Integrated multi-omics analysis reveals apple mango leaf extract- induced dendritic cell maturation associated with Il1b upregulation and PU.1/ETS motif enrichment

Integrated multi-omics analysis reveals apple mango leaf extract- induced dendritic cell maturation associated with Il1b upregulation and PU.1/ETS motif enrichment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Integrated multi-omics analysis reveals apple mango leaf extract- induced dendritic cell maturation associated with Il1b upregulation and PU.1/ETS motif enrichment Suyoung Choi, Ju-Gyeong Kang, Taejun Seol, Jeong-Hwan Kim, Juyoung Kim, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8217935/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Bioactive compounds extracted from apple mango leaf exhibit notable phytochemical, biological, and pharmacological properties, including anti-oxidant and immunomodulatory effects, which contribute to their wide application potential. This study investigated the immunomodulatory potential of Apple mango leaf extract (AMLE) on bone marrow-derived dendritic cells (BMDCs) using integrated RNA-seq and ATAC-seq analyses with flow cytometry validation. Weighted gene co-expression network analysis identified the blue and turquoise modules that were differentially expressed after AMLE treatment. The blue (immune-activating) modules were enriched in NF-κB, MAPK, and PI3K signaling pathways, implying strong immune activation. Integrative transcriptomic and chromatin accessibility analyses revealed that AMLE treatment upregulated Il1b , Flt3 , and Irf8 , which was accompanied by increased accessibility of PU.1- and ETS1-binding motifs. Notably, AMLE consistently downregulated cell cycle-related transcription factors (e.g., the E2F family), indicating a shift from proliferation/immaturity to functional maturity. Il1b emerged as a central regulator of transcriptional and epigenetic responses. Flow cytometry confirmed that AMLE enhanced the maturation of MHCII + CD11c + CD11b lo BMDCs with elevated MHCII, CD40, and CD80 expression. These findings indicate that AMLE induces concerted transcriptional and chromatin-remodeling events that drive DC activation. Overall, AMLE enhances dendritic cell (DC) maturation through the IL1b–PU.1 regulatory network, highlighting its potential as a natural immune-modulatory candidate. Biological sciences/Cell biology Biological sciences/Computational biology and bioinformatics Biological sciences/Immunology Biological sciences/Molecular biology apple mango leaf extract bone marrow-derived dendritic cells RNA-seq ATAC-seq Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Apple mangoes ( Mangifera indica L. ) are a widely cultivated tropical fruit with increasing global demand 1 . Global mango production is projected to rise from 58.3 million tons (2021) to 65 million tons by 2026 2 . This inevitably generates significant agricultural waste, including mango leaves, stems, peels, and unused fruit 3 . However, Mangifera indica leaves harbor a rich diversity of bioactive compounds, including mangiferin, phenolic acids, benzophenones, flavonoids, carotenoids, and tocopherols 3 . These phytochemicals have been reported to exert multiple biological benefit, such as anti-inflammatory, antitumor, immunomodulatory, and anti-allergic activities 4 – 6 . Previous studies have suggested that mango leaf extracts can modulate innate and adaptive immune responses, particularly by influencing macrophages and T and B lymphocytes; however, the precise mechanism of action in dendritic cells (DCs), the key initiators of immune activation, remains largely unexplored 7 , 8 . Therefore, natural food–derived compounds have emerged as promising candidates for immune modulation 9 . It is critical to determine whether Apple mango leaf extract (AMLE) directly engages or modulates central DC activation pathways such as the NF-κB, MAPK, and PI3K signaling cascades; however, this remains to be determined. DCs are a type of professional antigen-presenting cell that plays a key role in regulating immune responses and coordinating T cell-mediated immunity 10 . Immature DCs process and present antigens as they migrate to secondary lymphoid organs, where they determine whether immune activation or tolerance will occur 11 , 12 . The chemokine receptor CCR7 plays a crucial role in DC migration and inflammatory signaling through the PI3K/AKT, MAPK/NF-κB, and HIF-1α pathways 13 . Additionally, transcription factors such as E2F1, PU.1, and ELF4 modulate immune responses through transcriptional regulation 13 – 15 . Furthermore, transcription factors such as E2F1, PU.1, and ELF4 regulate antiviral and inflammatory responses by modulating activation-inhibitory signals and transcriptional activity 16 – 18 . Bone marrow-derived dendritic cells (BMDCs) cultured with granulocyte-macrophage colony-stimulating factor (GM-CSF) have been macrophages and DCs expressing both CD11c and major histocompatibility complex class II (MHCII) 19 , 20 . Immature BMDCs exhibit high antigen processing and presentation capabilities, whereas stimulation with lipopolysaccharide (LPS) drives their maturation into CD11c + MHCII hi cells, which are typically associated with enhanced potent T-cell priming capacity of DC. The expression of different surface molecules was used to distinguish between the two subsets. The tolerogenic subset is characterized by high expression of PD-L1, IL-10, TGF-β, and C1q. In contrast, the inflammatory subset is characterized by the expression of surface molecules and cytokines, including CD40, CD80, Flt, IL-1α/β, IL-12, IL-6, TNF-α, and MCP-1 21,22 . BMDCs are a good model for studying immune regulation by natural compounds, allowing us to investigate dynamic changes in morphology, transcriptomics, and immune function induced by these stimuli 11 . Correlation networks, in particular, weighted gene co-expression network analysis (WGCNA), are widely used in bioinformatics to analyze gene correlations based on “guilt-by-association” relationships in microarray samples 23 . WGCNAs are used to identify gene modules, summarize these clusters using unique or hub genes, associate modules with external traits, and calculate module membership 24 . These networks aid in identifying biomarkers and therapeutic targets across various biological contexts, including cancer, mouse and yeast genetics, and brain imaging analysis 25 , 26 . This study aimed to clarify how AMLE influences DC transcription and epigenetic regulatory mechanisms, to identify key target genes in DC subpopulations, and to validate these findings using flow cytometry to assess changes in DC maturation and subpopulations. Results 2.1. WGCNA network analysis shows AMLE-induced BMDC maturation and immune activation BMDCs were cultured and treated with AMLE (400 µg/mL) or DMSO for 24 h ( Fig. 1 a ). We analyzed RNA sequencing data from the AMLE and DMSO groups using WGCNA to generate network modules, including 17,934 of the 24,419 protein-coding genes ( Fig. 1 b ). WGCNA was performed to generate co-expression network modules based on 17,934 protein-coding genes out of 24,419 genes. To optimize WGCNA, the RNA-seq data were normalized using DEGseq2, achieving a scale-independence of 0.85 at a soft threshold of nine ( Fig. 2 a ). Network modules were clustered using the dynamic tree-cut algorithm, initially yielding 15 modules. Small and highly similar modules were subsequently merged using a dissimilarity cut-off of 0.1 (corresponding to a high correlation of 0.9), resulting in 12 final modules ( Fig. 2 b ). Module analysis confirmed that the blue ( p = 0.028) module significantly increased following AMLE treatment, whereas the turquoise ( p = 0.0014) module significantly decreased. ( Fig. 2 c, d, Fig. S1 ) . To identify the hub gene for each module network, a correlation cutoff of 0.95 was used (Table S1 ) . We performed WGCNA to identify the two most significant co-expression modules, blue and turquoise, and elucidated their characteristics using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) and STRING (Functional Protein Association Networks). Within the blue module (1243 genes), the largest protein-protein interaction (PPI) cluster, Cluster 1 (141 genes), was identified as the central regulatory node (Fig. S2 ) . STRING analysis demonstrated that this cluster was strongly associated with critical immunological and oncological processes, specifically the "Pathways in cancer" and “Cytokine signaling in immune system pathways,” with KEGG pathway analysis confirming "Pathways in cancer" as the top-ranked term (Table S2 , 3) . Further, DAVID analysis of the blue module components provided mechanistic support for the observed immune activation. The highly enriched Gene Ontology Biological Process (GOBP) terms were “positive regulation of transcription by RNA polymerase II” ( p < 8.63E-21), “canonical NF-kappaB signal transduction” ( p < 1.32E-06), which included numerous transcription factors and signaling components such as IKZF2, NFKB1, NFKB2, STAT5A/B, and IL6. Additionally, other significant sub-clusters reinforced the immune-focused nature of the blue module, highlighting key roles in immune cell activation and signaling, including “T cell activity” (Clusters 9 and 11), “CCR5 chemokine receptor binding” (Cluster 17), and the “Interleukin 15-mediated signaling pathway” (Cluster 30). In contrast to the blue module, the turquoise module (1499 genes) was predominantly associated with cellular proliferation and cell cycle regulation (Fig. S2 ) . Cluster 1 (629 genes) showed strong enrichment for genes involved in RNA metabolism (Table S3 , 4) . The turquoise module activity reflected the essential transcriptional/epigenetic maintenance of the basal state. Specifically, the “Transcriptional Regulation by E2F6” (Cluster 3) was highly enriched in categories related to RNA metabolism, E2F6 (E2F transcription factor 6), and the Histone H3 complex (Cluster 4). These components emphasize the main function of the module in controlling essential gene silencing and chromatin structure. E2F6, a repressor that mediates polycomb group repression, is actively involved in transcriptional tuning to maintain the basal state. 2.2. AMLE promotes BMDC immune activation Principal component analysis (PCA) plots following AMLE treatment revealed a transcript expression pattern distinct from that observed following DMSO treatment ( Fig. 3 a ) . Using a volcano plot, we identified 167 downregulated and 45 upregulated DEGs (FC > 2, log10, P -value > 1.5). The upregulated DEGs were associated with DC development (Irf8 and Flt3), inflammatory responses (Nos2, Il1a, and Il6), cell adhesion/migration (Ccl22 and Cxcl5), and co-stimulation (CD40). Conversely, downregulated DEGs included those involved in the complement system (C1qa/b/c), DC inhibition (Mpo), and cell proliferation (Mki67 and Cdc6) ( Fig. 3 b ) . The cytokine pathway (Cytokine-Cytokine Receptor interaction and chemokine signaling pathway) and focal adhesion PI3K-AKT-mTOR signaling pathway were found to be overrepresented, along with pathways related to inflammation, such as JAK-STAT signaling, TNF signaling, IL1 signaling pathway, and the inflammatory response pathway. Other enriched pathways included T-cell receptor signaling, suggesting enhanced DC-mediated T-cell immune responses (Fig. 3 c-d). Additionally, the downregulated pathways were strongly linked to the cell cycle, DNA replication, and the TCA cycle, indicating changes in the stable cellular state in response to AMLE treatment. These findings strongly suggested that AMLE influenced the immune activity of DCs. GO analysis was conducted using the top 500 DEGs with different cut-offs (FDR 2), and the relationship between Genes and GO terms was confirmed for the pathway of interest. Cell chemotaxis is linked to Ccl22, Enpp2, and Epa2, whereas Prc1 is associated with cytokinesis. Additionally, the upregulation of Afap1l2, a regulator of Il6 production related to inflammatory signals, was verified, consistent with previous observations (Supplementary Fig. S4 ). 