Summary of
Abstract
Multilocus sequence analysis (MLSA) and multilocus sequence typing (MLST) are nowadays considered as gold standards in the study of microbial systematic, being both techniques based on the interpretation of the sequences of several housekeeping genes. In this context, the sequences can be analyzed from different points of view. On the one hand, the phylogeny of the bacterial species can be estimated using the MLSA approach and on the other hand, the structure of the population can be inferred by means of MLST. Moreover, most species display some degree of population structure that can be interpreted in geographic and chronological contexts, that is, phylogeographic studies. In this review, the phylogeny and population structure of two important fish and shellfish pathogens, Yersinia ruckeri and Vibrio tapetis, exhibiting very different evolutive patterns will be analyzed. In both cases, the species form robust and monophyletic groups from a phylogenetic point of view. Regarding to the population structure, very different results were found. While Y. ruckeri follows an epidemic model of clonal expansion with well‐adapted clones that explode to be widely distributed, V. tapetis appears to have a mixed structure in where the paradox of clonality and high level of variability coexist. Furthermore, phylogeographical studies provided the evolutionary and geographical context for the species, allowing the determination of historical and spatial influences on the diversification of both species.
Keywords
- Yersinia ruckeri
- Vibrio tapetis
- MLST
- population diversity
- population dynamics
- mutation
- recombination.
1. Introduction
Phylogenetic analysis has long played a central role in basic microbiology. Sequence data offer direct genealogical information that can be efficiently used to estimate phylogenetic relationships and parameters associated with population dynamics. Furthermore, sequencing methods provide standardized and unambiguous data that are portable through online databases with direct access to the information needed to identify and monitor emerging pathogenic agents [1, 2]. Reconstructing the patterns of descent for a group of organisms can yield important awareness into why and how members of that group have specific characteristics and how those organisms are distributed across the environment. Integrating population patterns with phylogeny knowledge provides insights into epidemiological tracking of an organism at different evolutionary scales, from a single host to across the globe [3, 4]. On the other hand, more recently emerging fields of microbiology, including comparative genomics and phylogenomics, require substantial expertise in phylogenetic analysis and computational skills to handle the large‐scale data involved [5]. Understanding the ways in which current and emerging technologies can be used to maximize phylogenetic knowledge is advantageous only with a complete proficiency of the strengths and weaknesses of these methods.
Since the conception of phylogenetic trees [6], morphological comparisons have been utilized to determine patterns of descent. Historically, numerous DNA‐based approaches have been used to discriminate, subtype and build phylogenies for groups of organisms. Multilocus sequence analysis (MLSA) represents the standard in microbial molecular systematics. In this context, MLSA is implemented in a relatively straightforward way, consisting essentially in the concatenation of several gene fragments for the same set of organisms, resulting in one matrix which is used to infer a phylogeny by means of purely algorithmic methods [7–9].
For microbial pathogens, phylogenetic analyses are often conducted in order to determinate whether one particular outbreak may be related to another during times of an epidemic. While the clonal nature of an outbreak could be readily measured and predicted, Maynard‐Smith et al. [10] pointed out the potential importance of homologous recombination as a determinant in the overall population structure of many bacterial species. These notions are now supported by several typing methods including multilocus sequence typing (MLST). Unambiguous genotyping systems are a key to describing epidemiological and ecological patterns and highlighting the evolutionary processes that shape microbial populations. Levels of genetic diversity are sufficiently high in most of microbial taxa that the sequences of several housekeeping gene fragments can provide a medium‐resolution overview of their population genetic structure [11]. For the pathogenic bacteria whose members exhibit varying degrees of virulence, the integration of population genetic, evolutionary and epidemiological studies can provide important insights into the origins and spread of bacterial disease.
