Methods to detect avian influenza virus for food safety surveillance

Journal of Integrative Agriculture 2015, 14(11): 2296–2308 Available online at www.sciencedirect.com

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REVIEW

Methods to detect avian influenza virus for food safety surveillance SHI Ping1*, Shu Geng2, 3, 1*, LI Ting-ting1, LI Yu-shui1, FENG Ting1, WU Hua-nan1 1

School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, P.R.China Sino-US Joint Food Safety Research Center, Northwest A&F University, Yangling 712100, P.R.China 3 Department of Plant Science, University of California, Davis, CA 95616, USA 2

Abstract Avian influenza (AI), caused by the influenza A virus, has been a global concern for public health. AI outbreaks not only impact the poultry production, but also give rise to a risk in food safety caused by viral contamination of poultry products in the food supply chain. Distinctions in AI outbreak between strains H5N1 and H7N9 indicate that early detection of the AI virus in poultry is crucial for the effective warning and control of AI to ensure food safety. Therefore, the establishment of a poultry surveillance system for food safety by early detection is urgent and critical. In this article, methods to detect AI virus, including current methods recommended by the World Health Organization (WHO) and the World Organisation for Animal Health (Office International des Epizooties, OIE) and novel techniques not commonly used or commercialized are reviewed and evaluated for feasibility of use in the poultry surveillance system. Conventional methods usually applied for the purpose of AI diagnosis face some practical challenges to establishing a comprehensive poultry surveillance program in the poultry supply chain. Diverse development of new technologies can meet the specific requirements of AI virus detection in various stages or scenarios throughout the poultry supply chain where onsite, rapid and ultrasensitive methods are emphasized. Systematic approaches or integrated methods ought to be employed according to the application scenarios at every stage of the poultry supply chain to prevent AI outbreaks. Keywords: avian influenza, food safety, detection methods, poultry supply chain, surveillance system

1. Introduction The avian influenza (AI) virus, belonging to the species influenza A virus, is usually adapted to bird hosts and is

Received 26 December, 2014 Accepted 27 May, 2015 SHI Ping, E-mail: [email protected]; Shu Geng, E-mail: [email protected]; Correspondence WU Hua-nan, Tel: +86-755-26032670, Fax: +86-755-26035332, E-mail: [email protected] * These authors contributed equally to this study. © 2015, CAAS. All rights reserved. Published by Elsevier Ltd. doi: 10.1016/S2095-3119(15)61122-4

a devastating pathogen in the poultry production system (Harder and Werner 2006). Based on their antigenic relationships, influenza A virus strains are classified into serological subtypes between the primary surface glycoproteins, the hemagglutinin (HA), and the neuraminidase (NA). So far, a total of 18 HA subtypes (H1–H18) and 11 NA subtypes (N1–N11) have been identified, including two influenza-like virus genomes (designated H17N10 and H18N11) recently isolated from bats (Tong et al. 2012, 2013). Combinations of HA and NA proteins are possible and each combination represents a viral subtype (Harder and Werner 2006). The AI virus is chiefly found in birds, but the infection of an AI virus in humans can occur following direct or close contact with infected poultry. Genetic recombination of the AI virus during the virus replication process can result in mutations

