Towards nutrition with precision: unlocking biomarkers as dietary assessment tools.

Precision nutrition requires precise tools to monitor dietary habits. Yet current dietary assessment instruments are subjective, limiting our understanding of the causal relationships between diet and health. Biomarkers of food intake (BFIs) hold promise to increase the objectivity and accuracy of dietary assessment, enabling adjustment for compliance and misreporting. Here, we update current concepts and provide a comprehensive overview of BFIs measured in urine and blood. We rank BFIs based on a four-level utility scale to guide selection and identify combinations of BFIs that specifically reflect complex food intakes, making them applicable as dietary instruments. We discuss the main challenges in biomarker development and illustrate key solutions for the application of BFIs in human studies, highlighting different strategies for selecting and combining BFIs to support specific study designs. Finally, we present a roadmap for BFI development and implementation to leverage current knowledge and enable precision in nutrition research.

The Postprandial Calcium Absorption of a Milk-Derived Calcium Permeate - The Acute RENEW Double-Blinded Randomized Controlled Cross-Over Study.

BACKGROUND: Studies suggest that dairy-derived calcium supplements have additional beneficial properties compared with other calcium supplements in relation to bone health. OBJECTIVES: We investigated the postprandial calcium absorption from a milk-derived calcium permeate (CP) compared with calcium carbonate (CC). METHODS: In this randomized double-blinded cross-over study, 10 healthy postmenopausal females (age 50-65 y) received maltodextrin (placebo), 800 mg calcium from CP or from CC provided in 6 capsules on separate days. A fasting blood sample was collected at baseline, 60, 120, 240, and 360 min after ingestion. At baseline and 360 min, spot-urine samples were collected. Serum-ionized calcium, intact parathyroid hormone, phosphorus, and magnesium were analyzed, as were urinary calcium, phosphorus, and magnesium. A linear mixed model was applied. RESULTS: Serum-ionized calcium concentration after the CC supplement was higher at 240 min compared with the CP supplement [between-group difference; 95% confidence interval (CI): 0.039 mmol/L; 95% CI: 0.017-0.061; P = 0.00078]. Serum-ionized calcium concentration after the CC supplement was significantly higher than placebo at all postprandial time points except at 60 min. Urinary calcium concentration in 360 min spot urine was higher after intake of CC compared with CP [between-group difference; 95% CI: 2.47 mmol/L; 95% CI: 1.90-3.03; P = 0.0042]. CONCLUSIONS: Postprandial calcium absorption from CP was lower than that of CC, and concurrently, urinary concentration reflected increased serum appearance by CC compared with CP, highlighting different metabolic responses. The long-term and clinical implications should be studied further.

A Comprehensive Database for DNA Adductomics.

The exposure of human DNA to genotoxic compounds induces the formation of covalent DNA adducts, which may contribute to the initiation of carcinogenesis. Liquid chromatography (LC) coupled with high-resolution mass spectrometry (HRMS) is a powerful tool for DNA adductomics, a new research field aiming at screening known and unknown DNA adducts in biological samples. The lack of databases and bioinformatics tool in this field limits the applicability of DNA adductomics. Establishing a comprehensive database will make the identification process faster and more efficient and will provide new insight into the occurrence of DNA modification from a wide range of genotoxicants. In this paper, we present a four-step approach used to compile and curate a database for the annotation of DNA adducts in biological samples. The first step included a literature search, selecting only DNA adducts that were unequivocally identified by either comparison with reference standards or with nuclear magnetic resonance (NMR), and tentatively identified by tandem HRMS/MS. The second step consisted in harmonizing structures, molecular formulas, and names, for building a systematic database of 279 DNA adducts. The source, the study design and the technique used for DNA adduct identification were reported. The third step consisted in implementing the database with 303 new potential DNA adducts coming from different combinations of genotoxicants with nucleobases, and reporting monoisotopic masses, chemical formulas, .cdxml files, .mol files, SMILES, InChI, InChIKey and IUPAC nomenclature. In the fourth step, a preliminary spectral library was built by acquiring experimental MS/MS spectra of 15 reference standards, generating in silico MS/MS fragments for all the adducts, and reporting both experimental and predicted fragments into interactive web datatables. The database, including 582 entries, is publicly available (https://gitlab.com/nexs-metabolomics/projects/dna_adductomics_database). This database is a powerful tool for the annotation of DNA adducts measured in (HR)MS. The inclusion of metadata indicating the source of DNA adducts, the study design and technique used, allows for prioritization of the DNA adducts of interests and/or to enhance the annotation confidence. DNA adducts identification can be further improved by integrating the present database with the generation of authentic MS/MS spectra, and with user-friendly bioinformatics tools.

