Quantitative Real-Time PCR Detection of Porphyromonas gingivalis and Filifactor alocis in Peri- Implantitis

Quantitative Real-Time PCR Detection of Porphyromonas gingivalis and Filifactor alocis in Peri- Implantitis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Quantitative Real-Time PCR Detection of Porphyromonas gingivalis and Filifactor alocis in Peri- Implantitis Ioannis Fragkioudakis, Georgios Konstantopoulos, Christine Kottaridi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5798452/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Aim: To assess the prevalence and levels of P. gingivalis and F. alocis in peri-implantitis and healthy peri-implant sites using quantitative real-time PCR (qPCR) . Materials and Methods: This cross-sectional study included 110 participants, 52 with peri-implantitis and 58 with healthy peri-implant sites. Clinical parameters were recorded, including probing depth, clinical attachment level, and bleeding on probing. Microbiological samples were analyzed using qPCR, and significance was tested using the Mann-Whitney U test and Spearman’s rank correlation. Results: P. gingivalis and F. alocis levels were significantly higher in peri-implantitis sites, with P. gingivalis at 4.80 × 10⁶ ± 4.78 × 10⁶ copies/µL and F. alocis at 4.58 × 10⁵ ± 3.40 × 10⁵ copies/µL, compared to healthy sites, with P. gingivalis at 2.09 × 10³ ± 1.26 × 10³ copies/µL and F. alocis at 2.45 × 10³ ± 1.64 × 10³ copies/µL, with p < 0.001. P. gingivalis strongly correlated with clinical parameters, such as probing depth, clinical attachment level, and bleeding on probing. F. alocis showed moderate correlations with probing depth and clinical attachment level but not bleeding on probing. Conclusion: The findings suggest a potential synergistic role of P. gingivalis and F. alocis in peri-implantitis, emphasizing the importance of therapies targeting these pathogens. Clinicians might explore antimicrobial strategies disrupting biofilm formation and microbial synergy to improve outcomes. Further research is needed to refine treatment approaches and understand these bacteria's contributions to disease progression. Peri-implantitis Porphyromonas gingivalis Filifactor alocis biofilm quantitative real-time PCR peri-implant inflammation Figures Figure 1 Figure 2 Introduction Peri-implantitis is an inflammatory condition involving the mucosa and supporting bone around osseointegrated dental implants, characterized by increased probing depths, bleeding on probing, and radiographic bone loss (Schwarz et al. 2018 ). This condition represents a major complication in implant dentistry, with prevalence rates ranging from 10–47%. This variability can be attributed to factors such as differences in diagnostic criteria, population diversity, and study methodologies (Berglundh et al. 2024; Diaz et al. 2022 ). If left untreated, peri-implantitis can lead to implant failure. Despite advances in implant technology, surgical techniques, and primordial care protocols, peri-implantitis remains a significant clinical challenge, highlighting the need for a deeper understanding of the microbial etiology and prevention underlying this disease (Berglundh et al. 2024; Herrera et al. 2023; Khoury et al. 2019 ). Among the microorganisms implicated in peri-implantitis, Porphyromonas gingivalis ( P. gingivalis ) has long been recognized as a critical periodontal pathogen (Socransky and Haffajee 2005 ). It is a member of the “red complex,” a group of bacteria strongly associated with periodontitis and peri-implantitis (Belibasakis and Manoil 2021 ; Persson and Renvert 2014; Socransky et al. 1998 ). P. gingivalis possesses multiple virulence factors, including fimbriae, capsules, and gingipains, which allow it to invade host tissues, evade immune responses, and contribute to tissue destruction through the modulation of host immune responses (Darveau 2009 ; Socransky and Haffajee 2005 ). Its role in biofilm formation and its association with clinical parameters like probing depth and attachment loss underscore its significance in peri-implant disease progression. Recent research has highlighted the presence of Filifactor alocis ( F. alocis ) as an emerging pathogen in periodontal and peri-implant diseases (Aruni et al. 2015 ). Unlike many Gram-negative pathogens, F. alocis is a Gram-positive anaerobic bacterium that exhibits unique traits, including resistance to oxidative stress and the ability to thrive in inflamed environments. These characteristics allow it to persist within the biofilm of peri-implant pockets, contributing to chronic inflammation and tissue damage (Aja et al. 2021 ; Aruni et al. 2015 ). A notable aspect of F. alocis is its ability to synergize with P. gingivalis . Studies have demonstrated that P. gingivalis facilitates the colonization of F. alocis by altering the local environment, creating conditions favorable for its growth (Hajishengallis et al. 2011 ). In turn, F. alocis enhances the inflammatory response through immune modulation and oxidative stress resistance. Together, these two species form biofilms with increased virulence compared to when they are cultured individually. This cooperation highlights the need to understand their combined role in peri-implantitis to guide the development of more effective treatment strategies (Aruni et al. 2015 ). The aim of this study is to evaluate the prevalence and levels of P. gingivalis and F. alocis in peri-implantitis sites compared to healthy peri-implant sites using quantitative real-time polymerase chain reaction (qPCR). By focusing on their microbial synergy and specific contributions to peri-implant inflammation, this research seeks to inform targeted therapeutic strategies for managing peri-implantitis and improving patient outcomes Materials & Methods This study was designed as a cross-sectional investigation. All participants were recruited from the Department of Periodontology and Implant Biology at the School of Dentistry, Aristotle University of Thessaloniki, Greece. According to the 2018 classification criteria, patients were classified into two groups: those with peri-implantitis and those with healthy implants or peri-implant mucositis (Schwarz et al. 2018 ). The healthy/mucositis group included individuals with either healthy peri-implant tissues or signs of peri-implant mucositis, with no radiographic bone loss or clinical indicators of peri-implantitis. The Ethical Committee of the School of Dentistry, Aristotle University of Thessaloniki (115/25-05-21), approved the study, which was registered in the ClinicalTrialsGov.gr database under ID: NCT05711407. Study Timeline The study timeline included two appointments and subsequent laboratory analyses. Patients were diagnosed and recruited during the first appointment based on inclusion criteria, followed by a comprehensive clinical examination. The study took place between January 2023 and March 2024. A second appointment was scheduled one week later for biological sample collection. All samples were gathered between 8:00 AM and 10:00 AM to minimize diurnal fluctuations in microbial levels, and participants were instructed to fast for at least eight hours before sampling. Patients were also asked not to brush their teeth on the morning of sampling to avoid disturbing the biofilm. Participant Inclusion Criteria and Sample Size Participants were selected based on predefined inclusion criteria. Eligible individuals had at least one implant loaded for over a year and were systemically healthy, which is defined as those without any known chronic systemic conditions, such as diabetes mellitus, cardiovascular disease, autoimmune disorders, or other diseases that could influence periodontal or peri-implant health. They were either periodontally healthy or demonstrated stable periodontal disease as per Lang and Bartold (Lang and Bartold 2018). Patients who had taken antibiotics in the last six months were excluded, while smokers were permitted to participate. All participants provided informed consent before enrolling in the study. The sample size was determined through a power analysis to detect significant differences in the relative abundance of key periodontal pathogens between the peri-implantitis and healthy implant groups. The analysis indicated that a minimum of 54 participants per group was necessary to achieve 80% power at a 5% significance level, using data from previous research on P. gingivalis levels in peri-implantitis (Ito et al 2021 ). Clinical Examination Clinical parameters were recorded during the examination phase. The clinical examination included bleeding on probing (BOP), which was noted as present (+) or absent (−) and expressed as a percentage, observed 30 seconds after a periodontal probe was inserted into the peri-implant pocket. Probing depth (PD) was measured from the mucosal margin to the base of the peri-implant pocket. Recession (REC) was recorded as the distance from the shoulder of the prosthetic crown to the mucosal margin, while clinical attachment level (CAL) represented the distance from the shoulder of the prosthetic crown to the base of the sulcus or peri-implant pocket. All measurements were made at six sites per implant using a 15-mm scale periodontal probe (Hu-Friedy® CP-12, #30), graded in 1-mm increments. All examinations were conducted by the same examiner (I.F.), and intra-examiner reproducibility