Population genomics studies identify signatures of global dispersal and drug resistance in Plasmodium vivax (original) (raw)

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Acknowledgements

We acknowledge J. Bochicchio and S. Chapman for project management, A. Gnirke for technical support, and members of the Broad Institute Genomics Platform and NYU's Genomics Core for data generation. We thank F. Santillan and P. Michon for technical assistance and MR4 for providing us with malaria parasites deposited by W.E. Collins. The following grants supported this work: National Institute of Allergy and Infectious Diseases (NIAID)/National Institutes of Health (NIH) International Centers of Excellence for Malaria Research U19AI089676 to J.M.C.; U19AI089681, K24AI068903 and D43TW007120 to J.M.V.; U19AI089672 to L.C.; São Paulo Research Foundation 2009/52729-9 to M.U.F.; National Council for Science and Technology Mexico 29005-M SALUD-2004-119 and National Institute of Public Health Mexico project 476191 to L.G.-C.; Victorian State Government Operational Infrastructure Support and Australian Government National Health and Medical Research Council Independent Medical Research Institutes Infrastructure Support Scheme (NHMRC IRIISS) to A.B. and I.M.; 5U19AI089702 to S.H. and M.A.-H.; Armed Forces Health Surveillance Center, Global Emerging Infections Surveillance and Response System and US NIH grant D43TW007393 to A.G.L.; NIH U19AI089686 to J.W.K.; and Bill and Melinda Gates Foundation grant to J.S. Sequencing and analysis work at the Broad Institute was supported by federal funds from the NIAID, NIH, US Department of Health and Human Services, under contract HHSN272200900018C. M.U.F. is supported by a senior research scholarship from the Conselho Nacional de Desenvolvimento Científico e Tecnológico of Brazil, I.M. is supported by NHMRC senior research fellowship 1043345 and D.N.H. is supported by NIH training grant T32AI007180. The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official policy or position of the US Department of the Navy, the US Department of Defense, the US government or the National Institutes of Health.

Author information

Authors and Affiliations

1. Department of Biology, Center for Genomics and Systems Biology, New York University, New York, New York, USA
   Daniel N Hupalo, Zunping Luo, Patrick L Sutton & Jane M Carlton
2. Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
   Alexandre Melnikov, Peter Rogov, Bruce W Birren & Daniel E Neafsey
3. Department of Biology, Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, Pennsylvania, USA
   Ananias Escalante
4. Caucaseco Scientific Research Center, Cali, Colombia
   Andrés F Vallejo, Sócrates Herrera & Myriam Arévalo-Herrera
5. Faculty of Health, Universidad del Valle, Cali, Colombia
   Myriam Arévalo-Herrera
6. Dalian Institute of Biotechnology, Dalian, Liaoning, China
   Qi Fan
7. Third Military Medical University, Shapingba, Chongqing, China
   Ying Wang
8. Department of Entomology, Pennsylvania State University, University Park, Pennsylvania, USA
   Liwang Cui
9. US Naval Medical Research Unit No. 6, Callao, Peru
   Carmen M Lucas, Salomon Durand, Juan F Sanchez, G Christian Baldeviano & Andres G Lescano
10. Papua New Guinea Institute of Medical Research, Madang, Papua New Guinea
    Moses Laman
11. Vector Borne Diseases Unit, Papua New Guinea Institute of Medical Research, Goroka, Papua New Guinea
    Celine Barnadas
12. Division of Infection and Immunity, Walter & Eliza Hall Institute of Medical Research, Parkville, Australia
    Celine Barnadas
13. Division of Population Health and Immunity, Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
    Alyssa Barry & Ivo Mueller
14. Department of Medical Biology, University of Melbourne, Carlton, Victoria, Australia
    Alyssa Barry & Ivo Mueller
15. Institute of Global Health (ISGLOBAL), Barcelona, Spain
    Ivo Mueller
16. Center for Global Health and Diseases, Case Western Reserve University, Cleveland, Ohio, USA
    James W Kazura
17. National Institute of Malaria Research Field Unit, Indian Council of Medical Research, National Institute of Epidemiology Campus, Chennai, Tamil Nadu, India
    Alex Eapen & Deena Kanagaraj
18. National Institute of Malaria Research, Indian Council of Medical Research, New Delhi, India
    Neena Valecha
19. Department of Parasitology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
    Marcelo U Ferreira
20. Mahidol Vivax Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
    Wanlapa Roobsoong & Jetsumon Sattabonkot
21. Department of Molecular Tropical Medicine and Genetics, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
    Wang Nguitragool
22. Instituto de Medicine Tropical Alexander von Humboldt, Universidad Peruana Cayetano Heredia, Lima, Peru
    Dionicia Gamboa & Joseph M Vinetz
23. Departamento de Ciencias Celulares y Moleculares, Universidad Peruana Cayetano Heredia, Lima, Peru
    Dionicia Gamboa & Joseph M Vinetz
24. Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
    Margaret Kosek
25. Division of Infectious Diseases, Department of Medicine, University of California San Diego, La Jolla, California, USA
    Joseph M Vinetz
26. Regional Centre for Research in Public Health, National Institute for Public Health, Tapachula, Chiapas, México.,
    Lilia González-Cerón

