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The effect and mechanism of Jiao-tai-wan in the treatment of diabetes mellitus with depression based on network pharmacology and experimental analysis

Molecular Medicine volume 27, Article number: 154 (2021) Cite this article

Abstract

Background

The incidence of diabetes mellitus (DM) and depression is increasing year by year around the world, bringing a serious burden to patients and their families. Jiao-tai-wan (JTW), a well-known traditional Chinese medicine (TCM), has been approved to have hypoglycemic and antidepressant effects, respectively, but whether JTW has such dual effects and its potential mechanisms is still unknown. This study is to evaluate the dual therapeutic effects of JTW on chronic restraint stress (CRS)-induced DM combined with depression mice, and to explore the underlying mechanisms through network pharmacology.

Methods

CRS was used on db/db mice for 21 days to induce depression-like behaviors, so as to obtain the DM combined with depression mouse model. Mice were treated with 0.9% saline (0.1 ml/10 g), JTW (3.2 mg/kg) and Fluoxetine (2.0 mg/kg), respectively. The effect of JTW was accessed by measuring fasting blood glucose (FBG) levels, conducting behavioral tests and observing histopathological change. The ELISA assay was used to evaluate the levels of inflammatory cytokines and the UHPLC-MS/MS method was used to determine the depression-related neurotransmitters levels in serum. The mechanism exploration of JTW against DM and depression were performed via a network pharmacological method.

Results

The results of blood glucose measurement showed that JTW has a therapeutic effect on db/db mice. Behavioral tests and the levels of depression-related neurotransmitters proved that JTW can effectively ameliorate depression-like symptoms in mice induced by CRS. In addition, JTW can also improve the inflammatory state and reduce the number of apoptotic cells in the hippocampus. According to network pharmacology, 28 active compounds and 484 corresponding targets of JTW, 1407 DM targets and 1842 depression targets were collected by screening the databases, and a total of 117 targets were obtained after taking the intersection. JTW plays a role in reducing blood glucose level and antidepressant mainly through active compounds such as quercetin, styrene, cinnamic acid, ethyl cinnamate, (R)-Canadine, palmatine and berberine, etc., the key targets of its therapeutic effect include INS, AKT1, IL-6, VEGF-A, TNF and so on, mainly involved in HIF-1 signal pathway, pathways in cancer, Hepatitis B, TNF signal pathway, PI3K-Akt signal pathway and MAPK signaling pathway, etc.

Conclusion

Our experimental study showed that JTW has hypoglycemic and antidepressant effects. The possible mechanism was explored by network pharmacology, reflecting the characteristics of multi-component, multi-target and multi-pathway, which provides a theoretical basis for the experimental research and clinical application of JTW in the future.

Introduction

Diabetes mellitus (DM) is a metabolic disease characterized by hyperglycemia with a rapidly increasing prevalence. According to the International Diabetes Federation (IDF), about 463 million adults worldwide suffered from DM in 2019, and the number is expected to reach 700 million in 2045 (IDF 2019). China has the largest number of DM patients in the world, with an incidence of 11.2% (Li et al. 2020). Not only DM itself is seriously harmful to human health, but also the complications will bring a heavy burden to the family and society. Depression, one of the most prevalent disorders of mental health that limits psychosocial functioning and diminishes quality of life, is a common psychological complication of diabetes (Malhi and Mann 2018). A meta-analysis showed that 14.5% of patients with type 2 diabetes mellitus (T2DM) were complicated with depression (Wang et al. 2019), and depression is twice as common in people with DM as in the general population (Moulton et al. 2015). Long-term depression not only affects patients' compliance with treatment, but also causes neuroendocrine dysfunction and increases blood glucose level, and poor blood glucose control will aggravate patients' depression. Therefore, it is urgent to strengthen early identification and give corresponding psychological or drug intervention.

At present, the commonly used antidepressants are selective serotonin reuptake inhibitors (SSRIs) and serotonin and noradrenaline reuptake inhibitors (SNRIs). According to reports, psychopharmacological treatment with SSRIs medications has a moderate-to-large effect on depression with lesser effects on glycemic control (Sartorius 2018). Another cohort study showed that antidepressant use is associated with the risk of diabetes onset in a time- and dose-dependent manner, the adjusted hazard ratio is 3.95 for long-term high-dose antidepressant use (Miidera et al. 2020). Hence in-depth exploration of the pathogenesis and development of drugs with the dual effects of improving of DM and depression is imminent.

Traditional Chinese medicine (TCM) is a holistic medical system which uses experience-based therapies such as acupuncture and herbal medicine (Xu et al. 2013). Jiao-tai-wan (JTW), originated from the Han Shi Yi Tong in the Ming Dynasty, is composed of two herbal medicines: Huanglian (HL, Rhizoma Coptidis) and Rougui (RG, Cinnamon), and it has been used to treat insomnia since ancient times. With the development of science and technology and the verification of experiments, the therapeutic effect of JTW on DM has been discovered (Zou et al. 2017; Chen et al. 2013, 2017). Since insomnia and depression are closely related, in recent years, more and more studies have confirmed that JTW have obvious antidepressant effects in addition to reducing blood glucose level and improving sleep quality. Experimental research results showed that JTW can significantly alleviate the depressive-like behavior of mice (Zhe et al. 2017; Xiang et al. 2020). Therefore, JTW may have dual effects in the treatment of DM and depression. However, whether JTW has such dual effects and its related mechanisms are not yet clear, especially the molecular target mechanisms of its effective components, which needs to be further explored.

Network pharmacology is a new discipline based on the theories of systems biology, bioinformatics and classical pharmacology. The new method for analyzing the targets and mechanisms of drug treatment of diseases from multiple angles provides the possibility to reveal the mechanism of TCM, which is booming in recent years and has been widely used in the field of TCM (Chen et al. 2018). Network pharmacology, as a useful tool, can help us to further understand the role of drugs and how we can improve drug discovery for complex diseases (Hopkins 2007).

Therefore, in our present study, we established a mouse model of DM combined with depression induced by chronic restraint stress (CRS) to evaluate the dual therapeutic effects of JTW, and the underlying mechanisms were explored through network pharmacology. The study procedure is shown in Fig. 1.

Fig. 1
figure1

Workflow of the study of JTW on diabetes mellitus and depression and mechanism exploration based on network pharmacology analysis. TCMSP Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform; BATMAN-TCM Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine; OMIM Online Mendelian Inheritance in Man; TTD Therapeutic Target Database; JTW Jiao-tai-wan; PPI protein–protein interaction; D-C-T-D Drug-Compounds-Targets-Disease; GO Gene Ontology; KEGG Kyoto Encyclopedia of Genes and Genomes

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Materials and methods

Preparation of JTW and Fluoxetine

The ratio of HL and RG in JTW is 10:1 (w/w). Both HL and RG herbal concentrate-granules were purchased from China Resources Sanjiu Medical and Pharmaceutical Co., Ltd (Guangdong, China). As 1 g of HL and RG granules is as efficacious as 6 g of HL and 3 g of RG decoction pieces, the two granules were mixed at a ratio of 5:1 (w/w) and dissolved in ddH2O. Fluoxetine, which is the most commonly used drug for depression, was chosen as the positive control. The Fluoxetine pills were obtained from PATHEON FRANCE Co., Ltd. and were ground into powder and mixed with ddH2O sufficiently before gavage.

