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. 2019 Jun 6;9(31):17791–17800. doi: 10.1039/c8ra10443c

High throughput metabolomics-proteomics investigation on metabolic phenotype changes in rats caused by Radix Scrophulariae using ultra-performance liquid chromatography with mass spectrometry†

Fang Lu 1,‡, Ning Zhang 2,‡, Tao Ye 2,‡, Hongwei Zhao 1, Mu Pang 1, Shu-min Liu 1,3,✉
PMCID: PMC9064686  PMID: 35520561

Abstract

Radix Scrophulariae, a traditional Chinese herb, is used to treat various diseases, including H2O2-induced apoptosis in cardiomyocytes, HaCaT cells, hyperuricaemia, and depression. This study screened metabolites, proteins and common pathways to better understand both the therapeutic effects and side effects of this herb. Methods: Untargeted metabolomics based on UPLC-TOF-MS, coupled with proteomics based on nano-UPLC-Q-Exactive-MS/MS, were used to investigate the effects of R. Scrophulariae in rats. Fifty-one identified metabolites in urine samples and 76 modulated proteins in liver tissue were potential biomarkers for R. Scrophulariae treatment. The biomarkers and common pathways involved were steroid hormone biosynthesis, drug metabolism-cytochrome p450, drug metabolism-other enzymes, pentose and glucuronate interconversions, and starch and sucrose metabolism. Some biomarkers were beneficial for treating diseases such as cancer, tuberculosis and isovaleric acidaemia, while other biomarkers caused side effects. Metabolomic and proteomic analyses of R. Scrophulariae-treated rats provided valuable information on the biological safety and efficacy of using R. Scrophulariae clinically.

Radix Scrophulariae, a traditional Chinese herb, is used to treat various diseases, including H2O2-induced apoptosis in cardiomyocytes, HaCaT cells, hyperuricaemia, and depression.graphic file with name c8ra10443c-ga.jpg

1. Introduction

Radix Scrophulariae, the dried root of Scrophularia ningpoensis Hemsl., belongs to the Scrophulariaceae family and has been used in TCM for thousands of years. Per the Pharmacopoeia of the People's Republic of China (2015 Edition), the species' traditional functions include treating febrile diseases, constipation, hot eyes, pharyngalgia, diphtheria, and scrofula. Modern pharmacological research has shown that R. Scrophulariae inhibits ventricular remodelling,1–4 hypoxia-induced microglial activation and neurotoxicity,5 hypertension and attenuating arteriosclerosis,6 proliferation, apoptosis induction in cancer cell lines,7 and antioxidative activity.8 TCMs are administered orally; therefore, their metabolites and proteins are disturbed when the blood circulation contacts the target organs. TCMs are therapeutic but also have side effects. For clinical use, the safety and effectiveness of TCMs are most important. These fundamental rules will guide exploitation of the biological effects of R. Scrophulariae.

The integrated metabolomics and proteomics based on mass spectrometry represents an innovative approach to characterize molecule fingerprints related to the function.9–13 Here, metabolomics coupled with iTRAQ-based proteome profile analysis of these biological effects were employed to screen the key metabolites from urine samples and liver proteins by UPLC-TOF-MS and nano-UPLC-Q-Exactive-MS/MS, respectively.

2. Materials and methods

2.1. Plant material and extract preparation

The root of Scrophularia ningpoensis Hemsl. is a natural medicine. R. Scrophulariae was acquired from the Heilongjiang Province Drug Company (Harbin, PR China). The voucher specimen (hlj-20120623012) for the herb was authenticated by Prof. Zhenyue Wang of the Department of Resources and Development of TCM at Heilongjiang University of Traditional Chinese Medicine, meeting the standards of the Pharmacopoeia of the People's Republic of China (2015 edition), page 117.

Fatty oil of R. Scrophulariae was prepared with the 1 kg crude drug, which was extracted twice with 0.6 l petroleum ether for 12 h each. The two portions were mixed and concentrated into cream, then the drug residue was freeze-dried and extracted twice with 10 and 8 l distilled water (DW) for 1.5 h each, respectively. The two portions were mixed and concentrated into cream, comprising the aqueous extract of R. Scrophulariae. The eluates were freeze-dried to make extracts with a yield of 50.7%.

2.2. Rats and treatments

Healthy male Sprague-Dawley rats, weighing 200 ± 20 g each, were purchased from Liaoning Changsheng Biotechnology Co., Ltd. (PR China) (Animal Certificate No: SCXK [Liao] 2015-0001). Rats were fed a standard diet with free access to water and housed 1 per metabolic cage at a temperature of 21–23 °C and humidity at 40–50% in controlled rooms with a 12 h/12 h light/dark cycle. This study was conducted in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. The protocol was subject to approval by the Committee on the Ethics of Animal Experiments of the College of Pharmacy of Heilongjiang University of Chinese Medicine.

After acclimation for 1 week, 20 rats were randomly divided into two groups, the control group and the water decoction of R. Scrophulariae group (n = 10 per group). Rats in the R. Scrophulariae group received decoction of R. Scrophulariae (1350 mg crude drug per kg, i.g.) once daily for 15 consecutive days, and rats in the control group received the same volume of 0.9% saline once daily for 15 days.

2.3. Sample collection and preparation

For the metabolomic analysis, urine was collected from all rats in each group on days 0, 1, 3, 5, 7, 9, 11, 13 and 15. Urine was centrifuged twice at 10 000 g for 10 min at 4 °C to remove the solid residue. Supernatants were transferred to a 1.5 ml polypropylene tube and filtered through a syringe filter (0.22 μm). Two μl of the supernatant was injected into the UPLC/TOF-MS for analysis.

For the proteomic analysis, rats were anaesthetized with 1% sodium pentobarbital anaesthesia (0.15 ml/100 g), and liver samples were obtained. Each group was analysed in triplicate (n = 10 per group) and then mixed into 3 mixed samples and stored at −80 °C. After thawing, 100 μl of STD buffer (4% SDS [161-0302, Bio-Rad], 1 mM DTT [161-0404 Bio-Rad], 150 mM Tris–HCl pH 8.0) per 20 μg sample was added and homogenized with a tissue homogenizer for 5 min in a boiling water bath. The mix was ultrasonicated (80 W) for 10 s and intermittently for 15 s ten times, then incubated in boiling water for 5 min and centrifuged at 14 000 g for 10 min. The supernatant was removed and subjected to 12.5% SDS-PAGE electrophoresis.

