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Spatio-temporal Genetic Structuring of Leishmania major in Tunisia by Microsatellite Analysis.

Harrabi M, Bettaieb J, Ghawar W, Toumi A, Zaâtour A, Yazidi R, Chaâbane S, Chalghaf B, Hide M, Bañuls AL, Ben Salah A - PLoS Negl Trop Dis (2015)

Bottom Line: The genetic patterns of 2008-2012 isolates from the three governorates indicate that L. major populations did not spread gradually from the south to the center of Tunisia, according to a geographical gradient, suggesting that human activities might be the source of the disease expansion.Human activities as well as reservoir dynamics and the effects of environmental changes could explain how the disease progresses.This study provides new insights into the evolution and spread of L. major in Tunisia that might improve our understanding of the parasite flow between geographically and temporally distinct populations.

View Article: PubMed Central - PubMed

Affiliation: Institut Pasteur, Tunis, Tunisia; Faculté des Sciences de Bizerte-Université de Carthage, Tunis, Tunisia.

ABSTRACT
In Tunisia, cases of zoonotic cutaneous leishmaniasis caused by Leishmania major are increasing and spreading from the south-west to new areas in the center. To improve the current knowledge on L. major evolution and population dynamics, we performed multi-locus microsatellite typing of human isolates from Tunisian governorates where the disease is endemic (Gafsa, Kairouan and Sidi Bouzid governorates) and collected during two periods: 1991-1992 and 2008-2012. Analysis (F-statistics and Bayesian model-based approach) of the genotyping results of isolates collected in Sidi Bouzid in 1991-1992 and 2008-2012 shows that, over two decades, in the same area, Leishmania parasites evolved by generating genetically differentiated populations. The genetic patterns of 2008-2012 isolates from the three governorates indicate that L. major populations did not spread gradually from the south to the center of Tunisia, according to a geographical gradient, suggesting that human activities might be the source of the disease expansion. The genotype analysis also suggests previous (Bayesian model-based approach) and current (F-statistics) flows of genotypes between governorates and districts. Human activities as well as reservoir dynamics and the effects of environmental changes could explain how the disease progresses. This study provides new insights into the evolution and spread of L. major in Tunisia that might improve our understanding of the parasite flow between geographically and temporally distinct populations.

No MeSH data available.


Related in: MedlinePlus

Estimated population structure of L. major in Tunisia as inferred by the STRUCTURE software on the basis of the data on 10 microsatellite markers obtained for 137 recent isolates from the Gafsa (GF2008-2012; n = 66), Kairouan (KR2008-2012; n = 46) and Sidi Bouzid (SBZ2008-2012; n = 25) governorates.(A) Plot of the mean posterior probability (LnP(D)) values per clusters (K), based on 10 replicates per K, generated by the STRUCTURE software, and (B) delta-K analysis of LnP(K). (C) STRUCTURE plots grouped by Q-matrix (estimated membership coefficient for each sample) showing the distribution of genetic variation (C.1) at K = 2 and (C.2) at K = 4. Each strain is represented by a vertical line, which is partitioned into the colored segments that represent the parasite estimated membership fractions in K. The same color indicates that the isolates belong to the same group. Different colors for the same isolate indicate the percentage of the genotype shared with each group. Gene flow between populations is indicated with arrows.
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pntd.0004017.g002: Estimated population structure of L. major in Tunisia as inferred by the STRUCTURE software on the basis of the data on 10 microsatellite markers obtained for 137 recent isolates from the Gafsa (GF2008-2012; n = 66), Kairouan (KR2008-2012; n = 46) and Sidi Bouzid (SBZ2008-2012; n = 25) governorates.(A) Plot of the mean posterior probability (LnP(D)) values per clusters (K), based on 10 replicates per K, generated by the STRUCTURE software, and (B) delta-K analysis of LnP(K). (C) STRUCTURE plots grouped by Q-matrix (estimated membership coefficient for each sample) showing the distribution of genetic variation (C.1) at K = 2 and (C.2) at K = 4. Each strain is represented by a vertical line, which is partitioned into the colored segments that represent the parasite estimated membership fractions in K. The same color indicates that the isolates belong to the same group. Different colors for the same isolate indicate the percentage of the genotype shared with each group. Gene flow between populations is indicated with arrows.

