基于网络药理学和分子对接技术解读川皮苷改善代谢综合征的作用机制

doi:10.5246/jcps.2022.11.069

摘要/Abstract

摘要：

采用网络药理学方法探讨川皮苷改善代谢综合征的作用机制。首先利用TCMSP、TCMIP、TCMID、ETCM、HERB、NPASS和NPACT等数据库获取川皮苷作用靶点; 在DisGeNET、DrugBank等6个数据库中获取代谢综合征的相关靶点, 筛选出与川皮苷作用靶点的共同部分构建PPI网络, 并利用R语言对交集靶点进行GO和KEGG通路富集分析; 最后对川皮苷和关键疾病靶点进行分子对接验证。结果收集到川皮苷作用靶点105个, 代谢综合征相关靶点1975个。上述靶点取交集, 获得了60个川皮苷改善代谢综合征的潜在靶点。PPI分析发现, 川皮苷改善代谢综合征的关键靶点为TP53、MAPK8、AKT1、GSK3B、HSP90AA1、CTNNB1、JUN、AR、ESR1、CCND1、HRAS、TNF和PPARA。功能富集分析发现, 脂质和动脉粥样硬化通路及糖尿病并发症中的AGE-RAGE信号通路在川皮苷改善代谢综合征过程中发挥重要作用。分子对接结果显示, 川皮苷与上述13个核心基因具有很强的亲和力。综上所述, 推测川皮苷通过脂质和动脉粥样硬化通路及糖尿病并发症中的AGE-RAGE信号通路发挥改善代谢综合征的疗效。

关键词: 川皮苷, 代谢综合征, 网络药理学, 分子对接

Abstract:

Nobiletin (NOB) may have a potential effect on metabolic syndrome. In the present study, we aimed to explore the latent mechanisms of NOB for the treatment of metabolic syndrome based on network pharmacology and molecular docking methods. The potential targets of NOB were retrieved and identified from six databases, such as the Traditional Chinese Medicine Systems Pharmacology Database. The metabolic syndrome-related targets were retrieved from six databases as follows: the DrugBank database, GeneCards database, the Online Mendelian Inheritance in Man database, PharmGKB database, Therapeutic Target Database, and DisGeNet database. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of 60 intersected genes were performed in R software (Bioconductor, clusterProfiler) to investigate the molecular mechanisms. Subsequently, the ingredient-target-pathway network of NOB was constructed and visualized through Cytoscape 3.8.0 software. Protein-protein interaction network was constructed to screen hub genes in the treatment of NOB on metabolic syndrome through The Search Tool for the Retrieval of Interacting Genes/Proteins and visualized by Cytoscape 3.8.0 software. Afterward, molecular docking was used to analyze the score of the hub genes with NOB. Cumulatively, 105 targets of NOB were identified. Moreover, 1975 metabolic syndrome-related genes were acquired from six databases after combining and deleting the repeated items, and the overlap of metabolic syndrome-related genes with NOB-related target genes identified 60 intersection genes of NOB against metabolic syndrome. Moreover, 1858 GO entries of NOB on metabolic syndrome were identified, and 153 pathways were screened based on GO and KEGG analyses. The target hub genes of NOB in MetS treatment were TP53, MAPK8, AKT1, GSK3B, HSP90AA1, CTNNB1, JUN, AR, ESR1, CCND1, HRAS, TNF, and PPARA. It was confirmed that lipid and atherosclerosis, together with the AGE-RAGE signaling pathway in diabetic complications, were putatively critical pathways of NOB in the treatment of metabolic syndrome. The molecular docking results revealed that most of 13 hub genes had a strong binding to NOB. Due to the versatile actions of NOB, it had the potential action on metabolic syndrome by multiple targets and multiple pathways.

Key words: Nobiletin, Metabolic syndrome, Network pharmacology, Molecular docking

Supporting:

都晓辉, 杨宏艳, 王涛, 崔红霞, 林宇, 李宏铃. 基于网络药理学和分子对接技术解读川皮苷改善代谢综合征的作用机制[J]. 中国药学(英文版), 2022, 31(11): 803-823.

Xiaohui Du, Hongyan Yang, Tao Wang, Hongxia Cui, Yu Lin, Hongling Li. Deciphering the latent mechanism of nobiletin in the treatment of metabolic syndrome based on network pharmacology and molecular docking[J]. Journal of Chinese Pharmaceutical Sciences, 2022, 31(11): 803-823.

