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Mason, G. F.

Publications and source records attributed to Mason, G. F..

2 recordsLinked to original sources

Challenges and Solutions in Quantifying Brain β-Hydroxybutyrate (BHB) with 1H-MRS Following Oral Keto-Ester Consumption

Purpose{beta}-hydroxybutyrate (BHB), a ketone body and alternative cerebral energy substrate, can be measured in vivo using J-difference edited proton magnetic resonance spectroscopy (1H-MRS). Oral ketone supplementation with substrates such as the ketone monoester (R)-3-hydroxybutyl-(R)-3-hydroxybutyrate (KME) and 1,3-butanediol (BD) have gained attention as a mechanism to elevate circulating BHB and induce ketosis without dietary restrictions. Elevated brain ketone availability is of growing therapeutic interest as a strategy to support neuronal energetics in conditions such as epilepsy, neurodegenerative disease, and alcohol use disorder (AUD). However, both pathways introduce BD into the bloodstream, which crosses the blood-brain barrier. Critically, BD exhibits a spectral signature that closely resembles the prominent BHB peak in JDE-MR spectroscopic imaging (MRSI), identified in a pilot AUD study. MethodsTwo separate JDE-MRSI acquisitions tailored for BHB and BD editing were implemented, exploiting frequency separation between the BHB (4.14ppm) and BD (3.95ppm) coupling partners of the observed 1.2ppm resonance to independently quantify each metabolite. ResultsBrain BD concentrations (0.25-0.58mM) were comparable to or exceeded corresponding BHB concentrations (0.20-0.27mM) in all volunteers after consumption of a single dose of the KME, indicating that BD constitutes a major fraction of the signal conventionally attributed to BHB. Combined BHB+BD concentrations ([~]0.45-0.85mM) were consistent with brain BHB values reported in prior studies employing similar doses of the KME, indicating that those measurements likely reflect a combined BHB+BD signal. ConclusionsSeparate quantification of the two metabolites is important for interpreting brain ketone studies and for understanding the full pharmacology of KME supplementation.

neuroscience↗

Optimization of Experiment Design for Mass Spectrometric Isotopic Labeling Kinetics

Determination of metabolic fluxes by measurement of time-dependent sampling of isotopic enrichments during the administration of labeled substrates provides rich information. Because such experiments are resource-intensive and frequently push the limits of sensitivity of the measurement techniques, optimization of experiment design can improve feasibility with respect to financial and labor costs, time to completion, and increase precision and accuracy of the results. Here we used a previously published set of data acquired in cultured insulinoma cells to evaluate contributions to the sensitivity and variability of the rate of citrate synthase (CS). Specifically, we calculated changes in uncertainty in CS if sample times were dropped or new ones were added, and we observed that some sampling times can be dropped with little effect, while improvements can be made with a strategic choice of when to add samples. We measured the contributions of data sampled at different times on the sensitivity of CS, finding that CS had greater sensitivity at early time points. We tested the concept that if two estimated parameters are correlated significantly, then refining one might constrain the other. In this case, the rate of Beta-oxs was found to be correlated with CS, and narrower variability in Beta-ox did indeed improve the sensitivity of CS. The tests described here might be applied at the initial design stage and then after a pilot phase to improve sensitivities of targeted fluxes and the reduction of materials, time, labor, and other experimental resources. The correlation analyses can be used to consider what orthogonal measurements might be beneficial for further improvement of measurements. While this study used a specific example of a set of time-dependent kinetic isotopic measurements, the results illustrate some generalizable behaviors that can be tested in other experimental systems.

bioinformatics↗