A protocol using real-time polymerase chain reaction (PCR) for the direct detection and quantification of Plasmodiophora brassicae in soil samples was developed and used on naturally and artificially infested soil samples containing different concentrations of P. brassicae. Species-specific primers and a TaqMan fluorogenic probe were designed to amplify a small region of P. brassicae ribosomal DNA. Total genomic DNA was extracted and purified from soil samples using commercial kits. The amount of pathogen DNA was quantified using a standard curve generated by including reactions containing different amounts of a plasmid carrying the P. brassicae target sequence. The PCR assay was optimized to give high amplification efficiency and three to four copies of the target DNA sequence were detected. Regression analysis showed that the standard curve was linear over at least six orders of magnitude (R2 > 0·99) and that the amplification efficiency was >92%. The detection limit in soil samples corresponded to 500 resting spores g−1 soil. The intersample reproducibility was similar to, or higher than, that of assays for other pathogens quantified in soil samples. Bait plants were used to validate the real-time PCR assay. The protocol developed was used to investigate the spatial distribution of P. brassicae DNA in different fields and a significant difference was found between in-field sampling points. The reproducibility of soil sampling was evaluated and showed no significant differences for samples with low levels of inoculum, whereas at higher levels differences occurred. Indicator kriging was used for mapping the probability of detecting P. brassicae within a 2-ha area of a field. A threshold level of 5 fg plasmid DNA g−1 soil, corresponding to approximately 3 × 103P. brassicae resting spores g−1 soil, is suggested for growing resistant cultivars. The results provide a robust and reliable technique for predicting the risk of disease development and for assessing the distribution of disease within fields.
Clubroot in brassica crops, caused by the obligate endoparasite Plasmodiophora brassicae, is recognized as a serious soilborne disease (Buczacki, 1983), associated with appreciable yield losses (Wallenhammar, 1998) and is considered one of the most economically important diseases of cruciferous crops. The disease is found worldwide throughout the growing areas of oilseed rape and vegetable brassicas (Linnasalmi & Tovianinen, 1991; Engquist, 1994; Wallenhammar, 1996; Donald & Porter, 2009) and is becoming substantially important to emerging economies (Dixon, 2009). The reported outbreak of clubroot in parts of the rape-growing districts of western Canada is of particular concern (Tewari et al., 2005; Strelkov et al., 2007). In Sweden, outbreaks of clubroot in recent years have been reported to be more frequent in winter oilseed rape districts, and severe attacks have once again been reported from spring oilseed rape districts where the disease was observed 25 years ago (Wallenhammar, 1996). Host infection occurs through root hairs and rhizodermal cells and the pathogen subsequently induces local hypertrophy, resulting in gall formation. When infected roots decompose, the resting spores are released in large numbers into the soil, where they remain dormant for up to 17 years (Wallenhammar, 1996).
In cabbage crops, integrated management strategies and the use of resistant cultivars have been implemented (Donald & Porter, 2009), whereas in oilseed crops, chemical management strategies are not available for field-scale, commercial use. As the dissemination of virulent pathotypes seems to be slow in certain European areas, use of resistant cultivars is effective in an integrated control strategy in many cropping areas (Diederichsen et al., 2009). However, there are few clubroot-resistant cultivars of vegetable brassicas and oilseed rape available on the market (Idczak & Trautwein, 2008; Anonymous, 2009; Donald & Porter, 2009). Because of the lack of sensitive and reliable techniques for the detection of P. brassicae in soil, plants and growing media, spores can be spread inadvertently in soil and water from one farm to another (Donald & Porter, 2009) and the pathogen multiplies on susceptible weeds (Karling, 1968) and volunteers in addition to susceptible crops. The current strategy for controlling clubroot in brassica crops mainly consists of avoiding contaminated soils identified by advisory testing using soil bioassays (Brokenshire & Lewis, 1981; Wallenhammar, 1996). Although bioassays are sensitive at the level required to predict disease, in the range 102–105 spores g−1 soil dry weight (Friberg, 2005), they are also time-consuming and rely on personal judgement of disease scores. Improved methods are needed for rapid and reliable prediction of clubroot in field soils. A method using dot immunobinding developed by Lange et al. (1989) proved capable of detecting P. brassicae in infected roots. Resting spores have also been detected in artificially and naturally infested soils by indirect immunofluorescence and by indirect enzyme-linked immunosorbent assays (White & Wakeham, 1995). For various reasons, none of these methods has been further developed for routine testing of field soils. However, Wakeham et al. (2008) recently reported progress in the development of monoclonal antibodies that are highly specific and can detect P. brassicae in naturally infested soil.
