Multi-omics reveals Porphyromonas gingivalis-mediated exacerbation of depressive-like behaviors via the gut microbiota-metabolite-brain axis

Multi-omics reveals Porphyromonas gingivalis-mediated exacerbation of depressive-like behaviors via the gut microbiota-metabolite-brain axis | 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 Multi-omics reveals Porphyromonas gingivalis -mediated exacerbation of depressive-like behaviors via the gut microbiota-metabolite-brain axis Yan Li, Zhiyue Yang, Jiajun Cao, Yinzhi Jia, Zitong Zhang, Shouxia Qiao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9555082/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Periodontitis is a common chronic inflammatory disease and its comorbidity with major depressive disorder (MDD) is increasingly recognized, yet the underlying mechanisms remain unclear. This study investigated whether Porphyromonas gingivalis ( Pg ), a key periodontal pathogen, exacerbates depression-like behaviors in mice exposed to chronic unpredictable mild stress (CUMS) and explored the underlying mechanisms. C57BL/6J mice were divided into Control, CUMS, and CUMS with low- or high-dose Pg gavage groups. Depression-like behaviors were assessed by sucrose preference test (SPT) and open field test (OFT). Colonic intestinal flora and metabolites were analyzed using 16S rRNA sequencing and untargeted metabolomics. Inflammatory markers and colonic barrier integrity were evaluated by ELISA, histology, and immunohistochemistry. Pg gavage dose-dependently exacerbated anhedonia and exploratory deficits in CUMS mice. It reduced intestinal flora diversity, decreasing beneficial bacteria ( Akkermansia , Bifidobacterium ) while increasing opportunistic pathogens ( Desulfovibrio ). Metabolomic analysis revealed 451 differentially altered metabolites, enriched in pathways including tryptophan metabolism and neuroactive ligand-receptor interaction. Pg impaired colonic tight junctions, increased colonic and systemic proinflammatory cytokines, and upregulated hippocampal NLRP3 inflammasome and IL-1β expression. Correlation analyses linked beneficial bacteria with favorable behavioral and inflammatory profiles, and opportunistic pathogens with the opposite. In conclusion, Pg exacerbates depression-like behaviors in CUMS mice by disrupting intestinal flora and metabolites, impairing the intestinal barrier, and activating the gut-brain inflammatory axis. These findings provide a mechanistic basis linking periodontitis to MDD and suggest potential therapeutic targets. Health sciences/Diseases/Psychiatric disorders/Depression Biological sciences/Physiology Periodontitis MDD gut microbiota 16S and untargeted metabolomics Immune response Microbiota-gut-brain axis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction MDD is a prevalent psychiatric disorder characterized by persistent low mood, diminished interest, and anhedonia. Conventional research has focused on core mechanisms including neurotransmitter imbalances, hypothalamic-pituitary-adrenal axis dysfunction, and neuroinflammation 1 . In recent years, the concept of the "microbiota-gut-brain axis" has provided a novel perspective for understanding this complex disorder 2 . Trillions of microorganisms residing in the gastrointestinal tract engage in bidirectional, dynamic communication with the central nervous system via multiple pathways, including immune, neuroendocrine, and vagal nerve signaling, thereby profoundly influencing host emotion, cognition, and behavior 3 . Periodontitis is a chronic non-communicable disease caused by bacteria, characterized by gingival inflammation, loss of periodontal attachment, and alveolar bone resorption. It not only leads to progressive destruction of periodontal supporting tissues but is also closely associated with mental disorders such as MDD 4 . A questionnaire survey conducted by AlJameel et al. 5 among 614 university students examining oral health and psychological well-being showed that individuals with poor oral health were more likely to have MDD. A 10-year follow-up population-based cohort study demonstrated that periodontitis is an independent risk factor for MDD, with the incidence of MDD being significantly higher in the periodontitis group than in the non-periodontitis group 6 ; moreover, periodontitis may further exacerbate the severity of depressive symptoms in affected individuals 7 . However, most current studies have focused on confirming the clinical association between periodontitis and MDD using epidemiological surveys, health questionnaires, or scales, while research on the regulatory mechanisms linking these two diseases remains limited. Therefore, further investigation in this field is warranted. As a chronic bacterial infectious disease, periodontitis induces excessive expression of proinflammatory cytokines, leading to sustained damage to body tissues, and is considered a risk factor for various systemic diseases 8 . Pg is the major pathogen of periodontitis 9 . Oral microorganisms such as Pg present in the saliva of patients with periodontitis can enter the digestive system through swallowing. Pg has been detected in the ileum and colon of mice orally administered with periodontitis bacterial suspension, and this bacterial challenge was observed to induce dysbiosis of the gut microbiota 10 . Researchers collected salivary microbiota from healthy individuals and patients with periodontitis and administered them orally to mice exhibiting anxiety-like behaviors. The results showed that mice gavaged with saliva from patients with periodontitis exhibited exacerbated anxiety-like behaviors 11 . Numerous studies have confirmed that the gut microbiota can influence the host via the gut-brain axis, potentially triggering or even exacerbating depressive symptoms in patients 12 , 13 . Therefore, it is hypothesized that the impact of periodontitis on MDD may involve systemic immune-inflammatory responses or periodontal microorganisms. To this end, we established a CUMS mouse model and administered different concentrations of Pg solution via gavage. Behavioral tests, 16SrRNA gene analysis, untargeted metabolomics, and multi-level immunological analyses, aiming to investigate the influence of the major periodontopathogen Pg on MDD and its underlying mechanisms, aiming to provide a basis for the treatment of depression in patients with periodontitis and to offer theoretical guidance for improving both oral health and psychological well-being in clinical practice. 2 Materials and Methods 2.1 Animal Experiment and Grouping Animals and Ethics Male C57BL/6J mice, aged 8 weeks (body weight 20 ± 2 g), were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd. (Beijing, China). This study complied with the Chinese guidelines for the ethical review of laboratory animal welfare (GB/T 35892 − 2018) and was conducted in accordance with the protocol approved by the Medical Ethics Committee of Shanxi University of Medicine (Approval No. 2024016). CUMS Model Establishment Except for the Control group, mice were subjected daily to 1–2 randomly selected stressors, including cold exposure, heat exposure, restraint stress, noise exposure, foreign object exposure, tail clipping, reversed light-dark cycle, stroboscopic light exposure, wet bedding, cage tilting, and water or food deprivation. The stressors were applied continuously for 28 days with no repetition within any 3-day period. Pg Strain Culture and Gavage : Pg (ATCC 33277) was purchased from Shangcheng Beina Chuanglian Biotechnology Co., Ltd. (BNCC, Shangcheng, China). The Pg strain was revived on CDC anaerobic agar plates supplemented with 5 mg/L hemin, 500 µg/L vitamin K, 5% defibrinated sheep blood, and 0.1% L-cysteine, and cultured under anaerobic conditions at 37°C to the logarithmic growth phase. Bacterial cells were collected by centrifugation and resuspended in sterile PBS. The gavage concentrations were determined based on previous literature and preliminary experimental results 14 , 15 : the low-dose group received 1 × 10⁸ CFU/ml Pg , and the high-dose group received 1 × 10⁹ CFU/ml Pg . The gavage volume was 200 µl, administered every other day. The Control group and CUMS model group received an equal volume of PBS by gavage. Experimental Grouping : Mice were randomly divided into four groups: (1) Control group: no CUMS + PBS gavage; (2) CUMS group: CUMS + PBS gavage; (3) CUMS + Pg -L group: CUMS + low-dose Pg gavage; and (4) CUMS + Pg -H group: CUMS + high-dose Pg gavage (n = 5 per group). Gavage administration to CUMS mice was initiated at the third week of modeling (Fig. 1a). 2.2 Behavioral Tests Body weight was monitored throughout the experiment. All behavioral tests were conducted after stress exposure and gavage by investigators blinded to group allocation, with the OFT performed 24 hours after the SPT. SPT : Mice were individually acclimated to two bottles containing 1% sucrose solution for 24 hours. Following 12 hours of food and water deprivation, mice were allowed access to a bottle of 1% sucrose solution and a bottle of plain water, both of which had been pre-weighed.After 12 hours,the positions of the two bottleswere switched to prevent side preference. The sucrose preference rate (%) was calculated as: [sucrose solution consumption (g) / (sucrose solution consumption (g) + plain water consumption (g))] × 100%. OFT The OFT was undertaken in a square open-field chamber (100 cm × 100 cm × 40 cm). Each mouse was placed in the central area and allowed to explore freely for 5 minutes. Locomotor activity was recorded using the OFT-100 system (Chengdu Techman Software Co., Ltd., Chengdu, China). Outcome measures included total distance traveled (cm), immobility time (s), distance traveled in the central zone (cm), and time spent in the central zone (s). The apparatus was cleaned with 75% ethanol between trials. 2.3 Sample Collection Samples were collected within 24 hours after behavioral tests, with all procedures performed on ice. Mice were anesthetized with 1% sodium pentobarbital 50mg/kg,i.p, and blood was collected from the orbit. Serum was separated by centrifugation and stored at − 80°C. The entire colon and whole brain were rapidly dissected. Colonic contents were collected and stored at − 80°C for 16SrRNA sequencing and untargeted metabolomics (Shanghai Personal Biotechnology Co., Ltd., Shanghai, China). Colon tissues were either fixed in 4% paraformaldehyde for histological analysis or frozen at − 80°C. The hippocampus was isolated on ice and stored at − 80°C. 2.4 16SrRNA Gene Sequencing and Analysis Genomic DNA was extracted from colonic contents and subjected to 16SrRNA gene sequencing targeting the V3-V4 region using primers 338F/806R on the Illumina NovaSeq PE250 platform. Raw reads were processed using the QIIME2 pipeline with DADA2 for quality filtering, denoising, and chimera removal, generating an amplicon sequence variant table. Taxonomic assignment was performed against the Greengenes database. QIIME2 computes alpha diversity indices and beta diversity distance matrices, Differentially abundant taxa were identified by using linear discriminant anslysis effect size (LEfSe). 2.5 Untargeted Metabolomics Analysis Colonic contents were processed for LC-MS analysis using an UPLC-MS/MS system with an ACQUITY UPLC HSS T3 column. Data collection was carried out using both positive and negative ion modes. MS-DIAL was used to process raw date for peak extraction, alignment, and metabolite identification against the PSNGM database. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were conducted using SIMCA-P. Differential metabolites were identified based on fold change and false discovery rate, followed by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. 2.6 ELISA Analysis The levels of NLRP3, IL-1β, and IL-6 in mouse serum and hippocampal homogenates, as well as the levels of TNF-α and IL-6 in colon tissue homogenates, were measured using ELISA kits. 2.7 H&E Staining and Immunohistochemistry Colon tissues fixed in 4% paraformaldehyde were paraffin-embedded and sectioned. Sections were subjected to H&E staining for histopathological evaluation. For immunohistochemistry, sections were dewaxed, rehydrated, and subjected to antigen retrieval, followed by blocking. Sections were incubated overnight at 4°C with primary antibodies against Occludin and ZO-1, then with HRP-conjugated secondary antibodies for 1 hour at room temperature. Immunostaining was visualized using diaminobenzidine, and the positive staining area was quantified using ImageJ software. 2.8 Correlation Analysis Spearman's rank correlation analysis was employed to evaluate associations between differential bacterial genera and behavioral indices, inflammatory cytokines, and differential metabolites. Correlation coefficients were calculated using the "stats" package in R. Correlation network visualization was performed using the OmicStudio platform at https://www.omicstudio.cn . 2.9 Statistical Analysis Statistical analyses were conducted using GraphPad Prism 9.5.0. All data are presented as mean ± SD. Normality was assessed using the Shapiro-Wilk test. For normally distributed data, differences among groups were evaluated by ANOVA followed by Tukey's multiple comparisons test; otherwise, the Kruskal-Wallis test with Mann-Whitney U test was employed. A p-value < 0.05 was considered statistically significant. 