• Users Online: 120
  • Print this page
  • Email this page


 
 Table of Contents  
ORIGINAL ARTICLE
Year : 2021  |  Volume : 8  |  Issue : 1  |  Page : 13

Xiaochaihu decoction in diabetic kidney disease: A study based on network pharmacology and molecular docking technology


1 Clinical Direction of Integrated Traditional Chinese and Western Medicine, Beijing University of Chinese Medicine; Department of Nephrology, Beijing Key Laboratory for Immune Mediated Inflammatory Diseases, China Japan Friendship Hospital, Beijing, China
2 Department of Nephrology, Beijing Key Laboratory for Immune Mediated Inflammatory Diseases, China Japan Friendship Hospital, Beijing, China

Date of Submission16-Mar-2021
Date of Decision08-Oct-2021
Date of Acceptance30-Oct-2021
Date of Web Publication31-Dec-2021

Correspondence Address:
Dr. Ping Li
Department of Nephrology, Beijing Key Laboratory for Immune Mediated Inflammatory Diseases, China Japan Friendship Hospital, Beijing 100029
China
Dr. Tingting Zhao
Department of Nephrology, Beijing Key Laboratory for Immune Mediated Inflammatory Diseases, China Japan Friendship Hospital, Beijing 100029
China
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/imna.imna_21_21

Rights and Permissions
  Abstract 


Objective: To explore the potential mechanism of Xiaochaihu decoction (XCHD) in the treatment of diabetic kidney disease (DKD) by network pharmacology and molecular docking technology. Materials and Methods: Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform was used to screen out main active components of XCHD. Gene names of target proteins were obtained with UniProt database. DKD targets were collected by GeneCard database, and common targets were selected through jvenn platform. STRING database was used to construct a protein–protein interaction network. Enrichment analysis was carried out through the Metascape platform. The “drug–component–target” and “component–target–KEGG pathway” networks were constructed using Cytoscape software. Molecular docking analysis was carried out with AutoDockTool software. Results: A total of 195 active components were obtained for XCHD. There were 238 corresponding targets and 128 common targets associated with DKD, and the core targets involved IL6, AKT1, VEGFA, TNF, TP53, PTGS2, and JUN. Gene ontology enrichment analysis revealed 2242 entries for biological processes, 82 entries for cellular components, and 166 items of molecular functions. A total of 333 signal pathways were screened by KEGG pathway enrichment analysis. Molecular docking showed that quercetin, baicalin, luteolin, and wogonin were tightly bound to the key target proteins of PTGS2 and AKT1. Conclusions: 195 active components were screened from XCHD, among which 128 intersections with DKD were identified, and 333 signaling pathways were identified by KEGG pathway enrichment analysis.The key active components in XCHD, such as quercetin, baicalin, luteolin and wogonin, regulate multiple signaling pathways by acting on PTGS2, AKT1 and other targets, for anti-inflammatory, antioxidant, regulating cell factors, improving insulin resistance, and protecting renal function. This study provides a more in-depth scientific basis and research direction for the investigation on XCHD treatment of DKD.

Keywords: Diabetic kidney disease, molecular docking, network pharmacology, Xiaochaihu decoction


How to cite this article:
Wang Y, Zhou X, Luo M, Zhao T, Li P. Xiaochaihu decoction in diabetic kidney disease: A study based on network pharmacology and molecular docking technology. Integr Med Nephrol Androl 2021;8:13

How to cite this URL:
Wang Y, Zhou X, Luo M, Zhao T, Li P. Xiaochaihu decoction in diabetic kidney disease: A study based on network pharmacology and molecular docking technology. Integr Med Nephrol Androl [serial online] 2021 [cited 2022 May 23];8:13. Available from: http://www.journal-imna.com/text.asp?2021/8/1/13/334653




  Introduction Top


Diabetes is a global public health challenge. In 2019, the global prevalence of diabetes was approximately 9.3% (463 million people).[1] Diabetic kidney disease (DKD) is a microvascular complications and is the leading cause of morbidity and mortality in diabetes.[2] The pathogenesis of DKD is complex and likely involves the interaction of cytokines, inflammation, oxidative stress, abnormal glucose and lipid metabolism, and vascular endothelial growth factors.

