Design and Validation of Specific qPCR Primers for Soil-Borne and Apple Tree-Associated Phytopathogenic Fungi

Abstract

Soil-borne pathogenic fungi cause substantial economic losses worldwide by infecting the underground parts of plants. In fruit trees, infections are especially damaging, as they often result in the death of the entire plant. Therefore, early detection is essential for effective disease management caused by soil-borne pathogens. In this study, we designed and validated real-time PCR primers targeting eight soil-borne and apple tree-associated phytopathogenic fungi. Each primer set successfully detected 20 ng of target genomic DNA (gDNA) within 25 cycles, while the same amount of non-target gDNA mixture was detected only after 35 cycles of amplification. Moreover, target DNA amplification remained unaffected in the presence of mixed non-target gDNA background, confirming the high specificity of the primers. Sensitivity test showed that 1 fg of plasmid DNA, corresponding to about 290 copies, was detectable around 30 cycles with all primer sets. These primers support accurate pathogen detection and early diagnosis in various environmental samples.

Soil-borne phytopathogenic fungi infect the underground parts of plants and cause diseases such as root rot. These pathogens pose a serious threat to the productivity and security of major food crops worldwide (Hubballi et al., 2022; Savary et al., 2019). For instance, Fusarium species cause Fusarium wilt on Solanaceous crops, legumes, and banana, leading to yield losses of up to 50% in sweet potato and 80% in banana (Okungbowa and Shittu, 2012). Sclerotium rolfsii (Sr) has an exceptionally wide host range, from bean to apple tree, and can cause yield losses up to 60% in the field (Kator et al., 2015). Other notable soil-borne pathogens, such as Rosellinia necatrix (Rn), inflict fatal damages on fruit trees, resulting in enormous economic losses for farmers (Schena et al., 2008).

Because infections begin underground, diagnosing and controlling soil-borne disease are particularly challenging (Hubballi et al., 2022; Katan, 2017). Moreover, as soil-borne pathogens often cause the death of entire plants, economic losses on fruit trees can be devastating in fruit tree cultivation. Therefore, early detection is critical to prevent disease outbreaks and minimize economic impact. Numerous methods have been developed to detect plant pathogens and mitigate crop losses. Among these, nucleic acid-based assays including quantitative PCR (qPCR) are widely employed for the diagnosis of phytopathogenic fungi due to their high specificity and sensitivity (Kumar et al., 2020; Hariharan and Prasannath, 2021). However, development of specific and sensitive primer sets is a prerequisite for the success of PCR-based detection.

Previous studies have reported primer sets for detecting soil-borne phytopathogenic fungi. Lievens et al. (2006) demonstrated that specific primers targeting soil-borne fungal and oomycete pathogens of tomato are effective for detecting pathogens in both plant and soil samples. Sun et al. (2018) developed a multiplex PCR system to simultaneously detect five soil-borne fungal pathogens in wheat using specific primers. In this study, we designed detection primers for eight soil-borne or apple tree-infecting phytopathogenic fungi for qPCR-based detection: Alternaria tenuissima (At), Botryosphaeria dothidea (Bd), Fusarium oxysporum (Fo), Glomerella cingulata (Gc), Phytophthora cactorum (Pc), Rn, Sr, and Sclerotinia sclerotiorum (Ss).

For genomic DNA extraction, mycelia fragments grown on CM agar media (Molinari and Talbot, 2022) were ground with extraction buffer (1 M KCl, 100 mM Tris-Cl, 50 mM EDTA, pH 8.0) using an electric grinder and plastic pestle, as previously described (Chi et al., 2009). RNAs were removed by RNase A treatment, and gDNA was purified using phenol:chloroform:isoamyl alcohol (25:24:1) clean-up and ethanol precipitation.

Target gene sequences were obtained from the National Center for Biotechnology Information database and aligned using Clustal Omega in MEGA 11 program (Tamura et al., 2021). Specific primer sets producing amplicons of less than 200 bp were designed for each pathogen, based on the alignment of six different target gene sequences (Table 1, Supplementary Table 1). PCR reactions with designed primer sets were performed using 20 ng of extracted gDNA, and PCR products were confirmed on a 1.2% agarose gel (Supplementary Fig. 1).

Table 1.

