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Universidade de Lisboa, Faculdade de Ciências, Centro de Genética e Biologia Molecular and Instituto de Ciência Aplicada e Tecnologia, Edifício ICAT, Campus da FCUL, Campo Grande, 1749-016 Lisboa, Portugal
Correspondence
Rogério Tenreiro
rptenreiro{at}fc.ul.pt
| ABSTRACT |
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The GenBank/EMBL/DDBJ accession numbers for the sequences reported in this paper are DQ335652DQ335721, as indicated in Table 1
.
Primer details and phylogenetic trees based on amino acid sequences are available as supplementary material in IJSEM Online.
| INTRODUCTION |
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Although the taxonomy of these taxa seems to be well-established, their phylogenetic evaluation has been made almost exclusively from 16S rRNA gene sequences and there are still some controversial issues needing further clarification. These mainly concern the correct branching order of the three genera, as pointed out by Martinez-Murcia et al. (1993)
, and the relative rates of evolution revealed by phylogenies made with rpoC, encoding a
' subunit of the DNA-dependent RNA polymerase (Morse et al., 1996
). In this latter study, the tachytelic nature of O. oeni, as first hypothesized by Yang & Woese (1989)
based on comparative analysis of 16S rRNA gene sequences, was questioned, since it did not seem to be reflected in rpoC gene-based phylogenies. Recent developments coming from the genome analysis of O. oeni PSU-1 (Mills et al., 2005
) and comparison with other LAB strains indicate that the leuconostoc group has evolved rapidly in comparison with phylogenetically close groups, with Oenococcus as the most divergent genus.
16S rRNA gene sequence analysis is still of paramount importance in taxonomic resolution in bacteria (Stackebrandt et al., 2002
), but the use of different genes as phylogenetic markers, particularly protein-coding genes, is now a common approach (Yamamoto & Harayama, 1998
; Morse et al., 2002
; Adékambi & Drancourt, 2004
). In some cases, the use of other genes for phylogenetic inference even becomes essential, since it overcomes the limitation of 16S rRNA gene sequences in the phylogenetic resolution of close taxa (Konstantinidis & Tiedje, 2005
).
In this study we used partial sequences of dnaA (encoding chromosomal replication initiation protein), gyrB (encoding DNA gyrase B subunit) and dnaK (encoding the 70 kDa heat-shock protein) to clarify the intra- and intergeneric phylogenetic relationships inside the leuconostoc group. 16S rRNA gene and rpoC sequences were also used, but with an enlarged set of Leuconostoc and Weissella strains (11 from Leuconostoc and six from Weissella) in comparison with previous studies (Morse et al., 1996
). Phylogenetic resolution at different taxonomic ranks was also assessed by the additional inclusion of sequences belonging to different strains of the same species in Leuconostoc. This study provides (i) an assessment of confidence in the intrageneric resolution of the taxonomic structure of Leuconostoc and Weissella, (ii) a recognition of the relative timing of the events leading to the separation of Leuconostoc, Oenococcus and Weissella and (iii) an analysis of the adequacy of different genes, including the 16S rRNA gene, for phylogenetic inference in this group.
| METHODS |
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DNA sequencing.
In addition to full-length sequences, partially overlapping PCR products (amplified with internal primers) were also used in the sequencing process. DNA sequences used in this work were obtained using an automated DNA capillary sequencer CEQ 2000-XL (Beckman Coulter) by a dye-labelled dideoxy termination method (DTCS start kit; Beckman Coulter). Sequence accession numbers can be found in Table 1
.
Sequence alignment.
For phylogenetic inference, six different alignments were created, five corresponding to the alignment of sequences of each gene, 16S rRNA gene, dnaA, gyrB, rpoC and dnaK, and one corresponding to the concatenation of all gene sequences, which was used in a Bayesian approach. All alignments were made using CLUSTAL W program (Thompson et al., 1994
). For protein-coding genes, DNA alignments were made based on the corresponding amino acid alignments by using CodonAlign 2.0 (Hall, 2004
). Aligned regions with gaps or where the alignment was of low confidence, as a result of the presence of surrounding gaps, were removed before phylogenetic analyses.
Phylogenetic analyses.
