Dataset Information

Identification of Enterobacter sakazakii from closely related species: the use of artificial neural networks in the analysis of biochemical and 16S rDNA data.

ABSTRACT: BACKGROUND: Enterobacter sakazakii is an emergent pathogen associated with ingestion of infant formula and accurate identification is important in both industrial and clinical settings. Bacterial species can be difficult to accurately characterise from complex biochemical datasets and computer algorithms can potentially simplify the process. RESULTS: Artificial Neural Networks were applied to biochemical and 16S rDNA data derived from 282 strains of Enterobacteriaceae, including 189 E. sakazakii isolates, in order to identify key characteristics which could improve the identification of E. sakazakii. The models developed resulted in a predictive performance for blind (validation) data of 99.3 % correct discrimination between E. sakazakii and closely related species for both phenotypic and genotypic data. Three main regions of the partial rDNA sequence were found to be key in discriminating the species. Comparison between E. sakazakii and other strains also constitutively positive for expression of the enzyme alpha-glucosidase resulted in a predictive performance of 98.7 % for 16S rDNA sequence data and 100% for phenotypic data. CONCLUSION: The computationally based methods developed here show a remarkable ability in reducing data dimensionality and complexity, in order to eliminate noise from the system in order to facilitate the speed and reliability of a potential strain identification system. Furthermore, the approaches described are also able to provide valuable information regarding the population structure and distribution of individual species thus providing the foundations for novel assays and diagnostic tests for rapid identification of pathogens.

SUBMITTER: Iversen C

PROVIDER: S-EPMC1421405 | biostudies-literature | 2006

REPOSITORIES: biostudies-literature

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Identification of Enterobacter sakazakii from closely related species: the use of artificial neural networks in the analysis of biochemical and 16S rDNA data.

Iversen Carol C Lancashire Lee L Waddington Michael M Forsythe Stephen S Ball Graham G

BMC microbiology 20060313

<h4>Background</h4>Enterobacter sakazakii is an emergent pathogen associated with ingestion of infant formula and accurate identification is important in both industrial and clinical settings. Bacterial species can be difficult to accurately characterise from complex biochemical datasets and computer algorithms can potentially simplify the process.<h4>Results</h4>Artificial Neural Networks were applied to biochemical and 16S rDNA data derived from 282 strains of Enterobacteriaceae, including 189 ...[more]

PMID: 16533390

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Project description:BackgroundCronobacter spp. (formerly Enterobacter sakazakii), are a group of Gram-negative pathogens that have been implicated as causative agents of meningitis and necrotizing enterocolitis in infants. The pathogens are linked to infant formula; however, they have also been isolated from a wide range of foods and environmental samples.ResultsIn this study, 233 samples of food, infant formula and environment were screened for the presence of Cronobacter spp. in an attempt to find its source. Twenty nine strains were isolated from samples of spices, herbs, infant foods, and dust obtained from household vacuum cleaners. Among the 76 samples of infant food, infant formula, milk powder and non-milk dairy products tested, only one sample of infant food contained Cronobacter spp. (1.4%). The other Cronobacter spp. isolates recovered include two from household vacuum dust, and 26 from 67 samples of herbs and spices. Among the food categories analyzed, herbs and spices harbored the highest number of isolates, indicating plants as a possible reservoir of this pathogen. Initial screening with API 20E test strips yielded 42 presumptive isolates. Further characterization using 3 chromogenic media (alpha-MUG, DFI and EsPM) and 8 sets of PCR primers detecting ITS (internal transcribed spacer sequences), 16S rRNA, zpx, gluA, gluB, OmpA genes followed by nucleotide sequencing of some PCR amplicons did not confirm the identity of all the isolates as none of the methods proved to be free of both false positives or false negatives. The final confirmation step was done by 16S rRNA sequence analysis identifying only 29 of the 42 isolates as Cronobacter spp.ConclusionOur studies showed that Cronobacter spp. are highly diverse and share many phenotypic traits with other Enterobacteriaceae members highlighting the need to use several methods to confirm the identity of this pathogen. None of the biochemical, chromogenic or PCR primers proved to be a reliable method for confirmation of the identity of the isolates as all of them gave either false positives or false negatives or both. It is therefore concluded that 16S rRNA sequencing is pivotal to confirm the identity of the isolates.

Project description:BackgroundThe 5' region of cytochrome oxidase I (COI) is the standard marker for DNA barcoding. However, COI has proved to be of limited use in identifying some species, and for some taxa, the coding sequence is not efficiently amplified by PCR. These deficiencies lead to uncertainty as to whether COI is the most suitable barcoding fragment for species identification of ticks.MethodsIn this study, we directly compared the relative effectiveness of COI, 16S ribosomal DNA (rDNA), nuclear ribosomal internal transcribed spacer 2 (ITS2) and 12S rDNA for tick species identification. A total of 307 sequences from 84 specimens representing eight tick species were acquired by PCR. Besides the 1,834 published sequences of 189 tick species from GenBank and the Barcode of Life Database, 430 unpublished sequences representing 59 tick species were also successfully screened by Bayesian analyses. Thereafter, the performance of the four DNA markers to identify tick species was evaluated by identification success rates given by these markers using nearest neighbour (NN), BLASTn, liberal tree-based or liberal tree-based (+threshold) methods.ResultsGenetic divergence analyses showed that the intra-specific divergence of each marker was much lower than the inter-specific divergence. Our results indicated that the rates of correct sequence identification for all four markers (COI, 16S rDNA, ITS2, 12S rDNA) were very high (> 96%) when using the NN methodology. We also found that COI was not significantly better than the other markers in terms of its rate of correct sequence identification. Overall, BLASTn and NN methods produced higher rates of correct species identification than that produced by the liberal tree-based methods (+threshold or otherwise).ConclusionsAs the standard DNA barcode, COI should be the first choice for tick species identification, while 16S rDNA, ITS2, and 12S rDNA could be used when COI does not produce reliable results. Besides, NN and BLASTn are efficient methods for species identification of ticks.

Dataset Information

Identification of Enterobacter sakazakii from closely related species: the use of artificial neural networks in the analysis of biochemical and 16S rDNA data.

Publications

Identification of Enterobacter sakazakii from closely related species: the use of artificial neural networks in the analysis of biochemical and 16S rDNA data.

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