Increased rrn gene dosage causes intermittent transcription of rRNA in Escherichia coli

Understanding spatiotemporal coupling of gene expression using single molecule RNA imaging technologies

Across all kingdoms of life, gene regulatory mechanisms underlie cellular adaptation to ever-changing environments. Regulation of gene expression adjusts protein synthesis and, in turn, cellular growth. Messenger RNAs are key molecules in the process of gene expression. Our ability to quantitatively measure mRNA expression in single cells has improved tremendously over the past decades. This revealed an unexpected coordination between the steps that control the life of an mRNA, from transcription to degradation. Here, we provide an overview of the state-of-the-art imaging approaches for measurement and quantitative understanding of gene expression, starting from the early visualizations of single genes by electron microscopy to current fluorescence-based approaches in single cells, including live-cell RNA-imaging approaches to FISH-based spatial transcriptomics across model organisms. We also highlight how these methods have shaped our current understanding of the spatiotemporal coupling between transcriptional and post-transcriptional events in prokaryotes. We conclude by discussing future challenges of this multidisciplinary field.Abbreviations: mRNA: messenger RNA; rRNA: ribosomal rDNA; tRNA: transfer RNA; sRNA: small RNA; FISH: fluorescence in situ hybridization; RNP: ribonucleoprotein; smFISH: single RNA molecule FISH; smiFISH: single molecule inexpensive FISH; HCR-FISH: Hybridization Chain-Reaction-FISH; RCA: Rolling Circle Amplification; seqFISH: Sequential FISH; MERFISH: Multiplexed error robust FISH; UTR: Untranslated region; RBP: RNA binding protein; FP: fluorescent protein; eGFP: enhanced GFP, MCP: MS2 coat protein; PCP: PP7 coat protein; MB: Molecular beacons; sgRNA: single guide RNA.

rRNA operon multiplicity as a bacterial genome stability insurance policy

Quick growth restart after upon encountering favourable environmental conditions is a major fitness contributor in natural environment. It is widely assumed that the time required to restart growth after nutritional upshift is determined by how long it takes for cells to synthesize enough ribosomes to produce the proteins required to reinitiate growth. Here we show that a reduction in the capacity to synthesize ribosomes by reducing number of ribosomal RNA (rRNA) operons (rrn) causes a longer transition from stationary phase to growth of Escherichia coli primarily due to high mortality rates. Cell death results from DNA replication blockage and massive DNA breakage at the sites of the remaining rrn operons that become overloaded with RNA polymerases (RNAPs). Mortality rates and growth restart duration can be reduced by preventing R-loop formation and improving DNA repair capacity. The same molecular mechanisms determine the duration of the recovery phase after ribosome-damaging stresses, such as antibiotics, exposure to bile salts or high temperature. Our study therefore suggests that a major function of rrn operon multiplicity is to ensure that individual rrn operons are not saturated by RNAPs, which can result in catastrophic chromosome replication failure and cell death during adaptation to environmental fluctuations.

Trade-offs among transcription elongation rate, number, and duration of ubiquitous pauses on highly transcribed bacterial genes

In this paper, we study the limitations imposed on the transcription process by the presence of short ubiquitous pauses and crowding. These effects are especially pronounced in highly transcribed genes such as ribosomal genes (rrn) in fast growing bacteria. Our model indicates that the quantity and duration of pauses reported for protein-coding genes is incompatible with the average elongation rate observed in rrn genes. When maximal elongation rate is high, pause-induced traffic jams occur, increasing promoter occlusion, thereby lowering the initiation rate. This lowers average transcription rate and increases average transcription time. Increasing maximal elongation rate in the model is insufficient to match the experimentally observed average elongation rate in rrn genes. This suggests that there may be rrn-specific modifications to RNAP, which then experience fewer pauses, or pauses of shorter duration than those in protein-coding genes. We identify model parameter triples (maximal elongation rate, mean pause duration time, number of pauses) which are compatible with experimentally observed elongation rates. Average transcription time and average transcription rate are the model outputs investigated as proxies for cell fitness. These fitness functions are optimized for different parameter choices, opening up a possibility of differential control of these aspects of the elongation process, with potential evolutionary consequences. As an example, a gene's average transcription time may be crucial to fitness when the surrounding medium is prone to abrupt changes. This paper demonstrates that a functional relationship among the model parameters can be estimated using a standard statistical analysis, and this functional relationship describes the various trade-offs that must be made in order for the gene to control the elongation process and achieve a desired average transcription time. It also demonstrates the robustness of the system when a range of maximal elongation rates can be balanced with transcriptional pause data in order to maintain a desired fitness.

