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Use of transcriptional age grading technique to determine the chronological age of Sri Lankan Aedes aegypti and Aedes albopictus females

Parasites & Vectors volume 14, Article number: 493 (2021) Cite this article

Abstract

Background

Aedes aegypti and Ae. albopictus are important vectors of human diseases such as dengue, chikungunya, and zika. In Sri Lanka, they have been responsible for transmitting dengue virus. One of the most important parameters influencing the likelihood of arbovirus transmission is the age structure of the mosquito population. However, mosquito age is difficult to measure with accuracy. This study aims to construct multivariate calibration models using the transcriptional abundance of three age-responsive genes: Ae15848 (calcium-binding protein), Ae8505 (structural component of cuticle), and Ae4274 (fizzy cell cycle/cell division cycle 20).

Methods

The transcriptional age-grading technique was applied to determine the chronological age of Ae. aegypti and Ae. albopictus female mosquito populations from Sri Lanka using the age-responsive genes Ae15848, Ae8505, and Ae4274. Furthermore, Ae. aegypti samples obtained from colonies reared at two temperatures (23 and 27 °C) were used to investigate the influence of temperature on this age-grading technique. Expression levels of these three genes were quantified using reverse transcription qualitative PCR (qRT-PCR), and results were normalized against the housekeeping gene ribosomal gene S17 (RpS17).

Results

The expression of Ae15848 and Ae8505 decreased with the age of mosquitoes and showed the most significant and consistent change while expression of Ae4274 increased with age. The multivariate calibration models showed > 80% correlation between expression of these age-responsive genes and the age of female mosquitoes at both temperatures. At 27 °C the accuracy of age predictions using the models was 2.19 (± 1.66) days and 2.58 (± 2.06) days for Ae. aegypti and Ae. albopictus females, respectively. The accuracy of the model for Ae. aegypti at 23 °C was 3.42 (± 2.74) days.

Conclusions

An adult rearing temperature difference of 4 °C (23–27 °C) did not significantly affect the age predictions. The calibration models created during this study could be successfully used to estimate the age of wild Ae. aegypti and Ae. albopictus mosquitoes from Sri Lanka.

Graphical Abstract

Background

Aedes aegypti and Ae. albopictus are important vectors of human diseases such as dengue, chikungunya, and zika. Among these diseases, dengue is the most rapidly spreading mosquito-borne viral disease and is endemic in > 120 countries worldwide, including Sri Lanka. Among several factors, the age structure of the dengue vector population has been identified as one of the most important parameters influencing the epidemiology of the disease.

Based on experimental observations of the average extrinsic incubation period (EIP), the incubation period of the dengue virus within the mosquito’s body before it is transmitted to the human body is 8–12 days [1,2,3,4]. Hence, knowing the age structure of the wild populations in an area is vital in decision making, especially in vector control programmes.

As there is no specific antiviral vaccine against dengue, efficient and environmentally friendly vector control measures are needed to prevent disease outbreaks. Introduction of artificially infected Ae. aegypti females with wMel strain of Wolbachia into the field has been one of the most promising dengue control interventions in the world due to failures of traditional vector control measures. This wMel strain of Wolbachia can block the dengue virus inside the mosquito body. This environmentally friendly method of vector control has been successfully practiced in several areas in the world by introducing Ae. aegypti infected with Wolbachia (wMel strain) [5]. Knowledge of the age of the vector population is important as the age affects the fitness of the released mosquitoes in the natural environment.

Morphological, biochemical, and molecular-based age-grading techniques have been developed to determine the age of female mosquitoes. However, the use of both morphological and some biochemical methods has become questionable because of their inability and inaccuracy in measuring the age of mosquitoes older than the EIP. The transcriptional age-grading technique has now been identified as the most accurate and precise approach in determining the chronological age of mosquitoes [3, 6, 7].

Quantification of expression levels of genes that show variation in their expression with the age of the female mosquito is the basis of this technique. The transcription scores of these age-responsive genes of laboratory-reared mosquitoes of known ages are then fed into a multivariate calibration model, which can later be used in age predictions of field/wild individuals. The mosquitoes used in the construction of the calibration model must be from the same mosquito strain of the field population where the researcher is planning to apply the technique [6]. The analysis of age-responsive genes during these studies have shown that this technique accurately detects the age of An. gambiae [8,9,10] and Ae. aegypti [2,3,4, 11, 12] older than 15 days, which is more than the EIP period. Trials, using mosquitoes reared in field cages, have concluded that the gene expression profiles of Ae. aegypti female mosquitoes could determine the age with an accuracy of ± 5 days of the actual age [2, 11, 13].

The orthologues of the eight age-responsive genes, i.e. Ae4274, Ae4679, Ae4916, Ae6639, Ae7471, Ae8505, Ae12750, and Ae15848 selected from Drosophila melanogaster, were initially used to predict the age of female Ae. aegypti mosquitoes under both laboratory and field conditions [6, 14,15,16]. According to mosquito transcriptional age-grading studies CG8505/Ae-8505/AAEL003259 (pupal cuticle protein 78E putative) and SCP-1/Ae.-15,848/AAEL008844 (calcium-binding protein, putative) displayed the largest and most significant decrease in expression levels with the age of female mosquitoes while expression levels of fizzy/Ae-4274/AAEL014025 (fizzy cell cycle/cell physiology, putative) significantly increased with age. Hence, these three genes, Ae8505, Ae15848, and Ae4274, have been identified as the most informative age-responsive genes that can be used in the transcriptional profiling of mosquitoes [2, 11, 13]. Cook et al. [6] have identified Ae8505, Ae15848, and Ae4274 genes as the most reliable age-responsive genes and recommended using these three genes for future age determination studies. The gene RpS17 (40S ribosomal protein s17), which shows insignificant variation with age, has been exclusively used as the reference gene for normalizing the samples in these studies. Furthermore, the expression of these genes is not affected by blood feeding, egg laying, digestion, and reproductive status of the mosquitoes [6, 8, 17].