was assessed during two calibration sessions. The calibration sessions, conducted two weeks apart, involved repeated measurements on a sample of 10 patients to ensure consistency across time points. The intra-examiner agreement, determined using the intraclass correlation coefficient (ICC), showed an agreement of 0.93 (95% CI: 0.89 to 0.96), reflecting high reliability. Sample Collection Each implant was isolated using cotton rolls for microbiological sampling, and supragingival and marginal plaque were removed before sample collection. Biofilm samples were collected from the deepest peri-implant sulci or pockets by inserting three sterile endodontic paper points (No. 30) into the peri-implant crevice or pocket for 10 seconds. The paper points were placed into 1.5-mL microcentrifuge tubes and immediately stored at -80°C until further analysis. Whenever feasible, samples were collected following the removal of the prosthesis to ensure optimal sample quality. DNA Extraction and Quantitative Real-Time PCR Analysis DNA extraction was conducted using the ZymoBIOMICS™ DNA Miniprep Kit, with 4 µL of internal extraction control DNA added to the lysis buffer according to the manufacturer’s instructions. Quantitative real-time PCR (qPCR) was performed to detect and quantify P. gingivalis and F. alocis using the AccuPower® F. alocis Real-Time PCR Kit and the PorGin dtec-qPCR kit, respectively. The reaction mixes for F. alocis consisted of 12.5 µL of 2X Master Mix, 5 µL of Oligo Mix containing specific primers and probes, 1–5 µL of DNA template, and DEPC-treated water to a final volume of 25 µL. For P. gingivalis , the TargetSpecies dtec-qPCR-mix was reconstituted with 105 µL of Resuspension Buffer, and 1 µL of this mix was used in a 20 µL PCR reaction containing 15 µL of a mixture comprising DNase/RNase-free water (9 µL), GPS™-mix (5 µL), and TargetSpecies dtec-qPCR mix (1 µL). Positive controls were prepared using a reconstituted template buffer to generate a series of decimal dilutions for standard curve preparation. Negative controls were included in all runs to confirm the absence of contamination. The thermal cycling conditions for both targets involved an initial denaturation at 95°C for 2 minutes, followed by 45 cycles of denaturation at 95°C for 5 seconds and annealing/extension steps of 55°C for F. alocis and 60°C for P. gingivalis , both for 20 seconds. Fluorescence data was collected using the FAM channel for target detection and the HEX channel for internal control monitoring. Cq values were analyzed to confirm amplification, with controls ensuring the accuracy and reliability of the qPCR setup. This protocol allowed for sensitive and precise detection of P. gingivalis and F. alocis , supporting the accurate microbial load assessment required for this study. Statistical Analysis The statistical analysis was performed using SPSS 26 (IBM Corp., Armonk, NY, USA), with a significance level set at p ≤ 0.05 for all tests. Descriptive statistics were calculated for all variables, with continuous variables, such as probing depth (PD), clinical attachment level (CAL), and F. alocis and P. gingivalis loads, reported as means ± standard deviations, and categorical variables, such as smoking status, presented as frequencies and percentages. The assumption of normality for continuous variables was evaluated using the Shapiro-Wilk test. Since the data did not follow a normal distribution, non-parametric tests were applied. To compare differences between the healthy/mucositis group and the peri-implantitis group, the Mann-Whitney U test was used for continuous variables, while Pearson's chi-square test was applied to categorical variables. The correlation between F alocis and P. gingivalis levels and clinical parameters, including probing depth (PD), clinical attachment level (CAL), and bleeding on probing (BOP), was assessed using Spearman's rank correlation coefficient (ρ). Correlation values were interpreted as weak (0–0.3), moderate (0.3–0.7), or strong (> 0.7), with corresponding p-values used to determine statistical significance. Results Demographic Characteristics A total of 110 participants were included in the study, divided into two groups: peri-implantitis (n = 52) and healthy implants (n = 58). The demographic characteristics of participants, including age, sex, and smoking status, were balanced between the groups. No significant differences were found in terms of age or smoking status between the groups (Table 1 ). Table 1 Demographic characteristics of the patients Demographic Parameters Healthy/Mucositis (n = 58) Peri-implantitis (n = 52) Total (n = 110) Sex, n (%) Male 34 (58.6%) 31 (59.6%) 65 (59.1%) Female 24 (41.4%) 21 (40.4%) 45 (40.9%) Smoking Status, n (%) Non-smokers 34 (58.6%) 31 (59.6%) 65 (59.1%) Smokers 24 (41.4%) 21 (40.4%) 45 (40.9%) Age (years, Mean ± SD) 54.4 ± 11.63 65.1 ± 8.34 - Clinical Parameters Significant differences were observed in the clinical parameters between the healthy/mucositis and peri-implantitis groups. The peri-implantitis group exhibited significantly higher values for probing depth (PD), clinical attachment level (CAL), and bleeding on probing (BOP) compared to the healthy/mucositis group, as presented in Table 2 . Table 2 Comparison of Clinical Parameters between Healthy/Mucositis and Peri-implantitis Groups Clinical Parameter Healthy/Mucositis (Mean ± SD) Peri-implantitis (Mean ± SD) p-value Probing Depth (PD, mm) 3.42 ± 0.86 4.83 ± 1.51 < 0.001* Clinical Attachment Level (CAL, mm) 3.44 ± 0.90 6.33 ± 2.84 < 0.001* Bleeding on Probing (BOP, %) 28.8 ± 34.25 68.63 ± 35.19 < 0.001* PD (Probing Depth, mm) The distance from the gingival margin to the base of the pocket, CAL (Clinical Attachment Level, mm) : The measurement indicating the extent of attachment loss, BOP (Bleeding on Probing, %) : The percentage of sites that bled upon probing, SD (Standard Deviation) : Measure of variability in the data. Statistically significant differences were found using the Mann-Whitney U test , with significance set at the 0.05 level. Microbiological Findings. For F. alocis , the mean level in the peri-implantitis group was significantly higher (4.58 × 10 5 ± 3.40 × 10 5 copies/µL) compared to the healthy group (2.45 × 10 3 ± 1.64 × 10 3 copies/µL), with a p-value of less than 0.001. Similarly, for P. gingivalis , the peri-implantitis group had a significantly higher microbial load (4.80 × 10 6 ± 4.78 × 10 6 copies/µL) compared to the healthy group (2.09 × 10 3 ± 1.26 × 10 3 copies/µL), also with a p-value of less than 0.001 (Fig. 1 ). Statistical analysis showed significant differences between the two groups for numbers of both F. alocis (Man-Whitney U = 41.000, p < 0.001) and P. gingivalis (Man-Whitney U = 29.000, p = 0.014). Mann-Whitney U test, with p < 0.05 considered statistically significant. Correlation Analysis between P gingivalis, F.alocis , and Clinical Parameters P. gingivalis demonstrated statistically significant correlations with clinical parameters, such as probing depth (r = 0.474, p = 0.019) and clinical attachment level (r = 0.489, p = 0.015). Additionally, it showed a strong correlation with bleeding on probing (r = 0.575, p = 0.003). In contrast, F. alocis exhibited statistically significant but moderate correlations with probing depth (r = 0.419, p = 0.017) and clinical attachment level (r = 0.377, p = 0.033). However, it did not show significant correlations with bleeding on probing (r = 0.254, p = 0.161). The correlation between F. alocis and P. gingivalis was also not statistically significant (r = 0.346, p = 0.247). These correlations are illustrated in Fig. 2 . Impact of Smoking on Clinical Parameters and Microbial Presence No significant differences were observed between smokers and non-smokers regarding F.alocis, P.gingivalis , probing depth (PD), clinical attachment level (CAL), or bleeding on probing (BOP). Statistical analysis using the Kruskal-Wallis test indicated no significant associations for F.alocis (H = 1.638, df = 2, p = 0.441), P. gingivalis (H = 0.910, df = 2, p = 0.635), probing depth (PD) (H = 3.722, df = 2, p = 0.156), clinical attachment level (CAL) (H = 1.691, df = 2, p = 0.429), or bleeding on probing (BOP) (H = 0.667, df = 2, p = 0.716). These findings suggest that smoking status did not significantly influence microbial load or clinical parameters in the study population. Discussion The aim of the present study was to determine the prevalence and relative abundance of Porphyromonas gingivalis ( P. gingivalis ) and Filifactor alocis ( F. alocis ) in peri-implant and healthy peri-implant sites using quantitative real-time PCR. This is the first study to use real-time PCR to quantify F. alocis in peri-implantitis, providing new insights into the microbial dynamics of peri-implant disease. The results showed significant differences in microbial profiles between peri-implantitis and healthy sites, with higher abundances of P. gingivalis and F. alocis in diseased sites. The microbial load of both P. gingivalis and F. alocis was significantly higher in peri-implantitis compared to healthy sites, reinforcing their role as key contributors to peri-implant inflammation (Aruni et al. 2015 ; Berglundh et al. 2024; Savčić et al. 2022 ). P. gingivalis , a member of the "red complex," is well-known for its association with periodontal and peri-implant diseases, driven by its