RNA-binding proteins that are highly expressed and enriched in healthy cartilage but suppressed in osteoarthritis

Objectives: RNA-binding proteins (RBPs) have diverse and essential biological functions, but their role in cartilage health and disease is largely unknown. The objectives of this study were (i) map the global landscape of RBPs expressed and enriched in healthy cartilage and dysregulated in osteoarthritis (OA); (ii) prioritize RBPs for their potential role in cartilage and in OA pathogenesis and as therapeutic targets. Methods: Our published bulk RNA-sequencing (RNA-seq) data of healthy and OA human cartilage, and a census of 1,542 RBPs were utilized to identify RBPs that are expressed in healthy cartilage and differentially expressed (DE) in OA. Next, our comparison of healthy cartilage RNA-seq data to 37 transcriptomes in the Genotype-Tissue Expression (GTEx) database was used to determine RBPs that are enriched in cartilage. Finally, expression of RBPs was analyzed in our single cell RNA-sequencing (scRNA-seq) data from healthy and OA human cartilage. Results: Expression of RBPs was higher than nonRBPs in healthy cartilage. In OA cartilage, 188 RBPs were differentially expressed, with a greater proportion downregulated. Ribosome biogenesis was enriched in the upregulated RBPs, while splicing and transport were enriched in the downregulated. To further prioritize RBPs, we selected the top 10% expressed RBPs in healthy cartilage and those that were cartilage-enriched according to GTEx. Intersecting these criteria, we identified Tetrachlorodibenzodioxin (TCDD) Inducible Poly (ADP-Ribose) Polymerase (TIPARP) as a candidate RBP. TIPARP was downregulated in OA. scRNA-seq data revealed TIPARP was most significantly downregulated in the "pathogenic cluster". Conclusion: Our global analyses reveal expression patterns of RBPs in healthy and OA cartilage. We also identified TIPARP and other RBPs as novel mediators in OA pathogenesis and as potential therapeutic targets.

Improved Human Age Prediction by Using Gene Expression Profiles From Multiple Tissues

Studying transcriptome chronological change from tissues across the whole body can provide valuable information for understanding aging and longevity. Although there has been research on the effect of single-tissue transcriptomes on human aging or aging in mice across multiple tissues, the study of human body-wide multi-tissue transcriptomes on aging is not yet available. In this study, we propose a quantitative model to predict human age by using gene expression data from 46 tissues generated by the Genotype-Tissue Expression (GTEx) project. Specifically, the biological age of a person is first predicted via the gene expression profile of a single tissue. Then, we combine the gene expression profiles from two tissues and compare the predictive accuracy between single and two tissues. The best performance as measured by the root-mean-square error is 3.92 years for single tissue (pituitary), which deceased to 3.6 years when we combined two tissues (pituitary and muscle) together. Different tissues have different potential in predicting chronological age. The prediction accuracy is improved by combining multiple tissues, supporting that aging is a systemic process involving multiple tissues across the human body.