Integrating Network Pharmacology, Bioinformatics, and Mendelian Randomization Analysis to Identify Hub Targets and Mechanisms of Kunkui Baoshen Decoction in Treating Diabetic Kidney Disease


Дәйексөз келтіру

Толық мәтін

Аннотация

Objective:To uncover the potential hub targets of Kunkui Baoshen Decoction (KKBS) in alleviating Diabetic Kidney Disease (DKD).

Methods:Targets associated with KKBS and DKD were curated from TCMSP, GeneCards, OMIM, and Dis- GeNET databases. Common targets were identified through intersection analysis using a Venn diagram. Employing the \"Drug-component-target\" approach and constructing a Protein-protein Interaction (PPI) network, pivotal components and hub targets involved in KKBS's therapeutic action against DKD were identified. Functional enrichment and Gene Set Enrichment Analysis (GSEA) elucidated the potential mechanisms of these hub targets. Molecular docking simulations validated binding interactions. Subsequently, hub targets were validated using independent cohorts and clinical datasets. Immune cell infiltration in DKD samples was assessed using ESTIMATE, CIBERSORT, and IPS algorithms. A nomogram was developed to predict DKD prevalence. Finally, causal relationships between hub targets and DKD were explored through Mendelian randomization (MR) analysis at the genetic level.

Results:Jaranol, isorhamnetin, nobiletin, calycosin, and quercetin emerged as principal effective components in KKBS, with predicted modulation of the PI3K/Akt, MAPK, HIF-1, NF-kB, and IL-17 signaling pathways. The hub targets in the PPI network include proteins involved in regulating podocyte autophagy and apoptosis, managing antioxidant stress, contributing to insulin resistance, and participating in extracellular matrix deposition in DKD. Molecular docking affirmed favorable binding interactions between principal components and hub targets. Validation efforts across cohorts and databases underscored the potential of hub targets as DKD biomarkers. Among 20 model algorithms, the Extra Tree model yielded the largest Area Under the Curve (AUC) in receiver operating characteristic (ROC) analysis. MR analysis elucidated that the targets related to antioxidant stress had a positive impact on DKD, while the target associated with renal tubular basement membrane degradation had a negative impact.

Conclusion:Integration of Network Pharmacology, Bioinformatics, and MR analysis unveiled the capacity of KKBS to modulate pivotal targets in the treatment of DKD.

