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Published in final edited form as:
Acta Trop. 2007 June ; 102(3): 201–205. doi:10.1016/j.actatropica.2007.05.001.
Analysis of genetic polymorphism in select vaccine candidate
antigens and microsatellite loci in Plasmodium falciparum from
endemic areas at varying altitudes
Godfree Mlambo1,2, David Sullivan1, Susan L. Mutambu2, White Soko2, Joel Mbedzi2, James
Chivenga2, Thomas Jaenisch1, Armin Gemperli1, and Nirbhay Kumar1,*
1 Malaria Research Institute, Department of Molecular Microbiology and Immunology, Johns Hopkins
University, Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore MD 21205
2 National Institute of Health Research, P. O Box CY 573, Causeway, Harare Zimbabwe
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Abstract
Plasmodium falciparum parasites obtained from symptomatic patients attending clinics in Bindura
(altitude 1,100 m), Chiredzi (600 m) and Kariba (< 600 m), previously reported to differ in malaria
endemicity were genotyped on the msp-1, msp-2 and glurp loci to examine the extent of parasite
genetic diversity. While the parasites were monomorphic for msp-1 allele RO33 from the three
locations, the K1 allele was overrepresented in Kariba (p=0.02) and Mad20 alleles occurred at a
higher frequency in Bindura. A similar PCR analysis for glurp and the two main allelic families of
msp-2, i.e IC/3D7 and FC-27 revealed minimal differences in the parasite population. A total of 8
msp-1 Block 2 and 11 msp-2 genotypes were identified from the three locations combined. On the
glurp locus, thirteen different genotypes ranging in size from 660 to 1160 bp were detected in
parasites obtained from Bindura and Kariba. To gain further insight into P. falciparum genetic
diversity in the three different geographical locations, parasites were examined for neutral
microsatellite markers (C4M8, C13M30 and TA81). The number of microsatellite alleles ranged
from 8 to 17 and the average expected heterozygosity (HE) for the three areas combined was 0.83
suggesting that the parasite population of Zimbabwe is genetically heterogeneous. These findings
have implications in understanding the impact of genetic variation on immunity and possibly
emergence of drug resistance.
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Plasmodium falciparum parasites are highly genetically diverse and studies indicate that at any
given time, humans or mosquitoes can harbor a number of different parasite clones(Babiker et
al., 1991; Branch et al., 2001). The unique genetic characteristics of each parasite may
determine clinical or parasitological outcomes and properties such as cytoadherence, immune
evasion, resistance or susceptibility to drugs as well as infectivity to mosquitoes (Snounou et
al., 1999). Understanding the distribution of genetically diverse P. falciparum parasites is
important in malaria epidemiology as well as in designing vaccines as antigenic diversity
continues to pose a big challenge to vaccine development. Additionally, the performance of
*Corresponding author; Dr. Nirbhay Kumar, Malaria Research Institute, Department of Molecular Microbiology and Immunology, Johns
Hopkins University, Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore MD 21205, Tel: 410 955 7177, Fax: 410 955
0105, email: E-mail: nkumar@jhsph.edu.
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Mlambo et al.
Page 2
vaccine candidates employed will need to be monitored by assessing switching of allelic types
resulting from either natural or vaccine induced immune selection.
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Msp-1 and msp-2 are highly polymorphic single copy genes and have been employed to
investigate parasite genetic diversity (Anders and Smythe, 1989; Smythe et al., 1990). The
examination of polymorphic genes is also useful in estimating the number of concurrent
infections in a given individual, termed multiplicity of infection (MOI) (Smith et al., 1999).
Evidence from several studies suggests that people who are constantly exposed to malaria
caused by multiple parasite clones may have the advantage of clinical protection from future
malaria attacks (Smith et al., 1999; Branch et al., 2001; Henning et al., 2004). In addition to
genes such as msp-1 or msp-2 which are under extreme immune selection pressure (Cavanagh
et al., 1998), neutral microsatellite markers have also been employed to demonstrate parasite
genetic diversity.. Microsatellite analysis data has been used to estimate the expected
heterozygosisty (HE) index which represents the probability of picking two parasites with
different alleles at a given locus and it ranges in value from greater than 0 to less than 1
(Anderson et al., 1999; Su et al., 1999). Such HE calculations for parasites from South America,
Africa and Asia revealed that the extent of genetic diversity reflected transmission intensity of
the diseases with the highest diversity (HE=0.8) in Africa and the lowest diversity in South
America (HE=0.3) (Anderson et al., 2000).
