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Trypsin Digest Protocol to Analyze the Retinal Vasculature of a Mouse Model
Published on: June 13, 2013
Transcriptome sequencing and Mendelian randomization analysis identified biomarkers related to neutrophil
Linlin Hao1, Songhong Wang1, Lian Zhang1,2
1Department of Ophthalmology, the Second Hospital of Shandong University, Cheeloo College of Medicine, Shandong University, Jinan, China.
Summary:
In the development of diabetic retinopathy (DR), neutrophil infiltration hastens the adhesion between neutrophils and endothelial cells, leading to inflammation. Meanwhile, neutrophil extracellular traps (NETs) produced by neutrophils could clear aging blood vessels, setting the stage for retinal vascular regeneration. To explore the mechanism of NETs-related genes in DR, the transcriptome of NETs from normal and DR individuals were analyzed with gene sequencing and mendelian randomization (MR) analysis. Five NETs-related genes were identified as key genes. Among these genes, CLIC3, GBP2, and P2RY12 were found to be risk factors for Proliferative DR(PDR), whereas HOXA1 and PSAP were protective factors. Further verification by qRT-PCR recognized GBP2, P2RY12 and PSAP as NETs-associated biomarkers in PDR.
Purpose:
To investigate neutrophil extracellular traps (NETs) related genes as biomarkers in the progression of diabetic retinopathy (DR).
Methods:
We collected whole blood samples from 10 individuals with DR and 10 normal controls (NCs) for transcriptome sequencing. Following quality control and preprocessing of the sequencing data, differential expression analysis was conducted to identify differentially expressed genes (DEGs) between the DR and NC groups. Candidate genes were then selected by intersecting these DEGs with key module genes identified through weighted gene co-expression network analysis. These candidate genes were subjected to mendelian randomization (MR) analysis, then least absolute shrinkage and selection operator analysis to pinpoint key genes. The diagnostic utility of these key genes was evaluated using receiver operating characteristic curve analysis, and their expression levels were examined. Additional analysis, including nomogram construction, gene set enrichment analysis, drug prediction and molecular docking, were performed to investigate the functions and molecular mechanisms of the key genes. Finally, the expression of key genes was verified by qRT-PCR and biomarkers were identified.
Results:
Intersection of 1,004 DEGs with 1,038 key module genes yielded 291 candidate genes. Five key genes were identified: HOXA1, GBP2, P2RY12, CLIC3 and PSAP. Among them, CLIC3, GBP2, and P2RY12 were identified as risk factors for DR, while HOXA1 and PSAP were protective. These key genes demonstrated strong diagnostic performance for DR. With the exception of P2RY12, all other key genes exhibited down-regulation in the DR group. Furthermore, the nomogram incorporating multiple key genes demonstrated superior predictive capacity for DR compared to a single key genes. The identified key genes are involved in oxidative phosphorylation and ribosome functions. Drug predictions targeting P2RY12 suggested prasugrel, ticagrelor, and ticlopidine as potential options owing to their high binding affinity with this key genes. The qRT-PCR results revealed that the results of GBP2, PSAP and P2RY12 exhibited consistent expression patterns with the dataset.
Conclusion:
This study identified GBP2, P2RY12 and PSAP as NETs-associated biomarkers in the development of PDR, offering new insights for clinical diagnosis and potential treatment strategies for DR.
