A paired-end whole-genome sequencing approach enables comprehensive characterization of transgene integration in rice.
Wenting XuHanwen ZhangYuchen ZhangPing ShenXiang LiRong LiLitao YangPublished in: Communications biology (2022)
Efficient, accurate molecular characterization of genetically modified (GM) organisms is challenging, especially for those transgenic events transferred with genes/elements of recipient species. Herein, we decipher the comprehensive molecular characterization of one novel GM rice event G281 which was transferred with native promoters and an RNA interference (RNAi) expression cassette using paired-end whole genome sequencing (PE-WGS) and modified TranSeq approach. Our results show that transgenes integrate at rice chromosome 3 locus 16,439,674 included a 36 bp deletion of rice genomic DNA, and the whole integration contains two copies of the complete transfer DNA (T-DNA) in a head-to-head arrangement. No unintended insertion or backbone sequence of the transformed plasmid is observed at the whole genome level. Molecular characterization of the G281 event will assist risk assessment and application for a commercial license. In addition, we speculate that our approach could be further used for identifying the transgene integration of cisgenesis/intragenesis crops since both ends of T-DNA in G281 rice were from native gene or elements which is similar with that of cisgenesis/intrasgenesis. Our results from the in silico mimicking cisgenesis event confirm that the mimic rice Gt1 gene insertion and its flanking sequences are successfully identified, demonstrating the applicability of PE-WGS for molecular characterization of cisgenesis/intragenesis crops.
Keyphrases
- circulating tumor
- cell free
- risk assessment
- copy number
- single molecule
- genome wide
- nucleic acid
- escherichia coli
- poor prognosis
- genome wide identification
- high resolution
- crispr cas
- optic nerve
- heavy metals
- climate change
- multidrug resistant
- molecular dynamics simulations
- human health
- genetic diversity
- bioinformatics analysis
- genome wide analysis