Measurably recombining malaria parasites

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.

Malaria parasite phenotypic heterogeneity and the power of single-cell technologies

The two-host life cycle of the malaria parasite, combined with its ability to regulate gene expression and protein translation within a single clonal genotype, results in a remarkable potential for phenotypic heterogeneity. This heterogeneity presents underappreciated challenges to antimalarial interventions such as vaccines, drugs, and diagnostic tools, with parasites able to evolve resistance rapidly. Here we summarise current knowledge of the different mechanisms driving parasite phenotypic heterogeneity both at the gene and protein level. Centred on the most virulent human malaria parasite, Plasmodium falciparum, we explore the consequences of this diversity for antimalarial interventions and how single-cell technologies present an opportunity to study inter- and intra-clonal heterogeneity to better design future-proofed intervention strategies against this ancient disease.

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.

MalKinID: A classification model for identifying malaria parasite genealogical relationships using identity-by-descent

Pathogen genomics is a powerful tool for tracking infectious disease transmission. In malaria, identity-by-descent is used to assess the genetic relatedness between parasites and has been used to study transmission and importation. In theory, identity-by-descent can be used to distinguish genealogical relationships to reconstruct transmission history or identify parasites for QTL experiments. MalKinID (Malaria Kinship Identifier) is a new classification model designed to identify genealogical relationships among malaria parasites based on genome-wide identity-by-descent proportions and identity-by-descent segment distributions. MalKinID was calibrated to the genomic data from 3 laboratory-based genetic crosses (yielding 440 parent-child and 9060 full-sibling comparisons). MalKinID identified lab-generated F1 progeny with >80% sensitivity and showed that 0.39 (95% CI 0.28, 0.49) of the second-generation progeny of a NF54 and NHP4026 cross were F1s and 0.56 (0.45, 0.67) were backcrosses of an F1 with the parental NF54 strain. In simulated outcrossed importations, MalKinID reconstructs genealogy history with high precision and sensitivity, with F1-scores exceeding 0.84. However, when importation involves inbreeding, such as during serial co-transmission, the precision and sensitivity of MalKinID declined, with F1-scores (the harmonic mean of precision and sensitivity) of 0.76 (0.56, 0.92) and 0.23 (0.0, 0.4) for parent-child and full-sibling and <0.05 for second-degree and third-degree relatives. Disentangling inbred relationships required adapting MalKinID to perform multisample comparisons. Genealogical inference is most powered when (1) outcrossing is the norm or (2) multisample comparisons based on a predefined pedigree are used. MalKinID lays the foundations for using identity-by-descent to track parasite transmission history and for separating progeny for quantitative-trait-locus experiments.

Temporal genomics in Southern Zambia shows rising prevalence of Plasmodium falciparum mutations linked to delayed clearance after artemisinin-lumefantrine treatment

The emergence of antimalarial drug resistance is an impediment to malaria control and elimination in Africa. Analysis of temporal trends in molecular markers of resistance is critical to inform policy makers and guide malaria treatment guidelines. In a low and seasonal transmission region of southern Zambia, we successfully genotyped 85.5% (389/455) of Plasmodium falciparum samples collected between 2013 and 2018 from 8 spatially clustered health centres using molecular inversion probes (MIPs) targeting key drug resistance genes. Aside from one sample from 2016 carrying K13 622I, no other World Health Organization-validated or candidate artemisinin partial resistance (ART-R) mutations were observed. However, in the more recent years (2016-2017) five novel K13-propeller-domain mutations, C532S, A578S, Q613E, D680N and G718S were identified at low prevalence. Moreover, 13% (CI, 9.6-17.2) of isolates had the AP2MU 160N mutation, which has been associated with delayed clearance following artemisinin combination therapy in Africa. This mutation increased in prevalence between 2015 and 2018 and bears a genomic signature of selection. During this time period, there was an increase in the MDR1 NFD haplotype that is associated with reduced susceptibility to lumefantrine. Sulfadoxine-pyrimethamine polymorphisms were near fixation. While validated ART-R mutations are rare, a mutation associated with slow parasite clearance in Africa appears to be under selection in southern Zambia.

Peeling the onion: how complex is the artemisinin resistance genetic trait of malaria parasites?

The genetics of Plasmodium as an intracellular, mostly haploid, sexually reproducing, eukaryotic organism with a complex life cycle, presents unprecedented challenges in studying drug resistance. This article summarizes current knowledge on the genetic basis of artemisinin resistance (AR) - a main component of current drug therapies for falciparum malaria. Although centered on nonsynonymous single-nucleotide polymorphisms (nsSNPs), we describe multifaceted resistance mechanisms as part of a complex, cumulative genetic trait that involves regulation of expression by a wide array of polymorphisms in noncoding regions. These genetic variations alter transcriptome profiles linked to Plasmodium's development and population dynamics, ultimately influencing the emergence and spread of the resistance.

