Immune signature against Plasmodium falciparum antigens predicts clinical immunity in distinct Malaria endemic communities

Proietti, Carla, Krause, Lutz, Trieu, Angela, Dodoo, Daniel, Gyan, Ben, Koram, Kwadwo A., Rogers, William O., Richie, Thomas L., Crompton, Peter D., Felgner, Philip L., and Doolan, Denise L. (2020) Immune signature against Plasmodium falciparum antigens predicts clinical immunity in distinct Malaria endemic communities. Molecular and Cellular Proteomics, 19 (1). pp. 101-113.

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Abstract

We have established a predictive modelling framework to systematically analyze IgG antibody responses against a large panel of P. falciparum-specific antigens and identify a predictive signature of naturally acquired immunity to malaria. Our results show that an individual's immune status can be accurately predicted by measuring IgG antibody responses to a parsimonious set of 15 target antigens. The identified immune signature is highly versatile and capable of providing precise and accurate estimates of clinical protection from malaria in demographically distinct populations.

A large body of evidence supports the role of antibodies directed against the Plasmodium spp. parasite in the development of naturally acquired immunity to malaria, however an antigen signature capable of predicting protective immunity against Plasmodium remains to be identified. Key challenges for the identification of a predictive immune signature include the high dimensionality of data produced by high-throughput technologies and the limitation of standard statistical tests in accounting for synergetic interactions between immune responses to multiple targets. In this study, using samples collected from young children in Ghana at multiple time points during a longitudinal study, we adapted a predictive modeling framework which combines feature selection and machine learning techniques to identify an antigen signature of clinical immunity to malaria. Our results show that an individual's immune status can be accurately predicted by measuring antibody responses to a small defined set of 15 target antigens. We further demonstrate that the identified immune signature is highly versatile and capable of providing precise and accurate estimates of clinical protection from malaria in an independent geographic community. Our findings pave the way for the development of a robust point-of-care test to identify individuals at high risk of disease and which could be applied to monitor the impact of vaccinations and other interventions. This approach could be also translated to biomarker discovery for other infectious diseases.

Item ID: 62664
Item Type: Article (Research - C1)
ISSN: 1535-9484
Keywords: Biomarker: diagnostic, malaria, immunology, clinical data, modeling, antigen signature, feature selection, machine learning, Plasmodium falciparum, protein microarray
Copyright Information: © 2020 Proietti et al. Published by The American Society for Biochemistry and MolecularBiology, Inc. Final version open access under the terms of the Creative Commons CC-BY license.
Funders: National Health and Medical Research Council of Australia (NHMRC)
Projects and Grants: NHMRC Principal Research Fellowship
Date Deposited: 01 Apr 2020 07:37
FoR Codes: 32 BIOMEDICAL AND CLINICAL SCIENCES > 3207 Medical microbiology > 320704 Medical parasitology @ 33%
32 BIOMEDICAL AND CLINICAL SCIENCES > 3204 Immunology > 320405 Humoural immunology and immunochemistry @ 33%
31 BIOLOGICAL SCIENCES > 3101 Biochemistry and cell biology > 310114 Systems biology @ 34%
SEO Codes: 20 HEALTH > 2001 Clinical health > 200104 Prevention of human diseases and conditions @ 50%
20 HEALTH > 2001 Clinical health > 200101 Diagnosis of human diseases and conditions @ 50%
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