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Application of oligonucleotide microarrays for bacterial source tracking of environmental Enterococcus sp. isolates.

Indest KJ, Betts K, Furey JS - Int J Environ Res Public Health (2005)

Bottom Line: Principal Components Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were compared for their ability to visually cluster microarray hybridization profiles based on the environmental source from which the Enterococcus sp. isolates originated.The PCA was visually superior at separating origin-specific clusters, even for as few as 3 factors.The implication of these results for the application of random oligonucleotide microarrays for BST is that, given the reproducibility issues, factor-based variable selection such as in PCA and SIM greatly outperforms dendrogram-based similarity measures such as in HCA and K-Nearest Neighbor KNN.

View Article: PubMed Central - PubMed

Affiliation: U.S. Army Engineer Research and Development Center, Waterways Experiment Station, 3909 Halls Ferry Road, Vicksburg, MS 39180, USA. indestk@wes.army.mil

ABSTRACT
In an effort towards adapting new and defensible methods for assessing and managing the risk posed by microbial pollution, we evaluated the utility of oligonucleotide microarrays for bacterial source tracking (BST) of environmental Enterococcus sp. isolates derived from various host sources. Current bacterial source tracking approaches rely on various phenotypic and genotypic methods to identify sources of bacterial contamination resulting from point or non-point pollution. For this study Enterococcus sp. isolates originating from deer, bovine, gull, and human sources were examined using microarrays. Isolates were subjected to Box PCR amplification and the resulting amplification products labeled with Cy5. Fluorescent-labeled templates were hybridized to in-house constructed nonamer oligonucleotide microarrays consisting of 198 probes. Microarray hybridization profiles were obtained using the ArrayPro image analysis software. Principal Components Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were compared for their ability to visually cluster microarray hybridization profiles based on the environmental source from which the Enterococcus sp. isolates originated. The PCA was visually superior at separating origin-specific clusters, even for as few as 3 factors. A Soft Independent Modeling (SIM) classification confirmed the PCA, resulting in zero misclassifications using 5 factors for each class. The implication of these results for the application of random oligonucleotide microarrays for BST is that, given the reproducibility issues, factor-based variable selection such as in PCA and SIM greatly outperforms dendrogram-based similarity measures such as in HCA and K-Nearest Neighbor KNN.

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Related in: MedlinePlus

Oligonucleotide microarray replicate hybridization profiles resulting from hybridization with BOX-PCR amplification products from deer isolate 49.1.1.
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f3-ijerph-02-00175: Oligonucleotide microarray replicate hybridization profiles resulting from hybridization with BOX-PCR amplification products from deer isolate 49.1.1.

Mentions: Oligonucleotide microarrays were evaluated for their ability to resolve BOX PCR amplification products derived from environmental sources of Enterococcus sp. isolates originating from deer, bovine, gull, and human. Purified genomic DNA from Enterococcus sp. isolates was subjected to BOX PCR amplification and the resulting amplification products were visualized by agarose gel electrophoresis. The results of a typical experiment can be seen in Fig. 2, which represents the subset of samples originating from deer. Agar gel electrophoresis confirms amplification as well as consistency of the BOX PCR reaction. PCR products were fluorescently labelled with aminoallyl dUTP and Cy5 then resolved by hybridization to in house fabricated 9mer oligonucleotide microarrays (see Material & Methods). The results of a representative microarray experiment can be seen in Fig. 3, in which replicate BOX PCR reactions from Enterococcus sp. deer isolate 49.1.1 were hybridized to replicate oligonucleotide micro arrays. A histogram of fluorescent spot intensities indicates that these randomly selected nonamer intensities follow a lognormal distribution (data not shown). Of the 17 environmental isolates analysed, not all replicate microarrays were usable. For six of these isolates (4 human and 2 deer) a single microarray hybridization replicate, consisting of duplicate microarray spots, was available for analysis. For the remaining 11 isolates and their replicates, spots that exhibited extreme variability in normalized spot intensities among replicates within a specific source were identified and subsequently eliminated from analysis for all isolates. Normalized spot intensities with above median standard deviations > or = 0.7 within source-specific datasets (i.e. bovine, deer, etc.), were eliminated leaving 45 of the 200 probes. The remaining 45 probes were then used for analyzing all 17 isolates.


