Investigation of the Potential Mechanism of Alpinia officinarum Hance in Improving Type 2 Diabetes Mellitus Based on Network Pharmacology and Molecular Docking

Objective We used network pharmacology, molecular docking, and cellular analysis to explore the pharmacodynamic components and action mechanism of Alpinia officinarum Hance (A. officinarum) in improving type 2 diabetes mellitus (T2DM). Methods The protein-protein interaction (PPI) network, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to predict the potential targets and mechanism of A. officinarum toward improving T2DM. The first 9 core targets and potential active compounds were docked using Discovery Studio 2019. Finally, IR-HepG2 cells and qPCR were applied to determine the mRNA expression of the top 6 core targets of the PPI network. Results A total of 29 active ingredients and 607 targets of A. officinarum were obtained. T2DM-related targets overlapped with 176 targets. The core targets of the PPI network were identified as AKT serine/threonine kinase 1 (AKT1), an activator of transcription 3 (STAT3), tumor necrosis factor (TNF), tumor protein p53 (TP53), SRC proto-oncogene, nonreceptor tyrosine kinase (SRC), epidermal growth factor receptor (EGFR), albumin (ALB), mitogen-activated protein kinase 1 (MAPK1), and peroxisome proliferator-activated receptor gamma (PPARG). A. officinarum performs an antidiabetic role via the AGE-RAGE signaling pathway, the HIF-1 signaling pathway, the PI3K-AKT signaling pathway, and others, according to GO and KEGG enrichment analyses. Molecular docking revealed that the binding ability of diarylheptanoid active components in A. officinarum to core target protein was higher than that of flavonoids. The cell experiments confirmed that the A. officinarum extracts improved the glucose uptake of IR-HepG2 cells and AKT expression while inhibiting the STAT3, TNF, TP53, SRC, and EGFR mRNA expression. Conclusion A. officinarum Hance improves T2DM by acting on numerous components, multiple targets, and several pathways. Our results lay the groundwork for the subsequent research and broaden the clinical application of A. officinarum Hance.


Introduction
Type 2 diabetes mellitus (T2DM) has gradually become a global epidemic disease, infuencing global public health, and its incidence is rising. According to recent fgures issued by the International Diabetes Federation, 783 million adults are estimated to develop diabetes by 2045 [1]. Nevertheless, the pathogenesis of T2DM is intricate, including genetics, lifestyle, environment, and unidentifed factors [2]. It is characterized by persistently high blood sugar levels due to insulin defciency or insulin resistance. Currently, the treatment for T2DM is typically diet-based, supplemented with the use of oral drugs or insulin injections for patients who cannot regulate their blood glucose levels through diet management alone [3,4]. However, varying degrees of gastrointestinal events and other risks may still occur with the therapy of hypoglycemic Western medication, resulting in drug withdrawal [5]. Exploring a therapeutic drug with mild action, better efcacy, and higher safety is therefore critical for the treatment of T2DM patients.
Alpinia ofcinarum Hance (A. ofcinarum), also known as smaller galangal, is a dry rhizome of A. ofcinarum, a member of the Ginger Family Alpinia from Guangdong, Hainan, Guangxi, and Taiwan. Te main functions are to warm the stomach and stop vomiting, disperse cold, and relieve pain [6,7]. A. ofcinarum is commonly used in China and Europe to improve blood sugar levels, stomach pain, swelling, and cold [8]. It has been widely used in food favors and favorings [9,10]. Modern pharmacological studies have shown that A. ofcinarum and its extract can alleviate T2DM by promoting glucose metabolism and lowering the blood sugar levels [11][12][13]. However, the potential mechanism of A. ofcinarum against diabetes is unknown.
Owing to its characteristics of integrity and systematization, as well as based on the principle of traditional Chinese medicine (TCM) holistic view and syndrome differentiation, network pharmacology has gradually become one of the important means of predicting the mechanism of TCM treatment for various diseases [14,15]. Te molecular docking method employs a personal computer to simulate the binding conformation of ligand and receptor macromolecules and to predict their binding energies [16]. Terefore, accumulating studies have applied network pharmacology and molecular docking to clarify the pharmacodynamic basis and molecular mechanism of TCM/ drugs in treating diseases [17].
Te primary active components, prospective targets, and pharmacological mechanisms of A. ofcinarum were identifed using a method based on network pharmacology by integrating molecular docking in this study. IR-HepG2 cells and the qPCR method were employed to validate the proposed molecular mechanism. Tese fndings provide some predicted and inferred evidence for elucidating A. ofcinarum's material basis and the action mechanism in improving T2DM.