Impact of wheat aleurone on biomarkers of cardiovascular disease, gut microbiota and metabolites in adults with high body mass index: a double-blind, placebo-controlled, randomized clinical trial.

PURPOSE: Aleurone is a cereal bran fraction containing a variety of beneficial nutrients including polyphenols, fibers, minerals and vitamins. Animal and human studies support the beneficial role of aleurone consumption in reducing cardiovascular disease (CVD) risk. Gut microbiota fiber fermentation, polyphenol metabolism and betaine/choline metabolism may in part contribute to the physiological effects of aleurone. As primary objective, this study evaluated whether wheat aleurone supplemented foods could modify plasma homocysteine. Secondary objectives included changes in CVD biomarkers, fecal microbiota composition and plasma/urine metabolite profiles. METHODS: A parallel double-blind, placebo-controlled and randomized trial was carried out in two groups of obese/overweight subjects, matched for age, BMI and gender, consuming foods supplemented with either aleurone (27 g/day) (AL, n = 34) or cellulose (placebo treatment, PL, n = 33) for 4 weeks. RESULTS: No significant changes in plasma homocysteine or other clinical markers were observed with either treatment. Dietary fiber intake increased after AL and PL, animal protein intake increased after PL treatment. We observed a significant increase in fecal Bifidobacterium spp with AL and Lactobacillus spp with both AL and PL, but overall fecal microbiota community structure changed little according to 16S rRNA metataxonomics. Metabolomics implicated microbial metabolism of aleurone polyphenols and revealed distinctive biomarkers of AL treatment, including alkylresorcinol, cinnamic, benzoic and ferulic acids, folic acid, fatty acids, benzoxazinoid and roasted aroma related metabolites. Correlation analysis highlighted bacterial genera potentially linked to urinary compounds derived from aleurone metabolism and clinical parameters. CONCLUSIONS: Aleurone has potential to modulate the gut microbial metabolic output and increase fecal bifidobacterial abundance. However, in this study, aleurone did not impact on plasma homocysteine or other CVD biomarkers. TRIAL REGISTRATION: The study was registered at ClinicalTrials.gov (NCT02067026) on the 17th February 2014.

A Modular and Expandable Ecosystem for Metabolomics Data Annotation in R.

Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics experiments have become increasingly popular because of the wide range of metabolites that can be analyzed and the possibility to measure novel compounds. LC-MS instrumentation and analysis conditions can differ substantially among laboratories and experiments, thus resulting in non-standardized datasets demanding customized annotation workflows. We present an ecosystem of R packages, centered around the MetaboCoreUtils, MetaboAnnotation and CompoundDb packages that together provide a modular infrastructure for the annotation of untargeted metabolomics data. Initial annotation can be performed based on MS1 properties such as m/z and retention times, followed by an MS2-based annotation in which experimental fragment spectra are compared against a reference library. Such reference databases can be created and managed with the CompoundDb package. The ecosystem supports data from a variety of formats, including, but not limited to, MSP, MGF, mzML, mzXML, netCDF as well as MassBank text files and SQL databases. Through its highly customizable functionality, the presented infrastructure allows to build reproducible annotation workflows tailored for and adapted to most untargeted LC-MS-based datasets. All core functionality, which supports base R data types, is exported, also facilitating its re-use in other R packages. Finally, all packages are thoroughly unit-tested and documented and are available on GitHub and through Bioconductor.

Data sharing in PredRet for accurate prediction of retention time: Application to plant food bioactive compounds.

Prediction of retention times (RTs) is increasingly considered in untargeted metabolomics to complement MS/MS matching for annotation of unidentified peaks. We tested the performance of PredRet (http://predret.org/) to predict RTs for plant food bioactive metabolites in a data sharing initiative containing entry sets of 29-103 compounds (totalling 467 compounds, >30 families) across 24 chromatographic systems (CSs). Between 27 and 667 predictions were obtained with a median prediction error of 0.03-0.76 min and interval width of 0.33-8.78 min. An external validation test of eight CSs showed high prediction accuracy. RT prediction was dependent on shape and type of LC gradient, and number of commonly measured compounds. Our study highlights PredRet's accuracy and ability to transpose RT data acquired from one CS to another CS. We recommend extensive RT data sharing in PredRet by the community interested in plant food bioactive metabolites to achieve a powerful community-driven open-access tool for metabolomics annotation.

