Genetic relatedness analysis reveals the cotransmission of genetically related Plasmodium falciparum parasites in Thiès, Senegal

Two mosquito salivary antigens demonstrate promise as biomarkers of recent exposure to P. falciparum infected mosquito bites

Background

Measuring malaria transmission intensity using the traditional entomological inoculation rate is difficult. Antibody responses to mosquito salivary proteins such as SG6 have previously been used as biomarkers of exposure to Anopheles mosquito bites. Here, we investigate four mosquito salivary proteins as potential biomarkers of human exposure to mosquitoes infected with P. falciparum: mosGILT, SAMSP1, AgSAP, and AgTRIO.

Methods

We tested population-level human immune responses in longitudinal and cross-sectional plasma samples from individuals with known P. falciparum infection from low and moderate transmission areas in Senegal using a multiplexed magnetic bead-based assay.

Results

AgSAP and AgTRIO were the best indicators of recent exposure to infected mosquitoes. Antibody responses to AgSAP, in a moderate endemic area, and to AgTRIO in both low and moderate endemic areas, were significantly higher than responses in a healthy non-endemic control cohort (p-values = 0.0245, 0.0064, and <0.0001 respectively). No antibody responses significantly differed between the low and moderate transmission area, or between equivalent groups during and outside the malaria transmission seasons. For AgSAP and AgTRIO, reactivity peaked 2-4 weeks after clinical P. falciparum infection and declined 3 months after infection.

Discussion

Reactivity to both AgSAP and AgTRIO peaked after infection and did not differ seasonally nor between areas of low and moderate transmission, suggesting reactivity is likely reflective of exposure to infectious mosquitos or recent biting rather than general mosquito exposure. Kinetics suggest reactivity is relatively short-lived. AgSAP and AgTRIO are promising candidates to incorporate into multiplexed assays for serosurveillance of population-level changes in P. falciparum-infected mosquito exposure.

Plasmodium falciparum genetic diversity and multiplicity of infection based on msp-1, msp-2, glurp and microsatellite genetic markers in sub-Saharan Africa: a systematic review and meta-analysis

BACKGROUND

In sub-Saharan Africa (SSA), Plasmodium falciparum causes most of the malaria cases. Despite its crucial roles in disease severity and drug resistance, comprehensive data on Plasmodium falciparum genetic diversity and multiplicity of infection (MOI) are sparse in SSA. This study summarizes available information on genetic diversity and MOI, focusing on key markers (msp-1, msp-2, glurp, and microsatellites). The systematic review aimed to evaluate their influence on malaria transmission dynamics and offer insights for enhancing malaria control measures in SSA.

METHODS

The review was conducted following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. Two reviewers conducted article screening, assessed the risk of bias (RoB), and performed data abstraction. Meta-analysis was performed using the random-effects model in STATA version 17.

RESULTS

The review included 52 articles: 39 cross-sectional studies and 13 Randomized Controlled Trial (RCT)/cohort studies, involving 11,640 genotyped parasite isolates from 23 SSA countries. The overall pooled mean expected heterozygosity was 0.65 (95% CI: 0.51-0.78). Regionally, values varied: East (0.58), Central (0.84), Southern (0.74), and West Africa (0.69). Overall pooled allele frequencies of msp-1 alleles K1, MAD20, and RO33 were 61%, 44%, and 40%, respectively, while msp-2 I/C 3D7 and FC27 alleles were 61% and 55%. Central Africa reported higher frequencies (K1: 74%, MAD20: 51%, RO33: 48%) than East Africa (K1: 46%, MAD20: 42%, RO33: 31%). For msp-2, East Africa had 60% and 55% for I/C 3D7 and FC27 alleles, while West Africa had 62% and 50%, respectively. The pooled allele frequency for glurp was 66%. The overall pooled mean MOI was 2.09 (95% CI: 1.88-2.30), with regional variations: East (2.05), Central (2.37), Southern (2.16), and West Africa (1.96). The overall prevalence of polyclonal Plasmodium falciparum infections was 63% (95% CI: 56-70), with regional prevalences as follows: East (62%), West (61%), Central (65%), and South Africa (71%).

CONCLUSION

The study shows substantial regional variation in Plasmodium falciparum parasite genetic diversity and MOI in SSA. These findings suggest a need for malaria control strategies and surveillance efforts considering regional-specific factors underlying Plasmodium falciparum infection.

Evaluating the performance of Plasmodium falciparum genetic metrics for inferring National Malaria Control Programme reported incidence in Senegal

BACKGROUND

Genetic surveillance of the Plasmodium falciparum parasite shows great promise for helping National Malaria Control Programmes (NMCPs) assess parasite transmission. Genetic metrics such as the frequency of polygenomic (multiple strain) infections, genetic clones, and the complexity of infection (COI, number of strains per infection) are correlated with transmission intensity. However, despite these correlations, it is unclear whether genetic metrics alone are sufficient to estimate clinical incidence.

