Abstract
A major gap in the Plasmodium vivax elimination toolkit is the identification of individuals carrying clinically silent and undetectable liver-stage parasites, called hypnozoites. This study developed a panel of serological exposure markers capable of classifying individuals with recent P. vivax infections who have a high likelihood of harboring hypnozoites. We measured IgG antibody responses to 342 P. vivax proteins in longitudinal clinical cohorts conducted in Thailand and Brazil and identified candidate serological markers of exposure. Candidate markers were validated using samples from year-long observational cohorts conducted in Thailand, Brazil and the Solomon Islands and antibody responses to eight P. vivax proteins classified P. vivax infections in the previous 9 months with 80% sensitivity and specificity. Mathematical models demonstrate that a serological testing and treatment strategy could reduce P. vivax prevalence by 59–69%. These eight antibody responses can serve as a biomarker, identifying individuals who should be targeted with anti-hypnozoite therapy.
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Data and code availability
All data and code for reproducing this analysis are available online at https://github.com/MWhite-InstitutPasteur/Pvivax_sero_dx.
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Acknowledgments
We acknowledge the extensive field teams that contributed to sample collection and qPCR assays, including A. Kuehn, Y. W. Quah, P. Sripoorote and A. Waltmann. We thank all the individuals who participated in each of the studies and thank the Australian and Thai Red Cross and the RBB for donation of plasma samples. We thank the Volunteer Blood Donor Registry at WEHI for donation of plasma samples and L. Laskos and J. Harris for their collection and advice. We thank C. King (Case Western Reserve University) for provision of the Papua New Guinea control plasma pool. M. Bahlo (Walter and Eliza Hall Institute) is thanked for her advice on algorithm development. We acknowledge funding from the Global Health Innovative Technology Fund (T2015-142 to I.M.), the National Institute of Allergy and Infectious Diseases (National Institutes of Health grant 5R01 AI 104822 to J.S. and 5U19AI089686-06 to J.K.) and the National Health and Medical Research Council Australia (1092789 and 1134989 to I.M. and 1143187 to W.-H.T.). Cohort samples were derived from field studies originally funded by the TransEPI consortium (supported by the Bill and Melinda Gates Foundation). This work has been supported by FIND with funding from the Australian and British governments. We also acknowledge support from the National Research Council of Thailand. This work was made possible through Victorian State Government Operational Infrastructure Support and Australian Government National Health and Medical Research Council (NHMRC) Independent Research Institute Infrastructure Support Scheme. I.M. is supported by an NHMRC Senior Research Fellowship (1043345). D.L.D. is supported by an NHMRC Principal Research Fellowship (1023636). T.T. was supported in part by Japan Society for the Promotion of Science Grant-in-Aid for Scientific Research (KAKENHI, JP15H05276, JP16K15266). W.H.T. is a Howard Hughes Medical Institute–Wellcome Trust International Research Scholar (208693/Z/17/Z). R.J.L. received the Page Betheras Award from WEHI to provide funding for technical support for this project during parental leave. M.V.G.L. and W.M.M. are fellows of the Brazilian National Council for Scientific and Technological Development.
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R.J.L., M.T.W., T.T. and I.M. designed the study. W.N., W.M., J.K., M.L., J.S. and I.M. conducted the cohort studies. E.T., M.M., M.H., F.M., W.-H.T., J.H., C.H., C.E.C. and T.T. expressed the proteins. R.J.L., E.T., M.M., J.B. and Z.S.-J.L. performed antibody measurements. R.J.L., C.S.N.L.-W.-S. and M.T.W. conducted data management and analysis. M.T.W. and T.O. performed modeling. R.J.L., M.T.W. and I.M. wrote the draft of the report. L.J.R., C.P., D.L.D., X.C.D. and I.J.G. provided expert advice. All authors contributed to data interpretation and revision of the report.
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FIND contributed to early funding of this work and had a role in data interpretation, writing of the report and the decision to submit. No other funders of this study had any role in study design, data collection, data analysis, data interpretation, writing of the report or the decision to submit. R.J.L., M.W., T.T. and I.M. are inventors on patent PCT/US17/67926 on a system, method, apparatus and diagnostic test for P. vivax.
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Extended data
Extended Data Fig. 1 Study design and follow-up schedule.
a, Thai and Brazilian patients were enrolled following a clinical episode of P. vivax and treated according to the relevant National Guidelines, with directly observed treatment (DOT) to ensure compliance and reduced risk of relapse. Volunteers were followed for nine months after enrollment, with finger-prick blood samples collected at enrollment and week 1, then every two weeks for six months, then every month. Antibody levels were measured in a subset of 32 Thai and 33 Brazilian volunteers who were confirmed to be free of blood-stage Plasmodium parasites by analysing all samples by light microscopy and qPCR. b, 999 participants from Thailand, 1274 participants from Brazil, and 860 participants from the Solomon Islands were followed longitudinally for 12 months with active surveillance visits every month. For the analysis in the validation phase antibody levels were measured in plasma samples from the last visit. For the analysis in the application phase, antibody levels were measured in plasma samples from the first visit.
Extended Data Fig. 2 Measured antibody responses to 60 proteins on the Luminex® platform, stratified by geographical location and time since last PCR detected infection.
Antibody responses to 60 antigens measured in n = 2,281 biologically independent samples on the Luminex® platform, stratified by geographical location and time since last PCR detected infection. Boxplots denote median and interquartile ranges (IQR) of the data, with whiskers denoting the median ± 1.5*IQR.
Extended Data Fig. 3 Association between background reactivity in non-malaria exposed controls and ranking of candidate SEMs by area under the curve (AUC).
Mean relative antibody units (RAU) detected in malaria-naïve control panels from Melbourne, Australia (n=202), Bangkok, Thailand (n=72) and Rio de Janeiro Brazil (n = 96) compared to the AUC of the 60 candidate P.vivax proteins generated during the validation phase. WGCF expressed proteins are in black and E. coli or Baculovirus expressed proteins are in blue. RBP2b161-1454 (E. coli) is in red and RBP2b1986-2653 is in orange.
Extended Data Fig. 4 Breakdown of the classification of the Random Forests algorithm with target sensitivity and specificity of 80%.
The size of each rectangle is proportional to the number of samples in each category (See Supplementary Table 4 of accompanying manuscript). The coloured area represents the proportion correctly classified, and the shaded area represents the proportion mis-classified.
Extended Data Fig. 5 Receiver operating characteristic (ROC) curve for the composite classification algorithm.
All curves presented are the median of 1000 repeat cross-validations.
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Supplementary Methods and Results, Supplementary Figs. 1–12 and Tables 1–4.
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Longley, R.J., White, M.T., Takashima, E. et al. Development and validation of serological markers for detecting recent Plasmodium vivax infection. Nat Med 26, 741–749 (2020). https://doi.org/10.1038/s41591-020-0841-4
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DOI: https://doi.org/10.1038/s41591-020-0841-4
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