Project description:Recent genetic studies in mice have established a key role for the nuclear receptor coregulator Trim24 in liver tumor suppression and provided evidence that Trim24 suppresses hepatocarcinogenesis by inhibiting retinoic acid receptor alpha (Rara)-dependent transcription and cell proliferation. However, it is unknown which downstream targets of Rara regulated by Trim24 are critical for tumorigenesis. We report here that loss of Trim24 results in the overexpression of interferon (IFN)/STAT pathway genes in the liver, a process that occurs early in tumorigenesis and is more pronounced in tumors, despite the enhanced expression, late in the disease, of negative regulators such as Usp18, Socs1 and Socs2. Remarkably, Rara haplodeficiency, which was previously shown to suppress tumor development in Trim24-/- mice, also suppresses overexpression of the IFN/STAT pathway, thus providing evidence for a cross-pathway control that may be relevant to the transformation process. Biochemical studies revealed that Trim24 binds to the retinoic acid (RA)-responsive element in the Stat1 promoter in a RA-dependent manner and represses RA-induced transcription from this promoter. Together, these results identify Trim24 as a novel regulator of the IFN/STAT pathway and indicate that Trim24-mediated repression of the IFN/STAT signaling through Rara inhibition may play a critical role in preventing liver cancer. Generation of Trim24-/- mice has been described previously (Khetchoumian et al., 2007) by gene disruption. To generate compound mutant mice with a single allele of Rara deleted in the Trim24 -/- mutant background, we crossed Trim24 -/- mice with Rara+/- mice. The resulting Trim24+/- Rara+/- mice were generated in the hybrid (C57BL/6 (60%), 129/Sv (40%)) genetic background. These double heterozygous Trim24+/- Rara+/- mice were intercrossed to generate Trim24 -/-, Trim24 -/- Rara+/- and wild-type mice. Transcriptional profiling of mice at 5-weeks and 14-weeks of age.

Project description:Maier2022 - Stochastic Dynamics of Type I Interferon Responses Our study aims to determine whether and how biochemical noise affects the information transduced in the JAK-STAT signaling pathway and investigate the transition between basal and activated state. To this end, we studied the stochastic responses of MxA and IFIT1 expression in Huh7.5 cells stimulated with IFN-$\alpha$. Using fluorescent reporters under the control of the authentic promoter/enhancer region of IFIT1 and MxA we collected data displaying the differences between expressing and non-expressing cells for the marker genes in a time-course experiment. We hypothesize that the JAK-STAT signaling pathway efficiently transmits information under stochastic environments. To test our working hypothesis, we developed a detailed mathematical model using the obtained time-resolved flow cytometry data to describe the elements in the JAK-STAT signaling pathway at single-cell resolution. This model allowed us to systematically test the influence of intrinsic and extrinsic noise in the IFN response. The developed model consists of 42 species and 62 reactions (reactions m1 to m62). To name the variables in the model we used the following conventions: 1) variables referring to mRNA are denoted by $m$ prefix. 2) Variables in phosphorylated use $p$ as prefix. 3) Gene promoters are represented by the gene's name in lowercase. 4) R1, R2, IR, AR and RC, represent the IFN receptor subunits, inactive, active and complex forms, respectively. 5) The compartment is superscripted to the species if the species exist in multiple compartments. A graphical representation of the interaction between variables in the model is given in Maier et. al 2022 (Fig. 1) and all reactions are listed in the Supplementary Information, Section S2.1. Note that we publish the SBML model without the observables (e.g. exp_IRF9_n) as the change in the number of particles is defined in relation to the starting value in the COPASI model which is not supported in SBML format. In order to reproduce the parameter estimation without any extra worj, we recommend the direct download of the provided copasi model linked in the article. This model is described in the article: Stochastic Dynamics of Type I Interferon Responses Benjamin D. Maier(*), Luis U. Aguilera(*), Sven Sahle, Pascal Mutz, Priyata Kalra, Christopher Dächert, Ralf Bartenschlager, Marco Binder, Ursula Kummer PLOS Computational Biology, 2022 (*) Equally contributing authors Abstract: Interferon (IFN) activates the transcription of several hundred of IFN stimulated genes (ISGs) that constitute a highly effective antiviral defense program. Cell-to-cell variability in the induction of ISGs is well documented, but its source and effects are not completely understood. The molecular mechanisms behind this heterogeneity have been related to randomness in molecular events taking place during the JAK-STAT signaling pathway. Here, we study the sources of variability in the induction of the IFN-alpha response by using MxA and IFIT1 activation as read-out. To this end, we integrate time-resolved flow cytometry data and stochastic modeling of the JAK-STAT signaling pathway. The complexity of the IFN response was matched by fitting probability distributions to time-course flow cytometry snapshots. Both, experimental data and simulations confirmed that the MxA and IFIT1 induction circuits generate graded responses rather than all-or-none responses. Subsequently, we quantify the size of the intrinsic variability at different steps in the pathway. We found that stochastic effects are transiently strong during the ligand-receptor activation steps and the formation of the ISGF3 complex, but negligible for the final induction of the studied ISGs. We conclude that the JAK-STAT signaling pathway is a robust biological circuit that efficiently transmits information under stochastic environments.

Dataset Information

Mus musculus

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets