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Article

Integration of In Vitro Glucose Utilization, Metabolomics and Network Pharmacology Strategy to Explore Antidiabetic Mechanisms of Gunnera perpensa and Erythrina zeyheri Extracts

by
Oyinlola Oluwunmi Olaokun
Department of Biology and Environmental Science, Sefako Makgatho Health Science University, Molotlegi Street, Ga-Rankuwa, P.O. Box 139, Medunsa, Pretoria 0204, South Africa
Drugs Drug Candidates 2025, 4(4), 51; https://doi.org/10.3390/ddc4040051
Submission received: 8 October 2025 / Revised: 7 November 2025 / Accepted: 10 November 2025 / Published: 14 November 2025
(This article belongs to the Section Drug Candidates from Natural Sources)
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Abstract

Background: Type 2 diabetes mellitus (T2DM) is a complex metabolic disease requiring multi-targeted therapeutic strategies. Gunnera perpensa and Erythrina zeyheri are traditionally used in diabetes management, but their mechanisms remain poorly understood. Methods: This study used in vitro, metabolomics, and network pharmacology approaches to elucidate their antidiabetic potential. Leaf extracts were screened for glucose utilization in C2C12 cells, and cytotoxicity in Vero cells. Metabolites profiled via GC×GC-TOF-MS and those retrieved from Phytochemical Interaction Database were evaluated for drug-likeness and target prediction using SwissADME and SwissTargetPrediction. Diabetes-related targets were obtained from databases, and overlapping targets were used to construct interaction networks using Cytoscape and STRING. Functional enrichment analyses were conducted via DAVID for GO and KEGG pathways. Results: G. perpensa acetone and methanol extracts enhanced superior glucose utilization (IC50 = 78.5 and 94.8 µg/mL, respectively), with low cytotoxicity (LC50 > 600 µg/mL). Key compounds including arabinose, identified from both plants, showed multi-target binding potential against STAT3, PIK3RI and JAK2. Enrichment analyses revealed pathways related to insulin signaling, inflammation, and glucose metabolism. Conclusions: This study supports the therapeutic relevance of phytochemical synergy in the traditional use of both plants and demonstrated systems-level approaches for elucidating complex drug–target interactions in T2DM.

1. Introduction

Type 2 diabetes mellitus (T2DM) is a chronic, progressive, and multifactorial metabolic disorder characterized by persistent hyperglycemia resulting from a complex interaction between insulin resistance and/or impaired insulin secretion caused by pancreatic β-cell dysfunction. T2DM accounts for approximately 90% of global diabetes cases and represents a mounting public health concern, especially within the low- and middle- income countries where healthcare resources are limited [1,2]. According to the World Health Organization (WHO), the global prevalence of diabetes was estimated at 6.4% (about 285 million individuals) in 2010, which rose to 371 million by 2012 and is projected to reach 552 million by the year 2030 [3,4].
Although traditionally regarded as a condition of adulthood, the incidence of T2DM is increasingly being reported in younger individuals due to rising trends in obesity, poor dietary habits, and sedentary lifestyles. The pathogenesis of T2DM arises from a combination of genetic susceptibility, environmental exposures, and lifestyle choices [5]. In its early phase, the body compensates for insulin resistance by upregulating insulin production. However, over time, this compensatory mechanism becomes insufficient, leading to chronic hyperglycemia and the eventual need for external insulin administration [6,7].
T2DM is no longer viewed as a disease solely associated with disordered glucose metabolism. It is now well-recognized as a multisystemic disorder that contributes to progressive damage in vital organs such as the heart, liver, kidneys, eyes, and nervous system [6,8,9]. These complications often develop insidiously and may precede diagnosis, with progression continuing despite treatment. The burden is particularly profound in resource-limited regions, including many African countries, where over 336 million individuals are affected [10,11]. In such settings, the economic implications of T2DM extend beyond direct healthcare costs to include losses in household income, workforce productivity, and national development [10,11].
Although several conventional antidiabetic medications including metformin, insulin analogs, and sulfonylureas are clinically approved and widely used, they are often associated with considerable drawbacks. These include high treatment costs, undesirable side effects, low patient compliance, and limited availability in underserved communities. Additionally, long-term monotherapy has been found inadequate in curbing disease progression [12]. Consequently, there is a pressing need for alternative therapeutic interventions that are not only effective and accessible but also safe and affordable.
Medicinal plants have historically played a central role in the management of metabolic disorders, including T2DM. Ethnobotanical reports from diverse cultures, particularly across Africa and Asia, have documented the use of various plant species in managing diabetic symptoms. Many of these plants contain bioactive phytochemicals which exhibit promising antidiabetic properties [13,14]. The rising interest in phytotherapy is largely driven by its perceived safety, affordability, and potential for holistic efficacy [15].
The classical “one-drug, one-target” paradigm of drug discovery is increasingly being challenged in the context of complex diseases like T2DM, where multiple molecular and cellular pathways are dysregulated simultaneously. While combination therapies offer improved clinical outcomes by modulating different targets, they are often complicated by drug–drug interactions, toxicity, and difficulties in dose optimization [12]. A more promising strategy is the design of multi-target-directed ligands—single molecules capable of modulating multiple therapeutic targets. Natural products offer a rich source of structurally diverse compounds that can exert such pleiotropic effects, resulting in synergistic therapeutic outcomes [16]. However, the pharmacological complexity of plant extracts makes it difficult to fully elucidate the activity and mechanism of each constituent. The lack of standardized evaluation protocols and clarity on active ingredients has hindered the formal integration of many medicinal plants into modern drug discovery pipelines [17,18].
To address these limitations, network pharmacology has emerged as a promising strategy. It combines systems biology, bioinformatics, and cheminformatics to map the interactions between multiple compounds, target proteins, genes, and disease phenotypes. This holistic, in silico approach has proven particularly useful in studying multi-component systems such as traditional Chinese medicines (TCM), but its relevance extends to African and other indigenous medicinal systems [19]. Network pharmacology enables the prediction for target profile of complex herbal formulations, identify lead compounds, and understand the synergy among constituents. It marks a paradigm shift from the conventional “one-drug, one-target” model to a “multi-component, multi-target” strategy. When coupled with metabolomics, this approach becomes even more powerful. Metabolomics facilitates the profiling of diverse plant constituents and their metabolites, helping to establish quality standards and validate pharmacodynamic activity [20].
Furthermore, network pharmacology can construct intricate models that elucidate the relationships between drugs, pathways, and disease networks. This is especially valuable in understanding how medicinal plants may intervene in the pathophysiology of T2DM and its complications. By predicting the effects of specific plant-derived bioactive compounds on molecular targets, it offers insights into their therapeutic mechanisms and potential drug repositioning opportunities [21,22]. The synergy exhibited by multiple bioactive compounds in medicinal plants often results in enhanced therapeutic outcomes compared to isolated compounds. This synergistic effect, as revealed through network pharmacology together with metabolomics can be harnessed to develop novel therapeutic agents for T2DM obesity [23,24].
In this context, Gunnera perpensa and Erythrina zeyheri are two indigenous South African medicinal plants traditionally used by the Basotho people for managing diabetes and other related ailments. While their traditional use is well documented, scientific validation of their antidiabetic potential remains limited [25]. Thus, the current study aimed to evaluate the therapeutic potential of these plant species using a combination of in vitro and computational methods (Figure 1).

2. Results

2.1. Glucose Utilization Assay

The effect of the extracts of G. perpensa and E. zeyheri on glucose utilization of differentiated C2C12 muscle cells is presented in Figure 2. All the extracts of the plants to an extent enabled the glucose utilization activity of C2C12 muscle cells in a concentration dependent manner. With the exception of E. zeyheri acetone (EZA) extract (p = 0.236), all extracts enabled significant (p ˂ 0.05) glucose utilization of the muscle cells. The G. perpensa methanol extract (GPM) enhanced the highest (32.20 ± 1.75%) glucose utilization activity of the muscle cells at the highest concentration (500 µg/mL) and this equals to about 59.9% of those enhanced by insulin at 1 µM. The glucose utilization activity of the C2C12 muscle cells stimulated by E. zeyheri methanol extract (EZM) (29.63 ± 0.38%) and G. perpensa acetone extract (GPA) (30.42 ± 2.55%) at the highest concentration of 500 µg/mL equals to about 55.8% and 56.3%, respectively, of those stimulated by insulin at 1 µM. Insulin (positive control) at a concentration of 1 μM enhanced 55.01 ± 3.34% glucose utilization activity of the C2C12 muscle cells. The IC50 values showcased in Table 1 indicated the extracts of G. perpensa to demonstrate stronger inhibitory potential than those of E. zeyheri, with the acetone extract of G. perpensa and E. zeyheri in each case exhibited relatively better activity.

2.2. Cytotoxicity Assay

The effect of the extracts of G. perpensa and E. zeyheri on the viability of the Vero cells after 48 h exposure was investigated (Figure 3). The result showed that the Vero cells exposed to the Doxorubicin had a concentration-related decrease in cell viability from 103.19 ± 0.17% to 9.50 ± 0.05% as concentration was increased from 3.91 µg/mL to 125 µg/mL. However, the viability of the extracts was above 100% with the exception of EZA having viability at 98% at the concentration of 500 µg/mL and GPA having cell viability at a minimum of 92% after exposure from 62.5 µg/mL to 500 µg/mL. The result of the cytotoxicity assay (LC50) against Vero cells in Table 1 showed that all the extracts were less cytotoxic (LC50 > 1000 µg/mL) with the exception of acetone extract of G. perpensa (GPA) that had a relatively higher cytotoxicity LC50 value of 672 ± 42.6 µg/mL computed for it. Due to the lower cytotoxicity value, the methanol extract was selected for further metabolomics analysis.

2.3. Identification of Compounds of Extracts Using the GC×GC-TOF-MS

GC×GC-TOF-MS analysis was done to determine the phytochemical constituents of the plants. The result showed prevalent presence of many bioactive chemicals of both known and unknown components. In Figure 4 is the total ion chromatograms of G. perpensa (a) and E. zeyheri (b) while Table 2 showcases the compounds that were identified by comparing their fragmentation patterns and retention data against the NIST spectral database. A total of 70 unique mass (UM)-based compounds were detected, of which 56 were of known chemical structures (Table 2). After removing duplicates and reconciling multiple entries for compounds with varying UMs, 51 distinct compounds were confirmed across the two extracts. Among these, 39 metabolites were common to both species, with 10 and 2 compounds uniquely undetected in G. perpensa and E. zeyheri, respectively.
To ensure data reliability, compounds were screened against system and extraction blanks to eliminate potential contaminants. Peaks whose concentrations in the sample were ≥20 times higher than in system blanks were retained (superscript a), while those compounds shared between blanks and samples (superscript b) but present in significantly higher concentrations in the extracts are presented in Table 2. The major four bioactive compounds of G. perpensa were L-(-)-sorbose, pentakis(trimethylsilyl) ether, methyloxime (syn) (42,762.83 ng/mg), shikimic acid, 4TMS derivative (17,949.68 ng/mg), gallic acid, 4TMS derivative (12,821.30 ng/mg) and D-fructose, 1,3,4,5,6-pentakis-O-(trimethylsilyl)-, O-methyloxime (12,389.75 ng/mg). Those of E. zeyheri were aucubin, hexakis(trimethylsilyl) ether (8168.63 ng/mg), glycerol, 3TMS derivative (6281.66 ng/mg), D-fructose, 1,3,4,5,6-pentakis-O-(trimethylsilyl)-, O-methyloxime (3980.70 ng.mg) and 5-methyluridine, 3TMS derivative (3964.83 ng/mg). Compounds such as ribitol (5TMS derivative) and D-galactose derivatives appeared under multiple UMs, which were harmonized during data consolidation.
The other active compounds obtained for G. perpensa and E. zeyheri through the search of PCID and literature produced additional 20 compounds for G. perpensa and 11 for E. zeyheri (Table 3). This is to increase the number of compounds for prediction of the potential T2DM-related targets.

