Exploring Human Brain Metabolism via Genome-Scale Metabolic Modeling with Highlights on Multiple Sclerosis

Cerebral dysfunctions give rise to a wide range of neurological
diseases due to the structural and functional complexity of the human
brain stemming from the interactive cellular metabolism of its specific
cells, including neurons and glial cells. In parallel with advances
in isolation and measurement technologies, genome-scale metabolic
models (GEMs) have become a powerful tool in the studies of systems
biology to provide critical insights into the understanding of sophisticated
eukaryotic systems. In this study, brain cell-specific GEMs were reconstructed
for neurons, astrocytes, microglia, oligodendrocytes, and oligodendrocyte
precursor cells by integrating single-cell RNA-seq data and global
Human1 via a task-driven integrative network inference for tissues
(tINIT) algorithm. Then, intercellular reactions among neurons, astrocytes,
microglia, and oligodendrocytes were added to generate a combined
brain model, iHumanBrain2690. This brain network was used in the prediction
of metabolic alterations in glucose, ketone bodies, oxygen change,
and reporter metabolites. Glucose supplementation increased the subsystems’
activities in glycolysis, and ketone bodies elevated those in the
TCA cycle and oxidative phosphorylation. Reporter metabolite analysis
identified L-carnitine and arachidonate as the top reporter metabolites
in gray and white matter microglia in multiple sclerosis (MS), respectively.
Carbamoyl-phosphate was found to be the top reporter metabolite in
primary progressive MS. Taken together, single and integrated iHumanBrain2690
metabolic networks help us elucidate complex metabolism in brain physiology
and homeostasis in health and disease.