Mechanistic Analysis of Luteolin in Mitigating Dry Age-Related Macular Degeneration through Network Pharmacology and Experimental Validation

Mechanistic Analysis of Luteolin in Mitigating Dry Age-Related Macular Degeneration through Network Pharmacology and Experimental Validation | 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 Mechanistic Analysis of Luteolin in Mitigating Dry Age-Related Macular Degeneration through Network Pharmacology and Experimental Validation Maomei Luo, Min Zhang, Zhen Xing, Wei Yu, Hongbin Lv This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6948097/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Object Mechanistic analysis of luteolin’s protective effect against dry AMD via network pharmacology and experimental validation. Methods Luteolin's active ingredient and target information were retrieved from the publicly available database TCMSP. Disease genes associated with dry AMD were screened by GeneCards, OMIM, and DrugBank gene databases. The shared targets of luteolin and dry AMD were used to construct a protein-protein interaction network, followed by the implementation of Gene Ontology and pathway enrichment analyses. Finally, molecular docking of the active ingredient with core targets was validated. Sodium iodate was used to induce ARPE-19 cells and a mouse model. Cell viability was analyzed via CCK-8 assay. ROS levels were quantified using the DCFH-DA method, and mitochondrial membrane potential was detected via JC-1 staining. Results In the network pharmacology analysis, a total of 213 potential therapeutic targets associated with luteolin’s anti-dry AMD activity were identified. Among these, TP53, TNF, IL6, AKT1, BCL2, STAT3, JUN, and CASP3 were pinpointed as core therapeutic targets. These targets predominantly participated in pathways such as the lipid and atherosclerosis pathway, cancer pathways, and the AGE-RAGE signaling pathway in diabetic complications. Through molecular docking, strong binding affinities were identified between core targets and luteolin, the critical active moiety. This finding validated the molecular mechanism underlying luteolin’s efficacy against dry AMD. Experimental data demonstrated that luteolin not only attenuated sodium iodate–induced reduction in ARPE-19 cell viability but also decreased intracellular ROS levels and restored mitochondrial membrane potential, suggesting a protective role via oxidative stress regulation. Conclusion Luteolin exerts a protective effect against sodium iodate–induced damage in ARPE-19 cells. This protection is likely mediated through multi-target signaling pathways, potentially involving multiple molecular mechanisms. These findings suggest that luteolin has promising potential for the prophylaxis and therapy of dry AMD. Health sciences/Diseases Health sciences/Medical research network pharmacology luteolin dry age-related macular degeneration molecular docking oxidative stress Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Age-related macular degeneration (AMD) represents a progressive ophthalmic disorder. It mainly exerts an impact on the macular region of the retina, leading to the decline of central visual function 1 . As the global population ages at an accelerating pace, the incidence of AMD is on a continuous upward trend. This not only imposes substantial health-related challenges but also places a heavy economic burden on both individuals and society 2 , 3 . AMD is primarily categorized into two main subtypes: dry and wet. Among these, dry AMD constitutes approximately 85–90% of all AMD cases 4 , 5 . Dry AMD is marked by deposition of drusen and pigmentary alterations in fundus posterior pole. Concurrently, it features progressive atrophy of retinal pigment epithelial (RPE) cells and photoreceptor cells 6 . Oxidative stress is recognized to have a crucial role in the development of dry AMD 7 , 8 . RPE cells, given their high metabolic rate, are highly susceptible to generating reactive oxygen species (ROS). When ROS levels are imbalanced with the antioxidant system, oxidative stress ensues, inflicting damage on RPE cells 9 . Although the progression of dry AMD is relatively slow, it can progress to geographic atrophy in its late stage. In some cases, dry AMD may even transform into wet AMD, resulting in severe vision loss 10 , 11 . Currently, there remains a lack of effective therapeutic interventions for dry AMD 4 . In recent years, natural compounds have emerged as promising candidates for treating neurodegenerative and ocular diseases, drawing significant research interest 12 – 14 . Luteolin, a prevalent flavonoid compound in the plant kingdom, typically exists as glycosides. It is widely distributed across various plant sources, including vegetables (e.g., carrots, peppers, and celery), herbs (e.g., perilla and parsley), and flowers (e.g., honeysuckle and chrysanthemum) 15 . Luteolin exhibits a broad spectrum of bioactivities, such as antioxidant, anti-inflammatory, antitumor, and neuroprotective effects 16 – 18 . Mechanistically, luteolin suppresses protein acetylation and oxidative stress evoked by high glucose in ARPE-19 cells via the SIRT1/P53 pathway 19 . In rat models of corneal alkali burns, luteolin treatment effectively attenuates the inflammatory response mediated by cytokines and chemokines, thereby reducing corneal pathological damage 20 . When ARPE-19 cells are subjected to H₂O₂ treatment, luteolin significantly increases activities of superoxide dismutase and glutathione peroxidase and inhibits epithelial-mesenchymal transition 21 . Moreover, in mouse models of depression-related dry eye, luteolin downregulates the production and release of pro-inflammatory cytokines in the hippocampus and corneal tissues 22 . The herb extract of Lycii Fructus and Chrysanthemum Flos prevents sodium iodate-induced oxidative damage to the retina in mice, with luteolin potentially serving as the active components exerting this protective effect 23 . Collectively, these findings suggest that luteolin exerts ocular protective effects through multiple mechanisms, identifying it as a possible therapeutic candidate for dry AMD. Network pharmacology establishes a network model linking drug components, targets, and diseases, facilitating a comprehensive understanding of drug-disease interactions 24 , 25 . To elucidate luteolin's mechanism of action, we propose combining network pharmacology analysis with experimental validation to explore its potential in treating dry AMD. Methods ADME properties of luteolin The ADME characteristics of luteolin were determined by looking up chemical term “Luteolin” in TCMSP database ( https://www.tcmsp-e.com/ ), which provides information on its pharmacokinetics and other pertinent attributes. Target collection and construction of a “disease-target” network diagram for age-related macular degeneration Disease targets related to atrophic age-related macular degeneration, dry age- related macular degeneration, and non-exudative age-related macular degeneration were searched for in the GeneCards database ( https://www.genecards.org/ ), the OMIM database ( https://omim.org/ ), the DrugBank database ( https://www.drugbank.com/ ), and the TTD database ( https://db.idrblab.net/ttd/ ). In the GeneCards database, targets with a Relevance score of at least 5 were selected. The search outcomes from every database were combined, and redundant entries were eliminated to pinpoint disease targets. Subsequently, a network file was constructed using the collected disease targets and diseases. This file was then fed into Cytoscape 3.10.0 with the aim of generating a “disease-target” net. Target prediction of luteolin and construction of “Luteolin-target” network diagrams To identify possible targets of luteolin, we performed a search within CTD Database ( http://ctdbase.org/ ), Super–PRED ( https://prediction.charite.de/subpages/target_prediction.php ), and Swiss Target Prediction ( www.swisstargetprediction.ch ) databases. Following steps were implemented:Start by directly typing “Luteolin” into CTD database and downloading associated target genes. After that, obtain Canonical Smiles of luteolin from PubChem database ( https://pubchem.ncbi.nlm.nih.gov/ ). Import Canonical Smiles into Super-PRED and Swiss Target Prediction databases to predict luteolin targets. Conduct a screening in Swiss Target Prediction database for targets having probability values exceeding 0.1. Afterward, standardize the predicted target names to official gene-name abbreviations utilizing UniProt database ( https://www.uniprot.org/ ). Combine drug targets retrieved from the three databases and perform deduplication to remove redundant entries. Import luteolin and its targets into the Cytoscape 3.10.1 mapping tool for mapping analysis, constructing a “Luteolin-target” network diagram. To recognize intersecting targets of luteolin and disease, construct Venn diagrams by integrating the acquired compound targets with the relevant disease targets from appropriate databases. Building the PPI network and analyzing key genes The protein - protein interaction (PPI) network came into being when the intersecting targets were input into STRING 12.0 database ( https://cn.string - db.org/). The organism “Homo sapiens” was selected within STRING 12.0 database, with a confidence level set to medium (0.4) while keeping all other settings as default. The results were then exported as a TSV file. Next,to visualize the PPI network, the TSV file of PPI data was introduced into Cytoscape 3.10.1. CytoHubba plugin was employed to compute metrics like Degree, MNC, and MCC within PPI network. Network diagrams for top ten targets based on Degree, MNC, and MCC values were extracted and constructed within CytoHubba.The overlap among top ten targets for these three metrics was identified, and the targets within this intersection were considered core or important targets. Finally, MCODE plugin was used with default parameters (degree cutoff = 2, node score cutoff = 0.2, K-score = 2, and Max depth = 100) to identify the most important target modules. Molecular docking To forecast interactions of luteolin with related key proteins, an SDF file containing 3D structure of luteolin was acquired from PubChem database.Target proteins were searched for in UniProt Protein Data Bank ( https://www.uniprot.org/ ). For species, “Human” was selected, and PDB IDs in the range of 1–2 were chosen. Then, structure of core target was acquired by entering corresponding PDB ID in PDB database ( https://www.rcsb.org/ ). Finally, component structures and protein structures were imported into CB- DOCK2 ( https://cadd.labshare.cn/cb-dock2 ) for auto-blind docking. GO analysis and KEGG pathway enrichment analysis The potential effect of luteolin on dry AMD was explored through Gene Ontology(GO) analysis and Kyoto encyclopedia of genes and genomes (KEGG) analysis. Target genes common to luteolin and dry AMD were introduced into DAVID database ( https://david.ncifcrf.gov/ ), and “Homo sapiens” was selected as species for GO function and KEGG analysis.The GO enrichment analysis encompasses three aspects: biological process (BP), cellular component (CC), and molecular function (MF).Based on the pathway name and pathway ID, the corresponding pathway class was retrieved from KEGG PATHWAY database ( https://www.genome.jp/kegg/pathway.html).Wit h respect to P -value in GO analysis, 10 of the highest-ranked elements were picked respectively for BP, CC, and MF, while top 20 elements were singled out for KEGG analysis. KEGG bubble plots and bar graphs for GO-BP, GO-CC, and GO-MF were generated via the Wei Sheng Xin platform ( https://www.bioinformatics.com.cn/ ). Cell culture and cell viability assays The Chinese Academy of Sciences' Cell Bank provided the ARPE-19 cell line (GNHu45). The cells were cultivated in a suitable medium and then incubated within a cell incubator maintained at 37°C with an atmosphere of 5% CO₂. Cells were placed into 96-well