Animals

The animal experiment was overseen and approved by the Animal Ethics Committee of Tongji Medical College, Huazhong University of Science and Technology. Twenty-four db/db mice (male, aged 7 weeks) were purchased from GemPharmatech Co., Ltd (Jiangsu, China) and maintained in the experimental animal center of Tongji Hospital (SPF-grade) with environmental conditions of 20 ± 2 °C, 60 ± 5% relative humidity and 12 h dark/light cycle.

Preparation of animal model and treatment

Since db/db mice are spontaneous T2DM model mice, chronic restraint stress (CRS) was used to induce depression-like behaviors to obtain the DM combined with depression mouse model. After 1-week of acclimatization, the mice were randomly divided into four groups (six mice in each group): (1) Normal control group (Control), (2) Chronic restraint stress group (Model), (3) Jiao-tai-wan treatment group (JTW), (4) Fluoxetine treatment group (Fluoxetine). Mice in the JTW group and Fluoxetine group were gavaged daily with Jiao-tai-wan (3.2 mg/kg) and Fluoxetine (2.0 mg/kg), respectively, while the control and model groups received vehicle (0.9% saline). Drug treatment lasted from the 1st day of CRS until the end of this study. During the study, body weight of each mouse was measured every 3 days and fasting blood glucose (FBG) was measured once a week. At the last day of CRS, a glucose tolerance test (GTT) was performed on each mouse, all mice were fasted overnight and gavaged with glucose (2 g/kg), then the tail vein blood glucose was measured at 0, 30, 60, 90 and 120 min after gavage by glucose strip (ACCU-CHEK Performa, Roche).

Except for the control group, mice were exposed to CRS by placing in 100 ml plastic tubes with a few holes to keep air flow for 4 h per day for 21 days, they were able to move their forelimbs and head, but not their body. Mice in the control group were fasted at the same time without restraint, when the restraint procedure finished, the mice were returned to their cages immediately. Behavioral tests were then performed.

Behavioral tests

Forced swimming test (FST)

The FST was based on a previously described with slight modifications (Slattery and Cryan 2012). Mice were placed separately in a clear glass cylinder (30 cm in height and 18 cm in diameter) filled with 20 cm water (24 ± 2 °C). The first 2 min of the entire 6 min test was used for adaptation and the duration of immobility was recorded in the next 4 min. A mouse was considered immobility when it stayed moveless and floating, or moved only to keep it head above the water. The FST was carried out in a quiet environment, with no obvious changes in light. After each trial, the mouse was removed from the water and returned to the cage, and the water in the cylinder was changed.

Open field test (OFT)

The OFT was based on a previously described (Shieh and Yang 2020). The OFT apparatus consisted of a 50 × 50 × 35 cm square box, divided into 16 equal-size squares. The central area of the apparatus was defined as a central 25 cm × 25 cm square, and the rest was the peripheral area. Each mouse was placed in a corner of the box, and the total distance travelled were recorded during the 5 min test. The OFT was performed in a quiet environment, with no obvious changes in light. 75% ethanol was used to clean the apparatus to remove odors after each trial.

Tail suspension test (TST)

The TST was also based on a previously described (Iyer et al. 2019). The rear 1/3 of the tail of each mouse was affixed with adhesive tape and suspended about 40 cm above the floor for 6 min. The immobility time was measured for the last 4 min. Immobility was defined as a lack of active escape movements and maintaining a vertical posture during suspension.

Measurement of neurotransmitters and inflammatory cytokines

After all behavioral tests completed, all mice were anaesthetized with 1% pentobarbital (65 μl/10 g) to collect blood samples. Serum was obtained from the blood samples by centrifugation at 12,000 rpm for 15 min and stored at − 80 °C. Serum tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6) and high-sensitivity C reactive protein (hs-CRP) levels were determined using the ELISA kits (ABclonal Technology, Boster Biological Technology Co., Ltd), according to the manufacturer’s instructions. Depression-related neurotransmitters such as Noradrenaline (NE), serotonin (5-HT), and dopamine (DA) levels were measured using UHPLC-MS/MS method.

Hippocampus histopathology

The brain of each mouse was quickly isolated after blood collection for histological analysis. The hippocampus from mice in each group was obtained and fixed in 10% formalin, embedded in paraffin, and sliced into sections (4 μm thick). The sections were stained with Terminal Deoxynucleotidyl Transferase-Mediated dUTP Nick End-Labeling (TUNEL) according to the standard protocol. The hippocampus of each section was observed and photographed using an Olympus BX51 system (Olympus, Japan).

Network pharmacology

Screening of active compounds and targets of JTW

The active compounds and corresponding targets of JTW were screened using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP, https://tcmspw.com/tcmsp.php) (Ru et al. 2014). The oral bioavailability (OB) and drug-likeness (DL) contained in this database are important indicators for evaluating ADME attributes, which are often used as the key factors to screen active compounds of drugs (OB ≥ 30% and DL ≥ 0.18)(Lee et al. 2018; Tao et al. 2019). We also chose those two indicators as the screening criteria in our study. The Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine (BATMAN-TCM, http://bionet.ncpsb.org.cn/batman-tcm/) was also used to screen the active compounds and potential targets (Liu et al. 2016). According to the parameter information given by the BATMAN-TCM database, the Score cutoff ≥ 30 and p value ≤ 0.05 were considered as indicators when screening the active compounds of JTW.

In addition, we also searched the relevant literature on the compounds of JTW and completed the data collection to avoid omitting some important active compounds and corresponding target information due to the setting of screening criteria. Meanwhile, target genes of these active compounds were predicted by the above databases and corrected by the Uniprot database (https://www.uniprot.org/).

Collection of targets of DM and depression

The targets of DM and depression were also obtained by searching the databases. With the key words including “depression”, “depressive”, “depressive disorder”, “depressive illness”, “diabetes” and “diabetes mellitus”, targets related to DM and depression were founded in the GeneCards (https://www.genecards.org/, version 5.1), the Online Mendelian Inheritance in Man (OMIM, https://omim.org/), the DrugBank (https://go.drugbank.com/, version 5.1.8) and the Therapeutic Target Database (TTD, (Stelzer et al. 2016; Wang et al. 2020; Wishart et al. 2018; Hamosh et al. 2005).

Construction and analysis of the “D-C-T-D” network

The targets of active compounds and diseases were collected and sorted out to obtain the intersection targets of JTW, DM and depression. According to these intersection targets, the Venn diagram was obtained by using jvenn platform [(Bardou et al. 2014). Afterwards, we integrated the information of the intersection targets and input them into Cytoscape software (https://cytoscape.org/, version 3.8.2] to construct a “D-C-T-D” network and complete the subsequent analysis. Cytoscape is a software platform for the large-scale integration of molecular interaction network data, and can integrate these networks with annotations, gene expression profiles and other state data (Shannon et al. 2003).

Construction of the protein–protein interaction (PPI) network

The intersection targets of JTW, DM and depression were uploaded to the STRING database (https://www.string-db.org/, version 11.0) to construct the PPI network of protein–protein interaction and the key targets were screened and analyzed subsequently. We set the scoring condition to > 0.90, and the selected target proteins were limited to Homo sapiens. In the PPI network, the edges represent protein–protein associations, and the more lines, the greater the correlation (Szklarczyk et al. 2021).

