MetMSLine: an automated and fully integrated pipeline for rapid processing of high-resolution LC-MS metabolomic datasets

Atrazine exposure induces abnormal swimming behavior of tadpoles under light and/or dark stimuli: A comprehensive multi-omics insights from eyes and brain

Atrazine, a widely used pesticide, can damage organs and affect the respond ability of aquatic animals to environmental stimuli. To explore the amphibian response to light and/or dark stimuli following pesticide exposure, a comparative analysis was conducted on the swimming behavior of Pelophylax nigromaculatus tadpoles (Gs 8 - Gs 36, from fertilised egg to forelimb appearance) following a 60-day exposure period to atrazine. Additionally, an examination of ocular structures, eye metabolism, and brain transcription was undertaken across the treatment groups. This comprehensive approach aimed to elucidate how pollutants disrupt an individual's response to light-dark stimuli by interfering with both the light-sensing organs (eyes) and the signal-processing organ (brain). Under light conditions, atrazine exposure significantly increased the total movement distance of tadpoles. In contrast, under dark conditions, atrazine induced more pronounced hyperactivity, with significant elevations in moving distance, maximum acceleration, average activity, and moving frequency. Additionally, under light/dark alternating conditions, atrazine specifically enhanced moving frequency compared to control groups. Anatomical analysis of the eyes showed that atrazine exposure led to a notable increase in the thickness of the retinal pigmented epithelium (RPE), photoreceptor layer (PL), and inner plexiform layer (IPL) in tadpoles, while significantly decreasing the thickness of the inner nuclear layer (INL), outer nuclear layer (ONL), and ganglion cell layer (GCL). Metabolic analysis of the eyes indicated significant alterations in serotonergic synapse, arachidonic acid metabolism, and linoleic acid metabolism pathways due to atrazine exposure. Additionally, transcriptomic analysis of brain tissue revealed altered neutrophil activation, granulocyte activation, and leukocyte migration pathways, accompanied by upregulated gene expression of TNIP1, HAMP, CORO1A, LTA4H, RARRES2, and C1QA. The above multi omics evidence suggests that exposure to atrazine can cause structural damage and metabolic disorders in tadpole eyes, as well as abnormal expression of photosensitive genes in the brain, ultimately leading to abnormal photoresponsive behavior in amphibians. This discovery provides a new theoretical basis for the molecular mechanism of pesticide pollutants interfering with the environmental adaptability of aquatic animals.

The influences of pulse exposure versus continuous exposure to cadmium are different: Mechanisms elucidated from motor behavior and brain in amphibian larvae

Cadmium (Cd) is a common environmental pollutant in aquatic ecosystems, often present in the form of pulses. However, the toxic effects of Cd on aquatic animals have been found to come primarily from continuous exposure, and there is little research on the effects of pulse exposure on animals. Here, the different effects of Cd exposure patterns on the motor behavior, brain histology and brain metabolism of Pelophylax nigromaculatus tadpoles (20 per parallel group) were explored. Our study showed that both continuous (CECd) and pulse exposure of Cd (PECd) led to a significant reduction in the moving distance (57.7 % vs 42.5 %), average speed (57.7 % vs 42.6 %) and moving frequency (45.3 % vs 7.9 %). Furthermore, both CECd and PECd led to the expansion and enlargement of the perivascular space of the cerebrum. Cd exposure increased the blood-brain barrier permeability, leading to brain cell swelling, and destroyed brain granular cells, Purkinje cells and brain gliacytes. Non-targeted metabolomics found a significant effect of Cd exposure on nucleic acid and amino acid metabolism. The most significant increases were observed in adenosine (99.4 %), threonine (47.9 %), citrulline (123.9 %), and erythrose 4p (184.1 %). It is noteworthy that the CECd exerted a more pronounced influence on brain structure, metabolism, and movement behaviour than the PECd. This phenomenon can be attributed to the fact that in PECd exposure, the individual's intermittent exposure to clean water partially offsets the effects of previous Cd exposure.

