Predicting the age of mosquitoes using transcriptional profiles
Cook, Peter E., Hugo, Leon E., Iturbe-Ormaetxe, Inaki, Williams, Craig R., Chenoweth, Stephen F., Ritchie, Scott A., Ryan, Peter A., Kay, Brian H., Blows, Mark W., and O'Neill, Scott L. (2007) Predicting the age of mosquitoes using transcriptional profiles. Nature Protocols, 2 (11). pp. 2796-2806.
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View at Publisher Website: http://dx.doi.org/10.1038/nprot.2007.396
The use of transcriptional profiles for predicting mosquito age is a novel solution for the longstanding problem of determining the age of field-caught mosquitoes. Female mosquito age is of central importance to the transmission of a range of human pathogens. The transcriptional age-grading protocol we present here was developed in Aedes aegypti, principally as a research tool. Age predictions are made on the basis of transcriptional data collected from mosquitoes of known age. The abundance of eight candidate gene transcripts is quantified relative to a reference gene using quantitative reverse transcriptase-PCR (RT-PCR). Normalized gene expression (GE) measures are analyzed using canonical redundancy analysis to obtain a multivariate predictor of mosquito age. The relationship between the first redundancy variate and known age is used as the calibration model. Normalized GE measures are quantified for wild-caught mosquitoes, and ages are then predicted using this calibration model. Rearing of mosquitoes to specific ages for calibration data can take up to 40 d. Molecular analysis of transcript abundance, and subsequent age predictions, should take approx 3–5 d for 100 individuals.
|Item Type:||Article (Refereed Research - C1)|
|Keywords:||dengue; mosquito; Aedes aegypti; molecular biology|
|FoR Codes:||11 MEDICAL AND HEALTH SCIENCES > 1117 Public Health and Health Services > 111799 Public Health and Health Services not elsewhere classified @ 100%|
|SEO Codes:||92 HEALTH > 9204 Public Health (excl. Specific Population Health) > 920405 Environmental Health @ 51%|
92 HEALTH > 9299 Other Health > 929999 Health not elsewhere classified @ 49%
|Deposited On:||24 Aug 2009 15:28|
|Last Modified:||18 Oct 2013 00:28|
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