Login Register Cart 0
Generic placeholder image

Endocrine, Metabolic & Immune Disorders - Drug Targets

Editor-in-Chief

ISSN (Print): 1871-5303
ISSN (Online): 2212-3873

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Uncovering the Key miRNAs and Targets of the Liuwei Dihuang Pill in Diabetic Nephropathy-Related Osteoporosis based on Weighted Gene Co-Expression Network and Network Pharmacology Analysis

Author(s): Ming Ming Liu, Nan Ning Lv, Rui Geng, Zhen Hua, Yong Ma, Gui Cheng Huang, Jian Cheng* and Hai Yan Xu*

Volume 22, Issue 3, 2022

Published on: 15 February, 2021

Page: [274 - 289] Pages: 16

DOI: 10.2174/1871530321666210215161921

conference banner
Abstract

Background: Diabetic nephropathy-related osteoporosis (DNOP) is the most common comorbid bone metabolic disorder associated with diabetes mellitus (DM). The Liuwei Dihuang Pill (LWD) is a traditional Chinese herbal medicine widely used to treat diabetic complications, including diabetic nephropathy (DN). This study aimed to identify the biomarkers of the mechanisms of DNOP in LWD with systems biology approaches.

Methods: Herein, we performed an integrated analysis of the GSE51674 and GSE63446 datasets from the GEO database via weighted gene co-expression network and network pharmacology (WGCNA) analysis. In addition, a network pharmacology approach, including bioactive compounds, was used with oral bioavailability (OB) and drug-likeness (DL) evaluation. Next, target prediction, functional enrichment analysis, network analysis, and virtual docking were used to investigate the mechanisms of LWD in DNOP.

Results: WGCNA successfully identified 63 DNOP-related miRNAs. Among them, miR-574 was significantly upregulated in DN and OP samples. A total of 117 targets of 22 components associated with LWD in DNOP were obtained. The cellular response to nitrogen compounds, the AGERAGE signaling pathway in diabetic complications, and the MAPK signaling pathway were related to the main targets. Network analysis showed that kaempferol and quercetin were the most significant components. MAPK1 was identified as a potential target of miR-574 and the hub genes in the protein-protein interaction (PPI) network. The docking models demonstrated that kaempferol and quercetin had a strong binding affinity for Asp 167 of MAPK1.

Conclusion: This study demonstrated that miR-574 may play important roles in DNOP, and the therapeutic effects of kaempferol and quercetin on LWD in DNOP might be mediated by miR-574 by targeting MAPK1. Our results provide new perspectives for further studies on the anti-DNOP mechanism of LWD.

Keywords: Liuwei Dihuang, diabetic nephropathies, osteoporosis, WGCNA, network pharmacology, diabetes mellitus.

