Theo H. Reijmers

RPLC-ion-trap-FTMS method for lipid profiling of plasma : method validation and application to p53 mutant mouse model

A reversed-phase liquid chromatography-linear ion trap-Fourier transform ion cyclotron resonance-mass spectrometric method was developed for the profiling of lipids in human and mouse plasma. With the use of a fused-core C 8 column and a binary gradient, more than 160 lipids belonging to eight different classes were detected in a single LC-MS run. The method was fully validated and the analytical characteristics such as linearity ( R (2), 0.994-1.000), limit of detection (0.08-1.28 microg/mL plasma), repeatability (RSD, 2.7-7.9%) and intermediate precision (RSD, 2.7-15.6%) were satisfactory. The method was successfully applied to p53 mutant mice plasma for studying some phenotypic effects of p53 expression.

Metabolite Identification Using Automated Comparison of High-Resolution Multistage Mass Spectral Trees

Multistage mass spectrometry (MS(n)) generating so-called spectral trees is a powerful tool in the annotation and structural elucidation of metabolites and is increasingly used in the area of accurate mass LC/MS-based metabolomics to identify unknown, but biologically relevant, compounds. As a consequence, there is a growing need for computational tools specifically designed for the processing and interpretation of MS(n) data. Here, we present a novel approach to represent and calculate the similarity between high-resolution mass spectral fragmentation trees. This approach can be used to query multiple-stage mass spectra in MS spectral libraries. Additionally the method can be used to calculate structure-spectrum correlations and potentially deduce substructures from spectra of unknown compounds. The approach was tested using two different spectral libraries composed of either human or plant metabolites which currently contain 872 MS(n) spectra acquired from 549 metabolites using Orbitrap FTMS(n). For validation purposes, for 282 of these 549 metabolites, 765 additional replicate MS(n) spectra acquired with the same instrument were used. Both the dereplication and de novo identification functionalities of the comparison approach are discussed. This novel MS(n) spectral processing and comparison approach increases the probability to assign the correct identity to an experimentally obtained fragmentation tree. Ultimately, this tool may pave the way for constructing and populating large MS(n) spectral libraries that can be used for searching and matching experimental MS(n) spectra for annotation and structural elucidation of unknown metabolites detected in untargeted metabolomics studies.

Comparative transcriptional profiling and preliminary study on heterosis mechanism of super-hybrid rice.

Heterosis is a biological phenomenon whereby the offspring from two parents show improved and superior performance than either inbred parental lines. Hybrid rice is one of the most successful apotheoses in crops utilizing heterosis. Transcriptional profiling of F1 super-hybrid rice Liangyou-2186 and its parents by serial analysis of gene expression (SAGE) revealed 1183 differentially expressed genes (DGs), among which DGs were found significantly enriched in pathways such as photosynthesis and carbon-fixation, and most of the key genes involved in the carbon-fixation pathway exhibited up-regulated expression in F1 hybrid rice. Moreover, increased catabolic activity of corresponding enzymes and photosynthetic efficiency were also detected, which combined to indicate that carbon fixation is enhanced in F1 hybrid, and might probably be associated with the yield vigor and heterosis in super-hybrid rice. By correlating DGs with yield-related quantitative trait loci (QTL), a potential relationship between differential gene expression and phenotypic changes was also found. In addition, a regulatory network involving circadian-rhythms and light signaling pathways was also found, as previously reported in Arabidopsis, which suggest that such a network might also be related with heterosis in hybrid rice. Altogether, the present study provides another view for understanding the molecular mechanism underlying heterosis in rice.

Alkaloid profiling of the Chinese herbal medicine Fuzi by combination of matrix-assisted laser desorption ionization mass spectrometry with liquid chromatography-mass spectrometry

A matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) method was developed for the high throughput and robust qualitative profiling of alkaloids in Fuzi--the processed lateral roots of the Chinese herbal medicine Aconitum carmichaeli Debx (A. carmichaeli). After optimization, powdered roots--without any further sample preparation--could be used to screen for the presence of Aconitum alkaloids. Furthermore, the semi-quantitative potential of MALDI-MS was confirmed using liquid chromatography-mass spectrometry (LC-MS) as reference. In total over sixty alkaloids were detected by LC-MS and fifteen of them were tentatively identified. Both MALDI-MS and LC-MS analysis revealed significant variation in alkaloid content in different (commercial) samples. LC-MS analysis of three toxic alkaloids in 14 batches of Fuzi resulted in a variation of their concentrations expressed as RSDs of 138%, 99% and 221% for aconitine, hypaconitine and mesaconitine, respectively. The variation in concentrations (expressed as RSD) of about the ninety constituents detected were classified as follows: 13 constituents showed an RSD of 77-100%, 46 with an RSD of 100-150%, 21 with an RSD of 150-200% and 9 constituents with an RSD in concentration of 200-235%. These results demonstrate a strong difference in chemical composition of the various Fuzi and illustrate the necessity of adequate QA/QC procedures for both safety and efficiency of herbal medicine. The described analytical procedures for alkaloid profiling could play a role in these procedures.

