Introduction

Type 2 Diabetes Mellitus (T2DM) is a chronic condition characterized by high blood glucose levels due to the impaired regulation of glucose by insulin1. The global prevalence of T2DM has risen dramatically in recent decades, driven by genetic predisposition and lifestyle factors, such as high-fat/high-glucose diets, sedentary behaviour, and chronic stress2. These factors contribute to persistent glucose spikes, leading to prolonged insulin secretion, pancreatic β-cell dysfunction, and insulin resistance, ultimately exacerbating disease progression3. Despite the availability of pharmacological interventions - including metformin, sulfonylureas, and GLP-1 receptor agonists - many patients experience side effects, limited efficacy in advanced stages, or treatment non-adherence due to cost, accessibility, and cultural preferences4. Consequently, there is growing interest in complementary and alternative medicines (CAM), particularly polymedicinal plant formulations, which are used traditionally in diverse medical systems worldwide5.

Medicinal plants have long been investigated for their antidiabetic properties, with recent studies highlighting their ability to regulate key metabolic pathways, enhance insulin sensitivity, and provide pancreatic β-cell protection6,7,8. For instance, Marsupsin, isolated from the polyplant product ABPA, was shown to rejuvenate insulin resistance in skeletal muscle cells by activating the IRS1-PI3K-AKT-GLUT4 pathway, underscoring its potential to improve glucose metabolism8. Similarly, several Thai medicinal plants demonstrated α-glucosidase inhibitory activity, supporting their role in glycemic control7. The traditional Thai formulation Mathurameha (MT) has exhibited promising glucose-lowering effects in preclinical models9,10,11. However, their translational potential for wider applications remains limited by insufficient mechanistic insights, a gap addressed by recent advances in systems and integrative pharmacology. For example, studies on complex medicinal plant anti-obesity formulations12,13 and the Tibetan complex Compound Ruteng14 employed molecular and computational approaches to dissect the operating mechanisms, offering frameworks for characterizing traditional medicinal plant-based therapies. Similarly, the cardioprotective effects of the Chinese Erzhi Pill against diabetic complications were examined through integrative methods15. These efforts highlight the critical need for comprehensive molecular characterization to bridge traditional knowledge with mechanistic assessments, ensuring the clinical relevance of medicinal plant-based interventions.

One such promising complex medicinal plant-based formulation is Mathurameha (MT), a Thai traditional medicine composed of 26 medicinal plants (Table S1). MT is currently prescribed in Thailand (300 mg, twice daily) for T2DM management, particularly for newly diagnosed patients with fasting blood sugar (FBS) levels below 220 mg/dL, obesity (BMI > 30), and individuals younger than 60 years old9,16. Preliminary studies indicated that an aqueous extract of MT exerts significant glucose-lowering effects in streptozotocin-nicotinamide (STZ-Na)-induced diabetic rats, outperforming ethanolic extracts in oral glucose tolerance tests (OGTT), and showed no acute toxicity on male and female rats at a dose as high as 5 g/kg b.w9. Previous studies in this laboratory demonstrated that MT mitigates high-glucose-induced endothelial dysfunction through the EGF/NO/IL-1β regulatory axis16. Furthermore, an ethanol extract of MT modulated adipose tissue morphology and prevented fat accumulation in vascular tissues, indicating a broader spectrum of bioactive metabolites than aqueous extract17. Therefore, it became important to elucidate the role of the MT ethanol extract components for their actions on the diverse aspects of the pathological progression of diabetes. This research approach is crucial to meet the need for a deeper understanding of how complex medicinal plant formulations interact and regulate cellular pathways and biological targets.

In this study, MT was systematically characterized applying a multi-modal molecular approach. Ethanol and aqueous extracts were prepared, followed by bioactivity-guided fractionation of the ethanol extract into polarity-based fractions, and LC-MS/MS QTOF metabolomic analysis was performed. These extracts and fractions were evaluated for α-glucosidase and α-amylase inhibition, glucose uptake enhancement in L6 myotubes and 3T3-L1 adipocytes, and their ability to stimulate insulin secretion and protect pancreatic β-cells from oxidative stress. Finally, network pharmacology analysis of the most bioactive fraction (FrE) was integrated with gene expression profiling to elucidate the key molecular targets. This study builds on a previous proteomic and in vivo investigation16,18, and aims to provide deeper mechanistic insights into the antidiabetic potential of MT, contributing to the science-based development of this traditional formulation.

Methods

Materials and instruments

Methanol, ethyl acetate, hexane, ethanol, and dichloromethane (AR grade, Thermo Fisher Scientific, Waltham, MA, USA) were used for extraction and fractionation. Water, formic acid, and acetonitrile (LC-MS grade, Sigma-Aldrich, Burlington, MA, USA) were used for the LC-MS-QTOF analysis. The mobile phase was filtered using membrane filters (Millipore, 0.45 µm pore size, Merck KGaA, Darmstadt, Germany), and MT extracts were filtered using syringe filters (0.45 µm Millipak Express filter and 0.22 μm membrane filter for particulate-free and bacteria-free water, Merck KGaA, Darmstadt, Germany), and a Büchi rotary vacuum evaporator (R-200, CH-9230 Flawil, Switzerland) were used.

Acquisition of the Mathurameha formulation

The medicinal plants listed in Table S1 were purchased from Ayurawet Wattana Medicine Limited Partnership, Phichit, Thailand, and stored at room temperature (ca. 27–30 °C). Quality control assessment was carried out using physico-chemical methods. Microscopic examination, loss on drying, moisture content, extractive values, water content, volatile oil content, total ash, acid-insoluble ash, heavy metal content and total plate count were assessed by Ayurawet Wattana Medicine Limited Partnership, Phichit, Thailand following the Thai Herbal Pharmacopoeia 2021 and supplement 2022 protocols. Specimens of the 26 medicinal plants (voucher nos. MPIC/MT/22/05–31) were deposited in the Medicinal Plant Innovation Centre, Mae Fah Luang University, Chiang Rai, Thailand. The plant materials were each thoroughly washed with water; shade dried, and ground to a powder using an electronic mixer grinder (Mitsubishi CM, Charatchai Machinery Ltd., Bangkok, Thailand). The powdered crude drugs were combined according to the established formula to obtain the MT formulation.

