Metatranscriptomic Insights into Microbial Network Modulation and Pathogen Dynamics Underlying Healing Outcomes in Non-Surgical Periodontal Treatment

Metatranscriptomic Insights into Microbial Network Modulation and Pathogen Dynamics Underlying Healing Outcomes in Non-Surgical Periodontal Treatment | 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 Article Metatranscriptomic Insights into Microbial Network Modulation and Pathogen Dynamics Underlying Healing Outcomes in Non-Surgical Periodontal Treatment Ryota Kobayashi, Takahiko Shiba, Takahiko Nagai, Keiji Komatsu, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5764431/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 Periodontitis is a globally prevalent chronic inflammatory disease caused by dysbiosis of the oral microbiome. However, it remains unclear whether the bacterial communities of periodontitis and its precursor, gingivitis, transition to a state resembling healthy sites with no history of periodontitis following periodontal treatment or persist in a state prone to recurrence. Therefore, in this study, we performed metatranscriptomic analysis on subgingival plaque samples from the anterior teeth in healthy, gingivitis, and periodontitis sites before and after non-surgical treatment in 28 patients. To minimize inter-individual variability, all samples were collected from the same oral cavity in each patient. We revealed a new bacteriological characteristic of periodontitis, where periodontal pathogens emerge within the bacterial network alongside excessive and skewed interactions among bacterial taxa, such as those in the Streptococcus and Actinomyces genera. Furthermore, these imbalances were found improvable through non-surgical treatment. By comparing groups in which periodontitis resolved and those in which it did not, specific bacterial taxa, such as Neisseria elongata and Rothia aeria, were suggested to play a role in the periodontitis healing process, while the increase in functional genes encoding glycine dehydrogenase β-subunit and cleaved adhesin domain was implicated in inhibiting the healing process. However, even in clinically resolved gingivitis or periodontitis, the bacterial networks did not fully revert to the state observed in healthy sites. This was due to the persistence of periodontal pathogens, absent in the networks of healthy sites. As a result, continuous maintenance and monitoring are considered important to achieve sustained periodontal health. Biological sciences/Microbiology/Bacteria/Bacterial transcription Biological sciences/Microbiology/Bacteria/Bacterial structural biology Biological sciences/Microbiology/Bacteria/Bacterial pathogenesis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 8 Figure 10 INTRODUCTION Periodontitis, an irreversible disease characterized by gingival inflammation and alveolar bone resorption around the teeth, is an infectious disease caused by dysbiosis of the oral microbiome, 1 , 2 , 3 , 4 and its global prevalence is remarkably high. 5 , 6 Moreover, the disease is associated with several systemic diseases. 7 – 12 Gingivitis, which is considered a precursor to periodontitis, is a reversible disease that causes gingival inflammation. 13 Therefore, assessing periodontal health and diagnosing gingivitis early is necessary to prevent periodontitis-associated deterioration of the patient's quality of life. 14 , 15 These clinical conditions are reflected as oral microbiome dysbiosis, and etiological studies indicate that a specific bacterial species is associated with this dysbiosis. 16 – 18 The keystone pathogen is a microorganism that has a disproportionately large effect on its environment relative to its abundance, and the identification of both the keystone pathogen and the bacteria influenced by it is important for diagnosis and treatment of these oral conditions. 19 The identification of key pathogens require comprehensively capturing live bacterial taxa that are difficult to culture, using sequencing and bioinformatics analysis. Metatranscriptomic analysis enables comprehensive understanding of the pathogenesis of periodontitis in situ , as it can detect live bacterial species, active functional genes, and active metabolic pathways. 1 , 3 , 20 Using metatranscriptomic analysis, Nemoto et al . showed that the bacterial co-occurrence network at gingivitis sites was in a transitional state from healthy to periodontitis. 3 A study that performed metatranscriptomic analysis of both progressive and non-progressive sites within the same individuals with periodontitis identified differences in metabolic activities between these sites. 21 Non-surgical treatment involving mechanical debridement of both supra-and subgingival plaque and calculus is fundamental to the management of periodontal disease. 22 Diao et al . performed 16S ribosomal RNA (rRNA) sequencing to investigate the effect of non-surgical treatment on the oral microbiome in patients with periodontitis; they found that the bacterial composition of patients could not revert to a healthy state even after treatment. 23 Although the effects of non-surgical treatment on microbiome in periodontitis have been investigated, only a few studies have used metatranscriptomic analyses. Furthermore, whether the microbiome in periodontitis or gingivitis sites within the same patient shifts towards a healthy state with no history of periodontal disease after treatment or transitions into a state prone to recurrence remains uninvestigated. Notably, this remains unclear even within the context of the same patient and the same tooth types. This study hypothesizes that treatment interventions influence the ecological dynamics of the oral microbiome in diseased sites but that persistent bacterial groups and networks hinder the re-establishment of microbial homeostasis. Therefore, in this study, we aimed to clarify the bacteriological characteristics and treatment-induced changes in subgingival plaque under healthy, gingivitis, and periodontitis conditions by prospectively comparing samples obtained from the same patients and only anterior teeth, thereby eliminating inter- and intra-individual variability. Additionally, we sought to investigate the bacteriological features that contribute to differences in treatment efficacy, even in cases that appear to present the same clinical condition of periodontitis, with the goal of providing insights toward the development of an early prognostic method for treatment outcomes. RESULTS Study population and clinical parameters We collected baseline data from healthy (H1), gingivitis (G1), and periodontitis (P1) sites of 45 patients. The “1” indicates pre-treatment status. Owing to the read process, two G1 samples and one P1 sample did not produce sufficient read counts. Therefore, for the cross-sectional comparison of H1, G1, and P1, these three participants were excluded from all the groups, and the baseline data of the remaining 42 patients were used for the analyses (Supplementary Fig. S1). For longitudinal pre- and post-treatment bacteriological comparisons, these three participants were excluded from their respective groups. Seventeen patients were excluded from the study during the treatment process due to the use of antibiotics during periodontal treatment or loss of attendance. All H1 sites remained healthy after non-surgical treatment (H2). All of the G1 sites, which were characterized by bleeding on probing (BOP) improved after treatment and changed to a healthy state (G2). After treatment, P1 sites were categorized into diseased periodontitis sites (DP2), where probing pocket depth (PPD) improved but was ≥ 4 mm and BOP remained positive, or resolved periodontitis sites (RP2), where BOP was negative. P1 sites were divided into diseased periodontitis (DP1) and resolved periodontitis (RP1) groups based on the clinical status at re-evaluation. Finally, 28, 26, 13, and 14 samples from the H, G, DP, and RP groups, respectively, were analyzed. Tables 1 and 2 show the clinical data of patients as well as the changes in PPD, BOP, and radiographic bone loss (RBL) due to treatment. We observed no significant differences in the PPD and RBL before treatment between the DP1 and RP1 groups (adjusted p > 0.99 both). However, we observed significantly reduced PPDs after treatment in G, DP, and RP groups. Additionally, the DP2 and RP2 groups demonstrated differences in PPD after treatment (adjusted p = 0.02). All H2, G2, and RP2 sites were BOP-negative. In contrast, the percentage of BOP in DP2 was 100% (Table 2). Diversity analysis in each bacterial community For α diversity analysis, the number of operational taxonomic units (OTUs) assigned to reconstructed 16S rRNA (hereon referred to as rc-rRNA) obtained using expectation maximization iterative reconstruction of genes from the environment (EMIRGE) pipeline (24), as well as the Shannon index, were used as indices. Rarefaction curves and sequence read information indicated that the sequencing depth was sufficient (Supplementary Fig. S2, Table S1, S2). First, the number of OTUs was evaluated (Fig. 1a-d). We observed a significant increase in the number of OTUs in H and DP groups before and after treatment despite no change in clinical diagnosis in either group (Fig. 1a, c). Although the number of OTUs did not differ within G sites or within RP sites before and after treatment, we observed an increasing trend (Fig. 1b, d). We observed no significant differences in the number of OTUs in the cross-sectional comparisons among different periodontal tissue conditions, either before or after treatment (Fig. 1e, f). In addition, we observed no significant differences in the Shannon index within or between any of the groups before and after treatment; however, the Shannon index tended to increase after treatment, which is similar to the results for the number of OTUs (Fig. 1g-l). Further, we observed similar tendencies with no significant differences between the groups in the pre-treatment dataset, which included participants who withdrew during treatment (Supplementary Fig. S1a, b). Regarding β diversity, longitudinal bacteriological comparisons before and after treatment revealed significant differences in the G, DP, and RP groups (permutational multivariate analysis of variance [PERMANOVA], p = 0.024, p = 0.019, p = 0.021, respectively), but not in the H group ( p = 0.738) (Fig. 2a-d). In a cross-sectional comparison of the four pre-treatment groups, principal component analysis (PCA) showed that the distributions of DP1 and RP1 overlapped more than those between other group, with no statistically significant differences between them. However, we observed significant differences in the β diversities of H1 and DP1, H1 and RP1, and G1 and DP1 (PERMANOVA, adjusted p = 0.006, adjusted p = 0.006, and adjusted p = 0.042, respectively) (Fig. 2e). Conversely, the PCA plot indicated that the distributions of H2, G2, DP2, and RP2 were clustered together. We observed no significant differences in β diversities between H2, RP2, and DP2 after treatment, and a l difference between β diversities of G2 and DP2 (Fig. 2f). Differential abundance analysis based on bacterial abundance The OTUs were assigned to bacterial taxa based on the Human Oral Microbiome Database (HOMD) to obtain bacterial composition data for each group (Supplementary Fig. S3). Differentially abundant bacterial taxa (DAT) were evaluated by analyzing the Compositions of Microbiomes with Bias Correction 2 (ANCOM-BC2). 24 The numbers of DAT detected in the H, G, DP, and RP groups based on HOMD were 35, 33, 38, and 15, respectively (adjusted p < 0.05, Fig. 3a-d). In the H group, despite no significant difference in β diversity, we observed multiple DAT, and decreased proportion of facultative anaerobes as well as Tannerella forsythia , an obligate anaerobe and a member of the red complex at post-treatment compared with pre-treatment. Interestingly, by contrast, Porphyromonas gingivalis , a member of the red complex, increased in abundance after treatment (Fig. 3a). To elucidate the details of the unexpected increase in P. gingivalis after treatment, we evaluated DAT, identified by comparing H1 and H2, separately for individuals at sites DP2 and RP2. The results indicated an increase in P. gingivalis at H2 sites in individuals with DP2 sites and a decrease in T. forsythia at H2 sites in individuals with RP2 sites (Supplementary Fig. S4a, b). In the G group, the majority of bacteria that decreased after treatment were anaerobic, including the red complex. In contrast, the increased bacteria post-treatment included aerobic bacteria such as Neisseria sicca and Streptococcus pneumoniae (Fig. 3b). We also compared DAT detection between DP2 and RP2 individuals in the G group, identified by comparing G1 and G2. Individuals with RP2 demonstrated reduced proportions of several obligate anaerobes, including T. forsythia , P. gingivalis , Fusobacterium nucleatum subsp. vincentii and F. nucleatum subsp. polymorphum at post-treatment, whereas individuals with DP2 demonstrated a smaller reduction in the proportions of obligate anaerobes, such as Treponema denticola , post-treatment than the RP2 group (Supplementary Fig. S4c, d). In the DP group, the number of increased anaerobic bacteria among the DAT was only 3 bacterial taxa including T. denticola (Fig. 3c), whereas in the RP group, multiple aerobic bacteria, including Streptococcus mitis and Neisseria elongata , increased (Fig. 3d). Additionally, to focus on the bacterial taxa related to the prognosis and healing of periodontitis, we compared the DAT between the DP1 and RP1 groups and between the DP2 and RP2 groups. We found no DAT between the DP1 and RP1 groups; however, 18 DAT were in between the DP2 and RP2 groups. Compared with the RP2 group, the DP2 group demonstrated a significantly lower abundance of Rothia aeria and Lautropia mirabilis , and significantly higher abundances of Bacteroidetes [G-5] sp., Tannerella sp., and Streptococcus sp. (Fig. 3e). Interestingly, the proportion of Tannerella sp. and Streptococcus sp. was reduced after treatment in the RP group but not reduced after treatment in the DP group (Fig. 3c, d). Differential abundance analysis based on functional gene abundance Putative mRNA sequences were annotated as functional genes using Basic Local Alignment Search Tool X (BLASTX) with the National Center for Biotechnology Information non-redundant (NCBI nr) protein database, Virulence Factors of Pathogenic Bacteria database (VFDB), and Microbial Virulence Database (MvirDB). Principal coordinate analysis (PCoA) for the comparison of functional gene composition, which was detected in NCBI nr, showed significant differences between pre- and post-treatment in all groups (Supplementary Fig. S5a-d). In the pre-treatment cross-sectional analyses, we observed no significant differences between the H1 and G1 groups or between the DP1 and RP1 groups (Supplementary Fig. S5e). In contrast, there were significant differences between the H1 and DP1, H1 and RP1, G1 and DP1, and G1 and RP1 groups. After treatment, there were no significant differences between any of the groups (Supplementary Fig. S5f). The results obtained from the VFDB and MvirDB showed a tendency similar to that of NCBI nr (Supplementary Fig. S6-8). We observed no significant differences in the functional gene composition between DP1 and RP1 or between DP2 and RP2 in any of the databases. Differentially expressed genes (DEGs) were evaluated using R package ANCOM-BC2. 