Correlations between metabolites in the synovial fluid and serum: a mouse injury study

Abstract

Osteoarthritis, the most common degenerative joint disease, occurs more frequently in joints that have sustained injury. Currently, osteoarthritis is diagnosed with imaging that finds radiographic changes after the disease has already progressed to multiple tissues. The primary objective of this study was to compare potential metabolomic biomarkers of joint injury between the synovial fluid and serum in a mouse model of post-traumatic osteoarthritis. The secondary objective was to gain insight into the pathophysiology of osteoarthritis by examining metabolomic profiles after joint injury. 12-week-old adult female C57BL/6 mice (n=12) were randomly assigned to control, day 1 post injury, or day 8 post injury groups. Randomly selected stifle (i.e., knee) joints were placed into a non-invasive injury apparatus and subjected to a single dynamic axial compression causing anterior translation of the tibia relative to the femur to tear the anterior cruciate ligament. At days 1 and 8 post injury, serum was extracted then mice were immediately euthanized prior to synovial fluid collection. Metabolites were extracted and analyzed by liquid chromatography coupled to mass spectrometry. We detected ~2500 metabolites across serum and synovial fluid. Of these metabolites 179 were positively correlated and 51 were negatively correlated between synovial fluid and serum, indicating potential for the development of metabolomic biomarkers. Synovial fluid appeared to capture differences in metabolomic profiles between injured mice at both day 1 and 8 after injury whereas serum did not. However, synovial fluid and serum were distinct at both days 1 and 8 after injury. In the synovial fluid, pathways of interest across different time points mapped to amino acid synthesis and degradation, bupropion degradation, and the tRNA charging pathway. In the serum, notable pathways across time points were amino acid synthesis and degradation, the phospholipase pathway, and nicotine degradation. These results provide a rich picture of the injury response at early time points following traumatic joint injury. Furthermore, the correlations between synovial fluid and serum metabolites suggest that there is potential to gain insight into intra-articular pathophysiology through analysis of serum metabolites.

Ensemble Clustering Combined with Clustering Optimization – A Novel Workflow for Analyzing Metabolomics Data

“…When performing an ensemble of clustering solutions there are a bevy of potential clustering methods including partitional, hierarchical, and density-based methods that can be utilized. In this implementation we focused on a combination of hierarchical clustering solutions as they are commonly used clustering method in the metabolomics field(Carlson et al ., 2018; Carlson et al ., 2019a; Carlson et al ., 2019b; Hahn et al ., 2021; Hislop et al ., 2022; Kim et al ., 2014; Pang et al ., 2021; Wallace et al ., 2022; Wang-Sattler et al ., 2012; Zignego et al ., 2015). However, we acknowledge that there are many other potential combinations of clustering solutions that can be explored to provide a consensus on the appropriate number of clusters within a given dataset.…”

The impact of omics research on our understanding of osteoarthritis and future treatments

“…This study provides excellent baseline data on metabolic effects of these two lifestyle modifications on joint metabolism, as well as how these changes correlate to systemic parameters. A related study compared 2500 serum and synovial fluid metabolites after a single injury in mice and showed both similarities and differences between the two body fluids [21].…”