Neural and Molecular Mechanisms Underlying Stress-Induced Inflammatory Responses

项目摘要/摘要 心血管疾病是美国人的头号杀手，也造成了巨大的经济损失 献给这个国家。因此，在以下情况下，了解心血管疾病发展的机制至关重要 我们将成功地识别那些有心血管疾病风险的人，并进行干预，最终防止与心血管疾病相关的死亡和 对经济的影响。长期以来，心理应激反应一直被认为是负性心血管疾病的风险因素。 相关结果，以及最近的研究表明，对应激的炎症反应是一个关键的生物学因素 应激增加心血管疾病风险的机制。然而，我们目前的情况有很大差距 关于应激导致炎症的神经预测因子和分子途径的知识。 如果我们要从机械上全面理解压力是如何导致 对心血管疾病风险的影响，也可能有助于阐明未来的干预目标。因此，本项目将使用切割- 边缘计算方法识别与应激相关的炎症反应的神经特征，并将使用 阻断重要的应激信号通路(即，β-肾上腺素能信号)的药物工具和 研究其对应激的神经和炎症反应的影响。研究1(N=100)将使用功能磁共振成像检查 对社会评估应激任务的神经反应，在应激源前后采集血样 检测促炎基因表达和循环炎性蛋白。我们将使用创新的 多变量机器学习分析技术，以识别预测疾病变化的神经模式 炎症，以及来自数学的基于网络的分析工具，以检查大规模的大脑 网络在应对压力时会以与炎症相关的方式改变结构。研究2(N=120)， 我们将对β-肾上腺素能受体进行机械性、随机、双盲、安慰剂对照试验。 阻滞剂心得安检查阻断β-肾上腺素能信号对神经和炎症的影响 对社会评价压力任务的反应。结合起来，这两项研究将使我们能够建立 应激诱导的炎症增加的特征(目标1)，决定了β-肾上腺素能的影响 神经应激反应的信号转导(目标2)，并检测与β-肾上腺素能相关的神经介质 应激相关炎症反应的减弱(目标3)。通过这样做，该项目最终将有助于确定 应激相关炎症风险的神经信号，以及未来干预的新靶点 改善压力对大脑和身体的影响，减轻心血管疾病的健康和经济负担。

PROJECT SUMMARY/ABSTRACT Cardiovascular disease (CVD) is the number one killer of Americans, and also causes significant economic costs to the nation. Thus, understanding the mechanisms through which CVD develops is of paramount importance if we are to successfully identify those at risk for CVD, and intervene to ultimately prevent CVD-related death and economic impact. Psychological stress reactivity has long been appreciated as a risk factor for negative CVD- related outcomes, and recent work suggests that inflammatory reactivity to stress is a critical biological mechanism through which stress increases risk for CVD. However, there are significant gaps in our current knowledge regarding the neural predictors and molecular pathways through which stress leads to inflammation. These knowledge gaps are critical to fill if we are to develop a full mechanistic understanding of how stress leads to CVD risk, and may also shed light on future intervention targets. Thus, the present project will use cutting- edge computational methods to identify neural signatures of stress-related inflammatory reactivity, and will use pharmacological tools to block an important stress-signaling pathway (i.e., beta-adrenergic signaling) and examine its effects on neural and inflammatory reactivity to stress. Study 1 (N=100) will use fMRI to examine neural responses to a social evaluative stress task, with blood samples taken before and after the stressor assayed for pro-inflammatory gene expression and circulating inflammatory proteins. We will use innovative multivariate machine learning analytic techniques to identify the neural patterns that predict changes in inflammation, as well as network-based analytic tools from mathematics to examine how large-scale brain networks change configuration in response to stress in ways that are linked to inflammation. In Study 2 (N=120), we will conduct a mechanistic, randomized, double-blind, placebo-controlled trial of the beta-adrenergic receptor blocker propranolol to examine how blocking beta-adrenergic signaling impacts neural and inflammatory responses to the social evaluative stress task. Together, these two studies will allow us to establish the neural signatures of stress-induced increases in inflammation (Aim 1), determine the effects of beta-adrenergic signaling on neural responses to stress (Aim 2), and examine the neural mediators of beta-adrenergic related attenuations in stress-related inflammatory reactivity (Aim 3). In doing so, this project will ultimately help identify neural signatures of risk for stress-related inflammation, as well as novel targets for future intervention to ameliorate the impact of stress on the brain and body and reduce the health and economic burden of CVD.