Claudia Bertonati

Comprehensive analysis of the specificity of transcription activator-like effector nucleases

A key issue when designing and using DNA-targeting nucleases is specificity. Ideally, an optimal DNA-targeting tool has only one recognition site within a genomic sequence. In practice, however, almost all designer nucleases available today can accommodate one to several mutations within their target site. The ability to predict the specificity of targeting is thus highly desirable. Here, we describe the first comprehensive experimental study focused on the specificity of the four commonly used repeat variable diresidues (RVDs; NI:A, HD:C, NN:G and NG:T) incorporated in transcription activator-like effector nucleases (TALEN). The analysis of >15 500 unique TALEN/DNA cleavage profiles allowed us to monitor the specificity gradient of the RVDs along a TALEN/DNA binding array and to present a specificity scoring matrix for RVD/nucleotide association. Furthermore, we report that TALEN can only accommodate a relatively small number of position-dependent mismatches while maintaining a detectable activity at endogenous loci in vivo, demonstrating the high specificity of these molecular tools. We thus envision that the results we provide will allow for more deliberate choices of DNA binding arrays and/or DNA targets, extending our engineering capabilities.

BurrH: a new modular DNA binding protein for genome engineering

The last few years have seen the increasing development of new DNA targeting molecular tools and strategies for precise genome editing. However, opportunities subsist to either improve or expand the current toolbox and further broaden the scope of possible biotechnological applications. Here we report the discovery and the characterization of BurrH, a new modular DNA binding protein from Burkholderia rhizoxinica that is composed of highly polymorphic DNA targeting modules. We also engineered this scaffold to create a new class of designer nucleases that can be used to efficiently induce in vivo targeted mutagenesis and targeted gene insertion at a desired locus.

Structure of the Avrbs3-DNA Complex Provides New Insights Into the Initial Thymine-Recognition Mechanism

The crystal structure of the AvrBs3–DNA complex is reported.

BuD, a helix–loop–helix DNA-binding domain for genome modification

Crystal structures of BurrH and the BurrH–DNA complex are reported.

Efficient strategies for TALEN-mediated genome editing in mammalian cell lines

TALEN is one of the most widely used tools in the field of genome editing. It enables gene integration and gene inactivation in a highly efficient and specific fashion. Although very attractive, the apparent simplicity and high success rate of TALEN could be misleading for novices in the field of gene editing. Depending on the application, specific TALEN designs, activity assessments and screening strategies need to be adopted. Here we report different methods to efficiently perform TALEN-mediated gene integration and inactivation in different mammalian cell systems including induced pluripotent stem cells and delineate experimental examples associated with these approaches.

Efficient design of meganucleases using a machine learning approach

BackgroundMeganucleases are important tools for genome engineering, providing an efficient way to generate DNA double-strand breaks at specific loci of interest. Numerous experimental efforts, ranging from in vivo selection to in silico modeling, have been made to re-engineer meganucleases to target relevant DNA sequences.ResultsHere we present a novel in silico method for designing custom meganucleases that is based on the use of a machine learning approach. We compared it with existing in silico physical models and high-throughput experimental screening. The machine learning model was used to successfully predict active meganucleases for 53 new DNA targets.ConclusionsThis new method shows competitive performance compared with state-of-the-art in silico physical models, with up to a fourfold increase in terms of the design success rate. Compared to experimental high-throughput screening methods, it reduces the number of screening experiments needed by a factor of more than 100 without affecting final performance.