Exploring The Mechanism Of Action Of Chronic Heart Failure Curing By Desertliving Cistanche Based On Network Pharmacology Combined With Multi-chip Analysis Of GEO Database

Abstract

 

Objective To analyse the molecular mechanism of action of Desertliving cistanche against chronic heart failure (CHF) using bioinformatics and network pharmacology.

Methods The database of TCMSP, Swiss Target Prediction, ETCM, PharmMapper and literature search were used to screen the compounds and targets of Desertliving cistanche. The Gene Expression Omnibus (GEO) database was used to download the GSE16499, GSE42955, GSE84796 gene chips to screen the differential gene datasets, and the datasets from GeneCard, DisGeNET, DiGSeE, GEO and other databases were integrated to obtain the potential disease genes of CHF. The String database and Cytoscape 3.7.2 software were used to construct the active ingredient of Desertliving cistanche -target visualisation network, and the core targets were obtained by topological parameter analysis. The GO and KEGG pathway enrichment analyses were performed using the DVAID database to construct the active ingredient of Desertliving cistanche -target-pathway network. Finally, Autodock software was used to visualise the results of molecular docking between core targets and compounds. Results 55 compounds and 630 potential targets of Desertliving cistanche were screened, including key components such as Rhodiola rosea, gastrodynamics, batrachotoxin, and core targets such as glyceraldehyde-3-hosphate dehydrogenasem (GAPDH), interleukin 6 (IL-6), tumor necrosis factor (TNF), etc. The key pathways were mainly related to the phosphoinositide-3-kinase (PI3K) - protein kinase B (Akt) signaling pathway, central carbon metabolism in cancer, and the PI3K-Akt signaling pathway and central carbon metabolism in cancer. The key pathways mainly involved PI3K-Akt signaling pathway, central carbon metabolism in cancer, mitogen-activated protein kinase (MAPK) signaling pathway, etc. The molecular docking results showed that the key components and core targets were well combined. Conclusion A multicomponent, multi-target and multi-pathway study were conducted to investigate the molecular mechanism of Desertliving cistanche against CHF.The result suggested that the active ingredients of Desertliving cistanche might act on multiple targets and pathways to improve CHF.

Key words: Desertliving cistanche; chronic heart failure; network pharmacology; bioinformatics; GEO database; molecular docking; core target

 

Herbal Cistanche Extract For Treatment Of Chronic Heart Failure

 

Chronic heart failure (CHF) is a complex clinical syndrome mainly caused by abnormal cardiac structure and function, resulting in impaired ventricular filling or ejection capacity[1]. The latest epidemiological data show that CHF has become a global public health problem and is positively correlated with population aging[2-4]. Currently, existing internal and external treatments cannot cure CHF, and the disease is often accompanied by multiple complications, poor prognosis, and high mortality. At present, standard treatment plans have been developed for the treatment of CHF. According to relevant literature reports[5], traditional anti-heart failure drugs mainly include digitalis cardiotonic agents, diuretics, β-blockers, angiotensin converting enzyme inhibitors, angiotensin receptor antagonists, and mineralocorticoid receptor antagonists. New therapeutic drugs include angiotensin receptors, enkephalinase inhibitors, sodium-glucose cotransporter inhibitors, soluble guanylate cyclase agonists, cardiac myosin activators, and specific sinoatrial node If ion channel blockers. Although these drugs can improve the clinical symptoms of CHF patients, they are also accompanied by many adverse reactions. Professor Zhou Jingmin's research pointed out that the anti-CHF drugs currently used in clinical practice may cause dehydration or electrolyte imbalance, and in severe cases may cause angioedema, hypotension, tachycardia, arrhythmia, and liver and kidney function damage [6]. Therefore, there is an urgent need to develop safer and more effective drugs or treatments to prevent and treat CHF.

