Cluster size determines internal structure of transcription factories in human cells

TL;DR

This study uses a chromatin polymer model to show that cluster size influences whether transcription factories are specialized or mixed, with non-specific interactions promoting mixing in larger clusters, reconciling conflicting experimental observations.

Contribution

The paper introduces a polymer model demonstrating how cluster size determines the internal structure of transcription factories, highlighting the role of non-specific interactions in mixing.

Findings

Both specialized and mixed clusters can spontaneously form.

Cluster size primarily influences factory composition.

Non-specific interactions promote mixing in larger clusters.

Abstract

Transcription is a fundamental cellular process, and the first step of gene expression. In human cells, it depends on the binding to chromatin of various proteins, including RNA polymerases and numerous transcription factors (TFs). Observations indicate that these proteins tend to form macromolecular clusters, known as transcription factories, whose morphology and composition is still debated. While some microscopy experiments have revealed the presence of specialised factories, composed of similar TFs transcribing families of related genes, sequencing experiments suggest instead that mixed clusters may be prevalent, as a panoply of different TFs binds promiscuously the same chromatin region. The mechanisms underlying the formation of specialised or mixed factories remain elusive. With the aim of finding such mechanisms, here we develop a chromatin polymer model mimicking the chromatin binding-unbinding dynamics of different types of complexes of TFs. Surprisingly, both specialised (i.e., demixed) and mixed clusters spontaneously emerge, and which of the two types forms depends mainly on cluster size. The mechanism promoting mixing is the presence of non-specific interactions between chromatin and proteins, which become increasingly important as clusters become larger. This result, that we observe both in simple polymer models and more realistic ones for human chromosomes, reconciles the apparently contrasting experimental results obtained. Additionally, we show how the introduction of different types of TFs strongly affects the emergence of transcriptional networks, providing a pathway to investigate transcriptional changes following gene editing or naturally occurring mutations.