Mentions: The Bayesian model-based clustering analysis implemented in STRUCTURE indicated that our dataset (GF2008-2012, KR2008-2012 and SBZ2008-2012 isolates) could be organized in four (maximum L(K)) or two (maximum ΔK) clusters (K) (Fig 2A and 2B). For K = 2, 92% of L. major isolates from Gafsa, 10% from Kairouan and 38% from Sidi Bouzid were included in one of the inferred clusters and the remaining samples in the second one (Fig 2C.1). Based on the Q-matrix bar plots obtained for each isolate by calculating the posterior probabilities of belonging to each K cluster, the SBZ2008-2012 population showed a mixed membership to the inferred clusters (Fig 2C.1). For K = 4, the Q-matrix bar plots showed an increased separation of the L. major sample substructure. Although the most likely number of groups here suggested a total of four populations no strains were fully assigned to the fourth putative group (yellow group) (Fig 2C.2), suggesting that this was not a valid population for this set [34,35]. This “phantom” population suggesting a wider and deeper clinical sample collection may discover new diversity even in this small geographic area [36]. Based on the bar plots for the two assumptions, K = 2 seems to be the most probable partition for our data set. In agreement with the very low genetic differentiation between governorates, the analysis carried out with the STRUCTURE program did not divide the three populations according to their geographical origin. Nevertheless, most isolates from Gafsa and from Kairouan were grouped in cluster 1 and cluster 2, respectively, and the samples from Sidi Bouzid were distributed in the two clusters (38% in cluster 1 and 62% in cluster 2). Strains showing mixed membership were observed in each population, probably due to the low level of differentiation among populations. The occurrence of gene flow between GF2008-2012 and SBZ2008-2012, as well as between SBZ2008-2012 and KR2008-2012 can be clearly observed in Fig 2C.1. In the STRUCTURE assignment tests, we only reported the results for MIGPRIOR = 0.1, because migration appeared to occur frequently between governorates, thus the optimal MIGPRIOR value was likely to be the highest one [32]. Furthermore, when running the migration model at K = 3 (equal to the number of predefined populations), ten samples were identified as migrants. Among these migrants, two GF2008-2012 samples were assigned to the KR2008-2012 population and one GF2008-2012 sample to the SBZ2008-2012 group. Among the KR2008-2012 samples, only two showed a posterior probability of having recently migrated from Sidi Bouzid. In the SBZ2008-2012 group, five samples were assigned to both Gafsa and Kairouan. These results show a recent connectivity between localities. According to the Q-values of the samples, the KR2008-2012 and SBZ2008-2012 populations seemed to be more inter-connected than the GF2008-2012 population with either SBZ2008-2012 or KR2008-2012.


Spatio-temporal Genetic Structuring of Leishmania major in Tunisia by Microsatellite Analysis.

Harrabi M, Bettaieb J, Ghawar W, Toumi A, Zaâtour A, Yazidi R, Chaâbane S, Chalghaf B, Hide M, Bañuls AL, Ben Salah A - PLoS Negl Trop Dis (2015)

Estimated population structure of L. major in Tunisia as inferred by the STRUCTURE software on the basis of the data on 10 microsatellite markers obtained for 137 recent isolates from the Gafsa (GF2008-2012; n = 66), Kairouan (KR2008-2012; n = 46) and Sidi Bouzid (SBZ2008-2012; n = 25) governorates.(A) Plot of the mean posterior probability (LnP(D)) values per clusters (K), based on 10 replicates per K, generated by the STRUCTURE software, and (B) delta-K analysis of LnP(K). (C) STRUCTURE plots grouped by Q-matrix (estimated membership coefficient for each sample) showing the distribution of genetic variation (C.1) at K = 2 and (C.2) at K = 4. Each strain is represented by a vertical line, which is partitioned into the colored segments that represent the parasite estimated membership fractions in K. The same color indicates that the isolates belong to the same group. Different colors for the same isolate indicate the percentage of the genotype shared with each group. Gene flow between populations is indicated with arrows.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4547700&req=5

