Phospholipase C gamma 1 links Gordonibacter pamelaeae to osteoarthritis risk

Phospholipase C gamma 1 links Gordonibacter pamelaeae to osteoarthritis risk | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Phospholipase C gamma 1 links Gordonibacter pamelaeae to osteoarthritis risk Shengkun Li, Shaozi Zhong, Chun Zeng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9363368/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 The gut–joint axis has been increasingly implicated in osteoarthritis (OA), yet the causal contribution of specific gut microbes and their downstream molecular mechanisms remain unclear. We employed a multi-stage, two-sample Mendelian randomization (MR) framework. This approach utilized summary-level data from large-scale genome-wide association studies (GWAS) for gut microbiota, plasma proteins, and OA. The analysis involved three steps: (1) identifying gut microbial taxa causally associated with OA risk; (2) screening of pyroptosis-related proteins using pQTL data; and (3) performing mediation analysis to evaluate potential intermediate mechanisms. We also performed transcriptomic analysis of human cartilage and in vitro experiments in chondrocytes to support the biological relevance of our findings. Our MR analysis identified Gordonibacter pamelaeae as inversely associated with OA risk. Among 11 candidate pyroptosis-related proteins, only Phospholipase C Gamma 1 (PLCG1) showed a significant inverse association with OA. MR analysis further suggested that G. pamelaeae was positively associated with PLCG1 levels. Mediation analysis indicated that PLCG1 partially mediated the association between G. pamelaeae and OA. Consistent with these findings, PLCG1 mRNA levels were reduced in human OA cartilage. Furthermore, our in vitro experiments demonstrated that PLCG1 knockdown enhanced IL-1β-induced inflammatory and pyroptotic responses in chondrocytes. This study suggests a potential pathway linking gut microbiota to OA through PLCG1-related signaling. PLCG1 may act as a context-dependent regulator that limits excessive inflammatory responses under stress conditions. These findings refine the current understanding of the gut–joint axis and may help identify potential targets for OA intervention. Osteoarthritis Gut Microbiota Mendelian Randomization PLCG1 Pyroptosis Gut-Joint Axis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Osteoarthritis (OA) is a highly prevalent joint disorder characterized by progressive cartilage degeneration, chronic pain, and joint dysfunction, placing a substantial burden on patients and healthcare systems(Kloppenburg et al. 2025 ; Tang et al. 2025 ). Although mechanical loading contributes to disease onset, OA is increasingly recognized as a complex condition involving metabolic imbalance and persistent low-grade inflammation(Terkawi et al. 2022 ; Peng et al. 2025 ). Current therapies mainly provide symptomatic relief, and effective disease-modifying treatments are still lacking. This limitation highlights the need to identify upstream regulatory factors involved in OA development. In recent years, the role of the gut microbiota in musculoskeletal disorders has gained growing interest(Biver et al. 2019 ). Emerging evidence supports a gut–joint axis, in which alterations in microbial composition may influence joint homeostasis through systemic pathways(Deng et al. 2024 ; Yang et al. 2025 ; He et al. 2025 ). Observational studies have shown distinct gut microbiota profiles in people with OA compared with healthy individuals(Boer et al. 2019 ; Favazzo et al. 2020 ; Wang et al. 2023 ). Nevertheless, such associations are susceptible to confounding by diet, lifestyle, and comorbidities, which complicates efforts to determine causal links between specific microbial taxa and OA. At the cellular level, dysregulated chondrocyte death contributes directly to cartilage degeneration. Among the different forms of programmed cell death, pyroptosis has emerged as an important contributor to OA pathogenesis(Chen et al. 2024 ). The hallmarks of this process include inflammasome activation, caspase-1 cleavage, gasdermin D (GSDMD) pore formation, and the release of pro-inflammatory cytokines like interleukin-1β (IL-1β) and IL-18(Shi et al. 2015 ; Broz and Dixit 2016 ). Accumulating evidence indicates that pyroptosis occurs in chondrocytes and synovial cells in OA, amplifying local inflammation and accelerating cartilage degradation(Liu et al. 2023 ; Lin et al. 2025 ). However, the upstream regulators of pyroptosis in chondrocytes remain poorly defined. In particular, it is unclear whether systemic factors such as gut microbiota contribute to the regulation of pyroptotic signaling. Mendelian randomization (MR) provides an approach to estimate causal relationships. It leverages genetic variants as instrumental variables, thereby reducing confounding and reverse causation(Davey Smith and Hemani 2014 ; Sanderson et al. 2022 ). This approach is well suited to examining multi-layered biological pathways involving microbiota, host factors, and disease outcomes. In this study, we employed a multi-stage MR strategy to explore the causal relationship among gut microbiota, pyroptosis-related proteins, and OA risk. We further integrated transcriptomic analysis and in vitro experiments to evaluate the biological relevance of key findings. Our goal was to identify potential mediators linking gut microbiota to OA and to provide new insights into the gut–joint axis. Materials and Methods Study Design This investigation adopted a multi-stage, two-sample MR approach to systematically elucidate the causal relationship between gut microbiota and OA, and to further evaluate the mediating role of pyroptosis-related proteins. Our study protocol, which follows the STROBE-MR reporting guidelines, is schematically presented in Fig. 1 . The analysis proceeded sequentially: (1) We first conducted a two-sample MR to identify specific gut microbiota taxa causally associated with OA risk. (2) Next, we screened for causal links between a curated set of pyroptosis-related proteins and OA to identify potential mediators. (3) Finally, we performed a formal two-step mediation analysis to quantify the extent to which the identified pyroptosis-related protein, PLCG1, mediates the causal pathway from the exposure (gut microbiota) to the outcome (OA). In addition, public cartilage transcriptomic data and in vitro functional experiments were used to provide biological validation of the lead candidate. Data Acquisition Gut Microbiota and OA GWAS Data We obtained summary statistics for gut microbiota from genome-wide association studies (GWAS) Catalog (IDs: GCST90032172-GCST90032644). This dataset was determined through shotgun metagenomic sequencing of fecal samples, covering 11 phyla, 19 classes, 24 orders, 62 families, 146 genera, and 209 species of microorganisms. Supplementary Table S1 lists all analyzed gut microbial taxa. Genetic associations for OA were sourced from a GWAS meta-analysis involving 44,190 OA cases and 414,250 healthy controls of European ancestry. pQTL Data for Mediators To obtain genetic instruments for our potential mediators, we utilized protein quantitative trait loci (pQTL) dataset from the deCODE study in a population of 35,559 Icelanders(Ferkingstad et al. 2021 ). We initially downloaded summary statistics for 4,907 plasma proteins. Only proteins with available and valid instrumental variables after quality control were retained for subsequent MR analyses. Curation of the Pyroptosis-Related Gene Set Based on an extensive literature review, we compiled a candidate list of 33 key genes involved in the pyroptosis signaling pathway (e.g., GSDMD, CASP1, NLRP3, IL1B). We then intersected this gene list with the available pQTL data. This resulted in a final set of 11 pyroptosis-related proteins for which robust genetic instruments were available, thus enabling subsequent MR analysis. The curated list of pyroptosis-related genes is provided in Supplementary Table S4. Statistical Analysis Instrumental Variables Selection We selected genetic instruments (SNPs) based on the three key MR assumptions. Detailed information on instrumental variables used in MR analyses is provided in Supplementary Tables S2 and S5. We used a significance threshold of P < 5 × 10⁻⁸, which was relaxed to P < 5 × 10⁻⁵ for exposures with few instruments. We performed LD clumping (r² 10 to avoid weak instrument bias. MR and Sensitivity Analyses The random-effects Inverse Variance Weighted (IVW) method was used as the primary analysis to estimate the causal effects of (a) gut microbiota on OA, (b) pyroptosis proteins on OA, and (c) gut microbiota on pyroptosis proteins. