CRISPR Tools To Control Gene Expression in Bacteria

Abstract

CRISPR-Cas systems have been engineered as powerful tools to control gene expression in bacteria. The most common strategy relies on the use of Cas effectors modified to bind target DNA without introducing DNA breaks. These effectors can either block the RNA polymerase or recruit it through activation domains. Here, we discuss the mechanistic details of how Cas effectors can modulate gene expression by blocking transcription initiation or acting as transcription roadblocks. CRISPR-Cas tools can be further engineered to obtain fine-tuned control of gene expression or target multiple genes simultaneously. Several caveats in using these tools have also been revealed, including off-target effects and toxicity, making it important to understand the design rules of engineered CRISPR-Cas effectors in bacteria. Alternatively, some types of CRISPR-Cas systems target RNA and could be used to block gene expression at the posttranscriptional level. Finally, we review applications of these tools in high-throughput screens and the progress and challenges in introducing CRISPR knockdown to other species, including nonmodel bacteria with industrial or clinical relevance. A deep understanding of how CRISPR-Cas systems can be harnessed to control gene expression in bacteria and build powerful tools will certainly open novel research directions.

Keywords: CRISPR; gene silencing; transcriptional regulation.

FIG 1

General principle for the control of bacterial gene expression using reprogrammed CRISPR effectors. (A) Guide RNAs form complexes with natural or engineered CRISPR effectors. This results in a programmable ribonucleoprotein complex that will bind to either homologous DNA or RNA depending on the effector type. (B) Gene repression is possible by targeting either the promoter, the coding sequence, or mRNA. Gene activation is possible by fusing dCas9 to a transcriptional activator (such as ω, SoxS, or AsiA) and addressing it to a precise location upstream of the promoter.

FIG 2

Targeting the correct strand for efficient repression. When the target is within the promoter sequence, any strand can be targeted for strong repression. When the target is within the coding sequence, one orientation is typically much more effective than the other. The configuration depicted here is the one that leads to the strongest repression for each of the CRISPR types that have been tested.

FIG 3

Unnoticed “bad-seed” effect in the CRISPR screen of Wang et al. (51). Using CRISPR guides that target only nonessential genes, we calculated the average fitness scores (log₂ fold change) of guides depending on their seed sequence. The sequences AGGAA and ACCCA are the two most toxic bad seeds discovered by Cui et al. (46). In the CRISPR screen of Wang et al. (51), guides carrying a bad seed also have a strong average fitness defect, regardless of the genes that they target.

FIG 4

Overview of regulatory circuits that can be built using CRISPR repressors. As inputs, chemical inducers were used by Nielsen and Voigt (112). Metabolic burden was used by Ceroni et al. (131). The logic gates are from the study by Nielsen and Voigt (112). Since sgRNAs act as a NOT gate, and two identical sgRNAs act as NOR gates, all other basic logic gates like AND and OR can be constructed. As outputs, metabolic production is discussed by Ceroni et al. (131), replication is discussed by Wiktor et al. (49), filamentation is discussed by Mückl et al. (132), biofilm formation is discussed by Nielsen and Voigt (112), and cell shape is discussed by Elhadi et al. (136).