Cofactors revisited - Predicting the impact of flavoprotein-related diseases on a genome scale

Flavin adenine dinucleotide (FAD) and its precursor flavin mononucleotide (FMN) are redox cofactors that are required for the activity of more than hundred human enzymes. Mutations in the genes encoding these proteins cause severe phenotypes, including a lack of energy supply and accumulation of toxic intermediates. Ideally, patients should be diagnosed before they show symptoms so that treatment and/or preventive care can start immediately. This can be achieved by standardized newborn screening tests. However, many of the flavin-related diseases lack appropriate biomarker profiles. Genome-scale metabolic models can aid in biomarker research by predicting altered profiles of potential biomarkers. Unfortunately, current models, including the most recent human metabolic reconstructions Recon and HMR, typically treat enzyme-bound flavins incorrectly as free metabolites. This in turn leads to artificial degrees of freedom in pathways that are strictly coupled. Here, we present a reconstruction of human metabolism with a curated and extended flavoproteome. To illustrate the functional consequences, we show that simulations with the curated model - unlike simulations with earlier Recon versions - correctly predict the metabolic impact of multiple-acyl-CoA-dehydrogenase deficiency as well as of systemic flavin-depletion. Moreover, simulations with the new model allowed us to identify a larger number of biomarkers in flavoproteome-related diseases, without loss of accuracy. We conclude that adequate inclusion of cofactors in constraint-based modelling contributes to higher precision in computational predictions.FWN – Publicaties zonder aanstelling Universiteit Leide

Reconstruction of glutathione metabolism in the neuronal model of rotenone-induced neurodegeneration using mass isotopologue analysis with hydrophilic interaction liquid chromatography-Zeno high-resolution multiple reaction monitoring

Accurate reconstruction of metabolic pathways is an important prerequisite for interpreting metabolomics changes and understanding the diverse biological processes in disease models. A tracer-based metabolomics strategy utilizes stable isotope-labeled precursors to resolve complex pathways by tracing the labeled atom(s) to downstream metabolites through enzymatic reactions. Isotope enrichment analysis is informative and achieved by counting total labeled atoms and acquiring the mass isotopologue distribution (MID) of the intact metabolite. However, quantitative analysis of labeled metabolite substructures/moieties (MS2 fragments) can offer more valuable insights into the reaction connections through measuring metabolite transformation. In order to acquire the isotopic labeling information at the intact metabolite and moiety level simultaneously, we developed a method that couples hydrophilic interaction liquid chromatography (HILIC) with Zeno trap-enabled high-resolution multiple reaction monitoring (MRMHR). The method enabled accurate and reproducible MID quantification for intact metabolites as well as their fragmented moieties, with notably high sensitivity in the MS2 fragmentation mode based on the measurement of 13C- or 15N-labeled cellular samples. The method was applied to human-induced pluripotent stem cell-derived neurons to trace the fate of 13C/15N atoms from D-13C6-glucose/L-15N2-glutamine added to the media. With the MID analysis of both intact metabolites and fragmented moieties, we validated the pathway reconstruction of de novo glutathione synthesis in mid-brain neurons. We discovered increased glutathione oxidization from both basal and newly synthesized glutathione pools under neuronal oxidative stress. Furthermore, the significantly decreased de novo glutathione synthesis was investigated and associated with altered activities of several key enzymes, as evidenced by suppressed glutamate supply via glucose metabolism and a diminished flux of glutathione synthetic reaction in the neuronal model of rotenone-induced neurodegeneration.</p

LC-MS/MS analysis of the central energy and carbon metabolites in biological samples following derivatization by dimethylaminophenacyl bromide

Recent advances in metabolomics have enabled larger proportions of the human metabolome to be analyzed quantitatively. However, this usually requires the use of several chromatographic methods coupled to mass spectrometry to cover the wide range of polarity, acidity/basicity and concentration of metabolites. Chemical derivatization allows in principle a wide coverage in a single method, as it affects both the separation and the detection of metabolites: it increases retention, stabilizes the analytes and improves the sensitivity of the analytes. The majority of quantitative derivatization techniques for LC-MS in metabolomics react with amines, phenols and thiols; however, there are unfortunately very few methods that can target carboxylic acids at the same time, which contribute to a large proportion of the human metabolome. Here, we describe a derivatization technique which simultaneously labels carboxylic acids, thiols and amines using the reagent dimethylaminophenacyl bromide (DmPABr). We further improve the quantitation by employing isotope-coded derivatization (lCD), which uses internal standards derivatized with an isotopically-labelled reagent (DmPABr-D-6). We demonstrate the ability to measure and quantify 64 central carbon and energy-related metabolites including amino acids, N-acetylated amino acids, metabolites from the TCA cycle and pyruvate metabolism, acylcarnitines and medium-/long-chain fatty acids. To demonstrate the applicability of the analytical approach, we analyzed urine and SUIT-2 cells utilizing a 15-minute single UPLC-MS/MS method in positive ionization mode. SUIT-2 cells exposed to rotenone showed definitive changes in 28 out of the 64 metabolites, including metabolites from all 7 classes mentioned. By realizing the full potential of DmPABr to derivatize and quantify amines and thiols in addition to carboxylic acids, we extended the coverage of the metabolome, producing a strong platform that can be further applied to a variety of biological studies. (C) 2019 The Authors. Published by Elsevier B.V.Analytical BioScience