plates. Once reaching roughly 80% confluence, cells underwent treatment with sodium iodate (SI, Macklin, China) at different concentration gradients. In SI + Luteolin group(SI + LUT), luteolin (MCE, USA) was pretreated in advance for 24h, then SI was added. After 24h, a volume of 100 µL of a medium with 10% CCK − 8 (Beyotime, China) solution was introduced into every well, and plates were incubated within cell culture incubator for a period of 3h. Ultimately, absorbance readings were taken at 450 nm by means of a multifunctional microplate reader, and cell viability was computed. Detection of intracellular ROS levels Dilute DCFH-DA (Solarbio, China) 1000-fold with serum-free culture medium to a working concentration of 5 µM. Seed cells in six-well plates. After stimulating the cells, discard the original medium and add the freshly diluted DCFH-DA. Incubate the cells at 37℃ for 20 minutes. Subsequently, cleanse the cells thrice to thoroughly get rid of the un-internalized DCFH-DA. Visualize and record the fluorescence by employing a fluorescence microscope. Mitochondrial membrane potential level detection For a well in a six-well plate, aspirate the existing culture medium. Then, add 1 mL of cell culture medium and 1 mL of JC-1 staining working solution ((Solarbio, China)). Stir the components completely and incubate the plate at 37℃ for 20 minutes. Once the incubation is complete, aspirate the supernatant. Wash the cells two times using JC-1 staining buffer. Finally, cells were examined via a fluorescence microscope. Statistical analysis Each experiment was independently repeated on three separate occasions. The obtained data were statistically processed by means of SPSS 27.0 and presented visually with the help of GraphPad Prism 10.0. Data are displayed as the mean ± standard deviation. For the purpose of statistical comparisons between multiple groups, one-way ANOVA was implemented. Statistical significance was assumed when the P -value was below 0.05. Results ADME-related properties of luteolin ADME characteristics of Luteolin were retrieved from TCMSP database (Table 1 ), including DL, OB, Caco2, BBB, and “Lipinski's Rule of Five” (MW, AlogP, TPSA, Hdon, and Hacc). Table 1 The ADME properties of luteolin Name MW AlogP Hdon Hacc OB (%) Caco-2 BBB DL TPSA Luteolin 286.25 2.07 4 6 36.16 0.19 -0.84 0.25 111.13 Dry AMD targets, luteolin-target genes, and intersection analysis When searching for atrophic age-related macular degeneration in the GeneCards database, 2743 targets were retrieved. In OMIM database, 536 such targets were identified. For dry age-related macular degeneration, we used it as the search criterion. In the GeneCards database, 2751 targets were obtained; in the OMIM database, 536 targets were found. Additionally, DrugBank database yielded 1 target, and TTD database provided 1 target. When non-exudative age-related macular degeneration was used as the search criterion, GeneCards database provided 1 target, and OMIM database contributed 536 targets.Once all targets were combined and duplicates removed, 4005 unique targets remained. The “disease-target” network graph had 4006 nodes and 4005 edges. In the graph (Fig. 1 A), red rectangles represented diseases, while blue rectangles represented disease targets. A total of 383 target genes of luteolin from various sources were collected. Among these, 215 genes were from the CTD database, 107 were from the Super-PRED database, and 100 were from the Swiss Target Prediction database (Fig. 1 B). Subsequently, intersection analysis identified 213 genes shared between luteolin and AMD (Fig. 1 C). These genes were used to further explore the mechanism through which luteolin alleviates AMD. PPI network analysis The 213 intersecting targets underwent analysis within STRING database. Through this analysis, a PPI network consisting of 212 nodes and 5660 edges was created. In this network (Fig. 2 A), rectangles represent intersecting targets, and edges denote interactions between targets. Subsequently, CytoHubba analysis identified eight core genes: TP53, TNF, IL6, AKT1, BCL2, STAT3, JUN, and CASP3 (Fig. 2 B). Using the MCODE plugin in Cytoscape, 84 nodes with a score of 63.422 were identified in the PPI network. The identified module contained 2632 edges (Fig. 2 C). Notably, the most significant module comprised the eight core genes mentioned above. Molecular docking For predicting interaction of luteolin with target proteins, three-dimensional structure of luteolin was acquired from PubChem database. Target proteins in PDB format were also retrieved: TP53 (PDB ID: 1AIE), TNF (PDB ID: 2R32), IL6 (PDB ID: 1ALU), AKT1 (PDB ID: 1UNQ), BCL2 (PDB ID: 5VAU), STAT3 (PDB ID: 6NJS), JUN (PDB ID: 5T01), and CASP3 (PDB ID: 1NMS). Subsequently, the core targets screened above were subjected to docking analysis with luteolin using CB-DOCK2. The resulting docking complexes were visualized (Fig. 3 ). All complexes exhibited docking binding energies below − 5 kcal/mol, which suggested the validity of aforementioned screening results. (Table 2 ). Among these, JUN exhibited the best docking activity with luteolin, showing a binding energy of -8.9 kcal/mol. Table 2 Molecular docking analysis of luteolin binding energies with eight core anti-dry AMD targets. Gene Symbol Description PDB ID Affinity(kcal/mol) TP53 Tumor Protein P53 1AIE -5.9 TNF Tumor Necrosis Factor 2R32 -6.2 IL6 Interleukin 6 1ALU -6.5 AKT1 AKT Serine/Threonine Kinase 1 1UNQ -6.2 BCL2 BCL2 Apoptosis Regulator 5VAU -7.5 STAT3 Signal Transducer And Activator Of Transcription 3 6NJS -7.9 JUN Jun Proto-Oncogene 5T01 -8.9 CASP3 Caspase 3 1NMS -8.6 GO analysis and KEGG pathway enrichment analysis Upon inputting luteolin anti-AMD target genes into DAVID database, 308 BBP terms, 103 CC terms, and 109 MF terms were obtained. The P -value denoted the significance of the enrichment function. Subsequently, top 10 most significant entries were filtered, and GO bar graphs were plotted (Fig. 4 A). In the BP category, notable terms included responses to xenobiotic stimuli, negative regulation of apoptotic processes, and positive regulation of gene expression. For CC, terms covered cytoplasm, protein-containing complexes, extracellular space, and mitochondria. In the MF category, terms involved protein binding, enzyme binding, and cytokine activity. KEGG analysis identified 152 pathways. Prominent among these were the lipid and atherosclerosis pathway, cancer-related pathways, and AGE-RAGE signaling pathway in diabetic complications. These findings suggest that luteolin may act on these pathways, thereby exerting a therapeutic effect against AMD. To further visualize the significant pathways, Fig. 4 B depicts the top 20 significantly enriched KEGG signaling pathways. The lipid and atherosclerosis pathway exhibited the most significant P -value (Table 3 ). Table 3 KEGG pathway enrichment analysis of luteolin against dry AMD. Pathway ID KEGG_B_Class Pathway P Gene hsa05417 Cardiovascular disease Lipid and atherosclerosis 1.14E-39 GSK3B, CD40, CXCL8, TNF, ICAM1, CASP9, CASP7, TBK1, CASP8, CASP3, TNFSF10, CASP1, AKT1, HSP90AA1, MMP1, MMP3, PRKCA, FOS, MMP9, ERN1, IRF3, IL1B, DDIT3, PPARG, ATF6, TP53, SRC, AGER, MAPK8, CCL2, MAPK1, NLRP3, MAPK3, JUN, XBP1, VCAM1, HSPA5, IFNB1, NOS3, STAT3, SOD2, SELE, EIF2S1, NFKB1, MAPK10, IL6, CD40LG, CYP1A1, BCL2, BAX, FAS, NFE2L2, BCL2L1 hsa05200 Cancer Pathways in cancer 1.12E-34 ALK, GSK3B, CDKN1A, CDKN1B, CXCL8, PTEN, SLC2A1, KEAP1, FGF2, IGF1R, CASP9, CASP7, CASP8, CCND1, CDH1, CASP3, AKT1, JAK1, HSP90AA1, MMP1, MMP2, IL13, PRKCA, FOS, F2, MMP9, CCNA2, AR, IFNG, PPARG, MET, TP53, BIRC2, PDGFB, CXCR4, XIAP, PTGS2, HIF1A, EGFR, MAPK8, TERT, HMOX1, MAPK1, MAPK3, NQO1, JUN, TGFB1, NOS2, EGF, STAT1, STAT3, ESR1, MTOR, NFKB1, VEGFA, MAPK10, IL4, IL6, IL5, CXCL12, CDK6, CDK2, GSTA1, BCL2, MDM2, BAX, CTNNB1, FAS, NFE2L2, BCL2L1 hsa04933 Endocrine and metabolic disease AGE-RAGE signaling pathway in diabetic complications 7.59E-26 CDKN1B, CXCL8, SERPINE1, TNF, AGER, ICAM1, MAPK8, CCND1, CASP3, CCL2, AKT1, MAPK1, MAPK3, EGR1, JUN, TGFB1, VCAM1, NOS3, STAT1, MMP2, STAT3, PRKCA, SELE, NFKB1, VEGFA, MAPK10, IL6, IL1B, BCL2, BAX, NOX4 hsa04657 Immune system IL-17 signaling pathway 2.17E-25 GSK3B, CSF2, CXCL8, TNFAIP3, PTGS2, TNF, MAPK8, CASP8, TBK1, CASP3, CCL2, MAPK1, MAPK3, JUN, HSP90AA1, MMP1, IL13, MMP3, FOS, MMP9, NFKB1, MAPK10, IL4, CXCL10, IL6, MMP13, IL5, IFNG, IL1B, IL17A hsa05167 Infectious disease: viral Kaposi sarcoma-associated herpesvirus infection 4.40E-24 BECN1, GSK3B, CDKN1A, CSF2, CXCL8, SRC, PDGFB, PTGS2, FGF2, HIF1A, ICAM1, CASP9, MAP1LC3B, MAPK8, CASP8, TBK1, CCND1, CASP3, AKT1, MAPK1, JAK1, MAPK3, JUN, IFNB1, STAT1, STAT3, FOS, MTOR, NFKB1, VEGFA, MAPK10, IL6, CDK6, IRF3, BAX, CTNNB1, FAS, TP53 hsa05162 Infectious disease: viral Measles 1.52E-22 GSK3B, CDKN1B, TNFAIP3, CASP9, MAPK8, CASP8, TBK1, CCND1, CASP3, AKT1, JAK1, JUN, CSNK2A1, IFNB1, STAT1, STAT3, FOS, EIF2S1, NFKB1, MAPK10, IL6, CDK6, IRF3, IL1B, CSNK2B, CDK2, BCL2, BAX, FAS, TP53, BCL2L1, TP73 hsa05418 Cardiovascular disease Fluid shear stress and atherosclerosis 3.31E-21 PRKAA1, SRC, PDGFB, KEAP1, TNF, ICAM1, CDH5, MAPK8, KDR, CCL2, AKT1, HMOX1, NQO1, JUN, HSP90AA1, VCAM1, NOS3, MMP2, FOS, SELE, MMP9, NFKB1, VEGFA, MAPK10, IFNG, IL1B, GSTA1, BCL2, CTNNB1, TP53, NFE2L2 hsa04668 Signal transduction TNF signaling pathway 6.08E-20 CSF2, XIAP, TNFAIP3, PTGS2, TNF, ICAM1, CASP7, MAPK8, CASP8, CASP3, CCL2, AKT1, MAPK1, MAPK3, JUN, VCAM1, IFNB1, MMP3, FOS, SELE, MMP9, NFKB1, MAPK10, CXCL10, IL6, IL1B, FAS, BIRC2 hsa01521 Drug resistance: antineoplastic EGFR tyrosine kinase inhibitor resistance 1.19E-19 GSK3B, SRC, EGF, STAT3, PTEN, PDGFB, PRKCA, FOXO3, FGF2, EGFR, MTOR, IGF1R, VEGFA, IL6, AXL, KDR, BCL2, BAX, AKT1, MAPK1, MET, JAK1, BCL2L1, MAPK3 hsa05161 Infectious disease: viral Hepatitis B 2.66E-19 CDKN1A, CXCL8, SRC, TNF, CASP9, MAPK8, CASP8, TBK1, CASP3, AKT1, MAPK1, JAK1, MAPK3, JUN, TGFB1, IFNB1, STAT1, STAT3, PRKCA, FOS, MMP9, NFKB1, MAPK10, CCNA2, IL6, IRF3, CDK2, BCL2, BAX, FAS, TP53 hsa04210 Cell growth and death Apoptosis 2.43E-18 XIAP, TNF, CASP9, CASP7, MAPK8, CASP8, CASP3, TNFSF10, AKT1, MAPK1, CAPN1, CTSD, MCL1, MAPK3, JUN, PARP1, FOS, EIF2S1, NFKB1, ERN1, MAPK10, DDIT3, BCL2, BAX, FAS, TP53, BIRC2, BCL2L1 hsa05163 Infectious disease: viral Human cytomegalovirus infection 5.93E-18 GSK3B, CDKN1A, CXCL8, SRC, CXCR4, PTGS2, TNF, EGFR, CASP9, CASP8, TBK1, CCND1, CASP3, CCL2, AKT1, MAPK1, JAK1, MAPK3, IFNB1, STAT3, PRKCA, MTOR, NFKB1, VEGFA, IL6, CXCL12, CDK6, IRF3, IL1B, MDM2, BAX, CTNNB1, FAS, TP53 hsa05164 Infectious disease: viral Influenza A 1.34E-17 CXCL8, PLG, TNF, ICAM1, CASP9, CASP8, TBK1, CASP3, CASP1, TNFSF10, CCL2, AKT1, MAPK1, NLRP3, JAK1, MAPK3, IL33, IFNB1, STAT1, PRKCA, EIF2S1, NFKB1, CXCL10, IL6, CDK6, IFNG, IRF3, IL1B, BAX, FAS hsa05160 Infectious disease: viral Hepatitis C 1.54E-17 GSK3B, CDKN1A, TNF, EGFR, CASP9, CASP8, TBK1, CCND1, CASP3, AKT1, MAPK1, JAK1, MAPK3, IFNB1, EGF, STAT1, STAT3, EIF2S1, NFKB1, CXCL10, OCLN, CDK6, IFNG, IRF3, CDK2, BAX, CTNNB1, FAS, TP53 hsa05215 Cancer Prostate cancer 1.76E-17 GSK3B, CDKN1A, HSP90AA1, CDKN1B, EGF, MMP3, PTEN, PDGFB, MMP9, EGFR, MTOR, NFKB1, IGF1R, CASP9, AR, CCND1, CDK2, MDM2, BCL2, AKT1, MAPK1, CTNNB1, TP53, MAPK3 hsa05169 Infectious disease: viral Epstein-Barr virus infection 1.55E-16 CDKN1A, CD40, CDKN1B, TNFAIP3, TNF, ICAM1, CASP9, MAPK8, CASP8, TBK1, CCND1, CASP3, AKT1, JAK1, JUN, IFNB1, STAT1, STAT3, NFKB1, MAPK10, CCNA2, CXCL10, IL6, CDK6, IRF3, CDK2, BCL2, MDM2, BAX, FAS, TP53 hsa04066 Signal transduction HIF-1 signaling pathway 2.20E-16 CDKN1A, CDKN1B, PFKFB3, NOS2, NOS3, EGF, INSR, STAT3, SERPINE1, SLC2A1, PRKCA, HIF1A, EGFR, MTOR, NFKB1, IGF1R, VEGFA, IL6, IFNG, BCL2, AKT1, HMOX1, MAPK1, MAPK3 hsa05212 Cancer Pancreatic cancer 2.87E-16 CDKN1A, TGFB1, STAT1, EGF, STAT3, EGFR, MTOR, NFKB1, VEGFA, MAPK10, CASP9, MAPK8, CDK6, CCND1, BAX, AKT1, MAPK1, TP53, JAK1, BCL2L1, MAPK3 hsa05145 Infectious disease: parasitic Toxoplasmosis 3.37E-16 