Integrated Systems Pharmacology, Urinary Metabonomics, and Quantitative Real-Time PCR Analysis to Uncover Targets and Metabolic Pathways of the You-Gui Pill in Treating Kidney-Yang Deficiency Syndrome

Abstract

Kidney-yang deficiency syndrome (KYDS) is a metabolic disease caused by a neuro-endocrine disorder. The You-gui pill (YGP) is a classic traditional Chinese medicine (TCM) formula for the treatment of KYDS and has been widely used to warm and recuperate KYDS clinically for hundreds of years in China. However, it is unknown whetherthe corresponding targets and metabolic pathways can also be found via using metabonomics based on one platform (e.g., ¹H NMR) to study different biological samples of KYDS. At the same time, relevant reports on further molecular verification (e.g., RT-qPCR analysis) of these targets associated with biomarkers and metabolic pathways have not yet, to our knowledge, been seen in KYDS's research. In the present study, a comprehensive strategy integrating systems pharmacology and ¹H NMR-based urinary metabonomics analysis was proposed to identify the target proteins and metabolic pathways that YGP acts on KYDS. Thereafter, further validation of target proteins in kidney tissue was performed through quantitative real-time PCR analysis (RT-qPCR). Furthermore, biochemical parameters and histopathological analysis were studied. As a result, seven target proteins (L-serine dehydratase; phosphoenolpyruvate carboxykinase; spermidine synthase; tyrosyl-tRNA synthetase, glutamine synthetase; 3-hydroxyacyl-CoA dehydrogenase; glycine amidinotransferase) in YGP were discovered to play a therapeutic role in KYDS via affecting eight metabolic pathways (glycine, serine and threonine metabolism; butanoate metabolism; TCA cycle, etc.). Importantly, three target proteins (i.e., 3-hydroxyacyl-CoA dehydrogenase; glutamine synthetase; and glycine amidinotransferase) and two metabolic pathways (butanoate metabolism and dicarboxylate metabolism) related to KYDS, to our knowledge, had been newly discovered in our study. The mechanism of action mainly involved energy metabolism, oxidative stress, ammonia metabolism, amino acid metabolism, and fatty acid metabolism. In short, our study demonstrated that targets and metabolic pathways for the treatment of KYDS by YGP can be effectively found via combining with systems pharmacology and urinary metabonomics. In addition to this, common and specific targets and metabolic pathways of KYDS treated by YGP can be found effectively by integration with the analysis of different biological samples (e.g., serum, urine, feces, and tissue). It is; therefore, important that this laid the foundation for deeper mechanism research and drug-targeted therapy of KYDS in future.

Keywords: RT-qPCR; You-gui pill; kidney-yang deficiency syndrome; target protein; urinary metabonomics.

Figure 1

Typical histopathological changes of hypothalamic-pituitary-target gland axis (A), and the changes of biochemical indexes related to oxidative stress, and function of the liver and kidney (B). The magnification of hypothalamus, pituitary, adrenal gland, liver and kidney is 40×, while the magnification of thyroid and testis is 20×. * As compared with the control group (CON), * p < 0.05, ** p < 0.01, ^# as compared with the KYDS group (MOD), ^# p < 0.05, ^## p < 0.01.

Figure 2

(A) The interaction graph among YGP, nine kinds of TCM ingredients, and 61 kinds of active compounds. (B) Sixty-one active compounds (red) and 3180 targets (cyan) network. (C) Sixty-one active compounds (red) and 234 pathways network (cyan).

