Báo cáo khoa học: Combining theoretical analysis and experimental data generation reveals IRF9 as a crucial factor for accelerating interferon a-induced early antiviral signalling pdf

Combining theoretical analysis and experimental data generation reveals IRF9 as a crucial factor for accelerating interferon a-induced early antiviral signalling Tim Maiwald1,*, Annette Schneider2,*, Hauke Busch3, Sven Sahle1, Norbert Gretz4, Thomas S Weiss5, Ursula Kummer1 and Ursula Klingmuller2 ă Heidelberg University, Department Modeling of Biological Processes, BIOQUANT ⁄ Institute of Zoology, Germany German Cancer Research Center, Division Systems Biology of Signal Transduction ⁄ BIOQUANT, DKFZ-ZMBH Alliance, Heidelberg, Germany Freiburg Institute for Advanced Studies (FRIAS), Albertstraße 19 and Center for Biosystems Analysis (ZBSA), University of Freiburg, Germany Heidelberg University, Medical Faculty Mannheim, Germany University Medical Center Regensburg, Center for Liver Cell Research, Germany Keywords antiviral signalling; interferon a; IRF9; kinetic model; signal transduction Correspondence U Klingmuller, Division Systems Biology of ă Signal Transduction, German Cancer Research Center, Im Neuenheimer Feld 280, Heidelberg 69120, Germany Fax: +49 6221 42 4488 Tel: +49 6221 42 4481 E-mail: u.klingmueller@dkfz.de Database The mathematical model described here has been submitted to the Online Cellular Systems Modelling Database and can be accessed at http://jjj.biochem.sun.ac.za/ database/maiwald/index.html free of charge *These authors contributed equally to this work Type I interferons (IFN) are important components of the innate antiviral response A key signalling pathway activated by IFNa is the Janus kinase ⁄ signal transducer and activator of transcription (JAK ⁄ STAT) pathway Major components of the pathway have been identified However, critical kinetic properties that facilitate accelerated initiation of intracellular antiviral signalling and thereby promote virus elimination remain to be determined By combining mathematical modelling with experimental analysis, we show that control of dynamic behaviour is not distributed among several pathway components but can be primarily attributed to interferon regulatory factor (IRF9), constituting a positive feedback loop Model simulations revealed that increasing the initial IRF9 concentration reduced the time to peak, increased the amplitude and enhanced termination of pathway activation These model predictions were experimentally verified by IRF9 over-expression studies Furthermore, acceleration of signal processing was linked to more rapid and enhanced expression of IFNa target genes Thus, the amount of cellular IRF9 is a crucial determinant for amplification of early dynamics of IFNa-mediated signal transduction (Received 19 June 2010, revised 20 August 2010, accepted 13 September 2010) doi:10.1111/j.1742-4658.2010.07880.x Introduction Invading pathogens such as viruses elicit complex responses in host cells, and the outcome of an infection is critically determined by the dynamics of host defence mechanisms Important mediators of antiviral Abbreviations C ⁄ EBP-b, CCAAT-enhancer-binding protein b; IFN, interferon; IRF9, interferon regulatory factor 9; ISGF3, interferon-stimulated gene factor 3; ISG, interferon-stimulated gene; JAK, Janus kinase; PIAS, protein inhibitor of activated STATs; PKR, protein kinase R; SOCS, suppressor of cytokine signalling; STAT, signal transducer and activator of transcription; SHP-2, SH2-containing phosphatases; TYK2, tyrosine kinase 2; TFBS, transcription factor binding sites; USP18, ubiquitin specific peptidase 18 FEBS Journal 277 (2010) 4741–4754 ª 2010 The Authors Journal compilation ª 2010 FEBS 4741 IRF9 accelerates IFNa signal transduction T Maiwald et al responses are type I interferons (IFN), which are used in the treatment of hepatitis B and C virus infections However, success of the treatment is highly patientdependent [1] As a rapid IFN response may be decisive for a viral infection [2], it is important to identify factors that regulate IFN signalling kinetics and that may be a reason for patient-to-patient variations In general, several potential mechanisms are possible to expedite signal transduction As shown for the epidermal growth factor receptor system, increasing ligand concentrations result in earlier maximal pathway activation and an increase in signal amplitude [3,4] Furthermore, alterations in the activity of a kinase or phosphatase may affect the speed of signalling pathway activation However, theoretical studies by Heinrich et al [5] indicated that kinase activity primarily regulates signal amplitude rather than signalling time, whereas phosphatases enhance the signalling time, but lead to a decrease in signal amplitude Currently, very little is known regarding specific mechanisms that could be exploited to accelerate IFN signalling A key signalling pathway activated by type I IFN is the JAK ⁄ STAT pathway The Janus kinases JAK1 and tyrosine kinase (TYK2) are activated in response to ligand binding to the receptor, and these kinases phosphorylate signal transducers and activators of transcription STAT1 and STAT2 In contrast to other JAK ⁄ STAT signalling pathways, type I IFN signalling additionally involves interferon regulatory factor (IRF9 ⁄ p48), which, together with phosphorylated STAT1 ⁄ STAT2 dimers, forms the interferon-stimulated gene factor (ISGF3) complex ISGF3 is translocated to the nucleus and activates transcription of interferon-stimulated genes [6] Amongst others, this leads to induction of suppressor of cytokine signalling (SOCS) proteins that modulate termination of pathway activation Furthermore, the expression of IRF9 is induced, constituting a positive feedback loop IRF9 plays an important role in IFNa signalling [7] Increasing the amount of IRF9 by over-expression or prestimulation of cells with IFNc or interleukin-6 results in higher levels of transcription of IFN-stimulated genes [8–11] and an augmented antiviral response [11– 13] However, the specific impact of IRF9 on the dynamics of pathway activation, such as signalling speed and extent, remains to be identified To unravel the highly non-linear relations determining the timing and extent of signalling pathway activation, establishment of a dynamic pathway model is required Previous modelling approaches analysing the JAK ⁄ STAT pathway focussed on the impact of phosphatases and induced negative inhibitors [14–16] that primarily influence pathway termination Here, we 4742 have