Microbial Dominance in Diabetic Foot Osteomyelitis Determined With Nanopore Sequencing Techniques Predicts Positive Response to Surgical Intervention

利用纳米孔测序技术确定糖尿病足骨髓炎中的微生物优势可预测手术干预的积极反应

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Abstract

BACKGROUND: Tools to predict successful response to surgery for the treatment of diabetic foot osteomyelitis (OM) are currently lacking. Recent studies in nonbone infections have revealed that nanopore sequencing can provide real-time metagenomic identification of pathogens. In a cohort of patients with diabetic foot OM, we tested the feasibility of generating interpretable metagenomic data from surgically acquired osseous tissue, comparing bacterial community features (pathogen dominance) with clinical outcomes (resolution of infection). We hypothesized that nanopore-generated microbial data can be feasibly generated from surgically acquired bone, aligns with conventional culture results, and is predictive of clinical response. METHODS: We performed a pilot feasibility study of 10 consecutive patients hospitalized with diabetic foot OM who underwent surgery for OM. We performed metagenomic sequencing of surgical bone samples using the MinION (Oxford Nanopore). Our primary metagenomic index was community dominance (relative abundance of most abundant species). Our primary clinical end point was the clinical response to surgery, adjudicated at 1 year. RESULTS: We successfully generated interpretable metagenomic data from all 10 specimens, including 2 with negative culture growth. Among culture-positive specimens, the culture-identified pathogen was either the first or second most abundant organism in all cases. Patients with favorable clinical response exhibited greater pathogen dominance than those with unfavorable response (P = .002). CONCLUSIONS: In patients with surgically treated OM, nanopore sequencing can generate interpretable metagenomic data from bone specimens that are culture concordant and associated with clinical response. These findings support the feasibility and plausibility of using real-time metagenomic sequencing to improve the clinical management of OM.

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