Single DNA Change Can Affect Diabetes Drug Response
A new proof-of-concept study has demonstrated how small differences in DNA sequences in an individual can result in different antidiabetic drug effects. Findings from the study are published in Cell.
Researchers from the Perelman School of Medicine at the University of Pennsylvania analyzed the fat molecule PPAR-gamma, the target of thiazolidinedione (TZD) drugs indicated for type 2 diabetes. The study authors showed that natural genetic differences in DNA at gene regulatory switches could impact whether PPAR-gamma and TZD drugs could turn on other genes. TZDs are the only diabetes treatment that target fat cells and boost a diabetes patient's own response to insulin. The genetic differences are called single nucleotide polymorphisms (SNPs), and are variants in the DNA alphabet of A, T, C, and G molecules that occur naturally among individuals. These disease-related SNPs often reside in the “dark matter” of the genome that does not directly code for genes, but includes those switches that control genes.
The team of researchers showed that SNPs in PPAR-gamma switches provide a mechanism for the disease-risk associations. For example, one SNP was linked to blood lipids (eg, HDL, triglycerides), type 2 diabetes, and waist-hip ratio. A single DNA letter change could therefore affect whether PPAR-gamma binds to one regulatory site in fat tissue, which further impacts an individual's risk for metabolic syndrome.
In animal studies, the team showed how natural SNPs within mouse strains could determine whether PPAR-gamma made its way to its DNA regulatory switch in the genome's dark matter. Then they treated mice with TZDs and saw that the SNPs could also determine whether the drugs turned genes on.
Future studies need to determine the pattern of SNP variations that may show why TZD-like drugs benefit or harm one person and not another, researchers concluded. In this first-of-kind study, data showed natural genetic variation in PPAR-gamma binding to DNA in fat cells can determine individual disease risk and drug response.
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