Fig 1.
GSE30122 was acquired fromGEO Database. Post pre-processing data in R studio, differentially expressed genes (DEGs) were identified using “limma”. Gene set enrichment analysis (GSEA) and functional enrichment analysis was performed using “ClusterProfiler” to identify the hallmarks and pathways enriched in pathogenesis of DN. PPI network was generated in Cytoscape with the genes involved in top 5 pathways at 0.90 confidence level. Clustering analysis and analysis of topological parameters were performed to identify the core gene network using MCODE and Network Analyzer respectively. Cluepedia was used for visualising the genes and their pathway association.
Fig 2.
DEGs from the dataset were identified using “limma” package of R software with |log2FC| > 0 and p < 0.01 considered significant. Volcano plot for the DEGs were visualized using “ggplot2” package. 960 genes denoted in red were upregulated and 660 gene depicted in blue were downregulated and the non-significant genes are denoted in grey (|log2FC| > 0 and p < 0.01).
Table 1.
Top 5 pathways and their genes from KEGG analysis.
Table 2.
167 genes identified from KEGG pathway analysis that are closely associated with DN pathogenesis along with their module and topological properties.
Fig 3.
PI3K-Akt signaling pathway, inflammatory pathways and epithelial mesenchymal transition processes are most enriched in DN condition.
A. KEGG pathway analysis shows the top 15 enriched pathways are represented as bar plot. PI3K-AKT pathway, Cytokine-cytokine receptor interaction, MAPK signaling pathway, focal adhesion and regulation of actin cytoskeleton are the most enriched pathways in DN condition. B. Gene set enrichment analysis (GSEA) shows the complement, interferon gamma response, and epithelial mesenchymal transition hallmarks upregulated in DN pathogenesis based on NES, meaning that inflammation and fibrosis- related pathways are upregulated in DN condition.
Table 3.
58 genes from the condensed network.
Fig 4.
Chemokine markers, ECM/ fibrotic markers and stress response markers were the central players.
A. Network shows the interaction between the 58 core genes and the pathways. Edges in green, yellow and red depict activation, expression and inhibition respectively. The network depicts 3 major clusters- inflammatory cluster (CXCR4, CCR5, CXCL6, CCL19, TNFRSF1B, IKBKG, IRAK1), signaling cluster (PI3K-AKT signaling), and fibrosis cluster (COL1A2, COL6A3, LAMB1, ITGB7). B. Heatmap shows differential expression of the 58 genes in the condensed network based on their expression. Most of the genes were positively correlated except for IKBKG, LAMB1, MAGI2, and PAK4 suggesting that inflammation and ECM genes are tightly regulated together. C. Correlation plot highlight 27 genes whose p values were ≤ 1e-14. The upregulated genes (depicted in red) point at their potential involvement in the disease progression and may serve as biomarkers or therapeutic targets for AGE induced DN, while downregulated genes (in blue) indicate alteration and suppression in the signaling of the regulatory mechanism. D. The violin plot shows the distribution of expressional fold changes of the 12 driver genes across the microarray dataset (existing dataset) (blue violin) and regional transcriptomic dataset (independent dataset) (red violin) from KPMP public database. E. The scatter plot with connected lines shows difference in expression in the two datasets. The blue dots represent expression values of existing dataset and the red dot depicts expression values of independent dataset. The graph shows consistent pattern of expression in COL6A2, ITGB7, IRAK1, TNFRSF1B, GADD45B. CXCL8 is shows upregulation in both sets but the long connecting line indicates difference in the expression level.
Table 4.
List of the common genes between microarray dataset and RNA Seq dataset.
Fig 5.
15 genes were found to be common upon cross-platform validation with RNA-Seq data.
A. Venn diagram representing 15 genes are common between signifiant RNA seq dataset and the 167 genes related to AGEs obtained from microarray dataset. B: Heatmap representing the logFC of expression values 15 genes from microarray dataset. C. Heatmap representing the logFC of the quantified transcript expression values 15 genes from RNA Seq dataset. DDIT3, GADD45A, THBS2, CCL2, and CSF1R showed consistent expression trends across platforms. D. Volcano plot showing the differentially expressed genes from microarray dataset GSE30122. 58 core genes from the condensed gene network along with the 5 genes that were consistently altered across microarray and RNA-seq dataset are highlighted. Genes significantly upregulated are shown in red, while significantly downregulated genes are shown in blue. E. Volcano plot showing the differentially expressed genes from RNA-seq dataset GSE299230. 15 genes from the condensed gene network from microarray dataset GSE30122 and RNA-seq dataset GSE299230 including the 5 genes that were consistently altered across microarray and RNA-seq dataset are highlighted. Genes significantly upregulated are shown in red, while significantly downregulated genes are shown in blue.
Fig 6.
Interaction between AGE-RAGE induces ROS production and typically sets on PI3K-AKT pathway.
The simultaneous influence of both activates the NFKB signaling which elicits inflammatory responses. The induction of the NFKB signaling also triggers NICD1, thus leading to non-cannonical activation of the Notch signaling pathway. This reactivation suppresses PI3K-AKT signaling and disregulates the expression of integrins (ITGA4, ITGAL, ITGB7, ITGAX) and laminins (LAMA2, LAMB1, LAMC1), along with structural proteins like CCNE1 and MAGI2, leading to ECM remodeling and changes in cell adhesion and migration. These molecular changes contribute to EMT, where podocytes lose their epithelial characteristics and acquire mesenchymal-like properties, ultimately leading to podocyte injury.