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Discovery of new strains of bacteria that inhibit pathogen growth can facilitate improvements in biocontrol and probiotic strategies. Traditional, plate-based co-culture approaches that probe microbial interactions can impede this discovery as these methods are inherently low-throughput, labor-intensive, and qualitative. We report a second-generation, photo-addressable microwell device, developed to iteratively screen interactions between candidate biocontrol agents existing in bacterial strain libraries and pathogens under increasing pathogen pressure. Microwells (0.6 pl volume) provide unique co-culture sites between library strains and pathogens at controlled cellular ratios. During sequential screening iterations, library strains are challenged against increasing numbers of pathogens to quantitatively identify microwells containing strains inhibiting the highest numbers of pathogens. Ring-patterned 365 nm light is then used to ablate a photodegradable hydrogel membrane and sequentially release inhibitory strains from the device for recovery. Pathogen inhibition with each recovered strain is validated, followed by whole genome sequencing. To demonstrate the rapid nature of this approach, the device was used to screen a 293-membered biovar 1 agrobacterial strain library for strains inhibitory to the plant pathogen Agrobacterium tumefaciens sp. 15955. One iterative screen revealed nine new inhibitory strains. For comparison, plate-based methods did not uncover any inhibitory strains from the library (n = 30 plates). The novel pathogen-challenge screening mode developed here enables rapid selection and recovery of strains that effectively suppress pathogen growth from bacterial strain libraries, expanding this microwell technology platform toward rapid, cost-effective, and scalable screening for probiotics, biocontrol agents, and inhibitory molecules that can protect against known or emerging pathogens.
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Advances in Foodborne Pathogen Analysis
As the world population has grown, new demands on the production of foods have been met by increased efficiencies in production, from planting and harvesting to processing, packaging and distribution to retail locations. These efficiencies enable rapid intranational and global dissemination of foods, providing longer “face time” for products on retail shelves and allowing consumers to make healthy dietary choices year-round. However, our food production capabilities have outpaced the capacity of traditional detection methods to ensure our foods are safe. Traditional methods for culture-based detection and characterization of microorganisms are time-, labor- and, in some instances, space- and infrastructure-intensive, and are therefore not compatible with current (or future) production and processing realities. New and versatile detection methods requiring fewer overall resources (time, labor, space, equipment, cost, etc.) are needed to transform the throughput and safety dimensions of the food industry. Access to new, user-friendly, and point-of-care testing technologies may help expand the use and ease of testing, allowing stakeholders to leverage the data obtained to reduce their operating risk and health risks to the public. The papers in this Special Issue on “Advances in Foodborne Pathogen Analysis” address critical issues in rapid pathogen analysis, including preanalytical sample preparation, portable and field-capable test methods, the prevalence of antibiotic resistance in zoonotic pathogens and non-bacterial pathogens, such as viruses and protozoa.
