Enhanced Discrimination of Clostridioides difficile Transmission Using Whole Genome Sequencing and In-Silico Multi-locus Variable Number Tandem Repeat Analysis

Whole genome sequencing (WGS) is used to establish genetic relatedness of bacteria and track outbreaks in healthcare settings. While WGS provides sufficient discriminatory power to make inferences about genetic relatedness and transmission for most bacterial species, WGS for Clostridioides difficile often fails to provide discriminatory power, even at low single nucleotide polymorphism (SNP) differences. Multi-locus variable number tandem repeat analysis (MLVA), which analyzes rapidly mutating tandem repeat loci has previously been shown to be useful for this purpose for C. difficile. We investigated whether in silico MLVA can further elucidate genetic relatedness of C. difficile clusters identified by short-read WGS but lacking epidemiological links. Potential healthcare-associated toxin-positive C. difficile isolates were collected at our hospital from November 2016 to November 2019. Short-read WGS was performed on the Illumina platform to cluster isolates with [≤]2 SNPs, and Nanopore long-read sequencing was used to resolve MLVA loci within these clustered isolates. Among 666 isolates, 62 unique patient isolates met the [≤]2 SNP criterion and underwent MinION sequencing. Of the 105 pairs with 0-2 SNP differences, 79.0% had a summed tandem-repeat difference (STRD) of 0-5, 10.5% had an STRD of 6-10, and 10.5% had an STRD of 11-20. A significant correlation was found between a lower STRD value and the presence of a unit/procedure epidemiological link within low SNP clusters (OR: 0.45; 95% CI: 0.29, 0.70). Our findings demonstrate that MLVA provides additional genomic discrimination for closely related C. difficile isolates identified by WGS, enhancing outbreak investigation precision.