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in the HA or NA proteins, which may then lead to human infection (Kaverin et al. 1988; Chmielewski and Swayne 2011). To date, several subtypes of AI viruses have been found to infect humans, including H5N1, H6N1, H7N2, H7N3, H7N7, H7N9, H9N2, and H10N8 (Butt et al. 2005; Chmielewski and Swayne 2011; To et al. 2014; Zhang et al. 2014). Among these viruses, subtypes H5N1 and H7N9 are of the greatest concern since the outbreaks of these two subtypes have caused severe diseases and deaths in humans. Moreover, these subtypes strongly impact poultry production and raise problems in food safety with regard to poultry and egg consumption (Sonaiya 2007; Hou 2013; Qi et al. 2014). In accordance with differences in the pathogenicity of chicken and turkeys, AI virus strains are further classified into two types - highly pathogenic and low pathogenic. The definitive classification of pathogenicity is determined by an intravenous pathogenicity index (IVPI) which is greater than 1.2 for highly pathogenic avian influenza (HPAI) virus (Jin et al. 2004). Generally, infection by an HPAI virus in poultry can lead to an acute and fatal disease as the replicates of the virus occur in various organ systems. In contrast with HPAI viruses, infection by low pathogenic avian influenza (LPAI) viruses in poultry generally results in more moderate or asymptomatic problems as they mainly appear in the respiratory and enteric tracts, thus potentially causing respiratory diseases. As such, the clinical symptoms of LPAI in poultry are not obvious when compared to uninfected subjects on a poultry farm (Plague and Aviaire 2010; Rebel et al. 2011). For instance, it has been indicated that infection by the H7N9 virus in chickens, Japanese quail, pigeons, Peking ducks, Mallard ducks, Muscovy ducks, and Embden geese by intranasal inoculation does not produce any clinical signs of disease (Pantin-Jackwood et al. 2014). Nevertheless, it does not imply that the pathogenicity of LPAI viruses is equally low for humans. As reported by the World Health Organization (WHO), the infection of certain LPAI viruses has been found to result in serious illness and even death in humans (WHO 2003). Furthermore, the long-term existence of some subtypes of LPAI virus, such as H5 or H7, on a poultry farm would run an increased risk of becoming highly pathogenic through gene mutation or reassortment (Monne et al. 2014). Global poultry meat output was 94.2 million tonnes in 2013, according to data from the Food and Agriculture Organisation (FAO) (Sakhatskiy and Abdullaieva 2014). It is estimated that world poultry production will increase by 120% from 2010 to 2050, in which case, poultry would replace pork as the most globally consumed type of meat (O’Keefe 2014). Asia produces one-third of the world’s broilers, and China accounts for 15.6% of the global output of poultry meat (Sakhatskiy and Abdullaieva 2014).

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However, most AI virus infections in humans have been discovered in Asia, which is most likely attributable to the close relationship between live birds and humans in the poultry breeding and trade industries (McLeod et al. 2009; Magalhaes et al. 2010). On 31 March, 2013, the Chinese government reported that a novel strain of avian influenza (H7N9) had been confirmed, which led to the infection of three people in Shanghai City and Anhui Province (WHO 2013). The symptoms were severe and similar to those caused by other influenza viruses (Gao et al. 2013). This was the first time that infection by this virus was ever been reported in humans, and, within one month, the number of confirmed cases rapidly increased to 126, including 24 deaths (WHO 2013). As claimed by WHO on 23 February, 2015, the total number of confirmed human cases reached 571, including 212 deaths. The lethality of this virus quickly grabbed global attention to a degree that was matched only by the last avian influenza outbreak caused by virus H5N1 in 2003. Although direct transmission to humans seldom occurs through the consumption of poultry meat, serious public concerns have arisen due to the existence of the infection within the poultry supply chain. Therefore, it is necessary to establish an efficient surveillance program to promote the safety of poultry products and to protect public health. Currently, there are two modes of supply chains in the poultry industry, namely the traditional and vertically integrated modes (Fig. 1), both of which involve intensive interaction with humans. Increased human exposure to live poultry could lead to a greater risk of infection by AI virus, especially in the traditional mode (Fig. 1-B). The development of an effective surveillance program to detect AI virus at each stage of the poultry supply chain is essential. Moreover, detection technologies of AI virus have usually been summarized and categorized according to methodology, and rarely according to practicality. In this review, we will conduct a case study of AI viruses H5N1 and H7N9 to discuss whether present detection methods are adequate for the purposes of AI virus surveillance in the poultry supply chain and whether the current development and application of novel techniques are adoptable and sufficient.

2. Comparison of AI outbreak between H5N1 and H7N9 In 1997, the first AI outbreak in humans with virus subtype H5N1 occurred in Hong Kong, in which 18 cases were confirmed including 6 fatalities (Conly and Johnston 2004). Since then, the infection of humans by AI virus H5N1 occurs occasionally in various countries (WHO 2015). Thus far, according to data from the WHO that covered 16 countries in Asia, Africa, Europe, and the Americas, the number of cases of infection by H5N1 totaled 664, including 393 fatali-

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B

Breeding farms

Feed ingredients

Hatchery

Hatchery

Acquisition

Import

Export

Individual farms

Small scale farms

Grow out farms–contracted independent and company owned farms

Modern abattoirs

Breeding farms

Traditional slaughterhouses Processing company

Wholesale markets

Food outlets

Retailers

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Local retailers-wet markets

Food outlets

Consumers

Fig. 1 Two supply chain modes in the poultry industry. A, vertically integrated mode. B, traditional mode.

