Vitamin A Absorption Efficiency Determined by Compartmental Analysis of Postprandial Plasma Retinyl Ester Kinetics in Theoretical Humans

ABSTRACT Background Better methods are needed for determining vitamin A absorption efficiency in humans to support development of dietary recommendations and to improve the accuracy of predictions of vitamin A status. Objectives We developed and evaluated a method for estimating vitamin A absorption efficiency based on compartmental modeling of theoretical data on postprandial plasma retinyl ester (RE) kinetics. Methods We generated data on plasma RE and retinol kinetics (30 min to 8 h or 56 d, respectively) after oral administration of labeled vitamin A for 12 theoretical adults with a range of values assigned for vitamin A absorption (55–90%); we modeled all data to obtain best-fit values for absorption and other parameters using Simulation, Analysis, and Modeling software. We then modeled RE data only (16 or 10 samples), with or without added random error, and compared assigned to predicted absorption values. We also compared assigned values to areas under RE response curves (RE AUCs). Results We confirmed that a unique value for vitamin A absorption cannot be identified by modeling plasma retinol tracer kinetics. However, when RE data were modeled, predicted vitamin A absorptions were within 1% of assigned values using data without error and within 12% when 5% error was included. When the sample number was reduced, predictions were still within 13% for 10 of the 12 subjects and within 23% overall. Assigned values for absorption were not correlated with RE AUC (P = 0.21). Conclusions We describe a feasible and accurate method for determining vitamin A absorption efficiency that is based on compartmental modeling of plasma RE kinetic data collected for 8 h after a test meal. This approach can be used in a clinical setting after fasting subjects consume a fat-containing breakfast meal with a known amount of vitamin A or a stable isotope label.

Linking a rapid throughput plate-assay with high-sensitivity stable-isotope label LCMS quantification permits the identification and characterisation of low β-L-ODAP grass pea lines

Background Grass pea (Lathyrus sativus) is an underutilised crop with high tolerance to drought and flooding stress and potential for maintaining food and nutritional security in the face of climate change. The presence of the neurotoxin β-L-oxalyl-2,3-diaminopropionic acid (β-L-ODAP) in tissues of the plant has limited its adoption as a staple crop. To assist in the detection of material with very low neurotoxin toxin levels, we have developed two novel methods to assay ODAP. The first, a version of a widely used spectrophotometric assay, modified for increased throughput, permits rapid screening of large populations of germplasm for low toxin lines and the second is a novel, mass spectrometric procedure to detect very small quantities of ODAP for research purposes and characterisation of new varieties. Results A plate assay, based on an established spectrophotometric method enabling high-throughput ODAP measurements, is described. In addition, we describe a novel liquid chromatography mass spectrometry (LCMS)-based method for β-L-ODAP-quantification. This method utilises an internal standard (di-13C-labelled β-L-ODAP) allowing accurate quantification of β-L-ODAP in grass pea tissue samples. The synthesis of this standard is also described. The two methods are compared; the spectrophotometric assay lacked sensitivity and detected ODAP-like absorbance in chickpea and pea whereas the LCMS method did not detect any β-L-ODAP in these species. The LCMS method was also used to quantify β-L-ODAP accurately in different tissues of grass pea. Conclusions The plate-based spectrophotometric assay allows quantification of total ODAP in large numbers of samples, but its low sensitivity and inability to differentiate α- and β-L-ODAP limit its usefulness for accurate quantification in low-ODAP samples. Coupled to the use of a stable isotope internal standard with LCMS that allows accurate quantification of β-L-ODAP in grass pea samples with high sensitivity, these methods permit the identification and characterisation of grass pea lines with a very low ODAP content. The LCMS method is offered as a new ‘gold standard’ for β-L-ODAP quantification, especially for the validation of existing and novel low- and/or zero-β-L-ODAP genotypes.

Analytical Considerations of Stable Isotope Labelling in Lipidomics

Over the last two decades, lipids have come to be understood as far more than merely components of cellular membranes and forms of energy storage, and are now also being implicated to play important roles in a variety of diseases, with lipid biomarker research one of the most widespread applications of lipidomic techniques both in research and in clinical settings. Stable isotope labelling has become a staple technique in the analysis of small molecule metabolism and dynamics, as it is the only experimental setup by which biosynthesis, remodelling and degradation of biomolecules can be directly measured. Using state-of-the-art analytical technologies such as chromatography-coupled high resolution tandem mass spectrometry, the stable isotope label can be precisely localized and quantified within the biomolecules. The application of stable isotope labelling to lipidomics is however complicated by the diversity of lipids and the complexity of the necessary data analysis. This article discusses key experimental aspects of stable isotope labelling in the field of mass spectrometry-based lipidomics, summarizes current applications and provides an outlook on future developments and potential.

Relative and Absolute Quantitation in Mass Spectrometry–Based Proteomics

Mass spectrometry–based quantitative proteomics is a powerful tool for gaining insights into function and dynamics of biological systems. However, peptides with different sequences have different ionization efficiencies, and their intensities in a mass spectrum are not correlated with their abundances. Therefore, various label-free or stable isotope label–based quantitation methods have emerged to assist mass spectrometry to perform comparative proteomic experiments, thus enabling nonbiased identification of thousands of proteins differentially expressed in healthy versus diseased cells. Here, we discuss the most widely used label-free and metabolic-, enzymatic-, and chemical labeling–based proteomic strategies for relative and absolute quantitation. We summarize the specific strengths and weaknesses of each technique in terms of quantification accuracy, proteome coverage, multiplexing capability, and robustness. Applications of each strategy for solving specific biological complexities are also presented.