Potential Mechanisms of Yiqi Jiedu Huayu Decoction in the Treatment of Diabetic Microvascular Complications Based on Network Analysis, Molecular Docking, and Experimental Validation

Xuan, Chen; Ding, Weisen; Zhan, Ling; Xiong, Yanying; Yu, Xiao; Cao, Wenfu; Luo, Yan

doi:https://doi.org/10.1155/2023/5034687

Evidence-Based Complementary and Alternative Medicine

On this page

Abstract Introduction Methods Results Discussion Conclusions Data Availability Ethical Approval Disclosure Conflicts of Interest Authors’ Contributions Acknowledgments Supplementary Materials References Copyright Related Articles

Special Issue

Herbal Medicine and Natural Products for Metabolic Syndrome and Related Disorders

View this Special Issue

Research Article | Open Access

Volume 2023 | Article ID 5034687 | https://doi.org/10.1155/2023/5034687

Potential Mechanisms of Yiqi Jiedu Huayu Decoction in the Treatment of Diabetic Microvascular Complications Based on Network Analysis, Molecular Docking, and Experimental Validation

Chen Xuan,^1,2Weisen Ding,³Ling Zhan,³Yanying Xiong,³Xiao Yu,³Wenfu Cao,²and Yan Luo^2,3

Academic Editor: Nut Koonrungsesomboon

Received24 Jun 2022

Revised10 Nov 2022

Accepted19 Jan 2023

Published10 Feb 2023

Abstract

Background. Diabetic microvascular complications are the main causes of organ dysfunction and even death in diabetic patients. Our previous studies confirmed the beneficial effects of Yiqi Jiedu Huayu Decoction (YJHD) on diabetic cardiomyopathy and diabetic nephropathy. It is not clear whether YJHD can treat multiple diabetic microvascular complications including diabetic retinopathy, diabetic cardiomyopathy, and diabetic nephropathy through some common mechanisms. Methods. TCMSP, SymMap, STITCH, Swiss Target Prediction, and SEA databases were used to collect and analyze the components and targets of YJHD. GeneCards, DrugBank, DisGeNET, OMIM, and GEO databases were used to obtain target genes for diabetic retinopathy, diabetic cardiomyopathy, and diabetic nephropathy. The GO and KEGG enrichment analyses were performed on the DAVID and STRING platforms. Molecular docking was used to evaluate the binding sites and affinities of compounds and target proteins. Animal experiments were designed to validate the network pharmacology results. Results. Through network pharmacological analysis, oxidative stress, inflammatory response, and apoptosis were identified as key pathological phenotypes for the treatment of diabetic microvascular complications with YJHD. In addition, JNK, p38, and ERK1/2 were predicted as key targets of YJHD in regulating the abovementioned pathological phenotypes. The results of animal experiments showed that YJHD could ameliorate retinal pathological changes of diabetes rats. YJHD can inhibit oxidative stress and inflammation in heart and kidney of diabetic rats. Molecular docking showed strong binding between compounds and JNK, p38, and ERK1/2. Berlambine may play a key role in the treatment process and is considered as a promising regulator of MAPK protein family. The regulatory effects of YJHD on JNK, p38, and ERK1/2 were demonstrated in animal experiments. Conclusions. YJHD may play a therapeutic role in diabetic microvascular complications by regulating oxidative stress, inflammatory response, and apoptosis. The regulation of JNK, p38, and ERK1/2 phosphorylation may be the key to its therapeutic effect.

1. Introduction

As a disease characterized by hyperglycemia, diabetes is a great threat to human health. According to the International Diabetes Federation, approximately 463 million adults suffered from diabetes in 2019. Without effective control, this data will grow by 51% in 2045 to about 700 million people [1]. Diabetic microvascular complications are one of the main causes of organ dysfunction and even death in diabetic patients [2]. Long-term hyperglycemia can damage the structure and function of the eyes, heart, and kidneys, leading to diabetic microvascular complications [3, 4]. Diabetic retinopathy (DR), diabetic cardiomyopathy (DCM), and diabetic nephropathy (DN) are three major diabetic microvascular complications.

DR is a common microvascular complication of diabetes. Approximately 80% of all patients with diabetes will eventually develop to DR. Severe DR is often associated with irreversible retinal damage and even blindness [5, 6]. Approximately 4.2 million patients died of diabetes in 2019 alone. DCM and DN are the main causes of death in diabetic patients [7]. DCM affects approximately 70% of patients with diabetes. Pathological changes in DCM include myocardial fibrosis, left ventricular hypertrophy, and impaired left ventricular systolic and diastolic function. Without effective treatment, these pathological changes will eventually lead to cardiac dysfunction, heart failure, and even death [8, 9]. At least about 30% of diabetic patients suffer from DN [10]. DN is the leading cause of end-stage renal disease (ESRD). When renal disease progresses to the ESRD stage, it tends to mean irreversible damage to the kidney [11]. Patients with diabetic microvascular complications often have to bear serious consequences if they are not treated promptly and effectively. Unfortunately, the microvascular complications of diabetes involve multiple organs and tissues, the pathogenesis of which remains unclear, and lack of effective and specific therapeutic drugs. Therefore, it is particularly important to develop an early and effective treatment for diabetic complications. In recent years, more and more studies have shown that Chinese herbs and their compounds may be the potential candidates for the treatment of diabetic microvascular complications.

Over the years, Yiqi Jiedu Huayu Decoction (YJHD) has been used to treat diabetic microvascular complications with reliable results. As a Chinese herbal formula, YJHD consists of seven different herbs, including HuangQi (Astragalus mongholicus Bunge), GeGen (Pueraria montana var. lobata (Willd.) Maesen and S. M. Almeida ex Sanjappa and Predeep), HuangQin (Scutellaria baicalensis Georgi), HuangLian (Coptis chinensis Franch), SanLeng (Sparganium stoloniferum (Buch.-Ham. ex Graebn.) Buch.-Ham. ex Juz), JiangHuang (Curcuma longa L), and SangYe (Morus alba L). Our previous results showed that YJHD was effective in improving cardiac and renal damage in diabetic rats [12, 13]. By regulating the balance of the PI3K/Akt and AMPK signaling pathways and promoting autophagy, YJHD can ameliorate renal injury and protect renal function [12]. In the heart tissue of diabetic rats, YJHD can suppress the high expression of TNF-α, NF-κB, IL-1β, and IL-6 and ameliorate the heart injury by alleviating inflammatory response [13]. However, it remains unclear whether YJHD has the same therapeutic effect on DR. In this study, we focused on the common mechanisms involved in multiple diabetic microvascular complications, and the role of YJHD in these common mechanisms.

The aim of this study is to elucidate the potential mechanisms of YJHD against multiple diabetic microvascular complications. For the purpose of this study, network pharmacology analysis, molecular docking, and animal experiments were designed. The specific experimental process is shown in Figure 1.

