Source: CLEMSON UNIVERSITY submitted to
BIOSENSOR-BASED ASSAY FOR THE DETECTION AND QUANTIFICATION OF QUORUM SENSING AUTOINDUCERS (ACYL HOMOSERINE LACTONES AND AUTOINDUCER-2) PRODUCED IN PACKAGE FOODS
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
EXTENDED
Funding Source
Reporting Frequency
Annual
Accession No.
1014208
Grant No.
2018-67031-27359
Project No.
SC-2016-11391
Proposal No.
2016-11391
Multistate No.
(N/A)
Program Code
A1801
Project Start Date
Nov 1, 2017
Project End Date
Apr 30, 2020
Grant Year
2018
Project Director
Cooksey, K.
Recipient Organization
CLEMSON UNIVERSITY
(N/A)
CLEMSON,SC 29634
Performing Department
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
0%
Research Effort Categories
Basic
40%
Applied
0%
Developmental
60%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
71240101100100%
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.
Project Methods
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.

Progress 11/01/17 to 10/31/18

Outputs
Target Audience:The food industry will recieve beneficial feedback from this research. Food security is a topic of major concern as human population is estimated to increase over the next decades. A substantial percentage 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 the foundation for biosensors and ultimately intelligent packaging to effectively monitor changes in food microflora and improve food quality and safety. Changes/Problems:Change in the work plan as mentioned previously. What opportunities for training and professional development has the project provided?A postdoctoral fellow was trained in molecular food microbiology obtained in depth knowledge regarding microbial interactions in complex food systems. The findings can be applied not only in reducing microbial spoilage in packaged foods but also interfering with biofilm formation pathways. How have the results been disseminated to communities of interest?Study is not yet completed. In the end, the research will be published in the appropriate journal and also disseminated through poster and oral presentations for shareholdersat industry and scientific meetings. What do you plan to do during the next reporting period to accomplish the goals?Part of Objective 1 regarding Gram-positive quorum-sensing. We have isolated microbial cells (Enterococci and Streptococci) from packaged foods through co-culture assays that cause instant clumping to other cells. Specific microbial interactions are being assessed by microbial growth, turbidity assays and identification of the responsible compound. Part of Objective 1: Gram-positive bioreporters. A different strategy is employed: instead of cloning genetic elements in vectors, the lacZ cassette is inserted as gene fusion (shuttle vector constructed) in the cluster of genes of interest (e.g. comCDE for the competence stimulating peptide). These mutants will be tested by the same beta-galactosidase assay as described in the project, but experiments require more time since it involves homologous gene recombination. We are also working on transposon mutagenesis to identify new pathways for autoinducers. Part of Objective 1: Synthesize autoinducers and confirm activity for bioreporters. These are small peptides to be synthesized de novo and we will assess their biological activity on the microflora from packaged foods. Part of Objective 2: Complete standard curves and their response to autoinducers for the remaining Gram-positive bioreporter strains. Objective 3: Shelf-life study 1 for packaged foods. Packaged meat, poultry and fish (90 samples per set) will be stored in their normal and temperature abuse conditions (1 set each condition). Samples will be removed at select intervals and exposed to bioreporters in array format and compare the reporter response side-by-side by plate count. The bioreporter arrays will include Gram-negative and Gram-positive bacteria. Beta-galactosidase assay will be performed to determine standard curves for bioreporters Objective 3: Shelf-life study 2 for packaged foods. Packaged meat, poultry and fish (90 samples per set) will be stored in their normal and temperature abuse conditions (1 set per condition). Samples will be removed at select intervals and exposed to bioreporters in array format and compare the reporter response side-by-side by plate count. Beta-galactosidase assay will be performed to determine standard curves for bioreporters.

