PERSPECTIVE For reprint orders, please contact: [email protected]

One day to one hour: how quickly can foodborne pathogens be detected?

Arun K Bhunia*

ABSTRACT Foodborne pathogens pose serious public health risks. Rapid, accurate technologies to detect a low number of target cells (1 cell/25–325 g sample) and microbial toxins are in demand in order to assess product safety in hours to up to 1 day. Varied pathogen loads and the complexity of food present a major challenge. Current culture methods, while accurate, are lengthy. New methods, using brief culturing and detection kits (antibody based, nucleic acid amplification or nano/biosensors) or a culture-independent approach coupled with nucleic acid amplification, traditionally used for viruses/parasites, can be used to obtain results in hours. A strategic approach involving two-step, rapid, high-throughput screening to rule out negatives followed by a confirmatory test could accomplish product testing in 1 h to 1 day. Foodborne pathogens & current challenges Food- and water-borne pathogens are a global concern and, according to WHO, approximately 1.8 million deaths are attributed to contaminated food and water [1] . In the USA alone, foodborne pathogens are responsible for approximately 48 million illnesses, 128,000 hospitalizations and 3000 deaths each year, resulting in annual economic expenses of US$78 billion [2,3] . A core set of 31 bacterial (64%), viral (12%) and parasitic (25%) pathogens has been identified as causative agents for 9.4 million illnesses in the USA each year, and the remaining 38.6 million illnesses are attributed to unknown or emerging pathogens. Strict regulatory standards have been established for most pathogens, of which Listeria monocytogenes, Salmonella and the Shiga-toxigenic Escherichia coli are prohibited in some foods. While regulatory standards are more relaxed for other bacterial pathogens, such as Campylobacter spp., Shigella spp., Yersinia spp., Bacillus spp. and Vibrio spp., or viral pathogens, such as norovirus and Hepatitis A virus [4] , or parasitic pathogens, such as Toxoplasma, Cyclospora and Trichinella [5] , their presence in ready-to-eat food is still undesirable. In addition, mycotoxins associated with grains, nuts and spices may be widespread, since these serve as ingredients in a variety of food products [6] . Even though regulatory standards exist for different types of mycotoxins, it would be extremely difficult to monitor and enforce standards, since the raw materials are outsourced globally from producers who may not follow strict good agricultural practices or good manufacturing practices during food production. Mycotoxins are known carcinogens and are hepatotoxic and nephrotoxic [6] . Algal toxins associated with shellfish and seafoods are of major concern due to the increased consumption of seafood [7] . The majority of these toxins are neurotoxins and could be fatal if excess amounts are consumed without timely medical intervention. Emerging pathogens such as E. coli O104:H4, Brucella spp., Clostridium difficile, Mycobacterium paratuberculosis and Nipah virus are now associated with foodborne outbreaks and present a real threat because knowledge is limited about them.

KEYWORDS 

• detection • foodborne pathogen • high-throughput screening • rapid

*Molecular Food Microbiology Laboratory, Department of Food Science, Department of Comparative Pathobiology, Purdue University, West Lafayette, IN 47907, USA; [email protected]

10.2217/FMB.14.61 © 2014 Future Medicine Ltd

Future Microbiol. (2014) 9(8), 935–946

part of

ISSN 1746-0913

935

Perspective Bhunia The major factors that are likely to have impact on foodborne diseases and outbreaks in the coming years include climate change, water usage and global outsourcing of foods. Global warming and climate change will favor an increased association of pathogenic microorganisms, such as protozoan parasites (Cyclospora, Cryptosporidium and Giardia, among others), and mycotoxigenic molds, which are predominant in tropical climates [6,8,9] . Global outsourcing of food, especially from tropical countries, would increase the chance for intercontinental pathogen transmission [6,10] . Water is considered the ‘environmental common’ and serves as a major vehicle for pathogen transmission to foods [11] . Climate changes (e.g., drought) would have tremendous impacts on water usage and the availability of potable water for crop production and food manufacturing. Poor-quality water not only increases the chance of the transmission of pathogens, but also toxic chemicals, heavy metals and pesticides. In order to reduce the number of foodborne disease episodes from domestic or imported foods, the US government has passed legislation – the Food Safety Modernization Act (FSMA) of 2011 – that introduced several key elements [12,13] . The legislation emphasizes the supply of safe food to consumers by implementing critical preventative measures through five broad focus areas: prevention; inspection and compliance; response; imports; and enhanced partnership. Prevention emphasizes science-based preventative control across the food supply chain, including mandatory controls in food facilities, producing safety standards during production and packaging and the prevention of intentional contamination. Inspection and compliance emphasize the important of a mandatory inspection frequency and testing by accredited laboratories that meet high-quality standards. In the response category, the US FDA must have authority for the mandatory recall and suspension of the registration of a production facility and enhanced product tracing abilities for contaminated foods. For imports, the key elements are that the imported food must meet US standards, allowing third-party certification and the authority to deny the entry of food from a foreign producer who fails to comply with the US standards. Finally, the legislation category emphasizes enhanced partnership and collaboration among all food safety agencies, both domestic and foreign, for public health safety

936

Future Microbiol. (2014) 9(8)

[13] .

Pathogen detection, identification and an intervention plan to eliminate or reduce the pathogen load in products are integral to implementing the FSMA. Thus, improved, fast and accurate detection and diagnostic technologies are needed in order to address present needs and also future needs [14,15] . Two key performance standards are often used to evaluate a detection tool: time to result (TTR), starting with the food sample to obtain a result; and assay accuracy, to detect as little as one target cell per 25–325-g sample. A detection tool with a high false-negative rate may be of concern for potential foodborne outbreaks due to the distribution of contaminated foods and is more detrimental than a tool showing false-positive results. TTR may vary from hours to days. A comparable technology with similar accuracy rate but with a shorter TTR is highly attractive for the food industry since the product could be released promptly. Many detection methods are in use or under development with a wide range of TTRs. This article will examine the methods that present great potential for use in pathogen testing to provide results in 1 h to 1 day and the challenges associated with such approaches. The detection methods (Figure 1) could be arbitrarily grouped into: traditional culture-based methods (3 -7 days); rapid methods (18–24 h); ultra-rapid methods (4–8 h); and real-time or near-real-time methods. Detection approaches & how quickly a pathogen can be detected from food ●●Traditional culturing methods are still the

gold standard

Traditional culturing methods employ classical microbiological techniques and are considered the gold standard since they are highly accurate and dependable, but their major drawback that they involve multiple steps, making the process lengthy, taking up to 3–7 days to obtain negative results. For the confirmation of positive results using biochemical or molecular techniques, an additional 2–3 days or longer may be necessary [14] . Thus, these methods are unattractive from the food industry perspective, which desires more rapid testing. The methods may also fail to isolate viable but nonculturable cells, which are more widespread among food pathogens than previously thought [16,17] . Culturing methods employ a prolonged enrichment step (pre-enrichment and selective enrichment, each up to 24 h) or a single 24-h enrichment to ensure

future science group

One day to one hour: how quickly can foodborne pathogens be detected? 