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to represent effect sizes. We used MR-Egger regression, the Weighted Median method, and the MR-PRESSO test as sensitivity analyses to assess pleiotropy. Cochran's Q statistic was used to quantify heterogeneity. Mediation Analysis A two-step MR mediation framework was used to assess whether PLCG1 mediated the association between gut microbiota and OA. The total effect of a specific gut microbe on OA was denoted as β_total. The effect of the microbe on the mediator (PLCG1) was estimated as β₁, and the effect of the mediator on OA was estimated as β₂. The indirect (mediated) effect was calculated as β₁ × β₂, and the proportion of the total effect mediated by PLCG1 was calculated as (β₁ × β₂) / β_total. A significant indirect effect was interpreted as evidence supporting partial mediation. Bioinformatic Validation in Human Cartilage Tissue To determine the clinical relevance of our lead candidate gene, PLCG1, we analyzed its transcriptomic expression in human joint cartilage. We utilized the RNA-sequencing dataset GSE57218 from the GEO database. This dataset contains paired samples of OA-affected and preserved (macroscopically normal) cartilage from 33 patients. The significance level was set at P < 0.05. In Vitro Validation in Human Chondrocytes Cell Culture The immortalized human chondrocyte cell line IM-H488 (IMMOCELL, Xiamen, China) was cultured in Dulbecco's Modified Eagle Medium/F-12 (DMEM/F-12) supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin at 37°C in a 5% CO₂ incubator. Cells were passaged at approximately 80–90% confluence. To maintain phenotypic stability, all experiments were performed using cells prior to passage 3. Cell Transfection Small interfering RNA targeting human PLCG1 (siPLCG1) and a non-targeting negative control siRNA were purchased from RiboBio (Guangzhou, China). Chondrocytes were seeded in 6-well plates and transfected at approximately 60–70% confluence using riboFECT™ CP Transfection Reagent (RiboBio, Guangzhou, China). The final concentration of siRNA was 50 nM. Samples were collected 24–48 hours later for subsequent analyses. The effectiveness of the gene silencing was assessed by Quantitative Real-Time PCR (qRT-PCR) and Western blot. Only experiments with effective PLCG1 reduction (> 50% decrease) were included in downstream analyses. Inflammatory Stimulation and Experimental Grouping We established an in vitro OA model by treating cells with 10 ng/mL of IL-1β for 24 hours. For combined treatment experiments, chondrocytes were first transfected with siRNA for 24 hours before IL-1β was added. For experiments, cells were divided into four groups. The Control group received negative control siRNA. The IL-1β group received negative control siRNA followed by IL-1β stimulation. The siPLCG1 group was transfected with PLCG1-targeting siRNA alone. The IL-1β + siPLCG1 group was transfected with PLCG1-targeting siRNA and then stimulated with IL-1β. RNA Extraction and qRT-PCR We extracted total RNA from chondrocytes with TRIzol reagent following the supplier's protocol. A reverse transcription kit was then used to synthesize cDNA. We performed qRT-PCR using a SYBR Green master mix (Takara, Shiga, Japan) on a real-time PCR system. The relative expression of target genes (PLCG1, NLRP3, CASP1, IL-1β, and MMP13) was calculated via the 2^-ΔΔCt method, with β-actin serving as the internal control. All experiments were performed in triplicate. Primer sequences used for qRT-PCR are listed in Supplementary Table S10. Protein extraction and western blot analysis RIPA lysis buffer, supplemented with protease inhibitors, was used to extract total cellular protein. We then determined protein concentrations using a BCA protein assay kit (Solarbio, Beijing, China). Equal amounts of protein from each sample were separated by SDS-PAGE and subsequently transferred to PVDF membranes. Following blocking, the membranes were incubated overnight at 4°C with primary antibodies against PLCG1, NLRP3, cleaved caspase-1 (p20), GSDMD-N and β-actin. After incubation with HRP-conjugated secondary antibodies, we visualized protein bands using an enhanced chemiluminescence system. Band strengths were measured using ImageJ software. Target protein expression was normalized to β-actin. A detailed list of primary antibodies is provided in Supplementary Table S11. Statistical Analysis for In Vitro Experiments All in vitro experiments were independently repeated a minimum of three times. Results are shown as the mean ± standard deviation (SD). We used one-way analysis of variance (ANOVA) with Tukey's post-hoc test for comparisons between groups. The significance level was set at P < 0.05. Overall Statistical Analysis We performed all MR and bioinformatic analyses using R software. The TwoSampleMR package was used for MR analyses, and limma was used for transcriptomic differential expression analysis. All statistical tests were two-sided unless otherwise specified. Results Causal Associations Between Gut Microbiota and Osteoarthritis Risk To establish the causal foundation of our study, we first performed a two-sample MR analysis to assess the effects of gut microbiota taxa on the risk of OA. The primary analysis using the IVW method identified a diverse set of 26 taxa that were causally associated with OA (all P < 0.05). Among these, 9 taxa, such as Victivallis sp002998355 (OR = 1.342, 95% CI: 1.118–1.612, P = 0.002), were positively associated with OA risk. Conversely, 17 taxa were inversely associated with OA risk, including Gordonibacter pamelaeae (OR = 0.918, 95% CI: 0.853–0.988, P = 0.022) (Fig. 2 ). Notably, Gordonibacter pamelaeae showed a consistent inverse association with OA risk, suggesting a potential protective role. These results support a causal role of specific gut microbes in OA. Sensitivity analyses and complementary MR approaches produced consistent estimates, with no indication of heterogeneity or horizontal pleiotropy (Supplementary Figures S1 –S3; Supplementary Table S6), supporting the robustness of the observed associations. PLCG1 Identified as a Key Pyroptosis-Related Factor Associated With OA To explore whether pyroptosis-related proteins mediate the gut microbiota–OA relationship, we conducted an MR screening of 11 key pyroptosis-related proteins against OA. Among these, only Phospholipase C Gamma 1 (PLCG1) showed a significant inverse association with OA risk (OR = 0.894, 95% CI: 0.835–0.958, P = 0.002). No other protein in the curated panel demonstrated a significant causal link to OA risk (Fig. 3 ). Importantly, PLCG1 was the only protein that consistently showed a significant association across MR analyses, highlighting its potential as a key regulatory molecule linking gut microbiota and OA. Full results for all screened proteins are provided in Supplementary Table S7, while representative results, including those with suggestive associations (P < 0.1), are presented in Table 2 . These results positioned PLCG1 as the primary candidate mediator for subsequent analyses. Table 2 Mendelian randomization results for selected pyroptosis-related proteins associated with osteoarthritis risk. Protein nsnp Method OR 95% CI P value Elastase 74 IVW 1.023 0.998 0.0707 Caspase 8 17 IVW 0.972 0.885 0.558 IL-1β 26 IVW 1.008 0.974 0.643 Caspase_3 11 IVW 1.019 0.949 0.612 PLCG1 20 IVW 0.894 0.835 0.002 TIRAP 15 IVW 1.048 0.999 0.056 Odds ratios (ORs), 95% confidence intervals (CIs), and P values were estimated using the inverse variance weighted (IVW) method. Among 11 pyroptosis-related proteins with available pQTL instruments, selected proteins with suggestive or significant associations (P < 0.1) are presented in this table, while the complete results are provided in Supplementary Table S7. PLCG1 was the only protein significantly associated with OA risk. PLCG1 Partially Mediates the Effect of Gordonibacter pamelaeae on OA Having identified both OA-associated microbiota and a key pyroptosis-related protein, we proceeded to connect these elements. An MR analysis was conducted to evaluate the causal effect of OA-related gut taxa on PLCG1 levels. This revealed that Gordonibacter pamelaeae was positively associated with PLCG1 levels (OR = 1.107, 95% CI: 1.010–1.214, P = 0.031) (Fig. 4 ), suggesting a potential upstream regulatory relationship. Similar effect directions were observed across different MR models (Supplementary Table S8). To formally quantify this triangular relationship, we performed a two-step mediation analysis focusing on the Gordonibacter pamelaeae –PLCG1–OA pathway. The analysis confirmed a significant indirect effect channeled through PLCG1. Specifically, PLCG1 was found to mediate 13.28% of the total protective effect of Gordonibacter pamelaeae on OA (Table 1 ). These findings suggest that the association between Gordonibacter pamelaeae and reduced OA risk is partly mediated through PLCG1. Detailed mediation results are provided in Supplementary Table S9. Table 1 Mediation analysis of the Gordonibacter pamelaeae –PLCG1–osteoarthritis pathway. Gut Microbiota Mediator Outcome Total effect β_total Direct effect Indirect effect Mediated proportion (%) Path a β1 Path b β2 Gordonibacter pamelaeae PLCG1 Osteoarthritis -0.086 -0.074 -0.011 13.28% 0.102 -0.112 Estimates of the total, direct, and indirect (mediated) effects of Gordonibacter pamelaeae on OA risk. The proportion mediated by PLCG1 was calculated as the ratio of the indirect effect to the total effect. PLCG1 Expression is Reduced in Human OA Cartilage To validate the biological relevance of PLCG1 at the tissue level, we analyzed its expression in human cartilage using the GEO dataset GSE57218. This dataset contains paired OA-affected and normal-appearing cartilage samples from the same patients. The differential expression analysis demonstrated that PLCG1 mRNA levels were significantly reduced in OA-affected cartilage in comparison with the paired preserved cartilage ( P < 0.05) (Fig. 5 ). This reduction in PLCG1 expression in diseased tissue suggests a context-dependent alteration of PLCG1 signaling during OA progression, rather than a simple linear protective effect. This observation provided important biological support for the MR findings while also indicating potential complexity in PLCG1 function. PLCG1 knockdown enhances IL-1β-induced inflammatory and pyroptosis-related responses in chondrocytes To provide functional support for the role of PLCG1 in chondrocyte inflammatory responses, we performed in vitro experiments using human chondrocytes (Fig. 6 ). As shown in Fig. 6 A-B, Western blot analysis demonstrated that transfection with PLCG1-targeting siRNA effectively reduced PLCG1 protein levels. This finding was further confirmed at the mRNA level by qRT-PCR (Fig. 6 F), indicating successful knockdown efficiency. Stimulation with IL-1β alone significantly increased the protein expression of key pyroptosis-related markers, including NLRP3, cleaved Caspase-1 (p20), and GSDMD-N (Fig. 6 A, C–E), confirming activation of the pyroptotic pathway. Importantly, PLCG1 knockdown markedly enhanced this response. Compared with the IL-1β group, the combined treatment group (siPLCG1 + IL-1β) showed further increased expression of NLRP3, cleaved Caspase-1, and GSDMD-N (Fig. 6 A, C–E). Consistent with these findings, qRT-PCR analysis showed that IL-1β stimulation upregulated the mRNA levels of NLRP3, CASP1, IL1B, and MMP13 (Fig. 6 G–J). This effect was significantly amplified in PLCG1-silenced cells. Notably, PLCG1 knockdown alone had no significant effect on the basal expression levels of these genes, suggesting that PLCG1 primarily modulates inflammatory responses under stress conditions rather than affecting baseline cellular activity. Collectively, these findings suggest that PLCG1 deficiency increases the sensitivity of chondrocytes to inflammatory stimulation, leading to enhanced activation of pyroptotic and catabolic pathways. Discussion This study provides genetic evidence supporting a potential link between gut microbiota and OA. Using a multi-stage MR framework, we identified Gordonibacter pamelaeae as being associated with reduced OA risk and highlighted PLCG1 as a candidate mediator within this pathway. By integrating genetic inference with transcriptomic and in vitro validation, our study supports the existence of a gut microbiota–PLCG1–inflammatory signaling axis in OA. Several studies, including those by Boer and others, have reported changes in gut microbial composition in OA subjects, but most were observational and could not establish causality(Boer et al. 2019 ; Favazzo et al. 2020 ; Wang et al. 2023 ). Interventional studies using probiotics such as Lactobacillus or Bifidobacterium showed modest symptomatic benefits, yet the specific causal taxa remained unclear(Karim 2025 ; Zhao et al. 2026 ). In contrast, our MR analysis reduces confounding from diet, medication, and lifestyle, enabling more robust causal inference(Sanderson et al. 2022 ). A key finding of our study is the inverse association between Gordonibacter pamelaeae and OA risk, which is biologically plausible. Gordonibacter species are anaerobic gut bacteria commonly found in the human colon and are renowned for their involvement in polyphenol metabolism, particularly the conversion of ellagic acid into bioactive urolithins(Yang et al. 2024 ; Bae et al. 2024 ; Dong et al. 2025 ). These microbial metabolites, especially urolithin A, can be absorbed into systemic circulation and have been reported to exert anti-inflammatory and antioxidant effects(Zhao et al. 2023 ). Previous studies suggest that urolithins may contribute to cartilage protection by improving mitochondrial function and reducing inflammatory signaling(He et al. 2021 ; D’Amico et al. 2022 ). Taken together, our findings provide genetic evidence supporting a potential link between a urolithin-producing bacterium and reduced OA risk, suggesting that specific microbial metabolic functions may contribute to joint health. This study's primary contribution is identifying PLCG1 as a potential mediator linking gut microbiota to OA. Earlier studies have mostly focused on classical pyroptosis components, including NLRP3, CASP1, and GSDMD, which are typically upregulated in OA cartilage and promote inflammation(Huang et al. 2021 ; Tang et al. 2024 ; Chen et al. 2024 ). In contrast, PLCG1 has rarely been studied in OA and is not a canonical pyroptosis executor. It is a signaling enzyme that regulates phosphoinositide metabolism, intracellular calcium flux, and downstream kinase pathways(Kadamur and Ross 2013 ; Zhao et al. 2024 ). Interestingly, intracellular calcium signaling has been increasingly recognized as an important modulator of inflammasome activation(Murakami et al. 2012 ; Horng 2014 ; Zhou et al. 2024 ), suggesting that PLCG1 may influence pyroptosis through upstream signaling rather than acting as a direct executor. Our MR results indicate that genetically higher PLCG1 levels are associated with reduced OA risk, suggesting that PLCG1 may participate in maintaining inflammatory signaling balance. Our mediation analysis further demonstrated that 13.28% of the protective effect of Gordonibacter pamelaeae on OA is mediated through PLCG1. Although this proportion is modest, it reflects the multifactorial nature of the gut–joint axis. Recent studies (e.g., Favazzo et al., Liu et al.) suggest that gut microbiota influence OA via multiple parallel mechanisms, involving the modulation of systemic inflammation, metabolic homeostasis, and immune cell function(Favazzo et al. 2020 ; Liu et al. 2025 ; Sun et al. 2025 ; You et al. 2025 ). PLCG1-mediated signaling therefore represents one component of a broader regulatory network instead of a single dominant pathway. To support the biological relevance of our genetic findings, we examined PLCG1 expression in human cartilage and performed functional experiments. This integrative approach allows for a more coherent interpretation of the relationship between genetic associations and cellular responses. Consistent with our MR results suggesting a protective role, PLCG1 expression was reduced in OA cartilage. This observation suggests that reduced PLCG1 expression may contribute to increased susceptibility to inflammatory stress in chondrocytes. Although PLCG1 has been reported to facilitate inflammasome activation in immune cells(Kang et al. 2018 ; Tao et al. 2023 ), accumulating evidence indicates that inflammasome regulation is highly cell-type- and context-dependent(Kelley et al. 2019 ; Feng et al. 2023 ; Vande Walle and Lamkanfi 2024 ). In non-immune cells such as chondrocytes, chronic inflammatory stress may shift PLCG1 toward a role in maintaining intracellular signaling homeostasis(Zhao et al. 2024 ). In this context, reduced PLCG1 expression may increase the sensitivity to inflammasome activation upon IL-1β stimulation. Our in vitro findings further support this interpretation. PLCG1 knockdown alone did not significantly alter basal expression of pyroptosis-related markers. However, under IL-1β stimulation, PLCG1 deficiency markedly amplified the levels of NLRP3, CASP1, IL-1β, GSDMD-N, and MMP13. Importantly, these experimental observations provide functional support for the MR-derived association, linking genetic evidence with cellular responses under inflammatory conditions. Taken together, these findings suggest that PLCG1 may act as a modulatory factor that influences the sensitivity of chondrocytes to inflammatory stimuli, rather than directly driving pyroptosis. Collectively, our findings from genetic, transcriptomic, and functional analyses support a model of a gut–joint axis in which Gordonibacter pamelaeae is associated with PLCG1-related signaling, which may modulate inflammatory and pyroptosis-related responses (Fig. 7 ). From a translational perspective, our findings may have several potential applied and biotechnological implications. First, microbiota-based interventions, potentially including probiotic- or prebiotic-related approaches, could represent a promising strategy for OA prevention or management. Strategies targeting urolithin-producing bacteria, such as Gordonibacter pamelaeae , or dietary approaches aimed at enhancing polyphenol metabolism (e.g., ellagic acid from berries and nuts) may promote the production of beneficial metabolites such as urolithins, which have been reported to exert anti-inflammatory effects and may contribute to joint health(D’Amico et al. 2022 ). Second, specific microbial taxa such as Gordonibacter pamelaeae , along with PLCG1-related signaling pathways, may serve as potential biomarkers for OA susceptibility or progression. Finally, PLCG1-associated signaling pathways may represent a potential therapeutic target, and modulating PLCG1-related inflammatory responses in chondrocytes could help limit cartilage degeneration and pyroptosis-related processes. However, further experimental and clinical studies are required to validate these potential applications. This study has several limitations. First, our MR analyses were primarily based on GWAS data from European populations, which may limit generalizability. Second, although multiple sensitivity analyses were performed, horizontal pleiotropy cannot be completely excluded(Sanderson et al. 2022 ). Third, while MR provides strong evidence for causality, it cannot fully elucidate the underlying biological mechanisms. The specific microbial metabolites linking Gordonibacter pamelaeae to PLCG1 regulation remain to be identified. Finally, although our in vitro experiments provide functional support for the role of PLCG1 in regulating inflammatory and pyroptotic responses, further validation using primary human chondrocytes and animal models of OA is required to verify its physiological relevance. In conclusion, this study suggests a potential causal pathway linking gut microbiota to OA through PLCG1-related signaling. PLCG1 may act as a context-dependent regulator that modulates inflammatory responses in chondrocytes. These findings provide new insights into the gut–joint axis and may inform future strategies for OA prevention and treatment. Declarations Funding This research was funded by the Natural Science Foundation of Guangdong Province (Grant No. 2024A1515011231). Declarations of interest The authors declare that they have no competing interests. Ethics approval Not applicable. This study was a secondary analysis of existing, publicly available, and anonymized summary-level data. All contributing studies had previously obtained the necessary ethics approvals and participant consent. Authorship contribution statement Shengkun Li designed the study, performed the MR and bioinformatic analyses, and wrote the original draft. Shaozi Zhong supervised and analyzed the in vitro experiments. Chun Zeng conceived the study, supervised the project, and reviewed & edited the manuscript. Shengkun Li and Shaozi Zhong contributed equally to this work. All authors read and approved the final manuscript. Data availability statements The GWAS summary statistics for gut microbiota and OA are publicly available from the GWAS Catalog. The pQTL data are available from the deCODE genetics website. The GEO dataset (GSE57218) is accessible from the NCBI GEO database. All other data generated or analyzed during this study are available from the corresponding author on reasonable. request. References Bae M, Le C, Mehta RS, Dong X, Pieper LM, Ramirez L, Alexander M, Kiamehr S, Turnbaugh PJ, Huttenhower C, Chan AT, Balskus EP (2024) Metatranscriptomics-guided discovery and characterization of a polyphenol-metabolizing gut microbial enzyme. 