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Gene expression is an inherently stochastic process that depends on the structure of the biochemical regulatory network in which the gene is embedded. Here we study the dynamical consequences of the interplay between stochastic gene switching and the widespread negative feedback regulatory loop in a simple model of a biochemical regulatory network. Using a simplified hybrid simulation approach, in which only the gene activation is modeled stochastically, we find that stochasticity in gene switching by itself can induce pulses in the system, providing also analytical insights into their origin. Furthermore, we find that this simple network is able to reproduce both exponential and peaked distributions of gene active and inactive times similar to those that have been observed experimentally. This simplified hybrid simulation approach also allows us to link these patterns to the dynamics of the system for each gene state.

Objective Trained immunity (TI) is a de facto memory program of innate immune cells, characterized by immunometabolic and epigenetic changes sustaining enhanced production of cytokines. TI evolved as a protective mechanism against infections; however, inappropriate activation can cause detrimental inflammation and might be implicated in the pathogenesis of chronic inflammatory diseases. In this study, we investigated the role of TI in the pathogenesis of giant cell arteritis (GCA), a large-vessel vasculitis characterized by aberrant macrophage activation and excess cytokine production. Methods Monocytes from GCA patients and from age- and sex-matched healthy donors were subjected to polyfunctional studies, including cytokine production assays at baseline and following stimulation, intracellular metabolomics, chromatin immunoprecipitation-qPCR, and combined ATAC/RNA sequencing. Immunometabolic activation (i.e. glycolysis) was assessed in inflamed vessels of GCA patients with FDG-PET and immunohistochemistry (IHC), and the role of this pathway in sustaining cytokine production was confirmed with selective pharmacologic inhibition in GCA monocytes. Results GCA monocytes exhibited hallmark molecular features of TI. Specifically, these included enhanced IL-6 production upon stimulation, typical immunometabolic changes (e.g. increased glycolysis and glutaminolysis) and epigenetic changes promoting enhanced transcription of genes governing pro-inflammatory activation. Immunometabolic changes of TI (i.e. glycolysis) were a feature of myelomonocytic cells in GCA lesions and were required for enhanced cytokine production. Conclusions Myelomonocytic cells in GCA activate TI programs sustaining enhanced inflammatory activation with excess cytokine production. [ABSTRACT FROM AUTHOR]

Several transcription factors (TFs) oscillate, periodically relocating between the cytoplasm and the nucleus. NF-κB, which plays key roles in inflammation and cancer, displays oscillations whose biological advantage remains unclear. Recent work indicated that NF-κB displays sustained oscillations that can be entrained, that is, reach a persistent synchronized state through small periodic perturbations. We show here that for our GFP-p65 knock-in cells NF-κB behaves as a damped oscillator able to synchronize to a variety of periodic external perturbations with no memory. We imposed synchronous dynamics to prove that transcription of NF-κB-controlled genes also oscillates, but mature transcript levels follow three distinct patterns. Two sets of transcripts accumulate fast or slowly, respectively. Another set, comprising chemokine and chemokine receptor mRNAs, oscillates and resets at each new stimulus, with no memory of the past. We propose that TF oscillatory dynamics is a means of segmenting time to provide renewing opportunity windows for decision. [ABSTRACT FROM AUTHOR]

Abstract: The mathematical modeling of the oscillations has attracted considerable attention in recent times, but there is a lack of simple models in the literature that can capture the main features of the dynamics of this important transcription factor. For this reason we propose a simple model that summarizes the key steps of the pathway. We show that the resulting 5-dimensional dynamical system can reproduce different phenomena observed in experiments. Our model can display smooth and spiky oscillations in the amount of nuclear and can reproduce the variety of dynamics observed when different stimulations such as and LPS are used. Furthermore we show that the model can be easily extended to reproduce the expression of early, intermediate and late genes upon stimulation. As a final example we show that our simple model can mimic the different transcriptional outputs observed when cells are treated with two different drugs leading to nuclear localization of : Leptomycin B and Cycloheximide. [Copyright &y& Elsevier]