Location: Characterization and Interventions for Foodborne Pathogens
2017 Annual Report
Objectives
1: Development and evaluation of technologies for sample preparation, detection (label-based and label-free), and characterization of microbial, chemical, and biological contaminants of concern in foods that can be implemented for improved food safety and food defense.
1A. Spectroscopy-based identification of foodborne pathogens, toxins, and chemical contaminants.
1B. Antibody-based detection of foodborne pathogens and toxins.
1C. DNA - based detection of foodborne pathogens.
1D. Phage-based detection of foodborne pathogens.
1E. Cell-based detection of foodborne pathogens and toxins.
1F. Enabling technologies for pathogen detection.
2: Application of CFSE developed technologies either alone or in combination with existing methods to evaluate microbial populations and the microbial ecology of foods during production and processing.
2A. Expand the databases for BARDOT and HESPI techniques using pure cultures of known microorganisms (foodborne pathogens and indicator microorganisms)
2B. Apply BARDOT and HESPI techniques to analyze microbial populations in foods
Approach
The food supply must be protected from pathogens, toxins, and chemical contamination that cause disease or illness in humans. Detection technologies are a critical component for identifying and controlling the potentially harmful food contaminants. The overarching goal of the Center for Food Safety Engineering (CFSE), working in collaboration with USDA-ARS scientists, is to develop, validate, and implement new technologies and systematic approaches for improving food safety. We propose to develop a variety of timely, accurate, and cost-effective technologies for the pre-screening, detection, characterization, and classification of foodborne hazards. Our prototype pre-screening and detection technologies include hyperspectral light scattering, metal-enhanced plasma spectroscopy, phage-based detectors, cell-based assays, antibody- and DNA-probe inkjet-printed test strips, plasmonic ELISA, and enhanced lateral flow immunosensors. The accompanying algorithms and software for data processing, analysis, and interpretation of colorimetric, fluorometric, light-intensity, light-scattering, and spectroscopy-based assays, along with time-temperature tracking devices, will enable and enhance these technologies. These methods will detect Listeria monocytogenes, Shiga toxin-producing Escherichia coli (STEC), Campylobacter jejuni, and Salmonella enterica serovars, with demonstrated applications in meat, poultry, and produce, as well as detect toxins, metals, and chemicals of concern in foods. An experienced multidisciplinary team of investigators from Purdue University, the University of Illinois, and USDA will produce and evaluate operational technologies, and engage stakeholders and industry, in an integrated effort to validate and implement technologies for better detection of foodborne hazards along the food production continuum.
Progress Report
Progress was made on all objectives, all of which fall under National Program
108 – Food Safety, Component 1 Foodborne Contaminants; and Problem Statement 3, Microbial Contaminants: Technologies for Detection and Characterization and Problem Statement 4, Chemical and Biological Contaminants: Detection and Characterization Methodology, Toxicology and Toxinology.
We are developing advanced technologies for sample handling and the detection of foodborne pathogens and toxins, and demonstrating their applications for meats, poultry, ready to eat (RTE) products, and produce. Progress was made on all objectives and subobjectives. Our elastic light scatter “BARDOT” (Bacterial Rapid Detection using Optical Scattering Technology) system uses light scattering techniques to differentiate and classify bacterial colonies grown on Petri-dishes. The BARDOT successfully differentiated bacteria at genus, species, and serovar/serotype levels. BARDOT analysis resulted in correct classification often above 90% for foodborne pathogens (Listeria monocytogenes, Salmonella, Shiga toxin-producing E. coli (STEC), Vibrio, and Bacillus species). A light scattering image library of indicator bacteria representing the Enterobacteriaceae family continues to be expanded (containing over 1600 scatter images from 14 genera and 31 species), and a library of Staphylococcus species was created and shown to have a high positive predictive value (>87.5%) and low false-positive rate (6.6%) for detecting Staphylococcus. Numerous hardware and software upgrades have been made for improved data collection and analysis, including replacing the single-wavelength laser currently used in the BARDOT with a supercontinuum laser in our new hyperspectral elastic scatter phenotyping instrument (HESPI). Efforts are ongoing to validate pathogen identification by BARDOT/HESPI in real-world samples, emphasizing leafy greens and poultry, by coupling laser scatter patterns with DNA sequence verification and macro- and micro-morphology of the colonies. Additionally, the application of BARDOT/HESPI was expanded from bacteria to also include analysis of fungal species. We have cataloged 645 bacterial strains and 48 fungal species found in conventional and organic grown lettuces. The best BARDOT/HESPI patterns for yeasts were collected from yeast species grown on corn meal agar. Benchtop laser induced breakdown spectroscopy (LIBS) methods along with spark-induced breakdown spectroscopy (SIBS) technologies were applied to our labeled heavy metal probes to capture and identify foodborne pathogens and toxins, and preliminary data look promising. Several lateral flow immunochromatography assay (LFIA) approaches are in development. One LFIA that combines magnetic-focusing has been demonstrated to have a detection limit of <50 CFU/ml and an analysis time of 30 minutes. Printing processes in development successfully jetted drop of biologically active materials (including antibodies) onto substrates in predesigned geometric patterns, which will better facilitate pathogen capture for detection. The chemistry of our plasmonic ELISA system (PES) was optimized by conjugating streptavidin with catalase and observing the kinetics of color development in the presence of variable concentrations of hydrogen peroxide. For the cell-based assay for rapid high-throughput detection (CARD) technology for detecting Shiga-toxins (Stx), phage induction enrichment methods were used, Vero cells were successfully grown in 3D format, and the current detection limit is 10X6 CFU/ml with a 12-16 hour assay time. For monitoring the temperature of food products through distribution and retail, a high-resolution (1 deg C), low-powered, networked sensor for time-temperature monitoring was developed that can transmit the obtained measurements in real time to a base station. Work is ongoing to correlate microbial growth profiles to temperature histories. Advances in bacteriophage luminescence-based detection of E.coli O157:H7 were tied to developments in cellphone-based miniature optical attachments, and the proof of principle conducted with a surrogate organism had a 10X6 CFU/ml detection limit using a smartphone for the assay.