Specific primer sets for fungal pathogen detection

Target pathogen	Target genea (accession no.)	Sequence		Tm (°C)b	GC ratio (%)	Dimer formationc	Size (bp)	Amplicon GC ratio (%)
A. tenuissima (At)	ITS (KX664408.1)	Fw	ctttgctggagactcgccttaaa	60.1	47.8	No	177	48.02
		Rv	ttcctccgccttattgatatgctt	59.2	41.7	No
B.dothidea (Bd)	EF1 (OQ032606.1)	Fw	gccttatcactctggtgagggg	61.1	59.1	No	164	57.93
		Rv	tcgcatagacgaacgtgagtgg	61.7	54.5	No
F. oxysporum (Fo)	RPB1 (MT568951.1)	Fw	cggccaactgatgtatggtct	59.2	52.4	No	195	48.72
		Rv	gccgcatcgggaattgtatca	60.2	52.4	No
G. cingulata (Gc)	β-Tubulin (KJ638947.1)	Fw	ccgaatgattccttttccgtga	58	45.5	No	192	54.69
		Rv	aggtggaggacatcttgaggc	60.5	57.1	No
P. cactorum (Pc)	Ypt1 (OR614386.1)	Fw	gaccatcggtgtggactttgtg	60.7	54.5	No	199	50.25
		Rv	gttgaataggaaacacgccacgt	60.9	47.8	Yes
R. necatrix (Rn)	Cox1 (NC_087850.1)	Fw	ggagcttagcggtcctggtg	61.6	65	No	143	29.37
		Rv	tctgcgcacaagatagcgaatt	60	45.5	Yes
S. rolfsii (Sr)	ITS (HQ420816.1)	Fw	gttggtgtgataatatgtctacgcc	58.9	44	No	167	39.52
		Rv	cctccgcttattgatatgcttaagttc	59.4	40.7	No
S. sclerotiorum (Ss)	β-Tubulin (JX181762.1)	Fw	ccgtcttcatttcttcatggttgga	60.6	44	No	194	46.91
		Rv	gcagacgggtaatatggcaaactt	60.7	45.8	No

^aITS, internal transcribed spacer; EF1, translation elongation factor 1-α; RPB1, RNA polymerase II largest subunit; Cox1, cytochrome c oxidase subunit I.

^bThe Tm value of each primer was predicted using Bioneer’s online tool (Daejeon, South Korea), from which the primers were also ordered.

^cPrimer dimer formation was predicted using Oligo Evaluater provided by Sigma-Aldrich.

Primer specificity was validated via qPCR using 20 ng of either target pathogen gDNA or a mixture of non-target pathogen gDNA. qPCR was performed using 2× Real-Time PCR Master Mix For SYBR Green I (BioFACT, Daejeon, Korea) and Bio-Rad CFX96 real-time PCR system (Bio-Rad Laboratories, Hercules, CA, USA). The reaction condition was: 95°C for 15 min (initial denaturation), followed by 40 cycles of 95°C for 20 s, 55°C for 30 s, and 72°C for 20 s. A melting curve analysis was conducted from 55°C to 95°C and mean quantification cycle (Cq) value was obtained from at least 3 technical replicates for each biological replicate. As a result of qPCR, target pathogen samples yielded Cq values below 25, whereas non-target samples had Cq values above 35, indicating high specificity of the primers (Fig. 1). Primers targeting At, Fo, Rn, and Sr exhibited notably low Cq values—17.40, 19.09, 19.19, and 18.91, respectively—resulting in a clear distinction from the Cq values of the non-target gDNA mixture (Table 2). Although the Ss primers showed a relatively higher Cq value of 24.71 for the target pathogen, no amplification was observed for non-targets, indicating high specificity. Among all tested primer sets, the primers targeting ypt1 of Pc showed the lowest specificity, with only an 11-cycle difference between the Cq values of target and non-target templates. Melting peak data from each qPCR assay confirmed single-product amplification by the designed primer sets (Supplementary Fig. 2).

Fig. 1.

Quantitative PCR analysis showing primer specificity for each target pathogen. At, Alternaria tenuissima; Bd, Botryosphaeria dothidea; Fo, Fusarium oxysporum; Gc, Glomerella cingulata; Pc, Phytophthora cactorum; Rn, Rosellinia necatrix; Sr, Sclerotium rolfsii; Ss, Sclerotinia sclerotiorum.

Table 2.

Mean Cq value of qPCR result for primer specificity test

Template	At	Bd	Fo	Gc	Pc	Rn	Sr	Ss
Target	17.40 ± 0.03	22.98 ± 0.09	19.09 ± 0.05	24.08 ± 0.30	24.07 ± 0.07	19.19 ± 0.01	18.91 ± 0.22	24.71 ± 0.01
Non-targets	35.38 ± 0.27	35.76 ± 0.28	38.15 ± 0.71	36.73 ± 0.23	35.02 ± 0.14	37.09 ± 0.15	36.26 ± 0.44	N/A

qPCR, quantitative PCR; At, Alternaria tenuissima; Bd, Botryosphaeria dothidea; Fo, Fusarium oxysporum; Gc, Glomerella cingulata; Pc, Phytophthora cactorum; Rn, Rosellinia necatrix; Sr, Sclerotium rolfsii; Ss, Sclerotinia sclerotiorum; N/A, not available.

