Malaria: A genetic intervention

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.
  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

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.

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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
    didier.menard@pasteur.fr
    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)

Copyright

© 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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  1. Colin Sutherland
  2. Didier Menard
(2021)
Malaria: A genetic intervention
eLife 10:e72000.
https://doi.org/10.7554/eLife.72000

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    Background:

    Associations between attention-deficit/hyperactivity disorder (ADHD) and brain morphology have been reported, although with several inconsistencies. These may partly stem from confounding bias, which could distort associations and limit generalizability. We examined how associations between brain morphology and ADHD symptoms change with adjustments for potential confounders typically overlooked in the literature (aim 1), and for the intelligence quotient (IQ) and head motion, which are generally corrected for but play ambiguous roles (aim 2).

    Methods:

    Participants were 10-year-old children from the Adolescent Brain Cognitive Development (N = 7722) and Generation R (N = 2531) Studies. Cortical area, volume, and thickness were measured with MRI and ADHD symptoms with the Child Behavior Checklist. Surface-based cross-sectional analyses were run.

    Results:

    ADHD symptoms related to widespread cortical regions when solely adjusting for demographic factors. Additional adjustments for socioeconomic and maternal behavioral confounders (aim 1) generally attenuated associations, as cluster sizes halved and effect sizes substantially reduced. Cluster sizes further changed when including IQ and head motion (aim 2), however, we argue that adjustments might have introduced bias.

    Conclusions:

    Careful confounder selection and control can help identify more robust and specific regions of associations for ADHD symptoms, across two cohorts. We provided guidance to minimizing confounding bias in psychiatric neuroimaging.

    Funding:

    Authors are supported by an NWO-VICI grant (NWO-ZonMW: 016.VICI.170.200 to HT) for HT, LDA, SL, and the Sophia Foundation S18-20, and Erasmus University and Erasmus MC Fellowship for RLM.