Food Microbiology 44 (2014) 142e148

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Rapid detection of aflatoxin producing fungi in food by real-time quantitative loop-mediated isothermal amplification Jie Luo, Rudi F. Vogel, Ludwig Niessen* €t München, Lehrstuhl für Technische Mikrobiologie, Gregor-Mendel-Straße 4, 85354 Freising, Germany Technische Universita

a r t i c l e i n f o

a b s t r a c t

Article history: Received 4 December 2013 Received in revised form 10 April 2014 Accepted 6 June 2014 Available online 14 June 2014

Aflatoxins represent a serious risk for human and animal health. They are mainly produced by Aspergillus flavus and Aspergillus parasiticus but also by Aspergillus nomius. Three species specific turbidimeter based real-time LAMP (loop-mediated isothermal amplification) assays were developed to quantify the three species individually in conidial solutions and to define contamination levels in samples of shelled Brazil nuts, maize, and peanuts. Standard curves relating spore numbers to time to threshold (Tt) values were set up for each of the species. Assays had detection limits of 10, 100 and 100 conidia per reaction of A. flavus, A. parasiticus, and A. nomius, respectively. Analysis of contaminated sample materials revealed that the A. nomius specific real-time LAMP assay detected a minimum of 10 conidia/g in Brazil nuts while assays specific for A. flavus and A. parasiticus had detection limits of 102 conidia/g and 105 conidia/g, respectively in peanut samples as well as 104 conidia/g and 104 conidia/g, respectively in samples of maize. The real-time LAMP assays developed here appear to be promising tools for the prediction of potential aflatoxigenic risk at an early stage and in all critical control points of the food and feed production chain. © 2014 Elsevier Ltd. All rights reserved.

Keywords: Aspergillus Aflatoxin Spore Food Real-time LAMP

1. Introduction Aflatoxins are a class of fungal secondary metabolites with high toxicity and carcinogenicity toward animals and humans and are thus a topic of high concern in the food and feed industry. Aflatoxins B1, B2, G1 and G2 are of significance as contaminants of commodities used in the production of foods and feeds. Aflatoxin B1 (AFB1) has been classified as a human carcinogen (group 1A) by the International Agency for Research on Cancer (IARC, 1993). The evidence regarding the potential carcinogenicity of aflatoxins has forced governmental regulatory agencies to establish very low tolerances in food, including peanuts and related products, in order to prevent trade of commodities contaminated by these toxins at levels exceeding a maximum limit (van Egmond and Jonker, 2004). The Food and Agriculture Organization (FAO) estimates that 25% of the world's food crops are affected by aflatoxins, which have significant effects also on livestock and poultry (Adams and Motarjemi, 1999). In addition to health concerns related to aflatoxins, the rejection of contaminated bulk commodities such as peanuts, rice, sorghum and maize as well as products of smaller concern like pistachios, hazelnuts, Brazil nuts and nutmeg with * Corresponding author. E-mail address: [email protected] (L. Niessen). 0740-0020/© 2014 Elsevier Ltd. All rights reserved.

aflatoxin concentrations exceeding the maximum acceptable level results in large economic losses, worldwide. Aflatoxigenic species occur in sections Flavi, Nidulantes and Ochraceorosei of the genus Aspergillus (Varga et al., 2009), with section Flavi containing the majority of potential producers (Cary et al., 2005; Pildain et al., 2008). Aspergillus flavus and Aspergillus parasiticus are the most common filamentous fungi associated with aflatoxin contamination of commercially important agricultural commodities (i.e., groundnut kernels, maize, rice and sorghum grain) (Kumar et al., 2008). In addition, Olsen et al. (2008) suggested that the less regularly occurring Aspergillus nomius may be highly important as producer of aflatoxins in Brazil nuts. As a consequence, also this fungus as well as other potential producers of B and G aflatoxins should be carefully examined since the findings may influence strategies for prevention and control of aflatoxins in Brazil nuts. Only recently, Calderari et al. (2013) verified that A. nomius and A. flavus are the main aflatoxin producers at different stages of the Brazil nut production chain. The level of fungal infection in food and feed commodities as well as the identification of major species is important since the result can give an indication of the food quality as well as of the potential future risk of mycotoxins being present in a sample (Suanthie et al., 2009). Possible interventions aimed at lowering or preventing aflatoxin contamination in agricultural commodities

