RESUMO
Amyotrophic lateral sclerosis (ALS) is a fatal and incurable neurodegenerative disease caused by the selective and progressive death of motor neurons (MNs). Understanding the genetic and molecular factors influencing ALS survival is crucial for disease management and therapeutics. In this study, we introduce a deep learning-powered genetic analysis framework to link rare noncoding genetic variants to ALS survival. Using data from human induced pluripotent stem cell (iPSC)-derived MNs, this method prioritizes functional noncoding variants using deep learning, links cis-regulatory elements (CREs) to target genes using epigenomics data, and integrates these data through gene-level burden tests to identify survival-modifying variants, CREs, and genes. We apply this approach to analyze 6,715 ALS genomes, and pinpoint four novel rare noncoding variants associated with survival, including chr7:76,009,472:C>T linked to CCDC146. CRISPR-Cas9 editing of this variant increases CCDC146 expression in iPSC-derived MNs and exacerbates ALS-specific phenotypes, including TDP-43 mislocalization. Suppressing CCDC146 with an antisense oligonucleotide (ASO), showing no toxicity, completely rescues ALS-associated survival defects in neurons derived from sporadic ALS patients and from carriers of the ALS-associated G4C2-repeat expansion within C9ORF72. ASO targeting of CCDC146 may be a broadly effective therapeutic approach for ALS. Our framework provides a generic and powerful approach for studying noncoding genetics of complex human diseases.
RESUMO
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease caused by many diverse genetic etiologies. Although therapeutics that specifically target causal mutations may rescue individual types of ALS, such approaches cannot treat most patients since they have unknown genetic etiology. Thus, there is a critical need for therapeutic strategies that rescue multiple forms of ALS. Here, we combine phenotypic chemical screening on a diverse cohort of ALS patient-derived neurons with bioinformatic analysis of large chemical and genetic perturbational datasets to identify broadly effective genetic targets for ALS. We show that suppressing the gene-encoding, spliceosome-associated factor SYF2 alleviates TDP-43 aggregation and mislocalization, improves TDP-43 activity, and rescues C9ORF72 and causes sporadic ALS neuron survival. Moreover, Syf2 suppression ameliorates neurodegeneration, neuromuscular junction loss, and motor dysfunction in TDP-43 mice. Thus, suppression of spliceosome-associated factors such as SYF2 may be a broadly effective therapeutic approach for ALS.