Collection of Active Compounds and Targets of
A. ofcinarum. Te active compounds of A. ofcinarum were screened through the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) database (https://old.tcmsp-e.com/tcmsp.php) [18], the Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine (BATMAN-TCM) platform database (https://bionet.ncpsb.org/batmantcm/) [19], and the Encyclopedia of Traditional Chinese Medicine (ETCM) database (https://www.tcmip.cn/ETCM/ index.php/Home/Index/index.html) [20]. Furthermore, the compounds were supplemented by reviewing the related relevant articles [21][22][23]. After merging the results to remove the duplicates, the pharmacokinetic parameters of the collected components were evaluated using the Swiss ADME (https://www.swissadme.ch/) database, which could predict the components based on their chemical structures. In vivo pharmacokinetics-related information includes gastrointestinal (GI) absorption and Lipinski's 5 rules as the most important indicators [24]. Based on the output results, the active components of A. ofcinarum were screened preliminarily.
TCMSP, BATMAN-TCM, ETCM, and the Swis-sTargetPrediction database (https://www. swisstargetprediction.ch/) [25] were referred to search for targets corresponding to the active components of A. ofcinarum. Subsequently, the target protein was transformed into the corresponding target gene through the UniProt database (https://www.uniprot.org/) [26]. Te database of A. ofcinarum compounds and their targets was constructed. Finally, the resultant components and the targets were employed to construct a component-target (C-T) network diagram of A. ofcinarum by using the Cytoscape 3.8.2 software (https://apps.cytoscape.org) [27].

Protein-Protein Interaction (PPI)
Analysis. PPI data for the intersection targets of A. ofcinarum and T2DM were obtained from the STRING database (https://string-db.org) [34]. Te protein interaction data were gathered and imported into the Cytoscape 3.8.2 software (https://apps. cytoscape.org) to create the PPI network diagram. Ten, the core targets of the network were screened by applying the degree (D), betweenness centrality (BC), and closeness centrality (CC) in the CytoNCA analysis [35,36].

Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Enrichment Analyses.
GO functional analysis and the KEGG pathway enrichment analysis were performed on the potential targets by using the Metascape database (https://metascape.org/gp/index.html#/) 2 Evidence-Based Complementary and Alternative Medicine [37]. P < 0.05 was used as the screening condition, and the top 20 signaling pathways and the top 10 GO terms were shortlisted. Moreover, the GO terms were classifed into 3 aspects, including cellular component (CC), molecular function (MF), and biological process (BP). Finally, the bioinformatics software platform (https://www. bioinformatics.com.cn/login/) was applied to visualize the ultimate outcomes.
2.1.6. Construction of the Network of "Component-Target-Pathways". Te "component-target-pathway" (C-T-P) network of A. ofcinarum to improve T2DM was constructed by the Cytoscape 3.8.2 software. Te nodes of the network were mainly composed of A. ofcinarum, active components, potential targets, the top 20 signaling pathways, and their targets screened by the KEGG pathway enrichment analysis. Moreover, the core active component was further determined based on the degree value of the node. were downloaded from the RCSB database (https://www. RCSB.org). Te proteins were added to the Discovery Studio software to remove water and ligands from the environment to defne their binding sites. After preparing the receptors and ligands, the (LibDock) docking mode was selected to perform semifexible and fast molecular docking, with the conformation set to "BEST" and the remaining parameters set to default. After the docking was complete, the successfully docked compounds were sorted in descending order by score. Te CDOCKER mode was selected to better understand the ligand-binding mechanism, and the protein receptor was docked with the ligands and positive drugs with the highest LibDock scores for fexible molecular docking. Te "Pose Cluster Radius" was set to 0.5, and the other parameters were left alone. After the docking was complete, the binding force of the successfully docked compounds was expressed as "CDOCKER ENERGY."  Figure S1.

Cell Culture and Treatment.
HepG2 cells were procured from the Zhong Qiao Xin Zhou Biotechnology (Shanghai, China) and cultured in high-glucose Dulbecco's modifed essential medium (DMEM, Gibco, United States) supplemented with 10% fetal bovine serum (FBS, Gibco) and 1% penicillin-streptomycin (Biosharp, Anhui, China) under 37°C in a 5% CO 2 cell incubator. Te cells in the log phase were used for subsequent experiments. To investigate the efect of AOE on IR, the cells from the past methods were grown in 50 mM glucose for 48 h to establish an IR-HepG2 cell model [39]. In addition, HepG2 cells were classifed into 3 groups: control group (5.5 mM DMEM), model group (50 mM DMEM), and AOE group (50 g/mL AOE + 50 mM DMEM).