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include good agricultural practice with early harvesting and proper drying of the commodity, biological and chemical control, and breeding for resistance (Turner et al., 2005). All these strategies require effective and rapid control schemes. Rapid and sensitive methods for detection and differentiation of potential aflatoxigenic species in raw commodities and in food and feed products in order to estimate any potential health risk are highly needed (Valasek and Repa, 2005). For the detection of molds, methods such as the traditional mycological methods (Pitt and Hocking, 2009), enzymelinked immunosorbent assay (Notermans et al., 1986) as well as PCR, qPCR, RT-PCR and multiplex qPCR (Shapira et al., 1996; ~ as et al., 2011; Haugland et al., 2002; Rodríguez et al., Sardin ldez et al., 2014) have been developed and applied. 2012a; Berna Also DNA-based biosensors have been recently developed for the detection of aflatoxin producing molds (Tombelli et al., 2009). However, the traditional mycological methods being used to assess mold presence in commodities are time-consuming, labor-intensive, require lab facilities and mycological expertise and, above all, do not readily allow for the identification of mycotoxigenic strains. The molecular detection methods named above are costly and require trained personnel, although they have been described as more rapid, sensitive and specific as compared to microbiological methods. As an alternative technology, loop-mediated isothermal amplification (LAMP) of DNA was described as a specific, rapid, cost-effective, and easy-to-use method by Notomi et al. (2000). Only few applications of LAMP for the detection of fungal organisms have been described so far. Most recently, Niessen et al. (2013) reviewed the application of LAMP-based methods for the detection and identification of food borne bacterial pathogens and toxicants as well as mycotoxin producing food-borne fungi. The aim of the present work was to use three sets of LAMP primers previously introduced by Luo et al. (2012) for the development of turbidimeter based real-time LAMP assays specific for A. flavus, A. parasiticus and A. nomius, respectively, and to apply those new assays for the quantification of these three important aflatoxin-producing species in sample materials. 2. Materials and methods 2.1. Material 2.1.1. Preparation of spore suspensions Fungal colonies of A. flavus CBS 113.32, A. parasiticus CBS 126.62, and A. nomius CBS 260.86 were grown on MEA plates (3% (w/v) malt extract, 0.3% (w/v) soy peptone, pH 5.2) at ambient temperature under diffuse daylight until abundant sporulation occurred. Conidial suspensions were prepared using the method described by Luo et al. (2014). Conidia were harvested from plates and spun at 5 000x g for 5 min at ambient temperature. The pellet was washed twice with sterile deionized water and intermediate centrifugation under the conditions used previously. Washed conidia were resuspended in 2 ml sterile deionized water. Conidial concentrations were assessed by counting of an appropriate dilution in a Thoma type counting chamber (depth 0.1 mm). 2.1.2. Preparation of contaminated sample materials Shelled Brazil nuts, maize and peanuts with their red seed hulls (teguments) removed were ground in a coffee grinder (MKM 6003, Bosch, Germany) at maximum speed for 3 min. One gram of ground samples were inoculated with 200 ml conidial suspensions of either of the three fungi at concentrations 5  106, 5  105, 5  104, 5  103, 5  102 and 5  101 conidia/ml to result in conidial loads of 106e10 conidia/g, respectively. DNA was extracted from samples immediately after inoculation with conidia in order to prevent germination.


2.2. DNA extraction Highly purified fungal genomic DNA (gDNA) of reference strains A. flavus CBS 113.32, A. parasiticus CBS 126.62, and A. nomius CBS 260.86 was used as positive controls throughout the study. The mycelia were finely ground using the method described by Luo et al. (2012). Ground mycelia were subjected to DNA-extraction according to the method described by Niessen and Vogel (2010). A rapid extraction protocol as described in Luo et al. (2014) was used to prepare gDNA from 10-fold serial dilutions of conidia of the three reference strains involving vortexing for 10 min at maximum speed for cell disruption and boiling for 10 min. DNA extraction from samples of Brazil nuts, peanuts and maize with and without artificial inoculation with a 10-fold serial dilution of conidia of the three reference species was performed according to the CTAB method described by Alary et al. (2002) with some modifications. 5 ml of CTAB extraction buffer (20 g/l CTAB, 1.4 M NaCl, 100 mM Tris HCl, 20 mM EDTA, pH 8.0) were added to 1 g of finely ground sample in a 50 ml Falcon tube, homogenized by vortexing and treated with ultrasonic for 3 min. A sonotrode S14 connected to a UP200S ultrasonication apparatus (Dr. Hielscher, Berlin, Germany) was used at 50% intensity with maximum amplitude. Following ultrasonication, samples were incubated for 30 min in a water bath at 65  C. During incubation the tubes were mixed every 5 min by inversion. The solution was centrifuged at 15 000 g for 15 min at 20  C to pellet the solid debris followed by transfer of the supernatant to a new sterile 15 ml Falcon tube before adding an equal volume of chloroform-isoamyl alcohol (24:1). The mixture was homogenized by vortexing for 30 s and phases were separated by centrifugation at 12 000 g for 15 min at 20  C. The upper aqueous phase was transferred to a new 15 ml Falcon tube and 2 volumes of CTAB precipitation buffer (5 g/l CTAB, 40 mM NaCl, pH 8.0) were added. The mixture was homogenized and incubated at room temperature for 1 h before centrifugation at 12 000 g for 15 min at 20  C. The supernatant was discarded and the pellet was dissolved in 1 ml of 1.2 M NaCl. One milliliter of chloroform-isoamyl alcohol (24:1) was added and the mixture was homogenized by vortexing for 30 s before centrifugation at 12 000 g for 10 min at 20  C. The upper phase was transferred to a new 1.5 ml reaction tube, 0.6 vol of isopropanol at ambient temperature was added and the mixture was mixed by thoroughly inverting before centrifugation at 17 000 g for 15 min at 20  C. The DNA pellet was washed with 700 ml of 70% ethanol at 20  C, and centrifuged again and washing was repeated with 500 ml of ice cold 70% ethanol. The DNA pellet was dried under a fume cabinet and re-dissolved in 30 ml of sterile deionized water. 2.3. Real-time LAMP reaction The A. flavus ID58, A. parasiticus ID153 and A. nomius ID9 LMAP assays as described in Luo et al. (2012) were used for real-time LAMP with minor modifications. The LAMP reaction was carried out in a total 25 mL reaction volume using the mixture containing per reaction: 2.5 ml 10  LAMP buffer (200 mM MOPS, 100 mM KCl, 100 mM (NH4)2SO4, pH 8.8) (all chemicals from SigmaeAldrich, Taufkirchen, Germany), 3.5 ml dNTP mix (10 mM each dGTP, dATP, dTTP, dCTP, Fermentas, St. Leon-Rot, Germany), 1 ml 200 mM MgCl2, 2.6 ml primer mix (1.6 mM FIP, 1.6 mM BIP, 0.8 mM Loop F, 0.8 mM Loop B, 0.2 mM F3 and 0.2 mM B3, see Luo et al. (2012) for primer sequences), 1.0 ml (8 U) Bst 2.0 DNA polymerase, large fragment (8000 U/ml, New England Biolabs, Frankfurt, Germany). Three sets of LAMP primers targeting the A. flavus acl-1 gene, the A. parasiticus amy-1 gene, and the A. nomius amy-1 gene as described previously (Luo et al., 2012) were used. For the assays, 5.5% (v/v), 3.7%, 5.5% formamide was added, respectively. Sterile deionized water was added to result in a 25 ml total reaction volume including 5 ml DNA