To further assess specificity, we tested primers in mixtures containing 2 ng/μL of target pathogen alongside gDNAs of seven non-target pathogens at three different ratios. One microliter of the mixture of target and non-target pathogens gDNA was used for qPCR analysis. The results showed minimal variation in Cq values and quantification curves, even in the presence of a 100-fold excess of non-target gDNA (Fig. 2). All three ratio samples for each pathogen were grouped into a homogeneous subset by Tukey’s test at a 95% confidence level, except for Pc, in which the 1:10 ratio sample showed a significant difference. Nevertheless, the Cq value differences among the tested gDNA ratio samples for Pc were less than 0.3, indicating that all primers effectively detected the target pathogens even in complex DNA backgrounds (Supplementary Table 2). When comparing the Cq values obtained from 20 ng of pure target gDNA to those from average Cq values with 2 ng of target gDNA with background, the smallest difference was observed for Ss (1.36 cycles) and the largest for Fo (5.07 cycles). The average difference was 3.5 cycles, which is consistent with the 10-fold difference in target DNA concentration. Collectively, these results indicate that the primer sets exhibit high specificity and are suitable for the accurate detection of each target pathogen.

Fig. 2.

Primer specificity test using genomic DNA mixtures of target and non-target pathogens at varying ratios. At, Alternaria tenuissima; Bd, Botryosphaeria dothidea; Fo, Fusarium oxysporum; Gc, Glomerella cingulata; Pc, Phytophthora cactorum; Rn, Rosellinia necatrix; Sr, Sclerotium rolfsii; Ss, Sclerotinia sclerotiorum.

For sensitivity testing, PCR products amplified by each primer set were purified by NucleoSpin Gel & PCR Clean-up kit (Macherey-Nagel, Düren, Germany) and cloned into the pGEM-T easy vector (Promega, Madison, WI, USA). Recombinant clones were verified by sequencing (Macrogen, Seoul, South Korea). Plasmid DNA was extracted using NucleoSpin Plasmid Mini kit for plasmid DNA (Macherey-Nagel) and serially diluted 10-fold from 1 ng/μL to 0.1 pg/μL. Sensitivity test was performed by qPCR with 1 μL of plasmid template under same conditions (Fig. 3A). qPCR results demonstrated that 1 femtogram (fg) of plasmid DNA—corresponding to approximately 290 copies—was detected within 30 cycles using the At, Gc, and Ss primer sets (Table 3). For the remaining primers, 10 fg of plasmid DNA yielded Cq values below 30, indicating that all tested primer sets can reliably detect at least 1,000 target copies within 30 cycles. Standard regression lines were generated by plotting the mean Cq values from the sensitivity tests (Fig. 3B). All primer sets showed R² values exceeding 0.99 and efficiency between 90–110% indicating high reliability of the sensitivity measurements across primer sets.

Fig. 3.

Primer sensitivity test by quantitative PCR (qPCR) analysis. (A) Work flow of qPCR for primer sensitivity test. (B) Standard regression line of qPCR analysis result for primer sensitivity test. At, Alternaria tenuissima; Bd, Botryosphaeria dothidea; Fo, Fusarium oxysporum; Gc, Glomerella cingulata; Pc, Phytophthora cactorum; Rn, Rosellinia necatrix; Sr, Sclerotium rolfsii; Ss, Sclerotinia sclerotiorum.

Table 3.

