RNA-sequencing and metabolomics data of "A clinical-stage direct Nrf2 activator suppresses osteoclast differentiation via iron-ornithine axis"
Description
This dataset includes the raw RNA-sequencing (counts) and metabolomics data generated in the study. All the RNA-sequencing and metabolomic assays were performed on bone marrow derived macrophages of wildtype or Nrf2-KO C57BL6J mice. For RNA sequencing, BMDMs were seeded on 6-well plates with 1×10^6 cells per well. Each group had five replicates from different mice and cells were collected after three days of culture and intervention. Total RNA was extracted with TRIzol (Takara, Japan). For the metabolomic assay, BMDMs were cultured in 10-cm dishes with 1×10^7 cells per dish. After intervention, cells were collected in prechilled 80% methanol and were subjected to three freeze-thaw cycles in liquid nitrogen. LC-MS/MS was used for the untargeted metabolomic assay. All assays were performed by Novogene (Beijing, China).
Files
Steps to reproduce
RNA sequencing data were analyzed using R software (version 4.1.3). We identified the differentially expressed genes (DEGs) by setting the threshold of |log2Foldchange| as 0.5 and the P value as 0.05. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was then performed to determine the significantly enriched pathways of the DEGs. Protein-protein interaction (PPI) networks of the DEGs were constructed in the STRING database (https://www.string-db.org/). The PPI networks were then subjected to Cytoscape software. Then, the top 10 hub genes of the PPI network were determined by the Maximal Clique Centrality (MCC) algorithm in the CytoHubba plugin of the software. In addition, we also performed GSEA for two-group sequencing and GSVA analysis for three-group sequencing to investigate the changes in the pathways of interest. The analysis of the metabolomics data was performed in the MetaboAnalyst platform (V5.0) (https://www.metaboanalyst.ca/). In the platform, the statistical analysis module was used for principal component analysis (PCA) and fold-change calculation. In this module, the orthogonal Partial Least Squares - Discriminant Analysis (orthoPLS-DA) model was used to calculate variable importance in projection (VIP). We used VIP > 1, |log2(fold change)| > 0.5, and P < 0.05 as benchmarks to screen for differential metabolites. Finally, we analyzed the involved pathway of the identified differential metabolites in the enrichment analysis module.