developed a mathematical model of IFNa signal transduction, including the known key players and feedback mechanisms, to identify systems properties that facilitate accelerated IFNa signalling Using this approach, IRF9 was predicted to not only increase the amount of active ISGF3, but also to accelerate signal transduction into the nucleus, as verified experimentally by IRF9 over-expression studies Moreover, the accelerated signal processing also resulted in faster and increased expression of target genes Thus, we identified IRF9 as a pivotal player for the speed and efficiency of IFNa signal transduction Results A data-based mathematical model of IFNa signalling with predictive power To investigate the dynamic properties of IFNa-mediated JAK ⁄ STAT activation, we developed a mathematical model comprising the known key components, feedback responses and constitutive regulatory mechanisms (Fig 1A, Table S1 and Appendix S1) Specifically, we included constitutive negative regulations by general phosphatases and protein inhibitor of activated STATs (PIAS), as well as constitutive degradation of receptors, IRF9 and mRNA Receptor dephosphorylation by SH2-containing phosphatase (SHP-2) was represented by a constant kinetic parameter independent of SHP-2 concentration, as changes in SHP-2 concentration were assumed to be negligible during the measured timescale Furthermore, the negative feedback loop of ISGF3-mediated SOCS induction was incorporated IRF9 synthesis was included as a positive feedback mechanism as IFN-dependent expression of IRF9 was experimentally observed within the relevant timescale (Fig 1B) Further features of the model were based on literature evidence: (i) IRF9 is constitutively bound to unphosphorylated STAT2 in the unstimulated system [17], (ii) unphosphorylated STAT1 and STAT2 shuttle constantly between the cytoplasm and nucleus, and nuclear import of STAT2 is increased by IRF9 binding [18], (iii) free IRF9 is mainly localized to the nucleus [19], and (iv) phosphorylated STAT1 ⁄ STAT2 heterodimers require IRF9 to bind IFN-stimulated response elements [20] IRF9 independent DNAbinding of STAT heterodimers was not considered, as these complexes bind to different DNA elements, the c activation sites that are involved in IFNc signalling Thus, in the model, no gene expression occurs in the absence of IRF9 (Table S1 and Appendix S1) For model calibration, kinetic parameters were taken from the literature (Table S2) or trained against FEBS Journal 277 (2010) 4741–4754 ª 2010 The Authors Journal compilation ª 2010 FEBS T Maiwald et al IRF9 accelerates IFNa signal transduction A B Fig Kinetic model of the IFNa signalling pathway (A) Simplified view of the model architecture is shown, with the activation of STAT proteins summarized as one reaction and omitting receptor endocytosis, constitutive IRF9 degradation and nuclear translocation of phosphorylated STAT1 ⁄ STAT2 heterodimers For details, see Table S1 Circle-headed lines, reaction catalysis; lines with perpendicular bars, reaction inhibition; single-dotted arrows, transcription mRNA to IRF9 ⁄ SOCS; TFBS, transcription factor binding site The scheme was generated using CellDesigner [53] (B) Dynamic behaviour of IFNa signalling Activation of cytoplasmic pJAK1, pSTAT1 and nuclear IRF9 measured by quantitative immunoblotting after stimulating Huh7.5 cells with 500 mL)1 IFNa To facilitate direct comparison, the same scale was used on the y axis for the experimental data as used for the model simulation Minor levels of basal phosphorylation could not be eliminated by starvation for h For phosphorylated JAK1, the background signal (defined as the signal for the immunoblot in areas other than the protein bands) was subtracted to better distinguish background noise from the actual signal The background level in the data for phosphorylated STAT proteins was low, so background corrections did not alter the results A representative plot is shown in each case, and the experiment was repeated at least three times (see Fig S3A for additional data) The error bars represent a technical relative error of 18%, derived from multiple measurements (Fig S1) Filled circles, experimental data; dashed lines, smoothing splines; a.u., arbitrary units The model simulation (line) for pJAK1, pSTAT1 and IRF9 was performed using COPASI [23] The simulations are within the range of data reproducibility FEBS Journal 277 (2010) 4741–4754 ª 2010 The Authors Journal compilation ª 2010 FEBS 4743 IRF9 accelerates IFNa signal transduction T Maiwald et al experimental data (Fig 1B), with all kinetic parameters being estimated within a physiologically meaningful range Model simulations start in the steady state, without any IFNa-dependent phosphorylation of signalling components The minor amounts of basal phosphorylation of STAT1 shown in the data (Fig 1B) were not considered for calibration This phosphorylation was not affected by h starvation, a time period that is sufficient for decay of IFNa signalling in Huh7.5 cells, and therefore appeared to be independent of a major IFNa stimulus The initial concentrations of STAT1, STAT2, JAK1, TYK2 and IRF9 were experimentally determined (Fig S2 and Table S3) Experiments were performed in Huh7.5 human hepatocarcinoma cells, which show dynamic behaviour comparable to that of primary human hepatocytes (Fig S3A,C) Previous studies suggested that approximately 30% of the total amount of STAT molecules is phosphorylated after IFNa stimulation [21] Finally, the major signalling peak was assumed to occur between 20 and 60 after IFNa stimulation The inclusion of various feedback mechanisms is necessary for analysis of their specific impact, but leads to an underdetermined system due to the number of unknown kinetic parameters However, the established model is consistent with the experimental data (Fig 1B), and permits qualitative predictions Identification of IRF9 as an accelerator of IFNa signalling To systematically identify components that control the timing and extent of IFNa signalling, a sensitivity analysis was performed with initial protein concentrations as input (Fig 2A) As output, both the peak time and the integrated response of the DNA-bound pSTAT1–pSTAT2–IRF9 (ISGF3) complex were analysed These system quantities that represent the speed and the extent of signal transduction were selected as they are likely to be crucial for an efficient antiviral response In contrast to other systems, for which control is widely distributed [22], only a few molecules controlled the systems behaviour