To comprehensively understand organisms and their physiological and pathological states it is crucial to determine how their constituting networks operate and are controlled. Examples of biochemical protein networks include metabolic networks like the one shown in Fig. 1, signalling networks, patterns of cell development, cell division, stress response or cell death.
Of high biomedical relevance and so far poorly characterized are protein networks that accompany the onset of neurotoxic cell death, in neurodegenerative proteinopathies, and their dynamics. Many neurodegenerative disorders are associated to aggregates of abnormally folded proteins, that accumulate intra- or extra-cellularly and are critically involved in neuronal death (for example, α-synuclein-containing Lewy bodies in most types of Parkinsonism or neurofibrillary deposits of the tau protein in the Alzheimer’s disease). Strikingly, a characterization of the protein networks involved in such protein-oriented scenarios and how they trigger cell death is still incomplete, mostly due to the lack of suitable approaches.
Recently, a targeted proteomic technique, based on selected reaction monitoring (SRM)-mass spectrometry, emerged as a powerful tool to study the dynamics of cellular protein networks. The essence of the approach is the generation of specific, quantitative assays for each protein of interest and their subsequent application to multiple samples. The approach can be used to monitor the abundance and modifications of sets of biologically relevant proteins (see Fig. 2), spanning a broad range of abundances, across numerous conditions, at high speed and accuracy.
The SRM approach can provide a detailed picture of how protein networks adapt to changing conditions and respond to pathological stimuli, at high temporal resolution (see example in Fig. 3).
In the group we will apply targeted proteomic and phosphoproteomic techniques based on SRM to studying the molecular mechanisms associated to the onset of neurodegenerative diseases. The SRM approach will be integrated with classical proteomic approaches, biochemical and genetic tools and computational analyses.
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