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Adaptation of exercise-induced stress in well-trained healthy young men
Janssen Duijghuijsen, L.M. ; Keijer, J. ; Mensink, M.R. ; Lenaerts, Kaatje ; Ridder, L.O. ; Nierkens, Stefan ; Kartaram, Shirley ; Verschuren, Martie C.M. ; Pieters, Raymond ; Bas, Richard ; Witkamp, R.F. ; Wichers, H.J. ; Norren, K. van - \ 2017
Experimental Physiology 102 (2017)1. - ISSN 0958-0670 - p. 86 - 99.
Strenuous exercise induces different stress-related physiological changes, potentially including changes in intestinal barrier function. In the Protégé Study (ISRCTN14236739; www.isrctn.com) we determined the test-retest repeatability in responses to exercise in well-trained individuals.
Eleven well-trained males (27±4 years old) completed an exercise protocol that consisted of intensive cycling intervals, followed by an overnight fast and an additional 90 min cycling phase at 50% Wmax the next morning. The day before (rest), and immediately after the exercise protocol (exercise) a lactulose/rhamnose solution was ingested. Markers of energy metabolism, lactulose/rhamnose ratio, several cytokines and potential stress-related markers were measured at rest and during exercise. In addition, untargeted urine metabolite profiles were obtained. The complete procedure (Test) was repeated one week later (Retest) to assess repeatability.
Metabolic effect parameters with regard to energy metabolism and urine metabolomics were similar for both the Test and Retest period, underlining comparable exercise load. Following exercise, intestinal permeability (one hour plasma lactulose/rhamnose ratio), serum interleukin-6, interleukin-10, fibroblast growth factor-21, and muscle creatine kinase levels were only significantly increased compared to rest during the first test and not when the test was repeated.
Responses to strenuous exercise in well-trained young men, as indicated by intestinal markers and myokines, show adaptation in Test-Retest outcome. This might be due to a carry-over effect of the defense mechanisms triggered during the Test. This finding has implications for the design of studies aimed at evaluating physiological responses to exercise.
Transcriptional Analysis of serk1 and serk3 coreceptor mutants
Esse, Wilma van; Hove, Colette A. ten; Guzzonato, Francesco ; Esse, Peter van; Boekschoten, Mark ; Ridder, Lars ; Vervoort, Jacques ; Vries, Sacco C. de - \ 2016
Plant Physiology 172 (2016)4. - ISSN 0032-0889 - p. 2516 - 2529.
Somatic embryogenesis receptor kinases (SERKs) are ligand-binding coreceptors that are able to combine with different ligandperceiving receptors such as BRASSINOSTEROID INSENSITIVE1 (BRI1) and FLAGELLIN-SENSITIVE2. Phenotypical analysis of serk single mutants is not straightforward because multiple pathways can be affected, while redundancy is observed for a single phenotype. For example, serk1serk3 double mutant roots are insensitive toward brassinosteroids but have a phenotype different from bri1 mutant roots. To decipher these effects, 4-d-old Arabidopsis (Arabidopsis thaliana) roots were studied using microarray analysis. A total of 698 genes, involved in multiple biological processes, were found to be differentially regulated in serk1-3serk3-2 double mutants. About half of these are related to brassinosteroid signaling. The remainder appear to be unlinked to brassinosteroids and related to primary and secondary metabolism. In addition, methionine-derived glucosinolate biosynthesis genes are up-regulated, which was verified by metabolite profiling. The results also show that the gene expression pattern in serk3-2 mutant roots is similar to that of the serk1-3serk3-2 double mutant roots. This confirms the existence of partial redundancy between SERK3 and SERK1 as well as the promoting or repressive activity of a single coreceptor in multiple simultaneously active pathways.
Enhanced Acylcarnitine Annotation in High-Resolution Mass Spectrometry Data: Fragmentation Analysis for the Classification and Annotation of Acylcarnitines
Hooft, J.J.J. van der; Ridder, L.O. ; Barrett, M.P. ; Burgess, K.E.V. - \ 2015
Frontiers in Bioengineering and Biotechnology 3 (2015). - ISSN 2296-4185 - 15 p.
