bioRxivMolecular BiologyPreprint
Performance of Core Genome Multilocus Sequence Typing Compared to Capillary-Electrophoresis PCR Ribotyping and SNP Analysis of Clostridioides difficile
Amoe Baktash, Jeroen CorveriD, Céline HarmanusiD, Wiep Klaas SmitsiD, Warren N. Fawley, MH Wilcox, Nitin KumariD, David W. EyreiD, A. IndraiD, Alexander MellmanniD, Ed J. KuijperiD
Abstract
Clostridioides difficile is the most common cause of antibiotic-associated gastrointestinal infections. Capillary-electrophoresis (CE)-PCR ribotyping is currently the gold standard for C. difficile typing but lacks discriminatory power to study transmission and outbreaks in detail. New molecular methods have the capacity to differentiate better, but backward compatibility with CE-PCR ribotyping must be assessed. Using a well-characterized collection of diverse strains (N=630; 100 unique ribotypes [RTs]), we aimed to investigate PCR ribotyping prediction from core genome multilocus sequence typing (cgMLST). Additionally, we compared the discriminatory power of cgMLST (SeqSphere & EnteroBase) and whole genome MLST (wgMLST) (EnteroBase) with single nucleotide polymorphism (SNP) analysis). A unique cgMLST profile (>6 allele differences) was observed in 82/100 ribotypes, indicating sufficient backward compatibility. Intra-RT allele difference varied per ribotype and MLST clade. Application of cg/wgMLST and SNP analysis in two outbreak settings with ribotypes RT078 and RT181 (known with a low intra-ribotype allele difference) showed no distinction between outbreak- and non-outbreak strains, in contrast to wgMLST and SNP analysis. We conclude that cgMLST has the potential to be an alternative to CE-PCR ribotyping. The method is reproducible, easy to standardize and offers higher discrimination. However, in some ribotype complexes adjusted cut-off thresholds and epidemiological data are necessary to recognize outbreaks. We propose to decrease the current threshold of 6 to 3 alleles to better identify outbreaks.
- assay: DNA methylation
- research focus: variant calling
- research focus: benchmarking
- approach: statistical model
- Citations
- 1
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