Background

The Anopheles Hyrcanus Group in the Republic of Korea (ROK) includes five species (Anopheles sinensis, Anopheles pullus, Anopheles kleini, Anopheles belenrae, and Anopheles lesteri), which cannot be identified morphologically, and another member, Anopheles sineroides, which can be identified morphologically when specimens are not damaged during the collection process [1, 2]. Species identification is important because individual species within the Hyrcanus Group overlap geographically and demonstrate differences in their seasonal distributions, vector competence, and insecticide resistance [3,4,5]. Based on preliminary studies, An. kleini, and An. lesteri are primary vectors of Plasmodium vivax in the ROK, whereas An. pullus, An. belenrae, and An. sinensis are poor vectors [3,4,5]. As An. kleini is more commonly collected near the demilitarized zone (DMZ) where the majority of malaria cases occur, the density of this vector species is a primary covariate for identifying vivax malaria risk [6]. Since the L1014F mutation of voltage-sensitive sodium channel associated with pyrethroid insecticide resistance has been found only in An. sinensis, the efficacy of vector control may depend on the relative composition of vector species [7]. Therefore, information on the abundance of Hyrcanus Group member species in areas of likely malaria transmission is crucial for understanding the population dynamics of vector populations and epidemiology of malaria for the development and implementation of an efficient vector management strategies.

Since five members of the Hyrcanus Group cannot be identified morphologically, DNA barcoding based on the internal transcribed spacer 2 (ITS2) markers have been widely used for species identification [8, 9]. However, this molecular identification is based on individual genotyping requiring extensive labour and financial resources to conduct individual specimen DNA extraction, PCR, and sequencing gene fragments for large numbers of mosquitoes. Recently, detection of Plasmodium species and insecticide resistance genes have been routinely conducted using pooled mosquito specimens, where > 30 mosquitoes are homogenized, DNA extracted, and processed for subsequent analyses [10, 11]. If the species-specific molecular loci are identified and a protocol to distinguish and quantify their frequencies is developed, the DNA or specimen pooling technique can be employed to estimate the proportion of each species within a species complex.

Species-specific loci of the mitochondrial cytochrome C oxidase subunit I (COI) gene and the ITS2 rRNA gene that can distinguish each member of the Hyrcanus Group present in the ROK were first identified. Subsequently, a quantitative sequencing (QS) protocol was developed to estimate the proportion of each species in pooled samples using individual species-specific nucleotide signals at multiple loci of the COI or ITS2 genes. This two-step method was shown to provide a rapid and reliable estimation of the species composition ratios for members of the Hyrcanus Group, and thus is useful for studying vector ecology and epidemiology of vivax malaria in the ROK.

Methods

Anopheles genomic DNA extraction and target gene amplification

Six members of the Hyrcanus Group were collected from Paju, Gyeonggi province, ROK. For molecular identification, genomic DNA (gDNA) was individually extracted using DNeasy Blood & Tissue Kit (QIAGEN, Germany). Then, the ITS2 region of each specimen was amplified using rDNA 5.8 S forward (5′-TGTGAACTGCAGGACACATGAA-3′) and rDNA 28 S reverse (5′-ATGCTTAAATTTAGGGGGTAGTC-3′) primers [12]. The reaction mixture (25 µl) contained 10 ng of template DNA, 2 µl of 2.5 mM dNTP, 2.5 µl of 10X buffer, 0.4 µM of each primer, 0.12 µl of EX Taq polymerase (Takara Biotechnology, Japan) and double distilled water (ddH2O). A 3 min preincubation at 95 ℃ was followed by 34 cycles at 95 ℃ for 20 s, 55 ℃ for 30 s, and 72 ℃ for 1 min, with a final extension at 72 ℃ for 5 min. PCR products were purified using a Monarch Clean up kit (New England Biolabs, USA) and sequenced using an ABI3730xl sequencer at the National Instrumentation Center for Environmental Management (NICEM, Korea). Sequences from each Anopheles specimen were submitted as queries to Basic Local Alignment Search Tool (BLAST) to search similar data in GenBank. A maximum-likelihood (ML) phylogenetic tree for the ITS2 sequences (455–492 bp) for each of the six Hyrcanus Group species was created along with the reference sequences (1624–1651 bp) obtained from GenBank using MEGA-X (ver.10.0.5) (iGEM, USA).