2.4. Prediction of T2DM-Related Targets of G. perpensa and E. zeyheri Compounds

To elucidate the potential therapeutic mechanisms of the bioactive compounds identified in G. perpensa and E. zeyheri, a target prediction analysis was performed. A total of 82 unique compounds were identified across the two-plant species, comprising 51 from GC×GC-TOF-MS analysis (Table 2) and 31 from literature and PCID (Table 3). Specifically, 63 compounds were for E. zeyheri and 61 for G. perpensa. The canonical SMILES notations of all these compounds were retrieved from the PubChem database, except for Z-methyl lespedezate and Rans-phyt-2-enol; two compounds from G. perpensa whose SMILES data were unavailable. After screening 79 of the compounds with complete SMILES information by Lipinski’s selection criterion (Table 4), another three compounds from G. perpensa including 3,3′,4′-tri-O-methyl ellagic acid 4-O-α-D-glucopyranoside, punicalagin, and punicalin failed to meet the criteria. This leaves 76 compounds eligible for downstream analysis. Among the 76 compounds subjected to target prediction using the Swiss Target Prediction database, nine compounds yielded no predicted targets due to lack of structural similarity with known metabolites in this database. These excluded compounds included; arabinofuranose, 1,2,3,5-tetrakis-O-(trimethylsilyl)-, 1,3-dioxolane, arabinonic acid, 2,3,5-tris-O-(trimethylsilyl), γ-lactone, D-, methyl galactoside, 4TMS derivative, epigallocatechin (6TMS), D-xylopyranose, 4TMS derivative, 5-methyluridine, 3TMS derivative, 2-(2-methoxyethoxy)acetic acid, TMS derivative, (2-ethoxyethoxy)acetic acid, TMS derivative. Other compounds were excluded as their targets failed the prediction probability (≥0.1) threshold. Target prediction for the remaining 46 compounds (Table 5) yielded 542 unique protein targets after removal of duplicates. Specifically, 351 targets were linked to 30 compounds from G. perpensa, while 449 targets were associated with 31 from E. zeyheri (Table 5).

2.5. Interception Analysis Between G. perpensa and E. zeyheri and T2DM-Related Targets

For disease association, T2DM-related gene targets were retrieved from OMIM, DisGeNET, and GeneCards databases and after deduplication, a total of 4420 diabetes-related targets were identified. To determine the potential therapeutic relevance of the plant-derived compounds, their predicted targets were intersected with the diabetes-related genes. This comparison revealed 131 intercepting targets, and are considered as candidate therapeutic targets for T2DM (Figure 5a). Of these, 18 targets were uniquely associated with G. perpensa, 49 with E. zeyheri, and 64 shared by both species (Figure 5b). This integrative approach highlights the potential of these medicinal plants as sources of multi-target therapeutic agents for the management of T2DM.
To elucidate the core molecular interactions linking the bioactive compounds of G. perpensa and E. zeyheri to T2DM, the 131 hub gene targets were submitted to the STRING database, with the species parameter set to Homo sapiens. The resulting protein–protein interaction (PPI) network comprised 130 nodes representing target proteins (Figure 6). However, only 100 of these nodes, connected via 644 interaction edges, met the interaction confidence threshold and were visualized using Cytoscape (Figure 7a; Table S1). The average node connectivity was seven neighbors per protein, and the network diameter was also determined to be seven. The remaining 31 targets, which did not exhibit interactions with other proteins in the network, were excluded from further analysis.
Thereafter, the CytoHubba plugin in Cytoscape was used to rank the top hub genes using the Maximal Clique Centrality (MCC) algorithm. This analysis identified 21 key targets with high centrality values: STAT3, PIK3R1, JAK2, PTPN11, ERBB2, JAK1, PDGFRB, PDGFRA, FYN, PTPN6, IGF1R, SYK, KIT, ESR1, MAPK1, MTOR, RELA, CCND1, RAF1, MAPK8, and PRKCD (Figure 7b; Table S2). In the network visualization, each target is represented with color intensity reflecting the degree of connectivity—darker shades indicating higher degree values. These findings suggest that the bioactive compounds present in G. perpensa and E. zeyheri potentially exert their hypoglycemic effects through the modulation of multiple molecular targets.

2.6. Construction and Topological Analysis of the Extract–Compound–Target Network

To further elucidate the therapeutic potential of major bioactive constituents from G. perpensa and E. zeyheri in the management of T2DM, an extract–compound–target interaction network was constructed using Cytoscape (Figure 8; Table S3). The topological features of this network, including betweenness centrality and degree values, were computed to assess the relevance of each node within the system (Table 6). Betweenness centrality quantifies the extent to which a node lies on the shortest path between other nodes, thereby reflecting its control over information flow in the network. The degree value, on the other hand, indicates the number of direct connections a node has to other nodes. Higher values in both parameters suggest a node’s pivotal role in maintaining network integrity and its potential functional significance in disease modulation.
Analysis of the target nodes based on their degree values revealed the top ten most influential targets: STAT3, MAPK1, ESR1, JAK2, PIK3R1, PTPN11, ERBB2, PRKACA, CASP3, and CCND1. These targets may serve as critical molecular mediators in the antidiabetic action of the plant extracts. Similarly, compound node analysis identified ten prominent bioactive constituents with high degree values, indicating their likely contribution to T2DM modulation. These compounds include: EZ-sotrin (erysotrine); EZ-riboni (ribonic acid, 2,3,4,5-tetrakis-O-(trimethylsilyl)-, trimethylsilyl ester); EZ-zerinE (eryzerin E, (6aS,11aS)-3,6a-dihydroxy-9-methoxy-4,10-diprenylpterocarpan); EZ-galinA (erystagallin A) and EZ-phaseo (phaseollidin) from E. zeyheri extract, and GP-ursol (ursolic acid); and GP-pelt (β-peltoboykinolic acid, 3-β-hydroxyolean-12-en-27-oic acid); and GP-dihypo (3α-3, 19-dihydroxyurs-12-en-28-oic acid, pomolic acid) from G. perpensa extract. While arabinose has been identified in the extract of both plant species.

2.7. Gene Ontology (GO) and KEGG Pathway Enrichment Analyses of T2DM-Associated Targets

Gene Ontology (GO) enrichment analysis was conducted to explore the biological significance of the 130 putative therapeutic targets identified from the extracts of G. perpensa and E. zeyheri in relation to type 2 diabetes mellitus (T2DM). A total of 253 GO terms were significantly enriched (p < 0.05), comprising 143 biological process (BP), 24 cellular component (CC), and 86 molecular function (MF) terms (Table S4). The top 10 most significant GO terms were selected and visualized (Figure 9). Within the biological process category, the five most enriched terms were protein phosphorylation (GO:0006468), insulin-like growth factor receptor signaling pathway (GO:0048009), platelet-derived growth factor receptor-beta signaling pathway (GO:0035791), insulin receptor signaling pathway (GO:0008286), and epidermal growth factor receptor signaling pathway (GO:0007173). These processes are central to cellular signaling and metabolic regulation, which are commonly dysregulated in T2DM. For the cellular component category, key enriched terms included cytosol (GO:0005829), plasma membrane (GO:0005886), receptor complex (GO:0043235), nucleus (GO:0005634), and nucleoplasm (GO:0005654), reflecting the subcellular locations involved in signal transduction and gene expression. In the molecular function category, the top terms were protein kinase activity (GO:0004672), protein tyrosine kinase activity (GO:0004713), ATP binding (GO:0005524), protein serine/threonine kinase activity (GO:0004674), and platelet-derived growth factor alpha-receptor activity (GO:0005018), highlighting the predominance of enzymatic and receptor-mediated mechanisms among the targets.
To further understand the functional implications of these targets, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed. A total of 114 pathways were significantly enriched (p < 0.01) (Table S5). The most relevant pathways associated with T2DM pathophysiology included: EGFR tyrosine kinase inhibitor resistance (hsa01521), Pancreatic cancer (hsa05212), Endocrine resistance (hsa01522), MAPK signaling pathway (hsa04010), AGE-RAGE signaling pathway in diabetic complications (hsa04933), and Insulin resistance (hsa04931). The top 20 enriched pathways were selected for graphical representation (Figure 10), with EGFR tyrosine kinase inhibitor resistance emerging as the most significantly enriched. This pathway influences the ERBB signaling axis through the modulation of several key genes, including PDGFRA/B, STAT3, FGF, IGFR, VEGFR, JAK, and FGFR (Figure 11a). Additionally, downstream signaling cascades such as the JAK-STAT, mTOR, PI3K-Akt, and MAPK pathways were implicated. The AGE-RAGE signaling pathway is also enriched and significant in the pathogenesis of T2DM (Figure 11b) with genes like STAT3, FGF, IGFR, VEGFR, JAK and PI3K-Akt involved. These findings suggest that the identified targets from G. perpensa and E. zeyheri extracts may exert antidiabetic effects by modulating multiple signaling pathways, many of which are known to contribute to insulin sensitivity, cellular proliferation, oxidative stress, and inflammation which are the hallmarks of T2DM and its complications.

3. Discussion

This study utilized an integrative strategy combining in vitro glucose utilization assays, cytotoxicity testing, untargeted metabolomics, and network pharmacology to uncover the potential antidiabetic mechanisms of G. perpensa and E. zeyheri. The use of C2C12 skeletal muscle cells in glucose uptake assays was instrumental, given that skeletal muscle tissue accounts for approximately 75–80% of insulin-mediated glucose disposal. The results demonstrated that extracts from both plants enhanced glucose uptake, implying that the bioactive components may possess insulin-mimetic or insulin-sensitizing properties. These effects could stem from the modulation of the insulin signaling cascade, a hypothesis supported by prior studies indicating similar activities for plant-derived phytochemicals [26,27].
Cytotoxicity evaluation using Vero cells established the safety profile of the active extracts within therapeutic concentrations. This reinforces their traditional usage for the management of T2DM among the Basotho people of South Africa. The safety of these extracts aligns with earlier ethnopharmacological records, affirming their suitability for oral consumption [25].
Physiologically, the regulation of glucose homeostasis involves a complex network of interactions. Insulin facilitates glucose entry primarily through binding to insulin receptors on cell membranes, which subsequently triggers the autophosphorylation of tyrosine residues and activation of the insulin receptor substrate-1 (IRS-1)/PI3K/Akt signaling pathway. This cascade promotes GLUT4 translocation to the cell surface, thereby enabling glucose uptake, particularly in skeletal muscle, adipose tissue and liver [28,29]. Increased in levels of inflammation mediators such as tumor necrosis factor (TNF)-α, interleukin (IL)-6, C-reactive protein and plasminogen activator inhibitor are associated with insulin resistance in adipose tissue [1]. Furthermore, chronic hyperglycemia-induced oxidative stress can impair these pathways by activating JNK and NF-κB signaling, leading to serine phosphorylation of IRS-1, GLUT4 inhibition, and subsequent insulin resistance [30,31].
In vitro findings suggest that the tested extracts might counteract these impairments. This is particularly relevant since mitochondrial dysfunction, ROS overproduction, and ectopic lipid accumulation have all been linked to insulin resistance in skeletal muscle [32,33]. Notably, Plant phytochemicals have been observed to increase glucose uptake probably by regulating the expression of genes involved or by reversing the metabolic processes of T2DM [34,35,36,37].
A high-resolution comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOF-MS) was used for untargeted metabolite profiling of methanol extracts from the leaves of G. perpensa and E. zeyheri. This approach enabled the detection of trace-level compounds with improved sensitivity and resolution. Metabolomic profiling revealed a wide range of metabolites including sugars, amino acids, and fatty acids which are molecules essential to cellular signaling and metabolism. Phytochemicals such as gallic acid, epigallocatechin, myristic acid, and malic acid, detected in the extracts, have previously been associated with insulin sensitization, enzyme inhibition, and anti-inflammatory effects [37,38,39,40]. These findings indicate that the potential benefits observed in vitro are mediated through a complex array of biochemical interactions.
From the pool of detected compounds, 76 met the drug-likeness criteria based on Lipinski’s rule, suggesting a high probability of oral bioavailability. Further filtering using a threshold set at probability of ≥0.1 in Swiss Target Prediction database, resulted in 46 active compounds with identifiable protein targets. This step, however, highlights a key limitation in in silico workflows, where database-dependent target prediction may exclude novel or rare metabolites due to lack of structural references.
Using network pharmacology, 131 gene targets were identified at the intersection of phytochemical constituents and T2DM-related genes. STRING-based PPI analysis revealed 100 interconnected nodes forming 644 interactions. Among the central nodes were STAT3, PIK3R1, MAPK1, JAK2, and ESR1 emerging as top-ranking hubs in the network due to their high degree value and betweenness centrality. These molecules are extensively implicated in insulin signaling, inflammation, and cell survival pathways [41].
Among the 21 core genes identified using the CytoHubba MCC algorithm, STAT3, PIK3R1, JAK2, ERBB2, PDGFRB, and MTOR were particularly prominent. These genes play pivotal roles in inflammatory signaling, insulin action, and cell survival, all of which are dysregulated in T2DM [41]. Of particular importance are STAT3, JAK2, and PIK3R1. Notably, STAT3 emerged as a key hub, reinforcing its documented role in integrating cytokine and growth factor signaling, promoting β-cell function, and maintaining insulin sensitivity [41]. The ranking of PIK3R1 and JAK2 further highlights the role of PI3K-Akt and Jak-STAT pathways in mediating glucose uptake and insulin responsiveness. These targets are central to T2DM pathogenesis, and their modulation by plant-derived compounds suggests mechanistic relevance for the observed in vitro activities.
The extract–compound–target interaction network further highlighted ten key bioactive phytochemicals, among which was predominantly from E. zeyheri, including erysotrine, eryzerinE, phaseollidin, erystagallin A, and ribonic acid derivatives, alongside ursolic acid, β-peltoboykinolic acid and pomolic acid from G. perpensa. These compounds exhibited multi-target activity and ursolic acid, for instance has previously been demonstrated to enhance insulin sensitivity and downregulate inflammatory responses via NF-κB and PI3K-Akt signaling pathway [42].
Gene Ontology (GO) analysis provided further insight into the molecular basis of these interactions. Of the 253 enriched GO terms, critical biological processes included protein phosphorylation, insulin receptor signaling, and growth factor receptor signaling. Cellular components such as the cytosol, nucleus, and plasma membrane were prominent, emphasizing the subcellular loci of insulin activity. At the molecular function level, kinase activity and ATP binding were predominant, reflecting enzymatic regulation critical to signal transduction.
Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed 114 significant pathways, among which EGFR tyrosine kinase inhibitor resistance, MAPK signaling, AGE-RAGE signaling, and JAK-STAT signaling were most enriched. The EGFR resistance pathway, in particular, included central targets such as STAT3, PDGFR, JAK1/2, MAPK8, and PI3K, indicating that the extracts may modulate cytokine and growth factor resistance mechanisms that underlie insulin desensitization in T2DM [41].
Importantly, the AGE-RAGE signaling pathway has been recognized as a central mediator of oxidative stress and chronic inflammation in diabetes, contributing to vascular complications such as nephropathy and retinopathy [8]. The MAPK signaling cascade is similarly involved in insulin resistance through persistent activation during hyperglycemia, while the Jak-STAT pathway links metabolic dysfunction to immune signaling [8]. The modulation of the mTOR pathway, which integrates nutrient and insulin signals, further supports the extracts’ potential in restoring β-cell function and metabolic homeostasis [43].
Collectively, these findings support the hypothesis that medicinal plants exert therapeutic effects through a poly-pharmacological mechanism and simultaneously target multiple dysfunctional pathways. This contrasts with conventional single-target therapies, which often fail to halt disease progression or manage its complications comprehensively [21].
The observed multitarget effects and pathway enrichment strongly suggest that the phytochemicals in G. perpensa and E. zeyheri act in synergy to modulate the interconnected signaling networks that underlie T2DM. The convergence of network pharmacology, in vitro glucose uptake assays, and metabolomic profiling thus provides compelling evidence for the therapeutic relevance of these traditional medicinal plants.

