Maimendong decoction suppresses non-small cell lung cancer growth by promoting dendritic cell maturation via the SIRT1/p65 acetylation pathway

Summary

Maimendong Decoction (MMDD), a classical traditional Chinese medicine, has shown promising clinical efficacy against non-small cell lung cancer (NSCLC), yet its immunological mechanisms remain unclear. Using a Lewis lung carcinoma (LLC) mouse model, we found that MMDD markedly suppressed tumor growth, enhanced CD8⁺ T cell infiltration, and promoted dendritic cell (DC) maturation. Mechanistic analyses revealed that MMDD inhibited SIRT1 expression, increased p65 acetylation, and elevated pro-inflammatory cytokines, indicating the modulation of the SIRT1/acetyl-p65 pathway. In vitro, MMDD-treated bone marrow-derived DCs exhibited increased maturation and improved CD8⁺ T cell activation and IFN-γ secretion. Notably, SIRT1 overexpression attenuated the MMDD-induced DC maturation and CD8⁺ T cell activation, confirming the involvement of SIRT1/acetyl-p65 signaling. Collectively, MMDD exerts potent anti-NSCLC activity by facilitating DC maturation via the SIRT1/acetyl-p65 axis, highlighting its potential as an immunomodulatory therapeutic strategy for NSCLC.

Graphical abstract

Highlights

• •Impaired dendritic cell maturation and reduced CD8⁺ T cell infiltration in lung cancer
• •Maimendong Decoction enhances dendritic cell maturation and CD8⁺ T cell activation
• •SIRT1 inhibition increases p65 acetylation to promote dendritic cell maturation
• •Maimendong Decoction suppresses NSCLC growth via the SIRT1/acetyl-p65 pathway

Immunology; Pharmacology

Introduction

Lung cancer ranks as the leading cause of global cancer-related mortality and remains among the most prevalent malignancies worldwide. Non-small cell lung cancer (NSCLC), the predominant pathological subtype, exhibits a dismal 5-year survival rate of only 26%.¹ Current therapeutic strategies, including radiotherapy, chemotherapy, and surgical intervention, yield suboptimal outcomes. Immune evasion, a hallmark of tumorigenesis, represents a critical mechanism underlying NSCLC progression, making the targeted disruption of this process a cornerstone of immunotherapy.^2,3

Despite advances in NSCLC immunotherapy, heterogeneous treatment responses and adverse effects underscore the complexity of its mechanisms.⁴ The functional state of immune cells within the tumor microenvironment (TME) critically determines therapeutic efficacy. Dendritic cells (DCs), the most potent antigen-presenting cells (APCs), drive antitumor immunity by uniquely activating naive T cells and priming cytotoxic T lymphocytes (CTLs) through MHC-II-mediated antigen presentation, co-stimulatory signaling (CD40, CD80, and CD83), and cytokine secretion.^5,6 However, immunosuppressive factors in the TME impair DCs' maturation and antigen presentation, compromising T cell activation. Lu et al. revealed NSCLC cell-mediated DC dysfunction via the suppression of co-stimulatory molecules (CD80, CD86) and pro-inflammatory cytokines such as IL-12, IL-23, impairing antitumor immunity.⁷ Targeting DC maturation thus emerges as a promising strategy to alleviate TME immunosuppression and enhance NSCLC treatment.

Maimendong Decoction (MMDD), listed in the First Catalog of Ancient Classical Formulas, has demonstrated clinical efficacy in NSCLC management in traditional Chinese medicine (TCM).^8,9 Originating from Zhang Zhongjing’s Synopsis of Prescriptions of the Golden Chamber over 1000 years ago, MMDD comprises six medicinal materials: Ophiopogon japonicus (Thunb.) Ker Gawl., Pinellia ternata (Thunb.) Breit., Panax ginseng C.A.Mey., Glycyrrhiza uralensis Fisch., Oryza sativa L., and Ziziphus jujuba Mill.^10,11 The detailed composition of MMDD is summarized in Table 1. As modern pharmacological research progresses, MMDD has demonstrated various pharmacological effects in the treatment of pulmonary diseases, including the regulation of pulmonary surfactant secretion, cough suppression, promotion of airway clearance, and alleviation of respiratory allergic reactions.⁹ Moreover, in TCM, the therapeutic effects for patients with lung cancer are often closely related to immune modulation, which can typically improve the quality of life and extend survival.¹² Studies have shown that MMDD can enhance the activity of natural killer (NK) cells, thereby improving their cytotoxic effects against lung cancer cells, effectively inhibiting lung cancer metastasis and prolonging the survival of mice.¹³ However, the potential inhibitory effects of MMDD on lung cancer and its specific mechanisms in tumor immune modulation have not been thoroughly explored.

Table 1.

Composition of MMDD

Botanical Name	Chinese name	Plant part used	Dosage used
Ophiopogon japonicus (Thunb.) Ker Gawl.	Mai-Dong	Root tuber	42g
Pinellia ternata (Thunb.) Breit.	Ban-Xia	Tuber	6g
Panax ginseng C.A.Mey.	Ren-Shen	Root	9g
Glycyrrhiza uralensis Fisch.	Gan-Cao	Root and rhizome	6g
Oryza sativa L.	Jing-Mi	Seed	6g
Ziziphus jujuba Mill.	Dao-Zao	Fruit	20g

This study aims to investigate the potential mechanisms of MMDD in NSCLC from the perspective of regulating DCs, with a particular focus on the maturation process of DCs and their interactions with tumor-infiltrating T lymphocytes. By elucidating this critical immune regulatory pathway, we aim to provide a more robust theoretical basis and experimental support for the rational application of MMDD in the clinical treatment of lung cancer.

Results

Qualitative chemical profiling of Maimendong Decoction

Chemical constituents were characterized using ultra-performance liquid chromatography coupled with high-resolution mass spectrometry (UPLC-HRMS). Figure 1A displays the base peak chromatogram (BPC), with compounds effectively identified via chromatographic-mass spectrometric alignment. Figure S1 displays extracted ion chromatograms (EICs) and secondary mass spectrometry of 11 key bioactive compounds identified in the analysis, including Gallic acid, Isoferulic acid, Liquiritigenin, Luteolin, Ophiopogonin D, 17-Hydroxy Sprengerinin C, Ginsenoside Rg1, Ginsenoside Rb1 et al. Figure 1B summarizes the proportion of major chemical constituents in MMDD, predominantly including flavonoids, glycosides, terpenes, phenylpropanoids, and amino acids or peptides. Furthermore, the BPC profiles of MMDD decoction and MMDD-containing serum exhibited highly similar chromatographic patterns, and the major bioactive constituents identified in MMDD were also detected in serum, indicating that these compounds remain stable and bioavailable after absorption (Figure S2).

Figure 1.