Ethephon-induced changes in antioxidants and phenolic compounds in anthocyanin-producing black carrot hairy root cultures.

Hairy root (HR) cultures are quickly evolving as a fundamental research tool and as a bio-based production system for secondary metabolites. In this study, an efficient protocol for establishment and elicitation of anthocyanin-producing HR cultures from black carrot was established. Taproot and hypocotyl explants of four carrot cultivars were transformed using wild-type Rhizobium rhizogenes. HR growth performance on plates was monitored to identify three fast-growing HR lines, two originating from root explants (lines NB-R and 43-R) and one from a hypocotyl explant (line 43-H). The HR biomass accumulated 25- to 30-fold in liquid media over a 4 week period. Nine anthocyanins and 24 hydroxycinnamic acid derivatives were identified and monitored using UPLC-PDA-TOF during HR growth. Adding ethephon, an ethylene-releasing compound, to the HR culture substantially increased the anthocyanin content by up to 82% in line 43-R and hydroxycinnamic acid concentrations by >20% in line NB-R. Moreover, the activities of superoxide dismutase and glutathione S-transferase increased in the HRs in response to ethephon, which could be related to the functionality and compartmentalization of anthocyanins. These findings present black carrot HR cultures as a platform for the in vitro production of anthocyanins and antioxidants, and provide new insight into the regulation of secondary metabolism in black carrot.

Two apples a day modulate human:microbiome co-metabolic processing of polyphenols, tyrosine and tryptophan.

PURPOSE: Validated biomarkers of food intake (BFIs) have recently been suggested as a useful tool to assess adherence to dietary guidelines or compliance in human dietary interventions. Although many new candidate biomarkers have emerged in the last decades for different foods from metabolic profiling studies, the number of comprehensively validated biomarkers of food intake is limited. Apples are among the most frequently consumed fruits and a rich source of polyphenols and fibers, an important mediator for their health-protective properties. METHODS: Using an untargeted metabolomics approach, we aimed to identify biomarkers of long-term apple intake and explore how apples impact on the human plasma and urine metabolite profiles. Forty mildly hypercholesterolemic volunteers consumed two whole apples or a sugar and energy-matched control beverage, daily for 8 weeks in a randomized, controlled, crossover intervention study. The metabolome in plasma and urine samples was analyzed via untargeted metabolomics. RESULTS: We found 61 urine and 9 plasma metabolites being statistically significant after the whole apple intake compared to the control beverage, including several polyphenol metabolites that could be used as BFIs. Furthermore, we identified several endogenous indole and phenylacetyl-glutamine microbial metabolites significantly increasing in urine after apple consumption. The multiomic dataset allowed exploration of the correlations between metabolites modulated significantly by the dietary intervention and fecal microbiota species at genus level, showing interesting interactions between Granulicatella genus and phenyl-acetic acid metabolites. Phloretin glucuronide and phloretin glucuronide sulfate appeared promising biomarkers of apple intake; however, robustness, reliability and stability data are needed for full BFI validation. CONCLUSION: The identified apple BFIs can be used in future studies to assess compliance and to explore their health effects after apple intake. Moreover, the identification of polyphenol microbial metabolites suggests that apple consumption mediates significant gut microbial metabolic activity which should be further explored.

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.

Unravelling wine volatile evolution during Shiraz grape ripening by untargeted HS-SPME-GC × GC-TOFMS.

The influence of grape maturity on wine volatome was investigated using HS-SPME-GC × GC-TOFMS. Shiraz wines were made from grapes harvested from four different vineyards from two berry maturity levels. A total of 1276 putative compounds were detected in at least one of the wine samples and 175 showed significant trends related to grape maturity. The first two dimensions of the Principal component analysis accounted for 57% of the variation and separated the samples according to the harvest date. Wines from the first harvest date were characterised by an abundance of lipoxygenase derived compounds, norisoprenoids and sulfur-containing compounds whereas a significant increase in some acetate esters was observed in wines produced from the more mature grapes. This study demonstrated a common evolution of grape volatiles for Shiraz inside the same mesoclimate. During the late ripening stage of the grape, a direct nexus between sugar concentration and wine volatile evolution was not observed.

