Capture and concentration of viral and bacterial foodborne pathogens using apolipoprotein H
Introduction
Foodborne pathogens are responsible for approximately 9.4 million illnesses each year in the United States, with an estimated total illness cost of $14.0 billion annually (Batz et al., 2012, Scharff, 2012). About 58% (5.5 million) of these illnesses are caused by human norovirus (Scallan et al., 2011). While the bacterial foodborne pathogens are responsible for fewer illnesses (3.6 million each year), they constitute the majority of serious outcomes (Scallan et al., 2011). Nontyphoidal Salmonella spp., the most common bacterial causative agent, is the leading cause of food-related hospitalizations (35%) and deaths (28%) (Centers for Disease Control & Prevention, 2013), while Listeria monocyotogenes is the third leading cause of foodborne disease deaths (19%) (Scallan et al., 2011, Gandhi and Chikindas, 2007). Two other important foodborne pathogens are shiga toxin-producing Escherichia coli (STEC, for which E. coli O157:H7 is the most common serovar.), and Staphylococcus aureus, which causes self-limiting food intoxication associated with food handling and subsequent temperature abuse (Rangel et al., 2005, Kadariya et al., 2014).
Regulators and industry alike use microbiological testing as one tool to manage these foodborne disease agents. The inability to cultivate human norovirus in vitro means that separation and concentration of low levels of virus particles from food matrices is necessary prior to detection, which is done using molecular amplification methods. Conversely, it is possible to enrich low concentrations of most bacterial pathogens, however enrichment steps are time-consuming, often taking up to two days. Waiting for test results has many implications, both financial (e.g., storage and warehousing, reduced product shelf-life) and public health in nature. In fact, users of testing methods almost universally recognize that the enrichment and/or concentration process is the single most limiting factor in the development of near real-time methods for foodborne pathogen detection (Dwivedi and Jaykus, 2011).
There is a need to develop methods that either shorten enrichment times, or allow bypass of enrichment steps. Separation and concentration of pathogens from the sample matrix prior to detection is necessary in order to achieve these goals. This so-called “pre-analytical sample processing” is of increasing interest to the detection sector and approaches have been reviewed elsewhere (Stevens and Jaykus, 2008, Dwivedi and Jaykus, 2011). The most commonly used methods for pathogen separation and concentration are immuno-magnetic separation (antibody-conjugated paramagnetic beads) or affinity magnetic separation (ligand-conjugated paramagnetic beads) (Fluit et al., 1993a, Fluit et al., 1993b, Liebana et al., 2009, Stevens and Jaykus, 2008). However, depending on the ligand, these methods have their disadvantages, and none is ideal (Dwidedi and Jaykus, 2011). For example, it is very difficult to find broadly reactive ligands for capture of all human norovirus genotypes (and for some bacteria as well); binding affinities can be poor; and cost and/or shelf-life can be problematic. No ligand has binding specificity to a comprehensive and diverse group of foodborne pathogens.
Apolipoprotein H (ApoH), also known as beta2-glycoprotein I, is an acute phase protein found circulating in human plasma (Bouma et al., 1999). ApoH has already been shown to interact directly with a number of different viruses such as hepatitis B, rotavirus and human immunodeficiency virus (Adlhoch et al., 2011, Stefas et al., 1997, Stefas et al., 2001). It also binds with high affinity to LPS on Gram-negative bacteria (Agar et al., 2011) and specific proteins on two Gram-positive pathogens, S. aureus (Zhang et al., 1999) and Streptococcus pyogenes (Nilsson et al., 2008). The broad range specificity of ApoH may provide a viable alternative to current organism-specific techniques (e.g., antibody capture) for the concentration and separation of viral and bacterial pathogens from food or environmental samples.
The purpose of this study was to determine whether ApoH-conjugated magnetic beads could be used to capture norovirus (representative human genotypes and the Tulane virus, a cultivable surrogate) and select Gram-negative (E. coli O157:H7 and S. enterica serovar Enteritidis) and Gram-positive (L. monocytogenes and S. aureus) bacteria from solution. The efficiency of this concentration and separation technique was also determined.
Section snippets
Virus strains
The genogroup I, genotype 1 (GI.1) Norwalk virus (NV) (courtesy of Dr. Christine Moe, Emory University, Atlanta, GA) and a GII.4 New Orleans outbreak strain (courtesy of Dr. Shermalyn Greene, North Carolina State Laboratory of Public Health, Raleigh, NC) were obtained as stool samples and diluted 10–20% in phosphate buffered saline (PBS) (MP Biomedicals, Santa Ana, CA). Tulane virus was propagated in the LLC-MK2 cell line as previously described (Farkas et al., 2008) and recovered after three
ApoH bead-based capture of whole virus particles
A comparison of the RT-qPCRU corresponding to whole virus particles before and after capture using ApoH-coated beads is presented in Fig. 1. In virtually all instances, there was no statistically significant difference between input virus concentration and that recovered using the ApoH beads (p > 0.05), indicating a high degree of capture efficiency. On a percentage basis, > 23% of the input virus was captured by the beads; hence, virus loss during capture was always < 1 Log10 RT-qPCRU. The limit
Discussion
The need to shorten and simplify methods to detect bacterial and viral pathogens in food and environmental samples is well established. The most commonly used approach to achieve this is concentration and purification of the target from the sample matrix just prior to detection. Pathogen concentration methods range from general (e.g. high speed centrifugation, filtration) to specific (immunomagnetic separation), with a emphasis on separating the organisms from sample matrices, removing
Acknowledgements
We thank E. Benedict for her help troubleshooting buffer conditions. This work was supported in part by the Agriculture and Food Research Initiative Competitive Grant no. 2011-68003-30395 from the U.S. Department of Agriculture, National Institute of Food Agriculture through the NoroCORE project. Additional support was provided by the U.S. Air Force Institute of Technology Civilian Institute Program Scholarship. The views expressed in this article are those of the authors and do not reflect the
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