ties, a death rate of over 50%. Simultaneously, devastating outbreaks of the HPAI virus H5N1 occurred in poultry more than 7 000 times in various places from the end of 2003 to 17 July, 2014, as reported by the World Organisation for Animal Health (Office International des Epizooties, OIE), leading to huge economic losses (Elci 2006). According to the OIE and the WHO (Fig. 2), AI outbreaks most frequently emerged in Vietnam, which accounted for 36% of the total number of global outbreaks. Asia is the region most severely affected by AI outbreaks, especially South Asia, where more than half of the countries in the region experienced H5N1 infections in poultry and humans. As a result, a surveillance system for AI viruses was developed for the entire poultry supply chain that incorporated various laws, regulation standards, and detection technologies. For instance, the standards for HPAI control were published by the OIE (2000, 2001). These manuals detailed methods for the diagnosis, surveillance and monitoring of HPAI viruses. While AI by H5N1 was gradually brought under control, the unexpected outbreak of AI virus H7N9 in China drew grave attention in 2013 as many cases of human infection were confirmed within the short period of about one month in a relatively concentrated area (Chen et al. 2013; Gao et al. 2013; Uyeki and Cox 2013; Zhuang et al. 2013). The reported cases were distributed throughout 16 provinces

and cities in China but were mostly localized in Zhejiang and Guangdong provinces (Fig. 3). However, the mode of the H7N9 outbreak is completely distinct from that of the H5N1. As an LPAI virus, no epidemic outbreak by H7N9 in avians has been reported. No obvious signs or symptoms appeared in avians infected by H7N9, making an epidemic caused by H7N9 far more difficult to prevent. Therefore, extensive procedures identifying H7N9 virus-carrying birds within healthy-looking birds need to be conducted. The Ministry of Agriculture (MOA) of the People’s Republic of China issued an urgent circular on 9 April, 2013 concerning the monitoring scheme of H7N9 infections within animals (MOA 2013). In this scheme, serological and etiological methods are applied for virus detection. It was reported that the H7N9 virus was detected in samples taken from 15 provinces, which roughly coincided with the regions in which human outbreaks were discovered (Fig. 3). The rapid spread of the H7N9 virus, across almost half of China, is likely attributable to transportation in the poultry industry (Gao 2014; Liu and Gao 2014). The majority of positive samples were collected from live-poultry markets. Thus, live-poultry markets are the principal sites in which the risk of infection not only by HPAI viruses (Abdelwhab et al. 2010; Sanchez-Vizcaino et al. 2010) but also LPAI viruses with high pathogenicity in humans, such as H7N9 (Gao 2014), is the greatest. The

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85 12 63 4 Azerbaijan Turkey 32 Irak

47 30 127 Pakistan 31

Egypt 1 Djibouti

11 Nigeria

197 165

China Laos

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56 Myanmar1 22 37 25 17 Thailand Vietnam Cambodia Indonesia

Outbreaks of H5N1 in poultry 1–10 10–50 50–100 100–250 250–500

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500–1 000 H5N1 in human 1 000–2 000

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Fig. 2 Outbreaks by avian influenza (AI) virus H5N1 in poultry and the number of confirmed cases of infection by H5N1 in humans globally from 2003 to 2014. Data source: World Organisation for Animal Health (Office International des Epizooties, OIE) and World Health Organization (WHO).

Beijing Shandong Henan Hubei

The detection frequencies in poultry Month

Guizhou

Hunan

Anhui

Shanghai

Jiangxi

Guangxi

<2

Taiwan Hongkong

2–4 4–8 ≥8

Confirmed cases in human

Fig. 3 The detection frequencies of AI virus H7N9 in poultry and the number of confirmed cases of human infection by H7N9 in China from 2013 to 2014. The frequency is calculated as the number of months in which detection of H7N9 in poultry was reported. Data source: Chinese Center for Disease Control and Prevention (China CDC) and the Ministry of Agriculture of the People’s Republic of China (MOA of China).

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number of infections in humans declined quickly and the spread of the virus was finally brought under control (He et al. 2014; Yu et al. 2014) after the shutdown of live-poultry markets was carried out. However, total shutdown of live-poultry markets cannot be a long-term solution due to traditional dietary habits in China. In addition, the AI virus can survive low temperature treatments (e.g., refrigeration and freezing) (Chmielewski and Swayne 2011). To reduce the risk of infection, live poultry and poultry products must be subjected to rigorous AI surveillance procedures at each stage of the poultry supply chain. Therefore, novel scientific poultry surveillance methods have to be established to ensure food safety. These methods ought to involve both conventional and new detection methods, which may be different from the measures currently used to control outbreaks of HPAI.