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses

The database for annotation, visualization and integrated discovery (DAVID, https://david.ncifcrf.gov/, version 6.8) was used to carry out GO enrichment analysis and KEGG enrichment analysis on the intersection targets, which is a web-based online bioinformatics resource that can be used to explain the functions of large lists of genes/proteins (Jiao et al. 2012). The GO enrichment analysis includes three different biological aspects: biological process (BP), molecular function (MF) and cellular component (CC) (Ashburner et al. 2000). KEGG is a knowledge base for systematic analysis of gene functions (Kanehisa and Goto 2000).

Statistical analysis

Statistical analyses were performed by the GraphPad Prism 8 software and all the data are presented as the mean ± standard deviation (S.D.). Significant differences among the groups were evaluated with a one-way analysis of variance (ANOVA) and Dunnett’s t-test, and p < 0.05 was considered as statistically significant.

Results

JTW has a therapeutic effect on db/db mice

In our study, the well-accepted and spontaneous T2DM model, db/db mice, were used to explore the effect of JTW in vivo. We recorded the body weight and FBG levels to evaluate the therapeutic effects of JTW, it can be seen that JTW had a trend of weight loss although it was not statistically significant (Fig. 2A). In terms of FBG levels, there was no difference in FBG between different groups at the beginning, while FBG in JTW and Fluoxetine groups were significantly decreased compared to Model group with the drug intervention (Fig. 2B). In addition, the serum samples were used to examine fasting insulin (FINS) level and calculated the homeostasis model assessment insulin resistance (HOMA-IR) index. According to following formula: HOMA-IR = FBG (mmol) × fasting insulin (mU/l)/22.5, we found that JTW obviously improved fasting insulin level and decreased HOMA-IR index (Fig. 2C, D). What’s more, we also conducted GTT to access glucose metabolism state, the changes were also similar with above parameters (Fig. 2E). All above indicated that JTW has a therapeutic effect on diabetic mice.

Fig. 2
figure2

A Body weight of each mouse was recorded every 3 days (n = 5–6). B Fasting blood glucose (FBG) of each mouse was determined once a week (n = 5–6). C Fasting insulin (FINS) of each mouse was determined at the ending of study (n = 5–6). D HOMA-IR index was calculated according to standard formula: HOMA-IR = FBG (mmol) × FINS (mU/l)/22.5(n = 5–6). E For glucose tolerance test (GTT), all mice were fasted overnight and gavaged with glucose (2 g/kg), then the tail vein blood glucose was measured at 0, 30, 60, 90 and 120 min after gavage; the bar graph represents average area under the curve (n = 5–6). All data are presented as means ± SD. Compared to control group, *p < 0.05, **p < 0.01; Compared to model group, #p < 0.05, ##p < 0.01, ###p < 0.001

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JTW ameliorates depression-like behavior induced by CRS

Mice received JTW or Fluoxetine 1 h before the restraint stress for 21 days to access the effect of JTW on CRS-induced depressive symptoms. During the FST for testing levels of depression, the immobility time was increased significantly in the model group compared to the control group, which was attenuated by both JTW and Fluoxetine (Fig. 3A). The results of the TST were similar to the FST, the model group showed obviously higher immobility time compared to the control group, while JTW and Fluoxetine groups showed significant reductions in immobility time compared to the model group (Fig. 3C). In OFT as shown in Fig. 3B, treatment with JTW or Fluoxetine significantly increased the total distance travelled when compared to the model group. These results suggest that JTW could alleviate depression-like behavior in mice induced by CRS.

Fig. 3
figure3

A The forced swimming test (FST) were conducted after chronic restraint stress (CRS) treatment for 21 days, the immobility time of each mouse was recorded in the last 4 min (n = 5–6). B The open field test (OFT) were performed after chronic CRS treatment for 21 days, the total distance travelled of each mouse was recorded during the 5 min test (n = 5–6). C The tail suspension test (TST) were conducted after CRS treatment for 21 days, the immobility time of each mouse was recorded in the last 4 min (n = 5–6). D, E Serum depression-related neurotransmitters such as Noradrenaline (NE), serotonin (5-HT), and dopamine (DA) levels were detected at the end of the experiment (n = 5–6). G, H, I Serum tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6) and high-sensitivity C reactive protein (hs-CRP) levels were determined at the end of the experiment (n = 5–6). All data are presented as means ± SD. Compared to control group, *p < 0.05, **p < 0.01; Compared to model group, #p < 0.05, ##p < 0.01

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Effects of JTW on depression-related neurotransmitters

In addition to behavioral tests, we also measured the depression-related neurotransmitters such as 5-HT, DA and NE levels in the serum. As shown in Fig. 3D–F, exposure to chronic restraint stress obviously suppressed serum 5-HT, DA and NE levels, which could be significantly improved following Fluoxetine and JTW treatment.

Effects of JTW on serum inflammatory biomarkers

Due to the close relationship between inflammation, DM and depression, we determined the inflammatory biomarkers levels using the ELISA kits. The pro-inflammation cytokines TNF-α, IL-6 and hs-CRP levels increased significantly after 21 days of CRS in the model group mice. Treatment with JTW and Fluoxetine decreased the cytokines in the serum, suggesting that JTW may play an effective role in attenuating inflammation (Fig. 3G–I).

Effects of JTW on CRS-induced apoptosis in hippocampus

The apoptosis level in the hippocampus of each mouse in different groups was detected via TUNEL staining. It can be seen that the number of TUNEL-positive cells in the hippocampus of mice in the model group was obviously higher than that of the control group, while almost no TUNEL-positive cells was observed in the hippocampus of mice in JTW and Fluoxetine groups (Fig. 4). The results revealed that CRS intervention markedly increase the apoptotic rate of hippocampal neurons, which can be significantly reversed by JTW and Fluoxetine treatment.

Fig. 4
figure4

TUNEL staining was used to detect the apoptosis in the hippocampus of each mouse in different groups, the green dots indicate the TUNEL-positive apoptotic cell

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Screening of active compounds and targets of JTW

After searching the TCMSP database and the BATMAN-TCM database with the criteria of OB ≥ 30%, DL ≥ 0.18, and Score cutoff ≥ 30, combined with the results of related literature(Kin et al. 2013; Ma et al. 2018; Chae et al. 2019), a total of 28 active compounds of JTW were initially obtained, namely 14 of HL and 14 of RG. The relevant information of these 28 active compounds can be found in the Additional file 1: Table S1. What’s more, 526 corresponding targets were collected from the two databases, including 179 of HL and 347 of RG. After standardizing and unifying target names and deleting duplicate targets, a total of 484 drug targets were obtained.

Collection of therapeutic targets of DM and depression

After simplifying the results of each database and screening and removing duplicate targets, a total of 1842 targets of depression were obtained with the key words such as “depression”, “depressive”, “depressive disorder” and “depressive illness”. Similarly, 1407 targets of DM were obtained by using “diabetes” and “diabetes mellitus” as the key words to search in the databases.

Screening of the intersection targets and constructing the D-C-T-D network

Through taking the intersection of 484 drug targets, 1842 depression targets and 1407 DM targets, we obtained a total of 117 targets (Fig. 5A), which correspond to 17 active compounds (Table 1), suggesting that JTW probably performs the therapeutic effect via modulating the 117 genes. Subsequently, we input the information of the intersection targets into Cytoscape software for analysis in order to explore the possible mechanism of the therapeutic effect of JTW. Figure 5B shows the D-C-T-D network constructed by the Cytoscape, which fully reveals the multi-component and multi-target mechanism of JTW in the treatment of DM and depression. In addition, the analyzer tool that comes with the software was used to analyze the active compounds. The results showed that the active compounds with the highest number of targets included quercetin, styrene, cinnamic acid, ethyl-cinnamate, (R)-Canadine, palmatine, berberine, etc., indicating that the above compounds may be the main components of JTW in treating DM and depression.

