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Research Article
Network pharmacology exploration to reveal molecular insights of Phyllanthus niruri in non-alcoholic fatty liver: In vitro and in silico evidence
expand article infoAnuragh Singh‡, Vellapandian Chitra‡, Kaliappan Ilango§
‡ SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur, India
§ Tagore College of Pharmacy, Chennai, India

Corresponding author: Vellapandian Chitra ( dean.pharmacy.ktr@srmist.edu.in )

Corresponding author: Kaliappan Ilango ( ilangok67@gmail.com )

Academic editor: Ivan Dimitrov
© 2024 Anuragh Singh, Vellapandian Chitra, Kaliappan Ilango.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation: Singh A, Chitra V, Ilango K (2024) Network pharmacology exploration to reveal molecular insights of Phyllanthus niruri in non-alcoholic fatty liver: In vitro and in silico evidence. Pharmacia 71: 1-16. https://doi.org/10.3897/pharmacia.71.e124595
Open Access

Abstract

The hepatic manifestation of metabolic syndrome, associated with various metabolic diseases such as type 2 diabetes, insulin resistance, and high cholesterol, is called non-alcoholic fatty liver disease (NAFLD). Despite several research efforts, no approved medicine is currently available for the treatment of this illness. Swiss Target Prediction was used to screen phytochemicals. To examine potential targets, the protein-protein interaction (PPI) network was developed. Cytoscape was used to create the component-target-pathway (C-T-P) network, and AutoDock was used to assess molecular docking. Antioxidant and anti-inflammatory qualities were tested in vitro. Naringenin, ellagic acid, and cyanidin were found to be the main active components. As important targets, PPARA, PPARG, and AKT1 were selected. Through enrichment analysis, a total of 20 crucial signaling pathways, including insulin resistance (IR), NAFLD, relaxin, PI3K-Akt, HIF-1, AGE-RAGE, and MAPK, were identified. The in silico computational techniques predicted the molecular pathway for the active ingredients and the disease targets, thus helping to further research.

Keywords

NAFLD, network pharmacology, gene ontology, KEGG, protein targets

Introduction

Non-alcoholic fatty liver disease (NAFLD) is the hepatic manifestation of metabolic syndrome, which is associated with a number of metabolic diseases, such as type 2 diabetes (T2DM), insulin resistance (IR), and hyperlipidemia (Abenavoli et al. 2016; Ugbaja et al. 2020; Shiri-Shahsavar et al. 2023). In the past two decades, its prevalence has reached alarming levels, affecting 25.2% of the world’s population, including 20–30% of adults and 3–10% of minors in Western countries (Milić and Štimac 2012), and it remains a “silent epidemic.” Estimates indicate that NAFLD affects up to two billion people globally, making it the most prevalent form of liver disease in humans. However, the general public, politicians, and even the international public health profession are mostly unaware of it. It affects a startling number of people and is only likely to increase in the future decades, posing a serious threat to public health, straining health care systems, and leading to significant economic and quality of life losses. (Sivell 2019; Singh et al. 2021).

Phyllanthus niruri is a coastal invasive plant belonging to the Euphorbiaceae family. It is also known as a windstorm or a stonebreaker. Its leaves and fruit have been used medicinally since antiquity (Sakthivel and Nisha 2019; Adedotun et al. 2022). It is extensively used to treat inflammation, diarrhea, eye soreness, burns, suppurations, and skin chafing (Colpo et al. 2014). Researchers have used a variety of scientific study techniques, including in vivo, in vitro, and in silico (computational) research methods, in their effort to identify long-lasting remedies for the primary cause of chronic liver illnesses.

The new field of network pharmacology examines disease mechanisms and medication action mechanisms in the context of larger biological networks (Niu et al. 2021). Molecular docking can elucidate the molecular mechanism of action between ligands and receptors so that the implications of network pharmacology can be evaluated and determined. Network pharmacology research based on a complex mathematical network model may abstractly represent the interaction of multiple biological systems as networks. Utilizing both molecular docking technology and network pharmacology, network pharmacology research provides a high degree of precision when predicting the function and mechanism of drug therapy for diseases. Moreover, to accomplish therapeutic goals and reflect the characteristics of multiple components and innumerable targets, several active components regulate a variety of target proteins to operate along a variety of pathways (Yao et al. 2020). Methods of network pharmacology and molecular docking were used to investigate the molecular mechanism of HAEPN in the treatment of NAFLD and lay the groundwork for the clinical application of the two substances together. Fig. 1 depicts the functional flowchart.

Figure 1. 

Flow chart of the research.

