Identification of Analytical Factors Affecting Complex Proteomics Profiles Acquired in a Factorial Design Study with Analysis of Variance: Simultaneous Component Analysis.

Complex shotgun proteomics peptide profiles obtained in quantitative differential protein expression studies, such as in biomarker discovery, may be affected by multiple experimental factors. These preanalytical factors may affect the measured protein abundances which in turn influence the outcome of the associated statistical analysis and validation. It is therefore important to determine which factors influence the abundance of peptides in a complex proteomics experiment and to identify those peptides that are most influenced by these factors. In the current study we analyzed depleted human serum samples to evaluate experimental factors that may influence the resulting peptide profile such as the residence time in the autosampler at 4 °C, stopping or not stopping the trypsin digestion with acid, the type of blood collection tube, different hemolysis levels, differences in clotting times, the number of freeze-thaw cycles, and different trypsin/protein ratios. To this end we used a two-level fractional factorial design of resolution IV (2(IV)(7-3)). The design required analysis of 16 samples in which the main effects were not confounded by two-factor interactions. Data preprocessing using the Threshold Avoiding Proteomics Pipeline (Suits, F.; Hoekman, B.; Rosenling, T.; Bischoff, R.; Horvatovich, P. Anal. Chem. 2011, 83, 7786-7794, ref 1) produced a data-matrix containing quantitative information on 2,559 peaks. The intensity of the peaks was log-transformed, and peaks having intensities of a low t-test significance (p-value > 0.05) and a low absolute fold ratio (<2) between the two levels of each factor were removed. The remaining peaks were subjected to analysis of variance (ANOVA)-simultaneous component analysis (ASCA). Permutation tests were used to identify which of the preanalytical factors influenced the abundance of the measured peptides most significantly. The most important preanalytical factors affecting peptide intensity were (1) the hemolysis level, (2) stopping trypsin digestion with acid, and (3) the trypsin/protein ratio. This provides guidelines for the experimentalist to keep the ratio of trypsin/protein constant and to control the trypsin reaction by stopping it with acid at an accurately set pH. The hemolysis level cannot be controlled tightly as it depends on the status of a patient's blood (e.g., red blood cells are more fragile in patients undergoing chemotherapy) and the care with which blood was sampled (e.g., by avoiding shear stress). However, its level can be determined with a simple UV spectrophotometric measurement and samples with extreme levels or the peaks affected by hemolysis can be discarded from further analysis. The loadings of the ASCA model led to peptide peaks that were most affected by a given factor, for example, to hemoglobin-derived peptides in the case of the hemolysis level. Peak intensity differences for these peptides were assessed by means of extracted ion chromatograms confirming the results of the ASCA model.

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.

MetiTree: a web application to organize and process high-resolution multi-stage mass spectrometry metabolomics data.

UNLABELLED: Identification of metabolites using high-resolution multi-stage mass spectrometry (MS(n)) data is a significant challenge demanding access to all sorts of computational infrastructures. MetiTree is a user-friendly, web application dedicated to organize, process, share, visualize and compare MS(n) data. It integrates several features to export and visualize complex MS(n) data, facilitating the exploration and interpretation of metabolomics experiments. A dedicated spectral tree viewer allows the simultaneous presentation of three related types of MS(n) data, namely, the spectral data, the fragmentation tree and the fragmentation reactions. MetiTree stores the data in an internal database to enable searching for similar fragmentation trees and matching against other MS(n) data. As such MetiTree contains much functionality that will make the difficult task of identifying unknown metabolites much easier. AVAILABILITY: MetiTree is accessible at http://www.MetiTree.nl. The source code is available at https://github.com/NetherlandsMetabolomicsCentre/metitree/wiki.

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.

Elemental composition determination based on MS(n).

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.

Fragmentation trees for the structural characterisation of metabolites.

Metabolite identification plays a crucial role in the interpretation of metabolomics research results. Due to its sensitivity and widespread implementation, a favourite analytical method used in metabolomics is electrospray mass spectrometry. In this paper, we demonstrate our results in attempting to incorporate the potentials of multistage mass spectrometry into the metabolite identification routine. New software tools were developed and implemented which facilitate the analysis of multistage mass spectra and allow for efficient removal of spectral artefacts. The pre-processed fragmentation patterns are saved as fragmentation trees. Fragmentation trees are characteristic of molecular structure. We demonstrate the reproducibility and robustness of the acquisition of such trees on a model compound. The specificity of fragmentation trees allows for distinguishing structural isomers, as shown on a pair of isomeric prostaglandins. This approach to the analysis of the multistage mass spectral characterisation of compounds is an important step towards formulating a generic metabolite identification method.