2.4. Metabolic profiling platform

2.4.1. LC-MS analysis

Metabolomic analysis was performed on a Waters ACQUITY UPLC system coupled with time-of-flight mass spectrometry. Chromatography was performed using an ACQUITY BEH C18 chromatography column (2.1 mm × 100 mm, 1.7 μm). Column and sample temperatures were set at 40.0 °C and 4.0 °C, respectively. The gradient mobile phase conditions consisted of solvent A (0.05% FA-ACN) and solvent B (0.05% FA-H2O). The urine sample gradient was as follows: 0–8 min, 2.0–40% A; 8–10 min, 40.0–98% A; 10–13 min, 98.0–100.0% A; 13–14 min, 100.0–2% A; 14–17 min, 2% A. The flow rate was 0.400 ml min−1. To guarantee system stability and repeatability, quality control (QC) samples were inserted every 10 samples, which was urine, and not a single sample collected in the experiment, but a mixed one of 10 samples per group taken from 100 to 200 μl of each. MS parameters were established as follows. Mass range was from 100 to 1500 in the full scan mode. Desolvation temperature and source temperature were set at 350.0 °C and 110.0 °C, respectively. Cone gas flow and desolvation gas flow rate were maintained at 20.0 l h−1 and 750.0 l h−1, respectively. Capillary voltage was set at 1300.0 V in the positive ions (ESI+) mode and 1500.0 V in the negative ions (ESI−) mode. Sample cone voltage was set to 60.0 V (ESI+) and 70.0 V (ESI−). Ion energy voltage was set to 35.0 V (ESI+) and 34.0 V (ESI−). Scan duration time and inter-scan delay were set to 0.200 s and 0.010 s, respectively. Leucine-enkephalin was the lock-mass compound (556.2771 [M + H]+ and 554.2615 [M − H]−).

2.4.2. Multivariate data analysis

Raw data acquired by UPLC-TOF-MS were exported to the Progenesis QI v2.3 (Nonlinear Dynamics, Waters Company) workstation for peak alignment, peak picking, and deconvolution. The data matrix (Rt-m/z, normalised abundance, and adducts) were exported to Ezinfo 3.0.3.0 software for principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA). We first performed a non-discriminatory PCA analysis. If the identified metabolites were Scrophulariaceae components, they would be removed from the original data and then analyzed by PCA. Of these analyses, 2D or 3D-PCA score plots reflected the clustering degree of each group. To analyse the urine metabolic profiles between the experimental and control groups, OPLS-DA score plots were constructed to obtain the VIP-plot, S-plot and loading plot. Variables farther from the origin contributed significantly in these plots. Using P < 0.05 and variables important for the projection (VIP) value > 1 as the standards, potential biomarkers were selected and compared with HMDB (http://www.hmdb.ca/) and progenesis metascope. Metabolic pathway analysis was performed using KEGG (http://www.kegg.jp/).

2.5. Proteomics analysis

The electrophoretic conditions were kept at a constant current for 90 min. The 300 μg samples were filter-aided for sample preparation (FASP). Eighty μg peptide samples from each group were labelled per the iTRAQ Reagent-8plex Multiplex Kit (AB SCIEX) instructions. Strong cation exchange (SCX) chromatography was performed on an AKTA Purifier 100 (GE Healthcare) coupled with a polysulfoethyl 4.6 × 100 mm column (5 μm, 200 Å) (PolyLC, Inc., Maryland, U.S.A.). Buffers were composed of buffer A (10 mM KH2PO4 pH 3.0, 25% ACN) and buffer B (10 mM KH2PO4 pH 3.0, 500 mM KCl, 25% ACN) with a flow rate of 1000 μl min−1. SCX gradient was set as follows: 0–25.01 min, 100% A; 25.01–32.00 min, 100–90% A; 32.01–42.00 min, 90–80% A; 42.01–47.00 min, 80–55% A; 47.01–52 min, 55–0% A; 52–60 min, 0% A; 60–60.01 min, 0–100% A; 60.01–75.00 min, 100% A.

Samples were separated by an automated Easy-nLC system coupled with a Q-Exactive spectrometer (Thermo Finnigan, USA). Buffer was composed of solution A (water containing 0.1% FA) and solution B (84% ACN containing 0.1% FA). Protein samples were performed on a thermo scientific EASY C18 column (2 cm × 100 μm, 5 μm), and separated on a thermo scientific EASY C18 column (75 μm × 100 mm, 3 μm). The flow rate was 300 nL min−1. The gradient elution procedure was as follows: 0–55 min, 0–40% B; 55–58 min, 40–100% B; 58–60 min, 100% B. The scan range was set to 300–1800 m/z in positive ion mode. The AGC target was set to 3 × 106. The maximum injection time was 10 ms. The normalized collision energy was 30 eV. The underfill ratio was 0.1%. The mass resolution for full MS and dd-MS2 were 70 000 and 17 500, respectively.

2.5.1. Data processing

Raw peptide files were searched with the Mascot 2.2 and Proteome Discoverer 1.4 (thermo) and a database of uniprot_rat_35830_20160326 fasta. Mascot search parameters were set as follows: fixed modifications were carbamidomethyl (C), iTRAQ8plex (N-term), iTRAQ8plex (K) and variable modification was oxidation (M); enzyme was set to trypsin; mass values were set to monoisotopic; max missed cleavages were set to 2; peptide mass tolerance was ± 20 ppm; fragment mass tolerance was 0.1 Da; and the database pattern was a decoy. Heatmaps were constructed by MetaboAnalyst 3.0 online (http://www.metaboanalyst.ca/). Protein identities were linked to the following databases: Quick GO (Gene Ontology Analysis), KEGG Pathway (Pathway Analysis) and STRING (Protein–Protein Interaction Analysis) for downstream analysis.

3. Results and discussion

3.1. Metabolomic profile analysis

In OPLS-DA, urine samples from the R. Scrophulariae group were separated from the control group, revealing that the rats' metabolic profiles changed after R. Scrophulariae treatment (Fig. 1A). VIP-plot and S-plot for evaluating contribution degree are shown in Fig. 1B and C. Fifty-one differential metabolites were consistent with P < 0.05, VIP > 1, and maximum fold change > 1 after being retrieved and matched by Metlin, HMDB and KEGG (Fig. 2A and Table 1).Endogenous small metabolites were classified using the HMDB database, and 26% were classified as amino acids, peptides, and analogues, 23% were benzenoids, and 19% were lipids (Fig. 2B). For biofunction, 16% were subgrouped into waste products, and cell signalling, fuel and energy storage, fuel or energy source and membrane integrity/stability accounted for 15% each (Fig. 2C). These metabolites were primarily located in the cytoplasm, membrane, extracellular matrix and mitochondria (Fig. 2D).

