Indomethacin (IM, purity ≥98%) was purchased from Aladdin (Shanghai, China), and lipopolysaccharide (LPS) was obtained from Biosharp (Guangzhou, China). Secondary antibodies, including HRP-conjugated goat anti-rabbit antibodies (BS13278) and HRP-conjugated goat anti-mouse antibodies (BS12478), were sourced from Bioworld (Nanjing, China). PDE4B-IN-3 (IN-3, purity ≥98%) was purchased from MedChemExpress (Shanghai, China).

Menisoxoisoaporphine A (MA, purity ≥98%) was extracted and isolated by our research group (Guo et al., 2024). Briefly, milled rhizomes of Menispermum dauricum DC. (100 kg) were extracted with 95% ethanol (50 L × 3, 4 h each) to obtain the crude extract (30 kg). The alcohol extract (30 kg) was dissolved in acid, precipitated with alkali, and subsequently extracted with dichloromethane to yield a total alkaloid extract (2.4 kg). The total alkaloids were isolated systematically and purified using silica gel column chromatography as well as preparative and semi-preparative high-performance liquid chromatography. MA (25 mg, t_R 36.6 min) was purified by preparative HPLC with the mobile phase 65% MeOH/H₂O (v/v). The structures of MA was shown in Figure 1A. Figure 1B displays the HPLC chromatogram of MA detected at 260 nm.

Mouse peritoneal macrophage cells RAW264.7, sourced from the Cell Bank of the Chinese Academy of Sciences in Shanghai, were maintained in Roswell Park Memorial Institute 1640 medium (RPMI1640, KeyGen, Nanjing, China), supplemented with 10% (v/v) fetal bovine serum (FBS, Procell, China), penicillin (100 U·mL^-1), and streptomycin (100 μg·mL^-1) in a humidified 5% CO₂ incubator at 37°C.

Cell viability were determined by tetrazolium compound 3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazolium bromide (MTT) assay. Briefly, 6 × 10³ cells were seeded in a 96-well plate. After 12 h, the cells were exposed to LPS at a concentration of 1 μg·mL^-1, along with varying concentrations of MA, for a duration of 24 h. Then, the cells were incubated with MTT reagent for another 4 h. After that, the media with the reagent was carefully discarded, and 100 μL of dimethyl sulfoxide (DMSO, Solarbio, Beijing, China) was added to each well in order to dissolve the formazan crystals. The absorbance of each well was quantified using a microplate reader (SpectraMax 190, Molecular Devices) set to a wavelength of 570 nm. Cell viability was calculated as follows: Cell viability (%) = (OD_{treatment group} - OD_{blank group}/OD_control - OD_{blank group}) × 100%. Experiments were performed at least for 3 times using 6 replicates for each sample.

Briefly, cells were seeded into 6-well plates at a density of 6 × 10³ per well, with a final volume of 2 mL. After the cells became adherent, the original medium was removed, and cells were stimulated with 1 μg·mL^-1 LPS in the presence of MA (3, 6, 12 μM) or IM (10 μM) for 24 h. Cell morphology was examined utilizing an inverted microscope (DMi8, Leica, Germany).

The concentrations of IL-6 and TNF-α in cell culture supernatants were quantified through the utilization of Enzyme-linked immunosorbent assay (ELISA) kits, following the guidelines provided by the manufacturers (MultiSciences Biotech, Hangzhou, China). Nitric oxide (NO) production was quantified utilizing the Total Nitric Oxide Assay Kit (Beyotime, Shanghai, China) in accordance with the manufacturer’s instructions.

Total RNA extraction was conducted using TRIzol reagent (Vazyme, Nanjing, China), followed by cDNA synthesis using the HiScript II reverse transcriptase kit (Vazyme, Nanjing, China). Quantitative PCR was performed using ChamQ SYBR qPCR Master Mix (Vazyme, Nanjing, China) in accordance with the manufacturer’s guidelines on a real-time-PCR system (QuantStudio 3, Life Tech, United States). The mRNA levels of the target genes were normalized to β-actin and quantified using the 2^−ΔΔCt method. Primer sequences were shown in Table 1.

