Electropenetrography with Alternating Current Reveals In Situ Changes of Aedes aegypti Probing Behaviors Associated with Dengue Virus Infection.

Human infection with dengue virus (DENV) results in significant morbidity and mortality around the world. Current methods to investigate virus-associated changes in insect feeding behaviors are largely restricted to video analysis of feeding events outside of the host or intravital microscopy. Electropenetrography, a method originally developed for plant-feeding insects, offers a promising alternative by allowing high-resolution recording of voltage changes across the insect bite interface. We compared recordings from DENV-infected Aedes aegypti mosquitoes feeding on uninfected mice and uninfected A. aegypti feeding on DENV-infected mice to controls of uninfected A. aegypti feeding on uninfected mice. We found significant mosquito behavioral changes in both DENV-infected groups compared with controls including longer feeding times and longer preingestion probing events for A. aegypti feeding on DENV-infected mice and a higher number of sequential probing events in DENV-infected A. aegypti feeding on uninfected mice. By recording mosquito feeding and probing events beneath the surface of the skin, we have been able to both confirm and add new dimensions to previous findings regarding DENV-associated behavior changes in A. aegypti. This provides a foundation for increasingly in-depth studies focusing on the transmission of the DENV between vectors and hosts.

Development of an automated biomaterial platform to study mosquito feeding behavior.

Mosquitoes carry a number of deadly pathogens that are transmitted while feeding on blood through the skin, and studying mosquito feeding behavior could elucidate countermeasures to mitigate biting. Although this type of research has existed for decades, there has yet to be a compelling example of a controlled environment to test the impact of multiple variables on mosquito feeding behavior. In this study, we leveraged uniformly bioprinted vascularized skin mimics to create a mosquito feeding platform with independently tunable feeding sites. Our platform allows us to observe mosquito feeding behavior and collect video data for 30-45 min. We maximized throughput by developing a highly accurate computer vision model (mean average precision: 92.5%) that automatically processes videos and increases measurement objectivity. This model enables assessment of critical factors such as feeding and activity around feeding sites, and we used it to evaluate the repellent effect of DEET and oil of lemon eucalyptus-based repellents. We validated that both repellents effectively repel mosquitoes in laboratory settings (0% feeding in experimental groups, 13.8% feeding in control group, p < 0.0001), suggesting our platform's use as a repellent screening assay in the future. The platform is scalable, compact, and reduces dependence on vertebrate hosts in mosquito research.