Introduction
Insecticide resistance is a widespread phenomenon documented in a great number of insect species (Arthropod Pesticide Resistance Database; http://www.pesticideresistance.com). Most represent a serious threat for agricultural production, and others are of medical and veterinarian importance (Whalon et al., Reference Whalon, Mota-Sanchez, Hollingworth, Whalon, Mota-Sanchez and Hollingworth2008). Despite the large diversity of insect pests, resistance mechanisms that have so far been identified can roughly be gathered together just in a few groups: reduced penetration; enhanced production of metabolic enzymes excluding, sequestering and destroying the insecticide; target-site insensitivity (Feyereisen et al., Reference Feyereisen, Dermauw and Van Leeuwen2015).
The discovery of point mutations in target proteins associated with insecticide resistance mechanisms has increased in recent years and the coexistence of different mutations within a single specimen has been documented in several species. The establishment of different genotypes associated with resistant phenotypes depends on the life cycle, the fecundity and the reproductive mode of the insects. In particular, mating is responsible for the production of different genotype combinations and can explain the presence of different resistance mechanisms within the individual, each contributing to enhance resistance factors (Fenton et al., Reference Fenton, Margaritopoulos, Malloch and Foster2010; Hardstone & Scott, Reference Hardstone and Scott2010; Feyereisen et al., Reference Feyereisen, Dermauw and Van Leeuwen2015).
Monitoring and detection of insecticide resistance is currently one of the most important aspects for insect pest management.
In recent years, we have experienced an increase of resistant cases of agricultural pests that are important for some local crop productions. The most representative example is the green peach aphid Myzus persicae (Sulzer) (Hemiptera: Aphididae), which is considered one of the most widely and strongly resistant species worldwide. Since 2010, we have received a considerable number of requests for single nucleotide polymorphisms (SNP) genotyping for samples collected throughout Italy (Panini et al., Reference Panini, Dradi, Marani, Butturini and Mazzoni2014), with particular attention paid to neonicotinoid resistance. In fact, the assessment of the presence and distribution of resistant alleles is of particular importance to understand the reasons for control failures causing consistent re-infestation of this pest and to create and implement effective resistance management strategies, avoiding inefficacious insecticide applications.
Analogous considerations are true for other insect species important for urban and livestock environments, like the housefly Musca domestica L. (Diptera: Muscidae). As it can transmit several diseases it represent a serious pest for public health, poultry and livestock farming, and insecticide applications are commonly used for its control. In particular, previously, pyrethroids were the chemical control of choice, leading to serious resistance problems. Target-site mutations responsible for pyrethroid resistance have been documented in samples collected in different locations worldwide (Rinkevich et al., Reference Rinkevich, Hedtke, Leichter, Harris, Su, Brady, Taskin, Qiu and Scott2012; Mazzoni et al., Reference Mazzoni, Chiesa, Puggioni, Panini, Manicardi and Bizzaro2015). Despite this, they still remain one of the main chemicals adopted for housefly control. For this reason the presence and diffusion of any target-site mechanisms, which could affect this class of product must be continuously considered and monitored to assess the resistance status of this pest.
In both the above-mentioned species, there is a consistent number of SNPs that must be checked to estimate the presence of target-site resistance mechanisms. In addition, it is possible to find different polymorphisms for a specific locus, e.g. M918T/L in the green peach aphid s-kdr locus (Panini et al., Reference Panini, Anaclerio, Puggioni, Stagnati, Nauen and Mazzoni2015) or L1014F/H in the housefly kdr locus (Liu & Pridgeon, Reference Liu and Pridgeon2002). Furthermore, an important aspect to consider is the large number of samples that have to be analyzed in order to obtain frequencies of the resistant alleles, which can be representative of the real situation in the field. All of these aspects contribute to increase the number of analyses that must be performed.
A significant amount of research carried out in recent years has provided several advances in understanding the evolution of resistance mechanisms and a variety of molecular and biochemical techniques has been developed allowing the detection of resistance-associated mutations. In particular, the increasing number of insect genomic sequences available has enabled the development of several SNP genotype detection methods.
