the P-group, respectively. Among them, 19 were present in both the P-group and C-group (e.g., Rothia mucilaginosa, Solobacterium moorei, or Streptococcus sanguinis), 10 were found only in controls (e.g., Veillonella atypica, Veillonella parvula, and P. micra), and 7 were present only in the P-group but did not correspond to periodontal pathogens (e.g., Gemella morbillorum, Gemella sanguinis, and Streptococcus oralis). Nevertheless, the prevalence of these bacteria of potential oral origin in feces was <50% in both groups, and their mean relative abundance was <0.5%, with the exception of Dialister invisus, whose mean relative abundance was actually higher in fecal (2.38% C-group, 3.59% P-group) than in oral samples (0.1% saliva and 0.29% subgingival sulcus in the C-group, 0.45% and 0.43% respectively in the P-group). Indeed, the oral bacterial content within the fecal microbiome exceeded 5% in only 4 controls and 6 periodontal patients (Fig. 4).

Oral-Gut Leakage

Nevertheless, as demonstrated previously (Asnicar et al. 2017; Schmidt et al. 2019), it becomes necessary to track populations at the resolution of strains rather than species to accurately establish and quantify microbial transmission. As the FL-16S rRNA gene sequence allowed single-nucleotide resolution (Callahan et al. 2019), we used ASVs to compare the strains found in the oral cavity and the gut. Among the oral species detected in feces, we selected the most abundant ones, S. salivarius and D. invisus. In our data set, 28 different ASVs corresponded to S. salivarius, but only 7 were found in oral and fecal samples. The detection of the same ASV in both niches was found in 25% of the P-group and 40% of the controls. Interestingly, the relative abundance of S. salivarius in fecal samples correlates only with the amount of saliva in controls (Spearman’s rho 0.52, P = 0.02, Appendix Fig. 1) but not in the P-group. In the case of D. invisus, which had higher relative abundance in feces than in oral samples, up to 104 ASVs were detected, but only 5 of them were present in the fecal and oral samples of the same individual, and their fecal abundance did not correlate with the abundance on the oral cavity (P = 0.11 and 0.8 for saliva-fecal and subgingival-fecal correlations, respectively). In a similar way, when we try to estimate the contribution of oral source in fecal samples using FEAST based on ASV data, a mean of 0.124 and 0.612 was attributed to saliva and 0.126 and 0.016 to subgingival source in the C-group and P-group, respectively. These results confirm a low transmission rate from the oral cavity to the gut.

We hypothesized that if the leakage of oral bacteria to the gut is greater in the P-group, the oral and fecal microbial communities of these patients should be more similar. To evaluate this, Bray Curtis (BC) distances between the saliva and fecal microbiomes or between the subgingival and the fecal microbiome of each participant were measured. No significant differences were found among groups in any case (Fig. 5). Thus, our results suggest that translocation of oral bacteria to the gut occurs at a very low rate both in individuals with good oral health and in those with periodontitis, at least when no concomitant intestinal diseases are present.

When examining the impact of other clinical factors, no significant differences were observed in BC distances based on sex, diet, physical activity, or alcohol consumption. However, it was noteworthy that the BC distance between subgingival plaque and fecal microbiomes was lower in periodontal patients who had taken antibiotics in the previous year (mean 0.4 in the antibiotic use group v. 0.5 in the nonantibiotic use group, P < 0.01). In addition, the BC distance between saliva and fecal microbiomes in the P-group was negatively correlated with water intake (Spearman’s rho −0.55, P = 0.01), indicating a higher rate of bacterial translocation as water consumption increased in the P-group. In the C-group, the BC distance between subgingival plaque and fecal samples correlated negatively with age (Spearman’s rho −0.5, P = 0.03; Appendix Table 4).

Discussion

The increasing evidence linking periodontal pathogens to the development of CRC underscores the significance of understanding the impact of oral health status on the translocation of these bacteria to the gut. In the present study, we first confirmed that, as previously reported, periodontal patients have higher microbial diversity (using Shannon indexes) in the subgingival sulcus as well as in the saliva than periodontally healthy individuals do (Díaz and Kolenbrander 2009; Bao et al. 2022). As expected, their oral microbiota was enriched in periodontal pathogens such as T. forsythia, P. gingivalis, F. nucleatum, or P. micra. Nevertheless, the microbial diversity of the fecal samples did not differ between groups, in agreement with another exploratory study in humans (Lourenςo et al. 2018). In our cohort, the differential abundance analysis of the fecal samples did not identify any significantly enriched species in the P-group. This contrasts with the findings of a previous study (Bao et al. 2022), which reported the enrichment of Erysipelotrichaceae, Lachnospiracea incertae sedis, and Blautia in a Chinese cohort. In addition to the different geographic origin, these differences could arise from the different sequencing approach (FL-16S rRNA v. Illumina sequencing) and a different downstream analysis, as we use ANCOM-BC to account for the compositional nature of microbiome data (Lin and Peddada 2020). The small sample size in our study (N = 20 per group) may have also limited the statistical power to detect differences.

