Molecular Biology and EvolutionEvolutionary BiologyPeer-reviewed journal article
How the Structure of Signaling Regulation Evolves: Insights from an Evolutionary Model
Abstract
To remain responsive to environmental changes, signaling pathways attenuate their activity with negative feedback loops (NFLs), where proteins produced upon stimulation downregulate the response. NFLs function both upstream of signaling to reduce input and downstream to reduce output. Unlike upstream NFLs, downstream NFLs directly regulate gene expression without the involvement of intermediate proteins. Thus, we hypothesized that downstream NFLs evolve under more stringent selection than upstream NFLs. Indeed, genes encoding downstream NFLs exhibit a slower evolutionary rate than upstream genes. Such differences in selective pressures could result in the robust evolution of downstream NFLs while making the evolution of upstream NFLs more sensitive to changes in signaling proteins and stimuli. Here, we test these assumptions within the context of immune signaling. Our minimal model of immune signaling predicts robust evolution of downstream NFLs to changes in model parameters. This is consistent with their critical role in regulating signaling and the conservative rate of evolution. Furthermore, we show that the number of signaling steps needed to activate a downstream NFL is influenced by the cost of signaling. Our model predicts that upstream NFLs are more likely to evolve under a shorter half-life of signaling proteins, absence of host-pathogen co-evolution, and a high infection rate. Although it has been proposed that NFLs evolve to reduce the cost of signaling, we show that a high cost does not necessarily predict the evolution of upstream NFLs. The insights from our model have broad implications for understanding the evolution of regulatory mechanisms across signaling pathways.
- research focus: host-pathogen & immune response
- research focus: chromatin & epigenetic regulation
- approach: statistical model
- approach: mechanistic / dynamical model
- Citations
- 2
- FWCI
- 0.52
- Field percentile
- top 42%
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