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Research Article

Multilocus Sequence Typing (MLST) for Lineage Assignment and High Resolution Diversity Studies in Trypanosoma cruzi

  • Matthew Yeo mail,

    Matthew.yeo@lshtm.ac.uk

    Affiliation: London School of Hygiene and Tropical Medicine, London, United Kingdom

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  • Isabel L. Mauricio,

    Affiliation: Instituto de Higiene e Medicina Tropical, Lisboa, Portugal

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  • Louisa A. Messenger,

    Affiliation: London School of Hygiene and Tropical Medicine, London, United Kingdom

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  • Michael D. Lewis,

    Affiliation: London School of Hygiene and Tropical Medicine, London, United Kingdom

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  • Martin S. Llewellyn,

    Affiliation: London School of Hygiene and Tropical Medicine, London, United Kingdom

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  • Nidia Acosta,

    Affiliations: London School of Hygiene and Tropical Medicine, London, United Kingdom, Departamento de Medicina Tropical, Instituto de Investigaciones en Ciencias de la Salud, Universidad Nacional de Asuncion, Asuncion, Paraguay

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  • Tapan Bhattacharyya,

    Affiliation: London School of Hygiene and Tropical Medicine, London, United Kingdom

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  • Patricio Diosque,

    Affiliation: Unidad de Epidemiología Molecular (UEM), Instituto de Patología Experimental, Universidad Nacional de Salta, Salta, Argentina

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  • Hernan J. Carrasco,

    Affiliation: Instituto de Medicina Tropical, Universidad Central de Venezuela, Los Chaguaramos, Caracas, Venezuela

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  • Michael A. Miles

    Affiliation: London School of Hygiene and Tropical Medicine, London, United Kingdom

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  • Published: June 21, 2011
  • DOI: 10.1371/journal.pntd.0001049

Abstract

Background

Multilocus sequence typing (MLST) is a powerful and highly discriminatory method for analysing pathogen population structure and epidemiology. Trypanosoma cruzi, the protozoan agent of American trypanosomiasis (Chagas disease), has remarkable genetic and ecological diversity. A standardised MLST protocol that is suitable for assignment of T. cruzi isolates to genetic lineage and for higher resolution diversity studies has not been developed.

Methodology/Principal Findings

We have sequenced and diplotyped nine single copy housekeeping genes and assessed their value as part of a systematic MLST scheme for T. cruzi. A minimum panel of four MLST targets (Met-III, RB19, TcGPXII, and DHFR-TS) was shown to provide unambiguous assignment of isolates to the six known T. cruzi lineages (Discrete Typing Units, DTUs TcI-TcVI). In addition, we recommend six MLST targets (Met-II, Met-III, RB19, TcMPX, DHFR-TS, and TR) for more in depth diversity studies on the basis that diploid sequence typing (DST) with this expanded panel distinguished 38 out of 39 reference isolates. Phylogenetic analysis implies a subdivision between North and South American TcIV isolates. Single Nucleotide Polymorphism (SNP) data revealed high levels of heterozygosity among DTUs TcI, TcIII, TcIV and, for three targets, putative corresponding homozygous and heterozygous loci within DTUs TcI and TcIII. Furthermore, individual gene trees gave incongruent topologies at inter- and intra-DTU levels, inconsistent with a model of strict clonality.

Conclusions/Significance

We demonstrate the value of systematic MLST diplotyping for describing inter-DTU relationships and for higher resolution diversity studies of T. cruzi, including presence of recombination events. The high levels of heterozygosity will facilitate future population genetics analysis based on MLST haplotypes.

Author Summary

The single-celled parasite Trypanosoma cruzi occurs in animals and insect vectors in the Americas. When transmitted to humans it causes a major public health problem, Chagas disease (American trypanosomiasis). T. cruzi is genetically diverse and currently split into six groups, known as TcI to TcVI. Multilocus sequence typing (MLST) is a method used for studying the population structure and diversity of pathogens. MLST involves sequencing the DNA of several different genes and comparing the sequences between isolates. MLST has not yet been developed and systematically applied to T. cruzi. He, we sequence nine T. cruzi genes, selecting a panel of four for lineage assignment and six for higher resolution studies of genetic diversity. Our results showed that one of the T. cruzi genetic groups is further subdivided into North and South American subpopulations. Furthermore, comparative analyses of the gene sequences gave new evidence of genetic exchange in T. cruzi. Application of MLST for assigning field isolates of T. cruzi to genetic groups and for detailed investigation of diversity provides a valuable approach to understanding the taxonomy, population structure, genetics, ecology and epidemiology of this important human pathogen.

Introduction

Trypanosoma cruzi is the causative agent of Chagas disease and the most important parasitic infection in Latin America. Approximately 8 million people are thought to be infected [1]. Chagas disease is a zoonosis: T. cruzi infects many mammal species [2] and is transmitted to humans primarily by the infected faeces of haematophagous triatomine bugs coming into contact with mucosal membranes or broken skin. Transmission may also be by blood transfusion, congenitally or, rarely, by ingestion of food contaminated by infected triatomine faeces [3].

T. cruzi is monophyletic but genetically heterogeneous with at least six phylogenetic lineages (discrete typing units, DTUs) previously designated TcI, and TcIIa-e. A recent review meeting on T. cruzi intraspecific nomenclature reached an international consensus that these six DTUs should be renamed (former nomenclature in parenthesis): TcI (TcI), TcII (TcIIb), TcIII (TcIIc), TcIV (TcIIa), TcV (TcIId) and TcVI (TcIIe), to remove the presumptive sublineage designations within TcII [4]. Here we will adopt this new consensus nomenclature.

TcI and TcII are the most genetically distant groups. The evolutionary ancestry of TcIII and TcIV is presently a debated issue. Based on sequencing of individual nuclear genes Westernberger et al. [5] suggested an ancient hybridisation event occurred between TcI and TcII followed by a long period of clonal propagation leading to the extant DTUs TcIII and TcIV. Alternatively, de Freitas et al. [6] suggested that TcIII and TcIV have a separate evolutionary ancestry with mitochondrial sequences that are similar to each other but distinct from both TcI and TcII. Less controversially it is clear, using an array of molecular markers [6], [7], [8] that TcV and TcVI are hybrid lineages sharing haplotypes from both TcII and TcIII, with both DTUs retaining the mitochondrial genome of TcIII. Production of experimental hybrids [9] proved that T. cruzi has an extant capacity for genetic exchange (at least within TcI) but the mechanism of recombination is not fully understood, appearing to involve nuclear fusion followed by genome erosion leading to relatively stable aneuploid hybrids, a process distinct from classical meiosis [10].