Traditional culture (3–10 days)

Sample processing

Preenrichment

Selective enrichment

Plating on agar plate

Rapid method (24–48 h)

Sample processing

Preenrichment

Selective enrichment

Detection

Ultra-rapid (4–8 h)

Sample processing

Enrichment

Real-time or near-real-time (seconds-minutes)

Perspective

Detection

Detection

Screening

Time (minute – hour – day)

Figure 1. Steps involved in foodborne pathogen detection depending on the speed of the assay.

resuscitation of stressed or sublethally injured cells encountered during food processing and storage. Stressed or sublethally injured cells grow slowly, hence prolonged enrichment is necessary to achieve a detectable level. Furthermore, selective enrichment suppresses background competitive microbiota, facilitating the isolation of target organisms on the agar plate. Moreover, the enrichment step enables the release of intimately adherent cells through outgrowth from the food matrices, which are difficult to extricate even by a Stomacher® (Seward Ltd, Worthing, UK) or a Pulsifer ® (Microgen Bioproducts, Surrey, UK) instrument. Enrichment may pose some limitations, such as competition for nutrients, among closely related microorganisms, leading to the outgrowth of competitors (e.g., L. monocytogenes can be outcompeted by Listeria innocua, a nonpathogenic relative), thus potentially affecting results [18–20] . Moreover, culture dynamics may be unpredictable since different strains of the same species may respond differently in this artificial growth environment. Advancements in differential chromogenic media formulations with selective antimicrobial agents and specialized substrates have facilitated the improved isolation of putative target organisms from the agar plate. This is one of the growing areas within the field of classical microbiology and, depending on the pathogen, visually distinct colonies may be obtained within 24–48 h. The suspect isolated colonies could be further verified by molecular methods (e.g., PCR), immunological methods or biosensor-based methods [14,15,21] . The major

future science group

advantage of culture-based methods is that they yield a pure isolated colony of the target pathogen, which could be further characterized by molecular fingerprinting, genome sequencing, mass spectrometry, antibiotic resistance or metabolic profiling for epidemiological study or for source tracking (Figure 2) . If a pathogen is implicated in an outbreak, it is often necessary to obtain a pure isolated culture of the offending organism to initiate product recalls and to implicate a food producer/processor in an outbreak. Furthermore, a culturing method is also essential to enforce a federally mandated ‘zero-tolerance policy’ for certain foodborne pathogens. Is it possible to execute the culture-based method in 1 day? Samples enriched in a single enrichment broth with accelerated growth-promoting agents for 2–4 h followed by plating on an agar plate may yield colonies in 24 h that can be tested using advanced, sensitive, rapid methods (discussed later). However, one needs to be cautious about the recovery rate of sublethally injured, stressed cells or naturally slow-growing microorganisms during shortened enrichment and plating. The nature of food matrix can affect detection & assay performance

The complexity of food matrices, fat and salt contents, preservatives, raw versus cooked foods and the diversity and levels of background microbiota may also dictate the choice of a detection method for use. In raw samples, background microbiota levels are generally high, which may interfere with detection. Thus, the

www.futuremedicine.com

937

Perspective Bhunia

Scatter images

PCR

Genome sequencing

MALDI-TOF MS

Homogenized sample

BARDOT

Spectral-based sensors

Figure 2. Strategic approach in high-throughput screening of agar plates containing bacterial colonies using a light scattering sensor (bacterial rapid detection using optical scattering technology) and subsequent verification using molecular or biosensor-based methods. Representative colony scatter images of some bacteria (Salmonella serovars, Citrobacter and Hafnia grown on XLT4 media) are presented. BARDOT: Bacterial rapid detection using optical scattering technology; MALDI-TOF MS: Matrixassisted laser desorption/ionization time-of-flight mass spectrometry. Data taken from [21].

use of a selective enrichment broth coupled with immunomagnetic separation may help concentrate the target pathogen [22–24] . The accuracy of an assay also depends on the sampling scheme, such as sample collection, composite sampling, sample handling and preparation, swabbing and blending [22,23] . For example, before swabbing a surface, chemical or enzyme pretreatment may be necessary to release intimately attached microbes. Blending or homogenization of intact samples (fruits, vegetables or meats) may release cellular enzymes, antimicrobial components and salts, which may inhibit or slow down pathogen growth during enrichment, thus potentially affecting pathogen detection (e.g., PCR inhibitors). Most importantly, microbial stress and injury should be accounted for during the implementation of an assay procedure in order to avoid any false-negative results, which are more detrimental than obtaining a false-positive result. ●●Nonculture-based approaches

Recently, culture-independent methods for food pathogen detection have been discussed and debated in terms of obtaining results quickly (within hours). In such cases, a highly efficient

