Molecular profiling of diabetic mouse kidney reveals novel genes linked to glomerular disease

K Susztak, E Bottinger, A Novetsky, D Liang, Y Zhu… - Diabetes, 2004 - Am Diabetes Assoc
K Susztak, E Bottinger, A Novetsky, D Liang, Y Zhu, E Ciccone, D Wu, S Dunn, P McCue
Diabetes, 2004Am Diabetes Assoc
To describe gene expression changes that characterize the development of diabetic
nephropathy, we performed microarray and phenotype analysis on kidneys from db/db mice
(a model of type 2 diabetes), streptozotocin-induced diabetic C57BL/6J mice (a model of
type 1 diabetes), and nondiabetic controls. Statistical comparisons were implemented based
on phenotypic outcome characteristics of the animals. We used weighted vote-based
supervised analytical methods to find genes whose expression can classify samples based …
To describe gene expression changes that characterize the development of diabetic nephropathy, we performed microarray and phenotype analysis on kidneys from db/db mice (a model of type 2 diabetes), streptozotocin-induced diabetic C57BL/6J mice (a model of type 1 diabetes), and nondiabetic controls. Statistical comparisons were implemented based on phenotypic outcome characteristics of the animals. We used weighted vote-based supervised analytical methods to find genes whose expression can classify samples based on the presence or absence of mesangial matrix expansion, the best indicator for the development of end-stage renal disease in humans. We identified hydroxysteroid dehydrogenase-3β isotype 4 and osteopontin as lead classifier genes in relation to the mesangial matrix expansion phenotype. We used the expression levels of these genes in the kidney to classify a separate group of animals for the absence or presence of diabetic glomerulopathy with a high degree of precision. Immunohistochemical analysis of murine and human diabetic kidney samples showed that both markers were expressed in podocytes in the glomeruli and followed regulation similar to that observed in the microarray. The application of phenotype-based statistical modeling approaches has led to the identification of new markers for the development of diabetic kidney disease.
Am Diabetes Assoc