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Studies have emphasised the importance of combustion-derived particles in eliciting adverse health effects, especially those produced by diesel vehicles. In contrast, few investigations have explored the potential toxicity of particles derived from tyre and brake wear, despite their significant contributions to total roadside particulate mass. The objective of this study was to compare the relative toxicity of compositionally distinct brake abrasion dust (BAD) and diesel exhaust particles (DEP) in a cellular model that is relevant to human airways. Although BAD contained considerably more metals/metalloids than DEP (as determined by inductively coupled plasma mass spectrometry) similar toxicological profiles were observed in U937 monocyte-derived macrophages following 24 h exposures to 4–25 μg ml−1 doses of either particle type. Responses to the particles were characterised by dose-dependent decreases in mitochondrial depolarisation (p ≤ 0.001), increased secretion of IL-8, IL-10 and TNF-α (p ≤ 0.05 to p ≤ 0.001) and decreased phagocytosis of S. aureus (p ≤ 0.001). This phagocytic deficit recovered, and the inflammatory response resolved when challenged cells were incubated for a further 24 h in particle-free media. These responses were abrogated by metal chelation using desferroxamine. At minimally cytotoxic doses both DEP and BAD perturbed bacterial clearance and promoted inflammatory responses in U937 cells with similar potency. These data emphasise the requirement to consider contributions of abrasion particles to traffic-related clinical health effects.

Acyl glucuronide metabolites have been implicated in the toxicity of several carboxylic acid-containing drugs, and the rate of their degradation via intramolecular transacylation and hydrolysis has been associated with the degree of protein adduct formation. Although not yet proven, the formation of protein adducts in vivo - and subsequent downstream effects - has been proposed as a mechanism of toxicity for carboxylic acid-containing xenobiotics capable of forming acyl glucuronides. A structurally-related series of metabolites, the acyl glucosides, have also been shown to undergo similar degradation reactions and consequently the potential to display a similar mode of toxicity. Here we report detailed kinetic models of each transacylation and hydrolysis reaction for a series of phenylacetic acid acyl glucuronides and their analogous acyl glucosides. Differences in reactivity were observed for the individual transacylation steps between the compound series; our findings suggest that the charged carboxylate ion and neutral hydroxyl group in the glucuronide and glucoside conjugates, respectively, are responsible for these differences. The transacylation reaction was modelled using density functional theory and the calculated activation energy for this reaction showed a close correlation with the degradation rate of the 1-β anomer. Comparison of optimised geometries between the two series of conjugates revealed differences in hydrogen bonding which may further explain the differences in reactivity observed. Together, these models may find application in drug discovery for prediction of acyl glucuronide and glucoside metabolite behaviour.

Nuclear magnetic resonance (NMR) spectroscopy remains one of the core analytical platforms for metabolomics, providing complementary chemical information to others, such as mass spectrometry, and offering particular advantages in some areas of research on account of its inherent robustness, reproducibility, and phenomenal dynamic range [...]

Chirality plays a fundamental role in nature, but its detection and quantification still face many limitations. To date, the enantiospecific analysis of mixtures necessarily requires prior separation of the individual components. The simultaneous enantiospecific detection of multiple chiral molecules in a mixture represents a major challenge, which would lead to a significantly better understanding of the underlying biological processes; for example, via enantiospecifically analysing metabolites in their native environment. Here, we report on the first in situ enantiospecific detection of a thirty-nine-component mixture. As a proof of concept, eighteen essential amino acids at physiological concentrations were simultaneously enantiospecifically detected using NMR spectroscopy and a chiral solvating agent. This work represents a first step towards the simultaneous multicomponent enantiospecific analysis of complex mixtures, a capability that will have substantial impact on metabolism studies, metabolic phenotyping, chemical reaction monitoring, and many other fields where complex mixtures containing chiral molecules require efficient characterisation.

After over 60 years of therapeutic use in the UK, paracetamol (acetaminophen, N-acetyl-p-aminophenol, APAP) remains the subject of considerable research into both its mode of action and toxicity. The pharmacological properties of APAP are the focus of some activity, with the role of the metabolite N-arachidonoylaminophenol (AM404) still a topic of debate. However, that the hepatotoxicity of APAP results from the production of the reactive metabolite N-acetyl-p-benzoquinoneimine (NAPQI/NABQI) that can deplete glutathione, react with cellular macromolecules, and initiate cell death, is now beyond dispute. The disruption of cellular pathways that results from the production of NAPQI provides a source of potential biomarkers of the severity of the damage. Research in this area has provided new diagnostic markers such as the microRNA miR-122 as well as mechanistic biomarkers associated with apoptosis, mitochondrial dysfunction, inflammation and tissue regeneration. Additionally, biomarkers of, and systems biology models for, glutathione depletion have been developed. Furthermore, there have been significant advances in determining the role of both the innate immune system and genetic factors that might predispose individuals to APAP-mediated toxicity. This perspective highlights some of the progress in current APAP-related research.

