Epigenetically regulated digital signaling defines epithelial innate immunity at the tissue level

Helen R. Clark, Connor McKenney, Nathan M. Livingston, Ariel Gershman, Seema Sajjan, Isaac S. Chan, Andrew J. Ewald, Winston Timp, Bin Wu, Abhyudai Singh and Sergi Regot ()
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Helen R. Clark: Johns Hopkins University School of Medicine
Connor McKenney: Johns Hopkins University School of Medicine
Nathan M. Livingston: Johns Hopkins University School of Medicine
Ariel Gershman: Johns Hopkins University School of Medicine
Seema Sajjan: Johns Hopkins University School of Medicine
Isaac S. Chan: Johns Hopkins University School of Medicine
Andrew J. Ewald: Johns Hopkins University School of Medicine
Winston Timp: Johns Hopkins University School of Medicine
Bin Wu: Johns Hopkins University School of Medicine
Abhyudai Singh: University of Delaware
Sergi Regot: Johns Hopkins University School of Medicine

Nature Communications, 2021, vol. 12, issue 1, 1-13

Abstract: Abstract To prevent damage to the host or its commensal microbiota, epithelial tissues must match the intensity of the immune response to the severity of a biological threat. Toll-like receptors allow epithelial cells to identify microbe associated molecular patterns. However, the mechanisms that mitigate biological noise in single cells to ensure quantitatively appropriate responses remain unclear. Here we address this question using single cell and single molecule approaches in mammary epithelial cells and primary organoids. We find that epithelial tissues respond to bacterial microbe associated molecular patterns by activating a subset of cells in an all-or-nothing (i.e. digital) manner. The maximum fraction of responsive cells is regulated by a bimodal epigenetic switch that licenses the TLR2 promoter for transcription across multiple generations. This mechanism confers a flexible memory of inflammatory events as well as unique spatio-temporal control of epithelial tissue-level immune responses. We propose that epigenetic licensing in individual cells allows for long-term, quantitative fine-tuning of population-level responses.

Date: 2021
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DOI: 10.1038/s41467-021-22070-x

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