Fig. 1. Urine sample score plots for the R. Scrophulariae and control groups. (A) OPLS-DA score plots for the urine samples between the two groups, K represents the control, and Q represents the R. Scrophulariae group; (B) S-plots of urine samples between two groups; (C) VIP-plots of urine. Samples between the two groups.

Fig. 1

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Fig. 2. Metabolites expression profiling and pathway analysis of the R. Scrophulariae and control groups. (A) Heatmap of urine metabolites between the two groups. (B–D) Classification of potential biomarkers related to R. Scrophulariae in urine samples by chemical taxonomy, bio-function and cellular components based on HMDB annotations. (E) Topological mapping of potential biomarkers based on METPA analysis. (1) Nicotinate and nicotinamide metabolism; (2) phenylalanine metabolism; (3) tyrosine metabolism; (4) pyrimidine metabolism; (5) phenylalanine, tyrosine and tryptophan biosynthesis; (6) arginine and proline metabolism; (7), ubiquinone and other terpenoid-quinone biosynthesis; (8), drug metabolism – other enzymes; (9) steroid hormone biosynthesis; (10) starch and sucrose metabolism; (11) pentose and glucuronate interconversions; (12) cysteine and methionine metabolism; (13), tryptophan metabolism; (14) drug metabolism – cytochrome P450; (15) amino sugar and nucleotide sugar metabolism.

Fig. 2

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Significant differential metabolites produced in the urine after the intervention of Scrophulariaceae in normal ratsa.

No. Rt-m/z HMDB ID Ions mode KEGG Formula Metabolites VIP value Trends (Q/K) Anova (p) q value Max fold change
U1 10.70_207.1034 HMDB11603 pos C16453 C10H13N3O2 4-(Methylnitrosamino)-1-(3-pyridyl)-1-butanone 1.1366 Down* 0.0171 0.0450 1.5678
U2 4.45_202.1225 HMDB00792 pos C08277 C10H18O4 Sebacic acid 1.9215 Up* 0.0145 0.0402 1.4905
U3 1.39_153.0816 HMDB04825 pos C04227 C8H11NO2 p-Octopamine 1.1147 Up** 0.0029 0.0123 1.2995
U4 2.56_160.1227 HMDB02038 pos C02728 C7H16N2O2 N(6)-Methyllysine 1.0838 Up** 0.0000 0.0009 1.3617
U5 2.56_143.0966 HMDB04827 pos C10172 C7H13NO2 Proline betaine 3.5748 Up* 0.0109 0.0325 1.3654
U6 3.50_275.1305 HMDB13209 pos C00806 C14H17N3O3 Alanyltryptophan 2.5677 Up** 0.0000 0.0000 5.6666
U7 3.96_286.1515 HMDB00343 pos C05298 C18H22O3 2-Hydroxyestrone 1.0815 Up** 0.0028 0.0120 1.4051
U8 0.93_172.0633 HMDB01138 pos C00624 C7H11NO5 N-Acetylglutamic acid 1.4061 Up** 0.0000 0.0007 1.5997
U9 0.96_202.1108 HMDB00216 pos C00547 C8H11NO3 Norepinephrine 1.3643 Up** 0.0000 0.0005 1.5478
U10 0.97_284.1007 HMDB00472 pos C01017 C11H12N2O3 5-Hydroxy-l-tryptophan 1.1434 Up** 0.0016 0.0080 1.8401
U11 1.81_267.1359 HMDB05056 pos C18166 C18H22O4 Enterodiol 1.7389 Up** 0.0004 0.0031 1.4417
U12 1.84_144.0672 HMDB01514 pos C00329 C6H13NO5 Glucosamine 1.9440 Up** 0.0000 0.0000 2.5749
U13 2.23_275.1260 HMDB00273 pos C00214 C10H14N2O5 Thymidine 1.0469 Up** 0.0022 0.0103 1.4222
U14 2.31_297.1462 HMDB06344 pos C04148 C13H16N2O4 Alpha-N-phenylacetyl-l-glutamine 13.5022 Up** 0.0009 0.0055 1.4501
U15 2.92_267.1004 HMDB00933 pos C16308 C12H20O4 Traumatic acid 1.2758 Up** 0.0000 0.0008 2.1394
U16 4.24_254.1154 HMDB41959 pos C11785 C16H17NO3 Normorphine 2.1217 Down* 0.0388 0.0802 1.5050
U17 10.65_190.0521 HMDB01553 pos C01180 C5H8O3S 2-Oxo-4-methylthiobutanoic acid 1.3511 Up* 0.0287 0.0664 1.7704
U18 10.09_151.0469 HMDB02091 pos C03033 C11H18O8 Isovalerylglucuronide 2.2095 Down** 0.0001 0.0013 36.6655
U19 6.49_316.1969 HMDB00306 pos C00483 C8H11NO Tyramine 1.0905 Down* 0.0350 0.0748 1.3971
U20 5.54_246.1721 HMDB02176 pos C18319 C5H10O2 Ethylmethylacetic acid 1.1394 Down** 0.0009 0.0055 1.5188
U21 5.35_278.1073 HMDB10328 pos C03033 C14H19NO7 Tyramine glucuronide 1.0563 Down** 0.0015 0.0080 1.4124
U22 4.79_158.0630 HMDB00821 pos C05598 C10H11NO3 Phenylacetylglycine 1.2436 Up** 0.0000 0.0008 3.3653
U23 4.33_348.1204 HMDB01476 pos C00632 C7H7NO3 3-Hydroxyanthranilic acid 1.7225 Down** 0.0003 0.0027 3.1281
U24 3.36_153.0933 HMDB00784 pos C08261 C9H16O4 Azelaic acid 1.1179 Up* 0.0142 0.0398 2.6331
U25 2.73_245.1513 HMDB00201 pos C02571 C9H17NO4 l-Acetylcarnitine 2.8023 Up** 0.0090 0.0282 1.4032
U26 2.62_197.0838 HMDB02035 pos C00811 C9H8O3 4-Hydroxycinnamic acid 1.0570 Up** 0.0001 0.0012 1.6887
U27 2.49_230.1422 HMDB04063 pos C05588 C10H15NO3 Metanephrine 1.8485 Up* 0.0144 0.0400 1.1881
U28 2.45_243.1353 HMDB13248 pos C03343 C16H22O4 Monoethylhexyl phthalic acid 2.7083 Up** 0.0007 0.0048 1.3543
U29 2.27_200.0756 HMDB00462 pos C01551 C4H6N4O3 Allantoin 1.1143 Up** 0.0000 0.0002 1.8271
U30 2.18_261.0885 HMDB00181 pos C00355 C9H11NO4 l-Dopa 2.0865 Up** 0.0000 0.0002 1.5815
U31 2.05_413.1250 HMDB10334 pos C03033 C22H22O9 Ketoprofen glucuronide 1.1382 Up** 0.0084 0.0268 1.3105
U32 1.68_255.0778 HMDB01858 pos C01468 C7H8O p-Cresol 1.0839 Up** 0.0001 0.0011 1.5645
U33 1.65_265.1570 HMDB00010 pos C05299 C19H24O3 2-Methoxyestrone 1.3779 Up** 0.0006 0.0040 1.5384
U34 1.40_204.1350 HMDB00450 pos C16741 C6H14N2O3 5-Hydroxylysine 11.9554 Up** 0.0000 0.0003 1.5922
U35 1.34_173.0461 HMDB12710 pos C00944 C7H10O6 3-Dehydroquinate 1.3519 Up** 0.0012 0.0069 2.1674
U36 1.30_259.1671 HMDB00824 pos C03017 C10H19NO4 Propionylcarnitine 1.4708 Up** 0.0021 0.0100 1.5050
U37 1.22_215.1049 HMDB32049 pos C06354 C13H10O Benzophenone 1.3260 Up** 0.0000 0.0003 2.9951
U38 1.22_166.0736 HMDB02303 pos C00580 C2H6S Dimethylsulfide 5.9549 Up** 0.0000 0.0001 1.4267
U39 1.20_158.0904 HMDB00904 pos C00327 C6H13N3O3 Citrulline 2.6437 Up** 0.0000 0.0001 1.7277
U40 1.05_168.0684 HMDB00742 pos C05330 C4H9NO2S Homocysteine 1.0606 Up** 0.0028 0.0122 1.3436
U41 0.95_261.1481 HMDB13835 pos C15205 C16H22O4 Diisobutyl phthalate 1.1423 Up** 0.0014 0.0076 1.2734
U42 0.90_269.1267 HMDB00014 pos C00881 C9H13N3O4 Deoxycytidine 3.0487 Up** 0.0000 0.0001 1.5761
U43 0.87_227.0455 HMDB01890 pos C06809 C5H9NO3S Acetylcysteine 3.1726 Up** 0.0001 0.0012 1.6787
U44 0.80_212.1014 HMDB41821 pos C07585 C8H9N3O2 Acetylisoniazid 14.5218 Up** 0.0000 0.0003 1.6217
U45 0.74_144.1041 HMDB01010 pos C00745 C10H13N2 Nicotine imine 2.8013 Up** 0.0048 0.0179 1.3663
U46 0.68_176.0135 HMDB00875 pos C01004 C7H7NO2 Trigonelline 1.2566 Up** 0.0008 0.0049 1.4882
U47 7.86_253.1075 HMDB02004 neg C08309 C13H18N2O 5-Methoxydimethyltryptamine 1.4738 Down* 0.0235 0.5348 1.3094
U48 4.02_290.0707 HMDB00855 neg C03150 C11H15N2O5 Nicotinamide riboside 1.2816 Up* 0.0252 0.5368 1.6344
U49 3.72_247.0284 HMDB04983 neg C11142 C2H6O2S Dimethyl sulfone 13.8303 Up** 0.0000 0.0000 12.5966
U50 1.72_238.0757 HMDB13318 neg C00977 C11H13N3O Tryptophanamide 1.6212 Up* 0.0113 0.4388 1.8896
U51 1.72_483.1785 HMDB10317 neg C03033 C24H32O8 17-Beta-estradiol glucuronide 1.3788 Up* 0.0194 0.5261 2.7223
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a

