| Literature DB >> 26850066 |
Otchere Addai-Mensah1,2,3, Melanie Seidel4, Nafiu Amidu5, Dominika J Maskus6, Stephanie Kapelski7, Gudrun Breuer8, Carmen Franken9, Ellis Owusu-Dabo10, Margaret Frempong11, Raphaël Rakotozandrindrainy12, Helga Schinkel13, Andreas Reimann14, Torsten Klockenbring15, Stefan Barth16,17,18, Rainer Fischer19,20, Rolf Fendel21,22,23.
Abstract
BACKGROUND: Malaria still represents a major cause of morbidity and mortality predominantly in several developing countries, and remains a priority in many public health programmes. Despite the enormous gains made in control and prevention the development of an effective vaccine represents a persisting challenge. Although several parasite antigens including pre-erythrocytic antigens and blood stage antigens have been thoroughly investigated, the identification of solid immune correlates of protection against infection by Plasmodium falciparum or clinical malaria remains a major hurdle. In this study, an immuno-epidemiological survey was carried out between two populations naturally exposed to P. falciparum malaria to determine the immune correlates of protection.Entities:
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Year: 2016 PMID: 26850066 PMCID: PMC4743426 DOI: 10.1186/s12936-016-1112-1
Source DB: PubMed Journal: Malar J ISSN: 1475-2875 Impact factor: 2.979
Fig. 1Antigen reactivity of plasma samples from the Ghanaian and Malagasy study population. Plasma samples were collected from 136 Ghanaian and Malagasy volunteers, and the respective reactivity towards the malarial antigens AMA-1 (a), MSP1-19 (b), MSP3 (c) and total 3D7 parasite lysate (d) quantified using standard ELISA towards 50 ng coated antigen each. As negative control, a pool of plasma samples from malaria-naïve European volunteers was used (dashed line). Sample reactivity was quantified after addition of goat anti-human IgG antibody (Thermo Scientific) at a dilution of 1:5000 and subsequent addition of pNPP. The arbitrary units on the y-axis are the sample reactivity divided by the reactivity of the negative control. For statistical analysis of the populations, the non-parametric Kruskal–Wallis and Dunn’s post test was used, asterisks indicate the level of statistical significance
Fig. 2Prevalence of antibodies in plasma samples from the Ghanaian and Malagasy study population recognizing antigens a AMA1, b MSP1-19, c MSP3 and d 3D7 lysate stratified by country and gender. The antibody titers of the plasma samples were determined by ELISA. Positive reactivity was defined as reactivity higher than the respective negative control plus two times the value of the standard deviation of the negative control. Differences in the antibody prevalence were estimated by the Fisher’s exact test and in case of statistical significance corresponding p values are given
Reactivity to AMA1, MSP1-19, MSP3 and 3D7 lysate stratified by country and gender
| Ghana | Madagascar | Kuskal–Wallis | Dunn‘s post test | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Female (A) | Male (B) | Female (C) | Male (D) | p (A vs. B) | p (C vs. D) | P (A vs. C) | P (B vs. D) | ||
| Age (years, ± SD) | 32.2 ± 7.4 | 29.2 ± 4.7 | 37.5 ± 5.1 | 38.7 ± 5.7 | <0.0001 | 0.1617 | >0.9999 | 0.001 | <0.0001 |
| AMA1 (AU) | 1.6 (1.2–1.9) | 3.0 (1.9–4.2) | 0.7 (0.1–1.3) | 0.9 (0.4–1.4) | <0.0001 | 0.5952 | 0.2926 | <0.0001 | 0.0011 |
| MSP1-19 (AU) | 0.7 (0.5–1.0) | 1.5 (0.9–2.0) | 0.7 (0.0–1.4) | 0.8 (−0.3–1.9) | 0.0002 | 0.3937 | >0.9999 | 0.0294 | 0.0043 |
| MSP3 (AU) | 1.0 (0.5–1.4) | 2.0 (1.5–2.6) | 1.0 (0.3–1.7) | 0.9 (−0.2–2.1) | <0.0001 | 0.0141 | >0.9999 | 0.2725 | 0.0002 |
| 3D7 lysate (AU) | 3.5 (2.6–4.4) | 5.2 (4.1–6.3) | 2.3 (0.9–3.7) | 2.4 (1.1–3.7) | <0.0001 | 0.0423 | 0.701 | 0.003 | 0.0005 |
Continuous data are presented as mean ± SD for the age and as means with 95 % confidence interval for the antigens reactivity and analyzed using the Kruskal–Wallis rank sum test and Dunn’s post test. Categorical data are presented as proportions and analyzed using a Chi square test
Fig. 3Box-plots of percentage invasion inhibition, stratified by country and gender. The invasion inhibition potential was estimated by a standard invasion inhibition assay using Plasmodium falciparum strain 3D7 and readout by flow cytometry after staining of the parasites with ethidium bromide. Groups were compared using non-parametric Kruskal–Wallis test. There was no statistically significant difference between the median invasion inhibition of the Ghanaians in comparison to the Malagasy, and the males in comparison to the females. The lower and upper margins of the box represent the 25th and 75th percentiles, and the extended arms represent the 10th and 90th percentiles. The median is shown as the horizontal line within each box
Fig. 4Linear regression between reactivity to the malarial antigens AMA1, MSP1-19, MSP3 and 3D7 lysate and erythrocyte invasion inhibition. Invasion inhibition activities of the antibodies, as well as specific antibody concentration in the plasma samples were estimated. The correlation of the data sets was estimated using the Spearman’s Rank Correlation Coefficient. The data shown represent the correlation of the invasion inhibition and a the concentration of AMA1-specific antibodies, b the concentration of MSP1-19-specific antibodies, c the concentration of MSP3-specific antibodies and d the concentration of 3D7 lysate-specific antibodies. The lines are the result of a linear fitting. 95 % confidence intervals of the linear fitting model are shown. Respective p values are given
Fig. 5Relationship between invasion inhibition and the breadth of the antibody response in the Ghanaian and Malagasy study population. The breadth of the antibody response towards the antigens AMA1, MSP1-19, MSP3 and 3D7 lysate, each defined as a reactivity superior of more than two standard deviations above the negative control, was determined. To test for the dependence of the invasion inhibition on the breadth of antibody response, a Kruskal–Wallis test was performed