24 (Fig. 4, Supplementary Fig. S9, S10). In the NCBI nr protein database, when comparing pre- and post-treatment, the numbers of significantly enriched DEGs were 881, 644, 274, and 456 in the H, G, DP, and RP groups, respectively. The top 15 DEGs with the largest absolute log-fold changes were selected and are shown in Figure 4. Among the selected DEGs, genes encoding amino acid kinase family proteins and polysaccharide biosynthesis proteins were downregulated after treatment in both the H and G groups (Fig. 4a, b). Five DEGs were commonly detected in the DP and RP groups: genes encoding fatty acid-binding protein DegV, glucose-1-phosphate cytidylyltransferase, nicotinamide adenine dinucleotide hydrogen (NADH) pyrophosphatase, glycine dehydrogenase (decarboxylating) beta subunit, and partial cleavage of the adhesin domain protein (Fig. 4c, d). The expression of the first three DEGs increased after treatment in the RP group and decreased after treatment in the DP group, whereas the expression of the latter two DEGs decreased after treatment in the RP group and increased after treatment in the DP group. We observed that no DEGs differed between DP1 and RP1 in any of the reference databases. In contrast, 10 and 12 DEGs identified in the NCBI nr protein and MvirDB differed between DP2 and RP2, respectively (Fig. 4e, Supplementary Fig. S10). However, we observed no DEGs that differed between DP2 and RP2 based on the results obtained from the VFDB. In the NCBI nr database, genes encoding the C-terminal domain protein and type VI secretion protein were upregulated in DP2 compared to RP2. Similarly, in MvirDB, genes encoding endopeptidase and SubE were more upregulated in DP2 than in RP2. Bacterial correlation network analysis The taxonomic origin of each gene in the mRNA cluster was determined using functionally annotated data from the NCBI nr protein database. Taxa detected in both rc-rRNA and mRNA profiles were defined as viable taxa with in situ functions (VTiFs), and network analyses were conducted targeting only VTiF. 1,25 Two main genera were present in the network before treatment in all groups: Streptococcus genus group, mainly consisting of Streptococcus mitis, Streptococcus oralis , Streptococcus pneumoniae , and the Actinomyces genus group, mainly consisting of Actinomyces naeslundii , Actinomyces johsonii , Actinomyces oris , and others (Fig. 5a-d). The network structure of H1 consisted primarily of these groups. In the G1 network structure, we observed Capnocytophaga gingivalis and Selenomonas sp., in addition to the taxa detected in the H1 network structure. Moreover, we observed an increased in both number of nodes constituting the G1 network structure and its network complexity compared with the H1 network structure. In the DP1 and RP1 network structures, more taxa were added to the taxa comprising the G1 network structure, such as the red complex and Prevotella genera; however, these main network structures in DP1 and RP1 still comprised Streptococcus and Actinomyces genera. In the network structure of each group, the number of nodes post-treatment were more than that of the pre-treatment network except for the G group; however, the key species central to the pre-treatment network remained largely unchanged. The number of edges and the value of clustering coefficients decreased in the H and G groups after treatment. In contrast, these values were higher in the DP and RP groups after treatment. Network density and centralization values decreased after treatment in all groups. We observed negative correlations among the network structures of G1, DP2, RP1, and RP2. In the network structure of G1, Selenomonas sp., an anaerobic bacterium and Rothia aeria , an aerobic bacterium, were negatively correlated. Moreover, we found negative correlations between obligate anaerobic bacteria, such as Treponema and Tannerella genera, and obligate aerobic and facultative anaerobic bacteria, such as Neisseria , Actinomyces , and Streptococcus genera in the network structures of DP2, RP1, and RP2. In terms of the difference between DP1 and RP1 in the network, all values of the network structure were greater in the DP1 than RP1, with the number of edges and clustering coefficient being particularly elevated. Additionally, P. gingivalis , T. denticola, and T. forsythia had positive correlations with each other and formed the same cluster in the network structure of DP1. However, we observed no positive or negative correlations between P. gingivalis and different taxa in the network structure of RP1. Furthermore, we observed no positive or negative correlations between T. denticola and T. forsythia , and these taxa had few connections with other taxa. T. forsythia and Fusobacterium sp., which are known to cause periodontal disease, were negatively correlated with aerobic bacteria in the RP1 network, whereas DP1 network structure showed no negative correlation. The number of edges, network density, and centralization values were much lower in the RP2 network than in the DP2 network. In the network structure of DP2, the overly dense edge aggregation in the cluster comprising primarily of Streptococcus and Actinomyces genera still remained similar to that in the network structure of DP1. In contrast, dense edge aggregation in the network structure of RP2 was clearly reduced compared to that of RP1. DISCUSSION Periodontitis is the most prevalent chronic inflammatory condition worldwide. 5 , 6 Factors that influencing periodontitis treatment outcomes include pre-treatment BOP, tooth movement, and smoking. 26 Additionally, risk factors for periodontal disease progression post-treatment include residual PPD ≥ 5 mm. 26 , 27 Periodontitis progression may be related to differences in composition and proportions of bacterial species 28 . We hypothesized that the bacterial community might differ between cured and non-cured sites after periodontitis treatment, and underwent metatranscriptomic and network analyses. Our findings show differences in healing outcomes between DP and RP groups even in the absence of differences in PPD, BOP, smoking history, and RBL at baseline. These variations in healing may be partially explained by differences in the bacterial network structure at baseline, despite the similarities in the bacterial composition. In the network analysis, bacterial groups centered on Streptococcus and Actinomyces genera were observed in all groups. As the progression moved from healthy sites to gingivitis sites, and from gingivitis to periodontitis sites, densely connected edges tended to form within these bacterial groups, and network centralization increased. Additionally, disease-associated bacteria, such as C. gingivalis and Selenomonas genus at gingivitis sites and the red complex at periodontitis sites, were added to the network. Therefore, the network structure of each group exhibited a transitional arrangement, supporting similar observations from the PCA. In the diversity analysis, we observed an increasing trend in α diversity from pre- to post-treatment in all groups, whereas a study that performed sequencing of the 16S rRNA gene library prepared from bacterial DNA indicated a decrease in α diversity at the site of periodontitis after treatment. 14 , 29 , 30 This discrepancy appears to arise from the presence of dead or inactive microbes in DNA-based studies. Indeed, the discrepancies between DNA- and RNA-based bacterial analysis results, as reported in other studies, 31 suggest that the findings of the present study do not contradict those of the previous studies. Based on our RNA-based results, the richness of active bacterial species and their transcriptional activity after treatment were possibly elevated. In the β diversity analysis based on bacterial and functional data, differences in bacterial composition observed between pre-treatment groups were no longer apparent post-treatment, which suggests that both bacterial and functional gene compositions at diseased sites became more similar to those in a healthy state after periodontal treatment. In bacterial networks of DP1 and RP1, although pathogenic bacteria including the red complex were present, their correlations were not dense. Instead, the emergence of the red complex disrupts homeostasis by causing health-associated bacteria such as those of the Streptococcus and Actinomyces genera 32 to become excessively active and influential. Health-associated bacteria are also present at sites of periodontitis. 33 , 34 , 35 Although whether excessive interactions within these commensal bacterial networks cause or are a consequence of periodontitis remains unclear, these findings represent a "disruption" not observed in clinically healthy states such as H2, G2, or RP2, providing a new perspective on periodontitis as a dysbiosis-related disease. In H2, G2, and RP2, the formation of densely connected edges within these bacterial groups, including Streptococcus and Actinomyces genera, was alleviated. This was accompanied by a reduction in network density and centralization, and edges that were previously concentrated around these bacterial taxa became distributed among multiple other taxa. These findings suggest that such a stabilized network structure may indicate a healthy bacterial network state. Duran-Pinedo et al . found that the number of hubs increased in stable periodontitis sites after treatment, enhancing their resilience against future relapse, which aligns with our findings. 28 In both DP and RP, the number of negatively correlated edges increased after treatment. This is thought to result from the resolution of edge localization symbolized by clusters of Streptococcus and Actinomyces genera due to treatment, allowing these bacteria to antagonize periodontal pathogens. Non-surgical treatment increased the DAT of aerobic and health-related bacteria, such as Neisseria 32 and decreased the DAT of anaerobic and periodontal pathogens in each group, consistent with previous reports. 14 , 28 , 36 The treatment reduced the abundance of many periodontal causative bacteria, including the red complex, in RP2 compared with RP1, while the DP2 group showed no increase in health-related bacteria. This suggests that successful healing involves changes in bacterial composition closer to that of healthy sites, characterized by an abundance of health-associated bacteria and fewer periodontal pathogens. 32 Other studies have also indicated that changes in the bacterial community occur because of a proportional reduction in periodontal pathogens, which lose their anaerobic niche provided by deep periodontal pockets. 37 , 38 We observed no statistical differences in β diversity between DP2 and RP2; however, we observed differences in DAT and network structure. These differences may contribute to the persistence of periodontitis. For example, bacteria such as Streptococcu s sp., Tannerella sp., Bacteroidetes [G-5] sp., and Porphyromonas endodontalis were more abundant in DP2 than RP2. Among these, Streptococcus sp. and Tannerella sp. demonstrated a greater reduction in RP2 than in RP1; however, their abundance did not decrease from DP1 to DP2. This suggests that the persistence of these bacterial taxa may be a potential factor in preventing recovery from periodontitis. Additionally, Bacteroidetes [G-5] sp. and P. endodontalis were identified as DAT and were more abundant in P1 than in G1. These bacteria are highly prevalent in patients with periodontitis. 39 , 40 Additionally, P. endodontalis plays a crucial role in the onset and progression of periodontitis by acquiring iron and evading immune responses. 41 Therefore, these bacteria are suspected to be involved in the onset of periodontitis and interfere with its treatment. In the cross-sectional comparison of DP1 and RP1, the analyses of α and β diversity, bacterial composition, and functional gene expression did not reveal significant differences, making it challenging to reveal bacteria associated with poor prognosis challenging. However, using the longitudinal design employed in this study, we successfully highlighted bacterial taxa described above that may facilitate or impede the resolution of periodontitis. After treatment, Neisseria elongata and Capnocytophaga sputigena were identified as DAT, which increased in the RP group but not in the DP group. Notably, these two bacterial taxa were absent from the DP2 network, while in the RP2 network structure, they exhibited negative correlations and antagonistic interactions with anaerobic groups such as Prevotella oralis and Prevotella dentalis . Similarly, Rothia aeria was identified as a significantly more abundant DAT at RP2 than at DP2. This bacterial taxon was present in the networks of DP1, DP2, RP1, and RP2; however, only RP2 was negatively correlated with anaerobic bacteria. Pozhitkov et al . reported that in patients with healthy oral conditions included contributions from several bacteria, such as N. elongata . 