图/表 11

Figure 1. Workflow of the study. Note: The figure indicates the potential action and mechanism of NOB against MetS using the network pharmacology and computational bioinformatics analysis approach.

Figure 2. The structure of NOB. Note: Molecular weight of NOB is 402.39, and its chemical and molecular formulas are 5,6,7,8,3,4-hexamethoxy flavone and C21H22O8, respectively. Chromene and arene rings of NOB are on the same plane. The C atoms of two methoxy groups in the arene ring are on the same plane.

Table 1. Details of target genes of NOB.

Figure 3. MetS-related gene set and NOB-MetS interaction gene set. Note: (A) Identification of MetS-related genes by combining all results from six databases; (B) Identification of NOB-MetS interaction genes by taking an intersection of NOB-related and MetS-related target genes.

Figure 4. The GO enrichment analysis. Note: (A) GO-barplot, top 15 entries of GO enrichment analysis (BP, CC, and MF); (B) GO-bubble, top 15 entries of GO enrichment analysis (BP, CC, and MF).

Figure 5. The KEGG enrichment analysis. Note: (A) KEGG-barplot, top 30 entries of KEGG enrichment analysis; (B) KEGG-bubble, top 30 entries of KEGG enrichment analysis; (C) schematic diagram of lipid and atherosclerosis (hsa05417) and AGE-RAGE signaling pathway in diabetic complications (hsa04933), the potential pathways involved in NOB against MetS.

Figure 6. I-T-D-P network. Note: MetS = metabolic syndrome; NOB = nobiletin; Round rectangle represents disease; Diamond represents ingredient; V represents gene; Hexagon represents pathway.

Figure 7. PPI network and target hub genes. Note: (A) the PPI network of 60 overlapping genes; (B) PPI network was visualized by Cytoscape; (C) A subnetwork of 60 overlapping genes was constructed by the filtration via CytoNCA and R language.

Table 2. The topological parameters of target hub genes.

Figure 8. Molecular docking. Note: Binding of NOB to 13 hub genes, including TP53, MAPK8, AKT1, GSK3B, HSP90AA1, CTNNB1, JUN, AR, ESR1, CCND1, HRAS, TNF, and PPARA, were established by molecular docking analysis, respectively.

Table 3. Molecular docking results.