There is currently no consensual discriminatory typing method applicable to T. cruzi. Previous methods for characterisation have included multilocus enzyme electrophoresis [11], [12], [13], [14], PCR amplification of single gene loci, [15] and various PCR based assays [16], [17]. Lewis et al. [18] recommended the use of a triple-assay comprising SSU rDNA, HSP60 and GPI markers, allowing reliable and low cost typing to DTU level. Microsatellite typing (MLMT) provides a high resolution method for fine scale population genetics analysis [19]. What is still lacking is an unequivocally reproducible and standardised method that can simultaneously distinguish the known genetic lineages, describe inter-DTU relationships, and define high resolution intra-DTU diversity for population genetics studies.

Multilocus sequence typing (MLST) has been applied to a variety of bacterial [20], [21] and yeast species [22], [23] and typically involves sequencing internal fragments of six single copy housekeeping genes per strain [24]. The method is potentially highly discriminatory, when sequence polymorphisms within each housekeeping locus are categorised as distinct alleles. Sequence data for all loci are usually concatenated to produce a unique allelic profile (sequence type, ST). A major advantage of MLST analysis is that sound sequence data are unambiguous and suitable for population structure and epidemiological studies. Results are particularly relevant if easily accessible international databases are constructed such as MLST.net [25] which contains MLST typing schemes and data for a growing number of pathogens.

MLST was originally designed for haploid species, T. cruzi is diploid organism and as such heterozygosity renders MLST analysis more complicated. Heterozygosity from electropherograms can be inferred by a double peak (with two bases) at the same variable bi-allelic site [23]. One consequence of multiple bi-alleic sites is that of ambiguous allelic phase within loci and also ambiguous combinations of alleles across separate loci. However, it is possible for diploid sequence data, (without phase resolution) to be modified, concatenated across multiple loci [26] and applied in distance based phylogenetic methods for lineage assignment (see Materials and Methods). From these data one can also infer evolutionary relatedness, and detect gene mosaics, within or between homozygous gene loci [27]. A MLST approach specific to T. cruzi was first utilised by Machado and Ayala using two nuclear loci [8] in their study of the genetic recombination in natural T. cruzi populations and this is now being expanded by others to additional targets [28]. However the use of multicopy target genes is not recommended due to the possibility of non-identical paralogous copies confusing phylogenetic signals. The repetitive nature of the T. cruzi genome has previously hampered the search for suitable single copy targets; at least 50% of the T. cruzi genome consists of tandemly repeated genes [29]. However, the recent publication of a draft chromosomal level assembly by Weatherly et al. [30] has enabled both the determination of copy number and chromosomal distribution of markers to be established with reasonable confidence. A standardised panel of suitable MLST gene loci has yet to be developed for T. cruzi. Here we address this omission by sequencing and assessing 9 nuclear targets and evaluating them with a cohort of reference strains representing the known lineages. We demonstrate the potential of this formalised MLST for describing lineage assignment, describing inter-DTU relationships and for high resolution population genetic analysis of T. cruzi.

Materials and Methods

Isolates

A panel of 39 individual isolates (Table 1) was assembled, consisting of cloned reference strains encompassing all of the known DTUs (Table 1) spanning wide geographical and varied ecological origins. Isolates were previously characterised to DTU level by amplified fragment length polymorphisms (RFLP) in the D7 divergent domain of the 24Sα rRNA, and restriction fragment length polymorphism (RFLP) in the heat shock protein 60 (HSP60) and GPI genes [18]. Parasites were cultivated in supplemented RPMI liquid medium at 28°C, as described previously [31]. Genomic DNA was prepared from logarithmic phase cultures using Dneasy kits (Qiagen, UK).

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Table 1. Cohort of reference clonal isolates representing the six known T. cruzi lineages (DTUs).

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Choice of loci

Initially, 11 genes were investigated. This number was subsequently reduced to 9 single copy targets after screening for reliability of PCR amplification. The excluded genes were trypanothione-dependent glyoxalase I (gene ID, Tc00.1047053510659.240)and cyclophilin (gene ID, Tc00.1047053510947.50). Targets were verified by PCR amplification and sequences submitted for BLAST (blastn) analyses hosted at NCBI. Copy number of targets was verified by submission of gene IDs to TriTyrpDB 2.2 (http://TriTrypDB.org). The 9 single copy gene fragments amplified for MLST analysis were ascorbate-dependent haemoperoxidase (TcAPX), dihydrofolate reductase-thymidylate synthase (DHFR-TS), glutathione-dependent peroxidase II (TcGPXII), mitochondrial peroxidase (TcMPX), trypanothione reductase (TR), RNA-binding protein-19 (RB19), metacyclin-II (Met-II), metacyclin-III (Met-III) and LYT1. The ratio of nonsynonymous to synonymous amino acid changes (dN/dS) was calculated according to the Nei-Gojobori method [32] using SNAP software available at http://www.hiv.lanl.gov, [33] to infer relative selection pressures. Genes possessing a dN/dS ratio <1 meet the criteria for stabilising selection for the conservation of metabolic function. Taylor & Fisher [34] recommended the incorporation of some loci with a dN/dS ratio of >1 in order to obtain sufficient sequence diversity.

PCR amplification

Primers and annealing temperatures for PCR amplification are given in Table 2. For DHFR-TS and TR, cyclic amplifications were performed with an initial denaturation step for three minutes at 94°C, followed by 30 amplification cycles (94°C for 30 seconds, 58°C for 1 minute, 72°C for 2 minutes). Annealing temperatures were 55°C for TcAPX, 50°C for TcMPX and 62°C TcGPXII. Reaction conditions for Met-II, and RB19 were as follows: 3 minutes at 94°C, followed by 30 amplification cycles (94°C for 30 seconds, 53°C for 30 seconds, 72°C for 45 seconds; annealing temperatures were 51°C for Met-III and 56°C for LYT1). All reactions had a final ten minute elongation step at 72°C. Each 20 µl total reaction volume contained: 125 ng genomic DNA, 1 µl of each primer (20 pmol/µl), 2 µl dNTPs (2 mM), 0.8 µl (50 mM) MgCl2 and 5U Taq (BIO-21086, Bioline, UK).

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Table 2. Details of gene targets.

