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1 State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, PR China
2 Department of Biology, Graduate University of Chinese Academy of Sciences, Beijing 100049, PR China
Correspondence
Ying Huang
huangy{at}im.ac.cn
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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The GenBank/EMBL/DDBJ accession numbers for the sequence data reported in this paper are listed in Table 1
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A series of supplementary figures showing the chromosomal locations of the genes used, and phylogenetic trees for each protein-coding locus and for concatenated multiple genes are available with the online version of this paper.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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). Many members of this genus also play important roles in biodegradation and bioremediation (Ishiyama et al., 2004), such as degrading lignin and recalcitrant aromatic compounds (Phelan et al., 1979; Grund et al., 1990). Notwithstanding these findings, it has been predicted that less than 10 % of streptomycete bioactive metabolites have been discovered (Watve et al., 2001; Clardy et al., 2006).
As a rich source of novel bioactive, commercially significant compounds, the genus Streptomyces has been subjected to intensive isolation and screening, resulting in more than 3000 species being described in the literature (including patents) from the 1970s. The large number of isolates and poor species definition caused overspeciation and taxonomic chaos within this genus, the largest genus in the prokaryotes. The International Streptomyces Project was launched to establish standard phenotypic criteria for Streptomyces species determination during 1964–1972. Several numerical taxonomic and chemotaxonomic studies followed, albeit with limited success (Anderson & Wellington, 2001). Since the 1980s, the advent of molecular techniques has provided a number of genotypic approaches to investigate the taxonomy of Streptomyces, including rRNA sequence comparison, DNA–DNA hybridization and DNA fingerprinting (Watve et al., 2001; Lanoot et al., 2004, 2005a; Song et al., 2004). However, due to the respective drawbacks of each method, i.e. insufficient resolution, labour-intensive and high error rate, and low inter-laboratory reproducibility, all have their limitations in routine use.
Despite the current availability of almost all 16S rRNA gene sequences of Streptomyces type strains in public databases contributed by researchers from several countries, many species relationships within Streptomyces remain unclear. The Streptomyces griseus 16S rRNA gene clade has become one of the most taxonomically complex groups, and one which has proved most prolific in the production of bioactive secondary metabolites (Iwamoto et al., 1990; Strohl, 2003; Piel, 2004). The majority of the members of this group share highly similar phenotypes and 16S rRNA sequences. However, reorganization of species within the S. griseus clade is a common occurrence. For example, based on DNA–DNA relatedness data, ‘Streptomyces caviscabies’, ‘Streptomyces setonii’ and ‘Streptomyces argenteolus’ have been transferred to S. griseus (Liu et al., 2005); ‘S. griseus subsp. alpha’ and ‘S. griseus subsp. cretosus’ have been transferred to Streptomyces microflavus (Lanoot et al., 2005b); and ‘Streptomyces citreofluorescens’, ‘Streptomyces chrysomallus subsp. chrysomallus’ and ‘Streptomyces fluorescens’ have been reassigned to Streptomyces anulatus (Lanoot et al., 2005b).
It is important to unravel the taxonomic relationships of reference streptomycetes at species level to guide the species discrimination and the discovery of potentially novel species for ecological reasons and industrial purposes. MLST (Multilocus Sequence Typing) (Maiden et al., 1998; Enright & Spratt, 1999; Urwin & Maiden, 2003) has been successfully applied to the unambiguous characterization of bacterial pathogens (Dingle et al., 2001; Adiri et al., 2003; Baldwin et al., 2005; Coffey et al., 2006), and to phylogenetic analysis of highly diverse bacterial groups, such as the genera Mycobacterium (Devulder et al., 2005), Pseudomonas (Hilario et al., 2004) and Haemophilus (Nørskov-Lauritsen et al., 2005), because of its lab-to-lab portability and reproducibility, and the demonstrated efficiency of inter- and intra-species resolution. In the present investigation, a streptomycete multilocus sequence analysis scheme was developed to clarify the bona fide taxonomic structure of the S. griseus 16S rRNA gene clade. We selected six genes: atpD, gyrB, recA, rpoB, trpB and 16S rRNA, compared the phylogenetic trees derived from the sequence data, constructed a finer and more robust phylogeny, and set up a multilocus sequence database with open access on the internet. This study offers a primary multi-sequence-based framework for amending the systematics of Streptomyces, which facilitates our understanding of phylogeny and evolution of this genus.