Compared with control group, *p < 0.05,**p < 0.01. U represents urine. K represents control group, Q represents R. Scrophulariae group.

Pathway analysis was performed using MetaboAnalyst 3.0 software, revealing that endogenous small molecule metabolites were concentrated in the metabolisms of nicotinate and nicotinamide, phenylalanine, tyrosine and pyrimidine, and in phenylalanine, tyrosine and tryptophan biosynthesis (Fig. 2E).

3.2. Liver proteomic profile

Seventy-six significant proteins were selected by P < 0.05 and ratio >1.2 or <0.833 and are listed in Table 2. Compared with the control, expression levels of 31 proteins were down-regulated, while 45 were up-regulated. Seventy-six changed proteins were further enriched using the Bonferroni correction for multiple testing (P < 0.05) through GO analysis, and the GO terms for fold enrichment are shown in Fig. 3A. Seventy-six proteins participated in single-organism metabolic processes. These proteins were primarily involved in catalytic and electron carrier activities and were mainly located in the organelles. It suggested that the function of R. Scrophulariae is related to a complicated biological process. Twelve significant KEGG pathways were selected by −log p value > 2 and are shown in Fig. 3B. The protein–protein interaction (PPI) network was analysed by the publicly available programme STRING (http://string-db.org/). STRING is a database of known and predicted protein interactions. PPI nodes such as COX2, ND2, Cyp17a1, Hsd17b2, Mgst3, Ugt2b, Cyp2c13, RT1-CE1, Mn1 and Psmb8 might play key roles in the functional mechanism of R. Scrophulariae (Fig. 3C).

Significantly different proteins produced in the liver after the intervention of Scrophulariaceae in normal rats (difference multiple >1.2 or <0.8)a.

