Aedes aegypti, the primary vector of dengue virus, is well established throughout urban areas of the Southwestern US, including Tucson, AZ. Local transmission of the dengue virus, however, has not been reported in this area. Although many factors influence the distribution of the dengue virus, we hypothesize that one contributing factor is that the lifespan of female Ae. aegypti mosquitoes in the Southwestern US is too short for the virus to complete development and be transmitted to a new host. To test this we utilized two age grading techniques. First, we determined parity by analyzing ovarian tracheation and found that only 40% of Ae. aegypti females collected in Tucson, AZ were parous. The second technique determined transcript levels of an age-associated gene, Sarcoplasmic calcium-binding protein 1 (SCP-1). SCP-1 expression decreased in a predictable manner as the age of mosquitoes increased regardless of rearing conditions and reproductive status. We developed statistical models based on parity and SCP-1 expression to determine the age of individual, field collected mosquitoes within three age brackets: nonvectors (0–5 days post-emergence), unlikely vectors (6–14 days post-emergence), and potential vectors (15+ days post-emergence). The statistical models allowed us to accurately group individual wild mosquitoes into the three age brackets with high confidence. SCP-1 expression levels of individual, field collected mosquitoes were analyzed in conjunction with parity status. Based on SCP-1 transcript levels and parity data, 9% of collected mosquitoes survived more than 15 days post emergence.
Citation: Joy TK, Jeffrey Gutierrez EH, Ernst K, Walker KR, Carriere Y, Torabi M, et al. (2012) Aging Field Collected Aedes aegypti to Determine Their Capacity for Dengue Transmission in the Southwestern United States. PLoS ONE7(10): e46946. https://doi.org/10.1371/journal.pone.0046946
Editor: Nikos Vasilakis, University of Texas Medical Branch, United States of America
Received: June 14, 2012; Accepted: September 10, 2012; Published: October 12, 2012
Copyright: © Joy et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by National Institutes of Health grant R56AI091843 and the Center for Insect Science. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
The worldwide incidence of dengue has increased steadily since the 1960's. Currently, an estimated 100 million people are infected annually and serious outbreaks are becoming more common , . All four serotypes of dengue are now found in the Americas, Asia and Africa, which greatly increases the risk of dengue hemorrhagic fever (DHF) –. Furthermore, the worldwide direct and indirect costs of dengue infections amount to billions of dollars annually . In the Americas, estimates range from one to three billion dollars annually depending on frequency and severity of outbreaks. A majority of these costs are due to lost productivity, as many infections prevent people from working for a week or more .
Local transmission of dengue in the continental United States has occurred in the border towns of Brownsville, Texas and Matamoros, Chihuahua in 2005 and Key West, Florida and Miami-Dade County, FL in 2010 and 2011 –. Another likely route of dengue entry into the United States is through the southwest border, including Arizona. Like the sites in Texas and Florida, southeastern Arizona has well established populations of Aedes aegypti, the primary vector of dengue and DHF. Furthermore, dengue is endemic in the Mexican state of Sonora which borders Arizona. Since the 1990's Hermosillo, Sonora, just 178 miles south of the Arizona/Sonora border, has experienced seasonal dengue transmission and several large outbreaks . Arizona/Sonora border towns are major hubs of travel, immigration, and trade with Mexico, increasing the likelihood of dengue introduction into the United States through southern Arizona.
Even though Ae. aegypti can be found throughout urban areas of the Southwestern US including Nogales, Tucson, and Phoenix, local transmission of dengue has yet to be demonstrated. Multiple factors must be in place for dengue to emerge in a given region; dengue virus must be introduced into the population, interactions between a competent vector and susceptible human population must be present and introduction must coincide with the Ae. aegypti season. In the southwestern United States there may be limited water sources for oviposition decreasing overall vector density and high quality housing may reduce contact with the vector minimizing but not eliminating transmission potential, . Another contributing factor could be that the hot, dry climate of southeastern Arizona, which represents the northern edge of the mosquito's range, prevents Ae. aegypti from surviving long enough to effectively transmit the virus. Even relatively small reductions in lifespan can significantly impact transmission potential given the exponential relationship between vectorial capacity and survival.
Following an infective bloodmeal, the dengue virus must complete an extrinsic incubation period (EIP) averaging 10–14 days in the mosquito vector before it can be transmitted to a human host. This EIP is temperature dependent, and has been shown to increase as temperatures decrease . At high temperatures (≥32°C) the EIP of dengue can be as short as seven days. At 30°C the EIP was found to be as long as 12 days. Mean temperatures in Tucson, AZ during the peak of Ae. aegypti activity range from 30.5°C in July to 27.7°C in September , suggesting an EIP in our study area of ∼12 days. Since host seeking in Ae. aegypti does not begin until 36–48 h post emergence , mosquitoes in the study site are unlikely to be dengue vectors until they are ∼14 days old.
