Network Pharmacology-Based Prediction of Mechanism of Shenzhuo Formula for Application to DKD

Wang, Xinmiao; Yang, Haoyu; Zhang, Lili; Han, Lin; Di, Sha; Wei, Xiuxiu; Wu, Haoran; Zhang, Haiyu; Zhao, Linhua; Tong, Xiaolin

doi:https://doi.org/10.1155/2021/6623010

Evidence-Based Complementary and Alternative Medicine

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Abstract Introduction Methods Results Discussion Conclusion Data Availability Conflicts of Interest Authors’ Contributions Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2021 | Article ID 6623010 | https://doi.org/10.1155/2021/6623010

Network Pharmacology-Based Prediction of Mechanism of Shenzhuo Formula for Application to DKD

Xinmiao Wang,¹Haoyu Yang,^1,2Lili Zhang,¹Lin Han,¹Sha Di,¹Xiuxiu Wei,^1,2Haoran Wu,^1,2Haiyu Zhang,^1,2Linhua Zhao ,³and Xiaolin Tong⁴

Academic Editor: Daniel Dias Rufino Arcanjo

Received03 Oct 2020

Revised19 Feb 2021

Accepted12 Apr 2021

Published21 Apr 2021

Abstract

Background. Shenzhuo formula (SZF) is a traditional Chinese medicine (TCM) prescription which has significant therapeutic effects on diabetic kidney disease (DKD). However, its mechanism remains unknown. Therefore, this study aimed to explore the underlying anti-DKD mechanism of SZF. Methods. The active ingredients and targets of SZF were obtained by searching TCMSP, TCMID, SwissTargetPrediction, HIT, and literature. The DKD target was identified from TTD, DrugBank, and DisGeNet. The potential targets were obtained and PPI network were built after mapping SZF targets and DKD targets. The key targets were screened out by network topology and the “SZF-key targets-DKD” network was constructed by Cytoscape. GO analysis and KEGG pathway enrichment analysis were performed by using DAVID, and the results were visualized by Omicshare Tools. Results. We obtained 182 potential targets and 30 key targets. Furthermore, a “SZF-key targets-DKD” network topological analysis showed that active ingredients like M51, M21, M5, M71, and M28 and targets like EGFR, MMP9, MAPK8, PIK3CA, and STAT3 might play important roles in the process of SZF treating in DKD. GO analysis results showed that targets were mainly involved in positive regulation of transcription from RNA polymerase II promoter, inflammatory response, lipopolysaccharide-mediated signaling pathway, and other biological processes. KEGG showed that DKD-related pathways like TNF signaling pathway and PI3K-Akt signaling pathway were at the top of the list. Conclusion. This research reveals the potential pharmacological targets of SZF in the treatment of DKD through network pharmacology and lays a foundation for further studies.

1. Introduction

Diabetic kidney disease (DKD) is one of the most common chronic microvascular complications of diabetes. It may be caused and shaped by the interaction of many factors such as endoplasmic reticulum dysfunction, high sugar-mediated generation of terminal advanced glycation endproducts (AGE), increased activation of the renin angiotensin aldosterone system, increased generation of reactive oxygen species (ROS), and activation of extracellular matrix (ECM) and protein kinase C [1, 2]. It is reported that the incidence of DKD is about 40% in the diabetic population [3]. Furthermore, with the increasing incidence of diabetes, the incidence of DKD is increasing yearly [4]. Therefore, it is important to intensify studies of the pathogenesis of DKD and the search for effective intervention targets.

Shenzhuo formula (SZF) as a traditional Chinese medicine (TCM) prescription has certain advantages in the treatment of DKD [5].It is created by Tong Xiaolin, an academician at the Chinese Academy of Sciences, and his team. This formula was based on the pathogenesis of qi deficiency blood stasis, and the classic prescription of Didang decoction. Years of clinical studies have shown that SZF can effectively increase the glomerular filtration rate, reduce 24-hour urinary protein and kidney damage, and reverse kidney disease when used early [5, 6]. However, due to the diversity of TCM compounds and complexity of in vivo processes, the systematic mechanism research of SZF has been hindered.

