Aim. While metagenomic (microbial DNA) sequencing technologies can detect the presence of microbes in a clinical sample, it is unknown whether this signal represents dead or live organisms. Metatranscriptomics (sequencing of RNA) offers the potential to detect transcriptionally “active” organisms within a microbial community, and map expressed genes to functional pathways of interest (e.g. antibiotic resistance). We used this approach to evaluate the utility of metatrancriptomics to diagnose PJI and predict antibiotic resistance. Method. In this prospective study, samples were collected from 20 patients undergoing revision TJA (10 aseptic and 10 infected) and 10 primary TJA. Synovial fluid and peripheral blood samples were obtained at the time of surgery, as well as negative field controls (skin swabs, air swabs, sterile water). All samples were shipped to the laboratory for metatranscriptomic analysis. Following microbial RNA extraction and host analyte subtraction, metatranscriptomic sequencing was performed. Bioinformatic analyses were implemented prior to mapping against curated microbial sequence databases– to generate taxonomic expression profiles. Principle Coordinates Analysis (PCoA) and Partial Least Squares-Discriminant Analysis were utilized to ordinate metatranscriptomic profiles, using the 2018 definition of PJI as the gold-standard. Results. After RNA metatranscriptomic analysis, blinded PCoA modeling revealed accurate and distinct clustering of samples into 3 separate cohorts (infected, aseptic, and primary joints) – based on their active transcriptomic profile, both in synovial fluid and blood (synovial anosim p=0.001; blood anosim p=0.034). Differential metatranscriptomic signatures for infected versus noninfected cohorts enabled us to train machine learning algorithms to 84.9% predictive accuracy for infection. Multiple antibiotic resistance genes were expressed, with high concordance to conventional antibiotic sensitivity data. Conclusions. Our findings highlight the potential of metatranscriptomics for infection diagnosis. To our knowledge, this is the first report of RNA sequencing in the orthopaedic literature. Further work in larger patient cohorts will better inform deep learning approaches to improve accuracy, predictive power, and clinical utility of this technology

Aim. The aim of this study was to gain insight into the in vivo expression of virulence and metabolic genes of Staphylococcus aureus in a prosthetic joint infection in a human subject. Method. Deep RNA sequencing (RNA-seq) was used for transcriptome profile of joint fluid obtained from a patient undergoing surgery due to acute S. aureus prosthetic joint infection. The S. aureus gene expression in the infection was compared with exponential culture of a S. aureus isolate obtained from the same sample using EdgeR. In addition, the genome of the isolate was sequenced on Miseq, assembled in CLC genomics workbench and annotated by MaGe. Moreover, using nuclear magnetic resonance (NMR) spectroscopy we analysed the metabolites in the joint fluid and in the culture supernatants to determine the biochemical composition of the environments. Results. Antibiotic susceptibility testing by disk diffusion (EUCAST) demonstrated that the strain was susceptible to β-lactams (penicillin and cefoxitin) and macrolides (erythromycin and roxitromycin). This was indirectly confirmed by the annotated genome, because of absence of known resistant genes. The patient showed no signs of improvement during 2-days treatment with antibiotics (different β-lactams and gentamicin) prior to the surgery. The RNA-seq data indicated that the strategy employed by S. aureus to survive and proliferate in the host during antibiotic treatment involved overexpression of various enzymes related to cell-wall synthesis and multidrug efflux pumps. Interestingly, these efflux pumps are only known to be related to fluoroquinolone resistance. Many of the genes encoding virulence factors were upregulated, including toxins and superantigen-like proteins, hemolysins, and immune evasion proteins. A number of chaperones and stress related genes were overexpressed indicating a stress response. Furthermore, the RNA-seq data provided clues of the potential major nutrient sources for the pathogen in vivo. Several amino acid degradation pathways were highly upregulated, e.g. arginine, histidine. Additional carbon sources included N-acetylneuraminate and purine/pyrimidine deoxyribonucleosides as indicated by the upregulation of the genes involved in the degradation pathways of these compounds and higher concentration of these substances in the joint fluid compared to culture supernatants. Conclusions. Our results show that the gene expression pattern of S. aureusin vivo is vastly different from that of an in vitro grown exponential culture, indicating that the pathogen adapts to host environmental conditions by altering gene expression. Finally our study emphasizes the importance of in vivo study in elucidating pathogenesis of S. aureus in prosthetic joint infections

Aims

This study explored the shared genetic traits and molecular interactions between postmenopausal osteoporosis (POMP) and sarcopenia, both of which substantially degrade elderly health and quality of life. We hypothesized that these motor system diseases overlap in pathophysiology and regulatory mechanisms.

Aims

The aim of this study was to analyze the prevalence of culture-negative periprosthetic joint infections (PJIs) when adequate methods of culture are used, and to evaluate the outcome in patients who were treated with antibiotics for a culture-negative PJI compared with those in whom antibiotics were withheld.