A targeted screening approach to systematically identify the absorbed

substances of Poria cocos in vivo using ultra-high performance liquid. 2 ... Poria cocos are extensively used as nutritious food, dietary supplements ...
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Article Cite This: J. Agric. Food Chem. 2018, 66, 8319−8327

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Targeted Screening Approach to Systematically Identify the Absorbed Effect Substances of Poria cocos in Vivo Using Ultrahigh Performance Liquid Chromatography Tandem Mass Spectrometry Guifang Feng,†,‡ Shizhe Li,†,‡,§ Shu Liu,*,† Fengrui Song,† Zifeng Pi,† and Zhiqiang Liu*,†

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†

State Key Laboratory of Electroanalytical Chemistry, National Center of Mass Spectrometry in Changchun, Jilin Province Key Laboratory of Chinese Medicine Chemistry and Mass Spectrometry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, P. R. China ‡ University of Science and Technology of China, Hefei 230026, P. R. China § College of Chemistry, Jilin University, Changchun 130012, China S Supporting Information *

ABSTRACT: Poria cocos are extensively used as nutritious food, dietary supplements, and oriental medicine in Asia. However, the effect substances are still not very clear. In this study, a targeted screening approach was developed to systematically identify absorbed constituents of Poria cocos in vivo using ultrahigh performance liquid chromatography tandem mass spectrometry combined with UNIFI software. First, incubation reactions in vitro with rat intestinal microflora and rat liver microsomes were conducted to sum up metabolic rules of main constituents. Second, the absorbed constituents in vivo were picked out and identified based on the results of metabolic study in vitro. Finally, the absorbed active constituents in the treatment of Alzheimer’s disease were screened by targeted network pharmacology analysis. A total of 62 absorbed prototypes and 59 metabolites were identified and characterized in dosed plasma. Thirty potential active constituents were screened, and 86 drugtargets shared by absorbed constituents and Alzheimer’s disease were discovered by targeted network pharmacology analysis. In general, this proposed targeted strategy comprehensively provides new insight for active ingredients of Poria cocos. KEYWORDS: Poria cocos, absorbed constituents, ultrahigh performance liquid chromatography tandem mass spectrometry, targeted network pharmacology analysis

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INTRODUCTION Mushrooms have been widely used as nutritious food, dietary supplements, and oriental medicine. Poria cocos is a medicinal fungus of the family Polyporaceae that grows on the roots of old, dead pine trees.1−3 Previous studies have shown that triterpene acids are the principle active components of Poria cocos.4,5 They were widely applied to treat many kinds of disease, such as cancer, diabetes, inflammatory, loss of memory, etc.6,7 Poria cocos has also been used to make food supplements, such as soups, dishes, tea, snacks, and desserts. It also was used for making biscuits, cakes and bread owing to its potential promotion benefits for health. These potential application of Poria cocos attribute to the continued considerable levels of attention.8−12 It is worthwhile to analyze the absorbed substances of Poria cocos in vivo systematically. Clarifying absorbed substances in vivo is a key step to study food metabolism.13,14 Unfortunately, the complexity of ingredients greatly restricts the study of its metabolic components.15,16 The oral administration of them makes it more complex to study these components in vivo.17,18 All foods taken orally must be exposed in the intestinal tract and be transformed by a series of intestinal bacteria. The transformed constituents would then be converted under the action of liver microsomes before entering into the blood circulation. The main enzyme in the system of liver microsomes was cytochrome P-450 (CYP450).19,20 They play a great role in compound metabolism in vivo through all kinds of metabolic © 2018 American Chemical Society

pathways, mainly oxygenation metabolism. These metabolites absorbed into plasma can play a better pharmacological effect in specified sites of organism in vivo. Few studies pay considerable attention to these progressive metabolic processes for the identification of absorbed constituents in plasma. In fact, the metabolic types of each type structure of nature compounds varied with different characteristic chemical group. For example, glycosylated saponins are metabolized via deglycosylation under the action of intestinal microflora, such as ginsenosides.21,22 However, the sapogenins were transformed via the oxygenation, methylation, or other metabolic pathways under the action of CYP450.23 The major challenge for complex drug metabolism is how to categorize metabolic pathways of each compound and clarify the metabolic mechanism in possible specific sites. Development of various analytical technologies facilitates the identification and quantification of unknown and trace substances in complex samples.24,25 Quadrupole-time-of-flight mass spectrometer, taken as an example, needs a small amount of sample for analysis with its well-known selectivity and sensitivity.26−31 Besides, this machine has developed several scan functions, as it shared a quadrupole, T-wave element and Received: Revised: Accepted: Published: 8319

May 29, 2018 June 28, 2018 July 9, 2018 July 9, 2018 DOI: 10.1021/acs.jafc.8b02753 J. Agric. Food Chem. 2018, 66, 8319−8327

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Journal of Agricultural and Food Chemistry

