RESEARCH LETTER
Development of a qPCR assay for speci¢c quanti¢cation of Botrytis
cinerea on grapes
Camélia Filofteia Diguta1, Sandrine Rousseaux1, Stéphanie Weidmann1, Nicolas Bretin1, Béatrice
Vincent2, Michèle Guilloux-Benatier1 & Hervé Alexandre1
1
Institut Universitaire de la Vigne et du Vin Jules Guyot, Université de Bourgogne, Dijon, France; and 2Institut Français de la Vigne et du Vin Pôle
Bourgogne-Beaujolais-Jura-Savoie, Beaune, France
Correspondence: Hervé Alexandre, Institut
Universitaire de la Vigne et du Vin Jules
Guyot, Université de Bourgogne, rue Claude
Ladrey, BP 27877-21078 Dijon, Cedex,
France. Tel.: 133 0 3 80 39 63 93 fax: 133 0
3 80 39 62 65; e-mail: rvalex@u-bourgogne.fr
Received 5 July 2010; revised 16 September
2010; accepted 16 September 2010.
Final version published online 14 October 2010.
DOI:10.1111/j.1574-6968.2010.02127.x
Editor: Bernard Paul
MICROBIOLOGY LETTERS
Keywords
qPCR; Botrytis cinerea; quantification; grapes.
Abstract
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.
Introduction
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 browncoloured compounds. Furthermore, the association of B.
FEMS Microbiol Lett 313 (2010) 81–87
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
2010 Federation of European Microbiological Societies
Published by Blackwell Publishing Ltd. All rights reserved
c
82
C.F. Diguta et al.
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.
were maintained and grown on yeast peptone dextrose
(YPD) medium at 28 1C for 24–72 h.
Materials and methods
To prepare the standard curve, B. cinerea strains were grown
on PDA at 25 1C 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 mL1 was generated and stored at 4 1C.
Spore concentrations between 102 and 107 mL1 were
obtained by 10-fold serial dilutions. DNA was extracted
and used to generate a spore standard curve by qPCR.
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 1C 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
2010 Federation of European Microbiological Societies
Published by Blackwell Publishing Ltd. All rights reserved
c
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, L110
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 1C until laboratory
analysis (2–4 h after harvest). Each field trial was realized in
triplicate: the 200 berries sampled were an average sample.
Spores and/or mycelium were released from the surface of
berries as per a previously described protocol (DoaréLebrun et al., 2006; Laforgué et 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 1C
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
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.
FEMS Microbiol Lett 313 (2010) 81–87
83
qPCR detection and quantification of Botrytis cinerea
Table 1. Treatment in trials to evaluate the effects of different strategies for control of grey mould (Botrytis cinerea) in vineyards with Pinot Noir grapes
Timing of treatment
Treatment
Stage F (flowering)
Stage L (bunch closure)
Stage L110 (bunch closure110 days)
Stage M (veraison)
AB1 = control
AB2
AB3
AB4
AB5
AB6
AB7
AB8
AB9
AB10
AB11
AB12
AB13
AB14
–
Fludioxonil
Fenhexamid
Fenhexamid
Fenhexamid
Fenhexamid
Fenhexamid
Fenhexamid
Boscalid
–
Boscalidw
Boscalid
–
–
Pyrimethanil
Pyrimethanil
Thinning out of leaves (defoliation)
Boscalid
Pyrimethanil
Pyrimethanil
Pyrimethanil
Bacillus subtilis
Bacillus subtilis
Bacillus subtilis
Boscalid
Boscalid
Boscalid
Bentonite clay
Bacillus subtilis
Boscalid
Bacillus subtilisz
Bentonite clay
Fludioxonil, pyrimethanil and fenhexamid: contact fungicide.
w
Boscalid: systemic fungicide.
Bacillus subtilis: biofungicide.
z
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 1C at 140 r.p.m. After 48 h of incubation, a working solution of 1010 CFU mL1 was generated
and cell suspension concentrations ranging from 101 to
108 mL1 were obtained by 10-fold serial dilutions. DNA
was extracted and used to generate a cell standard curve by
qPCR.
DNA extraction
DNA extraction from B. cinerea spores, Y. lipolitica cells and
washing suspension was performed using a fungal DNA kit
(EZNAs, 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 mL
Buffer FG1 and 5 mL RNase (20 mg mL1) for 1 min. 2mercaptoethanol (10 mL) was added and the mix was
incubated at 65 1C for at least 5 min. Then 140 mL 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-bondsspin column. In the final
step, DNA was eluted in 100 mL 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 0 -GCTGTAATTT
CAATGTGCAGAATCC-3 0 ) and Bc3R (5 0 -GGAGCAA
CAATTAATCGCATTTC-3 0 ). Yarrowia lipolytica-specific
FEMS Microbiol Lett 313 (2010) 81–87
primers YALF (5 0 -ACGCATCTGATCCCTACCAAGG-3 0 )
and YALR (5 0 -CATCCTGTCGCTCTTCCAGGTT-3 0 ), 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 mL) was mixed in a final volume of
25 mL with 10 B. cinerea or Y. lipolytica primer mixture
containing 0.56 mM of either, 2 IQTMSYBR 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 1C, followed
by 40 cycles of 15 s at 95 1C and 30 s at 62 1C. A melting
curve was established by decreasing the temperature from
90 1C by 0.5 1C 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 = [101/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.
