Person:

Valim, Clarissa

Plasmodium falciparum infected erythrocytes (IE) accumulate in the placenta through the interaction between Duffy-binding like (DBL) domains of parasite-encoded ligand VAR2CSA and chondroitin sulphate-A (CSA) receptor. Polymorphisms in these domains, including DBL2X and DBL3X, may affect their antigenicity or CSA-binding affinity, eventually increasing parasitemia and its adverse effects on pregnancy outcomes. A total of 373 DBL2X and 328 DBL3X sequences were obtained from transcripts of 20 placental isolates infecting Mozambican women, resulting in 176 DBL2X and 191 DBL3X unique sequences at the protein level. Sequence alignments were divided in segments containing combinations of correlated polymorphisms and the association of segment sequences with placental parasite density was tested using Bonferroni corrected regression models, taking into consideration the weight of each sequence in the infection. Three DBL2X and three DBL3X segments contained signatures of high parasite density (P<0.003) that were highly prevalent in the parasite population (49–91%). Identified regions included a flexible loop that contributes to DBL3X-CSA interaction and two DBL3X motifs with evidence of positive natural selection. Limited antibody responses against signatures of high parasite density among malaria-exposed pregnant women could not explain the increased placental parasitemia. These results suggest that a higher binding efficiency to CSA rather than reduced antigenicity might provide a biological advantage to parasites with high parasite density signatures in VAR2CSA. Sequences contributing to high parasitemia may be critical for the functional characterization of VAR2CSA and the development of tools against placental malaria.

Multiplex bead-based immunoassays are used to measure concentrations of several analytes simultaneously. These assays include control standard curves (SC) to reduce between-plate variability and normalize quantitation of analytes of biological samples. Suboptimal calibration might result in large random error and decreased number of samples with analyte concentrations within the limits of quantification. Suboptimal calibration may be a consequence of poor fitness of the functions used for the SC, the treatment of the background noise and the method used to estimate the limits of quantification. Currently assessment of fitness of curves is largely dependent on operator and that may add additional error. Moreover, there is no software to automate data managing and quality control. In this article we present a R package, drLumi, with functions for managing data, calibrating assays and performing quality control. To optimize the assay the package implements: i) three dose-response functions, ii) four approaches for treating background noise and iii) three methods for estimating limits of quantifications. Other implemented functions are focused on the quality control of the fitted standard curve: detection of outliers, estimation of the confidence or prediction interval, and estimation of summary statistics. With demonstration purpose, we apply the software to 30 cytokines, chemokines and growth factors measured in a multiplex bead-based immunoassay in a study aiming to measure correlates of risk or protection from malaria of the RTS,S malaria vaccine nested in the Phase 3 randomized controlled trial of this vaccine.