J. Luo et al. / Food Microbiology 44 (2014) 142e148

sample. Two microliter highly purified DNA was added in the positive control reactions. Water instead of DNA was added in the negative control reactions. Reactions were incubated at a constant temperature of 65.5  C for 60 min (Afla ID58), 67  C for 80 min (Apara ID153) and 66.5  C for 60 min (Anom ID9) in a Loopamp LA320C real-time turbidimeter (EIKEN Chemical Co., LTD, Tokyo, Japan). The LA-320CE software package (EIKEN Chemical Co., LTD, Tokyo, Japan) was used for control of the turbidimeter and realtime turbidity measurement. Turbidity was monitored at 600 nm in a maximum of 32 parallel reactions in 4 independent heating blocks. Measurement was done in intervals of 6 s. A threshold of 0.05 for the first derivative of turbidity against time was set to measure time to threshold (Tt) throughout all experiments. 2.4. Evaluation of the real-time LAMP assays Sensitivity of the LAMP assays for purified gDNA has been established in Luo et al. (2012). In order to analyze the sensitivity of the assays on the turbidimeter platform, DNA extracted from 10fold serial dilutions of pure conidial suspensions of A. flavus strain CBS 113.32, A. parasiticus strain CBS 126.62 and A. nomius strain CBS 260.86 as previously described in Luo et al. (2014) was used as amplification target. Each reaction was repeated three times and the average value was used for further calculations. The reaction time after which the deviation of turbidity (OD600 nm) as a function of time (dturb./dt) reached a threshold level of 0.05 (time to threshold, Tt) was plotted against log conidial numbers used for DNA extraction. Plotting of x versus y resulted in a standard curve which was used to determine the conidial concentrations of unknown samples from their Tt value. To determine the influence of background sample DNA on the real-time LAMP assays, 5 ml of DNA extracted from non-infected peanuts, maize and Brazil nuts were mixed with the LAMP reaction mixture after 1, 2, 5 and 10-fold dilution in sterile deionized water. Concentrations of DNA extracted from peanuts, maize and Brazil nuts were 5.90 mg/ml, 1.57 mg/ml and 1.08 mg/ml, respectively. Two ml of purified DNA from reference strains with DNA concentrations of 1.6  104 mg/ml for A. flavus, 2.6  104 mg/ml for A. nomius, and 1.0  104 mg/ml for A. parasiticus, respectively, were added to the reaction mixtures containing background DNA. Samples were then measured by real-time LAMP to compare with reactions containing reference DNA with no background DNA added. Each measurement was repeated in triplicates and mean values were calculated and compared. In order to analyze correlations between Tt values and the corresponding contamination levels in sample materials, 5 ml of DNA were added which had been extracted from artificially contaminated peanuts, maize, and Brazil nuts (106e10 conidia/g in 10-fold serial dilution) immediately after inoculation using the modified CTAB method described previously. 3. Results 3.1. Quantitative analysis of conidial suspensions with real-time LAMP assays Specificity of the three sets of LAMP primers used during the current study has previously been established using calcein based indirect in-tube detection of LAMP signals. However, since conditions of the assays had to be slightly adjusted to run them in a realtime turbidimeter, specificity of the assays was re-assessed using DNA isolated from the same array of tester strains as described in Luo et al. (2012). The specificity of all three assays was fully verified as previously established. The time to threshold (Tt) method was used to qualify the signal intensity of the LAMP reaction. Turbidity