Statistics
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.
2010 Federation of European Microbiological Societies
Published by Blackwell Publishing Ltd. All rights reserved
c
84
C.F. Diguta et al.
Results
SYBR Green, the maximum Ct value that could be used was
30, which corresponds to a DNA concentration of 6.3 pg.
Specificity
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).
Calibration curves
Botrytis cinerea
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 1C. 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
Yarrowia lypolitica
Yarrowia lypolitica genomic DNA extracted from 10-fold
serial dilutions of Y. lypolitica cells ranging from 8 103 to
8 107 cells mL1 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 1C
(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:
normalized Ct Botrytis ¼ ð29:4=Ct Yarrowia in grape
sampleÞ Ct Botrytis in grape sample
The resultant Ct values were converted into DNA concentrations by extrapolation to a standard curve generated
40.0
y = –3.39x + 30.30
R 2 = 0.99
30.0
30.0
C t value
C t value
40.0
20.0
y = –3.51x + 44.55
R 2 = 0.99
10.0
10.0
0.0
0.0
0
1
2
3
4
5
Log10 (amount of DNA, pg)
6
Fig. 1. Standard curve generated from the amplification of 10-fold
dilutions of target genomic Botrytis cinerea DNA. This curve revealed a
good linear relationship (R2 = 0.99) between the log10 value of the
starting DNA concentration and the threshold cycle.
2010 Federation of European Microbiological Societies
Published by Blackwell Publishing Ltd. All rights reserved
c
20.0
3
3.5
4
4.5
5
5.5
6
Log10 (no. CFUs)
6.5
7
7.5
Fig. 2. Standard curve generated from the amplification of 10-fold
dilutions of target genomic Yarowia lipolitica DNA. This curve revealed a
good linear relationship (R2 = 0.99) between the log10 value of the
starting cell concentration and the threshold cycle.
FEMS Microbiol Lett 313 (2010) 81–87
85
qPCR detection and quantification of Botrytis cinerea
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.
Discussion
We developed a highly specific and sensitive qPCR protocol
for the detection and quantification of B. cinerea contam-
ination 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.
10 00 000.00
100 000.00
**
**
**
*
*
**
Fig. 3. DNA concentration of Botrytis cinerea
on grape berries determined by qPCR. DNA
concentration was determined for each trial
described Table 1. Values followed by
P o 0.05 or P o 0.001 were significantly
different from the control with the ANOVA test. ND,
no detection [inferior to the detection limit: 6.3
pg DNA (corresponding to 540 spores)].
FEMS Microbiol Lett 313 (2010) 81–87
Log DNA (pg)
*
10 000.00
1000.00
100.00
10.00
*
ND
1.00
AB1 AB2 AB3 AB4 AB5 AB6 AB7 AB8 AB9 AB10 AB11 AB12 AB13 AB14
Treatment number
2010 Federation of European Microbiological Societies
Published by Blackwell Publishing Ltd. All rights reserved
c
86
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.
References
Alaei H, Baeven S, Maes M, Höfte M & Heungens K (2009)
Molecular detection of Puccinia horiana in
Chrysanthemum morifolium through conventional and realtime PCR. J Microbiol Meth 76: 136–145.
Aylor AE (1998) The aerobiology of apple scab. Plant Dis 82:
838–849.
Cadle-Davidson L (2008) Monitoring pathogenesis of natural
Botrytis cinerea infections in developing grape berries. Am J
Enol Viticult 59: 387–395.
Calderon C, Ward E, Freeman J & McCartney A (2002) Detection
of airborne fungal spores sampled by rotating-arm and Hirsttype spore traps using polymerase chain reaction assays.
J Aerosol Sci 33: 283–296.
Carisse O, Tremblay DM, Lévesque CA, Gindro K, Ward P &
Houde A (2009) Development of a TaqMan real-time PCR
assay for quantification of airborne conidia of Botrytis
squamosa and management of Botrytis leaf blight of onion.
Phytopathology 99: 1273–1280.
Celik M, Kalpulov T, Zutahy Y, Ish-shalom S, Lurie S & Lichter A
(2009) Quantitative and qualitative analysis of Botrytis
inoculated on table grapes by qPCR and antibodies.
Postharvest Biol Tec 52: 235–239.
Chew GL, Wilson J, Rabito FA, Grimsley F, Iqbal S, Reponen T,
Muilenberg ML, Thorne PS, Dearborn DG & Morley RL
(2006) Mold and endotoxin levels in the aftermath of
hurricane Katrina: a pilot project of homes in New
Orleans undergoing renovation. Environ Health Persp 114:
1883–1889.