The Compound Characteristics Comparison (CCC) approach: a tool for improving confidence in natural compound identification.

Compound identification is the main hurdle in LC-HRMS-based metabolomics, given the high number of 'unknown' metabolites. In recent years, numerous in silico fragmentation simulators have been developed to simplify and improve mass spectral interpretation and compound annotation. Nevertheless, expert mass spectrometry users and chemists are still needed to select the correct entry from the numerous candidates proposed by automatic tools, especially in the plant kingdom due to the huge structural diversity of natural compounds occurring in plants. In this work, we propose the use of a supervised machine learning approach to predict molecular substructures from isotopic patterns, training the model on a large database of grape metabolites. This approach, called 'Compounds Characteristics Comparison' (CCC) emulates the experience of a plant chemist who 'gains experience' from a (proof-of-principle) dataset of grape compounds. The results show that the CCC approach is able to predict with good accuracy most of the sub-structures proposed. In addition, after querying MS/MS spectra in Metfrag 2.2 and applying CCC predictions as scoring terms with real data, the CCC approach helped to give a better ranking to the correct candidates, improving users' confidence in candidate selection. Our results demonstrated that the proposed approach can complement current identification strategies based on fragmentation simulators and formula calculators, assisting compound identification. The CCC algorithm is freely available as R package (https://github.com/lucanard/CCC) which includes a seamless integration with Metfrag. The CCC package also permits uploading additional training data, which can be used to extend the proposed approach to other systems biological matrices. List of abbreviations: Acidic: acidic moiety; aliph: aliphatic chain; AUC: area under the ROC curve; bs: best glycosidic structure; CCC: Compounds' Characteristics Comparison; Cees: Carbons estimation errors; CO: Carbon to Oxygen ratio; Het: Heterocyclic moiety; IMD: Isotopic Mass Defect (and Pattern); LC-HRMS: Liquid Chromatography - High Resolution Mass Spectrometry; md: mass defect; MM: Monoisotopic Mass; MS: Mass Spectrometry; MSE: Mean Squared Error; nC: number of Carbons; NN: Nitrogen; pC: percentage of Carbon mass on the total mass; Pho: Phosphate; PLSr: Partial Least Square regression; ppm: parts per million; QSRR: Quantitative structure-retention relationship; RMD: Relative Mass Defect; ROC: Receiver Operating Characteristics; rRMD: residual Relative Mass Defect; RT: retention time; Sul: Sulphur; UPLC-ESI-Q-TOF-MS: Ultra Performance Liquid Chromatography - ElectroSpray Ionization -Quadropole - Time of Flight - Mass Spectrometry; VAT: Vitis arizonica Texas.

Host: Microbiome co-metabolic processing of dietary polyphenols - An acute, single blinded, cross-over study with different doses of apple polyphenols in healthy subjects.

Apples are one of the most commonly consumed fruits and their high polyphenol content is considered one of the most important determinants of their health-promoting activities. Here we studied the nutrikinetics of apple polyphenols by UHPLC-HRMS metabolite fingerprinting, comparing bioavailability when consumed in a natural or a polyphenol-enriched cloudy apple juice. Twelve men and women participated in an acute single blind controlled crossover study in which they consumed 250 mL of cloudy apple juice (CAJ), Crispy Pink apple variety, or 250 mL of the same juice enriched with 750 mg of an apple polyphenol extract (PAJ). Plasma and whole blood were collected at time 0, 1, 2, 3 and 5 h. Urine was collected at time 0 and 0-2, 2-5, 5-8, and 8-24 h after juice consumption. Faecal samples were collected from each individual during the study for 16S rRNA gene profiling. As many as 110 metabolites were significantly elevated following intake of polyphenol enriched cloudy apple juice, with large inter-individual variations. The comparison of the average area under the curve of circulating metabolites in plasma and in urine of volunteers consuming either the CAJ or the PAJ demonstrated a stable metabotype, suggesting that an increase in polyphenol concentration in fruit does not limit their bioavailability upon ingestion. Faecal bacteria were correlated with specific microbial catabolites derived from apple polyphenols. Human metabolism of apple polyphenols is a co-metabolic process between human encoded activities and those of our resident microbiota. Here we have identified specific blood and urine metabolic biomarkers of apple polyphenol intake and identified putative associations with specific genera of faecal bacteria, associations which now need confirmation in specifically designed mechanistic studies.

