Impact of Multiplicity of Plasmodium falciparum Infection on Clinical Disease in Malawi

Strong isolation by distance and evidence of population microstructure reflect ongoing Plasmodium falciparum transmission in Zanzibar

Background

The Zanzibar archipelago of Tanzania has become a low-transmission area for Plasmodium falciparum. Despite being considered an area of pre-elimination for years, achieving elimination has been difficult, likely due to a combination of imported infections from mainland Tanzania and continued local transmission.

Methods

To shed light on these sources of transmission, we applied highly multiplexed genotyping utilizing molecular inversion probes to characterize the genetic relatedness of 282 P. falciparum isolates collected across Zanzibar and in Bagamoyo district on the coastal mainland from 2016 to 2018.

Results

Overall, parasite populations on the coastal mainland and Zanzibar archipelago remain highly related. However, parasite isolates from Zanzibar exhibit population microstructure due to the rapid decay of parasite relatedness over very short distances. This, along with highly related pairs within shehias, suggests ongoing low-level local transmission. We also identified highly related parasites across shehias that reflect human mobility on the main island of Unguja and identified a cluster of highly related parasites, suggestive of an outbreak, in the Micheweni district on Pemba island. Parasites in asymptomatic infections demonstrated higher complexity of infection than those in symptomatic infections, but have similar core genomes.

Conclusions

Our data support importation as a main source of genetic diversity and contribution to the parasite population in Zanzibar, but they also show local outbreak clusters where targeted interventions are essential to block local transmission. These results highlight the need for preventive measures against imported malaria and enhanced control measures in areas that remain receptive to malaria reemergence due to susceptible hosts and competent vectors.

Funding

This research was funded by the National Institutes of Health, grants R01AI121558, R01AI137395, R01AI155730, F30AI143172, and K24AI134990. Funding was also contributed from the Swedish Research Council, Erling-Persson Family Foundation, and the Yang Fund. RV acknowledges funding from the MRC Centre for Global Infectious Disease Analysis (reference MR/R015600/1), jointly funded by the UK Medical Research Council (MRC) and the UK Foreign, Commonwealth & Development Office (FCDO), under the MRC/FCDO Concordat agreement and is also part of the EDCTP2 program supported by the European Union. RV also acknowledges funding by Community Jameel.

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.

A Comparative Study of Genetic Diversity and Multiplicity of Infection in Uncomplicated Plasmodium falciparum Infections in Selected Regions of Pre-Elimination and High Transmission Settings Using MSP1 and MSP2 Genes

BACKGROUND

Understanding the genetic structure of P. falciparum population in different regions is pivotal to malaria elimination. Genetic diversity and the multiplicity of infection are indicators used for measuring malaria endemicity across different transmission settings. Therefore, this study characterized P. falciparum infections from selected areas constituting pre-elimination and high transmission settings in South Africa and Nigeria, respectively.

METHODS

Parasite genomic DNA was extracted from 129 participants with uncomplicated P. falciparum infections. Isolates were collected from 78 participants in South Africa (southern Africa) and 51 in Nigeria (western Africa). Allelic typing of the msp1 and msp2 genes was carried out using nested PCR.

RESULTS

In msp1, the K1 allele (39.7%) was the most common allele among the South African isolates, while the RO33 allele (90.2%) was the most common allele among the Nigerian isolates. In the msp2 gene, FC27 and IC3D7 showed almost the same percentage distribution (44.9% and 43.6%) in the South African isolates, whereas FC27 had the highest percentage distribution (60.8%) in the Nigerian isolates. The msp2 gene showed highly distinctive genotypes, indicating high genetic diversity in the South African isolates, whereas msp1 showed high genetic diversity in the Nigerian isolates. The RO33 allelic family displayed an inverse relationship with participants' age in the Nigerian isolates. The overall multiplicity of infection (MOI) was significantly higher in Nigeria (2.87) than in South Africa (2.44) (p < 0.000 *). In addition, heterozygosity was moderately higher in South Africa (1.46) than in Nigeria (1.13).

CONCLUSIONS

The high genetic diversity and MOI in P. falciparum that were observed in this study could provide surveillance data, on the basis of which appropriate control strategies should be adopted.