Extraction and fractionation

Powdered MT mixture (1.0 kg) was successively extracted for 24 h at room temperature with EtOH (3 × 1 L) and distilled water (3 × 1 L). Following filtration, the EtOH extracts were combined and evaporated under reduced pressure to afford the crude EtOH extract (104.4 g, 26%). The combined aqueous extracts were freeze-dried, yielding the aqueous extract (18.5 g, 9.3%). The extracts were stored at 4 °C until tested in the biological assays and the LC/QTOF-MS analysis. Given the promising results in the safety and glucose uptake assays (Figs. 1a-b), the crude EtOH extract was further fractionated. The crude EtOH extract (104.4 g) was dissolved in water (500 mL) and partitioned with hexane (500 mL, 3 times) to remove chlorophyll and the lipophilic constituents. The hexane extract was evaporated under reduced pressure to yield a hexane-soluble fraction (FrH) (1.99 g). The aqueous portion was further partitioned successively with dichloromethane, EtOAc, and n-butanol (each 500 mL). The extracts were evaporated under reduced pressure to yield a dichloromethane fraction (FrD) (1.54 g), an EtOAc fraction (FrE) (40.6 g), and a n-butanol fraction (FrB) (1.42 g) (see Supplementary Material, Scheme 1).

Fig. 1
figure 1

Experimental Assessment of antidiabetic potential of MT. (a) The effect of MT remedy extracts and fractions on the stimulation of glucose uptake on L6 myotube cells. One-way ANOVA followed by Tukey test; ** P < 0.001 compared with control, ## P < 0.001 compared with metformin. (b) INS-1 pancreatic β-cell viability after treatment with MT remedy extracts and FrE. Apoptosis assay using flow cytometry after staining with annexin V-FITC/propidium iodide (PI). INS-1 cells were treated with 250 µM H2O2 for 48 h to induce hypoxic stress mediated apoptosis. The effect of MT extracts and FrE in apoptosis protection on INS-1 cells. (c) Percentage of viable, early apoptotic and late apoptotic cells. (d) Representative scatter plots of PI (y-axis) vs. annexin V(x-axis).

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α-Glucosidase and α-Amylase Inhibition activity

The α-glucosidase and α-amylase inhibitory effects of the MT samples were assessed as described19. MT sample concentrations were 25, 50, 100, 250, and 500 µg/mL of the EtOH extract, water extract, FrD, FrE, and FrB. Acarbose (3.125–250 µg/mL) was utilized as the positive control for both assays as it inhibits both enzymes and is prescribed to manage postprandial hyperglycemia20.The absorbance reactions were measured using a microplate reader (EnVision, Perkin Elmer, Waltham, MA, USA). The IC₅₀ values were determined by plotting the inhibition percentages of the samples at various concentrations in an Excel spreadsheet and fitting a trendline to the data. The concentration at which the trendline indicates 50% inhibition corresponds to the IC₅₀ value.

Cell culture and treatment conditions

Three cell models were utilized: 3T3-L1 pre-adipocytes (ATCC® CL-173™): Cultured in DMEM (3 mg/mL glucose) supplemented with 10% FBS, 2 mM glutamine, and 1% penicillin/streptomycin at 37 °C under 5% CO₂. L6 myotubes (ATCC® CRL-1458™): Maintained in DMEM (10% FBS, 1% penicillin/streptomycin) at 37 °C in 95% air/5% CO₂. Differentiation into multinucleated myotubes was induced by switching to DMEM with 2% FBS at 80% confluence, with media changes every 48 h over 7 days. INS-1 pancreatic β-cells: Cultured as previously described19. Cells were treated with MT ethanol/water extracts or the polarity-based fractions (FrB, FrD, and FrE; 25–100 µg/mL) for 24 h. Metformin (CAS: 1115-70-4) (1-755 µM) served as a positive control.

Cell viability assay

Cells (4 × 10⁴/well in 96-well plates) were treated with MT samples for 24 h, followed by incubation with 0.5 mM MTT for 4 h. Formazan crystals were solubilized in DMSO (100 µL/well), and the absorbance was measured at 570 nm using a microplate reader (EnVision, Perkin Elmer). Viability was normalized to untreated controls20.

Glucose uptake in L6 myotubes

Differentiated myotubes were exposed to MT samples for 24 h, washed with Kreb’s Ringer buffer solution, and incubated with 80 µM 2-NBDG fluorescent glucose analog for 60 min. Fluorescence intensity (485/530 nm excitation/emission) was quantified to assess the glucose uptake.

Glucose consumption in 3T3-L1 adipocytes

Adipocytes were treated with the MT samples in glucose-enriched medium (3 mg/mL) for 24 h. Residual glucose was quantified using a glucose oxidase/peroxidase assay (510 nm absorbance). IC₅₀ values were derived from the dose-response curves.

Insulin secretion in INS-1 β-Cells

Cells were stimulated with 1000 mg/mL glucose post-MT treatment. Supernatants were analyzed using the AlphaLISA Insulin Detection Kit (Perkin Elmer), with luminescence measured at 615 nm.

Caspase-3 activity assay

H₂O₂-Stressed INS-1 cells (250 µM, 24 h) were lysed, and caspase-3 activity was quantified using Ac-DEVD-pNA substrate (CASP3C Kit, Sigma-Aldrich). Absorbance at 405 nm was proportional to enzymatic activity21.