2.3. ATAC-seq analysis profiled AMLE-driven chromatin accessibility changes in BMDC Chromatin accessibility was analyzed using ATAC-seq, which revealed 21,345 differentially accessible regions (DAR) peaks in the DMSO samples and 18,135 in AMLE samples ( Fig. 4 a ). When comparing the locations of the individual peaks, a slight increase in the promoter region was observed in the AMLE-treated samples (DMSO: 6.57%; AMLE: 6.97%) ( Fig. 4 b ) 27 . We also identified 590 increased and 685 decreased differentially accessible regions (DARs) in the AMLE-treated samples compared to the DMSO-treated control samples ( Fig. 4 c ). Gene Ontology (GO) analysis was performed to explore the functional implications of these genes; Gene Ontology Biological Process (GOBP) analysis confirmed a strong association between increased DAR in the AMLE-treated sample and immune system processes. Furthermore, Gene Ontology Molecular Function (GOMF) analysis revealed that they were also related to catalytic activity and RNA binding (Fig. S5 ). Additionally, analysis showed that the more accessible chromatin in the AMLE-treated sample was enriched with NFκB-p65 and ETS family members such as PU.1, ETS1, SpiB, Elf4, ELF1, GABPA, FLI1, Etv2, and EHF as the top 10 abundant transcription factor binding motifs. In contrast, the SFPI1, BHLHE41, and PU.1 were detected in the more closed chromatin region in the AMLE sample ( Fig. 4 d ) . In particular, the enrichment of NF-κB p65 and PU.1 motifs, known as key transcription factors in DC activation, suggests a potential contribution to the inflammatory response, innate immune activation, and enhanced antigen presentation. 2.4. Integrative transcriptomic and epigenetic analysis reveals IL1b as a key factor of AMLE-mediated immune modulation Both analyses identified common genes with adj- P < 0.01 and abs(logFC) < 0.7. We also identified 33 co-expressed genes among the significantly detected genes, including 665 by RNA-seq and 549 by ATAC-seq. Among these, we detected 33 common genes, of which 25 (e.g., Arg1, Il1b, Cd38, and Pik3r1) were upregulated, while eight genes were downregulated (Cdc25b, Ly6e, Lyz2, Pak1, Slc43a3, Timeless, and Umps) ( Fig. 5 a-b ). We identified Il1b and Umps as key hub genes among the upregulated and downregulated genes, respectively ( Fig. 5 c-d ). Notably, Il1b plays a key role in interacting with immune regulators, such as Arg1, Cx3cl1, Cd38, and Prdm1. In the turquoise module, Nckap1, Pik3r1, and Pdgfb have been implicated in cytoskeletal remodeling and intracellular signaling, suggesting a coordinated regulatory network that governs immune responses and cellular communication. Among the upregulated genes, Cx3cl1 was particularly relevant for the migration of activated DC to lymph nodes, reinforcing the role of Il1b in antigen-specific T-cell immune responses, inflammatory signaling, and DC maturation. 2.5. GSEA leading edge analysis (LEA) analysis shows gene expression and TF expression associated with DC maturation. To identify these signaling pathway sets in AMLE-treated BMDCs, Gene set enrichment analysis (GSEA) leading-edge analysis (LEA) was performed using three gene sets of C2 signaling pathways (KEGG, Reactome, and Wikipathway) and C3 TF sets ( p < 0.05; FDR q-val 1.2). LEA analysis of ATAC-seq revealed that DCs exhibited a functional mature phenotype in RNA-seq (A1). Signals indicating DC activation (B1), cell adhesion (B3), costimulation (B3), antigen recognition (B4), and chemotaxis (B5) were identified. ( Fig. 6 a ). Concurrently, the ATAC-seq LEA results showed the presence of VEGF signaling, ether lipid metabolism, the FC Epsilon RI Signaling Pathway (B1), cytokine production and T cell stimulation (B2), and collagen synthesis and formation (B3), indicating the migratory properties of DCs. ( Fig. 6 b ). LEA analysis of transcription factors ( p < 0.05; FDR q-value < 0.1) showed significant downregulation of the E2F family (specifically E2F1 and E2F4) in both ATAC-seq and RNA-seq ( Fig. 7 a-c ). Given that ETS plays a well-known role in regulating DC maturation, these results suggest that AMLE treatment promotes DC development by inhibiting E2F activity 28 – 30 . 2.6. In vitro treatment of AMLE induced the maturation of BMDCs and promoted adaptive immunity signals. In light of the findings of our transcriptomic analysis, which highlighted the immunological benefits of AMLE, including its effects on DC maturation, we hypothesized that AMLE would strongly activate DCs 4 – 6 . AMLE- and DMSO-treated BMDCs were stained with monoclonal antibodies against the antigen-presenting molecule major histocompatibility complex class II (MHCII), co-stimulatory molecules CD40 and CD80, and inhibitory molecules PD-L1 and PD-L2. This analysis revealed a concentration-dependent increase in two distinct AMLE-stimulated mature DC subpopulations: CD11c lo MHCII hi and CD11c + CD11b lo ( Fig. 8 a ) . We then measured molecular marker changes within these mature populations using marker brightness histograms ( Fig. 8 b ). The results showed that AMLE-treated mature DC populations (indicated by the red line) significantly increased the expression of the key maturation markers MHCII, CD40, and CD80, while the levels of CD11c and CD11b decreased. Additionally, expression of the inhibitory molecule PDL1 was elevated in mature cells ( Fig. 8 c ). Importantly, the pattern of marker changes in these two distinct populations was constant, suggesting that they may be divided into an MHCII + CD11c + CD11b lo mature population and an MHCII + CD11c + CD11b hi immature population. 2.7. WGCNA modules are associated with specific BMDC maturation subpopulations and markers. The upregulated blue module, "Cytokine signaling in the immune system" and “positive regulation of transcription by RNA polymerase II,” showed a strong positive correlation with the AMLE-induced mature DC populations, specifically the CD11c + MHCII hi subgroups and CD11c + CD40, CD80, MHCII, PDL1 PDL2 ( Fig. 9 ). Conversely, the turquoise module, which was highly expressed in the DMSO control state and associated with RNA Metabolism and Transcriptional Regulation by E2F6 (reflecting basal cellular stability), exhibited a significant negative correlation with AMLE-treated DC populations. Discussion This study demonstrated that AMLE promotes the functional maturation of BMDCs through coordinated transcriptional and epigenetic reprogramming. WGCNA revealed the upregulation of blue module genes and downregulation of turquoise module genes after AMLE treatment, indicating a shift from cell maintenance to immune activation. Integrative multi-omics analysis revealed enhanced NF-κB, MAPK, and PI3K signaling, with Il1b emerging as the central hub gene. Meanwhile, the reduced expression of E2F transcription factors, which are known inhibitors of DC maturation, suggests a response to transcriptional repression. These molecular changes were consistent with the increased expression of maturation markers, such as MHCII, CD40, and CD80, as confirmed by flow cytometry. DCs play a pivotal role in maintaining the balance between immunity and tolerance by transitioning from an immature state that senses and transmits information to a mature state that shapes immune responses 31 . Our results demonstrate that AMLE elicits the functional maturation of BMDCs primarily through the activation of the blue module and suppression of the turquoise module, distinguishing its regulatory profile from that of other plant-derived immunomodulators. Notably, Il1b acts as a central hub gene, driving downstream activation of NF-κB and PU.1 and repression of E2F1, a known inhibitor of DC maturation. Specifically, the MEblue module identified by WGCNA was strongly linked to NF-κB and MAPK signaling. Crucial activation and maturation functions of DCs, especially the PI3K/NF-κB axis, are frequently modulated by various natural compounds, underscoring the therapeutic potential of food-derived materials. For instance, indole compounds from cruciferous vegetables have been reported to modulate the PI3K/Akt/mTOR/NF-κB signaling pathway, which plays critical roles in both cancer therapy and DC function, including antigen acquisition and migration to secondary lymphoid organs. 32 . This pathway is a significant target in cancer therapy involving DCs and plays a critical role in facilitating antigen acquisition and subsequent migration to secondary lymphoid organs 32 , 33 . Because BCAP suppresses DC responses through MyD88-dependent NF-κB and PI3K/AKT signaling downstream of TLR activation 34 , AMLE modulation of these pathways may suggest a potential TLR-like upstream mechanism. DCs comprise a heterogeneous population with distinct subpopulations that are phenotypically, anatomically, and functionally specialized to respond to different immunological threats within the body 35 . The development of these DC subsets is critically regulated by the spatiotemporal activity of key transcription factors that act on bone marrow-derived progenitors 36 . Motif analysis of ATAC-seq data from AMLE-treated BMDCs revealed enrichment of chromatin-binding motifs for the E26 transformation-specific (ETS) family, including PU.1 (Spi1), ETS1, ELF4, ELF1, GABPA, and ETV2, which are pivotal for DC maturation, antigen presentation, and cytokine production 28 , 30 , 37 , 38 . Conversely, the E2F family members E2F1 and E2F4 act as suppressors of dendritic cell maturation within the DC population (Fig. 7 d) 29 . Our RNA expression analysis revealed that AMLE treatment upregulated Il1b, Flt3, and Irf8 39,40 . This suggests that AMLE enhances the functional maturation of DCs by inducing epigenetic changes that promote transcriptional activation. Apple mango leaf extract contains multiple bioactive compounds including mangiferin, norathyriol, phenolic acids, and flavonoids, which collectively may contribute to its immunomodulatory properties 1 , 3 , 5 . The observed immunomodulatory efficacy of AMLE on DC activation is consistent with the established literature on phytocompounds, many of which regulate DC maturation, migration, and T cell priming through mechanisms involving both immunostimulatory and immunosuppressive signals. 41 , 42 . For example, anapsos (an extract derived from Polypodium leucotomos ) has been proposed to promote monocyte and DC activation by increasing IL-1α, IL-1β, and TNF-α in human leukocyte fractions 43 , 44 . This is consistent with our observation that AMLE enhanced Il1b expression in BMDCs ( Fig. 5 c ) , suggesting its potential role in T and NK cell activation through pro-inflammatory cytokine-mediated DC activation. In addition, polysaccharides from red ginseng have been shown to activate BMDCs and promote their maturation via TLR4 45–47 . Our previous work has also shown that carrot polysaccharides enhance IL-12 and IFN-γ expression in BMDC, highlighting their potential as vaccine adjuvants 45 . Similarly, AMLE may regulate TLR signaling and the NF-κB pathway to promote IRF8-mediated DC maturation. Indeed, Lycium barbarum polysaccharides have been shown to induce DC maturation via the TLR2/4-NF-κB pathway, which may be mechanistically linked to AMLE-mediated upregulation of Flt3 and Irf8 expression 48 . Conversely, certain natural compounds exert immunosuppressive effects on DCs. Resveratrol inhibits CD80/CD86 expression and reduces IL-12 production 49 , 50 , whereas curcumin suppresses DC maturation and blocks the LPS response 51 . A study of mangiferin, the lead active compound of AMLE, has shown that it alleviates excessive inflammation, such as atopic dermatitis and TNBS-induced colitis, by modulating MAPK/NF-κB signaling in macrophages 52 . However, AMLE, used in this study, increased MHCII expression without reducing CD40 or CD80 levels, suggesting a potential role in enhancing antigen presentation and immune activation via increased Il1b, Flt3, and Irf8 expression. Thus, the multi-component nature of AMLE likely underlies its immunostimulatory efficacy, potentially through synergistic interactions among its bioactive constituents. We concluded that AMLE stimulates the immune system differently from mangiferin in Inflammatory Bowel Disease (IBD), suggesting its potential use as an immune adjuvant for cancer and immunity, as opposed to its anti-inflammatory effects that have been used for cancer 8 , 53 . Future studies should explore (1) whether Il1b–PU.1 signaling represents a conserved mechanism among phytochemicals, (2) how AMLE-induced transcriptional and epigenomic changes translate into T cell priming, (3) in vivo validation of the Il1b–PU.1–ETS regulatory network, and (4) bioassay-guided isolation of active AMLE constituents. Establishing these mechanisms will advance the development of AMLE-based immunomodulators with translational potential for vaccine adjustment and cancer immunotherapies. A major limitation of this study is that the functional and epigenetic effects of AMLE were assessed using in vitro mouse BMDCs, which may not fully mimic the complexity and diversity of in vivo immune environments or human DC subsets. Additionally, while integrated multi-omics analysis was used, bulk-level profiling can conceal the variability and unique responses of rare DC subpopulations, and the relatively small sample size makes it difficult to generalize the findings. Moreover, since AMLE is a complex mixture, the specific active components responsible for the observed effects have not been precisely identified or quantified, which limits understanding of the mechanisms and standardization. Future research should address these issues by employing single-cell multi-omics techniques, in vivo validation in disease models, and bioassay-guided fractionation to identify active compounds. In conclusion, AMLE is a natural material that induces BMDC maturation and enhances the immune response, demonstrating its potential as a therapeutic agent for modulating or enhancing the immune response. Materials and methods 3.1. Apple mango leaves water extract (AMLE) Mangifera indica leaves used in this study were obtained in January 2021 from a privately owned commercial mango orchard in Geumsan-gun, South Chungcheong Province (central region of South Korea), with the permission of the owner. Apple Mango leaves (100 g) were boiled in 2 L of distilled water for 2 h. The mixture was filtered, vacuum-filtered, and freeze-dried for 7 days. The hot-water extraction method yielded 11.254% yield 54 . AMLE was processed and provided by Professor Keekwang Kim’s laboratory. 