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Development and validation of serological markers for detecting recent Plasmodium vivax infection

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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Fig. 1: Flow diagram of antigen downselection pipeline.
Fig. 2: Antigen discovery phase: estimating the time since last P. vivax infection.
Fig. 3: Validation phase.
Fig. 4: Performance of the top eight P. vivax proteins for classifying recent infection and modeled ability to identify hypnozoite carriers.
Fig. 5: Pilot application phase.

Data and code availability

All data and code for reproducing this analysis are available online at https://github.com/MWhite-InstitutPasteur/Pvivax_sero_dx.

References

  1. Sutanto, I. et al. Negligible impact of mass screening and treatment on mesoendemic malaria transmission at West Timor in eastern Indonesia: a cluster-randomized trial. Clin. Infect. Dis. 67, 1364–1372 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Waltmann, A. et al. High rates of asymptomatic, sub-microscopic Plasmodium vivax infection and disappearing Plasmodium falciparum malaria in an area of low transmission in Solomon Islands. PLoS Negl. Trop. Dis. 9, e0003758 (2015).

    PubMed  PubMed Central  Google Scholar 

  3. Mueller, I. et al. Key gaps in the knowledge of Plasmodium vivax, a neglected human malaria parasite. Lancet Infect. Dis. 9, 555–566 (2009).

    CAS  PubMed  Google Scholar 

  4. Moreira, C. M., Abo-Shehada, M., Price, R. N. & Drakeley, C. J. A systematic review of sub-microscopic Plasmodium vivax infection. Malar. J. 14, 360 (2015).

    PubMed  PubMed Central  Google Scholar 

  5. Robinson, L. J. et al. Strategies for understanding and reducing the Plasmodium vivax and Plasmodium ovale hypnozoite reservoir in Papua New Guinean children: a randomised placebo-controlled trial and mathematical model. PLoS Med. 12, e1001891 (2015).

    PubMed  PubMed Central  Google Scholar 

  6. Parker, D. M. et al. Microgeography and molecular epidemiology of malaria at the Thailand-Myanmar border in the malaria pre-elimination phase. Malar. J. 14, 198 (2015).

    PubMed  PubMed Central  Google Scholar 

  7. Mogeni, P. et al. Detecting malaria hotspots: a comparison of rapid diagnostic test, microscopy, and polymerase chain reaction. J. Infect. Dis. 216, 1091–1098 (2017).

    PubMed  PubMed Central  Google Scholar 

  8. Ding, X. C. et al. Defining the next generation of Plasmodium vivax diagnostic tests for control and elimination: target product profiles. PLoS Negl. Trop. Dis. 11, e0005516 (2017).

    PubMed  PubMed Central  Google Scholar 

  9. World Health Organization. Malaria Surveillance, Monitoring & Evaluation: A Reference Manual (2018).

  10. Harris, I. et al. A large proportion of asymptomatic Plasmodium infections with low and sub-microscopic parasite densities in the low transmission setting of Temotu Province, Solomon Islands: challenges for malaria diagnostics in an elimination setting. Malar. J. 9, 254 (2010).

    PubMed  PubMed Central  Google Scholar 

  11. Sutanto, I. et al. Negligible impact of mass screening and treatment on meso-endemic malaria transmission at West Timor in eastern Indonesia: a cluster-randomised trial. Clin. Infect. Dis. 67, 1364–1372 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Ong, K. I. C. et al. Systematic review of the clinical manifestations of glucose-6-phosphate dehydrogenase deficiency in the Greater Mekong subregion: implications for malaria elimination and beyond. BMJ Glob. Health 2, e000415 (2017).

    PubMed  PubMed Central  Google Scholar 

  13. Longley, R. J. et al. Asymptomatic Plasmodium vivax infections induce robust IgG responses to multiple blood-stage proteins in a low-transmission region of western Thailand. Malar. J. 16, 178 (2017).

    PubMed  PubMed Central  Google Scholar 

  14. Longley, R. J. et al. Acquisition and longevity of antibodies to Plasmodium vivax preerythrocytic antigens in western Thailand. Clin. Vaccine Immunol. 23, 117–124 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Helb, D. A. et al. Novel serologic biomarkers provide accurate estimates of recent Plasmodium falciparum exposure for individuals and communities. Proc. Natl Acad. Sci. USA 112, E4438–E4447 (2015).

    CAS  PubMed  Google Scholar 

  16. Welch, R. J., Anderson, B. L. & Litwin, C. M. Rapid immunochromatographic strip test for detection of anti-K39 immunoglobulin G antibodies for diagnosis of visceral leishmaniasis. Clin. Vaccine Immunol. 15, 1483–1484 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Gwyn, S. et al. Lateral flow-based antibody testing for Chlamydia trachomatis. J. Immunol. Methods 435, 27–31 (2016).

    CAS  PubMed  Google Scholar 

  18. Landry, M. L. Diagnostic tests for influenza infection. Curr. Opin. Pediatr. 23, 91–97 (2011).

    CAS  PubMed  Google Scholar 

  19. Greenhouse, B., Smith, D. L., Rodriguez-Barraquer, I., Mueller, I. & Drakeley, C. J. Taking sharper pictures of malaria with CAMERAs: combined antibodies to measure exposure recency assays. Am. J. Trop. Med. Hyg. 99, 1120–1127 (2018).

