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Predicting the age of mosquitoes using transcriptional profiles

Nature Protocols volume 2, pages 2796–2806 (2007)Cite this article

Abstract

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 ∼3–5 d for 100 individuals.

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Figure 1: Overview of the transcriptional age-grading technique.
Figure 2: SAS output file showing the percentage of variance in gene expression measures (highlighted with red box) explained by adult female mosquito age.
Figure 3: SAS output showing the regression statistics for linear regression of the first redundancy variate on adult female mosquito age.
Figure 4: Microsoft Excel worksheet exported from the second SAS procedure.

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Acknowledgements

We are grateful to Dr. Jeremy C. Brownlie, Conor J. McMeniman and Grant Hughes for constructive reviews of earlier drafts of this manuscript. This research was funded by grants from the Australian Research Council (LP0455732), the Mosquito and Arbovirus Research Committee, and the Foundation for the National Institutes of Health through the Grand Challenges in Global Health Initiative.

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Authors and Affiliations

  1. School of Integrative Biology, The University of Queensland, Brisbane, 4072, Queensland, Australia

    Peter E Cook, Leon E Hugo, Iñaki Iturbe-Ormaetxe, Stephen F Chenoweth, Mark W Blows & Scott L O'Neill

  2. Queensland Institute of Medical Research, Brisbane, 4029, Queensland, Australia

    Leon E Hugo, Peter A Ryan & Brian H Kay

  3. Sansom Institute, School of Pharmacy & Medical Sciences, University of South Australia, Adelaide, 5000, South Australia, Australia

    Craig R Williams

  4. Anton Breinl Centre for Public Health and Tropical Medicine, James Cook University, Cairns, 4870, Queensland, Australia

    Scott A Ritchie

  5. Tropical Population Health Unit, Queensland Health, Cairns, 4870, Queensland, Australia

    Scott A Ritchie

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  1. Peter E Cook
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Correspondence to Scott L O'Neill.

Supplementary information

Supplementary Note

SAS syntax required to implement statistical analyses (PDF 31 kb)

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Cook, P., Hugo, L., Iturbe-Ormaetxe, I. et al. Predicting the age of mosquitoes using transcriptional profiles. Nat Protoc 2, 2796–2806 (2007). https://doi.org/10.1038/nprot.2007.396

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