Hanna Salman

Toward an artificial cell based on gene expression in vesicles.

We present a new experimental approach to build an artificial cell using the translation machinery of a cell-free expression system as the hardware and a DNA synthetic genome as the software. This approach, inspired by the self-replicating automata of von Neumann, uses cytoplasmic extracts, encapsulated in phospholipid vesicles, to assemble custom-made genetic circuits to develop the functions of a minimal cell. Although this approach can find applications, especially in biotechnology, the primary goal is to understand how a DNA algorithm can be designed to build an operating system that has some of the properties of life. We provide insights on this cell-free approach as well as new results to transform step by step a long-lived vesicle bioreactor into an artificial cell. We show how the green fluorescent protein can be anchored to the membrane and we give indications of a possible insertion mechanism of integral membrane proteins. With vesicles composed of different phospholipids, the fusion protein alpha-hemolysin-eGFP can be expressed to reveal patterns on the membrane. The specific degradation complex ClpXP from E. coli is introduced to create a sink for the synthesized proteins. Perspectives and subsequent limitations of this approach are discussed.

A concentration-dependent switch in the bacterial response to temperature

We observed that bacteria grown below a critical concentration, in batch-mode cultures, swim towards warm regions when subjected to a temperature gradient. Above that concentration, they swim towards colder regions. Our findings indicate that the secreted intercellular signal, glycine, mediates this switch through methylation of Tsr receptors. At high bacterial concentration, the switch is reinforced by an inversion of the Tar/Tsr expression ratio.

Effects of population density and chemical environment on the behavior of Escherichia coli in shallow temperature gradients

In shallow temperature gradients, changes in temperature that bacteria experience occur over long time scales. Therefore, slow processes such as adaptation, metabolism, chemical secretion and even gene expression become important. Since these are cellular processes, the cell density is an important parameter that affects the bacterias response. We find that there are four density regimes with distinct behaviors. At low cell density, bacteria do not cause changes in their chemical environment; however, their response to the temperature gradient is strongly influenced by it. In the intermediate cell-density regime, the consumption of nutrients becomes significant and induces a gradient of nutrients opposing the temperature gradient due to higher consumption rate at the high temperature. This causes the bacteria to drift toward low temperature. In the high cell-density regime, interactions among bacteria due to secretion of an attractant lead to a strong local accumulation of bacteria. This together with the gradient of nutrients, resulted from the differential consumption rate, creates a fast propagating pulse of bacterial density. These observations are a result of classical nonlinear population dynamics. At extremely high cell density, a change in the physiological state of the bacteria is observed. The bacteria, at the individual level, become cold seeking. This appears initially as a result of a change in the methylation level of the two most abundant sensing receptors, Tsr and Tar. It is further enforced at an even higher cell density by a change in the expression level of these receptors.

Precision and Variability in Bacterial Temperature Sensing

In Escherichia coli, the ratio of the two most abundant chemoreceptors, Tar/Tsr, has become the focus of much attention in bacterial taxis studies. This ratio has been shown to change under various growth conditions and to determine the response of the bacteria to the environment. Here, we present a study that makes a quantitative link between the ratio Tar/Tsr and the favored temperature of the cell in a temperature gradient and in various chemical environments. From the steady-state density-profile of bacteria with one dominant thermo-sensor, Tar or Tsr, we deduce the response function of each receptor to temperature changes. Using the response functions of both receptors, we determine the relationship between the favored temperature of wild-type bacteria with mixed clusters of receptors and the receptor ratio. Our model is based on the assumption that the behavior of a wild-type bacterium in a temperature gradient is determined by a linear combination of the independent responses of the two receptors, factored by the receptors relative abundance in the bacterium. This is confirmed by comparing our model predictions with measurements of the steady-state density-profile of several bacterial populations in a temperature gradient. Our results reveal that the density-profile of wild-type bacteria can be accurately described by measuring the distribution of the ratio Tar/Tsr in the population, which is then used to divide the population into groups with distinct Tar/Tsr values, whose behavior can be described in terms of independent Gaussian distributions. Each of these Gaussians is centered about the favored temperature of the subpopulation, which is determined by the receptor ratio, and has a width defined by the temperature-dependent speed and persistence time.

Universal protein distributions in a model of cell growth and division.