Авторлар туралы

Siyuan Song

Department of Endocrinology, Nanjing University of Chinese Medicine

Email: info@benthamscience.net

Jiangyi Yu

Department of Endocrinology, Nanjing University of Chinese Medicine,

Хат алмасуға жауапты Автор.
Email: info@benthamscience.net

Әдебиет тізімі

  1. Rayego-Mateos S, Rodrigues-Diez RR, Fernandez-Fernandez B, et al. Targeting inflammation to treat diabetic kidney disease: The road to 2030. Kidney Int 2023; 103(2): 282-96. doi: 10.1016/j.kint.2022.10.030 PMID: 36470394
  2. He F, Ng Yin Ling C, Nusinovici S, et al. Development and external validation of machine learning models for diabetic microvascular complications: Cross-sectional study with metabolites. J Med Internet Res 2024; 26: e41065. doi: 10.2196/41065 PMID: 38546730
  3. Ilyas Z, Chaiban JT, Krikorian A. Novel insights into the pathophysiology and clinical aspects of diabetic nephropathy. Rev Endocr Metab Disord 2017; 18(1): 21-8. doi: 10.1007/s11154-017-9422-3 PMID: 28289965
  4. Sawaf H, Thomas G, Taliercio JJ, Nakhoul G, Vachharajani TJ, Mehdi A. Therapeutic advances in diabetic nephropathy. J Clin Med 2022; 11(2): 378. doi: 10.3390/jcm11020378 PMID: 35054076
  5. Wang N, Zhang C. Recent advances in the management of diabetic kidney disease: Slowing progression. Int J Mol Sci 2024; 25(6): 3086. doi: 10.3390/ijms25063086 PMID: 38542060
  6. Wei C, Wang C, Li R, et al. The pharmacological mechanism of Abelmoschus manihot in the treatment of chronic kidney disease. Heliyon 2023; 9(11): e22017. doi: 10.1016/j.heliyon.2023.e22017 PMID: 38058638
  7. Tan Y, Li R, Zhou P, et al. Huobahuagen tablet improves renal function in diabetic kidney disease: A real-world retrospective cohort study. Front Endocrinol (Lausanne) 2023; 14: 1166880. doi: 10.3389/fendo.2023.1166880 PMID: 37404303
  8. Han H, Cao A, Wang L, et al. Huangqi decoction ameliorates streptozotocin-induced rat diabetic nephropathy through antioxidant and regulation of the TGF-β/MAPK/PPAR-γ signaling. Cell Physiol Biochem 2017; 42(5): 1934-44. doi: 10.1159/000479834 PMID: 28793292
  9. Xu H, Shen J, Liu H, Shi Y, Li L, Wei M. Morroniside and loganin extracted from Cornus officinalis have protective effects on rat mesangial cell proliferation exposed to advanced glycation end products by preventing oxidative stress. Can J Physiol Pharmacol 2006; 84(12): 1267-73. doi: 10.1139/y06-075 PMID: 17487235
  10. Gan X, Shu Z, Wang X, et al. Network medicine framework reveals generic herb-symptom effectiveness of traditional Chinese medicine. Sci Adv 2023; 9(43): eadh0215. doi: 10.1126/sciadv.adh0215 PMID: 37889962
  11. Burgess S, Davey Smith G, Davies NM, et al. Guidelines for performing Mendelian randomization investigations: Update for summer 2023. Wellcome Open Res 2019; 4: 186. doi: 10.12688/wellcomeopenres.15555.3 PMID: 32760811
  12. Qin C, Chen M, Yu Q, et al. Causal relationship between the blood immune cells and intervertebral disc degeneration: Univariable, bidirectional and multivariable Mendelian randomization. Front Immunol 2024; 14: 1321295. doi: 10.3389/fimmu.2023.1321295 PMID: 38268919
  13. Ru J, Li P, Wang J, et al. TCMSP: A database of systems pharmacology for drug discovery from herbal medicines. J Cheminform 2014; 6(1): 13. doi: 10.1186/1758-2946-6-13 PMID: 24735618
  14. Li J, Zhao P, Li Y, Tian Y, Wang Y. Systems pharmacology-based dissection of mechanisms of Chinese medicinal formula Bufei Yishen as an effective treatment for chronic obstructive pulmonary disease. Sci Rep 2015; 5(1): 15290. doi: 10.1038/srep15290 PMID: 26469778
  15. Murad AM, Rech EL. NanoUPLC-MSE proteomic data assessment of soybean seeds using the Uniprot database. BMC Biotechnol 2012; 12(1): 82. doi: 10.1186/1472-6750-12-82 PMID: 23126227
  16. Stelzer G, Rosen N, Plaschkes I, et al. The GeneCards suite: From gene data mining to disease genome sequence analyses. Curr Protoc Bioinformatics 2016; 54: 1.30.1-1.30.33. doi: 10.1002/cpbi.5