Quantification of plant pathogens in soil DNA extracts using real-time PCR has been applied to a number of soilborne pathogens, including Aphanomyces euteiches, Helminthosporium solani, Colletotrichum coccodes, Pythium spp., Polymyxa graminis, Rhizoctonia solani and Verticillium dahliae (Cullen et al., 2001, 2002; Lees et al., 2002; Ratti et al., 2004; Schroeder et al., 2006; Heyman, 2008; Bilodeau et al., 2010). Furthermore, in order to assist farmers in predicting the impact of different soilborne diseases, a soil testing service using real-time PCR is now operating in Australia (Ophel-Keller et al., 2008), offering tests for e.g. Gaeumannomyces graminis var. tritici, R. solani AG-8 and Fusarium pseudograminearum.
Infection by soilborne pathogens is often observed to occur in patches in fields (Gilligan et al., 1996; Hornby, 1998). When the distribution of a pathogen is patchy, it is appropriate to sample as much of the area as possible (Gilligan, 1982). Controlled navigation systems or GNSS (Global Navigation Satellite Systems) provide an improved sampling technique with the potential to accurately determine the position of sampling points within fields and resample at the same spot even after many years.
The objectives of this study were to develop and validate a P. brassicae-specific real-time PCR assay, and to use this assay as a quantitative measure for direct detection of P. brassicae in naturally infested soil samples. An additional objective was to combine these quantitative measures with methods for spatial prediction to assess the risk of disease development within fields.
Materials and methods
Fungal species and isolates
The species and isolates used for studying the specificity of the primers and the probe evaluated in this study are listed in Table 1. The plant pathogenic species tested were either soilborne pathogens or pathogens that are commonly found in Sweden. Culturable isolates were grown on oatmeal agar (Sigma), potato dextrose agar (Acumedia) or malt extract agar (Merck). DNA of P. brassicae was extracted from infected roots, while DNA of Sclerotinia sclerotiorum was extracted from apothecia. DNA of the remaining species was extracted from fungal material cultured in a peptone-glucose broth (20 g peptone and 5 g glucose per L distilled water) or from material collected directly from agar plates by scraping. DNA was extracted using the method described below.
Table 1. Fungal and plant species used to test the specificity of the Plasmodiophora brassicae-specific assay
Plasmodiophora brassicae (infected roots)
Aphanomyces euteiches SE 5
Botrytis fabae CBS 108·57
Drechslera tritici-repentis CBS 265·80
Gaeumannomyces graminis var. tritici CBS 186·65
Gaeumannomyces graminis var. avenae CBS 187·65
Gaeumannomyces graminis var. graminis CBS 387·81
Sclerotinia sclerotiorum (apothecia)
Septoria tritici CBS 292·69
Stagonospora nodorum Vadstena
Stagonospora avenae CBS 288·69
Verticillium albo-atrum CBS 234
Verticillium tricorpus CBS 11
Brassica rapa var. pekininsis
Triticum vulgare var. hibernum
Lantmännen SW Seeda
Artificially infested soil samples
A series of soil samples containing different concentrations of P. brassicae resting spores were analysed using real-time PCR. Samples that contained 0, 1, 10, 102, 5 × 102, 103, 104, 105, 106 and 107 resting spores g−1 soil (d.w.) were prepared according to Friberg (2005). The inoculated soil X, a loamy sand soil [organic matter (OM) 2·4%, clay 4·5%, pH 7·5] originated from Uppsala, Sweden. The soil samples were stored at −20°C until DNA extraction. To evaluate the possibility of detecting DNA from P. brassicae in different soil types, soil X containing either 0, 103 or 104 resting spores g−1 soil was mixed 1:1 (w/w) with two different soils: soil Y (OM 7·7%, clay 9·0%, pH 6·3) and soil Z (OM 4·3%, clay 14%, pH 5·9), previously tested and confirmed free from clubroot spores (i.e. below the detection limit), to produce soil mixtures containing 0, 5 × 102 or 103 resting spores g−1 soil.
Sampling of naturally infested soil
The location of the sampled fields and information about the different soil types are shown in Table 2. A total of 140 soil samples were collected in 2006, 2007 and 2009 from seven fields across three locations in southern and central Sweden where symptoms of clubroot were reported. One pooled sample of approximately 2 L, consisting of 30 randomly distributed subsamples (consisting of one core) taken from within a 3-m radius, was collected per hectare after harvest in late August. A soil auger with a diameter of 22 mm and a volume of 76 mm3 was used to extract samples representing the upper 20 cm of the soil profile. Each pooled sample was positioned by GPS (MobileMapper CE with Egnos DGPS correction; Magellan Navigation Inc.). Samples were transported to a storage building and stored for a maximum of 2 weeks at a temperature of approximately 10°C.