pntd.0004017.g002: Estimated population structure of L. major in Tunisia as inferred by the STRUCTURE software on the basis of the data on 10 microsatellite markers obtained for 137 recent isolates from the Gafsa (GF2008-2012; n = 66), Kairouan (KR2008-2012; n = 46) and Sidi Bouzid (SBZ2008-2012; n = 25) governorates.(A) Plot of the mean posterior probability (LnP(D)) values per clusters (K), based on 10 replicates per K, generated by the STRUCTURE software, and (B) delta-K analysis of LnP(K). (C) STRUCTURE plots grouped by Q-matrix (estimated membership coefficient for each sample) showing the distribution of genetic variation (C.1) at K = 2 and (C.2) at K = 4. Each strain is represented by a vertical line, which is partitioned into the colored segments that represent the parasite estimated membership fractions in K. The same color indicates that the isolates belong to the same group. Different colors for the same isolate indicate the percentage of the genotype shared with each group. Gene flow between populations is indicated with arrows.
Mentions: The Bayesian model-based clustering analysis implemented in STRUCTURE indicated that our dataset (GF2008-2012, KR2008-2012 and SBZ2008-2012 isolates) could be organized in four (maximum L(K)) or two (maximum ΔK) clusters (K) (Fig 2A and 2B). For K = 2, 92% of L. major isolates from Gafsa, 10% from Kairouan and 38% from Sidi Bouzid were included in one of the inferred clusters and the remaining samples in the second one (Fig 2C.1). Based on the Q-matrix bar plots obtained for each isolate by calculating the posterior probabilities of belonging to each K cluster, the SBZ2008-2012 population showed a mixed membership to the inferred clusters (Fig 2C.1). For K = 4, the Q-matrix bar plots showed an increased separation of the L. major sample substructure. Although the most likely number of groups here suggested a total of four populations no strains were fully assigned to the fourth putative group (yellow group) (Fig 2C.2), suggesting that this was not a valid population for this set [34,35]. This “phantom” population suggesting a wider and deeper clinical sample collection may discover new diversity even in this small geographic area [36]. Based on the bar plots for the two assumptions, K = 2 seems to be the most probable partition for our data set. In agreement with the very low genetic differentiation between governorates, the analysis carried out with the STRUCTURE program did not divide the three populations according to their geographical origin. Nevertheless, most isolates from Gafsa and from Kairouan were grouped in cluster 1 and cluster 2, respectively, and the samples from Sidi Bouzid were distributed in the two clusters (38% in cluster 1 and 62% in cluster 2). Strains showing mixed membership were observed in each population, probably due to the low level of differentiation among populations. The occurrence of gene flow between GF2008-2012 and SBZ2008-2012, as well as between SBZ2008-2012 and KR2008-2012 can be clearly observed in Fig 2C.1. In the STRUCTURE assignment tests, we only reported the results for MIGPRIOR = 0.1, because migration appeared to occur frequently between governorates, thus the optimal MIGPRIOR value was likely to be the highest one [32]. Furthermore, when running the migration model at K = 3 (equal to the number of predefined populations), ten samples were identified as migrants. Among these migrants, two GF2008-2012 samples were assigned to the KR2008-2012 population and one GF2008-2012 sample to the SBZ2008-2012 group. Among the KR2008-2012 samples, only two showed a posterior probability of having recently migrated from Sidi Bouzid. In the SBZ2008-2012 group, five samples were assigned to both Gafsa and Kairouan. These results show a recent connectivity between localities. According to the Q-values of the samples, the KR2008-2012 and SBZ2008-2012 populations seemed to be more inter-connected than the GF2008-2012 population with either SBZ2008-2012 or KR2008-2012.

Bottom Line: The genetic patterns of 2008-2012 isolates from the three governorates indicate that L. major populations did not spread gradually from the south to the center of Tunisia, according to a geographical gradient, suggesting that human activities might be the source of the disease expansion.Human activities as well as reservoir dynamics and the effects of environmental changes could explain how the disease progresses.This study provides new insights into the evolution and spread of L. major in Tunisia that might improve our understanding of the parasite flow between geographically and temporally distinct populations.

View Article: PubMed Central - PubMed

Affiliation: Institut Pasteur, Tunis, Tunisia; Faculté des Sciences de Bizerte-Université de Carthage, Tunis, Tunisia.

ABSTRACT
In Tunisia, cases of zoonotic cutaneous leishmaniasis caused by Leishmania major are increasing and spreading from the south-west to new areas in the center. To improve the current knowledge on L. major evolution and population dynamics, we performed multi-locus microsatellite typing of human isolates from Tunisian governorates where the disease is endemic (Gafsa, Kairouan and Sidi Bouzid governorates) and collected during two periods: 1991-1992 and 2008-2012. Analysis (F-statistics and Bayesian model-based approach) of the genotyping results of isolates collected in Sidi Bouzid in 1991-1992 and 2008-2012 shows that, over two decades, in the same area, Leishmania parasites evolved by generating genetically differentiated populations. The genetic patterns of 2008-2012 isolates from the three governorates indicate that L. major populations did not spread gradually from the south to the center of Tunisia, according to a geographical gradient, suggesting that human activities might be the source of the disease expansion. The genotype analysis also suggests previous (Bayesian model-based approach) and current (F-statistics) flows of genotypes between governorates and districts. Human activities as well as reservoir dynamics and the effects of environmental changes could explain how the disease progresses. This study provides new insights into the evolution and spread of L. major in Tunisia that might improve our understanding of the parasite flow between geographically and temporally distinct populations.

No MeSH data available.


Related in: MedlinePlus