The human RNA polymerase I structure reveals an HMG-like transcription factor docking domain specific to metazoans

Transcription of the ribosomal RNA precursor by RNA polymerase (Pol) I is a major determinant of cellular growth and dysregulation is observed in many cancer types. Here, we present the purification of human Pol I from cells carrying a genomic GFP-fusion on the largest subunit allowing the structural and functional analysis of the enzyme across species. In contrast to yeast, human Pol I carries a single-subunit stalk and in vitro transcription indicates a reduced proofreading activity. Determination of the human Pol I cryo-EM reconstruction in a close-to-native state rationalizes the effects of disease-associated mutations and uncovers an additional domain that is built into the sequence of Pol I subunit RPA1. This "dock II" domain resembles a truncated HMG-box incapable of DNA-binding which may serve as a downstream-transcription factor binding platform in metazoans. Biochemical analysis and ChIP data indicate that Topoisomerase 2a can be recruited to Pol I via the domain and cooperates with the HMG-box domain containing factor UBF. These adaptations of the metazoan Pol I transcription system may allow efficient release of positive DNA supercoils accumulating downstream of the transcription bubble.

Structural origins of Escherichia coli RNA polymerase open promoter complex stability

The first step in gene expression in all organisms requires opening the DNA duplex to expose one strand for templated RNA synthesis. In Escherichia coli, promoter DNA sequence fundamentally determines how fast the RNA polymerase (RNAP) forms “open” complexes (RPo), whether RPo persists for seconds or hours, and how quickly RNAP transitions from initiation to elongation. These rates control promoter strength in vivo, but their structural origins remain largely unknown. Here, we use cryoelectron microscopy to determine the structures of RPo formed de novo at three promoters with widely differing lifetimes at 37 °C: λPR (t1/2 ∼10 h), T7A1 (t1/2 ∼4 min), and a point mutant in λPR (λPR-5C) (t1/2 ∼2 h). Two distinct RPo conformers are populated at λPR, likely representing productive and unproductive forms of RPo observed in solution studies. We find that changes in the sequence and length of DNA in the transcription bubble just upstream of the start site (+1) globally alter the network of DNA–RNAP interactions, base stacking, and strand order in the single-stranded DNA of the transcription bubble; these differences propagate beyond the bubble to upstream and downstream DNA. After expanding the transcription bubble by one base (T7A1), the nontemplate strand “scrunches” inside the active site cleft; the template strand bulges outside the cleft at the upstream edge of the bubble. The structures illustrate how limited sequence changes trigger global alterations in the transcription bubble that modulate the RPo lifetime and affect the subsequent steps of the transcription cycle.

Structural origins of Escherichia coli RNA polymerase open promoter complex stability

The first step of gene expression in all organisms requires opening the DNA duplex to expose one strand for templated RNA synthesis. In Escherichia coli, promoter DNA sequence fundamentally determines how fast the RNA polymerase (RNAP) forms “open” complexes (RPo), whether RPo persists for seconds or hours, and how quickly RNAP transitions from initiation to elongation. These rates control promoter strength in vivo but their structural origins remain largely unknown. Here we use cryo-electron microscopy to determine structures of RPo formed de novo at three promoters with widely differing lifetimes at 37°C: λPR (t1/2 ∼ 10 hours), T7A1 (t1/2 ∼ 4 minutes), and a point mutant in λPR (λPR-5C) (t1/2 ∼ 2 hours). Two distinct RPo conformers are populated at λPR, likely representing productive and unproductive forms of RPo observed in solution studies. We find that changes in the sequence and length of DNA in the transcription bubble just upstream of the start site (+1) globally alter the network of DNA-RNAP interactions, base stacking, and strand order in the single-stranded DNA of the transcription bubble; these differences propagate beyond the bubble to upstream and downstream DNA. After expanding the transcription bubble by one base (T7A1), the nontemplate-strand “scrunches” inside the active site cleft; the template-strand bulges outside the cleft at the upstream edge of the bubble. The structures illustrate how limited sequence changes trigger global alterations in the transcription bubble that modulate RPo lifetime and affect the subsequent steps of the transcription cycle.

