Free RNA polymerase in Escherichia coli

Functionalizing DNA Origami by Triplex-Directed Site-Specific Photo-Cross-Linking

Here, we present a cross-linking approach to covalently functionalize and stabilize DNA origami structures in a one-pot reaction. Our strategy involves adding nucleotide sequences to adjacent staple strands, so that, upon assembly of the origami structure, the extensions form short hairpin duplexes targetable by psoralen-labeled triplex-forming oligonucleotides bearing other functional groups (pso-TFOs). Subsequent irradiation with UVA light generates psoralen adducts with one or both hairpin staples leading to site-specific attachment of the pso-TFO (and attached group) to the origami with ca. 80% efficiency. Bis-adduct formation between strands in proximal hairpins further tethers the TFO to the structure and generates "superstaples" that improve the structural integrity of the functionalized complex. We show that directing cross-linking to regions outside of the origami core dramatically reduces sensitivity of the structures to thermal denaturation and disassembly by T7 RNA polymerase. We also show that the underlying duplex regions of the origami core are digested by DNase I and thus remain accessible to read-out by DNA-binding proteins. Our strategy is scalable and cost-effective, as it works with existing DNA origami structures, does not require scaffold redesign, and can be achieved with just one psoralen-modified oligonucleotide.

High-throughput, fluorescent-aptamer-based measurements of steady-state transcription rates for the Mycobacterium tuberculosis RNA polymerase

The first step in gene expression is the transcription of DNA sequences into RNA. Regulation at the level of transcription leads to changes in steady-state concentrations of RNA transcripts, affecting the flux of downstream functions and ultimately cellular phenotypes. Changes in transcript levels are routinely followed in cellular contexts via genome-wide sequencing techniques. However, in vitro mechanistic studies of transcription have lagged with respect to throughput. Here, we describe the use of a real-time, fluorescent-aptamer-based method to quantitate steady-state transcription rates of the Mycobacterium tuberculosis RNA polymerase. We present clear controls to show that the assay specifically reports on promoter-dependent, full-length RNA transcription rates that are in good agreement with the kinetics determined by gel-resolved, α-32P NTP incorporation experiments. We illustrate how the time-dependent changes in fluorescence can be used to measure regulatory effects of nucleotide concentrations and identity, RNAP and DNA concentrations, transcription factors, and antibiotics. Our data showcase the ability to easily perform hundreds of parallel steady-state measurements across varying conditions with high precision and reproducibility to facilitate the study of the molecular mechanisms of bacterial transcription.

spotter: a single-nucleotide resolution stochastic simulation model of supercoiling-mediated transcription and translation in prokaryotes

Stochastic simulation models have played an important role in efforts to understand the mechanistic basis of prokaryotic transcription and translation. Despite the fundamental linkage of these processes in bacterial cells, however, most simulation models have been limited to representations of either transcription or translation. In addition, the available simulation models typically either attempt to recapitulate data from single-molecule experiments without considering cellular-scale high-throughput sequencing data or, conversely, seek to reproduce cellular-scale data without paying close attention to many of the mechanistic details. To address these limitations, we here present spotter (Simulation of Prokaryotic Operon Transcription & Translation Elongation Reactions), a flexible, user-friendly simulation model that offers highly-detailed combined representations of prokaryotic transcription, translation, and DNA supercoiling. In incorporating nascent transcript and ribosomal profiling sequencing data, spotter provides a critical bridge between data collected in single-molecule experiments and data collected at the cellular scale. Importantly, in addition to rapidly generating output that can be aggregated for comparison with next-generation sequencing and proteomics data, spotter produces residue-level positional information that can be used to visualize individual simulation trajectories in detail. We anticipate that spotter will be a useful tool in exploring the interplay of processes that are crucially linked in prokaryotes.