Antibiotic Treatment Preventing Necrotising Enterocolitis Alters Urinary and Plasma Metabolomes in Preterm Pigs.

Necrotising enterocolitis (NEC) is a serious gut inflammatory condition in premature neonates, onset and development of which depend on the gut microbiome. Attenuation of the gut microbiome by antibiotics can reduce NEC incidence and severity. However, how the antibiotics-suppressed gut microbiome affects the whole-body metabolism in NEC-sensitive premature neonates is unknown. In formula-fed preterm pigs, used as a model for preterm infants, plasma and urinary metabolomes were investigated by LC-MS and 1H NMR, with and without antibiotic treatment immediately after birth. While it reduced the gut microbiome density and NEC lesions as previously reported, the antibiotic treatment employed in the current study affected the abundance of 44 metabolites in different metabolic pathways. In antibiotics-treated pigs, tryptophan metabolism favored the kynurenine pathway, relative to the serotonin pathway, as shown by specific metabolites. Metabolites associated with the gut microbiome, including 3-phenyllactic acid, 4-hydroxyphenylacetic acid, and phenylacetylglycine, all from phenylalanine, and three bile acids showed lower levels in the antibiotics-treated pigs where the gut microbiome was extensively attenuated. Findings in the current study warrant further investigation of metabolic and developmental consequences of antibiotic treatment in preterm neonates.

The metabolomic profile of red non-V. vinifera genotypes.

Wild American genotypes represent an important part of the Vitis germplasm in relation to grape improvement. Today, these genotypes are currently involved in breeding programmes in order to introgress traits resistant to pests and diseases in V. vinifera cultivars. Nevertheless, the metabolic composition of their grapes has not been widely investigated. This study aimed to explore in detail the metabolomic profile in terms of simple phenolic, proanthocyanidin, anthocyanin and lipid compounds in two hybrids and five American genotypes. The results were compared with those of two V. vinifera cultivars. A multi-targeted metabolomics approach using a combination of LC-MS and LC-DAD methods was used to identify and quantify 124 selected metabolites. The genotypes studied showed considerable variability in the metabolomic profile according to the grape composition of V. vinifera and other Vitis genotypes. As regards the composition of anthocyanins, not all wild genotypes contained both mono- and di-glucoside derivatives. Wild genotype 41B and V. vinifera cultivars contained only monoglucoside anthocyanins. The proanthocyanidins of non-V. vinifera genotypes were mainly rich in oligomers and short-chain polymers. The analysis of lipids in wild Vitis genotypes, here reported for the first time, showed the existence of a certain diversity in their composition suggesting a strong influence of the environmental conditions on the general lipid pattern.

Progressive Changes in the Plasma Metabolome during Malnutrition in Juvenile Pigs.

Severe acute malnutrition (SAM) is one of the leading nutrition-related causes of death in children under five years of age. The clinical features of SAM are well documented, but a comprehensive understanding of the development from a normal physiological state to SAM is lacking. Characterizing the temporal metabolomic change may help to understand the disease progression and to define nutritional rehabilitation strategies. Using a piglet model we hypothesized that a progressing degree of malnutrition induces marked plasma metabolite changes. Four-week-old weaned pigs were fed a nutrient-deficient maize diet (MAL) or nutritionally optimized reference diet (REF) for 7 weeks. Plasma collected weekly was subjected to LC-MS for a nontargeted profiling of metabolites with abundance differentiation. The MAL pigs showed markedly reduced body-weight gain and lean-mass proportion relative to the REF pigs. Levels of eight essential and four nonessential amino acids showed a time-dependent deviation in the MAL pigs from that in the REF. Choline metabolites and gut microbiomic metabolites generally showed higher abundance in the MAL pigs. The results demonstrated that young malnourished pigs had a profoundly perturbed metabolism, and this provides basic knowledge about metabolic changes during malnourishment, which may be of help in designing targeted therapeutic foods for refeeding malnourished children.

PredRet: prediction of retention time by direct mapping between multiple chromatographic systems.