A comprehensive list of these techniques has been reviewed by Kwok (Reference Kwok2001) and Black & Vontas (Reference Black and Vontas2007), while a cost, timing and performance comparison has been presented by Bass et al. (Reference Bass, Nikou, Donnelly, Williamson, Ranson, Ball, Vontas and Field2007) and, more recently, by Bai et al. (Reference Bai, Zhu, Zhou, Tang, Li, Xu, Zhang, Yao, Huang, Wang, Zhang, Wang, Cao and Gao2014). Some of the high-throughput technologies (e.g. TaqMan assay or direct sequencing reactions) are limited due to expensive equipment and/or the high costs and expiring date of the reagents. Other affordable assays (e.g. allele-specific PCRs or PCR–RFLP) require several steps (amplification, restriction analysis, PCR and visualization in gel electrophoresis), limiting the number of samples that can be analyzed in a short time.
In this work, we combine the basics of real-time PCRs and allele-specific PCRs to develop a new affordable assay (QSGG: Qualitative Sybr Green Genotyping) to detect known target-site mutations in M. persicae and M. domestica (Nabeshima et al., Reference Nabeshima, Kozaki, Tomita and Kono2003; Bass et al., Reference Bass, Puinean, Andrews, Cutler, Daniels, Elias, Paul, Crossthwaite, Denholm, Field, Foster, Lind, Williamson and Slater2011; Rinkevich et al., Reference Rinkevich, Du and Dong2013; Panini et al., Reference Panini, Anaclerio, Puggioni, Stagnati, Nauen and Mazzoni2015). The approach links rapid detection of the Sybr Green real-time technique with the capability of PASA–PCRs to discern univocally the presence of a particular SNP of interest.
Materials and methods
Insects
M. persicae populations were collected around Italy from different hosts. During field sampling aphids were directly stored in acetone and then kept at −20°C till DNA extraction. Populations of reference clones were available in the rearing collection of the Department of Sustainable Crop Production (Università Cattolica del Sacro Cuore, Piacenza, Italy), where each strain is maintained as a colony of parthenogenetic females under controlled conditions (Mazzoni & Cravedi, Reference Mazzoni and Cravedi2002).
M. domestica populations were collected from various sites in Northern Italy, stored in acetone and kept at −20°C till DNA extraction. A reference susceptible strain S-WHO was kindly provided by Ralf Nauen (Bayer Crop Science, Monheim, Germany) and reared as described in Mazzoni et al. (Reference Mazzoni, Chiesa, Puggioni, Panini, Manicardi and Bizzaro2015).
Reference specimens
Specimens of M. persicae and M. domestica with well-defined genotypes were used as references (table 1). They were previously characterized by direct sequencing (Sanger method) for the presence of the following mutation of interest: L1014F/H (kdr and kdr-his) and M918T (s-kdr) for houseflies (Mazzoni et al., Reference Mazzoni, Chiesa, Puggioni, Panini, Manicardi and Bizzaro2015); L1014F (kdr), M918T/L (s-kdr), R81T for aphids (Panini et al., Reference Panini, Dradi, Marani, Butturini and Mazzoni2014, Reference Panini, Anaclerio, Puggioni, Stagnati, Nauen and Mazzoni2015). The same procedure was here adopted to characterize the presence of MACE (S431F).
Table 1. Reference specimens and known nucleotide polymorphisms associated with insecticide resistance in M. domestica and M. persicae.
DNA extraction
Genomic DNA was extracted from single specimens of M. persicae or from the head of individual adults of M. domestica by a ‘salting-out’ protocol, as already described (Panini et al., Reference Panini, Dradi, Marani, Butturini and Mazzoni2014). After some trials with different DNA dilutions without quantification, real-time PCR analyses were performed using 1:10 and 1:5 dilutions for aphid and housefly samples, respectively.
PCR protocols for SNP detection
The presence of target-site mutations was assessed by real-time PCR. Different assays were developed for different loci. A full list of primers used is reported in table 2. In some allele-specific primers a mismatch was incorporated in position 4, starting from 3′ end, to improve their specificity.
Table 2. Sequences of the primers used.
