In the midst of success with malaria reduction in Uganda, there are areas that still have high prevalence of malaria parasitaemia. This project aimed at investigating factors associated with this prevalence and its relationship with anaemia.This is a secondary data analysis of the 2014 Malaria Indicator Survey dataset of children under 5 years. All had a blood sample taken by finger or heel prick for determination of malaria parasitaemia and estimation of haemoglobin level for anaemia status. The main outcome was the presence of malaria parasitaemia by microscopy and independent variables included: age, gender, residence , use of a long-lasting, insecticidal-treated net, indoor residual spraying (IRS) of household in the past 6 months, mother’s highest education level, mother heard malaria prevention message in the past 6 months, and household wealth status.The analysis included 4930 children and of these, 938 (19.04%: 95% CI 16.63–21.71) tested positive for malaria parasites. Malaria parasite prevalence significantly increased from 11.08 (95% CI 9.12–13.40) among children with no anaemia to 50.99% (95% CI 39.13–62.74) with severe anaemia . Additionally, prevalence significantly rose from the youngest age group (under 6 months) by 1.62 times  among the age group of 7–12 months and to four times  among those who were between 49 and 59 months. The following were associated with reduced parasitaemia: IRS use , educated mothers , mother heard malaria message , and wealthier households .Increasing malaria parasite prevalence among children under 5 years is still related to increasing age and severity of anaemia even in the context of decreasing malaria prevalence. Designing interventions that include the use of IRS and behaviour change communication tailored to include older children, especially in areas with high malaria prevalence, could be of added value. All this should be done in an environment that improves the socio-economic status and equity of such populations. Uganda is one of the sub-Saharan African countries where malaria is still endemic in over 90% of the countries regions –3. In suSo far, the NMCP has led a number of efforts to reduce this malaria burden, which include universal coverage of long-lasting insecticidal nets (LLINs), indoor residual spraying (IRS), case management, and intermittent preventive therapy in pregnant women . AlreadyThis was a secondary data analysis project using the 2014 MIS datasets, a description of which is given below.The survey was conducted during December 2014 and January 2015 . HousehoFor the specifics of this study, information from two 2014 MIS datasets was used: children under 5 years and individual member datasets. The children under 5 years dataset contained information related to a child’s individual characteristics, health and treatment of childhood diseases. The individual member dataset had one record for every household member and included additional variables such as mother’s highest education attainment, mother’s treatment-seeking behaviour, use of LLINs, use of IRS and household wealth.Plasmodium parasite. Additionally, a malaria rapid diagnostic test was performed in order to offer prompt treatment to participants that were positive for malaria.During the survey, capillary blood samples were taken from the fingers or heels of children age 0 up to 59 months. Testing for anaemia was done by Hb analysis which was carried out on-site using a battery-operated portable HemoCue analyser, which produces a result in less than 1 min. Malaria testing by microscopy involved preparing both the thick and thin blood smears using 2% Giemsa stain. As a quality control measure, each smear was read by two independent expert microscopists who were not involved with the participant respondent interview. A third reader was only involved to settle parasite presence discrepancy between the two readers. A positive smear parasite density (parasites/µl) was estimated by counting the number of asexual parasites per 200 white blood cells, assuming a white blood cell count of 8000 cells/µl. A smear was only considered negative if no parasites were seen after viewing 100 high-powered fields. Thin smears of positive thick smears were read to determine the species of The dependent variables of interest in this analysis were the presence of malaria parasites by microscopy and prevalence of anaemia among children under 5 years of age. Using similar cut-offs as the 2014 MIS , anaemiaIndependent variables considered for presence of malaria parasites included: age, gender, residence , use of an LLIN, IRS of household in the past 6 months, mother’s highest education level attained, if mother has heard malaria prevention message in the past 6 months, and the extent of the household wealth.Stata version 14  was used for all data analysis. In order to create the dataset for this analysis, two 2014 MIS datasets: the children and individual member datasets were merged. Due to the non-proportional allocation of sample in the different regional domains at the second sampling stage , the sample was not self-weighting. Weighting factors were calculated based on the population of the selected regional domains and added to the MIS datasets so that any results with the regional weight factored into it would be representative at national and regional level as well as survey domain level. Details of how the weighting for the different regional domains was estimated are available in the 2014 MIS report ) children under 5 years, out of the 4930 in the sample had malaria parasitaemia by microscopy, on the day of the survey; 95.65% of all these cases of parasitaemia were due to Figure Using the age group up to 6 months as a reference category, there was a significant rising trend in odds of having malaria parasitaemia with each increasing age category, from 1.62 times among those aged 7–12 months to four times among those who were between 49 and 59 months . Just as in previous studies , 29, theThe second strategy is the use of BCC, which is supported by the finding that children from mothers who had heard malaria-related messages in the 6 months prior to the survey had 22% reduced odds of having malaria parasites. The use of BCC in the prevention of malaria is already well documented –32, and The significance of a higher education attainment of a child’s mother and a better wealth status of a household in which a child dwells in reducing the risk of a child’s ill health, including malaria, is well published –35. MothDue to the fact this was an observational study design, there is the possibility that some factors associated with malaria parasitaemia were not considered in this analysis because of reliance on variables collected only during the survey.This project has shown that malaria parasitaemia among children under 5 years in this setting is still related to increasing age and anaemia severity even in the context of decreasing trends of parasitaemia, suggesting that designing interventions that include older children who are seemingly well could be of added value. The use of IRS and BCC should be part of these interventions, especially in areas of high malaria prevalence. All this should be a holistic approach, which includes improving the socio-economic status and equity of such populations. However, the use of LLINs was not associated with a reduction in prevalence suggesting that further studies to explore proper LLIN use, reporting and efficacy in such settings are recommended. These findings are applicable to the Ugandan population since the 2014 MIS had an unbiased sampled population covering all regions of the country.


Plasmodium falciparum infection, varies among P. falciparum strains. Relatively little is known regarding markers of virulence capable of identifying strains responsible for severe malaria. We investigated the effects of genetic variations in the P.f. merozoite surface protein 2 gene (msp2) on virulence, as it was previously postulated as a factor. We analyzed 300 msp2 sequences of single P. falciparum clone infection from patients with uncomplicated disease as well as those admitted for severe malaria with and without cerebral disease. The association of msp2 variations with disease severity was examined. We found that the N allele at codon 8 of Block 2 in the FC27-like msp2 gene was significantly associated with severe disease without cerebral complications , while the K allele at codon 17 of Block 4 in the 3D7-like msp2 gene was associated with cerebral malaria . The data suggests possible roles for the associated alleles on parasite invasion processes and immune-mediated pathogenicity. Multiplicity of infection was found to associate with severe disease without cerebral complications, but not cerebral malaria. Variations in the msp2-FC27-block 2-8N and 3D7-block 4-17K allele appear to be parasite virulence markers, and may be useful in determining the likelihood for severe and cerebral malaria. Their interactions with potential host factors for severe diseases should also be explored.Parasite virulence, an important factor contributing to the severity of  P. falciparum ranges from asymptomatic infection to moderate acute febrile illness to severe complicated disease with organ failure, including life-threatening cerebral malaria. Although factors contributing to this wide spectrum of severity have not been well characterized to date, parasite virulence is believed to be an important contributor  n] . Similarn(R2)n] . Consideequences  , 11 (T11), and 14 (T14) Threonine repeats that were encoded by 2–4 copies of nanomer (ACT ACC ACA) followed by ACT ACT (msp2 strains (GenBank accession numbers JX8858919-JX885980). Haplotype analysis of polymorphisms in each block, except for the R1 region are shown in For the 3D7 family, nucleotide alignment of 163 3D7-liked  block 3 . In bloc block 3 . Based o block 3 . In R2,  ACT ACT , Table 5msp2 based on malaria severity are shown in msp2 sequences of P. falciparum isolates from mild, severe and cerebral malaria patients. When allele frequenices in the severe and mild malaria groups were compared, a significantly higher frequency of the block 2-8N alelle of FC27-like msp2 was found in patients with severe malaria (46.4%) compared to mild malaria (24.1%) with an odds ratio of 2.73 (P = 0.039). No significant difference was detected for alleles of 3D7-like msp2. However, when comparing the cerebral and mild malaria groups, a significant 2-fold higher frequency of the K allele was found for block 4-17K/N of 3D7-like msp2 in patients with cerebral malaria (23.9%) compared to mild malaria (12.5%). The odds ratio for the K allele in patients with cerebral malaria was 3.52 (P = 0.024). These findings suggest that block 2-8N of FC27 family and block4-17K of the 3D7 family may represent virulent genotypes for severe, and cerebral malaria, respectively. Moreover, bias in allele frequency distribution between severe and cerebral malaria was also observed for block 2-8N allele of FC27 family, as well as other loci in FC27 and 3D7–like msp2 . For 3D7-like msp2 which exhibited much greater diversity, haplotypes of each block were analyzed separately. For block 3, haplotypes contained only the NR and R2 region. R1 was not included given large variation (54 types) which obviated meaningful comparisons. Distinct haplotypes of 5, 15 and 12 were identified for block 2, 3, and 4, respectively (P = 0.02). The apparent association seems to come from the presence of the block 4-17K allele in this haplotype since the P value and OR were equal to those obtained for the block 4-17K allele alone.Given several polymorphisms detected in ike msp2 . Of thesectively . We founmsp2 sequences was analyzed based on r2 values .To clarify the contribution of virulence-associated alleles to disease progression of severe and cerebral malaria, their associations with parasitemia were analyzed. No association was detected for block 2-8K/N/T of FC27-like P. falciparum infection. Although several studies have tried to characterize virulent strains using polymorphic genes as markers, they analyzed only size variation and bi-allelic families of genes which is insufficient to identify suitable virulence markers [msp2_FC27_block 2-8N allele and 3D7_block 4-17K allele associated with severe and cerebral malaria, respectively, in Thailand. Lack of linkage disequilibrium between the associated alleles and other polymorphisms in msp2 indicated that the association is independent of other polymorphisms. This is the first study to reveal allelic sequences that could be potential markers for severe and cerebral malaria. In addition to virulence-associated alleles, interestingly, a number of polymorphic loci including severe malaria associated locus FC27_block 2-8N showed differences in allele frequencies between severe and cerebral malaria. This implies that parasite genetic factors responsible for cerebral malaria are distinct from those that cause severe symptoms involving other organs. Moreover, we found a clinical association of multiple infections with increased risk for non-cerebral severe malaria, but a much lower frequency of multiple infections observed in cerebral malaria patients. These findings correspond with previous studies where the epidemiological observations and parasite genetic characterization demonstrated the association of cerebral malaria with emergence of a few distinct virulent strains [Parasite virulence is thought to be responsible for severity of  markers , 24–27.  