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Multiple Streptomyces species with distinct secondary metabolomes have identical 16S rRNA gene sequences.

ABSTRACT: Microbial diversity studies using small subunit (SSU) rRNA gene sequences continue to advance our understanding of biological and ecological systems. Although a good predictor of overall diversity, using this gene to infer the presence of a species in a sample is more controversial. Here, we present a detailed polyphasic analysis of 10 bacterial strains isolated from three coastal lichens Lichina confinis, Lichina pygmaea and Roccella fuciformis with SSU rRNA gene sequences identical to the type strain of Streptomyces cyaneofuscatus. This analysis included phenotypic, microscopic, genetic and genomic comparisons and showed that despite their identical SSU rRNA sequences the strains had markedly different properties, and could be distinguished as 5 different species. Significantly, secondary metabolites profiles from these strains were also found to be different. It is thus clear that SSU rRNA based operational taxonomy units, even at the most stringent cut-off can represent multiple bacterial species, and that at least for the case of Streptomyces, strain de-replication based on SSU gene sequences prior to screening for bioactive molecules can miss potentially interesting novel molecules produced by this group that is notorious for the production of drug-leads.

SUBMITTER: Antony-Babu S

PROVIDER: S-EPMC5593946 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Multiple Streptomyces species with distinct secondary metabolomes have identical 16S rRNA gene sequences.

Antony-Babu Sanjay S Stien Didier D Eparvier Véronique V Parrot Delphine D Tomasi Sophie S Suzuki Marcelino T MT

Scientific reports 20170911 1

Microbial diversity studies using small subunit (SSU) rRNA gene sequences continue to advance our understanding of biological and ecological systems. Although a good predictor of overall diversity, using this gene to infer the presence of a species in a sample is more controversial. Here, we present a detailed polyphasic analysis of 10 bacterial strains isolated from three coastal lichens Lichina confinis, Lichina pygmaea and Roccella fuciformis with SSU rRNA gene sequences identical to the type ...[more]

PMID: 28894255

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Project description:16S rRNA gene sequences are commonly analyzed for taxonomic and phylogenetic studies because they contain variable regions that can help distinguish different genera. However, intra-genus distinction using variable region homology is often impossible due to the high overall sequence identities among closely related species, even though some residues may be conserved within respective species. Using a computational method that included the allelic diversity within individual genomes, we discovered that certain Escherichia and Shigella species can be distinguished by a multi-allelic 16S rRNA variable region single nucleotide polymorphism (SNP). To evaluate the performance of 16S rRNAs with altered variable regions, we developed an in vivo system that measures the acceptance and distribution of variant 16S rRNAs into a large pool of natural versions supporting normal translation and growth. We found that 16S rRNAs containing evolutionarily disparate variable regions were underpopulated both in ribosomes and in active translation pools, even for an SNP. Overall, this study revealed that variable region sequences can substantially influence the performance of 16S rRNAs and that this biological constraint can be leveraged to justify refining taxonomic assignments of variable region sequence data. IMPORTANCE This study reevaluates the notion that 16S rRNA gene variable region sequences are uninformative for intra-genus classification and that single nucleotide variations within them have no consequence to strains that bear them. We demonstrated that the performance of 16S rRNAs in Escherichia coli can be negatively impacted by sequence changes in variable regions, even for single nucleotide changes that are native to closely related Escherichia and Shigella species; thus, biological performance is likely constraining the evolution of variable regions in bacteria. Further, the native nucleotide variations we tested occur in all strains of their respective species and across their multiple 16S rRNA gene copies, suggesting that these species evolved beyond what would be discerned from a consensus sequence comparison. Therefore, this work also reveals that the multiple 16S rRNA gene alleles found in most bacteria can provide more informative phylogenetic and taxonomic detail than a single reference allele.

Project description:BackgroundThe phylogeny of the genus Methanobrevibacter was established almost 25 years ago on the basis of the similarities of the 16S rRNA oligonucleotide catalogs. Since then, many 16S rRNA gene sequences of newly isolated strains or clones representing the genus Methanobrevibacter have been deposited. We tried to reorganize the 16S rRNA gene sequences of this genus and revise the taxonomic affiliation of the isolates and clones representing the genus Methanobrevibacter.ResultsThe phylogenetic analysis of the genus based on 786 bp aligned region from fifty-four representative sequences of the 120 available sequences for the genus revealed seven multi-member groups namely, Ruminantium, Smithii, Woesei, Curvatus, Arboriphilicus, Filiformis, and the Termite gut symbionts along with three separate lineages represented by Mbr. wolinii, Mbr. acididurans, and termite gut flagellate symbiont LHD12. The cophenetic correlation coefficient, a test for the ultrametric properties of the 16S rRNA gene sequences used for the tree was found to be 0.913 indicating the high degree of goodness of fit of the tree topology. A significant relationship was found between the 16S rRNA sequence similarity (S) and the extent of DNA hybridization (D) for the genus with the correlation coefficient (r) for logD and logS, and for [ln(-lnD) and ln(-lnS)] being 0.73 and 0.796 respectively. Our analysis revealed that for this genus, when S = 0.984, D would be <70% at least 99% of the times, and with 70% D as the species "cutoff", any 16S rRNA gene sequence showing <98% sequence similarity can be considered as a separate species. In addition, we deduced group specific signature positions that have remained conserved in evolution of the genus.ConclusionsA very significant relationship between D and S was found to exist for the genus Methanobrevibacter, implying that it is possible to predict D from S with a known precision for the genus. We propose to include the termite gut flagellate symbiont LHD12, the methanogenic endosymbionts of the ciliate Nyctotherus ovalis, and rat feces isolate RT reported earlier, as separate species of the genus Methanobrevibacter.

Project description:BACKGROUND: The intra- and inter-species genetic diversity of bacteria and the absence of 'reference', or the most representative, sequences of individual species present a significant challenge for sequence-based identification. The aims of this study were to determine the utility, and compare the performance of several clustering and classification algorithms to identify the species of 364 sequences of 16S rRNA gene with a defined species in GenBank, and 110 sequences of 16S rRNA gene with no defined species, all within the genus Nocardia. METHODS: A total of 364 16S rRNA gene sequences of Nocardia species were studied. In addition, 110 16S rRNA gene sequences assigned only to the Nocardia genus level at the time of submission to GenBank were used for machine learning classification experiments. Different clustering algorithms were compared with a novel algorithm or the linear mapping (LM) of the distance matrix. Principal Components Analysis was used for the dimensionality reduction and visualization. RESULTS: The LM algorithm achieved the highest performance and classified the set of 364 16S rRNA sequences into 80 clusters, the majority of which (83.52%) corresponded with the original species. The most representative 16S rRNA sequences for individual Nocardia species have been identified as 'centroids' in respective clusters from which the distances to all other sequences were minimized; 110 16S rRNA gene sequences with identifications recorded only at the genus level were classified using machine learning methods. Simple kNN machine learning demonstrated the highest performance and classified Nocardia species sequences with an accuracy of 92.7% and a mean frequency of 0.578. CONCLUSION: The identification of centroids of 16S rRNA gene sequence clusters using novel distance matrix clustering enables the identification of the most representative sequences for each individual species of Nocardia and allows the quantitation of inter- and intra-species variability.

Dataset Information

Multiple Streptomyces species with distinct secondary metabolomes have identical 16S rRNA gene sequences.

Publications

Multiple Streptomyces species with distinct secondary metabolomes have identical 16S rRNA gene sequences.

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