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Bacterial messenger RNA (mRNA) synthesis by RNA polymerase (RNAP) and first-round translation by the ribosome are often coupled to regulate gene expression, yet how coupling is established and maintained is ill understood. Here, we develop biochemical and single-molecule fluorescence approaches to probe the dynamics of RNAP–ribosome interactions on an mRNA with a translational preQ₁-sensing riboswitch in its 5′ untranslated region. Binding of preQ₁ leads to the occlusion of the ribosome binding site (RBS), inhibiting translation initiation. We demonstrate that RNAP poised within the mRNA leader region promotes ribosomal 30S subunit binding, antagonizing preQ₁-induced RBS occlusion, and that the RNAP–30S bridging transcription factors NusG and RfaH distinctly enhance 30S recruitment and retention, respectively. We further find that, while 30S–mRNA interaction significantly impedes RNAP in the absence of translation, an actively translating ribosome promotes productive transcription. A model emerges wherein mRNA structure and transcription factors coordinate to dynamically modulate the efficiency of transcription–translation coupling.

Biochemical and genetic data show that ribosomes closely follow RNA polymerases that are transcribing protein-coding genes in bacteria. At the same time, electron and fluorescence microscopy have revealed that ribosomes are excluded from the Escherichia coli nucleoid, which seems to be inconsistent with fast translation initiation on nascent mRNA transcripts. The apparent paradox can be reconciled if translation of nascent mRNAs can start throughout the nucleoid before they relocate to the periphery. However, this mechanism requires that free ribosomal subunits are not excluded from the nucleoid. Here, we use single-particle tracking in living E. coli cells to determine the fractions of free ribosomal subunits, classify individual subunits as free or mRNA-bound, and quantify the degree of exclusion of bound and free subunits separately. We show that free subunits are not excluded from the nucleoid. This finding strongly suggests that translation of nascent mRNAs can start throughout the nucleoid, which reconciles the spatial separation of DNA and ribosomes with cotranscriptional translation. We also show that, after translation inhibition, free subunit precursors are partially excluded from the compacted nucleoid. This finding indicates that it is active translation that normally allows ribosomal subunits to assemble on nascent mRNAs throughout the nucleoid and that the effects of translation inhibitors are enhanced by the limited access of ribosomal subunits to nascent mRNAs in the compacted nucleoid.

In bacteria, the first two steps of gene expression—transcription and translation—are spatially and temporally coupled. Uncoupling may lead to the arrest of transcription through RNA polymerase backtracking, which interferes with replication forks, leading to DNA double-stranded breaks and genomic instability. How transcription–translation coupling mitigates these conflicts is unknown. Here we show that, unlike replication, translation is not inhibited by arrested transcription elongation complexes. Instead, the translating ribosome actively pushes RNA polymerase out of the backtracked state, thereby reactivating transcription. We show that the distance between the two machineries upon their contact on mRNA is smaller than previously thought, suggesting intimate interactions between them. However, this does not lead to the formation of a stable functional complex between the enzymes, as was once proposed. Our results reveal an active, energy-driven mechanism that reactivates backtracked elongation complexes and thus helps suppress their interference with replication.

Transcription of operons is initiated at the promoter of the first gene in the operon, continues through cistron junctions, and terminates at the end of the operon, generating a full-length mRNA. Here, we show that Rho-dependent termination of transcription occurs stochastically at a cistron junction, generating a stable mRNA that is shorter than the full-length mRNA. In Escherichia coli , transcription is coupled with translation. The polar gal operon is transcribed galE-galT-galK-galM ; however, about 10% of transcription terminates at the end of galE because of Rho-dependent termination (RDT). When galE translation is complete, galT translation should begin immediately. It is unclear whether RDT at the end of galE is due to decoupling of translation termination and transcription at the cistron junction. In this study, we show that RDT at the galE/galT cistron junction is linked to the failure of translation initiation at the start of galT , rather than translation termination at the end of galE . We also show that transcription pauses 130 nucleotides downstream from the site of galE translation termination, and this pause is required for RDT. IMPORTANCE Transcription of operons is initiated at the promoter of the first gene in the operon, continues through cistron junctions, and terminates at the end of the operon, generating a full-length mRNA. Here, we show that Rho-dependent termination of transcription occurs stochastically at a cistron junction, generating a stable mRNA that is shorter than the full-length mRNA. We further show that stochastic failure in translation initiation of the next gene, rather than the failure of translation termination of the preceding gene, causes the Rho-dependent termination. Thus, stochastic failure in translation initiation at the cistron junction causes the promoter-proximal gene to be transcribed more than promoter-distal genes within the operon.

