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Current application and future perspectives of molecular typing methods to study Clostridium difficile infections
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View Affiliations Hide AffiliationsE KuijperE.J.Kuijper lumc.nl
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Citation style for this article: . Current application and future perspectives of molecular typing methods to study Clostridium difficile infections. Euro Surveill. 2013;18(4):pii=20381. https://doi.org/10.2807/ese.18.04.20381-en Received: 25 Jul 2012
Abstract
Molecular typing is an essential tool to monitor Clostridium difficile infections and outbreaks within healthcare facilities. Molecular typing also plays a key role in defining the regional and global changes in circulating C. difficile types. The patterns of C. difficile types circulating within Europe (and globally) remain poorly understood, although international efforts are under way to understand the spatial and temporal patterns of C. difficile types. A complete picture is essential to properly investigate type-specific risk factors for C. difficile infections (CDI) and track long-range transmission. Currently, conventional agarose gel-based polymerase chain reaction (PCR) ribotyping is the most common typing method used in Europe to type C. difficile. Although this method has proved to be useful to study epidemiology on local, national and European level, efforts are made to replace it with capillary electrophoresis PCR ribotyping to increase pattern recognition, reproducibility and interpretation. However, this method lacks sufficient discriminatory power to study outbreaks and therefore multilocus variable-number tandem repeat analysis (MLVA) has been developed to study transmission between humans, animals and food. Sequence-based methods are increasingly being used for C. difficile fingerprinting/typing because of their ability to discriminate between highly related strains, the ease of data interpretation and transferability of data. The first studies using whole-genome single nucleotide polymorphism typing of healthcare-associated C. difficile within a clinically relevant timeframe are very promising and, although limited to select facilities because of complex data interpretation and high costs, these approaches will likely become commonly used over the coming years.
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