3 Results 3.1 Detection of Depression-Like Behaviors in Mice Following Pg Gavage No significant differences in initial body weight were observed among the four groups (Fig. 1b). After the gavage period, significant differences in body weight were found between the Control group and the CUMS group \(\:\text{p}\text{<0.0001}\) , as well as between the CUMS group and the CUMS+ Pg -L group \(\:\text{p}\text{=0.0418}\) and between the CUMS group and the CUMS + Pg -H group \(\:\text{p}\text{=0.0004}\) . CUMS stress significantly reduced body weight in mice, and Pg gavage further decreased body weight in a dose-dependent manner (Fig. 1c). In the SPT (Fig. 1d), the CUMS group exhibited a significantly lower sucrose preference rate compared with the Control group \(\:\text{p}\text{=0.0005}\) . The sucrose preference rate was significantly decreased in the CUMS+ Pg -H group compared with the CUMS group \(\:\text{p}\text{=0.0001}\) , and a more pronounced decrease was observed in the CUMS + Pg -H group compared with the CUMS+ Pg -L group \(\:\text{p}\text{=0.0280}\) , indicating a dose-dependent trend. These findings suggest that Pg gavage exacerbated anhedonia in depressed mice. In the OFT, CUMS mice exhibited reduced total distance traveled \(\:\text{p}\text{=0.0004}\) , increased immobility time \(\:\text{p}\text{=0.0094}\) , and decreased central zone activity (both distance and time, p < 0.01) compared with controls (Fig. 1e-h). Pg gavage further impaired spontaneous activity and exploration, as total distance and central zone parameters were significantly lower in the CUMS+ Pg -H group than in the CUMS group (total distance, p = 0.0001; central zone measures, p < 0.01). Representative locomotor tracks are shown in Fig. 1i, and behavioral data are summarized in Table 1 . Collectively, CUMS intervention induced significant depression-like behaviors (anhedonia, reduced spontaneous activity, and impaired exploration), confirming successful model establishment. Pg gavage dose-dependently aggravated these behaviors. 3.2 Structure and Composition of gut microbiota in CUMS Mice Following Pg Gavage To investigate the effects of Pg gavage on the gut microbiota, we performed 16SrRNA gene sequencing analysis on colonic contents from the Control, CUMS, and CUMS + Pg -H groups. Alpha diversity analysis reflects changes in community richness and evenness (Fig. 2a). In comparison with the Control group, the CUMS group exhibited a markedly lower Shannon index \(\:\text{p}\text{<0.05}\) . This trend was further amplified following Pg gavage, as the Chao1 index in the CUMS+ Pg -H group was significantly lower than that in the CUMS group \(\:\text{p}\text{<0.05}\) , confirming that Pg gavage exacerbated the loss of gut microbiota diversity. To explore the effects of CUMS and Pg on the β-diversity of the intestinal bacterial community in mice, we calculated the Bray-Curtis dissimilarity matrix based on the OTU abundance table and PCoA (Fig. 2b). Sample points within each group were relatively clustered, indicating consistent community structure within groups. The sample points of the Control group and the CUMS group formed distinctly separated spatial clusters, whereas the sample points of the CUMS + Pg -H group were separated from both but located closer to those of the CUMS group. These results suggest that CUMS intervention altered the structure of the intestinal microbiota in mice, and Pg gavage also significantly influenced the diversity of the intestinal microbiota in CUMS mice. Furthermore, we investigated the bacterial abundance at the phylum and genus levels in the mouse intestine. At the phylum level (Fig. 2c), Firmicutes and Bacteroidota were dominant in all groups. Compared with the Control group (0.47), the Firmicutes / Bacteroidota (F/B) ratio was increased in the CUMS group (0.87) and the CUMS + Pg -H group (0.71). Compared with the Control group, the CUMS group exhibited a 12.66% decrease in the relative abundance of Verrucomicrobiota and a 2.89% increase in the relative abundance of Proteobacteria . This trend was further amplified following Pg gavage, as the CUMS + Pg -H group showed a 5.20% decrease in Verrucomicrobiota and a 4.64% increase in Proteobacteria compared with the CUMS group. At the genus level (Fig. 2d), the relative abundance of Akkermansia was decreased in the CUMS group (6.26%) and the CUMS + Pg -H group (1.06%) compared with the Control group (18.92%). Escherichia accounted for only 0.13% in the Control group, whereas its relative abundance was 7.17% in the CUMS + Pg -H group. Bacteroide exhibited the highest relative abundance in the CUMS group (5.83%). To accurately identify the characteristic intestinal bacterial genera affected by Pg , we performed LEfSe analysis (LDA score > 3.0, p < 0.05, Fig. 2e). The results showed that the Control group was significantly enriched in commensal and probiotic bacteria, including Akkermansia , Alloprevotella , Clostridioides_A , Parasutterella , Bifidobacterium , Clostridium_Q , and CAG-485 . The CUMS group was significantly enriched in opportunistic pathogens such as CryptoBacteroide , CAG-95 , Lawsonibacter , and 14 − 2 (family Lachnospiraceae ). In addition, anaerobic bacteria including Ligilactobacillus, Limosilactobacillus, Desulfovibrio_R, Berryella, Acutalibacter, Malacoplasma_A, Rikenella, Nanosyncoccus , and Schaedlerella were significantly enriched in the CUMS + Pg -H group. Collectively, these results indicate that Pg gavage reshaped the intestinal microecology of CUMS mice at the species level. 3.3 Metabolite Profiles in the Intestine of CUMS Mice Following Pg Gavage To elucidate the potential effects of Pg gavage on intestinal physiological function in mice, untargeted metabolomics analysis was performed on colonic contents, with a focus on the metabolic distinct separation between the CUMS group and the CUMS + Pg -H group. First, PCA was performed to evaluate the overall structure of the metabolic profiles of colonic contents in each group. As shown in Fig. 3a (positive ion mode) and Fig. 3b (negative ion mode), the sample points from the Control, CUMS, and CUMS + Pg -H groups formed three clearly distinguishable clusters, indicating significant differences in metabolic profiles among the groups. The sample clusters of the CUMS group and the CUMS + Pg -H group were relatively close in spatial position but were completely separated from the Control group. The CUMS + Pg -H group deviated further from the CUMS group along the PC1 axis, suggesting that Pg gavage induced a distinct and more pronounced metabolic remodeling against the background of depression. Next,to maximize the discrimination between the CUMS group and the CUMS + Pg -H group and to remove orthogonal variation, an OPLS-DA model was subsequently established. The resulting score plots (Fig. 3c, d) revealed a clear separation between the two groups. Specifically, the CUMS + Pg -H group fell predominantly along the negative semi-axis, whereas the CUMS group clustered along the positive semi-axis. No sample overlap was observed within the 95% confidence interval, indicating significant metabolic differences between the two groups. Furthermore, to accurately identify specific metabolites regulated by Pg gavage, we merged differentially abundant metabolites from positive and negative ion modes and generated a volcano plot based on stringent screening thresholds (|log₂(Fold Change)| > 1, FDR < 0.05) (Fig. 3e). Compared with the CUMS group, a total of 451 metabolites showed significant alterations in abundance in the CUMS + Pg -H group, of which 210 were upregulated and 241 were downregulated, indicating widespread disruption of intestinal metabolic homeostasis in CUMS mice following Pg gavage. These metabolic changes were significantly associated with dysbiosis of the gut microbiota. Detailed information for the top 10 differentially abundant metabolites between groups is presented in Table 2. We annotated and performed enrichment analysis on the differentially abundant metabolites using the KEGG database (Fig. 3f). Among the most significantly enriched pathways were ABC transporters, tryptophan metabolism, and neuroactive ligand-receptor interaction. In addition, pathways such as serotonergic synapse, D-amino acid metabolism, and mineral absorption were also enriched. 3.4 Colonic Mucosal and Immune Status in CUMS Mice Following Pg Gavage ELISA results (Fig. 4a-f) showed that in colon tissues, compared with the CUMS group,the level of TNF-α and IL-6 of the CUMS + Pg -H group were significantly increased \(\:\text{bot}\text{h}\text{}\text{p}\text{<0.0001}\) . Serum IL-6 was also higher in the CUMS+ Pg -H group than in the CUMS group \(\:\text{p}\text{<0.0001}\) , indicating systemic spread of inflammation. In the hippocampus, NLRP3 and IL-1β expression were elevateg in the CUMS group versus controls, and further elevated in the CUMS + Pg -H group versus the CUMS group \(\:\text{all}\text{}\text{p}\text{<0.001}\) ; IL-6 levels were also increased across groups \(\:\text{p}\text{<0.01}\) . These findings suggest peripheral inflammation affected the brain. Colonic H&E staining (Fig. 4g-j) showed that CUMS mice exhibited mild mucosal damage (disorganized villi, reduced goblet cells, mild inflammatory infiltration), which was aggravated in the CUMS + Pg -L group (increased inflammation, epithelial shedding) and most severe in the CUMS + Pg -H group (extensive villous collapse, crypt disruption, marked epithelial loss, and massive inflammatory infiltration). Immunohistochemistry (Fig. 4k-n, o-r) revealed that tight junction protein (ZO-1, Occludin) expression was progressively reduced from the Control group (positive area: 24.52%, 19.66%) to the CUMS group (13.46%, 11.05%), CUMS + Pg -L group (8.27%, 6.19%), and CUMS + Pg -H group (5.53%, 3.86%), indicating a dose-dependent loss of intestinal barrier integrity. These results indicate that Pg gavage impaired colonic mucosal barrier function in CUMS mice in a dose-dependent manner and activated inflammatory responses in the colon, serum, and hippocampus. 3.5 Spearman Correlation Analysis 4 Discussion MDD is a common mental disorder that severely impairs the normal daily functioning of affected individuals. For a long time, research on MDD has primarily focused on intrinsic brain mechanisms. The gut microbiota, representing the largest microbial community in the human body, has been demonstrated to be deeply involved in the onset and progression of MDD. The oral cavity serves as the second largest reservoir of microbial communities after the intestine, with saliva containing a substantial number of oral bacteria. The major periodontopathogen Pg can translocate to the intestine through daily swallowing and, acting as an invader, disturb the distal intestinal microecology 16 . However, evidence regarding how this process influences the development and progression of MDD remains limited. In this study, we administered different concentrations of Pg via gavage to CUMS mice for the first time, aiming to elucidate the mechanisms by which periodontitis exacerbates MDD and to address a critical gap in this field. The gut microbiota is a critical environmental factor regulating host behavior and mood. In this study, Pg gavage resulted in reduced diversity of the gut microbiota in CUMS mice, which is consistent with the intestinal dysbiosis observed in patients with depression 17 . Specifically, we found that in the CUMS group, the opportunistic pathogen Bacteroide invaded the intestine, while the abundances of beneficial bacteria such as Akkermansia and Bifidobacterium were decreased. As a typical opportunistic pathogen, Bacteroide undergoes conditional expansion and mucosal invasion under CUMS induction, disrupting intestinal barrier integrity and triggering chronic low-grade inflammation. Its overgrowth may further exacerbate intestinal barrier damage and neuroinflammation, increasing susceptibility to depression through inflammatory pathways mediated by the gut-brain axis 18 . Akkermansia is widely recognized as a beneficial bacterium that enhances intestinal barrier function and exerts anti-inflammatory effects, and its reduced abundance is closely associated with anxiety- and depression-like behaviors 19 , 20 . Bifidobacterium possesses anti-inflammatory properties, modulates immune responses, produces beneficial metabolites, strengthens the intestinal barrier, and exerts antidepressant effects through regulation of tryptophan metabolism, serotonin synthesis, and the hypothalamic-pituitary-adrenal axis 21 . We observed that the CUMS + Pg -H group specifically exhibited enrichment of multiple bacterial genera associated with intestinal inflammation and barrier impairment. Desulfovibrio 22 is a typical opportunistic pathogen; excessive Desulfovibrio produces hydrogen sulfide (H₂S), and high concentrations of H₂S can damage intestinal epithelial cells, disrupt the mucus layer and colonic epithelial tight junctions, and exacerbate intestinal mucosal