Structural basis of λCII-dependent transcription activation

The CII protein of bacteriophage λ activates transcription from the phage promoters PRE, PI, and PAQ by binding to two direct repeats that straddle the promoter -35 element. Although genetic, biochemical, and structural studies have elucidated many aspects of λCII-mediated transcription activation, no precise structure of the transcription machinery in the process is available. Here, we report a 3.1-Å cryo-electron microscopy (cryo-EM) structure of an intact λCII-dependent transcription activation complex (TAC-λCII), which comprises λCII, E. coli RNAP-σ70 holoenzyme, and the phage promoter PRE. The structure reveals the interactions between λCII and the direct repeats responsible for promoter specificity and the interactions between λCII and RNAP α subunit C-terminal domain responsible for transcription activation. We also determined a 3.4-Å cryo-EM structure of an RNAP-promoter open complex (RPo-PRE) from the same dataset. Structural comparison between TAC-λCII and RPo-PRE provides new insights into λCII-dependent transcription activation.

High-throughput, fluorescent-aptamer-based measurements of steady-state transcription rates for Mycobacterium tuberculosis RNA polymerase

The first step in gene expression is the transcription of DNA sequences into RNA. Regulation at the level of transcription leads to changes in steady-state concentrations of RNA transcripts, affecting the flux of downstream functions and ultimately cellular phenotypes. Changes in transcript levels are routinely followed in cellular contexts via genome-wide sequencing techniques. However, in vitro mechanistic studies of transcription have lagged with respect to throughput. Here, we describe the use of a real-time, fluorescent-aptamer-based method to quantitate steady-state transcription rates of the Mycobacterium tuberculosis RNA polymerase. We present clear controls to show that the assay specifically reports on promoter-dependent, full-length RNA transcription rates that are in good agreement with the kinetics determined by gel-resolved, α- 32 P NTP incorporation experiments. We illustrate how the time-dependent changes in fluorescence can be used to measure regulatory effects of nucleotide concentrations and identity, RNAP and DNA concentrations, transcription factors, and antibiotics. Our data showcase the ability to easily perform hundreds of parallel steady-state measurements across varying conditions with high precision and reproducibility to facilitate the study of the molecular mechanisms of bacterial transcription.

Significance Statement

RNA polymerase transcription mechanisms have largely been determined from in vitro kinetic and structural biology methods. In contrast to the limited throughput of these approaches, in vivo RNA sequencing provides genome-wide measurements but lacks the ability to dissect direct biochemical from indirect genetic mechanisms. Here, we present a method that bridges this gap, permitting high-throughput fluorescence-based measurements of in vitro steady-state transcription kinetics. We illustrate how an RNA-aptamer-based detection system can be used to generate quantitative information on direct mechanisms of transcriptional regulation and discuss the far-reaching implications for future applications.

Analytical kinetic model of native tandem promoters in E. coli

Closely spaced promoters in tandem formation are abundant in bacteria. We investigated the evolutionary conservation, biological functions, and the RNA and single-cell protein expression of genes regulated by tandem promoters in E. coli. We also studied the sequence (distance between transcription start sites ‘d_TSS’, pause sequences, and distances from oriC) and potential influence of the input transcription factors of these promoters. From this, we propose an analytical model of gene expression based on measured expression dynamics, where RNAP-promoter occupancy times and d_TSS are the key regulators of transcription interference due to TSS occlusion by RNAP at one of the promoters (when d_TSS ≤ 35 bp) and RNAP occupancy of the downstream promoter (when d_TSS > 35 bp). Occlusion and downstream promoter occupancy are modeled as linear functions of occupancy time, while the influence of d_TSS is implemented by a continuous step function, fit to in vivo data on mean single-cell protein numbers of 30 natural genes controlled by tandem promoters. The best-fitting step is at 35 bp, matching the length of DNA occupied by RNAP in the open complex formation. This model accurately predicts the squared coefficient of variation and skewness of the natural single-cell protein numbers as a function of d_TSS. Additional predictions suggest that promoters in tandem formation can cover a wide range of transcription dynamics within realistic intervals of parameter values. By accurately capturing the dynamics of these promoters, this model can be helpful to predict the dynamics of new promoters and contribute to the expansion of the repertoire of expression dynamics available to synthetic genetic constructs.

Tandem promoters are common in nature, but investigations on their dynamics have so far largely relied on synthetic constructs. Thus, their regulation and potentially unique dynamics remain unexplored. We first performed a comprehensive exploration of the conservation of genes regulated by these promoters in E. coli and the properties of their input transcription factors. We then measured protein and RNA levels expressed by 30 Escherichia coli tandem promoters, to establish an analytical model of the expression dynamics of genes controlled by such promoters. We show that start site occlusion and downstream RNAP occupancy can be realistically captured by a model with RNAP binding affinity, the time length of open complex formation, and the nucleotide distance between transcription start sites. This study contributes to a better understanding of the unique dynamics tandem promoters can bring to the dynamics of gene networks and will assist in their use in synthetic genetic circuits.