Traditional Chinese medicine (TCM) compounds exert their efficacy through multiple targets and multiple pathways, which have significant advantages over pharmaceutical medications in the treatment of DKD. Xiaochaihu decoction (XCHD) is a TCM composed of seven herbs: radix bupleuri (Bupleurum root; Bupleurum chinense DC.), Scutellaria (Scutellaria root; Scutellaria baicalensis Georgi), Pinellia ternata (unprepared Pinellia rhizome; P. ternata (Thunb.) Makino), Codonopsis pilosula (Codonopsis root; C. pilosula (Franch.) Nannf.), licorice (licorice root; Glycyrrhiza uralensis Fisch.), Zingiber (dried ginger rhizome; Zingiber officinale Roscoe), and jujube (sour jujube seed; Ziziphus jujuba var. spinosa (Bunge) Hu ex H. F. Chow). In Chinese medicine theory, XCHD is used to harmonize the interior and exterior of the body and to regulate the liver and spleen. In China, there are many studies on XCHD in the treatment of proteinuria or DKD.[3],[4],[5]

Network pharmacology is a methodology based on systems biology, proteomics, and pharmacology and uses computer technology and software to perform analyses. By constructing a network of “disease, gene, target, and drug” and emphasizing their relationships from the perspective of the whole, the intervention of drugs on diseases can be explained systematically and comprehensively to reveal the pharmacologic mechanism acting on the body. Therefore, network pharmacology coincides with the holistic view advanced by TCM. For this study, we applied network pharmacology to predict the relationship between the active components and targets of XCHD and explored the pharmacologic mechanism of XCHD in treating DKD [Figure 1].
Figure 1: Flow diagram of the pharmacology-based study of Xiaochaihu decoction used in the treatment of diabetic kidney disease

Click here to view



  Materials And Methods Top


Screening of active components and target protein

The keywords “radix bupleuri,” “Scutellaria baicalensis,” “Pinellia ternata,” “Codonopsis pilosula,” “licorice,” “Zingiber,” and “red jujube” were used to retrieve the compound components in the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) database (https://tcmspw.com/tcmsp.php). Oral bioavailability (OB) and drug likeness (DL) were selected as screening indexes.[6],[7] The target protein corresponding to the component was then obtained from the TCMSP database.

Construction of drug–component–target network

Next, the UniProt database (https://www.uniprot.org) was used to standardize the target proteins associated with active components and to obtain the corresponding gene name of the target protein. The active components and their corresponding target data were imported into Cytoscape 3.7.1 (http://www.cytoscape.org) software to construct the drug–component–target network of XCHD.

Collection of disease targets

The keyword “diabetic kidney diseases” was searched in the GeneCard database (https://www.genecards.org) to collect DKD-related disease targets. After UniProt correction, jvenn platform (http://jvenn.toulouse.inra.fr/app/index.html) was applied to acquire the common targets between the components and disease.

Construction of the protein–protein interaction network

The common targets were then imported into the STRING database (https://string-preview.org) to obtain the protein interaction relationship. Cytoscape 3.7.1 software was used for visualization processing of the obtained results. Network topology parameters such as degree, betweenness centrality, and closeness centrality were then analyzed with built-in tools to determine the main active components of the efficacy of core targets.

Analysis of gene ontology functional and KEGG pathway enrichment

Gene ontology (GO) functional and KEGG pathway enrichment analyses were performed on the common targets through the Metascape platform (http://metascape.org). GO function enrichment analysis was conducted from three aspects: molecular function (MF), cellular component (CC), and biological process (BP). The resulting bubble charts were illustrated using the online tool (http://www.bioinformatics.com.cn).

Construction of component–target–KEGG pathway network

Using the merge function in Cytoscape 3.7.1, a component–target–KEGG pathway network was constructed. In the resulting output, different colors and shapes of nodes were expressed drug components, targets, and pathways.