inflammation by activating inflammatory signaling pathways 23 . The increase in Desulfovibrio may be related to alterations in the local redox potential or sulfate availability in the intestine following Pg invasion. Spearman correlation analysis showed that Desulfovibrio was significantly positively correlated with proinflammatory cytokines and significantly negatively correlated with behavioral parameters, indicating that Desulfovibrio , as a potential pathogen, may participate in the impairment of host behavioral function by promoting inflammatory responses. This study found that Pg gavage exacerbated structural changes in the gut microbiota of CUMS mice, characterized by a reduction in probiotics and an increase in pathogenic bacteria, driving the compositional shift of the flora toward a proinflammatory phenotype. Dysbiosis of the gut microbiota in depressed mice leads to abnormalities in key metabolic functions. Untargeted metabolomics analysis revealed that Pg gavage profoundly disrupted intestinal metabolic homeostasis in CUMS mice. KEGG enrichment analysis of differentially abundant metabolites showed significant enrichment of the ABC transporter pathway. This pathway plays a critical role in the secretion of virulence factors and drug resistance in pathogens such as Pg 24 . Its enrichment suggests that Pg colonizes the intestine and continuously releases harmful components, which in turn may activate host immunity. In the present study, the tryptophan metabolism pathway was also enriched. Ding et al. 25 found that Pg can promote local and systemic inflammation and induce dysbiosis of both oral and intestinal microbiota by enhancing its own tryptophan-indole metabolism, which is consistent with our findings. Additionally, the D-amino acid metabolism pathway, neuroactive ligand-receptor interaction, and serotonergic synapse pathway were enriched. Alterations in the serotonergic synapse pathway directly affect serotonergic neurotransmission, while disruption of the neuroactive ligand-receptor interaction pathway may involve functional abnormalities in multiple neurotransmitter systems, further indicating that Pg can influence neurological function through metabolic pathways. Among the top 10 differentially abundant metabolites, we observed a significant downregulation of melatonin levels. The intestine is an important source of melatonin synthesis. Chen et al. found that compared with healthy mice, menopausal depression model mice exhibited reduced intestinal melatonin levels and aggravated depressive symptoms, which is consistent with our findings. Furthermore, they demonstrated that melatonin supplementation alleviated depressive symptoms 26 . In this study, we propose that the downregulation of melatonin is associated with the increase in Desulfovibrio abundance and alterations in the tryptophan metabolism pathway. Under normal physiological conditions, tryptophan tends to be metabolized into neuroprotective indole derivatives, as well as the key neurotransmitter serotonin (5-HT) and melatonin. In the CUMS + Pg -H group, the increased abundance of Desulfovibrio under inflammatory conditions may lead to the utilization of tryptophan for bacterial protein synthesis and energy metabolism. This could induce the activity of indoleamine 2,3-dioxygenase, causing the metabolism of tryptophan to shift toward the kynurenine pathway, thereby decreasing the synthesis of melatonin and 5-HT. In addition, melatonin, as a potent antioxidant, is extensively consumed under inflammatory conditions to neutralize free radicals and protect the intestinal barrier. The combination of reduced synthesis and increased consumption likely accounts for the decreased melatonin levels observed in the intestine. The intestinal barrier is a critical structure for maintaining intestinal homeostasis and preventing the entry of harmful substances into the bloodstream. Metabolic disturbances, together with dysbiosis, collectively weaken this intestinal defense barrier. Histological and immunohistochemical results from this study showed that Pg gavage exacerbated damage to colonic mucosal structure, goblet cell loss, and immune cell infiltration in a dose-dependent manner, and the expression of the tight junction proteins Occludin and ZO-1 was significantly downregulated, leading to severe breaches in the intestinal physical barrier. This “leaky gut” phenomenon allows bacteria and their metabolites (e.g., LPS) to translocate into the bloodstream, triggering systemic inflammation 27 . ELISA results demonstrated that Pg not only elevated local colonic levels of TNF-α and IL-6 but also induced a sharp increase in serum IL-6 levels, indicating that inflammation had spread from the local intestinal site to the systemic circulation. More importantly, this peripheral inflammatory signal successfully transmitted to the central nervous system, manifested as activation of the NLRP3 inflammasome and its downstream effector IL-1β, along with elevated IL-6 levels, in the hippocampus. The hippocampus is a key brain region regulating mood and cognition and is highly sensitive to inflammatory signals. Chronic low-grade inflammation can induce inhibition of hippocampal neurogenesis, impaired synaptic plasticity, and neuronal dysfunction, changes that are closely associated with the pathophysiology of MDD 28 . The NLRP3/IL-1β axis represents a central pathway in neuroinflammation, and its excessive activation has been confirmed to induce depression-like behaviors 29 . We hypothesize that Pg -induced peripheral inflammation may affect the central nervous system through multiple pathways: on the one hand, circulating immune mediators, such as cytokines derived from intestinal immune cells, can directly or indirectly act on the central nervous system interface to modulate neuroimmune homeostasis. On the other hand, intestinal inflammatory signals can be sensed by the intestinal sensory neural network, including enteroendocrine cells and nociceptors, and transmitted primarily via vagal afferent pathways to the brainstem, subsequently influencing the function of limbic system structures such as the amygdala and hippocampus, thereby participating in the regulation of mood and behavioral responses. Ultimately, the perturbation originating from Pg gradually triggered neurological alterations in the brain. Elevated proinflammatory cytokines, particularly IL-1β, in the hippocampus can persistently activate microglia, the brain’s resident immune cells. Once chronically activated, microglia secrete additional inflammatory mediators and reactive oxygen species, thereby creating a vicious cycle that compromises neurogenesis, synaptic pruning, and plasticity, and may eventually result in neuronal death 30 . Concurrently, the reduction in melatonin resulting from the altered tryptophan metabolism pathway under inflammatory conditions further exacerbates depressive symptoms. This is behaviorally reflected in CUMS mice as worsened anhedonia, decreased spontaneous activity, and diminished exploratory behavior. Correlation analyses offer a logical framework connecting these events, demonstrating that the abundance of beneficial bacteria such as Akkermansia is positively correlated with favorable behavioral indicators and anti‑inflammatory status, whereas bacteria like Desulfovibrio are tightly linked to proinflammatory states and behavioral deterioration. 5 Conclusion Synthesizing the findings of this study, we conclude that Pg exacerbates depression-like behaviors in CUMS mice through the gut-brain axis. Pg gavage reshapes the composition of the gut microbiota in CUMS mice, characterized by a reduction in beneficial commensal bacteria and an enrichment of potential pathogens. This dysbiosis is accompanied by profound alterations in the metabolic profile, particularly disruptions in pathways such as tryptophan metabolism and neuroactive ligand-receptor interaction. Together, dysbiosis and metabolic disturbances impair intestinal barrier function, leading to decreased expression of tight junction proteins and disruption of mucosal integrity. Impaired intestinal barrier promotes the translocation of bacterial products and endotoxins, activates local intestinal immune cells, and releases proinflammatory cytokines. Inflammatory signals are transmitted to the central nervous system via the bloodstream and neural pathways, inducing NLRP3 inflammasome activation and neuroinflammation in key mood-regulating brain regions such as the hippocampus. The peripheral and central inflammatory environment further affects neurotransmitter metabolism, neurogenesis, and synaptic plasticity, ultimately resulting in aggravated depression-like behaviors. Limitations This study reveals the potential mechanisms by which Pg , a major pathogen of periodontitis, exacerbates depression through the microbiota-gut-brain axis; however, certain limitations remain. Although the mechanistic investigation confirmed correlations among differential gut microbiota, differential metabolites, inflammation, and central alterations following Pg gavage, fecal microbiota transplantation in germ-free mice was not performed to more definitively validate the role of the gut microbiota. Furthermore, untargeted metabolomics only provided pathway enrichment analysis, and targeted verification of key tryptophan metabolites and neurotransmitters has not yet been conducted, which represents a direction for our future research. Declarations CRediT authorship contribution statement Yan Li : Writing – original draft, Methodology, Supervision, Investigation. Zhiyue Yang : Writing – original draft, Formal analysis, Data curation, Conceptualization. Jiajun Cao , Yinzhi Jia , Zitong Zhang , Shouxia Qiao : Data curation, Conceptualization. Conflict of Interest All authors declare no conflict of interest related to this project. Acknowledgments We thank all individuals who participated in this study.This study was supported by Shanxi University of Medicine (Grant No. 2025A08) and the Science and Technology Innovation Program of Shanxi Province (Grant No. 2025L189). References Cui, L. et al. Major depressive disorder: hypothesis, mechanism, prevention and treatment. Signal Transduct Target Ther 9, 30 (2024). https://doi.org/10.1038/s41392-024-01738-y Loh, J. S. et al. Microbiota-gut-brain axis and its therapeutic applications in neurodegenerative diseases. Signal Transduct Target Ther 9, 37 (2024). https://doi.org/10.1038/s41392-024-01743-1 Liu, P. et al. Immunoregulatory role of the gut microbiota in inflammatory depression. Nat Commun 15, 3003 (2024). https://doi.org/10.1038/s41467-024-47273-w Zheng, D. X. et al. Periodontal disease and emotional disorders: A meta-analysis. J Clin Periodontol 48, 180–204 (2021). https://doi.org/10.1111/jcpe.13395 AlJameel, A. H., AlSaleh, L. S., Bawazir, N. H., AlOmair, A. S. & Almalki, S. A. How Mental Health Correlates with Subjective Oral Health Status: A Cross-Sectional Study among a Group of University Students. Niger J Clin Pract 26, 1716–1722 (2023). https://doi.org/10.4103/njcp.njcp_330_23 Hsu, C. C. et al. Association of Periodontitis and Subsequent Depression: A Nationwide Population-Based Study. Medicine (Baltimore) 94, e2347 (2015). https://doi.org/10.1097/md.0000000000002347 Deng, Y., He, S. & Wang, J. Validation of the Hospital Anxiety and Depression Scale and the Perceived Stress Scale and psychological features in patients with periodontitis. J Periodontol 92, 1601–1612 (2021). https://doi.org/10.1002/jper.20-0756 Hajishengallis, G. & Chavakis, T. Local and systemic mechanisms linking periodontal disease and inflammatory comorbidities. Nat Rev Immunol 21, 426–440 (2021). https://doi.org/10.1038/s41577-020-00488-6 Akkaoui, J. et al. Contribution of Porphyromonas gingivalis lipopolysaccharide to experimental periodontitis in relation to aging. Geroscience 43, 367–376 (2021). https://doi.org/10.1007/s11357-020-00258-1 Arimatsu, K. et al. Oral pathobiont induces systemic inflammation and metabolic changes associated with alteration of gut microbiota. Sci Rep 4, 4828 (2014). https://doi.org/10.1038/srep04828 Qian, J. et al. Periodontitis salivary microbiota exacerbates colitis-induced anxiety-like behavior via gut microbiota. NPJ Biofilms Microbiomes 9, 93 (2023). https://doi.org/10.1038/s41522-023-00462-9 Toader, C. et al. Mind, Mood and Microbiota-Gut-Brain Axis in Psychiatric Disorders. Int J Mol Sci 25 (2024). https://doi.org/10.3390/ijms25063340 Socała, K. et al. The role of microbiota-gut-brain axis in neuropsychiatric and neurological disorders. Pharmacol Res 172, 105840 (2021). https://doi.org/10.1016/j.phrs.2021.105840 Park, S. et al. Oral Porphyromonas gingivalis infection affects intestinal microbiota and promotes atherosclerosis. J Clin Periodontol 50, 1553–1567 (2023). https://doi.org/10.1111/jcpe.13864 Gao, Y. et al. Porphyromonas gingivalis exacerbates alcoholic liver disease by altering gut microbiota composition and host immune response in mice. J Clin Periodontol 50, 1253–1263 (2023). https://doi.org/10.1111/jcpe.13833 Lou, F. et al. Oral microbiota dysbiosis alters chronic restraint stress-induced depression-like behaviors by modulating host metabolism. Pharmacol