IL10, CD40, TGFB1, NOS2, STAT1, STAT3, XIAP, TNF, NFKB1, MAPK10, CASP9, MAPK8, CASP8, CD40LG, IFNG, CASP3, ALOX5, BCL2, AKT1, MAPK1, BIRC2, JAK1, BCL2L1, MAPK3 hsa05208 Cancer Chemical carcinogenesis - reactive oxygen species 5.12E-16 SRC, PTEN, KEAP1, AHR, FOXO3, HIF1A, EGFR, MAPK8, CYP1B1, AKT1, HMOX1, MAPK1, MAPK3, NQO1, JUN, EGF, AKR1A1, PTPN11, FOS, SOD2, NFKB1, SOD1, VEGFA, MAPK10, CYP1A2, CAT, GSTA1, CYP1A1, NOX4, CYP2E1, MET, NFE2L2 3.6 Network Diagram of "luteolin-dry AMD-target-pathway" Cytoscape 3.10.1 was employed to visualize the "luteolin-target-pathway" network, and major biological mechanisms were identified (Fig. 5 ). The analysis revealed that network comprised 235 nodes and 1071 linkages.. These findings identified crucial signaling pathways through which luteolin exerts its therapeutic effects against AMD. Pathway diagram of dry AMD and luteolin related targets. Analysis of the PPI network identified TP53, TNF, IL6, AKT1, BCL2, STAT3, JUN, and CASP3 as the eight key genes involved in luteolin’s treatment of AMD. This finding aligns well with the findings of KEGG analysis. The lipid and atherosclerosis pathways emerged as the most significantly enriched pathway. Notably, all eight of the aforementioned genes were associated with this pathway (Fig. 6 ). Luteolin attenuated sodium iodate-induced apoptosis in ARPE-19 cells To evaluate the detrimental effects of sodium iodate on ARPE-19 cells, cells were exposed to varying concentrations for 24h. Findings indicated that at a sodium iodate concentration of 7.5 mM, cell viability dropped to approximately 50% (Fig. 7 A). Therefore, 7.5 mM was selected as the concentration for model establishment. For cytotoxicity evaluation of luteolin, cells were administered with various concentrations.The CCK-8 results indicate that luteolin is safe for ARPE-19 cells and has a slight pro-proliferative effect (Fig. 7 B). To determine luteolin's protective effect against sodium iodate-induced damage, ARPE-19 cells were pretreated with different luteolin concentrations for 24h, followed by 7.5 mM sodium iodate addition. The results demonstrated that 40 µM luteolin exhibited the most pronounced protective effect(Fig. 7 C). This finding suggests that luteolin pretreatment can shield ARPE-19 cells from sodium iodate-induced cellular damage. Antioxidant properties of luteolin Overproduction of ROS and impairment of antioxidant defense mechanism result in redox imbalance and heightened oxidative stress. For assessing ROS levels in cells, the DCFH–DA assay was employed. Compared with control group, sodium iodate-treated group exhibited notably elevated ROS levels. However, pretreatment with luteolin effectively reduced the ROS levels (Fig. 8 ). Protective effect of luteolin on mitochondrial membrane potentials JC-1 staining was employed to evaluate mitochondrial membrane potential in each group. An increase in green fluorescence signified a reduction in mitochondrial membrane potential, suggesting that cells might be undergoing early apoptosis. Conversely, an increase in red fluorescence indicated a normal mitochondrial membrane potential. In comparison with control group, sodium iodate-treated group showed a marked reduction in mitochondrial membrane potential. By contrast, pretreatment with luteolin led to an elevation in the mitochondrial membrane potential of ARPE-19 cells (Fig. 9 ). These findings suggest that luteolin can protect against sodium iodate-induced mitochondrial damage and help alleviate early-stage cell apoptosis. Discussion Dry AMD is a prevalent and complex ocular degenerative disorder. It severely threatens the visual health of the elderly. The pathological mechanism of dry AMD encompasses oxidative stress, inflammation, and abnormal activation of the complement system 26 , 27 . Luteolin, a natural flavonoid, has garnered extensive attention in recent years due to its diverse biological activities. These include antioxidative stress capabilities, attenuation of inflammatory responses, and regulation of cellular metabolism 28 . Luteolin shows potential applications in the prevention and treatment of dry AMD. Network pharmacology analysis enables the construction of network models, facilitating the exploration of associations among targets, drugs, and diseases 29 , 30 . To identify the key targets of luteolin against dry AMD, we adopted a network pharmacology approach. As a result, 213 overlapping targets of luteolin for dry AMD treatment were obtained. KEGG analysis indicated that luteolin might exert beneficial effects primarily through the lipid-arteriosclerosis pathway. AMD is significantly associated with lipid metabolism disorders 31 and shares numerous risk factors with atherosclerosis 32 , 33 . Additionally, dry AMD patients who receive high-dose statin therapy have shown improvements in visual acuity 34 . The PPI network analysis suggested that luteolin alleviates AMD symptoms by modulating multiple targets. Subsequently, CytoHubba analysis screened eight core gene targets: TP53, TNF, IL6, AKT1, BCL2, STAT3, JUN, and CASP3. Docking results demonstrated that luteolin has a high affinity for JUN. c-Jun N-terminal kinase (JNK) functions as a critical activator of the JUN protein. Disruptions in ocular microcirculation can trigger JNK signaling activation. Experimental evidence has demonstrated that inhibiting JNK signaling alleviates symptoms in dry AMD model mice. This finding suggests that targeting the JNK signaling pathway could be a promising therapeutic strategy for dry AMD 35 . TNF and IL6 are key inflammatory mediators, playing pivotal parts in inflammatory process. Within dry AMD context, heightened plasma IL-6 levels correlate strongly with geographic atrophy progression, a late-stage manifestation of the disease 36 . STAT3 is a pivotal protein in cell signaling pathways and gene expression regulation. It can be activated by inflammation-related cytokines like IL6 37,38 . Once activated, STAT3 mitigates all-trans retinaldehyde-induced ferroptosis in 661W photoreceptor cells. This discovery suggests the possibility of targeting STAT3 as a treatment strategy for dry AMD 39 . functions as a crucial anti-apoptotic protein. In the dry AMD model, BCL2 expression is downregulated 40 , 41 . This downregulation leads to increased apoptosis, contributing to the progression of the disease. Collectively, the above results suggest that luteolin can alleviate AMD symptoms by targeting multiple proteins and intervening in various pathological processes. Prior to investigating the effects of luteolin, we evaluated its safety in ARPE-19 cells. Results of the safety assessment demonstrated that luteolin was non-cytotoxic to ARPE-19 cells and has a slight pro-proliferative effect. Subsequently, we determined that sodium iodate exerted concentration-dependent cytotoxic effects on ARPE-19 cells. Notably, pretreatment with luteolin effectively enhanced cell viability, counteracting the detrimental effects of sodium iodate. AMD pathogenesis is closely associated with oxidative stress in RPE cells 42 . RPE cells possess endogenous antioxidant defense mechanisms to counteract oxidative damage. However, when the production of ROS overwhelms the normal cellular and tissue antioxidant capacity, oxidative stress is triggered 8 , 43 . This, in turn, initiates a cascade of pathophysiological processes within cells and tissues. Mitochondria are the primary energy providers for cells. A decrease in the mitochondrial membrane potential indicates mitochondrial dysfunction, which may lead to cell damage and apoptosis 44 , 45 . The transition from red to green fluorescence in JC-1 staining clearly indicated a drop in mitochondrial membrane potential. These observations demonstrated that treatment with sodium iodate resulted in oxidative stress damage within ARPE-19 cells. This led to elevated levels of cellular ROS, a reduction in mitochondrial membrane potential, and ultimately, cell apoptosis. In contrast, pretreatment with luteolin mitigated ROS production, restored mitochondrial membrane potential, and alleviated oxidative stress, thereby protecting ARPE-19 cells. Luteolin exhibits robust antioxidant activities, able to eliminate free radicals, suppress lipid peroxidation, and protect cells from oxidative harm 46 . Its antioxidant mechanisms primarily encompass direct free radical scavenging, metal ion chelation, and upregulation of antioxidant enzyme activities 47 , 48 . By modulating the nuclear factor Nrf2 pathway, luteolin upregulates antioxidant activities like superoxide dismutase and glutathione, in rats with polycystic ovary syndrome, thereby exerting remarkable antioxidant effects 49 . Similarly, in type 2 diabetic rats, luteolin treatment leads to significantly decreased malondialdehyde levels and markedly increased superoxide dismutase, catalase, and glutathione levels compared to untreated counterparts. This indicates that luteolin can effectively alleviate oxidative stress in type 2 diabetic rats 50 . Oxidative stress and inflammation are closely interrelated. Oxidative stress often serves as a trigger for chronic inflammatory diseases 51 , 52 . Inflammatory cytokines including TNF-α and IL-6 can be stimulated for production by ROS. The upsurge in these cytokines may trigger inflammation and cell death 53 , whereas luteolin exhibits remarkable anti-inflammatory properties and can inhibit production of ROS and inflammatory cytokines 54 . Moreover, luteolin inhibits the generation of other inflammatory mediators, such as prostaglandin E2 and nitric oxide 55 , 56 . This study systematically explored the potential mechanisms by which luteolin ameliorates dry AMD. However, its limitation lies in the inability of in vitro models to fully recapitulate the in vivo pathological environment of animals. Future in vivo studies are warranted to further validate its efficacy. Conclusion Utilizing network pharmacology approaches alongside in vitro assays, we predicted and validated that luteolin, a natural bioactive compound, protects against sodium iodate-induced damage in ARPE-19 cells. Our findings strongly indicate that luteolin exerts its protective effect through the modulation of multiple targets and signaling pathways. This study has established a theoretical and experimental foundation, laying the groundwork for future research on the therapeutic potential of luteolin in the treatment of dry AMD. Declarations The authors declare no conflict of interest. Acknowledgements This work was supported by the Supported by the Sichuan Provincial Department of Science and Technology (No. 2024ZYD0114) and Sichuan Medical Association (No. S2024001). Author contributions Maomei Luo: Writing – original draft, Software, Methodology, Formal analysis, Conceptualization. Min Zhang: Software, Methodology, Formal analysis, Investigation. Zhen Xing: Software, Methodology, Formal analysis. Wei Yu: Software, Methodology. Hongbin Lv: Supervision Project administration, Funding acquisition, Conceptualization. Data availability The primary data of this study can be obtained from the corresponding author upon reasonable request. Footnotes Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Maomei Luo and Min Zhang contributed equally to this work. Contributor Information Hongbin Lv, Email: [email protected] . References Ruan, Y., Jiang, S. & Gericke, A. Age-Related Macular Degeneration: Role of Oxidative Stress and Blood Vessels. Int J. Mol. Sci 22 (2021). Guymer, R. H. & Campbell, T. G. Age-related macular degeneration. Lancet 401 , 1459–1472 (2023). Thomas, C. J., Mirza, R. G. & Gill, M. K. Age-Related Macular Degeneration. Med. Clin. North. Am. 105 , 473–491 (2021). Fernandes, A. R. et al., Exudative versus Nonexudative Age-Related Macular Degeneration: Physiopathology and Treatment Options. Int J. Mol. Sci 23 (2022). Schultz, N. M., Bhardwaj, S., Barclay, C., Gaspar, L. & Schwartz, J. Global Burden of Dry Age-Related Macular Degeneration: A Targeted Literature Review. Clin. Ther. 43 , 1792–1818 (2021). 