参考文献 76

[1]	Dabke, K.; Hendrick, G.; Devkota, S. The gut microbiome and metabolic syndrome. J. Clin. Investig. 2019, 129, 4050–4057.
[2]	Scholze, J.; Alegria, E.; Ferri, C.; Langham, S.; Stevens, W.; Jeffries, D.; Uhl-Hochgraeber, K. Epidemiological and economic burden of metabolic syndrome and its consequences in patients with hypertension in Germany, Spain and Italy; a prevalence-based model. BMC Public Heal. 2010, 10, 529.
[3]	Cioffi, G.; Viapiana, O.; Tarantini, L.; Orsolini, G.; Idolazzi, L.; Ognibeni, F.; Dalbeni, A.; Gatti, D.; Fassio, A.; Adami, G.; Rossini, M.; Giollo, A. The troubling liaison between cancer and metabolic syndrome in chronic inflammatory rheumatic diseases. Arthritis Res. Ther. 2021, 23, 89.
[4]	Bishehsari, F.; Voigt, R.M.; Keshavarzian, A. Circadian rhythms and the gut microbiota: from the metabolic syndrome to cancer. Nat. Rev. Endocrinol. 2020, 16, 731–739.
[5]	Feigin, V.L.; Roth, G.A.; Naghavi, M.; Parmar, P.; Krishnamurthi, R.; Chugh, S.; Mensah, G.A.; Norrving, B.; Shiue, I.; Ng, M.; Estep, K.; Cercy, K.; Murray, C.J.L.; Forouzanfar, M.H.;. Global burden of stroke and risk factors in 188 countries, during 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet Neurol. 2016, 15, 913–924.
[6]	Eckel, R.H.; Grundy, S.M.; Zimmet, P.Z. The metabolic syndrome. Lancet. 2005, 365, 1415–1428.
[7]	Tang, C.; Hoo, P.C.X.; Tan, L.T.H.; Pusparajah, P.; Khan, T.M.; Lee, L.H.; Goh, B.H.; Chan, K.G. Golden needle mushroom: a culinary medicine with evidenced-based biological activities and health promoting properties. Front. Pharmacol. 2016, 7, 474.
[8]	Gupta, B.; Sadaria, D.; Warrier, V.U.; Kirtonia, A.; Kant, R.; Awasthi, A.; Baligar, P.; Pal, J.K.; Yuba, E.J.; Sethi, G.; Garg, M.; Gupta, R.K. Plant lectins and their usage in preparing targeted nanovaccines for cancer immunotherapy. Semin. Cancer Biol. 2022, 80, 87–106.
[9]	Chang, H.Y.; Wu, J.R.; Gao, W.Y.; Lin, H.R.; Chen, P.Y.; Chen, C.N.; Wu, M.J.; Yen, J. The cholesterol-modulating effect of methanol extract of pigeon pea (cajanus Cajan (L.) millsp.) leaves on regulating LDLR and PCSK₉ expression in HepG2 cells. Molecules, 2019, 24, 493.
[10]	Banik, K.; Ranaware, A.M.; Harsha, C.; Nitesh, T.; Girisa, S.; Deshpande, V.; Fan, L.; Nalawade, S.P.; Sethi, G.; Kunnumakkara, A.B. Piceatannol: a natural stilbene for the prevention and treatment of cancer. Pharmacol. Res. 2020, 153, 104635.
[11]	Xie, Q.; Chen, Y.; Tan, H.D.; Liu, B.; Zheng, L.L.; Mu, Y.D. Targeting autophagy with natural compounds in cancer: a renewed perspective from molecular mechanisms to targeted therapy. Front. Pharmacol. 2021, 12, 748149.
[12]	Kashyap, D.; Tuli, H.S.; Yerer, M.B.; Sharma, A.; Sak, K.; Srivastava, S.; Pandey, A.; Garg, V.K.; Sethi, G.; Bishayee, A. Natural product-based nanoformulations for cancer therapy: opportunities and challenges. Semin. Cancer Biol. 2021, 69, 5–23.
[13]	Huang, H.; Li, L.F.; Shi, W.M.; Liu, H.; Yang, J.Q.; Yuan, X.L.; Wu, L.H. The multifunctional effects of nobiletin and its metabolites in vivo and in vitro. Evid. Based Complementary Altern. Med. 2016, 2016, 2918796.
[14]	Alam, M.N.; Almoyad, M.; Huq, F. Polyphenols in colorectal cancer: current state of knowledge including clinical trials and molecular mechanism of action. Biomed. Res. Int. 2018, 2018, 4154185.