Table 2. Characteristics of farm fields sampled in 2006 (farms 1 and 2) and 2007 (farm 3) for detection of Plasmodiophora brassicae
Farm and location
Number of samples
Organic matter %
Previous brassica crop and year of cropping
aWinter oilseed rape.
1. Falkenberg 56°N, 12°E
2. Götene 58°5′N,13°E
3. Linköping 58°5′N,15°E
Farm 1 (Falkenberg)
Soils were taken from three different fields (A, B and C). In 2006, 27 samples were analysed for the presence of P. brassicae (Table 2).
Farm 2 (Götene)
A total of 33 samples was taken from three fields (D, E and F) in 2006. These fields were sampled at each intersection of a grid with a distance of 50 m between the sampling sites (Table 2). In order to validate the reproducibility within a sampling site, four different pooled samples were collected from each of three sites in field E and field F in 2007. In 2009, sampling density was increased to include 40 samples in field D with the aim of improving the description of in-field variation in P. brassicae.
Farm 3 (Linköping)
In 2007, 28 soil samples from field G were analysed (Table 2). One sample per hectare was positioned with GPS as described above.
Determination of soil properties
Measurements of selected physiochemical parameters in the soil were performed by Eurofins Food and Agro Sweden AB. Soil pH values in fresh soil were determined potentiometrically in water (1·0:1·5 w/w), while OM and clay content were determined as described by Wetterlind et al. (2008).
Bait plants were used to validate the real-time PCR assay developed. The level of P. brassicae in naturally infested soil samples was assessed using a bioassay method previously developed by Wallenhammar (1996) and modified by Wallenhammar et al. (2000). The pots used were 90 × 90 × 95 mm3 (Vefi). Infected plants were scored according to the following classes: 0 = no galls; 1 = enlarged lateral roots; 2 = enlarged tap root; 3 = enlarged napiform tap root; 4 = enlarged napiform tap root, lateral roots healthy; and 5 = enlarged napiform tap root, lateral roots infected. A disease severity index (DSI) was calculated according to the equation:
Subsamples of approximately 500 g soil were withdrawn for further DNA detection and dried and stored at room temperature until extraction.
DNA extraction from infected roots, fungal material and plant material
A modified E.Z.N.A. SP Plant DNA Miniprep Kit extraction method (Omega Bio-Tek) was used to extract DNA from plant and fungal material. A total of 30–130 mg of fresh mycelia, apothecia or infected root tissue was incubated with two to six stainless steel beads (2·3 mm in diameter) in 1·5–mL cetyltrimethyl ammonium bromide (CTAB) solution (20 g CTAB, 12·11 g Tris, 81·8 g NaCl and 7·4 g Na2-ethylene diamine tetraacetic acid (EDTA) L−1; pH = 8·0) and 20 μL proteinase K (20 mg mL−1, GeneScan) at 60°C for 2 h after 30 s shaking at speed setting 4·5–5·5 using a Hybaid Ribolyser (Hybaid). The suspension was then centrifuged at >10 000 g for 5 min and 1 mL of the supernatant was mixed with 5 μL RNase A (Omega Bio-Tek) in a 1·5-mL tube and incubated at 60°C for 15 min. After centrifugation at 20 000 g for 1 min, 600 μL supernatant were mixed with 210 μL SP2 buffer (Omega Bio-Tek) and incubated in a refrigerator for 5 min. The suspension was centrifuged at >10 000 g for 10 min, and a 400-μL portion of the supernatant was transferred to a 1·5-mL tube and mixed with 600 μL SP3 buffer (Omega Bio-Tek). The supernatant was then transferred to a HiBind DNA Mini Column, washed and eluted in 100 μL elution buffer according to the manufacturer’s protocol. All DNA solutions were stored at −20°C.
DNA extraction from soil
Soil samples were air-dried at room temperature (20°C), plant material was removed by hand and the soil was pulverized using a mallet. DNA was extracted from 350-mg soil samples (two replicates per sample) using the FastDNA SPIN Kit for Soil (MP Biomedicals) according to the manufacturer’s instructions with the following modifications. After cell lysis for 30 s at speed setting 5·5, samples were centrifuged at 14 000 g for 5 min. At the 10th step, 600 μL of the supernatant were discarded and 600 μL of the resuspended mixture transferred to a SPIN Filter. DNA was eluted in 100 μL DES. Further purification of the eluate was performed using the Wizard DNA Clean-Up System (Promega Corporation) and Illustra MicroSpin S-300 HR Columns (GE Healthcare) according to the manufacturers’ instructions with the following modifications. In the Promega kit, 900 μL of the resin was used and incubated for 5 min at 60°C together with the DNA solution. DNA was eluted in 100 μL. The MicroSpin S-300 HR Columns were centrifuged for 2 min before adding the DNA eluate.