LC-MS chemical profiling of MMDD

(A) BPC in negative and positive ion modes.

(B) The proportion of major chemical constituents in MMDD.

See also Figures S1 and S2.

Maimendong Decoction significantly suppresses non-small cell lung cancer tumor growth

As shown in Figure 2, MMDD exhibited dose-dependent antitumor efficacy in an LLC subcutaneous model. Tumor volume comparisons revealed that all MMDD treatment groups (low, medium, and high doses) significantly inhibited tumor growth compared to the vehicle control (Figure 2A), with high-dose MMDD achieving comparable inhibition to the DDP positive control. Final tumor volumes and weights were markedly reduced across MMDD dose groups (Figures 2B and 2C). Throughout the treatment period, no significant changes in body weight were observed among the experimental groups, indicating that MMDD was well tolerated and did not cause overt systemic toxicity in tumor-bearing mice (Figure S3). Consistently, histopathological evaluation of major organs, including the heart, liver, spleen, lung, and kidney, showed no noticeable tissue damage or inflammatory infiltration following MMDD administration, further supporting its favorable systemic safety profile (Figure S4). H&E-stained sections revealed a dose-dependent increase in tumor necrosis, most evident in the high-dose MMDD group. The treated tumor tissues exhibited disrupted architecture, markedly reduced cellular density, nuclear condensation and fragmentation, and extensive necrotic regions (Figure 2D), indicating progressive tumor cell death following MMDD treatment. Apoptosis quantification via TUNEL staining demonstrated a significant increase in apoptotic cell percentages in MMDD-treated tumors (Figures 2E and 2F).

Figure 2.

MMDD dose-dependently suppresses tumor growth in an LLC subcutaneous model

(A) Tumor volume comparisons across treatment groups: vehicle control (sterile deionized water), low-dose MMDD (L-MMDD), medium-dose MMDD (M-MMDD), high-dose MMDD (H-MMDD), and positive control (DDP), scale bar, 10 mm.

(B and C) Final tumor volumes and weights at experimental endpoint (n = 6).

(D) Representative H&E-stained tumor sections (scale bar, 100 μm).

(E) TUNEL staining images of tumor sections across groups (scale bar, 50 μm).

(F) Quantitative analysis of TUNEL-positive apoptotic cells (%) (n = 3). Data are represented as mean ± SD. One-way ANOVA was used for comparison among multiple groups. ∗p < 0.05, ∗∗p < 0.01.

See also Figures S3 and S4.

Maimendong Decoction enhances antitumor immunity by promoting CD8⁺ T cell infiltration and dendritic cell maturation

CD8⁺ T cells serve as primary cytotoxic effectors, eliminating malignant cells through tumor antigen-MHC class I complexes, while mature dendritic cells (mDCs) orchestrate tumor-specific T cell activation via antigen presentation and co-stimulatory signaling.¹⁴ Building on this synergistic axis, we investigated MMDD’s immunomodulatory effects. As shown in Figure 3, MMDD dose-dependently amplified tumor-specific immunity. Immunohistochemical analysis revealed progressive intratumoral infiltration of CD8⁺ T cells, visualized via brown chromogenic staining, in low-, medium-, and high-dose MMDD groups compared to controls (Figures 3A and 3B). Flow cytometric analysis further validated enhanced DC maturation following MMDD treatment, as indicated by significantly increased co-expression of CD40, CD80, CD83, and MHC-II on CD11c⁺ DCs in the high-dose MMDD group relative to controls (Figures 3C and 3D).

Figure 3.

MMDD enhances antitumor immunity by promoting CD8⁺ T cell infiltration and DC maturation

(A) Representative immunohistochemical staining of CD8⁺ T cells in tumor tissues from vehicle control (sterile deionized water), L-MMDD, M-MMDD, and H-MMDD groups (scale bar, 50 μm).

(B) Quantitative analysis of CD8⁺ T cell density (n = 3).

(C) Flow cytometry gating strategy for mature DCs (CD11c⁺CD40⁺CD80⁺CD83⁺MHC-II⁺) in tumor tissues.

(D) Proportions of CD40⁺, CD80⁺, CD83⁺, and MHC-II⁺ cells among CD11c⁺ DCs (n = 3). Data are represented as mean ± SD. One-way ANOVA was used for comparison among multiple groups.∗p < 0.05 and ∗∗p < 0.01.

Network pharmacology of Maimendong Decoction in the treatment of non-small cell lung cancer

To explore the potential mechanisms of MMDD in treating NSCLC, we conducted a network pharmacology analysis to identify key targets and signaling pathways. First, disease-related targets of Non-Small Cell Lung Cancer were retrieved from the Genecards and OMIM database, yielding 1,930 disease targets. Based on 11 chemical components identified in MMDD through LC-MS/MS analysis, intersection targets between the drug and NSCLC were obtained, resulting in 231 shared targets (Figure 4A). Subsequently, the target network of active drug components was constructed using Cytoscape 3.9.1 software (Figure 4B). The intersection targets were then imported into the STRING database to generate a PPI network. Using Cytoscape 3.7.0 software, centrality analysis and evaluation of all target genes revealed a core target network comprising 46 nodes (Figure 4C), including SIRT1, NFKB1, TLR4, FOS, mTOR, JUN, and others.

Figure 4.

Network pharmacology of MMDD in the treatment of NSCLC

(A) Venn diagram of active ingredients and NSCLC targets.

(B) MMDD active ingredient-target network.

(C) Core target PPI network.

(D) GO enrichment analysis bar plot.

(E) KEGG pathway enrichment bubble plot.

GO enrichment analysis of the intersection targets revealed 1,102 GO terms, including 738 associated with biological processes, 115 related to cellular components, and 249 linked to molecular functions (Figure 4D). Additionally, KEGG pathway analysis highlighted several key pathways associated with NSCLC, such as EGFR tyrosine kinase inhibitor resistance, NSCLC, HIF-1 signaling pathway, small cell lung cancer, nuclear factor κB (NF-κB) signaling pathway, PI3K-Akt signaling pathway, pathways in cancer, microRNAs in cancer, AMPK signaling pathway, JAK-STAT signaling pathway, MAPK signaling pathway, and Wnt signaling pathway (Figure 4E). These findings suggest that MMDD may exert therapeutic effects by modulating these pathways. Among these, the NF-κB signaling pathway plays a pivotal role in DC activation. As a key transcription factor, NF-κB regulates the expression of numerous immune-related genes, and its activation represents a critical step in the maturation and functional activation of DCs.^15,16 The NAD⁺-dependent deacetylase SIRT1 has been widely recognized as a negative regulator of NF-κB transcriptional activity, as it specifically deacetylates the p65 subunit at lysine 310 (K310).¹⁷ Collectively, these data indicate that MMDD may exert its antitumor effects through multiple signaling pathways, among which the SIRT1-mediated regulation of NF-κB signaling may represent a key immunomodulatory mechanism.

