Multi‐layered stochasticity and paracrine signal propagation shape the type‐I interferon response
2012; Springer Nature; Volume: 8; Issue: 1 Linguagem: Inglês
10.1038/msb.2012.17
ISSN1744-4292
AutoresUlfert Rand, Melanie Rinas, Johannes Schwerk, Gesa Nöhren, Melanie Linnes, Andrea Kröger, Michael Floßdorf, Kristóf Kály‐Kullai, H. Häuser, Thomas Höfer, Mario Köster,
Tópico(s)Immunotherapy and Immune Responses
ResumoArticle22 May 2012Open Access Multi-layered stochasticity and paracrine signal propagation shape the type-I interferon response Ulfert Rand Ulfert Rand Department of Gene Regulation and Differentiation, Helmholtz Centre for Infection Research, Braunschweig, Germany Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ) and BioQuant Center, Heidelberg, Germany Search for more papers by this author Melanie Rinas Melanie Rinas Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ) and BioQuant Center, Heidelberg, Germany Search for more papers by this author Johannes Schwerk Johannes Schwerk Department of Gene Regulation and Differentiation, Helmholtz Centre for Infection Research, Braunschweig, Germany Search for more papers by this author Gesa Nöhren Gesa Nöhren Department of Gene Regulation and Differentiation, Helmholtz Centre for Infection Research, Braunschweig, Germany Search for more papers by this author Melanie Linnes Melanie Linnes Department of Gene Regulation and Differentiation, Helmholtz Centre for Infection Research, Braunschweig, Germany Search for more papers by this author Andrea Kröger Andrea Kröger Department of Gene Regulation and Differentiation, Helmholtz Centre for Infection Research, Braunschweig, Germany Search for more papers by this author Michael Flossdorf Michael Flossdorf Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ) and BioQuant Center, Heidelberg, Germany Search for more papers by this author Kristóf Kály-Kullai Kristóf Kály-Kullai Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ) and BioQuant Center, Heidelberg, Germany Search for more papers by this author Hansjörg Hauser Hansjörg Hauser Department of Gene Regulation and Differentiation, Helmholtz Centre for Infection Research, Braunschweig, Germany Search for more papers by this author Thomas Höfer Corresponding Author Thomas Höfer Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ) and BioQuant Center, Heidelberg, Germany Search for more papers by this author Mario Köster Corresponding Author Mario Köster Department of Gene Regulation and Differentiation, Helmholtz Centre for Infection Research, Braunschweig, Germany Search for more papers by this author Ulfert Rand Ulfert Rand Department of Gene Regulation and Differentiation, Helmholtz Centre for Infection Research, Braunschweig, Germany Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ) and BioQuant Center, Heidelberg, Germany Search for more papers by this author Melanie Rinas Melanie Rinas Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ) and BioQuant Center, Heidelberg, Germany Search for more papers by this author Johannes Schwerk Johannes Schwerk Department of Gene Regulation and Differentiation, Helmholtz Centre for Infection Research, Braunschweig, Germany Search for more papers by this author Gesa Nöhren Gesa Nöhren Department of Gene Regulation and Differentiation, Helmholtz Centre for Infection Research, Braunschweig, Germany Search for more papers by this author Melanie Linnes Melanie Linnes Department of Gene Regulation and Differentiation, Helmholtz Centre for Infection Research, Braunschweig, Germany Search for more papers by this author Andrea Kröger Andrea Kröger Department of Gene Regulation and Differentiation, Helmholtz Centre for Infection Research, Braunschweig, Germany Search for more papers by this author Michael Flossdorf Michael Flossdorf Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ) and BioQuant Center, Heidelberg, Germany Search for more papers by this author Kristóf Kály-Kullai Kristóf Kály-Kullai Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ) and BioQuant Center, Heidelberg, Germany Search for more papers by this author Hansjörg Hauser Hansjörg Hauser Department of Gene Regulation and Differentiation, Helmholtz