2. Methods

2.1. Identification of Active Compounds in YJHD

As a systems pharmacology platform of Chinese herbal medicines, TCMSP provides ADME information of compounds, which facilitates researchers to screen compounds [14]. Among these information, Oral Bioavailability (OB) and Drug-Like (DL) are parameters of interest in Chinese herbal medicine studies. Diabetes microangiopathy is a chronic complication of diabetes. Half Life (HL) is an important parameter of drug for chronic diseases. In this study, OB ≥ 30%, DL ≥ 0.18, and HL > 4 were used as criteria for screening active compounds from TCMSP [15, 16]. In addition, some compounds, such as astragaloside IV, although not meeting the screening parameters in this study, have been confirmed to be associated with the pharmacological effects of herbs. To avoid the deviation from missing these compounds, the PubMed platform was used to collect these compounds [17]. In our previous study, 15 key compounds of YJHD were identified by ultrahigh performance liquid chromatography (UHPLC), and these compounds were also included in this study (Supplementary 1) [12].

2.2. Targets Prediction and Analysis of YJHD

TCMSP, SymMap, STITCH, Swiss Target Prediction, and SEA are five different comprehensive databases that predict, screen, and collect targets based on different underlying logic [14, 18–21]. To avoid bias introduced by using a single database, these five databases were simultaneously adopted in this study.

All targets of YJHD were input into STRING platform for their protein-protein interaction (PPI) information [22]. The network connectivity degree (Degree) of these targets was scored in Cytoscape software [23]. The part of targets with degree ≥2 times of the median of all targets was considered to play a core connecting role in the PPI network. These targets were extracted and analyzed by gene ontology (GO) enrichment based on DAVID platform [24].

2.3. Target Genes Collection and Analysis of Diabetic Microvascular Complications

Target genes of diabetic microvascular complications were also derived from five commonly used databases, including GeneCards, DrugBank, DisGeNET, OMIM, and GEO [25–29]. Each of these databases has its own characteristics and limitations. To avoid the limitations introduced by different databases, this study adopted a strategy of integrating the information of these databases. Different from the other four databases, GEO provides high-throughput sequencing results of retinal tissues of DR, heart tissues of DCM, and kidney tissues of DN. These results demonstrate the difference of gene expression between the tissues with diabetic microvascular complications and normal tissues. Venn diagram was used to obtain the intersection genes of DR, DCM, and DN. These genes were then imported into DAVID platform for GO enrichment.

2.4. Analysis of Therapeutic Mechanisms Based on BP

Biological process (BP) items of YJHD targets and diabetic microvascular complications target genes were ranked by -Log10 (p.adjust), respectively. The top 20 BP were extracted as the main biological processes. Next, the common BP of YJHD and diabetic microvascular complications were obtained by intersection. Based on these BP and targets, a Targets-BP network was constructed.

2.5. Construction and Analysis of Protein-Protein Interaction Networks

The targets of YJHD and the common target genes of DR, DCM, and DN were intersected to obtain potential therapeutic targets. These targets were extracted and input into STRING platform for PPI information. Based on the PPI information of targets, Cytoscape software was used to construct the PPI network and perform topological analysis. Similarly, targets with Degree ≥2 times the median of all targets were extracted as the core targets to construct the Hub gene database.

2.6. Enrichment Analysis Based on Hub Genes

Hub genes are the focus of this research on YJHD in the treatment of diabetic microvascular complications. In this part, the GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were used to analyze Hub genes. DAVID is a comprehensive set of functional annotation tools, facilitating researchers’ understanding of the biological significance behind a large number of genes [24]. In this study, Hub genes were input into DAVID platform for GO enrichment analysis. Similarly, STRING platform was used for KEGG enrichment analysis of hub genes. The number of genes enriched in signaling pathways was used to rank the KEGG pathways. To further analyze the role of genes in pathways, Hub genes were mapped in the KEGG pathways.

2.7. Molecular Docking

The affinity between compounds and target proteins is very important information for their pharmacological effects. Autodock 4 and Autodock Vina are two commonly used software for predicting binding site and binding energy of compounds to target proteins. Compared with Autodock 4, Autodock Vina has better docking accuracy and shorter docking time [30]. Therefore, Autodock Vina was used for molecular docking in this study. In the Herbs-Compounds-Hub genes-Pathways/BP-Disease network, the corresponding relationships between JNK1, p38, ERK1, ERK2, and compounds were extracted. Then, MOL2 format files of compounds and PDB format files of proteins were obtained from ZINC and RCSB PDB databases, respectively [31, 32]. Based on these files, Autodock Vina was used for molecular docking to acquire binding site and binding energy. Finally, the binding patterns of the most affinity compounds to proteins were visualized using Discovery Studio software.

2.8. Animal Experiments

Ten-week-old male Sprague–Dawley rats (250 ± 30 g) used in this study were obtained from the animal experimental center of Chongqing Medical University. These rats were kept in a specific-pathogen-free level animal room at animal experimental center of Chongqing Medical University (SYXK 2018–0003). The animal center provides an environment of constant temperature (21 ± 1°C), constant humidity (55 ± 5%), and 12 hour alternating light and dark. All rats were free to get enough food and water. This study was approved by the ethics committee of the First Affiliated Hospital of Chongqing Medical University (permit no. 2020–454). All experimental operations in this study were carried out in accordance with the guidelines for the care and use of laboratory animals of the National Institutes of health.

A total of 28 rats were randomly divided into three groups: normal control group (8), diabetic group (10), and YJHD group (10). To construct the diabetic rat models, streptozotocin dissolved in citric acid solution was injected into the peritoneal cavity of the rats in the diabetic and YJHD groups at a dose of 60 mg/kg [12]. In order to avoid the influence of citric acid solution on the experimental results, the rats in the normal control group received intraperitoneal injection with a corresponding volume of citric acid solution. One week after streptozotocin injection, the animal model was evaluated by detecting fasting blood glucose twice per rat. When both fasting blood glucose tests were greater than 16.7 mmol/l, the diabetic model was considered to be established [12]. The drug intervention was administered by gavage once a day for 12 weeks after the establishment of the diabetic rat model. In the clinical use of YJHD granules, the daily dose for adults is 11.5 g. According to the surface area conversion ratio between rats and humans (6.3), the daily dose of rats is calculated as 1.2 g/kg. The YJHD group rats were gavaged with YJHD granule aqueous solution, while the normal control group and diabetic group rats were gavaged with corresponding doses of pure water [12]. Two rats in each of the diabetic and YJHD groups died during the 12 weeks drug intervention.

At the end of drug intervention, all rats were sacrificed under anesthesia. The eyeballs, heart, kidneys, and blood of rats were extracted and stored under suitable conditions. The eyeballs were fixed in 4% paraformaldehyde solution. The retina was then stripped for PAS staining. Part of the heart and kidney tissues was fixed in 4% paraformaldehyde solution for subsequent immunohistochemical staining. Another part of the heart and kidney tissues was snap frozen by liquid nitrogen and transferred to a −80°C refrigerator for storage. The serum was extracted from blood samples and stored in a −80°C refrigerator.