938

Future Microbiol. (2014) 9(8)

sample processing step must be used so that the food matrices and inhibitors are removed and pathogens are concentrated in a small volume before testing with a detection platform. A PCR method (see below) is ideal for such an application and can be used to determine the presence or absence of a pathogen or to quantify the pathogen load using a quantitative real-time PCR. Partial- or whole-genome sequencing with mixed cultures can be conducted in order to confirm results. Culture-independent methods are proposed for foodborne molds [25,26] , viruses [27,28] and parasites (protozoa or nematodes) [29] , especially for the latter two, since they do not multiply in vitro. In clinical microbiology, specimens often contain high microbial loads, thus it is more appropriate to use a nonculture-based method. However, for foodborne bacterial pathogen testing, routine culture-independent approaches may not be suitable for many foods, since pathogen loads are low, especially in processed foods. Hence, a large volume of sample needs to be processed and target pathogens concentrated in order to meet the detection threshold of the assay platform employed. One of the unintended consequences of this approach is

future science group

One day to one hour: how quickly can foodborne pathogens be detected?  the build-up of background microbiota and the dead or lethally injured target pathogen, which may yield false-positive results. In such cases, paramagnetic bead-based separation methods can be used to selectively capture the target pathogen for testing [22,23] ; however, dead cells can also be concentrated by paramagnetic bead-based separation. Furthermore, current microbial community analysis using highthroughput partial genome sequencing (16S) could be used to assess the presence of target pathogens. This approach may yield results very quickly and may be economically feasible in the near future. ●●Rapid & ultra-rapid methods

Rapid methods

A majority of rapid methods require at least 24 h (1 day) or 48 h to complete. ‘One-day methods’ are highly attractive, since results can be obtained sooner than the conventional cultural methods, but the accuracy of results may suffer. These methods include a combination of traditional culturing (i.e., one-step enrichment in a liquid broth [18–20 h]) followed by detection using an antibody-based method (e.g., lateral-flow dipstick or enzyme immunoassays) or nucleic acid amplification (e.g., PCR). Numerous commercial kits are available for the detection of single pathogens. In recent years, however, the trend has been to develop methods for multipathogen detection so that food testing could be economical. For such an approach, one needs a multipathogen enrichment broth in order to enable the simultaneous enrichment of all putative target pathogens to a level at which a multiplex detection platform can be applied [30] . Many multipathogen enrichment broths have been formulated for different combinations of pathogenic microorganisms that include both Grampositive and Gram-negative pathogens [19,31,32] . One of the major drawbacks of these media is that they are less effective against diversebackground microbiota since a reduced level of antimicrobials are used in order to allow the growth of closely related target pathogens [19] . Furthermore, competition among target pathogens and growth dynamics can also be problematic in mixed-culture enrichment [20,33] . The physiological status of the pathogen may also affect detection efficacy since injured and stressed cells require a lengthy recovery period in a nonselective pre-enrichment broth prior to

future science group

Perspective

introducing a selective enrichment step, thus extending the TTR to 48 h or longer. Ultra-rapid methods

The TTR can be shortened substantially if the sensitivity of an assay is significantly improved. Some novel PCR methods and nano/biosensors have remarkably low detection limit (102 –104 cells or picograms of toxins), thus a shorter enrichment step or a nonculture-based approach may be suitable. In such cases, pathogens can be detected within 4–8 h. However, a major concern would be the reproducibility and accuracy of such assays owing to the presence of potential PCR inhibitors and stressed and injured cells. To overcome such problems, most commercial PCR kits recommend a 24-h enrichment. A brief description of select detection platforms that are suitable for both rapid and ultra-rapid detection is presented below. PCR & genome sequencing

PCR relies on the specific amplification of a target gene by several billion-fold so that the amplified product is readily detected. Assuming a single gene is representing a single cell, this technique provides an opportunity to detect low levels of pathogens in food, thus prolonged culture enrichment may not be necessary. PCR is one of the most widely used methods in food testing today. It requires a cocktail of reagents (polymerase enzyme, divalent cation as a catalyst, salts, nucleotides and the template nucleic acid) and involves multiple steps, thus making it prone to assay failure due to foodorigin PCR inhibitors or incomplete nucleic acid extraction from the sample. Optimized standard assay kits are now available to overcome these problems. Since both live and dead cells can give PCR-amplified products, steps are taken to amplify only mRNA transcripts that are specific for a gene (present only in live cells) using reverse transcriptase PCR or quantitative reverse transcriptase PCR. Specific staining with propidium or ethidium monoazide coupled with a real-time PCR can be used to detect live cells [34–36] . Enumeration of pathogen is of significant interest, especially when nonculturing methods are desired. In such cases, real-time quantitative PCR is most appropriate. Loop-mediated isothermal amplification of DNA has been developed for foodborne pathogen detection in order to improve assay sensitivity and specificity [37,38] . Whole- or

www.futuremedicine.com

939

Perspective Bhunia partial-genome sequencing is used to identify the pathogens [39] . The genome sequence information could also be used to track the pathogen to its source of contamination in a product in order to implement proper corrective actions during food product preparation. Lateral flow immunoassay

Lateral flow immunoassays (LFIs) or dipstick tests require only 10–15 min to display results [40] . In LFIs, the captured antibody is immobilized on a nitrocellulose membrane. The detection antibody conjugated to the colloidal gold or latex particles is placed in an area near the sample application port. A blotting paper placed at the opposite end of the sample application port facilitates fluid movement as it wicks through the membrane. When a liquid sample is applied to the sample port, the antigen binds to the goldor latex-conjugated detection antibody, and the antigen–antibody complex migrates laterally on the membrane by capillary action. The membrane contains two capturing zones, one specific for the target pathogen and another specific for unbound antibodies coupled to the gold or latex (control line). The appearance of two lines within 10–15 min indicates a positive result, while only one line (control) indicates a negative result. This method requires high bacterial cell concentrations of approximately 107–109 cells for a positive reaction. However, the application of magnetic nanoparticles replacing gold particles and chemiluminescence has been used to improve the assay sensitivity by several-fold, and the signal is collected using an automated reader [41,42] . LFI is one of the most widely used methods for food and clinical diagnostics today. Nano/biosensor approaches

Nano/biosensor platforms employ a combination of biological recognition molecules and physicochemical transducers in order to produce an electronic signal proportional to the interaction of a specific analyte with the sensor [23,43,44] . The biorecognition molecules (antibodies, aptamers, bacteriophages or their tail proteins, enzymes, antimicrobial peptides, host cell receptors and nucleic acid probes) provide specificity [45–48] . Biosensors are grouped into optical, optochemical, electrical and mass-based types and show utility in detecting foodborne pathogens [15,23] . Among the optical sensors, surface plasmon resonance (SPR) sensors [49] , evanescent wave sensors [43] , Fourier