Phylogenetic analyses including neighbour-joining (NJ), maximum-parsimony (MP) and maximum-likelihood (ML) trees of DNA sequence alignments were conducted in PAUP* 4.0b10 software (Swofford, 2003
). Genetic distances used in NJ trees are Kimura two-parameter-model distances with a transition/transversion ratio of 2 : 1. Analyses of protein sequences were made using several programs present in the PHYLIP software package, version 3.6 (Felsenstein, 2002
). Bootstrap analysis was made with 1000 replicates except in ML, where only 100 replicates were generated. For the Bayesian approach, MrBayes software version 3.0b4 (Huelsenbeck & Ronquist, 2001
) was used. Each analysis consisted of 2.0x107 generations from a random starting tree and four Markov chains (with default heating values) sampled every 100 generations. The first 8000 sampled trees were discarded, resulting in a set of 12 000 analysed trees sampled after stationarity. To prevent a point of only apparent stationarity being reached, two separate runs were made for each analysis. Hierarchical likelihood-ratio tests were conducted with a batch file supplied with ModelTest 3.06 (Posada & Crandall, 1998
) to provide the evolutionary models used in ML and Bayesian analysis. Lactobacillus plantarum WCFS1, Lactobacillus johnsonii NCC 533, Lactococcus lactis Il1403 and Enterococcus faecalis V583 were used as outgroups (note that the abbreviation L. is used throughout for Leuconostoc). For the comparison of genus rates of evolution, Kimura two-parameter distances were used to calculate 95 % confidence limits taken from Student's t distributions in Leuconostoc and Weissella.
| RESULTS AND DISCUSSION |
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0.001, indicating heterogeneity of the tested partitions (i.e. codon positions), thus preventing combination of the data.
Phylogenetic analysis of the 16S rRNA gene
Phylogenetic analysis of 16S rRNA gene sequences resulted in the NJ tree (Fig. 1
) that is usually regarded as representative of the phylogenetic relationships of leuconostocs. The genus Weissella constitutes one line of descent and the other line includes both Oenococcus and Leuconostoc. In Leuconostoc, most of the species cluster in one group, here represented by Leuconostoc mesenteroides, Leuconostoc pseudomesenteroides, Leuconostoc citreum and Leuconostoc gelidum. The remaining species form another group (represented by Leuconostoc ficulneum, Leuconostoc pseudoficulneum and Leuconostoc fructosum), with the exception of Leuconostoc fallax, which constitutes the most peripheral line in the genus. As found previously (Yang & Woese, 1989
; Martinez-Murcia & Collins, 1990
; Collins et al., 1991
), in Weissella the first branching pattern leads to the formation of two groups, one with Weissella paramesenteroides and Weissella confusa (together with Weissella hellenica) and the other including Weissella viridescens, the type species of the genus, and Weissella halotolerans plus Weissella kandleri. Also, as reported by Collins et al. (1991)
but not so clear in the other studies, the three genera have distinct branch lengths. A long branch leading to O. oeni reveals its fastest evolutionary rate, which is followed by Leuconostoc, although L. fallax seems to be remarkably slower than the remaining Leuconostoc species.
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) variation and a proportion of invariable sites (I) was used. Groups are only considered when bootstrap support was above 70 % for the three methods. The rationale for this approach rests on the fact that, if a clade is supported by different methods of tree reconstruction (with their specific advantages and pitfalls), we can be confident that the assessment of monophyly comes from the data under analysis and not from the method in use. Inevitably, this will lead to conservative results in the estimation of topology, but reduces the eventual necessity for a precise interpretation of evolutionary reality, such as rate heterogeneity among sites and lineages. Still, some situations (such as differences in group evolutionary rates associated with extreme disproportion of internal and terminal branch lengths) may lead to the recovery of the same wrong tree or monophyletic group in a well-known phylogenetic artefact called long-branch attraction (Felsenstein, 1978It is noteworthy that, although the monophyly of the LeuconostocOenococcusWeissella clade is clear, as well as that of two of the main groups in Leuconostoc, there is no complete evidence of the monophyly of either Leuconostoc or Weissella. This is due mainly to the ML approach, which takes into account the existence of rate variation among sites and among lineages, which are estimated from the data. Nevertheless, there is still strong evidence for the evolutionary line leading to Oenococcus and Leuconostoc. It should be noticed that the position of L. fallax as an outgroup of the remaining Leuconostoc species is not clear from this analysis.