Material and methods

Preparation of the extract and preliminary analysis

Prof. P. Jayaraman, taxonomists, plant anatomy, and research center authenticated the plant Phyllanthus niruri (PARC/2021/4526), and a voucher specimen was deposited in the department of pharmacognosy, SRM College of Pharmacy, for future reference. After authentication, the entire plant was cleaned systematically, shaded, and dried at room temperature. Dried leaves were separated from stalks and ground to a coarse powder using a porcelain mortar and pestle. Then the powder was weighed as 500 g dry powder, soaked in 70% ethanol and 30% distilled water, and subjected to Soxhlet for 72 hours at 60–70 °C. The filtrate was then concentrated and evaporated at 40 °C under reduced pressure using a rotary evaporator to obtain the crude extract. Phytochemical tests and other screenings were carried out on the extract of P. niruri to identify the constituents using standard procedures (Shaikh and Patil 2020). Table 1.

Table 1.
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Preliminary analysis.

Test Standard Results
Description Brown colored fine powder Pass
Loss on drying NMT 7% w/w 2.40%
Total Ash NMT 20% w/w 16.08%
Acid insoluble ash NMT 7% 1.20%
pH (1% w/v Aqueous) 3–7 5.70
Water soluble extractive NLT 80% w/w 89.35%
TLC Complies Complies
Total Bacterial content NMT 10000 CFU/gm 450 CFU/gm

Determination of DPPH radical scavenging activity

Utilizing DPPH (2,2-diphenyl-1-picrylhydrazyl) free radicals, antioxidant activity in Phyllanthus niruri was calculated for free radical scavenging activity. One milliliter of DPPH solution (0.2 mM in methanol) was mixed with sample solutions at different concentrations (25, 50, 75, 100, and 200 µg/mL in methanol), and the reaction was allowed to carry out for 30 minutes at room temperature. After measuring the absorbencies of the solutions at 517 nm, the samples were examined for discoloration. Purple to yellow and pale pink were considered positive in strong and mild proportions, respectively (Asadujjaman et al. 2013).

To determine the sample’s IC50 value and the concentration of the sample required to inhibit 50% of the DPPH free radical, the log dosage inhibition curve was employed. The reaction mixture’s decreased absorbance revealed higher free radical activity. By contrasting the absorbance of each sample with that of a blank solution (a solution without any samples), the capacity of each sample to scavenge free radicals was calculated. Standard ascorbic acid was used as a point of comparison. The average findings (mean ± SD) of each analysis were obtained after it was done in triplicate.

The following equation calculated radical scavenging activity.

DPPH radical scavenging activity (%) = [(Absorbance of control - Absorbance of the test sample) / (Absorbance of control)] × 100.

Determination of the scavenging of superoxide radicals by alkaline DMSO

Scavenging of the superoxide (O2•-) anion radical was assessed by reducing nitroblue tetrazolium (NBT) using a previously published method (Fontana et al. 2001). 0.3 mL of crude samples at varying concentrations (10, 50, and 100 µg/mL) and standard ascorbic acid in DMSO at varying concentrations were added to a reaction mixture containing 1 mL of alkaline dimethyl sulfoxide (DMSO), followed by 0.1 mL of NBT (0.1 mg) to produce a final volume of 1.4 mL. The calculated absorbance was 560 nm. Every test was run in triplicate (Dhanaraj and Jebapriya 2021).

% SO radical scavenging activity = (Control OD - Sample OD) × 100/Control OD.

Anti-inflammatory activity by inhibition of albumin denaturation

The reaction mixture comprises 2800 μL of phosphate-buffered saline, 200 μL of egg albumin or 450 μL of bovine serum albumin (5% w/v aqueous solution), and 1000 μL of plant extract (10–50 μg/mL). A small amount of 0.1N HCl was used to adjust the pH of the solution (6.3), which was heated to 57 °C for 30 minutes after the first 20-minute correction. The extracts in the suggested combination are replaced with distilled water as a negative control. After 15 minutes at 37 degrees, the mixtures are then incubated for 5 minutes at 70 degrees. Before measuring the solution’s absorbance at 660 nm, transfer the solution to a 96-well plate when it has been cooled. Diclofenac sodium was utilized as a standard (Tona et al. 2020).

The following formula calculates the percentage inhibition of albumin denaturation.

Percentage of inhibition denaturation (%) = [(A control – A sample) / A control] × 100

where A control is absorbance above all mixtures except drugs, and A sample is the absorbance reaction mixture with the sample.

Active substance screening and target prediction

The hydroalcoholic extract of Phyllanthus niruri (HAEPN) was subjected to gas chromatography-mass spectrometry (GC/MS) analysis using a Shimadzu 17A GC in conjunction with a Shimadzu QP2010 plus (quadrupole) Mass Spectrometer (Shimadzu, Japan), equipped with EI and a fused silica column DB-5 (30 m × 0.25 mm i.d.) of film thickness. The oven was preheated for 40 minutes at a temperature of 50–28,000 °C after being preheated for 5 minutes at 5000 °C. High-purity helium was used as a carrier gas in this experiment. The ionization voltage of the MS-analysis was adjusted to 70 eV using the EI method, the helium gas flow rate was set to 2 mL/min, and a split proportion of 1:30 mode was employed for sample injection of 1 μl. Using computer searches on the National Institute of Standards and Technology (NIST) Ver. 11 MS data library database and comparing the GC-MS spectrum to the spectrum of the known components stored in the NIST library, the names, molecular weights, and nature/structure of the phytoconstituents were confirmed (Etl et al. 2022).