strains .msp2’s well characterized role in RBC invasion, the virulent allele is located in a specific RBC binding region [While it remains unclear whether these associated alleles were the primary cause of virulence or just markers based on LD to a causal variant in another locus, their contribution to severe malaria pathogenesis is hypothesized. Amino acid changes from positively charged lysine (K) to an uncharged asparagine (N) or vice versa may have effects on protein function. Given g region  and may g region , 16. Antg region . A priorg region . Viruleng region , 38. Thumsp2 [msp2 and virulence, and this served as the basis for our analysis to this gene. Therefore, type I error  was less of a concern than type II error . Given these caveats, correction for multiple comparisons was omitted here, but replicating the study may be required to confirm our results [Although the present study demonstrates the use of candidate gene association analysis to identify parasite genetic determinants of malaria severity, factors complicating study interpretation should be considered. Theoretically, when several polymorphic markers within a given candidate region are examined for association with a disease of interest, correction for multiple testing should be taken to account for spurious associations (type I error). However, multiple correction remains a problematic issue, especially for markers having several polymorphic loci or multiple alleles, as msp2 . By redu results , 41. In  results .msp2 family and size variation to characterize parasite genetics associated with malaria severity. The study provided limited information on the dimorphic family and overall variant size of the gene. Sequence variations that might be related to disease severity were not revealed. We observed limited capacity of gel electrophoresis to differentiate distinct variants with similar length, as described previously [msp2 family and size variation is likely to be underestimated, and may explain inconclusive results from previous studies attempting to determine allelic variants based on family/size variation. Recently, massively parallel pyrosequencing tools have been used to characterize parasite diversity in individual infections [P. falciparum within clinical infection [msp2, but other unidentified markers contribute to malaria pathogenesis. Genome-wide association analysis would be a powerful tool to discover other markers associated to malaria severity, and identify parasite factors which contribute to severe and cerebral malaria.Here we discerned the limitations of employing eviously . Our anafections . This tofections –47. Althnfection , 49, themsp2 in P. falciparum isolates from patients with different clinical presentations, suggesting that the K allele at codon17 of block 4 in 3D7 family and the N allele at codon8 of block 2 in FC27 family may be associated with increased risk for cerebral malaria and other severe complications, respectively. Differences in allele frequencies of several polymorphic loci between cerebral malaria and non-cerebral severe malaria implied parasite genetic factors responsible for cerebral malaria may be distinct from those that cause severe symptoms involving other organs. Their interactions with potential host factors for severe disease should also be explored. Functional study of this polymorphism may help to better understand parasite virulence leading to severe complications. This is the first study to our knowledge that identifies msp2 sequence polymorphisms as candidate virulence markers.The present study characterized sequence variants of S1 Figmsp2 variants into FC27 (red bars) or 3D7 (blue bars) families, respectively. The frequencies of variants sized by gel electrophoresis is shown by green bars in the lower panel.Sequence analysis differentiated (PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.


Submicroscopic malaria parasitaemia is common in both high- and low-endemicity settings, but its clinical consequences are unclear.A cohort of 364 children (0.5–10 years of age) and 106 adults was followed from 2011 to 2016 in Tororo District, Uganda using passive surveillance for malaria episodes and active surveillance for parasitaemia. Participants presented every 90 days for routine visits (n = 9075); a subset was followed every 30 days. Participants who presented with fever and a positive blood smear were treated for malaria. At all routine visits microscopy was performed and samples from subjects with a negative blood smear underwent loop-mediated isothermal amplification for detection of plasmodial DNA.Submicroscopic parasitaemia was common; the proportion of visits with submicroscopic parasitemia was 25.8% in children and 39.2% in adults. For children 0.5–10 years of age, but not adults, having microscopic and submicroscopic parasitaemia at routine visits was significantly associated with both fever  and non-febrile illness , compared to not having parasitaemia. After stratifying by age, significant associations were seen between submicroscopic parasitaemia and fever in children aged 2–< 5 and 5–10 years , and submicroscopic parasitaemia and non-febrile illness in children aged 5–10 years . These associations were maintained after excluding individuals with a malaria episode within the preceding 14 or following 7 days, and after adjusting for household wealth.Submicroscopic malaria infections were associated with fever and non-febrile illness in Ugandan children. These findings support malaria control strategies that target low-density infections.The online version of this article (10.1186/s12936-018-2221-9) contains supplementary material, which is available to authorized users. Despite coordinated efforts in vector-control and improved treatment of symptomatic disease, malaria infection remains common worldwide, causing an estimated 200 million illnesses per year . The useTo assess associations between submicroscopic parasitaemia and clinical outcomes, repeated surveys measuring parasite prevalence were performed in a cohort of Ugandan children and adults. We hypothesized that submicroscopic malaria infection would be associated with clinical outcomes, including fever, in children.The study protocol was reviewed and approved by the Uganda National Council of Science and Technology  and the institutional review boards of the University of California—San Francisco , and Makerere University . Informed consent was obtained from the parent or guardian of all participating children.The study took place in Nagongera sub-county, Tororo District, Uganda, an area where malaria transmission has been reported as high and perennial , with anCohort enrolment, follow-up, and clinical care have been described previously , 10. BriParticipants presented to the clinic for routine visits every 90 days (every 30 days for a subset of 204 patients starting in 2015). At each routine visit, clinical interviews were performed, and blood smears and dried blood spots (DBS) were collected, regardless of symptoms. For samples with negative blood smears, DNA extracted from DBS was tested for the presence of submicroscopic parasitaemia using LAMP. Participants were invited to visit the clinic any time they were ill, and microscopy was performed when there was a reported or documented fever (temperature of ≥ 38.0 °C). Patients with reported or documented fever and a positive blood smear were treated with standard dosing of artemether–lumefantrine. Participants with asymptomatic parasitaemia were not provided anti-malarial therapy in accordance with local standard-of-care.Loopamp™ Malaria Pan Detection Kit reaction tubes and 15 µL of extracted DNA, per manufacturer’s guidelines. The LAMP primer set targets a mitochondrial DNA sequence that is conserved in four major human malaria species [Plasmodium falciparum densities , as well as one positive and one negative control from the Eiken kit.Thick smears were prepared with 2% Giemsa. Two expert microscopists evaluated each smear separately, and a third resolved discrepancies. DBS were prepared by spotting approximately 25 μL of blood onto filter paper, drying completely, and storing at room temperature. DNA was extracted using Chelex, as previously described , yieldin species . LAMP reData were analysed using STATA . The proportion of visits with no detectable parasitaemia (negative blood smear and negative LAMP reaction), submicroscopic parasitaemia (negative blood smear and positive LAMP reaction), and microscopic parasitaemia (positive blood smear) were made for participants in different age strata. Age strata were pre-specified as <2, 2–< 5, 5–10, and ≥ 18 years, based on local epidemiology explored in prior work . The priOver 5 years of longitudinal follow-up of 364 children and 106 adults, there were a total of 9075 routine visits, including 7342 among children 0.5–10 years, and 1733 among adults ≥ 18 years  had an expected association between microscopically detected parasitaemia and both fever and non-febrile illness. However, in contrast to older children, in these young children submicroscopic infection was not associated with either febrile or non-febrile illness. Low numbers of children in this age group may have prevented the identification of an association, but it is possible that the persistence of fetal haemoglobin, or other age-related differences in the immune system of infants protected against clinical effects of low-density infections , 22. In The concept that submicroscopic infection has clinical consequences strengthens arguments for malaria control strategies designed to eliminate all malaria parasitaemia. These strategies may include mass drug administration, which has been used in Africa intermittently for decades , 27, or This study demonstrates a compelling association between submicroscopic malaria infection and clinical illness in children in a high-burden country. These findings highlight the importance of defining the submicroscopic malaria parasite reservoir, provide evidence for individual benefit from treatment of low-density malaria parasitaemia, and support aggressive interventions geared to eliminate submicroscopic malaria infections.