3.2 Mice Female C57BL/6 mice (8–10 weeks old and weighing 20–25 g) were purchased from Damul Science (Daejeon, Korea) and housed at the Preclinical Experimental Center of Chungnam National University under specific pathogen-free conditions. The mice were acclimated for at least one week before experimentation, maintained on a 12-h light-dark cycle, and provided ad libitum access to food and water. A total of 3 mice were used in the study for in vitro BMDC culture and for an independent experiment. All procedures involving animals, including housing, care before tissue collection, and mouse sacrifice, were approved. Protocols approved by the Animal Use and Care Committee of Chungnam National University Hospital (AUCUC; CNUH-2023-IA0108-00) and performed in accordance with the relevant guidelines and regulations. When the mice were sacrificed, they were humanely euthanized by exposure to carbon dioxide (CO 2 ) gas using a gradual fill method with a displacement rate of 30–70% of the chamber volume per minute, followed by cervical dislocation to confirm death, in accordance with the approved protocols and relevant guidelines. All methods were carried out in accordance with applicable guidelines and regulations, and were reported in accordance with the ARRIVE guidelines ( https://arriveguidelines.org ). 3.3 BMDC culture and AMLE treatment Bone marrow cells were isolated from the femurs and tibias of mice and differentiated into BMDCs as previously described 55 . Briefly, cells were cultured in cRPMI medium (RPMI 1640 supplemented with 10% FBS, 1% penicillin/streptomycin, 50 µM 2-mercaptoethanol, and 20 ng/mL GM-CSF) for 3 days. The medium was then replaced with fresh cRPMI, and the cells were cultured for an additional three days. Non-adherent cells were collected on day 6 as immature BMDCs, treated with AMLE (400 µg/mL) or 0.04% DMSO for 24 h, and analyzed for next-generation sequencing (NGS) or flow cytometry. 3.4. Bulk RNA sequencing (RNA-seq) Total RNA was extracted using the TRIzol method (Invitrogen, AM9738) and used to construct sequencing libraries according to the manufacturer’s protocol for the TruSeq Stranded mRNA Library Prep Kit (Guide #1000000040498). RNA sequencing was performed using the Illumina platform, generating raw fastq.gz files 1.7–2.2 G per sample, with 67–81 million reads after merging. Quality control (QC) and read-count matrix generation were performed using Python (v3.8.10) and R in a Linux environment. Trim Galore (v0.6.7) was used to filter low-quality reads. The reads were aligned to the mm10 reference genome using the STAR aligner (v2.7.11b). SAMtools (v1.7) was used to convert SAM to BAM. Gene count matrices were generated for downstream analysis using EdgeR, DESeq2, and WGCNA. 3.5. WGCNA analysis Annotation DBI (v1.68.0) was built into BiomaRt (v2.62.0) using 24,419 genes for WGCNA and Differential Expression analysis. We constructed a DESeq DataSet, performed VST normalization, transposed the data frame, and passed it to the WGCNA package (v1.73). SoftThreshold configures the network using the optimal power value calculated by pickSoftThreshold with a signed option. Unique genes were identified in each module, assigned a module color, and visualized using a dendrogram. The TopHubInEach module was used to identify the hub genes with the highest connectivity within the module. Groups of genes were subjected to biological pathway analysis in Database for Annotation, Visualization, and Integrated Discovery (DAVID) using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) database, after removing poorly expressed genes, to identify the signaling pathways regulated by the module. Protein-Protein Interaction (PPI) network figures for the significant genes were constructed using STRING (v12.0) 3.6. Differentially expressed genes (DEG) analysis DEG analysis was performed in R using edgeR (v4.4.1) with 24,419 genes. For normalization, genes with low expression (CPM < 1) were removed after conversion to CPM values, resulting in 12,107 genes. These genes were normalized to the library size. PCA was conducted to evaluate group differences based on gene expression composition. Differentially expressed genes were visualized in a volcano plot using the log2 fold change and average expression. ClusterProfiler (v4.14.4) was used for DEG analysis (|log2(FoldChange)| ≥ 1, adjusted p -value ≤ 0.05). GSEA ( https://www.gsea-msigdb.org/gsea/index.jsp ) was performed using the GO and KEGG databases. The results were visualized based on normalized enrichment scores (NES) and adjusted p -values. 3.7. The assay for transposase-accessible chromatin with sequencing (ATAC-seq) ATAC-seq libraries were prepared using the ATAC-seq kit (AT53150, Active Motif) and the Omni-ATAC protocol (Corces et al., 2017, Nature Methods 14, 959–962. 10.1038/nmeth.4396 ). Briefly, nuclear suspensions obtained from 100,000 BMDC samples treated with AMLE or DMSO control were incubated with assembled Tn5 transposase for 30 min at 37°C. Tagged DNA was purified using a MinElute PCR Purification Kit, amplified using Nextera PCR primers, purified using SPRI beads, and sequenced using a NovaSeq 6000 sequencer (Illumina). First, FastQC was used to check library quality and adapter contamination. Reads with adapter sequences or low-quality bases were trimmed and filtered using Trim Galore (v0.6.7). Next, the reads were mapped to the mm10 genome using Bowtie2 (v2.4.4), and only reads with MAPQ > 30 were retained. Picard MarkDuplicates(v2.26.3) were used to remove PCR duplicates from the library. MACS3 v3.0.0a7 and Deeptools v3.5.1 were used to call different peaks and generate a signal track. Using peak files, the R package csaw (version 1.28.0) was used for peak differential analysis, and ChIPseeker (version 1.30.0) was employed for annotation and visualization. Motif abundance analysis was performed using the HOMER function findMotifsGenome.pl with default options, and motifs with a p -value of less than 0.01 were considered abundant. 3.8 Integration analysis of bulk RNA-seq and ATAC-seq To assess the association between chromatin accessibility and changes in gene expression, we used the ChIPseeker R package to annotate the transcription start site (TSS) closest to the differentially accessible peaks. Regions within 3 kb upstream and downstream of the TSS were defined as promoter regions, whereas other regions were considered distal regulatory regions. Genes with the closest TSS to an accessible region within 100 kb were considered target genes. Among the common significant genes (adjusted p -value < 0.01 and logFC < 0.7), we evaluated the relationship between promoter region accessibility and gene expression. Genes with a fold change (FC) of 0.7 or higher in both analyses were considered to exhibit significant correlations. STRING (v12.0) was used to identify the functional interactions among these genes. 3.9 Gene set enrichment analysis (GSEA) and leading-edge analysis For both RNA-seq and ATAC-seq data, leading-edge analysis was performed using the GSEA pathway and transcription factor databases to identify the hub genes that were most involved in the biological roles of each gene set. GSEA was conducted using the Molecular Signatures Database (MSigDB): C2 (Reactome, WikiPathways, KEGG) and C3 ("tft.tft_legacy") gene sets. Significant gene sets (cut-off: p < 0.05; FDR < 0.25) were selected for leading-edge analysis. 3.10. Flow cytometry BMDCs were also used for flow cytometry analysis to evaluate changes in subtype and marker expression. After 24 h of treatment with the test substance, the BMDCs were collected as suspended cells. The medium and test substances were removed by washing the cells with PBS. To exclude cells with damaged membranes, the cells were stained with a fixable live/dead dye (Aqua kit; Thermo Fisher Scientific) at room temperature for 20 min. Mouse Fc receptors were blocked using Mouse Fc Block™ (553142, BD Biosciences, San Jose, CA, USA) for 15 minutes at 4°C to prevent nonspecific binding. Cells were then stained with specific fluorescent-conjugated monoclonal antibodies for 40 minutes at 4°C: CD11c (20-0114-U100, Tonbo Bioscience), CD11b (557657, BD Biosciences), MHCII (562363, BD Biosciences), co-stimulatory molecules CD40 (562846, BD Biosciences) and CD80 (25-0801-82, eBioscience), and inhibitory molecules PD-L1 (558091, BD Biosciences) and PD-L2 (11-9972-85, eBioscience). After staining, cells were filtered through a 70 µm strainer. All data were acquired using a BD Fortessa-X20 flow cytometer equipped with three lasers and analyzed using the FlowJo software (Tree Star, Ashland, OR, USA). 3.11. Statistical analysis Statistical analyses were performed using GraphPad Prism 8.0. Marker expression in flow cytometry was compared between the two groups using a one-tailed unpaired t -test. Two-tailed independent t -tests were used to compare changes in gene expression between differentially accessible promoters and distal regions. ANOVA with Dunnett’s post-hoc test was used to compare marker expression among the different concentration groups. Declarations Acknowledgements We thank members of Department of Infection Biology, College of Medicine, Chungnam National University for helpful assistance during the course of this study. In addition, we would like to thank Keekwang Kim for kindly providing the AMLE used in this experiment. We would like to thank Editage (www.editage.co.kr) for English language editing. Author contributions Suyoung Choi: Writing – original draft, Writing – review & editing, Methodology, Formal analysis, Conceptualization, Investigation; Ju-Gyeong Kang: Writing – review & editing, Methodology, Data curation, Conceptualization; Taejun Seol: Methodology, Formal analysis, Data curation, Conceptualization; Jeonghwan Kim: Methodology; Juyoung Kim: Writing – review & editing; Daesik Lim: Writing – review & editing; Seon-Young Kim: Writing – review & editing, Funding acquisition; Bo-In Kwon: Writing – review & editing, Validation, Funding acquisition, Conceptualization. Jaeyul Kwon: Project administration, Writing – review & editing, Validation, Supervision, Funding acquisition, Conceptualization. All authors have read and agreed to the published version of the manuscript. Data availability statement The datasets used in this analysis have been submitted to the NCBI Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE292962. Data analysis code for RNA-seq is available on this git-hub page “https://github.com/Syoung-Choi/AMLE_RNA_ATAC”. Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper. Funding segment This research was supported by the Regional Innovation System and Education (RISE) program through the Gangwon RISE Center, funded by the Ministry of Education (MOE) and Gangwon State (G.S.), Republic of Korea (2025-RISE-10-005). It was also supported by the Brain Korea 21 FOUR Project for Medical Science at Chungnam National University (S.Y.C. and J.K.). Supplementary materials Supplementary material associated with this article can be found online using the link below (TBA). References Barreto, J. C. et al. Characterization and quantitation of polyphenolic compounds in bark, kernel, leaves, and peel of mango (Mangifera indica L). J. Agric. 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Investigation on the effect of water extracts of Mangifera indica leaves on the hair loss-related genes in human dermal papilla cells. Korea J. Herbology . 36 , 7 (2021). Choi, Y. et al. Simple Nutrient-Based Rules vs. a Nutritionally Rich Plant-Centered Diet in Prediction of Future Coronary Heart Disease and Stroke: Prospective Observational Study in the US. Nutrients 14 10.3390/nu14030469 (2022). Additional Declarations No competing interests reported. 