Epigenetic factor competition reshapes the EMT landscape

Abstract

The emergence of and transitions between distinct phenotypes in isogenic cells can be attributed to the intricate interplay of epigenetic marks, external signals, and gene-regulatory elements. These elements include chromatin remodelers, histone modifiers, transcription factors, and regulatory RNAs. Mathematical models known as gene-regulatory networks (GRNs) are an increasingly important tool to unravel the workings of such complex networks. In such models, epigenetic factors are usually proposed to act on the chromatin regions directly involved in the expression of relevant genes. However, it has been well-established that these factors operate globally and compete with each other for targets genome-wide. Therefore, a perturbation of the activity of a regulator can redistribute epigenetic marks across the genome and modulate the levels of competing regulators. In this paper, we propose a conceptual and mathematical modeling framework that incorporates both local and global competition effects between antagonistic epigenetic regulators, in addition to local transcription factors, and show the counterintuitive consequences of such interactions. We apply our approach to recent experimental findings on the epithelial–mesenchymal transition (EMT). We show that it can explain the puzzling experimental data, as well as provide verifiable predictions.

Document Details

Document Type
Pub Defense Publication
Publication Date
Oct 10, 2022
Source ID
10.1073/pnas.2210844119

Entities

People

  • Eduardo D. Sontag
  • Herbert Levine
  • M. Ali Al-radhawi
  • Shubham Tripathi
  • Yun Zhang

Organizations

  • Air Force Office of Scientific Research
  • Harvard Medical School
  • National Science Foundation
  • Northeastern University
  • Peking Union Medical College
  • Rice University
  • Susan G. Komen for the Cure
  • Whitehead Institute

Tags

Fields of Study

  • Biology

Readers

  • Molecular Biology and Genetics
  • Molecular and genetic basis of cancer.
  • Neural Network Machine Learning.