Verification of molecular docking

The PDB-ID of the key targets of the component–target–KEGG pathway network was obtained from the PDB database (http://www.rcsb.org/pdb) and saved in 3D structured pdb format. The key active component in 2D structured was saved in sdf format in the PubChem database (https://pubchem.ncbi.nlm.nih.gov). The key active components and key target proteins were analyzed by virtual molecular docking using the AutoDockTool (http://autodock.scripps.edu/resources/adt). Affinity was applied as an index to evaluate the binding of small molecule ligands to receptors.


  Results Top


Screening of potential effective components

A total of 1420 active components of XCHD were collected. These included 349 related to radix bupleuri, 143 related to S. baicalensis, 116 related P. ternata, 134 related to C. pilosula, 280 related to licorice, 265 related to Zingiber, and 133 related to red jujube. After screening with OB > 30% and DL ≥0.18, 213 components were obtained, including 17 related to radix bupleuri, 36 related to S. baicalensis, 13 related to P. ternata, 21 related to C. pilosula, 92 related to licorice, 5 related to Zingiber, and 29 related to red jujube. Among these, 11 components were common to two or more TCMs. A total of 195 components were obtained after eliminating the duplicates [Table 1].
Table 1: Potential effective components of Xiaochaihu decoction

Click here to view


Target prediction and network construction of components

A total of 238 target proteins obtained from XCHD were input into UniProt database and converted into corresponding gene names. A drug–component–target network was constructed using Cytoscape software and includes 409 nodes (7 herb nodes, 164 component nodes, and 238 target nodes) and 1462 sides [Figure 2]. Based on the degree value, the top 10 components are stigmasterol, quercetin, beta-sitosterol, baicalein, kaempferol, luteolin, wogonin, 7-methoxy-2-methyl isoflavone, isorhamnetin, and cavidine [Table 2].
Figure 2: Network diagram of drug–component–target network

Click here to view
Table 2: Main active components in Xiaochaihu decoction

Click here to view



  Disease target retrieval results Top


A total of 1661 potential targets were screened out by the median of correlation scores in the GeneCard database using the keyword “diabetic kidney disease.” The active components targets were intersected with disease targets, and 128 common targets that might be related to the effect of XCHD on DKD were obtained [Figure 3].
Figure 3: Venn diagram of the common targets of Xiaochaihu decoction for diabetic kidney disease

Click here to view


Protein–protein interaction network of Xiaochaihu decoction in the treatment of diabetic kidney disease

After importing 128 common targets into the STRING database, there were 2747 interaction edges in the protein–protein interaction (PPI) network, with an average degree value of 42.9. Among these, IL6, AKT1, VEGFA, TNF, TP53, PTGS2, and Jun had strong interactions with other proteins [Figure 4] and [Table 3].
Figure 4: Network diagram of the protein–protein interaction network

Click here to view
Table 3: Network topology parameters of core targets

Click here to view


Gene ontology enrichment analysis and KEGG pathway mapping

By inputting 128 targets into the Metascape platform, GO enrichment analysis and KEGG pathway mapping were performed. A total of 2490 GO items (P < 0.01) were obtained. A total of 2242 BP items were acquired, including response to inorganic substances, and wounding and toxic substances. There were 82 CC items, including membrane rafts, vesicle lumen, and perinuclear region of cytoplasm. There were 166 MF items, including transcription factor binding, protein domain-specific binding, and protein kinase binding. KEGG pathway enrichment analysis screened 333 signal pathways (P < 0.01), involving cancer, PI3K-AKT signaling, hepatitis B, AGE-RAGE signaling in diabetes complications, and HIF-1 signaling. The top 20 items were selected for visualization as bubble charts [Figure 5].
Figure 5: Gene ontology enrichment analysis and KEGG pathway mapping of Xiaochaihu decoction in the treatment of diabetic kidney disease

Click here to view


Component–target–KEGG pathway network

To construct the network of component–target–KEGG pathway, 20 core components and 23 core targets (degree ≥70) were selected and imported into Cytoscape 3.7.1 software [Figure 6]. Each core component can act on multiple targets. Based on the degree value, the top five targets are PTGS2, AKT1, TP53, CASP3, and MAPK1 [Table 4]. The top four components were A3, B1, DS6, and HQ2, namely quercetin, baicalein, luteolin, and wogonin [Table 5], all in line with Lipinski's Rule of Five].
Figure 6: Network diagram of the component–target–KEGG pathway