« Previous Next »
Graphical Abstract

[1]
Zhao, Z. Correlation analysis of urine proteins and inflammatory cytokines with osteoporosis in patients with diabetic nephropathy. J. Musculoskelet. Neuronal Interact., 2018, 18(3), 348-353.
[PMID: 30179212]
[2]
Ji, L.; Chen, Y.; Wang, H.; Zhang, W.; He, L.; Wu, J.; Liu, Y. Overexpression of Sirt6 promotes M2 macrophage transformation, alleviating renal injury in diabetic nephropathy. Int. J. Oncol., 2019, 55(1), 103-115.
[http://dx.doi.org/10.3892/ijo.2019.4800] [PMID: 31115579]
[3]
Chen, X.; Zhao, L.; Xing, Y.; Lin, B. Down-regulation of microRNA-21 reduces inflammation and podocyte apoptosis in diabetic nephropathy by relieving the repression of TIMP3 expression. Biomed. Pharmacother., 2018, 108, 7-14.
[http://dx.doi.org/10.1016/j.biopha.2018.09.007] [PMID: 30212710]
[4]
Bahrambeigi, S.; Yousefi, B.; Rahimi, M.; Shafiei-Irannejad, V. Metformin; an old antidiabetic drug with new potentials in bone disorders. Biomed. Pharmacother., 2019, 109, 1593-1601.
[http://dx.doi.org/10.1016/j.biopha.2018.11.032] [PMID: 30551413]
[5]
Ying, X.; Chen, X.; Wang, T.; Zheng, W.; Chen, L.; Xu, Y. Possible osteoprotective effects of myricetin in STZ induced diabetic osteoporosis in rats. Eur. J. Pharmacol., 2020, 866, 172805.
[http://dx.doi.org/10.1016/j.ejphar.2019.172805] [PMID: 31756333]
[6]
Paschou, S.A.; Dede, A.D.; Anagnostis, P.G.; Vryonidou, A.; Morganstein, D.; Goulis, D.G. Type 2 diabetes and osteoporosis: a guide to optimal management. J. Clin. Endocrinol. Metab., 2017, 102(10), 3621-3634.
[http://dx.doi.org/10.1210/jc.2017-00042] [PMID: 28938433]
[7]
Cheng, L.; Wang, P.; Tian, R.; Wang, S.; Guo, Q.; Luo, M.; Zhou, W.; Liu, G.; Jiang, H.; Jiang, Q. LncRNA2Target v2.0: a comprehensive database for target genes of lncRNAs in human and mouse. Nucleic Acids Res., 2019, 47(D1), D140-D144.
[http://dx.doi.org/10.1093/nar/gky1051] [PMID: 30380072]
[8]
Clough, E.; Barrett, T. The gene expression omnibus database. Methods Mol. Biol., 2016, 1418, 93-110.
[http://dx.doi.org/10.1007/978-1-4939-3578-9_5] [PMID: 27008011]
[9]
Ismail, S.M.; El Boghdady, N.A.; Hamoud, H.S.; Shabayek, M.I. Evaluation of circulating miRNA-208a-3p, miRNA-155-5p and miRNA-637 as potential non-invasive biomarkers and the possible mechanistic insights into pre- and postmenopausal osteoporotic females. Arch. Biochem. Biophys., 2020, 684, 108331.
[http://dx.doi.org/10.1016/j.abb.2020.108331] [PMID: 32151564]
[10]
Yang, F.; Cui, Z.; Deng, H.; Wang, Y.; Chen, Y.; Li, H.; Yuan, L. Identification of miRNAs-genes regulatory network in diabetic nephropathy based on bioinformatics analysis. Medicine (Baltimore), 2019, 98(27), e16225.
[http://dx.doi.org/10.1097/MD.0000000000016225] [PMID: 31277135]
[11]
Liu, X.; Li, X. Key genes involved in diabetic nephropathy investigated by microarray analysis. J. Comput. Biol., 2019, 26(12), 1438-1447.
[http://dx.doi.org/10.1089/cmb.2019.0182] [PMID: 31356112]
[12]
Yu, T.; You, X.; Zhou, H.; He, W.; Li, Z.; Li, B.; Xia, J.; Zhu, H.; Zhao, Y.; Yu, G.; Xiong, Y.; Yang, Y. MiR-16-5p regulates postmenopausal osteoporosis by directly targeting VEGFA. Aging (Albany NY), 2020, 12(10), 9500-9514.
[http://dx.doi.org/10.18632/aging.103223] [PMID: 32427128]
[13]
Mao, J.H.; Sui, Y.X.; Ao, S.; Wang, Y.; Liu, Y.; Leng, H. miR-140-3p exhibits repressive functions on preosteoblast viability and differentiation by downregulating MCF2L in osteoporosis. In Vitro Cell. Dev. Biol. Anim., 2020, 56(1), 49-58.
[http://dx.doi.org/10.1007/s11626-019-00405-9] [PMID: 31732956]
[14]
