Processing stimulus dynamics by the NF-B network in single cells

Cells at the site of an infection experience numerous biochemical signals that vary in amplitude, space, and time. Despite the diversity of dynamic signals produced by pathogens and sentinel cells, information-processing pathways converge on a limited number of central signaling nodes to ultimately control cellular responses. In particular, the NF-kappa B pathway responds to dozens of signals from pathogens and self, and plays a vital role in processing proinflammatory inputs. Studies addressing the influence of stimulus dynamics on NF-kappa B signaling are rare due to technical limitations with live-cell measurements. However, recent advances in microfluidics, automation, and image analysis have enabled investigations that yield high temporal resolution at the single-cell level. Here, we summarize the recent research which measures and models the NF-kappa B response to pulsatile and fluctuating stimulus concentrations, as well as different combinations and sequences of signaling molecules. Collectively, these studies show that the NF-kappa B network integrates external inflammatory signals and translates these into downstream transcriptional responses. Minjun Son's research focuses on the intricate regulation of NF-kappa B, a central transcription factor governing inflammatory responses, under diverse stimulus dynamics and combinations. These stimuli include cytokines (small secreted proteins for cell-cell signaling) and other secretions from pathogens. While the precise mechanisms remain unknown, previous and ongoing studies have endeavored to elucidate NF-kappa B's response to short bursts, fluctuations, or combinations of stimuli. Through advanced technologies and analytical methods, researchers discovered that the NF-kappa B response is significantly influenced by the dynamics and combinations of stimuli. This variance in NF-kappa B response resulted in distinct gene expression patterns, underscoring the pivotal role of the NF-kappa B pathway in interpreting the dynamics of signal molecules and determining cell behavior. Future studies employing mouse models would bridge the gap between these experimental findings and physiological processes in vivo.This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.