Accomplishments
1. Cellphone based technology for bioluminescence detection. Bacteriophage are viruses that infect specific bacteria, and they have been used to detect foodborne pathogens. One such approach has added a light-producing bioluminescent indicator into the bacteriophage that becomes detectable once the bacteriophage has infected its target pathogen. A challenge for this bacteriophage bioluminescence detection is that it requires sensitive and costly instruments to detect the extremely dim light produced. ARS-funded scientists at the Center for Food Safety Engineering at Purdue University in West Lafayette, Indiana, have developed a smartphone-based bioluminescence detection system called BAQS (Bioluminiscent-based Analyte Quantification by Smartphone). The proof of concept was demonstrated with an indicator organism and resulted in the detection of the organism at levels comparable to some currently employed on-site detection methods.
2. Comparison of Salmonella prevalence between organic and conventional chicken. Salmonella is an important pathogen to control in poultry products, and organic poultry products are becoming more common. Therefore, it is important to determine the effectiveness of current processing methods on Salmonella prevalence in these products. ARS-funded scientists at the Center for Food Safety Engineering at Purdue University in West Lafayette, Indiana, collected samples from organic and conventional chicken during processing at a commercial facility and determined the prevalence of Salmonella at various processing steps. Results showed that Salmonella prevalence was higher in organic chickens compared to conventional chickens during early processing steps, but prevalence was the same for both processing methods after the final chill step. This indicates that organic raised chickens are similar to conventional raised ones.
3. BARDOT-based screening of pathogens and indicator bacteria from foods. In order to minimize disease outbreaks and financial losses, the food industry requires methods to rapidly and accurately detect disease-causing bacteria (pathogens) and indicator bacteria that are used to monitor industrial hygiene, sanitization practices, process verification, and indirectly reflect the likely presence pathogenic bacteria. Using the elastic light scatter “BARDOT” (Bacterial Rapid Detection using Optical Scattering Technology) technique developed by ARS-funded scientists at the Center for Food Safety Engineering (CFSE) at Purdue University in West Lafayette, Indiana, CFSE scientists have developed methods to identify harmful Staphylococcus bacteria and indicator organisms belonging a large bacterial family, the Enterobacteriaceae (EB). Numerous culture media were tested to identify a medium that allows highly accurate classification [positive predictive values (PPV) of 87-100%] for six different Staphylococcus species, and the developed Staphylococcus scatter pattern library was successfully used for the identification of Staphylococcus isolates from chicken salad and bovine raw milk. After comparing the numerous BARDOT scatter images for the various EB (31 different species from 14 genera) grown on several different culture media, one culture medium (Rapid EB) was found to yield the best results. The medium was used to identify EB with PPVs of 83-100% using individual cultures and 33%-96.5% using mixed cultures or artificially and naturally contaminated food samples. This novel label-free on-plate colony BARDOT screening technology has the potential for adoption by the food industry and public health laboratories for screening samples for the presence of both bacterial pathogens, such as Staphylococcus, and indicator organisms, such as EB, for hygiene monitory during food processing.
4. Improved method for detection of Shiga-toxin from pathogenic E. coli. Shiga-toxin producing Escherichia coli (STEC) are of major concern, since they are responsible for about 176,000 foodborne illness with more than 2,500 hospitalizations and 20 deaths annually in the US. STEC infection is characterized by severe intestinal illness sometimes leading to deadly kidney infections. Kidney toxicity is correlated with the production of the Shiga toxin (Stx). Immunological or molecular approaches, although rapid, cannot provide a correlation with the disease or presence of active Stx. The Vero cell (cultured mammalian kidney cells) cytotoxicity assay is the gold standard for Stx detection. ARS-funded scientists at the Center for Food Safety Engineering at Purdue University in West Lafayette, Indiana, modified this Vero cell assay for improved Stx detection. A variety of treatments [chloroform. ultraviolet light, and antibiotics (ciprofloxacin, polymyxin B, and mitocysin C)] that increase Stx production by E. coli cells were tested. Using a modified method that incorporated Vero cells after antibiotic and chloroform treatment, it was possible to detect active Stx from test samples within 16-18 hours. This reduced detection time for the active toxin can help ensure food safety, reduce outbreaks, and lessen the economic burden of human illness.
