Visual processing in mosquito larvae

Background.Malaria is a disease that affects half of the world population and their livestock.Currently the progress in malaria elimination has stalled, partly due to emerging insecticide resistance.New intervention strategies are urgently needed, and targeting larvae is a promising approach that has been briefly explored in the past.Now it is timely to revisit the idea of targeting mosquito larvae incorporating new genetic tools and a deeper understanding of larval behaviour.In addition, mosquito larvae have recently become a new genetically tractable neuroscience model organism, that will help us gain fundamental insights into the evolution and function of sensory systems.
This project will investigate responses of larval Anopheles gambiae(the malaria vector) to visual stimuli.
Mosquito larvae and pupae are very mobile and readily respond to visual stimuli.Work on Zika mosquitoes(e.g. Mysore etal, 2014, Dev Dyn) indicated that, uniquely, larvae possess both the larval and the developing adult eyes, and both are functional in the later stages of larval development.Larval and adult eyes are predicted to express different photoreceptor types(Rocha etal, 2015, JEB), which, if true in malaria mosquitoes as well, will allow an easy genetic way to differentially target the two visual systems. In addition, work on adult (but not yet in larval) Zika mosquitoes demonstrated modulation of responses to visual stimuli by simultaneously presented smells(Vinauger etal, 2019, Curr Biol).
Aims. 1)To characterise behavioural responses of A. gambiae larvae to visual stimuli from a broad range of spectral components, spatial and temporal frequencies. 2)To characterise the anatomy and function of larval and developing adult visual system by tracing neuronal projections into the larval brain, and by using RNAi against photoreceptor opsin genes. 3)To investigate modulation of visual responses by concurrent olfactory stimuli.
Methodology. Behavioural assays will be conducted in transparent chambers, placed on top of small computer screens displaying visual stimuli, programmed in MATLAB.The larvae will be video-recorded and analysed in DeepLabCut, a Python-based open-source software that employs machine-learning based approach to behavioural classification.The student will be trained to program in MATLAB and use DeepLabCut by VN, who is an expert in binocular insect vision and cognition.The stimuli will be developed in collaboration with NHI, who is an expert in insect colour vision and visually-guided behaviours.The contribution of olfactory stimuli will be assessed by placing larvae in a solution of an odorant while presenting visual stimuli. RNAi knockdowns will be conducted by feeding larvae with dsRNA/chitosan nanoparticles.The anatomy of the visual system will be analysed by immunohistochemistry and confocal imaging at all larval stages.Projections of visual neurons into the brain will be imaged in transgenic larvae on a light-sheet microscope.
Timetable(excluding general DTP events). Year 1, M0-3: Learning MATLAB and DeepLabCut in VN lab(Newcastle) and visiting NHI(Exeter) to design visual stimuli; M4-12: Aim 1 in Durham; M10: Progress meeting with NHI; M10: Light Microscopy School(York); Year 2, M1: SfN conference, USA; M1-3: PIPS placement; M4-12: Aim 2 in Durham; M8: Optical Imaging and Electrophysiological Recordings in Neuroscience School, Paris; M10: Neuroethology Congress, Germany; Year 3, M1: visiting VN(Newcastle) to modify stimuli presentation software for visual-olfactory assays; M1-12: Aim 3 in Durham; M8-10: MBL course on Neural System and Behavior, Woods Hole, USA; Year 4, M1-12: finishing up experiments and writing up.
Novelty.Previous research has mostly focussed on adult mosquitoes, but very little is known about visual anatomy and behaviours in mosquito larvae.Given recent developments in microscopy and genetic tools, it is timely to address significant gaps in the study of larval vision in holometabolic insects