BACKGROUND New genetic and genomic resources have identified multiple genetic risk factors for late-onset Alzheimer's disease (LOAD) and characterized this common dementia at the molecular level. Experimental studies in model organisms can validate these associations and elucidate the links between specific genetic factors and transcriptomic signatures. Animal models based on LOAD-associated genes can potentially connect common genetic variation with LOAD transcriptomes, thereby providing novel insights into basic biological mechanisms underlying the disease. METHODS We performed RNA-Seq on whole brain samples from a panel of six-month-old female mice, each carrying one of the following mutations: homozygous deletions of Apoe and Clu; hemizygous deletions of Bin1 and Cd2ap; and a transgenic APOEε4. Similar data from a transgenic APP/PS1 model was included for comparison to early-onset variant effects. Weighted gene co-expression network analysis (WGCNA) was used to identify modules of correlated genes and each module was tested for differential expression by strain. We then compared mouse modules with human postmortem brain modules from the Accelerating Medicine's Partnership for AD (AMP-AD) to determine the LOAD-related processes affected by each genetic risk factor. RESULTS Mouse modules were significantly enriched in multiple AD-related processes, including immune response, inflammation, lipid processing, endocytosis, and synaptic cell function. WGCNA modules were significantly associated with Apoe-/-, APOEε4, Clu-/-, and APP/PS1 mouse models. Apoe-/-, GFAP-driven APOEε4, and APP/PS1 driven modules overlapped with AMP-AD inflammation and microglial modules; Clu-/- driven modules overlapped with synaptic modules; and APP/PS1 modules separately overlapped with lipid-processing and metabolism modules. CONCLUSIONS This study of genetic mouse models provides a basis to dissect the role of AD risk genes in relevant AD pathologies. We determined that different genetic perturbations affect different molecular mechanisms comprising AD, and mapped specific effects to each risk gene. Our approach provides a platform for further exploration into the causes and progression of AD by assessing animal models at different ages and/or with different combinations of LOAD risk variants.

中文翻译：

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小鼠疾病风险基因的遗传扰动捕获了晚发性阿尔茨海默病的转录组特征。

背景新的遗传和基因组资源已经确定了迟发性阿尔茨海默病（LOAD）的多种遗传风险因素，并在分子水平上表征了这种常见的痴呆症。模型生物体的实验研究可以验证这些关联并阐明特定遗传因素和转录组特征之间的联系。基于 LOAD 相关基因的动物模型可以潜在地将常见的遗传变异与 LOAD 转录组联系起来，从而为该疾病的基本生物学机制提供新的见解。方法 我们对一组 6 个月大雌性小鼠的全脑样本进行了 RNA 测序，每只小鼠均携带以下突变之一：Apoe 和 Clu 纯合缺失；Bin1 和 Cd2ap 的半合子缺失；和转基因APOEε4。来自转基因 APP/PS1 模型的类似数据也被纳入其中，用于与早发变异效应进行比较。使用加权基因共表达网络分析（WGCNA）来识别相关基因的模块，并测试每个模块的菌株差异表达。然后，我们将小鼠模块与来自 AD 加速医学合作伙伴关系 (AMP-AD) 的人类死后大脑模块进行比较，以确定受每个遗传风险因素影响的 LOAD 相关过程。结果 小鼠模块在多个 AD 相关过程中显着富集，包括免疫反应、炎症、脂质加工、内吞作用和突触细胞功能。WGCNA 模块与 Apoe-/-、APOEε4、Clu-/- 和 APP/PS1 小鼠模型显着相关。Apoe-/-、GFAP 驱动的 APOEε4 和 APP/PS1 驱动的模块与 AMP-AD 炎症和小胶质细胞模块重叠；Clu-/-驱动模块与突触模块重叠；APP/PS1 模块分别与脂质加工和代谢模块重叠。结论 这项基因小鼠模型研究为剖析 AD 风险基因在相关 AD 病理中的作用提供了基础。我们确定不同的遗传扰动会影响 AD 的不同分子机制，并将具体影响映射到每个风险基因。我们的方法通过评估不同年龄和/或不同 LOAD 风险变异组合的动物模型，为进一步探索 AD 的病因和进展提供了一个平台。