Click here to view
Table 4: Key components and targets of Xiaochaihu decoction in the treatment of diabetic kidney disease

Click here to view
Table 5: Molecular docking results of key components and target proteins

Click here to view


Docking results of key components and target molecules

To better explain the binding activity between the key targets and the corresponding active components of XCHD in the treatment of DKD, AutoDockTool was used to conduct virtual molecular docking analysis of the key components and target proteins [Figure 7], [Figure 8], [Figure 9], [Figure 10] and [Table 6]. The smaller the free energy of binding, the stronger is the binding ability of ligand and receptor [Table 7]. Molecular docking between the key active components and target proteins indicated that quercetin had the best binding activity to PTGS2, which provided a theoretical reference for further elaboration of the pharmacologic substances in XCHD in the treatment of DKD.
Figure 7: Molecular docking mode of PTGS2 with quercetin, baicalein, luteolin, and wogonin. (a) Quercetin; (b) baicalein; (c) luteolin; (d) wogonin

Click here to view
Figure 8: Visualization of molecular docking binding sites of PTGS2 with quercetin, baicalein, luteolin, and wogonin. (a) Quercetin; (b) baicalein; (c) luteolin; (d) wogonin

Click here to view
Figure 9: Molecular docking mode of AKT1 with quercetin, baicalein, luteolin, and wogonin. (a) Quercetin; (b) baicalein; (c) luteolin; (d) wogonin

Click here to view
Figure 10: Visualization of molecular docking binding sites of AKT1 with quercetin, baicalein, luteolin, and wogonin. (a) Quercetin; (b) baicalein; (c) luteolin; (d) wogonin

Click here to view
Table 6: Binding sites of key components and target

Click here to view
Table 7: Molecular docking results of key components and target proteins

Click here to view



  Discussion Top


DKD is a serious complication of diabetic patients with edema, proteinuria, renal function damage, and so on. A number of Chinese herbal formulas have long been used to treat diabetes and DKD. Among these is XCHD. While individual herbs in XCHD have been investigated for their mechanism of action in DKD, how they exert their activities in toto in the formula remains unclear. Thus, applying network pharmacology is an ideal approach to explore and validate the mechanisms of action of individual compounds, and more importantly, their combined pharmaceutical activity.

In this study, based on the systems biology and network pharmacology, the active components of XCHD for DKD treatment are mainly quercetin, baicalein, and luteolin, all of which are flavonoids. Studies have shown that quercetin can directly scavenge reactive oxygen free radicals to prevent oxidation.[8] It also inhibits inflammatory factors,[9] nuclear factor-κB, and mitogen-activated protein kinase signaling pathways.[10] Quercetin has also been found to lower blood glucose and lipids by improving insulin resistance.[11]

Research on baicalein suggests that it can improve diabetes-related complications by regulating protein kinase R-like endoplasmic reticulum kinase/nuclear factor red related factor 2 pathway.[12] In the rat model of type 2 DKD established by high-fat and high-sugar diet and streptozotocin (STZ) injection, baicalein treatment protected renal function through adenylate activated protein kinase α expression and inflammatory response.[13] Furthermore, baicalein appears to promote glucose uptake and glycolysis to improve glucose metabolism and attenuate insulin resistance.[14]

Studies in db/db mice showed that luteolin treatment can reduce DKD by inhibiting the inflammatory and oxidative responses and the inflammasome pathway of pyrin domain protein 3 of the nucleotide binding oligomerized domain-like receptor family.[15] Luteolin has also been found to alleviate podocyte injury induced by high glucose.[16]

In this study, construction of the PPI network demonstrated that the targets of XCHD in treating DKD involve IL6, AKT1, and VEGFA. IL6 is a multifunctional cytokine. In the presence of high glucose, cells are stimulated to produce and secrete IL6 by autocrine or paracrine signaling and then to activate the tyrosine protein kinase 2 and signal transduction and transcriptional activation factor 3 pathway. IL6 also promotes an increase in mast cells in vitro, which can lead to podocyte injury.[17] It can induce expression of cytokine signaling suppressor protein-3 to disrupt the phosphorylation of insulin receptor and insulin receptor substrate 1 to cause insulin resistance.[18] Studies have confirmed that IL6 receptor blockers can inhibit the activation of NLRP3 inflammasome by inhibiting IL17A to block the IL6 signaling pathway and alleviate renal injury of DKD.[19]