  17. Amberger JS, Bocchini CA, Schiettecatte F, Scott AF, Hamosh A. OMIM.org: Online Mendelian Inheritance in Man (OMIM®), an online catalog of human genes and genetic disorders. Nucleic Acids Res 2015; 43(D1): D789-98. doi: 10.1093/nar/gku1205 PMID: 25428349
  18. Piñero J, Ramírez-Anguita JM, Saüch-Pitarch J, et al. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res 2020; 48(D1): D845-55. PMID: 31680165
  19. Gao CH, Yu G, Cai P. ggVennDiagram: An intuitive, easy-to-use, and highly customizable R package to generate venn diagram. Front Genet 2021; 12: 706907. doi: 10.3389/fgene.2021.706907 PMID: 34557218
  20. Sherman BT, Huang DW, Tan Q, et al. DAVID Knowledgebase: A gene-centered database integrating heterogeneous gene annotation resources to facilitate high-throughput gene functional analysis. BMC Bioinformatics 2007; 8(1): 426. doi: 10.1186/1471-2105-8-426 PMID: 17980028
  21. Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2019; 47(D1): D607-13. doi: 10.1093/nar/gky1131 PMID: 30476243
  22. Liu Z, Li Y, Han L, et al. PDB-wide collection of binding data: Current status of the PDBbind database. Bioinformatics 2015; 31(3): 405-12. doi: 10.1093/bioinformatics/btu626 PMID: 25301850
  23. Seeliger D, de Groot BL. Ligand docking and binding site analysis with PyMOL and AutoDock/Vina. J Comput Aided Mol Des 2010; 24(5): 417-22. doi: 10.1007/s10822-010-9352-6 PMID: 20401516
  24. Mooers BHM. Shortcuts for faster image creation in PyMOL. Protein Sci 2020; 29(1): 268-76. doi: 10.1002/pro.3781 PMID: 31710740
  25. Shuyuan L, Haoyu C. Mechanism of Nardostachyos Radix et Rhizoma-Salidroside in the treatment of premature ventricular beats based on network pharmacology and molecular docking. Sci Rep 2023; 13(1): 20741. doi: 10.1038/s41598-023-48277-0 PMID: 38007574
  26. Kawada J, Takeuchi S, Imai H, et al. Immune cell infiltration landscapes in pediatric acute myocarditis analyzed by CIBERSORT. J Cardiol 2021; 77(2): 174-8. doi: 10.1016/j.jjcc.2020.08.004 PMID: 32891480
  27. Abdel Razek AAK, ElKhamary S, Al-Mesfer S, AlKatan HM. Correlation of apparent diffusion coefficient at 3T with prognostic parameters of retinoblastoma. AJNR Am J Neuroradiol 2012; 33(5): 944-8. doi: 10.3174/ajnr.A2892 PMID: 22241394
  28. Lay AC, Hale LJ, Stowell-Connolly H, et al. IGFBP-1 expression is reduced in human Type 2 diabetic glomeruli and modulates β1-integrin/FAK signalling in human podocytes. Diabetologia 2021; 64(7): 1690-702. doi: 10.1007/s00125-021-05427-1 PMID: 33758952
  29. Zhou X, Du J, Liu C, et al. A pan-cancer analysis of CD161, a potential new immune checkpoint. Front Immunol 2021; 12: 688215. doi: 10.3389/fimmu.2021.688215 PMID: 34305920
  30. Fornes O, Castro-Mondragon JA, Khan A, et al. JASPAR 2020: Update of the open-access database of transcription factor binding profiles. Nucleic Acids Res 2020; 48(D1): D87-92. PMID: 31701148
  31. Coutinho de Almeida R, Ramos YFM, Mahfouz A, et al. RNA sequencing data integration reveals an miRNA interactome of osteoarthritis cartilage. Ann Rheum Dis 2019; 78(2): 270-7. doi: 10.1136/annrheumdis-2018-213882 PMID: 30504444
  32. Zhou G, Soufan O, Ewald J, Hancock REW, Basu N, Xia J. NetworkAnalyst 3.0: A visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res 2019; 47(W1): W234-41. doi: 10.1093/nar/gkz240 PMID: 30931480
  33. Wu J, Zhang H, Li L, et al. A nomogram for predicting overall survival in patients with low‐grade endometrial stromal sarcoma: A population‐based analysis. Cancer Commun (Lond) 2020; 40(7): 301-12. doi: 10.1002/cac2.12067 PMID: 32558385
  34. Robin X, Turck N, Hainard A, et al. pROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 2011; 12(1): 77. doi: 10.1186/1471-2105-12-77 PMID: 21414208
  35. Võsa U, Claringbould A, Westra HJ, et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat Genet 2021; 53(9): 1300-10. doi: 10.1038/s41588-021-00913-z PMID: 34475573