Aspirin Eugenol Ester Alleviates Energy Metabolism Disorders by Reducing Oxidative Damage and Inflammation in the Livers of Broilers Under High-Stocking-Density Stress

This study aimed to evaluate the effects of aspirin eugenol ester (AEE) on growth performance, oxidative liver damage, inflammation, and liver metabolomics in broilers under high-stocking-density (HSD) stress. A total of 360 broilers were divided into four groups: normal density (ND, 14/m2), high density (HD, 22/m2), ND-AEE (ND + 0.01% AEE), and HD-AEE (HD + 0.01% AEE). HSD decreased total antioxidant capacity, increased malondialdehyde (MDA) levels, and elevated the expression of cyclooxygenase-2 (COX-2) and microsomal prostaglandin E synthase-1 (mPGES-1) mRNA, which contributed to the reduced performance of broilers. Specifically, HSD caused abnormalities in linoleic acid metabolism, leading to elevated levels of Prostaglandin E2 (PGE2) and Leukotriene B4 (LTB4) synthesis, which aggravated inflammation, increased liver lipid levels, and impaired ATP production. AEE counteracted the decline in broiler production performance induced by HSD by enhancing total antioxidant capacity, reducing MDA levels, protecting the liver from oxidative damage, and maintaining mitochondrial oxidative phosphorylation. AEE positively regulated the linoleic acid metabolism by promoting the synthesis of γ-linolenic acid and phosphatidylcholine, which reduced the synthesis of COX-2 and mPGES-1. AEE alleviated the metabolic imbalance caused by HSD stress and enhanced the efficiency of mitochondrial fatty acid oxidation, which reduced excess lipid accumulation in the liver and promoted ATP production. In summary, this study provides strong support for the dietary addition of AEE to alleviate liver oxidative damage, inflammation, and energy metabolism disorders caused by HSD stress.

Different software processing affects the peak picking and metabolic pathway recognition of metabolomics data

In untargeted liquid chromatography‒mass spectrometry (LC‒MS) metabolomics studies, data preprocessing and metabolic pathway recognition are crucial for screening important pathways that are disturbed by diseases or restored by drugs. Here, we collected high-resolution mass spectrometry data of serum samples from 221 coronary heart disease (CHD) patients under two different chromatographic columns (BEH amide and C₁₈ column) and evaluated the three commonly used software programs (XCMS, Progenesis QI, MarkerView) from four aspects (including signal drift, peak number, metabolite annotation and metabolic pathway enrichment). The results showed that the data preprocessed by the three software programs have different degrees of signal drift, but the StatTarget could improve the data quality to meet the data analysis requirement after correction. In addition, XCMS surpassed other software in detection of real chromatographic peaks and Progenesis QI was the best performer in terms of the number of metabolite annotation. XCMS and Progenesis QI showed different performance in pathway enrichment. However, metabolic pathways based on the combination of XCMS and Progenesis QI had a high coincidence with Progenesis QI. In addition, we also reported that C₁₈ and amide columns were highly complementary and have great potential for cooperation in the context of metabolic pathways. In this study, the effects of different chromatographic columns and software pretreatments on metabolomics data were evaluated based on clinical large cohort samples, which will provide a reference for the metabolomics of clinical samples and guide subsequent mechanistic research.

Effects of antimony stress on growth, structure, enzyme activity and metabolism of Nipponbare rice (Oryza sativa L.) roots