and humidity (50% ± 10%). The animals were fasted overnight with free access to water before any experiment. All rats were randomly divided into three groups (n = 6 for each group), one group was selected as control group and administrated with normal saline, the rest were marked as 1 and 2 h groups after oral administered with Poria cocos (3 g kg−1). All the experimental procedures were performed in accordance with the Guide for the Care and Use of Laboratory Animals of Jilin University. Incubations of Poria cocos Standards with Intestinal Microflora in Vitro. This part work was completed referring to the previous work, including collection and preparation of intestinal bacteria mixture, incubation of Poria cocos standards with intestinal bacteria in vitro, namely, tumulosic acid, poricoic acid B, and pachymic acid.16 Incubations of Poria cocos with Rat Liver Microsomes (RLMs) in Vitro. The preparation of RLMs and determination of protein content was completed referring to the reported experimental method.39 The process of incubation is roughly as follows. All incubations were performed at 37 °C in a shaker. All stock solution of standards was prepared in methanol solution. The final concentration of methanol in the incubation was less than 0.2% (v/v). The prepared RLMs were carefully thawed on ice before the experiment. RLM proteins (0.5 mg/mL) were added to a solution of standard (20 mM) in a medium containing 100 mM potassium phosphate buffer (pH 7.4) and 10 mM MgCl2. The total incubation volume was 150 μL. After preincubation for 3 min at 37 °C, the incubation reactions were initiated by the addition of NADPH (1.0 mM). Control groups containing no NADPH or substrates were conducted; each incubation was performed in duplicate. After a continuous incubation for 60 min, the reactions were terminated with an equal volume of ice-cold acetonitrile. The resulting mixture was centrifuged at 13,000 rpm for 10 min at 4 °C to pellet protein. Then the supernatants were transferred to another centrifuge tube, and 5 μL samples were used for UHPLC−Q−TOF−MS (ultrahigh performance liquid chromatography tandem quadrupole−time−of−flight mass spectrometry) analysis. Sample Collection and Preparation. The blood samples were collected in a 10 mL centrifuge tube with 10 μL of heparin sodium (1%). The plasma was obtained from the whole blood by centrifugation at 3500 rpm at 4 °C for 10 min. The plasma samples were stored at −80 °C immediately. The protocol of the sample preparation was described as below: 1 mL of plasma was mixed with 4 mL of water-saturated butanol, vortexed for 30 min, and centrifuged at 13000 rpm for 10 min. The supernatant was dried with nitrogen gas at 40 °C. The residue was dissolved with 100 μL of methanol and centrifuged at 13000 rpm for 10 min at 4 °C. Finally, five microliters of supernatant was used for UHPLC−Q−TOF−MS analysis. UHPLC−Q−TOF−MS Analysis. An ultrahigh performance liquid chromatography system (Waters ACQUITY UHPLC core system, Waters), coupled with a Q−TOF SYNAPT G2 High Definition Mass Spectrometer in electrospray ionization mode (Waters, Milford, MA, USA), was used to obtain the specific and accurate masses of all samples. The source temperature was set at 110 °C, and the desolvation gas temperature was 350 °C. The flow rates of cone and desolvation gas were set at 50 and 600 L h−1, respectively. The voltages of capillary and cone in negative ion mode were set at 2.0 kV and 25 V, respectively. Mass spectra were acquired over the m/z 50− 700 range with a scan speed of 0.2 s per scan in continuum mode. The targeted precursor ions were fragmented in the first T-wave element (Trap) to generate the low-energy spectra. The collision energy in the trap cell was set up at 10 V to maintain metastable precursor ions. The precursor ions in metastable state can be fragmented in the trap cell further. The collision energy in the trap cell ranged from 35 to 45 V to generate the high-energy spectra. Leucine enkephalin (m/z 554.2615 in negative ion mode, 0.2 ng μL−1) was used as lockspray for real− time correction at a flow rate of 5 μL min−1. Sodium formate was used to set up mass spectrometer calibration in negative ion mode. The separation was performed by a Waters ACQUITY UHPLC BEH C18 Column (2.1 mm × 50 mm, 1.7 μm) at 30 °C. Aqueous formic acid (0.1% V/V) (A) and acetonitrile (B) were used as the

time-of-flight cells. The derived developed data-independent acquisitions, such as data-dependent acquisition (DDA) and data-independent mass spectrometry (MSE), can collect all mass data of precursor and fragment ions fully.32,33 In general, several acquisition modes provide a powerful platform for compound detection with rich chemical mass information. Based on these acquisition methods, more automated software has been developed for detection and analysis, such as QI and UNIFI software.34 The development of software based on mass spectrometry has also verified the importance of compound metabolism from another perspective. For every component detected in plasma, it must have one or more targets for therapy in vivo, which is known as an effect substance.35,36 The more components detected in plasma in vivo, the more targets possibly involved in therapy. Herein, the action mechanism of absorbed constituents in vivo is a complex biological active network. Elucidation of these effect substances and related targets by some methods is conducive to better clinical application. Fortunately, collecting potential drugtargets and clarifying complex molecular mechanisms is available, with the development of bioinformatic database.37,38 Much collection work is still needed considering the complexity of multiple components and multiple drug-targets. In this study, a targeted screening strategy was developed to systematically identify the absorbed effect substances of Poria cocos in vivo. The targeted screening of metabolites from in vitro to in vivo was accomplished under the assist of UNIFI software. The final objective of this study was to describe the absorbed effect substances of Poria cocos by using targeted network pharmacology analysis. We believe that this exploration of absorbed substances would promote better application of Poria cocos as supplement food and herbal medicine.

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MATERIALS AND METHODS

Materials and Reagents. Poria cocos was purchased from Hebei Kaida Traditional Chinese Medicine Co. Ltd. (Hebei China). All herb medicines were identified by Prof. Zhiqiang Liu (Changchun Institute of Applied Chemistry, Chinese Academy of Sciences). Eight reference standards of dehydrotrametenolic acid (1), 3α,16β-dihydroxylanosta7,9(11),24-trien-21-oic acid (2), polyporenic acid C (3), poricoic B (4), dehydrotumulosic acid (5), tumulosic acid (6), 3-O-acetyl-16αhydroxydehydrotrametenolic acid (7), and pachymic acid (8) were obtained from Purification Engineering Technology Research Center of Sichuan Province Natural Medicine (Sichuan China). NADPH (Nicotinamide Adenine Dinucleotide Phosphate) was purchased from R&D. The analytical-grade reagents, ethyl acetate, n-butanol, and absolute ethanol were provided by Beijing Chemical Works (Beijing China), and deionized water was purified using a Milli-Q water purification system (Milford, MA, USA). HPLC-grade acetonitrile and formic acid were obtained from Fisher Scientific (Loughborough, UK). Leucine enkephalin and sodium formate was purchased from Waters (Milford, USA). All other reagents were of analytical grade. Preparation of Poria cocos Extract. The Poria cocos powder was immersed in eight times of 75% ethanol aqueous solution for 2 h and then refluxed twice for 2 h each. The combined ethanol extracts were filtered with gauze to remove any solids and concentrated by rotary evaporation under vacuum to a certain volume. One part was kept to feed rats (equivalent to 0.4 g of crude powder per milliliter), while the rest were lyophilized to get extract powder. All of them were stored at −20 °C before the experiment. Animals. Male Sprague−Dawley rats (weights 200 ± 20 g) were obtained by Dalian Medical University (Dalian, China) (SCXK (Liao) 2015−0001). They were provided with standard laboratory food and water and maintained on a 12 hour light/dark cycle in an air-conditioned animal quarter at constant temperature (22−24 °C) 8320

DOI: 10.1021/acs.jafc.8b02753 J. Agric. Food Chem. 2018, 66, 8319−8327

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Figure 1. Systematic workflow for targeted screening to systematically identify the metabolites of Poria cocos in rats using ultrahigh performance liquid chromatography tandem mass spectrometry.

