Screening hepatoprotective effective components of Lonicerae japonica Flos based on the spectrum-effect relationship and its mechanism exploring

Song Wng, Lin Yng, Ajio Hou, Songto Liu, Liu Yng, Hixue Kung,*, Hi Jing,*

a Key Laboratory of Chinese Materia Medica, Heilongjiang University of Chinese Medicine, Ministry of Education, Harbin 150040, China

b School of Pharmacy, Jiangxi University of Traditional Chinese Medicine, Nanchang 330004, China

Keywords:Lonicerae japonicae Flos Hepatoprotective effect Spectrum-effect relationship Molecular docking Mechanism

A B S T R A C T Lonicerae japonicae Flos (LF) is a kind of healthcare food with hepatoprotective function. This study was designed to explore the spectrum-effect relationships between UPLC f ingerprints and the hepatoprotective effects of LF. Fingerprints of ten batches of LF were established by UPLC-PDA. The inhibitory levels of AST and ALT were used as pharmacological indexes, and secoxyloganin, isochlorogenic acid A and isochlorogenic acid C were screened as hepatoprotective active compounds by grey relational analysis (GRA) and partial least squares regression analysis (PLSR). Caspase-3 was obtained by network pharmacology as a key target of hepatoprotective active compounds. Molecular docking is used to explore the interaction between small molecules and proteins. This work provided a general model of the combination of UPLC-PDA and hepatoprotective effect to study the spectrum-effect relationship of LF, which can be used to considerable methods and insight for the fundamental research of the material basis of similar healthcare food.

1. Introduction

Liver disease is a common disease, the main liver diseases include hepatitis B, hepatitis C, alcoholic liver disease, non-alcoholic fatty liver disease [1]. By 2020, the total number of people with chronic liver disease will reach 447 million. Liver injury leads to hepatic fibrosis and later liver cirrhosis, which kills more than 1 million people a year. After a liver injury occurs, hepatic stellate cells are activated and induced to have a contractile, proliferative,and f ibroblast-like myof ibroblast-like phenotype, which leads to the accumulation of collagen and other extracellular matrix components,the continuous stimulation and accumulation of these substances lead to the destruction of liver structure and liver nerve function, resulting in apoptosis of liver cells and decline of liver function [2]. At present,there is no particularly good plan to protect the liver daily. People take treatment drugs usually after the liver injury has already occurred,which can lead to several drug reactions. Interestingly, people in the Asian region have a habit of eating healthcare food. Many healthcare foods have the function of improving human immunity, improving the gastrointestinal tract, protecting the liver and gallbladder, etc.

In China,Lonicerae japonicae Flos(LF) has been used as tea for more than a thousand years [3,4]. In modern times, products such as healthcare herbal tea, hot pot seasoning and toothpaste made from LF are widely used. For example, Jia Duo Bao herbal tea with LF as the main ingredient has been on the market for 26 years. LF is also an important traditional Chinese medicine (TCM) [5]. It has the functions of clearing away heat and detoxification and dispersing wind and heat [6]. Pharmacological studies have shown that LF has the effects of anti-oxidation [7], anti-tumor [8], anti-bacterial [9],antiviral [10], anti-inf lammation [11], protecting liver and gallbladder [12],hypolipidemic [13], immune regulation [14] and so on. Phytochemical studies have shown that LF has volatile oils [15], organic acids [16],flavonoids [17], iridoids [18], saponins [19] and so on. Miao et al.found that LF can inhibit HSC activation, reverse EMT and reduce liver oxidative stress damage by inducing Nrf2activation, thereby reducing CCl4induced liver fibrosis in mice [12]. Compared with Western medicine to protect the liver, LF tea has the advantages of health, no adverse reactions, cheap and delicious. However, LF still has serious drawbacks: it is difficult to control its quality because of its complex chemical composition. Fortunately, the presence of fingerprints provided insights into the study of mixtures.

Fingerprints can effectively evaluate the authenticity and consistency of the intrinsic quality of TCMs [20]. Since 2000,fingerprints have become an important research method to identify and fully reflect the quality of the intrinsic compounds of TCMs [21].However, the chromatographic fingerprint can only reflect the content of the compound, and it is not clear whether the chemical components reflected in the fingerprint of TCMs are medicinal ingredients.Besides, the degree of correlation between chemical components and efficacy has not been elucidated. Therefore, the fingerprint study alone cannot screen out the effective active compounds of TCMs. In recent years, the successful establishment of the spectrum-effect relationship has solved this problem to a certain degree [22-24]. In this paper,the studied pharmacodynamic model is established, pharmacological data is obtained, and appropriate mathematical statistics methods are used to correlate fingerprints and pharmacological data, which can be screened the effective compounds of TCMs are developed, which provides help for the follow-up basic research of medicinal materials.However, the mechanism of action of the screened active compounds is still unknown. Therefore, it is necessary to explore the mechanism of action through rapid and roundly network pharmacology and molecular docking methods [25].

