The origins and relatedness structure of mixed infections vary with local prevalence of P. falciparum malaria

Systematic bias in malaria parasite relatedness estimation

Genetic studies of Plasmodium parasites increasingly feature relatedness estimates. However, various aspects of malaria parasite relatedness estimation are not fully understood. For example, relatedness estimates based on whole-genome-sequence (WGS) data often exceed those based on sparser data types. Systematic bias in relatedness estimation is well documented in the literature geared towards diploid organisms, but largely unknown within the malaria community. We characterize systematic bias in malaria parasite relatedness estimation using three complementary approaches: theoretically, under a non-ancestral statistical model of pairwise relatedness; numerically, under a simulation model of ancestry; and empirically, using data on parasites sampled from Guyana and Colombia. We show that allele frequency estimates encode, locus-by-locus, relatedness averaged over the set of sampled parasites used to compute them. Plugging sample allele frequencies into models of pairwise relatedness can lead to systematic underestimation. 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 relatedness is estimated using WGS data under a hidden Markov model (HMM) that exploits linkage between proximal markers. The extent of mitigation is unknowable when a HMM is fit to sparser data, but downstream analyses that use high relatedness thresholds are relatively robust regardless. In summary, practitioners can either resolve to use relative relatedness estimated under independence, or try to estimate absolute relatedness under a HMM. We propose various tools to help practitioners evaluate their situation on a case-by-case basis.

Potential and pitfalls of using identity-by-descent for malaria genomic surveillance

The ability to genotype malaria parasites on an epidemiological scale is crucial for genomic surveillance as it aids in understanding malaria transmission dynamics and parasite demography changes in response to antimalarial interventions. Identity-by-descent (IBD)-based methods have demonstrated potential in various aspects of malaria genomic surveillance. However, there is a need for validation of existing approaches and development of new techniques to address challenges posed by the parasites' unique evolutionary dynamics and complex biological characteristics, which differ markedly from organisms like humans. This review examines current IBD use cases, identifies limitations of IBD-based methods, and explores promising future directions to enhance malaria genomic surveillance.

Protective targets of PfSPZ vaccines identified from whole-genome sieve analysis of isolates from malaria vaccine efficacy trials in West Africa

Identification of antigens targeted by a protective response is a central quest in malaria vaccinology. Whole-genome sieve analysis (SAWG) in samples collected from placebo-controlled field trials of Plasmodium falciparum (Pf) sporozoite (SPZ) vaccines may enable identification of Pf pre-erythrocytic antigens. We applied SAWG to genomic data generated from Pf isolates collected during two field trials measuring the efficacy, in malaria-exposed African adults, of two PfSPZ vaccines. These randomized, double-blind, placebo-controlled trials were conducted in regions of Mali and Burkina Faso characterized by high seasonal transmission, where parasite genetic diversity is high. Genomic sites in which the vaccine allelic state was significantly underrepresented among breakthrough infections in vaccinees relative to placebo recipients were termed "target sites". Protein-coding loci containing target sites that changed amino acids were termed "target loci". The SAWG conducted on clinical trial samples from the Burkina Faso and Mali trials identified 138 and 80 single-copy protein-coding target loci in the Burkinabe and Malian data sets, respectively, with twelve common to both, a number significantly higher than expected (E = 3.9; 99%CI = [0, 9]). Among these was the thrombospondin-related anonymous protein locus, which encodes PfSSP2|TRAP, one of the most abundant and well-characterized pre-erythrocytic stage antigen as well as other genes encoding membrane-associated proteins of unknown function. These results identify SAWG as a potentially powerful tool for identifying protective vaccine antigens in recombining pathogens with large genome size and reveals potential new protective Pf antigens.

Intrinsic multiplication rate variation of Plasmodium falciparum in clinical isolates prior to elimination in Malaysia

Replication rates and virulence of pathogens are hypothesised to evolve in response to varying intensity of transmission and competition among genotypes. Under exponential growth conditions in culture, clinical isolates of the malaria parasite Plasmodium falciparum have variable intrinsic multiplication rates, but comparisons of samples from different areas are needed. To analyse parasites from an area of low endemicity, Malaysian clinical isolates cryopreserved prior to malaria elimination were studied. The mean and range of P. falciparum multiplication rates in Malaysian isolates were no less than that seen among isolates from more highly endemic populations in Africa, which does not support a hypothesis of adaptation to prevailing levels of infection endemicity. Moreover, the distribution of multiplication rates was similar between isolates with single parasite genotypes and those containing multiple genotypes, which does not support a hypothesis of facultative adjustment to competing parasites. Based solely on clinical isolates, the findings indicate that parasites may not evolve lower multiplication rates under conditions of reduced transmission, and that the virulence potential is likely to be undiminished in pre-elimination settings. This encourages efforts to eliminate endemic infection completely, as has been achieved at the national level in Malaysia.

Genomic epidemiology of Plasmodium knowlesi reveals putative genetic drivers of adaptation in Malaysia

Sabah, Malaysia, has amongst the highest burden of human Plasmodium knowlesi infection in the world, associated with increasing encroachment on the parasite's macaque host habitat. However, the genomic make-up of P. knowlesi in Sabah was previously poorly understood. To inform on local patterns of transmission and putative adaptive drivers, we conduct population-level genetic analyses of P. knowlesi human infections using 52 new whole genomes from Sabah, Malaysia, in combination with publicly available data. We identify the emergence of distinct geographical subpopulations within the macaque-associated clusters using identity-by-descent-based connectivity analysis. Secondly, we report on introgression events between the clusters, which may be linked to differentiation of the subpopulations, and that overlap genes critical for survival in human and mosquito hosts. Using village-level locations from P. knowlesi infections, we also identify associations between several introgressed regions and both intact forest perimeter-area ratio and mosquito vector habitat suitability. Our findings provide further evidence of the complex role of changing ecosystems and sympatric macaque hosts in Malaysia driving distinct genetic changes seen in P. knowlesi populations. Future expanded analyses of evolving P. knowlesi genetics and environmental drivers of transmission will be important to guide public health surveillance and control strategies.

Review of MrsFreqPhase methods: methods designed to estimate statistically malaria parasite multiplicity of infection, relatedness, frequency and phase

Malaria parasites are haploid within humans, but infections often contain genetically distinct groups of clonal parasites. When the per-infection number of genetically distinct clones (i.e., the multiplicity of infection, MOI) exceeds one, and per-infection genetic data are generated in bulk, important information are obfuscated. For example, the MOI, the phases of the haploid genotypes of genetically distinct clones (i.e., how the alleles concatenate into sequences), and their frequencies. This complicates many downstream analyses, including relatedness estimation. MOIs, parasite sequences, their frequencies, and degrees of relatedness are used ubiquitously in malaria studies: for example, to monitor anti-malarial drug resistance and to track changes in transmission. In this article, MrsFreqPhase methods designed to estimate statistically malaria parasite MOI, relatedness, frequency and phase are reviewed. An overview, a historical account of the literature, and a statistical description of contemporary software is provided for each method class. The article ends with a look towards future method development, needed to make best use of new data types generated by cutting-edge malaria studies reliant on MrsFreqPhase methods.

MOIRE: a software package for the estimation of allele frequencies and effective multiplicity of infection from polyallelic data

MOTIVATION

Malaria parasite genetic data can provide insight into parasite phenotypes, evolution, and transmission. However, estimating key parameters such as allele frequencies, multiplicity of infection (MOI), and within-host relatedness from genetic data is challenging, particularly in the presence of multiple related coinfecting strains. Existing methods often rely on single nucleotide polymorphism (SNP) data and do not account for within-host relatedness.

