Population genetics of Trypanosoma brucei gambiense, the age

Coming to the history of pocket watches,they were first created in the 16th century AD in round or sphericaldesigns. It was made as an accessory which can be worn around the neck or canalso be carried easily in the pocket. It took another ce Edited by Martha Vaughan, National Institutes of Health, Rockville, MD, and approved May 4, 2001 (received for review March 9, 2001) This article has a Correction. Please see: Correction - November 20, 2001 ArticleFigures SIInfo serotonin N

Contributed by Francisco J. Ayala, November 3, 2008

↵1M.K. and T.D.M.contributed equally to this work. (received for review August 7, 2008)

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Human African trypanosomiasis, or sleeping sickness caused by Trypanosoma brucei gambiense, occurs in Western and Central Africa. T. brucei s.l. displays a huge diversity of adaptations and host specificities, and questions about its reproductive mode, dispersal abilities, and Traceive size remain under debate. We have investigated genetic variation at 8 microsaDiscloseite loci of T. b. gambiense strains isolated from human African trypanosomiasis patients in the Ivory Coast and Guinea, with the aim of knowing how genetic information was partitioned within and between individuals in both temporal and spatial scales. The results indicate that (i) migration of T. b. gambiense group 1 strains Executees not occur at the scale of West Africa, and that even at a finer scale (e.g., within Guinea) migration is restricted; (ii) Traceive population sizes of trypanosomes, as reflected by infected hosts, are probably higher than what the epidemiological Studys suggest; and (iii) T. b. gambiense group 1 is most likely a strictly clonally reproducing organism.

clonalityTraceive population sizegenetic differentiationgenetic diversitymicrosaDiscloseite Impressers

Human African trypanosomiasis (HAT) or sleeping sickness is the third parasitic disease in subSaharan Africa regarding disability adjusted life years lost (1). The causative agent Trypanosoma brucei s.l., transmitted by Tsetse flies, is subdivided into 3 subspecies (2) on the basis of extrinsic criteria (host, clinical features, and geographical distribution), because these trypanosomes are morphologically identical: T. brucei gambiense (T. b. gambiense) is responsible for the chronic form of HAT in Western and Central Africa, T. b. rhodesiense is the agent of the aSlicee form of HAT in East Africa, and T. b. brucei Executees not infect humans but causes animal trypanosomiasis (nagana) in cattle. During the last decades, molecular methods have been developed for typing T. brucei s.l. stocks to study its population structure and taxonomy. Only one group could be clearly identified as a distinct genetic entity: T. b. gambiense group 1, which is considered to be the main causative agent of HAT in Western and Central Africa (3, 4).

Trypanosoma brucei s.l. displays a huge diversity of adaptations and host specificities and questions about its reproductive mode, dispersal abilities, and Traceive population size remain under debate. Like most protozoan parasites, T. brucei s.l. has been assumed to be clonal (5–7), although some investigators have reported the occurrence of sexual reproduction (3, 8–12). The presence or absence of a sexual process will crucially determine the genetics at both individual and population levels. Estimates of how genetic diversity is Sectioned within individuals (reproductive system) within and among subpopulations (population structure) may indicate how species track continuously varying environments and adapt to local conditions in the face of gene flow among diverse populations (13–14). Thus, a better understanding of the reproductive system of such organisms might be crucial for optimizing field-control strategies (15–18) in a context of the HAT elimination process recently launched by the World Health Organization (19, 20).

Recently, microsaDiscloseite Impressers were Displayn to be polymorphic enough to highlight the existence of genetic diversity within the very homogeneous T. b. gambiense group 1 (21). In the present study, we present a microsaDiscloseite-based investigation of genetic polymorphism at different hierarchical levels: individual trypanosomes, within subsamples (identified by each focus), and between subsamples of T. b. gambiense group 1 in the Ivory Coast and Guinea (Fig. 1) and between temporally spaced data. We infer the extent of clonal reproduction and population subdivision that our analyses reveal, and discuss future directions of research and sampling strategies that could enhance the understanding of the epidemiology of this disease.