Phylogenetic analysis of protein-coding genes
Fig. 2(ad)
shows NJ phylogenies made with nucleotide data from the different protein-coding genes. Phylogenetic analysis based on protein sequence trees led to the same overall conclusions, and these trees are provided as Supplementary Figs S1S12 in IJSEM Online. ML trees were made using the TrN (Tamura & Nei, 1993
) model for dnaA sequences and GTR model for gyrB, rpoC and dnaK sequences, with gamma rate variation and a proportion of invariable sites. NJ trees from the different genes resulted in the formation of the same phylogenetic groups when there was good bootstrap support. The only exception refers to the clustering of W. viridescens with W. paramesenteroides, W. hellenica and W. confusa with bootstrap values of 99/84/85 % in rpoC analysis (Fig. 2c
), in disagreement with its clustering with W. halotolerans in the dnaK tree (Fig. 2d
) with bootstrap values of 100/100/96 %.
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Another uncertainty revealed by the analysis of protein-coding genes concerns the evolutionary line leading to O. oeni. In the majority of cases, O. oeni appears as an outgroup to both Leuconostoc and Weissella, although never with strong support; the highest bootstrap values (77/77/69 %) come from the dnaA phylogeny (Fig. 2a
). In this aspect, only the gyrB phylogeny is in agreement with the 16S rRNA gene tree, but still unsupported. As expected in such uncertain cases, the branch leading to the ingroup in question (Leuconostoc and Weissella or Leuconostoc and O. oeni) is distinguished by being of short length, revealing the proximity of the two branching events.
Another discrepancy of the phylogenies obtained with the 16S rRNA gene and protein-coding genes concerns the recognition of the most divergent line in Leuconostoc. This can be identified in cases where there is strong support in the monophyly both for the genus Leuconostoc as a whole and the intrageneric clade that does not include the divergent line. In all protein-coding gene phylogenies, the clade of L. fructosum, L. ficulneum and L. pseudoficulneum appears as the first divergent line, although this becomes evident only in the dnaK phylogeny, with bootstrap values of 100/91/99 % supporting the monophyly of the other Leuconostoc species (the gyrB phylogeny does not support a Leuconostoc monophyly indisputably). Still, as can be seen in the dnaA and rpoC phylogenies, L. fallax constitutes the next divergent line in Leuconostoc. It should also be noted that the marked differences observed in evolutionary rates in the 16S rRNA gene phylogeny are no longer clear. In this respect, the lengths of branches of Leuconostoc and Weissella species are more alike, and the difference from O. oeni is reduced and even inverted in the dnaA phylogeny.
Simultaneous analysis of all genes
Since there seems to be a general agreement of the different gene phylogenies, we combined all genes in one single analysis in order to obtain a tree taking into account all of the data. By using MrBayes software and the data partitioned in sets concerning the different gene sequences, we were aiming to obtain posterior probability values to support clades, taking in account gene-specific evolutionary rates and among-site rate patterns (Castoe et al., 2004
). Fig. 3
shows the result of this combined approach. It should be stated that, although Bayesian posterior probabilities are not the same as bootstrap values in confidence evaluation, they can be the result of an even more sensitive process in the evaluation of the phylogenetic signal (Alfaro et al., 2003
).
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To assess further the adequacy of the Bayesian tree to explain the evolution of each gene, we performed SH tests with RELL optimization (Shimodaira & Hasegawa, 1999
; see also Goldman et al., 2000
). In this test, different tree topologies are compared and their adequacy to fit the data is tested based upon the comparison of their log-likelihood values. Usually, a value of P
0.05 is used for the rejection of a given tree topology, representing a hypothesis that might explain the data in the form of an alignment. Beyond the topology given by the Bayesian tree, the set of trees tested included the ML tree of each gene and the NJ trees presented in Figs 1 and 2![]()
. The Bayesian tree was rejected only when the test was made to the 16S rRNA gene data (result not shown). This might be due to the different phylogenetic position of L. fallax in the combined and 16S rRNA gene phylogenies, since its removal from the analysis was enough to prevent the rejection of the Bayesian tree (whereas this did not happen with the removal of any other strain). These results indicate that the phylogenetic tree presented in Fig. 3
provides a good explanatory hypothesis for the evolution of the genes under study and that the observed differences are the result of a lack of phylogenetic information needed for their resolution.
