High-Density Guide RNA Tiling and Machine Learning for Designing CRISPR Interference in Synechococcus sp. PCC 7002.
ACS Synth Biol
; 12(4): 1175-1186, 2023 04 21.
Article
in En
| MEDLINE
| ID: mdl-36893454
ABSTRACT
While CRISPRi was previously established in Synechococcus sp. PCC 7002 (hereafter 7002), the design principles for guide RNA (gRNA) effectiveness remain largely unknown. Here, 76 strains of 7002 were constructed with gRNAs targeting three reporter systems to evaluate features that impact gRNA efficiency. Correlation analysis of the data revealed that important features of gRNA design include the position relative to the start codon, GC content, protospacer adjacent motif (PAM) site, minimum free energy, and targeted DNA strand. Unexpectedly, some gRNAs targeting upstream of the promoter region showed small but significant increases in reporter expression, and gRNAs targeting the terminator region showed greater repression than gRNAs targeting the 3' end of the coding sequence. Machine learning algorithms enabled prediction of gRNA effectiveness, with Random Forest having the best performance across all training sets. This study demonstrates that high-density gRNA data and machine learning can improve gRNA design for tuning gene expression in 7002.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Synechococcus
/
CRISPR-Cas Systems
Language:
En
Journal:
ACS Synth Biol
Year:
2023
Document type:
Article
Affiliation country:
United States