Anat Bren

A comprehensive library of fluorescent transcriptional reporters for Escherichia coli

E. coli is widely used for systems biology research; there exists a need, however, for tools that can be used to accurately and comprehensively measure expression dynamics in individual living cells. To address this we present a library of transcriptional fusions of gfp to each of about 2,000 different promoters in E. coli K12, covering the great majority of the promoters in the organism. Each promoter fusion is expressed from a low-copy plasmid. We demonstrate that this library can be used to obtain highly accurate dynamic measurements of promoter activity on a genomic scale, in a glucose-lactose diauxic shift experiment. The library allowed detection of about 80 previously uncharacterized transcription units in E. coli, including putative internal promoters within previously known operons, such as the lac operon. This library can serve as a tool for accurate, high-resolution analysis of transcription networks in living E. coli cells.

How Signals Are Heard during Bacterial Chemotaxis: Protein-Protein Interactions in Sensory Signal Propagation

Chemotaxis is a mechanism by which bacteria efficiently and rapidly respond to changes in the chemical composition of their environment, approaching chemically favorable environments and avoiding unfavorable ones. This behavior is achieved by integrating signals received from receptors that sense the environment and modulating the direction of flagellar rotation accordingly (for reviews, see references 39, 43, and 100). Early studies in the modern era, initiated some 4 decades ago (1), uncovered the behavioral response of cells to changes in the chemical composition of their environment and the correlation between flagellar rotation and the swimming mode of the cells. They also identified most of the gene products involved in chemotaxis (for reviews, see references 50 and 56). The mode of signal transduction began to be understood only in the mid-1980s, when the possibilities of electrical signaling and a direct interaction between the receptors and flagella were eliminated (for a review, see reference 41). The possibility of indirect interaction between the receptors and flagella via a protein that is activated by the receptors and inactivated as it diffuses through the cytoplasm was then raised (96). Subsequently, sequential transient phosphorylation of chemotaxis proteins was found to be a key process in signal transduction (for a review, see reference 25). During the last decade, it was established that the signal in bacteria such as Escherichia coli and Salmonella enterica serovar Typhimurium is transduced via protein-protein interactions. These interactions have been extensively studied, contributing greatly to the elucidation of the chemotaxis-signaling cascade. The chemotactic response in bacteria such as E. coli and Salmonella serovar Typhimurium is accomplished by signal transmission between two supramolecular complexes—the receptor complexes, located mainly at the pole(s) of the cell, and the flagellar-motor complexes (usually 5 to 10 complexes per cell), randomly distributed around the cell and embedded within the cell membrane. A messenger protein, CheY, shuttles back and forth between the complexes and transduces the signal from the receptors to the flagella (Fig. 1). The interaction of this messenger protein with the flagellar-motor supramolecular complex increases the probability of shifting the direction of flagellar rotation from the default direction, counterclockwise (CCW), to clockwise (CW) (for a review see reference 38). The consequence of CW rotation is an abrupt turning motion (tumbling), after which (when the default direction resumes) the cell swims in a new direction. Here we review the protein-protein interactions involved in chemotactic signaling, including interactions within the supramolecular complexes, interactions between the complexes and the messenger protein CheY, and interactions between CheY and the proteins that regulate its signaling state. Interactions involved in the signaling pathway leading to adaptation will also be reviewed. We will mainly focus on functional aspects of the interactions. The reader is referred to references 13, 35, 43, 54, and 81 for more-detailed structural aspects. Because this is a minireview, the reference list is incomplete. Whenever possible, reference is made to reviews or papers that provide access to the original literature.

The incoherent feed‐forward loop can generate non‐monotonic input functions for genes

Gene regulation networks contain recurring circuit patterns called network motifs. One of the most common network motif is the incoherent type 1 feed‐forward loop (I1‐FFL), in which an activator controls both gene and repressor of that gene. This motif was shown to act as a pulse generator and response accelerator of gene expression. Here we consider an additional function of this motif: the I1‐FFL can generate a non‐monotonic dependence of gene expression on the input signal. Here, we study this experimentally in the galactose system of Escherichia coli, which is regulated by an I1‐FFL. The promoter activity of two of the gal operons, galETK and galP, peaks at intermediate levels of the signal cAMP. We find that mutants in which the I1‐FFL is disrupted lose this non‐monotonic behavior, and instead display monotonic input functions. Theoretical analysis suggests that non‐monotonic input functions can be achieved for a wide range of parameters by the I1‐FFL. The models also suggest regimes where a monotonic input‐function can occur, as observed in the mglBAC operon regulated by the same I1‐FFL. The present study thus experimentally demonstrates how upstream circuitry can affect gene input functions and how an I1‐FFL functions within its natural context in the cell.