Equating, or correction for between-block effects with application to body fluid LC-MS and NMR metabolomics data sets.

Combination of data sets from different objects (for example, from two groups of healthy volunteers from the same population) that were measured on a common set of variables (for example, metabolites or peptides) is desirable for statistical analysis in "omics" studies because it increases power. However, this type of combination is not directly possible if nonbiological systematic differences exist among the individual data sets, or "blocks". Such differences can, for example, be due to small analytical changes that are likely to accumulate over large time intervals between blocks of measurements. In this article we present a data transformation method, that we will refer to as "quantile equating", which per variable corrects for linear and nonlinear differences in distribution among blocks of semiquantitative data obtained with the same analytical method. We demonstrate the successful application of the quantile equating method to data obtained on two typical metabolomics platforms, i.e., liquid chromatography-mass spectrometry and nuclear magnetic resonance spectroscopy. We suggest uni- and multivariate methods to evaluate similarities and differences among data blocks before and after quantile equating. In conclusion, we have developed a method to correct for nonbiological systematic differences among semiquantitative data blocks and have demonstrated its successful application to metabolomics data sets.

STATegra, a comprehensive multi-omics dataset of B-cell differentiation in mouse.

Multi-omics approaches use a diversity of high-throughput technologies to profile the different molecular layers of living cells. Ideally, the integration of this information should result in comprehensive systems models of cellular physiology and regulation. However, most multi-omics projects still include a limited number of molecular assays and there have been very few multi-omic studies that evaluate dynamic processes such as cellular growth, development and adaptation. Hence, we lack formal analysis methods and comprehensive multi-omics datasets that can be leveraged to develop true multi-layered models for dynamic cellular systems. Here we present the STATegra multi-omics dataset that combines measurements from up to 10 different omics technologies applied to the same biological system, namely the well-studied mouse pre-B-cell differentiation. STATegra includes high-throughput measurements of chromatin structure, gene expression, proteomics and metabolomics, and it is complemented with single-cell data. To our knowledge, the STATegra collection is the most diverse multi-omics dataset describing a dynamic biological system.

Similarities and differences in lipidomics profiles among healthy monozygotic twin pairs.

Differences in genetic background and/or environmental exposure among individuals are expected to give rise to differences in measurable characteristics, or phenotypes. Consequently, genetic resemblance and similarities in environment should manifest as similarities in phenotypes. The metabolome reflects many of the system properties, and is therefore an important part of the phenotype. Nevertheless, it has not yet been examined to what extent individuals sharing part of their genome and/or environment indeed have similar metabolomes. Here we present the results of hierarchical clustering of blood plasma lipid profile data obtained by liquid chromatography-mass spectrometry from 23 healthy, 18-year-old twin pairs, of which 21 pairs were monozygotic, and 8 of their siblings. For 13 monozygotic twin pairs, within-pair similarities in relative concentrations of the detected lipids were indeed larger than the similarities with any other study participant. We demonstrate such high coclustering to be unexpected on basis of chance. The similarities between dizygotic twins and between nontwin siblings, as well as between nonfamilial participants, were less pronounced. In a number of twin pairs, within-pair dissimilarity of lipid profiles positively correlated with increased blood plasma concentrations of C-reactive protein in one twin. In conclusion, this study demonstrates that in healthy individuals, the individual genetic background contributes to the blood plasma lipid profile. Furthermore, lipid profiling may prove useful in monitoring health status, for example, in the context of personalized medicine.

Multiple testing issues in discriminating compound-related peaks and chromatograms from high frequency noise, spikes and solvent-based noise in LC-MS data sets.

