Panaxatriol exerts anti-senescence effects and alleviates osteoarthritis and cartilage repair fibrosis by targeting UFL1
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May 02, 2025 version files 42.60 MB
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README.md
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RNA-Sequencing_all_compare_BK.xls
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Abstract
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.
Panaxatriol exerts anti-senescence effects and alleviates osteoarthritis and cartilage repair fibrosis by targeting UFL1
https://doi.org/10.5061/dryad.8cz8w9gzp
Quantification of gene expression level feature Counts 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. FPKM, expected number of Fragments Per Kilobase of transcript sequence per Millions base pairs sequenced, considers the effect of sequencing depth and gene length for the reads count at the same time, and is currently the most commonly used method for estimating gene expression levels.
Differential expression analysis Differential expression analysis of two conditions/groups (three biological replicates per condition) was performed using the DESeq2 R package (1.20.0). DESeq2 provide statistical routines for determining differential expression in digital gene expression data using a model based on the negative binomial distribution. The resulting P-values were adjusted using the Benjamini and Hochberg’s approach for controlling the false discovery rate . Genes with an adjusted P-value <=0.05 found by DESeq2 were assigned as differentially expressed.
The significant differences in each gene in all comparison combinations are shown in this dataset.
Column A：gene_id: gene ID
Column B：NC_1_count: The original readcount value of NC1 sample which is normal C28/I2 cells.
Column C：NC_2_count: The original readcount value of NC2 sample which is normal C28/I2 cells.
Column D：NC_3_count: The original readcount value of NC3 sample which is normal C28/I2 cells.
Column E：UFL1_KO_1_count: The original readcount value of UFL1-KO1 sample which is UFL1 konck-out C28/I2 cells.
Column F：UFL1_KO_2_count: The original readcount value of UFL1-KO2 sample which is UFL1 konck-out C28/I2 cells.
Column G：UFL1_KO_3_count: The original readcount value of UFL1-KO3 sample which is UFL1 konck-out C28/I2 cells.
Column H：NC_1 _fpkm: The fpkm value of NC1 sample which is normal C28/I2 cells.
Column I：NC_2 _fpkm: The fpkm value of NC2 sample which is normal C28/I2 cells.
Column J：NC_3 _fpkm: The fpkm value of NC3 sample which is normal C28/I2 cells.
Column K：UFL1_KO_1 _fpkm: The fpkm value of UFL1-KO1 sample which is UFL1 konck-out C28/I2 cells.
Column L：UFL1_KO_1 _fpkm: The fpkm value of UFL1-KO1 sample which is UFL1 konck-out C28/I2 cells.
Column M：UFL1_KO_1 _fpkm: The fpkm value of UFL1-KO1 sample which is UFL1 konck-out C28/I2 cells.
Column N：UFL1_KOvsNC_UFL1_KO: the average of standardized readcounts for UFL1** **konck-out group
Column O：UFL1_KOvsNC_NC: the mean of the standardized readcount of the control (NC) group.
Column P：UFL1_KOvsNC_log2FoldChange: the ratio of gene expression levels between UFL1 knock-out group and NC group, with a base of 2 for the logarithm, the symbol "-" in this column means minus.
Column Q：UFL1_KOvsNC_pvalue: the p-value of a significant test for the comparison between UFL1 knock-out group and NC group.
Column R：UFL1_KOvsNC_padj: the corrected p-value of a multiple hypothesis test for the comparison between UFL1 knock-out group and NC group.
Column S：gene_name: gene name, the symbol "-" in this column means none.
Column T：gene_chr: the name of the chromosome where the gene is located
Column U：gene_start: The starting position of the gene on the chromosome
Column V：gene_end: The end position of the gene on the chromosome
Column W：gene_strand: information about the positive(+) and negative(-) strands of the chromosome where the gene is located
Column X：gene_length: gene length, the sum of all non-overlapping regions from the start to the end of the gene
Column Y：gene_biotype: gene type, such as protein-coding genes, long non-coding genes, etc.
Column Z：gene_description: gene function description, the first && symbol represents the reference genome annotation information, the first && symbol represents the swissprot database annotation, and the second && symbol represents the pfam database annotation
Column AA：gene_tf_family: gene transcription factor family annotation
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.
- Kuang, Biao; Geng, Nana; Yi, Miao et al. (2024). Panaxatriol exerts anti-senescence effects and alleviates osteoarthritis and cartilage repair fibrosis by targeting UFL1. Journal of Advanced Research. https://doi.org/10.1016/j.jare.2024.10.016