However, this approach needs further validation and optimization based on the geographical region, as the age-responsive genes within these mosquito populations may have sequence polymorphisms that may affect the application of gene expression analysis. Therefore, Cook et al. [6] suggested creating separate models for mosquitoes in different geographical regions. Furthermore, fluctuations in environmental parameters, such as temperature, were flagged as being potentially important to the successful application of the technique as these may affect the transcription of age-responsive genes [6]. Hence, models constructed considering all or most of these limiting factors will increase the accuracy and precision of the transcriptional age-grading method.

At present, dengue has become one of the major causes of hospitalization and death in Sri Lanka. The largest dengue outbreak was recorded in 2017 (≈186,000 cases) in the country, which is a ≈4.3 times increase in the average number of cases that were reported from 2010 to the 2016 period [18]. Control of dengue vector populations in Sri Lanka is primarily based on the application of adulticides and larvicides. However, most of these programmes have been challenged because of the development of insecticide resistance by both dengue vectors [19, 20]. Hence, the Sri Lankan government has planned to release wMel-infected dengue vectors into the field [22]. However, so far no attempts have been made in Sri Lanka to determine the chronological age and age structure of any of the dengue vectors, which is essential before implementing this programme. Both primary and secondary dengue vectors, Ae. aegypti and Ae. albopictus, are found in Sri Lanka. Multivariate calibration models constructed so far for Ae. aegypti have used gene transcription measures of mosquitoes collected from geographical locations where the environmental and climatic conditions are different from those of Sri Lanka. Therefore, it is essential to validate the model for the Sri Lankan mosquito population.

Although Ae. albopictus was considered a rural secondary dengue vector in the past, it has now invaded urban areas as well and has become the dominant Aedes species across most of the country [20]. There has been relatively little research into age grading of Ae. albopictus [21], and none is based on the transcriptional age-grading technique. Furthermore, previous studies have focused only on one species where this study focuses on two vector species belonging to the same subgenus (Stegomyia) from the same geographical location. This will allow us to study the suitability of transcriptional age-grading across different species within this medically important subgenus.

Hence, the current study aimed to construct multivariate calibration models using the transcriptional abundance of three age-responsive genes, Ae15848, Ae8505, and Ae4274, to determine the age structure of the Ae. aegypti and Ae. albopictus female mosquito population from Sri Lanka and to investigate the influence of temperature on the expression levels of these age-responsive genes of Ae. aegypti females.

Methods

Establishment of mosquito colonies and sample collection

Blood-fed mosquitoes collected from Kandy district, Sri Lanka, were used to obtain eggs to establish initial colonies of Ae. aegypti and Ae. albopictus. Colonies were maintained at an insectary with ambient conditions of 27 ± 2 °C temperature, 70 ± 10% relative humidity (RH), and a photoperiod of 12:12 (L:D); these are similar to the field site conditions.

Another Ae. aegypti colony (using the eggs from the above Ae. aegypti colony at 27 °C) was established at 23 ± 2 °C (70 ± 10% RH and 12:12 photoperiod) to determine the effect of temperature on the transcriptional age-grading technique. The larvae in all colonies were given similar conditions (similar larval densities and larval food to each tray).

Unfed adult females of both species reared at 27 °C were collected at eight time points: 1, 5, 9, 13, 17, 21, 25, and 29 days. Unfed Ae. aegypti females reared at 23 °C were collected at five time points: 1, 5, 9, 13, and 17 days. Thirty individuals were collected at each time point. All samples were snap-frozen at − 80 °C for molecular analysis.

RNA extraction and cDNA synthesis

RNA was extracted from pools of ten individual mosquitoes of a given age group (3 replicates per each age class) using the Arcturus® PicoPure RNA Isolation kit (Thermo Fisher Scientific, Waltham, MA, USA) following the manufacturer's protocol. RNA was eluted in 30 µl of elution buffer provided with the isolation kit. All the steps were carried out while keeping the samples on ice, and centrifugation steps were carried out at 4 °C. After adding the wash buffer, the RNA purification columns were treated with RNase-free DNase (Qiagen Hilden Germany) and incubated for 15 min at room temperature to remove DNA contamination from the RNA samples. Samples were stored at − 80 °C for molecular analysis.

cDNA was synthesized by reverse transcription using 1 ng extracted RNA as the template. SuperScript III First-Strand Synthesis System (Invitrogen) was used for cDNA synthesis following the manufacturer's protocol. One microlitre of Oligo (dt)20 (50 µM), 3 µl of DEPC-treated water, and 1 µl of 10 mM dNTP mix were added to the sample and the mixture was incubated for 5 min at 65 °C. Four microlitres of 5× first-strand buffer, 1 µl of 0.1 M DDT, 1 µl of RNaseOUT, and 1.5 µl of Superscript III RT were added to the initial reaction. Sample was then incubated at 25 °C for 5 min, followed by 50 °C for 60 min and 70 °C for 15 min. The c-DNA product was treated with 1 µl Rnase H and incubated at 37 °C for 20 min to remove RNA.

