Anand Pai

Optimality and robustness in quorum sensing (QS)-mediated regulation of a costly public good enzyme

Bacteria secrete a variety of public good exoproducts into their environment. These exoproducts are typically produced under the control of quorum sensing (QS), a signaling mechanism by which bacteria sense and respond to changes in their density. QS seems to provide an advantageous strategy to regulate these costly but beneficial exoproducts: it delays production until sufficiently high cell density, when the overall benefit of exoproducts outweighs cost of their production. This notion raises several fundamental questions about QS as a general control strategy adopted by bacteria. How much delay is advantageous? Under what conditions does QS-mediated regulation become advantageous? How does this advantage depend on the kinetic properties of QS? How robust is a given QS system to the stochastic events that occur over bacterial lifecycles? To quantitatively address these questions, we engineered a gene circuit in Escherichia coli to control the synthesis and secretion of a costly but beneficial exoenzyme. We show that exoenzyme production is overall advantageous only if initiated at a sufficiently high density. This property sets the potential advantage for QS-mediated regulation when the initial density is low and the growth cycle is sufficiently long compared with the exoenzyme response time. This advantage of QS-mediated regulation is robust to varying initial cell densities and growth durations, and it is particularly striking when bacteria face uncertainty, such as from stochastic dispersal during their lifecycle. We show, however, that, for QS to be optimal, its kinetic properties must be appropriately tuned; this property has implications for antibacterial treatments that target QS.

Optimal tuning of bacterial sensing potential

Through production and sensing of small signal molecules, quorum sensing (QS) enables bacteria to detect changes in their density and regulate their functions accordingly. QS systems are tremendously diverse in terms of their specific sensory components, the biochemical and transport properties of signaling molecules, their target functions and the context in which QS‐mediated functions are activated. Cutting across this diversity, however, the central architecture of QS systems is universal; it comprises signal synthesis, secretion, degradation and detection. We are thus able to derive a general metric for QS ‘sensing potential’ based on this ‘core’ module. The sensing potential quantifies the ability of a single bacterium to sense the dimensions of its microenvironment. This simple metric captures the dominant activation properties of diverse QS systems, giving a concise description of the sensing characteristics. As such, it provides a convenient quantitative framework to study the phenotypic effects of QS characteristics. As an example, we show how QS characteristics uniquely determine the scenarios in which regulation of a typical QS‐controlled function, such as exoenzyme secretion, becomes advantageous.

A noisy linear map underlies oscillations in cell size and gene expression in bacteria

During bacterial growth, a cell approximately doubles in size before division, after which it splits into two daughter cells. This process is subjected to the inherent perturbations of cellular noise and thus requires regulation for cell-size homeostasis. The mechanisms underlying the control and dynamics of cell size remain poorly understood owing to the difficulty in sizing individual bacteria over long periods of time in a high-throughput manner. Here we measure and analyse long-term, single-cell growth and division across different Escherichia coli strains and growth conditions. We show that a subset of cells in a population exhibit transient oscillations in cell size with periods that stretch across several (more than ten) generations. Our analysis reveals that a simple law governing cell-size control—a noisy linear map—explains the origins of these cell-size oscillations across all strains. This noisy linear map implements a negative feedback on cell-size control: a cell with a larger initial size tends to divide earlier, whereas one with a smaller initial size tends to divide later. Combining simulations of cell growth and division with experimental data, we demonstrate that this noisy linear map generates transient oscillations, not just in cell size, but also in constitutive gene expression. Our work provides new insights into the dynamics of bacterial cell-size regulation with implications for the physiological processes involved.