No. Accession Gene name Description Average ratio Q/K Trends Q/K P value
L1 Q6MG32 RT1-CE12 RT1 class I, CE12 0.4685 Down** 0.0002
L2 Q63042 Gfer FAD-linked sulfhydryl oxidase ALR 0.5479 Down** 0.0022
L3 A0A0G2JSK1 Serpina3c Protein Serpina3c 0.5502 Down** 0.0001
L4 P35286 Rab13 Ras-related protein Rab-13 0.6139 Down* 0.0458
L5 M0R9Q1 Rbm14 Protein Rbm14 0.6297 Down** 0.0005
L6 Q4VBH1 Ighg Ighg protein 0.6415 Down** 0.0004
L7 A0A023IKI3 Psmb8 Proteasome subunit beta type 0.6454 Down** 0.0047
L8 A0A0G2JX10 Anks3 Ankyrin repeat and SAM domain-containing protein 3 0.6764 Down** 0.0022
L9 P70473 Amacr Alpha-methylacyl-CoA racemase 0.6981 Down** 0.0003
L10 M0RAJ5 Prr14l Protein Prr14l 0.7129 Down** 0.0069
L11 Q6P756 Necap2 Adaptin ear-binding coat-associated protein 2 0.7407 Down* 0.0151
L12 Q80W92 Vac14 Protein VAC14 homolog 0.7429 Down* 0.0338
L13 A0A097PE04 COX2 Cytochrome c oxidase subunit 2 0.7470 Down** 0.0005
L14 Q5RK24 Pmvk Phosphomevalonate kinase 0.7552 Down** 0.0019
L15 Q99MS0 Sec14l2 SEC14-like protein 2 0.7571 Down** 0.0009
L16 E9PU17 Abca17 ATP-binding cassette sub-family A member 17 0.7599 Down* 0.0271
L17 F1LM99 Phf12 PHD finger protein 12 0.7733 Down** 0.0045
L18 A0A0G2JVQ0 Rnf111 Protein Rnf111 0.7744 Down* 0.0444
L19 D3ZTW7 Atpaf2 ATP synthase mitochondrial F1 complex assembly factor 2 (predicted), isoform CRA_c 0.7886 Down** 0.0012
L20 P43424 Galt Galactose-1-phosphate uridylyltransferase 0.7907 Down** 0.0076
L21 A0A0G2JV37 LOC100910040 Carboxylic ester hydrolase 0.7914 Down* 0.0305
L22 P49889 Ste Estrogen sulfotransferase, isoform 3 0.7941 Down** 0.0050
L23 Q6AYW2 Pah Phenylalanine hydroxylase 0.7994 Down** 0.0088
L24 P55006 Rdh7 Retinol dehydrogenase 7 0.8006 Down** 0.0001
L25 P0C5E9 Crygs Beta-crystallin S 0.8026 Down** 0.0075
L26 A0A0G2KA12 Kif1b Kinesin-like protein KIF1B 0.8111 Down* 0.0244
L27 F1LRB8 Mat2a S-Adenosylmethionine synthase 0.8113 Down** 0.0057
L28 B2GV29 Trmt13 Ccdc76 protein 0.8139 Down** 0.0016
L29 Q4QR81 Rbms2 Protein Rbms2 0.8203 Down* 0.0224
L30 D4AAP6 Mn1 Protein Mn1 0.8215 Down** 0.0016
L31 D4AB73 Sprtn Putative uncharacterized protein RGD1559496_predicted 0.8298 Down* 0.0177
L32 Q5XHZ8 Cog3 Component of oligomeric golgi complex 3 1.2021 Up** 0.0011
L33 P00502 Gsta1 Glutathione S-transferase alpha-1 1.2046 Up** 0.0000
L34 P19488 Ugt2b37 UDP-glucuronosyltransferase 2B37 1.2057 Up** 0.0045
L35 Q6AXQ0 Sae1 SUMO-activating enzyme subunit 1 1.2057 Up* 0.0347
L36 F1LNM4 LOC103689965 Complement C4 (fragment) 1.2075 Up** 0.0039
L37 F1LU27 Focad Protein Focad 1.2138 Up* 0.0489
L38 Q32PY9 Idnk Probable gluconokinase 1.2167 Up** 0.0068
L39 G3V647 Pdxk Pyridoxal kinase 1.2222 Up** 0.0010
L40 P05545 Serpina3k Serine protease inhibitor A3K 1.2300 Up** 0.0100
L41 G3V9N9 Man1a1 Alpha-1,2-mannosidase 1.2303 Up** 0.0040
L42 Q566C7 Nudt3 Diphosphoinositol polyphosphate phosphohydrolase 1 1.2349 Up** 0.0001
L43 F1LN59 Eif4g2 Protein Eif4g2 1.2408 Up** 0.0084
L44 D4A284 Nell1 NEL-like 1 (chicken), isoform CRA_a 1.2417 Up* 0.0180
L45 D3ZNJ5 Inmt Protein Inmt 1.2494 Up** 0.0027
L46 A0A0G2JU41 Dyrk4 Protein Dyrk4 1.2503 Up** 0.0015
L47 D4ADS4 Mgst3 Protein Mgst3 1.2523 Up** 0.0007
L48 A0A0G2JSR8 Cyp17a1 Cytochrome P450, family 17, subfamily a, polypeptide 1 1.2576 Up** 0.0077
L49 A2VCW9 Aass Alpha-aminoadipic semialdehyde synthase, mitochondrial 1.2637 Up** 0.0000
L50 F1M7N8 Ugt2b37 UDP-glucuronosyltransferase 1.2647 Up** 0.0010
L51 P38659 Pdia4 Protein disulfide-isomerase A4 1.2670 Up** 0.0018
L52 Q6AXR4 Hexb Beta-hexosaminidase subunit beta 1.2712 Up** 0.0021
L53 D4A3E8 Mrps27 Mitochondrial ribosomal protein S27 (predicted), isoform CRA_b 1.2733 Up* 0.0453
L54 D3ZES7 Plxna4 Protein Plxna4 1.2853 Up** 0.0001
L55 P05183 Cyp3a2 Cytochrome P450 3A2 1.3085 Up** 0.0002
L56 Q5XIG0 Nudt9 ADP-ribose pyrophosphatase, mitochondrial 1.3121 Up** 0.0001
L57 Q920L7 Elovl5 Elongation of very long chain fatty acids protein 5 1.3239 Up** 0.0021
L58 P08290 Asgr2 Asialoglycoprotein receptor 2 1.3334 Up* 0.0127
L59 A0A023IM45 Psmb8 Proteasome subunit beta type 1.3358 Up** 0.0020
L60 Q62730 Hsd17b2 Estradiol 17-beta-dehydrogenase 2 1.3607 Up** 0.0000
L61 Q31256 N/A MHC class I RT1.Au heavy chain 1.3778 Up** 0.0003
L62 A0A0A1G491 ND2 NADH-ubiquinone oxidoreductase chain 2 1.3851 Up* 0.0195
L63 P20814 Cyp2c13 Cytochrome P450 2C13, male-specific 1.4111 Up** 0.0000
L64 F1LMF4 Fat3 Protocadherin fat 3 1.4253 Up** 0.0060
L65 Q4V797 RGD1309362 Interferon-gamma-inducible GTPase Ifgga1 protein 1.4272 Up** 0.0000
L66 P50169 Rdh3 Retinol dehydrogenase 3 1.4563 Up** 0.0000
L67 A0A0G2K222 N/A Uncharacterized protein 1.5162 Up 0.0272
L68 Q5UAJ6 COX2 Cytochrome c oxidase subunit 2 1.5316 Up** 0.0001
L69 M0RC39 Olr796 Olfactory receptor 1.5880 Up* 0.0484
L70 D3ZMQ0 Mga Protein Mga 1.6374 Up* 0.0312
L71 Q6T5E9 Ugt1a6 UDP-glucuronosyltransferase 1.7279 Up** 0.0003
L72 A1XF83 Ugt2b UDP-glucuronosyltransferase 1.8256 Up** 0.0001
L73 D3ZXC8 Ebpl Emopamil binding protein-like (predicted), isoform CRA_a 1.8324 Up** 0.0018
L74 F1LM22 Ugt2b UDP-glucuronosyltransferase 1.8894 Up** 0.0000
L75 Q63002 Igf2r Mannose 6-phosphate/insulin-like growth factor II receptor 2.1130 Up** 0.0080
L76 Q5BK88 Amacr Alpha-methylacyl-CoA racemase 2.5845 Up** 0.0001
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a

Compared with control group, *p < 0.05,**p < 0.01. L represents liver. K represents control group, Q represents R. Scrophulariae group.

