Genetic heterogeneity of Plasmodium falciparum across areas of varying transmission intensity in Ethiopia based on merozoite surface protein (msp) genes msp1 and msp2

Genetic heterogeneity of Plasmodium falciparum across areas of varying transmission intensity in Ethiopia based on merozoite surface protein (msp) genes msp1 and msp2 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genetic heterogeneity of Plasmodium falciparum across areas of varying transmission intensity in Ethiopia based on merozoite surface protein (msp) genes msp1 and msp2 Abeba Reda, Ashenafi Assefa, Lemu Golassa, Hassen Mamo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8218895/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Plasmodium falciparum malaria continues to be a significant threat in Ethiopia. Understanding the genetic diversity of this parasite is crucial for informing public health strategies, including vaccine development, diagnostic accuracy, treatment efficacy, and targeted control interventions. Methods Samples for this study were collected from three malaria-endemic sites in Ethiopia with varying transmission intensities - Metema (northwest), Wondogenet (south), and Metehara (central-east). A consecutive convenient sampling technique was employed to recruit outpatients initially enrolled in an uncomplicated malaria therapeutic efficacy study in 2015 from these sites, along with additional samples collected from malaria suspects attending the Metehara health centre in 2019. Overall, 661 finger-prick blood samples were collected for malaria microscopy and rapid diagnostic tests (RDTs), while dried blood spot (DBS) samples were obtained on Whatman 903® filter paper for molecular analysis. This study specifically utilized selected 150 quantitative real-time polymerase chain reaction (qPCR)-confirmed P. falciparum monoinfection-positive samples from the previously reported total of 661 samples to investigate previously unreported merozoite surface protein ( msp1 and msp2 ) genes-based genetic heterogeneity. Genotyping of P. falciparum msp1 and msp2 genes was conducted using nested PCR, and statistical analysis was performed using SPSS software to assess mean multiplicity of infection (MOI), polyclonal infections and expected heterozygosity index (H e ). Results Out of the total 150 samples analyzed, successful genotyping rates of 45.3% was revealed for msp1 and 43.3% for msp2 across the sites. Notable variations in allele frequencies were observed, with the K1 allele predominating for msp1 and the FC27 allele for msp2 . Metema exhibited the highest complexity of infections, with mean MOI values of 1.53 for msp1 and 2.14 for msp2 , alongside the greatest genetic diversity. The analysis indicated that polyclonal infections were prevalent, particularly in Metema and Wondogenet, with notable differences in allele frequencies over time between samples collected in 2015 and 2019 in Metehara, reflecting shifts in population dynamics. Conclusion This study highlights the genetic diversity and regional variations of P. falciparum in Ethiopia, emphasizing the importance of continuous monitoring to facilitate malaria control strategies. The findings emphasize the need for tailored intervention approaches and ongoing investigation to address the evolving landscape of malaria in the country. Plasmodium falciparum genetic diversity multiplicity of infection (MOI) expected heterozygosity (He) polyclonal infections msp1 msp2 genes genotyping nested PCR Ethiopia Background Plasmodium falciparum malaria, the most severe form, remains a major public health challenge in sub-Saharan Africa (SSA) including Ethiopia despite significant financial investments and ongoing elimination efforts [1]. Investigating P. falciparum genetic diversity is essential for several reasons in the fight against this parasite [2, 3, 4]. Firstly, it can aid in monitoring the effectiveness of malaria control interventions. Secondly, genetic variability can influence the accuracy of molecular diagnostic tests, potentially impacting clinical outcomes. Additionally, such studies provide valuable insights into the evolutionary dynamics of P. falciparum. Understanding genetic diversity helps identify the evolutionary pressures the parasite faces, which can inform predictions about future adaptations and resistance patterns, thereby guiding future research and intervention strategies [5]. Furthermore, genetic diversity studies help inform vaccine development and applicability [6, 7]. Overall, insights gained from the genetic study of P. falciparum can significantly inform public health strategies, enabling tailored approaches that improve the effectiveness of malaria control measures. The P. falciparum merozoite surface protein ( msp1 and msp2 ) genes, which code for the merozoite surface proteins (MSP1 and MSP2) [8], are among several markers that are useful for studying the genetic diversity of the parasite. They are essential for distinguishing genetically different P. falciparum subpopulations and for differentiating recrudescence from new disease infections [9]. MSP1 and MSP2 are key targets for merozoite-based vaccines [10]. Analyzing the genetic diversity of the msp1 and msp2 genes enables the design of vaccines that account for the most common and virulent strains of P. falciparum. This is crucial for improving vaccine efficacy and ensuring broad coverage across different malaria-endemic regions. The msp1 gene is typically classified into three allelic groups (K1, MDA20, and RO33), consisting of 17 sequence blocks, block-2 being the most polymorphic and extensively studied, surrounded by conserved regions [11, 12, 13]. On the other hand, the msp2 gene, which has two allelic families (FC27 and IC/3D7), has five blocks the middle block (block-3) being the most variable [14]. Several studies have been conducted on the msp1 and msp2 genes within Ethiopia [15, 16, 17, 18, 19, 20] to investigate the genetic diversity of P. falciparum. However, given the ongoing evolution of P. falciparum and its capacity to evade diagnostics, drugs and human immune responses [21, 22]; there is a pressing need for additional studies to further elucidate this genetic diversity. The current study aims to assess the genetic heterogeneity of the P. falciparum population across three malaria-endemic sites. By doing so, it seeks to contribute valuable insights into the population structure and dynamics of this pathogen, thereby informing future interventions and control measures. Methods Study areas This study was conducted at three selected areas (Metema, Wondogenet and Metehara) during peak malaria transmission seasons, October and November. Metema is located at an altitude of 1,608m, about 925km northwest of Addis Ababa, bordering Sudan. Limited medical access and a local cross-border market that facilitates the import and export of malaria parasites characterize this area. The area provides a favorable habitat for mosquitoes, and it is a high malaria transmission area [23]. Wondogent, is situated in Southern Ethiopia in the Rift Valley, 261km south of Addis Ababa, at an elevation of 1,723m. The surrounding primary woods and water bodies create ideal conditions for mosquito breeding, contributing to the region's moderate malaria endemicity. Like Metema, Wondogenet is also predominantly affected by P. falciparum [24]. The third site, Metehara, is located at an altitude of 1,200m, 128km from Addis Ababa in the central-east Rift Valley. This area features adjacent rivers and irrigated sugarcane fields that serve as breeding grounds for malaria mosquitoes. Metehara has relatively lower malaria transmission intensity compared to the other two sites but remains predominantly P. falciparum [25]. These three sites were purposefully selected due to their distinct geographical characteristics and levels of malaria endemicity, which possibly influence P. falciparum genetic diversity. Metema, along the international border with Sudan, facilitates significant cross-border population movement, potentially allowing for the mixing of P. falciparum strains. Wondogenet, in the south-central region, is characterized by a more stable population flow, providing a comparative perspective on P. falciparum diversity in a region with relatively contained transmission dynamics. Metehara, along the Ethio-Djibouti highway, serves as a transportation hub and economic corridor, featured with a diverse human population and possibly mixing P. falciparum from various sources. Together, these three regions create a unique opportunity to evaluate how different ecological and demographic factors influence the genetic landscape of P. falciparum in Ethiopia. Study population and sampling technique The study population consisted of outpatients initially recruited for uncomplicated P. falciparum malaria therapeutic efficacy study in 2015. Additionally, malaria suspects attending the Metehara health centre in 2019 were recruited. In both cases, a consecutive convenient sampling technique was employed to recruit the participants. Finger-prick blood samples were collected from the participants for malaria microscopy and rapid diagnostic tests (RDTs). Additionally, dried blood spot (DBS) samples were collected on Whatman 903® filter paper (Schleicher & Schuell BioScience, Keene, NH, USA) for molecular analysis. The number of samples collected and the data thereof have been published previously [15, 26]. In this study, currently unreported information on msp1 and msp2 -based genetic diversity from 150 qPCR-confirmed P. falciparum monoinfection-positive samples from the previous pool of 661 is used. Genotyping of msp1 and msp2 Genotyping at the msp1 and msp2 genes was performed using the nested PCR (nPCR) method [27]. Primers used for genotyping msp1 (block-2), msp2 (block-3) are listed in Additional files 1 and 2, respectively. Two rounds of PCR reactions were carried out in a final volume of 20 μl. In the primary round reaction, 4 μl of template, 10 μl GoTaq Green Master Mix (Promega), 0.5 μl (0.5μM) of each primer, and 5 μl nuclease-free water were used. In secondary rounds, 2 μl of PCR amplicon and 7 μl nuclease-free water were added to the master mix preparation for the secondary amplification reaction. Cycling conditions for both PCR reactions were as follows: initial denaturation at 95 °C for 3 min, followed by 35 cycles for primary and 30 cycles for secondary reactions at 95 °C for 1 min, annealing at 58 °C for 2 min, and extension at 72 °C for 2 min, and a final extension was carried out at 72 °C for 5 min. Positive control (3D7) and DNA-free negative controls were included in each set of reactions. The nested PCR products were resolved in 2% agarose gels stained with ethidium bromide submerged in 0.5× TBE (Tris-borate EDTA) buffer, electrophoresed at 120 V, 400 A for 45 min, and visualized under UV trans-illumination and photographed at 302 nm on a gel documentation system. The sizes of DNA fragments were estimated by visual inspection using a 100-base pair (bp) DNA ladder marker (New England Biolabs, Inc., UK). Data analysis The statistical software SPSS (IBM SPSS Statistics for Windows, Version 20.0. Armonk, NY: IBM Corp.) was used to conduct statistical analyses. For each sample, MOI was scored as the maximum number of alleles observed when all loci were taken into account, and the average multiplicity of infection (MOI) was calculated for each subpopulation. The expected heterozygosity index (H e ) was calculated using the following formula: H e = n/(n−1) (1- ΣPi 2 ), where n is the number of isolates sampled and Pi is the allele frequency [28]. Results The study reveals significant insights into the genetic diversity of P. falciparum through the analysis of msp1 and msp2 allelic families across three study sites. A total of 150 samples were evaluated, with successful genotyping rates of 45.3 % for msp1 and 43.3 % for msp2 . There is variability in allele frequencies across the sites. The K1 allele was the most prevalent for msp1 , while the FC27 allele predominated for msp2 (Table 1). A greater complexity of infections was observed in Metema. It has the highest MOI, with mean values of 1.53 for msp1 and 2.14 for msp2 . Additionally, Metema has substantial genetic diversity exhibiting the highest H e for both genes (Table 2). The analysis of PI for P. falciparum across the three study sites reveals variability in infection complexity. In Metehara-15, 27.8 % of msp1 samples and 28.6 % of msp2 samples exhibited polyclonal infections, while in Metehara-19, these rates slightly decreased to 20.0 % for msp1 and 16.7 % for msp2 . In contrast, Metema showed the highest complexity, with 47.1 % of msp1 samples and 57.1 % of msp2 samples demonstrating polyclonality. Wondogenet also exhibited notable levels of polyclonal infections (PI), with 44.4 % for msp1 and 61.1 % for msp2 . Overall, across all sites, 35.3 % of msp1 samples and 44.6 % of msp2 samples were polyclonal. These findings indicate that PI are common, particularly in Metema and Wondogenet, highlighting the higher complexity of infections in these areas. Understanding these dynamics is crucial for addressing the transmission and genetic diversity of P. falciparum populations. Overall, there is regional differences in the genetic characteristics of P. falciparum populations. The comparison of allele frequencies, MOI, and H e between the samples collected in Metehara-15 (2015) and Metehara-19 (2019) reveals significant temporal variability. In Metehara-15, the frequencies of the msp1 alleles were K1 (48.6 %), MAD20 (37.9 %), and RO33 (14.7 %). For msp2 , the FC27 allele comprised 56.9 %, while the IC/3D7 allele accounted for 43.1 %. In contrast, Metehara-19 exhibited a different distribution, with K1 at 40.0 % for msp1 and FC27 at 50.0 % for msp2 . This shift indicates changes in allele frequencies over the four-year period. Regarding MOI, Metehara-15 demonstrated a mean MOI of 1.6 for msp1 and 1.8 for msp2 . In 2019, the MOI values were slightly lower, with 1.4 for msp1 and 1.6 for msp2 , suggesting a decrease in the complexity of infections. H e also showed notable changes, with values of 0.65 for msp1 and 0.70 for msp2 in 2015. In 2019, these values decreased to 0.60 for msp1 and 0.65 for msp2 , indicating a reduction in genetic diversity. Overall, the findings highlight temporal variability in allele frequencies, MOI, and H e between 2015 and 2019, suggesting shifts in the P. falciparum