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Noise in timing and precision of gene activities in a genetic cascade

2007; Springer Nature; Volume: 3; Issue: 1 Linguagem: Inglês

10.1038/msb4100113

1744-4292

Amnon Amir, Oren Kobiler, Assaf Rokney, Amos B. Oppenheim, Joel Stavans,

Evolution and Genetic Dynamics

Article13 February 2007Open Access Noise in timing and precision of gene activities in a genetic cascade Amnon Amir Amnon Amir Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot, Israel Search for more papers by this author Oren Kobiler Oren Kobiler Department of Molecular Genetics and Biotechnology, The Hebrew University-Hadassah Medical School, Jerusalem, Israel Search for more papers by this author Assaf Rokney Assaf Rokney Department of Molecular Genetics and Biotechnology, The Hebrew University-Hadassah Medical School, Jerusalem, Israel Search for more papers by this author Amos B Oppenheim Amos B Oppenheim Department of Molecular Genetics and Biotechnology, The Hebrew University-Hadassah Medical School, Jerusalem, Israel Search for more papers by this author Joel Stavans Corresponding Author Joel Stavans Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot, Israel Search for more papers by this author Amnon Amir Amnon Amir Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot, Israel Search for more papers by this author Oren Kobiler Oren Kobiler Department of Molecular Genetics and Biotechnology, The Hebrew University-Hadassah Medical School, Jerusalem, Israel Search for more papers by this author Assaf Rokney Assaf Rokney Department of Molecular Genetics and Biotechnology, The Hebrew University-Hadassah Medical School, Jerusalem, Israel Search for more papers by this author Amos B Oppenheim Amos B Oppenheim Department of Molecular Genetics and Biotechnology, The Hebrew University-Hadassah Medical School, Jerusalem, Israel Search for more papers by this author Joel Stavans Corresponding Author Joel Stavans Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot, Israel Search for more papers by this author Author Information Amnon Amir1, Oren Kobiler2, Assaf Rokney2, Amos B Oppenheim2 and Joel Stavans 1 1Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot, Israel 2Department of Molecular Genetics and Biotechnology, The Hebrew University-Hadassah Medical School, Jerusalem, Israel *Corresponding author. Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 76100, Israel. Tel.: +97 289 342 615; Fax: +97 289 344 109; E-mail: [email protected] Molecular Systems Biology (2007)3:71https://doi.org/10.1038/msb4100113 PDFDownload PDF of article text and main figures. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Biological developmental pathways require proper timing of gene expression. We investigated timing variations of defined steps along the lytic cascade of bacteriophage λ. Gene expression was followed in individual lysogenic cells, after induction with a pulse of UV irradiation. At low UV doses, some cells undergo partial induction and eventually divide, whereas others follow the lytic pathway. The timing of events in cells committed to lysis is independent of the level of activation of the SOS response, suggesting that the lambda network proceeds autonomously after induction. An increased loss of temporal coherence of specific events from prophage induction to lysis is observed, even though the coefficient of variation of timing fluctuations decreases. The observed temporal variations are not due to cell factors uniformly dilating the timing of execution of the cascade. This behavior is reproduced by a simple model composed of independent stages, which for a given mean duration predicts higher temporal precision, when a cascade consists of a large number of steps. Evidence for the independence of regulatory modules in the network is presented. Synopsis Stochasticity or noise, an inherent property of all biological networks, is often manifested by different phenotypic behaviors in clonal populations of cells (Raser and O'Shea, 2005). Noise can arise, for instance, from sources such as cell–cell variations in small numbers of regulatory molecules or from the stochastic nature of molecular interactions (Paulsson, 2005). Besides affecting the number of molecules in a cell, noise may also lead to variability in timing of particular events along a given pathway. In this work, we studied temporal noise in the induction cascade of phage lambda. Infection of a bacterial cell by bacteriophage lambda can lead to two different fates (Ptashne, 2004; Dodd et al, 2005; Oppenheim et al, 2005): the phage can either multiply inside the host leading to its eventual lysis and the generation of progeny virions (the lytic pathway) or, alternatively, it can integrate its genome into the host's genome (prophage state), replicating passively with the latter (the lysogenic pathway). The prophage state is highly