Naringenin Restores Osteogenic Differentiation in TNF-α-Treated Bone Marrow Mesenchymal Stem Cells by Targeting AKR1B1

Naringenin Restores Osteogenic Differentiation in TNF-α-Treated Bone Marrow Mesenchymal Stem Cells by Targeting AKR1B1 | 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 Naringenin Restores Osteogenic Differentiation in TNF-α-Treated Bone Marrow Mesenchymal Stem Cells by Targeting AKR1B1 Bin He, Feng He, Huimin Li, Leyi Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6241423/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Chronic inflammatory microenvironments, driven by cytokines like TNF-α, disrupt bone remodeling by suppressing osteogenic differentiation, contributing to inflammatory bone diseases. While TNF-α inhibitors mitigate inflammation, they lack osteoprotective effects. Naringenin, a citrus flavonoid with antioxidative and anti-inflammatory properties, shows potential but its mechanism in counteracting TNF-α-induced osteogenic suppression remains unclear. This study aimed to elucidate how naringenin rescues osteogenesis in TNF-α-treated hBMSCs and identify its molecular targets. Methods hBMSCs were treated with TNF-α and naringenin. Cell viability and osteogenic differentiation were assessed via Live/Dead staining, CCK-8, ALP/ARS staining, and qRT-PCR. Antioxidant activity was evaluated using free radical scavenging assays, alongside SOD/CAT activity measurements. Bioinformatics analyses identified TNF-α-regulated differentially expressed genes. GO, KEGG, and PPI network analyses highlighted key pathways. Molecular docking (CB-Dock2) predicted naringenin’s binding to target proteins. Results Naringenin reversed TNF-α-induced suppression of osteogenic markers and restored mineralization in BMSCs. Naringenin also scavenged free radicals, reduced ROS, enhanced SOD/CAT activity, and attenuated TNF-α-driven inflammatory cytokines. Bioinformatic analyses revealed TNF-α-induced enrichment of NF-κB and cytokine pathways, while GSEA highlighted suppressed ossification. TNF-α upregulated AKR1B1, an enzyme linked to oxidative stress, which naringenin suppressed. Molecular docking confirmed strong binding between naringenin and AKR1B1 (Vina score: -10). Conclusion Naringenin rescues TNF-α-impaired osteogenesis by targeting AKR1B1, restoring redox balance, and inhibiting NF-κB-mediated inflammation. This positions naringenin as a therapeutic candidate for inflammatory bone disorders. The study identifies AKR1B1 as a novel target for mitigating inflammation-driven bone loss, bridging phytochemical research and clinical orthopedics. Naringenin AKR1B1 Osteogenic differentiation TNF-α hBMSCs Oxidative stress Molecular docking Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Chronic inflammatory microenvironments are pivotal drivers of impaired bone metabolism, contributing to clinically significant challenges such as osteoporosis, delayed fracture healing, and inflammatory osteolysis in conditions like rheumatoid arthritis and diabetic bone disease[ 1 ]. Pro-inflammatory cytokines disrupt the balance between osteogenesis and osteoclastogenesis, favoring bone resorption while suppressing osteoblast differentiation. Among these cytokines, tumor necrosis factor-alpha (TNF-α) plays a central role in bone remodeling. Elevated TNF-α levels inhibit osteogenic differentiation of bone marrow mesenchymal stem cells (BMSCs) by downregulating key transcription factors (e.g., RUNX2, Osterix) and activating NF-κB signaling, which amplifies inflammatory cascades and promotes osteoclastogenesis[ 2 ]. Clinical evidence further links TNF-α overexpression to poor bone repair outcomes, underscoring its therapeutic relevance[ 3 ]. While TNF-α inhibitors (e.g., biologics) mitigate inflammation, their immunosuppressive risks and limited capacity to directly restore osteogenesis necessitate alternative strategies targeting the molecular crosstalk between inflammation and osteoblast dysfunction. Natural flavonoids with dual anti-inflammatory and osteoprotective properties have emerged as promising candidates to address this unmet need. Naringenin, a bioactive compound abundant in citrus fruits, exhibits potent antioxidative and anti-inflammatory effects, including scavenging reactive oxygen species (ROS), suppressing NLRP3 inflammasome activation, and inhibiting pro-inflammatory cytokine release[ 4 ]. Notably, recent studies suggest its regulatory effects on bone metabolism, such as enhancing osteoblast mineralization under physiological conditions[ 5 – 8 ]. These properties position naringenin as a potential therapeutic agent to counteract TNF-α-driven osteogenic suppression. However, its precise molecular mechanisms in rescuing osteoblast function under inflammatory conditions remain unclear, particularly regarding direct interactions with TNF-α-mediated pathways. This knowledge gap limits the translation of naringenin’s potential into targeted therapies for inflammatory bone disorders. To investigate this, our study employed an integrative approach combining bioinformatics analyses with experimental validation. We first utilized the GEO dataset GSE176086 to confirm that TNF-α treatment in BMSCs induced transcriptional reprogramming characterized by suppressed osteogenic genes and upregulated inflammatory pathways, consistent with our in vitro findings. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses highlighted pathways related to oxidative stress and NF-κB signaling, while protein-protein interaction (PPI) networks identified hub genes implicated in TNF-α-mediated osteogenic inhibition. Cross-referencing naringenin’s predicted molecular targets with these TNF-α-regulated differentially expressed genes revealed AKR1B1 as a key overlapping candidate. Experimental validation demonstrated that TNF-α significantly upregulated AKR1B1 expression in BMSCs, which was effectively reversed by naringenin treatment. Further structural analysis via molecular docking predicted stable binding between naringenin and AKR1B1, suggesting a direct interaction. In summary, this study establishes naringenin as a potent modulator of TNF-α-induced osteogenic suppression, primarily through targeting AKR1B1. By integrating multi-omics data with functional experiments, we provide mechanistic insights into how naringenin rescues osteoblast differentiation in inflammatory microenvironments. These findings not only advance our understanding of flavonoid-based interventions for bone disorders but also highlight AKR1B1 as a novel therapeutic target for inflammatory bone diseases. This work bridges phytochemical research and clinical orthopedics, offering a foundation for developing naringenin as a multi-target agent to combat inflammation-driven bone loss. Materials and methods In vitro biocompatibility test Human bone marrow-derived mesenchymal stem cells (hBMSCs) were maintained in α-MEM complete medium supplemented with 10% fetal bovine serum and 1% antibiotics. For the live/dead staining assay, cells were seeded into 24-well plates and divided into four groups: a control group, a group treated with 20 ng/mL TNF-α alone, a group receiving 20 ng/mL TNF-α with 20 µM naringenin (designated as “+”), and another group treated with 20 ng/mL TNF-α plus 50 µM naringenin (designated as “++”). After incubating for 48 hours, the cells were incubated in the dark for 30 minutes with a staining solution containing 5 µM Calcein-AM and 0.6 µM propidium iodide (Life Technologies), following the manufacturer’s protocol. The resulting images of live and dead cells were then captured using an inverted microscope (LSM700, Zeiss, Germany). For the CCK-8 assay, hBMSCs were first detached using trypsin and subsequently reseeded into 96-well plates. At time points of 24, 48, and 72 hours, 10 µL of CCK-8 reagent was added to each well, and the plates were incubated at 37°C. Finally, the absorbance was measured at 450 nm with a microplate reader, allowing for quantitative assessment of cell viability and proliferation under the different treatment conditions. Cell staining In a 24-well plate, hBMSCs were initially plated at a density of 50,000 cells per well. On the first day, the cells were maintained in complete medium to facilitate proper adhesion. Beginning on day two, the complete medium was replaced with an osteogenic induction medium, which was refreshed every two days for durations of either 7 or 14 days, depending on the experimental setup. After the induction period, the cells underwent three successive washes with PBS to remove any residual medium, followed by fixation in neutral formalin at room temperature for 30 minutes. Finally, the cells were stained with ALP and ARS solutions as per the standard protocol, and images were subsequently captured using a stereomicroscope. Free radical scavenging test A DPPH• solution at a concentration of 125 µmol/mL was prepared and then mixed in equal volumes with different concentrations of naringenin. The mixture was subsequently incubated at 37°C for 15 minutes to allow the reaction to proceed. After the incubation period, the absorbance was measured at 517 nm using a UV-vis spectrophotometer, providing a quantitative assessment of the radical scavenging activity of the naringenin. To evaluate the scavenging activity of naringenin against hydrogen peroxide, a commercial H₂O₂ detection kit was employed. In this assay, H₂O₂ reacts with ammonium molybdate to generate a stable yellow complex that displays a characteristic absorbance peak at 405 nm. Various concentrations of naringenin were mixed with a 2 mM H₂O₂ solution and incubated at 37°C for 24 hours. After incubation, the residual H₂O₂ was quantified according to the kit protocol provided by the manufacturer. The extent to which naringenin removed H₂O₂ was then determined by comparing the measured concentration with the initial 2 mM level. Superoxide anions (O₂ •− ) were generated via the enzymatic oxidation of xanthine by xanthine oxidase. These radicals subsequently interacted with water-soluble tetrazolium salt-1 (WST-1) to produce WST-1 formazan, which exhibits a distinct absorption peak at 450 nm. The concentration of the formed formazan was then quantified using a microplate reader set to measure absorbance at 450 nm. A solution containing 1 mM FeSO₄ and 2 mM H₂O₂ was prepared in sodium acetate buffer, after which various concentrations of naringenin were introduced. The reaction was allowed to proceed for 5 minutes before the absorbance at 650 nm was measured using TMB (3,3',5,5'-tetramethylbenzidine) as the chromogenic substrate. In addition, the hydroxyl radical (•OH) scavenging activity of naringenin was further confirmed by electron spin resonance (ESR) spectroscopy. In this complementary assay, •OH radicals produced from the FeSO₄/H₂O₂ reaction in sodium acetate buffer were captured using DMPO (5,5-dimethyl-1-pyrroline N-oxide). Following the addition of different concentrations of naringenin to the reaction system, the reduction in the ESR peak intensity was analyzed to quantify the scavenging effect. Antioxidant capacity test hBMSCs were initially seeded in 24-well plates and divided into four distinct groups: a negative control group, a positive control group treated with 50 mg/mL Rosup, a group exposed solely to 500 µM H₂O₂, and a group receiving a combined treatment of 500 µM H₂O₂ with 50 µM naringenin. Following a 48-hour incubation period, the culture medium was aspirated, and the cells were incubated with a ROS detection solution at 37°C for 30 minutes. Subsequent fluorescence imaging was performed using a fluorescence microscope, and the intensity of the fluorescent signals was semi-quantitatively analyzed using ImageJ software. To determine the total antioxidant capacity imparted by naringenin, hBMSCs were first exposed to 50 µM naringenin for 48 hours. After treatment, the cells were harvested and resuspended in 200 µL of cold PBS. The cell suspension was then thoroughly homogenized to achieve complete lysis and to liberate the intracellular antioxidants. The lysate was centrifuged at 12,000g for 5 minutes at 4°C, and the supernatant was collected for further analysis. A standard curve was generated using a Trolox standard solution, after which 170 µL of the assay’s working solution was added to each sample. The mixture