Xiao, B.; Wang, G.; Li, W. Weighted gene correlation network analysis reveals novel biomarkers associated with mesenchymal stromal cell differentiation in early phase. PeerJ, 2020, 8, e8907.
[http://dx.doi.org/10.7717/peerj.8907] [PMID: 32280568]
[15]
Qian, G.F.; Yuan, L.S.; Chen, M.; Ye, D.; Chen, G.P.; Zhang, Z.; Li, C.J.; Vijayan, V.; Xiao, Y. PPWD1 is associated with the occurrence of postmenopausal osteoporosis as determined by weighted gene co‑expression network analysis. Mol. Med. Rep., 2019, 20(4), 3202-3214.
[http://dx.doi.org/10.3892/mmr.2019.10570] [PMID: 31432133]
[16]
Pang, B.; Zhao, L.H.; Zhou, Q.; Zhao, T.Y.; Wang, H.; Gu, C.J.; Tong, X.L. Application of berberine on treating type 2 diabetes mellitus. Int. J. Endocrinol., 2015, 2015, 905749.
[http://dx.doi.org/10.1155/2015/905749] [PMID: 25861268]
[17]
Lin, L.; Wang, Q.; Yi, Y.; Wang, S.; Qiu, Z. Liuwei dihuang pills enhance the effect of western medicine in treating diabetic nephropathy: a meta-analysis of randomized controlled trials. Evid. Based Complement. Alternat. Med., 2016, 2016, 1509063.
[http://dx.doi.org/10.1155/2016/1509063] [PMID: 26997962]
[18]
Shi, R.; Wang, Y.; An, X.; Ma, J.; Wu, T.; Yu, X.; Liu, S.; Huang, L.; Wang, L.; Liu, J.; Ge, J.; Qiu, S.; Yin, H.; Wang, X.; Wang, Y.; Yang, B.; Yu, J.; Sun, Z. Efficacy of Co-administration of Liuwei Dihuang Pills and Ginkgo Biloba Tablets on Albuminuria in Type 2 Diabetes: A 24-Month, Multicenter, Double-Blind, Placebo-Controlled, Randomized Clinical Trial. Front. Endocrinol. (Lausanne), 2019, 10, 100.
[http://dx.doi.org/10.3389/fendo.2019.00100] [PMID: 30873118]
[19]
Ge, J.R.; Xie, L.H.; Chen, J.; Li, S.Q.; Xu, H.J.; Lai, Y.L.; Qiu, L.L.; Ni, C.B. Liuwei Dihuang Pill () Treats postmenopausal osteoporosis with shen (kidney) yin deficiency via janus kinase/signal transducer and activator of transcription signal pathway by up-regulating cardiotrophin-like cytokine factor 1 expression. Chin. J. Integr. Med., 2018, 24(6), 415-422.
[http://dx.doi.org/10.1007/s11655-016-2744-2] [PMID: 28028720]
[20]
Guo, M.F.; Dai, Y.J.; Gao, J.R.; Chen, P.J. Uncovering the Mechanism of Astragalus membranaceus in the Treatment of Diabetic Nephropathy Based on Network Pharmacology. J. Diabetes Res., 2020, 2020, 5947304.
[http://dx.doi.org/10.1155/2020/5947304] [PMID: 32215271]
[21]
Conserva, F.; Barozzino, M.; Pesce, F.; Divella, C.; Oranger, A.; Papale, M.; Sallustio, F.; Simone, S.; Laviola, L.; Giorgino, F.; Gallone, A.; Pontrelli, P.; Gesualdo, L. Urinary miRNA-27b-3p and miRNA-1228-3p correlate with the progression of Kidney Fibrosis in Diabetic Nephropathy. Sci. Rep., 2019, 9(1), 11357.
[http://dx.doi.org/10.1038/s41598-019-47778-1] [PMID: 31388051]
[22]
Langfelder, P.; Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics, 2008, 9, 559.
[http://dx.doi.org/10.1186/1471-2105-9-559] [PMID: 19114008]
[23]
Davis, S.; Meltzer, P.S. GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics, 2007, 23(14), 1846-1847.
[http://dx.doi.org/10.1093/bioinformatics/btm254] [PMID: 17496320]
[24]
Xu, X.; Zhang, W.; Huang, C.; Li, Y.; Yu, H.; Wang, Y.; Duan, J.; Ling, Y. A novel chemometric method for the prediction of human oral bioavailability. Int. J. Mol. Sci., 2012, 13(6), 6964-6982.
[http://dx.doi.org/10.3390/ijms13066964] [PMID: 22837674]
[25]
Yang, H.; Zhang, W.; Huang, C.; Zhou, W.; Yao, Y.; Wang, Z.; Li, Y.; Xiao, W.; Wang, Y. A novel systems pharmacology model for herbal medicine injection: a case using Reduning injection. BMC Complement. Altern. Med., 2014, 14, 430.
[http://dx.doi.org/10.1186/1472-6882-14-430] [PMID: 25366653]
[26]