In this article, we first adopted the UPLC-PDA method to establish fingerprints of ten batches of LF. Then we establish a mice model of acute liver injury and determine the pharmacological data of each batch. Use grey relational analysis (GRA) [26] and partial least squares regression analysis (PLSR) [27] to establish the spectrumeffect relationship. Finally, the key targets of hepatoprotective compounds were searched through network pharmacology and molecular docking technology was used to reveal the interaction between active small molecules and the position of the protein target.The purpose of this article is to find the effective ingredients and to explore the mechanism of LF hepatoprotective effect.

2. Materials and methods

2.1 Instruments

The Waters ACQUITY UPLC system was used for fingerprint analysis, which was equipped with a vacuum degasser, quaternary pump, sample manager, and photodiode array (PDA) detector (Waters, Milford, MA, USA).MassLynx V4.1 software was used for instrument control and data acquisition. A microplate reader was obtained from BioTek(Winooski, VT, USA). A tissue grinder was obtained from Coyote Bioscience (Beijing, China). Upright Metallurgical Microscope was obtained from Nikon (Japan).

2.2 Materials and reagents

Ten batches of LF (S1-S10) from different origins were purchased from various provinces in China. Table 1 shows their origins. According to the identification by Professor Su Lianjie from Heilongjiang University of Chinese medicine, the ten batches of TCMs from different origins are all LF. Secoxyloganin (CAS: 58822-47-2) was purchased from Chengdu push bio-technology co., LTD.Isochlorogenic acid A (CAS: 89919-62-0), and Isochlorogenic acid C(CAS: 57378-72-0) were purchased from Chengdu purechem-standard co., LTD. All standards are of chromatographic purity. Distilled water was purchased from Hangzhou Wahaha Company. The purity of other reagents is greater than 98%.

Table 1 Origin of the 10 batches of LF samples.

2.3 Animals

104 ICR mice (20-22 g) were purchased from Liaoning Changsheng Biotechnology Co., Ltd. The indoor temperature is maintained at (25 ± 2) °C, the humidity is maintained at (50 ± 10)%.All mice under a standard light-dark cycle. The animal study was performed according to the international rules considering animal experiments and the internationally accepted ethical principles for laboratory animal use and care. Animal welfare and experimental procedures were carried out following the ethical regulations of Heilongjiang University of Chinese Medicine (License number:SCXK (Hei) 2021-004.). The mice were randomly divided into 13 groups with 8 mice in each group. They are model group, control group, positive group, and LF S1-S10 group.

2.4 UPLC-PDA fingerprints

2.4.1 Establishment of UPLC conditions

The chromatogram was analyzed using a Thermo Hypersil GOLD column (100 mm × 2.1 mm, 1.9 μm) (Thermo Scientific TM,Waltham, MA, USA) at 40 °C. The mobile phase consists of methanol(A) and 0.1% formic acid-water (B). The flow rate is 0.3 mL/min,and each injection volume is 2 μL. The optimized gradient condition was: 0-2 min: 10%-20% A, 2-4 min: 20%-22% A, 4-9 min: 22%A, 9-17 min: 22%-30% A, 17-29 min: 30%-55% A, 29-30 min:55%-100% A.

2.4.2 Preparation of sample solutions

The dry powder of 0.5 g LF sample is placed in a 15 mL centrifuge tube, 50% methanol/water is added, and ultrasonic extraction is carried out at a frequency of 35 Hz for 30 min. After centrifugation(5 000 r/min) for 15 min, the supernatant was taken, filtered with a 0.22 μm filter membrane, and stored in a refrigerator at 4 °C for analysis.All samples were processed using the above method.

2.4.3 Preparation of standard solutions

Three standard compounds (secoxyloganin, isochlorogenic acid A and isochlorogenic acid C) were prepared into a storage solution with a concentration of 1 mg/mL. The standard solution was stored in a refrigerator at 4 °C for analysis.

