A Network Pharmacology-Based Strategy For Predicting Therapy Targets Of Beta-Sitosterol In Treating Diabetic Retinopathy

The IDF estimates 10% of adults might be diagnosed with DM in 2040. There will be 629 million people with diabetes worldwide in 2045. At that time, the death rate of DM will be greater than the death rate combined with AIDS, tuberculosis, and malaria. According to the research data of “China's Medium- to-Long Term Plan for the Prevention and Treatment of Chronic Diseases (2017-2025) under the Healthy China Initiative” published by the Chinese Preventive Medicine Association and “IDF Diabetes Atlas (10TH EDITION)”, until 2020, the number of people with DM in China is about 35 million, and the prevalence rate is 10.9%, which is the highest in the world (Figure 1A). 

With the development of the economy and society and the increasingly aging society of population, the global prevalence of DM and the prevalence of diabetes-related complications are continually increasing (Figure 1B). Since 2013, the total health expenditure related to DM in the USA has exceeded 24% of the total healthcare expenditure, which is nearly 2.4% of the national GDP. In the same year, the global total health expenditure related to DM was about 548 billion US dollars (USD); in China, this expenditure was about 98.4 billion USD. By 2020, this expenditure exceeded 294.6 billion USD in the USA, 140.9 billion USD in China, and worldwide expend more than 820 billion USD. According to the model prediction analysis, the global diabetes’ rate will reach 12.20% in 2045, the prevalence of complications will reach 11.2%, and the domestic prevalence will be about 11.9% and 9.8%. The global and domestic medical financial pressure will reach 845 billion US dollars and 533.8 billion US dollars (Figure 1C). Figure 1

Figure 1.Prevalence and forecast of diabetes patients in China and globally. 1A. The DM prevalence from 2010 to 2045. 1B. The comparative prevalence 2017~2045. 1C. DM-related total health expenditure from 2013 to 2045.

Figure 2A shows keywords co-occurrence cluster of diabetic retinopathy research based on bibliometric analysis in recent 20 years. A total of 27564 publications were retrieved after refinement. To date, numerous DR-related studies had been reported. Numbers of studies focus on clinical diagnosis.

Intravitreal Injection of anti-VEGF (intravitreal vascular endothelial growth factor inhibitors) or steroid hormone is a simple but highly effective treatment in DR, but up to 50% of patients failed to respond. And repeated injection leads to many complications like cataracts or vitreous opacity.

The patients of DM had great individual differences in the course and severity of DR. Some patients with long-term diabetes do not develop diabetic retinopathy despite poor glycemic control, at the same time, others develop diabetic retinopathy rapidly. Some researchers believe that blood glucose level, the course of diabetes, smoking, alcohol, blood pressure, blood lipids, and other clinical factors might influence the occurrence and development of DR. Because DM is a complex, more complicated, and genetically heterogeneous disorder, its etiology, and pathogenesis are still unclear. 

Traditional Chinese medicine (TCM) is a system based on the holistic view, which is composed of Yin and Yang and the five basic elements. And these concepts and thinking are used to explain the human body structure, the physiological functions, pathological changes, diagnosis, and treatment in multi factors, different levels, which focus on the five internal organs. Therefore, based on the mechanism research and treatment of DR, it is a helpful way to find out a useful drug by the biological network of TCM.

The bibliometric analysis developed in this research was based on the online database of the Web of Science (https://www.webofscience.com/wos/alldb/basic-search). These search expressions included applying the terms “diabetic retinopathy” OR “DR”, which seeks words on titles, and abstracts, from 2002 to 2022. A total of 27564 publications were retrieved after refinement. All analyses were performed using the "Analyzing Results" tool provided by WoS with MS Excel support (v. 2016) to perform calculations and to develop graphs (visualization of bibliometric indicators) using the following information: i) several publications, ii) several citations iii) impact factor (for journals) and iv) h-index (for journals and authors).

The DR-related targets were screened 3 types from 9 databases sources:

(1) Database based: ①MedGene (https://www.ncbi.nlm.nih.gov/), ②Gendome (https://ai.citexs.com/), ③HGNC (https://www.genenames.org/), ④OMIM (http://www.omim.org/), ⑤GeneCards (https://www.genecards.org/), ⑥PheGenI (https://www.ncbi.nlm.nih.gov/gap/phegeni) These searches expressions consisted in applying the terms “diabetic retinopathy” OR “DR”, only “Homo sapiens” proteins linked to DR were selected. We got 4400 individual genes total, and details are described in Supply 1. We next use the R package UpSetR to visualise these related targets. 