[15]	Dachineni, R.; Kumar, D.R.; Callegari, E.; Kesharwani, S.S.; Sankaranarayanan, R.; Seefeldt, T.; Tummala, H.; Bhat, G.J. Salicylic acid metabolites and derivatives inhibit CDK activity: novel insights into aspirin’s chemopreventive effects against colorectal cancer. Int. J. Oncol. 2017, 51, 1661–1673.
[16]	Sankaranarayanan, R.; Valiveti, C.; Kumar, D.; van slambrouck, S.; Kesharwani, S.; Seefeldt, T.; Scaria, J.; Tummala, H.; Bhat, G. The flavonoid metabolite 2,4,6-trihydroxybenzoic acid is a CDK inhibitor and an anti-proliferative agent: a potential role in cancer prevention. Cancers. 2019, 11, 427.
[17]	Chen, S.P.; Cai, D.C.; Pearce, K.; Sun, P.Y.W.; Roberts, A.C.; Glanzman, D.L. Reinstatement of long-term memory following erasure of its behavioral and synaptic expression in Aplysia. ELife. 2014, 3, e03896.
[18]	Uckoo, R.M.; Jayaprakasha, G.K.; Vikram, A.; Patil, B.S. Polymethoxyflavones isolated from the peel of miaray mandarin (Citrus miaray) have biofilm inhibitory activity in Vibrio harveyi. J. Agric. Food Chem. 2015, 63, 7180–7189.
[19]	Itoh, N.; Iwata, C.; Toda, H. Molecular cloning and characterization of a flavonoid-O-methyltransferase with broad substrate specificity and regioselectivity from Citrus depressa. BMC Plant Biol. 2016, 16, 180.
[20]	Abe, S.; Hirose, S.; Nishitani, M.; Yoshida, I.; Tsukayama, M.; Tsuji, A.; Yuasa, K. Citrus peel polymethoxyflavones, sudachitin and nobiletin, induce distinct cellular responses in human keratinocyte HaCaT cells. Biosci. Biotechnol. Biochem. 2018, 82, 2064–2071.
[21]	Wang, Y.B.; Xie, J.; Ai, Z.X.; Su, J.S. Nobiletin-loaded micelles reduce ovariectomy-induced bone loss by suppressing osteoclastogenesis. Int. J. Nanomed. 2019, 14, 7839–7849.
[22]	Liao, W.Z.; Liu, Z.J.; Zhang, T.T.; Sun, S.X.; ye, J.F.; Li, Z.Y.; Mao, L.Z.; Ren, J.Y. Enhancement of anti-inflammatory properties of nobiletin in macrophages by a nano-emulsion preparation. J. Agric. Food Chem. 2018, 66, 91–98.
[23]	Zhang, B.F.; Jiang, H.; Chen, J.; Guo, X.; Li, Y.; Hu, Q.; Yang, S. Nobiletin ameliorates myocardial ischemia and reperfusion injury by attenuating endoplasmic reticulum stress-associated apoptosis through regulation of the PI3K/AKT signal pathway. Int. Immunopharmacol. 2019, 73, 98–107.
[24]	Morrow, N.M.; Telford, D.E.; Sutherland, B.G.; Edwards, J.Y.; Huff, M.W. Nobiletin corrects intestinal lipid metabolism in ldlr-/ - mice fed a high-fat diet. Atheroscler. Suppl. 2018, 32, 28.
[25]	Yuk, T.; Kim, Y.; Yang, J.; Sung, J.; Jeong, H.S.; Lee, J. Nobiletin inhibits hepatic lipogenesis via activation of AMP-activated protein kinase. Evid. Based Complementary Altern. Med. 2018, 2018, 7420265.
[26]	Ren, J.; Sowers, J.R. Application of a novel curcumin analog in the management of diabetic cardiomyopathy. Diabetes. 2014, 63, 3166–3168.
[27]	Zhang, Y.F.; Yang, H.Y.; Du, Y.; Liu, P.P.; Zhang, J.; Li, Y.; Shen, H.T.; Xing, L.X.; Xue, X.Y.; Chen, J.; Zhang, X.H. Long noncoding RNA TP53TG1 promotes pancreatic ductal adenocarcinoma development by acting as a molecular sponge of microRNA-96. Cancer Sci. 2019, 110, 2760–2772.
[28]	Kaneda, H.; Otomo, R.; Sasaki, N.; Omi, T.; Sato, T.; Kaneda, T. Endothelium-independent vasodilator effects of nobiletin in rat aorta. J. Pharmacol. Sci. 2019, 140, 48–53.
[29]	Youn, K.; Lee, S.; Jun, M. Discovery of nobiletin from citrus peel as a potent inhibitor of β-amyloid peptide toxicity. Nutrients. 2019, 11, 2648.
