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

There is a News and Views associated with this document.

Amnon Amir¹, Oren Kobiler², Assaf Rokney², Amos B Oppenheim² & Joel Stavans¹

Article highlights

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.

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.

Full figure and legend (298K)Figures & Tables index

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.

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 defined as the ratio between the standard deviation over the mean time of different stages along the lytic cascade. The value of 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 between two adjacent points is less than 0.02 in all cases (see Materials and methods).

Full figure and legend (106K)Figures & Tables index

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).

MORE ARTICLES LIKE THIS

These links to content published by NPG are automatically generated.