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mobile phase at a flow rate of 0.3 mL/min. Gradient programs were as follows for intestinal and liver microsomal samples: 0−2 min, 10− 45% B; 2−9 min, 45−70% B; 9−10 min, 70−100% B; 10−11 min, 100% B. The gradient program was as follows for plasma samples: 0− 5 min, 10−35% B; 5−10 min, 35−50% B; 10−20 min, 50−100% B; 20−21 min, 100% B. Targeted Network Pharmacology Analysis. All the constituents absorbed in plasma were regarded as targets to retrieval in network database, including STITCH (http://stitch.embl.de/, ver.5.0), TCMSP (http://lsp.nwu.edu.cn/tcmsp.php), and BATMAN−TCM (http://bionet.ncpsb.org/batman-tcm/). STITCH is a relatively authoritative resource to integrate interactions among metabolic pathways, crystal structures, and drug−target relationships. In fact, the number of drug-targets reported were far more than that of recorded in above database because of unresponsive update of these database. The systemic hunt for drug-targets was conducted in the SciFinder Scholar, which is a largest and most comprehensive database of compounds in the world. All chemical structures of constituents absorbed in plasma were imported into SciFinder Scholar for retrieval. The AD targets were gathered from DisGeNET (http://www. disgenet.org/web/DisGeNET/menu/home, ver. 5.0), which is one of the largest discovery platform containing available genes and variants associated with human diseases. A total of 2245 AD targets were gathered from the DisGeNET. Then the common targets shared by drugs and disease were picked up. Cytoscape 3.6.0 was applied to visualize the interaction among the compounds, drug−targets, and diseases.40 Cytoscape is a software platform that offers functions for visualizing data and biological pathways.36 In the graphic network, the compounds, targets and diseases were regarded as nodes, and the interactions between these nodes were linked by edges. The degree of a node is defined as the number of the edges linked to it. The Database for Annotation, Visualization, and Integrated Discovery (DAVID, https://david-d.ncifcrf.gov/, ver. 6.7) was applied for gene ontology (GO) enrichment analysis. Homo sapiens were chosen as the current background. Enriched GO terms (pathways) with p-value less than 0.001 (corrected with Benjamin step down) were collected and analyzed. These terms were integrated to interpret the biological meanings of these target gene data sets with a comprehensive set of functional annotation tools of DAVID and KEGG.

RESULTS AND DISCUSSION Systematic Workflow for Characterize of Metabolites of Poria cocos from in Vitro to in Vivo. The systematic workflow is schematically depicted in Figure 1. In step 1, the intestinal microflora metabolites in vitro for the major triterpene acids (poricoic acid B, pachymic acid, and tumulosic acid) were identified and structurally characterized based on the accurate MSE data acquired by UHPLC−Q−TOF−MS. Then the metabolic rules under the action of intestinal microflora metabolism in vitro for triterpene acids were summed up. In step 2, the CYP450 metabolites in vitro for the major triterpene acids were studied (dehydrotrametenolic acid, 16α-hydroxytrametenolic acid, polyporenic acid C, poricoic acid B, dehydrotumulosic acid, tumulosic acid, 3-Oacetyl-16α-hydroxydehydrotrametenolic acid, and pachymic acid). The metabolites in the RLMs in vitro were identified and characterized. The main metabolic pathways through the action of RLMs were summed up to construct a database of metabolic pathways. In step 3, the chemical profile of dosed rat plasma was described using UHPLC−Q−TOF−MS. Two screening methods were developed by using the UNIFI software, namely, screening of prototypes and metabolites. The prototype screening was based on a compound library, while metabolite screening was based on the prototype compounds and metabolic pathways. All absorbed constituents were picked out, which were detected in experimental group and not in blank group. The mass behaviors and liquid retention time were applied for confirming all absorbed constituents. In step 4, a targeted network pharmacological analysis was conducted based on the absorbed constituents in plasma. Characterize Metabolites of Representative Triterpene Acids Transformed by Intestinal Microflora in Vitro. Three representative triterpene acids of Poria cocos were selected to identify their metabolites and explore metabolic rules in intestinal microflora. They represent different structural types of triterpene acids and are also the major 8321

DOI: 10.1021/acs.jafc.8b02753 J. Agric. Food Chem. 2018, 66, 8319−8327

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Journal of Agricultural and Food Chemistry constituents of Poria cocos. Triterpene acids in Poria cocos mainly are divided into two types, namely, 3,4-secolanostane (poricoic acid B) and closed-lanostane (pachymic acid and tumulosic acid). Notably, the existence of the acetyl group at C-3-OH position distinguished pachymic acid from others. However, only pachymic acid could be biotransformed by deacetylation under the action of intestinal microflora in vitro owing to the acetyl group linked to C-3-OH. It was explained that the intestinal microflora composited of large amounts of anaerobic flora, which live on sugars, acids, proteins, and so on, as a source of energy.41−43 There was no glycosyl group and proteins in the 75% alcohol extract of Poria cocos. In general, few metabolites of Poria cocos could be detected under the action of intestinal microflora in vitro. It was also suggested that the triterpene acids of Poria cocos are mainly absorbed in the form of prototype components. Characterization of Metabolites of Representative Triterpene Acids Transformed by RLMs in Vitro. Eight representative triterpene acids in Poria cocos were selected to identify their metabolites and explore metabolic rules in RLMs (Figure 2). The stock standards (2 mM) were incubated with a

