Metabolite Identification
Evaluation of approaches for quantitative targeted profiling of complex compositions using 1D 1H NMR spectroscopy
NMR-based profiling of low molecular weight (LMW) compounds provides an ‘unbiased’ and broad view on the composition of foods and biofluids and has been proven beneficial to control the quality and safety of foods as well as to assess the effect of foods/nutrients in biological systems. The quantification of LMW compounds from 1D 1H NMR profiles has recently gained in importance for the analysis of complex mixtures, yet still suffers from major hurdles such as signal overlap preventing the accurate integration of NMR signals.
A systematic approach to obtain validated partial least square models for predicting lipoprotein subclasses from serum NMR spectra
Superinduction of estrogen receptor mediated gene expression in luciferase based reporter gene assays is mediated by a post-transcriptional mechanism
Several estrogenic compounds including the isoflavonoid genistein have been reported to induce a higher maximal response than the natural estrogen 17β-estradiol in in vitro luciferase based reporter gene bioassays for testing estrogenicity. The phenomenon has been referred to as superinduction.
LC-MS-SPE-NMR for the isolation and characterization of neo-clerodane diterpenoids from Teucrium luteum subsp. flavovirens (perpendicular)
Identification of nevadensin as an important herb-based constituent inhibiting estragole bioactivation and physiology-based biokinetic modeling of its possible in vivo effect
Estragole is a natural constituent of several herbs and spices including sweet basil. In rodent bioassays, estragole induces hepatomas, an effect ascribed to estragole bioactivation to 1'-sulfooxyestragole resulting in DNA adduct formation.
The pipelined metabolite identification based on MS fragmentation
Expanding and understanding metabolite space
Novel key metabolites reveal further branching of the roquefortine/meleagrin biosynthetic pathway
PMG: Multi-core metabolite identification
Distributed computing has been considered for decades as a promising way of speeding up software execution, resulting in a valuable collection of safe and efficient concurrent algorithms. With the pervasion of multi-core processors, parallelization has moved to the center of attention with new challenges, especially regarding scalability to tens or even hundreds of parallel cores. In this paper, we present a scalable multi-core tool for the metabolomics community. This tool addresses the problem of metabolite identification which is currently a bottleneck in metabolomics pipeline.