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Zimbabwe lies on the southern border of malaria transmission in Africa and little is known
about the genetic diversity of parasites in different endemic areas of the country. We describe
an analysis of the genetic variation of parasites in three areas of Zimbabwe using glurp,
msp-1, msp-2 and neutral polymorphic microsatellite markers. These areas, Bindura, Chiredzi
and Kariba, were described in a single study two decades ago to be of hypo-, meso- and
hyperendemic transmission, respectively (Taylor and Mutambu, 1986). Transmission is
seasonal in all these areas and the main mosquito vector is Anopheles arabiensis. National
control for malaria at the time of sample collection consisted of chloroquine as first line
treatment and second line treatment comprised of a combination of chloroquine and
sulfadoxine/pyrimethamine. Clinical data for the three catchment areas for the years 1997 to
2003 revealed that the incidence of malaria was 152/1,000, 394/1,000 and 419/1,000 for
Bindura, Chiredzi and Kariba respectively (Ministry of Health, Zimbabwe). Population
characteristics and health center catchment sizes for the three areas has been described
elsewhere (Mlambo et al., 2006).
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Dried finger-prick blood on filter papers was obtained and a total of 112 samples from
microscopy positive symptomatic patients from the three different locations were examined.
These samples were collected between March and April of 2003 with ethical permission from
the Medical Research Council of Zimbabwe and approval from Johns Hopkins School of Public
Health IRB.
DNA was extracted from approximately 50 μl blood spotted on filter papers using the chelex
method (Plowe et al., 1995). For PCR analysis of glurp, msp-1 and msp-2, 2.5 μl of DNA was
used as template in a 50 μl total reaction volume mix with 1.25 units of enzyme Taq polymerase,
buffer (50 mM KCl, 10mM Tris-HCl pH 8.3, 1.5 mM MgCl2), 200 μM dNTPs and 0.25 μM
each of reverse and forward primer. The primer sequences in nested PCR amplification and
cycling conditions for each genetic marker have been published elsewhere and modifications
are as indicated, i.e glurp (Ranford-Cartwright et al., 1997), msp-1 (Branch et al., 2001), first
PCR annealing at 50°C, 30 cycles, Mad20 annealing at 55°C) and msp-2 (Snounou et al.,
1999) first PCR annealing at 50°C, 30 cycles, second PCR annealing at 55°C ). In the second
round of PCR, 2 μl from the first PCR amplified products was used as template and the PCR
products were resolved on a 2.5% agarose gel stained with ethidium bromide. The glurp,
msp-1 and msp-2 genotypes were grouped into bins ranging from 20 to 50 bp with the aid of
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Mlambo et al.
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Kodak ID analysis image software version 2.0.2 (USA). The term genotype in this paper refers
to the allele bins and not the specific allele size in bp. The mean multiplicity of infection (MOI)
was calculated by adding up all the number of different msp-1 genotypes (i.e allelic types, K1,
RO33 and MAD20 as well as allele bin sizes) present per sample and dividing by the total
number of samples analyzed. Msp-1 genotypes were used to calculate MOI because in our
analysis it revealed more mixed infections compared to msp-2. Expected heterozygosity (HE),
a measure of diversity for msp-1 and msp-2 genotypes was calculated using the formula;
where p is the frequency of the ith genotype and n is the number of genotypes in the sample
population.
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DNA samples extracted by the chelex method (above) were used as template to amplify and
examine neutral polymorphic microsatellite markers. The microsatellite markers that were
analyzed are TA81, C4M8 and C13M30 (Gene Bank Acc. Nos. G3886, G37937 and G44421
respectively) and the primer sequences are published elsewhere (Jain et al., 2005). A heminested PCR approach was employed to amplify microsatellite markers. Briefly, 2 μl of DNA
was amplified in a total reaction volume of 15 μl containing 1.5 μl 10X buffer (50 mM KCl,
10mM Tris-HCl pH 8.3, 1.5 mM MgCl2), 0.2 units Taq polymerase, 0.5 pM reverse and
forward primer and 200 μM of dNTPs. For the second round of PCR, 2 μl from the primary
reaction was used as template with an internal fluorescently labeled primer and the reverse
primer used in the primary PCR. The cycling conditions for the primary PCR and the secondary
PCR steps were as follows; initial denaturation at 94°C for 2 min followed by 25 cycles
consisting of denaturation (94°C for 30 sec), annealing steps (42°C for 30 sec and 40°C for 30
sec), extension (68°C for 30 sec) and a final extension at 68°C for 2 min. PCR products were
analyzed by capillary electrophoresis using the 3100 ABI capillary electrophoresis system in
the presence of ROX500 size standards (Applied Biosystems, USA). Peak heights for each
allele were captured with the aid of ABI GeneScan 3.7 software (Applied Biosystems, USA)
and imported into Genotyper software, version 3.1 for further analysis. Alleles with peak
heights of at least 500 arbitrary fluorescence units and base pair sizes corresponding to the
expected allele sizes based on P. falciparum 3D7 from the PlasmoDB were considered for
analysis. The HE was calculated as above. All the statistical analyses were performed with the
aid of the statistical software package SAS version 9.1 (USA).