ELISA kits were used to detect the expressions of AGEs, MDA, TNF-α, IL-1β, and IL-6 in serum and tissue supernatants of the heart and kidney according to the manufacturer’s instructions. RIPA lysate with phosphatase inhibitor was used to extract proteins from heart and kidney tissues. The concentrations of these proteins were detected by BCA kit. Finally, western blotting was used to detect the protein expressions of JNK (1 : 1000; Cell Signaling Technology 9252), p-JNK (1 : 1000; Cell Signaling Technology 4668), p38 (1 : 1000; Cell Signaling Technology 8690), p-p38 (1 : 1000; Cell Signaling Technology 4511), ERK1/2 (1 : 1000; Cell Signaling Technology 4695), p-ERK1/2 (1 : 2000; Cell Signaling Technology 4370), and β-actin (1 : 1000; Cell Signaling Technology 4970) in heart and kidney tissues.

2.9. Statistical Analysis

SPSS 24.0 software (Inc., Chicago, IL, USA) was used to statistically analyze the experimental data in this study. All statistical analyses were performed using one-way ANOVA followed by the least significant difference (LSD) test or Dunnett’s T3 test. All data were expressed as the mean ± standard deviation. Results were considered statistically significant when < 0.05. GraphPad prism 8.0.1 (San Diego, CA) was used for image production.

3. Results

3.1. Active Compounds of YJHD

A total of 96 compounds were identified from YJHD based on TCMSP, PubMed, and UHPLC. OB indicates the percentage of oral drug reaching the systemic circulation. High OB is often a key indicator for oral therapeutics screening [33]. In this study, OB ≥ 30% was used as the screening standard for compounds in TCMSP. Among them, 17 compounds have OB > 60%, which have the basic characteristics and great potential to be developed into oral drugs. DL is the similarity of a compound to a known drug. Compounds with high DL have a greater possibility of becoming drugs [15]. In traditional Chinese medicine studies, DL is usually set at ≥0.18, which is also the compound screening criterion adopted in this study. HL refers to the time taken for the amount of compound in the body to fall by half, which determines the duration of the therapeutic effect that a compound can exert, HL ≤ 4 hours for rapid elimination, 4∼8 hours for intermediate elimination, and ≥8 hours for slow elimination [34]. HL > 4 hours was used as one of the compound screening criteria in this study. In addition, 47 of all 96 compounds had HL ≥ 8 hours. The YJHD-Herbs-Compound network is shown in Figure 2(a). Notably, 37 of the all 96 compounds are flavonoids, accounting for 38.5% of the total number of compounds (Figure 2(b)). Flavonoids have been shown to be beneficial in diabetes and diabetic complications [35, 36].

(a)

(b)

3.2. Putative Targets of YJHD

Based on TCMSP, SymMap, STITCH, Swiss Target Prediction, and SEA databases, there are 1,699 targets of YJHD which were extracted (Figure 3(a)). Different databases contained varying numbers of YJHD targets, of which 34 targets are common to the five databases (Figure 3(b)). Among all the YJHD targets, a total of 1,649 targets were identified by the STRING database. Based on the information from STRING database, PPI networks of the YJHD targets were constructed. In PPI network, the median target Degree was 39. In addition, 420 targets had Degree ≥78. The top 10 targets in terms of Degree were AKT1, GAPDH, TP53, ALB, TNF, IL6, INS, CTNNB1, SRC, and MYC. The DAVID platform was used for the enrichment of the YJHD targets. A total of 601 of the YJHD targets were enriched under the disease item Type 2 Diabetes. In addition, BP items were extracted from the results of the GO enrichment analysis. The top 20 BP are shown in Figure 3(c).

(a)

(b)

(c)

3.3. Target Genes for Diabetic Microvascular Complications

Based on GeneCards, DrugBank, DisGeNET, OMIM, and GEO databases, a total of 8,281 target genes of diabetic microvascular complications were extracted (Figure 4(a)). It contained 4,448 target genes for DR, 4,952 target genes for DCM, and 4,217 target genes for DN. In the GEO database, high-throughput sequencing data for DR (GSE12610), DCM (GSE4745), and DN (GSE1009) were extracted (Figure 4(b)). These data consisted of 849 differentially expressed genes for diabetic microvascular complications. Based on the Venn diagram, 1,520 common target genes of DR, DCM, and DN were extracted (Figure 4(c)). These common target genes were entered into the DAVID platform for GO enrichment. BP items were extracted from the results of GO enrichment. The top 20 BP are presented in Figure 4(d).

(a)

(b)

(c)

(d)

3.4. BP-Based Analysis of Treatment Mechanisms

The first 20 BP of YJHD and the first 20 BP of diabetic microvascular complications were taken as intersections. Based on the Venn diagram, 10 overlapping BP items were extracted (Figure 5(a)). These items were related to oxidative stress, cellular energy metabolism, and immune response. There were 527 YJHD targets and 502 target genes for diabetic microvascular complications enriched on these 10 BP entries (Figure 5(b)). Of these, 277 were identified as intersecting targets for YJHD and diabetic microvascular complications (Figure 5(c)). These intersecting targets are the bridge for YJHD to exert influence on the biological processes and pathological phenotypes of diabetic microvascular complications. Among these intersecting targets, 33 targets were identified as differentially expressed genes for diabetic microvascular complications (Figure 5(c)).

(a)

(b)

(c)

Figure 5

BP-based analysis of therapeutic mechanisms. (a) Intersection of BP items for YJHD targets and diabetic microvascular complications target genes. (b) Targets-BP network. Blue indicates the YJHD targets, purple indicates target genes of diabetic microvascular complications, and green indicates the intersection of the YJHD targets and diabetic microvascular complication target genes. (c) Intersection targets-BP network. Red indicates high expression genes and blue indicates low expression genes.

3.5. Hub Genes of YJHD for Diabetic Microvascular Complications

Based on the Venn diagram, 1,699 targets of YJHD and 1,520 common target genes of diabetic microvascular complications were taken to intersect. A total of 512 targets were extracted (Figure 6(a)). Among these, 509 targets were identified by STRING database. Based on the information provided by STRING database, the PPI network of intersecting targets was constructed (Figure 6(b)). In the PPI network, target Degree had a maximum of 336, a minimum of 1, and a median of 56. Among them, 105 targets had Degree ≥112, which were identified as the Hub genes of YJHD for the treatment of diabetic microvascular complications (Figure 6(c)). Nine Hub genes were identified as differentially expressed genes for diabetic microvascular complications by taking intersection between Hub genes and differentially expressed genes of diabetic microvascular complications.