Centre for Infection Research, Braunschweig, Germany Search for more papers by this author Thomas Höfer Corresponding Author Thomas Höfer Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ) and BioQuant Center, Heidelberg, Germany Search for more papers by this author Mario Köster Corresponding Author Mario Köster Department of Gene Regulation and Differentiation, Helmholtz Centre for Infection Research, Braunschweig, Germany Search for more papers by this author Author Information Ulfert Rand1,2,‡, Melanie Rinas2,‡, Johannes Schwerk1, Gesa Nöhren1, Melanie Linnes1, Andrea Kröger1, Michael Flossdorf2, Kristóf Kály-Kullai2, Hansjörg Hauser1, Thomas Höfer 2 and Mario Köster 1 1Department of Gene Regulation and Differentiation, Helmholtz Centre for Infection Research, Braunschweig, Germany 2Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ) and BioQuant Center, Heidelberg, Germany ‡These authors contributed equally to this work *Corresponding authors. Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ) and BioQuant Center, Im Neuenheimer Feld 280, Heidelberg 69120, Germany. Tel.:+49 6221 5451380; Fax:+49 6221 5451487; E-mail: [email protected] of Gene Regulation and Differentiation, Helmholtz Centre for Infection Research, Inhoffenstr. 7, Braunschweig 38124, Germany. Tel.:+49 531 61815091; Fax:+49 531 61815002; E-mail: [email protected] Molecular Systems Biology (2012)8:584https://doi.org/10.1038/msb.2012.17 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info The cellular recognition of viruses evokes the secretion of type-I interferons (IFNs) that induce an antiviral protective state. By live-cell imaging, we show that key steps of virus-induced signal transduction, IFN-β expression, and induction of IFN-stimulated genes (ISGs) are stochastic events in individual cells. The heterogeneity in IFN production is of cellular—and not viral—origin, and temporal unpredictability of IFN-β expression is largely due to cell-intrinsic noise generated both upstream and downstream of the activation of nuclear factor-κB and IFN regulatory factor transcription factors. Subsequent ISG induction occurs as a stochastic all-or-nothing switch, where the responding cells are protected against virus replication. Mathematical modelling and experimental validation show that reliable antiviral protection in the face of multi-layered cellular stochasticity is achieved by paracrine response amplification. Achieving coherent responses through intercellular communication is likely to be a more widely used strategy by mammalian cells to cope with pervasive stochasticity in signalling and gene expression. Synopsis Live-cell imaging and mathematical modelling of the type-I interferon response to viral infection reveal that multiple layers of the cellular response are stochastic events in individual cells, while paracrine propagation of the IFN signal results in reliable antiviral protection. Heterogeneous expression of IFN-β after viral infection is an intrinsic property of the host cell. Individual cell behaviour is inherently stochastic on multiple levels, encompassing virus-induced signal transduction, IFN-β gene expression, and the induction of an antiviral gene programme by secreted IFN-β. Nevertheless, paracrine response amplification can result in reliable and efficient antiviral protection. These results show how pervasive stochasticity in signalling and gene regulation in mammalian cells can be controlled by cell-to-cell communication. Introduction The type-I interferon (IFN) system provides a powerful defence against viral infections (Kunzi and Pitha, 2003; Sadler and Williams, 2008; Takeuchi and Akira, 2009). Pathogen recognition receptors, such as RIG-I, stimulate an intracellular signalling cascade that leads to the activation of nuclear factor κB (NF-κB) and IFN regulatory factors (IRFs) 3 and 7 (Brennan and Bowie, 2010). Subsequent induction of the IFN-β gene and other type-I IFNs is a hallmark of the early response to infection (Theofilopoulos et al, 2005). Upon secretion and binding of type-I IFNs to their specific receptor, the Jak/STAT signalling pathway is activated to reprogram gene expression (Brierley