2.4.4 Method validation of fingerprint analysis

Method validation examines the retention time and peak area of the sample respectively. Precision investigates the RSD of 6 consecutive injections. Repeatability investigates the RSD value of repeated injections of the same sample. Stability examines the RSD values of samples injected at different times (0, 2, 4, 8, 12, 24 and 48 h).

2.4.5 Similarity analysis of UPLC fingerprints

The Chinese medicine chromatographic fingerprint similarity evaluation system (version: 2012) was used to establish the fingerprint of ten batches of LF samples and check the similarity between the ten batches of samples.

2.5 Experiments of pharmacodynamic effects

2.5.1 Preparation of gavage

Eight volumes of 50% ethanol/water was added into the appropriate amount of LF, heated to boiling, extracted 3 times for 1.5 h each time. The three extracts were combined and concentrated to a suitable volume by a rotary evaporator. Stored in 4 °C refrigerators before use.

2.5.2 Evaluation of hepatoprotective activity

To explore the hepatoprotective effect of LF on the liver, we established a mice model of acute CCl4poisoning. The ICR mice were adapted to the laboratory environment for 7 days, and then the LF gavage was given continuously for 7 days (22.75 g/kg). The positive group was given silymarin (36.4 mg/kg) by gavage. Then, 1% CCl4olive oil diluent was injected intraperitoneally, the injection volume was 0.2 mL/10 g. After 24 h, blood was taken and the mice were sacrificed, and the liver was taken out. The blood was centrifuged at 12 000 r/min and 4 °C for 15 min immediately, and the serum was collected and stored at 4 °C for testing. Take an appropriate size of liver tissue and add 9 times volumes of normal saline. After grinding with a tissue grinding machine for 3 min, 10% tissue homogenate was prepared. After taking it out, it was immediately centrifuged at 12 000 r/min at 4 °C for 15 min. The tissue supernatant was taken and stored at 4 °C for testing.

The contents of AST, ALT, TBIL and TNF-α in the serum of mice were determined. The tissue supernatant was taken to determine the content of glutathione (GSH) in the liver. All indicators are tested according to the kit instructions. A pathological examination of liver tissue was performed. All grouped data and CCl4-treated groups were tested for statistical row differences. WhenP< 0.05, the difference was considered significant.

2.5.3 HE staining

The dewaxing process of the paraffin section is as follows:Xylene I for 20 min, Xylene II for 20 min, 100% ethanol I for 5 min,100% ethanol II for 5 min, 75% ethanol for 5 min and rinsing with tap water. Then dyed with hematoxylin and eosin dyes in turn. After dehydration with microscope observation, image acquisition and analysis.

2.6 Analysis of spectrum-effect relationships

2.6.1 GRA analysis

The GRA method is a multi-factor analysis method, and its basic principle is to distinguish the closeness of the multi-factor relationship in the system by comparing the geometric relationship of the statistical sequence. The closer the geometric shapes of the sequence curves are,the greater the correlation between them, and vice versa. In the study of the relationship between the fingerprint spectrum of TCMs and the efficacy, GRA is a commonly used mathematical research method,which can reveal the relationship between the compounds and the efficacy of TCMs, to achieve the purpose of screening effective active compounds of TCMs.

2.6.2 PLSR analysis

PLSR is a multivariate statistical analysis method with wide applicability. It can use the data information in the system to decompose and filter, extract the comprehensive variables with the strongest explanation to the dependent variable, and identify the information and noise in the system. Similarly, PLSR is also a commonly used mathematical-statistical method in the study of the relationship between spectrum effects of TCMs.

2.7 Network pharmacology

2.7.1 Targets for active compounds in LF

Compounds secoxyloganin, isochlorogenic acid A and isochlorogenic acid C were screened based on the spectrum-effect relationship. To obtain targets of hepatoprotective compounds in LF, we used the TCMSP (https://tcmsp-e.com/), the Swiss Target Prediction (http://www.swisstargetprediction.ch/) and the similarity ensemble approach (https://sea.bkslab.org/) database. We obtained the 2D chemical structure of the active ingredients on the website of PubChem and imported it into the Swiss Target Prediction database to obtain the relevant Target proteins. The Smiles structure of active ingredients was imported into SEA database to obtain relevant targets. All relevant targets were sorted out and the only one was retained, which turned out to be the relevant target of hepatoprotective ingredients in LF.

2.7.2 Target of liver injury

Information regarding liver injury target genes was gathered from the Genecards (https://www.genecards.org/) database. Use liver injury as the search term, targets related to liver injury were obtained. The Calculate and draw custom Venn diagrams (http://bioinformatics.psb.ugent.be/webtools/Venn/) was used to construct the Venn diagram, and the potential targets of active compounds in LF were obtained.