population dynamics in Metehara. Discussion This study presents data on the genetic diversity of P. falciparum isolates across low, moderate, and high transmission settings in Ethiopia. The findings improve our understanding of malaria transmission dynamics and show the need for tailored interventions to address specific challenges in different endemic settings, ultimately aiming for disease elimination. The current results show that the K1 allelic family (42.6 %) predominantly represents the msp1 gene allele frequency, while the msp2 allelic family FC27 makes up 56.9 % across the three sites. This allelic distribution is consistent with patterns observed in previous studies in Ethiopia [16, 17, 18, 29, 30, 31, 32, 33], and largely agrees with reports from SSA [34, 35, 36, 37, 38; 39, 40, 41, 42, 43, 44, 45, 46, 47]. A meta-analysis by Mwesigwa et al. (2024) [48], involving 52 articles and 11,640 genotyped parasite isolates from 23 SSA countries, reported pooled allele frequencies of 61% for msp1 alleles K1, MAD20, and RO33, and 61 % and 55 % for msp2 alleles I/C 3D7 and FC27, respectively. Notably, Central Africa reported higher frequencies compared to East Africa. The findings are also compatible with those from Asia [49, 50, 51, 52] and South America [53], with few inconsistencies. Notable exceptions include a study in Vietnam [54], where no samples tested positive for RO33, and in northern India, where a diverse range of msp1 alleles was observed, with RO33 being the least common [55]. Discrepancies in msp1 and msp2 alleles among different studies may be attributed to variations in disease transmission intensity, changes in drug policy, and local environmental factors. This emphasizes the importance of localized studies to inform malaria control strategies. Further research is needed to explore these variables and their implications for malaria management. The analysis of allelic families reveals significant genetic diversity and ongoing transmission dynamics of P. falciparum . Temporal data from Metehara, comparisons between samples collected in 2015 and 2019, indicate a slight decline in the frequencies of both msp1 and msp2 alleles. While K1 and FC27 alleles remain dominant, this overall decrease suggests a potential shift in the genetic landscape, possibly due to effective malaria control measures or changes in transmission dynamics. The stability of dominant alleles, despite a decrease in overall frequency, indicates that while control efforts are effective, the existing alleles are well adapted to the local environment. This stresses the need for continuous monitoring. Conversely, Metema shows a higher prevalence of msp2 alleles (52.5 %) compared to msp1 (42.5 %), with the dominance of the FC27 allele suggesting a conducive transmission environment. This elevation in msp2 frequency may reflect local adaptations or selective pressures favoring these alleles, critical for informing targeted interventions in Metema. Wondogenet's unique allele frequency distribution, particularly the high prevalence of the MAD20 allele in msp1 , distinguishes it from the other two sites. This suggests differing ecological or epidemiological factors at play, leading to a distinct local parasite population. The diversity in allele frequencies emphasizes the necessity of localized approaches in malaria control, as genetic variability can significantly impact treatment outcomes and vaccine development efforts. Overall, these findings highlight the dynamic nature of P. falciparum populations and the importance of integrating genetic data into public health strategies. Understanding trends in allele frequencies aids in tracking control measure effectiveness and informs vaccine design. The results stress the need for ongoing surveillance and adaptive strategies to combat falciparum malaria effectively. In this study, the overall mean MOI for msp1 is 2.5±0.64, while for msp2 , it is 2.3±0.45, indicating high genetic diversity within P. falciparum populations in Ethiopia. However, the level of PI varies across sites. Our data is in line with findings from various regions in Ethiopia [15, 16, 17, 20], but is higher than those reported from Boset and Badewacho Districts in Southern Ethiopia [19]. In comparison, a study in South Africa and Nigeria found Nigeria's MOI (2.87) significantly higher than South Africa’s (2.44), while our findings slightly exceed South Africa's values. Mwesigwa et al. (2024) [48] conducted a meta-analysis revealing a pooled mean H e of 0.65 (95 % CI: 0.51–0.78) for the msp1 , msp2 , and glurp genes. Region-specific values were: East (0.58), Central (0.84), Southern (0.74), and West Africa (0.69). 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 PI was 63% (95 % CI: 56-70), highlighting substantial regional variation in genetic diversity and MOI in SSA. Whether MOI values are considered ‘high’ depends on the study's context, including region and historical data. Generally, an MOI greater than 1 indicates multiple genotypes coexisting within individual infections, signifying genetic diversity [56, 57, 58, 59, 60, 61, 62]. Low MOI (e.g., <1.5) may suggest limited genetic diversity, while ‘moderate to high MOI’ (e.g., ≥2-5) reflects a more diverse population often linked to higher transmission rates. The observed MOI values (2.5 and 2.3) suggest ‘moderate to high’ genetic diversity, supporting previously reported values in the areas [15, 26]. More specifically, the observed MOI values indicate varying levels of genetic diversity across regions. In Metehara, a decline in MOI from 2015 to 2019 suggests reduced transmission intensity or genetic diversity over time, accompanied by a lower prevalence of PI. This may reflect improved malaria control measures or changes in population immunity. The stability of H e indicates that while infections with multiple genotypes may have decreased, the genetic diversity within remaining infections has remained consistent. In contrast, Metema exhibits higher MOI values and a greater proportion of PI, indicating a richer genetic landscape and potentially higher transmission rates. Elevated He values highlight the complexity of the P. falciparum population, posing challenges for effective treatment and control strategies. The presence of multiple genotypes complicates vaccine development, as diverse genetic backgrounds may result in varied immune responses. Tailored interventions addressing the local parasite population are essential in Metema, given its location along an international border, facilitating significant mixing of parasite strains from neighboring countries. Superinfection and cotransmission contribute to this genomic diversity. While Kwiatkowski (2024) [63] explored how transmission dynamics and migration impact parasite genomic diversity, the role of new infections and the duration of infections and growth rate is investigated by Pinkevych et al. (2015) [64]. Wondogenet presents a unique case with lower MOI for msp1 but comparable values for msp2 , alongside a high prevalence of PI. This suggests that while overall genetic diversity may be lower for msp1 , there remains a substantial presence of multiple genotypes in the population. Moderate H e values indicate balanced genetic diversity, which could complicate treatment efforts. Overall, these findings stress the importance of ongoing surveillance and monitoring of P. falciparum populations. Effective malaria control measures can lead to reductions in transmission, as evidenced by trends in Metehara, but continuous efforts are necessary to maintain these gains. In regions like Metema, where genetic diversity is high, targeted strategies such as scaled-up vector control and tailored treatment protocols are critical. Understanding the genetic variability of P. falciparum is vital for vaccine development, informing designs that effectively target a broad range of genotypes. By integrating these findings into public health strategies, stakeholders can better address the challenges posed by malaria, working towards more effective control and eventual elimination. Consistent with nearly all previous studies in Ethiopia, we found no significant relationship between MOI and patient age or parasite density. The relationship between MOI, patient age, parasitemia, and clinical outcomes in malaria is complex and remains inconclusive. In high-transmission settings, older individuals often develop acquired immunity, potentially leading to lower MOI as they can clear infections more efficiently. Conversely, younger children, with less developed immunity, may exhibit higher MOI. Research findings vary, with some studies showing a negative correlation between age and MOI, while others find no significant relationship, indicating that malaria endemicity and host genetics play crucial roles. A study in Burkina Faso demonstrated a negative correlation between MOI and both host age and parasite density, suggesting within-host competition among co-infecting genetically distinct P. falciparum variants [65]. In a southern district of Brazzaville, Republic of Congo, a correlation was observed with parasite density, although no statistically significant correlation was found between MOI and patient age [66]. A significant positive correlation between MOI and parasite density was identified along the China-Myanmar border [51], while no significant associations were observed in Laos [49]. Overall, our data further demonstrate the complexity of the relationship between MOI, age, and parasite density across different regions. Apparent inconsistencies in the findings could possibly be influenced by study design, population characteristics, genetic markers used, and host immune status. Additionally, asymptomatic infections and the impact of malaria control interventions complicate the picture, underscoring the need for comprehensive studies to clarify these intricate relationships and their implications for malaria transmission and management. A study by Mayor et al. (2003) [67] observed that MOI did not vary significantly during the first year of life but increased thereafter, only to decrease during adulthood to levels similar to those seen in infants. The researchers found that higher MOI correlated with a decreased risk of submicroscopic infections and was associated with parasite density in infants, children under 4, and adults. Notably, the relationship between PI and malaria morbidity was age-dependent; while infants' risk of subsequent clinical malaria episodes was related to parasite density, older children with a higher number of clones were more likely to experience clinical malaria episodes. Pinkevych et al. (2015) [64] employed mathematical modeling to analyze how variations in the growth rates of blood-stage P. falciparum affect MOI and its relationship with the risk of subsequent malaria. Their findings indicated that PI proportions vary with age, linking these infections to a reduction in blood-stage parasite growth with age. Conversely, a longitudinal study by Earland et al. (2019) [68] revealed no significant association between MOI and clinical disease after controlling for demographic factors and parasite density. Supporting this, Mwesigwa et al. (2024) [69] reported similar MOI in both symptomatic (1.92) and asymptomatic cases (2.10). In contrast, a study in Ethiopia [31] found that higher MOI was significantly associated with severe cases (3.0) compared to uncomplicated infections (2.0), noting that patients with severe malaria were more likely to present PI. This suggests a complex and often contradictory relationship between MOI and disease severity, as discussed by Pacheco et al. (2016) [70]. The relationship between MOI and malaria transmission intensity is also complex. In western Ethiopia, P. falciparum demonstrates low genetic diversity and MOI, regardless of transmission intensity [33]. In contrast, Eswatini exhibits high parasite diversity, significant PI, and high MOI even in low transmission settings, with varying MOI between imported and local cases [71]. This challenges the notion that P. falciparum diversity decreases with lower transmission intensity. Consequently, determinants of MOI, such as age, parasite density, and transmission intensity, appear inconsistent and complex. Additionally, the role of MOI in malaria clinical outcomes and disease severity is not straightforward, underscoring the need for more controlled and locality-specific longitudinal studies. Limitations of the Study A larger sample size representative of various levels of endemicity could provide a more comprehensive understanding of P. falciparum genetic diversity and transmission dynamics. Including asymptomatic cases, considering different levels of disease severity, accounting for seasonal variations, and sociodemographic factors are essential. Longitudinal follow-up studies are needed to derive deeper insights from P. falciparum genetic diversity data. By employing novel molecular tools and more reliable genetic markers, we can effectively monitor control strategies and work towards elimination. Conclusion Differences in allele frequencies among the study sites indicate adaptation to local pressures. The predominance of msp2 FC27 alleles in Metehara (57.1 %) suggests differing selective pressures or transmission dynamics. Certain alleles may confer advantages regarding survival, drug resistance, or immune evasion. Understanding these genetic differences is crucial for informing vaccine development and addressing potential drug resistance, key components in the fight against malaria. Temporal variations enrich our understanding of malaria dynamics, with high MOI values, particularly in Metema for the msp2 gene, suggesting a complex landscape characterized by multiple strains. This complexity has implications for treatment efficacy, disease severity, and drug resistance likelihood. Moreover, the significant genetic diversity indicated by high H e values may increase the parasite's adaptability, complicating control measures and necessitating ongoing monitoring. Overall, these findings emphasize the complex nature of malaria epidemiology in the studied areas, highlighting the need for tailored intervention strategies, scaled-up surveillance systems, and continued investigation to effectively combat malaria. Abbreviations PI: Polyclonal infections H e : Expected heterozygosity; MOI: Multiplicity of infection; MSP1: Merozoite surface protein 1; MSP2: Merozoite surface protein 2; msp1: merozoite surface protein 1 gene; msp2 : merozoite surface protein 2 gene; DBS: Dry Blood Spot qPCR: Quantitative real-time polymerase chain reaction nPCR: nested PCR Declarations Supplementary Information Additional File 1. Primers used for msp1 genotyping. Additional File 2. Primer used for msp2 genotyping. Acknowledgments The authors would like to thank the study participants. We would like to thank MR4 (now BEI Resources, American Type Culture Collection (ATCC), Manassas, VA, USA) for its kind donation of positive control P. falciparum 3D7 strain, primers for msp1 and msp2 , and genotyping. Authors’ contributions AGR was involved in data collection, laboratory work, drafting of the manuscript, and writing of the manuscript. AA was involved in data analysis and interpretation and critically revised the manuscript. LG supervised data generation, analysis, and interpretation, and also critically revised the manuscript. HM conceived the research idea, designed the study, was involved in data analysis and interpretation and overall supervision, and critically revised the manuscript. All authors have read and approved the final version of the manuscript. Funding Funding was partially obtained from Addis Ababa University and Human Heredity and Health in Africa (H3Africa) [H3A-18-002]. H3Africa is managed by the Science for Africa Foundation (SFA Foundation) in partnership with Welcome, NIH, and AfSHG, which did not play a role in the design of the study, collection of samples, analysis, and interpretation of the data, and writing of the manuscript. Availability of data and materials The dataset supporting the conclusion of this article is included within the article, and the primers used to genotype the two genes are presented in the attached Additional files 1, 2. Ethics approval and consent to participate The study was ethically approved by the Institutional Review Board (IRB) of the College of Natural and Computational Sciences, Addis Ababa University, certificate reference number IRB/033/2018. Written informed consent/assent was obtained from participants or parents/guardians for minors. Malaria-positive cases were treated as per the national treatment guideline. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References Oladipo HJ, Tajudeen YA, Oladunjoye IO, Yusuf SI, Yusuf RO, Oluwaseyi EM, et al. Increasing challenges of malaria control in sub-Saharan Africa: priorities for public health research and policymakers. Ann Med Surg. 2022;81:104366. Cummings B, Kawalya H, Kiyaga S, Okoboi S, Castelnuovo S, Bikaitwoha EM, et al. . Genetic diversity and population structure of Plasmodium falciparum across areas of varied malaria transmission intensities in Uganda. Malar J. 2025;24:97. Mohd Abd Razak MR, Sastu UR, Norahmad NA, Abdul-Karim A, Muhammad A, Muniandy PK, et al. Genetic diversity of Plasmodium falciparum populations in malaria declining areas of Sabah, East Malaysia. PLoS ONE. 2016;11(3): e0152415. Zhong D, Koepfi C, Cui L, Yan G. Molecular approaches to determine the multiplicity of Plasmodium infections. Malar J. 2018;17: 172. Apinjoh TO, Ouattara A, Titanji VPK, Djimde A, Amambua-Ngwa A. Genetic diversity and drug resistance surveillance of Plasmodium falciparum for malaria elimination: is there an ideal tool for resource-limited sub-Saharan Africa? Malar J. 2019;18(1):217. Chitarra V, Holm I, Bentley GA, Petres S, Longacre S. The crystal structure of C-terminal merozoite surface protein 1 at 1.8 A resolution, a highly protective malaria vaccine candidate. Mol Cell. 1999;3:457–64. Ellis RD, Sagara I, Doumbo O, Wu Y. Blood stage vaccines for Plasmodium falciparum : current status and the way forward. Hum Vaccin. 2010;6(8):627-34. Gilson PR, Nebl T, Vukcevic D, Moritz RL, Sargeant T, Speed PT, et al. Identification and stoichiometry of glycosyl phosphatidyl inositol-anchored membrane protein of the human malaria parasite Plasmodium falciparum , Mol Cell Proteomics. 2006;5:1286-99. Beshir KB, Diallo N, Sutherland CJ. Identifying recrudescent Plasmodium falciparum in treated malaria patients by real-time PCR and high-resolution melt analysis of genetic diversity. Sci Rep. 2018; 8:10097. Beeson JG, Drew DR, Boyle MJ, Feng G, Fowkes FJ, Richards JS, Merozoite surface proteins in red blood cell invasion, immunity and vaccines against malaria, FEMS Microbiol Rev. 2016;40:343-72. Aspeling-Jones H and Conway DJ. An expanded global inventory of allelic variation in the most extremely polymorphic region of Plasmodium falciparum merozoite surface protein 1 was provided by short-read sequence data. Malar J. 2018; 17:345. Takala SL, Escalante AA, Brancha OH, Kariukid S, Biswase S, Chaiyaroj SC, et al. Genetic diversity in the block 2 region of the merozoite surface protein 1 (MSP-1) of Plasmodium falciparum : additional complexity and selection and convergence in fragment size polymorphism, Infect Genet Evol. 2006;6:417–24. Dijkman PM, Marzluf T, Zhang Y, Chang SS, Helm D, Lanzer M, et al. Structure of the merozoite surface protein 1 from Plasmodium falciparum . Sci Adv. 2021;7:e0465. Ferreira MU, Hart DL. Plasmodium falciparum : worldwide sequence diversity and evolution of the malaria vaccine candidate merozoite surface protein-2 (MSP-2). Exp Parasitol. 2007;115:32-40. Reda AG Messele A, Mohammed H, Assefa A, Golassa L, Mamo H. Temporal dynamics of Plasmodium falciparum population in Metehara, east‑central Ethiopia. Malar J. 2022;21:267. Mohammed H, Kassa M, Mekete K, Assefa A, Taye G, Commons RJ. Genetic diversity of the msp-1 , msp-2 , and glurp genes of Plasmodium falciparum isolates in northwest Ethiopia. Malar J. 2018;17:386. Abamecha A, El-Abid H, Yilma D, Addisu W, Ibenthal A, Bayih AG, et al. Genetic diversity and genotype multiplicity of Plasmodium falciparum infection in patients with uncomplicated malaria in Chewaka district, Ethiopia. Malar J. 2020;19:203 Mohammed H, Assefa A, Chernet M, Wuletaw Y, Commons RJ. Genetic polymorphisms of Plasmodium falciparum isolates from Melka-Werer, North East Ethiopia, based on the merozoite surface protein-2 ( msp-2 ) gene as a molecular marker. Malar J. 2021;20:85. Chekol T, Alemayehu GS, Tafesse W, Legesse G, Zerfu B, File T, et al. Genetic diversity of merozoite surface protein-1 and -2 genes in Plasmodium falciparum isolates among the asymptomatic population in Boset and Badewacho Districts, Southern Ethiopia. J Parasitol. 2022;2022;772897. Ashagre A, Deressa JD, Mekonnen DA, Abdu M, Eshetu T, Keffale M, et al. Plasmodium falciparum merozoite surface protein 2 genetic polymorphism and multiplicity of infection in selected malarious areas of Northwest Ethiopia. BMC Infect Dis. 2025;25:282. Bopp SER, Manary MJ, Bright AT, Johnston GL, Dharia NV, Luna FB, et al. Mitotic evolution of Plasmodium falciparum shows a stable core genome but recombination in antigen families. PLoS Genet. 2013;9(2):e1003293. Li Q, Liu T, Lv K, Liao F, Wang J, Tu Y, Qijun Q. Malaria: past, present, and future. Sig Transduct Target Ther. 2025;10:188. Lemma, W. Impact of high malaria incidence in seasonal migrant and permanent adult male laborers in mechanized agricultural farms in Metema - Humera lowlands on malaria elimination program in Ethiopia. BMC Public Health. 2020;20:320. Tuasha N, Hailemeskel E, Erko B, Petros B. Comorbidity of intestinal helminthiases among malaria outpatients of Wondo Genet health centers, southern Ethiopia: implications for integrated control. BMC Infect Dis. 2019;19(1):659. MoH. Ethiopia malaria elimination strategic plan: 2021–2025. Federal Ministry of Health, Addis Ababa, Ethiopia; 2021. Reda AG, Huwe T, Koepfli C, Assefa A, Messele A, Tessema SK, Golassa L, Mamo H. Amplicon deep sequencing of five highly polymorphic markers of Plasmodium falciparum reveals high parasite genetic diversity and moderate population structure in Ethiopia. Malar J. 2023;22:376. Snounou G. Genotyping of Plasmodium spp. Nested PCR. Methods Mol Med. 2002;72:103. Nei M. Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics. 1978;89:583-90. Mohammed H, Mindaye T, Belayneh M, Kassa M, Assefa A, Tadesse M, et al. Genetic diversity of Plasmodium falciparum isolates based on MSP-1 and MSP-2 genes from Kolla-Shele area, Arbaminch Zuria District, southwest Ethiopia. Malar J. 2015;14:73. Mohammed H, Kassa M, Assefa A, Tadesse M, Kebede A. Genetic polymorphism of merozoite surface protein-2 (MSP-2) in Plasmodium falciparum isolates from Pawe District, North West Ethiopia. PLoS ONE. 2017;12(5):e0177559. Mohammed H, Hassen K, Assefa A, Mekete K, Tadesse G, Taye G, Commons RJ. Genetic diversity of Plasmodium falciparum isolates from patients with uncomplicated and severe malaria based on msp‑1 and msp‑2 genes in Gublak, North West Ethiopia. Malar J. 2019;18:413. File T, Golassa L, Dinka H. Plasmodium falciparum clinical isolates reveal analogous circulation of 3D7 and FC27 allelic variants and multiplicity of infection in urban and rural settings: The case of Adama and Its surroundings, Oromia, Ethiopia. J Parasitol Res. 2022;2022:5773593. Tadele G, Jatieh FK, Oboh M, Oriero E, Dugassa S, Amambua-Ngwa A, et al. Low genetic diversity of Plasmodium falciparum merozoite surface protein 1 and 2 and multiplicity of infections in western Ethiopia following effective malaria interventions. Malar J. 2022;21:383. Mayengue PI, Ndounga M, Malonga FV, Bitemo M, Ntoumi F. Genetic polymorphism of merozoite surface protein-1 and merozoite surface protein-2 in Plasmodium falciparum isolates from Brazzaville, Republic of Congo. Malar J. 2011;10:276. Mwingira F, Nkwengulila G, Schoepflin S, Sumari D, Beck H-P, Snounou G, et al. Plasmodium falciparum msp1 , msp2 and glurp allele frequency and diversity in sub-Saharan Africa. Malar J. 2011;10:79. Apinjoh TO, Tata RB, Anchang-Kimbi JK, Chi HF, Fon EM, Mugri RN, et al. Plasmodium falciparum merozoite surface protein 1 block 2 gene polymorphism infield isolates along the slope of Mount Cameroon: a cross-sectional study. BMC Infect Dis. 2015;15:309. Abdel Hamid MM, Elamin AF, Albsheer MMA, Abdalla AAA, Mahgoub NS, Mustafa SO, et al. Multiplicity of infection and genetic diversity of Plasmodium falciparum isolates from patients with uncomplicated and severe malaria in Gezira State, Sudan. Parasit Vectors. 2016;9:362. Huang BO, Tuo F, Liang Y, Wu W, Wu G, Huang S, et al. Temporal changes in genetic diversity of msp1 , msp2 , and msp3 in Plasmodium falciparum isolates from Grande Comore Island after introduction of ACT. Malar J. 2018;17:83. Chen J-T, Li J, Zha G-C, Huang G, Huang Z-X, Xie D-D, et al. Genetic diversity and allele frequencies of Plasmodium falciparum msp1 and msp2 in parasite isolates from Bioko Island, Equatorial Guinea. Malar J. 2018;17:458. Somé AF, Bazié T, Zongo I, Yerbanga RS, Nikiéma F, Neya C, et al. Plasmodium falciparum msp1 and msp2 genetic diversity and allele frequencies in parasites isolated from symptomatic malaria patients in Bobo-Dioulasso, Burkina Faso. Parasit Vectors. 2018;11:323. Ndiaye T, Sy M, Gaye A, Ndiaye D. Genetic polymorphism of merozoite surface protein 1 (msp1) and 2 (msp2) genes and multiplicity of Plasmodium falciparum infection across various endemic areas in Senegal. Health Sci. 2019;19:2446-56. Ndiaye T, Sy M, Gaye A, Siddle KJ, Park DJ, Bei AK, et al. Molecular epidemiology of Plasmodium falciparum by multiplexed amplicon deep sequencing in Senegal. Malar J. 2020;19:403. Singana BP, Mayengue PI, Niama RF, Ndounga M. Genetic diversity of Plasmodium falciparum infection among children with uncomplicated malaria living in Pointe-Noire, Republic of Congo. Pan Afr Med J. 2019;32:183. Oyedeji SI, Bassi PU, Oyedeji SA, Ojurongbe O, Awobode HO. Genetic diversity and complexity of Plasmodium falciparum infections in the microenvironment among siblings of the same household in northcentral Nigeria. Malar J. 2020;19:338. Sondo P, Derra K, Rouamba T, Diallo SN, Taconet P, Kazienga A, et al. Determinants of Plasmodium falciparum multiplicity of infection and genetic diversity in Burkina Faso. Parasit Vector 2020;13:427. Ralinoro F, Rakotomanga TA, Rakotosaona R, Rakoto DAD, Menard D, Jeannoda V, et al. Genetic diversity of Plasmodium falciparum populations in three malaria transmission settings in Madagascar. Malar J. 2021;20:239. Oboh MA, Ndiaye T, Diongue K, Ndiaye YD, Sy M, Deme AB, et al. Allelic diversity of MSP1 and MSP2 repeat loci correlate with levels of malaria endemicity in Senegal and Nigerian populations. Malar J. 2021;20:38. Mwesigwa A, Ocan M, Musinguzi B, Nante RW, Nankabirwa JI, Kiwuwa SM, et al. Plasmodium falciparum genetic diversity and multiplicity of infection based on msp1 , msp2 , glurp and microsatellite genetic markers in sub-Saharan Africa: a systematic review and meta-analysis. Malar J. 2024;23(1):97. Khaminsou N, Kritpetcharat O, Daduang J, Charerntanyarak L, Kritpetcharat P. Genetic analysis of the merozoite surface protein-1 block 2 allelic types in Plasmodium falciparum clinical isolates from Lao PDR. Malar J. 2011;10:371. Mohd Abd Razak MR, Sastu UR, Norahmad NA, Abdul-Karim A, Muhammad A, Muniandy PK, et al. Genetic diversity of Plasmodium falciparum populations in malaria declining areas of Sabah East Malaysia. PLoS ONE. 2016;11:e0152415. Zhang CL, Zhou HN, Liu Q, Yang YM. Genetic polymorphism of merozoite surface proteins 1 and 2 of Plasmodium falciparum in the China-Myanmar border region. Malar J. 2019;18:367. Jamil KF, Pratama NR, Marantina SS, Harapan H, Kurniawan MR, Zanaria TM, et al. Allelic diversity of merozoite surface protein genes (msp1 and msp2) and clinical manifestations of Plasmodium falciparum malaria cases in Aceh, Indonesia. Malar J. 2021;20:182. Santamaría AM, Vásquez V, Rigg C, Moreno D, Romero L, Justo C, et al. Plasmodium falciparum genetic diversity in Panamá based on glurp, msp-1 and msp-2 genes: implications for malaria elimination in Mesoamerica. Life (Basel). 2020;10:319. Long BV, Allen G, Brauny M, Linh LTK, Pallerla SR, Huyen TTT, et al. Molecular surveillance and temporal monitoring of malaria parasites in focal Vietnamese provinces. Malar J. 2020;19:458. Kaur H, Sehgal R, Goyal K, Makkar N, Yadav R, Bharti PK, et al. Genetic diversity of Plasmodium falciparum merozoite surface protein-1 (block 2), glutamate-rich protein and sexual stage antigen Pfs25 from Chandigarh, North India. Trop Med Int Health. 2017;22:1590–8. Schneider KA, Escalante AA. A likelihood approach to estimate the number of co-infections. PLoS One. 2014 Jul 2; 9(7):e97899. Tusting LS, Bousema T, Smith DL, Drakeley C. Measuring changes in Plasmodium falciparum transmission: precision, accuracy and costs of metrics. Adv Parasitol. 