Biology subjects

Dondorp, A. M.

Publications and source records attributed to Dondorp, A. M..

8 recordsLinked to original sources

bioRxiv2024

A complex Plasmodium falciparum cryptotype circulating at low frequency across the African continent

The population structure of the malaria parasite Plasmodium falciparum can reveal underlying demographic and adaptive evolutionary processes. Here, we analyse population structure in 4,376 P. falciparum genomes from 21 countries across Africa. We identified a strongly differentiated cluster of parasites, comprising [~]1.2% of samples analysed, geographically distributed over 13 countries across the continent. Members of this cluster, named AF1, carry a genetic background consisting of a large number of highly differentiated variants, rarely observed outside this cluster, at a multitude of genomic loci distributed across most chromosomes. At these loci, the AF1 haplotypes appear to have common ancestry, irrespective of the sampling location; outside the shared loci, however, AF1 members are genetically similar to their sympatric parasites. AF1 parasites sharing up to 23 genomic co-inherited regions were found in all major regions of Africa, at locations over 7,000 km apart. We coined the term cryptotype to describe a complex common background which is geographically widespread, but concealed by genomic regions of local origin. Most AF1 differentiated variants are functionally related, comprising structural variations and single nucleotide polymorphisms in components of the MSP1 complex and several other genes involved in interactions with red blood cells, including invasion and erythrocyte antigen export. We propose that AF1 parasites have adapted to some as yet unidentified evolutionary niche, by acquiring a complex compendium of interacting variants that rarely circulate separately in Africa. As the cryptotype spread across the continent, it appears to have been maintained mostly intact in spite of recombination events, suggesting a selective advantage. It is possible that other cryptotypes circulate in Africa, and new analysis methods may be needed to identify them.

genomics↗

bioRxiv2021

The mechanism of artemisinin resistance of Plasmodium falciparum malaria parasites originates in their initial transcriptional response.

The emergence and spread of artemisinin resistant Plasmodium falciparum, first in the Greater Mekong Subregion (GMS), and now in East Africa, is a major threat to global malaria eliminations ambitions. To investigate the artemisinin resistance mechanism, transcriptome analysis was conducted of 577 P. falciparum isolates collected in the GMS between 2016-2018. A specific artemisinin resistance-associated transcriptional profile was identified that involves a broad but discrete set of biological functions related to proteotoxic stress, host cytoplasm remodeling and REDOX metabolism. The artemisinin resistance-associated transcriptional profile evolved from initial transcriptional responses of susceptible parasites to artemisinin. The genetic basis for this adapted response is likely to be complex. One sentence summaryThe transcriptional profile that characterize artemisinin resistant infections with malaria parasites Plasmodium falciparum originates in the initial transcriptional response to the drug.

systems biology↗

bioRxiv2020

Modulation of triple artemisinin-based combination therapy pharmacodynamics by Plasmodium falciparum genotype

The first-line treatments for uncomplicated Plasmodium falciparum malaria are artemisinin-based combination therapies (ACTs), consisting of an artemisinin derivative combined with a longer acting partner drug. However, the spread of P. falciparum with decreased susceptibility to artemisinin and partner drugs presents a significant challenge to malaria control efforts. To stem the spread of drug resistant parasites, novel chemotherapeutic strategies are being evaluated, including the implementation of triple artemisinin-based combination therapies (TACTs). Currently, there is limited knowledge on the pharmacodynamics and pharmacogenetic interactions of proposed TACT drug combinations. To evaluate these interactions, we established an in vitro high-throughput process for measuring the drug dose-response to three distinct antimalarial drugs present in a TACT. Sixteen different TACT combinations were screened against fifteen parasite lines from Cambodia, with a focus on parasites with differential susceptibilities to piperaquine and artemisinins. Analysis revealed drug-drug interactions unique to specific genetic backgrounds, including antagonism between piperaquine and pyronaridine associated with gene amplification of plasmepsin II/III, two aspartic proteases that localize to the parasite digestive vacuole. From this initial study, we identified parasite genotypes with decreased susceptibility to specific TACTs, as well as potential TACTs that display antagonism in a genotype-dependent manner. Our assay and analysis platform can be further leveraged to inform drug implementation decisions and evaluate next-generation TACTs.One Sentence Summary In vitro process to evaluate triple-drug combinations for prioritizing antimalarial combinations for in vivo evaluation.Competing Interest StatementThe authors have declared no competing interest.View Full Text

microbiology↗

bioRxiv2020

A cautionary note on the use of machine learning algorithms to characterise malaria parasite population structure from genetic distance matrices