    PubMed  PubMed Central  Google Scholar 

  20. White, N. J. Determinants of relapse periodicity in Plasmodium vivax malaria. Malar. J. 10, 297 (2011).

    PubMed  PubMed Central  Google Scholar 

  21. Battle, K. E. et al. Geographical variation in Plasmodium vivax relapse. Malar. J. 13, 144 (2014).

    PubMed  PubMed Central  Google Scholar 

  22. White, M. T., Shirreff, G., Karl, S., Ghani, A. C. & Mueller, I. Variation in relapse frequency and the transmission potential of Plasmodium vivax malaria. Proc. Biol. Sci. 283, 20160048 (2016).

    PubMed  PubMed Central  Google Scholar 

  23. Longley, R. J. et al. Naturally acquired antibody responses to more than 300 Plasmodium vivax proteins in three geographic regions. PLoS Negl. Trop. Dis. 11, e0005888 (2017).

    PubMed  PubMed Central  Google Scholar 

  24. Tsuboi, T. et al. Wheat germ cell-free system-based production of malaria proteins for discovery of novel vaccine candidates. Infect. Immun. 76, 1702–1708 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Franca, C. T. et al. Identification of highly-protective combinations of Plasmodium vivax recombinant proteins for vaccine development. eLife 6, e28673 (2017).

    PubMed  PubMed Central  Google Scholar 

  26. Franca, C. T. et al. Plasmodium vivax reticulocyte binding proteins are key targets of naturally acquired immunity in young Papua New Guinean children. PLoS Negl. Trop. Dis. 10, e0005014 (2016).

    PubMed  PubMed Central  Google Scholar 

  27. Lu, F. et al. Profiling the humoral immune responses to Plasmodium vivax infection and identification of candidate immunogenic rhoptry-associated membrane antigen (RAMA). J. Proteomics 102, 66–82 (2014).

    CAS  PubMed  Google Scholar 

  28. Franca, C. T. et al. An antibody screen of a Plasmodium vivax antigen library identifies novel merozoite proteins associated with clinical protection. PLoS Negl. Trop. Dis. 10, e0004639 (2016).

    PubMed  PubMed Central  Google Scholar 

  29. Gruszczyk, J. et al. Transferrin receptor 1 is a reticulocyte-specific receptor for Plasmodium vivax. Science 359, 48–55 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Soares, I. S. et al. A Plasmodium vivax vaccine candidate displays limited allele polymorphism, which does not restrict recognition by antibodies. Mol. Med. 5, 459–470 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Wang, B. et al. Immunoprofiling of the tryptophan-rich antigen family in Plasmodium vivax. Infect. Immun. 83, 3083–3095 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Cheng, Y. et al. Naturally acquired humoral and cellular immune responses to Plasmodium vivax merozoite surface protein 8 in patients with infection. Malar. J. 16, 211 (2017).

    PubMed  PubMed Central  Google Scholar 

  33. Hester, J. et al. De novo assembly of a field isolate genome reveals novel Plasmodium vivax erythrocyte invasion genes. PLoS Negl. Trop. Dis. 7, e2569 (2013).

    PubMed  PubMed Central  Google Scholar 

  34. Jiang, J., Barnwell, J. W., Meyer, E. V. & Galinski, M. R. Plasmodium vivax merozoite surface protein-3 (PvMSP3): expression of an 11 member multigene family in blood-stage parasites. PLoS ONE 8, e63888 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Ntege, E. H. et al. Blood-stage malaria vaccines: post-genome strategies for the identification of novel vaccine candidates. Expert Rev. Vaccines 16, 769–779 (2017).

    CAS  PubMed  Google Scholar 

  36. White, M. T. et al. Mathematical modelling of the impact of expanding levels of malaria control interventions on Plasmodium vivax. Nat. Commun. 9, 3300 (2018).

    PubMed  PubMed Central  Google Scholar 

  37. Rabinovich, R. N. et al. malERA: an updated research agenda for malaria elimination and eradication. PLoS Med. 14, e1002456 (2017).

    PubMed  PubMed Central  Google Scholar 

  38. Cook, J. et al. Using serological measures to monitor changes in malaria transmission in Vanuatu. Malar. J. 9, 169 (2010).

    PubMed  PubMed Central  Google Scholar 

  39. Ladeia-Andrade, S. et al. Naturally acquired antibodies to merozoite surface protein (MSP)-1(19) and cumulative exposure to Plasmodium falciparum and Plasmodium vivax in remote populations of the Amazon Basin of Brazil. Mem. Inst. Oswaldo Cruz 102, 943–951 (2007).

    PubMed  Google Scholar 

  40. Cook, J. et al. Sero-epidemiological evaluation of changes in Plasmodium falciparum and Plasmodium vivax transmission patterns over the rainy season in Cambodia. Malar. J. 11, 86 (2012).