numerous virulence factors, including fimbriae, gingipains, and capsules, which allow it to colonize, evade immune responses, and contribute to tissue destruction (Hajishengallis and Diaz 2020 ). The significantly elevated presence of P. gingivalis in peri-implantitis sites aligns with existing literature, further supporting its involvement in the pathogenesis of peri-implant disease (Carvalho et al. 2023 ). The role of F. alocis in peri-implantitis, however, is less investigated in the literature, and previous studies have largely focused on F. alocis in the context of periodontal disease (Aruni et al. 2015 ; Manenzhe et al. 2024 ). In the present study, F. alocis was found in statistically significantly higher numbers in peri-implantitis sites, highlighting its emerging role as a possible pathogen in both peri-implant and periodontal diseases. The current study is among the first to use quantitative real-time PCR aiming to quantify F. alocis in peri-implantitis, thus contributing to our understanding of peri-implant microbiology. Present data exhibited statistically significant positive correlations with clinical parameters such as probing depth (PD) and clinical attachment level (CAL) for both investigated species, while F. alocis failed to demonstrate this correlation for bleeding on probing. This finding might suggest that while P. gingivalis is closely associated with clinical disease severity, F. alocis may play a more complex, indirect role in modulating the peri-implant environment, contributing to biofilm persistence and immune modulation rather than directly influencing clinical parameters.The weak statistical correlation between numbers of P. gingivalis and F. alocis observed in the present study also suggests that while they co-exist and enhance each other’s pathogenic potential, they may contribute to the disease through distinct mechanisms. While this study focused on P. gingivalis and F. alocis, the results should be interpreted in light of the broader complexity of the peri-implant microbiota. Peri-implantitis and mucositis are associated with shifts in the microbial ecosystem that involve multiple bacterial and fungal species, as well as viruses. Future studies utilizing metagenomic sequencing and broader microbial profiling could help elucidate how these organisms interact and contribute to disease progression. The findings from this study contribute to understanding the roles of P. gingivalis and F. alocis but underscore the need to see these pathogens within the larger microbiological context of dysbiosis in peri-implant environments. Our findings are consistent with studies that describe the microbial profile of peri-implantitis as being characterized by an increased presence of species such as P. gingivalis and F. alocis (Belibasakis and Manoil 2021 ; Carvalho et al. 2023 ; Lafaurie et al. 2017 ). Sanz-Martin et al. ( 2017 ) and Kensara et al. ( 2024 ) have also reported on the increased prevalence of P. gingivalis in peri-implantitis, but F. alocis was either not investigated or its role was not well characterized in peri-implant studies (Kensara et al. 2024 ; Sanz-Martin et al. 2017 ). This study, therefore, contributes to the growing understanding of F. alocis in peri-implantitis, alongside the well-established role of P. gingivalis . Interestingly, smoking did not appear to have a significant impact on microbial loads or clinical parameters in this study. While smoking is a recognized risk factor for peri-implant disease, this finding may reflect the specific characteristics of the study population, such as the relatively small number of smokers or variability in smoking intensity. Larger, stratified cohorts may be needed to clarify the role of smoking in peri-implant microbial dynamics and clinical outcomes. The cross-sectional design of this study does not allow for establishing causal relationships between microbial shifts and disease progression. Longitudinal studies are needed to better understand the temporal dynamics of microbial changes and their direct impact on peri-implant health and disease. The use of real-time PCR in this study provided key advantages for assessing microbial loads of investigated species. Real-time PCR's closed system also reduced contamination risk compared to conventional PCR, enhancing data reliability. However, quantitative real-time PCR's limitation lies in its inability to analyze the broader microbiome, which future studies using next-generation sequencing could address. More advanced molecular techniques, such as next-generation sequencing, could provide a more comprehensive profile of the peri-implant microbiome, offering insights into the bacterial composition and their functional contributions to disease. Metagenomic approaches could further elucidate the interactions and metabolic pathways that underpin the pathogenic synergy between P. gingivalis and F. alocis , ultimately guiding more effective therapeutic interventions (Kensara et al. 2024 ). In addition, it is crucial to note that despite its association with peri-implantitis, as observed in the current study, there is not yet conclusive evidence that F. alocis is a pathogenic organism directly causing disease. Its presence may indicate dysbiosis, but it remains uncertain whether it plays an active role in pathogenesis or is simply a bystander within a disturbed microbial environment. This distinction is critical in evaluating its role in peri-implantitis, as the mere presence of F. alocis does not necessarily confirm its pathogenicity. The findings of the present study have several clinical implications. The significantly higher prevalence of P. gingivalis and F. alocis in peri-implantitis sites indicates that these pathogens may require targeted antimicrobial strategies to reduce their levels specifically. For instance, antimicrobials that disrupt biofilm formation or selectively target P. gingivalis and F. alocis could be beneficial in managing peri-implantitis and preventing further progression. Early identification of F. alocis could enable more tailored and effective treatment strategies to halt the progression of peri-implant disease. The association between the presence or counts of P. gingivalis and F. alocis and a history of previously treated periodontal disease warrants further consideration. Notably, the study successfully recruited many individuals who were periodontally healthy in this age group, potentially reflecting stringent inclusion criteria or targeted recruitment strategies. However, future studies could explore whether residual effects of previously treated periodontal disease might influence peri-implant microbial profiles, even in individuals classified as periodontally healthy. In conclusion, the present study highlights the significant role of P. gingivalis and F. alocis in peri-implantitis. The possible synergistic relationship between these pathogens enhances the pathogenicity of the peri-implant biofilm, underscoring the need for targeted therapeutic strategies that disrupt these microbial interactions. Future research should focus on developing novel antimicrobial therapies to reduce the pathogenic burden of these key bacteria and restore the microbial homeostasis necessary for peri-implant health. Declarations Acknowledgments The authors wish to acknowledge the valuable assistance from the General Microbiology Laboratory at Aristotle University for their technical support in real-time PCR assays. We express our gratitude to the participants who contributed to the study. The authors declare that no external funding was used to support this study. Suppliers of materials: ZymoBIOMICS™ DNA Miniprep Kit was supplied by Zymo Research (Irvine, CA, USA). AccuPower® Real-Time PCR Kits were supplied by Bioneer Corporation (Daejeon, South Korea). Author Contributions Ioannis Fragkioudakis : Conceptualization of the study, experiment design, clinical sample collection, data acquisition, and manuscript drafting. Georgios Konstantopoulos : Microbiological analysis, real-time PCR execution, and data interpretation. Christine Kottaridi : Assisted with microbiological assays, provided technical support in PCR methodology, and contributed to data analysis. Leonidas Batas : Clinical examination of participants, assistance with data collection, and manuscript revision for critical intellectual content. Dimitra Sakellari : Supervision of the entire study, study design, interpretation of results, and manuscript revision. All authors read and approved the final manuscript. Ethics and Integrity Statements Data Availability Statement : The data that support the findings of this study are openly available. Funding Statement : This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Conflict of Interest Disclosure : The authors declare no conflict of interest. Ethics Approval Statement : The study was reviewed and approved by the Ethics Committee of the School of Dentistry, Aristotle University of Thessaloniki (115/25-05-21). All participants provided informed consent before their inclusion in the study. Patient Consent Statement : All participants provided written informed consent in accordance with the Declaration of Helsinki. Permission to Reproduce Material : Permission to reproduce previously published material is not required for this study, as all figures, tables, and data are original to this manuscript. Clinical Trial Registration : This study is registered at ClinicalTrials.gov under ID: NCT05711407. All authors read and approved the final manuscript. References Aja, E., M. Mangar, H. M. Fletcher, and A. 