was continuously monitored and OD600 nm was plotted against time. Tt was defined as dturb./dt ¼ 0.05. In order to analyze quantitative correlations between conidal numbers and Tt for the three species, DNA extracted from solutions with defined conidial numbers for each species was used as template in a real-time LAMP using the respective primer set. Calibration curves were generated for A. flavus, A. parasiticus, and A. nomius real-time LAMP by plotting log conidia/reaction against Tt. Each conidial dilution was analyzed three times in individual experiments. As shown in Fig. 1, the calibration curve for A. flavus conidial suspensions in the real-time LAMP assay spanned a range from 10 to 105 conidia/reaction corresponding to average Tt values between 21.0 and 40.1 min. When testing suspensions of A. parasiticus (Fig. 1B) and A. nomius (Fig. 1C), conidia in a concentration range from 102 to 105 conidia/reaction were detected corresponding to Tt values from 52.7 min to 68.3 min and from 20.9 min to 28.8 min, respectively. In all assays, conidial numbers and Tt values were positively correlated with coefficients of correlation (R2) being 0.9258, 0.9352, and 0.9333 for A. flavus, A. parasiticus, and A. nomius, respectively. 3.2. Effect of background DNA on real-time LAMP assays DNA extracted from food samples usually contains excess amounts of background DNA derived from the sample matrix. In order to study the effect of background DNA on the quantification capability of the three real-time LAMP reactions, mixtures of fungal reference DNA with DNA isolated from the different matrices used during the current study were analyzed. The results given in Fig. 2 show threshold times of the three LAMP reactions obtained with different ratios between reference and background DNA. Amplification of reference DNA of A. flavus and A. nomius was obtained independently from the concentration of Brazil nut background DNA added even when the ratio of reference DNA to background DNA was 1:1  104. Addition of peanut DNA and maize DNA to A. flavus and A. parasiticus reference DNA (1:4.6  104 ratio for A. flavus DNA:background DNA, 1:1  104 ratio for A. parasiticus DNA:background DNA) revealed that no amplification occurred or that Tt values of the respective LAMP assays were considerably longer as compared to a sample with no background DNA added, respectively. Addition of 5-fold and 10-fold dilutions of peanut and maize background DNA showed negligible inhibition of the realtime LAMP assays for A. flavus and A. parasiticus, respectively. No cross reactions occurred in none of the three LAMP reactions when pure background DNA was added instead of template DNA. 3.3. Estimation of infection levels in artificially infected samples In order to evaluate the capacity of the turbidimetric real-time LAMP methods for detection of three aflatoxigenic molds in artificially infected commodities, Brazil nuts, peanuts, and maize were inoculated with conidial solutions of A. flavus, A. parasiticus, and A. nomius, respectively. Solutions contained a serial 10-fold dilution of conidia resulting in concentrations ranging from 1  106 to 10 conidia per gram of sample. Conidia were added immediately after sample grinding and mixed well with the samples by vortexing for a few seconds. DNA was extracted immediately after mixing in order to prevent spore germination. Disruption of conidia was accomplished using ultrasonication and total DNA was extracted using CTAB precipitation of DNA. The results of real-time LAMP done with the total DNA isolated from inoculated samples confirmed the detection limits found for pure spore solutions in all tested commodities. In Brazil nut samples, LAMP assays for A. flavus and A. nomius showed a detection limit of 10 conidia/g, respectively. When samples of peanuts and maize were analyzed, the real-time LAMP assay for A. flavus had detection limits of 102 conidia/g and

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Fig. 1. Linear correlation between spore numbers (log per reaction) and Tt (min) in real-time LAMP assays for A. flavus, A. parasiticus, and A. nomius. A. Correlation for A. flavus specific real-time LAMP and spore numbers ranging from 10 to 105 spores/reaction; B. Correlation for A. parasiticus real-time LAMP and spore numbers ranging from 102 to 105 spores/reaction; C. Correlation for A. nomius LAMP and spore numbers ranging from 102 to 105 spores/reaction.

104 conidia/g in peanuts and maize, respectively. In the same commodities, the real-time LAMP assay specific for A. parasiticus had detection limits of 105 conidia/g and 104 conidia/g in peanuts and in maize, respectively (see Table 1). 4. Discussion In the present study, we used our previously designed LAMP primers (Luo et al., 2012) to specifically target the amy-1 or acl-1 genes in three common aflatoxin producing fungi, i.e. A. flavus, A. nomius, and A. parasiticus. We developed these LAMP assays

further by running them on a turbidity-based real-time platform in order to quantitatively detect these three aspergilli in pure conidial solutions and in artificially contaminated samples of Brazil nut, peanut and maize. This is the first report examining the quantitative capability of real-time LAMP for detecting A. flavus, A. nomius and A. parasiticus in samples of food commodities. Among a total of 39 Aspergillus spp. including three target fungi in these LAMP assays and 135 other species as described in previous research (Luo et al., 2012), the level of specificity of the real-time LAMP assays was the same as that of the respective conventional LAMP assays reported earlier.

