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    Results of canonical correspondence analysis of mono-infected isolates of each population at 16 loci (C4M79, Pf2689, TRAP, Pf2802, 7A11, C4M69, PE14F, 3E7, C3M27, CAL, C3M35, RRR1, SSP, C9M57, MMSA, and Pf9735). Centroids of populations are surrounded by 95% confidence intervals (○ and dashed oval = Djibouti; + and solid oval = Niamey; × and dotted and dashed oval = Dakar).

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GENETIC DIVERSITY AND STRUCTURE OF AFRICAN PLASMODIUM FALCIPARUM POPULATIONS IN URBAN AND RURAL AREAS

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  • 1 Institut de Médecine Tropicale du Service de Santé des Armées, Unité de Recherche en Biologie et Epidémiologie Parasitaire, Marseille, France; Institut de Recherche pour le Développement, Génétique des Maladies Infectieuses, Montpellier, France; Institut Pierre Richet, Bouaké, Côte d’Ivoire; Centre Muraz, Bobo-Dioulasso, Burkina-Faso; Hôpital d’Instruction des Armées Alphonse Laveran, Service de Biologie Médicale, Marseille, France; Hôpital Principal de Dakar, Dakar, Senegal; Hôpital National de Niamey, Niamey, Niger; Laboratoire de Parasitologie et Mycologie, Institut National de la Santé et de la Recherche Médicale, Unite 399, Institut Fédératif 48, Faculté de Médecine, Marseille, France; Direction et Laboratoire d’Epidémiologie et d’Hygiène Publique, Djibouti Ville, République de Djibouti; Institut Pasteur, Paris, France

The genetic variability and population structure of Plasmodium falciparum are key factors in malaria control strategies. Studies have suggested no P. falciparum population structure although linkage disequilibrium was observed in some African areas. We have assessed length polymorphism at 6–22 microsatellites in four urban and rural sites (Djibouti, Dakar, Niamey, and Zouan-Hounien, n = 240 blood samples). Results have shown a P. falciparum population structure in Africa (Fst = 0.17–0.24), lower genetic diversity in Djibouti (He = 0.53) than in the other sites (He = 0.73–0.76), and 3) significant linkage disequilibrium in Djibouti. These results could be related to geographic isolation and low flow of parasites between sites. They also suggest a potential effect of rural suburbs to generate genetic diversity in towns. This could affect the dispersal of selected drug resistance and should be considered when adapting urban malaria control strategies.

INTRODUCTION

Plasmodium falciparum, one of the causes of malaria, still remains a major public health problem. Although many efforts have been made to control this pathogen, between 300 and 500 million clinical cases are still encountered each year. This parasite exhibits developed and complex genetic polymorphism that confers the ability to develop multiple drug resistance1 or to circumvent vaccines.2 The emergence and spreading of resistant strains hamper efforts to control malaria and the situation is getting worse in some areas.3 The spread of drug resistance is due to gene flow and the scale of the P. falciparum population structure. A better understanding of P. falciparum population genetics is necessary to adjust control measures.

Several studies have investigated P. falciparum population structure. Most have analyzed antigen-coding genes or drug resistance–associated genes and such loci are under selective pressure.4,5 However, analyses are impaired by immunologic effects on selection6 or drug resistance, which reduces genetic diversity.7

Genetic analysis of P. falciparum populations with putative neutral microsatellites has been performed in Africa,811 where > 90% of malaria mortality occurs,3 and where the dynamic and genetics of P. falciparum populations remain unclear. Anderson and others have reported that low geographic genetic differentiation and high within-population genetic variability prevail in areas with a high degree of malaria transmission, with little linkage disequilibrium.8 Such linkage disequilibrium between loci, which suggests non-random genotype distribution, has been observed in Zimbabwe, Congo,10 and Senegal.9 Moreover, geographic genetic differentiation exists in Sudan between rural and urban sites.11 Such findings appear to be inconsistent and new data are necessary to assess the structure and diversity of P. falciparum in Africa.

Approximately 25% of the African population lives in cities, and the urbanization rate (2–6% per year) in developing countries remains high.12 Urbanization impedes malaria transmission and would increase the number of non-immune individuals in urban settings,13 with most being at risk of contracting potentially severe forms of the disease.12 Thus, the specificity of the urban epidemiology of malaria must be considered in developing control strategies.14

To assess these issues, we conducted a study of P. falciparum diversity and structure. Our objectives were to assess the genetic diversity, structure, and differentiation of P. falciparum populations according to geographic distances, accessibility, and level of malaria transmission in urban and rural sites. We report the results of genetic analysis and discuss parameters that could explain the observed patterns of diversity and geographic structure.

