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. 2018 Aug 1;8(48):27448-27463.
doi: 10.1039/c8ra04358b. eCollection 2018 Jul 30.

Combined systems pharmacology and fecal metabonomics to study the biomarkers and therapeutic mechanism of type 2 diabetic nephropathy treated with Astragalus and Leech

Affiliations

Combined systems pharmacology and fecal metabonomics to study the biomarkers and therapeutic mechanism of type 2 diabetic nephropathy treated with Astragalus and Leech

Ruiqun Chen et al. RSC Adv. .

Abstract

In our study, systems pharmacology was used to predict the molecular targets of Astragalus and Leech, and explore the therapeutic mechanism of type 2 diabetic nephropathy (T2DN) treated with Astragalus and Leech. Simultaneously, to reveal the systemic metabolic changes and biomarkers associated with T2DN, we performed 1H NMR-based metabonomics and multivariate analysis to analyze fecal samples obtained from model T2DN rats. In addition, ELISA kits and histopathological studies were used to examine biochemical parameters and kidney tissue, respectively. Striking differences in the Pearson's correlation of 22 biomarkers and 9 biochemical parameters were also observed among control, T2DN and treated rats. Results of systems pharmacology analysis revealed that 9 active compounds (3,9-di-O-methylnissolin; (6aR,11aR)-9,10-dimethoxy-6a,11a-dihydro-6H-benzofurano[3,2-c]chromen-3-ol; hirudin; l-isoleucine; phenylalanine; valine; hirudinoidine A-C) and 9 target proteins (l-serine dehydratase; 3-hydroxyacyl-CoA dehydrogenase; tyrosyl-tRNA synthetase; tryptophanyl-tRNA synthetase; branched-chain amino acid aminotransferase; acetyl-CoA C-acetyltransferase; isovaleryl-CoA dehydrogenase; pyruvate dehydrogenase E1 component alpha subunit; hydroxyacylglutathione hydrolase) of Astragalus and Leech were closely associated with the treatment of T2DN. Using fecal metabonomics analysis, 22 biomarkers were eventually found to be closely associated with the occurrence of T2DN. Combined with systems pharmacology and fecal metabonomics, these biomarkers were found to be mainly associated with 6 pathways, involving amino acid metabolism (leucine, valine, isoleucine, alanine, lysine, glutamate, taurine, phenylalanine, tryptophan); energy metabolism (lactate, succinate, creatinine, α-glucose, glycerol); ketone body and fatty acid metabolism (3-hydroxybutyrate, acetate, n-butyrate, propionate); methylamine metabolism (dimethylamine, trimethylamine); and secondary bile acid metabolism and urea cycle (deoxycholate, citrulline). The underlying mechanisms of action included protection of the liver and kidney, enhancement of insulin sensitivity and antioxidant activity, and improvement of mitochondrial function. To the best of our knowledge, this is the first time that systems pharmacology combined with fecal metabonomics has been used to study T2DN. 6 metabolites (n-butyrate, deoxycholate, propionate, tryptophan, taurine and glycerol) associated with T2DN were newly discovered in fecal samples. These 6 metabolites were mainly derived from the intestinal flora, and related to amino acid metabolism, fatty acid metabolism, and secondary bile acid metabolism. We hope the results of this study could be inspirational and helpful for further exploration of T2DN treatment. Meanwhile, our results highlighted that exploring the biomarkers of T2DN and therapeutic mechanisms of Traditional Chinese Medicine (TCM) formulas on T2DN by combining systems pharmacology and fecal metabonomics methods was a promising strategy.

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Conflict of interest statement

The authors declare no competing financial interest.