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We first analyzed parasites from the three locations for polymorphisms at the msp-1 locus for
the presence of 3 possible allele families, RO33, Mad20 and K1 and the allele sizes in base
pairs. As has been reported (Ariey, 1999; Mawili-Mboumba et al., 2003; Legrand et al.,
2005), the RO33 allele was monomorphic (140 bp PCR product) whereas block 2 genotypes
for K1 varied in size from 200 bp to 350 bp and the Mad20 alleles ranged from 160 bp to 220
bp in size. As shown in table 1, more than 50% of samples in all three areas revealed mixed
msp-1 genotypes. The presence of K1 alleles appeared to be influenced by location as there
were statistically significant differences in the proportions between samples from Kariba
(p=0.02) or Chiredzi (p=0.005) and Bindura (Table 1).
The data on msp-1 block 2 genotypes was used to calculate mean MOI for all the samples and
compared among the three areas. Values of MOI were approximately 2 for patients both below
and above 15 years for Chiredzi and Bindura and children less than 15 years in Kariba.
Interestingly, patients above 15 years (Table 1) in Kariba, an area previously reported to
experience high malaria transmission (Taylor and Mutambu, 1986) revealed mean MOI of 1.3.
It is not known why the adult sample population in Kariba had a lower MOI compared to the
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Mlambo et al.
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younger age group; and though our data is preliminary, it is possible that MOI in adults might
be regulated by strain specific immunity. Though the mean age of subjects less than 15 years
for the three areas was similar (i.e 11, 8.5 and 10.5 years for Bindura, Chiredzi and Kariba,
respectively), we did not see a similar trend in Bindura and Chiredzi probably because of low
statistical power or likely malaria transmission differences.
Our analysis on msp-1 genotypes and MOI did not reveal major differences in the genotypes
of parasites analyzed from the three areas, therefore samples were pooled for all the subsequent
genotype analysis. Since RO33 has only one allele size, it was excluded in the analysis of
genetic diversity. For the K1 allele, a 250 bp genotype was detected in 50% of the samples
while the 180 bp Mad20 genotype was the predominant genotype in all the three areas analyzed
(Table 2). For msp-2 locus, a 400 bp genotype of the FC-27 type and a 600 bp genotype of IC/
3D7 was common in all the three locations (Table 2). Combining all the three areas, a total of
8 msp-1 and 11 msp-2 genotypes were scored. P. falciparum samples from Kariba and Bindura
were analyzed for polymorphism in the glurp gene and the results revealed a spectrum of 13
genotypes ranging from 660 bp to 1170 bp (Table 2). Based on msp-1 block 2, msp-2 and the
glurp loci, our results revealed considerable genetic variation in parasites in the three locations.
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We attempted to further analyze distribution of msp-1 and msp-2 genotypes in patients grouped
below and above 15 years and did not find any significant differences for msp-1 K1, R033 and
msp-2 genotypes. This analysis did reveal that the msp-1 Mad20 allele prevalence was higher
in patients less than 15 years compared to patients above 15 years (data not shown) supporting
a previous suggestion that there could be host factors that help maintain Mad20 genotypes in
children below 15 years (Ntoumi et al., 1997).
After analyzing msp-1, msp-2 and glurp loci which code for immunogenic antigens, we
hypothesized that genetic diversity based on these markers would differ from neutral
microsatellite loci analysis. We therefore employed 3 neutral polymorphic microsatellite
markers to examine parasite samples in a hemi-nested PCR approach to gain insights into the
population structure and genetic diversity. Our results revealed that C4M8 was the most
polymorphic marker which had 17 alleles while C13M30 and TA81 had 14 and 8 alleles,
respectively. These results were then used to calculate HE and the HE was 0.80, 0.80 and 0.90
for TA81, 13M30 and C4M8, respectively. The mean HE for the three locations was 0.83.