Primers and probes
Primers and a probe with putative specificity to P. brassicae (Table 3) were designed using a ribosomal RNA gene sequence available in GenBank (accession number AF231027) and amplified a 103-bp fragment. The forward primer was based on the sequence of the forward primer used by Faggian et al. (1999). The 5′ terminal reporter dye of the probe was VIC and the 3′ quencher was TAMRA (Applied Biosystems).
Table 3. Primer and probe sequences used for the amplification and detection of the Plasmodiophora brassicae target sequence
Real-time quantitative PCR was performed using the 7300 Real Time PCR System (Applied Biosystems) in a total volume of 25 μL. The reaction mixture included 1× TaqMan Universal PCR Master Mix No AmpErase UNG (Applied Biosystems), 0·12 μm PbF, 0·6 μm PbR, 0·2 μm PbP and 5 μL template DNA. Soil DNA extracts were diluted fivefold before analysis and were analysed in duplicate. The thermal cycling conditions consisted of an initial denaturation for 10 min at 95°C followed by 45 cycles at 95°C for 15 s and finally 59°C for 60 s.
Since P. brassicae is an obligate biotroph, it is difficult and laborious to extract pure genomic DNA from spores to use as a standard. Therefore, the amount of pathogen DNA was quantified using a standard curve generated by including reactions containing different amounts of a plasmid carrying the P. brassicae target sequence. The target DNA used to generate the calibration curves was a 607-bp sequence, amplified using a universal forward primer (NS7: 5′-GAGGCAATAACAGGTCTGTGATGC-3′) (White et al., 1990), and a P. brassicae-specific reverse primer (Pb3R: 5′-ACCATACCCAGGGCGATTG-3′), which was cloned into pGEM-T vectors (Promega Corporation) according to the manufacturer’s instructions. Plasmid DNA was prepared using a QIAprep Spin Miniprep Kit (QIAGEN) and the DNA concentration was determined spectrophotometrically. A 10-fold dilution series of the plasmid standard was analysed in triplicate using the real-time PCR assay and Ct values were then plotted against the amount of plasmid DNA to create a standard curve. Soil samples with low levels of P. brassicae DNA outside the range of the standard curve were quantified by extrapolating the standard curve to facilitate analysis of the results. The starting quantity of DNA in soil samples was transformed from ag (10−18 g) plasmid DNA to fg (10−15 g) plasmid DNA g−1 soil by taking into account that 350 mg soil were used for the DNA extraction and that 1 μL of the original DNA extract was analysed. By analysing the series of artificially infested soil samples containing different amounts of P. brassicae resting spores, it was possible to convert fg plasmid DNA g−1 soil to number of P. brassicae resting spores g−1 soil. Amplification efficiency (E) was calculated as a percentage using the equation:
where k is the slope of the equation describing the plot of Ct versus the logarithm of the DNA amount.
Evaluation of inhibition and repeatability
Inhibition of the PCR reaction by contaminants in the DNA extracts was assessed for the soil samples analysed in this study by analysing a separate PCR reaction containing the soil DNA extract and 2 μL of the P. brassicae plasmid standard containing approximately 150 gene copies. The same amount of plasmid standard was added to a water control. The percentage inhibition effect (I) was calculated using the equation:
where Qs is the quantity of the unknown sample, Qsp is the quantity of the standard spiked in water and Qs + sp is the quantity of the unknown sample spiked with the standard.
The intersample repeatability was evaluated on separate extractions of 10 soil subsamples quantified in a single real-time PCR assay.
Distribution of P. brassicae inoculum and risk assessment
Indicator kriging was used for mapping the probability of detecting P. brassicae within a 2-ha area of field D at farm 2 (Götene). This method is a nonlinear form of ordinary kriging in which the original data are transformed to a binary scale (0–1) (Burrough & McDonnell, 1998). This approach, which has been applied frequently in soil mapping (e.g. Van Meirvenne & Goovaerts, 2001; Lark & Ferguson, 2004), is appropriate when the goal is to produce an estimate of the probability that a given threshold is exceeded. In this case, observations with a data value (z) of less than or equal to a specified threshold (T) (here T was set to 5·0 fg plasmid DNA g−1 soil) were assigned a value of 0. Other observations were given a value of 1. The transformed data were then used in variogram analysis and ordinary kriging (Webster & Oliver, 1992; Burrough & McDonnell, 1998). The map displays the probability of T being exceeded. The software GS+ (Gamma Design) was used for the geostatistical analysis and ArcGIS (ESRI) was used for mapping.
