Desarrollo de métodos analíticos metabolómicos y metalómicos para el estudio de la enfermedad de Alzheimerdiseño de nuevos biomarcadores químicos de diagnosis

Resumen

Alzheimer�s disease (AD) is the most common neurodegenerative disorder among older people, which is characterized by an insidious onset and progressive decline of cognitive functions. Nowadays there is no cure for this disease principally because its etiology is still unknown, although there is growing evidence that multiple pathological processes may be involved, with the confluence of genetic, environmental and aging-related factors. Furthermore, current diagnostic tests for AD show great limitations, including low sensitivity and specificity as well as the impossibility of early detection of the characteristic symptoms of this disease. Therefore, the identification of new biomarkers for diagnosis is critical. The main objective of this Thesis was the optimization of metabolomic and metallomic approaches, and subsequent application for studying the etiology of Alzheimer's disease and the discovery of potential diagnostic biomarkers. Analytical methods developed in works comprised in this Thesis are based on the use of mass spectrometry as detection technique, due to its wide range of applicability, sensitivity and specificity. In order to get a comprehensive metabolomic coverage we optimized multiple complementary analytical platforms, including procedures for direct analysis by mass spectrometry (DI- ESI-MS, FIA-APPI-MS) and its coupling with orthogonal separation techniques (UHPLC-MS, GC-MS, CE-MS). Direct analysis platforms showed a great potential to perform a first metabolic screening in multiple biological fluids and tissues, due to its reduced analysis time and instrumental simplicity. Subsequently, coupled techniques allowed carrying out a more comprehensive investigation of the whole metabolome by using complementary mechanisms of retention. Alternatively, we also developed a metallomic procedure based on the fractionation of metallo-species according to their molecular weight by means of protein precipitation in non-denaturing conditions and subsequent ICP-MS analysis. The application of these metabolomic and metallomic techniques in serum samples from patients with Alzheimer's disease and mild cognitive impairment allowed the identification of numerous pathological mechanisms related to the pathogenesis of this disorder and its progression from pre-clinical stages. Thus, some of the most important findings of this study were the detection of significant changes in the composition of membrane lipids, deficits in energy metabolism and neurotransmitter systems, oxidative stress, hyperammonemia, hyperlipidemia, or an altered homeostasis of multiple metallic and metalloid elements, among others. In turn, these metabolic disturbances were also observed in multiple biological compartments from the APPswe/PSlAE9 model, including serum, brain, liver, kidney, spleen and thymus, thus demonstrating the utility of this transgenic mouse for modelling AD. The comparison of different brain regions showed that the areas most affected by the characteristic neuropathology of this disease were hippocampus and cortex, although other regions were also disrupted to a lesser extent, including the striatum, cerebellum and olfactory bulbs. Furthermore, alterations detected in peripheral organs (i.e. liver, kidney, spleen and thymus) confirm the systemic nature of this neurodegenerative disorder. Finally, a new model of impaired immune function was developed based on the depletion of interleukin-4 gen in APP/PS1 (APP/PS1/TL4-KO) in order to investigate mechanisms underlying to the inflammatory component of AD. Thus, the application of metabolomic techniques revealed alterations in the biosynthesis of histamine, amino acid metabolism, production of eicosanoids and failures in the urea cycle. Therefore, it can be concluded that the combined application of multiple metabolomic and metallomic approaches in different biological matrices, from both human patients and animal models, allows to study in depth the etiology associated with Alzheimer's disease, and discover potential biomarkers of diagnosis.