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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-8217935","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":586326840,"identity":"f0e8c783-14d8-4128-8ad6-3073b046a5cb","order_by":0,"name":"Suyoung Choi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYBACxoYEgwMfKmzq+0nRYvhwxpk0xpkNxNuTYGzM23aYccMBYjUwtydvk+BhS2M2vt38TOLjHgZ5fjECmhl7npVJSPDYsJndOWYmOeMZg+HM2QkEtMzIMZMwkEjjMbsBdCHPAYYEg9vEaEkwOCxhPCP9s/EfIrUYGxxIOGxgIJFj+JiBKC09zwofNhxIS5C4kVP4sOeABGG/GLYnbzj8959NAv+M9A0HfhywkeeXJqSlAZUvgV85CMgTVjIKRsEoGAUjHgAAFJ1I3F5ogu8AAAAASUVORK5CYII=","orcid":"","institution":"Chungnam National University","correspondingAuthor":true,"prefix":"","firstName":"Suyoung","middleName":"","lastName":"Choi","suffix":""},{"id":586326841,"identity":"f9819607-8542-4dec-b520-61c94d17b8bf","order_by":1,"name":"Ju-Gyeong Kang","email":"","orcid":"","institution":"Korea Advanced Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ju-Gyeong","middleName":"","lastName":"Kang","suffix":""},{"id":586326842,"identity":"21ef6d6d-d1e3-4299-963e-bedfbea944dc","order_by":2,"name":"Taejun Seol","email":"","orcid":"","institution":"Korea Advanced Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Taejun","middleName":"","lastName":"Seol","suffix":""},{"id":586326843,"identity":"85f9ee10-da62-4f0f-895e-d08b8901ab72","order_by":3,"name":"Jeong-Hwan Kim","email":"","orcid":"","institution":"Korea Research Institute of Bioscience and Biotechnology","correspondingAuthor":false,"prefix":"","firstName":"Jeong-Hwan","middleName":"","lastName":"Kim","suffix":""},{"id":586326844,"identity":"f0780cda-d3a8-4500-9aa5-afa63a84b13a","order_by":4,"name":"Juyoung Kim","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Juyoung","middleName":"","lastName":"Kim","suffix":""},{"id":586326845,"identity":"13d98db6-e708-4323-b1d9-73a0cd1e46fa","order_by":5,"name":"Daesik Lim","email":"","orcid":"","institution":"Korea Advanced Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Daesik","middleName":"","lastName":"Lim","suffix":""},{"id":586326846,"identity":"e655c4f6-350b-4539-a7cb-054a60cf0508","order_by":6,"name":"Seon-Young Kim","email":"","orcid":"","institution":"Korea Research Institute of Bioscience and Biotechnology","correspondingAuthor":false,"prefix":"","firstName":"Seon-Young","middleName":"","lastName":"Kim","suffix":""},{"id":586326847,"identity":"4fd8618f-97da-4b94-9985-71107fae3c62","order_by":7,"name":"Bo-In Kwon","email":"","orcid":"","institution":"Sangji University","correspondingAuthor":false,"prefix":"","firstName":"Bo-In","middleName":"","lastName":"Kwon","suffix":""},{"id":586326848,"identity":"4fefb5db-4bb0-48a7-8c16-dc252ba16568","order_by":8,"name":"Jaeyul Kwon","email":"","orcid":"","institution":"Chungnam National University","correspondingAuthor":false,"prefix":"","firstName":"Jaeyul","middleName":"","lastName":"Kwon","suffix":""}],"badges":[],"createdAt":"2025-11-27 04:53:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8217935/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8217935/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102072014,"identity":"37477273-8fa3-4a40-9bac-24f5eaec305f","added_by":"auto","created_at":"2026-02-06 19:54:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":21176,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExperimental framework integrating transcriptomic and chromatin accessibility analysis in BMDCs. \u003c/strong\u003e(A) Experimental workflow illustrating BMDC culture and treatment with AMLE (400 µg/ml) or DMSO (0.04%), followed by transcriptomic profiling using RNA-seq and flow cytometry. (B) RNA and nuclear chromatin were extracted from BMDCs for transcriptomic (RNA-seq) and chromatin accessibility (ATAC-seq) analyses. Protein-coding genes from RNA-seq were subjected to weighted gene co-expression network analysis (WGCNA). Differentially accessible regions (DARs) and differentially expressed genes (DEGs) were integrated to identify correlated gene networks across both datasets. Functionally enriched pathways were characterized using gene set enrichment analysis.\u003c/p\u003e","description":"","filename":"OnlineFig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-8217935/v1/59d3d4e069ce64a90d2d449a.png"},{"id":102072004,"identity":"269bc0f9-46c6-48c7-8c91-f074b4b547ee","added_by":"auto","created_at":"2026-02-06 19:54:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":20700,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWGCNA reveals AMLE-induced gene modules associated with DC functional regulation.\u003c/strong\u003e (A) Scale-free topology model fit analysis at various soft threshold powers to determine an optimal power for network construction. The independence of the module (scale-free topology model fit) increased with higher soft threshold values, reaching 0.85 at a threshold of 9, which was the closest to the predefined cutoff (red line). (B) Cluster dendrogram of genes grouped into co-expression 15 number of modules, with displaying distinct expression patterns in WGCNA analysis. (C) Module–sample correlation heatmap based on Pearson correlation, illustrating the association between identified modules and experimental conditions. The MEblue and MEturquoise modules, which exhibit the most significant differences between groups, are highlighted with black boxes (D) Expression patterns of genes within the MEblue and MEturquoise modules, highlighting the most differentially regulated modules in response to AMLE treatment (n=3).\u003c/p\u003e","description":"","filename":"OnlineFig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-8217935/v1/d1150eedec8d4e5f1a14d8cb.png"},{"id":102295875,"identity":"f8589895-1ea8-43bd-a872-0af0be3494b7","added_by":"auto","created_at":"2026-02-10 10:15:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":27730,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAMLE treatment enhances genes associated with inflammatory immune response.\u003c/strong\u003e (A) Principal component analysis (PCA) of AMLE treatment BMDC and DMSO control. (B) Volcanoplot of DEG. The red dot represents AMLE upregulated genes and blue dot represent DMSO upregulated genes. Barplot of differently expressed KEGG (C) and (D) Wiki pathway gene set. The bars on the right are pathways that were upregulated in AMLE and the bars on the left are pathways that were downregulated. The color of the bar indicates statistical significance, with red being more significant.\u003c/p\u003e","description":"","filename":"OnlineFig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-8217935/v1/f7d1de3aa26a1876b516d445.png"},{"id":102072018,"identity":"e70864e7-84ed-43e9-ae6b-c55cd1a467c1","added_by":"auto","created_at":"2026-02-06 19:54:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":45699,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAMLE treatment shows immunomodulation through microregulation of promoter sites in BMDCs.\u003c/strong\u003e (A) Peak distribution landscape on chromosome of DMSO (blue) and AMLE (red). (B) Pie chart of DARs location distribution percentages of DMSO and AMLE. (C) Maplot of enriched or decreased DARs in AMLE vs. DMSO control. (C) MAplot of enriched or decreased DARs in AMLE and DMSO control comparison (D) HOMER known motif enrichment analysis using all DARs by HOMER. Red bars indicate more opened and blue bars indicate more closed\u003cstrong\u003e \u003c/strong\u003ein AMLE treated BMDCs.\u003c/p\u003e","description":"","filename":"OnlineFig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-8217935/v1/32015c7dbfd8611842215f81.png"},{"id":102295779,"identity":"fa3077f5-a8cd-4838-9490-3a111ebd322b","added_by":"auto","created_at":"2026-02-10 10:15:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":22085,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntegration analysis of ATAC-seq and RNA-seq.\u003c/strong\u003e (A) Venn diagram showing the overlapping significant genes between DARs identified by ATAC-seq and DEGs identified by RNA-seq (adj.pval \u0026lt;0.01 and logFC\u0026lt;1). (B) Scatterplot showing the correlation of significantly different (|log FC| \u0026gt; 0.7 both RNA-seq and ATAC-seq) RNA-seq and ATAC-seq genes. (C) STRING analysis of co-upregulated genes with confidence 0.4 and (D) co-downregulated genes with confidence 0.15. Protein-protein interaction networks were marked by removing unlinked genes.\u003c/p\u003e","description":"","filename":"OnlineFig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-8217935/v1/07c0f19f9c67391940e77b51.png"},{"id":102295788,"identity":"22f35093-5ea7-4760-a06b-2febf3753be3","added_by":"auto","created_at":"2026-02-10 10:15:06","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":25499,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGSEA leading-edge analysis reveals that AMLE induced functional maturation.\u003c/strong\u003e (A) RNA-seq and (B) ATAC-seq GSEA leading-edge analysis was performed on significantly enriched gene sets derived from the KEGG, Reactome, and WikiPathways databases within the built-in C2 curated collection. The findings are visualized as a set-to-set diagram, where the intensity of the green shading reflects the degree of overlap among leading-edge core genes from different gene sets. A darker hue indicates a higher intersection between subsets.\u003c/p\u003e","description":"","filename":"OnlineFig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-8217935/v1/7d8111a17cac710656e951f7.png"},{"id":102072006,"identity":"13b300a6-eeed-464e-bcd4-0b6d81bf035c","added_by":"auto","created_at":"2026-02-06 19:54:38","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":24361,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAMLE activated dendritic cells via E2F family downregulation.\u003c/strong\u003e (A) RNA-seq GSEA result TF geneset. (B) ATAC-seq GSEA result TF gene set. (C) ATAC-seq Leading-edge analysis for enriched GSEA gene sets. The bar represents the gene set, and its height indicates the number of genes it contains. The concentrated blue region in the gene heatmap at the bottom indicates downregulation of E2F-related genes following AMLE treatment.\u003c/p\u003e","description":"","filename":"OnlineFig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-8217935/v1/c24425a58c9148c229629430.png"},{"id":102072012,"identity":"e37cda62-6fe3-43d6-894a-77db0fdff49c","added_by":"auto","created_at":"2026-02-06 19:54:39","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":41494,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAMLE-induced dendritic cell maturation is associated with changes in the frequency of the BMDC subpopulation.\u003c/strong\u003e(A) GM-CSF-derived BMDC cells were treated with AMLE (10, 100, 200, 400µg/mL) or DMSO 0.04% control for 24 h, and flow cytometry analysis was performed. (B) Three subsets of BMDC were investigated for frequency and (C) Overlapping histograms by marker. Statistically significant differences were calculated by one-way ANOVA with Dunnett's post hoc test (n=3). *\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"OnlineFig.8.png","url":"https://assets-eu.researchsquare.com/files/rs-8217935/v1/43c9ef93c216ed48dd48eeb5.png"},{"id":102072016,"identity":"41acecd2-a35b-4154-baba-bb13d17dd45c","added_by":"auto","created_at":"2026-02-06 19:54:40","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":69335,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eModule-Trait relationship analysis between modules and dendritic Cell subtype and molecule expression.\u003c/strong\u003e A correlation coefficients heatmap between the module eigengenes (ME) and various dendritic cell phenotypic metrics (rows, e.g., cell percentages, Mean Fluorescence Intensity (MFI) of surface markers). Each cell displays the correlation between a module and a trait, with color intensity and the scale bar indicating the direction and strength of the correlation (blue: strong positive correlation; turquoise: strong negative correlation with CD11c\u003csup\u003e+\u003c/sup\u003eMHCII\u003csup\u003ehi\u003c/sup\u003e). Key traits, such as the MFI of MHCII in specific DC subsets, are highlighted.