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(T2D)与AD的相关性最强 然而，在精确的机制水平上，我们对这种前驱代谢是如何发生的知之甚少。 表型实际上会导致神经退行性改变，最终导致阿尔茨海默病。我们同样对此知之甚少 性行为如何有助于加速或减缓这一进程，以及已知的性别差异如何 METS/T2D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。 患有METS/T2D的人，AD的高危人群。然而，使用单一目标来解决这些问题 考虑到潜在相互作用的多因素性质(多种疾病)，基于假设的方法是困难的 因素、时间和性别)，并且由于动物实验设计的改变/不完整而进一步混淆 学习。为了应对这些挑战，我们试图研究METS/T2D进展和 AD以更全球化的全基因组方式，加上强大的统计和生化测量，以 更清楚地了解METS/T2D-AD的关系。具体地说，我们建议确定在 转录组，AD和蛋氨酸相关的线粒体，代谢和病理变化，在两性，随着时间的推移。 我们将使用生物信息学和大规模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(在这两种疾病中早期受到影响)和通过RNA序列发现的新基因 在体外机械地审问这些关系。

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.