and Fish, 2005). By activation of a wide set of these genes, IFNs act directly by establishing an antiviral state and indirectly through recruiting innate and adaptive immune cells. On the one hand, IFN production must be tightly regulated to avoid harmful inflammation and autoimmune disease (Trinchieri, 2010). On the other hand, pathogenic viruses inhibit the expression of IFNs or IFN-stimulated genes (ISGs) by diverse mechanisms, indicating that maintaining the efficiency of the IFN system in the face of these viral challenges might be an overriding objective of its evolution (Versteeg and Garcia-Sastre, 2010). Therefore, the finding that only a fraction of cells in a virus-infected cell population expresses IFNs has been surprising (Zawatzky et al, 1985; Hu et al, 2007). Biochemical studies have suggested that stochastic IFN induction results from host cell-intrinsic causes, such as a mechanism of IFN gene induction (Hu et al, 2007; Apostolou and Thanos, 2008) or cellular heterogeneity in expression of the viral sensor RIG-I (Hu et al, 2011). Alternatively, heterocellularity of IFN expression could be caused by the infecting virus (Chen et al, 2010; Killip et al, 2011). It is conceivable that several factors might shape the dynamics of IFN induction, depending on host cell type and virus. Live imaging provides a suitable tool to study dynamics and variability at single-cell resolution (Spiller et al, 2010), but, such an analysis has not yet been carried out for the IFN system. To understand the functional consequences of cell-to-cell variability in IFN induction, the cellular response to IFNs, particularly the expression of ISGs, must also be defined. Two recent quantitative analyses of IFN-stimulated signalling have modelled the dynamics at the cell-population level (Maiwald et al, 2010) and considered effects of cell-to-cell variability (Levin et al, 2011). However, the expression of ISGs and the resulting cell fate have not yet been characterized at single-cell level. Here, we study in living cells and at single-cell resolution both type-I IFN induction and the cellular response to secreted IFN. Using fluorescent reporters based on bacterial artificial chromosomes (BACs) and chimeric transcription factors (Supplementary Figure S1), we imaged successive key steps of IFN induction and response in a prototypical model system, the infection of murine cells in culture with a single-stranded RNA virus. To link our single-cell data to antiviral protection at the cell-population level, we developed a mathematical model based on these data and tested its predictions experimentally. Our results show that cell-to-cell heterogeneity is a pervasive feature of the IFN system. This heterogeneity manifests itself not only in the virus-induced expression of IFN but also in the IFN-induced protective response. It is to a large part due to cell-intrinsic stochasticity occurring in three key steps: virus-induced signalling, IFN gene induction, and expression of ISGs. In contrast to gene-expression noise described in bacteria and yeast that causes protein levels to fluctuate around a mean value in individual cells (Raj and van Oudenaarden, 2008), the stochasticity found here in the IFN system is an all-or-nothing phenomenon: cells switch on signalling or gene expression or not, and the switching cells do so at widely variable time points. Nevertheless, we find that a reliable antiviral response is achieved through powerful paracrine propagation of the signal. Thus, our results show that the functional dynamics of the IFN response must be understood in terms of the collective spatio-temporal dynamics of stochastically reacting single cells. Results Cell-to-cell heterogeneity in IFN induction In order to monitor authentic IFN-β expression in real time, we stably transfected murine fibroblasts with a BAC-encoded reporter expressing TurboGFP under the control of the Ifnb promoter (IFN-β–tGFP). A representative cell clone was selected showing stable expression of the reporter. These cells were infected with Newcastle Disease Virus (NDV), which replicates and induces IFN in the cells via the double-stranded RNA sensor RIG-I (Kato