Some antimony (Sb) contaminated areas are used for rice cultivation in response to economic demands. However, little is known about the effects of Sb stress on the growth and metabolism of rice roots. Thus, a hydroponic experiment was carried out on the growth, root anatomy, enzyme activity, and metabolism of Nipponbare rice (Oryza sativa L. ssp. japonica cv. Nipponbare) under varying levels of Sb (III) stress (0 mg L^-1, 10 mg L^-1, and 50 mg L^-1). With the increase of Sb concentration, rice root length and root fresh weight declined by 67.8 % and 90.5 % for 10 mg L^-1 Sb stress and 94.1 % and 98.4 % for 50 mg L^-1 Sb stress, respectively. Anatomical analysis of cross-sections of Sb-treated roots showed an increase in cell wall thickness and an increase in the number of cell mitochondria. The 10 mg L^-1 and 50 mg L^-1 Sb stress increased the activity of enzyme superoxide dismutase (SOD) in root cells by 1.94 and 2.40 times, respectively. Compared to the control, 10 mg L^-1 Sb treatment increased the activity of catalase (CAT) and peroxidase (POD), as well as the concentrations of antioxidant glutathione (GSH) in the root by 1.46, 1.38, and 0.52 times, respectively. However, 50 mg L^-1 Sb treatment significantly decreased the activity or content of CAT, POD and GSH by 28.1 %, 13.5 % and 28.2 %, respectively. Nontargeted LC/MS-based metabolomics analysis identified 23 and 13 significantly differential metabolites in rice roots exposed to 10 mg L^-1 and 50 mg L^-1 Sb, respectively, compared to the control. These differential metabolites were involved in four main metabolic pathways including the tricarboxylic acid cycle (TCA cycle), butanoate metabolism, alanine, aspartate and glutamate metabolism, and alpha-linolenic acid metabolism. Taken together, these findings indicate that Sb stress destroys the structure of rice roots, changes the activity of enzymes, and affects the metabolic pathway, thereby reducing the growth of rice roots and leading to toxicity.

Novel Biomarkers of Habitual Alcohol Intake and Associations With Risk of Pancreatic and Liver Cancers and Liver Disease Mortality

BACKGROUND

Alcohol is an established risk factor for several cancers, but modest alcohol-cancer associations may be missed because of measurement error in self-reported assessments. Biomarkers of habitual alcohol intake may provide novel insight into the relationship between alcohol and cancer risk.

METHODS

Untargeted metabolomics was used to identify metabolites correlated with self-reported habitual alcohol intake in a discovery dataset from the European Prospective Investigation into Cancer and Nutrition (EPIC; n = 454). Statistically significant correlations were tested in independent datasets of controls from case-control studies nested within EPIC (n = 280) and the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC; n = 438) study. Conditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for associations of alcohol-associated metabolites and self-reported alcohol intake with risk of pancreatic cancer, hepatocellular carcinoma (HCC), liver cancer, and liver disease mortality in the contributing studies.

RESULTS

Two metabolites displayed a dose-response association with self-reported alcohol intake: 2-hydroxy-3-methylbutyric acid and an unidentified compound. A 1-SD (log2) increase in levels of 2-hydroxy-3-methylbutyric acid was associated with risk of HCC (OR = 2.54, 95% CI = 1.51 to 4.27) and pancreatic cancer (OR = 1.43, 95% CI = 1.03 to 1.99) in EPIC and liver cancer (OR = 2.00, 95% CI = 1.44 to 2.77) and liver disease mortality (OR = 2.16, 95% CI = 1.63 to 2.86) in ATBC. Conversely, a 1-SD (log2) increase in questionnaire-derived alcohol intake was not associated with HCC or pancreatic cancer in EPIC or liver cancer in ATBC but was associated with liver disease mortality (OR = 2.19, 95% CI = 1.60 to 2.98) in ATBC.

CONCLUSIONS

2-hydroxy-3-methylbutyric acid is a candidate biomarker of habitual alcohol intake that may advance the study of alcohol and cancer risk in population-based studies.

A New Pipeline for the Normalization and Pooling of Metabolomics Data

Pooling metabolomics data across studies is often desirable to increase the statistical power of the analysis. However, this can raise methodological challenges as several preanalytical and analytical factors could introduce differences in measured concentrations and variability between datasets. Specifically, different studies may use variable sample types (e.g., serum versus plasma) collected, treated, and stored according to different protocols, and assayed in different laboratories using different instruments. To address these issues, a new pipeline was developed to normalize and pool metabolomics data through a set of sequential steps: (i) exclusions of the least informative observations and metabolites and removal of outliers; imputation of missing data; (ii) identification of the main sources of variability through principal component partial R-square (PC-PR2) analysis; (iii) application of linear mixed models to remove unwanted variability, including samples' originating study and batch, and preserve biological variations while accounting for potential differences in the residual variances across studies. This pipeline was applied to targeted metabolomics data acquired using Biocrates AbsoluteIDQ kits in eight case-control studies nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Comprehensive examination of metabolomics measurements indicated that the pipeline improved the comparability of data across the studies. Our pipeline can be adapted to normalize other molecular data, including biomarkers as well as proteomics data, and could be used for pooling molecular datasets, for example in international consortia, to limit biases introduced by inter-study variability. This versatility of the pipeline makes our work of potential interest to molecular epidemiologists.