Fundamental limits on the rate of bacterial growth and their influence on proteomic composition

Despite abundant measurements of bacterial growth rate, cell size, and protein content, we lack a rigorous understanding of what sets the scale of these quantities and when protein abundances should (or should not) depend on growth rate. Here, we estimate the basic requirements and physical constraints on steady-state growth by considering key processes in cellular physiology across a collection of Escherichia coli proteomic data covering ≈4,000 proteins and 36 growth rates. Our analysis suggests that cells are predominantly tuned for the task of cell doubling across a continuum of growth rates; specific processes do not limit growth rate or dictate cell size. We present a model of proteomic regulation as a function of nutrient supply that reconciles observed interdependences between protein synthesis, cell size, and growth rate and propose that a theoretical inability to parallelize ribosomal synthesis places a firm limit on the achievable growth rate. A record of this paper's transparent peer review process is included in the supplemental information.

The Context-Dependent Influence of Promoter Sequence Motifs on Transcription Initiation Kinetics and Regulation

The fitness of an individual bacterial cell is highly dependent upon the temporal tuning of gene expression levels when subjected to different environmental cues. Kinetic regulation of transcription initiation is a key step in modulating the levels of transcribed genes to promote bacterial survival. The initiation phase encompasses the binding of RNA polymerase (RNAP) to promoter DNA and a series of coupled protein-DNA conformational changes prior to entry into processive elongation. The time required to complete the initiation phase can vary by orders of magnitude and is ultimately dictated by the DNA sequence of the promoter. In this review, we aim to provide the required background to understand how promoter sequence motifs may affect initiation kinetics during promoter recognition and binding, subsequent conformational changes which lead to DNA opening around the transcription start site, and promoter escape. By calculating the steady-state flux of RNA production as a function of these effects, we illustrate that the presence/absence of a consensus promoter motif cannot be used in isolation to make conclusions regarding promoter strength. Instead, the entire series of linked, sequence-dependent structural transitions must be considered holistically. Finally, we describe how individual transcription factors take advantage of the broad distribution of sequence-dependent basal kinetics to either increase or decrease RNA flux.

A Decrease in Transcription Capacity Limits Growth Rate upon Translation Inhibition

Exposure of bacteria to sublethal concentrations of antibiotics can lead to bacterial adaptation and survival at higher doses of inhibitors, which in turn can lead to the emergence of antibiotic resistance. The presence of sublethal concentrations of antibiotics targeting translation results in an increase in the amount of ribosomes per cell but nonetheless a decrease in the cells’ growth rate. In this work, we have found that inhibition of ribosome activity can result in a decrease in the amount of free RNA polymerase available for transcription, thus limiting the protein expression rate via a different pathway than what was expected. This result can be explained by our observation that long genes, such as those coding for RNA polymerase subunits, have a higher probability of premature translation termination in the presence of ribosome inhibitors, while expression of short ribosomal genes is affected less, consistent with their increased concentration.

In bacterial cells, inhibition of ribosomes by sublethal concentrations of antibiotics leads to a decrease in the growth rate despite an increase in ribosome content. The limitation of ribosomal activity results in an increase in the level of expression from ribosomal promoters; this can deplete the pool of RNA polymerase (RNAP) that is available for the expression of nonribosomal genes. However, the magnitude of this effect remains to be quantified. Here, we use the change in the activity of constitutive promoters with different affinities for RNAP to quantify the change in the concentration of free RNAP. The data are consistent with a significant decrease in the amount of RNAP available for transcription of both ribosomal and nonribosomal genes. Results obtained with different reporter genes reveal an mRNA length dependence on the amount of full-length translated protein, consistent with the decrease in ribosome processivity affecting more strongly the translation of longer genes. The genes coding for the β and β' subunits of RNAP are among the longest genes in the Escherichia coli genome, while the genes coding for ribosomal proteins are among the shortest genes. This can explain the observed decrease in transcription capacity that favors the expression of genes whose promoters have a high affinity for RNAP, such as ribosomal promoters.

IMPORTANCE Exposure of bacteria to sublethal concentrations of antibiotics can lead to bacterial adaptation and survival at higher doses of inhibitors, which in turn can lead to the emergence of antibiotic resistance. The presence of sublethal concentrations of antibiotics targeting translation results in an increase in the amount of ribosomes per cell but nonetheless a decrease in the cells’ growth rate. In this work, we have found that inhibition of ribosome activity can result in a decrease in the amount of free RNA polymerase available for transcription, thus limiting the protein expression rate via a different pathway than what was expected. This result can be explained by our observation that long genes, such as those coding for RNA polymerase subunits, have a higher probability of premature translation termination in the presence of ribosome inhibitors, while expression of short ribosomal genes is affected less, consistent with their increased concentration.

Chromosomal Organization and Regulation of Genetic Function in Escherichia coli Integrates the DNA Analog and Digital Information

In this article, we summarize our current understanding of the bacterial genetic regulation brought about by decades of studies using the Escherichia coli model. It became increasingly evident that the cellular genetic regulation system is organizationally closed, and a major challenge is to describe its circular operation in quantitative terms. We argue that integration of the DNA analog information (i.e., the probability distribution of the thermodynamic stability of base steps) and digital information (i.e., the probability distribution of unique triplets) in the genome provides a key to understanding the organizational logic of genetic control. During bacterial growth and adaptation, this integration is mediated by changes of DNA supercoiling contingent on environmentally induced shifts in intracellular ionic strength and energy charge. More specifically, coupling of dynamic alterations of the local intrinsic helical repeat in the structurally heterogeneous DNA polymer with structural-compositional changes of RNA polymerase holoenzyme emerges as a fundamental organizational principle of the genetic regulation system. We present a model of genetic regulation integrating the genomic pattern of DNA thermodynamic stability with the gene order and function along the chromosomal OriC-Ter axis, which acts as a principal coordinate system organizing the regulatory interactions in the genome.

Heterogeneity coordinates bacterial multi-gene expression in single cells

For a genetically identical microbial population, multi-gene expression in various environments requires effective allocation of limited resources and precise control of heterogeneity among individual cells. However, it is unclear how resource allocation and cell-to-cell variation jointly shape the overall performance. Here we demonstrate a Simpson’s paradox during overexpression of multiple genes: two competing proteins in single cells correlated positively for every induction condition, but the overall correlation was negative. Yet this phenomenon was not observed between two competing mRNAs in single cells. Our analytical framework shows that the phenomenon arises from competition for translational resource, with the correlation modulated by both mRNA and ribosome variability. Thus, heterogeneity plays a key role in single-cell multi-gene expression and provides the population with an evolutionary advantage, as demonstrated in this study.

Microbes perform multitasking for a wide range of purposes, including survival, adaptation, colonization, and evolution. Both modelling and experimental results at the ensemble level reveal trade-offs between different tasks due to resource competition, but it is unclear how single cells allocate limited intracellular resources to perform multitasking, and how does a population coordinate single cell performances during multitasking to maximize population efficiencies. In this study, we address this question by using bacterial multi-gene overexpression as the basic form of multitasking. We discovered and analyzed a statistical phenomenon called Simpson’s paradox, where competing proteins in single cells correlate positively at each constant condition, although the proteins correlate negatively when all conditions are combined. We demonstrate that the phenomenon arises from competition for translational resources, with the correlation modulated by heterogeneity of both mRNA and ribosomes. We further show that heterogeneity coordinates multiple functional modules, conferring an evolutionary advantage on the population. Our work discloses that heterogeneity in the form of Simpson’s paradox is an important phenomenon in coordinating multi-gene expression.

Transcription closed and open complex formation coordinate expression of genes with a shared promoter region

Many genes are spaced closely, allowing coordination without explicit control through shared regulatory elements and molecular interactions. We study the dynamics of a stochastic model of a gene-pair in a head-to-head configuration, sharing promoter elements, which accounts for the rate-limiting steps in transcription initiation. We find that only in specific regions of the parameter space of the rate-limiting steps is orderly coexpression exhibited, suggesting that successful cooperation between closely spaced genes requires the coevolution of compatible rate-limiting step configuration. The model predictions are validated using in vivo single-cell, single-RNA measurements of the dynamics of pairs of genes sharing promoter elements. Our results suggest that, in E. coli, the kinetics of the rate-limiting steps in active transcription can play a central role in shaping the dynamics of gene-pairs sharing promoter elements.

