BioModelsBoyu JiangNon-curatedconstraint-based modelL3V1https://www.ebi.ac.uk/biomodels/MODEL2606170001falseBioModelsSBMLModelsiNeuron GEMMODEL2606170001Boyu Jiang, Shuang Wang, Junkai Xie, Hyunjin Kim, Anke Tukker, Juexin Wang, Aaron Bowman, Chongli Yuan, Priyanka BaloniBoyu Jiang10.1101/2025.11.03.686281, Understanding how distinct neuronal subtypes contribute to Alzheimer’s disease (AD) pathology remains a major challenge. Patient-derived induced pluripotent stem cell (iPSC) studies have shown neuronal subtype-specific molecular and pathological signatures, yet the underlying metabolic shifts driving this selective vulnerability are not completely understood. Here we present iNeuron-GEM, the first manually curated, genome-scale metabolic network of human neurons that integrates transcriptomic and metabolic knowledge to resolve subtype-specific metabolic states. By coupling iNeuron-GEM with single nucleus RNA sequencing data from post-mortem human cohort studies, ROSMAP and SEA-AD, we capture neuronal subtype-specific metabolic features and fluxes and identify perturbations in lipid and energy metabolism across excitatory and inhibitory neurons. Integrative analysis with NPS-AD data shows overlapping metabolic disruptions in AD and schizophrenia (SCZ), suggesting shared molecular vulnerabilities between neurodegenerative and neuropsychiatric disorders. We also developed a computational pipeline to infer transcriptional regulation of metabolic pathways and identify NR6A1 and NR3C1 as important regulators of lipid dysregulation in AD neurons. Our study establishes iNeuron-GEM as a framework to identify neuronal subtype-specific metabolic vulnerabilities in complex brain disorders.. null, null. 1 School of Health Sciences, Purdue University 2 PULSe, Purdue University 3 Biomedical Engineering and Informatics, Indiana University 4 Chemical Engineering, Purdue University 5 Purdue Institute for Integrative Neuroscience, Purdue Universityalvis_jiang@hotmail.com10.1101/2025.11.03.686281Purdue UniversityfalseiNeuron-GEMThis model was reconstructed from single-nucleus RNA-seq (snRNA-seq) data of human postmortem brain and curated using published metabolomics data from human iPSC-derived neurons and cerebrospinal fluid (CSF), together with gene expression data from the Human Protein Atlas. It more accurately simulates human neuronal metabolic flux in neurodegenerative conditions such as Alzheimer's disease 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