The exposome is defined as “the totality of environmental exposures encountered from birth to death” and was developed to address the need for comprehensive environmental exposure assessment to better understand disease etiology. Due to the complexity of the exposome, significant efforts have been made to develop technologies for longitudinal, internal and external exposure monitoring, and bioinformatics to integrate and analyze datasets generated. Our objectives were to bring together leaders in the field of exposomics, at a recent Symposium on “Lifetime Exposures and Human Health: The Exposome,” held at Yale School of Public Health. Our aim was to highlight the most recent technological advancements for measurement of the exposome, bioinformatics development, current limitations, and future needs in environmental health. In the discussions, an emphasis was placed on moving away from a one-chemical one-health outcome model toward a new paradigm of monitoring the totality of exposures that individuals may experience over their lifetime. This is critical to better understand the underlying biological impact on human health, particularly during windows of susceptibility. Recent advancements in metabolomics and bioinformatics are driving the field forward in biomonitoring and understanding the biological impact, and the technological and logistical challenges involved in the analyses were highlighted. In conclusion, further developments and support are needed for large-scale biomonitoring and management of big data, standardization for exposure and data analyses, bioinformatics tools for co-exposure or mixture analyses, and methods for data sharing.

Metabolic profiling (metabonomics/metabolomics) is now used routinely as a tool to provide information-rich datasets for biomarker discovery, prompting and augmenting detailed mechanistic studies. The experimental design and focus of any individual study will be reflected in the types of biomarkers that can be detected; toxicological studies will likely focus on markers of response to insult, whereas clinical case-control studies may yield diagnostic markers of disease. Population studies can make use of omics analyses, including metabonomics, to provide mechanistically-relevant markers that link environmental exposures to chronic disease endpoints. In this article, examples of how metabolic profiling has played a key role in molecular epidemiological analyses of chronic disease are presented, and how these reflect different aspects of the causal pathway. A commentary on the nature of metabolome analysis as a complex mixture problem as opposed to a coded, sequence or template problem is provided, alongside an overview of current and future analytical platforms that are being applied to meet this analytical challenge. Epidemiological studies are an important nexus for integrating various measures of the human exposome, and the ubiquity, diversity and functions of small molecule metabolites, represent an important way to link individual exposures, genetics and phenotype.

NMR spectroscopy can provide a wealth of information on cellular metabolism and is frequently used in metabolomics application that use cultured cells, tissues, and whole organisms. Central to these analyses are the protocols for sample harvest, which incorporate procedures for quenching metabolic processes to preserve samples in a state that is representative of their source. In this chapter, the main considerations are discussed with reference to literature exemplars. In the latter half of the chapter, less commonly studied biofluids that also have specific sample preparation requirements are discussed, with a focus on cerebrospinal fluid, faeces, bile, seminal fluid, and milk.

Small molecular species represent environmental/nutritional exposures as well as downstream mediators and their modulation reflects consequences of those exposures; metabolome analyses are therefore critical in efforts to characterise the internal chemical milieu to complement genomic profiles. As in other areas of health research, NMR spectroscopy is a primary platform for biofluid analysis, benefitting from good reproducibility and robustness, wide metabolome coverage, and the capacity to provide quantitative data. In this chapter, we highlight some of the main applications of NMR in environmental and nutritional research related to human health, which include analysis of dietary components, to molecular phenotyping, and structure elucidation of novel metabolites.

Epidemiological studies provide evidence that environmental exposures may affect health through complex mixtures. Formal investigation of the effect of exposure mixtures is usually achieved by modelling interactions, which relies on strong assumptions relating to the identity and the number of the exposures involved in such interactions, and on the order and parametric form of these interactions. These hypotheses become difficult to formulate and justify in an exposome context, where influential exposures are numerous and heterogeneous. To capture both the complexity of the exposome and its possibly pleiotropic effects, models handling multivariate predictors and responses, such as partial least squares (PLS) algorithms, can prove useful. As an illustrative example, we applied PLS models to data from a study investigating the inflammatory response (blood concentration of 13 immune markers) to the exposure to four disinfection by-products (one brominated and three chlorinated compounds), while swimming in a pool. To accommodate the multiple observations per participant (n=60; before and after the swim), we adopted a multilevel extension of PLS algorithms, including sparse PLS models shrinking loadings coefficients of unimportant predictors (exposures) and/or responses (protein levels). Despite the strong correlation among co-occurring exposures, our approach identified a subset of exposures (n=3/4) affecting the exhaled levels of 8 (out of 13) immune markers. PLS algorithms can easily scale to high-dimensional exposures and responses, and prove useful for exposome research to identify sparse sets of exposures jointly affecting a set of (selected) biological markers. Our descriptive work may guide these extensions for higher dimensional data.

Background Biomarkers of Alzheimer’s disease (AD) pathology and progression have now been identified across various modalities. The aims of the two studies presented (ANM-MMB and EMIF-AD biomarker discovery) are to discover and replicate previously identified biomarkers of disease pathology and progression, and moreover to determine whether a multimodal biomarker signature may add value in comparison to a biomarker of a single modality. Methods The ANM-MMB cohort is comprised of 718 AD, MCI converters and non-converters, and control subjects selected from the AddNeuroMed, Alzheimer’s research trust and Dementia case register cohorts. Cognitive measures, serum and urine metabolomics, structural MRI, genomics, whole blood transcriptomics and plasma proteomics data was available. The EMIF-AD biomarker discovery cohort consists of 1221 AD, MCI and control subjects, selected from the EMIF catalogue. All subjects had existing amyloid measures (CSF Aβ or amyloid-PET), structural MRI and clinical data, and furthermore plasma proteomics (targeted and untargeted), CSF proteomics (targeted), metabolomics, genomics and epigenetics data were generated. For both studies univariate and multivariate statistics were utilised to identify candidate biomarkers of AD pathology (neurodegeneration and/or brain amyloid burden), rates of cognitive decline, and MCI progression to dementia. pQTL-eQTL-mQTL analyses, network/pathway analysis, and multimodal classifiers were employed to detect multimodal signatures. Results Initial analyses indicate that in the ANM-MMB study a serum and urine derived 15 metabolite classifier predicts MCI progression to AD with 72% accuracy, and the biological significance of the metabolites included in the biomarker panel was identified. Further analyses will examine whether a multimodal classifier is able to predict with even greater accuracy. We will then seek to replicate this in the EMIF-AD biomarker discovery study. Further analyses will also examine single and multimodal biomarker classifiers of other endophenotypes. Conclusions These two studies could be used to identify novel and replicate previously identified single modality biomarker findings. Furthermore the impact of combining the additional modalities with these findings will be discussed. Computational and technical challenges encountered and the bioinformatics pipeline devised in the multimodal analysis of the ANM-MMB cohort will be used to inform the analysis pipeline of the EMIF-AD biomarker discovery study as a replication.