Fig. 5
figure5

A The 117 intersection targets of Jiao-tai-wan, diabetes mellitus and depression. B The Drug-Compounds-Targets-Disease (D-C-T-D) network

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Table 1 Seventeen active compounds of Jiao-tai-wan and their corresponding oral bioavailability (OB) and drug-likeness (DL)
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Construction and analysis of the PPI Network

117 intersection targets were uploaded to the STRING database to obtain the PPI network. There are 117 nodes and 1907 edges in the PPI network, and we screened out the top 10 targets after analyzing each node, which had a node degree greater than 64 (Fig. 6A, B). Therefore, we speculate that JTW may play a role in the treatment of DM and depression through the key targets such as INS, AKT1, IL-6, VEGF-A, TNF and so on.

Fig. 6
figure6

A The protein–protein interaction (PPI) network of 117 intersection targets. B The bar plot of the top 10 targets in the PPI network

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GO enrichment analysis

The DAVID database was used to perform GO enrichment analysis of 117 intersection targets to explore the relationship between these targets and diseases, including three aspects of BP, MF and CC. The top ten results were output after ranking according to the p value from small to large (Fig. 7). It can be seen that the occurrence of DM and depression involves many biological processes, and JTW can achieve the purpose of treatment by regulating multiple biological processes. Additional file 2: Fig. S1 shows the bubble chart results of the top 20 in the GO enrichment analysis.

Fig. 7
figure7

The top 10 biological process (BP), molecular function (MF) and cellular component (CC) in GO analysis of 117 intersection targets

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KEGG enrichment analysis

We also conducted KEGG enrichment analysis through the DAVID database, and the results showed that these targets involved 124 pathways. We selected the top 20 according to the p value from small to large for further analysis (Fig. 8). It can be seen that JTW mainly regulates Chagas disease (American trypanosomiasis), HIF-1 signaling pathway, pathways in cancer, Hepatitis B and so on to treat DM and depression. In addition, there are also many targets enriched in pathways such as TNF signaling pathway, PI3K-Akt signaling pathway and MAPK signaling pathway, indicating that they may also play an important role in the treatment.

Fig. 8
figure8

The top 20 signaling pathways in KEGG enrichment analysis of 117 intersection targets

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Afterwards, in order to explore the relationship between the top 20 pathways, drugs and intersection targets, we integrated the collected information and conducted a network by using the Cytoscape (Additional file 3: Fig. S2). It is obvious from the network that each pathway can correspond to multiple targets, and each target can also connect to multiple pathways. Different pathways can be connected to each other through the intersection targets, which fully reflects the multi-component, multi-target, and multi-pathway mechanism of JTW in treating DM and depression.

Discussion

Both DM and depression are serious chronic diseases, which can lead to a serious decline in the quality of life, increase functional disability and costs of care than many other chronic diseases (O'Connor et al. 2009). The bidirectional link between DM and depression has been confirmed. An epidemiological study has shown that the prevalence rate of depression is more than three times higher in people with type 1 diabetes mellitus (T1DM) and nearly twice as high in people with T2DM than those without (Roy and Lloyd 2012). Another research also indicated that the presence of diabetes doubles the odds of comorbid depression (Anderson et al. 2001). The results of a meta-analysis carried out by Mezuk et al. showed that compared with people without diabetes, people with T2DM had a 15% increased risk of depression, while those with depression had a 60% increased risk of developing T2DM (Mezuk et al. 2008). However, the current treatments for these two diseases are relatively single and there is a lack of the comprehensive treatment needed to improve clinical outcomes (Lloyd et al. 2018).

JTW, one of the most classical Chinese prescription, has been used to treat insomnia for hundreds of years. In recent years, more and more experiments have confirmed that JTW can not only improve the quality of sleep, but also has the dual effects of hypoglycemic and antidepressant (Hu et al. 2013; Dong et al. 2013; Jiao et al. 2021). In the traditional theories of TCM, different diseases may have similar etiology, pathogenesis, symptoms, and disease location during the occurrence and development, so that different diseases can be cured with the same prescription, which fully reflects the advantages of TCM in syndrome differentiation, holistic treatment and comprehensive treatment (Yu et al. 2020). Our study is to evaluate the dual therapeutic effects of JTW, and to explore the potential mechanisms via network pharmacology.

The results of analysis using Cytoscape showed that the active compounds with the highest number of targets included quercetin, styrene, cinnamic acid, ethyl-cinnamate, (R)-Canadine, palmatine, berberine and so on. First of all, quercetin, one of the active compounds of HL, has been confirmed had multiple pharmacological effects (Duarte et al. 2001; Kumar et al. 2020; Sharma et al. 2018; D'Andrea 2015). Studies have found that quercetin could ameliorate metabolic derangements in diabetes and effectively improve dyslipidemia in T2DM (Roslan et al. 2017; Jeong et al. 2012). Not only that, quercetin has also been found to alleviate LPS-induced depression-like behaviors in rats (Fang et al. 2019), and can dose-dependently decrease the immobility time of diabetic mice in FST and this effect is comparable to that of fluoxetine, a traditional antidepressant (Anjaneyulu et al. 2003; Bhutada et al. 2010).

Berberine, also an active compound of HL, has a wide range of pharmacological effects (Wang et al. 2017; Fan et al. 2019). The results of a clinical trial showed that berberine can significantly reduce FBG and HbA1c in patients with diabetes, and its hypoglycemic effect was similar to that of metformin (Yin et al. 2008). The therapeutic effect of berberine on depression has also been confirmed by numerous experiments. Studies have found that berberine can exert antidepressant effect by inhibiting neuroinflammation and regulating brain biogenic amines (Yin et al. 2008; Hu et al. 2019). What’s more, berberine can greatly shorten the immobility time of mice in FST and TST in animal experiments (Peng et al. 2007; Lee et al. 2012).

Cinnamic acid, one of the effective compounds of RG, is a natural aromatic carboxylic acid. Research results show that cinnamic acid can regulate glycogen production and gluconeogenesis (Huang and Shen 2012), and can also significantly enhance insulin secretion in isolated islets (Hafizur et al. 2015), thereby exerting anti-diabetic activity. In addition, Hemmati et al. found that the administration of cinnamic acid can inhibit the FBG level in diabetic mice (Hemmati et al. 2018). Although there are few researches on the therapeutic effect of cinnamic acid in depression, derivatives of cinnamic acid and other natural products can exert antidepressant effects and have potential applicability as candidates for antidepressant drugs (Diniz et al. 2019).

