Skip to content
2000
  1. Home
  2. A-Z Publications
  3. Current Pharmaceutical Design
  4. Volume 30, Issue 42
  5. Article
Volume 30, Issue 42
  • ISSN: 1381-6128
  • E-ISSN: 1873-4286

Abstract

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 DisGeNET 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.

© 2024 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cpd/10.2174/0113816128331463240816145054
2024-09-05
2024-11-21

  • From This Site

    /content/journals/cpd/10.2174/0113816128331463240816145054
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Rayego-MateosS. Rodrigues-DiezR.R. Fernandez-FernandezB. Targeting inflammation to treat diabetic kidney disease: The road to 2030.Kidney Int.2023103228229610.1016/j.kint.2022.10.030 36470394
     [Google Scholar]
  2. HeF. Ng Yin LingC. NusinoviciS. Development and external validation of machine learning models for diabetic microvascular complications: Cross-sectional study with metabolites.J. Med. Internet Res.202426e4106510.2196/41065 38546730
     [Google Scholar]
  3. IlyasZ. ChaibanJ.T. KrikorianA. Novel insights into the pathophysiology and clinical aspects of diabetic nephropathy.Rev. Endocr. Metab. Disord.2017181212810.1007/s11154‑017‑9422‑3 28289965
     [Google Scholar]
  4. SawafH. ThomasG. TaliercioJ.J. NakhoulG. VachharajaniT.J. MehdiA. Therapeutic advances in diabetic nephropathy.J. Clin. Med.202211237810.3390/jcm11020378 35054076
     [Google Scholar]
  5. WangN. ZhangC. Recent advances in the management of diabetic kidney disease: Slowing progression.Int. J. Mol. Sci.2024256308610.3390/ijms25063086 38542060
     [Google Scholar]
  6. WeiC. WangC. LiR. The pharmacological mechanism of Abelmoschus manihot in the treatment of chronic kidney disease.Heliyon2023911e2201710.1016/j.heliyon.2023.e22017 38058638
     [Google Scholar]
  7. TanY. LiR. ZhouP. Huobahuagen tablet improves renal function in diabetic kidney disease: A real-world retrospective cohort study.Front. Endocrinol. (Lausanne)202314116688010.3389/fendo.2023.1166880 37404303
     [Google Scholar]
  8. HanH. CaoA. WangL. Huangqi decoction ameliorates streptozotocin-induced rat diabetic nephropathy through antioxidant and regulation of the TGF-β/MAPK/PPAR-γ signaling.Cell. Physiol. Biochem.20174251934194410.1159/000479834 28793292
     [Google Scholar]
  9. XuH. ShenJ. LiuH. ShiY. LiL. WeiM. 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.200684121267127310.1139/y06‑075 17487235
     [Google Scholar]
  10. GanX. ShuZ. WangX. Network medicine framework reveals generic herb-symptom effectiveness of traditional Chinese medicine.Sci. Adv.2023943eadh021510.1126/sciadv.adh0215 37889962
     [Google Scholar]
  11. BurgessS. Davey SmithG. DaviesN.M. Guidelines for performing Mendelian randomization investigations: Update for summer 2023.Wellcome Open Res.2019418610.12688/wellcomeopenres.15555.3 32760811
     [Google Scholar]
  12. QinC. ChenM. YuQ. Causal relationship between the blood immune cells and intervertebral disc degeneration: Univariable, bidirectional and multivariable Mendelian randomization.Front. Immunol.202414132129510.3389/fimmu.2023.1321295 38268919
     [Google Scholar]
  13. RuJ. LiP. WangJ. TCMSP: A database of systems pharmacology for drug discovery from herbal medicines.J. Cheminform.2014611310.1186/1758‑2946‑6‑13 24735618
     [Google Scholar]
  14. LiJ. ZhaoP. LiY. TianY. WangY. Systems pharmacology-based dissection of mechanisms of Chinese medicinal formula Bufei Yishen as an effective treatment for chronic obstructive pulmonary disease.Sci. Rep.2015511529010.1038/srep15290 26469778
     [Google Scholar]
  15. MuradA.M. RechE.L. NanoUPLC-MSE proteomic data assessment of soybean seeds using the Uniprot database.BMC Biotechnol.20121218210.1186/1472‑6750‑12‑82 23126227
     [Google Scholar]
  16. StelzerG RosenN PlaschkesI The GeneCards suite: From gene data mining to disease genome sequence analyses.Curr Protoc Bioinformatics2016541.30.11.30.3310.1002/cpbi.5
     [Google Scholar]