COI and ITS2 sequence alignment

Although COI gene is a generally used marker for the identification of mosquito species, ITS2 was additionally used since the differences in COI sequence were insufficient to distinguish all the sibling species within Hyrcanus group. To detect any intra-species sequence polymorphism, five to seven COI sequences and three to five ITS2 sequences of each Hyrcanus Group species were downloaded from National Center for Biotechnology Information (NCBI) (Additional file 1). COI sequences and ITS2 sequences were aligned respectively using DNAstar MegAlign software (DNASTAR Inc., USA) by ClustalW methods. The ITS2 sequences obtained from collected mosquito samples were also aligned with downloaded sequences. From the alignment data, species-specific or group-specific nucleotide sequences were identified (Table 1).

Table. 1 Species-specific nucleotide sequence loci of Anopheles species in COI and ITS2
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QS primer design for the amplification of COI and ITS2 fragments

A set of primers (An_COI-F and An_COI-R) were designed from the conserved sequence regions across all six Anopheles species to equally amplify the target COI fragments among each of the mosquito species (Table 2). For ITS2 amplification, a set of primers (Anskb_ITS2-F and Anskb_ITS2-R) were designed from the conserved sequence regions of An. sinensis, An. kleini, and An. belenrae to block the amplification in An. sineroides, An. pullus, and An. lesteri (36 ~ 45 % sequence identity for Anskb_ITS2-F; 68.2 % sequence identity for Anskb_ITS2-R).

Table. 2 Designed primer sets used for predicting relative species composition ratios
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The target DNA fragments were amplified from pooled DNA samples using the An_COI-F vs. An_COI-R and Anskb_ITS2-F vs. Anskb_ITS2-R primer sets, respectively. The COI amplification reaction mixture contained 20 ng of each gDNA template, 0.2 mM of dNTP, 2.5 µl of 10X buffer, 0.5 µM of each primer, 0.12 µl of EX Taq polymerase (Takara), and ddH2O up to 25 µl. PCR cycling conditions included preincubation for at 95℃ for 3 min, followed by 32 cycles at 95℃ for 20 s, 56℃ for 30 s, and 72℃ for 1 min, with a final extension at 72℃ for 5 min. To amplify the ITS2 fragment from three species (An. sineroides, An. pullus, and An. lesteri), the reaction mixture contained 10 ng of gDNA template, 0.2 mM of dNTP, 2.5 µl of 10X buffer, 0.25 µM of each primer, 4 % of DMSO, 0.12 µl of EX Taq polymerase, and ddH2O up to 25 µl. PCR cycling conditions included a preincubation at 95℃ for 3 min, followed by 34 cycles at 95 ℃ for 20 s, 64℃ for 25 s, 72 ℃ for 50 s, with a final extension at 72 ℃ for 5 min.

Establishment of a two-step QS protocol for estimating species composition ratios

A two-step QS workflow was developed using PCR-amplified fragments of the COI and ITS2 genes (Fig. 1). The species composition ratios between An. sineroides, An. pullus, and An. lesteri were first estimated using QS of the COI amplicons that included target DNA fragments from all the six Anopheles species, if present. The species composition ratios of An. sinensis, An. kleini, and An. belenrae were predicted using QS of the ITS2 amplicons that did not contain amplified target DNA fragments from An. sineroides, An. pullus, or An. lesteri.

Fig. 1
figure 1

Schematic diagram of the workflow for predicting species composition ratio. The COI fragment were amplified and sequenced from pooled gDNA specimens. Based on the sequencing chromatograms, the nucleotide signals of three COI loci were quantified to estimate the composition ratios of Anopheles sineroides, Anopheles pullus, and Anopheles lesteri. Next, the ITS2 gene fragments were selectively amplified and sequenced for Anopheles sinensis, Anopheles kleini, and Anopheles belenrae. The nucleotide signals of two ITS2 loci were quantified from the sequence chromatograms and the composition ratios of An. sinensis, An. kleini, and An. belenrae were deduced

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Based on the characteristics of Sanger sequencing that the nucleotide signal intensity is affected by the surrounded nucleotide bases [13], equally diluted PCR products of each species were mixed in various ratios to prepare standard DNA templates for QS (Additional file 2) and sequenced to establish the nucleotide signal prediction equations based on linear regression analysis. In preparing the standard DNA templates, the proportion of An. sineroides was limited to 10–50 % since collected numbers are usually < 5 % of all Anopheles species collected in the ROK [14], whereas other species were mixed at 10–90 % proportions. Nucleotide base signal ratios in sequencing data were analysed using Chromas (ver. 2.6.6) and linear regression analysis was done using GraphPad Prism (ver. 6, GraphPad Inc., USA). Linear regression analysis was performed for all the species-specific nucleotide loci of COI and ITS2 to select the most reliable locus based on the R2 criteria and standard error of estimate (Sest), which were the statistical measures for goodness-of-fit.

