Characterizing the evolutionary relationships and population structure of parasites can provide important insights into the epidemiology of human disease. We examined 142 isolates of Trypanosoma brucei from all over sub-Saharan Africa using three distinct classes of genetic markers (kinetoplast CO1 sequence, nuclear SRA gene sequence, eight nuclear microsatellites) to clarify the evolutionary history of Trypanosoma brucei rhodesiense (Tbr) and T. b. gambiense (Tbg), the causative agents of human African trypanosomosis (sleeping sickness) in sub-Saharan Africa, and to examine the relationship between Tbr and the non-human infective parasite T. b. brucei (Tbb) in eastern and southern Africa. A Bayesian phylogeny and haplotype network based on CO1 sequences confirmed the taxonomic distinctness of Tbg group 1. Limited diversity combined with a wide geographical distribution suggested that this parasite has recently and rapidly colonized hosts across its current range. The more virulent Tbg group 2 exhibited diverse origins and was more closely allied with Tbb based on COI sequence and microsatellite genotypes. Four of five COI haplotypes obtained from Tbr were shared with isolates of Tbb, suggesting a close relationship between these taxa. Bayesian clustering of microsatellite genotypes confirmed this relationship and indicated that Tbr and Tbb isolates were often more closely related to each other than they were to other members of the same subspecies. Among isolates of Tbr for which data were available, we detected just two variants of the SRA gene responsible for human infectivity. These variants exhibited distinct geographical ranges, except in Tanzania, where both types co-occurred. Here, isolates possessing distinct SRA types were associated with identical COI haplotypes, but divergent microsatellite signatures. Our data provide strong evidence that Tbr is only a phenotypic variant of Tbb; while relevant from a medical perspective, Tbr is not a reproductively isolated taxon. The wide distribution of the SRA gene across diverse trypanosome genetic backgrounds suggests that a large amount of genetic diversity is potentially available with which human-infective trypanosomes may respond to selective forces such as those exerted by drugs.
Trypanosoma brucei is a unicellular flagellated parasite restricted to sub-Saharan Africa by the distribution of its tsetse vector (Glossina spp.) [1]. It has caused periodically devastating epidemics of human sleeping sickness. In the last decade, the annual number of new cases has decreased [2], [3]; currently, the World Health Organization estimates that among the millions of people at risk across 36 countries, sleeping sickness causes approximately 50,000 deaths each year [4], [5]. However, geographically restricted outbreaks can still cause severe economic and social disruption [6], [7] and past disease cycles suggest that new epidemics could occur at any time [8]. In addition, appropriate drugs to treat the disease are still lacking [9]. Taxonomically, T. brucei is divided into three subspecies, largely based on their geographical origin, infectivity to humans and severity of disease. T. b. gambiense (Tbg) is restricted to West and Central Africa, where it causes a chronic form of sleeping sickness in humans. The Gambian form of sleeping sickness, caused by Tbg, was traditionally viewed as primarily a human infection, but it has become clear that a broad range of wild and domestic animal reservoirs also harbor the parasite [10], [11], [12]. A second human-infective subspecies, T. b. rhodesiense (Tbr), is found in eastern and southern Africa and causes an acute form of sleeping sickness. Tbr is a zoonotic disease for which non-human vertebrates are the primary reservoir. The third subspecies, T. b. brucei (Tbb), is distributed across sub-Saharan Africa, and is restricted to non-human vertebrates, in which it can cause nagana, a chronic wasting disease [13]. Over the last three decades, population genetic research has provided important insights into the biology of T. brucei and the epidemiology of sleeping sickness [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24]. But the fine scale ecological and evolutionary processes underlying disease dynamics and the distinction of the different parasite forms are still not very well understood. From a taxonomic standpoint, this previous work has clearly established that Tbg is genetically distinct from Tbr and Tbb [14], [15], [24], [25]. However, there is still a debate whether Tbg is evolutionarily older than Tbb/Tbr. As Tbg is less virulent than Tbr, there is a widespread belief that Tbg is evolutionarily older than Tbb/Tbr, based on the assumption that parasites generally evolve towards becoming more benign as they adapt to their host, an assumption that not supported by evidence [26], [27]. In addition, the evolutionary relationship between Tbr and Tbb remains enigmatic; they are morphologically indistinguishable, sympatric in large parts of eastern Africa, and differentiated solely by their capacity to infect humans. Epidemics involving Tbr tend to occur in more or less discrete foci and may involve multiple Tbr lineages [28], [29], , but sometimes, a single lineage of Tbr may clonally expand to high frequency. Consequently, when population genetic structure is characterized over a small geographical range or over a small time frame, Tbr and Tbb may appear deceptively isolated from each other [31], [32]. On the other hand, Tbr may evolve through frequent genetic exchange with sympatric Tbb, leading to a mosaic of different Tbr genotypes distributed throughout endemic regions of eastern Africa [30], [33]. Laboratory studies have demonstrated that T. brucei is capable of sexual reproduction [34] and that crosses between Tbb and Tbr can produce viable progeny [35]. The extent to which this occurs in nature is unknown, but concurrent infections with multiple T. brucei genotypes are common [36], providing ample opportunity for genetic exchange. The finding that isolates of Tbr from Uganda were more closely related to sympatric Tbb than to Tbr from Zambia supports the hypothesis that human-infective parasite may have had multiple origins in Africa [37]. Human infectivity in Tbr has been attributed to the serum resistance associated (SRA) gene [38], [39]. The SRA gene has been PCR-amplified exclusively from human-infective trypanosome stocks [40] and transfection of Tbb with the SRA gene is sufficient to confer resistance to human serum [39]. Therefore, in eastern Africa, the SRA gene has emerged as a useful marker for identifying human-infective trypanosomes in their animal reservoir [33], [40], [41]. Given the potential for recombination between Tbr and Tbb [35], the SRA gene could potentially occur on all genetic backgrounds (i. e. turning Tbb into Tbr through recombination). This would imply that the standing genetic variation and associated phenotypic variation present in all T. brucei parasites in eastern and southern Africa, could eventually occur in a human-infective form. The questions remain if T. brucei lineages exist that are incapable of hosting the SRA gene and if Tbb and Tbr are simply host-range variants. Insights into these questions will be critical for more clearly defining the pool of parasites responsible for human disease, for understanding the emergence of new disease foci, and for eventually understanding how human-infectivity interacts with the evolution of other important traits such as animal host range [42], parasite fitness [43], virulence [44], [45], [46] and drug-resistance [47]. Our goal in this study was to clarify the evolutionary history of T. brucei and to more finely resolve the relationships between Tbr and Tbb from eastern and southern Africa, explicitly accounting for the SRA status of isolates. To accomplish this, we estimated phylogenetic relationships of all three subspecies using kinetoplast (mitochondrial) DNA sequence and integrated this with estimates of population structure based on nuclear microsatellite variation. We then examined the extent to which the distribution of the two existing lineages of the SRA gene among Tbr isolates matched the phylogenetic and population genetic patterns.