Multi-parameter photon-by-photon hidden Markov modeling

Single molecule FRET (smFRET) is a useful tool for studying biomolecular sub-populations and their dynamics. Advanced smFRET-based techniques often track multiple parameters simultaneously, increasing the information content of the measurement. Photon-by-photon hidden Markov modelling (H2MM) is a smFRET analysis tool that quantifies FRET dynamics of single biomolecules, even if they occur in sub-milliseconds. However, sub-populations can be characterized by additional experimentally-derived parameters other than the FRET efficiency. We introduce multi-parameter H2MM (mpH2MM) that identifies sub-populations and their transition dynamics based on multiple experimentally-derived parameters, simultaneously. We show the use of this tool in deciphering the number of underlying sub-populations, their mean characteristics and the rate constants of their transitions for a DNA hairpin exhibiting milliseconds FRET dynamics, and for the RNA polymerase promoter open complex exhibiting sub-millisecond FRET dynamics of the transcription bubble. Overall, we show that using mpH2MM facilitates the identification and quantification of biomolecular sub-populations in smFRET measurements that are otherwise difficult to identify. Finally we provide the means to use mpH2MM in analyzing FRET dynamics in advanced multi-color smFRET-based measurements.

The formation of the bacterial RNA polymerase–promoter open complex involves a branched pathway

The expression of most bacterial genes commences with the binding of RNA polymerase (RNAP)–σ70 holoenzyme to the promoter DNA. This initial RNAP–promoter closed complex undergoes a series of conformational changes, including the formation of a transcription bubble on the promoter and the loading of template DNA strand into the RNAP active site; these changes lead to the catalytically active open complex (RPO) state. Recent cryo-electron microscopy studies have provided detailed structural insight on the RPO and putative intermediates on its formation pathway. Here, we employ single-molecule fluorescence microscopy to interrogate the conformational dynamics and reaction kinetics during real-time RPO formation. We find that the RPO pathway is branched, generating RPO complexes with different stabilities. The RNAP cleft loops, and especially the β’ rudder, stabilise the transcription bubble. The RNAP interactions with the promoter upstream sequence (beyond −35) stimulate transcription bubble nucleation and tune the reaction path towards stable forms of the RPO. The mechanistic heterogeneity of the RPO pathway may be a prerequisite for its regulation since such heterogeneity allows the amplification of small promoter sequence or transcription-factor-dependent changes in the free energy profile of the RPO pathway to large differences in transcription efficiency.

Transcription activation by a sliding clamp

Transcription activation of bacteriophage T4 late genes is accomplished by a transcription activation complex containing RNA polymerase (RNAP), the promoter specificity factor gp55, the coactivator gp33, and a universal component of cellular DNA replication, the sliding clamp gp45. Although genetic and biochemical studies have elucidated many aspects of T4 late gene transcription, no precise structure of the transcription machinery in the process is available. Here, we report the cryo-EM structures of a gp55-dependent RNAP-promoter open complex and an intact gp45-dependent transcription activation complex. The structures reveal the interactions between gp55 and the promoter DNA that mediate the recognition of T4 late promoters. In addition to the σR2 homology domain, gp55 has a helix-loop-helix motif that chaperons the template-strand single-stranded DNA of the transcription bubble. Gp33 contacts both RNAP and the upstream double-stranded DNA. Gp45 encircles the DNA and tethers RNAP to it, supporting the idea that gp45 switches the promoter search from three-dimensional diffusion mode to one-dimensional scanning mode.

Direct binding of TFEα opens DNA binding cleft of RNA polymerase

Opening of the DNA binding cleft of cellular RNA polymerase (RNAP) is necessary for transcription initiation but the underlying molecular mechanism is not known. Here, we report on the cryo-electron microscopy structures of the RNAP, RNAP-TFEα binary, and RNAP-TFEα-promoter DNA ternary complexes from archaea, Thermococcus kodakarensis (Tko). The structures reveal that TFEα bridges the RNAP clamp and stalk domains to open the DNA binding cleft. Positioning of promoter DNA into the cleft closes it while maintaining the TFEα interactions with the RNAP mobile modules. The structures and photo-crosslinking results also suggest that the conserved aromatic residue in the extended winged-helix domain of TFEα interacts with promoter DNA to stabilize the transcription bubble. This study provides a structural basis for the functions of TFEα and elucidates the mechanism by which the DNA binding cleft is opened during transcription initiation in the stalk-containing RNAPs, including archaeal and eukaryotic RNAPs.