[Significance]: The environment can elicit biological responses such as oxidative stress (OS) and inflammation as a consequence of chemical, physical, or psychological changes. As population studies are essential for establishing these environment-organism interactions, biomarkers of OS or inflammation are critical in formulating mechanistic hypotheses., [Recent Advances]: By using examples of stress induced by various mechanisms, we focus on the biomarkers that have been used to assess OS and inflammation in these conditions. We discuss the difference between biomarkers that are the result of a chemical reaction (such as lipid peroxides or oxidized proteins that are a result of the reaction of molecules with reactive oxygen species) and those that represent the biological response to stress, such as the transcription factor NRF2 or inflammation and inflammatory cytokines., [Critical Issues]: The high-throughput and holistic approaches to biomarker discovery used extensively in large-scale molecular epidemiological exposome are also discussed in the context of human exposure to environmental stressors., [Future Directions]: We propose to consider the role of biomarkers as signs and to distinguish between signs that are just indicators of biological processes and proxies that one can interact with and modify the disease process., L.S. is supported by an Integrative Toxicology Training Partnership (ITTP) PhD studentship from the Medical Research Council (MRC). A.H. is recipient of a PhD studentship from the BHF. A.C. and G.M. are funded by SAF2013-43271-R of the Spanish Ministry of Economy and Competitiveness and European Regional Development Fund, Competitiveness Operational Program 2014–2020, grant P_37_732/2016 REDBRAIN. D.B. is supported by the H2020 MSCA project PANDORA (GA 671881) and by the Cluster project Medintech granted by the Italian MIUR. A.H. is a recipient of a BHF PhD studentship.

The formation, maintenance, and repair of epithelial barriers are of critical importance for whole-body homeostasis. However, the molecular events involved in epithelial tissue maturation are not fully established. To this end, we investigated the molecular processes involved in renal epithelial proximal-tubule monolayer maturation utilizing transcriptomic, metabolomic, and functional parameters. We uncovered profound dynamic alterations in transcriptional regulation, energy metabolism, and nutrient utilization over the maturation process. Proliferating cells exhibited high glycolytic rates and high transcript levels for fatty acid synthesis genes (FASN), whereas matured cells had low glycolytic rates, increased oxidative capacity, and preferentially expressed genes for beta oxidation. There were dynamic alterations in the expression and localization of several adherens (CDH1, -4, and -16) and tight junction (TJP3 and CLDN2 and -10) proteins. Genes involved in differentiated proximal-tubule function, cilium biogenesis (BBS1), and transport (ATP1A1 and ATP1B1) exhibited increased expression during epithelial maturation. Using TransAM transcription factor activity assays, we could demonstrate that p53 and FOXO1 were highly active in matured cells, whereas HIF1A and c-MYC were highly active in proliferating cells. The data presented here will be invaluable in the further delineation of the complex dynamic cellular processes involved in epithelial cell regulation.

The use of omics represents a shift in approach for environmental epidemiology and exposure science. In this article, the aspects of the use of omics that will require further development in the near future are discussed, including (a) the underlying causal interpretation and models; (b) the “meet-in-the-middle” concept, with examples; (c) the role of “calibration” of measurements; and (d) the role of life-course epidemiology and the related development of adequate biostatistical models. Environ. Mol. Mutagen. 54:461-467, 2013. © 2013 Wiley Periodicals, Inc.

A novel stepwise classification approach for predicting the metabolic fate of substituted anilines, based on calculated physicochemical parameters of the parent anilines, was developed. Based on multivariate pattern recognition methods (PLS-DA or soft independent modelling of class analogy [SIMCA]), these models allowed prediction of N-acetylation and subsequent N-oxanilic acid formation. These classification methods provided an improved classification success when compared with existing quantitative structure-metabolism relationship models for substituted anilines. Modelling the physicochemical properties of the N-acetylated compounds was considered as an addition to the stepwise model. Inclusion of parameters describing the N-acetyl moiety had little effect on the predictive ability of a stepwise parent to N-acetyl to N-oxanilic acid PLS-DA model, and had a negative impact on that of SIMCA models. This was attributed to the relatively small contribution to the total parameter variance caused by differences arising as a result of N-acetylation compared to the contribution made by the substituent effects. Calculation of physicochemical properties incorporating the effect of solvation using ab initio methods improved the classification model in terms of both the visual separation in multivariate projections and prediction accuracy.

Dietary fiber may modulate the environment of the intestinal lumen, alter the intestinal microflora populations, and influence the immune response and disease risk. Epidemiological investigations have suggested that higher fiber intake is associated with lower overall mortality, in particular from cardiovascular and digestive tract diseases. Here a panel of 17 cytokines and chemokines were measured in plasma of 88 cancer-free subjects sampled within the Italian EPIC-Italy cohort. A statistically significant inverse association (p-trend = 0.01) was observed for cereal fiber and cytokines included in the main factor in factor analysis (IL-1β, IL-4, IL-5, IL-6, IL-13, and TNF-α), which alone explained 35.5% of variance. Our study suggests that fiber intake, especially cereal fiber, may be associated with a decreased level of pro-inflammatory cytokines.