Metabolite annotation and identification are primary challenges in untargeted metabolomics experiments. Rigorous workflows for reliable annotation of mass features with chemical structures or compound classes are needed to enhance the power of untargeted mass spectrometry. High-resolution mass spectrometry considerably improves the confidence in assigning elemental formulas to mass features in comparison to nominal mass spectrometry, and embedding of fragmentation methods enables more reliable metabolite annotations and facilitates metabolite classification. However, the analysis of mass fragmentation spectra can be a time-consuming step and requires expert knowledge. This study demonstrates how characteristic fragmentations, specific to compound classes, can be used to systematically analyze their presence in complex biological extracts like urine that have undergone untargeted mass spectrometry combined with data dependent or targeted fragmentation. Human urine extracts were analyzed using normal phase liquid chromatography (hydrophilic interaction chromatography) coupled to an Ion Trap-Orbitrap hybrid instrument. Subsequently, mass chromatograms and collision-induced dissociation and higher-energy collisional dissociation (HCD) fragments were annotated using the freely available MAGMa software1. Acylcarnitines play a central role in energy metabolism by transporting fatty acids into the mitochondrial matrix. By filtering on a combination of a mass fragment and neutral loss designed based on the MAGMa fragment annotations, we were able to classify and annotate 50 acylcarnitines in human urine extracts, based on high-resolution mass spectrometry HCD fragmentation spectra at different energies for all of them. Of these annotated acylcarnitines, 31 are not described in HMDB yet and for only 4 annotated acylcarnitines the fragmentation spectra could be matched to reference spectra. Therefore, we conclude that the use of mass fragmentation filters within the context of untargeted metabolomics experiments is a valuable tool to enhance the annotation of small metabolites.
candYgene: enabling precision breeding through FAIR Data
Kuzniar, A. ; Gavai, A.K. ; Ridder, L.O. ; Bonino da Silva Santos, L.O. ; Singh, G. ; Visser, R.G.F. ; Finkers, H.J. - \ 2015
Genetics research is focusing more and more on mining fully sequenced genomes and their annotations to identify the causal genes associated with specific traits (phenotypes) of interest. However, a complex trait is typically associated with multiple quantitative trait loci (QTLs), each with hundreds of genes positively/negatively affecting the desired trait(s). Our aim is to develop a Big data analytics & semantic interoperability infrastructure for candidate gene prioritization that will aid breeders in the design of an optimal genotype with a desired trait(s) for a given environment.
Automated Annotation of Microbial and Human Flavonoid-Derived Metabolites
Mihaleva, V.V. ; Ünlü, F. ; Vervoort, J.J.M. ; Ridder, L.O. - \ 2015
In: Metabonomics and Gut Microbiota in Nutrition and Disease / Kochhar, S., Martin, F.P., London : Springer Verlag (Molecular and Integrative Toxicology ) - ISBN 9781447165385 - p. 109 - 124.
Flavonoids are a class of natural compounds essentially produced by plants that are part of animal and human diets and have assumed health-promoting benefits. Upon human consumption, these flavonoids are to a modest extent absorbed in the small intestines. The major part arrives in the colon where the microflora utilises and converts the flavonoids to a wide range of products. Many of these products are absorbed in the major intestines and subsequently metabolised by the host. To understand the impact of the microflora on the metabolism and possible effects on human health, complete (and quantitative) identification of the microbial as well as human metabolic conversion products of flavonoids is required. This is a challenging task, as these bioconversion products are often present in relatively small amounts, making classical identification strategies based on (accurate) mass information or nuclear magnetic resonance, not straightforward. In the absence of reference compounds, annotation of a component may be achieved by detailed expert evaluation, e.g. by searching for similar fragmentation patterns in spectral databases of known compounds. However, such manual analysis is a tedious task, and in advanced metabolite profiling experiments, with large numbers of unknown metabolites, this is a major bottleneck. Therefore, new strategies are needed for quick and reliable identification of the diverse range of molecules in complex matrices (faeces, blood, urine). Intelligent software for annotation and identification of unknowns is crucial to fully exploit complex datasets. We developed a new software tool (MAGMA) for (sub)structure-based annotation of LC-MSn datasets which, combined with a newly established database for phenolic molecules (MetIDB), enables semiautomated identification of flavonoid derivatives.
Automatic Compound Annotation from Mass Spectrometry Data Using MAGMa.
Ridder, L.O. ; Hooft, J.J.J. van der; Verhoeven, S. - \ 2014
Mass Spectrometry 3 (2014)3. - ISSN 1340-8097 - 7 p.