Determination of Cell
Viability. Cell viability was measured by using the Cell Counting Kit-8 assay (CCK-8 assay; Beyotime, Shanghai, China). Ten, the HepG2 cells were seeded at the concentration of 1 × 10 4 cells/well in a 96well plate and cultured in the DMEM medium supplemented with 10% CS. After 24 h of cell adhesion, the cells were treated with diferent concentrations of AOE (1-200 µg/mL) for 48 h. Ten, 10 μL of the CCK8 solution was added to each well and the plate was incubated at 37°C for 1 h. Te absorbance was measured at 450 nm by using the Spectra Max plus Automatic Plate Reader (Molecular Devices, Sunnyvale, CA, USA).

Glucose Uptake Analysis.
Te glucose uptake assay was performed using the fuorescence probe (2-NBDG, Invitrogen, USA), and the HepG2 cells were cultured in a 6well plate until the cells reached >70% confuence. After IR induction and AOE treatment, the cells were stimulated in a low-glucose DMEM containing 100 nmol/L insulin for 30 min, followed by treatment with 25 mM of 2-NBDG/well for 30 min. Next, the cells were washed with cold PBS until they became colorless. Te fuorescence intensity was then read with the Gemini XPS fuorescence microplate (Molecular Devices) reader with the excitation and emission wavelengths of 485 and 535 nm, respectively.

Statistical Analysis.
Te data were expressed as the mean ± SD (n ≥ 3). Diferences between the samples were analyzed by one-way ANOVA with GraphPad Prism 8.0.1. P < 0.05 was considered to indicate statistical signifcance.

Identifcation of the Potential Targets of A. ofcinarum
Improves T2DM. Te screening of a multisource database yielded 29 active components of A. ofcinarum. As shown in Table 1, the majority of the 29 compounds were favonoids and diarylheptanoids, volatile oils, and sterols, which were regarded as the active substances of A. ofcinarum in the treatment of T2DM. After eliminating the duplicates, a total of 607 potential targets of A. ofcinarum were obtained. Te C-T network graph also includes 637 nodes (29 active ingredients, 607 related targets, and 1 plant) and 1,168 edges ( Figure 1(a)).
A total of 625, 8, 95, 125, and 78 potential T2DM therapeutic targets were screened using the GeneCards, PharmGKB, TTD, DrugBank, and OMIM databases, respectively. After removing the duplicates, 824 targets were considered potential T2DM therapeutic targets (Figure 1(b)). Subsequently, the intersection of A. ofcinarum and T2DM targets was obtained, yielding a total of 176 targets that may be regarded as A. ofcinarumpotential targets for enhancing T2DM (Figure 1(c)).

PPI Network of Potential Targets.
A PPI network was built and evaluated using the Cytoscape 3.8.2 software after predicting reliable interaction information for 176 potential targets from the STRING database. After hiding the unconnected nodes, a PPI network consisting of 170 nodes and 2,814 edges was obtained (Figure 2(a)). Te CytoNCA app was applied to analyze the centrality of each node in the PPI network, namely, the degree, betweenness centrality, and closeness centrality, to further investigate the key targets in the PPI network. Terefore, according to the results of CytoNCA, a total of 9 key nodes were further screened out, which were considered to be the core targets for A. ofcinarum against T2DM, including AKT1 (AKT serine/ threonine kinase 1), STAT3 (signal transducer and activator of transcription 3), TNF (tumor necrosis factor), TP53 (tumor protein P53), SRC (SRC proto-oncogene, nonreceptor tyrosine kinase), EGFR (epidermal growth factor receptor), ALB (albumin), MAPK1 (mitogen-activated protein kinase 1), and PPARG (peroxisome proliferatoractivated receptor gamma). Furthermore, the magnitude of the degree value indicates the importance of the node, and Figure 2(b) shows the degree values of the 9 core target proteins. It was discovered that AKT1 had the highest degree value. As a result, AKT1 was determined to be the most central target in the PPI network.

