A research team led by David E. Cohen, MD, PhD, director of Hepatology at BWH, and director of the Harvard-MIT Division of Health Sciences and Technology, has found that a phospholipid-binding protein known as phosphatidylcholine transfer protein, together with its partner thioesterase superfamily member 2, suppresses insulin-signaling activity.
Their work will further the understanding of how insulin action is regulated, and could provide new modes of diabetes treatment. Insulin helps liver, muscle and fat tissues absorb glucose (sugar) from the blood for energy. However, those living with diabetes do not make enough insulin to control their blood sugar concentrations. This suggests that effective therapies could be aimed at increasing the sensitivity of cells to the available insulin.
The researchers observed that phosphatidylcholine transfer protein blocked the activation of insulin receptor substrate 2, a key effector molecule in insulin signaling. Insulin receptor substrate 2 was activated after genetic elimination of either phosphatidylcholine transfer protein or its binding partner thioesterase superfamily member 2.
In addition, when phosphatidylcholine transfer protein was prevented from interacting with thioesterase superfamily member 2 and another protein known as tuberous sclerosis complex 2, insulin-signaling activity was further increased through the activation of mammalian target of rapamycin complex 1, another key effector molecule in insulin signaling.
These findings help to explain prior observations that mice lacking either phosphatidylcholine transfer protein or thioesterase superfamily member 2 exhibit improved hepatic glucose homeostasis and are resistant to diet-induced diabetes.
“A particularly exciting aspect of this study was the demonstration that a chemical inhibitor of phosphatidylcholine transfer protein previously discovered by our laboratory was effective at promoting insulin signaling, potentially providing a new pharmaceutical approach to diabetes treatment,” said Cohen.
The study was published on July 30, 2013, in Science Signaling.