was incubated at room temperature for 6 minutes, and the absorbance was recorded at 414 nm. The total antioxidant capacity was then calculated based on the absorbance values and the established calibration curve, following the manufacturer’s protocol. For the assessment of superoxide dismutase (SOD) activity, hBMSCs were processed in a similar manner. The cells were first washed three times with PBS to remove any residual media and then lysed using an SOD sample preparation solution. After the addition of the assay’s working reagent, the samples were incubated at 37°C for 30 minutes. The absorbance was subsequently measured at 450 nm, and the SOD activity was calculated and expressed in enzyme activity units in accordance with the kit instructions. In addition, the catalase (CAT) activity was measured by mixing 0.1 mL of the cell lysate supernatant with 1.1 mL of the working solution. The reaction mixture was incubated at 37°C for 1 minute, and then a stop solution was added to terminate the reaction. The final absorbance was measured at 405 nm, and the CAT enzyme activity was calculated based on the absorbance change and the provided protocol. Quantitative real-time PCR Total RNA was extracted from cells or tissues using TRIzol reagent (Invitrogen) in accordance with the manufacturer's protocol. The isolated RNA was then reverse transcribed into cDNA using a qScript cDNA Synthesis kit (Takara, Shiga, Japan). Quantitative PCR was conducted on an ABI Step-One Plus™ Real-Time PCR System with SYBR® Premix Ex TaqTM (Takara, Shiga, Japan). The expression levels of the target genes were normalized to Gapdh, and the relative quantification was performed using the 2 −ΔΔCt method. Table 1 contained a list of all primers. Table 1 Primers for qRT-PCR. Gene Forward Reverse GAPDH GGAGCGAGATCCCTCCAAAAT GGCTGTTGTCATACTTCTCATGG RUNX2 TGGTTACTGTCATGGCGGGTA TCTCAGATCGTTGAACCTTGCTA OPN GAAGTTTCGCAGACCTGACAT GTATGCACCATTCAACTCCTCG OCN CACTCCTCGCCCTATTGGC CCCTCCTGCTTGGACACAAAG ALP ACTGGTACTCAGACAACGAGAT ACGTCAATGTCCCTGATGTTATG iNOS GGACTTTTGTACTCATCTGCAC GTGGACGGGTCGATGTCAC TNF-α GGCCAATGTGAGGGAGTTGAT CCCGCTTTATCTGTGAGCCC COX-2 GCACCCCGACATAGAGAGC CTGCGGAGTGCAGTGTTCT IL-6 CACTGGTCTTTTGGAGTTTGAG GGACTTTTGTACTCATCTGCAC AKR1B1 TTTTCCCATTGGATGAGTCGG CCTGGAGATGGTTGAAGTTGG SNCA AAGAGGGTGTTCTCTATGTAGGC GCTCCTCCAACATTTGTCACTT Gene expression data collection and preprocessing The GSE176086 dataset was obtained from the Gene Expression Omnibus (GEO) database ( http://www.ncbi.nlm.nih.gov/geo ) and is based on the GPL21185 platform. This dataset comprises 12 hBMSC samples, including six samples treated with TNF-α and six untreated controls. The online analysis tool GEO2R ( https://www.ncbi.nlm.nih.gov/geo/geo2r ) was employed for data normalization and differential gene expression (DEG) analysis. The selection criteria for DEGs were set as an absolute log2 fold change (logFC) greater than 1 and a P-value less than 0.05. Furthermore, GEO2R was also utilized to generate visualizations such as volcano plots to provide an intuitive representation of gene expression significance and variation magnitude. Functional analysis The Metascape database ( http://metascape.org ) was utilized to conduct Gene Ontology (GO) enrichment analyses, encompassing biological processes, cellular components, and molecular functions, for the previously identified differentially expressed genes (DEGs). Additionally, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to further elucidate the functional roles of these DEGs. A significance threshold of p < 0.05 was applied for all analyses. Gene Set Enrichment Analysis (GSEA) GSEA was performed using the GOBP_OSSIFICATION and CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION gene sets obtained from the Molecular Signatures Database (MSigDB). The DEGs identified in the GSE176086 dataset were uploaded to the Wei Sheng Xin online platform ( https://www.bioinformatics.com.cn/ ) to conduct GSEA, enabling systematic evaluation of pathway enrichment patterns associated with osteogenic suppression and inflammatory signaling. Protein-Protein Interaction (PPI) Network Analysis We used STRING 11.0 ( https://string-db.org/ ), an online tool for analyzing protein interactions, to identify the network PPI. All DEGs were uploaded for PPI analysis. To further capture the relationships between the terms, a subset of enriched terms has been selected and rendered as a network plot, where terms with a similarity > 0.3 are connected by edges. We select the terms with the best p-values from each of the 20 clusters, with the constraint that there are no more than 15 terms per cluster and no more than 250 terms in total. The three most significantly enriched clusters were selected for further refined PPI network analysis. Cytoscape v3.9.1 ( https://www.cytoscape.org/ ), a tool for analyzing and visualizing protein interaction networks, was used to display the nodes and connections in the PPI network. Naringenin target prediction We utilized SwissTargetPrediction ( http://www.swisstargetprediction.ch/ ) to identify potential target proteins of naringenin and conducted enrichment analysis on all predicted targets. To refine our results, we intersected the top 100 predicted target genes with the top 200 DEGs from the GSE176086 dataset. This intersection analysis revealed two potential target genes, AKR1B1 and SNCA, which may play a critical role in the mechanism of naringenin's action. Molecular docking To predict the binding interactions between naringenin and its potential target proteins AKR1B1 and SNCA, molecular docking analysis was performed using the CB-Dock2 online tool ( http://clab.labshare.cn/cb-dock2/ ). The three-dimensional (3D) structure of naringenin was retrieved from the PubChem database, while the protein structures of AKR1B1 and SNCA were obtained from the Protein Data Bank (PDB). Before docking, the protein structures were prepared by removing water molecules and heteroatoms, followed by energy minimization. CB-Dock2 employs blind docking to automatically predict the binding pocket and perform flexible docking simulations. The docking scores, which represent the binding affinity, were recorded, and the docking conformations with the lowest binding energy were selected for further analysis. Molecular dynamic simulation Molecular dynamics (MD) simulations were conducted on the WebGro online platform with the GROMOS96 43a1 force field and the TIP4P water model. The system was solvated in a dodecahedron box, and 0.15 M NaCl ions were added to mimic physiological ionic strength. Long-range electrostatic interactions were calculated using the Particle Mesh Ewald method with a cutoff distance of 1.2 nm, while short-range van der Waals interactions were truncated at 1.0 nm. Bond lengths involving hydrogen atoms were constrained using the LINCS algorithm, enabling a 2 fs integration time step. The system underwent energy minimization using the steepest descent algorithm to eliminate steric clashes, followed by a two-step equilibration protocol: (1) 100 ps of NVT (constant particle number, volume, and temperature) equilibration with the V-rescale thermostat to stabilize the temperature at 298 K, and (2) 100 ps of NPT (constant particle number, pressure, and temperature) equilibration using the Berendsen barostat to adjust the pressure to 1 bar. Subsequently, a production MD simulation was carried out for 50 ns under NPT conditions, with coordinates saved every 10 ps for trajectory analysis. Trajectory analysis included root mean square deviation (RMSD) and root mean square fluctuation (RMSF). Statistical Analysis All statistical analyses were performed using GraphPad Prism (version 8.2.0, GraphPad Software, CA, USA). Data from each experiment were expressed as means ± standard deviation (SD), and all experiments were conducted in triplicate or more. For comparisons between two groups, the Student's t-test was used. For comparisons involving multiple groups, One-way analysis of variance (ANOVA) followed by Dunnett’s multiple comparisons test was employed. Statistical significance was considered for p-values < 0.05. Results Naringenin ameliorates TNF-α-induced suppression of osteogenic differentiation in hBMSCs To evaluate the potential of naringenin in reversing TNF-α-induced inhibition of osteogenic differentiation, we first assessed its effects on the survival and proliferation of hBMSCs. Live/Dead staining (Fig. 1 A-B) and CCK-8 assays (Fig. 1 C) revealed that TNF-α did not significantly affect the cell viability or proliferation at the tested concentrations, and similarly, both low and high doses of naringenin did not show any adverse effects on cell growth. Next, we explored whether naringenin could rescue osteogenic differentiation that was suppressed by TNF-α. Both ALP and ARS staining demonstrated that TNF-α significantly inhibited osteogenic differentiation, whereas naringenin effectively reversed this inhibition in a dose-dependent manner (Fig. 1 D). Additionally, qRT-PCR analysis confirmed that the addition of naringenin led to a marked upregulation of osteogenic markers (Fig. 1 E). These findings collectively suggest that naringenin can counteract the inhibitory effects of TNF-α on osteogenesis, supporting its potential as a therapeutic agent for conditions involving osteogenic dysfunction. Anti-inflammatory and antioxidant properties of naringenin Having established that naringenin can reverse TNF-α-induced inhibition of osteogenesis, we proceeded to investigate the underlying molecular mechanisms through which naringenin exerts its beneficial effects on osteogenic differentiation. Given that TNF-α is a well-known inflammatory mediator involved in various pathophysiological processes, we aimed to explore whether naringenin also possesses anti-inflammatory and antioxidant properties that could further support its role in modulating osteogenesis. To assess the antioxidant capacity of naringenin, we initially evaluated the ability of naringenin to scavenge various free radicals, including DPPH•, •OH, O 2 •− and H 2 O 2 (Fig. 2 A). The results demonstrated that naringenin effectively neutralizes these free radicals in a dose-dependent manner. Then we measured the intracellular ROS levels using fluorescence detection (Fig. 2 B). The results revealed a significant reduction in ROS content following naringenin treatment, indicating its potential to alleviate oxidative stress (Fig. 2 C). To further evaluate its antioxidant potential, we performed three distinct assays: total antioxidant capacity, SOD activity, and CAT activity (Fig. 2 D-F). The findings consistently demonstrated the robust antioxidant effects of naringenin. Moreover, we investigated the impact of naringenin on inflammatory cytokine expression under oxidative stress conditions induced by H 2 O 2 . Exposure to H 2 O 2 led to a marked upregulation of several inflammatory mediators, including TNF-α, iNOS, COX-2, and IL-6 (Fig. 2 G). However, naringenin treatment significantly attenuated the expression of these inflammatory cytokines, indicating that naringenin possesses potent anti-inflammatory properties. These results suggest that naringenin not only mitigates oxidative stress but also effectively modulates inflammation, highlighting its potential as a therapeutic agent for inflammatory-related bone disorders. TNF-α-induced transcriptional reprogramming of hBMSCs impairs osteogenesis and enhances inflammatory pathways Next, through GEO database, we found and downloaded the mRNA microarray analysis dataset GSE176086 after TNF-α treatment of hBMSCs. After normalizing the data, differential expression analysis revealed 1,095 significantly upregulated genes and 1,083 downregulated genes, which were visualized in a volcano plot (Fig. 3 A-C). GO enrichment analysis of the top three biological processes indicated significant associations with viral response (e.g., defense response to virus, type I interferon signaling), chemotaxis (e.g., granulocyte migration), and inflammatory activation (e.g., response to lipopolysaccharide), reflecting TNF-α’s role in triggering immune-inflammatory cascades (Fig. 3 D). KEGG pathway analysis further confirmed enrichment in pathways related to cytokine-cytokine receptor interaction, NF-κB signaling, and TNF signaling (Fig. 