et al, 2005), without viral interference with this pathway (Childs et al, 2007). As the newly generated viral particles cannot re-infect the mouse cells (Rott, 1979), this system allows us to study in a controlled manner the IFN induction elicited by the primary infection. To quantitatively determine the kinetics and dose response of IFN-β–tGFP expression, reporter cells were infected with NDV and subjected to flow cytometry (Figure 1A). We observed a rise in IFN-β–tGFP-positive cells that faithfully reflected the accumulation of type-I IFNs in the supernatant (Figure 1B). The fraction of IFN producers increased nearly linearly over a broad range of NDV dose, whereas the average expression level already reached ∼70% of its maximal value at very low NDV dose (Figure 1C). The frequency of IFN-β expression was very similar for different clones, showing the highly reproducible behaviour of the BAC-based IFN-β–tGFP reporter (Supplementary Figure S2). Thus, the production of IFN-β in response to viral infection is controlled by the fraction of responding cells. Figure 1.Quantitative and temporal heterogeneity of IFN-β induction. A BAC-based reporter construct in which the IFN-β gene is replaced by TurboGFP was integrated into murine NIH3T3 fibroblasts. A cell clone with a stable integration of the BAC and representative response towards NDV infection was used (error bars represent ±s.d. of triplicates). (A) Induction of IFN-β–tGFP expression upon NDV infection. IFN-β reporter cells were infected with NDV for 1 h. Expression of tGFP was determined 24 h post-infection by flow cytometry. Representative dot plots are shown for 10, 20, und 40 HAU/ml NDV. (B) IFN-β reporter reflects endogenous IFN production. IFN-β–tGFP expression frequencies after infection with 40 HAU/ml NDV were detected at various time points by flow cytometry. Frequencies were plotted against time points post-infection (black circles) and compared with titres of type-I IFN in the supernatant (grey rhombs). (C) IFN-β expression frequency increases with viral titre. Reporter cells infected with increasing concentrations of NDV (HAU/ml) were subjected to flow cytometry 24 h post-infection. Frequency of IFN-β–tGFP expression (circles) following infection with 1, 2, 5, 10, 20, 40, 80, and 100 HAU/ml and the geometric mean of their fluorescence intensity (triangles) are presented. Source data is available for this figure in the Supplementary Information. Source data for Figure 1B [msb201217-sup-0001-SourceData-S1.txt] Source data for Figure 1C [msb201217-sup-0002-SourceData-S2.txt] Download figure Download PowerPoint To examine whether IFN expression correlates with viral replication, we jointly measured the viral protein, hemagglutinin-neuraminidase (HN), and IFN-β–tGFP by flow cytometry. In agreement with previous findings (Kumagai et al, 2009; Rehwinkel et al, 2010), only cells with replicating virus expressed IFN-β–tGFP. However, a large proportion of cells with replicating virus did not activate the Ifnb promoter. Strikingly, we observed no correlation between the extent of replication and the fraction of IFN-β–tGFP-expressing cells (Figure 2A and B; Supplementary Figure S3). These observations suggest that the presence of replicating virus in a cell is necessary but not sufficient to induce IFN-β. Figure 2.Viral replication is necessary but not sufficient to induce IFN-β expression. (A) Fractional IFN-β expression among productively infected cells. Reporter cells were infected with 40 HAU/ml NDV for 1 h. IFN-β–tGFP reporter expression and intracellular NDV HN protein was measured by flow cytometry at indicated time post-infection. Dot plots show IFN-β–tGFP expression among productively infected (NDV HN+) cells at indicated time post-infection. (B) Separate kinetics of viral replication and IFN-β expression. Frequency of IFN-β–tGFP (black circles) and NDV HN expression (grey squares) over time. (C) Unresponsiveness is not caused by the absence of inducing viral RNA. NDV-infected (80 HAU/ml) IFN-β–tGFP reporter cells were separated into GFP+ and GFP− fractions. Total RNA was isolated and transfected into naive IFN-β–tGFP reporter cells (lower graphs). RNA