2.7.3 PPI and network construction

To evaluate potential-protein interactions (PPI) among the targets,all gene symbols of the protein targets including hepatoprotective compounds and liver injury were submitted to the String Database(https://string-db.org/). Compound target-disease target networks were obtained from the network visualization software Cytoscape(version 3.7.2).

2.8 Molecular docking

To explore the interaction and affinity between the active compounds screened through the spectrum-effect relationship and the key target, we adopted the strategy of molecular docking with the basic rigidity of the receptor protein and the flexible ligands for molecular docking. First of all, according to the following conditions: 1) The protein structure obtained by X crystal diffraction method; 2) The crystal resolution of the protein is less than 3?;3) The protein with a clear type. The 3D structure of small molecules was obtained from PubChem. The docking results were analyzed by AutoDockTools, and Pymol was used for mapping.

3. Results

3.1 Results of UPLC fingerprints

3.1.1 Optimization of UPLC chromatographic conditions

To optimize the UPLC method, we investigated the influence of different columns, different column temperatures, different mobile phases, and different wavelengths. The results show that the separation effect of Thermo Hypersil GOLD column (100 mm ×2.1 mm, 1.9 μm) is better than that of the Waters Acquity UHPLC HSS T3 column (50 mm × 2.1 mm, 1.8 μm) (Waters, Milford, MA,USA). The peak width and resolution are both good at 40 °C. 0.1%formic acid-water has a better peak shape than distilled water because formic acid promotes the ionization of the compound. Comparing the chromatograms at different wavelengths of 254, 230, 270 and 327 nm, there are more chromatographic peaks at 254 nm. Therefore,we choose Thermo Hypersil GOLD column (100 mm × 2.1 mm, 1.9 μm)for analysis, the column temperature is 40 °C, the mobile phase is methanol (A) and 0.1% formic acid-water (B), the injection volume is 2 μL, the flow rate is 0.3 mL/min, and the analysis is performed at 254 nm. The optimized gradient condition was: 0-2 min:10%-20%A, 2-4 min: 20%-22% A, 4-9 min: 22% A, 9-17 min: 22%-30% A,17-29 min: 30%-55% A, 29-30 min: 55%-100% A.

3.1.2 UPLC fingerprints of LF samples

The above conditions were used to analyze ten batches of LF samples from different origins, and the Chinese medicine chromatographic fingerprint similarity evaluation system (version:2012) was used for analysis. As shown in Fig. 1, a total of 13 common peaks are generated, and the content of the peak areas of the common chromatographic peaks are different.

Fig. 1 Chromatography fingerprint of 10 batches of samples.

3.1.3 Similarity analysis of fingerprints

Taking the control fingerprint as a reference, the similarity between each batch of samples is shown in Table 2. The results show that most of the samples have a high degree of similarity.However, the similarities of S1 and S6 samples are 0.885 and 0.786,respectively, which are quite different from other batches of samples.The similarities of S2 and S3, S4 and S5, S7 and S8 from the same province are relatively close, which may be due to the different lighting and harvesting periods in different regions, resulting in different content of different components of the same plant.

Table 2 Similarity analysis of 10 LF samples.

3.1.4 Method validation of fingerprint analysis

In Table 3, the method verification results show that the precision of the relative retention time of the sample is in the range of 0.13%-0.34%, the repeatability is in the range of 0.16%-0.40%,and the stability is in the range of 0.12%-0.38%. The precision of the relative peak area of the sample is in the range of 0.12%-6.01%,the repeatability is in the range of 0.25%-4.70%, and the stability is in the range of 0.33%-4.38%. The results show that the established fingerprint profile is effective.

Table 3 Results of relative retention times and relative peak areas of precision,repeatability, and stability.

3.2 Results of pharmacological experiments

3.2.1 AST, ALT, TBIL and TNF-α

Fig. 2 Results of AST (A), ALT (B), TNF-α (D), TBIL (D) and GSH (E) test. (t-test; * P ≤ 0.05, ** P ≤ 0.01; # P ≤ 0.05, ## P ≤ 0.01).

The results of the pharmacological experiment are shown in Fig. 2.Compared with the control group, the contents of AST, ALT, TNF-α and TBIL in the model group were significantly increased. It shows that CCl4has a significant damaging effect on the liver of mice and caused inflammation in mice. The LF group had obvious callback effects on AST, ALT and TBIL in mice, indicating that LF reduced liver cell damage. In addition, LF also reduced inflammation in mice.In summary, LF has the effect of hepatoprotective.