Toxicity testing is vital to protect human health from exposure to toxic chemicals in the environment. Furthermore, combining novel cellular models with molecular profiling technologies, such as metabolomics can add new insight into the molecular basis of toxicity and provide a rich source of biomarkers that are urgently required in a 21st Century approach to toxicology. We have used an NMR-based metabolic profiling approach to characterise for the first time the metabolome of the RPTEC/TERT1 cell line, an immortalised non-tumour human renal epithelial cell line that recapitulates phenotypic characteristics that are absent in other in vitro renal cell models. RPTEC/TERT1 cells were cultured with either the dosing vehicle (DMSO) or with exposure to one of six compounds (nifedipine, potassium bromate, monuron, D-mannitol, ochratoxin A and sodium diclofenac), several of which are known to cause renal effects. Aqueous intracellular and culture media metabolites were profiled by (1)H NMR spectroscopy at 6, 24 and 72 hours of exposure to a low effect dose (IC(10)). We defined the metabolome of the RPTEC/TERT1 cell line and used a principal component analysis approach to derive a panel of key metabolites, which were altered by chemical exposure. By considering only major changes (±1.5 fold change from control) across this metabolite panel we were able to show specific alterations to cellular processes associated with chemical treatment. Our findings suggest that metabolic profiling of RPTEC/TERT1 cells can report on the effect of chemical exposure on multiple cellular pathways at low-level exposure, producing different response profiles for the different compounds tested with a greater number of major metabolic effects observed in the toxin treated cells. Importantly, compounds with established links to chronic renal toxicity produced more diverse and severe perturbations to the cellular metabolome than non-toxic compounds in this model. As these changes can be rationalised with the different pharmacological and toxicity profiles of the chemicals it is suggested that metabolic profiling in the RPTEC/TERT1 model would be useful in investigating the mechanism of action of toxins at a low dose.

1. A study of the rates and routes of excretion of 3-fluoro-[U-(14)C]aniline following intraperitoneal administration to male bile-cannulated rats by liquid scintillation counting (LSC) gave a total recovery of approximately 90% in the 48 h following dosing, with the majority of the dose being excreted in the urine during the first 24 h (approximately 49%). 2. The total recovery as determined by (19)F-nuclear magnetic resonance ((19)F-NMR) was approximately 49%, with the majority of the dose excreted in the first 24 h (approximately 41%). The comparatively low recovery in comparison to that obtained from LSC was due to matrix effects in bile and a contribution from metabolic defluorination. 3. High-performance liquid chromatography with radiometric profiling of urine and bile revealed a complex pattern of metabolites with the bulk of the dose excreted as a single peak. 4. Ultra-performance liquid chromatography-orthogonal acceleration time of flight mass spectrometry profiling also showed a complex pattern of metabolites, detecting approximately 21 metabolites of 3-fluoroaniline (3-FA) with six of these detected only in urine and four solely in bile. 5. (19)F-NMR revealed the presence of the parent compound and 15 metabolites in urine collected during the first 24 h after -dosing. The matrix effects of bile on (19)F-NMR spectroscopy made metabolite profiling impractical for this biofluid. The major metabolite of 3-FA was identified as 2-fluoro-4-acetamidophenol-sulfate.

The non-selective β-adrenergic receptor antagonist propranolol [1-(isopropylamino)-3-(1-naphthoxy)-2-propanol] is metabolised extensively in vivo . Enumerating and identifying the many metabolites that result from multiple biotransformations provides a considerable analytical challenge, greatly aided by efficient chromatography coupled to sensitive mass spectrometric detection. Here the use of the newly introduced high-resolution technique of “ultra performance liquid chromatography” (UPLC) linked to quadrupole time-of-flight mass spectrometry (TOFMS) with simultaneous [ 14 C]-radioflow detection was applied to rapid metabolite profiling. [ 14 C]-propranolol, dosed intraperitoneally to rat at 25 mg kg −1 and 200 μCi kg −1 was used as a model compound for this evaluation. Some 14 metabolites were detected in the urine by this technique including a number of conjugated metabolites such as sulphates, several isobaric glucuronides and two novel di-glucuronides.

In biofluid NMR spectroscopy, the frequency of each resonance is typically calibrated by addition of a reference compound such as 3-(trimethylsilyl)-propionic acid- d 4 (TSP) to the sample. However biofluids such as serum cannot be referenced to TSP, due to shifts resonance caused by binding to macromolecules in solution. In order to overcome this limitation we have developed algorithms, based on analysis of derivative spectra, to locate and calibrate (1)H NMR spectra to the alpha-glucose anomeric doublet. We successfully used these algorithms to calibrate 77 serum (1)H NMR spectra and demonstrate the greater reproducibility of the calculated chemical-shift corrections ( r = 0.97) than those generated by manual alignment ( r = 0.8-0.88). Hence we show that these algorithms provide robust and reproducible methods of calibrating (1)H NMR of serum, plasma, or any biofluid in which glucose is abundant. Precise automated calibration of complex biofluid NMR spectra is an important tool in large-scale metabonomic or metabolomic studies, where hundreds or even thousands of spectra may be analyzed in high-resolution by pattern recognition analysis.