Apoptosis detection through flow cytometry

H₂O₂-treated INS-1 cells were stained with Annexin V-FITC/PI (ANNEXIN V: FITC Kit, Bio-Rad) and analyzed on a DxFlex flow cytometer (Beckman Coulter). Apoptotic (Annexin V⁺/PI⁻) and necrotic (Annexin V⁺/PI⁺) populations were quantified using CyExpert 2.5 software22.

Statistical analysis

Data were analyzed in GraphPad Prism 8.0.1. All experiments were performed in triplicate. Statistical analysis was conducted using one-way ANOVA followed by Tukey’s post hoc test, with p-values < 0.05 considered significant.

LC/QTOF-MS analysis

The phytochemical constituents of the MT extracts were investigated using a LC-MS-QTOF-ESI-MS instrument (Agilent 6500 Series, Santa Clara, CA, USA). A small amount of the mobile phase ACN-H2O (1:1%v/v) was used to dissolve and dilute all samples to a concentration of 200 µg/mL.Before chromatographic analysis, the material was re-filtered through a 0.22 m PTFE membrane syringe filter. The chromatographic separation was conducted using an Agilent Zorbax Eclipse Plus - C18 column (2.1 × 50 mm, 1.8 µm) eluting with water containing 0.1% formic acid (A) and acetonitrile (0.1% formic acid) (B) as the mobile phase. The flow rate was 1 mL/min: 5% mobile phase B for one minute, increasing linearly to 17% B in 13 min. The eluent was increased to 100% mobile phase B within 22 min, and the eluent composition was then held steady for two minutes before being reduced to 5% mobile phase B over two minutes. Both positive and negative ion modes were used in the mass range m/z 100–1000 amu. Identification of the chromatographic peaks was performed by matching retention times and mass spectra to compounds in the library using the PCDL database (TCM-database, phenolic acids, and Pubchem chemical database) with a high accuracy (error < 5 ppm). MS/MS fragment matching was performed for each identified component23. Mass Profiler Professional (Agilent, Santa Clara, CA, USA) was used to perform chemometric differential MS-data analysis to derive the relationship between the extracts and their bioactivity. The retention times (RT), predicted and measured errors (in ppm), m/z values, chemical formulae, sigma values (an exact numerical comparison between the theoretical and measured isotope patterns), and MS/MS fragmentation patterns were used to identify the chemical components. Molecular feature extraction was applied to determine the metabolites in the EtOH extract (Pos 1389, Neg-656), the H2O extract (Pos-558, Neg-1527), and FrE (Pos-248, Neg-150). Compounds were identified using the height of 100,000, RT > 3.3, and a mass error of < 5 ppm to confirm their elemental composition.

Network Pharmacology analysis

Metabolites identified as present in the FrE fraction were subjected to target prediction using BindingDB (threshold 0.7)24SwissTargetPrediction (> 0.1)25, and LigTMap26. Human target IDs with UniProtKB references were retrieved. SwissADME27 analysis assessed oral bioavailability and druggability (Table S4). Venny 2.1 was used for Venn diagram analysis. The corresponding targets identified were searched in DisGeNet28 to identify 701 unique diabetes-associated genes. Protein-protein interaction (PPI) analysis was performed using STRING, and hub-module topology was examined through NetworkAnalyst (http://www.networkanalyst.ca)29,30. Functional annotation of molecular targets was assessed through DAVID 6.831, incorporating pathways from Gene Ontology, KEGG32,33and BioCarta34. Network enrichment was analyzed using MCODE35,36with scoring based on physical interactions from Biogrid37,38. Cytoscape was employed for network visualization, mapping hierarchical target relationships and protein-protein interaction linkages39,40.

Quantitative PCR for gene expression analysis

Based on the network analysis results, PCR analysis was carried out to investigate the expression of genes in glucose metabolism. L6 cells were treated with MT extracts and metformin (100 µg/mL). mRNA (1 µg) was transformed to cDNA using a cDNA synthesis kit. PCR was performed using specific primers (Fig. 7). The amplified cDNA products were separated electrophoretically using a 1% agarose gel. Normalization with the GAPDH gene as the internal control was used to calculate the relative expression level of a target gene. The ImageJ program (National Institutes of Health, Bethesda, MD, USA) was used to quantify the gene expression.

Results

In vitro antidiabetic activity of MT extracts and fractions

Glucose control in diabetes involves inhibiting α-glucosidase and α-amylase to reduce postprandial glucose, enhancing muscle glucose consumption, increasing insulin secretion, and protecting pancreatic β-cells from oxidative damage. The EtOH and aqueous extracts, along with fractions obtained from the EtOH extract, the hexane (FrH), dichloromethane (FrD), ethyl acetate (FrE), and n-butanol (FrB) fractions, were evaluated for their antidiabetic effects using the abovementioned assays.

Enzyme Inhibition assay

The results presented in Table 1 showed that the FrE and EtOH extracts exhibited the lowest IC₅₀ values for inhibiting α-glucosidase and α-amylase, respectively. All of the MT samples and acarbose inhibited α-glucosidase more effectively than α-amylase, except for the aqueous extract and FrH, which showed weak or no inhibition of α-amylase. These observations indicate that the constituents in these extracts do not interact with α-amylase. The superior inhibition of α-glucosidase aligns with previous reports that α-amylase inhibition is limited by poor ligand binding affinities to high molecular weight ligands compared to α-glucosidase41.