Pleione: A tool for statistical and multi-objective calibration of Rule-based models

Mathematical models based on Ordinary Differential Equations (ODEs) are frequently used to describe and simulate biological systems. Nevertheless, such models are often difficult to understand. Unlike ODE models, Rule-Based Models (RBMs) utilise formal language to describe reactions as a cumulative number of statements that are easier to understand and correct. They are also gaining popularity because of their conciseness and simulation flexibility. However, RBMs generally lack tools to perform further analysis that requires simulation. This situation arises because exact and approximate simulations are computationally intensive. Translating RBMs into ODEs is commonly used to reduce simulation time, but this technique may be prohibitive due to combinatorial explosion. Here, we present the software called Pleione to calibrate RBMs. Parameter calibration is essential given the incomplete experimental determination of reaction rates and the goal of using models to reproduce experimental data. The software distributes stochastic simulations and calculations and incorporates equivalence tests to determine the fitness of RBMs compared with data. The primary features of Pleione were thoroughly tested on a model of gene regulation in Escherichia coli. Pleione yielded satisfactory results regarding calculation time and error reduction for multiple simulators, models, parameter search strategies, and computing infrastructures.

Component Characterization in a Growth-Dependent Physiological Context: Optimal Experimental Design

Synthetic biology design challenges have driven the use of mathematical models to characterize genetic components and to explore complex design spaces. Traditional approaches to characterization have largely ignored the effect of strain and growth conditions on the dynamics of synthetic genetic circuits, and have thus confounded intrinsic features of the circuit components with cell-level context effects. We present a model that distinguishes an activated gene’s intrinsic kinetics from its physiological context. We then demonstrate an optimal experimental design approach to identify dynamic induction experiments for efficient estimation of the component’s intrinsic parameters. Maximally informative experiments are chosen by formulating the design as an optimal control problem; direct multiple-shooting is used to identify the optimum. Our numerical results suggest that the intrinsic parameters of a genetic component can be more accurately estimated using optimal experimental designs, and that the choice of growth rates, sampling schedule, and input profile each play an important role. The proposed approach to coupled component–host modelling can support gene circuit design across a range of physiological conditions.

Added value of autoregulation and multi-step kinetics of transcription initiation

Bacterial gene expression regulation occurs mostly during transcription, which has two main rate-limiting steps: the close complex formation, when the RNA polymerase binds to an active promoter, and the subsequent open complex formation, after which it follows elongation. Tuning these steps' kinetics by the action of e.g. transcription factors, allows for a wide diversity of dynamics. For example, adding autoregulation generates single-gene circuits able to perform more complex tasks. Using stochastic models of transcription kinetics with empirically validated parameter values, we investigate how autoregulation and the multi-step transcription initiation kinetics of single-gene autoregulated circuits can be combined to fine-tune steady state mean and cell-to-cell variability in protein expression levels, as well as response times. Next, we investigate how they can be jointly tuned to control complex behaviours, namely, time counting, switching dynamics and memory storage. Overall, our finding suggests that, in bacteria, jointly regulating a single-gene circuit's topology and the transcription initiation multi-step dynamics allows enhancing complex task performance.

Homeostasis of protein and mRNA concentrations in growing cells

Many experiments show that the numbers of mRNA and protein are proportional to the cell volume in growing cells. However, models of stochastic gene expression often assume constant transcription rate per gene and constant translation rate per mRNA, which are incompatible with these experiments. Here, we construct a minimal gene expression model to fill this gap. Assuming ribosomes and RNA polymerases are limiting in gene expression, we show that the numbers of proteins and mRNAs both grow exponentially during the cell cycle and that the concentrations of all mRNAs and proteins achieve cellular homeostasis; the competition between genes for the RNA polymerases makes the transcription rate independent of the genome number. Furthermore, by extending the model to situations in which DNA (mRNA) can be saturated by RNA polymerases (ribosomes) and becomes limiting, we predict a transition from exponential to linear growth of cell volume as the protein-to-DNA ratio increases.

