1. Epidemiology and Global Health
  2. Microbiology and Infectious Disease
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Malaria: A genetic intervention

  1. Colin Sutherland
  2. Didier Menard  Is a corresponding author
  1. Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, United Kingdom
  2. Malaria Genetics and Resistance Unit, INSERM U1201, France
  3. Department of Parasites and Insect Vectors, Institut Pasteur, France
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Cite this article as: eLife 2021;10:e72000 doi: 10.7554/eLife.72000


A tool that analyzes the genome of parasites found in the blood of malaria patients can help inform policy decisions on how best to tackle the rise in drug-resistant infections.

Main text

Malaria cases may have decreased over the last 15 years, but the number of parasites resistant to treatment is rising, particularly in Southeast Asia. Indeed, some Plasmodium falciparum parasites (which cause the most severe form of the disease) no longer respond to the most widely used antimalarial drugs (Blasco et al., 2017; Menard and Dondorp, 2017). This includes artemisinin-based combination therapy (ACT), the first line of defense recommended by National Malaria Control Programs (Noedl et al., 2008; Dondorp et al., 2009; Cheeseman et al., 2012; Ariey et al., 2014; Takala-Harrison et al., 2015; Amato et al., 2018; Hamilton et al., 2019; Imwong et al., 2020; Stokes et al., 2021). The reduced susceptibility to artemisinin and its partner drugs has resulted in ACT therapy failing to treat over 50% of malaria cases in some regions of Cambodia, Thailand and Vietnam (van der Pluijm et al., 2020van der Pluijm et al., 2019).

In order to make rapid, effective decisions on how best to tackle this rise in resistance, policymakers need to be aware of which strains are present in different regions. However, clinical studies of malaria are logistically difficult and expensive to implement. An alternative approach is to study molecular markers in the blood of malaria patients, which can also provide an earlier indication of where resistant parasites have emerged (World Health Organization, 2020). Now, in eLife, Olivo Miotto and colleagues – including Christopher Jacob (Wellcome Sanger Institute) as first author – report a new genetic surveillance platform called ‘GenRe-Mekong’ which monitors the spread of resistant parasites in the Greater Mekong Subregion (Jacob et al., 2021).

Jacob et al. collaborated with various National Malaria Control Programs and scientific partners to collect 9,623 blood samples from patients diagnosed with P. falciparum malaria in eight countries (Vietnam, Laos, Cambodia, Thailand, Myanmar, Bangladesh, India and Democratic Republic of Congo). A cutting-edge sequencing technology was then applied to extract and amplify specific genes from the parasitic genome. Jacob et al. analyzed this genetic data for variants which are known to reduce parasites’ susceptibility to the most widely used treatments, including artemisinin (Figure 1). Based on the proportion of variants present in each gene, each sample was then classified as having a ‘sensitive’, ‘resistant’ or ‘undetermined’ response to an antimalarial drug.

Bridging the gap between research and malaria surveillance in the Greater Mekong Subregion.

Previous research studying the genome of parasites has led to the identification of genetic variants which reduce parasites’ susceptibility to antimalarial drugs (blue box, left). Jacob et al. used this data to create a genetic surveillance platform called GenRe-Mekong, which analyzes the blood samples of malaria patients for these genetic variants (green box, right). Data from this platform is then regularly shared with local research organizations and various National Malaria Control Programs (NMCPs) in the Greater Mekong Subregion, who can use this information to help inform their policy decisions for tackling drug-resistant infections.

Data from the genetic surveillance platform were delivered to National Malaria Control Programs as maps which simply describe how the presence and absence of resistant parasites is changing over time. Notably, the team (who are based in the United States, United Kingdom, Vietnam, Laos, Cambodia, Thailand, Bangladesh, Switzerland, Democratic Republic of Congo, France, Myanmar and India) provided concrete examples of how this information can be translated into policy decisions. For example, the database found that parasites less susceptible to artemisinin and one of its partner drugs (originally identified in Cambodia and Thailand) had spread to southern provinces in Vietnam and Laos which were previously unaffected by these resistant strains. This led National Malaria Control Programs in these regions to reassess which frontline therapies to use and where to allocate resources to help combat the rise in drug resistance.