We present a novel application of the heteronuclear statistical total correlation spectroscopy (HET-STOCSY) approach utilizing statistical correlation between one-dimensional 19F/1H NMR spectroscopic data sets collected in parallel to study drug metabolism. Parallel one-dimensional (1D) 800 MHz 1H and 753 MHz 19F{1H} spectra (n = 21) were obtained on urine samples collected from volunteers (n = 6) at various intervals up to 24 h after oral dosing with 500 mg of flucloxacillin. A variety of statistical relationships between and within the spectroscopic datasets were explored without significant loss of the typically high 1D spectral resolution, generating 1H-1H STOCSY plots, and novel 19F-1H HET-STOCSY, 19F-19F STOCSY, and 19F-edited 1H-1H STOCSY (X-STOCSY) spectroscopic maps, with a resolution of approximately 0.8 Hz/pt for both nuclei. The efficient statistical editing provided by these methods readily allowed the collection of drug metabolic data and assisted structure elucidation. This approach is of general applicability for studying the metabolism of other fluorine-containing drugs, including important anticancer agents such as 5-fluorouracil and flutamide, and is extendable to any drug metabolism study where there is a spin-active X-nucleus (e.g., 13C, 15N, 31P) label present.

Metabolic profiling (metabonomics/metabolomics) is the untargeted analysis of metabolic composition in a biological sample, and is principally aimed at biomarker discovery. The frequent use of noninvasive biofluid analysis in metabonomics is suited to the clinic and facilitates dynamic monitoring. Analytical protocols for metabolic biomarkers are potentially robust because a metabolite is the same chemical entity irrespective of its origin, facilitating ‘bench-to-bedside’ translational research. Metabonomics can make an impact at several points in the drug-development process: target identification; lead discovery and optimization; preclinical efficacy and safety assessment; mode-of-action and mechanistic toxicology; patient stratification; and clinical pharmacological monitoring. This review describes and exemplifies the latest developments in each of these areas, including the impact of new data and chemical analytical techniques. The future goals for metabonomics are the validation of existing biomarkers, in terms of mechanism and translation to man, together with a focus on characterizing the individual (‘personalized healthcare’).

Isotopic enrichment of compounds dosed in drug metabolism studies greatly enhances the spectroscopic differentiation of xenobiotic and endogenous metabolites. We show the application of 13 C 15 N labelling of a model drug (4-cyanoaniline) as an aid to metabonomic studies of UPLC-MS-generated metabolic data sets.

Background Preterm birth (PB) and fetal growth restriction (FGR) convey the highest risk of perinatal mortality and morbidity, as well as increasing the chance of developing chronic disease in later life. Identifying early in pregnancy the unfavourable maternal conditions that can predict poor birth outcomes could help their prevention and management. Here we used an exploratory metabolic profiling approach (metabolomics) to investigate the association between birth outcomes and metabolites in maternal urine collected early in pregnancy as part of the prospective mother–child cohort Rhea study. Metabolomic techniques can simultaneously capture information about genotype and its interaction with the accumulated exposures experienced by an individual from their diet, environment, physical activity or disease (the exposome). As metabolic syndrome has previously been shown to be associated with PB in this cohort, we sought to gain further insight into PB-linked metabolic phenotypes and to define new predictive biomarkers. Methods Our study was a case–control study nested within the Rhea cohort. Major metabolites (n = 34) in maternal urine samples collected at the end of the first trimester (n = 438) were measured using proton nuclear magnetic resonance spectroscopy. In addition to PB, we used FGR in weight and small for gestational age as study endpoints. Results We observed significant associations between FGR and decreased urinary acetate (interquartile odds ratio (IOR) = 0.18 CI 0.04 to 0.60), formate (IOR = 0.24 CI 0.07 to 0.71), tyrosine (IOR = 0.27 CI 0.08 to 0.81) and trimethylamine (IOR = 0.14 CI 0.04 to 0.40) adjusting for maternal education, maternal age, parity, and smoking during pregnancy. These metabolites were inversely correlated with blood insulin. Women with clinically induced PB (IPB) had a significant increase in a glycoprotein N-acetyl resonance (IOR = 5.84 CI 1.44 to 39.50). This resonance was positively correlated with body mass index, and stratified analysis confirmed that N-acetyl glycoprotein and IPB were significantly associated in overweight and obese women only. Spontaneous PB cases were associated with elevated urinary lysine (IOR = 2.79 CI 1.20 to 6.98) and lower formate levels (IOR = 0.42 CI 0.19 to 0.94). Conclusions Urinary metabolites measured at the end of the first trimester are associated with increased risk of negative birth outcomes, and provide novel information about the possible mechanisms leading to adverse pregnancies in the Rhea cohort. This study emphasizes the potential of metabolic profiling of urine as a means to identify novel non-invasive biomarkers of PB and FGR risk.

Background Observational evidence suggests that intake of fruits, and of dietary antioxidants would be protective against symptoms and severity of asthma in children. Intervention studies with single or combined antioxidant supplements have so far been disappointing. To date, there have been no interventions using fresh fruit on asthmatic children. We plan to carry out a 3 month randomised controlled trial (RCT) in 360 children, to establish whether increasing fruit intake could be useful in the management of childhood asthma.