Application of oligonucleotide microarrays for bacterial source tracking of environmental Enterococcus sp. isolates.

Indest KJ, Betts K, Furey JS - Int J Environ Res Public Health (2005)

Oligonucleotide microarray replicate hybridization profiles resulting from hybridization with BOX-PCR amplification products from deer isolate 49.1.1.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3814713&req=5

f3-ijerph-02-00175: Oligonucleotide microarray replicate hybridization profiles resulting from hybridization with BOX-PCR amplification products from deer isolate 49.1.1.
Mentions: Oligonucleotide microarrays were evaluated for their ability to resolve BOX PCR amplification products derived from environmental sources of Enterococcus sp. isolates originating from deer, bovine, gull, and human. Purified genomic DNA from Enterococcus sp. isolates was subjected to BOX PCR amplification and the resulting amplification products were visualized by agarose gel electrophoresis. The results of a typical experiment can be seen in Fig. 2, which represents the subset of samples originating from deer. Agar gel electrophoresis confirms amplification as well as consistency of the BOX PCR reaction. PCR products were fluorescently labelled with aminoallyl dUTP and Cy5 then resolved by hybridization to in house fabricated 9mer oligonucleotide microarrays (see Material & Methods). The results of a representative microarray experiment can be seen in Fig. 3, in which replicate BOX PCR reactions from Enterococcus sp. deer isolate 49.1.1 were hybridized to replicate oligonucleotide micro arrays. A histogram of fluorescent spot intensities indicates that these randomly selected nonamer intensities follow a lognormal distribution (data not shown). Of the 17 environmental isolates analysed, not all replicate microarrays were usable. For six of these isolates (4 human and 2 deer) a single microarray hybridization replicate, consisting of duplicate microarray spots, was available for analysis. For the remaining 11 isolates and their replicates, spots that exhibited extreme variability in normalized spot intensities among replicates within a specific source were identified and subsequently eliminated from analysis for all isolates. Normalized spot intensities with above median standard deviations > or = 0.7 within source-specific datasets (i.e. bovine, deer, etc.), were eliminated leaving 45 of the 200 probes. The remaining 45 probes were then used for analyzing all 17 isolates.

Bottom Line: Principal Components Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were compared for their ability to visually cluster microarray hybridization profiles based on the environmental source from which the Enterococcus sp. isolates originated.The PCA was visually superior at separating origin-specific clusters, even for as few as 3 factors.The implication of these results for the application of random oligonucleotide microarrays for BST is that, given the reproducibility issues, factor-based variable selection such as in PCA and SIM greatly outperforms dendrogram-based similarity measures such as in HCA and K-Nearest Neighbor KNN.

View Article: PubMed Central - PubMed

Affiliation: U.S. Army Engineer Research and Development Center, Waterways Experiment Station, 3909 Halls Ferry Road, Vicksburg, MS 39180, USA. indestk@wes.army.mil

ABSTRACT
In an effort towards adapting new and defensible methods for assessing and managing the risk posed by microbial pollution, we evaluated the utility of oligonucleotide microarrays for bacterial source tracking (BST) of environmental Enterococcus sp. isolates derived from various host sources. Current bacterial source tracking approaches rely on various phenotypic and genotypic methods to identify sources of bacterial contamination resulting from point or non-point pollution. For this study Enterococcus sp. isolates originating from deer, bovine, gull, and human sources were examined using microarrays. Isolates were subjected to Box PCR amplification and the resulting amplification products labeled with Cy5. Fluorescent-labeled templates were hybridized to in-house constructed nonamer oligonucleotide microarrays consisting of 198 probes. Microarray hybridization profiles were obtained using the ArrayPro image analysis software. Principal Components Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were compared for their ability to visually cluster microarray hybridization profiles based on the environmental source from which the Enterococcus sp. isolates originated. The PCA was visually superior at separating origin-specific clusters, even for as few as 3 factors. A Soft Independent Modeling (SIM) classification confirmed the PCA, resulting in zero misclassifications using 5 factors for each class. The implication of these results for the application of random oligonucleotide microarrays for BST is that, given the reproducibility issues, factor-based variable selection such as in PCA and SIM greatly outperforms dendrogram-based similarity measures such as in HCA and K-Nearest Neighbor KNN.

Show MeSH
Related in: MedlinePlus