Identification of human pathogens isolated from blood using microarray hybridisation and signal pattern recognition

Abstract

Background: Pathogen identification in clinical routine is based on the cultivation of microbes with subsequent morphological and physiological characterisation lasting at least 24 hours. However, early and accurate identification is a crucial requisite for fast and optimally targeted antimicrobial treatment. Molecular biology based techniques allow fast identification, however discrimination of very closely related species remains still difficult.

Results: A molecular approach is presented for the rapid identification of pathogens combining PCR amplification with microarray detection. The DNA chip comprises oligonucleotide capture probes for 25 different pathogens including Gram positive cocci, the most frequently encountered genera of Enterobacteriaceae, non-fermenter and clinical relevant Candida species. The observed detection limits varied from 10 cells (e.g. E. coli) to 10(5) cells (S. aureus) per mL artificially spiked blood. Thus the current low sensitivity for some species still represents a barrier for clinical application. Successful discrimination of closely related species was achieved by a signal pattern recognition approach based on the k-nearest-neighbour method. A prototype software providing this statistical evaluation was developed, allowing correct identification in 100 % of the cases at the genus and in 96.7 % at the species level (n = 241).

Conclusion: The newly developed molecular assay can be carried out within 6 hours in a research laboratory from pathogen isolation to species identification. From our results we conclude that DNA microarrays can be a useful tool for rapid identification of closely related pathogens particularly when the protocols are adapted to the special clinical scenarios.

Figure 1

Normalised signal intensities of all hybridisation experiments listed by probe and species. The raw signal values were first normalised using quantile normalisation, and then averaged across spot-replicates and hybridisation-replicates (real values were divided by 1000 for better visualisation). No cut off value or signal limit was set in order to use absolute intensities for normalisation and table calculation. Background signals were subtracted prior to statistical evaluation. Background corrected hybridisation signals of 5001 – 10000, 10001 – 20000, and > 20001, are indicated in grey, dark grey and black, respectively. Normalised values lower than 5000 are not colour-coded. For calculations absolute values were used without defining a threshold that led to indication of low signals even when signals were flagged negative by the GenePix analysis software. Species are listed according to the phylogenetic relation of 16S and 18S rRNA sequences. Probes are sorted by species specificity. Abbreviations of probe names are listed in table 1.

Figure 2

Examples of individual hybridisation results. Experiments using E. coli (A) and S. aureus (B) were done as dilution series near their limit of detection. E. coli shows a much lower detection limit of 10 bacteria per assay than S. aureus with 10³ bacteria per assay. Even at lowest sensitivity level specific hybridisation patterns can still be obtained. Grey, black and white bars represent specific and non-specific signals as well as positive controls (BSrev is the hybridisation control and pr_FW and pr_FW T7 are PCR amplification controls). Error bars represent the mean of 6 replicate spots on the microarray. These figures only display intensities of spot signals which were flagged positive automatically by the GenePix Software. However for statistical analysis (quantile normalisation) all signals were evaluated. Results were controlled by visual inspection of image files.

Figure 3

Comparison of different parallel identifications of pathogens. Heatmap was drawn after hierarchical clustering. Each target combination was compared with hybridisation results of single cultures under equal experimental conditions. Rows correspond to probes and columns correspond to hybridisations. Colours correspond to signal values. Blue displays high signal value and red no signal value.

Figure 4

A: Hybridisation signals of E. coli isolated from whole blood. Despite the great background of human DNA in blood no interference (non-specific signals would be displayed in black) were observed. These results prove the high specificity of the protocol for clinical pathogens. Specific signals are shown as grey and positive controls as white bars. B: Isolation of bacterial DNA from blood spiked with E. coli and P. mirabilis, simulating a multi-microbial infection. It clearly shows the possibility of parallel detection of different microbes even from one human blood sample. Abbreviations of probe names are listed in table 1. Grey, black and white bars represent specific and non-specific signals as well as positive controls. These figures only display intensities of spot signals which were flagged positive automatically by the GenePix Software. However for statistical analysis all (both negative and positive flagged) signals were evaluated. Results were controlled by visual inspection of image files.

Figure 5

Results of all hybridisation experiments displayed as a heatmap. Columns correspond to probes and rows correspond to hybridisations. Colours correspond to signal values so that red indicates no signal succeeding to white for low signal strengths to blue indicating strong signal values (shown by the colour bar on the left side of the figure). The coefficient of variation of the different assays was already given along with the table of normalised signal values.