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- Award ID(s):
- 1826801
- PAR ID:
- 10227903
- Date Published:
- Journal Name:
- Foods
- Volume:
- 9
- Issue:
- 11
- ISSN:
- 2304-8158
- Page Range / eLocation ID:
- 1635
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Antimicrobial resistance (AMR) is arguably one of the major health and economic challenges in our society. A key aspect of tackling AMR is rapid and accurate detection of the emergence and spread of AMR in food animal production, which requires routine AMR surveillance. However, AMR detection can be expensive and time-consuming considering the growth rate of the bacteria and the most commonly used analytical procedures, such as Minimum Inhibitory Concentration (MIC) testing. To mitigate this issue, we utilized machine learning to predict the future AMR burden of bacterial pathogens. We collected pathogen and antimicrobial data from >600 farms in the United States from 2010 to 2021 to generate AMR time series data. Our prediction focused on five bacterial pathogens (Escherichia coli, Streptococcus suis, Salmonella sp., Pasteurella multocida, andBordetella bronchiseptica). We found that Seasonal Auto-Regressive Integrated Moving Average (SARIMA) outperformed five baselines, including Auto-Regressive Moving Average (ARMA) and Auto-Regressive Integrated Moving Average (ARIMA). We hope this study provides valuable tools to predict the AMR burden not only of the pathogens assessed in this study but also of other bacterial pathogens.more » « less
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Detection of bacterial pathogens is significant in the fields of food safety, medicine, and public health, just to name a few. If bacterial pathogens are not properly identified and treated promptly, they can lead to morbidity and mortality, also possibly contribute to antimicrobial resistance. Current bacterial detection methodologies rely solely on laboratory-based techniques, which are limited by long turnaround detection times, expensive costs, and risks of inadequate accuracy; also, the work requires trained specialists. Here, we describe a cost-effective and portable 3D-printed electrochemical biosensor that facilitates rapid detection of certain Escherichia coli (E. coli) strains (DH5α, BL21, TOP10, and JM109) within 15 min using 500 μL of sample, and costs only USD 2.50 per test. The sensor displayed an excellent limit of detection (LOD) of 53 cfu, limit of quantification (LOQ) of 270 cfu, and showed cross-reactivity with strains BL21 and JM109 due to shared epitopes. This advantageous diagnostic device is a strong candidate for frequent testing at point of care; it also has application in various fields and industries where pathogen detection is of interest.more » « less
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For thousands of years, humans have enjoyed the novel flavors, increased shelf-life, and nutritional benefits that microbes provide in fermented foods and beverages. Recent sequencing surveys of ferments have mapped patterns of microbial diversity across space, time, and production practices. But a mechanistic understanding of how fermented food microbiomes assemble has only recently begun to emerge. Using three foods as case studies (surface-ripened cheese, sourdough starters, and fermented vegetables), we use an ecological and evolutionary framework to identify how microbial communities assemble in ferments. By combining in situ sequencing surveys with in vitro models, we are beginning to understand how dispersal, selection, diversification, and drift generate the diversity of fermented food communities. Most food producers are unaware of the ecological processes occurring in their production environments, but the theory and models of ecology and evolution can provide new approaches for managing fermented food microbiomes, from farm to ferment.more » « less
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Access to safe and nutritious food is critical for maintaining life and supporting good health. Eating food that is contaminated with pathogens leads to serious diseases ranging from diarrhea to cancer. Many foodborne infections can cause long-term impairment or even death. Hence, early detection of foodborne pathogens such as pathogenicEscherichia colistrains is essential for public safety. Conventional methods for detecting these bacteria are based on culturing on selective media and following standard biochemical identification. Despite their accuracy, these methods are time-consuming. PCR-based detection of pathogens relies on sophisticated equipment and specialized technicians which are difficult to find in areas with limited resources. Whereas CRISPR technology is more specific and sensitive for identifying pathogenic bacteria because it employs programmable CRISPR-Cas systems that target particular DNA sequences, minimizing non-specific binding and cross-reactivity. In this project, a robust detection method based on CRISPR-Cas12a sensing was developed, which is rapid, sensitive and specific for detection of pathogenicE. coliisolates that were collected from the fecal samples from adult goats from 17 farms in Tennessee. Detection reaction contained amplified PCR products for the pathogenic regions, reporter probe, Cas12a enzyme, and crRNA specific to three pathogenic genes—stx1, stx2, and hlyA. The CRISPR reaction with the pathogenic bacteria emitted fluorescence when excited under UV light. To evaluate the detection sensitivity and specificity of this assay, its results were compared with PCR based detection assay. Both methods resulted in similar results for the same samples. This technique is very precise, highly sensitive, quick, cost effective, and easy to use, and can easily overcome the limitations of the present detection methods. This project can result in a versatile detection method that is easily adaptable for rapid response in the detection and surveillance of diseases that pose large-scale biosecurity threats to human health, and plant and animal production.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/foods9111635