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Phytomedicine 150:157679. https://doi.org/10.1016/j.phymed.2025.157679 Zhao Q, Chen X, Qu N, Qiu J, Zhang B, Xia C (2024) PLCγ1 deficiency in chondrocytes accelerates the age-related changes in articular cartilage and subchondral bone. J Cell Mol Med 28(16):e70027. https://doi.org/10.1111/jcmm.70027 Zhou R, Fu W, Vasylyev D, Waxman SG, Liu C (2024) Ion channels in osteoarthritis: emerging roles and potential targets. Nat Rev Rheumatol 20(9):545–564. https://doi.org/10.1038/s41584-024-01146-0 Additional Declarations No competing interests reported. Supplementary Files 1.SupplementaryfileTables.xlsx 2.SupplementaryfileFigures.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 18 May, 2026 Reviews received at journal 30 Apr, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviewers invited by journal 22 Apr, 2026 Editor assigned by journal 16 Apr, 2026 Submission checks completed at journal 16 Apr, 2026 First submitted to journal 12 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9363368","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":627775519,"identity":"3246a6e7-1327-420c-b226-3692273b2d0c","order_by":0,"name":"Shengkun Li","email":"","orcid":"","institution":"Third Affiliated Hospital of Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shengkun","middleName":"","lastName":"Li","suffix":""},{"id":627775520,"identity":"4fd5fb5b-fe84-4253-b0d6-8c31bb149c27","order_by":1,"name":"Shaozi Zhong","email":"","orcid":"","institution":"Third Affiliated Hospital of Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shaozi","middleName":"","lastName":"Zhong","suffix":""},{"id":627775521,"identity":"45901a7f-3f01-4d8c-8e38-859ec826d91c","order_by":2,"name":"Chun Zeng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYLACHhA63tj44ANpWs4cbjacQYoWBoYb6W3SHMSolm/vPfziTc1hGb6bDxukGRjs5HQbCGhh7DmXZjnn2GEeyduJDcYFDMnGZgcIaGGWyDEz5mFL4zEAakmewXAgcRshLWzyb4Ba/gG13DzYcJiHGC08EjzGj3nbbHgMbjA2NhOlRYInx4xxbp8Nj+SZxGbGGQZE+EW+/YzxhzffJOz5jh9//uNDhZ0cQS0g70gg2AaElYMAM/HJZBSMglEwCkYmAADW70Je9XcwawAAAABJRU5ErkJggg==","orcid":"","institution":"Third Affiliated Hospital of Southern Medical University","correspondingAuthor":true,"prefix":"","firstName":"Chun","middleName":"","lastName":"Zeng","suffix":""}],"badges":[],"createdAt":"2026-04-09 05:10:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9363368/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9363368/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108494355,"identity":"2a2f5de8-65c0-436d-9ce1-d4770c507fb7","added_by":"auto","created_at":"2026-05-05 10:04:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":11405893,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy design and mediation framework for the gut microbiota–pyroptosis–osteoarthritis pathway.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA multi-stage Mendelian randomization (MR) framework was applied to investigate the causal relationships among gut microbiota, pyroptosis-related proteins, and osteoarthritis (OA). Path a represents the effect of gut microbiota on pyroptosis-related proteins (βa); path b represents the effect of pyroptosis-related proteins on OA risk (βb); path c represents the total effect of gut microbiota on OA (βc), including the direct effect (c′) and the indirect (mediated) effect through pyroptosis-related proteins (ab).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9363368/v1/3c3f569a9c262723fca7ca60.png"},{"id":108494358,"identity":"bb1a370e-68d0-45bc-aaf6-b7aac33665b8","added_by":"auto","created_at":"2026-05-05 10:04:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":48801739,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCausal effects of gut microbiota on osteoarthritis risk estimated by the inverse variance weighted (IVW) method.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eForest plot showing the causal associations between gut microbial taxa and OA risk. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using the IVW method. Taxa with OR \u0026gt; 1 indicate increased OA risk, whereas taxa with OR \u0026lt; 1 indicate decreased risk.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9363368/v1/86056912f66f1eac5592213f.png"},{"id":108494347,"identity":"2a083e63-8efa-4e6b-8f7c-ad9f95ed031b","added_by":"auto","created_at":"2026-05-05 10:04:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":20745103,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCausal effects of pyroptosis-related proteins on osteoarthritis risk estimated by the IVW method.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eForest plot illustrating the associations between genetically predicted levels of pyroptosis-related proteins and OA risk. Among the tested proteins, PLCG1 showed a significant inverse association with OA risk, while other proteins were not significantly associated.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9363368/v1/de8c3189f6024d5344fe4784.png"},{"id":108494361,"identity":"47ce352a-213c-4de8-97a5-f6f2dadb6cae","added_by":"auto","created_at":"2026-05-05 10:04:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":11451927,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCausal effects of gut microbiota on PLCG1 levels estimated by the IVW method.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eForest plot showing the causal associations between gut microbial taxa and PLCG1 levels. \u003cem\u003eGordonibacter pamelaeae\u003c/em\u003e demonstrated a significant positive association with PLCG1, suggesting a potential upstream regulatory relationship.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9363368/v1/b1db2e1ba28d66b1abe9d79a.png"},{"id":108494351,"identity":"da28785f-445b-47e6-9e8b-edc46a24856b","added_by":"auto","created_at":"2026-05-05 10:04:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":10686367,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReduced expression of PLCG1 in osteoarthritis cartilage based on GEO transcriptomic analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferential expression analysis of PLCG1 between OA-affected cartilage and paired preserved cartilage from the GEO dataset (GSE57218). (*\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-9363368/v1/2b81674d14678782079e0b55.png"},{"id":108494359,"identity":"d01b9b58-1218-4d75-9ca9-f6a16b4b9bd1","added_by":"auto","created_at":"2026-05-05 10:04:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":21304844,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePLCG1 knockdown enhances IL-1β-induced inflammatory and pyroptosis-related responses in human chondrocytes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHuman IM-H488 chondrocytes were transfected with either non-targeting control siRNA (Control and IL-1β groups) or PLCG1-targeting siRNA (siPLCG1 and siPLCG1 + IL-1β groups) for 24 hours, followed by stimulation with or without 10 ng/mL IL-1β for another 24 hours. (A) Representative Western blot images showing the protein expression of PLCG1, NLRP3, cleaved Caspase-1 (p20), and GSDMD-N. β-actin served as the loading control. (B–E) Semi-quantitative analysis of the protein levels shown in (A). (F–J) qRT-PCR analysis of the relative mRNA expression of PLCG1, NLRP3, CASP1, IL1B, and MMP13. Data are presented as mean ± SD from three independent experiments. Statistical significance was determined by one-way ANOVA with Tukey’s post hoc test (*\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":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-9363368/v1/93486066966fd05d251ac265.png"},{"id":108494360,"identity":"a1612463-58e2-47b0-ac5c-1dee4a7938ff","added_by":"auto","created_at":"2026-05-05 10:04:06","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":33202443,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProposed gut microbiota–PLCG1–inflammatory signaling axis in osteoarthritis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSchematic diagram illustrating a potential pathway by which gut microbiota may influence OA progression. \u003cem\u003eGordonibacter pamelaeae\u003c/em\u003e is associated with increased PLCG1 levels, which may modulate inflammatory and pyroptosis-related responses in chondrocytes under stress conditions. Dysregulation of this pathway may contribute to cartilage degeneration and OA progression.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-9363368/v1/c4721bcaff7dd82fc81704c6.png"},{"id":108216023,"identity":"8807a9a1-d522-420e-b45b-6c11abd270ae","added_by":"auto","created_at":"2026-04-30 14:34:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":289034,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9363368/v1/5f67a1b2-eb57-40c1-b778-72f2fc6aae80.pdf"},{"id":108494357,"identity":"136dbd38-a452-4418-8622-88e188d234f2","added_by":"auto","created_at":"2026-05-05 10:04:05","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":230967,"visible":true,"origin":"","legend":"","description":"","filename":"1.SupplementaryfileTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9363368/v1/071f1fc3cc21313f72de89a0.xlsx"},{"id":108494362,"identity":"ab0a83a6-4af9-479b-9008-352430b3cae5","added_by":"auto","created_at":"2026-05-05 10:04:06","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":487771,"visible":true,"origin":"","legend":"","description":"","filename":"2.SupplementaryfileFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-9363368/v1/afb3a9e4cc26dc73f129277e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Phospholipase C gamma 1 links Gordonibacter pamelaeae to osteoarthritis risk","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOsteoarthritis (OA) is a highly prevalent joint disorder characterized by progressive cartilage degeneration, chronic pain, and joint dysfunction, placing a substantial burden on patients and healthcare systems(Kloppenburg et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Tang et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Although mechanical loading contributes to disease onset, OA is increasingly recognized as a complex condition involving metabolic imbalance and persistent low-grade inflammation(Terkawi et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Peng et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Current therapies mainly provide symptomatic relief, and effective disease-modifying treatments are still lacking. This limitation highlights the need to identify upstream regulatory factors involved in OA development.