Optimisation and validation of capture mNGS for predicting antimicrobial resistance

Area of Science:

• Genomics
• Microbiology
• Clinical Diagnostics

Background:

• Antibiotic resistance poses a critical threat to bacterial infection treatment.
• Traditional antimicrobial susceptibility testing (AST) is time-consuming due to its culture-dependent nature.
• Clinical metagenomic next-generation sequencing (mNGS) offers rapid pathogen detection but faces challenges in ARG detection sensitivity and genotype-phenotype correlation.

Purpose of the Study:

• To develop a capture mNGS approach for enhanced ARG detection and precise ARG-bacteria linkage.
• To evaluate the clinical utility and predictive value of ARGs detected by capture mNGS compared to phenotypic AST.
• To assess the impact of capture mNGS on guiding antibiotic therapy.

Main Methods:

• Development of a capture mNGS approach with probe-based ARG enrichment and a host-attribution algorithm.
• Comparative analysis of ARG detection sensitivity against standard mNGS.
• Validation of capture mNGS in retrospective and prospective clinical cohorts, using phenotypic AST as a reference.

Main Results:

• Capture mNGS demonstrated a 44-fold increase in sequencing depth and significantly enhanced ARG detection sensitivity compared to standard mNGS.
• Detected ARGs, such as blaCTX-M, blaKPC, blaOXA-23, and mecA, accurately predicted phenotypic resistance with high sensitivity and specificity.
• Capture mNGS significantly reduced turnaround time (median 24.71 h) compared to AST (median 73.16 h), enabling faster antibiotic therapy guidance.

Conclusions:

• Antibiotic resistance genes (ARGs) detected by capture mNGS can rapidly and accurately predict bacterial resistance phenotypes.
• The capture mNGS approach offers high sensitivity and specificity for ARG detection, improving clinical utility.
• This method holds significant potential for guiding antibiotic management in clinical practice, leading to more timely and effective treatments.