42 , 43 Rothia and Neisseria have an inverse relationship with inflammatory cytokines, and their growth in the presence of nitrates may alleviate inflammation. 44 These findings suggest that these bacteria contribute to the resolution of periodontitis by exhibiting antagonistic interactions with anaerobic bacteria in the post-treatment environment. The downregulation of genes involved in polysaccharide biosynthesis suggests evasion of the host immune system through polysaccharide biosynthesis, 45 which was observed in both the H and G groups after treatment. The fact that the expression of genes encoding polysaccharide biosynthesis proteins also decreased in the H group indicates that even in the H sites, treatment led to a reduction in pathogenicity, suggesting that the treatment was effective. DEGs common to the DP and RP groups before and after treatment included genes encoding fatty acid-binding protein DegV, glucose-1-phosphate cytidylyltransferase, NADH pyrophosphatase, glycine dehydrogenase (decarboxylating) β subunit, and cleaved adhesin domain protein. The first three genes increased following treatment in the RP group but decreased in the DP group, whereas the latter two parameters exhibited opposite trends. The first three are involved in lipid metabolism, diversity and function of surface polysaccharide structures, and energy metabolism, respectively, 46 , 47 , 48 and are increased in healed sites and decreased in unhealed sites. NADH pyrophosphatase has been reported to preferentially utilize reduced NADH. 49 This suggests the possibility that the reduced power required for energy metabolism and growth of periodontal pathogens is diminished as a result. Therefore, these functions may contribute to the healing of periodontitis. In contrast, the latter two genes encoding proteins are related to amino acid metabolism by glycine degradation and increased infectivity by adhesion to the tooth surface and host cells, respectively, 50 , 51 which suggests that the functional genes may inhibit the healing of periodontitis. In the comparison between the DP and RP groups, genes encoding the C-terminal domain protein, type VI secretion protein, and endopeptidase were detected as DEGs that were upregulated in DP2 compared with RP2, identifying them as characteristic genes of unhealed periodontitis sites after treatment. C-terminal domain is considered to contribute to the virulence of P. gingivalis , as found in its genes including gingipains and peptidyl deaminases. 52 The type VI secretion system is a gene cluster of gram-negative bacteria that encodes a protein complex responsible for injecting effector molecules into other bacteria and eukaryotes. 53 Endopeptidases, including gingipain and collagenase, are involved in the destruction of periodontal tissues. 54 , 55 Based on these findings and previous reports, these genes are suggested to inhibit the healing of periodontitis. Targeting these genes may provide a potential strategy to promote healing in periodontitis. In the H sites, where no clinical changes were observed between H1 and H2, the increase in α diversity, changes in β diversity of functional genes, the presence of DAT and DEGs, and the resolution of network centralization indicate that the bacterial community changed following treatment. Huang et al . demonstrated that changes in the microbiome occur before the onset of gingival inflammation during the pre-phase of experimental gingivitis, which indicates a variation in the composition of the bacterial community even within a healthy state. 15 In the RP2 network, even within the same healthy state, anaerobic bacterial species that were not present within H2 or G2 of the network were observed. Through RNA-level analysis we succeeded in observing such distinct differences, highlighting the functional changes in the bacterial community. These findings suggest that even if periodontitis is resolved and, due to the disappearance of beta diversity differences and the increase in health-associated bacteria, the condition appears healthy, the bacterial network structure differs from that of the originally healthy sites. This may explain one of the reasons why teeth with a history of periodontitis are more prone to recurrence. The fact that the network structure of RP2 differs from that of H2 and G2 can serve as one piece of evidence supporting the importance of continuous maintenance. One limitation of this study is that the post-treatment samples used were collected three months or later after treatment, which may indicate that the periodontal bacterial community had not yet fully stabilized at the time of analysis. Additionally, the findings were based solely on RNA analysis, and a more comprehensive assessment of the bacterial community can be achieved using a multi-omics approach. 56 Furthermore, dual sequencing approaches that include host response analysis should be considered because periodontitis is fundamentally driven by host immune reactions. 28 MATERIALS AND METHODS Ethical statement This research was conducted according to the Ethical Guidelines for Clinical Studies (Ministry of Health, Labor, and Welfare notification number 415, 2008). Approval was obtained from the Ethics Committee of the Tokyo Medical and Dental University (currently Institute of Science Tokyo), Tokyo, Japan (D2015-535). Written informed consent was obtained from all participants before their participation in the study. This study adhered to the principles outlined in the Declaration of Helsinki, which was amended in 2013. Study population and non-surgical treatment We included 45 patients who visited the Tokyo Medical and Dental University Hospital (currently Institute of Science Tokyo Hospital) for periodontal treatment, 21 of whom had participated in our previous study, 3 and 24 were newly enrolled. These patients had healthy (PPD ≤ 3 mm without BOP), gingivitis (PPD ≤ 3 mm with BOP), and periodontitis (PPD ≥ 4 mm with BOP, clinical attachment loss, and RBL) sites in maxillary or mandibular anterior teeth. 57,58 All patients underwent pre-treatment sampling, followed by non-surgical periodontal treatment. Subgingival plaque samples were collected from the deepest pockets at the healthy, gingivitis, and periodontitis sites (three individual sites per patient). None of the participants received systemic antibiotics or anti-inflammatory agents were administered from 3 months before the baseline examination until post-treatment sampling. The participants had no systemic problems or a history of smoking. 1,3,59 The patients received non-surgical treatment, which comprised oral hygiene instructions, scaling and root planing of all sites, and occlusal adjustment when necessary. Post-treatment sampling was conducted at least three months after treatment completion. Sample collection and RNA extraction The supragingival plaque was removed using sterile cotton pellets, and ten sterilized paper points were inserted into the pocket for 60 seconds. The points were collected in a sterilized tube, immediately immersed in liquid nitrogen, and then stored at –80 °C until RNA extraction. RNA was extracted using the PowerMicrobiome RNA Isolation Kit (Qiagen, Venlo, Netherlands). Purified RNA was quantified using a Quantus fluorometer (Promega, Madison, WI, USA), and RNA quality was evaluated using an Agilent 2100 bioanalyzer system (Agilent Technologies, Santa Clara, CA, USA) as described in previous studies. 1,3,25 Complementary DNA synthesis, library preparation, and Illumina sequencing Purified RNA was polyadenylated using an A-Plus Poly(A) Polymerase Tailing Kit (Epicentre Biotechnologies, Madison, WI, USA) and concentrated by ethanol precipitation using the Dr. GenTLE Precipitation Carrier (Takara Bio, Shiga, Japan). The polyadenylated RNA was reverse-transcribed into complementary DNA (cDNA) using the SMART-Seq v4 Ultra® Low Input RNA Kit for Sequencing (Takara). 3 Metatranscriptome sequencing libraries were constructed using a Nextera XT DNA Library Preparation Kit (Illumina, San Diego, CA, USA). The amplified cDNA was quantified by performing real-time polymerase chain reaction on a LightCycler (Roche Diagnostics, Mannheim, Germany) using a KAPA Library Quantification Kit (KAPA Biosystems, Wilmington, MA, USA). Library quality was evaluated using the Agilent 2100 Bioanalyzer. The prepared samples were pooled, and the Illumina MiSeq system platform was used to generate 300-base pair (bp) paired-end reads. Processing and analyzing Illumina sequencing data The sequencing data obtained in this study were analyzed together with data downloaded from the DNA Data Bank of Japan (DDBJ) (DRA011737), which were derived from 21 patients before non-surgical treatment in a previous study. The Illumina sequencing data were processed and analyzed as described previously. 1,25 Raw reads were trimmed using the Trimmomatic software version 0.32. 60 Sequences derived from the host were removed using DeconSeq software, version 0.4.3. 61 The trimmed data were separated into paired and unpaired reads using cmpfastq software. 62 Paired reads were then merged using fastq-join 63 to facilitate downstream taxonomic and functional analysis. 16S rRNA analysis and OTUs identification were conducted using EMIRGE pipeline with only paired reads. 64 Reconstructed 16S rRNA genes (rc-rRNA) are nearly full-length sequences assembled from paired-end reads. The number of reads for each rc-rRNA was calculated as the abundance value of its corresponding 16S rRNA OTU. We classified the representative sequence of each rc-rRNA OTU as similar to that in HOMD, version 13.2, 65 using the Basic Local Alignment Search Tool N (BLASTN). 66,67 The number of OTUs and Shannon index were used to estimate the α diversity indices. Rarefaction curves were drawn from abundance values calculated from the number of OTUs and species aligned to HOMD utilizing the rarefaction single command in Mothur software, version 1.48.0. 68 The abundance of all rc-rRNA OTUs was normalized using centered log-ratio (CLR) values. 69 Subsequently, community diversity was compared using PERMANOVA and visualized using PCA, both based on the Aitchison distance. In addition, all reads, including non-merged and unpaired reads, were formed into OTUs using Cluster Database at High Identity with Tolerance software. The 16S rRNA OTUs were removed by similarity comparison using BLASTN against SILVA (release 119). 70 The remaining OTUs were assumed to be derived from mRNA. Based on previous studies, 1,25 the mRNA OTUs were used to identify putative virulence factors using BLASTX 71 against the NCBI nr protein database (as of October 31, 2014), VFDB (as of February 9, 2015), and MvirDB (as of October 9, 2014), and protein function profiles were obtained. The abundance values of all mRNA OTUs were normalized by conversion to transcripts per million (TPM) to account for differences in gene length and library size. Only the mRNA OTUs with a prevalence of at least 50% in at least one group were included in the analysis. Additionally, batch effects were recognized between the data from previous studies and those used in this study; 72 therefore, the Conditional Quantile Regression (ConQuR) approach was employed to remove these batch effects. 73 The Bray–Curtis distance was used for PERMANOVA and PCoA. The Bray–Curtis dissimilarity of each group was calculated using the R package vegan (version 4.2.1; R Foundation for Statistical Computing, Vienna, Austria). We used the R packages tidyverse 74 and ggplot2 75 to generate the PCoA plots. Additionally, multi-group analysis of differentially abundant taxa from rc-rRNA and differentially expressed genes from mRNA was conducted using the R package ANCOM-BC2. 24 Only the taxa identified in both 16S rRNA and mRNA OTUs were included for further analyses, and these taxa were defined as viable taxa with in situ functions (VTiFs). 1,25 To understand the detailed positive and negative correlation relationships in the mRNA profiles of VTiF, we extracted only the VTiFs present in at least 50% of the participants in at least one group, and used them for the creation of network structures. Correlation coefficients were calculated using the Sparse Correlations for Compositional data (SparCC) software 76 based on mRNA taxonomic abundances. Taxon pairs with SparCC values of ≥ 0.85 and ≤ -0.8 were regarded as positive and negative relationships, respectively. Only taxon pairs with significance identified using the Benjamini–Hochberg (BH) method ( q < 0.05) were visualized using Cytoscape software, version 3.10.2. 77 The number of nodes and edges and the values of network density, clustering coefficient, and network centralization were also calculated for each group using Cytoscape. Statistical analysis For the statistics of clinical parameters and α diversities, Friedman and Kruskal–Wallis tests were used for paired and unpaired data, respectively. Dunn's multiple comparison test was used to identify significantly different groups. The Wilcoxon matched-pairs test was used to test for significant differences between groups. The tests were conducted using the statistical software GraphPad Prism (Version 10.1.1; San Diego, CA, USA). PERMANOVA was used to compare the similarity of the taxonomic and functional profiles of each group using the R package vegan:adonis2 with 999 permutations. This was followed by post hoc pairwise comparisons using the Bonferroni method with the R package pairwiseAdonis. To determine the significance of the abundance of DAT and DEGs in ANCOM-BC2, the BH method was used, with q < 0.05 considered statistically significant. Sensitivity analysis of the differentially abundant taxa was performed to assess the robustness of the results, including the impact of pseudo-count addition on zero-inflated data. For the correlation coefficients between taxon pairs in SparCC, the BH method was applied to manage the number of taxa included in the network, with q < 0.01 considered statistically significant. Declarations DATA AVAILABILITY The datasets generated for this study can be found in the DNA Data Bank of Japan (DDBJ) under the accession number for RNA sequencing PRJDB18491. ACKNOWLEDGEMENTS This work was supported by the Japan Society for the Promotion of Science (grant numbers 21K16987 to TS and 20K09934 to YT), the Japan Science and Technology Agency, Support for Pioneering Research Initiated by the Next Generation (grant numbers 51BA21K018 and 51BA216018 to RK). The authors extend their gratitude to the Data Science Center for providing supercomputing resources through the Human Genome Center at the Institute of Medical Science (University of Tokyo; http://sc.hgc.jp/shirokane.html ; December 1, 2022). Sequencing using Illumina MiSeq was performed at the Research Core of Institute of Science Tokyo. CONFLICT OF INTEREST We confirm that this research was conducted without any commercial or financial involvement, which could be interpreted as potential conflicts of interest. AUTHOR CONTRIBUTIONS R. K., T. Nemoto, T. Nagai, K. K, T. W., and T. S. performed the experiments and processed the sequence data. R. K. wrote the initial draft of the manuscript. T. 