Plasmodium falciparum infection accompanied by hemoglobin levels below 50 g/L, is a key manifestation of life-threatening malaria in high transmission regions. For this study sample, a prediction model incorporating cord blood levels of interleukin-1β provided the strongest discrimination of severe malarial anemia risk with a C-index of 0.77 (95% CI 0.70–0.84), whereas a pragmatic model based on sex, gravidity, transmission season at delivery, and bed net possession yielded a more modest C-index of 0.63 (95% CI 0.54–0.71). Although additional studies, ideally incorporating larger sample sizes and higher event per predictor ratios, are needed to externally validate these prediction models, the findings provide proof of concept that risk score-based screening programs could be developed to avert severe malaria cases in early childhood.As a pilot study to investigate whether personalized medicine approaches could have value for the reduction of malaria-related mortality in young children, we evaluated questionnaire and biomarker data collected from the Mother Offspring Malaria Study Project birth cohort  at the time of delivery as potential prognostic markers for pediatric severe malarial anemia. Severe malarial anemia, defined here as a  Global Technical Strategy for Malaria 2016–2030 endorsed in May 2015, the World Health Assembly set the goal of reducing the 2015 malaria mortality rates by a further 90% by 2030 [Plasmodium falciparum transmission suggest that scaling-up of existing population-wide interventions, including through expanded access to long-lasting insecticidal nets (LLINs), seasonal malaria chemoprevention in children aged 6 months to 5 years, and artesunate therapies, could reduce malaria mortality rates by up to an estimated 74% (95% CI 67–84%) [Although improved uptake of vector control measures and first-line antimalarial medication has reduced the number of deaths due to malaria in children younger than 5 years of age by nearly 60% since the year 2000, malaria still remains the fourth leading cause of child death in sub-Saharan Africa , 2. In tP. falciparum infection with hemoglobin concentrations below 50 g/L, is the most common manifestation of life-threatening malaria in young children [Using data collected at the time of birth as part of the Mother Offspring Malaria Study (MOMS) Project birth cohort in Muheza, United Republic of Tanzania, between 2002 and 2006, we sought to develop a prognostic model for severe malarial anemia as a pilot study for investigating whether personalized medicine approaches could have value for the reduction of malaria-related mortality. In regions with high malaria transmission, severe malarial anemia, here defined as a children , 8. Builα/α, α/α − 3.7, and α − 3.7/α − 3.7), birth weight, maternal age, gravidity , transmission season at delivery , late high season (August–October), early low season (November–January), and late low season (February–April), placental malaria status at delivery , household bed net possession at delivery , intermittent preventive treatment in pregnancy (IPTp) , and a panel of cord blood cytokines and receptors listed in ®, BioRad, Irvine, CA, USA) and custom-made assay kits as previously described [n = 76) and interferon-γ , these cytokines were evaluated as binary variables ; other cord cytokines and receptors were loge-transformed to obtain approximately normal distributions .The study setting, location, dates, and methods for data collection and follow-up in the MOMS Project have been described in detail previously . To be eescribed . SamplesThis study was approved by the US National Institutes of Health International Clinical Studies Review Committee of the Division of Microbiology and Infectious Diseases. Ethical clearance was provided by the Institutional Review Boards of the Seattle Biomedical Research Institute and the National Institute for Medical Research in Tanzania. Participating mothers provided written informed consent for themselves and their newborn child. Prompt care was provided to sick children in accordance with Tanzanian Ministry of Health protocols. All subsequent laboratory measurements were performed on de-identified samples.et al. [n = 781) who were followed between 2002 and 2006, and resources available to administer an intervention with 75% protective efficacy [Associations between the baseline risk markers and severe malarial anemia were quantified using Cox proportional hazard models. Candidate markers were investigated first individually using all of the available data for a given marker and then in combined analyses restricted to those individuals with complete information. A basic model using the pragmatic markers of sex, gravidity, transmission season at delivery, and bed net possession was also explored. Risk discrimination was assessed using Harrell's C-index, a generalization of the area under the receiver operating curve suitable for time-to-event analyses . The C-iet al. . For thiefficacy , 16 to 2During a median follow-up of 2.0  years, 37 incident severe malarial anemia events were recorded in the 880 children participating in the MOMS Project cohort. Out of the baseline demographic and clinical markers, the transmission season at delivery  provided the best discrimination of severe malarial anemia risk, and its associated C-index was 0.63 (95% CI 0.54–0.71) . HousehoOut of the cord blood markers, interleukin-1β yielded the strongest discrimination of risk with a C-index of 0.77 (95% CI 0.70–0.84) and had an average optimism of only 0.0044  and 2. IThese data provide proof of concept that a prognostic model based on measurements captured at the time of birth can be developed to facilitate the selection of high-risk children for additional targeted intervention against malaria. Despite the small sample size and number of cases in this pilot study, the prognostic associations, particularly with cord blood cytokines, were strong and had C-indices in the range of those observed for models of HIV acquisition  and mortPlasmodium parasites). Minimizing the potential for detection bias, the outcome of severe malarial anemia was independently diagnosed by clinical teams prior to the selection of potential risk predictors. Another strength of this analysis was that associations between the predictors and outcome were internally validated using a bootstrap resampling approach to investigate and correct for overfitting that could arise from the low event to predictor ratio [The current study had strengths and limitations. In line with the recommendations of the TRIPOD statement , candidaor ratio . Moving or ratio . In addiIn regions that are approaching the elimination of malaria, identifying high risk groups based on easily ascertainable and low cost indicators will be particularly relevant, and efforts should be made to enhance the feasibility of malaria screening programs. Specifically, future studies that aim to formally develop prognostic models of life-threatening malaria in childhood should evaluate additional pragmatic predictors, such as parental occupation  and houIn conclusion, this study provides valuable new evidence that the development of a prognostic model for severe malarial anemia during infancy and early childhood is scientifically plausible. By providing clinicians and families with an objectively estimated probability of a newborn's risk of developing life-threatening complications of infection, personalized medicine approaches have the potential to complement on-going population-wide approaches for malaria prevention.


Plasmodium vivax malaria burden has evoked enormous concern among the global research community. In this study, we have investigated the serum proteome alterations in non-severe vivax malaria patients before and during patient recuperation starting from the early febrile to the defervescence and convalescent stages of the infection. We have also performed an extensive quantitative proteomics analysis to compare the serum proteome profiles of vivax malaria patients with low (LPVM) and moderately-high (MPVM) parasitemia with healthy community controls. Interestingly, some of the serum proteins such as Serum amyloid A, Apolipoprotein A1, C-reactive protein, Titin and Haptoglobin, were found to be sequentially altered with respect to increased parasite counts. Analysis of a longitudinal cohort of malaria patients indicated reversible alterations in serum levels of some proteins such as Haptoglobin, Apolipoprotein E, Apolipoprotein A1, Carbonic anhydrase 1, and Hemoglobin subunit alpha upon treatment; however, the levels of a few other proteins did not return to the baseline even during the convalescent phase of the infection. Here we present the first comprehensive serum proteomics analysis of vivax malaria patients with different levels of parasitemia and during the acute and convalescent phases of the infection.The radial distribution of  Plasmodium vivax is the most widely distributed species among the five parasites responsible for malaria in humans. Even though this plasmodial infection was generally considered as benign, reported case series appeared mostly during the last 15 years from different endemic countries evidently indicated severe manifestations associated with P. vivax mono-infection1. Worrisomely, apart from the incidence of a very high level of parasitemia all the other complications of severe falciparum malaria, including cerebral syndromes and fatal outcomes have been observed in acute P. vivax infections3. Importantly, P. vivax causes severe and fatal manifestations even at a very low-grade parasitemia5 and elicits a greater host response than P. falciparum6. Insufficient knowledge about the invasion biology of P. vivax7, and poor understanding of host-parasite interactions are primarily due to the lack of an enduring in vitro culture system for this malaria parasite. The increasing global burden of vivax malaria, especially in infants and young children11 and emerging resistance of this pathogen against commonly used anti-malarials12 suggest an urgent need for intensive research in vivax malaria16.P. vivax preferentially invades reticulocytes, resulting in low levels of parasitemia, requiring the need for trained experts with good microscopic skills for proper diagnosis16. Polymerase chain reaction (PCR)-based molecular diagnostics are sensitive but not easy to use in point-of-care settings; while rapid diagnostic tests (RDTs) are routinely used for malaria diagnosis due to their ease and simplicity18. However, overall sensitivity of RDTs for low-parasite density in P. vivax samples are much lower than that of P. falciparum20, actuating the need for development of new diagnostic approaches. In this context, blood biomarkers and surrogate host markers for malaria could be used for early diagnosis, prognosis, monitoring responses to therapy and predicting outcomes.Early diagnosis and effective treatment against both the blood and liver stages of the parasite is an absolute necessity for vivax malaria control. To this end, microscopic examination of thick and thin blood smears often leads to inaccurate diagnosis, since 22. Earlier studies on the serum/plasma proteome of P. falciparum infected patients have led to the identification of multiple surrogate protein markers of infection and severity27. Presence of various muscle proteins in plasma samples of children with cerebral falciparum malaria identified through affinity proteomics indicate that plasma levels of carbonic anhydrase III, creatine kinase and myoglobin could serve as the indicators of cerebral malaria in children26. Compared to the studies