Long-Distance Cooperative and Antagonistic RNA Polymerase Dynamics via DNA Supercoiling

Genes are often transcribed by multiple RNA polymerases (RNAPs) at densities that can vary widely across genes and environmental conditions. Here, we provide in vitro and in vivo evidence for a built-in mechanism by which co-transcribing RNAPs display either collaborative or antagonistic dynamics over long distances (>2 kb) through transcription-induced DNA supercoiling. In Escherichia coli, when the promoter is active, co-transcribing RNAPs translocate faster than a single RNAP, but their average speed is not altered by large variations in promoter strength and thus RNAP density. Environmentally induced promoter repression reduces the elongation efficiency of already-loaded RNAPs, causing premature termination and quick synthesis arrest of no-longer-needed proteins. This negative effect appears independent of RNAP convoy formation and is abrogated by topoisomerase I activity. Antagonistic dynamics can also occur between RNAPs from divergently transcribed gene pairs. Our findings may be broadly applicable given that transcription on topologically constrained DNA is the norm across organisms.

Probing Mechanisms of Transcription Elongation Through Cell-to-Cell Variability of RNA Polymerase

The process of transcription initiation and elongation are primary points of control in the regulation of gene expression. Although biochemical studies have uncovered the mechanisms involved in controlling transcription at each step, how these mechanisms manifest in vivo at the level of individual genes is still unclear. Recent experimental advances have enabled single-cell measurements of RNA polymerase (RNAP) molecules engaged in the process of transcribing a gene of interest. In this article, we use Gillespie simulations to show that measurements of cell-to-cell variability of RNAP numbers and interpolymerase distances can reveal the prevailing mode of regulation of a given gene. Mechanisms of regulation at each step, from initiation to elongation dynamics, produce qualitatively distinct signatures, which can further be used to discern between them. Most intriguingly, depending on the initiation kinetics, stochastic elongation can either enhance or suppress cell-to-cell variability at the RNAP level. To demonstrate the value of this framework, we analyze RNAP number distribution data for ribosomal genes in Saccharomyces cerevisiae from three previously published studies and show that this approach provides crucial mechanistic insights into the transcriptional regulation of these genes.

A versatile cis-acting element reporter system to study the function, maturation and stability of ribosomal RNA mutants in archaea

General molecular principles of ribosome biogenesis have been well explored in bacteria and eukaryotes. Collectively, these studies have revealed important functional differences and few similarities between these processes. Phylogenetic studies suggest that the information processing machineries from archaea and eukaryotes are evolutionary more closely related than their bacterial counterparts. These observations raise the question of how ribosome synthesis in archaea may proceed in vivo. In this study, we describe a versatile plasmid-based cis-acting reporter system allowing to analyze in vivo the consequences of ribosomal RNA mutations in the model archaeon Haloferax volcanii. Applying this system, we provide evidence that the bulge-helix-bulge motif enclosed within the ribosomal RNA processing stems is required for the formation of archaeal-specific circular-pre-rRNA intermediates and mature rRNAs. In addition, we have collected evidences suggesting functional coordination of the early steps of ribosome synthesis in H. volcanii. Together our investigation describes a versatile platform allowing to generate and functionally analyze the fate of diverse rRNA variants, thereby paving the way to better understand the cis-acting molecular determinants necessary for archaeal ribosome synthesis, maturation, stability and function.

Decoding the grammar of transcriptional regulation from RNA polymerase measurements: models and their applications

The genomic revolution has indubitably brought about a paradigm shift in the field of molecular biology, wherein we can sequence, write and re-write genomes. In spite of achieving such feats, we still lack a quantitative understanding of how cells integrate environmental and intra-cellular signals at the promoter and accordingly regulate the production of messenger RNAs. This current state of affairs is being redressed by recent experimental breakthroughs which enable the counting of RNA polymerase molecules (or the corresponding nascent RNAs) engaged in the process of transcribing a gene at the single-cell level. Theorists, in conjunction, have sought to unravel the grammar of transcriptional regulation by harnessing the various statistical properties of these measurements. In this review, we focus on the recent progress in developing falsifiable models of transcription that aim to connect the molecular mechanisms of transcription to single-cell polymerase measurements. We discuss studies where the application of such models to the experimental data have led to novel mechanistic insights into the process of transcriptional regulation. Such interplay between theory and experiments will likely contribute towards the exciting journey of unfurling the governing principles of transcriptional regulation ranging from bacteria to higher organisms.

