Assessment of Plasmodium falciparum genetic diversity, multiplicity of infection and algorithms for distinguishing recrudescence from reinfection in antimalarial drug efficacy studies in Uganda

Abstract

Background: Despite renewed control efforts, malaria remains a major public health problem, contributing significantly to morbidity and mortality, especially in endemic countries. Effective control is hindered by several factors, including the high genetic diversity of Plasmodium falciparum, the presence of multiple parasite clones within a single host (referred to as multiplicity of infection (MOI)), and the persistence of parasite strains despite treatment (recrudescence). This PhD study aimed to assess P. falciparum genetic diversity and MOI with in sub-Saharan Africa (SSA), with a specific focus on areas of varying malaria transmission intensity in Uganda. Additionally, we evaluated the performance of different microsatellite markers and genotyping algorithms in distinguishing recrudescence from reinfection, an essential component of antimalarial drug efficacy monitoring. Methods: This study employed a combination of systematic review, meta-analysis, and secondary data analysis using both cross-sectional and longitudinal studies. To evaluate P.falciparum genetic diversity and MOI across different geographical regions in SSA (Objective 1), I conducted a systematic review and meta-analysis of published articles. The review focused on studies using common molecular markers, including genes of merozoite surface protein 1 and 2 (msp-1, msp-2), and microsatellites. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. To determine how P. falciparum genetic diversity and MOI vary by malaria transmission intensity, clinical disease status (asymptomatic versus symptomatic), and over time (Objectives 2, 3, and 4), I performed secondary analyses of data and dried blood spot (DBS) filter paper samples from comprehensive surveillance studies conducted between 2011 and 2017 in areas of varying transmission intensities in Uganda. To assess the performance of microsatellite markers and three genotyping algorithms (3/3 match-counting, ≥2/3 match￾xiii counting, and the Bayesian approach) in distinguishing between recrudescence and reinfection (Objective 5), I analyzed data and DBS filter paper samples from a therapeutic efficacy study (TES) conducted between September 2018 and February 2019 at three health centers located in high malaria transmission regions of Uganda. Demographic and clinical data were retrieved from primary study databases, while genetic data were generated through genotyping of P. falciparum isolates using the highly polymorphic markers msp-1, msp-2, and microsatellites. Genomic DNA was extracted from DBS samples using Chelex 100 Resin (Sigma-Aldrich, USA). Genotyping of msp-1, msp-2, and glurp was performed using nested PCR, with amplicon sizing on the QIAxcel Advanced System (QIAGEN). For microsatellites, seven loci (TA1, Poly-α, PfPK2, TA109, 2490, C2M34–313, and C3M69–383) were amplified using fluorescently labeled primers, and allele sizing was conducted via high-resolution capillary electrophoresis on an ABI 3730 Genetic Analyzer. P. falciparum genetic diversity was assessed by calculating allele count, effective allele number, allelic richness, and expected heterozygosity (He). P. falciparum MOI was estimated by determining the number of distinct genotypes per infection. Population structure was assessed using multilocus linkage disequilibrium (LD), genetic differentiation, analysis of molecular variance (AMOVA), and clustering analysis. Statistical comparisons were made using the Kruskal–Wallis test, Chi-square test, or Fisher’s exact test, as appropriate. Temporal trends were analyzed using the Jonckheere–Terpstra test, while genotyping algorithm performance was assessed using the Wilcoxon signed-rank test. For all analyses, 95% confidence intervals and/or standard deviations were reported where applicable, with statistical significance set at p < 0.05. Results: The systematic review and meta-analysis showed significant regional variation in P. falciparum genetic diversity and MOI across SSA. The pooled mean He was 0.65 (95% CI: 0.51–0.78), and the pooled mean MOI was 2.09 (CI: 1.88–2.30), with 63% (CI: 56–70%) of cases showing polyclonal infections. When P. falciparum genetic diversity was compared between areas with different malaria transmission intensities in Uganda, no statistically significant variation was observed across sites (p > 0.05), though Kanungu (a low transmission site) showed the highest mean He (0.81 ± 0.14), while the mean He was slightly lower and similar in both Jinja and Tororo (both 0.78 ± 0.16). Regarding MOI, Tororo, a high-transmission site, exhibited a significantly higher mean MOI (2.5 ± 0.5) and polyclonal infection rate (70%) compared to Kanungu (MOI: 2.0 ± 0.05; polyclonal rate: 56%) and Jinja (MOI: 1.9 ± 0.3; polyclonal rate: 58%) (p = 0.004). Genetic structure analysis indicated minimal differentiation (FST: 0.011–0.021). When comparing P. falciparum genetic diversity and MOI between asymptomatic and symptomatic participants, both groups exhibited high genetic diversity (He 0.79–0.81). Polyclonal infections were common in both groups (58.5% in symptomatic and 63% in asymptomatic). Asymptomatic participants had a higher mean MOI (2.10) than symptomatic individuals (1.92), though the difference was not statistically significant (p = 0.33). Temporal analyses showed a slight decline in He from 0.79 to 0.76, with polyclonal infections remaining above 50% throughout the period. Finally, the 3/3 match-counting algorithm identified significantly fewer recrudescence cases compared to the ≥2/3 match-counting and Bayesian algorithms (p < 0.001). Conclusions: This study shows significant regional variation in P. falciparum genetic diversity and MOI within SSA. Within Uganda, genetic diversity of P. falciparum did not differ significantly across sites, although MOI varied significantly by transmission intensity. Persistently high genetic diversity, minimal population differentiation, and sustained polyclonal infections were observed across clinical disease status and time, reflecting ongoing transmission pressures rather than uniform regional variation. Discrepancies in recrudescence classification between match-counting and Bayesian algorithms highlight the need for standardized PCR correction practices. These findings underscore the need for tailored malaria control strategies, including targeted interventions for asymptomatic carriers and continuous molecular surveillance to adapt to evolving transmission dynamics. Furthermore, the study emphasizes the importance of using robust analytical algorithms to accurately distinguish recrudescence from reinfection, an essential component of effective antimalarial drug efficacy monitoring.