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

Haystack, a web-based tool for metabolomics research

Background

Liquid chromatography coupled to mass spectrometry (LCMS) has become a widely used technique in metabolomics research for differential profiling, the broad screening of biomolecular constituents across multiple samples to diagnose phenotypic differences and elucidate relevant features. However, a significant limitation in LCMS-based metabolomics is the high-throughput data processing required for robust statistical analysis and data modeling for large numbers of samples with hundreds of unique chemical species.

Results

To address this problem, we developed Haystack, a web-based tool designed to visualize, parse, filter, and extract significant features from LCMS datasets rapidly and efficiently. Haystack runs in a browser environment with an intuitive graphical user interface that provides both display and data processing options. Total ion chromatograms (TICs) and base peak chromatograms (BPCs) are automatically displayed, along with time-resolved mass spectra and extracted ion chromatograms (EICs) over any mass range. Output files in the common .csv format can be saved for further statistical analysis or customized graphing. Haystack's core function is a flexible binning procedure that converts the mass dimension of the chromatogram into a set of interval variables that can uniquely identify a sample. Binned mass data can be analyzed by exploratory methods such as principal component analysis (PCA) to model class assignment and identify discriminatory features. The validity of this approach is demonstrated by comparison of a dataset from plants grown at two light conditions with manual and automated peak detection methods. Haystack successfully predicted class assignment based on PCA and cluster analysis, and identified discriminatory features based on analysis of EICs of significant bins.

Conclusion

Haystack, a new online tool for rapid processing and analysis of LCMS-based metabolomics data is described. It offers users a range of data visualization options and supports non-biased differential profiling studies through a unique and flexible binning function that provides an alternative to conventional peak deconvolution analysis methods.

Integrative cross-omics analysis in primary mouse hepatocytes unravels mechanisms of cyclosporin A-induced hepatotoxicity

The liver is responsible for drug metabolism and drug-induced hepatotoxicity is the most frequent reason for drug withdrawal, indicating that better pre-clinical toxicity tests are needed. In order to bypass animal models for toxicity screening, we exposed primary mouse hepatocytes for exploring the prototypical hepatotoxicant cyclosporin A. To elucidate the mechanisms underlying cyclosporin A-induced hepatotoxicity, we analyzed expression levels of proteins, mRNAs, microRNAs and metabolites. Integrative analysis of transcriptomics and proteomics showed that protein disulfide isomerase family A, member 4 was up-regulated on both the protein level and mRNA level. This protein is involved in protein folding and secretion in the endoplasmic reticulum. Furthermore, the microRNA mmu-miR-182-5p which is predicted to interact with the mRNA of this protein, was also differentially expressed, further emphasizing endoplasmic reticulum stress as important event in drug-induced toxicity. To further investigate the interaction between the significantly expressed proteins, a network was created including genes and microRNAs known to interact with these proteins and this network was used to visualize the experimental data. In total 6 clusters could be distinguished which appeared to be involved in several toxicity related processes, including alteration of protein folding and secretion in the endoplasmic reticulum. Metabonomic analyses resulted in 5 differentially expressed metabolites, indicative of an altered glucose, lipid and cholesterol homeostasis which can be related to cholestasis. Single and integrative analyses of transcriptomics, proteomics and metabonomics reveal mechanisms underlying cyclosporin A-induced cholestasis demonstrating that endoplasmic reticulum stress and the unfolded protein response are important processes in drug-induced liver toxicity.

Review: toxicometabolomics

Metabolomics use in toxicology is rapidly increasing, particularly owing to advances in mass spectroscopy, which is widely used in the life sciences for phenotyping disease states. Toxicology has the advantage of having the disease agent, the toxicant, available for experimental induction of metabolomics changes monitored over time and dose. This review summarizes the different technologies employed and gives examples of their use in various areas of toxicology. A prominent use of metabolomics is the identification of signatures of toxicity - patterns of metabolite changes predictive of a hazard manifestation. Increasingly, such signatures indicative of a certain hazard manifestation are identified, suggesting that certain modes of action result in specific derangements of the metabolism. This might enable the deduction of underlying pathways of toxicity, which, in their entirety, form the Human Toxome, a key concept for implementing the vision of Toxicity Testing for the 21st century. This review summarizes the current state of metabolomics technologies and principles, their uses in toxicology and gives a thorough overview on metabolomics bioinformatics, pathway identification and quality assurance. In addition, this review lays out the prospects for further metabolomics application also in a regulatory context.