on P. falciparum, research on P. vivax is awfully limited, necessitating further investigation in this front. Previously, we have reported serum proteome analysis of vivax malaria patients with identification of several differentially abundant proteins and associated physiological pathways to provide some imperative insights into disease pathogenesis and host immune responses in P. vivax infection28, while proteomic analyses of the parasite directly isolated from the human blood and Saimiri boliviensis monkey host have further strengthened our understanding of pathogenesis and host-parasite interactions in vivax malaria30. In this present study we investigated serum proteome profiles in low and moderately-high parasitemic vivax malaria patients to evaluate whether there is any possible correlation between serum abundance of diverse classes of proteins and parasite levels in peripheral blood. Furthermore, we aimed to explore the alterations in the host serum proteome profiles during the acute and remission phases (pre- and post-treatment time points) of the infection through analysis of a longitudinal cohort of vivax malaria patients.In recent years, proteome level analyses are found to be informative to comprehend different aspects of malaria pathogenesisThis study was approved by the Institutional Ethics Committees of Seth GS Medical College & King Edward Memorial (KEM) Hospital, Mumbai and Grant Govt. Medical College and Sir JJ Group of Hospitals, Mumbai. After providing detailed explanations about the experimental procedure in the language best understood by the potential participants, written informed consent was obtained from each individual before recruitment. Experiments involving human subjects were carried out in accordance with the relevant guidelines and regulations.19 along with 40 age and gender-matched healthy controls (HC) were enrolled for the study from Seth GS Medical College & King Edward VII Memorial Hospital. The cross-sectional vivax malaria cohorts were treated with Coartem [artemether + lumefantrine (20 mg + 120 mg)]; total course over 3 days, 24 tablets). Patients exhibiting symptoms for severe malaria in accordance with the WHO standard guidelines were excluded from this study. In addition, 7 patients suffering from dengue fever (DF) were also enrolled in this study to serve as a non-malaria febrile infection for a comparative analysis.Twenty-three low parasitemic  and 40 moderately-high parasitemic  non-severe vivax malaria patients classified according to the World Health Organization (WHO) guidelinesP. vivax. Demographic, epidemiological and clinicopathological details, together with past history of diseases of all the malaria patients and controls enrolled for this study were documented. Sample collection and storage was performed as described previously32.In order to perform a longitudinal study, 15 non-severe vivax malaria patients admitted at Grant Govt. Medical College and Sir JJ Group of Hospitals were followed up. Serum samples were collected during early febrile phase immediately after diagnosis , defervescence  and convalescence  stages. We also recruited age and gender-matched 15 HC participants for a comparative analysis. Diagnosis was confirmed by microscopic examination of thick and thin peripheral blood smear by trained microscopists and RDT. Those patients who were recruited for the longitudinal analysis were treated with intravenous Artesunate (2.4 mg/kg) given after confirmation of diagnosis (time = 0), which was repeated after 12 and 24 h, and then once a day from day 1–3. This was followed by an oral Artemesinin-based Combination Therapy (ACT), which includes Artemether (20 mg) and Lumefantrine (120 mg) for three days. Additionally, 14 daily doses of primaquine (0.50 mg/kg) were provided to the vivax malaria patients as a therapy for radical cure of st hr) and RBC count (millions/µL). Biochemical parameters including liver function tests  were measured in serum samples. In the longitudinal study, hematological parameters such as hemoglobin (g/dL), platelets (thousand/µL), erythrocyte sedimentation rate (mm in 1st hr), RBC (millions/µL), total WBC (counts/µL), neutrophils (%), monocytes (%), eosinophils (%) and lymphocytes (%), and biochemical parameters such as renal function tests  and liver function tests  were measured in non-severe vivax patients at three different time points. Hematological investigations were carried out in a fully automated cell counter , ESR was measured by Westergreen’s method33, and biochemical tests were carried out using a fully automated chemical analyzer . Kruskal–Wallis test was carried out to find out any statistically significant difference among the multiple study cohorts; if this multiple comparison test exhibited a significant difference, further statistical analysis was performed using Mann Whitney U test at 5% significance. GraphPad Prism software package (version 6.0) was used to generate graphical representations of the datasets.Hematological parameters analyzed in the blood samples collected from LPVM, MPVM, DF patients and HC subjects included hemoglobin level (g/dL), platelet count (thousand/µL), erythrocyte sedimentation rate  following the manufacturer’s instructions. Protein extraction from depleted serum samples was performed using trichloroacetic acid (TCA)-acetone precipitation method. Extracted serum proteins  were labeled with fluorescent dyes Cy3 and Cy5, while a mixture of equal amounts from each sample to be analyzed was regarded as an internal standard and was labeled with Cy2 according to the manufacturer’s instructions . After labeling, protein samples were pooled, diluted with rehydration buffer and loaded onto 18 cm, 4–7 pH immobilized pH gradient (IPG) strips. Subsequently, isoelectric focusing (IEF) and SDS-PAGE separation were performed following the same protocol as reported earlier32.The optimal sample size required to present sufficient statistical power at our selected level of significance in two-dimensional difference in-gel electrophoresis (2D-DIGE) analysis was calculated following the protocol as described by Hunt In order to perform a comparative serum proteomic analysis of a longitudinal cohort of vivax malaria patients at three different time points against healthy controls, three different sets of experiments were carried out. Sera from HC were pooled and labeled with Cy3 and sera obtained from patients during the FEB, DEF and CON stages were each labeled with Cy5, and individually used in different sets of experiments in comparison with HC . In every DIGE experiment, dye swapping was carried out while labeling the malaria and control samples to avoid labeling bias.vs. HC and LPVM vs. MPVM was performed separately, and subsequently the multiple biological variation analysis (BVA) modules were combined for a cross comparison of the abundances of serum proteins across HC, LPVM and MPVM study populations. In the longitudinal analysis, comparison of the three stages  individually against the HC was performed using differential in-gel analysis (DIA) and biological variation analysis (BVA) modules. Differentially abundant protein spots for subsequent mass spectrometric analysis (MS) were selected on the basis of statistical significance (p < 0.05) of their differential abundance using Student’s t-test and one-way ANOVA.A Typhoon 9400 variable mode imager  employing suitable excitation/emission wavelengths for CyDyes  was used to scan the 2D-DIGE gels, which were further analyzed by DeCyder 2D software, version 7.0 . Initially, a comparative analysis of LPVM p < 0.05) were manually excised from GelCode Blue stained preparative gels and subjected to in-gel digestion and matrix-assisted laser desorption/ionization–time-of-flight tandem mass spectrometry (MALDI-TOF/TOF) analysis as described earlier32. Protein identification was performed by MS/MS ion search using MASCOT version 2.1 (http://www.martixscience.com) search engine against the Swiss-Prot database with the following parameters: all entries taxonomy, trypsin digestion with one missed cleavage, fixed modifications: carbamidomethylation of cysteine residues, variable modifications: oxidation of methionine residues, mass tolerance 75 ppm for MS and 0.4 Da for MS/MS. Identified proteins having at least two unique matched peptides were selected for further analysis. Only those proteins with a protein identification confidence interval of ≥95% were considered for further analysis.Differentially abundant protein spots -based quantitative proteomics analysis was performed on HC, LPVM and MPVM cohorts using the pooled samples (each pool consists of 20 samples). Sample labeling strategy for differential proteomic analysis was; HC-114, LPVM-115 and MPVM-116. Comparative serum proteome analysis of vivax malaria patients at three different time points against healthy controls was also performed using iTRAQ. 10 selected serum samples from each of the study cohorts  were split into three pools- Set 1(n = 4); Set 2 (n = 3) and Set 3 (n = 3). Apart from these biological replicates, a pool containing all 10 samples  was also analyzed. HC samples were labeled with the 114 iTRAQ reagent, while the three different time-point samples  were labeled with 115, 116 and 117 labels, respectively. Buffer exchange (from rehydration solution to TEAB buffer) for all the samples was performed using Amicon Ultra 0.5 mL centrifugal 3 kDa filters  prior to in-solution digestion.35. Briefly, 75 μg of protein from each sample was digested using Trypsin  at a 1: 20 trypsin: protein ratio. The resulting peptides were iTRAQ labelled following the manufacturer’s instructions . All the labeled samples were pooled and concentrated using a speed vacuum centrifuge. Pre-fractionation of the labeled peptides was carried out using a 3100 OFFGEL fractionator  with high-resolution  IPG strips.In-solution digestion, iTRAQ labeling and OFFGEL fractionation were performed as previously described35. The data files obtained were processed by the Spectrum Mill Protein Identification software  using the Paragon algorithm and Mascot v2.2  and were searched against the UniProt database  using the following parameters: Data extraction was carried out between MH+ 600 and 4000, IAA for cysteine and iTRAQ  were specified as the fixed modifications and oxidized methionine as a variable modification. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE36 partner repository with the dataset identifier PXD005267.Analysis of the iTRAQ-labelled samples was performed using two mass spectrometry (MS) platforms; Agilent 6550 Quadrupole Time-of-Flight (Q-TOF) and Thermo Scientific Q-Exactive. Agilent 6550 iFunnel Q-TOF LC-MS/MS instrument  equipped with a Chip-Cube controlled by the Mass hunter Acquisition software was operated in a positive ion mode for data acquisition. Details of the liquid chromatography (LC) and MS parameters have been described previously elsewhereversion 1.5.5.3)37. Reverse and contaminant database hits were removed before executing subsequent statistical analyses. Reporter ion intensity values were log2 transformed, and were normalized by “subtract (mean)” followed by Z score normalization. Proteins groups were selected for valid values, and p-values obtained from a paired t-test were used to estimate significance of differences in the protein abundances between HC and different study cohorts . P-values (adjusted) ≤0.05 were considered to be statistically significant.Normalization and statistical analysis of the quantitative proteomics datasets was carried out using the Perseus workstation (38. Proteome discoverer 1.4 (Thermo Fisher Scientific) was applied for processing of the raw. msf files; MASCOT 2.2.4 and SEQUEST were used for database searching against the Uniprot Homo sapiens FASTA. Database searching parameters included precursor ion mass tolerance of 5 ppm and fragment mass tolerance of 0.02 Da. N-terminal modifications were selected as iTRAQ 4-plex reaction, with dynamic modifications at oxidation (M), deamination  and iTRAQ 4-plex (K) in addition to the static modifications at Methylthio (C).The iTRAQ labeled samples were also analyzed using a Q-Exactive mass spectrometer  for increasing the proteome coverage. Chromatographic separation and MS parameters were specified following the same method as described earlier39.Five selected targets namely, Serum amyloid A , Hemopexin , Apolipoprotein E , Haptoglobin , and Apolipoprotein A1  were quantified using AssayMax ELISA kits  in serum samples from LPVM, MPVM and DF patients, and healthy controls following the manufacturer’s instructions. Serum abundances of these five proteins along with three other proteins Ceruloplasmin , Plasma retinol binding protein , and Plasminogen  were measured in the FEB, DEF, and CON stages of the infection and HC following the same assay protocol as described earliervs. false positives (1- specificity) for each possible cutoff] were used to analyze the efficiency of the differentially abundant serum proteins  in prediction of low and moderately-high parasitemic cohorts as well as the longitudinal cohorts of vivax malaria patients. ROC curves were plotted using GraphPad Prism software package (version 6). Sensitivity and specificity values for these serum proteins were calculated at different threshold points. Two-sided p-values less than 0.05 were considered statistically significant. The area under the ROC curve (AUC) was also calculated as a measure of the accuracy of the test.ROC curves [plot of true positives (sensitivity) www.ingenuity.com). Pathway analysis was also performed using DAVID  database version 6.7 (http://david.abcc.ncifcrf.gov/home.jsp)40, and PANTHER  system, version 7 (http://www.pantherdb.org)41.Differentially abundant serum proteins identified in the comparative quantitative proteomics analysis in the longitudinal cohort and different parasitemic vivax malaria patients were subjected to further bioinformatics analysis using Ingenuity Pathway Analysis (IPA) version 9.0  in the malaria patients (both LPVM and MPVM), while hemoglobin (Hb) levels were found to be significantly lower (p < 0.0001) only in MPVM and DF patients , but did not exhibit any notable correlation with the parasite counts. Total bilirubin was also found to be significantly higher (p < 0.05) in MPVM and DF as compared to HC  Fig. . 6–8% ofnts Fig. . Interesnts Fig. . Similar HC Fig. . Since m HC Fig.  and S2B,Clinicopathological parameters were also analyzed in the longitudinal cohort of non-severe vivax malaria patients at three time points Fig. . Among ap < 0.05 significance level . Among the differentially abundant proteins, 5 were up-regulated, and the remaining 7 were down-regulated   Table . Detailsd) Table . Interesd) Table . Many ofp values versus difference of group means of FEB-HC, DEF-HC and CON-HC obtained in Q-TOF analysis are represented in Fig. p-value ≤0.05) of 25 proteins in FEB (12 up-regulated and 13 down-regulated), 28 proteins in DEF (8 up-regulated and 20 down-regulated), and 4 proteins in CON (1 up-regulated and 3 down-regulated) stages of the disease  only in the FEB stage, while serum abundances for two proteins were found to be dysregulated across all the three stages , but not in the FEB stage of the infection    Fig. . ROC cur05) Fig. . Serum l001 Fig. . Serum l001 Fig. . Additioia) Fig. .Figure 5p > 0.05)  in the serum abundances of HP, ApoE and Apo A1 during the acute and remission phases of the infection Table . Serum l) Tables  and S13C) Tables . Taken t44. To this end, in recent years, several research groups including ours have investigated alterations in serum/plasma proteome in severe and non-severe falciparum32 and vivax malaria45 to study malaria pathogenesis. In all these studies, serum/plasma proteome of the malaria patients have been analyzed during the febrile stages of the infection, either at the onset of the disease or at the fastigium stage. However, temporal profiling of serum/plasma proteome during acute and remission stages in malaria, which can provide snapshots of the transient and enduring alterations in serum proteome during the FEB, DEF and CON stages, has not been reported hitherto. Here, for the first time we report serum proteomic alterations in a longitudinal cohort of P. vivax infected patients to elucidate host responses when fever is established , during the stage when the temperature comes down to normal, and also during the gradual recovery of health after the illness. The three stages discussed in our study have been categorically chosen depending upon the clinical course of uncomplicated vivax malaria. Analysis of the early febrile stage represents host proteome profile immediately after onset of the infection, before administering any anti-malarial drugs. The second, defervescence stage, reflects any immediate change in blood proteome at early recovery phase, while the convalescent stage indicates a phase after administration of 14 days radical cure treatment with primaquine and a complete recovery, when none of the patients displayed any apparent symptoms of malaria.Identification of serum/plasma proteins, which exhibit altered abundance at the onset and during the acute phase of any infection, could be informative to understand the pathobiology of different infectious diseases and host responses against the invading pathogens47. Likewise, platelet counts, which were reported earlier to be consistently low in vivax malaria50, were also found to be reduced in malaria patients in our study, reflecting the possibilities of sequestration of platelets by macrophages in the spleen due to immune mediated injury as well as platelet clump formation with the infected erythrocytes52. Increased levels of liver enzymes were observed with an increase in parasitemia and degree of hemolysis, as reflected by the higher AST level in MPVM compared to the HC and LPVM cohorts. Liver function derangements have been studied earlier in malaria53. Other biochemical parameters such as bilirubin and ALP were also found to be elevated in vivax malaria, but returned to the normal levels during the convalescent stage indicating possibilities of liver involvement in malaria that could be attributed to mononuclear infiltration of the liver leading to an intrahepatic cholestasis.In this study, we have analyzed correlations of dysregulated serum proteins with different clinicopathological parameters and investigated their involvements in diverse physiological pathways and biological processes. Reduction in the hemoglobin level during the early stage of plasmodial infection and its gradual recovery to the normal level with the disease remission, as well as its reduction with the increase in parasitemia are suggestive of increased hemolysis and decreased rate of erythrocyte production in malaria patients. This observation is consistent with earlier reportsComparative analysis of the serum proteome profiles of non-severe vivax malaria patients with varying levels of parasitemia indicated some prominent differences in the serum proteome patterns of low and moderately-high parasitemic patients. Some of the differentially abundant proteins such as SAA, CRP, Titin, Apo E exhibited gradual alterations in their serum abundances with an increase in parasitemia. However, some of the identified proteins such as HPX, Vitronectin, Clusterin and Apo E exhibited nearly equal levels of differential serum abundance in both patient groups as compared to healthy controls, indicating some possibilities of differential host responses due to the varying levels of parasitemia.54. In this direction, an earlier study describing proteomics analysis of longitudinal cohorts of dengue fever and dengue hemorrhagic fever patients also reported altered serum levels of a large number of acute phase reactants and cytokines55. Non-specific resistance against the pre-erythrocytic stages of Plasmodium can be generated by APPs56. In malaria patients, the parasite selectively invades the red blood cells, multiplies within them and ultimately ruptures to release merozoites into circulation58. In this process, several proteins such as hemoglobin subunits, which remain confined in the interior of RBCs, are also released in the bloodstream. Consequently, inflammatory responses are triggered by the body against the parasites59, which can lead to the activation of various complement proteins61. Extreme dysregulation in the serum levels of several APPs including SAA, CRP, Leucine-rich alpha-2-glycoprotein (P02750), Alpha-1-antichymotrypsin, and Alpha-1-antitrypsin (P01009) observed during the FEB stage of the infection clearly indicates generation of strong inflammatory responses against the malaria parasites almost immediately after onset of the infection.Many of the dysregulated proteins were found to be acute phase reactants or acute phase proteins (APPs), followed by the proteins involved in complement and coagulation cascades. Previously, a time course analysis of falciparum malaria patients during antiparasitic therapy demonstrated complex interactions of inflammatory and coagulatory factors during the acute phase of the diseasede novo cholesterol synthetic pathway, and therefore need uptake of cholesterol and other nutrients through parasitophorous vacuolar membrane (PVM) to ensure their survival and propagation62. Our study indicates that apolipoproteins of high-density lipoproteins-cholesterol (HDL-C) such as Apolipoprotein CI , Apolipoprotein C2 , Apolipoprotein A4  and Paraoxonase 1 were down-regulated along with the alterations in HDL-C transport (LCAT) in vivax malaria. To this end, lower HDL level in malaria patients has been reported earlier64. An earlier report on the meta-analysis of serum lipid and lipoprotein changes indicates that the normalization of lipid profiles happens quite slowly in malaria patients, and takes over one to six months to reach the basal levels following the infection65. Intriguingly, we observed that serum levels of the proteins involved in HDL metabolism and transport remained lower not only in the FEB stage, but modulations in their serum levels were also observed during the DEF and CON stages of malaria.Bioinformatics analysis on the basis of our identified differentially abundant serum proteins indicates modulations in lipid metabolism and transport in the vivax malaria patients Fig. . Lipids ex vivo grown malaria parasites may provide some further insights, and could be an interesting continuation of this present study. Taken together, we are able to get a glimpse of the composite depictions of vivax malaria pathogenesis through a proteome level analysis. This study may pave the way for future proteomics and integrated multi-omics investigations on both the host and the parasite for obtaining a better perspective of vivax malaria pathogenesis.This study provided a comprehensive representation of the diverse alterations in serum proteome profiles of vivax malaria patients with low and moderately-high parasitemia, as well as regarding the phase-specific temporal protein profiles during the acute and convalescent phases of the infection. More importantly, our bioinformatics analyses provided evidences of intricate associations of many of the identified dysregulated proteins with crucial biological processes and physiological pathways such as blood coagulation and plasminogen activating cascade, complement systems, lipid metabolism and molecular transport, and acute phase response signalling. However, it is certainly difficult to speculate the exact mechanisms behind such diverse alterations in blood proteome as the factors introducing these alterations could be partly host related, or might be parasite-related (secondary to parasite metabolism), and there could also be some cumulative effect of interactions between the host and the parasite. Therefore, it remains challenging to unravel the precise mechanisms behind such observations through analysis of clinical specimens due to the presence of an entangled web of physiological networks that are controlled by both host and parasite under the complicated diseased conditions. Specific functional assays with the Supplementary Information