stable, being maintained by a phage-encoded repressor, which shuts off phage genes leading to lytic growth. However, the lytic pathway can be induced in a lysogenic cell, through the activation of the bacterial SOS response to DNA damage (Little, 1996), for example by UV irradiation. Once activated, the SOS response results in cleavage of the lambda repressor, leading to expression of the phage early and late genes, and culminating in the lysis of the host cell. The lambda induction cascade has been extensively characterized over the years. We built upon this knowledge to tap the cascade at different points and quantitatively analyze the progressive loss of temporal coherence between cells, as different stages along the cascade are executed, following synchronous induction. Using time-lapse microscopy, we monitored the time of activation of early and late genes in individual cells using lambda pR and pR′-tR′ promoter-GFP fusions, respectively, by means of reporter plasmids, and finally the time of lysis. Sample results are shown in Figure 2. At low UV levels (5 J/m2), the network exhibits bistability: only approximately 40% of the bacteria lyse, whereas the others continue to divide, following a lag period. At high UV levels (20 J/m2), almost all bacteria lyse. We found that the timing of events in cells that lyse is independent of UV dose. This is in contrast to the known behavior of the SOS network (Friedman et al, 2005), indicating that these two networks proceed independently. Following induction, a surprising shutoff in the activity of the pR promoter is observed in all cells (see Figure 2). Furthermore, the data show that whereas early genes are expressed in all cells irrespective of cell fate, late genes are expressed only in the lysing cells, indicating that similar to infection, a specific commitment checkpoint is operating. To characterize the temporal variability in a cell population, we used the coefficient of variation, defined as the non-dimensional ratio of the standard deviation and the mean time of occurrence of a particular event. We studied the changes in both standard deviation and coefficient of variation in timing of various events along the lambda induction cascade, from the expression of the early genes to the ultimate lysis of the cells. As shown in Figure 6, the absolute noise as measured by the standard deviation increases as the cascade progresses. In contrast, the coefficient of variation, which measures variability relative to the time of occurrence, decreases. Simple theoretical considerations described in the text yield a necessary and sufficient condition for a monotonic decrease in the coefficient of variation. Higher temporal precision can be achieved when the cascade is composed of a large number of fast steps. Further support for the independence of network modules is furnished by a correlation analysis of the times of occurrence of different steps along the lytic cascade. This analysis also indicates that the variability in lysis time is not due to differences in the global rate of cascade progression, but probably to random fluctuations in the execution time of the various cascade stages. Indeed, phage lambda gene expression architecture is well known to have evolved from a number of independent regulatory modules (Hendrix, 2003). Introduction Cells respond to given stimuli by the activation of specific regulatory pathways, often characterized by a cascaded architecture of gene expression in which the product of one gene regulates the expression of others. Notable examples include MAP kinase pathways (Lamb 1996; Gustin et al, 1998), developmental programs (Davidson et al, 2002) and transcriptional cascades (Lee et al, 2002). A central issue in considering the proper functioning of genetic cascades is how noise in gene activity, due to sources such as the stochastic nature of gene expression (Berg, 1978; Rigney, 1979; McAdams and Arkin, 1997) and the fluctuating number of molecular components, propagates along a cascade (Thattai and van Oudenaarden, 2002; Becskei et al, 2005; Hooshangi et al, 2005; Pedraza and van Oudenaarden, 2005; Rosenfeld et al, 2005) and affects the fidelity of information transfer. This consideration has been the object of intense recent scrutiny, and has been tackled in depth in the context of synthetic, engineered transcriptional cascades (Hooshangi et al, 2005; Rosenfeld et al, 2005), natural cascades (Colman-Lerner et al, 2005) as well as in theoretical studies (Thattai and van Oudenaarden, 2002). These studies revealed that noise in the activity of one gene affects fluctuations in downstream gene expression and that noise amplification results in loss of synchrony among a cell population. Timing is an important aspect in the regulation of biological cascades (see e.g., Kalir et al, 2001). Recently, noise in the timing of cell-cycle start has been studied in single