학술논문
Identification of Therapeutic Targets for Amyotrophic Lateral Sclerosis Using PandaOmics – An AI-Enabled Biological Target Discovery Platform
Document Type
article
Author
Frank W. Pun; Bonnie Hei Man Liu; Xi Long; Hoi Wing Leung; Geoffrey Ho Duen Leung; Quinlan T. Mewborne; Junli Gao; Anastasia Shneyderman; Ivan V. Ozerov; Ju Wang; Feng Ren; Alexander Aliper; Evelyne Bischof; Evgeny Izumchenko; Xiaoming Guan; Ke Zhang; Bai Lu; Jeffrey D. Rothstein; Merit E. Cudkowicz; Alex Zhavoronkov
Source
Frontiers in Aging Neuroscience, Vol 14 (2022)
Subject
Language
English
ISSN
1663-4365
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 Drosophila model, we verified 8 unreported genes (KCNB2, KCNS3, ADRA2B, NR3C1, P2RY14, PPP3CB, PTPRC, and RARA) 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.