\u003c/p\u003e \u003cp\u003eIn recent years, the role of the gut microbiota in musculoskeletal disorders has gained growing interest(Biver et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Emerging evidence supports a gut\u0026ndash;joint axis, in which alterations in microbial composition may influence joint homeostasis through systemic pathways(Deng et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; He et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Observational studies have shown distinct gut microbiota profiles in people with OA compared with healthy individuals(Boer et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Favazzo et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Nevertheless, such associations are susceptible to confounding by diet, lifestyle, and comorbidities, which complicates efforts to determine causal links between specific microbial taxa and OA.\u003c/p\u003e \u003cp\u003eAt the cellular level, dysregulated chondrocyte death contributes directly to cartilage degeneration. Among the different forms of programmed cell death, pyroptosis has emerged as an important contributor to OA pathogenesis(Chen et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The hallmarks of this process include inflammasome activation, caspase-1 cleavage, gasdermin D (GSDMD) pore formation, and the release of pro-inflammatory cytokines like interleukin-1β (IL-1β) and IL-18(Shi et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Broz and Dixit \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Accumulating evidence indicates that pyroptosis occurs in chondrocytes and synovial cells in OA, amplifying local inflammation and accelerating cartilage degradation(Liu et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lin et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, the upstream regulators of pyroptosis in chondrocytes remain poorly defined. In particular, it is unclear whether systemic factors such as gut microbiota contribute to the regulation of pyroptotic signaling.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) provides an approach to estimate causal relationships. It leverages genetic variants as instrumental variables, thereby reducing confounding and reverse causation(Davey Smith and Hemani \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sanderson et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This approach is well suited to examining multi-layered biological pathways involving microbiota, host factors, and disease outcomes.\u003c/p\u003e \u003cp\u003eIn this study, we employed a multi-stage MR strategy to explore the causal relationship among gut microbiota, pyroptosis-related proteins, and OA risk. We further integrated transcriptomic analysis and in vitro experiments to evaluate the biological relevance of key findings. Our goal was to identify potential mediators linking gut microbiota to OA and to provide new insights into the gut\u0026ndash;joint axis.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThis investigation adopted a multi-stage, two-sample MR approach to systematically elucidate the causal relationship between gut microbiota and OA, and to further evaluate the mediating role of pyroptosis-related proteins. Our study protocol, which follows the STROBE-MR reporting guidelines, is schematically presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The analysis proceeded sequentially: (1) We first conducted a two-sample MR to identify specific gut microbiota taxa causally associated with OA risk. (2) Next, we screened for causal links between a curated set of pyroptosis-related proteins and OA to identify potential mediators. (3) Finally, we performed a formal two-step mediation analysis to quantify the extent to which the identified pyroptosis-related protein, PLCG1, mediates the causal pathway from the exposure (gut microbiota) to the outcome (OA). In addition, public cartilage transcriptomic data and in vitro functional experiments were used to provide biological validation of the lead candidate.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Acquisition\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGut Microbiota and OA GWAS Data\u003c/h2\u003e \u003cp\u003eWe obtained summary statistics for gut microbiota from genome-wide association studies (GWAS) Catalog (IDs: GCST90032172-GCST90032644). This dataset was determined through shotgun metagenomic sequencing of fecal samples, covering 11 phyla, 19 classes, 24 orders, 62 families, 146 genera, and 209 species of microorganisms. Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e lists all analyzed gut microbial taxa. Genetic associations for OA were sourced from a GWAS meta-analysis involving 44,190 OA cases and 414,250 healthy controls of European ancestry.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003epQTL Data for Mediators\u003c/h3\u003e\n\u003cp\u003eTo obtain genetic instruments for our potential mediators, we utilized protein quantitative trait loci (pQTL) dataset from the deCODE study in a population of 35,559 Icelanders(Ferkingstad et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). We initially downloaded summary statistics for 4,907 plasma proteins. Only proteins with available and valid instrumental variables after quality control were retained for subsequent MR analyses.\u003c/p\u003e\n\u003ch3\u003eCuration of the Pyroptosis-Related Gene Set\u003c/h3\u003e\n\u003cp\u003eBased on an extensive literature review, we compiled a candidate list of 33 key genes involved in the pyroptosis signaling pathway (e.g., GSDMD, CASP1, NLRP3, IL1B). We then intersected this gene list with the available pQTL data. This resulted in a final set of 11 pyroptosis-related proteins for which robust genetic instruments were available, thus enabling subsequent MR analysis. The curated list of pyroptosis-related genes is provided in Supplementary Table S4.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eInstrumental Variables Selection\u003c/h2\u003e \u003cp\u003eWe selected genetic instruments (SNPs) based on the three key MR assumptions. Detailed information on instrumental variables used in MR analyses is provided in Supplementary Tables S2 and S5. We used a significance threshold of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10⁻⁸, which was relaxed to \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10⁻⁵ for exposures with few instruments. We performed LD clumping (r\u0026sup2; \u0026lt; 0.001, 10,000 kb window) to ensure SNP independence. All instruments had an F-statistic\u0026thinsp;\u0026gt;\u0026thinsp;10 to avoid weak instrument bias.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eMR and Sensitivity Analyses\u003c/h3\u003e\n\u003cp\u003eThe random-effects Inverse Variance Weighted (IVW) method was used as the primary analysis to estimate the causal effects of (a) gut microbiota on OA, (b) pyroptosis proteins on OA, and (c) gut microbiota on pyroptosis proteins. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to represent effect sizes. We used MR-Egger regression, the Weighted Median method, and the MR-PRESSO test as sensitivity analyses to assess pleiotropy. Cochran's Q statistic was used to quantify heterogeneity.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMediation Analysis\u003c/h2\u003e \u003cp\u003eA two-step MR mediation framework was used to assess whether PLCG1 mediated the association between gut microbiota and OA. The total effect of a specific gut microbe on OA was denoted as β_total. The effect of the microbe on the mediator (PLCG1) was estimated as β₁, and the effect of the mediator on OA was estimated as β₂. The indirect (mediated) effect was calculated as β₁ \u0026times; β₂, and the proportion of the total effect mediated by PLCG1 was calculated as (β₁ \u0026times; β₂) / β_total. A significant indirect effect was interpreted as evidence supporting partial mediation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eBioinformatic Validation in Human Cartilage Tissue\u003c/h2\u003e \u003cp\u003eTo determine the clinical relevance of our lead candidate gene, PLCG1, we analyzed its transcriptomic expression in human joint cartilage. We utilized the RNA-sequencing dataset GSE57218 from the GEO database. This dataset contains paired samples of OA-affected and preserved (macroscopically normal) cartilage from 33 patients. The significance level was set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eIn Vitro Validation in Human Chondrocytes\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003eCell Culture\u003c/h2\u003e \u003cp\u003eThe immortalized human chondrocyte cell line IM-H488 (IMMOCELL, Xiamen, China) was cultured in Dulbecco's Modified Eagle Medium/F-12 (DMEM/F-12) supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin at 37\u0026deg;C in a 5% CO₂ incubator. Cells were passaged at approximately 80\u0026ndash;90% confluence. To maintain phenotypic stability, all experiments were performed using cells prior to passage 3.