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Hämmerle, Ronald E. Jung, and Flavia R. F. Teles. 2017. “Exploring the Microbiome of Healthy and Diseased Peri-Implant Sites Using Illumina Sequencing.” Journal of Clinical Periodontology 44(12):1274–84. doi: 10.1111/jcpe.12788. Savčić, Nikolija, Damir Henjaš, Marija Jezdić, Ana Đinić Krasavčević, and Iva Milinković. 2022. “Porphyromonas Gingivalis in Different Peri-Implant Conditions: A Pilot Cross - Sectional Study.” Acta Stomatologica Croatica 56(4):387–94. doi: 10.15644/asc56/4/5. Schwarz, Frank, Jan Derks, Alberto Monje, and Hom Lay Wang. 2018. “Peri-Implantitis.” Journal of Clinical Periodontology 45 Suppl 20:S246–66. doi: 10.1111/JCPE.12954. Socransky, S. S., A. D. Haffajee, M. A. Cugini, C. Smith, and R. L. Kent. 1998. “Microbial Complexes in Subgingival Plaque.” Journal of Clinical Periodontology 25(2):134–44. doi: 10.1111/j.1600-051x.1998.tb02419.x. Socransky, Sigmund S., and Anne D. Haffajee. 2005. “Periodontal Microbial Ecology.” Periodontology 2000 38:135–87. doi: 10.1111/J.1600-0757.2005.00107.X. Teles, Ricardo, Flavia Teles, Jorge Frias-Lopez, Bruce Paster, and Anne Haffajee. 2013. “Lessons Learned and Unlearned in Periodontal Microbiology.” Periodontology 2000 62(1):95–162. doi: 10.1111/PRD.12010. Yoo, Hyun-Jun, and Sung-Hoon Lee. 2022. “Virulence of Filifactor Alocis Lipoteichoic Acid on Human Gingival Fibroblast.” Archives of Oral Biology 135:105370. doi: https://doi.org/10.1016/j.archoralbio.2022.105370. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5798452","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":401509548,"identity":"01e7664a-4543-412d-8b2b-aefbf8e56870","order_by":0,"name":"Ioannis Fragkioudakis","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYBACNuYDQPKABAM/iJdQQIwWtgSIFskGkBYDoqwBa2FgMADZxkCMFj423ocfv5yxkDM+vzrxwwMDBnl+sQOEHMZuLC1zQ8LY7MbbzRJAhxnOnJ1AQIt8G4O0xAeJxG03zm4AaUkwuE1ICxsb82+Qls0zzm7+QawWNskPNyQSN/D3biPaFjZrhjMSxhI3eLdZJBhIEPaLfBsb880fx+rk+PvPbr75o8JGnl+agBYQYOYBkRJglRKElYMA4w8QyX+AONWjYBSMglEw8gAADb4/u3Pw1jcAAAAASUVORK5CYII=","orcid":"","institution":"Aristotle University of Thessaloniki","correspondingAuthor":true,"prefix":"","firstName":"Ioannis","middleName":"","lastName":"Fragkioudakis","suffix":""},{"id":401509549,"identity":"269a76d2-c9ee-4168-9e57-9a1df38db409","order_by":1,"name":"Georgios Konstantopoulos","email":"","orcid":"","institution":"Aristotle University of Thessaloniki","correspondingAuthor":false,"prefix":"","firstName":"Georgios","middleName":"","lastName":"Konstantopoulos","suffix":""},{"id":401509550,"identity":"8b96adf9-1ec8-4cd3-a836-d99cbe59ce0a","order_by":2,"name":"Christine Kottaridi","email":"","orcid":"","institution":"Aristotle University of Thessaloniki","correspondingAuthor":false,"prefix":"","firstName":"Christine","middleName":"","lastName":"Kottaridi","suffix":""},{"id":401509551,"identity":"bc7cb7c0-5f5b-4ac5-b1db-f09e33e8536b","order_by":3,"name":"Leonidas Batas","email":"","orcid":"","institution":"Aristotle University of Thessaloniki","correspondingAuthor":false,"prefix":"","firstName":"Leonidas","middleName":"","lastName":"Batas","suffix":""},{"id":401509552,"identity":"fc1c2a64-e956-4a46-a89e-126770734787","order_by":4,"name":"Dimitra Sakellari","email":"","orcid":"","institution":"Aristotle University of Thessaloniki","correspondingAuthor":false,"prefix":"","firstName":"Dimitra","middleName":"","lastName":"Sakellari","suffix":""}],"badges":[],"createdAt":"2025-01-09 17:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5798452/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5798452/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73865798,"identity":"e5308333-26e2-420b-80fc-7e15eef11b3e","added_by":"auto","created_at":"2025-01-15 11:51:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":51177,"visible":true,"origin":"","legend":"\u003cp\u003eMicrobial levels in peri-implant health and disease\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5798452/v1/87a19bc04b974bc5ad29b1d0.png"},{"id":73865800,"identity":"c096d515-2a90-40d9-b7d8-75d98d70c6f7","added_by":"auto","created_at":"2025-01-15 11:51:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":62756,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations of microbial and clinical parameters\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5798452/v1/dfe94f10e689828691862d9c.png"},{"id":74296776,"identity":"55f8281c-9045-49fd-af2d-a10ea8fb285d","added_by":"auto","created_at":"2025-01-20 18:24:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1010272,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5798452/v1/1113f236-65d3-431a-855f-7bfafd5c6e6a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quantitative Real-Time PCR Detection of Porphyromonas gingivalis and Filifactor alocis in Peri- Implantitis","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003c/p\u003e \u003cp\u003ePeri-implantitis is an inflammatory condition involving the mucosa and supporting bone around osseointegrated dental implants, characterized by increased probing depths, bleeding on probing, and radiographic bone loss (Schwarz et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This condition represents a major complication in implant dentistry, with prevalence rates ranging from 10\u0026ndash;47%. This variability can be attributed to factors such as differences in diagnostic criteria, population diversity, and study methodologies (Berglundh et al. 2024; Diaz et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). If left untreated, peri-implantitis can lead to implant failure. Despite advances in implant technology, surgical techniques, and primordial care protocols, peri-implantitis remains a significant clinical challenge, highlighting the need for a deeper understanding of the microbial etiology and prevention underlying this disease (Berglundh et al. 2024; Herrera et al. 2023; Khoury et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong the microorganisms implicated in peri-implantitis, \u003cem\u003ePorphyromonas gingivalis\u003c/em\u003e (\u003cem\u003eP. gingivalis\u003c/em\u003e) has long been recognized as a critical periodontal pathogen (Socransky and Haffajee \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). It is a member of the \u0026ldquo;red complex,\u0026rdquo; a group of bacteria strongly associated with periodontitis and peri-implantitis (Belibasakis and Manoil \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Persson and Renvert 2014; Socransky et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). \u003cem\u003eP. gingivalis\u003c/em\u003e possesses multiple virulence factors, including fimbriae, capsules, and gingipains, which allow it to invade host tissues, evade immune responses, and contribute to tissue destruction through the modulation of host immune responses (Darveau \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Socransky and Haffajee \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Its role in biofilm formation and its association with clinical parameters like probing depth and attachment loss underscore its significance in peri-implant disease progression.\u003c/p\u003e \u003cp\u003eRecent research has highlighted the presence of \u003cem\u003eFilifactor alocis\u003c/em\u003e (\u003cem\u003eF. alocis\u003c/em\u003e) as an emerging pathogen in periodontal and peri-implant diseases (Aruni et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Unlike many Gram-negative pathogens, \u003cem\u003eF. alocis\u003c/em\u003e is a Gram-positive anaerobic bacterium that exhibits unique traits, including resistance to oxidative stress and the ability to thrive in inflamed environments. These characteristics allow it to persist within the biofilm of peri-implant pockets, contributing to chronic inflammation and tissue damage (Aja et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Aruni et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA notable aspect of \u003cem\u003eF. alocis\u003c/em\u003e is its ability to synergize with \u003cem\u003eP. gingivalis\u003c/em\u003e. Studies have demonstrated that \u003cem\u003eP. gingivalis\u003c/em\u003e facilitates the colonization of \u003cem\u003eF. alocis\u003c/em\u003e by altering the local environment, creating conditions favorable for its growth (Hajishengallis et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In turn, \u003cem\u003eF. alocis\u003c/em\u003e enhances the inflammatory response through immune modulation and oxidative stress resistance. Together, these two species form biofilms with increased virulence compared to when they are cultured individually. This cooperation highlights the need to understand their combined role in peri-implantitis to guide the development of more effective treatment strategies (Aruni et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe aim of this study is to evaluate the prevalence and levels of \u003cem\u003eP. gingivalis\u003c/em\u003e and \u003cem\u003eF. alocis\u003c/em\u003e in peri-implantitis sites compared to healthy peri-implant sites using quantitative real-time polymerase chain reaction (qPCR). By focusing on their microbial synergy and specific contributions to peri-implant inflammation, this research seeks to inform targeted therapeutic strategies for managing peri-implantitis and improving patient outcomes\u003c/p\u003e"},{"header":"Materials \u0026 Methods","content":"\u003cp\u003eThis study was designed as a cross-sectional investigation. All participants were recruited from the Department of Periodontology and Implant Biology at the School of Dentistry, Aristotle University of Thessaloniki, Greece. According to the 2018 classification criteria, patients were classified into two groups: those with peri-implantitis and those with healthy implants or peri-implant mucositis (Schwarz et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The healthy/mucositis group included individuals with either healthy peri-implant tissues or signs of peri-implant mucositis, with no radiographic bone loss or clinical indicators of peri-implantitis. The Ethical Committee of the School of Dentistry, Aristotle University of Thessaloniki (115/25-05-21), approved the study, which was registered in the ClinicalTrialsGov.gr database under ID: NCT05711407.