Metabolomics reveals the "Invisible" detoxification mechanisms of Amaranthus hypochondriacus at three ages upon exposure to different levels of cadmium

To decipher the Cd hyperaccumulation and tolerance mechanisms of plants and increase phytoremediation efficiency, in this study, the physiological effects induced by environmentally relevant concentrations (0, 25 and 200 mg/kg) of Cd were characterized in Amaranthus hypochondriacus (K472) at three growth stages using LC/MS-based metabolomics. Metabolomic analysis identified 31, 29 and 30 significantly differential metabolites (SDMs) in K472 exposed to Cd at the early, intermediate and late stages of vegetative growth, respectively. These SDMs are involved in nine metabolic pathways responsible for antioxidation, osmotic balance regulation, energy supplementation and the promotion of metabolites that participate in phytochelatin (PC) synthesis. K472 at the intermediate stage of vegetative growth had the strongest tolerance to Cd with the combined action of Ala, Asp and Glu metabolism, purine metabolism, Gly, Ser and Thr metabolism and Pro and Arg metabolism. Among these crucial metabolic biomarkers, purine metabolism could be the primary regulatory target for increasing the Cd absorption of K472 for the restoration of Cd-contaminated soil.

A Metabolomic Study of Biomarkers of Habitual Coffee Intake in Four European Countries

SCOPE

The goal of this work is to identify circulating biomarkers of habitual coffee intake using a metabolomic approach, and to investigate their associations with coffee intake in four European countries.

METHODS AND RESULTS

Untargeted mass spectrometry-based metabolic profiling is performed on serum samples from 451 participants of the European Prospective Investigation on Cancer and Nutrition (EPIC) originating from France, Germany, Greece, and Italy. Eleven coffee metabolites are found to be associated with self-reported habitual coffee intake, including eight more strongly correlated (r = 0.25-0.51, p < 10E-07 ). Trigonelline shows the highest correlation, followed by caffeine, two caffeine metabolites (paraxanthine and 5-Acetylamino-6-amino-3-methyluracil), quinic acid, and three compounds derived from coffee roasting (cyclo(prolyl-valyl), cyclo(isoleucyl-prolyl), cyclo(leucyl-prolyl), and pyrocatechol sulfate). Differences in the magnitude of correlations are observed between countries, with trigonelline most highly correlated with coffee intake in France and Germany, quinic acid in Greece, and cyclo(isoleucyl-prolyl) in Italy.

CONCLUSION

Several biomarkers of habitual coffee intake are identified. No unique biomarker is found to be optimal for all tested populations. Instead, optimal biomarkers are shown to depend on the population and on the type of coffee consumed. These biomarkers should help to further explore the role of coffee in disease risk.

SimExTargId: a comprehensive package for real-time LC-MS data acquisition and analysis

Summary

Liquid chromatography mass spectrometry (LC-MS) is the favored method for untargeted metabolomic analysis of small molecules in biofluids. Here we present SimExTargId, an open-source R package for autonomous analysis of metabolomic data and real-time observation of experimental runs. This simultaneous, fully automated and multi-threaded (optional) package is a wrapper for vendor-independent format conversion (ProteoWizard), xcms- and CAMERA- based peak-picking, MetMSLine-based pre-processing and covariate-based statistical analysis. Users are notified of detrimental instrument drift or errors by email. Also included are two shiny applications, targetId for real-time MS2 target identification, and peakMonitor to monitor targeted metabolites.

