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Diabetes 53:784-794, 2004
© 2004 by the American Diabetes Association, Inc.

Molecular Profiling of Diabetic Mouse Kidney Reveals Novel Genes Linked to Glomerular Disease

Katalin Susztak1, Erwin Böttinger1,2, Akiva Novetsky1, Dan Liang1, Yanqing Zhu3, Emilio Ciccone3, Dona Wu1,2, Stephen Dunn3, Peter McCue4, and Kumar Sharma3

1 Department of Medicine, Division of Nephrology, Albert Einstein College of Medicine, Bronx, New York
2 Department of Molecular Genetics, Albert Einstein College of Medicine, Bronx, New York
3 Department of Medicine, Division of Nephrology, Dorrance Hamilton Research Laboratories, Thomas Jefferson University, Philadelphia, Pennsylvania
4 Department of Anatomy, Cell Biology and Pathology, Division of Nephrology, Dorrance Hamilton Research Laboratories, Thomas Jefferson University, Philadelphia, Pennsylvania

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-3ß 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.


Address correspondence and reprint requests to Kumar Sharma, MD, Associate Professor of Medicine, or Erwin Böttlinger, MD, Dorrance Hamilton Research Laboratories, Division of Nephrology, Department of Medicine, Thomas Jefferson University, Suite 353, Jefferson Alumni Hall, 1020 Locust St., Philadelphia, PA 19107. E-mail: kumar.sharma{at}jefferson.edu Or to Erwin Bottinger, Professor of Medicine, Mount Sinai Medical Center, One Gustave L. Levy Place, Box 1118, New York, NY 10029. E-mail: erwin.bottinger{at}mssm.edu


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