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Medicine

Behavioral and Network Pharmacology-Based Analyses for the Traditional Mongolian Medicine Zadi-5 in a Rat Model of Depression

Published: February 24, 2023 doi: 10.3791/64832
Automatic Translation
*1, *1, 1, 1, 1, 1, 1, 2, 1
1Faculty of Mongolian Medicine, Inner Mongolia Medical University, 2Affiliated Hospital of Inner Mongolia Medical University
* These authors contributed equally

Summary

The present protocol describes a method for the behavioral test validation and bioinformatical prediction of the therapeutic efficacy of Zadi-5, a traditional Mongolian medicine, in depression.

Abstract

Zadi-5 is a traditional Mongolian medicine that is widely used for the treatment of depression and symptoms of irritation. Although the therapeutic effects of Zadi-5 against depression have been indicated in previously reported clinical studies, the identity and impact of the active pharmaceutical compounds present in the drug have not been fully elucidated. This study used network pharmacology to predict the drug composition and identify the therapeutically active compounds in Zadi-5 pills. Here, we established a rat model of chronic unpredicted mild stress (CUMS) and conducted an open field test (OFT), Morris water maze (MWM) analysis, and sucrose consumption test (SCT) to investigate the potential therapeutic efficacy of Zadi-5 in depression. This study aimed to demonstrate Zadi-5's therapeutic effects for depression and predict the critical pathway of the action of Zadi-5 against the disorder. The vertical and horizontal scores (OFT), SCT, and zone crossing numbers of the fluoxetine (positive control) and Zadi-5 groups were significantly higher (P < 0.05) than those of the CUMS group rats without treatment. According to the results of network pharmacology analysis, the PI3K-AKT pathway was found to be essential for the antidepressant effect of Zadi-5.

Introduction

Depression, also known as major depressive disorder (MDD), is a severe neuropsychiatric disease responsible for growing medical and economic burdens on society. Due to the associated complexity, morbidity, and mortality rates, a significant amount of research has been conducted to find remedies for the disorder1,2. According to a mental health survey by the World Health Organization, around 350 million people currently suffer from depression and its associated symptoms worldwide. It is predicted that depression will overtake cancer and cardiovascular diseases as the leading cause of disease burden globally by 2030. Thus, the prevention and treatment of depression will become a global priority in the near future3. The pathogenesis of MDD has not yet been elucidated. Still, it is commonly attributed to the following factors: genetic predisposition, dysfunction of the hypothalamus-pituitary-adrenal axis, reductions in neurotransmitter secretion, neuroimmune dysregulation-induced neuroinflammation, cell apoptosis, and reduced cell proliferation4,5.

Among these factors, neuroimmune dysregulation-induced neuroinflammation and altered secretion of neurotrophic factors have received particular attention for their roles in the development of depression and many other psychiatric diseases6. In the past decade, scholars have demonstrated that the hippocampus is the dominant site for regenerative nerve functions and is involved in regulating emotion and cognition. In this regard, the hippocampal neurons are recognized as novel therapeutic targets for antidepressant medicines under development7,8. Moreover, the hippocampus is also reported to be involved in short-term and long-term memory in learning and consolidating memories. Specifically, the shortage of pyramidal neurons in the CA1 region of the hippocampus causes retrograde and anterograde amnesia9. A typical antidepressant therapeutic strategy aims to enhance cell proliferation and neurogenesis in the dentate gyrus of the hippocampus. Natural product-derived compounds and small molecules synthesized based on medicinal chemistry techniques are considered the primary sources of innovative therapeutic agents for various neuropsychiatric conditions.

Traditional Mongolian medicines, which have a long history and a well-supported theoretical medical system, have descended from the nomads of the Mongolian plateau These medicines display multi-target and multi-pathway effects due to the various medicinal components that act in concert to generate synergistic functions. Zadi-5 is a well-established formulation among such drugs and was first recorded in "Clinical Experience of Dr. Gao Shi," written by an outstanding Mongolian clinician called Dr. Gao Shi (1804-1876). It has been clinical practice for a long time in Mongolia to use these pills to treat the symptoms of distress, palpitation, irritation, and cardiac stabbing pain10,11. Moreover, Zadi-5 has proven effects on alleviating post-stroke depression in affected patients12. The recent experimental research on CUMS has revealed that the Zadi-5 formulation alleviates depression by regulating the central neurotransmitters13; indeed, with Zadi-5, increased levels of brain-derived neurotrophic factor (BDNF) and tyrosine kinase receptor B (TrkB) have been detected and correlated with improved learning and memory in a rat model of depression14. However, the exact mechanism of action of Zadi-5 for such alleviation of depression has not been elucidated.

