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Metagenomics NGS for identification of prosthetic joint infection pathogens

ABSTRACT: metagenomics NGS for identification of prosthetic joint infection pathogens

PROVIDER: PRJEB36849 | ENA |

REPOSITORIES: ENA

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Application mNGS for identification of prosthetic joint infection pathogens

Project description:Application of next generation sequencing (NGS) for identification of prosthetic joint infection pathogens: a diagnostic evaluation study

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metagenomics NGS study

Project description:metagenomics NGS study

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In situ Staphylococcus aureus gene expression in a human prosthetic joint infection

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

ONT MinION sequencing of prosthetic joint infection sonication fluids

Project description:ONT MinION sequencing of prosthetic joint infection sonication fluids

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Identification of Prosthetic Joint Infection Pathogens Using a Shotgun Metagenomics Approach.

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

Staphylococcus aureus from prosthetic joint infections and blood cultures

Project description:Staphylococcus aureus from prosthetic joint infections and blood cultures display the same genetic background

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Shotgun metagenomics from positive blood culture bottles inoculated with prosthetic joint tissues positive for S. aureus

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

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SHOTGUN-METAGENOMICS ON BLOOD CULTURE BOTTLES WITH TISSUE

Project description:PROOF OF CONCEPT OF SHOTGUN-METAGENOMICS ON BLOOD CULTURE BOTTLES INOCULATED WITH PROSTHETIC JOINT TISSUE

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Analysis of peri-prosthetic breast tissue bewteen subjects with and without breast implant illness (BII)

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

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

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.

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