16S ribosomal DNA sequence analysis of a large collection of environmental and clinical unidentifiable bacterial isolates

Abstract

Some bacteria are difficult to identify with phenotypic identification schemes commonly used outside reference laboratories. 16S ribosomal DNA (rDNA)-based identification of bacteria potentially offers a useful alternative when phenotypic characterization methods fail. However, as yet, the usefulness of 16S rDNA sequence analysis in the identification of conventionally unidentifiable isolates has not been evaluated with a large collection of isolates. In this study, we evaluated the utility of 16S rDNA sequencing as a means to identify a collection of 177 such isolates obtained from environmental, veterinary, and clinical sources. For 159 isolates (89.8%) there was at least one sequence in GenBank that yielded a similarity score of > or =97%, and for 139 isolates (78.5%) there was at least one sequence in GenBank that yielded a similarity score of > or =99%. These similarity score values were used to defined identification at the genus and species levels, respectively. For isolates identified to the species level, conventional identification failed to produce accurate results because of inappropriate biochemical profile determination in 76 isolates (58.7%), Gram staining in 16 isolates (11.6%), oxidase and catalase activity determination in 5 isolates (3.6%) and growth requirement determination in 2 isolates (1.5%). Eighteen isolates (10.2%) remained unidentifiable by 16S rDNA sequence analysis but were probably prototype isolates of new species. These isolates originated mainly from environmental sources (P = 0.07). The 16S rDNA approach failed to identify Enterobacter and Pantoea isolates to the species level (P = 0.04; odds ratio = 0.32 [95% confidence interval, 0.10 to 1.14]). Elsewhere, the usefulness of 16S rDNA sequencing was compromised by the presence of 16S rDNA sequences with >1% undetermined positions in the databases. Unlike phenotypic identification, which can be modified by the variability of expression of characters, 16S rDNA sequencing provides unambiguous data even for rare isolates, which are reproducible in and between laboratories. The increase in accurate new 16S rDNA sequences and the development of alternative genes for molecular identification of certain taxa should further improve the usefulness of molecular identification of bacteria.

FIG. 1

Identification scheme for 177 phenotypically unidentifiable bacterial isolates.

FIG. 2

Distance-related trees indicating the phylogenetic relationships of 18 unidentified isolates referred as strain numbers. (A) Low-percent-G+C-content gram-positive isolates; (B) high-percent-G+C-content gram-positive isolates; (C) gamma subgroup Proteobacteria isolates; (D) Bacteroides-Cytophaga subgroup isolates. The numbers at nodes are the proportions of 100 bootstrap resamplings that support the topology shown. Only bootstrap values of >90% are indicated. Bars, 1% divergence.

FIG. 2

Distance-related trees indicating the phylogenetic relationships of 18 unidentified isolates referred as strain numbers. (A) Low-percent-G+C-content gram-positive isolates; (B) high-percent-G+C-content gram-positive isolates; (C) gamma subgroup Proteobacteria isolates; (D) Bacteroides-Cytophaga subgroup isolates. The numbers at nodes are the proportions of 100 bootstrap resamplings that support the topology shown. Only bootstrap values of >90% are indicated. Bars, 1% divergence.

FIG. 2

Distance-related trees indicating the phylogenetic relationships of 18 unidentified isolates referred as strain numbers. (A) Low-percent-G+C-content gram-positive isolates; (B) high-percent-G+C-content gram-positive isolates; (C) gamma subgroup Proteobacteria isolates; (D) Bacteroides-Cytophaga subgroup isolates. The numbers at nodes are the proportions of 100 bootstrap resamplings that support the topology shown. Only bootstrap values of >90% are indicated. Bars, 1% divergence.

FIG. 2

Distance-related trees indicating the phylogenetic relationships of 18 unidentified isolates referred as strain numbers. (A) Low-percent-G+C-content gram-positive isolates; (B) high-percent-G+C-content gram-positive isolates; (C) gamma subgroup Proteobacteria isolates; (D) Bacteroides-Cytophaga subgroup isolates. The numbers at nodes are the proportions of 100 bootstrap resamplings that support the topology shown. Only bootstrap values of >90% are indicated. Bars, 1% divergence.