3.2.2 Results of GSH content

The content of GSH in liver tissue is shown in Fig. 2. GSH can neutralize the oxides produced by CCl4, thereby protecting lymphocytes from damage. In addition, maintaining the content of GSH can also enhance the activity of immune cells. Compared with the control group, the GSH content in the liver tissue of the model group was significantly reduced, indicating that the oxide produced by CCl4consumed a large amount of GSH. The LF group recalled the content of GSH in the liver tissues of mice, indicating that LF has the effect of hepatoprotective.

3.2.3 Results of HE staining

Pathology slices of mice livers (magnification of 200 ×) are shown in Fig. 3. In Fig. 3A, the capsule of liver tissue is composed of dense connective tissue with uniform thickness and rich elastic fiber. The liver lobule is centered on the central vein, surrounded by liver cells and hepatic sinuses that are roughly arranged radially. The liver cells are arranged regularly and neatly. In Fig. 3B, there are two inflammatory foci in the liver tissue (black arrow). A large number of hepatocytes swelled around the portal area,and the cytoplasm is loose and lightly stained (red arrow). A small amount of hepatocyte swelled around the central vein(yellow arrow). In Fig. 3C, Moderate amounts of vacuolar degeneration of hepatocytes are seen in the liver tissue. Various sizes of vacuoles are seen in the cytoplasm (blue arrow). In Fig. 3D, there are numerous vacuolar degenerations of hepatocytes in the liver tissue. The vacuoles of varying sizes are seen in the cytoplasm (blue arrow). Here are a few hepatocytes with balloon-like changes with nuclei centered and vacuolized cytoplasm (green arrow).

Fig. 3 Pathology slices of mice livers (magnification of 200 ×). (A) Control Group; (B) CCl4-treated Group; (C) S5 LF Group; (D) S9 LF Group.

3.3 Analysis of spectrum–effect relationships

3.3.1 GRA results of AST

AST and ALT directly represent the degree of liver damage.Therefore, in the spectrum-effect relationship experiment, we choose the inhibition rate of AST and ALT as theYindex to screen out the active compounds in LF that protect the liver. The pharmacological data of AST is converted into inhibition rate,inhibition rate = (model group average value - LF group average value)/model group average value, the inhibition rate is taken asY,and the peak area of the sample is taken asX. The results of GRA are shown in Table 4. The results show that the inhibition rate of AST is used as the pharmacodynamic index, and the correlation from high to low is: P11 > P12 > P1 > P10 > P8 > P5 > P9 > P13 >P4 > P2 > P3 > P6 > P7, P11, P12, and P1 have the greatest correlation, indicating that P11, P12, and P1 may be important compounds that hepatoprotective effect.

Table 4 GRA results of liver injury experiments.

3.3.2 GRA results of ALT

Taking the inhibition rate of ALT as the pharmacodynamic index, the results of GRA are shown in Table 4. The correlation from high to low is P5 > P3 > P10 > P6 > P13 > P9 > P7 > P4 >P12 > P8 > P1 > P2 > P11, P5, P3, and P10 have the greatest correlation, indicating that P5, P3, and P10 may be important compounds with hepatoprotective effect. However, the correlation coefficients between the various compounds are not very different.A single mathematical-statistical method is not accurate to screen the effective components of LF. Therefore, this experiment combined with another mathematical-statistical method (PLSR) to jointly screen the activity ingredients to improve the accuracy of the results.

3.3.3 PLSR results of AST

Taking the inhibition rate of AST asYand the peak area of the sample asX, when the four principal components are extracted, the PLSR fitting curve shows good linearity, and the result is shown in Fig. 4A. When the regression coefficient is positive, it indicates that the compound is positively correlated with theYvalue. The regression equation of AST inhibition rate (Y)and compound peak area (X) isY1= 0.120 6X1- 0.235 0X2-0.326 4X3+ 0.022 9X4+ 0.015 8X5+ 0.038 8X6- 0.039 7X7+ 0.592 9X8+0.146 3X9- 0.741 5X10+ 0.311 7X11+ 0.250 5X12- 0.063 5X13.The results show that among the 13 compounds, P8, P11, and P12 have the largest positive correlation coefficients, indicating that these three compounds may be important hepatoprotective active substances in LF. Combining the results of GRA, it is found that P11 and P12 rank high in the two mathematical-statistical models. Therefore, P11 and P12 were finally screened out as hepatoprotective active compounds.