Distribution of Initiation Times Reveals Mechanisms of Transcriptional Regulation in Single Cells

Transcription is the dominant point of control of gene expression. Biochemical studies have revealed key molecular components of transcription and their interactions, but the dynamics of transcription initiation in cells is still poorly understood. This state of affairs is being remedied with experiments that observe transcriptional dynamics in single cells using fluorescent reporters. Quantitative information about transcription initiation dynamics can also be extracted from experiments that use electron micrographs of RNA polymerases caught in the act of transcribing a gene (Miller spreads). Inspired by these data, we analyze a general stochastic model of transcription initiation and elongation and compute the distribution of transcription initiation times. We show that different mechanisms of initiation leave distinct signatures in the distribution of initiation times that can be compared to experiments. We analyze published data from micrographs of RNA polymerases transcribing ribosomal RNA genes in Escherichia coli and compare the observed distributions of interpolymerase distances with the predictions from previously hypothesized mechanisms for the regulation of these genes. Our analysis demonstrates the potential of measuring the distribution of time intervals between initiation events as a probe for dissecting mechanisms of transcription initiation in live cells.

Effects of mRNA Degradation and Site-Specific Transcriptional Pausing on Protein Expression Noise

Genetically identical cells exhibit diverse phenotypes even when experiencing the same environment. This phenomenon in part originates from cell-to-cell variability (noise) in protein expression. Although various kinetic schemes of stochastic transcription initiation are known to affect gene expression noise, how posttranscription initiation events contribute to noise at the protein level remains incompletely understood. To address this question, we developed a stochastic simulation-based model of bacterial gene expression that integrates well-known dependencies between transcription initiation, transcription elongation dynamics, mRNA degradation, and translation. We identified realistic conditions under which mRNA lifetime and transcriptional pauses modulate the protein expression noise initially introduced by the promoter architecture. For instance, we found that the short lifetime of bacterial mRNAs facilitates the production of protein bursts. Conversely, RNA polymerase (RNAP) pausing at specific sites during transcription elongation can attenuate protein bursts by fluidizing the RNAP traffic to the point of erasing the effect of a bursty promoter. Pause-prone sites, if located close to the promoter, can also affect noise indirectly by reducing both transcription and translation initiation due to RNAP and ribosome congestion. Our findings highlight how the interplay between transcription initiation, transcription elongation, translation, and mRNA degradation shapes the distribution in protein numbers. They also have implications for our understanding of gene evolution and suggest combinatorial strategies for modulating phenotypic variability by genetic engineering.

Deciphering Transcriptional Dynamics In Vivo by Counting Nascent RNA Molecules

Deciphering how the regulatory DNA sequence of a gene dictates its expression in response to intra and extracellular cues is one of the leading challenges in modern genomics. The development of novel single-cell sequencing and imaging techniques, as well as a better exploitation of currently available single-molecule imaging techniques, provides an avenue to interrogate the process of transcription and its dynamics in cells by quantifying the number of RNA polymerases engaged in the transcription of a gene (or equivalently the number of nascent RNAs) at a given moment in time. In this paper, we propose that measurements of the cell-to-cell variability in the number of nascent RNAs provide a mostly unexplored method for deciphering mechanisms of transcription initiation in cells. We propose a simple kinetic model of transcription initiation and elongation from which we calculate nascent RNA copy-number fluctuations. To demonstrate the usefulness of this approach, we test our theory against published nascent RNA data for twelve constitutively expressed yeast genes. Rather than transcription being initiated through a single rate limiting step, as it had been previously proposed, our single-cell analysis reveals the presence of at least two rate limiting steps. Surprisingly, half of the genes analyzed have nearly identical rates of transcription initiation, suggesting a common mechanism. Our analytical framework can be used to extract quantitative information about dynamics of transcription from single-cell sequencing data, as well as from single-molecule imaging and electron micrographs of fixed cells, and provides the mathematical means to exploit the quantitative power of these technologies.

