Molecular mechanisms of Alzheimer's disease neuropathological endophenotypes

摘要 舒哈布拉塔·慕克吉是芝加哥大学医学系的研究助理教授 华盛顿。他寻求获得指导职业发展奖，以获得阿尔茨海默氏症的关键知识技能 疾病(AD)与衰老相关的生物学，特别关注复杂的遗传模型来分析 神经病理结果和独立职业生涯所需的研究经验 多学科研究人员。 培训计划详细说明了分子和生理学的正式和非正式指导的五年计划。 AD的方面。该计划包括由一个成熟的专家团队进行的指导研究、课程作业、研讨会、 以及衰老生物学和神经病理学以及统计遗传学方面的指导研究。该计划还包括 在动物模型中的功能验证和基因分子机制探索方面的经验 作为丰富的教育工作小组的合作。慕克吉博士将有机会与 全国会议上的同事们。慕克吉博士的短期职业目标包括：1)获得强大的 神经退行性疾病实验老化领域的知识基础，2) 接受实验老化研究数据分析和解释方面的培训，3)学习和应用新奇 在模式生物中整合遗传数据和验证技术的方法，4)改进手稿 并授予写作技能，以及5)继续进行负责任的研究工作培训。他长期的职业生涯 目标是成为一名独立的多学科研究人员，拥有包括尖端在内的各种工具箱 了解和揭示年龄的病理生理学的统计方法和实验途径- 相关的神经退行性疾病。 在过去的几年里，阿尔茨海默病的遗传学研究已经确定了大约20个易感基因座。翻译成英文 这一成功的可行的治疗和生物标记物的发现依赖于对 这些易感基因上的实际疾病基因和功能变异。要克服这些主要障碍 在后基因组广泛关联研究时代，需要多种替代方法，包括 使用神经病理内表型，这是生物学上相关的和可遗传的表型。 这项研究的具体目标是实现复杂的基因网络分析集成 先验生物学知识正确评估AD相关神经病理内表型和 获得新的见解。最有希望的神经病理学相关基因将通过动物模型进行验证。 最后，将在线虫长寿模型中探索这些有效基因的分子机制。 拟议目标的完成将为随后的独立资金使用网络奠定基础 进一步科学理解阿尔茨海默病遗传和内表型数据的分析方法 为新一代AD疗法奠定了基础。

ABSTRACT Shubhabrata Mukherjee is a Research Assistant Professor in the Department of Medicine at the University of Washington. He seeks a mentored career development award to obtain critical knowledge skills in Alzheimer's disease (AD) related biology of aging focusing particularly on sophisticated genetic models to analyze neuropathological outcomes and necessary research experience for an independent career as a multidisciplinary researcher. The training proposal details a five-year plan of formal and informal instruction in molecular and physiological aspect of AD. The plan includes mentored research by an established team of experts, coursework, seminars, and guided study in biology and neuropathology of aging and statistical genetics. The plan also includes experience in functional validation and exploration of molecular mechanisms of genes in animal models as well as collaborations in richly educational working groups. Dr. Mukherjee will have the opportunity to interact with colleagues at national meetings. Dr. Mukherjee's short-term career goals include 1) acquire a strong foundation of knowledge in the area of experimental aging as applied to neurodegenerative disorders, 2) receive training in the analysis and interpretation of experimental aging research data, 3) learn and apply novel ways of integrating genetic data followed by validation techniques in model organisms, 4) improve manuscript and grant writing skills, and 5) continue training in the responsible conduct of research. His long-term career goals are to be an independent multidisciplinary researcher with a diverse toolbox including sophisticated statistical methodologies and experimental approaches to understand and uncover pathophysiology of age- related neurodegenerative diseases. Genetic studies of AD have identified around 20 susceptibility loci in the past several years. The translation of this success to viable therapies and biomarker discovery depends on the identification and characterization of the actual disease genes and functional variants at these susceptibility loci. To overcome these major hurdles in the post-genome wide association studies era requires a multitude of alternative approaches including the use of neuropathological endophenotypes, which are biologically-relevant, and heritable phenotypes. The specific aims of the research proposed are to implement sophisticated gene-network analysis integrating prior biological knowledge properly to evaluate data from AD-related neuropathological endophenotypes and gain new insights. The most promising neuropathology-associated loci will be validated with animal models. Lastly, molecular mechanisms of these validated genes will be explored in C. elegans longevity models. Completion of the proposed aims will set the stage for subsequent independent funding to use network analysis approaches to further the scientific understanding of genetic and endophenotype data of AD, laying the groundwork for a new generation of AD therapeutics.