Sirtuin 1-targeted molecular docking analysis of 11 key active compounds in Maimendong Decoction

Molecular docking was performed using AutoDock and AutoDock Vina software, in which lower docking scores indicate stronger binding affinity between compounds and targets. Using SIRT1 as the target protein and 11 key active components from MMDD as small molecules, molecular docking results were obtained and summarized (Figure 5A). Binding energy, a critical metric for evaluating the interaction between small molecules and target proteins, suggests spontaneous binding when the energy value is below −5 kcal/mol, with lower values correlating to higher binding probability. The simulation results demonstrated favorable binding between the small molecules and the target protein. Based on the docking outcomes, the top five target protein-compound conformations with the strongest binding affinities were visualized using PyMOL software (Figures 5B–5F).

Figure 5.

SIRT1-targeted molecular docking analysis of 11 key active compounds in MMDD

(A) Binding energy table of key components with SIRT1.

(B–F) Molecular docking of SIRT1 with the active compound. In docking diagrams: Green stick models represent active molecules; pink stick structures denote amino acid residues on the SIRT1 protein; dashed lines indicate hydrogen bonds between active compounds and amino acid residues.

Maimendong Decoction suppresses tumor growth via sirtuin 1 downregulation and enhanced p65 acetylation

Dysregulated SIRT1 has been closely linked to tumor immune evasion, while enhanced p65 acetylation relieves NF-κB inhibition and triggers pro-inflammatory.^18,19 In order to further verify the molecular mechanisms underlying the antitumor effects of MMDD, western blot analysis revealed the progressive downregulation of SIRT1 and concomitant elevation of the acetyl-p65/p65 ratio across low-, medium-, and high-dose MMDD groups (Figures 6A–6D). Immunofluorescence co-staining confirmed dose-dependent reductions in SIRT1 fluorescence intensity alongside increased acetyl-p65 signals (Figures 6E and 6F). ELISA quantification of tumor homogenates demonstrated dose-dependent increases in pro-inflammatory cytokines, including IL-1β, IL-6, IL-12, TNF-α, and IFN-γ, particularly in the high-dose MMDD group compared with controls (Figure 6G).

Figure 6.

MMDD suppresses tumor growth via SIRT1 downregulation and enhanced p65 acetylation

(A and C) Western blot analysis of SIRT1 and acetyl-p65 protein levels in tumor tissues from vehicle control (sterile deionized water), L-MMDD, M-MMDD, and H-MMDD groups. GAPDH served as the internal loading control for SIRT1, acetyl-p65, and p65.

(B and D) Quantitative analysis of relative SIRT1 and acetyl-p65/p65 expression levels in tumor tissues using ImageJ software (n = 3).

(E) Representative immunofluorescence images of SIRT1 (red) and acetyl-p65 (green) in tumor sections. Nuclei were counterstained with DAPI (blue) (scale bar, 50 μm).

(F) Quantification of mean fluorescence intensity for SIRT1 and acetyl-p65 (n = 3).

(G) ELISA quantification of cytokine levels (IL-1β, IL-6, IL-12, TNF-α, and IFN-γ) in tumor homogenates (n = 6). Data are represented as mean ± SD. One-way ANOVA was used for comparison among multiple groups.∗p < 0.05 and ∗∗p < 0.01.

Maimendong Decoction-containing serum promotes dendritic cell maturation and enhances CD8⁺ T cell effector functions in vitro

To evaluate MMDD’s impact on DC maturation in vitro, BMDCs were generated from C57BL/6 mice and cultured for 7 days in complete RPMI-1640 medium supplemented with GM-CSF and IL-4. On day 7, the cells were collected and treated with MMDD-containing serum (low-, medium-, or high-dose) for 24 h. After treatment, the cells were harvested for subsequent analyses of DC maturation markers and functional assays. Experimental groups included: control, low-, medium-, and high-concentration MMDD-containing serum. Flow cytometry confirmed >95% purity of CD11c⁺ DCs (Figure 7A). Treatment with MMDD-containing serum promoted DC maturation in a dose-dependent manner, as evidenced by increased expression of surface markers CD40, CD80, CD83, and MHC-II (Figures 7B and 7C). To assess DC-mediated CD8⁺ T cell activation, a transwell co-culture system was employed. CD8⁺ T cells co-cultured with MMDD-treated DCs exhibited enhanced migration and invasion capabilities compared with the control group (Figures 8A and 8B). Moreover, flow cytometric analysis demonstrated that the supernatant from MMDD-treated DCs dose-dependently enhanced IFN-γ production in activated CD8⁺ T cells, with the highest levels observed in the high-dose group (Figures 8C and 8D).

Figure 7.

MMDD-containing serum promotes DC maturation in vitro

(A) Flow cytometry analysis of BMDCs purity.

(B) Flow cytometry profiles of maturation markers (CD40, CD80, CD83, and MHC-II) in DCs treated with control (drug-free serum from vehicle-treated rats), low-, medium-, and high-dose MMDD-containing serum.

(C) Quantitative analysis of CD40⁺, CD80⁺, CD83⁺, and MHC-II⁺ cell proportions among DCs (n = 3). Data are represented as mean ± SD. One-way ANOVA was used for comparison among multiple groups.∗p < 0.05 and ∗∗p < 0.01.

Figure 8.

MMDD-containing serum-treated DCs enhance the migration, invasion, and activation of CD8⁺ T cells

(A) Representative transwell assay images show the migration and invasion capacities of CD8⁺T cells co-cultured with DCs pretreated with control (drug-free serum from vehicle-treated rats), low-, medium-, or high-dose MMDD-containing serum (scale bar, 50 μm).

(B) Quantification of migrated and invaded CD8⁺ T cells (n = 3).

(C) Flow cytometry analysis of IFN-γ expression in CD8⁺ T cells co-cultured with supernatant of MMDD-treated DCs.

(D) Quantification of the proportion of IFN-γ⁺ cells among CD8⁺ T cells (n = 3). Data are represented as mean ± SD. One-way ANOVA (D) and two-way ANOVA (B) are used for comparison among multiple groups.∗p < 0.05 and ∗∗p < 0.01.

Maimendong Decoction exerts antitumor activity by modulating the Sirtuin 1/acetyl-p65 axis in dendritic cells

As shown in Figures 9A and 9B, Western blot analysis revealed progressive downregulation of SIRT1 accompanied by a concomitant elevation of the acetyl-p65/p65 ratio in low-, medium-, and high-dose MMDD-treated DCs compared with controls (Figures 9C and 9D). To exclude the possibility that MMDD-containing serum directly affected tumor cells, LLC cells were treated with low, medium, or high concentrations of MMDD-containing serum. No significant changes in cell viability or SIRT1 protein expression were observed, suggesting that MMDD-containing serum did not directly influence the proliferation or SIRT1 signaling of LLC cells (Figure S5). ELISA quantification further demonstrated significantly elevated levels of pro-inflammatory cytokines (IL-1β, IL-6, IL-12, and TNF-α) and IFN-γ in supernatants from MMDD-treated DCs, with maximal effects observed in the high-dose group (Figure 9E).