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PCR products were purified with QIAQuick PCR purification kits (Qiagen) or SureClean (Bioline, UK). Bi-directional sequencing was performed with Big Dye Terminator Cycle Sequencing V3.1 (Applied Biosystems) in ABI PRISM 377 DNA Sequencers (Applied Biosystems) according to the manufacturer's protocol. Gene fragments were sequenced in both directions (5′ and 3′) with the PCR primers described in Table 2. Additionally, in the case of LYT1, Met-II, Met-III, DHFR-TS and TR internal primers were used to obtain full sequence coverage. Sequence data were assembled manually in BioEdit v7.0.9.0 sequence alignment editor software (Ibis Biosciences, USA) and ambiguous peripheral regions of aligned sequences discarded to produce unambiguous partial gene sequences for each isolate. Chromatograms were examined visually in both directions and in most instances the results easily interpreted as heterozygous when two peaks in a chromatogram overlap. Re-sequencing was undertaken if results were ambiguous.

Strain differentiation by diploid sequence typing

The number of sequence types (STs) for each gene fragment was identified from SNP data across the panel of isolates and the discriminatory power (DP, genotypes recovered per unique isolate tested) for each gene determined. STs were conjoined across gene fragments in order to identify a diploid sequence type (DST) and to assess overall discriminatory power (DP). DSTs were applied to eBURST software to infer evolutionary relationships and founders. The eBURST algorithm (http://eburst.mlst.net) identifies related sequences and predicts a founding genotype (based on the most overrepresented genotype) with variants identified depending on the number of different loci.

Phylogenetic analysis was performed on modified sequences using the methodology described by Tavanti et al. [26] which renders diplotypic data suitable for use in MEGA analysis [35] to produce distance based dendrograms. Briefly, variable loci between isolates can be considered either homozygous or heterozygous, assuming that T. cruzi is minimally diploid. For example, a homozygous variable locus scored as A (adenine) was modified by duplication to AA, and a heterozygous locus, for example Y (C or T, in accordance with IUPAC nomenclature), scored as CT, effectively creating a difference matrix across the panel. Phylogenetically the data were examined in two different ways. Firstly sequences were examined at the level of individual genes by generating neighbor-joining trees (MEGA v4.0.2) in order to assess the “usefulness” of gene fragments in the context of lineage assignment, intralineage resolution and associated bootstrap values. Secondly, sequence data were concatenated across multiple gene fragments to produce MLST neighbor-joining trees.

Analysis of recombination at the level of individual genes was applied to isolates with unambiguous phase applied through the software package RDP3 [36] incorporating the following methods: RDP [37], Bootscanning [38], GENECONV [39], Maximum Chi Square method [40], [41], the Chimaera method [40], the Sister Scanning Method [42], the 3SEQ method [43], the Reticulate compatibility matrix method [44] and the TOPAL DSS method [45], [46],

Results

Chromosomal localisation of markers

Gene IDs were confirmed by BLAST searches and submitted to the TriTypDB 1.3 (http://TriTrypDB.org) to identify chromosomal location. Locations for each of the the genes under study are shown in Table 2. Two pairs of genes were physically linked: genes LYT1 (367579–369237) and TcMPX (P:120685–121365) on chromosome 22, and MET-III (P:945156–945731) and TcAPX (P:1142201–1143187) on chromosome 36. The remaining genes were on independent chromosomes. The ratios of nonsynonymous to synonymous amino acid substitutions, resulting from sequence polymorphisms, were 1.0 or less for 7 genes, two genes possessed dN/dS ratios of above 1 (Table 3).

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Table 3. Properties of nine T. cruzi MLST targets.

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Nucleotide polymorphisms and amino acid changes

The 304 nucleotide polymorphisms among the nine sequenced fragments resulted in 121 nonsynonymous changes in amino acids encoded by sequence-variable triplets. Up to three different amino acids were present in different isolates at the same locus across the panel. A deletion of 3 nucleotides (AAA = Lysine at position 179–181) was present in C8, X10/1 and SAXP18 (TcI) within LYT1. Within TcMPX, a single non-synonymous nucleotide polymorphism resulted in a conservative amino acid change between an aspartic acid residue and a glutamic acid residue was evident (position 21). All TcV representatives possessed glutamic acid but all other panel isolates (including TcVI) possessed aspartic acid residues at the corresponding position.

Discriminatory power of MLST targets by diploid sequence typing

Diploid sequence typing using 9 genes was able to discriminate 38 of 39 isolates (DP 0.97, Table 4). Importantly, using only 6 genes (Met-II, Met-III, RB19, TcMPX, DHFR-TS and TR) the same number of DSTs could be identified. Only two isolates, Sc43 and Vinch101, shared a DST (29). Both are positioned within the DTU TcV which is known to possess a particularly homogenous population structure by microsatellite analysis [10]. All other isolates, notably even those those within the relatively homogenous clade TcVI, produced a unique isolate specific DST. Discriminatory power was determined for each of the 9 gene fragments (Table 4), in decreasing power, as follows: Met-II, Met-III and RB19 (0.59), LYT1 (0.56), TR (0.54), DHFR-TS and TcGPXII (0.49), TcMPX (0.41), and TcAPX (0.38). No single gene was able to distinguish all 39 reference strains. Met-II, Met-III and RB19, taken individually, were the most discriminatory genes, all three identifying 24 separate genotypes from the panel of 39. TcAPX was the least resolutive marker distinguishing 15 of 39 isolates (DP 0.38). Table 4 shows in detail the number of STs (sequence types) that each individual gene fragment resolved, and also the derived DSTs obtained from the concatenation of all 9 STs for each isolate. Table 4 also indicates the reduced panel of 6 genes required to obtain the same 38 DSTs. eBURST analysis of the genotypes and DSTs for 39 T. cruzi isolates from the panel revealed one cluster of 7 DSTs within TcV as the only related set to emerge from this analysis. Vinch101 (Limari, Chile) and Sc43 (Santa Cruz, Bolivia), both DST 29 (Table 4) are the predicted genetic founders of isolates of all other TcV isolates. The remaining isolates across other DTUs appeared as unrelated singletons (isolates that do not belong to any cluster). Overall the results reveal that diploid sequence typing using just 6 genes is highly discriminatory.

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Table 4. Sequence types (STs) and diploid sequence types (DSTs) for nine gene fragments.

