Probing networks underlying sleep and stress with multiscale data

描述(由申请人提供)：临床和实验文献已经迅速融合，证明睡眠和昼夜节律障碍在广泛的慢性病的发生和维持中起着不可或缺的作用。睡眠障碍在神经精神障碍患者中尤其常见，神经精神障碍是美国疾病负担的主要原因，是心血管疾病的两倍多。流行病学数据表明，睡眠障碍是焦虑、抑郁和创伤后应激障碍的先兆，并往往使人容易患上创伤后应激障碍，这表明这些复杂的特征高度相互交织。解决睡眠和压力表型的复杂性需要新的方法来利用和整合多种形式的数据。我的建议侧重于确定睡眠和压力特征的共同因果分子网络，以研究与神经精神障碍相关的新的疾病机制和治疗策略。通过详尽的表型分析(479个睡眠和压力特征)和集成的多尺度计算方法，我们将利用这些特征的复杂性来探索分子路径作为一个协调单元是如何自然相互作用的，而不是当它们被人为操纵时它们是如何反应的。通过整合来自F2小鼠群体(&gt；100小鼠)的遗传、基因表达和共表达数据，我们可以使用贝叶斯重建来识别作为压力和睡眠表型因果调节的分子网络。由于睡眠和压力特征的复杂性，我们必须在多基因网络的背景下理解单个基因，并将表型视为这些网络的紧急属性。通过了解这些分子网络是如何作为表型的传感器和驱动器的，我们就可以适当地考虑药物干预的目标，并利用新的计算策略来重新调整药物的用途。我们预计，这项工作将为未来与抑郁、焦虑、压力易感性(PTSD)、 然后睡觉。

DESCRIPTION (provided by applicant): Clinical and experimental literature have rapidly converged to demonstrate that sleep and circadian dysfunction play an integral role in the onset and maintenance of a broad spectrum of chronic diseases. Sleep dysfunction is especially common among patients with neuropsychiatric disorders, which is the leading contributor of disease burden in the United States, more than twice as much as cardiovascular disease. Epidemiological data demonstrates that sleep disruption precedes and often predisposes people to anxiety, depression, and PTSD, suggesting that these complex traits are highly intertwined. Addressing the complexity of sleep and stress phenotypes requires novel approaches that leverage and integrate multiple forms of data. My proposal focuses on identifying causal molecular networks common to sleep and stress traits in order to investigate novel disease mechanisms and therapeutic strategies relevant to neuropsychiatric disorders. With an exhaustive phenotypic assay (479 sleep and stress traits) and an integrated multi-scale computational approach, we will leverage the complexity of these traits to probe how molecular pathways naturally interact as a coordinated unit, rather than how they react when they are artificially manipulated. By integrating genetic, gene expression, and co-expression data from F2 mouse populations (>100 mice), we can use Bayesian reconstruction to identify molecular subnetworks that act as causal regulators of stress and sleep phenotypes. Because of the complexity of sleep and stress traits, it is imperative that we understand individual genes in the context of polygenic networks and treat phenotypes as emergent properties of these networks. By understanding how these molecular networks act as sensors and drivers of phenotypes, we can then appropriately consider targets for pharmacological interventions and utilize novel computational strategies for repurposing drugs. We anticipate that this work will provide the foundation for future in vivo studies related to depression, anxiety, stress susceptibility (PTSD), and sleep.