Beyond providing actionable information on the spread of resistance, the approach could also shed light on the complexity of infection, revealing whether a patient was carrying different species or strains of parasites at the same time. It could also be used to infer where the strains detected originated, which could help to reconstruct how parasites resistant to multiple drugs spread to different regions.

Although this platform could rapidly become an essential, complementary strategy for eliminating malaria, additional work is needed to overcome some drawbacks. First, the changes in drug susceptibility caused by the genetic variants is not directly tested, but assumed on the basis of earlier research (Figure 1). Second, the platform will be unable to capture new resistant strains, unless newly discovered variants are continuously added to the GenRe-Mekong database. Last, the current approach is unlikely to be applicable to countries in the African continent. This is because patients are often infected by multiple strains of P. falciparum at the same time, which makes it difficult to apply the method across many variant genes and classify the resistance profile. Nevertheless, the work by Jacob et al. demonstrates how genetic data can be a tremendously practical tool that can help policy makers rapidly adapt their treatment strategies in response to rising levels of drug resistance.


    1. van der Pluijm RW
    2. Tripura R
    3. Hoglund RM
    4. Pyae Phyo A
    5. Lek D
    6. ul Islam A
    7. Anvikar AR
    8. Satpathi P
    9. Satpathi S
    10. Behera PK
    11. Tripura A
    12. Baidya S
    13. Onyamboko M
    14. Chau NH
    15. Sovann Y
    16. Suon S
    17. Sreng S
    18. Mao S
    19. Oun S
    20. Yen S
    21. Amaratunga C
    22. Chutasmit K
    23. Saelow C
    24. Runcharern R
    25. Kaewmok W
    26. Hoa NT
    27. Thanh NV
    28. Hanboonkunupakarn B
    29. Callery JJ
    30. Mohanty AK
    31. Heaton J
    32. Thant M
    33. Gantait K
    34. Ghosh T
    35. Amato R
    36. Pearson RD
    37. Jacob CG
    38. Gonçalves S
    39. Mukaka M
    40. Waithira N
    41. Woodrow CJ
    42. Grobusch MP
    43. van Vugt M
    44. Fairhurst RM
    45. Cheah PY
    46. Peto TJ
    47. von Seidlein L
    48. Dhorda M
    49. Maude RJ
    50. Winterberg M
    51. Thuy-Nhien NT
    52. Kwiatkowski DP
    53. Imwong M
    54. Jittamala P
    55. Lin K
    56. Hlaing TM
    57. Chotivanich K
    58. Huy R
    59. Fanello C
    60. Ashley E
    61. Mayxay M
    62. Newton PN
    63. Hien TT
    64. Valecha N
    65. Smithuis F
    66. Pukrittayakamee S
    67. Faiz A
    68. Miotto O
    69. Tarning J
    70. Day NPJ
    71. White NJ
    72. Dondorp AM
    73. van der Pluijm RW
    74. Tripura R
    75. Hoglund RM
    76. Phyo AP
    77. Lek D
    78. ul Islam A
    79. Anvikar AR
    80. Satpathi P
    81. Satpathi S
    82. Behera PK
    83. Tripura A
    84. Baidya S
    85. Onyamboko M
    86. Chau NH
    87. Sovann Y
    88. Suon S
    89. Sreng S
    90. Mao S
    91. Oun S
    92. Yen S
    93. Amaratunga C
    94. Chutasmit K
    95. Saelow C
    96. Runcharern R
    97. Kaewmok W
    98. Hoa NT
    99. Thanh NV
    100. Hanboonkunupakarn B
    101. Callery JJ
    102. Mohanty AK
    103. Heaton J
    104. Thant M
    105. Gantait K
    106. Ghosh T
    107. Amato R
    108. Pearson RD
    109. Jacob CG
    110. Gonçalves S
    111. Mukaka M
    112. Waithira N
    113. Woodrow CJ
    114. Grobusch MP
    115. van Vugt M
    116. Fairhurst RM
    117. Cheah PY
    118. Peto TJ
    119. von Seidlein L
    120. Dhorda M
    121. Maude RJ
    122. Winterberg M
    123. Thuy-Nhien NT
    124. Kwiatkowski DP
    125. Imwong M
    126. Jittamala P
    127. Lin K
    128. Hlaing TM
    129. Chotivanich K
    130. Huy R
    131. Fanello C
    132. Ashley E
    133. Mayxay M
    134. Newton PN
    135. Hien TT
    136. Valeche N
    137. Smithuis F
    138. Pukrittayakamee S
    139. Faiz A
    140. Miotto O
    141. Tarning J
    142. Day NPJ
    143. White NJ
    144. Dondorp AM
    (2020) Triple artemisinin-based combination therapies versus artemisinin-based combination therapies for uncomplicated Plasmodium falciparum malaria: a multicentre, open-label, randomised clinical trial
    The Lancet 395:1345–1360.
  1. Report
    1. World Health Organization
    Report on Antimalarial Drug Efficacy, Resistance and Response: 10 Years of Surveillance (2010–2019) Geneva
    World Health Organization.

