Mass spectrometry and nuclear magnetic resonance based metabolomics applied to the study of polycystic ovary syndrome

• Autores: Sara Samino Gené
• Directores de la Tesis: Oscar Yanes (dir. tes.)
• Lectura: En la Universitat Rovira i Virgili ( España ) en 2013
• Idioma: inglés
• Tribunal Calificador de la Tesis: Josep Ribalta Vives (presid.), Marta Cascante Serratosa (secret.), Reza Mohammadi Salek (voc.)
• Materias:
  • Química
    • Química analítica
      • Espectroscopia de resonancia magnética
      • Espectroscopia de masas
    • Bioquímica
• Enlaces
  • Tesis en acceso abierto en: TDX
• Resumen

  • Objectives: Three objectives of this thesis have been: (i) Mastering of the main analytical platforms used in metabolomics, (ii) Developing an untargeted metabolomic workflow, involving novel aspects of sample preparation, and data processing for metabolite identification, (iii) Implementing our untargeted metabolomic workflow to the study of human patients with Polycystic Ovary Syndrome (PCOS) and their response to drug treatment Results: In Work 1: Optimization metabolite extraction conditions for NMR analysis, followed by LC/ESI-MS by using the same sample extract with no need for solvent exchange or further pretreatment. In Work 2: Investigate the impact of different aspects of univariate statistical analysis on untargeted LC-MS based metabolomic experiments. In Work 3: Implementation of GC-MS untargeted metabolomic approach to provide new insights on the impact that obesity exerts on the metabolic derangements associated with PCOS. In Work 4: Implementation of multiplatform metabolomics approach based on NMR and LC-MS to provide new insights in PCOS disease in a cohort of young lean PCOS patients. In Work 5: Implementation of multiplatform metabolomics approach based on NMR, GC-MS and LC-MS to provide new insights on the action of drug polytherapy to PCOS disorder. Conclusion: Metabolomics can be consider as a powerful tool for the study of metabolic disorders. Furthermore, metabolite profiling has demonstrated feasibility and flexibility for revealing new mechanistic insights in metabolic disorders that are not been consider when classical analysis is used. Therefore, our metabolomic analysis have demonstrated a great potential as a useful diagnostic technique and can facilitate monitoring of both disease progression and effects of therapeutic treatment.