MATERIALS AND METHODS

The study was conducted in three urban sites in Africa (Dakar, Senegal, Niamey, Niger, and Djibouti City, Republic of Djibouti) and in a rural area (Zouan-Hounien, Danané region, Côte d’Ivoire). Plasmodium falciparum populations were characterized by multilocus microsatellite genotyping.

Plasmodium falciparum isolates.

Blood samples were collected during the rainy season from P. falciparum-infected urban dwellers who came to health centers in Dakar (14°40′N, 17°26′E), Niamey (13°31′N, 2°06′E), and Djibouti (11°35′N, 43°08′E), as well as villagers in the rural area of Zouan-Hounien, Côte d’Ivoire (6°55′N, 8°09′E). Because of their lack of immunity, most of P. falciparum-infected urban dwellers were symptomatic.15,16 Approximately 500 μL of blood were collected from each volunteer after informed consent had been obtained. Samples were frozen and kept at −20°C. Ethical clearance was obtained from local ethics committees (Ministries of Health of Djibouti, Senegal, Côte d’Ivoire, and Niger).

Malaria in Dakar is hypo-endemic and transmission is seasonal, remaining less than one infected bite/person/year. The P. falciparum prevalence rate is less than 5% in the general population.13,16,17 Thirty-seven infected blood samples were obtained in September 2002. Malaria in Niamey is mainly hypo-endemic and transmission is estimated to be less than one infected bite/person/year in most of the city, but it may be higher on the Niger River banks. The P. falciparum prevalence rate in the general population is generally less than 5%, particularly during the dry season, but may reach 30–50% during the rainy season at a few river banks sites.18 Forty-three infected blood samples were obtained in December 2001. Malaria in Djibouti is hypo-endemic with local transmission, mostly epidemic, less than one infected bite/person/ year.19 The P. falciparum prevalence rate in the general population is less than 5%.20,21 Forty-two infected blood samples were obtained in September and October 2001.22 Malaria in the rural area of Zouan-Hounien is holo-endemic, with high transmission (> 300 infected bites/person/year), and perennial. The P. falciparum prevalence rate exceeds 80%.23,24 One hundred eighteen infected blood samples were obtained in July 2001. Malaria endemicity at the four sample sites shows the following pattern: Djibouti < Dakar < Niamey << rural area of Zouan-Hounien.

Genotyping by polymerase chain reaction (PCR).

DNA was extracted from blood samples by using the ENZA blood DNA kit according to the manufacturer’s recommendations (Biofidal, Vaulx en Velin, France) and eluted in 100 μL of elution buffer per 250 μL of whole blood. DNA repeats (microsatellites) were amplified by PCR with fluorescent end-labeled primers from flanking sequences (Table 1). The primers and PCR conditions are shown in Table 1. All primers were synthesized and purified in the primers production laboratory at the Institute of Tropical Medicine (Marseille, France). The PCR products were subjected to electrophoresis on polyacrylamide gels with internal size standards (Tamra 500; Applied Biosystems, Foster City, CA) using GENSCAN® software (Applied Biosystems).

Molecular markers.

Twenty-two microsatellites (Table 1) previously described25,26 were analyzed. Panel A was composed of six microsatellites (C4M79, Pf2689, TRAP, Pf2802, 7A11, and C4M69) from P. falciparum populations from all four studied sites. Panel B was composed of panel A plus 10 additional microsatellites (C4M79, Pf2689, TRAP, Pf2802, 7A11, C4M69, PE14F, 3E7, C3M27, CAL, C3M35, RRR1, SSP, C9M57, MMSA, and Pf9735) from P. falciparum populations from Dakar, Djibouti and Niamey. Panel C was composed of six microsatellites (TA40, TA42, TA81, TA87, Pfg377, and 2490), previously used by Anderson and others,8 were analyzed to compare our results with those of previous P. falciparum genetic population studies. All genetic analyses were performed with panels A, B, and C.

Multiplicity of infections.