(a)

(b)

(c)

3.6. Hub Gene-Based Enrichment Analysis

Hub genes were entered into the DAVID platform for GO enrichment analysis. The results of GO enrichment included three parts, BP, molecular function (MF), and cellular component (CC). The top 10 items of BP, MF, and CC are shown in Figure 7(a). Hub genes are widely expressed in the extracellular space, cell membrane, and cytoplasm and are associated with the binding of various cytokines, receptors, and enzymes. Based on the abovementioned biological properties, Hub genes are involved in regulating various biological processes such as cell proliferation, apoptosis, and metabolism. Notably, all 105 Hub genes were enriched in the top 10 BP items (Figure 7(b)).

(a)

(b)

(c)

(d)

(e)

(f)

The STRING database was used for the KEGG pathway enrichment. A total of 102 Hub genes were identified by STRING database. The top 20 KEGG pathways are shown in Figure 7(c). Among them, the PI3K/Akt signaling pathway has the most enriched genes with 38. The AGE-RAGE signaling pathway in diabetic complications has the smallest p adjust value and also has 31 Hub genes. In addition, several signaling pathways are associated with cellular energy metabolism, immune, and inflammatory response. 83 Hub genes are enriched in the top 10 KEGG pathways, which together form the Hub genes-pathways network (Figure 7(d)).

In the KEGG platform, the specific role of Hub genes in the AGE-RAGE signaling pathway was demonstrated by pathway mapping (Figure 7(e)). Notably, JNK, p38, and ERK1/2 play central signaling roles in a variety of biological functions mediated by the AGE-RAGE signaling pathway. Based on the integration of the abovementioned information, the Herbs-Compounds-Hub genes-BP/pathways-Disease network was constructed (Figure 7(f)).

3.7. Molecular Docking

Autodock Vina software was used for molecular docking. 59 compounds were extracted from the Herbs-Compounds-Hub genes-BP/pathways-Disease network. These compounds constitute 93 correspondences with JNK1, p38, ERK1, and ERK2. Based on the correspondence between compounds and target proteins, the Compounds-Target proteins molecular docking network was constructed (Figure 8(a)). The binding between compounds and target proteins is considered stable when binding energy < −5 kcal/mol. Among all 93 docking relations, the binding energy was as low as −5.4 kcal/mol and as high as −10.5 kcal/mol. In addition, JNK1, p38, ERK1, and ERK2 had the highest binding energy with oxysanguinarine (−10.5 kcal/mol), inophyllum E (−9.7 kcal/mol), calycosin (−8.4 kcal/mol), baicalein (−8.4 kcal/mol), and berlambine (−9.6 kcal/mol), respectively. Among them, calycosin and baicalein had the same binding energy with ERK1, both at −8.4 kcal/mol (Figure 8(a)). Notably, berlambine targeted both JNK1 (−9.5 kcal/mol), p38 (−8.8 kcal/mol), ERK1 (−7.6 kcal/mol), and ERK2 (−9.6 kcal/mol), and all had binding energies < −7 kcal/mol. Therefore, berlambine may be a potential regulator of the MAPK protein family. The 3D structure of the berlambine molecule is shown in Figure 8(b). Discovery Studio software was used to visualize the molecular docking results.

(a)

(b)

3.8. YJHD Improves the Pathological Changes of Diabetic Microvascular Complications

In previous studies, we confirmed the therapeutic effect of YJHD on DN and DCM [12, 13]. In the kidney of diabetic rats, YJHD ameliorated pathological changes such as renal enlargement, mesangial area expansion, extracellular matrix accumulation, basement membrane thickening, and podocyte injury caused by diabetes [12]. In the heart tissue of diabetic rats, YJHD ameliorated pathological changes such as cardiomyocyte hypertrophy, increased intracellular fat vacuolization, myocardial fiber disarray, and fibroplasia induced by diabetes [13]. To assess the effect of YJHD on DR, PAS staining was used to examine the vascular system of the retina in diabetic rats. Pericyte loss is one of the early features of DR and is associated with retinal vascular damage [37]. Compared with the normal control group, the retinal vascular system in the diabetic group rats showed an increase in acellular neocapillaries and a decrease in the number of pericytes. YJHD inhibited acellular capillary angiogenesis and alleviated the decrease in pericyte number compared to the diabetic group (Figure 9).

3.9. Effects of YJHD on Inflammation and Oxidative Stress

Based on network pharmacology results, inflammatory response, oxidative stress, and apoptosis were identified as key pathological phenotypes for YJHD in the treatment of diabetic microvascular complications. The inflammatory response, oxidative stress, and apoptosis interact with each other and together lead to the development and progression of diabetic microvascular complications [38, 39]. The inflammatory response and oxidative stress induced by diabetes are key factors leading to apoptosis. In the present study, the effects of YJHD on inflammatory response and oxidative stress in diabetic rats were evaluated.

The levels of AGEs and MDA correlated with the degree of oxidative stress [40]. The expression levels of AGEs and MDA were significantly elevated in the heart, kidney, and serum of diabetic rats. AGEs and MDA were significantly decreased in the YJHD group compared with the diabetic group (Figures 10(a)–10(f)). This suggests that YJHD may improve oxidative stress in diabetic rats. MPO is rich in neutrophils. In inflammatory response, MPO is released by neutrophils and thus participates in the clearance of pathogens [41]. The increase of MPO content in heart and kidney of diabetic rats indicates the occurrence of inflammation. Compared with the diabetic group, YJHD decreased the content of MPO in the heart and kidney (Figures 10(g)–10(i)). TNF-α, IL-1β, and IL-6 are three proinflammatory cytokines [42]. Our previous studies demonstrated that YJHD was effective in reducing TNF-α, IL-1β, and IL-6 overexpression in the heart of diabetic rats. In this study, we also found increased expression of TNF-α, IL-1β, and IL-6 in the kidney tissue. The expression levels of TNF-α, IL-1β, and IL-6 in the rat kidneys of the YJHD group were significantly lower than those in the diabetic group (Figures 10(j)–10(l)). This suggests that YJHD inhibits inflammation in the heart and kidney of diabetic rats.

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

(j)

(k)

(l)

3.10. Effect of YJHD on MAPK Protein Family

JNK, p38, and ERK1/2 in the MAPK protein family play central signaling roles in the AGE-RAGE signaling pathway and are the focus of this study. Western blotting was used to detect protein expression levels of JNK, p-JNK, p38, p-p38, ERK1/2, and p-ERK1/2 in the heart and kidney tissues. The expression levels of JNK, p38, and ERK1/2 in the heart and kidney of rats did not differ significantly between the groups. Compared with the normal control group, the expression levels of p-JNK, p-p38, and p-ERK1/2 in the kidney and heart of diabetic rats were increased to a different extent. This suggests that JNK, p38, and ERK1/2 are activated by phosphorylation. Compared with the diabetic group, the expression levels of p-JNK, p-p38, and p-ERK1/2 in the heart and kidney of YJHD-treated rats were significantly decreased (Figure 11). This suggests that YJHD inhibits phosphorylation of JNK, p38, and ERK1/2.