MLSA and MLST are based on housekeeping genes, which are subject to purifying selection and slow evolution and the variation within these genes is nearly neutral [12]. Although there are normally fewer polymorphic sites in individual housekeeping genes compared with hypervariable genes, the use of the combined sequences of multiple housekeeping genes has been shown to provide high discriminatory power while retaining signatures of longer‐term evolutionary relationships or clonal stability. Furthermore, analysis of multiple loci can buffer against potentially skewed evolutionary pictures obtained by single‐locus analysis [13, 14].
Most species display some degree of population structure that can be interpreted in geographical and chronological context [15]. Phylogeography uses genetic information to study the geographical distribution of genealogical lineages, especially those found within species [16, 17]. Because the discipline has deep roots in historical biogeography and population genetics, phylogeography was heralded as a bridge linking the study of micro‐ and macroevolutionary processes providing the empirical and conceptual link between systematics and population genetics. Based on appropriated sampling of individuals and genes, this approach allows the assessment of the biogeographic hypothesis, the description of the evolution of isolated reproductive population units and the inference of processes underlying the origin, distribution and maintenance of diversity [18]. Detecting concordance of geographical variation in genotypes, or their genealogies and the environment is therefore at the core of phylogeographic studies.
The generation of large volumes of sequence data, combined with the development of novel analytical techniques and conceptual advances, promises a better understanding of the complexity of the evolution of bacterial populations. The application, advantages and constrains of the MLSA, MLST and phylogeographic analysis in taxonomic studies will be illustrated in this chapter with two examples:
2. MLSA: inferring phylogeny of bacteria species
16S rRNA gene was the most common phylogenetic marker during 40 years. This molecule has a slow rate of evolution so very often it is difficult to establish phylogenetic relationships among taxa with recent divergence. MLSA represents nowadays the novel standard in microbial molecular systematics [19]. This is a rapid and robust classification method to study phylogenetic relationships of very diverse taxa of prokaryotes, including entire genera, by combining the information contained in the sequences of several specific genes [19]. This technique consists essentially in the concatenation of the sequence of several housekeeping genes (more than five), being the relationships among taxa established by phylogenetic inference [7, 8, 20, 21]. The use of such amount of data provides increased resolution power than the use of a unique gene as in the case of 16S rRNA gene, although this marker is considered still useful at taxonomic levels above the species.
MLSA can be used for bacterial identification and classification as well as for inferring evaluative relationships and variability among different groups of bacteria. At identification level, there are several studies demonstrating that MLSA using the concatenation of eight housekeeping genes provides a robust phylogenetic resolution for microorganisms sharing 70–95% of average nucleotide identity (ANI) and therefore, it could distinguish species of the same genus [22]. It has been even proposed to replace DNA‐DNA hybridization (DDH), although concerns have arisen about this replacement [23–26]. At systematic level, MLSA is considered an intermediate resolution technique between the 16S rRNA gene and the whole‐genome‐based approaches [19]. At evolutionary level, MLSA is a useful tool for studying the variability of different evolutionary identities, from families to species, as long as the selected genes for the analysis reflect properly the similarity of the complete genome among the studied group and the evolutionary ratios of the genes represent the evolution of the species.
The critical point for the MLSA is the suitability of the genes chosen for the analysis. In fact, genes that are perfectly informative within a given species, genus, or family may not be useful or even present in other taxa [19, 27]. Ideally, the best strategy to get a reasonable estimation of the species tree is to consider multiple genealogies inferred from unlinked loci and to use multiple individuals per species [28–30]. To date, there is not a general criterion for determining which genes are more useful for taxonomic purposes, but some attributes have been described for the genes to be used in the analysis [9, 26, 31, 32]. Genes should contain enough genetic information and although there is not a specification regarding to length, they should be small enough to be easily sequenced. Very often the fragments used in phylogenetic reconstructions are the same of those employed for MLST, resulting in too short fragments of the studied genes. The genes should also reflect the evolutionary history of the studied taxon [33]. Therefore, conserved genes must be selected for higher taxa and more evolved genes for species or subspecies levels. In that concern, the so‐called core genes, the orthologous genes, should be used preferably than the accessory genes [34].