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.

Geographical and temporal dynamics of genetic diversity of Plasmodium falciparum merozoite surface proteins 1/2 in India

The high genetic diversity of Plasmodium falciparum (Pf) is a big obstacle to successful vaccine development programs. Here, the geographical and temporal dynamics of the genetic diversity of Indian Pf isolates from patients living in Ranchi, Raipur, Mewat, and Rourkela were analyzed. Typing and frequency of merozoite surface protein 1 and 2 genes (pfmsp1/2), their genotypes, clonality, heterozygosity, multiplicity of infection, and neutral evolution metrics were computed. A phylogenetic analysis was also performed for these two genes. The dominant allelic types were K1 (55%) and MAD20 (55%) for msp1, and FC27 (64.7%) for msp2. Infections were mainly monoclonal in Ranchi and Mewat while polyclonal in Raipur and Rourkela. Polyclonal infections dropped from 57.1 to 71.3% in 2013 to 33.3-33.4% in 2016 in Raipur. K1 and MAD20 sequences were highly diverse due to the organization of the amino acid units SGG, SVA, SVT, and SGN. The IC/3D7-related G,S,A-rich region showed a large variation of four to eight amino acid repeats, including mostly GAVASA (81.8%), GSGA (54.5%), and GASGSA (45.5%). The 32-amino acid sequence of the FC27 type was present in all isolates with several mutations. The msp1/2 sequences were not under neutral evolution, except the K1 family, which is under balancing selection. The msp1/2 sequences are phylogenetically closer to previous Indian sequences than those from Africa, Asia, the Americas, and Oceania. This study outlines a high genetic diversity of Pf infections with complex structure, and evolutionary signature changed with time.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12639-024-01698-8.

Benchmarking and Optimization of Methods for the Detection of Identity-By-Descent in High-Recombining Plasmodium falciparum Genomes

Genomic surveillance is crucial for identifying at-risk populations for targeted malaria control and elimination. Identity-by-descent (IBD) is increasingly being used in Plasmodium population genomics to estimate genetic relatedness, effective population size (N e ), population structure, and signals of positive selection. Despite its potential, a thorough evaluation of IBD segment detection tools for species with high recombination rates, such as P. falciparum, remains absent. Here, we perform comprehensive benchmarking of IBD callers - probabilistic (hmmIBD, isoRelate), identity-by-state-based (hap-IBD, phased IBD) and others (Refined IBD) - using population genetic simulations tailored for high recombination, and IBD quality metrics at both the IBD segment level and the IBD-based downstream inference level. Our results demonstrate that low marker density per genetic unit, related to high recombination relative to mutation, significantly compromises the accuracy of detected IBD segments. In genomes with high recombination rates resembling P. falciparum, most IBD callers exhibit high false negative rates for shorter IBD segments, which can be partially mitigated through optimization of IBD caller parameters, especially those related to marker density. Notably, IBD detected with optimized parameters allows for more accurate capture of selection signals and population structure; IBD-based N e inference is very sensitive to IBD detection errors, with IBD called from hmmIBD uniquely providing less biased estimates of N e in this context. Validation with empirical data from the MalariaGEN Pf 7 database, representing different transmission settings, corroborates these findings. We conclude that context-specific evaluation and parameter optimization are essential for accurate IBD detection in high-recombining species and recommend hmmIBD for quality-sensitive analysis, such as estimation of N e in these species. Our optimization and high-level benchmarking methods not only improve IBD segment detection in high-recombining genomes but also enhance overall genomic analysis, paving the way for more accurate genomic surveillance and targeted intervention strategies for malaria.

Analytic Approaches in Genomic Epidemiological Studies of Parasitic Protozoa

Whole genome sequencing (WGS) plays an important role in the advanced characterization of pathogen transmission and is widely used in studies of major bacterial and viral diseases. Although protozoan parasites cause serious diseases in humans and animals, WGS data on them are relatively scarce due to the large genomes and lack of cultivation techniques for some. In this review, we have illustrated bioinformatic analyses of WGS data and their applications in studies of the genomic epidemiology of apicomplexan parasites. WGS has been used in outbreak detection and investigation, studies of pathogen transmission and evolution, and drug resistance surveillance and tracking. However, comparative analysis of parasite WGS data is still in its infancy, and available WGS data are mainly from a few genera of major public health importance, such as Plasmodium, Toxoplasma, and Cryptosporidium. In addition, the utility of third-generation sequencing technology for complete genome assembly at the chromosome level, studies of the biological significance of structural genomic variation, and molecular surveillance of pathogens has not been fully exploited. These issues require large-scale WGS of various protozoan parasites of public health and veterinary importance using both second- and third-generation sequencing technologies.

