In Brain : a journal of neurology
Amyotrophic lateral sclerosis (ALS) is a fatal and incurable neurodegenerative disease that mainly affects the neurons of the motor system. Despite the increasing understanding of its genetic components, their biological meanings are still poorly understood. Indeed, it is still not clear to which extent the pathological features associated with ALS are commonly shared by the different genes causally linked to this disorder. To address this point, we combined multi-omics analysis covering the transcriptional, epigenetic and mutational aspects of heterogenous hiPSC-derived C9orf72-, TARDBP-, SOD1- and FUS-mutant motor neurons as well as datasets from patients' biopsies. We identified a common signature, converging toward increased stress and synaptic abnormalities, which reflects a unifying transcriptional program in ALS despite the specific profiles owing to the underlying pathogenic gene. In addition, whole genome bisulfite sequencing linked the altered gene expression observed in mutant cells to their methylation profile, highlighting deep epigenetic alterations as part of the abnormal transcriptional signatures linked to ALS. We then applied multi-layer deep machine-learning to integrate publicly-available blood and spinal cord transcriptomes and found a statistically significant correlation between their top predictor gene sets, which were significantly enriched in toll-like receptor signaling. Notably, the overrepresentation of this biological term also correlated with the transcriptional signature identified in mutant hiPSC-derived motor neurons, highlighting novel insights into ALS marker genes in a tissue-independent manner. Finally, using whole genome sequencing in combination with deep learning, we generated the first mutational signature for ALS and defined a specific genomic profile for this disease, which is significantly correlated to aging signatures, hinting at age as a major player in ALS. All in all, this work describes innovative methodological approaches for the identification of disease signatures through the combination of multi-omics analysis and provides novel knowledge on the pathological convergencies defining ALS.
Catanese Alberto, Rajkumar Sandeep, Sommer Daniel, Masrori Pegah, Hersmus Nicole, Van Damme Philip, Witzel Simon, Ludolph Albert, Ho Ritchie, Boeckers Tobias M, Mulaw Medhanie
2023-Mar-08
ALS, deep learning, motor neurons, omics