METHODS

This study examined parasites from 3147 clinical infections sampled between the years 2012-2020 through passive case detection (PCD) across 16 clinic sites spread throughout Senegal. Samples were genotyped with a 24 single nucleotide polymorphism (SNP) molecular barcode that detects parasite strains, distinguishes polygenomic (multiple strain) from monogenomic (single strain) infections, and identifies clonal infections. To determine whether genetic signals can predict incidence, a series of Poisson generalized linear mixed-effects models were constructed to predict the incidence level at each clinical site from a set of genetic metrics designed to measure parasite clonality, superinfection, and co-transmission rates.

RESULTS

Model-predicted incidence was compared with the reported standard incidence data determined by the NMCP for each clinic and found that parasite genetic metrics generally correlated with reported incidence, with departures from expected values at very low annual incidence (< 10/1000/annual [‰]).

CONCLUSIONS

When transmission is greater than 10 cases per 1000 annual parasite incidence (annual incidence > 10‰), parasite genetics can be used to accurately infer incidence and is consistent with superinfection-based hypotheses of malaria transmission. When transmission was < 10‰, many of the correlations between parasite genetics and incidence were reversed, which may reflect the disproportionate impact of importation and focal transmission on parasite genetics when local transmission levels are low.

Antigenic strain diversity predicts different biogeographic patterns of maintenance and decline of antimalarial drug resistance

The establishment and spread of antimalarial drug resistance vary drastically across different biogeographic regions. Though most infections occur in sub-Saharan Africa, resistant strains often emerge in low-transmission regions. Existing models on resistance evolution lack consensus on the relationship between transmission intensity and drug resistance, possibly due to overlooking the feedback between antigenic diversity, host immunity, and selection for resistance. To address this, we developed a novel compartmental model that tracks sensitive and resistant parasite strains, as well as the host dynamics of generalized and antigen-specific immunity. Our results show a negative correlation between parasite prevalence and resistance frequency, regardless of resistance cost or efficacy. Validation using chloroquine-resistant marker data supports this trend. Post discontinuation of drugs, resistance remains high in low-diversity, low-transmission regions, while it steadily decreases in high-diversity, high-transmission regions. Our study underscores the critical role of malaria strain diversity in the biogeographic patterns of resistance evolution.

Malaria surveillance reveals parasite relatedness, signatures of selection, and correlates of transmission across Senegal

We here analyze data from the first year of an ongoing nationwide program of genetic surveillance of Plasmodium falciparum parasites in Senegal. The analysis is based on 1097 samples collected at health facilities during passive malaria case detection in 2019; it provides a baseline for analyzing parasite genetic metrics as they vary over time and geographic space. The study's goal was to identify genetic metrics that were informative about transmission intensity and other aspects of transmission dynamics, focusing on measures of genetic relatedness between parasites. We found the best genetic proxy for local malaria incidence to be the proportion of polygenomic infections (those with multiple genetically distinct parasites), although this relationship broke down at low incidence. The proportion of related parasites was less correlated with incidence while local genetic diversity was uninformative. The type of relatedness could discriminate local transmission patterns: two nearby areas had similarly high fractions of relatives, but one was dominated by clones and the other by outcrossed relatives. Throughout Senegal, 58% of related parasites belonged to a single network of relatives, within which parasites were enriched for shared haplotypes at known and suspected drug resistance loci and at one novel locus, reflective of ongoing selection pressure.

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 ( e M O I ) , 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, e M O I 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/.

Whole-genome surveillance identifies markers of Plasmodium falciparum drug resistance and novel genomic regions under selection in Mozambique

IMPORTANCE

Malaria is a devastating disease caused by Plasmodium parasites. The evolution of parasite drug resistance continues to hamper progress toward malaria elimination, and despite extensive efforts to control malaria, it remains a leading cause of death in Mozambique and other countries in the region. The development of successful vaccines and identification of molecular markers to track drug efficacy are essential for managing the disease burden. We present an analysis of the parasite genome in Mozambique, a country with one of the highest malaria burdens globally and limited available genomic data, revealing current selection pressure. We contribute additional evidence to limited prior studies supporting the effectiveness of SWGA in producing reliable genomic data from complex clinical samples. Our results provide the identity of genomic loci that may be associated with current antimalarial drug use, including artemisinin and lumefantrine, and reveal selection pressure predicted to compromise the efficacy of current vaccine candidates.

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.

