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High blood pressure is a major risk factor for heart disease, which is one of the leading causes of death worldwide. While drugs are available to control blood pressure, male and female patients can respond differently to treatment. However, the biological mechanisms behind this sex difference are not fully understood.
Blood pressure is controlled by cells lining the artery walls called smooth muscle cells which alter the width of blood vessels. On the surface of smooth muscle cells are potassium and calcium channels which control the cell’s electrical activity. When calcium ions enter the cell via calcium channels, this generates an electrical signal that causes the smooth muscle to contract and narrow the blood vessel. Potassium ions then flood out of the cell via potassium channels to dampen the rise in electrical activity, causing the muscle to relax and widen the artery.
There are various sub-types of potassium and calcium channels in smooth muscle cells. Here, Hernandez-Hernandez et al. set out to find how these channels differ between male and female mice, and whether these sex differences could alter the response to blood pressure medication.
The team developed a computational model of a smooth muscle cell, incorporating data from laboratory experiments measuring differences in cells isolated from the arteries of male and female mice. The model predicted that the sub-types of potassium and calcium channels in smooth muscle cells varied between males and females, and how the channels impacted electrical activity also differed. For instance, the potassium channel Kv2.1 was found to have a greater role in controlling electrical activity in female mice, and this sex difference impacted blood vessel contraction. The model also predicted that female mice were more sensitive than males to calcium channel blockers, a drug commonly prescribed to treat high blood pressure.
The findings by Hernandez-Hernandez et al. provide new insights into the biological mechanisms underlying sex differences in response to blood pressure medication. They also demonstrate how computational models can be used to predict the effects of drugs on different individuals. In the future, these predictions may help researchers to identify better, more personalized treatments for blood pressure.
