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

阿尔茨海默病连接体改变背后的基因共表达

• 批准号: 10017854
• 负责人: Jingwen Yan
• 金额: $ 23.39万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-15 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10017854
• 关键词: Address; Affect; Alzheimer' s Disease; Alzheimer' s disease risk; Biological; Biomedical Research; Brain; Brain Diseases; Brain region; Clinical; Cognitive; Complex; Coupling; DNA Sequence Alteration; Data; Data Set; Development; Disease; Disease Progression; Functional Magnetic Resonance Imaging; Gene Expression; Genes; Genetic; Genetic Markers; Genetic Transcription; Genetic study; Health; Heritability; Human; Information Networks; Learning; Light; Link; Literature; Maps; Measures; Meta-Analysis; Modality; Modeling; Mutation; Network-based; Ontology; Outcome; Paper; Pathway interactions; Pattern; Pharmaceutical Preparations; Play; Prevention; Process; Property; Public Health; Rest; Risk; Role; Single Nucleotide Polymorphism; Structure; System; Therapeutic; Variant; Work; aging population; base; cohort; complex data; connectome; diagnostic biomarker; genetic association; genetic information; high dimensionality; interest; mild cognitive impairment; network architecture; novel; novel diagnostics; novel strategies; public health relevance; relating to nervous system; risk variant; therapeutic development; transcriptome

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.

摘要 在宏观尺度上，大脑连接体通常被表示为网络，其中节点是大脑 感兴趣区域（ROI）和链接指示它们的功能或结构连接。功能性 和结构性脑网络结构是可遗传的，并在AD或其前驱期被发现中断 阶段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，它捕获基因表达的空间变化， 连接作为ROI之间的转录偶联。一些研究表明，共表达网络是 与大脑的结构和功能网络紧密相连。然而，诱导这种基因 联系仍然未知。对这些基因的鉴定将改变我们对这些基因的理解。 生物学基础的改变神经系统的AD，并可以发挥巨大的影响， 开发新的AD诊断、治疗和预防方法。 然而，网络数据的复杂性已经提出了关键的计算挑战，需要新的 概念和扶持办法。为了应对这些挑战，我们提出了一种新的综合方案， 方法和执行以下两个任务：1）识别功能和结构的大脑网络 通过荟萃分析发现AD中的基因改变，2）确定共同 表达网络和AD改变的网络。 通过利用全脑范围的转录组数据，我们将了解一小部分基因，它们的共表达 跨ROI的模式可以最好地解释AD中它们改变的连接。如果成功的话， 该项目将改变我们对AD中基因和大脑区域之间相互作用的理解， 因此，预计将影响一般的生物医学研究，并有利于公共卫生成果。

项目成果

Fused multi-modal similarity network as prior in guiding brain imaging genetic association.

• DOI: 10.3389/fdata.2023.1151893
• 发表时间: 2023
• 期刊: FRONTIERS IN BIG DATA
• 影响因子: 3.1
• 作者: He, Bing;Xie, Linhui;Varathan, Pradeep;Nho, Kwangsik L.;Risacher, Shannon L. J.;Saykin, Andrew J.;Yan, Jingwen
• 通讯作者: Yan, Jingwen