APPROACHES TO THE MOLECULAR BASIS OF GENETIC DISEASES

Throughout my PhD period, I experimented different approaches to a complex problem, that is, assessing molecular basis of genetic diseases. In the present thesis I will show how both experimental and computational techniques can contribute to the study of typical biochemistry topics, providing non-conventional points of view and advanced technical and technological supports. The first part of my PhD was focused on physico-chemical investigation, while the second part was devoted to big-data and network analysis. For this reason my thesis is divided into two chapters. The former describes a typical bottom-up approach. I applied the physical technique of laser light scattering to study the first stages of aggregation of configurational variants of the beta-amyloid peptide, recognized to be either promoting or preventing the Alzheimer Disease, as compared with the wild type peptide. The variants correspond to genetic mutations giving rise to either familial cases of Alzheimer Disease or protection from the pathology. I carried out experiments aimed to assess similarities and differences in the kinetics of evolution of the aggregated species in very dilute solution, mimicking the physiological and pathological conditions. The outcome of this study is the discovery of a correlation between the molecular structure and the physico-chemical behavior, in this case aggregation, constituting the hallmark of the disease. The results obtained have been published in a peer reviewed journal (Biophysical Chemistry), with the title: “Pathogenic Aβ A2V versus protective Aβ A2T mutation: early stage aggregation and membrane interaction” (2017). The latter is a top-down approach. I defined the criteria and developed an algorithm for searching big databases with respect to: a) the clinical features of inherited diseases and b) the proteins that are known to be involved in genetically determined diseases. I established searching criteria aiming at regrouping extracted data according to similarity classes in each database. Then, the developed method involves assessing the existence and degree of similarity within and between different clusters. As a result of this approach, we have discovered a correlation between similarity classes extracted from the different databases (the clinical and the biological), thus establishing or suggesting the existence of a biological basis for a genetic disease. The obtained results have been submitted for publication in a peer reviewed journal with the title: “The Disease Similarity Networks: Correlating the Clinical and Biological Similarity of Inherited Diseases”, while a second manuscript is currently under preparation. Based on different disciplines, and designed with the typical instruments and methodologies of physics and statistics, both approaches give non-conventional hints for the understanding of the molecular basis of complex genetic diseases.