Research ArticleSystems Biology

A predictive model of gene expression reveals the role of network motifs in the mating response of yeast

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Science Signaling  16 Feb 2021:
Vol. 14, Issue 670, eabb5235
DOI: 10.1126/scisignal.abb5235

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Motifs in mating responses

In the mating response in yeast, pheromone stimulation initiates a MAPK signaling cascade that alleviates the repression of the transcription factor Ste12 by Dig1 and Dig2, resulting in transcriptional changes, and that activates a factor called Far1, which promotes Ste12 degradation. Pomeroy et al. found that pheromone-induced transcriptional regulation showed persistence after pheromone removal and long-term adaptation upon prolonged pheromone exposure. They generated a model trained on transcriptional data generated in response to different temporal patterns of pheromone stimulation. The model predicted that persistence and long-term adaptation required four network motifs: feedforward, positive feedback, and negative feedback loops between MAPKs, Ste12, and Far1; and the rebinding of Dig1 and Dig2 to Ste12. These predictions were experimentally validated with mutant yeast strains. Thus, multiple network motifs ensure that gene expression is appropriately regulated in response to different patterns of pheromone exposure.


Cells use signaling pathways to receive and process information about their environment. These nonlinear systems rely on feedback and feedforward regulation to respond appropriately to changing environmental conditions. Mathematical models describing signaling pathways often lack predictive power because they are not trained on data that encompass the diverse time scales on which these regulatory mechanisms operate. We addressed this limitation by measuring transcriptional changes induced by the mating response in Saccharomyces cerevisiae exposed to different dynamic patterns of pheromone. We found that pheromone-induced transcription persisted after pheromone removal and showed long-term adaptation upon sustained pheromone exposure. We developed a model of the regulatory network that captured both characteristics of the mating response. We fit this model to experimental data with an evolutionary algorithm and used the parameterized model to predict scenarios for which it was not trained, including different temporal stimulus profiles and genetic perturbations to pathway components. Our model allowed us to establish the role of four architectural elements of the network in regulating gene expression. These network motifs are incoherent feedforward, positive feedback, negative feedback, and repressor binding. Experimental and computational perturbations to these network motifs established a specific role for each in coordinating the mating response to persistent and dynamic stimulation.

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