Availability and implementation

SimExTargId is publicly available under GNU LGPL v3.0 license at https://github.com/JosieLHayes/simExTargId, which includes a vignette with example data. SimExTargId should be installed on a dedicated data-processing workstation or server that is networked to the LC-MS platform to facilitate MS1 profiling of metabolomic data.

Supplementary information

Supplementary data are available at Bioinformatics online.

The metaRbolomics Toolbox in Bioconductor and beyond

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub.

Smooth Factor Analysis (SFA) to Effectively Remove High Levels of Noise from Spectral Data Sets

Smooth factor analysis (SFA) is introduced as an effective method of removing heavy noise from spectral data sets. A modified form of the nonlinear iterative partial least squares (NIPALS) algorithm involving the smoothing of factors at each step is used in SFA. Compared with the conventional smoothing techniques for individual spectra, SFA is much more effective in the treatment of very noisy spectra (∼40% noise level). Smooth factor analysis invokes a large number of smooth factors to retain pertinent spectral information for high fidelity without distortion. This approach can be used as an effective general pretreatment procedure for multivariate spectral data analysis, such as principal component analysis (PCA) and partial least squares (PLS). This SFA method was also applied to the real experimental data, and its results successfully demonstrated the powerful potential for effective noise removal. Furthermore, this treatment is found to be very helpful to assist effective interpretation of two-dimensional correlation spectroscopy (2D-COS) spectra with very high noise level, which was not possible before.

From chromatogram to analyte to metabolite. How to pick horses for courses from the massive web resources for mass spectral plant metabolomics

The grand challenge currently facing metabolomics is the expansion of the coverage of the metabolome from a minor percentage of the metabolic complement of the cell toward the level of coverage afforded by other post-genomic technologies such as transcriptomics and proteomics. In plants, this problem is exacerbated by the sheer diversity of chemicals that constitute the metabolome, with the number of metabolites in the plant kingdom generally considered to be in excess of 200 000. In this review, we focus on web resources that can be exploited in order to improve analyte and ultimately metabolite identification and quantification. There is a wide range of available software that not only aids in this but also in the related area of peak alignment; however, for the uninitiated, choosing which program to use is a daunting task. For this reason, we provide an overview of the pros and cons of the software as well as comments regarding the level of programing skills required to effectively exploit their basic functions. In addition, the torrent of available genome and transcriptome sequences that followed the advent of next-generation sequencing has opened up further valuable resources for metabolite identification. All things considered, we posit that only via a continued communal sharing of information such as that deposited in the databases described within the article are we likely to be able to make significant headway toward improving our coverage of the plant metabolome.

compMS2Miner: An Automatable Metabolite Identification, Visualization, and Data-Sharing R Package for High-Resolution LC-MS Data Sets