Res 204, 107214 (2024). https://doi.org/10.1016/j.phrs.2024.107214 Liśkiewicz, P. et al. Analysis of gut microbiota and intestinal integrity markers of inpatients with major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry 106, 110076 (2021). https://doi.org/10.1016/j.pnpbp.2020.110076 Zhang, Y. et al. Bacteroides species differentially modulate depression-like behavior via gut-brain metabolic signaling. Brain Behav Immun 102, 11–22 (2022). https://doi.org/10.1016/j.bbi.2022.02.007 Jiang, Z. M. et al. Hypericum perforatum L. attenuates depression by regulating Akkermansia muciniphila, tryptophan metabolism and NFκB-NLRP2-Caspase1-IL1β pathway. Phytomedicine 132, 155847 (2024). https://doi.org/10.1016/j.phymed.2024.155847 Khalili, L., Park, G., Nagpal, R., Bhide, P. & Salazar, G. The Impact of Akkermansia muciniphila on Mouse Models of Depression, Anxiety, and Stress: A Systematic Review and Meta-Analysis. Curr Neuropharmacol 23, 1423–1441 (2025). https://doi.org/10.2174/011570159x360149250225041829 Li, J. et al. Bifidobacterium: a probiotic for the prevention and treatment of depression. Front Microbiol 14, 1174800 (2023). https://doi.org/10.3389/fmicb.2023.1174800 Zhou, H., Huang, D., Sun, Z. & Chen, X. Effects of intestinal Desulfovibrio bacteria on host health and its potential regulatory strategies: A review. Microbiol Res 284, 127725 (2024). https://doi.org/10.1016/j.micres.2024.127725 Blachier, F. et al. Production of hydrogen sulfide by the intestinal microbiota and epithelial cells and consequences for the colonic and rectal mucosa. Am J Physiol Gastrointest Liver Physiol 320, G125-g135 (2021). https://doi.org/10.1152/ajpgi.00261.2020 How, S. S., Nathan, S., Lam, S. D. & Chieng, S. ATP-binding cassette (ABC) transporters: structures and roles in bacterial pathogenesis. J Zhejiang Univ Sci B 26, 58–75 (2024). https://doi.org/10.1631/jzus.B2300641 Ding, J. et al. Regulation of tryptophan-indole metabolic pathway in Porphyromonas gingivalis virulence and microbiota dysbiosis in periodontitis. NPJ Biofilms Microbiomes 11, 37 (2025). https://doi.org/10.1038/s41522-025-00669-y Zheng, K. Y. et al. Melatonin Ameliorates Depressive-Like Behaviors in Ovariectomized Mice by Improving Tryptophan Metabolism via Inhibition of Gut Microbe Alistipes Inops. Adv Sci (Weinh) 11, e2309473 (2024). https://doi.org/10.1002/advs.202309473 Aburto, M. R. & Cryan, J. F. Gastrointestinal and brain barriers: unlocking gates of communication across the microbiota-gut-brain axis. Nat Rev Gastroenterol Hepatol 21, 222–247 (2024). https://doi.org/10.1038/s41575-023-00890-0 Xie, C. et al. Periodontitis-induced neuroinflammation impacts dendritic spine immaturity and cognitive impairment. Oral Dis 30, 2558–2569 (2024). https://doi.org/10.1111/odi.14674 Xia, C. Y. et al. The NLRP3 inflammasome in depression: Potential mechanisms and therapies. Pharmacol Res 187, 106625 (2023). https://doi.org/10.1016/j.phrs.2022.106625 Lopez-Rodriguez, A. B. et al. Acute systemic inflammation exacerbates neuroinflammation in Alzheimer's disease: IL-1β drives amplified responses in primed astrocytes and neuronal network dysfunction. Alzheimers Dement 17, 1735–1755 (2021). https://doi.org/10.1002/alz.12341 Tables Table 1 is available in the Supplementary Files section. Table 2 Traceability table of differential metabolites(Top 10). Compounds Class I Class II Formula CAS Regulation Hippuric acid Amino acid and Its metabolites Amino acid derivatives C₉H₉NO₃ 495-69-2 Up Epicatechin 5,3'-dimethyl ether Flavonoids Flavanols C₁₇H₁₈O₆ 1429-40-3 Up 13-hydroxy-12,12-dimethyl-4-oxo-3,11-dioxatricyclo[8.4.0.0²,⁷]tetradeca-1,5,7,9-tetraen-14-yl (2Z)-2-methylbut-2-enoate (abbreviated as Compound 1) Macrolides Dioxatricyclic macrolides C₂₀H₂₂O₇ 1259626-12-4 Down Melatonin Indole derivatives Indole hormones C₁₃H₁₆N₂O₂ 73-31-4 Down (R)-Norisocorydine Alkaloids Isoquinoline alkaloids C₂₀H₂₃NO₄ 21415-68-5 Down 1-Methyluric acid Purine derivatives Methylated uric acids C₆H₆N₄O₃ 611-55-0 Down Genistein 7-sulfate Isoflavones Sulfated isoflavones C₁₅H₁₀O₉S 6045-48-1 Down R-1 Methanandamide phosphate Endocannabinoid derivatives Phosphorylated endocannabinoids C₂₃H₄₀NO₅P 649569-33-5 Down Daidzein Isoflavones Aglycone isoflavones C₁₅H₁₀O₅ 486-66-8 Up Creatinine Amino acid and Its metabolites Guanidine derivatives C₄H₇N₃O 60-27-5 Up Additional Declarations The authors have declared there is NO conflict of interest to disclose All authors declare no conflict of interest related to this project. Supplementary Files Table1.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewer # 2 agreed at journal 13 May, 2026 Reviewer # 1 agreed at journal 09 May, 2026 Reviewers invited by journal 01 May, 2026 Editor assigned by journal 30 Apr, 2026 Submission checks completed at journal 30 Apr, 2026 First submitted to journal 29 Apr, 2026 Unknown event 29 Apr, 2026 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9555082","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":632860045,"identity":"5963e585-0bb9-41bd-8b8f-8cb5309b7b92","order_by":0,"name":"Yan Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIie3RsQrCMBCA4UihLsGuV9SKb3BQKD5OQsFJoZNkECkodhBxrW/RSRwrSqe4d2zfQHFxcLC4KqZuDvnm/FwuIUTT/pDZnB8LQHAswzgUTEzVSYtmQxwEA9eOlj4WMlMnDowQxEXwRErPLhdGjYvRFDFH4Nt45AkemsSKVkyxS8jKGMHdwHCS832HgDwnqimpW63f3cb+LufSJAhjRQI8bD8QGknOvIAvjTqJT6Ca0k/kySP1EpqZWCWvRwYmM6rcpRdtbgU8Zq+vvN7F1LGi9ffkDf3tuKZpmvbRE4nsSvIjO/F0AAAAAElFTkSuQmCC","orcid":"","institution":"Shanxi University of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Yan","middleName":"","lastName":"Li","suffix":""},{"id":632860046,"identity":"333a1fc3-70b7-423f-b8a0-10f886e63b54","order_by":1,"name":"Zhiyue Yang","email":"","orcid":"","institution":"Shanxi University of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zhiyue","middleName":"","lastName":"Yang","suffix":""},{"id":632860047,"identity":"e408efa5-3b7d-44b4-ab66-a79e4645c000","order_by":2,"name":"Jiajun Cao","email":"","orcid":"","institution":"Shanxi University of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jiajun","middleName":"","lastName":"Cao","suffix":""},{"id":632860048,"identity":"614336c8-10b6-4fab-8a25-051852ec4655","order_by":3,"name":"Yinzhi Jia","email":"","orcid":"","institution":"Shanxi University of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yinzhi","middleName":"","lastName":"Jia","suffix":""},{"id":632860049,"identity":"b098dd92-5715-42cd-8317-bb99c5cb5156","order_by":4,"name":"Zitong Zhang","email":"","orcid":"","institution":"Shanxi University of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zitong","middleName":"","lastName":"Zhang","suffix":""},{"id":632860050,"identity":"86259d99-bdbe-4a10-b0d1-950843f9721e","order_by":5,"name":"Shouxia Qiao","email":"","orcid":"","institution":"Shanxi University of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Shouxia","middleName":"","lastName":"Qiao","suffix":""}],"badges":[],"createdAt":"2026-04-28 13:54:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9555082/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9555082/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108978816,"identity":"3836d7c7-f8c4-4134-8d69-4fa7e39e4ed1","added_by":"auto","created_at":"2026-05-11 11:49:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":7726350,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of \u003cstrong\u003ePg\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003egavage on body weight and depression-like behaviors in CUMS mice.\u003cbr\u003e\n(a) Experimental timeline. (b) Initial body weight. (c) Body weight change after gavage. (d) Sucrose preference rate in SPT. (e) Total distance traveled, (f) immobility time, (g) distance traveled in central zone, and (h) time spent in central zone in OFT. (i) Representative locomotor tracks. Data are presented as mean ± SD.*\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001, ****\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-9555082/v1/daaafb8284a43a35839ce463.png"},{"id":108978734,"identity":"0fd082b3-7ad9-4ddb-9065-df0b1d9e6e8a","added_by":"auto","created_at":"2026-05-11 11:48:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":8027257,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePg\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003egavage alters intestinal flora composition in CUMS mice.\u003cbr\u003e\n(a) Shannon and Chao1 indices (α-diversity). (b) Principal coordinate analysis (PCoA) based on Bray-Curtis distance (β-diversity). (c) Relative abundance at the phylum level (top 10). (d) Relative abundance at the genus level (top 10). (e) Linear discriminant analysis effect size (LEfSe) histogram (LDA score \u0026gt; 3.0,\u003cem\u003e p\u003c/em\u003e\u0026lt; 0.05).*\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-9555082/v1/3f2097b04f89889db28a4c3c.png"},{"id":108978765,"identity":"321d9d41-5b37-4cc1-a2b7-324f21f25e7b","added_by":"auto","created_at":"2026-05-11 11:48:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5498341,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePg\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003egavage remodels colonic metabolomic profiles in CUMS mice.\u003c/p\u003e\n\u003cp\u003e(a) PCA score plot in positive ion mode. (b) PCA score plot in negative ion mode. (c) OPLS-DA score plot in positive ion mode. (d) OPLS-DA score plot in negative ion mode. (e) Volcano plot of differentially abundant metabolites (|log₂FC| \u0026gt; 1, FDR \u0026lt; 0.05). (f) KEGG pathway enrichment analysis of differential metabolites (top 20).\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-9555082/v1/ae782c6fee6ff06147c0cc51.png"},{"id":108978741,"identity":"7c6bc247-dd2b-4af3-ad03-2e8f2765ca08","added_by":"auto","created_at":"2026-05-11 11:48:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":29195240,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePg\u003c/em\u003e gavage induces intestinal barrier damage and inflammatory responses.\u003c/p\u003e\n\u003cp\u003e(a,b) Colonic TNF-α and IL-6 levels measured by ELISA. (c) Serum IL-6 levels.(d-f) Hippocampal NLRP3, IL-1β, and IL-6 levels. (g-j) Representative H\u0026amp;E-stained colon sections (scale bar = 100 μm). (k-r) Immunohistochemical staining of ZO-1 and Occludin in colon tissues (scale bar = 100 μm).Representative images are shown in order of Control, CUMS, CUMS+\u003cem\u003ePg\u003c/em\u003e-L, and CUMS+\u003cem\u003ePg\u003c/em\u003e-H groups. Data are mean ± SD. *\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001, ****\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-9555082/v1/ddcb8bcf5ce06cdc7c81b5ad.png"},{"id":108978808,"identity":"b5dcc563-7d8c-4e47-974c-dbf4895f6586","added_by":"auto","created_at":"2026-05-11 11:48:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3033155,"visible":true,"origin":"","legend":"\u003cp\u003eSpearman correlation analysis among intestinal flora, behavioral parameters, inflammatory cytokines, and metabolites.\u003c/p\u003e\n\u003cp\u003eHeatmap showing correlation coefficients (r) between differential bacterial genera (rows) and selected phenotypes (columns): behavioral parameters , inflammatory factors and differential metabolites. Red indicates positive correlation, blue indicates negative correlation. *\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-9555082/v1/d92f26942bd66ae7548d6b57.png"},{"id":108981127,"identity":"fb73e16a-4070-4e0a-b68b-4eb5a6e8426a","added_by":"auto","created_at":"2026-05-11 12:14:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":72025679,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9555082/v1/aa9fa200-0421-405f-9562-7a583d747c1d.pdf"},{"id":108978751,"identity":"27f02186-c77a-4a5f-bd75-f3eaecee912f","added_by":"auto","created_at":"2026-05-11 11:48:22","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21068,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9555082/v1/3b7e0ab2a9da8b62d1db679a.docx"}],"financialInterests":"The authors have declared there is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose\nAll authors declare no conflict of interest related to this project.","formattedTitle":"\u003cp\u003eMulti-omics reveals \u003cem\u003ePorphyromonas gingivalis\u003c/em\u003e-mediated exacerbation of depressive-like behaviors via the gut microbiota-metabolite-brain axis\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eMDD is a prevalent psychiatric disorder characterized by persistent low mood, diminished interest, and anhedonia. Conventional research has focused on core mechanisms including neurotransmitter imbalances, hypothalamic-pituitary-adrenal axis dysfunction, and neuroinflammation\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. In recent years, the concept of the \"microbiota-gut-brain axis\" has provided a novel perspective for understanding this complex disorder\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Trillions of microorganisms residing in the gastrointestinal tract engage in bidirectional, dynamic communication with the central nervous system via multiple pathways, including immune, neuroendocrine, and vagal nerve signaling, thereby profoundly influencing host emotion, cognition, and behavior\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePeriodontitis is a chronic non-communicable disease caused by bacteria, characterized by gingival inflammation, loss of periodontal attachment, and alveolar bone resorption. It not only leads to progressive destruction of periodontal supporting tissues but is also closely associated with mental disorders such as MDD\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. A questionnaire survey conducted by AlJameel et al.