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Proceedings of the National Academy of Sciences of the United States of America logoLink to Proceedings of the National Academy of Sciences of the United States of America
. 2009 Jun 4;106(25):10224–10229. doi: 10.1073/pnas.0904420106

Extreme inbreeding in Leishmania braziliensis

Virginie Rougeron a,1, Thierry De Meeûs a,b, Mallorie Hide a, Etienne Waleckx a, Herman Bermudez c, Jorge Arevalo d, Alejandro Llanos-Cuentas d, Jean-Claude Dujardin e, Simone De Doncker e, Dominique Le Ray e, Francisco J Ayala f,1, Anne-Laure Bañuls a
PMCID: PMC2700931  PMID: 19497885

Abstract

Leishmania species of the subgenus Viannia and especially Leishmania braziliensis are responsible for a large proportion of New World leishmaniasis cases. The reproductive mode of Leishmania species has often been assumed to be predominantly clonal, but remains unsettled. We have investigated the genetic polymorphism at 12 microsatellite loci on 124 human strains of Leishmania braziliensis from 2 countries, Peru and Bolivia. There is substantial genetic diversity, with an average of 12.4 ± 4.4 alleles per locus. There is linkage disequilibrium at a genome-wide scale, as well as a substantial heterozygote deficit (more than 50% the expected value from Hardy−Weinberg equilibrium), which indicates high levels of inbreeding. These observations are inconsistent with a strictly clonal model of reproduction, which implies excess heterozygosity. Moreover, there is large genetic heterogeneity between populations within countries (Wahlund effect), which evinces a strong population structure at a microgeographic scale. Our findings are compatible with the existence of population foci at a microgeographic scale, where clonality alternates with sexuality of an endogamic nature, with possible occasional recombination events between individuals of different genotypes. These findings provide key clues on the ecology and transmission patterns of Leishmania parasites.

Keywords: clonality, microsatellites, population genetics, endogamyl, heterozygote defiency

Leishmaniases are worldwide vector-borne diseases of humans and domestic animals, caused by protozoan parasites of the genus Leishmania. These parasitoses are a serious public health problem, with about 350 million persons at risk and 2,357,000 new cases per year (1). Leishmaniases occur on all continents except Antarctica. There are more than 20 described species causing human infections (review in ref. 2). Clinical symptoms range from asymptomatic, cutaneous, and mucocutaneous to visceral forms, depending on the Leishmania species. Visceral leishmaniasis is mainly caused by species from the Leishmania donovani complex; cutaneous and mucocutaneous forms are associated with species from the Viannia and Leishmania subgenera (3–5). L. braziliensis causes cutaneous and mucocutaneous leishmaniases in South America, where these are a severe public health problem.

Despite numerous studies and recent advances in the molecular genetics of these organisms, the reproductive mode of these parasites remains unsettled. Tibayrenc and Ayala (6) proposed that all (or most) Leishmania species are clonal. Other authors have challenged this hypothesis, based on pulse field gel electrophoresis (PFGE) data, and argued that some Leishmania species are potentially automictic, with frequent genetic exchanges (7). Several studies suggest that recombination may occur in Leishmania, and that other complexities may exist (see review in ref. 2). For example, based on evidence from PFGE analyses, Bañuls et al. (8) have proposed the occurrence of pseudorecombination in Leishmania populations. Moreover, several genetic studies indicate genetic recombination between Leishmania individuals, despite lack of evidence for a sexual stage (9–16). In any case, the molecular data suggest that, after a hybridization event, hybrids propagate clonally in natural populations (9, 12).

The prevailing hypothesis is that Leishmania displays a clonal mode of reproduction with occasional pseudorecombination and intragenic recombination, which mimic sexual reproduction processes, and that infrequent genetic exchanges take place in wild populations. Nevertheless, much remains to be elucidated as this interpretation is challenged by certain data, such as the absence of large excess in heterozygosity, as expected in clonal diploids (17, 18), and the lack of a clear structure in individualized lineages at the intraspecific level (2). Indeed, in a clonal model, an excess of heterozygotes and significant linkage disequilibrium are expected. Thus, the known results have failed to resolve the issue of clonality vs. sexuality in these protozoan parasites. Improved knowledge of the population structure and reproductive strategy of Leishmania parasites would provide a better understanding of their transmission patterns, as well as useful information for diagnostic purposes, epidemiological surveys, and drug and vaccine development.

Microsatellite loci are highly polymorphic, codominant, abundant throughout the genome, and relatively easy to assay (19, 20). In Leishmania, microsatellite studies are relatively recent; a small number of polymorphic microsatellites have been described for Leishmania species of the Viannia subgenus and especially for L. braziliensis (21). We analyze the population structure of L. braziliensis in several natural populations from South America (Peru and Bolivia), based on 12 microsatellite loci previously described (22). Peru and Bolivia are 2 of 7 world countries that report 90% of cutaneous leishmaniasis cases. Our population genetics analysis may be the first study of this kind for this Leishmania species. It reveals an unexpectedly high level of inbreeding within local samples, a large part of which is explained by local heterogeneity (Wahlund effect), probably due to a microgeographic population substructure, but also to the occurrence of mixed-mating events that include a significant contribution of endogamy (i.e., recombination between 2 genetically identical cells).