Statistical analyses were conducted with jmp® (SAS Institute Inc.).
Real-time PCR assay
The target sequence was located in an insert at the 3′ end of the 18S rRNA gene that is not present in other common plasmodiophorids such as Polymyxa graminis and Polymyxa betae (Ward & Adams, 1998). The specificity of primers PbF and PbR and of the probe PbP was assessed on DNA extracted from P. brassicae-infected root tissues, as well as on DNA extracted from several other common plant pathogens. Only DNA from P. brassicae was amplified and detected using this assay. Searches using blastn (http://www.ncbi.nlm.nih.gov/blast/Blast.cgi) confirmed the specificity of the primers and probe.
The standard curves used to calculate the starting quantity of P. brassicae DNA in soil samples had high amplification efficiencies (92–104%). The correlation was linear, with a correlation coefficient of R2 > 0·99. The target sequence was regularly detected even at the highest template dilution of the standard curve, representing only three or four copies of the target sequence and corresponding to approximately 4·5 fg plasmid DNA g−1 soil. The assay was linear over at least six orders of magnitude, which covered the range of natural P. brassicae infestation levels observed in this study.
Regression analysis of the number of added P. brassicae spores and the quantity of plasmid DNA produced a linear function (Fig. 1; R2 > 0·99) on both occasions. P. brassicae DNA was successfully detected at levels of 500 resting spores g−1 soil and the detection was linear from 1000 to 107 resting spores g−1 soil The artificially infested samples were purified once using the Promega kit and twice using the kit from GE Healthcare. The effect of inhibition, evaluated for half the number of extracts analysed, ranged from −30% to 8%, with a mean value of −4%.
The consistency of detection was investigated in three different soil types (X, X + Y and X + Z) and the equivalent of 1000 resting spores g−1 soil was detected in all three soil mixtures in 10 out of 10 replicates (Table 4). At a level of 500 resting spores g−1 soil P. brassicae DNA was detected in seven out of 12 replicates.
Table 4. Consistency of detection in soils inoculated with 500 or 1000 Plasmodiophora brassicae spores g−1 soil
P. brassicae spores g−1 soil
Number of extractions with P. brassicae detection
aFour subsamples were extracted and analysed on two different occasions.
bSix subsamples were extracted and analysed on three different occasions.
cTwo subsamples were extracted and analysed on the same occasion.
dFour subsamples were extracted and analysed on the same occasion.
3 of 4
6 of 6
0 of 2
Soil X + Y
2 of 4
2 of 2
0 of 2
Soil X + Z
2 of 4
2 of 2
0 of 2
7 of 12
10 of 10
0 of 6
The results from the intersample repeatability test showed that the relative standard deviation for the 10 subsamples analysed was 22·5% (Table 5).
Table 5. Intersample repeatability test of 10 separate soil DNA extracts of Plasmodiophora brassicae from one soil sample analysed in the same real-time PCR run
P. brassicae DNA (fg plasmid DNA g−1 soil)
aCt values represent the mean of two replicates.
bCorresponding to approximately 13 000 resting spores g−1 soil.
Relative standard deviation
Naturally infested soil samples
The levels of inhibition were evaluated for half the naturally infested soil samples analysed in this study. At farm 1, all soil samples were purified once using the Promega kit and twice using the kit from GE Healthcare. The effect of the inhibition ranged from −43·5% to 15·1%, with a mean value of −7·3%. The soil samples from farm 2 were purified in two or three steps (first once using the Promega kit followed by one or two rounds using the GE Healthcare kit). The effect of the inhibition ranged from −35·1% to 32·9%, with a mean value of −6·4%. The majority of the soil samples from farm 3 needed purification in three steps. The mean effect of inhibition was −10·7%, with a similar variation as for the other two sites. As an example, for this assay a value of −18% of the effect of inhibition for a soil sample with no P. brassicae infection corresponds to a Ct difference of only 0·3 in the spiked sample compared with the spiked water control. Consequently, a value of −49% corresponds to a Ct difference of approximately 1. A positive value of the level of inhibition only shows the effect of errors, such as technical and handling errors.