We obtained 142 isolates of T. b. brucei, T. b. rhodesiense and T. b. gambiense live, lysed, or as extracted DNA from the Swiss Tropical Institute Basel (STIB, Reto Brun), University of Bristol (Wendy Gibson), CIRAD-IRD/LRCT, Montpellier (Pascal Grébaut), IRD, Montpellier (Anne Clarisse Lekane), and Yale University (Serap Aksoy) (see supplementary material, Table S1). All isolates had been expanded in mice or in axenic culture in the past. Consequently, the diversity of parasite genotypes occurring in the wild may have been reduced by artificial selection pressures while cultures were maintained in an atypical environment [48]. All isolates were isolated in previous studies in adherence with national and institutional guidelines. Trypanosome isolates from patients were collected in previous studies as part of diagnostic procedures according to local ethical guidelines and were treated anonymously. Of those isolates with known host species, 16 (11%) were originally isolated from tsetse flies, 73 (52%) were from humans, and 52 (37%) from other vertebrate hosts. The geographical origin of these isolates, which spans sub-Saharan Africa, is indicated in Figure 1. Isolates of T. b. gambiense had been previously assigned five different taxonomic labels: Tbg, Tbg group 1, Tbg group 2, Tbg “non group 1” and “Tb non-gambiense group 1”. Tbg and Tbg group 1 were considered to be synonymous here and are referred to collectively as Tbg group 1. This group, which comprises classical Tbg, is distinguished from the more virulent and genetically distinct taxon Tbg group 2, which was originally found in Ivory Coast [11], [49]. Isolates originally classified as “Tbg non group 1” or “Tb non-gambiense group 1” (ob152–ob155), for which human infectivity has not been established, were treated as Tbb. Depending on the quality of the material, DNA was extracted using either a DNA extraction kit (Qiagen) or using phenol/chloroform extraction. Partial cytochrome c oxidase subunit I (CO1) was amplified from the kinetoplast (kDNA) genome of a subset of samples using primers Max1 (5′-ccctacaacagcaccaagt) and Max2 (5′-ttcacatgggttgattatgg) designed to the CO1 open reading frame contained in the maxicircle sequence of T. b. brucei 427 (GenBank accession no. M94286) and sequenced on an ABI3730 Genetic Analyzer (Applied Biosystems Inc.). Sequences were aligned by eye with Sequencher 4. 2 (Gene Codes Corporation, Ann Arbor, MI). All isolates were also typed at eight dinucleotide microsatellite loci (TB1/8, TB2/19, TB5/2, TB6/7, TB8/11, TB9/6, TB10/5, TB11/13) using conditions described previously [50]. These loci, which are located on eight different chromosomes are not physically linked [51]. Isolates exhibiting three or more alleles at any locus were considered to harbor multiple infections [36], [50] and were excluded from this analysis. We tested samples identified as Tbb and Tbr for the presence of the SRA gene. We performed PCR detection using the primers and protocols developed by Gibson et al. [33] (primers SRA A/E) and Radwanska et al. [52] (primers SRA F/R). Products from primers SRA A/E were sequenced on an ABI3730 Genetic Analyzer. As a control to help ensure that failure to amplify SRA using either of these primer sets was not attributable to poor DNA quality, we also tested the same samples for amplification of a single-copy microsatellite (Tb 9/6, [50]). For some isolates we incorporated the results of prior typing efforts [33], [52]. We limited this analysis to Tbb and Tbr since SRA has not been detected in Tbg groups 1 and 2 [33], [52], [53]. A phylogenetic tree of kinetoplast sequences was estimated using the Bayesian approach implemented in MrBayes [54]. Plotting of the appropriate maximum likelihood (ML) distance as determined by Modeltest [55] against the uncorrected p-distance for all sample pairs revealed saturation of the third codon position between ingroup and outgroup. Therefore third codon positions and combined first and second codon positions were treated as two separate partitions. The hierarchical likelihood ratio test implemented in MrModeltest [56] identified the Hasegawa, Kishino and Yano [57] model with gamma (HKY85+G) as the most appropriate nucleotide substitution model for the data in both partitions. Phylogenetic relationships were also estimated using maximum parsimony as implemented in PAUP* [58]. Bootstrap support was estimated using 1000 replicates. Trees were rooted with available sequences of T. cruzi (GenBank accession no. DQ343646), T. vivax, and T. congolense as outgroups (the two latter sequences were produced by the Pathogens Sequencing Group at the Sanger Sequencing Centre and can be obtained from GeneDB. org). We assessed geographical and taxonomic patterns in haplotype distribution using a haplotype network constructed using the statistical parsimony approach implemented in the program TCS 1. 