3 E), underscoring TNF-α’s dual role in suppressing osteogenesis and enhancing pro-inflammatory signaling. To explore the mechanistic relationship between TNF-α and osteogenic inhibition, GSEA was conducted using the GOBP_OSSIFICATION and CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION gene sets. The results showed that TNF-α treatment negatively correlated with GOBP_OSSIFICATION (NES = -2.1, FDR < 0.05; Fig. 3 F), indicating a suppression of osteogenic differentiation. Conversely, CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION exhibited significant positive enrichment (NES = 2.6, FDR < 0.05; Fig. 3 G), consistent with TNF-α-induced inflammatory activation. Furthermore, we examined the association between the top ten enriched pathways and representative DEGs with significant expression changes (Fig. 4 A). Concurrently, a PPI network was constructed using an online database, followed by functional enrichment analysis of the identified DEGs. The results indicated that these DEGs were primarily enriched in 20 biological categories (Fig. 4 B). To refine our analysis, we selected the top three most significantly enriched categories for further PPI network investigation. The findings revealed that these categories were highly correlated with key DEGs, predominantly associated with cytokine signaling in the immune system, the network map of SARS-CoV-2 signaling, and the innate immune response (Fig. 4 C). Collectively, these findings demonstrate that TNF-α drives a transcriptional reprogramming of BMSCs, shifting them towards an inflammatory phenotype while impairing their osteogenic potential, providing a molecular framework for its detrimental effects on bone formation. Naringenin reverses TNF-α-induced osteogenic inhibition by regulating AKR1B1 To further elucidate the molecular mechanism underlying naringenin’s protective effects, we first predicted its potential targets (PTs) based on its chemical structure and performed enrichment analysis on the identified targets (Fig. 5 A-B). The results revealed that the PTs were primarily enriched in kinase, enzyme, and lyase categories, suggesting that naringenin predominantly interacts with enzymatic proteins. Next, we intersected the top 100 PTs with the top 200 DEGs identified in the GSE176086 dataset, leading to the identification of two potential key genes: AKR1B1 and SNCA (Fig. 5 C). quantitative RT-PCR analysis demonstrated that TNF-α treatment significantly upregulated AKR1B1 expression, whereas the addition of naringenin effectively suppressed this effect (Fig. 5 D). In contrast, SNCA expression did not exhibit significant changes upon treatment (Fig. 5 E). To gain further insights into the interaction between naringenin and these candidate genes, molecular docking simulations were conducted. The docking results indicated that naringenin exhibited a strong binding affinity for AKR1B1 (Vina score = -10), whereas its binding affinity for SNCA was relatively weaker (Vina score = -5) (Fig. 5 F-G). Furthermore, the naringenin-AKR1B1 complex was selected for molecular dynamics simulations. RMSD and RMSF served as critical metrics to evaluate system stability, where values within 0.5 nm were considered indicative of a stable system. Throughout the simulation, the naringenin-AKR1B1 complex exhibited favorable stability, with both RMSD and RMSF consistently maintained below the threshold of 0.5 nm, reflecting robust structural integrity and minimal conformational fluctuations under the defined simulation conditions (Fig. 5 H-I). These findings strongly suggest that naringenin may exert its biological effects by directly binding to AKR1B1. Functionally, AKR1B1 encodes aldose reductase, an enzyme that catalyzes the conversion of glucose to sorbitol, consuming NADPH in the process[ 9 ]. This diminishes glutathione (GSH) regeneration capacity and exacerbates oxidative stress. Our results suggest that naringenin may attenuate the activation of the polyol pathway by directly binding to the active site of AKR1B1, thereby preserving NADPH for GSH regeneration and reducing ROS levels. Additionally, naringenin may inhibit AKR1B1 expression through its antioxidant (ROS-reducing) and anti-inflammatory (NF-κB nuclear translocation-inhibiting) properties. Collectively, our findings provide strong evidence that naringenin reverses TNF-α-induced osteogenic inhibition by regulating AKR1B1, highlighting its potential as a therapeutic agent against inflammation-induced bone loss (Fig. 6 ). Discussion Chronic inflammatory microenvironments, driven by cytokines such as TNF-α, disrupt bone remodeling by skewing the balance toward osteoclastogenesis and suppressing osteoblast differentiation, ultimately leading to pathological bone loss[ 10 , 11 ]. This study elucidates the therapeutic potential of naringenin, a citrus-derived flavonoid, in counteracting TNF-α-induced osteogenic suppression in hBMSCs. Our findings highlight a novel mechanism by which naringenin rescues osteoblast differentiation through direct interaction with AKR1B1, a key enzyme implicated in oxidative stress and inflammation. These results not only advance our understanding of flavonoid-based interventions for inflammatory bone disorders but also underscore the translational potential of targeting AKR1B1 in metabolic and inflammatory bone diseases. The clinical relevance of this work lies in the urgent need for therapies that address the dual challenges of inflammation and impaired osteogenesis in conditions such as rheumatoid arthritis, diabetic osteopathy, and osteoporosis[ 12 , 13 ]. While TNF-α inhibitors like biologics alleviate inflammation, their immunosuppressive risks and inability to directly restore osteoblast function limit their utility[ 14 ]. Natural compounds like naringenin, with dual anti-inflammatory and osteoprotective properties, offer a promising alternative. Our study demonstrates that naringenin reverses TNF-α-driven suppression of osteogenic markers (e.g., RUNX2, OPN, ALP) while mitigating oxidative stress and inflammatory cytokine production. This multifaceted action positions naringenin as a therapeutic candidate capable of addressing both the inflammatory and metabolic dysregulation underlying bone loss. Central to this mechanism is the identification of AKR1B1 as a critical mediator of TNF-α’s detrimental effects. AKR1B1, encoding aldose reductase, catalyzes the polyol pathway, consuming NADPH to convert glucose into sorbitol[ 9 , 15 ]. This process depletes cellular NADPH reserves, impairing GSH regeneration and exacerbating ROS accumulation. Elevated ROS levels activate NF-κB, further amplifying inflammatory cascades and suppressing osteogenic transcription factors[ 16 – 18 ]. Our data reveal that TNF-α significantly upregulates AKR1B1 expression in hBMSCs, aligning with prior studies linking AKR1B1 to diabetic complications and oxidative tissue damage. Crucially, naringenin suppresses AKR1B1 at both transcriptional and functional levels. Molecular docking analysis revealed a strong binding affinity (Vina score = -10) between naringenin and AKR1B1’s active site, suggesting direct inhibition of its enzymatic activity. By preserving NADPH for GSH synthesis, naringenin reduces oxidative burden, thereby restoring redox homeostasis—a finding corroborated by its dose-dependent scavenging of free radicals (DPPH•, •OH, O₂•⁻) and enhancement of SOD/CAT activity. The rescue of osteogenesis by naringenin cannot be attributed solely to AKR1B1 inhibition. Its anti-inflammatory effects, particularly the suppression of TNF-α-induced NF-κB activation, play a complementary role. TNF-α triggers NF-κB nuclear translocation, upregulating pro-inflammatory cytokines (e.g., IL-6, COX-2) that further inhibit osteoblast differentiation[ 19 – 21 ]. By reducing ROS levels, naringenin disrupts ROS-dependent NF-κB activation, while its potential interaction with IKK or IκBα may block downstream signaling[ 22 , 23 ]. This dual inhibition of oxidative and inflammatory pathways creates a permissive microenvironment for osteogenic differentiation, as evidenced by the restoration of RUNX2 and Osterix expression in naringenin-treated hBMSCs. Notably, our bioinformatics approach intersecting naringenin’s predicted targets with TNF-α-regulated DEGs initially identified SNCA as a potential candidate. However, experimental validation revealed no significant changes in SNCA expression, suggesting that naringenin’s osteoprotective effects are predominantly mediated through AKR1B1. This discrepancy may arise from tissue-specific regulatory mechanisms or post-transcriptional modifications not captured in our assays, warranting further investigation into SNCA’s role in BMSC differentiation. The pivotal role of AKR1B1 in naringenin’s mechanism highlights its potential as a therapeutic target for inflammatory bone diseases. AKR1B1 inhibitors, such as epalrestat, are already clinically approved for diabetic neuropathy or lung cancer, underscoring the enzyme’s druggability[ 24 – 26 ]. Our work extends this paradigm to bone metabolism, proposing that AKR1B1 inhibition could mitigate inflammation-driven osteopenia. However, several unanswered questions remain. While molecular docking supports naringenin-AKR1B1 binding, structural validation (e.g., X-ray crystallography) is needed to confirm interaction details. Additionally, the contribution of AKR1B1-independent pathways, such as naringenin’s modulation of BMP/Smad or MAPK signaling, requires exploration. Furthermore, our study focused on in vitro models, which may not fully recapitulate the complexity of bone tissue microenvironments. Co-culture systems or 3D bone organoids could better model interactions between osteoblasts, osteoclasts, and immune cells. Despite these limitations, our findings provide compelling evidence for naringenin’s osteoprotective effects. However, several gaps must be addressed to advance translational applications. First, the dose-dependent effects of naringenin were only partially characterized. A broader concentration range and pharmacokinetic studies are needed to establish optimal therapeutic doses. Second, the lack of in vivo data limits clinical relevance. Future work should assess naringenin’s efficacy in murine models of osteoporosis or fracture healing, particularly in the context of chronic inflammation. Third, the role of SNCA, though not validated here, warrants further investigation. Its involvement in protein aggregation and mitochondrial function suggests potential crosstalk with oxidative stress pathways in BMSCs. CRISPR-based knockout or overexpression studies could clarify its contribution to osteogenic differentiation. In conclusion, this study establishes naringenin as a potent modulator of TNF-α-induced osteogenic suppression, primarily through targeting AKR1B1. By inhibiting AKR1B1, naringenin restores NADPH availability, attenuates oxidative stress, and disrupts NF-κB-driven inflammation, thereby rescuing osteoblast differentiation. These findings bridge the gap between phytochemical research and clinical orthopedics, offering a mechanistic rationale for repurposing naringenin or AKR1B1 inhibitors in inflammatory bone diseases. Future studies should prioritize in vivo validation and explore combinatorial therapies targeting both oxidative stress and inflammatory pathways. Ultimately, this work advances our understanding of flavonoid-based interventions and underscores the therapeutic potential of dual-action agents in metabolic and inflammatory bone disorders. Conclusion This study establishes naringenin as a potent modulator of TNF-α-induced osteogenic suppression, primarily through targeting AKR1B1. By inhibiting AKR1B1, naringenin restores NADPH availability, attenuates oxidative stress, and disrupts NF-κB-driven inflammation, thereby rescuing osteoblast differentiation. These findings bridge the gap between phytochemical research and clinical orthopedics, offering a mechanistic rationale for repurposing naringenin or AKR1B1 inhibitors in inflammatory bone diseases. Future studies should prioritize in vivo validation and explore combinatorial therapies targeting both oxidative stress and inflammatory pathways. Ultimately, this work advances our understanding of flavonoid-based interventions and underscores the therapeutic potential of dual-action agents in metabolic and inflammatory bone disorders. Abbreviations TNF-α Tumor Necrosis Factor-alpha BMSCs Bone Marrow Sesenchymal Stem Cells ROS Reactive Oxygen Species GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes PPI Protein-protein interaction WST-1 Water-soluble tetrazolium salt-1 SOD Superoxide dismutase CAT Catalase GEO Gene Expression Omnibus DEGs Differentially Expressed Genes GSEA Gene Set Enrichment Analysis PTs Potential Targets GSH Glutathione MD Molecular Dynamics RMSD Root Mean Square Deviation RMSF Root Mean Square Fluctuation Declarations Acknowledgements Not applicable. Author contributions B.H. designed the study and literature research. H.L. defined the intellectual content. F.H. performed experiment. L.H. analyzed the data. B.H. and F.H. wrote the main manuscript text. All authors reviewed the manuscript. Funding This study was supported by the Zhejiang basic public welfare research program (NO. LTGY24H060001). Data availability For those requiring further elaboration or wishing to engage in deeper discourse, the corresponding author remains available for direct inquiry. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Redlich K, Smolen JS. Inflammatory bone loss: pathogenesis and therapeutic intervention. Nat Rev Drug Discov. 2012;11(3):234–50. Yao Q, et al. The role of TNF-α in osteoporosis, bone repair and inflammatory bone diseases: A review. Tissue Cell. 2024;89:102422. Fu SC, et al. The associations of TNF-α gene polymorphisms with bone mineral density and risk of osteoporosis: A meta-analysis. Int J Rheum Dis. 2019;22(9):1619–29. Cai J, et al. Naringenin: A flavanone with anti-inflammatory and anti-infective properties. Biomed Pharmacother. 2023;164:114990. Zhou X, et al. Naringenin is a Potential Anabolic Treatment for Bone Loss by Modulating Osteogenesis, Osteoclastogenesis, and Macrophage Polarization. Front Pharmacol. 2022;13:872188. Nor Muhamad ML et al. Scoping Rev Skeletal Eff Naringenin Nutrients, 2022. 14(22). Oršolić N et al. Antioxidative and Anti-Inflammatory Activities of Chrysin and Naringenin in a Drug-Induced Bone Loss Model in Rats. Int J Mol Sci, 2022. 23(5). Pan Z, et al. Naringenin protects against iron overload-induced osteoarthritis by suppressing oxidative stress. Phytomedicine. 2022;105:154330. Di Benedetto C, et al. The p53 tumor suppressor regulates AKR1B1 expression, a metastasis-promoting gene in breast cancer. Front Mol Biosci. 2023;10:1145279. Wang Q, et al. Diabetes fuels periodontal lesions via GLUT1-driven macrophage inflammaging. Int J Oral Sci. 2021;13(1):11. Straub RH, Cutolo M, Pacifici R. Evolutionary medicine and bone loss in chronic inflammatory diseases–A theory of inflammation-related osteopenia. Semin Arthritis Rheum. 2015;45(2):220–8. Gibon E, et al. Inflammation, ageing, and bone regeneration. J Orthop Translat. 2017;10:28–35. Gibon E, Lu L, Goodman SB. Aging, inflammation, stem cells, and bone healing. Stem Cell Res Ther. 2016;7:44. Jang DI et al. The Role of Tumor Necrosis Factor Alpha (TNF-α) in Autoimmune Disease and Current TNF-α Inhibitors in Therapeutics. Int J Mol Sci, 2021. 22(5). Balestri F et al. In Search of Differential Inhibitors of Aldose Reductase. Biomolecules, 2022. 12(4). Morgan MJ, Liu ZG. Crosstalk of reactive oxygen species and NF-κB signaling. Cell Res. 2011;21(1):103–15. Priya Dharshini LC, et al. Oxidative stress responsive transcription factors in cellular signalling transduction mechanisms. Cell Signal. 2020;72:109670. Nakano H, et al. Reactive oxygen species mediate crosstalk between NF-kappaB and JNK. Cell Death Differ. 2006;13(5):730–7. Sun SC. The non-canonical NF-κB pathway in immunity and inflammation. Nat Rev Immunol. 2017;17(9):545–58. Aoki T, et al. Inhibition of non-canonical NF-κB signaling suppresses periodontal inflammation and bone loss. Front Immunol. 2023;14:1179007. Zhang P, et al. Exosomes derived from LPS-preconditioned bone marrow-derived MSC modulate macrophage plasticity to promote allograft survival via the NF-κB/NLRP3 signaling pathway. J Nanobiotechnol. 2023;21(1):332. Perkins ND. Integrating cell-signalling pathways with NF-kappaB and IKK function. Nat Rev Mol Cell Biol. 2007;8(1):49–62. Karin M. How NF-kappaB is activated: the role of the IkappaB kinase (IKK) complex. Oncogene. 1999;18(49):6867–74. Zhang KR, et al. Targeting AKR1B1 inhibits glutathione de novo synthesis to overcome acquired resistance to EGFR-targeted therapy in lung cancer. Sci Transl Med. 2021;13(614):eabg6428. Bailly C. Moving toward a new horizon for the aldose reductase inhibitor epalrestat to treat drug-resistant cancer. Eur J Pharmacol. 2022;931:175191. Kallinikou D, et al. Diabetic neuropathy in children and adolescents with type 1 diabetes mellitus: Diagnosis, pathogenesis, and associated genetic markers. Diabetes Metab Res Rev. 2019;35(7):e3178. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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-6241423","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":430776424,"identity":"90063c57-4395-4837-8933-841f4e50a1a4","order_by":0,"name":"Bin He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIie3RsQrCMBCA4SuFTpU4ngj6CicugoKDL1IXMwlO4thQSBcfIODgM/gGKQGnUlcHh/oGjg4KVienGjfB/NvBfdxwAC7XD8aYECXSqAOgqzGwIC1lEhosZn17QprL5upipvFrtCGgMwlHMnwrcoLL0gDbxPXCS4Q8K+JzEefkqcIAnnQ98SFL+0jDeQI5+Q1pgDCqJwFMZftGPg+e5G5Dwoog0iQKn8SzIYhZQkiznoL9IlsXPMTjBzI+pOcSb6NuV5ldeV0OO0x9IG/39OuZoe1+FYu/WHa5XK6/6gF9qEaTGHXnMgAAAABJRU5ErkJggg==","orcid":"","institution":"The Fourth Affiliated Hospital of School of Medicine, International Institutes of Medicine, Zhejiang University","correspondingAuthor":true,"prefix":"","firstName":"Bin","middleName":"","lastName":"He","suffix":""},{"id":430776426,"identity":"8e2d0bbb-e7fe-43a0-b874-c8b40811644a","order_by":1,"name":"Feng He","email":"","orcid":"","institution":"The Fourth Affiliated Hospital of School of Medicine, International Institutes of Medicine, Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"He","suffix":""},{"id":430776428,"identity":"f5788dc7-7a23-4c04-9b1e-b6eabf5028a8","order_by":2,"name":"Huimin Li","email":"","orcid":"","institution":"The Fourth Affiliated Hospital of School of Medicine, International Institutes of Medicine, Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Huimin","middleName":"","lastName":"Li","suffix":""},{"id":430776429,"identity":"5e69f3a6-1a4d-495d-93cc-4d2903571fe1","order_by":3,"name":"Leyi Huang","email":"","orcid":"","institution":"The Fourth Affiliated Hospital of School of Medicine, International Institutes of Medicine, Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Leyi","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2025-03-17 06:08:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6241423/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6241423/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78914495,"identity":"8ced1642-2e97-486e-a683-df62f8cdd225","added_by":"auto","created_at":"2025-03-20 18:01:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":659816,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNaringenin ameliorates TNF-α-induced suppression of osteogenic differentiation in hBMSCs.\u003c/strong\u003e(A-B) Live/Dead images of hBMSCs cultured for 48 h. Scale bar: 200 μm. (C) Viability of hBMSCs quantified by the CCK-8 assay. (D) ARS and ALP staining of hBMSCs cultured for 14 days and 7 days respectively. Scale bar: 100 μm. (E) qRT-PCR analysis of osteogenic markers, including RUNX2, OPN, OCN and ALP. All data are shown as the means ± SD (n = 3) (∗p \u0026lt; 0.05, ∗∗p \u0026lt; 0.01, ∗∗∗p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6241423/v1/865bd528c91da011c6945324.png"},{"id":78914497,"identity":"83d665e0-fbd6-442b-a249-13a78c543727","added_by":"auto","created_at":"2025-03-20 18:01:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":387195,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnti-inflammatory and antioxidant properties of naringenin. \u003c/strong\u003e(A) Scavenging effect of naringenin with different concentration on free radicals. (B-C) Fluorescence detection of ROS. Scale bar: 100 μm. (D) Total antioxidant capacity of nomilin. (E) CAT activity of nomilin. (F) SOD activity of nomilin. (G) Gene expression analysis of inflammatory markers. All data are shown as the means ± SD (n = 3) (∗p \u0026lt; 0.05, ∗∗p \u0026lt; 0.01, ∗∗∗p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6241423/v1/bf394fe131c2ca7177da1866.png"},{"id":78914498,"identity":"bb83356b-e451-4f2b-a1b3-7da6561c6cfb","added_by":"auto","created_at":"2025-03-20 18:01:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":418740,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTNF-α-induced transcriptional reprogramming of hBMSCs impairs osteogenesis and enhances inflammatory pathways. \u003c/strong\u003e(A) Standardization of GSE176086 datasets. (B) UMAP of GSE176086 datasets. (C) Volcano plot of the DEGs in GSE176086. (D) GO functional annotation (BP, CC, and MF) for the DEGs. (E) KEGG pathway enrichment analysis for the DEGs. (F-G) Gene set enrichment analysis (GSEA) analysis.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6241423/v1/a46661cef01c681545f107dc.png"},{"id":78915051,"identity":"2222117e-f01b-4750-a254-65eb2e8103eb","added_by":"auto","created_at":"2025-03-20 18:17:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":647226,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Protein-Protein Interaction network. \u003c/strong\u003e(A) Enrichment analysis string diagram of difference categories and high significance DEGs. (B) TOP 20 enriched biological categories. (C) PPI networks of the TOP 3 enriched biological categories.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6241423/v1/8b4cae31840bc5fc3788ab9d.png"},{"id":78914500,"identity":"ddcd00da-8419-4378-814d-ac9cb7998789","added_by":"auto","created_at":"2025-03-20 18:01:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":385461,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNaringenin's target prediction and molecular structure analysis. \u003c/strong\u003e(A) The chemical structure (a) and molecular structure (b) of naringenin. (B) Target prediction and enrichment analysis of naringenin. (C) Wayne diagram between DEGs and the predicted targets. (D-E) qRT-PCR analysis of AKR1B1 and SNCA. (F-G) Molecular docking simulations of naringenin-AKR1B1 and naringenin-SNCA. (H-I) RMSD and RMSF analysis of naringenin-AKR1B1 complex. All data are shown as the means ± SD (n = 3) (∗p \u0026lt; 0.05, ∗∗p \u0026lt; 0.01, ∗∗∗p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6241423/v1/a02639e3c9f402a531fde99d.png"},{"id":78914794,"identity":"e4ed482b-12e5-4e34-95ec-c843aa7b47c9","added_by":"auto","created_at":"2025-03-20 18:09:23","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":205716,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNaringenin exerts antioxidant and anti-inflammatory effects by regulating AKR1B1 and reverses the osteogenic inhibition induced by TNF-α.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6241423/v1/2dff5f44d5a98aab21589985.png"},{"id":79840544,"identity":"d817c60a-4ba4-409b-bf52-d1461e400bc2","added_by":"auto","created_at":"2025-04-03 12:31:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3740128,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6241423/v1/5efc7866-5b0a-4eae-bc10-4a38da40fb30.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Naringenin Restores Osteogenic Differentiation in TNF-α-Treated Bone Marrow Mesenchymal Stem Cells by Targeting AKR1B1","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic inflammatory microenvironments are pivotal drivers of impaired bone metabolism, contributing to clinically significant challenges such as osteoporosis, delayed fracture healing, and inflammatory osteolysis in conditions like rheumatoid arthritis and diabetic bone disease[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Pro-inflammatory cytokines disrupt the balance between osteogenesis and osteoclastogenesis, favoring bone resorption while suppressing osteoblast differentiation. Among these cytokines, tumor necrosis factor-alpha (TNF-α) plays a central role in bone remodeling. Elevated TNF-α levels inhibit osteogenic differentiation of bone marrow mesenchymal stem cells (BMSCs) by downregulating key transcription factors (e.g., RUNX2, Osterix) and activating NF-κB signaling, which amplifies inflammatory cascades and promotes osteoclastogenesis[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Clinical evidence further links TNF-α overexpression to poor bone repair outcomes, underscoring its therapeutic relevance[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. While TNF-α inhibitors (e.g., biologics) mitigate inflammation, their immunosuppressive risks and limited capacity to directly restore osteogenesis necessitate alternative strategies targeting the molecular crosstalk between inflammation and osteoblast dysfunction.