| Abstract |
Fungal bloodstream infections (BSIs), particularly those caused by Candida species, represent a major clinical challenge associated with substantial morbidity and mortality. Timely initiation of appropriate antifungal therapy is critical for patient survival; however, conventional diagnostic methods rely on blood culture positivity and downstream identification procedures, resulting in delays that limit early, targeted treatment. In this study, we developed and validated an optimized workflow for rapid identification of fungal pathogens directly from blood culture samples collected prior to positivity in automated blood culture systems using real-time nanopore sequencing. The workflow consists of selective depletion of host genomic DNA, microbial DNA extraction, and species identification via random PCR-based whole-genome amplification, followed by sequencing on the MinION platform and real-time taxonomic classification using a custom database. Host DNA was effectively removed using saponin-mediated cell lysis and benzonase treatment, enabling enrichment of microbial DNA. Using this approach, species-level identification was achieved within 1.5 h from sequencing initiation, and accurate identification was possible with as few as 4,000 sequencing reads. The total turnaround time of the workflow was approximately 7 h. Validation using 48 clinical blood culture samples demonstrated high concordance with routine clinical diagnoses, and the method reliably detected both monomicrobial and polymicrobial infections, including fungal-fungal and fungal-bacterial co-infections. Genome-wide sequencing data enabled detection of candidate resistance-associated variants in clinically relevant genes. Overall, this workflow provides a clinically actionable solution for early diagnosis of fungal BSIs, with the potential to reduce diagnostic delays, inform antifungal selection, and improve patient outcomes.IMPORTANCEFungal bloodstream infections (BSIs) are serious and often fatal conditions that require prompt antifungal treatment. However, conventional diagnostic approaches depend on blood culture positivity and subsequent identification steps, leading to delays that can compromise patient outcomes. Here, we present a rapid sequencing-based workflow that enables direct identification of fungal pathogens from blood culture samples before they become positive. By combining host DNA depletion with real-time nanopore sequencing, our method delivers species-level results within hours and accurately detects both single-species and mixed infections. This capability is particularly important in clinical settings where early identification of fungal species can inform antifungal choice. Importantly, the workflow is compatible with existing laboratory infrastructure and requires only a limited number of sequencing reads, supporting routine use. By substantially shortening the time to actionable results, this approach has the potential to improve clinical decision-making, optimize antifungal therapy, and reduce morbidity and mortality from fungal BSIs.
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