\u003csup\u003e5\u003c/sup\u003e among 614 university students examining oral health and psychological well-being showed that individuals with poor oral health were more likely to have MDD. A 10-year follow-up population-based cohort study demonstrated that periodontitis is an independent risk factor for MDD, with the incidence of MDD being significantly higher in the periodontitis group than in the non-periodontitis group\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e; moreover, periodontitis may further exacerbate the severity of depressive symptoms in affected individuals\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. However, most current studies have focused on confirming the clinical association between periodontitis and MDD using epidemiological surveys, health questionnaires, or scales, while research on the regulatory mechanisms linking these two diseases remains limited. Therefore, further investigation in this field is warranted.\u003c/p\u003e \u003cp\u003eAs a chronic bacterial infectious disease, periodontitis induces excessive expression of proinflammatory cytokines, leading to sustained damage to body tissues, and is considered a risk factor for various systemic diseases\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003ePg\u003c/em\u003e is the major pathogen of periodontitis\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Oral microorganisms such as \u003cem\u003ePg\u003c/em\u003e present in the saliva of patients with periodontitis can enter the digestive system through swallowing. \u003cem\u003ePg\u003c/em\u003e has been detected in the ileum and colon of mice orally administered with periodontitis bacterial suspension, and this bacterial challenge was observed to induce dysbiosis of the gut microbiota\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Researchers collected salivary microbiota from healthy individuals and patients with periodontitis and administered them orally to mice exhibiting anxiety-like behaviors. The results showed that mice gavaged with saliva from patients with periodontitis exhibited exacerbated anxiety-like behaviors\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Numerous studies have confirmed that the gut microbiota can influence the host via the gut-brain axis, potentially triggering or even exacerbating depressive symptoms in patients\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Therefore, it is hypothesized that the impact of periodontitis on MDD may involve systemic immune-inflammatory responses or periodontal microorganisms.\u003c/p\u003e \u003cp\u003eTo this end, we established a CUMS mouse model and administered different concentrations of \u003cem\u003ePg\u003c/em\u003e solution via gavage. Behavioral tests, 16SrRNA gene analysis, untargeted metabolomics, and multi-level immunological analyses, aiming to investigate the influence of the major periodontopathogen \u003cem\u003ePg\u003c/em\u003e on MDD and its underlying mechanisms, aiming to provide a basis for the treatment of depression in patients with periodontitis and to offer theoretical guidance for improving both oral health and psychological well-being in clinical practice.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Animal Experiment and Grouping\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eAnimals and Ethics\u003c/strong\u003e \u003cp\u003eMale C57BL/6J mice, aged 8 weeks (body weight 20\u0026thinsp;\u0026plusmn;\u0026thinsp;2 g), were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd. (Beijing, China). This study complied with the Chinese guidelines for the ethical review of laboratory animal welfare (GB/T 35892\u0026thinsp;\u0026minus;\u0026thinsp;2018) and was conducted in accordance with the protocol approved by the Medical Ethics Committee of Shanxi University of Medicine (Approval No. 2024016).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCUMS Model Establishment\u003c/strong\u003e \u003cp\u003eExcept for the Control group, mice were subjected daily to 1\u0026ndash;2 randomly selected stressors, including cold exposure, heat exposure, restraint stress, noise exposure, foreign object exposure, tail clipping, reversed light-dark cycle, stroboscopic light exposure, wet bedding, cage tilting, and water or food deprivation. The stressors were applied continuously for 28 days with no repetition within any 3-day period.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePg\u003c/b\u003e \u003cb\u003eStrain Culture and Gavage\u003c/b\u003e: \u003cem\u003ePg\u003c/em\u003e(ATCC 33277) was purchased from Shangcheng Beina Chuanglian Biotechnology Co., Ltd. (BNCC, Shangcheng, China). The \u003cem\u003ePg\u003c/em\u003e strain was revived on CDC anaerobic agar plates supplemented with 5 mg/L hemin, 500 \u0026micro;g/L vitamin K, 5% defibrinated sheep blood, and 0.1% L-cysteine, and cultured under anaerobic conditions at 37\u0026deg;C to the logarithmic growth phase. Bacterial cells were collected by centrifugation and resuspended in sterile PBS. The gavage concentrations were determined based on previous literature and preliminary experimental results\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e: the low-dose group received 1 \u0026times; 10⁸ CFU/ml \u003cem\u003ePg\u003c/em\u003e, and the high-dose group received 1 \u0026times; 10⁹ CFU/ml \u003cem\u003ePg\u003c/em\u003e. The gavage volume was 200 \u0026micro;l, administered every other day. The Control group and CUMS model group received an equal volume of PBS by gavage.\u003c/p\u003e \u003cp\u003e \u003cb\u003eExperimental Grouping\u003c/b\u003e: Mice were randomly divided into four groups: (1) Control group: no CUMS\u0026thinsp;+\u0026thinsp;PBS gavage; (2) CUMS group: CUMS\u0026thinsp;+\u0026thinsp;PBS gavage; (3) CUMS\u0026thinsp;+\u0026thinsp;\u003cem\u003ePg\u003c/em\u003e-L group: CUMS\u0026thinsp;+\u0026thinsp;low-dose \u003cem\u003ePg\u003c/em\u003e gavage; and (4) CUMS\u0026thinsp;+\u0026thinsp;\u003cem\u003ePg\u003c/em\u003e-H group: CUMS\u0026thinsp;+\u0026thinsp;high-dose \u003cem\u003ePg\u003c/em\u003e gavage (n\u0026thinsp;=\u0026thinsp;5 per group). Gavage administration to CUMS mice was initiated at the third week of modeling (Fig.\u0026nbsp;1a).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Behavioral Tests\u003c/h2\u003e \u003cp\u003eBody weight was monitored throughout the experiment. All behavioral tests were conducted after stress exposure and gavage by investigators blinded to group allocation, with the OFT performed 24 hours after the SPT.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSPT\u003c/b\u003e: Mice were individually acclimated to two bottles containing 1% sucrose solution for 24 hours. Following 12 hours of food and water deprivation, mice were allowed access to a bottle of 1% sucrose solution and a bottle of plain water, both of which had been pre-weighed.After 12 hours,the positions of the two bottleswere switched to prevent side preference. The sucrose preference rate (%) was calculated as: [sucrose solution consumption (g) / (sucrose solution consumption (g) + plain water consumption (g))] \u0026times; 100%.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eOFT\u003c/strong\u003e \u003cp\u003eThe OFT was undertaken in a square open-field chamber (100 cm \u0026times; 100 cm \u0026times; 40 cm). Each mouse was placed in the central area and allowed to explore freely for 5 minutes. Locomotor activity was recorded using the OFT-100 system (Chengdu Techman Software Co., Ltd., Chengdu, China). Outcome measures included total distance traveled (cm), immobility time (s), distance traveled in the central zone (cm), and time spent in the central zone (s). The apparatus was cleaned with 75% ethanol between trials.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Sample Collection\u003c/h2\u003e \u003cp\u003eSamples were collected within 24 hours after behavioral tests, with all procedures performed on ice. Mice were anesthetized with 1% sodium pentobarbital 50mg/kg,i.p, and blood was collected from the orbit. Serum was separated by centrifugation and stored at \u0026minus;\u0026thinsp;80\u0026deg;C. The entire colon and whole brain were rapidly dissected. Colonic contents were collected and stored at \u0026minus;\u0026thinsp;80\u0026deg;C for 16SrRNA sequencing and untargeted metabolomics (Shanghai Personal Biotechnology Co., Ltd., Shanghai, China). Colon tissues were either fixed in 4% paraformaldehyde for histological analysis or frozen at \u0026minus;\u0026thinsp;80\u0026deg;C. The hippocampus was isolated on ice and stored at \u0026minus;\u0026thinsp;80\u0026deg;C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 16SrRNA Gene Sequencing and Analysis\u003c/h2\u003e \u003cp\u003eGenomic DNA was extracted from colonic contents and subjected to 16SrRNA gene sequencing targeting the V3-V4 region using primers 338F/806R on the Illumina NovaSeq PE250 platform. Raw reads were processed using the QIIME2 pipeline with DADA2 for quality filtering, denoising, and chimera removal, generating an amplicon sequence variant table. Taxonomic assignment was performed against the Greengenes database. QIIME2 computes alpha diversity indices and beta diversity distance matrices, Differentially abundant taxa were identified by using linear discriminant anslysis effect size (LEfSe).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Untargeted Metabolomics Analysis\u003c/h2\u003e \u003cp\u003eColonic contents were processed for LC-MS analysis using an UPLC-MS/MS system with an ACQUITY UPLC HSS T3 column. Data collection was carried out using both positive and negative ion modes. MS-DIAL was used to process raw date for peak extraction, alignment, and metabolite identification against the PSNGM database. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were conducted using SIMCA-P. Differential metabolites were identified based on fold change and false discovery rate, followed by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 ELISA Analysis\u003c/h2\u003e \u003cp\u003eThe levels of NLRP3, IL-1β, and IL-6 in mouse serum and hippocampal homogenates, as well as the levels of TNF-α and IL-6 in colon tissue homogenates, were measured using ELISA kits.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 H\u0026amp;E Staining and Immunohistochemistry\u003c/h2\u003e \u003cp\u003eColon tissues fixed in 4% paraformaldehyde were paraffin-embedded and sectioned. Sections were subjected to H\u0026amp;E staining for histopathological evaluation. For immunohistochemistry, sections were dewaxed, rehydrated, and subjected to antigen retrieval, followed by blocking. Sections were incubated overnight at 4\u0026deg;C with primary antibodies against Occludin and ZO-1, then with HRP-conjugated secondary antibodies for 1 hour at room temperature. Immunostaining was visualized using diaminobenzidine, and the positive staining area was quantified using ImageJ software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Correlation Analysis\u003c/h2\u003e \u003cp\u003eSpearman's rank correlation analysis was employed to evaluate associations between differential bacterial genera and behavioral indices, inflammatory cytokines, and differential metabolites. Correlation coefficients were calculated using the \"stats\" package in R. Correlation network visualization was performed using the OmicStudio platform at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.omicstudio.cn\u003c/span\u003e\u003cspan address=\"https://www.omicstudio.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Statistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were conducted using GraphPad Prism 9.5.0. All data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Normality was assessed using the Shapiro-Wilk test. For normally distributed data, differences among groups were evaluated by ANOVA followed by Tukey's multiple comparisons test; otherwise, the Kruskal-Wallis test with Mann-Whitney U test was employed. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Detection of Depression-Like Behaviors in Mice Following Pg Gavage\u003c/h2\u003e\n \u003cp\u003eNo significant differences in initial body weight were observed among the four groups (Fig. 1b). After the gavage period, significant differences in body weight were found between the Control group and the CUMS group \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{p}\\text{\u0026lt;0.0001}\\)\u003c/span\u003e\u003c/span\u003e, as well as between the CUMS group and the CUMS+\u003cem\u003ePg\u003c/em\u003e-L group \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{p}\\text{=0.0418}\\)\u003c/span\u003e\u003c/span\u003e and between the CUMS group and the CUMS\u0026thinsp;+\u0026thinsp;\u003cem\u003ePg\u003c/em\u003e-H group \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{p}\\text{=0.0004}\\)\u003c/span\u003e\u003c/span\u003e. CUMS stress significantly reduced body weight in mice, and \u003cem\u003ePg\u003c/em\u003e gavage further decreased body weight in a dose-dependent manner (Fig. 1c).