Feed-forward regulation adaptively evolves via dynamics rather than topology when there is intrinsic noise

Abstract

In transcriptional regulatory networks (TRNs), a canonical 3-node feed-forward loop (FFL) is hypothesized to evolve to filter out short spurious signals. We test this adaptive hypothesis against a novel null evolutionary model. Our mutational model captures the intrinsically high prevalence of weak affinity transcription factor binding sites. We also capture stochasticity and delays in gene expression that distort external signals and intrinsically generate noise. Functional FFLs evolve readily under selection for the hypothesized function but not in negative controls. Interestingly, a 4-node “diamond” motif also emerges as a short spurious signal filter. The diamond uses expression dynamics rather than path length to provide fast and slow pathways. When there is no idealized external spurious signal to filter out, but only internally generated noise, only the diamond and not the FFL evolves. While our results support the adaptive hypothesis, we also show that non-adaptive factors, including the intrinsic expression dynamics, matter.

Differences in evolutionary accessibility determine which equally effective regulatory motif evolves to generate pulses

“…We used a previously described computational model to simulate the expression of genes in a TRN, parameterized by available Saccharomyces cerevisiae data (Xiong et al, 2019). The TRN evolves under a realistic mutational spectrum including de novo appearance of transcription factor binding sites and frequent gene duplication and deletion.…”

“…Binding sites with up to 2 mismatches are still recognized, with each mismatch reducing binding affinity according to a thermodynamic model (Xiong et al, 2019, Supplementary Methods/TF Binding). The concentrations of TFs are used to calculate the probabilities that each cis-regulatory region is bound by a given number of activators and repressors (Xiong et al, 2019, Supplementary Methods/TF Occupancy).…”

“…Most transition rates depend on whether activators and/or repressors are bound (Xiong et al, 2019, Methods/Transcriptional regulation, but with no AND-gate capability), with the fastest transition rate to the active state occurring when at least one activator and no repressors are bound. Transcription initiation from the active chromatin state is simulated stochastically, creating transcriptional bursts whose mean duration depends on an evolvable gene-specific parameter (Xiong et al, 2019, Supplementary Methods/rAct_to_Int).…”

“…However, adaptationist claims about TRN organization have also been accused of being just-so stories, with adaptive hypotheses still in need of testing against an appropriate null model of network evolution (Artzy-Randrup et al, 2004;Jenkins & Stekel, 2010;Knabe et al, 2008;Kuo et al, 2006;Lynch, 2007;Mazurie et al, 2005;Payne & Wagner, 2015;Ruths & Nakhleh, 2013;Solé & Valverde, 2006;Tsuda & Kawata, 2010;Wagner, 2003;Widder et al, 2012). We recently generated such a null model and used it to show that coherent type 1 feedforward loops can, as hypothesized, evolve adaptively in response to selection to filter out short spurious signals, by combining a fast signaling pathway and a slow signaling pathway with an AND gate (Xiong et al, 2019). Testing the hypothesis in this way was not merely confirmatory, but generated other insights about the existence and nature of alternative adaptive solutions, especially when slow transcriptional regulation is combined with faster response mechanisms such as post-translational regulation (Xiong et al, 2019).…”

“…We recently generated such a null model and used it to show that coherent type 1 feedforward loops can, as hypothesized, evolve adaptively in response to selection to filter out short spurious signals, by combining a fast signaling pathway and a slow signaling pathway with an AND gate (Xiong et al, 2019). Testing the hypothesis in this way was not merely confirmatory, but generated other insights about the existence and nature of alternative adaptive solutions, especially when slow transcriptional regulation is combined with faster response mechanisms such as post-translational regulation (Xiong et al, 2019). Other network motifs and properties have not yet received similar treatment.…”