Temporal and spatial dynamics of<i>Plasmodium falciparum</i>clonal lineages in Guyana

Vanhove, Mathieu; Schwabl, Philipp; Clementson, Colette; Early, Angela M.; Laws, Margaret; Anthony, Frank; Florimond, Célia; Mathieu, Luana; James, Kashana; Knox, Cheyenne; Singh, Narine; Buckee, Caroline O.; Musset, Lise; Cox, Horace; Niles-Robin, Reza; Neafsey, Daniel E.

doi:10.1101/2024.01.31.578156

Cited by 4 publications

(1 citation statement)

References 76 publications

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“…Clonal group membership detection was often spatiotemporally aggregated (e.g., >50% of group members detected within 4 months per epidemiological zone, Supplementary Fig. 7), consistent with outbreak-like patterns of prevalence 35 . The largest clonal groups spread across multiple geographic areas (Supplementary Fig.…”

Section: Discrepant Genomic Diversity and Relatedness Between Speciessupporting

confidence: 60%

Contrasting genomic epidemiology between sympatricPlasmodium falciparumandPlasmodium vivaxpopulations

Schwabl,

Camponovo,

Clementson

et al. 2024

Preprint

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The malaria parasitesPlasmodium falciparumandPlasmodium vivaxdiffer in key biological processes and associated clinical effects, but consequences on population-level transmission dynamics are difficult to predict. This co-endemic malaria study from Guyana details important epidemiological contrasts between the species by coupling population genomics (1,396 spatiotemporally-matched parasite genomes) with sociodemographic analysis (nationwide patient census). We describe howP. falciparumforms large, interrelated subpopulations that sporadically expand but generally exhibit restrained dispersal, whereby spatial distance and patient travel statistics predict parasite identity-by-descent (IBD). Case bias towards working-age adults is also strongly pronounced.P. vivaxexhibits 46% higher average diversity (π) and 6.5x lower average IBD. It occupies a wider geographic range, without evidence for outbreak-like expansions, only microgeographic patterns of isolation-by-distance, and weaker case bias towards adults. Possible latency-relapse effects also manifest in various analyses. For example, 11.0% of patients diagnosed withP. vivaxin Greater Georgetown report no recent travel to endemic zones, andP. vivaxclones recur in 11/46 patients incidentally sampled twice during the study. Polyclonality rate is also 2.1x higher than inP. falciparum, does not trend positively with estimated incidence, and correlates uniquely to selected demographics. We discuss possible underlying mechanisms and implications for malaria control.

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Section: Discrepant Genomic Diversity and Relatedness Between Speciessupporting

confidence: 60%

Contrasting genomic epidemiology between sympatricPlasmodium falciparumandPlasmodium vivaxpopulations

Schwabl,

Camponovo,

Clementson

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

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Systematic bias in malaria parasite relatedness estimation

Mehra,

Neafsey,

White

et al. 2024

Preprint

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Genetic studies of malaria parasites increasingly feature estimates of relatedness. However, various aspects of malaria parasite relatedness estimation are not fully understood. For example, estimates of relatedness based on whole-genome-sequence (WGS) data often exceed those based on more sparse data types. We explore systematic bias in relatedness estimation using theoretical, numerical and empirical approaches. Specifically, we use a non-ancestral model of pairwise relatedness to derive theoretical results; a simulation model of ancestry to independently verify and expand our theoretical results; and data on parasites sampled from Guyana to explore how theoretical and numerical results translate empirically. We show that allele frequencies encode, locus-by-locus, relatedness averaged over the set of sampled parasites used to compute them. These sample allele frequencies are typically plugged into the models used to estimate pairwise relatedness. Consequently, models of pairwise relatedness are misspecified and pairwise relatedness values are systematically underestimated. However, systematic underestimation can be viewed as population-relatedness calibration, i.e., a way of generating measures of relative relatedness. Systematic underestimation is unavoidable when relatedness is estimated assuming independence between genetic markers. It is mitigated when estimated using WGS data under a hidden Markov model (HMM), which exploits linkage between proximal markers. Estimates of absolute relatedness generated under a HMM using relatively sparse data should be treated with caution because the extent to which underestimation is mitigated is unknowable. That said, analyses dependent on absolute values and high relatedness thresholds are relatively robust. In summary, practitioners have two options: resolve to use relative relatedness estimated under independence or try to estimate absolute relatedness under a HMM. We propose various practical tools to help practitioners evaluate their situation on a case-by-case basis.

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Temporal patterns of haplotypic and allelic diversity reflect the changing selection landscape of the malaria parasitePlasmodium falciparum

Early,

Pelleau,

Musset

et al. 2024

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Populations of the malaria parasitePlasmodium falciparumregularly confront orchestrated changes in frontline drug treatment that drastically alter the parasite’s selection landscape. When this has occurred, the parasite has successfully adapted to the new drugs through novel resistance mutations. These novel mutations, however, may emerge in a genetic background already shaped by prior drug selection. In some instances, selection imposed by distinct drugs has targeted the same loci in either synergistic or antagonistic ways, resulting in genomic signatures that can be hard to attribute to a specific agent. Here, we use two approaches for detecting sequential bouts of drug adaptation: haplotype-based selection testing and temporal changes in allele frequencies. Using a set of longitudinally acquired samples from French Guiana, we determine that since the introduction of the drug artemether-lumefantrine (AL) in 2007 there have been rapid hard selective sweeps at both known and novel loci. We additionally identify genomic regions where selection acted in opposing directions before and after widespread AL introduction. At four high-profile genes with demonstrated involvement in drug resistance (crt,mdr1,aat1, andgch1), we saw strong selection before and after drug regime change; however, selection favored different haplotypes in the two time periods. Similarly, the allele frequency analysis identified coding variants whose frequency trajectory changed sign under the new drug pressure. These selected alleles were enriched for genes implicated in artemisinin and/or partner drug resistance in other global populations. Overall, these results suggest that drug resistance inP. falciparumis governed by known alleles of large effect along with a polygenic architecture of more subtle variants, any of which can experience fitness reversals under distinct drug regimes.

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Temporal and spatial dynamics ofPlasmodium falciparumclonal lineages in Guyana

Cited by 4 publications

References 76 publications

Contrasting genomic epidemiology between sympatricPlasmodium falciparumandPlasmodium vivaxpopulations

Contrasting genomic epidemiology between sympatricPlasmodium falciparumandPlasmodium vivaxpopulations

Systematic bias in malaria parasite relatedness estimation

Temporal patterns of haplotypic and allelic diversity reflect the changing selection landscape of the malaria parasitePlasmodium falciparum

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