Broad-scale redistribution of mRNA abundance and transcriptional machinery in response to growth rate in Salmonella enterica serovar Typhimurium

We have investigated the connection between the four-dimensional architecture of the bacterial nucleoid and the organism's global gene expression programme. By localizing the transcription machinery and the transcriptional outputs across the genome of the model bacterium Salmonella enterica serovar Typhimurium at different stages of the growth cycle, a surprising disconnection between gene dosage and transcriptional output was revealed. During exponential growth, gene output occurred chiefly in the Ori (origin), Ter (terminus) and NSL (non-structured left) domains, whereas the Left macrodomain remained transcriptionally quiescent at all stages of growth. The apparently high transcriptional output in Ter was correlated with an enhanced stability of the RNA expressed there during exponential growth, suggesting that longer mRNA half-lives compensate for low gene dosage. During exponential growth, RNA polymerase (RNAP) was detected everywhere, whereas in stationary phase cells, RNAP was concentrated in the Ter macrodomain. The alternative sigma factors RpoE, RpoH and RpoN were not required to drive transcription in these growth conditions, consistent with their observed binding to regions away from RNAP and regions of active transcription. Specifically, these alternative sigma factors were found in the Ter macrodomain during exponential growth, whereas they were localized at the Ori macrodomain in stationary phase.

Stochastic simulation of multiscale complex systems with PISKaS: A rule-based approach

Computational simulation is a widely employed methodology to study the dynamic behavior of complex systems. Although common approaches are based either on ordinary differential equations or stochastic differential equations, these techniques make several assumptions which, when it comes to biological processes, could often lead to unrealistic models. Among others, model approaches based on differential equations entangle kinetics and causality, failing when complexity increases, separating knowledge from models, and assuming that the average behavior of the population encompasses any individual deviation. To overcome these limitations, simulations based on the Stochastic Simulation Algorithm (SSA) appear as a suitable approach to model complex biological systems. In this work, we review three different models executed in PISKaS: a rule-based framework to produce multiscale stochastic simulations of complex systems. These models span multiple time and spatial scales ranging from gene regulation up to Game Theory. In the first example, we describe a model of the core regulatory network of gene expression in Escherichia coli highlighting the continuous model improvement capacities of PISKaS. The second example describes a hypothetical outbreak of the Ebola virus occurring in a compartmentalized environment resembling cities and highways. Finally, in the last example, we illustrate a stochastic model for the prisoner's dilemma; a common approach from social sciences describing complex interactions involving trust within human populations. As whole, these models demonstrate the capabilities of PISKaS providing fertile scenarios where to explore the dynamics of complex systems.

Rate-limiting steps in transcription dictate sensitivity to variability in cellular components

Cell-to-cell variability in cellular components generates cell-to-cell diversity in RNA and protein production dynamics. As these components are inherited, this should also cause lineage-to-lineage variability in these dynamics. We conjectured that these effects on transcription are promoter initiation kinetics dependent. To test this, first we used stochastic models to predict that variability in the numbers of molecules involved in upstream processes, such as the intake of inducers from the environment, acts only as a transient source of variability in RNA production numbers, while variability in the numbers of a molecular species controlling transcription of an active promoter acts as a constant source. Next, from single-cell, single-RNA level time-lapse microscopy of independent lineages of Escherichia coli cells, we demonstrate the existence of lineage-to-lineage variability in gene activation times and mean RNA production rates, and that these variabilities differ between promoters and inducers used. Finally, we provide evidence that this can be explained by differences in the kinetics of the rate-limiting steps in transcription between promoters and induction schemes. We conclude that cell-to-cell and consequent lineage-to-lineage variability in RNA and protein numbers are both promoter sequence-dependent and subject to regulation.