The relationship between real-time qPCR (fg plasmid DNA g−1 soil) and DSI for 87 naturally infested soil samples is presented in Fig. 2a (y =0·0279x + 12·378, R2 = 0·35). At DNA levels >1000 fg plasmid DNA g−1 soil (equivalent to approximately >660 000 spores g−1 soil), all corresponding bioassays showed DSI >25. When the scale of the x-axis was changed (Fig. 2b), it appeared that at levels <25 fg plasmid DNA g−1 soil (corresponding to <16 000 spores g−1 soil), there were two observations (from field 1-C) with DSI >40. The levels of plasmid DNA were low or nondetectable and corresponding DSI values were close to zero (data not shown) when these sites were resampled in the following 2 years.
The spatial distribution of P. brassicae detected in soil samples taken from farm 1 and the corresponding DSI values are presented in Fig. 3. Statistical analysis of DSI showed that the level of infestation varied significantly between sampling sites within each of the fields. The evaluation of the reproducibility within selected sampling sites is presented in Table 6. Statistically significant differences in DSI were shown for two samples at site E2 and in fg plasmid DNA g−1 soil for one sample at site E1.
Table 6. Plasmid equivalents (fg plasmid DNA g−1 soil) of Plasmodiophora brassicae in soil and disease severity index (DSI) for four samples collected in 2007 from each of three sampling sites in two fields (E and F) at farm 2
Plasmid equivalents (fg plasmid DNA g−1 soil)a
aDifferent letters indicate statistically significant differences within each sampling site according to Tukey’s range test (P <0·05).
Disease risk assessment
The estimated probability of detecting >5 fg plasmid DNA of P. brassicae g−1 soil (corresponding to 3000 spores g−1 soil), based on an increased sampling density (n = 40), is presented as an indicator kriging map in Fig. 4. There was an overall spatial trend in the variation of the amount of P. brassicae DNA detected, with the lowest amounts of P. brassicae detected in the north-eastern part of the 2-ha area. In addition, short-range variation occurred in some parts of the field. For example, values >500 fg plasmid DNA g−1 soil (corresponding to 350 000 spores g−1 soil) were observed in the transition zone between sampling sites with low inoculum density in the central part of the sampled area and sampling sites with high inoculum density in the western part.
No procedure using real-time PCR for direct quantification of P. brassicae DNA in soil for risk assessment of clubroot has apparently been reported previously. This paper presents the development of a robust and useful method for detection and quantification of P. brassicae in soil samples and describes mapping of in-field variations, which can assist in management of infested fields. Real-time PCR is much faster than the conventional soil bioassays available today and differs from previously reported PCR assays (Ito et al., 1997; Faggian et al., 1999; Wallenhammar & Arwidsson, 2001; Cao et al., 2007; Sundelin et al., 2010) in that it allows the quantity of P. brassicae DNA in a naturally infested soil sample to be determined. DNA extraction and purification followed by PCR analysis can be performed in less than one working day, which makes this technique appropriate for routine use in commercial laboratories.
Direct detection of pathogen DNA in soil requires an efficient DNA extraction method and a suitable purification procedure to remove contaminants that may inhibit the PCR reaction, especially if a correct quantitative measurement is needed. In this study, a DNA extraction kit involving bead beating was used to disrupt cells and extract DNA from P. brassicae in soil samples of 350 mg. Cao et al. (2007) tested several methods for DNA extraction and concluded that the Fast DNA Spin Kit gave the most satisfactory results. The present study used the Fast DNA Spin Kit for Soil, which resulted in a relatively high total DNA yield. However, because of impurities in the DNA extracts PCR inhibition posed a significant problem in the present study (data not shown). Further purification was needed to improve the quality of the DNA extracts and it was concluded that the inhibition should be routinely monitored to avoid false negative results. On the other hand, excessive purification leads to loss of DNA, so caution is needed when deciding the degree of purification for a particular soil type. The DNA extraction kit used required a small starting sample, which may negatively affect both the detection limit of the assay and the representativeness of the sample. In this case, however, the main factor affecting the detection level is the P. brassicae DNA yield in the extraction procedure rather than the size of the soil sample used. Calculation of the number of copies of the target sequence in the DNA extracts from the artificially infested soils showed that the target sequence is present in approximately 0·4 copies per resting spore. However, it is likely that the ribosomal RNA gene is present in several copies, since it is localized on at least two different chromosomes (Graf et al., 2004). The samples studied in this paper can also be considered representative since the 350-mg soil sample used for DNA extraction was withdrawn from a dried and homogenized 500-g sample, which in turn was subsampled from approximately 2 L.