After calculating the HE based on microsatellite markers, we went back and calculated the HE
based on msp-1 block 2, msp-2 and glurp genotypes which was 0.70, 0.86 and 0.89 for
msp-1 block 2 locus, msp-2 and glurp, respectively.
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Finally, we compared MOI values based on msp-1, msp-2 and microsatellite data. For this
analysis we included only those samples for which we had data for the three markers. The 3
markers revealed similar MOI values for parasites analyzed from the three different locations
(data not shown). One simple interpretation of our data is that the 3 areas at present do not
differ in malaria transmission intensity and a single parameter like MOI alone may not be
sufficient to assess transmission differences. Studies in Tanzania have previously revealed very
similar MOI among areas differing by over 1000 fold in EIR values (Babiker et al., 1997;
Bendixen et al., 2001). Thus our study although revealing high genetic diversity in the three
areas of Zimbabwe will need to be backed up by direct EIR data to evaluate any correlation
between MOI and transmission intensity differences.
The data reported here provides a first comprehensive attempt to define genetic diversity of
malaria parasites in Zimbabwe. Parameters assessed here such as MOI, age biased distribution
of msp-1 Mad20 genotypes, microsatellite markers and the percentage of mixed infections have
been investigated in many other malarious areas and suggest that transmission conditions in
Zimbabwe are comparable with that of other medium to high transmission areas such as
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Mlambo et al.
Page 5
Tanzania, Papau New Guinea and Senegal (Paul et al., 1995; Babiker et al., 1997; Ntoumi et
al., 1997; Zwetyenga et al., 1998).
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In conclusion, our analysis suggest high parasite genetic diversity as assessed by examining
msp-1, msp-2 and glurp loci plus neutral polymorphic microsatellite markers in the 3 areas that
differ in terms of altitude. Although these three areas differed markedly in patterns of
transmission as reported 2 decades ago, this study on genetic diversity neither corroborate nor
dispute this previous report. Furthermore, we do not know whether migration among the three
sites may impact overall parasite genetic diversity. More studies are warranted to examine
whether these areas indeed still differ in transmission and if there are any biological
implications such as development of protective immunity for the differences in the distribution
of genotypes observed in this study.
Acknowledgments
We would like to thank the National Institute of Health Research in Zimbabwe for facilitating sample collection. We
also acknowledge the Fogarty International Center Training Grant (TW001587) for funding and training of Godfree
Mlambo. Research in the Kumar and Sullivan labs is supported by research grants from the NIH.
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Table 1
Percentage distributionof MSP-1 block 2mixed and single alleleinfections ofK1 (K), RO33 (R) and Mad20 (M) and multiplicity of
infection (MOI) in the two age groups (<15 and >15 years) in samples from Bindura, Chiredzi and Kariba.
Allele(s)
MOI ± SEM
M
R
K
R/M
K/M
K/R
K/R/M
<15 years
> 15 years
Bindura (n=16, 20)*
36
0
0
30
22
0
11
1.9 ± 0.2
1.8 ± 0.1
Chiredzi (n=16, 15)
10
7
7
13
35
0
29
2.2 ± 0.2
2.0 ± 0.2
Kariba (n= 18, 27)
10
15
25
8
25
5
12
2.2 ± 0.2
1.3 ± 0.1**
Mlambo et al.
Health Centers
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*
(n = subjects < 15 years, subjects > 15 years)
**
For Kariba samples, mean MOI was higher for patients less than 15 years compared with those above 15 years ( p < 0.05 by the t-test).
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Table 2
Distribution of msp-1, msp-2 and glurp genotypes (bp) in samples pooled from the three locations. Number of samples for a given allele
size is indicated in parenthesis.
Allele type
MSP-1
MSP-2
GLURP*
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K1
Mad 20
FC-27
IC/3D7
GLURP
200 (10)
160 (1)
300 (13)
450 (9)
660 (1)
250 (31)
180 (75)
350 (9)
500 (17)
720 (2)
300 (17)
200 (9)
400 (16)
550 (15)
750 (3)
350 (4)
220 (6)
450 (5)
600 (40)
780 (4)
500 (10)
650 (4)
830 (8)
700 (12)
880 (1)
Mlambo et al.
Marker
900 (9)
930 (3)
950 (5)
980 (5)
1030 (2)
1050 (5)
1170 (4)
*
Data on glurp genotypes was for samples pooled from Bindura and Kariba.
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