to generate a series of extracted ion chromatograms in each group. Liver microsomes, also known as monooxygenase, can catalyze the oxidation process of hundreds of compounds through the cytochrome P-450 (CYP450) in vivo. Eight reference compounds were selected to explore as rich metabolic rules as possible through the action of CYP450, considering little metabolites detected in the metabolic incubation with the action of intestinal microflora. There were 27 metabolic types identified from metabolism of Poria cocos in RLMs in vitro as shown in Table. 1. It was obvious that almost all standards were metabolized via four pathways, namely, M + O−H2, M + O, M + O2−H2, and M + O2, which indicate that CYP450 metabolism in Poria cocos was featured with oxidation action. As the difference of every triterpene acid of Poria cocos in chemical structure, the metabolic types and numbers of each compound varied. Poricoic acid B was a typical 3,4-seco-lanosta-type triterpene acid. Four metabolic types of poricoic acid B were detected when incubating with RLMs in vitro, namely, M + O−H2, M + O, M + O2−H2, and M + O2. Among them were two monooxygenated products extracted, the conversion yield of which reached at 85.5% and 108.0% (Figure 3A,B). This result explicated that the 3,4-seco-lansta structure shared more activity sites and higher transformation yield. Poricoic acid B generated a characteristic fragment ion at m/z 409.2768 [M− CH3CH2COOH]− at C−10 position in the negative ion mode as the base peak in the MS/MS spectra.4,5 As Figure 3C,D shows, these two metabolites M + O (m/z 499.3090) at 3.02 and 3.29 min in liquid chromatography shared three typical fragment ions at m/z 481.2905, m/z 425.2694, and m/z 409.2600, corresponding to [M−H2O]−, [M− CH3CH2COOH]−, and [M−H2O−CH3CH2COOH]−. It was speculated that the hydroxyl group was substituted to a methyl group, which would be fragmented easily. The 3,4-secolansta structure makes two methyl-independent ends compared with the closed structure of triterpene acids. It was explained that an independent end was likely to be oxidized.19 As there was a lack of standard reference material, the metabolic sites could not be confirmed absolutely and completely. Polyporenic acid C, with an acyl group linked to the C-3 position and distinguished from other triterpene acids, was used as an example to explain the metabolic rule. The acyl group was proposed to be transformed to a hydroxyl through hydrogenation. In fact, polyporenic acid C was biotransformed through four metabolic pathways, namely, M + H2, M + O, M + H 2 O, M + O 2 , and M + H2 O 2 . Apparently, the hydrogenation reaction happening to polyporenic acid C was common. This result demonstrated that the metabolic sites of polyporenic acid C was related with the acyl group linked to the C-3 position. At the same time, the metabolic yield was calculated based on the peak area of the liquid chromatograph of the parent compound, which was normalized as 100%. It was noteworthy that the peak area percent of biotransformed metabolites M + O, M + H2O, M + H2O2, M + O2, M + H2O2, and M + H2O2 was 23.6%, 28.5%, 23.9%, 38.1%, and 27.4%, respectively, compared with the final peak area of prototype compound polyporenic acid C. This indicted that polyporenic acid C possessed high conversion yield and activity in the RLMs. In general, the metabolic pathway of polyporenic acid C in RLMs in vitro took M + O2 and M + H2O2 as the main metabolic pathways.

Figure 2. Main structures of triterpene acids of Poria cocos, which were incubated with RLMs in vitro.

NADPH system at different time points (30, 60, and 90 min). The mass data were obtained through the UHPLC−Q−TOF− MS under the MSE acquiring mode. Metabolites screening was carried out with the aid of UNIFI software, which combined with an extensive list of parent compounds and potential biotransformation reactions (e.g., dehydrogenation, oxygenation, methylation, or arbitrary combination). The main filtering parameters were set as follows: the mass error, 10 ppm, and the retention time error, 0.2 min. The fragment information on all metabolites was presented in two spectra, namely, high-energy spectrum and low-energy spectrum. More importantly, these metabolites could be confirmed manually through the rationality of every fragment ion recognized automatically by mass fragment function in the UNIFI software. All identified metabolites were probably extracted 8322

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Journal of Agricultural and Food Chemistry Table 1. Main Metabolic Types and Numbers of Triterpene Acids in Poria cocos Biotransformed in RLMs in Vitro numbersa peak

metabolic types

G

S

C

B

Q

T

A

P

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 sum

M−C2H2O−H2O−H2 + O2 M−C2H2O2−H2 + O2 M−C2H2O2 + O2 M−COO + H2O M−COO−H2 + O2 M−C2H2O−H2 + O2 M−C2H2O + O2 M−C2H2O−H2 + O2 + CH2 M−H2 M + O−H2−H2 M + O−H2 M+O M + H2O M + O2−H2−H2 M + O2−H2 M + O + CH2−H2 M + O + CH2 M + O2 M + H2O2 M+O−H2+O2−H2 M−H2 + O2 + O M + H2O2 + O2−H2 M + H2O2 + O M−H2+O2+CH2 M + H2O2−H2 + CH2 M + CH2O2 + H2 M + H2O−H2 + O2 + CH2 metabolites

− − − − − − − − − − 1 2 − − 1 − − − − − − − − − − − − 4

− − − − − − − − − 1 5 5 − − 5 − 1 5 − − 1 − − 2 − − − 25

− − − − − − − − − − 1 3 2 − 2 − − 7 5 − 1 5 1 − − − − 27

− − − − − − − − 4 − 4 6 − − − − − 4 − − − − − − − − − 18

− − − − − − − − − − 3 3 − 3 8 − − 5 − 1 4 − − − − − − 27

− − − − − − − − − 1 4 3 − 1 8 − − 5 − 1 7 1 − − 1 − − 32

1 3 2 1 2 3 4 1 − − 1 3 − − 1 1 − 3 − − − − 1 − − − 1 28

− 2 1 − − − 2

− − − − 1 1 − − 3 1 2 − − − − − − − 13

a G, dehydrotrametenolic acid; S, 16α-hydroxytrametenolic acid; C, polyporenic acid C; B, poricoic B; Q, dehydrotumulosic; T, tumulosic acid; A, : 3-O-acetyl-16α-hydroxydehydrotrametenolic acid; P, pachymic acid.

Figure 3. Base peak chromatograms of control and experimental groups of poricoic acid B in RLMs in vitro (A); extract ion chromatograms of metabolites M + O (m/z 499.306, mass window, 0.02 Da) of control and experimental groups of poricoic acid B in RLMs in vitro (B); proposed fragmentation pathway for metabolites M + O of poricoic acid B at 3.2 min (C) and 3.29 min (D) in liquid chromatography.

Pachymic acid and 3-O-acetyl-16α-hydroxydehydrotrametenolic acid shared an acetyl group at C-3-OH, which could be translated partially through the action of deacetylation under the action of intestinal microflora. The deacetylation metabolite would be transformed further under the action of CYP450. The conversion yield of pachymic acid is very low

under the action of CYP450, only the yield of double oxygenated metabolites can reach 2.7%. The mass data of all metabolites of pachymic acid are listed in Table S1. A few metabolites of pachymic acid were detected in vitro under the action of CYP450. However, it was revealed that pachymic acid in prototype state was the actual active ingredient in vivo. The 8323