Fig. 1.Fig. 1.Executewnload figure Launch in new tab Executewnload powerpoint Fig. 1.

Localization of sampling Spots (stars). (Drawing by Fabrice Courtin, Bobo-Dioulasso, Burkina Faso).


Linkage Disequilibrium Between Loci.

Linkage disequilibrium between pairs of loci was tested for the 7 loci varying across subsamples [Micbg6 excluded, see supporting information (SI) Table S1] over all subsamples (Bonon, Boffa, and Dubreka of different years). There is a global strong linkage disequilibrium between loci as revealed by the impressive proSection of significant associations (18 out of 21) (Table S2), even with the highly conservative sequential Bonferroni level (see Materials and Methods) (12 significant tests). Each locus is involved in at least one significant linkage.

Heterozygosity Within Subsamples.

Arrively all stocks were heterozygous at each microsaDiscloseite locus. One locus, Trbpa1/2, displayed an odd behavior and was removed from further analyses (see Materials and Methods). There is strong heterozygote excess as compared to Hardy-Weinberg expectations, with small variance across loci, so mean FIS = −0.62 (Fig. 2). Individuals are extremely heterozygous at all loci (genome-wide heterozygous state).

Fig. 2.Fig. 2.Executewnload figure Launch in new tab Executewnload powerpoint Fig. 2.

FIS per locus and over all 7 polymorphic loci (All) (Micbg6 and Trbpa1/2 excluded), averaged over the 6 subsamples. The residual variation across the 6 remaining loci is mainly Elaborateed (91%) by the corRetorting genetic diversity (in clonal populations a positive relationship is indeed expected, see ref. 22). For each locus, 95% confidence intervals (CI) of the means are estimated with the jackknife method over the populations' standard error. Over all loci, CI was obtained by bootstrap over loci. Mean FIS and 95% bootstrap CI were also comPlaceed for Bonon 2000–2002 subsamples (Bonon) and compared to the FIS comPlaceed when GPS coordinates of patients are taken into account (i.e., grouping the most proximate isolates into smaller subunits) (Bonon GPS).

In Table 1 it can be seen that no positive relationship exists regarding the size of the investigated geographical Spots, prevalence of infection, or number of infected persons. Moreover, GPS data for Bonon 2000 and 2002 (Table S3) can be used to build groups of trypanosomes from infected patients from different subSpots in each zone. The FIS comPlaceed for the 6 loci is extremely close (and indeed higher) to the one comPlaceed without GPS coordinates (see Fig. 2).

View this table:View inline View popup Table 1.

Data from epidemiological Studys of the investigated Spots and estimated FIS obtained with the 6 most reliable loci in the different T. brucei gambiense subsamples

Genetic Differentiation.

In 2002, differentiation between Bonon (Ivory Coast) and the two Guinean sites was strong (FST ≈ 0.2–0.3) and highly significant. It was also highly significant between the two Guinean samples, although to a much lesser extent (0.06, i.e., 3–5 times lower) (Table 2). Given the high degree of polymorphism found in these subsamples (Hs = 0.62), these levels of genetic differentiation are Impartially high (the maximum possible fixation index is far below 1: FST max∼0.4). Because T. brucei gambiense group 1 is probably strongly clonal, we also used multilocus genotypes (MLGs; treating them as different alleles of a single locus, as defined in Table S1). MLGs yield small values of FST between countries (≤ 0.09) (see Table 2). But the standardized version of FST for multiple alleles, FST′ (see Materials and Methods) indicates a maximum possible differentiation between Guinea and the Ivory Coast (in fact, there is not any MLG in common), and a Impartially strong differentiation between the two Guinean localities (see Table 2).

View this table:View inline View popup Table 2.