Plasmodium falciparum apical membrane antigen 1 (pfama1) was utilized to (1) investigate the relationship between P. falciparum prevalence and COI, (2) to explore the population genetic structure of P. falciparum parasites from malaria asymptomatic individuals participating in the 2007 Demographic and Health Survey (DHS) in the Democratic Republic of Congo (DRC), and (3) to explore selection pressures on geospatially divergent parasite populations by comparing AMA1 amino acid frequencies in the DRC and Mali.Humans living in regions with high falciparum malaria transmission intensity harbour multi-strain infections comprised of several genetically distinct malaria haplotypes. The number of distinct malaria parasite haplotypes identified from an infected human host at a given time is referred to as the complexity of infection (COI). In this study, an amplicon-based deep sequencing method targeting the P. falciparum infections across 11 DRC provinces were examined. Deep sequencing of both individuals, for COI analysis, and pools of individuals, to examine population structure, identified 77 unique pfama1 haplotypes. The majority of individual infections (64.5%) contained polyclonal (COI > 1) malaria infections based on the presence of genetically distinct pfama1 haplotypes. A minimal correlation between COI and malaria prevalence as determined by sensitive real-time PCR was identified. Population genetic analyses revealed extensive haplotype diversity, the vast majority of which was shared across the sites. AMA1 amino acid frequencies were similar between parasite populations in the DRC and Mali.A total of 900 Amplicon-based deep sequencing is a useful tool for the detection of multi-strain infections that can aid in the understanding of antigen heterogeneity of potential malaria vaccine candidates, population genetics of malaria parasites, and factors that influence complex, polyclonal malaria infections. While AMA1 and other diverse markers under balancing selection may perform well for understanding COI, they may offer little geographic or temporal discrimination between parasite populations.The online version of this article (10.1186/s12936-017-2137-9) contains supplementary material, which is available to authorized users. Malaria caused an estimated 429,000 deaths worldwide in 2015, with the overwhelming majority of deaths occurring in sub-Saharan Africa . IndividPlasmodium falciparum strains circulate in malaria endemic regions and that this genetic heterogeneity contributes to the ability of P. falciparum to evade the host immune response and develop resistance to anti-malarial drugs [P. falciparum infections increase the likelihood of inter-strain genetic recombination during the sexual stage in the anopheline vector, resulting in the generation of genetically diverse P. falciparum strains and facilitating parasite evolution [P. falciparum infections can occur either via multiple mosquito bites each with a different strain of P. falciparum or via a single mosquito bite containing multiple P. falciparum strains [P. falciparum strains present within a single individual is defined as the complexity of infection (COI) [P. falciparum COI, with malaria holoendemic regions typically experiencing higher P. falciparum COIs compared to areas with seasonal or low malaria endemicity [P. falciparum COI [P. falciparum COI are, therefore, needed to better understand the relationship between malaria parasite genetic diversity and transmission dynamics and the potential utility of COI as a measure of change in malaria prevalence.Early molecular studies revealed that genetically diverse al drugs –22. It hal drugs , and mayal drugs . Furthervolution –29. Mult strains , 30, 31.on (COI) . The reldemicity , 33–39. demicity , 40–42. arum COI –45. AddiP. falciparum infections, including targeting size polymorphisms of the merozoite surface proteins  and GLURP [P. falciparum strains that differ by only a few nucleotides in length or that contain single nucleotide polymorphisms (SNPs). Also, these methods have poor sensitivity in terms of detecting less abundant strains [P. falciparum strains as well as discriminate SNPs and small indels. These deep sequencing technologies provide a more accurate determination of the COI within an individual or population thereby improving subsequent population genetic analyses [Several genetic tools and strategies have been employed to detect multiclonal nd GLURP , 46–49.  strains –53, and  strains . Novel aanalyses , 55, 56.P. falciparum [P. falciparum parasite populations are dispersed across seven geographical areas, likely due to movement of human populations between provinces in the DRC and the region [P. falciparum sulfadoxine drug resistance between western and eastern DRC [P. falciparum haplotype diversity are, therefore, warranted to inform malaria control strategies and to monitor changes in malaria parasite population structure in response to malaria control efforts in the DRC.In the Democratic Republic of Congo, malaria is a leading cause of morbidity and mortality with over 95% of malaria infections due to lciparum . The DRClciparum . The 200lciparum , 59. Sevlciparum . For inse region . Additiotern DRC . FurtherP. falciparum apical membrane antigen 1 (pfama1) gene in order to (1) examine the relationship between P. falciparum COI and P. falciparum prevalence as determined previously by real-time PCR [P. falciparum population genetic structure at both the individual and population level in the DRC, and (3) to explore AMA1 amino acid frequencies and potential selection pressures between geographically distinct malaria parasite populations in the DRC and Mali. The authors hypothesized that P. falciparum COI would be positively correlated with P. falciparum prevalence in a region, and that similar pfama1 haplotypes would be identified at the individual and population level in the DRC and Mali. In order to investigate pfama1 diversity at both the individual and population level, individual samples , and pooled samples (representing population cluster samples) were targeted in this study. Pooling samples is a cost-effective approach to amplicon-based deep sequencing as it reduces the number of PCR reactions and library preparations, and this pooled approach has been utilized in several malaria population genetic studies [In this study, a PCR amplicon-based deep sequencing approach was utilized to target the extensive allelic diversity of the time PCR , (2) to  studies –71. Thispfama1 haplotypes were identified across DRC provinces. The vast majority of individual malaria infections were polyclonal (COI > 1), and no correlation was found between COI and malaria prevalence at sites/regions. Population genetic analyses revealed extensive genetic diversity of P. falciparum parasites based on the pfama1 gene and similar amino acid frequencies between malaria parasite populations in the DRC and Mali. Herein, this manuscript highlights the utility of combining individual and pooled amplicon-based deep sequencing methods for population genetic analyses layered onto the infrastructure and sample collection process of a routine Demographic and Health Survey. This manuscript also describes the spatial and genetic diversity of pfama1 haplotypes circulating in the DRC and Mali to improve the understanding of malaria transmission dynamics that could potentially inform future malaria control and elimination efforts in the region.Overall, a total of 77 unique Participants included in the Demographic and Health Survey (DHS) provided verbal informed consent as described previously . Study eP. falciparum, Plasmodium ovale, and Plasmodium malariae parasites [The 2007 Democratic Republic of the Congo Demographic and Health Survey (DHS) was conducted to collect health indicator data from across the DRC. Within 300 clusters, survey teams went from house to house and enrolled women aged 15–49 years, and men aged 15–59. The age distribution was constant across sites. The survey in urban Kinshasa occurred during the rainy season . The remainder of the country was surveyed during the dry season  , 60. GenP. falciparum infection were identified based on the following criteria: (1) positive for P. falciparum and negative for P. ovale and P. malariae by species-specific qPCR and (2) geographically representative of the eleven DRC provinces. Eighty-four population cluster samples that represented pooled asymptomatic P. falciparum samples of 2–25 individuals were also chosen based on geographical proximity to the individual samples  gene by qPCR as described [In the present study, 115 individual samples positive for asymptomatic escribed .Fig. 1Inpfama1 gene (GenBank Reference XM_001347979.1)  Fig. . The fir.1) Fig. . In orde.1) Fig. . PCR amppfama1 PCR assay and included in downstream sequencing library preparations. Sequencing control samples contained P. falciparum DNA from V1S, RO33, Dd2, 7g8, and K1 strains at 5, 10, 15, 30, and 40 percent, respectively .Purified PCR amplicons containing barcodes were pooled into sets (up 24 amplicons each based on the 24 unique MIDs) at equal concentration. Each pool was then used to generate a sequencing library with a unique index to allow for the unambiguous identification of the sequences for every amplicon. Specifically, ten nanograms of pooled purified PCR amplicons was ligated with the appropriate index and the DNA concentration of each indexed amplicon pool was determined using the Agilent High Sensitivity D1000 ScreenTape Assay on the 2200 TapeStation  according to manufacturer’s instruction in order to confirm equal DNA concentrations across the 18 indexed amplicon pools. Six sequencing control samples were run as template in duplicate with the Ion Torrent library preparation was conducted following the “Preparing Short Amplicon < 350) Libraries Using the Ion Plus Fragment Library Kit” manual  for each of the 18 indexed PCR pools. DNA concentrations of the 18 resulting libraries were determined using the Agilent High Sensitivity D1000 ScreenTape Assay according to manufacturer’s protocol. Equal concentrations of each library were pooled and split across two Ion 318 Chips  utilizing 400 bp chemistry on the Ion Torrent PGM platform (Life Technologies) at the University of North Carolina Chapel Hill Microbiome Core Facility. Deep sequence data extraction, processing, and analyses were performed using the SeekDeep targeted amplicon bioinformatics pipeline –79. A wo50 Librarpfama1 sequences (GenBank FJ898536–FJ899041) previously published by Takala et al. were retrieved [Pfama1 sequences from Mali were trimmed and aligned to pfama1 sequences from the DRC using Geneious (v 9.1.5) [In order to compare AMA1 amino acid frequencies between the