Abstract Introduction: Adipose tissue functions as a key endocrine organ which releases multiple bioactive substances and regulate obesity-linked complications. Dysregulation of adipocyte differentiation, triglyceride metabolism, adipokines production and lipid transport contributes to impaired lipid metabolism resulting in obesity, insulin resistance and type 2 diabetes. Gymnema sylvestre plant is frequently used in Ayurveda for treatment of diabetes and obesity. Gymnemagenin is a major bioactive compound of Gymnema sylvestre. The present study was undertaken to elucidate the role of gymnemagenin in lipid metabolism by in vitro and computational approaches. Methods: A panel of twelve genes viz., Fasn, Lipe, Lpl, Pparg, Plin2, Cidea, Scd1, Adipoq, Lep, Ccl2, Fabp4, and Slc2a4, essential in lipid metabolism were selected and gene expression pattern and triglyceride content were checked in adipocytes (3T3L1 cells) with/without treatment of gymnemagenin by Real time PCR and colorimetric estimation, respectively. Mode of action of gymnemagenin on Pparg and Fabp4 was accomplished by computational studies. Gene set enrichment and network pharmacology were performed by STRING and Cytoscape. Molecular docking was performed by AutoDock vina by POAP pipeline. Molecular dynamics, MM-PBSA were done by Gromacs tool. Results: In vitro study showed that gymnemagenin improved triglyceride metabolism by up regulating the expression of lipase genes viz., Lipe and Lpl which hydrolyse triglyceride. Gymnemagenin also up regulated the expression of anti-inflammatory gene Adipoq. Importantly, gymnemagenin treatment up regulated the expression of Pparg gene and the downstream target genes (Plin2, Cidea, and Scd1) which are associated with adipogenesis. However, gymnemagenin has no effect on expression of Fabp4, codes for a lipid transport protein. In silico study revealed that gymnemagenin targeted 12 genes were modulating 6 molecular pathways involved in diabetes and obesity. Molecular docking and dynamics revealed that gymnemagenin stably bind to active site residue of Pparg and failed to bind to Fabp4 active site compared to its standard molecules throughout 100 ns MD production run. Gymnemagenin scored binding free energy of −177.94 and −25.406 kJ/mol with Pparg and Fabp4, respectively. Conclusion: Gymnemagenin improved lipid metabolism by increasing triglyceride hydrolysis (lipolysis), up regulating the crucial gene of adipogenesis and increasing the expression of anti-inflammatory adipokine proving its therapeutic importance as anti-obesity and anti-diabetic phytocompound. Keywords: gymnemagenin, lipid metabolism, obesity, in vitro study, in silico pharmacology, type 2 diabetes mellitus 1 Introduction Type 2 diabetes (T2D) is a prevalent metabolic disorder. More than 95% of people having diabetes are diagnosed with T2D. T2D is commonly asymptomatic and frequently recognized by the manifestation of excess body weight and elevation of random blood glucose. It is often diagnosed several years after the onset, when many other complications have already arisen ([36]Cole and Florez, 2020). T2D starts with the onset of insulin resistance, which is a cumulative health consequence of obesity, dysfunctional adipose tissue, chronic inflammation, and decrease in pancreatic β-cell mass and consecutive failure in the production of insulin. In addition, prolonged uncontrolled blood glucose levels have harmful effects on multiple tissues, including kidney, cardiovascular tissue, eye, neurons, skeletal muscles, and lower limbs ([37]Davoudi and Sobrin, 2015; [38]Sheleme et al., 2020). This disease is associated with several regulatory factors that affect various metabolic pathways in different important organs of the human body. It is evident that obesity plays a crucial role in the etiology of T2D. According to the data of the National Family Health Survey (2019–2021) obtained from the Global Obesity Observatory, the prevalence of obesity is 22.9% for men and 24% for women, whereas it was 11% and 15% in men and women, respectively, in the 2014–2015 report. These data clearly indicate that the global impact of obesity is increasing at an alarming rate. Thus, this disease requires multifactorial risk reduction strategies and continuous physical and medical care ([39]Altaf et al., 2015). Plants have been used as medicine since