940

Future Microbiol. (2014) 9(8)

transformed infrared sensors, Raman spectroscopy, hyperspectral imaging sensors [50,51] and light-scattering sensors [52] are capable of producing results in real time or near-real time. The latter four methods generally do not require any pathogen-specific biorecognition molecules, but are dependent on spectral libraries, thus being suitable for rapid, high-throughput screening. These are also suitable for multianalyte interrogation in a cost-effective manner [53–55] . The presumptive positive samples from initial screening could be further verified by using a pathogenspecific assay kit [52] . A brief description of select biosensors is provided below. Light-scattering sensors

Wyatt was the first person to use light-scattering sensors to detect bacteria in liquid suspensions [56] . The operating principle was to measure the intensities of scattered light from a single-wavelength laser onto bacterial cells in suspension without using any labeling reagent. Recently, a commercial device (MIT® 1000; Micro Imaging Technology, CA, USA) was built that utilizes 32 photodiodes in a spherical format in order to capture scatter signals from bacterial cells in suspension and compare them with a scatter image library for identification. It can detect approximately 1000 cells/ml in approximately 3–5 min. However, this method requires pure isolated cells from colonies on agar plates; thus, the actual TTR with food samples could vary at between 24 and 48 h. Recently, our team developed a modified light-scattering sensor system called BARDOT (standing for: bacterial rapid detection using optical scattering technology) in order to detect bacterial colonies on solid agar plates [57,58] . When a low-powered red diode laser beam (1 mW, 635 nm) is passed through the center of an isolated bacterial colony on a Petri dish, it generated unique scatter signatures for different phylogenetic groups. Scatter image libraries are constructed and used for the detection and identification of unknown pathogens or nonpathogenic bacteria using advanced classification algorithms at the genus, species or even serovar level [21,59,60] . Since there is a strong correlation between bacterial genotype and phenotype and the differential utilization of substrates in growth media, chromogenic or highly selective pathogen-specific media can be used for the high-throughput screening of closely related bacteria using BARDOT (Figure 2) . The steps

future science group

One day to one hour: how quickly can foodborne pathogens be detected?  involve in BARDOT-based detection include a brief enrichment in broth, followed by plating on selective/differential agar media for colony growth. A colony diameter of 1.1 ± 0.2 mm is needed, thus, depending on the growth rate of microbes, the assay time may vary. Some foodborne pathogens, such as Vibrio spp. [60] , Salmonella spp. [21] , E. coli O157:H7 and Bacillus spp., among others, from dairy, meat, vegetables and seafoods were detected within 24 h [59] . A slow-growing organism such as Listeria spp. may require more than 24 h for detection. Most importantly, BARDOT is based on the goldstandard culturing method and is suitable for the detection of 1 cell/g. BARDOT-positive colonies can be further verified by PCR, genetic fingerprinting, matrix-assisted laser desorption/ionization (MALDI), immunoassays or whole- or partial-genome sequencing (Figure 2) . Fiberoptic sensors

Fiberoptic biosensors, also known as evanescent-wave fluoroimmunosensors, generate evanescent waves after the interaction of incoming light with fluorophore-bound target molecules on the surface of the waveguide [61] . The evanescent wave undergoes total internal reflection within the waveguide and is detected by a fluorometer in real time. The signal strength is proportional to the amount of analyte bound to the surface. It has been used for the detection of toxins, bacteria, viruses, spores and other small molecules from food and clinical specimens. Two systems are commercially available: Analyte 2000™ and R APTOR™ (both from Research International, WA, USA). R APTOR is equipped with a microf luidic setup and is fully automated, portable and compact. It holds a cartridge containing four waveguides and each is precoated with analyte-specific antibody probes. This sensor has been used for foodborne pathogen detection with a sensitivity ranging from 102 –10 4 cells or nanogram quantities of toxins [43,55] . The planar array waveguide format is designed for multiple analytes and is suitable for highthroughput screening [62] . A combination of brief broth enrichment and a fiberoptic immunosensor can provide results in 4–6 h. A sample concentration step using paramagnetic beads can be included in order to improve fiberoptic sensitivity and specificity. This biosensors have been used for the detection of L. monocytogenes, E. coli O157:H7, Salmonella spp. and

future science group

Perspective

staphylococcal enterotoxins and mycotoxins from various food matrices. SPR sensors

SPR biosensors measure the interaction between two molecules in real time and have been widely used for the detection of small molecules, including microbial cells, toxins, proteins and chemicals, among others [63] . The ligands (capture molecules) are immobilized on a thin metal (gold) film mounted on a prism. When light interacts with the analyte–ligand complex, it alters the refractive index at the metal–dielectric interface, generating evanescent waves or an SPR response. As a result, the wavelength of the outgoing light shifts, which is then detected by a photodiode or a camera. The height of the evanescent wave is very shallow, thus being unsuitable for the accurate detection of large molecules, such as bacterial cells. Usually, small molecules (proteins or toxins) and viruses are detected more efficiently and reproducibly. These sensors are well suited for yielding results in hours, starting with food samples. Raman spectroscopy

Raman spectroscopy is one of the nondestructive chemical imaging techniques that uses diode lasers (785 nm) to record the vibrational and rotational properties of molecules. When the laser interacts with microbial cells or their components, it generates Raman scattering, termed inelastic scatter. In Raman spectroscopy, the analyte-specific spectral scatter signature is collected in the library and used for the identification of unknown agents. Generally, Raman scatter signals are very weak, and so to amplify the signal strength, gold or silver nanoparticles conjugated to biorecognition molecules, such as antibodies, are used to interact with the analyte before analysis by Raman spectroscopy. This is called surface-enhanced Raman spectroscopy [64] . Raman spectroscopy has been used for the detection of low concentrations (102 –104 cells) of biothreatening agents, including Bacillus anthracis, Yersinia pestis, Burkholderia mallei, Francisella tularensis and Brucella abortus, as well as foodborne pathogens, such as L. monocytogenes, E. coli O157:H7 and Salmonella spp., in hours [65] . Hyperspectral imaging

