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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. 

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. 

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. 

Analysis of interdigitated transducers often relies on phenomenological models to approximate device electrical performance. While these approaches prove essential for signal processing applications, phenomenological models provide limited information on the device’s mechanical response and physical characteristics of the generated acoustic field. Finite element method modeling, in comparison, offers a robust platform to study the effects of the full device geometry on critical performance parameters of interdigitated transducer devices. In this study, we fabricate a surface acoustic wave resonator on semi-insulating GaAs [Formula: see text], which consists of an interdigitated transducer and acoustic mirror assembly. The device is subsequently modeled using fem software. A vector network analyzer is used to measure the experimental device scattering response, which compares well with the simulated results. The wave characteristics of the experimental device are measured by contact-mode atomic force microscopy, which validates the simulation’s mechanical response predictions. We further show that a computational parametric analysis can be used to optimize device designs for series resonance frequency, effective coupling coefficient, quality factor, and maximum acoustic surface displacement. 

Free energy functionals of the Ginzburg–Landau type lie at the heart of a broad class of continuum dynamical models, such as the Cahn–Hilliard and Swift–Hohenberg equations. Despite the wide use of such models, the assumptions embodied in the free energy functionals frequently either are poorly justified or lead to physically opaque parameters. Here, we introduce a mathematically rigorous pathway for constructing free energy functionals that generalizes beyond the constraints of Ginzburg–Landau gradient expansions. We show that the formalism unifies existing free energetic descriptions under a single umbrella by establishing the criteria under which the generalized free energy reduces to gradient-based representations. Consequently, we derive a precise physical interpretation of the gradient energy parameter in the Cahn–Hilliard model as the product of an interaction length scale and the free energy curvature. The practical impact of our approach is demonstrated using both a model free energy function and the silicon–germanium alloy system.