The above averages, however, mask great diurnal variability and temperatures frequently range 10°C over the course of the day in Tucson. Average high temperatures reach 37.6°C in July and 34.7°C in September, with temperatures often exceeding 40°C . Extreme high temperatures such as this have a great impact on adult survivorship. For example, 50% of adult Ae. aegypti perish in less than an hour when maintained at 40°C . Mark-release-recapture studies show the adult lifespan of Ae. aegypti to be relatively short, although in dengue endemic areas a significant portion of the population does survive the EIP. In some urban neighborhoods of Rio de Janeiro where dengue is endemic, as many as half of mosquitoes survived more than ten days post-emergence during the dry season, while approximately a third survived the ten day EIP during the wet season . In contrast, suburban neighborhoods in Rio de Janeiro had fewer mosquitoes surviving the 10 day EIP with the first having approximately 10% of the Ae. aegypti population surviving ten days and the second having 4% to 26% survivorship after ten days , . Finally, a mark-release-recapture study in a high income, low density neighborhood in Rio de Janeiro found the 10 day survivorship was only between 0.6 and 13% . Dengue transmission intensity was highest in neighborhoods with higher survival rates but this may be confounded by other factors that differed between these neighborhoods. Studies conducted by McDonald (1977)  in Mombasa, Kenya predicted daily survivorship to be 89%, meaning that approximately 19% to 31% of female Ae. aegypti would survive the 10 to 14-day EIP. Daily survivorship of Ae. aegypti ranged from 86 to 91% in northeastern Australia, where sporadic dengue outbreaks occur, indicating that 12 to 27% of the population could survive a 14-day EIP . These survival rates from endemic areas suggest that transmission can be well established when as little as 10–27% of the Ae. aegypti population survives past the 10–14 day EIP. However, estimating the age structure of wild mosquito populations is difficult. Mark-release-recapture studies are valuable to determine mosquito survivorship, but these studies are laborious, difficult to conduct in some locations, and often use laboratory-reared mosquitoes that may not accurately represent wild mosquito populations. Such studies also tend to assume that mosquito survivorship is constant, although Styer et al. (2007) found that the mortality rate increased with age . The ability to rapidly determine the age structure of wild Ae. aegypti populations in endemic and non-endemic areas would allow us to better understand how adult survivorship impacts dengue transmission.
Several techniques have been developed to determine the chronological or physiological age of arthropod vectors. Physiological age grading techniques are largely based on morphological changes that mosquitoes undergo during different developmental stages and physiological processes such as reproduction. These include the dissection of mosquito ovaries and analysis of ovarian tracheae to determine the parity status of individual females , counting ovarioles to approximate the number of egg clutches a female has laid , and counting growth lines on the insect cuticle , . The first technique, tracheae analysis, provides the most reliable indicator of parity and is logistically easier than the second and third techniques .
Three well documented techniques have been used to determine a mosquito's chronological age and whether they can survive a pathogen's EIP: gas-liquid chromatography to determine the composition of cuticular hydrocarbons, near-infrared spectroscopy (NIRS), and gene transcription –. The analysis of cuticular hydrocarbon composition in Ae. aegypti was shown to accurately age mosquitoes up to 15 days post-emergence , . However, to estimate the risk of dengue transmission it is important to accurately identify those mosquitoes older than 15 days post emergence, as these old mosquitoes are the primary vectors of the virus. In addition, expression of cuticular hydrocarbon abundance has been shown to change as the ratio between n-alkanese and n-alkene shifts according to external temperatures. NIRS has been shown to be cost effective after the initial purchase of the equipment, and not as time consuming as many other methods , . One drawback is that considerable effort is required to calibrate models for particular mosquito species before using the technique on a large scale, although once this is accomplished subsequent analysis is rapid and inexpensive. It is also not possible to store field collected mosquitoes for extended periods of time before analysis. Finally, at this time NIRS can only differentiate female mosquitoes into young (<7 d) and old (>7 d) groups. Gene transcription analysis may provide more accurate information on the chronological age of mosquitoes –. Microarray data from the fruit fly Drosophila melanogaster and mosquito Anopheles gambiae has paved the way for identification of age associated genes in invertebrates , –. Ae. aegypti orthologues of D. melanogaster and An. gambiae genes have been used to age mosquitoes in the lab and under field conditions , , .
Here we used two techniques to age Ae. aegypti populations in Tucson, Arizona. We analyzed ovary tracheae to establish parity and transcript analysis of the Sarcoplasmic calcium binding protein 1 (SCP-1) to assess age , . These two techniques allowed us to accurately group individual wild mosquitoes into non-vectors (≤5 days post-emergence), unlikely vectors (6–14 days post-emergence), and potential vectors (≥15 days post-emergence).
Materials and Methods
Ae. aegypti mosquitoes (UGAL strain) were reared and maintained at 27°C and 70% RH with a 16 h light and 8 h dark photoperiod. Larval mosquitoes were reared at a density of approximately 150 larvae per liter of water, and maintained on cat chow to ensure optimal growth. Adult mosquitoes were given access to a 10% dextrose solution ad libitum. Mosquitoes used in blood feeding assays were given access to porcine blood containing 1% sodium citrate (obtained from the University of Arizona Meat Science Lab; IACUC: 05-182; PHS Assurance #A3248-01). Blood meals were offered weekly until mosquitoes were collected from the final time point. Oviposition substrates were offered 48 h after each blood meal for 24 h.