  17. AmbergerJ.S. BocchiniC.A. SchiettecatteF. ScottA.F. HamoshA. OMIM.org: Online Mendelian Inheritance in Man (OMIM®), an online catalog of human genes and genetic disorders.Nucleic Acids Res.201543D1D789D79810.1093/nar/gku1205 25428349
     [Google Scholar]
  18. PiñeroJ. Ramírez-AnguitaJ.M. Saüch-PitarchJ. The DisGeNET knowledge platform for disease genomics: 2019 update.Nucleic Acids Res.202048D1D845D855 31680165
     [Google Scholar]
  19. GaoC.H. YuG. CaiP. ggVennDiagram: An intuitive, easy-to-use, and highly customizable R package to generate venn diagram.Front. Genet.20211270690710.3389/fgene.2021.706907 34557218
     [Google Scholar]
  20. ShermanB.T. HuangD.W. TanQ. DAVID Knowledgebase: A gene-centered database integrating heterogeneous gene annotation resources to facilitate high-throughput gene functional analysis.BMC Bioinformatics20078142610.1186/1471‑2105‑8‑426 17980028
     [Google Scholar]
  21. SzklarczykD. GableA.L. LyonD. STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets.Nucleic Acids Res.201947D1D607D61310.1093/nar/gky1131 30476243
     [Google Scholar]
  22. LiuZ. LiY. HanL. PDB-wide collection of binding data: Current status of the PDBbind database.Bioinformatics201531340541210.1093/bioinformatics/btu626 25301850
     [Google Scholar]
  23. SeeligerD. de GrootB.L. Ligand docking and binding site analysis with PyMOL and AutoDock/Vina.J. Comput. Aided Mol. Des.201024541742210.1007/s10822‑010‑9352‑6 20401516
     [Google Scholar]
  24. MooersB.H.M. Shortcuts for faster image creation in PyMOL.Protein Sci.202029126827610.1002/pro.3781 31710740
     [Google Scholar]
  25. ShuyuanL. HaoyuC. Mechanism of Nardostachyos Radix et Rhizoma-Salidroside in the treatment of premature ventricular beats based on network pharmacology and molecular docking.Sci. Rep.20231312074110.1038/s41598‑023‑48277‑0 38007574
     [Google Scholar]
  26. KawadaJ. TakeuchiS. ImaiH. Immune cell infiltration landscapes in pediatric acute myocarditis analyzed by CIBERSORT.J. Cardiol.202177217417810.1016/j.jjcc.2020.08.004 32891480
     [Google Scholar]
  27. Abdel RazekA.A.K. ElKhamaryS. Al-MesferS. AlKatanH.M. Correlation of apparent diffusion coefficient at 3T with prognostic parameters of retinoblastoma.AJNR Am. J. Neuroradiol.201233594494810.3174/ajnr.A2892 22241394
     [Google Scholar]
  28. LayA.C. HaleL.J. Stowell-ConnollyH. IGFBP-1 expression is reduced in human Type 2 diabetic glomeruli and modulates β1-integrin/FAK signalling in human podocytes.Diabetologia20216471690170210.1007/s00125‑021‑05427‑1 33758952
     [Google Scholar]
  29. ZhouX. DuJ. LiuC. A pan-cancer analysis of CD161, a potential new immune checkpoint.Front. Immunol.20211268821510.3389/fimmu.2021.688215 34305920
     [Google Scholar]
  30. FornesO. Castro-MondragonJ.A. KhanA. JASPAR 2020: Update of the open-access database of transcription factor binding profiles.Nucleic Acids Res.202048D1D87D92 31701148
     [Google Scholar]
  31. Coutinho de AlmeidaR. RamosY.F.M. MahfouzA. RNA sequencing data integration reveals an miRNA interactome of osteoarthritis cartilage.Ann. Rheum. Dis.201978227027710.1136/annrheumdis‑2018‑213882 30504444
     [Google Scholar]
  32. ZhouG. SoufanO. EwaldJ. HancockR.E.W. BasuN. XiaJ. NetworkAnalyst 3.0: A visual analytics platform for comprehensive gene expression profiling and meta-analysis.Nucleic Acids Res.201947W1W234-4110.1093/nar/gkz240 30931480
     [Google Scholar]
  33. WuJ. ZhangH. LiL. A nomogram for predicting overall survival in patients with low‐grade endometrial stromal sarcoma: A population‐based analysis.Cancer Commun. (Lond.)202040730131210.1002/cac2.12067 32558385
     [Google Scholar]
  34. RobinX. TurckN. HainardA. pROC: An open-source package for R and S+ to analyze and compare ROC curves.BMC Bioinformatics20111217710.1186/1471‑2105‑12‑77 21414208
     [Google Scholar]
  35. VõsaU. ClaringbouldA. WestraH.J. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression.Nat. Genet.20215391300131010.1038/s41588‑021‑00913‑z 34475573
     [Google Scholar]
  36. SakaueS. KanaiM. TanigawaY. A cross-population atlas of genetic associations for 220 human phenotypes.Nat. Genet.202153101415142410.1038/s41588‑021‑00931‑x 34594039
