Integrative genome-scale metabolic model of GABAergic neurons reveals metabolic signatures across the mild cognitive impairment – Alzheimer disease continuum

Abstract

Introduction Mild cognitive impairment (MCI) represents a prodromal stage of Alzheimer’s disease (AD), but the metabolic mechanisms underlying early neuronal dysfunction remain incompletely understood. GABAergic neurons, which maintain excitatory–inhibitory balance and network stability, exhibit early vulnerability during neurodegeneration, although the metabolic alterations associated with their dysfunction remain poorly characterized. Methods We developed a context-specific genome-scale metabolic model (GEM) of human GABAergic neurons across the MCI–AD continuum using deconvolved hippocampal transcriptomic data. By integrating transcriptomic deconvolution with constraint-based modeling, including flux balance analysis (FBA) and flux variability analysis (FVA), we inferred disease-stage-associated metabolic alterations under Control, early MCI (E-MCI), advanced MCI (A-MCI), and AD conditions. Results Our analyses suggest progressive remodeling of energy metabolism, the glutamate–glutamine–GABA cycle, redox homeostasis, lipid metabolism, and neuron–astrocyte metabolic interactions. FVA identified reaction-specific changes in feasible flux ranges, indicating remodeling of the feasible metabolic solution space rather than a uniform contraction across pathways. These predicted metabolic alterations were accompanied by transcriptional changes in GABAergic markers and showed qualitative agreement with independent metabolomic observations, supporting their biological plausibility. Discussion Overall, this work provides a systems-level computational framework linking transcriptomic alterations with predicted metabolic remodeling in GABAergic neurons and generates experimentally testable hypotheses regarding metabolic dysfunction during progression from MCI to AD.