Hyperspectral imaging technology collects the spatial intensity information and generates

www.futuremedicine.com

941

Perspective Bhunia complete a spatiospectral map of the object using a small-wavelength bandwidth (several nm) from the visible to infrared ranges in real time [66,67] . It has been widely used in crop production and disease monitoring in agriculture and land surveillance, but most recently, it has been used in biosensor development. Hyperspectral imaging does not require any labeling reagents or biological probes, thus being suitable for the real-time examination of food products online. It has been used for the examination of poultry and beef carcasses, apples, cucumbers, tomatoes and seafoods [51,68] . Hyperspectral imaging has also been used to screen for biofilm formation or pathogens, but mostly under laboratory conditions. It remains to be seen whether such approaches can be used for the detection of a specific pathogens on food or produce surfaces under natural conditions. Mass spectrometry

Mass–spectrometry, such as MALDI time-offlight mass spectrometry, generates spectral signatures for individual pathogens based on protein profiles [69] . This system requires purified colony isolates from test specimens. MALDI time-of-f light mass spectrometry has been used to identify specific biomarkers in order to differentiate or identify various species of Campylobacter [70] and Salmonella enterica [71] . This technology is gaining significant interest for its ability to detect multiple pathogens at one time, and should be useful for high-throughput screening [72] . Since it relies on pure isolated samples (colonies), it may require days to provide results. ●●Real-time or near-real-time assays

Rapid high-throughput screening is needed to reduce assay time from hours to minutes

Automation in food manufacturing operations, starting with harvesting, transportation, processing operations, storage and distribution, is common. Efforts are being made to improve the online inspection, sorting (based on shape and size) and monitoring of product defects, including the contamination of products with soils, fecal materials or gross pathological changes in a diseased carcass or produce as indirect evidence for pathogen contamination [51,66] . Since the target pathogens are present at low levels and are associated with high background microbiota, it is nearly impossible to develop an online monitoring system for specific

942

Future Microbiol. (2014) 9(8)

pathogens, which may further be hindered by the complexity of food matrices, solid versus liquid foods, the fat contents and the speed at which the products move through a conveyor system. Therefore, pathogen testing is typically carried out in a laboratory, where representative sample portions from product lots are collected for testing. With improved sensitivity, high-throughput screening platforms equipped with electronic sensors and specific biomolecules (biosensors) for the detection of whole microorganisms, toxins, antigens or nucleic acids can further be used to shorten assay times. These platforms could possibly be used directly against food without preculturing or on culture-enriched samples [53] . Spectral-based sensors, such as light-scattering sensors [59] , hyperspectral imaging [51] and Raman spectroscopy [73] , are suitable for the real-time or near-real-time detection of pathogens, since they are highly sensitive, have no requirement for a pathogen-specific probe, maintain the integrity of target pathogens, are fast (requiring seconds to minutes) and are highly specific [44] . Furthermore, biosensor platforms amenable to automation that can also be configured for multipathogen and multisample screening could provide low-cost sample testing [53] . These approaches are highly desirable for assessing not only food safety, but also any potential biothreatening agents in food or water. Any samples with an indication of contamination can then be verified further by employing pathogen-specific assay kits [52] . Conclusion & future perspective In this article, an attempt was made to answer the question of how quickly one can detect foodborne pathogens. Assays yielding results in real time or near-real time are most desirable, but how realistic is it that they will obtain results so quickly from food samples, considering the inherent challenges of this task, such as low levels of pathogens or toxins, the physiological status of the microorganisms, the complexity of food matrices, the presence of inhibitors, salts, preservatives and dryness, among other factors? Furthermore, it is difficult to prescribe a single method that can be used for all food types or to confirm the presence of absence of the diverse range of foodborne pathogens. Since the majority of our test materials are going to be negative, it would be sensible to employ or develop screening technologies

future science group

One day to one hour: how quickly can foodborne pathogens be detected?  (mentioned above) that can rule out negative samples very quickly, preferably in hours, so that efforts could be directed towards identifying pathogens in positive samples, which may require lengthy analysis, providing results in days. It is rational to develop screening technologies in order to obtain indirect evidence for the potential presence of pathogens through monitoring fecal or soil contamination, gross pathological abnormalities on carcasses or abnormal appearance as indicators. Cell-based sensors, such as those that monitor pathogen–toxin interactions with mammalian cells, can provide valuable information about the presence of virulent pathogens or toxins in foods or beverages in hours [45,74] . Hyperspectral imaging technologies are being developed for the online screening of meat and poultry during slaughtering and fruits and vegetables during processing in order to provide such information [50,51,67,75] . The suspect samples are then rejected from the line in order to reduce the number of contaminated products. In addition, rapid high-throughput screening technology platforms can be used in the laboratory to expedite pathogen testing in order to provide results in hours. However, many challenges remain to be addressed in order to detect pathogens in hours to days, such sample processing, target recognition

Perspective

and signal generation and data analysis. With a certain amount of automation in all of these three areas, the high-throughput screening of microbial samples from food matrices is possible. In particular, automation of sample handling/processing is crucial, since any change or manipulation will affect downstream detection and analysis. Automation of detection steps will provide higher reproducibility, enabling a more quantitative sample assessment compared with manual detection and testing, which is prone to operational errors. Furthermore, automation of data analysis and bioinformatics would enable one to identify pathogens very quickly or may even predict potential contaminants in products. Acknowledgements The editorial assistance of V Ryan is acknowledged.

Financial & competing interests disclosure Research in the author’s laboratory is funded by grants from the US Department of Agriculture project number 193542000-035, 106037 and the Center for Food Safety Engineering at Purdue University. The author has no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript.

EXECUTIVE SUMMARY Foodborne pathogens & current challenges ●●

Food- and water-borne pathogens are responsible for 1.8 million deaths annually.

●●

Annual economic losses in the USA have been estimated to be US$78 billion.

●●

Besides standard foodborne pathogens, emerging pathogens also pose a serious public health concern.

●●

Increased foodborne pathogen outbreaks may be expected in the near future due to climate change, changing water usage and global outsourcing of foods.

Detection approaches & speed with which a pathogen can be detected from food ●●

Depending on the speed of the assay, the detection methods can be grouped into: traditional culture-based methods (3–7 days); rapid methods (18–24 h); ultra-rapid methods (4–8 h); and real-time or near-real-time methods.

●●

Assay sensitivity and accuracy are important attributes while choosing a method for food testing.

●●

Nonculture-based methods are appealing since the assay times can be shortened substantially in these approaches, but they may not be suitable for all food types or pathogens.