Fig. 4 The analysis of pharmacological experiments by PLSR. PLS linear regression (A1, B1), regression coefficients (A2, B2) of the 13 compounds analyzed.

3.3.4 PLSR results of ALT

The same processing method is used to analyze ALT, and the result is shown in Fig. 4B. The regression equation of ALT inhibition rate (Y) and compound peak area (X) isY2= 0.191 5X1- 0.743 4X2+0.096 1X3+ 0.010 2X4- 0.460 4X5+ 0.167 1X6- 0.584 6X7- 0.070 2X8-0.332 7X9+ 0.232 0X10- 1.043 3X11+ 0.332 5X12+ 0.955 4X13. The results show that among the 13 compounds, P13, P12, and P10 have the largest positive correlation coefficients, indicating that these three compounds may be important hepatoprotective active substances in LF. Combined with the results of GRA, it was found that P10 ranks high in the two mathematical-statistical models.Therefore, P10 was finally screened out as a hepatoprotective active compound.

In summary, the results of the spectrum-effect relationship indicate that P10, P11 and P12 are the liver-protecting active compounds of LF. After comparing the standard products, we found that the three compounds are secoxyloganin, isochlorogenic acid A and isochlorogenic acid C. The chromatogram of the standards is shown in Fig. 5.

Fig. 5 Chromatogram of LF sample (A), secoxyloganin (B), isochlorogenic acid A (C) and isochlorogenic acid C (D).

3.4 Results of network pharmacology

A total of 66 related targets of secoxyloganin, isochlorogenic acid A and isochlorogenic acid C were obtained. Intersected with 8 054 targets obtained through GeneCards, 56 potential action targets were obtained. Venn diagram is shown in Fig. 6A. PPI network diagram is shown in Fig. 6B. The results showed that caspase-3 was the target with the highest degree value, and caspase-3 was considered to be the key action target of hepatoprotective compounds of LF. Compound target-disease target networks are shown in Fig. 6C, which contains a total of 60 nodes and 133 edges. In conclusion, the interaction between the three compounds and caspase-3 can be explored to further explore the mechanism of LF’s hepatoprotective action.

3.5 Results of molecular docking

Using small molecule inhibitors to inhibit caspase-3 can effectively protect the liver, emricasan is the first caspase inhibitor tested in humans which have received orphan drug status by FDA. It is developed by Pfizer and made in such a way that it protects liver cells from excessive apoptosis [28]. Using molecular docking [29],can reveal the interaction and affinity between small molecule ligands and caspase-3. By comparing with emricasan, we can preliminarily infer the liver-protecting mechanism of small molecules in LF.

The protein with the PDB number 2H65 is selected, and all water molecules are removed from the original Protein Data Bank file.Then, AutoDockTools was used to pretreat the protein. Including removing ligands, adding polar hydrogen, correcting missing atoms,calculating and adding atomic gasteiger charges. The initial relative dihedrals of the ligand are set to random. Use the grid box to generate the receptor grid and determine the binding site. The grid coordinates are set tox= 20.71,y= 11.34,z= 37.059, and the grid spacing is set to 0.375 ?. The docking adopts a genetic algorithm, and the number of GA runs is set to 200. Both the LF hepatoprotective compounds and the caspase-3 inhibitor emricasan were docked with the caspase-3 protein under the above conditions.

The 3D and 2D diagrams of the docking between ligands and caspase-3 are shown in Fig. 7. The results show that the F atom on the benzene of emricasan generates halogen bonds with GLN217,ASN208 and GLU248; emricasan generates hydrogen bonding with TRP214 and ARG207 It has a pi-pi T-shape effect with PHE247, a pi-cation effect with TRP206, a pi-alkyl effect with CYS163, and van der Waals interactions with ASP211, TYR204, MET61, SER205,TRP206, PHE250, SER209, SER249 and GLN161. These forces make emricasan bind to caspase-3 and produce an inhibitory effect.

Secoxyloganin has hydrogen bonding with GLU248, PHE250,ARG207, TRP214 and SER209, and has Pi-Alkyl with PHE247, and TRP206, GLU246, ASN208, GLN217 and LYS210 produce van der Waals interactions, and SER249 produces carbon-hydrogen bond.These forces make secoxyloganin bind to caspase-3 and produce an inhibitory effect.

