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@ARTICLE{Catanese:258910,
      author       = {Catanese, Alberto and Rajkumar, Sandeep and Sommer, Daniel
                      and Masrori, Pegah and Hersmus, Nicole and Van Damme, Philip
                      and Witzel, Simon and Ludolph, Albert and Ho, Ritchie and
                      Boeckers, Tobias M and Mulaw, Medhanie},
      title        = {{M}ultiomics and machine-learning identify novel
                      transcriptional and mutational signatures in amyotrophic
                      lateral sclerosis.},
      journal      = {Brain},
      volume       = {146},
      number       = {9},
      issn         = {0006-8950},
      address      = {Oxford},
      publisher    = {Oxford Univ. Press},
      reportid     = {DZNE-2023-00683},
      pages        = {3770 - 3782},
      year         = {2023},
      abstract     = {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.},
      keywords     = {Humans / Amyotrophic Lateral Sclerosis: metabolism /
                      Multiomics / Neurodegenerative Diseases: metabolism /
                      C9orf72 Protein: genetics / Superoxide Dismutase-1: genetics
                      / Induced Pluripotent Stem Cells: metabolism / Motor
                      Neurons: metabolism / C9orf72 Protein (NLM Chemicals) / ALS
                      (Other) / deep learning (Other) / motor neurons (Other) /
                      omics (Other) / Superoxide Dismutase-1 (NLM Chemicals)},
      cin          = {AG Böckers / Clinical Study Center Ulm},
      ddc          = {610},
      cid          = {I:(DE-2719)1910002 / I:(DE-2719)5000077},
      pnm          = {352 - Disease Mechanisms (POF4-352) / 353 - Clinical and
                      Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-352 / G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)16},
      pmc          = {pmc:PMC10473564},
      pubmed       = {pmid:36883643},
      doi          = {10.1093/brain/awad075},
      url          = {https://pub.dzne.de/record/258910},
}