Characterization of age-associated genes
Female Ae. aegypti mosquitoes (10–15 individuals) that had not been previously blood fed were collected 1, 3, 5, 10, 15, 20, or 35 days post-emergence. Total RNA from the pooled mosquito samples was isolated using the RNeasy Total RNA kit (Qiagen, Valencia, CA). Total RNA was treated with DNAse (Fermentas; Thermo Scientific Inc., Glen Burnie, MD) to eliminate genomic DNA contamination and converted into cDNA using the High Capacity cDNA Reverse Transcription kit (Applied Biosystems, Carlsbad, CA). Quantitative real-time PCR (qRT-PCR) was performed using primers against eight previously identified age-associated genes (Table 1). Three of these genes, SCP-1/Ae-15848, CG-8505/Ae-8505, and fizzy/Ae-4274, and one reference gene RPS17/Ae-RPS17 were identified in Ae. aegypti  and the primers used were identical to those previously reported. An additional five age associated genes, AGAP009551, AGAP011615, AGAP002827, AGAP005501, and AGAP009790 were adopted from An. gambiae . Putative orthologs of these genes were identified in Ae. aegypti and new primers were developed (Table S1). The Ae. aegypti RPS17 gene was used as a loading control in qRT-PCR as described by Cook et al. (2006). The qRT-PCR reactions were performed using the Maxima SYBR Green/ROX qPCR Master Mix (2×) kit (Fermentas; Thermo Scientific Inc., Glen Burnie, MD) and a Mastercycler® ep realplex real-time PCR machine (Eppendorf, Hauppauge, NY) at the following cycling conditions: stage 1, 50°C for 2 min; stage2, 95°C for 10 min; stage 3, 95°C for 0:15 s, and 65°C for 1 min; for 40 cycles. If an age-associated expression pattern was observed, five additional replicates were combined to form a standard curve of gene expression with pooled mosquito samples.
To verify that expression of the age associated genes was not influenced by reproductive status or environmental conditions, we replicated the above assays with lab-reared mosquitoes provided with multiple blood meals and mosquitoes reared under semi-field conditions, but not blood fed. Blood fed mosquitoes were provided with weekly blood meals and oviposition substrates (blood meal and eggs were extracted from each mosquito prior to processing). For semi-field studies adult mosquitoes were maintained in 5 L plastic cages (Rubbermaid #3922 modified with screen top and stocking net access) exposed to the Tucson, AZ environment. Cages were stored under a small shelter to protect them from direct sunlight and rain and mosquitoes were provided with a 10% sucrose solution. Semi-field studies were conducted from May to September of 2010, which corresponds to southern Arizona's monsoon season and represents the annual peak of Ae. aegypti activity in Tucson .
Validation of age-associated gene expression using individual mosquitoes
To be of optimal use in the field, age grading individual mosquitoes, not just pooled samples, is essential. To analyze gene expression, 127 individual mosquitoes were collected at various adult ages (3, 4, 7, 8, 10, 12, 13, 14, 15, 19, 20, 24, 27, 30, 35 and 39 d after adult emergence) from multiple generations of lab reared mosquitoes. Total RNA was isolated and cDNA synthesized for each individual mosquito as described above, followed by qRT-PCR using SCP-1 and RPS17 primers as discussed above. The predicted age of the individual mosquitoes based on statistical analyses of SCP expression levels (see below) was compared with their known age. Twenty seven additional individual mosquitoes from these time points, reared under semi-field conditions, were also analyzed as described above for a total of 154 individuals.
Collection of wild Ae. aegypti mosquitoes
A residential site in downtown Tucson, AZ was chosen for trapping mosquitoes. This area showed a high density of Ae. aegypti in a previous city wide survey using oviposition traps in 2004 . Mosquitoes were trapped weekly, from July 20th to October 3rd 2009, using the BG Sentinel trap (BioQuip Products, Inc., Dominguez, CA). The trap was activated on Monday evenings and remained active until Friday evenings. Mosquitoes were collected from the trap twice daily, once in the morning before 8:00 am and once in the afternoon after 6:00 pm to minimize desiccation and mortality in trapped mosquitoes. Collected mosquitoes were individually labeled and stored at −80°C until analyzed for parity status and SCP-1 transcript expression.
Determining parity status and SCP-1 transcipt levels of field collected mosquitoes
We determined the parity status of Ae. aegypti mosquitoes collected in the field in 2009 (N = 219) using a slightly modified version of previously established protocols , . Briefly, ovaries were dissected directly into nanopure water droplets, rinsed with nanopure water, separated, and allowed to completely dry on a clean microscope slide. Parity status was determined by analyzing each dried ovary under an inverted microscope and observing trachea coiling. Images of each ovary were archived at 100× and 400× magnification using a Spot RT color camera (Diagnostic Instruments Inc., Sterling Heights, MI). Mosquitoes were scored as nulliparous if multiple trachea coils were observed on the surface of the ovary. Mosquitoes without coiled trachea indicating a previously completed reproductive cycle and gravid mosquitoes were scored as parous. Investigators scoring the ovaries first performed blind assays on mosquitoes of known parity until their accuracy rate was approximately 95%.
The remaining carcass (ie whole body minus ovaries) of field collected mosquitoes was processed for cDNA synthesis as described above. All cDNA samples were archived at −20°C for subsequent qRT-PCR assays. SCP-1 expression levels were determined by qRT-PCR as described above. To compare the two approaches, the predicted age of the mosquitoes based on statistical analyses of SCP expression levels (see below) was determined for nulliparous and parous mosquitoes.