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Timeline\u003c/h2\u003e \u003cp\u003eThe study timeline included two appointments and subsequent laboratory analyses. Patients were diagnosed and recruited during the first appointment based on inclusion criteria, followed by a comprehensive clinical examination. The study took place between January 2023 and March 2024. A second appointment was scheduled one week later for biological sample collection. All samples were gathered between 8:00 AM and 10:00 AM to minimize diurnal fluctuations in microbial levels, and participants were instructed to fast for at least eight hours before sampling. Patients were also asked not to brush their teeth on the morning of sampling to avoid disturbing the biofilm.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipant Inclusion Criteria and Sample Size\u003c/h3\u003e\n\u003cp\u003eParticipants were selected based on predefined inclusion criteria. Eligible individuals had at least one implant loaded for over a year and were systemically healthy, which is defined as those without any known chronic systemic conditions, such as diabetes mellitus, cardiovascular disease, autoimmune disorders, or other diseases that could influence periodontal or peri-implant health. They were either periodontally healthy or demonstrated stable periodontal disease as per Lang and Bartold (Lang and Bartold 2018). Patients who had taken antibiotics in the last six months were excluded, while smokers were permitted to participate. All participants provided informed consent before enrolling in the study. The sample size was determined through a power analysis to detect significant differences in the relative abundance of key periodontal pathogens between the peri-implantitis and healthy implant groups. The analysis indicated that a minimum of 54 participants per group was necessary to achieve 80% power at a 5% significance level, using data from previous research on P. gingivalis levels in peri-implantitis (Ito et al \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eClinical Examination\u003c/h3\u003e\n\u003cp\u003eClinical parameters were recorded during the examination phase. The clinical examination included bleeding on probing (BOP), which was noted as present (+) or absent (\u0026minus;) and expressed as a percentage, observed 30 seconds after a periodontal probe was inserted into the peri-implant pocket. Probing depth (PD) was measured from the mucosal margin to the base of the peri-implant pocket. Recession (REC) was recorded as the distance from the shoulder of the prosthetic crown to the mucosal margin, while clinical attachment level (CAL) represented the distance from the shoulder of the prosthetic crown to the base of the sulcus or peri-implant pocket. All measurements were made at six sites per implant using a 15-mm scale periodontal probe (Hu-Friedy\u0026reg; CP-12, #30), graded in 1-mm increments.\u003c/p\u003e \u003cp\u003eAll examinations were conducted by the same examiner (I.F.), and intra-examiner reproducibility was assessed during two calibration sessions. The calibration sessions, conducted two weeks apart, involved repeated measurements on a sample of 10 patients to ensure consistency across time points. The intra-examiner agreement, determined using the intraclass correlation coefficient (ICC), showed an agreement of 0.93 (95% CI: 0.89 to 0.96), reflecting high reliability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eSample Collection\u003c/h3\u003e\n\u003cp\u003eEach implant was isolated using cotton rolls for microbiological sampling, and supragingival and marginal plaque were removed before sample collection. Biofilm samples were collected from the deepest peri-implant sulci or pockets by inserting three sterile endodontic paper points (No. 30) into the peri-implant crevice or pocket for 10 seconds. The paper points were placed into 1.5-mL microcentrifuge tubes and immediately stored at -80\u0026deg;C until further analysis. Whenever feasible, samples were collected following the removal of the prosthesis to ensure optimal sample quality.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eDNA Extraction and Quantitative Real-Time PCR Analysis\u003c/h3\u003e\n\u003cp\u003eDNA extraction was conducted using the ZymoBIOMICS\u0026trade; DNA Miniprep Kit, with 4 \u0026micro;L of internal extraction control DNA added to the lysis buffer according to the manufacturer\u0026rsquo;s instructions. Quantitative real-time PCR (qPCR) was performed to detect and quantify \u003cem\u003eP. gingivalis\u003c/em\u003e and \u003cem\u003eF. alocis\u003c/em\u003e using the AccuPower\u0026reg; \u003cem\u003eF. alocis\u003c/em\u003e Real-Time PCR Kit and the PorGin dtec-qPCR kit, respectively.\u003c/p\u003e \u003cp\u003eThe reaction mixes for \u003cem\u003eF. alocis\u003c/em\u003e consisted of 12.5 \u0026micro;L of 2X Master Mix, 5 \u0026micro;L of Oligo Mix containing specific primers and probes, 1\u0026ndash;5 \u0026micro;L of DNA template, and DEPC-treated water to a final volume of 25 \u0026micro;L. For \u003cem\u003eP. gingivalis\u003c/em\u003e, the TargetSpecies dtec-qPCR-mix was reconstituted with 105 \u0026micro;L of Resuspension Buffer, and 1 \u0026micro;L of this mix was used in a 20 \u0026micro;L PCR reaction containing 15 \u0026micro;L of a mixture comprising DNase/RNase-free water (9 \u0026micro;L), GPS\u0026trade;-mix (5 \u0026micro;L), and TargetSpecies dtec-qPCR mix (1 \u0026micro;L).\u003c/p\u003e \u003cp\u003ePositive controls were prepared using a reconstituted template buffer to generate a series of decimal dilutions for standard curve preparation. Negative controls were included in all runs to confirm the absence of contamination. The thermal cycling conditions for both targets involved an initial denaturation at 95\u0026deg;C for 2 minutes, followed by 45 cycles of denaturation at 95\u0026deg;C for 5 seconds and annealing/extension steps of 55\u0026deg;C for \u003cem\u003eF. alocis\u003c/em\u003e and 60\u0026deg;C for \u003cem\u003eP. gingivalis\u003c/em\u003e, both for 20 seconds. Fluorescence data was collected using the FAM channel for target detection and the HEX channel for internal control monitoring.\u003c/p\u003e \u003cp\u003eCq values were analyzed to confirm amplification, with controls ensuring the accuracy and reliability of the qPCR setup. This protocol allowed for sensitive and precise detection of \u003cem\u003eP. gingivalis\u003c/em\u003e and \u003cem\u003eF. alocis\u003c/em\u003e, supporting the accurate microbial load assessment required for this study.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe statistical analysis was performed using SPSS 26 (IBM Corp., Armonk, NY, USA), with a significance level set at p\u0026thinsp;\u0026le;\u0026thinsp;0.05 for all tests. Descriptive statistics were calculated for all variables, with continuous variables, such as probing depth (PD), clinical attachment level (CAL), and \u003cem\u003eF. alocis\u003c/em\u003e and \u003cem\u003eP. gingivalis\u003c/em\u003e loads, reported as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations, and categorical variables, such as smoking status, presented as frequencies and percentages.\u003c/p\u003e \u003cp\u003eThe assumption of normality for continuous variables was evaluated using the Shapiro-Wilk test. Since the data did not follow a normal distribution, non-parametric tests were applied. To compare differences between the healthy/mucositis group and the peri-implantitis group, the Mann-Whitney U test was used for continuous variables, while Pearson's chi-square test was applied to categorical variables.