Integrative Biology Laboratory, Salk Institute for Biological Studies, 10010 N. Torrey Pines Road, La Jolla, CA 92037, USA; Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA 92093, USA; Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, NY 11724, USA.
Integrative Biology Laboratory, Salk Institute for Biological Studies, 10010 N. Torrey Pines Road, La Jolla, CA 92037, USA; School of Biological Sciences, University of California, San Diego, La Jolla, CA 92037, USA; Halicioglu Data Science Institute, University of California, San Diego, La Jolla, CA 92093, USA; Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
Cell Genom. 2024 Nov 13;4(11):100672. doi: 10.1016/j.xgen.2024.100672. Epub 2024 Oct 14.
A single gene may have multiple enhancers, but how they work in concert to regulate transcription is poorly understood. To analyze enhancer interactions throughout the genome, we developed a generalized linear modeling framework, GLiMMIRS, for interrogating enhancer effects from single-cell CRISPR experiments. We applied GLiMMIRS to a published dataset and tested for interactions between 46,166 enhancer pairs and corresponding genes, including 264 "high-confidence" enhancer pairs. We found that enhancer effects combine multiplicatively but with limited evidence for further interactions. Only 31 enhancer pairs exhibited significant interactions (false discovery rate <0.1), none of which came from the high-confidence set, and 20 were driven by outlier expression values. Additional analyses of a second CRISPR dataset and in silico enhancer perturbations with Enformer both support a multiplicative model of enhancer effects without interactions. Altogether, our results indicate that enhancer interactions are uncommon or have small effects that are difficult to detect.
单个基因可能有多个增强子，但它们如何协同工作以调节转录的机制还知之甚少。为了分析整个基因组中的增强子相互作用，我们开发了一种广义线性建模框架 GLiMMIRS，用于从单细胞 CRISPR 实验中检测增强子的影响。我们将 GLiMMIRS 应用于已发表的数据集，并测试了 46,166 对增强子对及其相应基因之间的相互作用，其中包括 264 对“高可信度”增强子对。我们发现，增强子的作用是相乘的，但进一步相互作用的证据有限。只有 31 对增强子表现出显著的相互作用（错误发现率<0.1），其中没有一对来自高可信度集，并且 20 对是由异常表达值驱动的。对第二个 CRISPR 数据集和使用 Enformer 的计算机模拟增强子扰动的额外分析都支持增强子作用的乘法模型而没有相互作用。总的来说，我们的结果表明，增强子相互作用不常见或作用很小，难以检测。