Validation and quantification of age-responsive genes using qRT-PCR assay

Three age-responsive genes, Ae. 15848 (a gene involved in calcium-binding), Ae. 8505 (a gene involved in producing a structural component of cuticle), Ae. 4274 (a gene involved in fizzy cell cycle/cell physiology), and a housekeeping gene, RpS17, were selected for qRT-PCR analysis according to previous studies [6]. Primer sequences given in Cook et al. [6] were used to amplify these three genes in Ae. aegypti. Primers for Ae. albopictus were designed using Primer 3 software (version 0.4.0) [23, 24] (Table 1).

Table 1 Primer sequences and product length of the candidate genes and housekeeping genes
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After validating the three age-responsive genes and the housekeeping gene of both species, quantitative real-time polymerase chain reaction (qRT-PCR) assays were used to quantify the expression levels of all these genes using the MX3005 qPCR system (Agilent Technologies). Each qRT-PCR reaction mixture (20 µl) was prepared using 1 µl cDNA, 10 µl Brilliant green Ultra-fast SyBr Green qPCR master mix (Agilent), 0.6 µl of primer (10 mM), and 7.8 µl nuclease-free water. The thermal cycling conditions were conducted with denaturation at 95 °C for 3 min followed by 40 cycles of 10 s at 95 °C, 10 s at 60 °C, and a last step of 95 °C for 1 min, 55 °C for 30 s, and 95 °C for 30 s. Three biological and three technical replicates were performed for each age class and each gene.

Data analysis

Data were analyzed using MxPro qPCR software (Agilent Technologies) to obtain the Cycle Threshold (Ct) value, which is the measure of the expression of genes. Fold change (FC) of each age-responsive gene at each time point, relative to the 1-day-old mosquitoes, was calculated using the 2−ΔΔCT method [25] incorporating the PCR efficiency. Gene expression value/Ct value of each candidate gene at each time point was normalized to the reference gene (RpS17) by calculating the log contrast values using the equation given below [6].

Log contrast Xi = log 10 [(Xi /Xtotal)/(Xref/Xtotal)].

Where, Xi = mean Ct value of gene X.

Xref = mean Ct of the reference gene RpS17.

Xtotal = sum of the Ct values for all genes from an individual.

One-way ANOVA was conducted (using the transcriptional abundance data/log contrast values of each experiment) to determine any significant variation in the expression of the gene with the age of mosquitoes using MiniTab 15.

A multivariate calibration method described by Cook et al. [3, 6] was used to predict mosquito age. Canonical redundancy analysis was conducted for each experimental design using the normalized gene expression values (log contrast) of all three candidate genes using the SAS statistical software (SAS University edition). Syntax previously written by Cook et al. [6] was modified and used in all the analyses. A calibration model, which explains the strength of the linear relationship among the expression of all three candidate genes and mosquito age, was constructed using the linear regression of the first redundancy variate generated during the redundancy analysis. A nonparametric bootstrapping (1000 bootstraps) method was used to assess the sampling error [95% confidence intervals (CI)] to validate the constructed calibration models and to predict mosquito pool ages. The median point of the CI intervals was considered as the likely predicted ages of mosquitoes [6]. The residual value (difference between the predicted age and actual age) was used to assess the accuracy of each model. The expression data of one experimental design were cross-checked with other models, e.g. Ae. albopictus age was predicted using the Ae. aegypti model and vice versa, to determine the possible use of one model for age prediction.

Results

Expression levels of age-responsive genes of Ae. aegypti and Ae. albopictus at 27 °C

All three genes, Ae15848, Ae8505, and Ae4274, were differentially expressed in both species and the fold changes relative to the expression of 1-day-old mosquitoes obtained are presented in Fig. 1. Underexpression of both Ae15848 and Ae8505 in all the age groups compared to day 1 and a strong drop in expression were observed from day 1 to day 5 for both species. The fold changes of genes Ae15848 and Ae8505 of 5-day-old mosquitoes compared to day 1 of Ae. aegypti were respectively 0.2 and 0.01. For Ae. albopictus day-5 FC values relative to day 1 for Ae15848 and Ae8505 respectively were 0.10 and 0.03. For Ae. aegypti, the FC of gene Ae4274 was highest at day 21 (FC = 8.80) and the lowest FC of 2.94 at day 5. Aedes albopictus showed the lowest FC at day 13 (FC = 7.97) while the largest FC of 14.88 at day 21.

Fig. 1
figure 1

Fold change of age-responsive genes Ae15848, Ae8505, and Ae4274 relative to the expression of 1-day-old mosquitoes of Ae. aegypti and Ae. albopictus, as determined by qRT-PCR analyses (error bars represent SEM)

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The transcriptional profiles and abundance values (log contrast of Ct) of each candidate gene at each experimental design are shown in Fig. 2. The expression levels are inversely proportional to the log contrast of normalized Ct values/transcriptional abundance. The lowest log contrast value of Ae15848 gene for both Ae. aegypti [(− 0.090) ± 0.016] and Ae. albopictus [(− 0.074) ± 0.012] was at day 1 and highest was at day 29 (Ae. aegypti = 0.032 ± 0.033 and Ae. albopictus = 0.30 ± 0.004). The transcriptional abundance of genes Ae15848 and Ae8505 increased with the age of both species, which indicates the expression of both genes decreased with mosquito age. Expression levels of Ae15848 were always greater than those of Ae8505 at each time point.

