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Abstract Cholesterol is a vital component of the cell membrane and plays an essential role in mediating integral membrane protein function. Altered cholesterol regulation has been implicated in neurological diseases that are associated with blood–brain barrier breakdown. However, the role of brain barrier function in inherited disorders of cholesterol metabolism, such as Niemann-Pick disease C1 (NP-C1), remains unclear. In this study, we determined how cholesterol depletion with U18666A, a chemical inhibitor of NPC1 protein, as well as with the cholesterol-depleting agent methyl-β cyclodextrin (MβCD), impacted brain endothelial cell barrier function. We hypothesized that cholesterol depletion would decrease barrier integrity by disrupting tight junction protein continuity. To test this hypothesis, we differentiated human-induced pluripotent stem cells into brain microvascular endothelial cells (hiBMECs). We then assessed barrier integrity by quantifying trans-endothelial electrical resistance (TEER), small fluorescent molecule permeability, and tight junction continuity and protein levels. We now show that U18666A-treated hiBMECs demonstrated a 75% decrease in TEER and 9-fold increase in sodium fluorescein permeability. Similar trends were observed for hiBMECs treated with MβCD, which showed significantly lowered TEER (93% decrease) and increased sodium fluorescein permeability (20-fold higher). We also observed decreased continuity of the tight junction proteins occludin (13% lower) and claudin-5 (8% lower) as well as a 53% decrease in claudin-5 protein with U18666A treatment. Co-treating U18666A-treated hiBMECs with hydroxypropyl-β cyclodextrin (HPβCD), which releases lysosomal cholesterol, prevented these changes. Together, our results demonstrate that cholesterol is vital for hiBMEC barrier function and tight junction continuity. This study highlights the potential of therapeutics targeted to brain endothelium in NP-C1 and other cholesterol metabolism disorders. 

Abstract Glucose transport from the blood into the brain is tightly regulated by brain microvascular endothelial cells (BMEC), which also use glucose as their primary energy source. To study how BMEC glucose transport contributes to cerebral glucose hypometabolism in diseases such as Alzheimer’s disease, it is essential to understand how these cells metabolize glucose. Human primary BMEC (hpBMEC) can be used for BMEC metabolism studies; however, they have poor barrier function and may not recapitulate in vivo BMEC function. iPSC-derived BMEC-like cells (hiBMEC) are readily available and have good barrier function but may have an underlying epithelial signature. In this study, we examined differences between hpBMEC and hiBMEC glucose metabolism using a combination of dynamic metabolic measurements, metabolic mass spectrometry, RNA sequencing, and Western blots. hiBMEC had decreased glycolytic flux relative to hpBMEC, and the overall metabolomes and metabolic enzyme levels were different between the two cell types. However, hpBMEC and hiBMEC had similar glucose metabolism, including nearly identical glucose labeled fractions of glycolytic and TCA cycle metabolites. Treatment with astrocyte conditioned media and high glucose increased glycolysis in both hpBMEC and hiBMEC, though hpBMEC decreased glycolysis in response to fluvastatin while hiBMEC did not. Together, these results suggest that hiBMEC can be used to model cerebral vascular glucose metabolism, which expands their use beyond barrier models. 

Human primary (hpBMEC) and induced pluripotent stem cell (iPSC)-derived brain microvascular endothelial-like cells (hiBMEC) are interchangeably used in blood-brain barrier models to study neurological diseases and drug delivery. Both hpBMEC and hiBMEC use glutamine as a source of carbon and nitrogen to produce metabolites and build proteins essential to cell function and communication. We used metabolomic, transcriptomic, and computational methods to examine how hpBMEC and hiBMEC metabolize glutamine, which may impact their utility in modeling the blood-brain barrier. We found that glutamine metabolism was systemically different between the two cell types. hpBMEC had a higher metabolic rate and produced more glutamate and GABA, while hiBMEC rerouted glutamine to produce more glutathione, fatty acids, and asparagine. Higher glutathione production in hiBMEC correlated with higher oxidative stress compared to hpBMEC. α-ketoglutarate (α-KG) supplementation increased glutamate secretion from hiBMEC to match that of hpBMEC; however, α-KG also decreased hiBMEC glycolytic rate. These fundamental metabolic differences between BMEC types may impactin vitroblood-brain barrier model function, particularly communication between BMEC and surrounding cells, and emphasize the importance of evaluating the metabolic impacts of iPSC-derived cells in disease models. 

Cell metabolism represents the coordinated changes in genes, proteins, and metabolites that occur in health and disease. The metabolic fluxome, which includes both intracellular and extracellular metabolic reaction rates (fluxes), therefore provides a powerful, integrated description of cellular phenotype. However, intracellular fluxes cannot be directly measured. Instead, flux quantification requires sophisticated mathematical and computational analysis of data from isotope labeling experiments. In this review, we describe isotope-assisted metabolic flux analysis (iMFA), a rigorous computational approach to fluxome quantification that integrates metabolic network models and experimental data to generate quantitative metabolic flux maps. We highlight practical considerations for implementing iMFA in mammalian models, as well as iMFA applications in in vitro and in vivo studies of physiology and disease. Finally, we identify promising new frontiers in iMFA which may enable us to fully unlock the potential of iMFA in biomedical research.