Engineered ribosomal RNA operon copy-number variants of E. coli reveal the evolutionary trade-offs shaping rRNA operon number

Ribosomal RNA (rrn) operons, characteristically present in several copies in bacterial genomes (7 in E. coli), play a central role in cellular physiology. We investigated the factors determining the optimal number of rrn operons in E. coli by constructing isogenic variants with 5–10 operons. We found that the total RNA and protein content, as well as the size of the cells reflected the number of rrn operons. While growth parameters showed only minor differences, competition experiments revealed a clear pattern: 7–8 copies were optimal under conditions of fluctuating, occasionally rich nutrient influx and lower numbers were favored in stable, nutrient-limited environments. We found that the advantages of quick adjustment to nutrient availability, rapid growth and economic regulation of ribosome number all contribute to the selection of the optimal rrn operon number. Our results suggest that the wt rrn operon number of E. coli reflects the natural, ‘feast and famine’ life-style of the bacterium, however, different copy numbers might be beneficial under different environmental conditions. Understanding the impact of the copy number of rrn operons on the fitness of the cell is an important step towards the creation of functional and robust genomes, the ultimate goal of synthetic biology.

Stochastic models of transcription: from single molecules to single cells

Genes in prokaryotic and eukaryotic cells are typically regulated by complex promoters containing multiple binding sites for a variety of transcription factors leading to a specific functional dependence between regulatory inputs and transcriptional outputs. With increasing regularity, the transcriptional outputs from different promoters are being measured in quantitative detail in single-cell experiments thus providing the impetus for the development of quantitative models of transcription. We describe recent progress in developing models of transcriptional regulation that incorporate, to different degrees, the complexity of multi-state promoter dynamics, and its effect on the transcriptional outputs of single cells. The goal of these models is to predict the statistical properties of transcriptional outputs and characterize their variability in time and across a population of cells, as a function of the input concentrations of transcription factors. The interplay between mathematical models of different regulatory mechanisms and quantitative biophysical experiments holds the promise of elucidating the molecular-scale mechanisms of transcriptional regulation in cells, from bacteria to higher eukaryotes.

Growth rate regulation in Escherichia coli

Growth rate regulation in bacteria has been an important issue in bacterial physiology for the past 50 years. This review, using Escherichia coli as a paradigm, summarizes the mechanisms for the regulation of rRNA synthesis in the context of systems biology, particularly, in the context of genome-wide competition for limited RNA polymerase (RNAP) in the cell under different growth conditions including nutrient starvation. The specific location of the seven rrn operons in the chromosome and the unique properties of the rrn promoters contribute to growth rate regulation. The length of the rrn transcripts, coupled with gene dosage effects, influence the distribution of RNAP on the chromosome in response to growth rate. Regulation of rRNA synthesis depends on multiple factors that affect the structure of the nucleoid and the allocation of RNAP for global gene expression. The magic spot ppGpp, which acts with DksA synergistically, is a key effector in both the growth rate regulation and the stringent response induced by nutrient starvation, mainly because the ppGpp level changes in response to environmental cues. It regulates rRNA synthesis via a cascade of events including both transcription initiation and elongation, and can be explained by an RNAP redistribution (allocation) model.

Varying rate of RNA chain elongation during rrn transcription in Escherichia coli

The value of the rRNA chain elongation rate in bacteria is an important physiological parameter, as it affects not only the rRNA promoter activity but also the free-RNA polymerase concentration and thereby the transcription of all genes. On average, rRNA chains elongate at a rate of 80 to 90 nucleotides (nt) per s, and the transcription of an entire rrn operon takes about 60 s (at 37 degrees C). Here we have analyzed a reported distribution obtained from electron micrographs of RNA polymerase molecules along rrn operons in E. coli growing at 2.5 doublings per hour (S. Quan, N. Zhang, S. French, and C. L. Squires, J. Bacteriol. 187:1632-1638, 2005). The distribution exhibits two peaks of higher polymerase density centered within the 16S and 23S rRNA genes. An evaluation of this distribution indicates that RNA polymerase transcribes the 5' leader region at speeds up to or greater than 250 nt/s. Once past the leader, transcription slows down to about 65 nt/s within the 16S gene, speeds up in the spacer region between the 16S and 23S genes, slows again to about 65 nt/s in the 23S region, and finally speeds up to a rate greater than 400 nt/s near the end of the operon. We suggest that the slowing of transcript elongation in the 16S and 23S sections is the result of transcriptional pauses, possibly caused by temporary interactions of the RNA polymerase with secondary structures in the nascent rRNA.