GO and KEGG Enrichment
Analyses. GO enrichment analysis was performed to explore the functions of 176 targets shared by A. ofcinarum and T2DM, yielding 2,631 GO items, including 2,324 BPs, 118 CCs, and 189 MFs terms. Considering P < 0.05 as the screening criterion, the top 10 BPs indicated that the common genes of A. ofcinarum and T2DM were mainly involved in hormone response, peptide response, organic nitrogen compound response, and circulatory system process (Figure 3(a)). Te GO keywords enriched for MFs in the enrichment analysis of CCs including membrane raft, membrane microdomain, caveola, and plasma membrane raft were mainly ligand-activated transcription factor activity, nuclear receptor activity, kinase binding, and transcription factor binding. Meanwhile, we constructed a chord diagram of the core targets and the top 10 GO terms to determine whether they are involved in the top 10 GO terms. As shown in Figure 3(b), the core targets were all involved in the top 10 GO terms, with AKT1, TNF, and SRC being implicated the most.
Te KEGG pathway enrichment analysis revealed a total of 81 pathways. Considering the enrichment value as the reference basis, we determined the top 20 pathways as the main drug pathways in Figure 3(c). Te relative enrichment analysis revealed the following signaling pathways: AGE-RAGE, HIF-1, PI3K-AKT, TNF, MAPK, and various other hormone transduction signaling pathways. Furthermore, to investigate whether core targets are involved in the top 10 signaling pathways, we constructed a chord diagram of the core targets and the top 10 signaling pathways. As shown in Figure 3(d), the core targets were all involved in the top 10 KEGG terms. AKT1 and MAPK1 are involved in the largest number of the top 10 signaling pathways. Te abovementioned results indicated that A. ofcinarum exerts anti-T2DM efcacy in multiple biological processes and signaling pathways.

Construction of the Network of "Component-Target-Pathways".
Te "component-target-pathway" network was constructed based on the top 20 signifcant KEGG signaling pathways and their corresponding targets to further investigate the molecular mechanism of A. ofcinarum to improve T2DM (Figure 4). Tis network included 225 nodes     pathways, and 1 disease). Te size of the compound node (orange node) in the network was proportional to its topological score, and the higher the degree value, the larger the node shape, the lower the degree value, and the smaller the node shape. As a result, the top 6 active ingredients (quercetin, kaempferol, 5-hydroxy-7-(4″-hydroxy-3″methoxy-phenyl)-1-phenyl-3-heptanone, 7-(4″-hydroxy-3″-methoxyphenyl)-1-phenyl-hept-4-en-3-one, galangin, and medicarpin) were screened based on the size of the network nodes, which were considered to be the main active compounds to be benefcial in the treatment of A. ofcinarum. According to the network analysis, 29 active components of A. ofcinarum might regulate multiple key proteins and act on multiple pathways to help alleviate T2DM.
To better understand the ligand-binding mechanism of A. ofcinarum active compounds, we selected the compounds with the highest ligand-binding scores from each core protein and docked them again in the CDOCKER mode. Figure 6 shows that the active ingredient in A. ofcinarum binds to the core protein primarily carbonhydrogen bond, conventional hydrogen bond, van der Waals, and others. To compare the molecular docking results, we selected resveratrol, napabucasin, thalidomide, 5fuorouracil, bosutinib, celecoxib, ulixertinib, warfarin, and rosiglitazone as positive drugs with their corresponding proteins [40][41][42][43][44][45][46][47][48]. Te active ingredient binds to AKT1, STAT3, TNF, MAPK1, ALB, and PPARG were found to be more efective than the positive drug, while binding to TP53, SRC, and EGFR was found to be slightly weaker ( Table 2). Tis observation suggested that A. ofcinarum has a benefcial efect in the treatment of IR.
Overall, the molecular docking results were consistent with the network pharmacology results, indicating that A. ofcinarum exerted an antidiabetic efect by using numerous components, multiple targets, and several pathways.

Efect of AOE on HepG2 Cell Viability.
We analyzed the viability of HepG2 cells at diferent AOE concentrations to determine the AOE concentrations that did not alter cell viability. As shown in Figure 7(a), the cell viability of each group did not change signifcantly when compared to that of the normal control group treated with AOE at diferent drug concentrations for 48 h. Terefore, the results showed that AOE at concentrations of 1-200 μg/mL had no obvious toxic efect on the HepG2 cells. Terefore, a dose of 50 μg/mL of AOE was selected for subsequent analyses.

Efect of AOE on Glucose Uptake in IR-HepG2 Cells.
To explore the efect of AOE on IR-HepG2 cells, 50 mM glucose was used to induce IR, and intracellular glucose uptake was assessed by using the 2-NBDG method. Te glucose uptake level of 3T3-L1 adipocytes was signifcantly decreased after 48 h of 50 mM glucose induction (P < 0.001) (Figure 7(b)), indicating that the IR model was successfully established. However, the results indicate that 50 μM AOE might reverse the high glucose-induced decline caused by glucose uptake (P < 0.05).

