New era of cystic fibrosis: full mutational analysis and personalized therapy

Despite its apparently simple genetics, cystic fibrosis (CF) is a rather complex genetic disease. A lot of variability in the steps of the path from the cystic fibrosis transmembrane conductance regulator (CFTR ) gene to the clinical manifestations originates an uncertain genotype - phenotype relationship. A major determinant of this uncertainty is the incomplete knowledge of the CFTR mutated genotypes, due to the high number of CFTR mutations and to the higher number of their combinations in trans and in cis. Also the very limited knowledge of functional effects of CFTR mutated alleles severely impairs our diagnostic and prognostic ability. The final phenotypic modulation exerted by CFTR modifier genes and interactome further complicates the framework. The next generation sequencing approach is a rapid, lowcost and high-throughput tool that allows a near complete structural characterization of CFTR mutated genotypes, as well as of genotypes of several other genes cooperating to the final CF clinical manifestations. This powerful method perfectly complements the new personalized therapeutic approach for CF. Drugs active on specific CFTR mutational classes are already available for CF patients or are in phase 3 trials. A complete genetic characterization has been becoming crucial for a correct personalized therapy. However, the need of a functional classification of each CFTR mutation potently arises. Future big efforts towards an ever more detailed knowledge of both structural and functional CFTR defects, coupled to parallel personalized therapeutic interventions decisive for CF cure can be foreseen

Mutational Analysis of Uterine Cervical Cancer That Survived Multiple Rounds of Radiotherapy

学位記番号：医博甲175

Degeneracy: a design principle for achieving robustness and evolvability

Robustness, the insensitivity of some of a biological system's functionalities to a set of distinct conditions, is intimately linked to fitness. Recent studies suggest that it may also play a vital role in enabling the evolution of species. Increasing robustness, so is proposed, can lead to the emergence of evolvability if evolution proceeds over a neutral network that extends far throughout the fitness landscape. Here, we show that the design principles used to achieve robustness dramatically influence whether robustness leads to evolvability. In simulation experiments, we find that purely redundant systems have remarkably low evolvability while degenerate, i.e. partially redundant, systems tend to be orders of magnitude more evolvable. Surprisingly, the magnitude of observed variation in evolvability can neither be explained by differences in the size nor the topology of the neutral networks. This suggests that degeneracy, a ubiquitous characteristic in biological systems, may be an important enabler of natural evolution. More generally, our study provides valuable new clues about the origin of innovations in complex adaptive systems.Comment: Accepted in the Journal of Theoretical Biology (Nov 2009