\u003c/p\u003e","description":"","filename":"OnlineFig.9.png","url":"https://assets-eu.researchsquare.com/files/rs-8217935/v1/9b09a7ddf508a38d04649796.png"},{"id":102397424,"identity":"97934e62-54b8-492f-a62a-ecfc98c1b0a6","added_by":"auto","created_at":"2026-02-11 10:16:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1913049,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8217935/v1/e6a99ec5-8cfc-4078-8b67-a1ac850aa25a.pdf"},{"id":102072000,"identity":"5a70ed59-5ef4-426f-aaa5-97067d141522","added_by":"auto","created_at":"2026-02-06 19:54:38","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":187532,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S1.tif","url":"https://assets-eu.researchsquare.com/files/rs-8217935/v1/f8a0275088df0f9a97ec6cdf.tif"},{"id":102072001,"identity":"c229c8c9-a7cf-4744-9759-59411f21678f","added_by":"auto","created_at":"2026-02-06 19:54:38","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":295556,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S2.tif","url":"https://assets-eu.researchsquare.com/files/rs-8217935/v1/685623e79ed1f4b57472c05f.tif"},{"id":102072015,"identity":"d163f3d9-a400-46ce-ac11-1b236b8fee85","added_by":"auto","created_at":"2026-02-06 19:54:39","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":469280,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S3.tif","url":"https://assets-eu.researchsquare.com/files/rs-8217935/v1/f0dd3cc6a3b7303a3a2dc734.tif"},{"id":102295790,"identity":"6e029626-2dbc-4b94-a507-ccdafe25acb1","added_by":"auto","created_at":"2026-02-10 10:15:06","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":148952,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S4.tif","url":"https://assets-eu.researchsquare.com/files/rs-8217935/v1/0a42d765e4234554f9c44a82.tif"},{"id":102298568,"identity":"d129df94-0256-4112-93f5-c257a04ec16f","added_by":"auto","created_at":"2026-02-10 10:47:52","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":197314,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S5.tif","url":"https://assets-eu.researchsquare.com/files/rs-8217935/v1/806c279cf5db883c297ce024.tif"},{"id":102072017,"identity":"8f494fb8-ebe9-440e-b2a1-7d04f5203545","added_by":"auto","created_at":"2026-02-06 19:54:40","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":302520,"visible":true,"origin":"","legend":"","description":"","filename":"251107Tables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8217935/v1/a87cdb52b68125e13e4e28c6.xlsx"},{"id":102072010,"identity":"92c37bae-06df-40ad-97d7-2462cc76281d","added_by":"auto","created_at":"2026-02-06 19:54:38","extension":"tsv","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":133449,"visible":true,"origin":"","legend":"","description":"","filename":"stringkmeansclustersblue.tsv","url":"https://assets-eu.researchsquare.com/files/rs-8217935/v1/ba78bbf39979ff0af8784422.tsv"},{"id":102072013,"identity":"7cdc58ed-c7ce-4f18-a6f1-7a6ff0614a39","added_by":"auto","created_at":"2026-02-06 19:54:39","extension":"tsv","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":353834,"visible":true,"origin":"","legend":"","description":"","filename":"stringkmeansclustersteqouis.tsv","url":"https://assets-eu.researchsquare.com/files/rs-8217935/v1/b4c33d2b1ec30f3a5a66d971.tsv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated multi-omics analysis reveals apple mango leaf extract- induced dendritic cell maturation associated with Il1b upregulation and PU.1/ETS motif enrichment","fulltext":[{"header":"Introduction","content":"\u003cp\u003eApple mangoes (\u003cem\u003eMangifera indica L.\u003c/em\u003e) are a widely cultivated tropical fruit with increasing global demand \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Global mango production is projected to rise from 58.3\u0026nbsp;million tons (2021) to 65\u0026nbsp;million tons by 2026 \u003csup\u003e2\u003c/sup\u003e. This inevitably generates significant agricultural waste, including mango leaves, stems, peels, and unused fruit \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, \u003cem\u003eMangifera\u003c/em\u003e indica leaves harbor a rich diversity of bioactive compounds, including mangiferin, phenolic acids, benzophenones, flavonoids, carotenoids, and tocopherols \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. These phytochemicals have been reported to exert multiple biological benefit, such as anti-inflammatory, antitumor, immunomodulatory, and anti-allergic activities \u003csup\u003e\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Previous studies have suggested that mango leaf extracts can modulate innate and adaptive immune responses, particularly by influencing macrophages and T and B lymphocytes; however, the precise mechanism of action in dendritic cells (DCs), the key initiators of immune activation, remains largely unexplored \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Therefore, natural food\u0026ndash;derived compounds have emerged as promising candidates for immune modulation \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. It is critical to determine whether Apple mango leaf extract (AMLE) directly engages or modulates central DC activation pathways such as the NF-κB, MAPK, and PI3K signaling cascades; however, this remains to be determined.\u003c/p\u003e \u003cp\u003eDCs are a type of professional antigen-presenting cell that plays a key role in regulating immune responses and coordinating T cell-mediated immunity \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Immature DCs process and present antigens as they migrate to secondary lymphoid organs, where they determine whether immune activation or tolerance will occur \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. The chemokine receptor CCR7 plays a crucial role in DC migration and inflammatory signaling through the PI3K/AKT, MAPK/NF-κB, and HIF-1α pathways \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Additionally, transcription factors such as E2F1, PU.1, and ELF4 modulate immune responses through transcriptional regulation \u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Furthermore, transcription factors such as E2F1, PU.1, and ELF4 regulate antiviral and inflammatory responses by modulating activation-inhibitory signals and transcriptional activity \u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBone marrow-derived dendritic cells (BMDCs) cultured with granulocyte-macrophage colony-stimulating factor (GM-CSF) have been macrophages and DCs expressing both CD11c and major histocompatibility complex class II (MHCII) \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Immature BMDCs exhibit high antigen processing and presentation capabilities, whereas stimulation with lipopolysaccharide (LPS) drives their maturation into CD11c\u003csup\u003e+\u003c/sup\u003e MHCII\u003csup\u003ehi\u003c/sup\u003e cells, which are typically associated with enhanced potent T-cell priming capacity of DC. The expression of different surface molecules was used to distinguish between the two subsets. The tolerogenic subset is characterized by high expression of PD-L1, IL-10, TGF-β, and C1q. In contrast, the inflammatory subset is characterized by the expression of surface molecules and cytokines, including CD40, CD80, Flt, IL-1α/β, IL-12, IL-6, TNF-α, and MCP-1 \u003csup\u003e21,22\u003c/sup\u003e. BMDCs are a good model for studying immune regulation by natural compounds, allowing us to investigate dynamic changes in morphology, transcriptomics, and immune function induced by these stimuli \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCorrelation networks, in particular, weighted gene co-expression network analysis (WGCNA), are widely used in bioinformatics to analyze gene correlations based on \u0026ldquo;guilt-by-association\u0026rdquo; relationships in microarray samples \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. WGCNAs are used to identify gene modules, summarize these clusters using unique or hub genes, associate modules with external traits, and calculate module membership \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. These networks aid in identifying biomarkers and therapeutic targets across various biological contexts, including cancer, mouse and yeast genetics, and brain imaging analysis \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study aimed to clarify how AMLE influences DC transcription and epigenetic regulatory mechanisms, to identify key target genes in DC subpopulations, and to validate these findings using flow cytometry to assess changes in DC maturation and subpopulations.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. WGCNA network analysis shows AMLE-induced BMDC maturation and immune activation\u003c/h2\u003e \u003cp\u003eBMDCs were cultured and treated with AMLE (400 \u0026micro;g/mL) or DMSO for 24 h \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea\u003cb\u003e).\u003c/b\u003e We analyzed RNA sequencing data from the AMLE and DMSO groups using WGCNA to generate network modules, including 17,934 of the 24,419 protein-coding genes \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb\u003cb\u003e).\u003c/b\u003e WGCNA was performed to generate co-expression network modules based on 17,934 protein-coding genes out of 24,419 genes. To optimize WGCNA, the RNA-seq data were normalized using DEGseq2, achieving a scale-independence of 0.85 at a soft threshold of nine \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea\u003cb\u003e).\u003c/b\u003e Network modules were clustered using the dynamic tree-cut algorithm, initially yielding 15 modules. Small and highly similar modules were subsequently merged using a dissimilarity cut-off of 0.1 (corresponding to a high correlation of 0.9), resulting in 12 final modules \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb\u003cb\u003e).\u003c/b\u003e Module analysis confirmed that the blue (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028) module significantly increased following AMLE treatment, whereas the turquoise (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0014) module significantly decreased. \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec, d, \u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/b\u003e. To identify the hub gene for each module network, a correlation cutoff of 0.95 was used \u003cb\u003e(Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe performed WGCNA to identify the two most significant co-expression modules, blue and turquoise, and elucidated their characteristics using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) and STRING (Functional Protein Association Networks). Within the blue module (1243 genes), the largest protein-protein interaction (PPI) cluster, Cluster 1 (141 genes), was identified as the central regulatory node \u003cb\u003e(Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e)\u003c/b\u003e. STRING analysis demonstrated that this cluster was strongly associated with critical immunological and oncological processes, specifically the \"Pathways in cancer\" and \u0026ldquo;Cytokine signaling in immune system pathways,\u0026rdquo; with KEGG pathway analysis confirming \"Pathways in cancer\" as the top-ranked term \u003cb\u003e(Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, 3)\u003c/b\u003e. Further, DAVID analysis of the blue module components provided mechanistic support for the observed immune activation. The highly enriched Gene Ontology Biological Process (GOBP) terms were \u0026ldquo;positive regulation of transcription by RNA polymerase II\u0026rdquo; (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;8.63E-21), \u0026ldquo;canonical NF-kappaB signal transduction\u0026rdquo; (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1.32E-06), which included numerous transcription factors and signaling components such as IKZF2, NFKB1, NFKB2, STAT5A/B, and IL6. Additionally, other significant sub-clusters reinforced the immune-focused nature of the blue module, highlighting key roles in immune cell activation and signaling, including \u0026ldquo;T cell activity\u0026rdquo; (Clusters 9 and 11), \u0026ldquo;CCR5 chemokine receptor binding\u0026rdquo; (Cluster 17), and the \u0026ldquo;Interleukin 15-mediated signaling pathway\u0026rdquo; (Cluster 30).\u003c/p\u003e \u003cp\u003eIn contrast to the blue module, the turquoise module (1499 genes) was predominantly associated with cellular proliferation and cell cycle regulation \u003cb\u003e(Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e)\u003c/b\u003e. Cluster 1 (629 genes) showed strong enrichment for genes involved in RNA metabolism \u003cb\u003e(Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e, 4)\u003c/b\u003e. The turquoise module activity reflected the essential transcriptional/epigenetic maintenance of the basal state. Specifically, the \u0026ldquo;Transcriptional Regulation by E2F6\u0026rdquo; (Cluster 3) was highly enriched in categories related to RNA metabolism, E2F6 (E2F transcription factor 6), and the Histone H3 complex (Cluster 4). These components emphasize the main function of the module in controlling essential gene silencing and chromatin structure. E2F6, a repressor that mediates polycomb group repression, is actively involved in transcriptional tuning to maintain the basal state.