Screening method of acrolein-induced retinal injury potential treatment target

Abstract

The invention discloses a method for screening potential treatment targets of acrolein-induced retinal injury, and relates to the field of acrolein toxicity prediction. According to the method disclosed by the invention, a retinal injury related differential expression gene and an acrolein related target spot are obtained, an intersection is taken, protein interaction relationship network analysis is carried out, and a potential mechanism of acrolein-induced retinal injury is predicted. GO functional clustering analysis results show that acrolein is mainly subjected to biological processes such as apoptosis signal channel regulation and epithelial cell development, and KEGG key signal channel enrichment analysis results show that acrolein may induce retinal injury through IL-17 signal channels, human cytomegalovirus infection and cell senescence cell signal channels. The predicted key genes comprise CXCR4, EDN1, ITGA6 and PECAM1, and a new target is provided for subsequent prevention and treatment of acrolein-induced retinal injury.

Description

Technical Field

[0001] The present invention relates to prediction of acrolein toxicity, and in particular to prediction of potential signal pathways and key targets in the process of acrolein-induced retinopathy. Background Art

[0002] Diabetic retinopathy is one of the most common and serious complications of diabetic patients. It is a vascular disease characterized by microvascular damage. Complications such as retinal neovascularization and macular edema can seriously affect the patient's vision. It is also one of the most common causes of blindness in the elderly, mainly due to macular ischemia. Studies have shown that the blockage, loss or degeneration of capillaries in the macular area can lead to macular microcirculation disorders, which in turn lead to damage and decreased density of the macular capillary network, which in turn leads to decreased vision in patients. The main manifestation is age-related macular degeneration (AMD). AMD is a neurodegenerative disease and is also the leading cause of blindness in people over 50 years old in developed countries. In patients with advanced AMD, the retinal neuroepithelial cells and retinal pigment epithelial cells (RPE) in the macular area degenerate, leading to vision loss in patients. With the advancement of population aging, the incidence of AMD in people over 50 years old has increased from 6.41% in 1987 to 15.5% in 2005. In 15 years, the incidence has increased by about 2.4 times; in 2005 alone, AMD caused 1.064 million low-vision patients and 217,000 blind people in people over 50 years old. It is estimated that by 2040, the number of AMD patients worldwide will reach 280 million, becoming one of the diseases that seriously threaten public health. Patients are under tremendous physical and mental pressure and heavy economic burdens, which seriously affect their quality of life. Therefore, it is of great significance to continuously explore new strategies and new ideas for the diagnosis and treatment of AMD and actively explore new targets for disease prevention and treatment, which are of great significance for the prevention and treatment of AMD and improving the quality of life of patients.

[0003] Acrolein is a highly reactive, electron-philic α,β-unsaturated aldehyde and a ubiquitous environmental pollutant. Although the pathogenesis of AMD is still unclear, smoking has been recognized by domestic and foreign scholars as the primary risk factor. Cigarette smoke refers to a mixture of smoke produced by the burning of tobacco products and smoke exhaled by smokers. It is a concentrated, complex and dynamic aerosol composed of more than 7,000 chemical substances, including more than 250 aldehydes, nitrides and other toxic or carcinogenic substances that irritate the respiratory tract. It is listed as a Class A carcinogen by the US Environmental Protection Agency. Studies have found that cigarette smoke can induce a significant decrease in ascorbic acid levels and hydrogen sulfide protein content in RPE cells, leading to the oxidation of fats and proteins. And with the increase in the smoking dose, the concentration of macular pigment in patients gradually decreases, and the concentration of nicotine and cotinine in plasma increases, further leading to the production of inflammatory mediators and ultimately cell apoptosis. Studies have reported that cigarette smoke can also cause oxidative damage and apoptosis of mouse retinal pigment epithelial cells, leading to the formation of drusen in smokers, and is also the basis for susceptibility to AMD-related gene mutations. Therefore, smoking has become a serious risk factor for inducing AMD. Cigarette smoke can spread to almost all organs of the body through blood flow or direct contact. Among the more than 7,000 components it contains, aldehydes such as acrolein, formaldehyde and acetaldehyde have been identified as the most dangerous toxic substances in cigarette smoke. Compared with formaldehyde and acetaldehyde, acrolein has a higher hazard index, and its toxicity is about 10 times that of formaldehyde and 100 times that of acetaldehyde. Studies to determine the hazard index of mainstream smoke components have shown that acrolein is the main cause of non-cancer disease risk, accounting for about 90% of the overall theoretical hazard index. In view of the toxic effects of acrolein on the retina, screening possible signaling pathways and key targets in acrolein-induced retinal damage has become a direction that needs to be urgently explored.