RESULTS

We present Multiplicity Of Infection and allele frequency REcovery (MOIRE), a Bayesian approach to estimate allele frequencies, MOI, and within-host relatedness from genetic data subject to experimental error. MOIRE accommodates both polyallelic and SNP data, making it applicable to diverse genotyping panels. We also introduce a novel metric, the effective MOI (eMOI), which integrates MOI and within-host relatedness, providing a robust and interpretable measure of genetic diversity. Extensive simulations and real-world data from a malaria study in Namibia demonstrate the superior performance of MOIRE over naive estimation methods, accurately estimating MOI up to seven with moderate-sized panels of diverse loci (e.g. microhaplotypes). MOIRE also revealed substantial heterogeneity in population mean MOI and mean relatedness across health districts in Namibia, suggesting detectable differences in transmission dynamics. Notably, eMOI emerges as a portable metric of within-host diversity, facilitating meaningful comparisons across settings when allele frequencies or genotyping panels differ. Compared to existing software, MOIRE enables more comprehensive insights into within-host diversity and population structure.

AVAILABILITY AND IMPLEMENTATION

MOIRE is available as an R package at https://eppicenter.github.io/moire/.

MOIRE: A software package for the estimation of allele frequencies and effective multiplicity of infection from polyallelic data

Malaria parasite genetic data can provide insight into parasite phenotypes, evolution, and transmission. However, estimating key parameters such as allele frequencies, multiplicity of infection (MOI), and within-host relatedness from genetic data has been challenging, particularly in the presence of multiple related coinfecting strains. Existing methods often rely on single nucleotide polymorphism (SNP) data and do not account for within-host relatedness. In this study, we introduce a Bayesian approach called MOIRE (Multiplicity Of Infection and allele frequency REcovery), designed to estimate allele frequencies, MOI, and within-host relatedness from genetic data subject to experimental error. Importantly, MOIRE is flexible in accommodating both polyallelic and SNP data, making it adaptable to diverse genotyping panels. We also introduce a novel metric, the effective MOI (eMOI), which integrates MOI and within-host relatedness, providing a robust and interpretable measure of genetic diversity. Using extensive simulations and real-world data from a malaria study in Namibia, we demonstrate the superior performance of MOIRE over naive estimation methods, accurately estimating MOI up to 7 with moderate sized panels of diverse loci (e.g. microhaplotypes). MOIRE also revealed substantial heterogeneity in population mean MOI and mean relatedness across health districts in Namibia, suggesting detectable differences in transmission dynamics. Notably, eMOI emerges as a portable metric of within-host diversity, facilitating meaningful comparisons across settings, even when allele frequencies or genotyping panels are different. MOIRE represents an important addition to the analysis toolkit for malaria population dynamics. Compared to existing software, MOIRE enhances the accuracy of parameter estimation and enables more comprehensive insights into within-host diversity and population structure. Additionally, MOIRE's adaptability to diverse data sources and potential for future improvements make it a valuable asset for research on malaria and other organisms, such as other eukaryotic pathogens. MOIRE is available as an R package at https://eppicenter.github.io/moire/.

Modelling transmission dynamics and genomic diversity in a recombining parasite population

The genomic diversity of a parasite population is shaped by its transmission dynamics but superinfection, cotranmission and recombination make this relationship complex and hard to analyse. This paper aims to simplify the problem by introducing the concept of a genomic transmission graph with three basic parameters: the effective number of hosts, the quantum of transmission and the crossing rate of transmission chains. This enables rapid simulation of coalescence times in a recombining parasite population with superinfection and cotransmission, and it also provides a mathematical framework for analysis of within-host variation. Taking malaria as an example, we use this theoretical model to examine how transmission dynamics and migration affect parasite genomic diversity, including the effective recombination rate and haplotypic metrics of recent common ancestry. We show how key transmission parameters can be inferred from deep sequencing data and as a proof of concept we estimate the Plasmodium falciparum transmission bottleneck. Finally we discuss the potential applications of this novel inferential framework in genomic surveillance for malaria control and elimination. Online tools for exploring the genomic transmission graph are available at d-kwiat.github.io/gtg.

Phylogenomic reconstruction of Cryptosporidium spp. captured directly from clinical samples reveals extensive genetic diversity

Cryptosporidium is a leading cause of severe diarrhea and mortality in young children and infants in Africa and southern Asia. More than twenty Cryptosporidium species infect humans, of which C. parvum and C. hominis are the major agents causing moderate to severe diarrhea. Relatively few genetic markers are typically applied to genotype and/or diagnose Cryptosporidium. Most infections produce limited oocysts making it difficult to perform whole genome sequencing (WGS) directly from stool samples. Hence, there is an immediate need to apply WGS strategies to 1) develop high-resolution genetic markers to genotype these parasites more precisely, 2) to investigate endemic regions and detect the prevalence of different genotypes, and the role of mixed infections in generating genetic diversity, and 3) to investigate zoonotic transmission and evolution. To understand Cryptosporidium global population genetic structure, we applied Capture Enrichment Sequencing (CES-Seq) using 74,973 RNA-based 120 nucleotide baits that cover ~92% of the genome of C. parvum. CES-Seq is sensitive and successfully sequenced Cryptosporidium genomic DNA diluted up to 0.005% in human stool DNA. It also resolved mixed strain infections and captured new species of Cryptosporidium directly from clinical/field samples to promote genome-wide phylogenomic analyses and prospective GWAS studies.

Genomics reveals heterogeneous Plasmodium falciparum transmission and selection signals in Zambia

BACKGROUND

Genomic surveillance is crucial for monitoring malaria transmission and understanding parasite adaptation to interventions. Zambia lacks prior nationwide efforts in malaria genomic surveillance among African countries.

METHODS

We conducted genomic surveillance of Plasmodium falciparum parasites from the 2018 Malaria Indicator Survey in Zambia, a nationally representative household survey of children under five years of age. We whole-genome sequenced and analyzed 241 P. falciparum genomes from regions with varying levels of malaria transmission across Zambia and estimated genetic metrics that are informative about transmission intensity, genetic relatedness between parasites, and selection.

RESULTS

We provide genomic evidence of widespread within-host polygenomic infections, regardless of epidemiological characteristics, underscoring the extensive and ongoing endemic malaria transmission in Zambia. Our analysis reveals country-level clustering of parasites from Zambia and neighboring regions, with distinct separation in West Africa. Within Zambia, identity by descent (IBD) relatedness analysis uncovers local spatial clustering and rare cases of long-distance sharing of closely related parasite pairs. Genomic regions with large shared IBD segments and strong positive selection signatures implicate genes involved in sulfadoxine-pyrimethamine and artemisinin combination therapies drug resistance, but no signature related to chloroquine resistance. Furthermore, differences in selection signatures, including drug resistance loci, are observed between eastern and western Zambian parasite populations, suggesting variable transmission intensity and ongoing drug pressure.

CONCLUSIONS

Our findings enhance our understanding of nationwide P. falciparum transmission in Zambia, establishing a baseline for analyzing parasite genetic metrics as they vary over time and space. These insights highlight the urgency of strengthening malaria control programs and surveillance of antimalarial drug resistance.

Strong positive selection biases identity-by-descent-based inferences of recent demography and population structure in Plasmodium falciparum

Malaria genomic surveillance often estimates parasite genetic relatedness using metrics such as Identity-By-Decent (IBD), yet strong positive selection stemming from antimalarial drug resistance or other interventions may bias IBD-based estimates. In this study, we use simulations, a true IBD inference algorithm, and empirical data sets from different malaria transmission settings to investigate the extent of this bias and explore potential correction strategies. We analyze whole genome sequence data generated from 640 new and 3089 publicly available Plasmodium falciparum clinical isolates. We demonstrate that positive selection distorts IBD distributions, leading to underestimated effective population size and blurred population structure. Additionally, we discover that the removal of IBD peak regions partially restores the accuracy of IBD-based inferences, with this effect contingent on the population's background genetic relatedness and extent of inbreeding. Consequently, we advocate for selection correction for parasite populations undergoing strong, recent positive selection, particularly in high malaria transmission settings.