Statistical analyses were performed for all eight genes from the pooled mosquito trials. The effect of age on the Ct ratio (putative age associated gene/RPS17) from pooled mosquitoes was analyzed with a one-way ANOVA. Further analyses were performed on the SCP-1 gene which showed the most consistent change with age. To assess whether SCP-1 expression changed as mosquitoes aged, the Ct ratio (SCP-1/RPS17) from pooled mosquitoes was analyzed with a two-way ANOVA (JMP 2010). The response variable was the Ct ratio (log10 transformed), and explanatory variables were treatment (lab-reared non-blood fed, lab-reared blood fed, and semi-field reared non-blood fed mosquitos), age (treated as a categorical variable), and the interaction between these factors. As neither treatment nor the interaction significantly affected the Ct ratio (see Results), a two-way ANOVA without the interaction was fit, and Tukey tests were used to compare least squares means of the Ct ratio among mosquito age. Logistic regression was used to evaluate the association between SCP-1 expression and age of individual mosquitoes to classify them based on SCP-1 expression (SPSS 2010). Mosquito age was treated as a binary response variable (mosquitoes ≤14 days vs. >14 days). Preliminary analyses showed that the association between SCP-1 expression and mosquito age did not differ significantly between lab and semi-field mosquitoes (lab vs. semi-field, Wald χ2 = 0.41, P = 0.52; experimental condition×SCP-1 expression, Wald χ2 = 2.19, P = 0.14), but that a negative association between SCP-1 expression and age of the mosquitoes occurred (Wald χ2 = 26.51, P<0.001). Lab and semi-field mosquitoes (N = 154) were thus pooled for analyses and the association between SCP-1 expression and mosquito age evaluated. Three outliers were identified at this point. Due to our inability to find an error in collecting and processing these samples, each outlier was incorporated in the statistical analysis. Coordinates of the Receiver Operating Characteristic (ROC) curve and associated sensitivity and 1- specificity values were used to determine appropriate SCP-1 expression cutoff points to classify mosquito age. A random sample of 62% (n = 96) of the observations was selected (Bernoulli distribution) to generate a predictive model. Accuracy of the predictive model was assessed using the remaining mosquitoes of known age (n = 58).
Mosquitoes may be nulliparous because they are too young to have reproduced, or older but were unable to find a blood meal. To use SCP-1 values to better understand the origin of field collected nulliparous mosquitoes, a second logistic regression model was fit, using the binary response variable mosquito ≤5 days vs. >5 days. We did not use a random sample of the data to validate the SCP-1 cutoff point resulting from this model because only 22 mosquitoes were ≤5 day old.
Using SCP-1 to determine the age of lab reared mosquito pools
Of the eight genes assayed (Table 1), the Ae. aegypti gene SCP-1/Ae-15848 showed the most consistent change in expression as mosquitoes aged. Expression of SCP-1 in pooled mosquitoes did not differ among lab reared, semi-field and blood fed lab reared mosquitoes (F = 0.20, P = 0.81), nor was the change in expression with age significantly different among these three groups (F = 0.42, P = 0.93). However, expression of SCP-1 was significantly reduced in older mosquitoes (F = 18.71, P<0.001, Figure 1). Among-age comparisons revealed that SCP-1 expression was highest in 3- and 5-day old mosquitoes, intermediate in 10-, 15-, and 20-day old mosquitoes, and lowest in 35-day old mosquitoes (Figure 1).
To evaluate effects of post emergence age and treatment on the Ct ratio (SCP-1/RPS17), groups of mosquitoes were compared using a two-way ANOVA followed by Tukey tests on least squares means. The means presented in this figure are untransformed. Different letters indicate significant difference in gene expression (p<0.05). Seven replicates were used for each time period in sugarfed, lab- reared mosquitoes; 3 replicates were used for each time period in bloodfed and semi-field reared mosquitoes. Each group consisted of 10–15 female mosquitoes. Bars indicate standard error.
Two other genes AGAP002827 and AGAP011615 showed significant changes in gene expression with age (Figure S1).Expression of AGA011615 was significantly higher in pooled mosquito samples in 35 day old mosquitoes (Figure S1D). Unfortunately, it was highly variable when we tested individual 35 day old mosquitoes instead of pooled mosquito samples (data not shown), making it unsuitable for age-grading older individual mosquitoes from the field. Expression of AGAP002827 was significantly different in 1 day old mosquitoes (Figure S1E). However, discrimination of newly emerged mosquitoes is not useful in addressing dengue transmission potential, so further characterization of this gene's expression pattern was not pursued. The other five genes did not show significant changes in expression with age (Figure S1).
Using SCP-1 to determine age of individual mosquitoes
The logistic regression model for mosquitoes greater than 14 days of age (log odds of >14 day old mosquitoes = −3.69+17.37 (SCP-1 expression), Likelihood Ratio χ2 = 31.98, P<0.0001) had a good fit (Hosmer–Lemeshow Chi-Square = 2.65, P = 0.95, Nagelkerke's R-square = 0.74) and an overall classification accuracy of 87.0%. Area under the ROC curve was large (area = 0.95, SE = 0.016, Asymptotic Significance <0.001). Ninety-one percent of mosquitoes ≤14 days had SCP-1 values ≥0.46 (Sensitivity = 0.91), while 13% of mosquitoes >14 days had SCP-1 values ≥0.46 (1 - Specificity = 0.13). Thus, using SCP-1≥0.46 as a cutoff point is expected to misclassify 9% of mosquitoes ≤14 days as being older and 13% of mosquitoes >14 days as being younger. Results from validation of the model with a random sample of the data show that the model predicted the age group 9 out of 10 times (overall classification accuracy = 90%; 80% for older mosquitoes; 97% for younger mosquitoes).