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

(j)

(k)

(l)

(m)

(n)

4. Discussion

Diabetic microvascular complications have complicated pathogenesis. Multiple factors are thought to be associated with the progression of diabetic microvascular complications. DR, DCM, and DN are three common diabetic microvascular complications [39, 43]. Based on five different databases, a total of 1,520 genes were identified as common target genes for DR, DCM, and DN. These genes hide the common pathogenesis of diabetic microvascular complications. As a traditional Chinese medicine compound composed of seven herbs, YJHD has complex chemical composition and active targets. Based on five different databases, 96 compounds met the screening criteria for this study, targeting 1,699 putative targets. By influencing these targets, YJHD is able to exert its possible pharmacological effects. Analysis of therapeutic mechanisms based on BP and Hub genes was used to elucidate the possible mechanisms of YJHD in the treatment of diabetic microvascular complications. The results of enrichment analysis consisted of multiple BP items and the KEGG pathways. They point to three key pathological phenotypes: oxidative stress, inflammatory response, and apoptosis. By cross-talk between the multiple signaling pathways, oxidative stress, inflammation, and apoptosis can affect each other [44]. Oxidative stress and inflammation are important inducers of apoptosis. Therefore, this study focused on the role of oxidative stress and inflammation.

Oxidative stress is a specific state in which the relative balance of oxidation and antioxidation in the body is tilted towards the oxidative side and is thought to play an important role in the development of diabetes and its complications [45, 46]. In the diabetic state, prolonged hyperglycemia and hyperlipidemia can cause a substantial production of reactive oxygen species (ROS) in mitochondria or cytoplasm, thereby activating oxidative stress [47]. A large number of oxidative intermediates caused by persistent oxidative stress damage the bioactive molecules of cells, including proteins, lipids, and DNA [48, 49]. The impairment of these bioactive molecules is associated with pancreatic islets β cells damage, insulin signaling dysfunction, cell apoptosis, and tissue inflammation [50]. The abnormal state of these tissues and cells plays an important role in the development of diabetes and its complications. In this study, the Hub genes were enriched in several BP items associated with oxidative stress. These items include response to oxidative stress and reactive oxygen species metabolic process. In the diabetic state, oxidative stress promotes the production of AGEs. Large amounts of AGEs activate the AGE-RAGE signaling pathway by binding to RAGE receptors, further promoting high expression of RAGE, which in turn increases tissue damage due to oxidative stress [51]. In this study, AGEs and MDA were used to evaluate oxidative stress levels. We observed significantly elevated levels of AGEs and MDA in the heart, kidney, and serum of diabetic rats, suggesting the occurrence of oxidative stress. YJHD improves oxidative stress in these tissues, consistent with our network pharmacological prediction.

Inflammation plays an important role in the occurrence and progression of many chronic diseases. The shadow of inflammation can be seen in both diabetes and diabetic complications [52]. In the persistently high glucose state, glucose polymerizes with protein, resulting in large amounts of AGEs. Overexpressed AGEs specifically bind to RAGE receptors present on the surface of fibroblasts, smooth muscle cells, endothelial cells, monocytes/macrophages, and lymphocytes, activate NF-κB through a series of intracellular signaling [53, 54]. Activated NF-κB promotes transcription of multiple genes including endothelin-1, VCAM-1, ICAM-1, E-selectin, thrombomodulin, TF, vascular endothelial growth factor, IL-1, IL-6, TNF-α, and RAGE. High expression of these cytokines induces an inflammatory response and procoagulant state leading to vascular damage [54]. In this study, Hub genes were enriched in multiple KEGG pathways associated with inflammatory responses. In the AGE-RAGE signaling pathway, YJHD may not target the RAGE receptor, but exerts an influence on the signal transduction process after activation of RAGE. YJHD target NF-κB, its upstream signaling proteins and downstream inflammatory cytokines. This may be the way it interferes with the inflammatory response. MPO is a protein rich in neutrophils. In inflammatory response, MPO is released by neutrophils and thus participates in the clearance of pathogen [41]. The level of MPO expression correlates with the degree of inflammatory response. TNF-α, IL-1β, and IL-6 are three inflammatory cytokines involved in inducing and promoting inflammation [42]. Our previous study demonstrated that YJHD was effective in reducing TNF-α, IL-1β, and IL-6 overexpression in the heart of diabetic rats [13]. In this study, we observed elevated expression levels of MPO in the heart and kidney of diabetic rats. In addition, TNF-α, IL-1β, and IL-6 were also elevated in kidney tissues of diabetic rats. After YJHD treatment, MPO in the heart and kidney, and TNF-α, IL-1β, and IL-6 in the kidney of rats all showed different degrees of reduction. This suggests that YJHD can effectively improve the inflammatory response in heart and kidney of diabetic rats. This result is consistent with the prediction of network pharmacology.

Apoptosis is one of the mechanisms of islet β cell destruction in both T1DM and T2DM and is thought to be associated with the occurrence of diabetes and its complications [55]. In addition, long-term hyperglycemia causes increased production of ROS, and a large number of ROS activates oxidative stress, further inducing islet β cell apoptosis, leading to aggravation of diabetes. Except to destroying islet β cells, apoptosis due to diabetes causes damage to the retina, heart, and kidney [56]. Inhibition of apoptosis has been confirmed to be associated with improvements in DR, DCM, and DN [57]. In this study, Hub genes were enriched in multiple BP items associated with apoptosis. These items include apoptotic process, programmed cell death, and regulation of programmed cell death. This suggests that apoptosis may be the key to YJHD in the treatment of diabetes and diabetic complications.

MAPK is a highly conserved protein class in eukaryotes. Human MAPK consists of four classical protein subfamilies, including ERK1/2 (MAPK3/1), p38 (MAPK 11-14), JNK (MAPK 8-10), and ERK5 (MAPK7). The MAPK protein family is associated with a variety of biological processes in cells [58]. Studies have shown that the MAPK protein family is activated by phosphorylation in the development of diabetes, which may be associated with tissue injury induced by diabetes [59]. In this study, the compounds in YJHD target JNK, p38, and ERK1/2 of the MAPK protein family. Notably, JNK, p38, and ERK1/2 play a central signaling role in the AGE-RAGE signaling pathway mediated diabetic complications. As shown in Figure 12, hyperglycemia, oxidative stress, and inflammation caused by diabetes increase the production of AGEs. A large number of AGEs bind to RAGE receptors and further induce and exacerbate oxidative stress, inflammatory response, and cell apoptosis by activating or upregulating the expression of RAGE, TNF-α, IL-1, IL-6, IL-8, and Caspase 3 [51]. This process requires the participation of JNK, p38, and ERK1/2. This means that JNK, p38, and ERK1/2 may be key therapeutic targets for blocking AGEs-induced tissue damage. This is the reason why the MAPK protein family is attracting increasing attention in the study of diabetes and diabetic complications. In this study, we observed increased phosphorylation of JNK, p38, and ERK1/2 in the heart and kidney of diabetic rats. In addition, YJHD inhibited phosphorylation activation of JNK, p38, and ERK1/2 to varying degrees. This suggests that the ameliorative effects of YJHD on oxidative stress and inflammatory response, as well as the potential regulatory effects on apoptosis, may be achieved by inhibiting phosphorylation of JNK, p38, and ERK1/2.