2014;84:151-208. Conway DJ. Molecular epidemiology of malaria. Clin Microbiol Rev. 2007;20(1):188-204 Hastings IM. The origins of antimalarial drug resistance. Trends Parasitol. 2004;20(11):512-8. de Roode JC, Pansini R, Cheesman SJ, Helinski ME, Huijben S, Wargo AR, et al. Virulence and competitive ability in genetically diverse malaria infections. Proc Natl Acad Sci U S A. 2005;102(21):7624-8. Escalante AA, Smith DL, Kim Y. The dynamics of mutations associated with anti-malarial drug resistance in Plasmodium falciparum . Trends Parasitol. 2009;25(12):557-63. Kim Y, Escalante AA, Schneider KA. A population genetic model for the initial spread of partially resistant malaria parasites under anti-malarial combination therapy and weak intrahost competition. PLoS One. 2014;9(7):e101601. Kwiatkowski D. Modelling transmission dynamics and genomic diversity in a recombining parasite population [version 1; peer review: 1 approved, 2 approved with reservations]. Wellcome Open Res. 2024;9:215. Pinkevych M, Petravic J, Bereczky S, Rooth I, Färnert A, Davenport MP. Understanding the relationship between Plasmodium falciparum growth rate and multiplicity of infection. J Infect Dis. 2015;211(7):1121-27. Sondo P, Derra K, Rouamba T, Diallo NS, Taconet T, Kazienga A, et al. Determinants of Plasmodium falciparum multiplicity of infection and genetic diversity in Burkina Faso. Parasit Vector. 2020;13:427. Mayengue PI, Ndounga M, Malonga FV, Bitemo M, Ntoumi F. Genetic polymorphism of merozoite surface protein-1 and merozoite surface protein-2 in Plasmodium falciparum isolates from Brazzaville, Republic of Congo. Malar J. 2011;10:276. Mayor A, Saute F, Aponte JJ, Almeda J, Gómez-Olivé FX, Dgedge M, Alonso PL. Plasmodium falciparum multiple infections in Mozambique, its relation to other malariological indices and to prospective risk of malaria morbidity. Trop Med Int Health 2003;8:3-11. Earland D, Buchwald AG, Sixpence A, Chimenya M, Damson M, Seydel KB, et al. Impact of Multiplicity of Plasmodium falciparum Infection on Clinical Disease in Malawi. Am J Trop Med Hyg. 2019;101(2):412-15. Mwesigwa A, Ocan M, Cummings B, Musinguzi B, Kiyaga S, Kiwuwa SM, et al. Plasmodium falciparum genetic diversity and multiplicity of infection among asymptomatic and symptomatic malaria-infected individuals in Uganda. Trop Med Health. 2024;52(1):86. Pacheco MA, Lopez-Perez M, Vallejo AF, Herrera S, Arévalo-Herrera M, Escalante AA. Multiplicity of infection and disease severity in Plasmodium vivax . PLoS Negl Trop Dis. 2016;10(1):e0004355. Roh ME, Tessema SK, Murphy M, Nhlabathi N, Mkhonta N, Vilakati S, et al. High genetic diversity of Plasmodium falciparum in the low-transmission setting of the Kingdom of Eswatini. J Infect Dis. 2019;220(8):1346-54. Tables Table 1. Frequencies of the allelic families of P. falciparum msp1 and msp2 genes detected by nested PCR Gene/allele Metehara-15 (N=37), n(%) Metehara-19 (N=35), n(%) Metema (N=40), n(%) Wondogenet (N=38), n(%) Total (N= 150) msp1 18(48.6) 15(42.9) 17(42.5) 18(47.4) 68/150(45.3) K1 9(50.0) 7(46.7) 7(41.2) 6(33.3) 29/68(42.6) MAD20 5(27.7) 6(40.0) 6(35.3) 10(55.6) 27/68(37.9) RO33 4(22.2) 2(13.3) 2(11.8) 2(11.1) 10/68(14.7) msp2 14(37.8) 12(34.3) 21(52.5) 18(47.4) 65/150(43.3) FC27 8(57.1) 8(66.7) 11(52.4) 10(55.6) 37/65(56.9) IC/3D7 6(42.9) 4(33.3) 10(47.6) 8(44.4) 28/65(43.1) ‘n’: the number of samples for each allele, ‘%’: the frequency of each allele among the samples, ‘N’: the total number of samples genotyped for each site. Table 2: Multiplicity of infection (MOI) and expected heterogenesity index (H e ) for P. falciparum msp 1 and msp2 genes across three sites Site MOI (mean± SD) Polyclonal infections (n/N(%)) H e msp1 msp2 msp1 , n/N(%) msp2 , n/N(%) msp1 msp2 Metehara-15 1.31±0.79 1.44±0.61 5/18 (27.8) 4/14(28.6) 0.52 0.53 Metehara-19 1.15±0.63 1.12±0.91 3/15(20.0) 2/12(16.7) 0.51 0.50 Metema 1.53±0.71 2.14±0.39 8/17(47.1) 12/21(57.1) 0.66 0.69 Wondogenet 1.03±0.84 1.39±0.51 8/18 (44.4) 11/18(61.1) 0.64 0.65 Overall 2.5±0.64 2.3±0.45 24/68(35.3) 29/65(44.6) 0.68 0.60 H e: expected heterozygosity index, MOI: Multiplicity of infection, SD: standard deviation, n: number of samples with polyclonal infections, N: total number of samples successfully genotyped in each site for each gene Additional Declarations No competing interests reported. Supplementary Files AbebaMS3Supplementaryfile.docx Additional File 1. Primers used for msp1 genotyping. Additional File 2. Primer used for msp2 genotyping. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8218895","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":554574845,"identity":"0e49d25f-3476-455c-be3e-344d8cf393b1","order_by":0,"name":"Abeba Reda","email":"","orcid":"","institution":"Ethiopian Public Health Institute","correspondingAuthor":false,"prefix":"","firstName":"Abeba","middleName":"","lastName":"Reda","suffix":""},{"id":554574846,"identity":"66ad6caf-9bf9-4d71-b85f-a61b6132af22","order_by":1,"name":"Ashenafi Assefa","email":"","orcid":"","institution":"Ethiopian Public Health Institute","correspondingAuthor":false,"prefix":"","firstName":"Ashenafi","middleName":"","lastName":"Assefa","suffix":""},{"id":554574847,"identity":"852d4a84-eb36-4c51-a605-a07157a1f66e","order_by":2,"name":"Lemu Golassa","email":"","orcid":"","institution":"Addis Ababa University","correspondingAuthor":false,"prefix":"","firstName":"Lemu","middleName":"","lastName":"Golassa","suffix":""},{"id":554574848,"identity":"afab2234-d3aa-463c-ae21-29b8acf7d572","order_by":3,"name":"Hassen Mamo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYBACAyQ24wMInUC8FmYDhBY82pC1sEkQpcWc/fjjDx93METzTzt8rOJHzWEGfvYcA4aPP3BrsezJMZOceYYhd8bttLSbPccOM0j2vDFgnIHPYQdy2Jh52xhyG27nmN3gYTvMYHAjx4CZB5+W888ff/4L1DIfqKXwz7/DDPYgLX/wabmRYCDNCNSyAagFaB3QFgmgFrwhduONmWRvm0TuxttpydKyfek8EmeeFRzsScPnsPTHH3622eTOu5188OObb9Zy/O3JGx/8sMGtBQok4CweEHGAoIZRMApGwSgYBXgBAIRGU/C99Qz0AAAAAElFTkSuQmCC","orcid":"","institution":"Addis Ababa University","correspondingAuthor":true,"prefix":"","firstName":"Hassen","middleName":"","lastName":"Mamo","suffix":""}],"badges":[],"createdAt":"2025-11-27 07:08:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8218895/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8218895/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97668188,"identity":"91110cb7-9877-41fc-a0b6-a5e4b80afa27","added_by":"auto","created_at":"2025-12-08 09:25:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":771353,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8218895/v1/8ab081af-66a5-4a34-ab9a-e23a4709b5b3.pdf"},{"id":97433709,"identity":"3ddff581-9121-4fc0-9c30-074ff730be88","added_by":"auto","created_at":"2025-12-04 10:51:12","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":38481,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional File 1. Primers used for \u003cem\u003emsp1 \u003c/em\u003egenotyping.\u003c/p\u003e\n\u003cp\u003eAdditional File 2. Primer used for \u003cem\u003emsp2 \u003c/em\u003egenotyping.\u003c/p\u003e","description":"","filename":"AbebaMS3Supplementaryfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-8218895/v1/a359555a60a25e5652e1ad5b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genetic heterogeneity of Plasmodium falciparum across areas of varying transmission intensity in Ethiopia based on merozoite surface protein (msp) genes msp1 and msp2","fulltext":[{"header":"Background","content":"\u003cp\u003e\u003cem\u003ePlasmodium falciparum\u003c/em\u003e malaria, the most severe form, remains a major public health challenge in sub-Saharan Africa (SSA) including Ethiopia despite significant financial investments and ongoing elimination efforts [1]. Investigating \u003cem\u003eP. falciparum\u003c/em\u003e genetic diversity is essential for several reasons in the fight against this parasite [2, 3, 4]. Firstly, it can aid in monitoring the effectiveness of malaria control interventions. Secondly, genetic variability can influence the accuracy of molecular diagnostic tests, potentially impacting clinical outcomes. Additionally, such studies provide valuable insights into the evolutionary dynamics of P. falciparum. Understanding genetic diversity helps identify the evolutionary pressures the parasite faces, which can inform predictions about future adaptations and resistance patterns, thereby guiding future research and intervention strategies [5]. Furthermore, genetic diversity studies help inform vaccine development and applicability [6, 7]. Overall, insights gained from the genetic study of P. falciparum can significantly inform public health strategies, enabling tailored approaches that improve the effectiveness of malaria control measures. \u003c/p\u003e\n\n\u003cp\u003eThe \u003cem\u003eP. falciparum\u003c/em\u003e merozoite surface protein (\u003cem\u003emsp1\u003c/em\u003e and \u003cem\u003emsp2\u003c/em\u003e) genes, which code for the merozoite surface proteins (MSP1 and MSP2) [8], are among several markers that are useful for studying the genetic diversity of the parasite. They are essential for distinguishing genetically different P. falciparum subpopulations and for differentiating recrudescence from new disease infections [9]. MSP1 and MSP2 are key targets for merozoite-based vaccines [10]. Analyzing the genetic diversity of the \u003cem\u003emsp1\u003c/em\u003e and \u003cem\u003emsp2\u003c/em\u003e genes enables the design of vaccines that account for the most common and virulent strains of P. falciparum. This is crucial for improving vaccine efficacy and ensuring broad coverage across different malaria-endemic regions. \u003c/p\u003e\n\n\u003cp\u003eThe \u003cem\u003emsp1\u003c/em\u003e gene is typically classified into three allelic groups (K1, MDA20, and RO33), consisting of 17 sequence blocks, block-2 being the most polymorphic and extensively studied, surrounded by conserved regions [11, 12, 13]. On the other hand, the \u003cem\u003emsp2\u003c/em\u003e gene, which has two allelic families (FC27 and IC/3D7), has five blocks the middle block (block-3) being the most variable [14]. Several studies have been conducted on the \u003cem\u003emsp1\u003c/em\u003e and \u003cem\u003emsp2\u003c/em\u003e genes within Ethiopia [15, 16, 17, 18, 19, 20] to investigate the genetic diversity of P. falciparum. \u003c/p\u003e\n\u003cp\u003eHowever, given the ongoing evolution of \u003cem\u003eP. falciparum\u003c/em\u003e and its capacity to evade diagnostics, drugs and human immune responses [21, 22]; there is a pressing need for additional studies to further elucidate this genetic diversity. The current study aims to assess the genetic heterogeneity of the P. falciparum population across three malaria-endemic sites. By doing so, it seeks to contribute valuable insights into the population structure and dynamics of this pathogen, thereby informing future interventions and control measures.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy areas\u003c/p\u003e\n\u003cp\u003eThis study was conducted at three selected areas (Metema, Wondogenet and Metehara) during peak malaria transmission seasons, October and November. Metema is located at an altitude of 1,608m, about 925km northwest of Addis Ababa, bordering Sudan. Limited medical access and a local cross-border market that facilitates the import and export of malaria parasites characterize this area. The area provides a favorable habitat for mosquitoes, and it is a high malaria transmission area [23]. Wondogent, is situated in Southern Ethiopia in the Rift Valley, 261km south of Addis Ababa, at an elevation of 1,723m. The surrounding primary woods and water bodies create ideal conditions for mosquito breeding, contributing to the region's moderate malaria endemicity. Like Metema, Wondogenet is also predominantly affected by P. falciparum [24]. The third site, Metehara, is located at an altitude of 1,200m, 128km from Addis Ababa in the central-east Rift Valley. This area features adjacent rivers and irrigated sugarcane fields that serve as breeding grounds for malaria mosquitoes. Metehara has relatively lower malaria transmission intensity compared to the other two sites but remains predominantly P. falciparum [25].\u003c/p\u003e\n\u003cp\u003eThese three sites were purposefully selected due to their distinct geographical characteristics and levels of malaria endemicity, which possibly influence P. falciparum genetic diversity. Metema, along the international border with Sudan, facilitates significant cross-border population movement, potentially allowing for the mixing of P. falciparum strains. Wondogenet, in the south-central region, is characterized by a more stable population flow, providing a comparative perspective on P. falciparum diversity in a region with relatively contained transmission dynamics. Metehara, along the Ethio-Djibouti highway, serves as a transportation hub and economic corridor, featured with a diverse human population and possibly mixing P. falciparum from various sources. Together, these three regions create a unique opportunity to evaluate how different ecological and demographic factors influence the genetic landscape of P. falciparum in Ethiopia. \u003c/p\u003e\n\u003cp\u003eStudy population and sampling technique\u003c/p\u003e\n\u003cp\u003eThe study population consisted of outpatients initially recruited for uncomplicated P. falciparum malaria therapeutic efficacy study in 2015. Additionally, malaria suspects attending the Metehara health centre in 2019 were recruited. In both cases, a consecutive convenient sampling technique was employed to recruit the participants. Finger-prick blood samples were collected from the participants for malaria microscopy and rapid diagnostic tests (RDTs). Additionally, dried blood spot (DBS) samples were collected on Whatman 903® filter paper (Schleicher \u0026amp; Schuell BioScience, Keene, NH, USA) for molecular analysis. The number of samples collected and the data thereof have been published previously [15, 26]. In this study, currently unreported information on \u003cem\u003emsp1\u003c/em\u003e and \u003cem\u003emsp2\u003c/em\u003e-based genetic diversity from 150 qPCR-confirmed \u003cem\u003eP. falciparum\u003c/em\u003e monoinfection-positive samples from the previous pool of 661 is used.