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
U. Adiga, P. Supriya, S. Adiga et al.· Journal of environmental bio...· 0 citations
Aim: Metabolic syndrome (MetS) is a clustering of risk factors that increases susceptibility to type 2 diabetes and cardiovascular disease. This study aimed to perform a comprehensive bioinformatic analysis of genomic data to elucidate molecular pathways underlying MetS.
Methodology: GWAS data from previous MetS studies were analyzed using TargetScan, miRTarBase, Reactome Pathways, KEGG, protein–protein interaction (PPI) networks, and Gene Ontology (GO) mapping. Integration of these datasets identified key miRNAs, metabolic pathways, biological processes, and molecular activities associated with MetS.
Results: hsa-miR-126 was markedly enriched and strongly correlated with MetS. Pathway analysis highlighted cholesterol metabolism (p <0.05) and plasma lipoprotein remodeling (p <0.05) as significant contributors. GO analysis revealed triglyceride homeostasis (p <0.05) and very-low-density lipoprotein particle remodeling (p <0.05) as a key biological processes. Metabolomic analysis established strong links between triacylglycerol and glycerol metabolism. Lipid transport and metabolism emerged as central to MetS pathogenesis, with notable enrichments for high-density lipoprotein particles (p <0.05) and phosphatidylcholine-sterol O-acyltransferase activator activity (p <0.05).
Interpretation: This comprehensive analysis indicates that dysregulation of lipid metabolism is a major pathway in MetS, with specific miRNAs functioning as critical regulatory molecules. These insights suggest potential therapeutic strategies targeting miRNA-mediated regulation of lipid metabolism.
Key words: Cholesterol homeostasis, Lipid metabolism, Lipoprotein remodeling, Metabolic syndrome, miRNA regulation
C. Deepthi, E. V. Ravikanth, P. Reddemma et al.· Journal of environmental bio...· 0 citations
The identified genes, microRNAs, and pathways advance mechanistic understanding of disease vulnerability and may ultimately inform the development of biological markers and targeted therapeutic strategies for prostate cancer.
J. Brahmaiah, T. Govardhan, J. Kavya et al.· Journal of environmental bio...· 0 citations