Predict long-range enhancer regulation based on protein-protein interactions between transcription factors.
Predict long-range enhancer regulation based on protein-protein interactions between transcription factors.
复制标题
DOI：
10.1093/nar/gkab841
复制
发表时间：
2021-10-11
影响因子：
14.9
通讯作者：
Wang J
中科院分区：
文献类型：
--
作者：
Wang H;Huang B;Wang J
远端增强子的远程调控在细胞类型特异性转录程序中起着关键作用。全基因组增强子-启动子相互作用的计算预测仍然是具有挑战性的，由于有限的准确性和缺乏知识的分子机制。近年来的生物学研究发现，转录因子之间的蛋白质-蛋白质相互作用（PPI）参与了染色质环的调控。因此，我们开发了一种新的预测模型的细胞类型特异性增强子-启动子相互作用，利用TF PPI签名的信息。通过一系列严格的性能比较，新模型实现了上级性能优于其他方法。该模型还确定了特定的TF PPI，可能介导的远程监管的相互作用，揭示了新的机制的理解增强子调控。优先的TF PPI与不同生物学途径中的基因相关，并且预测的增强子-启动子相互作用强烈富集有顺式eQTL。最有趣的是，该模型发现增强子介导的转录因子和基因之间的反式调节联系，显着丰富的trans-eQTL。新的预测模型，沿着全基因组分析，提供了一个平台，系统地描绘复杂的相互作用之间的转录因子，增强子和基因的远程调控。新的预测还导致eQTL的机制解释，以解码与基因表达的遗传关联。
Long-range regulation by distal enhancers plays critical roles in cell-type specific transcriptional programs. Computational predictions of genome-wide enhancer–promoter interactions are still challenging due to limited accuracy and the lack of knowledge on the molecular mechanisms. Based on recent biological investigations, the protein–protein interactions (PPIs) between transcription factors (TFs) have been found to participate in the regulation of chromatin loops. Therefore, we developed a novel predictive model for cell-type specific enhancer–promoter interactions by leveraging the information of TF PPI signatures. Evaluated by a series of rigorous performance comparisons, the new model achieves superior performance over other methods. The model also identifies specific TF PPIs that may mediate long-range regulatory interactions, revealing new mechanistic understandings of enhancer regulation. The prioritized TF PPIs are associated with genes in distinct biological pathways, and the predicted enhancer–promoter interactions are strongly enriched with cis-eQTLs. Most interestingly, the model discovers enhancer-mediated trans-regulatory links between TFs and genes, which are significantly enriched with trans-eQTLs. The new predictive model, along with the genome-wide analyses, provides a platform to systematically delineate the complex interplay among TFs, enhancers and genes in long-range regulation. The novel predictions also lead to mechanistic interpretations of eQTLs to decode the genetic associations with gene expression.
登录
查看更多内容
遗传对人体组织基因表达的影响。
影响因子：
64.8
作者：
GTEx Consortium;Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group;Statistical Methods groups—Analysis Working Group;Enhancing GTEx (eGTEx) groups;NIH Common Fund;NIH/NCI;NIH/NHGRI;NIH/NIMH;NIH/NIDA;Biospecimen Collection Source Site—NDRI;Biospecimen Collection Source Site—RPCI;Biospecimen Core Resource—VARI;Brain Bank Repository—University of Miami Brain Endowment Bank;Leidos Biomedical—Project Management;ELSI Study;Genome Browser Data Integration &Visualization—EBI;Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz;Lead analysts:;Laboratory, Data Analysis &Coordinating Center (LDACC):;NIH program management:;Biospecimen collection:;Pathology:;eQTL manuscript working group:;Battle A;Brown CD;Engelhardt BE;Montgomery SB
通讯作者：
Montgomery SB
影响因子：
7
作者：
Battle A;Mostafavi S;Zhu X;Potash JB;Weissman MM;McCormick C;Haudenschild CD;Beckman KB;Shi J;Mei R;Urban AE;Montgomery SB;Levinson DF;Koller D
通讯作者：
Koller D
影响因子：
30.8
作者：
Fulco, Charles P.;Nasser, Joseph;Engreitz, Jesse M.
通讯作者：
Engreitz, Jesse M.
影响因子：
30.8
作者：
Cao, Qin;Anyansi, Christine;Yip, Kevin Y.
通讯作者：
Yip, Kevin Y.
影响因子：
7
作者：
Dryden NH;Broome LR;Dudbridge F;Johnson N;Orr N;Schoenfelder S;Nagano T;Andrews S;Wingett S;Kozarewa I;Assiotis I;Fenwick K;Maguire SL;Campbell J;Natrajan R;Lambros M;Perrakis E;Ashworth A;Fraser P;Fletcher O
通讯作者：
Fletcher O