1a. Objectives (from AD-416):
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
1b. Approach (from AD-416):
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.
3. Progress Report:
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. BARDOT analysis has successfully differentiated bacteria at genus, species, and serovar/serotype levels, with correct classification often above 90% for foodborne pathogens (Listeria monocytogenes, Salmonella, Shiga toxin-producing E. coli (STEC), Vibrio, Staphylococcus, and Bacillus species). BARDOT has also been used to study the effects of subinhibitory levels of antibiotic exposure on bacterial cell morphology and scatter signatures. 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. Data collected on over 1300 samples from poultry farms and 1000 samples from poultry processing facilities found 450 isolates of Salmonella and are being used for both risk-assessment and developing mitigation strategies. Additionally, expansion of BARDOT/HESPI to include analysis of fungal species continued. Within the 645 bacterial strains isolated from conventionally grown and organic romaine lettuce and identified using DNA sequencing methods, 44 different bacterial genera were found, and BARDOT analysis was able to classify all but one of the genera with >90% accuracy. More than 250 fungal strains were also isolated from these lettuce samples, identified to 49 fungal species. A photographic library of the colony morphology of these identified bacteria and fungi, along with the micro-morphology, ITS GenBank sequence accession number, and BARDOT scatter pattern images, was created to be used as a web-based identification guide for the fresh produce industry. 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. Significant effort this year went in to developing a SIBs instrument that does provide a low cost technical solution for using our lanthanide-conjugated antibodies to detect foodborne pathogens and toxins. This technology is based on use of a high voltage spark that disrupts the sample into a plasma that is subsequently measured using a spectrometer. Several lateral flow immunochromatography assay (LFIA) approaches are in development. Advances this year in the LFIA method that utilizes magnetic-focusing reduced the detection limit from 50 CFU/ml to 25 CFU/ml in pure culture, with a repeatable detection limit of 100 CFU/ml E.coli O157:H7 and Salmonella spp. in fruit juices within 45 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. Printed DNA and antibodies remained functional on natural cellulose paper, and lateral flow analysis using these printed test strips was able to detect 100 CFU/ml E.coli O157:H7. Further developments in the chemistry of our plasmonic ELISA system (PES) led to use of liposome encapsulating chemicals which trigger aggregation of the gold nanoparticles to generate an enhanced colorimetric signal and lower detection threshold, which is currently 100 CFU/ml E.coli O157:H7. Additional PES developments utilized Listeria adhesion protein as a ligand for detection of Listeria monocytogenes. 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, the optimal mammalian cell concentration needed to show a maximum cytotoxicity towards a membrane active detergent or STEC cell was determined, and the current detection limit is 106 CFU/ml with a 12-16 hour assay time. The CARD was tested for specificity against a panel of pathogenic and commensal bacteria in a buffer and in 27 raw ground beef samples (32 ± 3.2 g) that were inoculated at 4 CFU/g. For monitoring the temperature of food products through distribution and retail, a high-resolution (1°C), low-powered, networked sensor for time-temperature monitoring was developed that can transmit the obtained measurements in real time to a base station. The original rigid circuit board was replaced with a flexible polyethylene terephthalate substrate (a plastic approved for food contact), and conductive traces and pads were formed via inkjet printed silver ink. Extensive work was also done to improve integrity of data storage when the connection with the base station is lost for long periods of time, as could happen during transport. Work is ongoing to correlate microbial growth profiles to temperature histories: efforts this year documented the growth of Listeria monocytogenes in different ready-to-eat products under various time-temperature profiles. The potential for using the luminescent phage-based detection assay during sample shipment was explored using different luminescent reporter phage concentrations, temperatures, and media:sample ratios, and it was possible to detect 1 cell of E.coli O157:H7 in 325 grams of ground beef within 15 hours. These results suggest this approach is feasible and could allow the rapid detection of a presumptive positive upon arrival, assuming the sample was shipped at 5pm and arrived at 8am the following day (15h) and the shipping container was maintained near 37 degrees. Developments in cellphone-based miniature optical attachments for pathogen detection enabled colorimetric detection of inkjet printed ink patterns and colorimetric assay responses, as well as bioluminescence detection, for which a portable silicon photomultiplier sensor reduced the detection from 106 CFU/ml surrogate bioluminescent bacteria to 103 CFU/ml.
1. Insight into the low infectious dose and highly invasive nature of Listeria monocytogenes. Listeria monocytogenes is an important foodborne pathogenic bacterium. ARS-funded scientists in the Center for Food Safety Engineering at Purdue University are developing methods to detect L. monocytogenes in foods while improving our understanding of the pathogenicity of this deadly bacterium. They previously discovered a protein they called Listeria adhesion protein (LAP) the helps L. monocytogenes bind to intestinal cells and leveraged the unique properties of LAP to develop methods to detect L. monocytogenes from foods. Here they report studies on how LAP is involved in helping L. monocytogenes cause infection. Intestinal cells are the first line of defense against gut pathogens, yet some bacterial pathogens, such as L. monocytogenes, can cross the intestinal barrier during foodborne infection. Not only does LAP allow L. monocytogenes to bind to intestinal cells it induces a unique physiological change that allows L. monocytogenes to bypass the intestinal barrier and cause serious illness, often leading to death. This may explain the low infectious dose and highly invasive nature of L. monocytogenes. This important discovery about L. monocytogenes’ mechanisms of infection provides researchers valuable details that could lead to the development of therapeutic interventions to prevent or treat human illness.