21 [59]. Sub-networks were created using the 99% confidence limit settings. Subsequently, sub-networks were connected to each other by relaxing the confidence limit. Divergence between subnetworks was calculated in the program DnaSP [60]. We used the individual-based Bayesian clustering approach implemented in the program STRUCTURE [61] to explore the hierarchical genetic relationships among all parasite isolates. For sexually recombining organisms, STRUCTURE estimates the proportion of each individual' s genome that is derived from one of K pre-specified populations. In the case of an often clonal organism such as T. brucei, inferred “populations” are likely to reflect the major clades of the coalescent tree and these clusters can help to describe the structure of genetic variation (J. Pritchard, pers. comm.). To identify the most likely K, we conducted 3 independent runs for each K from 1 to 16, assuming an admixture model and correlated allele frequencies. We used a burn-in of 50,000 and replication values of 250,000. We used two methods to determine the most likely number of clusters given the data. In the first, the likelihood values of each K (i. e. L (K) ) were converted into posterior probabilities as suggested by Pritchard et al. [61] to assess which number of subpopulations is most probable given the data. In the second, the greatest value of delta K, the second order of change in L (K) divided by the standard deviation of L (K) was taken as indication for the optimal K as suggested by Evanno [62]. We examined whether clusters of genetically similar individuals within the Tbb/Tbr group were more similar in geographical origin than expected by chance, given our sampling. For this analysis, individuals were assigned to the single cluster in which they exhibited the highest membership probability. We calculated a statistic that measured the sum of all differences between country of origin (same = 0, different = 1) for all pairwise comparisons among individuals within clusters. We then randomly re-assigned individuals to clusters 1000 times and calculated the same statistic for each permutation. Significance was determined by comparing the observed value to the distribution generated by random permutation. We also performed a similar analysis using date of sampling, but here the statistic was the sum of differences between years of sampling (number of years difference between two isolation events) for all pairwise comparisons among isolates within clusters. Permutations were performed in SAS v 9. 1 (SAS Institute, Cary, NC). We further evaluated the genetic differentiation between subspecies of T. brucei using principal components analysis (PCA). This method, which makes no assumptions regarding Hardy-Weinberg or linkage equilibrium, reduces the dimensionality of microsatellite data to two axes, allowing for easy visualization of relative differentiation. PCA was performed in R [63] using the package adegenet [64]. Within subspecies of T. brucei, we estimated the differentiation between temporally and geographically cohesive subgroups using DEST, an estimate of Jost' s D [65] calculated with the program smogd [66]. DEST, which varies on a scale from 0 (no differentiation) to 1 (complete differentiation), provides a less biased estimate of differentiation than FST and related statistics, particularly when estimated using highly polymorphic microsatellite loci [67].
Sequencing of CO1 yielded 812 base pairs with no gaps or stop codons. We recovered a total of 19 distinct haplotypes from the 87 T. brucei isolates sequenced (Table S2). These haplotypes exhibited sequence divergence ranging from 0. 1% (1 nucleotide substitution) to 4. 2% (34 substitutions). With the exception of the placement of Hap13, topologies recovered from Bayesian analysis and from maximum parsimony analysis were almost identical; therefore, we present only the results of the former. The 50% majority rule tree resulting from the Bayesian analysis of kinetoplast haplotypes (Figure 2) revealed one well-differentiated high-level clade (Clade A, Hap1 to Hap 12). Clade A was composed of haplotypes recovered from each of the three subspecies of T. brucei, all of which were more closely related to each other than to haplotypes Hap 13 to Hap 19, which formed clades B and C. The latter haplotypes derived from one isolate of Tbr, as well as several isolates of Tbb that had been previously