The presence of ‘X’ letter in the primer names indicates a mismatch corresponding to the underlined nucleotide. Nucleotides in bold at 3′ end indicate the specificity for the wild-type or the mutant allele.
1 primer used for sequencing.
2 Cassanelli et al. (Reference Cassanelli, Cerchiari, Giannini, Bizzaro, Mazzoni and Manicardi2005).
3 Fontaine et al. (Reference Fontaine, Caddoux, Brazier, Bertho, Bertolla, Micoud and Roy2011).
4 Panini et al. (Reference Panini, Dradi, Marani, Butturini and Mazzoni2014).
5 Mazzoni et al. (Reference Mazzoni, Chiesa, Puggioni, Panini, Manicardi and Bizzaro2015).
All PCR reactions were performed using iTaq™ Universal SYBR® Green Supermix (Bio-Rad) and were run on a BIO-RAD CFX96™ Real-Time system. In general, PCR reactions (10 µl) contained 5 µl iTaq™, 0.4 µM of each primer and 3 µl of diluted genomic DNA, and were performed as follows: initial denaturation at 95°C (5 min) followed by 40 cycles of denaturation at 95°C (5 s), annealing temperature depending on the locus (30 s) and elongation at 72°C (60 s). A final melt curve step was included, ramping from 65 to 95°C by 0.5°C every 5 s.
The possible genotypes can be distinguished by calculating the difference between the Quantification Cycle (Cq for short) values obtained from PCRs to detect wild-type allele (PCR-W) and mutant allele (PCR-M), as follows:
$$\Delta {\rm C}{\rm q}_{{\rm W - M}}\, = \,{\rm C}{\rm q}_{\rm W}\, - \,{\rm C}{\rm q}_{\rm M},$$
ΔCqW–M data were analyzed with the IBM SPSS version 23 Statistical Package using EXAMINE, ONEWAY and NPAR TESTS procedures.
M. domestica s-kdr
To detect M918T (ATG→ACG) in para-type sodium channel gene (NaCh), the following PCR reactions (T annealing 62.3°C) were used: (a) PCR-WHfSK combines primer HF_Sk7_Re with allele-specific primer HF_SkX5s_FW to detect codon ATG (Met); (b) PCR-MHfSK combines primer HF_Sk7_Re with allele-specific primer HF_SkX6r_FW to detect codon ACG (Thr).
M. persicae MACE
To detect S431F (TCA→TTT) in acetylcholinesterase 1 gene (AChE1), the following PCR reactions (T annealing 60°C) were used: (a) PCR-WMACE combines primer AChE-F2 with allele-specific primer MpACEs-R to detect codon TCA (Ser); (b) PCR-MMACE combines primer AChE-F2 with allele-specific primer Mace-R-Rev to detect codon TTT (Phe).
M. persicae R81T
To detect R81T (AGA→ACA) in nicotinic acetylcholine receptor β1 subunit (nAChR β1), the following PCR reactions (T annealing 60.5°C) were used: (a) PCR-WR81T combines primer MpNACR-R514 with allele-specific primer MpNACRs-XFW-aga to detect codon AGA (Arg); (b) PCR-MR81T combines primer MpNACR-F52 with allele-specific primer MpNACRr-XRE-aca to detect codon ACA (Thr).
M. persicae kdr
To detect L1014F (CTC→TTC) in para-type sodium channel gene (NaCh) the following PCR reactions (T annealing 58°C) were used: (a) PCR-WMpK combines primer kdr-R4 with allele-specific primer MpKDR-XFW-ctc to detect codon CTC (Leu); (b) PCR-MMpK combines primer kdr-F1 with allele-specific primer MpKDR-XRE-ttc to detect codon TTC (Phe). In the latter the concentration of primers was increased to 0.5 µM.
M. domestica kdr
To detect L1014F/H (CTC→TTC or CTC→CAT) in para-type sodium channel gene (NaCh) the following PCR reactions (T annealing 58.5°C) were used: (a) PCR-W1HfK combines primer K2 with allele-specific primer K3 to detect nucleotide C in position 1; (b) PCR-M1HfK combines primer K2 with allele-specific primer K4 to detect nucleotide T in position 1; (c) PCR-W2HfK combines primer K2 with allele-specific primer HF_KsH_F to detect nucleotide T in position 2; (d) PCR-M2HfK combines primer K2 with allele-specific primer HF_KrH_F to detect nucleotide A in position 2.
