The electronic structure of TEMPO, its cation and anion

The electronic structure of TEMPO (2,2,6,6-Tetramethylpiperidine-N-oxyl) and its cation and anion were studied experimentally using the electron spectroscopy techniques, dissociative electron attachment (DEA) spectroscopy, electron energy-loss spectroscopy, measurement of elastic and vibrational excitation (VE) cross sections and HeI photoelectron spectroscopy. The experiments were supplemented by quantum-chemical calculations of excitation energies, ionisation potential and the Franck–Condon profile of the first photoelectron band. Electron energy-loss spectra were recorded up to 12 eV and revealed a number of bands that were assigned to two valence and a number of Rydberg transitions. VE cross sections reveal a broad band in the 3–12 eV range, assigned to σ* shape resonances and signals in the 0–1 eV range, assigned to a shape resonance corresponding to a temporary capture of the incident electron in the (already singly occupied) π* orbital. Narrow threshold peaks in the VE cross sections are assigned to dipole-bound resonances. The major DEA fragment was found to be O−, with bands at 5.0 and 6.87 eV, assigned to core excited resonances

A newborn screening approach to diagnose 3-hydroxy-3-methylglutaryl-CoA lyase deficiency

Contains fulltext : 225031.pdf (publisher's version ) (Open Access)3-Hydroxy-3-methylglutaryl-coenzyme A lyase deficiency (HMGCLD) is a rare autosomal recessively inherited metabolic disorder. Patients suffer from avoidable neurologically devastating metabolic decompensations and thus would benefit from newborn screening (NBS). The diagnosis is currently made by measuring dry blood spot acylcarnitines (C5OH and C6DC) followed by urinary organic acid profiling for the differential diagnosis from several other disorders. Using untargeted metabolomics (reversed-phase UHPLC coupled to an Orbitrap Elite hybrid mass spectrometer) of plasma samples from 5 HMGCLD patients and 19 age-matched controls, we found 3-methylglutaconic acid and 3-hydroxy-3-methylglutaric acid, together with 3-hydroxyisovalerylcarnitine as the most discriminating metabolites between the groups. In order to evaluate the NBS potential of these metabolites we quantified the most discriminating metabolites from untargeted metabolomics in 23 blood spots from 4 HMGCLD patients and 55 controls by UHPLC tandem mass spectrometry. The results provide a tool for expanded NBS of HMGCLD using tandem mass spectrometry. Selected reaction monitoring transition 262/85 could be used in a first-tier NBS analysis to screen for elevated 3-hydroxyisovalerylcarnitine. In a positive case, a second-tier analysis of 3-hydroxy-3-methylglutaric acid and 3-methylglutaconic acid in a dry blood spot using UHPLC tandem mass spectrometry instruments confirms the diagnosis. In conclusion, we describe the identification of new diagnostic biomarkers for HMGCLD and their application in NBS in dry blood spots. By using second-tier testing, all patients with HMGCLD were unequivocally and correctly diagnosed

Metabolic control of arginine and ornithine levels paces the progression of leaf senescence

Leaf senescence can be induced by stress or aging, sometimes in a synergistic manner. It is generally acknowledged that the ability to withstand senescence-inducing conditions can provide plants with stress resilience. Although the signaling and transcriptional networks responsible for a delayed senescence phenotype, often referred to as a functional stay-green trait, have been actively investigated, very little is known about the subsequent metabolic adjustments conferring this aptitude to survival. First, using the individually darkened leaf (IDL) experimental setup, we compared IDLs of wild-type (WT) Arabidopsis (Arabidopsis thaliana) to several stay-green contexts, that is IDLs of two functional stay-green mutant lines, oresara1-2 (ore1-2) and an allele of phytochrome-interacting factor 5 (pif5), as well as to leaves from a WT plant entirely darkened (DP). We provide compelling evidence that arginine and ornithine, which accumulate in all stay-green contexts—likely due to the lack of induction of amino acids (AAs) transport—can delay the progression of senescence by fueling the Krebs cycle or the production of polyamines (PAs). Secondly, we show that the conversion of putrescine to spermidine (SPD) is controlled in an age-dependent manner. Thirdly, we demonstrate that SPD represses senescence via interference with ethylene signaling by stabilizing the ETHYLENE BINDING FACTOR1 and 2 (EBF1/2) complex. Taken together, our results identify arginine and ornithine as central metabolites influencing the stress- and age-dependent progression of leaf senescence. We propose that the regulatory loop between the pace of the AA export and the progression of leaf senescence provides the plant with a mechanism to fine-tune the induction of cell death in leaves, which, if triggered unnecessarily, can impede nutrient remobilization and thus plant growth and survival

Normalization techniques for PARAFAC modeling of urine metabolomic data

Introduction One of the body fluids often used in metabolomics studies is urine. The concentrations of metabolites in urine are affected by hydration status of an individual, resulting in dilution differences. This requires therefore normalization of the data to correct for such differences. Two normalization techniques are commonly applied to urine samples prior to their further statistical analysis. First, AUC normalization aims to normalize a group of signals with peaks by standardizing the area under the curve (AUC) within a sample to the median, mean or any other proper representation of the amount of dilution. The second approach uses specific end-product metabolites such as creatinine and all intensities within a sample are expressed relative to the creatinine intensity. Objectives Another way of looking at urine metabolomics data is by realizing that the ratios between peak intensities are the information-carrying features. This opens up possibilities to use another class of data analysis techniques designed to deal with such ratios: compositional data analysis. The aim of this paper is to develop PARAFAC modeling of three-way urine metabolomics data in the context of compositional data analysis and compare this with standard normalization techniques. Methods In the compositional data analysis approach, special coordinate systems are defined to deal with the ratio problem. In essence, it comes down to using other distance measures than the Euclidian Distance that is used in the conventional analysis of metabolomic data. Results We illustrate using this type of approach in combination with three-way methods (i.e. PARAFAC) of a longitudinal urine metabolomics study and two simulations. In both cases, the advantage of the compositional approach is established in terms of improved interpretability of the scores and loadings of the PARAFAC model. Conclusion For urine metabolomics studies, we advocate the use of compositional data analysis approaches. They are easy to use, well established and proof to give reliable results