The results of molecular docking showed that the compounds in YJHD had high affinity to JNK, p38, ERK1, and ERK2, and stable binding could occur. This is the basis for YJHD to play its pharmacological role. Notably, where Notably, berlambine targets JNK, p38, ERK1, and ERK2, and all have binding energies < −7 kcal/mol. Berlambine is an alkaloid with molecular weight of 351.4 Dalton and is mainly rich in Coptis chinensis. According to Lipinski’s rule of five, compounds with a molecular weight of 180–500 Dalton have the potential to become oral drugs in humans [60]. In addition, OB and DL of berlambine were 36.7% and 0.82, which means that berlambine has great potential to become an oral drug. HL for berlambine is 7.3 hours, which means friendly dosing frequency for patients. All of these properties suggest that berlambine has the potential to become an oral drug. Therefore, berlambine is considered a promising regulator of the MAPK protein family. More specific effects of berlambine on JNK, p38, ERK1, and ERK2 warrant were further studied.

Overall, this study predicted and evaluated the common mechanisms of YJHD in the treatment of DR, DCM, and DN. However, there are still some deficiencies in this study. 96 compounds that meet the parameters set in this study are considered to be the main therapeutic component of YJHD, but they are not strictly representative of YJHD. In this study, network pharmacology was used to predict the mechanism of YJHD in the treatment of diabetic microvascular complications. Databases are the basis for all network pharmacology analyses. Due to the imperfection of the database, there is an inevitable deviation between the results of network pharmacology and the reality. This study minimizes the bias caused by database defects by integrating multiple databases. Molecular docking was used to validate the binding of compounds and proteins. In the process of molecular docking, compounds and proteins are placed in an ideal environment in which water molecules and charges are removed. However, unlike the ideal environment, the real situation is that compounds and proteins are in a complex environment of force and electric fields. These differences will inevitably lead to a discrepancy between the molecular docking results and the real situation.

5. Conclusions

YJHD may play a therapeutic role in diabetic microvascular complications including DR, DCM, and DN by regulating oxidative stress, inflammatory response, and apoptosis. The regulation of phosphorylation of JNK, p38, and ERK1/2 may be crucial for the therapeutic effects of YJHD. Berlambine may play a key role in the treatment and is a promising regulator of the MAPK protein family. Our results provide the basis for further research in the future.

Data Availability

The datasets used to support this study are available from the corresponding authors upon request.

Ethical Approval

The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). All animal experiments were conducted in compliance with the National Institute of Health Guide for the Care and Use of Laboratory Animals, and were approved by the Ethics Committee of The First Affiliated Hospital of Chongqing Medical University (permit no. 2020-454).

Disclosure

Chen Xuan and Weisen Ding are regarded as the co-first authors.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this article.

Authors’ Contributions

Chen Xuan and Weisen Ding contributed equally to this work.

Acknowledgments

This work was supported by the Foundation of Performance Incentive and Guidance Project of Chongqing Scientific Research Institutions (grant nos. cstc2021jxjlAX0003 and cstc2019jxjl130022); General Program of Chongqing Natural Science Foundation (grant no. cstc2020jcyjmsxmX0036), and Special program of Chengdu University of Traditional Chinese Medicine (grant nos. YYZX2021077, YYZX2020068, and YYZX2021068).

Supplementary Materials

Supplementary 1: TIC diagram of UHPLC-QExactive-MS detection of YJHD granule aqueous solution. (Supplementary Materials)