Diffusion of a Rouse chain in porous media: A mode-coupling-theory study

We use a kinetic mode-coupling theory (MCT) combining with generalized Langevin equation (GLE) to study the diffusion and conformational dynamics of a bead-spring Rouse chain (RC) dissolved in porous media. The media contains fluid particles and immobile matrix ones wherein the latter leads to the lack of translational invariance. The friction kernel ζ(t) used in the GLE can be obtained directly by adopting a simple density-functional approach in which the density correlators calculated by MCT equations of porous media serve as inputs. Due to cage effects generated by surrounding particles, ζ(t) shows a very long tail memory in the high volume fraction of fluid and matrix. It is found that the long-time center-of-mass diffusion constant D_{CM} of the RC decreases with the increment of volume fraction, influencing more strongly by the matrix particles than by the fluid ones. The auto-correlation function (ACF) of the end-to-end distance fluctuation can also be calculated theoretically based on GLE. Of particular interest is that the power-law region of ACF has a nearly fixed length in logarithmic scale when it shifts to longer time range, with increasing the volume fraction of media particles. Moreover, the effect of lack of translational invariance has been investigated by comparing the results between fluid-matrix and pure fluid cases under identical total volume fraction.

Bacterial RNA Polymerase-DNA Interaction-The Driving Force of Gene Expression and the Target for Drug Action

DNA-dependent multisubunit RNA polymerase (RNAP) is the key enzyme of gene expression and a target of regulation in all kingdoms of life. It is a complex multifunctional molecular machine which, unlike other DNA-binding proteins, engages in extensive and dynamic interactions (both specific and nonspecific) with DNA, and maintains them over a distance. These interactions are controlled by DNA sequences, DNA topology, and a host of regulatory factors. Here, we summarize key recent structural and biochemical studies that elucidate the fine details of RNAP-DNA interactions during initiation. The findings of these studies help unravel the molecular mechanisms of promoter recognition and open complex formation, initiation of transcript synthesis and promoter escape. We also discuss most current advances in the studies of drugs that specifically target RNAP-DNA interactions during transcription initiation and elongation.

A Synonymous Mutation Upstream of the Gene Encoding a Weak-Link Enzyme Causes an Ultrasensitive Response in Growth Rate

When microbes are faced with an environmental challenge or opportunity, preexisting enzymes with promiscuous secondary activities can be recruited to provide newly important functions. Mutations that increase the efficiency of a new activity often compromise the original activity, resulting in an inefficient bifunctional enzyme. We have investigated the mechanisms by which growth of Escherichia coli can be improved when fitness is limited by such an enzyme, E383A ProA (ProA*). ProA* can serve the functions of both ProA (required for synthesis of proline) and ArgC (required for synthesis of arginine), albeit poorly. We identified four genetic changes that improve the growth rate by up to 6.2-fold. Two point mutations in the promoter of the proBA* operon increase expression of the entire operon. Massive amplification of a genomic segment around the proBA* operon also increases expression of the entire operon. Finally, a synonymous point mutation in the coding region of proB creates a new promoter for proA* This synonymous mutation increases the level of ProA* by 2-fold but increases the growth rate by 5-fold, an ultrasensitive response likely arising from competition between two substrates for the active site of the inefficient bifunctional ProA*.

IMPORTANCE

The high-impact synonymous mutation we discovered in proB is remarkable for two reasons. First, most polar effects documented in the literature are detrimental. This finding demonstrates that polar effect mutations can have strongly beneficial effects, especially when an organism is facing a difficult environmental challenge for which it is poorly adapted. Furthermore, the consequence of the synonymous mutation in proB is a 2-fold increase in the level of ProA* but a disproportionately large 5.1-fold increase in growth rate. While ultrasensitive responses are often found in signaling networks and genetic circuits, an ultrasensitive response to an adaptive mutation has not been previously reported.

Structural basis of transcription activation

Class II transcription activators function by binding to a DNA site overlapping a core promoter and stimulating isomerization of an initial RNA polymerase (RNAP)-promoter closed complex into a catalytically competent RNAP-promoter open complex. Here, we report a 4.4 angstrom crystal structure of an intact bacterial class II transcription activation complex. The structure comprises Thermus thermophilus transcription activator protein TTHB099 (TAP) [homolog of Escherichia coli catabolite activator protein (CAP)], T. thermophilus RNAP σ(A) holoenzyme, a class II TAP-dependent promoter, and a ribotetranucleotide primer. The structure reveals the interactions between RNAP holoenzyme and DNA responsible for transcription initiation and reveals the interactions between TAP and RNAP holoenzyme responsible for transcription activation. The structure indicates that TAP stimulates isomerization through simple, adhesive, stabilizing protein-protein interactions with RNAP holoenzyme.