Identification of cross-species shared transcriptional networks of diabetic nephropathy in human and mouse glomeruli

Abstract

Murine models are valuable instruments in defining the pathogenesis of diabetic nephropathy (DN), but they only partially recapitulate disease manifestations of human DN, limiting their utility. To define the molecular similarities and differences between human and murine DN, we performed a cross-species comparison of glomerular transcriptional networks. Glomerular gene expression was profiled in patients with early type 2 DN and in three mouse models (streptozotocin DBA/2, C57BLKS db/db, and eNOS-deficient C57BLKS db/db mice). Species-specific transcriptional networks were generated and compared with a novel network-matching algorithm. Three shared human-mouse cross-species glomerular transcriptional networks containing 143 (Human-DBA STZ), 97 (Human-BKS db/db), and 162 (Human-BKS eNOS(-/-) db/db) gene nodes were generated. Shared nodes across all networks reflected established pathogenic mechanisms of diabetes complications, such as elements of Janus kinase (JAK)/signal transducer and activator of transcription (STAT) and vascular endothelial growth factor receptor (VEGFR) signaling pathways. In addition, novel pathways not previously associated with DN and cross-species gene nodes and pathways unique to each of the human-mouse networks were discovered. The human-mouse shared glomerular transcriptional networks will assist DN researchers in selecting mouse models most relevant to the human disease process of interest. Moreover, they will allow identification of new pathways shared between mice and humans.

FIG. 1.

Analytical strategy used to generate shared glomerular human–mouse transcriptional networks. Transcriptional networks were generated from significantly regulated genes in human and mouse glomeruli using Genomatix Bibliosphere software. These networks were then overlapped using TALE to define cross-species shared transcriptional networks.

FIG. 2.

Human–mouse shared glomerular transcriptional networks. Each circle represents a gene node, and the lines represent a connection between two gene nodes. Shared transcriptional networks for Human-DBA STZ (A), Human-BKS db/db (B), and Human-BKS eNOS^−/− db/db (C), as defined by TALE, are displayed with the most connected gene nodes in the center (>70 gene-gene connections) and the least connected gene nodes at the periphery. The overlap between the shared human–mouse networks is shown in D.

FIG. 3.

Shared human–mouse transcriptional networks centered on STAT3. Focusing on any gene of interest allows for comparison of neighborhoods of functionally related genes within each shared network. We performed this analysis using STAT3 given its likely central role in the pathogenesis of DN, as suggested by previous studies.

FIG. 4.

qRT-PCR confirmation. qRT-PCR was performed to confirm the direction of fold change in expression (shown as log₂ fold change in human and mouse models by microarray). Human comparisons for Halb vs. Lalb (A), Lalb vs. ND (B), and Halb vs. ND (C) by qRT-PCR were compared with differential expression from microarray data. Mouse comparisons for DBA STZ vs. control (D), BKS db/db vs. control (E), and BKS eNOS^−/− db/db (F) were also compared with significant differential expression from microarray data. *P < 0.05.