4D nucleome equation predicts gene expression controlled by long-range enhancer-promoter interaction

• Published in: PLoS computational biology Vol. 19; no. 12; p. e1011722
• Main Authors: Wang, Zihao, Luo, Songhao, Zhang, Zhenquan, Zhou, Tianshou, Zhang, Jiajun
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 18.12.2023; Public Library of Science (PLoS)
• Subjects: Analysis; Animals; Biology and Life Sciences; Chromatin; Chromatin - genetics; Chromosomes; Decision making; Embryo cells; Embryonic stem cells; Enhancer Elements, Genetic - genetics; Gene Expression; Genes; Genetic aspects; Genetic transcription; Genomics; Heavy metals; Messenger RNA; Mice; Physical Sciences; Promoter Regions, Genetic - genetics; Promoters (Genetics); RNA, Messenger - genetics; Statistical analysis; Statistical inference; Stem cells; Transcription activation; Transcription initiation; China
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1011722

Recent experimental evidence strongly supports that three-dimensional (3D) long-range enhancer-promoter (E-P) interactions have important influences on gene-expression dynamics, but it is unclear how the interaction information is translated into gene expression over time (4D). To address this question, we developed a general theoretical framework (named as a 4D nucleome equation), which integrates E-P interactions on chromatin and biochemical reactions of gene transcription. With this equation, we first present the distribution of mRNA counts as a function of the E-P genomic distance and then reveal a power-law scaling of the expression level in this distance. Interestingly, we find that long-range E-P interactions can induce bimodal and trimodal mRNA distributions. The 4D nucleome equation also allows for model selection and parameter inference. When this equation is applied to the mouse embryonic stem cell smRNA-FISH data and the E-P genomic-distance data, the predicted E-P contact probability and mRNA distribution are in good agreement with experimental results. Further statistical inference indicates that the E-P interactions prefer to modulate the mRNA level by controlling promoter activation and transcription initiation rates. Our model and results provide quantitative insights into both spatiotemporal gene-expression determinants (i.e., long-range E-P interactions) and cellular fates during development.