  36. Sakaue S, Kanai M, Tanigawa Y, et al. A cross-population atlas of genetic associations for 220 human phenotypes. Nat Genet 2021; 53(10): 1415-24. doi: 10.1038/s41588-021-00931-x PMID: 34594039
  37. Liu B, Lyu L, Zhou W, et al. Associations of the circulating levels of cytokines with risk of amyotrophic lateral sclerosis: A mendelian randomization study. BMC Med 2023; 21(1): 39. doi: 10.1186/s12916-023-02736-7 PMID: 36737740
  38. Zou M, Zhang W, Shen L, Xu Y, Zhu Y. Causal association between inflammatory bowel disease and herpes virus infections: a two-sample bidirectional Mendelian randomization study. Front Immunol 2023; 14: 1203707. doi: 10.3389/fimmu.2023.1203707 PMID: 37465669
  39. Borenstein M, Hedges LV, Higgins JPT, Rothstein HR. A basic introduction to fixed-effect and random-effects models for meta-analysis. Res Synth Methods 2010; 1(2): 97-111. doi: 10.1002/jrsm.12 PMID: 26061376
  40. Hartwig FP, Davey Smith G, Bowden J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol 2017; 46(6): 1985-98. doi: 10.1093/ije/dyx102 PMID: 29040600
  41. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: Effect estimation and bias detection through Egger regression. Int J Epidemiol 2015; 44(2): 512-25. doi: 10.1093/ije/dyv080 PMID: 26050253
  42. Li L, Ren Q, Zheng Q, et al. Causal associations between gastroesophageal reflux disease and lung cancer risk: A Mendelian randomization study. Cancer Med 2023; 12(6): 7552-9. doi: 10.1002/cam4.5498 PMID: 36479899
  43. Tang Y, Wan F, Tang X, et al. Celastrol attenuates diabetic nephropathy by upregulating SIRT1-mediated inhibition of EZH2 related Wnt/β-catenin signaling. Int Immunopharmacol 2023; 122: 110584. doi: 10.1016/j.intimp.2023.110584 PMID: 37454630
  44. Liu L, Sheng C, Lyu Z, Dai H, Chen K. Association between genetically proxied lipid-lowering drug targets and renal cell carcinoma: a mendelian randomization study. Front Nutr 2021; 8: 755834. doi: 10.3389/fnut.2021.755834 PMID: 34712689
  45. Liu HX, Lian L, Hou LL, et al. Herb pair of Huangqi‐Danggui exerts anti‐tumor immunity to breast cancer by upregulatingPIK3R1. Animal Model Exp Med 2024; 7(3): 234-58. doi: 10.1002/ame2.12434 PMID: 38863309
  46. Fang J, Wang C, Zheng J, Liu Y. Network pharmacology study of Yishen capsules in the treatment of diabetic nephropathy. PLoS One 2022; 17(9): e0273498. doi: 10.1371/journal.pone.0273498 PMID: 36094934
  47. Matboli M, Ibrahim D, Hasanin AH, et al. Epigenetic modulation of autophagy genes linked to diabetic nephropathy by administration of isorhamnetin in Type 2 diabetes mellitus rats. Epigenomics 2021; 13(3): 187-202. doi: 10.2217/epi-2020-0353 PMID: 33406900
  48. Wang L, Xie Y, Xiao B, et al. Isorhamnetin alleviates cisplatin-induced acute kidney injury via enhancing fatty acid oxidation. Free Radic Biol Med 2024; 212: 22-33. doi: 10.1016/j.freeradbiomed.2023.12.010 PMID: 38101584
  49. Xu M, Wang R, Fan H, Ni Z. Nobiletin ameliorates streptozotocin-cadmium-induced diabetic nephropathy via NF-κB signalling pathway in rats. Arch Physiol Biochem 2024; 130(1): 29-37. doi: 10.1080/13813455.2021.1959617 PMID: 34346259
  50. Qin Y, Yang J, Li H, Li J. Recent advances in the therapeutic potential of nobiletin against respiratory diseases. Phytomedicine 2024; 128: 155506. doi: 10.1016/j.phymed.2024.155506 PMID: 38522319
  51. Yosri H, El-Kashef DH, El-Sherbiny M, Said E, Salem HA. Calycosin modulates NLRP3 and TXNIP-mediated pyroptotic signaling and attenuates diabetic nephropathy progression in diabetic rats; An insight. Biomed Pharmacother 2022; 155: 113758. doi: 10.1016/j.biopha.2022.113758 PMID: 36271546
  52. Lei D, Chengcheng L, Xuan Q, et al. Quercetin inhibited mesangial cell proliferation of early diabetic nephropathy through the Hippo pathway. Pharmacol Res 2019; 146: 104320. doi: 10.1016/j.phrs.2019.104320 PMID: 31220559