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triterpene acids under the action of CYP450. The screening of metabolites was mainly based on four metabolic pathways, namely, M + O−H2, M + O, M + O2−H2, and M + O2. The neutral losses were applied to distinguish structure type of triterpene acids. The formation neutral loss 74 or 72 Da (CH3CH2COOH/CHCH2COOH) under MSE acquiring mode resulted from the 3,4-sec structure-type in mass spectrometry. For example, the precursor ion at m/z 453.33 was integrated in spectra for screening, including blank, control, and experimental groups, as described in Figure 4. Compound P1

difference between pachymic acid and 3-O-acetyl-16α-hydroxydehydrotrametenolic acid was the position of the alkene bond on the C-21 side chain substituents. The pachymic acid was a 24,31-ene-lanosta-type structure, while 3-O-acetyl-16α-hydroxydehydrotrametenolic acid was a 24,25-ene-lanosta-type structure. The translate yield of 3-O-acetyl-16α-hydroxydehydrotrametenolic acid under the action of CYP450 was a little higher than that of pachymic acid relatively. It was speculated that the double bond on the C-21 side chain substituents shared more activity of oxidation and that the metabolic types of 3-O-acetyl-16α-hydroxydehydrotrametenolic acid under the action of CYP450 were more than that of pachymic acid. In that way, the 24,25-ene-lanosta-type structure shared more active sites than that of 24,31-ene-lanosta-type structure in RLMs. The structure of dehydrotumulosic acid and tumulosic acid is different from the numbers of double bond on pentacyclic triterpenes. Tumulosic acid shares an 8-ene-lansta structure, while dehydrotumulosic acid shares a 7,9(11)-dien-lansta structure. The number and metabolic types of metabolites for these two compounds were almost the same when incubated with RLMs in vitro. It was speculated that the double bond on structure of pentacyclic triterpenes had nothing to do with the site of oxidation in RLMs. Characterization of Metabolites of Poria cocos in rats in Vivo. The metabolites in vivo of Poria cocos were analyzed by UHPLC−Q−TOF−MS in rat plasma after oral administration, which served as the experiment group. The plasma with the administration of distilled water to rats was served as the blank group. The blank plasma sample dissolved with the extract of Poria cocos served as the control group. The confirmation of prototypes was made by comparing with the data of accurate molecular mass and retention time of control group. The compounds that can be detected in both experimental and control groups but not the blank group were defined as prototypes. As almost all triterpene acids in the extract of Poria cocos share a carboxyl at the C−21 position, four types of neutral losses were observed in MS/MS spectra, namely, HCOOH (46 Da), CO2 (44 Da), CO2 + CH4 (60 Da), and HCOOH + CH4 (62 Da). These neutral losses were imported into UNIFI software for distinguishing triterpene acids in dosed plasma. However, the number of all triterpene acids detected in the extract of Poria cocos exceed more than that in the in-house database, so there must some compounds identified as isomers. A total of 62 prototype constituents were detected in the dosed plasma, as listed in Table S2. Eight compounds were confirmed with the reference compounds. These absorbed ingredients involved the main active substances in Poria cocos. Notably, the mass errors of all identified constituents were within 10 ppm of error. The constituents absorbed in vivo would be further metabolized by various of metabolic enzymes. The compounds not detected in the control and blank groups were defined as metabolites. As the above study in vitro, only pachymic acid, sharing an acetyl group at C-3 position, was partly transformed into tumulosic acid through the deacetylation under the action of intestinal microflora. Because both of tumulosic acid and pachymic acid can be absorbed in vivo as prototypes, the action of intestinal microflora could be ignored when screening metabolites in vivo. In that way, the screening of absorbed metabolites in vivo only need to consider the effect of CYP450, which play a key role in compound metabolism in vivo. In fact, the oxidation action plays a large part in transforming

Figure 4. Extract ion chromatograms of compounds M1 (A) and P1 (B) in plasma in the control, blank, and experimental groups through UHPLC−Q−TOF MS.

in Table S2 was selected based on compound screening under the UNIFI software. The response intensity of compound at m/z 453.33 at 16.68 min in the control group was relative high. This compound was identified as dehydrotrametenolic acid, which was compared with the retention time and accurate mass of the reference compound. Notably, there was a liquid peak in experimental group at 16.68 min detected. The mass behavior of compound P1 in experimental group was compared with that of dehydrotrametenolic acid in the control group. Compound P1 was identified as dehydrotrametenolic acid in dosed plasma in vivo. Although a liquid peak could be observed in blank group, its mass behavior in mass spectrometry was not constituent with that of reference of dehydrotrametenolic acid. This can be defined as a false positive interference due to the complex matrix. However, this result gave proof of the importance of mass spectrometry and reference in identifying compounds in complex matrix. More interestingly, there was another liquid peak M1 detected at 14.64 min in the experimental group but not in blank and control groups, as described in Figure 4B. Next the MS/MS spectrum of this compound was taken into analysis. Just three fragment ions were observed under the MSE acquiring mode based on the analysis of UNIFI software, namely, fragmentations at m/z 371.2571, m/z 359.2944, and m/z 319.2264. There was a neutral loss C6H10 (84) observed resulted from the elimination of side chain, corresponding to fragment ion 8324

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Journal of Agricultural and Food Chemistry

Figure 5. Interaction network of the effect substances (in orange), the related target proteins (in blue), and the common targets shared by drug and targets (in royal blue). A node stands for a constituent, a target, or disease, the interactions of two nodes were represented by a line, and the bigger size of a node refers to a greater degree.

Table 2. Disease Associated Pathways of PC through the GO Enrichment Analysis (P value < 0.001) GO term (pathway)

rate of targeted genes in pathway (%)

Alzheimer’s disease cardiac muscle contraction Huntington’s disease pathways in cancer Parkinson’s disease small cell lung cancer apoptosis prostate cancer oxidative phosphorylation steroid hormone biosynthesis adipocytokine signaling pathway nonsmall cell lung cancer colorectal cancer amyotrophic lateral sclerosis androgen and estrogen metabolism

16.8 11.2 14 18.2 11.2 8.4 8.4 8.4 9.8 6.3 7 5.6 6.3 4.9 4.2

P value 2.00 5.00 5.20 1.40 6.20 1.30 1.90 2.40 3.20 4.60 1.00 1.30 3.90 8.60 1.00

m/z 371.2571. However, the formation of compound M1 still remained unclear. Two causes can result in the formation of metabolite M1, the transformation of isomers, and the oxidation. As the lack of enough mass fragmentations and reference information, the compound M1 could not be characterized absolutely. Collectively, a total of 59 constituents were detected and characterized as metabolites of Poria cocos in vivo, which was based on the above analysis. Almost all metabolites were classified as the products of oxidation, and only a small part of them were confirmed as products of isomerization. That was a key problem for screening metabolites of triterpene acids as they share the same chemical formula and similar fragmentation behaviors in mass spectrometry. A lot of work is still needed for complete determination of the structures of all metabolites. Collectively, the metabolic profile of Poria cocos was described through a targeted analysis strategy, which considered the metabolic processes of foods taken orally. This result can also provide better healthy support for Poria cocos as supplement food and herbal medicine. Targeted Network Pharmacological Analysis of the Bioactive Constituents. There are so many compounds detected and characterized in the extract crude of Poria cocos. However, not all constituents of Poria cocos were reported with potential activity. In this section, we retrieved all components of Poria cocos absorbed in plasma in SciFinder Scholar. First, the chemical structures were imported into the SciFinder Scholar for retrieval. All literature that has recorded the information of targeted compound was listed. The potential