A long-standing challenge of untargeted metabolomic profiling by ultrahigh-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) is efficient transition from unknown mass spectral features to confident metabolite annotations. The compMS2Miner (Comprehensive MS2 Miner) package was developed in the R language to facilitate rapid, comprehensive feature annotation using a peak-picker-output and MS2 data files as inputs. The number of MS2 spectra that can be collected during a metabolomic profiling experiment far outweigh the amount of time required for pain-staking manual interpretation; therefore, a degree of software workflow autonomy is required for broad-scale metabolite annotation. CompMS2Miner integrates many useful tools in a single workflow for metabolite annotation and also provides a means to overview the MS2 data with a Web application GUI compMS2Explorer (Comprehensive MS2 Explorer) that also facilitates data-sharing and transparency. The automatable compMS2Miner workflow consists of the following steps: (i) matching unknown MS1 features to precursor MS2 scans, (ii) filtration of spectral noise (dynamic noise filter), (iii) generation of composite mass spectra by multiple similar spectrum signal summation and redundant/contaminant spectra removal, (iv) interpretation of possible fragment ion substructure using an internal database, (v) annotation of unknowns with chemical and spectral databases with prediction of mammalian biotransformation metabolites, wrapper functions for in silico fragmentation software, nearest neighbor chemical similarity scoring, random forest based retention time prediction, text-mining based false positive removal/true positive ranking, chemical taxonomic prediction and differential evolution based global annotation score optimization, and (vi) network graph visualizations, data curation, and sharing are made possible via the compMS2Explorer application. Metabolite identities and comments can also be recorded using an interactive table within compMS2Explorer. The utility of the package is illustrated with a data set of blood serum samples from 7 diet induced obese (DIO) and 7 nonobese (NO) C57BL/6J mice, which were also treated with an antibiotic (streptomycin) to knockdown the gut microbiota. The results of fully autonomous and objective usage of compMS2Miner are presented here. All automatically annotated spectra output by the workflow are provided in the Supporting Information and can alternatively be explored as publically available compMS2Explorer applications for both positive and negative modes ( https://wmbedmands.shinyapps.io/compMS2_mouseSera_POS and https://wmbedmands.shinyapps.io/compMS2_mouseSera_NEG ). The workflow provided rapid annotation of a diversity of endogenous and gut microbially derived metabolites affected by both diet and antibiotic treatment, which conformed to previously published reports. Composite spectra (n = 173) were autonomously matched to entries of the Massbank of North America (MoNA) spectral repository. These experimental and virtual (lipidBlast) spectra corresponded to 29 common endogenous compound classes (e.g., 51 lysophosphatidylcholines spectra) and were then used to calculate the ranking capability of 7 individual scoring metrics. It was found that an average of the 7 individual scoring metrics provided the most effective weighted average ranking ability of 3 for the MoNA matched spectra in spite of potential risk of false positive annotations emerging from automation. Minor structural differences such as relative carbon-carbon double bond positions were found in several cases to affect the correct rank of the MoNA annotated metabolite. The latest release and an example workflow is available in the package vignette ( https://github.com/WMBEdmands/compMS2Miner ) and a version of the published application is available on the shinyapps.io site ( https://wmbedmands.shinyapps.io/compMS2Example ).

metaX: a flexible and comprehensive software for processing metabolomics data

Background

Non-targeted metabolomics based on mass spectrometry enables high-throughput profiling of the metabolites in a biological sample. The large amount of data generated from mass spectrometry requires intensive computational processing for annotation of mass spectra and identification of metabolites. Computational analysis tools that are fully integrated with multiple functions and are easily operated by users who lack extensive knowledge in programing are needed in this research field.

Results

We herein developed an R package, metaX, that is capable of end-to-end metabolomics data analysis through a set of interchangeable modules. Specifically, metaX provides several functions, such as peak picking and annotation, data quality assessment, missing value imputation, data normalization, univariate and multivariate statistics, power analysis and sample size estimation, receiver operating characteristic analysis, biomarker selection, pathway annotation, correlation network analysis, and metabolite identification. In addition, metaX offers a web-based interface (http://metax.genomics.cn) for data quality assessment and normalization method evaluation, and it generates an HTML-based report with a visualized interface. The metaX utilities were demonstrated with a published metabolomics dataset on a large scale. The software is available for operation as either a web-based graphical user interface (GUI) or in the form of command line functions. The package and the example reports are available at http://metax.genomics.cn/.

Conclusions

The pipeline of metaX is platform-independent and is easy to use for analysis of metabolomics data generated from mass spectrometry.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1579-y) contains supplementary material, which is available to authorized users.

An untargeted metabolomics method for archived newborn dried blood spots in epidemiologic studies

INTRODUCTION

For pediatric diseases like childhood leukemia, a short latency period points to in-utero exposures as potentially important risk factors. Untargeted metabolomics of small molecules in archived newborn dried blood spots (DBS) offers an avenue for discovering early-life exposures that contribute to disease risks.

OBJECTIVES

The purpose of this study was to develop a quantitative method for untargeted analysis of archived newborn DBS for use in an epidemiological study (California Childhood Leukemia Study, CCLS).

