Comparative meta-omics for identifying pathogens associated with prosthetic joint infection

Area of Science:

• Microbiology
• Genomics
• Infectious Diseases

Background:

• Prosthetic joint infections (PJI) are a growing concern in the United States, leading to significant patient and economic burdens.
• Accurate pathogen identification is crucial for effective PJI treatment and management.
• Current diagnostic methods have limitations in comprehensively characterizing the microbial landscape of PJI.

Purpose of the Study:

• To compare the efficacy of 16S rRNA amplicon sequencing (16S), shotgun metagenomics (MG), and metatranscriptomics (MT) in identifying pathogens in prosthetic joint infections (PJI).
• To investigate the distinct microbial communities present in infected, aseptic, and primary joint arthroplasty samples.
• To evaluate the potential of RNA-based sequencing (MT) for PJI diagnostics.

Main Methods:

• Collected synovial fluid and peripheral blood samples from 30 patients undergoing various joint replacement surgeries (infected revision, aseptic revision, primary arthroplasty).
• Employed 16S rRNA amplicon sequencing, shotgun metagenomics, and metatranscriptomics for microbial analysis.
• Utilized PERMANOVA for statistical comparison of microbial communities and supervised learning for group differentiation.

Main Results:

• Distinct microbial communities were identified across primary, aseptic, and infected samples using MG, MT, and 16S sequencing.
• MG and MT demonstrated higher concordance with culture-based methods (83%) compared to 16S sequencing (0%).
• Metatranscriptomics (MT) most effectively differentiated between infected, primary, and aseptic sample groups and identified more antibiotic resistance genes.

Conclusions:

• A differential microbial ecology exists between infected and uninfected prosthetic joints.
• Metatranscriptomics (MT) offers a powerful, RNA-based approach for PJI diagnostics, surpassing 16S rRNA sequencing and showing comparable or superior performance to MG.
• Future research with larger cohorts and deep learning can further enhance the accuracy and clinical utility of MT for PJI detection.