Figure 9.

MMDD exerts antitumor activity by modulating the SIRT1/acetyl-p65 axis in DCs

(A and C) Western blot analysis of SIRT1, acetyl-p65, and p65 expression in DCs treated with control (drug-free serum from vehicle-treated rats), low-, medium-, or high-dose MMDD-containing serum. GAPDH served as the internal loading control for SIRT1, acetyl-p65, and p65.

(B and D) Quantitative analysis of relative SIRT1 and acetyl-p65/p65 expression levels in DCs using ImageJ software (n = 3).

(E) ELISA quantification of pro-inflammatory cytokines (IL-1β, IL-6, IL-12, and TNF-α) and IFN-γ in supernatants of MMDD-treated DCs (n = 6).

(F) Western blot analysis of SIRT1 expression in control, negative control (NC), and AAV-SIRT1-transduced (OE-SIRT1) BMDCs.

(G) Quantification of SIRT1 expression levels in DCs (n = 3).

(H) Western blot analysis of SIRT1, acetyl-p65, and p65 in DCs from the control, high-dose MMDD-containing serum, OE-SIRT1, or OE-SIRT1+high-dose MMDD-containing serum groups.

(I and J) Quantification of relative SIRT1, acetyl-p65/p65 levels in DCs (n = 3).

(K) ELISA analysis of pro-inflammatory cytokines (IL-1β, IL-6, IL-12, and TNF-α) and IFN-γ in DC supernatants (n = 6).

(L) Flow cytometry profiles of DCs maturation markers (CD40, CD80, CD83, and MHC-II) across groups.

(M) Quantitative analysis of CD40⁺, CD80⁺, CD83⁺, and MHC-II⁺DC proportions (n = 3).

(N) Flow cytometry analysis of IFN-γ expression in CD8⁺ T cells treated with DC-conditioned supernatant from different groups.

(O) Quantification of the proportion of IFN-γ⁺ cells among CD8⁺ T cells (n = 3). Data are represented as mean ± SD. One-way ANOVA was used for comparison among multiple groups.∗p < 0.05 and ∗∗p < 0.01.

See also Figure S5.

To further elucidate the mechanism underlying MMDD’s immunomodulatory effects, a rescue experiment was conducted using SIRT1-overexpressing mice. Western blot analysis confirmed the successful overexpression of SIRT1, as evidenced by a marked increase in its protein level (Figures 9F and 9G). Mechanistic analysis demonstrated that MMDD-containing serum significantly suppressed SIRT1 expression and elevated the acetyl-p65/p65 ratio in DCs compared with controls. Following the establishment of the SIRT1-overexpressing mouse model, BMDCs were isolated and treated with MMDD-containing serum. Notably, the MMMD-induced suppression of SIRT1 was markedly alleviated in DCs with SIRT1 overexpression (OE-SIRT1) (Figures 9H–9J). Consistently, ELISA results showed that MMDD treatment robustly elevated secretion of pro-inflammatory cytokines (IL-1β, IL-6, IL-12, and TNF-α) and IFN-γ in DCs supernatants (Figure 9K). Flow cytometric analysis revealed that MMDD-containing serum promoted DC maturation, characterized by the increased expression of CD40, CD80, CD83, and MHC-II (Figures 9L and 9M). Moreover, supernatants from MMDD-pretreated DCs significantly enhanced CD8⁺ T cell effector function, reflected by increased frequencies of IFN-γ⁺ T cells (Figures 9N and 9O). However, when MMDD-containing serum was administered on the basis of OE-SIRT1, the immunostimulatory effects exerted by MMDD were markedly attenuated, as evidenced by suppressed cytokine secretion, reduced expression of DCs maturation markers, and decreased frequencies of IFN-γ⁺ T cells compared with the high-dose MMDD group alone (Figures 9K–9O).

Discussion

To date, lung cancer remains one of the leading causes of global cancer-related morbidity and mortality.^20,21 Despite advancements in precision-targeted therapies and immune checkpoint inhibitors, the 5-year survival rate remains unsatisfactory across all disease stages, underscoring the limitations of current therapeutic paradigms.²⁰ TCM, with its abundant repertoire of natural compounds and multi-target regulatory mechanisms, has demonstrated significant clinical potential in lung cancer management.^22,23,24 MMDD, a classical formula, contains bioactive constituents such as Ophiopogonin D, Ginsenosides Rg1/Rb1, Liquiritin, and Isoferulic acid, which collectively underpin its multi-dimensional pharmacological effects. Although MMDD has shown clinical efficacy, its precise antitumor mechanisms require further elucidation. In this study, we revealed that MMDD exerts dose-dependent antitumor effects in an LLC model, with high-dose MMDD achieving comparable efficacy to DDP. Notably, H&E staining revealed nuclear condensation and extensive necrotic regions in MMDD-treated tumors, indicative of apoptosis, which was further confirmed by TUNEL staining.

TME plays a pivotal role in lung cancer progression and therapeutic resistance.^25,26 Accumulating evidence indicates that tumor-infiltrating dendritic cells (TIDCs) often exhibit an immature phenotype and possess constitutively immunosuppressive properties, thereby impairing the proliferation and activation of CD8⁺ T lymphocytes and facilitating tumor immune evasion.^27,28 In our study, Immunohistochemical quantification demonstrated dose-dependent intratumoral infiltration of CD8⁺ cytotoxic T lymphocytes following MMDD treatment. Multidimensional flow cytometry further demonstrated that MMDD enhances DC maturation by upregulating the co-expression of CD40⁺, CD80⁺, CD83⁺, and MHC-II⁺ on CD11c⁺ DCs.

To further investigate the molecular mechanisms underlying the anti-tumor effects of MMDD, we performed GO analysis and found that 11 active components in MMDD may modulate NSCLC progression through diverse cellular biological processes and molecular functions. It is well-established that DC activation and functional maturation are regulated by multiple signaling pathways, among which the NF-κB pathways serve as a central regulator.²⁹ Activation of NF-κB drives pro-inflammatory cytokine secretion such as IL-1β, IL-6, IL-12, TNF-α via phosphorylation or acetylation of its p65 subunit, thereby amplifying immune responses.^15,30 Notably, the SIRT1 acts as a negative regulator of NF-κB by selectively deacetylating p65, thereby suppressing its transcriptional activity, and this mechanism represents a well-known important signaling pathway.^31,32 KEGG and western blot analyses indicated that the SIRT1/NF-κB axis may mediate the biological effects observed in response to MMDD. Furthermore, Molecular docking analyses demonstrated that 11 active compounds in MMDD exhibited strong binding affinities to the target molecule SIRT1, supporting the hypothesis that MMDD may exert its effects through these active molecules via SIRT1.