The MAGMa software for automatic annotation of mass spectrometry based fragmentation data was applied to 16 MS/MS datasets of the CASMI 2013 contest. Eight solutions were submitted in category 1 (molecular formula assignments) and twelve in category 2 (molecular structure assignment). The MS/MS peaks of each challenge were matched with in silico generated substructures of candidate molecules from PubChem, resulting in penalty scores that were used for candidate ranking. In 6 of the 12 submitted solutions in category 2, the correct chemical structure obtained the best score, whereas 3 molecules were ranked outside the top 5. All top ranked molecular formulas submitted in category 1 were correct. In addition, we present MAGMa results generated retrospectively for the remaining challenges. Successful application of the MAGMa algorithm required inclusion of the relevant candidate molecules, application of the appropriate mass tolerance and a sufficient degree of in silico fragmentation of the candidate molecules. Furthermore, the effect of the exhaustiveness of the candidate lists and limitations of substructure based scoring are discussed.
In Silico Prediction and Automatic LC–MSn Annotation of Green Tea Metabolites in Urine
Ridder, L.O. ; Hooft, J.J.J. van der; Verhoeven, S. ; Vos, R.C.H. de; Vervoort, J.J.M. ; Bino, R.J. - \ 2014
Analytical Chemistry 86 (2014)10. - ISSN 0003-2700 - p. 4767 - 4774.
human fecal microbiota - mass-spectrometry - structural elucidation - human plasma - phenolic-compounds - spectral trees - polyphenols - identification - absorption - metabolomics
The colonic breakdown and human biotransformation of small molecules present in food can give rise to a large variety of potentially bioactive metabolites in the human body. However, the absence of reference data for many of these components limits their identification in complex biological samples, such as plasma and urine. We present an in silico workflow for automatic chemical annotation of metabolite profiling data from liquid chromatography coupled with multistage accurate mass spectrometry (LC-MSn), which we used to systematically screen for the presence of tea-derived metabolites in human urine samples after green tea consumption. Reaction rules for intestinal degradation and human biotransformation were systematically applied to chemical structures of 75 green tea components, resulting in a virtual library of 27¿245 potential metabolites. All matching precursor ions in the urine LC–MSn data sets, as well as the corresponding fragment ions, were automatically annotated by in silico generated (sub)structures. The results were evaluated based on 74 previously identified urinary metabolites and lead to the putative identification of 26 additional green tea-derived metabolites. A total of 77% of all annotated metabolites were not present in the Pubchem database, demonstrating the benefit of in silico metabolite prediction for the automatic annotation of yet unknown metabolites in LC–MSn data from nutritional metabolite profiling experiments.
Identification of drug metabolites in human plasma or serum integrating metabolite prediction, LC–HRMS and untargeted data processing.
Jacobs, P.L. ; Ridder, L.O. ; Ruijken, M. ; Rosing, H. ; Jager, N.G.L. ; Beijnen, J.H. ; Bas, R.R. ; Dongen, W.D. van - \ 2013
Bioanalysis 5 (2013)17. - ISSN 1757-6180 - p. 2115 - 2128.
ionization mass-spectrometry - preclinical safety - major metabolite - in-vivo - disposition - excretion - pharmacokinetics - inhibitor - annotation - validation
Background: Comprehensive identification of human drug metabolites in first-in-man studies is crucial to avoid delays in later stages of drug development. We developed an efficient workflow for systematic identification of human metabolites in plasma or serum that combines metabolite prediction, high-resolution accurate mass LC–MS and MS vendor independent data processing. Retrospective evaluation of predictions for 14 14C-ADME studies published in the period 2007–January 2012 indicates that on average 90% of the major metabolites in human plasma can be identified by searching for accurate masses of predicted metabolites. Furthermore, the workflow can identify unexpected metabolites in the same processing run, by differential analysis of samples of drug-dosed subjects and (placebo-dosed, pre-dose or otherwise blank) control samples. To demonstrate the utility of the workflow we applied it to identify tamoxifen metabolites in serum of a breast cancer patient treated with tamoxifen. Results & Conclusion: Previously published metabolites were confirmed in this study and additional metabolites were identified, two of which are discussed to illustrate the advantages of the workflow.
|The Large Scale Identification and Quantification of Conjugates of Intact and Gut Microbial Bioconversion Products of Polyphenols.
Hooft, J.J.J. van der; Vos, C.H. de; Bino, R.J. ; Mihaleva, V.V. ; Ridder, L.O. ; Roo, N. de; Jacobs, D.M. ; Duynhoven, J.P.M. van; Vervoort, J. - \ 2013
In: Magnetic Resonance in Food Science / Duynhoven, J., Belton, P.S., Webb, G.A., As, H., CBSG/NMC - ISBN 9781849736343 - p. 177 - 182.
