Artificial evolution with adeno-associated viral libraries

Abstract

After attracting the attention of the scientific community due to a number of favourable characteristics that make it an attractive vector for human gene therapy [1,2], AAV has been thoroughly investigated in the past two decades. Standard technologies for the manipulation of the viral genome and for efficient packaging and purification protocols have paved the road for trial and error manipulation by educated guesses to study viral infectious biology by reverse genetics and to generate improved vectors for human gene transfer. However, despite remarkable progress, our limited knowledge of molecular mechanisms implicated in virus-cell interactions has been a limiting factor. Combinatorial engineering and high-throughput selection techniques hold the potential to boost technological improvement by offering the possibility to screen large numbers of randomly generated clones by appropriate selection protocols. These approaches not only require lesser knowledge of viral biology, but can also be employed as valuable tools to investigate molecular mechanisms that drive the infection process. In this review we recapitulate the rationale for employment of combinatorial methods in AAV vector development and the accomplishments achieved so far, discussing current limitations and interesting developments that are in sight.