Project description:Interferons (IFNs) are key players in the antiviral response. IFN sensing by the cell activates transcription of IFN-stimulated genes (ISGs) able to induce an antiviral state by affecting viral replication and release. IFN also induces the expression of ISGs that function as negative regulators to limit the strength and duration of IFN response. The ISGs identified so far belong to coding genes. However, only a small proportion of the transcriptome corresponds to coding transcripts and it has been estimated that there could be as many coding as long non-coding RNAs (lncRNAs). To address whether IFN can also regulate the expression of lncRNAs, we analyzed the transcriptome of HuH7 cells treated or not with IFNα2 by expression arrays. Analysis of the arrays showed increased levels of several well-characterized coding genes that respond to IFN both at early or late times. Furthermore, we identified several IFN-stimulated or -downregulated lncRNAs (ISRs and IDRs). Further validation showed that ISR2, 8, and 12 expression mimics that of their neighboring genes GBP1, IRF1, and IL6, respectively, all related to the IFN response. These genes are induced in response to different doses of IFNα2 in different cell lines at early (ISR2 or 8) or later (ISR12) time points. IFNβ also induced the expression of these lncRNAs. ISR2 and 8 were also induced by an influenza virus unable to block the IFN response but not by other wild-type lytic viruses tested. Surprisingly, both ISR2 and 8 were significantly upregulated in cultured cells and livers from patients infected with HCV. Increased levels of ISR2 were also detected in patients chronically infected with HIV. This is relevant as genome-wide guilt-by-association studies predict that ISR2, 8, and 12 may function in viral processes, in the IFN pathway and the antiviral response. Therefore, we propose that these lncRNAs could be induced by IFN to function as positive or negative regulators of the antiviral response. HuH7 cells were treated with 10000 units/ml of IFN a2 and RNA was isolated 3 days post-treatment

Project description:Sensitivity to Interferon (IFN) is determined by a complex coordination of genetic and environmental factors. A previous experiment using two renal cancer cell lines differing markedly in their response to IFN were analyzed for their ISG profiles in order to determine gene expression changes associated with IFN sensitivity (Holko M, Williams BR. Functional annotation of IFN-alpha-stimulated gene expression profiles from sensitive and resistant renal cell carcinoma cell lines. J Interferon Cytokine Res 2006 Aug;26(8):534-47). Higher and more persistent expression of a subset of ISGs was noted in the IFN-sensitive RCC1 cells when compared with the relatively IFN-insensitive ACHN cells. A subset of Interferon stimulated genes (ISGs) whose sustained expression correlates with heightened IFN sensitivity were analyzed for functional and sequence similarities. This subset predominately contains genes involved in transcription, and most were found to contain in their promoters binding sites for promyleocytic zinc finger factor (PLZF), a transcriptional repressor identified by virtue of its role in the etiology of Acute Promyelocytic Leukemia (APL). Analysis of gene expression in a lymphoid cell line with inducible PLZF expression reveals that increased expression of immune system related genes including ISGs, depends on PLZF expression. Chromatin Immunoprecipitation assays show direct association between PLZF and in silico identified PLZF binding sites in ISG promoters. This study reveals a novel interaction between PLZF and IFN signaling. A time course of IFNa treatment (0, 6, 16, 24 hrs) was performed in tetetracycline induced PLZF overexpressing and control U937T:PLZF45 cells. For the 0 hr time point, PLZF overexpression (Cy5) was compared with control cells (Cy3). For the 6, 16, and 24 hr IFN treated time points, treated RNA was labelled with Cy5 and non-IFN treated RNA with Cy3. Each IFN treatment time point was performed on control and PLZF-overexpressing cells respectively.

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:Interferons (IFNs) are key players in the antiviral response. IFN sensing by the cell activates transcription of IFN-stimulated genes (ISGs) able to induce an antiviral state by affecting viral replication and release. IFN also induces the expression of ISGs that function as negative regulators to limit the strength and duration of IFN response. The ISGs identified so far belong to coding genes. However, only a small proportion of the transcriptome corresponds to coding transcripts and it has been estimated that there could be as many coding as long non-coding RNAs (lncRNAs). To address whether IFN can also regulate the expression of lncRNAs, we analyzed the transcriptome of HuH7 cells treated or not with IFNα2 by expression arrays. Analysis of the arrays showed increased levels of several well-characterized coding genes that respond to IFN both at early or late times. Furthermore, we identified several IFN-stimulated or -downregulated lncRNAs (ISRs and IDRs). Further validation showed that ISR2, 8, and 12 expression mimics that of their neighboring genes GBP1, IRF1, and IL6, respectively, all related to the IFN response. These genes are induced in response to different doses of IFNα2 in different cell lines at early (ISR2 or 8) or later (ISR12) time points. IFNβ also induced the expression of these lncRNAs. ISR2 and 8 were also induced by an influenza virus unable to block the IFN response but not by other wild-type lytic viruses tested. Surprisingly, both ISR2 and 8 were significantly upregulated in cultured cells and livers from patients infected with HCV. Increased levels of ISR2 were also detected in patients chronically infected with HIV. This is relevant as genome-wide guilt-by-association studies predict that ISR2, 8, and 12 may function in viral processes, in the IFN pathway and the antiviral response. Therefore, we propose that these lncRNAs could be induced by IFN to function as positive or negative regulators of the antiviral response.

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Genome-wide maps of SP100A binding chromatin.

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