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Basic Science and Pathogenesis.

Boyu Jiang1, Anke Marije Tukker1, Hyunjin Kim1

  • 1Purdue University, West Lafayette, IN, USA.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 24, 2025
PubMed
Summary
This summary is machine-generated.

Researchers developed iNeuron-GEM, a metabolic model for neurons, to study Alzheimer's Disease (AD). Excitatory neurons show significant metabolic changes in AD, offering potential therapeutic targets.

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Area of Science:

  • Neuroscience
  • Systems Biology
  • Metabolic Engineering

Background:

  • Metabolic dysfunctions are increasingly recognized in neurodegenerative diseases like Alzheimer's Disease (AD).
  • Neurons require substantial energy for neurotransmission and synaptic activity, relying on oxidative phosphorylation and glycolysis.
  • Specific mechanisms of metabolic alterations in distinct neuronal subtypes in AD remain poorly understood.

Purpose of the Study:

  • To create a comprehensive, subtype-specific metabolic network model for human neurons.
  • To investigate metabolic alterations in different neuronal subtypes in the context of Alzheimer's Disease.
  • To identify key metabolic pathways and potential therapeutic targets in AD.

Main Methods:

  • Generated iNeuron-GEM, a genome-scale metabolic network, using single-nucleus RNA-sequencing data from AD cohorts (ROSMAP, SEA-AD).
  • Validated and refined iNeuron-GEM using neuron-specific metabolic tasks and metabolomics data.
  • Performed in silico metabolic analysis and machine learning on AD patient data to identify subtype-specific metabolic changes.

Main Results:

  • Developed iNeuron-GEM, a metabolic reconstruction of neurons with 1339 metabolites and 3900 reactions.
  • Identified significantly greater metabolic alterations in excitatory neurons compared to inhibitory neurons in AD.
  • Discovered key changes in fatty acid synthesis, glycerophospholipid metabolism, and branched-chain amino acid catabolism in excitatory neurons during AD progression.

Conclusions:

  • Presented iNeuron-GEM, the first manually curated metabolic reconstruction for human neurons.
  • Demonstrated iNeuron-GEM's utility in integrating multi-omics data for predicting neuronal metabolic changes in AD.
  • Highlighted potential for identifying AD metabolic signatures and druggable targets through iNeuron-GEM analysis.