Food, Nutrition, Packaging Sci
Non Technical Summary
Food security is a topic of major concern as human population is estimated to increase over the next decades. A substantial percent of the food produced globally and in the United States is wasted at distribution, retail and consumer level. Microbial spoilage represents an important source of food waste.As microorganisms multiply, they synthesize and release small molecules called quorum sensing mediators. Quorum sensing is defined as the cell-to-cell communication within bacterial populations mediated by autoinducers. This project is based on the concept that growth of spoilage microorganisms in food systems is accompanied by an increase in the concentration of autoinducers, which in turn can be quantified. The main goal of this research study is to identify autoinducers present in packaged foods and design a biosensor array that can monitor microbial development. Three research objectives have been identified to achieve this goal: (1) Construct bioreporters capable to produce a measurable response in the presence of quorum sensing autoinducers (2) Identify and characterize quorum sensing mediators released in situ by the local microbiota in packaged food samples, and (3) Test the biosensors response in shelf life studies of packaged foods and model bacterial growth based on biosensor array response.Outcomes from proposed research objectives have the potential to offer important and novel insight into communication, interaction and physiological processes of the food microbiota. Results from the proposed research will serve as a foundation for biosensors and ultimately intelligent packaging to effectively monitor changes in food microflora and improve food quality and safety.
Animal Health Component
Research Effort Categories
Goals / Objectives
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.
Objective 1Bioreporters will be created in our laboratory using a promoterless multicopy plasmid thatencodes for a promoterless beta-galactosidase gene (lacZ), a multiple cloning site (MCS), pUC19 origin of replication and a kanamycin resistance gene. Transcriptional fusions will be created by cloning promoters of interest in MCS of pSF βGal, upstream oflacZand expressed inE coli.The bioreporters will be then exposed to known concentrations of their autoinducers and the beta-galactosidase activity will be quantified for standard dose-response curves.Standard molecular biology techniques will be used for DNA manipulation and directional cloning (i.e. primer selection and PCR amplification, DNA isolation and modification). Cloned fragments will be sequenced to determine correct fragment orientation and promoter sequence. When necessary, we will co-express in the same cell the promoter regulatory element in addition to promoter fusion. Determination of the beta-galactosidase activity expressed in theE. colitransformants will be performed by established protocols. Briefly, beta-galactosidase will be assayed by measuring hydrolysis of the chromogenic substrate, o-nitrophenyl-ß-D-galactoside (ONPG). The amount of o-nitrophenol formed from ONPG can be measured spectroscopically by determining the absorbance at 420 nm. In the presence of excess ONPG, the amount of o-nitrophenol produced is directly proportional to the amount of ß-galactosidase released by the cell and calculations will includecorrections for factors such as incubationtime for the reaction and initial cell number. Results obtained based on beta-galactosidase activity will be used to construct standard dose-response curves. Experiments will be performed at least three times for each type of reporter.Objective 2Beta-galactosidase assays will be performed with bioreporters exposed to food extracts to test the response to inducers producedin situby the food microbiota. Testing will be performed on packaged meat, fish and poultry samples (foods affected most by spoilage) on a minimum of 10 samples for each type of food. Total aerobic bacteria will be enumerated for each food sample. Food samples that will induce a significant bioreporter response will be further characterized regarding their microbiota. Specifically, we will select microbial species that elicit a strong bioreporterresponse by culturing individual microbial isolates and performing individual strain beta-galactosidase assays in 48-well microtiter plates. Microbial isolates with increased QS molecules production will be identified by cloning and sequencing of the 16S rRNA.Beta-galactosidase assays with food extracts will follow the same methodology as in Objective 1, except that filtered food extracts will be used to induce bioreporters. Experiments will include a standard curve for analytical accuracy. To determine the total plate count number, food samples will be serially diluted and plated on appropriate media. If a food sample induce a strong biosensor response, we will identify bacterial species (biomarkers) by performing beta- galactosidase assays against individual colonies isolated from the plate count.To identify the isolates, we will amplify the 16S rDNA sequences of the genomic DNA using the universal primers 27F and 1497R. Resulting fragments (1470 bp) will be cloned using the TA-TOPO-cloning kit (Invitrogen). Clones from this rDNA library will be selected by blue-white screening and plating on antibiotic. After sequencing, the fragments will be aligned and compared with the 16S rRNA database (Silva or Ribosomal Database Project). We expect in this objective to quantitatively determine AI molecules in complex food matrices and identify potential biomarkers for this assay or AI high-producing microbial strains.Objective 3Packaged food samples will be subjected to shelf life studies performed in normal and temperature abusive conditions. Briefly, a set of packaged food samples will be stored in the recommended conditions whereas a second set will be stored in an environmental chamber with controlled temperature (temperature abused) and relative humidity. Periodically samples will be removed and analyzed over time by total plate count (same as in Objective 2) and exposed to bioreporters placed in 48-well microtiter plates, in an array format, and then analyzed for beta-galactosidase activity. We will include standard dose-response curves for analytical accuracy. We will analyze at least 3 types of packaged foods from each category (fish, meat, poultry) and experiments will be performed twice. Bioreporters responses will be compared with total number of bacteria. Data obtained from plate counts and biosensor response will be modeled for microbial growth and their model parameters compared. Attest will be performed to calculate confidence intervals for the model parameters.