Table 1 α-Glucosidase and α-amylase inhibitory activities of MT samples.
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Glucose consumption and uptake assay

In the glucose consumption and uptake assays (Tables 2 and 3), high viability of 3T3-L1 (81.4–83.9%) and L6 myotubes (86.2–97.7%) was observed when exposed to MT samples for 24 h, indicating the absence of acute cytotoxic effects. Among all the MT samples, FrE exhibited the most potent effects on glucose uptake and consumption. In the glucose consumption assay, measured using the GOD-POD assay, FrE inhibited 50% of glucose consumption by 3T3-L1 adipocytes at a concentration of 6.78 µg/mL, followed by the aqueous extract. The inhibition effects of both were better than metformin (Table 2). In the glucose uptake assay, all MT samples increased glucose uptake in L6 myotubes compared to control (Table 3). FrE significantly enhanced glucose uptake beyond metformin (Fig. 1a), whereas the aqueous and EtOH extracts showed lower activity than metformin.

Table 2 Effect of MT extracts and fractions on cell viability and glucose uptake in 3T3-L1 cells.
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Table 3 Effect of MT extracts and fractions on cell viability and glucose consumption of L6 myotubes.
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Insulin secretion and β-Cell protection

Based on the promising effects observed in Tables 1, 2 and 3, the insulin secretion activities of the aqueous extract, the EtOH extract, and the FrE fraction were examined. Table 4 shows that the MT samples increased insulin secretion compared to the control, metformin. FrE enhanced insulin secretion to 2512.11 ± 26 µIU/mL, comparable to metformin (p > 0.05). To assess pancreatic β-cell protection, hypoxic stress was induced in INS-1 β-cells using 250 µM of H₂O₂, followed by treatment with the aqueous extract, the EtOH extract, and FrE for 24 h. Flow cytometry results showed that FrE significantly reduced H₂O₂-induced apoptosis (Figs. 1b-d). Furthermore, caspase-3 activation, a key indicator of apoptosis, was quantified by measuring p-nitroaniline (pNA) release after hydrolysis of the Ac-DEVD-pNA substrate (Table 5). FrE significantly reduced caspase-3 activity (30.65 ± 3.54%), though its protective effect was less than that of metformin (65.06 ± 2.32%). FrE exhibited superior anti-apoptotic effects compared to the EtOH and aqueous extracts, as shown by partial caspase-3 inhibition in INS-1 β-cells under H₂O₂-induced hypoxic stress (Table 5). These results suggest that MT extracts, especially FrE, protect against caspase-3-mediated apoptosis in pancreatic β-cells.

Table 4 The effects of MT aqueous extract, the EtOH extract, and FrE on insulin secretion.
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Table 5 The effects of MT aqueous extract, EtOH extract and FrE on caspase-3 activity.
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Phytochemical characterization of the MT aqueous extract, the EtOH extract, and FrE by LC-MS-QTOF chemometrics

Based on the biological assays results, the LC-MS/MS analyses of the H2O and EtOH extracts, and FrE were carried out. Table 6 summarizes the LC-MS/MS parameters of the metabolites from FrE, while the compounds present in the EtOH and H2O extracts are summarized in Tables S2-S3, Figure S2-S5. The Total Ion Chromatograms (TIC) of the MS spectra of FrE are presented in Fig. 2a. where the metabolites were identified (numbered as Table 6) using the height of 100,000, RT > 3.3, and a mass error of < 5 ppm to confirm their elemental composition. The metabolites which represented artefact-matching scores from non-natural sources and synthetic fragments were identified manually from PubChem, Scifinder, and ChemSpider, and were not considered for further network pharmacology analysis.

Table 6 The 34 metabolites identified from the LC-MS/MS-QTOF analysis of the Ethyl acetate fraction (FrE) of the ethanolic extract of Mathurameha (MT).
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Fig. 2
figure 2

Metabolite profiling of MT. (a) Total Ion Chromatogram (TIC) of different extracts and FrE (EtOAc fraction of EtOH). (b) Venn diagram analysis of compounds identified by LC-MS/MS different extracts and fractions. (c) Hierarchical clustering of the metabolites from different extracts based on different bioactivities.

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Chemometric analysis of the MS data was performed for the MS-based differential analysis to determine the relationships of metabolites in the EtOH and H2O extracts and FrE with the bioactivities evaluated in this study. The bioactivities are glucose consumption, insulin secretion, and caspase-3 inhibition. The Venn diagram (Fig. 2b) analysis showed that 12 compounds were common to all of the extracts, whereas 10 compounds in the FrE and 19 compounds in the H2O extract were extractive specific. Hierarchical clustering (Fig. 2c) of the compounds from the different extracts was done based on their normalized intensity values with sample variability- CV < 25.0. Clustering was conducted on different experiments measuring the similarity based on Euclidean distancing following the Wards linkage rule for class prediction. The clustering results showed that kushenol A, gallic acid, ellagic acid, coumurrayin, and neoandrographolide contributed to the bioactivity of FrE and the aqueous extract.

Network Pharmacology identifies the MT constituents targeting diabetes-related proteins

Network pharmacology analysis revealed significant binding similarities between the FrE constituents and the diabetes-associated targets (Fig. S1). A Venn diagram (Fig. 3a) highlighted the FrE targets overlapping across the BindingDB, SwissTargetPrediction, and LigTmap databases. The target-compound network (1844 edges, 745 nodes) identified hub proteins including MAPK14 (degree = 20), CA2 (20), PPARG (15), GSK3B (11), AMY2A (10), and CASP3 (6). Kinases, hydrolases, and G-protein-coupled receptors emerged as prominent targets, with key interactions observed for kushenol A, clerodin, and neoandrographolide. Notably, cinnamic acid, gallic acid, and strictinin targeted AMY2A, aligning with FrE’s α-amylase inhibitory activity41. SwissADME-based ADME profiling (Fig. 3b, Table S4) indicated favorable drug-likeness for most metabolites (average bioavailability score = 0.6; Lipinski violations = 0.16). Compounds such as gentianose and arctiin exhibited lower bioavailability and higher violations (Ghose = 1.54, Muegge = 1.02), suggesting reduced druggability. FrE constituents, including ethyl cinnamate and gallic acid, interacted with carbonic anhydrase isoforms (CA1, CA2, CA4, etc.), implicated in hepatic gluconeogenesis42,43. Tankyrase1 (TNKS1), a regulator of GLUT4-mediated insulin sensitivity, was also targeted, suggesting modulation of adipocyte glucose uptake44. The PPI-enriched pathways are shown in Table 7. The low p values in Table 7 (< 0.05) indicating the enrichment of the KEGG pathway32,33 or protein-protein interaction (PPI) network is unlikely to have occurred by chance, while a low FDR value (< 0.05) indicated that the results are reliable and that the false discovery rate is low. Pathways showing highly significant results include the MAPK signalling pathway (p-value = 1.29E-28), the FoxO signalling pathway (p-value = 8.09E-19), the HIF-1 signalling pathway (p-value = 9.65E-19), the PI3K-Akt signalling pathway (p-value = 1.09E-18), and the insulin signalling pathway (p-value = 2.87E-16).

