Real-time RT-PCR high-resolution melting curve analysis and multiplex RT-PCR to detect and differentiate grapevine leafroll-associated virus 3 variant groups I, II, III and VI
© Bester et al.; licensee BioMed Central Ltd. 2012
Received: 19 June 2012
Accepted: 18 September 2012
Published: 27 September 2012
Grapevine leafroll-associated virus 3 (GLRaV-3) is the main contributing agent of leafroll disease worldwide. Four of the six GLRaV-3 variant groups known have been found in South Africa, but their individual contribution to leafroll disease is unknown. In order to study the pathogenesis of leafroll disease, a sensitive and accurate diagnostic assay is required that can detect different variant groups of GLRaV-3.
In this study, a one-step real-time RT-PCR, followed by high-resolution melting (HRM) curve analysis for the simultaneous detection and identification of GLRaV-3 variants of groups I, II, III and VI, was developed. A melting point confidence interval for each variant group was calculated to include at least 90% of all melting points observed. A multiplex RT-PCR protocol was developed to these four variant groups in order to assess the efficacy of the real-time RT-PCR HRM assay.
A universal primer set for GLRaV-3 targeting the heat shock protein 70 homologue (Hsp70h) gene of GLRaV-3 was designed that is able to detect GLRaV-3 variant groups I, II, III and VI and differentiate between them with high-resolution melting curve analysis. The real-time RT-PCR HRM and the multiplex RT-PCR were optimized using 121 GLRaV-3 positive samples. Due to a considerable variation in melting profile observed within each GLRaV-3 group, a confidence interval of above 90% was calculated for each variant group, based on the range and distribution of melting points. The intervals of groups I and II could not be distinguished and a 95% joint confidence interval was calculated for simultaneous detection of group I and II variants. An additional primer pair targeting GLRaV-3 ORF1a was developed that can be used in a subsequent real-time RT-PCR HRM to differentiate between variants of groups I and II. Additionally, the multiplex RT-PCR successfully validated 94.64% of the infections detected with the real-time RT-PCR HRM.
The real-time RT-PCR HRM provides a sensitive, automated and rapid tool to detect and differentiate different variant groups in order to study the epidemiology of leafroll disease.
KeywordsGrapevine leafroll-associated virus 3 GLRaV-3 Real-time RT-PCR High-resolution melting curve analysis Multiplex RT-PCR Molecular variants Leafroll disease
Grapevine leafroll-associated virus 3 (GLRaV-3) is a positive-sense single-stranded RNA virus that is the type member of the genus Ampelovirus in the family Closteroviridae. This virus is phloem-limited and is considered the main contributing agent of leafroll disease worldwide with detrimental effects on both wine and table grapes. Six variant groups of GLRaV-3 have been identified of which four are known to be present in South Africa [2–8]. The genomes of at least one representative isolate of variant groups I, II, III and VI have been sequenced. These are isolates 621, WA-MR, NY-1 and CI-766 (group I) [2–5], 623 and GP18 (group II) [2, 6], and PL-20 (group III) . Recently, isolates GH11 and GH30 (group VI), were identified, and showed less than 70% nucleotide identity to other GLRaV-3 variant groups . Limited sequence information for GLRaV-3 variant groups IV and V is available and isolates from these groups are only represented by coat protein gene sequences in the GenBank database . All these genetic variants commonly occur as mixed infections. However, no specific disease symptoms or geographic distribution could so far be assigned to a specific variant group or cluster of variant groups. It is therefore necessary to develop an effective method that can detect all GLRaV-3 variants and differentiate between them. Previously, single-strand conformation polymorphism (SSCP) profiles have been used to investigate the population structure and genetic variability of GLRaV-3 variants [2, 9]. Although SSCP analysis is fast and cost effective for variant typing based on sequence heterogeneity, the technique is not as sensitive as RT-PCR and requires sequencing to verify new variants. Metagenomic sequencing or next generation sequencing is the most sensitive diagnostic tool available to detect and identify known and novel viruses [10–13]. Next generation sequencing can identify viral pathogens occurring at extremely low titers without the necessity of any prior sequence knowledge. Although this technique is unbiased, it is still too expensive to use for routine diagnostics. Reverse transcription polymerase chain reaction (RT-PCR) is a diagnostic tool capable of detecting virus sequences at low concentrations and can be designed to be genus-, species-, or isolate-specific [14, 15]. The design of optimal RT-PCR primers requires accurate sequence information. The recently sequenced GLRaV-3 group VI was found to be less than 70% similar to other GLRaV-3 variant groups and warrants a re-evaluation of existing GLRaV-3 RT-PCR diagnostic primers.