Authors

1. Daniel N Hupalo
2. Zunping Luo
3. Alexandre Melnikov
4. Patrick L Sutton
5. Peter Rogov
6. Ananias Escalante
7. Andrés F Vallejo
8. Sócrates Herrera
9. Myriam Arévalo-Herrera
10. Qi Fan
11. Ying Wang
12. Liwang Cui
13. Carmen M Lucas
14. Salomon Durand
15. Juan F Sanchez
16. G Christian Baldeviano
17. Andres G Lescano
18. Moses Laman
19. Celine Barnadas
20. Alyssa Barry
21. Ivo Mueller
22. James W Kazura
23. Alex Eapen
24. Deena Kanagaraj
25. Neena Valecha
26. Marcelo U Ferreira
27. Wanlapa Roobsoong
28. Wang Nguitragool
29. Jetsumon Sattabonkot
30. Dionicia Gamboa
31. Margaret Kosek
32. Joseph M Vinetz
33. Lilia González-Cerón
34. Bruce W Birren
35. Daniel E Neafsey
36. Jane M Carlton

Contributions

J.M.C., I.M. and D.E.N. conceived and conducted the study. A.M., P.L.S., P.R., A.F.V., Q.F., Y.W., C.M.L., S.D., J.F.S., M.L., C.B., D.K., W.R., W.N. and M.K. undertook field and/or wet-lab work and sequencing of the samples. D.N.H., Z.L., J.M.C. and D.E.N. analyzed data. D.N.H., J.M.C., Z.L. and D.E.N. wrote the manuscript, and A.E., S.H., M.A.-H., L.C., G.C.B., A.G.L., A.B., I.M., J.W.K., A.E., N.V., M.U.F., J.S., D.G., J.M.V., L.G.-C. and B.W.B. revised the manuscript and made comments.

Corresponding authors

Correspondence toDaniel E Neafsey or Jane M Carlton.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Evaluation of the performance of different hybrid selection baits on a sample with 0.27% initial P. vivax mappable reads.

(a) Enrichment of P. vivax reads using six different baits, including synthetic oligonucleotides (far left column), ‘whole-genome baits’ (WGB) constructed from individual sources (Brazil I, India VII, N. Korean and Mauritania) of genomic DNA, and a WGB mixture (far right column). The vertical axis indicates percent mappable reads following hybrid selection, and numerals above bars indicate fold enrichment in mappable reads. (b) The fold enrichment of P. vivax DNA using WGB (vertical axis) was compromised by contamination of genomic DNA with host (in this case, monkey) material (horizontal axis).

Supplementary Figure 2 Project workflow used in this study, including sample collection, wet-lab processing and subsequent in silico analyses.

Supplementary Figure 3 Determination of complexity of infection using variant calls from 195 P. vivax isolates.