Inflammatory mediators have always been considered to be important factors in promoting the development of insulin resistance (IR), which will lead to the occurrence of T2DM. The results of our PPI network analysis that IL-6 and TNF are the top five targets also confirmed this view, indicating that they may play an important role in the treatment of DM and depression. It was found that the levels of cytokines such as TNF-α and IL-6 were highest in non-treated diabetic rats, and decreased significantly following quercetin or glibenclamide treatments (Roslan et al. 2017). The systematic review conducted by Esser et al. directly showed that immune system activation and chronic low-grade inflammation are involved in the pathogenesis of IR and diabetes (Esser et al. 2014). Inflammation is also thought to have a bidirectional relationship with depression (Beurel et al. 2020). A meta-analysis demonstrated that there was a significant correlation between depression and C-reactive protein (CRP) and IL-6 in children and adolescents (Colasanto et al. 2020). IL-6 knockout mice exhibit resistance to stress-induced depression-like behavior and showed reduced despair in FST and TST (Chourbaji et al. 2006). Vascular endothelial growth factor (VEGF) is a key driver of neovascularization and vascular permeability (Abdelsaid and El-Remessy 2012; Kajdaniuk et al. 2011). A case–control study showed that altered VEGF secretion, caused by genetic variation in VEGF-A gene, is involved in T2DM pathogenesis (Sellami et al. 2018). In addition, VEGF exerts effective neurotrophic effects. In both major depressive disorder (MDD) subjects and rat depression models, the hippocampal VEGF and other growth factors are abnormally regulated (Carboni et al. 2018). Deyama et al. confirmed that VEGF signaling plays a crucial role in the antidepressant effects of brain-derived neurotrophic factor (BDNF) and ketamine (Deyama et al. 2019a, b). It can be seen that VEGF is closely related to DM and depression. The correlation between INS and DM has long been recognized worldwide, and AKT1, as a key factor in the PI3K-Akt signaling pathway, has been confirmed by many researches on its relationship with DM and depression, which will be discussed in detail below.

The results of KEGG enrichment analysis showed that the active compounds of JTW may play a therapeutic effect on DM and depression by regulating multiple pathways. The intersection targets are mainly enriched in HIF-1 signaling pathway, pathways in cancer, TNF signaling pathway, PI3K-Akt signaling pathway and MAPK signaling pathway, etc. Many studies have shown that HIF-1 signaling pathway is associated with DM and depression. HIF-1α is important for maintaining the function and survival of pancreatic β cells (Stokes et al. 2013) and the expression of HIF-1β mRNA in patients with T2DM is decreased (Gunton et al. 2005). Glucose-induced inhibition of HIF-1α protein stability may also accelerate the deterioration of β cell function and speed progression to diabetes (Cheng et al. 2010). In terms of depression, Li et al. established a depression model using chronic unpredictable mild stress (CUMS) and found that FG-4592 can reverse depressive behaviors by activating HIF-1 signaling pathway (Li et al. 2020). Kang et al. also proposed that interventions including the intermittent hypoxia conditioning and hyperbaric oxygen therapy to elevate the level of HIF-1 in the brain might be considered as new additional treatments for depression (Kang et al. 2021).

PI3K-Akt signaling pathway is the main downstream molecular pathway of insulin, which plays a crucial role in regulating glucose and lipid metabolism. PI3K-Akt signaling pathway block and abnormal function of downstream target proteins can cause IR (Bathina and Das 2018). Lots of studies have confirmed that the IR of diabetic mice can be improved by regulating the PI3K-Akt signaling pathway (Chen et al. 2019; Yan et al. 2018; Liao et al. 2019). AKT1 has been considered as a key mediator of insulin-stimulated glucose uptake, suppression of apoptosis, stimulation of glycolysis and the activation of glycogen and protein synthesis (Coffer et al. 1998). The activation of PI3K-Akt pathway can protect pancreatic β cells from the influence of different apoptotic stimuli (Tuttle et al. 2001). What’s more, Cao et al. found that the expression of PI3K in depressed rats were attenuated significantly (Cao et al. 2019). The study of Xie et al. showed that Crocin can ameliorate depression via PI3K-Akt mediated suppression of inflammation (Xie et al. 2019), these studies illustrate the close connection between PI3K-Akt signaling pathway and depression.

In mammalian cells, MAPK families has been divided into three categories, including p38, extracellular signal-related kinase (ERK) and c-Jun N-terminal kinase (JNK) (Dewanjee et al. 2018). Among them, ERK1/2 play a pivotal role in various neuropsychiatric disorders, including depression (Wang and Mao 2019). Regulating the CUMS-induced MAPK pathway and NF-κB protein complex activation can alleviate depression-like behavior in mice (Su et al. 2017). Paroxetine combined with fluorouracil, ketamine and ghrelin and other drugs can show antidepressant-like effects via the MAPK signaling pathway (Zhang et al. 2020; Humo et al. 2020; Han et al. 2019). Cui et al. found that HL can alleviate inflammation by regulating the expression of pro-inflammatory cytokines through MAPK signaling pathway, thereby inhibiting the occurrence and development of IR and diabetes (Cui et al. 2018). Gelidium elegans extract can ameliorate T2DM via regulation of MAPK and PI3K-Akt signaling pathways (Choi et al. 2018).

As expected in our study, JTW has a therapeutic effect on diabetic mice, and can ameliorate depression-like behaviors induced by CRS. We found that JTW reduced the serum IL-6, TNF-α and hs-CRP levels, suggesting that JTW could by modulating the inflammation-related pathways, which were also predicted in the follow-up study by network pharmacological approach.

In our animal experiment, CRS was used to establish a DM combined with depression mice model in db/db mice, after conducting the behavioral tests, detecting serum inflammatory biomarkers and depression-related neurotransmitters, and observing apoptotic cells in the hippocampus of each mouse in different groups, we found that JTW did have such dual effects in treating DM and depression. Meanwhile, we explored the potential mechanism through network pharmacology. However, due to the limitations of some databases, we cannot collect all the active compounds and targets of JTW, and the targets and pathways are interrelated and regulate each other, more in-depth and comprehensive study is still needed, and in vivo and in vitro experiments are necessary for exploring and verifying more extensive pharmacological effects and mechanisms of JTW.

Conclusion

Our findings suggested that JTW has a therapeutic effect on diabetic mice, and can ameliorate depression-like behaviors induced by CRS, that is to say, JTW has dual effects on DM and depression. Network pharmacology analysis revealed the multi-component, multi-target, and multi-pathway mechanism of JTW. The key targets of JTW in treating DM and depression probably were INS, AKT1, IL-6, VEGF-A and TNF, and the underlying mechanism would be associated with modulation on HIF-1 signal pathway, pathways in cancer, Hepatitis B, TNF signal pathway, PI3K-Akt signal pathway and MAPK signaling pathway and so on. Our study provides evidence for JTW in DM and depression therapy, and would provide a theoretical basis for the experimental research and clinical application of JTW in the future.

Availability of data and materials

The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation, to any qualified researcher.

References

  1. Abdelsaid MA, El-Remessy AB. S-glutathionylation of LMW-PTP regulates VEGF-mediated FAK activation and endothelial cell migration. J Cell Sci. 2012;125:4751–60.

    CAS  PubMed  Google Scholar 

  2. Anderson RJ, Freedland KE, Clouse RE, Lustman PJ. The prevalence of comorbid depression in adults with diabetes: a meta-analysis. Diabetes Care. 2001;24:1069–78.

    CAS  PubMed  Google Scholar 

  3. Anjaneyulu M, Chopra K, Kaur I. Antidepressant activity of quercetin, a bioflavonoid, in streptozotocin-induced diabetic mice. J Med Food. 2003;6:391–5.

    CAS  PubMed  Google Scholar 

  4. Ashburner M, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25:25–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Bardou P, Mariette J, Escudié F, Djemiel C, Klopp C. jvenn: an interactive Venn diagram viewer. BMC Bioinform. 2014;15:293.