× × × × × × × × × × × × × × ×

10−13 10−11 10−09 10−08 10−08 10−06 10−06 10−06 10−06 10−06 10−05 10−04 10−04 10−04 10−03

class of pathway human diseases; neurodegenerative diseases organismal systems; circulatory system human diseases; neurodegenerative diseases human diseases; neurodegenerative diseases human diseases; cancers cellular processes; cell growth and death human diseases; cancers metabolism; energy metabolism metabolism; lipid metabolism metabolism; lipid metabolism human diseases; cancers human diseases; cancers human diseases; neurodegenerative diseases

targets were selected out and collected sequentially. The related target proteins were also gathered from several open source databases. Finally, a total of 30 constituents of Poria cocos were summed to act with the potential targets. The targets of AD were also collected, as the constituents of Poria cocos possess the potential activity toward AD. Only the target proteins shared by drugs and disease were retained to construct the network as shown in Figure 5 (86 targets). It was obvious that the larger target shared more edges with other targets, such as CYP17A1, RARG, VDR, and NR3C1. A total of 198 targets were imported into the bioinformatics database for GO enrichment analysis. When the background was chosen to be Homo sapiens, only 143 targets remained for further analysis. As shown in Table 2, the pathways enriched with genes targeted by ingredients of Poria cocos were mainly involved in human disease and material metabolism. Among the human disease, these were main neurodegenerative disease and cancers. The problems in the nervous system, such as neurofibrillary tangles, neuroinflammation, and deposition of Beta amyloid, can cause the incidence of AD. The material metabolism mainly focused on energy metabolism and lipid metabolism. This result also reflected the multitarget and multifunction therapeutic feature for the diseases treatment through complex compounds. The deep insight for Poria cocos in treatment with AD can provide a new clue for further clinical application. 8325

DOI: 10.1021/acs.jafc.8b02753 J. Agric. Food Chem. 2018, 66, 8319−8327

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(8) Eom, S.; Kim, Y. S.; Lee, S. B.; Noh, S.; Yeom, H. D.; Bae, H.; Lee, J. H. Molecular Determinants of alpha 3 beta 4 Nicotinic Acetylcholine Receptors Inhibition by Triterpenoids. Biol. Pharm. Bull. 2018, 41 (1), 65−72. (9) Lee, S. G.; Kim, M. M. Pachymic acid promotes induction of autophagy related to IGF-1 signaling pathway in WI-38 cells. Phytomedicine 2017, 36, 82−87. (10) Zhu, L. X.; Xu, J.; Zhang, S. J.; Wang, R. J.; Huang, Q.; Chen, H. B.; Dong, X. P.; Zhao, Z. Z. Qualitatively and quantitatively comparing secondary metabolites in three medicinal parts derived from Poria cocos (Schw.) Wolf using UHPLC-QTOF-MS/MS-based chemical profiling. J. Pharm. Biomed. Anal. 2018, 150, 278−286. (11) Miao, H.; Zhao, Y. H.; Vaziri, N. D.; Tang, D. D.; Chen, H.; Chen, H.; Khazaeli, M.; Tarbiat-Boldaji, M.; Hatami, L.; Zhao, Y. Y. Lipidomics Biomarkers of Diet-Induced Hyperlipidemia and Its Treatment with Poria cocos. J. Agric. Food Chem. 2016, 64 (4), 969− 79. (12) Wang, M.; Chen, D. Q.; Chen, L.; Liu, D.; Zhao, H.; Zhang, Z. H.; Vaziri, N. D.; Guo, Y.; Zhao, Y. Y. Novel RAS Inhibitors Poricoic Acid ZG and Poricoic Acid ZH Attenuate Renal Fibrosis via a Wnt/ beta-Catenin Pathway and Targeted Phosphorylation of smad3 Signaling. J. Agric. Food Chem. 2018, 66 (8), 1828−1842. (13) Pereira-Caro, G.; Ordonez, J. L.; Ludwig, I.; Gaillet, S.; Mena, P.; Del Rio, D.; Rouanet, J.-M.; Bindon, K. A.; Moreno-Rojas, J. M.; Crozier, A. Development and validation of an UHPLC-HRMS protocol for the analysis of flavan-3-ol metabolites and catabolites in urine, plasma and feces of rats fed a red wine proanthocyanidin extract. Food Chem. 2018, 252, 49−60. (14) Song, W.; Qiao, X.; Chen, K.; Wang, Y.; Ji, S.; Feng, J.; Li, K.; Lin, Y.; Ye, M. Biosynthesis-Based Quantitative Analysis of 151 Secondary Metabolites of Licorice To Differentiate Medicinal Glycyrrhiza Species and Their Hybrids. Anal. Chem. 2017, 89 (5), 3146−3153. (15) Tang, S.; Liu, S.; Liu, Z.; Song, F.; Liu, S. Analysis and Identification of the Chemical Constituents of Ding-Zhi-Xiao-Wan Prescription by HPLC-IT-MSn and HPLC-Q-TOF-MS. Chin. J. Chem. 2015, 33 (4), 451−462. (16) Feng, G. F.; Liu, S.; Pi, Z. F.; Song, F. R.; Liu, Z. Q. Studies on the chemical and intestinal metabolic profiles of Polygalae Radix by using UHPLC-IT-MS(n) and UHPLC-Q-TOF-MS method coupled with intestinal bacteria incubation model in vitro. J. Pharm. Biomed. Anal. 2018, 148, 298−306. (17) Gong, P.; Cui, N.; Wu, L.; Liang, Y.; Hao, K.; Xu, X.; Tang, W.; Wang, G.; Hao, H. Chemicalome and metabolome matching approach to elucidating biological metabolic networks of complex mixtures. Anal. Chem. 2012, 84 (6), 2995−3002. (18) Xing, R.; Zhou, L.; Xie, L.; Hao, K.; Rao, T.; Wang, Q.; Ye, W.; Fu, H.; Wang, X.; Wang, G. Development of a systematic approach to rapid classification and identification of notoginsenosides and metabolites in rat feces based on liquid chromatography coupled triple time-of-flight mass spectrometry. Anal. Chim. Acta 2015, 867, 56−66. (19) Li, L. A.; Chen, X. Y.; Li, D.; Zhong, D. F. Identification of 20(S)-Protopanaxadiol Metabolites in Human Liver Microsomes and Human Hepatocytes. Drug Metab. Dispos. 2011, 39 (3), 472−483. (20) Watanabe, H.; Takakusa, T.; Kimura, S.; Inoue, H.; Kusuhara, O. Ando, Analysis of Mechanism-Based Inhibition of CYP 3A4 by a Series of Fluoroquinolone Antibacterial Agents. Drug Metab. Dispos. 2016, 44 (10), 1608−1616. (21) Feng, G.; Liu, S.; Pi, Z.; Song, F.; Liu, Z. Study on the I Phase Metabolism of Ginsenoside in vitro by Ultra Performance Liquid Chromatography Coupled with Mass Spectrometry. Journal of Chinese Mass Spectrometry Society 2017, 38 (4), 450−459. (22) Wang, H.; Wang, Q.; Xiao, S.-L.; Yu, F.; Ye, M.; Zheng, Y.-X.; Zhao, C.-K.; Sun, D.-A.; Zhang, L.-H.; Zhou, D.-M. Elucidation of the pharmacophore of echinocystic acid, a new lead for blocking HCV entry. Eur. J. Med. Chem. 2013, 64, 160−168. (23) Li, Y.; Ren, G.; Wang, Y. X.; Kong, W. J.; Yang, P.; Wang, Y. M.; Li, Y. H.; Yi, H.; Li, Z. R.; Song, D. Q.; Jiang, J. D. Bioactivities of