Once the compound was selected through the GC/MS, the SMILES of the eluted compound were collected, and they were run on bulk analysis for ADMET screening through (SwissADME) (Daina et al. 2017). The Lipinski principle states that small molecules have drug-likeness if they exceed three or more indicators, such as the molecular weight of ≤500, the lipid water partition coefficient of ≤5, the number of hydrogen bond donors at ≤5, the number of hydrogen bond receptors at ≤10, and the number of rotatable bonds at ≤10. This is used to make this determination. The other notable parameters, such as water solubility, pharmacokinetics, and lead likeness, were also used to rule out the best compound. Once the compound is screened by SwissADME, the selected SMILES are again run for target identification through SwissTargetPrediction (Gfeller et al. 2014).

NAFLD-related target screening

GeneCards (https://www.genecards.org) (Stelzer et al. 2016) and DisGeNET (Harishchander 2017) were indexed and deduplicated using the keywords “NAFLD” and “Non-Alcoholic Fatty Liver Disorder” as search terms. The UniProt database was used to normalize the acquired targets (https://www.uniprot.org/) (Consortium 2019), a network route for the ligand, target pathway, and gene was created, and the relevant targets of NAFLD therapy were obtained (Zhu et al. 2021).

Potential target acquisition of HAEPN for NAFLD therapy

The genes obtained through Swiss TargetPrediction for the phytochemicals filtered from ADMET filtration were selected for interactions. Further, the genes obtained from the gene card and DisGiNET were co-related with those obtained from phytochemicals by drawing a Venn diagram (http://bioinformatics.psb.ugent.be/webtools/Venn/) and (https://bioinformatics.com.c) (Bardou et al. 2014) to identify the intersecting target gene for the NAFLD and targeted compounds from GC/MS.

Establishing a protein-protein interaction network and identifying key targets

PPI analysis is critical for understanding complicated cell mechanisms and analyzing biological processes. The PPI network was built with STRING v11.5 (Szklarczyk et al. 2019). The intersecting targets from the preceding list were input into STRING to obtain the PPI network association. The species was assigned to “homo sapiens”, and the interaction score was adjusted to 0.4. The PPI network schematic was created using the Cytoscape 3.9.1 program (Otasek et al. 2019). Using the network analyzer function, the degree of the value was calculated. Key goals might be defined as the top 10 objectives with the highest degree ratings.

Functional enrichment in gene ontology (GO) and pathway analysis in the Kyoto Encyclopaedia of Genes and Genomes (KEGG)

The Metascape platform (https://metascape.org/) was used to import putative HAEPN targets for NAFLD therapy (Zhou et al. 2019). The bioinformatic enrichment study included the performance of GO studies to investigate biological functions, molecular processes, and cellular components. Additionally, a KEGG pathway enrichment analysis was conducted. The species Homo sapiens was selected, and a significance threshold of P < 0.01 was chosen. After doing the study, the researchers selected the top GO functions and the top 20 KEGG pathways. The findings were then recorded and displayed using a bioinformatics platform.

Architecture of the compound-target pathway network

The compound-target and pathway network link for the treatment of non-alcoholic fatty liver disease (NAFLD) employing the HAEPN active ingredient, possible targets, and KEGG pathway information was established using the Cytoscape 3.7.2 software program. A set of topological measures, such as degree, betweenness, and closeness, were computed for the network. The Network Analyzer analytical method was used to identify the key target and substantial active components of HAEPN for the treatment of NAFLD.

Molecular docking

The key targets identified from HAEPN for NAFLD therapy and their matching active components were molecularly docked. The PubChem database (https://pubchem.ncbi.nlm.nih.gov/) was used to obtain compound structures, while the PDB database (https://www.rcsb.org/) downloaded core proteins. The proteins were screened through PharmaMapper (Wang et al. 2017) for appropriate protein selection. 2ATH, 2ZNN, 3VI8, 4CI4, 3ET1, 3TKM, 5F9B, 1NFK, 1A3Q, 3RZF, 4KIK, 2H8R, 3096, and 5GTY were the proteins. Based on the Ramachandran plot, amino acid breaking chain, and chain resolution, the best six proteins (3VI8, 4CI4, 3ET1, 5F9B, 1NFK, and 3TKM) were chosen for the analysis. The binding activity between ligands and targets can be considered significant when the value is <-4.25 kcal/mol. A stronger binding activity is observed when the value is <-5.0 kcal/mol, while an even greater docking activity is observed when the value is <-7.0 kcal/mol. These observations are relevant in the context of screening active ingredients and targets (Hsin et al. 2013; Wu et al. 2022).

