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Comparative meta-omics for identifying pathogens associated with prosthetic joint infection
Karan Goswami1, Alexander J Shope1,2, Vasily Tokarev2
1Rothman Institute, Philadelphia, PA, USA.
Abstract:
Prosthetic joint infections (PJI) are economically and personally costly, and their incidence has been increasing in the United States. Herein, we compared 16S rRNA amplicon sequencing (16S), shotgun metagenomics (MG) and metatranscriptomics (MT) in identifying pathogens causing PJI. Samples were collected from 30 patients, including 10 patients undergoing revision arthroplasty for infection, 10 patients receiving revision for aseptic failure, and 10 patients undergoing primary total joint arthroplasty. Synovial fluid and peripheral blood samples from the patients were obtained at time of surgery. Analysis revealed distinct microbial communities between primary, aseptic, and infected samples using MG, MT, (PERMANOVA p = 0.001), and 16S sequencing (PERMANOVA p < 0.01). MG and MT had higher concordance with culture (83%) compared to 0% concordance of 16S results. Supervised learning methods revealed MT datasets most clearly differentiated infected, primary, and aseptic sample groups. MT data also revealed more antibiotic resistance genes, with improved concordance results compared to MG. These data suggest that a differential and underlying microbial ecology exists within uninfected and infected joints. This study represents the first application of RNA-based sequencing (MT). Further work on larger cohorts will provide opportunities to employ deep learning approaches to improve accuracy, predictive power, and clinical utility.
Insights
Metatranscriptomics (MT) shows promise for identifying prosthetic joint infection (PJI) pathogens, outperforming 16S rRNA sequencing and matching shotgun metagenomics. This RNA-based method offers a clearer view of joint microbial communities and antibiotic resistance genes.
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.
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