A generic approach for the design of whole-genome oligoarrays, validated for genomotyping, deletion mapping and gene expression analysis on Staphylococcus aureus

Abstract

Background: DNA microarray technology is widely used to determine the expression levels of thousands of genes in a single experiment, for a broad range of organisms. Optimal design of immobilized nucleic acids has a direct impact on the reliability of microarray results. However, despite small genome size and complexity, prokaryotic organisms are not frequently studied to validate selected bioinformatics approaches. Relying on parameters shown to affect the hybridization of nucleic acids, we designed freely available software and validated experimentally its performance on the bacterial pathogen Staphylococcus aureus.

Results: We describe an efficient procedure for selecting 40-60 mer oligonucleotide probes combining optimal thermodynamic properties with high target specificity, suitable for genomic studies of microbial species. The algorithm for filtering probes from extensive oligonucleotides libraries fitting standard thermodynamic criteria includes positional information of predicted target-probe binding regions. This algorithm efficiently selected probes recognizing homologous gene targets across three different sequenced genomes of Staphylococcus aureus. BLAST analysis of the final selection of 5,427 probes yielded >97%, 93%, and 81% of Staphylococcus aureus genome coverage in strains N315, Mu50, and COL, respectively. A manufactured oligoarray including a subset of control Escherichia coli probes was validated for applications in the fields of comparative genomics and molecular epidemiology, mapping of deletion mutations and transcription profiling.

Conclusion: This generic chip-design process merging sequence information from several related genomes improves genome coverage even in conserved regions.

Figure 1

Schematic representation of StaphChip probe selection. All ORFs of N315 were loaded into ArrayDesigner™ (a) to select oligonucleotides according to their thermodynamical properties (Step A). The 417,776 N315-derived probes were filtered for target specificity using BLAST against N315 genome (Step B). Each probe should recognize a single target yielding a defined signal intensity threshold, i.e. outside the green box (b), otherwise it is rejected (c). During Step C, all accepted probes are aligned against heterologous S. aureus genomes (i.e. Mu50 or COL) to annotate probes common to the other genomes (d, e). Each probe should recognize a single target yielding a defined signal intensity threshold, i.e. inside the red box (d), with no other signals outside the green box; otherwise it is ignored (e). The process is repeated from Step A to C with the two other strain databases. Final selection by spreadsheet analysis (Step D) yielded a total number of 5,427 probes hybridizing with one or more S. aureus genomes.

Figure 2

In silico specificity of selected probes. Venn diagram showing probes recognized by all strains or strain-specific. Whereas the vast majority of probes recognized target in all three strains (4,812 probes), other recognized only 1 or 2 strains (n = 523).

Figure 3

Mapping of a deleted gene region by StaphChip. Cy-5 labelled DNA of parental strain SA113 was co-hybridized with Cy-3 labelled DNA from its isogenic ica deletion mutant. Colored bars indicate the position of each probe used to map ica-related and adjacent ORFs. Background signals (green) were recorded from probes recognizing the ica-region known to be deleted in strain SA113ica (arrows), as opposed to positive red signals recorded in the wild-type strain. The tetracycline resistance marker used for the construction and selection of strain SA113ica is recorded in the green channel only. Dye swap experiments provided similar results (not shown). Data are raw signal intensities; background level is indicated by a dotted line.

Figure 4

Comparative genome hybridization using clustering analysis. Genomic DNA of each individual S. aureus strain was labelled with Cy3 and co-hybridized with equivalent amounts of Cy5-labelled genomic DNA pooled from N315, Mu50 and COL. Background-subtracted data were expressed as Log₁₀ ratios and analyzed by two-way clustering using GeneSpring 6.1. Probes yielding positive and negative signals are shown in blue and yellow, respectively. The significance of black bars (a, b, and c) is indicated in the text. Note that the figure resolution does not allow visualising single probe differences but only clusters of probes.

Figure 5

Reproducibility of fluorescent signals in replicate experiments. Signals generated on StaphChip by 10 μg Cy-3 labelled N315 cDNA hybridized at 60°C. Average fluorescence intensities from replicate experiments (n = 8) and their maximal relative errors on S. aureus (A) or E. coli (B) capture probe elements are presented as scatter plots. The cumulative distribution of maximal relative errors is shown for S. aureus (C) or E. coli (D).

Figure 6

Reproducibility of fluorescent signals recorded from multiple non-overlapping capture elements for common transcripts. 10 μg Cy-3 labelled N315 cDNA were hybridized at 60°C on StaphChip. For 2,269 selected transcripts detected by two or multiple probes (n = 5,079), average fluorescence intensities and their maximal relative errors are presented in panel (A), and the cumulative distribution of maximal relative errors in panel (B).

Figure 7

Comparison of gene expression changes by real-time quantitative PCR and microarray analysis. Fold changes of gene expression estimated by either technique are shown for a set of 18 genes of S. aureus tested in two metabolic conditions. Data represent average values ± standard error of the mean of three independent experiments performed in duplicates.