(2) GEO Chip based: Using the NCBI Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/), the GSE41019, GSE60436, and GSE146615 related chips were obtained using “diabetic retinopathy” OR “DR” as the search term in Homo sapiens. The chips correspond to eye tissues and cell lines. The 3 sets of chips included DNA and RNA experiments, and their annotation files are the platform GPL6884 and GLP10558. We combined the same platform information to analyze.

(3) STRING Connected based: Cytoscape (Version 3.9.1) is an open-source software used to analyze and visualize biological networks. We installed the String and String Enrichment Apps, search tools for retrieving interacting genes or proteins. According to the String disease query and String Pubmed query, using “diabetic retinopathy” OR “DR” as the keyword, all these results were imported into Cytoscape to detect the possible relationships.

Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP, https://tcmspw.com/tcmsp.php) is a database based on the framework of systems pharmacology for herbal medicine. The CloudPhar traditional Chinese medicine (TCM-LTM, https://cloud.tasly.com/#/tcm/home) is used as the discovery of the complex mechanism of action of TCM, which contains 48126 prescriptions, 9122 plants, 34967 active ingredients, and 13109 targets for user querying the symptom, prescription, herb, and ingredient of TCM. The keyword “diabetic retinopathy” or “diabetes + retina” was searched in both 2 databases. 

Wayne diagram (http://jvenn.toulouse.inra.fr/app/example.html) was used to display the intersection of DR putative TCM. 26 herbs were obtained. Next, all these herbs were retrieved in TCMSP and screened with OB≧ 30% and LD≧ 0.18. The chemical compounds of each herb were obtained. Sorting by occurrence times, we got TOP. 10. Beta-Sitosterol is the most vital compound in TCM of TCM, which is contained in 12 of 26 kinds of DR-treatment herbs. TCMSP website was used to predict potential targets of Beta-Sitosterol for treating DR. The UniProt database (https://www.uniprot.org/) was used to convert the target name into the official name.

Next step, we matched Beta-Sitosterol treatment targets with DR targets to obtain overlapping targets. 

The core targets of Beta-Sitosterol in DR treatment were imported into the Meta-scape platform, and analyze parameters of P value < 0.01, a minimum count of 4, and an enrichment factor >1.5 (the enrichment factor is the ratio between the observed counts and the counts expected by chance) were set. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and GSEA pathway enrichment analyses on core targets were performed.

We constructed two networks: (1) A compound-target network; and (2) A target-pathway network. Protein-protein interaction (PPI) data were attained by STRING. 

Only “Homo sapiens” targets linked to DR were selected in 6 different databases mentioned above. We got 4400 genes total, and details are described in Supplement 1. Each target got a score by the number of appearance times (Table 1, Fig. 2B-2C).

We next merge GSE41019 and GSE60436 which are from the same platform. Using R package ggplot2 to visualise these chips (Fig. 2D-2E). Using R package limma for the visualization results of analyzing the differential genes of microarray data (Fig. 2F-2G). Figure 2

Figure 2.DR-Targets Database Construction. 2A. Keyword co-occurrence cluster of DR research based on bibliometric analysis in recent 20 years. 2B-2C. The DR-related target from 6 Database. 2D-2E. Normalization of different batch samples. 2F-2G. The DR-related target from GEO Database.

Understanding of TCM on Diabetic Retinopathy

In total, 360 and 45 kinds of herbs were obtained from the TCMSP and TCM-LTM. Paeoniae Radix Alba, licorice, and other 26 herbs were included in both 2 databases (Fig. 3A, Supplement 2). According to OB ≥ 30% and DL ≥ 0.18, we listed all active compounds of these 26 kinds of herbs (Table 2, Supplement 3).

Beta-sitosterol (SIT) is a kind of sterol occurred in natural plants, like oranges, beans, and so on. It has been reported to be involved in bioactivities like anti-inflammatory, antioxidant, anticancer, angiogenic, and immunomodulatory activities. It is indicated for benign prostate hyperplasia, hypercholesterolemia, and coronary heart disease.

SIT has 2612 potential targets after removing duplicates. Based on the intersection of drug and disease targets, we obtained 23 genes (Figure. 3B, Figure. 5A-5B, Supplement 4), meaning these genes could play a major role in SIT treatment for DR.