Malaria surveillance reveals parasite relatedness, signatures of selection, and correlates of transmission across Senegal

Parasite genetic surveillance has the potential to play an important role in malaria control. We describe here an analysis of data from the first year of an ongoing, nationwide program of genetic surveillance of Plasmodium falciparum parasites in Senegal, intended to provide actionable information for malaria control efforts. Looking for a good proxy for local malaria incidence, we found that the best predictor was the proportion of polygenomic infections (those with multiple genetically distinct parasites), although that relationship broke down in very low incidence settings (r = 0.77 overall). The proportion of closely related parasites in a site was more weakly correlated ( r = -0.44) with incidence while the local genetic diversity was uninformative. Study of related parasites indicated their potential for discriminating local transmission patterns: two nearby study areas had similarly high fractions of relatives, but one area was dominated by clones and the other by outcrossed relatives. Throughout the country, 58% of related parasites proved to belong to a single network of relatives, within which parasites were enriched for shared haplotypes at known and suspected drug resistance loci as well as at one novel locus, reflective of ongoing selection pressure.

Ex vivo RSA and pfkelch13 targeted-amplicon deep sequencing reveal parasites susceptibility to artemisinin in Senegal, 2017

BACKGROUND

Malaria control is highly dependent on the effectiveness of artemisinin-based combination therapy (ACT), the current frontline malaria curative treatment. Unfortunately, the emergence and spread of parasites resistant to artemisinin (ART) derivatives in Southeast Asia and South America, and more recently in Rwanda and Uganda (East Africa), compromise their long-term use in sub-Saharan Africa, where most malaria deaths occur.

METHODS

Here, ex vivo susceptibility to dihydroartemisinin (DHA) was evaluated from 38 Plasmodium falciparum isolates collected in 2017 in Thiès (Senegal) expressed in the Ring-stage Survival Assay (RSA). Both major and minor variants were explored in the three conserved-encoding domains of the pfkelch13 gene, the main determinant of ART resistance using a targeted-amplicon deep sequencing (TADS) approach.

RESULTS

All samples tested in the ex vivo RSA were found to be susceptible to DHA (parasite survival rate < 1%). The non-synonymous mutations K189T and K248R in pfkelch13 were observed each in one isolate, as major (99%) or minor (5%) variants, respectively.

CONCLUSION

The results suggest that ART is still fully effective in the Thiès region of Senegal in 2017. Investigations combining ex vivo RSA and TADS are a useful approach for monitoring ART resistance in Africa.

Genomic variation during culture adaptation of genetically complex Plasmodium falciparum clinical isolates

Experimental studies on the biology of malaria parasites have mostly been based on laboratory-adapted lines, but there is limited understanding of how these may differ from parasites in natural infections. Loss-of-function mutants have previously been shown to emerge during culture of some Plasmodium falciparum clinical isolates in analyses focusing on single-genotype infections. The present study included a broader array of isolates, mostly representing multiple-genotype infections, which are more typical in areas where malaria is highly endemic. Genome sequence data from multiple time points over several months of culture adaptation of 28 West African isolates were analysed, including previously available sequences along with new genome sequences from additional isolates and time points. Some genetically complex isolates eventually became fixed over time to single surviving genotypes in culture, whereas others retained diversity, although proportions of genotypes varied over time. Drug resistance allele frequencies did not show overall directional changes, suggesting that resistance-associated costs are not the main causes of fitness differences among parasites in culture. Loss-of-function mutants emerged during culture in several of the multiple-genotype isolates, affecting genes (including AP2-HS, EPAC and SRPK1) for which loss-of-function mutants were previously seen to emerge in single-genotype isolates. Parasite clones were derived by limiting dilution from six of the isolates, and sequencing identified de novo variants not detected in the bulk isolate sequences. Interestingly, several of these were nonsense mutants and frameshifts disrupting the coding sequence of EPAC, the gene with the largest number of independent nonsense mutants previously identified in laboratory-adapted lines. Analysis of genomic identity by descent to explore relatedness among clones revealed co-occurring non-identical sibling parasites, illustrative of the natural genetic structure within endemic populations.

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.

Plasmodium falciparum genomic surveillance reveals spatial and temporal trends, association of genetic and physical distance, and household clustering

Molecular epidemiology using genomic data can help identify relationships between malaria parasite population structure, malaria transmission intensity, and ultimately help generate actionable data to assess the effectiveness of malaria control strategies. Genomic data, coupled with geographic information systems data, can further identify clusters or hotspots of malaria transmission, parasite genetic and spatial connectivity, and parasite movement by human or mosquito mobility over time and space. In this study, we performed longitudinal genomic surveillance in a cohort of 70 participants over four years from different neighborhoods and households in Thiès, Senegal—a region of exceptionally low malaria transmission (entomological inoculation rate less than 1). Genetic identity (identity by state, IBS) was established using a 24-single nucleotide polymorphism molecular barcode, identity by descent was calculated from whole genome sequence data, and a hierarchical Bayesian regression model was used to establish genetic and spatial relationships. Our results show clustering of genetically similar parasites within households and a decline in genetic similarity of parasites with increasing distance. One household showed extremely high diversity and warrants further investigation as to the source of these diverse genetic types. This study illustrates the utility of genomic data with traditional epidemiological approaches for surveillance and detection of trends and patterns in malaria transmission not only by neighborhood but also by household. This approach can be implemented regionally and countrywide to strengthen and support malaria control and elimination efforts.