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jafc.8b02753.

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UHPLC−Q−TOF−MS chromatograms and data (PDF)

AUTHOR INFORMATION

Corresponding Authors

*Tel: +86-431-85262613. Fax: +86-431-85262044. E-mail: [email protected]. *E-mail: [email protected]. ORCID

Shu Liu: 0000-0002-8848-6871 Fengrui Song: 0000-0001-7338-8139 Funding

This work was supported by grants from the National Natural Science Foundation of China Key Program (No. 81530094) and General Program (No. 81573574) and the Science and Technology Development Project of Jilin Province (20170623025TC). Notes

The authors declare no competing financial interest.

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ABBREVIATIONS AD, Alzheimer’s disease; CYP450, cytochrome P-450; UHPLC−Q−TOF−MS, ultrahigh performance liquid chromatography tandem quadrupole-time-of-flight mass spectrometry; MSE, data-independent mass spectrometry; RLMs, preparation of rat liver microsomes

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REFERENCES

(1) Lu, M.-K.; Cheng, J.-J.; Lin, C.-Y.; Chang, C.-C. Purification, structural elucidation, and anti-inflammatory effect of a water-soluble 1,6-branched 1,3-alpha-D-galactan from cultured mycelia of Poria cocos. Food Chem. 2010, 118, 349−356. (2) Xia, B.; Zhou, Y.; Tan, H. S.; Ding, L. S.; Xu, H. X. Advanced ultra-performance liquid chromatography-photodiode array-quadrupole time-of-flight mass spectrometric methods for simultaneous screening and quantification of triterpenoids in Poria cocos. Food Chem. 2014, 152, 237−244. (3) Lu, Y. T.; Kuan, Y. C.; Chang, H. H.; Sheu, F. Molecular cloning of a Poria cocos protein that activates Th1 immune response and allays Th2 cytokine and IgE production in a murine atopic dermatitis model. J. Agric. Food Chem. 2014, 62 (13), 2861−71. (4) Ling, Y.; Chen, M.; Wang, K.; Sun, Z.; Li, Z.; Wu, B.; Huang, C. Systematic screening and characterization of the major bioactive components of Poria cocos and their metabolites in rats by LC-ESIMSn. Biomed. Chromatogr. 2012, 26 (9), 1109−1117. (5) Wang, W.; Dong, H.; Yan, R.; Li, H.; Li, P.; Chen, P.; Yang, B.; Wang, Z. Comparative study of lanostane-type triterpene acids in different parts of Poria cocos (Schw.) Wolf by UHPLC−Fourier transform MS and UHPLC-triple quadruple MS. J. Pharm. Biomed. Anal. 2015, 102, 203−214. (6) Gao, A.-H.; Chen, X.; Chen, Y.; Xu, Z.-Z.; Liu, Y.-N.; Zhang, H.; Zhang, L. Inhibition of ovarian cancer proliferation and invasion by pachymic acid. Int. J. Clin Exp Pathol 2015, 8 (2), 2235−41. (7) Wang, J.; Zhang, P.; He, H.; Se, X.; Sun, W.; Chen, B.; Zhang, L.; Yan, X.; Zou, K. Eburicoic acid from Laetiporus sulphureus (Bull.:Fr.) Murrill attenuates inflammatory responses through inhibiting LPS-induced activation of PI3K/Akt/mTOR/NF-κB pathways in RAW264.7 cells. Naunyn-Schmiedeberg's Arch. Pharmacol. 2017, 390 (8), 845−856. 8326