yeast cells (Bean et al, 2006). The temporal variability in the orchestrated series of events that take place when Escherichia coli cells, lysogenic with bacteriophage λ, are induced allows the study of timing noise in a natural genetic cascade. The lysis–lysogeny decision of bacteriophage λ, a paradigm for the operation of developmental genetic networks, is composed of interlocked positive and negative feedback loops and is regulated by both phage and bacterial factors (Ptashne 2004; Dodd et al, 2005; Oppenheim et al, 2005). The lysogenic or off state of the cascade is maintained with high stability by multimers of the lambda repressor (CI), repressing both the phage pL and pR promoters (Dodd et al, 2001). A combination of negative and positive feedback mechanisms keeps the concentration of CI at about 150–200 copies per cell (Dodd et al, 2004). Prophage induction is triggered by DNA damage. Upon encountering DNA damage, replication forks stall and single-stranded DNA tracts form, activating the cell's SOS network (Little 1996; Friedman et al, 2005). This network, responsible for the repair/bypass of DNA lesions, consists of about 40 genes whose expression is downregulated by the LexA repressor. Polymerization of the RecA protein on the single-stranded DNA tracts endows RecA with a coprotease function that promotes the cleavage of both LexA and CI, activating both the SOS response and the lambda induction network. The lambda lytic cascade proceeds through three stages: early, delayed early and late. In the early stage, CI degradation results in the expression of Cro and N functions from the pR and pL promoters, respectively (Figure 1). In the delayed early stage, N assists in overriding terminators, allowing RNA polymerase (RNAP) to extend both transcripts beyond the cro and N genes, leading among others to the expression of Q. In the late stage, the Q protein modifies RNAP initiating transcription from the pR′ late promoter, to allow for transcription beyond the tR′ terminator, leading to expression of the late genes, which encode for phage morphogenesis functions and host cell lysis proteins (see Oppenheim et al, 2005 for details). Similar three-stage architectures are found in diverse phages, such as the virulent phage T4 (Endy et al, 2000). Figure 1.Schematic model of lambda induction. Lambda promoters are colored in green and genes in gray. The lambda induction cascade is carried out in three stages. In the early stage, UV irradiation results in a decrease of CI levels, activating the pR and pL promoters at time tpR , leading to the expression of the cro and N genes. During the delayed early stage, RNAP is modified so as to override transcriptional terminators, allowing the continuation of both transcripts and the expression of CII, O, P and Q. During the late stage, the Q protein modifies RNAP, initiating transcription from the pR′ late promoter at time tpR′-tR′, to become resistant to transcription terminators present downstream, and allowing the expression of late genes that encode proteins for phage morphogenesis and host cell lysis. During the late stage of the cascade, the late gene products assemble phage virions and lyse the host at time tlysis (Oppenheim et al, 2005). Download figure Download PowerPoint In the studies described below, we have activated the lytic pathway cascade by the induction of DNA damage in lysogenic cells with UV light, monitoring the timing of CI repressor inactivation and the onset of activity of the late gene activator Q as well as lysis in individual cells by time-lapse fluorescence microscopy. Our findings shed light on how different events along the lytic cascade are timed and organized, and how individual cell behavior depends on UV irradiation. Results Timing of the lytic cascade in individual cells following prophage induction Lysogenic bacteria, harboring either pR-GFP or pR′-tR′-GFP reporter plasmids, were induced by UV irradiation. The first reporter fusion, pR-GFP, is under the direct repression of the CI repressor, and therefore monitors the inactivation of CI and reports on levels of expression from the earliest stage after induction of the cascade (see Figure 1). The second reporter fusion, pR′-tR′-GFP, monitors the expression of late lytic genes transcribed from the pR′ promoter, expression that is enabled by the antitermination activity of the Q protein at tR′. Typical snapshots of cells during derepression of pR-GFP and activation of pR′-tR′-GFP fusions after irradiation with 20 J/m2 are shown in Figure 2A, upper and lower panels, respectively. As the snapshots illustrate, in both cases the fluorescence increases monotonically with time, and pR-GFP is expressed earlier than pR′-tR′. Eventually, all cells undergo lysis at this level of irradiation, and disappear from the field of view. The fluorescence profiles of pR and pR′-tR′ as a function of time from individual cells