There are three AKT subtypes: AKT1 (PKBα), AKT2 (PKBβ), and AKT3 (PKBγ) in mammals, each of which has different physiologic functions. AKT1 participates in cell survival and protein synthesis and inhibits cell apoptosis; AKT2 is a significant metabolic regulator; the specific function of AKT3 remains unclear.[20] AKT has been associated with DKD in many cell and animal models. Krepinsky et al. demonstrated that AKT activity is required both for mechanical stretching of mesangial cells and for collagen production induced by high glucose.[21] In DKD rats treated with the incretin mimetic liraglutide, expression of protein kinase B (AKT)/target of rapamycin was inhibited resulting in blood glucose reduction and improved renal function, which led to a protective effect on renal damage.[22]

VEGFA is a significant proangiogenic factor mainly expressed in glomerular podocytes and renal tubule cells in the kidney and participates in abnormal angiogenesis in DKD by promoting glomerular angiogenesis.[23] The VEGFA inhibitor soluble FMS-like tyrosine kinase receptor 1 was observed to reverse renal damage, reduce proteinuria, glomerular hypertrophy, and glomerular mesangial matrix dilatation in diabetic mice, to reduce endothelial cell activation and glomerular inflammation.[24]

Through GO and KEGG analyses, we found that the signaling pathways of XCHD in the treatment of DKD were mainly concentrated in the cancer pathway, AGE-RAGE signaling pathway in diabetic complications, hepatitis B signaling pathway, and PI3K-AKT signaling pathway. Except in the hepatitis B pathway, VEGFA targets were not found in it. IL6, AKT1, and VEGFA targets were found in all other pathways. Binding of AGEs receptor (RAGE) to AGEs causes oxidative stress and subsequently causes proliferative, inflammatory, and fibrotic reactions of various cells to aggravate the progression of DKD.[25] In addition, salvianolic acid A (SALA) was found to alleviate glomerular filtration dysfunction in STZ-induced type 2 DKD rats by inhibiting AGE-RAGE signal transduction, resulting in a renal protection.[26] In db/db mice, high DKD was established using glucose cultured glomerular mesangial cells (MC). Activation of PI3K-AKT pathway can inhibit the Hippo pathway, leading to nuclear YES-related protein accumulation and accelerated MC proliferation and DKD formation.[27] In STZ-induced diabetic mice, the xanthone mangiferin can reduce inflammation and oxidative stress in DKD by regulating the tension protein homologous gene/PI3K/AKT pathway, to inhibit renal interstitial fibrosis.[28]


  Conclusions Top


In summary, based on network pharmacologic analysis, 195 active components (OB ≥30% and DL ≥0.18) were screened from XCHD, among which 128 intersections with DKD were identified, and 333 signaling pathways were identified by KEGG pathway enrichment analysis. Active components in XCHD can regulate various DKD-related targets through different biologic processes and signaling pathways. XCHD protects the kidney and lowers blood glucose through diverse actions, including anti-oxidation, anti-inflammation, cytokine regulation, and attenuating insulin resistance. Thus, XCHD is an example of the multifunction and multitarget advantages of TCM. This study advances a scientific basis for the current clinical treatment of DKD and a starting point for in-depth discussion of the potential mechanism of XCHD in preventing and treating DKD. However, a limitation of this study is that there is no corresponding basic research to verify our results. Therefore, further experiments should be carried out to validate the pharmacologic effects and related mechanisms of XCHD in the treatment of DKD.

Acknowledgment

The authors thank Nissi S. Wang, MSc, for content editing of the manuscript.

Financial support and sponsorship

This work was supported by the National Science Foundation (grant number 81973627, 81620108031) and Beijing Natural Science Foundation (grant number 7212195).