Infected blood samples may contain one or several haploidic clones of P. falciparum. In the case of multi-infection, more than one allele can be distinguished at each locus and it is generally impossible to consider the actual multilocus genotype of each clone, i.e., it is unfeasible to match the alleles of distinct loci. However, when several alleles were observed at only one locus and only one allele was observed at each other locus, it was possible to infer reconstruction of multilocus genotypes. For example, if the polymorphic locus exhibited (n) alleles, we scored (n) multilocus genotypes with identical alleles for mono-allelic loci and different alleles corresponding to variant alleles for the only one polymorphic locus. We refer to such data reconstructed multilocus genotypes. The multiplicity of infection, i.e., the number of distinct parasite populations in each isolate, was estimated from the locus that exhibited the highest number of alleles in that given isolate.

We conducted separate analysis considering either mono-infected isolates, i.e., with only one allele shown at a locus, or reconstructed multilocus genotypes from isolates that were multi-allelic at only one locus. The isolates with more than one allele at more than one locus were systematically removed from the analysis.

Measurement of genetic diversity.

Genetic diversity was assessed by the number of alleles per locus (A) and Nei’s unbiased expected heterozygosity (He)27 from haploid data using FSTAT version 2.9.4.28 Differences between sites of the estimated He at each locus were tested by the Wilcoxon signed rank test using STATA 7 software (Stata Corporation, College Station, TX).

Population genetic structure.

Population genetic structure was investigated using Wright’s F-statistics.29 The index Fst measures the genetic differentiation between samples. This parameter was computed with FSTAT version 2.9.4.28,30

Canonical correspondence analysis (CCA) was carried out to illustrate measures of population structure using CANOCO® software.31,32 Only isolates scored at each locus were considered for CCA. Analysis was performed with both mono-infected isolates and reconstructed multilocus genotype data. The significance of the canonical axes was tested with a Monte Carlo permutation test.32 This also allowed estimation of the 95% confidence intervals of the centroid of each population.

Linkage disequilibrium.

Deviations from the hypothesis of random association of alleles at distinct loci was tested by permutation procedure using FSTAT version 2.9.4.28 for each parasite population. We conducted separate analysis of mono-infected isolates and reconstructed multilocus genotypes data. In the case of significant linkage disequilibrium, analyses were performed once again after removing repeated multilocus genotypes from the data set. Associations between all pairs of loci were also estimated by a R2 coefficient akin to a squared coefficient of correlation29 and was computed using FSTAT. The sequential Bonferroni procedure was applied to consider the multiple testing enhanced type I error.33,34

RESULTS

The number of infected blood samples in which P. falciparum populations were detected by PCR genotyping was 42, 37, 43 and 118 in the cities of Djibouti, Dakar, Niamey and the rural area of Zouan-Hounien, respectively (Table 2).

Multiplicity of infections.

The proportions of isolates that were multi-allelic and the mean multiplicities of infection estimated with each microsatellite locus and with the three panels are shown in Table 2. The proportions of multi-infected isolates did not differ significantly between cities, but did differ between the cities and the rural area of Zouan-Hounien (P < 0.003, by Fisher’s exact test). The same pattern was observed with the mean multiplicities of infection. The estimated percentage of multi-infected isolates and the mean multiplicity of infection were higher with panel B (16 loci) than with panel A (6 loci).

Genetic diversity.

The number of distinct alleles observed per locus, the unbiased expected heterozygosity (He) estimated per locus and with each panel of loci, are shown in Table 3. The mean He estimated with loci in panel A differed significantly between Djibouti and Niamey (P < 0.025) and between Djibouti and Zouan-Hounien (P < 0.005). It was similar in Dakar, Niamey, and the rural area of Zouan-Hounien. The mean He did not differ according to the panel of loci used in each site.

Genetic differentiations between geographic sites.

Table 4 shows a pairwise comparison between sites of the differentiation coefficients (Fst) estimated with mono-infected isolates and reconstructed multilocus genotypes. Figure 1 shows the results obtained with panel B loci. The centroid of each population is surrounded by its 95% confidence interval. Results of CCA were consistent with Fst estimates. A Monte Carlo test permuting genotypes among the populations (i.e., Dakar, Niamey, and Djibouti) showed that the axes of the CCA could explain differentiation between populations (P = 0.001 for 1,000 permutations). The analysis showed that 95% of the genetic diversity observed for the Djibouti population was much smaller than for the two others sites (Figure 1).

Linkage disequilibrium.

