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. 2024 Aug 12;10(17):e36111.
doi: 10.1016/j.heliyon.2024.e36111. eCollection 2024 Sep 15.

The contribution of adiponectin to diabetic retinopathy progression: Association with the AGEs-RAGE pathway

Min Fu  1 , Li Zhengran  1   2 , Li Yingli  1 , Wu Tong  1   3 , Cai Liyang  1 , Guo Xi  4 , Yang Xiongyi  1   2 , Cao Mingzhe  5 , Yi Guoguo  6   7
Affiliations

The contribution of adiponectin to diabetic retinopathy progression: Association with the AGEs-RAGE pathway

Min Fu et al. Heliyon. .

Abstract

Diabetic retinopathy (DR) is a chronic complication of diabetes. Given that adiponectin plays a key role in DR progression, this study aims to elucidate the molecular mechanisms of sDR progression related to adiponectin. First, we extracted the microarray dataset GSE60436 from the Gene Expression Omnibus (GEO) database to identify hub genes associated with DR. Pathway enrichment analysis revealed a focus on inflammation, oxidative stress, and metabolic disease pathways. Gene Set Enrichment Analysis (GSEA) identified nine significant pathways related to DR. Immune infiltration analysis indicated increased infiltration of fibroblasts and endothelial cells in DR patients. Second, at the gene level, single-cell RNA sequencing (scRNA-seq) results showed a decrease in ADIPOQ gene expression as the disease progressed in our mouse models. At the protein level, ELISA results from sera of 31 patients and 11 control subjects demonstrated significantly lower adiponectin expression in the proliferative diabetic retinopathy (PDR) group compared to controls. Our findings reveal that adiponectin is involved in the advanced glycation end products (AGEs) and receptor of advanced glycation end products (RAGE) axis, as evidenced by hub gene analysis, scRNA-seq, and ELISA. In conclusion, adiponectin acts as a central molecule in the AGEs-RAGE axis, regulated by ADIPOQ, to influence DR progression.

Keywords: Adiponectin; Advanced glycation end products; Diabetic retinopathy; ELISA; Eye; Genomics; Glucose; Inflammation; Single-cell RNA sequencing.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Image 1
Graphical abstract
Fig. 1
Fig. 1
Gene set enrichment analysis: (A) Enrichment plot of ‘OXIDATIVE_PHOSPHORYLATION’ with enrichment score −0.52, FDR q-value 0.0016. (B) Enrichment plot of ‘RETINOL_METABOLISM’ with enrichment score −0.59, FDR q-value 0.0016. (C) Enrichment plot of ‘ARGININE_AND_PROLINE_METABOLISM’ with enrichment score −0.56, FDR q-value 0.0016. (D) Enrichment plot of ‘TYROSINE_METABOLISM’ with enrichment score −0.73, FDR q-value 0.0016. (E) Enrichment plot of ‘PROXIMAL_TUBULE_BICARBONATE_RECLAMATION’ with enrichment score −0.77, FDR q-value 0.0016. (F) Enrichment plot of ‘PHENYLALANINE_METABOLISM’ with enrichment score −0.77, FDR q-value 0.0016. (G) Enrichment plot of ‘ALLOGRAFT_REJECTION’ with enrichment score 0.67, FDR q-value 0.0016. (H) Enrichment plot of ‘GRAFT_VERSUS_HOST_DISEASE’ with enrichment score 0.61, FDR q-value 0.0016. (I) Enrichment plot of ‘TYPE_I_DIABETES_MELLITUS’ with enrichment score 0.66, FDR q-value 0.0016.
Fig. 2
Fig. 2
(A) Volcano map shows expression levels of DEGs. (B) Heatmap of the top-50 DEGs. (C) Box map of selected samples and samples grouping.
Fig. 3
Fig. 3
GO-KEGG analysis for significant related genes: (A) GO enrichment analysis on the Biological Process (BP) terms of the significant related genes. (B) GO enrichment analysis on the Cellular Component (CC) terms of the significant related genes. (C) GO enrichment analysis on the Molecular Function (MF) terms of the significant related genes. (D) KEGG terms in the enrichment analysis of the significant related genes.
Fig. 4
Fig. 4
(A) Top-250 sites filtered into the PPI network that contained 205 nodes and 603 edges in DR. (B) Sub-network for 11 sites significantly related to AGER. (C) Sub-networks for MCODE sub-networks. (D) Venn map for hub genes five algorithms.
Fig. 5
Fig. 5
(A) Functionally similar sites interaction network for 11 sites significantly related to AGER. (B) Functionally similar sites interaction network for Collagen family.
Fig. 6
Fig. 6
(A) Umap plot for samples in GSE60436. (B) Correlation matrix of infiltration degree of immune cells in RA samples. (C) Box plot for immune cells infiltration degree.
Fig. 7
Fig. 7
DotPlot of key genes detected by single-cell sequencing (A) DotPlot of ADIPOR1 and ADIPOR2 in single-cell sequencing (B) DotPlot of PAQR4, PAQR6 and PAQR7 in single-cell sequencing (C) DotPlot of ADIPOQ in single-cell sequencing (D) DotPlot of COL20a1 in single-cell sequencing (E) DotPlot of VEGFA in single-cell sequencing.
Fig. 8
Fig. 8
The Box plot shows the ELISA detection results of 5 sites DR: Diabetes retinopathy, PDR: Proliferative diabetes retinopathy, NPDR: non-proliferative diabetes retinopathy, ADIPOQ: Adiponectin, AGEs: Advanced glycation end products, sRAGE: The receptor of advanced glycation end products, esRAGE: Endogenous secreted receptor of advanced glycation end products, VEGF: Vascular endothelial growth factor.
See this image and copyright information in PMC

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