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

被引：17

作者：

Goswami, Karan ^{[1
]}

Shope, Alexander J. ^{[1
,2
]}

Tokarev, Vasily ^{[2
]}

Wright, Justin R. ^{[2
]}

Unverdorben, Lavinia V. ^{[2
]}

Ly, Truc ^{[2
]}

Chen See, Jeremy ^{[2
]}

McLimans, Christopher J. ^{[2
]}

Wong, Hoi Tong ^{[2
]}

Lock, Lauren ^{[2
]}

Clarkson, Samuel ^{[1
]}

Parvizi, Javad ^{[1
]}

Lamendella, Regina ^{[2
]}

机构：

[1] Rothman Inst, Philadelphia, PA 19107 USA

[2] Contaminat Source Identificat LLC, Huntingdon, PA 16652 USA

来源：

SCIENTIFIC REPORTS | 2021年 / 11卷 / 01期

关键词：

DNA; RESISTANCE; DIAGNOSIS; DEPLETION; BACTERIAL;

D O I：

10.1038/s41598-021-02505-7

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页数：12

共 49 条

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