Identification of Cross-Species Shared Transcriptional Networks of Diabetic Nephropathy in Human and Mouse Glomeruli

Department of Pathology, University of Michigan, Ann Arbor, Michigan.
Diabetes (Impact Factor: 8.1). 11/2012; 62(1). DOI: 10.2337/db11-1667
Source: PubMed


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.

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Available from: James Cavalcoli, Oct 10, 2014
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    • "Total RNA isolated from microdissected glomeruli as previously described (39,40) was reverse transcribed and linearly amplified to be hybridized to Affymetrix HG-U133A microarrays for profiling mRNA expression using Affymetrix protocols. The microarray analysis was described before, and robust multichip average was used to normalize the data (41). "
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