By recruiting the essential HIS3 gene to the GAL regulatory system and switching to a repressing glucose medium, we confronted yeast cells with a novel challenge they had not encountered before along their history in evolution.Adaptation to this challenge involved a global transcriptional response of a sizeable fraction of the genome, which relaxed on the time scale of the population adaptation, of order of 10 generations.For a large fraction of the responding genes there is no simple biological interpretation, connecting them to the specific cellular demands imposed by the novel challenge.Strikingly, repeating the experiment did not reproduce similar transcription patterns neither in the transient phase nor in the adapted state in glucose.These results suggest that physiological selection operates on the new metabolic configurations generated by the non-specific large scale transcriptional response to eventually stabilize an adaptive state.

The recruitment of a gene to a foreign regulatory system is a major evolutionary event that can lead to novel phenotypes. However, the evolvability potential of cells depends on their ability to cope with challenges presented by gene recruitment. To study this ability, we combined synthetic gene recruitment with continuous culture and online measurements of the metabolic and regulatory dynamics over long timescales. The gene HIS3 from the histidine synthesis pathway was recruited to the GAL system, responsible for galactose utilization in the yeast S. cerevisiae. Following a switch from galactose to glucose-from induced to repressed conditions of the GAL system-in histidine-lacking chemostats (where the recruited HIS3 is essential), the regulatory system reprogrammed to adaptively tune HIS3 expression, allowing the cells to grow competitively in pure glucose. The adapted state was maintained for hundreds of generations in various environments. The timescales involved and the reproducibility of separate experiments render spontaneous mutations an unlikely underlying mechanism. Essentially all cells could adapt, excluding selection over a genetically variable population. The results reveal heritable adaptation induced by the exposure to glucose. They demonstrate that genetic regulatory networks have the potential to support highly demanding events of gene recruitment.

The copy number of any protein fluctuates among cells in a population; characterizing and understanding these fluctuations is a fundamental problem in biophysics. We show here that protein distributions measured under a broad range of biological realizations collapse to a single non-Gaussian curve under scaling by the first two moments. Moreover in all experiments the variance is found to depend quadratically on the mean, showing that a single degree of freedom determines the entire distribution. Our results imply that protein fluctuations do not reflect any specific molecular or cellular mechanism, and suggest that some buffering process masks these details and induces universality.The protein content of a cell is a primary determinant of its phenotype. However, protein copy number is subject to large cell-to-cell variation even among genetically identical cells grown under uniform conditions ([1-3] and references therein). This variation has been the subject of intensive research in recent years ([4-7] and references therein). Much of this previous work was devoted to characterizing the stochastic properties of various processes underlying gene expression, such as transcription and translation [8], or different stages of the cell cycle [9], and understanding their effect on protein variation. However, gene expression is generally coupled to all aspects of cell physiology, such as growth [10], metabolism [11], aging [12], division [13,14] and epigenetic processes [15,16], as well as gene location and function [17], all of which were shown to affect protein variation. The emerging picture is of a plethora of correlated mechanisms at different levels of organization; how they integrate to shape the total protein variation in a dividing population remains an open question [11,14].In this work we addressed this question by a phenomenological approach. We measured distributions of highly expressed proteins in proliferating clonal populations of bacteria and yeast under natural conditions, where gene expression is coupled to other cellular processes. By designing an array of different metabolic and regulatory conditions as well as growth environments, we collected a compendium of measurements which systematically covers the major processes of gene expression and 2 cell division, and compared the measured distributions in a wide range of biological realizations. More specifically, our comparisons included: (a) Two archetypical microorganisms, bacteria and yeast, with two well-studied regulatory systems of essential metabolic pathways: the LAC operon in E. coli [18] and the GAL system in S. cerevisiae [19]. Both systems were studied under environmental conditions in which expression is strongly coupled to metabolism, namely they control the utilization of an essential sugar (lactose and galactose, respectively) as the sole carbon The spectrum of our experiments spans an array of "control parameters" p which covers many of the essential processes affecting protein content in cells. The two organisms used, E. coli an...