Fig. 3
figure 3

Target Space Analysis and druggability of MT metabolites and targets. (a) Venn diagram of targets with diabetes-associated target space. (b) Radial graph of ADME profile. (c) Target-PPI–disease network showing the interaction between the Target proteins to diabetes and associated targets through protein-protein interaction analysis. (d) Pathway enrichment score plot. (e) Target-pathway network showing the targets in red circles and pathways in light orange sized based on enrichment. The edges represent separate pathways.

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Table 7 Enrichment of PPI KEGG pathways by MT (FrE).
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STRING-based PPI analysis (Fig. 4a) revealed enrichment in insulin signalling (GO:0032869), glucose transport, and catalytic activity. MCODE clustering identified critical hubs (PIK3CA, INSR, IGF1R) linked to the NRF2-ARE and PI3K/AKT pathways (Figs. 4c-d). Figures 4b-d show the enriched network clusters based on enrichment scores of pathway terms and genetic ontology terms which were used in the network of pathways and process enrichment analyses. The enriched terms were clustered according to their kappa scores with a similarity > 0.3, and nodes reflecting significant p-values from each of the top clusters are connected by edges. The top clusters of the enriched terms are shown in Table 8, where “Count” is the number of query genes associated with each ontology term. The number for “%” represents the percentage of all query genes that are present in the specified ontology term. The multi-test adjusted p-value in log base 10 is known as “Log10(q)” and is computed using the previously mentioned procedures. The term “GO:0043408 responses to peptide” refers to any process that causes a change in the state or activity of a cell, including movement, secretion, enzyme production, and gene expression, in response to a peptide stimulus that may be insulin-mediated. GO:1,901,652 denotes the regulation of MAPK, which is important for promoting the transcription of the genes encoding the glucose transporters, GLUT1 and GLUT445. P38/MAPK also boosts insulin-dependent glucose transport and increases glucose transfer that is insulin-independent45. PGC1 is induced by p-38/MAPK, which also increases oxidative metabolism, by co-activating mitochondrial biogenesis and activity. Interestingly, the “energy sensors” of the cell, AMPK and p38/MAPK, are co-expressed in the contracting skeletal muscle. The combined actions of these two kinases boost glucose import through a different insulin-dependent mechanism46. Experimental validation confirmed FrE-induced upregulation of IRS and AKT in L6 myotubes (Figs. 5a-b) and INS-1 cell protection (Fig. 1b), supporting insulin signalling restoration through TCPTP inhibition. DAVID functional clustering (24 clusters, highest score = 7.2) highlighted PI3K-AKT, mTOR, and AMPK pathways (Figs. 6a-f) using KEGG databases32. Key targets (PIK3CA, AKT2, GSK3B) interacted with 16-acetylgitoxigenin, ellagic acid, and clerodin, modulating GLUT4-mediated glucose uptake. Crosstalk between AMPK and PI3K-AKT signalling was linked to reduced gluconeogenesis (through PTPN1) and improved insulin sensitivity47,48.

Fig. 4
figure 4

Functional Association Enrichment Analysis of MT targets. (a) Protein-Protein interaction Network of targets related to diabetes interacting with MT molecules the borders in pie format show functional annotation. (b) Network of enriched terms colored by cluster ID, where nodes that share the same cluster ID are typically close to each other (BP), molecular functions (MF), and cellular processes (CC). (c) Network of enriched terms colored by p-value, where terms containing more genes tend to have a more significant p-value. (d) Protein-protein interaction enrichment analysis of MT and MCODE components identified in the gene lists. (e) Gene-functional annotation heatmap.

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Table 8 Top 20 clusters with their representative enriched terms associated with the MT target proteins involved in diabetes.
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Fig. 5
figure 5

MT induces insulin mediated glucose transport related gene expression in L6 myotubes. (a) Gel doc representation of expression of glucose metabolism genes on L6 myotubes after treating with MT extracts, fractions, and metformin at 100 µg/mL showing GLUT4, AMPK, IRS, PI3K and AKT gene expression using GAPDH as the internal control. (b) The relative expression of insulin signalling related genes accessed by image intensity analysis expressed as fold change compared to control. FrE represents the EtOH extract partitioned with EtOAc.

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Fig. 6
figure 6

Pathway Analysis of MT targets associated with T2DM. (a) PI3K-AKT signalling pathway. (b) mTOR signalling pathway. (c) MAP-kinase signalling pathway. (d) Insulin resistance pathway. (e) Diabetic cardiomyopathy (f) AMPK pathway. The red nodes in the pathway represent targets which interact with MT molecules.The KEGG pathway maps were obtained from KEGG database (https://www.kegg.jp/kegg/pathway.html) rendered using Pathview.