Each column along the x axis represents one isolate, and the scale along the y axis represents the number of high-quality variants annotated as heterozygous within that isolate. Isolates exhibiting more than 1,236 heterozygous calls, which represents twice the median observed in the population (vertical dotted line), were classified as complex infections containing more than one haploid parasite lineage.

Supplementary Figure 4 Region-specific projections of the variation data that use two principal components and are limited to Old World and New World populations.

(a) Two-eigenvector PCA limited to Old World isolates. (b) Two-eigenvector PCA limited to New World isolates. Both analyses show similar results to the PCA in Figure 3a that uses the global population of 195 P. vivax isolates.

Supplementary Figure 7 Admixture analysis of 195 isolates.

Admixture plots under six different K cluster values. Colors correspond to K clusters within each graph, whereas columns are consistent across graphs and correspond to geographical population. The tenfold cross-validation error associated with each admixture analysis was minimized at K = 5.

Supplementary Figure 8 A comparison of the per-gene fixation index (_F_ST) between New World and Old World isolates and per-gene nucleotide diversity calculated in each case from 73 single-infection, high-quality samples.

In blue are genes annotated as antigens, including members of the P. vivax serine-repeat antigen (SERA) family; members of the merozoite surface protein (MSP) superfamily, including single-copy genes MSP1, MSP4, MSP5, MSP8, MSP9, MSP10 and several members of the MSP3 multigene family and the MSP7 multigene family; members of the variant interspersed repeat (vir) gene family (excluding the most highly conserved and potential founder gene PVX_113230); members of the Pv-fam-a (PvTRAG), Pv-fam-b, Pv-fam-c, Pv-fam-d (HYPB), Pv-fam-e (RAD), Pv-fam-g, Pv-fam-h (HYP16) and Pv-fam-i (HYP11) gene families; and any gene annotated as an antigen in the Salvador I reference genome annotation. In green are genes identified as being putatively involved in antimalarial drug resistance, including dihydrofolate reductase–thymidylate synthase (DHFR-TS), dihydropteroate synthase (DHPS), multidrug resistance 1 protein (MDR1) and chloroquine resistance transporter gene (PvCRT). Gray dots represent all other annotated genes for which the two statistics could be calculated.

Supplementary Figure 9 Results from the McDonald–Kreitman (MK) test using a subset of single-infection and high-quality isolates from Colombia, Peru, Mexico, Thailand, Myanmar and Papua New Guinea.

Codon gapped-nucleotide alignments for each gene in the P. vivax genome were created and aligned to the nearest P. cynomolgi ortholog where available; genes without a one-to-one ortholog were excluded. Plotted along the y axis is the _χ_2 value associated with the MK test for each gene within the P. vivax alignment set. From this _χ_2 value, a P value was calculated. A _q_-value FDR correction was added to account for the effects of multiple sampling. Genes that were found to be significant (q value < 0.05) are shown in red. α, an estimate of the proportion of bases fixed by positive selection, is plotted along the x axis and ranges from ∞ (infinity) to 1. Values above 0 are inferred to be under positive selection, whereas values below zero are subject to purifying selection and/or balancing selection. Significant results are also listed in Supplementary Table 3.

Supplementary Figure 10 Plots of five different population genetic values across chromosome 5 of P. vivax.

In each case, the statistics were calculated using high-quality, single-infection isolates. Values of linkage disequilibrium (LD) are collapsed into a mean value across a window of 50 kb for each nucleotide. In the case of nucleotide diversity (π), Tajima’s D and _F_ST, values represent the mean value across all nucleotides within a 1-kb window. Plotted along the y axis in each figure is a trendline representing a moving average (period 10) for each population genetic statistic. The vertical red line indicates genes of interest identified through high _F_ST values as labeled in Figure 5 or genes with significant McDonald–Kreitman test results as listed in Supplementary Table 3. The thickness of the red line is an approximation of gene length.

Supplementary Figure 11 Plots of five different population genetic values across chromosome 7 of P. vivax.