Background: Developmental periods in early life may be particularly vulnerable to impacts of environmental exposures. Human research on this topic has generally focused on single exposure–health effect relationships. The “exposome” concept encompasses the totality of exposures from conception onward, complementing the genome. Objectives: The Human Early-Life Exposome (HELIX) project is a new collaborative research project that aims to implement novel exposure assessment and biomarker methods to characterize early-life exposure to multiple environmental factors and associate these with omics biomarkers and child health outcomes, thus characterizing the “early-life exposome.” Here we describe the general design of the project. Methods: In six existing birth cohort studies in Europe, HELIX will estimate prenatal and postnatal exposure to a broad range of chemical and physical exposures. Exposure models will be developed for the full cohorts totaling 32,000 mother–child pairs, and biomarkers will be measured in a subset of 1,200 mother–child pairs. Nested repeat-sampling panel studies (n = 150) will collect data on biomarker variability, use smartphones to assess mobility and physical activity, and perform personal exposure monitoring. Omics techniques will determine molecular profiles (metabolome, proteome, transcriptome, epigenome) associated with exposures. Statistical methods for multiple exposures will provide exposure–response estimates for fetal and child growth, obesity, neurodevelopment, and respiratory outcomes. A health impact assessment exercise will evaluate risks and benefits of combined exposures. Conclusions: HELIX is one of the first attempts to describe the early-life exposome of European populations and unravel its relation to omics markers and health in childhood. As proof of concept, it will form an important first step toward the life-course exposome. Citation: Vrijheid M, Slama R, Robinson O, Chatzi L, Coen M, van den Hazel P, Thomsen C, Wright J, Athersuch TJ, Avellana N, Basagaña X, Brochot C, Bucchini L, Bustamante M, Carracedo A, Casas M, Estivill X, Fairley L, van Gent D, Gonzalez JR, Granum B, Gražulevičienė R, Gutzkow KB, Julvez J, Keun HC, Kogevinas M, McEachan RR, Meltzer HM, Sabidó E, Schwarze PE, Siroux V, Sunyer J, Want EJ, Zeman F, Nieuwenhuijsen MJ. 2014. The Human Early-Life Exposome (HELIX): project rationale and design. Environ Health Perspect 122:535–544; http://dx.doi.org/10.1289/ehp.1307204

The development of hepatoma-based in vitro models to study hepatocyte physiology is an invaluable tool for both industry and academia. Here, we develop an in vitro model based on the HepG2 cell line that produces chenodeoxycholic acid, the main bile acid in humans, in amounts comparable to human hepatocytes. A combination of adenoviral transfections for CCAAT/enhancer-binding protein beta (C/EBP beta), hepatocyte nuclear factor 4 alpha (HNF4 alpha), and constitutive androstane receptor (CAR) decreased intracellular glutamate, succinate, leucine, and valine levels in HepG2 cells, suggestive of a switch to catabolism to increase lipogenic acetyl CoA and increased anaplerosis to replenish the tricarboxylic acid cycle. Transcripts of key genes involved in bile acid synthesis were significantly induced by approximately 160-fold. Consistently, chenodeoxycholic acid production rate was increased by more than 20-fold. Comparison between mRNA and bile acid levels suggest that 12-alpha hydroxylation of 7-alpha-hydroxy-4-cholesten-3-one is the limiting step in cholic acid synthesis in HepG2 cells. These data reveal that introduction of three hepatocyte-related transcription factors enhance anabolic reactions in HepG2 cells and provide a suitable model to study bile acid biosynthesis under pathophysiological conditions.

The use of omics represents a shift in approach for environmental epidemiology and exposure science. In this article, the aspects of the use of omics that will require further development in the near future are discussed, including (a) the underlying causal interpretation and models; (b) the "meet-in-the-middle" concept, with examples; (c) the role of "calibration" of measurements; and (d) the role of life-course epidemiology and the related development of adequate biostatistical models.

Biofluids are by far the most commonly studied sample type in metabolic profiling studies, encompassing blood, urine, cerebrospinal fluid, cell culture media and many others. A number of these fluids can be obtained at a high sampling frequency with minimal invasion, permitting detailed characterisation of dynamic metabolic events. One of the attractive properties of solution-state metabolomics is the ability to generate profiles from these fluids following simple preparation, allowing the analyst to gain a naturalistic, largely unbiased view of their composition that is highly representative of the in vivo situation. Solution-state samples can also be generated from the extraction of tissue or cellular samples that can be tailored to target metabolites with particular properties. Nuclear magnetic resonance (NMR) provides an excellent technique for profiling these fluids and is especially adept at characterising complex solutions. Profiling biofluid samples by NMR requires appropriate preparation and experimental conditions to overcome the demands of varied sample matrices, including those with high protein, lipid or saline content, as well as the presence of water in aqueous samples.

Background: Predictive disease risk biomarkers that can be linked to exposure have proved difficult to identify in case-control studies.Methods: Parallel statistical analysis of the correlation between 1H NMR profiles from plasma samples collected before disease onset (EPIC cohort), versus exposure to dietary compounds, and follow-up disease endpoints (colon and breast cancer) was performed.Results: Metabonomic signatures associated with colon cancer and dietary fiber intake (a protective factor according to epidemiological studies) were identified.Conclusion: This implementation of the novel “meet-in-the-middle” analytical strategy indicates how case-control studies nested in prospectively collected cohorts may reveal intermediate biomarkers linking exposure and disease.