Complete genotyping of the six loci of panel A identified 69 distinct multilocus genotypes among 86 mono-infected isolates from all sites. Using the genotypes of the mono-infected isolates at the six loci of panel A, we observed that the Djibouti P. falciparum population had three significantly associated pairs of loci (P < 0.0009) among 15 possible pairs. All associated loci were located on different chromosomes. Among the 14 distinct multilocus genotypes identified in 29 mono-infected isolates from Djibouti, five multiple repeated multilocus genotypes were observed. When we analyzed the reconstructed data set with panel A, nine pairs of loci among 15 possible pairs were associated (P < 0.0009).

Using the genotypes of the mono-infected isolates at the 16 loci of panel B and the 6 loci of panel C, we showed that the Djibouti P. falciparum population had 9 (58 with reconstructed data) and 0 (1 with reconstructed data) associated pairs of loci (P < 0.0009), among 120 and 15 possible pairs, respectively. These associations between loci do not remain significant when we counted only the repeated genotype once to perform linkage disequilibrium analysis. The P. falciparum populations of Dakar, Niamey, and the rural area of Zouan-Hounien did not exhibit linkage disequilibrium.

DISCUSSION

Our findings provide evidence for support structured P. falciparum populations in Africa, and suggest that malaria epidemiology in urban areas depends on local transmission, geographic isolation, and parasite flow between the city and the surrounding rural areas. The proportion of multi-infected isolates and the mean multiplicity of infection in urban dwellers were low and slightly increased in Djibouti, Dakar, and Niamey. This is consistent with the low level of malaria transmission and endemicity in these cities. Genetic diversity did not differ in Dakar, Niamey, and the rural area of Zouan-Hounien and was similar to that previously observed in Africa (Uganda, Congo, and Zimbabwe; He range = 0.76–0.80).8 In the rural site, the higher percentage of multi-infected isolates and higher mean multiplicity of infections were consistent with the level of transmission, which was100-fold higher than that in the three cities.

In Djibouti, the genetic diversity of P. falciparum populations was low. The expected heterozygosity was similar to that observed in Asia (Shoklo, Thailand; He = 0.51) and higher than that observed in South America (Colombia, Bolivia and Brazil; He range = 0.30–0.40).8 This could result from isolation and low levels of transmission. Allele fixation due to genetic drift limits genetic diversity. The low P. falciparum prevalence rate reported in Djibouti and the rest of the country suggests that the parasite population is small and drift may have a greater effect because the parasite population size is smaller. Low levels of malaria transmission near Djibouti may also limit acquiring genetic diversity from surrounding areas.

Genetic diversity of P. falciparum populations sampled in the cities did not depend solely on the low level of transmission in urban conditions. Because African cities are communication nodes and drain human populations from surrounding areas, genetic diversity observed in the cities could reflect genetic diversity of P. falciparum populations of surrounding areas. Malaria transmission and endemicity are higher in the savannah around Dakar and Niamey than in the sub-desert area of Djibouti, whereas malaria transmission, endemicity, and complexity of infections are similar in these three cities. Thus, flow of parasite populations between urban and rural areas might be important and explain the differences in genetic diversity observed between Dakar and Niamey, and Djibouti. This implies that the easy access to anti-malarial drugs in the cities and the resulting drug pressure could have an impact on the selection of drug resistance not only in the urban areas but also in the surrounding rural areas. Conversely, the flow of parasite populations from the rural to the urban areas could dilute drug-resistance traits.

The Fst coefficients show evidence of population structure among studied samples spread throughout Africa. Estimated Fsts ranged from 0.109 to 0.243 between Djibouti and the other sites. The Fst was 0.122 between the closest sites geographically, i.e., Niamey and the rural area of Zouan-Hounien. These Fsts were stronger than the estimation reported by Anderson and others8 in Africa (Fst < 0.01). African P. falciparum populations are structured, and at a continental scale these populations are obviously isolated among geographic areas, i.e., they are not panmictic. The slight geographic genetic differentiation between Dakar and Niamey may be related to the road network that covers west Africa at this latitude from Senegal to Niger and permits the easy exchange of people and goods. There was not such interconnection between the other pairs of sites. Moreover, the Fst estimated between Zouan-Hounien and the urban sites (range = 0.107–0.243) suggested substantial geographic isolation of this landlocked rural area. Our results imply that genetic flow between sites is not sufficient to homogenize parasite populations. Selection of drug-resistant P. falciparum populations could then act mainly at the local level and the spread of drug resistances in neighboring areas could depend on their isolation. It explains why the P. falciparum chloroquine resistance took 12 years to cross Africa from east to west.35

