An untargeted multi-technique metabolomics approach to studying intracellular metabolites of HepG2 cells exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin

<p>Abstract</p> <p>Background</p> <p><it>In vitro </it>cell systems together with omics methods represent promising alternatives to conventional animal models for toxicity testing. Transcriptomic and proteomic approaches have been widely applied <it>in vitro </it>but relatively few studies have used metabolomics. Therefore, the goal of the present study was to develop an untargeted methodology for performing reproducible metabolomics on <it>in vitro </it>systems. The human liver cell line HepG2, and the well-known hepatotoxic and non-genotoxic carcinogen 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), were used as the <it>in vitro </it>model system and model toxicant, respectively.</p> <p>Results</p> <p>The study focused on the analysis of intracellular metabolites using NMR, LC-MS and GC-MS, with emphasis on the reproducibility and repeatability of the data. State of the art pre-processing and alignment tools and multivariate statistics were used to detect significantly altered levels of metabolites after exposing HepG2 cells to TCDD. Several metabolites identified using databases, literature and LC-nanomate-Orbitrap analysis were affected by the treatment. The observed changes in metabolite levels are discussed in relation to the reported effects of TCDD.</p> <p>Conclusions</p> <p>Untargeted profiling of the polar and apolar metabolites of <it>in vitro </it>cultured HepG2 cells is a valid approach to studying the effects of TCDD on the cell metabolome. The approach described in this research demonstrates that highly reproducible experiments and correct normalization of the datasets are essential for obtaining reliable results. The effects of TCDD on HepG2 cells reported herein are in agreement with previous studies and serve to validate the procedures used in the present work.</p

An overview of NMR-based metabolomics to identify secondary plant compounds involved in host plant resistance

Secondary metabolites provide a potential source for the generation of host plant resistance and development of biopesticides. This is especially important in view of the rapid and vast spread of agricultural and horticultural pests worldwide. Multiple pests control tactics in the framework of an integrated pest management (IPM) programme are necessary. One important strategy of IPM is the use of chemical host plant resistance. Up to now the study of chemical host plant resistance has, for technical reasons, been restricted to the identification of single compounds applying specific chemical analyses adapted to the compound in question. In biological processes however, usually more than one compound is involved. Metabolomics allows the simultaneous detection of a wide range of compounds, providing an immediate image of the metabolome of a plant. One of the most universally used metabolomic approaches comprises nuclear magnetic resonance spectroscopy (NMR). It has been NMR which has been applied as a proof of principle to show that metabolomics can constitute a major advancement in the study of host plant resistance. Here we give an overview on the application of NMR to identify candidate compounds for host plant resistance. We focus on host plant resistance to western flower thrips (Frankliniella occidentalis) which has been used as a model for different plant species

1H NMR metabolite fingerprinting and metabolomic analysis of perchloric acid extracts from plant tissues

Metabolite fingerprinting provides a powerful method for discriminating between biological samples on the basis of differences in metabolism caused by such factors as growth conditions, developmental stage or genotype. This protocol describes a technique for acquiring metabolite fingerprints from samples of plant origin. The preferred method involves freezing the tissue rapidly to stop metabolism, extracting soluble metabolites using perchloric acid (HClO4) and then obtaining a fingerprint of the metabolic composition of the sample using 1D 1H NMR spectroscopy. The spectral fingerprints of multiple samples may be analyzed using either unsupervised or supervised multivariate statistical methods, and these approaches are illustrated with data obtained from the developing seeds of two genotypes of sunflower (Helianthus annuus). Preparation of plant extracts for analysis takes 2-3 d, but multiple samples can be processed in parallel and subsequent acquisition of NMR spectra takes approximately 30 min per sample, allowing 24-48 samples to be analyzed in a week