This study aimed to demonstrate the therapeutic effects of Zadi-5 against depression in rats using a behavioral test and identify the components of Zadi-5 using Traditional Chinese Medicine Systems Pharmacology (TCMSP) and Swiss Target Prediction to predict the potential mechanisms underlying the efficacy of Zadi-5, a traditional Mongolian medicine, in treating depression.

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Protocol

All the experimental protocols were approved by the Ethics of Animal Experiment Care Committee of Inner Mongolia Medical University and followed the guidelines of the National Institutes of Health on animal care and ethics. Male Sprague Dawley (SD) rats aged 8 weeks old (200 g ± 20 g) were housed in a room with a controlled temperature (22 °C ± 2 °C) and humidity (55% ± 15%) under a 12 h/12 h regulated light/dark cycle for 1 week. See Figure 1 for the workflow of the network pharmacology analysis.

1. Behavioral test in rats

  1. Establish a CUMS rat model
    1. Apply the following stimuli combined with isolation for 28 days to all the rats, except for the controls: inversion of the light/dark cycle for 24 h, food deprivation for 24 h, water deprivation for 24 h, high-speed level shaking for 15 min (one time/s), tail clamp for 2 min, swimming in cold water (4 °C) for 5 min, 45 °C heat stimulation, and wet padding for 24 h (Table 1). Raise the rats in individual cages.
      NOTE: Avoid repeating the same type of stimuli for consecutive days.  The above procedures to establish a CUMS rat model have been approved by the animal ethics committee and described previously15.
  2. Drug preparation
    1. Pulverize the Zadi-5 pill in a grinder, and prepare a 1.16 g/mL solution in distilled water. Separately prepare a fluoxetine solution of 0.36 mg/mL in distilled water.
  3. Drug administration
    1. Divide the rats randomly into six groups (n = 10): control (CON), model (MOD), Zadi-5 group (Zadi-5, 1.6 g of Zadi-5/kg16), fluoxetine group (Fluoxetine, 3.6 mg of fluoxetine/kg). One time per day for 28 days, administer 1 mL/g per rat of the appropriate drug solution by gavage and treat the CON and MOD groups with an equal volume of distilled water.
      NOTE: Gavage starts at the beginning of model establishment for all the groups.
  4. Open-field test (OFT)
    1. Divide a black box (50 cm x 50 cm x 30 cm) into nine square regions of equal area. Equip the box with a video tracking analysis system. One day after the last gavage, place the rat in the center square, and record its horizontal and vertical activities for 3 min.
    2. Score the number of squares crossed with all paws as a horizontal activity, and score standing and grooming as a vertical activity. After each test, clean the box with 75% alcohol to remove the smell of the rat for subsequent tests17.
  5. Sucrose consumption test (SCT)
    1. Weigh the respective bottles before and after consumption, and calculate the 60 min sucrose preference rates on day 0, day 7, day 14, day 21, and day 28 using equation (1):
      ​Sucrose consumption = Equation 1 × 100%     (1)
  6. Morris water maze (MWM)
    1. Divide the pool into four quadrants. Order the quadrants from one to four, and place the hidden platform in the third quadrant, 1 cm below the water surface.
    2. Place the rat subject into the maze in different quadrants to look for the platform for 120 s, and record the latency time using the MWM video trail analysis system.
    3. Place the rat subject in a fixed position in the pool. If the subject cannot find the hidden platform in 120 s, record the latency as 120 s.
    4. Next, dislodge the platform, place the rat in the water, and record the number of zone-crossings for 120 s.
    5. Add milk to the pool for some level of opacity. Maintain the water temperature at 23 °C ± 1 °C during the experiment.

