A Targeted Genome-scale Overexpression Platform for Proteobacteria
Banta, A. B.; Myers, K. S.; Ward, R. D.; Cuellar, R. A.; Freeh, C. C.; Bacon, E. E.; Peters, J. M.
Show abstract
Targeted, genome-scale gene perturbation screens using Clustered Regularly Interspaced Short Palindromic Repeats interference (CRISPRi) and activation (CRISPRa) have revolutionized eukaryotic genetics, advancing medical, industrial, and basic research. Although CRISPRi knockdowns have been broadly applied in bacteria, options for genome-scale gene overexpression face key limitations. Here, we develop a facile approach for genome-scale overexpression in bacteria we call, "CRISPRtOE" (CRISPR transposition and OverExpression). We first create a platform for comprehensive gene targeting using CRISPR-associated transposons (CAST) and show that transposition occurs at a higher frequency in non-transcribed DNA. We then demonstrate that CRISPRtOE can upregulate gene expression in Proteobacteria with medical and industrial relevance by integrating synthetic promoters of varying strength upstream of target genes. Finally, we employ CRISPRtOE screening at the genome-scale in the model bacterium Escherichia coli and the non-model biofuel producer Zymomonas mobilis, recovering known and novel antibiotic and engineering targets. We envision that CRISPRtOE will be a valuable overexpression tool for antibiotic mode of action, industrial strain optimization, and gene function discovery in bacteria. ImportanceSystematic alteration of bacterial gene expression enables identification of genes relevant to diverse fields of study and practical applications. Although many targeted, genome-scale genetic tools exist for reducing or eliminating gene expression, there are few facile approaches for systematic gene overexpression in bacteria. Here, we develop a targeted overexpression approach for Proteobacteria of medical and industrial importance that precisely inserts strong promoters upstream of genes using CRISPR-associated transposons. We demonstrate that this approach can be used to systematically overexpress genes in both model (Escherichia coli K-12) and non-model (Zymomonas mobilis) Proteobacteria for the purpose of understanding antibiotic resistance mechanisms and improving strain resilience in biofuel production conditions, respectively.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Arrayed in vivo barcoding for multiplexed sequence verification of plasmid DNA and demultiplexing of pooled libraries 97%
- Transposon mutagenesis libraries reveal novel molecular requirements during CRISPR RNA-guided DNA integration 96%
- A deep mutational scanning platform to characterize the fitness landscape of anti-CRISPR proteins 96%
Similar papers in this journal
- A CRISPR-based genetic screen in Bacteroides thetaiotaomicron reveals a small RNA modulator of bile susceptibility 97%
- Distinct evolutionary trajectories following loss of RNA interference in Cryptococcus neoformans 96%
- Genetic dominance governs the evolution and spread of mobile genetic elements in bacteria 96%
Similar papers in this journal
- Systematically attenuating DNA targeting enables CRISPR-driven editing in bacteria 96%
- CRISPR-Cas12a induced DNA double-strand breaks are repaired by locus-dependent and error-prone pathways in a fungal pathogen 96%
- Efflux pump gene amplifications bypass necessity of multiple target mutations for resistance against dual-targeting antibiotic 95%
Similar papers in this journal
- A randomized multiplex CRISPRi-Seq approach for the identification of critical combinations of genes 96%
- Genetic determinants facilitating the evolution of resistance to carbapenem antibiotics 95%
- High-density transposon mutagenesis in Mycobacterium abscessus identifies an essential penicillin-binding lipo-protein (PBP-lipo) involved in septal peptidoglycan synthesis and antibiotic sensitivity 95%
"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.