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCell Transfection\u003c/h2\u003e \u003cp\u003eSmall interfering RNA targeting human PLCG1 (siPLCG1) and a non-targeting negative control siRNA were purchased from RiboBio (Guangzhou, China). Chondrocytes were seeded in 6-well plates and transfected at approximately 60\u0026ndash;70% confluence using riboFECT\u0026trade; CP Transfection Reagent (RiboBio, Guangzhou, China). The final concentration of siRNA was 50 nM. Samples were collected 24\u0026ndash;48 hours later for subsequent analyses. The effectiveness of the gene silencing was assessed by Quantitative Real-Time PCR (qRT-PCR) and Western blot. Only experiments with effective PLCG1 reduction (\u0026gt;\u0026thinsp;50% decrease) were included in downstream analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eInflammatory Stimulation and Experimental Grouping\u003c/h2\u003e \u003cp\u003eWe established an in vitro OA model by treating cells with 10 ng/mL of IL-1β for 24 hours. For combined treatment experiments, chondrocytes were first transfected with siRNA for 24 hours before IL-1β was added. For experiments, cells were divided into four groups. The Control group received negative control siRNA. The IL-1β group received negative control siRNA followed by IL-1β stimulation. The siPLCG1 group was transfected with PLCG1-targeting siRNA alone. The IL-1β\u0026thinsp;+\u0026thinsp;siPLCG1 group was transfected with PLCG1-targeting siRNA and then stimulated with IL-1β.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eRNA Extraction and qRT-PCR\u003c/h2\u003e \u003cp\u003eWe extracted total RNA from chondrocytes with TRIzol reagent following the supplier's protocol. A reverse transcription kit was then used to synthesize cDNA. We performed qRT-PCR using a SYBR Green master mix (Takara, Shiga, Japan) on a real-time PCR system. The relative expression of target genes (PLCG1, NLRP3, CASP1, IL-1β, and MMP13) was calculated via the 2^-ΔΔCt method, with β-actin serving as the internal control. All experiments were performed in triplicate. Primer sequences used for qRT-PCR are listed in Supplementary Table S10.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eProtein extraction and western blot analysis\u003c/h2\u003e \u003cp\u003eRIPA lysis buffer, supplemented with protease inhibitors, was used to extract total cellular protein. We then determined protein concentrations using a BCA protein assay kit (Solarbio, Beijing, China). Equal amounts of protein from each sample were separated by SDS-PAGE and subsequently transferred to PVDF membranes. Following blocking, the membranes were incubated overnight at 4\u0026deg;C with primary antibodies against PLCG1, NLRP3, cleaved caspase-1 (p20), GSDMD-N and β-actin. After incubation with HRP-conjugated secondary antibodies, we visualized protein bands using an enhanced chemiluminescence system. Band strengths were measured using ImageJ software. Target protein expression was normalized to β-actin. A detailed list of primary antibodies is provided in Supplementary Table S11.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis for In Vitro Experiments\u003c/h2\u003e \u003cp\u003eAll in vitro experiments were independently repeated a minimum of three times. Results are shown as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). We used one-way analysis of variance (ANOVA) with Tukey's post-hoc test for comparisons between groups. The significance level was set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eOverall Statistical Analysis\u003c/h2\u003e \u003cp\u003eWe performed all MR and bioinformatic analyses using R software. The TwoSampleMR package was used for MR analyses, and limma was used for transcriptomic differential expression analysis. All statistical tests were two-sided unless otherwise specified.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eCausal Associations Between Gut Microbiota and Osteoarthritis Risk\u003c/h2\u003e \u003cp\u003eTo establish the causal foundation of our study, we first performed a two-sample MR analysis to assess the effects of gut microbiota taxa on the risk of OA. The primary analysis using the IVW method identified a diverse set of 26 taxa that were causally associated with OA (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Among these, 9 taxa, such as \u003cem\u003eVictivallis\u003c/em\u003e sp002998355 (OR\u0026thinsp;=\u0026thinsp;1.342, 95% CI: 1.118\u0026ndash;1.612, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), were positively associated with OA risk. Conversely, 17 taxa were inversely associated with OA risk, including \u003cem\u003eGordonibacter pamelaeae\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.918, 95% CI: 0.853\u0026ndash;0.988, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Notably, \u003cem\u003eGordonibacter pamelaeae\u003c/em\u003e showed a consistent inverse association with OA risk, suggesting a potential protective role. These results support a causal role of specific gut microbes in OA. Sensitivity analyses and complementary MR approaches produced consistent estimates, with no indication of heterogeneity or horizontal pleiotropy (Supplementary Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u0026ndash;S3; Supplementary Table S6), supporting the robustness of the observed associations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003ePLCG1 Identified as a Key Pyroptosis-Related Factor Associated With OA\u003c/h2\u003e \u003cp\u003eTo explore whether pyroptosis-related proteins mediate the gut microbiota\u0026ndash;OA relationship, we conducted an MR screening of 11 key pyroptosis-related proteins against OA. Among these, only Phospholipase C Gamma 1 (PLCG1) showed a significant inverse association with OA risk (OR\u0026thinsp;=\u0026thinsp;0.894, 95% CI: 0.835\u0026ndash;0.958, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002). No other protein in the curated panel demonstrated a significant causal link to OA risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Importantly, PLCG1 was the only protein that consistently showed a significant association across MR analyses, highlighting its potential as a key regulatory molecule linking gut microbiota and OA. Full results for all screened proteins are provided in Supplementary Table S7, while representative results, including those with suggestive associations (P\u0026thinsp;\u0026lt;\u0026thinsp;0.1), are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e. These results positioned PLCG1 as the primary candidate mediator for subsequent analyses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMendelian randomization results for selected pyroptosis-related proteins associated with osteoarthritis risk.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ensnp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElastase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0707\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaspase 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.558\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-1β\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaspase_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLCG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIRAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eOdds ratios (ORs), 95% confidence intervals (CIs), and \u003cem\u003eP\u003c/em\u003e values were estimated using the inverse variance weighted (IVW) method. Among 11 pyroptosis-related proteins with available pQTL instruments, selected proteins with suggestive or significant associations (P\u0026thinsp;\u0026lt;\u0026thinsp;0.1) are presented in this table, while the complete results are provided in Supplementary Table S7. PLCG1 was the only protein significantly associated with OA risk.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePLCG1 Partially Mediates the Effect of\u003c/b\u003e \u003cb\u003eGordonibacter pamelaeae\u003c/b\u003e \u003cb\u003eon OA\u003c/b\u003e\u003c/p\u003e \u003cp\u003eHaving identified both OA-associated microbiota and a key pyroptosis-related protein, we proceeded to connect these elements. An MR analysis was conducted to evaluate the causal effect of OA-related gut taxa on PLCG1 levels. This revealed that \u003cem\u003eGordonibacter pamelaeae\u003c/em\u003e was positively associated with PLCG1 levels (OR\u0026thinsp;=\u0026thinsp;1.107, 95% CI: 1.010\u0026ndash;1.214, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), suggesting a potential upstream regulatory relationship. Similar effect directions were observed across different MR models (Supplementary Table S8).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo formally quantify this triangular relationship, we performed a two-step mediation analysis focusing on the \u003cem\u003eGordonibacter pamelaeae\u003c/em\u003e\u0026ndash;PLCG1\u0026ndash;OA pathway. The analysis confirmed a significant indirect effect channeled through PLCG1. Specifically, PLCG1 was found to mediate 13.28% of the total protective effect of \u003cem\u003eGordonibacter pamelaeae\u003c/em\u003e on OA (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These findings suggest that the association between \u003cem\u003eGordonibacter pamelaeae\u003c/em\u003e and reduced OA risk is partly mediated through PLCG1. Detailed mediation results are provided in Supplementary Table S9.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMediation analysis of the \u003cem\u003eGordonibacter pamelaeae\u003c/em\u003e\u0026ndash;PLCG1\u0026ndash;osteoarthritis pathway.