Fig. 2
figure 2

Log contrast values obtained for the three age-responsive genes Ae15848, Ae8505, and Ae4274 at each time point of Ae. aegypti and Ae. albopictus female mosquitoes reared at 27 °C temperature

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The expression of Ae4274 increased with the age of mosquitoes unlike the other two genes. For both mosquito species the lowest expression of Ae4274 was at day 1 (0.138 ± 0.025 and 0.143 ± 0.013 respectively for Ae. aegypti and Ae. albopictus). The highest expression was at 21-day-old age class, and the log contrast values were 0.087 ± 0.012 for Ae. aegypti and 0.89 ± 0.007 for Ae. albopictus. The expression levels of this gene were lower compared to Ae15848 and Ae8505 for almost all time points tested.

According to one-way ANOVA test results, all three genes showed a significant change in expression with the age of female mosquitoes (Table 2). Aedes aegypti showed a significant and more consistent change in expression of genes Ae15848 (F = 12.34, p = 0.0001) and Ae8505 (F = 19.62, p = 0.0001) than Ae4274 (F = 8.50, p = 0.001). Aedes albopictus also had a similar pattern of change in the expression of all the genes of the mosquito (Ae1848 F = 43.94, p = 0.0001, Ae8505 F = 52.04, p = 0.0001 and Ae4274 F = 9.12, p = 0.001). Analysis using two-way ANOVA showed that the expression of Ae15848 (F = 34.99, p = 0.0001) and Ae8505 (F = 36.69, p = 0.0001) was significantly greater in Ae. aegypti than that for Ae. albopictus while Ae4274 did not show any significant difference in its expression between these two species (F = 0.37, p = 0.547).

Table 2 Results of one-way ANOVA of Ae. albopictus and Ae. aegypti from different experimental designs
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Expression levels of age-responsive genes of Ae. aegypti at 23 °C

Expression levels of the three age-responsive genes, Ae15848, Ae8505, and, Ae4274, were determined at 23 °C for five age classes (1, 5, 9, 13, and 17 days) of female Ae. aegypti and the transcriptional profiles obtained are shown in Fig. 3. The expression patterns of all three genes at 23 °C were similar to those of Ae. aegypti mosquitoes at 27 °C, that is, the expression of Ae15848 and Ae8505 decreased with mosquito age while Ae4274 showed an increase in its expression with the mosquito age. The expression of the Ae15848 gene was highest at all five time points (log contrast value ranging from 0.073 ± 0.005 at day 29 to − 0.071 ± 0.017 at day 1) compared to the other two genes. The transcriptional abundance of Ae8505 was highest in 29-day-old mosquitoes (0.151 ± 0.009) and lowest in 1-day-old mosquitoes (0.014 ± 0.038). The expression of Ae4274 increased with the mosquitoe age at both temperatures (log contrast values ranging from 0.138 ± 0.013 to 0.201 ± 0.008). Similar to 27 °C results, the change in expression levels of the three genes with female mosquito age was significant at 23 °C (Table 2). Ae15848 (23 °C F = 42.44, p = 0.0001) showed a more consistent change in its expression with mosquitoe age compared to Ae8505 (23 °C F = 11.55, p = 0.0001) and Ae4274 (23 °C F = 10.46, p = 0.001). Furthermore, the transcriptional abundance was greater for Ae. aegypti at 23 °C compared to 27 °C indicating a lower expression at 23 °C (Fig. 3).

Fig. 3
figure 3

Log contrast values obtained for the three age-responsive genes, Ae15848, Ae8505, and Ae4274, at five age classes (1, 5, 9, 13, and 17 days) of Ae. aegypti females reared at 27 and 23 °C

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Multivariate calibration models and age predictions

Aedes aegypti and Ae. albopictus from 27 °C

Calibration models were generated separately for each species using multivariate canonical redundancy analysis of the transcriptional abundance of the three genes, Ae15848, Ae8505, and Ae4274. Figure 4a and b shows the graphs drawn between the first redundancy variate and the actual age of Ae. aegypti and Ae. albopictus females.

Fig. 4
figure 4

Calibration models generated for a Ae. aegypti and b Ae. albopictus female mosquitoes using the transcriptional profiles of the three age-responsive genes, Ae15848, Ae8505, and Ae4274 c). Age predictions of c Ae. Aegypti, d Ae. albopictus female mosquitoes in eight age class; cross validation, e Ae. albopictus data checked on Ae. aegypti model f Ae. aegypti data checked on Ae. albopictus model. (Dashed lines indicate where predicted age equals actual age)

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A nonparametric bootstrapping (1000 bootstraps) procedure was used to validate the model generated for each species and to obtain the 95% confidence intervals (CI) and age predictions for the mosquito pools [6]. According to the calibration model results, the actual age of both Ae. aegypti (R2 = 0.8108, p = 0.0001) and Ae. albopictus (R2 = 0.8990, p = 0.0001) females from the tested mosquito pools showed a strong positive correlation with the first redundancy variates. The graphs between the predicted ages derived from the model and the actual age of Ae. aegypti and Ae. albopictus are shown in Fig. 4c and d. The mean residual value (difference between the actual age and predicted age of mosquitoes in the pools) was 2.19 (± 1.66) for Ae. aegypti and 2.58 (± 2.06) days for Ae. albopictus.