Temporal genomic analysis of Plasmodium falciparum reveals increased prevalence of mutations associated with delayed clearance following treatment with artemisinin-lumefantrine in Choma District, Southern Province, Zambia

The emergence of antimalarial drug resistance is an impediment to malaria control and elimination in Africa. Analysis of temporal trends in molecular markers of resistance is critical to inform policy makers and guide malaria treatment guidelines. In a low and seasonal transmission region of southern Zambia, we successfully genotyped 85.5% (389/455) of Plasmodium falciparum samples collected between 2013-2018 from 8 spatially clustered health centres using molecular inversion probes (MIPs) targeting key drug resistance genes. Aside from one sample carrying K13 R622I, none of the isolates carried other World Health Organization-validated or candidate artemisinin partial resistance (ART-R) mutations in K13. However, 13% (CI, 9.6-17.2) of isolates had the AP2MU S160N mutation, which has been associated with delayed clearance following artemisinin combination therapy in Africa. This mutation increased in prevalence between 2015-2018 and bears a genomic signature of selection. During this time period, there was an increase in the MDR1 NFD haplotype that is associated with reduced susceptibility to lumefantrine. Sulfadoxine-pyrimethamine polymorphisms were near fixation. While validated ART-R mutations are rare, a mutation associated with slow parasite clearance in Africa appears to be under selection in southern Zambia.

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.

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.

Genomic malaria surveillance of antenatal care users detects reduced transmission following elimination interventions in Mozambique

Routine sampling of pregnant women at first antenatal care (ANC) visits could make Plasmodium falciparum genomic surveillance more cost-efficient and convenient in sub-Saharan Africa. We compare the genetic structure of parasite populations sampled from 289 first ANC users and 93 children from the community in Mozambique between 2015 and 2019. Samples are amplicon sequenced targeting 165 microhaplotypes and 15 drug resistance genes. Metrics of genetic diversity and relatedness, as well as the prevalence of drug resistance markers, are consistent between the two populations. In an area targeted for elimination, intra-host genetic diversity declines in both populations (p = 0.002-0.007), while for the ANC population, population genetic diversity is also lower (p = 0.0004), and genetic relatedness between infections is higher (p = 0.002) than control areas, indicating a recent reduction in the parasite population size. These results highlight the added value of genomic surveillance at ANC clinics to inform about changes in transmission beyond epidemiological data.

Genomic malaria surveillance of antenatal care users detects reduced transmission following elimination interventions in Mozambique

Routine sampling of pregnant women at first antenatal care (ANC) visits could make Plasmodium falciparum genomic surveillance more cost-efficient and convenient in sub-Saharan Africa. We compared the genetic structure of parasite populations sampled from 289 first ANC attendees and 93 children from the community in Mozambique between 2015 and 2019. Samples were amplicon sequenced targeting 165 microhaplotypes and 15 drug resistance genes. Metrics of genetic diversity and relatedness, as well as the prevalence of drug resistance markers, were consistent between the two populations. In an area targeted for elimination, intra-host genetic diversity declined in both populations (p=0.002-0.007), while for the ANC population, population genetic diversity was also lower (p=0.0004), and genetic relatedness between infections were higher (p=0.002) than control areas, indicating a recent reduction in the parasite population size. These results highlight the added value of genomic surveillance at ANC clinics to inform about changes in transmission beyond epidemiological data.

Plasmodium falciparum resistant to artemisinin and diagnostics have emerged in Ethiopia

Diagnosis and treatment of Plasmodium falciparum infections are required for effective malaria control and are pre-requisites for malaria elimination efforts; hence we need to monitor emergence, evolution and spread of drug- and diagnostics-resistant parasites. We deep sequenced key drug-resistance mutations and 1,832 SNPs in the parasite genomes of 609 malaria cases collected during a diagnostic-resistance surveillance study in Ethiopia. We found that 8.0% (95% CI 7.0-9.0) of malaria cases were caused by P. falciparum carrying the candidate artemisinin partial-resistance kelch13 (K13) 622I mutation, which was less common in diagnostic-resistant parasites mediated by histidine-rich proteins 2 and 3 (pfhrp2/3) deletions than in wild-type parasites (P = 0.03). Identity-by-descent analyses showed that K13 622I parasites were significantly more related to each other than to wild type (P < 0.001), consistent with recent expansion and spread of this mutation. Pfhrp2/3-deleted parasites were also highly related, with evidence of clonal transmissions at the district level. Of concern, 8.2% of K13 622I parasites also carried the pfhrp2/3 deletions. Close monitoring of the spread of combined drug- and diagnostic-resistant parasites is needed.

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.