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METHODS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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), 53 were from CGMCC/AS (China General Microbiological Culture Collection Centre, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, PR China); the other two strains were from BCCM/LMG (Collection of the Laboratorium voor Microbiologie en Microbiele Genetica, Rijksuniversiteit, Ledeganckstraat 35, B-9000, Gent, Belgium) and JCM (Japan Collection of Microorganisms, Institute of Physical and Chemical Research (RIKEN), Wako, Saitama 351-0198, Japan), respectively. The majority were type strains. Strains were cultured in yeast extract/malt extract agar (ISP 2; Shirling & Gottlieb, 1966) at 28 °C.
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) and Streptomyces avermitilis (Ikeda et al., 2003), whose genomes are available in public databases. Software packages Primer premier 5.0 (Premier Biosoft International), Oligo 6.0 (Molecular Biology Insights) and SPCR 3.0 (http://moleco.sjtu.edu.cn/moleco/softwares.html) were used to design and evaluate the primers. For gyrB, amplification primers were derived from published primers PF-1 and PR-2, and sequencing primers from F-1 and R-4 (Hatano et al., 2003). The information for loci and primers used in this scheme are described in Table 2.
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. Each 50 µl amplification reaction contained 1 µl template DNA (50–200 ng), 5 µl 10x PCR buffer, 1 µl each PCR primer (20 µM), 1 µl dNTP mix (10 mM), 6 µl MgCl2 (25 mM), 2.5 U Taq DNA polymerase, 5 µl DMSO and 29 µl sterile MilliQ water. The reaction conditions were initial denaturation at 95 °C for 5 min, followed by 30 cycles of denaturation at 95 °C for 30 s, annealing for 30 s at the primer-pair-specific annealing temperature (Table 2) and extension at 72 °C for 90 s. A final extension was performed at 72 °C for 10 min. Reaction products were electrophoresed on a 1 % agarose gel and checked with ethidium bromide under UV light, and then purified and sequenced directly using a Taq DyeDeoxy Terminator Cycle Sequencing Kit and an ABI Prism 3730 automated DNA sequencer (Applied Biosystems). Both strands were sequenced as a cross-check by using forward and reverse sequencing primers.
Data analysis.
Sequences of each locus were aligned using MEGA 3.1 software (Kumar et al., 2004), and trimmed manually at the same position before being used for further analysis and to set up a database on the internet (http://pubmlst.org/streptomyces). Statistics for each locus, such as the number and proportion of polymorphic sites, mean G+C content, etc., were calculated using the START2 program (Jolley et al., 2001). Pairwise distances between sequences of each locus were calculated by using the Kimura two-parameter (K2P) model (Kimura, 1980). The dN/dS ratios (the ratios of non-synonymous to synonymous polymorphisms) were calculated by using the method of Nei & Gojobori (1986) in START2.
To ensure the stability and reliability of phylogenetic relationships among strains used in this study, phylogenetic trees were constructed by using three different methods, neighbour-joining (NJ), maximum-parsimony (MP) and maximum-likelihood (ML). The NJ and MP methods were from the MEGA 3.1 package with the option of complete deletion of gaps, and the ML method was from PHYLIP package version 3.66 (Felsenstein, 2006). The K2P model was chosen as a substitution model for all NJ tree constructions. Single-locus and sequence-concatenated trees, including concatenation of sequences of three and four random protein-coding loci and all five protein-coding loci, were inferred including coding positions (1st+2nd+3rd+non-coding). Concatenated sequences of all five protein-coding loci were joined head-to-tail in-frame. The bootstrap technique (Felsenstein, 1985) was employed to evaluate the tree topologies by resampling the sequence alignment 1000 times. Mycobacterium tuberculosis H37Rv was used as the outgroup.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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region (158–203) (Anderson & Wellington, 2001). GenBank accession numbers of the sequences used in this study are listed in Table 1.
