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使用PandaOmics(一个基于人工智能的生物靶点发现平台)鉴定肌萎缩侧索硬化症的治疗靶点。

Identification of Therapeutic Targets for Amyotrophic Lateral Sclerosis Using PandaOmics - An AI-Enabled Biological Target Discovery Platform.

作者信息

Pun Frank W, Liu Bonnie Hei Man, Long Xi, Leung Hoi Wing, Leung Geoffrey Ho Duen, Mewborne Quinlan T, Gao Junli, Shneyderman Anastasia, Ozerov Ivan V, Wang Ju, Ren Feng, Aliper Alexander, Bischof Evelyne, Izumchenko Evgeny, Guan Xiaoming, Zhang Ke, Lu Bai, Rothstein Jeffrey D, Cudkowicz Merit E, Zhavoronkov Alex

机构信息

Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, Hong Kong, Hong Kong SAR, China.

Department of Neuroscience, Mayo Clinic Florida, Jacksonville, FL, United States.

出版信息

Front Aging Neurosci. 2022 Jun 28;14:914017. doi: 10.3389/fnagi.2022.914017. eCollection 2022.

Abstract

Amyotrophic lateral sclerosis (ALS) is a severe neurodegenerative disease with ill-defined pathogenesis, calling for urgent developments of new therapeutic regimens. Herein, we applied PandaOmics, an AI-driven target discovery platform, to analyze the expression profiles of central nervous system (CNS) samples (237 cases; 91 controls) from public datasets, and direct iPSC-derived motor neurons (diMNs) (135 cases; 31 controls) from Answer ALS. Seventeen high-confidence and eleven novel therapeutic targets were identified and will be released onto ALS.AI (http://als.ai/). Among the proposed targets screened in the c9ALS model, we verified 8 unreported genes (, , , , , , , and ) whose suppression strongly rescues eye neurodegeneration. Dysregulated pathways identified from CNS and diMN data characterize different stages of disease development. Altogether, our study provides new insights into ALS pathophysiology and demonstrates how AI speeds up the target discovery process, and opens up new opportunities for therapeutic interventions.

摘要

肌萎缩侧索硬化症(ALS)是一种发病机制不明的严重神经退行性疾病,迫切需要开发新的治疗方案。在此,我们应用了PandaOmics(一个人工智能驱动的靶点发现平台)来分析来自公共数据集的中枢神经系统(CNS)样本(237例;91例对照)以及来自Answer ALS的直接诱导多能干细胞衍生的运动神经元(diMNs)(135例;31例对照)的表达谱。确定了17个高可信度和11个新的治疗靶点,并将在ALS.AI(http://als.ai/)上公布。在c9ALS模型筛选出的提议靶点中,我们验证了8个未报道的基因(、、、、、、和),其抑制作用能强烈挽救眼部神经变性。从CNS和diMN数据中确定的失调通路表征了疾病发展的不同阶段。总之,我们的研究为ALS病理生理学提供了新见解,展示了人工智能如何加速靶点发现过程,并为治疗干预开辟了新机会。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2841/9273868/994e87aa5415/fnagi-14-914017-g001.jpg

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