Genome-scale reconstruction of Escherichia coli's transcriptional and translational machinery: a knowledge base, its mathematical formulation, and its functional characterization

Metabolic network reconstructions represent valuable scaffolds for ‘-omics’ data integration and are used to computationally interrogate network properties. However, they do not explicitly account for the synthesis of macromolecules (i.e., proteins and RNA). Here, we present the first genome-scale, fine-grained reconstruction of Escherichia coli's transcriptional and translational machinery, which produces 423 functional gene products in a sequence-specific manner and accounts for all necessary chemical transformations. Legacy data from over 500 publications and three databases were reviewed, and many pathways were considered, including stable RNA maturation and modification, protein complex formation, and iron–sulfur cluster biogenesis. This reconstruction represents the most comprehensive knowledge base for these important cellular functions in E. coli and is unique in its scope. Furthermore, it was converted into a mathematical model and used to: (1) quantitatively integrate gene expression data as reaction constraints and (2) compute functional network states, which were compared to reported experimental data. For example, the model predicted accurately the ribosome production, without any parameterization. Also, in silico rRNA operon deletion suggested that a high RNA polymerase density on the remaining rRNA operons is needed to reproduce the reported experimental ribosome numbers. Moreover, functional protein modules were determined, and many were found to contain gene products from multiple subsystems, highlighting the functional interaction of these proteins. This genome-scale reconstruction of E. coli's transcriptional and translational machinery presents a milestone in systems biology because it will enable quantitative integration of ‘-omics’ datasets and thus the study of the mechanistic principles underlying the genotype–phenotype relationship.

Systems biology aims to understand the interactions of cellular components in a systemic manner. Mathematical modeling is critical to the integration and analysis of these components on a conceptual as well as mechanistic level. To date, detailed genome-scale reconstructions of metabolism have become available for a growing number of organisms. Although metabolism has an important role in cells, other cellular functions need to be considered as well, such as signaling, regulation, and macromolecular synthesis. For instance, the cellular machinery required for RNA and protein synthesis consists of a complex set of proteins. Here, we show that one can collect all of the necessary information for a prokaryotic organism to create a gene-specific, fine-grained representation of the macromolecular synthesis machinery. E. coli was chosen as a model organism because of the wealth of available information. The explicit representation of transcription and translation in terms of a mass-balanced network enables a detailed, quantitative accounting of the protein synthesis capabilities of E. coli in silico. Hence, this study demonstrates the feasibility of constructing very large networks and also represents a critical step toward building cellular models of growth that can account for gene-specific protein production in a stoichiometric fashion on the genome scale.

A coarse-grained biophysical model of E. coli and its application to perturbation of the rRNA operon copy number

We propose a biophysical model of Escherichia coli that predicts growth rate and an effective cellular composition from an effective, coarse-grained representation of its genome. We assume that E. coli is in a state of balanced exponential steady-state growth, growing in a temporally and spatially constant environment, rich in resources. We apply this model to a series of past measurements, where the growth rate and rRNA-to-protein ratio have been measured for seven E. coli strains with an rRNA operon copy number ranging from one to seven (the wild-type copy number). These experiments show that growth rate markedly decreases for strains with fewer than six copies. Using the model, we were able to reproduce these measurements. We show that the model that best fits these data suggests that the volume fraction of macromolecules inside E. coli is not fixed when the rRNA operon copy number is varied. Moreover, the model predicts that increasing the copy number beyond seven results in a cytoplasm densely packed with ribosomes and proteins. Assuming that under such overcrowded conditions prolonged diffusion times tend to weaken binding affinities, the model predicts that growth rate will not increase substantially beyond the wild-type growth rate, as indicated by other experiments. Our model therefore suggests that changing the rRNA operon copy number of wild-type E. coli cells growing in a constant rich environment does not substantially increase their growth rate. Other observations regarding strains with an altered rRNA operon copy number, such as nucleoid compaction and the rRNA operon feedback response, appear to be qualitatively consistent with this model. In addition, we discuss possible design principles suggested by the model and propose further experiments to test its validity.