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.

Comparative Analysis of Plasmodium falciparum Genotyping via SNP Detection, Microsatellite Profiling, and Whole-Genome Sequencing

Research efforts to combat antimalarial drug resistance rely on quick, robust, and sensitive methods to genetically characterize Plasmodium falciparum parasites. We developed a single-nucleotide polymorphism (SNP)-based genotyping method that can assess 33 drug resistance-conferring SNPs in dhfr, dhps, pfmdr1, pfcrt, and k13 in nine PCRs, performed directly from P. falciparum cultures or infected blood. We also optimized multiplexed fragment analysis and gel electrophoresis-based microsatellite typing methods using a set of five markers that can distinguish 12 laboratory strains of diverse geographical and temporal origin. We demonstrate how these methods can be applied to screen for the multidrug-resistant KEL1/PLA1/PfPailin (KelPP) lineage that has been sweeping across the Greater Mekong Subregion, verify parasite in vitro SNP-editing, identify novel recombinant genetic cross progeny, or cluster strains to infer their geographical origins. Results were compared with Illumina-based whole-genome sequence analysis that provides the most detailed sequence information but is cost-prohibitive. These adaptable, simple, and inexpensive methods can be easily implemented into routine genotyping of P. falciparum parasites in both laboratory and field settings.

Higher gametocyte production and mosquito infectivity in chronic compared to incident Plasmodium falciparum infections

Plasmodium falciparum gametocyte kinetics and infectivity may differ between chronic and incident infections. In the current study, we assess parasite kinetics and infectivity to mosquitoes among children (aged 5-10 years) from Burkina Faso with (a) incident infections following parasite clearance (n = 48) and (b) chronic asymptomatic infections (n = 60). In the incident infection cohort, 92% (44/48) of children develop symptoms within 35 days, compared to 23% (14/60) in the chronic cohort. All individuals with chronic infection carried gametocytes or developed them during follow-up, whereas only 35% (17/48) in the incident cohort produce gametocytes before becoming symptomatic and receiving treatment. Parasite multiplication rate (PMR) and the relative abundance of ap2-g and gexp-5 transcripts are positively associated with gametocyte production. Antibody responses are higher and PMR lower in chronic infections. The presence of symptoms and sexual stage immune responses are associated with reductions in gametocyte infectivity to mosquitoes. We observe that most incident infections require treatment before the density of mature gametocytes is sufficient to infect mosquitoes. In contrast, chronic, asymptomatic infections represent a significant source of mosquito infections. Our observations support the notion that malaria transmission reduction may be expedited by enhanced case management, involving both symptom-screening and infection detection.

Development of a new barcode-based, multiplex-PCR, next-generation-sequencing assay and data processing and analytical pipeline for multiplicity of infection detection of Plasmodium falciparum

BACKGROUND

Simultaneous infection with multiple malaria parasite strains is common in high transmission areas. Quantifying the number of strains per host, or the multiplicity of infection (MOI), provides additional parasite indices for assessing transmission levels but it is challenging to measure accurately with current tools. This paper presents new laboratory and analytical methods for estimating the MOI of Plasmodium falciparum.

METHODS

Based on 24 single nucleotide polymorphisms (SNPs) previously identified as stable, unlinked targets across 12 of the 14 chromosomes within P. falciparum genome, three multiplex PCRs of short target regions and subsequent next generation sequencing (NGS) of the amplicons were developed. A bioinformatics pipeline including B4Screening pathway removed spurious amplicons to ensure consistent frequency calls at each SNP location, compiled amplicons by SNP site diversity, and performed algorithmic haplotype and strain reconstruction. The pipeline was validated by 108 samples generated from cultured-laboratory strain mixtures in different proportions and concentrations, with and without pre-amplification, and using whole blood and dried blood spots (DBS). The pipeline was applied to 273 smear-positive samples from surveys conducted in western Kenya, then providing results into StrainRecon Thresholding for Infection Multiplicity (STIM), a novel MOI estimator.

RESULTS

The 24 barcode SNPs were successfully identified uniformly across the 12 chromosomes of P. falciparum in a sample using the pipeline. Pre-amplification and parasite concentration, while non-linearly associated with SNP read depth, did not influence the SNP frequency calls. Based on consistent SNP frequency calls at targeted locations, the algorithmic strain reconstruction for each laboratory-mixed sample had 98.5% accuracy in dominant strains. STIM detected up to 5 strains in field samples from western Kenya and showed declining MOI over time (q < 0.02), from 4.32 strains per infected person in 1996 to 4.01, 3.56 and 3.35 in 2001, 2007 and 2012, and a reduction in the proportion of samples with 5 strains from 57% in 1996 to 18% in 2012.