References

International Diabetes Federation, Global Fact Sheet 2019, IDF Diabetes Atlas, Brussels B-1000, Belgium, 9th Edition edition, 2019.
C. Maric-Bilkan, “Sex differences in micro- and macro-vascular complications of diabetes mellitus,” Clinical Science, vol. 131, no. 9, pp. 833–846, 2017.
View at: Publisher Site | Google Scholar
J. S. Skyler, G. L. Bakris, E. Bonifacio et al., “Differentiation of diabetes by pathophysiology, natural history, and prognosis,” Diabetes, vol. 66, no. 2, pp. 241–255, 2017.
View at: Publisher Site | Google Scholar
C. J. McElwain, F. P. McCarthy, and C. M. McCarthy, “Gestational diabetes mellitus and maternal immune dysregulation: what we know so far,” International Journal of Molecular Sciences, vol. 22, no. 8, p. 4261, 2021.
View at: Publisher Site | Google Scholar
K. Antosik and M. Borowiec, “Genetic factors of diabetes,” Archivum Immunologiae et Therapiae Experimentalis, vol. 64, no. S1, pp. 157–160, 2016.
View at: Publisher Site | Google Scholar
A. Karaa and A. Goldstein, “The spectrum of clinical presentation, diagnosis, and management of mitochondrial forms of diabetes,” Pediatric Diabetes, vol. 16, no. 1, pp. 1–9, 2015.
View at: Publisher Site | Google Scholar
S. Demir, P. P. Nawroth, S. Herzig, and B. Ekim Ustunel, “Emerging targets in type 2 diabetes and diabetic complications,” Advanced Science, vol. 8, no. 18, Article ID e2100275, 2021.
View at: Publisher Site | Google Scholar
J. Wang, X. Huang, H. Liu et al., “Empagliflozin ameliorates diabetic cardiomyopathy via attenuating oxidative stress and improving mitochondrial function,” Oxidative Medicine and Cellular Longevity, vol. 2022, Article ID 1122494, 16 pages, 2022.
View at: Publisher Site | Google Scholar
S. Boudina and E. D. Abel, “Diabetic cardiomyopathy, causes and effects,” Reviews in Endocrine & Metabolic Disorders, vol. 11, no. 1, pp. 31–39, 2010.
View at: Publisher Site | Google Scholar
J. E. Kanter and K. E. Bornfeldt, “Impact of diabetes mellitus,” Arteriosclerosis, Thrombosis, and Vascular Biology, vol. 36, no. 6, pp. 1049–1053, 2016.
View at: Publisher Site | Google Scholar
American Diabetes Association, R. A. DeFronzo, and M. J. Franz, “Nephropathy in diabetes,” Diabetes Care, vol. 27, no. 1, pp. S79–S83, 2004.
View at: Publisher Site | Google Scholar
C. Xuan, Y. M. Xi, Y. D. Zhang, C. H. Tao, L. Y. Zhang, and W. F. Cao, “Yiqi Jiedu Huayu decoction alleviates renal injury in rats with diabetic nephropathy by promoting autophagy,” Frontiers in Pharmacology, vol. 12, Article ID 624404, 2021.
View at: Publisher Site | Google Scholar
C. Xuan, Y. Luo, Y. Xiong, Y. Zhang, C. Tao, and W. Cao, “Multitarget mechanism of Yiqi Jiedu Huayu decoction on diabetic cardiomyopathy based on network pharmacology,” European Journal of Integrative Medicine, vol. 47, Article ID 101388, 2021.
View at: Publisher Site | Google Scholar
J. Ru, P. Li, J. Wang et al., “TCMSP: a database of systems pharmacology for drug discovery from herbal medicines,” Journal of Cheminformatics, vol. 6, no. 1, p. 13, 2014.
View at: Publisher Site | Google Scholar
W. Tao, X. Xu, X. Wang et al., “Network pharmacology-based prediction of the active ingredients and potential targets of Chinese herbal Radix Curcumae formula for application to cardiovascular disease,” Journal of Ethnopharmacology, vol. 145, no. 1, pp. 1–10, 2013.
View at: Publisher Site | Google Scholar
W. Guo, J. Huang, N. Wang et al., “Integrating network pharmacology and pharmacological evaluation for deciphering the action mechanism of herbal formula zuojin pill in suppressing hepatocellular carcinoma,” Frontiers in Pharmacology, vol. 10, p. 1185, 2019.
View at: Publisher Site | Google Scholar
N. Fiorini, D. J. Lipman, and Z. Lu, “Towards PubMed 2.0,” Elife, vol. 6, Article ID e28801, 2017.
View at: Publisher Site | Google Scholar
Y. Wu, F. Zhang, K. Yang et al., “SymMap: an integrative database of traditional Chinese medicine enhanced by symptom mapping,” Nucleic Acids Research, vol. 47, no. D1, pp. D1110–D1117, 2019.
View at: Publisher Site | Google Scholar
D. Szklarczyk, A. Santos, C. von Mering, L. J. Jensen, P. Bork, and M. Kuhn, “Stitch 5: augmenting protein-chemical interaction networks with tissue and affinity data,” Nucleic Acids Research, vol. 44, no. D1, pp. D380–D384, 2016.
View at: Publisher Site | Google Scholar
D. Gfeller, A. Grosdidier, M. Wirth, A. Daina, O. Michielin, and V. Zoete, “SwissTargetPrediction: a web server for target prediction of bioactive small molecules,” Nucleic Acids Research, vol. 42, no. 1, pp. W32–W38, 2014.
View at: Publisher Site | Google Scholar
M. J. Keiser, B. L. Roth, B. N. Armbruster, P. Ernsberger, J. J. Irwin, and B. K. Shoichet, “Relating protein pharmacology by ligand chemistry,” Nature Biotechnology, vol. 25, no. 2, pp. 197–206, 2007.
View at: Publisher Site | Google Scholar
D. Szklarczyk, A. L. Gable, K. C. Nastou et al., “The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets,” Nucleic Acids Research, vol. 49, no. 1, pp. D605–D612, 2021.
View at: Publisher Site | Google Scholar
N. T. Doncheva, J. H. Morris, J. Gorodkin, and L. J. Jensen, “Cytoscape StringApp: network analysis and visualization of proteomics data,” Journal of Proteome Research, vol. 18, no. 2, pp. 623–632, 2019.
View at: Publisher Site | Google Scholar
B. T. Sherman, M. Hao, and J. Qiu, “DAVID: A Web Server for Functional Enrichment Analysis and Functional Annotation of Gene Lists (2021 Update),” Nucleic Acids Res, vol. 50, no. 1, pp. W216–W221, 2022.
View at: Publisher Site | Google Scholar
M. Safran, I. Dalah, and J. Alexander, “GeneCards Version 3: the human gene integrator,” Database (Oxford), vol. 2010, Article ID baq020, 2010.
View at: Publisher Site | Google Scholar
D. S. Wishart, Y. D. Feunang, A. C. Guo et al., “DrugBank 5.0: a major update to the DrugBank database for 2018,” Nucleic Acids Research, vol. 46, no. D1, pp. D1074–D1082, 2018.
View at: Publisher Site | Google Scholar
J. Piñero, J. M. Ramírez-Anguita, J. Saüch-Pitarch et al., “The DisGeNET knowledge platform for disease genomics: 2019 update,” Nucleic Acids Research, vol. 48, no. 1, pp. D845–D855, 2020.
View at: Publisher Site | Google Scholar
J. S. Amberger, C. A. Bocchini, F. Schiettecatte, A. F. Scott, and A. Hamosh, “OMIM.org: online Mendelian Inheritance in Man (OMIM®) an online catalog of human genes and genetic disorders,” Nucleic Acids Research, vol. 43, no. 1, pp. D789–D798, 2015.
View at: Publisher Site | Google Scholar
T. Barrett, S. E. Wilhite, P. Ledoux et al., “NCBI GEO: archive for functional genomics data sets--update,” Nucleic Acids Research, vol. 41, no. 1, pp. D991–D995, 2012.
View at: Publisher Site | Google Scholar
O. Trott and A. J. Olson, “AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading,” Journal of Computational Chemistry, vol. 31, no. 2, pp. 455–461, 2010.
View at: Publisher Site | Google Scholar
J. J. Irwin and B. K. Shoichet, “ZINC--a free database of commercially available compounds for virtual screening,” Journal of Chemical Information and Modeling, vol. 45, no. 1, pp. 177–182, 2005.
View at: Publisher Site | Google Scholar