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Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27, 431–432 (2011). https://doi.org:10.1093/bioinformatics/btq675 Additional Declarations There is no conflict of interest. Tables 1-2 are available in the supplementary files section. Supplementary Files Tables.docx Tables 1-2 TableS1.xls Table S1 TableS2.xls Table S2 FigureS1.pdf Figure S1 FigureS2.pdf Figure S2 FigureS3.pdf Figure S3 FigureS4.pdf Figure S4 FigureS5.pdf Figure S5 FigureS6.pdf Figure S6 FigureS7.pdf Figure S7 FigureS8.pdf Figure S8 FigureS9.pdf Figure S9 FigureS10.pdf Figure S10 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-5764431","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":398564902,"identity":"991a9628-3088-4b37-8e4a-c274fe839f31","order_by":0,"name":"Ryota Kobayashi","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ryota","middleName":"","lastName":"Kobayashi","suffix":""},{"id":398564901,"identity":"1e7549c8-1e69-477c-a092-abeec7ae840e","order_by":1,"name":"Takahiko Shiba","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYDACCcY2CIOZ+QADAxuYmUCsFrbEBiK1QJUxMPAYNsDZ+ID87Oa2Bz8Y7siZt/N8f3SjjEGev4Hh2QN8WgzuHGw37GF4ZixzmHdjc845BsMZBxjSDfBqkUhsk+BhOJw4gxmoJbeNgXEDA0OaBF6HzUhsk/zDcLh+BjPPQ5AWe4JaGG4ktkkDbUmQYOZhBGlJJKjF4EZiu7GMwTPDGcxshrNzzkkkzzhMwC/yM9KfPXxTcUdegv/wg885ZTa2/e09aQ/wOgxi1wEYC+gkZp40wjoYGA4gc9iPEaNlFIyCUTAKRg4AAMQ1Ri3cLcJLAAAAAElFTkSuQmCC","orcid":"","institution":"Institute of Science Tokyo","correspondingAuthor":true,"prefix":"","firstName":"Takahiko","middleName":"","lastName":"Shiba","suffix":""},{"id":398564903,"identity":"09eabb18-20cc-455d-97b7-00aa816a0af3","order_by":2,"name":"Takahiko Nagai","email":"","orcid":"","institution":"Institute of Science Tokyo","correspondingAuthor":false,"prefix":"","firstName":"Takahiko","middleName":"","lastName":"Nagai","suffix":""},{"id":398564904,"identity":"4c76a349-8027-477b-83de-18d2d80cf6e8","order_by":3,"name":"Keiji Komatsu","email":"","orcid":"","institution":"Institute of Science Tokyo","correspondingAuthor":false,"prefix":"","firstName":"Keiji","middleName":"","lastName":"Komatsu","suffix":""},{"id":398564905,"identity":"350f3e57-f1d3-4039-a6b1-c780fae1236c","order_by":4,"name":"Shunsuke Matsumura","email":"","orcid":"","institution":"Institute of Science Tokyo","correspondingAuthor":false,"prefix":"","firstName":"Shunsuke","middleName":"","lastName":"Matsumura","suffix":""},{"id":398564906,"identity":"6fe6f644-1e81-4117-ab77-47394fbe7636","order_by":5,"name":"Takayasu Watanabe","email":"","orcid":"","institution":"Nihon University School of Dentistry","correspondingAuthor":false,"prefix":"","firstName":"Takayasu","middleName":"","lastName":"Watanabe","suffix":""},{"id":398564907,"identity":"715444c0-cc4a-4d2c-b710-1cfecc7c2588","order_by":6,"name":"Takashi Nemoto","email":"","orcid":"","institution":"Institute of Science Tokyo","correspondingAuthor":false,"prefix":"","firstName":"Takashi","middleName":"","lastName":"Nemoto","suffix":""},{"id":398564908,"identity":"620f21cc-944f-47b7-99a8-7248d7585ebc","order_by":7,"name":"Koki Takada","email":"","orcid":"","institution":"Institute of Science Tokyo","correspondingAuthor":false,"prefix":"","firstName":"Koki","middleName":"","lastName":"Takada","suffix":""},{"id":398564909,"identity":"947eec15-9a19-4a07-817e-7f10502361bc","order_by":8,"name":"Yasuo Takeuchi","email":"","orcid":"","institution":"Department of Periodontology Tokyo Medical and Dental University","correspondingAuthor":false,"prefix":"","firstName":"Yasuo","middleName":"","lastName":"Takeuchi","suffix":""},{"id":398564910,"identity":"7ed572c8-411a-422e-a53b-33ae9cec4cf1","order_by":9,"name":"Takanori Iwata","email":"","orcid":"","institution":"Department of Periodontology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University,","correspondingAuthor":false,"prefix":"","firstName":"Takanori","middleName":"","lastName":"Iwata","suffix":""}],"badges":[],"createdAt":"2025-01-04 15:50:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5764431/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5764431/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73413099,"identity":"6b3eef18-51d3-4cf5-9df6-d03b1bf779d6","added_by":"auto","created_at":"2025-01-09 16:30:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":55271,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the number of operational taxonomic units (OTUs) (\u003cstrong\u003ea-f\u003c/strong\u003e) and the shannon index (\u003cstrong\u003eg-l\u003c/strong\u003e) in each group. The boxes represent the interquartile range, extending from the 25th percentile to the 75th percentile, with the lines inside the boxes indicating the median value, and the whiskers extending to the 95% confidence interval. H, G, DP, and RP indicate healthy, gingivitis, diseased periodontitis, and resolved periodontitis sites, respectively. The numbers 1 and 2 after each group name represent before and after treatment, respectively. Statistical significance was indicated by * (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5764431/v1/792c8012b981124be6134dc3.png"},{"id":73413116,"identity":"61533e2f-c8e6-4033-856c-3028ff40ce41","added_by":"auto","created_at":"2025-01-09 16:30:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":92935,"visible":true,"origin":"","legend":"\u003cp\u003eβ diversity analysis conducted using principal component analysis (PCA) and permutational multivariate analysis of variance (PERMANOVA) in each group. Bacterial abundance was obtained using the Human Oral Microbiome Database. Light colors indicate post-treatment samples for each group, and darker colors indicate pre-treatment samples. The plots show each sample, and the ellipses indicate 95% confidence intervals. Group names are as indicated in Fig 1. (\u003cstrong\u003ea-d\u003c/strong\u003e) PCA and PERMANOVA show longitudinal bacterial changes in pre- and post-treatment using Aitchison distance for the H, G, DP, and RP groups, respectively. (\u003cstrong\u003ee, f\u003c/strong\u003e) Cross-sectional multi-group comparisons were conducted between groups at pre- and post-treatment using PCA and PERMANOVA with Aitchison distance based on bacterial abundance.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5764431/v1/99c26ddf9ccf6ab18bcf1c9e.png"},{"id":73413100,"identity":"613066e0-e216-4985-9b4d-d48830f1db2b","added_by":"auto","created_at":"2025-01-09 16:30:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":99064,"visible":true,"origin":"","legend":"\u003cp\u003eDifferentially abundant taxa (DAT) revealing significant differences in pre- and post-treatment comparisons for each group. Bacterial taxa that increased at post-treatment are shown with red bars, while those that decreased are shown with blue bars. The height of the bars represents the log fold change (LFC), and the whiskers indicate ±1 standard error. DAT that passed sensitivity analysis are indicated in light blue. Group names are as indicated in Fig. 1. (\u003cstrong\u003ea-d\u003c/strong\u003e) DAT in the H, G, DP, and RP groups, respectively. (\u003cstrong\u003ee\u003c/strong\u003e) DAT observed in DP2 compared to RP2.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5764431/v1/44747bb622a7922fd73d7320.png"},{"id":73413104,"identity":"03bf2578-db6e-417a-add5-4884d69c5185","added_by":"auto","created_at":"2025-01-09 16:30:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":164082,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmaps of differentially expressed genes (DEGs) with significant expression change in each group based on the National Center for Biotechnology Information non-redundant (NCBI nr) protein database. The numbers indicate the log fold change (LFC) with DEGs that passed sensitivity analysis marked in light blue. Group names are as indicated in Fig. 1. (\u003cstrong\u003ea-d\u003c/strong\u003e) DEGs in the H, G, DP, and RP groups between pre- and post-treatment. (\u003cstrong\u003ee\u003c/strong\u003e) DEGs between DP2 and RP2.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5764431/v1/b090d138e2eeaba92f3df148.png"},{"id":73413932,"identity":"f747ed18-f75e-491c-af76-3f7d9271e060","added_by":"auto","created_at":"2025-01-09 16:38:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":183953,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork analyses of subgingival bacterial community in pre- and post-treatment samples for each group. Group names are as indicated in Fig 1. Taxon pairs with SparCC values of ≥ 0.85 and ≤ −0.8 were considered as positive and negative relationships, respectively, with only significantly associated pairs included in the networks (\u003cem\u003eq\u003c/em\u003e \u0026lt; 0.05; Benjamini–Hochberg [BH] method). Nodes represent bacterial taxa, and their sizes indicate abundance based on mRNA read counts. The thickness of the edges represents the magnitude of the correlation coefficient, with red indicating positive correlations and blue indicating negative correlations. Network density represents the density of edges in each network, the clustering coefficient indicates the density of connections among neighboring nodes, and network centralization reflects the extent to which a few nodes have a large number of connections.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5764431/v1/28b3d8c129a561d72220f3f8.png"},{"id":73413143,"identity":"cf502efe-2fab-4aa0-bcd5-b40648dfde63","added_by":"auto","created_at":"2025-01-09 16:30:43","extension":"pdf","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":293116,"visible":true,"origin":"","legend":"β diversity analysis conducted using principal component analysis (PCA) and permutational multivariate analysis of variance (PERMANOVA) in each group. Bacterial abundance was obtained using the Human Oral Microbiome Database. Light colors indicate post-treatment samples for each group, and darker colors indicate pre-treatment samples. The plots show each sample, and the ellipses indicate 95% confidence intervals. Group names are as indicated in Fig.\u0026nbsp;. () PCA and PERMANOVA show longitudinal bacterial changes in pre- and post-treatment using Aitchison distance for the H, G, DP, and RP groups, respectively. () Cross-sectional multi-group comparisons were conducted between groups at pre- and post-treatment using PCA and PERMANOVA with Aitchison distance based on bacterial abundance.","description":"","filename":"FigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5764431/v1/3f0fd3ee63f0fb8a3f145cd2.pdf"},{"id":73413133,"identity":"1622df74-466c-4e39-bb65-5f750fbf49ae","added_by":"auto","created_at":"2025-01-09 16:30:42","extension":"pdf","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":126038,"visible":true,"origin":"","legend":"Heatmaps of differentially expressed genes (DEGs) with significant expression change in each group based on the National Center for Biotechnology Information non-redundant (NCBI nr) protein database. The numbers indicate the log fold change (LFC) with DEGs that passed sensitivity analysis marked in light blue. Group names are as indicated in Fig.\u0026nbsp;. () DEGs in the H, G, DP, and RP groups between pre- and post-treatment. () DEGs between DP2 and RP2.","description":"","filename":"FigureS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5764431/v1/dd10c7f634e5d8f20e1f70c3.pdf"},{"id":74365563,"identity":"3132414a-4f28-4ee5-95da-2001e2fe30c7","added_by":"auto","created_at":"2025-01-21 13:55:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1391007,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5764431/v1/4bda796a-be19-4d92-8a00-733a841d998f.pdf"},{"id":73414546,"identity":"5fea5250-2c3f-4799-a238-62d582c7be81","added_by":"auto","created_at":"2025-01-09 16:46:41","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":107964,"visible":true,"origin":"","legend":"\u003cp\u003eTables 1-2\u003c/p\u003e","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-5764431/v1/ddb50333e9d7dc5930b7388a.docx"},{"id":73413103,"identity":"a97d8678-286b-4bb5-959c-fc6a32eb8f58","added_by":"auto","created_at":"2025-01-09 16:30:40","extension":"xls","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":81408,"visible":true,"origin":"","legend":"\u003cp\u003eTable S1\u003c/p\u003e","description":"","filename":"TableS1.xls","url":"https://assets-eu.researchsquare.com/files/rs-5764431/v1/680890857f84a283d3feb68b.xls"},{"id":73413102,"identity":"5410bac3-579c-4166-9af8-5c9abee31f99","added_by":"auto","created_at":"2025-01-09 16:30:40","extension":"xls","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":63488,"visible":true,"origin":"","legend":"\u003cp\u003eTable S2\u003c/p\u003e","description":"","filename":"TableS2.xls","url":"https://assets-eu.researchsquare.com/files/rs-5764431/v1/b38fb32548e82e0e8f3cd909.xls"},{"id":73413121,"identity":"b0ca16ed-9dd7-44f5-9e51-7b9a947e54ff","added_by":"auto","created_at":"2025-01-09 16:30:42","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":293116,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S1\u003c/p\u003e","description":"","filename":"FigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5764431/v1/73f26d427eb663f7f84840b8.pdf"},{"id":73413936,"identity":"352cb04a-fbdb-40d7-8334-e46f205e18cf","added_by":"auto","created_at":"2025-01-09 16:38:42","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":126038,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S2\u003c/p\u003e","description":"","filename":"FigureS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5764431/v1/d9f41d313c8b94646c6904f9.pdf"},{"id":73413105,"identity":"b9d6aaaf-6ba2-43c1-846a-229185c28747","added_by":"auto","created_at":"2025-01-09 16:30:41","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":659487,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S3\u003c/p\u003e","description":"","filename":"FigureS3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5764431/v1/68dec06e3af7a60a9aec0529.pdf"},{"id":73413108,"identity":"b08355e2-7ad3-4b06-b378-4d38212402d3","added_by":"auto","created_at":"2025-01-09 16:30:41","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":195229,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S4\u003c/p\u003e","description":"","filename":"FigureS4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5764431/v1/17b4c0c5996b558726c51e42.pdf"},{"id":73413120,"identity":"355bad06-fa0a-4751-a878-7a101bab4b8b","added_by":"auto","created_at":"2025-01-09 16:30:41","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":291558,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S5\u003c/p\u003e","description":"","filename":"FigureS5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5764431/v1/43e8fbd050048829622d7834.pdf"},{"id":73413152,"identity":"3e6d7e8f-f7aa-4609-bf00-0900cf1da2b2","added_by":"auto","created_at":"2025-01-09 16:30:43","extension":"pdf","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":293198,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S6\u003c/p\u003e","description":"","filename":"FigureS6.