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Fig. 7
figure 7

Combination Synergy Network Analysis based on neighbourhood approach. (a) Multi-molecular combination network of compound-target-disease against diabetes. (b) Multi-molecular combination network of compound-target-disease against diabetes with cardiac protection mechanism.

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PCR determination of the MT-dependent regulation of glucose metabolism gene expression

Based on the in silico prediction that the MT fractions regulate glucose metabolism through modulation of PI3K-AKT/AMPK/GLUT4 signalling, quantitative PCR gene expression analysis of GLUT4, AMPK, PI3K, AKT, and IRS in MT-treated L6 cells was performed to test this hypothesis. The results showed that L6 myotubes had high levels of GLUT4 and AMPK expression after treatment with the MT extracts, and FrE provided the most significant up-regulation. In addition, up-regulation was observed for IRS, PI3K, and Akt gene expression in cells treated with MT extracts (Figs. 6a,-b).

Discussion

The antidiabetic potential of the MT extracts, particularly the ethyl acetate fraction (FrE) of the ethanol extract, lies in its broad capacity to simultaneously target enzymatic, metabolic, and cellular pathways critical for diabetes pathogenesis. Building on recent advances in this laboratory16,17 in elucidating the therapeutic mechanisms of traditional medicinal plant formulas, this study demonstrates that FrE outperforms conventional therapies, like metformin and acarbose, by integrating α-glucosidase inhibition, glucose transport modulation, β-cell protection, and insulin signalling enhancement and represents a multi-target strategy essential for addressing the multifactorial nature of diabetes. Previous studies, including Aluksanasuwan et al.. (2024)16 and Somsuan et al.. (2024)17established a foundation for this investigation by employing SWATH-proteomics to decode the vascular protective effects of MT, through the EGF/NO/IL-1β axis. These studies revealed how medicinal plant mixtures can restore endothelial homeostasis in hyperglycemic environments, bridging in vitro pathway dissection16 with the in vivo evaluation of cardiovascular protection in diabetic rats17. Here, this translational framework is applied to MT, leveraging its structural diversity and multi-target engagement to regulate glucose metabolism and mitigate diabetic complications. By combining high-resolution proteomics, network pharmacology, and functional assays, this work begins to address a critical gap in diabetes research, namely the need for therapeutic regimens that can holistically target both hyperglycemia and its systemic sequelae, while advancing the integration of traditional medicine into evidence-based, precision therapeutics.

Significance of enzyme Inhibition by MT in diabetes control

The in vitro findings establish that the MT extract exhibits holistic antidiabetic effects, surpassing metformin and acarbose by influencing multiple glucose regulation pathways. The α-glucosidase and α-amylase inhibition profiles of the MT extracts highlight their role in postprandial glucose control. Since α-glucosidase, primarily located in the small intestine, facilitates glucose absorption, its inhibition significantly impacts postprandial glucose levels more than α-amylase inhibition, which occurs in saliva and the pancreas43,44. FrE exhibited potent α-glucosidase inhibition (IC₅₀ 0.3 µg/mL), surpassing α-amylase inhibition, aligning with its role in mitigating postprandial hyperglycemia. The superior activity of FrE highlights its potential to reduce post-meal glucose spikes with fewer off-target effects, a critical advantage over broad-spectrum glucose inhibitors like acarbose.

MT modulate glucose uptake and consumption to modulate insulin sensitivity

Glucose uptake and consumption assays emphasize the ability of a fraction-enriched extract (i.e., FrE) to enhance glucose disposal, thereby reducing the risk of insulin resistance. FrE significantly enhanced glucose uptake in L6 myotubes (3.67 ± 0.23-fold vs. control) and suppressed adipocyte glucose consumption (IC₅₀ 6.78 µg/mL), suggesting the dual benefits of promoting peripheral glucose utilization while limiting lipid accumulation in adipose tissue, a key driver of insulin resistance46. Notably, FrE exceeded metformin in glucose uptake efficacy, implying insulin-mimetic or sensitizing effects. This aligns with its upregulation of the GLUT4, AMPK, and PI3K/AKT pathways in L6 cells, which are central to skeletal muscle glucose homeostasis mechanism48.

MT in pancreatic β-Cell protection and insulin secretion

INS-1 β-cell protection assays assessed MT’s therapeutic potential in diabetes management. Oxidative stress-induced β-cell apoptosis is a key factor in Type 2 Diabetes Mellitus (T2DM) progression. The ability of FrE to protect β-cells against H₂O₂-induced apoptosis through caspase-3 inhibition highlights its role in β-cell preservation. Additionally, the enhancement of insulin secretion by FrE suggests a direct stimulatory effect on β-cell function, further supporting its antidiabetic potential. FrE protected INS-1 β-cells from H₂O₂-induced apoptosis (30.65 ± 3.54% caspase-3 inhibition) and stimulated insulin secretion (2512.11 ± 26 µIU/mL), comparable to metformin. These effects are mediated through the PI3K/AKT and NRF2-ARE pathways, which mitigate oxidative stress and restore β-cell function49,50,51,52. The dual capacity to enhance insulin secretion while preserving β-cell viability also addresses a critical gap in current therapeutic options, which often fail to halt β-cell degeneration in T2DM.

