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1H NMR Data

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Kenji Mori – One of the best experts on this subject based on the ideXlab platform.

Hirosato Takikawa – One of the best experts on this subject based on the ideXlab platform.

Mitsuru Sasaki – One of the best experts on this subject based on the ideXlab platform.

Johan Lindberg – One of the best experts on this subject based on the ideXlab platform.

  • Automated annotation and quantification of metabolites in 1H NMR Data of biological origin.
    Analytical and bioanalytical chemistry, 2012
    Co-Authors: Erik Alm, K. Magnus Åberg, Tove Slagbrand, Erik Wahlström, Ingela Gustafsson, Johan Lindberg
    Abstract:

    In (1)H NMR metabolomic Datasets, there are often over a thousand peaks per spectrum, many of which change position drastically between samples. Automatic alignment, annotation, and quantification of all the metabolites of interest in such Datasets have not been feasible. In this work we propose a fully automated annotation and quantification procedure which requires annotation of metabolites only in a single spectrum. The reference Database built from that single spectrum can be used for any number of (1)H NMR Datasets with a similar matrix. The procedure is based on the generalized fuzzy Hough transform (GFHT) for alignment and on Principal-components analysis (PCA) for peak selection and quantification. We show that we can establish quantities of 21 metabolites in several (1)H NMR Datasets and that the procedure is extendable to include any number of metabolites that can be identified in a single spectrum. The procedure speeds up the quantification of previously known metabolites and also returns a table containing the intensities and locations of all the peaks that were found and aligned but not assigned to a known metabolite. This enables both biopattern analysis of known metabolites and Data mining for new potential biomarkers among the unknowns.

  • Time-resolved biomarker discovery in 1HNMR Data using generalized fuzzy Hough transform alignment and parallel factor analysis.
    Analytical and bioanalytical chemistry, 2010
    Co-Authors: Erik Alm, Ralf J. O. Torgrip, K. Magnus Åberg, Ina Schuppe-koistinen, Johan Lindberg
    Abstract:

    This work addresses the subject of time-series analysis of comprehensive 1HNMR Data of biological origin. One of the problems with toxicological and efficacy studies is the confounding of correlation between the administered drug, its metabolites and the systemic changes in molecular dynamics, i.e., the flux of drug-related molecules correlates with the molecules of system regulation. This correlation poses a problem for biomarker mining since this confounding must be untangled in order to separate true biomarker molecules from dose-related molecules. One way of achieving this goal is to perform pharmacokinetic analysis. The difference in pharmacokinetic time profiles of different molecules can aid in the elucidation of the origin of the dynamics, this can even be achieved regardless of whether the identity of the molecule is known or not. This mode of analysis is the basis for metabonomic studies of toxicology and efficacy. One major problem concerning the analysis of 1HNMR Data generated from metabonomic studies is that of the peak positional variation and of peak overlap. These phenomena induce variance in the Data, obscuring the true information content and are hence unwanted but hard to avoid. Here, we show that by using the generalized fuzzy Hough transform spectral alignment, variable selection, and parallel factor analysis, we can solve both the alignment and the confounding problem stated above. Using the outlined method, several different temporal concentration profiles can be resolved and the majority of the studied molecules and their respective fluxes can be attributed to these resolved kinetic profiles. The resolved time profiles hereby simplifies finding true biomarkers and bio-patterns for early detection of biological conditions as well as providing more detailed information about the studied biological system. The presented method represents a significant step forward in time-series analysis of biological 1HNMR Data as it provides almost full automation of the whole Data analysis process and is able to analyze over 800 unique features per sample. The method is demonstrated using a 1HNMR rat urine Dataset from a toxicology study and is compared with a classical approach: COW alignment followed by bucketing.

  • Proof of principle of a generalized fuzzy Hough transform approach to peak alignment of one-dimensional 1H NMR Data.
    Analytical and bioanalytical chemistry, 2007
    Co-Authors: Leonard Csenki, Erik Alm, Ralf J. O. Torgrip, K. Magnus Åberg, Lars I. Nord, Ina Schuppe-koistinen, Johan Lindberg
    Abstract:

    In metabolic profiling, multivariate Data analysis techniques are used to interpret one-dimensional (1D) 1H NMR Data. Multivariate Data analysis techniques require that peaks are characterised by t …

Mercedes Amat – One of the best experts on this subject based on the ideXlab platform.

  • Stereoselective Total Synthesis of the Putative Structure of Nitraraine
    The Journal of organic chemistry, 2014
    Co-Authors: Federica Arioli, Maria Pérez, Fabiana Subrizi, Núria Llor, Joan Bosch, Mercedes Amat
    Abstract:

    After the structure originally proposed for nitraraine was shown to be incorrect by total synthesis, the alternative structure 5 was recently suggested for the alkaloid on biosynthetic grounds and by comparison with the 1H NMR Data of tangutorine. The unambiguous synthesis of 5 is reported from tryptophanol and ketodiester 6, via oxazoloquinolone lactam 7. However, the melting point and 1H NMR Data of 5 did not match those reported for the natural product.