Integrating transcriptomics and metabonomics to unravel modes-of-action of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) in HepG2 cells

<p>Abstract</p> <p>Background</p> <p>The integration of different 'omics' technologies has already been shown in several <it>in vivo </it>studies to offer a complementary insight into cellular responses to toxic challenges. Being interested in developing <it>in vitro </it>cellular models as alternative to animal-based toxicity assays, we hypothesize that combining transcriptomics and metabonomics data improves the understanding of molecular mechanisms underlying the effects caused by a toxic compound also <it>in vitro </it>in human cells. To test this hypothesis, and with the focus on non-genotoxic carcinogenesis as an endpoint of toxicity, in the present study, the human hepatocarcinoma cell line HepG2 was exposed to the well-known environmental carcinogen 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD).</p> <p>Results</p> <p>Transcriptomics as well as metabonomics analyses demonstrated changes in TCDD-exposed HepG2 in common metabolic processes, e.g. amino acid metabolism, of which some of the changes only being confirmed if both 'omics' were integrated. In particular, this integrated analysis identified unique pathway maps involved in receptor-mediated mechanisms, such as the G-protein coupled receptor protein (GPCR) signaling pathway maps, in which the significantly up-regulated gene son of sevenless 1 (SOS1) seems to play an important role. SOS1 is an activator of several members of the RAS superfamily, a group of small GTPases known for their role in carcinogenesis.</p> <p>Conclusions</p> <p>The results presented here were not only comparable with other <it>in vitro </it>studies but also with <it>in vivo </it>studies. Moreover, new insights on the molecular responses caused by TCDD exposure were gained by the cross-omics analysis.</p

Ultra-fast searching assists in evaluating sub-ppm mass accuracy enhancement in U-HPLC/Orbitrap MS data

A strategy, detailed methodology description and software are given with which the mass accuracy of U-HPLC-Orbitrap data (resolving power 50,000 FWHM) can be enhanced by an order of magnitude to sub-ppm levels. After mass accuracy enhancement all 211 reference masses have mass errors within 0.5 ppm; only 14 of these are outside the 0.2 ppm error margin. Further demonstration of mass accuracy enhancement is shown on a pre-concentrated urine sample in which evidence for 89 (342 ions) potential hydroxylated and glucuronated DHEA-metabolites is found. Although most DHEA metabolites have low-intensity mass signals, only 11 out of 342 are outside the ±1 ppm error envelop; 272 mass signals have errors below 0.5 ppm (142 below 0.2 ppm). The methodology consists of: (a) a multiple internal lock correction (here ten masses; no identity of internal lock masses is required) to avoid suppression problems of a single internal lock mass as well as to increase lock precision, (b) a multiple external mass correction (here 211 masses) to correct for calibration errors, (c) intensity dependant mass correction, (d) file averaging. The strategy is supported by ultra-fast file searching of baseline corrected, noise-reduced metAlign output. The output and efficiency of ultra-fast searching is essential in obtaining the required information to visualize the distribution of mass errors and isotope ratio deviations as a function of mass and intensity

Ultra-Fast Retroactive Processing by MetAlign of Liquid-Chromatography High-Resolution Full-Scan Orbitrap Mass Spectrometry Data in WADA Human Urine Sample Monitoring Program

Rationale: The World Antidoping Agency (WADA) Monitoring program concentrates analytical data from the WADA Accredited Laboratories for substances which are not prohibited but whose potential misuse must be known. The WADA List of Monitoring substances is updated annually, where substances may be removed, introduced or transferred to the Prohibited List, depending on the prevalence of their use. Retroactive processing of old sample datafiles has the potential to create information for the prevalence of use of candidate substances for the Monitoring List in previous years. MetAlign is a freeware software with functionality to reduce the size of liquid chromatography (LC)/high-resolution (HR) full-scan (FS) mass spectrometry (MS) datafiles and to perform a fast search for the presence of substances in thousands of reduced datafiles. Methods: Validation was performed to the search procedure of MetAlign applied to Anti-Doping Lab Qatar (ADLQ)-screened LC/HR-FS-MS reduced datafiles originated from antidoping samples for tramadol (TRA), ecdysterone (ECDY) and the ECDY metabolite 14-desoxyecdysterone (DESECDY) of the WADA Monitoring List. Searching parameters were related to combinations of accurate masses and retention times (RTs). Results: MetAlign search validation criteria were based on the creation of correct identifications, false positives (FPs) and false negatives (FNs). The search for TRA in 7410 ADLQ routine LC/HR-FS-MS datafiles from the years 2017 to 2020 revealed no false identification (FPs and FNs) compared with the ADLQ WADA reports. ECDY and DESECDY were detected by MetAlign search in approximately 5% of the same cohort of antidoping samples. Conclusions: MetAlign is a powerful tool for the fast retroactive processing of old reduced datafiles collected in screening by LC/HR-FS-MS to reveal the prevalence of use of antidoping substances. The current study proposed the validation scheme of the MetAlign search procedure, to be implemented per individual substance in the WADA Monitoring program, for the elimination of FNs and FPs.</p

An inter-laboratory comparison demonstrates that [1H]-NMR metabolite fingerprinting is a robust technique for collaborative plant metabolomic data collection