DOI: 10.1021/acs.jafc.8b02753 J. Agric. Food Chem. 2018, 66, 8319−8327

Article

Journal of Agricultural and Food Chemistry berberine metabolites after transformation through CYP450 isoenzymes. J. Transl. Med. 2011, 9, 62. (24) Losito, L.; Facchini, A.; Valentini, T. R. I.; Cataldi, F. Palmisano, Fatty acidomics: Evaluation of the effects of thermal treatments on commercial mussels through an extended characterization of their free fatty acids by liquid chromatography - Fourier transform mass spectrometry. Food Chem. 2018, 255, 309−322. (25) Uchida, H.; Itabashi, Y.; Watanabe, R.; Matsushima, R.; Oikawa, H.; Suzuki, T.; Hosokawa, M.; Tsutsumi, N.; Ura, K.; Romanazzi, D.; Miller, M. R. Detection and identification of furan fatty acids from fish lipids by high-performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry. Food Chem. 2018, 252, 84−91. (26) Bauer, J.; Luetjohann, F. S.; Hanschen, M.; Schreiner, J.; Kuballa, E.; Jantzen, S. Rohn, Identification and characterization of pesticide metabolites in Brassica species by liquid chromatography travelling wave ion mobility quadrupole time-of-flight mass spectrometry (UPLC-TWIMS-QTOF-MS). Food Chem. 2018, 244, 292−303. (27) Righetti, L.; Fenclova, M.; Dellafiora, L.; Hajslova, J.; StranskaZachariasova, M.; Dall’Asta, C. High resolution-ion mobility mass spectrometry as an additional powerful tool for structural characterization of mycotoxin metabolites. Food Chem. 2018, 245, 768−774. (28) Gibbs, R. S.; Murray, S. L.; Watson, L. V.; Nielsen, B. P.; Potter, R. A.; Murphy, C. J. Development and Validation of a Hybrid Screening and Quantitative Method for the Analysis of Eight Classes of Therapeutants in Aquaculture Products by Liquid Chromatography-Tandem Mass Spectrometry. J. Agric. Food Chem. 2018, 66 (20), 4997−5008. (29) Enomoto, H.; Sato, K.; Miyamoto, K.; Ohtsuka, A.; Yamane, H. Distribution Analysis of Anthocyanins, Sugars, and Organic Acids in Strawberry Fruits Using Matrix-Assisted Laser Desorption/IonizationImaging Mass Spectrometry. J. Agric. Food Chem. 2018, 66 (19), 4958−4965. (30) Wylie, A.D.; Zandberg, W.F. Quantitation of Sialic Acids in Infant Formulas by Liquid Chromatography-Mass Spectrometry: An Assessment of Different Protein Sources and Discovery of New Analogues. J. Agric. Food Chem. 2018, DOI: 10.1021/acs.jafc.8b01042. (31) Song, S.; Zhu, K.; Han, L.; Sapozhnikova, Y.; Zhang, Z.; Yao, W. Residue Analysis of 60 Pesticides in Red Swamp Crayfish Using QuEChERS with High-Performance Liquid Chromatography-Tandem Mass Spectrometry. J. Agric. Food Chem. 2018, 66 (20), 5031− 5038. (32) Si, W.; Yang, W.; Guo, D.; Wu, J.; Zhang, J.; Qiu, S.; Yao, C.; Cui, Y.; Wu, W. Selective ion monitoring of quinochalcone Cglycoside markers for the simultaneous identification of Carthamus tinctorius L. in eleven Chinese patent medicines by UHPLC/QTOF MS. J. Pharm. Biomed. Anal. 2016, 117, 510−21. (33) Yang, W.; Zhang, J.; Yao, C.; Qiu, S.; Chen, M.; Pan, H.; Shi, X.; Wu, W.; Guo, D. Method development and application of offline two-dimensional liquid chromatography/quadrupole time-of-flight mass spectrometry-fast data directed analysis for comprehensive characterization of the saponins from Xueshuantong Injection. J. Pharm. Biomed. Anal. 2016, 128, 322−332. (34) Qiu, S.; Yang, W.-z.; Yao, C.-l.; Qiu, Z.-d.; Shi, X.-j.; Zhang, J.x.; Hou, J.-j.; Wang, Q.-r.; Wu, W.-y.; Guo, D.-a. Nontargeted metabolomic analysis and ″Commercial-homophyletic″ comparisoninduced biomarkers verification for the systematic chemical differentiation of five different parts of Panax ginseng. J. Chromatogr. A 2016, 1453, 78−87. (35) Fang, J.; Wang, L.; Wu, T.; Yang, C.; Gao, L.; Cai, H.; Liu, J.; Fang, S.; Chen, Y.; Tan, W.; Wang, Q. Network pharmacology-based study on the mechanism of action for herbal medicines in Alzheimer treatment. J. Ethnopharmacol. 2017, 196, 281−292. (36) Xu, T.; Li, S.; Sun, Y.; Pi, Z.; Liu, S.; Song, F.; Liu, Z. Systematically characterize the absorbed effective substances of Wutou Decoction and their metabolic pathways in rat plasma using

UHPLC-Q-TOF-MS combined with a target network pharmacological analysis. J. Pharm. Biomed. Anal. 2017, 141, 95−107. (37) Liu, J.; Liu, J.; Shen, F.; Qin, Z.; Jiang, M.; Zhu, J.; Wang, Z.; Zhou, J.; Fu, Y.; Chen, X.; Huang, C.; Xiao, W.; Zheng, C.; Wang, Y. Systems pharmacology analysis of synergy of TCM: an example using saffron formula. Sci. Rep. 2018, 8 (1), 380. (38) Luo, Y.; Wang, Q.; Zhang, Y. A systems pharmacology approach to decipher the mechanism of danggui-shaoyao-san decoction for the treatment of neurodegenerative diseases,. J. Ethnopharmacol. 2016, 178, 66−81. (39) Bi, Y.-F.; Zhu, H.-B.; Pi, Z.-F.; Liu, Z.-Q.; Song, F.-R. Effects of Flavonoides from the Leaves of Acanthopanax on the Activity of CYP450 Isozymes in Rat Liver Microsomes by a UPLC-MS/MS and Cocktail Probe Substrates Method. Chemical Journal of Chinese Universities-Chinese 2013, 34 (5), 1067−1071. (40) Liu, J.; Mu, J.; Zheng, C.; Chen, X.; Guo, Z.; Huang, C.; Fu, Y.; Tian, G.; Shang, H.; Wang, Y. Systems-Pharmacology Dissection of Traditional Chinese Medicine Compound Saffron Formula Reveals Multi-scale Treatment Strategy for Cardiovascular Diseases. Sci. Rep. 2016, 6, 1980910. (41) Keppler, K.; Humpf, H.-U. Metabolism of anthocyanins and their phenolic degradation products by the intestinal microflora. Bioorg. Med. Chem. 2005, 13 (17), 5195−5205. (42) Parkar, S. G.; Trower, T. M.; Stevenson, D. E. Fecal microbial metabolism of polyphenols and its effects on human gut microbiota. Anaerobe 2013, 23, 12−19. (43) Nakamura, K.; Nishihata, T.; Jin, J.-S.; Ma, C.-M.; Komatsu, K.; Iwashima, M.; Hattori, M. The C-glucosyl bond of puerarin was cleaved hydrolytically by a human intestinal bacterium strain PUE to yield its aglycone daidzein and an intact glucose. Chem. Pharm. Bull. 2011, 59 (1), 23−27.

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