Identification of immune signatures predictive of clinical protection from malaria
PLoS Computational Biology
Public Library of Science for International Society for Computational Biology (ISCB)
Copyright: © 2017 Valletta, Recker. This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Antibodies are thought to play an essential role in naturally acquired immunity to malaria. Prospective cohort studies have frequently shown how continuous exposure to the malaria parasite Plasmodium falciparum cause an accumulation of specific responses against various antigens that correlate with a decreased risk of clinical malaria episodes. However, small effect sizes and the often polymorphic nature of immunogenic parasite proteins make the robust identification of the true targets of protective immunity ambiguous. Furthermore, the degree of individual-level protection conferred by elevated responses to these antigens has not yet been explored. Here we applied a machine learning approach to identify immune signatures predictive of individual-level protection against clinical disease. We find that commonly assumed immune correlates are poor predictors of clinical protection in children. On the other hand, antibody profiles predictive of an individual's malaria protective status can be found in data comprising responses to a large set of diverse parasite proteins. We show that this pattern emerges only after years of continuous exposure to the malaria parasite, whereas susceptibility to clinical episodes in young hosts (< 10 years) cannot be ascertained by measured antibody responses alone.
This work has been funded by the Medical Research Council (https://www.mrc.ac.uk/) (grant MR/M003906/1) to MR and a Royal Society (https://royalsociety.org/) University Research Fellowship to MR. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
This is the final version of the article. Available from Public Library of Science via the DOI in this record.
Vol. 13 (10), article e1005812
Place of publication