Assessment of the lipidomic effects of environmental pollutants on exposed organisms using chemometric and analytical methods
- Autores: Eva Gorrochategui Matas
- Directores de la Tesis: Romà Tauler Ferré (dir. tes.), Silvia Lacorte Bruguera (dir. tes.)
- Lectura: En la Universitat de Barcelona ( España ) en 2018
- Idioma: español
- Tribunal Calificador de la Tesis: Héctor Gallart Ayala (presid.), Carmen Bedia Girbés (secret.), Oscar Yanes (voc.)
- Programa de doctorado: Programa de Doctorado en Química Analítica y Medio Ambiente por la Universidad de Barcelona
- Materias:
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- Resumen
Lipidomics is a subset of metabolomics, which in turn, is one of the categorical platforms that constitute omics. Omic sciences aim at the study of the abundance and (or) structural characterization of a broad range of molecules (e.g., lipids in the case of lipidomics) in organisms under distinct scenarios. In the environmental field, omic studies aim at the evaluation of the alterations that organisms might suffer after exposure to environmental stressors such as chemical pollutants, leading to exposomics. Thus, lipidomic data can be used to acquire fundamental understanding of the interaction between organisms and external environmental stimuli. Distinct analytical techniques can be used to obtain lipidomic data. Among them, liquid chromatography coupled to mass spectrometry (LC-MS) is one of the most powerful technologies due to its ability in the analysis of low molecular weight compounds in biological systems. However, LC-MS data sets are challenging to analyse because of their very large size and complexity and for this reason chemometric methods are proposed to reveal the information contained in LC-MS lipidomic (and metabolomic) data sets as much as possible.
This Thesis has dealt, on the one hand, with the development of an untargeted LC-MS data analysis strategy based on the use of powerful chemometric tools, with the ultimate goal of facilitating lipidomic studies and demonstrating the usefulness of Chemometrics in this field. The developed strategy covers most of the steps involved in the data analysis process: data storage and conversion, import, compression, normalization, scaling and transformation, peak resolution and biomarker detection. However, the main contributions of the present Thesis are related with the steps of data compression and resolution. Distinct data compression strategies have been compared such as the classical binning procedure or the time/mass windowing and a more recent strategy based on the search of the regions of interest or ROI, being the latter, the one that has provided better results. Concerning data resolution, the performance of Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) has been evaluated showing excellent results with LC-MS lipidomic data sets. Also, different methods for variable selection (biomarker screening) have been compared including the classical ANOVA statistical test followed by a multiple comparisons test and a chemometric method: Partial Least Squares-Discriminant Analysis (PLS-DA), using the variables importance in projection (VIP). Moreover, the study of the contribution of the factors underlying multifactorial experimental designs and the evaluation of the sources of variance has been performed by ANOVA-simultaneous component analysis (ASCA). A step-by-step protocol of the developed LC-MS data analysis strategy and a description of the corresponding methodology have been provided so that researchers in lipidomics (and metabolomics) can use them to analyse their own data without the requirement of external data analysis software.
On the other hand, this Thesis has aimed to generate lipidomic data from LC-MS techniques and from attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy and Surface Enhanced Raman Spectroscopy (SERS) combined with chemometric methods. The obtained lipidomic data have been used to provide knowledge of the effects of some widespread environmental pollutants on human and environmental modelbiosystems. The selected environmental pollutants in this Thesis include some perfluoroalkylated substances (PFASs), tributyltin (TBT), different forms of carbon-based nanoparticles (CBNs) and some proautophagic drugs. Moreover, the biological systems that have been exposed to these environmental stressors comprise three cell lines (a human placental chroriocarcinoma cell line (JEG-3), a human glioblastoma cell line (T98G) and a Xenopus laevis kidney epithelial cell line (A6)) and a model organism (zebrafish, Danio rerio). In order to extract more information of the effects of the chemicals on the exposed biological systems, some toxicological assays have also been performed. Overall, this Thesis pretends to provide a useful contribution to the untargeted lipidomic research.