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Intra DTU diversity

Table 5 describes the levels of diversity seen in each gene fragment represented as the number of variable sites per DTU (VS), the number of genotypes differentiated per DTU (GT) and the discriminatory power for each gene fragment for each DTU (DP). The number of genotypes identified at the intra DTU level, varied widely and is correlated to the SNP diversity within each gene fragment. The most and least discriminatory genes for each of the six DTUs are also shown in detail in Table 5. LYT1 and RB19 genes revealed the most diversity for TcI discriminating 7 of 8 isolates (DP 0.88). Two genes DHFRS-TS and Met-III gave the highest resolution for TcII (DP 0.80), generally considered to be a relatively homogenous clade, and revealed surprising discriminatory ability, distinguishing 4 of 5 isolates. A single gene (RB19) was able to distinguish all 8 reference isolates within TcIII (DP 1.0). Six of 9 genetic loci were able to discriminate individually the full panel of 5 TcIV reference strains (DP 1.0). DTUs TcV and TcVI have previously been shown to have homogenous population structures with low intralineage diversity. Nevertheless TcAPX resolved three of 5 isolates within TcV (DP 0.43) and TcMPX 5 of 6 (DP 0.83) isolates within DTU VI.

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Table 5. Intra-lineage diversity.

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Phylogenies and incongruence

In addition to the derivation of DSTs, neighbor-joining trees for individual and concatenated gene fragments were constructed, representatives of which are shown in Figures 1 and 2. Concatenation using all of the original 9 gene fragments generated phylogenies with the expected DTU assignments for all 39 isolates with much higher bootstrap values than for individual genes (supporting information, Figure S1); there were no unexpected outliers. However, bootstrap support distinguishing TcV and VI was low (21%), but to distinguish between these DTUs concatenation of just two genes (DHFR-TS and LYT1 sequences) generated robust lineage assignment, and also two separate clusters within TcV and TcVI (supporting information, Figure S1). Generally, individual trees assigned each of the reference DTUs to the predicted clade (TcI-TcIV). As expected, bootstrap support for individual gene loci was variable depending on locus. For example TcAPX, generated dendrograms with high bootstraps for lineage assignment (with the exception of TcV and TcIV) but low intralineage diversity. Other genes (for example Met-II and Met-III) generated trees with high bootstrap values for both lineage assignment and comparatively high bootstraps for intralineage diversity. Conversely, TcMPX produced the least phylogenetically informative dendrograms with low associated bootstrap values. Three genes (LYT1, DHFRS-TS, RB19) individually separated the difficult to distinguish homogenous clades TcV and TcVI with relatively high bootstraps. Loss of heterozygosity (LOH, the loss of one allele at a specific locus) was observed in DTUs TcV and TcVI, in some isolates, in 4 gene fragments (see below) and confused the phylogenetic assignment of those individual isolates affected. In these instances the remaining allelic profiles clustered with either the respective TcII or TcIII clades. One surprising output was the level of incongruent DTU topologies between individual genetic loci (Figure 1). For example TcAPX, TcMPX, Met-II, and Met-III generated TcI clades that were most closely associated to TcIV. In contrast, DHFR-TS, TR, and LYT1 dendrograms inferred a closer association of TcI to TcIII. TcGPXII inferred that TcI was more similar to TcIII. Similarly, at the intra DTU level there was also incongruence between dendrograms. For example, TcGPXII and Met-II each separated TcIII into two clusters but with incongruent isolates (for example M5631 and CM25, Figure 1).

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Figure 1. Phylogenetic DTU incongruence between two individual gene trees (TcI, TcIII and TcIV).

*loss of heterozygosity in isolates at the Met-II locus (EPV20-1, TcVI: Para 6, TcV).

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Figure 2. Concatenation and lineage assignment.

A combination of 4 genes allows for DTU assignment. 2A, Unrooted neighbor-joining diplotypic tree showing p-distance for 3 concatenated gene fragments (Met-III, RB19, and TcGPXII) which discriminate all lineages except TcV and VI. 2B, DHFR-TS differentiates between TcV and TcVI.

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Choice of loci for lineage assignment

The minimum number of genes required for phylogenetic assignment to DTU level was investigated according to the following criteria. Firstly, the minimum number of genes required to produce phylogenetically robust bootstrap support. Secondly, to choose those genes that produced the most consistent unambiguous sequences. Thirdly, to choose genes requiring the minimum number of internal primers. Using these criteria a minimum combination of 4 genes (Met-III, RB19, TcGPXII and DHFR-TS) enabled all reference isolates to be assigned to DTU level. Specifically, concatenated sequences of Met-III, RB19 and TcGPXII assigned isolates to DTUs TcI-TcIV (Figure 2A). Separately, DHFR-TS (the only gene requiring internal primers) distinguished the DTUs TcV and TcVI (Figure 2B, insert). Internal primers for Met-III, RB19 and TcGPXII were not required after initial optimisation (Table 2).

Intralineage phylogenies

Generally, bootstrap support within DTUs using concatenated sequences was lower than between DTUs (Figure 1) which is expected in data originating from gene fragments with non-contiguous topologies at the intralineage level. In most instances intralineage topology of individual gene dendrograms was generally low and/or not contiguous across loci (Figure 1). However, certain observations deserve closer scrutiny. Importantly, two distinct clusters within TcIV separated isolates from North America and South America. This genetic partitioning was detected in 8 out of 9 gene individual targets in the present study, indicating a robust cladistic subdivision within TcIV. Interestingly, two TcI isolates SAXP18 (Peru, Didelphis marsupialis) and C8 (Bolivia, Triatoma infestans) were isolated from silvatic and domestic sources respectively, and are genetically similar suggesting present or past overlapping transmission cycles. Relatively high bootstrap support for isolates of the TcII clade did not reveal evidence of geographical clustering and genetically similar isolates originated from distant localities. Within TcIII at least two clusters were seen (in concatenated data); silvatic isolates (from Dasypus novemcinctus) in the Paraguayan Chaco clustered with a domestic strain (SABP19) isolated from a domestic T. infestans in Peru. The 8 TcIII isolates included in our panel showed considerable diversity. However, numbers are insufficient to examine population substructuring.

Intralineage recombination

Analysis for the presence of mosaic alleles, at the level of individual genetic loci was performed using RDP [37], applied to a total of 240 representative sequences of known allelic phase (single SNP heterozygous and homozygous sequence profiles) encompassing DTUs TcI, TcII, TcIII and TcIV across the 9 genetic loci. We found no evidence of allelic mosaics within individual genes. However, diplotypic SNP data revealed for three genes (LYT1, TcGPXII and TcMPX) that putative donor homozygous SNP profiles and the corresponding heterozygous profiles were present as shown in Figure 3. Specifically, within LYT1 the heterozygous isolate (PII) and putative donors (B187 and PI) were identified within TcI. For TcGPXII, 2 heterozygous isolates (ARMA18 and M6421) possess SNP profiles of the putative donor isolates CM25 and 85/847 (TcIII). Lastly, for TcMPX, SABP19 was heterozygous at a single locus with potential donors in the same DTU (TcIII). In each instance individual isolates containing heterozygous and donor SNPs were present only in a single gene and were not contiguous across loci.