A human diet containing a significant amount of flavonoids, such as present in tea, red wine, apple, and cocoa has been associated with reduced disease risks. After consumption, a part of these flavonoids can be directly absorbed by the small intestine, but the greatest part passages towards the large intestine where microbes break the flavonoids down into phenolic metabolites. After absorption into the blood, both intact and metabolized flavonoids are subsequently methylated, sulphated, and glucuronidated or a combination thereof. The exact chemical structural elucidation and quantification of these conjugates present in the human body are key to identify potential bioactive components. However, this is still a tedious task due to their relative low abundance in a complex background of other high-abundant metabolites and the many possible isomeric forms. Therefore, we aimed to systematically identify these conjugates by using a combination of pre-concentration and separation by solid phase extraction (SPE) followed by LC-FTMSn and 1D 1H NMR. The combination of LC-FTMSn and HPLC-TOF-MS-SPE-NMR resulted in the efficient identification and quantification of low abundant polyphenol metabolites down to micromolar concentrations and thus opens up new perspectives for in depth studying of the bioavailability and the possible mode of action of flavonoids like flavan-3-ols and their gut-microbial break-down products circulating in the human body.
Automatic Chemical Structure Annotation of an LC-MSn Based Metabolic Profile from Green Tea
Ridder, L.O. ; Hooft, J.J.J. van der; Verhoeven, S. ; Vos, C.H. de; Bino, R.J. ; Vervoort, J. - \ 2013
Analytical Chemistry 85 (2013)12. - ISSN 0003-2700 - p. 6033 - 6040.
accurate mass-spectrometry - camelia-sinensis extracts - spectral trees - oolong tea - identification - fragmentation - elucidation - flavan-3-ols - polyphenols - software
Liquid chromatography coupled with multistage accurate mass spectrometry (LC–MSn) can generate comprehensive spectral information of metabolites in crude extracts. To support structural characterization of the many metabolites present in such complex samples, we present a novel method (http://www.emetabolomics.org/magma) to automatically process and annotate the LC–MSn data sets on the basis of candidate molecules from chemical databases, such as PubChem or the Human Metabolite Database. Multistage MSn spectral data is automatically annotated with hierarchical trees of in silico generated substructures of candidate molecules to explain the observed fragment ions and alternative candidates are ranked on the basis of the calculated matching score. We tested this method on an untargeted LC–MSn (n = 3) data set of a green tea extract, generated on an LC-LTQ/Orbitrap hybrid MS system. For the 623 spectral trees obtained in a single LC–MSn run, a total of 116¿240 candidate molecules with monoisotopic masses matching within 5 ppm mass accuracy were retrieved from the PubChem database, ranging from 4 to 1327 candidates per molecular ion. The matching scores were used to rank the candidate molecules for each LC–MSn component. The median and third quartile fractional ranks for 85 previously identified tea compounds were 3.5 and 7.5, respectively. The substructure annotations and rankings provided detailed structural information of the detected components, beyond annotation with elemental formula only. Twenty-four additional components were putatively identified by expert interpretation of the automatically annotated data set, illustrating the potential to support systematic and untargeted metabolite identification.
Structural elucidation of low abundant metabolites in complex sample matrices
Hooft, J.J.J. van der; Vos, R.C.H. de; Ridder, L.O. ; Vervoort, J. ; Bino, R.J. - \ 2013
Metabolomics 9 (2013)5. - ISSN 1573-3882 - p. 1009 - 1018.
ms-spe-nmr - nuclear-magnetic-resonance - solid-phase extraction - liquid-chromatography - spectral trees - circular-dichroism - plant metabolomics - crude extracts - mass-spectra - identification
Identification of metabolites is a major challenge in biological studies and relies in principle on mass spectrometry (MS) and nuclear magnetic resonance (NMR) methods. The increased sensitivity and stability of both NMR and MS systems have made dereplication of complex biological samples feasible. Metabolic databases can be of help in the identification process. Nonetheless, there is still a lack of adequate spectral databases that contain high quality spectra, but new developments in this area will assist in the (semi-)automated identification process in the near future. Here, we discuss new developments for the structural elucidation of low abundant metabolites present in complex sample matrices. We describe how a recently developed combination of high resolution MS multistage fragmentation (MSn) and high resolution one dimensional (1D)-proton (1H)-NMR of liquid chromatography coupled to solid phase extraction (LC–SPE) purified metabolites can circumvent the need for isolating extensive amounts of the compounds of interest to elucidate their structures. The LC–MS–SPE–NMR hardware configuration in conjunction with high quality databases facilitates complete structural elucidation of metabolites even at sub-microgram levels of compound in crude extracts. However, progress is still required to optimally exploit the power of an integrated MS and NMR approach. Especially, there is a need to improve and expand both MSn and NMR spectral databases. Adequate and user-friendly software is required to assist in candidate selection based on the comparison of acquired MS and NMR spectral information with reference data. It is foreseen that these focal points will contribute to a better transfer and exploitation of structural information gained from diverse analytical platforms
Substructure-based annotation of high-resolution multistage MSn spectral trees
Ridder, L.O. ; Hooft, J.J.J. van der; Verhoeven, S. ; Vos, R.C.H. de; Schaik, R. van; Vervoort, Jacques - \ 2012
Rapid Communications in Mass Spectrometry 26 (2012)20. - ISSN 0951-4198 - p. 2461 - 2471.