4. Materials and Methods

4.1. In Vitro Assays

4.1.1. Preparation of Plant Extracts

The leaves of Gunnera perpensa and Erythrina zeyheri were collected in 2019 from the SANBI Pretoria National botanical garden, and voucher specimens were deposited to the Department of Biology and Environmental, Sefako Makgatho Health Sciences where they are conserved. The dried and finely ground leaf materials each of G. perpensa and E. zeyheri (2 g) were extracted individually using 20 mL of water, methanol and acetone (technical grade, Merck, Darmstadt, Germany) in polyester centrifuge tubes. Extraction was carried out on a platform shaker (Labotec Model 20.2) at room temperature for 30 min. After centrifugation (Rotofix 32 A; Hettich, Kirchlengern, Germany), the supernatant was filtered through Whatman No. 1 filter paper. This extraction was repeated thrice on the same plant material using fresh solvent to ensure exhaustive recovery of phytoconstituents. The combined filtrates of each solvent were concentrated by air-drying at room temperature and stored in pre-weighed vials for subsequent biological assays.

4.1.2. Glucose Utilization Assay in C2C12 Muscle Cells

The effect of the extracts on glucose utilization was evaluated using murine C2C12 skeletal muscle cells, as previously described [44]. A 100 mg/mL stock solution of each extract was prepared in dimethyl sulfoxide (DMSO). C2C12 cells were maintained in Dulbecco’s Modified Eagle Medium (DMEM; Sigma-Aldrich, Darmstadt, Germany) containing 10% fetal bovine serum (FBS) and seeded into 96-well plates at a density of 25,000 cells/mL. Upon reaching confluence, cells were differentiated in DMEM supplemented with 2% FBS for six days at 37 °C in a 5% CO2 incubator. After differentiation, the medium was replaced with 100 µL of DMEM containing 0.25% bovine serum albumin (BSA) and various concentrations of the extracts (15.63–500 µg/mL). Plates were incubated for 1 h, after which glucose concentration in the medium was measured using the glucose oxidase method (Sigma GAGO-20 kit; Sigma-Aldrich, Darmstadt, Germany). Absorbance was read at 540 nm using an Epoch Microplate Reader (BioTek, Santa Clara, CA, USA). Insulin (1 µM) and 0.5% DMSO served as positive and solvent controls, respectively. All experiments were performed in triplicate and repeated twice. Glucose utilization was expressed as the percentage reduction in glucose concentration relative to untreated cells.

4.1.3. Cytotoxicity Assessment Using MTT Assay

The cytotoxic potential of the extracts was evaluated in Vero cells (African green monkey kidney cell line) using the MTT [3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide] reduction assay, according to Mosmann [45], with modifications [46]. A stock solution of each extract (100 mg/mL) was prepared in DMSO, and working concentrations (15.63–1000 µg/mL) were obtained by serial dilution in DMEM. Vero cells at sub-confluence were harvested, centrifuged (200× g, 5 min), and resuspended in DMEM supplemented with 5% FBS and 0.1% gentamicin at 5 × 104 cells/mL. A total of 200 µL of the cell suspension was seeded into columns 2–11 of 96-well microtiter plates. To minimize edge effects, 200 µL of DMEM alone was added to wells in columns 1 and 12. After 24 h of incubation at 37 °C in 5% CO2 incubator, the culture medium was replaced with extract-containing DMEM at the designated concentrations. The plates were further incubated for 48 h. Doxorubicin chloride (Pfizer Laboratories, New York, NY, USA) was included as a positive control. Thereafter, the wells of the plates were washed twice with 150 µL phosphate-buffered saline (PBS), and replaced with 200 µL fresh DMEM. Then, 30 µL of MTT solution (5 mg/mL in PBS) was added, followed by another 4 h incubation at 37 °C. The medium was carefully aspirated, and 50 µL DMSO was added to solubilize the formazan crystals. Absorbance was measured at 570 nm after a gentle shaking, using the Epoch Microplate Reader (BioTek). Blank controls consisted of wells containing medium and MTT without cells. The median lethal concentration (LC50) was defined as the concentration causing 50% reduction in cell viability relative to untreated controls.

4.2. GC×GC-TOFMS-Based Metabolomics Analysis

4.2.1. Reagents & Chemicals

BSTFA (N,O-Bis(trimethylsilyl)trifluoroacetamide) with 1% TMCS (trimethylchlorosilane) was purchased from Sigma Aldrich (St. Louis, MO, USA). All organic solvents used were ultra-pure Burdick & Jackson brands (Honeywell International Inc., Muskegon, MI, USA).

4.2.2. Extraction and Analysis

Metabolomic profiling was performed using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS). The analytical platform consisted of a Pegasus 4D GC×GC-TOFMS system (LECO Corporation, St. Joseph, MI, USA), equipped with an Agilent 7890A gas chromatograph (Agilent Technologies, Atlanta, GA, USA) and a LECO TOF-MS detector. Automated sample injection was carried out using a Gerstel Multi-Purpose Sampler (MPS) (Gerstel GmbH & Co. KG, Mülheim an der Ruhr, Germany). The entire system was fitted with a cryogenic modulator to enhance chromatographic resolution.
A modified single-phase extraction protocol adapted from Beukes et al. [47] was followed. Briefly, experimental samples were weighed, and 100 μL of internal standard (IS) solution containing 3-phenylbutyric acid (50 ppm) was added to each sample. Subsequently, 1 mL of an extraction solvent mixture (chloroform–methanol–water, 1:3:1, v/v/v) was added into each tube, along with a 3 mm tungsten carbide bead to aid mechanical disruption. Samples were homogenized using a vibration mill set at 30 Hz for 10 min, followed by centrifugation at 12,000 rpm for 5 min. An aliquot of 450 μL of the resulting supernatant was transferred to GC-MS vials and evaporated under a gentle nitrogen stream at 37 °C for 20 min. The dried extracts were derivatized in a two-step process. First, 50 μL of methoxyamine hydrochloride (150 mg/10 mL in pyridine) was added to each vial, followed by incubation at 60 °C for 60 min to convert carbonyl groups to methoximes. Thereafter, 40 μL of BSTFA (N,O-bis(trimethylsilyl)trifluoroacetamide) with 1% TMCS (trimethylchlorosilane) was added, and samples were incubated at 40 °C for an additional 60 min for silylation. The derivatized extracts were then transferred to 0.1 mL glass inserts, placed inside the GC-MS vials prior to GC × GC-TOFMS analysis.
The primary GC oven had the initial temperature at 70 °C (held for 2 min), ramped at 4.5 °C/min to 290 °C (held for 4 min), then increased at 3 °C/min to a final temperature of 305 °C (held for 2 min). For the secondary GC oven, the initiated at 85 °C (held for 2 min), ramped at 4.5 °C/min to 300 °C (held for 8 min), followed by a second ramp at 3 °C/min to 310 °C (held for 2 min). The modulator was started at 100 °C (held for 2 min), ramped at 4.5 °C/min to 310 °C (held for 12 min), followed by a final ramp at 3 °C/min to 320 °C (held for 1 min), while the cryogenic modulation was used with a nitrogen pulse duration of 0.5 s every 3 s to ensure efficient focusing and transfer of analytes from the primary to the secondary column. The data acquisition, deconvolution, and peak alignment were performed using ChromaTOF software (version 4.50) (LECO Corporation). Peak detection parameters were set at a signal-to-noise (S/N) ratio of 100, with a minimum requirement of three apexing data points per peak to ensure robustness. Compound identification was carried out using mass spectral matching and retention index comparison. Detected metabolites were annotated by comparing their fragmentation patterns and retention data against the NIST spectral libraries (mainlib and replib). Identifications were reported at Level 3 confidence, based on proposed criteria [48], indicating putative structure assignment via spectral similarity.

4.3. Network Pharmacology

4.3.1. Collation and In Silico Screening of Phytocompounds

Phytochemical constituents identified from the leaf extracts of G. perpensa and E. zeyheri using GC×GC-TOFMS analysis were compiled and matched with additional compounds retrieved from the Phytochemical Interaction Database (PCID; https://www.genome.jp/db/pcidb/, accessed on 2 May 2025) and previous literature reports [49,50,51,52,53]. Duplicate entries across sources were manually screened and removed. Subsequently, the canonical SMILES (Simplified Molecular-Input Line-Entry System) notations for all unique compounds were retrieved from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/, accessed on 2 May 2025). PubChem serves as a comprehensive resource for chemical structures, bioactivities, and molecular descriptors of small organic compounds. The collected SMILES were then imported into the Swiss ADME platform (http://www.swissadme.ch/, accessed on 2 May 2025), an open-access tool for evaluating pharmacokinetic profiles and molecular drug-likeness [54]. Each compound was subjected to Lipinski’s rule of five [55], which assesses the physicochemical parameters predictive of oral bioavailability. Only compounds that met the drug-likeness criteria involving molecular weight, hydrogen bond donors and acceptors, lipophilicity (LogP), and bioavailability score were selected for further evaluation.

4.3.2. Target Gene Mining and Integration

Compounds that satisfied Lipinski’s rule of five were subjected to target prediction analysis using the Swiss Target Prediction database (http://www.swisstargetprediction.ch/, accessed on 2 May 2025). The canonical SMILES of each compound was submitted in Homo sapiens mode, and only targets with a probability score ≥ 0.1 were retained for further consideration. This predictive model is based on the principle of chemical structure similarity, using the molecular features of small-molecule ligands to infer putative target proteins. To ensure consistency in gene nomenclature, predicted protein targets were standardized using the UniProt database (http://www.uniprot.org/, accessed on 3 May 2025) [56]. Redundant entries were removed, and a unified target gene list was generated for each plant extract.
Subsequently, genes associated with type 2 diabetes mellitus (T2DM) were retrieved from three complementary human disease-related databases of GeneCards (https://www.genecards.org/, accessed on 5 May 2025) [57], DisGeNET (http://www.disgenet.org/, accessed on 5 May 2025) [58], and Online Mendelian Inheritance in Man (OMIM) (https://www.omim.org/, accessed on 5 May 2025) [59]. The keyword terms “diabetes mellitus” and “type-2 diabetes mellitus” were used in Homo sapiens mode to extract disease-relevant genes. Duplicates across the databases were eliminated, and the remaining targets were maintained. In GeneCards, targets with relevance scores above the median value were considered to have stronger associations with T2DM and were therefore selected as high-confidence disease-related targets. Due to variability in gene/protein nomenclature, especially where multiple synonyms or identifiers exist for the same biomolecule, all retrieved targets were cross-referenced and standardized using UniProt to maintain consistency across datasets. This ensured uniform annotation and facilitated data integration.
To identify potential pharmacologically relevant target genes, Venn diagram analysis was conducted using Venny v2.1 (https://bioinfogp.cnb.csic.es/tools/venny/, accessed on 5 May 2025). This was done to determine the intersection between compound-predicted targets (from the plants) and T2DM-associated targets (from the disease databases). The resulting overlapping gene targets (called hub genes), which represent the likely mechanistic link between the bioactive phytochemicals and the pathology of type 2 diabetes, were selected for subsequent network construction and enrichment analyses.

