Application mNGS for identification of prosthetic joint infection pathogens
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PROVIDER: PRJEB35719 | ENA |
REPOSITORIES: ENA
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Project description:metagenomics NGS for identification of prosthetic joint infection pathogens
| PRJEB36849 | ENA
Project description:Little is known about regulation of gene activity of the major pathogen Staphylococcus aureus during actual human infection. Here we characterize the transcriptome using deep RNA sequencing and the metabolome using NMR of S. aureus infected joint fluid derived from an acute human prosthetic joint infection, and compare them with the genome, transcriptome and metabolome of an isolate obtained from the sample grown in vitro (LB medium). The transcriptome indicated that the bacterial infection sustained on a versatile human-cell-based diet consisting of amino acids, glycans and nucleosides, since significant upregulation of genes involved in the catabolic degradation pathways of these compounds were observed in situ. This is consistent with metabolite analysis of the infected joint fluid and of S. aureus culture supernatants where the concentration of most amino acids and some amino sugars were found to be higher in the joint fluid, whereas the concentration of glucose was higher in culture supernatant. Furthermore, presumably because of oxygen limitations in the joint fluid, transcriptomic evidence for fermentation was observed, consistent with the presence of fermentation products (ethanol) in situ. Moreover, many, but not all, of the known virulence factor genes were upregulated in situ as well as the nine genes encoding the iron uptaking siderophore synthesis system.
2015-10-01 | GSE62091 | GEO
Project description:ONT MinION sequencing of prosthetic joint infection sonication fluids
| PRJEB23460 | ENA
Project description:Staphylococcus aureus from prosthetic joint infections and blood cultures display the same genetic background
| PRJEB74467 | ENA
Project description:Peri-prosthetic breast tissues were obtained from women with breast implants. Total RNA was extracted and cDNA library was prepared.
2024-01-14 | GSE178425 | GEO
Project description:AimsThe aim of this study was to evaluate the performance of metagenomic next-generation sequencing (mNGS) in detecting pathogens from synovial fluid of prosthetic joint infection (PJI) patients.MethodsA group of 75 patients who underwent revision knee or hip arthroplasties were enrolled prospectively. Ten patients with primary arthroplasties were included as negative controls. Synovial fluid was collected for mNGS analysis. Optimal thresholds were determined to distinguish pathogens from background microbes. Synovial fluid, tissue, and sonicate fluid were obtained for culture.ResultsA total of 49 PJI and 21 noninfection patients were finally included. Of the 39 culture-positive PJI cases, mNGS results were positive in 37 patients (94.9%), and were consistent with culture results at the genus level in 32 patients (86.5%) and at the species level in 27 patients (73.0%). Metagenomic next-generation sequencing additionally identified 15 pathogens from five culture-positive and all ten culture-negative PJI cases, and even one pathogen from one noninfection patient, while yielding no positive findings in any primary arthroplasty. However, seven pathogens identified by culture were missed by mNGS. The sensitivity of mNGS for diagnosing PJI was 95.9%, which was significantly higher than that of comprehensive culture (79.6%; p = 0.014). The specificity is similar between mNGS and comprehensive culture (95.2% and 95.2%, respectively; p = 1.0).ConclusionMetagenomic next-generation sequencing can effectively identify pathogens from synovial fluid of PJI patients, and demonstrates high accuracy in diagnosing PJI.Cite this article: Bone Joint Res 2020;9(7):440-449.
| S-EPMC7437524 | biostudies-literature
Project description:Background:Metagenomic shotgun sequencing has the potential to change how many infections, particularly those caused by difficult-to-culture organisms, are diagnosed. Metagenomics was used to investigate prosthetic joint infections (PJIs), where pathogen detection can be challenging. Methods:Four hundred eight sonicate fluid samples generated from resected hip and knee arthroplasties were tested, including 213 from subjects with infections and 195 from subjects without infection. Samples were enriched for microbial DNA using the MolYsis basic kit, whole-genome amplified, and sequenced using Illumina HiSeq 2500 instruments. A pipeline was designed to screen out human reads and analyze remaining sequences for microbial content using the Livermore Metagenomics Analysis Toolkit and MetaPhlAn2 tools. Results:When compared to sonicate fluid culture, metagenomics was able to identify known pathogens in 94.8% (109/115) of culture-positive PJIs, with additional potential pathogens detected in 9.6% (11/115). New potential pathogens were detected in 43.9% (43/98) of culture-negative PJIs, 21 of which had no other positive culture sources from which these microorganisms had been detected. Detection of microorganisms in samples from uninfected aseptic failure cases was conversely rare (7/195 [3.6%] cases). The presence of human and contaminant microbial DNA from reagents was a challenge, as previously reported. Conclusions:Metagenomic shotgun sequencing is a powerful tool to identify a wide range of PJI pathogens, including difficult-to-detect pathogens in culture-negative infections.
| S-EPMC6186856 | biostudies-literature
Project description: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.
| S-EPMC8660779 | biostudies-literature
Project description:Typing and prediction of antibiotic resistance and virulence determinants in S. aureus using shotgun-metagenomics data from prosthetic joint tissue on blood culture bottles
| PRJEB43858 | ENA