Hasman, Henrik2; Saputra, Dhany3; Sicheritz-Pontén, Thomas3; Lund, Ole3; Svendsen, Christina Aaby1; Frimodt-Møller, Niels5; Aarestrup, Frank Møller1
1 National Food Institute, Technical University of Denmark2 Division of Epidemiology and Microbial Genomics, National Food Institute, Technical University of Denmark3 Department of Systems Biology, Technical University of Denmark4 Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark5 Copenhagen University Hospital
Whole genome sequencing (WGS) is becoming available as a routine tool for clinical microbiology. If applied directly on clinical samples this could further reduce diagnostic time and thereby improve control and treatment. A major bottle-neck is the availability of fast and reliable bioinformatics tools. This study was conducted to evaluate the applicability of WGS directly on clinical samples and to develop easy-to-use bioinformatics tools for analysis of the sequencing data. Thirty-five random urine samples from patients with suspected urinary tract infections were examined using conventional microbiology, WGS of isolated bacteria and by directly sequencing on pellets from the urine. A rapid method for analyzing the sequence data was developed. Bacteria were cultivated from 19 samples, but only in pure culture from 17. WGS improved the identification of the cultivated bacteria and almost complete agreement was observed between phenotypic and predicted antimicrobial susceptibility. Complete agreement was observed between species identification, multi-locus-sequence typing and phylogenetic relationship for the Escherichia coli and Enterococcus faecalis isolates when comparing the results of WGS of cultured isolates and directly from the urine samples. Sequencing directly from the urine enabled bacterial identification in polymicrobic samples. Additional putative pathogenic strains were observed in some culture negative samples. WGS directly on clinical samples can provide clinically relevant information and drastically reduce diagnostic time. This may prove very useful, but the need for data analysis is still a hurdle to clinical implementation. To overcome this problem a publicly available bioinformatics tool was developed in this study.
Journal of Clinical Microbiology, 2014, Vol 52, Issue 1, p. 139-146