the beginning of civilization. Recently, there is a global thrust in the usage of natural products such as herbs. These natural herbal products contain phytocompounds, which are the chemical compounds synthesized and preserved by plants through their various secondary metabolic pathways. Several of these phytocompounds have medicinal value, are used as crude ingredients in numerous pharmaceuticals and also have a foundational role in modern drug development ([40]Wang et al., 2013; [41]Tran et al., 2020; [42]Jugran et al., 2021). Gymnema sylvestre R. Br. is one of the major botanicals used to treat diabetes and obesity in Ayurveda, an Indian traditional system of medicine. Several group of scientists have investigated different formulations of this plant as well as different phytocompounds of which gymnemagenin is the most potent compound used in several antidiabetic Indian traditional AYUSH formulations (Ayurveda, Unani, Siddha, and Homeopathy) as well as nutraceuticals and food supplements ([43]Tiwari et al., 2014). Although numerous scientific studies have been carried out focusing on the hypoglycemic bioactivity of gymnemagenin, there are lacunae in the detailed scientific reports on antiobesity activity. Because obesity and T2D is correlated to a great extent, there is an urgent need to study the role of gymnemagenin in lipid metabolism in correlation with T2D, which could lead to cost-effective targeted phytomedicines with lesser side effects. The structure and function of adipose tissue are key regulatory factors in lipid metabolism. Adipocyte differentiation, lipid droplet biosynthesis, and lipid droplet size are directly linked with lipolysis, that is, triglyceride metabolism ([44]Albert et al., 2014; [45]Sanjabi et al., 2015). Adipokines, the cell signaling molecules secreted mainly by adipocytes, carry out inter-tissue communication functions, and an imbalance of pro- and anti-inflammatory adipokines contributes to metabolic dysfunction ([46]Ouchi et al., 2011). Lipid transport is associated with muscle insulin sensitivity, which, in turn, regulates glucose transporters and glucose disposal ([47]Hagberg et al., 2012). In this present study, a panel of twelve genes, Fatty acid synthase (Fasn), Lipase (Lipe), Lipoprotein lipase (Lpl), Peroxisome proliferator-activated receptor gamma (Pparg), Perilipin 2 (Plin2), Cell death-inducing DNA fragmentation factor, alpha subunit-like effector A (Cidea), Stearoyl-Coenzyme A desaturase 1 (Scd1), Adiponectin (Adipoq), Leptin (Lep), Chemokine (C-C motif) ligand 2 (Ccl2), Fatty acid binding protein 4 (Fabp4), Adipocyte solute carrier family 2 (facilitated glucose transporter), and member 4 (Slc2a4/GLUT4), which are essential in lipid metabolism and T2D, was selected from literature survey and taken for analysis by in vitro and computational approaches to elucidate the role of gymnemagenin in lipid metabolism. 2 Materials and methods 2.1 Cell culture and treatment The 3T3L1 mouse embryonic fibroblast cell line was obtained from NCCS, Pune, India. 3T3L1 pre-adipocytes were maintained in Dulbecco’s Modified Eagle’s Medium (DMEM) supplemented with 10% fetal bovine serum (FBS) in a humidified atmosphere containing 5% CO[2] at 37 °C. Cells were subcultured before the culture reached 70% confluence ([48]Rathinasabapathy et al., 2017). 3T3L1 pre-adipocytes were differentiated into adipocytes following the protocol developed by Rubin, Lai, and Rosen (1977) with minor modifications. Post-confluent pre-adipocytes were supplemented with DMEM (with 10% FBS) media having 1 µM dexamethasone (DEX), 0.5 mM L-methyl-3-isobutylxanthine (MIX) and10 µg/ml insulin. After 3 days, fresh media was added containing DMEM/10% FBS supplemented with 10 µg/ml insulin. The media was changed on alternate days until fully differentiated adipocytes showing well-developed lipid droplets were observed. 3T3L1 pre-adipocytes were plated in 12-well plates and kept for differentiation. Terminally differentiated adipocytes were treated with gymnemagenin in different concentrations (50, 25, 12.5, 6.25, 3.125, 1.56 μM) based on the cell viability data and incubated for 6 h at 37 °C in an incubator with 5% CO[2]. After the incubation, cells were used for RNA isolation and subsequent real-time PCR. 2.2 Cytotoxicity assay 3T3L1 cells (5,000 cells/well) were seeded in a 96-well plate and incubated in different concentrations of gymnemagenin (400, 200, 100, 50, 25, 12.5, 6.25 μM) for 24 h. A 10-μL aliquot of MTT (5 mg/ml) was added to each well and incubated for 4 h. Later, formazan crystals were dissolved in 100 μL DMSO, and OD values were taken at 570 nm. OD values were normalized with the blank, and the cell viability percentage was calculated and plotted in a graph. 