Recently, network pharmacology has been developed rapidly with the use of multiomics, high-throughput screening, network visualization and analysis, or other techniques [7–9]. It can help to reveal the network structure of drug action [10] and provide possibilities for exploring the mechanism of action of TCM compounds. Therefore, this study aimed to shed light on the underlying mechanisms of SZF in DKD treatment using a network pharmacology approach.

2. Methods

2.1. Research Tools

The Chinese Traditional Medicine System Pharmacological Database Analysis Platform (TCMSP, http://lsp.nwu.edu.cn/tcmsp.php) [11], Traditional Chinese Medicine Integrated Database (TCMID, http://www.megabionet.org/tcmid/) [12], SwissTargetPrediction (http://www.swisstargetprediction.ch/) [13], and HIT (http:lifecenter.biosino.org/hit/) [14] were used to access to SZF ingredients and targets. (2) The Therapeutic Target Database (TTD, http://bidd.nus.edu.sg/group/cjttd/) [15], DrugBank (https://www.drugbank.ca/) [16], and DisGeNet (http://www.disgenet.org/) [17] were used to get the targets’ proteins of DKD. (3) The protein-protein interaction (PPI) network was obtained online using STRING (http://string-db.org) [18]. Compositional software Cytoscape 3.2.1 (http://www.cytoscape.org/) [19] was used to carry out network topology analysis and construct SZF-key targets-DKD network. The Database for Annotation, Visualization and Integrated Discovery (DAVID, http://david.ncifcrf.Gov) [20] was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The Omicshare Tools (https://www.Omicshare.com/) were used for visual analysis of GO and KEGG results.

2.2. Collection of Major Chemical Constituents

We relied on TCMSP, TCMID database, and literatures mining to search for the chemical constituents of SZF (Hedysarum Multijugum Maxim, Radix Salviae, Hirudo, and Radix Rhei Et Rhizome).

2.3. Screening of Active Compounds

As we all know, TCM drugs enter human body and then take effect through absorption, distribution, metabolism, and excretion (ADME) processes. Among them, oral bioavailability (OB) and drug similarity (DL), the key parameters of ADME components, were used as the screening criteria for active ingredients in this study. In this section, we used TCMSP to collect active compounds and their ADME properties. And then the active compounds that meet “OB ≥ 30%, DL ≥ 0.18” were selected as potential active ingredients.

2.4. Prediction of Targets

SwissTargetPrediction and HIT databases were used to collect the drug targets. In addition, TTD, DrugBank and DisGeNet databases were used to search for DKD targets by entering the key words of “diabetic kidney disease” and “diabetic nephropathy.” Further, we matched SZF targets with DKD targets to obtain common targets.

2.5. Network Construction and Analysis

PPI network of common targets was obtained using STRING. Furthermore, the PPI network topology analysis was carried out using Cytoscape 3.2.1 software and then key targets were obtained. To further explore the interactions between the active ingredients and their related targets at a system level, a “SZF-key targets-DKD” network was constructed by Cytoscape3.2.1.

2.6. GO and KEGG Analysis

GO analysis is widely used for gene function classification and mainly includes the molecular function (MF), biological processes (BP), and cellular components (CC) [21]. In this step, we used the DAVID tool for GO and KEGG pathway analysis. Then, we used Omicshare Tools for visual display.

