Muscle transcriptome analysis following Total Knee Arthroplasty with Tourniquet
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ABSTRACT: Transcriptome profiling was performed on muscle biopsies from patients immediately before Total Knee Arthroplasty and two hours after TKA and tourniquet application.
Project description:Ischaemic preconditioning is a method of protecting tissue against ischaemia-reperfusion injury. It is an innate protective mechanism that increases a tissue's tolerance to prolonged ischaemia when it is first subjected to short burst of ischaemia and reperfusion. It is thought to provide this protection by increasing the tissue's tolerance to ischaemia, therby reducing oxidative stress, inflammation and apoptosis in the preconditioned tissue. We used microarrays to investigate the genomic response induced by ischaemic preconditioning in muscle biopsies taken from the operative leg of total knee arthroplasty patients in order to gain insight into the ischaemic preconditioning mechanism. Patients undergoing primary knee arthroplasty were randomised to control and treatment (ischaemic preconditioning) groups. Patients in the treatment group received a preconditioning stimulus immediately prior to surgery. The ischaemic preconditioning stimulus consisted of three five-minute periods of tourniquet insufflation on the lower operative limb, interrupted by five minute periods of reperfusion. All patients had a tourniquet applied to the lower limb after the administration of spinal anaesthesia, as per normal protocol for knee arthroplasty surgery. Muscle biopsies were taken from the quadriceps muscle of the operative knee at the immediate onset of surgery (T0) and at 1 hour into surgery (T1). Total RNA was extracted from biospies of four control and four treatment patients and hybridised to the Affymetrix Human U133 2.0 chip.
Project description:These data are from a preclinical model of total knee arthroplasty (TKA) performed in healthy rats. One day after surgery, ipsilateral dorsal root ganglia (DRG) from L3 and L4 were collected for RNA extraction and sequencing.
Project description:Arthrofibrosis is characterized by excessive extracellular matrix (ECM) deposition that results in restricted joint motion after total knee arthroplasty (TKA). Current surgical and pharmacologic treatment options are limited. Therefore, an in vitro model for joint myofibroblastogenesis is valuable to investigate the arthrofibrotic process and identify diagnostic biomarkers and treatment options. In this study, we obtained intraoperative posterior capsule (PC), quadriceps tendon (QT), and suprapatellar pouch (SP) tissue from knees of four patients undergoing primary TKA for osteoarthritis and characterized primary outgrowth cells from these tissues in the absence and presence of transforming growth factor beta 1 (TGFβ1), a pro-myofibroblastic cytokine. Light microscopy of knee outgrowth cells revealed spindle-shaped cells while immunofluorescence (IF) established staining for the fibroblast-specific antigen TE-7 and Vimentin, which are characteristics of fibroblasts. These fibroblasts differentiate readily into myofibroblasts as highlighted by enhanced alpha smooth muscle actin (ACTA2) mRNA and protein expression and increased collagen mRNA (i.e., collagen type 1 (COL1A1) and collagen type 3 (COL3A1)) expression and collagenous matrix deposition in the presence of TGFβ1. Of note, these studies also revealed that knee-derived fibroblasts are more sensitive to TGFβ1-mediated myofibroblastogenesis than adipose-derived mesenchymal stem cells. Importantly, while outgrowth fibroblasts isolated from four patients and three anatomical regions exhibit similar gene expression profiles, these knee fibroblasts form a unique gene expression cluster within the fibroblast niche as revealed by RNA-sequencing analysis. In conclusion, our study provides a fibroblast/myofibroblast model of outgrowth knee cells derived from patients undergoing primary TKA that can be employed to assess myofibroblast-related processes and test novel pharmacological strategies in vitro for arthrofibrosis.