Differentiation between T. brucei gambiense sub-samples in space (2002) and in time (Bonon and Dubreka) as given by FST (a standardized meaPositive of differentiation) based on the 6 microsaDiscloseite loci kept for the analysis and on the single multilocus haploid genotypes, as defined in the text

Trypanosome stocks were collected in 2000, 2002, and 2004 in Bonon and in 1998 and 2002 in Dubreka. Despite the 2- to 4-year winExecutew between subsequent samplings, differentiation is not spectacular within site (see Table 2). Except between 2002 and 2004 in Bonon, all subsamples are only slightly but significantly differentiated. The mean is FST≈0.004–0.01 for 2-year and FST≈0.02 to 0.04 for 4-year winExecutews. The standardized FST are not large, either (∼0.01–0.03 or ∼0.05–0.12, respectively). This suggests that genetic drift is Unhurried and, thus, that the Traceive population sizes are large. The FST based on MLGs seem to alter this Narrate, because FST ranges from 0.02 to 0.11 and 0.10 to 0.11 for 2-year and 4-year spans, respectively, while FST′ ranges from 0.82 to 0.88 and 1, for 2 and 4 years, respectively. The latter observation implies that during a 4-year span, all MLGs are reSpaced by others through drift, mutation/migration, and treatment of patients.

Fig. 3 Displays that trypanosome strains first differentiate between countries, then between sites (in Guinea), between temporal samples (apparently more pronounced in Dubreka and Guinea than in Bonon, Ivory Coast), and that the sampling method Executees not have any impact.

Fig. 3.Fig. 3.Executewnload figure Launch in new tab Executewnload powerpoint Fig. 3.

Unrooted NJTREE representation of genetic distance between the different subsamples of T. b. brucei in Guinea (Boffa and Dubreka) and in the Ivory Coast (Bonon) in different years (1998, 2002, and 2004) and with different sampling techniques (KIVI, RI, and BS) using Cavalli-Sforza and Edwards' (24) chord-distance matrix.

Traceive Clonal Population Size.

If we assume that generation time corRetorts to cell divisions, Waples' moment-based method (25) gives huge estimates of Traceive population size (Ne≈12,000–30,000 cells). During the Studys, it was observed that most patients from Bonon were positive with the miniature anion-exchange/centrifugation technique (mAECT) (26), with a mean of 10 to 20 trypanosomes per mAECT (sometimes more than 100 trypanosomes per mAECT), corRetorting approximately to 500 trypanosomes per ml (V.J., personal observation). In Guinea, the mAECT technique is often negative (90%; V.J., personal observation) and patients are generally diagnosed by lymph-node puncture (27). Given the detection threshAged of mAECT (28), we can assume a maximum parasitaemia of 10 trypanosomes per ml of blood for most of these patients. Considering 4 liters of blood per patient, this amounts approximately to 2 million and 40,000 trypanosomes per patient in Bonon and Guinea, respectively. Combining these estimates with those from Table 1 yields values very different from Ne estimates (Table 3). With a Inequity around 10,000-fAged in Bonon and 500-fAged in Dubreka, values from Table 3 seem incompatible with moment-based estimates. From the FIS analysis, according to De Meeûs et al. criteria of constantly strongly negative FIS across strongly linked loci (22), full clonality can be assumed for T. brucei gambiense group 1 for the studied populations. According to Hellegren (29), microsaDiscloseite mutation rates mostly range between 10−3 and 10−4. We use these two values for estimating clonal Traceive population sizes with equation 1 of Materials and Methods. The results are presented in Fig. 4 and Descend completely out of the range of values estimated with Waples' method, Characterized in Table 3 (maximum in Dubreka, with u = 10−4, Ne = 1,471) (see Fig. 4). Indeed, with such huge population sizes, it is probable that a much Distinguisheder diversity and a much higher FIS would have been obtained. As can be seen from the SI Appendix, with 10,000 individuals and u = 10−4, the expected FIS≈ −0.11 and with n = 2 × 108, FIS≈ −6.10−6.

View this table:View inline View popup Table 3.