[30]	Nohara, K.; Mallampalli, V.; Nemkov, T.; Wirianto, M.; Yang, J.; Ye, Y.Q.; Sun, Y.X.; Han, L.; Esser, K.A.; Mileykovskaya, E.; D’Alessandro, A.; Green, C.B.; Takahashi, J.S.; Dowhan, W.; Yoo, S.H.; Chen, Z. Nobiletin fortifies mitochondrial respiration in skeletal muscle to promote healthy aging against metabolic challenge. Nat. Commun. 2019, 10, 3923.
[31]	Hopkins, A.L. Network pharmacology: the next paradigm in drug discovery. Nat. Chem. Biol. 2008, 4, 682–690.
[32]	Ru, J.L.; Li, P.; Wang, J.N.; Zhou, W.; Li, B.H.; Huang, C.; Li, P.D.; Guo, Z.H.; Tao, W.Y.; Yang, Y.F.; Xu, X.; Li, Y.; Wang, Y.H.; Yang, L. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J. Cheminformatics. 2014, 6, 13.
[33]	Xu, H.Y.; Zhang, Y.Q.; Liu, Z.M.; Chen, T.; Lv, C.Y.; Tang, S.H.; Zhang, X.B.; Zhang, W.; Li, Z.Y.; Zhou, R.R.; Yang, H.J.; Wang, X.J.; Huang, L.Q. ETCM: an encyclopaedia of traditional Chinese medicine. Nucleic Acids Res. 2019, 47, D976–D982.
[34]	Mangal, M.; Sagar, P.; Singh, H.; Raghava, G.P.S.; Agarwal, S.M. NPACT: naturally occurring plant-based anti-cancer compound-activity-target database. Nucleic Acids Res. 2013, 41, D1124–D1129.
[35]	Wishart, D.S.; Feunang, Y.D.; Guo, A.C.; Lo, E.J.; Marcu, A.; Grant, J.R.; Sajed, T.; Johnson, D.; Li, C.; Sayeeda, Z.; Assempour, N.; Iynkkaran, I.; Liu, Y.F.; Maciejewski, A.; Gale, N.; Wilson, A.; Chin, L.; Cummings, R.; Le, D.A.; Pon, A.; Knox, C.; Wilson, M. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2018, 46, D1074–D1082.
[36]	Consortium, U. UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res. 2021, 49, D480–D489.
[37]	Kim, S.; Chen, J.; Cheng, T.J.; Gindulyte, A.; He, J.; He, S.Q.; Li, Q.L.; Shoemaker, B.A.; Thiessen, P.A.; Yu, B.; Zaslavsky, L.; Zhang, J.; Bolton, E.E. PubChem in 2021: new data content and improved web interfaces. Nucleic Acids Res. 2021, 49, D1388–D1395.
[38]	Stelzer, G.; Rosen, N.; Plaschkes, I.; Zimmerman, S.; Twik, M.; Fishilevich, S.; Stein, T.I.; Nudel, R.; Lieder, I.; Mazor, Y.; Kaplan, S.; Dahary, D.; Warshawsky, D.; Guan-Golan, Y.; Kohn, A.; Rappaport, N.; Safran, M.; Lancet, D. The GeneCards suite: from gene data mining to disease genome sequence analyses. Curr. Protoc. Bioinform. 2016, 54, 1.30.1–1.30.33.
[39]	Amberger, J.S.; Hamosh, A. Searching online Mendelian inheritance in man (OMIM): a knowledgebase of human genes and genetic phenotypes. Curr. Protoc. Bioinform. 2017, 58, 1.2.1–1.2.12.
[40]	Whirl-Carrillo, M.; Huddart, R.; Gong, L.; Sangkuhl, K.; Thorn, C.F.; Whaley, R.; Klein, T.E. An evidence-based framework for evaluating pharmacogenomics knowledge for personalized medicine. Clin. Pharmacol. Ther. 2021, 110, 563–572.
[41]	Wang, Y.X.; Zhang, S.; Li, F.C.; Zhou, Y.; Zhang, Y.; Wang, Z.W.; Zhang, R.Y.; Zhu, J.; Ren, Y.X.; Tan, Y.; Qin, C.; Li, Y.H.; Li, X.X.; Chen, Y.Z.; Zhu, F. Therapeutic target database 2020: enriched resource for facilitating research and early development of targeted therapeutics. Nucleic Acids Res. 2020, 48, D1031–D1041.
[42]	Piñero, J.; Ramírez-Anguita, J.M.; Saüch-Pitarch, J.; Ronzano, F.; Centeno, E.; Sanz, F.; Furlong, L.I. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res. 2020, 48, D845–D855.
[43]	Ruan, X.F.; Du, P.; Zhao, K.; Huang, J.C.; Xia, H.M.; Dai, D.; Huang, S.; Cui, X.; Liu, L.M.; Zhang, J.J. Mechanism of Dayuanyin in the treatment of coronavirus disease 2019 based on network pharmacology and molecular docking. Chin. Med. 2020, 15, 62.