Furthermore, to check the species specificity of the created models, data of Ae. albopictus were used as the test data for the model generated for Ae. aegypti to predict the ages of Ae. albopictus at each time point and vice versa. The age of Ae. albopictus predicted using the Ae. aegypti model overestimated (mean residual value of 6.77 ± 1.41 days) (Fig. 4e) the age of Ae. albopictus while the Ae. albopictus model estimated the age of Ae. aegypti 7.4 ± 1.41 days (residual value) lower than the actual ages of the mosquitoes in these pools (Fig. 4f).

Aedes aegypti from 27 °C and 23 °C

Linear age prediction models were generated separately for female Ae. aegypti at 23 °C and 27 °C using the normalized expressions of the age-responsive genes for five age classes, 1, 5, 9, 13, and 17 days. Similar to the calibration model for mosquitoes at 27 °C, the model developed at 23 °C also showed a strong positive linear correlation with the age of female mosquitoes (23 °C; R2 = 0.9222, p = 0.0001) (Fig. 5a and b). According to the nonparametric bootstrap method, the residual value for Ae. aegypti at 27 °C was 2.19 (± 1.66) days and at 23 °C was 3.42 (± 2.74) days (Fig. 5c and d).

Fig. 5
figure 5

Calibration models generated for female Ae. aegypti mosquitoes reared at a 23 °C and b 27 °C using the transcriptional profiles of the three age-responsive genes, Ae15848, Ae8505, and Ae4274. c Age predictions of female Ae. aegypti mosquitoes reared at c 23 °C, d 27 °C at five age classes; cross validation, e 27 °C data checked on 23 °C model f 23 °C data checked on 27 °C model. (The dashed lines indicate where predicted age equals actual age)

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To test the effect of temperature on the age prediction accuracy, cross predictions were conducted. When the age of Ae. aegypti females reared at 27 °C were cross-predicted with the model generated for Ae. aegypti females at 23 °C, the age of Ae. aegypti was overestimated [residual value 3.93 (± 3.00) days] (Fig. 5e). The 27 °C model under-estimated (mainly the younger age classes showed the highest deviation) the age of Ae. aegypti females from 23 °C [residual value of 5.82 (± 3.57)] (Fig. 5f).

Discussion

Several chemical and molecular methods have been developed to measure the age of insect vectors such as measuring pteridine concentration [26], detecting the proportions of different cuticular hydrocarbons [27], protein expression profiling [28], and near-infrared spectroscopy [28, 29]. Among these, transcriptional age grading is a molecular-based technique with high precision and accuracy that can be used to determine the chronological age of mosquitoes. However, the models developed using this approach should be validated and optimized for mosquito populations from different geographical areas and adjusted for variations of environmental factors; especially temperature prevails in such areas [6]. Hence, the present study was conducted to develop a multivariate calibration model for Ae. aegypti and Ae. albopictus from Sri Lanka using transcriptional age grading, which could later be used in estimating the age structure of wild mosquito populations. Furthermore, the study attempted to understand the effect of temperature on this age-grading technique. This study basically followed the protocol described by Cook et al. [6] with some changes in the RNA extraction and qRT-PCR technique. Also RNA was extracted from pools of mosquitoes, similar to several other studies [2, 3, 10]. Therefore, the transcriptional abundance represented by the graphs represents broad trends among a pool of mosquito.

In Sri Lanka, the highest number of yearly dengue incidences and deaths is reported from the dry zone where the annual temperature is 27.5 °C and covers a major part of the island. Furthermore, the entire country experiences relatively high humidity averaging around 80% throughout the year [30]. Hence, the mosquitoes used in the present study were maintained in insectaries with 27 ± 2 °C and 80% ± 10 relative humidity, simulating the field conditions. Transcriptional age-grading studies on Ae. aegypti [2, 4] and An. gambiae [9] have clearly shown that the models constructed for mosquitoes reared in laboratory or semi-field conditions could be successfully used to assess the age of wild mosquito populations. Therefore, the multivariate calibration models constructed during this study will be a useful tool for predicting the age of wild populations of Ae. aegypti and Ae. albopictus in Sri Lanka.

For both Sri Lankan Ae. aegypti and Ae. albopictus, the pattern of gene expression was similar to those of previous reports, i.e. reduction in the expression of Ae15848 and Ae8505 genes and an increase in Ae4274 gene expression with the age of the female mosquito [3, 4, 6, 8, 11]. In all the experimental designs, the change in expression of these three genes was significantly related to the age of female mosquitoes, further proving that these are among the most informative age-responsive genes that can be successfully used in the transcriptional age-grading approach.