Results

We analyzed 124 human strains of L. braziliensis from 4 samples: 2 from the Pilcopata department in Peru, isolated in either 1993 or 1994, and 2 from Chapare Natural Park in Bolivia, isolated in either 1994 or 1998 (Tables 1 and 2). Both sites are located in the Amazonian forest and extend over large areas of great faunal and floral diversity.

Table 1.

Genetic diversity at 12 microsatellite loci in 124 strains of Leishmania braziliensis strains from 4 populations

Locus GenBank accession no. Allele size, bp N Hs FIS
AC01 AF139110 198–212 8 0.707 0.576
AC16 AF139112 147–161 11 0.754 0.341
AC52 AF139111 098–126 22 0.914 0.501
ARP AF045249 121–157 18 0.874 0.441
ITSbraz AJ300483 100–108 6 0.603 0.923
Ibh3 AF044682 116–136 9 0.584 0.599
LRC BX544585 118–134 15 0.826 0.424
CAK BX544561 152–170 13 0.743 0.676
EMI BX541508 183–189 14 0.809 0.645
LBA BX539885 168–180 14 0.803 0.225
GO9 BX539509 148–168 10 0.673 0.466
E11 BX542509 096–108 9 0.715 0.618
Mean ± SE 12.4 ± 4.4 0.750 ± 0.100 0.537 ± 0.040
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N, number of alleles; Hs, Nei's unbiased genetic diversity within subsamples (23); FIS, deviation from panmixia.

Table 2.

Data set with each sample code, the country, and the year of collection and all genotypes obtained at each locus by PCR