[44]	Li, R.; Li, Y.; Liang, X.; Yang, L.; Su, M.; Lai, K.P. Network Pharmacology and bioinformatics analyses identify intersection genes of niacin and COVID-19 as potential therapeutic targets. Brief. Bioinform. 2021, 22, 1279–1290.
[45]	Su, G.; Morris, J.H.; Demchak, B.; Bader, G.D. Biological network exploration with cytoscape 3. Curr. Protoc. Bioinform. 2014, 4747, 8.13.11–24.
[46]	Doncheva, N.T.; Morris, J.H.; Gorodkin, J.; Jensen, L.J. Cytoscape StringApp: network analysis and visualization of proteomics data. J. Proteome Res. 2019, 18, 623–632.
[47]	Szklarczyk, D.; Gable, A.L.; Nastou, K.C.; Lyon, D.; Kirsch, R.; Pyysalo, S.; Doncheva, N.T.; Legeay, M.; Fang, T.; Bork, P.; Jensen, L.J.; von Mering, C. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021, 49, D605–D612.
[48]	Tang, Y.; Li, M.; Wang, J.X.; Pan, Y.; Wu, F.X. CytoNCA: a cytoscape plugin for centrality analysis and evaluation of protein interaction networks. Biosystems. 2015, 127, 67–72.
[49]	Xiao, H.; Qin, X.Y.; Wan, J.P.; Li, R. Pharmacological targets and the biological mechanisms of formononetin for Alzheimer’s disease: a network analysis. Med. Sci. Monit. 2019, 25, 4273–4277.
[50]	Burley, S.K.; Bhikadiya, C.; Bi, C.X.; Bittrich, S.; Chen, L.; Crichlow, G.V.; Christie, C.H.; Dalenberg, K.; di Costanzo, L.; Duarte, J.M.; Dutta, S.; Feng, Z.K.; Ganesan, S.; Goodsell, D.S.; Ghosh, S.; Green, R.K.; Guranović, V.; Guzenko, D.; Hudson, B.P.; Lawson, C.L.; Liang, Y.H.; Lowe, R.; Namkoong, H.; Peisach, E.; Persikova, I.; Randle, C.; Rose, A.; Rose, Y.; Sali, A.; Segura, J.; Sekharan, M.; Shao, C.H.; Tao, Y.P.; Voigt, M.; Westbrook, J.D.; Young, J.Y.; Zardecki, C.; Zhuravleva, M. RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. Nucleic Acids Res. 2021, 49, D437–D451.
[51]	Xia, Q.D.; Xun, Y.; Lu, J.L.; Lu, Y.C.; Yang, Y.Y.; Zhou, P.; Hu, J.; Li, C.; Wang, S.G. Network pharmacology and molecular docking analyses on Lianhua Qingwen capsule indicate Akt1 is a potential target to treat and prevent COVID-19. Cell Prolif. 2020, 53, e12949.
[52]	Zimmet, P.; Alberti, K.G.M.M.; Shaw, J. Global and societal implications of the diabetes epidemic. Nature. 2001, 414, 782–787.
[53]	Hu, W.; Chen, S.; Thorne, R.F.; Wu, M. TP53, TP53 Target Genes (DRAM, TIGAR), and Autophagy. Adv. Exp. Med. Biol. 2019, 1206, 127–149.
[54]	Lacroix, M.; Riscal, R.; Arena, G.; Linares, L.K.; le Cam, L. Metabolic functions of the tumor suppressor p53: implications in normal physiology, metabolic disorders, and cancer. Mol. Metab. 2020, 33, 2–22.
[55]	Gungl, A.; Biasin, V.; Wilhelm, J.; Olschewski, A.; Kwapiszewska, G.; Marsh, L.M. Fra2 overexpression in mice leads to non-allergic asthma development in an IL-13 dependent manner. Front. Immunol. 2018, 9, 2018.
[56]	Trop-Steinberg, S.; Azar, Y. AP-1 expression and its clinical relevance in immune disorders and cancer. Am. J. Med. Sci. 2017, 353, 474–483.
[57]	Uluçkan, Ö.; Guinea-Viniegra, J.; Jimenez, M.; Wagner, E.F. Signalling in inflammatory skin disease by AP-1 (Fos/Jun). Clin. Exp. Rheumatol. 2015, 33, S44–S49.
[58]	Pishgahi, A.; Abolhasan, R.; Danaii, S.; Amanifar, B.; Soltani-Zangbar, M.S.; Zamani, M.; Kamrani, A.; Ghorbani, F.; Mehdizadeh, A.; Kafil, H.S.; Jadidi-Niaragh, F.; Yousefi, B.; Hajialiloo, M.; Yousefi, M. Immunological and oxidative stress biomarkers in Ankylosing Spondylitis patients with or without metabolic syndrome. Cytokine. 2020, 128, 155002.
