Comparison of Whole-Genome Sequence-Based Methods and PCR Ribotyping for Subtyping of Clostridioides difficile

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 the discriminatory power to study transmission and outbreaks in detail. New molecular methods have the capacity to differentiate better and provide standardized and interlaboratory exchangeable data. Using a well-characterized collection of diverse strains (N = 630; 100 unique ribotypes [RTs]), we compared the discriminatory power of core genome multilocus sequence typing (cgMLST) (SeqSphere and EnteroBase), whole-genome MLST (wgMLST) (EnteroBase), and single-nucleotide polymorphism (SNP) analysis. A unique cgMLST profile (more than six allele differences) was observed in 82 of 100 RTs, indicating that cgMLST could distinguish most, but not all, RTs. Application of cgMLST in two outbreak settings with RT078 and RT181 (known to have low intra-RT allele differences) showed no distinction between outbreak and nonoutbreak 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, adjusted cutoff thresholds and epidemiological data are necessary to recognize outbreaks of some specific RTs. We propose to use an allelic threshold of three alleles to identify outbreaks.

Automated summary

Clostridioides difficile is the most common cause of antibiotic-associated gastrointestinal infections. Capillary electrophoresis-PCR ribotyping is the current gold standard for C. difficile typing, but lacks the ability to study transmission and outbreaks in detail. New molecular methods, such as core genome multilocus sequence typing (cgMLST), have the potential to provide better differentiation and standardized data exchange. However, adjusted cutoff thresholds and epidemiological data are necessary to recognize outbreaks of specific ribotypes.