from non-infected cells served as a control (upper graph). The frequency of IFN-β–tGFP-expressing cells 20 h after transfection is presented. Source data is available for this figure in the Supplementary Information. Source data for Figure 2B [msb201217-sup-0003-SourceData-S3.txt] Download figure Download PowerPoint As an alternative explanation of heterogeneous IFN expression, it has been suggested that defective viruses are primarily responsible for inducing IFN during parainfluenza virus type 5 (PIV5) infection while during normal replication of PIV5 effective pathogen-associated molecular patterns (PAMPs) are not produced or exposed (Killip et al, 2011). Therefore, we examined the intracellular RNA of non-responding virus-infected cells for its ability to induce IFN in naive cells (Figure 2C; Supplementary Figure S4). RNA from non-responding cells induced IFN to a comparable extent as RNA from responding cells. This finding shows that the viral RNA in IFN-producing and non-producing cells is equally capable of inducing IFN and thus the heterogeneity of IFN-β induction occurs despite the presence of IFN-inducing viral RNA. Time-lapse microscopic data revealed that the onset of IFN-β expression after infection of cells with NDV varied strongly among the IFN-β producers, with cell-to-cell differences as large as 20 h (Figure 3A; Supplementary Figure S11; Supplementary Movie S1). While the increase in viral load results in earlier onset, the relative temporal variability of IFN-β–tGFP expression differs only slightly. To dissect whether this variability is due to variable time points of infection or reflects cell-intrinsic properties, we bypassed viral infection by liposome-transfecting the cells with the dsRNA analogue poly I:C (liposome-free delivery of poly I:C did not lead to IFN-β expression). Among the IFN producers and independent of the poly I:C concentration, the onset time of IFN-β expression varied strongly and was quantitatively comparable to the case of viral infection, as seen by the same order of magnitude of the coefficient of variation (CV) (Figure 3A). Furthermore, unchanged temporal variability was obtained when synchronizing viral entry by a temperature shift (Supplementary Figure S5). Our data show that the IFN-β gene is induced with widely varying time delays in the producing cells that are not due to variable infection times. In summary, we conclude that the cell-to-cell heterogeneity in IFN-β expression is predominantly of cellular origin. Figure 3.Temporal variability in cellular IFN-β induction. (A) IFN-β expression onset in single cells. Variability of response timing is virus-independent. IFN-β–tGFP reporter cells infected for 1 h with indicated concentrations of NDV or transfected with poly I:C at given concentrations were subjected to time-lapse microscopy (15 min picture intervals). Distribution of tGFP expression onset over time (scatter plot, n=456 (NDV), n=140 (poly I:C)) and CVs are shown. (B) Synchronous activation of NF-κB and IRF-7. NIH3T3 cell clone stably expressing the fusion proteins IRF-7–CFP and NF-κB/p65–YFP were infected with 80 HAU/ml NDV for 1 h and subjected to time-lapse microscopy. Fluorescence pictures for CFP and YFP were taken every 20 min. Subcellular localization of IRF-7–CFP (left column) and p65–YFP (right column) at indicated time after infection. The diagram shows relative nuclear fluorescence for IRF-7–CFP and p65–YFP from sister cells. (C) Synchronicity is independent of response time. IRF-7–CFP and p65–YFP initial nuclear translocation were determined in individual cells and plotted against each other (n=65). Coloured dots indicate the frequency of data points. (D) Expression delay of an individual cell. NIH3T3 cell clone stably expressing IRF-7–CFP together with IFN-β–tGFP were infected with 80 HAU/ml NDV. Fluorescence pictures for CFP and GFP were taken every 20 min. Subcellular localization of IRF-7–CFP (left column) and IFN-β–tGFP expression (right column) at indicated time after infection. Graphs show relative IRF-7–CFP nuclear fluorescence and tGFP intensity. Tsig: time interval between infection and IRF-7–CFP nuclear translocation. Tgen: time