Cilindre A, Castro AJ, Clément C, Jeandet P & Marchal R (2007)
Influence of Botrytis cinerea infection on Champagne wine
proteins (characterized by two-dimensional electrophoresis/
immunodetection) and wine foaming properties. Food Chem
103: 139–149.
Dewey FM & Meyer U (2004) Rapid, quantitative tube
immunoassays for on-site detection of Botrytis, Aspergillus
and Penicillium antigens in grape juice. Anal Chim Acta 513:
11–19.
Doaré-Lebrun E, El Arbi A, Charlet M, Guérin L, Pernelle JJ,
Ogier JC & Bouix M (2006) Analysis of fungal diversity of
grapes by application of temporal temperature gradient gel
electrophoresis – potentialities and limits of the method. J Appl
Microbiol 101: 1340–1350.
2010 Federation of European Microbiological Societies
Published by Blackwell Publishing Ltd. All rights reserved
c
C.F. Diguta et al.
Gindro K, Pezet R, Viret O & Richter H (2005) Development of a
rapid and highly sensitive direct-PCR assay to detect a single
conidium of Botrytis cinerea Pers:Fr in vitro and quiescent
forms in planta. Vitis 44: 139–142.
Hartman LJ, Coyne SR & Norwood DA (2005) Development of a
novel internal positive control for Taqman based assays. Mol
Cell Probe 19: 51–59.
Hunter T, Coker RR & Royle DL (1999) The teleomorph
stage, Mycosphaerella graminicola, in epidemics of
Septoria tritici blotch on winter wheat in UK. Plant Pathol 48:
51–57.
Keller M, Viret O & Cole FM (2003) Botrytis cinerea infection in
grape flowers: defense reaction, latency, and disease
expression. Phytopathology 93: 316–322.
Kennedy R, Wakeham AJ, Byrne KG, Meyer UM & Dewey FM
(2000) A new method to monitor airborne inoculums of the
fungal plant pathogens Mycosphaerella brassicicola and Botrytis
cinerea. Appl Environ Microb 66: 2960–3000.
Komárek M, Cadková E, Chrastný V, Bordas F & Bollinger JC
(2010) Contamination of vineyard soils with fungicides: a
review of environmental and toxicological aspects. Environ Int
36: 138–151.
Laforgué R, Guérin L, Pernelle JJ, Monnet C, Dupont J & Bouix
M (2009) Evaluation of PCR-DGGE methodology to monitor
fungal communities on grapes. J Appl Microbiol 107:
1208–1218.
La Guerche S, Dauphin B, Pons M, Blancard D & Darriet P (2006)
Characterization of some mushroom and earthy off-odors
microbially induced by the development of rot on grapes. J Agr
Food Chem 54: 9193–9200.
Leroux P (2004) Chemical control of Botrytis and its resistance to
chemical fungicides. Botrytis: Biology, Pathology and Control
(Elad Y, Williamson B, Tudzynski P & Delen N, eds), pp.
195–222. Kluwer Academic Publishers, Dordrecht, the
Netherlands.
Luo Y, Gao W, Doster M & Michailides TJ (2010) Quantification
of conidial density of Aspergillus flavus and A. parasiticus in
soil from almond orchards using real-time PCR. J Appl
Microbiol 106: 1649–1660.
Martinez J, Simon V, Gonzalez B & Conget P (2010) A real-time
PCR-based strategy for the detection of Paenibacillus larvae
vegetative cells and spores to improve the diagnosis and the
screening of American foulbrood. Lett Appl Microbiol 50:
603–610.
Meyer UM, Spotts RA & Dewey FM (2000) Detection and
quantification of Botrytis cinerea by ELISA in pear stems
during cold storage. Plant Dis 84: 1099–1103.
Suarez BM, Walsh K, Boonham N, O’Neill T, Pearson S & Barker I
(2005) Development of real-time PCR (TaqMan) assays for the
detection and quantification of Botrytis cinerea in planta. Plant
Physiol Bioch 43: 890–899.
Tessonniere H, Vidal S, Barnavon L, Alexandre H & Remize F
(2009) Design and performance of a real-time PCR assay for
sensitive and reliable direct quantification of Brettanomyces in
wine. Int J Food Microbiol 129: 237–243.
FEMS Microbiol Lett 313 (2010) 81–87
87
qPCR detection and quantification of Botrytis cinerea
West JS, Atkins SD, Emberlin J & Fitt BDL (2008) PCR to predict
risk of airborne disease. Trends Microbiol 16: 380–387.
Zar JH (1996) Biostatistical Analysis. Prentice-Hall, Upper Saddle
River, NJ.
FEMS Microbiol Lett 313 (2010) 81–87
Zhou G, Whong WZ, Ong T & Chen B (2000) Development
of a fungus-specific PCR assay for detecting low-level
fungi in an indoor environment. Mol Cell Probe 14:
339–348.
2010 Federation of European Microbiological Societies
Published by Blackwell Publishing Ltd. All rights reserved
c