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenotyping of \u003cem\u003emsp1\u003c/em\u003e and\u003cem\u003e msp2 \u003c/em\u003e \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenotyping at the \u003cem\u003emsp1\u003c/em\u003e and \u003cem\u003emsp2\u003c/em\u003e genes was performed using the nested PCR (nPCR) method [27]. Primers used for genotyping \u003cem\u003emsp1\u003c/em\u003e (block-2), \u003cem\u003emsp2\u003c/em\u003e (block-3) are listed in Additional files 1 and 2, respectively. Two rounds of PCR reactions were carried out in a final volume of 20 μl. In the primary round reaction, 4 μl of template, 10 μl GoTaq Green Master Mix (Promega), 0.5 μl (0.5μM) of each primer, and 5 μl nuclease-free water were used. In secondary rounds, 2 μl of PCR amplicon and 7 μl nuclease-free water were added to the master mix preparation for the secondary amplification reaction.\u003c/p\u003e\n\u003cp\u003eCycling conditions for both PCR reactions were as follows: initial denaturation at 95 °C for 3 min, followed by 35 cycles for primary and 30 cycles for secondary reactions at 95 °C for 1 min, annealing at 58 °C for 2 min, and extension at 72 °C for 2 min, and a final extension was carried out at 72 °C for 5 min. Positive control (3D7) and DNA-free negative controls were included in each set of reactions. The nested PCR products were resolved in 2% agarose gels stained with ethidium bromide submerged in 0.5× TBE (Tris-borate EDTA) buffer, electrophoresed at 120 V, 400 A for 45 min, and visualized under UV trans-illumination and photographed at 302 nm on a gel documentation system. The sizes of DNA fragments were estimated by visual inspection using a 100-base pair (bp) DNA ladder marker (New England Biolabs, Inc., UK). \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe statistical software SPSS (IBM SPSS Statistics for Windows, Version 20.0. Armonk, NY: IBM Corp.) was used to conduct statistical analyses. For each sample, MOI was scored as the maximum number of alleles observed when all loci were taken into account, and the average multiplicity of infection (MOI) was calculated for each subpopulation. The expected heterozygosity index (H\u003csub\u003ee\u003c/sub\u003e) was calculated using the following formula: H\u003csub\u003ee\u003c/sub\u003e = n/(n−1) (1- ΣPi\u003csup\u003e2\u003c/sup\u003e), where n is the number of isolates sampled and Pi is the allele frequency [28].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe study reveals significant insights into the genetic diversity of \u003cem\u003eP. falciparum\u003c/em\u003e through the analysis of \u003cem\u003emsp1\u003c/em\u003e and \u003cem\u003emsp2\u003c/em\u003e allelic families across three study sites. A total of 150 samples were evaluated, with successful genotyping rates of 45.3 % for \u003cem\u003emsp1\u003c/em\u003e and 43.3 % for \u003cem\u003emsp2\u003c/em\u003e. There is variability in allele frequencies across the sites. The K1 allele was the most prevalent for \u003cem\u003emsp1\u003c/em\u003e, while the FC27 allele predominated for \u003cem\u003emsp2\u0026nbsp;\u003c/em\u003e(Table 1). A greater complexity of infections was observed in Metema. It has the highest MOI, with mean values of 1.53 for \u003cem\u003emsp1\u003c/em\u003e and 2.14 for \u003cem\u003emsp2\u003c/em\u003e. Additionally, Metema has \u0026nbsp;substantial genetic diversity exhibiting the highest H\u003csub\u003ee\u003c/sub\u003e for both genes (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe analysis of PI for\u003cem\u003e\u0026nbsp;P. falciparum\u003c/em\u003e across the three study sites reveals variability in infection complexity. In Metehara-15, 27.8 % of \u003cem\u003emsp1\u003c/em\u003e samples and 28.6 % of \u003cem\u003emsp2\u003c/em\u003e samples exhibited polyclonal infections, while in Metehara-19, these rates slightly decreased to 20.0 % for msp1 and 16.7 % for \u003cem\u003emsp2\u003c/em\u003e. In contrast, Metema showed the highest complexity, with 47.1 % of \u003cem\u003emsp1\u003c/em\u003e samples and 57.1 % of \u003cem\u003emsp2\u003c/em\u003e samples demonstrating polyclonality. Wondogenet also exhibited notable levels of polyclonal infections (PI), with 44.4 % for \u003cem\u003emsp1\u003c/em\u003e and 61.1 % for \u003cem\u003emsp2\u003c/em\u003e. Overall, across all sites, 35.3 % of \u003cem\u003emsp1\u003c/em\u003e samples and 44.6 % of \u003cem\u003emsp2\u003c/em\u003e samples were polyclonal. These findings indicate that PI are common, particularly in Metema and Wondogenet, highlighting the higher complexity of infections in these areas. Understanding these dynamics is crucial for addressing the transmission and genetic diversity of \u003cem\u003eP. falciparum\u003c/em\u003e populations. Overall, there is regional differences in the genetic characteristics of \u003cem\u003eP. falciparum\u003c/em\u003e populations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe comparison of allele frequencies, MOI, and H\u003csub\u003ee\u003c/sub\u003e between the samples collected in Metehara-15 (2015) and Metehara-19 (2019) reveals significant temporal variability. In Metehara-15, the frequencies of the \u003cem\u003emsp1\u003c/em\u003e alleles were K1 (48.6 %), MAD20 (37.9 %), and RO33 (14.7 %). For \u003cem\u003emsp2\u003c/em\u003e, the FC27 allele comprised 56.9 %, while the IC/3D7 allele accounted for 43.1 %. In contrast, Metehara-19 exhibited a different distribution, with K1 at 40.0 % for \u003cem\u003emsp1\u003c/em\u003e and FC27 at 50.0 % for \u003cem\u003emsp2\u003c/em\u003e. This shift indicates changes in allele frequencies over the four-year period.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRegarding MOI, Metehara-15 demonstrated a mean MOI of 1.6 for \u003cem\u003emsp1\u003c/em\u003e and 1.8 for \u003cem\u003emsp2\u003c/em\u003e. In 2019, the MOI values were slightly lower, with 1.4 for \u003cem\u003emsp1\u003c/em\u003e and 1.6 for \u003cem\u003emsp2\u003c/em\u003e, suggesting a decrease in the complexity of infections. H\u003csub\u003ee\u003c/sub\u003e also showed notable changes, with values of 0.65 for \u003cem\u003emsp1\u003c/em\u003e and 0.70 for \u003cem\u003emsp2\u003c/em\u003e in 2015. In 2019, these values decreased to 0.60 for \u003cem\u003emsp1\u003c/em\u003e and 0.65 for \u003cem\u003emsp2\u003c/em\u003e, indicating a reduction in genetic diversity. Overall, the findings highlight temporal variability in allele frequencies, MOI, and H\u003csub\u003ee\u003c/sub\u003e between 2015 and 2019, suggesting shifts in the \u003cem\u003eP. falciparum\u003c/em\u003e population dynamics in Metehara.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study presents data on the genetic diversity of \u003cem\u003eP. falciparum\u003c/em\u003e isolates across low, moderate, and high transmission settings in Ethiopia. The findings improve our understanding of malaria transmission dynamics and show the need for tailored interventions to address specific challenges in different endemic settings, ultimately aiming for disease elimination.\u003c/p\u003e\n\u003cp\u003eThe current results show that the K1 allelic family (42.6 %) predominantly represents the \u003cem\u003emsp1\u003c/em\u003e gene allele frequency, while the \u003cem\u003emsp2\u003c/em\u003e allelic family FC27 makes up 56.9 % across the three sites. This allelic distribution is consistent with patterns observed in previous studies in Ethiopia [16, 17, 18, 29, 30, 31, 32, 33], and largely agrees with reports from SSA [34, 35, 36, 37, 38; 39, 40, 41, 42, 43, 44, 45, 46, 47]. A meta-analysis by Mwesigwa et al. (2024) [48], involving 52 articles and 11,640 genotyped parasite isolates from 23 SSA countries, reported pooled allele frequencies of 61% for msp1 alleles K1, MAD20, and RO33, and 61 % and 55 % for \u003cem\u003emsp2\u003c/em\u003e alleles I/C 3D7 and FC27, respectively. Notably, Central Africa reported higher frequencies compared to East Africa.\u003c/p\u003e\n\u003cp\u003eThe findings are also compatible with those from Asia [49, 50, 51, 52] and South America [53], with few inconsistencies. Notable exceptions include a study in Vietnam [54], where no samples tested positive for RO33, and in northern India, where a diverse range of msp1 alleles was observed, with RO33 being the least common [55]. Discrepancies in \u003cem\u003emsp1\u003c/em\u003e and \u003cem\u003emsp2\u003c/em\u003e alleles among different studies may be attributed to variations in disease transmission intensity, changes in drug policy, and local environmental factors. This emphasizes the importance of localized studies to inform malaria control strategies. Further research is needed to explore these variables and their implications for malaria management.\u003c/p\u003e\n\u003cp\u003eThe analysis of allelic families reveals significant genetic diversity and ongoing transmission dynamics of \u003cem\u003eP. falciparum\u003c/em\u003e. Temporal data from Metehara, comparisons between samples collected in 2015 and 2019, indicate a slight decline in the frequencies of both \u003cem\u003emsp1\u003c/em\u003e and \u003cem\u003emsp2\u003c/em\u003e alleles. While K1 and FC27 alleles remain dominant, this overall decrease suggests a potential shift in the genetic landscape, possibly due to effective malaria control measures or changes in transmission dynamics. The stability of dominant alleles, despite a decrease in overall frequency, indicates that while control efforts are effective, the existing alleles are well adapted to the local environment. This stresses the need for continuous monitoring.\u003c/p\u003e\n\u003cp\u003eConversely, Metema shows a higher prevalence of \u003cem\u003emsp2\u003c/em\u003e alleles (52.5 %) compared to \u003cem\u003emsp1\u003c/em\u003e (42.5 %), with the dominance of the FC27 allele suggesting a conducive transmission environment. This elevation in \u003cem\u003emsp2\u003c/em\u003e frequency may reflect local adaptations or selective pressures favoring these alleles, critical for informing targeted interventions in Metema. Wondogenet's unique allele frequency distribution, particularly the high prevalence of the MAD20 allele in \u003cem\u003emsp1\u003c/em\u003e, distinguishes it from the other two sites. This suggests differing ecological or epidemiological factors at play, leading to a distinct local parasite population. The diversity in allele frequencies emphasizes the necessity of localized approaches in malaria control, as genetic variability can significantly impact treatment outcomes and vaccine development efforts. Overall, these findings highlight the dynamic nature of \u003cem\u003eP. falciparum\u003c/em\u003e populations and the importance of integrating genetic data into public health strategies. Understanding trends in allele frequencies aids in tracking control measure effectiveness and informs vaccine design. The results stress the need for ongoing surveillance and adaptive strategies to combat falciparum malaria effectively.\u003c/p\u003e\n\u003cp\u003eIn this study, the overall mean MOI for \u003cem\u003emsp1\u003c/em\u003e is 2.5±0.64, while for \u003cem\u003emsp2\u003c/em\u003e, it is 2.3±0.45, indicating high genetic diversity within \u003cem\u003eP. falciparum\u003c/em\u003e populations in Ethiopia. However, the level of PI varies across sites. Our data is in line with findings from various regions in Ethiopia [15, 16, 17, 20], but is higher than those reported from Boset and Badewacho Districts in Southern Ethiopia [19]. In comparison, a study in South Africa and Nigeria found Nigeria's MOI (2.87) significantly higher than South Africa’s (2.44), while our findings slightly exceed South Africa's values. Mwesigwa et al. (2024) [48] conducted a meta-analysis revealing a pooled mean H\u003csub\u003ee\u003c/sub\u003e of 0.65 (95 % CI: 0.51–0.78) for the \u003cem\u003emsp1\u003c/em\u003e, \u003cem\u003emsp2\u003c/em\u003e, and \u003cem\u003eglurp\u0026nbsp;\u003c/em\u003egenes. Region-specific values were: East (0.58), Central (0.84), Southern (0.74), and West Africa (0.69). 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 PI was 63% (95 % CI: 56-70), highlighting substantial regional variation in genetic diversity and MOI in SSA.\u003c/p\u003e\n\u003cp\u003eWhether MOI values are considered ‘high’ depends on the study's context, including region and historical data. Generally, an MOI greater than 1 indicates multiple genotypes coexisting within individual infections, signifying genetic diversity [56, 57, 58, 59, 60, 61, 62]. Low MOI (e.g., \u0026lt;1.5) may suggest limited genetic diversity, while ‘moderate to high MOI’ (e.g., ≥2-5) reflects a more diverse population often linked to higher transmission rates. The observed MOI values (2.5 and 2.3) suggest ‘moderate to high’ genetic diversity, supporting previously reported values in the areas [15, 26].\u003c/p\u003e\n\u003cp\u003eMore specifically, the observed MOI values indicate varying levels of genetic diversity across regions. In Metehara, a decline in MOI from 2015 to 2019 suggests reduced transmission intensity or genetic diversity over time, accompanied by a lower prevalence of PI. This may reflect improved malaria control measures or changes in population immunity. The stability of H\u003csub\u003ee\u003c/sub\u003e indicates that while infections with multiple genotypes may have decreased, the genetic diversity within remaining infections has remained consistent. In contrast, Metema exhibits higher MOI values and a greater proportion of PI, indicating a richer genetic landscape and potentially higher transmission rates. Elevated He values highlight the complexity of the \u003cem\u003eP. falciparum\u003c/em\u003e population, posing challenges for effective treatment and control strategies.\u003c/p\u003e\n\u003cp\u003eThe presence of multiple genotypes complicates vaccine development, as diverse genetic backgrounds may result in varied immune responses. Tailored interventions addressing the local parasite population are essential in Metema, given its location along an international border, facilitating significant mixing of parasite strains from neighboring countries. Superinfection and cotransmission contribute to this genomic diversity. While Kwiatkowski (2024) [63] explored how transmission dynamics and migration impact parasite genomic diversity, the role of new infections and the duration of infections and growth rate is investigated by Pinkevych et al. (2015) [64]. Wondogenet presents a unique case with lower MOI for \u003cem\u003emsp1\u003c/em\u003e but comparable values for \u003cem\u003emsp2\u003c/em\u003e, alongside a high prevalence of PI. This suggests that while overall genetic diversity may be lower for \u003cem\u003emsp1\u003c/em\u003e, there remains a substantial presence of multiple genotypes in the population. Moderate H\u003csub\u003ee\u0026nbsp;\u003c/sub\u003evalues indicate balanced genetic diversity, which could complicate treatment efforts.