Transcriptome analysis of RAW264.7 cells was performed using the RNA-sequencing method (RNA-seq) by BGI Technology Services Co., Ltd. (Shenzhen, China). In brief, total RNA was extracted utilizing Trizol (Invitrogen, Carlsbad, CA, United States). The concentration of total RNA was determined using the Equalbit RNA BR Assay Kit (Invitrogen, MA, United States). The qualified RNA samples were used for single-strand circle DNA (ssCir DNA) library construction. The sequencing substrate DNA nanoballs (DNBs) were generated from ssCir DNA by rolling circle amplification. Then, the DNBs were then incorporated into the structured nanoarray through the utilization of high-density DNA nanochip technology. Finally, library sequencing was performed using a combinatorial probe-anchor synthesis (cPAS)-based sequencer. To acquire clean data, the raw data underwent filtration utilizing using the SOAPnuke Toolkit software (v1.5.6). Then, all the clean data were analyzed on the Dr. Tom network platform (https://biosys.bgi.com/) provided by BGI. Clean data were mapped to the reference genome via HISAT2 (v2.1.0) and aligned to the reference genome set using Bowtie2 (v2.2.5). RSEM (v1.3.1) was used to calculate the expression of genes and transcripts. Fragments per kilobase of transcript per million fragments mapped (FPKM) was utilized as a metric for gene expression levels. DEseq2 (v1.4.5) was employed to detect differentially expressed genes (DEGs) with a Q-value ≤0.05. Volcano plots were acquired based on DEGs with |log2-fold change (FC)| ≥ 1.5. Venn diagrams and hierarchical clustering were generated using the DEGs between control-model and model-MA to obtain common DEGs. Three biological replicates were prepared in each group.

Cells were lysed with ice-cold radio immunoprecipitation assay (RIPA) buffer (Beyotime, Hangzhou, China) containing protease inhibitor (Beyotime, Hangzhou, China) and phosphatase inhibitor (Beyotime, Hangzhou, China). Total protein concentration was determined with a bicinchoninic acid (BCA) protein assay kit (Beyotime, Hangzhou, China). All proteins were resolved in 10% SDS-polyacrylamide gel electrophoresis (SDS-PAGE) gels and transferred to polyvinylidene fluoride (PVDF) membranes (Millipore, United States). The membrane was blocked for 2 h at room temperature using a 5% skim milk solution prior to incubation with the primary antibody at 4°C overnight. Then secondary antibodies were incubated for 2 h at room temperature. Immunoreactive bands were visualized by using chemiluminescence (Elabscience, Wuhan, China), followed by quantitative analysis using ImageJ software (NIH, United States). Primary antibodies were shown in Supplementary Table S1.

To silence the expression of PDE4B, a Pde4b small interfering RNA (siRNA) and a control siRNA were chemically synthesized by GenePharma Co., Ltd. (Shanghai, China) and transfected into RAW264.7 cells using Lipofectamine 2000 Transfection Reagent (Invitrogen, CA, United States) according to the manufacturer’s instructions. The siRNA sequences were shown in Table 2. For PDE4B overexpression, the pIRES2-EGFP-PDE4B plasmid and empty pIRES2-EGFP plasmid were constructed by Public Protein/Plasmid Library (PPL, China).

The predicted structures of PDE4B were generated by AlphaFold. The protonation state of all the compounds was adjusted to pH 7.4, and their molecular structures were converted into three-dimensional representations using Open Babel (O'Boyle et al., 2011). AutoDock Tools (ADT3) were utilized for the preparation and parameterization of the receptor protein and ligands. The docking grid documents were created using AutoGrid of SiteMap, and the docking simulations were conducted using AutoDock Vina (1.2.0) (Eberhardt et al., 2021; Trott and Olson, 2010). The optimal pose was chosen for the analysis of interaction. Finally, the protein-ligand interaction diagram was produced using PyMOL.

The interaction between PDE4B and MA was studied by molecular dynamics (MD) simulation. The results of molecular docking was used as the initial wild-type conformation, and the tyrosine residue at position 405 (Tyr405) was replaced with alanine by SPDBV (Johansson et al., 2012) software to form an alanine mutant system. Gromacs 2023.1 (Mark et al., 2023) version was used for molecular dynamics simulation, and Amber ff14sb (He et al., 2020b) force field and GAFF (Wang et al., 2004) force field were used to parameterize proteins and small molecules, respectively. Using the TIP3P (Van Der Spoel and Van Maaren, 2006) water model, the two complex systems were explicitly solvated in a regular dodecahedron of appropriate periodic size. The steepest descent method was employed to optimize the energy of 0.1 ns in order to mitigate the presence of an unjustifiable energy barrier within the simulation. Na ions were added to both simulations to maintain the electric neutrality of the simulation system, and the energy of another 0.1 ns was minimized by conjugate gradient method. The system temperature was then raised from 0 K to 310 K for 1 ns in a constant temperature and volume (NVT) ensemble, controlled by a V-rescale thermostat. A 1 ns pre-balance was then performed in a constant temperature and constant pressure (NPT) system at 1 atm and 310 K, with a Berendsen pressure controller and a V-rescale thermostat controlling the temperature. Finally, the MD running time for each system was set to 100 ns. The entire simulation process was run in a NPT ensemble with a pressure of 1 atm and a temperature of 310 K, the step size was set to 2 fs, and the trajectory coordinates were recorded every 10 ps. In order to avoid the edge effect, the whole simulation process adopts periodic boundary conditions. After the simulation, VMD (Humphrey et al., 1996) software was used for visual analysis and trajectory animation, while the gmx_MMPBSA script (Valdés-Tresanco et al., 2021) was employed for combined free energy calculations.