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Cite Share Download PDF Status: Published Journal Publication published 29 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 21 Jul, 2025 Reviews received at journal 20 Jul, 2025 Reviewers agreed at journal 19 Jul, 2025 Reviews received at journal 10 Jul, 2025 Reviewers agreed at journal 09 Jul, 2025 Reviewers agreed at journal 09 Jul, 2025 Reviewers invited by journal 09 Jul, 2025 Editor assigned by journal 09 Jul, 2025 Editor invited by journal 26 Jun, 2025 Submission checks completed at journal 25 Jun, 2025 First submitted to journal 22 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-6948097","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":483384394,"identity":"696b3eea-a9bd-4185-9db2-9722a7b9110b","order_by":0,"name":"Maomei Luo","email":"","orcid":"","institution":"The Affiliated Hospital of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Maomei","middleName":"","lastName":"Luo","suffix":""},{"id":483384395,"identity":"6a39f9b9-ca4a-4f3c-944e-a43be6c4b7d7","order_by":1,"name":"Min Zhang","email":"","orcid":"","institution":"The Affiliated Hospital of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Zhang","suffix":""},{"id":483384396,"identity":"705a9000-96cb-4c29-9898-09c8e108ab7c","order_by":2,"name":"Zhen Xing","email":"","orcid":"","institution":"The Affiliated Hospital of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Xing","suffix":""},{"id":483384397,"identity":"416968aa-a7c9-4a57-b22e-a0e552761418","order_by":3,"name":"Wei Yu","email":"","orcid":"","institution":"The Affiliated Hospital of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Yu","suffix":""},{"id":483384398,"identity":"f0179cb3-dc92-4d51-a181-250e27b946aa","order_by":4,"name":"Hongbin Lv","email":"data:image/png;base64,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","orcid":"","institution":"The Affiliated Hospital of Southwest Medical University","correspondingAuthor":true,"prefix":"","firstName":"Hongbin","middleName":"","lastName":"Lv","suffix":""}],"badges":[],"createdAt":"2025-06-22 07:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6948097/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6948097/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-21730-y","type":"published","date":"2025-10-29T15:56:54+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86507185,"identity":"26327872-b35d-4556-8031-bcce59259645","added_by":"auto","created_at":"2025-07-11 12:12:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":344870,"visible":true,"origin":"","legend":"\u003cp\u003e(A)Network Diagrams Visualizing “Dry AMD-Target” Associations. (B)Network diagram depicting the relationship between luteolin and its targets. (C) Intersecting targets shared by luteolin and dry AMD.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6948097/v1/baf06c9cc5082089542e31c3.png"},{"id":86506163,"identity":"85c1c631-9d26-47eb-b5c8-717754d6bbb1","added_by":"auto","created_at":"2025-07-11 12:04:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":344559,"visible":true,"origin":"","legend":"\u003cp\u003ePPI network of luteolin in the context of dry AMD and its core targets. (A) A PPI network comprising 213 intersection targets was created via STRING database version 3.10.1. (B) Eight core genes were identified via the CytoHubba plugin in Cytoscape. (C) Utilizing MCODE plugin within Cytoscape, the most prominent target modules were determined.\u003c/p\u003e","description":"","filename":"Onlinefloatimage210.png","url":"https://assets-eu.researchsquare.com/files/rs-6948097/v1/d05e4af8a73de523197b89d6.png"},{"id":86506164,"identity":"6ee39776-85a5-45ae-9978-c6ba2ea21b6f","added_by":"auto","created_at":"2025-07-11 12:04:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":223916,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking of luteolin with target proteins. (A: TP53; B: TNF; C: IL6; D: AKT1; E: BCL2; F: STAT3; G: JUN; H: CASP3)\u003c/p\u003e","description":"","filename":"Onlinefloatimage36.png","url":"https://assets-eu.researchsquare.com/files/rs-6948097/v1/8a8c83dfa1473b351ac3ed37.png"},{"id":86506166,"identity":"a17d2506-b720-4eee-a0df-bc8eec1ca9f4","added_by":"auto","created_at":"2025-07-11 12:04:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":191986,"visible":true,"origin":"","legend":"\u003cp\u003eGO and KEGG Enrichment Analyses. (A) GO enrichment analysis bar graph. (B) Top-20 KEGG pathway enrichment analysis bubble diagram.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6948097/v1/6feb167d899cb40f18bf5e8a.png"},{"id":86506170,"identity":"4fe4e6b8-a779-4bfb-98bf-17c07b7533b9","added_by":"auto","created_at":"2025-07-11 12:04:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":80091,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork diagram of luteolin-dry AMD-target-pathway interactions.\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6948097/v1/c76b551862a55431d4b5d0f5.png"},{"id":86506167,"identity":"80fd8b44-9ce4-468f-b155-8e13997a125c","added_by":"auto","created_at":"2025-07-11 12:04:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":142977,"visible":true,"origin":"","legend":"\u003cp\u003eThe lipid and atherosclerosis pathway diagram, with red marking the intersection targets of the analysis.\u003c/p\u003e","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6948097/v1/34086f4e5092e2c61bc437f7.png"},{"id":86506168,"identity":"cd48bd04-78e4-4046-a225-7ed2092b893b","added_by":"auto","created_at":"2025-07-11 12:04:59","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":83593,"visible":true,"origin":"","legend":"\u003cp\u003eImpact of luteolin on ARPE-19 cell viability. (A) Cells were incubated with varying concentrations of sodium iodate for 24h. (B) The cells underwent treatment with varying concentrations of luteolin for a consistent 24h period. (C) Cells underwent pretreatment with various luteolin concentrations prior to being exposed to 7.5 mM sodium iodate for 24h. *\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001, n=5.\u003c/p\u003e","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6948097/v1/e64fcd6db0f9c0f998504ff2.png"},{"id":86507186,"identity":"ec4934f6-43ee-433a-aa7c-bd260ad51dea","added_by":"auto","created_at":"2025-07-11 12:12:59","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":584733,"visible":true,"origin":"","legend":"\u003cp\u003eLuteolin's effect on ROS levels in ARPE-19 cells. (A) DCFH-DA staining visualized intracellular ROS levels. Scale bar: 200μm. (B) Fluorescence intensity quantification revealed luteolin suppressed ROS production. *\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001，n=3.\u003c/p\u003e","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-6948097/v1/daef20abb0b74084251b72a4.png"},{"id":86506172,"identity":"c8f64ffd-9eec-4d9e-8e35-3b3b2e5c5aff","added_by":"auto","created_at":"2025-07-11 12:04:59","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":889737,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of luteolin on mitochondrial membrane potential within ARPE-19 Cells. (A) JC-1 staining evaluated mitochondrial membrane potential. Red to green fluorescence shift indicates decreasing potential. Scale bar: 125μm. (B) Fluorescence intensity analysis showed luteolin increased mitochondrial membrane potential.*\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001, n=3.\u003c/p\u003e","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-6948097/v1/fc153267cca44885a394c21f.png"},{"id":95039777,"identity":"d1bd9511-989f-498e-9175-5d57af58ce74","added_by":"auto","created_at":"2025-11-03 16:01:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4634346,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6948097/v1/5cabec29-6083-44c0-8ec5-6af1a7dd44ca.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mechanistic Analysis of Luteolin in Mitigating Dry Age-Related Macular Degeneration through Network Pharmacology and Experimental Validation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAge-related macular degeneration (AMD) represents a progressive ophthalmic disorder. It mainly exerts an impact on the macular region of the retina, leading to the decline of central visual function\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. As the global population ages at an accelerating pace, the incidence of AMD is on a continuous upward trend. This not only imposes substantial health-related challenges but also places a heavy economic burden on both individuals and society\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. AMD is primarily categorized into two main subtypes: dry and wet. Among these, dry AMD constitutes approximately 85\u0026ndash;90% of all AMD cases\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDry AMD is marked by deposition of drusen and pigmentary alterations in fundus posterior pole. Concurrently, it features progressive atrophy of retinal pigment epithelial (RPE) cells and photoreceptor cells\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Oxidative stress is recognized to have a crucial role in the development of dry AMD\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. RPE cells, given their high metabolic rate, are highly susceptible to generating reactive oxygen species (ROS). When ROS levels are imbalanced with the antioxidant system, oxidative stress ensues, inflicting damage on RPE cells\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Although the progression of dry AMD is relatively slow, it can progress to geographic atrophy in its late stage. In some cases, dry AMD may even transform into wet AMD, resulting in severe vision loss\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Currently, there remains a lack of effective therapeutic interventions for dry AMD\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn recent years, natural compounds have emerged as promising candidates for treating neurodegenerative and ocular diseases, drawing significant research interest\u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Luteolin, a prevalent flavonoid compound in the plant kingdom, typically exists as glycosides. It is widely distributed across various plant sources, including vegetables (e.g., carrots, peppers, and celery), herbs (e.g., perilla and parsley), and flowers (e.g., honeysuckle and chrysanthemum)\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Luteolin exhibits a broad spectrum of bioactivities, such as antioxidant, anti-inflammatory, antitumor, and neuroprotective effects\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. Mechanistically, luteolin suppresses protein acetylation and oxidative stress evoked by high glucose in ARPE-19 cells via the SIRT1/P53 pathway\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. In rat models of corneal alkali burns, luteolin treatment effectively attenuates the inflammatory response mediated by cytokines and chemokines, thereby reducing corneal pathological damage\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. When ARPE-19 cells are subjected to H₂O₂ treatment, luteolin significantly increases activities of superoxide dismutase and glutathione peroxidase and inhibits epithelial-mesenchymal transition\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Moreover, in mouse models of depression-related dry eye, luteolin downregulates the production and release of pro-inflammatory cytokines in the hippocampus and corneal tissues\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The herb extract of Lycii Fructus and Chrysanthemum Flos prevents sodium iodate-induced oxidative damage to the retina in mice, with luteolin potentially serving as the active components exerting this protective effect\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Collectively, these findings suggest that luteolin exerts ocular protective effects through multiple mechanisms, identifying it as a possible therapeutic candidate for dry AMD.