Fig. 2. Influence of background DNA from different sample matrices on the performance of real-time LAMP assays for A. flavus, A. parasiticus, and A. nomius. Time to threshold (Tt (min), mean values from three repetitions with error bar) of LAMP assays performed with constant concentrations of the respective target DNA and addition of 1e10-fold diluted DNA extracted from uninfected samples of three different commodities are compared to untreated control reactions. Signals with Tt values exceeding 90 min were defined as negative. The concentrations of A. flavus, A. parasiticus, and A. nomius DNA were 1.6  104 mg/ml, 1  104 mg/ml and 2.6  104 mg/ml, respectively. The original concentrations of DNA from Brazil nut, peanut and maize were 1.08 mg/ml, 5.9 mg/ml and 1.57 mg/ml, respectively.


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Table 1 Limits of detection (spores/g) of three real-time LAMP assays for spores of A. flavus, A. parasiticus, and A. nomius in different commodities after inoculation with spores of the respective fungal species. Commodity

Inoculated mold strain

Detection limit (spores/g)

Brazil nut

Aspergillus nomius CBS 260.86 A. flavus CBS 113.32 A. flavus CBS 113.32 A. parasiticus CBS 126.62 A. flavus CBS 113.32 A. parasiticus CBS 126.62

10 10 102 105 104 104

Peanut Maize

With regard to sensitivity of the real-time LAMP assays, the developed method was able to detect conidia previously harvested from pure cultures of A. flavus, A. nomius and A. parasiticus, respectively, with high sensitivity. Assays had detection limits of 10, 102 and 102 conidia/reaction for A. flavus, A. nomius, and A. parasiticus, respectively. The method used during the current study for preparation of template DNA from pure culture conidia involved treatment with a bead-beater for cell disruption and release of DNA from conidia (Luo et al., 2014). All assays applied were sensitive enough to detect target DNA directly from the treated solution without any further manipulations for DNA extraction, purification or concentration. Comparison of results obtained with the real-time LAMP assays and the corresponding conventional LAMP assays showed that the sensitivity for detection of A. flavus increased about 10-fold while sensitivity of the A. nomius assay was 10-fold lower in the real-time assay. The sensitivity of the A. parasiticus specific real-time LAMP assay was comparable to the conventional LAMP assay for that species. The coefficients of correlation between conidial numbers used as template in the real-time LAMP reaction and the corresponding Tt values over a range of 105e101 conidia/reaction were found to be in a range of R2 ¼ 0.92e0.94 indicating a high degree of relatedness between parameters. This illustrates the quantitative capability of the real-time LAMP assays when detecting their respective target organism in pure cultures or in conidial solutions. Very few reports have examined the quantitative ability of LAMP for the detection of fungi. Recently, Denschlag et al. (2013) published results of a study in which real-time LAMP was applied to monitor gushing-inducing Fusarium spp. using fluorescence and turbidity based LAMP assays. The authors reported good quantitative capabilities with a range of quantification between 2.17  106 and 695 copies of the hyd5 target gene per reaction. Studying the influence of matrix or background DNA on the effectiveness of real-time PCR, Mayer et al. (2003) reported that during analysis of samples containing low concentrations of fungal DNA, high concentrations of unspecific matrix DNA may act as an inhibitor of DNA amplification, apparently by competition between specific primer binding to target DNA and unspecific binding to non-target DNA resulting in a decrease of primer concentrations accessible to specific amplification. During the current study we tested for the presence of inhibitory effects of background DNA by parallel amplification of pure DNA of A. flavus, A. nomius or A. parasiticus in a mix with different dilutions of DNA extracted from uninfected commodities. No influence of DNA and other food components co-extracted from Brazil nut on the sensitivity of the method was observed, while addition of DNA from peanut and maize resulted in gradual inhibition of the LAMP reactions. In order to minimize inhibition of amplification during analysis of peanut and maize samples, sample DNA was diluted 5 fold prior to addition to the real-time LAMP assays. According to calculations, there was an inhibition when the amount of background DNA from peanuts was 3  104 fold in excess of target DNA,