All results were presented as the mean ± standard error of the mean (SEM) derived from a minimum of three distinct experiments. Statistical analyses were carried out utilizing the GraphPad software (v. 7.0.0, San Diego, CA, United States). The statistical methods of the Student’s t-test or one-way ANOVA were employed to compare groups within the study. Values of p < 0.05 indicated statistical significance.

To investigate whether MA can suppress LPS-induced inflammation in RAW264.7 macrophages, we preferentially tested the cytotoxicity of MA. The findings of the MTT assay indicated that MA exhibited no statistically significant cytotoxic effects on RAW264.7 cells at concentrations below 12.5 μM when compared to the control group (Figure 2A). Likewise, the selected concentrations of MA (3 μM, 6 μM, and 12 μM) were found to be non-cytotoxic for RAW264.7 macrophages challenged with LPS (Figure 2B). Indomethacin (IM; 10 μM) was employed as a positive control.

We then examined the in-vitro anti-inflammatory activities of MA in LPS-induced RAW264.7 macrophages. As shown in Figure 2C, RAW264.7 cells were small and round under normal conditions and became larger and extended pseudopods after LPS stimulation for 12 h. However, MA treatment markedly suppressed differentiation in RAW264.7 cells. We also found that pro-inflammatory macrophages upregulated the levels of inflammatory factors (IL-6, NO and TNF-α) after LPS stimulation, while MA effectively inhibited the release of these inflammatory factors (p < 0.05) (Figures 2D–F). To further assess the effects of MA on LPS-induced inflammatory response in RAW264.7, we examined mRNA expression for key inflammatory cytokine genes. As expected, MA significantly decreased the mRNA levels of Il-6, Nos2, and Tnf-α in a concentration-dependent manner (p < 0.05) (Figures 2G–I). These results indicated that MA could inhibit LPS-induced inflammation in RAW264.7 cells, suggesting its potential applications in the treatment of inflammatory diseases and the modulation of immune responses. Nonetheless, additional research is required to comprehensively elucidate the mechanisms underlying the action of MA.

To further probe the underlying inhibitory mechanisms against LPS, RNA-Seq was employed to evaluate transcriptome profiling in RAW264.7 cells. According to the established criteria of q-value ≤0.05 and log2-|fold change| ≥ 1.5, a total of 3,193 differentially expressed genes (DEGs) were detected in the comparison between the control and model groups. Among these DEGs, 1706 were found to be upregulated and 1,487 were downregulated (Figure 3A). Furthermore, treatment with MA resulted in alterations in 203 genes compared to the model group, with 3 genes showing upregulation and 200 genes showing downregulation (Figure 3B). Of them, 117 overlapping DEGs between the control vs. model and model vs. MA groups were identified by Venn analysis and visualized using a Heatmap (Figures 3C, D; Supplementary Table S2). To assess the credibility of the CO-DEGs identified in our RNA-Seq analysis, a subset of 25 genes was chosen for validation through qRT-PCR analysis. Among these 25, the expression results of 11 genes (Pde4b, Igf2r, Slc6a12, Itgb7, Tlr9, Nox1, Mmp12, Abca1, Cd80, Il11, and Tnfrsf9) were consistent with RNA-seq data (Figure 3E). The phosphodiesterases (PDEs) constitute a diverse group of isozymes, with PDE4B being the primary isoform expressed in various inflammatory cells and known to regulate the inflammatory response. Therefore, we focused on PDE4B. Overall, these results suggested that the anti-inflammatory effect on RAW264.7 macrophages of MA may be highly associated with PDE4B function, indicating that MA may exert anti-inflammatory effects by inhibiting the activity of PDE4B.

Utilizing transcriptome data, we conducted a detailed analysis of the molecular mechanisms involved in the regulation of PDE4B by MA. It is well known that PDE4B exhibits a high degree of specificity in degrading cAMP, and the increase in cAMP levels resulting from the use of PDE4B inhibitors plays a significant role in inhibiting the release of inflammatory factors from macrophage cells. Increased concentrations of cAMP have been correlated with the stimulation of PKA and the suppression of NF-κB, thus establishing the PDE4B-cAMP-PKA-NF-κB signaling cascade. PDE4B-IN-3, an inhibitor of PDE4B, is used as the positive drug. As shown in Figures 4A, B, PDE4B was upregulated by 2 fold in response to LPS in RAW264.7 cells but significantly decreased following MA treatment (p < 0.05). We found that LPS-induced levels of cAMP and p-PKA were significantly reduced compared with the control group, while these proteins were all dose-dependently increased in response to MA treatment (p < 0.05) (Figures 4A, C, D). Moreover, the phosphorylation of IκB and P65 was markedly induced by LPS but significantly inhibited by MA treatment (p < 0.05) (Figures 4A, E, F). Collectively, these results indicated that MA alleviates the LPS-triggered inflammatory response in RAW264.7 cells through the PDE4B-cAMP-PKA-NF-κB axis. This study not only offers novel insights into the anti-inflammatory mechanisms of MA, but also establishes a theoretical foundation and provides empirical support for the application of MA and analogous compounds in the treatment of inflammatory diseases.