assigned to the “Sindo” (Hap13) or “Kiboko” groups (Hap14 to Hap19) by kDNA typing [68] or isoenzyme analysis [15] (Table S1). Within Clade A, haplotypes were further structured, with Clade A2 exhibiting strong Bayesian and bootstrap support. Subclade A2 was composed of all three subspecies of T. brucei and contained all haplotypes recovered from Tbg. Tbg group 1 was represented by only two closely related haplotypes (Hap8 and Hap9), which differed by just one nucleotide. Hap8 was recovered from 34 out of 35 Tbg group 1 isolates. Isolates classified as Tbg group 2 were represented by three different haplotypes (Hap6, Hap10 and Hap12), each of which was also found in Tbb and one of which (Hap6) was also recovered from Tbr. A close relationship between Tbr and Tbb was supported by the fact that four out of the five haplotypes recovered from Tbr were also recovered from Tbb, and these haplotypes were distributed across the phylogeny. The structure observed in the Bayesian phylogeny was reiterated in a haplotype network (Figure 3A). Haplotype network construction resulted in three separate subnetworks, reflecting the relatively large divergence (∼3%) observed between Clades A, B and C. Clade A was composed of isolates found across all of Africa while Clade C appeared to be restricted to eastern and southern Africa (Figure 3B). Isolates of Tbb or Tbr from Kenya, Tanzania and Zambia were represented in both Clades A and C. The most commonly recovered haplotype of Tbg (Hap8) was found across most of central and western Africa and from every country in which Tbg was sampled (Figure 3B). We used microsatellites to genotype 27 isolates of Tbr, 55 isolates of Tbb and 58 isolates of Tbg collected across Africa (Figure 1B and Table S1). The L (K) values derived from STRUCTURE analysis indicated that the probability of our data was maximized by K = 11 partitions. Alternatively, ignoring the strong signal derived from the obvious division between parasites identified as Tbg group 1 and all other parasites, Evanno' s criterion (delta K) indicated that our data were most consistent with K = 5 partitions. To capture the hierarchical relationships among genotypes, Figure 4 shows the clustering results for K = 5 and 11, as well as K = 3, corresponding to the number of classically-defined subspecies presumed to be present in our sample. Nesting of clusters (from K = 11 to K = 3) reflects the hierarchical relationships among parasite genotypes. Among isolates of Tbb and Tbr, only one cluster (Cluster 2, Figure 4) exhibited strong cohesion across various levels of K. This cluster was composed exclusively of Tbb from Kenya and Tanzania, and contained all individuals of Tbb that had been previously identified as the “Kiboko B” group by isoenzyme and kDNA analysis (Table S1). These isolates also possessed a discrete group of closely related kDNA haplotypes (Hap14–Hap16) that were not shared by any isolates outside of this cluster. The relative differentiation of this group compared to other Tbb/Tbr and to Tbg is visualized in Figure 5, in which the first two axes accounted for 40% of the overall genetic variance. None of the isolates belonging to Cluster 2 tested positive for the SRA gene; however, clustering of isolates at K = 3 and K = 5, as well as the PCA analysis, identified two isolates of Tbr (ob065, ob066) that were closely related to isolates in Cluster 2. Outside of Cluster 2, Tbb and Tbr exhibited strong genetic similarity as reflected in broadly overlapping 95% ellipses in PCA analysis (Figure 5). At finer scales, clustering of genotypes indicated that in many cases, Tbb and Tbr isolates are more closely related to each other than they are to other isolates of the same subspecies. Clusters 1,3, 4,5, 6,7, 8 and 9 were each composed of isolates of both Tbb and Tbr (Figure 4) and in Cluster 8, two isolates of Tbb and Tbr differed from each other by just one allele at one locus (b179 = RUMP 503 (Tbb) and b021 = STIB 391 (Tbr) ). We detected the SRA gene in all isolates of Tbr except for isolate KETRI 2538, which had been designated as Tbr based on isolation from a human patient. Thus, the SRA gene occurred in seven of the ten genetic clusters containing at least one isolate of Tbb/Tbr from eastern Africa (Figure 4). Among the samples for which we were able to generate SRA sequence with primers A/E, we detected just two sequence variants. Across the 420 bp of sequence, SRA type 1 was identical to the sequence previously deposited under GenBank accession no. AJ345057 and SRA type 2 was identical to GenBank accession no. AJ345058. These two sequence fragments differed by just three polymorphic sites. SRA