近日，美国斯坦福大学医学院教授Jesse M. Engreitz及其课题组研究出人类增强基因调控相互作用的百科全书。2026年7月15日，国际知名学术期刊《自然》发表了这一成果。

这里研究小组创建并评估超过92个资源 通过整合预测模型、染色质状态、三维接触和ENCODE Consortium产生的大规模遗传扰动，研究了覆盖369种细胞类型和组织的1458个生物样本中的数百万个增强子基因调控相互作用。

小组首先创建了一个系统的基准管道来比较预测模型，组装了在CRISPR扰动实验中测量的10,356个元件-基因对的数据集，超过30,000个精细定位的表达定量性状位点和569个精细定位的全基因组关联研究（GWAS）变体，这些变体与可能的致病基因相关。利用这个框架，小组开发了ENCODE-rE2G，这是一个预测模型，在多个预测任务中实现了最先进的性能，证明了迭代扰动和监督机器学习可以构建越来越准确的增强子调节预测模型。

利用ENCODE-rE2G，该研究团队构建了人类基因组中增强子-基因调控相互作用的百科全书，揭示了增强子网络的全局特性，确定了基因间调控复杂性的差异，并改进了将非编码变异与靶基因和常见复杂疾病的细胞类型联系起来的分析。通过解释该模型，该研究组发现除了增强子活性和增强子-启动子三维接触之外，指导增强子-启动子交流的附加特征包括启动子类别和增强子-增强子协同作用。这些增强子-基因调控相互作用的全基因组图谱、基准测试软件、预测模型和对增强子功能的见解为未来基因调控和人类遗传学的研究提供了有价值的依据。

据介绍，鉴定转录增强子及其靶基因对于理解基因调控和人类遗传变异对疾病的影响至关重要。

附：英文原文

Title: An encyclopedia of human enhancer–gene regulatory interactions

Author: Gschwind, Andreas R., Mualim, Kristy S., Karbalayghareh, Alireza, Sheth, Maya U., Dey, Kushal K., Jagoda, Evelyn, Nurtdinov, Ramil N., Xi, Wang, Tan, Anthony S., Galante, James, Jones, Hank, Ma, X. Rosa, Yao, David, Amgalan, Dulguun, Ray, Judhajeet, Munger, Chad J., Nasser, Joseph, Avsec, iga, James, Benjamin T., Shamim, Muhammad S., Durand, Neva C., Rao, Suhas S. P., Mahajan, Ragini, Doughty, Benjamin R., Andreeva, Kalina, Ulirsch, Jacob C., Fan, Kaili, Perez, Elizabeth M., Nguyen, Tri C., Kelley, David R., Finucane, Hilary K., Moore, Jill E., Weng, Zhiping, Kellis, Manolis, Bassik, Michael C., Ustun, Berk, Price, Alkes L., Beer, Michael A., Guig, Roderic, Stamatoyannopoulos, John A., Lieberman Aiden, Erez, Greenleaf, William J., Leslie, Christina S., Steinmetz, Lars M., Kundaje, Anshul, Engreitz, Jesse M.

Issue&Volume: 2026-07-15

Abstract: Identifying transcriptional enhancers and their target genes is essential for understanding gene regulation and the effect of human genetic variation on disease1,2,3,4,5,6. Here we create and evaluate a resource of more than 92million enhancer–gene regulatory interactions across 1,458 biosamples covering 369 cell types and tissues, by integrating predictive models, chromatin states, three-dimensional contacts and large-scale genetic perturbations generated by the ENCODE Consortium7. We first create a systematic benchmarking pipeline to compare predictive models, assembling a dataset of 10,356 element–gene pairs measured in CRISPR perturbation experiments, more than 30,000 fine-mapped expression quantitative trait loci and 569 fine-mapped genome-wide association study (GWAS) variants linked to a probable causal gene. Using this framework, we develop ENCODE-rE2G, a predictive model achieving state-of-the-art performance across several prediction tasks, demonstrating that iterative perturbations and supervised machine learning can build increasingly accurate predictive models of enhancer regulation. Using ENCODE-rE2G, we build an encyclopedia of enhancer–gene regulatory interactions in the human genome, revealing global properties of enhancer networks, identifying differences in regulatory complexity across genes and improving analyses linking noncoding variants to target genes and cell types for common complex diseases. By interpreting the model, we find that beyond enhancer activity and three-dimensional enhancer–promoter contacts, additional features that guide enhancer–promoter communication include promoter class and enhancer–enhancer synergy. These genome-wide maps of enhancer–gene regulatory interactions, benchmarking software, predictive models and insights about enhancer function provide a valuable resource for future studies of gene regulation and human genetics.

DOI: 10.1038/s41586-026-10781-4

Source: https://www.nature.com/articles/s41586-026-10781-4