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Figure 3. Raw SNP data showing putative donor and recipient isolates.

Sequences containing heterozygous SNP's (R) and putative homozygous donors (D) within a single DTU. Genetic loci are LYT1 (top), TcGPXII (bottom left) and TcMPX (bottom right).

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SNP data and loss of heterozygosity

Our panel of isolates included 13 representatives of DTUs V and VI, which are known to be genetic hybrids of TcII and TcIII [8] Across all 9 loci the expected allelic heterozygous profile was observed in most individual isolates. However, at four gene loci complete LOH was detected for individual isolates within the hybrid lineages. The observations are as follows: within Met-III, two TcVI isolates (Tula cl2 and P251 cl7) possessed a TcII-like allele but not a TcIII-like allele as did an individual TcV isolate for Met-II (PARA6 cl4). In contrast a single TcVI isolate at the locus RB19 (VFRA cl1) possessed only the TcIII-like allele. At the TcMPX locus, all TcV isolates (with the exception of PAH179) appeared to have lost the TcIII-like allele and 2 representatives of TcVI (P251 and LHVA) lost the TcII-like allele; the remaining 4 TcVI isolates retained heterozygosity. LOH among individual isolates in genetic loci could be clearly visualised when dendrograms were constructed, for example, hybrid isolates PARA6 (TcV) and EPV20-1(TcVI) for Met-II clustered within TcII (Figure 1). There was no continuity of allelic loss across different genes for individual isolates. For example, the two aforementioned isolates exhibiting homozygosity in Met-III were heterozygous in TcAPX, which are linked on chromosome 36. It is therefore evident that heterozygosity has been maintained elsewhere on the same chromosome. Taken together the results indicate a return to homozygosity in 6.24% of TcV and TcIV (combined) isolates when considering the four affected gene fragments.

Discussion

Discriminatory power and diploid sequence typing

Diploid sequence typing of 39 reference isolates indicates that together the nine genes under study were highly discriminatory, 38 of 39 reference isolates generated a unique DST. It was apparent that the number of STs identified for each gene fragment varied considerably (Table 5) despite the fact that all but two of the genes (TcAPX and TR) were under stabilising selection. However, by using just 6 of the original panel of nine genes the same 38 DSTs could be identified. The reduced panel consisted of those genes with the highest overall DPs (Met-II, Met-III and Rb19) with additional genes (TcMPX, DHFR-TS and TR) resolving further STs for the genetically homogenous DTUs TcV and TcVI. The reduction in the number of genes would represent a considerable saving in sequencing effort if applied to new samples. Furthermore, the reduced panel consists primarily of genes that are easily amplified and sequenced. The derived STs and DSTs applied to eBURST software, designed specifically for MLST data to infer evolutionary relationships and genetic founders, identified one cluster encompassing all of the TcV reference isolates. The inferred founder of this group (DST29) was found in 2 isolates (Vinch101 and Sc43). Both of these isolates were originally isolated from domestic T. infestans from Chile and Bolivia respectively. The ability to differentiate isolates within TcV is an important development, as this DTU is found throughout the Southern Cone countries in domestic cycles. Currently other methods to demonstrate diversity within TcV have been lacking. The high number of singletons observed in remaining DTUs was surprising, although this is probably a consequence of reference isolates spanning diverse spatial and ecological origins and diversity found within our panel. It is likely that high intensity localised sampling would reveal more easily genetic relationships between isolates. High numbers of singletons are often observed in other MLST typing schemes (for example Candida species) when applied to small datasets. Furthermore distance based dendrograms have been shown to correlate well with clonal clusters generated by eBURST when applied to large numbers [27]. It is also worth noting that high rates of singletons are typical of populations with a high rate of recombination relative to mutation [27]. Although eBURST is an established software for diplotyping and haplotyping pathogens [47], [48] until analysis has been applied to a larger number of sympatric T. cruzi isolates it is difficult to evaluate the usefulness of the analysis as a tool for predicting founders and clusters in DTUs other than TcV, and other approaches should be considered in parallel.

Phylogenies and incongruence

A separate approach for the analysis of diplotypic MLST data is to generate phylogenetic trees utilising distance based methods and modified sequences, adapted for diplotyping. At the level of individual gene fragments, isolates were generally assigned to the predicted clades, although bootstap support varied widely and no single gene was able to assign all isolates robustly. A noticeable observation was that of incongruent topologies in individual gene trees and in particular between DTUs TcI, TcIII, and TcIV. At the intra DTU level there was incongruence between dendrograms (Figure 1). The bootstraps supporting incongruence varied widely between genes and DTUs and similar patterns of incongruence have been previously observed in nuclear genes [5], [49]. Specifically, our data revealed that DTUs TcIII and TcIV possessed split affinities to DTUs TcI and TcII. For 4 genes, DTUs TcIII and TcIV showed greater affinity to TcI. These split affinities of TcIII and TcIV to TcI and TcII are in broad agreement with the spectrum of polymorphism observed by Westenberger et al. [5]. Such incongruence is not immediately compatible with a model of strict clonality; genealogical relationships where sequences vary in topological position is a classical marker in populations that have undergone recombination.

Choice of loci for lineage assignment

Sequence data concatenated across all 9 genes produced robust phylogenetic assignment for all DTUs except for TcV and TcVI, which could be resolved using two concatenated genes (DHFR-TS and LYT1 sequences). Importantly, this number could subsequently be reduced to a combination of just 4 genes (Met-III, RB19, TcGPXII and DHFR-TS) while maintaining high bootstrap support. The first three aforementioned gene sequences were concatenated to differentiate DTUs TcI-TcIV, with DHFR-TS dendrograms separately able to distinguish DTUs TcV and VI. Notably concatenated data implies a cladistic subdivision within TcIV, separating North American from South American isolates. Bootstrap support is a requirement for any MLST scheme, but of equal importance are reliable PCR amplifications which generate unambiguous sequences in both forward and reverse directions. All four targets meet this criterion. Met-III, RB19 and TcGPXII do not require internal primers, although some were used initially in the optimisation process, and generate small amplicons (824 bp, 408 bp and 487 bp respectively). DHFR-TS alone requires the use of internal sequencing primers. LYT1 was considered to be an alternative candidate, also able to distinguish TcV and TcVI, but it was technically more difficult to obtain sequences in the forward direction despite multiple attempts at optimisation.

