Cross-Species Modeling Identifies Gene Signatures in Type 2 Diabetes Mouse Models Predictive of Inflammatory and Estrogen Signaling Pathways Associated with Alzheimer's Disease Outcomes in Humans.

Q2 Computer Science
Brendan K Ball, Elizabeth A Proctor, Douglas K Brubaker
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Abstract

Alzheimer's disease (AD), the predominant form of dementia, is influenced by several risk factors, including type 2 diabetes (T2D), a metabolic disorder characterized by the dysregulation of blood sugar levels. Despite mouse and human studies reporting this connection between T2D and AD, the mechanism by which T2D contributes to AD pathobiology is not well understood. A challenge in understanding mechanistic links between these conditions is that evidence between mouse and human experimental models must be synthesized, but translating between these systems is difficult due to evolutionary distance, physiological differences, and human heterogeneity. To address this, we employed a computational framework called translatable components regression (TransComp-R) to overcome discrepancies between pre-clinical and clinical studies using omics data. Here, we developed a novel extension of TransComp-R for multi-disease modeling to analyze transcriptomic data from brain samples of mouse models of AD, T2D, and simultaneous occurrence of both disease (ADxT2D) and postmortem human brain data to identify enriched pathways predictive of human AD status. Our TransComp-R model identified inflammatory and estrogen signaling pathways encoded by mouse principal components derived from models of T2D and ADxT2D, but not AD alone, predicted with human AD outcomes. The same mouse PCs predictive of human AD outcomes were able to capture sex-dependent differences in human AD biology, including significant effects unique to female patients, despite the TransComp-R being derived from data from only male mice. We demonstrated that our approach identifies biological pathways of interest at the intersection of the complex etiologies of AD and T2D which may guide future studies into pathogenesis and therapeutic development for patients with T2D-associated AD.

跨物种模型确定2型糖尿病小鼠模型中的基因特征,预测与人类阿尔茨海默病结局相关的炎症和雌激素信号通路
阿尔茨海默病(AD)是痴呆症的主要形式,受多种风险因素的影响,其中包括 2 型糖尿病(T2D),这是一种以血糖水平失调为特征的代谢紊乱。尽管小鼠和人体研究报告了 T2D 与老年痴呆症之间的这种联系,但人们对 T2D 促成老年痴呆症病理生物学的机制还不甚了解。要了解这些病症之间的机理联系所面临的一个挑战是,必须综合小鼠和人类实验模型之间的证据,但由于进化距离、生理差异和人类异质性,在这些系统之间进行转化非常困难。为了解决这个问题,我们采用了一种名为可转化成分回归(TransComp-R)的计算框架,利用omics数据克服临床前研究与临床研究之间的差异。在这里,我们开发了用于多疾病建模的 TransComp-R 的新扩展功能,以分析来自 AD、T2D 和同时发生这两种疾病(ADxT2D)的小鼠模型脑样本的转录组数据以及死后人脑数据,从而确定可预测人类 AD 状态的富集通路。我们的 TransComp-R 模型确定了由小鼠主成分编码的炎症和雌激素信号通路,这些主成分来源于 T2D 和 ADxT2D 模型,但不是单独的 AD 模型,可预测人类 AD 的结果。尽管TransComp-R是根据雄性小鼠的数据得出的,但预测人类AD结果的相同小鼠主成分能够捕捉到人类AD生物学中的性别差异,包括女性患者特有的显著效应。我们的研究表明,我们的方法可以识别出在注意力缺失症和 T2D 复杂病因交叉点上的生物通路,这些通路可以指导未来对 T2D 相关注意力缺失症患者的发病机制和疗法开发的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
4.50
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