\u003c/p\u003e \u003cp\u003eThe correlation between \u003cem\u003eF alocis\u003c/em\u003e and \u003cem\u003eP. gingivalis\u003c/em\u003e levels and clinical parameters, including probing depth (PD), clinical attachment level (CAL), and bleeding on probing (BOP), was assessed using Spearman's rank correlation coefficient (ρ). Correlation values were interpreted as weak (0\u0026ndash;0.3), moderate (0.3\u0026ndash;0.7), or strong (\u0026gt;\u0026thinsp;0.7), with corresponding p-values used to determine statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDemographic Characteristics\u003c/h2\u003e \u003cp\u003eA total of 110 participants were included in the study, divided into two groups: peri-implantitis (n\u0026thinsp;=\u0026thinsp;52) and healthy implants (n\u0026thinsp;=\u0026thinsp;58). The demographic characteristics of participants, including age, sex, and smoking status, were balanced between the groups. No significant differences were found in terms of age or smoking status between the groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic characteristics of the patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographic Parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealthy/Mucositis (n\u0026thinsp;=\u0026thinsp;58)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePeri-implantitis (n\u0026thinsp;=\u0026thinsp;52)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;110)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34 (58.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31 (59.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65 (59.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24 (41.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21 (40.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45 (40.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking Status, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-smokers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34 (58.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31 (59.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65 (59.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmokers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24 (41.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21 (40.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45 (40.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54.4\u0026thinsp;\u0026plusmn;\u0026thinsp;11.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eClinical Parameters\u003c/h2\u003e \u003cp\u003eSignificant differences were observed in the clinical parameters between the healthy/mucositis and peri-implantitis groups. The peri-implantitis group exhibited significantly higher values for probing depth (PD), clinical attachment level (CAL), and bleeding on probing (BOP) compared to the healthy/mucositis group, as presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eTable 2 Comparison of Clinical Parameters between Healthy/Mucositis and Peri-implantitis Groups\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical Parameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealthy/Mucositis (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePeri-implantitis (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProbing Depth (PD, mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical Attachment Level (CAL, mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBleeding on Probing (BOP, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e28.8\u0026thinsp;\u0026plusmn;\u0026thinsp;34.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e68.63\u0026thinsp;\u0026plusmn;\u0026thinsp;35.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePD (Probing Depth, mm)\u003c/strong\u003e \u003cp\u003eThe distance from the gingival margin to the base of the pocket,\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCAL (Clinical Attachment Level, mm)\u003c/b\u003e: The measurement indicating the extent of attachment loss, \u003cb\u003eBOP (Bleeding on Probing, %)\u003c/b\u003e: The percentage of sites that bled upon probing,\u003cb\u003eSD (Standard Deviation)\u003c/b\u003e: Measure of variability in the data.\u003c/p\u003e \u003cp\u003e \u003cem\u003eStatistically significant differences were found using the\u003c/em\u003e \u003cb\u003eMann-Whitney U test\u003c/b\u003e, \u003cem\u003ewith significance set at the 0.05 level.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eMicrobiological Findings.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFor \u003cem\u003eF. alocis\u003c/em\u003e, the mean level in the peri-implantitis group was significantly higher (4.58 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e \u0026plusmn; 3.40 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e copies/\u0026micro;L) compared to the healthy group (2.45 \u0026times; 10\u003csup\u003e3\u003c/sup\u003e \u0026plusmn; 1.64 \u0026times; 10\u003csup\u003e3\u003c/sup\u003e copies/\u0026micro;L), with a p-value of less than 0.001.\u003c/p\u003e \u003cp\u003eSimilarly, for \u003cem\u003eP. gingivalis\u003c/em\u003e, the peri-implantitis group had a significantly higher microbial load (4.80 \u0026times; 10\u003csup\u003e6\u003c/sup\u003e \u0026plusmn; 4.78 \u0026times; 10\u003csup\u003e6\u003c/sup\u003e copies/\u0026micro;L) compared to the healthy group (2.09 \u0026times; 10\u003csup\u003e3\u003c/sup\u003e \u0026plusmn; 1.26 \u0026times; 10\u003csup\u003e3\u003c/sup\u003e copies/\u0026micro;L), also with a p-value of less than 0.001 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStatistical analysis showed significant differences between the two groups for numbers of both \u003cem\u003eF. alocis\u003c/em\u003e (Man-Whitney U\u0026thinsp;=\u0026thinsp;41.000, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and \u003cem\u003eP. gingivalis\u003c/em\u003e (Man-Whitney U\u0026thinsp;=\u0026thinsp;29.000, p\u0026thinsp;=\u0026thinsp;0.014).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMann-Whitney U test, with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCorrelation Analysis between\u003c/b\u003e \u003cb\u003eP gingivalis, F.alocis\u003c/b\u003e, \u003cb\u003eand Clinical Parameters\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eP. gingivalis\u003c/em\u003e demonstrated statistically significant correlations with clinical parameters, such as probing depth (r\u0026thinsp;=\u0026thinsp;0.474, p\u0026thinsp;=\u0026thinsp;0.019) and clinical attachment level (r\u0026thinsp;=\u0026thinsp;0.489, p\u0026thinsp;=\u0026thinsp;0.015). Additionally, it showed a strong correlation with bleeding on probing (r\u0026thinsp;=\u0026thinsp;0.575, p\u0026thinsp;=\u0026thinsp;0.003). In contrast, \u003cem\u003eF. alocis\u003c/em\u003e exhibited statistically significant but moderate correlations with probing depth (r\u0026thinsp;=\u0026thinsp;0.419, p\u0026thinsp;=\u0026thinsp;0.017) and clinical attachment level (r\u0026thinsp;=\u0026thinsp;0.377, p\u0026thinsp;=\u0026thinsp;0.033). However, it did not show significant correlations with bleeding on probing (r\u0026thinsp;=\u0026thinsp;0.254, p\u0026thinsp;=\u0026thinsp;0.161). The correlation between F. alocis and P. gingivalis was also not statistically significant (r\u0026thinsp;=\u0026thinsp;0.346, p\u0026thinsp;=\u0026thinsp;0.247). These correlations are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eImpact of Smoking on Clinical Parameters and Microbial Presence\u003c/h2\u003e \u003cp\u003eNo significant differences were observed between smokers and non-smokers regarding \u003cem\u003eF.alocis, P.gingivalis\u003c/em\u003e, probing depth (PD), clinical attachment level (CAL), or bleeding on probing (BOP). Statistical analysis using the Kruskal-Wallis test indicated no significant associations for \u003cem\u003eF.alocis\u003c/em\u003e (H\u0026thinsp;=\u0026thinsp;1.638, df\u0026thinsp;=\u0026thinsp;2, p\u0026thinsp;=\u0026thinsp;0.441), \u003cem\u003eP. gingivalis\u003c/em\u003e (H\u0026thinsp;=\u0026thinsp;0.910, df\u0026thinsp;=\u0026thinsp;2, p\u0026thinsp;=\u0026thinsp;0.635), probing depth (PD) (H\u0026thinsp;=\u0026thinsp;3.722, df\u0026thinsp;=\u0026thinsp;2, p\u0026thinsp;=\u0026thinsp;0.156), clinical attachment level (CAL) (H\u0026thinsp;=\u0026thinsp;1.691, df\u0026thinsp;=\u0026thinsp;2, p\u0026thinsp;=\u0026thinsp;0.429), or bleeding on probing (BOP) (H\u0026thinsp;=\u0026thinsp;0.667, df\u0026thinsp;=\u0026thinsp;2, p\u0026thinsp;=\u0026thinsp;0.716). These findings suggest that smoking status did not significantly influence microbial load or clinical parameters in the study population.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe aim of the present study was to determine the prevalence and relative abundance of \u003cem\u003ePorphyromonas gingivalis\u003c/em\u003e (\u003cem\u003eP. gingivalis\u003c/em\u003e) and \u003cem\u003eFilifactor alocis\u003c/em\u003e (\u003cem\u003eF. alocis\u003c/em\u003e) in peri-implant and healthy peri-implant sites using quantitative real-time PCR. This is the first study to use real-time PCR to quantify \u003cem\u003eF. alocis\u003c/em\u003e in peri-implantitis, providing new insights into the microbial dynamics of peri-implant disease. The results showed significant differences in microbial profiles between peri-implantitis and healthy sites, with higher abundances of \u003cem\u003eP. gingivalis\u003c/em\u003e and \u003cem\u003eF. alocis\u003c/em\u003e in diseased sites.