3. Current methods for detection of the AI virus 3.1. Etiological methods AI viral isolation, via cultivation in embryonated chicken eggs and further identification of the virus subtypes, is the gold-standard technique as it is the most sensitive and accurate method, generating very high titers of all types of AI viruses except the H17N10 virus (Newton et al. 2000; Tong et al. 2012). To obtain high titers of the virus, either specific pathogen-free chicken eggs or specific antibody-negative eggs are inoculated using samples taken from the trachea and cloacae of live birds or from the feces and organs of dead birds for 4 to 7 days (Swayne et al. 2008; Woolcock 2008). During the propagation, a readily available and continuous supply of fertilized chicken eggs is required (Cattoli et al. 2004). This method is very time-consuming, laborious, and resource-intensive. Furthermore, due to viral infectivity and high pathogenicity, biosecurity is an important concern and all facilities should be classified at least as biosafety class II (Woolcock 2008). Thus, virus isolation is not suitable for fast detection and routine diagnosis, but rather should be used to obtain live virus isolates for further, detailed laboratory analysis. Other detection techniques, such as agar gel immunodiffusion (AGID), various immunoassays, and real-time RT-PCR, can confirm and subtype the virus isolates (Woolcock 2008).

3.2. Serological methods Antibodies to HA, NA, nucleoprotein, and matrix protein, produced after the onset of influenza virus illness in poultry, can be detected by using serological diagnostic techniques like hemagglutination inhibition (HI) assay, neuraminidase

inhibition (NI) assay, enzyme-linked immunoassay (ELISA), AGID, complement fixation (CF), and neutralization test (Cox 1999; El Zowalaty et al. 2013). Due to their high specificity, the HI and NI tests are mostly used in subtype identification and for measuring the levels of respective antibodies to HA and NA in serum samples (Julkunen et al. 1985; Prince and Leber 2003). The HI assay is based on the fact that antibodies to the influenza virus will prevent attachment of the virus to red blood cells and therefore inhibit hemagglutination (Li et al. 2013). It has been widely applied as it is rather inexpensive. For instance, this method is used by the MOA of China to detect antibodies to the H7 subtype of AI virus in serums during the surveillance of H7N9 in poultry (MOA 2013). However, the HI assay is regarded as a difficult diagnostic tool as it is labor-intensive and requires two serum samples taken with accurate timing (Cox 1999); the same goes for the NI assay. The ELISA technique is another common method for the detection of AI virus antibodies based on high titres. Commercial ELISA kits are available and have been widely used for general serological monitoring of vaccinal responses (Rosenqvist et al. 1983). The antigens in the kits are broadly cross-reactive among subtypes (Ding et al. 2011), so an HI test or virus isolation must be performed following ELISA tests if there is any further requirement for subtyping of HA or for final diagnosis. ELISA kits are of moderate cost and are adaptable to high-throughput screening for influenza A virus infections (Mendoza et al. 1999). Thus, this method is more suitable for the surveillance of AI viruses or vaccination responses on large-scale poultry farms. The AGID test is able to detect IgMs and some IgGs. Although it is a low cost serological screening test, a high dose of antigens is required and the results can be observed after approximately 24–48 h (Higgins 1998; Woolcock 2008). Like ELISA methods, downstream subtype identification would need to be supplemented by other techniques. Moreover, this test lacks sensitivity and is liable to yield inconsistent results as the presence and duration of precipitating antibodies may vary among individual birds (Brown et al. 2010). The CF test is an immunological medical test that can be used to detect the presence of either a specific antibody or a specific antigen in serum (Tesh and McCammon 1979). However, owing to the unreliable indicator of immunity or response to vaccination, this test has not been offered for the diagnosis of acute influenza infection since 2009 (El Zowalaty et al. 2013). The virus neutralization test is a highly sensitive and specific assay applicable to the identification of virus-specific antibodies in animals and humans, which works by inoculating the mixture of the virus and the appropriate antibody reagents into host systems (e.g., cell cultures, embryonated eggs, or animals) to determine residual virus infectivity (Huang et al. 2007; Kashyap et al. 2008). Although the micro-neutralization

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test was verified to be much more sensitive during the 1997 outbreak of AI by H5N1 in Hong Kong of China (Rowe et al. 1999; Papenburg et al. 2011), this method is still seldom used due to its labor-intensive, time-consuming, and highly technical nature as well as its strict environmental biosafety requirements (Papenburg et al. 2011).