2. Network pharmacological prediction

  1. Screen the active components in Zadi-5.
    1. Browse the Traditional Chinese Medicine Systems Pharmacology (TCMSP, https://old.tcmsp-e.com/tcmsp.php), and input "Myristicae Semen Seeds," "Aucklandiae Radix roots," and "Piperis Longi Fructus" in the "herb name" section to obtain the names of chemicals. Set the pharmacokinetic index of oral bioavailability (OB) to be >30% and the drug-like (DL) index to be >0.18 (Supplementary File 1).
    2. Search "Rou Dou Kou" (Myristica fragrans Houtt), "Tu Mu Xiang" (Inula helenium L.), "Mu Xiang" (Aucklandia lappa Decne.), "Guang Zao" (Choerospondias axillaris Roxb. Burtt Hill), and "Bi Ba" (Piper longum L.) in the Chinese Medicine Pharmacopeia (http://www.zhongyaocai360.com/zhongguoyaodian/) to identify the chemical names of each component.
    3. Search the identified chemical names in PubChem (https://pubchem.ncbi.nlm.nih.gov/) to find the isomeric SMILES or InChIkey
  2. Identify the target proteins of the active components in Zadi-5.
    1. Identify the target proteins of the active components using SEA (http://sea.bkslab.org/), BATMAN (http://bionet.ncpsb.org.cn/batman-tcm/), and Swiss Target Prediction (http://www.swisstargetprediction.ch/) with isomeric SMILES or InChIkey, and find the overlapping proteins.
    2. Use the protein database UniProt (http://www.uniprot.org/uploadlists/) to convert the identified targets to unified gene names.
  3. Search for the target proteins for depression.
    1. Search and identify the potential protein targets for depression by using the keywords "depression" and "depressive disorder" in Genecards (https://www.genecards.org/), Disgenet (https://www.disgenet.org/), and Drugbank (https://www.drugbank.com/).
  4. Find the target genes.
    1. Browse the Venn diagram (http://bioinformatics.psb.ugent.be/webtools/Venn/), upload the targets of the active components of Zadi-5 in List-1, upload the targets for depression in List-2, and submit. Obtain the Venn diagram, and filter out the overlapping target candidates.
  5. Construct the network.
    1. Construct a spreadsheet called "Type and Network" (Supplementary File 2). "Type" is the network's signature, and "Network" illustrates the relationship between the signs.
    2. Export the "Type and Network" to Cytoscape v3.9.0 to construct the network "Zadi-5 herbs-ingredients-disease targets."
  6. Analyze the protein-protein interaction (PPI) network of the target candidates.
    1. Set the common targets in the STRING database (https://cn.string- db.org/) to analyze their interactions. Set the protein type as "homo sapiens." Set the interaction threshold value to 0.9, and select only the experimentally verified types. Do not display the lonely island nodes.
  7. Conduct a Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses of the target-related pathways.
    1. Paste 86 potential antidepressant targets of Zadi-5 into the start analysis bracket in DAVID (https:// david.ncifcrf.gov/) to study the related signaling pathways by performing the Gene Ontology (GO) function-including biological process, cellular component, and molecular function-and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses.
      NOTE: KEGG is visualized in the bubble chart using an online IMageGP (http://www.ehbio.com /ImageGP/index.php/Home/Index/). The bubble size represents the number of targets enriched in the indicated pathway, and the bubble color represents the enrichment’s ​P-value.
  8. Construct the network to illustrate the active compounds in Zadi-5 that interact with the PI3K-AKT signaling pathway.
    1. Download the KEGG pathway document, select the genes of the PI3K-AKT pathway from the enrichment analysis, and paste them on the spreadsheet to construct a "Type and Network" document.
    2. Export the "Type and Network" document to the Cytoscape to generate the "PI3K-AKT visualized compounds-targets-pathways network" (Supplementary File 3).
      ​NOTE: "Type" is the network's signature, and "Network" illustrates the relationship between the signs.

3. Statistical analysis

  1. Use a one-way analysis of variance (ANOVA), followed by Duncan's post hoc test, to determine the significant differences in biochemical and gene expression parameters. Calculate the mean ± standard deviation (SD), and visualize the data. Consider P < 0.05 as statistically significant.

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Representative Results

Behavioral test in animals
Results of the behavioral tests in the CUMS-induced rat depression model
No significant differences between the tested groups were found for the OFT score, sucrose consumption, and MWM analysis before CUMS stimulation. After establishing the CUMS model, the MOD group's vertical and horizontal scores were lower than those of the CON group (P < 0.05). Compared with the MOD group, the vertical and horizontal scores of the POS and Zadi-5 groups were significantly higher (P < 0.05) (Figures 2A,B).

On day 0, the tested groups had no significant difference in sucrose consumption (%). The sucrose consumption (%) of the Zadi-5 and POS groups was higher than that of the MOD group on day 28 (Figure 2C).

Compared with the CON group, the latency of the MOD group to find the platform in the MWM was significantly higher (P < 0.01). The latency in the POS group was markedly lower than in the MOD group. The latencies of the rats in the Zadi-5 group were lower than in the MOD group but not to a significant degree. Regarding the zone-crossing counts, the MOD group showed fewer crossings than the CON group. The POS and Zadi-5 groups showed more crossings than the MOD group (Figure 2D,E).