M. persicae s-kdr
To detect M918T (ATG→ACG) in para-type sodium channel gene (NaCh) the following PCR reactions (T annealing 68.7°C) were used: (a) PCR-W2MpSK combines primer MpSK-F25 with allele-specific primer MpSKs-RE to detect nucleotide T in position 2; (b) PCR-M2MpSK combines primer MpSK-F25 with allele-specific primer MpSKr-RE to detect nucleotide C in position 2.
To detect M918L (ATG→CTG or TTG) in the same locus the following PCR reactions (T annealing 63°C) were used: (a) PCR-W1MpSK combines primer MpSK-F25 with allele-specific primer MpSKL-XRE-atg to detect nucleotide A in position 1; (b) PCR-M1CMpSK combines primer MpSK-R3292 with allele-specific primer MpSKL-XFW-ctg to detect nucleotide C in position 1; (c) PCR-M1TMpSK combines primer MpSK-R3292 with allele-specific primer MpSKL-XFW-ttg to detect nucleotide T in position 1.
Results
For each target, PCR for the wild-type and the mutant alleles were set up. The reaction conditions were optimized using preliminary gradient PCRs to get the best thermal conditions as well as the optimal primer concentrations (data not shown). After this, PCRs on a certain number of referent specimens where run for 40 cycles and their Cqs were evaluated using threshold automatically calculated by the Bio-Rad CFX Manager™ software.
When both PCRs (wild-type and mutant) gave Cqs higher than 30 they were scored as negative amplification and not considered in our statistics. When only one reaction gave positive amplification, the sample was definitively scored as homozygous, wild-type (W) or mutant (M), according to the primer set used (fig. 1a). In all the remaining cases, the difference between Cq wild-type and Cq mutant values (ΔCqW–M) was calculated (fig. 1b) even if one of the Cqs was higher than 30 (fig. 1c) and used to score the correct genotype. Final melt curves always confirmed the absence of non-specific amplification products (data not shown).
Fig. 1. Examples of real-time fluorescence curves (i.e. M. persicae MACE) obtained in presence of different genotypes: (a) homozygous wild-type; (b) heterozygous; (c) mutant homozygous (PCR-W, PCR with specific primer for wild-type allele; PCR-M, PCR with specific primer for mutant allele).
ΔCqW–M values are expected to be negative when both alleles are wild-type, near 0 for heterozygous genotype or positive when both alleles are mutant.
A range of 20–40 reactions were performed for each target, and data were validated as follow. The Shapiro–Wilk test (P > 0.05) showed that ΔCqW–M values for reference samples were approximately normally distributed for each target and genotype (Supplementary table 1). The same sets of ΔCqW–M values were analyzed with one way analysis of variance (ANOVA) comparing different genotypes within each target. F statistics revealed extremely significant differences of ΔCqW–M means (table 3). The comparison using the Student–Newman–Keuls (SNK) test confirmed that ΔCqW–M means of homozygous wild-type, heterozygous and homozygous mutated genotype were statistically different (P < 0.05).
Table 3. Analysis of variance (ANOVA) performed on ΔCqW–M values obtained from reference samples.
Discrimination of one polymorphism
Target-site resistance to neonicotinoids (R81T), dimethyl-carbamates (MACE: S431F) and pyrethroids (kdr: L1014F in M. persicae; s-kdr: M918T in M. domestica) are caused by one amino acidic substitution, giving three possible genotypes: homozygous wild-type (W/W), heterozygous (W/M), mutant homozygous (M/M). These targets were characterized as above described and, as expected, homozygous wild-type genotypes produced negative ΔCqW–M; heterozygous samples produced ΔCqW–M mean values near 0; mutant homozygous genotypes gave positive ΔCqW–M mean values depending on the considered target (table 4; Supplementary fig. 1).
Table 4. ΔCqW–M mean values measured for targets with one polymorphism in the corresponding codons.
SD, standard deviation; n, number of reference samples analyzed.
