skip to main content

Title: 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 more » field-capable test methods, the prevalence of antibiotic resistance in zoonotic pathogens and non-bacterial pathogens, such as viruses and protozoa. « less
; ; ;
Award ID(s):
Publication Date:
Journal Name:
Page Range or eLocation-ID:
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    Molecular technologies have revolutionized the field of wildlife disease ecology, allowing the detection of outbreaks, novel pathogens, and invasive strains. In particular, metabarcoding approaches, defined here as tools used to amplify and sequence universal barcodes from a single sample (e.g., 16S rRNA for bacteria, ITS for fungi, 18S rRNA for eukaryotes), are expanding our traditional view of host–pathogen dynamics by integrating microbial interactions that modulate disease outcome. Here, I provide an analysis from the perspective of the field of amphibian disease ecology, where the emergence of multi-host pathogens has caused global declines and species extinctions. I reanalyzed an experimental mesocosm dataset to infer the functional profiles of the skin microbiomes of coqui frogs (Eleutherodactylus coqui), an amphibian species that is consistently found infected with the fungal pathogen Batrachochytrium dendrobatidis and has high turnover of skin bacteria driven by seasonal shifts. I found that the metabolic activities of microbiomes operate at different capacities depending on the season. Global enrichment of predicted functions was more prominent during the warm-wet season, indicating that microbiomes during the cool-dry season were either depauperate, resistant to new bacterial colonization, or that their functional space was more saturated. These findings suggest important avenues to investigate howmore »microbes regulate population growth and contribute to host physiological processes. Overall, this study highlights the current challenges and future opportunities in the application of metabarcoding to investigate the causes and consequences of disease in wild systems.

    « less
  2. Hyperspectral imaging (HSI) is a spectroscopic technique which captures images at a high contrast over a wide range of wavelengths to show pixel specific composition. Traditional uses of HSI include: satellite imagery, food distribution quality control and digital archaeological reconstruction. Our lab has focused on developing applications of HSI fluorescence imaging systems to study molecule-specific detection for rapid cell signaling events or real-time endoscopic screening. Previously, we have developed a prototype spectral light source, using our modified imaging technique, excitationscanning hyperspectral imaging (HIFEX), coupled to a commercial colonoscope for feasibility testing. The 16 wavelength LED array was combined, using a multi-branched solid light guide, to couple to the scope’s optical input. The prototype acquired a spectral scan at near video-rate speeds (~8 fps). The prototype could operate at very rapid wavelength switch speeds, limited to the on/off rates of the LEDs (~10 μs), but imaging speed was limited due to optical transmission losses (~98%) through the solid light guide. Here we present a continuation of our previous work in performing an in-depth analysis of the solid light guide to optimize the optical intensity throughput. The parameters evaluated include: LED intensity input, geometry (branch curvature and combination) and light propagation usingmore »outer claddings. Simulations were conducted using a Monte Carlo ray tracing software (TracePro). Results show that transmission within the branched light guide may be optimized through LED focusing lenses, bend radii and smooth tangential branch merges. Future work will test a new fabricated light guide from the optimized model framework.« less
  3. Due to Wildfire's huge destructive impacts on agriculture and food production, wildlife habitat, climate, human life and ecosystem, timely discovery of fires enable swift response to fires before they go out of control, in order to minimize the resulting damage and impacts. One of the emerging technologies for fire monitoring is deploying Unmanned Aerial Vehicles, due to their high flexibility and maneuverability, less human risk, and on-demand high quality imaging capabilities. In order to realize a real-time system for fire detection and expansion analysis, fast and high-accuracy image-processing algorithms are required. Several studies have shown that deep learning methods can provide the most accurate response, however the training time can be prohibitively long, especially when using online learning for constant refinement of the developed model. Another challenge is the lack of large datasets for training a deep learning algorithm. In this respect, we propose to use a pretrained mobileNetV2 architecture to implement transfer learning, which requires a smaller dataset and reduces the computational complexity while not compromising the accuracy. In addition, we conduct an effective data augmentation pipeline to simulate some extreme scenarios, which could promise the robustness of our approach. The testing results illustrate that our method maintains amore »high identification accuracy in different situations - original dataset (99.7%), adding Gaussian blurred (95.3%), and additive Gaussian noise (99.3%).« less
  4. 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.
  5. In September 2018, Hurricane Florence caused extreme flooding in eastern North Carolina, USA, a region highly dense in concentrated animal production, especially swine and poultry. In this study, floodwater samples (n=96) were collected as promptly post-hurricane as possible and for up to approx. 30 days, and selectively enriched for Campylobacter using Bolton broth enrichment and isolation on mCCDA microaerobically at 42°C. Only one sample yielded Campylobacter , which was found to be Campylobacter jejuni with the novel genotype ST-2866. However, the methods employed to isolate Campylobacter readily yielded Arcobacter from 73.5% of the floodwater samples. The Arcobacter isolates failed to grow on Mueller-Hinton agar at 25, 30, 37 or 42°C microaerobically or aerobically, but could be readily subcultured on mCCDA at 42°C microaerobically. Multilocus sequence typing of 112 isolates indicated that all were Arcobacter butzleri. The majority (85.7%) of the isolates exhibited novel sequence types (STs), with 66 novel STs identified. Several STs, including certain novel ones, were detected in diverse waterbody types (channel, isolated ephemeral pools, floodplain) and from multiple watersheds, suggesting the potential for regionally-dominant strains. The genotypes were clearly partitioned into two major clades, one with high representation of human and ruminant isolates and another with anmore »abundance of swine and poultry isolates. Surveillance of environmental waters and food animal production systems in this animal agriculture-dense region is needed to assess potential regional prevalence and temporal stability of the observed A. butzleri strains, as well as their potential association with specific types of food animal production. IMPORTANCE Climate change and associated extreme weather events can have massive impacts on the prevalence of microbial pathogens in floodwaters. However, limited data are available on foodborne zoonotic pathogens such as Campylobacter or Arcobacter in hurricane-associated floodwaters in rural regions with intensive animal production. With high density of intensive animal production as well as pronounced vulnerability to hurricanes, Eastern North Carolina presents unique opportunities in this regard. Our findings revealed widespread incidence of the emerging zoonotic pathogen Arcobacter butzleri in floodwaters from Hurricane Florence. We encountered high and largely unexplored diversity while also noting the potential for regionally-abundant and persistent clones. We noted pronounced partitioning of the floodwater genotypes in two source-associated clades. The data will contribute to elucidating the poorly-understood ecology of this emerging pathogen, and highlight the importance of surveillance of floodwaters associated with hurricanes and other extreme weather events for Arcobacte r and other zoonotic pathogens.« less