5. Development of high-resolution, low-powered, networked, time-temperature monitoring sensor. Temperature abuse of food products has been established as one of the most important causes for foodborne disease outbreaks. Regulatory efforts are focused on food production but there is a lack of control measures outside the production plant. Therefore, distribution and retail are considered the weakest links in the food safety management system with regard to temperature abuses, creating a pressing need for new management tools. A high-resolution, low-powered, networked sensor for time-temperature monitoring (TTM) was developed. Correlation of microbial growth profiles to temperature history requires a sensor that can collect and transmit measurements without interfering with the conditions of the sample under study. ARS-funded scientists at the Center for Food Safety Engineering at Purdue University in West Lafayette, Indiana, developed time-temperature monitoring (TTM) sensors that provide temperature measurements with high resolution and transfer them wirelessly to a basestation. The sensors have been calibrated in a wide range of temperatures (-40 to 140 deg F) and have exhibited resolution better than 2 deg F. These TTM sensors are ready for application in different environments and have the potential to assist in efforts to monitor and manage the environmental conditions to which food products are exposed during distribution and retail, providing crucial information on the quality and handling of the product.
Review Publications
Dak, P., Ebrahimi, A., Swaminathan, V., Duarte-Guevara, C., Bashir, R., Alam, M.A. 2016. Droplet-based biosensing for lab-on-a-chip, open microfluidics platforms. Biosensors. doi: 10.3390/bios6020014.
Bashir, R. 2016. Microcantilevers track single-cell mass. Nature Biotechnology. 34(11):1125-1126.
Duarte-Guevara, P., Duarte-Guevara, C., Ornob, A., Bashir, R. 2016. On-chip PMA labeling of foodborne pathogenic bacteria for viable qPCR and qLAMP detection. Microfluidics and Nanofluidics. doi: 10.1007/s104016-1778-2.
Mendonça, M., Moreira, G., Conceição, F., Hust, M., Mendonça, K., Moreira, Â., França, R., Padilha Da Silva, W., Bhunia, A.K., Aleixo, J. 2016. Fructose 1,6-Bisphosphate aldolase, a novel immunogenic surface protein on Listeria species. PLoS One. doi: 10.1371/journal.pone.0160544.
Kim, H., Doh, I., Sturgis, J., Bhunia, A.K., Robinson, J.P., Bae, E. 2016. Reflected scatterometry for noninvasive interrogation of bacterial colonies. Journal of Biomedical Optics. doi: 10.1117/1.JBO.21.10.107004.
Kim, H., Jung, Y., Doh, I., Lozano-Mahecha, R.A., Applegate, B., Bae, E. 2017. Smartphone-based low light detection for bioluminescence application. Scientific Reports. doi: 10.1038/srep40203.
Kim, H., Rajwa, B., Bhunia, A.K., Robinson, J.P., Bae, E. 2016. Development of a multispectral light-scatter sensor for bacterial colonies. Journal of Biophotonics. 10:634–644.
Ku, S., Ximenes, E., Kreke, T., Foster, K., Deering, A.J., Ladisch, M.R. 2016. Microfiltration of enzyme treated egg whites for accelerated detection of viable Salmonella. Biotechnology Progress. 32(6):1464-1471.
Rajwa, B. 2017. Effect-size measures as descriptors of assay quality in high-content screening: A brief review of some available methodologies. Assay and Drug Development Technologies. 15(1):15-29.
Shenoy, A.G., Oliver, H.F., Deering, A.J. 2017. Listeria monocytogenes internalizes in Romaine Lettuce grown in greenhouse conditions. Journal of Food Protection. 80(4):573–581.
Wang, R., Xu, Y., Liu, H., Peng, J., Irudayaraj, J., Cui, F. 2017. An integrated microsystem with dielectrophoresis enrichment and impedance detection for detection of Escherichia coli. Biomedical Microdevices. 19(2):34.
Ximenes, E., Hoagland, L., Ku, S., Ladisch, M. 2017. Human pathogens in plant biofilms: Formation, physiology, and detection. Biotechnology and Bioengineering. 114:1403–1418.
Ren, W., Liu, W., Irudayaraj, J. 2017. A net fishing enrichment strategy for colorimetric detection of E. coli O157:H7. Sensors and Actuators B: Chemical. 247:923-929.
Kim, H., Awofeso, O., Choi, S., Jung, Y., Bae, E. 2016. Colorimetric analysis of saliva–alcohol test strips by smartphone-based instruments using machine-learning algorithms. Applied Optics. doi: org/10.1364/AO.56.000084.
Reddy, B., Salm, E., Bashir, R. 2016. Electrical chips for biological point-of-care detection. Annals of Biomedical Engineering. 18:329-355.