Conflicts of interest

Ping Li is a Co-Editor-in-Chief of the journal and Tingting Zhao is an Editorial Board Member. The article was subject to the journal's standard procedures, with peer review handled independently of these editors and their research groups.



 
  References Top

1.
Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract 2019;157:107843.  Back to cited text no. 1
    
2.
Warren AM, Knudsen ST, Cooper ME. Diabetic nephropathy: An insight into molecular mechanisms and emerging therapies. Expert Opin Ther Targets 2019;23:579-91.  Back to cited text no. 2
    
3.
Ding SY, Zheng PD, He LQ, Hou WG, Zou Y, Gao JD. The research on xiaochalhu decoction improving the inflammation of chronic glomerulonephritis patients and relieving the proteinuria. Zhongguo Zhong Xi Yi Jie He Za Zhi 2013;33:21-6.  Back to cited text no. 3
    
4.
Li P, Kawachi H, Morioka T, Orikasa M, Oite T, Shi ZS, et al. Suppressive effects of sairei-to on monoclonal antibody 1-22-3-induced glomerulonephritis: Analysis of effective components. Pathol Int 1997;47:430-5.  Back to cited text no. 4
    
5.
Lin CC, Lin LT, Yen MH, Cheng JT, Hsing CH, Yeh CH. Renal protective effect of xiao-chai-hu-tang on diabetic nephropathy of type 1-diabetic mice. Evid Based Complement Alternat Med 2012;2012:984024.  Back to cited text no. 5
    
6.
Xu X, Zhang W, Huang C, Li Y, Yu H, Wang Y, et al. A novel chemometric method for the prediction of human oral bioavailability. Int J Mol Sci 2012;13:6964-82.  Back to cited text no. 6
    
7.
Yamanishi Y, Kotera M, Kanehisa M, Goto S. Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework. Bioinformatics 2010;26:i246-54.  Back to cited text no. 7
    
8.
Lespade L. Ab initio molecular dynamics of the reaction of quercetin with superoxide radical. Chem Phys 2016;475:32-8.  Back to cited text no. 8
    
9.
Lee JY, Park W. Anti-inflammatory effect of wogonin on RAW 264.7 mouse macrophages induced with polyinosinic-polycytidylic acid. Molecules 2015;20:6888-900.  Back to cited text no. 9
    
10.
Si TL, Liu Q, Ren YF, Li H, Xu XY, Li EH, et al. Enhanced anti-inflammatory effects of DHA and quercetin in lipopolysaccharide-induced RAW264.7 macrophages by inhibiting NF-κB and MAPK activation. Mol Med Rep 2016;14:499-508.  Back to cited text no. 10
    
11.
Vidyashankar S, Sandeep Varma R, Patki PS. Quercetin ameliorate insulin resistance and up-regulates cellular antioxidants during oleic acid induced hepatic steatosis in HepG2 cells. Toxicol In Vitro 2013;27:945-53.  Back to cited text no. 11
    
12.
Dong Y, Xing Y, Sun J, Sun W, Xu Y, Quan C. Baicalein alleviates liver oxidative stress and apoptosis induced by high-level glucose through the activation of the PERK/Nrf2 signaling pathway. Molecules 2020;25:599.  Back to cited text no. 12
    
13.
Sun P, Lu L, Chen J, Liu XD, Zhang Q, Wang X. AMPKα, hs-CRP and FcγR in diabetic nephropathy and drug intervention. Exp Ther Med 2018;15:4659-64.  Back to cited text no. 13
    
14.
Yang Z, Huang W, Zhang J, Xie M, Wang X. Baicalein improves glucose metabolism in insulin resistant HepG2 cells. Eur J Pharmacol 2019;854:187-93.  Back to cited text no. 14
    
15.
Zhang M, He L, Liu J, Zhou L. Luteolin attenuates diabetic nephropathy through suppressing inflammatory response and oxidative stress by inhibiting STAT3 pathway. Exp Clin Endocrinol Diabetes 2021;129:729-39.  Back to cited text no. 15
    
16.
Yu Q, Zhang M, Qian L, Wen D, Wu G. Luteolin attenuates high glucose-induced podocyte injury via suppressing NLRP3 inflammasome pathway. Life Sci 2019;225:1-7.  Back to cited text no. 16
    
