Usefulness of McRAPD for typing and importance of biofilm production in a case of nosocomial ventriculoperitoneal shunt infection caused by Candida lusitaniae.

A case report of ventriculoperitoneal shunt infection caused by Candida lusitaniae in a 6-year-old patient with cerebral astrocytoma and obstructive hydrocephalus is presented briefly with emphasis on the course of antifungal treatment. Seven isolates recovered subsequently from the cerebrospinal fluid were studied retrospectively. To confirm identity, isolates were typed using pulsed-field gel electrophoresis and melting curve of random amplified polymorphic DNA (McRAPD). Further, the ability to form biofilm and its susceptibility to systemic antifungals were evaluated. Using McRAPD, identity of C. lusitaniae isolates showing slight microevolutionary changes in karyotypes was undoubtedly confirmed; successful application of numerical interpretation of McRAPD for typing is demonstrated here for the first time. The strain was also recognized as a strong biofilm producer. Moreover, minimum biofilm inhibitory concentrations were very high, in contrast to low antifungal minimum inhibitory concentrations of isolates. It can be concluded that McRAPD seems to be a simple and reliable method not only for identification but also for typing of yeasts. A ventriculoperitoneal shunt colonized by C. lusitaniae was revealed as the source of this nosocomial infection, and the ability of the strain to form biofilm on its surface likely caused treatment failure.

The potential of high resolution melting analysis (hrma) to streamline, facilitate and enrich routine diagnostics in medical microbiology.

BACKGROUND: Routine medical microbiology diagnostics relies on conventional cultivation followed by phenotypic techniques for identification of pathogenic bacteria and fungi. This is not only due to tradition and economy but also because it provides pure culture needed for antibiotic susceptibility testing. This review focuses on the potential of High Resolution Melting Analysis (HRMA) of double-stranded DNA for future routine medical microbiology. METHODS AND RESULTS: Search of MEDLINE database for publications showing the advantages of HRMA in routine medical microbiology for identification, strain typing and further characterization of pathogenic bacteria and fungi in particular. The results show increasing numbers of newly-developed and more tailor-made assays in this field. For microbiologists unfamiliar with technical aspects of HRMA, we also provide insight into the technique from the perspective of microbial characterization. CONCLUSIONS: We can anticipate that the routine availability of HRMA in medical microbiology laboratories will provide a strong stimulus to this field. This is already envisioned by the growing number of medical microbiology applications published recently. The speed, power, convenience and cost effectiveness of this technology virtually predestine that it will advance genetic characterization of microbes and streamline, facilitate and enrich diagnostics in routine medical microbiology without interfering with the proven advantages of conventional cultivation.

Performance of optimized McRAPD in identification of 9 yeast species frequently isolated from patient samples: potential for automation.

BACKGROUND: Rapid, easy, economical and accurate species identification of yeasts isolated from clinical samples remains an important challenge for routine microbiological laboratories, because susceptibility to antifungal agents, probability to develop resistance and ability to cause disease vary in different species. To overcome the drawbacks of the currently available techniques we have recently proposed an innovative approach to yeast species identification based on RAPD genotyping and termed McRAPD (Melting curve of RAPD). Here we have evaluated its performance on a broader spectrum of clinically relevant yeast species and also examined the potential of automated and semi-automated interpretation of McRAPD data for yeast species identification. RESULTS: A simple fully automated algorithm based on normalized melting data identified 80% of the isolates correctly. When this algorithm was supplemented by semi-automated matching of decisive peaks in first derivative plots, 87% of the isolates were identified correctly. However, a computer-aided visual matching of derivative plots showed the best performance with average 98.3% of the accurately identified isolates, almost matching the 99.4% performance of traditional RAPD fingerprinting. CONCLUSION: Since McRAPD technique omits gel electrophoresis and can be performed in a rapid, economical and convenient way, we believe that it can find its place in routine identification of medically important yeasts in advanced diagnostic laboratories that are able to adopt this technique. It can also serve as a broad-range high-throughput technique for epidemiological surveillance.