The logistic regression model for mosquitoes less than five days of age (log odds of <5 day old mosquitoes = 5.98−5.56 (SCP-1 expression), Likelihood Ratio χ2 = 20.89, P<0.001) had a good fit (Hosmer–Lemeshow Chi-Square = 2.52, P = 0.96, Nagelkerke's R-square = 0.77) and an overall classification accuracy of 94.8%. Area under the ROC curve was 0.98 (SE = 0.009, Asymptotic Significance <0.001). Eighty-nine percent of mosquitoes ≤5 days had SCP-1 values≥0.94 (Sensitivity = 0.89), while no mosquitoes >5 days had SCP-1 values≥0.94 (1 - Specificity = 0). Thus, using SCP-1≥0.94 as a cutoff point is expected to misclassify 11% of mosquitoes ≤5 days as being older and 0% of mosquitoes >5 days as being younger. Using cutoff points from both logistic regression models, we were thus able to classify mosquitoes as ≤5 day old if SCP-1>0.94, 6 to 14 day old if 0.46<SCP-1≤0.94, and >14 day old if SCP-1≤0.46. Average SCP-1 expression for these age classes is presented in Figure S2.
Determining parity status of field collected mosquitoes
The percentage of trapped female mosquitoes that were parous averaged 40% (Figure 2) over the course of the season, ranging from 18% in the second week of September to 57% in the third week of August. There were 1.3 times more nulliparous mosquitoes (n = 110) trapped throughout the season than parous mosquitoes (n = 88). Included in the parous category were nine individuals (4.6%) with some degree of ovarian development. We were unable to confirm parity status of approximately 10% (n = 21) of the collected mosquitoes due to the poor state of the mosquito at the time of collection or damage/stretching of the ovaries during dissection (Figure 2).
The parity status of field caught mosquitoes was established by analyzing ovary tracheation. Individual mosquitoes were separated according to the week they were trapped. The parity status could not be determined for approximately 10% of mosquitoes (n = 21).
Determining age in field collected mosquitoes by combining gene expression and parity
A subset of nulliparous (n = 47) and parous (n = 39) mosquitoes were randomly selected to test for SCP-1 expression. According to the cutoff SCP-1 values determined in the logistic regression model, 100% of the nulliparous mosquitoes were less than 15 days post adult emergence (Figure 3a). The majority of nulliparous mosquitoes (91%) were found to be 5 days or less post adult emergence. In contrast, most of the parous mosquitoes (69%) were between 6 and 14 days post adult emergence. Only 10% of the tested parous mosquitoes were age graded as less than five days old, and only 21% of parous mosquitoes tested were found to be older than 14 days post adult emergence (Figure 3b). Combining the percentage of parous mosquitoes surviving at least 15 days with the average parity rate of 40%, we find that only ∼9% of the field collected mosquitoes in our sample survived long enough to be potential vectors of dengue.
Based on cutoff points from logistic regression models, the Ct value of each mosquito was assigned into one of three catagories: 0–5 days post emergence, 6–14 days post emergence, or 15+ days accordingly. A. Nulliparous mosquitoes (n = 47) were randomly chosen from three, three-week time periods throughout the 2009 monsoon season. Based on cutoff points, all 47 nulliparous mosquitoes were less than 15 days post emergence, and 91% of them were less than 5 days post-emergence. B. Parous mosquitoes (n = 39) were randomly chosen from the same three time periods. Based on cutoff points, 79% of the parous mosquitoes were 14 days or less post emergence, while 21% were greater than 14 days post emergence.
The age structure of mosquito populations is a vital component in the vector's ability to efficiently transmit the dengue virus between human hosts. If insufficient numbers of mosquitoes survive the EIP of the virus, transmission is unlikely to occur. Aging individual wild mosquitoes can be used to validate outputs from models that use climatic inputs to predict the proportion of Ae. aegypti that survive the EIP. Incorporating data that quantifies mosquitoes older than the EIP is especially beneficial for modeling to predict the transmission potential of dengue in a given area. One such model, DyMSim, has been calibrated for use in both Ae. aegypti and Culex quinquefasciatus . By comparing the monthly density of trapped mosquitoes to factors including regional geography and climate data such as temperature, relative humidity, rainfall, and solar radiation, the DyMSim model has been shown to accurately predict inter and intra-annual shifts in Cx. quinquefasciatus population densities . If in addition to mosquito density, associations can be found between population longevity and climatic factors, models of vector competency and dengue risk across geographic regions will be estimated with increased accuracy.
From this research we determined the parity status of Ae. aegypti in Tucson, AZ based on ovary tracheation. Over the 2009 field season, we found that only 40% of trapped mosquitoes were parous or gravid, suggesting that most mosquitoes are very young or have decreased access to hosts compared to other areas. This parity level is similar to results from another study conducted in Tucson, in which 44% of female Ae. aegypti trapped between August and September were parous . The percent of parous females trapped in Tucson is much lower than recorded percentages in some dengue endemic regions such as Rio de Janeiro, Brazil (92.9%) , , and in Trinidad, West Indies (99%) . A recent study conducted in dengue-free Al-Madinah, Saudi Arabia found parity rates as low as 29.1% . This would seem to support the hypothesis that the aridity of desert environments does not allow Ae. aegypti to survive long enough to transmit dengue, however dengue is endemic in the neighboring cities of Jeddah and Makkah, Saudi Arabia, illustrating the complex factors involved in dengue emergence .
