Högskolan i Skövde

Sepsis, a life-threatening condition with alarmingly high mortality rates, demands the development of improved diagnostic methods to better understand and manage the disease. Enterococcus spp., significant contributors to sepsis and known for their multidrug resistance, urgently require thorough and detailed investigation to devise effective treatment strategies and healthcare interventions. This study explores the genomic characterization of Enterococcus spp. isolates from patients with suspected community-onset sepsis in Sweden, using whole-genome sequencing. Data was processed through an in-house developed bioinformatics pipeline, including quality control with FastQC, sequence trimming with Trimmomatic, and assembly with Unicycler and QUAST, generating reliable FASTA files for the downstream analysis. Subsequently, different gene annotation tools were applied for the genotypic species identification, prediction of antibiotic resistance genes, plasmid replicons, and determination of sequence type. Moreover, the results from the genotypic characterization were compared to those obtained using routine microbiological methods based on cultures followed by MALDI-TOF MS for species identification and disk diffusion for antimicrobial resistance testing. The results revealed a high prevalence of resistance genes, particularly for macrolide, lincosamide, and streptogramin antibiotics. A notable finding was the high discordance (83.2%) between phenotypic and genotypic methods in detecting resistance, highlighting the complexity of correlating phenotypic antibiotic resistance with genotypic predictions. Additionally, a statistically significant higher prevalence of antibiotic resistance genes was observed in E. faecium compared to E. faecalis (p=0.001). Furthermore, a high diversity of sequence types among Enterococcus spp. isolates was detected by multi-locus sequencing typing, with ST6 (14%) as the most prevalent for E. faecalis and ST192 (37%) for E. faecium. In conclusion, this comprehensive genomic approach enhances the understanding of antibiotic resistance spread and informs strategies for improved clinical and public health interventions in sepsis management. The study also underscores the importance of integrating genomic data with traditional diagnostic methods to develop effective strategies for managing antibiotic resistant infections.