Title: Computational Drug Repurposing for Alzheimer's Disease via Sheaf Theoretic Population-Scale Analysis of snRNA-seq Data Show Chinese title

Title: 基于小规模RNA测序数据的层论种群大规模分析的阿尔茨海默病计算药物再利用

Abstract: Single-cell and single-nucleus RNA sequencing (scRNA-seq /snRNA-seq) are widely used to reveal heterogeneity in cells, showing a growing potential for precision and personalized medicine. Nonetheless, sustainable drug discovery must be based on a population-level understanding of molecular mechanisms, which calls for the population-scale analysis of scRNA-seq/snRNA-seq data. This work introduces a sequential target-drug selection model for drug repurposing against Alzheimer's Disease (AD) targets inferred from population-level snRNA-seq studies of AD progression in microglia cells as well as different cell types taken from an AD affected brain vascular tissue atlas, involving hundreds of thousands of nuclei from multi-patient and multi-regional studies. We utilize Persistent Sheaf Laplacians (PSL) to facilitate a Protein-Protein Interaction (PPI) analysis of AD targets inferred from differential gene expression (DEG), and then use machine learning models to predict repurpose-able DrugBank compounds for molecular targeting. We screen the efficacy of different DrugBank small compounds and further examine their central nervous system (CNS)-relevant ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity), resulting in a list of lead candidates for AD treatment. The list of significant genes establishes a target domain for effective machine learning based AD drug repurposing analysis of DrugBank small compounds to treat AD related molecular targets. Show Chinese abstract

Abstract: 单细胞和单核RNA测序（scRNA-seq/snRNA-seq）被广泛用于揭示细胞的异质性，显示出在精准和个性化医学中的巨大潜力。 然而，可持续的药物发现必须基于对分子机制的群体水平理解，这需要对scRNA-seq/snRNA-seq数据进行群体规模的分析。 本研究介绍了一种顺序目标-药物选择模型，用于针对从微胶质细胞中AD进展的群体水平snRNA-seq研究以及来自AD影响的脑血管组织图谱的不同细胞类型中推断出的AD靶点进行药物再利用，该模型涉及来自多患者和多区域研究的数十万核。 我们利用持久层拉普拉斯算子（PSL）来促进从差异基因表达（DEG）中推断出的AD靶点的蛋白质-蛋白质相互作用（PPI）分析，然后使用机器学习模型预测可再利用的DrugBank化合物用于分子靶向。 我们筛选了不同DrugBank小分子化合物的效果，并进一步检查它们与中枢神经系统（CNS）相关的ADMET（吸收、分布、代谢、排泄和毒性），从而得到一组AD治疗的候选药物。 显著基因列表为有效的基于机器学习的DrugBank小分子化合物AD药物再利用分析建立了目标领域，以治疗与AD相关的分子靶点。