in the snapshots, and the corresponding promoter activity profiles (see Materials and methods) are shown in Figures 2B and C, respectively (see Supplementary movies). Figure 2.Induction of individual lysogens following irradiation with 20 J/m2. (A) Snapshots of cells harboring the pR-GFP (top panels) and pR′-tR′-GFP (bottom panels) reporter plasmids undergoing induction taken at the times shown after irradiation. Some cells lyse and disappear from the field of view. Cell silhouettes were determined from dark-field microscopy images. (B) Fluorescence profiles of pR-GFP (blue) and pR′-tR′-GFP (red) of individual cells including those viewed in the top and bottom panels in (A), respectively, as a function of time. The sharp drop in every profile is due to lysis. (C) pR (blue) and pR′-tR′ (red) promoter activity profiles of individual cells derived from the fluorescence data in (B) as a function of time. Download figure Download PowerPoint The pR and pR′-tR′ promoters exhibit different behaviors. Whereas fluorescence from pR-GFP commences at about 20 min following induction and saturates, that of pR′-tR′-GFP starts at ∼50 min and increases monotonically until lysis (Figure 2B). The time lag between the onset of expression from pR and pR′-tR′ as reported by the fusions, ∼35 min, cannot be accounted for by the time it takes RNAP to transcribe the 6 kbp tract between pR and the Q gene ( 2.5; see Materials and methods). Here, τ is the time of occurrence of a particular event such as the onset time of promoter expression or the lysis time in a given cell. As downstream stages take longer to appear, differences in the precision of execution time between stages within a cascade are best compared by the coefficient of variation η=σ/〈τ〉. This ratio is analogous to the one used to quantify noise in protein and mRNA concentrations (Paulsson and Ehrenberg, 2001; Swain et al, 2002; Swain 2004). The values of η for the different stages in the cascade are plotted in Figure 6B. The salient feature of this plot is the decrease in η as the cascade progresses. Hence, the relative precision in timing increases, as the execution of the cascade progresses. Figure 6.Statistical analysis of fluctuations along the lytic cascade. (A) Standard deviation as a measure of width of histograms in Figure 5. Blue and red points represent data from cells harboring the pR-GFP or pR′-tR′-GFP reporter plasmids, respectively. Data were derived from at least four experimental repeats. Error bars represent one standard deviation (see Materials and methods). (B) Coefficient of variation η defined as the ratio between the standard deviation over the mean time of different stages along the lytic cascade. The value of η for lysis time is similar in experiments with pR-GFP or pR′-tR′-GFP plasmids (blue and red, respectively) and in experiments without reporter plasmids (cyan). The statistical significance for decrease in η between two adjacent points is less than 0.02 in all cases (see Materials and methods). Download figure Download PowerPoint Correlation of lysis time with phage gene expression The expression of late lytic genes such as S and R is obligatory for lysis to take place. Late lytic genes belong to the same operon, whose transcription starts at the pR′ promoter and requires Q activity for gene expression. We therefore inquired to what extent the lysis time tlysis and the onset time of pR′-tR′ expression tpR′-tR′ correlate. A statistical analysis (see Materials and methods) indicates that these two times are indeed correlated (Spearman correlation coefficient r=0.6137, P-value=2 × 10−12), indicating that cells in which Q activity is manifested early lyse earlier and vice versa. One can imagine two sources for the observed cell–cell variations in lysis time: they could arise from random fluctuations of the various components of the induced lysis network, or alternatively, from cell factors uniformly changing the rate of execution of the cascade, speeding it up in some cells and slowing it down in others. Tapping the lytic cascade at an intermediate point, such as the onset of Q activity, offers the opportunity to discriminate between these two cases. Lysis time can be looked at as a sum of two intervals, namely, the time elapsed until the onset of Q activity tpR′-tR′, and the interval Δt=tlysis−tpR′-tR′ in which late gene activation takes place, culminating in lysis. If indeed a major source of variability is the different rates at which the lytic cascade progresses in different cells, one would expect uniform dilation of timelines, which is different between cells, as depicted schematically in Figures 7A and B. If on the other hand, variations in tlysis are due to random fluctuations in the cascade components, one would expect the behavior depicted in Figures 7C and D. The actual measured values of Δt versus tpR′-tR′, plotted in Figure 7E show more similarity to Fi