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Abstract Background Shotgun and targeted metagenomic sequencing have been shown in separate studies to be potentially useful for culture-free pathogen identification in blood and/or plasma of patients with infective endocarditis (IE). However, the 2 approaches have not been directly compared. The aim of this study was to compare shotgun metagenomic sequencing with targeted metagenomic sequencing (tMGS) for organism identification in blood or plasma of patients with IE. Methods Patients with possible or definite IE were prospectively enrolled from October 2020 to July 2021. Shotgun metagenomic sequencing was performed with the Karius test, which uses microbial cell-free DNA (mcfDNA) sequencing to detect, identify, and quantitate DNA-based pathogens in plasma. tMGS was performed using a 16S ribosomal RNA (rRNA) polymerase chain reaction assay targeting the V1 to V3 regions of the 16S rRNA gene. Results were compared using the McNemar test of paired proportions. Results Samples from 34 patients were investigated. The Karius test was positive in 24/34 (71%), including 3/6 (50%) with blood culture–negative endocarditis (BCNE), which was not significantly different from the positivity rate of tMGS (P = .41). Results of the Karius test were concordant with tMGS in 75% of cases. The Karius test detected 2 cases of methicillin-resistant Staphylococcus aureus among the 7 S. aureus detections, in accordance with results of phenotypic susceptibility testing. The combination of blood cultures, the Karius test, and tMGS found a potential causative pathogen in 33/34 (97%), including 5/6 with BCNE. Conclusions The Karius test and tMGS yielded comparable detection rates; however, beyond organism identification, the Karius test generated potentially useful antibiotic resistance data.

AbstractPurposeThis pilot study aimed to use proteomic profiling of sonicate fluid samples to compare host response during Staphylococcus aureus‐associated periprosthetic joint infection (PJI) and non‐infected arthroplasty failure (NIAF) and identify potential novel biomarkers differentiating the two.Experimental designIn this pilot study, eight sonicate fluid samples (four from NIAF and four from S. aureus PJI) were studied. Samples were reduced, alkylated, and trypsinized overnight, followed by analysis using liquid chromatography‐tandem mass spectrometry (LC‐MS/MS) on a high‐resolution Orbitrap Eclipse mass spectrometer. MaxQuant software suite was used for protein identification, filtering, and label‐free quantitation.ResultsPrincipal component analysis of the identified proteins clearly separated S. aureus PJI and NIAF samples. Overall, 810 proteins were identified based on their detection in at least three out of four samples from each group; 35 statistically significant differentially abundant proteins (DAPs) were found (two‐sample t‐test p‐values ≤0.05 and log2fold‐change values ≥2 or ≤−2). Gene ontology pathway analysis found that microbial defense responses, specifically those related to neutrophil activation, to be increased in S. aureus PJI compared to NIAF samples.Conclusion and clinical relevanceProteomic profiling of sonicate fluid using LC‐MS/MS differentiated S. aureus PJI and NIAF in this pilot study. Further work is needed using a larger sample size and including non‐S. aureus PJI and a diversty of NIAF‐types.

Arthroplasty failure is a major complication of joint replacement surgery. It can be caused by periprosthetic joint infection (PJI) or non-infectious etiologies, and often requires surgical intervention and (in select scenarios) resection and reimplantation of implanted devices. Fast and accurate diagnosis of PJI and non-infectious arthroplasty failure (NIAF) is critical to direct medical and surgical treatment; differentiation of PJI from NIAF may, however, be unclear in some cases. Traditional culture, nucleic acid amplification tests, metagenomic, and metatranscriptomic techniques for microbial detection have had success in differentiating the two entities, although microbiologically negative apparent PJI remains a challenge. Single host biomarkers or, alternatively, more advanced immune response profiling-based approaches may be applied to differentiate PJI from NIAF, overcoming limitations of microbial-based detection methods and possibly, especially with newer approaches, augmenting them. In this review, current approaches to arthroplasty failure diagnosis are briefly overviewed, followed by a review of host-based approaches for differentiation of PJI from NIAF, including exciting futuristic combinational multi-omics methodologies that may both detect pathogens and assess biological responses, illuminating causes of arthroplasty failure.

Abstract Differentiation of aseptic and septic causes of arthroplasty failure is essential due to their different treatment schemes. We hypothesized that predicted cellularity profiling of sonicate fluid using the RNASeq-based the cellular deconvolution tools CIBERSORTx and ABIS-seq can differentiate between PJI and non-infected arthroplasty failure (NIAF). Profiles were created for 93 sonicate fluid samples (40 from NIAF and 53 from PJI patients) that had been subjected to RNASeq analysis. CIBERSORTx provided 22 total predicted cell types; 12 predicted cellular abundances were differentially expressed in PJI versus NIAF, including increased neutrophils, activated mast cells, and eosinophils (p≤0.0004) and decreased M0 macrophages, M2 macrophages, and Treg cells (p≤0.0001). ABIS-seq provided 17 total predicted cell types; seven predicted cellular abundances were differentially expressed in PJI versus NIAF, including increased neutrophils, Vd2 γδ T cells, and basophils (p-values of <0.0001, 0.0003, and 0.0263, respectively) and decreased nonclassical/intermediate (NC+I) monocytes, mucosal-associated invariant T (MAIT) cells, and myeloid dendritic cells (mDC) (p-values ≤0.0001). Receiver operative characteristic area under the curve (AUC) analysis identified cells types most predictive of PJI (CIBERSORTx: neutrophils [AUC = 94], activated mast cells [AUC = 93]; ABIS-seq: neutrophils [AUC = 88]) and NIAF (ABIS-seq: NC+I monocytes [AUC = 85], MAIT cells [AUC = 80], mDCs [AUC = 81]). PJI and NIAF samples were differentially clustered by principal component analysis using both tools. Overall, cellularity profiling using RNASeq-based cellular deconvolution can differentiate between PJI and NIAF sonicate fluid samples. Work supported by NIAMS (NIH R01 AR056647). CF was supported by the Mayo Clinic Graduate School of Biomedical Sciences and the Ph.D. Training Grant in Basic Immunology (NIH R25 GM055252 24).