Role for gene conversion in the evolution of cell-surface antigens of the malaria parasite Plasmodium falciparum

While the malaria parasite Plasmodium falciparum has low average genome-wide diversity levels, likely due to its recent introduction from a gorilla-infecting ancestor (approximately 10,000 to 50,000 years ago), some genes display extremely high diversity levels. In particular, certain proteins expressed on the surface of human red blood cell-infecting merozoites (merozoite surface proteins (MSPs)) possess exactly 2 deeply diverged lineages that have seemingly not recombined. While of considerable interest, the evolutionary origin of this phenomenon remains unknown. In this study, we analysed the genetic diversity of 2 of the most variable MSPs, DBLMSP and DBLMSP2, which are paralogs (descended from an ancestral duplication). Despite thousands of available Illumina WGS datasets from malaria-endemic countries, diversity in these genes has been hard to characterise as reads containing highly diverged alleles completely fail to align to the reference genome. To solve this, we developed a pipeline leveraging genome graphs, enabling us to genotype them at high accuracy and completeness. Using our newly- resolved sequences, we found that both genes exhibit 2 deeply diverged lineages in a specific protein domain (DBL) and that one of the 2 lineages is shared across the genes. We identified clear evidence of nonallelic gene conversion between the 2 genes as the likely mechanism behind sharing, leading us to propose that gene conversion between diverged paralogs, and not recombination suppression, can generate this surprising genealogy; a model that is furthermore consistent with high diversity levels in these 2 genes despite the strong historical P. falciparum transmission bottleneck.

Genomics reveals heterogeneous Plasmodium falciparum transmission and population differentiation in Zambia and bordering countries

Genomic surveillance plays a critical role in monitoring malaria transmission and understanding how the parasite adapts in response to interventions. We conducted genomic surveillance of malaria by sequencing 241 Plasmodium falciparum genomes from regions with varying levels of malaria transmission across Zambia. We found genomic evidence of high levels of within-host polygenomic infections, regardless of epidemiological characteristics, underscoring the extensive and ongoing endemic malaria transmission in the country. We identified country-level clustering of parasites from Zambia and neighboring countries, and distinct clustering of parasites from West Africa. Within Zambia, our identity by descent (IBD) relatedness analysis uncovered spatial clustering of closely related parasite pairs at the local level and rare cases of long-distance sharing. Genomic regions with large shared IBD segments and strong positive selection signatures identified genes involved in sulfadoxine-pyrimethamine and artemisinin combination therapies drug resistance, but no signature related to chloroquine resistance. Together, our findings enhance our understanding of P. falciparum transmission nationwide in Zambia and highlight the urgency of strengthening malaria control programs and surveillance of antimalarial drug resistance.

Genomic analysis of Plasmodium vivax describes patterns of connectivity and putative drivers of adaptation in Ethiopia

Ethiopia has the greatest burden of Plasmodium vivax in Africa, but little is known about the epidemiological landscape of parasites across the country. We analysed the genomic diversity of 137 P. vivax isolates collected nine Ethiopian districts from 2012 to 2016. Signatures of selection were detected by cross-country comparisons with isolates from Thailand (n = 104) and Indonesia (n = 111), representing regions with low and high chloroquine resistance respectively. 26% (35/137) of Ethiopian infections were polyclonal, and 48.5% (17/35) of these comprised highly related clones (within-host identity-by-descent > 25%), indicating frequent co-transmission and superinfection. Parasite gene flow between districts could not be explained entirely by geographic distance, with economic and cultural factors hypothesised to have an impact on connectivity. Amplification of the duffy binding protein gene (pvdbp1) was prevalent across all districts (16-75%). Cross-population haplotype homozygosity revealed positive selection in a region proximal to the putative chloroquine resistance transporter gene (pvcrt-o). An S25P variant in amino acid transporter 1 (pvaat1), whose homologue has recently been implicated in P. falciparum chloroquine resistance evolution, was prevalent in Ethiopia (96%) but not Thailand or Indonesia (35-53%). The genomic architecture in Ethiopia highlights circulating variants of potential public health concern in an endemic setting with evidence of stable transmission.

Genomics of Plasmodium vivax in Colombia reveals evidence of local bottle-necking and inter-country connectivity in the Americas

Colombia aims to eliminate malaria by 2030 but remains one of the highest burden countries in the Americas. Plasmodium vivax contributes half of all malaria cases, with its control challenged by relapsing parasitaemia, drug resistance and cross-border spread. Using 64 Colombian P. vivax genomes collected between 2013 and 2017, we explored diversity and selection in two major foci of transmission: Chocó and Córdoba. Open-access data from other countries were used for comparative assessment of drug resistance candidates and to assess cross-border spread. Across Colombia, polyclonal infections were infrequent (12%), and infection connectivity was relatively high (median IBD = 5%), consistent with low endemicity. Chocó exhibited a higher frequency of polyclonal infections (23%) than Córdoba (7%), although the difference was not significant (P = 0.300). Most Colombian infections carried double pvdhfr (95%) and single pvdhps (71%) mutants, but other drug resistance mutations were less prevalent (< 10%). There was no evidence of selection at the pvaat1 gene, whose P. falciparum orthologue has recently been implicated in chloroquine resistance. Global population comparisons identified other putative adaptations. Within the Americas, low-level connectivity was observed between Colombia and Peru, highlighting potential for cross-border spread. Our findings demonstrate the potential of molecular data to inform on infection spread and adaptation.

Strong Positive Selection Biases Identity-By-Descent-Based Inferences of Recent Demography and Population Structure in Plasmodium falciparum

Malaria genomic surveillance often estimates parasite genetic relatedness using metrics such as Identity-By-Decent (IBD). Yet, strong positive selection stemming from antimalarial drug resistance or other interventions may bias IBD-based estimates. In this study, we utilized simulations, a true IBD inference algorithm, and empirical datasets from different malaria transmission settings to investigate the extent of such bias and explore potential correction strategies. We analyzed whole genome sequence data generated from 640 new and 4,026 publicly available Plasmodium falciparum clinical isolates. Our findings demonstrated that positive selection distorts IBD distributions, leading to underestimated effective population size and blurred population structure. Additionally, we discovered that the removal of IBD peak regions partially restored the accuracy of IBD-based inferences, with this effect contingent on the population's background genetic relatedness. Consequently, we advocate for selection correction for parasite populations undergoing strong, recent positive selection, particularly in high malaria transmission settings.

Performance of SNP barcodes to determine genetic diversity and population structure of Plasmodium falciparum in Africa

Panels of informative biallelic single nucleotide polymorphisms (SNPs) have been proposed to be an economical method to fast-track the population genetic analysis of Plasmodium falciparum in malaria-endemic areas. Whilst used successfully in low-transmission areas where infections are monoclonal and highly related, we present the first study to evaluate the performance of these 24- and 96-SNP molecular barcodes in African countries, characterised by moderate-to-high transmission, where multiclonal infections are prevalent. For SNP barcodes it is generally recommended that the SNPs chosen i) are biallelic, ii) have a minor allele frequency greater than 0.10, and iii) are independently segregating, to minimise bias in the analysis of genetic diversity and population structure. Further, to be standardised and used in many population genetic studies, these barcodes should maintain characteristics i) to iii) across various iv) geographies and v) time points. Using haplotypes generated from the MalariaGEN P. falciparum Community Project version six database, we investigated the ability of these two barcodes to fulfil these criteria in moderate-to-high transmission African populations in 25 sites across 10 countries. Predominantly clinical infections were analysed, with 52.3% found to be multiclonal, generating high proportions of mixed-allele calls (MACs) per isolate thereby impeding haplotype construction. Of the 24- and 96-SNPs, loci were removed if they were not biallelic and had low minor allele frequencies in all study populations, resulting in 20- and 75-SNP barcodes respectively for downstream population genetics analysis. Both SNP barcodes had low expected heterozygosity estimates in these African settings and consequently biased analyses of similarity. Both minor and major allele frequencies were temporally unstable. These SNP barcodes were also shown to identify weak genetic differentiation across large geographic distances based on Mantel Test and DAPC. These results demonstrate that these SNP barcodes are vulnerable to ascertainment bias and as such cannot be used as a standardised approach for malaria surveillance in moderate-to-high transmission areas in Africa, where the greatest genomic diversity of P. falciparum exists at local, regional and country levels.