Efects of AOE on the mRNA Expression in IR-HepG2
Cells. Te frst six-core targets (i.e., AKT, STAT3, TNF, TP53, SRC, and EGFR) were determined by qPCR to validate the core targets predicted by network pharmacology. As shown in Figures 7(c)-7(h), the model group signifcantly suppressed the AKT mRNA expression when compared to the control group, indicating the occurrence of insulin signaling disorder, that is, insulin resistance. Furthermore, 50 ug/mL AOE may signifcantly increase the mRNA expression of AKT, promoting insulin signal transduction. Te STAT3, TP53, and TNF mRNA expression were signifcantly decreased under high-glucose treatment but signifcantly increased after 48 h of AOE treatment. Tese results suggest that AOE can improve insulin resistance by lowering infammation. SRC and EGFR are both involved in angiogenesis, and in the experiment, the mRNA expressions of SRC and EGFR in the model group were signifcantly decreased, which AOE signifcantly reversed. Tis fnding suggests that AOE could improve angiogenesis and alleviate T2DM complications. Furthermore, these results were consistent with those predicted by network pharmacology.
AKT1, STAT3, TNF, TP53, SRC, EGFR, PPARG, ALB, and MAPK1 were identifed as the main targets after a topological analysis of the PPI network. Te molecular docking results showed that the active components of A. ofcinarum exhibited good binding ability to the core target protein. More importantly, in vitro experiments revealed that AOE may signifcantly increase the AKT mRNA expression while inhibiting the STAT3, TNF, TP53, SRC, and EGFR mRNA expression. Phosphorylation of AKT1 by phosphoinositide 3-kinase (PI3K) promotes the   translocation of GLUT4 to the cell surface, which regulates glucose uptake, and GSK3B regulates gluconeogenesis [63]. Phosphorylation of STAT3 has been found in studies to aggravate lung injury in mice with T2DM-associated pulmonary tuberculosis [64]. TNF induces insulin resistance in adipocytes by inhibiting insulin-induced IRS1 tyrosine phosphorylation and insulin-induced glucose uptake [65].
In STZ-treated and db/db mice, P53 inhibition ameliorates mitochondrial dysfunction, and glucose intolerance [66]. Excessive SRC activation produces reactive oxygen species and impairs the pancreatic β-cells metabolic-secretory coupling [67]. In type 2 diabetic mice, Choung et al. revealed that inhibiting the EGFR activity increased glucose tolerance and insulin sensitivity [68]. Terefore, the results of the in vitro experiments indicated that AOE improves insulin resistance in IR-hepG2 cells by regulating the abovementioned core targets, further demonstrating that A. ofcinarum exerts anti-T2DM efects through multiple target multipath ways. Tis study has some limitations. First, only molecular docking and in vitro experimental validation were performed. Terefore, subsequent validation should be combined with animal models. Second, we only verifed the frst 6 core targets by qPCR. Terefore, other targets (such as PPARG and ALB) or specifc pathways (such as PI3K/AKT signaling pathway) can be selected for further verifcation. We plan to pursue these gaps as a focus of our future work so as to provide more reliable data supporting the elucidation of the treatment of T2DM with A. ofcinarum.

Conclusions
Te active ingredients of A. ofcinarum Hance were used for network pharmacological analysis and cell experimentation in this study, and the main active components, action targets, and main signaling pathways of A. ofcinarum Hance in the improvement of type 2 DM were predicted. Te main targets were AKT1, STAT3, TNF, TP53, SRC, EGFR, ALB, MAPK1, and PPARG, and the core pathways were the AGE-RAGE signaling pathway, HIF-1 signaling pathway, PI3K-Akt signaling pathway, TNF signaling pathway, and MAPK signaling pathway. Furthermore, cell experiments revealed that AOE may increase glucose uptake and AKT mRNA expression, while inhibiting the STAT3, TNF, TP53, SRC, and EGFR mRNA expression. Tese results suggest that A. ofcinarum Hance plays a role in the amelioration of T2DM through a variety of components, multiple targets, and several pathways, providing sufcient reference and guidance for future experimental research and clinical applications of A. ofcinarum Hance.

Data Availability
Te raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Disclosure
Xuguang Zhang and Xiangyi Li are co-frst authors.

Conflicts of Interest
Te authors declare that they have no conficts of interest.