Step emulsification is an attractive method for production of monodisperse drops. Its main advantage is the ability to parallelize many step emulsifier nozzles to achieve high production rates. However, step emulsification is sensitive to any obstructions at the nozzle exit. At high production rates, drops can accumulate at nozzle exits, disturb the formation of subsequent drops and impair monodispersity. As a result, parallelized step emulsifier devices typically do not work at maximum productivity. Here a design is introduced that parallelizes hundreds of step emulsifier nozzles, and effectively removes drops from the nozzle exits. The drop clearance is achieved by an open collecting channel, and is aided by buoyancy. Importantly, this clearance method avoids the use of a continuous phase flow for drop clearance and hence no shear is applied on the forming drops. The method works well for a wide range of drops, sizing from 30 to 1000 μm at production rates of 0.03 and 10 L per hour and achieved by 400 and 120 parallelized nozzles respectively.

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Three-dimensional, time-dependent direct simulations of step emulsification micro-devices highlight two essential mechanisms for droplet formation: first, the onset of an adverse pressure gradient driving a backflow of the continuous phase from the external reservoir to the micro-channel. Second, the striction of the flowing jet which leads to its subsequent rupture. It is also shown that such a rupture is delayed and eventually suppressed by increasing the flow speed of the dispersed phase within the channel, due to the stabilising effect of dynamic pressure. This suggests a new criterion for dripping-jetting transition, based on local values of the Capillary and Weber numbers. * Electronic address: and.

The phenotypic state of the cell is commonly thought to be determined by the set of expressed genes. However, given the apparent complexity of genetic networks, it remains open what processes stabilize a particular phenotypic state. Moreover, it is not clear how unique is the mapping between the vector of expressed genes and the cell's phenotypic state. To gain insight on these issues, we study here the expression dynamics of metabolically essential genes in twin cell populations. We show that two yeast cell populations derived from a single steady-state mother population and exhibiting a similar growth phenotype in response to an environmental challenge, displayed diverse expression patterns of essential genes. The observed diversity in the mean expression between populations could not result from stochastic cell-to-cell variability, which would be averaged out in our large cell populations. Remarkably, within a population, sets of expressed genes exhibited coherent dynamics over many generations. Thus, the emerging gene expression patterns resulted from collective population dynamics. It suggests that in a wide range of biological contexts, gene expression reflects a self-organization process coupled to population-environment dynamics.

Frequently during evolution, new phenotypes evolved due to novelty in gene regulation, such as that caused by genome rewiring. This has been demonstrated by comparing common regulatory sequences among species and by identifying single regulatory mutations that are associated with new phenotypes. However, while a single mutation changes a single element, gene regulation is accomplished by a regulatory network involving multiple interactive elements. Therefore, to better understand regulatory evolution, we have studied how mutations contributed to the adaptation of cells to a regulatory challenge. We created a synthetic genome rewiring in yeast cells, challenged their gene regulation, and studied their adaptation. HIS3, an essential enzyme for histidine biosynthesis, was placed exclusively under a GAL promoter, which is induced by galactose and strongly repressed in glucose. Such rewired cells were faced with significant regulatory challenges in a repressive glucose medium. We identified several independent mutations in elements of the GAL system associated with the rapid adaptation of cells, such as the repressor GAL80 and the binding sites of the activator GAL4. Consistent with the extraordinarily high rate of cell adaptation, new regulation emerged during adaptation via multiple trajectories, including those involving mutations in elements of the GAL system. The new regulation of HIS3 tuned its expression according to histidine requirements with or without these significant mutations, indicating that additional factors participated in this regulation and that the regulatory network could reorganize in multiple ways to accommodate different mutations. This study, therefore, stresses network plasticity as an important property for regulatory adaptation and evolution.