Fig. 3. Analysis of enriched gene ontology (A), KEGG pathway (B) and protein–protein interaction (C).

Fig. 3

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3.3. Common pathways analysis

The common KEGG pathways between proteins and metabolism were steroid hormone biosynthesis, drug metabolism – cytochrome p450, drug metabolism – other enzymes, pentose and glucuronate interconversions, and starch and sucrose metabolism. Among them, the proteins engaged in steroid hormone biosynthesis were Ste, Ugt2b37, Cyp17a1, Cyp3a2, Hsd17b2, Cyp2c13, Ugt1a6 and Ugt2b, and the metabolites were 2-hydroxyestrone and 2-methoxyestrone. The proteins related to drug metabolism – cytochrome p450 were Gsta1, Ugt2b37, mgst3, Ugt1a6 and Ugt2b, and the metabolite was normorphine. The proteins participating in drug metabolism – other enzymes were carboxylic ester hydrolase, Ugt2b37, Ugt1a6 and Ugt2b. The proteins associated with the two pathways were Ugt2b37, Ugt1a6 and Ugt2b, and the metabolites were isovalerylglucuronide and tyramine glucuronide.

R. Scrophulariae enhanced 2-hydroxyestrone and 2-methoxyestrone expression in the urine. The direct precursor of 2-methoxyestrone is 2-hydroxyestrone, while the direct precursor of the latter is estrone. Ste levels decreased in the liver. Ste catalyses the transfer reaction from estrone to estrone sulfate and adenosine 3′,5′-diphosphate (PAP).14 PAP accumulation is toxic to several cellular systems.15 In addition, R. Scrophulariae enhanced the levels of Ugt2b37, Ugt1a6, Ugt2b, Cyp3a2 and Cyp2c13 in liver tissue. UDPGT16 is important in the conjugation and subsequent elimination of potentially toxic xenobiotics and endogenous compounds, which catalyse the transfer of glucuronic acid from uridine diphosphoglucuronic acid to a variety of substrates, including steroid hormones. Ugt2b37 participates in the glucuronidation of testosterone and dihydrotestosterone, Ugt1a6 transforms small lipophilic molecules into water-soluble and excretable metabolites, Ugt2b conjugates lipophilic aglycon substrates with glucuronic acid;17 thus, R. Scrophulariae may detoxify liver tissue. Cyp3a2 and Cyp2c13 are important drug metabolic enzymes in rat livers. Cyp3a2 activity was suppressed and appeared in cases of acute formaldehyde poisoning,18 5 week-old Zucker fatty diabetic rats,19 and human immunodeficiency virus-infected rats.20 One previous study has shown that CYP2C13 was absent in male hyperlipidaemic Sprague-Dawley rats.21 However, R. Scrophulariae enhanced CYP2C13 levels in liver tissue, indicating that R. Scrophulariae may have protective effects.

However, down-regulation of carboxylic ester hydrolase and tyramine glucuronide by R. Scrophulariae may be toxic. Carboxylic ester hydrolase participates in phase I metabolism of xenobiotics such as toxins or drugs, and the resulting carboxylates are conjugated by other enzymes to increase solubility and are eventually excreted.22 Tyramine glucuronide is a natural body metabolite of tyramine generated in the liver by UDP glucanosyltransferase.23 Glucuronidation assists in excreting toxic substances, drugs and other substances that cannot be used as an energy source.24 Glucuronic acid25 attaches to the substance via a glycosidic bond, and the resulting glucuronide, which has a higher water solubility than the original substance, is eventually excreted by the kidneys. Therefore, further studies should be conducted on R. Scrophulariae toxicity.

R. Scrophulariae enhanced the level of Hsd17b2, which promotes the interconversion of estrone and oestradiol and regulates the biological activity of sex hormones.26 Oestradiol is essential for reproductive and sexual functioning in women, and it also affects other organs including bones.27 Thus, R. Scrophulariae may generate an oestrogen-like effect by raising Hsd17b2 levels. In addition, oestrogen assimilates protein in the liver and can also impact the male reproductive system, including androgen levels, causing testicular tissue structural changes and testicular cancer, reducing sperm counts, developing male breasts and leading to endocrine disorders.28 Therefore, we must administer R. Scrophulariae appropriately to take advantage of its assimilation rather than its side effects.

In this study, Cyp17a1 and Gsta1 levels were increased by R. Scrophulariae. Cyp17a1 is a prominent inhibitory target in treating prostate cancer because it produces the androgen required for tumour cell growth.29 Studies found that Gsta1 was involved in metabolizing carcinogenic compounds.30 These results may suggest that R. Scrophulariae has potential anti-cancer effects. In urine samples, R. Scrophulariae inhibited normorphine expression, a major metabolite of morphine. It acts directly on the central nervous system (CNS) to diminish sensations of pain.31,32 The analgesic effect from R. Scrophulariae was minimal and likely related to the dosage.

R. Scrophulariae enhanced Gsta1 and Mgst3 expression in liver tissues. Gsta1 exhibits glutathione peroxidase activity, thereby protecting cells from reactive oxygen species and peroxidation products.33 Mgst3 (microsomal glutathione s-transferase 3) is involved in the producing leukotrienes and prostaglandin E, important mediators of inflammation, and it demonstrates glutathione-dependent peroxidase activity towards lipid hydroperoxides.34 Thus, R. Scrophulariae may produce antioxidant effects. However, it is worth noting that increases in serum and urinary Gsta1 have been found associated with hepatocyte and renal proximal tubular necrosis, respectively, and show potential for monitoring injury to these tissues.35R. Scrophulariae reduced isovalerylglucuronide expression. Elevated isovalerylglucuronide was reported in isovaleric acidaemia,36 indicating that R. Scrophulariae may be used to treat isovaleric acidaemia by decreasing isovalerylglucuronide.

