Genomic expression profiling and bioinformatics analysis on diabetic nephrology with ginsenoside Rg3

Wang,Juan; Cui,Chunli; Fu,Li; Xiao,Zili; Xie,Nanzi; Liu,Yang; Yu,Lu; Wang,Haifeng; Luo,Bangzhen

doi:10.3892/mmr.2016.5349

Genomic expression profiling and bioinformatics analysis on diabetic nephrology with ginsenoside Rg3

Authors:
- Juan Wang
- Chunli Cui
- Li Fu
- Zili Xiao
- Nanzi Xie
- Yang Liu
- Lu Yu
- Haifeng Wang
- Bangzhen Luo
View Affiliations

Affiliations: Department of Geriatric, Shanghai Tongji Hospital Affiliated to Tongji University, Shanghai 200065, P.R. China, Department of Nephrology, Shanghai Tongji Hospital Affiliated to Tongji University, Shanghai 200065, P.R. China, Dalian Fusheng Natural Pharmaceutical Development Co. Ltd., Dalian, Liaoning 116600, P.R. China, Shanghai Jinfang Biotechnology Co. Ltd., Shanghai 201210, P.R. China
Published online on: May 27, 2016 https://doi.org/10.3892/mmr.2016.5349
Pages: 1162-1172
Copyright: © Wang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )

Metrics: Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )

Cited By (CrossRef): 0 citations Loading Articles...

Abstract

Diabetic nephropathy (DN), a common diabetes-related complication, is the leading cause of progressive chronic kidney disease (CKD) and end‑stage renal disease. Despite the rapid development in the treatment of DN, currently available therapies used in early DN cannot prevent progressive CKD. The exact pathogenic mechanisms and the molecular events underlying DN development remain unclear. Ginsenoside Rg3 is a herbal medicine with numerous pharmacological effects. To gain a greater understanding of the molecular mechanism and signaling pathway underlying the effect of ginsenoside Rg3 in DN therapy, an RNA sequencing approach was performed to screen differential gene expression in a rat model of DN treated with ginsenoside Rg3. A combined bioinformatics analysis was then conducted to obtain insights into the underlying molecular mechanisms of the disease development, in order to identify potential novel targets for the treatment of DN. Six Sprague‑Dawley male rats were randomly divided into 3 groups: Normal control group, DN group and ginsenoside‑Rg3 treatment group, with two rats in each group. RNA sequencing was adopted for transcriptome profiling of cells from the renal cortex of DN rat model. Differentially expressed genes were screened out. Cluster analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were used to analyze the differentially expressed genes. In total, 78 differentially expressed genes in the DN control group were identified when compared with the normal control group, of which 52 genes were upregulated and 26 genes were downregulated. Differential expression of 43 genes was observed in the ginsenoside‑Rg3 treatment group when compared with the DN control group, consisting of 10 upregulated genes and 33 downregulated genes. Notably, 21 that were downregulated in the DN control group compared with the control were then shown to be upregulated in the ginsenoside‑Rg3 treatment group compared with the DN control group. In addition, 7 upregulated genes in the DN control group compared with the control were then shown to be downregulated in the ginsenoside‑Rg3 treatment group compared with the DN control group. Cluster analysis based on differentially expressed genes indicated that the transcriptomes are quite different among the samples. Distinct GO terms associated with these groups of genes were shown to be enriched. KEGG pathway analysis demonstrated that differentially expressed genes were predominantly involved in the fatty acid metabolism pathway and peroxisome proliferator‑activated receptor (PPAR) signaling pathway. To the best of our knowledge, this study was the first to present whole genome expression profiling in DN with ginsenoside‑Rg3 treatment by RNA‑Seq. A set of differentially expressed genes and pathways were identified. These data provided an insight into understanding the molecular mechanisms underlying the effect of ginsenoside‑Rg3 treatment of DN.