\u003c/p\u003e\u003cp\u003eNetwork pharmacology establishes a network model linking drug components, targets, and diseases, facilitating a comprehensive understanding of drug-disease interactions\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. To elucidate luteolin's mechanism of action, we propose combining network pharmacology analysis with experimental validation to explore its potential in treating dry AMD.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eADME properties of luteolin\u003c/h2\u003e\u003cp\u003eThe ADME characteristics of luteolin were determined by looking up chemical term \u0026ldquo;Luteolin\u0026rdquo; in TCMSP database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.tcmsp-e.com/\u003c/span\u003e\u003cspan address=\"https://www.tcmsp-e.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which provides information on its pharmacokinetics and other pertinent attributes.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eTarget collection and construction of a “disease-target” network diagram for age-related macular degeneration\u003c/h3\u003e\n\u003cp\u003eDisease targets related to atrophic age-related macular degeneration, dry age- related macular degeneration, and non-exudative age-related macular degeneration were searched for in the GeneCards database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the OMIM database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://omim.org/\u003c/span\u003e\u003cspan address=\"https://omim.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the DrugBank database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.drugbank.com/\u003c/span\u003e\u003cspan address=\"https://www.drugbank.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the TTD database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://db.idrblab.net/ttd/\u003c/span\u003e\u003cspan address=\"https://db.idrblab.net/ttd/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). In the GeneCards database, targets with a Relevance score of at least 5 were selected. The search outcomes from every database were combined, and redundant entries were eliminated to pinpoint disease targets. Subsequently, a network file was constructed using the collected disease targets and diseases. This file was then fed into Cytoscape 3.10.0 with the aim of generating a \u0026ldquo;disease-target\u0026rdquo; net.\u003c/p\u003e\n\u003ch3\u003eTarget prediction of luteolin and construction of “Luteolin-target” network diagrams\u003c/h3\u003e\n\u003cp\u003eTo identify possible targets of luteolin, we performed a search within CTD Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ctdbase.org/\u003c/span\u003e\u003cspan address=\"http://ctdbase.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Super\u0026ndash;PRED (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://prediction.charite.de/subpages/target_prediction.php\u003c/span\u003e\u003cspan address=\"https://prediction.charite.de/subpages/target_prediction.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and Swiss Target Prediction (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.swisstargetprediction.ch\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.swisstargetprediction.ch\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) databases. Following steps were implemented:Start by directly typing \u0026ldquo;Luteolin\u0026rdquo; into CTD database and downloading associated target genes. After that, obtain Canonical Smiles of luteolin from PubChem database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Import Canonical Smiles into Super-PRED and Swiss Target Prediction databases to predict luteolin targets. Conduct a screening in Swiss Target Prediction database for targets having probability values exceeding 0.1. Afterward, standardize the predicted target names to official gene-name abbreviations utilizing UniProt database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uniprot.org/\u003c/span\u003e\u003cspan address=\"https://www.uniprot.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Combine drug targets retrieved from the three databases and perform deduplication to remove redundant entries. Import luteolin and its targets into the Cytoscape 3.10.1 mapping tool for mapping analysis, constructing a \u0026ldquo;Luteolin-target\u0026rdquo; network diagram. To recognize intersecting targets of luteolin and disease, construct Venn diagrams by integrating the acquired compound targets with the relevant disease targets from appropriate databases.\u003c/p\u003e\n\u003ch3\u003eBuilding the PPI network and analyzing key genes\u003c/h3\u003e\n\u003cp\u003eThe protein - protein interaction (PPI) network came into being when the intersecting targets were input into STRING 12.0 database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string\u003c/span\u003e\u003cspan address=\"https://cn.string\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e - db.org/). The organism \u0026ldquo;Homo sapiens\u0026rdquo; was selected within STRING 12.0 database, with a confidence level set to medium (0.4) while keeping all other settings as default. The results were then exported as a TSV file. Next,to visualize the PPI network, the TSV file of PPI data was introduced into Cytoscape 3.10.1. CytoHubba plugin was employed to compute metrics like Degree, MNC, and MCC within PPI network. Network diagrams for top ten targets based on Degree, MNC, and MCC values were extracted and constructed within CytoHubba.The overlap among top ten targets for these three metrics was identified, and the targets within this intersection were considered core or important targets. Finally, MCODE plugin was used with default parameters (degree cutoff\u0026thinsp;=\u0026thinsp;2, node score cutoff\u0026thinsp;=\u0026thinsp;0.2, K-score\u0026thinsp;=\u0026thinsp;2, and Max depth\u0026thinsp;=\u0026thinsp;100) to identify the most important target modules.\u003c/p\u003e\n\u003ch3\u003eMolecular docking\u003c/h3\u003e\n\u003cp\u003eTo forecast interactions of luteolin with related key proteins, an SDF file containing 3D structure of luteolin was acquired from PubChem database.Target proteins were searched for in UniProt Protein Data Bank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uniprot.org/\u003c/span\u003e\u003cspan address=\"https://www.uniprot.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e For species, \u0026ldquo;Human\u0026rdquo; was selected, and PDB IDs in the range of 1\u0026ndash;2 were chosen. Then, structure of core target was acquired by entering corresponding PDB ID in PDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e Finally, component structures and protein structures were imported into CB- DOCK2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cadd.labshare.cn/cb-dock2\u003c/span\u003e\u003cspan address=\"https://cadd.labshare.cn/cb-dock2\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e for auto-blind docking.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eGO analysis and KEGG pathway enrichment analysis\u003c/h2\u003e\u003cp\u003eThe potential effect of luteolin on dry AMD was explored through Gene Ontology(GO) analysis and Kyoto encyclopedia of genes and genomes (KEGG) analysis. Target genes common to luteolin and dry AMD were introduced into DAVID database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and \u0026ldquo;Homo sapiens\u0026rdquo; was selected as species for GO function and KEGG analysis.The GO enrichment analysis encompasses three aspects: biological process (BP), cellular component (CC), and molecular function (MF).Based on the pathway name and pathway ID, the corresponding pathway class was retrieved from KEGG PATHWAY database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genome.jp/kegg/pathway.html).Wit\u003c/span\u003e\u003cspan address=\"https://www.genome.jp/kegg/pathway.html).Wit\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003eh respect to \u003cem\u003eP\u003c/em\u003e-value in GO analysis, 10 of the highest-ranked elements were picked respectively for BP, CC, and MF, while top 20 elements were singled out for KEGG analysis. KEGG bubble plots and bar graphs for GO-BP, GO-CC, and GO-MF were generated via the Wei Sheng Xin platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bioinformatics.com.cn/\u003c/span\u003e\u003cspan address=\"https://www.bioinformatics.com.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCell culture and cell viability assays\u003c/h3\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eThe Chinese Academy of Sciences' Cell Bank provided the ARPE-19 cell line\u003c/h2\u003e\u003cp\u003e(GNHu45). The cells were cultivated in a suitable medium and then incubated within a cell incubator maintained at 37\u0026deg;C with an atmosphere of 5% CO₂. Cells were placed into 96-well plates. Once reaching roughly 80% confluence, cells underwent treatment with sodium iodate (SI, Macklin, China) at different concentration gradients. In SI\u0026thinsp;+\u0026thinsp;Luteolin group(SI\u0026thinsp;+\u0026thinsp;LUT), luteolin (MCE, USA) was pretreated in advance for 24h, then SI was added. After 24h, a volume of 100 \u0026micro;L of a medium with 10% CCK \u0026minus;\u0026thinsp;8 (Beyotime, China) solution was introduced into every well, and plates were incubated within cell culture incubator for a period of 3h. Ultimately, absorbance readings were taken at 450 nm by means of a multifunctional microplate reader, and cell viability was computed.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eDetection of intracellular ROS levels\u003c/h2\u003e\u003cp\u003eDilute DCFH-DA (Solarbio, China) 1000-fold with serum-free culture medium to a working concentration of 5 \u0026micro;M. Seed cells in six-well plates. After stimulating the cells, discard the original medium and add the freshly diluted DCFH-DA. Incubate the cells at 37℃ for 20 minutes. Subsequently, cleanse the cells thrice to thoroughly get rid of the un-internalized DCFH-DA. Visualize and record the fluorescence by employing a fluorescence microscope.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eMitochondrial membrane potential level detection\u003c/h2\u003e\u003cp\u003eFor a well in a six-well plate, aspirate the existing culture medium. Then, add 1 mL of cell culture medium and 1 mL of JC-1 staining working solution ((Solarbio, China)). Stir the components completely and incubate the plate at 37℃ for 20 minutes. Once the incubation is complete, aspirate the supernatant. Wash the cells two times using JC-1 staining buffer. Finally, cells were examined via a fluorescence microscope.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eEach experiment was independently repeated on three separate occasions. The obtained data were statistically processed by means of SPSS 27.0 and presented visually with the help of GraphPad Prism 10.0. Data are displayed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. For the purpose of statistical comparisons between multiple groups, one-way ANOVA was implemented. Statistical significance was assumed when the \u003cem\u003eP\u003c/em\u003e-value was below 0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eADME-related properties of luteolin\u003c/h2\u003e\u003cp\u003eADME characteristics of Luteolin were retrieved from TCMSP database (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), including DL, OB, Caco2, BBB, and \u0026ldquo;Lipinski's Rule of Five\u0026rdquo; (MW, AlogP, TPSA, Hdon, and Hacc).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe ADME properties of luteolin\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eName\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAlogP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHdon\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHacc\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOB (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCaco-2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBBB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eDL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eTPSA\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLuteolin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e286.