Neural and Molecular Mechanisms Underlying Stress-Induced Inflammatory Responses

项目概要/摘要 心血管疾病（CVD）是美国人的头号杀手，也造成巨大的经济损失 给国家。因此，了解 CVD 发生的机制至关重要，如果 我们要成功识别那些有 CVD 风险的人，并进行干预以最终预防 CVD 相关的死亡和 经济影响。长期以来，心理应激反应一直被认为是心血管疾病阴性的危险因素。 相关的结果，最近的工作表明，对压力的炎症反应是一个关键的生物学 压力增加 CVD 风险的机制。但我们目前的水平还存在很大差距 有关压力导致炎症的神经预测因素和分子途径的知识。 如果我们要对压力如何导致疾病有一个完整的机械理解，那么填补这些知识空白至关重要 CVD 风险，也可能为未来的干预目标指明方向。因此，本项目将使用切割- 边缘计算方法来识别压力相关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-肾上腺素信号传导）的药理学工具和 检查它对神经和炎症对压力反应的影响。研究 1 (N=100) 将使用 fMRI 来检查 对社会评价压力任务的神经反应，在压力源之前和之后采集血液样本 测定促炎基因表达和循环炎症蛋白。我们将用创新的 多元机器学习分析技术来识别预测变化的神经模式 炎症，以及基于网络的数学分析工具来检查大规模大脑如何 网络以与炎症相关的方式改变配置以应对压力。在研究 2 (N=120) 中， 我们将对β-肾上腺素能受体进行机械、随机、双盲、安慰剂对照试验 阻断剂普萘洛尔用于检查阻断 β 肾上腺素信号传导如何影响神经和炎症 对社会评价压力任务的反应。这两项研究共同将使我们能够建立神经网络 压力引起的炎症增加的特征（目标 1），确定 β-肾上腺素能的作用 压力神经反应的信号传导（目标 2），并检查 β-肾上腺素能相关的神经介质 与压力相关的炎症反应性减弱（目标 3）。在此过程中，该项目最终将有助于确定 压力相关炎症风险的神经特征，以及未来干预的新目标 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.