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Objective: Obesity is a leading risk factor for both the incidence and progression of osteoarthritis (OA). Omic technologies, including transcriptomics and metabolomics are capable of identifying RNA and metabolite profiles in tissues and biofluids of OA patients. The objective of this review is to highlight studies using transcriptomics and metabolomics that contribute to our understanding of OA pathology in relation to obesity. Design: We conducted a targeted search of PUBMED for articles, and GEO for datasets, published up to February 13, 2024, screening for those using high-throughput transcriptomic and metabolomic techniques to study human or pre-clinical animal model tissues or biofluids related to obesity-associated OA. We describe relevant studies and discuss challenges studying obesity as a disease-related factor in OA. Results: Of the 107 publications identified by our search criteria, only 15 specifically used transcriptomics or metabolomics to study joint tissues or biofluids in obesity-related OA. Specific transcriptomic and metabolomic signatures associated with obesity-related OA have been defined in select local joint tissues, biofluids and other biological material. However, considerable challenges exist in understanding contributions of obesity-associated modifications of transcriptomes and metabolomes related to OA, including sociodemographic, anthropometric, dietary and molecular redundancy-related factors. Conclusions: A number of additional transcriptomic and metabolomic studies are needed to comprehensively understand how obesity affects OA incidence, progression and outcomes. Integration of transcriptome and metabolome signatures from multiple tissues and biofluids, using network-based approaches will likely help to better define putative therapeutic targets that could enable precision medicine approaches to obese OA patients.

Purpose: Up to 30% of spine facet osteoarthritis patients with lumbar spinal stenosis (SF-OA ​+ ​LSS) have little to no improvement in their pain after surgery. Lack of meaningful improvement in pain following surgery provides a unique opportunity to identify specific predictive biomarker signatures that might be associated with the outcomes of surgical treatment. The objective of the present study was to determine whether a microRNA (miRNA) biomarker signature could be identified in presurgical blood plasma that corresponded with levels of SF-OA ​+ ​LSS patient post-surgical pain intensity one year later. Methods: RNA was extracted from baseline plasma of SF-OA ​+ ​LSS patients and prepared for miRNA sequencing. Statistical approaches were performed to identify differentially expressed miRNAs associated with reduced 1-year postsurgical pain (n ​= ​56). Using an integrated computational approach, we further created predicted gene and pathway networks for each identified miRNA. Results: We identified a panel of 4 circulating candidate miRNAs (hsa-miR-155-5p, hsa-let-7e-5p, hsa-miR-125a-5p, hsa-miR-99b-5p) with higher levels at presurgical baseline that were associated with greater changes in % NPRS20Δ, reflecting reduced pain intensity levels at one year. Genes encoding hsa-let-7e-5p, hsa-miR-125a-5p, and hsa-miR-99b-5p are part of an evolutionarily conserved miRNA cluster. Using integrated computational analyses, we showed that mammalian target of rapamycin, transforming growth factor-β1 receptor, Wnt signaling, epithelial–mesenchymal transition regulators, and cholecystokinin signaling were enriched pathways of predicted gene targets. Conclusions: Taken together, our findings suggest that 4 presurgical baseline circulating miRNAs correlate with 1-year postsurgical SF-OA ​+ ​LSS patient pain intensity and represent possible candidate biomarker signature of surgical pain response.

The rapid expansion of Next Generation Sequencing (NGS) data availability has made exploration of appropriate bioinformatics analysis pipelines a timely issue. Since there are multiple tools and combinations thereof to analyze any dataset, there can be uncertainty in how to best perform an analysis in a robust and reproducible manner. This is especially true for newer omics applications, such as miRNomics, or microRNA-sequencing (miRNA-sequencing). As compared to transcriptomics, there have been far fewer miRNA-sequencing studies performed to date, and those that are reported seldom provide detailed description of the bioinformatics analysis, including aspects such as Unique Molecular Identifiers (UMIs). In this article, we attempt to fill the gap and help researchers understand their miRNA-sequencing data and its analysis. This article will specifically discuss a customizable miRNA bioinformatics pipeline that was developed using miRNA-sequencing datasets generated from human osteoarthritis plasma samples. We describe quality assessment of raw sequencing data files, reference-based alignment, counts generation for miRNA expression levels, and novel miRNA discovery. This report is expected to improve clarity and reproducibility of the bioinformatics portion of miRNA-sequencing analysis, applicable across any sample type, to promote sharing of detailed protocols in the NGS field.

Summary: Objective: Osteoarthritis (OA) is a degenerative joint disease with no approved disease modifying therapy. The enzyme autotaxin (ATX) converts lysophoshatidylcholine analogues to lysophosphatidic acid. Systemic inhibition of ATX reduces pain in animal models of OA; however, OA disease-modifying effects associated with ATX inhibition remain unknown. Here, we sought to determine whether local (knee joint) injection of an ATX inhibitor attenuates surgically-induced OA in mice. Methods: ATX expression was evaluated in human knee OA cartilage. Ten-week-old mice were subjected to surgically-induced OA. ATX inhibitor (PF-8380, 2.5ng/joint) was injected intra-articularly either at three and five weeks post-surgery or at two, four, six and eight weeks post-surgery and knee joints were evaluated by histopathology and immunohistochemistry to study the expression of catabolic and cell death markers. mRNA sequencing of human OA chondrocytes treated with/without ATX inhibitor was performed to identify differentially expressed transcripts, followed by pathway enrichment analysis. Results: ATX expression was elevated in severely degenerated cartilage compared to less degenerated human OA cartilage. In surgically-induced OA mice, intra-articular injection of ATX inhibitor at three and five weeks post-surgery partially protected knee joints from cartilage degeneration. Interestingly, earlier and more frequent ATX inhibitor injections did not confer significant protection. Immunohistochemical analysis showed decreased expression of catabolic and apoptotic markers with two ATX inhibitor injections. mRNA sequencing followed by pathway analysis identified pathways related to cholesterol analogue metabolism and cell-cycle that could be modulated by ATX inhibition in human OA chondrocytes. Conclusion: Local delivery of ATX inhibitor partially attenuates surgically-induced OA in mice.