Back

NFκB dynamics determine stimulus-responsive epigenomic reprogramming in macrophages

Cheng, Q. J.; Ohta, S.; Sheu, K. M.; Spreafico, R.; Adelaja, A.; Taylor, B.; Hoffmann, A.

2020-02-19 molecular biology
10.1101/2020.02.18.954602 bioRxiv
Show abstract

The epigenome defines the cell type, but also shows plasticity that enables cells to tune their gene expression potential to the context of extracellular cues. This is evident in immune sentinel cells such as macrophages, which can respond to pathogens and cytokines with phenotypic shifts that are driven by epigenomic reprogramming1. Recent studies indicate that this reprogramming arises from the activity of transcription factors such as nuclear factor kappa-light-chain-enhancer of activated B cells (NF{kappa}B), which binds not only to available enhancers but may produce de novo enhancers in previously silent areas of the genome2. Here, we show that NF{kappa}B reprograms the macrophage epigenome in a stimulus-specific manner, in response only to a subset of pathogen-derived stimuli. The basis for these surprising differences lies in the stimulus-specific temporal dynamics of NF{kappa}B activity. Testing predictions of a mathematical model of nucleosome interactions, we demonstrate through live cell imaging and genetic perturbations that NF{kappa}B promotes open chromatin and formation of de novo enhancers most strongly when its activity is non-oscillatory. These de novo enhancers result in the activation of additional response genes. Our study demonstrates that the temporal dynamics of NF{kappa}B activity, which encode ligand identity3, can be decoded by the epigenome through de novo enhancer formation. We propose a mechanistic paradigm in which the temporal dynamics of transcription factors are a key determinant of their capacity to control epigenomic reprogramming, thus enabling the formation of stimulus-specific memory in innate immune sentinel cells.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.