Real-time RT-PCR is another technique that has been successfully utilized to detect various plant viruses, including GLRaV-3 . It is a rapid, reliable and quantitative detection method that is more sensitive than conventional RT-PCR. It has the potential for multiplexing and is therefore able to detect several pathogens in the same reaction . The development of high-resolution melting (HRM) curve analysis, as an extension to real-time RT-PCR, provides a rapid, high-throughput, cost effective and single tube approach to discriminate and genotype strains of bacteria and viruses . The genotyping of variants does not require a labeled probe and sequence variants can be distinguished from each other based on their individual melting temperatures . High-resolution melting (HRM) curve analysis was effectively applied in diagnostics for viruses affecting humans  as well as for phytopathogenic bacteria .
The aim of this study was to develop a simple and reliable one-step real-time RT-PCR assay with high-resolution melting (HRM) curve analysis (RT-PCR HRM) for the simultaneous detection and identification of GLRaV-3 variants of groups I, II, III and VI, all four previously detected in South African vineyards. To achieve this, a universal primer set, able to detect and differentiate these variant groups, was designed. A multiplex RT-PCR was also developed to validate the RT-PCR HRM. The application of these protocols will aid in the understanding of the molecular epidemiology of GLRaV-3 and leafroll disease and assist programmes focused at managing and controlling the spread of GLRaV-3.
Results and discussion
Primer design and evaluation
Descriptive statistics of melting points generated by real-time RT-PCR HRM assays with primer pairs LR3.HRM4 and LR3.HRM6
Number of data pointsa
Temperature range between upper and lower limit
Pairwise comparison of LR3.HRM4 amplicon (226 nt segment of Hsp70h) for each variant group
Variant group representative isolates
Group III_GQ352633.1_GLRaV-3_Isolate PL-20
Group VI_JQ655295_GLRaV-3_Isolate GH11
Verification of one-step real-time RT-PCR HRM assay
Real-time RT-PCR and HRM analysis
Analysis of grapevine leafroll-associated virus 3 (GLRaV-3) single and mixed variant group infections
Number of infections
I + II
I + III
I + VI
II + III
II + VI
III + VI
I + II + III
I + II + VI
II + III + VI
I + II + III + VI
Calculation of the melting point confidence interval for each variant group based on real-time RT-PCR HRM curve analysis using LR3.HRM4 or LR3.HRM6 primer pairs a
Interquartile range (IQR) (75%-25%)d
Number of outliers (>±1.5xIQR)
Shapiro- Wilk test of normality (p)e
Melting point interval without overlaps
To differentiate groups I and II, primer pair LR3.HRM6 was used. The melting points of both groups I and II were also not normally distributed and the confidence intervals were calculated using the 2.5th to 97.5th percentile range. The group I interval was calculated from 84.79°C to 85.39°C (95% confidence) and for group II from 86.01°C to 86.78°C (95% confidence).
Outliers were identified within variant groups III and VI for primer pair LR3.HRM4 and within variant group I for primer pair LR3.HRM6 (Table 4). The comparatively high number of outliers identified within variant group VI resulted in a lower confidence level for this variant group compared to the other groups.
The Rotor-Gene software can perform automated variant classification based on the melting point interval calculated from the derivative melting curve (dF/dT) profile for each sample. Bins were programmed based on the data set for each variant group that consisted of a calculated midpoint with a 95% confidence interval width. This allows the software to automatically classify each melting peak observed according to the bins programmed. To avoid unnecessary peak calling, the temperature threshold can be set at 83°C, because none of the variant groups is expected to have a melting point below 83°C.
These confidence intervals for both primer pairs LR3.HRM4 and LR3.HRM6 were calculated, based on data generated from RNA extracted using the CTAB method. It was observed that when a different RNA extraction protocol was used, the melting points for each variant group shifted proportionally (unpublished data). This is probably the result of the interaction of the intercalating SYTO 9 dye which is influenced by inhibitors and salt concentration in the RNA extract.