    PubMed  PubMed Central  Google Scholar 

  41. White, M. T. et al. Plasmodium vivax and Plasmodium falciparum infection dynamics: re-infections, recrudescences and relapses. Malar. J. 17, 170 (2018).

    PubMed  PubMed Central  Google Scholar 

  42. White, M. T. et al. Dynamics of the antibody response to Plasmodium falciparum infection in African children. J. Infect. Dis. 210, 1115–1122 (2014).

    CAS  PubMed  Google Scholar 

  43. Boyle, M. J., Reiling, L., Osier, F. H. & Fowkes, F. J. Recent insights into humoral immunity targeting Plasmodium falciparum and Plasmodium vivax malaria. Int. J. Parasitol. 47, 99–104 (2017).

    CAS  PubMed  Google Scholar 

  44. Richards, J. S. et al. Association between naturally acquired antibodies to erythrocyte-binding antigens of Plasmodium falciparum and protection from malaria and high-density parasitemia. Clin. Infect. Dis. 51, e50–e60 (2010).

    CAS  PubMed  Google Scholar 

  45. Arama, C. et al. Genetic resistance to malaria is associated with greater enhancement of immunoglobulin (Ig)M than IgG responses to a broad array of Plasmodium falciparum antigens. Open Forum Infect. Dis. 2, ofv118 (2015).

    PubMed  PubMed Central  Google Scholar 

  46. Woodberry, T. et al. Antibodies to Plasmodium falciparum and Plasmodium vivax merozoite surface protein 5 in Indonesia: species-specific and cross-reactive responses. J. Infect. Dis. 198, 134–142 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Douglas, N. M. et al. Plasmodium vivax recurrence following falciparum and mixed species malaria: risk factors and effect of antimalarial kinetics. Clin. Infect. Dis. 52, 612–620 (2011).

    PubMed  PubMed Central  Google Scholar 

  48. Looareesuwan, S., White, N. J., Chittamas, S., Bunnag, D. & Harinasuta, T. High rate of Plasmodium vivax relapse following treatment of falciparum malaria in Thailand. Lancet 2, 1052–1055 (1987).

    CAS  PubMed  Google Scholar 

  49. Fornace, K. M. et al. Exposure and infection to Plasmodium knowlesi in case study communities in Northern Sabah, Malaysia and Palawan, the Philippines. PLoS Negl. Trop. Dis. 12, e0006432 (2018).

    PubMed  PubMed Central  Google Scholar 

  50. Herman, L. S. et al. Identification and validation of a novel panel of Plasmodium knowlesi biomarkers of serological exposure. PLoS Negl. Trop. Dis. 12, e0006457 (2018).

    PubMed  PubMed Central  Google Scholar 

  51. White, N. J. & Imwong, M. Relapse. Adv. Parasitol. 80, 113–150 (2012).

    PubMed  Google Scholar 

  52. Alonso, P. L. et al. A research agenda to underpin malaria eradication. PLoS Med. 8, e1000406 (2011).

    PubMed  PubMed Central  Google Scholar 

  53. Hietanen, J. et al. Gene models, expression repertoire, and immune response of Plasmodium vivax reticulocyte binding proteins. Infect. Immun. 84, 677–685 (2015).

    PubMed  Google Scholar 

  54. Cole-Tobian, J. L. et al. Strain-specific duffy binding protein antibodies correlate with protection against infection with homologous compared to heterologous Plasmodium vivax strains in Papua New Guinean children. Infect. Immun. 77, 4009–4017 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Nguitragool, W. et al. Highly heterogeneous residual malaria risk in western Thailand. Int. J. Parasitol. 49, 455–462 (2019).

    PubMed  PubMed Central  Google Scholar 

  56. Quah, Y. W. et al. Molecular epidemiology of residual Plasmodium vivax transmission in a paediatric cohort in Solomon Islands. Malar. J. 18, 106 (2019).

    PubMed  PubMed Central  Google Scholar 

  57. Wampfler, R. et al. Strategies for detection of Plasmodium species gametocytes. PLoS ONE 8, e76316 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Rosanas-Urgell, A. et al. Comparison of diagnostic methods for the detection and quantification of the four sympatric Plasmodium species in field samples from Papua New Guinea. Malar. J. 9, 361 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Healer, J. et al. Vaccination with conserved regions of erythrocyte-binding antigens induces neutralizing antibodies against multiple strains of Plasmodium falciparum. PLoS ONE 8, e72504 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Borremans, B., Hens, N., Beutels, P., Leirs, H. & Reijniers, J. Estimating time of infection using prior serological and individual information can greatly improve incidence estimation of human and wildlife infections. PLoS Comput. Biol. 12, e1004882 (2016).

    PubMed  PubMed Central  Google Scholar 

  61. Kirkpatrick, S., Gelatt, C. D. Jr. & Vecchi, M. P. Optimization by simulated annealing. Science 220, 671–680 (1983).

    CAS  PubMed  Google Scholar 

  62. Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer, 2009).

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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.

Author information

Authors and Affiliations

Authors

Contributions

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.

Corresponding author

Correspondence to Ivo Mueller.

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Competing interests

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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Peer review information Alison Farrell is the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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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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