  53. Li T, Li Y. Quercetin acts as a novel anti-cancer drug to suppress cancer aggressiveness and cisplatin-resistance in nasopharyngeal carcinoma (NPC) through regulating the yes-associated protein/Hippo signaling pathway. Immunobiology 2023; 228(2): 152324. doi: 10.1016/j.imbio.2022.152324 PMID: 36608594
  54. Wang X, Jiang L, Liu X, et al. Paeoniflorin binds to VEGFR2 to restore autophagy and inhibit apoptosis for podocyte protection in diabetic kidney disease through PI3K-AKT signaling pathway. Phytomedicine 2022; 106: 154400. doi: 10.1016/j.phymed.2022.154400 PMID: 36049428
  55. Xuan C, Xi YM, Zhang YD, Tao CH, Zhang LY, Cao WF. Yiqi Jiedu Huayu decoction alleviates renal injury in rats with diabetic nephropathy by promoting autophagy. Front Pharmacol 2021; 12: 624404. doi: 10.3389/fphar.2021.624404 PMID: 33912044
  56. Harris RC. The role of the epidermal growth factor receptor in diabetic kidney disease. Cells 2022; 11(21): 3416. doi: 10.3390/cells11213416 PMID: 36359813
  57. Wang Y, Liu T, Ma F, et al. A Network pharmacology-based strategy for unveiling the mechanisms of Tripterygium wilfordii Hook F against diabetic kidney disease. J Diabetes Res 2020; 2020: 1-14. doi: 10.1155/2020/2421631 PMID: 33274236
  58. Mao C, Gu Z. Puerarin reduces increased c-fos, c-jun, and type IV collagen expression caused by high glucose in glomerular mesangial cells. Acta Pharmacol Sin 2005; 26(8): 982-6. doi: 10.1111/j.1745-7254.2005.00133.x PMID: 16038632
  59. Chen X, Cobbs A, George J, Chima A, Tuyishime F, Zhao X. Endocytosis of albumin induces matrix metalloproteinase-9 by activating the ERK signaling pathway in renal tubule epithelial cells. Int J Mol Sci 2017; 18(8): 1758. doi: 10.3390/ijms18081758 PMID: 28805677
  60. Du B, Yin Y, Wang Y, et al. Calcium dobesilate efficiency in the treatment of diabetic kidney disease through suppressing MAPK and chemokine signaling pathways based on clinical evaluation and network pharmacology. Front Pharmacol 2022; 13: 850167. doi: 10.3389/fphar.2022.850167 PMID: 36160448
  61. Zhang SJ, Zhang YF, Bai XH, et al. Integrated network pharmacology analysis and experimental validation to elucidate the mechanism of acteoside in treating diabetic kidney disease. Drug Des Devel Ther 2024; 18: 1439-57. doi: 10.2147/DDDT.S445254 PMID: 38707616
  62. Ahluwalia TS, Rönkkö TKE, Eickhoff MK, et al. Randomized trial of SGLT2 inhibitor identifies target proteins in diabetic kidney disease. Kidney Int Rep 2023; 9(2): 334-46. doi: 10.1016/j.ekir.2023.11.020 PMID: 38344728
  63. Gonzalez FJ, Xie C, Jiang C. The role of hypoxia-inducible factors in metabolic diseases. Nat Rev Endocrinol 2019; 15(1): 21-32. doi: 10.1038/s41574-018-0096-z PMID: 30275460
  64. Zhou XF, Zhou WE, Liu WJ, et al. A network pharmacology approach to explore the mechanism of HuangZhi YiShen capsule for treatment of diabetic kidney disease. J Transl Int Med 2021; 9(2): 98-113. doi: 10.2478/jtim-2021-0020 PMID: 34497749
  65. Fawaz S, Martin Alonso A, Qiu Y, et al. Adiponectin reduces glomerular endothelial glycocalyx disruption and restores glomerular barrier function in a mouse model of Type 2 diabetes. Diabetes 2024; 73(6): 964-76. doi: 10.2337/db23-0455 PMID: 38530908
  66. Sanajou D, Ghorbani Haghjo A, Argani H, Aslani S. AGE-RAGE axis blockade in diabetic nephropathy: Current status and future directions. Eur J Pharmacol 2018; 833: 158-64. doi: 10.1016/j.ejphar.2018.06.001 PMID: 29883668
  67. Chow F, Ozols E, Nikolic-Paterson DJ, Atkins RC, Tesch GH. Macrophages in mouse Type 2 diabetic nephropathy: Correlation with diabetic state and progressive renal injury. Kidney Int 2004; 65(1): 116-28. doi: 10.1111/j.1523-1755.2004.00367.x PMID: 14675042
  68. Bessho R, Takiyama Y, Takiyama T, et al. Hypoxia-inducible factor-1α is the therapeutic target of the SGLT2 inhibitor for diabetic nephropathy. Sci Rep 2019; 9(1): 14754. doi: 10.1038/s41598-019-51343-1 PMID: 31611596

Қосымша файлдар

Қосымша файлдар
Әрекет
1. JATS XML
Жүктеу

© Bentham Science Publishers, 2024