4. Conclusions

Untargeted urine metabolomics were performed by UPLC-TOF-MS, and proteomic liver profiling of R. Scrophulariae-treated rats was detected by nano-UPLC-Q- Exactive-MS/MS. We found that 5 common pathways were targeted by R. Scrophulariae, including steroid hormone biosynthesis, drug metabolism – cytochrome p450, drug metabolism – other enzymes, pentose and glucuronate interconversions, and starch and sucrose metabolism. These results show therapeutic effects as well as side effects. When administering R. Scrophulariae treatment, we should focus on its side effects. Curative effects of R. Scrophulariae included detoxification, anti-cancer, antioxidant and isovaleric acidaemia treatment. Since R. Scrophulariae was characterized by multiple targets and multiple pathways, finding the appropriate basis for its specific pharmacological effects is vital, as this process lays the foundation for clinically accurate and safe medication.

Conflicts of interest

There are no conflicts to declare.

Supplementary Material

RA-009-C8RA10443C-s001
†

Electronic supplementary information (ESI) available. See DOI: 10.1039/c8ra10443c

References

  1. Zhang C. C. Gu W. L. Wu X. M. Li Y. M. Chen C. X. Huang X. Y. Active components from Radix Scrophulariae inhibits the ventricular remodeling induced by hypertension in rats. SpringerPlus. 2016;5:358. doi: 10.1186/s40064-016-1985-z. doi: 10.1186/s40064-016-1985-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Gu W. L. Chen C. X. Huang X. Y. Gao J. P. The effect of angoroside C on pressure overload-induced ventricular remodeling in rats. Phytomedicine. 2015;22(7–8):705–712. doi: 10.1016/j.phymed.2015.05.002. doi: 10.1016/j.phymed.2015.05.002. [DOI] [PubMed] [Google Scholar]
  3. Gu W. L. Chen C. X. Wu Q. Lu J. Liu Y. Zhang S. J. Effects of Chinese herb medicine Radix Scrophulariae on ventricular remodeling. Pharmazie. 2010;65(10):770–775. [PubMed] [Google Scholar]
  4. Huang X. Y. Chen C. X. Zhang X. M. Liu Y. Wu X. M. Li Y. M. Effects of ethanolic extract from Radix Scrophulariae on ventricular remodeling in rats. Phytomedicine. 2012;19(3–4):193–205. doi: 10.1016/j.phymed.2011.09.079. doi: 10.1016/j.phymed.2011.09.079. [DOI] [PubMed] [Google Scholar]
  5. Sheu S. Y. Hong Y. W. Sun J. S. Liu M. H. Chen C. Y. Ke C. J. Radix Scrophulariae extracts (harpagoside) suppresses hypoxia-induced microglial activation and neurotoxicity. BMC Complementary Altern. Med. 2015;15:324. doi: 10.1186/s12906-015-0842-x. doi: 10.1186/s12906-015-0842-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Chen C. Chen C. X. Wu X. M. Wang R. Li Y. M. Effects of extracts of Radix Scrophulariae on blood pressure in spontaneously hypertensive rats and the underlying mechanisms. Zhongxiyi Jiehe Xuebao. 2012;10(9):1009–1017. doi: 10.3736/jcim20120910. [DOI] [PubMed] [Google Scholar]
  7. Shen X. Eichhorn T. Greten H. J. Efferth T. Effects of Scrophularia ningpoensis Hemsl. on Inhibition of Proliferation, Apoptosis Induction and NF-kappaB Signaling of Immortalized and Cancer Cell Lines. Pharmaceuticals. 2012;5(2):189–208. doi: 10.3390/ph5020189. doi: 10.3390/ph5020189. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Li Y. M. Han Z. H. Jiang S. H. Jiang Y. Yao S. D. Zhu D. Y. Fast repairing of oxidized OH radical adducts of dAMP and dGMP by phenylpropanoid glycosides from Scrophularia ningpoensis Hemsl. Acta Pharmacol. Sin. 2000;21(12):1125–1128. [PubMed] [Google Scholar]
  9. Wang X. Zhang A. Wang P. et al., Metabolomics coupled with proteomics advancing drug discovery toward more agile development of targeted combination therapies. Mol. Cell. Proteomics. 2013;12(5):1226–1238. doi: 10.1074/mcp.M112.021683. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Qiu S. Zhang A. Zhang T. et al., Dissect new mechanistic insights for geniposide efficacy on the hepatoprotection using multiomics approach. Oncotarget. 2017;8(65):108760–108770. doi: 10.18632/oncotarget.21897. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Zhang A. Zhou X. Zhao H. et al., Metabolomics and proteomics technologies to explore the herbal preparation affecting metabolic disorders using high resolution mass spectrometry. Mol. BioSyst. 2017;13(2):320–329. doi: 10.1039/C6MB00677A. [DOI] [PubMed] [Google Scholar]
  12. Cao H. Zhang A. Sun H. et al., Metabolomics-proteomics profiles delineate metabolic changes in kidney fibrosis disease. Proteomics. 2015;15(21):3699–3710. doi: 10.1002/pmic.201500062. [DOI] [PubMed] [Google Scholar]
  13. Wang X. Zhang A. Yan G. et al., Metabolomics and proteomics annotate therapeutic properties of geniposide: targeting and regulating multiple perturbed pathways. PLoS One. 2013;8(8):e71403. doi: 10.1371/journal.pone.0071403. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Thomas M. P. Potter B. V. The structural biology of oestrogen metabolism. J. Steroid Biochem. Mol. Biol. 2013;137:27–49. doi: 10.1016/j.jsbmb.2012.12.014. doi: 10.1016/j.jsbmb.2012.12.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Yenush L. Belles J. M. Lopez-Coronado J. M. Gil-Mascarell R. Serrano R. Rodriguez P. L. A novel target of lithium therapy. FEBS Lett. 2000;467(2–3):321–325. doi: 10.1016/S0014-5793(00)01183-2. [DOI] [PubMed] [Google Scholar]
  16. Salinas S. R. Petruk A. A. Brukman N. G. Bianco M. I. Jacobs M. Marti M. A. et al., Binding of the substrate UDP-glucuronic acid induces conformational changes in the xanthan gum glucuronosyltransferase. Protein Eng., Des. Sel. 2016;29(6):197–207. doi: 10.1093/protein/gzw007. doi: 10.1093/protein/gzw007. [DOI] [PubMed] [Google Scholar]