Fig. 6 Results of network pharmacology: Venn diagram (A); PPI network (B); Compound target-disease target networks (C).

Fig. 7 The 3D and 2D diagrams of the docking between ligands and caspase-3. The ligands are emricasan (A1, B1), secoxyloganin (A2, B2), isochlorogenic acid A(A3, B3) and isochlorogenic acid C (A4, B4).

Isochlorogenic acid A has hydrogen bonding with ARG64,GLN161, SER120, HIS121, ASN208, GLU248 and SER251. It produces pi-cation and attractive charge with ARG207, produces van der Waals interactions with ALA162, GLY122, MET61, TRP206,PHE256, PHE250, TRP214, SER205, TYR204 and SER209, and produces a carbon-hydrogen bond with SER249. These forces make isochlorogenic acid A bind to caspase-3 and produce an inhibitory effect.

Isochlorogenic acid C has hydrogen bonding with SER209,ARG64, GLN161, SER120 and HIS121. It produces salt bridge and pi-cation with ARG207, Pi-Alkyl with CYS163, and van der Waals interactions with PHE250, TRP214, SER249, TRP206, ASN208,SER205, ALA162, MET61, GLY122, TYR204, SER63, SER65, and THR62. These forces make isochlorogenic acid C bind to caspase-3 and produce an inhibitory effect.

The docking scores (estimated free energy of binding) of the four compounds are shown in Table 5. Compared with the classic caspase-3 inhibitor emricasan, the docking scores of isochlorogenic acid A and isochlorogenic acid C are both higher than emricasan, and secoxyloganin is lower than emricasan.

4. Discussion

In this paper, UPLC-PDA was used to establish fingerprints of LF in different regions, and it was found that LF has 13 common peaks. However, the content of compounds from different producing areas varies greatly, and the difference in active ingredients will lead to differences in their hepatoprotective effect. Then, this article uses AST and ALT as indicators to investigate the pharmacological activities of each batch of LF. The levels of AST and ALT are by far the most widely used test to reflect liver cell damage. The expression level of AST and ALT in the liver can reach 100 times in serum.ALT is only present in the cytoplasm, while AST only accounts for 20% in the liver cytoplasm, and the remaining 80% is present in the mitochondria. CCl4has a direct dissolving effect on liver cell membranes. At the same time, it produces a large number of active metabolites such as electrophilic groups and free radicals, which directly attack phospholipid molecules on the endoplasmic reticulum,causing membrane lipid peroxidation, changing the structure and function of the membrane, and damaging liver cells. Finally, causing ALT and AST in the cytoplasm to enter the blood.

Caspase-3 is one of the most important apoptotic proteases in the cysteine-containing aspartate-specific protease (caspase) family [30]. As an apoptotic effector, located downstream of the cascade reaction, it can be activated by the upstream initiator and act on the specific substrate,causing morphological changes of cells, leading to apoptosis [31].Under normal circumstances, caspase-3 in the cytoplasm exists as an inactive zymogen [32]. Tumor necrosis factor (TNF) combines with Fas-associated death domain (FADD) to form a death complex,which activates upstream caspase-2, caspase8 and other enzyme sources and then activates downstream caspase-3 through the process of transactivation [33]. Caspase-3 can cleave structural proteins and regulatory proteins in the nucleus and cytoplasm, thereby regulating cell apoptosis [34]. In the clinic, caspase-3 inhibitor is an important direction in the research of hepatoprotective drugs.

In addition, TNF is a cytokine with an anti-tumor effect. CCl4can significantly increase the level of TNF-α in animal serum and liver tissue. TNF-α can stimulate monocytes and other immune-related cells to produce a large number of cytokines, such as IL-1, IL-6, IL-8 and lead to local inflammation. The death domains contained in the transmembrane receptor protein of TNF can aggregate with each other, combine with FADD to form a polymer, activate caspase-2, caspase-8 and otherenzymes, and then activate the downstream caspase-3. The activated caspase-3 can cleave structural proteins and regulatory proteins in the nucleus and cytoplasm, thereby regulating cell apoptosis.

Table 5 The docking scores (estimated free energy of binding) of the four compounds with caspase-3.