interval between IRF-7–CFP nuclear translocation and onset of IFN-β–tGFP gene expression. (E) Response variation at distinct stages of IFN induction. The starting times for IRF-7–CFP nuclear translocation were plotted against the times of IFN-β–tGFP expression for individual cells (n=315). Source data is available for this figure in the Supplementary Information. Source data for Figure 3A [msb201217-sup-0004-SourceData-S4.txt] Source data for Figure 3C [msb201217-sup-0005-SourceData-S5.txt] Source data for Figure 3E [msb201217-sup-0006-SourceData-S6.txt] Download figure Download PowerPoint Both virus-induced signal transduction and Ifnb gene expression are sources of heterogeneity To analyse mechanistically how cell-to-cell heterogeneity in IFN induction arises, we monitored the activation of the key transcription factors NF-κB and IRF-7 in dual reporter cells, representing the IKKα/β/γ–NF-κB and IKKε/TBK-1–IRF-3/IRF-7 pathways downstream of the dsRNA sensor RIG-I. Fluorescent protein-tagged NF-κB and IRF-7 (p65–YFP and IRF-7–CFP) were localized predominantly in the cytoplasm in uninfected cells and accumulated in the nucleus after NDV infection (Figure 3B). Nuclear translocation of the two transcription factors occurred at the same time in a given cell, but the joint translocation time varied widely between cells (Figure 3C). This finding was corroborated by antibody staining of endogenous IRF-3 and NF-κB (p65), showing that the activation of both factors upon NDV infection or poly I:C stimulation correlated in individual cells (Supplementary Figure S6). Thus, strong cell-to-cell variability arises in the shared upstream activation pathway of NF-κB and IRF-7. To relate IFN induction to the activation of the latent transcription factors we used dual reporter cells expressing the IRF-7–CFP fusion protein and the transcriptional reporter IFN-β–tGFP (Figure 3D; Supplementary Figure S7). We found that the majority of cells translocating IRF-7 to the nucleus also activated the Ifnb promoter (91% at 80 HAU/ml NDV) (Figure 3E). Only few cells (9%) did not express IFN-β–tGFP upon IRF-7 nuclear accumulation, showing the same distribution of IRF-7 translocation times as the IFN-β-expressing cells (Figure 3E, bottom part). No IFN-β–tGFP induction was observed without preceding IRF-7 nuclear accumulation. Thus, IRF-7 nuclear translocation is strictly required for IFN-β expression. The tight coupling of the two events also shows the validity of the reporters used. In agreement with the results in Figure 3C, there was strong temporal heterogeneity in IRF-7 translocation between the cells (signalling delay from viral infection to IRF-7 translocation Tsig=11.7±4.0 h). The interval between IRF-7 translocation and the onset of IFN-β–tGFP gene expression also varied considerably (gene expression delay, Tgen=3.4±1.5 h; note that the CVs are similar, σsig/Tsig=0.34, σgen/Tgen=0.44). These quantitative data show that both cytoplasmic signalling from viral entry to the activation of latent transcription factors and induction of IFN-β expression cause strong heterogeneity in IFN-β production. Sister-cell analysis reveals cell-intrinsic stochasticity Cell-to-cell variability can arise from the intrinsic noise of biochemical reactions and from extrinsic factors, such as differences in cell-cycle stage or cellular environment (Elowitz et al, 2002; Maheshri and O'Shea, 2007; Paixão et al, 2007; Raj and van Oudenaarden, 2008; Snijder and Pelkmans, 2011). To minimize extrinsic cell-to-cell differences, we analysed sister cells after division (Spencer et al, 2009). Cells that divided after the 1 h period of infection were followed. IRF-7 activation in sister cells occurred mainly asynchronously, differing by >2 h in ∼50% of cell pairs (Figure 4A). The time between IRF-7–CFP activation and IFN-β–tGFP expression correlated even less in sister cells (Figure 4B), consistent with the previously described stochastic transcription of the Ifnb gene (Apostolou and Thanos, 2008). The coefficient of determination r2 was 0.6 for Tsig and 0.34 for Tgen, indicating that 40% of the variability in signalling and 66% of the variability in Ifnb gene expression