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGut Microbiota\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMediator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal effect\u003c/p\u003e \u003cp\u003eβ_total\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDirect effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIndirect effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMediated proportion (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePath a\u003c/p\u003e \u003cp\u003eβ1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePath b\u003c/p\u003e \u003cp\u003eβ2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGordonibacter pamelaeae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePLCG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOsteoarthritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.28%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eEstimates of the total, direct, and indirect (mediated) effects of \u003cem\u003eGordonibacter pamelaeae\u003c/em\u003e on OA risk. The proportion mediated by PLCG1 was calculated as the ratio of the indirect effect to the total effect.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003ePLCG1 Expression is Reduced in Human OA Cartilage\u003c/h2\u003e \u003cp\u003eTo validate the biological relevance of PLCG1 at the tissue level, we analyzed its expression in human cartilage using the GEO dataset GSE57218. This dataset contains paired OA-affected and normal-appearing cartilage samples from the same patients. The differential expression analysis demonstrated that PLCG1 mRNA levels were significantly reduced in OA-affected cartilage in comparison with the paired preserved cartilage (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This reduction in PLCG1 expression in diseased tissue suggests a context-dependent alteration of PLCG1 signaling during OA progression, rather than a simple linear protective effect. This observation provided important biological support for the MR findings while also indicating potential complexity in PLCG1 function.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003ePLCG1 knockdown enhances IL-1β-induced inflammatory and pyroptosis-related responses in chondrocytes\u003c/h2\u003e \u003cp\u003eTo provide functional support for the role of PLCG1 in chondrocyte inflammatory responses, we performed in vitro experiments using human chondrocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-B, Western blot analysis demonstrated that transfection with PLCG1-targeting siRNA effectively reduced PLCG1 protein levels. This finding was further confirmed at the mRNA level by qRT-PCR (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF), indicating successful knockdown efficiency. Stimulation with IL-1β alone significantly increased the protein expression of key pyroptosis-related markers, including NLRP3, cleaved Caspase-1 (p20), and GSDMD-N (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, C\u0026ndash;E), confirming activation of the pyroptotic pathway.\u003c/p\u003e \u003cp\u003eImportantly, PLCG1 knockdown markedly enhanced this response. Compared with the IL-1β group, the combined treatment group (siPLCG1\u0026thinsp;+\u0026thinsp;IL-1β) showed further increased expression of NLRP3, cleaved Caspase-1, and GSDMD-N (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, C\u0026ndash;E). Consistent with these findings, qRT-PCR analysis showed that IL-1β stimulation upregulated the mRNA levels of NLRP3, CASP1, IL1B, and MMP13 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG\u0026ndash;J). This effect was significantly amplified in PLCG1-silenced cells. Notably, PLCG1 knockdown alone had no significant effect on the basal expression levels of these genes, suggesting that PLCG1 primarily modulates inflammatory responses under stress conditions rather than affecting baseline cellular activity.\u003c/p\u003e \u003cp\u003eCollectively, these findings suggest that PLCG1 deficiency increases the sensitivity of chondrocytes to inflammatory stimulation, leading to enhanced activation of pyroptotic and catabolic pathways.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides genetic evidence supporting a potential link between gut microbiota and OA. Using a multi-stage MR framework, we identified \u003cem\u003eGordonibacter pamelaeae\u003c/em\u003e as being associated with reduced OA risk and highlighted PLCG1 as a candidate mediator within this pathway. By integrating genetic inference with transcriptomic and in vitro validation, our study supports the existence of a gut microbiota\u0026ndash;PLCG1\u0026ndash;inflammatory signaling axis in OA.\u003c/p\u003e \u003cp\u003eSeveral studies, including those by Boer and others, have reported changes in gut microbial composition in OA subjects, but most were observational and could not establish causality(Boer et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Favazzo et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Interventional studies using probiotics such as \u003cem\u003eLactobacillus\u003c/em\u003e or \u003cem\u003eBifidobacterium\u003c/em\u003e showed modest symptomatic benefits, yet the specific causal taxa remained unclear(Karim \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). In contrast, our MR analysis reduces confounding from diet, medication, and lifestyle, enabling more robust causal inference(Sanderson et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A key finding of our study is the inverse association between \u003cem\u003eGordonibacter pamelaeae\u003c/em\u003e and OA risk, which is biologically plausible. \u003cem\u003eGordonibacter\u003c/em\u003e species are anaerobic gut bacteria commonly found in the human colon and are renowned for their involvement in polyphenol metabolism, particularly the conversion of ellagic acid into bioactive urolithins(Yang et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Bae et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Dong et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These microbial metabolites, especially urolithin A, can be absorbed into systemic circulation and have been reported to exert anti-inflammatory and antioxidant effects(Zhao et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Previous studies suggest that urolithins may contribute to cartilage protection by improving mitochondrial function and reducing inflammatory signaling(He et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; D\u0026rsquo;Amico et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Taken together, our findings provide genetic evidence supporting a potential link between a urolithin-producing bacterium and reduced OA risk, suggesting that specific microbial metabolic functions may contribute to joint health.\u003c/p\u003e \u003cp\u003eThis study's primary contribution is identifying PLCG1 as a potential mediator linking gut microbiota to OA. Earlier studies have mostly focused on classical pyroptosis components, including NLRP3, CASP1, and GSDMD, which are typically upregulated in OA cartilage and promote inflammation(Huang et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tang et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In contrast, PLCG1 has rarely been studied in OA and is not a canonical pyroptosis executor. It is a signaling enzyme that regulates phosphoinositide metabolism, intracellular calcium flux, and downstream kinase pathways(Kadamur and Ross \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Interestingly, intracellular calcium signaling has been increasingly recognized as an important modulator of inflammasome activation(Murakami et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Horng \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zhou et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), suggesting that PLCG1 may influence pyroptosis through upstream signaling rather than acting as a direct executor. Our MR results indicate that genetically higher PLCG1 levels are associated with reduced OA risk, suggesting that PLCG1 may participate in maintaining inflammatory signaling balance.\u003c/p\u003e \u003cp\u003eOur mediation analysis further demonstrated that 13.28% of the protective effect of \u003cem\u003eGordonibacter pamelaeae\u003c/em\u003e on OA is mediated through PLCG1. Although this proportion is modest, it reflects the multifactorial nature of the gut\u0026ndash;joint axis. Recent studies (e.g., Favazzo et al., Liu et al.) suggest that gut microbiota influence OA via multiple parallel mechanisms, involving the modulation of systemic inflammation, metabolic homeostasis, and immune cell function(Favazzo et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sun et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; You et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). PLCG1-mediated signaling therefore represents one component of a broader regulatory network instead of a single dominant pathway.\u003c/p\u003e \u003cp\u003eTo support the biological relevance of our genetic findings, we examined PLCG1 expression in human cartilage and performed functional experiments. This integrative approach allows for a more coherent interpretation of the relationship between genetic associations and cellular responses. Consistent with our MR results suggesting a protective role, PLCG1 expression was reduced in OA cartilage. This observation suggests that reduced PLCG1 expression may contribute to increased susceptibility to inflammatory stress in chondrocytes. Although PLCG1 has been reported to facilitate inflammasome activation in immune cells(Kang et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tao et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), accumulating evidence indicates that inflammasome regulation is highly cell-type- and context-dependent(Kelley et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Feng et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Vande Walle and Lamkanfi \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In non-immune cells such as chondrocytes, chronic inflammatory stress may shift PLCG1 toward a role in maintaining intracellular signaling homeostasis(Zhao et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this context, reduced PLCG1 expression may increase the sensitivity to inflammasome activation upon IL-1β stimulation.