Immune and Repair Responses in Joint Tissues and Lymph Nodes after Knee Arthroplasty Surgery in Mice
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA729169 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR14517900.fastq.gz | Fastqsanger.gz | |||
| SRR14517901.fastq.gz | Fastqsanger.gz | |||
| SRR14517902.fastq.gz | Fastqsanger.gz | |||
| SRR14517903.fastq.gz | Fastqsanger.gz | |||
| SRR14517904.fastq.gz | Fastqsanger.gz |
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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.
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Project description:Transcriptome profiling was performed on muscle biopsies from patients immediately before Total Knee Arthroplasty and two hours after TKA and tourniquet application.
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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.
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Project description:The importance of a local tissue immune response in healing injured tissues such as skin and lung is well established. Little is known about whether sterile wounds elicit lymph node (LN) responses and inflammatory responses after injury of musculoskeletal tissues that are mechanically loaded during the repair response. We investigated LN and tissue immune responses in a tibial implant model of joint replacement surgery where wounded tissue is subjected to movement and mechanical loading postoperatively. Draining inguinal and iliac LNs expanded postoperatively, including increases in regulatory T cells and activation of a subset of T cells. Thus, tissue injury was actively sensed in secondary lymphoid organs, with the potential to activate adaptive immunity. Joint tissues exhibited three temporally distinct immune response components, including a novel interferon (IFN) response with activation of signal transducer and activator of transcription (STAT) and interferon regulatory factor (IRF) pathways. Fibrovascular tissue formation was not associated with a macrophage type 2 (M2) reparative immune response, but instead with delayed induction of interleukin-1 family (IL-1β, IL-33, IL-36), IL-17, and prostaglandin pathway genes concomitant with transforming growth factor (TGF)-β and growth factor signaling, fibroblast activation, and tissue formation. Tissue remodeling was associated with activity of the HOX antisense intergenic RNA (HOTAIR) pathway. These results provide insights into immune responses and regulation of tissue healing after knee arthroplasty that potentially can be used to develop therapeutic strategies to improve healing, prevent arthrofibrosis, and improve surgical outcomes. © 2021 American Society for Bone and Mineral Research (ASBMR).
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Project description:Gene Expression of Fibroblasts Derived from Knee Tissue of Patients Undergoing Primary Total Knee Arthroplasty for Osteoarthritis
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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.
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