Sample code Country Year Loci
AC01 AC16 AC52 ARP ITSbraz Ibh3 LRC CAK EMI LBA G09 E11
LC1568 Peru 1993 202–202 149–161 104–104 139–139 102–102 130–130 124–124 162–162 165–165 180–180 150–152 102–102
LC2231 Peru 1994 202–202 149–155 084–098 139–145 106–106 130–130 132–132 162–162 189–189 178–178 150–154 096–096
LC2280 Peru 1994 198–210 151–161 084–084 137–139 104–106 130–130 132–132 158–158 191–191 176–182 150–162 098–102
LC2282 Peru 1994 206–212 149–149 100–100 151–153 102–102 128–128 120–126 162–162 165–165 176–180 156–156 096–096
LC2291 Peru 1994 202–202 151–151 120–120 133–149 102–106 130–130 128–128 162–162 175–185 174–182 148–148 100–100
LC2292 Peru 1994 204–210 149–149 084–120 139–139 104–104 130–130 132–132 160–160 189–189 174–180 148–152 096–096
LC2293 Peru 1994 202–202 149–151 098–098 133–149 102–106 130–130 128–128 164–168 175–185 174–174 150–152 100–102
LC2308 Peru 1994 204–210 151–169 100–100 149–149 100–102 116–130 120–128 160–160 185–185 174–180 154–154 098–100
LC2310 Peru 1994 204–210 159–159 084–094 137–139 102–102 130–130 132–132 160–160 189–189 176–182 148–156 100–100
LC2315 Peru 1994 202–210 151–161 108–108 141–141 102–102 130–130 130–134 162–162 189–189 176–182 154–154 100–104
LC2316 Peru 1994 200–204 147–161 088–088 135–135 100–100 118–130 130–138 160–160 185–185 176–180 148–154 098–098
LC2318 Peru 1994 202–202 149–167 092–108 139–139 102–102 130–130 130–130 162–162 177–195 174–182 152–152 100–100
LC2319 Peru 1994 200–200 149–157 104–110 141–147 104–108 130–130 124–124 164–164 193–193 174–184 152–152 100–100
LC2320 Peru 1994 202–210 149–155 118–118 155–155 104–104 128–128 132–132 162–162 189–189 174–182 150–154 102–102
LC2321 Peru 1994 208–208 149–149 086–106 149–155 100–100 128–128 122–130 162–162 189–189 176–176 148–156 098–106
LC2352 Peru 1994 202–212 149–161 110–110 143–143 102–104 130–130 126–130 162–162 191–191 178–180 150–154 098–098
LC2353 Peru 1994 202–212 149–161 084–094 129–139 102–102 128–128 132–132 158–162 191–191 176–180 150–152 098–098
LC2355 Peru 1994 210–210 149–159 098–122 129–137 102–102 128–128 132–132 158–160 189–189 176–180 150–150 100–100
LC2367 Peru 1994 202–202 149–161 084–098 131–139 102–106 130–130 132–132 158–164 191–191 174–180 148–148 098–098
LC2368 Peru 1994 202–202 149–161 084–098 135–141 102–104 130–130 132–132 158–162 191–191 174–180 148–148 100–100
LC2369 Peru 1994 200–210 149–161 096–096 135–141 102–106 130–130 126–132 158–164 189–189 174–182 150–150 098–098
LC2371 Peru 1994 200–200 151–161 084–098 135–141 100–106 130–130 132–132 158–164 189–189 174–180 148–148 098–098
LC2373 Peru 1994 202–202 151–161 084–098 133–139 102–104 130–130 132–132 162–162 191–191 174–180 150–150 100–100
LC2284 Peru 1994 210–210 151–161 086–116 139–139 102–102 130–130 130–130 158–160 189–189 174–180 150–152 100–102
LC2322 Peru 1994 202–202 149–161 094–110 149–149 104–104 130–130 130–130 160–160 191–193 174–182 154–154 100–102
CH12B Bolivia 1994 204–206 149–159 126–126 155–157 102–102 116–130 124–130 168–168 183–183 174–180 154–154 100–100
CH15 Bolivia 1994 206–206 147–157 124–124 155–157 102–102 116–130 122–134 164–164 189–189 168–174 154–154 100–106
CH17 Bolivia 1994 202–202 147–161 096–096 131–131 102–102 130–130 132–132 164–164 185–191 168–168 154–154 098–102
CH25B Bolivia 1994 202–204 149–149 100–100 121–121 102–102 130–130 124–134 158–164 179–187 168–172 152–168 100–102
CH29B Bolivia 1994 204–206 149–159 126–126 125–157 102–102 128–128 130–134 158–166 183–185 174–180 154–154 100–100
CUM106 Bolivia 1994 204–206 147–159 100–100 129–131 102–102 128–128 124–128 164–164 185–189 174–174 152–154 100–100
CUM107 Bolivia 1994 202–202 149–149 092–092 121–121 106–106 120–120 118–120 156–156 189–189 170–170 144–144 090–090
CUM31 Bolivia 1994 206–206 149–159 126–126 135–137 102–102 116–128 122134 162–172 187189 168–174 154–154 102–108
CUM38 Bolivia 1994 202–204 149–149 098–104 129–135 102–102 130–130 114134 160–160 185–185 168–168 154–154 102–102
CUM41 Bolivia 1994 202–202 149–149 114–120 131–131 102–102 118–128 126–130 160–160 185187 168–172 154–154 100–102
CUM50 Bolivia 1994 206–206 149–159 100–110 129–147 102–102 116–128 122–124 164–164 185–187 174–174 154–154 100–102
CUM52 Bolivia 1994 206–206 149–149 100–110 129–147 102–102 116–128 122–124 164–164 185–187 174–174 154–154 100–102
CUM53 Bolivia 1994 206–206 149–149 112–112 129–147 104–104 116–116 122–132 164–170 185–189 174–174 154–154 098–104
CUM55 Bolivia 1994 206–206 149–149 126–126 137–137 102–102 128–130 114–130 158–170 187–189 162–168 154–154 098–102
CUM59 Bolivia 1994 206–206 149–159 124–124 129–129 102–102 136–136 122–134 162–174 187–189 168–174 154–154 102–108
CUM65 Bolivia 1994 202–202 149–159 114–114 129–137 102–102 116–128 122–130 164–164 179–185 174–174 154–154 102–102
CUM67 Bolivia 1994 208–208 149–149 088–108 149–155 102–106 128–128 122–130 160–166 187–193 176–178 150–156 100–108
CUM68 Bolivia 1994 202–206 151–151 128–128 129–129 102–108 128–128 122–130 164–168 187–189 168–174 152–154 102–102
CUM82 Bolivia 1994 202–206 149–149 098–112 131–131 100–102 136–136 124–132 164–164 179–189 180–180 154–154 098–100
CUM84 Bolivia 1994 206–206 149–149 096–096 131–131 100–100 116–130 130–130 158–166 181–189 174–174 152–152 100–100
CUM97 Bolivia 1994 206–206 149–159 100–110 129–147 102–102 116–128 122–124 164–164 185–187 174–174 154–154 100–102
CUM96 Bolivia 1994 204–204 149–161 084–098 129–137 102–102 128–128 130–130 162–172 191–193 168–182 154–154 100–102
