Patel, Robin

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 Antimicrobial resistance (AMR) among bacteria is an escalating public health emergency that has worsened during the COVID-19 pandemic. When making antibiotic treatment decisions, clinicians rely heavily on determination of antibiotic susceptibility or resistance by the microbiology laboratory, but conventional methods often take several days to identify AMR. There are now several commercially available molecular methods that detect antibiotic resistance genes within hours rather than days. While these methods have limitations, they offer promise for optimizing treatment and patient outcomes, and reducing further emergence of AMR. This review provides an overview of commercially available genotypic assays that detect individual resistance genes and/or resistance-associated mutations in a variety of specimen types and discusses how clinical outcomes studies may be used to demonstrate clinical utility of such diagnostics.

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.

Leptotrichia species are anaerobic, Gram-negative bacilli increasingly recognized as pathogens capable of causing invasive infections such as bloodstream infection (BSI), particularly among immunocompromised patients. However, there is a paucity of data regarding epidemiology, antimicrobial susceptibility, optimal treatment, and clinical outcomes among patients with Leptotrichia bacteremia.

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.

Abstract Background We assessed how laboratories use and handle reporting of results of rapid diagnostics performed on positive blood culture broths, with a focus on antimicrobial resistance (AMR) markers. Methods A survey assembled by the Antibacterial Resistance Leadership Group Diagnostics Committee was circulated from December 2020 to May 2021. The survey was sent to local hospitals, shared on the ClinMicroNet and Division C listservs, and included in a College of American Pathologists proficiency testing survey. Results Ninety-six laboratories of various sizes across the United States (95%) and outside of the United States (5%) participated. Of the laboratories that had at least 1 rapid diagnostic in place (94%), significant heterogeneity in methods used and reporting practices was found across community (52%) and academic (40%) laboratories serving hospitals of various sizes. Respondents had implemented 1 to 6 different panels/platforms for a total of 31 permutations. Methods of reporting rapid organism identification and AMR results varied from listing all targets as “detected”/“not detected” (16–22%) without interpretive guidance, to interpreting results (23–42%), or providing therapeutic guidance comments to patient-facing healthcare teams (3–17%). Conclusions Current approaches to reporting molecular AMR test results from positive blood culture vary significantly across clinical laboratories. Providing interpretative comments with therapeutic guidance alongside results reported may assist clinicians who are not well-versed in genetic mechanisms of AMR. However, this is currently not being done in all clinical laboratories. Standardized strategies for AMR gene result reporting are needed.

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.