Lipidomic approach to evaluate rosuvastatin and atorvastatin at various dosages: investigating differential effects among statins

ABSTRACT Objective: Lipid profiling (lipidomics) may be useful in revealing detailed information with regard to the effects on lipid metabolism, the cardiovascular risk and to differentiate between therapies. The aims of the present study were to: (1) analyze in depth the lipid changes induced by rosuvastatin and atorvastatin at different dosages; (2) compare differences between the two drugs with respect to the lipid profile change; (3) relate the findings with meaningful pathological mechanisms of coronary artery disease. Research design and methods: Liquid chromatography–mass spectrometry was applied to obtain the metabolite profiles of plasma samples taken from a prospectively defined subset (n = 80) of participants in the RADAR study where a randomly assigned treatment with rosuvastatin or atorvastatin in increasing dosages was administered during an 18-week period. Results: A number of sphingomyelins (SPMs) and phosphatidylcholines (PCs) correlate with the different effects of the two statins on the LDL-C/HDL-C ratio. Rosuvastatin increased the plasma concentration of PCs after 6 and 18 weeks, while atorvastatin reduced the plasma concentrations of PCs at both timepoints and dosages ( p < 0.01 for between-treatment comparison). Both atorvastatin and rosuvastatin lowered plasma SPMs concentrations, but atorvastatin demonstrated a more pronounced effect with the highest dose ( p = 0.03). Rosuvastatin resulted in a significantly more effective lowering of the [SPMs/(SPMs + PCs)] ratio than atorvastatin at any dose/timepoint ( p < 0.05), a ratio reported to be of clinical importance in coronary artery disease. Conclusions: The lipidomic technique has revealed that statins are different with regards to the effect on detailed lipid profile. The observed difference in lipids may be connected with different clinical outcomes as suggested by the [SPMs/(SPMs + PCs)] ratio.

Elemental composition determination based on MSn

MOTIVATION Identification of metabolites is essential for its use as biomarkers, for research in systems biology and for drug discovery. The first step before a structure can be elucidated is to determine its elemental composition. High-resolution mass spectrometry, which provides the exact mass, together with common constraint rules, for rejecting false proposed elemental compositions, cannot always provide one unique elemental composition solution. RESULTS The Multistage Elemental Formula (MEF) tool is presented in this article to enable the correct assignment of elemental composition to compounds, their fragment ions and neutral losses that originate from the molecular ion by using multistage mass spectrometry (MS(n)). The method provided by MEF reduces the list of predicted elemental compositions for each ion by analyzing the elemental compositions of its parent (precursor ion) and descendants (fragments). MS(n) data of several metabolites were processed using the MEF tool to assign the correct elemental composition and validate the efficacy of the method. Especially, the link between the mass accuracy needed to generate one unique elemental composition and the topology of the MS(n) tree (the width and the depth of the tree) was addressed. This method makes an important step toward semi-automatic de novo identification of metabolites using MS(n) data. AVAILABILITY Software available at: http://abs.lacdr.gorlaeus.net/people/rojas-cherto CONTACT m.rojas@lacdr.leidenuniv.nl; t.reijmers@lacdr.leidenuniv.nl SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Characterization of Rheumatoid Arthritis Subtypes Using Symptom Profiles, Clinical Chemistry and Metabolomics Measurements

Objective The aim is to characterize subgroups or phenotypes of rheumatoid arthritis (RA) patients using a systems biology approach. The discovery of subtypes of rheumatoid arthritis patients is an essential research area for the improvement of response to therapy and the development of personalized medicine strategies. Methods In this study, 39 RA patients are phenotyped using clinical chemistry measurements, urine and plasma metabolomics analysis and symptom profiles. In addition, a Chinese medicine expert classified each RA patient as a Cold or Heat type according to Chinese medicine theory. Multivariate data analysis techniques are employed to detect and validate biochemical and symptom relationships with the classification. Results The questionnaire items ‘Red joints’, ‘Swollen joints’, ‘Warm joints’ suggest differences in the level of inflammation between the groups although c-reactive protein (CRP) and rheumatoid factor (RHF) levels were equal. Multivariate analysis of the urine metabolomics data revealed that the levels of 11 acylcarnitines were lower in the Cold RA than in the Heat RA patients, suggesting differences in muscle breakdown. Additionally, higher dehydroepiandrosterone sulfate (DHEAS) levels in Heat patients compared to Cold patients were found suggesting that the Cold RA group has a more suppressed hypothalamic-pituitary-adrenal (HPA) axis function. Conclusion Significant and relevant biochemical differences are found between Cold and Heat RA patients. Differences in immune function, HPA axis involvement and muscle breakdown point towards opportunities to tailor disease management strategies to each of the subgroups RA patient.

