Title : Dengue Injection in 3D Skin Model Mimics Mosquito Infection in Humans
Abstract:
Dengue virus poses a significant global health challenge, causing over 14 million infections and 10,000 dengue-related deaths in 2024 alone. While most infections result in mild to moderate illness, a subset progresses to severe syndromes such as dengue hemorrhagic fever or dengue shock syndrome. The early mechanisms of dengue viral infection and dissemination from the injection site remain poorly understood, primarily because initial infections are asymptomatic and innate immune responses are difficult to disentangle from adaptive immunity. To address this, our lab developed a structurally and functionally mature 3D primary human skin model (OTE) that lacks immune cells. We hypothesize that this model provides a unique platform to investigate innate antiviral responses in human skin tissue.
We found that injecting 2.0 x 107 infectious units of dengue virus into the epidermis of the OTE–mimicking a proboscis-like inoculation–elicits a more robust infection compared to apical exposures of dengue infection. Our results indicate that keratinocytes, fibroblasts and possibly adipocytes are also susceptible to infection. Using immunohistochemistry, we visualized localized dengue infection, forming an aggregation of virus in the basal epithelium, with dengue specific markers, alongside and highly specific markers of differentiated skin epithelium. To confirm viral infection and viral gene expression, we quantified the presence of negative strand RNA using quantitative PCR and RNAscope.
Building on this established 3D infection model, we are currently analyzing RNA sequencing data to validate its relevance to human dengue infection. This analysis aims to uncover intrinsic innate immune pathways that may drive dengue pathogenesis and contribute to viremia. By untangling innate immune signaling and functions, we can use these findings to target pathways of infection for therapeutics and understand potential drivers of severe infection outcomes.
Acknowledgements: This work was supported by the Defense Threat Reduction Agency—Joint Science and Technology Office (Project MCDC2201-002)