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Macrophages must respond appropriately to pathogens and other pro-inflammatory stimuli in order to perform their roles in fighting infection. One way in which inflammatory stimuli can vary is in their dynamics--that is, the amplitude and duration of stimulus experienced by the cell. In this study, we performed long-term live cell imaging in a microfluidic device to investigate how the pro-inflammatory genes IRF1, CXCL10, and CXCL9 respond to dynamic interferon-gamma (IFN) stimulation. We found that IRF1 responds to low concentration or short duration IFN stimulation, whereas CXCL10 and CXCL9 require longer or higherconcentration stimulation to be expressed. We also investigated the heterogeneity in the expression of each gene and found that CXCL10 and CXCL9 have substantial cell-to-cell variability. In particular, the expression of CXCL10 appears to be largely stochastic with a subpopulation of nonresponding cells across all the stimulation conditions tested. We developed both deterministic and stochastic models for the expression of each gene. Our modeling analysis revealed that the heterogeneity in CXCL10 can be attributed to a slow chromatin-opening step that is on a similar timescale to that of adaptation of the upstream signal. In this way, CXCL10 expression in individual cells can remain stochastic in response to each pulse of repeated stimulation, which we also validated by experiments. Together, we conclude that pro-inflammatory genes in the same signaling pathway can respond to dynamic IFN stimulus with very different response features and that upstream signal adaptation can contribute to shaping heterogeneous gene expression. [ABSTRACT FROM AUTHOR]

Information about environmental stimuli often can be encoded by the dynamics of signaling molecules or transcription factors. In the yeast Saccharomyces cerevisiae, different types of stresses induce distinct nuclear translocation dynamics of the general stress-responsive transcription factor Msn2, but the underlying mechanisms remain unclear. Using deterministic and stochastic modeling, we reproduced in silico the different dynamic responses of Msn2 to glucose limitation and osmotic stress observed in vivo and found that a positive feedback loop on protein kinase A mediated by the AMP-activated protein kinase Snf1 is coupled with a negative feedback loop to generate the characteristic pulsatile dynamics of Msn2. The model predicted that the stimulus-specific positive feedback loop could be responsible for the difference between Msn2 dynamics induced by glucose limitation and osmotic stress. This prediction was further verified experimentally by time-lapse microscopic examinations of the snf1Δ strain. In this mutant lacking the Snf1-mediated positive feedback loop, Msn2 responds similarly to glucose limitation and osmotic stress, and its pulsatile translocation is largely abrogated. Our combined computational and experimental analysis reveals a regulatory mechanism by which cells can encode information about environmental cues into distinct signaling dynamics through stimulus-specific network architectures. [ABSTRACT FROM AUTHOR]