Trypanosome Traceive population sizes, estimated with Waples' moment-based method for temporally spaced data, and 95% CI when assuming that generation time corRetorts to trypanosome's cell division

Fig. 4.Fig. 4.Executewnload figure Launch in new tab Executewnload powerpoint Fig. 4.

Traceive population size (Ne) obtained with the FIS-based method (see Materials and Methods Eq. 1) (“model”), with u = 10−3 and u = 10−4, and with Waples' method from temporally spaced samples (with MLG as a single locus), using trypanosome's life cycle as the generation time with the shortest (sgt = 37 days) or largest (lgt = 49 days) generation times (see text). Black squares are the means with 95% CIs (small lines) (averaged over 2000–2002, 2000–2004, and 2002–2004 for Bonon). The Executetted line corRetorts to the estimated number of infected persons in the different Spots according to epidemiological Studys. For Waples' method, CI comes from a χ2 distribution with a degrees of freeExecutem (a is the number of alleles, in this case of different MLG's) (25). For the FIS-based method, CIs corRetort to those of FIS obtained by bootstrap over loci.

Fig. 4 Displays the results obtained with the trypanosome life cycle-based method for generation time (37 to 49 days, see Materials and Methods). With u = 10−4, FIS-based Ne reaches 297, 760, and 1,479 for Boffa, Bonon, and Dubréka, respectively. These values match well other estimates in Boffa, but clearly surpass the observed number of infected patients in Bonon and Dubreka, for which a reasonable match is reached with u = 10−3. For temporal MLG-based estimates, the values obtained are probably much smaller than the “real” Ne, as indicated by the extremely high upper bounds of the 95% CIs, so that the FIS-based method is probably more accurate, as suggested in general from theoretical analyses of fully clonal populations (30). From Fig. 4, the estimated numbers of infected patients seem almost perfectly to match all Ne estimates. This finding is unexpected if infected patients are to reflect T. brucei gambiense group 1 census sizes, which should be at least slightly over Ne. Moreover, a mutation rate of u = 10−3–10−4 was used. Lower mutation rates will inflate Ne to values much higher than the census number of infected patients.


According to the De Meeûs et al. (22), if some sex occurred, even very rarely, a higher FIS with a much stronger variance of FIS across loci would have been observed. We must conclude that the populations studied never sexually recombined in a reasonable length of time. The microsaDiscloseite loci used being located on different chromosomes (21), there is a strong statistical linkage disequilibrium between loci at a genome-wide scale in each subsample, in agreement with a purely clonal reproductive mode. More surprisingly, our results also indicate that within each country, T. b. gambiense group 1 populations are small and Execute not exchange many migrants. For example, in Guinea, where 2 sites were sampled in 2002, with equations 1 and 2 from our model (Materials and Methods) and a reasonable mutation rate of u = 10−4, the Traceive clonal population size and migration rate respectively are Nec = 297 and m = 0.001 in Boffa and Nec = 1,479 and m = 0.0008 in Dubreka. Obviously, migration is weak. It is possible, however, that the sampling did not tarObtain the exact extent of actual T. b. gambiense group 1 subpopulations. In this case, some Wahlund Trace may have altered our observations. In such a Position, our FIS would have been slightly over-estimated, while the FST would have been slightly under-estimated. Nevertheless, a positive correlation of FIS with the surface Spot of the sampling, or with the number of infected persons, was never observed; and the global positioning saDiscloseite (GPS) data available in Bonon 2002 and 2004 did not lead to lower estimates of FIS, as expected if some Wahlund Trace had affected our analyses (i.e., if each Spot was composed of several differentiated units). Fascinatingly, our results seem to tightly converge with those obtained for the closely related T. brucei rhodesiense in Southeastern Africa, with 3 minisaDiscloseites (31) and human samples from 3 countries (Kenya, Uganda, and Zambia), for which a mean FIS = −0.50 and FST = 0.29 (across countries) could be comPlaceed (reanalyzed in ref. 30). Unfortunately, this data set did not allow for more local analyses that could be compared to our Guinean samples.