Means with different letters are statistically different (SNK test).
Discrimination of two or three alternative polymorphisms
Other assays were developed to characterize target-site mutations, which are due to multiple possible nucleotide substitutions involving different positions within the same codon, like kdr in M. domestica (L1014F and L1014H) or s-kdr in M. persicae (M918T and M918L).
In M. domestica, the presence of L1014F/H (kdr/kdr-his) was assessed combining results from four different allele-specific real-time PCR reactions, organized in two separate groups to detect nucleotide substitutions in the first and the second position of the codon, respectively.
The first PCR group combines the reverse common primer K2 and the forward primers K3 (PCR-W1HfK ) or K4 (PCR-M1HfK ), specifically designed to detect nucleotide C or T in the first position of the codon. This reaction detects the Phe substitution (TTT) but it is not able to discriminate between wild-type susceptible Leu (CTT) and resistant His (CAT).
Smaller ΔCqW1–M1 values were obtained in comparison to those observed for the other considered targets. They ranged from 3.35 in the presence of ‘thymine’ to −5.16 in the presence of ‘cytosine’ (table 5).
Table 5. M. domestica kdr locus.
Mean values of ΔCqW–M recorded for the two PCR groups to detect SNPs in positions 1 and 2. Means with different letters are statistically different (SNK test) (SD, standard deviation; n, number of reference samples considered for each genotype).
The second PCR group combines the reverse common primer K2 and the forward primers HF_KsH_F (PCR-W2HfK ) or HF_KrH_F (PCR-M2HfK ), specifically designed to detect nucleotide T or A in the second position of the codon. This reaction detects the His substitution (CAT) but it is not able to discriminate between wild-type susceptible Leu (CTT) and resistant Phe (TTT). A wider range of values was obtained and ΔCqW2–M2 mean values were more clearly separated, ranging from −13.6 in the presence of ‘thymine’ to 9.18 in the presence of ‘adenine’ (table 5).
The combination of the results allows the identification of the correct genotype (table 6; Supplementary fig. 2).
Table 6. Schematic representation of PCRs used to detect known mutations in M. domestica kdr locus.
Ticks indicate positive amplification for the corresponding genotype.
In M. persicae, the presence of M918T/L (s-kdr) is further complicated by two possible polymorphisms (Italian and French) for the leucine. For this reason, five different specific real-time PCRs are needed, organized in two separate groups to detect the nucleotide substitutions in the first and second position of the codon.
PCR group checking the ‘classic’ s-kdr (M918T) includes two different reactions to determine the second position of the codon. It combines the forward common primer MpSK-F25 with the reverse primers MpSKs-RE (PCR-W2MpSK ) or MpSKr-RE (PCR-M2MpSK ), specifically designed to detect nucleotide T or C. It allows the determination of the Thr substitution (ACG) (fig. 2a), but it is not able to discriminate between wild-type susceptible Met (ATG) and resistant Leu (CTG or TTG).
Fig. 2. Schematic representations of allele-specific primers for s-kdr locus in M. persicae. (a) ‘classic’ s-kdr (M918T). (b) ‘new’ s-kdr (M918L, codons CTG and TTG). Mismatch in position 4 from 3′ end is indicated.
The other PCR group, which evaluates the presence of the ‘new’ s-kdr (M918L), includes three different reactions to determine the first position of the codon. The first PCR combines the forward primer MpSK-F25 with the reverse primer MpSKL-XRE-atg (W1MpSK ) specifically designed to detect nucleotide A. The second combines the reverse primer MpSK-R3292 with the forward primer MpSKL-XFW-ctg (M1CMpSK ) specifically designed to detect nucleotide C. The third combines the reverse primer MpSK-R3292 with the forward primer MpSKL-XFW-ttg (M1TMpSK ) specifically designed to detect nucleotide T (fig. 2b). These reactions identify Leu substitution (CTG or TTG) and the presence of wild-type susceptible Met (ATG) but it is not able to discriminate between mutant homozygous (CTG/CTG and TTG/TTG) and mutant heterozygous (ACG/CTG and ACG/TTG) because the primer MpSKL-XRE-atg cannot amplify codon ACG (fig. 3). The combination of the results allows the identification of the correct genotype (table 7; Supplementary figs 3 and 4).