2. A net fishing enrichment strategy for pathogen detection. The relatively low numbers and uneven distribution of pathogenic microorganisms in foods create challenges for reliable and rapid pathogen detection. ARS-funded scientists in the Center for Food Safety Engineering have developed a unique strategy for the enrichment of pathogens based on a concept that captures target bacterial using a net fishing approach. The functionalized ‘net’ is immersed into a contaminated liquid food sample and captures the target pathogenic bacteria. The net fishing enrichment requires 2 hours for pathogen capture and 30 minutes for detection of the foodborne pathogen (such as E. coli O157:H7). Lower numbers of pathogens can be detected sooner using this approach because they are captured by the ‘net’ and then detected. Rapid and more sensitive detection of foodborne pathogens could lead to enhanced food safety and improve public health outcomes.
3. New species of yeast commonly associated with romaine lettuce. Yeast and molds are commonly associated with plants and foods of plant origin. Yeast in the order Sporidiobolales, commonly called red yeast because they produce a red carotenoid pigment, have been reported from numerous ecosystems, including marine, soil, leaves, polar ice, and many others. ARS-funded scientists in the Center for Food Safety Engineering at Purdue University present several analyses drawing on nearly 600 new isolates collected from various substrates around the globe. An analysis of niche preferences in Sporidiobolales shows they are ubiquitous on plant surfaces and in commercial crops and food products. The study revealed numerous interesting results. For example, the red yeast species Sporobolomyces cf. roseus was recovered from every Romaine lettuce sample tested, and cross analyses with isolates recovered from other foods show that this new species is ubiquitous on plant surfaces, yet had not previously been scientifically characterized. Researchers will use these data to study and better understand the interactions between microorganisms on common crops and how these may impact produce shelf-life and human health.
4. A robust system for molecular printing of biosensing test strips. While many rapid and sensitive biosensing technologies for foodborne pathogen detection have been developed at the laboratory level, the transition to the market has been difficult to accomplish. ARS-funded scientists in the Center for Food Safety Engineering at Purdue University are overcoming obstacles in the way of mass production of foodborne pathogen sensors by designing inkjet patterning methodologies for the mass fabrication of test strips comprising biologically active nanomaterials that can perform sensing functions. Some sensing molecules (e.g., antibodies and DNA aptamers) remain active for pathogen detection after being inkjet printed in targeted bioink quantities in controlled design printed patterns. The developed procedure was proven to fabricate both antibody-based and aptamer-based color-changing biosensing test strips reproducibly and reliably, detecting E. coli O157:H7 at low concentrations. The development of printable test strips for foodborne pathogen detection should be broadly applicable and will allow the rapid and reproducible production of inexpensive detection devices.
5. Recognition of emerging food-pathogens using the tools of artificial intelligence. Pathogen detection and data analysis are often limited to the types of samples present in a database, and problems are often encountered when new bacteria not present in the database are encountered. ARS-funded scientists at the Center for Food Safety Engineering, at Purdue University in West Lafayette, Indiana, explored the application of an artificial intelligence (AI) system to phenotypic characteristics of various food-borne pathogens to determine the ability of the AI to identify the number of present pathogenic classes, and recognition of new, unknown classes of food-borne pathogens that were not present in the databases. The scientists were able to develop a functional prototype of an emerging pathogen detection system using AI methodology primarily based on the pattern-recognition neural network created by data scientists at Google initially for the goal of natural images classification. The technology developed at Purdue integrates the cutting-edge machine learning tools with a unique optical phenotypic biosensing device created at Purdue using ARS funding. The result demonstrates tremendous potential of the AI technology in the areas of biosurveillance, biothreat detection, and agricultural biosafety. Additionally, it must be emphasized that leveraging the existing state-of-art informatics tools employed by the leading U.S. data management companies will lower the cost of adoption of the new AI technologies by food producers and regulatory agencies.
Alsulami, T.S., Zhu, X., Abdelhaseib, M.U., Singh, A.K., Bhunia, A.K. 2018. Rapid detection and differentiation of Staphylococcus colonies using an optical scattering technology. Analytical and Bioanalytical Chemistry. https://doi.org/10.1007/s00216-018-1133-4.
Redemann, M.A., Brar, J., Niebuhr, S.E., Lucia, L.M., Acuff, G.R., Dickson, J.S., Singh, M. 2017. Evaluation of thermal process lethality for non-pathogenic Escherichia coli as a surrogate for Salmonella in ground beef. LWT - Food Science and Technology. 90:290-296.