Djibouti was the only site that showed linkage disequilibrium, which is consistent with the population structure described earlier in this report. Whatever its origin, linkage disequilibrium will remain longer in an isolated population such as seen in Djibouti. Several non-exclusive hypotheses such as selfing or the Wallhund effect can explain such linkage disequilibrium. An epidemic may lead to a high proportion of a few major multilocus genotypes in remote parasite populations, such as in Djibouti.19 Moreover, we observed a low number of different multilocus genotypes within blood sample from Djibouti. Therefore, we can expect a high proportion of mating between similar strains (during the sexual phase within the vector) and a limited effect of genetic recombination. If we consider several malaria foci in Djibouti and an epidemic that occurs within one of them, this leads to local selfing that could mime clonal expansion. This would be consistent with the disappearance of linkage disequilibrium after removing the repeated genotype.19 Although we cannot reject statistical type II error when repeated genotypes are removed, a local epidemic may explain most of the linkage disequilibrium36 observed in this city. It can be hypothesized that malaria transmission in Djibouti occurs usually in the form of localized micro-epidemics, possibly related to the introduction of new P. falciparum populations or to a brief increase in vector densities around focused vector breeding sites.19 We did not reproduce the linkage disequilibrium observed in Dakar in a previous study,9 possibly because of differences in sampling.

Our results showed fairly structured P. falciparum populations at the scale of the African continent that could be related to geographic isolation and insufficient flow of parasites between sites. Furthermore, we pointed out the potential effect of rural suburbs in generating genetic diversity in towns where malaria transmission is usually low.13 If one considers urban malaria as malaria of tomorrow, control strategies in towns should take into consideration the malaria situation in rural surroundings and parasite flow between rural and urban areas, which could affect genetic diversity and dispersal of selected drug resistance.

Table 1

Primer sequences and amplification conditions of 22 microsatellite loci from the Plasmodium falciparum genome*