Trichostatin A (TSA) is a histone deacetylase inhibitor that has antiproliferative and differentiation-inducing effects on cancer cells, and in cultures of primary hepatocytes has been shown to maintain xenobiotic metabolic capacity. Using an NMR-based metabolic profiling approach, we evaluated if the endogenous metabolome was stabilized and the normal metabolic phenotype retained in this model. Aqueous soluble metabolites were extracted from isolated rat hepatocytes after 44 and 92 h exposure to TSA (25 muM) together with time-matched controls and measured by (1)H NMR spectroscopy. Multivariate analysis showed a clear difference in the global metabolic profile over time in control samples, while the TSA treated group was more closely clustered at both time points, suggesting that treatment reduced the time related effect on metabolism that was observed in the control. TSA treatment was associated with decreases in glycerophosphocholine, 3-hydroxybutyric acid, glycine and adenosine, an increase in glycogen, and a reduction in the decrease of inosine, hypoxanthine, and glutathione over time. Collectively, our data suggest that TSA treatment reduces the loss of a normal metabolic phenotype in cultured primary hepatocytes, improving the model as a tool to study endogenous liver metabolism, xenobiotic metabolism, and potentially affecting the accuracy of all biological assays in this system.

2,3-dimethoxy-1,4-naphthoquinone (CAS-RN 6959-96-3) (DMNQ) and 2-methyl-1,4-naphthoquinone (CAS-RN 58-27-5) (MNQ:menadione) are effective one electron redox cycling chemicals in vitro. In addition, in vitro MNQ forms a thioether conjugate with glutathione by nucleophilic attack at the third carbon. In contrast, here we demonstrate that in vivo the major metabolic route is directly to the dihydronaphthoquinone for both DMNQ and MNQ followed by conjugation to mono- and di-glucuronides and sulfate. Analysis of urine and bile showed that glutathione conjugation of MNQ was only a very minor route of metabolism. DMNQ was distributed to all tissues including the brain, and MNQ was much less widely distributed. For DMNQ tissue half-life, in particular for the heart, was considerably longer than the plasma half-life. For both DMNQ and MNQ, urine 8-oxo-7,8-dihydro-2'-deoxyguanosine and liver transcriptomic analysis failed to show any evidence of redox stress. Oxidized glutathione (GSSG) in liver increased significantly at the 10 min postdosing time point only. Metabonomic analysis 96 h after DMNQ administration indicated decreased liver glucose and increased lactate and creatine suggesting an impairment of oxidative metabolism. We conclude that in vivo DMNQ and MNQ are primarily two electron reduced to the dihydronaphthoquinones and undergo little one electron redox cycling. For DMNQ, disruption of cellular oxidative metabolism may be a primary mechanism of toxicity rather than redox stress.

We demonstrate here a new variant on a statistical spectroscopic method for recovering structural information on unstable intermediates formed in reaction mixtures. We exemplify this approach with respect to the internal acyl migration reactions of 1-beta-O-acyl glucuronides (AGs), which rearrange at neutral or slightly alkaline pH on a minute to hour time scale to yield a series of positional glucuronide ring isomers and alpha/beta anomers from the 1-beta (starting material), i.e. 2-beta, 2-alpha, 1-alpha, 3-beta, 3-alpha, and 4-beta, 4-alpha isomers together with the aglycon and alpha- and beta-glucuronic acid hydrolysis products. Multiple sequential 800 MHz cryoprobe (1)H NMR spectra (1D and 2D J-resolved, JRES) were collected on a 5.1 mM solution of a synthetic model drug glucuronide, 1-beta-O-acyl (S)-alpha-methyl phenylacetyl glucuronide (MPG) in 0.1 M sodium phosphate buffer in D2O at pD 7.4 over 18 h to monitor the reaction which leads to the formation of the eight positional isomers and hydrolysis products. As the reaction proceeds and new isomers form, the NMR signal intensities vary accordingly allowing the application of a novel kinetic variant on statistical total correlation spectroscopy (K-STOCSY) method to recover the connectivities between proton signals on the same reacting molecule based on their intensity covariance through time. We performed K-STOCSY analysis on both the standard 1D NMR spectra and the skyline projected singlets of the (1)H-(1)H JRES NMR spectra through time, i.e. the K-JRES-STOCSY experimental variant, which increases the effective spectral dispersion and is ideally suited for the analysis of heavily overlapped spin systems. High statistical correlations were observed between mutarotated alpha- and beta-anomers of individual positional isomers, as well as directly acyl migrated products and anticorrelation observed between signals from compounds that were being depleted as others increased, e.g. between the 1-beta and 2-alpha/2-beta isomers. This statistical kinetic approach enabled the recovery of structural connectivity information on all isomers allowing unequivocal resonance assignment, and this approach to spectroscopic information recovery has wider potential uses in the study of reactions that occur on the second-to-minute time scale in conditions where multiple sequential NMR spectra can be collected. JRES-STOCSY is also of potential use as a method for recovering spectroscopic information in highly overlapped NMR signals and spin systems in other types of complex mixture analysis.

Recent changes in the European legislation of chemical-related substances have forced the scientific community to speed up the search for alternative methods that could partly or fully replace animal experimentation. The Sixth Framework Program project carcinoGENOMICS was specifically raised to develop omics-based in vitro screens for testing the carcinogenic potential of chemical compounds in a pan-European context. This paper provides an in-depth analysis of the complexity of choosing suitable reference compounds used for creating and fine-tuning the in vitro carcinogenicity assays. First, a number of solid criteria for the selection of the model compounds are defined. Secondly, the strategy followed, including resources consulted, is described and the selected compounds are briefly illustrated. Finally, limitations and problems encountered during the selection procedure are discussed. Since selecting an appropriate set of chemicals is a frequent impediment in the early stages of similar research projects, the information provided in this paper might be extremely valuable. Crown Copyright © 2008.