are uncorrelated between sister cells and thus provide an estimate for the cell-intrinsic stochasticity. To examine whether different viral replication kinetics in sister-cell pairs are a source of variability, we subjected cells to poly I:C stimulation. Also, under these conditions cells displayed largely uncorrelated IRF-7 signalling and IFN-β gene expression (Figure 4E and F), with similar coefficients of determination (r2=0.54 for Tsig and 0.11 for Tgen) as with viral infection. Taken together, these findings show that cell-intrinsic stochasticity is a strong source of cell-to-cell heterogeneity in IFN-β expression. Figure 4.Temporal variability of signalling events in sister cells reveals stochasticity. NIH3T3 cell clone stably expressing IRF-7–CFP or IRF-7–TagRFP together with IFN-β–tGFP were infected with 80 HAU/ml NDV for 1 h (A–D, n=38 sister-cell pairs) or transfected with poly I:C (5 μg/ml) (E–H, n=36 sister-cell pairs) and subjected to time-lapse microscopy (20 min interval). Sister-pair analysis was carried out for IRF-7 nuclear translocation and IFN-β expression onset. Coloured dots indicate the frequency of data points. (A, E) Time of IRF-7 nuclear translocation onset (Tsig) in sister-cell pairs. (B, F) Time intervals between IRF-7 nuclear translocation and IFN-β–tGFP expression onset (Tgen) among sister cells. (C, G) Time elapsed from cell division to IRF-7 nuclear translocation (Tsig−Tdiv) of sister cells. (D, H) Time elapsed from cell division to IFN-β–tGFP expression (Tsig+Tgen−Tdiv). Source data is available for this figure in the Supplementary Information. Source data for Figure 4ABCD [msb201217-sup-0007-SourceData-S7.txt] Source data for Figure 4EFGH [msb201217-sup-0008-SourceData-S8.txt] Download figure Download PowerPoint We checked whether the observed differences between sister cells relate to their time of division. For this purpose, we plotted the time differences between sister cells (ΔTsig and ΔTgen) versus the time elapsed since cell division. The very weak correlations argue against strong control of heterogeneity by the cell cycle (Figure 4C and D for NDV infection and Figure 4G and H for poly I:C stimulation). Taken together, the sister-cell analysis indicates a role for both ‘extrinsic’ variability between cells and cell-intrinsic stochasticity. The cell-intrinsic component is strong, accounting for approximately half of the variability in the kinetics of antiviral signalling and IFN-β induction in individual cells. This intrinsic stochasticity provides a rationale for the lack of correlation of IFN-β expression with the extent of viral replication (cf. Figure 2A). Antiviral protection is an IFN concentration-dependent switch in individual cells The intrinsic stochasticity indicates that the responsiveness of IFN-β induction towards virus is not maximized (i.e., many cells with replicating virus do not express IFN-β or do so only very late after infection). Therefore, we asked how IFN-β production translates into antiviral protection. We chose IRF-7 as a prototypical ISG (Honda et al, 2005) and measured its expression upon IFN-β stimulation in cells stably transfected with a BAC-encoded IRF-7–mCherry fusion gene (Supplementary Movie S2). Flow-cytometric analysis showed a digital pattern of IRF-7–mCherry levels, with distinct expressing and non-expressing subpopulations, where the expressing subpopulation increased with extracellular IFN-β concentration (Figure 5A). The binary IRF-7 response was consistently found for several IRF-7–mCherry clones (Supplementary Figure S8); it was not caused by competition of cells for IFN-β, as IFN-β was still detectable in the supernatant for >30 h (Supplementary Figure S9). Figure 5.Bimodal antiviral response towards IFN. A BAC-based reporter construct containing mCherry fused to the C-terminus of the chromosomal IRF-7 gene was integrated into NIH3T3 cells. Experiments were performed with a cell clone exhibiting a stable integration of the BAC and a representative response towards IFN. (A) Binary dose- and time-dependent IRF-7–mCherry expression. IRF-7–mCherry reporter cells were stimulate
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