Figure 3

Typical ¹H NMR spectra of urine sample (CON: Control group; MOD: KYDS group; YGP: You-gui pill group). (1) 2-hydroxyisovalerate; (2) 3-methyl-2-ketovalerate; (3) 2-hydroxyvalerate; (4) 2-oxoisocaproate; (5) valine; (6) isobutyrate; (7) 3-hydroxybutyrate; (8) lactate; (9) alanine; (10) acetate; (11) N-acetyl glycoprotein; (12) acetone; (13) acetoacetate; (14) succinate; (15) α-ketoglutarate; (16) citrate; (17) dimethylamine (DMA); (18) dimethylglycine (DMG); (19) methylguanidine; (20) trimethylamine (TMA); (21) creatinine; (22) trimethylamine-N-oxide (TMAO); (23) choline, (24) glycine; (25) glycerol; (26) creatine; (27) α-glucose; (28) β-glucose; (29) betaine; (30) hippurate; (31) allantoin; (32) fumarate; (33) N(1)-methyl-4-pyridone-5-carboxamide; (34) 3-hydroxyphenyl propionic acid; (35) tryptophan; (36) phenylalanine; (37) phenylacetylglycine; (38) indoxyl sulfate; (39) uridine; (40) hypoxanthine; (41) formate. The numbers “×8” and “×2” stand for the amplification factor of spectra.

Figure 4

Results of multivariate statistical analysis. Principle component analysis (PCA) score plot and 3D-PCA score plot among CON, MOD, and YGP groups (A1,A2, R²X = 0.856, Q² = 0.520). Orthogonal partial least squares discriminant analysis (OPLS-DA) score plot and S-plot between CON and MOD groups (B1,B2, R²X = 0.842, R²Y = 0.993, Q² = 0.840, p-value of CV-ANOVA = 0.0298); OPLS-DA score plot and S-plot between MOD and YGP groups (C1,C2, R²X = 0.683, R²Y = 0.987, Q² = 0.714, p-value of CV-ANOVA = 0.0370). The greater the distance of the red dots in S-plots from the origin, the greater the contribution to the grouping. Variables labeled with red dots could be considered as differential metabolites (B2,C2) and the labels next to red dots correspond to the numbers of metabolites in Table S1. All the points of the important variable values (VIP) < 1 in the S-plots were removed, leaving only the variables with VIP > 1.

Figure 5

The distribution of 27 differential metabolites. The background color represents the MOD group vs. CON group, and color of font represents the YGP group vs. MOD group. Red means ascending, green means descending, and grey meansno significant change.

Figure 6

(A) The metabolic pathways corresponding to differential metabolites in urine sample; and (B) overlapping pathways between systems pharmacology and urinary metabonomics analysis. The numbers in A correspond to the serial numbers of the metabolic pathway in Table S2, while the white pathway in B represents seven overlapping metabolic pathways. (C) The network of YGP–herbs–active compounds–target proteins–pathways. (D) Comparison of metabonomics and systems pharmacology studies between urine and serum samples.

Figure 7

YGP regulates KYDS-related differential gene expression. All genes expression are examined by RT-qPCR and normalized to β-actin expression. Data represent mean ± SEM for at least three independent experiments; * p < 0.05, ** p < 0.01; ^# p < 0.05, ^## p < 0.01.

Figure 8

Pearson’s correlation analysis of 27 differential metabolites and nine biochemical parameters related to the pathogenesis axis of KYDS in the CON, MOD, and YGP groups, respectively. * p < 0.05, ** p < 0.01 (two-sided test), red indicates positive correlation, blue represents a negative correlation. 27 differential metabolites were clustered analysis by Euclidean distance.

Figure 9

The overall interactive network diagram (herbs, active compounds, target proteins, pathways, and metabolites). SZY: Fructus Corni; GQ: Fructus Lycii; SY: Rhizoma Dioscoreae; FZ: Radix Aconiti Lateralis Prreparata; RG: Cinnamomi cortex. GTP: phosphoenolpyruvate carboxykinase; Yars: tyrosyl-tRNA synthetase; Srm: spermidine synthase; Sds: L-serine dehydratase; Hadh: 3-hydroxyacyl-CoA dehydrogenase; Glul: glutamine synthetase; Gatm: glycine amidinotransferase.