Jul 2026· Journal of environmental biology· Vol 47, pp. 921-927· 0 citations
TL;DR
These findings underscore the multifactorial nature of ALS across genetic, molecular and metabolic dimensions, identifying potential molecular targets to guide future therapeutic development.
Abstract
Aim: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterised by progressive motor neuron degeneration. Despite identification of multiple susceptibility loci, the genetic and molecular basis of ALS pathogenesis remains incompletely understood. The present study aimed to integrate functional and metabolic analyses with GWAS-derived variants to investigate key genes, pathways, and cellular mechanisms underlying ALS.
Methodology: Genetic variants associated with ALS were retrieved from published GWAS datasets and analysed through integrative bioinformatic approaches, including Gene Ontology (GO) enrichment and KEGG pathway mapping. Cell Marker enrichment assessed immune cell involvement, whilst metabolomic profiling examined lipid metabolism alterations. Unsupervised machine learning, encompassing clustering and principal component analysis (PCA), identified patterns across susceptibility loci on chromosomes 9, 12 and 19.
Results: Core susceptibility genes identified included C9orf72, UNC13A and ITPR2. Enrichment analyses revealed disruptions in synaptic vesicle docking, neurotransmitter release, calcium homeostasis, oxidative stress, and neuroinflammation. Metabolomic profiling implicated disturbed lipid metabolism, whilst chromosomal clustering highlighted a genetic basis for disease heterogeneity.
Interpretation: These findings underscore the multifactorial nature of ALS across genetic, molecular and metabolic dimensions, identifying potential molecular targets to guide future therapeutic development.
Key words: ALS, Calcium signalling, GWAS, Neurodegeneration, Synaptic dysfunction
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