The features of each locus are displayed in Table 3. The proportion of variable sites in the alleles varied from 20.4 % (16S rRNA allele) to 48 % (gyrB), in contrast with 15.5 % variability in full-length 16S rRNA sequences. The higher percentage of variable sites led to wider ranges of distance and higher mean K2P distances, which made it possible to obtain finer relationships between close strains. Among the five protein-coding genes, gyrB had the largest mean K2P distance (0.140), significantly larger than that of the full-length 16S rRNA sequence; atpD and recA shared a similar distance range and mean K2P distance, as did rpoB and trpB. With the exception of 16S rRNA, the G+C contents of the loci were close to the genomic G+C content range of the genus Streptomyces, 67–78 mol%. The dN/dS ratio is used to estimate the degree of selection operating on each locus. All of the dN/dS values shown in Table 3 were much less than 1, indicating that most of the sequence variability identified is selectively neutral at protein level, validating their suitability for this multilocus sequence analysis scheme.
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). Bootstrap support for the trees was low: nodes with values less than 60 % accounted for 21 out of 40 nodes (52.5 %) in the NJ tree, 41 out of 53 nodes (77.4 %) in the MP tree and 100 % in the ML tree; in addition it cannot be neglected that 10 nodes in the MP tree have a bootstrap value of 0 %.
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); within each of these clusters the strains group together without divergence, sharing completely identical 16S rRNA gene sequences. S. griseus subsp. griseus forms cluster IV (red) with Streptomyces erumpens and ‘Streptomyces ornatus’, while ‘S. griseus subsp. alpha’ and ‘S. griseus subsp. cretosus’ belong to cluster III (green). It is interesting that S. griseus subsp. solvifaciens forms a distinct cluster with ‘S. argenteolus’, Streptomyces galilaeus and Streptomyces vinaceus at the bottom of the tree, and shares an equally low sequence identity of 95.4 % with S. griseus subsp. griseus, S. griseus subsp. alpha and S. griseus subsp. cretosus. However, the levels of identity between S. griseus subsp. griseus and S. griseus subsp. alpha and S. griseus subsp. cretosus are 99.9 % in both cases. Strains in the lower part of the tree were generally well separated, except Streptomyces spororaveus and Streptomyces nojiriensis, and S. galilaeus, S. griseus subsp. solvifaciens and S. vinaceus. 16S rRNA gene sequence identities between strains in this part ranged from 87.1 to 100 %, with a mean distance of 0.033. However, without S. flavidofuscus, the range of sequence identities narrowed to 95.0–100 %.
Single and concatenated protein-coding-gene trees
Phylogenetic trees for each protein-coding locus were constructed by using the three different methods (Supplementary Figs S2–S6, available with the online version of this paper). These trees, based on different loci, show general congruence. Clusters of strains indicated by different colours in the 16S rRNA gene tree (Fig. 1) are largely maintained in each single-gene tree, but with better discrimination. However, none of these genes alone contains enough phylogenetic information to reliably discriminate all species in the trees. The congruence among single-gene trees allows concatenation of multigene sequences for global analyses.
Sequences of three, four and five protein-coding genes were concatenated for phylogenetic analysis. It can be seen from the resultant trees (Supplementary Figs S7–S9, available with the online version of this paper) that, with the addition of each gene, the discriminatory power and the robustness of the trees increases slowly and regularly. Compared to the three- and four-gene trees, the five-gene tree holds bootstrap support that is slightly increased in the upper part, but greatly increased for the lower part, especially for the higher nodes, and gives enough resolution to separate closely related Streptomyces species.
Six-gene trees
The concatenated alignment of six loci contains 2900 nt with a mean G+C content of 67 mol% and a mean pairwise distance of 0.079. The sequence identities ranged from 88.3 to 100 %. Two-section division and a similar tendency of congruence and incongruence of each section were also observed for six-gene trees constructed by different methods (Fig. 2). The sequence identity range was 91.2–100 % for the upper part and 88.3–99.7 % for the lower part.
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In the lower part of the tree, bootstrap values were generally higher than those in five-gene trees, as demonstrated by 80 % of nodes holding values over 60 % in the NJ tree. The topological congruence between the three trees constructed by different methods is better than that between trees of five loci: high bootstrap values support the resemblance between deep branches of each tree, while the topology of some higher nodes remains unstable.