The availability of a universal set of conserved orthologous loci on a given taxon and, therefore, a set of primers that could amplified them across the studied group often precludes the comparative analysis of evolutionary process and patterns among closely related species and genera [26]. The strongest conflicting signals are usually derived from the existence of horizontal gene transfer (HGT) events in the dataset [35–37]. The resulting phylogenetic hypothesis may be distorted since standard treeing methods assume a single underlying evolutionary history [20, 38, 39].
There are no official recommendations about the inclusion of amino acid‐base sequence analysis in MLSA studies although it is recommended because the study of the nucleotide sequences by themselves can lead to an “overinterpretation” of phylogenetic differentiation in closely related taxa [32]. Usually, the exchange in a base on the third position of a given codon has no influence in the resulting protein sequence and therefore in the structure and/or function of the protein, but also it can have the opposite effect. Because of that, nucleotide alignments should be done regarding their amino acid sequence. It must be taking into account that a bacterium is not only a sequence of DNA and for taxonomic purposes, the living unit at all its levels should be considered.
3. MLST: establishing the bacterial population structure
Nucleotide sequence data from multiple housekeeping genes in an appropriately sampled population can be used in a variety of analyses to determine population structure. The simplest of these analyses is MLST, which establishes the allele present at each locus and use a clustering algorithm to determine the relationships among strains from the matrix of pairwise differences between their allelic profiles [40]. The major advantage of MLST over others typing methods, such as multilocus enzyme electrophoresis (MLEE), is the unambiguous nature of the data obtained and the simple storage and electronically exchange, meaning that any isolate that is typed using the method can be rapidly compared with all previously typed strains.
The number of alleles obtained for each locus is much higher using MLST than MLEE and the information obtained by MLST is more precise. Publically available databases such as
Unweighted pair group method with arithmetic mean (UPGMA) dendrograms based on pairwise comparisons among allelic profiles can be structured on the website to detect relationships between query and/or isolates database. However, although clustering algorithms are useful for detecting the genetic relatedness of small number of isolates, they can result infeasible when visualizing larger sample sizes (e.g., >1000) in MLST database. As these methods are not based on an evolutionary model, they are often inaccurate in reconstructing evolutionary events [44]. The recent development of the algorithm eBURST [1] has addressed both issues. The model incorporated into eBURST assumes that, due to selection or genetic drift, some genotypes will occasionally increased the frequency in the population and then gradually diversify by the accumulation of mutation(s) and/or recombinational replacements, resulting in slight variants of the founding genotype. Using allelic profile data, one sequence type (ST) is assigned to each isolate. STs sharing high genetic similarity are grouped into clonal complexes (CCs). The founding genotype for each CC is then identified parsimoniously as the genotype differs from the highest number of the other genotypes in the CC at only one locus. Further diversification will produce variants of the founder allelic profile that differ at two or more locus. Thus, the simple principal underlying eBURST is that bacterial populations will consist of a series of clonal complexes (set of variants of a funding genotype) that can be recognized from the allelic profiles of the strains within a MLST database [1].
While MLST is very effective for establishing which isolates are identical or closely related, the approach will not provide major information about the relationships between more distantly related isolates, unless the population is strictly clonal. However, additional phylogenetic information can be gathered if the nucleotide sequences themselves are studied by analyzing the extent of linkage disequilibrium between alleles and looking for recombination by the congruence of gene trees, or the presence of mosaic structures [45, 46]. Knowledge of the recombination extent in bacterial pathogens is important since low levels of recombination result in a highly clonal population, where lineages persist with little variation over hundreds or thousands of years. At the other extreme, high rates of recombination lead to weakly and/or non‐clonal populations in which lineages diversify so rapidly that the isolates recovered in one decade may be completely different from those recovered in the next [47].
