RNA-seq analysis

Total RNA was extracted using RNeasy Mini Kit (Qiagen, USA), and RNA integrity was assessed using an Agilent 2100 Bioanalyzer (Agilent Technologies, USA), and only samples with RNA integrity number (RIN) > 7.0 were used for library preparation. RNA-seq libraries were prepared using the TruSeq Standard mRNA Library Preparation Kit v2 (Illumina, USA) and libraries were size-selected and purified using AMPure XP beads (Beckman Coulter, USA) as previously reported (Yoshida Y et al., 2024). Paired-end 150-bp reads were sequenced on a NovaSeq 6000 platform (Illumina). Raw sequence reads were trimmed and quality-checked using FastQC. Reads were aligned to the mouse reference genome (GRCm38/mm10) using HISAT2. Gene expression was quantified using featureCounts, and differentially expressed genes (DEGs) were identified using DESeq2 with standard normalization and dispersion estimation. Genes with a Benjamini–Hochberg adjusted p-value (FDR) < 0.05 and absolute log2 fold change > 1 were considered significant. All samples were processed in a single batch; no batch correction was applied. Visualization of DEG results (volcano plots, heatmaps) was performed using R (ggplot2, pheatmap) and Olvtool (Olvtool Inc.). Gene Ontology (GO) enrichment analysis was conducted using the Database for Annotation, Visualization and Integrated Discovery (DAVID) Bioinformatics Resources (Sherman et al., 2022, Huang et al., 2009) with an expressed-gene background set. Functional annotation clustering was performed using default settings, and terms with p < 0.05 were reported Fraction3_control VS Ctnnb1_CKO (1).xlsx,Fraction4_control VS Ctnnb1_CKO (1).xlsx,Fraction5_control VS Ctnnb1_CKO (1).xlsx are to Figure.3 shCtnnb1VS Ctnnb1_CA (1).csv,control VS shCtnnb1 (1).csv,control VS Ctnnb1_CA (1).csv are to Figure.6