Buddhari, D. | Search bioRxiv

bioRxiv2025

Antibody response to Aedes aegypti D7L1+2 salivary proteins as marker of aggregate vector exposure and correlate of dengue virus susceptibility

Aedes aegypti mosquitoes transmit several arboviruses of public health importance. Among these is dengue virus (DENV), a flavivirus whose global infection rates continue to rise each year. With limited options available for preventing or treating DENV infections, mosquito control remains the most widely implemented strategy to combat DENV transmission. Due to the global distribution of DENV, which infects an estimated 400 million people per year, vector suppression practices vary drastically by country and/or region and even small differences in microenvironment can significantly impact vector abundance. There remains a significant need to better understand vector exposure rates at an individual level to disentangle vector exposure and arboviral infection rates. To this end, we have optimized a serologic assay to assess the abundance of antibodies directed against the mosquito salivary proteins AeD7L1+2 as a surrogate metric of vector exposure. Utilizing this assay, we found that anti-D7L1+2 IgG levels were unable to identify low levels of Aedes exposure in individuals with limited prior Aedes exposure, indicating they are unreliable markers of an individuals recent exposure to low levels of these vectors. However, antibody levels against D7L1+2 were robust in plasma samples from individuals living in Aedes endemic regions. These antibody levels reflected seasonal changes in Aedes abundance and exposure, indicating their potential for use as an aggregate marker of vector exposure. Additionally, we found that there were slight negative associations with anti-D7L1+2 IgG levels and age in our cohort. Interestingly, we also found that lower titers of anti-AeD7L1+2 IgG correlated with higher infection burden in households. This finding has implications for the potential interaction between AeD7 proteins and DENV during infection events that will require further study.

immunology↗

bioRxiv2024

Predicting the infecting dengue serotype from antibody titre data using machine learning

The development of a safe and efficacious vaccine that provides immunity against all four dengue virus serotypes is a priority, and a significant challenge for vaccine development has been defining and measuring serotype-specific outcomes and correlates of protection. The plaque reduction neutralisation test (PRNT) is the gold standard assay for measuring serotype-specific antibodies, but this test cannot differentiate homotypic and heterotypic antibodies and characterising the infection history is challenging. To address this, we present an analysis of pre- and post-infection antibody titres measured using the PRNT, collected from a prospective cohort of Thai children. We applied four machine learning classifiers and multinomial logistic regression to the titre data to predict the infecting serotype. The models were validated against the true infecting serotype, identified using RT-PCR. Model performance was calculated using 100 bootstrap samples of the train and out-of-sample test sets. Our analysis showed that, on average, the greatest change in titre was against the infecting serotype. However, in 53.4% (109/204) of the subjects, the highest titre change did not correspond to the infecting serotype, including in 34.3% (12/35) of dengue-naive individuals. The highest post-infection titres of seropositive cases were more likely to match the serotype of the highest pre-infection titre than the infecting serotype, consistent with original antigenic sin. Despite these challenges, the best performing machine learning algorithm achieved 76.3% (95% CI 57.9-89.5%) accuracy on the out-of-sample test set in predicting the infecting serotype from PRNT data. Incorporating additional spatiotemporal data improved accuracy to 80.6% (95% CI 63.2-94.7%), while using only post-infection titres as predictor variables yielded an accuracy of 71.7% (95% CI 57.9-84.2%). These results show that machine learning classifiers can be used to overcome challenges in interpreting PRNT titres, making them useful tools in investigating dengue immune dynamics, infection history and identifying serotype-specific correlates of protection, which in turn can support the evaluation of clinical trial endpoints and vaccine development.

immunology↗