Molecular fingerprinting of Clostridium difficile isolates: pulsed-field gel electrophoresis versus amplified fragment length polymorphism

Abstract

Two molecular fingerprinting techniques, pulsed-field gel electrophoresis (PFGE) and amplified fragment length polymorphism (AFLP), were used to investigate the epidemiological relatedness among Clostridium difficile isolates from suspected outbreaks in three general hospitals. Analysis by PFGE yielded inconclusive data as a result of extensive DNA degradation. Although this degradation could be prevented to a certain extent by the inclusion of thiourea in the electrophoresis buffer, the weak DNA banding patterns obtained in this way were still far from optimal. AFLP data were obtained by using fluorescently labeled PCR primers and analysis on an ABI PRISM automated DNA analysis platform. AFLP analysis yielded high resolution and highly reproducible DNA fingerprinting patterns from which the epidemiological relatedness among the isolates could easily be determined. AFLP results could be readily obtained within 24 h, whereas 3 to 4 days were routinely required to complete the lengthy PFGE protocol. AFLP clearly proved to be a much more fail-safe fingerprinting method for C. difficile isolates, especially for those isolates for which a standard PFGE procedure yielded inconclusive results due to DNA degradation.

FIG. 1.

Representative PFGE results obtained for 14 clinical C. difficile isolates (lanes 1 to 14) and 2 unrelated C. difficile strains (lanes U1 and U2) after the inclusion of 100 μM thiourea to the electrophoresis buffer. The marker lanes (M) contain bacteriophage lambda concatemers (approximately 0.05 to 1.0 Mbp).

FIG. 2.

AFLP fingerprints obtained from 30 clinical C. difficile isolates (A1 to A15, B1 to B11, and C1 to C4) from three general hospitals and an unrelated control strain (U1). The fingerprints were obtained using a fluorescently labeled EcoRI primer and an unlabeled MseI-plus-G primer. The dendrogram was created by unweighted pair group method with arithmetic mean clustering using the Pearson correlation coefficient.