The present protocol describes an integrated strategy for exploring the key targets and mechanisms of Fructus Phyllanthi against hyperlipidemia based on network pharmacology prediction and metabolomics verification.
Hyperlipidemia has become a leading risk factor for cardiovascular diseases and liver injury worldwide. Fructus Phyllanthi (FP) is an effective drug against hyperlipidemia in Traditional Chinese Medicine (TCM) and Indian Medicine theories, however the potential mechanism requires further exploration. The present research aims to reveal the mechanism of FP against hyperlipidemia based on an integrated strategy combining network pharmacology prediction with metabolomics validation. A high-fat diet (HFD)-induced mice model was established by evaluating the plasma lipid levels, including total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C). Network pharmacology was applied to find out the active ingredients of FP and potential targets against hyperlipidemia. Metabolomics of plasma and liver were performed to identify differential metabolites and their corresponding pathways among the normal group, model group, and intervention group. The relationship between network pharmacology and metabolomics was further constructed to obtain a comprehensive view of the process of FP against hyperlipidemia. The obtained key target proteins were verified by molecular docking. These results reflected that FP improved the plasma lipid levels and liver injury of hyperlipidemia induced by a HFD. Gallic acid, quercetin, and beta-sitosterol in FP were demonstrated as the key active compounds. A total of 16 and six potential differential metabolites in plasma and liver, respectively, were found to be involved in the therapeutic effects of FP against hyperlipidemia by metabolomics. Further, integration analysis indicated that the intervention effects were associated with CYP1A1, AChE, and MGAM, as well as the adjustment of L-kynurenine, corticosterone, acetylcholine, and raffinose, mainly involving tryptophan metabolism pathway. Molecular docking ensured that the above ingredients acting on hyperlipidemia-related protein targets played a key role in lowering lipids. In summary, this research provided a new possibility for preventing and treating hyperlipidemia.
Hyperlipidemia is a common metabolic disease with serious impacts on human health, and is also the primary risk factor for cardiovascular diseases1. Recently, there has been a downward age-related trend for this disease, and younger people have become more susceptible because of long-term irregular lifestyles and unhealthy eating habits2. In the clinic, various drugs have been used to treat hyperlipidemia. For example, one of the most commonly used drugs for patients with hyperlipidemia and related atherosclerotic disorders is statins. However, long-term use of statins has side effects that can't be neglected, which lead to a poor prognosis, such as intolerance, treatment resistance, and adverse events3,4. These shortcomings have become additional pains for hyperlipidemia patients. Therefore, novel treatments for stable lipid-lowering efficacy and fewer side effects should be proposed.
Traditional Chinese Medicine (TCM) has been widely used to treat diseases because of its good efficacy and few side effects5. Fructus Phyllanthi (FP), the dried fruit of Phyllanthus emblica Linn. (popularly known as amla berry or Indian gooseberry), is a famous medicine and food homologous material of traditional Chinese and India medicines6,7. This medicine has been used for clearing heat, cooling blood, and promoting digestion, as per TCM theories8. Modern pharmacological studies have shown that FP is rich in bioactive compounds such as gallic acids, ellagic acids, and quercetin9, which are responsible for a range of multifaceted biological properties, by acting as an antioxidant, an anti-inflammatory, liver protection, an anti-hypolipidaemic, and so on10. Recent research has also showed that FP could effectively regulate the blood lipids of patients with hyperlipidemia. For example, Variya et al.11 have demonstrated that FP fruit juice and its main chemical ingredient of gallic acid can decrease plasma cholesterol and reduce oil infiltration in the liver and aorta. The therapeutic efficacy was related to FP's regulation in increasing the expression of peroxisome proliferator-activated receptor-alpha and decreasing hepatic lipogenic activity. However, the underlying mechanism of FP in improving hyperlipidemia should be further investigated, because its bioactive ingredients are quite extensive. We sought to explore the potential mechanism of FP's therapeutic efficacy, which may be beneficial for the further development and utilization of this medicine.