MT targets Multi-Target networks against diabetes

Unlike single-target drugs, such as metformin (primarily activating AMPK) and acarbose (inhibiting α-glucosidase), MT as a complex of 26 medicinal plants and innumerable metabolites, exhibits multi-target mechanisms of action. It enhances glucose uptake, stimulates insulin secretion, protects β-cells, and inhibits digestive enzymes, offering a comprehensive strategy for blood sugar regulation. These findings position MT as a promising multifunctional antidiabetic agent, necessitating further in vivo and clinical studies to establish translational potential. Network analysis identified the interactions of FrE with 745 nodes, including hub targets like MAPK14, CA2, PPARG, and GSK3B. Carbonic anhydrase isoforms (CA1, CA2, and CA4) and TNKS1 emerged as novel targets, linking MT to hepatic gluconeogenesis suppression and GLUT4-mediated insulin sensitivity47. Key metabolites, including gallic acid, ellagic acid, and 16-acetylgitoxigenin, modulated the PI3K/AKT/GLUT4 and AMPK/mTOR pathways, synergistically enhancing glucose uptake53 and suppressing gluconeogenesis54,. Crosstalk between these pathways restores energy homeostasis, a hallmark of effective antidiabetic agents49,50,51,52. SwissADME analysis signifies drug-likeness for most FrE metabolites (bioavailability score: 0.6; Lipinski violations: 0.16). However, compounds like gentianose and arctiin showed reduced bioavailability, guiding future optimization efforts. The structural diversity of the MT metabolites enables multi-target engagement, circumventing the biological limitations of single-agent therapies.

Findings from this study align with prior research on the 26 medicinal plants comprising the Wang-Nam-Yen preparation, which demonstrated antidiabetic activity through the terpenoid and flavonoid constituents55. Network analysis identified MT bioactive metabolites targeting multiple diabetes-related pathways, including diabetic retinopathy, nephropathy, and cardiovascular diseases24. The multi-molecular combination network (MCN) of MT (Fig. 7a) reveals interactions with 14 diabetes-related sites through 22 metabolites. Compounds such as rutamarin, arctiin, 16-acetylgitoxigenin, and glyuranolide interact with key glucose metabolism regulators, including GCK, INS, TNF, and PPARG56. Rutamarin’s interaction with protein tyrosine phosphatase 1B (PTP1B) enhances insulin-induced GLUT4 translocation through the PI3K/AKT pathway, increasing glucose uptake. Additionally, network mapping identified cardio-protective interactions involving phenolics such as gallic acid, ellagic acid, and neo-andrographolide, which target ACE, TNF, MMP9, and GSK3B - key mediators of diabetic cardiomyopathy. Clinical studies associate T2DM with elevated MMP-7 and MMP-12 levels, contributing to atherosclerosis and coronary events49,50,51,52. Network analysis suggests that MT may mitigate diabetes-associated cardiomyopathy by modulating MMP9, PIK3CA, AGTR1, ACE, INSR, AKT2, PTEN, PRKCB, RELA, and GSK3B. The enrichment of the MAPK, FoxO, HIF-1, and PI3K-Akt signalling pathways (Table 7) suggests that MT (FrE) targets critical hubs governing cellular proliferation, stress adaptation, and metabolic regulation. The prominence of MAPK signalling in KEGG32 pathways (Table 7) and GO clusters (Table 8) aligns with its established role in insulin resistance and inflammation, key drivers of diabetes pathogenesis. Similarly, the PI3K-Akt and FoxO pathways, central to glucose homeostasis and apoptosis, further implicate MT in modulating insulin signalling and β-cell survival.

The significant enrichment of pathways like “endocrine resistance” and “AGE-RAGE signalling in diabetic complications” (Table 7) highlights the potential intersections between diabetes progression and therapeutic resistance or oxidative stress. This is reinforced by the cluster “chemical carcinogenesis - reactive oxygen species” (Table 8), linking oxidative damage to diabetic complications. The association of lipid metabolism and carbohydrate processes (Table 8) underscores the potential role of MT in addressing metabolic dysregulation, another hallmark of diabetes.

Clusters such as “regulation of hormone levels” and “response to peptide” (Table 8) may reflect MT’s influence on insulin and incretin signalling, while terms like “negative regulation of response to external stimulus” suggest protective mechanisms against stressors exacerbating diabetes. The overlap between the enriched pathways (e.g., MAPK and AMPK) and disease-specific processes (e.g., cardiac hypertrophy) implies systemic effects, possibly connecting diabetes to cardiovascular comorbidities. The statistical robustness of these findings (e.g., FDR < 1E-14 for top pathways) strengthens confidence in the multi-target engagement capacity of MT. However, further experimentation is needed to establish causality. Collectively, these results position the multimetabolite composition of MT as a modulator of interconnected signalling networks influencing diabetes progression, and offering insights for therapeutic strategies targeting metabolic, inflammatory, and stress-response pathways.

Mechanistic insights: PI3K/AKT/GLUT4 signalling and insulin sensitivity

The PI3K/AKT/GLUT4 pathway plays a crucial role in glucose metabolism, enhancing glucose consumption and alleviating insulin resistance in obesity-related diabetes8. Skeletal muscle glucose uptake, primarily mediated by GLUT4, is essential for systemic glucose homeostasis. Upregulation of GLUT4, AMPK, AKT, and INS mRNA expression supports increased glucose uptake through the insulin and AMPK pathways. Activation of the IRS1-PI3K-AKT-GLUT4 pathway in L6 cells further supports reduced insulin resistance and enhanced glucose absorption. Up-regulation was observed for IRS, PI3K, and Akt gene expression in cells treated with MT extracts (Figs. 6a,-b). Based on this evidence, the cells showed enhanced glucose uptake through the insulin and AMPK pathways. MT extracts might therefore enhance glucose uptake, which is then translocated into the cells through the GLUT4 receptor, and subsequently metabolized through AMPK activation56. AMPK stimulation promotes the AMP/ATP ratio and increases intracellular calcium levels57. The AMPK domain regulates glucose uptake by TBC1D4 and TBC1D1 phosphorylation, this allows Rab proteins and glucose transport 4 (GLUT4) translocation, improving glucose uptake into the cells58. Interestingly, insulin plays a critical role in glucose regulation59. In addition, insulin secretion is activated by the insulin receptor (IRS) PI3K and AKT54. This correlates with the high level of expression of these genes in MT-treated L6 cells in this study. The glucose uptake by skeletal muscle cells, and the upregulation of GLUT4, AMPK, AKT, and IRS gene expression after MT treatment, similar to that of metformin, suggests a beneficial role of the MT metabolites in overcoming insulin resistance and diabetic complications by modulating the PI3K-AKT/AMPK/GLUT4 signalling cascade48.The experimental observations align with network pharmacology predictions, which indicate enrichment in insulin secretion (GO:0030073) and PI3K/AKT pathway involvement. Prior studies on β-cells subjected to STZ-induced damage suggest that the PI3K/AKT/insulin pathway plays a protective role against apoptosis60. Moreover, the NRF2 pathway, known for regulating oxidative stress, modulates apoptosis through enzyme activity, receptor signalling, and ROS reduction61. Enrichment of the NRF2-ARE regulatory network (WP4357) and insulin signalling (WP481) highlights the antioxidative and insulin-preserving mechanisms of MT extracts supported by previous reports which support Nrf2 activation through the actions of metabolites such as naringin and epigallocatechin-3-gallate62.

