Dataset Information

Analysis of secA1 gene sequences for identification of Nocardia species.

ABSTRACT: Molecular methodologies, especially 16S rRNA gene sequence analysis, have allowed the recognition of many new species of Nocardia and to date have been the most precise methods for identifying isolates reliably to the species level. We describe here a novel method for identifying Nocardia isolates by using sequence analysis of a portion of the secA1 gene. A region of the secA1 gene of 30 type or reference strains of Nocardia species was amplified using secA1-specific primers. Sequence analysis of 468 bp allowed clear differentiation of all species, with a range of interspecies similarity of 85.0% to 98.7%. Corresponding 16S rRNA gene sequences of a 1,285-bp region for the same isolates showed a range of interspecies similarity of 94.4 to 99.8%. In addition to the type and reference strains, a 468-bp fragment of the secA1 gene was sequenced from 40 clinical isolates of 12 Nocardia species previously identified by 16S rRNA gene sequence analysis. The secA1 gene sequences of most isolates showed >99.0% similarity to the secA1 sequences of the type or reference strain to which their identification corresponded, with a range of 95.3 to 100%. Comparison of the deduced 156 amino acid sequences of the SecA1 proteins of the clinical isolates showed between zero and two amino acid residue differences compared to that of the corresponding type or reference strain. Sequencing of the secA1 gene, and using deduced amino acid sequences of the SecA1 protein, may provide a more discriminative and precise method for the identification of Nocardia isolates than 16S rRNA gene sequencing.

SUBMITTER: Conville PS

PROVIDER: S-EPMC1594632 | biostudies-literature | 2006 Aug

REPOSITORIES: biostudies-literature

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Analysis of secA1 gene sequences for identification of Nocardia species.

Conville Patricia S PS Zelazny Adrian M AM Witebsky Frank G FG

Journal of clinical microbiology 20060801 8

Molecular methodologies, especially 16S rRNA gene sequence analysis, have allowed the recognition of many new species of Nocardia and to date have been the most precise methods for identifying isolates reliably to the species level. We describe here a novel method for identifying Nocardia isolates by using sequence analysis of a portion of the secA1 gene. A region of the secA1 gene of 30 type or reference strains of Nocardia species was amplified using secA1-specific primers. Sequence analysis o ...[more]

PMID: 16891489

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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

Analysis of secA1 gene sequences for identification of Nocardia species.

Publications

Analysis of secA1 gene sequences for identification of Nocardia species.

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