LociPanelGenBank Accession no.Size, basepairsChromosomePrimer sequencesTa, °C
* Panel A (C4M79, Pf2689, TRAP, Pf2802, 7A11, and C4M69); panel B (C4M79, Pf2689, TRAP, Pf2802, 7A11, C4M69, PE14F, 3E7, C3M27, CAL, C3M35, RRR1, SSP, C9M57, MMSA, and Pf9735); panel C (TA40, TA42, TA81, TA87, Pfg377, and 2490). Size in basepairs from 3D7 clone. Primer sequences are 5′ → 3′ with fluorescent label (HEX, TET, 6-FAM) and annealing temperature (Ta, °C). Polymerase chain reaction (PCR) conditions for panels A and B; the PCR (15 μL) contained 2 μL of template, 1.5 μL of 10× buffer, 0.1 units Taq polymerase (Eurogentec, Seraing, Belgium), 1.5 mM MgCl2, and 0.3 μM of each primer. Thermocycling was performed in a Biometra (Goettingen, Germany) 96-well T3 thermocycler with an initial denaturation at 94°C for 2 minutes; 30 cycles at 94°C for 20 seconds, the locus-specific annealing temperature for 30 seconds, 72°C for 30 seconds, and a final elongation step at 72°C for 30 seconds. Programs were ended at 60°C for 30 minutes. PCR conditions for panel C, volume reaction = 20 μL, 2 μL of template, 2.5 μL of 10× buffer, 2.5 mM MgCl2, 0.2 mM dNTP, primers 0.075 μM, and 0.5 units Taq polymerase (Eurogentec). Themocycling conditions were an initial denaturation at 94°C for 2 minutes; 30 cycles at 94°C for 20 seconds, 45°C for 10 seconds, 40°C for 10 seconds, and 60°C for 30 seconds, and a final elongation step at 72°C for 15 minutes.
C3M27BG377691543ATGATCATATTTGGTTAGATC
 HEX—TTTGGTTAACAAATTTCCTAC58
C4M79A and BG427262203TTTATATCAAGAATGACAACC
 HEX—TAGCAACAATAAACAATATGG55
C3M35BG379532184GGAAATATATATCATACTTGG
 6-FAM—TTTTTGGTGTCGGTTATTTTT55
C4M69A and BG379563624GAAATGGAGATAAACTATTAC
 TET—AATTACACAACAGATGTGAA57
SSPBG377731884AATACAGATGAAGGAGAC
 6-FAM—TTGCAACGAACAGTCATC59
Pf2802A and BG378181365GTATAAAAGGAAATACCTA
 TET—CAGACTATCTTAAGGGAA54
3E7BG377851887AAGAATGAAAGTATTTTTAGC
 TET—CCCCTTCAAAAAGGAAATAACAC59
7A11A and BG38831927ATGTGTAAGGAGATAGTATA
 6-FAM—CAACTTTCTCTTTTTAAATATTAC56
PE14FBG388461267CTGTGGATAATGATATTC
 6-FAM—GTCCATTGAAAAGATAGG54
C9M57BG444792029TGCTTTTTATGTATGCGTAAA
 TET—TTCTTCTTTCTTTTCAAGTTC59
MMSABG378341319TGTAGGAGTAAAATATGT
 HEX—AATATCACTATTCCTGTA49
Pf9735BG3779111411TATATCATCGGGATGCTA
 TET—AGGTAATAAGAGTGTTAA55
Pf2689A and BG378548612TATGCACACACGTTTCTA
 6-FAM—CTCCAAGGCATTCACGTA57
TRAPA and BG3785813413CATAATAGTAGCAAGAGA
 HEX—GATTATATATAGCGATTTAC49
CALBG3787025614CATAATAGTAGCAAGAGA
 TET—GATTATATATAGCGATTTAC49
RRR1BG3786513714GTTGTTATAGCTAATGAG
 TET—ATTATGAACAATTCAGAC55
TA40CG38858124310TET—GAAATTGGCACCACCACA
 AAGGGATTGCTGCAAGGT54
TAA42CG3883220056-FAMTAGAAACAGGAATGATACG
 GTATTATTACTACTACTAAAG52
TAA81CG388361185HEX—TGGACAAATGGGAAAGGATA
 TTTCACACAACACAGGATT56
TAA87CG388389566-FAMAATGGCAACACCATTCAAC
 ACATGTTCATATTACTCAC54
Pfg377CG378519512GATCTCAACGGAAATTAT
 TET—TTATCCCTACGATTAACA48
2490CG377908010TTCTAAATAGATCCAAAG
 HEX—TAGAATTATTGAATGCAC46
Table 2

Multiplicity of Plasmodium falciparum populations from Djibouti, Dakar, Niamey, and Zouan Hounien*

Djibouti n = 42Dakar n = 37Niamey n = 43Zouan Hounien n = 118
PanelLocin% Multi-infectionMean multiplicityn% Multi-infectionMean multiplicityn% Multi-infectionMean multiplicityn% Multi-infectionMean multiplicity
* n = number of isolates with detected P. falciparum population; % Multi-infection = percentage of isolates that exhibit more than one population; Mean multiplicity = mean number of parasite populations per isolate. The 22 microsatellite loci are grouped together in panels A, B, and C. NA = not analyzed. Multi-infections and mean multiplicity from each panel with the locus that exhibits the highest number of alleles are shown in the bottom three rows.
A and BC4M794201.03713.51.14320.91.27648.71.7
Pf26894211.91.13713.81.24323.31.3100561.8
TRAP424.81.1373.71.14327.91.310658.52.0
Pf28024112.21.13711.41.243311.39551.61.9
7A11422.41.03716.21.14223.81.310963.32.2
C4M694214.31.13715.21.24235.71.49164.82.2
BPE14F402.51.02913.51.24323.31.3NANANA
3E73801.0278.31.041221.3NANANA
C3M274101.03518.91.14017.51.2NANANA
CAL3910.31.1338.11.24240.51.5NANANA
C3M35422.41.13611.11.14318.61.3NANANA
RRR14012.51.13610.81.14027.51.3NANANA
SSP422.41.0375.41.13915.41.2NANANA
C9M574201.0372.71.1352.91.0NANANA
MMSA4001.03718.91.03813.21.1NANANA
Pf97354001.0326.31.14334.91.3NANANA
CTA403324.21.225201.2NANANANANANA
TAA423716.21.2316.51.1NANANANANANA
TAA81378.11.13231.31.3NANANANANANA
TAA87385.31.13718.91.2NANANANANANA
Pfg377402.51.03312.11.1NANANANANANA
24903901.03215.61.2NANANANANANA
Panel A4226.21.33737.81.44348.81.611575.42.7
Panel B4235.71.43740.51.54362.81.8NANANA
Panel C4133.31.33735.11.3NANANANANANA
Table 3