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5764431/v1/1edb3bae96a6e48dec671028.pdf"},{"id":73413107,"identity":"a62a2318-c397-48ff-bbfa-9ab3d3b4357c","added_by":"auto","created_at":"2025-01-09 16:30:41","extension":"pdf","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":293734,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S7\u003c/p\u003e","description":"","filename":"FigureS7.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5764431/v1/3de4a2663bcee723a4baef20.pdf"},{"id":73413114,"identity":"99bb1ed7-0c17-469e-aa34-9cdc078f06ee","added_by":"auto","created_at":"2025-01-09 16:30:41","extension":"pdf","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":125180,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S8\u003c/p\u003e","description":"","filename":"FigureS8.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5764431/v1/c7793a619c1434bec99d5863.pdf"},{"id":73413938,"identity":"e7fcd605-3d89-41d2-9002-50deffc8a22b","added_by":"auto","created_at":"2025-01-09 16:38:42","extension":"pdf","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":162293,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S9\u003c/p\u003e","description":"","filename":"FigureS9.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5764431/v1/4e359df2f860322e9f16a5a3.pdf"},{"id":73413939,"identity":"55473db6-ec86-42b4-be39-39bcc8c1877a","added_by":"auto","created_at":"2025-01-09 16:38:42","extension":"pdf","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":204375,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S10\u003c/p\u003e","description":"","filename":"FigureS10.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5764431/v1/f43338e615774aade9960fb2.pdf"}],"financialInterests":"\u003cp\u003eThere is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTables 1-2 are available in the supplementary files section.\u003c/p\u003e","formattedTitle":"Metatranscriptomic Insights into Microbial Network Modulation and Pathogen Dynamics Underlying Healing Outcomes in Non-Surgical Periodontal Treatment","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003ePeriodontitis, an irreversible disease characterized by gingival inflammation and alveolar bone resorption around the teeth, is an infectious disease caused by dysbiosis of the oral microbiome,\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e and its global prevalence is remarkably high.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Moreover, the disease is associated with several systemic diseases.\u003csup\u003e\u003cspan additionalcitationids=\"CR8 CR9 CR10 CR11\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e Gingivitis, which is considered a precursor to periodontitis, is a reversible disease that causes gingival inflammation.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Therefore, assessing periodontal health and diagnosing gingivitis early is necessary to prevent periodontitis-associated deterioration of the patient's quality of life.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e These clinical conditions are reflected as oral microbiome dysbiosis, and etiological studies indicate that a specific bacterial species is associated with this dysbiosis.\u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e The keystone pathogen is a microorganism that has a disproportionately large effect on its environment relative to its abundance, and the identification of both the keystone pathogen and the bacteria influenced by it is important for diagnosis and treatment of these oral conditions.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e The identification of key pathogens require comprehensively capturing live bacterial taxa that are difficult to culture, using sequencing and bioinformatics analysis.\u003c/p\u003e \u003cp\u003eMetatranscriptomic analysis enables comprehensive understanding of the pathogenesis of periodontitis \u003cem\u003ein situ\u003c/em\u003e, as it can detect live bacterial species, active functional genes, and active metabolic pathways.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Using metatranscriptomic analysis, Nemoto \u003cem\u003eet al\u003c/em\u003e. showed that the bacterial co-occurrence network at gingivitis sites was in a transitional state from healthy to periodontitis.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e A study that performed metatranscriptomic analysis of both progressive and non-progressive sites within the same individuals with periodontitis identified differences in metabolic activities between these sites.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eNon-surgical treatment involving mechanical debridement of both supra-and subgingival plaque and calculus is fundamental to the management of periodontal disease.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Diao \u003cem\u003eet al\u003c/em\u003e. performed 16S ribosomal RNA (rRNA) sequencing to investigate the effect of non-surgical treatment on the oral microbiome in patients with periodontitis; they found that the bacterial composition of patients could not revert to a healthy state even after treatment.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e Although the effects of non-surgical treatment on microbiome in periodontitis have been investigated, only a few studies have used metatranscriptomic analyses. Furthermore, whether the microbiome in periodontitis or gingivitis sites within the same patient shifts towards a healthy state with no history of periodontal disease after treatment or transitions into a state prone to recurrence remains uninvestigated. Notably, this remains unclear even within the context of the same patient and the same tooth types.\u003c/p\u003e \u003cp\u003eThis study hypothesizes that treatment interventions influence the ecological dynamics of the oral microbiome in diseased sites but that persistent bacterial groups and networks hinder the re-establishment of microbial homeostasis. Therefore, in this study, we aimed to clarify the bacteriological characteristics and treatment-induced changes in subgingival plaque under healthy, gingivitis, and periodontitis conditions by prospectively comparing samples obtained from the same patients and only anterior teeth, thereby eliminating inter- and intra-individual variability. Additionally, we sought to investigate the bacteriological features that contribute to differences in treatment efficacy, even in cases that appear to present the same clinical condition of periodontitis, with the goal of providing insights toward the development of an early prognostic method for treatment outcomes.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003eStudy population and clinical parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe collected baseline data from healthy (H1), gingivitis (G1), and periodontitis (P1) sites of 45 patients. The \u0026ldquo;1\u0026rdquo; indicates pre-treatment status. Owing to the read process, two G1 samples and one P1 sample did not produce sufficient read counts. Therefore, for the cross-sectional comparison of H1, G1, and P1, these three participants were excluded from all the groups, and the baseline data of the remaining 42 patients were used for the analyses (Supplementary Fig. S1). For longitudinal pre- and post-treatment bacteriological comparisons, these three participants were excluded from their respective groups. Seventeen patients were excluded from the study during the treatment process due to the use of antibiotics during periodontal treatment or loss of attendance. All H1 sites remained healthy after non-surgical treatment (H2). \u0026nbsp;All of the G1 sites,\u0026nbsp;which were characterized by\u0026nbsp;bleeding on probing\u0026nbsp;(BOP) improved after treatment and changed to a healthy state (G2). After treatment,\u0026nbsp;P1 sites\u0026nbsp;were categorized into diseased periodontitis sites (DP2), where\u0026nbsp;probing pocket depth (PPD) improved\u0026nbsp;but was \u0026ge; 4\u0026nbsp;mm and BOP\u0026nbsp;remained positive,\u0026nbsp;or\u0026nbsp;resolved periodontitis sites (RP2), where BOP was negative. P1 sites were divided into diseased periodontitis (DP1) and resolved periodontitis (RP1) groups based on the clinical status at re-evaluation.\u0026nbsp;Finally,\u0026nbsp;28, 26, 13, and 14 samples from the H, G, DP, and RP groups, respectively, were analyzed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Tables 1 and 2 show the clinical data of patients as well as the changes in PPD, BOP, and radiographic bone loss (RBL) due to treatment. We observed no significant differences in the PPD and RBL before treatment between the DP1 and RP1 groups (adjusted \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.99 both). However, we observed significantly reduced PPDs after treatment in G, DP, and RP groups. Additionally, the DP2 and RP2 groups demonstrated differences in PPD after treatment (adjusted\u0026nbsp;\u003cem\u003ep\u003c/em\u003e = 0.02). All H2, G2, and RP2\u0026nbsp;sites were BOP-negative.\u0026nbsp;In contrast, the percentage of BOP in\u0026nbsp;DP2 was 100% (Table 2).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiversity analysis in each bacterial community\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor \u0026alpha; diversity analysis, the number of operational taxonomic units (OTUs) assigned to reconstructed 16S rRNA (hereon referred to as rc-rRNA) obtained using expectation maximization iterative reconstruction of genes from the environment (EMIRGE) pipeline (24), as well as the Shannon index, were used as indices. Rarefaction curves and sequence read information indicated that the sequencing depth was sufficient (Supplementary Fig. S2, Table S1, S2). First, the number of OTUs was evaluated (Fig. 1a-d). We observed a significant increase in the number of OTUs in H and DP groups before and after treatment despite no change in clinical diagnosis in either group (Fig. 1a, c). Although the number of OTUs did not differ within G sites or within RP sites before and after treatment, we observed an increasing trend (Fig. 1b, d). We observed no significant differences in the number of OTUs in the cross-sectional comparisons among different periodontal tissue conditions, either before or after treatment (Fig. 1e, f). In addition, we observed no significant differences in the Shannon index within or between any of the groups before and after treatment; however, the Shannon index tended to increase after treatment, which is similar to the results for the number of OTUs (Fig. 1g-l). Further, we observed similar tendencies with no significant differences between the groups in the pre-treatment dataset, which included participants who withdrew during treatment (Supplementary Fig. S1a, b).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRegarding \u0026beta; diversity, longitudinal bacteriological comparisons before and after treatment revealed significant differences in the G, DP, and RP groups (permutational multivariate analysis of variance [PERMANOVA],\u0026nbsp;\u003cem\u003ep\u003c/em\u003e = 0.024, \u003cem\u003ep\u003c/em\u003e = 0.019, \u003cem\u003ep\u003c/em\u003e = 0.021, respectively), but not in the H group\u0026nbsp;(\u003cem\u003ep\u003c/em\u003e = 0.738)\u0026nbsp;(Fig. 2a-d). In a cross-sectional comparison of the four pre-treatment\u0026nbsp;groups, principal component analysis (PCA) showed that the distributions of DP1 and RP1 overlapped\u0026nbsp;more than those between other group, with no statistically significant differences between them.\u0026nbsp;However,\u0026nbsp;we observed significant differences in the\u0026nbsp;\u0026beta;\u0026nbsp;diversities of H1 and DP1, H1 and RP1, and G1 and DP1 (PERMANOVA, adjusted \u003cem\u003ep\u003c/em\u003e = 0.006, adjusted \u003cem\u003ep\u003c/em\u003e = 0.006,\u0026nbsp;and adjusted\u0026nbsp;\u003cem\u003ep\u003c/em\u003e = 0.042, respectively) (Fig. 2e).\u0026nbsp;Conversely, the PCA plot indicated that the distributions of H2, G2, DP2, and RP2 were clustered together. We observed no significant differences\u0026nbsp;in\u0026nbsp;\u0026beta; diversities\u0026nbsp;between H2, RP2, and DP2\u0026nbsp;after treatment, and a l difference between\u0026nbsp;\u0026beta;\u0026nbsp;diversities of G2 and DP2 (Fig. 2f).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferential abundance analysis\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;based on bacterial abundance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe OTUs were assigned to bacterial taxa based on the Human Oral Microbiome Database (HOMD)\u0026nbsp;to obtain bacterial composition data for each group (Supplementary Fig. S3).