In this study, preliminary data on the incidence of GLRaV-3 variants in the Western Cape of South Africa were collected using RT-PCR HRM analysis. A previous study, using SSCP, identified variant group II as the most prevalent, with 54% of a sample size of 80 being infected by this variant . In the present study, variant groups II and VI were equally distributed with a 39% infection rate each. Of the 224 infections detected in 121 positive samples, 21% were single variant infections, with half of these classified as group VI. These preliminary data is not necessarily an indication of the distribution of GLRaV-3, since more than halve of the grapevine samples came from only three severely infected vineyards and the rest were from greenhouse isolate collections that decreases the complexity of mix infections. However, it confirms the presence of four GLRaV-3 variant groups in South Africa and that the technique can successfully be applied to study the distribution of GLRaV-3 variants.
Variant status confirmation using multiplex RT-PCR
In order to investigate the spread and impact of different GLRaV-3 variants in vineyards, sensitive diagnostic techniques are a necessity. Serological tests like ELISA is one of the preferred detection methods for plant viral disease diagnostics due to its simplicity and effectiveness . However, as viral sequences become available, virus-specific primers can be designed to be used in RT-PCR or real-time RT-PCR that is more sensitive than serological tests. In this study, a real-time RT-PCR was designed that is able to detect GLRaV-3 variant groups I, II, III and VI, using a single primer pair targeting the Hsp70h gene of GLRaV-3. If HRM curve analysis is added to the real-time RT-PCR, it is possible to differentiate between variant groups based on three melting point intervals. An additional primer pair was identified that is able to differentiate between variant groups I and II. The RT-PCR HRM assay provides a more sensitive, automated and rapid tool to detect and differentiate between different GLRaV-3 variant groups. The multiplex RT-PCR offers an end-point PCR alternative to differentiate between the variant groups present in South African or to be used as a validation method for the RT-PCR HRM. The abovementioned tools will contribute to the understanding of the pathogenesis of leafroll disease and aid epidemiology studies to investigate how these different GLRaV-3 variant groups are spreading.
Materials and methods
Virus source and sample preparation
Plant material from 173 grapevine plants was used to establish and validate the RT-PCR HRM. Forty vines from a study in 2008, where the distribution of GLRaV-3 variants in disease clusters were investigated, were re-collected from a vineyard in the Worcester vine growing region . Ninety grapevine plants were randomly selected during a field survey in 2008 from two severely infected vineyards in the Stellenbosch area and 39 grapevine samples were from a virus isolate collection (Vitis Laboratory, Stellenbosch University, South Africa), maintained in V. vinifera, grown in the greenhouse. An additional GLRaV-3 positive sample for each variant group, singly infected with only that variant (Group I, II, III and VI), were obtained from a virus isolate collection (ARC-Plant Protection Research Institute, Pretoria, South Africa). Phloem scrapings were prepared from cane material collected during winter. Total RNA was extracted from 2.5g phloem tissue using an adapted Cetyltrimethylammonium bromide (CTAB) method (2% CTAB, 2.5% PVP-40, 100mM Tris-HCL pH8, 2M NaCl, 25mM EDTA pH8 and 3% β-mercaptoethanol) .
List of primers used with the real-time RT-PCR HRM assay and the end-point multiplex RT-PCR protocol
Amplicon size (bp)
V. vinifera predicted actin-7
Verification of one-step real-time RT-PCR assay with melting curves generated from plasmid DNA
Real-time RT-PCR amplicons of GLRaV-3 variant groups I, II, III and VI were cloned into a pGEM-T-easy Vector (Promega) and sequenced to obtain variant-specific plasmid DNA. Artificial in vitro mixed infections between the variant-specific plasmid DNA were made to determine whether the chosen primer pair could differentiate between variants if mixed infections would be present in field plants. Duplex infections were made in a 1:3, 1:1 and 3:1 ratio for each combination of two variant groups. Reaction mixtures of all variant-specific plasmid DNA PCR HRM assays contained 1x KAPA Taq Buffer A (KAPA Biosystems), 0.4 μM reverse primer (IDT), 0.4 μM forward primer (IDT), 0.2 mM dNTP mix (Fermentas) 1 μM SYTO 9 (Invitrogen), 0.04 U/ μl KAPA Taq DNA polymerase (KAPA Biosystems) and 0.01 ng/μl plasmid DNA. Cycle conditions included an initial denaturation step at 94°C for 5 minutes, followed by 45 cycles of 94°C for 10 seconds, annealing at 55°C for 10 seconds and elongation at 72°C for 20 seconds. Acquisition on the green channel was recorded at the end of the extension step. High-resolution melting curves of PCR amplicons were obtained with temperatures ranging from 70°C to 90°C with a 0.1°C increase in temperature every two seconds.