[0004] Bioinformatics has gradually developed into an interdisciplinary subject involving life sciences, statistics, informatics, computational biology and mathematics. Its application is mainly through the calculation and analysis of biological big data to conduct deeper and more meaningful research on basic life sciences. Researchers usually use programming tools to complete specific and detailed visualization analysis. At present, the specific mechanism and key targets of acrolein-induced retinal damage are still unclear. Therefore, the present invention predicts the potential signal pathways and key targets in the process of retinal damage induced by the environmental pollutant acrolein through bioinformatics technology, further explores the toxicity of acrolein, and provides new targets and new ideas for the prevention and treatment of acrolein-induced retinal damage. Summary of the invention

[0005] The purpose of the present invention is to provide a possible toxic mechanism of acrolein-induced retinal damage and key prevention and treatment targets, to provide data support for the study of the toxic mechanism of acrolein, and to provide new ideas for the prevention and treatment of related diseases induced by acrolein.

[0006] The technical solution adopted by the present invention is as follows: a method for screening potential therapeutic targets for acrolein-induced retinal damage, the steps of which are:

[0007] 1) Visualization analysis of differentially expressed genes:

[0008] 1.1) A retinal injury dataset was selected from the database, differentially expressed genes were obtained as disease targets of retinal injury based on the set screening conditions, and a volcano plot was drawn to represent the differential expression of genes in the dataset; the database was the GEO database, and the retinal injury dataset was GSE34379; the set screening conditions were Adj.P Value<0.05,|log2FC|>1.5.

[0009] 1.2) Obtain targets related to acrolein toxicity in the CTD database and screen the common intersections between acrolein and retinal damage datasets using a Venn diagram.

[0010] 2) Construction of protein interaction network and GO functional clustering analysis:

[0011] 2.1) The targets obtained in step 1.2) were imported into Cytoscape software. The isolated nodes in the preliminary protein interaction network needed to be deleted. After removing the individual nodes, a protein interaction network of potential targets was constructed to analyze the potential mechanism of acrolein-induced retinal damage.

[0012] 2.2) GO functional clustering analysis was performed on potential targets to obtain molecular function MF, biological process BP and cellular composition CC.

[0013] 3) Enrichment of KEGG key signaling pathways and analysis of Hub genes;

[0014] 3.1) The differentially expressed genes obtained in step 1.2) were subjected to KEGG signal pathway enrichment analysis to identify the biological processes and signal transduction pathways that may be involved in acrolein-induced retinal membrane damage.

[0015] The biological processes and signal transduction pathways that may be involved in the process of acrolein-induced retinal membrane damage include Kaposl sarcoma-associated herpes infection, IL-17 signaling pathway, human cytomegalovirus infection, measles, fluid shear stress and atherosclerosis, small cell lung cancer, lipids and atherosclerosis, AGE-RAGE signaling pathway, cell senescence and C-type lectin receptor signaling pathway.

[0016] 3.2) Using the MCC algorithm in the Cytohubba plug-in, the top 5% of Hub genes in the protein interaction network were selected for GO functional clustering and KEGG signaling pathway enrichment analysis, and key targets were screened including CXCR4, EDN1, ITGA6 and PECAM1 genes.

[0017] The beneficial effects of the present invention are:

[0018] The present invention uses bioinformatics technology to comprehensively and rapidly screen out key targets of acrolein-induced retinal damage from a large amount of data, and predicts possible biological processes and signal transduction pathways, thereby effectively improving research efficiency and revealing the molecular mechanism of acrolein-induced retinal damage. It not only provides new ideas and methods for the study of acrolein-induced retinal damage, but also provides an indication for exploring the occurrence and development of related diseases, and provides a data basis for subsequent drug development and treatment strategy formulation, which has significant scientific value and social significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Volcano plot of gene expression for retinal damage and common targets of acrolein and retinal damage;

[0020] Among them, A is the volcano map of retinal damage gene expression; B is the common target of acrolein and retinal damage.

[0021] Figure 2 To construct protein interaction network and conduct GO functional clustering analysis;

[0022] Among them, A is the protein interaction network; B is the GO functional clustering analysis.

[0023] Figure 3 To obtain and analyze the enrichment of KEGG key signaling pathways and Hub genes;

[0024] Among them, A is the bubble diagram of KEGG key signal pathway enrichment analysis of acrolein-induced retinal damage targets; B is the acquisition and analysis diagram of Hub genes of acrolein-induced retinal damage targets. DETAILED DESCRIPTION

[0025] Embodiment 1:

[0026] This example uses the GEO database and the CTD database as data sources to predict potential signal pathways and targets for acrolein-induced retinal damage. The specific experimental steps are as follows:

[0027] 1) Visual analysis of differentially expressed genes (volcano plot and Venn diagram analysis)

[0028] The retinal injury dataset GSE34379 was selected from the GEO database (https: / / www.ncbi.nlm.nih.gov / geo / ), and 676 differentially expressed genes were obtained based on the screening conditions of Adj.P Value<0.05,|log2FC|>1.5 as disease targets of retinal injury, and a volcano map was drawn to represent the differential expression of genes in the dataset. Figure 1 As can be seen from A, red represents up-regulated genes, blue represents down-regulated genes, and gray represents genes with |log2FC| < 1.5. At the same time, 2373 targets related to acrolein toxicity were obtained from the CTD database (https: / / ctdbase.org / ). The common intersection of acrolein and retinal damage datasets was screened through the Venn diagram. The results are as follows Figure 1 As shown in B, the 118 overlapping targets shared by the two may play an important role in the process of acrolein-induced retinal damage.

