Editor: Bernard Paul
Development of a qPCR assay for specific quantification of Botrytis cinerea on grapes
Version of Record online: 14 OCT 2010
© 2010 Federation of European Microbiological Societies. Published by Blackwell Publishing Ltd. All rights reserved
FEMS Microbiology Letters
Volume 313, Issue 1, pages 81–87, December 2010
How to Cite
Diguta, C. F., Rousseaux, S., Weidmann, S., Bretin, N., Vincent, B., Guilloux-Benatier, M. and Alexandre, H. (2010), Development of a qPCR assay for specific quantification of Botrytis cinerea on grapes. FEMS Microbiology Letters, 313: 81–87. doi: 10.1111/j.1574-6968.2010.02127.x
- Issue online: 9 NOV 2010
- Version of Record online: 14 OCT 2010
- Accepted manuscript online: 27 SEP 2010 12:00AM EST
- Received 5 July 2010; revised 16 September 2010; accepted 16 September 2010.Final version published online 14 October 2010.
- Botrytis cinerea;
The aim of this study was to develop a system for rapid and accurate real-time quantitative PCR (qPCR) identification and quantification of Botrytis cinerea, one of the major pathogens present on grapes. The intergenic spacer (IGS) region of the nuclear ribosomal DNA was used to specifically detect and quantify B. cinerea. A standard curve was established to quantify this fungus. The qPCR reaction was based on the simultaneous detection of a specific IGS sequence and also contained an internal amplification control to compensate for variations in DNA extraction and the various compounds from grapes that inhibit PCR. In these conditions, the assay had high efficiency (97%), and the limit of detection was estimated to be 6.3 pg DNA (corresponding to 540 spores). Our method was applied to assess the effects of various treatment strategies against Botrytis in the vineyard. Our qPCR assay proved to be rapid, selective and sensitive and may be used to monitor Botrytis infection in vineyards.
Many fungal and bacterial organisms, of which Botrytis cinerea is the most important, can infect grapes and cause a ‘bunch rot’ (Keller et al., 2003). The disease caused by B. cinerea, also known as ‘grey mould’, is arguably the most significant disease problem confronting the wine industry worldwide. The presence of grey mould on grapes is undesirable, as it lowers the quality of wines. Depending on the vintage, fungal infection rates can reach 15–25% of grapes, and wines prepared from infected grapes usually exhibit organoleptic defects, such as colour oxidation or the appearance of typical aromatic notes (‘moldy’, ‘rotten’), which are not appreciated by consumers (Cilindre et al., 2007).
The colour of red and white wines is affected, with the main acids such as tartaric and malic acid degraded together with aromatic compounds. Concerning the colour, the fungus B. cinerea can attack the grape berry and introduce the oxidative enzyme laccase into the berry and hence into grape juice. Laccase targets phenolics such as the red colour compounds in red wine and oxidizes them into brown-coloured compounds. Furthermore, the association of B. cinerea with other, less visible, fungi frequently leads to the development of organoleptic defects in grapes and sometimes in wines (La Guerche et al., 2006).
The strategy most widely adopted by winegrowers to reduce the impact of grey mould is the systematic application of chemical fungicides, based on a preset calendar that takes into account the phenological growth stages of the grapevine. This reduction policy will have an impact on Botrytis resistance to fungicides (Leroux, 2004) and on the environment. Indeed, the contamination of agricultural soils with inorganic (Cu-based) and organic pesticides (including their residues) presents a major environmental and toxicological concern (Komárek et al., 2010). Although there are alternative methods to synthetic fungicides, such as the application of antagonistic microorganisms and the application of natural antimicrobial substances, it is essential to monitor the disease development and particularly the concentration of fungal spores. Indeed, monitoring disease development will allow better disease management, and will reduce cost and improve grape quality.
Spores can be identified and quantified by light microscopy (Aylor, 1998; Hunter et al., 1999). However, this is not straightforward. Indeed, it is a time-consuming technique that needs expertise for the accurate identification of spores. Antibody immunoassays have been used for the early detection of B. cinerea (Kennedy et al., 2000). However, taking into account the low sensitivity and the limited dynamic range of the method, it is not well adapted for quantification, although it can be used to confirm the nature of the agent (Suarez et al., 2005). Molecular techniques for the identification of spores have already been published (West et al., 2008), most of which are based on detection by standard PCR methods (Zhou et al., 2000; Calderon et al., 2002; Chew et al., 2006). However, under these conditions, quantification is not precise. One way to assess for the presence of specific spores more accurately and to avoid some of the problems that accompany the other methodologies is real-time quantitative PCR (qPCR). Numerous quantitative assays utilizing real-time PCR have been developed to specifically detect microbial targets in many types of samples, including, but not limited to, moulds (Alaei et al., 2009; Carisse et al., 2009; Luo et al., 2010).