Determination of copy numbers of COI and ITS2

Copy numbers of COI (or mitochondria number) and ITS2 loci were measured by quantitative real-time PCR (qPCR) using the LightCycler 96 (Roche, Basel, Switzerland). Ribosomal protein S7 (RPS7) and ribosomal protein L8 (RPL8) were used as reference genes since they are known as single-copy genes [15, 16]. gDNA was extracted from individual mosquitoes, and species was identified based on ITS2 markers [8] as described earlier. Following species identification, 3 ~ 5 individual gDNA samples were combined and used as templates for qPCR. The qPCR reaction mixture (10 µl) contained 10 ng of gDNA template, 0.5 µM of each primer (Additional file 3) and 5 µl of TB Green™ Premix Ex Taq™ II (Takara biotechnology, Japan). qPCR for each species was conducted with 7 ~ 20 replicates using the amplification condition of 95 °C denaturation step for 1 min followed by 35 cycles of 95 °C for 15 s, 57 °C for 20 s, and 72 °C for 30 s. Melting curve analysis was conducted to confirm the integrity of PCR product by starting the melting step at 60 °C and increasing to 95 °C with a ramping temperature of 0.2 °C/s. The copy number of COI was estimated for all the six mosquito species. In case of ITS2, however, the copy number was only determined for An. sinensis, An. kleini and An. belenrae, as ITS2 was used for the prediction of species composition ratio in the three species.

Verification assay using pooled DNA and pooled mosquito specimens

Based on the previous study that the primary Anopheles species collected in various traps in the ROK are An. sinensis, An. kleini, and An. pullus, the accuracy of the QS protocol was evaluated with serial gDNA mixtures of the three species. The gDNA ratios between two species in all the three combinations (An. pullus:An. sinensis, An. pullus:An. kleini, and An. sinensis:An. kleini) were prepared at ratios of 2:8, 3:7, 4:6, 6:4, 7:3 and 8:2, and three species combinations of 2:3:5 and 1:2:7 was amplified using COI and ITS2 primer sets. The signal intensity was obtained from two nucleotide positions of COI [mC.122 (the nucleotide number 122 of mitochondrial COI gene; the same rule of nomenclature applies hereafter) and mC.525] and a single locus (rI.370; the nucleotide number of 370 for ribosomal genes in the ITS2 region; the same rule of nomenclature applies hereafter) of ITS2. Predicted composition ratios were obtained by inversely substituting the gDNA ratios into regression equations (Tables 3, 4). The observed values of the three species were calculated by the peak of the signal intensity chromatogram. The Pearson correlation coefficient (r) and errors between observed and predicted values were calculated using GraphPad Prism (Table 5).

To confirm that the established protocol is valid with gDNA samples prepared from pooled mosquito specimens, groups of 20 ~ 24 individual mosquitoes collected from six different collection times and two different locations (DMZ-0619, 0712, 0828, 0911; Pyeongtaek-0706, 0815) were randomly selected and pooled. gDNA was released from one wing of each specimen using DNA releasing buffer (5 % of DMSO, 5 % of PEG 200, 20 mM of NaOH, and 1 mM of EDTA) for species identification. In brief, each wing was incubated in 15 µl of DNA releasing buffer for 20 min at room temperature and then diluted with 25 µl of water for PCR reaction. Following species identification based on the ITS2 marker [8], the remaining mosquito bodies were combined and homogenized together by plastic pestle in liquid nitrogen. Species composition ratios were predicted by inversely substituting the gDNA ratios into regression equations and compared to actual species composition data.