Currently, network pharmacology is regarded as a holistic and efficient technique to study the therapeutic mechanism of TCM. Instead of looking for single disease-causing genes and drugs treating solely an individual target, a complete drug-ingredients-genes-diseases network is constructed to find the multi-target mechanism of the multi-ingredient drug regarding their comprehensive treatment12. This technique is especially suitable for TCM, as their chemical compositions are massive. Unfortunately, network pharmacology can only be used to forecast targets affected by chemical ingredients in theory. The endogenous metabolites in the disease model should be observed to validate the effectiveness of network pharmacology. The metabolomics method, which emerges with the development of systems biology, is an important tool for monitoring the changes in endogenous metabolites13. The changes in metabolites reflect the steady state changes of the host, which is also an important indicator for studying the internal mechanism. Some researchers have successfully integrated network pharmacology and metabolomics to explore the interaction mechanism between drugs and diseases14,15.
This article explores the mechanistic basis of FP against hyperlipidemia by integrating network pharmacology and metabolomics techniques. Network pharmacology was applied to analyze the relationship between the main active ingredients in FP and molecular targets for hyperlipidemia. Subsequently, metabolomics was performed to observe the change of endogenous metabolites in the animal model, which can explain the medicine actions at the metabolic level. Compared with the application of network pharmacology or metabonomics alone, this integrated analysis provided a more specific and comprehensive research mechanism. Additionally, the molecular docking strategy was used to analyze the interaction between active ingredients and key proteins. In general, this integrated approach could compensate for the lack of experimental evidence for network pharmacology and the lack of an endogenous mechanism for the metabolomics method, and can be used for the therapeutic mechanism analysis of natural medicine. The main schematic flowchart of the protocol is shown in Figure 1.
All procedures involving the handling of animals were conducted in accordance with the Chengdu University of Traditional Chinese Medicine Guide for the Care and Use of Laboratory Animals and were approved by the Institutional Ethics Committee of the Chengdu University of Traditional Chinese Medicine (Protocol number 2020-36). Male C57BL/6 mice (20 ± 2 g) were used for the present study. The mice were obtained from a commercial source (see Table of Materials).
1. Network pharmacology-based prediction
NOTE: The network pharmacology is used to predict the active ingredients and their key targets of FP against hyperlipidemia.
2. Experimental design
3. Metabolomic validation
NOTE: The metabolomic profiling data of plasma and liver metabolites are imported into Compound Discoverer software to perform the metabolic feature extraction by adopting a molecular feature extraction algorithm. Set the parameters as follows: mass deviation, 5 x 10-6; mass range, 100-1,500; signal to noise ratio (SNR) threshold, 3; and retention time deviation, 0.05. Evaluate the stability and repeatability of metabolomics by the relative standard deviation (RSD) of QC peak areas.
4. Molecular docking
5. Statistical analysis
NOTE: Use SPSS statistical software (see Table of Materials) for data analysis. Consider the value of p < 0.05 as statistically significant.
Network pharmacology
A total of 18 potential ingredients in FP were screened according to their pharmacokinetic and pharmacodynamic properties from the database and LC-MS analysis (the total ion chromatograms are shown in Supplementary Figure 1). Through relevant literature, the content of gallic acid is much higher than other ingredients and is effective in lowering lipids9,11. Therefore, this ingredient was considered a potential ingredient too. In total, 19 ingredients and 134 ingredient-related targets of FP have been founded. All 19 ingredients are shown in Table 1. To select the most representative ingredients for further analysis, these ingredients were imported into Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine database (BATMAN-TCM; http://bionet.ncpsb.org/batman-tcm/). According to the ingredient-target-pathway-disease network, some bioactive ingredients, such as gallic acid, quercetin, and beta-sitosterol, were identified as the most important ingredients of FP related to hypercholesterolemia and coronary atherosclerosis (Supplementary Figure 2). Among these, gallic acid is one of the most widely studied phenolic acids; it is the main bioactive ingredient presented in FP18. Meanwhile, gallic acid is also the highest content ingredient in FP; its concentration is usually 1% to 3%. El-Hussainy et al.19 have revealed that gallic acid can limit cardiac injury, improve lipid profile, and downregulate cardiac inflammatory markers. The contents of quercetin and beta-sitosterol are lower, but some studies have proved their effect on lowering lipids. Quercetin, as an important flavonoid that widely exists in plants, has various properties, such as antioxidant, anti-inflammatory and cardiovascular protection effects20. Lu et al.21 have studied that the quercetin-enriched juice can attenuate TC, LDL-C, and HDL-C levels in healthy individuals with mild hypercholesterolemia. As for beta-sitosterol, clinical studies have shown that plant sterol can significantly prevent hypercholesterolemia and cardiovascular disease22,23. Althwab et al.24proved that beta-sitosterol could improve lipid profile and atherogenic index in HFD rats. It can be seen that the lipid-lowering effect of FP may be related to these three ingredients.