type 1 was found in 12 isolates from Uganda, Kenya and Tanzania, while SRA type 2 was found in 11 isolates from Zambia, Ethiopia and Tanzania (Table S1). In the one location where both types occurred sympatrically (Serengeti National Park, Tanzania), we detected isolates in which the two different SRA types associated with the same kDNA lineage (Hap1), but these isolates belonged to different clusters based on their microsatellite genotypes. Across our wider sampling, SRA type 1 was associated with two different kDNA lineages (Hap1 and Hap5), while SRA type 2 was associated with four different lineages (Hap1, Hap4, Hap6 and Hap19; Figure 3 and Figure 4). Permutation tests indicated that isolates of Tbb/Tbr found within the same genetic cluster were more likely to originate from the same country than expected by chance alone (p<0. 001). This relationship remained significant after excluding individuals from Cluster 2 (p<0. 001), i. e. when only those clusters were considered that contained both Tbb and Tbr. Similarly, individuals from the same genetic cluster were more likely to have been sampled within a similar time period than expected by chance (p<0. 001). This, too, remained significant after excluding individuals from Cluster 2, all of which had been isolated between 1970 and 1973 (p = 0. 009). The broad geographical and temporal scale over which samples were collected limited our ability to quantify genetic differences among populations defined by narrow sampling in time and space. Among the groups of isolates that were most cohesive, we observed strong differentiation between isolates from the “Kiboko B” cluster of Tbb (Cluster 2) sampled in Tanzania between 1970–1971 and other isolates of Tbb sampled in the same place and time (DEST = 0. 59±0. 11; Table 1). Isolates of Tbr sampled concurrently in Tanzania were similarly divergent from the “Kiboko B” cluster (DEST = 0. 53±0. 12), but exhibited lower differentiation from other Tbb (DEST = 0. 10±0. 06). The low differentiation observed between Tbr and Tbb (excluding “Kiboko B”) in Tanzania was similar to that observed between isolates of Tbr sampled 30 years apart in Uganda (DEST = 0. 08±0. 08). Across all levels of partitioning, isolates of Tbg group 1 formed a single uniform cluster in STRUCTURE analyses (Cluster 11; Figure 4). Isolates of Tbg group 1 also formed a relatively tight and distinct group of genotypes in PCA analysis (Figure 5). Only one isolate identified as Tbg group 1 (b028 = STIB 368) did not join this cluster. Within Cluster 11, genetic divergence was low between groups of isolates defined by disease focus and collection date (Table 2). The average pairwise differentiation among all foci was DEST = 0. 12. Reflecting this low level of genetic divergence among Tbg group 1, we identified just 21 multilocus genotypes among the 54 isolates sampled across central and western Africa. While most of the genotypes that were recovered more than once originated in the same or adjacent countries, two multilocus genotypes were shared between the Ivory Coast and either Equatorial Guinea or the Democratic Republic of Congo (Table S3). One of these multilocus genotypes had persisted for a period of about 18 years (1960–1978), and we identified a second multilocus genotype that had persisted for at least 22 years (1968–1990). Clustering of microsatellite genotypes from isolates identified as Tbg group 2, all originating from the Ivory Coast, indicated a close association between these parasites and isolates of Tbb and Tbr (Figure 4). At K = 11, two Tbg group 2 isolates clustered together with isolates of Tbb from Uganda, Burkina Faso and Cameroon (Cluster 10). One of these Tbg group 2 isolates (b151 = TH02) shared a kDNA haplotype (Hap6) with Tbb isolates from Uganda and Tanzania, and Tbr isolates from Tanzania and Ethiopia. The other isolate (b032 = STIB386) shared a kDNA haplotype with Tbb. The remaining isolate representing Tbg group 2 (b146 = TH113) also shared a haplotype (Hap12) with an isolate of Tbb (b152 = TSW65, isolated from a pig in the Ivory Coast) and exhibited a signal of mixed ancestry between Tbg group 1 (Cluster 11) and Tbb/Tbr (Cluster 9) based on STRUCTURE analysis (Figure 4). Assignment probabilities for this isolate exhibited 95% credible limits that excluded zero for membership in both Cluster 9 and Cluster 11 (data not shown). The results of Bayesian clustering were reflected in the PCA plot, which placed Tbg group 2 genotypes intermediate to Tbb/Tbr and Tbg group 1 (Figure 5). The two Tbb genotypes most closely related to the Tbg group 2 cluster derived from Uganda (b009) and Ivory Coast (b152).