In any metabolomics experiment, robustness and reproducibility of data collection is of vital importance. These become more important in collaborative studies where data is to be collected on multiple instruments. With minimisation of variance in sample preparation and instrument performance it is possible to elucidate even subtle differences in metabolite fingerprints due to genotype or biological treatment. In this paper we report on an inter laboratory comparison of plant derived samples by [1H]-NMR spectroscopy across five different sites and within those sites utilising instruments with different probes and magnetic field strengths of 9.4 T (400 MHz), 11.7 T (500 MHz) and 14.1 T (600 MHz). Whilst the focus of the study is on consistent data collection across laboratories, aspects of sample stability and the requirement for sample rotation within the NMR magnet are also discussed. Comparability of the datasets from participating laboratories was exceptionally good and the data were amenable to comparative analysis by multivariate statistics. Field strength differences can be adjusted for in the data pre-processing and multivariate analysis demonstrating that [1H]-NMR fingerprinting is the ideal technique for large scale plant metabolomics data collection requiring the participation of multiple laboratories

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

Inter-laboratory reproducibility of fast gas chromatography–electron impact–time of flight mass spectrometry (GC–EI–TOF/MS) based plant metabolomics

The application of gas chromatography–mass spectrometry (GC–MS) to the ‘global’ analysis of metabolites in complex samples (i.e. metabolomics) has now become routine. The generation of these data-rich profiles demands new strategies in data mining and standardisation of experimental and reporting aspects across laboratories. As part of the META-PHOR project’s (METAbolomics for Plants Health and OutReach: http://www.meta-phor.eu/) priorities towards robust technology development, a GC–MS ring experiment based upon three complex matrices (melon, broccoli and rice) was launched. All sample preparation, data processing, multivariate analyses and comparisons of major metabolite features followed standardised protocols, identical models of GC (Agilent 6890N) and TOF/MS (Leco Pegasus III) were also employed. In addition comprehensive GC×GC–TOF/MS was compared with 1 dimensional GC–TOF/MS. Comparisons of the paired data from the various laboratories were made with a single data processing and analysis method providing an unbiased assessment of analytical method variants and inter-laboratory reproducibility. A range of processing and statistical methods were also assessed with a single exemplary dataset revealing near equal performance between them. Further investigations of long-term reproducibility are required, though the future generation of global and valid metabolomics databases offers much promise

A large scale multi-laboratory suspect screening of pesticide metabolites in human biomonitoring: From tentative annotations to verified occurrences

Within the Human Biomonitoring for Europe initiative (HBM4EU), a study to determine new biomarkers of exposure to pesticides and to assess exposure patterns was conducted. Human urine samples (N = 2,088) were collected from five European regions in two different seasons. The objective of the study was to identify pesticides and their metabolites in collected urine samples with a harmonized suspect screening approach based on liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS) applied in five laboratories. A combined data processing workflow included comprehensive data reduction, correction of mass error and retention time (RT) drifts, isotopic pattern analysis, adduct and elemental composition annotation, finalized by a mining of the elemental compositions for possible annotations of pesticide metabolites. The obtained tentative annotations (n = 498) were used for acquiring representative data-dependent tandem mass spectra (MS2) and verified by spectral comparison to reference spectra generated from commercially available reference standards or produced through human liver S9 in vitro incubation experiments. 14 parent pesticides and 71 metabolites (including 16 glucuronide and 11 sulfate conjugates) were detected. Collectively these related to 46 unique pesticides. For the remaining tentative annotations either (i) no data-dependent MS2 spectra could be acquired, (ii) the spectral purity was too low for sufficient matching, or (iii) RTs indicated a wrong annotation, leaving potential for more pesticides and/or their metabolites being confirmed in further studies. Thus, the reported results are reflecting only a part of the possible pesticide exposure

Ultrafast PubChem Searching Combined with Improved Filtering Rules for Elemental Composition Analysis

A new and improved software tool for elemental composition annotation of molecular ions detected in mass spectrometry, based on improved filtering rules followed by ultrafast querying in publicly available compound databases, is provided. Pubchem is used as a general source of 1.3 million unique chemical formulas. A plant metabolomics database containing ca. 100 000 formulas is used as a source of naturally occurring compounds. Four modes with different sets of rules for heuristic filtering of candidate formulas coming from elemental composition analysis are incorporated and tested on both databases. The elemental composition analysis is then coupled to ultrafast PubChem searching based on a mass-indexed intermediate system. The performance of the filters is compared and discussed. When reactive compounds are assumed not to be present, 99.95% of the 1.3 million PubChem formulas is correctly found, while ca. 30% less formulas per mass are given compared to previously published rules. For the ca. 100 000 plant metabolomics based formulas, 100% fit the improved rules

Validation of an automated screening method for persistent organic contaminants in fats and oils by GC × GC-ToFMS

An screening method, comprised of straightforward sample treatment based on silica clean-up, GC × GC-ToFMS detection and automated data processing with the non-proprietary free downloadable software MetAlignID, has been successfully validated with respect to false negatives for the sum PCB 28, 52, 101, 138, 153 and 180), for the sum of BDE 28, 47, 99, 100, 153, 154 and 183, for the four markers of PAHs and for a number of emerging brominated flame retardants. A screening detection limit (SDL) equal to or lower than the maximum regulatory level was always achieved. MetAlignID considerably decreased the time needed for data treatment from 20 to 5 min/file. Automated identification of the signature mass spectral patterns was applied to identify chlorinated- and brominated-containing substances with more than two halogen atoms, and PAH derivates. Although the success rate was variable and needs to be further improved, the tool was considered to be of added value