Protein Identified in Diabetic Kidney Disease Using New Bioinformatic Tool

November 16, 2016
Protein Identified in Diabetic Kidney Disease Using New Bioinformatic Tool
Source:NIH

Researchers at the University of California San Diego (UCSD) School of Medicine have just identified key proteins significantly altered at the gene-expression level in biopsied tissue from patients with diabetic kidney disease while using a new bioinformatic framework—a result that may reveal new therapeutic targets. The findings from this new study were published recently in JCI Insights through an article entitled “Systems biology analysis reveals role of MDM2 in diabetic nephropathy.”

The researchers used the new "MetBridge Generator" bioinformatics framework to identify the relevant enzymes and bridge proteins that link human metabolomics data to the pathophysiology of diabetic kidney disease at a molecular level. What the investigators found was that the protein MDM2 was consistently down-regulated and played a key role in diabetic kidney disease progression.

“MetBridge Generator allows for efficient, focused analysis of urine metabolomics data from patients with diabetic kidney disease, providing researchers an opportunity to develop new hypotheses based on the possible cellular or physiological role of key proteins," explained senior study investigator Kumar Sharma, MD, professor of medicine and director of the Institute for Metabolomic Medicine and the Center for Renal Translational Medicine at UCSD School of Medicine. "The framework may also be used in the interpretation of other metabolomic signatures from a variety of diseases. For example, MDM2 is also involved in regulating tumor protein p53, which is a target for cancer treatments."

In previous work the UCSD researchers identified 13 metabolites that were found to be altered in patients with diabetic kidney disease. Combining this information and publicly available data on metabolic pathways, the researchers tested their hypothesis that some proteins act as bridges creating less well-defined pathways. The framework then created a map of metabolic and protein-protein interaction (PPI) networks. This allowed the team to look deeper into relevant bridges with the greatest number of interactions with enzymes that regulate the 13-metabolite signature of diabetic kidney disease.

“To derive new insights in diabetic complications, we integrated publicly available human PPI networks with global metabolic networks using metabolomic data from patients with diabetic nephropathy,” the authors wrote. “We focused on the participating proteins in the network that were computationally predicted to connect the urine metabolites. Thus, our bioinformatics tool combined with multi-omics studies identified an important functional role for MDM2 in glomeruli and tubules of the diabetic nephropathic kidney and links MDM2 to a reduction in 2 key metabolite biomarkers.”

The authors also went on to state that the identification of MDM2 in kidney disease progression will be combined with the previously identified protein-RNA interactions as possible sources for additional key pathways underlying disease progression—all of which will be added to the MetBridge Generator network This growth will continue to expand the possible therapeutic targets for disease treatment.