CUM99 Bolivia 1994 204–204 149–159 088–088 121–121 102–102 122–122 132–132 176–176 185–191 162–162 150–156 098–098
CUM32 Bolivia 1994 204–204 147–161 124–124 129–147 102–102 118–130 120–134 164–172 189–189 168–174 152–152 100–106
CUM46 Bolivia 1994 202–202 149–149 118–118 127–127 102–102 136–136 120–128 152–152 185–187 168–182 154–154 100–100
CUM30 Bolivia 1994 200–200 149–149 090–090 129–131 104–104 122–122 114–130 164–164 179–179 170–170 150–150 096–096
CUM24 Bolivia 1994 204–204 149–149 110–110 129–129 102–102 132–132 124–134 158–164 189–189 174–174 166–166 102–102
CUM42 Bolivia 1994 204–204 149–159 098–110 129–147 100–100 116–128 124–124 164–164 183–183 174–174 152–154 100–100
CUM26 Bolivia 1994 202–204 151–151 118–118 129–137 102–102 116–116 120–132 164–164 183–183 174–180 152–152 102–102
CUM39 Bolivia 1994 204–204 151–151 098–110 135–137 100–100 116–128 120–122 164–164 189–189 172–172 152–152 098–102
CH22B Bolivia 1994 204–204 149–159 118–118 129–129 102–102 128–128 120–128 162–162 185–187 168–182 154–154 102–102
CUM49 Bolivia 1994 206–206 151–151 122–122 129–129 102–102 130–130 122–124 164–164 185–187 174–174 154–154 100–102
CUM51 Bolivia 1994 202–204 151–161 098–118 135–137 102–102 116–130 132–134 162–162 187–191 168–174 152–154 100–102
CUM251 Bolivia 1998 200–200 149–149 092–092 143–143 102–102 122–122 128–128 166–166 189–189 172–172 152–152 096–096
CUM252 Bolivia 1998 200–200 153–153 116–116 141–141 106–106 122–122 128–128 166–166 183–183 170–172 152–152 096–096
CUM263 Bolivia 1998 204–204 161–161 100–116 131–147 102–102 116–130 122–122 162–162 185–185 176–182 154–154 100–100
CUM266 Bolivia 1998 200–200 149–149 116–116 145–145 106–106 122–122 116–130 164–164 183–189 168–172 152–152 098–098
CUM271 Bolivia 1998 200–200 153–153 098–118 143–143 106–106 122–122 116–128 164–164 181–181 166–170 152–152 096–096
CUM274 Bolivia 1998 200–200 153–157 098–108 139–139 106–106 122–122 116–118 164–164 183–183 170–172 152–152 096–096
CUM275 Bolivia 1998 200–200 151–151 092–092 143–143 106–106 122–122 120–120 164–164 181–181 170–172 152–152 096–096
CUM276 Bolivia 1998 200–200 151–151 092–092 143–143 106–106 122–122 116–130 164–164 183–183 172–172 152–152 096–096
CUM277 Bolivia 1998 204–206 151–151 100–110 131–147 102–102 130–130 116–126 162–162 185–185 168–174 154–154 100–100
CUM279 Bolivia 1998 200–200 151–161 094–094 145–145 100–102 122–122 116–128 164–164 183–183 170–172 152–152 096–096
CUM280 Bolivia 1998 200–200 151–151 092–092 145–145 106–106 122–122 128–128 164–164 183–183 174–174 154–154 096–096
CUM281 Bolivia 1998 200–200 149–149 092–116 145–145 106–106 124–124 132–132 164–164 181–181 187–189 150–150 096–096
CUM282 Bolivia 1998 200–200 149–159 090–090 139–139 106–106 122–122 130–130 164–164 181–181 180–180 152–152 100–100
CUM285 Bolivia 1998 200–200 151–151 092–092 139–149 106–106 122–122 130–130 164–164 183–183 170–170 152–152 096–096
CUM286 Bolivia 1998 200–200 151–151 092–092 141–149 106–106 122–122 130–130 164–164 183–183 170–170 152–152 100–100
CUM288 Bolivia 1998 200–200 149–149 092–092 139–139 106–106 122–122 120–120 164–164 183–183 172–172 152–152 096–096
CUM289 Bolivia 1998 200–200 161–161 092–092 141–149 102–102 122–122 130–130 166–166 183–189 170–170 152–152 098–098
CUM290 Bolivia 1998 200–200 149–159 092–092 145–145 106–106 122–122 118–128 166–166 183–183 172–172 152–152 096–096
CUM291 Bolivia 1998 200–200 149–159 092–092 145–145 106–106 116–130 132–132 164–164 187–187 170–170 152–152 096–096
CUM294 Bolivia 1998 202–202 149–149 088–088 147–147 102–102 122–122 130–130 174–174 189–189 164–164 150–150 094–094
CUM295 Bolivia 1998 202–202 151–151 088–088 131–131 102–102 122–122 132–132 164–164 191–191 164–164 152–152 096–096
CUM310 Bolivia 1998 202–202 151–151 112–112 129–147 102–102 116–130 124–132 162–162 185–189 174–180 154–156 102–102
CUM311 Bolivia 1998 200–200 149–149 100–108 131–131 102–102 116–128 132–132 162–162 189–189 174–174 152–152 098–100
CUM323 Bolivia 1998 206–208 159–159 100–110 131–137 102–102 130–130 124–130 164–164 187–187 174–182 154–154 098–102
CUM324 Bolivia 1998 206–206 161–161 098–098 131–137 100–100 128–128 132–132 166–168 185–185 174–182 154–154 096–096
CUM327 Bolivia 1998 204–204 151–161 104–108 137–137 102–102 128–128 132–132 162–162 181–181 178–180 150–152 102–102
CUM329 Bolivia 1998 204–204 149–149 116–116 145–145 102–102 116–130 124–128 162–162 185–185 174–180 154–154 100–102
CUM331 Bolivia 1998 200–200 149–149 098–112 145–145 106–106 122–122 116–130 164–164 181–189 170–170 152–152 096–096
CUM346 Bolivia 1998 204–204 151–161 104–108 131–137 102–102 130–130 132–132 162–162 185–185 174–180 152–152 098–098
CUM381 Bolivia 1998 200–200 149–153 100–110 145–145 106–106 122–122 116–118 166–166 183–183 168–168 152–152 096–096
CUM384 Bolivia 1998 198–204 149–159 116–116 129–135 102–102 116–130 122–122 162–162 185–185 174–180 154–154 100–102
CUM388 Bolivia 1998 200–200 149–149 098–110 131–137 100–102 116–116 122–132 162–162 183–183 172–180 150–150 098–098
CUM389 Bolivia 1998 204–212 151–151 118–118 131–137 102–102 116–116 122–132 162–162 183–183 172–180 152–152 098–098
CUM393 Bolivia 1998 200–200 153–153 098–098 149–149 106–106 128–128 130–130 164–164 185–185 168–172 148–156 100–100
CUM396 Bolivia 1998 200–200 151–153 116–116 143–143 102–102 122–122 116–118 164–164 183–183 170–170 152–152 096–096
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Genetic Diversity and Heterozygote Deficiency.