DRC and Mali, 506 etrieved . Pfama1 v 9.1.5) .ST) comparisons were calculated between DRC provinces using the Analysis of Molecular Variance (AMOVA) tool in the Arlequin (v3.5.2.2) population genetics data analysis program [pfama1 haplotypes [Molecular population genetic analyses were conducted using MEGA version 7  and DnaS program . Networkplotypes . Isolatiplotypes , 89.Statistical analyses were performed in GraphPad Prism (v6), SPSS (v22), R , and MicP. falciparum based on the lactate dehydrogenase (pfldh) gene. Conventional PCR based on the pfama1 gene was performed on all individual samples regardless of pfldh qPCR results and on all geographical cluster samples (n = 84). Table pfldh qPCR and pfama1 PCR for both sample types.Real-time PCR (qPCR) was performed for all 115 individual samples to confirm the presence of pfama1 deep sequencing reads were obtained using the Ion Torrent PGM platform. Using the SeekDeep targeted amplicon analysis pipeline, reads with missing barcodes, short reads (< 50 bp), poor quality, and chimeric reads were filtered out, resulting in 4,879,911 remaining reads. The IonTorrent PGM platform is based on the “sequencing by synthesis” principle (detecting H ion release on a semiconductor matrix when a base is added during synthesis), which can result in variable quality reads particularly within homopolymer repeats. Thus, sequencing read quality varies in the proportion of reads that are poor quality or truncated and is mainly dependent on the quality of the input library as well as the specific run rather than one sequencing platform over another. These sequencing reads were subsequently de-multiplexed (separated by input amplicon based on index and MID), clustered according to samples and replicates, and haplotypes estimated using a 2.5% minimum haplotype frequency cutoff. Based on these criteria, a total of 3,754,497 reads was obtained for downstream haplotype analysis. Analysable deep sequencing reads of the target pfama1 region were generated for 79 (68.7%) of the individual samples, 73 (86.9%) of the population cluster samples, and six sequencing control samples. Analysis of the six sequencing control samples revealed similar haplotype frequencies between the expected haplotype percentage and the actual haplotype percentage determined by the SeekDeep targeted amplicon analysis pipeline    defined as a COI > 1 . Difference in observed COI and bifurcated prevalence based on the mean (0.4413) was also tested. These high and low prevalence groups had a mean of 2.75 and 2.00, respectively, with suggestive significance .The majority of > 1 Fig. a. The me> 1 Fig. . Not sur> 1 Fig. b. Compar> 1 Fig. . In ordete study . As showyclonal 6.5% definyclonal 6.5% definPfama1 amplicon deep sequencing, like all single locus methods, will underestimate the true COI when strains share the same haplotype. A permutation model was utilized to correct for COI when strains share the same pfama1 haplotype. Based on the observed pfama1 haplotype frequencies, the probability of true COI of 2, 3, 4, 5, 6, 7, 8, 9, and 10 being undercalled (missing one or more strains) is 4.5, 12.7, 24, 37, 50, 62, 73, 82 and 88 percent, respectively. While these values demonstrate that the highest COIs are usually underestimated, these high COIs represent a low proportion of the observed samples in this study. Using both the probability of undercalling and the observed distribution, the corrected COIs were simulated and the average correction plotted  due to increased variance resulting from modeling the imprecision of the COI measurements.pfama1 haplotypes from all sample types as well as between individual samples and population cluster samples (Table pfama1 haplotypes and high haplotype diversity (Hd) were found in both sample types  was determined between DRC provinces from haplotypes identified in both sample types  analysis of individual samples showed no correlation of genetic distance and spatial distance (R2 = 7.7 × 10−5), indicating that genetically similar pfama1 haplotypes are not found closer together spatially. A Median-Joining Network Diagram was also constructed using the 60 haplotypes found in individual samples to examine whether related haplotypes are spatially clustered in the DRC  [pfama1 sequences to match the 162 bp region sequenced in the DRC samples resulted in 58 distinct Mali pfama1 sequences. The trimmed sequences were then aligned with the DRC pfama1 sequences and 32 (55%) of the DRC pfama1 sequences were found to be 100% identical over a 162 bp region to the Mali pfama1 sequences previously identified. Nucleic acid sequence identity between the DRC and Mali pfama1 sequences ranged from 91.4 to 100%. Analysis of the DRC and Mali pfama1 sequence heterogeneity at the amino acid level  . Trimminpfama1 genes from asymptomatic malaria infections at both the individual and population cluster level from across the DRC and in Mali. Overall, a total of 77 unique pfama1 haplotypes were identified and the majority of individual infections in the DRC were polyclonal (64.5%). Population genetic analyses revealed pfama1 haplotypes are not isolated based on distance or province within the DRC. These results align with a previous study in the DRC, which found a lack of spatial restriction of malaria parasite populations. This diversity, however, may not be due to the extensive movement of P. falciparum parasites with their human hosts between provinces and neighbouring countries [pfama1 haplotype diversity identified in the DRC in this study include human host immune selection that maintains the antigenic diversity of pfama1  and spatially restrictive protein–protein interactions [In this study, an amplicon-based deep sequencing was utilized to investigate the diversity of ountries . Rather,ractions , 93.pfama1 diversity between geographically divergent malaria endemic regions, haplotype frequencies were compared at the amino acid level in parasite populations from the DRC and Mali. Highly similar amino acid frequencies were observed between parasite populations in the DRC and Mali , another hypervariable surface antigen, also showed shared amino acid frequencies between two geographically separated malaria parasite populations [In order to more fully explore ali Fig. , suggestulations . Highly P. falciparum prevalence [P. falciparum strains and varying malaria transmission intensity by region. Previous studies that reported significant correlations between COI and malaria prevalence typically compare low and high malaria transmission areas [P. falciparum prevalence in this study compared to other studies could be because due to the high stable malaria transmission across the DRC. Additional research studies including larger sample sizes and additional markers are needed to further explore the potential relationship between COI and malaria prevalence and how population diversity indices could be utilized to monitor changes in malaria transmission intensity in the DRC and other malaria endemic regions. However, given the wide variance observed in the correlation between COI and prevalence, it may not be a reliable surrogate in differentiating malaria transmission levels within the DRC.In contrast to several recent studies , 33–36, evalence –45. Poteon areas –37, 39. P. falciparum haplotypes and characterize malaria COI from a variety of sample types including dried blood spots [P. falciparum infections for several reasons, including its cost-effectiveness compared to whole genome sequencing and the ability to utilize barcoding and pool several dozen samples thereby increasing sample size. The SeekDeep bioinformatics pipeline is designed for analysis of haplotype frequency from amplicon-based deep sequencing data and has been used successfully in several studies investigating malaria population genetics globally [Deep sequencing technologies have enhanced ability to detect low frequency, minor variant od spots , 55, 56.globally , 78, 79.Pfama1 was chosen for amplicon-based deep sequencing based on several factors. First, pfama1 is a highly polymorphic gene, containing several single nucleotide polymorphisms (SNPs), likely maintained via balancing selection due to immune pressure in the human host [pfama1 [pfama1 haplotypes [P. falciparum AMA1 antigen is also a highly-studied malaria vaccine antigen candidate. Vaccine studies have demonstrated that AMA1 based vaccine protection against clinical malaria is extremely strain-specific and, therefore, a clear understanding of AMA1 diversity is critical to develop an effective malaria vaccine based on this polymorphic antigen [pfama1 haplotypes in the DRC and surrounding malaria endemic regions.man host , 94–96.  [pfama1 . Similarplotypes . The P.  antigen –105. Thepfama1 sequence polymorphisms in primer binding sites, malaria parasite nucleic acid degradation stored on dried blood spots, and pfama1 haplotype frequency below the limit of detection of the PCR assay or 2.5% cut off for sequencing analysis. In addition, this study focused on a subset of asymptomatic malaria samples collected as part of the 2007 DHS in the DRC. The inclusion of more malaria positive samples, including symptomatic as well as asymptomatic malaria infections, would provide a more comprehensive description of the P. falciparum population genetic structure in the DRC. Another potential limitation is that this study targeted a region in the highly polymorphic pfama1 gene as surrogate for the entire P. falciparum genome. As such, the true genetic heterogeneity of P. falciparum parasites circulating in the DRC is underestimated. Further, as the number of polymorphic sites (S) was unexpectedly higher in the individual samples compared to the pooled population cluster samples, it is important to note that pooled sampling likely missed some variants occurring at low frequency within one or a few individuals within the population  COI results . Given the P. falciparum recent speciation bottleneck and limited diversity compared to other species, selection of more appropriate genetic markers of diversity may be a challenge. Sensitive detection methods, such as amplicon-based deep sequencing, can improve the understanding of malaria strain diversity as it relates to potential malaria vaccine antigen candidates and monitor for changes in parasite genetic diversity.This study describes the use of amplicon-based deep sequencing for the detection and relative quantification of Additional file 1. Population cluster sample characteristics. Asterisks (*) indicate population cluster samples that failed initial pfama1 PCR amplification (n=5). Diamonds (♦) indicate a population cluster sample that was not successfully sequenced (n=6).Additional file 2.Pfama1 heminested primer sequences and Multiplex identifier (MID) sequences.Additional file 3. A workflow diagram outlines the steps from nested PCR to bioinformatic analyses (left boxes) and provides corresponding background, reasoning, and details at each step in the process (right boxes).Additional file 4. Six internal quality control samples were PCR amplified and deep sequenced in duplicate. Expected (first column) and actual  haplotype percentages are similar. The average percent error between duplicates was 4.4% (range 0.4-13.6%).Additional file 5.Pfama1 haplotype sequences and NCBI GenBank accession numbers.Additional file 6. Comparison of haplotype frequencies between all sample types, individual samples, and population cluster samples.Additional file 7. Comparison of demographic factors between sample sequenced individually (n=79) and pooled population cluster samples (N=821) with analysable sequence reads.