Cistanche deserticola is the dried fleshy stem with scaly leaves of Cistanche deserticola Y.C.Ma or Cistanche tubulosa (Schenk) Wight, a plant of the Orobanchaceae family. It has the characteristics of being both a medicine and a food, and has been used clinically for more than 2,000 years. According to the "Shennong Bencao Jing", Cistanche deserticola tastes sweet, salty, and warm, and has the effects of tonifying kidney yang, benefiting essence and blood, and moistening the intestines and relieving constipation [7-8]. From the perspective of traditional Chinese medicine, the treatment of CHF mainly focuses on invigorating qi and warming yang, promoting blood circulation and removing blood stasis, and promoting diuresis and removing dampness [9-12]. As a good medicine for warming yang, nourishing blood and removing blood stasis, Cistanche deserticola has shown multiple pharmacological effects, such as anti-coronary atherosclerosis, protection against ischemic myocardial reperfusion injury, anti-oxidation, anti-inflammation, regulation of cell autophagy and promotion of gastrointestinal motility [13-15]. Clinical trials and pharmacological studies have shown that the main active ingredients of Cistanche deserticola, such as salidroside, ferulic acid, leonurine, gallic acid, succinic acid, caffeic acid and motilin, have shown significant improvement in CHF-related animal and cell experimental models, helping to delay the structural changes of myocardial cells [16-22]. However, research on the mechanism of action of Cistanche deserticola in the treatment of CHF is still relatively scarce. Based on this, this paper uses GEO database multi-chip joint analysis, network pharmacology and molecular docking technology to analyze the relationship between drugs and diseases from multiple perspectives such as drug targets, disease targets, and signal pathways, and explores the potential mechanism of action of Cistanche deserticola in improving CHF symptoms, providing a theoretical basis for subsequent experimental research [23].

 

1 Materials and methods

1.1 Screening of active ingredients of Cistanche deserticola and collection of potential targets

Using "Cistanche deserticola" as the keyword, the relevant chemical components were searched through the TCMSP database, and the active ingredients of Cistanche deserticola were screened according to the standards of oral bioavailability (OB) ≥ 30% and drug likeness (DL) ≥ 0.18. The active ingredients in Cistanche deserticola were further supplemented by searching the published relevant literature. The PubChem platform (https://pubchem.ncbi.nlm.nih.gov/) was used to obtain the CAS numbers of all the main active ingredients screened out, and then their SMILES chemical formulas were obtained. Download the SDF file of the 2DStructure of the potential active ingredients, and import the SMILES chemical formula of all ingredients into the SwissADME analysis platform (http://www.swissadme.ch/) in batches. Screening was performed based on the five principles of Lipinski (at least two "yes" conditions were met, and Gl absorption was "high"), and a total of 49 active ingredients were screened out. Using platforms such as Swiss Target Prediction (http://www.swisstargetprediction.ch/), PharmMapper (http://www.lilab-ecust.cn/pharmmapper/), Stitch http://stitch.embl.de/), Super-PRED (http://predi
ction.charite.de/) and SEA Search Server https://sea.docking.org/search), set the species to "human" and add "Probability">0 as the screening condition to collect and organize the potential targets of Cistanche deserticola. Finally, the targets corresponding to each active compound component were input through the UniProt database (https://www.uniprot.org/), and the species was selected as "homo sapiens". All target names were deduplicated and standardized to obtain gene abbreviations.

 

1.2 CHF gene chip and differentially expressed gene screening

Using "Chronic Heart Failure" as the key search term, the GEO database (https://www.ncbi.nlm.nih.gov/geo/) was searched, and "Organism" was set to "Homo sapiens" to extract the GSE42955, GSE16499 and GSE84796 gene chip data sets to analyze the differentially expressed gene data of heart tissues of CHF patients and healthy controls. The limma package in R language was used for paired differential analysis. The screening criteria were set as [log2(FC)]＞0.5 and corrected P＜0.05 to determine differentially expressed genes (DEGs). Volcano plots and gene heat maps were made to obtain CHF disease-related differentially expressed genes screened by multi-chip.

 

1.3 Screening of potential disease targets and Cistanche deserticola components-CHF common targets

Based on disease databases such as DisGeNET (https://www.disgenet.org/), Drugbank (https://www.drugbank.com/datasets), DiGSeEhttp://210.107.182.61/geneSearch/), GeneCards
(https://www.genecards.org/) and GEO (https://www.aclbi.com/), "Chronic Heart Failure" was used as the search keyword to screen out genes related to CHF. Subsequently, these genes were imported into the UniProt database to obtain gene symbols, and after taking the union, duplicate gene targets were removed to obtain gene targets for CHF disease. Apply
Venny 2.1 online mapping software (https://bioinfogp.cnb.csic.es/tools/venny/) to draw the intersection of Cistanche deserticola ingredients and drug target genes, obtain the common targets and generate Venny diagram.