2.3 Determination of triglyceride content by colorimetric estimation 3T3L1 cells were plated in 12-well plates and kept for differentiation. On Day 1 of differentiation, cells were treated with gymnemagenin at different concentrations (50, 25, 12.5, 6.25, 3.125, 1.56 μM). Triglyceride was measured on Day 4 and Day 8 of differentiation using a triglyceride colorimetric assay kit following the manufacturer’s protocol (Elabscience). 2.4 RNA preparation and real-time PCR analysis Total RNA was extracted from cells using TRI reagent (Sigma-Aldrich) following the manufacturer’s protocol with minor modifications. The RNA pellet was dissolved in nuclease-free water. RNA was quantified using Nanodrop. RNA integrity was checked by running the samples in 1% agarose/formaldehyde gel containing 0.5 µg/ml ethidium bromide. cDNAs were synthesized using 1 μg of RNA for each sample using PrimeScript RT Reagent Kit (DSS Takara Bio India), following the manufacturer’s protocol. cDNA was amplified by real-time quantitative PCR (RT PCR) using SYBR green PCR Master Mix (DSS Takara Bio India). Primers of selected genes under this study were procured from Merck (KiCqStart^® SYBR^® Green Primers) (tabulated in [49]Table 1). RT PCR amplifications were performed on BIORAD CFX Maestro (Version 1.1). Cycle conditions were 95 °C for 30 s, followed by 40 cycles at 95 °C for 15 s and 54°C for 30 s and 72°C for 30 s. mRNA expression was analyzed using the ΔΔCT method and normalized with respect to the expression of the μ-actin using BIORAD CFX Maestro Software (Version 1.1). Amplification of specific transcripts was further confirmed by obtaining dissociation (melting) curve profiles with 1 cycle of 1 min at 95 °C, 30 s at 55 °C, and 30 s at 95 °C ([50]Cho et al., 2009). TABLE 1. Sequences of primers used for RT PCR. Gene Primer ID Primer sequence (5’…3′) Fasn M_Fas_1 F: TGA​ATG​CCT​CAA​ATC​TTA​GC R: TTT​TAG​CTT​CCT​GGA​TTG​TC Lipe M_Lipe_1 F: AAC​TCC​TTC​CTG​GAA​CTA​AG R: CTT​CTT​CAA​GGT​ATC​TGT​GC Lpl M_Lpl_1 F: GAG​ACT​CAG​AAA​AAG​GTC​ATC R: GTC​TTC​AAA​GAA​CTC​AGA​TGC Glut4/Slc2a4 M_Slc2a4_1 F: CAA​TGG​TTG​GGA​AGG​AAA​AG R: AAT​GAG​TAT​CTC​ATA​GGA​GGC Pparg M_Pparg_1 F: AAA​GAC​AAC​GGA​CAA​ATC​AC R: GGG​ATA​TTT​TTG​GCA​TAC​TCT​G Plin2 M_Plin2_1 F: ATA​AGC​TCT​ATG​TCT​CGT​GG R: GCC​TGA​TCT​TGA​ATG​TTC​TG Cidea M_Cidea_1 F: GTG​TTA​AGG​AAT​CTG​CTG​AG R: CTA​TAA​CAG​AGA​GCA​GGG​TC Scd1 M_Scd1_1 F: GTG​GGG​TAA​TTA​TTT​GTG​ACC R: TTT​TTC​CCA​GAC​AGT​ACA​AC AdiQ M_Adipoq_1 F: CCA​CTT​TCT​CCT​CAT​TTC​TG R: CTA​GCT​CTT​CAG​TTG​TAG​TAA​C Lep M_Lep_1 F: CTT​TGG​TCC​TAT​CTG​TCT​TAT​G R: TCT​TGG​ACA​AAC​TCA​GAA​TG Ccl2 (MCP-1) M_Ccl2_1 F: CAA​GAT​GAT​CCC​AAT​GAG​TAG R: TTG​GTG​ACA​AAA​ACT​ACA​GC Fabp4 M_Fabp4_1 F: GTA​AAT​GGG​GAT​TTG​GTC​AC R: TAT​GAT​GCT​CTT​CAC​CTT​CC Β-Actin M_Actb_1 F: GAT​GTA​TGA​AGG​CTT​TGG​TC R: TGT​GCA​CTT​TTA​TTG​GTC​TC [51]Open in a new tab 2.5 Enrichment analysis of gymnemagenin-regulated targets Based on the in vitro analysis, we subjected 12 genes expressed by gymnemagenin in 3T3L1 cells, Fabp4, Fasn, Lipe, Lpl, Slc2a4, Pparg, Plin2, Cidea, Scd1, Adiq, Lep, and Ccl2, to molecular pathway enrichment analysis. The 12 genes were queried into STRING ([52]Szklarczyk et al., 2016; [53]https://string-db.org/) for Homo sapiens, Mus musculus, and Rattus norvegicus ([54]Khanal et al., 2020). Furthermore, we identified modulated pathways with reference to the Kyoto Encyclopedia of Genes and Genomes (KEGG; [55]https://www.genome.jp/kegg/) pathway database. The pathways associated with diabetes mellitus and obesity were traced ([56]Patil et al., 2020). 2.6 Network construction and analysis The network of gymnemagenin, regulated genes, and modulated pathways in Homo sapiens, Mus musculus, and Rattus norvegicus was constructed by Cytoscape ([57]Shannon et al., 2003) ver. 3.6.1 ([58]https://cytoscape.org/). During analysis, the network was treated as direct and analyzed using the edge count topological parameter. To find the hub gene and pathway within the network, the node color and size were set as “low values to bright colors” and “low values to small size,” respectively ([59]Dwivedi et al., 2022). 