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

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.