Lee Meelim J, Wang Chuangqi, Carroll Molly J, Brubaker Douglas K, Hyman Bradley T, Lauffenburger Douglas A
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States.
Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States.
Front Neurosci. 2021 Sep 30;15:727784. doi: 10.3389/fnins.2021.727784. eCollection 2021.
Mouse models are vital for preclinical research on Alzheimer's disease (AD) pathobiology. Many traditional models are driven by autosomal dominant mutations identified from early onset AD genetics whereas late onset and sporadic forms of the disease are predominant among human patients. Alongside ongoing experimental efforts to improve fidelity of mouse model representation of late onset AD, a computational framework termed Translatable Components Regression (TransComp-R) offers a complementary approach to leverage human and mouse datasets concurrently to enhance translation capabilities. We employ TransComp-R to integratively analyze transcriptomic data from human postmortem and traditional amyloid mouse model hippocampi to identify pathway-level signatures present in human patient samples yet predictive of mouse model disease status. This method allows concomitant evaluation of datasets across different species beyond observational seeking of direct commonalities between the species. Additional linear modeling focuses on decoupling disease signatures from effects of aging. Our results elucidated mouse-to-human translatable signatures associated with disease: excitatory synapses, inflammatory cytokine signaling, and complement cascade- and TYROBP-based innate immune activity; these signatures all find validation in previous literature. Additionally, we identified agonists of the Tyro3 / Axl / MerTK (TAM) receptor family as significant contributors to the cross-species innate immune signature; the mechanistic roles of the TAM receptor family in AD merit further dedicated study. We have demonstrated that TransComp-R can enhance translational understanding of relationships between AD mouse model data and human data, thus aiding generation of biological hypotheses concerning AD progression and holding promise for improved preclinical evaluation of therapies.
小鼠模型对于阿尔茨海默病（AD）病理生物学的临床前研究至关重要。许多传统模型由早发性AD遗传学中鉴定出的常染色体显性突变驱动，而晚发性和散发性形式的疾病在人类患者中占主导地位。除了正在进行的提高小鼠模型对晚发性AD代表性保真度的实验努力外，一种称为可翻译成分回归（TransComp-R）的计算框架提供了一种补充方法，可同时利用人类和小鼠数据集来增强翻译能力。我们使用TransComp-R对来自人类尸检和传统淀粉样蛋白小鼠模型海马体的转录组数据进行综合分析，以识别存在于人类患者样本中但可预测小鼠模型疾病状态的通路水平特征。这种方法允许对不同物种的数据集进行伴随评估，而不仅仅是观察物种之间的直接共性。额外的线性建模专注于将疾病特征与衰老效应解耦。我们的结果阐明了与疾病相关的小鼠到人类的可翻译特征：兴奋性突触、炎性细胞因子信号传导以及基于补体级联和TYROBP的先天免疫活性；这些特征均在先前文献中得到验证。此外，我们将酪氨酸激酶3/AXL/巨噬细胞清道夫受体酪氨酸激酶（TAM）受体家族的激动剂鉴定为跨物种先天免疫特征的重要贡献者；TAM受体家族在AD中的机制作用值得进一步专门研究。我们已经证明，TransComp-R可以增强对AD小鼠模型数据与人类数据之间关系的翻译理解，从而有助于生成关于AD进展的生物学假设，并有望改善治疗的临床前评估。