Evaluation of evolutionary rates
The genetic distances from the three genera under study to the four outgroups were plotted (Fig. 4
) in order to obtain relative rates of group evolution that are unbiased by phylogenetic reconstruction. Genetic distances of Leuconostoc and Weissella from outgroup species were considered as Student's t distributions resulting from individual species distances. In all but one case (distances based on dnaA), Oenococcus appears as the most distant group, supporting its aforementioned fast evolutionary nature. Distances based on dnaA (Fig. 4b
) show that this gene presents the highest rate of divergence, which could preclude its use for this comparison. Leuconostoc and Weissella are in general at the same distance from the outgroup species, although Leuconostoc sometimes presents greater distances. A particular case refers to the 16S rRNA gene distances, in which there is no overlapping of the 95 % confidence intervals. This fact, allied to the highest rate differences shown by O. oeni, suggests uniqueness of the evolutionary rate of the 16S rRNA gene in this species, indicating that care should be taken in conclusions based on this gene. In fact, the greater distances presented by O. oeni and Leuconostoc associated with the existence of a short internal branch (as suggested in phylogenies from protein-coding genes) could lead to their incorrect grouping in 16S rRNA gene trees, as a result of long-branch attraction. It has been shown that high (>95 %) posterior probability values could be incorrectly assigned in low-rate trees with short branch lengths (Alfaro et al., 2003
), which could justify the high support of Leuconostoc and Oenococcus clustering in MrBayes tree. The difference in branch- or group-specific evolutionary rates could also explain the position of L. fallax in 16S rRNA gene phylogenies, as the result of a relatively low rate of evolution in comparison with the other Leuconostoc species (see Fig. 1
).
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In cases of low rates of evolution, there is a tendency for the slow-evolving taxa to resemble outgroup sequences, leading to their phylogenetic attraction (Philippe et al., 2005
). There is a strong indication that, in this case, this is due to the proximity of Weissella and Lactobacillus species. In Table 3 of Yang & Woese (1989)
, we can see that, of the nine signature positions of lactobacilli (all differing from Leuconostoc species), only two differ in the L. paramesenteroides group (Weissella). Furthermore, in the study of Collins et al. (1991)
, where more than 40 other Lactobacillus species were used, the discrepancy of branch lengths of Weissella and Leuconostoc was greater than in the study of Martinez-Murcia & Collins (1990)
, where most of the outgroup sequences belonged to other bacterial genera. Our statement is further corroborated by the work of Morse et al. (1996)
in which, in contradiction to many other studies, a monophyly of Weissella and Leuconostoc is suggested, albeit with low bootstrap values (65 %). In this last case, most of the outgroup sequences belong to phylogenetically distant species. In spite of the misleading information in the 16S rRNA gene data, this does not mean that the suggested monophyly is wrong, but that it should continue to be treated as a so-far unsupported hypothesis.
The genus-specific rate of evolution of the 16S rRNA gene prevents its use in the determination of the precise branching order and in the evaluation of rates of evolution at genomic level. However, this does not preclude its use in intrageneric phylogenetic evaluation, as shown by the general agreement with other gene-based phylogenies. Another outcome of the specific evolution of the 16S rRNA gene is its possible association with the underlying cladogenic process, even if its meaning is unclear. As to the presence of tachytely in Oenococcus, this study partially corroborates the findings of Morse et al. (1996)
, as there seems to be no exceptionally fast nature in the evolution of this genus. Although we found it to be generally more distant than Leuconostoc and Weissella from the outgroups used, the difference is never as large as in 16S rRNA gene-based phylogenies. In addition, the fact that O. oeni is the sole species in this genus presents the drawback of reduced taxon sampling, the effect of which on the accuracy of estimation of the evolutionary rate is still unknown.
| ACKNOWLEDGEMENTS |
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