Human infective trypanosomes from eastern Africa fall into two groups based on clinical characteristics and are characterized by two SRA variants [33], [44]. Our results generally confirm the previously observed geographical partitioning: we found SRA type 1 in Uganda, Kenya and Tanzania, and SRA type 2 in Tanzania, Zambia and Ethiopia. While prior detection of SRA type 2 had been limited to patients sampled in Zambia, Malawi and Ethiopia, we have extended the known range of SRA type 2 to wildlife reservoir hosts in northwest Tanzania. Consequently, Tanzania appears to be a rare location where both SRA types co-occur. Here, trypanosome lineages with SRA type 1 and type 2 were associated with the same kDNA haplotype but distinct microsatellite genotypes. Presuming that an opportunity for dispersal exists, the distinct SRA types may eventually be expected to co-occur elsewhere, raising the potential need for diagnostics that differentiate between these two types. If the SRA gene, which is responsible for human infectivity of Tbr [39], [71], is freely transferable across trypanosome genomes via sexual recombination, then the SRA gene should be associated with trypanosome genetic backgrounds that encompass the diversity observed in Tbb. Our results largely corroborate this scenario. We detected SRA in trypanosomes from both of the well-sampled kDNA clades and in seven of the ten genetic clusters inferred from microsatellite-based analysis that contained Tbb and/or Tbr isolates. Among Tbb/Tbr, only one cluster of isolates (Cluster 2, Figure 4) appeared to lack SRA while also exhibiting strong differentiation at microsatellite loci. These trypanosomes also possessed a unique group of kDNA haplotypes, potentially indicating that they have not exchanged genes with the other trypanosome lineages represented in our sample. Therefore, this group, containing individuals previously identified as “Kiboko B”, and isolated in the early 1970' s from Kenya and Tanzania, may represent true animal-restricted trypanosomes, i. e. Tbb [68]. However, this would be a surprising outcome given that at least one cross between the “Kiboko B” group and an unrelated trypanosome lineage (TREU927×STIB386) has been demonstrated in the laboratory [72]. Furthermore, we identified two isolates of Tbr that possessed kDNA haplotypes distinct from those possessed by the “Kiboko B” group, but exhibited nuclear genotypes very similar to the “Kiboko B” group (Figure 5). This is consistent with a recombination event between the “Kiboko B” group and an unrelated SRA-positive trypanosome lineage. Assessed more broadly, our results suggest that SRA has been gained (by recombination) or lost (e. g. by gene conversion) during multiple independent events in the past. For example, Cluster 5 and Cluster 8 are each composed of SRA-positive (type 1) and SRA-negative trypanosomes that are more closely related to each other than they are to trypanosomes in the other cluster. The same is true for Cluster 7 and Cluster 9. Previous work has revealed that human infective and animal-restricted trypanosomes from the same focus showed distinct allele sets, suggesting little recent exchange [18], [19]. On the other hand, our results, which place the results from individual foci in the context of broader geographical sampling, demonstrate that parasites sampled in a restricted time and space often consist of SRA-positive and SRA-negative individuals that may be more closely related to each other than to SRA-positive and SRA-negative parasites recovered from another time and place. In other words, human infective and animal-restricted trypanosomes represent phenotypic variation in a single structured species [73], [74]. Reconciling the apparent lack of interaction between Tbb and Tbr in a single focus with the capacity for the two to share genes will require more in depth ecological and functional molecular work. Nonetheless, the wide distribution of the SRA gene across trypanosome genotypes has important consequences for the evolution of human infectivity in Tbb/Tbr as it suggests that a large amount of genetic diversity is potentially available with which human-infective trypanosomes may eventually respond to selective pressures such as those exerted by drugs. Understanding the time-frame in which SRA can move between trypanosome groups will become particularly important as these genetic groups become better defined with respect to underlying phenotypes of importance, such as drug resistance and disease severity. High throughput next generation sequencing technologies offer the possibility of generating thousands of markers with which to more precisely circumscribe trypanosome groups. Linking these groups to important phenotypes will require large-scale field collections combined with dedicated collaborations with medical staff in disease-endemic countries. Tbg group 1 is the most common form of Tbg and is