Protein distributions measured under a broad set of conditions in bacteria and yeast were shown to exhibit a common skewed shape, with variances depending quadratically on means. For bacteria these properties were reproduced by temporal measurements of protein content, showing accumulation and division across generations. Here we present a stochastic growth-and-division model with feedback which captures these observed properties. The limiting copy number distribution is calculated exactly, and a single parameter is found to determine the distribution shape and the variance-to-mean relation. Estimating this parameter from bacterial temporal data reproduces the measured distribution shape with high accuracy and leads to predictions for future experiments.

Single-cell protein dynamics reproduce universal fluctuations in cell populations.

Protein variability in single cells has been studied extensively in populations, but little is known about temporal protein fluctuations in a single cell over extended times. We present here traces of protein copy number measured in individual bacteria over multiple generations and investigate their statistical properties, comparing them to previously measured population snapshots. We find that temporal fluctuations in individual cells exhibit the same properties as those previously observed in populations. Scaled fluctuations around the mean of each trace exhibit the universal distribution shape measured in populations under a wide range of conditions and in two distinct microorganisms; the mean and variance of the traces over time obey the same quadratic relation. Analyzing the individual protein traces reveals that within a cell cycle protein content increases exponentially, with a rate that varies from cycle to cycle. This leads to a compact description of the trace as a 3-variable stochastic process —exponential rate, cell cycle duration and value at the cycle start— sampled once a cycle. This description is sufficient to reproduce both universal statistical properties of the protein fluctuations. Our results show that the protein distribution shape is insensitive to sub-cycle intracellular microscopic details and reflects global cellular properties that fluctuate between generations.Graphical abstract

Cell-cell communication enhances bacterial chemotaxis toward external attractants

Bacteria are able to coordinate their movement, growth and biochemical activities through cell-cell communication. While the biophysical mechanism of bacterial chemotaxis has been well understood in individual cells, the role of communication in the chemotaxis of bacterial populations is not clear. Here we report experimental evidence for cell-cell communication that significantly enhances the chemotactic migration of bacterial populations, a finding that we further substantiate using numerical simulations. Using a microfluidic approach, we find that E. coli cells respond to the gradient of chemoattractant not only by biasing their own random-walk swimming pattern through the well-understood intracellular chemotaxis signaling, but also by actively secreting a chemical signal into the extracellular medium, possibly through a hitherto unknown communication signal transduction pathway. This extracellular signaling molecule is a strong chemoattractant that attracts distant cells to the food source. The observed behavior may represent a common evolved solution to accelerate the function of biochemical networks of interacting cells.

Individuality and slow dynamics in bacterial growth homeostasis

Significance Microbial cells go through repeated cycles of growth and division. These cycles are not perfect: the time and size at division can fluctuate from one cycle to the next. Still, cell size is kept tightly controlled, and fluctuations do not accumulate to large deviations. How this control is implemented in single cells is still not fully understood. We performed experiments that follow individual bacteria in microfluidic traps for hundreds of generations. This enables us to identify distinct individual dynamic properties that are maintained over many cycles of growth and division. Surprisingly, we find that each cell suppresses fluctuations with a different strength; this variability defines an “individual” behavior for each cell, which is inherited along many generations. Microbial growth and division are fundamental processes relevant to many areas of life science. Of particular interest are homeostasis mechanisms, which buffer growth and division from accumulating fluctuations over multiple cycles. These mechanisms operate within single cells, possibly extending over several division cycles. However, all experimental studies to date have relied on measurements pooled from many distinct cells. Here, we disentangle long-term measured traces of individual cells from one another, revealing subtle differences between temporal and pooled statistics. By analyzing correlations along up to hundreds of generations, we find that the parameter describing effective cell size homeostasis strength varies significantly among cells. At the same time, we find an invariant cell size, which acts as an attractor to all individual traces, albeit with different effective attractive forces. Despite the common attractor, each cell maintains a distinct average size over its finite lifetime with suppressed temporal fluctuations around it, and equilibration to the global average size is surprisingly slow (>150 cell cycles). To show a possible source of variable homeostasis strength, we construct a mathematical model relying on intracellular interactions, which integrates measured properties of cell size with those of highly expressed proteins. Effective homeostasis strength is then influenced by interactions and by noise levels and generally varies among cells. A predictable and measurable consequence of variable homeostasis strength appears as distinct oscillatory patterns in cell size and protein content over many generations. We discuss implications of our results to understanding mechanisms controlling division in single cells and their characteristic timescales.