tandem mass-spectrometry - metabolite identification - discriminating signals - fragmentation trees - accurate mass - metabolomics - dissociation - elucidation - information - software
RATIONALE High-resolution multistage MSn data contains detailed information that can be used for structural elucidation of compounds observed in metabolomics studies. However, full exploitation of this complex data requires significant analysis efforts by human experts. In silico methods currently used to support data annotation by assigning substructures of candidate molecules are limited to a single level of MS fragmentation. METHODS We present an extended substructure-based approach which allows annotation of hierarchical spectral trees obtained from high-resolution multistage MSn experiments. The algorithm yields a hierarchical tree of substructures of a candidate molecule to explain the fragment peaks observed at consecutive levels of the multistage MSn spectral tree. A matching score is calculated that indicates how well the candidate structure can explain the observed hierarchical fragmentation pattern. RESULTS The method is applied to MSn spectral trees of a set of compounds representing important chemical classes in metabolomics. Based on the calculated score, the correct molecules were successfully prioritized among extensive sets of candidates structures retrieved from the PubChem database. CONCLUSIONS The results indicate that the inclusion of subsequent levels of fragmentation in the automatic annotation of MSn data improves the identification of the correct compounds. We show that, especially in the case of lower mass accuracy, this improvement is not only due to the inclusion of additional fragment ions in the analysis, but also to the specific hierarchical information present in the MSn spectral trees. This method may significantly reduce the time required by MS experts to analyze complex MSn data. Copyright (c)proves 2012 John Wiley & Sons, Ltd.
Structural elucidation and quantification of phenolic conjugates present in human urine after tea intake
Hooft, J.J.J. van der; Vos, R.C.H. de; Mihaleva, V. ; Bino, R.J. ; Ridder, L.O. ; Roo, N. de; Jacobs, D.M. ; Duynhoven, J.P.M. van; Vervoort, J.J.M. - \ 2012
Analytical Chemistry 84 (2012)16. - ISSN 0003-2700 - p. 7263 - 7271.
tandem mass-spectrometry - green tea - black tea - metabolite identification - ellagic acid - metabolomics - polyphenols - nmr - ingestion - phytochemicals
In dietary polyphenol exposure studies, annotation and identification of urinary metabolites present at low (micromolar) concentrations are major obstacles. In order to determine the biological activity of specific components, it is necessary to have the correct structures and the quantification of the polyphenol-derived conjugates present in the human body. We present a procedure for identification and quantification of metabolites and conjugates excreted in human urine after single bolus intake of black or green tea. A combination of a solid phase extraction (SPE) preparation step and two high pressure liquid chromatography (HPLC)-based analytical platforms was used; namely, accurate mass fragmentation (HPLC-FTMSn) and mass-guided SPE-trapping of selected compounds for nuclear magnetic resonance spectroscopy (NMR) measurements (HPLC-TOFMS-SPE-NMR). HPLC-FTMSn analysis led to the annotation of 138 urinary metabolites, including 48 valerolactone and valeric acid conjugates. By combining the results from MSn fragmentation with the one dimensional (1D)-1H-NMR spectra of HPLC-TOFMS-SPE trapped compounds, we elucidated the structures of 36 phenolic conjugates, including the glucuronides of 3’,4’-di, and 3’,4’,5’-trihydroxyphenyl-¿-valerolactone, three urolithin glucuronides, and indole-3-acetic acid glucuronide. We also obtained 26 hours of quantitative excretion profiles for specific valerolactone conjugates. The combination of the HPLC-FTMSn and HPLC-TOFMS-SPE-NMR platforms results in the efficient identification and quantification of low abundant phenolic conjugates down to nanomoles of trapped amounts of metabolite corresponding to micromolar metabolite concentrations in urine