Additionally, 1,552 targets of hyperlipidemia-related from the Genecards, OMIM, and TTD databases were collected. After matching the 134 FP-related targets with the hyperlipidemia-related targets, 62 targets were identified as potential targets for FP against hyperlipidemia (Figure 2A). All the intersected targets were normalized to their official symbols, according to the UniProt database. Subsequently, the PPI network was constructed by STRING (Figure 2B) and Cytoscape (Figure 2C). Combining the scores of computational methods, the top 10 targets were ESR1, RELA, FOS, EGFR, HIF1A, AR, CCND1, IL6, MAPK8, and MYC. The details are presented in Supplementary Figure 3. All of these 62 targets are the basis of further analysis, which is integrated with the results of metabonomics.
GO and KEGG pathways were performed by enrichment analysis. The top 15 pathways, according to the number of targets, were selected for analysis according to the p-value. The GO enrichment results suggested that the biological processes and the molecular function of FP against hyperlipidemia were mainly related to gene expression and protein binding (Figure 2D). The KEGG enrichment proved that FP could intervene in the process of lipid metabolism and atherosclerosis (Figure 2E), which means FP relieves hyperlipidemia through affecting the lipid metabolism.
The effect of FP on plasma lipid levels and liver index
To test the improved effect of FP on hyperlipidemia, the changes in TC, TG, LDL-C, HDL-C, and liver index (the ratio of liver weight to body weight) were first measured. Compared with the NC group, mice in the HFD group showed a significant increase in the plasma levels of TC (p < 0.001), LDL-C (p < 0.001) and TG (p < 0.05), indicating that long-term HFD intervention can increase lipid levels and induce hyperlipidemia (Figure 3).
After administering FP aqueous extract, the levels of TC in FP_L and FP_H groups were significantly (p < 0.05) reduced by 18.8% and 12.4%, respectively (Figure 3A). The levels of LDL-C in FP_L and FP_H groups were significantly (p < 0.05) reduced by 13.7% and 21.8%, respectively (Figure 3B). Regarding HDL-C level, the FP_H group was significantly (p < 0.01) increased from 1.81 ± 0.08 mmol/L to 2.65 ± 0.16 mmol/L, compared with the HFD group (Figure 3C). Although the TG level remained insignificant after FP intervention, it was reduced compared with the HFD group (Figure 3D). Recent studies have indicated that the index of LDL-C/HDL-C ratio is a better index than LDL-C or HDL-C alone in predicting cardiovascular disease25,26. Compared with the HFD group, the LDL-C/HDL-C ratio was significantly (p < 0.01) reduced by 46.3% in the FP_H group (Figure 3E), meaning that FP intervention reduced bad cholesterol and increased good cholesterol levels. As the main fat metabolic organ, the liver weight reflects fat storage in mice to a certain extent27. After 12 weeks, the liver indexes in the FP_L group and FP_H group were significantly (p < 0.01) reduced compared with the HFD group (Figure 3F). The PC group also showed different degrees of reduction in these indicators above, demonstrating that FP had similar effects to statins, and the protective effect showed a dose-response relationship.
Various clinical studies revealed that, after taking either extract or whole FP for a while, TC and LDL-C levels were significantly decreased. Meanwhile, the HDL-C level was remarkably raised upon long-term administration of FP28,29. Nambiar and Shetty30 found that FP juice could reduce oxidized low-density lipoproteins, therefore greatly reducing the risk of atherosclerosis. Gopa et al.31 evaluated the hypolipidemic effect of FP in patients with hyperlipidemia and compared it with simvastatin. Treatment with FP resulted in a considerable reduction in TC, LDL-C, and TG, and a significant increase in HDL-C levels, similar to that of simvastatin. In this research, FP and simvastatin also had similar therapeutic effects, and the LDL-C-lowering effect and hepatic repairing action of FP were superior to simvastatin.