Advantages of utilizing qPCR for spore enumeration over classic culture-based methods include its enhanced specificity and reduced processing time, leading to quicker results. Cadle-Davidson (2008) reported a qPCR method based on Taqman chemistry for monitoring B. cinerea infection. However, this protocol uses a long freezing assay protocol and does not include internal control. Celik et al. (2009) have also developed a quantitative analysis of Botrytis by qPCR but only on artificially contaminated table grapes. We developed a quantitative assay for the enumeration of B. cinerea utilizing the fluorescent dye SYBR Green I and PCR primers designed to specifically target B. cinerea DNA. This method was then applied to assess different control strategies against Botrytis in vineyards.
Materials and methods
Strains and culture conditions
Various fungal strains were used in this study: Aspergillus carbonarius MUCL 44624, B. cinerea MUCL 28920, Cladiosporium cladiosporoides MUCL 30838, Fusarium oxysporum MUCL 792, Penicillium crustosum MUCL 14155, Penicillium expansum MUCL 29192, Penicillium minioluteum MUCL 28666, Penicillium spinulosum MUCL 13911, Penicillium thomii MUCL 31204 and Trichoderma harzianum MUCL 29707. All fungi were grown on potato dextrose agar (PDA, Difco, Fisher Bioblock Scientific, Illkirch, France) dishes at 25 °C and maintained by a monthly transfer of mycelia plugs onto fresh dishes.
Two yeasts were also used: Saccharomyces cerevisiae BM 45 (Lallemand SA, Blagnac, France) as a reference strain, and Yarrowia lipolytica W29 (ATCC 20460), a strain found in soil, as internal control in qPCR assays. These two yeasts were maintained and grown on yeast peptone dextrose (YPD) medium at 28 °C for 24–72 h.
Samples and washing of grapes
Grape samples (Pinot noir grape variety) were collected at technological maturity from vineyards of the Burgundy area. A total of 14 control strategies against B. cinerea with different combinations of fungicides were applied in vineyards. Fungicide applications were performed at various phenological stages of vine: after flowering, at bunch closure, 10 days after bunch closure and at the beginning of veraison (colour change), corresponding to stages I, L, L+10 and M, respectively, on the international Baggiolini scale (Table 1). For each plot, several bunches of grapes were cut at random using shears sterilized with ethanol. The bunches were collected in sterilized plastic bags without any hand contact and placed in a cooler at 4 °C until laboratory analysis (2–4 h after harvest). Each field trial was realized in triplicate: the 200 berries sampled were an average sample.
|Treatment||Timing of treatment|
|Stage F (flowering)||Stage L (bunch closure)||Stage L+10 (bunch closure+10 days)||Stage M (veraison)|
|AB6||Fenhexamid||Thinning out of leaves (defoliation)||Pyrimethanil|
|AB8||Fenhexamid||Bacillus subtilis||Bacillus subtilis|
|AB13||Boscalid||Bacillus subtilis||Bacillus subtilis‡|
|AB14||Bentonite clay||Bentonite clay|
Spores and/or mycelium were released from the surface of berries as per a previously described protocol (Doaré-Lebrun et al., 2006; Laforguéet al., 2009) using the following solution: 200 mL sterile distilled water containing 0.9% (w/v) NaCl and 0.2% (v/v) Tween 80 to wash 200 berries. This mix was sonicated for 1 min and then shaken for 30 min to put the microorganisms in suspension. The washing suspension took place in sterilized flasks at 4 °C before use. Botrytis populations ranging between 2 × 106 and 1.6 × 104 CFU per 200 berries in function of different strategies were recovered by direct plating.
Preparation of spores of B. cinerea for the qPCR standard curve
To prepare the standard curve, B. cinerea strains were grown on PDA at 25 °C for 2 weeks and collected from the agar plate using sterile distilled water containing 0.05% (w/v) Tween 80. The number of spores was counted under a light microscope at × 400 magnification. A working solution of 107 spores mL−1 was generated and stored at 4 °C.
Spore concentrations between 102 and 107 mL−1 were obtained by 10-fold serial dilutions. DNA was extracted and used to generate a spore standard curve by qPCR.
Internal control for DNA extraction and amplification
An internal control was included in the assay by adding 8 × 106 CFU of the yeast Y. lipolytica to 2 mL of washing solution of grape as described before (Tessonniere et al., 2009). The yeast was added to the sample before DNA extraction to ensure that controls for DNA preparation and PCR amplification were available.
To prepare the cell standard curve, Yarowia lipolitica was grown on YPD (yeast extract 0.5% w/v, peptone 1% w/v, dextrose 2% wv) at 28 °C at 140 r.p.m. After 48 h of incubation, a working solution of 1010 CFU mL−1 was generated and cell suspension concentrations ranging from 101 to 108 mL−1 were obtained by 10-fold serial dilutions. DNA was extracted and used to generate a cell standard curve by qPCR.
