A meta-analysis of expression signatures in glomerular disease

Glomerular diseases represent major diagnostic and therapeutic challenges with classification of these diseases largely relying on clinical and histological findings. Elucidation of molecular mechanisms of progressive glomerular disease could facilitate quicker development. High-throughput expression profiling reveals all genes and proteins expressed in tissue and cell samples. These methods are very appropriate for glomerular disease as pure glomeruli can be obtained from kidney biopsies. To date, proteome profiling data are only available for normal glomeruli, but more robust transcriptome methods have been applied to many mouse model and a few human glomerular diseases. Here, we have carried out a meta-analysis of currently available glomerular expression data in normal and diseased glomeruli from mice, rats, and humans using a standardized protocol. The results suggest a potential for glomerular transcriptomics in identifying pathogenic pathways, disease monitoring, and the feasibility to use animal models to study human glomerular disease. We also found that currently there are no specific consensus biomarkers or pathways among different disease data sets, indicating there are likely disease-specific mechanisms and expression profiles. Thus, further transcriptomics and proteomics analysis, especially that of dynamic changes in the diseases, may lead to novel diagnostics tools and specific pharmacologic therapies.