17.
Jo HA, Kim JY, Yang SH, Han SS, Joo KW, Kim YS, et al. The role of local IL6/JAK2/STAT3 signaling in high glucose-induced podocyte hypertrophy. Kidney Res Clin Pract 2016;35:212-8.  Back to cited text no. 17
    
18.
Rehman K, Akash MS, Liaqat A, Kamal S, Qadir MI, Rasul A. Role of interleukin-6 in development of insulin resistance and type 2 diabetes mellitus. Crit Rev Eukaryot Gene Expr 2017;27:229-36.  Back to cited text no. 18
    
19.
Wu R, Liu X, Yin J, Wu H, Cai X, Wang N, et al. IL-6 receptor blockade ameliorates diabetic nephropathy via inhibiting inflammasome in mice. Metabolism 2018;83:18-24.  Back to cited text no. 19
    
20.
Piao C, Zhang Q, Jin D, Wang L, Tang C, Zhang N, et al. A study on the mechanism of milkvetch root in the treatment of diabetic nephropathy based on network pharmacology. Evid Based Complement Alternat Med 2020;2020:6754761.  Back to cited text no. 20
    
21.
Krepinsky JC, Li Y, Chang Y, Liu L, Peng F, Wu D, et al. Akt mediates mechanical strain-induced collagen production by mesangial cells. J Am Soc Nephrol 2005;16:1661-72.  Back to cited text no. 21
    
22.
Liao TT, Zhao LB, Liu H, He RL, Wang YQ, Li J. Liraglutide protects from renal damage via Akt-mTOR pathway in rats with diabetic kidney disease. Eur Rev Med Pharmacol Sci 2019;23:117-25.  Back to cited text no. 22
    
23.
Zhang A, Fang H, Chen J, He L, Chen Y. Role of VEGF-A and LRG1 in abnormal angiogenesis associated with diabetic nephropathy. Front Physiol 2020;11:1064.  Back to cited text no. 23
    
24.
Bus P, Scharpfenecker M, Van Der Wilk P, Wolterbeek R, Bruijn JA, Baelde HJ. The VEGF-A inhibitor sFLT-1 improves renal function by reducing endothelial activation and inflammation in a mouse model of type 1 diabetes. Diabetologia 2017;60:1813-21.  Back to cited text no. 24
    
25.
Yamagishi S, Fukami K, Matsui T. Crosstalk between advanced glycation end products (AGEs)-receptor RAGE axis and dipeptidyl peptidase-4-incretin system in diabetic vascular complications. Cardiovasc Diabetol 2015;14:2.  Back to cited text no. 25
    
26.
Hou B, Qiang G, Zhao Y, Yang X, Chen X, Yan Y, et al. Salvianolic acid A protects against diabetic nephropathy through ameliorating glomerular endothelial dysfunction via inhibiting AGE-RAGE signaling. Cell Physiol Biochem 2017;44:2378-94.  Back to cited text no. 26
    
27.
Qian X, He L, Hao M, Li Y, Li X, Liu Y, et al. YAP mediates the interaction between the Hippo and PI3K/Akt pathways in mesangial cell proliferation in diabetic nephropathy. Acta Diabetol 2021;58:47-62.  Back to cited text no. 27
    
28.
Song Y, Liu W, Tang K, Zang J, Li D, Gao H. Mangiferin alleviates renal interstitial fibrosis in streptozotocin-induced diabetic mice through regulating the PTEN/PI3K/Akt signaling pathway. J Diabetes Res 2020;2020:9481720.  Back to cited text no. 28
    


    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7], [Figure 8], [Figure 9], [Figure 10]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7]



 

Top
 
 
  Search
 
Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

 
  In this article
   Abstract
  Introduction
   Materials And Me...
  Results
   Disease target r...
  Discussion
  Conclusions
   References
   Article Figures
   Article Tables

 Article Access Statistics
    Viewed991    
    Printed14    
    Emailed0    
    PDF Downloaded86    
    Comments [Add]    

Recommend this journal


[TAG2]
[TAG3]
[TAG4]