coiaf: Directly estimating complexity of infection with allele frequencies

In malaria, individuals are often infected with different parasite strains. The complexity of infection (COI) is defined as the number of genetically distinct parasite strains in an individual. Changes in the mean COI in a population have been shown to be informative of changes in transmission intensity with a number of probabilistic likelihood and Bayesian models now developed to estimate the COI. However, rapid, direct measures based on heterozygosity or FwS do not properly represent the COI. In this work, we present two new methods that use easily calculated measures to directly estimate the COI from allele frequency data. Using a simulation framework, we show that our methods are computationally efficient and comparably accurate to current approaches in the literature. Through a sensitivity analysis, we characterize how the distribution of parasite densities, the assumed sequencing depth, and the number of sampled loci impact the bias and accuracy of our two methods. Using our developed methods, we further estimate the COI globally from Plasmodium falciparum sequencing data and compare the results against the literature. We show significant differences in the estimated COI globally between continents and a weak relationship between malaria prevalence and COI.

Comparison of molecular surveillance methods to assess changes in the population genetics of Plasmodium falciparum in high transmission

A major motivation for developing molecular methods for malaria surveillance is to measure the impact of control interventions on the population genetics of Plasmodium falciparum as a potential marker of progress towards elimination. Here we assess three established methods (i) single nucleotide polymorphism (SNP) barcoding (panel of 24-biallelic loci), (ii) microsatellite genotyping (panel of 12-multiallelic loci), and (iii) varcoding (fingerprinting var gene diversity, akin to microhaplotyping) to identify changes in parasite population genetics in response to a short-term indoor residual spraying (IRS) intervention. Typical of high seasonal transmission in Africa, multiclonal infections were found in 82.3% (median 3; range 1-18) and 57.8% (median 2; range 1-12) of asymptomatic individuals pre- and post-IRS, respectively, in Bongo District, Ghana. Since directly phasing multilocus haplotypes for population genetic analysis is not possible for biallelic SNPs and microsatellites, we chose ~200 low-complexity infections biased to single and double clone infections for analysis. Each genotyping method presented a different pattern of change in diversity and population structure as a consequence of variability in usable data and the relative polymorphism of the molecular markers (i.e., SNPs < microsatellites < var). Varcoding and microsatellite genotyping showed the overall failure of the IRS intervention to significantly change the population structure from pre-IRS characteristics (i.e., many diverse genomes of low genetic similarity). The 24-SNP barcode provided limited information for analysis, largely due to the biallelic nature of SNPs leading to a high proportion of double-allele calls and a view of more isolate relatedness compared to microsatellites and varcoding. Relative performance, suitability, and cost-effectiveness of the methods relevant to sample size and local malaria elimination in high-transmission endemic areas are discussed.

Measurably recombining malaria parasites

Genomic epidemiology has guided research and policy for various viral pathogens and there has been a parallel effort towards using genomic epidemiology to combat diseases that are caused by eukaryotic pathogens, such as the malaria parasite. However, the central concept of viral genomic epidemiology, namely that of measurably mutating pathogens, does not apply easily to sexually recombining parasites. Here we introduce the related but different concept of measurably recombining malaria parasites to promote convergence around a unifying theoretical framework for malaria genomic epidemiology. Akin to viral phylodynamics, we anticipate that an inferential framework developed around recombination will help guide practical research and thus realize the full public health potential of genomic epidemiology for malaria parasites and other sexually recombining pathogens.

Evolutionary genetics of malaria

Many standard-textbook population-genetic results apply to a wide range of species. Sometimes, however, population-genetic models and principles need to be tailored to a particular species. This is particularly true for malaria, which next to tuberculosis and HIV/AIDS ranks among the economically most relevant infectious diseases. Importantly, malaria is not one disease-five human-pathogenic species of Plasmodium exist. P. falciparum is not only the most severe form of human malaria, but it also causes the majority of infections. The second most relevant species, P. vivax, is already considered a neglected disease in several endemic areas. All human-pathogenic species have distinct characteristics that are not only crucial for control and eradication efforts, but also for the population-genetics of the disease. This is particularly true in the context of selection. Namely, fitness is determined by so-called fitness components, which are determined by the parasites live-history, which differs between malaria species. The presence of hypnozoites, i.e., dormant liver-stage parasites, which can cause disease relapses, is a distinct feature of P. vivax and P. ovale sp. In P. malariae inactivated blood-stage parasites can cause a recrudescence years after the infection was clinically cured. To properly describe population-genetic processes, such as the spread of anti-malarial drug resistance, these features must be accounted for appropriately. Here, we introduce and extend a population-genetic framework for the evolutionary dynamics of malaria, which applies to all human-pathogenic malaria species. The model focuses on, but is not limited to, the spread of drug resistance. The framework elucidates how the presence of dormant liver stage or inactivated blood stage parasites that act like seed banks delay evolutionary processes. It is shown that, contrary to standard population-genetic theory, the process of selection and recombination cannot be decoupled in malaria. Furthermore, we discuss the connection between haplotype frequencies, haplotype prevalence, transmission dynamics, and relapses or recrudescence in malaria.

A maximum-likelihood method to estimate haplotype frequencies and prevalence alongside multiplicity of infection from SNP data

The introduction of genomic methods facilitated standardized molecular disease surveillance. For instance, SNP barcodes in Plasmodium vivax and Plasmodium falciparum malaria allows the characterization of haplotypes, their frequencies and prevalence to reveal temporal and spatial transmission patterns. A confounding factor is the presence of multiple genetically distinct pathogen variants within the same infection, known as multiplicity of infection (MOI). Disregarding ambiguous information, as usually done in ad-hoc approaches, leads to less confident and biased estimates. We introduce a statistical framework to obtain maximum-likelihood estimates (MLE) of haplotype frequencies and prevalence alongside MOI from malaria SNP data, i.e., multiple biallelic marker loci. The number of model parameters increases geometrically with the number of genetic markers considered and no closed-form solution exists for the MLE. Therefore, the MLE needs to be derived numerically. We use the Expectation-Maximization (EM) algorithm to derive the maximum-likelihood estimates, an efficient and easy-to-implement algorithm that yields a numerically stable solution. We also derive expressions for haplotype prevalence based on either all or just the unambiguous genetic information and compare both approaches. The latter corresponds to a biased ad-hoc estimate of prevalence. We assess the performance of our estimator by systematic numerical simulations assuming realistic sample sizes and various scenarios of transmission intensity. For reasonable sample sizes, and number of loci, the method has little bias. As an example, we apply the method to a dataset from Cameroon on sulfadoxine-pyrimethamine resistance in P. falciparum malaria. The method is not confined to malaria and can be applied to any infectious disease with similar transmission behavior. An easy-to-use implementation of the method as an R-script is provided.

