Telomere length analysis in amyotrophic lateral sclerosis using large-scale whole genome sequence data

Recent advances in the genetics of familial and sporadic ALS

ALS shows complex genetic inheritance patterns. In about 5% to 10% of cases, there is a family history of ALS or a related condition such as frontotemporal dementia in a first or second degree relative, and for about 80% of such people a pathogenic gene variant can be identified. Such variants are also seen in people with no family history because of factor influencing the expression of genes, such as age. Genetic susceptibility factors also contribute to risk, and the heritability of ALS is between 40% and 60%. The genetic variants influencing ALS risk include single base changes, repeat expansions, copy number variants, and others. Here we review what is known of the genetic landscape and architecture of ALS.

Molecular hallmarks of ageing in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a fatal, severely debilitating and rapidly progressing disorder affecting motor neurons in the brain, brainstem, and spinal cord. Unfortunately, there are few effective treatments, thus there remains a critical need to find novel interventions that can mitigate against its effects. Whilst the aetiology of ALS remains unclear, ageing is the major risk factor. Ageing is a slowly progressive process marked by functional decline of an organism over its lifespan. However, it remains unclear how ageing promotes the risk of ALS. At the molecular and cellular level there are specific hallmarks characteristic of normal ageing. These hallmarks are highly inter-related and overlap significantly with each other. Moreover, whilst ageing is a normal process, there are striking similarities at the molecular level between these factors and neurodegeneration in ALS. Nine ageing hallmarks were originally proposed: genomic instability, loss of telomeres, senescence, epigenetic modifications, dysregulated nutrient sensing, loss of proteostasis, mitochondrial dysfunction, stem cell exhaustion, and altered inter-cellular communication. However, these were recently (2023) expanded to include dysregulation of autophagy, inflammation and dysbiosis. Hence, given the latest updates to these hallmarks, and their close association to disease processes in ALS, a new examination of their relationship to pathophysiology is warranted. In this review, we describe possible mechanisms by which normal ageing impacts on neurodegenerative mechanisms implicated in ALS, and new therapeutic interventions that may arise from this.

Unsupervised machine learning identifies distinct ALS molecular subtypes in post-mortem motor cortex and blood expression data

Amyotrophic lateral sclerosis (ALS) displays considerable clinical and genetic heterogeneity. Machine learning approaches have previously been utilised for patient stratification in ALS as they can disentangle complex disease landscapes. However, lack of independent validation in different populations and tissue samples have greatly limited their use in clinical and research settings. We overcame these issues by performing hierarchical clustering on the 5000 most variably expressed autosomal genes from motor cortex expression data of people with sporadic ALS from the KCL BrainBank (N = 112). Three molecular phenotypes linked to ALS pathogenesis were identified: synaptic and neuropeptide signalling, oxidative stress and apoptosis, and neuroinflammation. Cluster validation was achieved by applying linear discriminant analysis models to cases from TargetALS US motor cortex (N = 93), as well as Italian (N = 15) and Dutch (N = 397) blood expression datasets, for which there was a high assignment probability (80-90%) for each molecular subtype. The ALS and motor cortex specificity of the expression signatures were tested by mapping KCL BrainBank controls (N = 59), and occipital cortex (N = 45) and cerebellum (N = 123) samples from TargetALS to each cluster, before constructing case-control and motor cortex-region logistic regression classifiers. We found that the signatures were not only able to distinguish people with ALS from controls (AUC 0.88 ± 0.10), but also reflect the motor cortex-based disease process, as there was perfect discrimination between motor cortex and the other brain regions. Cell types known to be involved in the biological processes of each molecular phenotype were found in higher proportions, reinforcing their biological interpretation. Phenotype analysis revealed distinct cluster-related outcomes in both motor cortex datasets, relating to disease onset and progression-related measures. Our results support the hypothesis that different mechanisms underpin ALS pathogenesis in subgroups of patients and demonstrate potential for the development of personalised treatment approaches. Our method is available for the scientific and clinical community at https://alsgeclustering.er.kcl.ac.uk .