Mean Cq value of qPCR result for primer sensitivity test

DNA	copy no.	At	Bd	Fo	Gc	Pc	Rn	Sr	Ss
1 ng	2.9 × 108	10.34 ± 0.09	11.02 ± 0.14	11.71 ± 0.12	10.93 ± 0.08	12.31 ± 0.08	12.43 ± 0.20	10.65 ± 0.01	10.34 ± 0.11
100 pg	2.9 × 107	12.88 ± 0.07	15.00 ± 0.06	15.18 ± 0.02	13.53 ± 0.05	15.94 ± 0.09	15.62 ± 0.06	14.27 ± 0.30	13.25 ± 0.04
10 pg	2.9 × 106	16.10 ± 0.09	17.82 ± 0.21	18.52 ± 0.06	16.29 ± 0.06	19.14 ± 0.09	18.98 ± 0.08	17.25 ± 0.03	16.17 ± 0.18
1 pg	2.9 × 105	19.61 ± 0.04	21.37 ± 0.01	22.02 ± 0.12	19.71 ± 0.03	22.48 ± 0.05	22.27 ± 0.07	21.31 ± 0.23	19.37 ± 0.14
100 fg	2.9 × 104	22.81 ± 0.12	24.30 ± 0.09	24.93 ± 0.04	23.03 ± 0.04	25.16 ± 0.05	25.53 ± 0.03	24.82 ± 0.15	22.85 ± 0.05
10 fg	2.9 × 103	26.23 ± 0.03	27.35 ± 0.15	28.74 ± 0.09	26.65 ± 0.06	28.49 ± 0.05	28.82 ± 0.14	27.04 ± 0.02	26.05 ± 0.19
1 fg	2.9 × 102	29.49 ± 0.15	30.45 ± 0.02	31.78 ± 0.05	29.63 ± 0.11	31.43 ± 0.02	31.79 ± 0.24	30.76 ± 0.10	29.27 ± 0.12
0.1 fg	2.9 × 10	32.87 ± 0.35	34.05 ± 0.35	34.97 ± 0.16	33.21 ± 0.42	34.60 ± 0.01	34.68 ± 0.39	34.79 ± 0.33	32.66 ± 0.37

qPCR, quantitative PCR; At, Alternaria tenuissima; Bd, Botryosphaeria dothidea; Fo, Fusarium oxysporum; Gc, Glomerella cingulata; Pc, Phytophthora cactorum; Rn, Rosellinia necatrix; Sr, Sclerotium rolfsii; Ss, Sclerotinia sclerotiorum.

Several studies have previously reported and evaluated detection primers for the pathogens examined in this study. For Alternaria spp., internal transcribed spacer (ITS)-based primers detected 100 ng of At gDNA with Cq values of 11–12 (Pavón et al., 2012). In comparison, our newly designed primers detected 20 ng of At gDNA within 18 cycles. Although the previous primers appear more sensitive, our primers amplify a shorter 178 bp product—approximately half the size of previously reported ones—making them more suitable for SYBR Green-based qPCR and allowing for shorter reaction times. For Bd, EF1-targeting primers detected 1 ng of gDNA at over 26 cycles in simplex qPCR (Romero-Cuadrado et al., 2023). Primers targeting beta-tubulin of two Gc complex species showed Cq values around 26 for 1 ng of gDNA (Cosseboom and Hu, 2021). In comparison, our Bd and Gc primers detected 20 ng of gDNA at Cq values of around 23 and 24, indicating similar sensitivity despite targeting different regions. For Fo, EF1-based primers showed Cq value of about 25 for 1 ng of gDNA (Haegi et al., 2013). On the other hand, our Fo primers, targeting RPB1, detected 20 ng at a Cq value of 19.09, suggesting higher sensitivity.

In the case of Pc, both ITS- and Ypt1-based primers were tested, and Ypt1-based primers offer higher specificity, but lower sensitivity (Verdecchia et al., 2021). In our study, the Pc Ypt1-targeting primers showed relatively lower specificity and sensitivity among all tested sets, highlighting the importance of primer selection based on application purpose. For Rn, ITS-based primers with a TaqMan probe detected 0.4 ng of gDNA within 18 cycles (Shishido et al., 2012). Although our primers for Rn showed relatively lower sensitivity, they target a different gene (Cox1) and demonstrated high specificity, making them a valuable alternative for pathogen detection. Regarding Sr, ITS-based primers were estimated to detect 1,000 DNA copies at a Cq value of over 35 (Gao et al., 2015). In contrast, our primers detected as few as tens of copies within 35 cycles, indicating greater sensitivity. For Ss, previously designed beta-tubulin primers required over 25 cycles to detect 30 ng of gDNA (Ramiro et al., 2019). Conversely, our primer set for Ss, also targeting beta-tubulin, detected 20 ng of gDNA below 25 cycles. Moreover, the high specificity of our primers against non-target pathogens supports their reliability in Ss detection.

In conclusion, we developed specific qPCR primers for the detection of eight phytopathogenic fungi associated with apple trees and soil-borne diseases. These primer sets exhibit high specificity and sensitivity, and reliably detect target gDNA even in the presence of non-target pathogen DNA. Given the diverse microbial communities in soil in addition to target phytopathogenic fungi (Tedersoo et al., 2014), we anticipate that these primers, combined with appropriate gDNA extraction protocols, will facilitate effective pathogen detection in soil samples.

Electronic Supplementary Material

Supplementary materials are available at The Plant Pathology Journal website (http://www.ppjonline.org/).