Back

Insights on recurrent and sequential Clostridioides difficile infections from genomic surveillance in Minnesota, USA, 2019-2021

Evans, D.; Friedman, B.; Pung, K.; Weber, B.; Plumb, M.; Lees, C.; Holzbauer, S.; Lynfield, R.; Wang, X.

2025-05-15 infectious diseases
10.1101/2025.05.13.25327503 medRxiv
Show abstract

BackgroundThe frequent temporal recurrence of Clostridioides difficile infection (CDI) may be the result of relapse with the same strain or reinfection with a different strain; the role of genetic evolution of C. difficile in relapse is poorly understood. We used whole-genome sequencing (WGS) to determine the frequency of same strain relapse among CDI recurrences and changes in the genetic diversity of individual strains responsible for relapses over time. MethodsWe analyzed data from active population- and laboratory-based surveillance of CDI in Minnesota, USA. We performed WGS on isolates collected from 306 patients with multiple CDI events during 2019-2021. We identified multi-locus sequence types (MLSTs), nucleotide variants, and putative mobile genetic elements (MGEs) from WGS data to study the genetic similarity and evolution of those C. difficile genomes. ResultsAmong patients with multiple CDI events in the total surveillance period, 198 (64.7%) had multiple infections of the same MLST. Of these patients, 17.6% had multiple same-MLST CDI events >8 weeks apart. Among 232 temporally defined cases of recurrent CDI, 155 (66.8%) involved isolates of the same MLST. Among same-MLST isolates, there were no statistically significant correlations between accumulated mutations and elapsed time between CDI events. Analysis of sequential same-MLST C. difficile genomes showed evidence of gain or loss of putative mobile genetic elements (MGEs) in 45.6% of genome pairs. ConclusionsLeveraging the largest CDI genomic dataset to date, our results confirm prior findings that recurrent CDI is a combination of reinfection and/or change in the ascendant strain in mixed infection, and relapse, while expanding knowledge on the evolution of pathogenic C. difficile strains in the human gastrointestinal tract.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.