\u003c/p\u003e \u003cp\u003eThe microbial load of both \u003cem\u003eP. gingivalis\u003c/em\u003e and \u003cem\u003eF. alocis\u003c/em\u003e was significantly higher in peri-implantitis compared to healthy sites, reinforcing their role as key contributors to peri-implant inflammation (Aruni et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Berglundh et al. 2024; Savčić et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). \u003cem\u003eP. gingivalis\u003c/em\u003e, a member of the \"red complex,\" is well-known for its association with periodontal and peri-implant diseases, driven by its numerous virulence factors, including fimbriae, gingipains, and capsules, which allow it to colonize, evade immune responses, and contribute to tissue destruction (Hajishengallis and Diaz \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The significantly elevated presence of \u003cem\u003eP. gingivalis\u003c/em\u003e in peri-implantitis sites aligns with existing literature, further supporting its involvement in the pathogenesis of peri-implant disease (Carvalho et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe role of \u003cem\u003eF. alocis\u003c/em\u003e in peri-implantitis, however, is less investigated in the literature, and previous studies have largely focused on \u003cem\u003eF. alocis\u003c/em\u003e in the context of periodontal disease (Aruni et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Manenzhe et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In the present study, \u003cem\u003eF. alocis\u003c/em\u003e was found in statistically significantly higher numbers in peri-implantitis sites, highlighting its emerging role as a possible pathogen in both peri-implant and periodontal diseases. The current study is among the first to use quantitative real-time PCR aiming to quantify \u003cem\u003eF. alocis\u003c/em\u003e in peri-implantitis, thus contributing to our understanding of peri-implant microbiology.\u003c/p\u003e \u003cp\u003ePresent data exhibited statistically significant positive correlations with clinical parameters such as probing depth (PD) and clinical attachment level (CAL) for both investigated species, while \u003cem\u003eF. alocis\u003c/em\u003e failed to demonstrate this correlation for bleeding on probing. This finding might suggest that while \u003cem\u003eP. gingivalis\u003c/em\u003e is closely associated with clinical disease severity, \u003cem\u003eF. alocis\u003c/em\u003e may play a more complex, indirect role in modulating the peri-implant environment, contributing to biofilm persistence and immune modulation rather than directly influencing clinical parameters.The weak statistical correlation between numbers of \u003cem\u003eP. gingivalis\u003c/em\u003e and \u003cem\u003eF. alocis\u003c/em\u003e observed in the present study also suggests that while they co-exist and enhance each other\u0026rsquo;s pathogenic potential, they may contribute to the disease through distinct mechanisms.\u003c/p\u003e \u003cp\u003eWhile this study focused on P. gingivalis and F. alocis, the results should be interpreted in light of the broader complexity of the peri-implant microbiota. Peri-implantitis and mucositis are associated with shifts in the microbial ecosystem that involve multiple bacterial and fungal species, as well as viruses. Future studies utilizing metagenomic sequencing and broader microbial profiling could help elucidate how these organisms interact and contribute to disease progression. The findings from this study contribute to understanding the roles of P. gingivalis and F. alocis but underscore the need to see these pathogens within the larger microbiological context of dysbiosis in peri-implant environments.\u003c/p\u003e \u003cp\u003eOur findings are consistent with studies that describe the microbial profile of peri-implantitis as being characterized by an increased presence of species such as \u003cem\u003eP. gingivalis\u003c/em\u003e and \u003cem\u003eF. alocis\u003c/em\u003e (Belibasakis and Manoil \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Carvalho et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lafaurie et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Sanz-Martin et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Kensara et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) have also reported on the increased prevalence of \u003cem\u003eP. gingivalis\u003c/em\u003e in peri-implantitis, but \u003cem\u003eF. alocis\u003c/em\u003e was either not investigated or its role was not well characterized in peri-implant studies (Kensara et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sanz-Martin et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This study, therefore, contributes to the growing understanding of \u003cem\u003eF. alocis\u003c/em\u003e in peri-implantitis, alongside the well-established role of \u003cem\u003eP. gingivalis\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eInterestingly, smoking did not appear to have a significant impact on microbial loads or clinical parameters in this study. While smoking is a recognized risk factor for peri-implant disease, this finding may reflect the specific characteristics of the study population, such as the relatively small number of smokers or variability in smoking intensity. Larger, stratified cohorts may be needed to clarify the role of smoking in peri-implant microbial dynamics and clinical outcomes.\u003c/p\u003e \u003cp\u003eThe cross-sectional design of this study does not allow for establishing causal relationships between microbial shifts and disease progression. Longitudinal studies are needed to better understand the temporal dynamics of microbial changes and their direct impact on peri-implant health and disease. The use of real-time PCR in this study provided key advantages for assessing microbial loads of investigated species. Real-time PCR's closed system also reduced contamination risk compared to conventional PCR, enhancing data reliability. However, quantitative real-time PCR's limitation lies in its inability to analyze the broader microbiome, which future studies using next-generation sequencing could address.\u003c/p\u003e \u003cp\u003eMore advanced molecular techniques, such as next-generation sequencing, could provide a more comprehensive profile of the peri-implant microbiome, offering insights into the bacterial composition and their functional contributions to disease. Metagenomic approaches could further elucidate the interactions and metabolic pathways that underpin the pathogenic synergy between \u003cem\u003eP. gingivalis\u003c/em\u003e and \u003cem\u003eF. alocis\u003c/em\u003e, ultimately guiding more effective therapeutic interventions (Kensara et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition, it is crucial to note that despite its association with peri-implantitis, as observed in the current study, there is not yet conclusive evidence that \u003cem\u003eF. alocis\u003c/em\u003e is a pathogenic organism directly causing disease. Its presence may indicate dysbiosis, but it remains uncertain whether it plays an active role in pathogenesis or is simply a bystander within a disturbed microbial environment. This distinction is critical in evaluating its role in peri-implantitis, as the mere presence of \u003cem\u003eF. alocis\u003c/em\u003e does not necessarily confirm its pathogenicity.\u003c/p\u003e \u003cp\u003eThe findings of the present study have several clinical implications. The significantly higher prevalence of P. gingivalis and F. alocis in peri-implantitis sites indicates that these pathogens may require targeted antimicrobial strategies to reduce their levels specifically. For instance, antimicrobials that disrupt biofilm formation or selectively target P. gingivalis and F. alocis could be beneficial in managing peri-implantitis and preventing further progression. Early identification of F. alocis could enable more tailored and effective treatment strategies to halt the progression of peri-implant disease. The association between the presence or counts of P. gingivalis and F. alocis and a history of previously treated periodontal disease warrants further consideration. Notably, the study successfully recruited many individuals who were periodontally healthy in this age group, potentially reflecting stringent inclusion criteria or targeted recruitment strategies. However, future studies could explore whether residual effects of previously treated periodontal disease might influence peri-implant microbial profiles, even in individuals classified as periodontally healthy.\u003c/p\u003e \u003cp\u003eIn conclusion, the present study highlights the significant role of \u003cem\u003eP. gingivalis\u003c/em\u003e and \u003cem\u003eF. alocis\u003c/em\u003e in peri-implantitis. The possible synergistic relationship between these pathogens enhances the pathogenicity of the peri-implant biofilm, underscoring the need for targeted therapeutic strategies that disrupt these microbial interactions. Future research should focus on developing novel antimicrobial therapies to reduce the pathogenic burden of these key bacteria and restore the microbial homeostasis necessary for peri-implant health.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to acknowledge the valuable assistance from the General Microbiology Laboratory at Aristotle University for their technical support in real-time PCR assays. We express our gratitude to the participants who contributed to the study. The authors declare that no external funding was used to support this study.\u003c/p\u003e\n\u003cp\u003eSuppliers of materials:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eZymoBIOMICS\u0026trade; DNA Miniprep Kit was supplied by Zymo Research (Irvine, CA, USA).\u003c/li\u003e\n \u003cli\u003eAccuPower\u0026reg; Real-Time PCR Kits were supplied by Bioneer Corporation (Daejeon, South Korea).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eIoannis Fragkioudakis\u003c/strong\u003e: Conceptualization of the study, experiment design, clinical sample collection, data acquisition, and manuscript drafting.