Greenwood‐Quaintance, Kerryl E.

Sequencing is increasingly used for infective endocarditis (IE) diagnosis. Here, the performance of 16S rRNA gene PCR/sequencing of heart valves utilized in routine clinical practice was compared with conventional IE diagnostics.

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.

AbstractBiofilms formed by antibiotic‐resistant bacteria in wound beds challenge the treatment of wound infections. In this work, the activity of a novel electrochemical bandage (e‐bandage) composed of carbon fabric and controlled by a wearable potentiostat, designed to continuously deliver low amounts of hydrogen peroxide (H2O2), is evaluated against methicillin‐resistant Staphylococcus aureus (MRSA), multidrug‐resistant Pseudomonas aeruginosa (MDR‐PA) and mixed‐species (MRSA and MDR‐PA) wound infections. Wounds created on Swiss Webster mice are infected with the above‐named bacteria and biofilms allow to establish in wound beds for 3 days. e‐Bandages, which electrochemically reduce dissolved oxygen to H2O2 when polarized at −0.6 VAg/AgCl, are placed atop infected wound beds and polarized continuously for 48 h. Polarized e‐bandage treatment results in significant reductions (p <0.001) of both monospecies and mixed‐species wound infections. After e‐bandage treatment, electron microscopy shows degradation of bacterial cells, and histopathology shows no obvious alterations to the inflammatory host response. Blood biochemistries show no abnormalities. Taken all together, results of this work suggest that the described H2O2‐producing e‐bandage can reduce in vivo MRSA, MDR‐PA, and mixed‐species wound biofilms, and should be further developed as a potential antibiotic‐free strategy for treatment of wound infections.

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 &lt;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).

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.

The antibiofilm activity of a hypochlorous acid (HOCl)-producing electrochemical bandage (e-bandage) was assessed against 14 yeast isolates in vitro . The evaluated e-bandage was polarized at +1.5 V Ag/AgCl to allow continuous production of HOCl.

The diagnosis of periprosthetic joint infection (PJI) is challenging, often requiring multiple clinical specimens and diagnostic techniques, some with prolonged result turnaround times. Here, the diagnostic performance of the Investigational Use Only (IUO) BioFire Joint Infection (JI) Panel was compared to 16S rRNA gene-based targeted metagenomic sequencing (tMGS) applied to synovial fluid for PJI diagnosis.

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.

Abstract Background Ceftriaxone-resistant (CRO-R) Escherichia coli bloodstream infections (BSIs) are common. Methods This is a prospective cohort of patients with E coli BSI at 14 United States hospitals between November 2020 and April 2021. For each patient with a CRO-R E coli BSI enrolled, the next consecutive patient with a ceftriaxone-susceptible (CRO-S) E coli BSI was included. Primary outcome was desirability of outcome ranking (DOOR) at day 30, with 50% probability of worse outcomes in the CRO-R group as the null hypothesis. Inverse probability weighting (IPW) was used to reduce confounding. Results Notable differences between patients infected with CRO-R and CRO-S E coli BSI included the proportion with Pitt bacteremia score ≥4 (23% vs 15%, P = .079) and the median time to active antibiotic therapy (12 hours [interquartile range {IQR}, 1–35 hours] vs 1 hour [IQR, 0–6 hours]; P &lt; .001). Unadjusted DOOR analyses indicated a 58% probability (95% confidence interval [CI], 52%–63%) for a worse clinical outcome in CRO-R versus CRO-S BSI. In the IPW-adjusted cohort, no difference was observed (54% [95% CI, 47%–61%]). Secondary outcomes included unadjusted and adjusted differences in the proportion of 30-day mortality between CRO-R and CRO-S BSIs (−5.3% [95% CI, −10.3% to −.4%] and −1.8 [95% CI, −6.7% to 3.2%], respectively), postculture median length of stay (8 days [IQR, 5–13 days] vs 6 days [IQR, 4–9 days]; P &lt; .001), and incident admission to a long-term care facility (22% vs 12%, P = .045). Conclusions Patients with CRO-R E coli BSI generally have poorer outcomes compared to patients infected with CRO-S E coli BSI, even after adjusting for important confounders.

Chronic wound biofilm infections represent a major clinical challenge which results in a substantial burden to patients and healthcare systems. Treatment with topical antibiotics is oftentimes ineffective as a result of antibiotic‐resistant microorganisms and biofilm‐specific antibiotic tolerance. Use of biocides such as hypochlorous acid (HOCl) has gained increasing attention due to the lack of known resistance mechanisms. An HOCl‐generating electrochemical bandage (e‐bandage) is designed that delivers HOCl continuously at low concentrations targeting infected wound beds in a similar manner to adhesive antimicrobial wound dressings. A battery‐operated wearable potentiostat is developed that controls the e‐bandage electrodes at potentials suitable for HOCl generation. It is demonstrated that e‐bandage treatment is tunable by changing the applied potential. HOCl generation on electrode surfaces is verified using microelectrodes. The developed e‐bandage shows time‐dependent responses against in vitro Acinetobacter baumannii and Staphylococcus aureus biofilms, reducing viable cells to nondetectable levels within 6 and 12 h of treatment, respectively. The developed e‐bandage should be further evaluated as an alternative to topical antibiotics to treat wound biofilm infections.