3. Results

3.1. Screening of Candidate Components in SZF

Through TCMSP and TCMID database, a total of 87 active compounds of Hedysarum Multijugum Maxim, 210 of Radix Salviae, 35 of Hirudo, and 92 of Radix Rhei Et Rhizome were obtained. Then by ADME (OB ≥ 30%, DL ≥ 0.18) screening, a total of 101 active compounds were selected, including 20 active compounds of Hedysarum Multijugum Maxim, 65 of Radix Salviae, and 16 of Radix Rhei Et Rhizome (in this section, because Hirudo could not be found in TCMSP database, its ADME parameters could not be obtained and did not participate in screening). In addition, through literature mining, another 4 active compounds were collected, including 2 active compounds of Hedysarum Multijugum Maxim [22, 23], 1 of Radix Salviae [24], and 1 of Radix Rhei Et Rhizome [25].

3.2. Target Prediction

After matching SZF targets with DKD genes, a total of 182 common targets of SZF were obtained. We only show 50 of them in Table 1. And full information of 182 common targets is displayed in Table 2.

3.3. Construction and Analysis of Network Maps

The PPI network of the 182 common targets was obtained using STRING (Figure 1). Then, we used Cytoscape 3.2.1 to obtain 30 key targets by network topology analysis with inclusion criteria of “degree ≥ 2 times of the median, closeness centrality ≥ median, betweenness centrality ≥ median” (Table 3). Next, we constructed a “SZF-key targets-DKD” network by Cytoscape3.2.1 (Figure 2).

3.4. GO and KEGG Analysis

The DAVID was used to carry out GO analysis. And the GO terms were visualized by the Omicshare Tools (Figure 3). The GO analysis results showed that targets were mainly involved in positive regulation of transcription from RNA polymerase II promoter, inflammatory response, lipopolysaccharide-mediated signaling pathway, positive regulation of peptidyl-serine phosphorylation, and other biological processes. As the top 20 GO enrichment items listed, DKD is relevant to kinds of BP in body abnormalities, and SZF is likely to regulate these items and then play an anti-DKD role.

KEGG pathway enrichment analysis showed that a total of 104 pathways were obtained. The top 20 pathways are displayed in Figure 4, which include TNF signaling pathway, HIF-1 signaling pathway, Toll-like receptor signaling pathway, FoxO signaling pathway, NOD-like receptor signaling pathway, and so on.

4. Discussion

Previous studies have suggested that SZF has a therapeutic effect on DKD [5,6]. However, the potential mechanisms of SZF treating in DKD have not been fully explained. In this study, we mainly applied network pharmacology to explore it. Firstly, a total of 140 potential active compounds and 182 common targets of SZF and DKD were obtained after screening of active compounds and mapping of targets. Then, we constructed two networks, including the PPI network of 182 common targets and SZF-key targets-DKD network, and then applying GO and KEGG enrichment analysis to explore the regulation mechanism of SZF in treating DKD.

Through the SZF-key targets-DKD network, we could know that most active ingredients were linked with no less than one target, which indicated the character of multi-target of TCM active ingredients. In the meanwhile, different active compounds from different herbs acted on the same targets, which demonstrated that SZF had a synergistic effect in treating DKD. In addition, there were 8 active ingredients whose degrees were greater than 2 times of average in SZF-key targets-DKD network topology analysis. Interestingly, 3 of them had been proven to have kidney protection effect by experiments. For example, quercetin liposomes had renal protective effects of reducing oxidative stress, attenuating AGE expression, and delaying the progression of DKD [26]. Luteolin attenuated DKD mainly via suppression of inflammatory response and oxidative response [27]. Ursolic acid alleviated renal damage in type 2 diabetic db/db mice by downregulating proteins in the angiotensin II type 1 receptor-associated protein/angiotensin II type 1 receptor signaling pathway to inhibit extracellular matrix accumulation, renal inflammation, fibrosis, and oxidative stress [28]. These results were coincident with our predictions, which suggested that active ingredients with higher degree might play an important role in the treatment of DKD. Meanwhile, we discovered five active ingredients (M5, M27, M28, M60, and M70) that were likely to have renal protection effect but had not been verified up to now.