4.3.3. Construction of an Active Compound–Target Network

The bioactive compounds and potential targets of G. perpensa and E. zeyheri, were matched with targets associated with T2DM in the Cytoscape software (version 3.10.1) to construct a compound–target interaction network. The Network Analyzer plugin was employed to compute topological parameters and then the compound-target network was analyzed according to the degree and betweenness centrality values. Compounds and targets with values greater than the median for two parameters were considered as core nodes.

4.3.4. Construction of the Protein–Protein Interaction (PPI) Network

The overlapping targets (hub genes) shared between bioactive compounds of plants and T2DM-associated genes were used to construct a protein–protein interaction (PPI) network. These targets were submitted to the STRING database (Search Tool for the Retrieval of Interacting Genes) (version 12.0; https://string-db.org/, accessed on 7 May 2025), with the organism set to Homo sapiens and the minimum required interaction confidence score set at 0.7 to ensure high-quality interaction data [60]. The PPI data was inputted into Cytoscape (version 3.10.1) from STRING using the send network to Cytoscape feature [61]. Targets whose degree value was greater than the median was selected for visualization and a PPI network was constructed. The MCODE and CytoHubba plugins were then used to obtain key modules and screen key targets. This enabled the identification of key regulatory proteins potentially involved in the pharmacological action of the plant-derived compounds against T2DM.

4.3.5. Gene Ontology (GO) Functional Annotation and KEGG Pathway Enrichment Analysis

To gain insights into the biological roles and signaling pathways associated with the predicted 131 hub genes, Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) Bioinformatics database (https://davidbioinformatics.nih.gov/summary.jsp/ accessed on 10 May 2025) [62]. The pathway with the lowest false discovery rate (FDR < 0.05) was taken as the most enriched and significantly associated with the intersecting targets. The search parameters were set to Homo sapiens and the selected gene identifier was the official gene symbol. Gene target list was generated in a consistent format and thereafter analyzed using the functional annotation tool [63]. A p value < 0.05 and a p value < 0.01 were used as the thresholds for GO terms and KEGG pathways, respectively, with some top entries chosen for visualization. GO analysis categorize target genes into three functional domains: biological processes (BP), cellular components (CC), and molecular functions (MF). KEGG enrichment pathway identified relevant biological signaling pathways potentially implicated in type 2 diabetes mellitus (T2DM). The top-ranked 20 entries (KEGG) and top 10 entries for GO were selected for visualization using the Science and Research online plot (SRplot) platform (https://bioinformatics.com.cn/en, accessed on 12 May 2025).

4.4. Statistical Analysis

The experimental data of the in vitro glucose utilization and cytotoxicity data were presented as mean ± standard error of the mean (SEM). Each experiment was conducted in triplicate and repeated on at least three occasions. The data were subjected to one-way analysis of variance (ANOVA) (Welch’s) using the Jamovi open statistical software version 2.6.26 solid. A statistically significant p < 0.05 was subjected to a Tukey post hoc test.

5. Conclusions

This study provides scientific validation for the antidiabetic potential of G. perpensa and E. zeyheri, two medicinal plants traditionally used by the Basotho people of South Africa. Integrated network pharmacology approach supported by in vitro and metabolomic data, identified multiple bioactive compounds capable of modulating key targets and signaling pathways associated with T2DM, including PI3K-Akt, MAPK, JAK-STAT, and AGE-RAGE. Core targets such as STAT3, JAK2, and PIK3R1 were found to be central in mediating the extracts’ effects on insulin signaling and inflammation. The identification of compounds like ursolic acid, arabinose and erysotrine, and their ability to interact with multiple targets highlights the poly-pharmacological nature of these extracts. GO and KEGG enrichment analyses revealed that these phytochemicals influence essential biological processes and molecular functions relevant to glucose metabolism, insulin sensitivity, and β-cell preservation. This study supports the therapeutic relevance of phytochemical synergy in traditional medicine and demonstrates the value of systems-level approaches in elucidating complex drug–target interactions. Further in vivo validation and clinical studies are essential to advance these findings toward the development of safe, cost-effective, and accessible antidiabetic therapies based on indigenous medicinal knowledge.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ddc4040051/s1, Table S1: String database prediction of 100 nodes and 644 edges; Table S2: The 21 core target genes identified by MCC analysis of Hub genes PPI data; Table S3: Extracts-compounds-targets network; Table S4: The hub genes of complete GO analysis; Table S5: Hub genes and type 2 diabetes mellitus (T2DM) related KEGG enrichment pathways.

Funding

This research received no external funding.

Data Availability Statement

The original contributions presented in this study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The author declares no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
AGE-RAGEAdvanced Glycation End products–Receptor for Advanced Glycation End products
AktProtein Kinase B
EGFREpidermal Growth Factor Receptor
ERBBErb-b2 Receptor Tyrosine Kinase
FGFFibroblast Growth Factor
FGFRFibroblast Growth Factor Receptor
IGFRInsulin-like Growth Factor Receptor
JAKJanus Kinase
MAPKMitogen-Activated Protein Kinase
mTORMechanistic Target of Rapamycin
NISTNational Institute of Standards and Technology
OMIMOnline Mendelian Inheritance in Man
PDGFRA/BPlatelet-Derived Growth Factor Receptor Alpha/Beta
PI3KPhosphoinositide 3-Kinase
PIK3R1Phosphoinositide-3-Kinase Regulatory Subunit 1
STAT3Signal Transducer and Activator of Transcription 3
UMUnique Mass (used as compound identifier)
VEGFRVascular Endothelial Growth Factor Receptor