CONCLUSION

The combined approach of new multiplex PCRs and NGS, the unique bioinformatics pipeline and STIM could identify 24 barcode SNPs of P. falciparum correctly and consistently. The methodology could be applied to field samples to reliably measure temporal changes in MOI.

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.

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.

Genetic analysis reveals unique characteristics of Plasmodium falciparum parasite populations in Haiti

Background

With increasing interest in eliminating malaria from the Caribbean region, Haiti is one of the two countries on the island of Hispaniola with continued malaria transmission. While the Haitian population remains at risk for malaria, there are a limited number of cases annually, making conventional epidemiological measures such as case incidence and prevalence of potentially limited value for fine-scale resolution of transmission patterns and trends. In this context, genetic signatures may be useful for the identification and characterization of the Plasmodium falciparum parasite population in order to identify foci of transmission, detect outbreaks, and track parasite movement to potentially inform malaria control and elimination strategies.

Methods

This study evaluated the genetic signals based on analysis of 21 single-nucleotide polymorphisms (SNPs) from 462 monogenomic (single-genome) P. falciparum DNA samples extracted from dried blood spots collected from malaria-positive patients reporting to health facilities in three southwestern Haitian departments (Nippes, Grand’Anse, and Sud) in 2016.

Results

Assessment of the parasite genetic relatedness revealed evidence of clonal expansion within Nippes and the exchange of parasite lineages between Nippes, Sud, and Grand'Anse. Furthermore, 437 of the 462 samples shared high levels of genetic similarity–at least 20 of 21 SNPS–with at least one other sample in the dataset.

Conclusions

These results revealed patterns of relatedness suggestive of the repeated recombination of a limited number of founding parasite types without significant outcrossing. These genetic signals offer clues to the underlying relatedness of parasite populations and may be useful for the identification of the foci of transmission and tracking of parasite movement in Haiti for malaria elimination.

Genomic and chemical evidence for local adaptation in resistance to different herbivores in Datura stramonium

Because most species are collections of genetically variable populations distributed to habitats differing in their abiotic/biotic environmental factors and community composition, the pattern and strength of natural selection imposed by species on each other's traits are also expected to be highly spatially variable. Here, we used genomic and quantitative genetic approaches to understand how spatially variable selection operates on the genetic basis of plant defenses to herbivores. To this end, an F₂ progeny was generated by crossing Datura stramonium (Solanaceae) parents from two populations differing in their level of chemical defense. This F₂ progeny was reciprocally transplanted into the parental plants' habitats and by measuring the identity by descent (IBD) relationship of each F₂ plant to each parent, we were able to elucidate how spatially variable selection imposed by herbivores operated on the genetic background (IBD) of resistance to herbivory, promoting local adaptation. The results highlight that plants possessing the highest total alkaloid concentrations (sum of all alkaloid classes) were not the most well-defended or fit. Instead, specific alkaloids and their linked loci/alleles were favored by selection imposed by different herbivores. This has led to population differentiation in plant defenses and thus, to local adaptation driven by plant-herbivore interactions.

Genetic evidence for imported malaria and local transmission in Richard Toll, Senegal

BACKGROUND

Malaria elimination efforts can be undermined by imported malaria infections. Imported infections are classified based on travel history.

METHODS

A genetic strategy was applied to better understand the contribution of imported infections and to test for local transmission in the very low prevalence region of Richard Toll, Senegal.

RESULTS

Genetic relatedness analysis, based upon molecular barcode genotyping data derived from diagnostic material, provided evidence for both imported infections and ongoing local transmission in Richard Toll. Evidence for imported malaria included finding that a large proportion of Richard Toll parasites were genetically related to parasites from Thiès, Senegal, a region of moderate transmission with extensive available genotyping data. Evidence for ongoing local transmission included finding parasites of identical genotype that persisted across multiple transmission seasons as well as enrichment of highly related infections within the households of non-travellers compared to travellers.

CONCLUSIONS

These data indicate that, while a large number of infections may have been imported, there remains ongoing local malaria transmission in Richard Toll. These proof-of-concept findings underscore the value of genetic data to identify parasite relatedness and patterns of transmission to inform optimal intervention selection and placement.

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.

Co-transmission of Related Malaria Parasite Lineages Shapes Within-Host Parasite Diversity

In high-transmission regions, we expect parasite lineages within complex malaria infections to be unrelated due to parasite inoculations from different mosquitoes. This project was designed to test this prediction. We generated 485 single-cell genome sequences from fifteen P. falciparum malaria patients from Chikhwawa, Malawi-an area of intense transmission. Patients harbored up to seventeen unique parasite lineages. Surprisingly, parasite lineages within infections tend to be closely related, suggesting that superinfection by repeated mosquito bites is rarer than co-transmission of parasites from a single mosquito. Both closely and distantly related parasites comprise an infection, suggesting sequential transmission of complex infections between multiple hosts. We identified tetrads and reconstructed parental haplotypes, which revealed the inbred ancestry of infections and non-Mendelian inheritance. Our analysis suggests strong barriers to secondary infection and outbreeding amongst malaria parasites from a high transmission setting, providing unexpected insights into the biology and transmission of malaria.