In order for the cell's genome to be passed intact from one generation to the next, the events of the cell cycle (DNA replication, mitosis, cell division) must be executed in the correct order, despite the considerable molecular noise inherent in any protein‐based regulatory system residing in the small confines of a eukaryotic cell. To assess the effects of molecular fluctuations on cell‐cycle progression in budding yeast cells, we have constructed a new model of the regulation of Cln‐ and Clb‐dependent kinases, based on multisite phosphorylation of their target proteins and on positive and negative feedback loops involving the kinases themselves. To account for the significant role of noise in the transcription and translation steps of gene expression, the model includes mRNAs as well as proteins. The model equations are simulated deterministically and stochastically to reveal the bistable switching behavior on which proper cell‐cycle progression depends and to show that this behavior is robust to the level of molecular noise expected in yeast‐sized cells (∼50 fL volume). The model gives a quantitatively accurate account of the variability observed in the G1‐S transition in budding yeast, which is governed by an underlying sizer+timer control system. Synopsis Progression through the eukaryotic cell cycle is governed by the activation and inactivation of a family of cyclin‐dependent kinases (CDKs) and auxiliary proteins that regulate CDK activities (Morgan, 2007 ). The many components of this protein regulatory network are interconnected by positive and negative feedback loops that create bistable switches and transient pulses (Tyson and Novak, 2008 ). The network must ensure that cell‐cycle events proceed in the correct order, that cell division is balanced with respect to cell growth, and that any problems encountered (in replicating the genome or partitioning chromosomes to daughter cells) are corrected before the cell proceeds to the next phase of the cycle. The network must operate robustly in the context of unavoidable molecular fluctuations in a yeast‐sized cell. With a volume of only 5×10 −14 l, a yeast cell contains one copy of the gene for each component of the network, a handful of mRNA transcripts of each gene, and a few hundreds to thousands of protein molecules carrying out each gene's function. How large are the molecular fluctuations implied by these numbers, and what effects do they have on the functioning of the cell‐cycle control system? To answer these questions, we have built a new model (Figure 1 ) of the CDK regulatory network in budding yeast, based on the fact that the targets of CDK activity are typically phosphorylated on multiple sites. The activity of each target protein depends on how many sites are phosphorylated. The target proteins feedback on CDK activity by controlling cyclin synthesis (SBF's role) and degradation (Cdh1's role) and by releasing a CDK‐counteracting phosphatase (Cdc14). Every reaction in Figure 1 can be described by a mass‐action rate law, with an accompanying rate constant that must be estimated from experimental data. As the transcription and translation of mRNA molecules have major effects on fluctuating numbers of protein molecules (Pedraza and Paulsson, 2008 ), we have included mRNA transcripts for each protein in the model. To create a deterministic model, the rate laws are combined, according to standard principles of chemical kinetics, into a set of 60 differential equations that govern the temporal dynamics of the control system. In the stochastic version of the model, the rate law for each reaction determines the probability per unit time that a particular reaction occurs, and we use Gillespie's stochastic simulation algorithm (Gillespie, 1976 ) to compute possible temporal sequences of reaction events. Accurate stochastic simulations require knowledge of the expected numbers of mRNA and protein molecules in a single yeast cell. Fortunately, these numbers are available from several sources (Ghaemmaghami et al , 2003 ; Zenklusen et al , 2008 ). Although the experimental estimates are not always in good agreement with each other, they are sufficiently reliable to populate a stochastic model with realistic numbers of molecules. By simulating thousands of cells (as in Figure 5 ), we can build up representative samples for computing the mean and s.d. of any measurable cell‐cycle property (e.g. interdivision time, size at division, duration of G1 phase). The excellent fit of simulated statistics to observations of cell‐cycle variability is documented in the main text and Supplementary Information . Of particular interest to us are observations of Di Talia et al (2007) of the timing of a crucial G1 event (export of Whi5 protein from the nucleus) in a population of budding yeast cells growing at a specific growth rate α=ln2/(mass‐doubling time). Whi5 export is a consequence of Whi5 phosphorylation, and it occurs simultaneously with the release (activation) of SBF (see Figure 1 ). Using fluorescently labeled Whi5, Di Talia et al could easily measure (in individual yeast cells) the time, T 1 , from cell birth to the abrupt loss of Whi5 from the nucleus. Correlating T 1 to the size of the cell at birth, V birth , they found that, for a sample of daughter cells, α T 1 versus ln( V birth ) could be fit with two straight lines of slope −0.7 and −0.3. Our simulation of this experiment (Figure 7 of the main text) compares favorably with Figure 3d and e in Di Talia et al (2007) . The major sources of noise in our model (and in protein regulatory networks in yeast cells, in general) are related to gene transcription and the small number of unique mRNA transcripts. As each mRNA molecule may instruct the synthesis of dozens of protein molecules, the coefficient of variation of molecular fluctuations at the protein level (CV P ) may be dominated by fluctuations at the mRNA level, as expressed in the formula (Pedraza and Paulsson, 2008 ) where N M , N P denote the number of mRNA and protein molecules, respectively, and ρ=τ M /τ P is the ratio of half‐lives of mRNA and protein molecules. For a yeast cell, typical values of N M and N P are 8 and 800, respectively (Ghaemmaghami et al , 2003 ; Zenklusen et al , 2008 ). If ρ=1, then CV P ≈25%. Such large fluctuations in protein levels are inconsistent with the observed variability of size and age at division in yeast cells, as shown in the simplified cell‐cycle model of Kar et al (2009) and as we have confirmed with our more realistic model. The size of these fluctuations can be reduced to a more acceptable level by assuming a shorter half‐life for mRNA (say, ρ=0.1). There must be some mechanisms whereby yeast cells lessen the protein fluctuations implied by transcription–translation coupling. Following Pedraza and Paulsson (2008) , we suggest that mRNA gestation and senescence may resolve this problem. Equation (3) is based on a simple, one‐stage, birth–death model of mRNA turnover. In Supplementary Appendix 1 , we show that a model of mRNA processing, with 10 stages each of mRNA gestation and senescence, gives reasonable fluctuations at the protein level (CV P ≈5%), even if the effective half‐life of mRNA is 10 min. A one‐stage model with τ M =1 min gives comparable fluctuations (CV P ≈5%). In the main text, we use a simple birth–death model of mRNA turnover with an ‘effective’ half‐life of 1 min, in order to limit the computational complexity of the full cell‐cycle model. Multisite phosphorylation of CDK target proteins provides the requisite nonlinearity for cell cycle modeling using elementary reaction mechanisms. Stochastic simulations, based on Gillespie's algorithm and using realistic numbers of protein and mRNA molecules, compare favorably with single‐cell measurements in budding yeast. The role of transcription–translation coupling is critical in the robust operation of protein regulatory networks in yeast cells.

