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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.
Highlights
Prosthetic joint infections (PJI) are economically and personally costly, and their incidence has been increasing in the United States
A total of 30 synovial fluid samples from knee or hip joints and 30 paired blood samples were subjected to 16S rRNA gene amplicon (16S), metagenomics (MG), and metatranscriptomics (MT) library preparation and sequencing (Fig. 1)
PJIs are a complication for patients undergoing total hip (THA) and knee (TKA) arthroplasty and continue to pose a public health concern since current methods lack accuracy to identify causative pathogens
Summary
Prosthetic joint infections (PJI) are economically and personally costly, and their incidence has been increasing in the United States. We compared 16S rRNA amplicon sequencing (16S), shotgun metagenomics (MG) and metatranscriptomics (MT) in identifying pathogens causing PJI. MT data 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. Amplicon NGS methods, like 16S rRNA gene sequencing have demonstrated success in identifying causative organisms in culture-negative infections, but there are significant shortcomings like PCR biases and limited taxonomic. Shotgun sequencing circumvents culture and PCR-based limitations by enabling a comprehensive view of the identity and functional gene content of microbial consortia populating a clinical specimen[18]. Metagenomics analysis has previously shown high concordance with culture results for diagnosing P JIs19; to the best of our knowledge, no work has yet been done comparing metatranscriptomics results to culture results in this context
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