Genotyping of malaria parasite populations remains an important tool to determine the types and number of parasite clones in an infection. The present study aimed to evaluate the merozoite surface protein 1  and PCR products were analyzed by electrophoresis on a 2.5% agarose gel. Alleles were categorized according to their molecular weight.Dried blood spots (DBS) were collected at baseline from patients with uncomplicated malaria in urban health centers in Bobo-Dioulasso. Parasite DNA was extracted using chelex-100 and species were identified using nested PCR. P. falciparum-positive and one sample classified as mixed infection for P. malaria and P. falciparum. In msp1, the K1 allelic family was predominant with 77.4% (162/209) followed respectively by the MAD20 allelic family with 41.3% and R033 allelic family with 36%. In msp2, the 3D7 allelic family was the most frequently detected with 93.1 % compared to FC27 with 41.3%. Twenty-one different alleles were observed in msp1 with 9 alleles for K1, 8 alleles for MAD20 and 4 alleles for R033. In msp2, 25 individual alleles were detected with 10 alleles for FC27 and 15 alleles for 3D7. The mean multiplicity of falciparum infection was 1.95 with respectively 1.8 (1.76–1.83) and 2.1 (2.03–2.16) for msp1 and msp2 (P = 0.01).A total of 228 blood samples were analyzed out of which 227 (99.9%) were confirmed as msp1 and msp2 in Plasmodium falciparum isolates from symptomatic malaria patients in Bobo-Dioulasso.Our study showed high genetic diversity and allelic frequencies of  Despite substantial efforts to eliminate or control malaria, it remains the leading cause of morbidity and mortality in the world . In BurkPlasmodium falciparum blood-stage malaria vaccine targets [P. falciparum parasite sub-populations [msp1 gene located on chromosome 9 and contains 17 blocks of sequences flanked by conserved regions [msp2 gene located on chromosome 2 and composed by five blocks of which the central block is the most polymorphic [msp2 alleles are grouped into two allelic families, FC27 and 3D7/IC1 [Genotyping of malaria parasite populations remains an important tool to determine the types and number of parasite clones in an infection. In molecular epidemiological studies of malaria, this approach is used to investigate the genetic diversity of infections with consideration of various factors including transmission intensity and host immunity. The most widely used techniques for genotyping malaria infections are based on amplification by PCR of the polymorphic genes encoding the merozoite surface proteins 1 and 2 (MSP1 and MSP2) and the glutamate-rich protein (GLURP) –9. MSP 1 targets  playing ulations . They arulations  and are ulations , 14. MSP regions , 16. Bloymorphic . The msp 3D7/IC1 .msp1 and msp2 have been shown to be highly polymorphic in different geographical settings in malaria endemic countries [P. falciparum genome enables the comparison of the genotypes of isolates collected before the treatment and at the time of recurrent malaria infection. This comparison has been widely used to classify treatment outcomes in antimalarial clinical trials [msp1 and msp2 genetic diversity in Bobo-Dioulasso. The present study aimed to evaluate the genetic diversity and allele frequencies of msp1 and msp2 in malaria parasites isolated from symptomatic patients in Bobo-Dioulasso, Burkina Faso.Both ountries , 19, 20.ountries . The divl trials , 23. To Samples for this study were provided by a clinical trial conducted from October to December 2012 in two health facilities of Colsama and Sakaby in the district of Do in Bobo-Dioulasso, located in the western region of Burkina Faso. Malaria transmission is holoendemic with a peak around August to October and an estimated entomological inoculation rate (EIR) of 300–500 infective bites per person per year. In 2012, the population of the health district of Do was estimated to be about 504,895 inhabitants .Plasmodium spp. infection at parasite densities between 2000 and 200,000 parasites/μl and hemoglobin > 5g/dl were included in the clinical study after they or their parents/guardians had signed an informed consent form. Other inclusion criteria were: absence of known adverse events to study drugs, absence of non-malarial febrile diseases, absence of documented malaria treatment in the two weeks prior to enrolment, absence of danger signs or severe malaria. Details of the overall clinical study have been described elsewhere [All subjects aged 6 months or more and attending Colsama and Sakaby health centers with fever or history of fever in the last 24 h were referred by a clinician for screening of malaria infection using Giemsa-stained thick and thin blood smears. Participants with lsewhere .For all patients included in the clinical study, blood was also collected on filter papers  at any scheduled or unscheduled visit, labeled, air-dried and stored into sealed plastic bags at ambient temperature. Parasite DNA was subsequently extracted from day 0 samples using the QIAamp DNA Mini Kit  according to the Qiagen-DNA purification from dried blood spot protocol.Plasmodium species were detected using a modified protocol from Snounou et al. [18S ribosomal RNA using nested PCR with secondary primers specific to the species Plasmodium falciparum, Plasmodium malariae, Plasmodium ovale and Plasmodium vivax. Primary and nested PCR primer sets used are listed in Table www.eurogentec.com), 2.5 μl of 10× PCR buffer, 2.5 μl of each dNTP, 1.5 μl of 25 mM MgCl2, 0.2 μl of Go Taq DNA polymerase (www.promega.com) and 2 μl of Template DNA or primary PCR product. All PCRs were performed in a BIO-RAD S1000 thermal cycler  under the following conditions for primary (35 cycles) and secondary PCR (30 cycles): initial denaturation at 94 °C for 1 min, extension at 94 °C for 1 min, 58 °C for 2 min and 72 °C for 5 min, and final elongation at 72 °C for 5 min.u et al. . Brieflymsp1 and msp2 of all Plasmodium falciparum parasites were amplified using sequence-specific primers according to a modified protocol previously described [msp1 and block 3 for msp2. The second reaction primer set targets specific allelic families of msp1  or msp2 (IC/3D7 and FC27). Reactions for each set of primary and nested primers were performed separately. A template-free control was used in all reactions and genomic DNA from cloned laboratory strains was used as a positive control for respective alleles. Cycling conditions for both msp1 and msp2 as well as primer sequences are summarized in Table The escribed . In summPlasmodium species were identified by comparison of DNA fragments from the samples with DNA fragments from known positive controls (www.beiresource.org). Alleles of msp1 and msp2 were categorized according to their molecular weights.All PCR products were analyzed by electrophoresis on a 2.5% agarose gel. DNA fragments were stained with ethidium bromide and visualized by UV trans-illumination. The sizes of the amplicons were detected using a 100 bp DNA ladder . msp1 and msp2 genotypes by the number of positive samples for each marker. The mean MOI was calculated by dividing the total number of alleles detected in both msp1 and msp2 by the total number of positive samples for both markers. Samples were considered single infected when harboring only one allele at each of the genotyped loci. Multiclonal infections were defined as infections with more than one allele in at least one locus.MOIs were calculated by dividing the total number of distinct msp1 and msp2 was calculated as the proportion of the allele detected for each allelic family out of the total of alleles detected. The frequency of polyclonal infection was calculated using number of samples with more than one amplified fragment out of the total samples. The mean MOI was determined by dividing the total number of alleles detected in both msp1 and msp2 by the total number of positive samples for both markers. The Chi-square test was used to compare proportions. Statistical significance was defined as P < 0.05.Data were collected with Epidata and analyzed by R version 3.4.0 (2017-04-21). The allelic frequency of Overall, 441 subjects were screened for malaria infection out of which 244 subjects were included in the clinical study. Sixteen subjects were excluded  and the remaining 228 subjects completed the study follow-up. Details on baseline characteristics of the study population will be published elsewhere. Briefly, the study included 110 male and 118 female subjects. The mean age was 9.8 years (± 0.6) with minimum and maximum ages of 0.6 and 60 years, respectively. The mean body axillary temperature measured prior to blood sampling was 38.51 °C (± 1.1) and the mean parasite count was 52,543 parasites  per μl of blood.P. falciparum-positive by nested-PCR. Only one sample was classified as a mixed infection for P. malariae and P. falciparum. Plasmodium ovale and P. vivax were not detected in the samples.All 228 blood samples analyzed were confirmed as msp1 (91.67%) and 188 (82.8%) for msp2. In msp1, the K1 allelic family was predominant with 77.4% (162/209), followed by the MAD20 allelic family with 41.3% (86/209) and the R033 allelic family with 36% (75/209). Thirty-eight percent (38.1%) of the samples positive for msp1 were classified as monoclonal infection and the remaining 61.9% were classified as polyclonal infections with K1/MAD20, K1/R033 and MAD20/R033 representing 21.5, 22.3 and 11.3%, respectively. Only 6.9% of samples harbored together the K1, MAD20 and R033 allelic families  compared to FC27 with 52.9%. Allelic distributions of both msp1 and msp2 did not differ significantly when the study population was subdivided by age group , 8 alleles for MAD20 (fragment range 150–300 bp) and for 4 alleles for R033 (fragment range 120–200 bp). In msp2, the K1 200 bp, 250 bp and 240 bp; the MAD20 200 bp and 220 bp; and the R033 150 bp were the most prevalent with a frequency above 20%. The allele distribution of msp1 is represented in Fig. For both msp2, 25 individual alleles were detected with 10 alleles for FC27 (fragment range 250–550 bp) and 15 alleles for 3D7 (fragment range 100–450 bp). The frequency of all alleles detected in the 3D7 family was less than 20% while the allele with 400 bp (31%) was the most prevalent in the FC27 family. Allele distribution for both FC27 and 3D7 families is illustrated in Fig. With regard to msp1 and msp2, respectively. The MOIs for both msp1 and msp2 are summarized in Table msp2 was 2.1 (2.03–2.16) with a range of 1–6 strains, and was higher than MOI for msp1, i.e. 1.8 (1.76–1.83) with a range of 1–4 strains (P = 0.01).In total, 61.9% 153/209) and 34.4% (65/188) of the isolates contained multiple infections in 3/209 andPlasmodium falciparum msp1 and msp2 are currently being recommended in antimalarial clinical trials as standard markers to distinguish recrudescent from newly infecting malaria parasites [msp1 and msp2 in malaria parasites circulating in many endemic countries including Burkina Faso. Our study aimed to evaluate the genetic diversity and allelic frequency of msp1 and msp2 in malaria parasites isolated from symptomatic patients in Bobo-Dioulasso.arasites . Howevermsp1 was higher than msp2. This result is consistent with previous studies in a rural area of Burkina Faso [msp1 and msp2 as also demonstrated in previous studies in Africa including Burkina Faso [Our study showed that the frequency of successfully genotyped samples for ina Faso  and in Cina Faso . K1 and ina Faso , 30, Cotina Faso  and Ethiina Faso , but in ina Faso . These tina Faso , 33 and ina Faso , 35.msp1 and msp2, respectively, in parasites circulating in the southwest of Burkina Faso. This finding is consistent with those from previous studies in different malaria endemic countries in Africa [Our study reported high genetic diversity with 21 and 25 genotypes for n Africa , 36, 37.n Africa –40. Thisn Africa .msp1 and msp2 with nearly two alleles per locus for both markers. The overall MOI reported in this study is lower than that reported by Soulama et al. [Our study did not show any significant difference in MOI using a et al.  in parasa et al. . The difa et al. , 36, 41.a et al.  and intea et al.  but othea et al. , 45. Thea et al. , and theOne limitation of this study is that the allelic frequencies and genetic diversity reported may have been estimated incorrectly due to the detection limit of the PCR technique used in the study. Alleles with short differences in length (less than 10 bp) might not be clearly distinguished as separate alleles on agarose gel and this could lead to a misclassification of the genotype.msp1 and msp2 in Plasmodium falciparum isolates from uncomplicated malaria patients in Bobo-Dioulasso. K1 and 3D7 are the most prevalent allelic families. More than 50% of infections are classified as polyclonal infection for msp1 against one-third percent for msp2. The high diversity reported may reinforce the use of msp1 and msp2 to distinguish recrudescent from new infection in antimalarial efficacy trials in Burkina Faso. However, further investigations with more powerful techniques such as capillary electrophoresis and DNA sequencing are needed to better characterize the malaria parasites in the country.Our study showed high genetic diversity and allelic frequencies of