Liquid Chromatography--Mass Spectrometry (LC-MS) is a powerful method for sensitive detection and quantification of proteins and peptides in complex biological fluids like serum. LC-MS produces complex data sets, consisting of some hundreds of millions of data points per sample at a resolution of 0.1 amu in the m/z domain and 7000 data points in the time domain. However, the detection of the lower abundance proteins from this data is hampered by the presence of artefacts, such as high frequency noise and spikes. Moreover, not all of the tens of thousands of the chromatograms produced per sample are relevant for the pursuit of the biomarkers. Thus in analysing the LC-MS data, two critical pre-processing issues arise. Which of the thousands of the: 1. chromatograms per sample are relevant for the detection of the biomarkers?, and 2. signals per chromatogram are truly compound-related? Each of these issues involves assessing the significance (deviation from noise) of multiple observations and the issue of multiple comparisons arises. Current methods disregard the multiplicity and provide no concrete threshold for significance. However, with such procedures, the probability of one or more false-positives is high as the number of tests to be performed is large, and must be controlled. Realizing that the cut-offs for declaring a chromatogram (or a signal) to be compound-related can hugely influence which proteins are detected, it seems natural to define thresholds that are neither arbitrary nor subjective. We suggest the choice of thresholds guided by the critical aim of controlling the False Discovery Rate (FDR) in multiple hypotheses testing for significance over a large set of features produced per sample. This involves the use of the regression diagnostics to characterize the signals of a chromatogram (e.g. as outliers or influential) and to suggest suitable tests statistics for the multiple testing procedures (MTP) for discriminating noise and spikes from true signals. The role of the Generalized Linear Models (GLM) in this MTP is investigated. The method is applied to LC-MS datasets from trypsin-digested serum spiked with varying levels of horse heart cytochrome C (cytoc).

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

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.

Correlated measurement error hampers association network inference.

Modern chromatography-based metabolomics measurements generate large amounts of data in the form of abundances of metabolites. An increasingly popular way of representing and analyzing such data is by means of association networks. Ideally, such a network can be interpreted in terms of the underlying biology. A property of chromatography-based metabolomics data is that the measurement error structure is complex: apart from the usual (random) instrumental error there is also correlated measurement error. This is intrinsic to the way the samples are prepared and the analyses are performed and cannot be avoided. The impact of correlated measurement errors on (partial) correlation networks can be large and is not always predictable. The interplay between relative amounts of uncorrelated measurement error, correlated measurement error and biological variation defines this impact. Using chromatography-based time-resolved lipidomics data obtained from a human intervention study we show how partial correlation based association networks are influenced by correlated measurement error. We show how the effect of correlated measurement error on partial correlations is different for direct and indirect associations. For direct associations the correlated measurement error usually has no negative effect on the results, while for indirect associations, depending on the relative size of the correlated measurement error, results can become unreliable. The aim of this paper is to generate awareness of the existence of correlated measurement errors and their influence on association networks. Time series lipidomics data is used for this purpose, as it makes it possible to visually distinguish the correlated measurement error from a biological response. Underestimating the phenomenon of correlated measurement error will result in the suggestion of biologically meaningful results that in reality rest solely on complicated error structures. Using proper experimental designs that allow for the quantification of the size of correlated and uncorrelated errors, can help to identify suspicious connections in association networks constructed from (partial) correlations.

A new approach to untargeted integration of high resolution liquid chromatography-mass spectrometry data.

Because of its high sensitivity and specificity, hyphenated mass spectrometry has become the predominant method to detect and quantify metabolites present in bio-samples relevant for all sorts of life science studies being executed. In contrast to targeted methods that are dedicated to specific features, global profiling acquisition methods allow new unspecific metabolites to be analyzed. The challenge with these so-called untargeted methods is the proper and automated extraction and integration of features that could be of relevance. We propose a new algorithm that enables untargeted integration of samples that are measured with high resolution liquid chromatography-mass spectrometry (LC-MS). In contrast to other approaches limited user interaction is needed allowing also less experienced users to integrate their data. The large amount of single features that are found within a sample is combined to a smaller list of, compound-related, grouped feature-sets representative for that sample. These feature-sets allow for easier interpretation and identification and as important, easier matching over samples. We show that the automatic obtained integration results for a set of known target metabolites match those generated with vendor software but that at least 10 times more feature-sets are extracted as well. We demonstrate our approach using high resolution LC-MS data acquired for 128 samples on a lipidomics platform. The data was also processed in a targeted manner (with a combination of automatic and manual integration) using vendor software for a set of 174 targets. As our untargeted extraction procedure is run per sample and per mass trace the implementation of it is scalable. Because of the generic approach, we envision that this data extraction lipids method will be used in a targeted as well as untargeted analysis of many different kinds of TOF-MS data, even CE- and GC-MS data or MRM. The Matlab package is available for download on request and efforts are directed toward a user-friendly Windows executable.

Hierarchical clustering analysis of blood plasma lipidomics profiles from mono- and dizygotic twin families.