For highly clonal species such as
4. Phylogeography: putting the geography into phylogeny
Phylogeography attempts to infer history from the geographical variation of genes and genetically controlled characters. In the phylogenetic/population genetic approach, graphical phylogenetics trees, networks, or clades are visualized from the observed variation data [49–51]. Thus, the usefulness of this approach is to integrate both phylogeny and geography within a quantitative analytical framework that encompasses the diverse aspects of phylogeography concordance [16, 52]. In this context, several classes of analytical techniques are used according to their function. The first class of techniques (i.e., AMOVA, Wombling, Monmonier's maximum difference algorithm, cline model by maximum likelihood) extracts spatial pattern from geographically distributed genetic data to identify either geographical partitions or clines (first‐order pattern, in the terminology of spatial statistic), or alternatively, patterns of isolation‐by‐distance (second‐order pattern) [53–55]. The second class (i.e., analysis of distance matrices, allelic aggregation index) attempts to infer historical scenarios directly from observed distributions of genes or taxa and one or more phylogenetic model [56]. A third class of techniques, such as Slatkin's distribution, provides statistical testing for the previously inferred scenario [57]. Phylogenetic trees and networks are often visualized over a cartographic background. Spatial interpolation algorithms [58] estimate parameter values at unsampled locations from a spatial distribution of observed points, providing a mean of interpreting and visualization the sampled data at different sets of locations [59, 60].
Many species show pronounced phylogeographic structure, or even regional or continental endemism, which counteracts the previously held paradigm of continuous and global panmixia. However, biogeographic and macro‐ecological studies at the community level have shown that relatively few free‐living microbial eukaryotes have cosmopolitan distribution [61, 62]. However, prokaryotes are generally smaller and have faster reproduction cycles than eukaryotic microorganisms that were the subject of these biogeographic studies [63]. Several studies have reported clear phylogeographic structuring in bacterial communities including marine, soil and soil‐freshwater bacteria [64–66]. Conversely, the absence of spatial structuring in other prokaryotes has been corroborated by molecular data for bacteria from those same environments, including cyanobacteria [67–69]. For microorganisms occurring in extreme environments, phylogeographic structure indicates the effects of strong geographic isolation and dispersal constraints, although not all show clear spatial structure [70, 71]. For the more widely distributed bacteria, biogeographic patterns may result from historical and/or contemporary environmental processes. The importance of these processes in structuring microbial systems is still poorly understood [72] and few studies have focused on the phylogeographic structure and dispersal limitation in bacteria on a truly global scale in discontinuous but globally common habitats.
5. Case studies
5.1. Yersinia ruckeri