Figures

Fig. 1
Fig. 1. Box plots of concentration of biochemical and weight data from different groups of rats (above); * as compared with the control group, *P < 0.05, **P < 0.01; # as compared with the T2DN group, #P < 0.05, ##P < 0.01 (one-way ANOVA with a Bonferroni correction). Representative HE staining (magnification 20×) of kidney tissues among control, T2DN and treated group (below).
Fig. 2
Fig. 2. Typical 500 MHz 1H NMR spectra of fecal extracts: (A) control group; (B) T2DN group; (C) treated group. Keys: (1) deoxycholate; (2) n-butyrate; (3) alanine; (4) lactate; (5) propionate; (6) valine; (7) leucine; (8) isoleucine; (9) lysine; (10) cadaverine; (11) acetate; (12) glutamate; (13) N-acetyl-5-aminosalicylate; (14) succinate; (15) trimethylamine; (16) choline; (17) taurine; (18) glycine; (19) α-glucose/β-glucose; (20) tryptophan; (21) creatinine; (22) formate; (23) tyrosine; (24) fumarate; (25) benzoic acid; (26) phenylalanine; (27) phenylacetic acid; (28) glycerol; (29) 3-hydrobutyrate; (30) citrulline; (31) dimethylamine.
Fig. 3
Fig. 3. (A1 and A2): PCA score plot and 3D-PCA score plot for three groups (R2X = 0.963, Q2 = 0.941) based on 1H NMR spectra of fecal samples; (B1 and B2): OPLS-DA score plot and S-plot for control group and T2DN group (R2X = 0.932, R2Y = 0.993, Q2 = 0.970, p-value = 0.0001); (C1 and C2): OPLS-DA score plot and S-plot for T2DN group and treated group (R2X = 0.757, R2Y = 0.703, Q2 = 0.581, p-value = 0.0352). Control group (, green), T2DN group (, blue) and treated group (, red). The axes plotted in the S-plots from the predictive component are p[1] vs p(corr)[1], representing the magnitude and reliability, respectively. Variables labeled with orange squares () could be exploited as potential biomarkers and the labels next to the orange squares correspond to the numbers of metabolites in Table 1.
Fig. 4
Fig. 4. Pearson's correlation analysis of 22 biomarkers and 9 biochemical parameters in control, T2DN and treated groups, respectively. X1–X22 correspond to numbering of metabolites in Table 1. Y1–Y9 represent TC, TG, HDL, LDL, UREA, UA CREA, weight, and 24 h urine protein, respectively. Significantly correlated at *P < 0.05, **P < 0.01 (two-sided test).
Fig. 5
Fig. 5. The metabonome view above shows all matched pathways according to p values from pathway enrichment analysis (A) and pathway impact values from pathway topology analysis (B). The bubble size is proportional to the impact of each pathway and the bubble color denotes the significance, from highest in red to lowest in white. Labels correspond to numbering of pathways in Table 2. P values and pathway impact values (from 0 to 1) are corresponding to “P-value” and “Impact” column in Table 2, respectively.
Fig. 6
Fig. 6. Schematic diagram of the metabolic pathway alterations among the biomarkers associated with T2DN according to the KEGG database. “↑” represents a significant elevation in metabolites in T2DN group, while “↓” indicates a significant reduction. A red color () indicates increased metabolites in treated group, while a green color () shows decreased metabolites. The number in the pink region corresponds to the ID of target proteins in ESI, Table S4.
Fig. 7
Fig. 7. The total interaction network diagram. A1: 3,9-di-O-methylnissolin; A2: (6aR,11aR)-9,10-dimethoxy-6a,11a-dihydro-6H-benzofurano [3,2-c] chromen-3-ol; L1: hirudin; L2: l-isoleucine; L3: phenylalanine; L4: valine; L5: hirudinoidine A; L6: hirudinoidine B; L7: hirudinoidine C. Detailed information is available in ESI, Table S4.

References

    1. Conserva F. Gesualdo L. Papale M. J. Diabetes Res. 2016;2016:7934504. doi: 10.1155/2016/7934504. - DOI - PMC - PubMed
    1. Gross J. L. de Azevedo M. J. Silveiro S. P. Canani L. H. Caramori M. L. Zelmanovitz T. Diabetes Care. 2005;28:164–176. doi: 10.2337/diacare.28.1.164. - DOI - PubMed
    1. Kanwar Y. S. Sun L. Xie P. Liu F. Chen S. Annu. Rev. Pathol.: Mech. Dis. 2011;6:395–423. doi: 10.1146/annurev.pathol.4.110807.092150. - DOI - PMC - PubMed
    1. Singh D. K. Winocour P. Farrington K. Nat. Rev. Endocrinol. 2011;7:176–184. doi: 10.1038/nrendo.2010.212. - DOI - PubMed
    1. Dronavalli S. Duka I. Bakris G. L. Nat. Clin. Pract. Endocrinol. Metab. 2008;4:444–452. doi: 10.1038/ncpendmet0894. - DOI - PubMed