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Blood meal-induced changes to antennal transcriptome profiles reveal shifts in odor sensitivities in Anopheles gambiae
- Rinker, David C., Pitts, R. Jason, Zhou, Xiaofan, Suh, Eunho, Rokas, Antonis, Zwiebel, Laurence J.
- Proceedings of the National Academy of Sciences of the United States of America 2013 v.110 no.20 pp. 8260-8265
- Anopheles gambiae, blood, blood meal, cluster analysis, disease transmission, females, gene expression, genes, host seeking, insect vectors, malaria, messenger RNA, odor compounds, odors, oviposition, receptors, smell, tissues, transcriptome
- Olfactory-driven behaviors are central to the lifecycle of the malaria vector mosquito Anopheles gambiae and are initiated by peripheral signaling in the antenna and other olfactory tissues. To continue gaining insight into the relationship between gene expression and olfaction, we have performed cohort comparisons of antennal transcript abundances at five time points after a blood meal, a key event in both reproduction and disease transmission cycles. We found that more than 5,000 transcripts displayed significant abundance differences, many of which were correlated by cluster analysis. Within the chemosensory gene families, we observed a general reduction in the level of chemosensory gene transcripts, although a subset of odorant receptors (AgOrs) was modestly enhanced in post–blood-fed samples. Integration of AgOr transcript abundance data with previously characterized AgOr excitatory odorant response profiles revealed potential changes in antennal odorant receptivity that coincided with the shift from host-seeking to oviposition behaviors in blood-fed female mosquitoes. Behavioral testing of ovipositing females to odorants highlighted by this synthetic analysis identified two unique, unitary oviposition cues for An. gambiae, 2-propylphenol and 4-methylcyclohexanol. We posit that modest, yet cumulative, alterations of AgOr transcript levels modulate peripheral odor coding resulting in biologically relevant behavioral effects. Moreover, these results demonstrate that highly quantitative, RNAseq transcript abundance data can be successfully integrated with functional data to generate testable hypotheses.