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.

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

Individual malaria infections can carry multiple strains of Plasmodium falciparum with varying levels of relatedness. Yet, how local epidemiology affects the properties of such mixed infections remains unclear. Here, we develop an enhanced method for strain deconvolution from genome sequencing data, which estimates the number of strains, their proportions, identity-by-descent (IBD) profiles and individual haplotypes. Applying it to the Pf3k data set, we find that the rate of mixed infection varies from 29% to 63% across countries and that 51% of mixed infections involve more than two strains. Furthermore, we estimate that 47% of symptomatic dual infections contain sibling strains likely to have been co-transmitted from a single mosquito, and find evidence of mixed infections propagated over successive infection cycles. Finally, leveraging data from the Malaria Atlas Project, we find that prevalence correlates within Africa, but not Asia, with both the rate of mixed infection and the level of IBD.

Genetic diversity and drug resistance surveillance of Plasmodium falciparum for malaria elimination: is there an ideal tool for resource-limited sub-Saharan Africa?

The intensification of malaria control interventions has resulted in its global decline, but it remains a significant public health burden especially in sub-Saharan Africa (sSA). Knowledge on the parasite diversity, its transmission dynamics, mechanisms of adaptation to environmental and interventional pressures could help refine or develop new control and elimination strategies. Critical to this is the accurate assessment of the parasite’s genetic diversity and monitoring of genetic markers of anti-malarial resistance across all susceptible populations. Such wide molecular surveillance will require selected tools and approaches from a variety of ever evolving advancements in technology and the changing epidemiology of malaria. The choice of an effective approach for specific endemic settings remains challenging, particularly for countries in sSA with limited access to advanced technologies. This article examines the current strategies and tools for Plasmodium falciparum genetic diversity typing and resistance monitoring and proposes how the different tools could be employed in resource-poor settings. Advanced approaches enabling targeted deep sequencing is valued as a sensitive method for assessing drug resistance and parasite diversity but remains out of the reach of most laboratories in sSA due to the high cost of development and maintenance. It is, however, feasible to equip a limited number of laboratories as Centres of Excellence in Africa (CEA), which will receive and process samples from a network of peripheral laboratories in the continent. Cheaper, sensitive and portable real-time PCR methods can be used in peripheral laboratories to pre-screen and select samples for targeted deep sequence or genome wide analyses at these CEAs.

Intra-host dynamics of co-infecting parasite genotypes in asymptomatic malaria patients

Malaria-infected individuals often harbor mixtures of genetically distinct parasite genotypes. We studied intra-host dynamics of parasite genotypes co-infecting asymptomatic adults in an area of intense malaria transmission in Chikhwawa, Malawi. Serial blood samples (5 ml) were collected over seven consecutive days from 25 adults with asymptomatic Plasmodium falciparum malaria and analyzed to determine whether a single peripheral blood sample accurately captures within-host parasite diversity. Blood samples from three of the participants were also analyzed by limiting dilution cloning and SNP genotyping of the parasite clones isolated to examine both the number and relatedness of co-infecting parasite haplotypes. We observed rapid turnover of co-infecting parasite genotypes in 88% of the individuals sampled (n = 22) such that the genetic composition of parasites infecting these individuals changed dramatically over the course of seven days of follow up. Nineteen of the 25 individuals sampled (76%) carried multiple parasite genotypes at baseline. Analysis of serial blood samples from three of the individuals revealed that they harbored 6, 12 and 17 distinct parasite haplotypes respectively. Approximately 70% of parasite haplotypes recovered from the three extensively sampled individuals were unrelated (proportion of shared alleles <83.3%) and were deemed to have primarily arisen from superinfection (inoculation of unrelated parasite haplotypes through multiple mosquito bites). The rest were related at the half-sib level or greater and were deemed to have been inoculated into individual human hosts via parasite co-transmission from single mosquito bites. These findings add further to the growing weight of evidence indicating that a single blood sample poorly captures within-host parasite diversity and underscore the importance of repeated blood sampling to accurately capture within-host parasite ecology. Our data also demonstrate a more pronounced role for parasite co-transmission in generating within-host parasite diversity in high transmission settings than previously assumed. Taken together, these findings have important implications for understanding the evolution of drug resistance, malaria transmission, parasite virulence, allocation of gametocyte sex ratios and acquisition of malaria immunity.

Exploration of Plasmodium vivax transmission dynamics and recurrent infections in the Peruvian Amazon using whole genome sequencing

BACKGROUND

Plasmodium vivax poses a significant challenge to malaria elimination due to its ability to cause relapsed infections from reactivation of dormant liver parasites called hypnozoites. We analyzed 69 P. vivax whole genome sequences obtained from subjects residing in three different villages along the Peruvian Amazon. This included 23 paired P. vivax samples from subjects who experienced recurrent P. vivax parasitemia following observed treatment with chloroquine and primaquine.