NusA and Rho are essential Escherichia coli proteins that influence transcription elongation and termination. We show that an E. coli derivative unable to express NusA, because its sole nusA gene contains a large deletion/substitution, is viable providing that the bacterium also carries a rho mutation that reduces transcription termination. This Rho-mediated suppression is not allele specific, since either a mutation changing amino acid 134 [rho(E134D)] or a mutation changing amino acid 352 (rho1) allows growth of a nusA-deleted E. coli. However, both rho mutations similarly decrease transcription termination 8- to 9-fold. We propose that the essential role of NusA is to enhance pausing of RNA polymerase at certain sites, permitting tight coupling of transcription and translation. This coupling interferes with Rho access to and/or movement on the nascent RNA and blocks premature termination of transcription. Thus, NusA-dependent coupling should be less important in a mutant with low Rho activity. The fact that E. coli grows without NusA argues that NusA should be considered an accessory factor rather than a subunit of RNA polymerase.

This article will review our current understanding of transcription elongation and termination in E. coli. We discuss why transcription elongation complexes pause at certain template sites and how auxiliary host and phage transcription factors affect elongation and termination. The connection between translation and transcription elongation is described. Finally we present an overview indicating where progress has been made and where it has not.

Two kinds of signal-dependent transcription termination and RNA release mechanisms have been established in prokaryotes in vitro by: (i) binding of Rho to cytidine-rich nascent RNA [Rho-dependent termination (RDT)], and (ii) the formation of a hairpin structure in the nascent RNA, ending predominantly with uridine residues [Rho-independent termination (RIT)]. As shown here, the two signals act independently of each other and can be regulated (suppressed) by translation–transcription coupling in vivo. When not suppressed, both RIT- and RDT-mediated transcription termination do occur, but ribonucleolytic processing generates defined new 3′ ends in the terminated RNA molecules. The actual termination events at the end of transcription units are masked by generation of new processed 3′ RNA ends; thus the in vivo 3′ ends do not define termination sites. We predict generation of 3′ ends of mRNA by processing is a common phenomenon in prokaryotes as is the case in eukaryotes.