The PCR assay presented here was optimized to give high amplification efficiency and only three or four copies of the target DNA were needed for detection. The detection limit in the artificially infested soil samples corresponded to approximately 500–1000 P. brassicae resting spores g−1 soil, which is similar to detection limits reported in other studies (Faggian et al., 1999; Cao et al., 2007). At levels corresponding to the detection limit, the consistency of detection was also satisfactory for different soil mixtures (Table 4). Taking into account that DNA was extracted from 350 mg soil and assuming 100% recovery of DNA after extraction and purification, the detection limit was equivalent to approximately two resting spores per PCR reaction. The limit of quantification was approximately 4·5 fg plasmid DNA g−1 soil. To further improve the detection limit of the assay presented here, other DNA extraction protocols could be evaluated, e.g. the protocol developed by Brierley et al. (2009).
Previous analyses of real-time PCR assays for quantification of plant pathogens have shown that the intra-assay precision (between replicates in the same real-time PCR assay) and inter-assay precision (over separate real-time PCR assays) are often high, whereas the inter-sample reproducibility (between separate extractions of subsamples) can have a significant negative effect on the results (Valsesia et al., 2005; Chandelier et al., 2006). The inter-sample reproducibility (Table 5) for the assay developed in this study was similar to, or better than, that of assays for other plant pathogens quantified in soil samples (Cullen et al., 2001, 2002; Atkins et al., 2003, 2005; Zang et al., 2006).
Reproducibility was evaluated within sampling sites, and significant differences were found at sampling point E2 for DSI, whereas for fg plasmid DNA g−1 soil a difference was shown at site E1. For both methods no differences were found at the lowest level of P. brassicae (sampling point F1). These findings demonstrate the patchiness of inoculum distribution within a small area and emphasize the importance of including an adequate number of subsamples at sampling.
The use of bait plants has been considered the most reliable diagnostic method for assessing soils for the presence of P. brassicae and is still heavily relied upon, despite the availability of more modern techniques (Faggian & Strelkov, 2009). The DNA assay presented in this study is an approach to measure the quantity of genomic DNA of P. brassicae from resting spores in the soil. An important question concerns the relationship between DNA levels and disease expression (DSI) in the bioassay. As mentioned previously, two samples clearly diverged and showed high DSI levels although the corresponding DNA levels were low. The high DSI values in 2006 might be attributable to the procedure used for drawing the subsample for DNA analysis and the precrop grown in the field. During the sampling procedure in 2006 small amounts of plant material from infected winter rape roots still carrying high amounts of resting spores are likely to have been included in the soil used in the bioassay, while the sample of 350 mg withdrawn for the PCR assay was thoroughly cleansed of plant material according to the protocol. During the following years the plant material was degraded and the resting spores were more evenly distributed when the fields were cultivated. Hence, discrepancies did not occur for either DSI or PCR determination (data not shown). In contrast, the biosassay did not show infection for 11 out of 23 samples from farm 3, while DNA levels ranged from 2·4 to 933·3 fg plasmid DNA g−1 soil (Fig. 2a). The lack of a relationship between detection and disease is most likely the result of poor development of the root system of the bait plants caused by soil properties, such as high clay content or the presence of other soilborne pathogens such as R. solani. This was reported previously by Wallenhammar (1996), indicating that a bioassay does not give a proper predictive value in all situations.
The generally accepted threshold for symptom development is 1000 spores g−1 dry soil (Faggian & Strelkov, 2009). In field conditions, the threshold varies depending upon soil type and environmental factors, and symptoms have been reported for values lower than 10 spores g−1 soil (Murakami et al., 2000). Visible symptoms were observed by Friberg (2005) at 100 spores g−1 soil (d.w.) in artificially infested field soil. Sundelin et al. (2010) reported visible detection of symptoms at 100 spores g−1 in sterilized soil. As has been pointed out earlier (Wallenhammar, 1996, 1998), the main effective means of control is to avoid contaminated soil. Cultivation of brassica crops is possible when the present qPCR assay does not show detection. In soil samples containing P. brassicae DNA levels exceeding the detection limit (500 spores g−1 soil) but at <5 fg plasmid DNA g−1 soil (Fig. 2b), corresponding to 3000 spores g−1 soil, resistant cultivars can be grown without expecting yield losses (Wallenhammar et al., 2000). Based on previous studies in which the agronomic properties of one susceptible cultivar and three partly resistant lines of spring oilseed turnip rape (Brassica rapa) were investigated by Wallenhammar et al. (2000), the following recommendations are given: For DNA levels <5 fg plasmid DNA g−1 soil, there is a risk of yield loss <10% in susceptible crops. At DNA levels ranging from 5 to 200 fg plasmid DNA g−1 soil (corresponding to 3000 and 130 000 spores g−1 soil, respectively), there is a risk of crop losses >10% in susceptible crops, but resistant cultivars can be grown. At levels >200 fg plasmid DNA g−1, cultivation of resistant cultivars is not advisable because of a risk of great multiplication of inoculum. The multiplication of disease that occurs when susceptible and resistant cultivars are grown increases the risk of considerable yield losses in future brassica crops. A high reproduction rate combined with longevity is part of the survival strategy of P. brassicae. One gram of infected root tissue contains at least 1–2 × 108 spores (Diederichsen et al., 2009) and the half-life time is estimated to be 3·6 years (Wallenhammar, 1996).