The pedagogy of the concoction of macromolecules

The protein data bank (PDB) provided 3D protein structures in PDB format. The Biovia Discovery Studio Visualizer 2021 software was used to strip the proteins of ions, co-crystallized ligands, and water molecules. After that, missing hydrogens and Kollman partial charges were added to the grid in AutoDock. Tools 1.5.7 and non-polar hydrogens were combined with the matching carbons. A grid with the necessary size (60*60*60) and a grid spacing of 0.53 A° was created. The receptor was subsequently modified to be rigid and devoid of any flexible connections in order to undertake docking research. The structures were saved in PDB, partial charges (Q), and atom type (T) format files, also known as PDBQT protein receptor files, for use in future studies.

Preparation of the ligand file

The structure was created using ChemDraw Ultra 12.0 for a 2D structure after obtaining the ligand file from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/). The structure was then loaded into the Chem3D software for energy minimization using the MM2 force field. It was then translated into three-dimensional structures and saved as a PDB file. Then, ligands were made using the AutoDock tools 1.5.7 and M.G.L. tools 1.5.7. Hydrogen addition and protonation were used to alter the resulting 3D structures. To find the root, non-polar hydrogens were fused to the matching carbon rotations, and the program chose the torsion tree in the ligands as flexible. The structures were then stored as PDBQT ligand files for further study (Rahman et al. 2021; Siva Kumar et al. 2022).

The construction of mating pockets

To construct mating spaces for docking in the grid design, the PDBQT receptor and ligand structures were opened in AutoDock Tools 1.5.7. When building a mating box, the sensitivity was set to 2.00, the spacing (angstrom) was set to 0.53 A°, the center was placed on the macromolecule, and the macromolecule’s properties in the x, y, and z dimensions in the center grid box were adjusted to maintain the protein covered by the mating pocket. These parameters include the coordinates of the mating box’s center (x, y, and z), its dimensions (x, y, and z), the maximum number of binding modes to be generated (set to 20), the energy range (set to 5), and the exhaustiveness (set to 8). To do more research, the output file was saved as a grid parameter file (GPF). A good level of 0 and the log results were used in the AutoGrid 4.2.6 run on the prepared GPF file. For a successful execution, the GLG file was examined.

Docking and visualization

The prepared macromolecule and ligand file were chosen, the search parameters were set to GA, the docking parameters were set to default, and the output was set to Lamarckian 4.2 and saved as a docking parameter file (DPF). The AutoDock was initiated with the DPF file at a nice level of 20. Upon completion of the docking process, the data files were saved in the formats pdbqt* and log*. To determine the binding affinity score between the target receptor proteins and small-molecule ligands, the docking binding free energy method was used using DLG data. In this study, docking models exhibiting the most minimal binding affinities were visualized, and the hydrogen bonds were represented in a two-dimensional format using Discovery Studio1.

Results

Phytochemical screening and preliminary analysis

The presence of alkaloids, carbohydrates, tannins, terpenoids, phenol, phlobatanins, steroids, cardiac glycosides, and reducing sugars was found in a hydroalcoholic extract of P. niruri; however, saponins were not found. The preliminary examination revealed that the loss on drying, total ash, acid insoluble ash, TLC, total bacterial count, pH, bulked, and tap density met Indian pharmacopoeia standards (Table 1).

In vitro antioxidant study

Determination of DPPH radical scavenging activity

The absorbance at 517 nm measured using a UV-visible spectrophotometer was 0.06 for standard ascorbic acid and 0.04 for HAEPN, respectively. The IC50 values obtained were 10.89 and 10.27 µg/mL for ascorbic acid and HAEPN, respectively. That suggests that a higher concentration of hydroalcoholic extract from plants trapped more free radicals produced by DPPH, resulting in a drop in absorbance and an increase in percentage inhibition. The graph was plotted for the standard ascorbic acid (Fig. 2) and the test sample, P. niruri (Fig. 3).

Figure 2. 

DPPH radical scavenging activity of standard ascorbic acid with IC50 10.89 μg/mL and R2 = 0.8088, y = 0.2062x + 47.753.

Figure 3. 

DPPH radical scavenging activity of Phyllanthus niruri HAE with standard ascorbic acid. IC50 = 10.27 μg/mL and R2 = 0.8715, y = 0.2305x +47.632.

Determination of the scavenging of superoxide radicals by alkaline DMSO

The ability to neutralize superoxide radicals in the reaction mixture using the test material at different dosages, compared to standard ascorbic acid, is shown by the decrease in absorbance at 560 nm. The IC50 values for ascorbic acid and HAEPN, which exhibit superoxide radical scavenging action, were determined to be 4.1715 μg/mL and 7.6499 μg/mL, respectively (Fig. 4).

