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Development of High-Performance Whole Cell Biosensors Aided by Statistical Modeling.

ABSTRACT: Whole cell biosensors are genetic systems that link the presence of a chemical, or other stimulus, to a user-defined gene expression output for applications in sensing and control. However, the gene expression level of biosensor regulatory components required for optimal performance is nonintuitive, and classical iterative approaches do not efficiently explore multidimensional experimental space. To overcome these challenges, we used a design of experiments (DoE) methodology to efficiently map gene expression levels and provide biosensors with enhanced performance. This methodology was applied to two biosensors that respond to catabolic breakdown products of lignin biomass, protocatechuic acid and ferulic acid. Utilizing DoE we systematically modified biosensor dose-response behavior by increasing the maximum signal output (up to 30-fold increase), improving dynamic range (>500-fold), expanding the sensing range (?4-orders of magnitude), increasing sensitivity (by >1500-fold), and modulated the slope of the curve to afford biosensors designs with both digital and analogue dose-response behavior. This DoE method shows promise for the optimization of regulatory systems and metabolic pathways constructed from novel, poorly characterized parts.

SUBMITTER: Berepiki A

PROVIDER: S-EPMC7146887 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Development of High-Performance Whole Cell Biosensors Aided by Statistical Modeling.

Berepiki Adokiye A Kent Ross R Machado Leopoldo F M LFM Dixon Neil N

ACS synthetic biology 20200217 3

Whole cell biosensors are genetic systems that link the presence of a chemical, or other stimulus, to a user-defined gene expression output for applications in sensing and control. However, the gene expression level of biosensor regulatory components required for optimal performance is nonintuitive, and classical iterative approaches do not efficiently explore multidimensional experimental space. To overcome these challenges, we used a design of experiments (DoE) methodology to efficiently map g ...[more]

PMID: 32023410

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Project description:There is an urgent need to develop novel antibiotics since antibiotic resistance is an increasingly serious threat to global public health. Whole-cell biosensors are one of the promising strategies for new antibiotic discovery. The peptidoglycan (PG) of the bacterial cell wall is one of the most important targets for antibiotics. However, the biosensors for the detection of PG-targeting antibiotics in Gram-negative bacteria have not been developed, mainly because of the lack of the regulatory systems that sense and respond to PG stress. Recently, we identified a novel two-component signal transduction system (PghKR) that is responsible for sensing and responding to PG damage in the Gram-negative bacterium Shewanella oneidensis. Based on this system, we developed biosensors for the detection of PG-targeting antibiotics. Using ampicillin as an inducer for PG stress and the bacterial luciferase LuxCDABE as the reporter, we found that the PghKR biosensors are specific to antibiotics targeting PG synthesis, including β-lactams, vancomycin, and d-cycloserine. Deletion of genes encoding PG permease AmpG and β-lactamase BlaA improves the sensitivity of the biosensors substantially. The PghKR biosensor in the background of ΔblaA is also functional on agar plates, providing a simple method for screening bacteria that produce PG-targeting antibiotics. IMPORTANCE The growing problem of antibiotic resistance in Gram-negative bacteria urgently needs new strategies so that researchers can develop novel antibiotics. Microbial whole-cell biosensors are capable of sensing various stimuli with a quantifiable output and show tremendous potential for the discovery of novel antibiotics. As the Achilles' heel of bacteria, the synthesis of the peptidoglycan (PG) is targeted by many antibiotics. However, the regulatory systems that sense and respond to PG-targeting stress in Gram-negative bacteria are reported rarely, restricting the development of biosensors for the detection of PG-targeting antibiotics. In this study, we developed a highly sensitive and specific biosensor based on a novel two-component system in the Gram-negative bacterium Shewanella oneidensis that is responsible for the sensing and responding to PG stress. Our biosensors have great potential for discovering novel antibiotics and determining the mode of action of antibiotics.

Dataset Information

Development of High-Performance Whole Cell Biosensors Aided by Statistical Modeling.

Publications

Development of High-Performance Whole Cell Biosensors Aided by Statistical Modeling.

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