Gene regulatory logic of the interferon-β enhancer is characterized by two selectively deployed modes of transcription factor synergy
Abstract
Type I interferon IFNβ is a key immune response cytokine, and when its expression is dysregulated, it causes disease. The regulation of IFNβ enhancer has been a touchpoint of mammalian gene control research since the discovery of functional synergy between two stimulus-responsive transcription factors (TFs), nuclear factor kappa B (NFκB) and interferon regulatory factors (IRF). However, subsequent gene knockout studies revealed that in some conditions either NFκB or IRF activation can be dispensable, leaving the precise regulatory logic of IFNβ transcription an open question. Here, we developed a series of quantitative enhancer states models of IFNβ expression control and evaluated them with stimulus–response data from TF knockouts. Of these, our analysis reveals that two modes of TF synergy account for the available data and neither is based on binding cooperativity. The first involves two adjacent IRF dimers, with a sigmoidal binding curve at the distal site rendering it ultrasensitive and restricting it to conditions of high IRF activity upon viral infection. The second is driven by the proximal site, which has high affinity and synergizes with NFκB to enable about half-maximal expression in response to bacterial exposure. Its accessibility is controlled by the competitive repressor p50:p50, which prevents basal IRF from binding, such that NFκB-only stimuli do not induce IFNβ expression and may allow for prior-exposure-dependent tuning. The model explains how the regulatory logic of the IFNβ enhancer ensures invariant IFNβ expression in response to viral exposure, while providing tunable, context-dependent expression in response to bacterial exposure.
Article Details
Journal Info
Proceedings of the National Academy of Sciences
National Academy of Sciences
Authors (4)
Allison Schiffman
Signaling Systems Laboratory, Department of Microbiology, Immunology and Molecular Genetics, and the Institute for Quantitative and Computational Biosciences, University of California
Zhang Cheng
Signaling Systems Laboratory, Department of Microbiology, Immunology and Molecular Genetics, and the Institute for Quantitative and Computational Biosciences, University of California
Diana Ourthiague
Signaling Systems Laboratory, Department of Microbiology, Immunology and Molecular Genetics, and the Institute for Quantitative and Computational Biosciences, University of California
Alexander Hoffmann
Signaling Systems Laboratory, Department of Microbiology, Immunology and Molecular Genetics, and the Institute for Quantitative and Computational Biosciences, University of California