Deep Learning-Based Multimodal Clustering Model for Endotyping and Post-Arthroplasty Response Classification using Knee Osteoarthritis Subject-Matched Multi-Omic Data
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ABSTRACT:
PROVIDER: PRJNA924328 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR23094200.fastq.gz | Fastqsanger.gz | |||
| SRR23094300.fastq.gz | Fastqsanger.gz | |||
| SRR23094400.fastq.gz | Fastqsanger.gz | |||
| SRR23094500.fastq.gz | Fastqsanger.gz | |||
| SRR23094600.fastq.gz | Fastqsanger.gz |
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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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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: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.
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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: Endoprosthesis loosening is the major cause of arthroplasty failure. Currently, radiography and histo-pathological classification of the periprosthetic membrane are used retrospectively for diagnosis. Prospective options for early diagnosis and prevention of loosening are not available. In order to approach this challenge, the study presented here aimed to identify molecular biomarkers in single-centre prospectively collected cell and plasma samples from patients. Methods: Four patient cohorts (primary and revision arthroplasty of hip or knee) were defined. Aseptic loosening of implants was assessed by the standardised approach of the Knee Society Total Knee Arthroplasty Roentgenographic Evaluation and Scoring System (KSRESS) which is based on radiolucent lines. Synovial fluid, bone marrow, and blood were collected from a total of 96 patients. Bulk RNA-sequencing (RNA-seq) of mesenchymal stem cells (MSCs) isolated from the bone marrow of patients was performed for 28 samples. The data was analysed by bioinformatic tools. Quantitative real-time PCR served to validate and extend RNA-seq data. Western blots were performed for molecular differentiation. ELISAs were used to quantitate selected protein targets. Results: RNA-seq of MSCs identified glycoprotein non-metastatic melanoma protein B (GPNMB) as significantly upregulated in the cells from revision patients compared to patients with primary implantations. The protein encoded by this gene is plasma membrane-standing; its extracellular domain may be cleaved and secreted. In synovial fluid plasma, the soluble GPNMB protein exhibited increased levels in patients undergoing revision surgery compared to patients subjected to primary arthroplasty. No significant differences were observed in bone marrow plasma and blood plasma. The molecular species detected by Western blotting were similar in the plasma fractions of synovial fluid, bone marrow and blood. Conclusions: The use of GPNMB as a biomarker may be of prime importance for early detection of implant loosening, ahead of radiographic failure demonstration. Translational potential of this article: GPNMB has the potential to act as a prospective and quantitative parameter to assess different stages of implant loosening. Radiography and histo-pathological classification of the periprosthetic membrane, on the contrary, are used retrospectively for diagnosis and are subjective measures, performed mainly at the end of a full loosening process.
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