Moreover, the results of the SZF-key targets-DKD topology analysis also showed that there were 5 targets whose degrees were greater than 2 times of the average. Particularly, 3 of these had been proven to be closely related with DKD. For instance, EGFR activation had a significant role in activating pathways that mediate podocyte injury and loss in diabetic nephropathy [29]. Downregulated expression of MMP-9 could promote the process of DKD [30]. STAT3 inhibition could hinder the development and progression of DKD in diabetic patients [31].

As shown in GO analysis, the potential targets of SZF acting on DKD were mainly associated with various biological processes, such as lipopolysaccharide-mediated signaling pathway, inflammatory response, positive regulation of cyclase activity, protein kinase B signaling, positive regulation of MAP kinase activity, and response to estradiol, which had a strongly direct correlation with the pathogenesis of DKD [32–38].

Similarly, KEGG pathway enrichment analysis showed that SZF took an anti-DKD effect by multiple pathways. Through further research, we found that some pathways had been already verified to exert anti-DKD potential by experiments, such as TNF signaling pathway [39], HIF-1 signaling pathway [40], Toll-like receptor signaling pathway [41], FoxO signaling pathway [42], focal adhesion [43], and NOD-like receptor signaling pathway [44]. These results were also consistent with what we predicted. In addition, SZF might have potential therapeutic effects on diseases such as cancer, hepatitis, influenza, leishmaniasis, pertussis, and tuberculosis according to the KEGG enrichment analysis. Just as it was reported that different diseases had common or similar pathological changes and could be treated with the same prescription [45], the above results suggested that SZF concentrated more on the systematicness of the body when treating DKD. In other words, SZF possibly regulated the body to reach the balance state, then reaching the aim of treatment.

5. Conclusion

In conclusion, this study based on the network pharmacology had preliminarily explained the anti-DKD mechanism of SZF from the perspective of multi-active ingredients, multi-targets, and multi-pathway. In the future, we will further investigate its mechanism by molecular docking, using in vitro or in vivo studies.

Data Availability

The data used to support the results of this study can be obtained from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare that there are no conflicts of interest.

Authors’ Contributions

Wang Xin-miao, Yang Hao-yu, and Zhang Li-li contributed equally to this work.

Acknowledgments

This study was supported by the Beijing Natural Science Foundation (7212189), the Fundamental Research Funds for the Central Public Welfare Research Institutes (ZZ13-ZD-06), and the Fundamental Research Funds for the China Academy of Chinese Medical Sciences (ZZ0808004).

