A personalized metabolic modelling approach through integrated analysis of RNA-Seq-based genomic variants and gene expression levels in Alzheimer's disease

Abstract

Generating condition-specific metabolic models by mapping gene expression data to genome-scale metabolic models (GEMs) is a routine approach to elucidate disease mechanisms from a metabolic perspective. On the other hand, integrating variants that perturb enzyme functionality from the same RNA-seq data may enhance GEM accuracy, offering insights into genome-wide metabolic pathology. Our study pioneers the extraction of both transcriptomic and genomic data from the same RNA-seq data to reconstruct personalized metabolic models. We map genes with significantly higher load of pathogenic variants in Alzheimer's disease (AD) onto a human GEM together with the gene expression data. Comparative analysis of the resulting personalized patient metabolic models with the control models shows enhanced accuracy in detecting AD-associated metabolic pathways compared to the case where only expression data is mapped on the GEM. Besides, several otherwise would-be missed pathways are annotated in AD by considering the effect of genomic variants.