Project description:Purpose The effects of the commonly utilized intraoperative tourniquet on gene expression within the skeletal muscle cells are barely examined. Our group already showed that tourniquet application results in higher proteolytic activity within vastus medialis cells (KSSTA 2016), without influence on the amount and function of mitochondria (EJOT 2016). The purpose of the present study was to examine the gene expression within the skeletal muscle cells after tourniquet-induced ischemia and to identify differential expressed genes (DEGs). Methods As part of a randomized, controlled, monocentric trial (Clinical-Trials.gov NCT02475603) we included 20 patients scheduled to undergo primary total knee arthroplasty (TKA). The patients received a written consent and were randomly assigned to Group A (TKA with tourniquet) (n=10) and Group B (TKA without tourniquet) (n=10). Muscle biopsies of (5×5×5 mm) were obtained from the vastus medialis immediately after performing the surgical approach and exactly 60 min later. After preparation of a muscle homogenate, RNA extraction was performed (RNeasy Plus Universal Mini Kit Qiagen) and RNA integrity (RIN) was determined (Agilent 2100 Bioanalyzer,RNA 6000 Pico Kit). Gene expression profiling was performed methodological validated (GeneChip™ Human Transcriptome Array 2.0;Affymetrix). Statistical analysis include the number of significant DEGs with p-value < 0,05, number of DEGs with relative difference >25% and pathway analysis (SPSS-Version 24; SAS JMP10 Genomics, Version 6). The power analysis considered an effect size of 0,4 and a statistical power > 0,8, sample size n=10 for each group. The protocol was approved by our Institutional Ethics Committee (File reference 2012-334N-MA). Results and conclusion Numerous genes were differencially expressed during tourniquet induced ischemia. Further investigations and statistical analysis are ongoning.
Project description:Ischaemic preconditioning is a method of protecting tissue against ischaemia-reperfusion injury. It is an innate protective mechanism that increases a tissue's tolerance to prolonged ischaemia when it is first subjected to short burst of ischaemia and reperfusion. It is thought to provide this protection by increasing the tissue's tolerance to ischaemia, therby reducing oxidative stress, inflammation and apoptosis in the preconditioned tissue. We used microarrays to investigate the genomic response induced by ischaemic preconditioning in muscle biopsies taken from the operative leg of total knee arthroplasty patients in order to gain insight into the ischaemic preconditioning mechanism.
Project description:Background: Primary knee osteoarthritis (KOA) is a heterogeneous disease with clinical and molecular contributors. Biofluids contain microRNAs and metabolites that can be measured by omic technologies. Deep learning captures complex non-linear associations within multimodal data but, to date, has not been used for multi-omic-based endotyping of KOA patients. We developed a novel multimodal deep learning framework for clustering of multi-omic data from three subject-matched biofluids to identify distinct KOA endotypes and classify one-year post-total knee arthroplasty (TKA) pain/function responses. Materials and Methods: In 414 KOA patients, subject-matched plasma, synovial fluid and urine were analyzed by microRNA sequencing or metabolomics. Integrating 4 high-dimensional datasets comprising metabolites from plasma (n=151 features), along with microRNAs from plasma (n=421), synovial fluid (n=930), or urine (n=1225), a multimodal deep learning variational autoencoder architecture with K-means clustering was employed. Features influencing cluster assignment were identified and pathway analyses conducted. An integrative machine learning framework combining 4 molecular domains and a clinical domain was then used to classify WOMAC pain/function responses post-TKA within each cluster. Findings: Multimodal deep learning-based clustering of subjects across 4 domains yielded 3 distinct patient clusters. Feature signatures comprising microRNAs and metabolites across biofluids included 30, 16, and 24 features associated with Clusters 1-3, respectively. Pathway analyses revealed distinct pathways associated with each cluster. Integration of 4 multi-omic domains along with clinical data improved response classification performance, with Cluster 3 achieving AUC=0·879 for subject pain response classification and Cluster 2 reaching AUC=0·808 for subject function response, surpassing individual domain classifications by 12% and 15% respectively. Interpretation: We have developed a deep learning-based multimodal clustering model capable of integrating complex multi-fluid, multi-omic data to assist in KOA patient endotyping and test outcome response to TKA surgery.
Project description:Analysis of gene expression profiles of ACL-MSCs derived from total knee arthroplasty (TKA) and ACL reconstruction patients. We hypothesized that the proportion of MSCs in ACL samples correlates negatively with donor age, i.e., the proportion of MSCs in ACL samples are higher in younger patients undergoing ACL reconstruction than in older patients undergoing TKA. Results provide additional information on the the effect of age on MSC properties, in particular, the differential regulation of genes encoding components of the extracellular matrix (ECM).
Project description:Large scale RNA-Seq analysis was performed to investigate the transcriptomic response to osteoarthritis in cartilage and investigate potential subgroups of patients. Data were collected from intact knee cartilage (posterior lateral condyle) from at total of 60 patients with osteoarthritis (OA) following total knee replacement and 10 control non-OA patients following amputation.