RNA-sequencing analysis of TCDD-induced responses in zebrafish liver reveals high relatedness to in vivo mammalian models and conserved biological pathways

TCDD is one of the most persistent environmental toxicants in biological systems and its effect through aryl hydrocarbon receptor (AhR) has been well characterized. However, the information on TCDD-induced toxicity in other molecular pathways is rather limited. To fully understand molecular toxicity of TCDD in an in vivo animal model, adult zebrafish were exposed to TCDD at 10 nM for 96 h and the livers were sampled for RNA-sequencing based transcriptomic profiling. A total of 1,058 differently expressed genes were identified based on fold-change>2 and TPM (transcripts per million) >10. Among the top 20 up-regulated genes, 10 novel responsive genes were identified and verified by RT-qPCR analysis on independent samples. Transcriptomic analysis indicated several deregulated pathways associated with cell cycle, endocrine disruptors, signal transduction and immune systems. Comparative analyses of TCDD-induced transcriptomic changes between fish and mammalian models revealed that proteomic pathway is consistently up-regulated while calcium signaling pathway and several immune-related pathways are generally down-regulated. Finally, our study also suggested that zebrafish model showed greater similarity to in vivo mammalian models than in vitro models. Our study indicated that the zebrafish is a valuable in vivo model in toxicogenomic analyses for understanding molecular toxicity of environmental toxicants relevant to human health. The expression profiles associated with TCDD could be useful for monitoring environmental dioxin and dioxin-like contamination.

Metabolomics in toxicology and preclinical research

Metabolomics, the comprehensive analysis of metabolites in a biological system, provides detailed information about the biochemical/physiological status of a biological system, and about the changes caused by chemicals. Metabolomics analysis is used in many fields, ranging from the analysis of the physiological status of genetically modified organisms in safety science to the evaluation of human health conditions. In toxicology, metabolomics is the -omics discipline that is most closely related to classical knowledge of disturbed biochemical pathways. It allows rapid identification of the potential targets of a hazardous compound. It can give information on target organs and often can help to improve our understanding regarding the mode-of-action of a given compound. Such insights aid the discovery of biomarkers that either indicate pathophysiological conditions or help the monitoring of the efficacy of drug therapies. The first toxicological applications of metabolomics were for mechanistic research, but different ways to use the technology in a regulatory context are being explored. Ideally, further progress in that direction will position the metabolomics approach to address the challenges of toxicology of the 21st century. To address these issues, scientists from academia, industry, and regulatory bodies came together in a workshop to discuss the current status of applied metabolomics and its potential in the safety assessment of compounds. We report here on the conclusions of three working groups addressing questions regarding 1) metabolomics for in vitro studies 2) the appropriate use of metabolomics in systems toxicology, and 3) use of metabolomics in a regulatory context.

Identification of cisplatin-regulated metabolic pathways in pluripotent stem cells

The chemotherapeutic compound, cisplatin causes various kinds of DNA lesions but also triggers other pertubations, such as ER and oxidative stress. We and others have shown that treatment of pluripotent stem cells with cisplatin causes a plethora of transcriptional and post-translational alterations that, to a major extent, point to DNA damage response (DDR) signaling. The orchestrated DDR signaling network is important to arrest the cell cycle and repair the lesions or, in case of damage beyond repair, eliminate affected cells. Failure to properly balance the various aspects of the DDR in stem cells contributes to ageing and cancer. Here, we performed metabolic profiling by mass spectrometry of embryonic stem (ES) cells treated for different time periods with cisplatin. We then integrated metabolomics with transcriptomics analyses and connected cisplatin-regulated metabolites with regulated metabolic enzymes to identify enriched metabolic pathways. These included nucleotide metabolism, urea cycle and arginine and proline metabolism. Silencing of identified proline metabolic and catabolic enzymes indicated that altered proline metabolism serves as an adaptive, rather than a toxic response. A group of enriched metabolic pathways clustered around the metabolite S-adenosylmethionine, which is a hub for methylation and transsulfuration reactions and polyamine metabolism. Enzymes and metabolites with pro- or anti-oxidant functions were also enriched but enhanced levels of reactive oxygen species were not measured in cisplatin-treated ES cells. Lastly, a number of the differentially regulated metabolic enzymes were identified as target genes of the transcription factor p53, pointing to p53-mediated alterations in metabolism in response to genotoxic stress. Altogether, our findings reveal interconnecting metabolic pathways that are responsive to cisplatin and may serve as signaling modules in the DDR in pluripotent stem cells.