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eGeorgios Konstantopoulos\u003c/strong\u003e: Microbiological analysis, real-time PCR execution, and data interpretation.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eChristine Kottaridi\u003c/strong\u003e: Assisted with microbiological assays, provided technical support in PCR methodology, and contributed to data analysis.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eLeonidas Batas\u003c/strong\u003e: Clinical examination of participants, assistance with data collection, and manuscript revision for critical intellectual content.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDimitra Sakellari\u003c/strong\u003e: Supervision of the entire study, study design, interpretation of results, and manuscript revision.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthics and Integrity Statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e:\u003cbr\u003e\u0026nbsp;The data that support the findings of this study are openly available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e:\u003cbr\u003e\u0026nbsp;This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Disclosure\u003c/strong\u003e:\u003cbr\u003e\u0026nbsp;The authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval Statement\u003c/strong\u003e:\u003cbr\u003e\u0026nbsp;The study was reviewed and approved by the Ethics Committee of the School of Dentistry, Aristotle University of Thessaloniki (115/25-05-21). All participants provided informed consent before their inclusion in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient Consent Statement\u003c/strong\u003e:\u003cbr\u003e\u0026nbsp;All participants provided written informed consent in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePermission to Reproduce Material\u003c/strong\u003e:\u003cbr\u003e\u0026nbsp;Permission to reproduce previously published material is not required for this study, as all figures, tables, and data are original to this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Registration\u003c/strong\u003e:\u003cbr\u003e\u0026nbsp;This study is registered at ClinicalTrials.gov under ID: NCT05711407.\u003c/p\u003e\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAja, E., M. Mangar, H. M. Fletcher, and A. 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Keeve, Ausra Ramanauskaite, Frank Schwarz, Ki Tae Koo, Anton Sculean, and Georgios Romanos. 2019. \u0026ldquo;Surgical Treatment of Peri-Implantitis \u0026ndash; Consensus Report of Working Group 4.\u0026rdquo; \u003cem\u003eInternational Dental Journal\u003c/em\u003e 69(S2):18\u0026ndash;22. doi: 10.1111/idj.12505.\u003c/li\u003e\n\u003cli\u003eLafaurie, Gloria In\u0026eacute;s, Mar\u0026iacute;a Alejandra Sabogal, Diana Marcela Castillo, Mar\u0026iacute;a Victoria Rinc\u0026oacute;n, Luz Amparo G\u0026oacute;mez, Yamil Augusto Lesmes, and Leandro Chambrone. 2017. \u0026ldquo;Microbiome and Microbial Biofilm Profiles of Peri‐Implantitis: A Systematic Review.\u0026rdquo; \u003cem\u003eJournal of Periodontology\u003c/em\u003e 88(10):1066\u0026ndash;89. doi: 10.1902/jop.2017.170123.\u003c/li\u003e\n\u003cli\u003eLang, Niklaus P., and P. Mark Bartold. 2018. \u0026ldquo;Periodontal Health.\u0026rdquo; \u003cem\u003eJournal of Clinical Periodontology\u003c/em\u003e 45:S9\u0026ndash;16. doi: 10.1111/JCPE.12936.\u003c/li\u003e\n\u003cli\u003eManenzhe, Shumani Charlotte, Sandra Koutras, Nompumelelo Benedicta Zwane, Aubrey Isaac Masilana, and Sindisiwe Londiwe Shangase. 2024. \u003cem\u003eThe Impact of Filifactor Alocis on the Severity of Periodontitis among Diabetic and Non-Diabetic Patients: A Narrative Review\u003c/em\u003e. Vol. 5. Frontiers Media SA.\u003c/li\u003e\n\u003cli\u003eMombelli, Andrea, and Fabien D\u0026eacute;caillet. 2011. \u0026ldquo;The Characteristics of Biofilms in Peri-Implant Disease.\u0026rdquo; \u003cem\u003eJournal of Clinical Periodontology\u003c/em\u003e 38 Suppl 11(SUPPL. 11):203\u0026ndash;13. doi: 10.1111/J.1600-051X.2010.01666.X.\u003c/li\u003e\n\u003cli\u003eMombelli, Andrea, Nada M\u0026uuml;ller, and Norbert Cionca. 2012. \u0026ldquo;The Epidemiology of Peri-Implantitis.\u0026rdquo; \u003cem\u003eClinical Oral Implants Research\u003c/em\u003e 23 Suppl 6(SUPPL.6):67\u0026ndash;76. doi: 10.1111/J.1600-0501.2012.02541.X.\u003c/li\u003e\n\u003cli\u003ePersson, G. Rutger, and Stefan Renvert. 2014. \u0026ldquo;Cluster of Bacteria Associated with Peri-Implantitis.\u0026rdquo; \u003cem\u003eClinical Implant Dentistry and Related Research\u003c/em\u003e 783\u0026ndash;93. doi: 10.1111/cid.12052.\u003c/li\u003e\n\u003cli\u003eSanz-Martin, Ignacio, Janet Doolittle-Hall, Ricardo P. 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Kent. 1998. \u0026ldquo;Microbial Complexes in Subgingival Plaque.\u0026rdquo; \u003cem\u003eJournal of Clinical Periodontology\u003c/em\u003e 25(2):134\u0026ndash;44. doi: 10.1111/j.1600-051x.1998.tb02419.x.\u003c/li\u003e\n\u003cli\u003eSocransky, Sigmund S., and Anne D. Haffajee. 2005. \u0026ldquo;Periodontal Microbial Ecology.\u0026rdquo; \u003cem\u003ePeriodontology 2000\u003c/em\u003e 38:135\u0026ndash;87. doi: 10.1111/J.1600-0757.2005.00107.X.\u003c/li\u003e\n\u003cli\u003eTeles, Ricardo, Flavia Teles, Jorge Frias-Lopez, Bruce Paster, and Anne Haffajee. 2013. \u0026ldquo;Lessons Learned and Unlearned in Periodontal Microbiology.\u0026rdquo; \u003cem\u003ePeriodontology 2000\u003c/em\u003e 62(1):95\u0026ndash;162. doi: 10.1111/PRD.12010.\u003c/li\u003e\n\u003cli\u003eYoo, Hyun-Jun, and Sung-Hoon Lee. 2022. \u0026ldquo;Virulence of Filifactor Alocis Lipoteichoic Acid on Human Gingival Fibroblast.\u0026rdquo; \u003cem\u003eArchives of Oral Biology\u003c/em\u003e 135:105370. doi: https://doi.org/10.1016/j.archoralbio.2022.105370.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Peri-implantitis, Porphyromonas gingivalis, Filifactor alocis, biofilm, quantitative real-time PCR, peri-implant inflammation","lastPublishedDoi":"10.21203/rs.3.rs-5798452/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5798452/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eAim:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo assess the prevalence and levels of \u003cem\u003eP. gingivalis\u003c/em\u003e and \u003cem\u003eF. alocis\u003c/em\u003e in peri-implantitis and healthy peri-implant sites using \u003cb\u003equantitative real-time PCR (qPCR)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMaterials and Methods:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis cross-sectional study included 110 participants, 52 with peri-implantitis and 58 with healthy peri-implant sites. Clinical parameters were recorded, including probing depth, clinical attachment level, and bleeding on probing. Microbiological samples were analyzed using qPCR, and significance was tested using the Mann-Whitney U test and Spearman\u0026rsquo;s rank correlation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults:\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eP. gingivalis\u003c/em\u003e and \u003cem\u003eF. alocis\u003c/em\u003e levels were significantly higher in peri-implantitis sites, with \u003cem\u003eP. gingivalis\u003c/em\u003e at 4.80 \u0026times; 10⁶ \u0026plusmn; 4.78 \u0026times; 10⁶ copies/\u0026micro;L and \u003cem\u003eF. alocis\u003c/em\u003e at 4.58 \u0026times; 10⁵ \u0026plusmn; 3.40 \u0026times; 10⁵ copies/\u0026micro;L, compared to healthy sites, with \u003cem\u003eP. gingivalis\u003c/em\u003e at 2.09 \u0026times; 10\u0026sup3; \u0026plusmn; 1.26 \u0026times; 10\u0026sup3; copies/\u0026micro;L and \u003cem\u003eF. alocis\u003c/em\u003e at 2.45 \u0026times; 10\u0026sup3; \u0026plusmn; 1.64 \u0026times; 10\u0026sup3; copies/\u0026micro;L, with p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. \u003cem\u003eP. gingivalis\u003c/em\u003e strongly correlated with clinical parameters, such as probing depth, clinical attachment level, and bleeding on probing. \u003cem\u003eF. alocis\u003c/em\u003e showed moderate correlations with probing depth and clinical attachment level but not bleeding on probing.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe findings suggest a potential synergistic role of \u003cem\u003eP. gingivalis\u003c/em\u003e and \u003cem\u003eF. alocis\u003c/em\u003e in peri-implantitis, emphasizing the importance of therapies targeting these pathogens. Clinicians might explore antimicrobial strategies disrupting biofilm formation and microbial synergy to improve outcomes. Further research is needed to refine treatment approaches and understand these bacteria's contributions to disease progression.\u003c/p\u003e","manuscriptTitle":"Quantitative Real-Time PCR Detection of Porphyromonas gingivalis and Filifactor alocis in Peri- Implantitis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-15 11:51:13","doi":"10.21203/rs.3.rs-5798452/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cf651f09-4136-4b83-989a-0a56758eaea0","owner":[],"postedDate":"January 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-20T18:08:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-15 11:51:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5798452","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5798452","identity":"rs-5798452","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}