Evaluation of a Combined Multilocus Sequence Typing and Whole-Genome Sequencing Two-Step Algorithm for Routine Typing of Clostridioides difficile. Academic Article uri icon

Overview

abstract

  • Multilocus sequence typing (MLST) is a low-resolution but rapid genotyping method for Clostridioides difficile Whole-genome sequencing (WGS) has emerged as the new gold standard for C. difficile typing, but cost and lack of standardization still limit broad utilization. In this study, we evaluated the potential to combine the portability of MLST with the increased resolution of WGS for a cost-saving approach to routine C. difficile typing. C. difficile strains from two New York City hospitals (hospital A and hospital B) were selected. WGS single-nucleotide polymorphism (wgSNP) was performed using established methods. Sequence types (ST) were determined using PubMLST, while wgSNP analysis was performed using the Bionumerics software. An additional analysis of a subset of data (hospital A) was made comparing the Bionumerics software to the CosmosID pipeline. Cost and turnaround time to results were compared for the algorithmic approach of MLST followed by wgSNP versus direct wgSNP. Among the 202 C. difficile isolates typed, 91% (n = 185/203) clustered within the representative ST, showing a high agreement between MLST and wgSNP. While clustering was similar between the Bionumerics and CosmosID pipelines, large differences in the overall number of SNPs were noted. A two-step algorithm for routine typing results in significantly lower cost than routine use of WGS. Our results suggest that using MLST as a first step in routine typing of C. difficile followed by WGS for MLST concordant strains is a less technically demanding, cost-saving approach for performing C. difficile typing than WGS alone without loss of discriminatory power.

publication date

  • January 21, 2021

Research

keywords

  • Clostridioides
  • Clostridioides difficile

Identity

PubMed Central ID

  • PMC8111118

Scopus Document Identifier

  • 85099270438

Digital Object Identifier (DOI)

  • 10.1016/j.anaerobe.2019.102087

PubMed ID

  • 33177119

Additional Document Info

volume

  • 59

issue

  • 2