Precision of Tissue Patterning is Controlled by Dynamical Properties of Gene Regulatory Networks

Abstract

During development, gene regulatory networks allocate cell fates by par-titioning tissues into spatially organised domains of gene expression. How the sharp boundaries that delineate these gene expression patterns arise, despite the stochasticity associated with gene regulation, is poorly understood. We show, in the vertebrate neural tube, using perturbations of coding and regulatory regions, that the structure of the regulatory network contributes to boundary precision. This is achieved, not by reducing noise in individual genes, but by the configuration of the network modulating the ability of stochastic fluctuations to initiate gene expression changes. We use a computational screen to identify the properties of a network that influence boundary precision, revealing two dynamical mechanisms by which small gene circuits attenuate the effect of noise to increase patterning precision. These results establish design principles of gene regulatory networks that produce precise patterns of gene expression.

The developmental hourglass model is applicable to the spinal cord

“…The only exception is Irx-CRM1 , which exert no enhancer function in the chicken neural tube (data not shown). Enhancer function of Olig2-CRM1 was already validated previously (Exelby et al, 2021; Oosterveen et al, 2012; Peterson et al, 2012; Wang et al, 2011). These results indicate that these CRMs are functional in amniotes, but nevertheless, are lost in teleosts.…”

“…We also examined the transcriptional regulatory elements in the neural tube patterning genes based on ChIP-seq and ATAC-seq data and sequence conservation, suggesting that GRN upstream to the progenitor domain specification has been rewired during vertebrate evolution. Based on our findings and robust development of the neuronal progenitor specification (Balaskas et al, 2012; Delás and Briscoe, 2020; Exelby et al, 2021; Xiong et al, 2013; Zagorski et al, 2017), we propose that the progenitor domain configuration in the neural tube is less evolvable due to its canalization (Waddington, 1942).…”

“…These CRMs are open chromatin state in most cases, and are bound by Gli1, Gli3, Sox2, Neurog2, Pax6, Pax7, Olig2, Nkx2-2 and Nkx6-1 in various combinations (Fig.5). These TFs are essential components of GRN regulating the progenitor fate in the neural tube (Balaskas et al, 2012; Delás and Briscoe, 2020; Exelby et al, 2021; Kutejova et al, 2016). Thus, non-conservation of these CRMs and conservation of progenitor organization has necessarily entailed the CRM turnover and GRN rewiring during vertebrate evolution.…”

The nonlinearity of life

“…This gives us Langevin equations, similar to those used in statistical physics to describe the Brownian motion of particles jiggled randomly by a surrounding liquid or gas. Such a stochastic model can then capture the timing, position and, importantly, precision of tissue patterning [5]. We can study in particular how boundary precision is reduced by changes in the GRN such as deleting nodes (TFs) or edges (interactions), and the predicted effects compare well with experiments.…”

“…These transition rates can be worked out from our stochastic model by generalising Kramers' approach to the thermally activated escape of a particle from a potential well, and this then allows to predict boundary sharpness and its main drivers. We can even perform a systematic screen across a broad class of GRNs, to establish which network structures tend to produce sharp boundaries by the above mechanism [5].…”