Programming stress‐induced altruistic death in engineered bacteria

Programmed death is often associated with a bacterial stress response. This behavior appears paradoxical, as it offers no benefit to the individual. This paradox can be explained if the death is ‘altruistic’: the killing of some cells can benefit the survivors through release of ‘public goods’. However, the conditions where bacterial programmed death becomes advantageous have not been unambiguously demonstrated experimentally. Here, we determined such conditions by engineering tunable, stress‐induced altruistic death in the bacterium Escherichia coli. Using a mathematical model, we predicted the existence of an optimal programmed death rate that maximizes population growth under stress. We further predicted that altruistic death could generate the ‘Eagle effect’, a counter‐intuitive phenomenon where bacteria appear to grow better when treated with higher antibiotic concentrations. In support of these modeling insights, we experimentally demonstrated both the optimality in programmed death rate and the Eagle effect using our engineered system. Our findings fill a critical conceptual gap in the analysis of the evolution of bacterial programmed death, and have implications for a design of antibiotic treatment.

Programmed Allee effect in bacteria causes a tradeoff between population spread and survival

Significance Understanding how species spread and survive is important in many biological contexts. The ability to disperse has been shown to enhance spread in some species but detract in others. Theoretical studies have predicted that these observations may be due to the Allee effect. To test this theory, we engineered Escherichia coli to have an Allee effect. Using these bacteria, we found that if dispersal is too fast or too slow, a population cannot spread. By manipulating the number of patches, we uncovered tradeoffs that control spread and survival. Finally, we demonstrate that fluctuations in growth may serve to determine if spread occurs. Our results may be useful in controlling invasive species and the spread of infectious diseases. Dispersal is necessary for spread into new habitats, but it has also been shown to inhibit spread. Theoretical studies have suggested that the presence of a strong Allee effect may account for these counterintuitive observations. Experimental demonstration of this notion is lacking due to the difficulty in quantitative analysis of such phenomena in a natural setting. We engineered Escherichia coli to exhibit a strong Allee effect and examined how the Allee effect would affect the spread of the engineered bacteria. We showed that the Allee effect led to a biphasic dependence of bacterial spread on the dispersal rate: spread is promoted for intermediate dispersal rates but inhibited at low or high dispersal rates. The shape of this dependence is contingent upon the initial density of the source population. Moreover, the Allee effect led to a tradeoff between effectiveness of population spread and survival: increasing the number of target patches during dispersal allows more effective spread, but it simultaneously increases the risk of failing to invade or of going extinct. We also observed that total population growth is transiently maximized at an intermediate number of target patches. Finally, we demonstrate that fluctuations in cell growth may contribute to the paradoxical relationship between dispersal and spread. Our results provide direct experimental evidence that the Allee effect can explain the apparently paradoxical effects of dispersal on spread and have implications for guiding the spread of cooperative organisms.

Long-term growth data of Escherichia coli at a single-cell level

Long-term, single-cell measurement of bacterial growth is extremely valuable information, particularly in the study of homeostatic aspects such as cell-size and growth rate control. Such measurement has recently become possible due to the development of microfluidic technology. Here we present data from single-cell measurements of Escherichia coli growth over 70 generations obtained for three different growth conditions. The data were recorded every minute, and contain time course data of cell length and fluorescent intensity of constitutively expressed yellow fluorescent protein.

Neutrophil Motion, Adhesion and Activation in an In Vitro Micropipette Model of a Lung Capillary

White blood cell (WBC) sequestration in lung capillaries is a key step in the inflammatory response to lung infection. P-selectin and ICAM-1 have well-defined roles in WBC adhesion in venules but their role in pulmonary capillaries is still unclear. Here, a novel in vitro Micropipette Cell Adhesion Assay used P-selectin, ICAM-1 or BSA-coated capillary-sized glass micropipettes as an in vitro model of a lung capillary. WBC were aspirated into adhesion molecule-coated vessels of varying diameters. Cell velocities and activation times were determined under pressures representative of lung capillaries. WBC velocities in this assay were significantly lower on P-selectin than BSA and decreased with increasing P-selectin concentration. These results demonstrate that P-selectin at low density mediates WBC adhesion in the pulmonary capillary geometry. WBC can also become activated upon aspiration into micropipettes and under some circumstances can be seen to exhibit a cyclic migratory behavior. This work was supported by grant BES-0547165 from the National Science Foundation and by an award from the American Heart Association.© 2010 ASME