Gene co-expression underlying the connectomic alterations in Alzheimer's disease

摘要 宏观尺度上的大脑连接体通常被表示为网络，其中的节点是大脑 感兴趣区域(ROI)和链接表示它们的功能或结构连接。都是功能性的 和结构性脑网络结构是可遗传的，并在AD或其前驱症状中被发现破坏 舞台。最近获得的全脑转录组数据使另一种类型的大脑成为可能 连接体，大脑共表达网络，用来捕捉基因表达的空间差异 作为ROI之间转录偶联的链接。一些研究表明，共表达网络是 与结构和功能的大脑网络紧密相连。然而，导致这种情况的基因 目前尚不清楚是否存在联系。这些基因的识别将改变我们对 阿尔茨海默病神经系统改变的生物学基础，并可对 开发AD的新诊断、治疗和预防方法。 然而，网络数据的复杂性提出了关键的计算挑战，需要新的 概念和扶持方法。为了应对这些挑战，我们提出了新颖的一体化 方法并执行以下两项任务：1)识别功能和结构的大脑网络 通过荟萃分析在AD中发生改变，以及2)确定共同-基因之间的关联。 表达网络和AD改变的网络。 通过利用全脑的转录组数据，我们将了解到一小部分基因的共同表达 ROI的模式可以最好地解释它们在AD中改变的连接。如果成功，则结果为 该项目将改变我们对阿尔茨海默病基因和大脑区域之间相互作用的理解，以及 因此，预计将对整个生物医学研究产生影响，并使公共卫生成果受益。

Abstract A brain connectome at the macroscale is typically represented as networks, where nodes are brain regions of interest (ROIs) and links indicate their functional or structural connections. Both functional and structural brain network architecture are heritable and found disrupted in AD or its prodromal stage. Recent availability of brain-wide transcriptome data has made possible another type of brain connectome, brain co-expression network, which captures spatial variations in gene expression with links as transcriptional coupling between ROIs. Some studies showed that co-expression network is closely connected to structural and functional brain networks. However, the genes inducing such connection remains unknown. Identification of these genes will transform our understanding of the biological underpinnings of altered neural system in AD and can exert a huge impact on the development of new diagnostic, therapeutic and preventative approaches for AD. The complexity of network data, however, has presented critical computational challenge requiring new concepts and enabling approaches. To address these challenges, we propose novel integrative approaches and perform the following two tasks: 1) identifying functional and structural brain networks altered in AD via meta-analyses, and 2) identifying the genes underlying the association between co- expression networks and AD-altered networks. By leveraging the brain-wide transcriptome data, we will learn a small set of genes whose co-expression patterns across ROIs can best explain their altered connections in AD. If successful, results from this project will transform our understanding of the interplay between genes and brain regions in AD, and thus be expected to impact biomedical research in general and benefit public health outcomes.