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e36.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e111.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eDry AMD targets, luteolin-target genes, and intersection analysis\u003c/h2\u003e\u003cp\u003eWhen searching for atrophic age-related macular degeneration in the GeneCards database, 2743 targets were retrieved. In OMIM database, 536 such targets were identified. For dry age-related macular degeneration, we used it as the search criterion. In the GeneCards database, 2751 targets were obtained; in the OMIM database, 536 targets were found. Additionally, DrugBank database yielded 1 target, and TTD database provided 1 target. When non-exudative age-related macular degeneration was used as the search criterion, GeneCards database provided 1 target, and OMIM database contributed 536 targets.Once all targets were combined and duplicates removed, 4005 unique targets remained. The \u0026ldquo;disease-target\u0026rdquo; network graph had 4006 nodes and 4005 edges. In the graph (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), red rectangles represented diseases, while blue rectangles represented disease targets.\u003c/p\u003e\u003cp\u003eA total of 383 target genes of luteolin from various sources were collected. Among these, 215 genes were from the CTD database, 107 were from the Super-PRED database, and 100 were from the Swiss Target Prediction database (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Subsequently, intersection analysis identified 213 genes shared between luteolin and AMD (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). These genes were used to further explore the mechanism through which luteolin alleviates AMD.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003ePPI network analysis\u003c/h2\u003e\u003cp\u003eThe 213 intersecting targets underwent analysis within STRING database. Through this analysis, a PPI network consisting of 212 nodes and 5660 edges was created. In this network (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), rectangles represent intersecting targets, and edges denote interactions between targets. Subsequently, CytoHubba analysis identified eight core genes: TP53, TNF, IL6, AKT1, BCL2, STAT3, JUN, and CASP3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Using the MCODE plugin in Cytoscape, 84 nodes with a score of 63.422 were identified in the PPI network. The identified module contained 2632 edges (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Notably, the most significant module comprised the eight core genes mentioned above.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eMolecular docking\u003c/h2\u003e\u003cp\u003eFor predicting interaction of luteolin with target proteins, three-dimensional structure of luteolin was acquired from PubChem database. Target proteins in PDB format were also retrieved: TP53 (PDB ID: 1AIE), TNF (PDB ID: 2R32), IL6 (PDB ID: 1ALU), AKT1 (PDB ID: 1UNQ), BCL2 (PDB ID: 5VAU), STAT3 (PDB ID: 6NJS), JUN (PDB ID: 5T01), and CASP3 (PDB ID: 1NMS). Subsequently, the core targets screened above were subjected to docking analysis with luteolin using CB-DOCK2. The resulting docking complexes were visualized (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). All complexes exhibited docking binding energies below \u0026minus;\u0026thinsp;5 kcal/mol, which suggested the validity of aforementioned screening results. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among these, JUN exhibited the best docking activity with luteolin, showing a binding energy of -8.9 kcal/mol.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMolecular docking analysis of luteolin binding energies with eight core anti-dry AMD targets.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGene Symbol\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePDB ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAffinity(kcal/mol)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTP53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTumor Protein P53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1AIE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-5.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTNF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTumor Necrosis Factor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2R32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-6.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIL6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInterleukin 6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1ALU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-6.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAKT1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAKT Serine/Threonine Kinase 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1UNQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-6.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBCL2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBCL2 Apoptosis Regulator\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5VAU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSTAT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSignal Transducer And Activator Of Transcription 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6NJS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJUN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJun Proto-Oncogene\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5T01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-8.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCASP3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaspase 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1NMS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-8.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eGO analysis and KEGG pathway enrichment analysis\u003c/h2\u003e\u003cp\u003eUpon inputting luteolin anti-AMD target genes into DAVID database, 308 BBP terms, 103 CC terms, and 109 MF terms were obtained. The \u003cem\u003eP\u003c/em\u003e-value denoted the significance of the enrichment function. Subsequently, top 10 most significant entries were filtered, and GO bar graphs were plotted (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). In the BP category, notable terms included responses to xenobiotic stimuli, negative regulation of apoptotic processes, and positive regulation of gene expression. For CC, terms covered cytoplasm, protein-containing complexes, extracellular space, and mitochondria. In the MF category, terms involved protein binding, enzyme binding, and cytokine activity.\u003c/p\u003e\u003cp\u003eKEGG analysis identified 152 pathways. Prominent among these were the lipid and atherosclerosis pathway, cancer-related pathways, and AGE-RAGE signaling pathway in diabetic complications. These findings suggest that luteolin may act on these pathways, thereby exerting a therapeutic effect against AMD. To further visualize the significant pathways, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB depicts the top 20 significantly enriched KEGG signaling pathways. The lipid and atherosclerosis pathway exhibited the most significant \u003cem\u003eP\u003c/em\u003e-value (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eKEGG pathway enrichment analysis of luteolin against dry AMD.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePathway ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKEGG_B_Class\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePathway\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGene\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa05417\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCardiovascular disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLipid and atherosclerosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.14E-39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGSK3B, CD40, CXCL8, TNF, ICAM1, CASP9, CASP7, TBK1, CASP8, CASP3, TNFSF10, CASP1, AKT1, HSP90AA1, MMP1, MMP3, PRKCA, FOS, MMP9, ERN1, IRF3, IL1B, DDIT3, PPARG, ATF6, TP53, SRC, AGER, MAPK8, CCL2, MAPK1, NLRP3, MAPK3, JUN, XBP1, VCAM1, HSPA5, IFNB1, NOS3, STAT3, SOD2, SELE, EIF2S1, NFKB1, MAPK10, IL6, CD40LG, CYP1A1, BCL2, BAX, FAS, NFE2L2, BCL2L1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa05200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePathways in cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.12E-34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eALK, GSK3B, CDKN1A, CDKN1B, CXCL8, PTEN, SLC2A1, KEAP1, FGF2, IGF1R, CASP9, CASP7, CASP8, CCND1, CDH1, CASP3, AKT1, JAK1, HSP90AA1, MMP1, MMP2, IL13, PRKCA, FOS, F2, MMP9, CCNA2, AR, IFNG, PPARG, MET, TP53, BIRC2, PDGFB, CXCR4, XIAP, PTGS2, HIF1A, EGFR, MAPK8, TERT, HMOX1, MAPK1, MAPK3, NQO1, JUN, TGFB1, NOS2, EGF, STAT1, STAT3, ESR1, MTOR, NFKB1, VEGFA, MAPK10, IL4, IL6, IL5, CXCL12, CDK6, CDK2, GSTA1, BCL2, MDM2, BAX, CTNNB1, FAS, NFE2L2, BCL2L1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa04933\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEndocrine and metabolic disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAGE-RAGE signaling pathway in diabetic complications\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.59E-26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCDKN1B, CXCL8, SERPINE1, TNF, AGER, ICAM1, MAPK8, CCND1, CASP3, CCL2, AKT1, MAPK1, MAPK3, EGR1, JUN, TGFB1, VCAM1, NOS3, STAT1, MMP2, STAT3, PRKCA, SELE, NFKB1, VEGFA, MAPK10, IL6, IL1B, BCL2, BAX, NOX4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa04657\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eImmune system\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIL-17 signaling pathway\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.17E-25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGSK3B, CSF2, CXCL8, TNFAIP3, PTGS2, TNF, MAPK8, CASP8, TBK1, CASP3, CCL2, MAPK1, MAPK3, JUN, HSP90AA1, MMP1, IL13, MMP3, FOS, MMP9, NFKB1, MAPK10, IL4, CXCL10, IL6, MMP13, IL5, IFNG, IL1B, IL17A\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa05167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInfectious disease: viral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eKaposi sarcoma-associated herpesvirus infection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.40E-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBECN1, GSK3B, CDKN1A, CSF2, CXCL8, SRC, PDGFB, PTGS2, FGF2, HIF1A, ICAM1, CASP9, MAP1LC3B, MAPK8, CASP8, TBK1, CCND1, CASP3, AKT1, MAPK1, JAK1, MAPK3, JUN, IFNB1, STAT1, STAT3, FOS, MTOR, NFKB1, VEGFA, MAPK10, IL6, CDK6, IRF3, BAX, CTNNB1, FAS, TP53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa05162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInfectious disease: viral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMeasles\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.52E-22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGSK3B, CDKN1B, TNFAIP3, CASP9, MAPK8, CASP8, TBK1, CCND1, CASP3, AKT1, JAK1, JUN, CSNK2A1, IFNB1, STAT1, STAT3, FOS, EIF2S1, NFKB1, MAPK10, IL6, CDK6, IRF3, IL1B, CSNK2B, CDK2, BCL2, BAX, FAS, TP53, BCL2L1, TP73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa05418\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCardiovascular disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFluid shear stress and atherosclerosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.31E-21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePRKAA1, SRC, PDGFB, KEAP1, TNF, ICAM1, CDH5, MAPK8, KDR, CCL2, AKT1, HMOX1, NQO1, JUN, HSP90AA1, VCAM1, NOS3, MMP2, FOS, SELE, MMP9, NFKB1, VEGFA, MAPK10, IFNG, IL1B, GSTA1, BCL2, CTNNB1, TP53, NFE2L2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa04668\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSignal transduction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTNF signaling pathway\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.08E-20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCSF2, XIAP, TNFAIP3, PTGS2, TNF, ICAM1, CASP7, MAPK8, CASP8, CASP3, CCL2, AKT1, MAPK1, MAPK3, JUN, VCAM1, IFNB1, MMP3, FOS, SELE, MMP9, NFKB1, MAPK10, CXCL10, IL6, IL1B, FAS, BIRC2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa01521\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDrug resistance: antineoplastic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEGFR tyrosine kinase inhibitor resistance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.19E-19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGSK3B, SRC, EGF, STAT3, PTEN, PDGFB, PRKCA, FOXO3, FGF2, EGFR, MTOR, IGF1R, VEGFA, IL6, AXL, KDR, BCL2, BAX, AKT1, MAPK1, MET, JAK1, BCL2L1, MAPK3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa05161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInfectious disease: viral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHepatitis B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.66E-19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCDKN1A, CXCL8, SRC, TNF, CASP9, MAPK8, CASP8, TBK1, CASP3, AKT1, MAPK1, JAK1, MAPK3, JUN, TGFB1, IFNB1, STAT1, STAT3, PRKCA, FOS, MMP9, NFKB1, MAPK10, CCNA2, IL6, IRF3, CDK2, BCL2, BAX, FAS, TP53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa04210\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCell growth and death\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eApoptosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.43E-18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eXIAP, TNF, CASP9, CASP7, MAPK8, CASP8, CASP3, TNFSF10, AKT1, MAPK1, CAPN1, CTSD, MCL1, MAPK3, JUN, PARP1, FOS, EIF2S1, NFKB1, ERN1, MAPK10, DDIT3, BCL2, BAX, FAS, TP53, BIRC2, BCL2L1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa05163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInfectious disease: viral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHuman cytomegalovirus infection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.93E-18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGSK3B, CDKN1A, CXCL8, SRC, CXCR4, PTGS2, TNF, EGFR, CASP9, CASP8, TBK1, CCND1, CASP3, CCL2, AKT1, MAPK1, JAK1, MAPK3, IFNB1, STAT3, PRKCA, MTOR, NFKB1, VEGFA, IL6, CXCL12, CDK6, IRF3, IL1B, MDM2, BAX, CTNNB1, FAS, TP53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa05164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInfectious disease: viral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInfluenza A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.34E-17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCXCL8, PLG, TNF, ICAM1, CASP9, CASP8, TBK1, CASP3, CASP1, TNFSF10, CCL2, AKT1, MAPK1, NLRP3, JAK1, MAPK3, IL33, IFNB1, STAT1, PRKCA, EIF2S1, NFKB1, CXCL10, IL6, CDK6, IFNG, IRF3, IL1B, BAX, FAS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa05160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInfectious disease: viral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHepatitis C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.54E-17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGSK3B, CDKN1A, TNF, EGFR, CASP9, CASP8, TBK1, CCND1, CASP3, AKT1, MAPK1, JAK1, MAPK3, IFNB1, EGF, STAT1, STAT3, EIF2S1, NFKB1, CXCL10, OCLN, CDK6, IFNG, IRF3, CDK2, BAX, CTNNB1, FAS, TP53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa05215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProstate cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.76E-17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGSK3B, CDKN1A, HSP90AA1, CDKN1B, EGF, MMP3, PTEN, PDGFB, MMP9, EGFR, MTOR, NFKB1, IGF1R, CASP9, AR, CCND1, CDK2, MDM2, BCL2, AKT1, MAPK1, CTNNB1, TP53, MAPK3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa05169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInfectious disease: viral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEpstein-Barr virus infection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.55E-16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCDKN1A, CD40, CDKN1B, TNFAIP3, TNF, ICAM1, CASP9, MAPK8, CASP8, TBK1, CCND1, CASP3, AKT1, JAK1, JUN, IFNB1, STAT1, STAT3, NFKB1, MAPK10, CCNA2, CXCL10, IL6, CDK6, IRF3, CDK2, BCL2, MDM2, BAX, FAS, TP53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa04066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSignal transduction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHIF-1 signaling pathway\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.20E-16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCDKN1A, CDKN1B, PFKFB3, NOS2, NOS3, EGF, INSR, STAT3, SERPINE1, SLC2A1, PRKCA, HIF1A, EGFR, MTOR, NFKB1, IGF1R, VEGFA, IL6, IFNG, BCL2, AKT1, HMOX1, MAPK1, MAPK3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa05212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePancreatic cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.87E-16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCDKN1A, TGFB1, STAT1, EGF, STAT3, EGFR, MTOR, NFKB1, VEGFA, MAPK10, CASP9, MAPK8, CDK6, CCND1, BAX, AKT1, MAPK1, TP53, JAK1, BCL2L1, MAPK3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa05145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInfectious disease: parasitic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eToxoplasmosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.37E-16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIL10, CD40, TGFB1, NOS2, STAT1, STAT3, XIAP, TNF, NFKB1, MAPK10, CASP9, MAPK8, CASP8, CD40LG, IFNG, CASP3, ALOX5, BCL2, AKT1, MAPK1, BIRC2, JAK1, BCL2L1, MAPK3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa05208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChemical carcinogenesis - reactive oxygen species\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.12E-16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSRC, PTEN, KEAP1, AHR, FOXO3, HIF1A, EGFR, MAPK8, CYP1B1, AKT1, HMOX1, MAPK1, MAPK3, NQO1, JUN, EGF, AKR1A1, PTPN11, FOS, SOD2, NFKB1, SOD1, VEGFA, MAPK10, CYP1A2, CAT, GSTA1, CYP1A1, NOX4, CYP2E1, MET, NFE2L2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e3.6 Network Diagram of \"luteolin-dry AMD-target-pathway\"\u003c/p\u003e\u003cp\u003eCytoscape 3.10.1 was employed to visualize the \"luteolin-target-pathway\" network, and major biological mechanisms were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The analysis revealed that network comprised 235 nodes and 1071 linkages.. These findings identified crucial signaling pathways through which luteolin exerts its therapeutic effects against AMD.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePathway diagram of dry AMD and luteolin related targets.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAnalysis of the PPI network identified TP53, TNF, IL6, AKT1, BCL2, STAT3, JUN, and CASP3 as the eight key genes involved in luteolin\u0026rsquo;s treatment of AMD. This finding aligns well with the findings of KEGG analysis. The lipid and atherosclerosis pathways emerged as the most significantly enriched pathway. Notably, all eight of the aforementioned genes were associated with this pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eLuteolin attenuated sodium iodate-induced apoptosis in ARPE-19 cells\u003c/h2\u003e\u003cp\u003eTo evaluate the detrimental effects of sodium iodate on ARPE-19 cells, cells were exposed to varying concentrations for 24h. Findings indicated that at a sodium iodate concentration of 7.5 mM, cell viability dropped to approximately 50% (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Therefore, 7.5 mM was selected as the concentration for model establishment.\u003c/p\u003e\u003cp\u003eFor cytotoxicity evaluation of luteolin, cells were administered with various concentrations.The CCK-8 results indicate that luteolin is safe for ARPE-19 cells and has a slight pro-proliferative effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). To determine luteolin's protective effect against sodium iodate-induced damage, ARPE-19 cells were pretreated with different luteolin concentrations for 24h, followed by 7.5 mM sodium iodate addition. The results demonstrated that 40 \u0026micro;M luteolin exhibited the most pronounced protective effect(Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). This finding suggests that luteolin pretreatment can shield ARPE-19 cells from sodium iodate-induced cellular damage.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eAntioxidant properties of luteolin\u003c/h2\u003e\u003cp\u003eOverproduction of ROS and impairment of antioxidant defense mechanism result in redox imbalance and heightened oxidative stress. For assessing ROS levels in cells, the DCFH\u0026ndash;DA assay was employed. Compared with control group, sodium iodate-treated group exhibited notably elevated ROS levels. However, pretreatment with luteolin effectively reduced the ROS levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eProtective effect of luteolin on mitochondrial membrane potentials\u003c/h2\u003e\u003cp\u003eJC-1 staining was employed to evaluate mitochondrial membrane potential in each group. An increase in green fluorescence signified a reduction in mitochondrial membrane potential, suggesting that cells might be undergoing early apoptosis. Conversely, an increase in red fluorescence indicated a normal mitochondrial membrane potential. In comparison with control group, sodium iodate-treated group showed a marked reduction in mitochondrial membrane potential. By contrast, pretreatment with luteolin led to an elevation in the mitochondrial membrane potential of ARPE-19 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). These findings suggest that luteolin can protect against sodium iodate-induced mitochondrial damage and help alleviate early-stage cell apoptosis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDry AMD is a prevalent and complex ocular degenerative disorder. It severely threatens the visual health of the elderly. The pathological mechanism of dry AMD encompasses oxidative stress, inflammation, and abnormal activation of the complement system\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Luteolin, a natural flavonoid, has garnered extensive attention in recent years due to its diverse biological activities. These include antioxidative stress capabilities, attenuation of inflammatory responses, and regulation of cellular metabolism\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Luteolin shows potential applications in the prevention and treatment of dry AMD.