Mitochondrial translation modulate the mammalian cell metabolism
Description
High-throughput sequencing of total RNA (RNA-seq) from NIH3T3-WT and NIH3T3-MU cells showed strong concordance between replicates and identified 3582 protein-coding genes that were differentially expressed (1603 up-regulated genes and 1979 down-regulated genes).
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The total amount and integrity of RNA were assessed using an RNA Nano 6000 Assay Kit with a Bioanalyzer 2100 system (Agilent Technologies). Two to four micrograms of total RNA were used as the starting material to prepare libraries using Illumina TruSeq Stranded mRNA Library Prep Kit Set A (RS-122-2101, Illumina). The size of the libraries was selected by using Agencourt AMPure XP beads (Beckman Coulter, A63882) at an average sample size of 400 bp. The libraries were sequenced using Illumina NovaSeq 6000 (pair-end 150 bp). Bioinformatic analysis of RNA-seq data: the feature Counts v1.5.0-p3 was used to count the read numbers mapped to each gene. Then, the log2-transformed gene expression values (FPKM) of each gene were calculated based on the length of the gene and the reads mapped to this gene. FPKM, the expected number of fragments per kilobase of transcript sequence per million base pairs sequenced, was determined on the basis of the effect of sequencing depth and gene length on the read count at the same time and is currently the most commonly used method for estimating gene expression levels. Sequencing quality was evaluated by FastQC v.0.11.4. All reads were mapped to the reference genome at Illumina iGenomes UCSC mm10 using HISAT2 v.2.05.0. A differential expression analysis was performed using the DESeq2 R package (1.20.0). DESeq2 provides 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 Benjamini and Hochberg’s approach for controlling the false discovery rate. padj <= 0.05 & |log2(FoldChange)|> 0 was set as the threshold for identifying significantly different gene expressions. The FPKM values were normalized by subtracting the mean in every row and hierarchically clustering with a Pearson correlation algorithm.