Engineering a Glucose-Inducible Whole-Cell Biosensor via CRISPRi-Based Promoter Reprogramming

Abstract

Precise monitoring of intracellular glucose dynamics is essential for understanding carbon flux, optimizing microbial bioprocesses, and enabling responsive control of engineered metabolic pathways. Here, we develop a modular whole-cell biosensor in Escherichia coli that converts the native glucose repression phenotype of a CAP-sensitive promoter into a tunable, glucose-inducible output using CRISPR interference (CRISPRi). By placing a guide RNA (gRNA) under the control of the CAP promoter and positioning dCas9 to target the -10 region of a constitutive promoter driving sfGFP, we created an inversion circuit in which glucose suppresses gRNA expression, thereby relieving dCas9-mediated repression and activating fluorescence. Systematic evaluation of gRNA strand orientation and target site selection revealed that template-strand targeting yielded strong repression ([~]90 %) but reduced sensing range, whereas moderately repressive non-template gRNAs ([~]27-35 % repression) enabled optimal signal inversion. The resulting biosensor demonstrated a robust, linear fluorescence response across 200 M -50 mM glucose (R{superscript 2} > 0.97), with high specificity against other sugars and a strong correlation between glucose consumption and fluorescence accumulation (R{superscript 2} {approx} 0.996). To extend the functionality of the platform, we integrated the sensor with a secreted {beta}-glucosidase module that hydrolyzes cellobiose to glucose. The biosensor accurately quantified glucose released during cellobiose degradation, with engineered strains producing up to [~]33 mM glucose from 50 mM cellobiose in a two-plasmid system. This coupling of enzymatic conversion with intracellular sensing enabled real-time, non-destructive monitoring of metabolic transitions. Together, this work establishes a programmable CRISPRi-based strategy for inverting native promoter logic and provides a sensitive, specific, and modular platform for metabolite sensing in bacteria. The approach is broadly applicable for dynamic pathway regulation, monitoring carbon fluxes, and building responsive genetic circuits in metabolic engineering and synthetic microbial ecosystems. TOC O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=116 SRC="FIGDIR/small/717388v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@158eb53org.highwire.dtl.DTLVardef@c1a21eorg.highwire.dtl.DTLVardef@7a87f7org.highwire.dtl.DTLVardef@746e80_HPS_FORMAT_FIGEXP M_FIG C_FIG