References

A.-L. Cao, L. Wang, X. Chen et al., “Ursodeoxycholic acid and 4-phenylbutyrate prevent endoplasmic reticulum stress-induced podocyte apoptosis in diabetic nephropathy,” Laboratory Investigation, vol. 96, no. 6, pp. 610–622, 2016.
View at: Publisher Site | Google Scholar
G.-D. Sun, W.-P. Cui, Q.-Y. Guo, and L.-N. Miao, “Histone lysine methylation in diabetic nephropathy,” Journal of Diabetes Research, vol. 2014, Article ID 654148, 9 pages, 2014.
View at: Publisher Site | Google Scholar
R. Z. Alicic, M. T. Rooney, and K. R. Tuttle, “Diabetic kidney disease,” Clinical Journal of the American Society of Nephrology, vol. 12, no. 12, pp. 2032–2045, 2017.
View at: Publisher Site | Google Scholar
C. Magee, D. J. Grieve, C. J. Watson, and D. P. Brazil, “Diabetic nephropathy: a tangled web to unweave,” Cardiovascular Drugs and Therapy, vol. 31, no. 5-6, pp. 579–592, 2017.
View at: Publisher Site | Google Scholar
H. Chen, J. Guo, X. Zhao et al., “Retrospective analysis of the overt proteinuria diabetic kidney disease in the treatment of modified Shenzhuo formula for 2 years,” Medicine (Baltimore), vol. 96, no. 12, p. e6349, 2017.
View at: Publisher Site | Google Scholar
J. Tian, L. Zhao, Q. Zhou et al., “Retrospective analysis on modified Didang Tang for treating microalbuminuria of diabetic nephropathy,” Journal of Beijing University of Traditional Chinese Medicine, vol. 19, no. 6, pp. 7–10, 2012.
View at: Google Scholar
Y. Y. Jiang, M. Q. Sun, B. Ma et al., “The application and thinking of omics technologies in the modern pharmacology research of traditional Chinese medine,” World Science and Technology/Modernization of Traditional Chinese Medicine and Materia Medica, vol. 20, no. 8, pp. 1287–1295, 2018.
View at: Google Scholar
M. Isgut, M. Rao, C. Yang, V. Subrahmanyam, P. C. G. Rida, and R. Aneja, “Application of combination high-throughput phenotypic screening and target identification methods for the discovery of natural product-based combination drugs,” Medicinal Research Reviews, vol. 38, no. 2, pp. 504–524, 2018.
View at: Publisher Site | Google Scholar
G.-B. Zhang, Q.-Y. Li, Q.-L. Chen, and S.-B. Su, “Network pharmacology: a new approach for Chinese herbal medicine research,” Evidence-based Complementary and Alternative Medicine, vol. 2013, Article ID 621423, 9 pages, 2013.
View at: Publisher Site | Google Scholar
A. L. Hopkins, “Network pharmacology: the next paradigm in drug discovery,” Nature Chemical Biology, vol. 4, no. 11, pp. 682–690, 2008.
View at: Publisher Site | Google Scholar
J. Ru, P. Li, J. Wang et al., “TCMSP: a database of systems pharmacology for drug discovery from herbal medicines,” Journal of Cheminformatics, vol. 6, p. 13, 2014.
View at: Publisher Site | Google Scholar
R. Xue, Z. Fang, M. Zhang, Z. Yi, C. Wen, and T. Shi, “TCMID: traditional Chinese Medicine integrative database for herb molecular mechanism analysis,” Nucleic Acids Research, vol. 41, no. D1, pp. D1089–D1095, 2013.
View at: Publisher Site | Google Scholar
A. Daina, O. Michielin, and V. Zoete, “SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules,” Nucleic Acids Research, vol. 47, no. W1, pp. W357–W364, 2019.
View at: Publisher Site | Google Scholar
H. Ye, L. Ye, H. Kang et al., “HIT: linking herbal active ingredients to targets,” Nucleic Acids Research, vol. 39, no. suppl_1, pp. D1055–D1059, 2011.
View at: Publisher Site | Google Scholar
Y. H. Li, C. Y. Yu, X. X. Li et al., “Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics,” Nucleic Acids Research, vol. 46, no. D1, pp. D1121–D1127, 2018.
View at: Publisher Site | Google Scholar