In our cohort, the proportion of oral pathogens in the fecal content was very low. Studies of 2 other cohorts of healthy adults from China (n = 470) and from Europe (n = 66) had previously reported a limited presence of oral bacteria in the fecal samples, with S. salivarius (in the first case) and D. invisus (in the second case) being among the exceptions that do overlap between the 2 niches (Rashidi et al. 2021; Cheung et al. 2023). This supports the idea that the presence of oral commensals and especially periodontal pathogens in the gut is a rare event that could be exploited as a hallmark of disease. In fact, in our study, we did not detect F. nucleatum in any of the donors’ fecal samples, despite its relative abundance values of up to 11% in saliva and 15% in subgingival plaque in the P-group. When Schmidt et al. formulated their transmission score for each species, most F. nucleatum subspecies (nucleatum, polymorphum, and vincentii) were categorized as nontransmitters, and only F. nucleatum sp. animalis was categorized as an occasional transmitter (Schmidt et al. 2019). This suggests that some previous alteration or damage must occur for the translocation and colonization of this bacteria to the intestine and supports its identification in fecal samples as a biomarker of the disease (Zhang et al. 2022; Wang and Fang 2023). Similarly, when Kitamoto et al. (2020) studied the effect of periodontitis on gut inflammation in a mouse model, the ligature insertion alone (which simulates periodontal condition) did not lead to gut colonization by oral bacteria. However, when the mice were subjected to treatment with dextran sodium sulfate, which induces experimental colitis and gut inflammation, the translocation was significantly higher in periodontal mice than in oral healthy ones.

The above observations suggest that periodontitis alone might not be sufficient to cause significant translocation of oral bacteria to the gut in the absence of gastrointestinal impairment. Under this assumption, when a gut alteration exists, the translocation of oral bacteria to the intestine could be promoted, especially in individuals with periodontitis, and future studies should test the oral-gut translocation in periodontal patients with CRC or other intestinal conditions. Thus, understanding the host factors that could influence this process or the mechanisms that could be necessary for the translocation and colonization of oral microbes to the gut is vital, especially for prevention purposes, and should be studied in the future.

In relation to this, we found slight correlations of oral-gut transaction with water consumption and age. A recent study with a bigger cohort of healthy individuals in Japan (n = 146) confirmed that aging (as well as dental plaque accumulation) contributes to an increased abundance of oral microbes in the gut (Kageyama et al. 2023). Another interesting factor that could influence the translocation of oral microbes to the gut was the use of antibiotics. Antibiotics could disrupt the gut commensal microbiota, facilitating the colonization by other opportunistic microbes (Ramirez et al. 2020). In our study, antibiotic use in the previous 2 mo was an exclusion criterion, but participants reported whether they had used them in the past year. The P-group who reported the use of antibiotics had higher transmission based on the BC distances. This finding suggests a potential influence of antibiotic treatment on the oral-gut microbial connection and implies that the effects of taking antibiotics can last even more than a year. Therefore, further studies focused on the effects of antibiotics as well as other behavioral factors, host features, or clinical factors that could modulate the translocation of oral microbes to the gut deserve further investigation. From an applied viewpoint, the absence of oral pathogens in the gut of the P-group with healthy gastrointestinal conditions indicates that the presence of periodontal bacteria in the gut could be a specific hallmark of intestinal disease, including CRC.

Author Contributions

E. Buetas, contributed to conception, design, data analysis and interpretation, drafted the manuscript; M. Jordán López, contributed to data acquisition, critically revised the manuscript; A. López Roldán, contributed to design, critically revised the manuscript; A. Mira, contributed to conception, design, data interpretation, critically revised the manuscript; M. Carda-Diéguez, contributed to conception, design, data analysis and interpretation, critically revised the manuscript. All authors gave final approval and agree to be accountable for all aspects of the work.

Supplemental Material

sj-docx-1-jdr-10.1177_00220345231221709 – Supplemental material for Impact of Periodontitis on the Leakage of Oral Bacteria to the Gut

Supplemental material, sj-docx-1-jdr-10.1177_00220345231221709 for Impact of Periodontitis on the Leakage of Oral Bacteria to the Gut by E. Buetas, M. Jordán-López, A. López-Roldán, A. Mira and M. Carda-Diéguez in Journal of Dental Research

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: EB is supported by a grant from the Spanish Ministry of Science and Innovation with the reference PRE2019-088126. This work was supported by grant RTI2018- 102032-B-I00 from the Spanish Ministry of Innovation and Science to AM.

Availability of Data and Material

The original sequencing output generated during the current study is available in the SRA repository with the accession number PRJNA933120.

A supplemental appendix to this article is available online.

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Supplementary Material

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