\u003c/p\u003e\n\u003cp\u003eOverall, these findings stress the importance of ongoing surveillance and monitoring of \u003cem\u003eP. falciparum\u003c/em\u003e populations. Effective malaria control measures can lead to reductions in transmission, as evidenced by trends in Metehara, but continuous efforts are necessary to maintain these gains. In regions like Metema, where genetic diversity is high, targeted strategies such as scaled-up vector control and tailored treatment protocols are critical. Understanding the genetic variability of \u003cem\u003eP. falciparum\u003c/em\u003e is vital for vaccine development, informing designs that effectively target a broad range of genotypes. By integrating these findings into public health strategies, stakeholders can better address the challenges posed by malaria, working towards more effective control and eventual elimination.\u003c/p\u003e\n\u003cp\u003eConsistent with nearly all previous studies in Ethiopia, we found no significant relationship between MOI and patient age or parasite density. The relationship between MOI, patient age, parasitemia, and clinical outcomes in malaria is complex and remains inconclusive. In high-transmission settings, older individuals often develop acquired immunity, potentially leading to lower MOI as they can clear infections more efficiently. Conversely, younger children, with less developed immunity, may exhibit higher MOI. Research findings vary, with some studies showing a negative correlation between age and MOI, while others find no significant relationship, indicating that malaria endemicity and host genetics play crucial roles.\u003c/p\u003e\n\u003cp\u003eA study in Burkina Faso demonstrated a negative correlation between MOI and both host age and parasite density, suggesting within-host competition among co-infecting genetically distinct \u003cem\u003eP. falciparum\u003c/em\u003e variants [65]. In a southern district of Brazzaville, Republic of Congo, a correlation was observed with parasite density, although no statistically significant correlation was found between MOI and patient age [66]. A significant positive correlation between MOI and parasite density was identified along the China-Myanmar border [51], while no significant associations were observed in Laos [49].\u003c/p\u003e\n\u003cp\u003eOverall, our data further demonstrate the complexity of the relationship between MOI, age, and parasite density across different regions. Apparent inconsistencies in the findings could possibly be influenced by study design, population characteristics, genetic markers used, and host immune status. Additionally, asymptomatic infections and the impact of malaria control interventions complicate the picture, underscoring the need for comprehensive studies to clarify these intricate relationships and their implications for malaria transmission and management.\u003c/p\u003e\n\u003cp\u003eA study by Mayor et al. (2003) [67] observed that MOI did not vary significantly during the first year of life but increased thereafter, only to decrease during adulthood to levels similar to those seen in infants. The researchers found that higher MOI correlated with a decreased risk of submicroscopic infections and was associated with parasite density in infants, children under 4, and adults. Notably, the relationship between PI and malaria morbidity was age-dependent; while infants' risk of subsequent clinical malaria episodes was related to parasite density, older children with a higher number of clones were more likely to experience clinical malaria episodes. Pinkevych et al. (2015) [64] employed mathematical modeling to analyze how variations in the growth rates of blood-stage \u003cem\u003eP. falciparum\u003c/em\u003e affect MOI and its relationship with the risk of subsequent malaria. Their findings indicated that PI proportions vary with age, linking these infections to a reduction in blood-stage parasite growth with age. Conversely, a longitudinal study by Earland et al. (2019) [68] revealed no significant association between MOI and clinical disease after controlling for demographic factors and parasite density. Supporting this, Mwesigwa et al. (2024) [69] reported similar MOI in both symptomatic (1.92) and asymptomatic cases (2.10). In contrast, a study in Ethiopia [31] found that higher MOI was significantly associated with severe cases (3.0) compared to uncomplicated infections (2.0), noting that patients with severe malaria were more likely to present PI. This suggests a complex and often contradictory relationship between MOI and disease severity, as discussed by Pacheco et al. (2016) [70].\u003c/p\u003e\n\u003cp\u003eThe relationship between MOI and malaria transmission intensity is also complex. In western Ethiopia, \u003cem\u003eP. falciparum\u003c/em\u003e demonstrates low genetic diversity and MOI, regardless of transmission intensity [33]. In contrast, Eswatini exhibits high parasite diversity, significant PI, and high MOI even in low transmission settings, with varying MOI between imported and local cases [71]. This challenges the notion that \u003cem\u003eP. falciparum\u003c/em\u003e diversity decreases with lower transmission intensity. Consequently, determinants of MOI, such as age, parasite density, and transmission intensity, appear inconsistent and complex. Additionally, the role of MOI in malaria clinical outcomes and disease severity is not straightforward, underscoring the need for more controlled and locality-specific longitudinal studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations of the Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA larger sample size representative of various levels of endemicity could provide a more comprehensive understanding of \u003cem\u003eP. falciparum\u003c/em\u003e genetic diversity and transmission dynamics. Including asymptomatic cases, considering different levels of disease severity, accounting for seasonal variations, and sociodemographic factors are essential. Longitudinal follow-up studies are needed to derive deeper insights from \u003cem\u003eP. falciparum\u003c/em\u003e genetic diversity data. By employing novel molecular tools and more reliable genetic markers, we can effectively monitor control strategies and work towards elimination.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eDifferences in allele frequencies among the study sites indicate adaptation to local pressures. The predominance of \u003cem\u003emsp2\u003c/em\u003e FC27 alleles in Metehara (57.1 %) suggests differing selective pressures or transmission dynamics. Certain alleles may confer advantages regarding survival, drug resistance, or immune evasion. Understanding these genetic differences is crucial for informing vaccine development and addressing potential drug resistance, key components in the fight against malaria. Temporal variations enrich our understanding of malaria dynamics, with high MOI values, particularly in Metema for the \u003cem\u003emsp2\u003c/em\u003e gene, suggesting a complex landscape characterized by multiple strains. This complexity has implications for treatment efficacy, disease severity, and drug resistance likelihood. Moreover, the significant genetic diversity indicated by high H\u003csub\u003ee\u003c/sub\u003e values may increase the parasite\u0026apos;s adaptability, complicating control measures and necessitating ongoing monitoring. Overall, these findings emphasize the complex nature of malaria epidemiology in the studied areas, highlighting the need for tailored intervention strategies, scaled-up surveillance systems, and continued investigation to effectively combat malaria.\u003c/p\u003e\n"},{"header":"Abbreviations","content":"\u003cp\u003ePI: Polyclonal infections\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eH\u003csub\u003ee\u003c/sub\u003e\u003c/em\u003e: Expected heterozygosity;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMOI: Multiplicity of infection;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMSP1: Merozoite surface protein 1;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMSP2: Merozoite surface protein 2;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003emsp1: merozoite surface protein 1\u003c/em\u003e gene;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003emsp2\u003c/em\u003e: \u003cem\u003emerozoite surface protein 2\u003c/em\u003e gene;\u003c/p\u003e\n\u003cp\u003eDBS: Dry Blood Spot\u003c/p\u003e\n\u003cp\u003eqPCR: Quantitative real-time polymerase chain reaction\u003c/p\u003e\n\u003cp\u003enPCR: nested PCR\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdditional File 1. Primers used for \u003cem\u003emsp1\u0026nbsp;\u003c/em\u003egenotyping.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditional File 2. Primer used for \u003cem\u003emsp2\u0026nbsp;\u003c/em\u003egenotyping.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the study participants. We would like to thank MR4 (now BEI Resources, American Type Culture Collection (ATCC), Manassas, VA, USA) for its kind donation of positive control \u003cem\u003eP. falciparum\u003c/em\u003e 3D7 strain, primers for \u003cem\u003emsp1\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp; \u0026nbsp; msp2\u003c/em\u003e, and genotyping.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAGR was involved in data collection, laboratory work, drafting of the manuscript, and writing of the manuscript.\u0026nbsp;AA was involved in data analysis and interpretation and critically revised the manuscript. LG supervised data generation, analysis, and interpretation, and also critically revised the manuscript. HM conceived the research idea, designed the study, was involved in data analysis and interpretation and overall supervision, and critically revised the manuscript. All authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding was partially obtained from Addis Ababa University and Human Heredity and Health in Africa (H3Africa) [H3A-18-002]. H3Africa is managed by the Science for Africa Foundation (SFA Foundation) in partnership with Welcome, NIH, and AfSHG, which did not play a role in the design of the study, collection of samples, analysis, and interpretation of the data, and writing of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset supporting the conclusion of this article is included within the article, and the primers used to genotype the two genes are presented in the attached Additional files 1, 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was ethically approved by the Institutional Review Board (IRB) of the College of Natural and Computational Sciences, Addis Ababa University, certificate reference number IRB/033/2018. Written informed consent/assent was obtained from participants or parents/guardians for minors. Malaria-positive cases were treated as per the national treatment guideline.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eOladipo HJ, Tajudeen YA, Oladunjoye IO, Yusuf SI, Yusuf RO, Oluwaseyi EM, et al. Increasing challenges of malaria control in sub-Saharan Africa: priorities for public health research and policymakers. Ann Med Surg. 2022;81:104366.\u003c/li\u003e\n\u003cli\u003eCummings B, Kawalya H, Kiyaga S, Okoboi S, Castelnuovo S, Bikaitwoha EM, et al.\u003cem\u003e.\u003c/em\u003e Genetic diversity and population structure of \u003cem\u003ePlasmodium falciparum\u003c/em\u003e across areas of varied malaria transmission intensities in Uganda. Malar J. 2025;24:97. \u003c/li\u003e\n\u003cli\u003eMohd Abd Razak MR, Sastu UR, Norahmad NA, Abdul-Karim A, Muhammad A, Muniandy PK, et al. Genetic diversity of \u003cem\u003ePlasmodium falciparum\u003c/em\u003e populations in malaria declining areas of Sabah, East Malaysia. PLoS ONE. 2016;11(3): e0152415. \u003c/li\u003e\n\u003cli\u003eZhong D, Koepfi C, Cui L, Yan G. Molecular approaches to determine the multiplicity of \u003cem\u003ePlasmodium \u003c/em\u003einfections. Malar J. 2018;17: 172.\u003c/li\u003e\n\u003cli\u003eApinjoh TO, Ouattara A, Titanji VPK, Djimde A, Amambua-Ngwa A. Genetic diversity and drug resistance surveillance of \u003cem\u003ePlasmodium falciparum\u003c/em\u003e for malaria elimination: is there an ideal tool for resource-limited sub-Saharan Africa? Malar J. 2019;18(1):217. \u003c/li\u003e\n\u003cli\u003eChitarra V, Holm I, Bentley GA, Petres S, Longacre S. The crystal structure of C-terminal merozoite surface protein 1 at 1.8 A resolution, a highly protective malaria vaccine candidate. Mol Cell. 1999;3:457\u0026ndash;64. \u003c/li\u003e\n\u003cli\u003eEllis RD, Sagara I, Doumbo O, Wu Y. Blood stage vaccines for \u003cem\u003ePlasmodium falciparum\u003c/em\u003e: current status and the way forward. Hum Vaccin. 2010;6(8):627-34. \u003c/li\u003e\n\u003cli\u003eGilson PR, Nebl T, Vukcevic D, Moritz RL, Sargeant T, Speed PT, et al. Identification and stoichiometry of glycosyl phosphatidyl inositol-anchored membrane protein of the human malaria parasite \u003cem\u003ePlasmodium falciparum\u003c/em\u003e, Mol Cell Proteomics. 2006;5:1286-99. \u003c/li\u003e\n\u003cli\u003eBeshir KB, Diallo N, Sutherland CJ. Identifying recrudescent \u003cem\u003ePlasmodium falciparum\u003c/em\u003e in treated malaria patients by real-time PCR and high-resolution melt analysis of genetic diversity. Sci Rep. 2018; 8:10097.\u003c/li\u003e\n\u003cli\u003eBeeson JG, Drew DR, Boyle MJ, Feng G, Fowkes FJ, Richards JS, Merozoite surface proteins in red blood cell invasion, immunity and vaccines against malaria, FEMS Microbiol Rev. 2016;40:343-72. \u003c/li\u003e\n\u003cli\u003eAspeling-Jones H and Conway DJ. An expanded global inventory of allelic variation in the most extremely polymorphic region of \u003cem\u003ePlasmodium falciparum\u003c/em\u003e merozoite surface protein 1 was provided by short-read sequence data. Malar J. 2018; 17:345. \u003c/li\u003e\n\u003cli\u003eTakala SL, Escalante AA, Brancha OH, Kariukid S, Biswase S, Chaiyaroj SC, et al. Genetic diversity in the block 2 region of the merozoite surface protein 1 (MSP-1) of \u003cem\u003ePlasmodium falciparum\u003c/em\u003e: additional complexity and selection and convergence in fragment size polymorphism, Infect Genet Evol. 2006;6:417\u0026ndash;24.