Genetic surveillance of malaria parasites supports malaria control programmes, treatment guidelines and elimination strategies. Surveillance studies often pose questions about malaria parasite ancestry (e.g. how antimalarial resistance has spread) and employ statistical methods that characterise parasite population structure. Many of the methods used to characterise structure are unsupervised machine learning algorithms which depend on a genetic distance matrix, notably principal coordinates analysis (PCoA) and hierarchical agglomerative clustering (HAC). PCoA and HAC are sensitive to both the definition of genetic distance and algorithmic specification. Importantly, neither algorithm infers malaria parasite ancestry. As such, PCoA and HAC can inform (e.g. via exploratory data visualisation and hypothesis generation), but not answer comprehensively, key questions about malaria parasite ancestry. We illustrate the sensitivity of PCoA and HAC using 393 Plasmodium falciparum whole genome sequences collected from Cambodia and neighbouring regions (where antimalarial resistance has emerged and spread recently) and we provide tentative guidance for the use and interpretation of PCoA and HAC in malaria parasite genetic epidemiology. This guidance includes a call for fully transparent and reproducible analysis pipelines that feature (i) a clearly outlined scientific question; (ii) a clear justification of analytical methods used to answer the scientific question along with discussion of any inferential limitations; (iii) publicly available genetic distance matrices when downstream analyses depend on them; and (iv) sensitivity analyses. To bridge the inferential disconnect between the output of non-inferential unsupervised learning algorithms and the scientific questions of interest, tailor-made statistical models are needed to infer malaria parasite ancestry. In the absence of such models speculative reasoning should feature only as discussion but not as results. Author summaryGenetic epidemiology studies of malaria attempt to characterise what is happening in malaria parasite populations. In particular, they are an important tool to track the spread of drug resistance and to validate genetic makers of drug resistance. To make sense of parasite genetic data, researchers usually characterise the population structure using statistical methods. This is most often done as a two step process. The first is a data reduction step, whereby the data are summarised into a distance matrix (each entry represents the genetic distance between two isolates) and then the distance matrix is input into an unsupervised machine learning algorithm. Principal coordinates analysis and hierarchical agglomerative clustering are the two most popular unsupervised machine learning algorithms used for this purpose in malaria genetic epidemiology. We illustrate that this procedure is sensitive to the choice of genetic distance and to the specification of the algorithms. These unsupervised methods are useful for exploratory data analysis but cannot be used to infer historical events. We provide some guidance on how to make genetic epidemiology analyses more transparent and reproducible.

genetics↗

bioRxiv2020

Artemisinin-resistant malaria parasites show enhanced transmission to mosquitoes under drug pressure

Resistance to artemisinin combination therapy (ACT) in the Plasmodium falciparum parasite is threatening to reverse recent gains in reducing global deaths from malaria. Whilst resistance manifests as delayed asexual parasite clearance in patients following ACT treatment, the phenotype can only spread geographically via the sexual cycle and subsequent transmission through the mosquito. Artemisinin and its derivatives (such as dihydroartemisinin, DHA) as well as killing the asexual parasite form are known to sterilize male, sexual-stage gametes from activation. Whether resistant parasites overcome this artemisinin-dependent sterilizing effect has not, however, been fully tested. Here, we analysed five P. falciparum clinical isolates from the Greater Mekong Subregion, each of which demonstrated delayed clinical clearance and carried known resistance-associated polymorphisms in the Kelch13 gene (PfK13var). As well as demonstrating reduced sensitivity to artemisinin-derivates in in vitro asexual growth assays, certain PfK13var isolates also demonstrated a marked reduction in sensitivity to these drugs in an in vitro male gamete activation assay compared to a sensitive control. Importantly, the same reduction in sensitivity to DHA was observed when the most resistant isolate was assayed by standard membrane feeding assays using Anopheles stephensi mosquitoes. These results indicate that ACT use can favour resistant over sensitive parasite transmission. A selective advantage for resistant parasite transmission could also favour acquisition of further polymorphisms, such as mosquito host-specificity or antimalarial partner-drug resistance in mixed infections. Favoured transmission of resistance under ACT coverage could have profound implications for the spread of multidrug resistant malaria beyond Southeast Asia. ONE SENTENCE SUMMARYArtemisinin-resistant clinical isolates can also demonstrate resistance to the transmission-blocking effects of artemisinin-based drugs, favouring resistance transmission to the mosquito.