\u003c/p\u003e\u003cp\u003eNetwork pharmacology analysis enables the construction of network models, facilitating the exploration of associations among targets, drugs, and diseases\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. To identify the key targets of luteolin against dry AMD, we adopted a network pharmacology approach. As a result, 213 overlapping targets of luteolin for dry AMD treatment were obtained. KEGG analysis indicated that luteolin might exert beneficial effects primarily through the lipid-arteriosclerosis pathway. AMD is significantly associated with lipid metabolism disorders\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e and shares numerous risk factors with atherosclerosis\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Additionally, dry AMD patients who receive high-dose statin therapy have shown improvements in visual acuity\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. The PPI network analysis suggested that luteolin alleviates AMD symptoms by modulating multiple targets. Subsequently, CytoHubba analysis screened eight core gene targets: TP53, TNF, IL6, AKT1, BCL2, STAT3, JUN, and CASP3. Docking results demonstrated that luteolin has a high affinity for JUN. c-Jun N-terminal kinase (JNK) functions as a critical activator of the JUN protein. Disruptions in ocular microcirculation can trigger JNK signaling activation. Experimental evidence has demonstrated that inhibiting JNK signaling alleviates symptoms in dry AMD model mice. This finding suggests that targeting the JNK signaling pathway could be a promising therapeutic strategy for dry AMD\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. TNF and IL6 are key inflammatory mediators, playing pivotal parts in inflammatory process. Within dry AMD context, heightened plasma IL-6 levels correlate strongly with geographic atrophy progression, a late-stage manifestation of the disease\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. STAT3 is a pivotal protein in cell signaling pathways and gene expression regulation. It can be activated by inflammation-related cytokines like IL6\u003csup\u003e37,38\u003c/sup\u003e. Once activated, STAT3 mitigates all-trans retinaldehyde-induced ferroptosis in 661W photoreceptor cells. This discovery suggests the possibility of targeting STAT3 as a treatment strategy for dry AMD\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. functions as a crucial anti-apoptotic protein. In the dry AMD model, BCL2 expression is downregulated\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. This downregulation leads to increased apoptosis, contributing to the progression of the disease. Collectively, the above results suggest that luteolin can alleviate AMD symptoms by targeting multiple proteins and intervening in various pathological processes.\u003c/p\u003e\u003cp\u003ePrior to investigating the effects of luteolin, we evaluated its safety in ARPE-19 cells. Results of the safety assessment demonstrated that luteolin was non-cytotoxic to ARPE-19 cells and has a slight pro-proliferative effect. Subsequently, we determined that sodium iodate exerted concentration-dependent cytotoxic effects on ARPE-19 cells. Notably, pretreatment with luteolin effectively enhanced cell viability, counteracting the detrimental effects of sodium iodate.\u003c/p\u003e\u003cp\u003eAMD pathogenesis is closely associated with oxidative stress in RPE cells\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. RPE cells possess endogenous antioxidant defense mechanisms to counteract oxidative damage. However, when the production of ROS overwhelms the normal cellular and tissue antioxidant capacity, oxidative stress is triggered\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. This, in turn, initiates a cascade of pathophysiological processes within cells and tissues. Mitochondria are the primary energy providers for cells. A decrease in the mitochondrial membrane potential indicates mitochondrial dysfunction, which may lead to cell damage and apoptosis\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. The transition from red to green fluorescence in JC-1 staining clearly indicated a drop in mitochondrial membrane potential. These observations demonstrated that treatment with sodium iodate resulted in oxidative stress damage within ARPE-19 cells. This led to elevated levels of cellular ROS, a reduction in mitochondrial membrane potential, and ultimately, cell apoptosis. In contrast, pretreatment with luteolin mitigated ROS production, restored mitochondrial membrane potential, and alleviated oxidative stress, thereby protecting ARPE-19 cells.\u003c/p\u003e\u003cp\u003eLuteolin exhibits robust antioxidant activities, able to eliminate free radicals, suppress lipid peroxidation, and protect cells from oxidative harm\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Its antioxidant mechanisms primarily encompass direct free radical scavenging, metal ion chelation, and upregulation of antioxidant enzyme activities\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. By modulating the nuclear factor Nrf2 pathway, luteolin upregulates antioxidant activities like superoxide dismutase and glutathione, in rats with polycystic ovary syndrome, thereby exerting remarkable antioxidant effects\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Similarly, in type 2 diabetic rats, luteolin treatment leads to significantly decreased malondialdehyde levels and markedly increased superoxide dismutase, catalase, and glutathione levels compared to untreated counterparts. This indicates that luteolin can effectively alleviate oxidative stress in type 2 diabetic rats\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Oxidative stress and inflammation are closely interrelated. Oxidative stress often serves as a trigger for chronic inflammatory diseases\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Inflammatory cytokines including TNF-α and IL-6 can be stimulated for production by ROS. The upsurge in these cytokines may trigger inflammation and cell death\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e, whereas luteolin exhibits remarkable anti-inflammatory properties and can inhibit production of ROS and inflammatory cytokines\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Moreover, luteolin inhibits the generation of other inflammatory mediators, such as prostaglandin E2 and nitric oxide\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThis study systematically explored the potential mechanisms by which luteolin ameliorates dry AMD. However, its limitation lies in the inability of in vitro models to fully recapitulate the in vivo pathological environment of animals. Future in vivo studies are warranted to further validate its efficacy.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eUtilizing network pharmacology approaches alongside in vitro assays, we predicted and validated that luteolin, a natural bioactive compound, protects against sodium iodate-induced damage in ARPE-19 cells. Our findings strongly indicate that luteolin exerts its protective effect through the modulation of multiple targets and signaling pathways. This study has established a theoretical and experimental foundation, laying the groundwork for future research on the therapeutic potential of luteolin in the treatment of dry AMD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Supported by the Sichuan Provincial Department of Science and Technology (No. 2024ZYD0114) and Sichuan Medical Association (No. S2024001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMaomei Luo: Writing \u0026ndash; original draft, Software, Methodology, Formal analysis, Conceptualization. Min Zhang: Software, Methodology, Formal analysis, Investigation. Zhen Xing: Software, Methodology, Formal analysis. Wei Yu: Software, Methodology. Hongbin Lv: Supervision Project administration, Funding acquisition, Conceptualization.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary data of this study can be obtained from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFootnotes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublisher\u0026rsquo;s Note\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\u003c/p\u003e\n\u003cp\u003eMaomei Luo and Min Zhang contributed equally to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributor Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHongbin Lv, Email: [email protected].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRuan, Y., Jiang, S. \u0026amp; Gericke, A. 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(2015).\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":true,"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":"network pharmacology, luteolin, dry age-related macular degeneration, molecular docking, oxidative stress","lastPublishedDoi":"10.21203/rs.3.rs-6948097/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6948097/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObject\u003c/p\u003e\u003cp\u003eMechanistic analysis of luteolin\u0026rsquo;s protective effect against dry AMD via network pharmacology and experimental validation.\u003c/p\u003e\u003cp\u003eMethods\u003c/p\u003e\u003cp\u003eLuteolin's active ingredient and target information were retrieved from the publicly available database TCMSP. Disease genes associated with dry AMD were screened by GeneCards, OMIM, and DrugBank gene databases. The shared targets of luteolin and dry AMD were used to construct a protein-protein interaction network, followed by the implementation of Gene Ontology and pathway enrichment analyses. Finally, molecular docking of the active ingredient with core targets was validated. Sodium iodate was used to induce ARPE-19 cells and a mouse model. Cell viability was analyzed via CCK-8 assay. ROS levels were quantified using the DCFH-DA method, and mitochondrial membrane potential was detected via JC-1 staining.\u003c/p\u003e\u003cp\u003eResults\u003c/p\u003e\u003cp\u003eIn the network pharmacology analysis, a total of 213 potential therapeutic targets associated with luteolin\u0026rsquo;s anti-dry AMD activity were identified. Among these, TP53, TNF, IL6, AKT1, BCL2, STAT3, JUN, and CASP3 were pinpointed as core therapeutic targets. These targets predominantly participated in pathways such as the lipid and atherosclerosis pathway, cancer pathways, and the AGE-RAGE signaling pathway in diabetic complications. Through molecular docking, strong binding affinities were identified between core targets and luteolin, the critical active moiety. This finding validated the molecular mechanism underlying luteolin\u0026rsquo;s efficacy against dry AMD. Experimental data demonstrated that luteolin not only attenuated sodium iodate\u0026ndash;induced reduction in ARPE-19 cell viability but also decreased intracellular ROS levels and restored mitochondrial membrane potential, suggesting a protective role via oxidative stress regulation.\u003c/p\u003e\u003cp\u003eConclusion\u003c/p\u003e\u003cp\u003eLuteolin exerts a protective effect against sodium iodate\u0026ndash;induced damage in ARPE-19 cells. This protection is likely mediated through multi-target signaling pathways, potentially involving multiple molecular mechanisms. These findings suggest that luteolin has promising potential for the prophylaxis and therapy of dry AMD.\u003c/p\u003e","manuscriptTitle":"Mechanistic Analysis of Luteolin in Mitigating Dry Age-Related Macular Degeneration through Network Pharmacology and Experimental Validation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-11 12:04:54","doi":"10.21203/rs.3.rs-6948097/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-21T07:07:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-20T16:07:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"307145758356255591086744956170768425275","date":"2025-07-20T02:42:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-10T08:38:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"234808249764820582591344535969494284507","date":"2025-07-09T15:34:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"167479922570497144301906287968230339145","date":"2025-07-09T10:22:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-09T08:49:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-09T08:47:28+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-27T03:20:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-25T13:13:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-06-22T07:00:06+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":"b8bbae22-1b38-4404-8737-ff533e6495c0","owner":[],"postedDate":"July 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":51326516,"name":"Health sciences/Diseases"},{"id":51326517,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2025-11-03T15:59:14+00:00","versionOfRecord":{"articleIdentity":"rs-6948097","link":"https://doi.org/10.1038/s41598-025-21730-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-10-29 15:56:54","publishedOnDateReadable":"October 29th, 2025"},"versionCreatedAt":"2025-07-11 12:04:54","video":"","vorDoi":"10.1038/s41598-025-21730-y","vorDoiUrl":"https://doi.org/10.1038/s41598-025-21730-y","workflowStages":[]},"version":"v1","identity":"rs-6948097","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6948097","identity":"rs-6948097","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}