[0029] II) Construction of protein interaction network and GO functional clustering analysis

[0030] The 118 targets of acrolein-induced retinal damage were imported into Cytoscape software. The isolated nodes in the initial protein interaction network needed to be deleted. After removing 40 isolated nodes, a protein interaction network containing 78 targets was constructed to analyze the potential mechanism of acrolein-induced retinal damage. GO functional clustering analysis was performed on the 78 potential targets. The results are shown in Figure 2. Figure 2 As shown in Figure 2, Gene Ontology can be divided into three parts: molecular function (MF), biological process (BP) and cellular component (CC). Figure 2 As shown in B, the BP results showed that through various molecular activities, the acrolein-induced retinal damage targets mainly completed the biological processes of "apoptosis signaling pathway regulation" and "epithelial cell development". The CC results showed that the cell structure positions of the acrolein-induced retinal damage targets when performing functions were mainly in the "cell leading edge" and "lesion adhesion"; MF found that "DNA transcription factor binding" and "adhesion protein binding" played a significant role in the molecular functions of acrolein-induced retinal damage.

[0031] III) KEGG key signaling pathway enrichment and Hub gene acquisition analysis

[0032] The above 78 differentially expressed genes were subjected to KEGG signaling pathway enrichment analysis to predict the biological processes and signal transduction pathways that may be involved in acrolein-induced retinal membrane damage. Figure 3It can be seen from A that through the KEGG key signaling pathway enrichment analysis, it was found that Kaposl sarcoma-related herpes infection, IL-17 signaling pathway, human cytomegalovirus infection, measles, fluid shear stress and atherosclerosis, small cell lung cancer, lipids and atherosclerosis, AGE-RAGE signaling pathway, cell senescence and C-type lectin receptor signaling pathway were significantly enriched, and the Adj.P Value was lower, which was statistically significant, indicating that the above three pathways may play an important role in acrolein-induced retinal damage. Using the MCC algorithm in the Cytohubba plug-in, the top 5% (4 in total) Hub genes in the protein interaction network were taken for subsequent analysis. From Figure 3 As can be seen from B, the four Hub genes are all enriched in signaling pathways related to acrolein-induced retinal damage targets, among which CXCR4, EDN1, ITGA6 and PECAM1 are significantly enriched in signaling pathways such as human cytomegalovirus infection, fluid shear stress and atherosclerosis, small cell lung carcinoma and AGE-RAGE signaling pathway in diabetic complications, respectively, which will help to further explore the potential mechanism of acrolein-induced retinal damage.

Claims

1. A method for screening potential therapeutic targets for acrolein-induced retinal damage, characterized in that: The steps are: 1) Visualization analysis of differentially expressed genes: 1.1) Select a retinal injury dataset from the database, obtain differentially expressed genes as disease targets of retinal injury based on the set screening conditions, and draw a volcano map to represent the differential expression of genes in the dataset; 1.2) Obtain targets related to acrolein toxicity in the database and screen the common intersections of acrolein and retinal damage datasets through Venn diagrams; 2) Construction of protein interaction network and GO functional clustering analysis: 2.1) Import the targets obtained in step 1.2) into Cytoscape software. The isolated nodes in the preliminary protein interaction network need to be deleted. After removing the individual nodes, a protein interaction network of potential targets is constructed to analyze the potential mechanism of acrolein-induced retinal damage; 2.2) Perform GO functional clustering analysis on potential targets to obtain molecular function MF, biological process BP and cell composition CC; 3) Enrichment of KEGG key signaling pathways and analysis of Hub genes; 3.1) The differentially expressed genes obtained in step 1.2) were subjected to KEGG signal pathway enrichment analysis to analyze the biological processes and signal transduction pathways that may be involved in the process of acrolein-induced retinal membrane damage; 3.2) Using the MCC algorithm in the Cytohubba plug-in, the top 5% of Hub genes in the protein interaction network were selected for GO functional clustering and KEGG signaling pathway enrichment analysis, and key targets were screened including CXCR4, EDN1, ITGA6 and PECAM1 genes.

2. The method for screening potential therapeutic targets for acrolein-induced retinal damage according to claim 1, characterized in that: In the above 1.1), the database is the GEO database, and the retinal injury dataset is GSE34379.

3. The method for screening potential therapeutic targets for acrolein-induced retinal damage according to claim 1, characterized in that: In the above 1.1), the screening conditions are set as Adj.P Value<0.05,|log2FC|>1.

5.

4. The method for screening potential therapeutic targets for acrolein-induced retinal damage according to claim 1, characterized in that: In the above 1.2), the database is the CTD database.

5. The method for screening potential therapeutic targets for acrolein-induced retinal damage according to claim 1, characterized in that: In the above 3.1), the biological processes and signal transduction pathways that may be involved in the process of acrolein-induced retinal membrane damage include Kaposl sarcoma-associated herpes infection, IL-17 signaling pathway, human cytomegalovirus infection, measles, fluid shear stress and atherosclerosis, small cell lung cancer, lipids and atherosclerosis, AGE-RAGE signaling pathway, cell senescence and C-type lectin receptor signaling pathway.

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