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. AMLE promotes BMDC immune activation\u003c/h2\u003e \u003cp\u003ePrincipal component analysis (PCA) plots following AMLE treatment revealed a transcript expression pattern distinct from that observed following DMSO treatment \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. Using a volcano plot, we identified 167 downregulated and 45 upregulated DEGs (FC\u0026thinsp;\u0026gt;\u0026thinsp;2, log10, \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026gt;\u0026thinsp;1.5). The upregulated DEGs were associated with DC development (Irf8 and Flt3), inflammatory responses (Nos2, Il1a, and Il6), cell adhesion/migration (Ccl22 and Cxcl5), and co-stimulation (CD40). Conversely, downregulated DEGs included those involved in the complement system (C1qa/b/c), DC inhibition (Mpo), and cell proliferation (Mki67 and Cdc6) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe cytokine pathway (Cytokine-Cytokine Receptor interaction and chemokine signaling pathway) and focal adhesion PI3K-AKT-mTOR signaling pathway were found to be overrepresented, along with pathways related to inflammation, such as JAK-STAT signaling, TNF signaling, IL1 signaling pathway, and the inflammatory response pathway. Other enriched pathways included T-cell receptor signaling, suggesting enhanced DC-mediated T-cell immune responses (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec-d). Additionally, the downregulated pathways were strongly linked to the cell cycle, DNA replication, and the TCA cycle, indicating changes in the stable cellular state in response to AMLE treatment. These findings strongly suggested that AMLE influenced the immune activity of DCs.\u003c/p\u003e \u003cp\u003eGO analysis was conducted using the top 500 DEGs with different cut-offs (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, FC\u0026thinsp;\u0026gt;\u0026thinsp;2), and the relationship between Genes and GO terms was confirmed for the pathway of interest. Cell chemotaxis is linked to Ccl22, Enpp2, and Epa2, whereas Prc1 is associated with cytokinesis. Additionally, the upregulation of Afap1l2, a regulator of Il6 production related to inflammatory signals, was verified, consistent with previous observations \u003cb\u003e(Supplementary Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e).\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. ATAC-seq analysis profiled AMLE-driven chromatin accessibility changes in BMDC\u003c/h2\u003e \u003cp\u003eChromatin accessibility was analyzed using ATAC-seq, which revealed 21,345 differentially accessible regions (DAR) peaks in the DMSO samples and 18,135 in AMLE samples \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea\u003cb\u003e).\u003c/b\u003e When comparing the locations of the individual peaks, a slight increase in the promoter region was observed in the AMLE-treated samples (DMSO: 6.57%; AMLE: 6.97%) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb\u003cb\u003e)\u003c/b\u003e \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. We also identified 590 increased and 685 decreased differentially accessible regions (DARs) in the AMLE-treated samples compared to the DMSO-treated control samples \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec\u003cb\u003e).\u003c/b\u003e Gene Ontology (GO) analysis was performed to explore the functional implications of these genes; Gene Ontology Biological Process (GOBP) analysis confirmed a strong association between increased DAR in the AMLE-treated sample and immune system processes. Furthermore, Gene Ontology Molecular Function (GOMF) analysis revealed that they were also related to catalytic activity and RNA binding \u003cb\u003e(Fig. \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditionally, analysis showed that the more accessible chromatin in the AMLE-treated sample was enriched with NFκB-p65 and ETS family members such as PU.1, ETS1, SpiB, Elf4, ELF1, GABPA, FLI1, Etv2, and EHF as the top 10 abundant transcription factor binding motifs. In contrast, the SFPI1, BHLHE41, and PU.1 were detected in the more closed chromatin region in the AMLE sample \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed\u003cb\u003e)\u003c/b\u003e. In particular, the enrichment of NF-κB p65 and PU.1 motifs, known as key transcription factors in DC activation, suggests a potential contribution to the inflammatory response, innate immune activation, and enhanced antigen presentation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Integrative transcriptomic and epigenetic analysis reveals IL1b as a key factor of AMLE-mediated immune modulation\u003c/h2\u003e \u003cp\u003eBoth analyses identified common genes with adj-\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and abs(logFC)\u0026thinsp;\u0026lt;\u0026thinsp;0.7. We also identified 33 co-expressed genes among the significantly detected genes, including 665 by RNA-seq and 549 by ATAC-seq.\u0026nbsp;Among these, we detected 33 common genes, of which 25 (e.g., Arg1, Il1b, Cd38, and Pik3r1) were upregulated, while eight genes were downregulated (Cdc25b, Ly6e, Lyz2, Pak1, Slc43a3, Timeless, and Umps) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea-b\u003cb\u003e).\u003c/b\u003e We identified Il1b and Umps as key hub genes among the upregulated and downregulated genes, respectively \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec-d\u003cb\u003e).\u003c/b\u003e Notably, Il1b plays a key role in interacting with immune regulators, such as Arg1, Cx3cl1, Cd38, and Prdm1. In the turquoise module, Nckap1, Pik3r1, and Pdgfb have been implicated in cytoskeletal remodeling and intracellular signaling, suggesting a coordinated regulatory network that governs immune responses and cellular communication. Among the upregulated genes, Cx3cl1 was particularly relevant for the migration of activated DC to lymph nodes, reinforcing the role of Il1b in antigen-specific T-cell immune responses, inflammatory signaling, and DC maturation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e2.5. GSEA leading edge analysis (LEA) analysis shows gene expression and TF expression associated with DC maturation.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eTo identify these signaling pathway sets in AMLE-treated BMDCs, Gene set enrichment analysis (GSEA) leading-edge analysis (LEA) was performed using three gene sets of C2 signaling pathways (KEGG, Reactome, and Wikipathway) and C3 TF sets (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; FDR q-val\u0026thinsp;\u0026lt;\u0026thinsp;0.1, NES\u0026thinsp;\u0026gt;\u0026thinsp;1.2). LEA analysis of ATAC-seq revealed that DCs exhibited a functional mature phenotype in RNA-seq (A1). Signals indicating DC activation (B1), cell adhesion (B3), costimulation (B3), antigen recognition (B4), and chemotaxis (B5) were identified. \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea\u003cb\u003e).\u003c/b\u003e Concurrently, the ATAC-seq LEA results showed the presence of VEGF signaling, ether lipid metabolism, the FC Epsilon RI Signaling Pathway (B1), cytokine production and T cell stimulation (B2), and collagen synthesis and formation (B3), indicating the migratory properties of DCs. \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb\u003cb\u003e).\u003c/b\u003e LEA analysis of transcription factors (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; FDR q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.1) showed significant downregulation of the E2F family (specifically E2F1 and E2F4) in both ATAC-seq and RNA-seq \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea-c\u003cb\u003e).\u003c/b\u003e Given that ETS plays a well-known role in regulating DC maturation, these results suggest that AMLE treatment promotes DC development by inhibiting E2F activity \u003csup\u003e\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.6. In vitro treatment of AMLE induced the maturation of BMDCs and promoted adaptive immunity signals.\u003c/h2\u003e \u003cp\u003eIn light of the findings of our transcriptomic analysis, which highlighted the immunological benefits of AMLE, including its effects on DC maturation, we hypothesized that AMLE would strongly activate DCs \u003csup\u003e\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. AMLE- and DMSO-treated BMDCs were stained with monoclonal antibodies against the antigen-presenting molecule major histocompatibility complex class II (MHCII), co-stimulatory molecules CD40 and CD80, and inhibitory molecules PD-L1 and PD-L2. This analysis revealed a concentration-dependent increase in two distinct AMLE-stimulated mature DC subpopulations: CD11c\u003csup\u003elo\u003c/sup\u003eMHCII\u003csup\u003ehi\u003c/sup\u003e and CD11c\u003csup\u003e+\u003c/sup\u003eCD11b\u003csup\u003elo\u003c/sup\u003e \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. We then measured molecular marker changes within these mature populations using marker brightness histograms \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb\u003cb\u003e).\u003c/b\u003e The results showed that AMLE-treated mature DC populations (indicated by the red line) significantly increased the expression of the key maturation markers MHCII, CD40, and CD80, while the levels of CD11c and CD11b decreased. Additionally, expression of the inhibitory molecule PDL1 was elevated in mature cells \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec\u003cb\u003e).\u003c/b\u003e Importantly, the pattern of marker changes in these two distinct populations was constant, suggesting that they may be divided into an MHCII\u003csup\u003e+\u003c/sup\u003eCD11c\u003csup\u003e+\u003c/sup\u003eCD11b\u003csup\u003elo\u003c/sup\u003e mature population and an MHCII\u003csup\u003e+\u003c/sup\u003eCD11c\u003csup\u003e+\u003c/sup\u003eCD11b\u003csup\u003ehi\u003c/sup\u003e immature population.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.7. WGCNA modules are associated with specific BMDC maturation subpopulations and markers.\u003c/h2\u003e \u003cp\u003eThe upregulated blue module, \"Cytokine signaling in the immune system\" and \u0026ldquo;positive regulation of transcription by RNA polymerase II,\u0026rdquo; showed a strong positive correlation with the AMLE-induced mature DC populations, specifically the CD11c\u003csup\u003e+\u003c/sup\u003eMHCII\u003csup\u003ehi\u003c/sup\u003e subgroups and CD11c\u003csup\u003e+\u003c/sup\u003e CD40, CD80, MHCII, PDL1 PDL2 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e Conversely, the turquoise module, which was highly expressed in the DMSO control state and associated with RNA Metabolism and Transcriptional Regulation by E2F6 (reflecting basal cellular stability), exhibited a significant negative correlation with AMLE-treated DC populations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrated that AMLE promotes the functional maturation of BMDCs through coordinated transcriptional and epigenetic reprogramming. WGCNA revealed the upregulation of blue module genes and downregulation of turquoise module genes after AMLE treatment, indicating a shift from cell maintenance to immune activation. Integrative multi-omics analysis revealed enhanced NF-κB, MAPK, and PI3K signaling, with Il1b emerging as the central hub gene. Meanwhile, the reduced expression of E2F transcription factors, which are known inhibitors of DC maturation, suggests a response to transcriptional repression. These molecular changes were consistent with the increased expression of maturation markers, such as MHCII, CD40, and CD80, as confirmed by flow cytometry.\u003c/p\u003e \u003cp\u003eDCs play a pivotal role in maintaining the balance between immunity and tolerance by transitioning from an immature state that senses and transmits information to a mature state that shapes immune responses \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Our results demonstrate that AMLE elicits the functional maturation of BMDCs primarily through the activation of the blue module and suppression of the turquoise module, distinguishing its regulatory profile from that of other plant-derived immunomodulators. Notably, Il1b acts as a central hub gene, driving downstream activation of NF-κB and PU.1 and repression of E2F1, a known inhibitor of DC maturation. Specifically, the MEblue module identified by WGCNA was strongly linked to NF-κB and MAPK signaling. Crucial activation and maturation functions of DCs, especially the PI3K/NF-κB axis, are frequently modulated by various natural compounds, underscoring the therapeutic potential of food-derived materials. For instance, indole compounds from cruciferous vegetables have been reported to modulate the PI3K/Akt/mTOR/NF-κB signaling pathway, which plays critical roles in both cancer therapy and DC function, including antigen acquisition and migration to secondary lymphoid organs.