Table. 3 Linear regression analysisa results of species-specific nucleotide position of COI gene
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Table. 4 Linear regression analysisa results of species-specific nucleotide position of ITS2 gene
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Table. 5 Evaluation of the accuracy of the predicted composition ratios from the pooled gDNA and pooled mosquito specimens
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Results

Phylogenetic tree of collected specimens

Based on the COI phylogenetic tree, An. sineroides, An. pullus, and An. lesteri were clearly divided into separate clusters, whereas An. sinensis, An. kleini, and An. belenrae were clustered into a large monophyletic cluster (Additional file 4). In contrast, the ITS2 phylogenetic analysis demonstrated that all the collected specimens were clearly divided into separate clusters with corresponding GenBank references (Additional file 5).

Search for species-specific loci in COI and ITS2

The results of COI and ITS2 sequence alignment were organized with different color codes for each species (Additional files 6, 7). Residues that match the consensus sequences were marked as dots, and black background was applied for the sequences that differ from the consensus. The locations of species-specific COI sequence loci used for species discrimination are listed in Table 1. Nucleotide sequences at seven COI loci were found to be either species- or group-specific. The mC.122C (cytosine at the nucleotide number 122 of mitochondrial COI DNA) was only observed in An. pullus, and An. sineroides, whereas the remaining four species had mC.122T. At the mC.260, and mC.443 loci, An. sineroides was separated from other species by having adenine and cytosine, respectively, thus allowing mC.260A (adenine at the mC.260 locus; the same rule of nomenclature applies hereafter) and mC.525C as An. sineroides-specific alleles. The cytosine nucleotide bases at both mC.387 and mC.582 nucleotide positions were only specific to An. pullus, thus these alleles were used as An. pullus-specific markers. No species-specific nucleotides to An. sinensis, An. kleini, An. belenrae, or An. lesteri were found at any COI loci examined. However, group-specific nucleotides (mC.525C, mC.527T, and mC.590C) were identical in all three species of An. sinensis, An. kleini, and An. belenrae.

Nucleotide sequence alignment of ribosomal RNA genes from An. sinensis, An. kleini, and An. belenrae demonstrated that the longest fragment without any insertion/deletion (indel) was located in the region containing 5.8S rDNA and 28S rDNA of the ITS2 region (nucleotide number 208–446 of An. sinensis). Among a total of eight ITS2 nucleotide positions specific to individual species, the rI.377T (thymine at the nucleotide number 377 of ribosomal DNA ITS2 region; the same rule of nomenclature applies hereafter) was only specific to An. sinensis, whereas other two species had rI.377 C (Table 1). The nucleotide sequences at four positions (rI.370G, rI.372T, rI.380, and rI.384) were specific to An. kleini, whereas those at three nucleotide positions (rI.378G, rI.389T, and rI.400A) were specific to An. belenrae (Table 1).

The target COI fragment was equally amplified from all six species (Fig. 2A). Due to the substantial differences in the priming sequences when using the Anskb_ITS2-F and R primers, however, the ITS2 fragment was only amplified from An. sinensis, An. kleini, and An. belenrae but not from An. sineroides, An. pullus, and An. lesteri (Fig. 2B).

Fig. 2
figure 2

Amplification of gDNA fragments of COI and ITS2 genes. A The gDNA fragments of COI gene (594 bp) were equally amplified from all the six Anopheles species (s = An. sinensis; k = An. kleini; b = An. belenrae; p = An. pullus; l = An. lesteri; sd = An. sineroides). B The gDNA fragments of ITS2 gene (234–238 bp) were amplified from An. sinensis, An. kleini, and An. belenrae, but not amplified from An. pullus, An. lesteri, and An. sineroides

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Establishment of prediction equation

Linear regression analysis was performed for all the eight species-specific nucleotide loci of COI to select the most reliable loci based on the criteria (R2 and Sest) (Table 3). Between the two loci specific to An. sineroides (mC.260 and mC.443), the mC.260 locus was selected as the species-specific locus for the identification of An. sineroides because it was determined to be more reliable by showing better criteria values (0.995 and 1.49 for R2 and Sest, respectively). In the case of two alleles specific to An. pullus (mC.387C and mC.582C), they were excluded as species distinction alleles, since their Sest values (2.49 at mC.387C and 4.26 at mC.582C) were much larger than those of other loci. Instead, the mC.122C, showing better criteria values (0.997 and 1.78 for R2 and Sest, respectively), was selected as an alleles that can simultaneously distinguish both An. pullus and An. sineroides from other species. Among the three alleles specific to the combined group of An. sinensis, An. kleini, and An. belenrae (mC.525C, mC.527T, and mC.590C), mC.525C was determined to be the best allele to estimate the combined proportion of An. sinensis, An. kleini, and An. belenrae out of six candidate species (Fig. 3A–C). Since no nucleotide position was found to be only specific to An. lesteri, the composition of An. lesteri was deduced by subtracting the combined ratios of the other five species from 1.