Liver histopathological observation
The effect of FP on hepatic steatosis in HFD mice is shown in Figure 4. The liver pathological sections in the NC group expressed regular hepatocyte morphology, clearly defined cell borders, and no obvious fat vacuoles (Figure 4A,B). Comparatively, the HFD group had fat vacuoles of different sizes around the blood vessels and showed obvious hepatic damage, as characterized by cell swelling, fatty degeneration, loss of cellular boundaries, cellular contraction, and hepatocyte necrosis (Figure 4C,D). As shown in Figure 4E,F, FP intervention could improve liver steatosis, especially in the FP_L group. Compared with the HFD group, the FP_H (Figure 4G,H) and PC group (Figure 4I,J) had a certain degree of recovery of the liver cell structure, fat degeneration, and fat vacuole reduction. This means that FP intervention can protect liver tissue from HFD-induced hepatic injury.
Metabolomics profiling
According to plasma lipid level and liver histopathological observation, high-dose FP had a better effect on hyperlipidemia than low-dose FP. Therefore, NC, HFD and FP_H groups were chosen to analyze their change in the metabolism level. The total ion chromatograms of QC samples were shown in Supplementary Figure 4. To ensure the accuracy of the data, the features with RSD values >30% were removed from all the QC samples. PCA and ion chromatograms reflected that QC samples were stable during the process (Supplementary Figure 5). A total of 626 and 562 features in the plasma and liver were determined after the data preprocessing. Among them, 120 and 124 metabolites in the plasma and liver, respectively, were identified based on the KEGG database. OPLS-DA analysis was used to explore the separation among the NC, HFD, and FP_H groups. OPLS-DA showed that the same group samples clustered together and different group samples distinguished well (Figure 5A,B). These results indicated that the HFD and FP interventions caused obvious metabolic variations.
To identify the potential differential metabolites that contributed to the metabolic distinction, further OPLS-DA and t-test analyses of NC versus HFD and HFD versus FP_H were performed, respectively. The OPLS-DA results distinguished well, and showed significant differences between different groups of models14 (Supplementary Figure 6). Based on VIP (Variable important in projection) >1 and p < 0.05, 32 metabolites in the plasma showed differentiation between the NC and HFD group, and 72 metabolites showed differentiation between the HFD and FP_H group. In the liver, 38 metabolites showed differentiation between the NC and HFD group, and 17 metabolites showed differentiation between the HFD and FP_H group. Finally, a total of 16 and 6 metabolites were identified as differential metabolites in FP-affecting HFD mice in the plasma and liver, respectively (Supplementary Figure 7). The information on these metabolites is shown in Table 2.
To visualize the variation in metabolites among the three groups, heat maps were plotted by MetaboAnalyst 5.0. All of the differential metabolites in the plasma and liver were changed in the HFD group and most of them were reversed in the FP group, indicating that FP intervention can improve metabolic disorder (Figure 5C,D). Further, differential metabolites were imported into MetaboAnalyst 5.0 to explore the metabolic pathways of FP in HFD mice. Based on p < 0.05 and a pathway impact >0.10, tryptophan metabolism was affected significantly in the plasma, and the metabolites related to this pathway were D-tryptophan and L-kynurenine (Figure 5E). Jung et al.32 studied that prolonged hyperlipidemia may lower the serum levels of kynurenine. Taurine and hypotaurine metabolism was affected significantly in the liver, and the related metabolite related was taurine (Figure 5F). Taurine is an important and necessary amino acid in the animal body; Dong et al.33 studied that taurine could mildly reduce the damage of blood lipids and lower the atherosclerosis risk caused by HFD. In this research, FP intervention increased the content of L-kynurenine and taurine, which is positively related to the reduction of lipid levels, supporting the effectiveness of FP against hyperlipidemia.
Integrated analysis of network pharmacology and metabolomics
An integrated strategy of network pharmacology combined with metabolomics has become more and more indispensable in studying disease mechanisms and intervention strategies. The relevance between network pharmacology and metabolomics with limited evidence was established. To obtain a comprehensive view of the mechanism of FP against hyperlipidemia, the interaction networks based on network pharmacology and metabolomics were constructed. Differential metabolites were imported into the MetScape plugin in Cytoscape and matched the hub genes identified in network pharmacology to collect the compound-reaction-enzyme-gene networks (Figure 6). As shown in Table 3, in plasma metabolites, L-kynurenine and corticosterone were related to CYP1A1, which can catalyze lipid peroxidation and induce non-alcoholic fatty liver disease34,35; the affected pathways were tryptophan metabolism and steroid hormone biosynthesis, respectively. Acetylcholine was related to AChE and affected glycerophospholipid metabolism. In liver metabolites, MGAM and raffinose were related to galactose metabolism. Several studies have demonstrated that the intake of raffinose family oligosaccharides could improve metabolic disorders in HFD mice36.