Our in vivo studies demonstrated that MMDD dose-dependently inhibits SIRT1 expression, enhances p65 acetylation, and activates pro-inflammatory or pro-apoptotic cytokines such as IL-1β, IL-6, IL-12, TNF-α, and IFN-γ. More importantly, the in vitro experiments further confirmed that MMDD-containing serum promotes DC maturation through the same SIRT1/acetyl-p65 signaling pathway, thereby enhancing CD8⁺ T cell migration, invasion, and IFN-γ secretion. The increased migratory and invasive capacities of CD8⁺ T cells suggest that MMDD-treated DCs possess an enhanced chemotactic potential toward T cells. This effect is likely mediated indirectly through the elevated secretion of immune-activating cytokines, such as TNF-α and IFN-γ, which may facilitate T cell recruitment. Crucially, these effects were reversed by the OE-SIRT1, confirming the central regulatory role of SIRT1 in this process.

In conclusion, this study demonstrates that MMDD promotes DC maturation through the SIRT1/p65 acetylation axis, thereby enhancing CD8⁺ T cell cytotoxicity and suppressing NSCLC growth (Figure 10).

Figure 10.

Schematic illustration of the mechanism by which MMDD promotes DC maturation within the TME to suppress NSCLC progression

This diagram summarizes how MMDD enhances CD8⁺ T cell-mediated antitumor responses by promoting DC maturation via the SIRT1-p65 acetylation axis, ultimately exerting therapeutic effects against NSCLC.

Limitations of the study

In this study, several limitations should be acknowledged. Although numerous studies support the close relationship between the maturation of DCs and their antigen presentation capabilities, our current experiments did not directly evaluate the antigen presentation function of DCs. Future work will address this limitation by employing OVA-pulsed DCs co-cultured with OT-1/2 T cells to directly assess the effects of MMDD on DC antigen processing and presentation, thereby strengthening the mechanistic rigor of the study. Additionally, while our results suggest that MMDD may exert its effects through the DC-CD8⁺ T cell axis, this has not yet been validated by in vivo depletion experiments (such as using DC depletion or deficiency models, or InVivoMAb anti-mouse CD8α antibody treatment) or tumor-bearing nude mouse models. Future studies should consider employing these in vivo validation methods to rule out other potential anti-tumor mechanisms and further confirm the critical roles of DCs and CD8⁺ T cells in the anti-tumor effects of MMDD. Finally, given that chemotherapy remains an essential component of NSCLC treatment, future investigations could explore the combinational potential of MMDD with conventional chemotherapeutic agents such as DDP. Based on our findings that MMDD enhances immune activity through the SIRT1/acetyl-p65 pathway, we hypothesize that MMDD-chemotherapy combination therapy may not only potentiate antitumor immune responses but also alleviate chemotherapy-induced toxicity, thereby improving patients’ quality of life. Further preclinical and clinical studies are warranted to validate the immunoregulatory and protective potential of this combined therapeutic approach.

Resource availability

Lead contact

Further information and requests should be directed to the lead contact, Dawei Cui (daweicui@zju.edu.cn).

Materials availability

All unique reagents generated in this study are available from the lead contact without restriction.

Data and code availability

• •Original data, including raw western blot images, qualitative chemical profiling of MMDD, and identification of serum-absorbed components of MMDD, have been deposited in Mendeley Data: https://doi.org/10.17632/p9fkgm2wkj.2 and are publicly available.
• •Additional data reported in this article will be shared by the lead contact upon request.
• •This article does not report original code.
• •Any additional information required to reanalyze the data reported in this article is available from the lead contact upon request.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (82205211, 81871709), Jiangsu Provincial Health International Regional Exchange Support Program❲2023❳No.88, 2024 Key Project of University-Local Collaborative Innovation Research, Jiangsu Vocational College of Medicine (202490602), 2023 Medical Research Project Approved by Jiangsu Provincial Health Commission (Z2023037), and National Natural Science Foundation of Zhangjiagang (ZKYL2301). We also would like to sincerely acknowledge BioRender (https://www.biorender.com) for creating professional scientific illustrations and extend gratitude to Shanghai OE Biotech Co., Ltd. and Majorbio Bio-Pharm Technology Co., Ltd. for their valuable assistance with data analysis.

Author contributions

Q.Z.: original draft, investigation, and funding acquisition. Y.S.: methodology and data curation. Y.G.: methodology and formal analysis. Y.H.: data curation. Z.Z.: data acquisition. K.Z.: methodology. L.A.: methodology. K.W.: formal analysis and data curation. C.W.: methodology. L.W.: statistical analyses and funding acquisition. L.L.: software and funding acquisition. L.H.: software. W.J.: software. Y.M.: writing – review and editing and supervision. D.C.: writing – review and editing, supervision, and funding acquisition.

Declaration of interests

The authors declare no competing interests.