[59]	Arita, R.; Nakao, S.; Kita, T.; Kawahara, S.; Asato, R.; Yoshida, S.; Enaida, H.; Hafezi-Moghadam, A.; Ishibashi, T. A key role for ROCK in TNF-α-mediated diabetic microvascular damage. Investig. Ophthalmol. Vis. Sci. 2013, 54, 2373–2383.
[60]	Khaloo, P.; Qahremani, R.; Rabizadeh, S.; Omidi, M.; Rajab, A.; Heidari, F.; Farahmand, G.; Bitaraf, M.; Mirmiranpour, H.; Esteghamati, A.; Nakhjavani, M. Nitric oxide and TNF-α are correlates of diabetic retinopathy independent of hs-CRP and HbA1c. Endocrine. 2020, 69, 536–541.
[61]	Maruotti, N.; d'Onofrio, F.; Cantatore, F.P. Metabolic syndrome and chronic arthritis: effects of anti-TNF-α therapy. Clin. Exp. Med. 2015, 15, 433–438.
[62]	Eckel, R.H.; Barouch, W.W.; Ershow, A.G. Report of the national heart, lung, and blood institute-national institute of diabetes and digestive and kidney diseases working group on the pathophysiology of obesity-associated cardiovascular disease. Circulation. 2002, 105, 2923–2928.
[63]	Hotamisligil, G.S. Inflammation and metabolic disorders. Nature. 2006, 444, 860–867.
[64]	Petersen, M.C.; Shulman, G.I. Roles of diacylglycerols and ceramides in hepatic insulin resistance. Trends Pharmacol. Sci. 2017, 38, 649–665.
[65]	Prasun, P. Mitochondrial dysfunction in metabolic syndrome. Biochim. Biophys. Acta BBA Mol. Basis Dis. 2020, 1866, 165838.
[66]	Brownlee, M. Biochemistry and molecular cell biology of diabetic complications. Nature. 2001, 414, 813–820.
[67]	Tuleta, I.; Frangogiannis, N.G. Diabetic fibrosis. Biochim. Biophys. Acta BBA Mol. Basis Dis. 2021, 1867, 166044.
[68]	Peng, Y.Q.; Kim, J.M.; Park, H.S.; Yang, A.N.; Islam, C.; Lakatta, E.G.; Lin, L. AGE-RAGE signal generates a specific NF-κB RelA "barcode" that directs collagen I expression. Sci. Rep. 2016, 6, 18822.
[69]	Goldin, A.H.; Beckman, J.; Schmidt, A.; Creager, M. Advanced glycation end products: sparking the development of diabetic vascular injury. Circulation. 2006, 114, 597–605.
[70]	Yan, S.F.; Ramasamy, R.; Schmidt, A.M. The RAGE axis: a fundamental mechanism signaling danger to the vulnerable vasculature. Circ. Res. 2010, 106, 842–853.
[71]	Bierhaus, A.; Chevion, S.; Chevion, M.; Hofmann, M.; Quehenberger, P.; Illmer, T.; Luther, T.; Berentshtein, E.; Tritschler, H.; Müller, M.; Wahl, P.; Ziegler, R.; Nawroth, P.P. Advanced glycation end product-induced activation of NF-kappaB is suppressed by alpha-lipoic acid in cultured endothelial cells. Diabetes. 1997, 46, 1481–1490.
[72]	Wautier, M.P.; Chappey, O.; Corda, S.; Stern, D.M.; Schmidt, A.M.; Wautier, J.L. Activation of NADPH oxidase by AGE links oxidant stress to altered gene expression via RAGE. Am. J. Physiol. Endocrinol. Metab. 2001, 280, E685–E694.
[73]	Tonna, S.; El-Osta, A.; Cooper, M.E.; Tikellis, C. Metabolic memory and diabetic nephropathy: potential role for epigenetic mechanisms. Nat. Rev. Nephrol. 2010, 6, 332–341.
[74]	Amidon, G.L.; Lennernäs, H.; Shah, V.P.; Crison, J.R. A theoretical basis for a biopharmaceutic drug classification: the correlation of in vitro drug product dissolution and in vivo bioavailability. Pharm. Res. 1995, 12, 413–420.
[75]	Lipinski, C.A.; Lombardo, F.; Dominy, B.W.; Feeney, P.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 2012, 64, 4–17.
[76]	Júlio, A.; Caparica, R.; Costa Lima, S.A.; Fernandes, A.S.; Rosado, C.; Prazeres, D.M.F.; Reis, S.; Santos de Almeida, T.; Fonte, P. Ionic liquid-polymer nanoparticle hybrid systems as new tools to deliver poorly soluble drugs. Nanomaterials. 2019, 9, 1148.