Network pharmacology prediction
There were 134 active components in Zadi-5, and 220 target protein candidates were retrieved for these components. Moreover, 1,000 depression-related protein targets were predicted. According to the Venn diagram analysis, 86 overlapping targets were identified as the critical depression-related targets of Zadi-5 (Figure 3). Based on these findings, "Zadi-5 herbs-ingredients-disease targets" and a PPI network analysis of the target candidates were constructed (Figure 4A,B, Figure 5, and Table 2). The PI3K-AKT pathway showed relatively irregular edges, indicating that this pathway is essential for the antidepressant effect of Zadi-5. According to the KEGG pathway analysis, the PI3K-AKT signaling pathway was ranked seventh (Figure 6) and was associated with many signaling pathways. Accordingly, it was considered relatively more important than the other enrichment pathways (Figure 7A-C and Figure 8).

Figure 1
Figure 1: Workflow of the network pharmacology analysis for in vivo validation. Abbreviations: PPI = protein-protein interaction; KEGG = Kyoto Encyclopedia of Genes and Genomes; GO = Gene Ontology; OFT = open-field test; MWM = Morris Water Maze; SCT = sucrose consumption test. Please click here to view a larger version of this figure.

Figure 2
Figure 2: Effects of Zadi-5 on the CUMS-induced rat depression model. (A) OFT vertical scores. (B) OFT horizontal scores. (C) Sucrose consumption level (%) on day 0 and day 28. (D) Latency time for the MWM test. (E) Zone-crossing numbers for the MWM test. ##P < 0.01 indicates that the CUMS MOD group showed significant differences compared with the CON group. *P < 0.05 indicates that the POS group and Zadi-5 group showed significant differences compared with the MOD group. Please click here to view a larger version of this figure.

Figure 3
Figure 3: Venn diagram of the protein targets of Zadi-5 and depression-associated proteins. The red circle represents the targets of the active components in Zadi-5, while the blue circle represents the proteins associated with depression. The intersection of the two colors represents the overlapping proteins that can be identified as the therapeutic targets for the alleviation of depression by Zadi-5. Please click here to view a larger version of this figure.

Figure 4
Figure 4: Construction of the "Zadi-5 herbs-ingredients-disease targets network." The red parallelograms represent Zadi-5 and its herbs, while the pink, orange, yellow, green, and purple circles represent the components of each herb. The blue diamonds represent the proteins associated with depression, which is the green hexagon. Please click here to view a larger version of this figure.

Figure 5
Figure 5: PPI of the overlapping protein targets. Please click here to view a larger version of this figure.

Figure 6
Figure 6: The bubble chart for the top 20 KEGGs-enriched terms based on the 86 overlapping protein targets. Please click here to view a larger version of this figure.

Figure 7
Figure 7: The key bioinformatics results for the Zadi-5 components and depression-associated protein targets. (A) The top 20 overlapping molecular functions GO terms of Zadi-5 and depression. (B) The top 20 overlapping cellular components GO terms of Zadi-5 and depression. (C) The top 20 overlapping biological processes GO terms of Zadi-5 and depression. Please click here to view a larger version of this figure.

Figure 8
Figure 8: The Zadi-5 ingredients-PI3K-AKT signaling pathway enrichment targets network for the treatment of depression. The circle represents the ingredients of Zadi-5, the hexagons indicate each drug of Zadi-5, and the green diamonds represent the enriched targets of the PI3K-AKT signaling pathway. Please click here to view a larger version of this figure.

Table 1: Sequence and methods of application of stress stimuli for the induction of CUMS in rat subjects. Please click here to download this Table.

Table 2: The ID codes used in Figure 2. Abbreviations: MF = Myristica Fragrans Inula IH = Innula helenium L.; FC = Fructus Choerospondiatis; AL = Aucklandia lappa Decne.; PL = Piper Longum L. Please click here to download this Table.

Supplementary File 1: Screenshot images of the network pharmacology protocol. Please click here to download this File.

Supplementary File 2: Zadi-5 components-proteins-depression network. Please click here to download this File.

Supplementary File 3: Type of network. Please click here to download this File.

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Discussion

Depression is a mental disease characterized by low mood, anhedonia, and a lack of energy. This disorder is accompanied by distraction, cognitive dysfunction, social withdrawal, insomnia, sexual dysfunction, and gastrointestinal diseases18,19. In the study of depression, establishing an animal model is crucial for understanding the pathological mechanisms and effects of new drugs. In this study, a CUMS-induced rat depression model was established through the irritations described in protocol section 2 and Table 1 to simulate stress, attack, and frustration from sources related to society, family, and work. The appropriate sequence and adjustment of the intensities of the stimuli are critical to establishing this depression model. In addition, after each rat test of OFT, the box must be cleaned immediately to avoid the next rat's track being influenced. CUMS combined with isolation effectively creates a reliable rat depression model20 that simulates the development of depressive symptoms and biochemical signs in human patients. This model has been widely used to explore the pathophysiological mechanisms of depression and evaluate antidepressant drugs under development21,22. Tests such as the OFT, SCT, and MWM are simple, objective, and reasonable ways to assess the rat subjects' appetite, absence of motivation, learning ability, and memory. These tests are recognized as the gold standard for testing animal models of neurodisorders.