of IFNa signalling Among the identified proteins, nuclear phosphatases had a pronounced effect, positively influencing the peak time, but greatly decreasing the integrated response, in line with previous theoretical studies [5] A higher ligand dose resulted in increased signal amplitude, but had only a minor effect on signal duration, as confirmed experimentally (Fig S3B) Of the signal transducers, STAT1 and IRF9 exerted the greatest control STAT1 had a positive effect on the integrated response, but negatively influenced the peak time IRF9 was the only factor that had a substantial positive effect on the peak time and also increased the integrated response A B Fig Sensitivity analysis for peak time and integrated responses Initial concentrations of all players were varied to calculate their control coefficient on the kinetic behaviour of the system (A) Sensitivity analysis using the original parameter set (B) Global sensitivity analysis using 998 parameter sets 4744 FEBS Journal 277 (2010) 4741–4754 ª 2010 The Authors Journal compilation ª 2010 FEBS T Maiwald et al To confirm that the results derived by the sensitivity analysis were not restricted to the original parameter set, the same approach was repeated using diverse parameter sets For this purpose, a random search implemented in the optimization task of the simulation software copasi [23] was used to vary all model parameters between ±50% of their original value As fitting constraints, the resulting kinetic behaviours had to reproduce the experimental data (Fig 1B) Using this process, 998 parameter sets matching the given criteria were obtained Further analysis of these data sets showed that the kinetic parameters could vary quite substantially and still reproduce the experimental data Therefore, it is important to not only examine parameter sensitivities at a single point in parameter space, but also to use a more global approach The obtained parameter sets were used for a global sensitivity analysis As shown in Fig 2B, the most sensitive component in both analyses was IRF9, supporting its central role The influence of IFNa on the time of the signalling peak differed: in contrast to the previous analysis, increasing IFN concentrations led to a delayed peak for most parameter sets (Fig 2B) However, in the experimental data the peak time for different interferon doses was comparable (Fig S3B), and thus it was reasonable to retain the original parameter set for further analysis In conclusion, major sensitivities were conserved throughout the parameter space, confirming that IRF9 has an important impact on the kinetic behaviour of the system, independent of specific parameter sets We performed additional model simulations to qualitatively examine the impact of large variations in IRF9 expression levels on the dynamic behaviour of IFNa signalling, as sensitivity analyses describe only small changes at single points within the parameter space Indeed, a major increase in IRF9 levels accelerated signal transduction from the cytoplasm to the nucleus, resulting in a greater amount of active ISGF3 in the nucleus at earlier time points (Fig 3A) Furthermore, our model predicted a steeper signalling decline after the peak for cells with elevated IRF9 levels To determine whether this effect is the result of up-regulated transcription of negative inhibitors (SOCS proteins), we removed SOCS1 induction in silico Without this negative feedback, signal termination was attenuated in the IRF9 over-expressing cells, and de novo IRF9 synthesis in wild-type cells accounted for enhanced signalling during the analysed timescale (Fig S4A) To experimentally validate the model predictions, IRF9 was stably over-expressed in Huh7.5 cells by lentiviral transduction (Fig S5) The phosphorylation kinetics of nuclear STAT1 and STAT2 in response to IRF9 accelerates IFNa signal transduction stimulation with 500 mL)1 IFNa were determined by quantitative immunoblotting (Fig 3B) In line with the model analysis, cells over-expressing IRF9 showed a higher and earlier activation peak in the nucleus as well as a steeper peak decline compared to wild-type cells To determine whether different IRF9 induction rates have similar effects, we varied the parameter for IRF9 synthesis in silico More rapid IRF9 synthesis resulted in enhanced IFNa signalling, and eliminating the positive feedback dampened the response (Fig S4B) Complete absence of IRF9 was predicted to lead to a reduction in the amounts of phosphorylated STAT proteins (Fig S4C) In principle, the effects of IRF9 could be achieved by two mechanisms IRF9 could decelerate dephosphorylation of activated STAT1 ⁄ 2, as phosphorylated STAT1 ⁄ complexes can only bind specifically to DNA in combination with IRF9, and DNA-bound STAT proteins are protected from nuclear phosphatase activity [24] This mechanism was implemented in the model As a potential alternative mechanism, nuclear import of phosphorylated STAT1 ⁄ could be increased upon interaction with IRF9 This is based on the observation that IRF9 possesses a strong constitutive nuclear localization signal recognized by a variety of importins, whereas the nuclear localization signal of phosphorylated STAT1 ⁄ heterodimers is only recognized by importin a-5 [25] Therefore, complexes harbouring both types of nuclear localization signal would have an increased chance of interacting with a matching importin, resulting in enhanced nuclear translocation kinetics We performed model simulations to assess the impact of both effects In silico analysis indicated that increasing IRF9-dependent nuclear import kinetics while neglecting IRF9-mediated phosphatase protection could not represent the experimental data However, a model describing the observed dynamics solely on the basis of IRF9-dependent phosphatase protection of DNA-bound ISGF3 was necessary and sufficient to reproduce the observed kinetic data (Fig 3C) Hence, our analysis identified IRF9 as crucial for both rapid and efficient IFNa-mediated signal transduction, and suggests an increased probability of DNA-binding of ISGF3 as the underlying mechanism Over-expression of IRF9 accelerates and increases IFNa-stimulated gene expression To test whether the accelerated and enhanced nuclear presence of phosphorylated STAT1 ⁄ proteins upon IRF9 over-expression resulted in altered gene activation FEBS Journal 277 (2010) 4741–4754 ª 2010 The Authors Journal compilation ª 2010 FEBS 4745 IRF9 accelerates IFNa signal transduction A T Maiwald et al B C Fig IRF9 controls the dynamics of IFNa signalling (A) Model prediction of IFNa-dependent pSTAT1 ⁄ pSTAT2 accumulation in the nucleus, which is equivalent to the kinetics of ISGF3 (pSTAT1-pSTAT2-IRF9) Simulations (lines) were performed for wild-type cells (wt) and for cells with 32-fold IRF9 over-expression (IRF9oe) (B) Experimental validation of the model prediction Wild-type Huh7.5 cells (wt) or Huh7.5 cells stably over-expressing IRF9 32-fold (IRF9oe) were stimulated with 500 mL)1 IFNa, and phosphorylation of nuclear STAT proteins was measured by quantitative immunoblotting (see Fig S5) To facilitate direct comparison, the same scale was used on the y axis for the experimental data as used for the model simulation Over-expression of a control protein (GFP) had no effect on the dynamic behaviour (Fig S6) The error bars represent a technical relative error of 18%, derived from multiple measurements (Fig S1) Filled circles, experimental data; dashed lines, smoothing splines; a.u., arbitrary units (C) In silico analysis of two potential mechanisms underlying the effect of IRF9 Simulation of DNA-bound ISGF3 and pSTAT1–pSTAT2 heterodimers in the nucleus, representing situations where IRF9 leads to increased nuclear import of pSTAT1–pSTAT2 or provides protection from phosphatase degradation kinetics, we analysed the expression of IFNa-stimulated genes by quantitative real-time PCR RNA levels of the antiviral genes protein kinase R (PKR) [26] and interferon stimulated gene 56 (ISG56) [27], as well as the genes encoding negative inhibitors SOCS1 [28] and ubiquitin specific peptidase (USP18), were determined at various time points for up to 24 h USP18 is a protease that cleaves the IFN-induced, ubiquitin-like modifier ISG15 from its target proteins [29], and was also reported recently to block phosphorylation of JAK1 [30] 4746 The examined genes were strongly induced by IFNa (Fig 4A) Interestingly, each gene analysed displayed different expression kinetics SOCS1 exhibited very fast induction followed by rapid repression USP18, on the other hand, displayed increased expression for up to 24 h Similar to USP18, the antiviral genes ISG56 and PKR showed prolonged up-regulation Interestingly, for all genes investigated, induction of gene expression was faster when IRF9 levels were elevated, consistent with the general mRNA induction predicted by the model (Fig S4D) For ISG56, SOCS1 and USP18, a FEBS Journal 277 (2010) 4741–4754 ª 2010 The Authors Journal compilation ª 2010 FEBS T Maiwald et al IRF9 accelerates IFNa signal transduction A α α B C –2 –2 –2 –2 α α Fig IRF9 controls the dynamics of IFNa-induced gene expression Huh7.5 wild-type cells stably transduced with an empty vector (wt) or IRF9-over-expressing cells (IRF9oe) were stimulated with 500 mL)1 IFNa, and RNA was extracted at the indicated time points (A) Quantitative real-time PCR analysis of four sample genes For each gene, the integrated response was calculated for early (4 h) and late (24 h) time points (B,C) Time-resolved microarray analysis performed with one replicate per time point (B) Kinetics of representative genes in Huh7.5 wild-type cells stably transduced with an empty vector (wt) or in IRF9-over-expressing cells (IRF9oe) (C) Scatter plot showing the difference in gene induction time and mean fold expression in control or IRF9-over-expressing cells Positive values indicate accelerated and augmented gene expression in IRF9oe cells The genes indicated show an increased relative expression upon IFNa stimulation in either wild-type or IRF9-over-expressing cells and have a difference in gene induction time of less than h (257 genes) SOCS1 and IRF9 are also included There is a clear trend for faster and augmented gene expression in the IRF9-over-expressing cells, as demonstrated by the positive slope of a linear model y = b + m*x, which was fitted to the data points The variables x and y indicate the time difference of activation and the mean differential expression, respectively The slope (m = 0.08; P value = 0.00006, t test) and intercept (b = 0.25; P value < 2e-16, t test) were estimated using the lm() function in R, version 2.11.1 (http://www.R-project.org) high IRF9 level resulted in an increased peak amplitude, but the peak amplitude was unaltered for PKR The integrated response was larger for each of the four genes when IRF9 was overexpressed, with a more pronounced difference during the first h (Fig 4A) To confirm that the observed effect was not restricted to the tested genes, we investigated the global induction of IFNa-stimulated genes using a timeresolved microarray (Fig 4B,C) For data analysis, we selected genes that showed an increased relative FEBS Journal 277 (2010) 4741–4754 ª 2010 The Authors Journal compilation ª 2010 FEBS 4747 IRF9 accelerates IFNa signal transduction T Maiwald et al expression in both wild-type and IRF9-over-expressing cells upon stimulation with IFNa A gene ontology analysis using DAVID [31] showed that these 284 genes are related to immune and virus response as well as antigen processing and presentation, as expected (Table S4) Gene expression time series were characterized with respect to the differences in mean fold expression and temporal regulation (see Experimental procedures) There was an overall positive correlation between the level of gene expression and the expression kinetics: genes that were more strongly up-regulated in the IRF9-over-expressing cells were also induced earlier Remarkably, this was true for the majority of the genes in the IRF9-over-expressing cells compared to wild-type cells (160 out of 257) One exception was IRF9 itself, as it could not be induced much beyond the already high expression level in over-expressing cells Taken together, these data demonstrate that an elevated amount of IRF9 not only results in higher levels of transcription, but also in accelerated expression of IFNa target genes Discussion Here we describe the development of a comprehensive model of IFNa signalling and its experimental validation The aim of our modelling approach was to qualitatively predict the interplay between various molecules and feedback mechanisms, requiring consideration of all known pathway components Obviously, a mathematical model that includes all known negative and positive feedbacks represents an underdetermined system as it contains too many parameters that cannot be reliably estimated from the experimental data To verify the predictive power of our model, a sensitivity analysis of 998 parameter sets describing the experimental data was performed, and the results obtained were