●●

Genome sequencing could be an important approach in foodborne pathogen detection and tracking in future.

●●

Novel nano/biosensor tools are promising and may help with rapid food pathogen testing.

●●

Rapid, high-throughput screening technologies could be important in sorting out foods that are negative for any pathogens in order to expedite product release for retail distribution.

future science group

www.futuremedicine.com

943

Perspective Bhunia References Papers of special note have been highlighted as: • of interest; •• of considerable interest. 1

Newell DG, Koopmans M, Verhoef L et al. Food-borne diseases – the challenges of 20 years ago still persist while new ones continue to emerge. Int. J. Food Microbiol. 139(Suppl. 1), S3–S15 (2010).

2

Scharff R. Economic burden from health losses due to foodborne illness in the United States. J. Food Prot. 75(1), 123–131 (2012).

3

Scallan E, Hoekstra RM, Angulo FJ et al. Foodborne illness acquired in the United States – major pathogens. Emerg. Infect. Dis. 17(1), 7–15 (2011).

4

Rodriguez-Lazaro D, Cook N, Ruggeri FM et al. Virus hazards from food, water and other contaminated environments. FEMS Microbiol. Rev. 36(4), 786–814 (2012).

5

Dorny P, Praet N, Deckers N, Gabriel S. Emerging food-borne parasites. Vet. Parasitol. 163(3), 196–206 (2009).

6

Stoev SD. Food safety and increasing hazard of mycotoxin occurrence in foods and feeds. Crit. Rev. Food Sci. Nutr. 53(9), 887–901 (2013).

7

Kalaitzis JA, Chau R, Kohli GS, Murray SA, Neilan BA. Biosynthesis of toxic naturally-occurring seafood contaminants. Toxicon 56(2), 244–258 (2010).

8

Thompson RCA, Owen IL, Puana I, Banks D, Davis TME, Reid SA. Parasites and biosecurity – the example of Australia. Trends Parasitol. 19(9), 410–416 (2003).

9

Broglia A, Kapel C. Changing dietary habits in a changing world: emerging drivers for the transmission of foodborne parasitic zoonoses. Vet. Parasitol. 182(1), 2–13 (2011).

10 Patyal A, Rathore RS, Mohan HV, Dhama K,

Kumar A. Prevalence of Arcobacter spp. in humans, animals and foods of animal origin including sea food from India. Transboundary Emerg. Dis. 58(5), 402–410 (2011). 11 Pachepsky Y, Shelton DR, McLain JET,

Patel J, Mandrel RE. Irrigation waters as a source of pathogenic microorganisms in produce: a review. Adv. Agronomy 113, 75–141 (2011). •

Summarizes the transmission and source of pathogens in fruits and vegetables through irrigation water, soil and the environment.

12 Shapiro JT. Food Safety Modernization Act.

Manufact. Confect. 93(6), 41–45 (2013). 13 U.S. Department of Health & Human

Services/U.S. Food & Drug Administration. Background on the FDA Food Safety Modernization Act (FSMA).

944

concentration before being used for detection.

www.fda.gov/downloads/Food/ GuidanceRegulation/UCM263773.pdf  14 National Advisory Committee on

Microbiological Criteria For Foods. Response to questions posed by the food safety and inspection service regarding determination of the most appropriate technologies for the food safety and inspection service to adopt in performing routine and baseline microbiological analyses. J. Food Prot. 73(6), 1160–1200 (2010).  •• Summarizes various detection methods, including traditional culturing methods, rapid methods and advanced biosensor-based methods. 15 Velusamy V, Arshak K, Korostynska O,

Oliwa K, Adley C. An overview of foodborne pathogen detection: in the perspective of biosensors. Biotechnol. Adv. 28(2), 232–254 (2010).

23 Bhunia AK. Biosensors and bio-based

methods for the separation and detection of foodborne pathogens. Adv. Food Nutr. Res. 54, 1–44 (2008). •• Comprehensive review of pathogen separation from food matrices and biosensor-based detection platforms for foodborne pathogens. 24 Dwivedi HP, Jaykus L-A. Detection of

pathogens in foods: the current state-of-the-art and future directions. Crit. Rev. Microbiol. 37(1), 40–63 (2011). •

25 White PL, Archer AE, Barnes RA.

Comparison of non-culture-based methods for detection of systemic fungal infections, with an emphasis on invasive Candida infections. J. Clin. Microbiol. 43(5), 2181–2187 (2005).

•• Provides a detailed review on various biosensor-based methods for pathogens. 16 Nicolo MS, Gioffre A, Carnazza S,

Platania G, Silvestro ID, Guglielmino SPP. Viable but nonculturable state of foodborne pathogens in grapefruit juice: a study of laboratory. Foodborne Pathog. Dis. 8(1), 11–17 (2011).

26 Zhang SX. Non-culture-based methods in

diagnostic mycology. Clin. Microbiol. Newslett. 34(13), 101–105 (2012). 27 Stals A, Van Coillie E, Uyttendaele M. Viral

genes everywhere: public health implications of PCR-based testing of foods. Curr. Opin. Virol. 3(1), 69–73 (2013).

17 Dinu L-D, Bach S. Detection of viable but

non-culturable Escherichia coli O157:H7 from vegetable samples using quantitative PCR with propidium monoazide and immunological assays. Food Control 31(2), 268–273 (2013).

28 Katano H, Kano M, Nakamura T, Kanno T,

Asanuma H, Sata T. A novel real-time PCR system for simultaneous detection of human viruses in clinical samples from patients with uncertain diagnoses. J. Med. Virol. 83(2), 322–330 (2011).

18 Taskila S, Tuomola M, Ojamo H. Enrichment

cultivation in detection of food-borne Salmonella. Food Control 26(2), 369–377 (2012).

29 De Keuckelaere A, Stals A, Baert L,

Uyttendaele M. Performance of two real-time RT-PCR assays for the quantification of GI and GII noroviruses and hepatitis A virus in environmental water samples. Food Anal. Meth. 6(4), 1016–1023 (2013).

19 Kim H, Bhunia AK. SEL, a selective

enrichment broth for simultaneous growth of Salmonella enterica, Escherichia coli O157:H7, and Listeria monocytogenes. Appl. Environ. Microbiol. 74(15), 4853–4866 (2008).