The six-gene tree proved to be the most robust and reliable phylogenetic tree that differentiated most strains in this study well, with the exceptions of S. griseus subsp. griseus and ‘S. setonii’, ‘S. ornatus’ and S. erumpens, S. microflavus, ‘S. griseus subsp. alpha’ and ‘S. griseus subsp. cretosus’, and S. galilaeus and S. vinaceus.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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). The five protein-coding genes we selected are commonly used in the identification of bacteria, and some of them have been used individually for phylogenetic analyses of Streptomyces (Egan et al., 2001; Hatano et al., 2003; Kim et al., 2004). Each gene has only one copy on the S. coelicolor A3(2) genome, performs a different function in primary metabolism and is evolutionarily conserved. Primers designed for this multilocus sequence analysis scheme gave good results, but the sequencing primers of gyrB derived from the literature (Hatano et al., 2003) performed inconsistently, although sequences were eventually obtained. New sequencing primers should be designed for future phylogenetic research using gyrB.
Combinability of loci
The suitability of the loci for this study was validated by their remote positions on the chromosome, low dN/dS ratios and high content of informative sites (Table 3). The minimum distance between two loci (rrnE and recA) among the six distant genes is over 30 kb (Fig. S1), which means there is no genetic linkage between these loci theoretically. The low dN/dS ratios (far below 1) indicated that the five housekeeping genes are not subjected to selection pressure for amino acid changes – they are selectively neutral. These properties guarantee the independence of each locus as they underwent evolution. Moreover, the variability of the loci, as demonstrated by the high proportion of polymorphic sites, and larger distance range and larger mean distance for each locus than for full-length 16S rRNA gene sequences, implied that they would give a higher resolution for the phylogeny of the S. griseus clade than the 16S rRNA gene. The widespread congruence between phylogenies recovered from individual genes indicated that the phylogenetic signals would not be counteracted when the loci are concatenated, thus making the combined analysis of different loci possible in this study.
Phylogenetic resolution and reliability of single and combined datasets
The 16S rRNA gene tree is unreliable based on the conflicting topologies and the low bootstrap support for the trees generated by different algorithms (Fig. 1), pointing to a significant probability of support for incorrect relationships for the strains included. The relationships of strains in the upper section are poorly resolved, especially strains in the coloured clusters. In contrast, strains in the lower section which share low sequence identities are largely well distinguished. So we can assume that the 16S rRNA gene is more appropriate for discrimination of (moderately) distantly related streptomycetes, but is not efficient for closely related strains such as those in clusters I–IV.
We found all five protein-coding loci to be informative, but not powerful or reproducible for all reference strains; our effort to construct well-resolved, deep phylogenies of Streptomyces was plagued by rate heterogeneity of different loci and weak phylogenetic signals in single genes. Therefore, we used multiple genes and, as expected, the effect of these problems was reduced considerably. Considering the potential systematic biases in different datasets, concatenation of the six genes would be optimal in resolving the relationships among all the strains. The strains that could not be discriminated in the six-gene tree belong to same species (Fig. 2). Lanoot et al. (2005b) transferred ‘S. griseus subsp. alpha’ and ‘S. griseus subsp. cretosus’ into S. microflavus because of their 100 % DNA–DNA relatedness. ‘S. setonii’ has been transferred into S. griseus based on DNA–DNA relatedness (Liu et al., 2005) as well. In our study, all trees except the 16S rRNA gene tree reached a consensus that ‘S. setonii’ falls consistently into the closely related branch with S. griseus subsp. griseus, which is in agreement with the result of DNA–DNA relatedness. ‘S. ornatus’ and S. erumpens have identical six-gene concatenated sequences, with which S. griseus subsp. griseus shares 99.3 % sequence identity. DNA–DNA relatedness data from previous studies (Okanishi, 1972; Liu et al., 2005) support ‘S. ornatus’ belonging to the same genomic species as the type strain of S. griseus. Based on results of present and previous studies, we propose that ‘S. ornatus’, S. erumpens, ‘S. setonii’, and S. griseus subsp. griseus should be considered as members of the same genomic species, S. griseus, which however, should not encompass ‘S. caviscabies’ based on their well-separated locations in the multigene trees. The two remaining strains, S. galilaeus and S. vinaceus, which share identical sequences of all six genes, do not merit separate species status, because their 16S rRNA gene sequences (EU048542 and EU048541) determined in this study are evidently different from those (AB045878 and AB184394) of the respective type strains (with distances of 0.042–0.044), and they should be assigned to a single species with a different name.