\u003c/p\u003e \u003cp\u003eNatural flavonoids with dual anti-inflammatory and osteoprotective properties have emerged as promising candidates to address this unmet need. Naringenin, a bioactive compound abundant in citrus fruits, exhibits potent antioxidative and anti-inflammatory effects, including scavenging reactive oxygen species (ROS), suppressing NLRP3 inflammasome activation, and inhibiting pro-inflammatory cytokine release[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Notably, recent studies suggest its regulatory effects on bone metabolism, such as enhancing osteoblast mineralization under physiological conditions[\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These properties position naringenin as a potential therapeutic agent to counteract TNF-α-driven osteogenic suppression. However, its precise molecular mechanisms in rescuing osteoblast function under inflammatory conditions remain unclear, particularly regarding direct interactions with TNF-α-mediated pathways. This knowledge gap limits the translation of naringenin\u0026rsquo;s potential into targeted therapies for inflammatory bone disorders.\u003c/p\u003e \u003cp\u003eTo investigate this, our study employed an integrative approach combining bioinformatics analyses with experimental validation. We first utilized the GEO dataset GSE176086 to confirm that TNF-α treatment in BMSCs induced transcriptional reprogramming characterized by suppressed osteogenic genes and upregulated inflammatory pathways, consistent with our \u003cem\u003ein vitro\u003c/em\u003e findings. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses highlighted pathways related to oxidative stress and NF-κB signaling, while protein-protein interaction (PPI) networks identified hub genes implicated in TNF-α-mediated osteogenic inhibition. Cross-referencing naringenin\u0026rsquo;s predicted molecular targets with these TNF-α-regulated differentially expressed genes revealed AKR1B1 as a key overlapping candidate. Experimental validation demonstrated that TNF-α significantly upregulated AKR1B1 expression in BMSCs, which was effectively reversed by naringenin treatment. Further structural analysis via molecular docking predicted stable binding between naringenin and AKR1B1, suggesting a direct interaction.\u003c/p\u003e \u003cp\u003eIn summary, this study establishes naringenin as a potent modulator of TNF-α-induced osteogenic suppression, primarily through targeting AKR1B1. By integrating multi-omics data with functional experiments, we provide mechanistic insights into how naringenin rescues osteoblast differentiation in inflammatory microenvironments. These findings not only advance our understanding of flavonoid-based interventions for bone disorders but also highlight AKR1B1 as a novel therapeutic target for inflammatory bone diseases. This work bridges phytochemical research and clinical orthopedics, offering a foundation for developing naringenin as a multi-target agent to combat inflammation-driven bone loss.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e \u003cb\u003eIn vitro\u003c/b\u003e \u003cb\u003ebiocompatibility test\u003c/b\u003e\u003c/p\u003e \u003cp\u003eHuman bone marrow-derived mesenchymal stem cells (hBMSCs) were maintained in α-MEM complete medium supplemented with 10% fetal bovine serum and 1% antibiotics. For the live/dead staining assay, cells were seeded into 24-well plates and divided into four groups: a control group, a group treated with 20 ng/mL TNF-α alone, a group receiving 20 ng/mL TNF-α with 20 \u0026micro;M naringenin (designated as \u0026ldquo;+\u0026rdquo;), and another group treated with 20 ng/mL TNF-α plus 50 \u0026micro;M naringenin (designated as \u0026ldquo;++\u0026rdquo;). After incubating for 48 hours, the cells were incubated in the dark for 30 minutes with a staining solution containing 5 \u0026micro;M Calcein-AM and 0.6 \u0026micro;M propidium iodide (Life Technologies), following the manufacturer\u0026rsquo;s protocol. The resulting images of live and dead cells were then captured using an inverted microscope (LSM700, Zeiss, Germany).\u003c/p\u003e \u003cp\u003eFor the CCK-8 assay, hBMSCs were first detached using trypsin and subsequently reseeded into 96-well plates. At time points of 24, 48, and 72 hours, 10 \u0026micro;L of CCK-8 reagent was added to each well, and the plates were incubated at 37\u0026deg;C. Finally, the absorbance was measured at 450 nm with a microplate reader, allowing for quantitative assessment of cell viability and proliferation under the different treatment conditions.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCell staining\u003c/h2\u003e \u003cp\u003eIn a 24-well plate, hBMSCs were initially plated at a density of 50,000 cells per well. On the first day, the cells were maintained in complete medium to facilitate proper adhesion. Beginning on day two, the complete medium was replaced with an osteogenic induction medium, which was refreshed every two days for durations of either 7 or 14 days, depending on the experimental setup. After the induction period, the cells underwent three successive washes with PBS to remove any residual medium, followed by fixation in neutral formalin at room temperature for 30 minutes. Finally, the cells were stained with ALP and ARS solutions as per the standard protocol, and images were subsequently captured using a stereomicroscope.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFree radical scavenging test\u003c/h3\u003e\n\u003cp\u003eA DPPH\u0026bull; solution at a concentration of 125 \u0026micro;mol/mL was prepared and then mixed in equal volumes with different concentrations of naringenin. The mixture was subsequently incubated at 37\u0026deg;C for 15 minutes to allow the reaction to proceed. After the incubation period, the absorbance was measured at 517 nm using a UV-vis spectrophotometer, providing a quantitative assessment of the radical scavenging activity of the naringenin.\u003c/p\u003e \u003cp\u003eTo evaluate the scavenging activity of naringenin against hydrogen peroxide, a commercial H₂O₂ detection kit was employed. In this assay, H₂O₂ reacts with ammonium molybdate to generate a stable yellow complex that displays a characteristic absorbance peak at 405 nm. Various concentrations of naringenin were mixed with a 2 mM H₂O₂ solution and incubated at 37\u0026deg;C for 24 hours. After incubation, the residual H₂O₂ was quantified according to the kit protocol provided by the manufacturer. The extent to which naringenin removed H₂O₂ was then determined by comparing the measured concentration with the initial 2 mM level.\u003c/p\u003e \u003cp\u003eSuperoxide anions (O₂\u003csup\u003e\u0026bull;\u0026minus;\u003c/sup\u003e) were generated via the enzymatic oxidation of xanthine by xanthine oxidase. These radicals subsequently interacted with water-soluble tetrazolium salt-1 (WST-1) to produce WST-1 formazan, which exhibits a distinct absorption peak at 450 nm. The concentration of the formed formazan was then quantified using a microplate reader set to measure absorbance at 450 nm.\u003c/p\u003e \u003cp\u003eA solution containing 1 mM FeSO₄ and 2 mM H₂O₂ was prepared in sodium acetate buffer, after which various concentrations of naringenin were introduced. The reaction was allowed to proceed for 5 minutes before the absorbance at 650 nm was measured using TMB (3,3',5,5'-tetramethylbenzidine) as the chromogenic substrate. In addition, the hydroxyl radical (\u0026bull;OH) scavenging activity of naringenin was further confirmed by electron spin resonance (ESR) spectroscopy. In this complementary assay, \u0026bull;OH radicals produced from the FeSO₄/H₂O₂ reaction in sodium acetate buffer were captured using DMPO (5,5-dimethyl-1-pyrroline N-oxide). Following the addition of different concentrations of naringenin to the reaction system, the reduction in the ESR peak intensity was analyzed to quantify the scavenging effect.\u003c/p\u003e\n\u003ch3\u003eAntioxidant capacity test\u003c/h3\u003e\n\u003cp\u003ehBMSCs were initially seeded in 24-well plates and divided into four distinct groups: a negative control group, a positive control group treated with 50 mg/mL Rosup, a group exposed solely to 500 \u0026micro;M H₂O₂, and a group receiving a combined treatment of 500 \u0026micro;M H₂O₂ with 50 \u0026micro;M naringenin. Following a 48-hour incubation period, the culture medium was aspirated, and the cells were incubated with a ROS detection solution at 37\u0026deg;C for 30 minutes. Subsequent fluorescence imaging was performed using a fluorescence microscope, and the intensity of the fluorescent signals was semi-quantitatively analyzed using ImageJ software.\u003c/p\u003e \u003cp\u003eTo determine the total antioxidant capacity imparted by naringenin, hBMSCs were first exposed to 50 \u0026micro;M naringenin for 48 hours. After treatment, the cells were harvested and resuspended in 200 \u0026micro;L of cold PBS. The cell suspension was then thoroughly homogenized to achieve complete lysis and to liberate the intracellular antioxidants. The lysate was centrifuged at 12,000g for 5 minutes at 4\u0026deg;C, and the supernatant was collected for further analysis. A standard curve was generated using a Trolox standard solution, after which 170 \u0026micro;L of the assay\u0026rsquo;s working solution was added to each sample. The mixture was incubated at room temperature for 6 minutes, and the absorbance was recorded at 414 nm. The total antioxidant capacity was then calculated based on the absorbance values and the established calibration curve, following the manufacturer\u0026rsquo;s protocol.\u003c/p\u003e \u003cp\u003eFor the assessment of superoxide dismutase (SOD) activity, hBMSCs were processed in a similar manner. The cells were first washed three times with PBS to remove any residual media and then lysed using an SOD sample preparation solution. After the addition of the assay\u0026rsquo;s working reagent, the samples were incubated at 37\u0026deg;C for 30 minutes. The absorbance was subsequently measured at 450 nm, and the SOD activity was calculated and expressed in enzyme activity units in accordance with the kit instructions.\u003c/p\u003e \u003cp\u003eIn addition, the catalase (CAT) activity was measured by mixing 0.1 mL of the cell lysate supernatant with 1.1 mL of the working solution. The reaction mixture was incubated at 37\u0026deg;C for 1 minute, and then a stop solution was added to terminate the reaction. The final absorbance was measured at 405 nm, and the CAT enzyme activity was calculated based on the absorbance change and the provided protocol.\u003c/p\u003e\n\u003ch3\u003eQuantitative real-time PCR\u003c/h3\u003e\n\u003cp\u003eTotal RNA was extracted from cells or tissues using TRIzol reagent (Invitrogen) in accordance with the manufacturer's protocol. The isolated RNA was then reverse transcribed into cDNA using a qScript cDNA Synthesis kit (Takara, Shiga, Japan). Quantitative PCR was conducted on an ABI Step-One Plus\u0026trade; Real-Time PCR System with SYBR\u0026reg; Premix Ex TaqTM (Takara, Shiga, Japan). The expression levels of the target genes were normalized to Gapdh, and the relative quantification was performed using the 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e contained a list of all primers.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrimers for qRT-PCR.