Malaria, caused by Plasmodium falciparum, is especially severe in pregnant women because infected erythrocytes (IE) express VAR2CSA, a ligand that binds to chondroitin sulfate A (CSA) on trophoblasts, causing IE to accumulate in the placenta. As a result, inflammation and pathology occur, increasing the risk of spontaneous abortions, premature deliveries, and low birth weight babies. Fortunately, antibodies (Ab) against VAR2CSA significantly improve pregnancy outcomes. VAR2CSA-based vaccines are being designed with the goal of inducing high levels of protective Ab, and safety testing of one candidate vaccine will begin in humans this year. However, there is no method for determining if a woman is immune to placental malaria (PM). The availability of a cost-effective diagnostic approach that identifies a woman's level of immunity will allow 1) doctors to provide better prenatal care, 2) vaccine developers to assess the level of immunity women have before and after vaccination, and 3) government officials to make intelligent health policies for pregnant women, especially with the changing malaria landscape due to implementation of intervention strategies. Therefore, our goal is to use a combination of serological and functional assays to characterize Ab that mediate clearance of IE from the placenta and then use the data to develop statistical models that predict whether a woman has sufficient immunity to 1) prevent placental pathology and 2) prevent PM. Archival samples from pregnant Cameroonian women with different levels of immunity to PM will be screened in assays that measure different characteristics of Ab to VAR2CSA, including specificity, avidity, and function. This part of the study is straight-forward as the assays are already optimized in our laboratory. In addition, we seek to develop two new functional assays that measure the ability of Ab to (a) block the interaction of VAR2CSA with CSA and (b) prevent activation and dysregulation of placental trophoblasts exposed to IE, a mechanism that contributes to placental pathology. Results from the serological and functional assays will help identify characteristics of protective Ab. Then, th data will be used to build multi-assay statistical prediction models. Two statistical approaches will be used. First, multivariable logistic models will be built based on a combination of Ab characteristics and other key variables related to immunity (e.g., age, gravidity, hematocrit, length of gestation). This approach will result in two user friendly simple risk indexes (formulas) based on the least number of assays and variables that provide the optimal prediction for prevention of pathology and infection. Second, a recursive partitioning approach will be taken to develop classification and regression trees (CART) and random forests (RF) using the data. The resulting binary classification trees will be easy to interpret and allow more complex immunological pathways to be incorporated. Once the models are developed, data from archival samples collected monthly throughout pregnancy will be used to determine when during pregnancy the models have the best predictive value in high and low malaria transmission settings.
In developing countries, young pregnant women are at risk of spontaneous abortions, premature deliveries and low birth weight babies if they become infected with malaria. Fortunately, over several pregnancies women acquire antibodies that protect them from the severe effects of malaria, but there is no way to determine if a woman has adequate levels of protective antibodies. The goal of this project is to develop a way to predict a woman's level of immunity; thereby allowing physicians to provide improve health care for pregnant women exposed to malaria; a goal embraced by NIAID, NIH.