In light of the clear differentiation of all strains at species level by the six-gene tree and the accordance between data of this study and DNA–DNA relatedness studies, our multilocus sequence analysis scheme based on a combined analysis of the six loci gives credible and appropriate phylogenetic resolution at species level for the S. griseus 16S rRNA gene clade.
Comparison between 16S rRNA gene and concatenated trees
The upper parts of NJ trees include almost the same closely related strains except S. griseobrunneus, which failed to appear in the same position as in the 16S rRNA gene tree and moved to the upper part in the concatenated trees, with S. badius and S. sindenensis of cluster II as its consistent neighbours. Another strain with noticeably different positions within the upper part of the trees is S. griseoplanus (cluster I), as mentioned above. To check the incongruence observed for these two strains between the 16S rRNA gene and concatenated trees, we sequenced their 16S rRNA gene again, but obtained identical sequences as those retrieved from GenBank, which we had used. This result reveals the misleading probability of using the 16S rRNA gene solely for phylogenetic relationships.
Three species with abnormal taxonomic positions were observed in both the 16S rRNA gene and concatenated trees. One is ‘S. argenteolus’, which always clusters with S. griseus subsp. solvifaciens, S. galilaeus and S. vinaceus in the lower part of all trees, supported by high bootstrap values. Since it is separated from S. griseus subsp. griseus in the 16S rRNA gene tree with a phylogenetic distance of 0.046, and in the six-gene tree with a significant distance of 0.103, we suggest that ‘S. argenteolus’ AS 4.1693T merits species status distinct from S. griseus, rather than being a member of the latter species as proposed by Liu et al. (2005). The 16S rRNA gene sequence (EU048540) obtained for this strain in our study is markedly divergent (with a distance of 0.051) from those (AB045872 and AB184187) obtained previously for ‘S. argenteolus’ JCM 4623T (=NBRC 12841T). The second is S. griseus subsp. solvifaciens that was located unusually far from S. griseus subsp. griseus in all trees. Combined with its genotypical heterogeneity with other members of S. griseus as reported by Lanoot et al. (2005b), we propose that S. griseus subsp. solvifaciens should be removed from S. griseus. Additional analyses with more Streptomyces species are needed to clarify its taxonomic status. The third is S. flavidofuscus that was consistently located at the periphery of the other streptomycetes selected in this study. Further examination demonstrated that this type strain had sequences very different from that of other strains at all loci, but shared high 16S rRNA gene sequence similarities with strains of Nocardiopsis [e.g. 99.5 % identity with Nocardiopsis synnemataformans DSM 44143T (Y13593)] as determined by a BLAST search against the GenBank database. Therefore, we propose that S. flavidofuscus should be transferred to the genus Nocardiopsis. Therefore, S. griseobrunneus, S. griseoplanus, ‘S. argenteolus’, S. griseus subsp. solvifaciens and S. flavidofuscus, whose taxonomic positions have significantly changed, may be worth looking at again for potentially useful metabolites.
Several points can be obtained from comparison of the concatenated six-gene and 16S rRNA gene trees. First, the phylogenetic relationships among most strains in this study are generally congruent between the two trees. Second, the six-gene tree shows a much higher power of discrimination in that most species are clearly discriminated from each other, especially the closely related species in clusters I–IV, and the overall mean distance of the six-gene tree (0.079) is significantly greater than that of the 16S rRNA gene tree (0.020). Third, the topological structure of the six-gene tree, which is supported by noticeably higher bootstrap values, is much more stable than that of the 16S rRNA gene tree. These points emphasize the fact that the six-gene tree is obviously superior to the 16S rRNA gene tree in both resolution power and topological stability. Moreover, the concatenation of a sufficient number of genes overwhelms possible conflicting phylogenetic signals in different genes.
In conclusion, we have developed a pilot multilocus sequence analysis scheme for Streptomyces, and have shown its promising potential for refining the phylogeny of this genus. Our scheme, based on combined analysis of sequence variations at six different loci, can discriminate and define phylogenetic relationships among diverse and closely related species of the S. griseus 16S rRNA gene clade. We believe that this multilocus sequence analysis scheme can be applied further to more taxonomically diverse actinomycete taxa where identification and discrimination are valuable tools in the discovery of novel and commercially important metabolites.
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ACKNOWLEDGEMENTS |
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