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReverse\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAPDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGGAGCGAGATCCCTCCAAAAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGGCTGTTGTCATACTTCTCATGG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRUNX2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGGTTACTGTCATGGCGGGTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTCTCAGATCGTTGAACCTTGCTA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOPN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGAAGTTTCGCAGACCTGACAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGTATGCACCATTCAACTCCTCG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOCN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCACTCCTCGCCCTATTGGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCCCTCCTGCTTGGACACAAAG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACTGGTACTCAGACAACGAGAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACGTCAATGTCCCTGATGTTATG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eiNOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGGACTTTTGTACTCATCTGCAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGTGGACGGGTCGATGTCAC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNF-α\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGGCCAATGTGAGGGAGTTGAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCCCGCTTTATCTGTGAGCCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOX-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCACCCCGACATAGAGAGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCTGCGGAGTGCAGTGTTCT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCACTGGTCTTTTGGAGTTTGAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGGACTTTTGTACTCATCTGCAC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAKR1B1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTTTTCCCATTGGATGAGTCGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCCTGGAGATGGTTGAAGTTGG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAAGAGGGTGTTCTCTATGTAGGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGCTCCTCCAACATTTGTCACTT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eGene expression data collection and preprocessing\u003c/h3\u003e\n\u003cp\u003eThe GSE176086 dataset was obtained from the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and is based on the GPL21185 platform. This dataset comprises 12 hBMSC samples, including six samples treated with TNF-α and six untreated controls. The online analysis tool GEO2R (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/geo2r\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/geo2r\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was employed for data normalization and differential gene expression (DEG) analysis. The selection criteria for DEGs were set as an absolute log2 fold change (logFC) greater than 1 and a P-value less than 0.05. Furthermore, GEO2R was also utilized to generate visualizations such as volcano plots to provide an intuitive representation of gene expression significance and variation magnitude.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFunctional analysis\u003c/h2\u003e \u003cp\u003eThe Metascape database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://metascape.org\u003c/span\u003e\u003cspan address=\"http://metascape.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilized to conduct Gene Ontology (GO) enrichment analyses, encompassing biological processes, cellular components, and molecular functions, for the previously identified differentially expressed genes (DEGs). Additionally, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to further elucidate the functional roles of these DEGs. A significance threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was applied for all analyses.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGene Set Enrichment Analysis (GSEA)\u003c/h3\u003e\n\u003cp\u003eGSEA was performed using the GOBP_OSSIFICATION and CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION gene sets obtained from the Molecular Signatures Database (MSigDB). The DEGs identified in the GSE176086 dataset were uploaded to the Wei Sheng Xin online platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bioinformatics.com.cn/\u003c/span\u003e\u003cspan address=\"https://www.bioinformatics.com.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to conduct GSEA, enabling systematic evaluation of pathway enrichment patterns associated with osteogenic suppression and inflammatory signaling.\u003c/p\u003e\n\u003ch3\u003eProtein-Protein Interaction (PPI) Network Analysis\u003c/h3\u003e\n\u003cp\u003eWe used STRING 11.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), an online tool for analyzing protein interactions, to identify the network PPI. All DEGs were uploaded for PPI analysis. To further capture the relationships between the terms, a subset of enriched terms has been selected and rendered as a network plot, where terms with a similarity\u0026thinsp;\u0026gt;\u0026thinsp;0.3 are connected by edges. We select the terms with the best p-values from each of the 20 clusters, with the constraint that there are no more than 15 terms per cluster and no more than 250 terms in total. The three most significantly enriched clusters were selected for further refined PPI network analysis. Cytoscape v3.9.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cytoscape.org/\u003c/span\u003e\u003cspan address=\"https://www.cytoscape.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a tool for analyzing and visualizing protein interaction networks, was used to display the nodes and connections in the PPI network.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eNaringenin target prediction\u003c/h2\u003e \u003cp\u003eWe utilized SwissTargetPrediction (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.swisstargetprediction.ch/\u003c/span\u003e\u003cspan address=\"http://www.swisstargetprediction.ch/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to identify potential target proteins of naringenin and conducted enrichment analysis on all predicted targets. To refine our results, we intersected the top 100 predicted target genes with the top 200 DEGs from the GSE176086 dataset. This intersection analysis revealed two potential target genes, AKR1B1 and SNCA, which may play a critical role in the mechanism of naringenin's action.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMolecular docking\u003c/h2\u003e \u003cp\u003eTo predict the binding interactions between naringenin and its potential target proteins AKR1B1 and SNCA, molecular docking analysis was performed using the CB-Dock2 online tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://clab.labshare.cn/cb-dock2/\u003c/span\u003e\u003cspan address=\"http://clab.labshare.cn/cb-dock2/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The three-dimensional (3D) structure of naringenin was retrieved from the PubChem database, while the protein structures of AKR1B1 and SNCA were obtained from the Protein Data Bank (PDB). Before docking, the protein structures were prepared by removing water molecules and heteroatoms, followed by energy minimization. CB-Dock2 employs blind docking to automatically predict the binding pocket and perform flexible docking simulations. The docking scores, which represent the binding affinity, were recorded, and the docking conformations with the lowest binding energy were selected for further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMolecular dynamic simulation\u003c/h2\u003e \u003cp\u003eMolecular dynamics (MD) simulations were conducted on the WebGro online platform with the GROMOS96 43a1 force field and the TIP4P water model. The system was solvated in a dodecahedron box, and 0.15 M NaCl ions were added to mimic physiological ionic strength. Long-range electrostatic interactions were calculated using the Particle Mesh Ewald method with a cutoff distance of 1.2 nm, while short-range van der Waals interactions were truncated at 1.0 nm. Bond lengths involving hydrogen atoms were constrained using the LINCS algorithm, enabling a 2 fs integration time step.\u003c/p\u003e \u003cp\u003eThe system underwent energy minimization using the steepest descent algorithm to eliminate steric clashes, followed by a two-step equilibration protocol: (1) 100 ps of NVT (constant particle number, volume, and temperature) equilibration with the V-rescale thermostat to stabilize the temperature at 298 K, and (2) 100 ps of NPT (constant particle number, pressure, and temperature) equilibration using the Berendsen barostat to adjust the pressure to 1 bar. Subsequently, a production MD simulation was carried out for 50 ns under NPT conditions, with coordinates saved every 10 ps for trajectory analysis. Trajectory analysis included root mean square deviation (RMSD) and root mean square fluctuation (RMSF).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using GraphPad Prism (version 8.2.0, GraphPad Software, CA, USA). Data from each experiment were expressed as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), and all experiments were conducted in triplicate or more. For comparisons between two groups, the Student's t-test was used. For comparisons involving multiple groups, One-way analysis of variance (ANOVA) followed by Dunnett\u0026rsquo;s multiple comparisons test was employed. Statistical significance was considered for p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eNaringenin ameliorates TNF-α-induced suppression of osteogenic differentiation in hBMSCs\u003c/h2\u003e \u003cp\u003eTo evaluate the potential of naringenin in reversing TNF-α-induced inhibition of osteogenic differentiation, we first assessed its effects on the survival and proliferation of hBMSCs. Live/Dead staining (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-B) and CCK-8 assays (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC) revealed that TNF-α did not significantly affect the cell viability or proliferation at the tested concentrations, and similarly, both low and high doses of naringenin did not show any adverse effects on cell growth.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, we explored whether naringenin could rescue osteogenic differentiation that was suppressed by TNF-α. Both ALP and ARS staining demonstrated that TNF-α significantly inhibited osteogenic differentiation, whereas naringenin effectively reversed this inhibition in a dose-dependent manner (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Additionally, qRT-PCR analysis confirmed that the addition of naringenin led to a marked upregulation of osteogenic markers (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). These findings collectively suggest that naringenin can counteract the inhibitory effects of TNF-α on osteogenesis, supporting its potential as a therapeutic agent for conditions involving osteogenic dysfunction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAnti-inflammatory and antioxidant properties of naringenin\u003c/h2\u003e \u003cp\u003eHaving established that naringenin can reverse TNF-α-induced inhibition of osteogenesis, we proceeded to investigate the underlying molecular mechanisms through which naringenin exerts its beneficial effects on osteogenic differentiation. Given that TNF-α is a well-known inflammatory mediator involved in various pathophysiological processes, we aimed to explore whether naringenin also possesses anti-inflammatory and antioxidant properties that could further support its role in modulating osteogenesis.\u003c/p\u003e \u003cp\u003eTo assess the antioxidant capacity of naringenin, we initially evaluated the ability of naringenin to scavenge various free radicals, including DPPH\u0026bull;, \u0026bull;OH, O\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026bull;\u0026minus;\u003c/sup\u003e and H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The results demonstrated that naringenin effectively neutralizes these free radicals in a dose-dependent manner. Then we measured the intracellular ROS levels using fluorescence detection (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The results revealed a significant reduction in ROS content following naringenin treatment, indicating its potential to alleviate oxidative stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). To further evaluate its antioxidant potential, we performed three distinct assays: total antioxidant capacity, SOD activity, and CAT activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD-F). The findings consistently demonstrated the robust antioxidant effects of naringenin.