Large-scale in silico modeling of metabolic interactions between cell types in the human brain

Abstract

A workflow is presented that integrates gene expression data, proteomic data, and literature-based manual curation to construct multicellular, tissue-specific models of human brain energy metabolism that recapitulate metabolic interactions between astrocytes and various neuron types. Three analyses are applied for gene identification, analysis of omics data, and analysis of physiological states. First, we identify glutamate decarboxylase as a target that may contribute to cell-type and regional specificity in Alzheimer’s disease. Second, the decreased metabolic rate seen in affected brain regions in Alzheimer’s disease is consistent with a suppression of central metabolic gene expression in histopathologically normal neurons. Third, we identify pathways in cholinergic neurons that couple mitochondrial metabolism and cytosolic acetylcholine production, and subsequently find that cholinergic neurotransmission accounts for ∼3% of brain neurotransmission. Constraint-based modeling can thus contribute to the study and analysis of multicellular metabolic processes in human tissues, and provide detailed mechanistic insight into high-throughput data analysis.

Systems biology of stored blood cells: Can it help to extend the expiration date?

“…In fact, metabolomics, proteomics, and transcriptomics data obtained from a particular tissue or cell line in defined conditions can be used to generate a model which is not only specific for the tissue but for the conditions investigated as well. The procedure to obtain such a reconstruction is well defined and partially automated [16], leading to an exponential increase in the number of reconstructions and in silico modeling strategies [17,18].…”

Understanding the physiology of the ageing individual: computational modelling of changes in metabolism and endurance

“…An example of the prediction of fluxes starts from the measured exchange of oxygen, glucose, lactate, pyruvate, ketone bodies and amino acids in the brain of young and elderly people [69]. These measured exchange rates form the input for a prediction of the flux distribution in a model of brain metabolism [67,70]. Because of the many degrees of freedom involved in large biochemical systems, the distribution of metabolic fluxes inside the network can usually not be uniquely determined and a range of reaction velocities are still possible.…”

“…The distribution of fluxes has been predicted for large metabolic networks, for instance for brain and cancer cells [70,72,73] or for cells in the gut [74]. Changes in brain metabolism during Alzheimer's disease have initially been predicted based on the reduction of the maximal flux measured for one enzyme (2-oxoglutarate dehydrogenase) that catalyses a reaction midway in the Krebs or tricarboxylic acid cycle) [70].…”

Generation of hydrogen peroxide-resistant murine neuroblastoma cells: a target discovery platform for novel neuroprotective genes

“…AD is a human disease that mostly affects glutamatergic and cholinergic neurons and therefore it would be more than justifiable to use human primary cells (Greenamyre et al, 1987;Lewis et al, 2010;Pearson et al, 1983). Those cells would need to be made immortal by stable transfection of telomerase-based or oncogene-containing vectors (Davies et al, 2003).…”