We obtained clear electrophoregrams for all genotypes at all 12 loci investigated, with only 1 or 2 alleles per strain at each locus, which excludes events of aneuploidy (for which we would have expected individuals with 3 or 4 alleles). There is considerable genetic diversity, with an average number of alleles per locus of 12.4 ± 4.4, ranging from 6 (ITSbraz) to 22 (AC52), and a mean genetic diversity Hs = 0.750 ± 0.100 (Table 1).

There is large deficiency of heterozygotes compared with Hardy–Weinberg expectations in each population, for both the multilocus data and each locus separately. The population FIS ranges from FIS = 0.396 in Bolivia 1994, to FIS = 0.687 in Bolivia 1998 (Fig. 1). For individual loci, the average values range from FIS = 0.225 for LBA to FIS = 0.676 for CAK (Fig. 2). The overall mean value is FIS = 0.504 (95% CI = 0.427–0.577). All FIS values are significantly different from zero (P ≤ 0.001).

Fig. 1.

Fig. 1.

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FIS and 95% confidence intervals obtained by bootstrap over loci, for the 124 human Leishmania braziliensis strains collected in 2 different countries in 2 different years. FIS measures the local deficiency of heterozygous genotypes due to nonrandom mating. There is a large heterozygote deficiency in each population, shown because FIS is significantly greater than zero.

Fig. 2.

Fig. 2.

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FIS for each of 12 microsatellite loci in the 4 populations (and their mean) of Leishmania braziliensis collected in Peru and Bolivia. There is a large heterozygote deficiency at each locus.

The selfing rate (s) required to account for the heterozygote deficiency observed over all samples and loci is s = 0.67.

Wahlund Effect.

The heterozygote deficiency could possibly result from the Wahlund effect—that is, population subdivision within each subsample. This can be investigated with the Bayesian analysis of genetic population structure (BAPS) software. The 2 populations from Bolivia collected in 1994 and 1998 are composed of 15 (with probability PBAPS = 0.46) and 13 clusters (PBAPS = 0.69), respectively. The other 2 populations from Peru collected in 1993 and 1994 are composed of 18 (PBAPS = 0.60) and 11 clusters (PBAPS = 0.63), respectively. In each partition identified by BAPS in the 4 subsamples, the heterozygote deficit was calculated again. There was a decrease in FIS with respect to the initial data set. However, FIS_C = 0.307 (CI = 0.227–0.584) remains significant (P ≤ 0.001; Fig. 3). Moreover, analyses with the Wilcoxon test showed a significant decrease (Z = 1.657, P = 0.0488). Thus, ≈40% of total FIS can be explained by a Wahlund effect. The selfing rate required to account for this remaining FIS is high, sc = 0.47.

Fig. 3.

Fig. 3.

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FIS for Leishmania braziliensis strains in each population and within their subdivisions as identified by BAPS. The decrease of FIS in the subdivision suggests a Wahlund effect. However, the residual FIS values are still high, which suggests the persistence of nonrandom mating (due, for example, to selfing).

Population Differentiation.