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMoreover, we investigated the impact of naringenin on inflammatory cytokine expression under oxidative stress conditions induced by H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e. Exposure to H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e led to a marked upregulation of several inflammatory mediators, including TNF-α, iNOS, COX-2, and IL-6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). However, naringenin treatment significantly attenuated the expression of these inflammatory cytokines, indicating that naringenin possesses potent anti-inflammatory properties. These results suggest that naringenin not only mitigates oxidative stress but also effectively modulates inflammation, highlighting its potential as a therapeutic agent for inflammatory-related bone disorders.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eTNF-α-induced transcriptional reprogramming of hBMSCs impairs osteogenesis and enhances inflammatory pathways\u003c/h2\u003e \u003cp\u003eNext, through GEO database, we found and downloaded the mRNA microarray analysis dataset GSE176086 after TNF-α treatment of hBMSCs. After normalizing the data, differential expression analysis revealed 1,095 significantly upregulated genes and 1,083 downregulated genes, which were visualized in a volcano plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-C). GO enrichment analysis of the top three biological processes indicated significant associations with viral response (e.g., defense response to virus, type I interferon signaling), chemotaxis (e.g., granulocyte migration), and inflammatory activation (e.g., response to lipopolysaccharide), reflecting TNF-α\u0026rsquo;s role in triggering immune-inflammatory cascades (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). KEGG pathway analysis further confirmed enrichment in pathways related to cytokine-cytokine receptor interaction, NF-κB signaling, and TNF signaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE), underscoring TNF-α\u0026rsquo;s dual role in suppressing osteogenesis and enhancing pro-inflammatory signaling.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo explore the mechanistic relationship between TNF-α and osteogenic inhibition, GSEA was conducted using the GOBP_OSSIFICATION and CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION gene sets. The results showed that TNF-α treatment negatively correlated with GOBP_OSSIFICATION (NES = -2.1, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF), indicating a suppression of osteogenic differentiation. Conversely, CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION exhibited significant positive enrichment (NES\u0026thinsp;=\u0026thinsp;2.6, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG), consistent with TNF-α-induced inflammatory activation.\u003c/p\u003e \u003cp\u003eFurthermore, we examined the association between the top ten enriched pathways and representative DEGs with significant expression changes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Concurrently, a PPI network was constructed using an online database, followed by functional enrichment analysis of the identified DEGs. The results indicated that these DEGs were primarily enriched in 20 biological categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). To refine our analysis, we selected the top three most significantly enriched categories for further PPI network investigation. The findings revealed that these categories were highly correlated with key DEGs, predominantly associated with cytokine signaling in the immune system, the network map of SARS-CoV-2 signaling, and the innate immune response (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Collectively, these findings demonstrate that TNF-α drives a transcriptional reprogramming of BMSCs, shifting them towards an inflammatory phenotype while impairing their osteogenic potential, providing a molecular framework for its detrimental effects on bone formation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eNaringenin reverses TNF-α-induced osteogenic inhibition by regulating AKR1B1\u003c/h2\u003e \u003cp\u003eTo further elucidate the molecular mechanism underlying naringenin\u0026rsquo;s protective effects, we first predicted its potential targets (PTs) based on its chemical structure and performed enrichment analysis on the identified targets (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-B). The results revealed that the PTs were primarily enriched in kinase, enzyme, and lyase categories, suggesting that naringenin predominantly interacts with enzymatic proteins.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, we intersected the top 100 PTs with the top 200 DEGs identified in the GSE176086 dataset, leading to the identification of two potential key genes: AKR1B1 and SNCA (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). quantitative RT-PCR analysis demonstrated that TNF-α treatment significantly upregulated AKR1B1 expression, whereas the addition of naringenin effectively suppressed this effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). In contrast, SNCA expression did not exhibit significant changes upon treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003eTo gain further insights into the interaction between naringenin and these candidate genes, molecular docking simulations were conducted. The docking results indicated that naringenin exhibited a strong binding affinity for AKR1B1 (Vina score = -10), whereas its binding affinity for SNCA was relatively weaker (Vina score = -5) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF-G). Furthermore, the naringenin-AKR1B1 complex was selected for molecular dynamics simulations. RMSD and RMSF served as critical metrics to evaluate system stability, where values within 0.5 nm were considered indicative of a stable system. Throughout the simulation, the naringenin-AKR1B1 complex exhibited favorable stability, with both RMSD and RMSF consistently maintained below the threshold of 0.5 nm, reflecting robust structural integrity and minimal conformational fluctuations under the defined simulation conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH-I). These findings strongly suggest that naringenin may exert its biological effects by directly binding to AKR1B1.\u003c/p\u003e \u003cp\u003eFunctionally, AKR1B1 encodes aldose reductase, an enzyme that catalyzes the conversion of glucose to sorbitol, consuming NADPH in the process[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This diminishes glutathione (GSH) regeneration capacity and exacerbates oxidative stress. Our results suggest that naringenin may attenuate the activation of the polyol pathway by directly binding to the active site of AKR1B1, thereby preserving NADPH for GSH regeneration and reducing ROS levels. Additionally, naringenin may inhibit AKR1B1 expression through its antioxidant (ROS-reducing) and anti-inflammatory (NF-κB nuclear translocation-inhibiting) properties.\u003c/p\u003e \u003cp\u003eCollectively, our findings provide strong evidence that naringenin reverses TNF-α-induced osteogenic inhibition by regulating AKR1B1, highlighting its potential as a therapeutic agent against inflammation-induced bone loss (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eChronic inflammatory microenvironments, driven by cytokines such as TNF-α, disrupt bone remodeling by skewing the balance toward osteoclastogenesis and suppressing osteoblast differentiation, ultimately leading to pathological bone loss[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This study elucidates the therapeutic potential of naringenin, a citrus-derived flavonoid, in counteracting TNF-α-induced osteogenic suppression in hBMSCs. Our findings highlight a novel mechanism by which naringenin rescues osteoblast differentiation through direct interaction with AKR1B1, a key enzyme implicated in oxidative stress and inflammation. These results not only advance our understanding of flavonoid-based interventions for inflammatory bone disorders but also underscore the translational potential of targeting AKR1B1 in metabolic and inflammatory bone diseases.\u003c/p\u003e \u003cp\u003eThe clinical relevance of this work lies in the urgent need for therapies that address the dual challenges of inflammation and impaired osteogenesis in conditions such as rheumatoid arthritis, diabetic osteopathy, and osteoporosis[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. While TNF-α inhibitors like biologics alleviate inflammation, their immunosuppressive risks and inability to directly restore osteoblast function limit their utility[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Natural compounds like naringenin, with dual anti-inflammatory and osteoprotective properties, offer a promising alternative. Our study demonstrates that naringenin reverses TNF-α-driven suppression of osteogenic markers (e.g., RUNX2, OPN, ALP) while mitigating oxidative stress and inflammatory cytokine production. This multifaceted action positions naringenin as a therapeutic candidate capable of addressing both the inflammatory and metabolic dysregulation underlying bone loss.\u003c/p\u003e \u003cp\u003eCentral to this mechanism is the identification of AKR1B1 as a critical mediator of TNF-α\u0026rsquo;s detrimental effects. AKR1B1, encoding aldose reductase, catalyzes the polyol pathway, consuming NADPH to convert glucose into sorbitol[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This process depletes cellular NADPH reserves, impairing GSH regeneration and exacerbating ROS accumulation. Elevated ROS levels activate NF-κB, further amplifying inflammatory cascades and suppressing osteogenic transcription factors[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Our data reveal that TNF-α significantly upregulates AKR1B1 expression in hBMSCs, aligning with prior studies linking AKR1B1 to diabetic complications and oxidative tissue damage. Crucially, naringenin suppresses AKR1B1 at both transcriptional and functional levels. Molecular docking analysis revealed a strong binding affinity (Vina score = -10) between naringenin and AKR1B1\u0026rsquo;s active site, suggesting direct inhibition of its enzymatic activity. By preserving NADPH for GSH synthesis, naringenin reduces oxidative burden, thereby restoring redox homeostasis\u0026mdash;a finding corroborated by its dose-dependent scavenging of free radicals (DPPH\u0026bull;, \u0026bull;OH, O₂\u0026bull;⁻) and enhancement of SOD/CAT activity.\u003c/p\u003e \u003cp\u003eThe rescue of osteogenesis by naringenin cannot be attributed solely to AKR1B1 inhibition. Its anti-inflammatory effects, particularly the suppression of TNF-α-induced NF-κB activation, play a complementary role. TNF-α triggers NF-κB nuclear translocation, upregulating pro-inflammatory cytokines (e.g., IL-6, COX-2) that further inhibit osteoblast differentiation[\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. By reducing ROS levels, naringenin disrupts ROS-dependent NF-κB activation, while its potential interaction with IKK or IκBα may block downstream signaling[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This dual inhibition of oxidative and inflammatory pathways creates a permissive microenvironment for osteogenic differentiation, as evidenced by the restoration of RUNX2 and Osterix expression in naringenin-treated hBMSCs. Notably, our bioinformatics approach intersecting naringenin\u0026rsquo;s predicted targets with TNF-α-regulated DEGs initially identified SNCA as a potential candidate. However, experimental validation revealed no significant changes in SNCA expression, suggesting that naringenin\u0026rsquo;s osteoprotective effects are predominantly mediated through AKR1B1. This discrepancy may arise from tissue-specific regulatory mechanisms or post-transcriptional modifications not captured in our assays, warranting further investigation into SNCA\u0026rsquo;s role in BMSC differentiation.