Further, the ingredients-targets-metabolites-pathways network has been constructed (Figure 7). In the ingredients, quercetin connected the most edges, indicating that quercetin of FP plays the most important role in lowering lipids. The above-integrated analysis revealed the key targets, metabolites, and pathways of FP against hyperlipidemia, which could be the foundation of further study of this medicine's therapeutic mechanism and clinical application.
Molecular docking
To further investigate the possibility of interaction between the selected ingredients and the key targets, molecular docking was used to analyze their ligand-active site interactions. AutoDock Vina software (see Table of Materials) was used to perform molecular docking, and the first docking pose was outputted according to the rank of the scoring function. The docking results are shown in Figure 8.
In the integrated analysis, CYP1A1, AChE, and MGAM were related to differential metabolites; they built bridges between targets and metabolites. Further molecular docking was performed to verify the relation between the target and ingredients. The results of ingredient docking with CYP1A1 were as follows: gallic acid formed four hydrogen bonds through the amino acid residues Asn-185, Tyr-187, Asn-219, and His-500, and formed π-π stacking interaction through the amino acid residue Tyr-187 (Figure 8A); quercetin formed three hydrogen bonds through Asn-185, Asn-219, and His-500, hydrophobic interaction, and π-π stacking interaction through Tyr-187 (Figure 8B); beta-sitosterol formed four hydrogen bonds through Arg-362, Ser-363, Leu-365, and Arg-464, and hydrophobic interaction through Glu-369 and Ile-439 (Figure 8C). The binding energies were 5.3, 7.0, and 7.3 kcal/-mol, respectively. In the interaction with AChE, gallic acid was stabilized by hydrogen bonds with Arg-237, Arg-238, and Arg-480 (Figure 8D); quercetin was stabilized by hydrogen bonds with Arg-237 and Phe-474, by hydrophobic interaction with Phe-157, and by π-π stacking interaction with Tyr-478 (Figure 8E); beta-sitosterol was stabilized by hydrophobic interaction with Phe-157, Val-244, Ile-248, Phe-474, Ala477, and TYR478 (Figure 8F). The binding energies were 5.0, 6.5, and 8.0 kcal/- mol, respectively. In the interaction with MGAM, gallic acid was stabilized by hydrogen bonds with Ile-1716, Gly-1747, and Trp-1749, and by hydrophobic interaction with Tyr-1715 and Trp-1749 (Figure 8G); quercetin was stabilized by hydrogen bonds with Arg-1311, Thr-1726, Gln-1731, and Trp-1752, by hydrogen bonds with Arg-1730, and by π-π stacking with His-1727 (Figure 8H); beta-sitosterol was stabilized by hydrophobic interaction with Pro-1159, Trp-1355, Phe-1427, and Phe-1560, The binding energies were 5.9, 8.1, and 6.9 kcal/mol, respectively. Detailed information on the interactions and binding affinities is exhibited in Table 4. Multiple binding sites and high binding energies explain the high affinities between ingredients and protein targets, verifying that these ingredients play the role of lowering lipids by acting on hyperlipidemia-related targets.
Figure 1: Schematic flowchart of the integrated strategy. Hub ingredients and genes were extracted by network pharmacology (Part 1). Differential metabolites of FP against hyperlipidemia were analyzed by plasma and liver metabolomics (Part 2). Key targets, metabolites, and pathways were identified and linked based on an integrated analysis of Part 1 and Part 2 (Part 3). Please click here to view a larger version of this figure.
Figure 2: Target screening, network construction, and enrichment analysis of the effect of FP against hyperlipidemia. (A) Venn diagram of the FP-hyperlipidemia targets. (B) Potential active drug-ingredients-targets-disease network: different color symbols as mentioned here: disease (red), drug (blue), ingredients (green), and targets (yellow). (C) PPI network by STRING. (D) GO pathway enrichment analysis. (E) KEGG pathway enrichment analysis. Please click here to view a larger version of this figure.