Aging individual mosquitoes provides insight into age variation within a population. Thus, we validated the use of SCP-1 to age individual mosquitoes, instead of the pooled samples. We generated two logistic regression models that together differentiated well between nonvectors (0–5 days post-emergence), unlikely vectors (5–14 days post-emergence), and potential vectors (15+ days post-emergence) based on SCP-1 expression. Through the use of an established age grading technique (analysis of ovary tracheation) and SCP-1 expression, we then determined that only 9% of the mosquitoes tested from our Tucson, AZ site were older than 15 days post emergence, suggesting that most Ae. aegypti individuals residing in Tucson may be too young to transmit dengue.
It is important to note that the age structure of the Ae. aegypti population is only one factor that influences transmission potential in Tucson, AZ. Factors directly associated with the vector including Ae. aegypti population density and vector competence also play a role, though Ae. aegypti from Tucson have been shown to be competent transmitters of DENV 2 . Indirectly social factors may also play a significant role as modifiers of vector-human contact. As compared to endemic areas in low income countries, higher quality housing in the Arizona may prevent vector entrance into the homes while high temperatures may prevent individuals from frequent outdoor activity. Yet while social infrastructure has been associated with decreased intensity it has not completely prevented dengue emergence in the United States . At the same time, human behaviors associated with dengue risk may also influence vector longevity. Endophilic mosquitoes in dengue endemic regions may also survive better when sheltered from the environment than those mosquitoes forced outdoors due to improved housing. Yet this does not explain why autochthonous transmission has not yet been reported south of the border in Nogales, SN, MX where climatic conditions are similar to Tucson, AZ but social infrastructure is similar to other endemic regions in northern Mexico. Future studies will further explore these factors.
This proof of principle study confirms the usefulness of combining two age grading techniques, SCP-1 transcription and parity analysis. Not only can we accurately age single mosquitoes within the age groups, 1–14 days, or 15 days or more post emergence, but we can also identify mosquitoes less than 5 days post emergence. When coupled with parity analysis, the use of a single age-associated gene has proven to be a quick and efficient way to classify mosquitoes into these three age groups. A recent study by Hugo et al. (2010)  demonstrated that testing single mosquitoes on three genes (SCP-1/Ae-15848, Ae-8505, and Ae-4274) using a Taqman detection system aged 72% of single Ae. aegypti mosquitoes within ±6 days of their actual age. They also found no significant difference in SCP-1 expression between mosquitoes released into the wild and reared in semi-field conditions . These findings further validate the use of SCP-1 gene expression in semi-field reared Ae. aegypti to approximate the age of field caught individuals.
The identification of additional age-grading genes, especially those with increased expression in older mosquitoes, would refine the accuracy of this technique. In testing previously described age-associated genes, we only found a consistent and significant pattern with SCP-1, in contrast to other studies , , . One possible difference is that we isolated total RNA from the entire carcass minus the ovaries, whereas other studies utilized only the thorax and head. It is possible that variation in abdominal gene expression could have masked the age-associated expression patterns and we will explore this possibility in the future. In addition, microarray studies continue to be conducted on various mosquito species, introducing the possibility of identifying novel age-associated genes , , . Recently, microarray studies have identified 35 genes in Ae. aegypti with age related gene expression , while 179 age related An. gambiae genes were not only found to be unaffected by one or more blood meals but 112 of the 179 genes showed a monotonic increase or decrease with age . In the future it would be ideal to test more of these genes for similar expression trends in Ae. aegypti. The age grading genes that Wang et. al, 2010  identified as homologous to D. melanogaster may be of special interest to future studies because like SCP-1, they can be applied to a greater number of arthropod species.
The next step in our research is to test the combination of the aging techniques, SCP-1 gene expression and ovary tracheation, on a greater geographic scale across the southwestern United States and in nearby dengue endemic regions. Future testing of this set of techniques in endemic regions will reveal if differences in population ages exist between endemic and non-endemic areas. To establish the accuracy of age grading field caught mosquitoes with these techniques, one collection site was used during 2009. During the summer rainy seasons of 2010 and 2011, mosquitoes were sampled at multiple sites in both Tucson and Nogales, AZ to continue to test these aging techniques as well as our hypothesis that Ae. aegypti mosquitoes at the edge of their ecological range do not survive long enough to be efficient vectors of dengue.
Expression profiles of seven putative age associated genes. Three to nine replicates of pooled mosquito samples from various time points (1, 3, 5, 10, 15, 20, or 35 days post emergence) were tested for expression profiles of seven previously reported age associated genes. For AGA011615 (S1D) a significant increase in transcript expression was observed in 35 day old mosquitoes compared to 5-, 10-, and 20-day old mosquitoes. For AGAP002827 (S1E) a significant increase in expression was observed in one day old mosquitoes. The effect of age on gene expression was tested using a one-way ANOVA followed by Tukey tests. Letters above the bars indicate significant differences in gene expression (p<0.05). If no letters are present no significant differences were observed. Bars indicate standard error.
Average SCP-1 gene expression per age group. The average SCP-1 gene expression and standard error of single mosquitoes used in the aging model is presented here by age group, untransformed (n = 154).