METHODS

Genomic DNA was extracted from whole blood samples collected from subjects. P. vivax DNA was enriched using selective whole genome amplification and whole genome sequencing. We used single nucleotide polymorphisms (SNPs) from the core P. vivax genome to determine characteristics of the parasite population using discriminant analysis of principal components, maximum likelihood estimation of individual ancestries, and phylogenetic analysis. We estimated the relatedness of the paired samples by calculating the number of segregating sites and using a hidden Markov model approach to estimate identity by descent.

RESULTS

We present a comprehensive dataset of population genetics of Plasmodium vivax in the Peruvian Amazonian. We define the parasite population structure in this region and demonstrate a novel method for distinguishing homologous relapses from reinfections or heterologous relapses with improved accuracy. The parasite population in this area was quite diverse with an estimated five subpopulations and evidence of a highly heterogeneous ancestry of some of the isolates, similar to previous analyses of P. vivax in this region. Pairwise comparison of recurrent infections determined that there were 12 homologous relapses and 3 likely heterologous relapses with highly related parasites. To the best of our knowledge, this is the first large-scale study to evaluate recurrent P. vivax infections using whole genome sequencing.

CONCLUSIONS

Whole genome sequencing is a high-resolution tool that can identify P. vivax homologous relapses with increased sensitivity, while also providing data about drug resistance and parasite population genetics. This information is important for evaluating the efficacy of known and novel antirelapse medications in endemic areas and thus advancing the campaign to eliminate malaria.

Identity-by-descent analyses for measuring population dynamics and selection in recombining pathogens

Identification of genomic regions that are identical by descent (IBD) has proven useful for human genetic studies where analyses have led to the discovery of familial relatedness and fine-mapping of disease critical regions. Unfortunately however, IBD analyses have been underutilized in analysis of other organisms, including human pathogens. This is in part due to the lack of statistical methodologies for non-diploid genomes in addition to the added complexity of multiclonal infections. As such, we have developed an IBD methodology, called isoRelate, for analysis of haploid recombining microorganisms in the presence of multiclonal infections. Using the inferred IBD status at genomic locations, we have also developed a novel statistic for identifying loci under positive selection and propose relatedness networks as a means of exploring shared haplotypes within populations. We evaluate the performance of our methodologies for detecting IBD and selection, including comparisons with existing tools, then perform an exploratory analysis of whole genome sequencing data from a global Plasmodium falciparum dataset of more than 2500 genomes. This analysis identifies Southeast Asia as having many highly related isolates, possibly as a result of both reduced transmission from intensified control efforts and population bottlenecks following the emergence of antimalarial drug resistance. Many signals of selection are also identified, most of which overlap genes that are known to be associated with drug resistance, in addition to two novel signals observed in multiple countries that have yet to be explored in detail. Additionally, we investigate relatedness networks over the selected loci and determine that one of these sweeps has spread between continents while the other has arisen independently in different countries. IBD analysis of microorganisms using isoRelate can be used for exploring population structure, positive selection and haplotype distributions, and will be a valuable tool for monitoring disease control and elimination efforts of many diseases.

There are growing concerns over the emergence of antimicrobial drug resistance, which threatens the efficacy of treatments for infectious diseases such as malaria. As such, it is important to understand the dynamics of resistance by investigating population structure, natural selection and disease transmission in microorganisms. The study of disease dynamics has been hampered by the lack of suitable statistical models for analysis of isolates containing multiple infections. We introduce a statistical model that uses population genomic data to identify genomic regions (loci) that are inherited from a common ancestor, in the presence of multiple infections. We demonstrate its potential for biological discovery using a global Plasmodium falciparum dataset. We identify low genetic diversity in isolates from Southeast Asia, possibly from clonal expansion following intensified control efforts after the emergence of artemisinin resistance. We also identify loci under positive selection, most of which contain genes that have been associated with antimalarial drug resistance. We discover two loci under strong selection in multiple countries throughout Southeast Asia and Africa where the selection pressure is currently unknown. We find that the selection pressure at one of these loci has originated from gene flow, while the other loci has originated from multiple independent events.

hmmIBD: software to infer pairwise identity by descent between haploid genotypes

Background

A number of recent malaria studies have used identity by descent (IBD) to study epidemiological processes relevant to malaria control. In this paper, a software package, hmmIBD, is introduced for estimating pairwise IBD between haploid genomes, such as those of the malaria parasite, sampled from one or two populations. Source code is freely available.

Methods

The performance of hmmIBD was verified using simulated data and benchmarked against an existing method for detecting IBD within populations. Code for all tests is freely available. The utility of hmmIBD for detecting IBD across populations was demonstrated using Plasmodium falciparum data from Cambodia and Ghana.