Figure 4. 

Superoxide anion radical (O2*-) scavenging activity with standard ascorbic acid and HAEPN. The IC50 was found as 4.171 μg/mL and 7.6499 μg/mL, with Y = 0.3299x+48.653, R2 = 0.9999; y = 0.351x+46.944, R2 = 0.9976.

Anti-inflammatory activity by inhibition of albumin denaturation

HAEPN at concentrations ranging from 100 to 1000 µg/mL inhibited egg albumin denaturation, as evidenced by a concentration-dependent rise in solution absorbance. A comparable impact was seen with standard Diclofenac sodium concentrations ranging from 100 to 1000 µg/mL. HAEPN and diclofenac sodium had linear regression coefficients of R2 = 0.9906 and R2 = 0.9498, respectively, and the observed inhibition was dose dependent. The IC50 values of HAEPN and diclofenac sodium obtained from linear regression analysis were 64.30 µg/mL and 80.40 µg/mL, respectively. The plotted graph for the test sample is shown in Fig. 5 and for standard diclofenac sodium in Fig. 6.

Figure 5. 

Anti-inflammatory by protein denaturation assay with standard diclofenac sodium IC50 = 64.30 μg/mL and R2 = 0.9906, y = 0.0479x +46.952.

Figure 6. 

Anti-inflammatory by protein denaturation assay with Phyllanthus niruri HAE IC50 = 80.40 μg/mL and R2 = 0.9498, y = 0.0324x +47.628.

Active substance screening and target prediction

Fig. 7 depicts the chromatographic profile of a P. niruri hydroalcoholic extract. Table 2 contains a list of phytochemical substances. There are 17 bioactive compounds identified, corresponding to the area peaks and heights. However, 5 significant compounds were identified, which include 5,7-dihydroxy-2-(4-hydroxyphenyl) chroman-4-one (naringenin), 3,4-dihydroxybenzoic acid (protocatechuic acid), diethyl phthalate, 2-(3,4-dihydroxyphenyl)-3,5,7-trihydroxy-1-benzopyrylium (cyanidin), and ellagic acid through ADMET filtration.

Table 2.
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Phytochemical compounds form GC/MS.

SI.No. RT Compound Structure MW Formulae Peak area %
1 3.198 Glycerin 92.09 C3H8O3 0.81
2 5.649 2-Furanmethanol 98 C5H6O2 0.43
3 7.100 α-Angelica lactone 98 C5H6O2 0.20
4 8.385 5,7-Dihydroxy-2-(4-hydroxyphenyl) chroman-4-one 272 C15H12O5 0.02
5 10.870 (E)-1,2-ditert-butyldiazene 142 C8H18N2 0.34
6 11.267 4h-pyran-4-one, 3-hydroxy-2-methyl 126 C6H6O3 0.07
7 12.008 3,4-Dihydroxybenzoic acid 154.1 C7H6O4 0.09
8 20.337 1,2-Benzenedicarboxylic acid, diethyl ester 222 C12H14O4 6.13
9 27.416 Ellagic acid 302 C14H6 O8 0.29
10 29.884 8,5’-Diferulic acid 386 C20H18O8 2.27
11 32.295 7-Hydroxy-6,9a-dimethyl-3-methylene-decahydro-azuleno[4,5-b] furan-2,9-dione 264 C15H20O4 1.06
12 33.867 Quercetin-3-O-rhamnoside 448 C22H45Cl 1.61
13 36.020 Niruriflavone 364 C16H12O8S 3.78
14 37.011 2-(3,4-Dihydroxyphenyl)-3,5,7-trihydroxy-1-Benzopyrylium 282 C15H11O6 + 7.55
15 37.770 9-Bromononanoic acid 237 C9H17BrO2 0.80
16 38.927 18,19-Secoyohimban-19-oic acid, 16,17,20,21-tetradehydro-16-(hydroxymethyl)-, methyl ester 352 C21H24N2O3 1.93
17 39.220 Isobornyl acrylate 208 C13H20O2 0.46
Figure 7. 

Spectrum of P. niruri obtained from GC/MS.

NAFLD-related target screening

A total of 1,808 and 1,058 genes were obtained through gene cards and DisGeNET for NAFLD-related targets with ‘Non-Alcoholic Fatty Liver Disorder’ and ‘NAFLD’ as keywords. The duplicates were removed, and 1,713 were selected for further evaluation. The overall unique elements were 2,482.

Potential targets of HAEPN for NAFLD therapy

There were 59 common targets found when a total of 1713 NAFLD-related targets intersected with 521 GCMS targets that included five main bioactive chemicals that were discovered during screening. For the GCMS, DisGeNET, and gene cards, a Venn diagram was drawn (Fig. 8), and a potential target was discovered.

Figure 8. 

The intersection diagram is plotted for GC/MS, DisGiNET, and gene cards.

