Abstract
PURPOSE: Clostridioides difficile is a major cause of healthcare-associated infections, contributing to significant morbidity and mortality. This study aimed to investigate the genomic characteristics, antimicrobial resistance (AMR) profiles, and temporal dynamics of C. difficile strains isolated from hospitalized patients in a German tertiary hospital over nearly two decades (1997-2015). METHODS: Whole-genome sequencing was performed on 46 toxigenic C. difficile isolates to determine sequence types (STs) and phylogenetic relationships and these were compared to national surveillance data on C. dificile. AMR profiling was conducted to identify key resistance determinants at genetic level while epsilometer minimum inhibitory concentration (MIC) analyses were used to correlate genetic resistance markers with phenotypic resistance. Longitudinal antibiotic usage data were analysed to assess potential associations with resistance profiles and strains evolution. RESULTS: Five predominant STs were identified: ST1 (30%), ST54 (24%), ST3 (22%), ST11 (11%), and ST37 (4%). Phylogenetic analysis showed that ST1 (ribotype 027) emerged as the dominant and persistent lineage, replacing ST11 and ST54 over time. AMR profiling detected several resistance genetic markers such as CDD-1/CDD-2 (carbapenem resistance), ErmB (macrolide-lincosamide-streptogramin B resistance/MLS resistance), and mutations in gyrA (fluoroquinolone resistance) and rpoB (rifampicin resistance). MIC analyses confirmed high resistance rates to moxifloxacin (87%) and rifampicin (59%), while susceptibility to fidaxomicin, metronidazole, and vancomycin remained. The tetM gene, associated with doxycycline resistance, declined as ST11 and ST54 frequencies decreased. Longitudinal analysis revealed a reduction in moxifloxacin resistance following its decreased use, whereas increased doxycycline use paradoxically correlated with reduced resistance. CONCLUSION: This study highlights the dynamic strain evolution of C. difficile, reflecting national trends in strain evolution. The findings emphasize the strong correlation between epsilometer MIC values and molecular resistance markers. This observation reinforces the integration of genetic surveillance with antibiotic stewardship in the clinical routine to effectively mitigate CDI recurrence. Further research is needed to better understand the complex interactions between antibiotic exposure and strain evolution in hospital environments.