30 Gehring A, Barnett C, Chu T et al.

A high-throughput antibody-based microarray typing platform. Sensors 13(5), 5737–5748 (2013).

20 Besse NG, Barre L, Buhariwalla C et al.

The overgrowth of Listeria monocytogenes by other Listeria spp. in food samples undergoing enrichment cultivation has a nutritional basis. Int. J. Food Microbiol. 136(3), 345–351 (2010). 21 Singh AK, Bettasso AM, Bae E et al. Laser

optical sensor, a label-free on-plate Salmonella enterica colony detection tool. MBio 5(1), e01019-13 (2014). 22 Brehm-Stecher B, Young C, Jaykus L-A,

Tortorello ML. Sample preparation: the forgotten beginning. J. Food Prot. 72, 1774–1789 (2009). •• Describes various methods that are currently used for sample preparation and pathogen

Future Microbiol. (2014) 9(8)

Comprehensive review of foodborne pathogen detection.



Summarizes antibody-based high-throughput screening methods.

31 Gehring AG, Albin DM, Bhunia AK, Kim H,

Reed SA, Tu S-I. Mixed culture enrichment of Escherichia coli O157:H7, Listeria monocytogenes, Salmonella enterica, and Yersinia enterocolitica. Food Control 26(2), 269–273 (2012). 32 Yi-Gang Y, Hui W, Yuan-Yuan L, Su-Long L,

Xiao-Quan Y, Xing-Long X. A multipathogen selective enrichment broth for simultaneous growth of Salmonella enterica serovar

future science group

One day to one hour: how quickly can foodborne pathogens be detected?  foodborne pathogens and toxins, including detection limits assay specificities for different pathogens in various food matrices.

Enteritidis, Staphylococcus aureus, and Listeria monocytogenes. Can. J. Microbiol. 56(7), 585–597 (2010). 33 Koo OK, Aroonnual A, Bhunia AK. Human

heat-shock protein 60 receptor-coated paramagnetic beads show improved capture of Listeria monocytogenes in the presence of other Listeria in food. J. Appl. Microbiol. 111(1), 93–104 (2011). 34 Hruskova L, Mot’kova P, Vytrasova J.

Multiplex polymerase chain reaction using ethidium monoazide and propidium monoazide for distinguishing viable and dead cells of arcobacters in biofilm. Can. J. Microbiol. 59(12), 797–802 (2013). 35 Nkuipou-Kenfack E, Engel H, Fakih S,

44 Bae E, Bhunia AK. Nano optical sensors for

developments in the use of viability dyes and quantitative PCR in the food microbiology field. J. Appl. Microbiol. 116(1), 1–13 (2014). 37 Niessen L, Luo J, Denschlag C, Vogel RF.

The application of loop-mediated isothermal amplification (LAMP) in food testing for bacterial pathogens and fungal contaminants. Food Microbiol. 36(2), 191–206 (2013).

capture of pathogenic bacteria using a mammalian cell receptor coupled with dielectrophoresis on a biochip. Anal. Chem. 81(8), 3094–3101 (2009). 47 Chambers JP, Arulanandam BP, Matta LL,

Weis A, Valdes JJ. Biosensor recognition elements. Curr. Issues Mol. Biol. 10, 1–12 (2008). 48 Singh A, Arutyunov D, Szymanski CM,

Evoy S. Bacteriophage based probes for pathogen detection. Analyst 137(15), 3405–3421 (2012).

41 Anfossi L, Di Nardo F, Giovannoli C,

Passini C, Baggiani C. Increased sensitivity of lateral flow immunoassay for ochratoxin A through silver enhancement. Anal. Bioanal. Chem. 405(30), 9859–9867 (2013).

High-throughput SPR sensor for food safety. Biosens. Bioelectron. 24(5), 1399–1404 (2009). Chao K, Chan DE. Microbial biofilm detection on food contact surfaces by macro-scale fluorescence imaging. J. Food Eng. 99(3), 314–322 (2010).

•• Provides an updated review on biosensor-based methods for the detection of

future science group

differential light scattering. Nature 221(5187), 1257–1969 (1969). 57 Bae E, Banada PP, Huff K, Bhunia AK,

Robinson JP, Hirleman ED. Biophysical modeling of forward scattering from bacterial colonies using scalar diffraction theory. Appl. Opt. 46(17), 3639–3648 (2007). 58 Banada PP, Guo S, Bayraktar B et al. Optical

forward-scattering for detection of Listeria monocytogenes and other Listeria species. Biosens. Bioelectron. 22(8), 1664–1671 (2007). 59 Banada PP, Huff K, Bae E et al. Label-free

detection of multiple bacterial pathogens using light-scattering sensor. Biosens. Bioelectron. 24, 1685–1692 (2009). •

hyperspectral imaging in food safety inspection and control: a review. Crit. Rev. Food Sci. Nutr. 52(11), 1039–1058 (2012). •• Highlights the applications of hyperspectral imaging on the real-time or near-real-time safety and quality assessment of food products.

Light-scattering sensor for real-time identification of Vibrio parahaemolyticus, Vibrio vulnificus and Vibrio cholerae colonies on solid agar plate. Microb. Biotechnol. 5(5), 607–620 (2012). 61 Marazuela MD, Moreno-Bondi MC.

Fiber-optic biosensors – an overview. Anal. Bioanal. Chem. 372(5–6), 664–682 (2002). 62 Taitt CR, Shriver-Lake LC, Ngundi MM,

Ligler FS. Array biosensor for toxin detection: continued advances. Sensors 8(12), 8361–8377 (2008). •

52 Bhunia AK. Rapid pathogen screening tools for

food safety. Food Technol. 65(2), 38–43 (2011). •

Highlights the importance of high-throughput screening technologies in food safety applications using two novel biosensor technologies.



Highlights various high-throughput and multipathogen screening tools, including protein and nucleic acid microarrays.

Review of array-based biosensors for the detection of pathogens and toxins of biothreat and food safety importance.

63 Homola J. Surface plasmon resonance

sensors for detection of chemical and biological species. Chem. Rev. 108(2), 462–493 (2008). 64 Craig AP, Franca AS, Irudayaraj J.

Surface-enhanced Raman spectroscopy applied to food safety. Annu. Rev. Food Sci. Technol. 4, 369–380 (2013).

