Cut&RUN ACSS2 on liver tissue

Pre-processed CUT&RUN files for ACLY and GCN5 were normalized to effective genome size.For each file, background signal was calculated in heterochromatin regions using negativeATAC-seq signal mask, and subsequently removed from the overall coverage. CUT&RUNcoverage heatmaps and profiles around the TSS of expressed genes were produced usingcomputeMatrix, plotHeatmap functions from deepTools v3.5.2 package51. The list of expressedgenes was inferred from the mouse primary hepatocyte RNA-seq data using variancestabilizing transformation of the expression matrix and selecting genes with values > 0 for allreplicates. Peak calling on each replicate was performed using MACS252 v.2.2.7.1, and theconsensus overlapping peaks between all replicates were considered as reproducible for thecorresponding dataset. Number of overlapping peaks between conditions was calculated in Rwith subsetByOverlaps (GenomicRanges v.1.54.1)57, and nearest genes were annotated topeaks using biomaRt.For the correlation of ChIP peaks with gene expression, all expressed genes derived fromRNA-seq data were divided into 4 equal groups based on their expression levels (quartiles (Q)1 to 4). Promoter regions of those genes (± 1kb TSS) were extracted using the Rsubreadv.2.16.0)59 package, and the CUT&RUN signal was quantified with multiBigwigSummary fromdeepTools package针对ACLY与GCN5的预处理CUT&RUN测序（CUT&RUN）文件已按有效基因组大小完成标准化。对于每份文件，均采用阴性ATAC-seq信号掩码在异染色质区域计算背景信号，并随后从总覆盖度中扣除该背景信号。针对表达基因转录起始位点（Transcription Start Site, TSS）上下游区域的CUT&RUN覆盖度热图与信号分布图，通过deepTools v3.5.2工具包的computeMatrix与plotHeatmap函数51生成。表达基因列表通过小鼠原代肝细胞RNA测序（RNA-seq）数据推导得到：先对表达矩阵执行方差稳定变换，再筛选出所有生物学重复的表达值均大于0的基因。采用MACS2 v.2.2.7.1 52对每份生物学重复样本进行峰值识别（Peak calling），将所有重复样本间的一致性重叠峰视为对应数据集的可重复峰。各组间的重叠峰数量通过R语言中的subsetByOverlaps函数（GenomicRanges v.1.54.1）57计算得到，同时利用biomaRt工具为峰注释最近的编码基因。为分析染色质免疫沉淀（ChIP）峰与基因表达的相关性，将从RNA-seq数据中得到的所有表达基因按表达水平分为4个均等分组（四分位组Q1至Q4）。采用Rsubread v.2.16.0工具包59提取上述基因的启动子区域（转录起始位点上下游1kb范围），并通过deepTools工具包的multiBigwigSummary函数定量CUT&RUN信号。