Figure 3: The effect of FP on plasma lipid levels and liver index in mice with HFD-induced hyperlipidemia (n = 6). (A) Levels of TC. (B) Levels of LDL-C. (C) HDL-C level. (D) TG level. (E) The LDL-C/HDL-C ratio. (F) Liver indexes.*p < 0.05, **p < 0.01, ***p < 0.001. Statistically significant differences were evaluated using a one-way ANOVA followed by Dunnett's multiple comparisons test or post hoc analysis. Please click here to view a larger version of this figure.
Figure 4: The effect of FP on liver tissue in mice with HFD-induced hyperlipidemia (H&E staining). (A,B) NC group, (C,D) HFD group, (E,F) FP_L group, (G,H) FP_H group, (I,J) PC group (n = 6). Scale bar: (A,C,E = 200 µm; B,D,F = 50 µm). Please click here to view a larger version of this figure.
Figure 5: The OPLS-DA score plots, heat maps, and metabolic pathways of differential metabolites. The OPLS-DA score plots of FP on HFD mice in the plasma (A) and liver (B). The heat maps of differential metabolites in the plasma (C) and liver (D). The metabolic pathways of differential metabolites in the plasma (E) and liver (F). Please click here to view a larger version of this figure.
Figure 6: The compound-reaction-enzyme-gene networks of the key metabolites and targets. Low-degree nodes have been removed. The red hexagons, blue circles, round green rectangles, and grey diamonds represent the active compounds, genes, proteins, and reactions, respectively. The key targets and metabolites were magnified. The pathways with the white background are significantly regulated in the plasma. The pathway with the grey background is significantly regulated in the liver. Please click here to view a larger version of this figure.
Figure 7: The ingredients-targets-metabolites-pathways network. The darker the color, the more the connected edges, signifying the node is more important in this network. Please click here to view a larger version of this figure.
Figure 8: The interaction diagrams of FP ingredients and the key targets. (A) Gallic acid acting on CYP1A1. (B) Quercetin acting on CYP1A1. (C) Beta-sitosterol acting on CYP1A1. (D) Gallic acid acting on AChE. (E) Quercetin acting on AChE. (F) Beta-sitosteroling act on AChE. (G) Gallic acid acting on MGAM. (H) Quercetin acting on MGAM. (I) Beta-sitosterol acting on MGAM. Please click here to view a larger version of this figure.
Figure 9: Overview of FP against hyperlipidemia result. Please click here to view a larger version of this figure.
Table 1: The selected ingredients of FP aqueous extract. Please click here to download this Table.
Table 2: The differential metabolites between the three groups. Please click here to download this Table.
Table 3: The information on key targets, metabolites, and pathways. Please click here to download this Table.
Table 4: Binding sites and action forces between FP ingredients and target proteins. Please click here to download this Table.
Supplementary Figure 1: The positive and negative ion chromatograms of FP aqueous extract. Please click here to download this File.
Supplementary Figure 2: The FP ingredient-target-pathway-disease network by BATMAN-TCM. Please click here to download this File.
Supplementary Figure 3: The frequency analysis of hub genes in network pharmacology. Please click here to download this File.
Supplementary Figure 4: Ion chromatograms of plasma and liver QC samples. The representative positive (A) and negative (B) ion chromatograms of plasma QC samples. The representative positive (C) and negative (D) ion chromatograms of liver QC samples. Please click here to download this File.
Supplementary Figure 5: The PCA score plots of plasma (A) and liver (B) QC samples. Please click here to download this File.
Supplementary Figure 6: The OPLS-DA score plots of plasma (A and B) and liver (C and D) samples. Please click here to download this File.
Supplementary Figure 7: Venn diagrams of the differential metabolites in plasma (A) and liver (B) samples. Please click here to download this File.
In recent years, the incidence rate of hyperlipidemia has been increasing, mainly due to long-term unhealthy eating habits. TCM and its chemical ingredients have various pharmacological activities, which have been widely studied in recent years37,38. FP is a kind of fruit resource, used both as medicine and food, and has an important potential for treating hyperlipidemia. However, the potential therapeutic mechanism of FP against hyperlipidemia needs further study.