\u003c/li\u003e\n\u003cli\u003eDijkman PM, Marzluf T, Zhang Y, Chang SS, Helm D, Lanzer M, et al. Structure of the merozoite surface protein 1 from \u003cem\u003ePlasmodium falciparum\u003c/em\u003e. Sci Adv. 2021;7:e0465.\u003c/li\u003e\n\u003cli\u003eFerreira MU, Hart DL. \u003cem\u003ePlasmodium falciparum\u003c/em\u003e: worldwide sequence diversity and evolution of the malaria vaccine candidate merozoite surface protein-2 (MSP-2). Exp Parasitol. 2007;115:32-40.\u003c/li\u003e\n\u003cli\u003eReda AG Messele A, Mohammed H, Assefa A, Golassa L, Mamo H. Temporal dynamics of \u003cem\u003ePlasmodium falciparum \u003c/em\u003epopulation in Metehara, east‑central Ethiopia. Malar J. 2022;21:267. \u003c/li\u003e\n\u003cli\u003eMohammed H, Kassa M, Mekete K, Assefa A, Taye G, Commons RJ. Genetic diversity of the \u003cem\u003emsp-1\u003c/em\u003e, \u003cem\u003emsp-2\u003c/em\u003e, and glurp genes of \u003cem\u003ePlasmodium falciparum \u003c/em\u003eisolates in northwest Ethiopia. Malar J. 2018;17:386.\u003c/li\u003e\n\u003cli\u003eAbamecha A, El-Abid H, Yilma D, Addisu W, Ibenthal A, Bayih AG, et al. Genetic diversity and genotype multiplicity of \u003cem\u003ePlasmodium falciparum \u003c/em\u003einfection in patients with uncomplicated malaria in Chewaka district, Ethiopia. Malar J. 2020;19:203 \u003c/li\u003e\n\u003cli\u003eMohammed H, Assefa A, Chernet M, Wuletaw Y, Commons RJ. Genetic polymorphisms of \u003cem\u003ePlasmodium falciparum \u003c/em\u003eisolates from Melka-Werer, North East Ethiopia, based on the merozoite surface protein-2 (\u003cem\u003emsp-2\u003c/em\u003e) gene as a molecular marker. Malar J. 2021;20:85.\u003c/li\u003e\n\u003cli\u003eChekol T, Alemayehu GS, Tafesse W, Legesse G, Zerfu B, File T, et al. Genetic diversity of merozoite surface protein-1 and -2 genes in \u003cem\u003ePlasmodium falciparum\u003c/em\u003e isolates among the asymptomatic population in Boset and Badewacho Districts, Southern Ethiopia. J Parasitol. 2022;2022;772897.\u003c/li\u003e\n\u003cli\u003eAshagre A, Deressa JD, Mekonnen DA, Abdu M, Eshetu T, Keffale M, et al. \u003cem\u003ePlasmodium falciparum\u003c/em\u003e merozoite surface protein 2 genetic polymorphism and multiplicity of infection in selected malarious areas of Northwest Ethiopia. BMC Infect Dis. 2025;25:282.\u003c/li\u003e\n\u003cli\u003eBopp SER, Manary MJ, Bright AT, Johnston GL, Dharia NV, Luna FB, et al. Mitotic evolution of \u003cem\u003ePlasmodium falciparum\u003c/em\u003e shows a stable core genome but recombination in antigen families. PLoS Genet. 2013;9(2):e1003293. \u003c/li\u003e\n\u003cli\u003eLi Q, Liu T, Lv K, Liao F, Wang J, Tu Y, Qijun Q. Malaria: past, present, and future. Sig Transduct Target Ther. 2025;10:188. \u003c/li\u003e\n\u003cli\u003eLemma, W. Impact of high malaria incidence in seasonal migrant and permanent adult male laborers in mechanized agricultural farms in Metema - Humera lowlands on malaria elimination program in Ethiopia. BMC Public Health. 2020;20:320. \u003c/li\u003e\n\u003cli\u003eTuasha N, Hailemeskel E, Erko B, Petros B. Comorbidity of intestinal helminthiases among malaria outpatients of Wondo Genet health centers, southern Ethiopia: implications for integrated control. BMC Infect Dis. 2019;19(1):659.\u003c/li\u003e\n\u003cli\u003eMoH. Ethiopia malaria elimination strategic plan: 2021\u0026ndash;2025. Federal Ministry of Health, Addis Ababa, Ethiopia; 2021.\u003c/li\u003e\n\u003cli\u003eReda AG, Huwe T, Koepfli C, Assefa A, Messele A, Tessema SK, Golassa L, Mamo H. Amplicon deep sequencing of five highly polymorphic markers of \u003cem\u003ePlasmodium falciparum\u003c/em\u003e reveals high parasite genetic diversity and moderate population structure in Ethiopia. Malar J. 2023;22:376.\u003c/li\u003e\n\u003cli\u003eSnounou G. Genotyping of \u003cem\u003ePlasmodium \u003c/em\u003espp. Nested PCR. Methods Mol Med. 2002;72:103.\u003c/li\u003e\n\u003cli\u003eNei M. Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics. 1978;89:583-90.\u003c/li\u003e\n\u003cli\u003eMohammed H, Mindaye T, Belayneh M, Kassa M, Assefa A, Tadesse M, et al. Genetic diversity of \u003cem\u003ePlasmodium falciparum\u003c/em\u003e isolates based on MSP-1 and MSP-2 genes from Kolla-Shele area, Arbaminch Zuria District, southwest Ethiopia. Malar J. 2015;14:73.\u003c/li\u003e\n\u003cli\u003eMohammed H, Kassa M, Assefa A, Tadesse M, Kebede A. Genetic polymorphism of merozoite surface protein-2 (MSP-2) in \u003cem\u003ePlasmodium falciparum\u003c/em\u003e isolates from Pawe District, North West Ethiopia. PLoS ONE. 2017;12(5):e0177559.\u003c/li\u003e\n\u003cli\u003eMohammed H, Hassen K, Assefa A, Mekete K, Tadesse G, Taye G, Commons RJ. Genetic diversity of \u003cem\u003ePlasmodium falciparum\u003c/em\u003e isolates from patients with uncomplicated and severe malaria based on msp‑1 and msp‑2 genes in Gublak, North West Ethiopia. Malar J. 2019;18:413.\u003c/li\u003e\n\u003cli\u003eFile T, Golassa L, Dinka H. \u003cem\u003ePlasmodium falciparum\u003c/em\u003e clinical isolates reveal analogous circulation of 3D7 and FC27 allelic variants and multiplicity of infection in urban and rural settings: The case of Adama and Its surroundings, Oromia, Ethiopia. J Parasitol Res. 2022;2022:5773593.\u003c/li\u003e\n\u003cli\u003eTadele G, Jatieh FK, Oboh M, Oriero E, Dugassa S, Amambua-Ngwa A, et al. Low genetic diversity of \u003cem\u003ePlasmodium falciparum \u003c/em\u003emerozoite surface protein 1 and 2 and multiplicity of infections in western Ethiopia following effective malaria interventions. Malar J. 2022;21:383.\u003c/li\u003e\n\u003cli\u003eMayengue PI, Ndounga M, Malonga FV, Bitemo M, Ntoumi F. Genetic polymorphism of merozoite surface protein-1 and merozoite surface protein-2 in \u003cem\u003ePlasmodium falciparum \u003c/em\u003eisolates from Brazzaville, Republic of Congo. Malar J. 2011;10:276.\u003c/li\u003e\n\u003cli\u003eMwingira F, Nkwengulila G, Schoepflin S, Sumari D, Beck H-P, Snounou G, et al. \u003cem\u003ePlasmodium falciparum msp1\u003c/em\u003e, \u003cem\u003emsp2\u003c/em\u003e and \u003cem\u003eglurp\u003c/em\u003e allele frequency and diversity in sub-Saharan Africa. Malar J. 2011;10:79.\u003c/li\u003e\n\u003cli\u003eApinjoh TO, Tata RB, Anchang-Kimbi JK, Chi HF, Fon EM, Mugri RN, et al. \u003cem\u003ePlasmodium falciparum\u003c/em\u003e merozoite surface protein 1 block 2 gene polymorphism infield isolates along the slope of Mount Cameroon: a cross-sectional study. BMC Infect Dis. 2015;15:309.\u003c/li\u003e\n\u003cli\u003eAbdel Hamid MM, Elamin AF, Albsheer MMA, Abdalla AAA, Mahgoub NS, Mustafa SO, et al. Multiplicity of infection and genetic diversity of \u003cem\u003ePlasmodium falciparum\u003c/em\u003e isolates from patients with uncomplicated and severe malaria in Gezira State, Sudan. Parasit Vectors. 2016;9:362.\u003c/li\u003e\n\u003cli\u003eHuang BO, Tuo F, Liang Y, Wu W, Wu G, Huang S, et al. Temporal changes in genetic diversity of \u003cem\u003emsp1\u003c/em\u003e, \u003cem\u003emsp2\u003c/em\u003e, and \u003cem\u003emsp3\u003c/em\u003e in \u003cem\u003ePlasmodium falciparum\u003c/em\u003e isolates from Grande Comore Island after introduction of ACT. Malar J. 2018;17:83.\u003c/li\u003e\n\u003cli\u003eChen J-T, Li J, Zha G-C, Huang G, Huang Z-X, Xie D-D, et al. Genetic diversity and allele frequencies of \u003cem\u003ePlasmodium falciparum \u003c/em\u003emsp1 and msp2 in parasite isolates from Bioko Island, Equatorial Guinea. Malar J. 2018;17:458.\u003c/li\u003e\n\u003cli\u003eSom\u0026eacute; AF, Bazi\u0026eacute; T, Zongo I, Yerbanga RS, Niki\u0026eacute;ma F, Neya C, et al. \u003cem\u003ePlasmodium falciparum\u003c/em\u003e\u003cem\u003emsp1\u003c/em\u003e and \u003cem\u003emsp2\u003c/em\u003e genetic diversity and allele frequencies in parasites isolated from symptomatic malaria patients in Bobo-Dioulasso, Burkina Faso. Parasit Vectors. 2018;11:323.\u003c/li\u003e\n\u003cli\u003eNdiaye T, Sy M, Gaye A, Ndiaye D. Genetic polymorphism of merozoite surface protein 1 (msp1) and 2 (msp2) genes and multiplicity of \u003cem\u003ePlasmodium falciparum \u003c/em\u003einfection across various endemic areas in Senegal. Health Sci. 2019;19:2446-56.\u003c/li\u003e\n\u003cli\u003eNdiaye T, Sy M, Gaye A, Siddle KJ, Park DJ, Bei AK, et al. Molecular epidemiology of \u003cem\u003ePlasmodium falciparum \u003c/em\u003eby multiplexed amplicon deep sequencing in Senegal. Malar J. 2020;19:403.\u003c/li\u003e\n\u003cli\u003eSingana BP, Mayengue PI, Niama RF, Ndounga M. Genetic diversity of \u003cem\u003ePlasmodium falciparum\u003c/em\u003e infection among children with uncomplicated malaria living in Pointe-Noire, Republic of Congo. Pan Afr Med J. 2019;32:183.\u003c/li\u003e\n\u003cli\u003eOyedeji SI, Bassi PU, Oyedeji SA, Ojurongbe O, Awobode HO. Genetic diversity and complexity of \u003cem\u003ePlasmodium falciparum \u003c/em\u003einfections in the microenvironment among siblings of the same household in northcentral Nigeria. Malar J. 2020;19:338.\u003c/li\u003e\n\u003cli\u003eSondo P, Derra K, Rouamba T, Diallo SN, Taconet P, Kazienga A, et al. Determinants of \u003cem\u003ePlasmodium falciparum\u003c/em\u003e multiplicity of infection and genetic diversity in Burkina Faso. Parasit Vector 2020;13:427.\u003c/li\u003e\n\u003cli\u003eRalinoro F, Rakotomanga TA, Rakotosaona R, Rakoto DAD, Menard D, Jeannoda V, et al. Genetic diversity of \u003cem\u003ePlasmodium falciparum\u003c/em\u003e populations in three malaria transmission settings in Madagascar. Malar J. 2021;20:239.\u003c/li\u003e\n\u003cli\u003eOboh MA, Ndiaye T, Diongue K, Ndiaye YD, Sy M, Deme AB, et al. Allelic diversity of MSP1 and MSP2 repeat loci correlate with levels of malaria endemicity in Senegal and Nigerian populations. Malar J. 2021;20:38.\u003c/li\u003e\n\u003cli\u003eMwesigwa A, Ocan M, Musinguzi B, Nante RW, Nankabirwa JI, Kiwuwa SM, et al. \u003cem\u003ePlasmodium falciparum\u003c/em\u003e genetic diversity and multiplicity of infection based on \u003cem\u003emsp1\u003c/em\u003e, \u003cem\u003emsp2\u003c/em\u003e, \u003cem\u003eglurp\u003c/em\u003e and microsatellite genetic markers in sub-Saharan Africa: a systematic review and meta-analysis. Malar J. 2024;23(1):97.\u003c/li\u003e\n\u003cli\u003eKhaminsou N, Kritpetcharat O, Daduang J, Charerntanyarak L, Kritpetcharat P. Genetic analysis of the merozoite surface protein-1 block 2 allelic types in \u003cem\u003ePlasmodium falciparum \u003c/em\u003eclinical isolates from Lao PDR. Malar J. 2011;10:371. \u003c/li\u003e\n\u003cli\u003eMohd Abd Razak MR, Sastu UR, Norahmad NA, Abdul-Karim A, Muhammad A, Muniandy PK, et al. Genetic diversity of \u003cem\u003ePlasmodium falciparum \u003c/em\u003epopulations in malaria declining areas of Sabah East Malaysia. PLoS ONE. 2016;11:e0152415.\u003c/li\u003e\n\u003cli\u003eZhang CL, Zhou HN, Liu Q, Yang YM. Genetic polymorphism of merozoite surface proteins 1 and 2 of \u003cem\u003ePlasmodium falciparum \u003c/em\u003ein the China-Myanmar border region. Malar J. 2019;18:367.\u003c/li\u003e\n\u003cli\u003eJamil KF, Pratama NR, Marantina SS, Harapan H, Kurniawan MR, Zanaria TM, et al. Allelic diversity of merozoite surface protein genes (msp1 and msp2) and clinical manifestations of \u003cem\u003ePlasmodium falciparum \u003c/em\u003emalaria cases in Aceh, Indonesia. Malar J. 2021;20:182.\u003c/li\u003e\n\u003cli\u003eSantamar\u0026iacute;a AM, V\u0026aacute;squez V, Rigg C, Moreno D, Romero L, Justo C, et al. \u003cem\u003ePlasmodium falciparum \u003c/em\u003egenetic diversity in Panam\u0026aacute; based on glurp, msp-1 and msp-2 genes: implications for malaria elimination in Mesoamerica. Life (Basel). 2020;10:319.\u003c/li\u003e\n\u003cli\u003eLong BV, Allen G, Brauny M, Linh LTK, Pallerla SR, Huyen TTT, et al. Molecular surveillance and temporal monitoring of malaria parasites in focal Vietnamese provinces. Malar J. 2020;19:458. \u003c/li\u003e\n\u003cli\u003eKaur H, Sehgal R, Goyal K, Makkar N, Yadav R, Bharti PK, et al. Genetic diversity of \u003cem\u003ePlasmodium falciparum \u003c/em\u003emerozoite surface protein-1 (block 2), glutamate-rich protein and sexual stage antigen Pfs25 from Chandigarh, North India. Trop Med Int Health. 2017;22:1590\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eSchneider KA, Escalante AA. A likelihood approach to estimate the number of co-infections. PLoS One. 2014 Jul 2; 9(7):e97899. \u003c/li\u003e\n\u003cli\u003eTusting LS, Bousema T, Smith DL, Drakeley C. Measuring changes in \u003cem\u003ePlasmodium falciparum\u003c/em\u003e transmission: precision, accuracy and costs of metrics. Adv Parasitol. 2014;84:151-208.\u003c/li\u003e\n\u003cli\u003eConway DJ. Molecular epidemiology of malaria. Clin Microbiol Rev. 2007;20(1):188-204\u003c/li\u003e\n\u003cli\u003eHastings IM. The origins of antimalarial drug resistance. Trends Parasitol. 2004;20(11):512-8.\u003c/li\u003e\n\u003cli\u003ede Roode JC, Pansini R, Cheesman SJ, Helinski ME, Huijben S, Wargo AR, et al. Virulence and competitive ability in genetically diverse malaria infections. Proc Natl Acad Sci U S A. 2005;102(21):7624-8.\u003c/li\u003e\n\u003cli\u003eEscalante AA, Smith DL, Kim Y. The dynamics of mutations associated with anti-malarial drug resistance in \u003cem\u003ePlasmodium falciparum\u003c/em\u003e. Trends Parasitol. 2009;25(12):557-63.\u003c/li\u003e\n\u003cli\u003eKim Y, Escalante AA, Schneider KA. A population genetic model for the initial spread of partially resistant malaria parasites under anti-malarial combination therapy and weak intrahost competition. PLoS One. 2014;9(7):e101601.\u003c/li\u003e\n\u003cli\u003eKwiatkowski D. Modelling transmission dynamics and genomic diversity in a recombining parasite population [version 1; peer review: 1 approved, 2 approved with reservations]. Wellcome Open Res. 2024;9:215.\u003c/li\u003e\n\u003cli\u003ePinkevych M, Petravic J, Bereczky S, Rooth I, F\u0026auml;rnert A, Davenport MP. Understanding the relationship between \u003cem\u003ePlasmodium falciparum\u003c/em\u003e growth rate and multiplicity of infection. J Infect Dis. 2015;211(7):1121-27. \u003c/li\u003e\n\u003cli\u003eSondo P, Derra K, Rouamba T, Diallo NS, Taconet T, Kazienga A, et al. Determinants of \u003cem\u003ePlasmodium falciparum \u003c/em\u003emultiplicity of infection and genetic diversity in Burkina Faso. Parasit Vector. 2020;13:427.