Abstract Loosening of prosthetic joint implants is a leading cause of arthroplasty failure and the need for surgical revision. Though the mechanisms that induce loosening are complex and not fully understood, implant-derived wear debris has been suggested to be an important player in the interaction between the prosthesis and surrounding cells needed for post-operative immune response, bone remodeling and implant retention. In this study, MC-3T3 pre-osteoblasts were grown in media supplemented with wear particle analogs [titanium (II) oxide (TiO), chromium (III) oxide (Cr2O3), or cobalt (II, III) oxide (Co3O4) particles] for up to seven days and analyzed for apoptotic marker regulation and cell viability. An increase in apoptotic marker staining and a decrease in viability was found in osteoblasts cultured with TiO and Cr2O3 at concentrations >10 μg/ml after just one day and lasting through seven days of exposure. Similar apoptotic and viability response was observed in Co3O4 treated osteoblasts, but only after seven days of particle exposure. Osteoblasts exposed to 100 μg/ml of TiO, Cr2O3, or Co3O4 for seven days had AnnexinV+ frequencies of 60.2%, 74.7%, and 13.4%, respectively. No difference was detected in osteoblasts exposed to particle concentrations ≤10 μg/ml at any time point. Additionally, particles were found localized to cell membranes and the cytoplasm within a single day of treatment; internalization, specific intracellular location, and downstream processing of the particles are under active investigation. This work provides insight into interactions between prosthetic joint arthroplasty wear particles and the osteoblasts necessary for proper bone homeostasis and joint retention.

Background: Although cellularity is traditionally assessed morphologically, deep sequencing approaches being used for microorganism detection may be able to provide information about cellularity. We hypothesized that cellularity predicted using CIBERSORTx (Stanford University), a transcriptomic-based cellular deconvolution tool, would differentiate between infectious and non-infectious arthroplasty failure. Methods: CIBERSORTx-derived cellularity profiles of 93 sonicate fluid samples, including 53 from subjects who underwent failed arthroplasties due to periprosthetic joint infection (PJI) (abbreviated for the purpose of this study as PJIF) and 40 from subjects who had undergone non-infectious arthroplasty failure (abbreviated NIAF) that had been subjected to bulk RNA sequencing were evaluated. Results: Samples from PJIF and NIAF subjects were differentially clustered by principal component analysis based on the cellularity profile. Twelve of the 22 individual predicted cellular fractions were differentially expressed in the PJIF cases compared with the NIAF cases, including increased predicted neutrophils (mean and standard error, 9.73% ± 1.06% and 0.81% ± 0.60%), activated mast cells (17.12% ± 1.51% and 4.11% ± 0.44%), and eosinophils (1.96% ± 0.37% and 0.42% ± 0.21%), and decreased predicted M0 macrophages (21.33% ± 1.51% and 39.75% ± 2.45%), M2 macrophages (3.56% ± 0.52% and 8.70% ± 1.08%), and regulatory T cells (1.57% ± 0.23% and 3.20% ± 0.34%). The predicted total granulocyte fraction was elevated in the PJIF cases (32.97% ± 2.13% and 11.76% ± 1.61%), and the samples from the NIAF cases had elevated predicted total macrophage and monocyte (34.71% ± 1.71% and 55.34% ± 2.37%) and total B cell fractions (5.89% ± 0.30% and 8.62% ± 0.86%). Receiver operating characteristic curve analysis identified predicted total granulocytes, neutrophils, and activated mast cells as highly able to differentiate between the PJIF cases and the NIAF cases. Within the PJIF cases, the total granulocyte, total macrophage and monocyte, M0 macrophage, and M2 macrophage fractions were differentially expressed in Staphylococcus aureus compared with Staphylococcus epidermidis -associated samples. Within the NIAF cases, the predicted total B cell, naïve B cell, plasma cell, and M2 macrophage fractions were differentially expressed among different causes of failure. Conclusions: CIBERSORTx can predict the cellularity of sonicate fluid using transcriptomic data, allowing for the evaluation of the underlying immune response during the PJIF and NIAF cases, without a need to phenotypically assess cell composition.

Periprosthetic joint infection (PJI) is a dreadful complication of joint replacement. Noninvasive identification of infectious pathogens has been traditionnally limited to culture-based testing of synovial fluid which has poor sensitivity.

Initial microbiologic diagnosis of infective endocarditis (IE) relies on blood cultures and Bartonella and Coxiella burnetii serology. Small case series and one prospective study have preliminarily reported application of metagenomic sequencing on blood or plasma for IE diagnosis.

Wound infections are caused by bacteria and/or fungi. The presence of fungal biofilms in wound beds presents a unique challenge, as fungal biofilms may be difficult to eradicate.