STAR★Methods

Key resources table

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Antibodies
Anti-CD8α(D4W2Z) XP® Rabbit mAb	Cell Signaling Technology	Cat# 98941
Anti-SIRT1	Abcam	Cat# ab110304
Anti-NFκB p65	Abcam	Cat# ab32536
Anti-Acetyl-NFκB p65(Lys310)	ThermoFisher	Cat# PA5-17264
Anti-GAPDH	Cell Signaling Technology	Cat# 5174
Goat Anti-Mouse IgG H&L (Alexa Fluor® 594)	Abcam	Cat# ab150116
Goat Anti-Rabbit IgG H&L (Alexa Fluor® 488)	Abcam	Cat# ab150077
Anti-CD11c	Elabscience	Cat# E-AB-F0991H
Anti-CD40	Elabscience	Cat# E-AB-F1028C
Anti-CD80	Elabscience	Cat# E-AB-F0992C
Anti-CD83	Abcam	Cat# ab252822
Anti-MHC II	Elabscience	Cat# E-AB-F0990C
Goat F(ab’)2 Anti-rat IgG Fc (Alexa Fluor® 488)	Abcam	Cat# ab150161
Anti-CD45 (APC)	Elabscience	Cat# E-AB-F1136E
Anti-CD8a (FITC)	Elabscience	Cat# E-AB-F1104C
Anti-IFN-γ (PE-Cyanine7)	Invitrogen	Cat# 25-7311-82
Bacterial and virus strains
AAV-SIRT1	This paper	N/A
Chemicals, peptides, and recombinant proteins
DMEM	Gibco	Cat# 11965092
Fetal bovine serum	Gibco	Cat# 10099141
Penicillin-streptomycin	NCM Biotech	Cat# C100C5
RPMI-1640 medium	Shanghai Yuanpei Biotechnology	Cat# L210KJ
Recombinant mouse GM-CSF	MedChemExpress	Cat# HY-P7361
Recombinant mouse IL-4	MedChemExpress	Cat# HY-P70653
Cisplatin (DDP)	Macklin	Cat# D807330
Proteinase K	Beyotime	Cat# ST532
Bovine serum albumin	Cell Signaling Technology	Cat# 9998
Crystal violet	Merck	Cat# 32675
PMA	MedChemExpress	Cat# HY-18739
Ionomycin	MedChemExpress	Cat# HY-13434
Brefeldin A	MedChemExpress	Cat# HY-16592
Critical commercial assays
TUNEL Apoptosis Assay Kit	Beyotime	Cat# C1089
H&E Staining Kit	BaSO	Cat# BA4099
ELISA Kits (IL-1β, IL-6, IL-12, TNF-α, IFN-γ)	LiankeBio	Cat# EK201B; EK206; EK2183; EK282; EK280
MTT Cell Proliferation and Cytotoxicity Assay Kit	Beyotime	Cat# C0009S
Deposited data
Raw western blot images	Mendeley Data	https://doi.org/10.17632/p9fkgm2wkj.2
Qualitative chemical profiling of Maimendong Decoction	Mendeley Data	https://doi.org/10.17632/p9fkgm2wkj.2
Identification of serum-absorbed components of Maimendong Decoction	Mendeley Data	https://doi.org/10.17632/p9fkgm2wkj.2
Experimental models: Cell lines
Lewis Lung Carcinoma (LLC)cells	Procell	Cat# CL-0140; RRID:CVCL_4358
Experimental models: Organisms/strains
C57BL/6 mice	Jiangsu Huachuang-Sino Pharma Tech Co.,Ltd.	Cat# HCM004
Sprague-Dawley	Jiangsu Qinglongshan Biotechnology Co. LTD	Cat# SN525549603287109
Software and algorithms
GraphPad Prism 9.5.0	GraphPad Software	https://www.graphpad.com
FlowJo	BD Biosciences	https://www.flowjo.com
ImageJ	NIH	https://imagej.nih.gov/ij/
PubChem	NCBI	https://pubchem.ncbi.nlm.nih.gov
Swiss Target Prediction	Swiss Institute of Bioinformatics	http://www.swisstargetprediction.ch
ChEMBL	EMBL-EBI	https://www.ebi.ac.uk/chembl
GeneCards	Weizmann Institute of Science	https://www.genecards.org
OMIM	Johns Hopkins University	https://www.omim.org
Venny 2.1.0	Bioinformatics for Genomics & Proteomics	https://bioinfogp.cnb.csic.es/tools/venny
Cytoscape 3.9.1	Cytoscape Consortium	https://cytoscape.org
STRING Database	EMBL	https://string-db.org
DAVID database	NIAID, NIH	https://davidbioinformatics.nih.gov
Protein DataBank	RCSB	https://www.rcsb.org
AutoDock Vina	The Scripps Research Institute	https://github.com/ccsb-scripps/AutoDock-Vina
PyMOL	Schrödinger, LLC	https://pymol.org
Other
BioRender	BioRender	https://www.biorender.com
Majorbio Cloud Platform	Majorbio	https://www.majorbio.com

Experimental model and study participant details

Cell line

Lewis lung carcinoma (LLC) cells were obtained from Pricella Biotechnology Co.,Ltd. (Wuhan, China). Cells were cultured in DMEM medium supplemented with 10% fetal bovine serum (FBS) and 1% penicillin–streptomycin. The cultures were maintained at 37°C in a humidified atmosphere containing 5% CO₂. Cells were routinely tested to be free of mycoplasma contamination.

Tumor-bearing animal models

Male C57BL/6 mice (4 weeks old, 20 g) were housed under specific pathogen-free conditions with controlled temperature (22 ± 2°C), humidity (50 ± 10%), and a 12-hour light/dark cycle, with ad libitum access to standard chow and water. Mice were subcutaneously injected with 1 × 10⁶ LLC cells , into the right flank. Tumor volume was monitored daily and calculated as V=L×W²/2, where L and W represent the length and width, respectively. Drug treatment was initiated when the tumor volume reached 100 mm³. All animal experiments were approved by the Institutional Animal Care and Use Committee of Nanjing University of Chinese Medicine (Approval No.: 202504A091) and conducted in compliance with the ARRIVE guidelines.

Construction of the SIRT1-overexpressing animal models

Male C57BL/6 mice (4 weeks old, 20 g) were injected via the tail vein with AAV-SIRT1 and AAV-control separately at a dose of 1×10¹² vg/mouse, once. 14 days after injection, bone marrow cells were isolated and induced to differentiate into bone marrow–derived dendritic cells (BMDCs) in vitro for 7 days.

Isolation of bone marrow cells

Male C57BL/6 mice were euthanized by cervical dislocation. Both hind limbs were excised and immersed in 75% ethanol for 5 min. The femorotibial joint was disarticulated by reverse flexion of the knee joint. Muscles were stripped from the tibia by moving from the proximal to distal end. Residual muscles attached to the tibia and femur were removed, followed by excision of both ends of the femur and tibia. Bone marrow cells were flushed from the medullary cavity using a syringe filled with basic medium until the cavity turned pale white. The resulting cell suspension was collected.

Induction and culture of BMDCs

Bone marrow cells isolated from male C57BL/6 mice (2×10⁶ cells/well) were seeded in 6-well plates and cultured in complete RPMI-1640 medium medium supplemented with 10% FBS, 1% penicillin–streptomycin, 10 ng/ml recombinant mouse GM-CSF, and 10 ng/ml recombinant mouse IL-4. After 2 days of culture, the half medium was carefully removed and replaced with fresh complete medium containing the same concentrations of cytokines. On day 4, half of the medium was exchanged. By day 7, loosely adherent and suspended BMDCs were collected, while firmly adherent cells were discarded.

Isolation of primary murine CD8⁺T cells

Single-cell suspensions from the spleens of 6-8-week-old male C57BL/6 mice were resuspended in PEB buffer at a density of 1×10⁷–1×10⁸ cells/mL. Cells were incubated with FcR blocking reagent at 4°C for 10 min, followed by addition of CD8 microbeads and incubation at room temperature in the dark for 15 min. The bead-bound cells were resuspended in 1 mL PEB buffer and loaded onto a pre-cooled MS magnetic separation column placed in a magnetic stand. Unbound cells were removed by washing with PEB buffer until the effluent became clear. CD8⁺ T cells were eluted by adding 1 mL PEB buffer and flushing the column with a plunger. The purified cells were collected for subsequent co-culture experiments.

