Stage-dependent proteomic alterations in aqueous humor of diabetic retinopathy patients based on data-independent acquisition and parallel reaction monitoring

Abstract

Background: Diabetic retinopathy (DR), a microvascular complication of diabetes mellitus (DM), represents the predominant cause of preventable vision loss in working-age populations globally. While the pathophysiological mechanisms underlying DR progression remain incompletely understood, our study employs comprehensive proteomic profiling of aqueous humor (AH) to identify stage-specific biomarkers and therapeutic targets in type 2 diabetes mellitus (T2DM) patients across DR progression.

Methods: Utilizing data-independent acquisition (DIA) mass spectrometry, we quantified AH proteomes in a discovery cohort comprising 24 subjects: 18 T2DM patients stratified by DR severity [6 non-DR, 6 non-proliferative DR (NPDR), 6 proliferative DR (PDR)] and 6 cataract controls without diabetes (non-DM). Validation cohort analysis (including 10 AH samples in each group) was performed using parallel reaction monitoring (PRM) strategy for verification of target proteins. Comprehensive bioinformatics analyses included gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis, protein-protein interaction (PPI) network construction, receiver operating characteristic (ROC) curve analysis, and ConnectivityMap (Cmap)-based drug prediction.

Results: Proteomic profiling identified 739 quantifiable AH proteins (62% extracellular) with clear separation among the four clinical stages in the discovery cohort. GSEA uncovered altered expression of proteins mainly related to complement and coagulation cascades, folate metabolism, and the selenium micronutrient network in patients with DR. WGCNA-derived protein modules yielded 83 PRM-validated targets, including 5 hub proteins differentiating NPDR from non-DR and 33 hub proteins showed significant upregulation in PDR versus NPDR comparison. Clinical correlation analysis identified F2, FGG, FGB, RBP4, AMBP, VTN, C8A, CPB2, and C2 associated with clinical traits. C6, FAM3C, SPP1, and JCHAIN levels were altered post-anti-VEGF treatment. Pharmacological prediction identified potential therapeutic compounds, including perindopril, triciribine, and XAV-939 for NPDR, and topiramate, triciribine, and vecuronium for PDR.

Conclusion: This study established a comprehensive AH proteomic signature of DR progression, offering insights into the pathogenesis of DR and highlighting potential biomarkers and novel therapeutic targets.

Keywords: Aqueous humor; Data-independent acquisition; Diabetic retinopathy; Parallel reaction monitoring; Proteomics.

Fig. 1

Workflow of the study and protein identification in aqueous humor using DIA mode. (A) Overview of the study workflow. (B) Number of unique peptides examined in each sample. (C) Number of proteins identified in each sample. (D) Venn diagram showing unique proteins in aqueous humor from non-DM, non-DR, NPDR, and PDR groups. (E) PCA plots of non-DM, non-DR, NPDR, and PDR groups based on DIA analysis in the discovery cohort. DIA: data-independent acquisition; DM: diabetes mellitus; DR: diabetic retinopathy; NPDR: non-proliferative diabetic retinopathy; PDR: proliferative diabetic retinopathy; PCA: principal component analysis

Fig. 2

Key enriched pathways in DR vs. non-DR (A), NPDR vs. non-DR (B), PDR vs. NPDR (C) and non-DR vs. non-DM (D) using GSEA. DM: diabetes mellitus; DR: diabetic retinopathy; NPDR: non-proliferative diabetic retinopathy; PDR: proliferative diabetic retinopathy; GSEA: gene set enrichment analysis

Fig. 3

Identification of clinically significant protein modules by weighted correlation network analysis. (A) Module-trait correlation plot based on module eigengenes, where red and blue represent positive and negative correlations, respectively. Values in each grid represent the correlation coefficient and corresponding p-value in parentheses between modules and clinical traits. (B–D) KEGG analysis of the turquoise (B), blue (C), and brown (D) modules. (E) Venn diagram showing proteins with GS (PDR) > 0.5 and MM > 0.7 in the turquoise module. (F, G) Venn diagram showing proteins with GS (PDR) < − 0.5 and MM > 0.7 in the (F) blue and (G) brown modules. WGCNA: weighted correlation network analysis; KEGG: Kyoto Encyclopedia of Genes and Genomes; GS: gene significance; MM: module membership

Fig. 4

PRM-based validation in an independent cohort. (A) ROC curves show the ability of the top five hub proteins (ranked by AUC) to distinguish NPDR from non-DR. (B) Lollipop diagram showing key pathways enriched for the seven DEPs from NPDR vs. non-DR. (C) PPI network of the five hub proteins from NPDR vs. non-DR. (D) ROC curves show the ability of the top ten hub proteins (ranked by AUC) to distinguish PDR from NPDR. (E) Lollipop diagram showing key pathways enriched for the 33 DEPs from PDR vs. NPDR. (F) PPI network of the 33 DEPs from PDR vs. NPDR. PRM: parallel reaction monitoring; ROC: receiver operating characteristic; AUC: area under curve; DEPs: differentially expressed proteins; PPI: protein-protein interaction; DR: diabetic retinopathy; NPDR: non proliferative diabetic retinopathy; PDR: proliferative diabetic retinopathy

Fig. 5

Pearson correlation analysis between hub proteins and clinical traits. (A) F2, FGG, and FGB from NPDR vs. non-DR are significantly correlated with clinical data. (B) RBP4, AMBP, VTN, C8A, CPB2, and C2 from the top ten hub proteins in PDR vs. NPDR are significantly correlated with clinical data. DR: diabetic retinopathy; NPDR: non-proliferative diabetic retinopathy; PDR: proliferative diabetic retinopathy

Fig. 6

Raincloud plots show the response of hub proteins validated by PRM in PDR patients after anti-VEGF treatment. (A) C6 and FAM3C are significantly decreased, while SPP1 and JCHAIN are significantly increased after anti-VEGF treatment. (B) Eight of the top ten hub proteins between PDR and NPDR show no changes after anti-VEGF treatment. PRM: parallel reaction monitoring; PDR: proliferative diabetic retinopathy; VEGF: vascular endothelial growth factor

Fig. 7

CMap-based drug repurposing analysis for NPDR and PDR. (A) Barplot indicating the percentage of 15 predicted compounds for NPDR and PDR. Drugs validated in animal experiments, agents sharing common mechanisms of action with known drugs, and compounds with undetermined functions for DR are represented by orange, blue, and gray, respectively. (B) Venn diagram illustrating the shared and unique compounds for NPDR and PDR. (C, D) Mechanisms of action (MOA) of predicted drugs are represented in Sankey diagrams for NPDR (C) and PDR (D). NPDR: non-proliferative diabetic retinopathy; PDR: proliferative diabetic retinopathy