Using the method of spectrum-effect relationship, combined with the 13 compounds of LF and pharmacological parameters, this experiment found that P10 (Secoxyloganin), P11 (Isochlorogenic acid A) and P12 (Isochlorogenic acid C) are the most important indicators with hepatoprotective effect. For molecular docking with caspase-3,we found that the binding energy of P11 and P12 was lower than that of its inhibitor Emricasan, indicating that P11 and P12 bind well to caspase-3. The inhibitory positions of caspase-3 include amino acids such as CYS163, SER209, Phe250, ASN208, ARG207, SER205, HIS121,GLN161, ARG64 and ARG207 [35]. Hydrogen bonding is an important force of drug-receptor. The results of molecular docking showed that the three ligands and the inhibitory positions of caspase-3 produce a large number of hydrogen bonds and van der Waals forces. At the same time,it produces pi-alkyl and other hydrophobic effects, which reduce the entropy of the system and makes the system more stable.

In summary, this study found that secoxyloganin, isochlorogenic acid A and isochlorogenic acid C in LF may directly inhibit caspase-3 and reduce liver cell apoptosis. At the same time, these active compounds can inhibit liver inflammation, reduce the polymer formed by the combination of TNF-α and FADD, thereby reducing the activation of pro-caspase-2 and pro-caspase-8 upstream of caspase-3,and reducing the activation of pro-caspase-3, to reduce hepatocyte apoptosis. In addition, the LF group reduced the reduction of GSH in the liver significantly. GSH has a powerful antioxidant effect, it can metabolize free oxygen radicals produced by chemicals, drugs, and alcohol. Secoxyloganin, isochlorogenic acid A and isochlorogenic acid C may reduce the damage to glutathione reductase caused by metabolizing toxic substances. This ensures that toxic substances do not accumulate in the body and damage liver cells. Secoxyloganin,isochlorogenic acid A and isochlorogenic acid C may protect the liver from inflammation, oxidative stress, and apoptosis.

LF, as a kind of medicine-food homology plant and TCM,contains a complex system of multiple components and multiple targets. Different components may have synergistic or antagonistic effects. Therefore, the hepatoprotective effects of secoxyloganin,isochlorogenic acid A and isochlorogenic acid C still need to be verified by the method of monomer administration. This paper verified that LF have a hepatoprotective effect. Secoxyloganin, isochlorogenic acid A and isochlorogenic acid C are selected as active compounds based on the spectrum-effect relationship. In the fingerprinting results,this study found that there are differences in the content of different batches of LF. Therefore, in the future, secoxyloganin, isochlorogenic acid A and isochlorogenic acid C will be used for quality control of LF. Selecting three ingredients of LF from the production area with higher compound content as raw materials, making hepatoprotective health products or foods will have better results. This paper proved that the spectrum-effect relationship can be used as an effective method to search for active ingredients in complex compound systems. At the same time, it discussed the material basis of LF as a health care product, and provided theoretical support for the subsequent development of new drugs and targeted preparations for the treatment of the liver injury.

5. Conclusion

In this study, the method of spectral-effect relationship was used to combine the fingerprint of LF and the pharmacological parameters of liver protection. Combined with the results of GRA and PLSR,P10 (secoxyloganin), P11 (isochlorogenic acid A) and P12 (isochlorogenic acid C) were screened out as the liver-protecting active compounds of LF. These compounds may reduce liver cell apoptosis by inhibiting liver inflammation and directly inhibiting caspase-3.

Conflicts of interest:

All the authors have not submitted the manuscript to another journal and all authors have no conflict of interest.

Acknowledgement

This work was financially supported by the National Natural Science Foundation of China (81973604, 81803690 and 81703684),Special Funds from the Central Finance to Support the Development of Local Universities, the National Natural Science Foundation Matching Project (2018PT02), the Innovative Talents Funding of Heilongjiang University of Chinese Medicine (2018RCD25), the Postdoctoral Initial Fund of Heilongjiang Province (UNPYSCT 2017219), the University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province (UNPYSCT-2017215), the National Natural Science Foundation Matching Project (2017PT01),the Natural Science Foundation of Heilongjiang Province (H2015037),the Heilongjiang University of Chinese Medicine Doctoral Innovation Foundation (2014bs05), the Application Technology Research and Development Projects of Harbin Technology Bureau(2014RFQXJ149), the Heilongjiang Postdoctoral Scientific Research Developmental Fund (LBH-Q16210 and LBH-Q17161), the Heilongjiang University of Chinese Medicine Doctoral Innovation Foundation (2013bs04), the scientific research project of Heilongjiang Provincial Health Commission (20211313050171), Heilongjiang Touyan Innovation Team Program, National Famous Old Traditional Chinese Medecine Experts Inheritance Studio Construction Program of National Administration of TCM ([2022]No.75).