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

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.

Diagnosis and Treatment of Culture-Negative Periprosthetic Joint Infection

“…One of the cited criticisms related to the use of NGS is that it is unable to distinguish between active and treated infections, as genetic material may persist in the joint even after bacterial cell lysis [107,108]. Notwithstanding, a recent study from our institution explored this issue and found that the presence of DNA correlated with the presence of RNA (and active infection) in majority of patients with and without PJI [100]. Based on these findings, one is inclined to assume that the presence of any DNA in patients with PJI could be indicative of an active infection.…”

“…This RNA-based sequencing method offers a deeper insight on the clinical relevance of identified organism(s) in any given specimen, since active virulence and antimicrobial-resistance mechanisms can be determined [99]. Goswami et al [100] recently performed a comparison of 16S rRNA, MG and MT sequencing methods and showed that MT most accurately differentiated between primary, aseptic revision and PJI cases, concurrently revealing a greater number of resistance genes with higher concordance compared to MG sequencing.…”

Prospective Comparison Between Shotgun Metagenomics and Sanger Sequencing of the 16S rRNA Gene for the Etiological Diagnosis of Infections

“…The high performance (sensitivity and specificity) of SMg has been recently highlighted for the etiological diagnosis of a number of infectious diseases ( Miao et al, 2018 ; Filkins et al, 2020 ; Gu et al, 2021 ; Miller and Chiu, 2021 ), including bone and joint infections ( Street et al, 2017 ; Thoendel et al, 2018 ; Gamie et al, 2021 ; Goswami et al, 2021 ), meningitis and encephalitis ( Wilson et al, 2019 ; Rodino et al, 2020 ), or necrotizing fasciitis ( Rodriguez et al, 2020c ). Indeed, by allowing the detection and quantification of an unlimited panel of microorganisms, including bacteria, viruses, fungi, or parasites without a priori orientation, SMg is capable to identify the etiology of any infectious disease, including those due to microorganisms that have never been previously described in human infections ( Rodriguez et al, 2020b ).…”

“…Among them, NGS-based approaches, as shotgun metagenomics (SMg) or amplification of the universal bacterial 16S rRNA gene and NGS sequencing of the PCR products (NGS 16S), appear to be particularly promising. Focusing on SMg, recent studies have demonstrated its utility for the etiological diagnosis of infections ( Miao et al, 2018 ; Filkins et al, 2020 ; Gu et al, 2021 ; Miller and Chiu, 2021 ), including meningitidis/encephalitis ( Wilson et al, 2019 ; Rodino et al, 2020 ), necrotizing soft-tissue infections ( Rodriguez et al, 2020c ), pneumonia ( Hilton et al, 2016 ; Zhou et al, 2021 ), bloodstream infections ( Grumaz et al, 2016 ), bone and joint infections ( Street et al, 2017 ; Thoendel et al, 2018 ; Gamie et al, 2021 ; Goswami et al, 2021 ) or fever with unknown origin ( Fu et al, 2022 ). Moreover, because SMg has the capacity to generate the full-length sequence of bacterial genomes (unlike 16S approaches), this method could be used for the study of antimicrobial susceptibility and molecular typing.…”

cleanSURFACES® intervention reduces microbial activity on surfaces in a senior care facility

“…While MG is a comprehensive method that randomly sequences DNA to detect all microbes present in a sample, it is unable to differentiate between latent and active microbial communities. In clinically relevant settings, MT has been shown to be a promising method in its ability to identify active pathogens and antimicrobial-resistance mechanisms ( Goswami et al., 2021 ; Malone et al., 2021 ).…”