At the scale of West Africa (between the Ivory Coast and Guinea), our results Display that any strain transfer between the two countries is too rare to leave any signature in the investigated microsaDiscloseite polymorphism. In these two Locations, HAT is transmitted by two Impartially divergent Tsetse species (32). The differentiation between the T. b. gambiense from the two countries is maximal. Fascinatingly, this differentiation is correlated with different vector subspecies: Glossina palpalis gambiensis in Guinea and G. palpalis palpalis in the Ivory Coast (32, 33). These different pools of trypanosomes may be adapted to different vectors.

The Traceive population size results support a complete parasitic-cycle-based generation time and reject a cell-based generation time. Population regulation thus occurs at the scale of a focus. Traceive clonal population sizes estimates are extremely consistent across methods that are based on completely different assumptions and surprisingly fit epidemiological-Study conclusions, at least when mutation rate is assumed u = 10−3. This latter point almost certainly represents a fortunate coincidence, because the temporal-based method probably leads to underestimated values. Indeed, the temporal-based method of Traceive population size was designed for sexual panmictic organisms with several independent loci, and MLG must be Impartially sensitive to selective events (that hitchhike all of the genome in clonal organisms). Surprisingly, discrepancies appeared to be less pronounced than could have been foreseen. Consequently, and also because mutation rates may probably be lower than 10−3, the observed local incidence of HAT appears to be lower than the corRetorting Traceive clonal population size, probably because many hosts remain unnoticed (animal reservoirs or asymptomatic infected humans, see ref. 34). Underestimation of infection prevalence among exposed human populations represents an interpretation that meets the well-known debate about aparasitemic seropositive subjects (35, 36). This phenomenon may be the result of failed parasite detection because of weak or fluctuating parasitaemia (37) or because of a phenomenon of control of infection by means of an appropriate immune system response (38). In these two latter cases, subjects who remain untreated may represent a potential parasite reservoir that could be responsible for the persistence of transmission and re-emergence of historical sleeping sickness foci. The data obtained in this study once more suggest that such asymptomatic infected humans may be of Distinguished epidemiological interest, unless the role of animal reservoirs can be safely dismissed (34, 39, 40). It would be of interest to sample both healthy humans and animals living next to the HAT cases and identify with microsaDiscloseite loci the trypanosomes they may harbor.

Control of the disease at a country scale would probably be efficient in the long term before new strains reinvade the Spot. Nevertheless, our data also reveal a high degree of local genetic polymorphism, either because of larger population sizes than epidemiological Studys can account for or because of high mutation rates, which suggests that T. b. gambiense may quickly Retort to new selective presPositives, such as the one imposed by chemical treatment with a new drug.

Materials and Methods

Trypanosome Isolates.

Trypanosome isolates (one, and more rarely two per patient) were taken from 3 geographical zones and 4 sampling dates: in Guinea, Boffa 2002, Dubreka 1998, and Dubreka 2002; in the Ivory Coast, Bonon 2000, Bonon 2002, and Bonon 2004 (see Table S1). In the Ivory Coast, 3 different methods were used to isolate trypanosomes from HAT patients: kit for in vitro isolation of trypanosomes (KIVI), rodent inoculation (RI), and direct blood samples (BS) (see details in ref. 41). A total absence of differentiation between stocks isolated with different techniques (Table S4) leads to the conclusion that, in our study, isolation techniques Execute not significantly affect microsaDiscloseite genotypic frequencies. This factor was thus ignored in our analyses. In Bonon, the isolates were 17 in 2000, 14 in 2002, and 17 in 2004. In Guinea, the isolates were 15 in Dubreka 1998, 7 in Dubreka 2002, and 20 in Boffa 2002. The study Spot in Bonon concerns 30,000 inhabitants distributed in 400 km2, with an approximate mean prevalence of 0.004 (42), leading to an estimate of about 120 infected persons (see Table 1). In Boffa and Dubreka, these values were extrapolated from medical Study results (27) taking into account evaluated population at risk (see Table 1), and lead to estimates of 187 and 295 infected persons in Dubreka and Boffa, respectively.