Serum metabolomic pertubations among workers exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD)

Exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) has been associated with multiple health effects. Mechanistic studies using metabolomics could provide supporting evidence for such associations by identifying relevant biological pathways. In this study, we investigated metabolic perturbations in a cohort of TCDD exposed workers to better understand TCDD related health effects. Eighty one workers who had been exposed to TCDD in the past and 63 nonexposed workers were included in the study. Serum metabolites were detected using ultra high pressure liquid chromatography coupled online to a Q-TOF Premier mass spectrometer with a scan range of 70-1,000 m/z. Current plasma levels of TCDD were determined by high-resolution gas chromatography/isotope dilution high resolution mass spectrometry. TCDD blood levels at the time of last exposure were estimated using a one-compartment first order kinetic model. Differentially expressed metabolites were identified using linear regression models, partial least squares regression (PLSr) and a regression-based Bayesian variable selection approach. Features that were present in all quality control samples and had a coefficient of variation <30% were included in the analyses (n = 421 features). Adjusted linear regression analysis showed several significant perturbations (n = 27; P < 0.05) but these observations did not survive multiple testing correction (q value > 0.05). PLSr analyses and Bayesian variable selection regression analyses revealed no obvious metabolic perturbations associated with TCDD levels. This is the first metabolomic analysis related to TCDD exposure in humans. No significant metabolic features were identified. It is concluded that TCDD exposure at levels present in this study does not lead to significant perturbations of the serum metabolome.

Metabolomic profiling of in vivo plasma responses to dioxin-associated dietary contaminant exposure in rats: implications for identification of sources of animal and human exposure

Dioxin contamination of the food chain typically occurs when cocktails of combustion residues or polychlorinated biphenyl (PCB) containing oils become incorporated into animal feed. These highly toxic compounds are bioaccumulative with small amounts posing a major health risk. The ability to identify animal exposure to these compounds prior to their entry into the food chain may be an invaluable tool to safeguard public health. Dioxin-like compounds act by a common mode of action and this suggests that markers or patterns of response may facilitate identification of exposed animals. However, secondary co-contaminating compounds present in typical dioxin sources may affect responses to compounds. This study has investigated for the first time the potential of a metabolomics platform to distinguish between animals exposed to different sources of dioxin contamination through their diet. Sprague-Dawley rats were given feed containing dioxin-like toxins from hospital incinerator soot, a common PCB oil standard and pure 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) (normalized at 0.1 μg/kg TEQ) and acquired plasma was subsequently biochemically profiled using ultra high performance liquid chromatography (UPLC) quadropole time-of-flight-mass spectrometry (QTof-MS). An OPLS-DA model was generated from acquired metabolite fingerprints and validated which allowed classification of plasma from individual animals into the four dietary exposure study groups with a level of accuracy of 97-100%. A set of 24 ions of importance to the prediction model, and which had levels significantly altered between feeding groups, were positively identified as deriving from eight identifiable metabolites including lysophosphatidylcholine (16:0) and tyrosine. This study demonstrates the enormous potential of metabolomic-based profiling to provide a powerful and reliable tool for the detection of dioxin exposure in food-producing animals.

