Clinical Metagenomic Sequencing for Species Identification and Antimicrobial Resistance Prediction in Orthopedic Device Infection.

ABSTRACT

Diagnosis of orthopedic device-related infection is challenging, and causative pathogens may be difficult to culture. Metagenomic sequencing can diagnose infections without culture, but attempts to detect antimicrobial resistance (AMR) determinants using metagenomic data have been less successful. Human DNA depletion may maximize the amount of microbial DNA sequence data available for analysis. Human DNA depletion by saponin was tested in 115 sonication fluid samples generated following revision arthroplasty surgery, comprising 67 where pathogens were detected by culture and 48 culture-negative samples. Metagenomic sequencing was performed on the Oxford Nanopore Technologies GridION platform. Filtering thresholds for detection of true species versus contamination or taxonomic misclassification were determined. Mobile and chromosomal genetic AMR determinants were identified in Staphylococcus aureus-positive samples. Of 114 samples generating sequence data, species-level positive percent agreement between metagenomic sequencing and culture was 50/65 (77%; 95% confidence interval [CI], 65 to 86%) and negative percent agreement was 103/114 (90%; 95% CI, 83 to 95%). Saponin treatment reduced the proportion of human bases sequenced in comparison to 5-µm filtration from a median (interquartile range [IQR]) of 98.1% (87.0% to 99.9%) to 11.9% (0.4% to 67.0%), improving reference genome coverage at a 10-fold depth from 18.7% (0.30% to 85.7%) to 84.3% (12.9% to 93.8%). Metagenomic sequencing predicted 13/15 (87%) resistant and 74/74 (100%) susceptible phenotypes where sufficient data were available for analysis. Metagenomic nanopore sequencing coupled with human DNA depletion has the potential to detect AMR in addition to species detection in orthopedic device-related infection. Further work is required to develop pathogen-agnostic human DNA depletion methods, improving AMR determinant detection and allowing its application to other infection types.