Project description: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 stratified the transcriptomes of 148 ALS postmortem cortex samples into three distinct molecular subtypes. The largest cluster, identified in 61% of patient samples, displayed hallmarks of oxidative and proteotoxic stress. Another 19% of the samples showed predominant signatures of glial activation. Finally, a third group (20%) exhibited high levels of retrotransposon expression and signatures of TARDBP/TDP-43 dysfunction. We further demonstrated 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.
Project description: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.
Project description: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.
Project description: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.
Project description:Amyotrophic Lateral Sclerosis (ALS) is a rare neurodegenerative disease characterized by motor neuron dysfunction and loss, leading to progressive paralysis and death. A portion of ALS cases is caused by mutation of the proteasome shuttle factor Ubiquilin 2 (UBQLN2), but the molecular pathway leading from UBQLN2 dysfunction to neurodegenerative disease remains unclear. Here, we demonstrate a major function of UBQLN2 in regulating activity of the domesticated gag-pol retrotransposon ‘paternally expressed gene 10’ (PEG10) in human cells and tissues. UBQLN2 exclusively facilitates degradation of the frameshifted gag-pol form of PEG10 through recognition of a unique polyproline repeat. In cells, the PEG10 gag-pol protein cleaves itself in a mechanism reminiscent of retrotransposon self-processing to generate a liberated ‘nucleocapsid’ fragment, which uniquely localizes to the nucleus. Overexpression of the nucleocapsid fragment upregulates transcription of neuronal genes involved in axon remodeling, which were also affected in sporadic ALS (sALS) patient tissues. Finally, proteomics of spinal cords from ALS patients revealed that PEG10 gag-pol is significantly elevated in disease compared to healthy controls. These findings implicate the retrotransposon-like activity of PEG10 as a contributing mechanism in ALS through regulation of neuronal gene expression, and restraint of PEG10 as a primary function of UBQLN2.
Project description:Amyotrophic Lateral Sclerosis (ALS) is a complex syndrome with multiple genetic causes and wide variation in disease presentation. Despite this general heterogeneity, several common factors have been identified. For example, nearly all patients show pathological accumulations of phosphorylated TDP-43 protein in affected regions of the motor cortex and spinal cord. Moreover, large patient cohort studies have revealed that most patient samples can be grouped into a small number of ALS subtypes, as defined by their transcriptomic profiles. These ALS molecular subtypes can be grouped by whether postmortem motor cortex samples display signatures of: mitochondrial dysfunction and oxidative stress (ALS-Ox), microglial activation and neuroinflammation (ALS-Glia), or dense TDP-43 pathology and associated transposable element de-silencing (ALS-TE). In this study, we have built a deep layer ALS neural network classifier (DANcer) that has learned to accurately assign patient samples to these ALS subtypes, and which can be run on either bulk or single-cell datasets. Upon applying this classifier to an expanded ALS patient cohort from the NYGC ALS Consortium, we show that ALS Molecular Subtypes are robust across clinical centers, with no new subtypes appearing in a cohort that has quadrupled in size. Signatures from two of these molecular subtypes strongly correlate with disease duration: ALS-TE signatures in cortex and ALS-Glia signatures in spinal cord, revealing molecular correlates of clinical features. Finally, we use single nucleus RNA sequencing to reveal the cell type-specific contributions to ALS subtype, as determined by our single-cell classifier (scDANCer). Single-cell transcriptomes reveal that ALS molecular subtypes are recapitulated in neurons and glia, with both ALS-wide shared alterations in each cell type as well as ALS subtype- specific alterations. In summary, ALS molecular subtypes: (1) are robust across large cohorts of sporadic and familial ALS patient samples, (2) represent a combination of cellular, genetic, and pathological features, and (3) correlate with clinical features of ALS.