Identification of the N-Linked Glycosylation Sites of the Transcription Factor Rest and Effect of Glycosylation on DNA Binding and Transcriptional Activity

REST is a zinc-finger transcription factor implicated in several processes such as maintenance of embryonic stem cell pluripotency and regulation of mitotic fidelity in non-neuronal cells [Chong et al., 1995]. The gene encodes for a 116-kDa protein that acts as a molecular platform for co-repressors recruitment and promotes modifications of DNA and histones [Ballas, 2005]. REST showed different apparent molecular weights, consistent with the possible presence of post-translational modifications [Lee et al., 2000]. Among these the most common is glycosylation, the covalent attachment of carbohydrates during or after protein synthesis [Apweiler et al., 1999] My thesis has ascertained, for the first time, the presence of glycan chians in the transcription factor REST. Through enzymatic deglycosylation and MS, oligosaccharide composition of glycan chains was evaluated: a complex mixture of glycans, composed of N-acetylgalactosamine, galactose and mannose, was observed thus confirming the presence of O- and N-linked glycan chains. Glycosylation site mapping was done using a 18O-labeling method and MS/MS and twelve potential N-glycosylation sites were identified. The most probable glycosylation target residues were mutated through site-directed mutagenesis and REST mutants were expressed in different cell lines. Variations in the protein molecular weight and mutant REST ability to bind the RE-1 sequence were analyzed. Gene reporter assays showed that, altogether, removal of N-linked glycan chains causes loss of transcriptional repressor function, except for mutant N59 which showed a slight residual repressor activity in presence of IGF-I. Taken togheter these results demonstrate the presence of complex glycan chians in the transcription factor REST: I have depicted their composition, started defining their position on the protein backbone and identified their possible role in the transcription factor functioning. Considering the crucial role of glycosylation and transcription factors activity in the aetiology of many diseases, any further knowledge could find important and interesting pharmacological application.

Development of advanced methods for the simulation of the reacting mixture formation in internal combustion engines with the use of machine learning algorithms

Besides increasing the share of electric and hybrid vehicles, in order to comply with more stringent environmental protection limitations, in the mid-term the auto industry must improve the efficiency of the internal combustion engine and the well to wheel efficiency of the employed fuel. To achieve this target, a deeper knowledge of the phenomena that influence the mixture formation and the chemical reactions involving new synthetic fuel components is mandatory, but complex and time intensive to perform purely by experimentation. Therefore, numerical simulations play an important role in this development process, but their use can be effective only if they can be considered accurate enough to capture these variations. The most relevant models necessary for the simulation of the reacting mixture formation and successive chemical reactions have been investigated in the present work, with a critical approach, in order to provide instruments to define the most suitable approaches also in the industrial context, which is limited by time constraints and budget evaluations. To overcome these limitations, new methodologies have been developed to conjugate detailed and simplified modelling techniques for the phenomena involving chemical reactions and mixture formation in non-traditional conditions (e.g. water injection, biofuels etc.). Thanks to the large use of machine learning and deep learning algorithms, several applications have been revised or implemented, with the target of reducing the computing time of some traditional tasks by orders of magnitude. Finally, a complete workflow leveraging these new models has been defined and used for evaluating the effects of different surrogate formulations of the same experimental fuel on a proof-of-concept GDI engine model.