Although several real-time PCR assays have been developed for quantification of plant pathogens in soil, few are routinely and commercially used as predictive diagnostic tests or to quantify the inoculum density in soils (Ophel-Keller et al., 2008; Donald et al., 2009). Following the outbreak of clubroot in Alberta, Canada, a qualitative PCR method was developed for diagnosis (Cao et al., 2007) and is now available in commercial versions (Faggian & Strelkov, 2009). The assay presented in this paper is now commercially available in Sweden and can be used as guidance prior to growing susceptible crops.
Patchiness has been observed for soilborne pathogens, e.g. R. solani and G. graminis (Gilligan et al., 1996; Hornby, 1998) and was clearly demonstrated for P. brassicae in this study by both the 1-ha sampling (Fig. 3) and by the intensified randomized sampling (Fig. 4). Previous observations of within-field variation in an infested spring oilseed rape crop showed a variation in disease incidence ranging from 5% to 95% of diseased plants (Wallenhammar, 1998). This patchy distribution emphasizes the fact that one of the main challenges involved in testing a particular field for the presence of P. brassicae is the choice of sampling technique (Cao et al., 2007). Using the diagonal of the field was found to be applicable for detection in a general survey (Wallenhammar, 1996). Applied to the fields in this study, however, areas within a field with high inoculum density might have been excluded. A standardized pattern such as a ‘W’ transect, with subsamples collected at multiple points along the arms of each transect, is often considered to be ideal (Faggian & Strelkov, 2009). A W transect, consisting of 40 subsamples (soil cores) generating a general sample, complemented with samples from compacted, high-moisture areas, headlands and field entrances, is suggested for a general survey of a field. When additional information on the prevalence of P. brassicae is needed, i.e. when resistant brassica cultivars are available, intensified sampling of one sample per hectare is suggested. Even more intensive sampling might be needed depending on the variation in soil composition, e.g. clay content or pH value. High-resolution mapping of these soil properties can be undertaken using advanced precision agricultural devices such as sensors for apparent soil electrical conductivity and on-the-go pH measurements (Adamchuck et al., 2007; Wetterlind, 2009).
The results of this study will be of great value to growers in many clubroot-affected countries in predicting the risk of disease development in various cropping situations. The probability of infection is presented in a mode that enables the measured values to be applied in practice (Fig. 4). In situations where the main interest is whether the level exceeds a specific threshold rather than estimates of the actual values, various forms of probalistic kriging are useful (Burrough & McDonnell, 1998). A map showing the likelihood of the selected threshold of 5 fg P. brassicae plasmid DNA g−1 soil being exceeded was produced by indicator kriging. As Fig. 4 shows, an area in the north-east was identified as a place where cultivation of resistant cultivars is possible.
The integration of resistance as a management tool, along with a range of other control measures, can create a more robust management strategy (Donald & Porter, 2009). A prerequisite is to quantify soilborne inoculum. To fully accomplish this, additional work is needed to achieve detection at levels of 100 spores g−1 soil or less. Complementary knowledge of the prevalent pathotypes is also required, since tolerance in resistant cultivars seems to vary considerably in response to different pathotypes of clubroot (Osaki et al., 2008). The present results, based on naturally infested Swedish soils with a clay content ranging from 2% to 35% and an OM content ranging from 1·5% to 11% (Table 2), demonstrate that the qPCR assay developed in this study offers growers a quantitative, fast and reliable predictive test for P. brassicae levels in individual fields or parts of fields. The continual increase in the acreage of oilseed rape grown for food, feed and fuel and the introduction of clubroot-susceptible brassica crops as catch crops demands routine testing of soils. Using the threshold levels of inoculum suggested here, heavily infested soils can be avoided and the possibilities for integrating resistant brassica cultivars into crop rotations are enhanced.
The authors wish to thank the Swedish Farmers’ Foundation for Agricultural Research and the Foundation for Swedish Oil Plant Research for funding, Dr Hanna Friberg for preparing the artificially inoculated soil samples, Dr Caroline Filipsson and Åsa Fransson for technical assistance, Dr Eva Blixt and Professor Christina Dixelius for providing some of the fungal isolates, and Anna Nyberg, Dr Eva Stoltz and Amélie Wallenhammar for assistance with the bioassays.