The analysis of novel distal Cebpa enhancers and silencers using a transcriptional model reveals the complex regulatory logic of hematopoietic lineage specification.
The analysis of novel distal Cebpa enhancers and silencers using a transcriptional model reveals the complex regulatory logic of hematopoietic lineage specification.
复制标题
使用转录模型对新型远端CEBPA增强子和消音器的分析揭示了造血谱系规范的复杂调节逻辑。
DOI:
10.1016/j.ydbio.2016.02.030
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发表时间:
2016-05-01
影响因子:
2.7
通讯作者:
Manu
中科院分区:
文献类型:
--
作者:
Bertolino E;Reinitz J;Manu
C/EBPα plays an instructive role in the macrophage-neutrophil cell-fate decision and its expression is necessary for neutrophil development. How Cebpa itself is regulated in the myeloid lineage is not known. We decoded the cis-regulatory logic of Cebpa, and two other myeloid transcription factors, Egr1 and Egr2, using a combined experimental-computational approach. With a reporter design capable of detecting both distal enhancers and silencers, we analyzed 46 putative cis-regulatory modules (CRMs) in cells representing myeloid progenitors, and derived early macrophages or neutrophils. In addition to novel enhancers, this analysis revealed a surprisingly large number of silencers. We determined the regulatory roles of 15 potential transcriptional regulators by testing 32,768 alternative sequence-based transcriptional models against CRM activity data. This comprehensive analysis allowed us to infer the cis-regulatory logic for most of the CRMs. Silencer-mediated repression of Cebpa was found to be effected mainly by TFs expressed in non-myeloid lineages, highlighting a previously unappreciated contribution of long-distance silencing to hematopoietic lineage resolution. The repression of Cebpa by multiple factors expressed in alternative lineages suggests that hematopoietic genes are organized into densely interconnected repressive networks instead of hierarchies of mutually repressive pairs of pivotal TFs. More generally, our results demonstrate that de novo cis-regulatory dissection is feasible on a large scale with the aid of transcriptional modeling.