Metabolomics of biofluids : from analytical tools to data interpretation

The chapters that comprised this thesis cover a broad range of subjects from analytical method development to clinical application of metabolic profiling. They are united by the facts that all of these studies aimed at analysis of biological fluids and that the presented methods and approaches may ultimately become parts of a robust metabolomics workflow that might be used in a future personalized medicine.Bruker BioSpin GmbH, Germany; Dionex Benelux B.V.; Beckman Coulter (Nederland) B.V.; Bruker Nederland B.V.UBL - phd migration 201

Exploratory analysis of human urine by LC–ESI-TOF MS after high intake of olive oil: understanding the metabolism of polyphenols

Olive oil polyphenols have important biological properties which closely depend on their bioavailability, therefore it is essential to understand how polyphenols are absorbed, metabolized and eliminated from the body. An analytical methodology based on rapid resolution liquid chromatography (RRLC) coupled to mass spectrometry detection with a time of flight analyzer (RRLC-ESI-TOF MS) was developed for the determination of the main olive oil phenolic compounds and their metabolites in human urine. Urine samples from ten healthy volunteers were collected before and 2, 4 and 6 h after the intake of 50 mL of extra-virgin olive oil. The proposed method includes liquid-liquid extraction with ethyl acetate that provides extraction recoveries of the phenolic compounds studied between 35 and 75% from spiked urine samples. Good repeatability was obtained, since the relative standard deviations (RSDs) of peak areas in the intra- and inter-day studies were 4.3 and 6.5%, respectively. Statistical studies allowed us to discriminate between the urine samples before and after the intake, and facilitated to find out the m/z values responsible of this discrimination. Based on the very accurate mass information and the isotopic pattern provided by TOF-MS analyzer, together with other available information, ten of these biomarkers and more than 50 metabolites, obtained through phase I and phase II biotransformation reactions, were tentatively identified. Additionally, kinetic studies of the metabolites identified as possible biomarkers were developed, obtaining maximal values in the first two hours for most compounds.The authors are very grateful to Ministry of Education and Science (FPU, AP2005-4356 and Project AGL 2008- 05108-613 CO3-03/ALI), and Junta de Andalucía (P07-AGR-02629 and P09-FQM-5469)

Metabonomics and Intensive Care

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency medicine 2016. Other selected articles can be found online at http://www.biomedcentral.com/collections/annualupdate2016. Further information about the Annual Update in Intensive Care and Emergency Medicine is available from http://www.springer.com/series/8901

Metabolic biomarkers of ageing in C57BL/6J wild-type and flavin-containing monooxygenase 5 (FMO5)-knockout mice

It was recently demonstrated in mice that knockout of the flavin-containing monooxygenase 5 gene, Fmo5, slows metabolic ageing via pleiotropic effects. We have now used an NMR-based metabonomics approach to study the effects of ageing directly on the metabolic profiles of urine and plasma from male, wild-type C57BL/6J and Fmo5−/− (FMO5 KO) mice back-crossed onto the C57BL/6J background. The aim of this study was to identify metabolic signatures that are associated with ageing in both these mouse lines and to characterize the age-related differences in the metabolite profiles between the FMO5 KO mice and their wild-type counterparts at equivalent time points. We identified a range of age-related biomarkers in both urine and plasma. Some metabolites, including urinary 6-hydroxy-6-methylheptan-3-one (6H6MH3O), a mouse sex pheromone, showed similar patterns of changes with age, regardless of genetic background. Others, however, were altered only in the FMO5 KO, or only in the wild-type mice, indicating the impact of genetic modifications on mouse ageing. Elevated concentrations of urinary taurine represent a distinctive, ageing-related change observed only in wild-type mice