\u0026nbsp;Differentially abundant\u0026nbsp;bacterial taxa (DAT) were evaluated\u0026nbsp;by analyzing the Compositions of Microbiomes with Bias Correction 2 (ANCOM-BC2).\u003csup\u003e24\u003c/sup\u003e The numbers of DAT detected in the H, G, DP, and RP groups based on HOMD were 35, 33, 38, and 15, respectively (adjusted \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, Fig. 3a-d). In the H group, despite no significant difference in\u0026nbsp;\u0026beta;\u0026nbsp;diversity, we observed multiple DAT, and decreased proportion of facultative anaerobes as well as \u003cem\u003eTannerella forsythia\u003c/em\u003e,\u0026nbsp;an obligate anaerobe and a member of the red complex\u0026nbsp;at post-treatment compared with pre-treatment. Interestingly, by contrast, \u003cem\u003ePorphyromonas gingivalis\u003c/em\u003e, a member of the red complex, increased in abundance after treatment (Fig. 3a).\u0026nbsp;To elucidate the details of the unexpected increase in \u003cem\u003eP. gingivalis\u003c/em\u003e after treatment, we evaluated\u0026nbsp;DAT, identified by comparing H1 and H2,\u0026nbsp;separately for individuals at sites DP2 and RP2.\u0026nbsp;The results indicated an increase in \u003cem\u003eP. gingivalis\u003c/em\u003e at H2 sites in individuals with DP2 sites and a decrease in \u003cem\u003eT. forsythia\u003c/em\u003e at H2 sites in individuals with RP2 sites (Supplementary Fig. S4a, b). In the G group, the majority of bacteria that decreased after treatment were anaerobic, including the red complex. In contrast, the increased bacteria post-treatment included aerobic bacteria such as \u003cem\u003eNeisseria sicca\u003c/em\u003e and \u003cem\u003eStreptococcus pneumoniae\u0026nbsp;\u003c/em\u003e(Fig. 3b).\u0026nbsp;We also compared DAT detection between DP2 and RP2 individuals in the G group, identified by comparing G1 and G2. Individuals with RP2\u0026nbsp;demonstrated reduced proportions of several obligate anaerobes, including \u003cem\u003eT. forsythia\u003c/em\u003e, \u003cem\u003eP. gingivalis\u003c/em\u003e, \u003cem\u003eFusobacterium nucleatum\u0026nbsp;\u003c/em\u003esubsp.\u003cem\u003e\u0026nbsp;vincentii\u003c/em\u003e and \u003cem\u003eF.\u003c/em\u003e \u003cem\u003enucleatum\u003c/em\u003e subsp. \u003cem\u003epolymorphum\u0026nbsp;\u003c/em\u003eat post-treatment, whereas individuals with DP2 demonstrated a smaller reduction in the proportions of obligate anaerobes, such as \u003cem\u003eTreponema denticola\u003c/em\u003e, post-treatment than the RP2 group (Supplementary Fig. S4c, d).\u0026nbsp;In the DP group,\u0026nbsp;the number of\u0026nbsp;increased\u0026nbsp;anaerobic\u0026nbsp;bacteria among the DAT was only 3 bacterial taxa including \u003cem\u003eT. denticola\u003c/em\u003e (Fig. 3c), whereas in the RP group, multiple aerobic bacteria, including \u003cem\u003eStreptococcus mitis\u0026nbsp;\u003c/em\u003eand \u003cem\u003eNeisseria elongata\u003c/em\u003e, increased (Fig. 3d). Additionally, to focus on\u0026nbsp;the bacterial taxa related to the prognosis and healing of periodontitis, we compared the DAT between the DP1 and RP1 groups and between the\u0026nbsp;DP2 and RP2 groups. We found no DAT\u0026nbsp;between the DP1 and RP1 groups; however, 18 DAT\u0026nbsp;were in between the DP2 and RP2 groups.\u0026nbsp;Compared with the RP2 group, the DP2 group demonstrated\u0026nbsp;a significantly lower abundance\u0026nbsp;of \u003cem\u003eRothia aeria\u003c/em\u003e and \u003cem\u003eLautropia mirabilis\u003c/em\u003e, and significantly higher abundances of \u003cem\u003eBacteroidetes\u003c/em\u003e [G-5] sp., \u003cem\u003eTannerella\u003c/em\u003e sp., and \u003cem\u003eStreptococcus\u003c/em\u003e sp. (Fig. 3e).\u0026nbsp;Interestingly, the proportion of \u003cem\u003eTannerella\u003c/em\u003e sp. and \u003cem\u003eStreptococcus\u003c/em\u003e sp. was reduced after treatment in the RP group but not reduced after treatment in the DP group (Fig. 3c, d).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferential abundance analysis\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;based on functional gene abundance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePutative mRNA sequences were annotated as functional genes using\u0026nbsp;Basic Local Alignment Search Tool X (BLASTX) with the National Center for Biotechnology Information non-redundant (NCBI nr) protein database, Virulence Factors of Pathogenic Bacteria database (VFDB), and Microbial Virulence Database (MvirDB). Principal coordinate analysis (PCoA) for the comparison of functional gene composition, which was detected in NCBI nr, showed significant differences between pre- and post-treatment in all groups (Supplementary Fig. S5a-d). In the pre-treatment cross-sectional analyses, we observed no significant differences between the H1 and G1 groups or between the DP1 and RP1 groups (Supplementary Fig. S5e). In contrast, there were significant differences between the H1 and DP1, H1 and RP1, G1 and DP1, and G1 and RP1 groups. After treatment, there were no significant differences between any of the groups (Supplementary Fig. S5f). The results obtained from the VFDB and MvirDB showed a tendency\u0026nbsp;similar\u0026nbsp;to\u0026nbsp;that\u0026nbsp;of NCBI nr (Supplementary Fig. S6-8).\u0026nbsp;We observed no significant differences in the functional gene composition between DP1 and RP1 or between DP2 and RP2 in any of the databases.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDifferentially expressed genes (DEGs) were evaluated using R package ANCOM-BC2.\u003csup\u003e24\u003c/sup\u003e (Fig. 4, Supplementary Fig. S9, S10). In the NCBI nr protein database, when comparing pre- and post-treatment, the numbers of significantly enriched DEGs were 881, 644, 274, and 456 in the H, G, DP, and RP groups, respectively. The top 15 DEGs with the largest absolute log-fold changes were selected and are shown in Figure 4. Among the selected DEGs, genes encoding amino acid kinase family proteins and polysaccharide biosynthesis proteins were downregulated after treatment in both the H and G groups (Fig. 4a, b). Five DEGs were commonly detected in the DP and RP groups: genes encoding fatty acid-binding protein DegV, glucose-1-phosphate cytidylyltransferase, nicotinamide adenine dinucleotide hydrogen (NADH) pyrophosphatase, glycine dehydrogenase (decarboxylating) beta subunit, and partial cleavage of the adhesin domain protein (Fig. 4c, d). The expression of the first three DEGs increased after treatment in the RP group and decreased after treatment in the DP group, whereas the expression of the latter two DEGs decreased after treatment in the RP group and increased after treatment in the DP group. We observed that no DEGs differed between DP1 and RP1 in any of the reference databases. In contrast, 10 and 12 DEGs identified in the NCBI nr protein and MvirDB differed between DP2 and RP2, respectively (Fig. 4e, Supplementary Fig. S10). However, we observed no DEGs that differed between DP2 and RP2 based on the results obtained from the VFDB. In the NCBI nr database, genes encoding the C-terminal domain protein and type VI secretion protein were upregulated in DP2 compared to RP2. Similarly, in MvirDB, genes encoding endopeptidase and SubE were more upregulated in DP2 than in RP2.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBacterial correlation network analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe taxonomic origin of each gene in the mRNA cluster was determined using functionally annotated data from the NCBI nr protein database. Taxa detected in both rc-rRNA and mRNA profiles were defined as viable taxa with \u003cem\u003ein situ\u003c/em\u003e functions (VTiFs), and network analyses were conducted targeting only VTiF.\u003csup\u003e1,25\u003c/sup\u003e Two main genera were present in the network before treatment in all groups: \u003cem\u003eStreptococcus\u003c/em\u003e genus group, mainly consisting of \u003cem\u003eStreptococcus mitis,\u003c/em\u003e \u003cem\u003eStreptococcus oralis\u003c/em\u003e, \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e, and the \u003cem\u003eActinomyces\u003c/em\u003e genus group, mainly consisting of \u003cem\u003eActinomyces naeslundii\u003c/em\u003e, \u003cem\u003eActinomyces johsonii\u003c/em\u003e, \u003cem\u003eActinomyces oris\u003c/em\u003e, and others (Fig. 5a-d). The network structure of H1 consisted primarily of these groups. In the G1 network structure, we observed \u003cem\u003eCapnocytophaga gingivalis\u0026nbsp;\u003c/em\u003eand \u003cem\u003eSelenomonas\u003c/em\u003e sp., in addition to the taxa detected in the H1 network structure. Moreover, we observed an increased in both number of nodes constituting the G1 network structure and its network complexity compared with the H1 network structure. In the DP1 and RP1 network structures, more taxa were added to the taxa comprising the G1 network structure, such as the red complex and\u0026nbsp;\u003cem\u003ePrevotella\u003c/em\u003e genera; however, these main network structures in DP1 and RP1 still comprised \u003cem\u003eStreptococcus\u003c/em\u003e and \u003cem\u003eActinomyces\u003c/em\u003e genera.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; In the network structure of each group, the number of nodes post-treatment were more than that of the pre-treatment network except for the G group; however, the key species central to the pre-treatment network remained largely unchanged. The number of edges and the value of clustering coefficients decreased in the H and G groups after treatment. In contrast, these values were higher in the DP and RP groups after treatment. Network density and centralization values decreased after treatment in all groups. We observed negative correlations among the network structures of G1, DP2, RP1, and RP2. In the network structure of G1, \u003cem\u003eSelenomonas\u0026nbsp;\u003c/em\u003esp., an anaerobic bacterium and \u003cem\u003eRothia aeria\u003c/em\u003e, an aerobic bacterium, were negatively correlated. Moreover, we found negative correlations between obligate anaerobic bacteria, such as \u003cem\u003eTreponema\u003c/em\u003e and \u003cem\u003eTannerella\u003c/em\u003e genera, and obligate aerobic and facultative anaerobic bacteria, such as \u003cem\u003eNeisseria\u003c/em\u003e, \u003cem\u003eActinomyces\u003c/em\u003e, and \u003cem\u003eStreptococcus\u003c/em\u003e genera in the network structures of DP2, RP1, and RP2. In terms of the difference between DP1 and RP1 in the network, all values of the network structure were greater in the DP1 than RP1, with the number of edges and clustering coefficient being particularly elevated. Additionally, \u003cem\u003eP. gingivalis\u003c/em\u003e,\u0026nbsp;\u003cem\u003eT. denticola,\u003c/em\u003e and \u003cem\u003eT. forsythia\u003c/em\u003e had positive correlations with each other and formed the same cluster in the network structure of DP1. However, we observed no positive or negative correlations between \u003cem\u003eP. gingivalis\u003c/em\u003e and different taxa in the network structure of RP1. Furthermore, we observed no positive or negative correlations between \u003cem\u003eT. denticola\u003c/em\u003e and \u003cem\u003eT. forsythia\u003c/em\u003e, and these taxa had few connections with other taxa. \u003cem\u003eT. forsythia\u003c/em\u003e and \u003cem\u003eFusobacterium\u003c/em\u003e sp.,\u0026nbsp;which are known to cause periodontal disease,\u0026nbsp;were negatively correlated with aerobic bacteria in the RP1 network, whereas DP1 network structure showed no negative correlation. The number of edges, network density, and\u0026nbsp;centralization\u0026nbsp;values were\u0026nbsp;much lower in the RP2 network than in the\u0026nbsp;DP2 network. In the network structure of DP2, the overly dense edge aggregation in the cluster comprising primarily of \u003cem\u003eStreptococcus\u003c/em\u003e and \u003cem\u003eActinomyces\u003c/em\u003e genera still remained similar to that in the network structure of DP1. In contrast, dense edge aggregation in the network structure of RP2 was clearly reduced compared to that of RP1.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003ePeriodontitis is the most prevalent chronic inflammatory condition worldwide.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Factors that influencing periodontitis treatment outcomes include pre-treatment BOP, tooth movement, and smoking.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e Additionally, risk factors for periodontal disease progression post-treatment include residual PPD\u0026thinsp;\u0026ge;\u0026thinsp;5 mm.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Periodontitis progression may be related to differences in composition and proportions of bacterial species\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. We hypothesized that the bacterial community might differ between cured and non-cured sites after periodontitis treatment, and underwent metatranscriptomic and network analyses. Our findings show differences in healing outcomes between DP and RP groups even in the absence of differences in PPD, BOP, smoking history, and RBL at baseline. These variations in healing may be partially explained by differences in the bacterial network structure at baseline, despite the similarities in the bacterial composition.\u003c/p\u003e \u003cp\u003eIn the network analysis, bacterial groups centered on \u003cem\u003eStreptococcus\u003c/em\u003e and \u003cem\u003eActinomyces\u003c/em\u003e genera were observed in all groups. As the progression moved from healthy sites to gingivitis sites, and from gingivitis to periodontitis sites, densely connected edges tended to form within these bacterial groups, and network centralization increased. Additionally, disease-associated bacteria, such as \u003cem\u003eC. gingivalis\u003c/em\u003e and \u003cem\u003eSelenomonas\u003c/em\u003e genus at gingivitis sites and the red complex at periodontitis sites, were added to the network. Therefore, the network structure of each group exhibited a transitional arrangement, supporting similar observations from the PCA.\u003c/p\u003e \u003cp\u003eIn the diversity analysis, we observed an increasing trend in α diversity from pre- to post-treatment in all groups, whereas a study that performed sequencing of the 16S rRNA gene library prepared from bacterial DNA indicated a decrease in α diversity at the site of periodontitis after treatment.