Comparative interactomics analysis of different ALS-associated proteins identifies converging molecular pathways

Amyotrophic lateral sclerosis (ALS) is a devastating neurological disease with no effective treatment available. An increasing number of genetic causes of ALS are being identified, but how these genetic defects lead to motor neuron degeneration and to which extent they affect common cellular pathways remains incompletely understood. To address these questions, we performed an interactomic analysis to identify binding partners of wild-type (WT) and ALS-associated mutant versions of ATXN2, C9orf72, FUS, OPTN, TDP-43 and UBQLN2 in neuronal cells. This analysis identified several known but also many novel binding partners of these proteins

Using the structure of genome data in the design of deep neural networks for predicting amyotrophic lateral sclerosis from genotype

Motivation: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease caused by aberrations in the genome. While several disease-causing variants have been identified, a major part of heritability remains unexplained. ALS is believed to have a complex genetic basis where non-additive combinations of variants constitute disease, which cannot be picked up using the linear models employed in classical genotype-phenotype association studies. Deep learning on the other hand is highly promising for identifying such complex relations. We therefore developed a deep-learning based approach for the classification of ALS patients versus healthy individuals from the Dutch cohort of the Project MinE dataset. Based on recent insight that regulatory regions harbor the majority of disease-associated variants, we employ a two-step approach: first promoter regions that are likely associated to ALS are identified, and second individuals are classified based on their genotype in the selected genomic regions. Both steps employ a deep convolutional neural network. The network architecture accounts for the structure of genome data by applying convolution only to parts of the data where this makes sense from a genomics perspective. Results: Our approach identifies potentially ALS-associated promoter regions, and generally outperforms other classification methods. Test results support the hypothesis that non-additive combinations of variants contribute to ALS. Architectures and protocols developed are tailored toward processing population-scale, whole-genome data. We consider this a relevant first step toward deep learning assisted genotype-phenotype association in whole genome-sized data

Using the structure of genome data in the design of deep neural networks for predicting amyotrophic lateral sclerosis from genotype

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease caused by aberrations in the genome. While several disea

Facial onset sensory and motor neuronopathy: new cases, cognitive changes and pathophysiology

Purpose of review To improve our clinical understanding of facial onset sensory and motor neuronopathy (FOSMN). Recent findings We identified 29 new cases and 71 literature cases, resulting in a cohort of 100 patients with FOSMN. During follow-up, cognitive and behavioral changes became apparent in 8 patients, suggesting that changes within the spectrum of frontotemporal dementia (FTD) are a part of the natural history of FOSMN. Another new finding was chorea, seen in 6 cases. Despite reports of autoantibodies, there is no consistent evidence to suggest an autoimmune pathogenesis. Four of 6 autopsies had TAR DNA-binding protein (TDP) 43 pathology. Seven cases had genetic mutations associated with neurodegenerative diseases. Summary FOSMN is a rare disease with a highly characteristic onset and pattern of disease progression involving initial sensory disturbances, followed by bulbar weakness with a cranial to caudal spread of pathology. Although not conclusive, the balance of evidence suggests that FOSMN is most likely to be a TDP-43 proteinopathy within the amyotrophic lateral sclerosis–FTD spectrum

Advances in the genetic classification of amyotrophic lateral sclerosis

Purpose of review Amyotrophic lateral sclerosis (ALS) is an archetypal complex disease wherein disease risk and severity are, for the majority of patients, the product of interaction between multiple genetic and environmental factors. We are in a period of unprecedented discovery with new large-scale genome-wide association study (GWAS) and accelerating discovery of risk genes. However, much of the observed heritability of ALS is undiscovered and we are not yet approaching elucidation of the total genetic architecture, which will be necessary for comprehensive disease subclassification. Recent findings We summarize recent developments and discuss the future. New machine learning models will help to address nonlinear genetic interactions. Statistical power for genetic discovery may be boosted by reducing the search-space using cell-specific epigenetic profiles and expanding our scope to include genetically correlated phenotypes. Structural variation, somatic heterogeneity and consideration of environmental modifiers represent significant challenges which will require integration of multiple technologies and a multidisciplinary approach, including clinicians, geneticists and pathologists. Summary The move away from fully penetrant Mendelian risk genes necessitates new experimental designs and new standards for validation. The challenges are significant, but the potential reward for successful disease subclassification is large-scale and effective personalized medicine

Rare genetic variation in UNC13A may modify survival in amyotrophic lateral sclerosis

Our objective was to identify whether rare genetic variation in amyotrophic lateral sclerosis (ALS) candidate survival genes modifies ALS survival. Candidate genes were selected based on evidence for modifying ALS survival. Each tail of the extreme 1.5% of survival was selected from the UK MND DNA Bank and all samples available underwent whole genome sequencing. A replication set from the Netherlands was used for validation. Sequences of candidate survival genes were extracted and variants passing quality control with a minor allele frequency ≤0.05 were selected for association testing. Analysis was by burden testing using SKAT. Candidate survival genes UNC13A, KIFAP3, and EPHA4 were tested for association in a UK sample comprising 25 short survivors and 25 long survivors. Results showed that only SNVs in UNC13A were associated with survival (p = 6.57 × 10−3). SNV rs10419420:G > A was found exclusively in long survivors (3/25) and rs4808092:G > A exclusively in short survivors (4/25). These findings were not replicated in a Dutch sample. In conclusion, population specific rare variants of UNC13A may modulate survival in ALS

Analysis of shared common genetic risk between amyotrophic lateral sclerosis and epilepsy

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