Heterogeneous Timing of Gene Induction as a Regulation Strategy.

In response to environmental changes, cells often adapt by up-regulating genes to synthesize proteins that generate a benefit in the new environment. Several such cases of gene induction have been reported where the timing was heterogeneous, with some cells responding early and others responding late, although the microbial population was genetically homogeneous and the environment was well mixed. Here, we explore under which conditions heterogeneous timing of gene induction could be advantageous for the population as a whole. We base our study on a mathematical model that accounts for the cost of protein synthesis in terms of resources, which cells must provide immediately, whereas the associated benefit accumulates only slowly over the protein lifetime. Due to this delayed benefit, gene induction can be a risky investment, if resources are scarce and the environment fluctuates rapidly and unpredictably. Unprofitable gene induction then depletes the remaining limiting resource needed for maintenance of cell viability. We show that whenever gene induction is associated with a transient risk but beneficial in the long run, the stochastic timing of gene induction maximizes the reproductive success of a population. In particular, in an environment of stochastic periods of famine and feast, an optimum emerges from a trade-off between short-term growth, favoring rapid and homogeneous responses, and long-term survival, favoring a broadly heterogeneous response. Our analysis suggests that the optimal variability of induction times is just as large as the time required for the amortization of the initial investment into protein synthesis.

Size Laws and Division Ring Dynamics in Filamentous Escherichia coli cells.

Our understanding of bacterial cell size control is based mainly on stress-free growth conditions in the laboratory [1-10]. In the real world, however, bacteria are routinely faced with stresses that produce long filamentous cell morphologies [11-28]. Escherichia coli is observed to filament in response to DNA damage [22-25], antibiotic treatment [11-14, 28], host immune systems [15, 16], temperature [17], starvation [20], and more [18, 19, 21], conditions which are relevant to clinical settings and food preservation [26]. This shape plasticity is considered a survival strategy [27]. Size control in this regime remains largely unexplored. Here we report that E. coli cells use a dynamic size ruler to determine division locations combined with an adder-like mechanism to trigger divisions. As filamentous cells increase in size due to growth, or decrease in size due to divisions, its multiple Fts division rings abruptly reorganize to remain one characteristic cell length away from the cell pole and two such length units away from each other. These rules can be explained by spatiotemporal oscillations of Min proteins. Upon removal of filamentation stress, the cells undergo a sequence of division events, randomly at one of the possible division sites, on average after the time required to grow one characteristic cell size. These results indicate that E. coli cells continuously keep track of absolute length to control size, suggest a wider relevance for the adder principle beyond the control of normally sized cells, and provide a new perspective on the function of the Fts and Min systems.

Generation and filtering of gene expression noise by the bacterial cell cycle.

BACKGROUND: Gene expression within cells is known to fluctuate stochastically in time. However, the origins of gene expression noise remain incompletely understood. The bacterial cell cycle has been suggested as one source, involving chromosome replication, exponential volume growth, and various other changes in cellular composition. Elucidating how these factors give rise to expression variations is important to models of cellular homeostasis, fidelity of signal transmission, and cell-fate decisions. RESULTS: Using single-cell time-lapse microscopy, we measured cellular growth as well as fluctuations in the expression rate of a fluorescent protein and its concentration. We found that, within the population, the mean expression rate doubles throughout the cell cycle with a characteristic cell cycle phase dependent shape which is different for slow and fast growth rates. At low growth rate, we find the mean expression rate was initially flat, and then rose approximately linearly by a factor two until the end of the cell cycle. The mean concentration fluctuated at low amplitude with sinusoidal-like dependence on cell cycle phase. Traces of individual cells were consistent with a sudden two-fold increase in expression rate, together with other non-cell cycle noise. A model was used to relate the findings and to explain the cell cycle-induced variations for different chromosomal positions. CONCLUSIONS: We found that the bacterial cell cycle contribution to expression noise consists of two parts: a deterministic oscillation in synchrony with the cell cycle and a stochastic component caused by variable timing of gene replication. Together, they cause half of the expression rate noise. Concentration fluctuations are partially suppressed by a noise cancelling mechanism that involves the exponential growth of cellular volume. A model explains how the functional form of the concentration oscillations depends on chromosome position.

Stochasticity of metabolism and growth at the single-cell level.

Elucidating the role of molecular stochasticity in cellular growth is central to understanding phenotypic heterogeneity and the stability of cellular proliferation. The inherent stochasticity of metabolic reaction events should have negligible effect, because of averaging over the many reaction events contributing to growth. Indeed, metabolism and growth are often considered to be constant for fixed conditions. Stochastic fluctuations in the expression level of metabolic enzymes could produce variations in the reactions they catalyse. However, whether such molecular fluctuations can affect growth is unclear, given the various stabilizing regulatory mechanisms, the slow adjustment of key cellular components such as ribosomes, and the secretion and buffering of excess metabolites. Here we use time-lapse microscopy to measure fluctuations in the instantaneous growth rate of single cells of Escherichia coli, and quantify time-resolved cross-correlations with the expression of lac genes and enzymes in central metabolism. We show that expression fluctuations of catabolically active enzymes can propagate and cause growth fluctuations, with transmission depending on the limitation of the enzyme to growth. Conversely, growth fluctuations propagate back to perturb expression. Accordingly, enzymes were found to transmit noise to other unrelated genes via growth. Homeostasis is promoted by a noise-cancelling mechanism that exploits fluctuations in the dilution of proteins by cell-volume expansion. The results indicate that molecular noise is propagated not only by regulatory proteins but also by metabolic reactions. They also suggest that cellular metabolism is inherently stochastic, and a generic source of phenotypic heterogeneity.