Ae. aegypti orthologues of age associated genes. The five genes (AGAP009551, AGAP011615, AGAP002827, AGAP005501, and AGAP009790) adopted from Wang, 2010 were transformed to Ae. aegypti orthologues with the use of the NCBI Homologene database. The primer sequence and efficiency is provided.
We would like to thank Dr. Frank Ramberg for helping with mosquito rearing. We would also like to thank Donna Feng and Linda Ramirez for their assistance on this project.
Conceived and designed the experiments: KE KRW MAR. Performed the experiments: TKJ EHJ. Analyzed the data: TKJ YC MT MAR. Wrote the paper: TKJ KE KRW YC MAR.
- 1. Guzman A, Isturiz RE (2010) Update on the global spread of dengue. International Journal of Antimicrobial Agents 36 Suppl 1: S40–2.
- 2. Guzman MG, Halstead SB, Artsob H, Buchy P, Farrar J, et al. (2010) Dengue: A continuing global threat. Nature Reviews. Microbiology 8: S7–S16.
- 3. Guzman MG, Kouri G (2002) Dengue: An update. The Lancet Infectious Diseases 2: 33–42.
- 4. Gubler DJ (2006) Dengue/dengue hemorrhagic fever: History and current status. Novartis Foundation Symposium 277: 3–16 discussion 16-22, 71-3, 251-3.
- 5. Gubler DJ, Meltzer M (1999) Impact of dengue/dengue hemorrhagic fever on the developing world. Advances in Virus Research 53: 35–70.
- 6. Shepard DS, Coudeville L, Halasa YA, Zambrano B, Dayan GH (2011) Economic impact of dengue illness in the Americas. American Journal of Tropical Medical Hygiene 84: 200–207.
- 7. Brunkard JM, Robles Lopez JL, Ramirez J, Cifuentes E, Rothenberg SJ, et al. (2007) Dengue fever seroprevalence and risk factors, Texas-Mexico border, 2004. Emerging Infectious Diseases 13: 1477–1483.
- 8. Centers for Disease Control and Prevention (CDC) (2010) Locally acquired dengue–Key West, Florida, 2009–2010. MMWR. Morbidity and Mortality Weekly Report 59: 577–581.
- 9. Centers for Disease Control and Prevention (CDC) (2007) Dengue hemorrhagic fever–U.S.-Mexico border, 2005. MMWR. Morbidity and Mortality Weekly Report 56: 785–789.
- 10. Knowlton K, Solomon G, Rotkin-Ellman M (2009) Fever pitch: Mosquito-borne dengue fever threat spreading in the Americas.
- 11. Reiter P, Lathrop S, Bunning M, Biggerstaff B, Singer D, et al. (2003) Texas lifestyle limits transmission of dengue virus. Emerging Infectious Diseases 9: 86–89.
- 12. Watts DM, Burke DS, Harrison BA, Whitmire RE, Nisalak A (1987) Effect of temperature on the vector efficiency of Aedes aegypti for dengue 2 virus. The American Journal of Tropical Medicine and Hygiene 36: 143–152.
- 13. (2011) National weather service forecast office. monthly climate report and F-6 data for Tucson AZ. 2011.
- 14. Davis E (1983) Development of lactic acid-receptor sensitivity and host-seeking behavior in newly emerged female Aedes aegypti mosquitoes. Journal of Insect Physiology 30: 211–215.
- 15. Bar-Zeev M (1957) The effect of extreme temperatures on different stages of Aedes aegypti (L). Bulletin of Entomological Research 48: 593–599.
- 16. Maciel-de-Freitas R, Codeco CT, Lourenco-de-Oliveira R (2007) Daily survival rates and dispersal of Aedes aegypti females in Rio de Janeiro, Brazil. The American Journal of Tropical Medicine and Hygiene 76: 659–665.
- 17. Maciel-de-Freitas R, Eiras AE, Lourenço-de-Oliveira R (2008) Calculating the survival rate and estimated population density of gravid Aedes aegypti (diptera, culicidae) in Rio de Janeiro, Brazil. Cadernos De Saude Publica 24: 2747–2754.
- 18. David MR, Lourenco-de-Oliveira R, Freitas RM (2009) Container productivity, daily survival rates and dispersal of Aedes aegypti mosquitoes in a high income dengue epidemic neighborhood of Rio de Janeiro: Presumed influence of differential urban structure on mosquito biology. Memorias do Instituto Oswaldo Cruz 104: 927–932.
- 19. McDonald PT (1977) Population characteristics of domestic Aedes aegypti (diptera: Culicidae) in villages on the Kenya coast I. adult survivorship and population size. Journal of Medical Entomology 14: 42–48.
- 20. Muir LE, Kay BH (1998) Aedes aegypti survival and dispersal estimated by mark-release-recapture in northern Australia. The American Journal of Tropical Medicine and Hygiene 58: 277–282.
- 21. Styer LM, Carey JR, Wang JL, Scott TW (2007) Mosquitoes do senesce: Departure from the paradigm of constant mortality. The American Journal of Tropical Medicine and Hygiene 76: 111–117.
- 22. Detinova TS (1962) Age-grouping methods in diptera of medical importance with special reference to some vectors of malaria. Monograph Series. World Health Organization 47: 13–191.
- 23. Hoc TQ, Schaub GA (1996) Improvement of techniques for age grading hematophagous insects: Ovarian oil-injection and ovariolar separation techniques. Journal of Medical Entomology 33: 286–289.