  17. Izukawa T. Nakajima M. Fujiwara R. Yamanaka H. Fukami T. Takamiya M. et al., Quantitative analysis of UDP-glucuronosyltransferase (UGT) 1A and UGT2B expression levels in human livers. Drug Metab. Dispos. 2009;37(8):1759–1768. doi: 10.1124/dmd.109.027227. doi: 10.1124/dmd.109.027227. [DOI] [PubMed] [Google Scholar]
  18. Xu M. Tang H. Rong Q. Zhang Y. Li Y. Zhao L. et al., The Effects of Formaldehyde on Cytochrome P450 Isoform Activity in Rats. BioMed Res. Int. 2017;2017:6525474. doi: 10.1155/2017/6525474. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Park S. Y. Kim C. H. Lee J. Y. Jeon J. S. Kim M. J. Chae S. H. et al., Hepatic expression of cytochrome P450 in Zucker diabetic fatty rats. Food Chem. Toxicol. 2016;96:244–253. doi: 10.1016/j.fct.2016.08.010. doi: 10.1016/j.fct.2016.08.010. [DOI] [PubMed] [Google Scholar]
  20. Ghoneim R. H. Piquette-Miller M. Endotoxin-Mediated Downregulation of Hepatic Drug Transporters in HIV-1 Transgenic Rats. Drug Metab. Dispos. 2016;44(5):709–719. doi: 10.1124/dmd.115.067827. doi: 10.1124/dmd.115.067827. [DOI] [PubMed] [Google Scholar]
  21. Brassil P. J. Debri K. Nakura H. Kobayashi S. Davies D. S. Edwards R. J. Reduced hepatic expression of CYP7A1 and CYP2C13 in rats with spontaneous hyperlipidaemia. Biochem. Pharmacol. 1998;56(2):253–257. doi: 10.1016/S0006-2952(98)00015-X. [DOI] [PubMed] [Google Scholar]
  22. Chen Y. Black D. S. Reilly P. J. Carboxylic ester hydrolases: classification and database derived from their primary, secondary, and tertiary structures. Protein Sci. 2016;25(11):1942–1953. doi: 10.1002/pro.3016. doi: 10.1002/pro.3016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Wong K. P. The biosynthesis of tyramine glucuronide by liver microsomal fractions. Biochem. J. 1977;164(3):529–531. doi: 10.1042/bj1640529. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Kaeferstein H. Forensic relevance of glucuronidation in phase-II-metabolism of alcohols and drugs. Leg. Med. 2009;11(suppl. 1):S22–S26. doi: 10.1016/j.legalmed.2009.01.037. doi: 10.1016/j.legalmed.2009.01.037. [DOI] [PubMed] [Google Scholar]
  25. Lewis S. S. Hutchinson M. R. Zhang Y. Hund D. K. Maier S. F. Rice K. C. et al., Glucuronic acid and the ethanol metabolite ethyl-glucuronide cause toll-like receptor 4 activation and enhanced pain. Brain, Behav., Immun. 2013;30:24–32. doi: 10.1016/j.bbi.2013.01.005. doi: 10.1016/j.bbi.2013.01.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Hilborn E. Stal O. Jansson A. Estrogen and androgen-converting enzymes 17beta-hydroxysteroid dehydrogenase and their involvement in cancer: with a special focus on 17beta-hydroxysteroid dehydrogenase type 1, 2, and breast cancer. Oncotarget. 2017;8(18):30552–30562. doi: 10.18632/oncotarget.15547. doi: 10.18632/oncotarget.15547. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Tang L. Xia Z. Luo Z. Long H. Zhu Y. Zhao S. Low plasma PDGF-BB levels are associated with estradiol in postmenopausal osteoporosis. J. Int. Med. Res. 2017:300060517706630. doi: 10.1177/0300060517706630. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Lau K. M. To K. F. Importance of Estrogenic Signaling and Its Mediated Receptors in Prostate Cancer. Int. J. Mol. Sci. 2016;17(9):1434. doi: 10.3390/ijms17091434. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Maity S. N. Titus M. A. Gyftaki R. Wu G. Lu J. F. Ramachandran S. et al., Targeting of CYP17A1 Lyase by VT-464 Inhibits Adrenal and Intratumoral Androgen Biosynthesis and Tumor Growth of Castration Resistant Prostate Cancer. Sci. Rep. 2016;6:35354. doi: 10.1038/srep35354. doi: 10.1038/srep35354. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Martinez-Guzman C. Cortes-Reynosa P. Perez-Salazar E. Elizondo G. Dexamethasone induces human glutathione S transferase alpha 1 (hGSTA1) expression through the activation of glucocorticoid receptor (hGR) Toxicology. 2017;385:59–66. doi: 10.1016/j.tox.2017.05.002. doi: 10.1016/j.tox.2017.05.002. [DOI] [PubMed] [Google Scholar]
  31. Glare P. A. Walsh T. D. Pippenger C. E. Normorphine, a neurotoxic metabolite? Lancet. 1990;335(8691):725–726. doi: 10.1016/0140-6736(90)90840-2. [DOI] [PubMed] [Google Scholar]
  32. Li H. Tao R. Wang J. Xia L. Upregulation of miR-375 level ameliorates morphine analgesic tolerance in mouse dorsal root ganglia by inhibiting the JAK2/STAT3 pathway. J. Pain Res. 2017;10:1279–1287. doi: 10.2147/jpr.s125264. doi: 10.2147/jpr.s125264. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Higgins L. G. Hayes J. D. Mechanisms of induction of cytosolic and microsomal glutathione transferase (GST) genes by xenobiotics and pro-inflammatory agents. Drug Metab. Rev. 2011;43(2):92–137. doi: 10.3109/03602532.2011.567391. doi: 10.3109/03602532.2011.567391. [DOI] [PubMed] [Google Scholar]
  34. Liu F. P. Ma X. Li M. M. Li Z. Han Q. Li R. et al., Hepatoprotective effects of Solanum nigrum against ethanol-induced injury in primary hepatocytes and mice with analysis of glutathione S-transferase A1. J. Chin. Med. Assoc. 2016;79(2):65–71. doi: 10.1016/j.jcma.2015.08.013. doi: 10.1016/j.jcma.2015.08.013. [DOI] [PubMed] [Google Scholar]
  35. Amlabu V. Mulligan C. Jele N. Evans A. Gray D. Zar H. J. et al., Isoniazid/acetylisoniazid urine concentrations: markers of adherence to isoniazid preventive therapy in children. Int. J. Tuberc. Lung Dis. 2014;18(5):528–530. doi: 10.5588/ijtld.13.0730. doi: 10.5588/ijtld.13.0730. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Dorland L. Duran M. Wadman S. K. Niederwieser A. Bruinvis L. Ketting D. Isovalerylglucuronide, a new urinary metabolite in isovaleric acidemia. Identification problems due to rearrangement reactions. Clin. Chim. Acta; international journal of clinical chemistry. 1983;134(1–2):77–83. doi: 10.1016/0009-8981(83)90186-9. [DOI] [PubMed] [Google Scholar]

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