Review: Activities of IRF-1

Department of Gene Regulation and Differentiation, GBF, Gesellschaft für Biotechnologische Forschung, D 38124 Braunschweig Mascheroder Weg 1, Germany.
Journal of Interferon & Cytokine Research (Impact Factor: 2). 02/2002; 22(1):5-14. DOI: 10.1089/107999002753452610
Source: PubMed


Interferon (IFN) regulatory factor-1 (IRF-1) was isolated by virtue of its affinity to specific DNA sequences in the IFN-beta promoter that mediate virus responsiveness. IRF-1 was the first factor identified of the IRF family and was most extensively characterized at the molecular level. Also, its physiologic role in host defense against pathogens, tumor prevention, and development of the immune system was investigated in detail. Even though some of the functions first associated with IRF-1 were later found to be mediated in part or predominantly by other activators of the IRF family of transcription factors, IRF-1 has remained a central paradigm in the transcriptional regulation of the IFN response.

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    • "All these genes are closely or directly related to the antigen presentation process. Indeed, IRF-1 plays a crucial role in the regulation of the antigen presentation, being needed for the expression of LMP-2, TAP-1, and MHC-I (Kröger et al., 2002). Fig. 7. Expression analysis of different immune genes in head kidney samples at 48 h after pMCV1.4, "
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    ABSTRACT: Type I interferons (IFNs) are considered the main cytokines directing the antiviral immune response in vertebrates. These molecules are able to induce the transcription of interferon-stimulated genes (ISGs) which, using different blocking mechanisms, reduce the viral proliferation in the host. In addition, a contradictory role of these IFNs in the protection against bacterial challenges using murine models has been observed, increasing the survival or having a detrimental effect depending on the bacteria species. In teleosts, a variable number of type I IFNs has been described with different expression patterns, protective capabilities or gene induction profiles even for the different IFNs belonging to the same species. In this work, two type I IFNs (ifn1 and ifn2) have been characterized for the first time in turbot (Scophthalmus maximus), showing different properties. Whereas Ifn1 reflected a clear antiviral activity (over-expression of ISGs and protection against viral haemorrhagic septicaemia virus), Ifn2 was not able to induce this response, although both transcripts were up-regulated after viral challenge. On the other hand, turbot IFNs did not show any protective effect against the bacteria Aeromonas salmonicida, although they were induced after bacterial challenge. Both IFNs induced the expression of several immune genes, but the effect of Ifn2 was mainly limited to the site of administration (intramuscular injection). Interestingly, Ifn2 but not Ifn1 induced an increase in the expression level of interleukin-1 beta (il1b). Therefore, the role of Ifn2 could be more related with the immune regulation, being involved mainly in the inflammation process.
    Full-text · Article · Mar 2014 · Developmental and comparative immunology
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    • "Recent studies observed that the constitutive activation of the interferon (IFN)-g/signal transducers and activators of transcription (STAT)-1/IRF-1 axis (Th1 phenotype ) bears good prognostic connotation and predicts better responsiveness to anti-cancer therapy (Ascierto et al, 2011). IRF-1 is a nuclear transcription factor crucial to inflammation, immunity, cell proliferation and apoptosis (Kroger et al, 2002). Its synthesis is induced in response to IFN-g (Taniguchi et al, 2001). "
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    ABSTRACT: Background: Several lines of evidence suggest a dichotomy between immune active and quiescent cancers, with the former associated with a good prognostic phenotype and better responsiveness to immunotherapy. Central to such dichotomy is the master regulator of the acute inflammatory process interferon regulatory factor (IRF)-1. However, it remains unknown whether the responsiveness of IRF-1 to cytokines is able to differentiate cancer immune phenotypes. Methods: IRF-1 activation was measured in 15 melanoma cell lines at basal level and after treatment with IFN-γ, TNF-α and a combination of both. Microarray analysis was used to compare transcriptional patterns between cell lines characterised by high or low IRF-1 activation. Results: We observed a strong positive correlation between IRF-1 activation at basal level and after IFN-γ and TNF-α treatment. Microarray demonstrated that three cell lines with low and three with high IRF-1 inducible translocation scores differed in the expression of 597 transcripts. Functional interpretation analysis showed mTOR and Wnt/β-cathenin as the top downregulated pathways in the cell lines with low inducible IRF-1 activation, suggesting that a low IRF-1 inducibility recapitulates a cancer phenotype already described in literature characterised by poor prognosis. Conclusion: Our findings support the central role of IRF-1 in influencing different tumour phenotypes.
    Full-text · Article · Jun 2013 · British Journal of Cancer
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    • "This central function is supported by the finding that Fos expression is a pre-requisite for the reentry of quiescent cells into the cell cycle [72]. In the TFN, Fos is positively regulating Irf1 and Cebpd, both of which were found to be involved in proliferation and the cell cycle [73-75]. Interestingly, these two TFs can be found to negatively regulate Fos expression in turn. "
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    ABSTRACT: Background Network inference is an important tool to reveal the underlying interactions of biological systems. In the liver, a complex system of transcription factors is active to distribute signals and induce the cellular response following extracellular stimuli. Plenty of information is available about single transcription factors important for the different functions of the liver, but little is known about their causal relations to each other. Results Given a DNA microarray time series dataset of collagen monolayers cultured murine hepatocytes, we identified 22 differentially expressed genes for which the corresponding protein is known to exhibit transcription factor activity. We developed the Extended TILAR (ExTILAR) network inference algorithm based on the modeling concept of the previously published TILAR algorithm. Using ExTILAR, we inferred a transcription factor network based on gene expression data which puts these important genes into a functional context. This way, we identified a previously unknown relationship between Tgif1 and Atf3 which we validated experimentally. Beside its known role in metabolic processes, this extends the knowledge about Tgif1 in hepatocytes towards a possible influence of processes such as proliferation and cell cycle. Moreover, two positive (i.e. double negative) regulatory loops were predicted that could give rise to bistable behavior. We further evaluated the performance of ExTILAR by systematic inference of an in silico network. Conclusions We present the ExTILAR algorithm, which combines the advantages of the regression based inference algorithm TILAR, like large network sizes processable and low computational costs, with the advantages of dynamic network models based on ordinary differential equation (i.e. in silico knock-down simulations). Like TILAR, ExTILAR makes use of various prior-knowledge types such as transcription factor binding site information and gene interaction knowledge to infer biologically meaningful gene regulatory networks. Therefore, ExTILAR is especially useful when a large number of genes is modeled using a small number of experimental data points.
    Full-text · Article · Nov 2012 · BMC Systems Biology
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