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. This pathway is a significant target in cancer therapy involving DCs and plays a critical role in facilitating antigen acquisition and subsequent migration to secondary lymphoid organs \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Because BCAP suppresses DC responses through MyD88-dependent NF-κB and PI3K/AKT signaling downstream of TLR activation\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, AMLE modulation of these pathways may suggest a potential TLR-like upstream mechanism.\u003c/p\u003e \u003cp\u003eDCs comprise a heterogeneous population with distinct subpopulations that are phenotypically, anatomically, and functionally specialized to respond to different immunological threats within the body\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. The development of these DC subsets is critically regulated by the spatiotemporal activity of key transcription factors that act on bone marrow-derived progenitors \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Motif analysis of ATAC-seq data from AMLE-treated BMDCs revealed enrichment of chromatin-binding motifs for the E26 transformation-specific (ETS) family, including PU.1 (Spi1), ETS1, ELF4, ELF1, GABPA, and ETV2, which are pivotal for DC maturation, antigen presentation, and cytokine production\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Conversely, the E2F family members E2F1 and E2F4 act as suppressors of dendritic cell maturation within the DC population (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ed) \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Our RNA expression analysis revealed that AMLE treatment upregulated Il1b, Flt3, and Irf8\u003csup\u003e39,40\u003c/sup\u003e. This suggests that AMLE enhances the functional maturation of DCs by inducing epigenetic changes that promote transcriptional activation.\u003c/p\u003e \u003cp\u003eApple mango leaf extract contains multiple bioactive compounds including mangiferin, norathyriol, phenolic acids, and flavonoids, which collectively may contribute to its immunomodulatory properties\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The observed immunomodulatory efficacy of AMLE on DC activation is consistent with the established literature on phytocompounds, many of which regulate DC maturation, migration, and T cell priming through mechanisms involving both immunostimulatory and immunosuppressive signals.\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. For example, anapsos (an extract derived from \u003cem\u003ePolypodium leucotomos\u003c/em\u003e) has been proposed to promote monocyte and DC activation by increasing IL-1α, IL-1β, and TNF-α in human leukocyte fractions \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. This is consistent with our observation that AMLE enhanced Il1b expression in BMDCs \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec\u003cb\u003e)\u003c/b\u003e, suggesting its potential role in T and NK cell activation through pro-inflammatory cytokine-mediated DC activation. In addition, polysaccharides from red ginseng have been shown to activate BMDCs and promote their maturation via TLR4 \u003csup\u003e45\u0026ndash;47\u003c/sup\u003e. Our previous work has also shown that carrot polysaccharides enhance IL-12 and IFN-γ expression in BMDC, highlighting their potential as vaccine adjuvants \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Similarly, AMLE may regulate TLR signaling and the NF-κB pathway to promote IRF8-mediated DC maturation. Indeed, \u003cem\u003eLycium barbarum\u003c/em\u003e polysaccharides have been shown to induce DC maturation via the TLR2/4-NF-κB pathway, which may be mechanistically linked to AMLE-mediated upregulation of Flt3 and Irf8 expression \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Conversely, certain natural compounds exert immunosuppressive effects on DCs. Resveratrol inhibits CD80/CD86 expression and reduces IL-12 production \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, whereas curcumin suppresses DC maturation and blocks the LPS response \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. A study of mangiferin, the lead active compound of AMLE, has shown that it alleviates excessive inflammation, such as atopic dermatitis and TNBS-induced colitis, by modulating MAPK/NF-κB signaling in macrophages \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. However, AMLE, used in this study, increased MHCII expression without reducing CD40 or CD80 levels, suggesting a potential role in enhancing antigen presentation and immune activation via increased Il1b, Flt3, and Irf8 expression. Thus, the multi-component nature of AMLE likely underlies its immunostimulatory efficacy, potentially through synergistic interactions among its bioactive constituents.\u003c/p\u003e \u003cp\u003eWe concluded that AMLE stimulates the immune system differently from mangiferin in Inflammatory Bowel Disease (IBD), suggesting its potential use as an immune adjuvant for cancer and immunity, as opposed to its anti-inflammatory effects that have been used for cancer \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Future studies should explore (1) whether Il1b\u0026ndash;PU.1 signaling represents a conserved mechanism among phytochemicals, (2) how AMLE-induced transcriptional and epigenomic changes translate into T cell priming, (3) in vivo validation of the Il1b\u0026ndash;PU.1\u0026ndash;ETS regulatory network, and (4) bioassay-guided isolation of active AMLE constituents. Establishing these mechanisms will advance the development of AMLE-based immunomodulators with translational potential for vaccine adjustment and cancer immunotherapies.\u003c/p\u003e \u003cp\u003eA major limitation of this study is that the functional and epigenetic effects of AMLE were assessed using in vitro mouse BMDCs, which may not fully mimic the complexity and diversity of in vivo immune environments or human DC subsets. Additionally, while integrated multi-omics analysis was used, bulk-level profiling can conceal the variability and unique responses of rare DC subpopulations, and the relatively small sample size makes it difficult to generalize the findings. Moreover, since AMLE is a complex mixture, the specific active components responsible for the observed effects have not been precisely identified or quantified, which limits understanding of the mechanisms and standardization. Future research should address these issues by employing single-cell multi-omics techniques, in vivo validation in disease models, and bioassay-guided fractionation to identify active compounds. In conclusion, AMLE is a natural material that induces BMDC maturation and enhances the immune response, demonstrating its potential as a therapeutic agent for modulating or enhancing the immune response.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Apple mango leaves water extract (AMLE)\u003c/h2\u003e \u003cp\u003e Mangifera indica leaves used in this study were obtained in January 2021 from a privately owned commercial mango orchard in Geumsan-gun, South Chungcheong Province (central region of South Korea), with the permission of the owner. Apple Mango leaves (100 g) were boiled in 2 L of distilled water for 2 h. The mixture was filtered, vacuum-filtered, and freeze-dried for 7 days. The hot-water extraction method yielded 11.254% yield \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. AMLE was processed and provided by Professor Keekwang Kim\u0026rsquo;s laboratory.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Mice\u003c/h2\u003e \u003cp\u003eFemale C57BL/6 mice (8\u0026ndash;10 weeks old and weighing 20\u0026ndash;25 g) were purchased from Damul Science (Daejeon, Korea) and housed at the Preclinical Experimental Center of Chungnam National University under specific pathogen-free conditions. The mice were acclimated for at least one week before experimentation, maintained on a 12-h light-dark cycle, and provided ad libitum access to food and water. A total of 3 mice were used in the study for in vitro BMDC culture and for an independent experiment. All procedures involving animals, including housing, care before tissue collection, and mouse sacrifice, were approved. Protocols approved by the Animal Use and Care Committee of Chungnam National University Hospital (AUCUC; CNUH-2023-IA0108-00) and performed in accordance with the relevant guidelines and regulations. When the mice were sacrificed, they were humanely euthanized by exposure to carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) gas using a gradual fill method with a displacement rate of 30\u0026ndash;70% of the chamber volume per minute, followed by cervical dislocation to confirm death, in accordance with the approved protocols and relevant guidelines. All methods were carried out in accordance with applicable guidelines and regulations, and were reported in accordance with the ARRIVE guidelines (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://arriveguidelines.org\u003c/span\u003e\u003cspan address=\"https://arriveguidelines.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 BMDC culture and AMLE treatment\u003c/h2\u003e \u003cp\u003eBone marrow cells were isolated from the femurs and tibias of mice and differentiated into BMDCs as previously described \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Briefly, cells were cultured in cRPMI medium (RPMI 1640 supplemented with 10% FBS, 1% penicillin/streptomycin, 50 \u0026micro;M 2-mercaptoethanol, and 20 ng/mL GM-CSF) for 3 days. The medium was then replaced with fresh cRPMI, and the cells were cultured for an additional three days. Non-adherent cells were collected on day 6 as immature BMDCs, treated with AMLE (400 \u0026micro;g/mL) or 0.04% DMSO for 24 h, and analyzed for next-generation sequencing (NGS) or flow cytometry.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Bulk RNA sequencing (RNA-seq)\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted using the TRIzol method (Invitrogen, AM9738) and used to construct sequencing libraries according to the manufacturer\u0026rsquo;s protocol for the TruSeq Stranded mRNA Library Prep Kit (Guide #1000000040498). RNA sequencing was performed using the Illumina platform, generating raw fastq.gz files 1.7\u0026ndash;2.2 G per sample, with 67\u0026ndash;81\u0026nbsp;million reads after merging. Quality control (QC) and read-count matrix generation were performed using Python (v3.8.10) and R in a Linux environment. Trim Galore (v0.6.7) was used to filter low-quality reads. The reads were aligned to the mm10 reference genome using the STAR aligner (v2.7.11b). SAMtools (v1.7) was used to convert SAM to BAM. Gene count matrices were generated for downstream analysis using EdgeR, DESeq2, and WGCNA.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5. WGCNA analysis\u003c/h2\u003e \u003cp\u003eAnnotation DBI (v1.68.0) was built into BiomaRt (v2.62.0) using 24,419 genes for WGCNA and Differential Expression analysis. We constructed a DESeq DataSet, performed VST normalization, transposed the data frame, and passed it to the WGCNA package (v1.73). SoftThreshold configures the network using the optimal power value calculated by pickSoftThreshold with a signed option. Unique genes were identified in each module, assigned a module color, and visualized using a dendrogram. The TopHubInEach module was used to identify the hub genes with the highest connectivity within the module. Groups of genes were subjected to biological pathway analysis in Database for Annotation, Visualization, and Integrated Discovery (DAVID) using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) database, after removing poorly expressed genes, to identify the signaling pathways regulated by the module. Protein-Protein Interaction (PPI) network figures for the significant genes were constructed using STRING (v12.0)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Differentially expressed genes (DEG) analysis\u003c/h2\u003e \u003cp\u003eDEG analysis was performed in R using edgeR (v4.4.1) with 24,419 genes. For normalization, genes with low expression (CPM\u0026thinsp;\u0026lt;\u0026thinsp;1) were removed after conversion to CPM values, resulting in 12,107 genes. These genes were normalized to the library size. PCA was conducted to evaluate group differences based on gene expression composition. Differentially expressed genes were visualized in a volcano plot using the log2 fold change and average expression. ClusterProfiler (v4.14.4) was used for DEG analysis (|log2(FoldChange)| \u0026ge; 1, adjusted \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026le;\u0026thinsp;0.05). GSEA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/index.jsp\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/index.