Intralineage recombination

SNP data revealed that for three genes (LYT1, TcGPXII and TcMPX) putative donor homozygous SNP profiles and the corresponding heterozygous profiles, were present within TcI (a single heterozygous isolate) and TcIII (1 heterozygous isolate) one tentative explanation of the presence of heterozygous SNPs is that of a recombination signature within these DTUs. In the present paper examination 240 sequences (of known allelic phase) using an array of recombination detection algorithms did not detect evidence of allelic mosaics. This result is not unexpected as diplotyping is not as sensitive as haplotyping for detecting mosaic's or investigating the sexual reproduction in evolutionary history [50]. Future recombination analysis will include more refined phase resolution of sequences heterozygous at two or more loci, typically not required for diplotypic MLST typing schemes. However, haplotypes can be derived from current targets reconstructed by the program PHASE [51], or more recently fastPHASE (http://depts.washington.edu/uwc4c/expres​s-licenses/assets/fastphase/), by cloning, or by allelic specific PCR [52]. In a sexually reproducing population the frequencies of genotypes (defined by haplotypes) should be in Hardy-Weinberg equilibrium. New software to analyse haplotypic data include those incorporating Bayesian methodologies; Structure [53] and Beast [54] reconstruct phylogenies with epidemiologically and evolutionary informative results.

Loss of heterozygosity

LOH observed in 4 gene fragments affecting the hybrid lineages TcV and TcIV has potentially significant consequences for MLST and lineage assignment. For example, TcMPX hybrid isolates (TcV) would be incorrectly assigned if judgment was based on a single locus due to LOH (Figure 1). There was no continuity of allelic loss across different genes for individual isolates, linked genes (chromosome 36) were homozygous in Met-III but heterozygous in TcAPX indicating local rather than large scale events indicative of LOH seen in C. albicans [55]. Such apparent random loss is consistent with gene conversion. However, within TcMPX all TcV isolates (with the exception of PAH179) possessed only a TcII like allele suggesting clonal expansion subsequent to this LOH event. Discriminating between recombination, gene conversion, duplication or mitotic recombination requires analysis at the haplotypic level and should be possible by MLST, particularly in light of the considerable heterozgosity found in a T. cruzi (in particular DTUs TcI, TcIII, and TcIV). Use of a larger number of sympatric isolates and gene targets evenly distributed along those chromosomes where LOH has been previously identified (chromosomes 6, 36, 29, 22) would be a suitable approach, and one that has been exploited in C. albicans [55]. LOH in C. albicans [56], [57], [58] is one mechanism generating minor genetic changes which over time enable adaptation to new microenvironments [59], [60], [61], [62]. Such microvariation has been demonstrated by passage of C. albicans in vitro. Prolonged passage of MLST characterised T.cruzi isolates under a range of different environmental stresses could be applied to detect genetic basis for fitness. In the current panel 40% of the SNPs were non synonymous and resulted in amino acid changes. Some of these were nontrivial (e.g., acidic to basic side chains, aliphatic to aromatic side chains) which would be expected to result in significant changes in higher and secondary peptide structures and inter and intralineage catalytic/phenotypic differences.

Taken together we demonstrate that diploid sequence typing is a powerful and practical method to describe the extent of diversity in T. cruzi that can be integrated into population and evolutionary studies, particularly if the current protocol can be applied to an MLST database for T. cruzi. We propose that four genes (Met-III, RB19, TcGPXII and DHFR-TS) be used for the characterisation of isolates to DTU level. Additionally we propose that six gene fragments (Met-II, Met-III, RB19, TcMPX, DHFR-TS and TR) form the basis for a formalised MLST diplotyping scheme for further high resolution studies on the basis that DST diversity differentiated 38 out of 39 isolates. The proposed panel does not exclude the addition or replacement of new gene targets to further optimise diversity studies. eBURST analysis predicts that DST 29 isolated from T. infestans in Boliva and Chile is the genetic founder of all other TcV isolates in the reference panel (although isolate numbers were small), phylogenies also support a cladistic subdivision between North and South American TcIV isolates. SNP data revealed that for three genes (LYT1, TcGPXII and TcMPX) putative donor homozygous SNP profiles and the corresponding heterozygous profiles, were present within DTUs TcI and TcIII for single isolates. Incongruent topologies and the intra and inter DTU level is not consistent with a model of strict clonality. The high levels of heterozygosity detected in the gene targets offers the potential for a future deeper level of analysis of population genetics based on haplotypes.

Supporting Information

Figure S1.

Concatenation and lineage assignment. Unrooted neighbor-joining diplotypic tree showing p-distance for 9 concatenated gene fragments (A). Concatenation of LYT1 and DHFR-TS discriminate between DTUs V and VI (inset). Concatenated diplotypic tree using a reduced panel of 4 gene fragments (B). Concatenation of Met-III, RB19, and TcGPXII assign isolates to DTUs TcI-TcIV. DHFR-TS differentiates DTUs V and VI (B inset).

doi:10.1371/journal.pntd.0001049.s001

(0.41 MB DOC)

Acknowledgments

The authors thank Christian Barnabé and Michael Tibayrenc, who kindly provided T. cruzi strains. Also thanks to Urvish Mistry for his constructive comments.

Author Contributions

Conceived and designed the experiments: MY MAM. Performed the experiments: MY LAM. Analyzed the data: MY LAM. Contributed reagents/materials/analysis tools: MY LAM MDL MSL NA TB PD HJC. Wrote the paper: MY MAM. Input into design of experiments and technical advice: ILM.