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e This discrepancy appears to arise from the presence of dead or inactive microbes in DNA-based studies. Indeed, the discrepancies between DNA- and RNA-based bacterial analysis results, as reported in other studies,\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e suggest that the findings of the present study do not contradict those of the previous studies. Based on our RNA-based results, the richness of active bacterial species and their transcriptional activity after treatment were possibly elevated. In the β diversity analysis based on bacterial and functional data, differences in bacterial composition observed between pre-treatment groups were no longer apparent post-treatment, which suggests that both bacterial and functional gene compositions at diseased sites became more similar to those in a healthy state after periodontal treatment.\u003c/p\u003e \u003cp\u003eIn bacterial networks of DP1 and RP1, although pathogenic bacteria including the red complex were present, their correlations were not dense. Instead, the emergence of the red complex disrupts homeostasis by causing health-associated bacteria such as those of the \u003cem\u003eStreptococcus\u003c/em\u003e and \u003cem\u003eActinomyces\u003c/em\u003e genera\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e to become excessively active and influential. Health-associated bacteria are also present at sites of periodontitis.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e Although whether excessive interactions within these commensal bacterial networks cause or are a consequence of periodontitis remains unclear, these findings represent a \"disruption\" not observed in clinically healthy states such as H2, G2, or RP2, providing a new perspective on periodontitis as a dysbiosis-related disease. In H2, G2, and RP2, the formation of densely connected edges within these bacterial groups, including \u003cem\u003eStreptococcus\u003c/em\u003e and \u003cem\u003eActinomyces\u003c/em\u003e genera, was alleviated. This was accompanied by a reduction in network density and centralization, and edges that were previously concentrated around these bacterial taxa became distributed among multiple other taxa. These findings suggest that such a stabilized network structure may indicate a healthy bacterial network state. Duran-Pinedo \u003cem\u003eet al\u003c/em\u003e. found that the number of hubs increased in stable periodontitis sites after treatment, enhancing their resilience against future relapse, which aligns with our findings.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e In both DP and RP, the number of negatively correlated edges increased after treatment. This is thought to result from the resolution of edge localization symbolized by clusters of \u003cem\u003eStreptococcus\u003c/em\u003e and \u003cem\u003eActinomyces\u003c/em\u003e genera due to treatment, allowing these bacteria to antagonize periodontal pathogens.\u003c/p\u003e \u003cp\u003eNon-surgical treatment increased the DAT of aerobic and health-related bacteria, such as \u003cem\u003eNeisseria\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e and decreased the DAT of anaerobic and periodontal pathogens in each group, consistent with previous reports.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e The treatment reduced the abundance of many periodontal causative bacteria, including the red complex, in RP2 compared with RP1, while the DP2 group showed no increase in health-related bacteria. This suggests that successful healing involves changes in bacterial composition closer to that of healthy sites, characterized by an abundance of health-associated bacteria and fewer periodontal pathogens.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e Other studies have also indicated that changes in the bacterial community occur because of a proportional reduction in periodontal pathogens, which lose their anaerobic niche provided by deep periodontal pockets.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWe observed no statistical differences in β diversity between DP2 and RP2; however, we observed differences in DAT and network structure. These differences may contribute to the persistence of periodontitis. For example, bacteria such as \u003cem\u003eStreptococcu\u003c/em\u003es sp., \u003cem\u003eTannerella\u003c/em\u003e sp., \u003cem\u003eBacteroidetes\u003c/em\u003e [G-5] sp., and \u003cem\u003ePorphyromonas endodontalis\u003c/em\u003e were more abundant in DP2 than RP2. Among these, \u003cem\u003eStreptococcus\u003c/em\u003e sp. and \u003cem\u003eTannerella\u003c/em\u003e sp. demonstrated a greater reduction in RP2 than in RP1; however, their abundance did not decrease from DP1 to DP2. This suggests that the persistence of these bacterial taxa may be a potential factor in preventing recovery from periodontitis. Additionally, \u003cem\u003eBacteroidetes\u003c/em\u003e [G-5] sp. and \u003cem\u003eP. endodontalis\u003c/em\u003e were identified as DAT and were more abundant in P1 than in G1. These bacteria are highly prevalent in patients with periodontitis.\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e Additionally, \u003cem\u003eP. endodontalis\u003c/em\u003e plays a crucial role in the onset and progression of periodontitis by acquiring iron and evading immune responses. \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e Therefore, these bacteria are suspected to be involved in the onset of periodontitis and interfere with its treatment. In the cross-sectional comparison of DP1 and RP1, the analyses of α and β diversity, bacterial composition, and functional gene expression did not reveal significant differences, making it challenging to reveal bacteria associated with poor prognosis challenging. However, using the longitudinal design employed in this study, we successfully highlighted bacterial taxa described above that may facilitate or impede the resolution of periodontitis.\u003c/p\u003e \u003cp\u003eAfter treatment, \u003cem\u003eNeisseria elongata\u003c/em\u003e and \u003cem\u003eCapnocytophaga sputigena\u003c/em\u003e were identified as DAT, which increased in the RP group but not in the DP group. Notably, these two bacterial taxa were absent from the DP2 network, while in the RP2 network structure, they exhibited negative correlations and antagonistic interactions with anaerobic groups such as \u003cem\u003ePrevotella oralis\u003c/em\u003e and \u003cem\u003ePrevotella dentalis\u003c/em\u003e. Similarly, \u003cem\u003eRothia aeria\u003c/em\u003e was identified as a significantly more abundant DAT at RP2 than at DP2. This bacterial taxon was present in the networks of DP1, DP2, RP1, and RP2; however, only RP2 was negatively correlated with anaerobic bacteria. Pozhitkov \u003cem\u003eet al\u003c/em\u003e. reported that in patients with healthy oral conditions included contributions from several bacteria, such as \u003cem\u003eN. elongata\u003c/em\u003e.\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e\u003cem\u003eRothia\u003c/em\u003e and \u003cem\u003eNeisseria\u003c/em\u003e have an inverse relationship with inflammatory cytokines, and their growth in the presence of nitrates may alleviate inflammation.\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e These findings suggest that these bacteria contribute to the resolution of periodontitis by exhibiting antagonistic interactions with anaerobic bacteria in the post-treatment environment.\u003c/p\u003e \u003cp\u003eThe downregulation of genes involved in polysaccharide biosynthesis suggests evasion of the host immune system through polysaccharide biosynthesis,\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e which was observed in both the H and G groups after treatment. The fact that the expression of genes encoding polysaccharide biosynthesis proteins also decreased in the H group indicates that even in the H sites, treatment led to a reduction in pathogenicity, suggesting that the treatment was effective. DEGs common to the DP and RP groups before and after treatment included genes encoding fatty acid-binding protein DegV, glucose-1-phosphate cytidylyltransferase, NADH pyrophosphatase, glycine dehydrogenase (decarboxylating) β subunit, and cleaved adhesin domain protein. The first three genes increased following treatment in the RP group but decreased in the DP group, whereas the latter two parameters exhibited opposite trends. The first three are involved in lipid metabolism, diversity and function of surface polysaccharide structures, and energy metabolism, respectively,\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e and are increased in healed sites and decreased in unhealed sites. NADH pyrophosphatase has been reported to preferentially utilize reduced NADH.\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e This suggests the possibility that the reduced power required for energy metabolism and growth of periodontal pathogens is diminished as a result. Therefore, these functions may contribute to the healing of periodontitis. In contrast, the latter two genes encoding proteins are related to amino acid metabolism by glycine degradation and increased infectivity by adhesion to the tooth surface and host cells, respectively,\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e which suggests that the functional genes may inhibit the healing of periodontitis. In the comparison between the DP and RP groups, genes encoding the C-terminal domain protein, type VI secretion protein, and endopeptidase were detected as DEGs that were upregulated in DP2 compared with RP2, identifying them as characteristic genes of unhealed periodontitis sites after treatment. C-terminal domain is considered to contribute to the virulence of \u003cem\u003eP. gingivalis\u003c/em\u003e, as found in its genes including gingipains and peptidyl deaminases.\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e The type VI secretion system is a gene cluster of gram-negative bacteria that encodes a protein complex responsible for injecting effector molecules into other bacteria and eukaryotes.\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e Endopeptidases, including gingipain and collagenase, are involved in the destruction of periodontal tissues.\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e Based on these findings and previous reports, these genes are suggested to inhibit the healing of periodontitis. Targeting these genes may provide a potential strategy to promote healing in periodontitis.\u003c/p\u003e \u003cp\u003eIn the H sites, where no clinical changes were observed between H1 and H2, the increase in α diversity, changes in β diversity of functional genes, the presence of DAT and DEGs, and the resolution of network centralization indicate that the bacterial community changed following treatment. Huang \u003cem\u003eet al\u003c/em\u003e. demonstrated that changes in the microbiome occur before the onset of gingival inflammation during the pre-phase of experimental gingivitis, which indicates a variation in the composition of the bacterial community even within a healthy state.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e In the RP2 network, even within the same healthy state, anaerobic bacterial species that were not present within H2 or G2 of the network were observed. Through RNA-level analysis we succeeded in observing such distinct differences, highlighting the functional changes in the bacterial community. These findings suggest that even if periodontitis is resolved and, due to the disappearance of beta diversity differences and the increase in health-associated bacteria, the condition appears healthy, the bacterial network structure differs from that of the originally healthy sites. This may explain one of the reasons why teeth with a history of periodontitis are more prone to recurrence. The fact that the network structure of RP2 differs from that of H2 and G2 can serve as one piece of evidence supporting the importance of continuous maintenance.\u003c/p\u003e \u003cp\u003eOne limitation of this study is that the post-treatment samples used were collected three months or later after treatment, which may indicate that the periodontal bacterial community had not yet fully stabilized at the time of analysis. Additionally, the findings were based solely on RNA analysis, and a more comprehensive assessment of the bacterial community can be achieved using a multi-omics approach.\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e Furthermore, dual sequencing approaches that include host response analysis should be considered because periodontitis is fundamentally driven by host immune reactions.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003e\u003cstrong\u003eEthical statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was conducted according to the Ethical Guidelines for Clinical Studies (Ministry of Health, Labor, and Welfare notification number 415, 2008). Approval was obtained from the Ethics Committee of the Tokyo Medical and Dental University (currently Institute of Science Tokyo), Tokyo, Japan (D2015-535). Written informed consent was obtained from all participants before their participation in the study. This study adhered to the principles outlined in the Declaration of Helsinki, which was amended in 2013.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy population and non-surgical treatment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe included 45 patients who visited the Tokyo Medical and Dental University Hospital (currently Institute of Science Tokyo Hospital) for periodontal treatment, 21 of whom had participated in our previous study,\u003csup\u003e3\u003c/sup\u003e and 24 were newly enrolled. These patients had healthy (PPD \u0026le; 3 mm without BOP), gingivitis (PPD \u0026le; 3 mm with BOP), and periodontitis (PPD \u0026ge; 4 mm with BOP, clinical attachment loss, and RBL) sites in maxillary or mandibular anterior teeth.