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

PLoS Comput Biol. 2023 Dec 18;19(12):e1011722. doi: 10.1371/journal.pcbi.1011722. eCollection 2023 Dec.

Abstract

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.

MeSH terms

  • Animals
  • Chromatin* / genetics
  • Chromosomes*
  • Enhancer Elements, Genetic / genetics
  • Gene Expression
  • Mice
  • Promoter Regions, Genetic / genetics
  • RNA, Messenger / genetics

Substances

  • Chromatin
  • RNA, Messenger

Grants and funding

This work was supported by the National Key R&D Program of China (2021YFA1302500 to JZ), the Natural Science Foundation of P. R. China (12171494 to JZ;11931019 to TZ; 11775314 to TZ; 62373384 to TZ; 12301646 to ZZ), the Guangdong Basic and Applied Basic Research Foundation (2022A1515011540 to JZ; 2023A1515011982 to ZZ), Key-Area Research and Development Program of Guangzhou, P. R. China (2019B110233002 to JZ; 202007030004 to TZ), the Guangdong Province Key Laboratory of Computational Science at the Sun Yat-sen University (2020B1212060032 to JZ), the Fundamental Research Funds for the Central Universities, Sun Yat-sen University (23qnpy48 to ZW; 23qnpy49 to ZZ), and the China Postdoctoral Science Foundation (2023M734061 to ZW). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.