compared to those for the original parameter set (Fig 2A,B) The observations were comparable, indicating that they are intrinsic properties of the model structure The robustness of sensitivity against single parameter changes has been described by Gutenkunst et al [32], suggesting that model predictions are reasonable when they are derived from collective fits and can only be improved by precise and complete measurements of all kinetic parameters According to the sensitivity analysis, IRF9 is a decisive factor in IFNa signalling as it represents the only component that both augments and accelerates antiviral gene expression This was confirmed by in silico analysis simulating IRF9 overexpression and subsequently experimentally validated (Fig 3A,B) Our approach focused on the qualitative 4748 analysis of mechanisms that determine signalling speed and the extent of pathway activation The model was used to design experiments that would be most informative, and the experimental data were in qualitative agreement with the model predictions, although some deviations in quantitative terms were observed, such as smaller differences between peaks in experimental data compared to the model prediction Importantly, the main characteristics of signalling kinetics could be validated Increasing the initial IRF9 concentration by overexpression resulted in higher levels of phosphorylated STAT proteins in the nucleus, and consequently augmented expression of IFNa target genes This is consistent with previous reports describing the impact of IRF9 on the amount of active ISGF3 [8–11] However, in contrast to previous studies, we analysed the IFNa response in a time-resolved manner We showed that enhanced IFNa-induced gene expression not only applies at isolated time points but also for the overall integrated response In addition, we demonstrated that IRF9 is also crucial for the speed of the IFNa response, with higher IRF9 levels accelerating signal transduction and gene expression Theoretically, these effects of IRF9 could be achieved by two mechanisms: by increased nuclear import of the signal transducers or by IRF9-mediated protection from nuclear phosphatases Model analysis excluded accelerated nuclear import and indicated protection from nuclear phosphatases as the underlying mechanism These mechanisms are difficult to address experimentally, but, by disentangling the involved processes, the mathematical modelling approach provides important insights for further studies Both in wildtype and IRF9 overexpressing cells, the analysed genes showed different expression kinetics Possible mechanisms explaining this behaviour are varying production rates and differences in mRNA stability Furthermore, IFN-stimulated transcription factors may account for the sustained activation of certain genes, constituting positive feedback loops Regulatory networks in which individual genes are regulated by a cascade of multiple transcription factors were recently shown to play an important role in the antiviral response [33] Here, SOCS1 expression was rapidly activated and repressed, whereas the activation of USP18 was sustained These observations are in agreement with a recent report stating that SOCS1 is responsible for early inhibition of IFNa signalling, whereas USP18 mediates late inhibition [34] The sustained response could be explained by an additional positive feedback As shown in previous studies, expression of the IRF9 gene is regulated through a FEBS Journal 277 (2010) 4741–4754 ª 2010 The Authors Journal compilation ª 2010 FEBS T Maiwald et al positive feedback loop by the IFN-stimulated transcription factor CCAAT-enhancer-binding protein b (C ⁄ EBP-b) [35] The prolonged up-regulation of other IFN-stimulated genes could be mediated by IRF7, which is produced in response to IFNa and is able to bind promoters of IFN-inducible genes [36] The existence of positive feedback mechanisms could be a general design principle in IFN signalling to enhance the antiviral response In contrast to oncogenic pathways, augmented IFN signalling is not detrimental to an organism, as it does not lead to uncontrolled cell proliferation, but rather to apoptosis [37] While we were performing experiments in Huh7.5 cells to validate the model predictions, the enhanced IFN response as an effect of increased IRF9 levels was also demonstrated in various cell types, suggesting a general mechanism [11,12] Individual cells in a cell population may elicit different responses [38] Nevertheless, we aimed to develop a population-based model, as the IFNa response primarily occurs at the tissue level, comprising a population of individual cells Additionally, regulation of gene expression is likely to differ between individuals Therefore, variations in either IRF9 initial concentrations or the IRF9 induction rate may be one reason for patient-to-patient variations in responses to IFNa therapy As demonstrated by model simulations, not only higher IRF9 levels, but also faster IRF9 synthesis, significantly augment early IFN signalling Consequently, the balance between positive and negative feedback loops (e.g IRF9 ⁄ SOCS) may be decisive As a rapid IFN response could be crucial for a viral infection [2], the IRF9 level in a cell may play a pivotal role In line with this, IRF9 was shown to be targeted by several viruses in order to interfere with the cellular antiviral response, as demonstrated for human papillomavirus [39,40], reovirus [41], adenovirus [12], hepatitis B virus [42] and human cytomegalovirus [43] Moreover, it was shown that elevated IRF9 levels increase the antiviral response [10,12] Additionally, it was recently reported that IRF9 is necessary for the anti-proliferative activity of IFNa, as only RNAi against IRF9, but not against STAT1, inhibited IFNa-mediated apoptosis [44] In conclusion, our modelling approach, in combination with experimental analysis, confirmed that elevated IRF9 starting levels are a crucial determinant for amplified IFNa-mediated antiviral signalling, and additionally identified the IRF9 level to be vital for a rapid response As a key regulator shaping the early phase of IFNa signalling, IRF9 represents an appealing target for innovative therapeutic approaches IRF9 accelerates IFNa signal transduction Experimental procedures Cells and time-course experiments Huh7.5 cells (a kind gift from C M Rice, Laboratory of Virology and Infectious Disease, Rockefeller University, NY) were cultivated in Dulbecco’s modified Eagle’s medium (DMEM; Invitrogen, Carlsbad, CA, USA) supplemented