D. S. Wishart, Y. D. Feunang, A. C. Guo et al., “DrugBank 5.0: a major update to the DrugBank database for 2018,” Nucleic Acids Research, vol. 46, no. D1, pp. D1074–D1082, 2018.
View at: Publisher Site | Google Scholar
J. Piñero, À. Bravo, N. Queralt-Rosinach et al., “DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants,” Nucleic Acids Research, vol. 45, no. D1, pp. D833–D839, 2017.
View at: Publisher Site | Google Scholar
D. Szklarczyk, A. L. Gable, D. Lyon et al., “STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets,” Nucleic Acids Research, vol. 47, no. D1, pp. D607–D613, 2019.
View at: Publisher Site | Google Scholar
P. Shannon, A. Markiel, O. Ozier et al., “Cytoscape: a software environment for integrated models of biomolecular interaction networks,” Genome Research, vol. 13, no. 11, pp. 2498–2504, 2003.
View at: Publisher Site | Google Scholar
D. W. Huang, B. T. Sherman, and R. A. Lempicki, “Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources,” Nature Protocols, vol. 4, no. 1, pp. 44–57, 2009.
View at: Publisher Site | Google Scholar
M. Ashburner, C. A. Ball, J. A. Blake et al., “Gene ontology: tool for the unification of biology,” Nature Genetics, vol. 25, no. 1, pp. 25–29, 2000.
View at: Publisher Site | Google Scholar
L. Zhou, Z. Liu, Z Wang et al., “Astragalus polysaccharides exerts immunomodulatory effects via TLR4-mediated MyD88-dependent signaling pathway in vitro and in vivo,” Scientific Reports, vol. 7, p. 44822, 2017.
View at: Publisher Site | Google Scholar
L. Li, X. Hou, R. Xu, C. Liu, and M. Tu, “Research review on the pharmacological effects of astragaloside IV,” Fundamental and Clinical Pharmacology, vol. 31, no. 1, pp. 17–36, 2017.
View at: Publisher Site | Google Scholar
X.-Y. Bao, Q. Zheng, Q. Tong et al., “Danshensu for myocardial ischemic injury: precli-nical evidence and novel methodology of quality assessment tool,” Frontiers in Pharmacology, vol. 9, p. 1445, 2018.
View at: Publisher Site | Google Scholar
X. Dong, J. Fu, X. Yin et al., “Emodin: a review of its pharmacology, toxicity and pharmacokinetics,” Phytotherapy Research, vol. 30, no. 8, pp. 1217-1218, 2016.
View at: Publisher Site | Google Scholar
L. Tang, K. Li, Y. Zhang et al., “Quercetin liposomes ameliorate streptozotocin-induced diabetic nephropathy in diabetic rats,” Scientific Reports, vol. 10, no. 1, p. 2440, 2020.
View at: Publisher Site | Google Scholar
M. Zhang, L. He, J. Liu, and L. Zhou, “Luteolin attenuates diabetic nephropathy through suppressing inflammatory response and oxidative stress by inhibiting STAT3 pathway,” Experimental and Clinical Endocrinology and Diabetes, vol. 128, 2020.
View at: Publisher Site | Google Scholar
T. Ma, L. Xu, L. Lu et al., “Ursolic acid treatment alleviates diabetic kidney injury by regulating the ARAP1/AT1R signaling pathway,” Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, vol. 12, pp. 2597–2608, 2019.
View at: Publisher Site | Google Scholar
J. Chen, J.-K. Chen, and R. C. Harris, “EGF receptor deletion in podocytes attenuates diabetic nephropathy,” Journal of the American Society of Nephrology, vol. 26, no. 5, pp. 1115–1125, 2015.
View at: Publisher Site | Google Scholar
L. Liu and J Tan, “The relationship between TIMP-1, MMP-9 and diabetic nephropathy,” Chinese Journal of Clinical Rational Drug Use, vol. 7, no. 29, pp. 173-174, 2014.
View at: Google Scholar
E. Said, A. Z. Sawsan, M. Eldosoky, and N. M. Elsherbeny, “Nifuroxazide, a STAT3 inhibitor, mitigates inflammatory burden and protects against diabetes-induced nephropathy in rats,” Chemico-Biological Interactions, vol. 281, pp. 111–120, 2018.
View at: Publisher Site | Google Scholar