References

  1. Berbudi, A.; Rahmadika, N.; Tjahjadi, A.I.; Ruslami, R. Type 2 diabetes and its impact on the immune system. Curr. Diabetes Rev. 2020, 16, 442–449. [Google Scholar]
  2. International Diabetes Federation, 6th ed.; IDF Diabetes Atlas: Brussels, Belgium, 2013.
  3. Guariguata, L. By the numbers: New estimates from the IDF diabetes atlas update for 2012. Diabetes Res. Clin. Pract. 2012, 98, 524–525. [Google Scholar] [CrossRef]
  4. Whiting, D.R.; Guariguata, L.; Weil, C.; Shaw, J. IDF diabetes atlas: Global estimates of the prevalence of diabetes for 2011 and 2030. Diabetes Res. Clin. Pract. 2011, 94, 311–321. [Google Scholar] [CrossRef] [PubMed]
  5. Alberti, K.G.; Zimmet, P.Z. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: Diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet. Med. 1998, 15, 539–553. [Google Scholar] [CrossRef]
  6. Donath, M.Y.; Shoelson, S.E. Type 2 diabetes as an inflammatory disease. Nat. Rev. Immunol. 2011, 11, 98–107. [Google Scholar] [CrossRef] [PubMed]
  7. Lim, E.L.; Hollingsworth, K.G.; Aribisala, B.S.; Chen, M.J.; Mathers, J.C.; Taylor, R. Reversal of type 2 diabetes: Normalisation of beta cell function in association with decreased pancreas and liver triacylglycerol. Diabetologia 2011, 54, 2506–2514. [Google Scholar] [CrossRef]
  8. Demir, S.; Nawroth, P.P.; Herzig, S.; Ekim Üstünel, B. Emerging targets in type 2 diabetes and diabetic complications. Adv. Sci. 2021, 8, 2100275. [Google Scholar] [CrossRef]
  9. Deng, W.; Zhao, Z.; Zou, T.; Kuang, T.; Wang, J. Research advances in fusion protein-based drugs for diabetes treatment. Diabetes Metab. Syndr. Obes. 2024, 17, 343–362. [Google Scholar] [CrossRef]
  10. IDF International Diabetes Federation (IDF) Diabetes Atlas, 8th ed.; IDF: Brussels, Belgium, 2017.
  11. NCD Risk Factor Collaboration. Worldwide trends in diabetes since 1980: A pooled analysis of 751 population-based studies with 4.4 million participants. Lancet 2016, 387, 1513–1530. [Google Scholar] [CrossRef]
  12. Ajiboye, B.O.; Iwaloye, O.; Owolabi, O.V.; Ejeje, J.N.; Okerewa, A.; Johnson, O.O.; Udebor, A.E.; Oyinloye, B.E. Screening of potential antidiabetic phytochemicals from Gongronema latifolium leaf against therapeutic targets of type 2 diabetes mellitus: Multi-targets drug design. SN Appl. Sci. 2022, 4, 14. [Google Scholar] [CrossRef]
  13. Ge, Q.; Chen, L.; Yuan, Y.; Liu, L.; Feng, F.; Lv, P. Network pharmacology-based dissection of the anti-diabetic mechanism of Lobelia chinensis. Front. Pharmacol. 2020, 11, 347. [Google Scholar] [CrossRef]
  14. Eawsakul, K.; Ongtanasup, T.; Ngamdokmai, N.; Bunluepuech, K. Alpha-glucosidase inhibitory activities of astilbin contained in Bauhinia strychnifolia Craib. stems: An investigation by in silico and in vitro studies. BMC Complement. Med. Ther. 2023, 23, 25. [Google Scholar] [CrossRef]
  15. Moyo, B.; Madala, N.E. Appraisal of the anti-diabetic potential of polyphenolic compounds from Viscum combreticola Engl.: In Vitro α-glucosidase inhibition, LC-ESI-QTOF-MS coupled to pre-column DPPH antioxidative assay, network pharmacology and molecular docking analysis. S. Afr. J. Bot. 2024, 165, 1–3. [Google Scholar] [CrossRef]
  16. Kannan, K.; George, J.A.; Sahadevan, R.; Kothari, M.; Sadhukhan, S. Insights into one drug, multi-target aspects of polyphenols for diabetes management: In Vitro, in vivo, and clinical evidence. Phytochem. Rev. 2024, 23, 1–49. [Google Scholar] [CrossRef]
  17. Guo, X.X.; An, S.; Bao, F.; Xu, T.R. Challenges and perspectives in target identification and mechanism illustration for Chinese medicine. Chin. J. Integr. Med. 2023, 29, 644–654. [Google Scholar] [CrossRef] [PubMed]
  18. Zhang, Y.; Lu, Z.; Guo, J.; Wang, Q.; Zhang, X.; Yang, H.; Li, X. Advanced Carriers for Precise Delivery and Therapeutic Mechanisms of Traditional Chinese Medicines: Integrating Spatial Multi-Omics and Delivery Visualization. Adv. Healthc. Mater. 2025, 14, 2403698. [Google Scholar] [CrossRef]
  19. Li, S.; Zhang, B. Traditional Chinese medicine network pharmacology: Theory methodology application. Chin. J. Nat. Med. 2013, 11, 110–120. [Google Scholar] [CrossRef]
  20. Kar, A.; Mukherjee, P.K.; Saha, S.; Banerjee, S.; Goswami, D.; Matsabisa, M.G.; Charoensub, R.; Duangyod, T. Metabolite profiling and evaluation of CYP450 interaction potential of ‘Trimada’—An Ayurvedic formulation. J. Ethnopharmacol. 2021, 266, 113457. [Google Scholar] [CrossRef]
  21. Hopkins, A.L. Network pharmacology: The next paradigm in drug discovery. Nat. Chem. Biol. 2008, 4, 682–690. [Google Scholar] [CrossRef]
  22. Chandran, U.; Mehendale, N.; Patil, S.; Chaguturu, R.; Patwardhan, B. Network pharmacology. In Innovative Approaches in Drug Discovery; Elsevier: Amsterdam, The Netherlands, 2016; p. 127. [Google Scholar]
  23. Mukherjee, P.K.; Singha, S.; Kar, A.; Chanda, J.; Banerjee, S.; Dasgupta, B.; Haldar, P.K.; Sharma, N. Therapeutic importance of Cucurbitaceae: A medicinally important family. J. Ethnopharmacol. 2022, 282, 114599. [Google Scholar] [CrossRef]
  24. Das Gupta, B.; Kar, A.; Singha, S.; Gayen, S.; Jana, S.; Sharma, N.; Haldar, P.K.; Mukherjee, P.K. Metabolite Profiling and Integrated Network Pharmacology Based Mechanism of Benincasa hispida (Thunb.) Cogn. Fruit Against Non-insulin-Dependent Diabetes Mellitus. Phytochem. Anal. 2025, 36, 884–895. [Google Scholar] [CrossRef] [PubMed]
  25. Balogun, F.O.; Tshabalala, N.T.; Ashafa, A.O. Antidiabetic medicinal plants used by the Basotho tribe of Eastern Free State: A review. J. Diabetes Res. 2016, 2016, 4602820. [Google Scholar] [CrossRef] [PubMed]
  26. Rosenzweig, T.; Sampson, S.R. Activation of insulin signaling by botanical products. Int. J. Mol. Sci. 2021, 22, 4193. [Google Scholar] [CrossRef] [PubMed]
  27. Shehadeh, M.B.; Suaifan, G.A.; Abu-Odeh, A.M. Plants secondary metabolites as blood glucose-lowering molecules. Molecules 2021, 26, 4333. [Google Scholar] [CrossRef]
  28. Alkhalidy, H.; Wang, Y.; Liu, D. Dietary flavonoids in the prevention of T2D: An overview. Nutrients 2018, 10, 438. [Google Scholar] [CrossRef]
  29. Eid, H.M.; Haddad, P.S. Mechanisms of action of indigenous antidiabetic plants from the boreal Forest of northeastern Canada. Adv. Endocrinol. 2014, 2014, 272968. [Google Scholar] [CrossRef]
  30. Ighodaro, O.M. Molecular pathways associated with oxidative stress in diabetes mellitus. Biomed. Pharmacother. 2018, 108, 656–662. [Google Scholar] [CrossRef]
  31. Kaikini, A.A.; Kanchan, D.M.; Nerurkar, U.N.; Sathaye, S. Targeting mitochondrial dysfunction for the treatment of diabetic complications: Pharmacological interventions through natural products. Pharmacogn. Rev. 2017, 11, 128–139. [Google Scholar] [CrossRef]
  32. DeFronzo, R.A. From the triumvirate to the ominous octet: A new paradigm for the treatment of type 2 diabetes mellitus. Diabetes 2009, 58, 773–795. [Google Scholar] [CrossRef]
  33. Honka, M.J.; Latva-Rasku, A.; Bucci, M.; Virtanen, K.A.; Hannukainen, J.C.; Kalliokoski, K.K.; Nuutila, P. Insulin-stimulated glucose uptake in skeletal muscle, adipose tissue and liver: A positron emission tomography study. Eur. J. Endocrinol. 2018, 178, 523–531. [Google Scholar] [CrossRef]
  34. Aryaeian, N.; Sedehi, S.K.; Arablou, T. Polyphenols and their effects on diabetes management: A review. Med. J. Islam. Repub. Iran. 2017, 31, 134. [Google Scholar] [CrossRef]
  35. Vetterli, L.; Brun, T.; Giovannoni, L.; Bosco, D.; Maechler, P. Resveratrol potentiates glucose-stimulated insulin secretion in INS-1E beta-cells and human islets through a SIRT1-dependent mechanism. J. Biol. Chem. 2011, 286, 6049–6060. [Google Scholar] [CrossRef]
  36. Scazzocchio, B.; Vari, R.; Filesi, C.; D’Archivio, M.; Santangelo, C.; Giovannini, C.; Iacovelli, A.; Silecchia, G.; Volti, G.L.; Galvano, F.; et al. Cyanidin-3-O-β-glucoside and protocatechuic acid exert insulin-like effects by upregulating PPARγ activity in human omental adipocytes. Diabetes 2011, 60, 2234–2244. [Google Scholar] [CrossRef] [PubMed]
  37. Lin, C.-L.; Lin, J.-K. Epigallocatechin gallate (EGCG) attenuates high glucose-induced insulin signaling blockade in human hepG2 hepatoma cells. Mol. Nutr. Food Res. 2008, 52, 930–939. [Google Scholar] [CrossRef] [PubMed]
  38. Xu, Y.; Tang, G.; Zhang, C.; Wang, N.; Feng, Y. Gallic acid and diabetes mellitus: Its association with oxidative stress. Molecules 2021, 26, 7115. [Google Scholar] [CrossRef] [PubMed]
  39. Takato, T.; Iwata, K.; Murakami, C.; Wada, Y.; Sakane, F. Chronic administration of myristic acid improves hyperglycaemia in the Nagoya–Shibata–Yasuda mouse model of congenital type 2 diabetes. Diabetologia 2017, 60, 2076–2083. [Google Scholar] [CrossRef]
  40. Gou, L.; Zhan, Y.; Lee, J.; Li, X.; Lü, Z.R.; Zhou, H.M.; Lu, H.; Wang, X.Y.; Park, Y.D.; Yang, J.M. Effects of L-malic acid on alpha-glucosidase: Inhibition kinetics and computational molecular dynamics simulations. Appl. Biochem. Biotechnol. 2015, 175, 2232–2245. [Google Scholar] [CrossRef]
  41. Taniguchi, C.M.; Emanuelli, B.; Kahn, C.R. Critical nodes in signalling pathways: Insights into insulin action. Nat. Rev. Mol. Cell Biol. 2006, 7, 85–96. [Google Scholar] [CrossRef]
  42. Wang, T.Z.; Zuo, G.W.; Yao, L.; Yuan, C.L.; Li, H.F.; Lai, Y.; Chen, Z.W.; Zhang, J.; Jin, Y.Q.; Yamahara, J.; et al. Ursolic acid ameliorates adipose tissue insulin resistance in aged rats via activating the Akt-glucose transporter 4 signaling pathway and inhibiting inflammation. Exp. Ther. Med. 2021, 22, 1466. [Google Scholar] [CrossRef]
  43. Saxton, R.A.; Sabatini, D.M. mTOR signaling in growth, metabolism, and disease. Cell 2017, 168, 960–976. [Google Scholar] [CrossRef]
  44. Olaokun, O.O.; McGaw, L.J.; Janse van Rensburg, I.; Eloff, J.N.; Naidoo, V. Antidiabetic activity of the ethyl acetate fraction of Ficus lutea (Moraceae) leaf extract: Comparison of an in vitro assay with an in vivo obese mouse model. BMC Complement. Altern. Med. 2016, 16, 110. [Google Scholar] [CrossRef] [PubMed]
  45. Mosmann, T. Rapid colorimetric assay for cellular growth and survival: Application to proliferation and cytotoxicity assays. J. Immunol. Methods. 1983, 65, 55–63. [Google Scholar] [CrossRef] [PubMed]
  46. McGaw, L.J.; Steenkamp, V.; Eloff, J.N. Evaluation of Athrixia bush tea for cytotoxicity, antioxidant activity, caffeine content and presence of pyrrolizidine alkaloids. J. Ethnopharmacol. 2007, 110, 16–22. [Google Scholar] [CrossRef] [PubMed]