3.3. Molecular methods Molecular techniques have recently been introduced for the detection or diagnosis of AI viruses. Polymerase chain reaction (PCR) is the most basic of these methods and is regarded as a powerful technique. In 1991, a PCR-based assay was used for the first time for the detection of an AI virus (Zhang and Evans 1991). Because of their high sensitivity and short turnaround time, a range of different types of molecular diagnostic tests have been developed. Until recently, the most commonly used method was reverse-transcription PCR (RT-PCR), by which cDNA reverse-transcribed from the RNA of the virus is exponentially amplified using various specific primers for subtype identification (Suarez et al. 2007). However, the RT-PCR technique is laborious and easily contaminated due to the fact that the results are observed after agarose gel electrophoresis and staining with ethidium bromide (Lee et al. 2001; Cattoli and Capua 2006). Modified tests based on the RT-PCR methods have recently been developed to reduce the effect of inhibitory substances in the samples taken, the possible presence of contaminating nucleic acids, and the time required to perform the test. These methods include nucleic acid sequence-based amplification (NASBA) (Lau et al. 2004), loop-mediated isothermal amplification (LAMP) (Imai et al. 2006), and real-time RT-PCR. Real-time RTPCR is a preferred molecular detection test for AI viruses, since it can survey the increase of PCR products by means of one fluorescently-labeled probe, while the amplification is being performed with this probe simultaneously (Spackman et al. 2002). Therefore, this assay is recommended and widely-used for its minimal hands-on time, simplified process, and sensitive diagnostic ability when compared with other methods. In spite of this, there are still several limitations. Firstly, a change or reassortment of the viral sequence occurs very easily, including primer binding sites, thus timely primer updating is necessary (El Zowalaty et al. 2011). Secondly, false negative results may arise due to the presence of inhibitors such as low virus titers, low quality of extracted RNA, and potential bias during the reaction (Das et al. 2006; Van Borm et al. 2007). Thirdly, this method requires high-level technology, operation in strict accordance with standards, and expensive equipment. Therefore, the effectiveness of these methods is restricted for the detection of AI viruses in the field.

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All the methods mentioned above are recommended by the WHO and the OIE for the detection, diagnosis, and surveillance of AI viruses in humans, poultry, and wild birds. However, none of the diagnostic or detection assays can be easily carried out onsite or provide quick results. These conventional methods face some practical challenges to establishing a comprehensive poultry surveillance program in the poultry supply chain. Below, we will review some new techniques that have not been commonly used or commercialized and discuss their feasibility for use in the poultry surveillance system.

4. Novel techniques used for detection of AI viruses Detection techniques keep evolving toward the goal of AI virus identification with high sensitivity, specificity, and efficiency in terms of both time and cost. In addition to the improvement of commercialized methods such as RT-PCR and ELISA, emerging techniques aim to achieve portability, robustness, and simplicity. A portable impedance biosensor system demonstrated comparable sensitivity and specificity to a real-time RT-PCR. The detection process, which incorporates virus-capturing by magnetic nanobeads coated with antibodies specific to the AI virus H5 in a microfluidic chip and impedance measurements by an interdigitated microelectrode, takes less than 1 h (Wang et al. 2011). In a subsequent study, the second antibody to the AI virus N1 immobilized on the surface of the microelectrode led to specificity to H5N1. Red blood cells from chickens were used for signal amplification, but the detection time doubled as a result (Lum et al. 2012). It can be seen that the challenges persist for developing a method that will fulfill all the requirements for the surveillance of AI virus in the poultry supply chain, and thus new techniques should be categorized for different orientations, especially from the food safety point of view. Unlike diagnoses that require high specificity to distinguish among subtypes, food safety surveillance needs varying levels of specificity depending on the stage and channel of the poultry supply chain. Therefore, three scenarios for AI virus detection have been demonstrated below as easy to use onsite, rapid subtyping, and highly sensitive quantitation. The novel techniques that have been reported since 2012 will be discussed herein. Some studies on human influenza viruses and human infections are also included in the discussion.