    Google Scholar 

  6. Bathina S, Das UN. Dysregulation of PI3K-Akt-mTOR pathway in brain of streptozotocin-induced type 2 diabetes mellitus in Wistar rats. Lipids Health Dis. 2018;17:168.

    PubMed  PubMed Central  Google Scholar 

  7. Beurel E, Toups M, Nemeroff CB. The bidirectional relationship of depression and inflammation: double trouble. Neuron. 2020;107:234–56.

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Bhutada P, et al. Reversal by quercetin of corticotrophin releasing factor induced anxiety- and depression-like effect in mice. Prog Neuropsychopharmacol Biol Psychiatry. 2010;34:955–60.

    CAS  PubMed  Google Scholar 

  9. Cao LH, et al. PI3K-AKT signaling activation and icariin: the potential effects on the perimenopausal depression-like rat model. Molecules (Basel, Switzerland). 2019;24:3700.

    CAS  Google Scholar 

  10. Carboni L, et al. Cross-species evidence from human and rat brain transcriptome for growth factor signaling pathway dysregulation in major depression. Neuropsychopharmacology. 2018;43:2134–45.

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Chae HK, Kim W, Kim SK. Phytochemicals of cinnamomi cortex: cinnamic acid, but not cinnamaldehyde, attenuates oxaliplatin-induced cold and mechanical hypersensitivity in rats. Nutrients. 2019;11:432.

    CAS  PubMed Central  Google Scholar 

  12. Chen G, et al. The anti-diabetic effects and pharmacokinetic profiles of berberine in mice treated with Jiao-Tai-Wan and its compatibility. Phytomedicine. 2013;20:780–6.

    CAS  PubMed  Google Scholar 

  13. Chen G, et al. Jia-Wei-Jiao-Tai-Wan ameliorates type 2 diabetes by improving β cell function and reducing insulin resistance in diabetic rats. BMC Complement Altern Med. 2017;17:507.

    PubMed  PubMed Central  Google Scholar 

  14. Chen L, et al. Network pharmacology-based strategy for predicting active ingredients and potential targets of Yangxinshi tablet for treating heart failure. J Ethnopharmacol. 2018;219:359–68.

    PubMed  Google Scholar 

  15. Chen SH, Liu XN, Peng Y. MicroRNA-351 eases insulin resistance and liver gluconeogenesis via the PI3K/AKT pathway by inhibiting FLOT2 in mice of gestational diabetes mellitus. J Cell Mol Med. 2019;23:5895–906.

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Cheng K, et al. Hypoxia-inducible factor-1alpha regulates beta cell function in mouse and human islets. J Clin Invest. 2010;120:2171–83.

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Choi J, Kim KJ, Koh EJ, Lee BY. Gelidium elegans extract ameliorates Type 2 diabetes via regulation of MAPK and PI3K/Akt signaling. Nutrients. 2018;10:51.

    PubMed Central  Google Scholar 

  18. Chourbaji S, et al. IL-6 knockout mice exhibit resistance to stress-induced development of depression-like behaviors. Neurobiol Dis. 2006;23:587–94.

    CAS  PubMed  Google Scholar 

  19. Coffer PJ, Jin J, Woodgett JR. Protein kinase B (c-Akt): a multifunctional mediator of phosphatidylinositol 3-kinase activation. Biochem J. 1998;335(Pt 1):1–13.

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Colasanto M, Madigan S, Korczak DJ. Depression and inflammation among children and adolescents: a meta-analysis. J Affect Disord. 2020;277:940–8.

    CAS  PubMed  Google Scholar 

  21. Cui X, et al. Scutellariae radix and coptidis rhizoma improve glucose and lipid metabolism in T2DM rats via regulation of the metabolic profiling and MAPK/PI3K/Akt signaling pathway. Int J Mol Sci. 2018;19:3634.

    PubMed Central  Google Scholar 

  22. D’Andrea G. Quercetin: a flavonol with multifaceted therapeutic applications? Fitoterapia. 2015;106:256–71.

    CAS  PubMed  Google Scholar 

  23. Dewanjee S, et al. Molecular mechanism of diabetic neuropathy and its pharmacotherapeutic targets. Eur J Pharmacol. 2018;833:472–523.

    CAS  PubMed  Google Scholar 

  24. Deyama S, Bang E, Kato T, Li XY, Duman RS. Neurotrophic and antidepressant actions of brain-derived neurotrophic factor require vascular endothelial growth factor. Biol Psychiatry. 2019a;86:143–52.

    CAS  PubMed  Google Scholar 

  25. Deyama S, et al. Role of neuronal VEGF signaling in the prefrontal cortex in the rapid antidepressant effects of ketamine. Am J Psychiatry. 2019b;176:388–400.

    PubMed  PubMed Central  Google Scholar 

  26. Diniz LRL, Souza MTS, Barboza JN, Almeida RN, Sousa DP. Antidepressant potential of cinnamic acids: mechanisms of action and perspectives in drug development. Molecules (Basel, Switzerland). 2019;24:4469.

    CAS  Google Scholar 

  27. Dong H, et al. Jiaotai pill enhances insulin signaling through phosphatidylinositol 3-kinase pathway in skeletal muscle of diabetic rats. Chin J Integr Med. 2013;19:668–74.

    PubMed  Google Scholar 

  28. Duarte J, et al. Antihypertensive effects of the flavonoid quercetin in spontaneously hypertensive rats. Br J Pharmacol. 2001;133:117–24.

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Esser N, Legrand-Poels S, Piette J, Scheen AJ, Paquot N. Inflammation as a link between obesity, metabolic syndrome and type 2 diabetes. Diabetes Res Clin Pract. 2014;105:141–50.

    CAS  PubMed  Google Scholar 

  30. Fan J, et al. Pharmacological effects of berberine on mood disorders. J Cell Mol Med. 2019;23:21–8.

    PubMed  Google Scholar 

  31. Fang K, et al. Quercetin alleviates LPS-induced depression-like behavior in rats via regulating BDNF-related imbalance of Copine 6 and TREM1/2 in the hippocampus and PFC. Front Pharmacol. 2019;10:1544.

    PubMed  Google Scholar 

  32. Gunton JE, et al. Loss of ARNT/HIF1beta mediates altered gene expression and pancreatic-islet dysfunction in human type 2 diabetes. Cell. 2005;122:337–49.

    CAS  PubMed  Google Scholar 

  33. Hafizur RM, et al. Cinnamic acid exerts anti-diabetic activity by improving glucose tolerance in vivo and by stimulating insulin secretion in vitro. Phytomedicine. 2015;22:297–300.

    CAS  PubMed  Google Scholar 

  34. Hamosh A, Scott AF, Amberger JS, Bocchini CA, McKusick VA. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 2005;33:D514-517.

    CAS  PubMed  Google Scholar 

  35. Han QQ, et al. Ghrelin exhibited antidepressant and anxiolytic effect via the p38-MAPK signaling pathway in hippocampus. Prog Neuropsychopharmacol Biol Psychiatry. 2019;93:11–20.

    CAS  PubMed  Google Scholar 

  36. Hemmati AA, Alboghobeish S, Ahangarpour A. Effects of cinnamic acid on memory deficits and brain oxidative stress in streptozotocin-induced diabetic mice. Korean J Physiol Pharmacol. 2018;22:257–67.

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Hopkins AL. Network pharmacology. Nat Biotechnol. 2007;25:1110–1.

