近日，国际著名肿瘤学杂志Cancer Research发表了同济大学生命科学院刘小乐研究组关于核受体研究的最新成果“A Comprehensive View of Nuclear Receptor Cancer Cistromes”。该研究成果建立了一个新的模型预测转录因子直接调控的基因。
核受体是一类由配体激活的转录因子，在正常生理和如癌症的疾病中都扮演重要角色。通过整合和分析现有的核受体ChIP-chip/seq数据，该工作揭示了有关核受体调控机理的一些有意义现象：核受体识别的非典型模体，于其共同作用的先导因子特点，其结合位点的强度／保守性和功能性的关系，及其在转录调控中的作用。该工作通过整合同一细胞株和条件下同一转录因子的全部ChIP-chip/seq数据和与之匹配的全部差异表达数据，建立了一个新的模型预测转录因子直接调控的基因。该工作提出了不同类型高通量数据整合研究转录调控机制的新思路。
本研究工作是在刘小乐教授的指导下，主要由同济大学生命科学院博士研究生唐茜子完成。刘小乐教授是哈佛大学公共卫生学院生物统计系的副教授，同济大学生命科学与技术学院兼职教授，博士生导师。该研究工作得到了科技部的经费支持。(生物谷Bioon.com)
doi:10.1158/0008-5472.CAN-11-2091
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A Comprehensive View of Nuclear Receptor Cancer Cistromes
Qianzi Tang Yiwen Chen Clifford Meyer Tim Geistlinger Mathieu Lupien Qian Wang Tao Liu Yong Zhang Myles Brownand and Xiaole Shirley Liu
Nuclear receptors (NRs) comprise a superfamily of ligand-activated transcription factors that play important roles in both physiology and diseases including cancer. The technologies of Chromatin ImmunoPrecipitation followed by array hybridization (ChIP-chip) or massively parallel sequencing (ChIP-seq) has been used to map, at an unprecedented rate, the in vivo genome-wide binding (cistrome) of NRs in both normal and cancer cells. We developed a curated database of 88 NR cistrome datasets and other associated high-throughput datasets, including 121 collaborating factor cistromes, 94 epigenomes and 319 transcriptomes. Through integrative analysis of the curated NR ChIP-chip/seq datasets, we discovered novel factor-specific noncanonical motifs that may have important regulatory roles. We also revealed a common feature of NR pioneering factors to recognize relatively short and AT-rich motifs. Most NRs bind predominantly to introns and distal intergenetic regions, and binding sites closer to transcription start sites (TSSs) were found to be neither stronger nor more evolutionarily conserved. Interestingly, while most NRs appear to be predominantly transcriptional activators, our analysis suggests that the binding of ESR1, RARA and RARG has both activating and repressive effects. Through meta-analysis of different omic data of the same cancer cell line model from multiple studies, we generated consensus cistrome and expression profiles. We further made probabilistic predictions of the NR target genes by integrating cistrome and transcriptome data, and validated the predictions using expression data from tumor samples. The final database, with comprehensive cistrome, epigenome, transcriptome datasets, and downstream analysis results, constitutes a valuable resource for the nuclear receptor and cancer community.