Fig. 3. Possible pairing of primer MpSKL-XRE-atg. The cross indicates the natural mismatch which prevents amplification despite the presence of nucleotide A in position 1. Mismatch in position 4 from 3′ end is indicated.
Table 7. Schematic representation of the PCRs used to detect known mutations in M. persicae s-kdr locus.
Ticks indicate positive amplification for the corresponding genotype (*: never detected).
PCRs to detect M918T, gave ΔCqW2–M2 values widely separated, ranging from −10.2 in the presence of ‘thymine’ to 10.9 in the presence of ‘cytosine’.
To discriminate ΔCqW–M means measured in presence of codons CTG and TTG two separate ANOVA were applied.
Considering the A/C substitution, ΔCqW1–M1C mean values showed good differentiation between different genotypes: wild-type homozygous (ATG/ATG; ΔCqW1–M1C = −12.2), heterozygous (ATG/CTG; ΔCqW1–M1C = −1.7) and mutant homozygous genotype (CTG/CTG; ΔCqW1–M1C = 6.1). Finally, the heterozygous genotype generated by the presence of both mutant codons (ACG/CTG) produced ΔCqW1–M1C mean value equal to 8.1. Similar results were obtained analyzing the presence of A/T nucleotides in position 1 even if slightly lower ΔCqs were observed in samples with codons ACG/TTG (ΔCqW1–M1T = 5.9) (table 8).
Table 8. M. persicae s-kdr locus.
SD, standard deviation; n, number of reference samples considered for each genotype.
Mean values of ΔCqs recorded for the two PCR groups to detect SNPs in position 1 and 2. Means with different letters are statistically different (SNK test). Genotype TTG/TTG was never detected.
Application of QSGG method to unknown genotype samples
After the assessment of ΔCqs ranges calculated from the reference samples, a series of threshold values were defined using the limits of data distributions rounded to the nearest integer (table 9). It was decided to adopt this approach instead of using confidence intervals to limit the number of doubtful cases and because theoretical distribution limits were well separated.
Table 9. ΔCqW–M values used as thresholds for genotype assignment.
Several specimens of M. domestica and M. persicae were then analyzed with the QSGG method to verify its capability to characterize samples with unknown genotype. Sets of PCRs were performed as above described and ΔCq values were scored on the basis of the established threshold limits (table 9).
The distributions of unknown samples well fitted those estimated with reference populations (fig. 4). The statistical significance of this comparison was evaluated for each target and genotype with the non-parametric Mann–Whitney U test and in almost every case no statistically significant differences were found (Supplementary table 1). The only statistically significant difference was observed in MACE homozygous mutated distribution (M/M), due to the small number of samples analyze likely because of the rareness of this genotype in field populations. Nonetheless all the ΔCqW–M observed were without any doubt higher than the threshold limit (fig. 4d).
Fig. 4. Frequency distributions of ΔCqW–M measured in unknown samples compared with ΔCqW–M distribution from known genotypes for the investigated targets in M. domestica (a: kdr, L1014F, n = 107; b: kdr-his, L1014H, n = 107; c: s-kdr, M918T, n = 37) and M. persicae (d: MACE, n = 85; e: R81T, n = 65; f: kdr, L1014F, n = 270; g: s-kdr, M918L (ctg), n = 215; h: s-kdr, M918L (ttg), n = 215; i: s-kdr, M918T, n = 340).
When no statistically significant differences were detected, examples of small deviations from the predicted ranges were observed. Examples were represented by MACE homozygous wild-type (W/W) and R81T mutant homozygous (M/M) where some ΔCqs were higher than expected but in both case this did not affect genotype assignment.
Discussion
Recent advances in molecular biology allowed the development of several techniques for SNPs detection. The opportunity and feasibility to detect variation of individual nucleotides is well established and represents a key tool for multiple application, including genetic population studies and specific point mutation characterization (Tsuchihashi & Dracopoli, Reference Tsuchihashi and Dracopoli2002; Black & Vontas, Reference Black and Vontas2007). Many efforts have been finalized for the identification of target-site resistance in a wide number of insect pests and there is now an increasing consciousness of the importance of early detect resistance in field populations to prevent the misuse and abuse of insecticide products that are not efficacious anymore.
