Diabetic kidney disease, a complication that occurs in some people with diabetes, leads to kidney damage. In this condition the kidneys 'leak' abnormal amounts of protein from the blood into the urine.
A new bioinformatic framework developed by researchers at University
of California San Diego School of Medicine has identified key proteins
significantly altered at the gene-expression level in biopsied tissue
from patients with diabetic kidney disease, a result that may reveal new
‘The protein MDM2 was observed to be consistently down-regulated and played a key role in diabetic kidney disease progression.’
In a recently published paper in JCI Insights
led by Kumar Sharma, professor of medicine at UC San Diego School
of Medicine, revealed that the protein MDM2 was consistently
down-regulated and played a key role in diabetic kidney disease
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.
"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," said Sharma,
senior author and director of the Institute for Metabolomic Medicine and
the Center for Renal Translational Medicine at UC San Diego 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
In a previous study, the authors 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 an 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.
The authors already identified protein-RNA interactions as possible
sources for additional key pathways underlying disease progression that
could be added to the MetBridge Generator network. This growth will
continue to add to possible therapeutic targets for disease treatment.