The expression levels of all three genes were higher in both Ae. aegypti and Ae. albopictus from Sri Lanka than in mosquito populations from other countries. However, it cannot be confirmed that these results are solely due to population differences since there are some experimental protocol differences in the present study compared to that of the previous work. Furthermore, during the current study, the expression of Ae15848 was significantly higher than in the other two genes and showed a consistent change with mosquito age. This gene has shown a similar strong negative correlation with the chronological age of female Ae. aegypti [2, 4] and An. gambiae [9] mosquitoes from other parts of the world. Aedes aegypti has shown a four-fold increase in the log contrast of the same gene from 1- to 29-day-old mosquitoes in a study carried out in Northern Australia [4] and around 1.5-fold increase in mosquitoes from Central Vietnam [11] and Queensland, Australia [3]. Gene Ae8505 of both species showed a rapid decrease in gene expression from day 1 to day 5 and more consistent change thereafter, similar to previous reports. The expression change of gene Ae4274 with the age of mosquitoes was significant although it vaired along a small range as observed for Ae. aegypti females from Australia and Vietnam [3, 4, 11].

Unlike the calibration models developed previously using individual mosquitoes of Ae. aegypti by Cook et al. [3] (R2 = 0.73) and Hugo et al. [4] (R2 = 0.72), the two multivariate calibration models developed for both species during the present study had a strong correlation to the age of the mosquito (R2 for Ae. aegypti = 0.8108, p = 0.0001 and R2 for Ae. albopictus = 0.8990, p = 0.0001). Furthermore, in this study the accuracy of the models was greater for both species, 2.19 (± 1.66) days for Ae. aegypti and 2.58 (± 2.06) days for Ae. albopictus. These facts indicate the high precision and accuracy of the age prediction models generated during the current study. Hence, these multivariate calibration models generated from this study will be refined using data collected from mosquitoes maintained under a more natural environment. The refined models will be tested using field-collected mosquitoes. Furthermore, these findings provide strong evidence that the transcriptional age grading must be validated and optimized for mosquito strains from different geographical locations as stated by Cook et al. [6].

The age of Ae. albopictus was overestimated when the data were cross-predicted with the Ae. aegypti model and the age prediction accuracy decreases significantly from 2.58 (± 2.06) days to 6.77 (± 1.41) days. Ae. aegypti age will be underestimated and age prediction accuracy decreases from 2.19 (± 1.66) to 7.4 (± 3.41) days when cross-predicted with Ae. albopictus model. This analysis strongly supports the species-specific nature of the multivariate calibration models (although of the same genus) generated using the transcriptional age-grading technique.

Previous research work has reported a 4 °C temperature difference as the minimum range that could have a significant effect on the survival of both mosquito larvae and adults [31,32,33,34]. Hence, the model developed for Ae. aegypti colonies maintained at 27 °C was compared with mosquitoes reared at 23 °C (4 °C difference) to check the effect of temperature on this molecular-based age-grading approach. According to the results, the expression of the three age-responsive genes for mosquitoes from 23 °C was lower than for mosquitoes from 27 °C. Around 92% of the gene expressions were age related at 23 °C while it is 81.01% at 27 °C. According to the analysis conducted using nonparametric bootstrapping, the accuracy of age predictions did not show a considerable difference between the two temperatures (residual value 2.19 ± 1.66 days at 27 °C and 3.42 ± 2.74 days at 23 °C).

According to the results of the cross-validation, the age prediction accuracy of mosquitoes from 27 °C decreased slightly from 2.19 (± 1.66) days to 3.93 (± 3.00) days and from 3.42 (± 2.74) to 5.82 (± 3.57) days for mosquitoes reared in 23 °C. It could be stated that a temperature difference of 4 °C affects expression of age-responsive genes to a lesser extent than the difference exists between two species under the same temperature. Similar observations have been reported by Hugo et al. [4] and have suggested generating separate calibration models for inter-seasonal variations for different regions that show a greater variation in climatic factors. Sri Lanka is a small island and does not show drastic changes in temperature between areas within the country. Hence, the models developed during this study could be used to determine the age structure of wild mosquito populations from the whole country. Depending on the average temperature at the time of investigation the most appropriate model to be used can be determined.

Studies will be conducted in the future to determine the age of wild mosquitoes using the multivariate calibration models generated during this study after validating the models with mosquitoes reared under semi-field/field conditions.

Conclusion

This is the first report from Sri Lanka on the use of the transcriptional age-grading technique to determine the age of mosquitoes and the first study to develop a model for Aedes albopictus. The species-specific multivariate calibration models created using the age-responsive genes, Ae15858, Ae8505, and Ae4274, could successfully estimate the chronological age of wild Ae. aegypti and Ae. albopictus with higher accuracy than previously reported in other mosquito species. The age of mosquitoes could be predicted with an accuracy of nearly ± 3 days of their actual age across the age spectrum. A drop in temperature from 27 to 23 °C did not have a very strong effect on the multivariate calibration models.

Availability of data and materials

The datasets supporting the findings of this article are included within the article.

Abbreviations

EIP:

Extrinsic incubation period

RH:

Relative humidity

qRT-PCR:

Quantitative real-time polymerase chain reaction

Ct:

Cycle threshold

FC:

Fold change

CI:

Confidence interval

References

  1. Chan M, Johansson MA. The incubation periods of dengue viruses. PLoS ONE. 2012;7:e50972.

    Article  CAS  Google Scholar 

  2. Joy TK, Gutierrez EHJ, Ernst K, Walker KR, Carriere Y, Torabi M, Riechle MA. Aging field collected Aedes aegypti to determine their capacity for dengue transmission in the Southwestern United States. PLoS ONE. 2012;7(10):e46946.