\u003c/p\u003e\n \u003cp\u003eIn the SPT (Fig. 1d), the CUMS group exhibited a significantly lower sucrose preference rate compared with the Control group \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{p}\\text{=0.0005}\\)\u003c/span\u003e\u003c/span\u003e. The sucrose preference rate was significantly decreased in the CUMS+\u003cem\u003ePg\u003c/em\u003e-H group compared with the CUMS group \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{p}\\text{=0.0001}\\)\u003c/span\u003e\u003c/span\u003e, and a more pronounced decrease was observed in the CUMS\u0026thinsp;+\u0026thinsp;\u003cem\u003ePg\u003c/em\u003e-H group compared with the CUMS+\u003cem\u003ePg\u003c/em\u003e-L group \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{p}\\text{=0.0280}\\)\u003c/span\u003e\u003c/span\u003e, indicating a dose-dependent trend. These findings suggest that \u003cem\u003ePg\u003c/em\u003e gavage exacerbated anhedonia in depressed mice.\u003c/p\u003e\n \u003cp\u003eIn the OFT, CUMS mice exhibited reduced total distance traveled \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{p}\\text{=0.0004}\\)\u003c/span\u003e\u003c/span\u003e, increased immobility time \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{p}\\text{=0.0094}\\)\u003c/span\u003e\u003c/span\u003e, and decreased central zone activity (both distance and time, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) compared with controls (Fig. 1e-h). \u003cem\u003ePg\u003c/em\u003e gavage further impaired spontaneous activity and exploration, as total distance and central zone parameters were significantly lower in the CUMS+\u003cem\u003ePg\u003c/em\u003e-H group than in the CUMS group (total distance, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001; central zone measures, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Representative locomotor tracks are shown in Fig. 1i, and behavioral data are summarized in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eCollectively, CUMS intervention induced significant depression-like behaviors (anhedonia, reduced spontaneous activity, and impaired exploration), confirming successful model establishment. \u003cem\u003ePg\u003c/em\u003e gavage dose-dependently aggravated these behaviors.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Structure and Composition of gut microbiota in CUMS Mice Following Pg Gavage\u003c/h2\u003e\n \u003cp\u003eTo investigate the effects of \u003cem\u003ePg\u003c/em\u003e gavage on the gut microbiota, we performed 16SrRNA gene sequencing analysis on colonic contents from the Control, CUMS, and CUMS\u0026thinsp;+\u0026thinsp;\u003cem\u003ePg\u003c/em\u003e-H groups.\u003c/p\u003e\n \u003cp\u003eAlpha diversity analysis reflects changes in community richness and evenness (Fig. 2a). In comparison with the Control group, the CUMS group exhibited a markedly lower Shannon index \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{p}\\text{\u0026lt;0.05}\\)\u003c/span\u003e\u003c/span\u003e. This trend was further amplified following \u003cem\u003ePg\u003c/em\u003e gavage, as the Chao1 index in the CUMS+\u003cem\u003ePg\u003c/em\u003e-H group was significantly lower than that in the CUMS group \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{p}\\text{\u0026lt;0.05}\\)\u003c/span\u003e\u003c/span\u003e, confirming that \u003cem\u003ePg\u003c/em\u003e gavage exacerbated the loss of gut microbiota diversity.\u003c/p\u003e\n \u003cp\u003eTo explore the effects of CUMS and \u003cem\u003ePg\u003c/em\u003e on the \u0026beta;-diversity of the intestinal bacterial community in mice, we calculated the Bray-Curtis dissimilarity matrix based on the OTU abundance table and PCoA (Fig. 2b). Sample points within each group were relatively clustered, indicating consistent community structure within groups. The sample points of the Control group and the CUMS group formed distinctly separated spatial clusters, whereas the sample points of the CUMS\u0026thinsp;+\u0026thinsp;\u003cem\u003ePg\u003c/em\u003e-H group were separated from both but located closer to those of the CUMS group. These results suggest that CUMS intervention altered the structure of the intestinal microbiota in mice, and \u003cem\u003ePg\u003c/em\u003e gavage also significantly influenced the diversity of the intestinal microbiota in CUMS mice.\u003c/p\u003e\n \u003cp\u003eFurthermore, we investigated the bacterial abundance at the phylum and genus levels in the mouse intestine. At the phylum level (Fig. 2c), \u003cem\u003eFirmicutes\u003c/em\u003e and \u003cem\u003eBacteroidota\u003c/em\u003e were dominant in all groups. Compared with the Control group (0.47), the \u003cem\u003eFirmicutes\u003c/em\u003e/ \u003cem\u003eBacteroidota\u003c/em\u003e (F/B) ratio was increased in the CUMS group (0.87) and the CUMS\u0026thinsp;+\u0026thinsp;\u003cem\u003ePg\u003c/em\u003e-H group (0.71). Compared with the Control group, the CUMS group exhibited a 12.66% decrease in the relative abundance of \u003cem\u003eVerrucomicrobiota\u003c/em\u003e and a 2.89% increase in the relative abundance of \u003cem\u003eProteobacteria\u003c/em\u003e. This trend was further amplified following \u003cem\u003ePg\u003c/em\u003e gavage, as the CUMS\u0026thinsp;+\u0026thinsp;\u003cem\u003ePg\u003c/em\u003e-H group showed a 5.20% decrease in \u003cem\u003eVerrucomicrobiota\u003c/em\u003e and a 4.64% increase in \u003cem\u003eProteobacteria\u003c/em\u003e compared with the CUMS group. At the genus level (Fig. 2d), the relative abundance of \u003cem\u003eAkkermansia\u003c/em\u003e was decreased in the CUMS group (6.26%) and the CUMS\u0026thinsp;+\u0026thinsp;\u003cem\u003ePg\u003c/em\u003e-H group (1.06%) compared with the Control group (18.92%). \u003cem\u003eEscherichia\u003c/em\u003e accounted for only 0.13% in the Control group, whereas its relative abundance was 7.17% in the CUMS\u0026thinsp;+\u0026thinsp;\u003cem\u003ePg\u003c/em\u003e-H group. \u003cem\u003eBacteroide\u003c/em\u003e exhibited the highest relative abundance in the CUMS group (5.83%).\u003c/p\u003e\n \u003cp\u003eTo accurately identify the characteristic intestinal bacterial genera affected by \u003cem\u003ePg\u003c/em\u003e, we performed LEfSe analysis (LDA score\u0026thinsp;\u0026gt;\u0026thinsp;3.0, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig. 2e). The results showed that the Control group was significantly enriched in commensal and probiotic bacteria, including \u003cem\u003eAkkermansia\u003c/em\u003e, \u003cem\u003eAlloprevotella\u003c/em\u003e, \u003cem\u003eClostridioides_A\u003c/em\u003e, \u003cem\u003eParasutterella\u003c/em\u003e, \u003cem\u003eBifidobacterium\u003c/em\u003e, \u003cem\u003eClostridium_Q\u003c/em\u003e, and \u003cem\u003eCAG-485\u003c/em\u003e. The CUMS group was significantly enriched in opportunistic pathogens such as \u003cem\u003eCryptoBacteroide\u003c/em\u003e, \u003cem\u003eCAG-95\u003c/em\u003e, \u003cem\u003eLawsonibacter\u003c/em\u003e, and \u003cem\u003e14\u0026thinsp;\u0026minus;\u0026thinsp;2\u003c/em\u003e (family \u003cem\u003eLachnospiraceae\u003c/em\u003e). In addition, anaerobic bacteria including \u003cem\u003eLigilactobacillus, Limosilactobacillus, Desulfovibrio_R, Berryella, Acutalibacter, Malacoplasma_A, Rikenella, Nanosyncoccus\u003c/em\u003e, and \u003cem\u003eSchaedlerella\u003c/em\u003e were significantly enriched in the CUMS\u0026thinsp;+\u0026thinsp;\u003cem\u003ePg\u003c/em\u003e-H group.\u003c/p\u003e\n \u003cp\u003eCollectively, these results indicate that \u003cem\u003ePg\u003c/em\u003e gavage reshaped the intestinal microecology of CUMS mice at the species level.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Metabolite Profiles in the Intestine of CUMS Mice Following \u003cem\u003ePg\u003c/em\u003e Gavage\u003c/h2\u003e\n \u003cp\u003eTo elucidate the potential effects of \u003cem\u003ePg\u003c/em\u003e gavage on intestinal physiological function in mice, untargeted metabolomics analysis was performed on colonic contents, with a focus on the metabolic distinct separation between the CUMS group and the CUMS\u0026thinsp;+\u0026thinsp;\u003cem\u003ePg\u003c/em\u003e-H group.\u003c/p\u003e\n \u003cp\u003eFirst, PCA was performed to evaluate the overall structure of the metabolic profiles of colonic contents in each group. As shown in Fig.\u0026nbsp;3a (positive ion mode) and Fig.\u0026nbsp;3b (negative ion mode), the sample points from the Control, CUMS, and CUMS\u0026thinsp;+\u0026thinsp;\u003cem\u003ePg\u003c/em\u003e-H groups formed three clearly distinguishable clusters, indicating significant differences in metabolic profiles among the groups. The sample clusters of the CUMS group and the CUMS\u0026thinsp;+\u0026thinsp;\u003cem\u003ePg\u003c/em\u003e-H group were relatively close in spatial position but were completely separated from the Control group. The CUMS\u0026thinsp;+\u0026thinsp;\u003cem\u003ePg\u003c/em\u003e-H group deviated further from the CUMS group along the PC1 axis, suggesting that \u003cem\u003ePg\u003c/em\u003e gavage induced a distinct and more pronounced metabolic remodeling against the background of depression.\u003c/p\u003e\n \u003cp\u003eNext,to maximize the discrimination between the CUMS group and the CUMS\u0026thinsp;+\u0026thinsp;\u003cem\u003ePg\u003c/em\u003e-H group and to remove orthogonal variation, an OPLS-DA model was subsequently established. The resulting score plots (Fig.\u0026nbsp;3c, d) revealed a clear separation between the two groups. Specifically, the CUMS\u0026thinsp;+\u0026thinsp;\u003cem\u003ePg\u003c/em\u003e-H group fell predominantly along the negative semi-axis, whereas the CUMS group clustered along the positive semi-axis. No sample overlap was observed within the 95% confidence interval, indicating significant metabolic differences between the two groups.\u003c/p\u003e\n \u003cp\u003eFurthermore, to accurately identify specific metabolites regulated by \u003cem\u003ePg\u003c/em\u003e gavage, we merged differentially abundant metabolites from positive and negative ion modes and generated a volcano plot based on stringent screening thresholds (|log₂(Fold Change)| \u0026gt; 1, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig. 3e). Compared with the CUMS group, a total of 451 metabolites showed significant alterations in abundance in the CUMS\u0026thinsp;+\u0026thinsp;\u003cem\u003ePg\u003c/em\u003e-H group, of which 210 were upregulated and 241 were downregulated, indicating widespread disruption of intestinal metabolic homeostasis in CUMS mice following \u003cem\u003ePg\u003c/em\u003e gavage. These metabolic changes were significantly associated with dysbiosis of the gut microbiota. Detailed information for the top 10 differentially abundant metabolites between groups is presented in Table 2.\u003c/p\u003e\n \u003cp\u003eWe annotated and performed enrichment analysis on the differentially abundant metabolites using the KEGG database (Fig.\u0026nbsp;3f). Among the most significantly enriched pathways were ABC transporters, tryptophan metabolism, and neuroactive ligand-receptor interaction. In addition, pathways such as serotonergic synapse, D-amino acid metabolism, and mineral absorption were also enriched.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Colonic Mucosal and Immune Status in CUMS Mice Following \u003cem\u003ePg\u003c/em\u003e Gavage\u003c/h2\u003e\n \u003cp\u003eELISA results (Fig.\u0026nbsp;4a-f) showed that in colon tissues, compared with the CUMS group,the level of TNF-\u0026alpha; and IL-6 of the CUMS\u0026thinsp;+\u0026thinsp;\u003cem\u003ePg\u003c/em\u003e-H group were significantly increased \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{bot}\\text{h}\\text{}\\text{p}\\text{\u0026lt;0.0001}\\)\u003c/span\u003e\u003c/span\u003e. Serum IL-6 was also higher in the CUMS+\u003cem\u003ePg\u003c/em\u003e-H group than in the CUMS group \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{p}\\text{\u0026lt;0.0001}\\)\u003c/span\u003e\u003c/span\u003e, indicating systemic spread of inflammation. In the hippocampus, NLRP3 and IL-1\u0026beta; expression were elevateg in the CUMS group versus controls, and further elevated in the CUMS\u0026thinsp;+\u0026thinsp;\u003cem\u003ePg\u003c/em\u003e-H group versus the CUMS group \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{all}\\text{}\\text{p}\\text{\u0026lt;0.001}\\)\u003c/span\u003e\u003c/span\u003e; IL-6 levels were also increased across groups \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{p}\\text{\u0026lt;0.01}\\)\u003c/span\u003e\u003c/span\u003e. These findings suggest peripheral inflammation affected the brain.\u003c/p\u003e\n \u003cp\u003eColonic H\u0026amp;E staining (Fig.\u0026nbsp;4g-j) showed that CUMS mice exhibited mild mucosal damage (disorganized villi, reduced goblet cells, mild inflammatory infiltration), which was aggravated in the CUMS\u0026thinsp;+\u0026thinsp;\u003cem\u003ePg\u003c/em\u003e-L group (increased inflammation, epithelial shedding) and most severe in the CUMS\u0026thinsp;+\u0026thinsp;\u003cem\u003ePg\u003c/em\u003e-H group (extensive villous collapse, crypt disruption, marked epithelial loss, and massive inflammatory infiltration).