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Probing networks underlying sleep and stress with multiscale data

利用多尺度数据探索睡眠和压力背后的网络

基本信息

批准号：
9130893
负责人：
Joseph Scarpa
金额：
$ 5万
依托单位：
ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-09-03 至 2018-10-23
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9130893
关键词：
Address Affect Alzheimer&apos s Disease Animal Model Anticonvulsants Anxiety Behavior Behavioral Bioinformatics Biological Assay Brain region Cardiovascular Diseases Categories Cell physiology Chronic Disease Circadian Rhythms Clinical Complex Corpus striatum structure Data Disease Disease Pathway Drug Targeting Epidemiology FDA approved Foundations Functional disorder Future Gene Expression Genes Genetic Genetic Transcription Genetic study Goals Hippocampus (Brain)Human Human Genetics Huntington Disease Hypothalamic structure Individual Intervention Intestines Investigation Literature Maintenance Maps Mental Depression Mental disorders Methods Modality Modeling Molecular Molecular Probes Mus Network-based Ontology Pathway Analysis Pathway interactions Patients Pharmaceutical Preparations Pharmacology Phase Phenotype Play Population Population Group Post-Traumatic Stress Disorders Predisposition Prefrontal Cortex Property REM Sleep Regulation Research Role Sleep Stress Structure Testing Thalamic structure Therapeutic Transcript Tricyclic Antidepressive Agents United States Work addiction base burden of illness computer based statistical methods epidemiologic data exhaustion gene interaction genetic signature in vivo lung small cell carcinoma nervous system disorder neuropsychiatric disorder new therapeutic target novel novel strategies patient population public health relevance reconstruction sensor serological marker small molecule trait transcriptome sequencing

项目摘要

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.

描述（由申请人提供）：临床和实验文献已迅速汇集，证明睡眠和昼夜节律功能障碍在广泛的慢性疾病的发作和维持中起着不可或缺的作用。睡眠障碍在神经精神疾病患者中尤其常见，这是美国疾病负担的主要贡献者，是心血管疾病的两倍多。流行病学数据表明，睡眠中断先于焦虑，抑郁和创伤后应激障碍，并经常使人们容易患上焦虑，抑郁和创伤后应激障碍，这表明这些复杂的特征高度交织在一起。解决睡眠和压力表型的复杂性需要利用和整合多种形式数据的新方法。我的提案重点是识别睡眠和压力特征常见的因果分子网络，以研究与神经精神疾病相关的新型疾病机制和治疗策略。通过详尽的表型分析（479种睡眠和压力特征）和综合的多尺度计算方法，我们将利用这些特征的复杂性来探索分子通路如何作为一个协调单元自然地相互作用，而不是当它们被人为操纵时它们如何反应。通过整合来自F2小鼠群体（>100只小鼠）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充当压力和睡眠表型的因果调节器的分子子网络。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个体基因，并将表型视为这些网络的涌现特性。通过了解这些分子网络如何作为表型的传感器和驱动器，我们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利用药物。我们预计这项工作将为未来与抑郁、焦虑、压力易感性（PTSD）相关的体内研究提供基础，睡觉