ERM has been successfully controlled for decades by vaccination with commercial monovalent killed whole‐cell vaccines. Although formulations of most commercial vaccines are based only on serotype O1a (Hagerman strain), different degrees of cross‐protection among serotypes have been described [76]. In recent years, reports of ERM vaccine breakdown have emerged in Europe and USA mostly attributed to biotype 2 strains [80–82]. Other epizooties have occurred in vaccinated Atlantic salmon (
Molecular techniques have been used to study the intraspecific genetic variability showing a low genetic diversity. By using of MLEE was identified only four electropherotypes for 47 isolates of
In the context of the genetic approach, none of the studies have focused on the sequencing and analysis of housekeeping genes to understand the
Using a sequence‐based approach, new studies were developed by our research group to reconstruct the phylogeny and to characterize the molecular epidemiology and population structure with a collection of 103 strains of
Origin | Isolates ( |
Biotype | Serotype | ST* | Host/source |
---|---|---|---|---|---|
USA | 19 | 1, 2 | O1a, O1b, O2b, O3, O4 | 8 |
|
UK | 6 | 1, 2 | O1a, O1b | 4 |
|
Portugal | 21 | 1, 2 | O1a, O3 | 5 |
|
Chile | 11 | 1 | O1a, O1b, O2b | 3 |
|
Peru | 28 | 1, 2 | O1a | 7 |
|
Denmark | 4 | 1 | O1a, O2b, | 4 |
|
Spain | 4 | 1 | O1a, O2b | 2 |
|
Finland | 3 | 1, 2 | O1a, | 2 |
|
Canada | 4 | 1 | O1a, O1b, O2a | 3 |
|
Germany | 1 | 1 | O2b | 1 |
|
Scotland | 1 | 1 | O2b | 1 |
|
Norway | 1 | 1 | O1b | 1 |
|
Similarity matrix of intraspecific sequence for the individual genes ranged 97.2–100% for
The results of the MLSA analysis confirm that there is significant diversity within
Based on the sequences of the six housekeeping genes available in the public database htpp://publmst.org/yruckeri/, a MLST scheme for
Size of fragment (pb) | Alleles | Polymorphic sites |
|
|
|
|
|
|
---|---|---|---|---|---|---|---|---|
|
416 | 9 | 7 | 0.0064 | 0.2528 | 0.0042 | 0.0123 | 0.3427 |
|
454 | 6 | 8 | 0.0075 | 0.3107 | 0.0043 | 0.0194 | 0.2238 |
Y‐HSP60 | 509 | 4 | 4 | 0.0039 | 0.0577 | 0.0000 | 0.0159 | 0.0000 |
|
472 | 4 | 11 | 0.0125 | 0.5773 | 0.0047 | 0.0354 | 0.1339 |
|
632 | 7 | 16 | 0.0078 | 0.4717 | 0.0000 | 0.0340 | 0.0000 |
|
303 | 4 | 3 | 0.0055 | 0.4841 | 0.0074 | 0.0000 | ‐ |
Among all isolates of
ST | Profile | Frequency | % | |||||
---|---|---|---|---|---|---|---|---|
|
|
Y‐HPS60 |
|
|
|
|||
2 | 1 | 1 | 1 | 1 | 1 | 2 | 43 | 41.75 |
1 | 1 | 1 | 1 | 1 | 1 | 1 | 16 | 15.53 |
7 | 1 | 1 | 1 | 1 | 2 | 1 | 8 | 7.77 |
3 | 1 | 2 | 1 | 1 | 2 | 2 | 4 | 3.88 |
16 | 6 | 1 | 1 | 1 | 1 | 2 | 3 | 2.91 |
9 | 1 | 3 | 1 | 1 | 1 | 1 | 2 | 1.94 |
14 | 1 | 1 | 1 | 1 | 2 | 2 | 2 | 1.94 |
23 | 1 | 1 | 1 | 1 | 6 | 2 | 2 | 1.94 |
26 | 1 | 5 | 1 | 1 | 2 | 2 | 2 | 1.94 |
4 | 2 | 1 | 1 | 1 | 2 | 2 | 1 | 0.97 |
5 | 1 | 1 | 2 | 1 | 2 | 2 | 1 | 0.97 |
6 | 3 | 1 | 1 | 1 | 1 | 2 | 1 | 0.97 |
8 | 1 | 2 | 3 | 1 | 3 | 1 | 1 | 0.97 |
10 | 4 | 1 | 1 | 1 | 1 | 1 | 1 | 0.97 |
11 | 1 | 4 | 1 | 1 | 1 | 1 | 1 | 0.97 |
12 | 1 | 3 | 1 | 1 | 1 | 2 | 1 | 0.97 |
13 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 0.97 |
15 | 5 | 1 | 1 | 1 | 2 | 2 | 1 | 0.97 |
17 | 7 | 1 | 1 | 1 | 4 | 4 | 1 | 0.97 |
18 | 1 | 5 | 1 | 1 | 2 | 1 | 1 | 0.97 |
19 | 8 | 6 | 4 | 4 | 5 | 2 | 1 | 0.97 |
20 | 9 | 1 | 1 | 1 | 6 | 1 | 1 | 0.97 |
21 | 7 | 2 | 1 | 1 | 6 | 1 | 1 | 0.97 |
22 | 7 | 2 | 1 | 1 | 4 | 4 | 1 | 0.97 |
24 | 1 | 1 | 1 | 1 | 4 | 4 | 1 | 0.97 |
25 | 7 | 2 | 1 | 1 | 1 | 2 | 1 | 0.97 |
27 | 7 | 2 | 1 | 1 | 7 | 2 | 1 | 0.97 |
28 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 0.97 |
29 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 0.97 |