\u003c/p\u003e\n \u003cp\u003eImmunohistochemistry (Fig.\u0026nbsp;4k-n, o-r) revealed that tight junction protein (ZO-1, Occludin) expression was progressively reduced from the Control group (positive area: 24.52%, 19.66%) to the CUMS group (13.46%, 11.05%), CUMS\u0026thinsp;+\u0026thinsp;\u003cem\u003ePg\u003c/em\u003e-L group (8.27%, 6.19%), and CUMS\u0026thinsp;+\u0026thinsp;\u003cem\u003ePg\u003c/em\u003e-H group (5.53%, 3.86%), indicating a dose-dependent loss of intestinal barrier integrity.\u003c/p\u003e\n \u003cp\u003eThese results indicate that \u003cem\u003ePg\u003c/em\u003e gavage impaired colonic mucosal barrier function in CUMS mice in a dose-dependent manner and activated inflammatory responses in the colon, serum, and hippocampus.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Spearman Correlation Analysis\u003c/h2\u003e\n \u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/58893_b39df98f09c4a4bb/58893_custom_files/img1778476504.png\" width=\"541\" height=\"552\"\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eMDD is a common mental disorder that severely impairs the normal daily functioning of affected individuals. For a long time, research on MDD has primarily focused on intrinsic brain mechanisms. The gut microbiota, representing the largest microbial community in the human body, has been demonstrated to be deeply involved in the onset and progression of MDD. The oral cavity serves as the second largest reservoir of microbial communities after the intestine, with saliva containing a substantial number of oral bacteria. The major periodontopathogen \u003cem\u003ePg\u003c/em\u003e can translocate to the intestine through daily swallowing and, acting as an invader, disturb the distal intestinal microecology\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. However, evidence regarding how this process influences the development and progression of MDD remains limited. In this study, we administered different concentrations of \u003cem\u003ePg\u003c/em\u003e via gavage to CUMS mice for the first time, aiming to elucidate the mechanisms by which periodontitis exacerbates MDD and to address a critical gap in this field.\u003c/p\u003e \u003cp\u003eThe gut microbiota is a critical environmental factor regulating host behavior and mood. In this study, \u003cem\u003ePg\u003c/em\u003e gavage resulted in reduced diversity of the gut microbiota in CUMS mice, which is consistent with the intestinal dysbiosis observed in patients with depression\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Specifically, we found that in the CUMS group, the opportunistic pathogen \u003cem\u003eBacteroide\u003c/em\u003e invaded the intestine, while the abundances of beneficial bacteria such as \u003cem\u003eAkkermansia\u003c/em\u003e and \u003cem\u003eBifidobacterium\u003c/em\u003e were decreased. As a typical opportunistic pathogen, \u003cem\u003eBacteroide\u003c/em\u003e undergoes conditional expansion and mucosal invasion under CUMS induction, disrupting intestinal barrier integrity and triggering chronic low-grade inflammation. Its overgrowth may further exacerbate intestinal barrier damage and neuroinflammation, increasing susceptibility to depression through inflammatory pathways mediated by the gut-brain axis\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eAkkermansia\u003c/em\u003e is widely recognized as a beneficial bacterium that enhances intestinal barrier function and exerts anti-inflammatory effects, and its reduced abundance is closely associated with anxiety- and depression-like behaviors\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eBifidobacterium\u003c/em\u003e possesses anti-inflammatory properties, modulates immune responses, produces beneficial metabolites, strengthens the intestinal barrier, and exerts antidepressant effects through regulation of tryptophan metabolism, serotonin synthesis, and the hypothalamic-pituitary-adrenal axis\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. We observed that the CUMS\u0026thinsp;+\u0026thinsp;\u003cem\u003ePg\u003c/em\u003e-H group specifically exhibited enrichment of multiple bacterial genera associated with intestinal inflammation and barrier impairment. \u003cem\u003eDesulfovibrio\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e is a typical opportunistic pathogen; excessive \u003cem\u003eDesulfovibrio\u003c/em\u003e produces hydrogen sulfide (H₂S), and high concentrations of H₂S can damage intestinal epithelial cells, disrupt the mucus layer and colonic epithelial tight junctions, and exacerbate intestinal mucosal inflammation by activating inflammatory signaling pathways\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The increase in \u003cem\u003eDesulfovibrio\u003c/em\u003e may be related to alterations in the local redox potential or sulfate availability in the intestine following \u003cem\u003ePg\u003c/em\u003e invasion. Spearman correlation analysis showed that \u003cem\u003eDesulfovibrio\u003c/em\u003e was significantly positively correlated with proinflammatory cytokines and significantly negatively correlated with behavioral parameters, indicating that \u003cem\u003eDesulfovibrio\u003c/em\u003e, as a potential pathogen, may participate in the impairment of host behavioral function by promoting inflammatory responses. This study found that \u003cem\u003ePg\u003c/em\u003e gavage exacerbated structural changes in the gut microbiota of CUMS mice, characterized by a reduction in probiotics and an increase in pathogenic bacteria, driving the compositional shift of the flora toward a proinflammatory phenotype.\u003c/p\u003e \u003cp\u003eDysbiosis of the gut microbiota in depressed mice leads to abnormalities in key metabolic functions. Untargeted metabolomics analysis revealed that \u003cem\u003ePg\u003c/em\u003e gavage profoundly disrupted intestinal metabolic homeostasis in CUMS mice. KEGG enrichment analysis of differentially abundant metabolites showed significant enrichment of the ABC transporter pathway. This pathway plays a critical role in the secretion of virulence factors and drug resistance in pathogens such as \u003cem\u003ePg\u003c/em\u003e\u003csup\u003e24\u003c/sup\u003e. Its enrichment suggests that \u003cem\u003ePg\u003c/em\u003e colonizes the intestine and continuously releases harmful components, which in turn may activate host immunity. In the present study, the tryptophan metabolism pathway was also enriched. Ding et al.\u003csup\u003e25\u003c/sup\u003e found that \u003cem\u003ePg\u003c/em\u003e can promote local and systemic inflammation and induce dysbiosis of both oral and intestinal microbiota by enhancing its own tryptophan-indole metabolism, which is consistent with our findings. Additionally, the D-amino acid metabolism pathway, neuroactive ligand-receptor interaction, and serotonergic synapse pathway were enriched. Alterations in the serotonergic synapse pathway directly affect serotonergic neurotransmission, while disruption of the neuroactive ligand-receptor interaction pathway may involve functional abnormalities in multiple neurotransmitter systems, further indicating that \u003cem\u003ePg\u003c/em\u003e can influence neurological function through metabolic pathways.\u003c/p\u003e \u003cp\u003eAmong the top 10 differentially abundant metabolites, we observed a significant downregulation of melatonin levels. The intestine is an important source of melatonin synthesis. Chen et al. found that compared with healthy mice, menopausal depression model mice exhibited reduced intestinal melatonin levels and aggravated depressive symptoms, which is consistent with our findings. Furthermore, they demonstrated that melatonin supplementation alleviated depressive symptoms\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. In this study, we propose that the downregulation of melatonin is associated with the increase in \u003cem\u003eDesulfovibrio\u003c/em\u003e abundance and alterations in the tryptophan metabolism pathway. Under normal physiological conditions, tryptophan tends to be metabolized into neuroprotective indole derivatives, as well as the key neurotransmitter serotonin (5-HT) and melatonin. In the CUMS\u0026thinsp;+\u0026thinsp;\u003cem\u003ePg\u003c/em\u003e-H group, the increased abundance of \u003cem\u003eDesulfovibrio\u003c/em\u003e under inflammatory conditions may lead to the utilization of tryptophan for bacterial protein synthesis and energy metabolism. This could induce the activity of indoleamine 2,3-dioxygenase, causing the metabolism of tryptophan to shift toward the kynurenine pathway, thereby decreasing the synthesis of melatonin and 5-HT. In addition, melatonin, as a potent antioxidant, is extensively consumed under inflammatory conditions to neutralize free radicals and protect the intestinal barrier. The combination of reduced synthesis and increased consumption likely accounts for the decreased melatonin levels observed in the intestine.\u003c/p\u003e \u003cp\u003eThe intestinal barrier is a critical structure for maintaining intestinal homeostasis and preventing the entry of harmful substances into the bloodstream. Metabolic disturbances, together with dysbiosis, collectively weaken this intestinal defense barrier. Histological and immunohistochemical results from this study showed that \u003cem\u003ePg\u003c/em\u003e gavage exacerbated damage to colonic mucosal structure, goblet cell loss, and immune cell infiltration in a dose-dependent manner, and the expression of the tight junction proteins Occludin and ZO-1 was significantly downregulated, leading to severe breaches in the intestinal physical barrier. This \u0026ldquo;leaky gut\u0026rdquo; phenomenon allows bacteria and their metabolites (e.g., LPS) to translocate into the bloodstream, triggering systemic inflammation\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. ELISA results demonstrated that \u003cem\u003ePg\u003c/em\u003e not only elevated local colonic levels of TNF-α and IL-6 but also induced a sharp increase in serum IL-6 levels, indicating that inflammation had spread from the local intestinal site to the systemic circulation. More importantly, this peripheral inflammatory signal successfully transmitted to the central nervous system, manifested as activation of the NLRP3 inflammasome and its downstream effector IL-1β, along with elevated IL-6 levels, in the hippocampus. The hippocampus is a key brain region regulating mood and cognition and is highly sensitive to inflammatory signals. Chronic low-grade inflammation can induce inhibition of hippocampal neurogenesis, impaired synaptic plasticity, and neuronal dysfunction, changes that are closely associated with the pathophysiology of MDD\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The NLRP3/IL-1β axis represents a central pathway in neuroinflammation, and its excessive activation has been confirmed to induce depression-like behaviors\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. We hypothesize that \u003cem\u003ePg\u003c/em\u003e-induced peripheral inflammation may affect the central nervous system through multiple pathways: on the one hand, circulating immune mediators, such as cytokines derived from intestinal immune cells, can directly or indirectly act on the central nervous system interface to modulate neuroimmune homeostasis. On the other hand, intestinal inflammatory signals can be sensed by the intestinal sensory neural network, including enteroendocrine cells and nociceptors, and transmitted primarily via vagal afferent pathways to the brainstem, subsequently influencing the function of limbic system structures such as the amygdala and hippocampus, thereby participating in the regulation of mood and behavioral responses.\u003c/p\u003e \u003cp\u003eUltimately, the perturbation originating from \u003cem\u003ePg\u003c/em\u003e gradually triggered neurological alterations in the brain. Elevated proinflammatory cytokines, particularly IL-1β, in the hippocampus can persistently activate microglia, the brain\u0026rsquo;s resident immune cells. Once chronically activated, microglia secrete additional inflammatory mediators and reactive oxygen species, thereby creating a vicious cycle that compromises neurogenesis, synaptic pruning, and plasticity, and may eventually result in neuronal death\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Concurrently, the reduction in melatonin resulting from the altered tryptophan metabolism pathway under inflammatory conditions further exacerbates depressive symptoms. This is behaviorally reflected in CUMS mice as worsened anhedonia, decreased spontaneous activity, and diminished exploratory behavior. Correlation analyses offer a logical framework connecting these events, demonstrating that the abundance of beneficial bacteria such as \u003cem\u003eAkkermansia\u003c/em\u003e is positively correlated with favorable behavioral indicators and anti‑inflammatory status, whereas bacteria like \u003cem\u003eDesulfovibrio\u003c/em\u003e are tightly linked to proinflammatory states and behavioral deterioration.