Fig. 3
figure 3

Linear regression equations for the prediction of species-specific nucleotide frequencies at five different loci of COI and ITS2. Relationships between nucleotide signal intensities and species compositions of (A) An. sineroides, (B) An. sineroides + An. pullus, (C) An. sinensis + An. kleini + An. belenrae, (D) An. kleini, and (E) An. belenrae with 95 % CI in dotted lines

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The same analysis and screening were performed for the seven candidate nucleotide positions (rI.384 was excluded due to the unstable signal intensity) in the ITS2 amplicon (Table 4). Since the rI.370 nucleotide position exhibited the best criteria scores (1.00 and 0.65 for R2 and Sest, respectively) for An. kleini distinction among the three nucleotide positions (rI.370, rI.372 and rI.380), the rI.370G was used as the allele to estimate the ratio of An. kleini. Likewise, the rI.389T allele (0.999 and 1.33 for R2 and Sest, respectively) was selected out of the three alleles (rI.378G, rI.389T, and rI.400A) for estimating the proportion of An. belenrae (Fig. 3D, E). Because the An. sinensis-specific rI.377T position produced relatively lower criteria scores (0.894 and 12.4 for R2 and Sest, respectively), it was not used to estimate the proportion of An. sinensis. Instead, the proportion of An. sinensis was calculated by subtracting the combined proportions of An. kleini and An. belenrae from the total proportions of An. sinensis, An. kleini, and An. belenrae.

Copy numbers of COI and ITS2 loci between species in the Anopheles Hyrcanus group

The copy numbers of COI gene, which reflect the relative numbers of mitochondria, ranged from 69.9 (An. lesteri) to 99.8 (An. kleini), but were not significantly different between all the six Anopheles species examined (p = 0.232; Fig. 4A). However, the average ITS2 copy number of An. sinensis, An. kleini and An. belenrae was 795, 689 and 1054, respectively. The ITS2 copy number of An. belenrae was significantly higher (1.36-fold, p < 0.0001) from the other two species (Fig. 4B). Since the nucleotide signal ratio of rI.377T site can be overestimated by the copy number factor of 1.36, the predicted value of An. belenrae composition ratio was further adjusted by multiplying the factor of 0.7.

Fig. 4
figure 4

Copy numbers of COI and ITS2 loci of Anopheles Hyrcanus Group species. A The relative copy numbers of COI locus of six Anopheles species (s = An. sinensis; k = An. kleini; b = An. belenrae; p = An. pullus; l = An. lesteri; sd = An. sineroides). B The relative copy number of ITS2 locus of An. sinensis, An. kleini, and An. belenrae

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Evaluation of the QS accuracy

Validation of the QS method of three major species were performed using 15 sets of gDNA mixtures. To verify the correlation and error between the actual and predicted values, a total of 45 sets of data were plotted on a graph with an x-axis representing the actual species composition ratio and a y-axis representing the estimated ratios (Fig. 5A). In addition, separate analyses were conducted for the 21 and 30 data sets derived from COI and ITS2 genes, respectively, and 15 data sets from the major species composed of three members of the Hyrcarus Group (An. pullus, An. kleini, and An. sinensis) (Fig. 5B–F). Overall data sets were confirmed to have significant correlations (r > 0.977, p < 0.001, R2 > 0.955) between the actual and predicted composition ratios (Table 5). Mean absolute errors (MAE) of the QS prediction data of the three major species was 3.87 %, and MAE of COI and ITS2 loci were 4.45 and 4.02 %, respectively. The MAE rates for the prediction of An. pullus, An. kleini, and An. sinensis were 3.57 % (maximum 7.34 %), 3.28 % (maximum 10.8 %), and 4.76 % (maximum 12.2 %), respectively.

