ABSTRACT
Soman, an irreversible organophosphorus cholinesterase inhibitor, induces status epilepticus and, in sensitive brain areas, seizure-related brain damage (e.g. brain edema and neuronal loss). The brain metabolic disturbances associated with these events are ill known. In the present study, we thus evaluated these changes in a murine model of soman-induced status epilepticus up to 7 days after intoxication. Mice, protected by HI-6 and atropine methyl nitrate, were poisoned with soman (172 microg/kg) and then sacrificed at set time points, from 1 h to 7 days. Brain biopsies from the piriform cortex (Pir) and cerebellum (Cer) were analyzed by 1H HRMAS NMR spectroscopy. Spectra were then analyzed using both a supervised multivariate analysis and the QUEST procedure of jMRUI for the quantification of 17 metabolites. The multivariate analysis clearly showed the metabolic differences between a damaged structure (Pir) and a structure with less prominent changes (cerebellum) and helped to globally assess the time course of metabolic changes. Analysis of the individual metabolites showed that the major changes took place in the piriform cortex but that cerebellum was not change-free. The most prominent changes in the former were an early (1-4 h) increase in alanine and acetate, a delayed increase in lactate, glycerophosphocholine and glutamine as well as a delayed decrease in myo-inositol and N-acetylaspartate. A week after poisoning, some metabolic disturbances were still present. Further research will be necessary to clarify what could be the involvement of these metabolites in physiological processes and how they might become useful surrogate markers of brain damage and repair.
Subject(s)
Brain/metabolism , Cholinesterase Inhibitors/toxicity , Soman/toxicity , Animals , Brain/drug effects , Brain/pathology , Disease Models, Animal , Magnetic Resonance Imaging/methods , Male , Mice , Protons , Status Epilepticus/chemically induced , Status Epilepticus/pathologyABSTRACT
Quantitation of High Resolution Magic Angle Spinning (HRMAS) Nuclear Magnetic Resonance (NMR) signals enables establishing reference metabolite profiles of ex vivo tissues. Signals are often contaminated by a background signal originating mainly from macromolecules and lipids and by residual water which hampers proper quantitation. We show that automatic quantitation of HRMAS signals, even in the presence of a background, can be achieved by the semi-parametric algorithm QUEST based on prior knowledge of a metabolite basis-set. The latter was quantum-mechanically simulated with NMR-SCOPE and requires accurate spin parameters. The region of interest of spectra is a small part of the full spectral bandwidth. Reducing the computation time inherent to the large number of data-points is possible by using ER-Filter in a preprocessing step. Through Monte-Carlo studies, we analyze the performances of quantitation without and with ER-Filtering. Applications of QUEST to quantitation of 1H ex vivo HRMAS-NMR data of mouse brains after intoxication with soman, are demonstrated. Metabolic profiles obtained during status epilepticus and later when neuronal lesions are installed, are established. Acetate, Alanine, Choline and gamma-amino-butyric acid concentrations increase in the piriform cortex during the initial status epilepticus, when seizures are maximum; Lactate and Glutamine concentrations increase while myo-Inositol and N-acetylaspartate concentrations decrease when neuronal lesions are clearly installed.
Subject(s)
Brain/metabolism , Magnetic Resonance Spectroscopy/methods , Status Epilepticus/metabolism , Acetates/metabolism , Alanine/metabolism , Algorithms , Animals , Aspartic Acid/analogs & derivatives , Aspartic Acid/metabolism , Choline/metabolism , Glutamine/metabolism , Inositol/metabolism , Lactates/metabolism , Mice , Monte Carlo Method , Soman/toxicity , Status Epilepticus/chemically induced , gamma-Aminobutyric Acid/metabolismABSTRACT
Semi-parametric disentanglement of parametric parts from non-parametric parts of a signal is a universal problem. This study concerns estimation of metabolite concentrations from in vivo Magnetic Resonance Spectroscopy (MRS) signals. Due to in vivo conditions, so-called macro-molecules contribute non-parametric components to the signals. Disentanglement is achieved by exploiting prior knowledge about the parametric and non-parametric parts directly in the measurement domain. Moreover, Cramér-Rao bounds on the non-parametric part are derived. These expressions are used to automate the disentanglement procedure.
