Panaxatriol exerts anti-senescence effects and alleviates osteoarthritis and cartilage repair fibrosis by targeting UFL1

Osteoarthritis is the most commmon degenerative joint disease which can lead to disability eventually. However, there are currently no safe and effective interventions available. Therefore, it is urgent to develop effective drugs that can reduce cartilage damage and treat OA.With the in vitro culturing of human cartilage explants and mouse OA model, it was found that, panaxtriol, a natural small molecule drug, could promote chondrocyte anabolism and inhibit catabolism. It could also reduce the loss of cartilage matrix , decrease the subchondral osteosclerosis, and relieve pain in mice. Eventually, it could delay the progression of OA. Subsequently, the binding target of panaxtriol was found to be UFL1 through the target stability of drug affinity reaction (DARTS). The UFL1 knockout cell line was constructed using CRISPR-Cas9. Then, the regulation of chondrocyte metabolism by panaxtriol was found to dependent on UFL1. Transcriptome sequencing of UFL1 knockout cells were conducted. Through differential gene analysis, GO analysis and KEGG analysis, it showed that UFL1 was closely related to cell senescence. By using activators and inhibitors of the signaling pathway, panaxtriol was proved that it can inhibit chondrocyte senescence through UFL1/FOXO1/P21 and UFL1/NF-κB/SASPs signaling pathways to delay the progression of OA. It also could inhibit the formation of fibrocartilage during cartilage repair by UFL1/FOXO1/COL1 signaling pathway. Lastly, a sustained release system of panaxtriol was constructed based on PLGA-PEG. Through drug sustained release, the therapeutic effect was achieved while the number of intra-articular injection was reduced, thereby alleviating joint swelling and joint injury.

Methods
RNA sequencing: C28/I2 cells and UFL1 knock-out cells were send to RNA sequencing analysis, each group has 3 repeats. According to the technology vendor's process, RNA integrity was assessed using the RNA Nano 6000 Assay Kit of the Bioanalyzer 2100 system (Agilent Technologies, CA, USA). After the construction of the library, the library was initially quantified by Qubit2.0 Fluorometer, then diluted to 1.5ng/ul, and the insert size of the library is detected by Agilent 2100 bioanalyzer. After the library is qualified, the different libraries are pooling according to the effective concentration and the target amount of data off the machine, then being sequenced by the Illumina NovaSeq 6000. Raw data (raw reads) of fastq format were firstly processed through fastp software to get clean data with high quality. We selected Hisat2 as the mapping tool to read mapping to the reference genome. FeatureCounts (v1.5.0-p3) was used to count the reads numbers mapped to each gene. And then FPKM of each gene was calculated based on the length of the gene and reads count mapped to this gene. Differential expression analysis of two groups was performed using theDESeq2 R package (1.20.0). padj<=0.05 and |log2(foldchange)| >= 1 were set as the threshold for significantly differential expression. Gene Ontology (GO) enrichment analysis of differentially expressed genes wasimplemented by the clusterProfiler R package (3.8.1), in which gene length biaswascorrected. GO terms with corrected Pvalue less than 0.05 were consideredsignificantly enriched by differential expressed genes. We used clusterProfiler R package (3.8.1) to test the statistical enrichment of differential expression genes in KEGG pathways. Reactome pathways with corrected Pvalue less than 0.05 were considered significantly enriched by differential expressed genes. We use the local version of the GSEA analysis tool http://www.broadinstitute.org/gsea/index.jsp.