53 Gehring A, Tu SI. High-throughput

biosensors for multiplexed food-borne pathogen detection. Annu. Rev. Anal. Chem. 4, 151–172 (2011).

Describes the application of a laser-based, novel, on-plate, real-time colony screening tool for the detection and identification of pathogens.

60 Huff K, Aroonnual A, Littlejohn AEF et al.

51 Feng Y-Z, Sun D-W. Application of

43 Sharma H, Mutharasan R. Review of

biosensors for foodborne pathogens and toxins. Sens. Actuat. B Chem. 183, 535–549 (2013).

56 Wyatt PJ. Identification of bacteria by

50 Jun W, Kim MS, Cho B-K, Millner PD,

42 Joung H-A, Oh YK, Kim M-G. An automatic

enzyme immunoassay based on a chemiluminescent lateral flow immunosensor. Biosens. Bioelectron. 53, 330–335 (2014).

biosensor for detection of Listeria monocytogenes, Escherichia coli O157:H7 and Salmonella enterica from ready-to-eat meat samples. Food Microbiol. 33(2), 166–171 (2013).

49 Piliarik M, Párová L, Homola J.

40 Banada PP, Bhunia AK. Antibodies and

immunoassays for detection of bacterial pathogens. In: Principles of Bacterial Detection: Biosensors, Recognition Receptors and Microsystems. Zourob M, Elwary S, Turner A (Eds). Cambridge University Press, Cambridge, UK, 567–602 (2008).

55 Ohk S-H, Bhunia AK. Multiplex fiber optic

46 Koo OK, Liu Y, Shuaib S et al. Targeted

39 Boxrud D. Advances in subtyping methods of

foodborne disease pathogens. Curr. Opin. Biotechnol. 21(2), 137–141 (2010).

wave fluorescence biosensors. Biosens. Bioelectron. 20(12), 2470–2487 (2005).

cell-based biosensors for pathogens and toxins. Trends Biotechnol. 27(3), 179–188 (2009).

38 Soli KW, Kas M, Maure T et al. Evaluation of

colorimetric detection methods for Shigella, Salmonella, and Vibrio cholerae by loop-mediated isothermal amplification. Diag. Microbiol. Infect. Dis. 77(4), 321–323 (2013).

Provides a comprehensive description on optical biosensors and their operating principles.

45 Banerjee P, Bhunia AK. Mammalian

Nocker A. Improving efficiency of viability-PCR for selective detection of live cells. J. Microbiol. Methods 93(1), 20–24 (2013). 36 Elizaquivel P, Aznar R, Sanchez G. Recent

54 Taitt CR, Anderson GP, Ligler FS. Evanescent

food safety and security. In: Optochemical Nanosensors. Cusano A, Arregui FJ, Giordano M, Cutolo A (Eds). CRC Press, FL, USA, 497–512 (2013).  •

Perspective



Updated review on the Raman-based spectral sensor for the label-free detection of foodborne pathogens.

65 Golightly RS, Doering WE, Natan MJ.

Surface-enhanced Raman spectroscopy and

www.futuremedicine.com

945

Perspective Bhunia homeland security: a perfect match? ACS Nano 3(10), 2859–2869 (2009). 66 Gowen AA, O’Donnell CP, Cullen PJ,

Downey G, Frias JM. Hyperspectral imaging – an emerging process analytical tool for food quality and safety control. Trends Food Sci. Technol. 18(12), 590–598 (2007). 67 Yoon SC, Park B, Lawrence KC,

Windham WR, Heitschmidt GW. Line-scan hyperspectral imaging system for real-time inspection of poultry carcasses with fecal material and ingesta. Computers Electron. Agri. 79(2), 159–168 (2011). 68 Elmasry G, Kamruzzaman M, Sun D-W,

Allen P. Principles and applications of hyperspectral imaging in quality evaluation of agro-food products: a review. Crit. Rev. Food Sci. Nutr. 52(11), 999–1023 (2012). •• Highlights the principles and applications of hyperspectral imaging on the real-time or

946

near-real-time safety and quality assessment of food products. 69 Sandrin TR, Goldstein JE, Schumaker S.

MALDI TOF MS profiling of bacteria at the strain level: a review. Mass Spec. Rev. 32(3), 188–217 (2013). 70 Mandrell RE, Harden LA, Bates A,

Miller WG, Haddon WF, Fagerquist CK. Speciation of Campylobacter coli, C. jejuni, C. helveticus, C. lari, C. sputorum, and C. upsaliensis by matrix-assisted laser desorption Ionization-time of flight mass spectrometry. Appl. Environ. Microbiol. 71(10), 6292–6307 (2005). 71 Dieckmann R, Malorny B. Rapid screening of

epidemiologically important Salmonella enterica subsp enterica serovars by whole-cell matrix-assisted laser desorption ionizationtime of flight mass spectrometry. Appl. Environ. Microbiol. 77(12), 4136–4146 (2011).

Future Microbiol. (2014) 9(8)

72 Chan P-H, Wong S-Y, Lin S-H, Chen Y-C.

Lysozyme-encapsulated gold nanoclusterbased affinity mass spectrometry for pathogenic bacteria. Rapid Commun. Mass Spec. 27(19), 2143–2148 (2013). 73 Xiaomeng W, Chao X, Tripp RA,

Yao-Wen H, Yiping Z. Detection and differentiation of foodborne pathogenic bacteria in mung bean sprouts using field deployable label-free SERS devices. Analyst 138(10), 3005–3012 (2013). 74 Banerjee P, Franz B, Bhunia A. Mammalian

cell-based sensor system. In: Whole Cell Sensing Systems I. Belkin S, Gu MB (Eds). Springer, Germany, 21–55 (2010). 75 Hruska Z, Yao H, Kincaid R et al.

Fluorescence imaging spectroscopy (FIS) for comparing spectra from corn ears naturally and artificially infected with aflatoxin producing fungus. J. Food Sci. 78(8), T1313–T1320 (2013).

future science group

One day to one hour: how quickly can foodborne pathogens be detected?

Foodborne pathogens pose serious public health risks. Rapid, accurate technologies to detect a low number of target cells (1 cell/25-325 g sample) and...
2MB Sizes 0 Downloads 4 Views