while inhibition was found for the DNA from maize when the background DNA was 8  104 fold in excess. In the literature, an inhibitive effect on amplification efficiency of DNA polymerase was also described when high amounts of DNA from fresh figs were present during PCR-based detection of aflatoxigenic molds (F€ arber et al., 1997). Possible causes for this inhibitory effect may either be the amplification of non-target DNA which competes with amplification of the specific product or direct inhibition of DNA polymerase by compounds co-extracted during DNA preparation. The real-time LAMP assays developed during the current study were designed to detect fungal contamination at a level at which consumers might encounter increased risk of aflatoxin contamination in commodities. Levels of fungal contamination above which a consumer risk may be present have previously been analyzed in a Swedish study (Olsen et al., 1998) but can be widely generalized. From their results, authors concluded that there was a considerable risk of exceeding the Swedish and EU maximum limit for aflatoxins (4 mg/kg) at a level of A. flavus/parasiticus above 100 cfu/g of commodity. Moreover, the probability for total aflatoxin levels to exceed the EU legislative limit of 4 mg/kg increased rapidly from approx. 30% to above 80% when mold levels increased from 100 to 1000 cfu/g (Johnsson et al., 2008). Therefore, systems for early detection of aflatoxin-producing species are critical to have in order to prevent aflatoxins from entering the food chain. However, it seems mandatory that such systems should enable detection of target fungi at the levels discussed in previous studies. We described a real-time LAMP method for detection of A. flavus, A. nomius and A. parasiticus, respectively in artificially infected Brazil nut, peanut and maize representing some of the typical commodities which are prone to elevated levels of aflatoxin. Usefulness of the primer sets also for detection of natural contaminations has recently been shown for the A. flavus and A. nomius specific primers which were applied to the analysis of Brazil nut samples (Luo et al., 2014). Results obtained during the current study showed that the sensitivity of LAMP assays was lower when peanuts and maize were analyzed which was mainly due to the fact that DNA had to be diluted before being used as template in realtime LAMP. In general, comparison of the detection limits found for conidia of the three Aspergillus spp. with the cfu levels discussed previously (Johnsson et al., 2008) shows that assays for A. flavus and A. nomius in Brazil nut fully meet the suggested criteria. It can be concluded that a negative result with both assays in Brazil nut samples implies a safe product with very low potential to contain aflatoxins. The same holds true for detection limits of A. flavus in peanuts. However, limits of detection of A. parasiticus conidia in peanuts as well as for all species in maize samples did not meet the mentioned criteria which means that consumer risk cannot be ruled out even in samples with negative results in the real-time LAMP assays. In order to considerably lower the detection levels for assays applied to the analysis of maize, a more sophisticated protocol for DNA preparation must be elaborated in future studies in order to meet criteria of safety. According to Shapira et al. (1996) aflatoxin concentration can be correlated with the level of cfu of aflatoxigenic species detected on naturally contaminated samples. Although Gunterus et al. (2007) reported that no correlation was observed between the inoculum level and the level of aflatoxin accumulation in peanuts, the possibility to quantify contamination levels in food matrices is essential since previous studies have demonstrated that levels of mycotoxigenic fungi can be related with mycotoxin concentrations that exceed legal limits (Lund and Frisvad, 2003). However, the quantification of fungal contamination and its correlation to quality parameters such like mycotoxin contamination is a challenging task because of the nature of the fungal colony. Fungal colonies

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consist of filamentous multi-cellular mycelia as well as conidia consisting of one to few cells. Especially in genera such like Aspergillus and Penicillium, great bias exists between numbers of cfu detected after plating and the biomass actually present in a sample. Also during the current study a difference of up to 2 log was found between spore numbers added after counting in a Thoma type counting chamber and cfu values recovered by plating of samples directly after inoculation (data not shown). Moreover, since mycotoxins are produced by the vegetative mycelia of the fungus rather than by its spores or conidia, similar disproportionality exists between cfu found in a sample and the actual production of mycotoxins and their respective concentrations in many species. This may also explain why mycotoxin concentrations and real-time PCR or real-time LAMP results have been found to be rather highly correlated in samples contaminated with species of Fusarium because their level of sporulation is rather low as compared to Aspergillus spp. In Fusarium spp. this effect will presumably result in a more balanced relation between hyphal and spore biomass. Realtime PCR has been established as a method often used for prediction of the potential aflatoxinogenic risk in plant derived food such as maize, pepper, and paprika (Mayer et al., 2003; Mideros et al., ~ as et al. 2009) or peanuts (Passone et al., 2010). Recently, Sardin (2011) applied real-time PCR for the quantitative measurement of A. flavus and A. parasiticus with a detection limit at spore concentrations equal or higher than 106 conidia/g in flour samples. Comparing the detection limits of the LAMP assays given in Table 1 of the current study with the detection limit given for the recently published qPCR assay of Rodríguez et al. (2012a) and Rodríguez et al. (2012b), the A. flavus specific LAMP assay had comparable sensitivity only when the fungus was analyzed in Brazil nuts (LOD ¼ 10 spores/g). In peanuts, the LAMP assay detected a limit of 100 spores per gram which is a 10-fold lower sensitivity but would still suffice to detect samples with elevated risk of aflatoxin contamination according to Johnsson et al. (2008). In maize however, detection limits of LAMP assays for both A. flavus and A. parasiticus were comparably low. In that matrix detection limits would not be sufficiently low to readily detect samples harboring the risk of aflatoxin contamination. Moreover, detection limits for the A. parasiticus specific LAMP was generally lower as compared to the qPCR assays based on aflatoxin biosynthetic genes (Rodríguez et al., 2012a). However, taking into account that all LAMP assays were run with minimum effort in DNA extraction and cleanup, total time needed to complete the LAMP analysis was only 2.5 h using very simple lab equipment. Sensitivity of the LAMP assays described here could considerably be improved by addition of a DNA cleanup step during sample preparation and can be assumed to be similar to that of qPCR assays. However, this would nullify speed of analysis and ease of application as the major advantages of the LAMP method. The current study describes an alternative rapid and reliable method for the detection of the aflatoxigenic species A. flavus, A. nomius and A. parasiticus in order to estimate contamination levels in commodities such like Brazil nuts, peanuts or maize. The assays have a potential to improve fungal diagnosis at critical control points integrated in HACCP strategies in the food industry. In addition, these assays will be useful to improve knowledge about these species in relation to ecophysiological factors, distribution or host/matrix preferences in order to improve strategies to prevent and control fungal colonization and aflatoxin risks in food commodities. References Adams, M., Motarjemi, Y., 1999. World Health Organization WHO/SDE/PHE/FOS/ 99.1.