    Article  CAS  Google Scholar 

  3. Cook PE, Hugo LE, Iturbe-Ormaetxe I, Williams CR, Chenoweth SF, Ritchie SA, Ryan PA, Kay BH, Blows MW, O’Neill S. The use of transcriptional profiles to predict adult mosquito age under field conditions. Proc Natl Acad Sci U S A. 2006;103:18060–5.

    Article  CAS  Google Scholar 

  4. Hugo LE, Cook PE, Johnson PH, Rapley LP, Kay BH, Ryan PA, Ritchie SA, O’Neill SL. Field validation of a transcriptional assay for the prediction of age of uncaged Aedes aeygpti mosquitoes in Northern Australia. PLOS Negl Trop Dis. 2010a;4(2):e608.

    Article  Google Scholar 

  5. O’Neill SL, Ryan PA, Turley AP, Wilson G, Retzki K, Iturbe-Ormaetxe I, et al. Scaled deployment of Wolbachia to protect the community from dengue and other Aedes transmitted arboviruses. Gates Open Res. 2018;2:36.

    Article  Google Scholar 

  6. Cook PE, Hugo LE, Iturbe-Ormaetxe I, Williams CR, Chenoweth SF, Ritchie SA, Ryan PA, Kay BH, Blows MW, O’Neill SL. Predicting the age of mosquitoes using transcriptional profiles. Nat Protoc. 2007;2(11):2796–806.

    Article  CAS  Google Scholar 

  7. Gerade BB, Lee SH, Scott TW, Edman JD, Harrington LC, Kitthawee S, Jones JW, Clark JM. Field validation of Aedes aegypti (Diptera: Culicidae) age estimation by analysis of cuticular hydrocarbons. J Med Entomol. 2004;41(2):231–8.

    Article  CAS  Google Scholar 

  8. Cook PE, Sinkins SP. Transcriptional profiling of Anopheles gambiae mosquitoes for adult age estimation. Insect Mol Biol. 2010;19(6):45–751.

    Article  Google Scholar 

  9. Wang MH, Marinotti O, James AA, Walker E, Githure J, Yan G. Genome-wide patterns of gene expression during aging in the African malaria vector Anopheles gambiae. PLoS ONE. 2010;5:e13359.

    Article  Google Scholar 

  10. Marinotti O, Calvo E, Nauyen QK, Dissanayake S, Ribeiro JM, James AA. Genome-wide analysis of gene expression in adult Anopheles gambiae. Insect Mol Biol. 2006;15:1–12.

    Article  CAS  Google Scholar 

  11. Hugo LE, Jeffery JAL, Trewin BJ, Wockner LF, Ye NY, Le NH, Nghia LT, Hine E, Ryan PA, Kay BH. Adult survivorship of the Dengue mosquito Aedes aegypti varies seasonally in Central Vietnam. PLOS Negl Trop Dis. 2014;8(2):e2669.

    Article  Google Scholar 

  12. Caragata EP, Poinsignon A, Moreira LA, Johnson PH, Leong YS, Ritchie SA, O’Neill SL, McGraw EA. Improved accuracy of the transcriptional profiling method of age grading in Aedes aegypti mosquitoes under laboratory and semi-field cage conditions and in the presence of Wolbachia infection. Insect Mol Biol. 2011;20(2):215–24.

    Article  CAS  Google Scholar 

  13. Hugo LE, Kay BH, O’Neill SL, Rayn PA. Investigation of environmental influences on a transcriptional assay for the prediction of age of Aedes aegypti (Diptera: Culicidae) mosquitoes. J Med Entomol. 2010;47(6):1044–52.

    Article  CAS  Google Scholar 

  14. Lai CQ, Parnell LD, Layman RF, Ordovas JM, Mackay TF. Candidate genes affecting Drosophila life span identified by integrating microarray gene expression analysis and QTL mapping. Mech Ageing Dev. 2007;128:237–49.

    Article  CAS  Google Scholar 

  15. Arbeitman MN, Furlong EEM, Imam F, Johnson E, Null BH, Baker BS, Krasnow MA, Scott MP, Davis RW, White KP. Gene expression during the life cycle of Drosophila melanogaster. Science. 2002;297:2270–5.

    Article  CAS  Google Scholar 

  16. Pletcher SD, Macdonald SJ, Marguerie R, Certa U, Stearns SC, Goldstein DB, Partridge L. Genome-wide transcript profiles in aging and calorically restricted Drosophila melanogaster. Curr Biol. 2002;12:712–23.

    Article  CAS  Google Scholar 

  17. Wang M, Marinotti O, Zhong D, James AA, Walker E, Guda T, Kweka EJ, Githure J, Yan G. Gene expression-based biomarkers for Anopheles gambiae age grading. PLoS ONE. 2013;8(7):e69439.

    Article  CAS  Google Scholar 

  18. World Health Organization. http://www.who.int/csr/don/19-july-2017-dengue-sri-lanka/en/. Accessed 20 Apr 2020

  19. Karunaratne SHPP, De Silva WAPP, Weeraratne TC, Surendran SN. Insecticide resistance in mosquitoes: development, mechanisms and monitoring. Ceylon J Sci. 2018;47(4):299–309.