Nascent transcript folding plays a major role in determining RNA polymerase elongation rates

Transcription elongation rates are important for RNA processing, but sequence-specific regulation is poorly understood. We addressed this in vivo, analyzing RNAPI in S.cerevisiae. Analysis of Miller chromatin spreads and mapping RNAPI using UV crosslinking, revealed a marked 5' bias and strikingly uneven local polymerase occupancy, indicating substantial variation in transcription speed. Two features of the nascent transcript correlated with RNAPI distribution; folding energy and G+C content. In vitro experiments confirmed that strong RNA structures close to the polymerase promote forward translocation and limit backtracking, whereas high G+C within the transcription bubble slows elongation. We developed a mathematical model for RNAPI elongation, which confirmed the importance of nascent RNA folding in transcription. RNAPI from S.pombe was similarly sensitive to transcript folding, as were S.cerevisiae RNAPII and RNAPIII. For RNAPII, unstructured RNA, which favors slowed elongation, was associated with faster cotranscriptional splicing and proximal splice site usage indicating regulatory significance for transcript folding.

Approximate Bayesian computation of transcriptional pausing mechanisms

At a transcriptional pause site, RNA polymerase (RNAP) takes significantly longer than average to transcribe the nucleotide before moving on to the next position. At the single-molecule level this process is stochastic, while at the ensemble level it plays a variety of important roles in biological systems. The pause signal is complex and invokes interplay between a range of mechanisms. Among these factors are: non-canonical transcription events – such as backtracking and hypertranslocation; the catalytically inactive intermediate state hypothesised to act as a precursor to backtracking; the energetic configuration of basepairing within the DNA/RNA hybrid and of those flanking the transcription bubble; and the structure of the nascent mRNA. There are a variety of plausible models and hypotheses but it is unclear which explanations are better.We performed a systematic comparison of 128 kinetic models of transcription using approximate Bayesian computation. Under this Bayesian framework, models and their parameters were assessed by their ability to predict the locations of pause sites in the E. coli genome.These results suggest that the structural parameters governing the transcription bubble, and the dynamics of the transcription bubble during translocation, play significant roles in pausing. This is consistent with a model where the relative Gibbs energies between the pre and posttranslocated positions, and the rate of translocation between the two, is the primary factor behind invoking transcriptional pausing. Whereas, hypertranslocation, backtracking, and the intermediate state are not required to predict the locations of transcriptional pause sites. Finally, we compared the predictive power of these kinetic models to that of a non-explanatory statistical model. The latter approach has significantly greater predictive power (AUC = 0.89 cf. 0.73), suggesting that, while current models of transcription contain a moderate degree of predictive power, a much greater quantitative understanding of transcriptional pausing is required to rival that of a sequence motif.Author summaryTranscription involves the copying of a DNA template into messenger RNA (mRNA). This reaction is implemented by RNA polymerase (RNAP) successively incorporating nucleotides onto the mRNA. At a transcriptional pause site, RNAP takes significantly longer than average to incorporate the nucleotide. A model which can not only predict the locations of pause sites in a DNA template, but also explain how or why they are pause sites, is sought after.Transcriptional pausing emerges from cooperation between several mechanisms. These mechanisms include non-canonical RNAP reactions; and the thermodynamic properties of DNA and mRNA. There are many hypotheses and kinetic models of transcription but it is unclear which hypotheses and models are required to predict and explain transcriptional pausing.We have developed a rigorous statistical framework for inferring model parameters and comparing hypotheses. By applying this framework to published pause-site data, we compared 128 kinetic models of transcription with the aim of finding the best models for predicting the locations of pause sites. This analysis offered insights into mechanisms of transcriptional pausing. However, the predictive power of these models lacks compared with non-explanatory statistical models - suggesting the data contains more information than can be satisfied by current quantitative understandings of transcriptional pausing.