R H: a genetic metric for measuring intrahost Plasmodium falciparum relatedness and distinguishing cotransmission from superinfection

Multiple-strain (polygenomic) infections are a ubiquitous feature of Plasmodium falciparum parasite population genetics. Under simple assumptions of superinfection, polygenomic infections are hypothesized to be the result of multiple infectious bites. As a result, polygenomic infections have been used as evidence of repeat exposure and used to derive genetic metrics associated with high transmission intensity. However, not all polygenomic infections are the result of multiple infectious bites. Some result from the transmission of multiple, genetically related strains during a single infectious bite (cotransmission). Superinfection and cotransmission represent two distinct transmission processes, and distinguishing between the two could improve inferences regarding parasite transmission intensity. Here, we describe a new metric, R H, that utilizes the correlation in allelic state (heterozygosity) within polygenomic infections to estimate the likelihood that the observed complexity resulted from either superinfection or cotransmission. R H is flexible and can be applied to any type of genetic data. As a proof of concept, we used R H to quantify polygenomic relatedness and estimate cotransmission and superinfection rates from a set of 1,758 malaria infections genotyped with a 24 single nucleotide polymorphism (SNP) molecular barcode. Contrary to expectation, we found that cotransmission was responsible for a significant fraction of 43% to 53% of the polygenomic infections collected in three distinct epidemiological regions in Senegal. The prediction that polygenomic infections frequently result from cotransmission stresses the need to incorporate estimates of relatedness within polygenomic infections to ensure the accuracy of genomic epidemiology surveillance data for informing public health activities.

Dcifer: an IBD-based method to calculate genetic distance between polyclonal infections

An essential step toward reconstructing pathogen transmission and answering epidemiologically relevant questions from genomic data is obtaining pairwise genetic distance between infections. For recombining organisms such as malaria parasites, relatedness measures quantifying recent shared ancestry would provide a meaningful distance, suggesting methods based on identity by descent (IBD). While the concept of relatedness and consequently an IBD approach is fairly straightforward for individual parasites, the distance between polyclonal infections, which are prevalent in malaria, presents specific challenges, and awaits a general solution that could be applied to infections of any clonality and accommodate multiallelic (e.g. microsatellite or microhaplotype) and biallelic [single nucleotide polymorphism (SNP)] data. Filling this methodological gap, we present Dcifer (Distance for complex infections: fast estimation of relatedness), a method for calculating genetic distance between polyclonal infections, which is designed for unphased data, explicitly accounts for population allele frequencies and complexity of infection, and provides reliable inference. Dcifer’s IBD-based framework allows us to define model parameters that represent interhost relatedness and to propose corresponding estimators with attractive statistical properties. By using combinatorics to account for unobserved phased haplotypes, Dcifer is able to quickly process large datasets and estimate pairwise relatedness along with measures of uncertainty. We show that Dcifer delivers accurate and interpretable results and detects related infections with statistical power that is 2–4 times greater than that of approaches based on identity by state. Applications to real data indicate that relatedness structure aligns with geographic locations. Dcifer is implemented in a comprehensive publicly available software package.

A GPU-accelerated compute framework for pathogen genomic variant identification to aid genomic epidemiology of infectious disease: a malaria case study

As recently demonstrated by the COVID-19 pandemic, large-scale pathogen genomic data are crucial to characterize transmission patterns of human infectious diseases. Yet, current methods to process raw sequence data into analysis-ready variants remain slow to scale, hampering rapid surveillance efforts and epidemiological investigations for disease control. Here, we introduce an accelerated, scalable, reproducible, and cost-effective framework for pathogen genomic variant identification and present an evaluation of its performance and accuracy across benchmark datasets of Plasmodium falciparum malaria genomes. We demonstrate superior performance of the GPU framework relative to standard pipelines with mean execution time and computational costs reduced by 27× and 4.6×, respectively, while delivering 99.9% accuracy at enhanced reproducibility.

Design and implementation of multiplexed amplicon sequencing panels to serve genomic epidemiology of infectious disease: A malaria case study

Multiplexed PCR amplicon sequencing (AmpSeq) is an increasingly popular application for cost-effective monitoring of threatened species and managed wildlife populations, and shows strong potential for the genomic epidemiology of infectious disease. AmpSeq data from infectious microbes can inform disease control in multiple ways, such as by measuring drug resistance marker prevalence, distinguishing imported from local cases, and determining the effectiveness of therapeutics. We describe the design and comparative evaluation of two new AmpSeq assays for Plasmodium falciparum malaria parasites: a four-locus panel ("4CAST") composed of highly diverse antigens, and a 129-locus panel ("AMPLseq") composed of drug resistance markers, highly diverse loci for inferring relatedness, and a locus to detect Plasmodium vivax co-infection. We explore the performance of each panel in various public health use cases with in silico simulations as well as empirical experiments. The 4CAST panel appears highly suitable for evaluating the number of distinct parasite strains within samples (complexity of infection), showing strong performance across a wide range of parasitaemia levels without a DNA pre-amplification step. For relatedness inference, the larger AMPLseq panel performs similarly to two existing panels of comparable size, despite differences in the data and approach used for designing each panel. Finally, we describe an R package (paneljudge) that facilitates the design and comparative evaluation of genetic panels for relatedness estimation, and we provide general guidance on the design and implementation of AmpSeq panels for the genomic epidemiology of infectious disease.

Molecular Surveillance of Artemisinin-Based Combination Therapies Resistance in Plasmodium falciparum Parasites from Bioko Island, Equatorial Guinea

Artemisinin-based combination therapies (ACTs) resistance has emerged and could be diffusing in Africa. As an offshore island on the African continent, the island of Bioko in Equatorial Guinea is considered severely affected and resistant to drug-resistant Plasmodium falciparum malaria. However, the spatial and temporal distribution remain unclear. Molecular monitoring targeting the Pfcrt, Pfk13, Pfpm2, and Pfmdr1 genes was conducted to provide insight into the impact of current antimalarial drug resistance on the island. Furthermore, polymorphic characteristics, haplotype network, and the effect of natural selection of the Pfk13 gene were evaluated. A total of 152 Plasmodium falciparum samples (collected from 2017 to 2019) were analyzed for copy number variation of the Pfpm2 gene and Pfk13, Pfcrt, and Pfmdr1 mutations. Statistical analysis of Pfk13 sequences was performed following different evolutionary models using 96 Bioko sequences and 1322 global sequences. The results showed that the prevalence of Pfk13, Pfcrt, and Pfmdr1 mutations was 73.68%, 78.29%, and 75.66%, respectively. Large proportions of isolates with multiple copies of Pfpm2 were observed (67.86%). In Bioko parasites, the genetic diversity of Pfk13 was low, and purifying selection was suggested by Tajima's D test (-1.644, P > 0.05) and the dN/dS test (-0.0004438, P > 0.05). The extended haplotype homozygosity analysis revealed that Pfk13_K189T, although most frequent in Africa, has not yet conferred a selective advantage for parasitic survival. The results suggested that the implementation of continuous drug monitoring on Bioko Island is an essential measure. IMPORTANCE Malaria, one of the tropical parasitic diseases with a high transmission rate in Bioko Island, Equatorial Guinea, especially caused by P. falciparum is highly prevalent in this region and is commonly treated locally with ACTs. The declining antimalarial susceptibility of artemisinin-based drugs suggested that resistance to artemisinin and its derivatives is developing in P. falciparum. Copy number variants in Pfpm2 and genetic polymorphisms in Pfk13, Pfcrt, and Pfmdr1 can be used as risk assessment indicators to track the development and spread of drug resistance. This study reported for the first time the molecular surveillance of Pfpm2, Pfcrt, Pfk13, and Pfmdr1 genes in Bioko Island from 2017 to 2019 to assess the possible risk of local drug-resistant P. falciparum.