  47. Beukes, D.; Du Preez, I.; Loots, D.T. Total metabolome extraction from mycobacterial cells for GC-MS metabolomics analysis. In Microbial Metabolomics; Humana Press: New York, NY, USA, 2019; pp. 121–131. [Google Scholar]
  48. Schymanski, E.L.; Jeon, J.; Gulde, R.; Fenner, K.; Ruff, M.; Singer, H.P.; Hollender, J. Identifying small molecules via high resolution mass spectrometry: Communicating confidence. Env. Environ. Sci. Technol. 2014, 48, 2097–2098. [Google Scholar] [CrossRef]
  49. Maroyi, A. From Traditional Usage to Pharmacological Evidence: Systematic Review of Gunnera perpensa L. Evid. Based Complement. Altern. Med. 2016, 2016, 1720123. [Google Scholar] [CrossRef]
  50. Mammo, F.K. Isolation, Characterization and Biological Activities of Terpenoids from Gunnera perpensa. Doctoral Dissertation, Durban University of Technology, Durban, South Africa, 2018. [Google Scholar]
  51. Invernizzi, L.; Moyo, P.; Cassel, J.; Isaacs, F.J.; Salvino, J.M.; Montaner, L.J.; Tietjen, I.; Maharaj, V. Use of hyphenated analytical techniques to identify the bioactive constituents of Gunnera perpensa L., A South African medicinal plant, which potently inhibit SARS-CoV-2 spike glycoprotein–host ACE2 binding. Anal. Bioanal. Chem. 2022, 414, 3971–3985. [Google Scholar] [CrossRef]
  52. Sato, M.; Tanaka, H.; Oh-Uchi, T.; Fukai, T.; Etoh, H.; Yamaguchi, R. Antibacterial activity of phytochemicals isolated from Erythrina zeyheri against vancomycin-resistant enterococci and their combinations with vancomycin. Phytother. Res. 2004, 18, 906–910. [Google Scholar] [CrossRef]
  53. Tanaka, H.; Oh-Uchi, T.; Etoh, H.; Sako, M.; Asai, F.; Fukai, T.; Sato, M.; Murata, J.; Tateishi, Y. Isoflavonoids from roots of Erythrina zeyheri. Phytochem 2003, 64, 753–758. [Google Scholar] [CrossRef]
  54. Daina, A.; Michielin, O.; Zoete, V. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep. 2017, 7, 42717. [Google Scholar] [CrossRef]
  55. Lipinski, C.A.; Lombardo, F.; Dominy, B.W.; Feeney, P.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 1997, 23, 3–25. [Google Scholar] [CrossRef]
  56. Yuan, G.; Shi, S.; Jia, Q.; Shi, J.; Shi, S.; Zhang, X.; Shou, X.; Zhu, X.; Hu, Y. Use of network pharmacology to explore the mechanism of Gegen (Puerariae lobatae Radix) in the treatment of type 2 diabetes mellitus associated with hyperlipidemia. Evid. Based Complement. Altern. Med. 2021, 2021, 6633402. [Google Scholar] [CrossRef] [PubMed]
  57. Stelzer, G.; Rosen, N.; Plaschkes, I.; Zimmerman, S.; Twik, M.; Fishilevich, S.; Stein, T.I.; Nudel, R.; Lieder, I.; Mazor, Y.; et al. The GeneCards suite: From gene data mining to disease genome sequence analyses. Curr. Protoc. Bioinform. 2016, 54, 1–30. [Google Scholar] [CrossRef] [PubMed]
  58. Piñero, J.; Ramírez-Anguita, J.M.; Saüch-Pitarch, J.; Ronzano, F.; Centeno, E.; Sanz, F.; Furlong, L.I. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res. 2020, 48, D845–D855. [Google Scholar] [CrossRef] [PubMed]
  59. Amberger, J.S.; Bocchini, C.A.; Schiettecatte, F.; Scott, A.F. Online Mendelian Inheritance in Man (OMIM®), an online catalog of human genes and genetic disorders. Nucleic Acids Res. 2015, 43, D789–D798. [Google Scholar] [CrossRef]
  60. Szklarczyk, D.; Kirsch, R.; Koutrouli, M.; Nastou, K.; Mehryary, F.; Hachilif, R.; Gable, A.L.; Fang, T.; Doncheva, N.T.; Pyysalo, S.; et al. The STRING database in 2023: Protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 2023, 51, D638–D646. [Google Scholar] [CrossRef]
  61. Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N.S.; Wang, J.T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 2003, 13, 2498–2504. [Google Scholar] [CrossRef]
  62. Huang, D.W.; Sherman, B.T.; Tan, Q.; Kir, J.; Liu, D.; Bryant, D.; Guo, Y.; Stephens, R.; Baseler, M.W.; Lane, H.C.; et al. DAVID Bioinformatics Resources: Expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res. 2007, 35 (Suppl. S2), W169–W175. [Google Scholar] [CrossRef]
  63. Sherman, B.T.; Hao, M.; Qiu, J.; Jiao, X.; Baseler, M.W.; Lane, H.C.; Imamichi, T.; Chang, W. DAVID: A web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res. 2022, 50, W216–W221. [Google Scholar] [CrossRef]
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Figure 1. Schematic illustration of unraveling the mechanisms of G. perpensa and E. zeyheri action on T2DM.
Figure 1. Schematic illustration of unraveling the mechanisms of G. perpensa and E. zeyheri action on T2DM.
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Figure 2. Glucose utilization in differentiated C2C12 muscle cells (expressed as percentage of untreated control cells ± standard error of mean, n = 9) treated different concentrations of plants’ extracts and insulin (positive control). Key: GPA (G. perpensa acetone extract), GPM (G. perpensa methanol extract), EZA (E. zeyheri acetone extract), EZM (E. zeyheri methanol extract). The statistical analysis of glucose utilization by one-way ANOVA showed significant difference for GPA (p = 0.010), GPM (p = 0.040), and EZM (p ˂ 0.001) and none for EZA (p = 0.236). Key: a = p ˂ 0.001, b,b1 = p ˂ 0.01, and c,c1 = p ˂ 0.05. The concentrations with the same alphabet or alphabet with superscript are significantly different.
Figure 2. Glucose utilization in differentiated C2C12 muscle cells (expressed as percentage of untreated control cells ± standard error of mean, n = 9) treated different concentrations of plants’ extracts and insulin (positive control). Key: GPA (G. perpensa acetone extract), GPM (G. perpensa methanol extract), EZA (E. zeyheri acetone extract), EZM (E. zeyheri methanol extract). The statistical analysis of glucose utilization by one-way ANOVA showed significant difference for GPA (p = 0.010), GPM (p = 0.040), and EZM (p ˂ 0.001) and none for EZA (p = 0.236). Key: a = p ˂ 0.001, b,b1 = p ˂ 0.01, and c,c1 = p ˂ 0.05. The concentrations with the same alphabet or alphabet with superscript are significantly different.
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Figure 3. Effect of the extracts of G. perpensa and E. zeyheri on the viability of the Vero cells (expressed as percentage of untreated control cells ± standard error of mean, n = 6). Key: GPA (G. perpensa acetone extract), GPM (G. perpensa methanol extract), EZA (E. zeyheri acetone extract), EZM (E. zeyheri methanol extract). The statistical analysis of cell viability by one-way ANOVA showed no significant difference for GPA (p = 0.280), GPM (p = 0.051), EZA (p = 0.072), and EZM (p = 0.173).
Figure 3. Effect of the extracts of G. perpensa and E. zeyheri on the viability of the Vero cells (expressed as percentage of untreated control cells ± standard error of mean, n = 6). Key: GPA (G. perpensa acetone extract), GPM (G. perpensa methanol extract), EZA (E. zeyheri acetone extract), EZM (E. zeyheri methanol extract). The statistical analysis of cell viability by one-way ANOVA showed no significant difference for GPA (p = 0.280), GPM (p = 0.051), EZA (p = 0.072), and EZM (p = 0.173).
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Figure 4. GC×GC/TOF-MS total ion chromatograms (TIC) of extracts (a) G. perpensa and (b) E. zeyheri.
Figure 4. GC×GC/TOF-MS total ion chromatograms (TIC) of extracts (a) G. perpensa and (b) E. zeyheri.
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Figure 5. Venn diagram. (a) Combined extracts and T2DM intersection targets and (b) GP, EZ and T2DM intersection targets. G. perpensa (GP) and E. zeyheri (EZ). 2.6. Protein–Protein Interaction (PPI) Network Analysis and Identification of Core Targets.
Figure 5. Venn diagram. (a) Combined extracts and T2DM intersection targets and (b) GP, EZ and T2DM intersection targets. G. perpensa (GP) and E. zeyheri (EZ). 2.6. Protein–Protein Interaction (PPI) Network Analysis and Identification of Core Targets.
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Figure 6. PPI network of 130 targets for G. perpensa, and E. zeyheri against T2DM.
Figure 6. PPI network of 130 targets for G. perpensa, and E. zeyheri against T2DM.
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Figure 7. Network pharmacology MCC analysis results. (a) PPI network of 100 target genes and (b) PPI network of 21 core target genes; The colors (from red to orange to yellow) indicate the MCC values binding between proteins and depth represents its interaction strength, and the lines represent protein–protein interactions (edges).
Figure 7. Network pharmacology MCC analysis results. (a) PPI network of 100 target genes and (b) PPI network of 21 core target genes; The colors (from red to orange to yellow) indicate the MCC values binding between proteins and depth represents its interaction strength, and the lines represent protein–protein interactions (edges).
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Figure 8. Extracts-compound-target network. Green represents the plants species (G. perpensa, and E. zeyheri), brown represents compounds of G. perpensa, and E. zeyheri and red represents intercepting targets, while blue represents the other targets, respectively. The full name of coded compound can be found in Table 5.
Figure 8. Extracts-compound-target network. Green represents the plants species (G. perpensa, and E. zeyheri), brown represents compounds of G. perpensa, and E. zeyheri and red represents intercepting targets, while blue represents the other targets, respectively. The full name of coded compound can be found in Table 5.
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Figure 9. The top 10 results of GO enrichment analyses for 131 intercepting targets (hub genes) BP (Biological process), CC (Cellular component), and MF (Molecular function).
Figure 9. The top 10 results of GO enrichment analyses for 131 intercepting targets (hub genes) BP (Biological process), CC (Cellular component), and MF (Molecular function).
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Figure 10. The top 20 results of KEGG pathway enrichment analyses for 131 intercepting targets (hub genes). The color represents the different −log10(p value), the size of the circle represents the gene counts.
Figure 10. The top 20 results of KEGG pathway enrichment analyses for 131 intercepting targets (hub genes). The color represents the different −log10(p value), the size of the circle represents the gene counts.
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Figure 11. The T2DM pathways and the potential therapeutic module in the red rectangles representing the core enriched target genes; (a) EGFR tyrosine kinase inhibitor resistance pathway and (b) AGE-RAGE signaling pathway in diabetes complications.