     [Google Scholar]
  37. LiuB. LyuL. ZhouW. Associations of the circulating levels of cytokines with risk of amyotrophic lateral sclerosis: A mendelian randomization study.BMC Med.20232113910.1186/s12916‑023‑02736‑7 36737740
     [Google Scholar]
  38. ZouM. ZhangW. ShenL. XuY. ZhuY. Causal association between inflammatory bowel disease and herpes virus infections: a two-sample bidirectional Mendelian randomization study.Front. Immunol.202314120370710.3389/fimmu.2023.1203707 37465669
     [Google Scholar]
  39. BorensteinM. HedgesL.V. HigginsJ.P.T. RothsteinH.R. A basic introduction to fixed-effect and random-effects models for meta-analysis.Res. Synth. Methods2010129711110.1002/jrsm.12 26061376
     [Google Scholar]
  40. HartwigF.P. Davey SmithG. BowdenJ. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption.Int. J. Epidemiol.20174661985199810.1093/ije/dyx102 29040600
     [Google Scholar]
  41. BowdenJ. Davey SmithG. BurgessS. Mendelian randomization with invalid instruments: Effect estimation and bias detection through Egger regression.Int. J. Epidemiol.201544251252510.1093/ije/dyv080 26050253
     [Google Scholar]
  42. LiL. RenQ. ZhengQ. Causal associations between gastroesophageal reflux disease and lung cancer risk: A Mendelian randomization study.Cancer Med.20231267552755910.1002/cam4.5498 36479899
     [Google Scholar]
  43. TangY. WanF. TangX. Celastrol attenuates diabetic nephropathy by upregulating SIRT1-mediated inhibition of EZH2 related Wnt/β-catenin signaling.Int. Immunopharmacol.202312211058410.1016/j.intimp.2023.110584 37454630
     [Google Scholar]
  44. LiuL. ShengC. LyuZ. DaiH. ChenK. Association between genetically proxied lipid-lowering drug targets and renal cell carcinoma: a mendelian randomization study.Front. Nutr.2021875583410.3389/fnut.2021.755834 34712689
     [Google Scholar]
  45. LiuH.X. LianL. HouL.L. Herb pair of Huangqi‐Danggui exerts anti‐tumor immunity to breast cancer by upregulatingPIK3R1.Animal Model. Exp. Med.20247323425810.1002/ame2.12434 38863309
     [Google Scholar]
  46. FangJ. WangC. ZhengJ. LiuY. Network pharmacology study of Yishen capsules in the treatment of diabetic nephropathy.PLoS One2022179e027349810.1371/journal.pone.0273498 36094934
     [Google Scholar]
  47. MatboliM. IbrahimD. HasaninA.H. Epigenetic modulation of autophagy genes linked to diabetic nephropathy by administration of isorhamnetin in Type 2 diabetes mellitus rats.Epigenomics202113318720210.2217/epi‑2020‑0353 33406900
     [Google Scholar]
  48. WangL. XieY. XiaoB. Isorhamnetin alleviates cisplatin-induced acute kidney injury via enhancing fatty acid oxidation.Free Radic. Biol. Med.2024212223310.1016/j.freeradbiomed.2023.12.010 38101584
     [Google Scholar]
  49. XuM. WangR. FanH. NiZ. Nobiletin ameliorates streptozotocin-cadmium-induced diabetic nephropathy via NF-κB signalling pathway in rats.Arch. Physiol. Biochem.20241301293710.1080/13813455.2021.1959617 34346259
     [Google Scholar]
  50. QinY. YangJ. LiH. LiJ. Recent advances in the therapeutic potential of nobiletin against respiratory diseases.Phytomedicine202412815550610.1016/j.phymed.2024.155506 38522319
     [Google Scholar]
  51. YosriH. El-KashefD.H. El-SherbinyM. SaidE. SalemH.A. Calycosin modulates NLRP3 and TXNIP-mediated pyroptotic signaling and attenuates diabetic nephropathy progression in diabetic rats; An insight.Biomed. Pharmacother.202215511375810.1016/j.biopha.2022.113758 36271546
     [Google Scholar]
  52. LeiD. ChengchengL. XuanQ. Quercetin inhibited mesangial cell proliferation of early diabetic nephropathy through the Hippo pathway.Pharmacol. Res.201914610432010.1016/j.phrs.2019.104320 31220559
     [Google Scholar]
  53. LiT. LiY. 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.Immunobiology2023228215232410.1016/j.imbio.2022.152324 36608594
     [Google Scholar]
  54. WangX. JiangL. LiuX. Paeoniflorin binds to VEGFR2 to restore autophagy and inhibit apoptosis for podocyte protection in diabetic kidney disease through PI3K-AKT signaling pathway.Phytomedicine202210615440010.1016/j.phymed.2022.154400 36049428