Method details

Preparation of MMDD

MMDD was prepared according to its classical formula, as indicated in Table 1: Ophiopogon japonicus (Thunb.) Ker Gawl., Pinellia ternata (Thunb.) Breit., Panax ginseng C.A.Mey., Glycyrrhiza uralensis Fisch., Oryza sativa L. and Ziziphus jujuba Mill. The botanical names were verified through World Flora Online (www.worldfloraonline.org). Crude herbal materials were purchased from the National TCM Experts Clinic, Nanjing University of Chinese Medicine, and authenticated by Prof. Y. Guo, Nanjing University of Chinese Medicine. Voucher specimens with specific storage code were well-deposited at Nanjing university of Chinese medicine. The herbs were mixed, decocted twice with 10 volumes of deionized water (v/w; 1.5 h for the first and 1 h for the second), followed by filtration. The combined filtrates were concentrated using a rotary evaporator, sterilized through a 0.22 μm filter, and stored at 4°C for subsequent use.

Chemical profiling of MMDD decoction by liquid chromatography-tandem mass spectrometry (LC-MS/MS)

The contents of MMDD were characterized via LC-MS/MS (Shanghai OE Biotech Co., Ltd., Shanghai, China) using a Q-Exactive hybrid quadrupole-Orbitrap mass spectrometer (Thermo Fisher Scientific, USA) interfaced with an ACQUITY UPLC I-Class Plus system. Electrospray ionization (ESI) was applied in dual polarity modes. Separation was carried out on an ACQUITY UPLC HSS T3 column (2.1 mm × 100 mm, 1.8 μm particle size) maintained at 45°C. The mobile phase comprised water with 0.1% (v/v) formic acid and acetonitrile, delivered at a flow rate of 0.35 mL/min. During analysis, samples were stored at 4°C.

Identification of serum-absorbed components of MMDD

The serum composition of MMDD was analyzed by Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China) using UHPLC–MS/MS. MMDD decoction and MMDD-containing serum samples (n=3 per group) were extracted with methanol–acetonitrile (1:1, v/v), sonicated, centrifuged, dried under nitrogen, and reconstituted in acetonitrile–water (1:1, v/v) for analysis. Separation was performed on an ACQUITY UPLC BEH C18 column (100×2.1 mm, 1.7 μm) using a Vanquish UHPLC coupled to a Q Exactive mass spectrometer (Thermo Fisher Scientific, USA). Mobile phases were 0.1% formic acid in water (A) and acetonitrile (B), with a 0.5 mL/min flow rate at 40 °C. Mass spectra were acquired in both positive and negative ion modes (m/z 70–1050). Compounds were identified by matching ion fragmentation patterns with Progenesis QI v3.0 against the MJBIOTCM database, and data processing was performed using the Majorbio Cloud Platform (www.majorbio.com).

Network pharmacology-based analysis

Active ingredient targets in MMDD were predicted using PubChem (https://pubchem.ncbi.nlm.nih.gov), Swiss Target Prediction (http://www.swisstargetprediction.ch), and ChEMBL(https://www.ebi.ac.uk/chembl) databases, while disease targets for NSCLC were retrieved from GeneCards(http://www.Genecards.org) and OMIM (https://omim.org). Intersection targets were identified using Venny 2.1.0 tool and visualized as a component-target-disease network with Cytoscape 3.9.1 software. A protein-protein interaction (PPI) network was constructed via STRING database (https://string-db.org), and core targets were filtered by topological parameters (Betweenness Centrality, Closeness Centrality, and Degree Centrality). Functional enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways was performed using DAVID database(https://davidbioinformatics.nih.gov), with results visualized through a microbial bioinformatics platform.

Molecular docking and visualization analysis

Target proteins were obtained from the Protein Data Bank (PDB, https://www.rcsb.org), preprocessed in PyMOL, and converted to the PDBQT format using AutoDockTools. Compound structures were hydrogenated and assigned charges via AutoDockTools, followed by molecular docking of 11 active ingredients with key targets using AutoDock Vina. Interaction modes were analyzed with PyMOL.

Preparation of drug-containing serum

8-week-old male Sprague-Dawley (SD) rats were randomly allocated into two groups: control, high-dose MMDD (H-MMDD) groups. The intragastric administration volume (mL/kg) was calculated based on both the crude drug dosage (g/kg) and the concentration of the MMDD extract (g/mL). The MMDD-treated groups received 10mL/kg of the H-MMDD via oral gavage once daily for 7 consecutive days, while the control group was administered an equivalent volume of sterile deionized water (vehicle for MMDD). Two hours after the final dose, blood was collected from the abdominal aorta under isoflurane anesthesia. The serum was separated by centrifugation at 3500×g for 15 min, heat-inactivated at 56 °C for 30 min, sterilized by filtration through a 0.22 μm membrane, and stored at −80 °C until use. High-dose MMDD-containing serum was obtained from rats administered the H-MMDD as described. The medium- and low-dose serum used in our in vitro assays were generated by diluting the high-dose serum with drug-free blank serum obtained from vehicle-treated control rats, yielding final concentrations equivalent to 1/2 and 1/4 of the high-dose serum, respectively.

Experimental groups and interventions

Male C57BL/6 mice (4 weeks old, 20 g) bearing LLC subcutaneous tumors were randomly divided into five groups (n=6/group) and treated as follows: Control group: Received daily oral gavage with sterile deionized water (volume-matched to drug groups) for 14 days. Low-, Medium-, and High-dose groups were administered 4.5, 9, and 18 g/kg/day of MMDD, respectively, via oral gavage. Positive control group: Injected intraperitoneally with 5 mg/kg/day cisplatin (DDP), a standard chemotherapeutic agent, for 14 days. Tumor volumes and body weights were monitored throughout the treatment period to assess efficacy.

Hematoxylin-Eosin (H&E) staining

Tumor tissues, as well as the heart, liver, spleen, lung, and kidney, were collected from mice at the end of the experiment for histopathological evaluation. All samples were fixed in 4% paraformaldehyde for 48 hours, dehydrated through a graded ethanol series, and embedded in paraffin. 4 μm sections were sequentially stained with H&E, followed by mounting and microscopic examination under a light microscope.

TUNEL staining

Tumor tissues were fixed in 4% paraformaldehyde for 24 h, embedded in paraffin, and sectioned at a thickness of 4 μm. After deparaffinization and rehydration, sections were permeabilized with 0.1% Triton X-100 for 20 min and digested with proteinase K at 37°C for 15 min. TUNEL reaction mixture was applied and incubated at 37°C for 1 h in the dark. Nuclei were counterstained with DAPI. Apoptotic cells (red fluorescence) were visualized using a fluorescence microscope. The apoptotic rate was calculated as the ratio of TUNEL-positive cells to the total number of cells.