Demands in research investigating small molecules by applying untargeted approaches have been a key motivator for the development of repositories for mass spectrometry spectra and automated tools to aid compound identification. Comparatively little attention has been afforded to using retention times (RTs) to distinguish compounds and for liquid chromatography there are currently no coordinated efforts to share and exploit RT information. We therefore present PredRet; the first tool that makes community sharing of RT information possible across laboratories and chromatographic systems (CSs). At http://predret.org , a database of RTs from different CSs is available and users can upload their own experimental RTs and download predicted RTs for compounds which they have not experimentally determined in their own experiments. For each possible pair of CSs in the database, the RTs are used to construct a projection model between the RTs in the two CSs. The number of compounds for which RTs can be predicted and the accuracy of the predictions are dependent upon the compound coverage overlap between the CSs used for construction of projection models. At the moment, it is possible to predict up to 400 RTs with a median error between 0.01 and 0.28 min depending on the CS and the median width of the prediction interval ranging from 0.08 to 1.86 min. By comparing experimental and predicted RTs, the user can thus prioritize which isomers to target for further characterization and potentially exclude some structures completely. As the database grows, the number and accuracy of predictions will increase.

Comparing Wild American Grapes with Vitis vinifera: A Metabolomics Study of Grape Composition.

We analyzed via untargeted UHPLC-ESI-Q-TOF-MS the metabolome of the berry tissues (skin, pulp, seeds) of some American Vitis species (Vitis cinerea, Vitis californica, Vitis arizonica), together with four interspecific hybrids, and seven Vitis vinifera cultivars, aiming to find differences in the metabolomes of the American Vitis sp. versus Vitis vinifera. Apart from the known differences, that is, more complex content of anthocyanins and stilbenoids in the American grapes, we observed higher procyanidin accumulation (tens to hundreds of times) in the vinifera skin and seeds in comparison to American berries, and we confirmed this result via phloroglucinolysis. In the American grapes considered, we did not detect the accumulation of pleasing aroma precursors (terpenoids, glycosides), whereas they are common in vinifera grapes. We also found accumulation of hydrolyzable tannins and their precursors in the skin of the wild American grapes, which has never been reported earlier in any of the species under investigation. Such information is needed to improve the design of new breeding programs, lowering the risk of retaining undesirable characteristics in the chemical phenotype of the offspring.

Whey protein delays gastric emptying and suppresses plasma fatty acids and their metabolites compared to casein, gluten, and fish protein.

Whey protein has been demonstrated to improve fasting lipid and insulin response in overweight and obese individuals. To establish new hypotheses for this effect and to investigate the impact of stomach emptying, we compared plasma profiles after intake of whey isolate (WI), casein, gluten (GLU), and cod (COD). Obese, nondiabetic subjects were included in the randomized, blinded, crossover meal study. Subjects ingested a high fat meal containing one of the four protein sources. Plasma samples were collected at five time points and metabolites analyzed using LC-Q-TOF-MS. In contrast to previous studies, the WI meal caused a decreased rate of gastric emptying compared to the other test meals. The WI meal also caused elevated levels of a number of amino acids, possibly stimulating insulin release leading to reduced plasma glucose. The WI meal also caused decreased levels of a number of fatty acids, while the GLU meal caused elevated levels of a number of unidentified hydroxy fatty acids and dicarboxylic fatty acids. Also reported are a number of markers of fish intake unique to the COD meal.

Metabolite profiling and beyond: approaches for the rapid processing and annotation of human blood serum mass spectrometry data.

In this paper, we describe data processing and metabolite identification approaches which lead to a rapid and semi-automated interpretation of metabolomics experiments. Data from metabolite fingerprinting using LC-ESI-Q-TOF/MS were processed with several open-source software packages, including XCMS and CAMERA to detect features and group features into compound spectra. Next, we describe the automatic scheduling of tandem mass spectrometry (MS) acquisitions to acquire a large number of MS/MS spectra, and the subsequent processing and computer-assisted annotation towards identification using the R packages MetShot, Rdisop, and the MetFusion application. We also implement a simple retention time prediction model using predicted lipophilicity logD, which predicts retention times within 42 s (6 min gradient) for most compounds in our setup. We putatively identified 44 common metabolites including several amino acids and phospholipids at metabolomics standards initiative (MSI) levels two and three and confirmed the majority of them by comparison with authentic standards at MSI level one. To aid both data integration within and data sharing between laboratories, we integrated data from two labs and mapped retention times between the chromatographic systems. Despite the different MS instrumentation and different chromatographic gradient programs, the mapped retention times agree within 26 s (20 min gradient) for 90% of the mapped features.