DNA extraction from B. cinerea spores, Y. lipolitica cells and washing suspension was performed using a fungal DNA kit (EZNA®, Omega-Biotek). In detail, 2 mL of spore or cell solutions or 2 mL of the washing solution were centrifuged at 10 000 g for 20 min. The pellet was incubated with 600 μL Buffer FG1 and 5 μL RNase (20 mg mL−1) for 1 min. 2-mercaptoethanol (10 μL) was added and the mix was incubated at 65 °C for at least 5 min. Then 140 μL Buffer FG2 was added and the mix was incubated on ice for 5 min. After a centrifugation at 10 000 g for 10 min, the supernatant was transferred and 1/2 volume of Buffer FG3 and 1 volume of absolute ethanol were added. The following steps implies DNA cleanup through Hi-bond®spin column. In the final step, DNA was eluted in 100 μL of deionized water.
Real-time PCR amplification
Specific B. cinerea primers targeting the ribosomal region between 28S and 18S genes (intergenic spacer) reported by Suarez et al. (2005) were used: Bc3F (5′-GCTGTAATTTCAATGTGCAGAATCC-3′) and Bc3R (5′-GGAGCAACAATTAATCGCATTTC-3′). Yarrowia lipolytica-specific primers YALF (5′-ACGCATCTGATCCCTACCAAGG-3′) and YALR (5′-CATCCTGTCGCTCTTCCAGGTT-3′), were selected from the LIP4 gene (AJ549517) and were used to amplify a 106-bp fragment (Tessonniere et al., 2009). All primers were purchased from Invitrogen (Cergy, France).
The DNA sample (5 μL) was mixed in a final volume of 25 μL with 10 ×B. cinerea or Y. lipolytica primer mixture containing 0.56 μM of either, 2 × IQ™SYBR Green supermix (Bio-Rad, Marnes-la-coquette, France) or water. Reactions were performed in a Biorad iQ5 real-time PCR iCycler apparatus. We used a program of: 3 min at 95 °C, followed by 40 cycles of 15 s at 95 °C and 30 s at 62 °C. A melting curve was established by decreasing the temperature from 90 °C by 0.5 °C every 10 s. All reactions were performed in triplicate. The cycle threshold (Ct), or the PCR cycle where fluorescence first occurred, was determined automatically using bio-rad software after setting the baseline to 100. The efficiency (E) of the PCR assay was calculated using the formula, E=[10−1/slope−1] × 100, where the slope was extracted from the curve Ct=f(log Q0) and Q0 is the initial DNA or cell population in the assay. E was expressed as percentage.
All values are expressed as the mean ± SD. All data were analysed using sigmastat 3.0 statistical software from Systat Inc. Differences between groups were analysed by one-way anova. Post hoc comparisons were conducted using the Holm-Sidak comparison test as suggested by Zar (1996). A P value ≤0.001 or 0.05 was considered to be statistically significant.
The specificity of the primers Bc3F and Bc3R was studied by conventional PCR using B. cinerea MUCL 28920 and other genera and species of fungi potentially present on grapes. A single fragment of about 95 bp was amplified from B. cinerea genomic DNA. No product was observed with genomic DNA from isolates of the other species tested (data not shown).
Specific primers for the LIP4 gene were used as described in a previous study (Tessonniere et al., 2009), in which primers were already tested against Brettanomyces but not against fungi. So, in our study, the specificity of LIP4 primers was checked against a number of genera and species of different fungi from various origins. Apart from Yarrowia lypolitica, no amplification was observed for the tested microorganisms (data not shown).
Genomic DNA obtained from B. cinerea MUCL 28920 was used as a template for qPCR with primers Bc3F and Bc3R. As expected, the PCR product melting temperature was 83 ± 0.5 °C. The standard curve generated with the Bc3F/Bc3R pair in the conditions described above is shown in Fig. 1. The standard curve for B. cinerea was generated by plotting the log of DNA (pg) against the Ct value determined by qPCR. Linearity was observed across the whole range used and the very high correlation coefficient (R2=0.99) indicated very low interassay variability. The slope of the standard curve was −3.39, which corresponds to an amplification efficiency of 97%.
The limit of detection was defined as the lowest population of the microorganisms that could be detected using our SYBR Green qPCR method. Under conditions that include SYBR Green, the maximum Ct value that could be used was 30, which corresponds to a DNA concentration of 6.3 pg.
Yarrowia lypolitica genomic DNA extracted from 10-fold serial dilutions of Y. lypolitica cells ranging from 8 × 103 to 8 × 107 cells mL−1 was used as a template. Ct values were plotted against the logarithm of cell concentration. Under these conditions, PCR efficiency was 93% with a correlation coefficient of 0.99. The Tm of the product was 85 ± 0.5 °C (Fig. 2).