Automated pipeline for de novo metabolite identification using mass-spectrometry-based metabolomics.

Metabolite identification is one of the biggest bottlenecks in metabolomics. Identifying human metabolites poses experimental, analytical, and computational challenges. Here we present a pipeline of previously developed cheminformatic tools and demonstrate how it facilitates metabolite identification using solely LC/MS(n) data. These tools process, annotate, and compare MS(n) data, and propose candidate structures for unknown metabolites either by identity assignment of identical mass spectral trees or by de novo identification using substructures of similar trees. The working and performance of this metabolite identification pipeline is demonstrated by applying it to LC/MS(n) data of urine samples. From human urine, 30 MS(n) trees of unknown metabolites were acquired, processed, and compared to a reference database containing MS(n) data of known metabolites. From these 30 unknowns, we could assign a putative identity for 10 unknowns by finding identical fragmentation trees. For 11 unknowns no similar fragmentation trees were found in the reference database. On the basis of elemental composition only, a large number of candidate structures/identities were possible, so these unknowns remained unidentified. The other 9 unknowns were also not found in the database, but metabolites with similar fragmentation trees were retrieved. Computer assisted structure elucidation was performed for these 9 unknowns: for 4 of them we could perform de novo identification and propose a limited number of candidate structures, and for the other 5 the structure generation process could not be constrained far enough to yield a small list of candidates. The novelty of this work is that it allows de novo identification of metabolites that are not present in a database by using MS(n) data and computational tools. We expect this pipeline to be the basis for the computer-assisted identification of new metabolites in future metabolomics studies, and foresee that further additions will allow the identification of even a larger fraction of the unknown metabolites.

COordination of Standards in MetabOlomicS (COSMOS): facilitating integrated metabolomics data access

Abstract Metabolomics has become a crucial phenotyping technique in a range of research fields including medicine, the life sciences, biotechnology and the environmental sciences. This necessitates the transfer of experimental information between research groups, as well as potentially to publishers and funders. After the initial efforts of the metabolomics standards initiative, minimum reporting standards were proposed which included the concepts for metabolomics databases. Built by the community, standards and infrastructure for metabolomics are still needed to allow storage, exchange, comparison and re-utilization of metabolomics data. The Framework Programme 7 EU Initiative ‘coordination of standards in metabolomics’ (COSMOS) is developing a robust data infrastructure and exchange standards for metabolomics data and metadata. This is to support workflows for a broad range of metabolomics applications within the European metabolomics community and the wider metabolomics and biomedical communities’ participation. Here we announce our concepts and efforts asking for re-engagement of the metabolomics community, academics and industry, journal publishers, software and hardware vendors, as well as those interested in standardisation worldwide (addressing missing metabolomics ontologies, complex-metadata capturing and XML based open source data exchange format), to join and work towards updating and implementing metabolomics standards.

Purine Pathway Implicated in Mechanism of Resistance to Aspirin Therapy: Pharmacometabolomics-Informed Pharmacogenomics

Although aspirin is a well‐established antiplatelet agent, the mechanisms of aspirin resistance remain poorly understood. Metabolomics allows for measurement of hundreds of small molecules in biological samples, enabling detailed mapping of pathways involved in drug response. We defined the metabolic signature of aspirin exposure in subjects from the Heredity and Phenotype Intervention Heart Study. Many metabolites, including known aspirin catabolites, changed on exposure to aspirin, and pathway enrichment analysis identified purine metabolism as significantly affected by drug exposure. Furthermore, purines were associated with aspirin response, and poor responders had higher postaspirin adenosine and inosine levels than did good responders (n = 76; both P < 4 × 10−3). Using our established “pharmacometabolomics‐informed pharmacogenomics” approach, we identified genetic variants in adenosine kinase associated with aspirin response. Combining metabolomics and genomics allowed for more comprehensive interrogation of mechanisms of variation in aspirin response—an important step toward personalized treatment approaches for cardiovascular disease.