A synthetic gene circuit for measuring autoregulatory feedback control.

Abstract

These authors contributed equally to this work.Auto regulatory feedback loops occur in the regulation of molecules ranging from ATP to MAP kinases to zinc. Negative feedback loops can increase a system's robustness, while positive feedback loops can mediate transitions between cell states. Recent genome-wide experimental and computational studies predict hundreds of novel feedback loops. However, not all physical interactions are regulatory, and many experimental methods cannot detect self-interactions. Our understanding of regulatory feedback loops is therefore hampered by the lack of high-throughput methods to experimentally quantify the presence, strength, and temporal dynamics of auto regulatory feedback loops. Here we present a mathematical and experimental framework for high-throughput quantification of feedback regulation, and apply it to RNA binding proteins (RBPs) in yeast. Our method is able to determine the existence of both direct and indirect positive and negative feedback loops, and to quantify the strength of these loops. We experimentally validate our model using two RBPs which lack native feedback loops, and by the introduction of synthetic feedback loops. We find that the the RBP Puf3 does not natively participate in any direct or indirect feedback regulation, but that replacing the native 3'UTR with that of COX17 generates an auto-regulatory negative feedback loop which reduces gene expression noise. Likewise, the RBP Pub1 does not natively participate in any feedback loops, but a synthetic positive feedback loop involving Pub1 results in increased expression noise. Our results demonstrate a synthetic experimental system for quantifying the existence and strength of feedback loops using a combination of high-throughput experiments and mathematical modeling. This system will be of great use in measuring auto-regulatory feedback by RNA binding proteins, a regulatory motif that is difficult to quantify using existing high-throughput methods.

High Cooperativity in Negative Feedback can Amplify Noisy Gene Expression

“…Transcriptional feedback thus regulates the rate, or frequency, with which such translational bursts occur (Friedman et al, 2006). Post-transcriptional feedback, on the other hand, regulates the size of translational bursts (Schikora-Tamarit et al, 2016). As a specific example, RNA binding proteins reduce the size of translational bursts by enhancing the degradation of their mRNA transcript (Yates and Nomura, 1981;Swain, 2004;Schikora-Tamarit et al, 2016).…”

“…Post-transcriptional feedback, on the other hand, regulates the size of translational bursts (Schikora-Tamarit et al, 2016). As a specific example, RNA binding proteins reduce the size of translational bursts by enhancing the degradation of their mRNA transcript (Yates and Nomura, 1981;Swain, 2004;Schikora-Tamarit et al, 2016). Previous studies indicate that posttranscriptional feedback outperforms transcriptional feedback in terms of its noise-reducing capability (Swain, 2004;Singh, 2011;Bokes and Singh, 2016).…”

Heavy-tailed distributions in a stochastic gene autoregulation model

“…Such regulation can result from common transcriptional control mechanisms [27]. In addition to feedback in burst frequency, there is evidence of feedback mechanisms that act on burst size or protein stability [28][29][30]. The explicit stationary solution to the drift-jump model has been extended to the case of feedback in protein stability [31].…”

A genome-scale yeast library with inducible expression of individual genes

“…Since β-estradiol is not a yeast metabolite or signaling molecule, cellular metabolism is not perturbed [13] . ZEVs have been widely used for basic and applied research, including studies of gene regulatory networks [16][17][18] , individual gene function [19][20][21][22][23][24][25] , gene regulation [26][27][28][29][30] , metabolic engineering [31] , synthetic biology [32][33][34][35][36][37][38][39] , biocontainment [40] , living materials [41] , high-throughput screening [42,43] , and they have also been adapted to fission yeast [44][45][46] and Pichia pastoris [47] .…”