Article and author information

Author details

  1. Colin Sutherland

    Colin Sutherland is in the Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom

    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1592-6407
  2. Didier Menard

    Didier Menard is in the Malaria Genetics and Resistance Unit, INSERM U1201, and Department of Parasites and Insect Vectors, Institut Pasteur, Paris, France

    For correspondence
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1357-4495

Publication history

  1. Version of Record published: August 12, 2021 (version 1)


© 2021, Sutherland and Menard

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.


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Further reading

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    Transmission of respiratory pathogens such as SARS-CoV-2 depends on patterns of contact and mixing across populations. Understanding this is crucial to predict pathogen spread and the effectiveness of control efforts. Most analyses of contact patterns to date have focused on high-income settings.


    Here, we conduct a systematic review and individual-participant meta-analysis of surveys carried out in low- and middle-income countries and compare patterns of contact in these settings to surveys previously carried out in high-income countries. Using individual-level data from 28,503 participants and 413,069 contacts across 27 surveys, we explored how contact characteristics (number, location, duration, and whether physical) vary across income settings.


    Contact rates declined with age in high- and upper-middle-income settings, but not in low-income settings, where adults aged 65+ made similar numbers of contacts as younger individuals and mixed with all age groups. Across all settings, increasing household size was a key determinant of contact frequency and characteristics, with low-income settings characterised by the largest, most intergenerational households. A higher proportion of contacts were made at home in low-income settings, and work/school contacts were more frequent in high-income strata. We also observed contrasting effects of gender across income strata on the frequency, duration, and type of contacts individuals made.


    These differences in contact patterns between settings have material consequences for both spread of respiratory pathogens and the effectiveness of different non-pharmaceutical interventions.


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    1. Epidemiology and Global Health
    2. Genetics and Genomics
    Daniel W Belsky et al.
    Research Advance

    Background: Measures to quantify changes in the pace of biological aging in response to intervention are needed to evaluate geroprotective interventions for humans. Previously we showed that quantification of the pace of biological aging from a DNA-methylation blood test was possible (Belsky et al. 2020). Here we report a next-generation DNA-methylation biomarker of Pace of Aging, DunedinPACE (for Pace of Aging Calculated from the Epigenome).

    Methods: We used data from the Dunedin Study 1972-3 birth cohort tracking within-individual decline in 19 indicators of organ-system integrity across four time points spanning two decades to model Pace of Aging. We distilled this two-decade Pace of Aging into a single-time-point DNA-methylation blood-test using elastic-net regression and a DNA-methylation dataset restricted to exclude probes with low test-retest reliability. We evaluated the resulting measure, named DunedinPACE, in five additional datasets.

    Results: DunedinPACE showed high test-retest reliability, was associated with morbidity, disability, and mortality, and indicated faster aging in young adults with childhood adversity. DunedinPACE effect-sizes were similar to GrimAge Clock effect-sizes. In analysis of incident morbidity, disability, and mortality, DunedinPACE and added incremental prediction beyond GrimAge.

    Conclusions: DunedinPACE is a novel blood biomarker of the pace of aging for gerontology and geroscience.

    Funding: This research was supported by US-National Institute on Aging grants AG032282, AG061378, AG066887, and UK Medical Research Council grant MR/P005918/1.