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

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/.

Hyper-diverse antigenic variation and resilience to transmission-reducing intervention in falciparum malaria

Intervention efforts against falciparum malaria in high-transmission regions remain challenging, with rapid resurgence typically following their relaxation. Such resilience co-occurs with incomplete immunity and a large transmission reservoir from high asymptomatic prevalence. Incomplete immunity relates to the large antigenic variation of the parasite, with the major surface antigen of the blood stage of infection encoded by the multigene and recombinant family known as var. With a stochastic agent-based model, we investigate the existence of a sharp transition in resurgence ability with intervention intensity and identify molecular indicators informative of its proximity. Their application to survey data with deep sampling of var sequences from individual isolates in northern Ghana suggests that the transmission system was brought close to transition by intervention with indoor residual spraying. These results indicate that sustaining and intensifying intervention would have pushed malaria dynamics to a slow-rebound regime with an increased probability of local parasite extinction.

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/.

Measuring changes in Plasmodium falciparum census population size in response to sequential malaria control interventions

Here we introduce a new endpoint ″census population size″ to evaluate the epidemiology and control of Plasmodium falciparum infections, where the parasite, rather than the infected human host, is the unit of measurement. To calculate census population size, we rely on a definition of parasite variation known as multiplicity of infection (MOI var ), based on the hyper-diversity of the var multigene family. We present a Bayesian approach to estimate MOI var from sequencing and counting the number of unique DBLα tags (or DBLα types) of var genes, and derive from it census population size by summation of MOI var in the human population. We track changes in this parasite population size and structure through sequential malaria interventions by indoor residual spraying (IRS) and seasonal malaria chemoprevention (SMC) from 2012 to 2017 in an area of high-seasonal malaria transmission in northern Ghana. Following IRS, which reduced transmission intensity by > 90% and decreased parasite prevalence by ~40-50%, significant reductions in var diversity, MOI var , and population size were observed in ~2,000 humans across all ages. These changes, consistent with the loss of diverse parasite genomes, were short lived and 32-months after IRS was discontinued and SMC was introduced, var diversity and population size rebounded in all age groups except for the younger children (1-5 years) targeted by SMC. Despite major perturbations from IRS and SMC interventions, the parasite population remained very large and retained the var population genetic characteristics of a high-transmission system (high var diversity; low var repertoire similarity) demonstrating the resilience of P. falciparum to short-term interventions in high-burden countries of sub-Saharan Africa.

Two decades of molecular surveillance in Senegal reveal rapid changes in known drug resistance mutations over time

BACKGROUND

Drug resistance in Plasmodium falciparum is a major threat to malaria control efforts. Pathogen genomic surveillance could be invaluable for monitoring current and emerging parasite drug resistance.

METHODS

Data from two decades (2000-2020) of continuous molecular surveillance of P. falciparum parasites from Senegal were retrospectively examined to assess historical changes in malaria drug resistance mutations. Several known drug resistance markers and their surrounding haplotypes were profiled using a combination of single nucleotide polymorphism (SNP) molecular surveillance and whole genome sequence based population genomics.

RESULTS

This dataset was used to track temporal changes in drug resistance markers whose timing correspond to historically significant events such as the withdrawal of chloroquine (CQ) and the introduction of sulfadoxine-pyrimethamine (SP) in 2003. Changes in the mutation frequency at Pfcrt K76T and Pfdhps A437G coinciding with the 2014 introduction of seasonal malaria chemoprevention (SMC) in Senegal were observed. In 2014, the frequency of Pfcrt K76T increased while the frequency of Pfdhps A437G declined. Haplotype-based analyses of Pfcrt K76T showed that this rapid increase was due to a recent selective sweep that started after 2014.

DISCUSSION (CONCLUSION)

The rapid increase in Pfcrt K76T is troubling and could be a sign of emerging amodiaquine (AQ) resistance in Senegal. Emerging AQ resistance may threaten the future clinical efficacy of artesunate-amodiaquine (ASAQ) and AQ-dependent SMC chemoprevention. These results highlight the potential of molecular surveillance for detecting rapid changes in parasite populations and stress the need to monitor the effectiveness of AQ as a partner drug for artemisinin-based combination therapy (ACT) and for chemoprevention.

From multiplicity of infection to force of infection for sparsely sampled Plasmodium falciparum populations at high transmission