The human metabolome-the complement of small molecule metabolites present in biofluids and tissues-represents a significant part of the internal chemical milieu and is therefore an important aspect of the human exposome. Metabolic profiling approaches, commonly referred to as metabonomics or metabolomics, permit detailed and efficient characterisation of human biospecimens; application to population studies holds great promise for uncovering new associations and causal relationships between environmental factors and chronic disease. In addition to the insight metabolic information can provide, metabolic phenotypes anchor other molecular readouts and help formulate a systems-level interpretation of biological phenomena. In this commentary, we discuss the general approach for applying metabolic profiling in exposome studies, alongside recent exemplars. We also comment on the complexity and dynamism of the metabolome and highlight both the limitations such properties impart and the requirements for dealing with such issues in real-world phenotyping studies. Given that several large-scale exposome studies are now underway, we offer a perspective on current and future challenges that will need to be addressed to maximise their utility in environmental health research.

[14C]-5-chloro-1,3-benzodioxol-4-amine was administered intraperitoneally (i.p.) to bile duct-cannulated rats (Alpk:ApfSD, Wistar derived) at 25 mg kg−1 to determine the rates and routes of excretion of the compound and to investigate its metabolic fate. A total of 89.1% of the dose was excreted in the 48 h following administration, the majority being recovered in the urine during the first 12 h. The main metabolite in both urine and bile, detected by high-performance liquid chromatography (HPLC) with radioprofiling and mass spectrometry, was identified as a demethylenated monosulfate conjugate. Unchanged parent compound formed a major component of the radiolabel excreted in urine and, in addition to unchanged parent and demethylenated sulphate conjugate, a large number of minor metabolites were detected in urine and bile. The overall metabolic fate of 5-chloro-1,3-benzodioxol-4-amine in the rat was complex, with some similarities to previously studied methylenedioxyphenyl compounds.

As end products of xenobiotic metabolism via glutathione conjugation, mercapturic acids (MCAs) can be used as markers to indicate exposure to allylic compounds as well as the rate and efficiency of their excretion. In addition, the formation of certain MCAs indicates metabolism via the known toxin acrolein, a strong electrophile. High-resolution 1H NMR spectroscopy has been employed to quantitatively measure the presence of MCAs in the urine of Sprague-Dawley rats, collected in the 8 h following 25 and 50 mgkg(-1) i.p. doses of allyl formate (AF), a model toxin. 3-Hydroxypropylmercapturic acid (HPMA) was found to be the only 1H NMR-observable MCA excreted in the urine, exhibiting a percentage recovery of approximately 20% at the 25 mgkg(-1) dose level, and approximately 30% at the 50 mgkg(-1) dose level.

Breast cancer is associated with adverse outcomes in patients with the metabolic syndrome phenotype. To study this further, we examined the relationship between serum metabolite levels and the components of metabolic syndrome with treatment outcomes in breast cancer.A total of 88 women with measurable breast cancer were studied; their serum metabolites as assessed by (1)H nuclear magnetic resonance spectroscopy, blood pressure, lipids, glucose, body mass index and waist circumference were recorded and correlated with treatment response.We identified metabolic syndrome in approximately half of our cohort (42 patients) and observed a significant trend (P = 0.03) of increased incidence of metabolic syndrome in partial response (33.3%), stable disease (42.9%) and progressive disease groups (66.1%). High blood sugar predicted a poor response (P0.001). Logistic regression of metabonomic data demonstrated that high lactate (P = 0.03) and low alanine (P = 0.01) combined with high glucose (P = 0.01) were associated with disease progression.Metabolic syndrome is commonly observed in metastatic breast cancer and these patients have poorer outcomes. These data, which support our previous findings, suggest that high blood glucose as part of metabolic syndrome is associated with a poor response in breast cancer. They also validate new therapeutic approaches that focus on metabolism.

Using transcriptomic and metabolomic measurements from the NCI60 cell line panel, together with a novel approach to integration of molecular profile data, we show that the biochemical pathways associated with tumour cell chemosensitivity to platinum-based drugs are highly coincident, i.e. they describe a consensus phenotype. Direct integration of metabolome and transcriptome data at the point of pathway analysis improved the detection of consensus pathways by 76%, and revealed associations between platinum sensitivity and several metabolic pathways that were not visible from transcriptome analysis alone. These pathways included the TCA cycle and pyruvate metabolism, lipoprotein uptake and nucleotide synthesis by both salvage and de novo pathways. Extending the approach across a wide panel of chemotherapeutics, we confirmed the specificity of the metabolic pathway associations to platinum sensitivity. We conclude that metabolic phenotyping could play a role in predicting response to platinum chemotherapy and that consensus-phenotype integration of molecular profiling data is a powerful and versatile tool for both biomarker discovery and for exploring the complex relationships between biological pathways and drug response., Author Summary Resistance to chemotherapy drugs in cancer sufferers is very common. Using a panel of 59 cell lines obtained from different types of cancer we study the links between the genes and metabolites measured in these cells and the resistance the cells show to common cancer drugs containing platinum. In order to combine the information given by the genes and metabolites we introduce a new pathway-based approach, which allows us to explore synergy between the different types of data. We then extend the procedure to look at a wider panel of drugs and show that the pathways we found were associated with platinum are not just the pathways which are frequently selected for a large number of drugs. Given the increasing use of multiple sets of measurements (genes, metabolites, proteins etc.) in biological studies, we demonstrate a powerful, yet straightforward method for dealing with the resulting large datasets and integrating their knowledge. We believe that this work could contribute to developing a personalised medicine approach to treating tumours, where the genetic and metabolic changes in the tumour are measured and then used for prediction of the optimal treatment regime.