     [Google Scholar]
  55. XuanC. XiY.M. ZhangY.D. TaoC.H. ZhangL.Y. CaoW.F. Yiqi Jiedu Huayu decoction alleviates renal injury in rats with diabetic nephropathy by promoting autophagy.Front. Pharmacol.20211262440410.3389/fphar.2021.624404 33912044
     [Google Scholar]
  56. HarrisR.C. The role of the epidermal growth factor receptor in diabetic kidney disease.Cells20221121341610.3390/cells11213416 36359813
     [Google Scholar]
  57. WangY. LiuT. MaF. A Network pharmacology-based strategy for unveiling the mechanisms of Tripterygium wilfordii Hook F against diabetic kidney disease.J. Diabetes Res.2020202011410.1155/2020/2421631 33274236
     [Google Scholar]
  58. MaoC. GuZ. Puerarin reduces increased c-fos, c-jun, and type IV collagen expression caused by high glucose in glomerular mesangial cells.Acta Pharmacol. Sin.200526898298610.1111/j.1745‑7254.2005.00133.x 16038632
     [Google Scholar]
  59. ChenX. CobbsA. GeorgeJ. ChimaA. TuyishimeF. ZhaoX. Endocytosis of albumin induces matrix metalloproteinase-9 by activating the ERK signaling pathway in renal tubule epithelial cells.Int. J. Mol. Sci.2017188175810.3390/ijms18081758 28805677
     [Google Scholar]
  60. DuB. YinY. WangY. 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.20221385016710.3389/fphar.2022.850167 36160448
     [Google Scholar]
  61. ZhangS.J. ZhangY.F. BaiX.H. Integrated network pharmacology analysis and experimental validation to elucidate the mechanism of acteoside in treating diabetic kidney disease.Drug Des. Devel. Ther.2024181439145710.2147/DDDT.S445254 38707616
     [Google Scholar]
  62. AhluwaliaT.S. RönkköT.K.E. EickhoffM.K. Randomized trial of SGLT2 inhibitor identifies target proteins in diabetic kidney disease.Kidney Int. Rep.20239233434610.1016/j.ekir.2023.11.020 38344728
     [Google Scholar]
  63. GonzalezF.J. XieC. JiangC. The role of hypoxia-inducible factors in metabolic diseases.Nat. Rev. Endocrinol.2019151213210.1038/s41574‑018‑0096‑z 30275460
     [Google Scholar]
  64. ZhouX.F. ZhouW.E. LiuW.J. A network pharmacology approach to explore the mechanism of HuangZhi YiShen capsule for treatment of diabetic kidney disease.J. Transl. Int. Med.2021929811310.2478/jtim‑2021‑0020 34497749
     [Google Scholar]
  65. FawazS. Martin AlonsoA. QiuY. Adiponectin reduces glomerular endothelial glycocalyx disruption and restores glomerular barrier function in a mouse model of Type 2 diabetes.Diabetes202473696497610.2337/db23‑0455 38530908
     [Google Scholar]
  66. SanajouD. Ghorbani HaghjoA. ArganiH. AslaniS. AGE-RAGE axis blockade in diabetic nephropathy: Current status and future directions.Eur. J. Pharmacol.201883315816410.1016/j.ejphar.2018.06.001 29883668
     [Google Scholar]
  67. ChowF. OzolsE. Nikolic-PatersonD.J. AtkinsR.C. TeschG.H. Macrophages in mouse Type 2 diabetic nephropathy: Correlation with diabetic state and progressive renal injury.Kidney Int.200465111612810.1111/j.1523‑1755.2004.00367.x 14675042
     [Google Scholar]
  68. BesshoR. TakiyamaY. TakiyamaT. Hypoxia-inducible factor-1α is the therapeutic target of the SGLT2 inhibitor for diabetic nephropathy.Sci. Rep.2019911475410.1038/s41598‑019‑51343‑1 31611596
     [Google Scholar]
/content/journals/cpd/10.2174/0113816128331463240816145054
Loading
Integrating Network Pharmacology, Bioinformatics, and Mendelian Randomization Analysis to Identify Hub Targets and Mechanisms of Kunkui Baoshen Decoction in Treating Diabetic Kidney Disease
Curr. Pharm. Des. 30, 3367 (2024); https://doi.org/10.2174/0113816128331463240816145054
/content/journals/cpd/10.2174/0113816128331463240816145054
/content/journals/cpd/10.2174/0113816128331463240816145054
Loading

Data & Media loading...

Supplements

Supplementary material is available on the publisher’s website along with the published article.

file format download Download

  • Article Type:     Research Article
Keyword(s): bioinformatics; diabetic kidney disease; Kunkui Baoshen decoction; molecular docking; MR analysis; network pharmacology

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test