Alary, R., Serin, A., Maury, D., Ben Jouira, H., Sirven, J.P., Gautier, M.F., Joudrier, P., 2002. Comparison of simplex and duplex real-time PCR for the quantification of GMO in maize and soybean. Food Control 13, 235e244. ldez, V., Rodríguez, A., Martín, A., Lozano, D., Co rdoba, J.J., 2014. Development Berna of a multiplex qPCR method for simultaneous quantification in dry-cured ham of an antifungal-peptide Penicillium chrysogenum strain used as protective culture and aflatoxin-producing moulds. Food Control 36, 257e265. Calderari, T.O., Iamanaka, B.T., Frisvad, J.C., Pitt, J.I., Sartori, D., Pereira, J.L., Pelegrinelli Fungaro, M.H., Taniwaki, M.H., 2013. The biodiversity of Aspergillus section Flavi in Brazil nuts: from rainforest to consumer. Int. J. Food Microbiol. 160, 267e272. Cary, J.W., Klich, M.A., Beltz, S.B., 2005. Characterization of aflatoxin-producing fungi outside of Aspergillus section Flavi. Mycologia 97, 425e432. Denschlag, C., Vogel, R.F., Niessen, L., 2013. Hyd5 gene based analysis of cereals and malt for gushing-inducing Fusarium spp. by real-time LAMP using fluorescence and turbidity measurements. Int. J. Food Microbiol. 162, 245e251. €rber, P., Geisen, R., Holyapfel, W.H., 1997. Detection of aflatoxigenic fungi in figs by Fa PCR reaction. Int. J. Food Microbiol. 36, 215e220. Gunterus, A., Roze, L.V., Beaudry, R., Linz, J.E., 2007. Ethylene inhibits aflatoxin biosynthesis in Aspergillus parasiticus grown on peanuts. Food Microbiol. 24, 658e663. Haugland, R.A., Brinkman, N., Vesper, S.J., 2002. Evaluation of rapid DNA extraction methods for the quantitative detection of fungi using real-time PCR analysis. J. Microbiol. Methods 50, 319e323. IARC, 1993. International Agency for Research on Cancer: Monograph on the Evaluation of Carcinogenic Risk to Human, vol. 56, pp. 257e263. Lyon, France. Johnsson, P., Lindblad, M., Thim, A.M., Jonsson, N., Vargas, E.A., Medeiros, N.L., Brabet, C., Olsen, M., 2008. Growth of aflatoxigenic moulds and aflatoxin formation in Brazil nuts. World Mycotoxin J. 1, 127e137. Kumar, V., Basu, M.S., Rajendran, T.P., 2008. Crop Prot. 27, 891e905. Lund, F., Frisvad, J.C., 2003. Penicillium verrucosum in wheat and barley indicates presence of ochratoxin A. J. Appl. Microbiol. 95, 1117e1123. Luo, J., Vogel, R.F., Niessen, L., 2012. Development and application of a loopmediated isothermal amplification assay for rapid identification of aflatoxigenic molds and their detection in food samples. Int. J. Food Microbiol. 159, 214e224. Luo, J., Taniwaki, M.H., Iaman, B.T., Vogel, R.F., Niessen, L., 2014. Application of loopmediated isothermal amplification assays for direct identification of pure cultures of Aspergillus flavus, A. nomius, and A. caelatus and for their rapid detection in shelled Brazil nuts. Int. J. Food Microbiol. 172, 5e12. Mayer, Z., Bagnara, A., F€ arber, P., Geisen, R., 2003. Quantification of the copy number of nor-1, a gene of the aflatoxin biosynthetic pathway by real-time PCR, and its correlation to the cfu of Aspergillus flavus in foods. Int. J. Food Microbiol. 82, 143e151. Mideros, S.X., Windham, G.L., Williams, W.P., Nelson, R.J., 2009. Aspergillus flavus biomass in maize estimated by quantitative real-time polymerase chain reaction is strongly correlated with aflatoxin concentration. Plant Dis. 93, 1163e1170. Niessen, L., Vogel, R.F., 2010. Detection of Fusarium graminearum DNA using a loop-mediated isothermal amplification (LAMP) assay. Int. J. Food Microbiol. 140, 183e191. Niessen, L., Luo, J., Denschlag, C., Vogel, R.F., 2013. The application of loop-mediated isothermal amplification (LAMP) in food testing for bacterial pathogens and fungal contaminants. Food Microbiol. 36, 191e206. Notermans, S., Heuvelman, C.J., Beumer, R.R., Maas, R., 1986. Immunological detection of moulds in food: relation between antigen production and growth. Int. J. Food Microbiol. 3, 253e261. Notomi, T., Okayama, H., Masubuchi, H., Yonekawa, T., Watanabe, K., Amino, N., Hase, T., 2000. Loop-mediated isothermal amplification of DNA. Nucleic Acids Res. 28, e63. € ller, T., Thuvander, A., Salmonsson, A.-C., 1998. Mo €gelgifter-aflatoxiner Olsen, M., Mo €gelsvampar i kryddor-en riskva €rdering. V€ € da och aflatocinproducerande mo ar Fo 4, 33e35 (In Swedish). Olsen, M., Johnsson, P., Moeller, T., Paladino, R., Lindblad, M., 2008. Aspergillus nomius, an important aflatoxin producer in Brazil nuts? World Mycotoxin J. 1, 123e126. Passone, M.A., Rosso, L.C., Ciancio, A., Etcheverry, M., 2010. Detection and quantification of Aspergillus section Flavi spp. in stored peanuts by real-time PCR of nor-1 gene, and effects of storage conditions on aflatoxin production. Int. J. Food Microbiol. 138, 276e281. Pildain, M.B., Frisvad, J.C., Vaamonde, G., Cabral, D., Varga, J., Samson, R.A., 2008. Two novel aflatoxin producing Aspergillus species from Argentinean peanuts. Int. J. Syst. Evol. Microbiol. 58, 725e735. Pitt, J.I., Hocking, A.A.D., 2009. Fungi and Food Spoilage. Academic Press, Sydney, Australia, pp. 143e167.  rdoba, J.J., 2012a. Development of a Rodríguez, A., Rodríguez, M., Andrade, M.J., Co multiplex real-time PCR to quantify aflatoxin, ochratoxin A and patulin producing molds in foods. Int. J. Food Microbiol. 155, 10e18. ~ ez, F., Cordoba, J.J., 2012b. Evaluation of Rodríguez, A., Rodríguez, M., Martin, A., Nun hazard of aflatoxin B1, Ochratoxin A and patulin production in dry-cured ham and early detection of production of moulds by qPCR. Food Control 27, 118e126. ~ as, N., Va zquez, C., Gil-Serna, J., Gonz n, M.T., Patin ~ o, B., 2011. Specific Sardin alez-Jae detection and quantification of Aspergillus flavus and Aspergillus parasiticus in ® wheat flour by SYBR Green quantitative PCR. Int. J. Food Microbiol. 145, 121e125.