References

  1. 1. Rassi A Jr, Rassi A, Marin-Neto JA (2010) Chagas disease. Lancet 375: 1388–1402.
  2. 2. Yeo M, Acosta N, Llewellyn M, Sanchez H, Adamson S, et al. (2005) Origins of Chagas disease: Didelphis species are natural hosts of Trypanosoma cruzi I and armadillos hosts of Trypanosoma cruzi II, including hybrids. Int J Parasitol 35: 225–233.
  3. 3. Alarcon de Noya B, Diaz-Bello Z, Colmenares C, Ruiz-Guevara R, Mauriello L, et al. (2010) Large urban outbreak of orally acquired acute Chagas disease at a school in Caracas, Venezuela. J Infect Dis 201: 1308–1315.
  4. 4. Zingales B, Andrade SG, Briones MR, Campbell DA, Chiari E, et al. (2009) A new consensus for Trypanosoma cruzi intraspecific nomenclature: second revision meeting recommends TcI to TcVI. Mem Inst Oswaldo Cruz 104: 1051–1054.
  5. 5. Westenberger SJ, Barnabe C, Campbell DA, Sturm NR (2005) Two hybridization events define the population structure of Trypanosoma cruzi. Genetics 171: 527–543.
  6. 6. de Freitas JM, Augusto-Pinto L, Pimenta JR, Bastos-Rodrigues L, Goncalves VF, et al. (2006) Ancestral genomes, sex, and the population structure of Trypanosoma cruzi. PLoS Pathog 2: e24.
  7. 7. Brisse S, Henriksson J, Barnabe C, Douzery EJ, Berkvens D, et al. (2003) Evidence for genetic exchange and hybridization in Trypanosoma cruzi based on nucleotide sequences and molecular karyotype. Infect Genet Evol 2: 173–183.
  8. 8. Machado CA, Ayala FJ (2001) Nucleotide sequences provide evidence of genetic exchange among distantly related lineages of Trypanosoma cruzi. Proc Natl Acad Sci U S A 98: 7396–7401.
  9. 9. Gaunt MW, Yeo M, Frame IA, Stothard JR, Carrasco HJ, et al. (2003) Mechanism of genetic exchange in American trypanosomes. Nature 421: 936–939.
  10. 10. Lewis MD, Llewellyn MS, Gaunt MW, Yeo M, Carrasco HJ, et al. (2009) Flow cytometric analysis and microsatellite genotyping reveal extensive DNA content variation in Trypanosoma cruzi populations and expose contrasts between natural and experimental hybrids. Int J Parasitol 39: 1305–1317.
  11. 11. Barnabe C, Brisse S, Tibayrenc M (2000) Population structure and genetic typing of Trypanosoma cruzi, the agent of Chagas disease: a multilocus enzyme electrophoresis approach. Parasitology 120 ( Pt 5): 513–526.
  12. 12. Brisse S, Barnabe C, Banuls AL, Sidibe I, Noel S, et al. (1998) A phylogenetic analysis of the Trypanosoma cruzi genome project CL Brener reference strain by multilocus enzyme electrophoresis and multiprimer random amplified polymorphic DNA fingerprinting. Mol Biochem Parasitol 92: 253–263.
  13. 13. Miles MA, Souza A, Povoa M, Shaw JJ, Lainson R, et al. (1978) Isozymic heterogeneity of Trypanosoma cruzi in the first autochthonous patients with Chagas' disease in Amazonian Brazil. Nature 272: 819–821.
  14. 14. Tibayrenc M, Miles MA (1983) A genetic comparison between Brazilian and Bolivian zymodemes of Trypanosoma cruzi. Trans R Soc Trop Med Hyg 77: 76–83.
  15. 15. Broutin H, Tarrieu F, Tibayrenc M, Oury B, Barnabe C (2006) Phylogenetic analysis of the glucose-6-phosphate isomerase gene in Trypanosoma cruzi. Exp Parasitol 113: 1–7.
  16. 16. Brisse S, Barnabe C, Tibayrenc M (2000) Identification of six Trypanosoma cruzi phylogenetic lineages by random amplified polymorphic DNA and multilocus enzyme electrophoresis. Int J Parasitol 30: 35–44.
  17. 17. Brisse S, Verhoef J, Tibayrenc M (2001) Characterisation of large and small subunit rRNA and mini-exon genes further supports the distinction of six Trypanosoma cruzi lineages. Int J Parasitol 31: 1218–1226.
  18. 18. Lewis MD, Ma J, Yeo M, Carrasco HJ, Llewellyn MS, et al. (2009) Genotyping of Trypanosoma cruzi: systematic selection of assays allowing rapid and accurate discrimination of all known lineages. Am J Trop Med Hyg 81: 1041–1049.
  19. 19. Llewellyn MS, Miles MA, Carrasco HJ, Lewis MD, Yeo M, et al. (2009) Genome-scale multilocus microsatellite typing of Trypanosoma cruzi discrete typing unit I reveals phylogeographic structure and specific genotypes linked to human infection. PLoS Pathog 5: e1000410.
  20. 20. Dingle KE, Van Den Braak N, Colles FM, Price LJ, Woodward DL, et al. (2001) Sequence typing confirms that Campylobacter jejuni strains associated with Guillain-Barre and Miller-Fisher syndromes are of diverse genetic lineage, serotype, and flagella type. J Clin Microbiol 39: 3346–3349.
  21. 21. Enright MC, Spratt BG, Kalia A, Cross JH, Bessen DE (2001) Multilocus sequence typing of Streptococcus pyogenes and the relationships between emm type and clone. Infect Immun 69: 2416–2427.
  22. 22. Bougnoux ME, Tavanti A, Bouchier C, Gow NA, Magnier A, et al. (2003) Collaborative consensus for optimized multilocus sequence typing of Candida albicans. J Clin Microbiol 41: 5265–5266.
  23. 23. Tavanti A, Gow NA, Senesi S, Maiden MC, Odds FC (2003) Optimization and validation of multilocus sequence typing for Candida albicans. J Clin Microbiol 41: 3765–3776.
  24. 24. Spratt BG (1999) Multilocus sequence typing: molecular typing of bacterial pathogens in an era of rapid DNA sequencing and the internet. Curr Opin Microbiol 2: 312–316.
  25. 25. Aanensen DM, Spratt BG (2005) The multilocus sequence typing network: mlst.net. Nucleic Acids Res 33: W728–733.
  26. 26. Tavanti A, Davidson AD, Johnson EM, Maiden MC, Shaw DJ, et al. (2005) Multilocus sequence typing for differentiation of strains of Candida tropicalis. J Clin Microbiol 43: 5593–5600.
  27. 27. Odds FC, Jacobsen MD (2008) Multilocus sequence typing of pathogenic Candida species. Eukaryot Cell 7: 1075–1084.
  28. 28. Subileau M, Barnabe C, Douzery EJ, Diosque P, Tibayrenc M (2009) Trypanosoma cruzi: new insights on ecophylogeny and hybridization by multigene sequencing of three nuclear and one maxicircle genes. Exp Parasitol 122: 328–337.
  29. 29. El-Sayed NM, Myler PJ, Bartholomeu DC, Nilsson D, Aggarwal G, et al. (2005) The genome sequence of Trypanosoma cruzi, etiologic agent of Chagas disease. Science 309: 409–415.
  30. 30. Weatherly DB, Boehlke C, Tarleton RL (2009) Chromosome level assembly of the hybrid Trypanosoma cruzi genome. BMC Genomics 10: 255.
