The use of transcriptional profiles to predict adult mosquito age under field conditions

Abstract

Age is a critical determinant of an adult female mosquito's ability to transmit a range of human pathogens. Despite its central importance, relatively few methods exist with which to accurately determine chronological age of field-caught mosquitoes. This fact is a major constraint on our ability to fully understand the relative importance of vector longevity to disease transmission in different ecological contexts. It also limits our ability to evaluate novel disease control strategies that specifically target mosquito longevity. We report the development of a transcriptional profiling approach to determine age of adult female Aedes aegypti under field conditions. We demonstrate that this approach surpasses current cuticular hydrocarbon methods for both accuracy of predicted age as well as the upper limits at which age can be reliably predicted. The method is based on genes that display age-dependent expression in a range of dipteran insects and, as such, is likely to be broadly applicable to other disease vectors.

Fig. 1.

Calibration data from individual mosquitoes. Calibration models were developed from adult female Ae. aegypti of known age. Log-contrast normalized measures of GE (A and C) and CHC (B) abundance were entered into separate canonical redundancy analyses. Each analysis produced a redundancy variate that is the linear combination that maximizes correlation with mosquito age. (A and C) The calibration model is based on expression data from eight genes (Ae-4274, Ae-4679, Ae-4916, Ae-6639, Ae-7471, Ae-8505, Ae-12750, and Ae-15848) (A), and the calibration model restricted to the three most informative genes (Ae-4274, Ae-8505, and Ae-15848) (C).

Fig. 2.

Age predictions for individual mosquitoes. Age predictions were made for additional blind samples by using both GE (A and C) and CHC (B) measures. Log-contrast normalized GE and CHC measures were calculated from each mosquito and entered into a redundancy analysis. Age predictions were estimated by inverse regression of the respective calibration model shown in Fig. 1. Error bars indicate 95% CI for individual age predictions as determined by nonparametric bootstrap methods. The dashed line indicates where predicted age equals actual age. Age predictions derived from the calibration model indicated in Fig. 1C based on a reduced data set of the three most informative genes (C).