AMPK/PI3K-AKT crosstalk and energy homeostasis restoration

The results also reveal that the structural diversity of the metabolites in MT are operating at multiple sites and mechanisms of action to combat T2DM. Metformin increases glucose uptake in L6 myotube skeletal muscle cells8. This suggests that a combination of MT bioactive metabolites, i.e., gallic acid, kushenol A, 7-O-methylaromadendrin, paeonol, and astilbin may be more effective than a single compound, metformin, through a multi-combinatorial homeostatic mechanism of action49,50,51,52. The MT metabolites interact with TNFRSF1A, TNF, INSR, AKT2, CASP3, PRKCB, and RELA, which are all linked to MAP kinase signalling. Compound analysis further reveals that MT metabolites, particularly glyuranolide, interact with TNF, influencing insulin resistance. The target compound network highlights the AMPK pathway interactions (Fig. 5f), including PPARG engagement by bioactives such as schisanlactone E, kushenol A, muricatacin, 16-acetylgitoxigenin, and gallic acid. AMPK signalling enhances insulin sensitivity, increases GLUT4 expression, and stimulates glucose uptake in muscle cells while concurrently inhibiting mTOR to promote autophagy and mitigate insulin resistance. The crosstalk between the AMPK, PI3K-AKT, and insulin pathways suggests a metabolic shift that restores energy homeostasis. PPI and pathway enrichment analyses revealed the role of MT in mitigating diabetic cardiomyopathy through the modulation of MMP9, TNF, and AGTR1. The AGE-RAGE and HIF-1 signalling pathways, enriched in cells treated with FrE, further underscore its potential to address vascular and metabolic comorbidities.

The superior efficacy of FrE over metformin in glucose uptake assays, coupled with its β-cell protective effects, positions MT as a holistic alternative or adjunct to current therapies. The enrichment of the MAPK, FoxO, and PI3K-AKT pathways in proteomic networks (p < 0.05; FDR < 0.05) mirrors clinical T2DM signatures, suggesting translatability. However, in vivo assessment is critical to examine these effects in physiological contexts of metabolic dysregulation. In vivo studies will be a key focus of future studies and will also prioritize phosphoproteomics and single-cell transcriptomics to resolve cell-type-specific signalling dynamics through longer-term in vivo studies assessing the impact of FrEon diabetic cardiomyopathy and nephropathy.

Conclusions

This study provides a comprehensive molecular characterization of the antidiabetic potential of Mathurameha (MT) extracts and fractions. The ethyl acetate fraction (FrE) demonstrated the most potent activity, exhibiting significant α-glucosidase inhibition (IC₅₀ 0.3 µg/mL) and enhancing glucose uptake in L6 myotubes (3.67 ± 0.23-fold) and 3T3-L1 adipocytes (IC₅₀ 6.78 µg/mL). Additionally, FrE stimulated insulin secretion (1.42-fold), comparable to metformin (1.46-fold) at 100 µg/mL in INS-1 pancreatic β-cells. Notably, FrE protected INS-1 cells against H₂O₂-induced apoptosis (30.65 ± 3.54%) through partial caspase-3 inhibition, demonstrating superior cytoprotective effects compared to metformin (65.06 ± 2.32%).

Metabolomic profiling (LC-MS-QTOF) identified 73 known bioactive metabolites across the aqueous and ethanol extracts and the FrE fraction, with compounds such as ellagic acid, kushenol A, gallic acid, arctin, neoandrographolide, astilbin, paenol, muricatacin, coumarrayin, and zingerone playing key pharmacological roles. Network pharmacology and pathway enrichment analyses identified critical molecular targets (GSK3β, GLUT4, PPARG, INSR, AKT2, CASP3, and MMP9) and highlighted the involvement of key pathways, including MAPK, FoxO, HIF-1, and PI3K-Akt, suggesting that MT modulates the PI3K-AKT/AMPK/GLUT4 signalling axis. Gene expression studies in L6 myotubes treated with FrE further demonstrated significant upregulation of GLUT4, AMPK, IRS, PI3K, and AKT genes, reinforcing its role in glucose metabolism regulation.

Future research should focus on developing standardized formulations enriched with the most bioactive metabolites to optimize therapeutic efficacy. INS-1, L6, and 3T3-L1 are standard in vitro models; however, future work will involve human-derived organoids and preclinical mouse models to increase translational relevance In vivo assessment through pharmacokinetic studies, RT-PCR, Western blotting, and omics-based approaches (phosphoproteomics and transcriptomics) will further elucidate molecular mechanisms of MT. Sustainable sourcing of the 26 MT components is a practical challenge. Cultivation and quality assurance strategies are currently being explored to address this vital issue. These concerns are designed to ensure the long-term quality, safety, efficacy, consistency, and availability of MT-based interventions for type 2 Diabetes Mellitus.