Real-time RT-PCR and HRM analysis
The primer pair that could most effectively detect and differentiate between GLRaV-3 variant groups I, II, III and VI was used to screen the 173 samples to optimize the assay. Each reaction was performed in duplicate using the RT-PCR HRM on a Qiagen Rotor-Gene Q thermal cycler. Reaction mixtures contained 1x KAPA Taq Buffer A (KAPA Biosystems), 0.4 μM reverse primer (IDT), 0.4 μM forward primer (IDT), 0.2 mM dNTP mix (Fermentas), 1 μM SYTO 9 (Invitrogen), 0.04 U/μl KAPA Taq (KAPA Biosystems), 0.08 U/μl Avian Myeloblastosis Virus (AMV) reverse transcriptase (Fermentas) and 100 ng RNA. Optimized cycle conditions were a cDNA synthesis step at 48°C for 30 minutes, an initial denaturation step at 94°C for 5 minutes, followed by 45 cycles of 94°C for 10 seconds, annealing at 55°C for 10 seconds and elongation at 72°C for 20 seconds. Acquisition on the green channel was recorded at the end of the extension step. High-resolution melting curves of PCR amplicons were obtained with temperatures ranging from 70°C to 90°C with a 0.1°C increase in temperature every two seconds. HRM curve analysis was performed using the Rotor-Gene software version 1.7. In order to use the RT-PCR HRM to differentiate between variants, a melting point confidence interval had to be determined for each variant group. The data generated for each variant group were tested for normality using the Shapiro-Wilk algorithm and descriptive statistics were calculated using the SPSS statistics software package 19 (IBM).
Variant status conformation using multiplex RT-PCR
Variant-specific RT-PCR reverse primers (Table 5) targeting the 5’ UTR of the GLRaV-3 variant groups I, II, III and VI were designed to be used in a single reaction with one forward primer. This multiplex RT-PCR was designed to validate the HRM analysis and assign each sample to a specific variant group. A primer pair targeting the V. vinifera actin gene was also included in the multiplex RT-PCR to act as an RNA specific internal control. A two-step RT-PCR multiplex protocol was used and approximately 1000-1500 ng of total RNA was denatured at 65°C for 5 minutes with 2 μM of LR_ORF1aR primer (IDT) and 2 μM of Vv_Actin_R (IDT)  (Table 5) and incubated for 2 minutes on ice (5 μl final volume). The RNA was reverse-transcribed by incubation at 48°C for 1 h in a reaction mixture (10 μl final volume) containing 1x Avian Myeloblastosis Virus (AMV) reverse transcriptase buffer (Fermentas), 1 mM dNTP mix (Fermentas), 1U/μl Ribolock (Fermentas) and 0.5 U/μl AMV reverse transcriptase (Fermentas). A 2.5 μl aliquot of cDNA was subjected to PCR in a 25 μl reaction mixture containing 1x KAPA Taq buffer B (KAPA Biosystems), 0.4 mM dNTP mix (Fermentas), 0.4 μM LR_universal_F primer (IDT), 0.28 μM Vv_Actin F (IDT) , 0.28 μM Vv_Actin R (IDT) , 0.4 μM of each variant-specific reverse primer (IDT) (Table 5), 0.5μg/μl Bovine Serum Albumin (BSA) (Roche) and 0.08 U/μl KAPA Taq DNA polymerase (KAPA Biosystems). Cycle conditions included an initial denaturation step at 94°C for 5 minutes, followed by 35 cycles of 94°C for 30 seconds, annealing at 58°C for 20 seconds and elongation at 72°C for 40 seconds. Final extension was at 72°C for 7 minutes. Amplicons were visualized on an ethidium bromide-stained 2% TAE-agarose gel (2 M Tris, 1M glacial acetic acid, 0.05 M Na2EDTA, pH 8).
The financial assistance of the National Research Foundation (NRF) towards this research is hereby acknowledged. Opinions expressed and conclusions arrived at, are those of the author and are not necessarily to be attributed to the NRF. The authors thank Winetech for research funding, Yolandi Espach for her contribution to the sample preparation, Theo Pepler for his assistance in the statistical analysis of data and Dr. Dirk Stephan for critical reading of this manuscript.
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