Dataset Information

Identification by 16S rRNA gene analyses of a potential novel mycobacterial species as an etiological agent of canine leproid granuloma syndrome.

ABSTRACT: PCR amplifications of the 16S rRNA gene were performed on 46 specimens obtained from 43 dogs with canine leproid granuloma syndrome to help determine its etiology. Sequence capture PCR was applied to 37 paraffin-embedded specimens from 37 dogs, and nested PCR was attempted on DNA from 9 fresh tissue specimens derived from 3 of the 37 aforementioned dogs and from an additional 6 dogs. Molecular analyses of the paraffin-embedded tissues and fresh tissue specimen analyses were performed at separate institutions. PCR products with identical sequences over a 350-bp region encompassing variable regions 2 and 3 of the 16S rRNA gene were obtained from 4 of 37 paraffin-embedded specimens and from all 9 specimens of fresh tissue originating from 12 of the 43 dogs. Identical sequences were determined from amplicons obtained from paraffin-embedded and fresh specimens from one dog. The consensus DNA sequence, amplified from paraffin-embedded tissue and represented by GenBank accession no. AF144747, shared highest nucleotide identity (99.4% over 519 bp) with mycobacterial strain IWGMT 90413 but did not correspond exactly to any EMBL or GenBank database sequence. With a probe derived from the V2 region of the novel canine sequence, reverse cross blot hybridization identified an additional four paraffin-embedded specimens containing the same novel sequence. In total, molecular methodologies identified the proposed novel mycobacterial sequence in 16 of 43 dogs with canine leproid granuloma syndrome, indicating that the species represented by this sequence may be the principal etiological agent of canine leproid granuloma syndrome.

SUBMITTER: Hughes MS

PROVIDER: S-EPMC86311 | biostudies-literature | 2000 Mar

REPOSITORIES: biostudies-literature

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Identification by 16S rRNA gene analyses of a potential novel mycobacterial species as an etiological agent of canine leproid granuloma syndrome.

Hughes M S MS James G G Ball N N Scally M M Malik R R Wigney D I DI Martin P P Chen S S Mitchell D D Love D N DN

Journal of clinical microbiology 20000301 3

PCR amplifications of the 16S rRNA gene were performed on 46 specimens obtained from 43 dogs with canine leproid granuloma syndrome to help determine its etiology. Sequence capture PCR was applied to 37 paraffin-embedded specimens from 37 dogs, and nested PCR was attempted on DNA from 9 fresh tissue specimens derived from 3 of the 37 aforementioned dogs and from an additional 6 dogs. Molecular analyses of the paraffin-embedded tissues and fresh tissue specimen analyses were performed at separate ...[more]

PMID: 10698979

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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

Identification by 16S rRNA gene analyses of a potential novel mycobacterial species as an etiological agent of canine leproid granuloma syndrome.

Publications

Identification by 16S rRNA gene analyses of a potential novel mycobacterial species as an etiological agent of canine leproid granuloma syndrome.

Similar Datasets

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