W. Huang, F. Guo, Y. Long et al., “High glucose and lipopolysaccharide activate NOD1- RICK-NF-kappaB inflammatory signaling in mesangial cells,” Experimental and Clinical Endocrinology and Diabetes, vol. 124, no. 8, pp. 512–517, 2016.
View at: Publisher Site | Google Scholar
J. Donate-Correa, D. Luis-Rodríguez, E. Martín-Núñez et al., “Inflammatory targets in diabetic nephropathy,” Journal of Clinical Medicine, vol. 9, no. 2, p. 458, 2020.
View at: Publisher Site | Google Scholar
S. Xiao, Q. Li, L. Hu et al., “Soluble guanylate cyclase stimulators and activators: where are we and where to go?” Mini-Reviews in Medicinal Chemistry, vol. 19, no. 18, pp. 1544–1557, 2019.
View at: Publisher Site | Google Scholar
K. Sakamoto, K. Kuno, M. Takemoto et al., “Pituitary adenylate cyclase-activating polypeptide protects glomerular podocytes from inflammatory injuries,” Journal of Diabetes Research, vol. 2015, Article ID 727152, 10 pages, 2015.
View at: Publisher Site | Google Scholar
G. Wang, Y. Yan, N. Xu, Y. Hui, and D. Yin, “Upregulation of microRNA-424 relieved diabetic nephropathy by targeting Rictor through mTOR complex2/protein kinase B signaling,” Journal of Cellular Physiology, vol. 234, no. 7, pp. 11646–11653, 2019.
View at: Publisher Site | Google Scholar
R.-M. Wang, Z.-B. Wang, Y. Wang et al., “Swiprosin-1 promotes mitochondria-dependent apoptosis of glomerular podocytes via P38 MAPK pathway in early-stage diabetic nephropathy,” Cellular Physiology and Biochemistry, vol. 45, no. 3, pp. 899–916, 2018.
View at: Publisher Site | Google Scholar
A. Inada, O. Inada, N. L. Fujii et al., “Adjusting the 17β-Estradiol-to-Androgen ratio ameliorates diabetic nephropathy,” Journal of the American Society of Nephrology, vol. 27, no. 10, pp. 3035–3050, 2016.
View at: Publisher Site | Google Scholar
K. Omote, T. Gohda, M. Murakoshi et al., “Role of the TNF pathway in the progression of diabetic nephropathy in KK-Ay mice,” American Journal of Physiology-Renal Physiology, vol. 306, no. 11, pp. F1335–F1347, 2014.
View at: Publisher Site | Google Scholar
R. Bohuslavova, R. Cerychova, K. Nepomucka, and G. Pavlinkova, “Renal injury is accelerated by global hypoxia-inducible factor 1 alpha deficiency in a mouse model of STZ-induced diabetes,” BMC Endocrine Disorders, vol. 17, no. 1, p. 48, 2017.
View at: Publisher Site | Google Scholar
X. Y. Wu, J. Yu, and H. M Tian, “Effect of SOCS1 on diabetic renal injury through regulating TLR signaling pathway,” European Review for Medical and Pharmacological Sciences, vol. 23, no. 18, pp. 8068–8074, 2019.
View at: Publisher Site | Google Scholar
Y. A. Hong, J. H. Lim, M. Y. Kim et al., “Extracellular superoxide dismutase attenuates renal oxidative stress through the activation of adenosine monophosphate-activated protein kinase in diabetic nephropathy,” Antioxidants & Redox Signaling, vol. 28, no. 17, pp. 1543–1561, 2018.
View at: Publisher Site | Google Scholar
R. Yan, Y. Wang, M. Shi et al., “Regulation of PTEN/AKT/FAK pathways by PPARgamma impacts on fibrosis in diabetic nephropathy,” Journal of Cellular Biochemistry, vol. 120, 2019.
View at: Publisher Site | Google Scholar
P. Luan, J. Zhuang, J. Zou et al., “NLRC5 deficiency ameliorates diabetic nephropathy through alleviating inflammation,” FASEB Journal: Official Publication of the Federation of American Societies for Experimental Biology, vol. 32, no. 2, pp. 1070–1084, 2018.
View at: Publisher Site | Google Scholar
W.-Y. Jiang, “Therapeutic wisdom in traditional Chinese medicine: a perspective from modern science,” Trends in Pharmacological Sciences, vol. 26, no. 11, pp. 558–563, 2005.
View at: Publisher Site | Google Scholar

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