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eSynthesizing the findings of this study, we conclude that \u003cem\u003ePg\u003c/em\u003e exacerbates depression-like behaviors in CUMS mice through the gut-brain axis.\u003cem\u003ePg\u003c/em\u003e gavage reshapes the composition of the gut microbiota in CUMS mice, characterized by a reduction in beneficial commensal bacteria and an enrichment of potential pathogens. This dysbiosis is accompanied by profound alterations in the metabolic profile, particularly disruptions in pathways such as tryptophan metabolism and neuroactive ligand-receptor interaction. Together, dysbiosis and metabolic disturbances impair intestinal barrier function, leading to decreased expression of tight junction proteins and disruption of mucosal integrity. Impaired intestinal barrier promotes the translocation of bacterial products and endotoxins, activates local intestinal immune cells, and releases proinflammatory cytokines. Inflammatory signals are transmitted to the central nervous system via the bloodstream and neural pathways, inducing NLRP3 inflammasome activation and neuroinflammation in key mood-regulating brain regions such as the hippocampus. The peripheral and central inflammatory environment further affects neurotransmitter metabolism, neurogenesis, and synaptic plasticity, ultimately resulting in aggravated depression-like behaviors.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLimitations\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study reveals the potential mechanisms by which \u003cem\u003ePg\u003c/em\u003e, a major pathogen of periodontitis, exacerbates depression through the microbiota-gut-brain axis; however, certain limitations remain. Although the mechanistic investigation confirmed correlations among differential gut microbiota, differential metabolites, inflammation, and central alterations following \u003cem\u003ePg\u003c/em\u003e gavage, fecal microbiota transplantation in germ-free mice was not performed to more definitively validate the role of the gut microbiota. Furthermore, untargeted metabolomics only provided pathway enrichment analysis, and targeted verification of key tryptophan metabolites and neurotransmitters has not yet been conducted, which represents a direction for our future research.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYan Li\u003c/strong\u003e: Writing \u0026ndash; original draft, Methodology, Supervision, Investigation. \u003cstrong\u003eZhiyue Yang\u003c/strong\u003e: Writing \u0026ndash; original draft, Formal analysis, Data curation, Conceptualization. \u003cstrong\u003eJiajun Cao\u003c/strong\u003e, \u003cstrong\u003eYinzhi Jia\u003c/strong\u003e, \u003cstrong\u003eZitong Zhang\u003c/strong\u003e, \u003cstrong\u003eShouxia Qiao\u003c/strong\u003e: Data curation, Conceptualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no conflict of interest related to this project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all individuals who participated in this study.This study was supported by Shanxi University of Medicine (Grant No. 2025A08) and the Science and Technology Innovation Program of Shanxi Province (Grant No. 2025L189).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCui, L. \u003cem\u003eet al.\u003c/em\u003e Major depressive disorder: hypothesis, mechanism, prevention and treatment. \u003cem\u003eSignal Transduct Target Ther\u003c/em\u003e 9, 30 (2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41392-024-01738-y\u003c/span\u003e\u003cspan address=\"10.1038/s41392-024-01738-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoh, J. 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B. \u003cem\u003eet al.\u003c/em\u003e Acute systemic inflammation exacerbates neuroinflammation in Alzheimer's disease: IL-1β drives amplified responses in primed astrocytes and neuronal network dysfunction. \u003cem\u003eAlzheimers Dement\u003c/em\u003e 17, 1735\u0026ndash;1755 (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/alz.12341\u003c/span\u003e\u003cspan address=\"10.1002/alz.12341\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e\n\u003cp\u003eTable 2 Traceability table of differential metabolites(Top 10).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"632\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompounds\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClass I\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClass II\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFormula\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCAS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003eHippuric acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eAmino acid and Its metabolites\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eAmino acid derivatives\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eC₉H₉NO₃\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e495-69-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003eEpicatechin 5,3\u0026apos;-dimethyl ether\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eFlavonoids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eFlavanols\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eC₁₇H₁₈O₆\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1429-40-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003e13-hydroxy-12,12-dimethyl-4-oxo-3,11-dioxatricyclo[8.4.0.0\u0026sup2;,⁷]tetradeca-1,5,7,9-tetraen-14-yl (2Z)-2-methylbut-2-enoate (abbreviated as Compound 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eMacrolides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eDioxatricyclic macrolides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eC₂₀H₂₂O₇\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1259626-12-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003eMelatonin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eIndole derivatives\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eIndole hormones\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eC₁₃H₁₆N₂O₂\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e73-31-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003e(R)-Norisocorydine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eAlkaloids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eIsoquinoline alkaloids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eC₂₀H₂₃NO₄\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e21415-68-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003e1-Methyluric acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003ePurine derivatives\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eMethylated uric acids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eC₆H₆N₄O₃\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e611-55-0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003eGenistein 7-sulfate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eIsoflavones\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eSulfated isoflavones\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eC₁₅H₁₀O₉S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e6045-48-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003eR-1 Methanandamide phosphate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eEndocannabinoid derivatives\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003ePhosphorylated endocannabinoids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eC₂₃H₄₀NO₅P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e649569-33-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003eDaidzein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eIsoflavones\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eAglycone isoflavones\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eC₁₅H₁₀O₅\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e486-66-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003eCreatinine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eAmino acid and Its metabolites\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eGuanidine derivatives\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eC₄H₇N₃O\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e60-27-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"translational-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tp","sideBox":"Learn more about [Translational Psychiatry](http://www.nature.com/tp/)","snPcode":"41398","submissionUrl":"https://mts-tp.nature.com/cgi-bin/main.plex","title":"Translational Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Periodontitis, MDD, gut microbiota, 16S and untargeted metabolomics, Immune response, Microbiota-gut-brain axis","lastPublishedDoi":"10.21203/rs.3.rs-9555082/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9555082/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePeriodontitis is a common chronic inflammatory disease and its comorbidity with major depressive disorder (MDD) is increasingly recognized, yet the underlying mechanisms remain unclear. This study investigated whether \u003cem\u003ePorphyromonas gingivalis\u003c/em\u003e (\u003cem\u003ePg\u003c/em\u003e), a key periodontal pathogen, exacerbates depression-like behaviors in mice exposed to chronic unpredictable mild stress (CUMS) and explored the underlying mechanisms. C57BL/6J mice were divided into Control, CUMS, and CUMS with low- or high-dose \u003cem\u003ePg\u003c/em\u003e gavage groups. Depression-like behaviors were assessed by sucrose preference test (SPT) and open field test (OFT). Colonic intestinal flora and metabolites were analyzed using 16S rRNA sequencing and untargeted metabolomics. Inflammatory markers and colonic barrier integrity were evaluated by ELISA, histology, and immunohistochemistry. \u003cem\u003ePg\u003c/em\u003e gavage dose-dependently exacerbated anhedonia and exploratory deficits in CUMS mice. It reduced intestinal flora diversity, decreasing beneficial bacteria (\u003cem\u003eAkkermansia\u003c/em\u003e, \u003cem\u003eBifidobacterium\u003c/em\u003e) while increasing opportunistic pathogens (\u003cem\u003eDesulfovibrio\u003c/em\u003e). Metabolomic analysis revealed 451 differentially altered metabolites, enriched in pathways including tryptophan metabolism and neuroactive ligand-receptor interaction. \u003cem\u003ePg\u003c/em\u003e impaired colonic tight junctions, increased colonic and systemic proinflammatory cytokines, and upregulated hippocampal NLRP3 inflammasome and IL-1β expression. Correlation analyses linked beneficial bacteria with favorable behavioral and inflammatory profiles, and opportunistic pathogens with the opposite. In conclusion, \u003cem\u003ePg\u003c/em\u003e exacerbates depression-like behaviors in CUMS mice by disrupting intestinal flora and metabolites, impairing the intestinal barrier, and activating the gut-brain inflammatory axis. These findings provide a mechanistic basis linking periodontitis to MDD and suggest potential therapeutic targets.\u003c/p\u003e","manuscriptTitle":"Multi-omics reveals Porphyromonas gingivalis-mediated exacerbation of depressive-like behaviors via the gut microbiota-metabolite-brain axis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 05:51:57","doi":"10.21203/rs.3.rs-9555082/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-05-13T08:17:38+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-05-09T20:26:05+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2026-05-01T08:12:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-30T14:23:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-30T14:22:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Translational Psychiatry","date":"2026-04-30T03:38:24+00:00","index":"","fulltext":""},{"type":"checksFailed","content":"","date":"2026-04-29T15:08:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"translational-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tp","sideBox":"Learn more about [Translational Psychiatry](http://www.nature.com/tp/)","snPcode":"41398","submissionUrl":"https://mts-tp.nature.com/cgi-bin/main.plex","title":"Translational Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"71b78101-a9bb-409b-a458-ebca7af6acdd","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-05-13T08:17:38+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-05-09T20:26:05+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"17","date":"2026-05-01T08:12:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-30T14:23:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-30T14:22:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Translational Psychiatry","date":"2026-04-30T03:38:24+00:00","index":"","fulltext":""},{"type":"checksFailed","content":"","date":"2026-04-29T15:08:29+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67890739,"name":"Health sciences/Diseases/Psychiatric disorders/Depression"},{"id":67890740,"name":"Biological sciences/Physiology"}],"tags":[],"updatedAt":"2026-05-11T05:51:57+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 05:51:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9555082","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9555082","identity":"rs-9555082","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}