Developing a multivariate prediction model of antibody features associated with protection of malaria-infected pregnant women from placental malaria
- The University of Melbourne, Australia
- Royal Darwin Hospital, Australia
- Walter and Eliza Hall Institute of Medical Research, Australia
- University of Copenhagen, Denmark
- Seattle Children's Research Institute, United States
- Burnet Institute, Australia
Abstract
Background: Plasmodium falciparum causes placental malaria, which results in adverse outcomes for mother and child. P. falciparum infected erythrocytes that express the parasite protein VAR2CSA on their surface can bind to placental chondroitin sulfate-A. It has been hypothesized that naturally acquired antibodies towards VAR2CSA protect against placental infection, but it has proven difficult to identify robust antibody correlates of protection from disease. The objective of this study was to develop a prediction model using antibody features which could identify women protected from placental malaria.
Methods: We used a systems serology approach with elastic net-regularized logistic regression, Partial Least Squares Discriminant Analysis and a case control study design to identify naturally acquired antibody features mid pregnancy that were associated with protection from placental malaria at delivery in a cohort of 77 pregnant women from Madang, Papua New Guinea.
Results: The machine learning techniques selected six out of 169 measured antibody features towards VAR2CSA that could predict (with 86% accuracy) whether a woman would subsequently have active placental malaria infection at delivery. Selected features included previously described associations with inhibition of placental binding and/or opsonic phagocytosis of infected erythrocytes, and network analysis indicated that there are not one but multiple pathways to protection from placental malaria.
Conclusions: We have identified candidate antibody features which could accurately identify malaria-infected women as protected from placental infection. It is likely that there are multiple pathways to protection against placental malaria.
Funding: This study was supported by the National Health and Medical Research Council (No. APP1143946, GNT1145303, APP1092789, APP1140509 and APP1104975).
Data availability
All antibody feature data has been deposited in datadryad.
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Antibody features towards VAR2CSA and CSA binding infected erythrocytes in a cohort of pregnant women from PNGDryad Digital Repository, doi.org/10.5061/dryad.wpzgmsbkx.
Article and author information
Author details
Stephen J Rogerson
Funding
National Health and Medical Research Council (APP1143946)
- Elizabeth H Aitken
- Amy Chung
- Stephen J Rogerson
National Health and Medical Research Council (GNT1145303)
- P Mark Hogarth
- Bruce D Wines
National Health and Medical Research Council (APP1092789)
- Stephen J Rogerson
National Health and Medical Research Council (APP1140509)
- Amy Chung
University of Melbourne
- Amaya Ortega-Pajares
Australian Society for Parasitology
- Elizabeth H Aitken
National Health and Medical Research Council (APP1104975)
- Julie A Simpson
Bill and Melinda Gates Foundation (46099)
- Stephen J Rogerson
Miller Foundation Australia
- Amaya Ortega-Pajares
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: Collection and use of plasma samples from women in PNG was approved by the PNG Institute of Medical Research Institutional Review Board, the PNG Medical Research Advisory Council and the Melbourne Health Human Research Ethics Committee. All participants provided informed written consent. The use of blood products from donors in Melbourne for isolation of primary cells, culture of parasites and leukocytes and for use as negative controls was approved by the Melbourne Health Human Research Ethics committee and the University of Melbourne Human Research Ethics committee.
Copyright
© 2021, Aitken et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Developing a multivariate prediction model of antibody features associated with protection of malaria-infected pregnant women from placental malaria
eLife 10:e65776.
https://doi.org/10.7554/eLife.65776
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