2.7 Molecular docking Based on the in vitro and network analysis, we prioritized Pparg and Fabp4 for molecular docking studies using gymnemagenin and respective standard compounds as ligand molecules. Pioglitazone ([60]Charbonnel, 2009) and BMS-309403 ([61]Lan et al., 2011) compounds were used as standard molecules of Pparg and Fabp4, respectively. PubChem was used to retrieve the 3D structure of phytocompounds. The compounds were prepared using POAP ligand preparation “POAP_lig.bash” script ([62]Samdani and Vetrive, 2018). The structures were minimized by MMFF94 force field using the conjugate gradients algorithm and finally converted into a pdbqt molecule by adding the gasteiger charges and polar hydrogens for further molecular docking study. The 3D x-ray crystallographic structures of Pparg (PDB: 5Y2O) and Fabp4 (PDB: 3JS1) were retrieved from the Protein Data Bank. The PDB ID 5Y2O consisted of missing residues and was remodeled by the SWISS-MODEL ([63]Greenfield and Pietruszko, 1977) web server ([64]https://swissmodel.expasy.org/) using Uniprot ID [65]P37231 as the query sequence and 5Y2O chain A as a template. Furthermore, P2Rank ([66]Krivák and Hoksza, 2018) was employed to retrieve the active site residues information. AutoDock vina was executed using the POAP pipeline ([67]Samdani and Vetrive, 2018; [68]Patil et al., 2020) to perform the molecular docking. During docking, the exhaustiveness was set to 100 and generated nine docked conformations, of which the conformation with the lowest BE and least RMSD was chosen. The interaction between the compound and target was analyzed by BIOVIA Discovery Studio Visualizer 2019 ([69]https://discover.3ds.com/discovery-studio-visualizer-download). 2.8 Stability of the docked complexes To examine the docked complex structural and intermolecular interaction stabilities, an all-atom molecular dynamics (MD) simulation for 100 ns in an explicit solvent was performed. The GROMACS ([70]Van Der Spoel et al., 2005) ver 2021.3 ([71]https://www.gromacs.org) package was utilized to run MD simulations using the Amber ff99SB-ildn force field. The topological parameters of the ligands and the whole complex were generated using the AmberTools xleap module ([72]https://ambermd.org/AmberTools.php), and the partial charges of the ligand were generated using an antechamber with a “bcc” charge model. The prepared systems were solvated using the three-site water (TIP3P) model in a rectangular box with 10.0 Å boundary conditions from the protein edges in all directions. The prepared systems were neutralized by adding the required number of counter ions. The steepest descent and conjugate gradient energy minimization methods were used to obtain the near-global state least energy conformations. The systems were equilibrated using “canonical (NVT) and isobaric (NPT)” ensembles for 1 ns. A modified Berendsen thermostat method was used in NVT equilibration to keep the volume and temperature (300 K) constant. A Parrinello–Rahman barostat was used to keep the pressure constant at 1 bar during NPT equilibration. In addition, the particle–mesh Ewald approximation was used with a cut-off value of 1 nm to calculate the long-range electrostatic, van der Waals, and coulomb interactions. Similarly, bond length was constrained using the LINear Constraint Solver method. Finally, the system was subjected to a 100-ns MD production run, and the coordinates were recorded every 2 fs. The trajectories produced were examined using the built-in gromacs tools. RMSD, RMSF, Rg, SASA, and H-bond were used to investigate the stability and fluctuations of ligand-protein interactions using MD simulation. 2.9 Molecular mechanics Poisson–Boltzmann surface area (MM-PBSA): Investigation of binding affinity In MD simulations and thermodynamic calculations, the relative binding energies were calculated using the MM-PBSA method using the “g_mmpbsa” tool ([73]Kumari and Kumar, 2014). The parameters from past research were considered while calculating the binding energy ([74]Bhandare et al., 2019; [75]Dwivedi et al., 2022; [76]Khanal et al., 2022). The binding energy was determined throughout the steady trajectory observed between 50 and 100 ns using 50 representative snapshots. Binding free energy obtained from MM-PBSA was represented in kJ/mol units. 