Twin and family studies are typically used to elucidate the relative contribution of genetic and environmental variation to phenotypic variation. Here, we apply a quantitative genetic method based on hierarchical clustering, to blood plasma lipidomics data obtained in a healthy cohort consisting of 37 monozygotic and 28 dizygotic twin pairs, and 52 of their biological nontwin siblings. Such data are informative of the concentrations of a wide range of lipids in the studied blood samples. An important advantage of hierarchical clustering is that it can be applied to a high-dimensional 'omics' type data, whereas the use of many other quantitative genetic methods for analysis of such data is hampered by the large number of correlated variables. For this study we combined two lipidomics data sets, originating from two different measurement blocks, which we corrected for block effects by 'quantile equating'. In the analysis of the combined data, average similarities of lipidomics profiles were highest between monozygotic (MZ) cotwins, and became progressively lower between dizygotic (DZ) cotwins, among sex-matched nontwin siblings and among sex-matched unrelated participants, respectively. Our results suggest that (1) shared genetic background, shared environment, and similar age contribute to similarities in blood plasma lipidomics profiles among individuals; and (2) that the power of quantitative genetic analyses is enhanced by quantile equating and combination of data sets obtained in different measurement blocks.

OMG: Open Molecule Generator.

Computer Assisted Structure Elucidation has been used for decades to discover the chemical structure of unknown compounds. In this work we introduce the first open source structure generator, Open Molecule Generator (OMG), which for a given elemental composition produces all non-isomorphic chemical structures that match that elemental composition. Furthermore, this structure generator can accept as additional input one or multiple non-overlapping prescribed substructures to drastically reduce the number of possible chemical structures. Being open source allows for customization and future extension of its functionality. OMG relies on a modified version of the Canonical Augmentation Path, which grows intermediate chemical structures by adding bonds and checks that at each step only unique molecules are produced. In order to benchmark the tool, we generated chemical structures for the elemental formulas and substructures of different metabolites and compared the results with a commercially available structure generator. The results obtained, i.e. the number of molecules generated, were identical for elemental compositions having only C, O and H. For elemental compositions containing C, O, H, N, P and S, OMG produces all the chemically valid molecules while the other generator produces more, yet chemically impossible, molecules. The chemical completeness of the OMG results comes at the expense of being slower than the commercial generator. In addition to being open source, OMG clearly showed the added value of constraining the solution space by using multiple prescribed substructures as input. We expect this structure generator to be useful in many fields, but to be especially of great importance for metabolomics, where identifying unknown metabolites is still a major bottleneck.

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.

Metabolomics of cerebrospinal fluid reveals changes in the central nervous system metabolism in a rat model of multiple sclerosis.

Experimental Autoimmune Encephalomyelitis (EAE) is the most commonly used animal model for Multiple Sclerosis (MScl). CSF metabolomics in an acute EAE rat model was investigated using targetted LC-MS and GC-MS. Acute EAE in Lewis rats was induced by co-injection of Myelin Basic Protein with Complete Freund's Adjuvant. CSF samples were collected at two time points: 10 days after inoculation, which was during the onset of the disease, and 14 days after inoculation, which was during the peak of the disease. The obtained metabolite profiles from the two time points of EAE development show profound differences between onset and the peak of the disease, suggesting significant changes in CNS metabolism over the course of MBP-induced neuroinflammation. Around the onset of EAE the metabolome profile shows significant decreases in arginine, alanine and branched amino acid levels, relative to controls. At the peak of the disease, significant increases in concentrations of multiple metabolites are observed, including glutamine, O-phosphoethanolamine, branched-chain amino acids and putrescine. Observed changes in metabolite levels suggest profound changes in CNS metabolism over the course of EAE. Affected pathways include nitric oxide synthesis, altered energy metabolism, polyamine synthesis and levels of endogenous antioxidants. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0306-3) contains supplementary material, which is available to authorized users.

Lipidomics reveals multiple pathway effects of a multi-components preparation on lipid biochemistry in ApoE*3Leiden.CETP mice.

