bioRxivPhysiologyPreprint
Larval environment reshapes mosquito disease risk via phenotypic and molecular plasticity
Karthikeyan ChandrasegaraniD, Melody WalkeriD, Jeffrey M. MaranoiD, Spruha Rami, Adaline Bisese, James Weger‐LucarelliiD, Chloé LahondèreiD, Michael A. RobertiD, Lauren M. ChildsiD, Clément VinaugeriD
Abstract
Early-life environmental conditions can exert profound, lasting effects on adult phenotypes, with major consequences for fitness and disease transmission, especially in holometabolous insects like mosquitoes, which are a key vector species. Yet, the molecular mechanisms through which juvenile environments shape adult physiology and behavior via transstadial effects remain largely unresolved. Here, we demonstrate that larval competition, a key ecological stressor, profoundly alters adult body size, survival, reproductive output, host-seeking behavior, olfactory neurophysiology, and vector competence in the mosquito Aedes aegypti. Crucially, using transcriptomic profiling and integrative network analyses, we identify seven regulatory hub genes whose expression is strongly associated with size-dependent variation in olfactory behavior, reproductive investment, and Zika virus transmission potential. These hub genes belong to gene modules enriched for functions in chemosensory processing, metabolic regulation, and signal transduction, revealing a molecular framework mediating environmentally induced plasticity across metamorphosis. Integrating these empirical findings into a transmission model, we show that incomplete larval control can inadvertently increase outbreak risk by producing larger, longer-lived, and more competent vectors. Our results uncover molecular mechanisms underpinning phenotypic plasticity in disease vectors and highlight the critical need to account for transstadial effects in models of vector-borne disease transmission.
- organism: Arabidopsis
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: transcription & RNA processing
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 3
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