Back

Edition of complex gene families in tobacco with GoldenBraid 4.0, a multipurpose web-based platform for plant genome engineering

Vazquez-Vilar, M.; Garcia-Carpintero, V.; Selma, S.; Bernabe-Orts, J. M.; Sanchez-Vicente, J.; Salazar-Sarasua, B.; Ressa, A.; de Paola, C.; Ajenjo, M.; Fernandez-del-Carmen, A.; Granell, A.; Orzaez, D.

2020-10-06 molecular biology
10.1101/2020.10.06.327841 bioRxiv
Show abstract

CRISPR/Cas ability to target several loci simultaneously (multiplexing) is a game-changer in plant breeding. Multiplexing not only accelerates trait pyramiding but also can unveil traits hidden by functional redundancy in polyploid crops. Furthermore, multiplexing enhances dCas-based programmable gene expression and enables cascade-like gene regulation. However, multiplex constructs comprising tandemly arrayed gRNAs are difficult to assemble, this hampering more widespread use. Here we present a comprehensive upgrade of the popular cloning platform GoldenBraid (GB), in which, on top of its classical multigene cloning software, we integrate new assembly tools for two-dimensions gRNA multiplexing with both Cas9 and Cas12a, using the gRNA-tRNA-spaced and the gRNA unspaced approaches, respectively. As functional validation, we show, among others, the assembly of up to 17 tandemly-arrayed gRNAs constructs against a subset of the Squamosa-Promoter Binding Protein-Like (SPL) gene family in tobacco. With these constructs we generated a collection of Cas9-free SPL mutants harboring up to 9 biallelic mutations in a single generation. The functionality of GB-assembled dCas9 and dCas12a-based CRISPR activators and repressors using single and multiplexing gRNAs is also validated. With the incorporation of the new CRISPR tools and parts collection, GB4.0 turns an unprecedentedly comprehensive open platform for plant genetic engineering.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.