4.1. Easy to use onsite One of the challenges faced by current food safety surveillance methods is the reliance on specialized laboratory facilities and trained technicians, which inhibit practical applica-

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tion. Reducing this dependence is crucial in scenarios where fast screening in decentralized trading locations and markets is required. The techniques also have to be effective in notso-well-controlled environments. For example, immunoreactions coupled with nanotechnologies have shown great potential in various detection platforms. Immunomagnetic beads (IMBs) feature simultaneous virus capturing by antibodies and magnetic separation. Through conjugation with highly fluorescent quantum dots, this method demonstrated very high specificity, precision and reproducibility, a result further validated by throat swab samples (Zhao et al. 2012). Detection can be performed in a microfluidic chip integrated with a portable optical fiber spectrometer (Zhang et al. 2013). In order to reduce background noise signals caused by light scattering, electrochemical sensors were used to detect the AI virus captured by IMBs with the assistance of bienzymatic amplification (Zhou et al. 2013). For easier usage, immunoreactions involving gold nanoparticles were carried out on nitrocellulose membranes, forming immunochromatographic strips that enabled onsite naked eye detection with enhanced sensitivity (Li et al. 2013). Taking advantage of the specific binding between HA of influenza viruses and sialic acid-containing carbohydrates, a colorimetric sensor was developed based on the aggregation of gold nanoparticles induced by the thiolatedsialic acid derivative self-assembled on the gold surface (Marin et al. 2013). By simply mixing the glyconanoparticles with influenza virus samples, the color change could be observed in 30 min. Although the study focused on the alpha 2,6-thio-linkage of sialic acid to galactose dedicated for human influenza virus, potential exists for detecting an AI virus that would preferentially bind to sialic acid through alpha 2,3-thio-linkage (Takahashi et al. 2013). More sensitive colorimetric sensors were also developed based on non-aggregation gold-silver nanoparticles coupled to immunomagnetic separation. Metallic silver formed on the surface of gold nanoparticles led to significant color changes, where the metallization of silver ions was induced by an enzyme conjugated to AI virus-MIB composites. The detection limit was 17.5 pg mL–1 and detection time was 1.5 h (Zhou et al. 2014). Due to their high selectivity, some of these methods incorporate simplified pretreatment of complex samples such as mashed tissues and feces. Oligonucleotide probes have long been used in AI virus detection with the assistance of fluorophore-quencher labels (Ge et al. 2010). Upconversion nanoparticles are alternative luminescent labels with higher photostability, which is important for onsite applications. In a biosensor based on luminescence resonance energy transfer from BaGdF5:Yb/Erupconversion nanoparticles to gold nanoparticles, H7 subtype-specific gene sequences were successfully identified and quantified (Ye et al. 2014). Hybridization of oligonucleotide probes and targets produced capacitance

changes that could be measured by miniaturized complementary metal oxide semiconductor (CMOS) sensors. The impedimetric sensor realized label-free detection (Wei et al. 2012). However, DNA extraction is needed for assays based on oligonucleotide hybridization. For direct recognition of an AI virus, considerable effort has been directed towards the development of artificial oligonucleotides that bind to AI viruses in the same way as antibodies do. These oligonucleotides are also known as aptamers, and have advantages over antibodies in terms of in vitro design and synthesis without the usage of animals as well as thermal stability. Aptamers specific to an AI virus have been selected from a randomized library of single-stranded DNA (Wang et al. 2013). In an electrochemical impedance biosensor, aptamers were used to capture the H5N1 virus and induce ionic strength variations due to the enzymatic catalyzed production of gluconic acid. Measurement of the impedance changes on a bare electrode neglected the biochemical immobilization processes, which simplified the detection of the AI virus (Fu et al. 2014). Aptamers incorporated in hydrogels have done well in alternating the cross-lined polymeric structure in response to various physical and chemical changes, which is desired for visual detection as a biophysical sensor (Liu et al. 2012). Through computer simulations and statistical analyses, aptamers were found to be capable of generating significant mechanical stress upon binding to a virus. Deformation of the sensing matrix such as aptamer-containing hydrogels induced by the stress is a promising tool for macroscopic screening of viruses including AI viruses (Shin et al. 2014).

4.2. Rapid subtyping Another scenario for food safety surveillance is rapid detection for high throughput assays of an AI virus, especially HPAI viruses in a large number of samples. This may follow the onsite screening, when further analysis of uncertain samples is required for decision-making. Amplification of virus genes is the key step that determines the overall time consumption. Although PCR-based techniques have improved in the past decade, post-PCR processing and detection is still challenging due to the diversity of subtypes. A highly proficient DNAChip was developed for the typing and subtyping of the HPAI viruses based on 9G DNAChip technology. The high signal to background ratio enabled the simultaneous detection of H5N1 from China and Korea, H5N3, H7N7, H9N2, H9N4, and H9N6 using only HA probes. With hybridization, washing, and drying at 25°C, the final results were obtained in 40 min after PCR. The rapidness and mild conditions of this method are important for practical applications (Van Thuan et al. 2012). On the other hand, integrated amplification-detection techniques have

