Biosensor-Based Assay for the Detection and Quantification of Quorum Sensing Autoinducers (Acyl Homoserine Lactones and Autoinducer-2) Produced in Package Foods

Achieving food security in the changing climate and expanding human population are topics of concern for both producers and regulatory agencies. Food waste may have negative economic and environmental impacts; therefore, mitigation strategies are necessary to reduce microbial spoilage. Sensors that indicate bacterial spoilage have been developed. Typically, detection involves an 'electronic nose' or a device that senses amines as byproducts or changes in local pH, all resulting mostly from bacterial growth. However, from the food loss perspective, the detection of byproducts is intended more as a warning for the customers, since the chemical changes are irreversible. A useful approach in reducing meat loss would be to monitor bacterial growth directly and initiate actions before chemical changes, but to our knowledge there are no sensors that monitor directly bacterial growth. In this project we aim to exploit bacterial cell-to-cell communication system, to develop biosensors that accurately indicate bacterial community development in real time. In another words if bacteria communicate through chemical mediators, perhaps we should learn to 'listen-in'. The rationale to decipher microbial communications in food systems is based on the idea that quorum sensing (QS) common features should be exploited for both scientific and practical purposes. First, the autoinducers in QS systems such as AHLs, AI-2 or other molecules are synthesized intracellularly and then able to diffuse freely through the bacterial membrane, therefore their extracellular concentrations accumulate and are proportional with bacterial density. Therefore, the principle can be developed into a colorimetric assay, as a rapid detection method for spoilage microflora. Second, the assay can have specificity in identifying the types of microorganisms present in the sample, since AI-2 are bound by specific receptors that reside either in the inner membrane or in the cytoplasm. The presence and concentration of a particular AI can be the result of specific microbial species colonizing the food sample. Third, QS typically alters dozens to hundreds of genes that encode for various biological processes resulting in distinct possible methodologies to manipulate cell density (stop growth) or microbial physiology (biofilm formation). In our preliminary experiments with E. coli Top 10 pBG3 (lsrA bioreporter), cells stopped their growth in the exponential phase in the presence of AI-2. This concept can be utilized in active packaging or antimicrobial coatings to prevent microbial growth. Fourth, autoinduction, which is AI-driven activation of QS, stimulates the increased synthesis of the AI, which results in a synchronous gene expression in the population and therefore the possibility for improved molecular methods for pathogen identification (e.g. RT-PCR and TaqMan detection methods).Objective 1. Construct bioreporters capable to produce a measurable response in the presence of QS inducing molecules. Hypothesis: QS molecules (AHLs and AIs-2 type, or oligopeptides) can be detected by genetically engineered biosensors.Objective 2. Identify and characterize AI-QS molecules (AHLs, AI, oligopeptides) released in situ by the local microbiota in select packaged food samples. Hypothesis: Bioreporter technology can be used to quantify AI molecules produced in complex food matrices.Objective 3. Test the biosensors' response (in an array format) in shelf life studies of packaged foods and model bacterial growth based on array response. Hypothesis: Bioreporter technology can be used to monitor microbial growth in complex food matrices.