Results

Alongside an existing method, hmmIBD was highly accurate, sensitive and specific. It is fast, requiring only 70 s on average to analyse 50 whole genome sequences on a laptop computer, and scales linearly in the number of pairwise comparisons. Treatment of different populations under hmmIBD improves detection of IBD across populations.

Conclusion

Fast and accurate software for detecting IBD in malaria parasite genetic data sampled from one or two populations is presented. The latter will likely be a useful feature for malaria elimination efforts, since it could facilitate identification of imported malaria cases. Software is robust to possible misspecification of the genotyping error and the recombination rate. However, exclusion of data in regions whose rates vary greatly from their genome-wide average is recommended.

Electronic supplementary material

The online version of this article (10.1186/s12936-018-2349-7) contains supplementary material, which is available to authorized users.

Modeling the genetic relatedness of Plasmodium falciparum parasites following meiotic recombination and cotransmission

Unlike in most pathogens, multiple-strain (polygenomic) infections of P. falciparum are frequently composed of genetic siblings. These genetic siblings are the result of sexual reproduction and can coinfect the same host when cotransmitted by the same mosquito. The degree with which coinfecting strains are related varies among infections and populations. Because sexual recombination occurs within the mosquito, the relatedness of cotransmitted strains could depend on transmission dynamics, but little is actually known of the factors that influence the relatedness of cotransmitted strains. Part of the uncertainty stems from an incomplete understanding of how within-host and within-vector dynamics affect cotransmission. Cotransmission is difficult to examine experimentally but can be explored using a computational model. We developed a malaria transmission model that simulates sexual reproduction in order to understand what determines the relatedness of cotransmitted strains. This study highlights how the relatedness of cotransmitted strains depends on both within-host and within-vector dynamics including the complexity of infection. We also used our transmission model to analyze the genetic relatedness of polygenomic infections following a series of multiple transmission events and examined the effects of superinfection. Understanding the factors that influence the relatedness of cotransmitted strains could lead to a better understanding of the population-genetic correlates of transmission and therefore be important for public health.

Genomic studies of P. falciparum reveal that multi-strain infections can include genetically related strains. P. falciparum must reproduce sexually in the mosquito vector. One consequence of sexual reproduction is that parasites cotransmitted by the same mosquito are related to one another. The degree of genetic relatedness of these parasites can be as great as that of full-siblings. However, our understanding of the cotransmission process is incomplete, and little is known of the role of cotransmission in influencing population genomic processes. To help bridge this gap, we developed a simulation model to determine which of the steps involved in transmission have the greatest impact on the relatedness of parasites cotransmitted by a mosquito vector. The primary goal of this study is to characterize the outcomes of cotransmission following single or multiple transmission events. Our model yields new insights into the cotransmission process, which we believe will be useful for understanding the results from more complicated population models and epidemiological conditions. Such an understanding is important for the use of population genomics to inform public health decisions as well as for understanding of parasite evolution.

Quantifying connectivity between local Plasmodium falciparum malaria parasite populations using identity by descent

With the rapidly increasing abundance and accessibility of genomic data, there is a growing interest in using population genetic approaches to characterize fine-scale dispersal of organisms, providing insight into biological processes across a broad range of fields including ecology, evolution and epidemiology. For sexually recombining haploid organisms such as the human malaria parasite P. falciparum, however, there have been no systematic assessments of the type of data and methods required to resolve fine scale connectivity. This analytical gap hinders the use of genomics for understanding local transmission patterns, a crucial goal for policy makers charged with eliminating this important human pathogen. Here we use data collected from four clinics with a catchment area spanning approximately 120 km of the Thai-Myanmar border to compare the ability of divergence (FST) and relatedness based on identity by descent (IBD) to resolve spatial connectivity between malaria parasites collected from proximal clinics. We found no relationship between inter-clinic distance and FST, likely due to sampling of highly related parasites within clinics, but a significant decline in IBD-based relatedness with increasing inter-clinic distance. This association was contingent upon the data set type and size. We estimated that approximately 147 single-infection whole genome sequenced parasite samples or 222 single-infection parasite samples genotyped at 93 single nucleotide polymorphisms (SNPs) were sufficient to recover a robust spatial trend estimate at this scale. In summary, surveillance efforts cannot rely on classical measures of genetic divergence to measure P. falciparum transmission on a local scale. Given adequate sampling, IBD-based relatedness provides a useful alternative, and robust trends can be obtained from parasite samples genotyped at approximately 100 SNPs.

Malaria Epidemiology at the Clone Level

Genotyping to distinguish between parasite clones is nowadays a standard in many molecular epidemiological studies of malaria. It has become crucial in drug trials and to follow individual clones in epidemiological studies, and to understand how drug resistance emerges and spreads. Here, we review the applications of the increasingly available genotyping tools and whole-genome sequencing data, and argue for a better integration of population genetics findings into malaria-control strategies.