Androgen receptor profiling predicts prostate cancer outcome

Prostate cancer is the second most prevalent malignancy in men. Biomarkers for outcome prediction are urgently needed, so that high-risk patients could be monitored more closely postoperatively. To identify prognostic markers and to determine causal players in prostate cancer progression, we assessed changes in chromatin state during tumor development and progression. Based on this, we assessed genomewide androgen receptor/chromatin binding and identified a distinct androgen receptor/chromatin binding profile between primary prostate cancers and tumors with an acquired resistance to therapy. These differential androgen receptor/chromatin interactions dictated expression of a distinct gene signature with strong prognostic potential. Further refinement of the signature provided us with a concise list of nine genes that hallmark prostate cancer outcome in multiple independent validation series. In this report, we identified a novel gene expression signature for prostate cancer outcome through generation of multilevel genomic data on chromatin accessibility and transcriptional regulation and integration with publically available transcriptomic and clinical datastreams. By combining existing technologies, we propose a novel pipeline for biomarker discovery that is easily implementable in other fields of oncology

Statistical HOmogeneous Cluster SpectroscopY (SHOCSY): an optimized statistical approach for clustering of ¹H NMR spectral data to reduce interference and enhance robust biomarkers selection.

We propose a novel statistical approach to improve the reliability of (1)H NMR spectral analysis in complex metabolic studies. The Statistical HOmogeneous Cluster SpectroscopY (SHOCSY) algorithm aims to reduce the variation within biological classes by selecting subsets of homogeneous (1)H NMR spectra that contain specific spectroscopic metabolic signatures related to each biological class in a study. In SHOCSY, we used a clustering method to categorize the whole data set into a number of clusters of samples with each cluster showing a similar spectral feature and hence biochemical composition, and we then used an enrichment test to identify the associations between the clusters and the biological classes in the data set. We evaluated the performance of the SHOCSY algorithm using a simulated (1)H NMR data set to emulate renal tubule toxicity and further exemplified this method with a (1)H NMR spectroscopic study of hydrazine-induced liver toxicity study in rats. The SHOCSY algorithm improved the predictive ability of the orthogonal partial least-squares discriminatory analysis (OPLS-DA) model through the use of "truly" representative samples in each biological class (i.e., homogeneous subsets). This method ensures that the analyses are no longer confounded by idiosyncratic responders and thus improves the reliability of biomarker extraction. SHOCSY is a useful tool for removing irrelevant variation that interfere with the interpretation and predictive ability of models and has widespread applicability to other spectroscopic data, as well as other "omics" type of data

APOBEC3B-Mediated Cytidine Deamination Is Required for Estrogen Receptor Action in Breast Cancer.

Estrogen receptor α (ERα) is the key transcriptional driver in a large proportion of breast cancers. We report that APOBEC3B (A3B) is required for regulation of gene expression by ER and acts by causing C-to-U deamination at ER binding regions. We show that these C-to-U changes lead to the generation of DNA strand breaks through activation of base excision repair (BER) and to repair by non-homologous end-joining (NHEJ) pathways. We provide evidence that transient cytidine deamination by A3B aids chromatin modification and remodelling at the regulatory regions of ER target genes that promotes their expression. A3B expression is associated with poor patient survival in ER+ breast cancer, reinforcing the physiological significance of A3B for ER action

A unified conceptual framework for metabolic phenotyping in diagnosis and prognosis

Understanding metabotype (multicomponent metabolic characteristics) variation can help generate new diagnostic and prognostic biomarkers and models with the potential to impact patient management. Here we present a suite of conceptual approaches for the generation, analysis and understanding of metabotypes from body fluids and tissues. We describe and exemplify four fundamental approaches to the generation and utilization of metabotype data via multiparametric measurement of: i) metabolite levels; ii) metabolic trajectories; iii) metabolic entropies and iv) metabolic networks and correlations in space and time. This conceptual framework can underpin metabotyping in the scenario of personalised medicine, with the aim of improving clinical outcomes for patients, but it will have value and utility in all areas of metabolic profiling well beyond this exemplar