Characterization of transcriptome changes in diet-induced progression to METS/T2D to identify earliest and sex-specific neurodegenerative foci for AD development

项目摘要 代谢抵抗（METS）和II型糖尿病（T2 D）是与AD最强的关联之一 然而，我们对这种前驱代谢如何在精确的机械水平上发生知之甚少。 表型实际上导致最终导致AD的神经退行性变化。我们同样不知道 性别如何加速或减缓这一进程，以及已知的性别特异性差异， METS/T2 D和AD影响这种关系。鉴于这两种疾病的发病率都在飙升， 了解这些方面对于制定最佳的早期/预防性治疗干预措施至关重要， METS/T2 D患者，AD高风险。然而，使用单一目标解决这些问题 由于潜在相互作用的多因素性质（多种疾病）， 因素、时间和性别），并进一步受到动物实验设计的变化/不完整的混淆 问题研究为了应对这些挑战，我们寻求研究METS/T2 D进展与 AD以更全球性的基因组方式，加上强大的统计和生化测量， 更清楚地了解METS/T2 D-AD关系。具体而言，我们建议确定 转录组、AD和METS相关的线粒体、代谢和病理学变化。 我们将使用生物信息学和大规模的统计分析工具来确定这些之间的关联。 变量，除了性激素水平和时间。我们将把这些观察结果与对 线粒体和代谢基因（在两种疾病的早期受到影响）以及通过RNA测序发现的新基因， 在体外机械地询问这些关系。

Project Summary Metabolic resistance (METS) and type II diabetes (T2D) hold one of the strongest associations with AD development, however, we know little in terms of how, at a precise mechanistic level, this prodromal metabolic phenotype actually leads to neurodegenerative changes that ultimately result in AD. We know equally little about how sex contributes in accelerating or slowing this progression and how known sex-specific differences for METS/T2D and AD affect this relationship. Given that the incidences of both diseases are skyrocketing, understanding these aspects is critical to develop optimal early/preventative therapeutic interventions for individuals with METS/T2D, at high risk for AD. However, addressing these questions using single targeted hypothesis-based approaches is difficult given the multifactorial nature of potential interactions (multiple disease factors, time, and sex) and is further confounded by changing/incomplete experimental designs across animal studies. To address these challenges, we seek to study the relationship between METS/T2D progression and AD in a more global genome-wide manner, coupled with powerful statistics and biochemical measurements to gain a clearer picture of the METS/T2D-AD relationship. Specifically, we propose to determine alterations in transcriptome, AD and METS-related mitochondrial, metabolic, and pathology changes, in both sexes, over time. We will use bioinformatics and large-scale statistical analysis tools to identify associations across these variables, in addition to sex hormone levels, and time. We will couple these observations with modifications of mitochondrial and metabolic genes (affected early in both diseases) and novel ones discovered via RNA seq to interrogate these relationships mechanistically in vitro.