BACKGROUND: Causes and consequences of the complex changes in lipids occurring in the metabolic syndrome are only partly understood. Several interconnected processes are deteriorating, which implies that multi-target approaches might be more successful than strategies based on a limited number of surrogate markers. Preparations from Chinese Medicine (CM) systems have been handed down with documented clinical features similar as metabolic syndrome, which might help developing new intervention for metabolic syndrome. The progress in systems biology and specific animal models created possibilities to assess the effects of such preparations. Here we report the plasma and liver lipidomics results of the intervention effects of a preparation SUB885C in apolipoprotein E3 Leiden cholesteryl ester transfer protein (ApoE*3Leiden.CETP) mice. SUB885C was developed according to the principles of CM for treatment of metabolic syndrome. The cannabinoid receptor type 1 blocker rimonabant was included as a general control for the evaluation of weight and metabolic responses. METHODOLOGY/PRINCIPAL FINDINGS: ApoE*3Leiden.CETP mice with mild hypercholesterolemia were divided into SUB885C-, rimonabant- and non-treated control groups. SUB885C caused no weight loss, but significantly reduced plasma cholesterol (-49%, p<0.001), CETP levels (-31%, p<0.001), CETP activity (-74%, p<0.001) and increased HDL-C (39%, p<0.05). It influenced lipidomics classes of cholesterol esters and triglycerides the most. Rimonabant induced a weight loss (-9%, p<0.05), but only a moderate improvement of lipid profiles. In vitro, SUB885C extract caused adipolysis stimulation and adipogenesis inhibition in 3T3-L1 cells. CONCLUSIONS: SUB885C, a multi-components preparation, is able to produce anti-atherogenic changes in lipids of the ApoE*3Leiden.CETP mice, which are comparable to those obtained with compounds belonging to known drugs (e.g. rimonabant, atorvastatin, niacin). This study successfully illustrated the power of lipidomics in unraveling intervention effects and to help finding new targets or ingredients for lifestyle-related metabolic abnormality.

Understanding and classifying metabolite space and metabolite-likeness.

While the entirety of 'Chemical Space' is huge (and assumed to contain between 10(63) and 10(200) 'small molecules'), distinct subsets of this space can nonetheless be defined according to certain structural parameters. An example of such a subspace is the chemical space spanned by endogenous metabolites, defined as 'naturally occurring' products of an organisms' metabolism. In order to understand this part of chemical space in more detail, we analyzed the chemical space populated by human metabolites in two ways. Firstly, in order to understand metabolite space better, we performed Principal Component Analysis (PCA), hierarchical clustering and scaffold analysis of metabolites and non-metabolites in order to analyze which chemical features are characteristic for both classes of compounds. Here we found that heteroatom (both oxygen and nitrogen) content, as well as the presence of particular ring systems was able to distinguish both groups of compounds. Secondly, we established which molecular descriptors and classifiers are capable of distinguishing metabolites from non-metabolites, by assigning a 'metabolite-likeness' score. It was found that the combination of MDL Public Keys and Random Forest exhibited best overall classification performance with an AUC value of 99.13%, a specificity of 99.84% and a selectivity of 88.79%. This performance is slightly better than previous classifiers; and interestingly we found that drugs occupy two distinct areas of metabolite-likeness, the one being more 'synthetic' and the other being more 'metabolite-like'. Also, on a truly prospective dataset of 457 compounds, 95.84% correct classification was achieved. Overall, we are confident that we contributed to the tasks of classifying metabolites, as well as to understanding metabolite chemical space better. This knowledge can now be used in the development of new drugs that need to resemble metabolites, and in our work particularly for assessing the metabolite-likeness of candidate molecules during metabolite identification in the metabolomics field.

Sub-typing of rheumatic diseases based on a systems diagnosis questionnaire.

BACKGROUND: The future of personalized medicine depends on advanced diagnostic tools to characterize responders and non-responders to treatment. Systems diagnosis is a new approach which aims to capture a large amount of symptom information from patients to characterize relevant sub-groups. METHODOLOGY: 49 patients with a rheumatic disease were characterized using a systems diagnosis questionnaire containing 106 questions based on Chinese and Western medicine symptoms. Categorical principal component analysis (CATPCA) was used to discover differences in symptom patterns between the patients. Two Chinese medicine experts where subsequently asked to rank the Cold and Heat status of all the patients based on the questionnaires. These rankings were used to study the Cold and Heat symptoms used by these practitioners. FINDINGS: The CATPCA analysis results in three dimensions. The first dimension is a general factor (40.2% explained variance). In the second dimension (12.5% explained variance) 'anxious', 'worrying', 'uneasy feeling' and 'distressed' were interpreted as the Internal disease stage, and 'aggravate in wind', 'fear of wind' and 'aversion to cold' as the External disease stage. In the third dimension (10.4% explained variance) 'panting s', 'superficial breathing', 'shortness of breath s', 'shortness of breath f' and 'aversion to cold' were interpreted as Cold and 'restless', 'nervous', 'warm feeling', 'dry mouth s' and 'thirst' as Heat related. 'Aversion to cold', 'fear of wind' and 'pain aggravates with cold' are most related to the experts Cold rankings and 'aversion to heat', 'fullness of chest' and 'dry mouth' to the Heat rankings. CONCLUSIONS: This study shows that the presented systems diagnosis questionnaire is able to identify groups of symptoms that are relevant for sub-typing patients with a rheumatic disease.