Fig. 5
figure 5

Evaluation of accuracy between the actual and predicted composition ratios from pooled gDNA specimens. The x axis indicates actual species composition ratios obtained from gDNA proportions, whereas the y axis was predicted species composition ratios deduced from the prediction equations. A total of 45 estimates were obtained from the verification test and plotted against their corresponding actual values. The graphs show the linear regression between the actual and predicted values with (A) all data points, (B) values from COI gene, (C) values from ITS2 gene, and (D ~ F) values from An. pullus, An. kleini, and An. sinensis

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Validation experiments were also conducted using gDNA samples prepared from pooled mosquito specimens. Five species (An. sinensis, An. kleini, An. pullus, An. belenrae, and An. sineroides) were found in pooled mosquito populations with composition ratios of 0:16:3:4:1, 0:9:5:6:0, 16:0:6:0:1, 1:0:9:0:0, 3:0:5:0:0 and 3:2:0:0:0, respectively, as determined by individual genotyping (Additional file 8). When the actual and predicted data sets were plotted on the graph (Fig. 6), the pooled mosquito data sets also showed significant correlations (r > 0.984, p < 0.0002, R2 > 0.968) between the actual and predicted composition ratios of Anopheles species (Table 5). MAE of the all data was 2.9 %, which is less than that of the pooled gDNA data. MAE values of COI and ITS2 were also lower (3.59 and 2.21 %, respectively) than those from the pooled gDNA data. Since the species with low frequencies obtains lower error rates (An. belenrae 0.66 %, An. sineroides 1.09 %), their overall MAEs were lower in the pooled mosquito data compared to the pooled gDNA specimen data. The maximum prediction error generated from the all data set was estimated to 13.2 %.

Fig. 6
figure 6

Evaluation of accuracy between the actual and predicted composition ratios from pooled mosquito specimens. The x axis indicates actual species composition ratios obtained from individually identified mosquitoes in pooled mosquito specimens, whereas the y axis was predicted species composition ratios deduced from the prediction equations. A total of 36 estimates were obtained from the verification test and plotted against their corresponding actual values. The graphs show the linear regression between the actual and predicted values with (A) all data points, (B) values from COI gene, (C) values from ITS2 gene, and (D–F) values from An. pullus, An. kleini, and An. sinensis

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Discussion

Anopheles species have been conventionally identified by morphological characteristics. However, when this method fails (e.g., for members of the Hyrcanus Group in the ROK), individual genotyping has been used to identify each species, from which the relative overall species composition was determined. As a cost-effective alternative to individual genotyping (or barcoding), a population-based QS protocol was developed that can rapidly process large numbers of mosquitoes to estimate their relative species abundance. Since similar prediction error rates were found between the pooled DNA specimens and pooled mosquito specimens, mosquito samples can be pooled and processed for QS-based prediction of species composition, as previously reported in the QS-based prediction of resistance allele frequencies in head lice [17]. Since variations in DNA content and quality between species could influence the results as main error sources, however, mosquito specimens with the same body size and quality (live or preserved specimens under identical conditions) should be used for diagnosis. In addition, copy numbers of the target loci used for species prediction should be considered as they directly determine the DNA content: if one species has more copies of the DNA loci investigated, the assay would overestimate its abundance. In this study, the mitochondrial COI alleles could be safely employed as species distinction markers as the copy number of COI did not differ significantly between different species in Hyrcanus group. In case of ITS2, however, An. belenrae showed slightly higher copy numbers than An. sinensis and An. kelini, requiring an additional adjustment in the prediction of its species composition. Once wild-caught Anopheles are collected, 30–100 individual mosquitoes, depending on the relative size of the collections, can be pooled (whole bodies or isolated abdomens) and processed for downstream procedures, including gDNA extraction, PCR, and sequencing. Analysis of a single pooled mosquito sample provides information on the species composition that is nearly equivalent to that obtained by the multiple numbers of individual genotyping, thereby substantially saving time and resources. For example, QS-based analysis of one pool of 100 mosquitoes requires a total cost of approximately $7 and 1.5 days including sequencing cost and time, whereas separate individual genotyping with 100 individual mosquitoes would require much greater costs, particularly when sequencing is involved, and much longer time for gDNA extraction and downstream processes. This cost- and time-effectiveness is especially beneficial when processing large numbers of mosquitoes from different geographical locations and collection time points compared to individual genotyping.