Impacts
What was accomplished under these goals? Objective 1. In order to identify genetic elements of interest and autoinducers we followed 2 different approaches : (i) a literature search was performed and data already available in the public domain (such as microbial host, type of autoinducer, type of response and genetic information) was used to construct bioreporters and (ii) we isolated microbial species from packed foods (meat and poultry) and we tested their influence on other spoilage microorganisms in co-culture experiments. To construct bioreporters that respond to microbial quorum sensing elements we used a plasmid with a promoterless lacZ gene, pBG . Transcriptional fusions were created by cloning promoters of interest in multiple cloning site of pSF βGal, upstream of lacZ. These cloned elements can be expressed in generic laboratory strains such as E. coli DH5&. Also, it was necessary to co-express in the same cell the promoter regulatory element in addition to promoter fusion. These elements were cloned in compatible plasmids (pGEM T-easy or pCR TOPO) and transformed in the same cell with pBG plasmid. The bioreporters were exposed to known concentrations of their respective autoinducers and the beta-galactosidase activity was quantified for standard dose-response curves. Determination of the beta-galactosidase activity expressed in the E. coli transformants was performed by established protocol. Briefly, beta-galactosidase activity was 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 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 beta-galactosidase released by the cell and a function of the time of the reaction and initial cell number. The reaction is stopped by adding sodium carbonate which inactivates beta-galactosidase, stopping the reaction and converts most of the o-nitrophenol to a yellow colored anionic form (determined by absorbance at 420nm). We obtained E. coliwhole cell biosensing systems recognition and regulatory elements derived from thePseudomonas aeruginosa(gram-negative and a common spoilage microorganism) AHL-dependent RhlR/RhlI and LasR/LasI systems. The E coli strains lack therhlIandlasIgenes that code for the AHL synthase enzymes and, thus, exogenous AHL needs to be supplied in order for the sensing cells to synthesize the reporter gene lacZ. When AHL are present in the environment of the sensing cells, they bind to the regulatory proteins (rhlR and lasR) and induce the expression of thelacZ and production of the enzyme in a manner proportional to the concentration of AHLs present. With this strategy we cloned the AHL-responsive sdiA from E. coli and Salmonella spp. Both these gram-negative microorganisms encode the protein SdiA that recognizes to AHL. This is considered to be the LuxI homologue but when we tested in the laboratory our constructs, SidA reacted to both long and short chain AHL including oxoC10. In addition, we created a AI-2 response bioreporter by cloning the promoter for luxP from plasmid pGEX4T1-LuxP and and lsrB reporter system from Salmonella spp. Both luxP and lsrB respond to AI-2 autoinducer. Results obtained based on beta-galactosidase activity were used to construct standard dose-response curves, where the bioreporters were exposed to predetermined concentrations of autoinducers. Typically, based on the beta galactosidase assays, short chain AHLs were detected in the range of 10-9M to 10−5 M, long chain AHL in the range of 10-9 M to 10−6M, and AI-2 in the range of 10−9 M to 10−4 M. There were no false positives or false negatives observed for the analytical figures of merit for the constructed bioreporters. The highest correlation coefficient was R2=0.9930 and the lowest R2=0.8510. Beta-galactosidase assays were performed with bioreporters exposed to food extracts to test sensor response to inducers produced in situ by the food microbiota. Testing was performed on packaged meat, and poultry samples (foods affected most by spoilage). Beta-galactosidase assays with food extracts were performed by the same methodology as in Objective 1, except that filtered food extracts were used to induce bioreporters. To determine the total plate count number, food samples were diluted and plated on appropriate media. In addition, standard curves were performed side-by-side with the chemically purified bioinducers. Bacterial species (biomarkers) were identified by performing beta- galactosidase assays of the bioreporters against individual colonies isolated from the plate count. To identify the isolates, we amplified the 16S rDNA sequences of the genomic DNA using the universal primers 27F and 1497R (ref). 16S rRNA is a highly conserved gene that is used for taxonomic identification. We tested the response in food samples by (i) dose-response curves generated by spiking the food product with a known concentration of the bioinducer and, (ii) by testing the whole cell bioreporter response to the cell extract filtrate of the food product. Analytical characteristics of tests of the food samples were similar to those seen when analyzing standard curves with prepared standards solutions. In this instance, short chain AHL were detected in the range of 10-8 M to 1 × 10−7 M, long chain AHL in the range of 10-8 M to 1 × 10−6 M, and AI-2 in the range of 10−8 M to 10−5 M. Assays performed using filtrated food samples had lower limits of detection and dynamic ranges than the autoinducer spikes samples: 10−4 M for AHLs and 10−6 M for AI-2. Dynamic ranges spanned from 10−5 M to 1 × 10−4 M for AHL and 10−7 M to 10−6 M for AI-2. At least three sets of experiments were performed to verify reproducibility, both with standard solutions and in food matrices. These results prove that biosensing systems could be employed to detect spoilage in food matrices, and the autoinducer that can be detected mostly is AI-2. However, the high microbial load in the food samples correlated more with the presence of gram-positive bacteria in these samples. We concluded that the response we observed from a number of gram-negative microorganisms but in order to develop an early detection bioreporter system we decided to focus more on creating chromosomal lacZ translational fusions in gram-positive bacteria. Gram-positives can respond to AI-2 but their quorum sensing is based on extracellular peptides that interact with a 2-component system spanning the cell membrane. This work is on-going.

Publications