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 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

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

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

The automation of the development of classification models and improvement of model quality using feature engineering techniques

Recently pipelines of machine learning-based classification models have become important to codify, orchestrate, and automate the workflow to produce an effective machine learning model. In this article, we propose a framework that combines feature engineering techniques such as data imputation, transformation, and class balancing to compare the performance of different prediction models and select the best final model based on predefined parameters. The proposed framework is extendable and configurable by adding algorithms supported by the CARET package implemented in the R programming language. This framework can generate different machine learning models, which provide comparable results compared to other studies. The framework allows practitioners and researchers to automatically generate different classification models. This research used High-Resolution Orbitrap-based Mass Spectrometers (HRMS) data to create automated prediction models for the first time in literature. We demonstrated the applicability of feature engineering techniques such as data imputation, transformation (e.g., scaling, centering, etc.), and data balancing using several case studies and the proposed semi-automated framework. We showed how the initial prediction models can be improved using the proposed framework