PPI network construction

The STRING database was used to import the common 59 targets for PPI network data. The PPI network has 374 edges and 59 nodes, with an average node degree of 12.7 and a local clustering coefficient of 0.576. The nodes in the network represent proteins, while the edges indicate PPI. The PPI enrichment value was found to be < 1.0e-16. The network obtained from STRING is shown in Fig. 10. Later, the network was transferred from STRING to cystoscope for analysis, and the results were as follows: network density was 0.234, network heterogenicity was 0.690, network centralization was 0.516, and the betweenness by degree for the text mining and score a scattered plot with linear regression was plotted with R = 0.1741 (Fig. 9). The top 10 targets by their degree value are presented in Table 3. These objectives are crucial to the PPI network and serve as a connection between the other goals.

Table 3.
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The degree, average shortest path length, betweenness, and closeness centrality of the top 10 key targets.

Target Degree Betweenness Centrality Closeness Centrality Avg Shortest Path Length
AKT1 41 0.111 0.777 1.285
PPARG 33 0.103 0.777 1.428
VEGFA 32 0.103 0.674 1.482
EGFR 30 0.045 0.658 1.517
MMP9 29 0.039 0.658 1.517
ESR1 28 0.033 0.643 1.553
SIRT1 27 0.026 0.651 1.535
PPARA 26 0.038 0.629 1.589
TLR4 26 0.037 0.602 1.660
MMP2 22 0.007 0.589 1.696
Figure 9. 

Linear regression for the betweenness by degree exported from Cytoscape with R = 0.1741.

Figure 10. 

The PPI network of target proteins.

GO functional enrichment and KEGG pathway analysis

The Metascape platform was used to conduct GO and KEGG pathway enrichment analyses on 59 putative HAEPN targets for NAFLD treatment. The obtained GO enrichment analysis results were of biological function, cellular function, and molecular function. The top ten GO characteristics were chosen, and the bioinformatics platform was used to record and visualize the data (Fig. 11). According to the visualization, the primary biological processes at play were the cellular reaction to chemical stress, oxidative stress, cellular response to aging, positive regulation of protein/serine, threonine kinase activity, ROS on the metabolic process, and the metabolic process of fatty acids. The molecular function comprises nuclear receptor activity, ligand-induced transcription factor activity, phosphatase binding, deacetylase activity, etc. The cellular function was shown by the ficolin-1-rich granule, vesicle lumen, nuclear envelope, and cytoplasmic vesicle lumen. The top 20 elements of KEGG pathway data were selected for input and visualized using the bioinformatics platform during the KEGG analysis (Figs 12, 13). According to their strength score, the following KEGG pathways had the strongest correlations: AGE-RAGE signaling route (1.43), insulin resistance (1.34), non-alcoholic fatty liver disorder (1.26), PI3K-Akt signaling pathway, EGFR tyrosine kinase inhibitor (1.47), endocrine resistance (1.45), and AGE-RAGE signaling pathway.

Figure 11. 

The bar diagram illustrates the GO enrichment analysis of genes associated with NAFLD. The study includes the top 20 enrichment keywords in three categories: biological processes (BP), cellular components (CC), and molecular functions (MF). The abscissa represents the proportion of genes of interest inside each entry, whereas the ordinate denotes the individual entries. There is a positive correlation between the size of a bar and the number of genes identified in the corresponding entry. The -LogP denotes the enrichment score associated with each ontology, as seen in the color bar.

Figure 12. 

KEGG pathway enrichment analysis.

Figure 13. 

KEGG pathway enrichment score.

Network construction analysis

The Compound-Target Pathway network diagram (Fig. 14) was made using Cytoscape 3.9.1. In order to determine the intensity of the interactions between phytoconstituents and the target proteins, the network topological properties were examined using network analyzer tools. There are 249 nodes and 1011 edges in the network.

Figure 14. 

Network design of CTP construction.

Molecular docking assessment

The best six proteins were selected for the analysis (3VI8, 4CI4, 3ET1, 5F9B, 1NFK, and 3TKM), and five selected active ingredients (i.e., naringenin, protocatechuic acid, diethyl phthalate, ellagic acid, and cyanidin) underwent molecular docking separately. After that, 30 sets of receptor-ligand docking outcomes were produced similarly. Intermolecular forces must be evaluated during the molecular docking process. In other words, the study’s intermolecular forces are mostly hydrogen bonds. Table 4 displays the results of molecular docking. There were 11 groupings with affinity <−4 *<−5 kcal·mol−1, nine groups with affinity <−6 kcal·mol−1, and 10 groups with affinity <−7 and <−8 kcal·mol−1 among the 30 receptor-ligand findings. The screened putative key effective elements have a high binding activity towards important targets. (Tables 5, 6) depict the docking mechanisms of the top two core compounds. Naringenin showed hydrogen bonding interaction with 3ET1 at SER A:280 and GLU A: 269, with 3TKM at VAL A:286 and LEU A:294, and with 5F9B at LEU A: 228, ARG A:288, ILE A: 326 and SER:342. Cyanidin showed hydrogen bonding interaction with 3VI8 at ASN A:219 and LEU A: 331; MET A:192, GLU A: 255, GLU A: 259, MET A: 293. Each target ligand and each active chemical residue formed at least one hydrogen bond, confirming the accuracy and veracity of the study’s prediction.