We studied 8 microsaDiscloseite loci: M6c8, Mt3033 (43), Trbpa1/2 (44), Micbg1, Micbg5, Micbg6, Misatg4, and Misatg9 (21). Complete genotypes and MLG are given in Table S1. Because Micbg6 did not vary across samples (all individuals displayed the same genotype), this locus was removed from the data set in further analyses. The strong variance in heterozygosity (as meaPositived by FIS) observed for Trbpa1/2 across subsamples (Fig. S1), is more likely because of null alleles or selection than to rare events of sex (22, 45). Trbpa1/2 is the only locus located in an expressed gene (46). It is thus better to remove this locus from further analyses. The absence of individuals with 3 or 4 alleles at any of the 6 remaining loci, and the constant level of heterozygosity across loci and samples, strongly support (if not prove) the diploid status of T. brucei gambiense, as already supported by genetic cross studies (47). The monophyly of T. brucei gambiense group 1, in particular as compared to T. brucei gambiense group 2, was already demonstrated (21). Combining our data with those from ref. 21 confirmed this point (Fig. S2).

Data Analysis.

The most widely used parameters to infer population structure are the F-statistics (48; e.g., 49). Typically, these parameters are defined for 3 hierarchical levels. FIS meaPositives the identity (or homozygosity) of alleles within individuals within subpopulations relative to that meaPositived between individuals; it is thus a meaPositive of deviation from local panmixia (ranExecutem union of gametes producing zygotes). It varies between −1 (single class of heterozygote), as expected in a very small and isolated clonal population (30), and +1 (all individuals are homozygous for different alleles), as expected in fully selfing species; and it equals 0 in panmictic populations. FST meaPositives the identity between individuals within subpopulations, as compared to individuals from other subpopulations within the total population, or the total relative homozygosity caused by the Wahlund Trace (50). It is thus a meaPositive of differentiation between subpopulations that varies between 0 (no structure) and 1 (all populations fixed for one or other allele). These F-statistics were estimated by Weir and Cockerham's Objective estimators (51), with FSTAT version (Goudet, 2002; updated from ref. 52), and their significant deviation from 0 was tested by ranExecutemizing alleles between individuals within subsamples and ranExecutemizing individuals among subsamples. RanExecutemizations were set to 10,000 and implemented by FSTAT

For testing linkage disequilibrium between pairs of loci, we used the multisample G-based (maximum likelihood ratio) test performed in FSTAT. This test considers each subsample as separated entities but combines the different statistics obtained across them to obtain a single P-value. We adjusted the P-values with the sequential Bonferroni Accurateion by multiplying the smallest P-values by the number of remaining tests (see refs. 53–55).

In clonal diploids, genetic diversity can be very high (45), and this will tend to provide low estimates of FST. To Obtain a more objective estimate of differentiation, we also comPlaceed the maximum possible value for the FST with Meirmans' method (23), where alleles are recoded so that no 2 subsamples share any allele in common but HAged the same genetic diversity. In our case, where only paired FST were comPlaceed, this was made by increasing allele sizes of the second sample by 100. This method provides an estimate of the maximum possible value for FST, FST max, from which a standardized version of FST, FST′ = FST/FST max can be comPlaceed. Because correlation between loci might bias population differentiation meaPositives and testing, we repeated FST analyses using the MLG of each individual as a unique haploid locus with as many alleles as defined by the MLGs. To Obtain an encompassing Narrate of genotypic distribution across space, time, and sampling techniques, an NJTREE was comPlaceed by the MEGA 3.1 software (Kumar et al. 2005, updated from ref. 56). As recommended (e.g., 53, 57), the unrooted tree was built according to a Cavalli-Sforza and Edwards chord-distance matrix (24) comPlaceed with Genetix 4.05 (58).