\u003csup\u003e57,58\u003c/sup\u003e All patients underwent pre-treatment sampling, followed by non-surgical periodontal treatment. Subgingival plaque samples were collected from the deepest pockets at the healthy, gingivitis, and periodontitis sites (three individual sites per patient). None of the participants received systemic antibiotics or anti-inflammatory agents were administered from 3 months before the baseline examination until post-treatment sampling. The participants had no systemic problems or a history of smoking.\u003csup\u003e1,3,59\u003c/sup\u003e The patients received non-surgical treatment, which comprised oral hygiene instructions, scaling and root planing of all sites, and occlusal adjustment when necessary. Post-treatment sampling was conducted at least three months after treatment completion.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample collection and RNA extraction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe supragingival plaque was removed using sterile cotton pellets, and ten sterilized paper points were inserted into the pocket for 60 seconds. The points were collected in a sterilized tube, immediately immersed in liquid nitrogen, and then stored at \u0026ndash;80 \u0026deg;C until RNA extraction.\u003c/p\u003e\n\u003cp\u003eRNA was extracted using the PowerMicrobiome RNA Isolation Kit (Qiagen, Venlo, Netherlands). Purified RNA was quantified using a Quantus fluorometer (Promega, Madison, WI, USA), and RNA quality was evaluated using an Agilent 2100 bioanalyzer system (Agilent Technologies, Santa Clara, CA, USA) as described in previous studies.\u003csup\u003e1,3,25\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComplementary DNA synthesis, library preparation, and Illumina sequencing\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePurified RNA was polyadenylated using an A-Plus Poly(A) Polymerase Tailing Kit (Epicentre Biotechnologies, Madison, WI, USA) and concentrated by ethanol precipitation using the Dr. GenTLE Precipitation Carrier (Takara Bio, Shiga, Japan). The polyadenylated RNA was reverse-transcribed into complementary DNA (cDNA) using the SMART-Seq v4 Ultra\u0026reg; Low Input RNA Kit for Sequencing (Takara).\u003csup\u003e3\u003c/sup\u003e Metatranscriptome sequencing libraries were constructed using a Nextera XT DNA Library Preparation Kit (Illumina, San Diego, CA, USA). The amplified cDNA was quantified by performing real-time polymerase chain reaction on a LightCycler (Roche Diagnostics, Mannheim, Germany) using a KAPA Library Quantification Kit (KAPA Biosystems, Wilmington, MA, USA). Library quality was evaluated using the Agilent 2100 Bioanalyzer. The prepared samples were pooled, and the Illumina MiSeq system platform was used to generate 300-base pair (bp) paired-end reads.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProcessing and analyzing Illumina sequencing data\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sequencing data obtained in this study were analyzed together with data downloaded from the DNA Data Bank of Japan (DDBJ) (DRA011737), which were derived from 21 patients before non-surgical treatment in a previous study. The Illumina sequencing data were processed and analyzed as described previously.\u003csup\u003e1,25\u003c/sup\u003e Raw reads were trimmed using the Trimmomatic software version 0.32.\u003csup\u003e60\u003c/sup\u003e Sequences derived from the host were removed using DeconSeq software, version 0.4.3.\u003csup\u003e61\u003c/sup\u003e The trimmed data were separated into paired and unpaired reads using cmpfastq software.\u003csup\u003e62\u003c/sup\u003e Paired reads were then merged using fastq-join\u003csup\u003e63\u003c/sup\u003e to facilitate downstream taxonomic and functional analysis.\u003c/p\u003e\n\u003cp\u003e16S rRNA analysis and OTUs identification were conducted using EMIRGE pipeline with only paired reads.\u003csup\u003e64\u003c/sup\u003e Reconstructed 16S rRNA genes (rc-rRNA) are nearly full-length sequences assembled from paired-end reads. The number of reads for each rc-rRNA was calculated as the abundance value of its corresponding 16S rRNA OTU. We classified the representative sequence of each rc-rRNA OTU as similar to that in HOMD, version 13.2,\u003csup\u003e65\u003c/sup\u003e using the Basic Local Alignment Search Tool N (BLASTN).\u003csup\u003e66,67\u003c/sup\u003e The number of OTUs and Shannon index were used to estimate the \u0026alpha; diversity indices. Rarefaction curves were drawn from abundance values calculated from the number of OTUs and species aligned to HOMD utilizing the rarefaction single command in Mothur software, version 1.48.0.\u003csup\u003e68\u003c/sup\u003e The abundance of all rc-rRNA OTUs was normalized using centered log-ratio (CLR) values.\u003csup\u003e69\u003c/sup\u003e Subsequently, community diversity was compared using PERMANOVA and visualized using PCA, both based on the Aitchison distance. In addition, all reads, including non-merged and unpaired reads, were formed into OTUs using Cluster Database at High Identity with Tolerance software. The 16S rRNA OTUs were removed by similarity comparison using BLASTN against SILVA (release 119).\u003csup\u003e70\u003c/sup\u003e The remaining OTUs were assumed to be derived from mRNA. Based on previous studies,\u003csup\u003e1,25\u003c/sup\u003e the mRNA OTUs were used to identify putative virulence factors using BLASTX\u003csup\u003e71\u003c/sup\u003e against the NCBI nr protein database (as of October 31, 2014), VFDB (as of February 9, 2015), and MvirDB (as of October 9, 2014), and protein function profiles were obtained.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe abundance values of all mRNA OTUs were normalized by conversion to transcripts per million (TPM) to account for differences in gene length and library size. Only the mRNA OTUs with a prevalence of at least 50% in at least one group were included in the analysis. Additionally, batch effects were recognized between the data from previous studies and those used in this study;\u003csup\u003e72\u003c/sup\u003e therefore, the Conditional Quantile Regression (ConQuR) approach was employed to remove these batch effects.\u003csup\u003e73\u003c/sup\u003e The Bray\u0026ndash;Curtis distance was used for PERMANOVA and PCoA. The Bray\u0026ndash;Curtis dissimilarity of each group was calculated using the R package vegan (version 4.2.1; R Foundation for Statistical Computing, Vienna, Austria). We used the R packages tidyverse\u003csup\u003e74\u003c/sup\u003e and ggplot2\u003csup\u003e75\u003c/sup\u003e to generate the PCoA plots. Additionally, multi-group analysis of differentially abundant taxa from rc-rRNA and differentially expressed genes from mRNA was conducted using the R package ANCOM-BC2.\u003csup\u003e24\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eOnly the taxa identified in both 16S rRNA and mRNA OTUs were included for further analyses, and these taxa were defined as viable taxa with \u003cem\u003ein situ\u003c/em\u003e functions (VTiFs).\u003csup\u003e1,25\u003c/sup\u003e To understand the detailed positive and negative correlation relationships in the mRNA profiles of VTiF, we extracted only the VTiFs present in at least 50% of the participants in at least one group, and used them for the creation of network structures. Correlation coefficients were calculated using the Sparse Correlations for Compositional data (SparCC) software\u003csup\u003e76\u003c/sup\u003e based on mRNA taxonomic abundances. Taxon pairs with SparCC values of\u0026nbsp;\u0026nbsp;\u0026ge; 0.85 and \u0026le; -0.8 were regarded as positive and negative relationships, respectively. Only taxon pairs with significance identified using the Benjamini\u0026ndash;Hochberg (BH) method (\u003cem\u003eq\u003c/em\u003e \u0026lt; 0.05) were visualized using Cytoscape software, version 3.10.2.\u003csup\u003e77\u003c/sup\u003e The number of nodes and edges and the values of network density, clustering coefficient, and network centralization were also calculated for each group using Cytoscape.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the statistics of clinical parameters and \u0026alpha; diversities, Friedman and Kruskal\u0026ndash;Wallis tests were used for paired and unpaired data, respectively. Dunn\u0026apos;s multiple comparison test was used to identify significantly different groups. The Wilcoxon matched-pairs test was used to test for significant differences between groups. The tests were conducted using the statistical software GraphPad Prism (Version 10.1.1; San Diego, CA, USA). PERMANOVA was used to compare the similarity of the taxonomic and functional profiles of each group using the R package vegan:adonis2 with 999 permutations. This was followed by post hoc pairwise comparisons using the Bonferroni method with the R package pairwiseAdonis. To determine the significance of the abundance of DAT and DEGs in ANCOM-BC2, the BH method was used, with \u003cem\u003eq\u003c/em\u003e \u0026lt; 0.05 considered statistically significant. Sensitivity analysis of the differentially abundant taxa was performed to assess the robustness of the results, including the impact of pseudo-count addition on zero-inflated data. For the correlation coefficients between taxon pairs in SparCC, the BH method was applied to manage the number of taxa included in the network, with \u003cem\u003eq\u003c/em\u003e \u0026lt; 0.01 considered statistically significant.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated for this study can be found in the DNA Data Bank of Japan (DDBJ) under the accession number for RNA sequencing PRJDB18491.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Japan Society for the Promotion of Science (grant numbers 21K16987 to TS and 20K09934 to YT), the Japan Science and Technology Agency, Support for Pioneering Research Initiated by the Next Generation (grant numbers 51BA21K018 and 51BA216018 to RK). The authors extend their gratitude to the Data Science Center for providing supercomputing resources through the Human Genome Center at the Institute of Medical Science (University of Tokyo;\u0026nbsp;\u003ca href=\"http://sc.hgc.jp/shirokane.html\"\u003ehttp://sc.hgc.jp/shirokane.html\u003c/a\u003e; December 1, 2022). Sequencing using Illumina MiSeq was performed at the Research Core of Institute of Science Tokyo.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe confirm that this research was conducted without any commercial or financial involvement, which could be interpreted as potential conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eR. K., T. Nemoto, T. Nagai, K. K, T. W., and T. S. performed the experiments and processed the sequence data. R. K. wrote the initial draft of the manuscript. T. Nemoto, T. Nagai, S. M., K. T., T. W., Y. T., T. S, and T. I. reviewed the manuscript. Y. T., T. S., and T. I. supervised the experiments. All the authors have read and approved the final version of this manuscript.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eShiba, T. \u003cem\u003eet al.\u003c/em\u003e Distinct interacting core taxa in co-occurrence networks enable discrimination of polymicrobial oral diseases with similar symptoms. 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Bioinformatics 27, 431\u0026ndash;432 (2011). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1093/bioinformatics/btq675\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1093/bioinformatics/btq675\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-5764431/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5764431/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePeriodontitis is a globally prevalent chronic inflammatory disease caused by dysbiosis of the oral microbiome. However, it remains unclear whether the bacterial communities of periodontitis and its precursor, gingivitis, transition to a state resembling healthy sites with no history of periodontitis following periodontal treatment or persist in a state prone to recurrence. Therefore, in this study, we performed metatranscriptomic analysis on subgingival plaque samples from the anterior teeth in healthy, gingivitis, and periodontitis sites before and after non-surgical treatment in 28 patients. To minimize inter-individual variability, all samples were collected from the same oral cavity in each patient. We revealed a new bacteriological characteristic of periodontitis, where periodontal pathogens emerge within the bacterial network alongside excessive and skewed interactions among bacterial taxa, such as those in the Streptococcus and Actinomyces genera. Furthermore, these imbalances were found improvable through non-surgical treatment. By comparing groups in which periodontitis resolved and those in which it did not, specific bacterial taxa, such as Neisseria elongata and Rothia aeria, were suggested to play a role in the periodontitis healing process, while the increase in functional genes encoding glycine dehydrogenase β-subunit and cleaved adhesin domain was implicated in inhibiting the healing process. However, even in clinically resolved gingivitis or periodontitis, the bacterial networks did not fully revert to the state observed in healthy sites. This was due to the persistence of periodontal pathogens, absent in the networks of healthy sites. As a result, continuous maintenance and monitoring are considered important to achieve sustained periodontal health.\u003c/p\u003e","manuscriptTitle":"Metatranscriptomic Insights into Microbial Network Modulation and Pathogen Dynamics Underlying Healing Outcomes in Non-Surgical Periodontal Treatment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-09 16:30:35","doi":"10.21203/rs.3.rs-5764431/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":"99d99e18-8c52-4e20-95ae-7d57de5bf313","owner":[],"postedDate":"January 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":42460458,"name":"Biological sciences/Microbiology/Bacteria/Bacterial transcription"},{"id":42460459,"name":"Biological sciences/Microbiology/Bacteria/Bacterial structural biology"},{"id":42460460,"name":"Biological sciences/Microbiology/Bacteria/Bacterial pathogenesis"}],"tags":[],"updatedAt":"2025-01-10T03:30:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-09 16:30:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5764431","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5764431","identity":"rs-5764431","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}