Network pharmacology evaluates drug polypharmacological effects at a molecular level, and predicts the interaction of natural products and proteins to determine the major mechanism39. The first step is to select the active ingredients and key targets of the drug. In this research, nine active ingredients and 62 hub genes were found. To further understand the molecular mechanism of FP on hyperlipidemia, PPI and ingredient-target networks were established based on network pharmacology analysis. To narrow the scope of key ingredients and targets, three key ingredients (gallic acid, quercetin, and beta-sitosterol) related to hypercholesterolemia and coronary atherosclerosis have been founded by BATMAN-TCM. All these ingredients could reduce LDL-C levels or increase HDL-C levels, validating the specific effects of FP on hyperlipidemia. Besides, according to KEGG enrichment analysis, the function of FP on hyperlipidemia is related to the activity of the lipid and atherosclerosis pathway. Although this method depends too much on the database and lacks experimental verification, it has theoretical value and provides ideas for subsequent experimental verification research.
For further experimental validation, mice were fed with a fat-supplemented diet for 8 weeks to induce hyperlipidemia. The results showed that plasma TC, LDL-C, and TG levels were significantly increased. Although the level of HDL-C decreased significantly, the ratio of LDL-C to HDL-C increased significantly. The histopathological observations showed that the liver tissue of HFD mice was severely damaged, but there was no significant increase in liver index; it may be that changes in body weight and visceral weight take longer. The lipids and liver changes adequately showed the intervention effect of FP on hyperlipidemia. However, the internal mechanism of the intervention effect still needs further exploration.
Metabolomics provides a list of potential metabolites and related pathways, which aim to explore the mechanism of metabolic diseases and the action of therapeutic drugs40. The result of metabolomics can be affected by the type of sample. Considering the pathogenic characteristics of hyperlipidemia, plasma and liver samples were chosen for metabonomic analysis in this research. According to OPLS-DA results, the NC, HFD, and FP_H groups' metabolites were discriminated well. A total 16 differential metabolites were found in the plasma, and 6 differential metabolites were found in the liver. There were more affected metabolites in the plasma than in the liver, proving that blood is the main place of metabolic disturbance induced by hyperlipidemia. FP intervention can reverse the change of these metabolites under the influence of HFD. Furthermore, these differential metabolites were imported into the KEGG database. The significant metabolic pathways of differential metabolites in the plasma were tryptophan metabolism, and in the liver were taurine and hypotaurine metabolism. In this research, FP intervention increased the content of L-kynurenine of tryptophan metabolism and taurine content of taurine and hypotaurine metabolism, meaning FP could be effective in favorably adjusting metabolic disorders and hyperlipidemia. The metabolomics analysis revealed which metabolites were related to hyperlipidemia or FP intervention, and determined the downstream mechanism of the FP effect.
By combining the result of network pharmacology with metabolomics, three key targets (CYP1A1, AChE, and MGAM) were identified in the compound-reaction-enzyme-gene networks. According to molecular docking analysis, these targets showed high affinities with FP ingredients (gallic acid, quercetin, and beta-sitosterol). Four metabolites (L-kynurenine, corticosterone, acetylcholine, and raffinose) and four related pathways (tryptophan metabolism, steroid hormone biosynthesis, glycerophospholipid metabolism, and galactose metabolism) were identified as the key metabolites and metabolic pathways. Among these, quercetin was associated with the most targets, and tryptophan metabolism appeared in both metabonomics and integrated results. They play the most essential role in the therapeutic effect of FP against hyperlipidemia. Molecular docking result showed that CYP1A1, AChE, and MGAM have high affinities with ingredients. The above results prove that these screened targets are closely related to the therapeutic effect of FP.
In the present research, gallic acid, quercetin, and beta-sitosterol were identified as FP active ingredients toward anti-hyperlipidemia, and tryptophan metabolism is the main metabolic pathway of FP therapy in HFD mice. The overview of the result is shown in Figure 9. This research offered data and theoretical support for further studies of mechanisms and provided a foundation for the clinical application of FP medicine. It also proved that natural food might be a promising option with great prospects in clinical practice. However, there are still some shortcomings in this research. The therapeutic effect of the active ingredient alone on hyperlipidemia has not been verified. In addition, the pathway of key targets has not been studied; it also needs further systematic molecular biology experiments to verify the accurate mechanism.