microbiology↗

bioRxiv2019

Not all MDAs should be created equal-determinants of MDA impact and designing MDAs towards malaria elimination

Malaria remains at the forefront of scientific research and global political and funding agendas. Previous malaria models of mass-interventions have consistently oversimplified how mass interventions are implemented. We present an individual based, spatially explicit model of malaria transmission that includes all the programmatic implementation details of mass drug administration (MDA) campaigns. We uncover how the impact of MDA campaigns is determined by the interaction between implementation logistics, patterns of human mobility and how transmission risk is distributed over space. This translates into a higher likelihood of malaria elimination for areas with true prevalence under 3% with a faster implementation, in highly mobile populations. If populations are more static, deploying less interventions teams would be cost optimal and predicted to be as impactful. We conclude that mass drug interventions can be an invaluable tool towards malaria elimination in the right context, specifically when paired with effective vector control.

physiology↗

bioRxiv2019

Evolution and expansion of multidrug resistant malaria in Southeast Asia: a genomic epidemiology study

BackgroundA multidrug resistant co-lineage of Plasmodium falciparum malaria, named KEL1/PLA1, spread across Cambodia c.2008-2013, causing high treatment failure rates to the frontline combination therapy dihydroartemisinin-piperaquine. Here, we report on the evolution and spread of KEL1/PLA1 in subsequent years.\n\nMethodsWe analysed whole genome sequencing data from 1,673 P. falciparum clinical samples collected in 2008-2018 from northeast Thailand, Laos, Cambodia and Vietnam. By investigating genome-wide relatedness between parasites, we inferred patterns of shared ancestry in the KEL1/PLA1 population.\n\nFindingsKEL1/PLA1 spread rapidly from 2015 into all of the surveyed countries and now exceeds 80% of the P. falciparum population in several regions. These parasites maintained a high level of genetic relatedness reflecting their common origin. However, several genetic subgroups have recently emerged within this co-lineage with diverse geographical distributions. Some of these emerging KEL1/PLA1 subgroups carry recent mutations in the chloroquine resistance transporter (crt) gene, which arise on a specific genetic background comprising multiple genomic regions.\n\nInterpretationAfter emerging and circulating for several years within Cambodia, the P. falciparum KEL1/PLA1 co-lineage diversified into multiple subgroups and acquired new genetic features including novel crt mutations. These subgroups have rapidly spread into neighbouring countries, suggesting enhanced fitness. These findings highlight the urgent need for elimination of this increasingly drug-resistant parasite co-lineage, and the importance of genetic surveillance in accelerating elimination efforts.\n\nFundingWellcome Trust, Bill & Melinda Gates Foundation, UK Medical Research Council, UK Department for International Development.\n\nResearch in context O_TEXTBOXEvidence before this studyThis study updates our previous work describing the emergence and spread of a multidrug resistant P. falciparum co-lineage (KEL1/PLA1) within Cambodia up to 2013. Since then, a regional genetic surveillance project, GenRe-Mekong, has reported that markers of dihydroartemisinin-piperaquine (DHA-PPQ) resistance have increased in frequency in neighbouring countries. A PubMed search (terms: \"artemisinin\", \"piperaquine\", \"resistance\", \"southeast asia\") for articles listed since our previous study (from 30/10/2017 to 05/01/2019) yielded 28 results, including reports of a recent sharp decline in DHA-PPQ clinical efficacy in Vietnam; the spread of genetic markers of DHA-PPQ resistance into neighbouring countries by Imwong and colleagues; and multiple reports associating mutations in the crt gene with piperaquine resistance, including newly emerging crt variants in Southeast Asia.\n\nAdded value of this studyWe analysed P. falciparum whole genomes collected up to early 2018 from Eastern Southeast Asia (Cambodia and surrounding regions), describing the fine-scale epidemiology of multiple KEL1/PLA1 genetic subgroups that have spread out from Cambodia since 2015 and taken over indigenous parasite populations in northeastern Thailand, southern and central Vietnam and parts of southern Laos. Several newly emerging crt mutations accompanied the spread and expansion of KEL1/PLA1 subgroups, suggesting an active proliferation of biologically fit, multidrug resistant parasites.\n\nImplications of all the available evidenceThe problem of P. falciparum multidrug resistance has dramatically worsened in Eastern Southeast Asia since previous reports. KEL1/PLA1 has diversified and spread widely across Eastern Southeast Asia since 2015, becoming the predominant parasite group in several regions. This may have been fuelled by continued parasite exposure to DHA-PPQ, resulting in sustained selection after KEL1/PLA1 became established. Continued drug pressure enabled the acquisition of further mutations, resulting in higher levels of resistance. These data demonstrate the value of pathogen genetic surveillance and the urgent need to eliminate these dangerous parasites.\n\nC_TEXTBOX