GSEA analysis was used first to analyze GEO database information, which was enriched to 33 pathways (Supplement 6). The top 5 main pathways included biocarta MAPK pathway, KEGG Alanine Aspartate, and Glutamate metabolism, Reactome export of viral ribonucleoproteins from the nucleus, Reactome Gene silencing by RNA and reaction HCMV early events (Figure 4A). 

The GO and KEGG pathway enrichment analysis was performed on the total related targets in DR and the above-mentioned 7 modules (Cluster GEO, Cluster GeneDoma, Cluster OMIM, Cluster PheGene, Cluster GeneCard, Cluster Pubmed, and Cluster String). Total DR-related targets enriched to Human cytomegalovirus infection, Apoptosis, NOD-like receptor signaling pathway, and other 12 pathways (Figure 4B, Supplement 7). Because there are more than 4700 genes related in DR, we next only analyzed those scores> 4. Next high correlational genes were enriched to 10 pathways, respectively, which were enriched mainly and significantly in the AGE-RAGE signaling pathway in diabetic complications (p= 3.90E-21), Non-alcoholic fatty liver disease (p= 4.45E-21), PI3K-Akt signaling pathway (p= 1.60E-20), HIF-1 signaling pathway (p= 9.88E-18), EGFR tyrosine kinase inhibitor resistance (p= 1.77E-17) (Figure 4D). 

Next, all Clusters were enriched to 11 pathways, respectively, which were enriched respectively and mainly in the PI3K-Akt signaling pathway, positive regulation of leukocyte cell-cell adhesion, and regulation of T cell activation Figure 4.

Figure 4.Enrichment analysis. 4A. GSEA analysis of GEO databased targets. 4B. The GO and KEGG pathway enrichment analysis on the total related targets in DR. 4C. The pathway enrichment analysis on the SIT targets. 4D. The pathway enrichment analysis on the high correlational genes of DR. 4E. The pathway of DR-SIT Network. 4F. The pathway enrichment analysis on the targets of Cluster groups. 4G. Pathway network in SIT treatment of DR.

The GO and KEGG pathway enrichment analysis was performed on the SIT (Figure 4E). Human cytomegalovirus infection (p= 1.19E-41) and hepatitis B ((p= 1.13E-46) are high enrichment levels. Lots of these pathways were relevant to inflammation.

Pathway Of DR-SIT Network

We first use the 23 genes of both DR-related and SIT-related to enrich, PI3K-Akt signaling pathway (p= 0.035), focal adhesion(p= 3.21E-06), platelet-derived growth factor binding (PDGF, p= 0.007), extracellular matrix binding (p= 0.007) are high enrichment level (Figure 4C), which means inflammatory reaction and angiogenesis might play a vital role in SIT treat DR.

In the DR-SIT network, some signaling pathways (such as the AGE-RAGE signaling pathway in diabetics, PI3K-Akt signaling pathway, and EGFR tyrosine kinase inhibitor resistance) with higher degrees are closely related to the development of DR (Figure 4G).

We use bioinformatics analysis to screen the core genes for SIT in treating DR, ERBB3, HLA-DRB1, SPP1, CXCL10, NOX4, LOX, FBLN1, LEP, CAV1, IGF2, CD28, UCHL1, COL4A1, EDN3, BIRC5, PDGFRA, LGALS1, GSN, AURKA, TPX2, SIRPA, RIPK2, ANXA2 in the cross pathway. COL4A1, ERBB3, IGF2, PDGFRA, and SPP1 related PI3K-Akt signaling pathways or EDN3, IGF2, CXCL10, LEP, and SPP1 related receptor ligand activity pathways might be the main pharmacological targets, and pathways in DR (Fig. 5C-5D). 

Figure.Predicting pathway and target gene point. 5A. DR high correlational genes in GEO database. 5B. DR-SIT cross gene in GEO database. 5C. PI3K-Akt signaling pathways related points in GEO database. 5D. receptor ligand activity pathways in GEO database.

Figure 5.Predicting pathway and target gene point. 5A. DR high correlational genes in GEO database. 5B. DR-SIT cross gene in GEO database. 5C. PI3K-Akt signaling pathways related points in GEO database. 5D. receptor ligand activity pathways in GEO database.

Kexin Sun involved in design and conduct of the study, and preparation of the manuscript, participated in the management of this program. Kexin Sun and Peiran Yu participated in the collection of the data. All authors review and approval of the final manuscript.

None.

The datasets generated and/or analysed in the current study are available from the corresponding author on reasonable request. The dataset is held at Chongqing Medical University (CQMU), access to it requiring approval of the Institutional Review Board of CQMU. The data underlying the results presented in the study are available within the article.