A great variety of techniques have been developed, which are based on different chemistries and signal detection methods. As consequence, some of them are efficient buy very costly, whilst others represent cheaper but time-consuming solutions.
Briefly, if we compare the most common methodologies today available for SNP genotyping (Bai et al., Reference Bai, Zhu, Zhou, Tang, Li, Xu, Zhang, Yao, Huang, Wang, Zhang, Wang, Cao and Gao2014), the two most specific and accurate are those which rely on the sequencing of DNA traits of interest: Sanger or pyro-sequencing. In addition to the obvious reliability of those techniques, it is worth to consider the high costs required per sample, together with the inconvenience of a certain waiting time for the results, especially if the sequences are produced by external sequencing services. Another important technique largely used to detect point mutations is the fluorescence assay based on TaqMan probes. Despite the previous ones, real-time TaqMan assay do not require any pre-amplification steps for templates preparation (like the linkage with streptavidin-coated magnetic beads as for the pyrosequencing), and results can be obtain in only one single step; also, using different dyes it is possible to combine different allele-specific probes in a single reaction. Despite those certainly advantages, it remains quite expensive because of the need of fluorescent probes and, although their costs become a minor problem if a high number of samples are analyzed in a reduced period of time, their performance is strictly dependent on their expiring date. Furthermore, if more than two alleles can be present, single tube multiplex TaqMan analysis with more than two probes could not work in some cases, as reported by Fontaine et al. (Reference Fontaine, Caddoux and Micoud2013) which reported unsatisfactory results with this technique for simultaneously detection of three codons of s-kdr locus in M. persicae.
Cheaper methods are available and routinely used for the same purposes, like the classic restriction length polymorphism (PCR–RFLP) and the allele-specific PCR-based assays (PASA–PCR). Excluding the former method, which is strictly dependent on the creation/destruction of restriction sites in case of nucleotide mutations, the latter has been widely used during the years for SNPs genotyping, as it represents one of the most feasible and economic technique, which do not require any specific laboratory equipment or costly kit reagents, as it is confirmed by Bai et al. (Reference Bai, Zhu, Zhou, Tang, Li, Xu, Zhang, Yao, Huang, Wang, Zhang, Wang, Cao and Gao2014) through the comparison of different genotyping assays. Nevertheless, limitations are impose by the timing of the experiment, which necessarily depends on a visualization step in agarose gel after the amplification. Recently, comparisons of costs and advantages of using allele-specific real-time PCR (similar to QSGG method), with restriction fragment length polymorphism (RFLP) and TaqMan techniques was discussed by Dhas et al. (Reference Dhas, Ashmi, Bhat, Parija and Banupriya2015). All the above mentioned techniques have been largely adopted for the characterization of point mutations related to insecticide resistance in several pests, including M. persicae and M. domestica. Specifically, most of the studies carried out in housefly populations relied on Sanger sequencing (Rinkevich et al., Reference Rinkevich, Hedtke, Leichter, Harris, Su, Brady, Taskin, Qiu and Scott2012), while pyrosequencing was applied to detect M918T/L and L1014F mutations in aphid samples (Panini et al., Reference Panini, Anaclerio, Puggioni, Stagnati, Nauen and Mazzoni2015). TaqMan assays were set up to detect other important point mutations found in M. persicae (Anstead et al., Reference Anstead, Williamson, Eleftherianos and Denholm2004, Reference Anstead, Williamson and Denholm2008; Puinean et al., Reference Puinean, Elias, Slater, Warren, Field, Williamson and Bass2013), as well as PCR–RFLP and PASA–PCRs were applied to both species (Huang et al., Reference Huang, Kristensen, Qiao and Jespersen2004; Cassanelli et al., Reference Cassanelli, Cerchiari, Giannini, Bizzaro, Mazzoni and Manicardi2005; Qiu et al., Reference Qiu, Pan, Li and Li2012; Panini et al., Reference Panini, Dradi, Marani, Butturini and Mazzoni2014; Voudouris et al., Reference Voudouris, Kati, Sadikoglou, Williamson, Skouras, Dimotsiou, Georgiou, Fenton, Skavdis and Margaritopoulos2016).