The genetic differentiation between Peru and Bolivia for both 1994 collections was small but significant (FST = 0.092, P < 0.001). There was also a small temporal differentiation between 1993 and 1994 in Peru (FST = 0.004, P < 0.001) and an apparently larger one between 1994 and 1998 in Bolivia (FST = 0.114, P < 0.001).

The differentiation between BAPS clusters, using the HIERFSTAT software, was very high (FCluster/Country = 0.31), as expected. The remaining variation between countries was smaller but significant (FCountry/Total = 0.07, P < 0.002).

Linkage Disequilibrium.

Linkage disequilibrium for all populations is significant for 46 of the 66 pairs of loci (70%), which is much higher than the 5% (about 3 loci pairs) expected by chance. After sequential Bonferroni correction, 13 pairs remain in significant linkage disequilibrium, so that each of the 12 loci is involved in at least one significant linkage. This cannot be attributed to close physical linkage between loci, as the 12 loci are distributed on different chromosomes (22). These findings indicate strong linkage at a genome-wide scale.

Discussion

Numerous studies published since 1990 suggest that Leishmania species may have a predominantly clonal mode of reproduction associated with rare sexual recombination events. The majority of these studies are, however, based on databases that may not be suitable to reach that conclusion. Clonality is mainly inferred from analyses of strong linkage disequilibria observed across loci (24). Yet, computer simulations show that linkage disequilibrium is not a reliable measure of the proportion of clonal versus sexual reproduction in a population (25) because it is too sensitive to population demographic parameters (see also refs. 26–28). Moreover, the genetic markers used (such as multilocus enzyme electrophoresis [MLEE], random amplified polymorphic DNA [RAPD], restriction fragment length polymorphism [RFLP], and pulse field gel electrophoresis [PFGE]) have inherent limitations for inferences on the population genetic structure. These molecular markers have little resolution power (MLEE or RFLP), are dominant (RAPD), or multifactorial (reflecting global genomic organization in PFGE). Thus, even if these approaches can give valuable information on the evolutionary history of Leishmania species, they do not allow definitive conclusions about the population structure and the mode of reproduction of such organisms.

Our findings reveal a strong deficiency of heterozygotes (as well as linkage disequilibrium), theoretically incompatible with a strictly clonal reproduction model. Theoretical studies have shown that diploid clones are expected to accumulate heterozygosity at every locus over time (29–33). Clonal diploids should therefore exhibit negative FIS values (17). There are several nonexclusive hypotheses that could account for heterozygote deficiency. They include the presence of null alleles, natural selection, genic conversion, the Wahlund effect, and inbreeding.

Null alleles are often encountered in population genetics studies. They may be frequent in allozymes (34, 35) and in such DNA markers as microsatellites (36–38). In our data, there is relatively little FIS variation across loci, and those loci displaying the strongest FIS variance are not necessarily those with the highest FIS (see Fig. 2), which is what would be expected if null alleles were present. Moreover, no blank has ever been observed in the genotypes (no missing data; i.e., all individuals were amplified at all loci), which, given the high FIS, makes the null allele explanation unlikely.

Selection can strongly affect allele and genotypic frequencies. Underdominance, which decreases the fitness of heterozygous individuals, would result in deficiency of heterozygous genotypes relative to Hardy–Weinberg expectations. Underdominance, however, is not expected to be frequently encountered in nature because it is highly unstable (the rarest allele tends to disappear). We have observed similar FIS patterns across all 12 (dinucleotide, noncoding) microsatellite loci. Widespread, almost genome-wide, underdominance would be required to fit our data, which does not seem reasonable.

Parasites of the Trypanosomatidae family, such as Leishmania species, are characterized by genetic plasticity, so that they can use different pathways to generate genetic diversity (e.g., gene conversion; refs. 39 and 40), a process of unidirectional transfer of genetic material between members of a multigenic family (41). Gene conversion generates a transition from the heterozygous stage to the homozygous stage, and thus can result in substantial heterozygote deficiency (40). Given that we obtained similar findings across the 12 independent microsatellite loci, gene conversion could account for our findings only if it occurred among all of the loci studied, across the entire genome, which seems unlikely. If gene conversion significantly affected microsatellite loci heterozygosity, a negative relationship would be expected between differences in allele size in heterozygous individuals and the number of such heterozygous individuals in the data set. This is because heterozygotes recently aroused through mutation have less chance of being immediately converted again compared with older heterozygotes, and with microsatellite loci in clonal organisms, old heterozygotes are expected to carry the most distant alleles in terms of size (30, 31). If gene conversion occurs frequently, microsatellite loci should restore heterozygosity through mutation and thus between alleles that are close in length. We obtained a significant negative relative relationship between the size difference in bases between alleles (Δ) and the number of heterozygous individuals, NHz, only for locus E11 (see Fig. S1 and SI Text). This locus is located 60 bp before the trifunctional enzyme alpha subunit mitochondrial precursor-like gene, and this observation might reflect frequent conversion in this genomic region. If we exclude E11 from our data, the overall high FIS values persist.