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also been widely studied. A one-step quantitative real-time RT-PCR assay detected the H7N9 virus in 2–3 h using the primer set specifically designed for the HA gene of H7N9 viruses, but mismatched to other H7 HA sequences (Wong et al. 2013). Replacement of the TaqMan hydrolysis probe with the low-cost SYBR green fluorescence dye simplified the amplification process while maintaining comparable performance (Zhu et al. 2013). The simpler and faster amplification-detection scheme termed a simple method for amplifying RNA targets (SMART) was based on an isothermal nucleic acid sequence-based amplification (NASBA). Magnetic beads conjugated with the capture probe formed a complex with virus RNA and amplification probes, followed by magnetic separation in a simple microfluidic device. At 41°C, amplification was finished in 90 min (Wang et al. 2013). Moreover, reverse transcription-loop-mediated isothermal amplification (RT-LAMP) is another alternative to the commonly used RT-PCR. It works at a constant temperature of 60–65°C without the requirement of thermal cyclers. By using a lateral flow device, the product of RT-LAMP from H7N9 virus was detected in less than 1 h, which is faster and more sensitive than the RT-PCR (Ge et al. 2013). A one-step RT-LAMP method was developed for detecting the HA and NA genes of the H7N9 virus. In less than 25 min, the process was complete with sensitivity for the N9 gene 100-fold higher than the real-time PCR recommended by the WHO (Zhang et al. 2013). Extensive studies have also been done on amplification-free methods. A gold nanoparticle-based genomic microarray was reported for the simultaneous detection and subtyping of influenza A viruses including the AI virus. Silver staining mediated by the gold nanoparticles allowed direct detection by light scattering with high sensitivity; therefore, RNA fragmentation, target amplification, and enzymatic reactions were not required (Zhao et al. 2010, 2012). Recognition agents immobilized on biosensors have been used to detect AI viruses without gene extraction. Anti-H5N1 antibodies immobilized on the indium-tin-oxide (ITO) channel between source/drain electrodes of ITO thin-film transistors (TFTs) caused a change in the electronic properties of the ITO TFT upon binding to H5N1 viruses. Devices with good reusability and stability can be easily fabricated in mass production (Guo et al. 2013). Quartz crystal microbalance (QCM) is a widely used biosensing tool due to its simplicity and sensitive response in the oscillation frequency to surface changes of the crystal. QCM sensors have been used for AI virus detection based on antibody-antigen interactions (Li et al. 2011) and aptamer incorporated hydrogels (Wang and Li 2013). In the latter case, aptamer-virus binding led to the swelling of hydrogels self-assembled on the sensor surface in the way described in the previous subsection. The results were obtained in 30 min, which was faster than

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the antibody-based counterpart.

4.3. Highly sensitive quantitation High sensitivity remains the most desirable characteristic for the early detection of an AI virus before it develops into a massive outbreak in the poultry supply chain. In order to enhance the sensitivity of the simple detection methods, one of the most straightforward approaches is transplanting the detection schemes to more sensitive detectors. For example, IMBs were used in combination with the resonance light scattering (RLS) technique. Owing to strong scattering near the absorption band of the nanoparticles, a lower detection limit and wider linear range were achieved (Zou et al. 2012). IMBs accumulated on the magnetic-gold electrode surface of an electrochemical sensor generated a catalytically reduced electrochemical signal of hydrogen peroxide, making it possible to develop a simple and reusable device that could detect AI virus in samples of chicken dung (Zhou et al. 2013). Field effect transistor (FET) detectors are also good candidates for a detection platform with high sensitivity. By using a FET as the detector, the sensitivity of detection methods based on binding between HA and sialic-acid-containing carbohydrates was brought down to attomolar (Hideshima et al. 2013). Simply adding the hydrophobic passivation layer to the popularly used hydrophilic passivation layer of FET sensors, a 100-fold improvement in the detection limit was achieved for antibody-antigen based detection (Kim et al. 2013). It can be expected that higher sensitivities will be realized if these two FET techniques are incorporated into the same device. Other approaches toward improving sensitivity involve specially-designed chemical agents or reactions. By using graphene oxide as carriers of bovine serum albumin (BSA), signals of electrochemical immunosensors were amplified due to the highly specific surface area of graphene oxide (Xie et al. 2014). Moreover, immunoassays can be improved by labeling antibodies with silver nanoparticles (Ag-NP) for signal amplification. Moreover, the dissolving of Ag-NP and releasing of Ag+ ions further induced chemiluminescence (Li et al. 2013) and fluorescence (Li et al. 2014) due to the formation of the Ag+-Na2S2O8-Mn2+-H3PO4-luminol system and o-phenylenediamine oxidation catalyzed by Ag+, respectively. Both methods demonstrated a detection limit as low as 0.1 pg mL–1 for H1N1, but the approaches should be easy to apply to AI virus detection. Furthermore, the sensitivity of oligonucleotide probes could also be improved in electrochemical sensors by attaching a redox label. Hybridization of target HA genes pushed away and consequently altered the redox activity of the label consisting of metallacarborane (Grabowska et al. 2014). In summary, new technologies have been developed to