with 10% fetal bovine serum (Invitrogen) and 1% penicillin ⁄ streptomycin (Invitrogen) One day before commencement of a time-course experiment, 1.7 · 106 cells were seeded into cm dishes Prior to stimulation with IFNa, the cells were washed three times by removing the culture medium and replacing it with DMEM, and afterwards cultivated in starvation medium for h [DMEM supplemented with mgỈmL)1 BSA (Sigma-Aldrich, St Louis, MO, USA) and 25 mm Hepes pH 7.0 (Invitrogen)] To stimulate cells, human leukocyte IFNa (R&D Systems, Minneapolis, MN, USA) was added to the medium to a final concentration of 500 mL)1 For each time point, the contents of one dish were lysed using 1% Nonidet P-40 lysis buffer (1% Nonidet P-40, 150 mm NaCl, 20 mm Tris pH 7.4, 10 mm NaF, mm EDTA pH 8.0, mm ZnCl2 pH 4.0, mm MgCl2, mm Na3VO4, 10% glycerol, freshly supplemented with lgỈmL)1 aprotinin and 200 lgỈmL)1 4-(2-aminoethyl)-benzensulfonylfluoride), and the lysates were used for immunoprecipitation or directly analysed by SDS ⁄ PAGE For cell fractionation, cells were lysed using 0.4% Nonidet P-40 cytoplasmic lysis buffer (0.4% Nonidet P-40, 10 mm Hepes pH 7.9, 10 mm KCl, 0.1 mm EDTA, 0.1 mm EGTA, freshly supplemented with lgỈmL)1 aprotinin, 200 lgỈmL)1 4-(2-aminoethyl)-benzensulfonylfluoride, mm dithiothreitol, mm NaF, 0.1 mm Na3VO4), and vortexed for 10 s After centrifugation (1 at 17 900 g, °C), supernatants were used as the cytoplasmic fraction and the nuclear pellet was resuspended in nuclear lysis buffer (20 mm Hepes pH 7.9, 25% glycerin, 400 mm NaCl, mm EDTA, mm EGTA, freshly supplemented with lgỈmL)1 aprotinin, 200 lgỈmL)1 4-(2-aminoethyl)-benzensulfonylfluoride, mm dithiothreitol, mm NaF, 0.1 mm Na3VO4) by repeated vortexing The suitability of the procedure was verified by confirming the presence of the nuclear marker protein poly [ADP-ribose] polymerase and the cytoplasmic marker protein Eps15 in the corresponding fractions Primary human hepatocytes were isolated and cultivated in serum-free Williams’ Medium E (Biochrom AG, Berlin, Germany) [45] The viability of isolated hepatocytes was determined by trypan blue exclusion Only cell preparations with a viability > 80% were used for experiments The isolated cells were seeded on collagen type I-coated culture dishes at a density of 1.2 · 105 cells per cm2 Tissue samples from human liver resection were obtained from patients undergoing partial hepatectomy for metastatic liver tumor secondary to colorectal cancer Experimental FEBS Journal 277 (2010) 4741–4754 ª 2010 The Authors Journal compilation ª 2010 FEBS 4749 IRF9 accelerates IFNa signal transduction T Maiwald et al procedures were performed according to the guidelines of the charitable state-controlled foundation Human Tissue and Cell Research, with the patient’s informed consent [46], as approved by the local ethical committee The day after isolation, the primary hepatocytes were cultivated for days in Williams Medium E supplemented with mm l-glutamine (Invitrogen), 100 nm dexomethasone (Sigma) and 1% penicillin ⁄ streptomycin (Invitrogen) Prior to stimulation with IFNa, the cells were washed three times by removing the culture medium and replacing it with Williams Medium E and afterwards cultivated in starvation medium for h (Williams Medium E supplemented with mm l-glutamine) The time-course experiment was performed according to the protocol for Huh7.5 cells Quantitative immunoblotting For immunoprecipitation, the lysates were incubated with anti-JAK1 serum (Upstate Millipore, Billerica, MA, USA) and anti-TYK2 polyclonal IgGs (Upstate Millipore) and protein A–Sepharose beads (GE Healthcare, Chalfont, NJ, United Kingdom) For cellular lysates, protein concentrations were measured using the BCA assay (Pierce, Thermo Fisher Scientific Inc., Waltham, MA, USA) Immunoprecipitated proteins, cytoplasmic (70–80 lg) or nuclear lysates (45 lg) were loaded in a randomized manner on a 10% SDS ⁄ polyacrylamide gel as described previously [47], separated by electrophoresis and transferred to poly(vinylidene difluoride) (STATs, IRF9) or nitrocellulose membranes (JAK1, TYK2) Proteins were immobilized using Ponceau S solution (Sigma-Aldrich) followed by immunoblotting analysis using anti-phosphotyrosine monoclonal IgG 4G10 (Upstate Millipore) for the phosphorylation signal of immunoprecipitated JAK1 and TYK2, anti-phospho-STAT1 IgG (Cell Signaling Technologies, Danvers, MA, USA), anti-phospho-STAT2 IgG (Cell Signaling Technologies) and anti-IRF9 IgG (BD Bioscience, Franklin Lakes, NJ, USA) Antibodies were removed by treating the blots with b-mercaptoethanol and SDS Reprobing was performed using anti-JAK1 (Cell Signaling Technologies), anti-TYK2 (Upstate Millipore), anti-STAT1 and anti-STAT2 (Upstate Millipore) For normalization, IgGs against calnexin (Stressgen, Enzo Life Sciences, Plymouth Meeting, PA, USA) and poly [ADP-ribose] polymerase (Roche, Basel, Switzerland) were used Secondary horseradish peroxidaseconjugated IgGs (anti-rabbit HRP, anti-goat HRP, protein A HRP) were purchased from GE Healthcare Immunoblots were incubated with enhanced chemiluminescence (ECL) or ECL Advance substrate (Amersham), and signals were detected using a CCD camera (LumiImager F1 workstation; Roche) This ensured measurements were in the linear range, avoiding saturation effects Data were quantified using lumianalyst 3.1 software (Roche) Quantitative immunoblotting data were processed using gelinspector software [48] The following normalizers were 4750 used: GST-TYK2DC or GST-JAK1DN for pJAK1, JAK1, pTYK2 and TYK2, calnexin for pSTAT1, STAT1, pSTAT2, STAT2 and IRF9, in the cytoplasm and poly [ADP-ribose] polymerase for pSTAT1, STAT1, pSTAT2, STAT2 and IRF9 in the nucleus To smooth spline estimates of the data, MATLAB (http://www.mathworks.com) csaps-splines with a smoothness between 0.7 and 0.9 were used Plasmids, recombinant proteins and lentiviral transduction Recombinant proteins were used as normalizers and as references to determine the number of molecules per cell To generate N-terminally GST- and SBP-tagged constructs, the pGEX system (GE Healthcare) and the derived pSBPEX system, in which glutathione S-transferase (GST) was replaced by strepatavidin binding tag (SBP), were used To construct GST-TYK2DC, the N-terminal FERM and SH2 domain of TYK2 (amino acid 1–586) were amplified by PCR, using human TYK2 cDNA (Open Biosystems, Huntsville, AL, USA, cDNA number 4591726) as template The resulting fragment was cloned into the BamHI–EcoRI site of pGEX2T GST-JAK1DN was generated by amplifying human