Genetic diversity of Plasmodiium falciparum populations from Djibouti, Dakar, Niamey, and Zouan Hounien*

DjiboutiDakarNiameyZouan Hounien
PanelLocinAHenAHenAHenAHe
A and BC4M794240.1837130.8143150.8576250.84
Pf26894230.443770.604340.54100100.56
TRAP4280.6837140.8343140.81106180.78
Pf28024280.703770.694390.7295170.76
7A114260.473760.644290.84109130.81
C4M694270.713770.8442100.8291100.85
BPE14F4040.392980.814390.81NANANA
3 E73840.2027100.5341140.88NANANA
C3M274170.4835150.9240190.94NANANA
CAL3960.6833220.9542360.98NANANA
C3M3542100.7336170.9143180.94NANANA
RRR14060.6136140.9340210.92NANANA
SSP4280.7537170.9239160.92NANANA
C9M574240.313740.263560.34NANANA
MMSA4050.513750.663850.54NANANA
Pf97354060.6932100.8543110.83NANANA
CTA 403360.7125150.95NANANANANANA
TA423750.423150.44NANANANANANA
TA 813730.5932100.84NANANANANANA
TA873850.433790.80NANANANANANA
Pfg3774040.213370.61NANANANANANA
24903940.163250.44NANANANANANA
Mean AMean HESDMean AMean HeSDMean AMean HESDMean AMean HeSD
* n = number of typed isolateds; A = number of alleles; He = biased expected heterozygosity1271 for haploid organisms are shown for each microsatellite loci. NA = not analyzed.
Panel A60.530.2190.730.1110.20.760.1215.50.760.11
Panel B60.530.19110.760.1913.50.800.18NANANA
Panel C4.50.410.218.50.670.22NANANANANANA
Table 4

Genetic differentiation of Plasmodium falciparum populations between Africans sites*

DjiboutiDakarNiamey
Sample sizeFst95% CIFst95% CIFst95% CI
Fst-statistic (Weir & Cockernams) computed with Fstat version 2.9.4.
* Results were obtained from mono-infected isolates and from reconstructed data (genotypes inferred from multi-infected isolates displayed at only one locus) are shown in the upper and lower parts of the table, respectively. For each pairwise comparison between sites, sample size, Fst and its 95% confidence interval (CI) are shown according to the three microsatellite panels: PA = panel A (C4M79, Pf2689, TRAP, Pf2802, 7A11, and C4M69); PB = panel B (C4M79, PE14F, 3EF, C3M27, Pf2689, CAL, TRAP, C3M35, Pf2802, 7A11, RRR1, SSP, C9M57, MMSA, C4M69, and Pf9735); PC = panel C (TA 40, TA 42, TA 81, TA 87, Pfg377, and 2490). Confidence intervals were estimated after 1,000 bootstrap simulations over loci. The departure of Fst from zero was tested after 10,000 boostrap simulations and using a Bonferroni corrected P value.
†Statistically significant.
‡Not significant.
Mono-infected isolates
    DjiboutiPA n = 52
PB n = 44
PC n = 54
    DakarPA n = 350.109†0.025–0.181
PB n = 330.173†0.112–0.236
PC n = 360.194†0.096–0.246
    NiameyPA n = 340.197†0.102–0.2800.042‡−0.008–0.094
PB n = 260.232†0.154–0.3250.020‡−0.003–0.049
    Zouan-HounienPA n = 410.243†0.155–0.3250.107†0.005–0.2370.122†0.015–0.252
Reconstructed Multilocus Genotypes
    DjiboutiPA n = 30
PB n = 22
PC n = 28
    DakarPA n = 230.128†0.053–0.220
PB n = 200.154†0.109–0.193
PC n = 240.182†0.102–0.262
    NiameyPA n = 210.195†0.104–0.2780.057†0.003–0.121
PB n = 160.202†0.151–0.2670.042†0.013–0.076
    Zouah-HounienPA n = 270.162†0.069–0.2920.044†0.008–0.0960.031†−0.005–0.086
Figure 1.
Figure 1.