widespread across West and Central Africa. With the exception of one anomalous isolate (STIB368), which is very old (collected in 1959) and may well have been mixed up during prolonged maintenance in the lab, trypanosomes identified as Tbg group 1 formed a cohesive genetic group. Tbg group 1 genotypes formed a single cluster at all levels of K in STRUCTURE analyses, and all isolates shared just two sister haplotypes within clade A of the kDNA phylogeny. Previous studies have used microsatellites to demonstrate limited genetic diversity within the nuclear DNA of Tbg group 1 [21], [23], [70]. Our data indicate that this taxon also shows limited diversity in kinetoplast DNA sequence and that extant Tbg group 1 kDNA haplotypes fall within a well supported clade representing just a fraction of overall Tbb/Tbr diversity. These results suggest that the mechanism governing human infectivity and reproductive isolation of Tbg group 1 arose relatively recently. The low virulence in this system is thus not correlated with age of the host-parasite association, as is sometimes suggested based on the wrong assumption [26], [27] that parasites generally evolve towards being more benign as they become better adapted to the host (and vice-versa). The low extant diversity in Tbg group 1 may be attributable to a recent and extreme bottleneck. Whatever the underlying cause of the low genetic diversity, the broad distribution of the most common Tbg group 1 haplotype across central and western Africa is consistent with the rapid colonization of hosts in this region. Tbg group 2 was originally identified among patient isolates from Ivory Coast; these trypanosomes do not share the low virulence of typical Tbg isolates, show variable resistance to the trypanolytic factor in human serum [11] and they do not possess the SRA gene [33], [52], [53]. Identical isolates were recovered from wild and domestic animals in Ivory Coast and Burkina Faso [11]. Relatively few isolates of this type have been recovered, but they have been reported to be genetically heterogeneous [70], distinct from Tbg group 1 [11], [75], and closely related to Tbb [17], [76]. In our analysis, kDNA haplotypes obtained from Tbg group 2 were distinct from haplotypes possessed by isolates of Tbg group 1 but fell within a single clade representing all three T. brucei subspecies. Each of the three haplotypes possessed by Tbg group 2 were shared with isolates classified as Tbb/Tbr. Clustering of microsatellite genotypes at K = 3 and K = 5 also supported a close ancestry between Tbg group 2 and Tbb or Tbr. At K = 11 in STRUCTURE analysis, two isolates of Tbg group 2 formed a discrete cluster with five isolates of Tbb. The remaining isolate exhibited approximately equal probability of membership in Tbg group 1 and Tbb/Tbr Cluster 9, supporting a hybrid origin for some members of Tbg group 2. Although many of the associations above point to close relationships between isolates of Tbg group 2 from western Africa and isolates of Tbb or Tbr originating in eastern Africa, these results are likely biased by a lack of sampling of Tbb in central and western Africa. Future sampling and genotyping of Tbb in these regions should help to resolve the evolutionary origins of human infectivity in the gambiense group of trypanosomes.
Trypanosoma brucei, the parasite causing human African trypanosomiasis (sleeping sickness) across sub-Saharan Africa is traditionally split into three subspecies: T. b. gambiense (Tbg), causing a chronic form of human disease in West and Central Africa; T. b. rhodesiense (Tbr), causing an acute form of human disease in East and Southern Africa; and T. b. brucei (Tbb), which is restricted to animals. Tbg is further split into Tbg group 1 and Tbg group 2. Better understanding the evolutionary relationships between these groups may help to shed light on the epidemiology of sleeping sickness. Here, we used three different types of genetic markers to investigate the phylogeographic relationships among the four groups across a large portion of their range. Our results confirm the distinctiveness of Tbg group 1 while highlighting the extremely close relationships among the other three taxa. In particular, Tbg group 2 was closely related to Tbb, while Tbr appeared to be a variant of Tbb, differing only in its phenotype of human infectivity. The wide geographic distribution of the gene conferring human infectivity (SRA) and the fact that it is readily exchanged among lineages of T. brucei in eastern Africa suggests that human-infective trypanosomes have access to an extensive gene pool with which to respond to selective pressures such as drugs.
ecology/evolutionary ecology
public health and epidemiology/epidemiology
public health and epidemiology/infectious diseases
infectious diseases/tropical and travel-associated diseases
genetics and genomics/population genetics
Phylogeography and Taxonomy of Trypanosoma brucei