    CAS  PubMed  Google Scholar 

  38. Hu N, et al. Anti-diabetic activities of Jiaotaiwan in db/db mice by augmentation of AMPK protein activity and upregulation of GLUT4 expression. Evid Based Complement Altern Med. 2013;2013:180721.

    Google Scholar 

  39. Hu S, et al. Preventive and therapeutic roles of berberine in gastrointestinal cancers. Biomed Res Int. 2019;2019:6831520.

    PubMed  PubMed Central  Google Scholar 

  40. Huang D-W, Shen S-C. Caffeic acid and cinnamic acid ameliorate glucose metabolism via modulating glycogenesis and gluconeogenesis in insulin-resistant mouse hepatocytes. J FunctFoods. 2012;4:358–66.

    CAS  Google Scholar 

  41. Humo M, et al. Ketamine induces rapid and sustained antidepressant-like effects in chronic pain induced depression: Role of MAPK signaling pathway. Prog Neuro-Psychopharmacol Biol Psychiatry. 2020;100:109898.

    CAS  Google Scholar 

  42. IDF. IDF diabetes atlas. 9th ed. Brussels: International Diabetes Federation; 2019.

    Google Scholar 

  43. Iyer KA, et al. Multi-modal antidepressant-like action of 6- and 7-chloro-2-aminodihydroquinazolines in the mouse tail suspension test. Psychopharmacology. 2019;236:2093–104.

    CAS  PubMed  Google Scholar 

  44. Jeong SM, Kang MJ, Choi HN, Kim JH, Kim JI. Quercetin ameliorates hyperglycemia and dyslipidemia and improves antioxidant status in type 2 diabetic db/db mice. Nurs Res Pract. 2012;6:201–7.

    CAS  Google Scholar 

  45. Jiao X, et al. DAVID-WS: a stateful web service to facilitate gene/protein list analysis. Bioinformatics (Oxford, England). 2012;28:1805–6.

    CAS  Google Scholar 

  46. Jiao Z, et al. An investigation of the antidepressant-like effect of Jiaotaiwan in rats by nontargeted metabolomics based on ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry. J Sep Sci. 2021;44:645–55.

    CAS  PubMed  Google Scholar 

  47. Kajdaniuk D, Marek B, Borgiel-Marek H, Kos-Kudła B. Vascular endothelial growth factor (VEGF)—part 1: in physiology and pathophysiology. Endokrynol Pol. 2011;62:444–55.

    CAS  PubMed  Google Scholar 

  48. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27–30.

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Kang I, et al. Elevating the level of hypoxia inducible factor may be a new potential target for the treatment of depression. Med Hypotheses. 2021;146:110398.

    CAS  PubMed  Google Scholar 

  50. Kin R, et al. Procyanidin C1 from cinnamomi cortex inhibits TGF-β-induced epithelial-to-mesenchymal transition in the A549 lung cancer cell line. Int J Oncol. 2013;43:1901–6.

    CAS  PubMed  Google Scholar 

  51. Kumar A, Gupta M, Sharma R, Sharma N. Deltamethrin-induced immunotoxicity and its protection by quercetin: an experimental study. Endocr Metab Immune Disord Drug Targets. 2020;20:67–76.

    CAS  PubMed  Google Scholar 

  52. Lee B, et al. Effect of berberine on depression- and anxiety-like behaviors and activation of the noradrenergic system induced by development of morphine dependence in rats. Korean J Physiol Pharmacol. 2012;16:379–86.

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Lee AY, Park W, Kang TW, Cha MH, Chun JM. Network pharmacology-based prediction of active compounds and molecular targets in Yijin-Tang acting on hyperlipidaemia and atherosclerosis. J Ethnopharmacol. 2018;221:151–9.

    PubMed  Google Scholar 

  54. Li G, et al. FG-4592 improves depressive-like behaviors through HIF-1-mediated neurogenesis and synapse plasticity in rats. Neurotherapeutics. 2020;17:664–75.

    PubMed  Google Scholar 

  55. Li Y, et al. Prevalence of diabetes recorded in mainland China using 2018 diagnostic criteria from the American Diabetes Association: national cross sectional study. BMJ (Clinical Research ed). 2020;369:m997.

    Google Scholar 

  56. Liao Z, et al. Polysaccharide from okra (Abelmoschus esculentus (L.) Moench) improves antioxidant capacity via PI3K/AKT pathways and Nrf2 translocation in a type 2 diabetes model. Molecules (Basel, Switzerland). 2019;24:1906.

    CAS  Google Scholar 

  57. Liu Z, et al. BATMAN-TCM: a bioinformatics analysis tool for molecular mechANism of traditional Chinese medicine. Sci Rep. 2016;6:21146.

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Lloyd CE, et al. Prevalence and correlates of depressive disorders in people with Type 2 diabetes: results from the International Prevalence and Treatment of Diabetes and Depression (INTERPRET-DD) study, a collaborative study carried out in 14 countries. Diabet Med. 2018;35:760–9.

    CAS  PubMed  Google Scholar 

  59. Ma N, et al. Chemical fingerprinting and quantification of Chinese cinnamomi cortex by ultra high performance liquid chromatography coupled with chemometrics methods. Molecules (Basel, Switzerland). 2018;23:2214.

    Google Scholar 

  60. Malhi GS, Mann JJ. Depression. Lancet (London, England). 2018;392:2299–312.

    Google Scholar 

  61. Mezuk B, Eaton WW, Albrecht S, Golden SH. Depression and type 2 diabetes over the lifespan: a meta-analysis. Diabetes Care. 2008;31:2383–90.

    PubMed  PubMed Central  Google Scholar 

  62. Miidera H, Enomoto M, Kitamura S, Tachimori H, Mishima K. Association between the use of antidepressants and the risk of type 2 diabetes: a large, population-based cohort study in Japan. Diabetes Care. 2020;43:885–93.

    CAS  PubMed  Google Scholar 

  63. Moulton CD, Pickup JC, Ismail K. The link between depression and diabetes: the search for shared mechanisms. Lancet Diabetes Endocrinol. 2015;3:461–71.

    PubMed  Google Scholar 

  64. O’Connor PJ, et al. Does diabetes double the risk of depression? Ann Fam Med. 2009;7:328–35.

    PubMed  PubMed Central  Google Scholar 

  65. Peng WH, Lo KL, Lee YH, Hung TH, Lin YC. Berberine produces antidepressant-like effects in the forced swim test and in the tail suspension test in mice. Life Sci. 2007;81:933–8.

    CAS  PubMed  Google Scholar 

  66. Roslan J, Giribabu N, Karim K, Salleh N. Quercetin ameliorates oxidative stress, inflammation and apoptosis in the heart of streptozotocin-nicotinamide-induced adult male diabetic rats. Biomed Pharmacother. 2017;86:570–82.

    CAS  PubMed  Google Scholar 

  67. Roy T, Lloyd CE. Epidemiology of depression and diabetes: a systematic review. J Affect Disord. 2012;142(Suppl):S8-21.

    PubMed  Google Scholar 

  68. Ru J, et al. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J Cheminform. 2014;6:13.

    PubMed  PubMed Central  Google Scholar 

  69. Sartorius N. Depression and diabetes. Dialogues Clin Neurosci. 2018;20:47–52.

    PubMed  PubMed Central  Google Scholar 

  70. Sellami N, et al. Association of VEGFA variants with altered VEGF secretion and type 2 diabetes: a case–control study. Cytokine. 2018;106:29–34.