epidemiology↗

bioRxiv2018

Herd protection against Plasmodium falciparum infections conferred by mass antimalarial drug administrations and the implications for malaria elimination

IntroductionAll nations of the Greater Mekong Subregion have committed to elimination of malaria by the year 2030. Elimination efforts rely on increasing access to diagnosis and treatment. However, asymptomatic infections pose a major challenge for these efforts. One approach towards eliminating asymptomatic reservoirs is targeted mass antimalarial drug administration (MDA). Here we present a fine scale spatiotemporal analysis of malaria incidence and prevalence in villages undergoing MDA.\n\nMethodsFour villages along the Myanmar-Thailand border were selected for a MDA pilot study based on clinical records and prevalence surveys. Passive detection of clinical episodes was facilitated through community based malaria clinics in each village. All villagers were screened using venous blood and ultra-sensitive qPCR (uPCR) for detection of parasites at baseline and every subsequent 3 months until month 18 (M18). A final screening was done at M24. MDAs were conducted on M0, M1 and M2 in two villages and on M9, M10, M11 in the remaining two villages. Which villages received early or deferred MDA was decided using restricted randomization. MDA participation, clinical episodes and uPCR detected infections were mapped to participant houses. Scan statistics were used to test for clusters of malaria episodes, malaria infections and non-adherence to MDA. Mixed effects regressions were used to test for risk factors for clinical episodes after MDA.\n\nResultsNeighborhood level MDA adherence was a major predictor for clinical episodes of malaria post-MDA, suggesting a strong herd effect. Each village had a cluster of P. falciparum infections at M0. After M0, there were no clusters of uPCR detectable P. falciparum infections. Clinical episodes of P. falciparum occurred in one village only which had a cluster of non-adherence to MDA and a high mosquito vector human biting rate. Individuals with subclinical P.falciparum infections were more likely to have subsequent clinical episodes than individuals who had no subclinical infections. Individuals who lived in a house with someone who had a clinical episode were more likely to also have a clinical episode subsequently than individuals residing in a house free of P.falciparum infections.\n\nConclusionTo our knowledge this is the first study to show a herd effect from MDA for malaria. These data and results have significance for spatial targeting of interventions for malaria elimination. Clusters of non-adherence to MDA participation can lead to failed elimination if they occur among individuals with asymptomatic infections and given sufficient mosquito vector exposure. Community participation, which can be facilitated through community engagement, is key to MDA success.

epidemiology↗