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meet the specific requirements of AI virus detection in various stages or scenarios throughout the poultry supply chain where onsite, rapid, and ultrasensitive methods are emphasized. The application of AI virus detection technologies will vary in accordance with different scenarios in the two modes of the poultry supply chain. In the vertically-integrated mode of the poultry supply chain (Fig. 1-A), detection of AI viruses should be initially carried out in the stage from poultry farms to abattoirs. The techniques, which can be easily executed onsite without requiring technical training or excessive expenditure, ought to be used for virus detection in contracted independent farms. Meanwhile, methods based on PCR assays could be applied for high throughput detection in virus testing laboratories of company owned farms. If there are demands for downstream diagnosis, the AI virus can be subtyped by using the current detection methods (including virus isolation and serological assays) as supplements. When the poultry products are prepared for sale, AI virus detection methods must be highly sensitive as this stage would be the last chance to ensure their safety. By contrast, a higher number of AI virus detections should be conducted during the late stages of the supply chain in the traditional mode due to the higher probability of exposure to live poultry and consequently of virus transmission. Techniques that feature ease of application are preferred for onsite AI virus detection. Integrated methods have shown great potential by combining the advantages of two or more techniques. The selection and combination of immunomagnetic beads, oligonucleotide probes, nanoparticles, and electrochemical sensor devices may be the trend that will dominate the research and development of new technologies.

5. Conclusion Avian influenza has been an issue of international concern and exemplifies a strong connection between food supply and human health. This review has focused on AI virus transmission from poultry to humans related to the poultry supply chain of the food industry. Due to the differing pathogenicity of AI virus in poultry and humans, special attention has to be paid to LPAI. The mode of poultry supply chain has been discussed to demonstrate that various scenarios of AI virus detection exist according to the probability of human exposure to poultry products. Currently, most of the conventional and standard techniques used in AI virus detection are well applied for the purpose of diagnosis with high accuracy. However, due to the complexity of the poultry supply chain, novel techniques are needed to detect the AI virus for the purpose of food safety surveillance. Recent research has been diversified to develop more convenient, more rapid, more sensitive, and less costly methods. For the first time, the novel techniques have been discussed in

an application-oriented way rather than a technology-based way. There has not been a method that can be used at all the stages of the poultry supply chain, so a set of separate or integrated techniques have to be employed according to the application scenario. Certainly, technical detection and surveillance of AI viruses in the poultry supply chain are not sufficient for the prevention of AI outbreaks. Relevant policies and standard regulations that ensure the competent execution of a surveillance system are equally important. In the traditional mode (Fig. 1-B) of the poultry supply chain, the higher the number of direct links between poultry farms and consumers, the more difficult it is to organize, survey, and standardize the industry. Governments should be responsible for enforcing more routine sample checks not only on the poultry farms but also in the markets, which is similar to what has been done in the status quo during AI outbreaks. In contrast, the vertically-integrated mode (Fig. 1-A) is considered to be the optimal mode for the poultry industry at present, since vertical integration offers the opportunity to combine different biosecurity and sanitation practices, housing technologies, and feeding regimens to improve food safety. It also allows the industry to maintain strict biosecurity measures, vaccination programs, and the ability to detect pathogenic microbes such as the AI virus (Costales et al. 2006; NCC 2012). Therefore, development of a vertically-integrated poultry industry could be another preventive measure for AI.

Acknowledgements This research is supported by the National Natural Science Foundation of China (21405008), the Shenzhen Municipal Government Subsidies for Postdoctoral Research, the Special Fund for Sino-US Joint Research Center for Food Safety in Northwest A&F University, China (A200021501) and the Start-up Funds for Talents in Northwest A&F University, China (Z111021403). The authors appreciate the thorough review by anonymous reviewers who have improved our paper considerably.

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