2.10 Analysis of principal component Principal component analysis (PCA) investigates molecular motion using MD trajectories. The “least square fit” to the reference structure is used to eliminate the molecule’s translational and rotational motion. A set of eigenvectors that reflect the motion of the molecule is produced by diagonalizing a covariance matrix generated by a linear transformation of cartesian coordinate space. The energy contribution of each eigenvector to the motion is shown by the eigenvalue associated with that eigenvector. The “time-dependent motions” that the components carry out in a certain vibrational mode are demonstrated by projecting the trajectory onto a particular eigenvector. The atomic vibrational components’ contribution to this form of coordinated motion is shown by the projection’s temporal average. Using the built-in gromacs utility “g_covar,” the eigenvectors and eigenvalues of the trajectory were produced by computing and diagonalizing the covariance matrix. Additionally, the eigenvectors were examined and shown using the “g_anaeig” tool ([77]Amadei et al., 1993; [78]Van Aalten et al., 1995; [79]Amadei et al., 1996; [80]Bhandare and Ramaswamy, 2018). The least squares fit, gromacs inbuilt utility g_covar, and g_anaeig tools were used for PCA. 2.11 Statistical analysis All experiments were done in triplicate. All statistical analyses were done by analyzing two-variable data with a simple t-test and using a one-way analysis of variance (ANOVA). The value of p < 0.05 was considered statistically significant. The network was analyzed by the “Edge count” topological parameter. The docking score was represented in kcal/mol units. 3 Results 3.1 Cytotoxicity assay The cytotoxic effects of gymnemagenin were tested in 3T3L1 cells by MTT assay. Only metabolically viable cells convert tetrazolium salts to formazan dye by cellular enzymes. Thus, the amount of formazan dye formed directly correlates to the number of viable cells in the culture and can be measured in a spectrophotometer, whereas cells exposed to toxins will have decreased activity. At the highest concentration of gymnemagenin, 86.8% of the cells were viable ([81]Figure 1). FIGURE 1. FIGURE 1 [82]Open in a new tab Determination of cytotoxic effects of gymnemagenin in 3T3L1 cells. 3T3L1 cell viability upon gymnemagenin treatment was assessed by MTT assay. 3.2 Gymnemagenin promotes triglyceride metabolism in 3T3L1 cells To examine the effects of gymnemagenin on triglyceride metabolism, the triglyceride content of 3T3L1 adipocytes differentiated from gymnemagenin untreated and treated pre-adipocytes was measured by colorimetric estimation. The triglyceride content of the gymnemagenin-treated group was less than that of the untreated group on the fourth day of the differentiation stage ([83]Figure 2A) as well as on the eighth day of the differentiation stage ([84]Figure 2B), depicting hydrolysis of more triglyceride by Lpl into glycerol and free fatty acids due to induction of gymnemagenin. This was further confirmed by gene expression studies. FIGURE 2. FIGURE 2 [85]Open in a new tab Effect of gymnemagenin on triglyceride content of adipocytes. (A) Triglyceride content after gymnemagenin treatment on the 4th day of differentiation in 3T3L1 adipocytes. (B) Triglyceride content after gymnemagenin treatment on the 8th day of differentiation in 3T3L1 adipocytes. 3.3 Gymnemagenin improves lipid metabolism in 3T3L1 cells To elucidate the effect of gymnemagenin on lipid metabolism, we examined the expression of 12 genes with and without gymnemagenin treatment in 3T3L1 adipocytes. Twelve genes were categorized into five categories: lipolysis or triglyceride metabolism, adipocyte differentiation, adipokine/adipocyte function, lipid transport, and insulin signaling. [86]Table 2 describes the function of selected genes and their expression pattern in obesity and T2D. In addition, the expression pattern of those genes after gymnemagenin treatment has also been tabulated in [87]Table 2. TABLE 2. Effect of gymnemagenin on the 12 gene panel of lipid metabolism and T2D. Category Gene symbol Gene full name Specific function Expression pattern in obesity/T2D Expression pattern after gymnemagenin treatment References