In vivo systems biology of neurodegenerative diseases

描述（由申请人提供）：Tau病是一组不同的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生神经原纤维缠结和其他Tau内含物。这些情况是渐进的，使人衰弱，最终是致命的。每种牛头病都有不同的临床和形态学特征，但都有影响独特神经元群的特点。在影响超过500万美国人的阿尔茨海默病（AD）中，牛头病发生在淀粉样蛋白沉积的环境中，导致边缘和联合皮层变性，而不影响邻近的运动和感觉区域。其他的牛头病变，包括额颞叶痴呆（FTD），是脑叶变性。由于缺乏与神经退行性疾病的解剖特异性和进展机制相关的分子机制，对有效治疗方法的持续探索受到了阻碍。我们在这项研究中的目标是通过精确定位在神经退行性疾病发生和发展过程中大脑特定区域中失调的信号通路来确定新的治疗靶点。我们识别这些通路的方法是在FTD和AD小鼠模型中开发定量的、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设是，神经变性与神经元多通路信号网络“状态”偏离正常有关，因此这种“状态”的扰动可以通过表征“网络-表型”关系的计算模型可预测的方式调节神经退行性疾病的发生和进展。计算建模需要提供新颖的见解，这些见解不易从相关复杂数据集的直观检查中确定，以了解控制神经元如何对最终导致细胞死亡的损伤作出反应的关键途径的综合运作。重要的是，这种基于生物工程的观点也将有助于产生与通路扰动的表型效应有关的新的、多变量的推论假设。从本质上讲，从小鼠神经变性模型产生的信号数据集衍生的计算模型将确定可以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.