Suppr超能文献

基于数据驱动发现针对阿尔茨海默病的细胞类型导向的网络校正联合疗法。

Data-driven discovery of cell-type-directed network-correcting combination therapy for Alzheimer's disease.

作者信息

Li Yaqiao, Serras Carlota Pereda, Blumenfeld Jessica, Xie Min, Hao Yanxia, Deng Elise, Chun You Young, Holtzman Julia, An Alice, Yoon Seo Yeon, Tang Xinyu, Rao Antara, Woldemariam Sarah, Tang Alice, Zhang Alex, Simms Jeffrey, Lo Iris, Oskotsky Tomiko, Keiser Michael J, Huang Yadong, Sirota Marina

机构信息

Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.

Pharmaceutical Science and Pharmacogenomics Graduate Program, University of California, San Francisco, CA, USA.

出版信息

bioRxiv. 2024 Dec 13:2024.12.09.627436. doi: 10.1101/2024.12.09.627436.

Abstract

Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder characterized by heterogeneous molecular changes across diverse cell types, posing significant challenges for treatment development. To address this, we introduced a cell-type-specific, multi-target drug discovery strategy grounded in human data and real-world evidence. This approach integrates single-cell transcriptomics, drug perturbation databases, and clinical records. Using this framework, letrozole and irinotecan were identified as a potential combination therapy, each targeting AD-related gene expression changes in neurons and glial cells, respectively. In an AD mouse model, this combination therapy significantly improved memory function and reduced AD-related pathologies compared to vehicle and single-drug treatments. Single-nuclei transcriptomic analysis confirmed that the therapy reversed disease-associated gene networks in a cell-type-specific manner. These results highlight the promise of cell-type-directed combination therapies in addressing multifactorial diseases like AD and lay the groundwork for precision medicine tailored to patient-specific transcriptomic and clinical profiles.

摘要

阿尔茨海默病(AD)是一种多因素神经退行性疾病,其特征是不同细胞类型中存在异质性分子变化,这给治疗开发带来了重大挑战。为解决这一问题,我们引入了一种基于人类数据和真实世界证据的细胞类型特异性、多靶点药物发现策略。这种方法整合了单细胞转录组学、药物扰动数据库和临床记录。利用这个框架,来曲唑和伊立替康被确定为一种潜在的联合疗法,它们分别针对神经元和神经胶质细胞中与AD相关的基因表达变化。在AD小鼠模型中,与赋形剂和单药治疗相比,这种联合疗法显著改善了记忆功能并减少了与AD相关的病理变化。单核转录组分析证实,该疗法以细胞类型特异性方式逆转了疾病相关基因网络。这些结果凸显了细胞类型导向的联合疗法在治疗AD等多因素疾病方面的前景,并为根据患者特异性转录组和临床特征量身定制精准医学奠定了基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1338/11661161/ba180201cbac/nihpp-2024.12.09.627436v1-f0002.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验