SUMMARY Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by the progressive loss of motor neurons. While several pathogenic mutations have been identified, the vast majority of ALS cases have no family history of disease. Thus, for most ALS cases, the disease may be a product of multiple pathways contributing to varying degrees in each patient. Using machine learning algorithms, we stratify the transcriptomes of 148 ALS postmortem cortex samples into three distinct molecular subtypes. The largest cluster, identified in 61% of patient samples, displays hallmarks of oxidative and proteotoxic stress. Another 19% of the samples shows predominant signatures of glial activation. Finally, a third group (20%) exhibits high levels of retrotransposon expression and signatures of TARDBP/TDP-43 dysfunction. We further demonstrate that TDP-43 (1) directly binds a subset of retrotransposon transcripts and contributes to their silencing in vitro, and (2) pathological TDP-43 aggregation correlates with retrotransposon de-silencing in vivo.
Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disease characterized by the progressive loss of motor neurons. While several inherited pathogenic mutations have been identified as causative, the vast majority of cases are sporadic with no family history of disease. Thus, for the majority of ALS cases, a specific causal abnormality is not known and the disease may be a product of multiple inter-related pathways contributing to varying degrees in different ALS patients. Using unsupervised machine learning algorithms, we stratified the transcriptomes of 148 ALS decedent cortex tissue samples into three distinct and robust molecular subtypes. The largest cluster, identified in 61% of patient samples, displayed hallmarks of oxidative and proteotoxic stress. Another 20% of the ALS patient samples exhibited high levels of retrotransposon expression and other signatures of TDP-43 dysfunction. Finally, a third group showed predominant signatures of glial activation (19%). Together these results demonstrate that at least three distinct molecular signatures contribute to ALS disease. While multiple dysregulated components and pathways comprising these clusters have previously been implicated in ALS pathogenesis, unbiased analysis of this large survey demonstrated that sporadic ALS patient tissues can be segregated into distinct molecular subsets.
AUTHOR CONTRIBUTIONS O.H.T., M.G.H., and J.D. designed the study. N.V.R. designed and performed the experiments identifying TDP-43 targets in SH-SY5Y cells. R.S. designed and performed the experiments on the UCSD ALS patient samples. J.R. provided the UCSD ALS patient samples and associated clinical and diagnostic data. In the NYGC ALS Consortium, members contributed ALS patient samples and clinical information. D.K. curated de-identified clinical data and C9orf72 genotype information. I.H. and N.P. coordinated study materials and processed samples for sequencing. S.F. oversees Consortium resources and data distribution. D.F. and H.P. designed the methodology, reviewed sample preparation and data quality, and coordinated the research activity of NYGC ALS Consortium postmortem core RNA-seq experiments. B.T.H. supervised the neuropathological analysis of the immunohistochemical staining results. L.W.O. coordinated the post-mortem tissue, slide, and data collection through the Target ALS Multicenter Post-Mortem Tissue Core and assisted in analysis of the immunohistochemical staining results. O.H.T. and M.G.H. analyzed the data. All authors contributed to the interpretation, writing, and editing of the manuscript.
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