Clustering within each sample, as performed by BAPS, results in a substantial (40%) decrease in FIS values. This is consistent with the existence of a strong Wahlund effect within each subsample. Considering the large areas investigated (100–200 km2), it is not unreasonable to expect geographic subdivision within our Leishmania samples. In addition to geographic barriers that could influence parasite distribution, the biology of vectors and reservoirs may strongly interfere with the homogeneous spread of genotypes across both regions sampled. We note, for example, that the overall flight distance traveled by a sandfly over its entire lifetime is estimated to be ≈1 km (42); the scale at which our samples were collected is far above this limit. The Wahlund effect we have detected indicates that the Bolivian and Peruvian samples are probably each composed of several strongly differentiated subpopulations. This substructure may result in very large global effective population sizes, as shown by the weak temporal differentiation we observed, and thus may contribute to maintaining the genetic diversity at the scale of each geographic population. This could provide Leishmania populations with an advantage in adaptability to environmental differences and changes. The modest but significant differentiation observed between countries (most of the variance is contained within each country) reflects that there is little migration between countries, as well as between subpopulations within each country, where most of the variance occurs (see SI).

Heterozygote deficiency remains high (above 0.307) for every sample and for every locus, even within the clusters defined by BAPS. These findings, together with the high linkage disequilibrium observed, support the idea that these parasites, known to reproduce by clonal fission, also often sexually cross with individuals from the same strain (endogamy), unless our sampling did not allow us to detect a more nested population structure than is apparent. To evaluate this possibility, we studied the distribution of the heterozygous loci in each individual and found a random distribution (adjusted to a Poisson distribution, Kolmogorov–Smirnoff test). A small simulation study undertaken with Easypop v. 2.0.1 (43) suggests that our data are compatible with partially clonal populations. More especially, it seems likely to correspond to very small subpopulations, well structured at a scale much smaller than what can be detected even with clustering procedures (see Figs. S2 and S3 and SI Text).

On the basis of the previous considerations, we propose that Leishmania parasites use alternative modes of reproduction: clonality in both the vertebrate host and the insect vector and occasional sexual fusion in the vector, as has been shown to occur for other kinetoplastid parasites, such as Trypanosoma brucei s.l. (44). However, in Leishmania, this fusion may frequently involve genetically related parasites or even genetically identical members of the same strain, given the very low incidence of Leishmania parasites generally observed in the sandfly vectors (45–47).

We are not aware of any previous evidence of such strong inbreeding in Leishmania. This changes the assumption that its mode of reproduction is overwhelmingly clonal. This finding is an important step toward an understanding of leishmaniasis epidemiology. Reproductive mode influences the distribution of alleles within individuals and impacts the rate of selection of recessive or dominant alleles.

An important observation is the high overall genetic diversity observed in each sample. Published studies suggest that there is a link between the genetic polymorphism of circulating strains of Leishmania and environmental diversity (48). The large diversity observed in the present samples may be related to the extremely diversified ecosystem (various host and vector species; ref. 49) of the Amazonian forest.

To conclude this detailed population genetics study of L. braziliensis, which we believe may be the first of its kind, it seems that these parasites alternate clonal and sexual, although endogamic, reproduction, with infrequent recombination events between different individuals. In addition, our findings show the existence of strong genetic heterogeneity within each country (Wahlund effect), suggesting a substantial population structure at a microgeographic scale. In future studies, it will be important to work at finer geographic scales to detect and delimit this substructuring. The approach used here needs to be applied to other species of Leishmania to ascertain the generality of our findings. In vitro experiments could explore whether sexual recombination readily occurs within the sandfly vectors.

Materials and Methods

Parasite Culture and DNA Extraction.

One hundred twenty-four human isolates of Leishmania (Viannia) braziliensis were cultured. Promastigote cultures were maintained at 26 °C by weekly subpassages in RPMI1640 medium, buffered with 25 mM Hepes, 2 mM NaHCO3, and supplemented with 20% heat-inactivated FCS, 2 mM glutamine, 100 U/mL penicillin, and 100 μg/mL streptomycin. Cultures were harvested by centrifugation and stored at −80 °C until DNA extraction. Fifty-six strains from Peru and 68 from Bolivia were isolated in the Laboratory of Biochemistry, the Alexander Von Humbolt Institute of Tropical Medicine of the University of Lima (Peru), and at the University of Cochabamba Medical Center (Bolivia). We characterized the 124 strains as L. braziliensis using the MLEE technique as described in ref. 50. DNA was extracted from parasite mass cultures (promastigotes), using the classical phenol/chloroform extraction technique (51).

Genotyping.