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was performed using the GO and KEGG databases. The results were visualized based on normalized enrichment scores (NES) and adjusted \u003cem\u003ep\u003c/em\u003e-values.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.7. The assay for transposase-accessible chromatin with sequencing (ATAC-seq)\u003c/h2\u003e \u003cp\u003eATAC-seq libraries were prepared using the ATAC-seq kit (AT53150, Active Motif) and the Omni-ATAC protocol (Corces et al., 2017, Nature Methods 14, 959\u0026ndash;962. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nmeth.4396\u003c/span\u003e\u003cspan address=\"10.1038/nmeth.4396\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Briefly, nuclear suspensions obtained from 100,000 BMDC samples treated with AMLE or DMSO control were incubated with assembled Tn5 transposase for 30 min at 37\u0026deg;C. Tagged DNA was purified using a MinElute PCR Purification Kit, amplified using Nextera PCR primers, purified using SPRI beads, and sequenced using a NovaSeq 6000 sequencer (Illumina).\u003c/p\u003e \u003cp\u003eFirst, FastQC was used to check library quality and adapter contamination. Reads with adapter sequences or low-quality bases were trimmed and filtered using Trim Galore (v0.6.7). Next, the reads were mapped to the mm10 genome using Bowtie2 (v2.4.4), and only reads with MAPQ\u0026thinsp;\u0026gt;\u0026thinsp;30 were retained. Picard MarkDuplicates(v2.26.3) were used to remove PCR duplicates from the library. MACS3 v3.0.0a7 and Deeptools v3.5.1 were used to call different peaks and generate a signal track. Using peak files, the R package csaw (version 1.28.0) was used for peak differential analysis, and ChIPseeker (version 1.30.0) was employed for annotation and visualization. Motif abundance analysis was performed using the HOMER function findMotifsGenome.pl with default options, and motifs with a \u003cem\u003ep\u003c/em\u003e-value of less than 0.01 were considered abundant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Integration analysis of bulk RNA-seq and ATAC-seq\u003c/h2\u003e \u003cp\u003eTo assess the association between chromatin accessibility and changes in gene expression, we used the ChIPseeker R package to annotate the transcription start site (TSS) closest to the differentially accessible peaks. Regions within 3 kb upstream and downstream of the TSS were defined as promoter regions, whereas other regions were considered distal regulatory regions. Genes with the closest TSS to an accessible region within 100 kb were considered target genes. Among the common significant genes (adjusted \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and logFC\u0026thinsp;\u0026lt;\u0026thinsp;0.7), we evaluated the relationship between promoter region accessibility and gene expression. Genes with a fold change (FC) of 0.7 or higher in both analyses were considered to exhibit significant correlations. STRING (v12.0) was used to identify the functional interactions among these genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Gene set enrichment analysis (GSEA) and leading-edge analysis\u003c/h2\u003e \u003cp\u003eFor both RNA-seq and ATAC-seq data, leading-edge analysis was performed using the GSEA pathway and transcription factor databases to identify the hub genes that were most involved in the biological roles of each gene set. GSEA was conducted using the Molecular Signatures Database (MSigDB): C2 (Reactome, WikiPathways, KEGG) and C3 (\"tft.tft_legacy\") gene sets. Significant gene sets (cut-off: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.25) were selected for leading-edge analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.10. Flow cytometry\u003c/h2\u003e \u003cp\u003eBMDCs were also used for flow cytometry analysis to evaluate changes in subtype and marker expression. After 24 h of treatment with the test substance, the BMDCs were collected as suspended cells. The medium and test substances were removed by washing the cells with PBS. To exclude cells with damaged membranes, the cells were stained with a fixable live/dead dye (Aqua kit; Thermo Fisher Scientific) at room temperature for 20 min. Mouse Fc receptors were blocked using Mouse Fc Block\u0026trade; (553142, BD Biosciences, San Jose, CA, USA) for 15 minutes at 4\u0026deg;C to prevent nonspecific binding. Cells were then stained with specific fluorescent-conjugated monoclonal antibodies for 40 minutes at 4\u0026deg;C: CD11c (20-0114-U100, Tonbo Bioscience), CD11b (557657, BD Biosciences), MHCII (562363, BD Biosciences), co-stimulatory molecules CD40 (562846, BD Biosciences) and CD80 (25-0801-82, eBioscience), and inhibitory molecules PD-L1 (558091, BD Biosciences) and PD-L2 (11-9972-85, eBioscience). After staining, cells were filtered through a 70 \u0026micro;m strainer. All data were acquired using a BD Fortessa-X20 flow cytometer equipped with three lasers and analyzed using the FlowJo software (Tree Star, Ashland, OR, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.11. Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using GraphPad Prism 8.0. Marker expression in flow cytometry was compared between the two groups using a one-tailed unpaired \u003cem\u003et\u003c/em\u003e-test. Two-tailed independent \u003cem\u003et\u003c/em\u003e-tests were used to compare changes in gene expression between differentially accessible promoters and distal regions. ANOVA with Dunnett\u0026rsquo;s post-hoc test was used to compare marker expression among the different concentration groups.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank members of Department of Infection Biology, College of Medicine, Chungnam National University for helpful assistance during the course of this study. In addition, we would like to thank Keekwang Kim for kindly providing the AMLE used in this experiment. We would like to thank Editage (www.editage.co.kr) for English language editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSuyoung Choi:\u003c/strong\u003e Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing, Methodology, Formal analysis, Conceptualization, Investigation; \u003cstrong\u003eJu-Gyeong Kang:\u003c/strong\u003e Writing \u0026ndash; review \u0026amp; editing, Methodology, Data curation, Conceptualization; \u003cstrong\u003eTaejun Seol:\u003c/strong\u003e Methodology, Formal analysis, Data curation, Conceptualization; \u003cstrong\u003eJeonghwan Kim:\u003c/strong\u003e Methodology; \u003cstrong\u003eJuyoung Kim:\u003c/strong\u003e Writing \u0026ndash; review \u0026amp; editing; Daesik Lim: Writing \u0026ndash; review \u0026amp; editing; \u003cstrong\u003eSeon-Young Kim:\u003c/strong\u003e Writing \u0026ndash; review \u0026amp; editing, Funding acquisition; \u003cstrong\u003eBo-In Kwon:\u003c/strong\u003e Writing \u0026ndash; review \u0026amp; editing, Validation, Funding acquisition, Conceptualization. \u003cstrong\u003eJaeyul Kwon:\u003c/strong\u003e Project administration, Writing \u0026ndash; review \u0026amp; editing, Validation, Supervision, Funding acquisition, Conceptualization. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used in this analysis have been submitted to the NCBI Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE292962. Data analysis code for RNA-seq is available on this git-hub page \u0026ldquo;https://github.com/Syoung-Choi/AMLE_RNA_ATAC\u0026rdquo;.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding segment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Regional Innovation System and Education (RISE) program through the Gangwon RISE Center, funded by the Ministry of Education (MOE) and Gangwon State (G.S.), Republic of Korea (2025-RISE-10-005). It was also supported by the Brain Korea 21 FOUR Project for Medical Science at Chungnam National University (S.Y.C. and J.K.).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary material associated with this article can be found online using the link below (TBA).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBarreto, J. C. et al. Characterization and quantitation of polyphenolic compounds in bark, kernel, leaves, and peel of mango (Mangifera indica L). \u003cem\u003eJ. Agric. 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Simple Nutrient-Based Rules vs. a Nutritionally Rich Plant-Centered Diet in Prediction of Future Coronary Heart Disease and Stroke: Prospective Observational Study in the US. \u003cem\u003eNutrients\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/nu14030469\u003c/span\u003e\u003cspan address=\"10.3390/nu14030469\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"apple mango leaf extract, bone marrow-derived dendritic cells, RNA-seq, ATAC-seq","lastPublishedDoi":"10.21203/rs.3.rs-8217935/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8217935/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBioactive compounds extracted from apple mango leaf exhibit notable phytochemical, biological, and pharmacological properties, including anti-oxidant and immunomodulatory effects, which contribute to their wide application potential. This study investigated the immunomodulatory potential of Apple mango leaf extract (AMLE) on bone marrow-derived dendritic cells (BMDCs) using integrated RNA-seq and ATAC-seq analyses with flow cytometry validation. Weighted gene co-expression network analysis identified the blue and turquoise modules that were differentially expressed after AMLE treatment. The blue (immune-activating) modules were enriched in NF-κB, MAPK, and PI3K signaling pathways, implying strong immune activation. Integrative transcriptomic and chromatin accessibility analyses revealed that AMLE treatment upregulated \u003cem\u003eIl1b\u003c/em\u003e, \u003cem\u003eFlt3\u003c/em\u003e, and \u003cem\u003eIrf8\u003c/em\u003e, which was accompanied by increased accessibility of PU.1- and ETS1-binding motifs. Notably, AMLE consistently downregulated cell cycle-related transcription factors (e.g., the E2F family), indicating a shift from proliferation/immaturity to functional maturity. Il1b emerged as a central regulator of transcriptional and epigenetic responses. Flow cytometry confirmed that AMLE enhanced the maturation of MHCII\u003csup\u003e+\u003c/sup\u003eCD11c\u003csup\u003e+\u003c/sup\u003eCD11b\u003csup\u003elo\u003c/sup\u003e BMDCs with elevated MHCII, CD40, and CD80 expression. These findings indicate that AMLE induces concerted transcriptional and chromatin-remodeling events that drive DC activation. Overall, AMLE enhances dendritic cell (DC) maturation through the IL1b\u0026ndash;PU.1 regulatory network, highlighting its potential as a natural immune-modulatory candidate.\u003c/p\u003e","manuscriptTitle":"Integrated multi-omics analysis reveals apple mango leaf extract- induced dendritic cell maturation associated with Il1b upregulation and PU.1/ETS motif enrichment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-06 19:54:28","doi":"10.21203/rs.3.rs-8217935/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-24T11:57:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-23T09:11:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-16T13:06:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-09T00:47:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"71462661908890555195923952326356905170","date":"2026-02-05T00:04:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"102841628549303267117494358398783071844","date":"2026-02-04T23:03:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"259870195879985336397489903253281534398","date":"2026-02-04T14:21:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"64812754360914100480105328095501664675","date":"2026-02-04T12:25:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-04T11:51:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-29T17:41:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-23T10:07:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-30T08:54:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-12-30T08:45:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cbb1f5b4-e398-411e-86b9-163578ec3906","owner":[],"postedDate":"February 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":62382956,"name":"Biological sciences/Cell biology"},{"id":62382957,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":62382958,"name":"Biological sciences/Immunology"},{"id":62382959,"name":"Biological sciences/Molecular biology"}],"tags":[],"updatedAt":"2026-05-19T00:23:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-06 19:54:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8217935","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8217935","identity":"rs-8217935","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}