- 24. Neville AC (1963) Daily growth layers for determining the age of grasshopper populations. Okios 14: 3/14/2011.
- 25. Schlein Y (1979) Age grouping of anopheline malaria vectors (diptera: Culicidae) by the cuticular growth lines. Journal of Medical Entomology 16: 502–506.
- 26. Hugo LE, Quick-Miles S, Kay BH, Ryan PA (2008) Evaluations of mosquito age grading techniques based on morphological changes. Journal of Medical Entomology 45: 353–369.
- 27. Mayagaya VS, Michel K, Benedict MQ, Killeen GF, Wirtz RA, et al. (2009) Non-destructive determination of age and species of Anopheles gambiae s.l. using near-infrared spectroscopy. The American Journal of Tropical Medicine and Hygiene 81: 622–630.
- 28. Sikulu M, Killeen GF, Hugo LE, Ryan PA, Dowell KM, et al. (2010) Near-infrared spectroscopy as a complementary age grading and species identification tool for African malaria vectors. Parasites & Vectors 3: 49.
- 29. Gerade BB, Lee SH, Scott TW, Edman JD, Harrington LC, et al. (2004) Field validation of Aedes aegypti (diptera: Culicidae) age estimation by analysis of cuticular hydrocarbons. Journal of Medical Entomology 41: 231–238.
- 30. Desena ML, Clark JM, Edman JD, Symington SB, Scott TW, et al. (1999) Potential for aging female Aedes aegypti (diptera: Culicidae) by gas chromatographic analysis of cuticular hydrocarbons, including a field evaluation. Journal of Medical Entomology 36: 811–823.
- 31. Cook PE, Hugo LE, Iturbe-Ormaetxe I, Williams CR, Chenoweth SF, et al. (2006) The use of transcriptional profiles to predict adult mosquito age under field conditions. Proceedings of the National Academy of Sciences of the United States of America 103: 18060–18065.
- 32. Hugo LE, Cook PE, Johnson PH, Rapley LP, Kay BH, et al. (2010) Field validation of a transcriptional assay for the prediction of age of uncaged Aedes aegypti mosquitoes in northern Australia. PLoS Neglected Tropical Diseases 4: e608.
- 33. Wang MH, Marinotti O, James AA, Walker E, Githure J, et al. (2010) Genome-wide patterns of gene expression during aging in the African malaria vector Anopheles gambiae. PloS One 5: e13359.
- 34. Caragata EP, Poinsignon A, Moreira LA, Johnson PH, Leong YS, et al. (2011) 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 Molecular Biology 20: 215–224.
- 35. Marinotti O, Calvo E, Nguyen QK, Dissanayake S, Ribeiro JM, et al. (2006) Genome-wide analysis of gene expression in adult Anopheles gambiae. Insect Molecular Biology 15: 1–12.
- 36. Zou S, Meadows S, Sharp L, Jan LY, Jan YN (2000) Genome-wide study of aging and oxidative stress response in Drosophila melanogaster. Proceedings of the National Academy of Sciences of the United States of America 97: 13726–13731.
- 37. Pletcher SD, Macdonald SJ, Marguerie R, Certa U, Stearns SC, et al. (2002) Genome-wide transcript profiles in aging and calorically restricted Drosophila melanogaster. Current Biology : CB 12: 712–723.
- 38. Lai CQ, Parnell LD, Lyman RF, Ordovas JM, Mackay TF (2007) Candidate genes affecting Drosophila life span identified by integrating microarray gene expression analysis and QTL mapping. Mechanisms of Ageing and Development 128: 237–249.
- 39. Arbeitman MN, Furlong EE, Imam F, Johnson E, Null BH, et al. (2002) Gene expression during the life cycle of Drosophila melanogaster. Science (New York, N.Y.) 297: 2270–2275.
- 40. Cook PE, Sinkins SP (2010) Transcriptional profiling of Anopheles gambiae mosquitoes for adult age estimation. Insect Molecular Biology 19: 745–751.
- 41. Walker KR, Joy TK, Ellers-Kirk C, Ramberg FB (2011) Human and environmental factors affecting Aedes aegypti distribution in an arid urban environment. Journal of the American Mosquito Control Association 27: 135–141.
- 42. Morin CW, Comrie AC (2010) Modeled response of the West Nile virus vector Culex quinquefasciatus to changing climate using the dynamic mosquito simulation model. International Journal of Biometeorology 54: 517–529.
- 43. Hoeck PA, Ramberg FB, Merrill SA, Moll C, Hagedorn HH (2003) Population and parity levels of Aedes aegypti collected in Tucson. Journal of Vector Ecology : Journal of the Society for Vector Ecology 28: 65–73.
- 44. Chadee DD, Ritchie SA (2010) Oviposition behavior and parity rates of Aedes aegypti collected in sticky traps in Trinidad, West Indies. Acta Tropica 116: 212–216.
- 45. El-Badry A, Al-Ali K (2010) Prevalence and seasonal distribution of dengue mosquito, Aedes aegypti (diptera: Culicidae) in Al-madinah Al-munawwarah, Saudi Arabia. Journal of Entomology 7: 80–88.
- 46. Bennett KE, Olson KE, Munoz Mde L, Fernandez-Salas I, Farfan-Ale JA, et al. (2002) Variation in vector competence for dengue 2 virus among 24 collections of Aedes aegypti from Mexico and the United States. The American Journal of Tropical Medicine and Hygiene 67: 85–92.