JAK1 cDNA (a kind gift from I Behrmann, Life Sciences Research Unit, University of Luxembourg) from the SH2 domain to the C-terminus (amino acids 421–1150) The resulting fragment was cloned into the BamHI–EcoRI site of pGEX-2T SBP-STAT1DN was generated by amplifying human STAT1 cDNA (a kind gift from H Hauser, Helmholtz Centre for Infection Research, Braunschweig, Germany), to yield a product corresponding to amino acids 131–750 The resulting fragment was cloned into the BamHI–EcoRI site of pSBPEX-2T SBP-STAT2DN was generated by amplifying human STAT2 cDNA (a kind gift from H Hauser, Helmholtz Centre for Infection Research, Braunschweig Germany), resulting in a product corresponding to amino acids 133–851 The resulting fragment was cloned into the BamHI–EcoRI site of pSBPEX-2T To express the recombinant proteins, the expression plasmids were transformed into competent Escherichia coli BL21(DE3) CodonPlusRIL (Stratagene, Agilent, Santa Clara, CA, USA), and proteins were purified using glutathione–Sepharose beads for GST-tagged proteins, or streptavidin–Sepharose beads for SBP-tagged proteins GST-tagged IRF9 was kindly provided by Rainer Zawatzky (German Cancer Research Center, Division of Viral Transformation Mechanisms, Germany) For over-expression studies, IRF9 cDNA was cloned into the lentiviral expression vector pRRLSIN.cPPT.PGKGFP.WPRE (deposited in the non-profit plasmid repository Addgene, number 12252) by PCR amplification of pCMVSport6-IRF9 (Open Biosystems) and digestion with BamHI and SalI, replacing the gene encoding GFP (green fluorescent protein) and resulting in pRRLSIN.cPPT.PGK-IRF9.WPRE To generate pRRLSIN.cPPT.PGK-MCS.WPRE, a multiple cloning site with the restriction sites BmtI, PacI, SmaI, PstI, FEBS Journal 277 (2010) 4741–4754 ª 2010 The Authors Journal compilation ª 2010 FEBS T Maiwald et al NdeI and BclI was introduced into the BamHI–SalI locus of pRRLSIN.cPPT.PGK-GFP.WPRE, replacing the GFP gene Lentiviral expression vectors in combination with the packaging plasmids pRSVRev (Addgene plasmid 12253) and pMDLg ⁄ pRRE (Addgene plasmid 12251) and the envelope plasmid pMD2.G (Addgene plasmid 12259) were transiently transfected into 293T cells using the calcium phosphate method For this, a mix of plasmid DNA and CaCl2 (2.5 m) was added to 2· HBS (280 mm NaCl, 50 mm Hepes, 1.5 mm Na2HPO4, pH 7.05), forming a precipitate The suspension was transferred dropwise to the cells, with the culture medium with 25 lm chloroquine Around 16 h after transfection, the medium was changed to mL DMEM, 10% fetal bovine serum, 1% penicillin ⁄ streptomycin in a 10 cm dish, and the virus-containing supernatant was collected 48 h after transfection and filtered through a 0.45 mm filter For over-expression experiments, · 105 Huh7.5 cells were seeded into six-well plates, and 24 h later cells were infected with 500 lL of virus supernatant diluted in 500 lL medium containing lgỈmL)1 Polybrene (Sigma-Aldrich) The medium was changed h post-infection, and cells were subsequently allowed to proliferate Flow cytometry To analyse intracellular IRF9 expression, cells were fixed with 4% paraformaldehyde in NaCl ⁄ Pi, washed with NaCl ⁄ Pi and 0.3% BSA, and then permeabilized with 0.1% saponin (Sigma-Aldrich), 0.3% BSA and NaCl ⁄ Pi The cells were incubated with anti-IRF9 IgGs (Santa Cruz, CA, USA, antibody 10793) as primary antibody and anti-rabbit Alexa Fluor 680 (Invitrogen) as secondary antibody, and analysed by flow cytometry using a FACSCalibur (Becton Dickinson, Franklin Lakes, NJ, USA) As a control, cells were incubated with the secondary antibody only RNA analysis Huh7.5 wild-type cells or cells over-expressing IRF9 were starved for h and stimulated using IFNa for 0, 1, 2, 3, 4, 8, 12 or 24 h, or left untreated as a control Per time point and cell-type, total RNA of cells in three independent dishes was isolated using an RNeasy Plus Mini Kit (Qiagen, Hilden, Germany) The RNA was used for the quantitative real-time PCR and microarray analysis To generate cDNA, lg of total RNA was transcribed using a QuantiTect reverse transcription kit (Qiagen) Quantitative PCR was performed using a LightCycler 480 (Roche) in combination with the hydrolysis-based Universal Probe Library (UPL) platform (Roche) Primer pairs were generated using the automated UPL Assay Design Center (Roche) Crossing point values were calculated using the second-derivative maximum method in the lightcycler 480 Basic Software (Roche) PCR efficiency correction was performed for each PCR set-up individually based on a IRF9 accelerates IFNa signal transduction dilution series of template cDNA Relative concentrations were normalized using hypoxanthine-guanine phosphoribosyltransferase (HPRT) as reference gene The microarray analysis was performed using the Affymetrix (Santa Clara, CA, USA) Human GeneChip 1.0 ST array system according to the manufacturer’s instructions Microarrays used in this paper will be uploaded to the Gene Expression Omnibus (http://www.ncbi.nlm.gov/ geo) Raw microarray data were processed using the R environment together with the Aroma.affymetrix R package [49] Normalization was performed using the robust multichip average (RMA) [50] for background adjustment, quantile normalization and summarization Subsequent gene annotation was performed using the Human Gene ST 1.0 annotation file (HuGene-1_0-st-v1.na30.hg19.transcript.csv) from Affymetrix, and the 22 118 transcript cluster IDs that have an assigned gene were used for further analysis The fold expression of each gene was calculated relative to the untreated controls at h for wild-type Huh7.5 and IRF9over-expressing cells, respectively The quality of the Human Gene ST 1.0 arrays was checked based on the normalized unscaled standard error and relative log expression of all chips [51] The normalized unscaled standard error indicates the standard error estimate distributions obtained for each gene on each array when performing RMA analysis Normalization ensures that the median standard error across all arrays is for each gene Problematic chips are identified on the basis of an increased median residuum The relative log expression compares the expression level for each chip with the median expression of all chips in the experiment From a biological point of view, the expression of only a small proportion of genes changes across experimental conditions Hence, the chip-wise gene expression distribution should be centered around the same values with a small inter-quartile range Large deviations of relative log expression box plots