Table 4.
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Binding energy (KJ/mol) of the 30 ligand-receptor interaction targets.

Ligand PPAR-A PPAR-D PPAR-G
3VI8 4CI4 3ET1 3TKM 5F9B
Naringenin -8.03 -7.79 -8.67 -7.12 -8.53
Protocatechuic acid -4 -5.09 -6.32 -5.14 -6.32
Diethyl Phthalate -5.4 -5.27 -5.34 -6.05 -6.87
Cyanidin -7.76 -7.42 -7.6 -6.47 -7.67
Ellagic acid -7.17 -6.32 -5.77 -6.21 -5.43
Table 5.
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Molecular docking visualization of key components and key targets.

Ligand Target Protein Pocket Atom 2D Interaction
Naringenin PPAR α 3ET1
PPAR δ 3TKM
PPAR γ 5F9B
Table 6.
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Molecular docking visualization of key components and key targets.

Ligand Target Protein Pocket Atom 2D Interaction
Cyanidin PPAR α 3VI8
PPAR δ 3TKM
PPAR γ 5F9B

Discussion

Non-alcoholic steatohepatitis may develop over time as a silent state of non-alcoholic fatty liver disease. In the future, it may significantly increase the risk of cirrhosis, liver transplantation, and death. The major causes of central obesity and NAFLD include a sedentary lifestyle and excessive consumption of high-calorie foods such as butter, cheese, egg yolk, and lard fat (Al Zarzour et al. 2017). Mitochondrial dysfunction, lipid overload, and insulin resistance are thought to be the primary pathogenic contributors to the development of NAFLD (Bessone et al. 2019). Fat accumulation causes endoplasmic reticulum (ER) stress, increasing reactive oxygen species (ROS) levels inside hepatocytes. In conjunction with other variables, this oxidative stress promotes inflammatory responses that can lead to fibrosis and/or hepatic cancer (Al Zarzour et al. 2018). In this present study, in silico computational techniques and in vitro studies were applied to evaluate the potency of the hydroalcoholic extract of P. niruri. Studies have been conducted on the plant’s traditional claims of being antiangiogenic, mild hypertension, hemorrhoids, anti-microbial, kidney stone, anti-inflammatory, anti-diabetic, NAFLD, etc. Plant metabolites include phenols, flavonoids, tannins, saponins, alkaloids, phlobatannins, steroids, anthraquinones, cardiac glycosides, and carbohydrates. Phytochemicals, often referred to as secondary metabolites, are acknowledged for their biological functionalities. (Soni and Sosa 2013). Tannins, phenols, phlobatannins, terpenoids, steroids, alkaloids, cardiac glycosides, and reducing sugars were found in the hydroalcoholic extract of P. niruri in this study. Since these chemicals are responsible for many biological activities in the human body, they have several therapeutic uses. Based on various literature, P. niruri has shown hepatoprotective properties in rats with hepatitis, and it is also abundant in phenols and flavonoids, which are essential for exerting a notable antioxidant effect. The intervention leads to a reduction in insulin resistance, levels of free fatty acids (FFAs), and glucose, as well as inhibition of α-glucosidase, cholesterol micellization, and pancreatic lipase. Additionally, it results in decreased serum fatty acid levels, thus contributing to a decrease in hepatic fat storage through the de novo lipogenesis pathway (Anuragh and Ilango 2022). In GC-MS analysis of HAEPN, 184 peaks were observed, and 17 phytoconstituents were screened through peak, area, and height ratio.

These 17 phytoconstituents were subjected to SwissADME tool screening, and 5 compounds were screened based on Lipinski’s principle and lead likeness. The targets of putative compounds were integrated with the NAFLD-associated gene from the DisGeNET and gene card databases, and a common target plot was constructed. The common target was then used to form a PPI network, and KEGG and GO enrichment analyses were constructed. The network construction for the ligand and pathway with the gene was explored. The compound-target-pathway network was constructed (Fig. 14), and the five selected compounds were molecularly docked with the selected target from the network construction. Cyanidin and naringenin have shown better binding affinity (Tables 5, 6) when compared to other compounds.

