In vivo systems biology of neurodegenerative diseases

描述(申请人提供)：Tau病是一组不同的与年龄相关的神经退行性疾病，其特征是在大脑的特定区域发展神经原纤维缠结和其他Tau包涵体。这些疾病是渐进性的，会使人虚弱，最终是致命的。每种对位疗法都有不同的临床和形态特征，但都有影响独特神经元群体的特点。在影响500多万美国人的阿尔茨海默病(AD)中，紧张性病变发生在淀粉样蛋白沉积的环境中，导致边缘皮质和联合皮质的退化，而不影响邻近的运动和感觉区域。其他神经官能症，包括额颞叶痴呆(FTD)，是大脑叶变性。由于缺乏与神经退行性疾病的解剖学特异性和进展机制相关的分子机制方面的知识，对有效治疗方法的持续探索受到阻碍。我们在这项研究中的目标是通过精确定位那些在神经变性的开始和进展过程中大脑特定区域调节失调的信号通路来确定新的治疗靶点。我们识别这些途径的方法是在FTD和AD模型的小鼠大脑中开发定量的、数据驱动的细胞信号计算模型。我们从生物工程分析的角度提出的驱动假说是，神经元退行性变与神经元多途径信号网络‘状态’偏离正常有关，因此这种‘状态’的扰动可以通过描述“网络-表型”关系的计算模型预测的方式来调节神经退行性疾病的发生和发展。需要计算模型来提供对相关复杂数据集的直观检查不容易确定的新见解，以了解控制神经元如何对最终导致细胞死亡的侮辱做出反应的关键通路的整合操作。重要的是，这种基于生物工程的观点也将有助于产生与途径扰动的表型效应相关的新的、多变量的推论假说。本质上，从小鼠神经退行性变模型产生的信号数据集得出的计算模型将识别可以被调节以控制疾病发生和进展的信号通路。

DESCRIPTION (provided by applicant): The tauopathies are a diverse group of age-related neurodegenerative diseases that are characterized by the development of neurofibrillary tangles and other Tau inclusions in specific regions of the brain. These conditions are progressive and debilitating, and are ultimately fatal. Each tauopathy has distinct clinical and morphologic features, but all have the characteristic of affecting unique neuronal populations. In Alzheimer's Disease (AD), which affects over 5 million Americans, the tauopathy occurs in the setting of amyloid depositions and causes degeneration of limbic and association cortices, sparing adjacent motor and sensory regions. Other tauopathies, including frontotemporal dementia (FTD), are lobar degenerations. The continuing search for effective therapies is crippled by the lack of knowledge pertaining to the molecular mechanisms underlying the anatomical specificity and mechanisms of progression of neurodegenerative disease. Our goal in this study is to identify novel therapeutic targets by pinpointing those signaling pathways that are dysregulated in specific regions of the brain during the onset and progression of neurodegeneration. Our approach to identifying these pathways is to develop quantitative, data-driven computational models of cellular signaling in the brains of mouse models of FTD and AD. Our driving hypothesis, arising from a bioengineering analysis perspective, is that neuro-degeneration is associated with a deviation of the neuronal multi-pathway signaling network 'state' from normal, such that perturbation of this 'state' can modulate the onset and progression of neurodegenerative disease in ways predictable from a computational model characterizing the "network-phenotype" relationship. Computational modeling is required to provide novel insights, not readily ascertained from intuitive inspection of the associated complex data-sets, into the integrative operation of key pathways that govern how neurons respond to the insults that ultimately result in cell death. Importantly, this bioengineering-based perspective will also help to generate new, multi-variate corollary hypotheses relating to the phenotypic effects of pathway perturbation. In essence, the computational models derived from signaling datasets generated from mouse models of neurodegeneration will identify signaling pathways that can be modulated to control disease onset and progression.