Genetic surveillance for monitoring the impact of drug use on Plasmodium falciparum populations

The use of antimalarial drugs is an effective strategy in the fight against malaria. However, selection of drug resistant parasites is a constant threat to the continued use of this approach. Antimalarial drugs are used not only to treat infections but also as part of population-level strategies to reduce malaria transmission toward elimination. While there is strong evidence that the ongoing use of antimalarial drugs increases the risk of the emergence and spread of drug-resistant parasites, it is less clear how population-level use of drug-based interventions like seasonal malaria chemoprevention (SMC) or mass drug administration (MDA) may contribute to drug resistance or loss of drug efficacy. Critical to sustained use of drug-based strategies for reducing the burden of malaria is the surveillance of population-level signals related to transmission reduction and resistance selection. Here we focus on Plasmodium falciparum and discuss the genetic signatures of a parasite population that are correlated with changes in transmission and related to drug pressure and resistance as a result of drug use. We review the evidence for MDA and SMC contributing to malaria burden reduction and drug resistance selection and examine the use and impact of these interventions in Senegal. Throughout we consider best strategies for ongoing surveillance of both population and resistance signals in the context of different parasite population parameters. Finally, we propose a roadmap for ongoing surveillance during population-level drug-based interventions to reduce the global malaria burden.

Single-genome sequencing reveals within-host evolution of human malaria parasites

Population genomics of bulk malaria infections is unable to examine intrahost evolution; therefore, most work has focused on the role of recombination in generating genetic variation. We used single-cell sequencing protocol for low-parasitaemia infections to generate 406 near-complete single Plasmodium vivax genomes from 11 patients sampled during sequential febrile episodes. Parasite genomes contain hundreds of de novo mutations, showing strong signatures of selection, which are enriched in the ApiAP2 family of transcription factors, known targets of adaptation. Comparing 315 P. falciparum single-cell genomes from 15 patients with our P. vivax data, we find broad complementary patterns of de novo mutation at the gene and pathway level, revealing the importance of within-host evolution during malaria infections.

Single-cell genome sequencing of protozoan parasites

Despite considerable genetic variation within hosts, most parasite genome sequencing studies focus on bulk samples composed of millions of cells. Analysis of bulk samples is biased toward the dominant genotype, concealing cell-to-cell variation and rare variants. To tackle this, single-cell sequencing approaches have been developed and tailored to specific host-parasite systems. These are allowing the genetic diversity and kinship in complex parasite populations to be deciphered and for de novo genetic variation to be captured. Here, we outline the methodologies being used for single-cell sequencing of parasitic protozoans, such as Plasmodium and Leishmania spp., and how these tools are being applied to understand parasite biology.

Elucidating relationships between P.falciparum prevalence and measures of genetic diversity with a combined genetic-epidemiological model of malaria

There is an abundance of malaria genetic data being collected from the field, yet using these data to understand the drivers of regional epidemiology remains a challenge. A key issue is the lack of models that relate parasite genetic diversity to epidemiological parameters. Classical models in population genetics characterize changes in genetic diversity in relation to demographic parameters, but fail to account for the unique features of the malaria life cycle. In contrast, epidemiological models, such as the Ross-Macdonald model, capture malaria transmission dynamics but do not consider genetics. Here, we have developed an integrated model encompassing both parasite evolution and regional epidemiology. We achieve this by combining the Ross-Macdonald model with an intra-host continuous-time Moran model, thus explicitly representing the evolution of individual parasite genomes in a traditional epidemiological framework. Implemented as a stochastic simulation, we use the model to explore relationships between measures of parasite genetic diversity and parasite prevalence, a widely-used metric of transmission intensity. First, we explore how varying parasite prevalence influences genetic diversity at equilibrium. We find that multiple genetic diversity statistics are correlated with prevalence, but the strength of the relationships depends on whether variation in prevalence is driven by host- or vector-related factors. Next, we assess the responsiveness of a variety of statistics to malaria control interventions, finding that those related to mixed infections respond quickly (∼months) whereas other statistics, such as nucleotide diversity, may take decades to respond. These findings provide insights into the opportunities and challenges associated with using genetic data to monitor malaria epidemiology.

An open dataset of Plasmodium falciparum genome variation in 7,000 worldwide samples

MalariaGEN is a data-sharing network that enables groups around the world to work together on the genomic epidemiology of malaria. Here we describe a new release of curated genome variation data on 7,000 Plasmodium falciparum samples from MalariaGEN partner studies in 28 malaria-endemic countries. High-quality genotype calls on 3 million single nucleotide polymorphisms (SNPs) and short indels were produced using a standardised analysis pipeline. Copy number variants associated with drug resistance and structural variants that cause failure of rapid diagnostic tests were also analysed.  Almost all samples showed genetic evidence of resistance to at least one antimalarial drug, and some samples from Southeast Asia carried markers of resistance to six commonly-used drugs. Genes expressed during the mosquito stage of the parasite life-cycle are prominent among loci that show strong geographic differentiation. By continuing to enlarge this open data resource we aim to facilitate research into the evolutionary processes affecting malaria control and to accelerate development of the surveillance toolkit required for malaria elimination.

An open dataset of Plasmodium falciparum genome variation in 7,000 worldwide samples

MalariaGEN is a data-sharing network that enables groups around the world to work together on the genomic epidemiology of malaria. Here we describe a new release of curated genome variation data on 7,000 Plasmodium falciparum samples from MalariaGEN partner studies in 28 malaria-endemic countries. High-quality genotype calls on 3 million single nucleotide polymorphisms (SNPs) and short indels were produced using a standardised analysis pipeline. Copy number variants associated with drug resistance and structural variants that cause failure of rapid diagnostic tests were also analysed.  Almost all samples showed genetic evidence of resistance to at least one antimalarial drug, and some samples from Southeast Asia carried markers of resistance to six commonly-used drugs. Genes expressed during the mosquito stage of the parasite life-cycle are prominent among loci that show strong geographic differentiation. By continuing to enlarge this open data resource we aim to facilitate research into the evolutionary processes affecting malaria control and to accelerate development of the surveillance toolkit required for malaria elimination.

Antimalarial Drug Resistance and Implications for the WHO Global Technical Strategy

PURPOSE OF REVIEW

Five years have passed since the World Health Organization released its Global Technical Strategy for Malaria (GTS). In that time, progress against malaria has plateaued. This review focuses on the implications of antimalarial drug resistance for the GTS and how interim progress in parasite genomics and antimalarial pharmacology offer a bulwark against it.

RECENT FINDINGS

For the first time, drug resistance-conferring genes have been identified and validated before their global expansion in malaria parasite populations. More efficient methods for their detection and elaboration have been developed, although low-density infections and polyclonality remain a nuisance to be solved. Clinical trials of alternative regimens for multidrug-resistant malaria have delivered promising results. New agents continue down the development pipeline, while a nascent infrastructure in sub-Saharan Africa for conducting phase I trials and trials of transmission-blocking agents has come to fruition after years of preparation.

SUMMARY

These and other developments can help inform the GTS as the world looks ahead to the next two decades of its implementation. To remain ahead of the threat that drug resistance poses, wider application of genomic-based surveillance and optimization of existing and forthcoming antimalarial drugs are essential.

Genome-Wide Scans for Ghanaian Plasmodium falciparum Genes Under Selection From Local and Chinese Host Populations

Initial malarial infection mostly causes symptomatic illness in humans. Infection that is not fatal induces complete protection from severe illness and death, and thus complete protection from severe illness or death is granted with sufficient exposure. However, malaria parasite immunity necessitates constant exposure. Therefore, it is important to evaluate lowered immunity and recurrent susceptibility to symptomatic disease in lower transmission areas. We aimed to investigate selection pressure based on transmission levels, antimalarial drug use, and environmental factors. We whole genome sequenced (WGS) P. falciparum clinical samples from Chinese hosts working in Ghana and compared the results with the WGS data of isolates from native Ghanaians downloaded from pf3k. The P. falciparum samples were generally clustered according to their geographic origin, and Chinese imported samples showed a clear African origin with a slightly different distribution from the native Ghanaian samples. Moreover, samples collected from two host populations showed evidence of differences in the intensity of selection. Compared with native Ghanaian samples, the China-imported isolates exhibited a higher proportion of monoclonal infections, and many genes associated with RBC invasion and immune evasion were found to be under less selection pressure. There was no significant difference in the selection of drug-resistance genes due to a similar artemisinin-based combination therapy medication profile. Local selection of malarial parasites is considered to be a result of differences in the host immunity or disparity in the transmission opportunities of the host. In China, most P. falciparum infections were imported from Africa, and under these circumstances, distinct local selective pressures may be caused by varying acquired immunity and transmission intensity. This study revealed the impact of host switching on the immune system, and it may provide a better understanding of the mechanisms that enable clinical immunity to malaria.