High multiplicity of infection or MOI, the number of genetically distinct parasite strains co-infecting a single human host, characterizes infectious diseases including falciparum malaria at high transmission. It accompanies high asymptomatic Plasmodium falciparum prevalence despite high exposure, creating a large transmission reservoir challenging intervention. High MOI and asymptomatic prevalence are enabled by immune evasion of the parasite achieved via vast antigenic diversity. Force of infection or FOI, the number of new infections acquired by an individual host over a given time interval, is the dynamic sister quantity of MOI, and a key epidemiological parameter for monitoring the impact of antimalarial interventions and assessing vaccine or drug efficacy in clinical trials. FOI remains difficult, expensive, and labor-intensive to accurately measure, especially in high-transmission regions, whether directly via cohort studies or indirectly via the fitting of epidemiological models to repeated cross-sectional surveys. We propose here the application of queuing theory to obtain FOI on the basis of MOI, in the form of either a two-moment approximation method or Little's law. We illustrate these methods with MOI estimates obtained under sparse sampling schemes with the recently proposed " v a r coding" method, based on sequences of the v a r multigene family encoding for the major variant surface antigen of the blood stage of malaria infection. The methods are evaluated with simulation output from a stochastic agent-based model, and are applied to an interrupted time-series study from Bongo District in northern Ghana before and immediately after a three-round transient indoor residual spraying (IRS) intervention. We incorporate into the sampling of the simulation output, limitations representative of those encountered in the collection of field data, including under-sampling of v a r genes, missing data, and usage of antimalarial drug treatment. We address these limitations in MOI estimates with a Bayesian framework and an imputation bootstrap approach. We demonstrate that both proposed methods give good and consistent FOI estimates across various simulated scenarios. Their application to the field surveys shows a pronounced reduction in annual FOI during intervention, of more than 70%. The proposed approach should be applicable to the many geographical locations where cohort or cross-sectional studies with regular and frequent sampling are lacking but single-time-point surveys under sparse sampling schemes are available, and for MOI estimates obtained in different ways. They should also be relevant to other pathogens of humans, wildlife and livestock whose immune evasion strategies are based on large antigenic variation resulting in high multiplicity of 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.

Hyper-diverse antigenic variation and resilience to transmission-reducing intervention in falciparum malaria

Intervention against falciparum malaria in high transmission regions remains challenging, with relaxation of control efforts typically followed by rapid resurgence. Resilience to intervention co-occurs with incomplete immunity, whereby children eventually become protected from severe disease but not infection and a large transmission reservoir results from high asymptomatic prevalence across all ages. Incomplete immunity relates to the vast antigenic variation of the parasite, with the major surface antigen of the blood stage of infection encoded by the multigene family known as var. Recent deep sampling of var sequences from individual isolates in northern Ghana showed that parasite population structure exhibited persistent features of high-transmission regions despite the considerable decrease in prevalence during transient intervention with indoor residual spraying (IRS). We ask whether despite such apparent limited impact, the transmission system had been brought close to a transition in both prevalence and resurgence ability. With a stochastic agent-based model, we investigate the existence of such a transition to pre-elimination with intervention intensity, and of molecular indicators informative of its approach. We show that resurgence ability decreases sharply and nonlinearly across a narrow region of intervention intensities in model simulations, and identify informative molecular indicators based on var gene sequences. Their application to the survey data indicates that the transmission system in northern Ghana was brought close to transition by IRS. These results suggest that sustaining and intensifying intervention would have pushed malaria dynamics to a slow-rebound regime with an increased probability of local parasite extinction.

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.

Evaluating the performance of Plasmodium falciparum genetics for inferring National Malaria Control Program reported incidence in Senegal

Genetic surveillance of the Plasmodium falciparum parasite shows great promise for helping National Malaria Control Programs (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. Here, we examined parasites from 3,147 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, we constructed a series of Poisson generalized linear mixed-effects models 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. We compared the model-predicted incidence 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 [‰]). 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 ‰, we found that many of the correlations between parasite genetics and incidence were reversed, which we hypothesize reflects the disproportionate impact of importation and focal transmission on parasite genetics when local transmission levels are low.

High Prevalence of Polyclonal Plasmodium falciparum Infections and Association with Poor IgG Antibody Responses in a Hyper-Endemic Area in Cameroon

Malaria remains a major public health problem worldwide, with eradication efforts thwarted by drug and insecticide resistance and the lack of a broadly effective malaria vaccine. In continuously exposed communities, polyclonal infections are thought to reduce the risk of severe disease and promote the establishment of asymptomatic infections. We sought to investigate the relationship between the complexity of P. falciparum infection and underlying host adaptive immune responses in an area with a high prevalence of asymptomatic parasitaemia in Cameroon. A cross-sectional study of 353 individuals aged 2 to 86 years (median age = 16 years) was conducted in five villages in the Centre Region of Cameroon. Plasmodium falciparum infection was detected by multiplex nested PCR in 316 samples, of which 278 were successfully genotyped. Of these, 60.1% (167/278) were polyclonal infections, the majority (80.2%) of which were from asymptomatic carriers. Host-parasite factors associated with polyclonal infection in the study population included peripheral blood parasite density, participant age and village of residence. The number of parasite clones per infected sample increased significantly with parasite density (r = 0.3912, p < 0.0001) but decreased with participant age (r = -0.4860, p < 0.0001). Parasitaemia and the number of clones per sample correlated negatively with total plasma levels of IgG antibodies to three highly reactive P. falciparum antigens (MSP-1p19, MSP-3 and EBA175) and two soluble antigen extracts (merozoite and mixed stage antigens). Surprisingly, we observed no association between the frequency of polyclonal infection and susceptibility to clinical disease as assessed by the recent occurrence of malarial symptoms or duration since the previous fever episode. Overall, the data indicate that in areas with the high perennial transmission of P. falciparum, parasite polyclonality is dependent on underlying host antibody responses, with the majority of polyclonal infections occurring in persons with low levels of protective anti-plasmodial antibodies.

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.