RNA was isolated from cultured cells with TRIzol reagent (Invitrogen) and purified using QIAGEN RNeasy Mini-kit columns (QIAGEN). RNA quality was confirmed using an Agilent 2100 Bioanalyzer. Mouse cDNA was reverse transcribed from 1 μg total RNA with SuperScript VILO Master Mix (Life Technologies). qPCR was performed in triplicate samples using SYBR Green PCR master mix (Applied Biosystems) according to the manufacturer’s instructions. For each transcript examined, raw data (CT) were obtained by qPCR, and the ΔΔCT method (ΔΔCT = ΔCT (experimental gene) − ΔCT (controlled gene)) was used to calculate the relative fold of gene expression (fold = 2ΔΔCT). ΔCT was calculated using a housekeeping gene (Gapdh; whose equivalent expression in wild-type and DKO cells was confirmed in our transcriptional profiling) and averaged (ΔCT = CTGAPDH − CTgene). For qPCR primers, see Table S2 .
For RNA-seq, the construction of libraries was generated using QuantSeq 3′ mRNA-Seq Library Prep Kit (Lexogen) according to the manufacturer’s instructions. High-throughput sequencing was performed as single-end 75 sequencing using NextSeq 500 (Illumina). Each sample was analyzed at the University of Michigan Advanced Genomics Core. Data were aligned using the STAR aligner and featureCounts v1.6.4 software, and reads per kilobase of transcript per million mapped read values on gene level were estimated for ensemble transcriptome (Dobin et al., 2013 (link); Liao et al., 2014 (link)). DESeq2 was used to estimate significance between any two experimental groups (Love et al., 2014 (link)). Principal component analysis was performed on the RNA-seq data to visualize sample-to-sample variance. Differentially expressed genes were analyzed using the DAVID Bioinformatics Resources 6.8 (Jiao et al., 2012 (link)).
For RNA-seq, the construction of libraries was generated using QuantSeq 3′ mRNA-Seq Library Prep Kit (Lexogen) according to the manufacturer’s instructions. High-throughput sequencing was performed as single-end 75 sequencing using NextSeq 500 (Illumina). Each sample was analyzed at the University of Michigan Advanced Genomics Core. Data were aligned using the STAR aligner and featureCounts v1.6.4 software, and reads per kilobase of transcript per million mapped read values on gene level were estimated for ensemble transcriptome (Dobin et al., 2013 (link); Liao et al., 2014 (link)). DESeq2 was used to estimate significance between any two experimental groups (Love et al., 2014 (link)). Principal component analysis was performed on the RNA-seq data to visualize sample-to-sample variance. Differentially expressed genes were analyzed using the DAVID Bioinformatics Resources 6.8 (Jiao et al., 2012 (link)).