 

1.4 Construct protein-protein interaction networks (PPI) of Cistanche deserticola active ingredients for potential therapeutic targets of CHF

Import the common target standard names obtained in the Venny diagram into the STRING database, select the "multiple proteins" mode, set the "minimum interaction score" to medium confidence (medium confidence) 0.400, and generate the intersection target interaction.

 

1.5 Screening of core targets of Cistanche deserticola components-CHF disease intersection targets

TSV format data were obtained through the above potential target interaction network diagram, and the exported TSV format data were analyzed and visualized using Cytoscape 3.7.2 software. The target connectivity (Degree) value was adjusted from small to large to adjust the color and size of each node (the color of the corresponding node changed from light to dark as the Degree value changed from small to large), and the connection coefficient (combined score) between targets was used as a quality control condition to control the thickness and color of the connection line (the combined score value changed from small to large to correspond to the color of the node changed from light to dark, and the Degree value changed from small to large to correspond to the node changed from small to large). Topological parameters such as betweenness centrality (BC), closeness centrality (CC) and Degree value were used as screening conditions, and then the network topological parameters of each node in the interaction network diagram were analyzed and calculated using the built-in tool Network Analyzer of cytoscape 3.7.2 software. At the same time, the target with a value greater than BC, CC, and Degree was selected. The median target was used as the core target in the regulation of CHF by Cistanche deserticola. The images were drawn and visualized using Cytoscape 3.7.2 software.

1.6 GO biological function and KEGG pathway enrichment analysis

The Cistanche deserticola-CHF common targets obtained from the Venny diagram were uploaded to the DAVID online analysis platform (https://david.ncifcrf.gov/). "Homo sapiens" was selected in the "Input species" and "Analysis species" options. After setting (P < 0.01), GO and KEGG enrichment analysis were performed. The top 10 biological processes (BP), cellular components and molecular functions (MF) and the top 20 significant pathways were screened out according to the P value. Then, the GO and KEGG functional enrichment maps were drawn through the microbial information mapping platform (https://www.bioinformatics.com.cn/) for visualization analysis.

 

1.7 Network construction of active ingredients-signaling pathways-core targets

Based on the results of KEGG pathway analysis, the research team extracted potential targets enriched in these pathways, and matched them with the corresponding active ingredients, imported them into Cytoscape 3.7.2 software for visualization, and constructed a network diagram of active ingredients-signaling pathways-core targets.

 

1.8 Molecular docking verification

The compound structure of the active ingredient of the drug was obtained through the PubChem database, and the SDF file was converted to PDB format using OpenBabel-3.1.1 software. Then, the structure of the key target receptor protein was retrieved through the PDB database (http://www.rcsb.org), and dehydration and deliganding were performed in PYMOL software. Then, the Autodock Tools software (Autodock Tools.exe) was used for hydrogenation and charge balance, and the receptor protein and ligand small molecules were finally converted to pdbqt format. Finally, Autodock Vina version 1.1.2 was used for molecular docking and prediction of binding free energy, and Pymol software (pymol.exe) was used to visualize a three-dimensional graph to display the key components with high molecular binding energy between targets-the core target combination.

 

2 Results

2.1 Main active ingredients of Cistanche deserticola

The TCMSP database was searched with the keyword "Cistanche deserticola" to obtain 75 chemical components. OB≥30% and DL≥0.18 were set as screening conditions, and 6 main active ingredients of Cistanche deserticola were finally screened out. In addition, 49 active ingredients such as salidroside, phenethyl caffeate, leonurin, ononin, cistanin, dauricine, and motilin were added through literature search, and a total of 55 active ingredients were finally obtained. See Table 1 for details.

 

Table 1 The main active ingredients of Cistanche deserticola

No.	Substance Category	Name	CAS No.
R1	Red Pigment	alizarin	103-58-8
R2	Phenolic Compound	phloretin	104-57-4
R3		caffeic acid	331-39-5
R4	Flavor Compound	kenuin	92432-60-5
R5		enone	101-62-6
R6		ferulic acid	1135-24-6
R7		salicylic acid	69-72-7
R8		coumarin	91-64-5
R9		kava lactone H1	68434-40-4
R10		kava lactone H2	17193-39-6
R11		kava lactone H3	17193-40-9
R12		kava lactone H4	17193-41-0
R13		3,4-dihydroxyphenylalanine	1194-00-1
R14		4-hydroxybenzyl alcohol	623-05-2