Immunohistochemistry (IHC)

Paraffin-embedded tumor sections were subjected to antigen retrieval in citrate buffer (pH 6.0). Endogenous peroxidase activity was blocked with 3% H₂O₂, followed by blocking with 5% bovine serum albumin (BSA) for 30 min at room temperature. Sections were incubated overnight at 4°C with CD8α (D4W2Z) XP® Rabbit mAb. After washing with PBS, HRP-conjugated goat anti-rabbit secondary antibody was applied for 1 h at room temperature. Signals were visualized using a DAB substrate, and nuclei were counterstained with hematoxylin.

Western blot

Tumor tissues or MMDD-treated DCs were lysed in RIPA buffer (containing 1% protease and phosphatase inhibitors) on ice for 30 min. Lysates were centrifuged at 12,000 ×g for 15 min, and supernatants were collected for protein quantification via BCA assay. Equal protein amounts were separated on 10% SDS-PAGE gels and transferred to PVDF membranes (400 mA, 45 min). Membranes were blocked with 5% skim milk for 1 h and incubated overnight at 4°C with primary antibodies: Sirtuin 1 (SIRT1), Acetyl-NFκB p65 (Lys310) , p65, and GAPDH. After TBST washing, HRP-conjugated secondary antibodies were applied for 1 h at room temperature. Bands were visualized using ECL reagent and quantified via ImageJ software.

Immunofluorescence

Paraffin-embedded tumor sections were deparaffinized, rehydrated, and subjected to antigen retrieval. Sections were permeabilized with 0.3% Triton X-100 for 20 min and blocked with 5% BSA for 1 h. Primary antibodies—anti-SIRT1 and anti- Acetyl-NFκB p65(Lys310)—were incubated overnight at 4°C. After PBS washing, fluorescent secondary antibodies were applied for 1 h in the dark. Nuclei were counterstained with DAPI, and slides were mounted with anti-fade medium. Fluorescence intensity was quantified using Image J.

Cytokine detection by ELISA

Tumor homogenates or cell culture supernatants were stored at -80°C until analysis. Cytokine levels, including IL-1β, IL-6, IL-12, TNF-α, and IFN-γ, were quantified using sandwich ELISA kits from LiankeBio with the following catalog numbers: EK201B for IL-1β, EK206 for IL-6, EK2183 for IL-12, EK282 for TNF-α, and EK280 for IFN-γ. Standards and samples were loaded into pre-coated 96-well plates and incubated at 37°C for 1 hour. Following five washes with wash buffer, biotinylated detection antibodies were introduced and incubated at 37°C for 30 minutes. Streptavidin-HRP was subsequently applied and allowed to react at 37°C for 10 minutes under dark conditions. TMB substrate was then added for a 15-minute incubation. Reactions were terminated with sulfuric acid, and absorbance values were recorded at 450 nm.

Flow cytometric analysis of BMDCs purity

BMDCs harvested on day 7 were washed twice with PBS and centrifuged. The pellet was resuspended and incubated with anti-CD11c antibody on ice in the dark for 15-30 min. After two additional PBS washes, cells were immediately analyzed using a flow cytometer. Data were processed with FlowJo software, with CD11c⁺ cells quantified as a percentage of total BMDCs cells.

Flow cytometric analysis of DC maturation markers

DCs (2×10⁵ cells) were resuspended in PBS containing 2% FBS and stained with the following antibodies at 4°C for 30 min in the dark: Anti-CD11c; Anti-CD40; Anti-CD80; Anti-CD83, Goat F(ab’)2 Anti-rat IgG Fc (Alexa Fluor® 488); Anti-MHC II. Cells were washed once with 2% FBS-PBS and analyzed on a flow cytometer. For flow cytometric analysis, fragments and doublets were first excluded using FSC/SSC gating. DCs were identified as CD11c⁺cells, followed by the analysis of co-expression levels of CD40⁺, CD80⁺, CD83⁺, and MHC-II⁺ to assess DC maturation.

Transwell co-culture system

Migration Assay: CD8⁺ T cells were resuspended in serum-free medium and seeded into the upper chamber of a Transwell insert. The lower chamber contained DCs pretreated with drug-containing serum in RPMI-1640 medium supplemented with 10% FBS. Invasion Assay: The upper chamber was pre-coated with Matrigel matrix to simulate the extracellular matrix barrier. Subsequent steps followed the migration assay protocol. After 24-hour incubation at 37°C with 5% CO₂, migrated and invasive CD8⁺ T cells in the lower chamber were fixed with 4% paraformaldehyde for 15 minutes, stained with 0.1% crystal violet for 20 minutes, and quantified under a light microscope.

Flow cytometric analysis of T cell activation modulated by MMDD-treated DCs

The MMDD-treated DC-conditioned supernatant and magnetic bead sorted mouse CD8⁺ T cells were cultured in RPMI-1640 medium with 10% FBS at 37°C and 5% CO₂ for 24 hours. 4 hours prior to harvest, cells were stimulated with 50 ng/mL PMA, 1 μg/mL ionomycin, and 1 μg/mL brefeldin A. Cells were harvested and surface-stained with anti-CD45 and anti-CD8α antibody at 4°C in the dark for 30 minutes. Following fixation/permeabilization using a commercial kit, intracellular staining was performed with anti-IFN-γ antibody at room temperature in the dark for 1 hour. Cells were washed once with 0.5% BSA and analyzed by flow cytometry. Single-cell populations were selected using FSC/SSC gating to exclude debris and doublets. Immune cells were identified by gating on CD45⁺ cells, and within this population, CD8⁺ IFN-γ⁺ double-positive cells were analyzed to assess the activation level of purified CD8⁺ T cells.

MTT assay

The MTT assay was performed to assess the viability of LLC cells treated with MMDD-containing serum. LLC cells were seeded into 96-well plates at a density of 5×10³ cells per well and incubated overnight for attachment. Cells were then treated with vehicle-treated serum, low-, medium-, or high-dose MMDD-containing serum for 24 h. Subsequently, 10 μL of MTT solution was added to each well and incubated for 4 h at 37°C. Afterward, 100 μL of formazan solubilization solution was added, and incubation continued until complete dissolution of formazan crystals (approximately 3–4 h). Absorbance was measured at 570 nm using a microplate reader, and cell viability was calculated relative to the control group.

Quantification and statistical analysis

Statistical analyses were performed using GraphPad Prism 9.5.0 software (GraphPad Software, USA). Data are presented as mean ± standard deviation (SD). Statistical differences among multiple groups were evaluated using one-way or two-way analysis of variance (ANOVA), followed by Tukey’s or Dunnett’s post hoc test, as appropriate. The specific statistical test and the exact sample size (n) for each experiment are provided in the corresponding figure legends. Here, n refers to the number of independent biological replicates or individual animals. Statistical significance was defined as p < 0.05.

Published: January 24, 2026