College of Computer Science and Technology, Jilin University, Changchun 130012, China.
Systems Biology Lab for Metabolic Reprogramming, Department of Human Genetics and Cell Biology, School of Medicine, Southern University of Science and Technology, Shenzhen 518055, China.
Int J Mol Sci. 2024 Aug 25;25(17):9211. doi: 10.3390/ijms25179211.
Alzheimer's disease (AD) is a multifaceted neurodegenerative disorder characterized by cognitive decline and neuronal loss, representing a most challenging health issue. We present a computational analysis of transcriptomic data of AD tissues vs. healthy controls, focused on the elucidation of functional roles played by long non-coding RNAs (lncRNAs) throughout the AD progression. We first assembled our own lncRNA transcripts from the raw RNA-Seq data generated from 527 samples of the dorsolateral prefrontal cortex, resulting in the identification of 31,574 novel lncRNA genes. Based on co-expression analyses between mRNAs and lncRNAs, a co-expression network was constructed. Maximal subnetworks with dense connections were identified as functional clusters. Pathway enrichment analyses were conducted over mRNAs and lncRNAs in each cluster, which served as the basis for the inference of functional roles played by lncRNAs involved in each of the key steps in an AD development model that we have previously built based on transcriptomic data of protein-encoding genes. Detailed information is presented about the functional roles of lncRNAs in activities related to stress response, reprogrammed metabolism, cell polarity, and development. Our analyses also revealed that lncRNAs have the discerning power to distinguish between AD samples of each stage and healthy controls. This study represents the first of its kind.
阿尔茨海默病（AD）是一种多方面的神经退行性疾病，其特征是认知能力下降和神经元丧失，是最具挑战性的健康问题之一。我们对 AD 组织与健康对照的转录组数据进行了计算分析，重点阐明了长非编码 RNA（lncRNA）在 AD 进展过程中所起的功能作用。我们首先从 527 个背外侧前额叶皮质的原始 RNA-Seq 数据中组装了自己的 lncRNA 转录本，鉴定出了 31574 个新的 lncRNA 基因。基于 mRNA 和 lncRNA 之间的共表达分析，构建了一个共表达网络。识别出具有密集连接的最大子网络作为功能簇。对每个簇中的 mRNA 和 lncRNA 进行了通路富集分析，这为推断我们之前基于蛋白质编码基因转录组数据构建的 AD 发展模型中每个关键步骤所涉及的 lncRNA 的功能作用提供了依据。详细介绍了 lncRNA 在与应激反应、重编程代谢、细胞极性和发育相关的活动中的功能作用。我们的分析还表明，lncRNA 具有区分每个阶段的 AD 样本和健康对照的能力。这项研究是同类研究中的首次。