According to certain studies, cyanidin and its glycosides are found in foods where lower levels of atherogenic lipoprotein and oxidized low-density lipoprotein (LDL) are produced both in vitro (Meyer et al. 1998) and in vivo (Abdel-Moemin 2011). Moreover, several studies have shown that the consumption of a diet rich in cyanidin effectively mitigates the adverse effects of a high-fat diet, including body weight increase, hyperglycemia, and hyperinsulinemia, via the enhancement of adipokine production (Décordé et al. 2009) and hormone-sensitive lipase (Dallas et al. 2008). Jia et al. discovered that cyanidin has the greatest affinity for direct binding to all three PPAR isoform subtypes and that this direct binding results in common agonistic effects. Cyanidin is a mild PPARγ and PPARβ/PPARδ agonist and a moderate PPARα agonist. Hence, it is plausible that the physiological impacts of cyanidin on lipid metabolism, insulin sensitivity, inflammation, and obesity might be modulated via the activation of peroxisome proliferator-activated receptors (PPARs) in metabolically active tissues (Jia et al. 2013).

The study conducted by Arul and Subramanian in 2013 revealed that the growth of HepG2 cells was effectively suppressed by naringenin in a concentration-dependent manner. The activation of p53 has been associated with cell cycle arrest in both the G1 and G2 stages of the cell cycle. Significantly, the accumulation of p53 was enhanced by naringenin in a way that was dependent on the dosage. This observation provides a potential explanation for the G0/G1 and G2/M phase arrests induced by naringenin in HepG2 cells (Arul and Subramanian 2013). Naringenin mitigates oxidative stress and inflammation associated with non-alcoholic fatty liver disease (NAFLD) by effectively decreasing liver lipid peroxidation, enhancing liver antioxidant levels, and inhibiting the activation of nuclear factor-kappa B (NF-kB), thereby suppressing the expression of pro-inflammatory genes such as tumor necrosis factor (TNF-α), interleukin-6 (IL-6), and IL-1β (Hernández-Aquino et al. 2019).

Notably, protocatechuic acid, ellagic acid, naringenin, cyanidin, and diethyl phthalate are the main active substances in HAEPN for the treatment of NAFLD. Such active ingredients may affect the PI3K-Akt, HIF-1, AGE-RAGE, and MAPK signaling pathways, as well as EGFR tyrosine kinase inhibitors, endocrine resistance, insulin resistance, non-alcoholic fatty liver disease, relaxin, and non-alcoholic fatty liver disease. The present research discusses a putative mechanism of action for HAEPN in the treatment of NAFLD utilizing network pharmacology and molecular docking technologies. This study also offers some conceptual and empirical support for the development of new NAFLD treatments.

Many constraints have plagued both molecular docking and network pharmacology. They can be utilized as a primary tool to sort out the active ingredient, specific targets, and their pathways through computational means. The currently accessible database has several uncertainties. At the same time, computation findings cannot completely replace experimental data; they must be combined with other methodologies and empirically confirmed in many circumstances for the logical design of polypharmacology (Jiao et al. 2021). Network pharmacology is commonly misinterpreted and confused with other areas of pharmacology due to its ambiguity. Scientific research is more challenging since it requires the use of specialized databases and analytical tools for data collection and processing. The target-oriented single-component chemical drug development model is no longer able to fulfill the needs of difficult diseases as a result of the change in the global model of new drug development, and compound drug development has taken over as the main area of new drug development.

To further corroborate our results, it is widely known that further studies such as hit/lead optimization, molecular dynamics, MMPBA (free energy calculation), and in vivo animal trials are required. Nevertheless, our breadth was constrained by time constraints in addition to other factors. Lead optimization comprises changing the chemical makeup of newly identified compounds to improve their toxicity, potency, and pharmacokinetics, thereby creating safer and more effective small molecules for the inhibition of NAFLD-relevant pathways.

Conclusion

Contrary to what was shown in the previous research, HAEPN did have higher levels of antioxidant and anti-inflammatory activity, thus creating a platform for further NAFLD activity augmentation. Nuclear receptor activity and ligand-induced transcription factor activity were shown to be significantly enriched in gene ontology’s molecular function. AKT1, PPARA, PPARG, MAPK8, GSK3B, INSR, NR1H3, and ERN1 all had improved connections in the PPI networks built for target proteins, suggesting they might be used to treat NAFLD. Based on our findings, we propose using network pharmacology, GO, KEGG, and molecular docking as the initial phase in doing this study. To develop a novel therapeutic agent for treating and managing NAFLD, we recommend further refining and enhancing the top two hits (naringenin and cyanidin). However, additional in vivo pharmacological research development of HAEPN and animal or cell line testing are needed to verify the reliability and rationality of the projected effects.

Acknowledgments

We thank the management, SRM College of Pharmacy, and SRMIST for providing facilities to carry out this work.

No funding was received to assist with the preparation of this manuscript.

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1 The processor used for the docking was 11 th Gen Intel (R) Core (TM), i5-1135G7@2.40GHz, 64-bit OS, x64 processor, RAM-16GB, GPU: Iris (R) Xe Graphics.
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