The Metabolomics Unit provides services to all areas of the centre in research related to metabolites and their variations in response to different causes. Metabolomics is within the omics sciences that studies the metabolome and the changes that occur in it when it is subjected to external stimuli: disease, per diem expenses, environmental conditions, etc.
Metabolomics is classified into two main approaches: targeted metabolomics and non-targeted metabolomics. In the first case, the quantification of specific, known metabolites is studied. In contrast, non-targeted metabolomics aims to answer the question: What is the global metabolic profile of a sample, and by extension, how is this metabolic profile affected by an external change?
Examples of initial hypotheses in an untargeted metabolomics study in the field of nutrition include:
How does an individual's metabolome change before and after a nutritional intervention study?
How does the intake of a food supplement (e.g. lipoic acid) affect individuals versus group placebo?
Which metabolites are the discriminants between the various groups on my project?
The two approaches are not mutually exclusive; initially a non-targeted metabolomics analysis can be performed to identify the metabolites of interest. Once these metabolites have been established, a targeted approach can be used to quantify these metabolites.
The Metabolomics Unit has specialised in programs of study untargeted metabolomics, as it is considered to be the approach that can provide value in the research of molecular mechanisms, and in the finding of new biomarkers of disease and per diem expenses.
A very important part, especially when working in non-targeted metabolomics, is the processing of the data. For this purpose, specific software is used to perform the alignment of the chromatographic peaks obtained in the analyses. Subsequently, for the final study of the data multivariate statistical tools are used; some of these tools are specific to Big Data, such as Principal Component Analysis (PCA), Partial Least Squares Regression-Discriminant Analysis (PLS-DA), model RandomForest, model Support Vector Machine (SVM), HeatMap, etc.
Ana Romo Hualde
manager of the Unit.
Dr. in Chemical Sciences
David Muñoz Prieto.Technician.
graduate in Chemical Sciences
→ HPLC - TOF: HPLC 1200 coupled to TOF 6220 Accurate -Mass LC/MS mass detector.
→ UPLC - Qtrap 5500: Acquity H-Class UPLC (Waters) coupled to QTRAP 5500 mass detector (Sciex).
→ microBIOMICS: Metabolomic study related to microbiota in various types of biological samples.
→ EHGNA: Non-targeted metabolomic study in serum in relation to fatty liver
→ PREDISMET: Non-targeted metabolomics study in culture medium of various strains
→ LIKIDI: Untargeted serum metabolomics study in humans
→ BIOPRO: Non-targeted metabolomic study in rat serum on polyphenol-rich extract.
→ NUTRICIA: Non-targeted metabolomic study in plasma at the beginning and end of the intervention in humans. Lipidomics study.
→ CIBENA pumpkin: Non-directed metabolomics study of in vitro fermentation processes.
→ BIOTAGUT: Non-targeted metabolomic study in faeces at the beginning and end of intervention in humans, mice and in vitro fermentative processes.
→ miRNABIOTA: Untargeted serum metabolomics study in humans before and after nutritional intervention.
→ NUTRAGEN: Non-targeted metabolomic study in rat serum on various plant extracts.
→ NUTRIGENIO: Non-targeted metabolomic study in plasma at the beginning and end of the intervention in humans.
→ OBEBACS: Non-targeted metabolomic study in rat serum on various plant extracts.
→ SIMBIOTPLUS: Untargeted metabolomics study carried out on feedstuffs