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

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

Population genomics in neglected malaria parasites

Malaria elimination includes neglected human malaria parasites Plasmodium vivax, Plasmodium ovale spp., and Plasmodium malariae. Biological features such as association with low-density infection and the formation of hypnozoites responsible for relapse make their elimination challenging. Studies on these parasites rely primarily on clinical samples due to the lack of long-term culture techniques. With improved methods to enrich parasite DNA from clinical samples, whole-genome sequencing of the neglected malaria parasites has gained increasing popularity. Population genomics of more than 2200 P. vivax global isolates has improved our knowledge of parasite biology and host-parasite interactions, identified vaccine targets and potential drug resistance markers, and provided a new way to track parasite migration and introduction and monitor the evolutionary response of local populations to elimination efforts. Here, we review advances in population genomics for neglected malaria parasites, discuss how the rich genomic information is being used to understand parasite biology and epidemiology, and explore opportunities for the applications of malaria genomic data in malaria elimination practice.

Analysis of Recurrent Times-to-Clinical Malaria Episodes and Plasmodium falciparum Parasitemia: A Joint Modeling Approach Applied to a Cohort Data

Background

Recurrent clinical malaria episodes due to Plasmodium falciparum parasite infection are common in endemic regions. With each infection, acquired immunity develops, making subsequent disease episodes less likely. To capture the effect of acquired immunity to malaria, it may be necessary to model recurrent clinical disease episodes jointly with P. falciparum parasitemia data. A joint model of longitudinal parasitemia and time-to-first clinical malaria episode (single-event joint model) may be inaccurate because acquired immunity is lost when subsequent episodes are excluded. This study's informativeness assessed whether joint modeling of recurrent clinical malaria episodes and parasitemia is more accurate than a single-event joint model where the subsequent episodes are ignored.

Methods

The single event joint model comprised Cox Proportional Hazards (PH) sub-model for time-to-first clinical malaria episode and Negative Binomial (NB) mixed-effects sub-model for the longitudinal parasitemia. The recurrent events joint model extends the survival sub-model to a Gamma shared frailty model to include all recurrent clinical episodes. The models were applied to cohort data from Malawi. Simulations were also conducted to assess the performance of the model under different conditions.

Results

The recurrent events joint model, which yielded higher hazard ratios of clinical malaria, was more precise and in most cases produced smaller standard errors than the single-event joint model; hazard ratio (HR) = 1.42, [95% confidence interval [CI]: 1.22, 2.03] vs. HR = 1.29, [95% CI:1.60, 2.45] among participants who reported not to use LLINs every night compared to those who used the nets every night; HR = 0.96, [ 95% CI: 0.94, 0.98] vs. HR = 0.81, [95% CI: 0.75, 0.88] for each 1-year increase in participants' age; and HR = 1.36, [95% CI: 1.05, 1.75] vs. HR = 1.10, [95% CI: 0.83, 4.11] for observations during the rainy season compared to the dry season.

Conclusion

The recurrent events joint model in this study provides a way of estimating the risk of recurrent clinical malaria in a cohort where the effect of immunity on malaria disease acquired due to P. falciparum parasitemia with aging is captured. The simulation study has shown that if correctly specified, the recurrent events joint model can give risk estimates with low bias.

Plasmodium falciparum msp1 and msp2 genetic diversity in parasites isolated from symptomatic and asymptomatic malaria subjects in the South of Benin

Symptomatic and asymptomatic malaria patients are considered as the reservoirs of human Plasmodium. In the present study, we have evaluated the Plasmodium falciparum merozoite surface protein-1 (Pfmsp1) and protein-2 (Pfmsp2) genetic diversity among the symptomatic and asymptomatic malaria infection from health facilities in Cotonou, Benin Republic. A cross-sectional study recruited 158 individuals, including 77 from the asymptomatic and 81 from the symptomatic groups. The parasites were genotyped using Nested Polymerase Chain Reaction. Samples identified as Plasmodium falciparum were genotyped for their genetic diversity. No significant difference was observed in the overall multiplicity of infection (MOI) between the asymptomatic and symptomatic groups. In the symptomatic group, the overall frequency of K1, MAD20, and RO33 allelic family was more predominant (98.5%) followed by 3D7 (87.3%) and FC27 (83.1%). However, in asymptomatic group, the K1 alleles were the most prevalent (100%) followed by FC27 (89.9%), 3D7 (76.8%), MAD20 (60.5%), and RO33 (35.5%). The frequency of multiple allelic types (K1+MAD20+RO33) at the Pfmsp1 loci in the symptomatic infections was significantly higher when compared to that of the asymptomatic ones (97% vs. 34%, p < 0.05), whereas no difference was observed in the frequency of multiple allelic types (3D7 and FC27) at the Pfmsp2 loci between the two groups. The high presence of msp1 multiple infections in the symptomatic group compared to asymptomatic ones suggests an association between the genetic diversity and the onset of malaria symptoms. These data can provide valuable information in the development of a vaccine that could reduce the symptomatic disease.