\u003c/p\u003e \u003cp\u003eOur in vitro findings further support this interpretation. PLCG1 knockdown alone did not significantly alter basal expression of pyroptosis-related markers. However, under IL-1β stimulation, PLCG1 deficiency markedly amplified the levels of NLRP3, CASP1, IL-1β, GSDMD-N, and MMP13. Importantly, these experimental observations provide functional support for the MR-derived association, linking genetic evidence with cellular responses under inflammatory conditions. Taken together, these findings suggest that PLCG1 may act as a modulatory factor that influences the sensitivity of chondrocytes to inflammatory stimuli, rather than directly driving pyroptosis. Collectively, our findings from genetic, transcriptomic, and functional analyses support a model of a gut\u0026ndash;joint axis in which \u003cem\u003eGordonibacter pamelaeae\u003c/em\u003e is associated with PLCG1-related signaling, which may modulate inflammatory and pyroptosis-related responses (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFrom a translational perspective, our findings may have several potential applied and biotechnological implications. First, microbiota-based interventions, potentially including probiotic- or prebiotic-related approaches, could represent a promising strategy for OA prevention or management. Strategies targeting urolithin-producing bacteria, such as \u003cem\u003eGordonibacter pamelaeae\u003c/em\u003e, or dietary approaches aimed at enhancing polyphenol metabolism (e.g., ellagic acid from berries and nuts) may promote the production of beneficial metabolites such as urolithins, which have been reported to exert anti-inflammatory effects and may contribute to joint health(D\u0026rsquo;Amico et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Second, specific microbial taxa such as \u003cem\u003eGordonibacter pamelaeae\u003c/em\u003e, along with PLCG1-related signaling pathways, may serve as potential biomarkers for OA susceptibility or progression. Finally, PLCG1-associated signaling pathways may represent a potential therapeutic target, and modulating PLCG1-related inflammatory responses in chondrocytes could help limit cartilage degeneration and pyroptosis-related processes. However, further experimental and clinical studies are required to validate these potential applications.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, our MR analyses were primarily based on GWAS data from European populations, which may limit generalizability. Second, although multiple sensitivity analyses were performed, horizontal pleiotropy cannot be completely excluded(Sanderson et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Third, while MR provides strong evidence for causality, it cannot fully elucidate the underlying biological mechanisms. The specific microbial metabolites linking \u003cem\u003eGordonibacter pamelaeae\u003c/em\u003e to PLCG1 regulation remain to be identified. Finally, although our in vitro experiments provide functional support for the role of PLCG1 in regulating inflammatory and pyroptotic responses, further validation using primary human chondrocytes and animal models of OA is required to verify its physiological relevance.\u003c/p\u003e \u003cp\u003eIn conclusion, this study suggests a potential causal pathway linking gut microbiota to OA through PLCG1-related signaling. PLCG1 may act as a context-dependent regulator that modulates inflammatory responses in chondrocytes. These findings provide new insights into the gut\u0026ndash;joint axis and may inform future strategies for OA prevention and treatment.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Natural Science Foundation of Guangdong Province (Grant No. 2024A1515011231).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study was a secondary analysis of existing, publicly available, and anonymized summary-level data. All contributing studies had previously obtained the necessary ethics approvals and participant consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShengkun Li designed the study, performed the MR and bioinformatic analyses, and wrote the original draft. Shaozi Zhong supervised and analyzed the in vitro experiments. Chun Zeng conceived the study, supervised the project, and reviewed \u0026amp; edited the manuscript. Shengkun Li and Shaozi Zhong contributed equally to this work. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe GWAS summary statistics for gut microbiota and OA are publicly available from the GWAS Catalog. The pQTL data are available from the deCODE genetics website. The GEO dataset (GSE57218) is accessible from the NCBI GEO database. All other data generated or analyzed during this study are available from the corresponding author on reasonable.\u003c/p\u003e\n\u003cp\u003erequest.\u003cbr clear=\"all\"\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBae M, Le C, Mehta RS, Dong X, Pieper LM, Ramirez L, Alexander M, Kiamehr S, Turnbaugh PJ, Huttenhower C, Chan AT, Balskus EP (2024) Metatranscriptomics-guided discovery and characterization of a polyphenol-metabolizing gut microbial enzyme. 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Nat Rev Rheumatol 20(9):545\u0026ndash;564. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41584-024-01146-0\u003c/span\u003e\u003cspan address=\"10.1038/s41584-024-01146-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":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":"amb-express","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ambe","sideBox":"Learn more about [AMB Express](http://amb-express.springeropen.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/AMBE/default.aspx","title":"AMB Express","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Osteoarthritis, Gut Microbiota, Mendelian Randomization, PLCG1, Pyroptosis, Gut-Joint Axis","lastPublishedDoi":"10.21203/rs.3.rs-9363368/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9363368/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe gut\u0026ndash;joint axis has been increasingly implicated in osteoarthritis (OA), yet the causal contribution of specific gut microbes and their downstream molecular mechanisms remain unclear. We employed a multi-stage, two-sample Mendelian randomization (MR) framework. This approach utilized summary-level data from large-scale genome-wide association studies (GWAS) for gut microbiota, plasma proteins, and OA. The analysis involved three steps: (1) identifying gut microbial taxa causally associated with OA risk; (2) screening of pyroptosis-related proteins using pQTL data; and (3) performing mediation analysis to evaluate potential intermediate mechanisms. We also performed transcriptomic analysis of human cartilage and in vitro experiments in chondrocytes to support the biological relevance of our findings. Our MR analysis identified \u003cem\u003eGordonibacter pamelaeae\u003c/em\u003e as inversely associated with OA risk. Among 11 candidate pyroptosis-related proteins, only Phospholipase C Gamma 1 (PLCG1) showed a significant inverse association with OA. MR analysis further suggested that G. pamelaeae was positively associated with PLCG1 levels. Mediation analysis indicated that PLCG1 partially mediated the association between G. pamelaeae and OA. Consistent with these findings, PLCG1 mRNA levels were reduced in human OA cartilage. Furthermore, our in vitro experiments demonstrated that PLCG1 knockdown enhanced IL-1β-induced inflammatory and pyroptotic responses in chondrocytes. This study suggests a potential pathway linking gut microbiota to OA through PLCG1-related signaling. PLCG1 may act as a context-dependent regulator that limits excessive inflammatory responses under stress conditions. These findings refine the current understanding of the gut\u0026ndash;joint axis and may help identify potential targets for OA intervention.\u003c/p\u003e","manuscriptTitle":"Phospholipase C gamma 1 links Gordonibacter pamelaeae to osteoarthritis risk","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-30 14:34:43","doi":"10.21203/rs.3.rs-9363368/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"296818534763256054351286774726165643857","date":"2026-05-18T12:50:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T17:13:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"177400515490155713987998429405816311813","date":"2026-04-30T17:11:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-22T08:17:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-16T14:31:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-16T11:29:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"AMB Express","date":"2026-04-12T06:03:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"amb-express","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ambe","sideBox":"Learn more about [AMB Express](http://amb-express.springeropen.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/AMBE/default.aspx","title":"AMB Express","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4ccd6cab-204e-4d29-95ca-5d4a46e08667","owner":[],"postedDate":"April 30th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"296818534763256054351286774726165643857","date":"2026-05-18T12:50:12+00:00","index":48,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T17:13:07+00:00","index":30,"fulltext":""},{"type":"reviewerAgreed","content":"177400515490155713987998429405816311813","date":"2026-04-30T17:11:15+00:00","index":29,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-30T14:34:43+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-30 14:34:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9363368","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9363368","identity":"rs-9363368","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}