J. Luo et al. / Food Microbiology 44 (2014) 142e148

Shapira, R., Paster, N., Eyal, O., Menasherov, M., Mett, A., Salomon, R., 1996. Detection of aflatoxigenic molds in grains by PCR. Appl. Environ. Microbiol. 62, 3270e3273. Suanthie, Y., Cousin, M.A., Woloshuk, C.P., 2009. Multiplex real-time PCR for detection and quantification of mycotoxigenic Aspergillus, Penicillium and Fusarium. J. Stored Prod. Res. 45, 139e145. Tombelli, S., Mascini, M., Scherm, B., Battacone, G., Migheli, Q., 2009. DNA biosensors for the detection of aflatoxin producing Aspergillus flavus and A. parasiticus. Mon. Chem. Chem. Mon. 140, 901e907. Turner, P., Sylla, A., Gong, Y., Diallo, M., Sutcliffe, A., Hall, A., Wild, C., 2005. Reduction in exposure to carcinogenic aflatoxins by postharvest intervention

measures in west Africa: a community-based intervention study. Lancet 365, 1950e1959. Valasek, M.A., Repa, J.J., 2005. The power of real-time PCR. Adv. Physiol. Educ. 29, 151e159. van Egmond, H.P., Jonker, M.A., 2004. Worldwide Regulations for Mycotoxins in Food and Feed in 2003. Food and Agriculture Organization of the United Nations, Rome, p. 165. Varga, J., Frisvad, J.C., Samson, R.A., 2009. A reappraisal of fungi producing aflatoxins. World Mycotoxin J. 2 (3), 263e277.

Rapid detection of aflatoxin producing fungi in food by real-time quantitative loop-mediated isothermal amplification.

Aflatoxins represent a serious risk for human and animal health. They are mainly produced by Aspergillus flavus and Aspergillus parasiticus but also b...
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