Evolutionary epigenetics of modern human populations

Epigenetic variability is a new mechanism for the study of human microevolution, because it creates both phenotypic diversity within an individual and within population. This mechanism constitutes an important reservoir for adaptation in response to new stimuli and recent studies have demonstrated that selective pressures shape not only the genetic code but also DNA methylation profiles. The aim of this thesis is the study of the role of DNA methylation changes in human adaptive processes, considering the Italian peninsula and macro-geographical areas. A whole-genome analysis of DNA methylation profile across the Italian penisula identified some genes whose methylation levels differ between individuals of different Italian districts (South, Centre and North of Italy). These genes are involved in nitrogen compound metabolism and genes involved in pathogens response. Considering individuals with different macro-geographical origins (individuals of Asians, European and African ancestry) more significant DMRs (differentially methylated regions) were identified and are located in genes involved in glucoronidation, in immune response as well as in cell comunication processes. A "profile" of each ancestry (African, Asian and European) was described. Moreover a deepen analysis of three candidate genes (KRTCAP3, MAD1L and BRSK2) in a cohort of individuals of different countries (Morocco, Nigeria, China and Philippines) living in Bologna, was performed in order to explore genetic and epigenetic diversity. Moreover this thesis have paved the way for the application of DNA methylation for the study of hystorical remains and in particular for the age-estimation of individuals starting from biological samples (such as teeth or blood). Noteworthy, a mathematical model that considered methylation values of DNA extracted from cementum and pulp of living individuals can estimate chronological age with high accuracy (median absolute difference between age estimated from DNA methylation and chronological age was 1.2 years).

Genome characterization through a mathematical model of the genetic code: an analysis of the whole chromosome 1 of A. thaliana

The objective of this work is to characterize the genome of the chromosome 1 of A.thaliana, a small flowering plants used as a model organism in studies of biology and genetics, on the basis of a recent mathematical model of the genetic code. I analyze and compare different portions of the genome: genes, exons, coding sequences (CDS), introns, long introns, intergenes, untranslated regions (UTR) and regulatory sequences. In order to accomplish the task, I transformed nucleotide sequences into binary sequences based on the definition of the three different dichotomic classes. The descriptive analysis of binary strings indicate the presence of regularities in each portion of the genome considered. In particular, there are remarkable differences between coding sequences (CDS and exons) and non-coding sequences, suggesting that the frame is important only for coding sequences and that dichotomic classes can be useful to recognize them. Then, I assessed the existence of short-range dependence between binary sequences computed on the basis of the different dichotomic classes. I used three different measures of dependence: the well-known chi-squared test and two indices derived from the concept of entropy i.e. Mutual Information (MI) and Sρ, a normalized version of the “Bhattacharya Hellinger Matusita distance”. The results show that there is a significant short-range dependence structure only for the coding sequences whose existence is a clue of an underlying error detection and correction mechanism. No doubt, further studies are needed in order to assess how the information carried by dichotomic classes could discriminate between coding and noncoding sequence and, therefore, contribute to unveil the role of the mathematical structure in error detection and correction mechanisms. Still, I have shown the potential of the approach presented for understanding the management of genetic information.