MethNet: a robust approach to identify regulatory hubs and their distal targets in cancer [pcHiC]. MethNet: a robust approach to identify regulatory hubs and their distal targets in cancer [pcHiC]

We present MethNet, a pipeline that integrates large-scale DNA methylation and gene expression data across multiple cancers, to uncover novel cis regulatory elements (CREs) in a 1Mb region around every promoter in the genome. MethNet identifies highly ranked CREs, referred to as ‘hubs’, which contribute to the regulation of multiple genes and strongly affect patient survival. Promoter-capture Hi-C confirmed that highly ranked associations involve physical interactions between CREs and their gene targets, and CRISPRi based scRNA Perturb-seq validated the functional impact of CREs. Thus, MethNet represents a valuable resource for unraveling complex mechanisms underlying gene expression, and for prioritizing the verification of predicted non-coding disease hotspots. Overall design: Promoter Capture Hi-C data was generated in K562 and A549 cell lines using the Arima Hi-C+ kit, the Arima Promoter Capture Module, and the Arima Library Prep Module according to the Arima Genomics manufacturer’s protocols. The A549 and K562 cell lines were purchased from ATCC. Two replicates of the Hi-C were performed in each cell line, and for each replicate 1 million cells were collected and double cross-linked using 3mM DSG (disuccinimidyl glutarate), followed by 1% formaldehyde. Samples were sequenced with Illumina Novaseq 6000 technology according to standard protocols with around 300 million (150bp paired-ends) reads per sample. The library preparation and sequencing were conducted by NYU Langone's Genome Technology Center.

本研究介绍了MethNet——一款整合多癌种大规模DNA甲基化与基因表达数据的分析流程，用于在基因组内每个启动子周边1Mb的区域中挖掘新型顺式调控元件（cis regulatory elements, CREs）。MethNet可识别被称为“枢纽”的高优先级顺式调控元件，这类元件可调控多个基因的表达，并对患者生存情况产生显著影响。启动子捕获Hi-C（Promoter-capture Hi-C）实验证实，高优先级的关联涉及顺式调控元件与其基因靶点之间的物理相互作用；而基于CRISPR干扰（CRISPRi）的单细胞RNA扰动测序（scRNA Perturb-seq）则验证了顺式调控元件的功能影响。因此，MethNet是解析基因表达背后复杂调控机制、优先验证预测性非编码疾病热点区域的宝贵资源。实验整体设计：本研究在K562与A549细胞系中生成启动子捕获Hi-C数据，实验严格遵循Arima Genomics厂商的操作流程，使用Arima Hi-C+试剂盒、Arima启动子捕获模块及Arima文库制备模块完成。A549与K562细胞系均购自ATCC。每个细胞系均设置2次Hi-C实验重复；每次重复收集100万个细胞，先用3mM 戊二酰亚胺二琥珀酸酯（disuccinimidyl glutarate, DSG）进行双交联，随后使用1%甲醛继续交联。按照标准流程，使用Illumina NovaSeq 6000测序平台对样本进行测序，每个样本约产生3亿条（150bp双端）测序读段。文库制备与测序工作由纽约大学朗格尼医学中心基因组技术中心完成。