Inherent regulatory asymmetry emanating from network architecture in a prevalent autoregulatory motif.

Robert C Brewster,Sandeep Choubey,Vinuselvi Parisutham,Md Zulfikar Ali

doi:10.7554/elife.56517

Abstract

Predicting gene expression from DNA sequence remains a major goal in the field of gene regulation. A challenge to this goal is the connectivity of the network, whose role in altering gene expression remains unclear. Here, we study a common autoregulatory network motif, the negative single-input module, to explore the regulatory properties inherited from the motif. Using stochastic simulations and a synthetic biology approach in E. coli, we find that the TF gene and its target genes have inherent asymmetry in regulation, even when their promoters are identical; the TF gene being more repressed than its targets. The magnitude of asymmetry depends on network features such as network size and TF-binding affinities. Intriguingly, asymmetry disappears when the growth rate is too fast or too slow and is most significant for typical growth conditions. These results highlight the importance of accounting for network architecture in quantitative models of gene expression.

Highlights

The genomics revolution has enabled biology with the ability to read, write and assemble DNA at the genome scale with single base pair resolution
We use a combination of theory and experimental in vivo measurements on engineered E. coli strains to study the interplay between transcription factors (TFs) gene, target gene, and additional binding sites of a negative autoregulatory singleinput module (SIM) network motif
While the role of TF autoregulation has been extensively studied (Acar et al, 2008; Assaf et al, 2011; Becskei and Serrano, 2000; Ochab-Marcinek et al, 2017; Rodrigo et al, 2016; Rosenfeld et al, 2002; Savageau, 1975; Semsey et al, 2009), the focus here is on the combined influence of an autoregulated TF and its target genes and how the shared need for that TF influences the quantitative features of its regulatory behaviors

Summary

Introduction

The genomics revolution has enabled biology with the ability to read, write and assemble DNA at the genome scale with single base pair resolution. We use stochastic simulations of kinetic models (Gillespie, 1977; Gillespie, 2007; Kaern et al, 2005; Shahrezaei and Swain, 2008), to predict how the overall level of gene expression depends on parameters characterizing cellular environment such as TF-binding affinities and the number of competing binding sites. Regulatory asymmetry is not captured by a simple deterministic model which is based on translating the stochastic reactions to kinetic rates through mass action equilibrium kinetics (which have been shown to accurately predict target gene expression in other studies [Brewster et al, 2014; Garcia and Phillips, 2011; Garcia et al, 2012; Jones et al, 2014; Razo-Mejia et al, 2018]). A revised deterministic model, which explicitly allows for different microenvironments in each ‘regulatory state’, predicts asymmetry, it still does not recover quantitative agreement with stochastic simulations

Results

Discussion

Materials and methods

Simulation methodology

Funding Funder