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.

Project description:Abstract Rationale: Interferon (IFN) plays a key role in immunotherapy by acting through interferon-stimulated genes. Interferon resistance limits its efficacy. Ubiquitination modification of key proteins is a popular research direction in hepatocellular carcinoma (HCC), and it is also one of the important mechanisms of interferon resistance. This study is dedicated to discovering the link between the pathogenesis of interferon resistance and ubiquitination modifications in HCC. Methods: We first combined the TCGA LIHC dataset and genes from the iUUCD 2.0 database to screen the most promising ubiquitination-related gene, UBE2O, in HCC using a bioinformatics approach. A combination of proteomic analysis, mass spectrometry, and survival analysis was used to screen for pathways and proteins negatively regulated by UBE2O. The effect of silencing UBE2O on interferon sensitivity in HCC cells was assessed using in vitro clone formation, migration, and wound healing assays and in vivo subcutaneous tumor xenograft models. Changes in the half-life of substrates following inhibition of UBE2O were assessed using a cycloheximide method. Two databases were used to predict ubiquitination sites on IFIT3. After rescue experiments to exclude the effect of substrate, proteomic analysis was reperformed to analyze the effect on the interferon pathway and phenotype. The effect on substrate and interferon sensitivity was observed using arsenic trioxide (ATO) to inhibit UBE2O. Results: Initially, UBE2O was deduced from 807 UUCRGs in the TCGA LIHC dataset by six consecutive screening steps. UBE2O was significantly overexpressed in HCC cell lines and tissues compared to the corresponding normal group, and low expression of UBE2O was associated with a better prognosis. UBE2O was knocked down for proteomic analysis, and the results suggested that UBE2O negatively regulates the interferon-alpha/beta signaling (p＜0.01) and the interferon pathway (p＜0.01). IFIT3 in the interferon pathway was identified as a possible ubiquitinated substrate of UBE2O by combining mass spectrometry, the human protein atlas (HPA), and TCGA-based survival analysis. There was a significant negative correlation between UBE2O and IFIT3, including survival analysis showing opposite trends and a negative correlation in protein expression. UBE2O binds to IFIT3, and interferon-α promotes this binding. The stability of IFIT3 protein increased with decreasing UBE2O (p＜0.05), and these effects disappeared with the mutational inactivation of UBE2O. K236 of IFIT3 is the active site of ubiquitination. By increasing IFIT3, the knockdown of UBE2O increases the sensitivity of HCC cells to interferon, leading to reduced proliferation and migration (all p＜0.05). These effects disappeared after the elimination of IFIT3. Additionally, ATO inhibited UBE2O and therefore increased interferon sensitivity in HCC cells. Conclusions: UBE2O exerts a protumor role and targets a new substrate, IFIT3, for ubiquitination and degradation, impeding the interferon pathway activation and interferon sensitization in HCC. The findings will shed light on the interferon-based targeted or combination therapeutic strategies for HCC in the future.

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Slfn2 Regulates Type I Interferon Responses by Modulating the NFĸB Pathway

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