Diverse Two-Dimensional Input Functions Control Bacterial Sugar Genes

Cells respond to signals by regulating gene expression. The relation between the level of input signals and the transcription rate of the gene is called the genes input function. Because most genes are regulated by more than one signal, the input functions are usually multidimensional. To understand cellular responses, it is essential to know the shapes of these functions. Here, we map the two-dimensional input functions of 19 sugar-utilization genes at high resolution in living E. coli cells. We find diverse, intricately shaped input functions, despite the similarity in the regulatory circuitry of these genes. Surprisingly, some of the input functions are nonmonotonic, peaking at intermediate signal levels. Furthermore, most of the input functions show separation of variables, in the sense that they can be described as the product of simple functions that depend on a single input. This first broad survey of two-dimensional input functions can be extended to map the logic of gene regulation in other systems.

Promoters maintain their relative activity levels under different growth conditions

Most genes change expression levels across conditions, but it is unclear which of these changes represents specific regulation and what determines their quantitative degree. Here, we accurately measured activities of ∼900 S. cerevisiae and ∼1800 E. coli promoters using fluorescent reporters. We show that in both organisms 60–90% of promoters change their expression between conditions by a constant global scaling factor that depends only on the conditions and not on the promoters identity. Quantifying such global effects allows precise characterization of specific regulation—promoters deviating from the global scale line. These are organized into few functionally related groups that also adhere to scale lines and preserve their relative activities across conditions. Thus, only several scaling factors suffice to accurately describe genome‐wide expression profiles across conditions. We present a parameter‐free passive resource allocation model that quantitatively accounts for the global scaling factors. It suggests that many changes in expression across conditions result from global effects and not specific regulation, and provides means for quantitative interpretation of expression profiles.

Invariant Distribution of Promoter Activities in Escherichia coli

Cells need to allocate their limited resources to express a wide range of genes. To understand how Escherichia coli partitions its transcriptional resources between its different promoters, we employ a robotic assay using a comprehensive reporter strain library for E. coli to measure promoter activity on a genomic scale at high-temporal resolution and accuracy. This allows continuous tracking of promoter activity as cells change their growth rate from exponential to stationary phase in different media. We find a heavy-tailed distribution of promoter activities, with promoter activities spanning several orders of magnitude. While the shape of the distribution is almost completely independent of the growth conditions, the identity of the promoters expressed at different levels does depend on them. Translation machinery genes, however, keep the same relative expression levels in the distribution across conditions, and their fractional promoter activity tracks growth rate tightly. We present a simple optimization model for resource allocation which suggests that the observed invariant distributions might maximize growth rate. These invariant features of the distribution of promoter activities may suggest design constraints that shape the allocation of transcriptional resources.

Robust control of nitrogen assimilation by a bifunctional enzyme in E. coli.

Bacteria regulate the assimilation of multiple nutrients to enable growth. How is balanced utilization achieved, despite fluctuations in the concentrations of the enzymes that make up the regulatory circuitry? Here we address this question by studying the nitrogen system of E. coli. A mechanism based on the avidity of a bifunctional enzyme, adenylyltransferase (AT/AR), to its multimeric substrate, glutamine synthetase, is proposed to maintain a robust ratio between two key metabolites, glutamine and α-ketoglutarate. This ratio is predicted to be insensitive to variations in protein levels of the core circuit and to the rate of nitrogen utilization. We find using mass spectrometry that the metabolite ratio is robust to variations in protein levels and that this robustness depends on the bifunctional enzyme. Moreover, robustness carries through to the bacteria growth rate. Interrupting avidity by adding a monofunctional AT/AR mutant to the native system abolishes robustness, as predicted by the proposed mechanism.

Hierarchy of non-glucose sugars in Escherichia coli

BackgroundUnderstanding how cells make decisions, and why they make the decisions they make, is of fundamental interest in systems biology. To address this, we study the decisions made by E. coli on which genes to express when presented with two different sugars. It is well-known that glucose, E. coli’s preferred carbon source, represses the uptake of other sugars by means of global and gene-specific mechanisms. However, less is known about the utilization of glucose-free sugar mixtures which are found in the natural environment of E. coli and in biotechnology.ResultsHere, we combine experiment and theory to map the choices of E. coli among 6 different non-glucose carbon sources. We used robotic assays and fluorescence reporter strains to make precise measurements of promoter activity and growth rate in all pairs of these sugars. We find that the sugars can be ranked in a hierarchy: in a mixture of a higher and a lower sugar, the lower sugar system shows reduced promoter activity. The hierarchy corresponds to the growth rate supported by each sugar- the faster the growth rate, the higher the sugar on the hierarchy. The hierarchy is ‘soft’ in the sense that the lower sugar promoters are not completely repressed. Measurement of the activity of the master regulator CRP-cAMP shows that the hierarchy can be quantitatively explained based on differential activation of the promoters by CRP-cAMP. Comparing sugar system activation as a function of time in sugar pair mixtures at sub-saturating concentrations, we find cases of sequential activation, and also cases of simultaneous expression of both systems. Such simultaneous expression is not predicted by simple models of growth rate optimization, which predict only sequential activation. We extend these models by suggesting multi-objective optimization for both growing rapidly now and preparing the cell for future growth on the poorer sugar.ConclusionWe find a defined hierarchy of sugar utilization, which can be quantitatively explained by differential activation by the master regulator cAMP-CRP. The present approach can be used to understand cell decisions when presented with mixtures of conditions.

Promoter activity dynamics in the lag phase of Escherichia coli

BackgroundLag phase is a period of time with no growth that occurs when stationary phase bacteria are transferred to a fresh medium. Bacteria in lag phase seem inert: their biomass does not increase. The low number of cells and low metabolic activity make it difficult to study this phase. As a consequence, it has not been studied as thoroughly as other bacterial growth phases. However, lag phase has important implications for bacterial infections and food safety. We asked which, if any, genes are expressed in the lag phase of Escherichia coli, and what is their dynamic expression pattern.ResultsWe developed an assay based on imaging flow cytometry of fluorescent reporter cells that overcomes the challenges inherent in studying lag phase. We distinguish between lag1 phase- in which there is no biomass growth, and lag2 phase- in which there is biomass growth but no cell division. We find that in lag1 phase, most promoters are not active, except for the enzymes that utilize the specific carbon source in the medium. These genes show promoter activities that increase exponentially with time, despite the fact that the cells do not measurably increase in size. An oxidative stress promoter, katG, is also active. When cells enter lag2 and begin to grow in size, they switch to a full growth program of promoter activity including ribosomal and metabolic genes.ConclusionsThe observed exponential increase in enzymes for the specific carbon source followed by an abrupt switch to production of general growth genes is a solution of an optimal control model, known as bang-bang control. The present approach contributes to the understanding of lag phase, the least studied of bacterial growth phases.

Mode of Regulation and the Insulation of Bacterial Gene Expression

A gene can be said to be insulated from environmental variations if its expression level depends only on its cognate inducers, and not on variations in conditions. We tested the insulation of the lac promoter of E. coli and of synthetic constructs in which the transcription factor CRP acts as either an activator or a repressor, by measuring their input function-their expression as a function of inducers-in different growth conditions. We find that the promoter activities show sizable variation across conditions of 10%-100% (SD/mean). When the promoter is bound to its cognate regulator(s), variation across conditions is smaller than when it is unbound. Thus, mode of regulation affects insulation: activators seem to show better insulation at high expression levels, and repressors at low expression levels. This may explain the Savageau demand rule, in which E. coli genes needed often in the natural environment tend to be regulated by activators, and rarely needed genes by repressors. The present approach can be used to study insulation in other genes and organisms.