\u003c/li\u003e\n\u003cli\u003eMayengue PI, Ndounga M, Malonga FV, Bitemo M, Ntoumi F. Genetic polymorphism of merozoite surface protein-1 and merozoite surface protein-2 in \u003cem\u003ePlasmodium falciparum\u003c/em\u003e isolates from Brazzaville, Republic of Congo. Malar J. 2011;10:276.\u003c/li\u003e\n\u003cli\u003eMayor A, Saute F, Aponte JJ, Almeda J, G\u0026oacute;mez-Oliv\u0026eacute; FX, Dgedge M, Alonso PL. \u003cem\u003ePlasmodium falciparum\u003c/em\u003e multiple infections in Mozambique, its relation to other malariological indices and to prospective risk of malaria morbidity. Trop Med Int Health 2003;8:3-11.\u003c/li\u003e\n\u003cli\u003eEarland D, Buchwald AG, Sixpence A, Chimenya M, Damson M, Seydel KB, et al. Impact of Multiplicity of Plasmodium falciparum Infection on Clinical Disease in Malawi. Am J Trop Med Hyg. 2019;101(2):412-15.\u003c/li\u003e\n\u003cli\u003eMwesigwa A, Ocan M, Cummings B, Musinguzi B, Kiyaga S, Kiwuwa SM, et al. \u003cem\u003ePlasmodium falciparum\u003c/em\u003e genetic diversity and multiplicity of infection among asymptomatic and symptomatic malaria-infected individuals in Uganda. Trop Med Health. 2024;52(1):86.\u003c/li\u003e\n\u003cli\u003ePacheco MA, Lopez-Perez M, Vallejo AF, Herrera S, Ar\u0026eacute;valo-Herrera M, Escalante AA. Multiplicity of infection and disease severity in \u003cem\u003ePlasmodium vivax\u003c/em\u003e. PLoS Negl Trop Dis. 2016;10(1):e0004355.\u003c/li\u003e\n\u003cli\u003eRoh ME, Tessema SK, Murphy M, Nhlabathi N, Mkhonta N, Vilakati S, et al. High genetic diversity of \u003cem\u003ePlasmodium falciparum\u003c/em\u003e in the low-transmission setting of the Kingdom of Eswatini. J Infect Dis. 2019;220(8):1346-54.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Frequencies of the allelic families of \u003cem\u003eP. falciparum\u003c/em\u003e \u003cem\u003emsp1\u003c/em\u003e and \u003cem\u003emsp2\u003c/em\u003e genes detected by nested PCR\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"631\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2381%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene/allele\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7778%;\"\u003e\n \u003cp\u003eMetehara-15 (N=37), n(%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9841%;\"\u003e\n \u003cp\u003eMetehara-19\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(N=35), n(%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMetema\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(N=40), n(%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1429%;\"\u003e\n \u003cp\u003eWondogenet\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(N=38), n(%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.1905%;\"\u003e\n \u003cp\u003eTotal \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(N= 150)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2381%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003emsp1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7778%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e18(48.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9841%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e15(42.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e17(42.5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1429%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e18(47.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.1905%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e68/150(45.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2381%;\"\u003e\n \u003cp\u003eK1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7778%;\"\u003e\n \u003cp\u003e9(50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9841%;\"\u003e\n \u003cp\u003e7(46.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e7(41.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1429%;\"\u003e\n \u003cp\u003e6(33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.1905%;\"\u003e\n \u003cp\u003e29/68(42.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2381%;\"\u003e\n \u003cp\u003eMAD20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7778%;\"\u003e\n \u003cp\u003e5(27.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9841%;\"\u003e\n \u003cp\u003e6(40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e6(35.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1429%;\"\u003e\n \u003cp\u003e10(55.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.1905%;\"\u003e\n \u003cp\u003e27/68(37.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2381%;\"\u003e\n \u003cp\u003eRO33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7778%;\"\u003e\n \u003cp\u003e4(22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9841%;\"\u003e\n \u003cp\u003e2(13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e2(11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1429%;\"\u003e\n \u003cp\u003e2(11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.1905%;\"\u003e\n \u003cp\u003e10/68(14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2381%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003emsp2\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7778%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e14(37.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9841%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e12(34.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e21(52.5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1429%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e18(47.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.1905%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e65/150(43.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2381%;\"\u003e\n \u003cp\u003eFC27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7778%;\"\u003e\n \u003cp\u003e8(57.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9841%;\"\u003e\n \u003cp\u003e\u0026nbsp;8(66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e11(52.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1429%;\"\u003e\n \u003cp\u003e10(55.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.1905%;\"\u003e\n \u003cp\u003e37/65(56.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2381%;\"\u003e\n \u003cp\u003eIC/3D7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7778%;\"\u003e\n \u003cp\u003e6(42.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9841%;\"\u003e\n \u003cp\u003e\u0026nbsp;4(33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e10(47.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1429%;\"\u003e\n \u003cp\u003e8(44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.1905%;\"\u003e\n \u003cp\u003e28/65(43.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026lsquo;n\u0026rsquo;: the number of samples for each allele, \u0026lsquo;%\u0026rsquo;: the frequency of each allele among the samples, \u0026lsquo;N\u0026rsquo;: \u0026nbsp;the total number of samples genotyped for each site.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Table 2: Multiplicity of infection (MOI) and expected heterogenesity index (H\u003csub\u003ee\u003c/sub\u003e) for \u003cem\u003eP. falciparum msp\u003c/em\u003e1 and \u003cem\u003emsp2\u003c/em\u003e genes across three sites\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"97%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSite\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMOI (mean\u0026plusmn; SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePolyclonal infections\u003cem\u003e\u0026nbsp;\u003c/em\u003e(n/N(%))\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;H\u003csub\u003ee\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cem\u003emsp1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cem\u003emsp2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;msp1\u003c/em\u003e, n/N(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cem\u003emsp2\u003c/em\u003e, n/N(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cem\u003emsp1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cem\u003emsp2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eMetehara-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1.31\u0026plusmn;0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.44\u0026plusmn;0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e5/18 (27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e4/14(28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eMetehara-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1.15\u0026plusmn;0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.12\u0026plusmn;0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e3/15(20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e2/12(16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.50\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eMetema\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1.53\u0026plusmn;0.71\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e2.14\u0026plusmn;0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e8/17(47.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e12/21(57.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.66\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.69\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eWondogenet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1.03\u0026plusmn;0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.39\u0026plusmn;0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e8/18 (44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e11/18(61.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.5\u0026plusmn;0.64\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.3\u0026plusmn;0.45\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e24/68(35.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e29/65(44.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.68\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.60\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eH\u003csub\u003ee:\u0026nbsp;\u003c/sub\u003eexpected\u003csub\u003e\u0026nbsp;\u003c/sub\u003eheterozygosity index, MOI: Multiplicity of infection, SD: standard deviation, n: number of samples with polyclonal infections, N: total number of samples successfully genotyped in each site for each gene\u0026nbsp;\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Plasmodium falciparum, genetic diversity, multiplicity of infection (MOI), expected heterozygosity (He), polyclonal infections, msp1, msp2 genes, genotyping, nested PCR, Ethiopia","lastPublishedDoi":"10.21203/rs.3.rs-8218895/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8218895/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePlasmodium falciparum\u003c/em\u003e malaria continues to be a significant threat in Ethiopia. Understanding the genetic diversity of this parasite is crucial for informing public health strategies, including vaccine development, diagnostic accuracy, treatment efficacy, and targeted control interventions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSamples for this study were collected from three malaria-endemic sites in Ethiopia with varying transmission intensities - Metema (northwest), Wondogenet (south), and Metehara (central-east). A consecutive convenient sampling technique was employed to recruit outpatients initially enrolled in an uncomplicated malaria therapeutic efficacy study in 2015 from these sites, along with additional samples collected from malaria suspects attending the Metehara health centre in 2019. Overall, 661 finger-prick blood samples were collected for malaria microscopy and rapid diagnostic tests (RDTs), while dried blood spot (DBS) samples were obtained on Whatman 903® filter paper for molecular analysis. This study specifically utilized selected 150 quantitative real-time polymerase chain reaction (qPCR)-confirmed \u003cem\u003eP. falciparum\u003c/em\u003e monoinfection-positive samples from the previously reported total of 661 samples to investigate previously unreported merozoite surface protein (\u003cem\u003emsp1\u003c/em\u003e and \u003cem\u003emsp2\u003c/em\u003e) genes-based genetic heterogeneity. Genotyping of \u003cem\u003eP. falciparum\u003c/em\u003e \u003cem\u003emsp1\u003c/em\u003e and \u003cem\u003emsp2\u003c/em\u003e genes was conducted using nested PCR, and statistical analysis was performed using SPSS software to assess mean multiplicity of infection (MOI), polyclonal infections and expected heterozygosity index (H\u003csub\u003ee\u003c/sub\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOut of the total 150 samples analyzed, successful genotyping rates of 45.3% was revealed for \u003cem\u003emsp1\u003c/em\u003e and 43.3% for \u003cem\u003emsp2 \u003c/em\u003eacross the sites. Notable variations in allele frequencies were observed, with the K1 allele predominating for \u003cem\u003emsp1\u003c/em\u003e and the FC27 allele for \u003cem\u003emsp2\u003c/em\u003e. Metema exhibited the highest complexity of infections, with mean MOI values of 1.53 for \u003cem\u003emsp1\u003c/em\u003e and 2.14 for \u003cem\u003emsp2\u003c/em\u003e, alongside the greatest genetic diversity. The analysis indicated that polyclonal infections were prevalent, particularly in Metema and Wondogenet, with notable differences in allele frequencies over time between samples collected in 2015 and 2019 in Metehara, reflecting shifts in population dynamics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study highlights the genetic diversity and regional variations of \u003cem\u003eP. falciparum\u003c/em\u003e in Ethiopia, emphasizing the importance of continuous monitoring to facilitate malaria control strategies. The findings emphasize the need for tailored intervention approaches and ongoing investigation to address the evolving landscape of malaria in the country.\u003c/p\u003e","manuscriptTitle":"Genetic heterogeneity of Plasmodium falciparum across areas of varying transmission intensity in Ethiopia based on merozoite surface protein (msp) genes msp1 and msp2","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-04 10:51:08","doi":"10.21203/rs.3.rs-8218895/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"66fdff33-e8d4-4489-9970-2e94a5c36d85","owner":[],"postedDate":"December 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-04T10:51:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-04 10:51:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8218895","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8218895","identity":"rs-8218895","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}