Back

Efficient Gene Editing and Overexpression of Gametophyte Transformation in a Model Fern

Jiang, W.; Deng, F.; Babla, M.; Chen, C.; Yang, D.; Tong, T.; Qin, Y.; Chen, G.; Marchant, B.; Soltis, P. S.; Soltis, D.; Zeng, F.; Chen, Z.

2024-04-11 plant biology
10.1101/2024.04.10.588889 bioRxiv
Show abstract

The clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-related nuclease (Cas) system allows precise and easy editing of genes in many plant species. However, this system has not yet been applied to any fern species due to the complex characteristics of fern genomes, genetics and physiology. Here, we established, for the first time, a protocol for gametophyte-based screening single-guide RNAs (sgRNAs) with high efficiency for CRISPR/Cas-mediated gene editing in a model fern species, Ceratopteris richardii. We utilized the C. richardii Actin promoter to drive sgRNA expression and enhanced CaMV 35S promoter to drive the expression of Streptococcus pyogenes Cas9 in this CRISPR-mediated editing system, which was employed to successfully edit a few genes (e.g., nucleotidase/phosphatase 1, CrSAL1; Cryptochrome 4, CRY4) and CrPDS, encoding a phytoene desaturase protein that resulted in an albino phenotype in C. richardii. Knockout of CrSAL1 resulted in significantly reduced stomatal conductance (gs), leaf transpiration rate (E), stomatal/pore length, and abscisic acid (ABA)-induced reactive oxygen species (ROS) accumulation in guard cells. Moreover, CrSAL1 overexpressing plants showed significantly increased net photosynthetic rate (A), gs, E and intrinsic water use efficiency (iWUE) as well as most of the stomatal traits and ROS production in guard cells compared to those in the wild-type (WT) plants. Taken together, the optimized CRISPR/Cas9 system provides a useful tool for functional genomics in a model fern species, allowing the exploration of fern gene functions for evolutionary biology, herbal medicine discovery and agricultural applications.

Matching journals

The top 5 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.