Developing a multivariate prediction model of antibody features associated with protection of malaria-infected pregnant women from placental malaria

Elizabeth H Aitken; Timon Damelang; Amaya Ortega-Pajares; Agersew Alemu; Wina Hasang; Saber Dini; Holger W Unger; Maria Ome-Kaius; Morten A Nielsen; Ali Salanti; Joe Smith; Stephen Kent; P Mark Hogarth; Bruce D Wines; Julie A Simpson; Amy W Chung; Stephen J Rogerson

doi:10.7554/eLife.65776

eLife (Jun 2021)

Developing a multivariate prediction model of antibody features associated with protection of malaria-infected pregnant women from placental malaria

Elizabeth H Aitken,
Timon Damelang,
Amaya Ortega-Pajares,
Agersew Alemu,
Wina Hasang,
Saber Dini,
Holger W Unger,
Maria Ome-Kaius,
Morten A Nielsen,
Ali Salanti,
Joe Smith,
Stephen Kent,
P Mark Hogarth,
Bruce D Wines,
Julie A Simpson,
Amy W Chung,
Stephen J Rogerson

Affiliations

Elizabeth H Aitken: ORCiD; Department of Medicine, University of Melbourne, the Doherty Institute, Melbourne, Australia
Timon Damelang: ORCiD; Department of Microbiology and Immunology, University of Melbourne, the Doherty Institute, Melbourne, Australia
Amaya Ortega-Pajares: Department of Medicine, University of Melbourne, the Doherty Institute, Melbourne, Australia
Agersew Alemu: Department of Medicine, University of Melbourne, the Doherty Institute, Melbourne, Australia
Wina Hasang: Department of Medicine, University of Melbourne, the Doherty Institute, Melbourne, Australia
Saber Dini: Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
Holger W Unger: Department of Medicine, University of Melbourne, the Doherty Institute, Melbourne, Australia; Department of Obstetrics and Gynaecology, Royal Darwin Hospital, Darwin, Australia; Menzies School of Health Research, Darwin, Australia
Maria Ome-Kaius: Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
Morten A Nielsen: ORCiD; Centre for Medical Parasitology, Department of Microbiology and immunology, University of Copenhagen, Copenhagen, Denmark
Ali Salanti: Centre for Medical Parasitology, Department of Microbiology and immunology, University of Copenhagen, Copenhagen, Denmark; Department of Infectious Disease, Copenhagen University Hospital, Copenhagen, Denmark
Joe Smith: ORCiD; Seattle Children’s Research Institute, Seattle, United States; Department of Pediatrics, University of Washington, Seattle, United States
Stephen Kent: Department of Microbiology and Immunology, University of Melbourne, the Doherty Institute, Melbourne, Australia
P Mark Hogarth: Seattle Children’s Research Institute, Seattle, United States; Immune Therapies Group, Centre for Biomedical Research, Burnet Institute, Melbourne, Australia; Department of Clinical Pathology, University of Melbourne, Melbourne, Australia; Department of Immunology and Pathology, Monash University, Melbourne, Australia
Bruce D Wines: Immune Therapies Group, Centre for Biomedical Research, Burnet Institute, Melbourne, Australia; Department of Clinical Pathology, University of Melbourne, Melbourne, Australia; Department of Immunology and Pathology, Monash University, Melbourne, Australia
Julie A Simpson: ORCiD; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
Amy W Chung: Department of Microbiology and Immunology, University of Melbourne, the Doherty Institute, Melbourne, Australia
Stephen J Rogerson: ORCiD; Department of Medicine, University of Melbourne, the Doherty Institute, Melbourne, Australia

DOI: https://doi.org/10.7554/eLife.65776
Journal volume & issue: Vol. 10

Abstract

Read online

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 that 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 6 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 that 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 (Nos. APP1143946, GNT1145303, APP1092789, APP1140509, and APP1104975).

Published in eLife

ISSN: 2050-084X (Online)
Publisher: eLife Sciences Publications Ltd
Country of publisher: United Kingdom
LCC subjects: Medicine; Science: Biology (General)
Website: https://elifesciences.org

About the journal

Abstract

Keywords