Ru, J.; Li, P.; Wang, J.; Zhou, W.; Li, B.; Huang, C.; Li, P.; Guo, Z.; Tao, W.; Yang, Y.; Xu, X.; Li, Y.; Wang, Y.; Yang, L. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J. Cheminform., 2014, 6, 13.
[http://dx.doi.org/10.1186/1758-2946-6-13] [PMID: 24735618]
[27]
Wang, Y.; Bryant, S.H.; Cheng, T.; Wang, J.; Gindulyte, A.; Shoemaker, B.A.; Thiessen, P.A.; He, S.; Zhang, J. PubChem BioAssay: 2017 update. Nucleic Acids Res., 2017, 45(D1), D955-D963.
[http://dx.doi.org/10.1093/nar/gkw1118] [PMID: 27899599]
[28]
Gilson, M.K.; Liu, T.; Baitaluk, M.; Nicola, G.; Hwang, L.; Chong, J. Binding DB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res., 2016, 44(D1), D1045-D1053.
[http://dx.doi.org/10.1093/nar/gkv1072] [PMID: 26481362]
[29]
Nickel, J; Gohlke, BO; Erehman, J SuperPred: update on drug classification and target prediction. Nucleic Acids Res., 2014, 42, W26-W31.
[http://dx.doi.org/10.1093/nar/gku477]
[30]
Piñero, J.; Ramírez-Anguita, J.M.; Saüch-Pitarch, J.; Ronzano, F.; Centeno, E.; Sanz, F.; Furlong, L.I. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res., 2020, 48(D1), D845-D855.
[PMID: 31680165]
[31]
Stelzer, G; Rosen, N; Plaschkes, I. The GeneCards Suite: From gene data mining to disease genome sequence analyses. Curr. Protoc. Bioinform., 2016, 54, 1.30.1-1.30.33..
[32]
Zhou, Y.; Zhou, B.; Pache, L.; Chang, M.; Khodabakhshi, A.H.; Tanaseichuk, O.; Benner, C.; Chanda, S.K. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun., 2019, 10(1), 1523.
[http://dx.doi.org/10.1038/s41467-019-09234-6] [PMID: 30944313]
[33]
Sticht, C.; De La Torre, C.; Parveen, A.; Gretz, N. miRWalk: An online resource for prediction of microRNA binding sites. PLoS One, 2018, 13(10), e0206239.
[http://dx.doi.org/10.1371/journal.pone.0206239] [PMID: 30335862]
[34]
Sankrityayan, H.; Kulkarni, Y.A.; Gaikwad, A.B. Diabetic nephropathy: The regulatory interplay between epigenetics and microRNAs. Pharmacol. Res., 2019, 141, 574-585.
[http://dx.doi.org/10.1016/j.phrs.2019.01.043] [PMID: 30695734]
[35]
Zhang, X.; Liang, H.; Kourkoumelis, N.; Wu, Z.; Li, G.; Shang, X. Comprehensive Analysis of lncRNA and miRNA Expression Profiles and ceRNA Network Construction in Osteoporosis. Calcif. Tissue Int., 2020, 106(4), 343-354.
[http://dx.doi.org/10.1007/s00223-019-00643-9] [PMID: 31858161]
[36]
He, M.; Wang, J.; Yin, Z.; Zhao, Y.; Hou, H.; Fan, J.; Li, H.; Wen, Z.; Tang, J.; Wang, Y.; Wang, D.W.; Chen, C. MiR-320a induces diabetic nephropathy via inhibiting MafB. Aging (Albany NY), 2019, 11(10), 3055-3079.
[http://dx.doi.org/10.18632/aging.101962] [PMID: 31102503]
[37]
Kong, Y.; Nie, Z.K.; Li, F.; Guo, H.M.; Yang, X.L.; Ding, S.F. MiR-320a was highly expressed in postmenopausal osteoporosis and acts as a negative regulator in MC3T3E1 cells by reducing MAP9 and inhibiting PI3K/AKT signaling pathway. Exp. Mol. Pathol., 2019, 110, 104282.
[http://dx.doi.org/10.1016/j.yexmp.2019.104282] [PMID: 31301305]
[38]
Guérit, D.; Philipot, D.; Chuchana, P.; Toupet, K.; Brondello, J.M.; Mathieu, M.; Jorgensen, C.; Noël, D. Sox9-regulated miRNA-574-3p inhibits chondrogenic differentiation of mesenchymal stem cells. PLoS One, 2013, 8(4), e62582.
[http://dx.doi.org/10.1371/journal.pone.0062582] [PMID: 23626837]
[39]
Jiang, Y.; Liu, J.; Zhou, Z.; Liu, K.; Liu, C. Fangchinoline protects against renal injury in diabetic nephropathy by modulating the MAPK signaling pathway. Exp. Clin. Endocrinol. Diabetes, 2018, 128(8), 499-505.
[http://dx.doi.org/10.1055/a-0636-3883] [PMID: 30049003]
[40]