A bacterium like E. coli can be thought of as a self-replicating factory, where inventory synthesis, degradation, and management is concerted according to a well-defined set of rules encoded in the organism's genome. Since the organism's survival depends on this set of rules, these rules were most likely optimized by evolution. Therefore, by writing down these rules, what could one learn about Escherichia coli? We examined E. coli growing in the simplest imaginable environment, one constant in space and time and rich in resources, and attempted to identify the rules that relate the genome to the cell composition and self-replication time. With more than 4,400 genes, a full-scale model would be prohibitively complicated, and therefore we “coarse-grained” E. coli by lumping together genes and proteins of similar function. We used this model to examine measurements of strains with reduced copy number of ribosomal-RNA genes, and also to show that increasing this copy number overcrowds the cell with ribosomes and proteins. As a result, there appears to be an optimum copy number with respect to the wild-type genome, in agreement with observation. We hope that this model will improve and further challenge our understanding of bacterial physiology, also in more complicated environments.

Active transcription of rRNA operons is a driving force for the distribution of RNA polymerase in bacteria: effect of extrachromosomal copies of rrnB on the in vivo localization of RNA polymerase

In contrast to eukaryotes, bacteria such as Escherichia coli contain only one form of RNA polymerase (RNAP), which is responsible for all cellular transcription. Using an RNAP-green fluorescent protein fusion protein, we showed previously that E. coli RNAP is partitioned exclusively in the nucleoid and that stable RNA synthesis, particularly rRNA transcription, is critical for concentrating a significant fraction of RNAP in transcription foci during exponential growth. The extent of focus formation varies under different physiological conditions, supporting the proposition that RNAP redistribution is an important element for global gene regulation. Here we show that extra, plasmid-borne copies of an rRNA operon recruit RNAP from the nucleoid into the cytoplasmic space and that this is accompanied by a reduction in the growth rate. Transcription of an intact rRNA operon is not necessary, although a minimal transcript length is required for this phenotype. Replacement of the ribosomal promoters with another strong promoter, Ptac, abolished the effect. These results demonstrate that active synthesis from rRNA promoters is a major driving force for the distribution of RNAP in bacteria. The implications of our results for the regulation of rRNA synthesis and cell growth are discussed.

Holoenzyme Switching and Stochastic Release of Sigma Factors from RNA Polymerase In Vivo

We investigated the binding of E. coli RNA polymerase holoenzymes bearing sigma70, sigma(S), sigma32, or sigma54 to the ribosomal RNA operons (rrn) in vivo. At the rrn promoter, we observed "holoenzyme switching" from Esigma70 to Esigma(S) or Esigma32 in response to environmental cues. We also examined if sigma factors are retained by core polymerase during transcript elongation. At the rrn operons, sigma70 translocates briefly with the elongating polymerase and is released stochastically from the core polymerase with an estimated half-life of approximately 4-7 s. Similarly, at gadA and htpG, operons that are targeted by Esigma(S) and Esigma32, respectively, we find that sigma(S) and sigma32 also dissociate stochastically, albeit more rapidly than sigma70, from the elongating core polymerase. Up to approximately 70% of Esigma70 (the major vegetative holoenzyme) in rapidly growing cells is engaged in transcribing the rrn operons. Thus, our results suggest that at least approximately 70% of cellular holoenzymes release sigma70 during transcript elongation. Release of sigma factors during each round of transcription provides a simple mechanism for rapidly reprogramming polymerase with the relevant sigma factor and is consistent with the occurrence of a "sigma cycle" in vivo.

RNA DNA discrimination by the antitermination protein NusB

The regulation of ribosomal RNA biosynthesis in Escherichia coli by antitermination requires binding of NusB protein to a dodecamer sequence designated boxA on the nascent RNA. The affinity of NusB protein for boxA RNA exceeds that for the homologous DNA segment by more than three orders of magnitude as shown by surface plasmon resonance measurements. DNA RNA discrimination by NusB protein was shown to involve methyl groups (i.e. discrimination of uracil versus thymine) and 2' hydroxyl groups (i.e. discrimination of ribose versus deoxyribose side-chains) in the RNA motif. Ligand perturbation experiments monitored by 1H15N correlation NMR experiments identified amide NH groups whose chemical shifts are affected selectively by ribose/deoxyribose exchange in the 5' and the central part of the dodecameric boxA motif respectively. The impact of structural modification of the boxA motif on the affinity for NusB protein as observed by 1H15N heterocorrelation was analysed by a generic algorithm.