    Article  Google Scholar 

  20. Karunaratne SHPP, Weeraratne TC, Perera MDB, Surendran SN. Insecticide resistance and efficacy of space spraying and larviciding in the control of dengue vectors Aedes aegypti and Aedes albopictus in Sri Lanka. Pestic Biochem Physiol. 2013;107(1):98–105.

    Article  CAS  Google Scholar 

  21. Ong OT, Kho EA, Esperança PM, Freebairn C, Dowell FE, Devine GJ, Churcher TS. Ability of near-infrared spectroscopy and chemometrics to predict the age of mosquitoes reared under different conditions. Parasit Vectors. 2020;13(1):1.

    Article  Google Scholar 

  22. World Mosquito Program. Sri Lanka. http://www.worldmosquitoprogram.org/en/global-progress/sri-lanka. Accessed 27 Aug 2020

  23. Untergasser A, Cutcutache I, Koressaar T, Ye J, Faircloth BC, Remm M, Rozen SG. Primer3- new capabilities and interfaces. Nucleic Acids Res. 2012;40(15):e115.

    Article  CAS  Google Scholar 

  24. Koressaar T, Remm M. Enhancements and modifications of primer design program Primer3. Bioinformatics. 2007;23(10):1289–91.

    Article  CAS  Google Scholar 

  25. Rao X, Huang X, Zhou Z, Lin X. An improvement of the 2^(-delta delta CT) method for quantitative real-time polymerase chain reaction data analysis. Biostat Bioinforma Biomath. 2013;3(3):71–85.

    PubMed  PubMed Central  Google Scholar 

  26. Křemenová J, Balvín O, Otti O, et al. Identification and age-dependence of pteridines in bed bugs (Cimex lectularius) and bat bugs (C. pipistrelli) using liquid chromatography-tandem mass spectrometry. Sci Rep. 2020;10:10146.

    Article  Google Scholar 

  27. Moore HE, Pechal JL, Benbow ME, et al. The potential use of cuticular hydrocarbons and multivariate analysis to age empty puparial cases of Calliphora vicina and Lucilia sericata. Sci Rep. 2017;7:1933.

    Article  Google Scholar 

  28. Johnson BJ, Hugo LE, Churcher TS, Ong OT, Devine GJ. Mosquito age grading and vector-control programmes. Trend Parasitol. 2020;36(1):39–51.

    Article  Google Scholar 

  29. Krajacich BJ, Meyers JI, Alout H, et al. Analysis of near infrared spectra for age-grading of wild populations of Anopheles gambiae. Parasites Vectors. 2017;10:552.

    Article  Google Scholar 

  30. Department of Meteorology Sri Lanka. http://www.meteo.gov.lk/index. Accessed 10 Apr 2020

  31. Christiansen-Jucht C, Parham PE, Saddler A, Koella JC, Basáñez M. Temperature during larval development and adult maintenance influences the survival of Anopheles gambiae s.s. Parasit Vectors. 2014;7:489.

    Article  Google Scholar 

  32. Lunde T, Balkew M, Korecha D, Gebre-Michael T, Massebo F, Sorteberg A, Lindtjørn B. A dynamic model of some malaria-transmitting anopheline mosquitoes of the Afrotropical region, validation of species distribution and seasonal variations. Malar J. 2013;12:78–91.

    Article  Google Scholar 

  33. Kirby MJ, Lindsay SW. Effect of temperature and inter-specific competition on the development and survival of Anopheles gambiae sensu strict and An. arabiensis larvae. Acta Trop. 2009;109(2):118–23.

    Article  Google Scholar 

  34. Bayoh MN, Lindsay SW. Temperature related duration of aquatic stages of the Afrotropical malaria vector mosquito Anopheles gambiae in the laboratory. Med Vet Entomol. 2004;18(2):174–9.

    Article  CAS  Google Scholar 

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Acknowledgements

The authors thank Mrs. Helen Irving (LSTM) for laboratory technical assistance.

Funding

Project financial support was provided by the, Commonwealth Association for the Split-Site Scholarship (2014) granted to TCW (reference no. LKCN-2014-263), Wellcome Trust Senior Research Fellowship in Biomedical Sciences to CSW (217188/Z/19/Z), and International Research Center (InRC), University of Peradeniya of Sri Lanka (grant no. InRC/RG/13/21).

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  1. Department of Zoology, Faculty of Science, University of Peradeniya, Peradeniya, Sri Lanka

    Thilini Chathurika Weeraratne, S. H. P. Parakrama Karunaratne & W. A. Priyanka P. de Silva

  2. Vector Biology Department, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, UK

    Lisa Reimer & Charles S. Wondji

  3. Medical Entomology Department, Centre for Research in Infectious Diseases (CRID), Yaoundé, Cameroon

    Charles S. Wondji

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Contributions

CSW, SHPPK, LR, and TCW conceptualized and designed the work. TCW, LR, and CSW conducted molecular laboratory work. TCW, WAPPS, and CW analyzed the data. All contributed to the discussion and writing the manuscript. All authors read and approved the final manuscript.

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Weeraratne, T.C., Karunaratne, S.H.P.P., Reimer, L. et al. Use of transcriptional age grading technique to determine the chronological age of Sri Lankan Aedes aegypti and Aedes albopictus females. Parasites Vectors 14, 493 (2021). https://doi.org/10.1186/s13071-021-04994-x

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Parasites & Vectors

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