Internal control for the detection and quantification of B. cinerea on grapes
To obtain an accurate estimate of the target molecules in the grape sample, different controls were needed: controls to test the efficiency of the PCR itself (PCR positive control) and controls for the effect of the grape matrix, which includes natural inhibitory compounds, on the recovery of DNA from the B. cinerea, as well as its effects on PCR. To achieve these goals, 2-mL samples were spiked with 8 × 106 cells of Y. lypolitica, a microorganism that is absent from grapes before nucleic acid extraction. The LIP4 gene from Y. lipolytica was used as an internal control. From the calibration curve of Y. lypolitica obtained previously, DNA extracted from 8 × 106 CFU per 2 mL of the yeast Y. lypolitica yielded a Ct of 29.4 ± 0.631.
We used this Ct value as a normalizer for the quantification of B. cinerea DNA concentration on grapes. Ct values obtained from B. cinerea were normalized according to the following equation:
The resultant Ct values were converted into DNA concentrations by extrapolation to a standard curve generated from qPCR analysis using 10-fold dilutions of between 102 and 106 pg B. cinerea DNA (Fig. 1).
Application of the B. cinerea quantification method to assess the effects of various anti-Botrytis treatments on vine
A total of 14 strategies, which included various fungicide treatments for controlling B. cinerea, were applied to grapes at different growing stages: flowering, bunch closure, 10 days after bunch closure and veraison (colour change) (Table 1). In each experimental plot, microbial communities on grape berries were assessed at harvest. Our qPCR method was used to assess the level of B. cinerea contamination in each treatment (spore and mycelium). The DNA concentration of B. cinerea present in each sample (200 berries) for each strategy is given Fig. 3. The type of treatment had a clear impact on B. cinerea contamination. In our case, the best strategy appeared to be AB6, which led to a significant decrease in B. cinerea contamination. This treatment used at least two chemical products during grape development with thinning out of leaves. This prophylactic method increases the efficiency of the treatment strategy as compared with AB5, in which the same chemical product was used (fenhexamid and pyrimethanil) but without thinning out of leaves. Nevertheless, the AB10 treatment, in which only one chemical product was used, also appeared to be efficient, i.e. a low level of B. cinerea DNA was detected. The low significant level of B. cinerea DNA concentration observed for strategy AB8 demonstrated that the association of a chemical product together with Bacillus subtilis improves anti-Botrytis treatment. Our trial underlined that bentonite clay (AB14) did not protect grapes from B. cinerea contamination.
We developed a highly specific and sensitive qPCR protocol for the detection and quantification of B. cinerea contamination in grapes. This method was developed to serve as an alternative to the various conventional methods: (1) counting spores with a microscope, which is time-consuming and has a low detection limit; (2) spread plate culture method, which underestimates the number of spores (Martinez et al., 2010); (3) classical methods such as isolation on selective media, which are useful but subject to limitations, i.e. many pathogens are masked by overgrowth of faster growing fungi; (4) use of antibodies, which has proven to be reliable for the detection and quantification of B. cinerea in juice and wine (Meyer et al., 2000; Dewey & Meyer, 2004), but lacks sensitivity to detect small quantities of fungal biomass; and (5) PCR, which has also been used successfully to detect low levels of B. cinerea (Gindro et al., 2005), but lacks precision for quantification. Thus, a rapid, selective method to detect and quantify B. cinerea was clearly required.
Our qPCR assay clearly distinguishes between B. cinerea and other fungi and even yeast present on grapes. The fungal DNA was isolated using a commercially available kit, which is an efficient and simple method, allowing the routine analysis of more samples per day. The robustness of our assay relies on our normalization procedure. Indeed, one of the main issues that arises when detecting fungi by PCR, using DNA as the target, is inhibition of the amplification reaction because of components of the matrix being tested (Hartman et al., 2005). False-negative results due to expired reagents, poor technique and other causes could be eliminated using a DNA standard. Therefore, it is imperative for these types of assays to include an internal amplification control (IAC) in each PCR reaction tube. This IAC ensures that variations in the efficiency of the DNA extraction are taken into account. We used exogenous DNA from Y. lipolytica in our assay. These applications highlight the value of this IAC in the detection of inhibitors in samples and provide a relatively simple solution to the issue of unforeseen false-negative reactions in PCR.
We used our assay to compare various treatment strategies. Our results demonstrate that qPCR could be useful to compare and choose the most efficient treatment. Furthermore, our qPCR assay could serve as a decision-making tool in vineyards, whereby the data obtained would help wine producers to assess the risk of contamination. Indeed, our protocol could be used to monitor the evolution of B. cinerea attack during the season and consequently to optimize the number of sprays and the concentration of fungicides used.
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