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000258910 1001_ $$0P:(DE-2719)9001873$$aCatanese, Alberto$$b0$$eFirst author
000258910 245__ $$aMultiomics and machine-learning identify novel transcriptional and mutational signatures in amyotrophic lateral sclerosis.
000258910 260__ $$aOxford$$bOxford Univ. Press$$c2023
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000258910 520__ $$a Amyotrophic lateral sclerosis 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 amyotrophic lateral sclerosis are commonly shared by the different genes causally linked to this disorder. To address this point, we combined multiomics analysis covering the transcriptional, epigenetic and mutational aspects of heterogenous human induced pluripotent stem cell-derived C9orf72-, TARDBP-, SOD1- and FUS-mutant motor neurons as well as datasets from patients' biopsies. We identified a common signature, converging towards increased stress and synaptic abnormalities, which reflects a unifying transcriptional program in amyotrophic lateral sclerosis despite the specific profiles due to the underlying pathogenic gene. In addition, whole genome bisulphite 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 amyotrophic lateral sclerosis. 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 signalling. Notably, the overrepresentation of this biological term also correlated with the transcriptional signature identified in mutant human induced pluripotent stem cell-derived motor neurons, highlighting novel insights into amyotrophic lateral sclerosis marker genes in a tissue-independent manner. Finally, using whole genome sequencing in combination with deep learning, we generated the first mutational signature for amyotrophic lateral sclerosis and defined a specific genomic profile for this disease, which is significantly correlated to ageing signatures, hinting at age as a major player in amyotrophic lateral sclerosis. This work describes innovative methodological approaches for the identification of disease signatures through the combination of multiomics analysis and provides novel knowledge on the pathological convergencies defining amyotrophic lateral sclerosis.
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000258910 650_2 $$2MeSH$$aHumans
000258910 650_2 $$2MeSH$$aAmyotrophic Lateral Sclerosis: metabolism
000258910 650_2 $$2MeSH$$aMultiomics
000258910 650_2 $$2MeSH$$aNeurodegenerative Diseases: metabolism
000258910 650_2 $$2MeSH$$aC9orf72 Protein: genetics
000258910 650_2 $$2MeSH$$aSuperoxide Dismutase-1: genetics
000258910 650_2 $$2MeSH$$aInduced Pluripotent Stem Cells: metabolism
000258910 650_2 $$2MeSH$$aMotor Neurons: metabolism
000258910 650_7 $$2NLM Chemicals$$aC9orf72 Protein
000258910 650_7 $$2Other$$aALS
000258910 650_7 $$2Other$$adeep learning
000258910 650_7 $$2Other$$amotor neurons
000258910 650_7 $$2Other$$aomics
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000258910 7001_ $$0P:(DE-HGF)0$$aRajkumar, Sandeep$$b1
000258910 7001_ $$0P:(DE-HGF)0$$aSommer, Daniel$$b2
000258910 7001_ $$0P:(DE-HGF)0$$aMasrori, Pegah$$b3
000258910 7001_ $$0P:(DE-HGF)0$$aHersmus, Nicole$$b4
000258910 7001_ $$0P:(DE-HGF)0$$aVan Damme, Philip$$b5
000258910 7001_ $$0P:(DE-HGF)0$$aWitzel, Simon$$b6
000258910 7001_ $$0P:(DE-2719)2812633$$aLudolph, Albert$$b7
000258910 7001_ $$0P:(DE-HGF)0$$aHo, Ritchie$$b8
000258910 7001_ $$0P:(DE-2719)2812855$$aBoeckers, Tobias M$$b9
000258910 7001_ $$0P:(DE-HGF)0$$aMulaw, Medhanie$$b10
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