Evaluating the Performance of Malaria Genetics for Inferring Changes in Transmission Intensity Using Transmission Modeling

Substantial progress has been made globally to control malaria, however there is a growing need for innovative new tools to ensure continued progress. One approach is to harness genetic sequencing and accompanying methodological approaches as have been used in the control of other infectious diseases. However, to utilize these methodologies for malaria, we first need to extend the methods to capture the complex interactions between parasites, human and vector hosts, and environment, which all impact the level of genetic diversity and relatedness of malaria parasites. We develop an individual-based transmission model to simulate malaria parasite genetics parameterized using estimated relationships between complexity of infection and age from five regions in Uganda and Kenya. We predict that cotransmission and superinfection contribute equally to within-host parasite genetic diversity at 11.5% PCR prevalence, above which superinfections dominate. Finally, we characterize the predictive power of six metrics of parasite genetics for detecting changes in transmission intensity, before grouping them in an ensemble statistical model. The model predicted malaria prevalence with a mean absolute error of 0.055. Different assumptions about the availability of sample metadata were considered, with the most accurate predictions of malaria prevalence made when the clinical status and age of sampled individuals is known. Parasite genetics may provide a novel surveillance tool for estimating the prevalence of malaria in areas in which prevalence surveys are not feasible. However, the findings presented here reinforce the need for patient metadata to be recorded and made available within all future attempts to use parasite genetics for surveillance.

Type I Interferons and Malaria: A Double-Edge Sword Against a Complex Parasitic Disease

Type I interferons (IFN-Is) are important cytokines playing critical roles in various infections, autoimmune diseases, and cancer. Studies have also shown that IFN-Is exhibit 'conflicting' roles in malaria parasite infections. Malaria parasites have a complex life cycle with multiple developing stages in two hosts. Both the liver and blood stages of malaria parasites in a vertebrate host stimulate IFN-I responses. IFN-Is have been shown to inhibit liver and blood stage development, to suppress T cell activation and adaptive immune response, and to promote production of proinflammatory cytokines and chemokines in animal models. Different parasite species or strains trigger distinct IFN-I responses. For example, a Plasmodium yoelii strain can stimulate a strong IFN-I response during early infection, whereas its isogenetic strain does not. Host genetic background also greatly influences IFN-I production during malaria infections. Consequently, the effects of IFN-Is on parasitemia and disease symptoms are highly variable depending on the combination of parasite and host species or strains. Toll-like receptor (TLR) 7, TLR9, melanoma differentiation-associated protein 5 (MDA5), and cyclic GMP-AMP synthase (cGAS) coupled with stimulator of interferon genes (STING) are the major receptors for recognizing parasite nucleic acids (RNA/DNA) to trigger IFN-I responses. IFN-I levels in vivo are tightly regulated, and various novel molecules have been identified to regulate IFN-I responses during malaria infections. Here we review the major findings and progress in ligand recognition, signaling pathways, functions, and regulation of IFN-I responses during malaria infections.

Single cell sequencing shines a light on malaria parasite relatedness in complex infections

Recent genomic studies are investigating the wide-ranging implications of malaria complex infections on parasite diversity, transmission, and downstream disease eradication efforts. Using single cell sequencing, new work by Nkhoma et al. provides evidence that there is unexpectedly frequent co-transmission of related parasites in the intense transmission setting in Malawi.

Intrinsic multiplication rate variation and plasticity of human blood stage malaria parasites

Pathogen multiplication rate is theoretically an important determinant of virulence, although often poorly understood and difficult to measure accurately. We show intrinsic asexual blood stage multiplication rate variation of the major human malaria parasite Plasmodium falciparum to be associated with blood-stage infection intensity in patients. A panel of clinical isolates from a highly endemic West African population was analysed repeatedly during five months of continuous laboratory culture, showing a range of exponential multiplication rates at all timepoints tested, mean rates increasing over time. All isolates had different genome sequences, many containing within-isolate diversity that decreased over time in culture, but increases in multiplication rates were not primarily attributable to genomic selection. New mutants, including premature stop codons emerging in a few isolates, did not attain sufficiently high frequencies to substantially affect overall multiplication rates. Significantly, multiplication rate variation among the isolates at each of the assayed culture timepoints robustly correlated with parasite levels seen in patients at clinical presentation, indicating innate parasite control of multiplication rate that contributes to virulence.

Lindsay Stewart et al. analyze clinical isolates of the human malaria parasite Plasmodium falciparum from a highly endemic West African population and show that intrinsic multiplication rate variation is associated with blood-stage infection intensity. Their results indicate that parasite control of multiplication contributes to virulence.

Host-Malaria Parasite Interactions and Impacts on Mutual Evolution

Malaria is the most deadly parasitic disease, affecting hundreds of millions of people worldwide. Malaria parasites have been associated with their hosts for millions of years. During the long history of host-parasite co-evolution, both parasites and hosts have applied pressure on each other through complex host-parasite molecular interactions. Whereas the hosts activate various immune mechanisms to remove parasites during an infection, the parasites attempt to evade host immunity by diversifying their genome and switching expression of targets of the host immune system. Human intervention to control the disease such as antimalarial drugs and vaccination can greatly alter parasite population dynamics and evolution, particularly the massive applications of antimalarial drugs in recent human history. Vaccination is likely the best method to prevent the disease; however, a partially protective vaccine may have unwanted consequences that require further investigation. Studies of host-parasite interactions and co-evolution will provide important information for designing safe and effective vaccines and for preventing drug resistance. In this essay, we will discuss some interesting molecules involved in host-parasite interactions, including important parasite antigens. We also discuss subjects relevant to drug and vaccine development and some approaches for studying host-parasite interactions.

A cautionary note on the use of unsupervised 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.

Predicting geographic location from genetic variation with deep neural networks

Most organisms are more closely related to nearby than distant members of their species, creating spatial autocorrelations in genetic data. This allows us to predict the location of origin of a genetic sample by comparing it to a set of samples of known geographic origin. Here, we describe a deep learning method, which we call Locator, to accomplish this task faster and more accurately than existing approaches. In simulations, Locator infers sample location to within 4.1 generations of dispersal and runs at least an order of magnitude faster than a recent model-based approach. We leverage Locator's computational efficiency to predict locations separately in windows across the genome, which allows us to both quantify uncertainty and describe the mosaic ancestry and patterns of geographic mixing that characterize many populations. Applied to whole-genome sequence data from Plasmodium parasites, Anopheles mosquitoes, and global human populations, this approach yields median test errors of 16.9km, 5.7km, and 85km, respectively.

Local emergence in Amazonia of Plasmodium falciparum k13 C580Y mutants associated with in vitro artemisinin resistance

Antimalarial drug resistance has historically arisen through convergent de novo mutations in Plasmodium falciparum parasite populations in Southeast Asia and South America. For the past decade in Southeast Asia, artemisinins, the core component of first-line antimalarial therapies, have experienced delayed parasite clearance associated with several pfk13 mutations, primarily C580Y. We report that mutant pfk13 has emerged independently in Guyana, with genome analysis indicating an evolutionary origin distinct from Southeast Asia. Pfk13 C580Y parasites were observed in 1.6% (14/854) of samples collected in Guyana in 2016-2017. Introducing pfk13 C580Y or R539T mutations by gene editing into local parasites conferred high levels of in vitro artemisinin resistance. In vitro growth competition assays revealed a fitness cost associated with these pfk13 variants, potentially explaining why these resistance alleles have not increased in frequency more quickly in South America. These data place local malaria control efforts at risk in the Guiana Shield.