30 | 1 | 1 | 1 | 3 | 2 | 2 | 1 | 0.97 |
All alleles analyzed showed to be in nonrandom distribution or linkage disequilibrium (
Based on the single‐locus variables (SLVs) found between the two clonal complexes and the different subgroups identified by eBURST algorithm, the variant alleles can be used to determine the events responsible for the evolution into the population [93]. Thus, the per‐allele and per‐site recombination/mutation (r/m) parameter was calculated empirically from 25 SLVs identified within the two clonal complexes in
Epidemic model is also consistent with the epidemiology of
The phylogeographic analysis showed concordance with the eBURST diagram obtained previously for the
The sequence dataset was divided into 29 predefined subpopulations consisting of sequences from STs that present in each geographical origin and the geographical distances between different populations were measured using geographical coordinates. The Mantel test (“isolation‐by‐distance” analysis) for the matrix of correlation between genetic and geographic distance showed no significant correlation positive for the full dataset (
5.2. Vibrio tapetis
Population structure and phylogenetic analysis (as well as its relationship with the geography) of
The partial sequences of ten housekeeping genes were used:
The phylogenetic reconstruction for the concatenated gene sequences was done using three different methods, NJ, MP and ML, using in all cases 1000 bootstraps. Topology of all the trees was the same, showing only some differences at bootstrap values. Visual inspection of the
In the biggest cluster, high diversity is observed regarding to their host and geographical origin, containing the isolates classified as group one (represented by the type strain) and group two (represented by the isolate GR0202RD) by Rodríguez et al. [103]. As can be observed, different branches are formed, most of them related with host origin: the adult Manila clam isolates together with those from cockle and Venus clam cluster in the major branch and related to them appears the corkwing wrasse isolate. The carpet shell clam isolates fall into an individual branch as well as the wedge sole isolates, which form a cluster close to shi drum isolates. The second cluster, formed by isolates HH6087 (halibut), 102 and 127 (
MLST analysis revealed the heterogeneity of the population of this clam pathogen. The high variability of the population is reflected in the number of identified alleles ranging from 3 to 9 depending on the gene analyzed. The allele combination leads to the description of 10 STs (Table 4), all of them constituting singletons. Even when the stringent SLV criterion was relaxed (from 9/10 to 1/10 shared alleles), none of the SLV or DLV was found (data not shown). This variability is also reflected in the 450 single‐nucleotide polymorphisms (SNPs) detected across the 5826 bp surveyed. The majority of the SNPs were biallelic, being only 7 of them were triallelic. The nucleotidic substitutions found throughout the concatenated sequence showed, as usual for housekeeping genes, more frequency in synonymous substitutions (
ST |
|
|
|
Y‐HSP60 |
|
|
|
|
|
|
n |
---|---|---|---|---|---|---|---|---|---|---|---|
1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 17 |
2 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 |
3 | 3 | 2 | 3 | 3 | 3 | 3 | 3 | 2 | 3 | 3 | 1 |
4 | 4 | 3 | 3 | 4 | 4 | 4 | 4 | 2 | 4 | 4 | 1 |
5 | 5 | 4 | 3 | 5 | 5 | 5 | 5 | 2 | 4 | 5 | 1 |