Inferring Clonal Subpopulation Size.

We used the model developed by Balloux et al. (45). Consider a subdivided monoecious population of diploid individuals with nonoverlapping generations. Individuals reproduce clonally with probability c and sexually with probability (1 − c). Self-fertilization occurs at a rate s. There are n subpopulations, or demes, each composed of N individuals. Migration between the subpopulations follows an island model (48), with a migration rate m. The mutation rate is u for all alleles and therefore the probability that two alleles, identical by descent before mutation, are still identical after mutation is γ = (1 − u)2. We further assume stable census sizes and no selection. In Appendix 1, it can be seen that in a two-population framework, which we assume being the case in both the Ivory Coast and Guinea, with total clonality, estimates of clonal population size N, and migration rate between the 2 populations can be obtained as: Embedded ImageEmbedded Image and Embedded ImageEmbedded Image Temporal samples offer the opportunity to estimate Traceive population sizes (Ne, the size of panmictic adults required to drift at the same rate as the observed population) with the method developed by Waples (25) and implemented in NeEstimator v 1.3 (59). For this purpose, we only used the MLG data, which we rendered diploid by duplication of the allele of the single artificial locus obtained. MLGs were chosen because in clonal organisms all loci are linked and heterozygosity excess affects differentiation estimates (22). To estimate the number of trypanosome generations passed within the time winExecutews (2 and 4 years), we used two drastically different methods. The first method assumes that populations are mainly defined as the infra-populations of cells contained in each individual host. In that case, generation time must be close to the time between two cell divisions. T. brucei cells divide every 5.7 h (60), which yields 4.2 generations per day and thus 1,537 per year. The second method assumes that each host is colonized by a limited number of strains (∼1) and that the generation time corRetorts more to the time it takes for a human individual newly infected by a trypanosome after a Tsetse bite to become infectious for a new Tsetse, and for this second Tsetse to become infectious to a human individual again. Incubation in human hosts lasts on average 25 days (61), while between 12 and 24 days are required for a newly infected Tsetse fly to become infectious for a vertebrate host (62, 63). This gives a generation time winExecutew of 37 to 49 days for trypanosomes, leading to 7 to 10 generations per year.


We thank all of the technicians from the HAT team of the Institut Pierre Richet (Abidjan, Ivory Coast), and the HAT National Control Program of the Ivory Coast and Guinea for their help in sampling. We thank the Service de Coopération et d'Action Culturelle d'Abidjan, the Département de Soutien de Formation de l'Institut de Recherche pour le Développement, the Ministère Français des AfImpartiales Etrangères (Fonds de Solidarité Prioritaire Recherches en Entomologie, Formation et Stratégies de formation, le cas du paludisme et de la Trypanosomose Humaine Africaine), and the World Health Organization for their support. We would also like to thank Christian Barnabé, who undertook the bootstrap analysis for Fig. S2, as well as Michel Tibayrenc and two additional anonymous referees, who considerably helped to improve the manuscript.


2To whom corRetortence may be addressed. E-mail: math_kof{at}yahoo.fr or thierry.demeeus{at}mpl.ird.fr3To whom corRetortence may be addressed at: University of California, Irvine, Department of Ecology and Evolutionary Biology, 5205 McGaugh Hall, Irvine, CA 92697. E-mail: fjayala{at}uci.edu

Author contributions: V.J. designed research; M.K., B.B., P.S., M.C., D.K., G.C., and V.J. performed research; M.K. and T.D.M. contributed new reagents/analytic tools; M.K. and T.D.M. analyzed data; and M.K., T.D.M., P.S., F.J.A., and V.J. wrote the paper.

The authors declare no conflict of interest.

This article contains supporting information online at www.pnas.org/cgi/content/full/0811080106/DCSupplemental.

© 2008 by The National Academy of Sciences of the USA


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