The 12 microsatellite loci investigated are listed in Table 1 (see ref. 22). A 30-μL reaction mix was made of 1.2 μL of each primer (10 μM), with the forward primer being labeled with fluorochrome, 100 ng template DNA, 0.9 μL dNTP mix (5 mM), 3 μL buffer 10× and 0.3 μL Taq polymerase (5 UI/μL; Roche Diagnostics). Amplifications were carried out in a thermal cycler: 30 cycles of 94 °C for 30 s, annealing temperature of each locus for 1 min at 72 °C, final extension at 72 °C for 7 min. The reaction products were visualized on a 1.5% agarose gel stained with ethidium bromide. Fluorescence-labeled PCR products were sized on Applied Biosystems Prism 310, with a Genescan 500 LIZ internal size standard. All 124 isolates were genotyped at all 12 loci.

Statistical Analyses.

Data were analyzed with the software FSTAT (version 2.9.3.2; ref. 52), which computes estimates and tests the significance of various population genetics parameters. Genetic polymorphism was measured by the number of alleles per locus (N) and by Nei's unbiased genetic diversity within subsamples Hs (23). We estimated Wright's F statistics (53) with Weir and Cockerham's method (54): FIS measures the relative inbreeding of individuals due to the local nonrandom union of gametes in each subpopulation; FIS_C measures the relative inbreeding of individuals clustered; and FST measures the relative inbreeding in subpopulations attributable to the subdivision of the total population into subpopulations of limited size. FST thus also measures genetic differentiation between subpopulations. FIS ranges between −1 and 1. A negative value corresponds to an excess of heterozygotes, a positive value to heterozygote deficiency; 0 is expected under panmixia. FST varies between 0, when genetic identity between individuals is independent from the subpopulation (no differentiation) and 1, when all individuals of the same subpopulation are homozygous for the same allele but differ from individuals of different subpopulations. The significance of the departure from 0 was tested by 10,000 randomizations of alleles within subpopulations (for FIS) and of individuals between subpopulations (for FST). For FIS, the statistic used was Weir and Cockerham's estimator f; for FST, the statistic used was the log-likelihood ratio G (55) summed over all loci. Confidence intervals were estimated by bootstrapping over loci or jackknifing over populations with FSTAT. From the FIS parameter, a potential selfing rate s was inferred using the formula s = (2 * FIS)/(1 + FIS) (e.g., ref. 29).

Linkage disequilibrium between pairs of loci (nonrandom association of alleles at different loci) was assessed with a randomization test (genotypes at 2 loci are associated at random a number of times). The statistic used was the log likelihood ratio G summed over all subpopulations. Because this procedure was repeated on all pairs of loci, we applied the sequential Bonferroni correction (56) to the P values (P value × number of tests).

The #3.2 software identifies a hidden structure within populations through a Bayesian analysis. It clusters individuals into genetically distinguishable groups based on allele frequencies. This software was used to detect possible Wahlund effects and has been successfully applied to other parasites (16, 57). The BAPS software used stochastic optimization to infer the posterior mode of the genetic structure. To obtain the best distribution of the 4 populations under study, we ran the program many times to obtain the number of clusters. We also checked that nonstructured populations would not give the same results as ours. This was done by running BAPS on populations simulated with EASYPOP (version 2.0.1). Each of the 4 samples was submitted to a clustering exploration by BAPS with 160 runs with a maximum number of clusters set to 20. FIS was recalculated in each best distribution identified by BAPS and compared FIS_C with the initial FIS using a unilateral Wilcoxon signed-rank test for paired data, the pairing units being the 12 loci. If FIS_C is lower than FIS, it is probable that the initial subsamples were composed of several genetically distinct entities (e.g., geographical microstructure or subpopulations).

To estimate the contribution of macrogeography (between Bolivia and Peru) corrected for the effect of the subpopulation structure (between BAPS clusters), we used HIERFSTAT (version 0.03–2) software (58). This test uses the same statistics as those used for FST analyses, but the permutation procedure takes into account the hierarchy of the population structure. Differentiation between clusters within countries, FCluster/Country, is tested by randomization of individuals between clusters of the same country. FCountry/Total, the fixation index due to the distribution of clusters into different countries, is tested by randomizing clusters (including all individuals) between countries.

Supplementary Material

Supporting Information
supp_106_25_10224__index.html (652B, html)

Acknowledgments.

The authors acknowledge F. Kjellberg, F. Renaud, M. Choisy, and F. Prugnolle for helpful discussions and for their assistance in the analysis and interpretation of the results. We also thank 2 anonymous referees who considerably helped improve the manuscript. We are grateful to the Institut de Recherche pour le Développement and the Centre National de la Recherche Scientifique for financial support. The strains were isolated as part of a European Community STD3 project (n8TS3*-CT92–0129). This work was also supported in a framework of a French National Project ANR SEST.

Footnotes

The authors declare no conflict of interest.

This article contains supporting information online at www.pnas.org/cgi/content/full/0904420106/DCSupplemental.

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