Falciparum malaria from coastal Tanzania and Zanzibar remains highly connected despite effective control efforts on the archipelago

BACKGROUND

Tanzania's Zanzibar archipelago has made significant gains in malaria control over the last decade and is a target for malaria elimination. Despite consistent implementation of effective tools since 2002, elimination has not been achieved. Importation of parasites from outside of the archipelago is thought to be an important cause of malaria's persistence, but this paradigm has not been studied using modern genetic tools.

METHODS

Whole-genome sequencing (WGS) was used to investigate the impact of importation, employing population genetic analyses of Plasmodium falciparum isolates from both the archipelago and mainland Tanzania. Ancestry, levels of genetic diversity and differentiation, patterns of relatedness, and patterns of selection between these two populations were assessed by leveraging recent advances in deconvolution of genomes from polyclonal malaria infections.

RESULTS

Significant decreases in the effective population sizes were inferred in both populations that coincide with a period of decreasing malaria transmission in Tanzania. Identity by descent analysis showed that parasites in the two populations shared long segments of their genomes, on the order of 5 cM, suggesting shared ancestry within the last 10 generations. Even with limited sampling, two of isolates between the mainland and Zanzibar were identified that are related at the expected level of half-siblings, consistent with recent importation.

CONCLUSIONS

These findings suggest that importation plays an important role for malaria incidence on Zanzibar and demonstrate the value of genomic approaches for identifying corridors of parasite movement to the island.

Resolving the cause of recurrent Plasmodium vivax malaria probabilistically

Relapses arising from dormant liver-stage Plasmodium vivax parasites (hypnozoites) are a major cause of vivax malaria. However, in endemic areas, a recurrent blood-stage infection following treatment can be hypnozoite-derived (relapse), a blood-stage treatment failure (recrudescence), or a newly acquired infection (reinfection). Each of these requires a different prevention strategy, but it was not previously possible to distinguish between them reliably. We show that individual vivax malaria recurrences can be characterised probabilistically by combined modelling of time-to-event and genetic data within a framework incorporating identity-by-descent. Analysis of pooled patient data on 1441 recurrent P. vivax infections in 1299 patients on the Thailand-Myanmar border observed over 1000 patient follow-up years shows that, without primaquine radical curative treatment, 3 in 4 patients relapse. In contrast, after supervised high-dose primaquine only 1 in 40 relapse. In this region of frequent relapsing P. vivax, failure rates after supervised high-dose primaquine are significantly lower (∼3%) than estimated previously.

Genomic Analysis of Plasmodium vivax in Southern Ethiopia Reveals Selective Pressures in Multiple Parasite Mechanisms

The Horn of Africa harbors the largest reservoir of Plasmodium vivax in the continent. Most of sub-Saharan Africa has remained relatively vivax-free due to a high prevalence of the human Duffy-negative trait, but the emergence of strains able to invade Duffy-negative reticulocytes poses a major public health threat. We undertook the first population genomic investigation of P. vivax from the region, comparing the genomes of 24 Ethiopian isolates against data from Southeast Asia to identify important local adaptions. The prevalence of the Duffy binding protein amplification in Ethiopia was 79%, potentially reflecting adaptation to Duffy negativity. There was also evidence of selection in a region upstream of the chloroquine resistance transporter, a putative chloroquine-resistance determinant. Strong signals of selection were observed in genes involved in immune evasion and regulation of gene expression, highlighting the need for a multifaceted intervention approach to combat P. vivax in the region.

Genetic mapping of fitness determinants across the malaria parasite Plasmodium falciparum life cycle

Determining the genetic basis of fitness is central to understanding evolution and transmission of microbial pathogens. In human malaria parasites (Plasmodium falciparum), most experimental work on fitness has focused on asexual blood stage parasites, because this stage can be easily cultured, although the transmission of malaria requires both female Anopheles mosquitoes and vertebrate hosts. We explore a powerful approach to identify the genetic determinants of parasite fitness across both invertebrate and vertebrate life-cycle stages of P. falciparum. This combines experimental genetic crosses using humanized mice, with selective whole genome amplification and pooled sequencing to determine genome-wide allele frequencies and identify genomic regions under selection across multiple lifecycle stages. We applied this approach to genetic crosses between artemisinin resistant (ART-R, kelch13-C580Y) and ART-sensitive (ART-S, kelch13-WT) parasites, recently isolated from Southeast Asian patients. Two striking results emerge: we observed (i) a strong genome-wide skew (>80%) towards alleles from the ART-R parent in the mosquito stage, that dropped to ~50% in the blood stage as selfed ART-R parasites were selected against; and (ii) repeatable allele specific skews in blood stage parasites with particularly strong selection (selection coefficient (s) ≤ 0.18/asexual cycle) against alleles from the ART-R parent at loci on chromosome 12 containing MRP2 and chromosome 14 containing ARPS10. This approach robustly identifies selected loci and has strong potential for identifying parasite genes that interact with the mosquito vector or compensatory loci involved in drug resistance.

Applying next-generation sequencing to track falciparum malaria in sub-Saharan Africa

Next-generation sequencing (NGS) technologies are increasingly being used to address a diverse range of biological and epidemiological questions. The current understanding of malaria transmission dynamics and parasite movement mainly relies on the analyses of epidemiologic data, e.g. case counts and self-reported travel history data. However, travel history data are often not routinely collected or are incomplete, lacking the necessary level of accuracy. Although genetic data from routinely collected field samples provides an unprecedented opportunity to track the spread of malaria parasites, it remains an underutilized resource for surveillance due to lack of local awareness and capacity, limited access to sensitive laboratory methods and associated computational tools and difficulty in interpreting genetic epidemiology data. In this review, the potential roles of NGS in better understanding of transmission patterns, accurately tracking parasite movement and addressing the emerging challenges of imported malaria in low transmission settings of sub-Saharan Africa are discussed. Furthermore, this review highlights the insights gained from malaria genomic research and challenges associated with integrating malaria genomics into existing surveillance tools to inform control and elimination strategies.

Estimating Relatedness Between Malaria Parasites

Understanding the relatedness of individuals within or between populations is a common goal in biology. Increasingly, relatedness features in genetic epidemiology studies of pathogens. These studies are relatively new compared to those in humans and other organisms, but are important for designing interventions and understanding pathogen transmission. Only recently have researchers begun to routinely apply relatedness to apicomplexan eukaryotic malaria parasites, and to date have used a range of different approaches on an ad hoc basis. Therefore, it remains unclear how to compare different studies and which measures to use. Here, we systematically compare measures based on identity-by-state (IBS) and identity-by-descent (IBD) using a globally diverse data set of malaria parasites, Plasmodium falciparum and P. vivax, and provide marker requirements for estimates based on IBD. We formally show that the informativeness of polyallelic markers for relatedness inference is maximized when alleles are equifrequent. Estimates based on IBS are sensitive to allele frequencies, which vary across populations and by experimental design. For portability across studies, we thus recommend estimates based on IBD. To generate estimates with errors below an arbitrary threshold of 0.1, we recommend ∼100 polyallelic or 200 biallelic markers. Marker requirements are immediately applicable to haploid malaria parasites and other haploid eukaryotes. C.I.s facilitate comparison when different marker sets are used. This is the first attempt to provide rigorous analysis of the reliability of, and requirements for, relatedness inference in malaria genetic epidemiology. We hope it will provide a basis for statistically informed prospective study design and surveillance strategies.