An Integrative Analysis of Metagenomic and Metabolomic Profiling Reveals Gut Microbiome Dysbiosis and Metabolic Alterations in ALS: Potential Biomarkers and Therapeutic Insights.

Abstract

ALS is a severe neurodegenerative disorder characterized by motor neuron degeneration, gut dysbiosis, immune dysregulation, and metabolic disturbances. In this study, shotgun metagenomics and ¹H nuclear magnetic resonance (NMR)-based metabolomics were employed to investigate the altered gut microbiome and metabolite profiles in individuals with ALS, household controls (HCs), and nonhousehold controls (NHCs). The principal component analysis (PCA) explained 33% of the variance, and the partial least-squares discriminant analysis (PLS-DA) model demonstrate R² and Q² values of 0.97 and 0.84, respectively, indicating an adequate model fit. The relative bacterial abundance was 99.34% in the ALS group and 98.94% in the HC group. Among the ten identified genera, Bifidobacterium, Lactobacillus, and Enterococcus were more prevalent in ALS individuals, while Lactiplantibacillus and Klebsiella were more abundant in the HC group. We identified 70 metabolites, including short-chain fatty acids (SCFAs), branched-chain amino acids (BCAAs), carbohydrates, and aromatic compounds, using NMR. Orthogonal partial least-squares discriminant analysis (O-PLS-DA) explained 15.8% of the variance, with a clear separation between the ALS and HC groups. Univariate receiver operating characteristic (ROC) analysis identified three fecal metabolites with AUC values above 0.70, including butyrate (0.798), propionate (0.727), and citrate (0.719). These metabolites may serve as potential biomarkers for ALS. The statistical model for metabolic pathway analysis revealed interconnected pathways, highlighting the complexity of metabolic dysregulation, as well as potential microbial and metabolic biomarkers in ALS. These results highlight the role of gut microbiome alterations in ALS and suggest potential avenues for therapeutic intervention.