Figure 11. The T2DM pathways and the potential therapeutic module in the red rectangles representing the core enriched target genes; (a) EGFR tyrosine kinase inhibitor resistance pathway and (b) AGE-RAGE signaling pathway in diabetes complications.
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Table 1. The inhibitory (IC50) and lethal (LC50) concentration of G. perpensa and E. zeyheri extracts.
Table 1. The inhibitory (IC50) and lethal (LC50) concentration of G. perpensa and E. zeyheri extracts.
ExtractsGlucose Utilization (µg/mL)Cytotoxicity (µg/mL)
G. perpensa acetone extract78.52 ± 21.6662 ± 42.6
G. perpensa methanol extract94.78 ± 15.7>1000
E. zeyheri acetone extract130.69 ± 23.3>1000
E. zeyheri methanol extract144.76 ± 9.7>1000
Table 2. Metabolite profiling of methanol extracts of G. perpensa and E. zeyheri.
Table 2. Metabolite profiling of methanol extracts of G. perpensa and E. zeyheri.
S/NName of CompoundsCompound Concentration (ng/mg Sample)
G. perpensaE. zeyheri
11,3-Dioxolane-2-methanol (UM73) a1606.05-
2Gallic acid, 4TMS derivative (UM281) a12,821.30432.78
3Benzene, 1,3,5-trichloro-(UM180) b222.4864.20
4Palmitic acid, TMS derivative (UM117) a1200.23722.10
52-Deoxypentofuranose, 3TMS derivative (UM73) b728.2157.26
61,8-cis-Undecadien-5-yne 3,7-bis-trimethylsilyl ether (UM129) b394.81163.42
7Levoglucosan, 3TMS derivative (UM204) a198.47131.53
8Myristic acid, TMS derivative (UM117) b73.47158.58
9Arabinofuranose, 1,2,3,5-tetrakis-O-(trimethylsilyl)-(UM217) b528.65469.59
10Propane, 2-methyl-1,2-bis(trimethylsiloxy)-(UM131)-147.88
11Glycerol, 3TMS derivative (UM73)6413.496281.66
12Glycerol, 3TMS derivative (UM117)666.841151.36
13Diglycolic acid, 2TMS derivative (UM73)135.6246.42
14Malic acid, 3TMS derivative (UM55)-115.84
15Ribitol, 5TMS derivative (UM73)3203.652951.96
16meso-Erythritol, 4TMS derivative (UM73)635.421239.36
17α-Ketoisovaleric acid, TMS derivative (UM73)3793.99103.73
181,3-Dioxolane (UM73)2131.09173.95
19Arabinonic acid, 2,3,5-tris-O-(trimethylsilyl)-, γ-lactone, d-(UM73)245.10137.49
20Ribitol, 5TMS derivative (UM103)1244.33678.84
21D-Arabinose, tetrakis(trimethylsilyl) ether, ethyloxime (isomer 1) (UM103)393.9892.94
222,3-Butanediol, O-(trimethylsilyl)-, monoacetate (UM117)-46.46
23L-(-)-Arabitol, 5TMS derivative (UM103)101.01536.18
24D-(-)-Rhamnose, tetrakis(trimethylsilyl) ether, methyloxime (syn) (UM117)-66.19
25D-(+)-Ribono-1,4-lactone (R,S,R)-, 3TMS derivative (UM73)783.92128.06
262-Desoxy-pentos-3-ulose, bis(methoxime),O,O’-bis(trimethylsilyl)-(UM73)1216.7761.16
27Ribonic acid, 2,3,4,5-tetrakis-O-(trimethylsilyl)-, trimethylsilyl ester (UM103)-117.28
28Epinephrine, (α)-, 3TMS derivative (UM73)1952.57-
29Shikimic acid, 4TMS derivative (UM204)17,949.6880.46
30Methyl galactoside, 4TMS derivative (UM73)6535.33221.94
31Hexanoic acid, 2-[(trimethylsilyl)oxy]-, trimethylsilyl ester (UM73)155.4645.58
32D-Fructose, 1,3,4,5,6-pentakis-O-(trimethylsilyl)-, O-methyloxime (UM103)12,389.753980.70
33L-(-)-Sorbose, pentakis(trimethylsilyl) ether, methyloxime (syn) (UM103)42,762.833273.19
34Ribitol, 5TMS derivative (UM217)-142.51
35D-Galactose, 2,3,4,5,6-pentakis-O-(trimethylsilyl)-, o-methyloxyme, (1E)-(UM205)55.1440.44
36D-Galactose, 2,3,4,5,6-pentakis-O-(trimethylsilyl)-, o-methyloxyme, (1E)-(UM201)-47.09
37D-Glucose, 2,3,4,5,6-pentakis-O-(trimethylsilyl)-, o-methyloxyme, (1E)-(UM205)5394.491338.29
38D-Mannose, 2,3,4,5,6-pentakis-O-(trimethylsilyl)-, o-methyloxyme, (1Z)-(UM103)1054.78478.84
39D-Mannitol, 6TMS derivative (UM103)-164.23
40D-Lyxose, 4TMS derivative (UM204)49.4674.01
41D-Gluconic acid, 6TMS derivative (UM147)12.59439.48
42Myo-Inositol, 6TMS derivative (UM217)-2564.45
439,12-Octadecadienoic acid (Z,Z)-, TMS derivative (UM67)140.6443.57
44Ethyl α-D-glucopyranoside, 4TMS derivative (UM204)114.78627.19
45D-Xylopyranose, 4TMS derivative (UM73)-427.36
46Aucubin, hexakis(trimethylsilyl) ether (UM73)2115.351282.54
471-Monopalmitin, 2TMS derivative (UM57)41.5010.34
48Aucubin, hexakis(trimethylsilyl) ether (UM361)144.828168.63
495-Methyluridine, 3TMS derivative (UM73)2399.243964.83
50Mannonic acid, γ-lactone, 4TMS derivative (UM217)-30.87
51Epigallocatechin (6TMS) (UM456)196.403.91
522-Oxopentanoic acid, TMS derivative (UM73)1996.7973.09
53α-D-(+)-Talopyranose, 5TMS derivative (UM204)-820.27
542-O-Glycerol-α-d-galactopyranoside, hexa-TMS (UM204)30.64876.82
552-(2-Methoxyethoxy)acetic acid, TMS derivative (UM73)582.75287.07
56(2-Ethoxyethoxy)acetic acid, TMS derivative (UM73)235.89121.90
Key: Superscript a: Concentration of compounds in the sample ≥20 times higher than in system blanks; Superscript b: Shared compounds between blanks and samples but present in significantly higher concentrations in the extracts samples.
Table 3. Compounds from plants species retrieved from PCID database and literature.
Table 3. Compounds from plants species retrieved from PCID database and literature.
G. perpensaE. zeyheri
PyrogallolEryzerin A/(+/−)-7,2′,4′-Trihydroxy-8,3′-diprenylisoflavanone
Succinic acidEryzerin B/(3R)-7,4′-Dihydroxy-2′-methoxy,6,8-diprenylisoflavanone
3,3′,4′-tri-O-methyl ellagic acidPhaseollidin
p-hydroxy-benzaldehydeEryzerin D/2′,4′-Dihydroxy-8-prenyl-6″,6″-dimethylpyrano [2,3″:7,6]isoflavan
Ellagic acidFolitenol
2-methyl-6-(-3-methyl-2-butenyl) benzo-1,4-quinoneErysotrine
3-hydroxy-2-methyl-5-(3-methyl-2-butenyl) benzo-1,4-quinoneErybraedin A/4-Prenylphaseollidin/(6aR,11aR)-3,9-Dihydroxy-4,10-diprenylpterocarpan
6-hydroxy-8-methyl-2,2-dimethyl-2H-benzopyranErystagallin A
1,1′-biphenyl-4,4′-diacetic acidEryzerin E/(6aS,11aS)-3,6a-Dihydroxy-9-methoxy-4,10-diprenylpterocarpan
Z-venusol, 7,8-dihydroxy-6-(hydroxymethyl)-3-[(Z)-(4-hydroxyphenyl)methylidene]tetrahydro-4aH-pyrano [2,3-b][1,4]dioxin-2-oneErythrabyssin II/3,9-Dihydroxy-2,10-diprenylpterocarpan
β-sitosterolEryzerin C/(3R)-7,2′,4′-Trihydroxy-6,8-diprenylisoflavan
3,3′,4′-tri-O-methyl ellagic acid 4-O-β-D-glucopyranoside
punicalagin
phytol
ursolic acid
β-peltoboykinolic acid, 3-β-hydroxyolean-12-en-27-oic acid
3α-3, 19-dihydroxyurs-12-en-28-oic acid, pomolic acid
punicalin
Z-methyl lespedezate
Rans-phyt-2-enol
Table 4. Information on the combined bioactive constituents of G. perpensa and E. zeyheri.
Table 4. Information on the combined bioactive constituents of G. perpensa and E. zeyheri.
S/NCompoundCanonical SMILESFormulaMW
≤ 500		HBA ≤ 10HBD
≤ 5		MLOG
≤ 4.15		L.V.
≤ 1		B.S.
> 0.1
11,3-Dioxolane-2-methanolOCC1OCCO1C4H8O3104.131−1.0500.55
2Gallic acid,OC(=O)c1cc(O)c(c(c1)O)OC7H6O5170.1254−0.1600.56
3Benzene, 1,3,5-trichloro-Clc1cc(Cl)cc(c1)ClC6H3Cl3181.45004.0600.55
4Palmitic Acid,CCCCCCCCCCCCCCCC(=O)OC16H32O2256.42214.1910.85
51,8-cis-Undecadien-5-yne 3,7-bis-trimethylsilyl etherCC/C=C\C(O[Si](C)(C)C)/C=C\CC(O[Si](C)(C)C)C=CC17H34O2Si2326.62204.1910.85
6Levoglucosan,O[C@H]1[C@H](O)[C@H]2CO[C@@H]([C@@H]1O)O2C6H10O5162.1453−1.9400.55
7Myristic acid,CCCCCCCCCCCCCC(=O)OC14H28O2228.37213.6900.85
8Arabinofuranose, 1,2,3,5-tetrakis-O-(trimethylsilyl)-O=CC(C(C(O[Si](C)(C)C)CO[Si](C)(C)C)O[Si](C)(C)C)O[Si](C)(C)CC17H42O5Si4438.85501.0600.55
9Propane, 2-methyl-1,2-bis(trimethylsiloxy)C[Si](OCC(O[Si](C)(C)C)(C)C)(C)CC10H26O2Si2234.48201.9100.55
10Glycerol,OCC(CO)OC3H8O392.0933−1.5100.55
11Diglycolic acid,OC(=O)COCC(=O)OC4H6O5134.0952−1.3700.56
12Malic acid, 3TMS derivativeOC(=O)CC(C(=O)O)OC4H6O5134.0953−1.3700.56
13Meso-Erythritol, 4TMS derivativeOC[C@H]([C@H](CO)O)OC4H10O4122.1244−1.9100.55
14α-Ketoisovaleric acid, TMS derivativeCC(C(=O)C(=O)O)CC5H8O3116.1231−0.0900.85
151,3-DioxolaneC1OCCO1C3H6O274.0820−0.6200.55
16Arabinonic acid, 2,3,5-tris-O-(trimethylsilyl)-, γ-lactone, d-O=C1O[C@@H]([C@H]([C@@H]1O[Si](C)(C)C)O[Si](C)(C)C)CO[Si](C)(C)CC14H32O5Si3364.66500.7400.55
17D-Arabinose, tetrakis(trimethylsilyl) ether, ethyloxime (isomer 1)CO/N=C/C(C(C(O[Si](C)(C)C)CO[Si](C)(C)C)O[Si](C)(C)C)O[Si](C)(C)CC18H45NO5Si4467.9601.300.55
182,3-Butanediol, O-(trimethylsilyl)-, monoacetate CC(=O)OC(C(O[Si](C)(C)C)C)CC9H20O3Si204.34301.400.55
19L-(-)-Arabitol, 5TMS derivativeOC[C@@H](C([C@H](CO)O)O)OC5H12O5152.1555−2.3300.55
20D-(-)-Rhamnose, tetrakis(trimethylsilyl) ether, methyloxime (syn)CON=CC(C(C(C(O[Si](C)(C)C)C)O[Si](C)(C)C)O[Si](C)(C)C)O[Si](C)(C)CC19H47NO5Si4481.92601.5300.55
21D-(+)-Ribono-1,4-lactone (R,S,R)-, 3TMS derivativeOC[C@H]1OC(=O)[C@@H]([C@@H]1O)OC5H8O5148.1153−2.0600.55
222-Desoxy-pentos-3-ulose, bis(methoxime),O,O’-bis(trimethylsilyl)-CO/N=C(\C(O[Si](C)(C)C)CO[Si](C)(C)C)/C/C=N\OCC13H30N2O4Si2334.5660−2.0600.55
23Ribonic acid, 2,3,4,5-tetrakis-O-(trimethylsilyl)-, trimethylsilyl esterO=C(C(C(C(O[Si](C)(C)C)CO[Si](C)(C)C)O[Si](C)(C)C)O[Si](C)(C)C)O[Si](C)(C)CC20H50O6Si5527.03601.7610.55
24Epinephrine, (α)-, 3TMS derivativeCNC[C@@H](c1ccc(c(c1)O)O)OC9H13NO3183.2440.0700.55
25Shikimic acid, 4TMS derivativeO=C(C1=CC(C(C(C1)O[Si](C)(C)C)O[Si](C)(C)C)O[Si](C)(C)C)O[Si](C)(C)CC19H42O5Si4462.88501.8500.55
26Methyl galactoside, 4TMS derivativeCOC1OC(CO[Si](C)(C)C)C(C(C1O[Si](C)(C)C)O[Si](C)(C)C)O[Si](C)(C)CC19H46O6Si4482.91600.8800.55
27Hexanoic acid, 2-[(trimethylsilyl)oxy]-, trimethylsilyl esterCCCCC(C(=O)O[Si](C)(C)C)O[Si](C)(C)CC12H28O3Si2276.52302.2700.55
28D-Fructose, 1,3,4,5,6-pentakis-O-(trimethylsilyl)-, O-methyloximeCON=C(C(C(C(O[Si](C)(C)C)CO[Si](C)(C)C)O[Si](C)(C)C)O[Si](C)(C)C)CO[Si](C)(C)CC22H55NO6Si5570.1701.410.55
29D-Mannose, 2,3,4,5,6-pentakis-O-(trimethylsilyl)-, o-methyloxyme, (1Z)-CO/N=C\C(C(C(C(O[Si](C)(C)C)CO[Si](C)(C)C)O[Si](C)(C)C)O[Si](C)(C)C)O[Si](C)(C)CC22H55NO6Si5570.1701.410.55
30D-Gluconic acid, 6TMS derivativeO=C(C(C(C(C(O[Si](C)(C)C)CO[Si](C)(C)C)O[Si](C)(C)C)O[Si](C)(C)C)O[Si](C)(C)C)O[Si](C)(C)CC24H60O7Si6629.24701.8310.55
319,12-Octadecadienoic acid (Z,Z)-, TMS derivativeCCCCC/C=C\C/C=C\CCCCCCCC(=O)O[Si](C)(C)CC21H40O2Si352.63201.8310.55
32Ethyl α-D-glucopyranoside, 4TMS derivative CCO[C@H]1O[C@@H](CO)[C@H]([C@@H]([C@@H]1O)O)OC8H16O6208.2164−2.0700.55
331-Monopalmitin, 2TMS derivative CCCCCCCCCCCCCCCC(=O)OCC(O[Si](C)(C)C)CO[Si](C)(C)CC25H54O4Si2474.86404.4810.55
34Aucubin, hexakis(trimethylsilyl) etherC[Si](OC1C(OC(C(C1O[Si](C)(C)C)O[Si](C)(C)C)CO[Si](C)(C)C)OC1OC=CC2C1C(=CC2O[Si](C)(C)C)CO[Si](C)(C)C)(C)CC33H70O9Si6779.42901.5310.55
35Mannonic acid, γ-lactone, 4TMS derivativeOC[C@H](C1OC(=O)[C@H]([C@H]1O)O)OC6H10O6178.1464−2.4900.55
36Epigallocatechin (6TMS)Oc1cc2O[C@H](c3cc(O)c(c(c3)O)O)[C@@H](Cc2c(c1)O)OC15H14O7306.2776−0.2910.55