To improve the diagnosis of target-site resistance in insect pests, in the present study we described an alternative approach for SNPs genotyping, which combines the specificity of PASA–PCR with the rapidity of the Sybr Green real-time detection. The increasing fluorescence is monitored, as in a classic real-time PCR, but it is considered just for the qualitative discrimination and not for quantitative purposes.
In this approach, the low-throughput of the classic allele-specific PCR is overcame, and 96-well microtiter plates can be used in order to process a high number of samples with a consistent reduction of the protocol run time. Furthermore, as the analyses are based on differences of Cq values (ΔCq), gDNA quantification is not compulsory. Initial efforts for the protocol optimization are needed (primer design – if they are not already available in the literature – and temperature annealing), but not more than other techniques. On the contrary, in the proposed QSGG analysis, the amplicon length is not a critical parameter and primers can be improved with the addition of mismatches to increase their allele specificity, whilst for example TaqMan probes must follow specific constraints. Finally, after the statistic validation of standard samples, the analyses can be easily performed when are needed, without any dependence with fluorescent probes availability.
Similar approaches were already been considered by other authors witch performed Sybr Green analysis combining allele-specific PCRs with quantitative real-time PCR with melt curve analysis for genotyping (Dall'Ozzo et al., Reference Dall'Ozzo, Andres, Bardos, Watier and Thibault2003; Papp et al., Reference Papp, Pinsonneault, Cooke and Sadée2003). Further improvements were obtained with high resolution melting (HRM) assays although it required more expensive equipment (Bass et al., Reference Bass, Nikou, Donnelly, Williamson, Ranson, Ball, Vontas and Field2007).
Other studies for different purposes were performed using similar strategies for example by Fraaije et al. (Reference Fraaije, Butters, Coelho, Jones and Hollomon2002) that obtained high accuracy in allele frequencies quantification and by Yu et al. (Reference Yu, Chen, Zhang and Yin2005) that discriminated different Bactrocera species amplifying a specific region of their mitochondrial cytochrome oxidase I gene (COI).
Here we developed similar protocols looking for the discrimination of different polymorphisms that are feasible for specific nucleotide positions. We presented different solutions for genotyping point mutations already described in M. persicae and M. domestica, starting from those with just one polymorphism to reach more complex situations with multiple possible variations in the same locus. Despite a large number of PCR are required for the latter condition, strategies can be adopted to optimize the workflow and reduce the analyses. For example, the detection of kdr and s-kdr mutations can be optimized taking into account that M918T was always found only in the presence of L1014F (Soderlund & Knipple, Reference Soderlund and Knipple2003; Eleftherianos et al., Reference Eleftherianos, Foster, Williamson and Denholm2008). Therefore, in M. persicae, checking first the presence of the classic s-kdr mutation (M918T), in case of homozygous mutated samples no further investigations are necessary, reducing the number of PCRs from 7 to just 2. Similar considerations could be done on the basis of the current knowledge of specific mutation and depending on the sampling areas. An example is the new s-kdr mutation in green peach aphids, which is known to be caused by an A/C substitution in the majority of the Italian populations while the most common French polymorphism is A/T substitution (Roy et al., Reference Roy, Fontaine, Caddoux, Micoud and Simon2013).
To summarize, the QSGG approach represents a valid SNP genotyping method which is high-throughput, rapid and very cost-effective. It can be easily adopted in monitoring surveys related to evaluation of target-site resistance spread and persistence, helping pest management strategies for the control of the insects here considered, as well as other possible targets or biological situations.
Supplementary material
The supplementary material for this article can be found at http://dx.doi.org/10.1017/S0007485316000675
Author contributions
All authors conceived and designed the experiments. V.P., O.C. and M.P. performed the experiments. E.M. and O.C. analyzed the data. All authors wrote, read and approved the manuscript.
Acknowledgements
The present work was supported by European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement n. 605740 (Project EcoSyn).