Genetic approaches to studying protein synthesis: effects of mutations at Psi516 and A535 in Escherichia coli 16S rRNA

A genetic system for the study of ribosomal RNA function and structure was developed. First, the ribosome binding sequence of the chloramphenicol acetyltransferase gene and the message binding sequence of 16S ribosomal RNA were randomly mutated and alternative highly functional sequences were selected and characterized. From this set of mutants, a single clone was chosen and subjected to a second round of mutagenesis to optimize the specificity of the system. In the resulting system, plasmid-encoded ribosomes efficiently and exclusively translate specific mRNA containing the appropriate ribosome binding sequences. This system allows facile isolation and analysis of mutations that would normally be lethal and allows direct selection of rRNA mutants with predetermined levels of ribosome function. The system was used to examine the effects of mutations at the sole pseudouridine (Psi) in Escherichia coli 16S rRNA which is located at position 516 of the conserved 530 loop. The nucleotide opposite Psi516 in the hairpin, A535, was also mutated. The data show that a pyrimidine (Psi or C) is required at position 516, while substitutions at position 535 reduce ribosome function by < 50%. A requirement for base pair formation between Psi516 and A535 was not indicated.

Proteins shared by the transcription and translation machines

It is becoming increasingly clear that the complex machines involved in transcription and translation, the two major activities leading to gene expression, communicate directly with one another by sharing proteins. For some proteins, such as ribosomal proteins S10 and L4, there is strong evidence of their participation in both processes, and much is known about their role in both activities. The exact roles and interactions of other proteins, such as Nus factors B and G, in both transcription and translation remain a mystery. Although there are not, at present, many examples of such shared proteins, the importance of understanding their behavior and intimate involvement with two major cellular machines is beginning to be appreciated. Studies related to the dual activities of these proteins and searches for more examples of proteins shared between the transcription and translation machines should lead to a better understanding of the communication between these two activities and the purposes it serves.

The feedback response of Escherichia coli rRNA synthesis is not identical to the mechanism of growth rate-dependent control

Growth rate-independent rrn P1 promoter mutants were tested for their ability to respond to changes in rrn gene dosage. Most were found to be normal for the feedback response. In addition, cellular levels of the initiating nucleoside triphosphates remained unchanged when the rrn gene dosage was altered. These results suggest that the feedback response cannot be the mechanism for growth rate-dependent control of rRNA synthesis and that the relationship between these two processes may be more complicated than is currently understood.

Analysis of the function of Escherichia coli poly(A) polymerase I in RNA metabolism

To help understand the role of polyadenylation in Escherichia coli RNA metabolism, we constructed an IPTG-inducible pcnB [poly(A) polymerase I, PAP I] containing plasmid that permitted us to vary poly(A) levels without affecting cell growth or viability. Increased polyadenylation led to a decrease in the half-life of total pulse-labelled RNA along with decreased half-lives of the rpsO, trxA, lpp and ompA transcripts. In contrast, the transcripts for rne (RNase E) and pnp (polynucleotide phosphorylase, PNPase), enzymes involved in mRNA decay, were stabilized. rnb (RNase II) and rnc (RNase III) transcript levels were unaffected in the presence of increased polyadenylation. Long-term overproduction of PAP I led to slower growth and irreversible cell death. Differential display analysis showed that new RNA species were being polyadenylated after PAP I induction, including the mature 3'-terminus of 23S rRNA, a site that was not tailed in wild-type cells. Quantitative reverse transcriptase-polymerase chain reaction (RT-PCR) demonstrated an almost 20-fold variation in the level of polyadenylation among three different transcripts and that PAP I accounted for between 94% and 98.6% of their poly(A) tails. Cloning and sequencing of cDNAs derived from lpp, 23S and 16S rRNA revealed that, during exponential growth, C and U residues were polymerized into poly(A) tails in a transcript-dependent manner.