Nevertheless, the information obtained from the QS-based prediction is not as accurate as that obtained from individual genotyping due to the prediction error. The prediction errors were on average 4.45 and 4.02 % when based on COI and ITS2 genes, respectively. The respective composition ratio of An. pullus, An. lesteri, An. kleini, An. belenrae, and An. sinensis was deduced from combinations of two or three alleles (mC.260A vs. mC.122C, mC.122C vs. mC.525C, mC.525C vs. rI.370G, mC.525C vs. rI.389T and mC.525C vs. rI.370G vs. rI.389T, respectively). Therefore, the error for predicting the relative ratios of these species is additive and thus becomes larger than directly predicting the composition of An. sineroides, where species composition is deduced from a single marker. Since the maximum error rates for prediction of An. pullus, An. kleini, and An. sinensis were 10.9 %, 12.8 %, and 12.2 %, respectively, the prediction may not be accurate when the composition ratios of these species are lower than their maximum error rates. It is also worth to note that if mosquito species other than the ones investigated, such as An. koreicus, a rarely collected species, is present in a pool, the prediction results would be influenced. Considering the prediction error, this QS-based protocol is better suited as a primary survey tool to rapidly assess species composition in multiple pooled specimens in a tier system. If more accurate information on species composition for any particular mosquito sample is needed, a second round of analysis based on the conventional individual genotyping can be conducted [18].

Insecticide resistance for members of the Hyrcanus Group has been reported to be widely distributed in the ROK. Interestingly, as determined by QS-based genotyping, the resistance mutation frequencies fluctuated significantly throughout the mosquito season [11]. Frequencies of L1014F/C and G119S mutations associated with resistance to pyrethroid and organophosphorus insecticides, respectively, dramatically decreased in the Hyrcanus Group toward the fall and became zero the following spring, suggesting a possible overwintering cost associated with insecticide resistance. However, since the resistance mutation frequency was highly proportional to the composition of An. sinensis within the Hyrcanus Group ([7]; Lee DE, unpublished data), rapid estimation of the proportion of An. sinensis is crucial for understanding the resistance dynamics of Anopheles mosquitoes throughout the season. With this in mind, the high-throughput prediction of species composition based on QS using pooled DNA will facilitate the understanding of differences in insecticide resistance potential between different Anopheles spp.

The information on the geographical and seasonal distributions of Anopheles mosquitoes is crucial for establishing an efficient malaria management program. Since the species belonging to the Hyrcanus Group varies depending on geographical location and collection season, it is essential to precisely identify the relative numbers and proportion of each species over time and geographical distributions. Information on Anopheles species composition in northern Gyeonggi province in the ROK, considered as a high-risk area for vivax malaria, is particularly critical as An. kleini and An. lesteri were reported as the primary vectors with significantly high sporozoite rate and infection rate than An. sinensis [3, 19]. Distribution and predominant species change throughout the year in northern Gyeonggi province, with An. lesteri being predominant along the western coastal areas, whereas An. kleini, An. belenrae, and An. sinensis are distributed more centrally [20]. In addition, An. pullus and An. belenrae are found in early summer, An. kleini in mid-summer, and An. sinensis is more abundant in the late summer [14]. Therefore, a larger scale information on species composition dynamics over time and distributions would enable in-depth understanding of ecology of Hyrcanus group mosquitoes and malaria epidemiology. With this in mind, the QS protocol developed in this study should facilitate to acquire large scale phenology information, which is fundamental for assessing the impact of climate change on the malaria epidemiology in the Korean Peninsula. Moreover, the principle of QS-based prediction method can be applied to other Anopheles spp., e.g., members of the Anopheles gambiae complex of sub-Saharan Africa, to estimate the composition ratio of individual species that exhibit different seasonal occurrence, vector competence, and insecticide resistance.

Conclusions

In this study, a rapid QS-based method for the prediction of species composition ratios in pooled specimens of members of the Hyrcanus Group was developed. Since this protocol can be adapted as a cost-effective high-throughput analysis tool, rapid processing of multiple Anopheles spp. samples from multiple geographical areas and time series is feasible for large-scale studies to better understand the ecology, phenology, and epidemiology of Anopheles mosquitoes. Together with molecular tools for the detection of Plasmodium spp. and insecticide resistance, this two-step prediction protocol will facilitate to elucidate any possible correlations between vector competence and resistance potential of the Hyrcanus Group. In addition, the same principle can be applied for the quantitative analysis of species composition in other morphologically indistinguishable mosquito species complexes or groups, including the An. gambiae complex.