Metabolically-targeted dCas9 expression in bacteria.
Gregory M PellegrinoTyler S BrowneKeerthana SharathKhaleda A BariSarah J VancurenEmma Allen-VercoeGregory B GloorDavid R EdgellPublished in: Nucleic acids research (2023)
The ability to restrict gene expression to a relevant bacterial species in a complex microbiome is an unsolved problem. In the context of the human microbiome, one desirable target metabolic activity are glucuronide-utilization enzymes (GUS) that are implicated in the toxic re-activation of glucuronidated compounds in the human gastrointestinal (GI) tract, including the chemotherapeutic drug irinotecan. Here, we take advantage of the variable distribution of GUS enzymes in bacteria as a means to distinguish between bacteria with GUS activity, and re-purpose the glucuronide-responsive GusR transcription factor as a biosensor to regulate dCas9 expression in response to glucuronide inducers. We fused the Escherichia coli gusA regulatory region to the dCas9 gene to create pGreg-dCas9, and showed that dCas9 expression is induced by glucuronides, but not other carbon sources. When conjugated from E. coli to Gammaproteobacteria derived from human stool, dCas9 expression from pGreg-dCas9 was restricted to GUS-positive bacteria. dCas9-sgRNAs targeted to gusA specifically down-regulated gus operon transcription in Gammaproteobacteria, with a resulting ∼100-fold decrease in GusA activity. Our data outline a general strategy to re-purpose bacterial transcription factors responsive to exogenous metabolites for precise ligand-dependent expression of genetic tools such as dCas9 in diverse bacterial species.
Keyphrases
- transcription factor
- poor prognosis
- endothelial cells
- escherichia coli
- gene expression
- cancer therapy
- binding protein
- genome wide
- long non coding rna
- dna methylation
- photodynamic therapy
- emergency department
- induced pluripotent stem cells
- pluripotent stem cells
- ms ms
- staphylococcus aureus
- dna binding
- drug delivery
- drinking water
- cystic fibrosis
- pseudomonas aeruginosa
- big data
- data analysis