37Ribitol, 5TMS derivativeC[Si](OC([C@H](O[Si](C)(C)C)CO[Si](C)(C)C)[C@@H](O[Si](C)(C)C)CO[Si](C)(C)C)(C)CC20H52O5Si5513.05501.9110.55
38L-(-)-Sorbose, pentakis(trimethylsilyl) ether, methyloxime (syn)CO/N=C(\C(C(C(O[Si](C)(C)C)CO[Si](C)(C)C)O[Si](C)(C)C)O[Si](C)(C)C)/CO[Si](C)(C)CC22H55NO6Si5570.1701.410.55
39D-Galactose, 2,3,4,5,6-pentakis-O-(trimethylsilyl)-, o-methyloxyme, (1E)-O=CC(C(C(C(O[Si](C)(C)C)CO[Si](C)(C)C)O[Si](C)(C)C)O[Si](C)(C)C)O[Si](C)(C)CC21H52O6Si5541.06601.1710.55
40D-Glucose, 2,3,4,5,6-pentakis-O-(trimethylsilyl)-, o-methyloxyme, (1E)-C[Si](OC(C(C(O[Si](C)(C)C)CO[Si](C)(C)C)O[Si](C)(C)C)C(O[Si](C)(C)C)C=NO[Si](C)(C)C)(C)CC24H61NO6Si6628.26701.8310.55
41D-Mannitol, 6TMS derivativeOC[C@H]([C@H]([C@@H]([C@@H](CO)O)O)O)OC6H14O6182.1766−2.7710.55
42D-Lyxose, 4TMS derivativeO[C@@H]1COC([C@H]([C@H]1O)O)OC5H10O5150.1354−2.3200.55
43Myo-Inositol, 6TMS derivative OC1C(O)C(O)C(C(C1O)O)OC6H12O6180.1666−3.1610.55
44D-Xylopyranose, 4TMS derivative C[Si](O[C@H]1C(OC[C@H]([C@@H]1O[Si](C)(C)C)O[Si](C)(C)C)O[Si](C)(C)C)(C)CC17H42O5Si4438.85501.2200.55
455-Methyluridine, 3TMS derivative OC[C@H]1O[C@H]([C@@H]([C@@H]1O)O)n1cc(C)c(=O)[nH]c1=OC10H14N2O6258.2364−1.9400.55
462-Oxopentanoic acid, TMS derivativeCCCC(=O)C(=O)OC5H8O3116.1231−0.0900.85
47α-D-(+)-Talopyranose, 5TMS derivativeC[Si](OC1OC(CO[Si](C)(C)C)C(C(C1O[Si](C)(C)C)O[Si](C)(C)C)O[Si](C)(C)C)(C)CC21H52O6Si5541.06601.3310.55
482-O-Glycerol-α-d-galactopyranoside, hexa-TMS C[Si](O[C@@H]1[C@H](OC(CO[Si](C)(C)C)CO[Si](C)(C)C)O[C@H]([C@H]([C@H]1O[Si](C)(C)C)O[Si](C)(C)C)CO[Si](C)(C)C)(C)CC27H66O8Si6687.3280110.55
492-(2-Methoxyethoxy)acetic acid, TMS derivative COCCOCC(=O)O[SiH2]CC6H14O4Si178.2640−0.4600.55
50(2-Ethoxyethoxy)acetic acid, TMS derivative CCOCCOCC(=O)O[Si](C)(C)CC9H20O4Si220.34400.5500.55
51PyrogallolOc1c(O)cccc1OC6H6O3126.11330.1800.55
52Succinic acidOC(=O)CCC(=O)OC4H6O4118.0942−0.5400.85
533,3’,4’-tri-O-methyl ellagic acid COc1cc2c(=O)oc3c4c2c(c1OC)oc(=O)c4cc(c3O)OCC17H12O8344.27810.8900.55
54p-hydroxy-benzaldehydeO=Cc1ccc(cc1)OC7H6O2122.12210.7900.55
55Ellagic acid Oc1cc2c(=O)oc3c4c2c(c1O)oc(=O)c4cc(c3O)OC14H6O8302.19840.1400.55
562-methyl-6-(-3-methyl-2-butenyl) benzo-1,4-quinoneCC(=CCC1=CC(=O)C=C(C1=O)C)CC12H14O2190.24201.5700.55
573-hydroxy-2-methyl-5-(3-methyl-2-butenyl) benzo-1,4-quinoneCC(=CCC1=CC(=O)C(=C(C1=O)O)C)CC12H14O3206.24310.6900.85
586-hydroxy-8-methyl-2,2-dimethyl-2H-benzopyranOc1cc(C)c2c(c1)C=CC(O2)(C)CC12H14O2190.24212.1900.55
591,1’ -biphenyl-4,4’ -diacetic acidOC(=O)Cc1ccc(cc1)c1ccc(cc1)CC(=O)OC16H14O4270.28422.6500.85
60Z-venusol, 7,8-dihydroxy-6-(hydroxymethyl)-3-[(Z)-(4-hydroxyphenyl)methylidene]tetrahydro-4aH-pyrano [2,3-b][1,4]dioxin-2-oneOC[C@H]1O[C@H]2O/C(=C\c3ccc(cc3)O)/C(=O)O[C@@H]2[C@H]([C@@H]1O)OC15H16O8324.2884−1.1200.55
61β-sitosterolCC[C@@H](C(C)C)CC[C@H]([C@H]1CC[C@@H]2[C@]1(C)CC[C@H]1[C@H]2CC=C2[C@]1(C)CC[C@@H](C2)O)CC29H50O414.71116.7310.55
623,3’,4’-Tri-O-methyl ellagic acid 4-O-α-D-glucopyranosideOC[C@H]1O[C@@H](O)[C@@H]([C@H]([C@@H]1O)Oc1cc2c(=O)oc3c4c2c(c1OC)oc(=O)c4cc(c3OC)OC)OC23H22O13506.41134−0.8920.17
63PunicalaginO=CC1OC(=O)c2cc(O)c(c(c2c2c(C(=O)OC1C1OC(=O)c3cc(O)c(c(c3c3c(O)c(O)c4c5c3c(=O)oc3c5c(c(c5c(C(=O)OCC1O)cc(O)c(c5O)O)c(O)c3O)c(=O)o4)O)O)cc(c(c2O)O)O)O)OC48H28O301084.723017−3.7630.17
64PhytolOC/C=C(/CCC[C@@H](CCC[C@@H](CCCC(C)C)C)C)\CC20H40O296.53115.2510.55
65Ursolic acidC[C@@H]1CC[C@]2([C@@H]([C@H]1C)C1=CC[C@H]3[C@@]([C@@]1(CC2)C)(C)CC[C@@H]1[C@]3(C)CC[C@@H](C1(C)C)O)C(=O)OC30H48O3456.7325.8210.85
66β-Peltoboykinolic acid, 3-β-hydroxyolean-12-en-27-oic acidO[C@H]1CC[C@]2([C@H](C1(C)C)CC[C@@]1([C@@H]2CC=C2[C@]1(CC[C@@]1([C@H]2CC(C)(C)CC1)C)C(=O)O)C)CC30H48O3456.7325.8210.85
673α-3, 19-dihydroxyurs-12-en-28-oic acid, pomolic acidO[C@H]1CC[C@]2([C@H](C1(C)C)CC[C@@]1([C@@H]2CC=C2[C@@]1(C)CC[C@@]1([C@H]2[C@](C)(O)[C@H](C)CC1)C(=O)O)C)CC30H48O4472.7434.9710.56
68 PunicalinOC1O[C@@H]2COC(=O)c3cc(O)c(c(c3c3c(O)c(O)c4c5c3c(=O)oc3c(c(c(c6c(C(=O)O[C@H]2[C@@H]([C@H]1O)O)cc(O)c(c6O)O)c(c(=O)o4)c53)O)O)O)OC34H22O22782.532213−2.8330.17
69Eryzerin A, (+/−)-7,2′,4′-Trihydroxy-8,3′-diprenylisoflavanoneCC(=CCc1c(O)ccc(c1O)[C@H]1COc2c(C1=O)ccc(c2CC=C(C)C)O)CC25H28O5408.49532.8200.55
70Eryzerin B, (3R)-7,4′-Dihydroxy-2′-methoxy,6,8-diprenylisoflavanoneCOc1cc(O)ccc1[C@@H]1COc2c(C1=O)cc(c(c2CC=C(C)C)O)CC=C(C)CC26H30O5422.51523.0300.55
71PhaseollidinCC(=CCc1c(O)ccc2c1O[C@@H]1[C@H]2COc2c1ccc(c2)O)CC20H20O4324.37422.7300.55
72Eryzerin D, 2′,4′-Dihydroxy-8-prenyl-6″,6″-dimethylpyrano [2″,3″:7,6]isoflavanCC(=CCc1c2OCC(Cc2cc2c1OC(C)(C)C=C2)c1ccc(cc1O)O)CC25H28O4392.49423.7300.55
73FolitenolCC(=CCc1cc2c(cc1O)OC[C@@H]1[C@H]2Oc2c1ccc1c2C=CC(O1)(C)C)CC25H26O4390.47413.7300.55
74ErysotrineCO[C@H]1C=CC2=CCN3[C@]2(C1)c1cc(OC)c(cc1CC3)OCC19H23NO3313.39402.1300.55
75Erybraedin A, 4-Prenylphaseollidin/(6aR,11aR)-3,9-Dihydroxy-4,10-diprenylpterocarpanCC(=CCc1c(O)ccc2c1OC[C@@H]1[C@H]2Oc2c1ccc(c2CC=C(C)C)O)CC25H28O4392.49423.7300.55
76Erystagallin ACOc1ccc2c(c1CC=C(C)C)O[C@@H]1[C@@]2(O)COc2c1cc(CC=C(C)C)c(c2)OC26H30O5422.51523.100.55
77Eryzerin E, (6aS,11aS)-3,6a-Dihydroxy-9-methoxy-4,10-diprenylpterocarpanCOc1ccc2c(c1CC=C(C)C)O[C@@H]1[C@@]2(O)COc2c1ccc(c2CC=C(C)C)OC26H30O5422.51523.100.55
78Erythrabyssin II, 3,9-Dihydroxy-2,10-diprenylpterocarpanCC(=CCc1cc2c(cc1O)OCC1C2Oc2c1ccc(c2CC=C(C)C)O)CC25H28O4392.49423.7300.55
79Eryzerin C, (3R)-7,2′,4′-Trihydroxy-6,8-diprenylisoflavanCC(=CCc1cc2C[C@@H](COc2c(c1O)CC=C(C)C)c1ccc(cc1O)O)CC25H30O4394.5433.7300.55
Key: MW = molecular weight, HBA = hydrogen bond acceptor; HBD = Hydrogen bond donor; L.V. = Lipinski violations; B.S. = Bioavailability score.
Table 5. Bioactive compounds of G. perpensa and E. zeyheri with predicted targets.
Table 5. Bioactive compounds of G. perpensa and E. zeyheri with predicted targets.
S/NCompoundsCode
1Gallic acid, 4TMS derivativegallic
2Palmitic Acid, TMS derivativepalmitic
3Myristic acid, TMS derivativemyristic
4D-Arabinose, tetrakis(trimethylsilyl) ether, ethyloxime (isomer 1)arabinose
5Shikimic acid, 4TMS derivativeshikimic
6D-Fructose, 1,3,4,5,6-pentakis-O-(trimethylsilyl)-, O-methyloximefructose
7Ribitol, 5TMS derivative (UM217)ribitol
8D-Mannose, 2,3,4,5,6-pentakis-O-(trimethylsilyl)-, o-methyloxyme, (1Z)-mannose
9D-Gluconic acid, 6TMS derivative (UM147) gluconic
109,12-Octadecadienoic acid (Z,Z)-, TMS derivativeoctadeca
111-Monopalmitin, 2TMS derivativemonopalm
12L-(-)-Sorbose, pentakis(trimethylsilyl) ether, methyloxime (syn)sorbose
13D-Galactose, 2,3,4,5,6-pentakis-O-(trimethylsilyl)-, o-methyloxyme, (1E)-galactose
14D-Glucose, 2,3,4,5,6-pentakis-O-(trimethylsilyl)-, o-methyloxyme, (1E)-glucose
15D-Lyxose, 4TMS derivativelyxose
16PyrogallolGP-pyrog
17Succinic acidGP-succ
183,3′,4′-tri-O-methyl ellagic acidGP-met_ellag
19Ellagic acidGP-ellagic
202-methyl-6-(-3-methyl-2-butenyl) benzo-1,4-quinoneGP-2methy
213-hydroxy-2-methyl-5-(3-methyl-2-butenyl) benzo-1,4-quinoneGP-3hydrx
22Epinephrine, (á)-, 3TMS derivativeGP-epine
236-hydroxy-8-methyl-2,2-dimethyl-2H-benzopyranGP-6hydrx
241,1′ -biphenyl-4,4′ -diacetic acidGP-biphen
25Z-venusol, 7,8-dihydroxy-6-(hydroxymethyl)-3-[(Z)-(4-hydroxyphenyl)methylidene]tetrahydro-4aH-pyrano [2,3-b][1,4]dioxin-2-oneGP-venu
26β-sitosterolGP-sitost
273,3′,4′-tri-O-methyl ellagic acid 4-O-β-D-glucopyranosideGP-metpyra
28ursolic acidGP-ursol
29β-peltoboykinolic acid, 3-β-hydroxyolean-12-en-27-oic acidGP-pelt
303α-3, 19-dihydroxyurs-12-en-28-oic acid, pomolic acidGP-dihypo
31Eryzerin A, (+/−)-7,2′,4′-Trihydroxy-8,3′-diprenylisoflavanoneEZ-zerinA
32D-Mannitol, 6TMS derivative EZ-manni
33Eryzerin B, (3R)-7,4′-Dihydroxy-2′-methoxy,6,8-diprenylisoflavanoneEZ-zerinB
34PhaseollidinEZ-phaseo
35Eryzerin D, 2′,4′-Dihydroxy-8-prenyl-6″,6″-dimethylpyrano [2″,3″:7,6]isoflavanEZ-zerinD
36FolitenolEZ-folit
37Myo-Inositol, 6TMS derivativeEZ-myoino
38ErysotrineEZ-sotrin
39Mannonic acid, ç-lactone, 4TMS derivativeEZ-manno
40Erybraedin A, 4-Prenylphaseollidin/(6aR,11aR)-3,9-Dihydroxy-4,10-diprenylpterocarpanEZ-braeA
41Erystagallin AEZ-galinA
42Eryzerin E, (6aS,11aS)-3,6a-Dihydroxy-9-methoxy-4,10-diprenylpterocarpanEZ-zerinE
43Erythrabyssin II, 3,9-Dihydroxy-2,10-diprenylpterocarpanEZ-thrab
44Ribonic acid, 2,3,4,5-tetrakis-O-(trimethylsilyl)-, trimethylsilyl esterEZ-riboni
45α-D-(+)-Talopyranose, 5TMS derivativeEZ-talopy
46Eryzerin C, (3R)-7,2′,4′-Trihydroxy-6,8-diprenylisoflavanEZ-zerinC
Table 6. Topology parameters of important compounds and targets in the network.
Table 6. Topology parameters of important compounds and targets in the network.
CompoundBetweenness
Centrality		DegreeTargetBetweenness
Centrality		Degree
EZ-sotrin0.1960113STAT30.008873
EZ-riboni0.1061111MAPK10.013354
EZ-zerinE0.1045111ESR10.017553
EZ-galinA0.1013109JAK20.005545
EZ-phaseo0.1615103PIK3R10.002444
Ez-zerinA0.112983PTPN110.004243
GP-ursol0.067978ERBB20.006142
Arabinose0.051575PRKACA0.006337
GP-pelt0.058274CASP30.003937
GP-dihypo0.053969CCND10.003036
Key: EZ-sotrin (Erysotrine); EZ-riboni (Ribonic acid, 2,3,4,5-tetrakis-O-(trimethylsilyl)-, trimethylsilyl ester); EZ-zerinE (Eryzerin E, (6aS,11aS)-3,6a-Dihydroxy-9-methoxy-4,10-diprenylpterocarpan); EZ-galinA (Erystagallin A); EZ-phaseo (Phaseollidin); GP-ursol (ursolic acid); Arabinose; GP-pelt (β-peltoboykinolic acid, 3-β-hydroxyolean-12-en-27-oic acid); and GP-dihypo (3α-3, 19-dihydroxyurs-12-en-28-oic acid, pomolic acid).
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Olaokun, O.O. Integration of In Vitro Glucose Utilization, Metabolomics and Network Pharmacology Strategy to Explore Antidiabetic Mechanisms of Gunnera perpensa and Erythrina zeyheri Extracts. Drugs Drug Candidates 2025, 4, 51. https://doi.org/10.3390/ddc4040051

AMA Style

Olaokun OO. Integration of In Vitro Glucose Utilization, Metabolomics and Network Pharmacology Strategy to Explore Antidiabetic Mechanisms of Gunnera perpensa and Erythrina zeyheri Extracts. Drugs and Drug Candidates. 2025; 4(4):51. https://doi.org/10.3390/ddc4040051

Chicago/Turabian Style

Olaokun, Oyinlola Oluwunmi. 2025. "Integration of In Vitro Glucose Utilization, Metabolomics and Network Pharmacology Strategy to Explore Antidiabetic Mechanisms of Gunnera perpensa and Erythrina zeyheri Extracts" Drugs and Drug Candidates 4, no. 4: 51. https://doi.org/10.3390/ddc4040051

APA Style

Olaokun, O. O. (2025). Integration of In Vitro Glucose Utilization, Metabolomics and Network Pharmacology Strategy to Explore Antidiabetic Mechanisms of Gunnera perpensa and Erythrina zeyheri Extracts. Drugs and Drug Candidates, 4(4), 51. https://doi.org/10.3390/ddc4040051

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