View Figures

View References

1	Vinik AI, Nevoret ML, Casellini C and Parson H: Diabetic neuropathy. Endocrinol Metab Clin North Am. 42:747–787. 2013. View Article : Google Scholar : PubMed/NCBI
2	Waanders F, Visser FW and Gans RO: Current concepts in the management of diabetic nephropathy. Neth J Med. 71:448–458. 2013.PubMed/NCBI
3	Zeng CC, Liu X, Chen GR, Wu QJ, Liu WW, Luo HY and Cheng JG: The molecular mechanism of rhein in diabetic nephropathy. Evid Based Complement Alternat Med. 2014:4870972014. View Article : Google Scholar : PubMed/NCBI
4	Yin J, Zhang H and Ye J: Traditional chinese medicine in treatment of metabolic syndrome. Endocr Metab Immune Disord Drug Targets. 8:99–111. 2008. View Article : Google Scholar : PubMed/NCBI
5	Lee JH, Choi SH, Kwon OS, Shin TJ, Lee JH, Lee BH, Yoon IS, Pyo MK, Rhim H, Lim YH, et al: Effects of ginsenosides, active ingredients of panax ginseng, on development, growth and life span of caenorhabditis elegans. Biol Pharm Bull. 30:2126–2134. 2007. View Article : Google Scholar : PubMed/NCBI
6	Trapnell C, Pachter L and Salzberg SL: TopHat: Discovering splice junctions with RNA-Seq. Bioinformatics. 25:1105–1111. 2009. View Article : Google Scholar : PubMed/NCBI
7	Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, Salzberg SL, Wold BJ and Pachter L: Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol. 28:511–515. 2010. View Article : Google Scholar : PubMed/NCBI
8	Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, Katayama T, Kawashima S, Okuda S, Tokimatsu T and Yamanishi Y: KEGG for linking genomes to life and the environment. Nucleic Acids Res. 36:D480–D484. 2008. View Article : Google Scholar :
9	Kanehisa M, Goto S, Sato Y, Furumichi M and Tanabe M: KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40:D109–D114. 2012. View Article : Google Scholar :
10	Chen YA, Tripathi LP and Mizuguchi K: TargetMine, an integrated data warehouse for candidate gene prioritisation and target discovery. PLoS One. 6:e178442011. View Article : Google Scholar : PubMed/NCBI
11	Wang Z, Gerstein M and Snyder M: RNA-Seq: A revolutionary tool for transcriptomics. Nat Rev Genet. 10:57–63. 2009. View Article : Google Scholar
12	Sutherland GT, Janitz M and Kril JJ: Understanding the pathogenesis of Alzheimer's disease: Will RNA-Seq realize the promise of transcriptomics? J Neurochem. 116:937–946. 2011. View Article : Google Scholar
13	Satoh J, Yamamoto Y, Asahina N, Kitano S and Kino Y: RNA-Seq data mining: Downregulation of NeuroD6 serves as a possible biomarker for Alzheimer's disease brains. Dis Markers. 2014:1231652014. View Article : Google Scholar : PubMed/NCBI
14	de Vries AP, Ruggenenti P, Ruan XZ, Praga M, Cruzado JM, Bajema IM, D'Agati VD, Lamb HJ, Pongrac Barlovic D, Hojs R, et al: Fatty kidney: Emerging role of ectopic lipid in obesity-related renal disease. Lancet Diabetes Endocrinol. 2:417–426. 2014. View Article : Google Scholar : PubMed/NCBI
15	Bu QT, Zhang WY, Chen QC, Zhang CZ, Gong XJ, Liu WC, Li W and Zheng YN: Anti-diabetic effect of ginsenoside Rb(3) in alloxan-induced diabetic mice. Med Chem. 8:934–941. 2012. View Article : Google Scholar : PubMed/NCBI
16	Jiang T, Wang XX, Scherzer P, Wilson P, Tallman J, Takahashi H, Li J, Iwahashi M, Sutherland E, Arend L and Levi M: Farnesoid X receptor modulates renal lipid metabolism, fibrosis and diabetic nephropathy. Diabetes. 56:2485–2493. 2007. View Article : Google Scholar : PubMed/NCBI
17	Herman-Edelstein M, Scherzer P, Tobar A, Levi M and Gafter U: Altered renal lipid metabolism and renal lipid accumulation in human diabetic nephropathy. J Lipid Res. 55:561–572. 2014. View Article : Google Scholar :
18	Isselmann I and Green S: Activation of a member of the steroid hormone receptor superfamily by peroxisome proliferators. Nature. 347:645–650. 1990. View Article : Google Scholar
19	Park CW, Kim HW, Ko SH, Chung HW, Lim SW, Yang CW, Chang YS, Sugawara A, Guan Y and Breyer MD: Accelerated diabetic nephropathy in mice lacking the peroxisome prolif-erator-activated receptor alpha. Diabetes. 55:885–893. 2006. View Article : Google Scholar : PubMed/NCBI
20	Duan SZ, Ivashchenko CY, Whitesall SE, D'Alecy LG, Duquaine DC, Brosius FC III, Gonzalez FJ, Vinson C, Pierre MA, Milstone DS and Mortensen RM: Hypotension, lipodystrophy and insulin resistance in generalized PPARgamma-deficient mice rescued from embryonic lethality. J Clin Invest. 117:812–822. 2007. View Article : Google Scholar : PubMed/NCBI
21	Deeb SS, Fajas L, Nemoto M, Pihlajamäki J, Mykkänen L, Kuusisto J, Laakso M, Fujimoto W and Auwerx J: A Pro12Ala substitution in PPARgamma2 associated with decreased receptor activity, lower body mass index and improved insulin sensitivity. Nat Genet. 20:284–287. 1998. View Article : Google Scholar : PubMed/NCBI
22	Kang KS, Yamabe N, Kim HY, Park JH and Yokozawa T: Therapeutic potential of 20(S)-ginsenoside Rg(3) against streptozotocin-induced diabetic renal damage in rats. Eur J Pharmacol. 591:266–272. 2008. View Article : Google Scholar : PubMed/NCBI
23	Kim M, Ahn BY, Lee JS, Chung SS, Lim S, Park SG, Jung HS, Lee HK and Park KS: The ginsenoside Rg3 has a stimulatory effect on insulin signaling in L6 myotubes. Biochem Biophys Res Commun. 389:70–73. 2009. View Article : Google Scholar : PubMed/NCBI
24	Hwang JT, Lee MS, Kim HJ, Sung MJ, Kim HY, Kim MS and Kwon DY: Antiobesity effect of ginsenoside Rg3 involves the AMPK and PPAR-gamma signal pathways. Phytother Res. 23:262–266. 2009. View Article : Google Scholar
25	Ni HX, Yu NJ and Yang XH: The study of ginsenoside on PPARgamma expression of mononuclear macrophage in type 2 diabetes. Mol Biol Rep. 37:2975–2979. 2010. View Article : Google Scholar
26	Sun HQ and Zhou ZY: Effect of ginsenoside-Rg3 on the expression of VEGF and TNF-α in retina with diabetic rats. Int J Ophthalmol. 3:220–223. 2010.
27	Lee OH, Lee HH, Kim JH and Lee BY: Effect of ginsenosides Rg3 and Re on glucose transport in mature 3T3-L1 adipocytes. Phytother Res. 25:768–773. 2011. View Article : Google Scholar : PubMed/NCBI