from zero and large inter-quartile ranges of the expression distributions indicate problematic chips Normalized unscaled standard error and relative log expression analyses (Fig S7) show that all microarrays have acceptable error bounds Estimation of gene induction times We estimated the gene induction times by fitting the mRNA fold expression g(t) to a logistic function: gtị ẳ a 1 ỵ expb ctị Parameters a, b and c were estimated using a Levenberg– Marquardt non-linear least-squares algorithm The start of gene regulation was defined as the time of maximal change in acceleration of the fitted function: i.e the up-regulation time for each gene was defined as the time of maximal acceleration of the logistic function g(t), which is calculated FEBS Journal 277 (2010) 4741–4754 ª 2010 The Authors Journal compilation ª 2010 FEBS 4751 IRF9 accelerates IFNa signal transduction T Maiwald et al from the first maximum of the third derivative of g(t) [33] The mean difference in gene expression time series was calculated from the mean of the fold expression differences at the respective experiment time points Modelling The IFNa model was created and graphical outputs of kinetic behaviours of the model were produced using Copasi [23] All reactions are defined as ordinary differential equations Time course data were computed using the deterministic LSODA algorithm [52] provided by Copasi LSODA solves systems dy ⁄ dt = f with a dense or banded Jacobian when the problem is stiff, but automatically selects between non-stiff (Adams) and stiff (BDF) methods It uses the non-stiff method initially, and dynamically monitors data in order to decide which method to use A detailed overview of the specific reactions defined in the model is provided in Table S1, and kinetic parameters are given in Table S2 The SBML version of the model is provided in Appendix S1, and will be deposited in the Biomodels Database (http://www.ebi.ac.uk/biomodels) Sensitivity analyses of the model were performed by numerical differentiation of simulation results on the basis of finite differences Obtaining several valid parameter sets was achieved by using the random search algorithm implemented in the optimization task of the simulation software copasi All model parameters were varied randomly between ±50% of their original value For selection, the resulting kinetic behaviours had to match the experimental data Matching was determined on the basis of several criteria First, the amount of maxima in the kinetic behaviour had to be identical Second, the initial and final concentration, as well as the time and height of the peak for each simulated species, had to fit into a ±20% threshold of the measured data Of 10 000 evaluated parameter sets, approximately 1000 valid sets were retrieved Acknowledgements We thank Katrin Hubner, Marcel Schilling and ă Simone Rosenberger (German Cancer Research Center, Division of Viral Transformation Mechanisms, Germany) for fruitful discussions, and Sebastian Bohl, Thomas Hofer (German Cancer Research Center, ă Modeling of biological systems, Germany) and Jens Timmer [University of Freiburg, FRIAS (Freiburg Institute for advanced studies), Germany] for critically reading the manuscript We are grateful to Rainer Zawatzky, Ralf Bartenschlager (Heidelberg University, Department of Molecular Virology, Germany), Charles M Rice, Iris Behrmann, Hansjorg Hauser and Didier ă ´ Trono (Ecole polytechnique federale de Lausanne EPFL, Lab of virology and genetics, Switzerland) for 4752 the supplied reagents We thank Sandra Manthey and Maria Saile for excellent technical assistance This work was funded by the German Ministry of Education and Research through the FORSYS centre ViroQuant and by the Excellence Initiative of the German Federal and State Governments References Manns MP, McHutchison JG, Gordon SC, Rustgi VK, Shiffman M, Reindollar R, Goodman ZD, Koury K, Ling M & Albrecht JK (2001) Peginterferon alfa-2b plus ribavirin compared with interferon alfa-2b plus ribavirin for initial treatment of 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ordinary differential equations SIAM J Sci Stat Comput 4, 136–148 53 Kitano H, Funahashi A, Matsuoka Y & Oda K (2005) Using process diagrams for the graphical representation of biological networks Nat Biotechnol 23, 961–966 Supporting information The following supplementary material is available: Fig S1 Determination of the technical error inherent in the immunoblotting technique Fig S2 Protein quantification of key pathway components Fig S3 Additional data describing the dynamics of IFNa signalling in Huh7.5 cells and primary human hepatocytes Fig S4 In silico analysis of changes in feedback control and their effects on kinetic behaviours of various components Fig S5 Characterization of IRF9-over-expressing Huh7.5 cells Fig S6 Additional time course data for IRF9-overexpressing Huh7.5 cells Fig S7 Microarray quality assessment using normalized unscaled standard error and relative log expression plots Table S1 Equation overview of the kinetic model describing the IFNa signalling pathway Table S2 Overview of kinetic parameters of the presented model and the sources used as reference values Table S3 Initial concentrations of model species Table S4 Gene ontology analysis of the 284 genes with an increased relative expression upon IFNa stimulation in both wild-type and IRF9-over-expressing cells Appendix S1 Model description This supplementary material can be found in the online version of this article Please note: As a service to our authors and readers, this journal provides supporting information supplied by the authors Such materials are peer-reviewed and may be re-organized for online delivery, but are not copy-edited or typeset Technical support issues arising from supporting information (other than missing files) should be addressed to the authors FEBS Journal 277 (2010) 4741–4754 ª 2010 The Authors Journal compilation ª 2010 FEBS ... increasing ligand concentrations result in earlier maximal pathway activation and an increase in signal amplitude [3,4] Furthermore, alterations in the activity of a kinase or phosphatase may affect... modelling approach, in combination with experimental analysis, confirmed that elevated IRF9 starting levels are a crucial determinant for amplified IFNa-mediated antiviral signalling, and additionally... the IRF9 level to be vital for a rapid response As a key regulator shaping the early phase of IFNa signalling, IRF9 represents an appealing target for innovative therapeutic approaches IRF9 accelerates