6 | 6 | 1 | 1 | 6 | 6 | 6 | 6 | 1 | 5 | 6 | 1 |
7 | 6 | 5 | 1 | 6 | 6 | 2 | 7 | 1 | 6 | 6 | 1 |
8 | 7 | 1 | 1 | 7 | 1 | 2 | 7 | 1 | 6 | 7 | 1 |
9 | 8 | 1 | 4 | 1 | 7 | 7 | 8 | 3 | 7 | 8 | 4 |
10 | 1 | 6 | 5 | 8 | 8 | 8 | 9 | 1 | 8 | 9 | 1 |
Gene | Loci | Polymorphisms | Index of diversity | Recombination | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Length (bp) | G+C content | Alleles | SNPs | SVS | PIS | Snonsyn |
|
|
|
Rmin | Phi test | |
|
600 | 46.51 | 8 | 25(25) | 2 | 23 | 1 | 0.01257 | 0.0087 | 0.9556 | 1 |
|
|
609 | 46.38 | 6 | 49(51) | 12 | 37 | 6 | 0.01291 | 0.0204 | 0.7778 | 0 |
|
|
588 | 45.23 | 5 | 8(10) | 0 | 8 | 0 | 0.00292 | 0.0000 | 0.8000 | 0 |
|
|
525 | 44.91 | 8 | 35(37) | 5 | 30 | 6 | 0.01167 | 0.0270 | 0.9556 | 0 |
|
|
516 | 48.42 | 8 | 38(38) | 2 | 36 | 1 | 0.01332 | 0.0036 | 0.9556 | 1 |
|
|
600 | 43.14 | 8 | 95(96) | 3 | 92 | 7 | 0.02975 | 0.0095 | 0.9333 | 1 |
|
|
588 | 45.86 | 9 | 64(66) | 4 | 60 | 6 | 0.02117 | 0.0237 | 0.9778 | 4 |
|
|
600 | 47.00 | 3 | 7(7) | 0 | 7 | 1 | 0.00226 | 0.0528 | 0.6000 | 0 |
|
|
600 | 43.52 | 8 | 47(47) | 2 | 45 | 13 | 0.01598 | 0.1111 | 0.9556 | 5 |
|
|
600 | 47.75 | 9 | 82(86) | 5 | 77 | 10 | 0.02633 | 0.0188 | 0.9778 | 1 |
|
The alleles showed to be in linkage disequilibrium (
The contradiction between the results inferred for
Since the three isolates of the cluster two of
The phylogeographic network is very useful to clarify all the previous data. First, the two groups of isolates that can be observed are the same two clusters of the phylogenetic study (MLSA) and in the inferred clonal genealogy. The node A in the clonal genealogy is represented in this phylograph by the long branch (note that this is not an evolutive method, so that length is not representative of distance) generated by 370 nucleotidic substitutions, which according to the reconstructed evolutionary study are likely mutations. At the ends of this branch are located nodes B and C on the clonal genealogy (and the two clusters in MLSA), which are generated essentially by recombination according to ClonalFrame. These recombinatory events can be seen in the topology of the graphic. On the other hand, the inconsistence between the results in I
To date, the groups defined for
6. Conclusions
In conclusion, MLSA, MLST and phylogeographic analysis are successful for (i) unambiguously genotyping both
The results obtained from our works suggest that the processes involved in the genetic variability and evolution in both species are different. Using the MLST approximation, two different expansion models of population were detected, a mutation‐based epidemic model for
The phylogeographic approach indicated that well‐adapted clones of
It is noteworthy that the observed diversification, no matter the process suffered, could be related with host specificity to some extent, which may be indicating the existence of certain degree of function specialization. Further studies using “omics” techniques will allow to confirm such hypothesis.
Acknowledgments
The studies of the University of Santiago reviewed in this chapter were supported in part by grants AGL2003‐09307‐C02‐01, AGL2006‐13208‐C02‐01 and AGL2010‐18438 from the Ministerio de Economía y Competitividad (Spain). A.B. and S.B. acknowledge the Fondo Nacional de Ciencia y Tecnología (Venezuela) and the Ministerio de Ciencia y Tecnología (Spain) for research fellowships.
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