\u003c/p\u003e \u003cp\u003eThe pivotal role of AKR1B1 in naringenin\u0026rsquo;s mechanism highlights its potential as a therapeutic target for inflammatory bone diseases. AKR1B1 inhibitors, such as epalrestat, are already clinically approved for diabetic neuropathy or lung cancer, underscoring the enzyme\u0026rsquo;s druggability[\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Our work extends this paradigm to bone metabolism, proposing that AKR1B1 inhibition could mitigate inflammation-driven osteopenia. However, several unanswered questions remain. While molecular docking supports naringenin-AKR1B1 binding, structural validation (e.g., X-ray crystallography) is needed to confirm interaction details. Additionally, the contribution of AKR1B1-independent pathways, such as naringenin\u0026rsquo;s modulation of BMP/Smad or MAPK signaling, requires exploration. Furthermore, our study focused on \u003cem\u003ein vitro\u003c/em\u003e models, which may not fully recapitulate the complexity of bone tissue microenvironments. Co-culture systems or 3D bone organoids could better model interactions between osteoblasts, osteoclasts, and immune cells.\u003c/p\u003e \u003cp\u003eDespite these limitations, our findings provide compelling evidence for naringenin\u0026rsquo;s osteoprotective effects. However, several gaps must be addressed to advance translational applications. First, the dose-dependent effects of naringenin were only partially characterized. A broader concentration range and pharmacokinetic studies are needed to establish optimal therapeutic doses. Second, the lack of \u003cem\u003ein vivo\u003c/em\u003e data limits clinical relevance. Future work should assess naringenin\u0026rsquo;s efficacy in murine models of osteoporosis or fracture healing, particularly in the context of chronic inflammation. Third, the role of SNCA, though not validated here, warrants further investigation. Its involvement in protein aggregation and mitochondrial function suggests potential crosstalk with oxidative stress pathways in BMSCs. CRISPR-based knockout or overexpression studies could clarify its contribution to osteogenic differentiation.\u003c/p\u003e \u003cp\u003eIn conclusion, this study establishes naringenin as a potent modulator of TNF-α-induced osteogenic suppression, primarily through targeting AKR1B1. By inhibiting AKR1B1, naringenin restores NADPH availability, attenuates oxidative stress, and disrupts NF-κB-driven inflammation, thereby rescuing osteoblast differentiation. These findings bridge the gap between phytochemical research and clinical orthopedics, offering a mechanistic rationale for repurposing naringenin or AKR1B1 inhibitors in inflammatory bone diseases. Future studies should prioritize \u003cem\u003ein vivo\u003c/em\u003e validation and explore combinatorial therapies targeting both oxidative stress and inflammatory pathways. Ultimately, this work advances our understanding of flavonoid-based interventions and underscores the therapeutic potential of dual-action agents in metabolic and inflammatory bone disorders.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study establishes naringenin as a potent modulator of TNF-α-induced osteogenic suppression, primarily through targeting AKR1B1. By inhibiting AKR1B1, naringenin restores NADPH availability, attenuates oxidative stress, and disrupts NF-κB-driven inflammation, thereby rescuing osteoblast differentiation. These findings bridge the gap between phytochemical research and clinical orthopedics, offering a mechanistic rationale for repurposing naringenin or AKR1B1 inhibitors in inflammatory bone diseases. Future studies should prioritize \u003cem\u003ein vivo\u003c/em\u003e validation and explore combinatorial therapies targeting both oxidative stress and inflammatory pathways. Ultimately, this work advances our understanding of flavonoid-based interventions and underscores the therapeutic potential of dual-action agents in metabolic and inflammatory bone disorders.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eTNF-α \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp;Tumor Necrosis Factor-alpha\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBMSCs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Bone Marrow Sesenchymal Stem Cells\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eROS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Reactive Oxygen Species\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGO \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Gene Ontology\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKEGG \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp;Kyoto Encyclopedia of Genes and Genomes\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePPI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Protein-protein interaction\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWST-1 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Water-soluble tetrazolium salt-1\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSOD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Superoxide dismutase\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCAT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Catalase\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGEO \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Gene Expression Omnibus\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDEGs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Differentially Expressed Genes\u003c/p\u003e\n\u003cp\u003eGSEA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Gene Set Enrichment Analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePTs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Potential Targets\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGSH \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Glutathione\u003c/p\u003e\n\u003cp\u003eMD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Molecular Dynamics\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRMSD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Root Mean Square Deviation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRMSF \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp;Root Mean Square Fluctuation\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eB.H. designed the study and literature research. H.L. defined the intellectual content. F.H. performed experiment. L.H. analyzed the data. B.H. and F.H. wrote the main manuscript text. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Zhejiang basic public welfare research program (NO.\u0026nbsp;LTGY24H060001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor those requiring further elaboration or wishing to engage in deeper discourse, the corresponding author remains available for direct inquiry.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRedlich K, Smolen JS. Inflammatory bone loss: pathogenesis and therapeutic intervention. Nat Rev Drug Discov. 2012;11(3):234\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYao Q, et al. The role of TNF-α in osteoporosis, bone repair and inflammatory bone diseases: A review. Tissue Cell. 2024;89:102422.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFu SC, et al. The associations of TNF-α gene polymorphisms with bone mineral density and risk of osteoporosis: A meta-analysis. Int J Rheum Dis. 2019;22(9):1619\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCai J, et al. Naringenin: A flavanone with anti-inflammatory and anti-infective properties. Biomed Pharmacother. 2023;164:114990.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou X, et al. Naringenin is a Potential Anabolic Treatment for Bone Loss by Modulating Osteogenesis, Osteoclastogenesis, and Macrophage Polarization. Front Pharmacol. 2022;13:872188.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNor Muhamad ML et al. Scoping Rev Skeletal Eff Naringenin Nutrients, 2022. 14(22).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOršolić N et al. Antioxidative and Anti-Inflammatory Activities of Chrysin and Naringenin in a Drug-Induced Bone Loss Model in Rats. Int J Mol Sci, 2022. 23(5).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePan Z, et al. Naringenin protects against iron overload-induced osteoarthritis by suppressing oxidative stress. Phytomedicine. 2022;105:154330.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDi Benedetto C, et al. The p53 tumor suppressor regulates AKR1B1 expression, a metastasis-promoting gene in breast cancer. Front Mol Biosci. 2023;10:1145279.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Q, et al. Diabetes fuels periodontal lesions via GLUT1-driven macrophage inflammaging. 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Sci Transl Med. 2021;13(614):eabg6428.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBailly C. Moving toward a new horizon for the aldose reductase inhibitor epalrestat to treat drug-resistant cancer. Eur J Pharmacol. 2022;931:175191.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKallinikou D, et al. Diabetic neuropathy in children and adolescents with type 1 diabetes mellitus: Diagnosis, pathogenesis, and associated genetic markers. Diabetes Metab Res Rev. 2019;35(7):e3178.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Naringenin, AKR1B1, Osteogenic differentiation, TNF-α, hBMSCs, Oxidative stress, Molecular docking","lastPublishedDoi":"10.21203/rs.3.rs-6241423/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6241423/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eChronic inflammatory microenvironments, driven by cytokines like TNF-α, disrupt bone remodeling by suppressing osteogenic differentiation, contributing to inflammatory bone diseases. While TNF-α inhibitors mitigate inflammation, they lack osteoprotective effects. Naringenin, a citrus flavonoid with antioxidative and anti-inflammatory properties, shows potential but its mechanism in counteracting TNF-α-induced osteogenic suppression remains unclear. This study aimed to elucidate how naringenin rescues osteogenesis in TNF-α-treated hBMSCs and identify its molecular targets.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003ehBMSCs were treated with TNF-α and naringenin. Cell viability and osteogenic differentiation were assessed via Live/Dead staining, CCK-8, ALP/ARS staining, and qRT-PCR. Antioxidant activity was evaluated using free radical scavenging assays, alongside SOD/CAT activity measurements. Bioinformatics analyses identified TNF-α-regulated differentially expressed genes. GO, KEGG, and PPI network analyses highlighted key pathways. Molecular docking (CB-Dock2) predicted naringenin\u0026rsquo;s binding to target proteins.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eNaringenin reversed TNF-α-induced suppression of osteogenic markers and restored mineralization in BMSCs. Naringenin also scavenged free radicals, reduced ROS, enhanced SOD/CAT activity, and attenuated TNF-α-driven inflammatory cytokines. Bioinformatic analyses revealed TNF-α-induced enrichment of NF-κB and cytokine pathways, while GSEA highlighted suppressed ossification. TNF-α upregulated AKR1B1, an enzyme linked to oxidative stress, which naringenin suppressed. Molecular docking confirmed strong binding between naringenin and AKR1B1 (Vina score: -10).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eNaringenin rescues TNF-α-impaired osteogenesis by targeting AKR1B1, restoring redox balance, and inhibiting NF-κB-mediated inflammation. This positions naringenin as a therapeutic candidate for inflammatory bone disorders. The study identifies AKR1B1 as a novel target for mitigating inflammation-driven bone loss, bridging phytochemical research and clinical orthopedics.\u003c/p\u003e","manuscriptTitle":"Naringenin Restores Osteogenic Differentiation in TNF-α-Treated Bone Marrow Mesenchymal Stem Cells by Targeting AKR1B1","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-20 18:01:18","doi":"10.21203/rs.3.rs-6241423/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e539d230-90a1-4182-b632-620aeebc22e1","owner":[],"postedDate":"March 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-03T04:53:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-20 18:01:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6241423","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6241423","identity":"rs-6241423","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}