In this study, the network pharmacology was based on the TCMSP database of online sites, which collects, stores, and handles data from many available sources, such as websites, books, and academic journals23. It effectively supports the exploration of traditional medicines that have complex mechanisms of action by reducing the time and costs needed for the investigation24,25. In network pharmacology, data are copied to the excel files and clearly classified based on each drug and database. Traditional Mongolian medicine is different from traditional Chinese medicine in that the components of each drug cannot be searched. In this regard, protocol step 2.2.1 lists a valuable database for identifying effective chemical components in this study. As described in protocol step 2.4, determining the overlapping target proteins of Zadi-5 and depression based on three distinct databases could ensure the accuracy and efficiency of the investigation. This represents a key modification of the experiment that is essential for improving the accuracy of the results. The PPI interaction, KEGGs, and GO functional enrichment analyses indicated that the PI3K-AKT signaling pathway plays a vital role in the therapeutic effects of Zadi-5 against depression. The pro-survival kinase-signaling cascade PI3K/AKT pathway is an intracellular signal transduction pathway that exerts a pivotal role in neuron injury through anti-oxidative stress and anti-apoptotic effects26. The PI3K-AKT signaling pathway is closely associated with fundamental cellular functions such as proliferation, survival, differentiation, and protein translation. Additionally, it plays a dominant role in the metabolism of cells in specific organs such as the heart and brain27. It has been reported that the activation of the PI3K-AKT pathway can control neuron functions and protect neurons from oxidative damage by regulating anti-oxidative Bcl-2 proteins to inhibit the generation of reactive oxygen species (ROS)28,29.

This study utilized a behavioral test and network pharmacological prediction to estimate the effects of Zadi-5. In the future, network pharmacology can be used for predicting the bioactive constituents of herbal medicines and their target proteins. Pathological assessment and key molecule tests will be undertaken to characterize the bioactive components of Zadi-5. Behavioral tests will be applied to validate the animals' motivation, appetite, and memory. To ensure the quality of validation, the prediction of the components of Zadi-5 based on online databases will be analyzed by liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) spectroscopy. The target proteins will be analyzed by western blot and quantitative polymerase chain reaction (qPCR) analyses30.

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Disclosures

The authors have no conflicts of interest to disclose.

Acknowledgments

We are grateful for the instrumentation and laboratory provided by the Mongolian medical faculty of the Inner Mongolian Medical University, China. This study was supported by the National Natural Sciences Foundation of China (81760762) and the Science and Technology Plan Project of the Health Commission of Inner Mongolia, China (202201300).

Materials

Name Company Catalog Number Comments
Cytoscape software  version 3.7.0
Fluoxetine Lilly Suzhou Pharmaceutical Co., Ltd J20160029
Morris water maze video trail analysing system  Tai Meng Tech Co., Ltd WMT-200
Sprague Dawley rats Beijing Biotechnology Co., Ltd, China  SCXK (JING) 2016-0002
 video tracking system Tai Meng Tech Co., Ltd ZH-ZFT
Zadi-5 pill Pharmaceutical Preparation Center of International Mongolian Hospital, Inner Mongolia, China M1301006

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Tags

Medicine Depression Treatment Therapeutic Effects Active Pharmaceutical Compounds Network Pharmacology Rat Model Chronic Unpredicted Mild Stress (CUMS) Open Field Test (OFT) Morris Water Maze (MWM) Analysis Sucrose Consumption Test (SCT) Therapeutic Efficacy Critical Pathway Fluoxetine PI3K-AKT Pathway
Behavioral and Network Pharmacology-Based Analyses for the Traditional Mongolian Medicine Zadi-5 in a Rat Model of Depression
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Cite this Article

Wu, R., Zu, W., Wu, L., Su, S., Su,… More

Wu, R., Zu, W., Wu, L., Su, S., Su, N., Qi, L., Wu, R., Sun, W., Hu, R. Behavioral and Network Pharmacology-Based Analyses for the Traditional Mongolian Medicine Zadi-5 in a Rat Model of Depression. J. Vis. Exp. (192), e64832, doi:10.3791/64832 (2023).

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