BioProject

We report using global regulator libraries based on the CRISPR-enabled trackable genome engineering (CREATE) method to engineer tolerance against multiple inhibitors in Escherichia coli. More...

We report using global regulator libraries based on the CRISPR-enabled trackable genome engineering (CREATE) method to engineer tolerance against multiple inhibitors in Escherichia coli. Deep mutagenesis libraries were rationally designed, constructed, and screened to target 34,340 mutations across 23 global regulators.These libraries were divided into G1, G2, G3, G4 and G5 sub-groups according to different gene functions (such as active sites, DNA binding sites, and predicted functional sites) . And we successively screened the libraries under different stress conditions (acetate, NaCl, furfural, high temperature and isobutanol), and enriched mutants able to tolerate multiple inhibitors. In order to determine the tolerance-conferring mutations screened under different stress conditions, deep sequencing and fitness analysis were used to detect the recombinant strains with good performance. Overall design: Mutations on chromosomes were consistent with those carried by the CREATE plasmids. Firstly, the CREATE plasmids of tolerant strains were extracted separately. Following extraction, the CREATE plasmids were used as templates for PCR using Illumina-compatible primers designed with Golay barcodes . CREATE library fragments were amplified in 16 cycles of PCR. At the first PCR step, each experimental sample was ligated with a unique barcode used to track the mutations. After that, PCR products were confirmed by electrophoresis and purified using an Agarose Gel DNA Extraction Kit. Deep sequencing was performed according to the Illumina protocol for amplicon sequencing. The editing cassette was used to quantify fitness associated with different mutations in the library. Fitness associated with all mutations in the library was calculated using log2-fold enrichment. Synonymous mutations of every homology arm were regard as a part of each corresponding library. Synonymous mutations were used to determine the standard deviation and mean value of the log2-fitness change of wild-type cells. Then, a mean ± 2σ cutoff (i.e., p=0.05 assuming a normal distribution) was used to estimate strongly enriched mutations in the selections. The confidence interval of 95% was used to calculate statistically significant differences between samples. Differences with p ≤ 0.05 were considered statistically significant. Less...