  31. 31. Carrasco HJ, Frame IA, Valente SA, Miles MA (1996) Genetic exchange as a possible source of genomic diversity in sylvatic populations of Trypanosoma cruzi. Am J Trop Med Hyg 54: 418–424.
  32. 32. Nei M, Gojobori T (1986) Simple methods for estimating the numbers of synonymous and nonsynonymous nucleotide substitutions. Mol Biol Evol 3: 418–426.
  33. 33. Korber B, Los Alamos National LaboratoryTheoretical Biology and Biophysics Group T-10 (2000) HIV molecular immunology 2000. Alamos Los, N.M.: Los Alamos National Laboratory, Theoretical Biology and Biophysics Group T-10.1 v. in various pagings p.
  34. 34. Taylor JW, Fisher MC (2003) Fungal multilocus sequence typing–it's not just for bacteria. Curr Opin Microbiol 6: 351–356.
  35. 35. Kumar S, Nei M, Dudley J, Tamura K (2008) MEGA: a biologist-centric software for evolutionary analysis of DNA and protein sequences. Brief Bioinform 9: 299–306.
  36. 36. Martin DP, Williamson C, Posada D (2005) RDP2: recombination detection and analysis from sequence alignments. Bioinformatics 21: 260–262.
  37. 37. Martin D, Rybicki E (2000) RDP: detection of recombination amongst aligned sequences. Bioinformatics 16: 562–563.
  38. 38. Salminen MO, Carr JK, Burke DS, McCutchan FE (1995) Identification of breakpoints in intergenotypic recombinants of HIV type 1 by bootscanning. AIDS Res Hum Retroviruses 11: 1423–1425.
  39. 39. Sawyer S (1989) Statistical tests for detecting gene conversion. Mol Biol Evol 6: 526–538.
  40. 40. Posada D, Crandall KA (2001) Evaluation of methods for detecting recombination from DNA sequences: computer simulations. Proc Natl Acad Sci U S A 98: 13757–13762.
  41. 41. Smith JM (1992) Analyzing the mosaic structure of genes. J Mol Evol 34: 126–129.
  42. 42. Gibbs MJ, Armstrong JS, Gibbs AJ (2000) Sister-scanning: a Monte Carlo procedure for assessing signals in recombinant sequences. Bioinformatics 16: 573–582.
  43. 43. Boni MF, Posada D, Feldman MW (2007) An exact nonparametric method for inferring mosaic structure in sequence triplets. Genetics 176: 1035–1047.
  44. 44. Jakobsen IB, Easteal S (1996) A program for calculating and displaying compatibility matrices as an aid in determining reticulate evolution in molecular sequences. Comput Appl Biosci 12: 291–295.
  45. 45. McGuire G, Wright F (1998) TOPAL: recombination detection in DNA and protein sequences. Bioinformatics 14: 219–220.
  46. 46. McGuire WL, DeLaGarza M (1973) Improved sensitivity in the measurement of estrogen receptor in human breast cancer. J Clin Endocrinol Metab 37: 986–989.
  47. 47. Feil EJ, Li BC, Aanensen DM, Hanage WP, Spratt BG (2004) eBURST: inferring patterns of evolutionary descent among clusters of related bacterial genotypes from multilocus sequence typing data. J Bacteriol 186: 1518–1530.
  48. 48. Spratt BG, Hanage WP, Li B, Aanensen DM, Feil EJ (2004) Displaying the relatedness among isolates of bacterial species – the eBURST approach. FEMS Microbiol Lett 241: 129–134.
  49. 49. Rozas M, De Doncker S, Coronado X, Barnabe C, Tibyarenc M, et al. (2008) Evolutionary history of Trypanosoma cruzi according to antigen genes. Parasitology 135: 1157–1164.
  50. 50. Tavanti A, Gow NA, Maiden MC, Odds FC, Shaw DJ (2004) Genetic evidence for recombination in Candida albicans based on haplotype analysis. Fungal Genet Biol 41: 553–562.
  51. 51. Stephens M, Smith NJ, Donnelly P (2001) A new statistical method for haplotype reconstruction from population data. Am J Hum Genet 68: 978–989.
  52. 52. Mauricio IL, Yeo M, Baghaei M, Doto D, Pratlong F, et al. (2006) Towards multilocus sequence typing of the Leishmania donovani complex: resolving genotypes and haplotypes for five polymorphic metabolic enzymes (ASAT, GPI, NH1, NH2, PGD). Int J Parasitol 36: 757–769.
  53. 53. Falush D, Stephens M, Pritchard JK (2007) Inference of population structure using multilocus genotype data: dominant markers and null alleles. Mol Ecol Notes 7: 574–578.
  54. 54. Drummond AJ, Rambaut A (2007) BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol Biol 7: 214.
  55. 55. Diogo D, Bouchier C, d'Enfert C, Bougnoux ME (2009) Loss of heterozygosity in commensal isolates of the asexual diploid yeast Candida albicans. Fungal Genet Biol 46: 159–168.
  56. 56. Cowen LE, Sanglard D, Calabrese D, Sirjusingh C, Anderson JB, et al. (2000) Evolution of drug resistance in experimental populations of Candida albicans. J Bacteriol 182: 1515–1522.
  57. 57. Forche A, May G, Magee PT (2005) Demonstration of loss of heterozygosity by single-nucleotide polymorphism microarray analysis and alterations in strain morphology in Candida albicans strains during infection. Eukaryot Cell 4: 156–165.
  58. 58. Sampaio P, Gusmao L, Correia A, Alves C, Rodrigues AG, et al. (2005) New microsatellite multiplex PCR for Candida albicans strain typing reveals microevolutionary changes. J Clin Microbiol 43: 3869–3876.
  59. 59. Lockhart SR, Fritch JJ, Meier AS, Schroppel K, Srikantha T, et al. (1995) Colonizing populations of Candida albicans are clonal in origin but undergo microevolution through C1 fragment reorganization as demonstrated by DNA fingerprinting and C1 sequencing. J Clin Microbiol 33: 1501–1509.
  60. 60. Lockhart SR, Reed BD, Pierson CL, Soll DR (1996) Most frequent scenario for recurrent Candida vaginitis is strain maintenance with "substrain shuffling": demonstration by sequential DNA fingerprinting with probes Ca3, C1, and CARE2. J Clin Microbiol 34: 767–777.
  61. 61. Pujol C, Joly S, Nolan B, Srikantha T, Soll DR (1999) Microevolutionary changes in Candida albicans identified by the complex Ca3 fingerprinting probe involve insertions and deletions of the full-length repetitive sequence RPS at specific genomic sites. Microbiology 145(Pt 10): 2635–2646.
  62. 62. Schroppel K, Rotman M, Galask R, Mac K, Soll DR (1994) Evolution and replacement of Candida albicans strains during recurrent vaginitis demonstrated by DNA fingerprinting. J Clin Microbiol 32: 2646–2654.