Mammalian cell metabolomics: experimental design and sample preparation

Metabolomics represents the global assessment of metabolites in a biological sample and reports the closest information to the phenotype of the biological system under study. Mammalian cell metabolomics has emerged as a promising tool with potential applications in many biotechnology and research areas. Metabolomics workflow includes experimental design, sampling, sample processing, metabolite analysis, and data processing. Given their influence on metabolite content and biological interpretation of data, a good experimental design and the appropriate choice of a sample processing method are prerequisites for success in any metabolomic study. The use of mammalian cells in the metabolomics field involves harder sample processing methods, including metabolism quenching and metabolite extraction, as compared to the use of body fluids, although such critical issues are frequently overlooked. This review aims to overview the common experimental procedures used in mammalian cell metabolomics based on mass spectrometry, by placing special emphasis on discussing sample preparation approaches, although other aspects, such as cell metabolomics applications, culture systems, cellular models, analytical platforms, and data analysis, are also briefly covered. This review intends to be a helpful tool to assist researchers in addressing decisions when planning a metabolomics study involving the use of mammalian cells.

Mass spectrometry-based metabolomics towards understanding of gene functions with a diversity of biological contexts

Currently, mass spectrometry-based metabolomics studies extend beyond conventional chemical categorization and metabolic phenotype analysis to understanding gene function in various biological contexts (e.g., mammalian, plant, and microbial). These novel utilities have led to many innovative discoveries in the following areas: disease pathogenesis, therapeutic pathway or target identification, the biochemistry of animal and plant physiological and pathological activities in response to diverse stimuli, and molecular signatures of host-pathogen interactions during microbial infection. In this review, we critically evaluate the representative applications of mass spectrometry-based metabolomics to better understand gene function in diverse biological contexts, with special emphasis on working principles, study protocols, and possible future development of this technique. Collectively, this review raises awareness within the biomedical community of the scientific value and applicability of mass spectrometry-based metabolomics strategies to better understand gene function, thus advancing this application's utility in a broad range of biological fields.

A rough guide to metabolite identification using high resolution liquid chromatography mass spectrometry in metabolomic profiling in metazoans

Compound identification in mass spectrometry based metabolomics can be a problem but sometimes the problem seems to be presented in an over complicated way. The current review focuses on metazoans where the range of metabolites is more restricted than for example in plants. The focus is on liquid chromatography with high resolution mass spectrometry where it is proposed that most of the problems in compound identification relate to structural isomers rather than to isobaric compounds. Thus many of the problems faced relate to separation of isomers, which is usually required even if fragmentation is used to support structural identification. Many papers report the use of MS/MS or MS² as an adjunct to the identification of known metabolites but there a few examples in metabolomics studies of metazoans of complete structure elucidation of novel metabolites or metabolites where no authentic standards are available for comparison.

Global metabolic profiling of animal and human tissues via UPLC-MS

Obtaining comprehensive, untargeted metabolic profiles for complex solid samples, e.g., animal tissues, requires sample preparation and access to information-rich analytical methodologies such as mass spectrometry (MS). Here we describe a practical two-step process for tissue samples that is based on extraction into 'aqueous' and 'organic' phases for polar and nonpolar metabolites. Separation methods such as ultraperformance liquid chromatography (UPLC) in combination with MS are needed to obtain sufficient resolution to create diagnostic metabolic profiles and identify candidate biomarkers. We provide detailed protocols for sample preparation, chromatographic procedures, multivariate analysis and metabolite identification via tandem MS (MS/MS) techniques and high-resolution MS. By using these optimized approaches, analysis of a set of samples using a 96-well plate format would take ~48 h: 1 h for system setup, 8-10 h for sample preparation, 34 h for UPLC-MS analysis and 2-3 h for preliminary/exploratory data processing, representing a robust method for untargeted metabolic screening of tissue samples.

MetAlign 3.0: performance enhancement by efficient use of advances in computer hardware

A new, multi-threaded version of the GC-MS and LC-MS data processing software, metAlign, has been developed which is able to utilize multiple cores on one PC. This new version was tested using three different multi-core PCs with different operating systems. The performance of noise reduction, baseline correction and peak-picking was 8-19 fold faster compared to the previous version on a single core machine from 2008. The alignment was 5-10 fold faster. Factors influencing the performance enhancement are discussed. Our observations show that performance scales with the increase in processor core numbers we currently see in consumer PC hardware development.