In each case, the statistics were calculated using high-quality, single-infection isolates. Values of linkage disequilibrium (LD) are collapsed into a mean value across a window of 50 kb for each nucleotide. In the case of nucleotide diversity (π), Tajima’s D and _F_ST, values represent the mean value across all nucleotides within a 1-kb window. Plotted along the y axis in each figure is a trendline representing a moving average (period 10) for each population genetic statistic. The vertical red line indicates genes of interest identified through high _F_ST values as labeled in Figure 5 or genes with significant McDonald–Kreitman test results as listed in Supplementary Table 3. The thickness of the red line is an approximation of gene length.

Supplementary Figure 12 Plots of six different population genetic values across chromosome 9 of P. vivax.

In each case, the statistics were calculated using high-quality, single-infection isolates. Values of linkage disequilibrium (LD) are collapsed into a mean value across a window of 50 kb for each nucleotide. In the case of nucleotide diversity (π), Tajima’s D and _F_ST, values represent the mean value across all nucleotides within a 1-kb window. Plotted along the y axis in each figure is a trendline representing a moving average (period 10) for each population genetic statistic. The vertical red line indicates genes of interest identified through high _F_ST values as labeled in Figure 5 or genes with significant McDonald–Kreitman test results as listed in Supplementary Table 3. The thickness of the red line is an approximation of gene length.

Supplementary Figure 13 Plots of six different population genetic values across chromosome 11 of P. vivax.

In each case, the statistics were calculated using high-quality, single-infection isolates. Values of linkage disequilibrium (LD) are collapsed into a mean value across a window of 50 kb for each nucleotide. In the case of nucleotide diversity (π), Tajima’s D and _F_ST, values represent the mean value across all nucleotides within a 1-kb window. Plotted along the y axis in each figure is a trendline representing a moving average (period 10) for each population genetic statistic. The vertical red line indicates genes of interest identified through high _F_ST values as labeled in Figure 5 or genes with significant McDonald–Kreitman test results as listed in Supplementary Table 3. The thickness of the red line is an approximation of gene length.

Supplementary Figure 14 Plots of six different population genetic values across chromosome 12 of P. vivax.

In each case, the statistics were calculated using high-quality, single-infection isolates. Values of linkage disequilibrium (LD) are collapsed into a mean value across a window of 50 kb for each nucleotide. In the case of nucleotide diversity (π), Tajima’s D and _F_ST, values represent the mean value across all nucleotides within a 1-kb window. Plotted along the y axis in each figure is a trendline representing a moving average (period 10) for each population genetic statistic. The vertical red line indicates genes of interest identified through high _F_ST values as labeled in Figure 5 or genes with significant McDonald–Kreitman test results as listed in Supplementary Table 3. The thickness of the red line is an approximation of gene length.

Supplementary Figure 15 Plots of six different population genetic values across chromosome 14 of P. vivax.

In each case, the statistics were calculated using high-quality, single-infection isolates. Values of linkage disequilibrium (LD) are collapsed into a mean value across a window of 50 kb for each nucleotide. In the case of nucleotide diversity (π), Tajima’s D and _F_ST, values represent the mean value across all nucleotides within a 1-kb window. Plotted along the y axis in each figure is a trendline representing a moving average (period 10) for each population genetic statistic. The vertical red line indicates genes of interest identified through high _F_ST values as labeled in Figure 5 or genes with significant McDonald–Kreitman test results as listed in Supplementary Table 3. The thickness of the red line is an approximation of gene length.

Supplementary Figure 16 Haplotype map of three genes found to exhibit signals of positive selection within P. vivax.

Shown in red are nonsynonymous alternative alleles within each gene. In gray are sites that are identical to the reference genome nucleotide. Blank entries (white) indicate missing data.

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Hupalo, D., Luo, Z., Melnikov, A. et al. Population genomics studies identify signatures of global dispersal and drug resistance in Plasmodium vivax.Nat Genet 48, 953–958 (2016). https://doi.org/10.1038/ng.3588

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• Received: 11 December 2015
• Accepted: 13 May 2016
• Published: 27 June 2016
• Issue date: August 2016
• DOI: https://doi.org/10.1038/ng.3588