To confirm the role of PDE4B activation in the effects of MA, we blocked PDE4B activation with siRNA-Pde4b. Knocking down PDE4B expression with four different siRNAs led to a robust decrease in the PDE4B protein levels (p < 0.05) (Figure 5A). Then, we selected siRNA-Pde4b #2 and siRNA-Pde4b #4 with the strongest knockdown efficiency for subsequent experiments. Both siRNA-Pde4b #2 and siRNA-Pde4b #4 reversed the protective effects of MA on LPS-induced the inhibition of the cAMP/PKA pathway and activation of the NF-κB signaling pathway in RAW264.7 cells (Figures 5B, C).

To clarify whether MA exerts an anti-inflammatory effect depending on PDE4B, we performed a PDE4B overexpression experiment on RAW264.7 cells. After transfecting the pIRES2-EGFP-PDE4B plasmid for 12 h, PDE4B expression increased significantly compared to the control group (p < 0.05) (Figure 6A). MA treatments significantly inhibited the PDE4B-cAMP-PKA-NF-κB signaling pathway, while PDE4B overexpression abolished the effect of MA on the cAMP-PKA pathway (p < 0.05) (Figure 6B). Moreover, the suppressive effect of MA on the NF-κB pathway was no longer observed upon the overexpression of PDE4B, which means that the ameliorating effects of MA on LPS-induced inflammation were abolished in RAW264.7 cells transfected with a PDE4B overexpression plasmid. These findings collectively suggest that MA suppresses inflammation mediated by the cAMP-PKA-NF-κB pathway through its interaction with PDE4B. This interaction may facilitate the development of novel therapeutic agents targeting diseases associated with PDE4B activity.

In order to probe whether MA is directly combined with PDE4B, we performed molecular docking experiments. Based on the PDE4B model, we docked MA using AutoDock. As shown in Figure 7A, stable hydrogen bonds between PDE4B and MA were found. MA could interact with the Tyr405 residue of PDE4B, and the bond length was found to be 4.8 Å. The calculated free energy of the interaction between MA and PDE4B was determined to be −8.2 kcal/mol, suggesting a favorable binding affinity. To further understand the interacting process of MA and PDE4B, we then conducted MD simulation experiments using the native (PDE4B^WT) and mutant protein (PDE4B^Y405A) structures. The conformational stability of the PDE4B-MA complexes was assessed by measuring the root mean square deviation (RMSD) values. The RMSD profile showed that PDE4B^WT and PDE4B^Y405A reached the equilibrium phase at 20 ns for MA (Figure 7B). As shown in Figure 7C, the RMSD of MA in the PDE4B^WT system was smaller than that in the PDE4B^Y405A system, indicating a decrease in the stability of MA after the Tyr405 site mutation of the PDE4B protein. Due to the system stabilizing after 20 ns, we selected trajectories in the range of 20–100 ns for the MM-PBSA binding free energy calculation. According to the data presented in Table 3, the binding free energy of the MA-PDE4B^WT system was lower than that of the MA-PDE4B^Y405A, indicating that MA-PDE4B^WT system is more stable. In order to conduct an in-depth examination of the interplay between MA and PDE4B, we analyzed the binding free energy of MA-PDE4B^WT and MA-PDE4B^Y405A complexes to assess the individual energy contributions of each protein residue, thereby identifying the critical binding residues. The results showed that in the MA-PDE4B^WT system, Tyr405 showed a favorable binding effect (−8.88 kcal/mol), while in the MA-PDE4B^Y405A system, Ala405 demonstrated an unfavorable binding effect (5.92 kcal/mol) (Figure 7D). These results indicated that the mutation of Tyr405 has a critical impact on the stability of MA-PDE4B. Overall, the findings suggested that MA could directly bind to the PDE4B Tyr405 site, indicating a potential mechanism through which MA may influence inflammation, immune function, and cardiovascular processes. Selective PDE4B inhibitors could provide benefits over non-selective PDE4 inhibitors by minimizing off-target effects and enhancing safety profiles.