Y. Rose, J. M. Duarte, R. Lowe et al., “RCSB protein data bank: architectural advances towards integrated searching and efficient access to macromolecular structure data from the PDB archive,” Journal of Molecular Biology, vol. 433, no. 11, Article ID 166704, 2021.
View at: Publisher Site | Google Scholar
X. Xu, W. Zhang, C. Huang et al., “A novel chemometric method for the prediction of human oral bioavailability,” International Journal of Molecular Sciences, vol. 13, no. 6, pp. 6964–6982, 2012.
View at: Publisher Site | Google Scholar
H. Yang, W. Zhang, C. Huang et al., “A novel systems pharmacology model for herbal medicine injection: a case using Reduning injection,” BMC Complementary and Alternative Medicine, vol. 14, no. 1, p. 430, 2014.
View at: Publisher Site | Google Scholar
A. Tanveer, K. Akram, U. Farooq, Z. Hayat, and A. Shafi, “Management of diabetic complications through fruit flavonoids as a natural remedy,” Critical Reviews in Food Science and Nutrition, vol. 57, no. 7, pp. 1411–1422, 2017.
View at: Publisher Site | Google Scholar
J. Chen, S. Mangelinckx, A. Adams, and Z. N. Wang, “Natural flavonoids as potential herbal medication for the treatment of diabetes mellitus and its complications,” Natural Product Communications, vol. 10, no. 1, pp. 187–200, 2015.
View at: Publisher Site | Google Scholar
H. P. Hammes, Y. Feng, F. Pfister, and M. Brownlee, “Diabetic retinopathy: targeting vasoregression,” Diabetes, vol. 60, no. 1, pp. 9–16, 2011.
View at: Publisher Site | Google Scholar
K. Turkmen, “Inflammation, oxidative stress, apoptosis, and autophagy in diabetes mellitus and diabetic kidney disease: the Four Horsemen of the Apocalypse,” International Urology and Nephrology, vol. 49, no. 5, pp. 837–844, 2017.
View at: Publisher Site | Google Scholar
A. Avogaro and G. P. Fadini, “Microvascular complications in diabetes: a growing concern for cardiologists,” International Journal of Cardiology, vol. 291, pp. 29–35, 2019.
View at: Publisher Site | Google Scholar
J. Frijhoff, P. G. Winyard, N. Zarkovic et al., “Clinical relevance of biomarkers of oxidative stress,” Antioxidants and Redox Signaling, vol. 23, no. 14, pp. 1144–1170, 2015.
View at: Publisher Site | Google Scholar
K. D. Metzler, T. A. Fuchs, W. M. Nauseef et al., “Myeloperoxidase is required for neutrophil extracellular trap formation: implications for innate immunity,” Blood, vol. 117, no. 3, pp. 953–959, 2011.
View at: Publisher Site | Google Scholar
M. Kapoor, J. Martel-Pelletier, D. Lajeunesse, J. P. Pelletier, and H. Fahmi, “Role of proinflammatory cytokines in the pathophysiology of osteoarthritis,” Nature Reviews Rheumatology, vol. 7, no. 1, pp. 33–42, 2011.
View at: Publisher Site | Google Scholar
E. J. Barrett, Z. Liu, M. Khamaisi et al., “Diabetic microvascular disease: an endocrine society scientific statement,” Journal of Clinical Endocrinology and Metabolism, vol. 102, no. 12, pp. 4343–4410, 2017.
View at: Publisher Site | Google Scholar
Y. Lin, M. Jiang, W. Chen, T. Zhao, and Y. Wei, “Cancer and ER stress: mutual crosstalk between autophagy, oxidative stress and inflammatory response,” Biomedicine & Pharmacotherapy, vol. 118, Article ID 109249, 2019.
View at: Publisher Site | Google Scholar
P. Rsen, P. P. Nawroth, G. King, W. Mller, H. J. Tritschler, and L. Packer, “The role of oxidative stress in the onset and progression of diabetes and its complications: a summary of a Congress Series sponsored by UNESCO-MCBN, the American Diabetes Association and the German Diabetes Society,” Diabetes Metab Res Rev, vol. 17, no. 3, pp. 189–212, 2001.
View at: Publisher Site | Google Scholar
H. Sies, “Oxidative stress: a concept in redox biology and medicine,” Redox Biology, vol. 4, pp. 180–183, 2015.
View at: Publisher Site | Google Scholar
F. Giacco and M. Brownlee, “Oxidative stress and diabetic complications,” Circulation Research, vol. 107, no. 9, pp. 1058–1070, 2010.
View at: Publisher Site | Google Scholar
D. Wu and A. I. Cederbaum, “Alcohol, oxidative stress, and free radical damage,” Alcohol Research & Health, vol. 27, no. 4, pp. 277–284, 2003.
View at: Google Scholar
J. R. Speakman and C. Selman, “The free-radical damage theory: accumulating evidence against a simple link of oxidative stress to ageing and lifespan,” BioEssays, vol. 33, no. 4, pp. 255–259, 2011.
View at: Publisher Site | Google Scholar
J. L. Evans, I. D. Goldfine, B. A. Maddux, and G. M. Grodsky, “Are oxidative stress-activated signaling pathways mediators of insulin resistance and beta-cell dysfunction?” Diabetes, vol. 52, no. 1, pp. 1–8, 2003.
View at: Publisher Site | Google Scholar
S. Yamagishi, K. Fukami, and T. Matsui, “Crosstalk between advanced glycation end products (AGEs)-receptor RAGE axis and dipeptidyl peptidase-4-incretin system in diabetic vascular complications,” Cardiovascular Diabetology, vol. 14, no. 1, p. 2, 2015.
View at: Publisher Site | Google Scholar
E. Lontchi-Yimagou, E. Sobngwi, T. E. Matsha, and A. P. Kengne, “Diabetes mellitus and inflammation,” Current Diabetes Reports, vol. 13, no. 3, pp. 435–444, 2013.
View at: Publisher Site | Google Scholar
S. Yamagishi and T. Matsui, “Advanced glycation end products, oxidative stress and diabetic nephropathy,” Oxidative Medicine and Cellular Longevity, vol. 3, no. 2, pp. 101–108, 2010.
View at: Publisher Site | Google Scholar
C. Giannini, A. Mohn, F. Chiarelli, and C. J. H. Kelnar, “Macrovascular angiopathy in children and adolescents with type 1 diabetes,” Diabetes Metab Res Rev, vol. 27, no. 5, pp. 436–460, 2011.
View at: Publisher Site | Google Scholar
S. C. Lee and S. Pervaiz, “Apoptosis in the pathophysiology of diabetes mellitus,” The International Journal of Biochemistry & Cell Biology, vol. 39, no. 3, pp. 497–504, 2007.
View at: Publisher Site | Google Scholar
C. M. O. Volpe, P. H. Villar-Delfino, P. M. F. Dos Anjos, and J. A. Nogueira-Machado, “Cellular death, reactive oxygen species (ROS) and diabetic complications,” Cell Death & Disease, vol. 9, no. 2, p. 119, 2018.
View at: Publisher Site | Google Scholar
S. Zheng, K. Huang, and T. Tong, “Efficacy and mechanisms of oleuropein in mitigating diabetes and diabetes complications,” Journal of Agricultural and Food Chemistry, vol. 69, no. 22, pp. 6145–6155, 2021.
View at: Publisher Site | Google Scholar
C. Zhang, Z. Huang, J. Gu et al., “Fibroblast growth factor 21 protects the heart from apoptosis in a diabetic mouse model via extracellular signal-regulated kinase 1/2-dependent signalling pathway,” Diabetologia, vol. 58, no. 8, pp. 1937–1948, 2015.
View at: Publisher Site | Google Scholar
J. Choi, K. J. Kim, E. J. Koh, and B. Y. Lee, “Gelidium elegans extract ameliorates type 2 diabetes via regulation of MAPK and PI3K/akt signaling,” Nutrients, vol. 10, no. 1, p. 51, 2018.
View at: Publisher Site | Google Scholar
C. A. Lipinski, F. Lombardo, B. W. Dominy, and P. J. Feeney, “Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings,” Advanced Drug Delivery Reviews, vol. 23, no. 1-3, pp. 3–25, 1997.
View at: Publisher Site | Google Scholar

Copyright

Copyright © 2023 Chen Xuan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation

Download other formats

Order printed copies

Views

327

Downloads

277

Citations