The authors have nothing to disclose.
This research was supported by the Product Development and Innovation Team of TCM Health Preservation and Rehabilitation (2022C005) and Research on New Business Cross-border Integration of "Health Preservation and Rehabilitation+".
101-3B Oven | Luyue Instrument and Equipment Factory | ||
80312/80302 Glass Slide | Jiangsu Sitai Experimental Equipment Co., LTD | ||
80340-1630 Cover Slip | Jiangsu Sitai Experimental Equipment Co., LTD | ||
AccucoreTM C18 (3 mm × 100 mm, 2. 6 μm) | Thermo Fisher Scientific | ||
Acetonitrile | Fisher Chemical | A998 | Version 1.5.6 |
ACQUITY UPLC HSS T3 Column (2.1 mm × 100 mm, 1.8 μm) | Thermo Fisher Scientific | ||
Aethanol | Fisher Chemical | A995 | Version 3.0 |
Ammonia Solution | Chengdu Cologne Chemicals Co., LTD | 1336-21-6 | Version 3.9.1 |
AutoDockTools | Scripps Institution of Oceanography | ||
BS-240VT Full-automatic Animal Biochemical Detection System | Shenzhen Mindray Bio-Medical Electronics Co., Ltd. | ||
Compound Discoverer | Thermo Fisher Scientific | ||
Cytoscape | Cytoscape Consortium | ||
DM500 Optical Microscope | Leica | ||
DV215CD Electronic Balance | Ohaus Corporation ., Ltd | T15A63 | |
Ethyl Alcohol | Chengdu Cologne Chemicals Co., LTD | 64-17-5 | |
Formic Acid | Fisher Chemical | A118 | |
HDL-C Assay Kit | Nanjing Jiancheng Bioengineering Institute | A112-1-1 | |
Hematoxylin Staining Solution | Biosharp | BL700B | |
High Fat Diet | ENSIWEIER | 202211091031 | |
Hitachi CT15E/CT15RE Centrifuge | Hitachi., Ltd. | ||
Homogenizer | Oulaibo Technology Co., Ltd | ||
Hydrochloric Acid | Chengdu Cologne Chemicals Co., LTD | 7647-01-0 | |
Image-forming System | LIOO | ||
JB-L5 Freezer | Wuhan Junjie Electronics Co., Ltd | ||
JB-L5 Tissue Embedder | Wuhan Junjie Electronics Co., Ltd | ||
JK-5/6 Microtome | Wuhan Junjie Electronics Co., Ltd | ||
JT-12S Hydroextractor | Wuhan Junjie Electronics Co., Ltd | ||
KQ3200E Ultrasonic Cleaner | Kun Shan Ultrasonic Instruments Co., Ltd | ||
LDL-C Assay Kit | Nanjing Jiancheng Bioengineering Institute | A113-1-1 | |
Male C57BL/6 Mice | SBF Biotechnology Co., Ltd. | Version 2.3.2 | |
Neutral Balsam | Shanghai Yiyang Instrument Co., Ltd | 10021190865934 | |
Pure Water | Guangzhou Watson's Food & Beverage Co., Ltd | GB19298 | |
PyMOL | DeLano Scientific LLC | Version 14.1 | |
RE-3000 Rotary Evaporator | Yarong Biochemical Instrument Factory ., Ltd | ||
RM2016 Pathological Microtome | Shanghai Leica Instruments Co., Ltd | Version 26.0 | |
SIMCA-P | Umetrics AB | ||
Simvastatin | Merck Sharp & Dohme., Ltd | 14202220051 | |
SPSS | International Business Machines Corporation | ||
TC Assay Kit | Nanjing Jiancheng Bioengineering Institute | A111-1-1 | |
TG Assay Kit | Nanjing Jiancheng Bioengineering Institute | A110-1-1 | |
UPLC-Q-Exactive Quadrupole Electrostatic Field Orbital Hydrazine High Resolution Mass Spectrometry | Thermo Fisher Scientific | ||
Vortex Vibrator | Beijing PowerStar Technology Co., Ltd. | LC-Vortex-P1 | |
Xylene | Chengdu Cologne Chemicals Co., LTD | 1330-20-7 |