Aging, Viral Immunity and Atherosclerosis

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DESCRIPTION (provided by applicant): Clinical Studies have demonstrated that aging increases the risk of atherosclerosis. As the number of older people continues to grow, morbidity from atherosclerosis in older adults will pose a heavy burden on our health care resources. There has been a growing appreciation that inflammation is a key pathopysiological mechanism that underpins the development of atherosclerosis. Importantly, both experimental and clinical studies provide evidence that microbial infections, including viruses, exacerbate the development of atherosclerosis. Prior studies have demonstrated that aging impairs immune function and leads to a reduced ability to clear viruses. Data from our laboratory, supported by a recently acquired R01 (R01AG028082), has demonstrated that aging impairs the function of plasmacytoid DCs (pDCs), critical innate sensors of viral infection. This K02 application will complement the currently funded R01 and will examine how aging accelerates the atherosclerotic process; in particular, it will elucidate the mechanisms by which viral infections exacerbate atherosclerosis with aging. Importantly, we will examine if TLR-based vaccines protect against subsequent viral infection with aging and ameliorate virus-exacerbated atherosclerosis. In testing these hypotheses, we will collaborate with two senior vascular biologists, Drs. Sessa and Tellides. Thus, this K02 proposal will allow me to focus on my research program and develop new scientific collaborations that will be instrumental to leading a future program project on the effects of aging on cardiovascular diseases. Relevance (see instructions): The number of older people in our society will continue to grow. As older people will have an increased probability of developing heart attacks and strokes, it will be imperative to understand how aging leads to increased cardiovascular diseases. This proposal will investigate mechanisms by which aging leads to cardiovascular diseases using murine experimental models.