Results of canonical correspondence analysis of mono-infected isolates of each population at 16 loci (C4M79, Pf2689, TRAP, Pf2802, 7A11, C4M69, PE14F, 3E7, C3M27, CAL, C3M35, RRR1, SSP, C9M57, MMSA, and Pf9735). Centroids of populations are surrounded by 95% confidence intervals (○ and dashed oval = Djibouti; + and solid oval = Niamey; × and dotted and dashed oval = Dakar).

Citation: The American Journal of Tropical Medicine and Hygiene Am J Trop Med Hyg 74, 6; 10.4269/ajtmh.2006.74.953

*

Address correspondence to Hervé Bogreau or Christophe Rogier, Institut de Médecine Tropicale du Service de Santé des Armées, Unité de Recherche en Biologie et Epidémiologie Parasitaire, Boulevard Charles Livon, Parc du Pharo, Marseille, France. E-mails: hervebogreau@yahoo.fr or christophe.rogier@wanadoo.fr

Authors’ addresses: Hervé Bogreau, Housem Bouchiba, Bruno Pradines, Thierry Fusai, and Christophe Rogier, Institut de Médecine Tropicale du Service de Santé des Armées, Unité de Recherche en Biologie et Epidémiologie Parasitaire, Boulevard Charles Livon, Parc du Pharo, Marseille, France, Telephone: 33-6-85-94-12-30 or 33-4-91-15-01-50, Fax: 33-4-91-15-01-64, E-mails: hervebogreau@yahoo.fr, housem_b@hotmail.com, bruno.pradines@free.fr, thierry.fusai@free.fr, and christophe.rogier@wanadoo.fr. François Renaud and Patrick Durand, Institut de Recherche pour le Développement, Génétique des Maladies Infectieuses, Unité Mixte de Recherche 2724, Institut de Recherche pour le Développement, Centre National de la Recherche Scientifique, 911 Avenue Agropolis, BP 64501, 34394 Montpellier CEDEX 5, France, E-mails: renaud@mpl.ird.fr and durand@mpl.ird.fr. Serge-Brice Assi, Institut Pierre Richet, Bouaké, Côte d’Ivoire, E-mail: assisergi@yahoo.fr. Marie-Claire Henry, Centre Muraz, 01 BP390, Bobo-Dioulasso 01, Burkina-Faso, E-mail: depauw.henry@fasonet.bf. Eric Garnotel, Hôpital d’Instruction des Armées Alphonse Laveran, Service de Biologie Médicale, 13013 Marseille, France, E-mail: eganotel@mageos.com. Boubacar Wade, Hôpital Principal de Dakar, 1 Avenue Nelson Mandela, BP3006, Dakar, Senegal, E-mail: bwade55@yahoo.com. Eric Adehossi, Département de Médecine Interne B3, BP238, Hôpital National de Niamey, Niamey, Niger, E-mail: eadehossi@yahoo.fr. Philippe Parola, Laboratoire de Parasitologie et Mycologie, Institut National de la Santé et de la Recherche Médicale, Unite 399, Institut Fédératif 48, Faculté de Médecine, Marseille, France, E-mail: philippe.parola@medecine.univ-mrs.fr. Mohammed Ali Kamil, Direction et Laboratoire d’Epidémiologie et d’Hygiène Publique, Djibouti Ville, République de Djibouti, E-mail: drmakamil@intnet.dj. Odile Puijalon, Institut Pasteur Immunologie Moléculaire des Parasites, Centre National de la Recherche Scientifique, Unité de Recherche Associée, 2581, 75015 Paris, France, E-mail: omp@pasteur.fr.

Acknowledgments: We thank Professor A. Buguet, V. Buguet, Professor J. Lebras, Dr. F. Ariey, and Dr. R. Prescott for helpful comments on the manuscript. We also thank the people of studied sites for their cooperation during the survey. We are also grateful to the team nurses, health workers, and microscopists who made this study possible.

Financial support. This study was undertaken within the framework of the PAL+ program (DYNAPOP) supported by the French Ministry of Research (PAL+). It is also supported by the Impact Malaria Program of Sanofi-Synthélabo, the Délégation Générale pour l’Armement (PEA 010808), and the Société de Pathologie Exotique (research grant 2003). Patrick Durand and François Renaud are supported by the Centre National de la Recherche Scientifique and the Institut de Recherche pour le Développement.

Disclosure: None of the authors has commercial or other associations that might pose a conflict of interest.

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