Assessment of compatibility between extraction methods for NMR- and LC/MS-based metabolomics

Because of the wide range of chemically and structurally diverse metabolites, efforts to survey the complete metabolome rely on the implementation of multiplatform approaches based on nuclear magnetic resonance (NMR) and mass spectrometry (MS). Sample preparation disparities between NMR and MS, however, may limit the analysis of the same samples by both platforms. Specifically, deuterated solvents used in NMR strategies can complicate LC/MS analysis as a result of potential mass shifts, whereas acidic solutions typically used in LC/MS methods to enhance ionization of metabolites can severely affect reproducibility of NMR measurements. These intrinsically different sample preparation requirements result in the application of different procedures for metabolite extraction, which involve additional sample and unwanted variability. To address this issue, we investigated 12 extraction protocols in liver tissue involving different aqueous/organic solvents and temperatures that may satisfy the requirements for both NMR and LC/MS simultaneously. We found that deuterium exchange did not affect LC/MS results, enabling the measurement of metabolites by NMR and, subsequently, the direct analysis of the same samples by using LC/MS with no need for solvent exchange. Moreover, our results show that the choice of solvents rather than the temperature determined the extraction efficiencies of metabolites, a combination of methanol/chloroform/water and methanol/water being the extraction methods that best complement NMR and LC/MS analysis for metabolomic studies.

A personalized life: biomarker monitoring from cradle to grave

Considering the holy grail of future medical treatment being personalized medicines, biomarker research will become more and more the focus for attention not only to develop new medical treatment regimes, based on changes in biomarker patterns, but also for nutritional advice to guarantee a lifelong optimized health condition. The current review gives an outline of how personalized medicine can become established for actual medical treatment using new biomarker concepts. Starting from the development of biomarker research using mainly immunological techniques, the review gives an overview about biomarkers of prediction evolved and focuses on new methodology for the identification of biomarkers using hyphenated analytical techniques like metabolomics and lipidomics. The actual use of multivariate statistical methods in combination with metabolomics and lipidomics is discussed not only for medical treatment but also for precautionary risk identification in human biomonitoring studies.

Comparative metabolomic and genomic analyses of TCDD-elicited metabolic disruption in mouse and rat liver

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) elicits a broad spectrum of species-specific effects that have not yet been fully characterized. This study compares the temporal effects of TCDD on hepatic aqueous and lipid metabolite extracts from immature ovariectomized C57BL/6 mice and Sprague-Dawley rats using gas chromatography-mass spectrometry and nuclear magnetic resonance-based metabolomic approaches and integrates published gene expression data to identify species-specific pathways affected by treatment. TCDD elicited metabolite and gene expression changes associated with lipid metabolism and transport, choline metabolism, bile acid metabolism, glycolysis, and glycerophospholipid metabolism. Lipid metabolism is altered in mice resulting in increased hepatic triacylglycerol as well as mono- and polyunsaturated fatty acid (FA) levels. Mouse-specific changes included the induction of CD36 and other cell surface receptors as well as lipases- and FA-binding proteins consistent with hepatic triglyceride and FA accumulation. In contrast, there was minimal hepatic fat accumulation in rats and decreased CD36 expression. However, choline metabolism was altered in rats, as indicated by decreases in betaine and increases in phosphocholine with the concomitant induction of betaine-homocysteine methyltransferase and choline kinase gene expression. Results from these studies show that aryl hydrocarbon receptor-mediated differential gene expression could be linked to metabolite changes and species-specific alterations of biochemical pathways.

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

BACKGROUND

The integration of different 'omics' technologies has already been shown in several in vivo studies to offer a complementary insight into cellular responses to toxic challenges. Being interested in developing in vitro 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 in vitro 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).

RESULTS

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.

CONCLUSIONS

The results presented here were not only comparable with other in vitro studies but also with in vivo studies. Moreover, new insights on the molecular responses caused by TCDD exposure were gained by the cross-omics analysis.