Study Info
INDA Accessions: INRP000279
INSDC Accessions: PRJEB85442, ERP168817
- Title: Developing Higher-order Knockout Population in Rice Through CRISPR-Cas9 Library Approach
- Data Type : Other
- Descriptive Title: Developing Higher-order Knockout Population in Rice Through CRISPR-Cas9 Library Approach
-
Organism:
Scientific Name(Taxon Id): Oryza sativa (4530) Common Name: Asian cultivated rice
Other Info
- Abstract: In higher plants like rice, genetic redundancy within gene families compensates for unintended genetic changes, but hinders functional genomics research, as single-gene knockouts often fail to produce visible phenotypes. To address this, we present a proof-of-concept for a CRISPR-Cas9 library approach to create higher-order knockouts by editing redundant genes in rice. We identified genome-wide sets of redundant genes based on similarities in sequences and expression patterns. To simultaneously knockout the sets of these redundant genes, sgRNAs targeting conserved DNA sequences were identified, pooled and cloned into a CRISPR-Cas9 vector. Based on tissue-specific expression patterns of the gene families, ten CRISPR-Cas9 plasmid libraries were developed. The accuracy and uniformity of these libraries were ascertained through Sanger sequencing and Next Generation Sequencing. To demonstrate the effectiveness of the approach, one library targeting seed-expressed gene families was transformed into rice and a small population of higher-order knockouts was developed. Genotyping of the developed plants revealed 83 percent genome-editing efficiency of the transformed library. Phenotypic analysis of the higher-order knockout plants identified lines with altered grain morphology and panicle length. Overall, this work provides foundation for developing higher-order knockout populations, and it will help to accelerate the functional genomics research in rice and other crops.
- Linked publications:
- Center Name: Hasthi Ram, NIPGR
- Number of Base(Total) Mbp: 1,318,085,196
- Size in bytes(Total): 478,412,970
- Number of sample:
- Number of Runs:
- Number of Sequences:
- Number of Assembly:
- Number of Variant files:
Back