Pan, B.L.; Tong, Z.W.; Li, S.D.; Wu, L.; Liao, J.L.; Yang, Y.X.; Li, H.H.; Dai, Y.J.; Li, J.E.; Pan, L. Decreased microRNA-182-5p helps alendronate promote osteoblast proliferation and differentiation in osteoporosis via the Rap1/MAPK pathway. Biosci. Rep., 2018, 38(6), BSR20180696.
[http://dx.doi.org/10.1042/BSR20180696] [PMID: 30413613]
[41]
He, X.; Zhu, L.; An, L.; Zhang, J. MiR-143 inhibits osteoclastogenesis by targeting RANK and NF-κB and MAPK signaling pathways. Curr. Mol. Pharmacol., 2020, 13(3), 224-232.
[http://dx.doi.org/10.2174/1874467213666200116113945] [PMID: 31951177]
[42]
Bai, L.; Li, X.; He, L.; Zheng, Y.; Lu, H.; Li, J.; Zhong, L.; Tong, R.; Jiang, Z.; Shi, J.; Li, J. Antidiabetic potential of flavonoids from traditional Chinese Medicine: A Review. Am. J. Chin. Med., 2019, 47(5), 933-957.
[http://dx.doi.org/10.1142/S0192415X19500496] [PMID: 31248265]
[43]
Chen, S.; Jiang, H.; Wu, X.; Fang, J. Therapeutic Effects of Quercetin on Inflammation, Obesity, and Type 2 Diabetes. Mediators Inflamm., 2016, 2016, 9340637.
[http://dx.doi.org/10.1155/2016/9340637] [PMID: 28003714]
[44]
Lu, Q.; Ji, X.J.; Zhou, Y.X.; Yao, X.Q.; Liu, Y.Q.; Zhang, F.; Yin, X.X. Quercetin inhibits the mTORC1/p70S6K signaling-mediated renal tubular epithelial-mesenchymal transition and renal fibrosis in diabetic nephropathy. Pharmacol. Res., 2015, 99, 237-247.
[http://dx.doi.org/10.1016/j.phrs.2015.06.006] [PMID: 26151815]
[45]
Sharma, D.; Gondaliya, P.; Tiwari, V.; Kalia, K. Kaempferol attenuates diabetic nephropathy by inhibiting RhoA/Rho-kinase mediated inflammatory signalling. Biomed. Pharmacother., 2019, 109, 1610-1619.
[http://dx.doi.org/10.1016/j.biopha.2018.10.195] [PMID: 30551415]
[46]
Lei, D.; Chengcheng, L.; Xuan, Q.; Yibing, C.; Lei, W.; Hao, Y.; Xizhi, L.; Yuan, L.; Xiaoxing, Y.; Qian, L. Quercetin inhibited mesangial cell proliferation of early diabetic nephropathy through the Hippo pathway. Pharmacol. Res., 2019, 146, 104320.
[http://dx.doi.org/10.1016/j.phrs.2019.104320] [PMID: 31220559]
[47]
Wong, S.K.; Chin, K.Y.; Ima-Nirwana, S. The osteoprotective effects of kaempferol: the evidence from in vivo and in vitro studies. Drug Des. Devel. Ther., 2019, 13, 3497-3514.
[http://dx.doi.org/10.2147/DDDT.S227738] [PMID: 31631974]
[48]
Zhao, J.; Wu, J.; Xu, B.; Yuan, Z.; Leng, Y.; Min, J.; Lan, X.; Luo, J. Kaempferol promotes bone formation in part via the mTOR signaling pathway. Mol. Med. Rep., 2019, 20(6), 5197-5207.
[http://dx.doi.org/10.3892/mmr.2019.10747] [PMID: 31638215]
[49]
Yuan, Z.; Min, J.; Zhao, Y.; Cheng, Q.; Wang, K.; Lin, S.; Luo, J.; Liu, H. Quercetin rescued TNF-alpha-induced impairments in bone marrow-derived mesenchymal stem cell osteogenesis and improved osteoporosis in rats. Am. J. Transl. Res., 2018, 10(12), 4313-4321.
[PMID: 30662673]
[50]
Vakili, S.; Zal, F.; Mostafavi-Pour, Z.; Savardashtaki, A.; Koohpeyma, F. Quercetin and vitamin E alleviate ovariectomy-induced osteoporosis by modulating autophagy and apoptosis in rat bone cells. J. Cell. Physiol., 2021, 236(5), 3495-3509.
[http://dx.doi.org/10.1002/jcp.30087] [PMID: 33030247]
[51]
Wang, B.; Yao, K.; Wise, A.F.; Lau, R.; Shen, H.H.; Tesch, G.H.; Ricardo, S.D. miR-378 reduces mesangial hypertrophy and kidney tubular fibrosis via MAPK signalling. Clin. Sci. (Lond.), 2017, 131(5), 411-423.
[http://dx.doi.org/10.1042/CS20160571] [PMID: 28053239]
[52]
Zhu, N.; Hou, J. Exploring the mechanism of action Xianlingubao Prescription in the treatment of osteoporosis by network pharmacology. Comput. Biol. Chem., 2020, 85, 107240.
[http://dx.doi.org/10.1016/j.compbiolchem.2020.107240] [PMID: 32126522]

Rights & Permissions Print Cite
Article Metrics
70
3
Wayfinder Image
© 2024 Bentham Science Publishers | Privacy Policy