METHODS

Using experimental DBS from the blood of an adult volunteer, we optimized extraction of small molecules and integrated measurement of potassium as a proxy for blood hematocrit. We then applied this extraction method to 4.7-mm punches from 106 control DBS samples from the CCLS. Sample extracts were analyzed with liquid chromatography high resolution mass spectrometry (LC-HRMS) and an untargeted workflow was used to screen for metabolites that discriminate population characteristics such as sex, ethnicity, and birth weight.

RESULTS

Thousands of small molecules were measured in extracts of archived DBS. Normalizing for potassium levels removed variability related to varying hematocrit across DBS punches. Of the roughly 1,000 prevalent small molecules that were tested, multivariate linear regression detected significant associations with ethnicity (3 metabolites) and birth weight (15 metabolites) after adjusting for multiple testing.

CONCLUSIONS

This untargeted workflow can be used for analysis of small molecules in archived DBS to discover novel biomarkers, to provide insights into the initiation and progression of diseases, and to provide guidance for disease prevention.

Computational Metabolomics: A Framework for the Million Metabolome

"Sola dosis facit venenum." These words of Paracelsus, "the dose makes the poison", can lead to a cavalier attitude concerning potential toxicities of the vast array of low abundance environmental chemicals to which humans are exposed. Exposome research teaches that 80-85% of human disease is linked to environmental exposures. The human exposome is estimated to include >400,000 environmental chemicals, most of which are uncharacterized with regard to human health. In fact, mass spectrometry measures >200,000 m/z features (ions) in microliter volumes derived from human samples; most are unidentified. This crystallizes a grand challenge for chemical research in toxicology: to develop reliable and affordable analytical methods to understand health impacts of the extensive human chemical experience. To this end, there appears to be no choice but to abandon the limitations of measuring one chemical at a time. The present review looks at progress in computational metabolomics to provide probability-based annotation linking ions to known chemicals and serve as a foundation for unambiguous designation of unidentified ions for toxicologic study. We review methods to characterize ions in terms of accurate mass m/z, chromatographic retention time, correlation of adduct, isotopic and fragment forms, association with metabolic pathways and measurement of collision-induced dissociation products, collision cross section, and chirality. Such information can support a largely unambiguous system for documenting unidentified ions in environmental surveillance and human biomonitoring. Assembly of this data would provide a resource to characterize and understand health risks of the array of low-abundance chemicals to which humans are exposed.

Polyphenol metabolome in human urine and its association with intake of polyphenol-rich foods across European countries

BACKGROUND

An improved understanding of the contribution of the diet to health and disease risks requires accurate assessments of dietary exposure in nutritional epidemiologic studies. The use of dietary biomarkers may improve the accuracy of estimates.

OBJECTIVE

We applied a metabolomic approach in a large cohort study to identify novel biomarkers of intake for a selection of polyphenol-containing foods. The large chemical diversity of polyphenols and their wide distribution over many foods make them ideal biomarker candidates for such foods.

DESIGN

Metabolic profiles were measured with the use of high-resolution mass spectrometry in 24-h urine samples from 481 subjects from the large European Prospective Investigation on Cancer and Nutrition cohort. Peak intensities were correlated to acute and habitual dietary intakes of 6 polyphenol-rich foods (coffee, tea, red wine, citrus fruit, apples and pears, and chocolate products) measured with the use of 24-h dietary recalls and food-frequency questionnaires, respectively.

RESULTS

Correlation (r > 0.3, P < 0.01 after correction for multiple testing) and discriminant [pcorr (1) > 0.3, VIP > 1.5] analyses showed that >2000 mass spectral features from urine metabolic profiles were significantly associated with the consumption of the 6 selected foods. More than 80 polyphenol metabolites associated with the consumption of the selected foods could be identified, and large differences in their concentrations reflecting individual food intakes were observed within and between 4 European countries. Receiver operating characteristic curves showed that 5 polyphenol metabolites, which are characteristic of 5 of the 6 selected foods, had a high predicting ability of food intake.

CONCLUSION

Highly diverse food-derived metabolites (the so-called food metabolome) can be characterized in human biospecimens through this powerful metabolomic approach and screened to identify novel biomarkers for dietary exposures, which are ultimately essential to better understand the role of the diet in the cause of chronic diseases.