    CAS  PubMed  Google Scholar 

  71. Shannon P, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504.

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Sharma A, Kashyap D, Sak K, Tuli HS, Sharma AK. Therapeutic charm of quercetin and its derivatives: a review of research and patents. Pharm Pat Anal. 2018;7:15–32.

    CAS  PubMed  Google Scholar 

  73. Shieh KR, Yang SC. Formosan wood mice (Apodemus semotus) exhibit more exploratory behaviors and central dopaminergic activities than C57BL/6 mice in the open field test. Chin J Physiol. 2020;63:27–34.

    CAS  PubMed  Google Scholar 

  74. Slattery DA, Cryan JF. Using the rat forced swim test to assess antidepressant-like activity in rodents. Nat Protoc. 2012;7:1009–14.

    CAS  PubMed  Google Scholar 

  75. Stelzer G, et al. The GeneCards suite: from gene data mining to disease genome sequence analyses. Curr Protoc Bioinform. 2016;54:13031–313033.

    Google Scholar 

  76. Stokes RA, et al. Hypoxia-inducible factor-1α (HIF-1α) potentiates β-cell survival after islet transplantation of human and mouse islets. Cell Transplant. 2013;22:253–66.

    PubMed  Google Scholar 

  77. Su WJ, et al. NLRP3 gene knockout blocks NF-κB and MAPK signaling pathway in CUMS-induced depression mouse model. Behav Brain Res. 2017;322:1–8.

    CAS  PubMed  Google Scholar 

  78. Szklarczyk D, et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021;49:D605-d612.

    CAS  PubMed  Google Scholar 

  79. Tao Y, et al. Network pharmacology-based prediction of the active compounds, potential targets, and signaling pathways involved in danshiliuhao granule for treatment of liver fibrosis. Evid Based Complement Altern Med. 2019;2019:2630357.

    Google Scholar 

  80. Tuttle RL, et al. Regulation of pancreatic beta-cell growth and survival by the serine/threonine protein kinase Akt1/PKBalpha. Nat Med. 2001;7:1133–7.

    CAS  PubMed  Google Scholar 

  81. Wang JQ, Mao L. The ERK pathway: molecular mechanisms and treatment of depression. Mol Neurobiol. 2019;56:6197–205.

    CAS  PubMed  PubMed Central  Google Scholar 

  82. Wang K, Feng X, Chai L, Cao S, Qiu F. The metabolism of berberine and its contribution to the pharmacological effects. Drug Metab Rev. 2017;49:139–57.

    PubMed  Google Scholar 

  83. Wang F, et al. Prevalence of comorbid major depressive disorder in Type 2 diabetes: a meta-analysis of comparative and epidemiological studies. Diabet Med. 2019;36:961–9.

    CAS  PubMed  Google Scholar 

  84. Wang Y, et al. Therapeutic target database 2020: enriched resource for facilitating research and early development of targeted therapeutics. Nucleic Acids Res. 2020;48:D1031-d1041.

    CAS  PubMed  Google Scholar 

  85. Wishart DS, et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2018;46:D1074-d1082.

    CAS  PubMed  Google Scholar 

  86. Xiang L, et al. Jiao-Tai-Wan ameliorates depressive-like behavior through the A(1)R pathway in ovariectomized mice after unpredictable chronic stress. Biomed Res Int. 2020;2020:1507561.

    PubMed  PubMed Central  Google Scholar 

  87. Xie Y, et al. Crocin ameliorates chronic obstructive pulmonary disease-induced depression via PI3K/Akt mediated suppression of inflammation. Eur J Pharmacol. 2019;862:172640.

    CAS  PubMed  Google Scholar 

  88. Xu Q, et al. The quest for modernisation of traditional Chinese medicine. BMC Complement Altern Med. 2013;13:132.

    PubMed  PubMed Central  Google Scholar 

  89. Yan J, et al. Catalpol ameliorates hepatic insulin resistance in type 2 diabetes through acting on AMPK/NOX4/PI3K/AKT pathway. Pharmacol Res. 2018;130:466–80.

    CAS  PubMed  Google Scholar 

  90. Yin J, Xing H, Ye J. Efficacy of berberine in patients with type 2 diabetes mellitus. Metabolism. 2008;57:712–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  91. Yu Y, Zhang G, Han T, Huang H. Analysis of the pharmacological mechanism of Banxia Xiexin decoction in treating depression and ulcerative colitis based on a biological network module. BMC Complement Med Ther. 2020;20:199.

    CAS  PubMed  PubMed Central  Google Scholar 

  92. Zhang H, et al. Paroxetine combined with fluorouracil plays a therapeutic role in mouse models of colorectal cancer with depression through inhibiting IL-22 expression to regulate the MAPK signaling pathway. Exp Ther Med. 2020;20:240.

    CAS  PubMed  PubMed Central  Google Scholar 

  93. Zhe Q, Sulei W, Weiwei T, Hongyan L, Jianwei W. Effects of Jiaotaiwan on depressive-like behavior in mice after lipopolysaccharide administration. Metab Brain Dis. 2017;32:415–26.

    CAS  PubMed  Google Scholar 

  94. Zou X, et al. The effects of Jiao-Tai-Wan on sleep, inflammation and insulin resistance in obesity-resistant rats with chronic partial sleep deprivation. BMC Complement Altern Med. 2017;17:165.

    PubMed  PubMed Central  Google Scholar 

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Acknowledgements

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Funding

This study was supported by the National Natural Science Foundation of China (No. 81904011) and the National Natural Science Foundation of China (Nos. 82174159, 81874382).

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  1. Yueheng Tang and Hao Su have contributed equally to this work and share first authorship

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  1. Institute of Integrated Traditional Chinese and Western Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China

    Yueheng Tang, Hao Su, Hongzhan Wang, Fuer Lu, Kexin Nie, Zhi Wang & Hui Dong

  2. Department of Integrated Traditional Chinese and Western Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China

    Wenya Huang

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Contributions

TYH, SH, HWY and DH contributed to the design of the study. TYH and SH wrote the paper. TYH, NKX and WHZ conducted the behavioral tests. NKX and WZ helped with network pharmacological analysis. LFE and DH contributed to the improvement of the manuscript. All authors have read and approved the final version of the manuscript.

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Correspondence to Wenya Huang or Hui Dong.

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The animal experiment was overseen and approved by the Animal Ethics Committee of Tongji Medical College, Huazhong University of Science and Technology.

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All data generated or analyzed during this study are included in this published article.

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The authors declare no competing interests.

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Supplementary Information

Additional file 1: Table S1.

Twenty-eight potential active compounds of Jiao-tai-wan and their corresponding OB and DL.

Additional file 2: Figure S1.

The top 20 BP, MF and CC in GO analysis of 117 intersection targets.

Additional file 3: Figure S2.

Drug-intersection targets-signaling pathways network of Jiao-tai-wan in the treatment of diabetes mellitus and depression.

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Tang, Y., Su, H., Wang, H. et al. The effect and mechanism of Jiao-tai-wan in the treatment of diabetes mellitus with depression based on network pharmacology and experimental analysis. Mol Med 27, 154 (2021). https://doi.org/10.1186/s10020-021-00414-z

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Keywords

  • Jiao-tai-wan
  • Diabetes mellitus
  • Depression
  • Chronic restraint stress
  • Network pharmacology
  • Mechanism

Molecular Medicine

ISSN: 1528-3658