Citations

AbstractArthrofibrosis, which is characterized by excessive scar tissue and limited motion, can complicate the daily functioning of patients after total knee arthroplasty (TKA). Molecular hallmarks of arthrofibrosis include pathologic accumulation of myofibroblasts and disproportionate collagen deposition. Epigenetic mechanisms, including posttranslation modification of histones, control gene expression and may regulate fibrotic events. This study assessed the role of the bromodomain and extra‐terminal (BET) proteins on myofibroblast differentiation. This group of epigenetic regulators recognize acetylated lysines and are targeted by a class of drugs known as BET inhibitors. RNA‐seq analysis revealed robust mRNA expression of three BET members (BRD2, BRD3, and BRD4) while the fourth member (BRDT) is not expressed in primary TKA knee outgrowth fibroblasts. RT‐qPCR and western blot analyses revealed that BET inhibition with the small molecule JQ1 impairs TGFβ1‐induced expression of ACTA2, a key myofibroblast marker, in primary outgrowth knee fibroblasts. Similarly, JQ1 administration also reduced COL3A1 mRNA levels and collagen deposition as monitored by picrosirius red staining. Interestingly, the inhibitory effects of JQ1 on ACTA2 mRNA and protein expression, as well as COL3A1 expression and collagen deposition, were paralleled by siRNA‐mediated depletion of BRD4. Together, these data reveal that BRD4‐mediated epigenetic events support TGFβ1‐mediated myofibroblast differentiation and collagen deposition as seen in arthrofibrosis. To our knowledge, these are the first studies that assess epigenetic regulators and their downstream events in the context of arthrofibrosis. Future studies may reveal clinical utility for drugs that target epigenetic pathways, specifically BET proteins, in the prevention and treatment of arthrofibrosis.

Aims As has been shown in larger animal models, knee immobilization can lead to arthrofibrotic phenotypes. Our study included 168 C57BL/6J female mice, with 24 serving as controls, and 144 undergoing a knee procedure to induce a contracture without osteoarthritis (OA). Methods Experimental knees were immobilized for either four weeks (72 mice) or eight weeks (72 mice), followed by a remobilization period of zero weeks (24 mice), two weeks (24 mice), or four weeks (24 mice) after suture removal. Half of the experimental knees also received an intra-articular injury. Biomechanical data were collected to measure passive extension angle (PEA). Histological data measuring area and thickness of posterior and anterior knee capsules were collected from knee sections. Results Experimental knees immobilized for four weeks demonstrated mean PEAs of 141°, 72°, and 79° after zero, two, and four weeks of remobilization (n = 6 per group), respectively. Experimental knees demonstrated reduced PEAs after two weeks (p < 0.001) and four weeks (p < 0.0001) of remobilization compared to controls. Following eight weeks of immobilization, experimental knees exhibited mean PEAs of 82°, 73°, and 72° after zero, two, and four weeks of remobilization, respectively. Histological analysis demonstrated no cartilage degeneration. Similar trends in biomechanical and histological properties were observed when intra-articular violation was introduced. Conclusion This study established a novel mouse model of robust knee contracture without evidence of OA. This was appreciated consistently after eight weeks of immobilization and was irrespective of length of remobilization. As such, this arthrofibrotic model provides opportunities to investigate molecular pathways and therapeutic strategies. Cite this article: Bone Joint Res 2023;12(1):58–71.

Objectives: To date, there are no specific guidelines on antithrombotic therapy (ATT) management, which includes both anticoagulation and antiplatelet medications, for open tracheostomy. The objective of this study was to evaluate whether the use of perioperative antithrombotic medication during open tracheostomy influences the incidence of perioperative or postoperative complications. Methods: A retrospective review was conducted of all patients who underwent open tracheostomies at a tertiary care medical center from January 2015 to December 2019. Charts were reviewed for demographics, comorbidities, indication for tracheostomy, ATT use, operative details, and complications. Results: A total of 217 tracheostomies were evaluated for this study, of which 148 (68.2%) were not on ATT and 69 (31.8%) were on ATT during surgery. No significant difference was observed based on ATT status in perioperative bleeding ( P = .983), postoperative bleeding ( P = .24), or median days to decannulation ( P = .5986). ATT patients were 2.67 times more likely to experience 30-day mortality than those non-ATT patients ( P = .035). There was only one death due to hemorrhage in the ATT group. This was unrelated to the tracheostomy. This compares to 2 hemorrhage-related deaths in those not on ATT. Conclusion: There was no significant difference in perioperative or postoperative bleeding based on ATT use. Patients on ATT were significantly more likely to experience 30-day mortality, however only one death was due to hemorrhage in the ATT group and was unrelated to tracheostomy. Therefore, continued perioperative ATT use appears to be safe when performing open tracheostomy. Level of Evidence: 4

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 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.