A comprehensive mechanism for 5-carboxylcytosine-induced transcriptional pausing revealed by Markov state models.

RNA polymerase II (Pol II) surveils the genome, pausing as it encounters DNA lesions and base modifications and initiating signals for DNA repair among other important regulatory events. Recent work suggests that Pol II pauses at 5-carboxycytosine (5caC), an epigenetic modification of cytosine, because of a specific hydrogen bond between the carboxyl group of 5caC and a specific residue in fork loop 3 of Pol II. This hydrogen bond compromises productive NTP binding and slows down elongation. Apart from this specific interaction, the carboxyl group of 5caC can potentially interact with numerous charged residues in the cleft of Pol II. However, it is not clear how other interactions between Pol II and 5caC contribute to pausing. In this study, we use Markov state models (a type of kinetic network models) built from extensive molecular dynamics simulations to comprehensively study the impact of 5caC on Pol II translocation. We describe two translocation intermediates with specific interactions that prevent the template base from loading into the Pol II active site. In addition to the previously observed state with 5caC constrained by fork loop 3, we discovered a new intermediate state with a hydrogen bond between 5caC and fork loop 2. Surprisingly, we find that 5caC may curb translocation by suppressing kinking of the helix bordering the active site (the bridge helix) because its high flexibility is critical to translocation. Our work provides new insights into how epigenetic modifications of genomic DNA can modulate Pol II translocation, inducing pauses in transcription.

Molecular mechanisms of RNA polymerase II transcription elongation elucidated by kinetic network models.

Transcription elongation cycle (TEC) of RNA polymerase II (Pol II) is a process of adding a nucleoside triphosphate to the growing messenger RNA chain. Due to the long timescale events in Pol II TEC, an advanced computational technique, such as Markov State Model (MSM), is needed to provide atomistic mechanism and reaction rates. The combination of MSM and experimental results can be used to build a kinetic network model (KNM) of the whole TEC. This review provides a brief protocol to build MSM and KNM of the whole TEC, along with the latest findings of MSM and other computational studies of Pol II TEC. Lastly, we offer a perspective on potentially using a sequence dependent KNM to predict genome-wide transcription error.

An RNA polymerase II-driven Ebola virus minigenome system as an advanced tool for antiviral drug screening.

Ebola virus (EBOV) causes a severe disease in humans with the potential for significant international public health consequences. Currently, treatments are limited to experimental vaccines and therapeutics. Therefore, research into prophylaxis and antiviral strategies to combat EBOV infections is of utmost importance. The requirement for high containment laboratories to study EBOV infection is a limiting factor for conducting EBOV research. To overcome this issue, minigenome systems have been used as valuable tools to study EBOV replication and transcription mechanisms and to screen for antiviral compounds at biosafety level 2. The most commonly used EBOV minigenome system relies on the ectopic expression of the T7 RNA polymerase (T7), which can be limiting for certain cell types. We have established an improved EBOV minigenome system that utilizes endogenous RNA polymerase II (pol II) as a driver for the synthesis of minigenome RNA. We show here that this system is as efficient as the T7-based minigenome system, but works in a wider range of cell types, including biologically relevant cell types such as bat cells. Importantly, we were also able to adapt this system to a reliable and cost-effective 96-well format antiviral screening assay with a Z-factor of 0.74, indicative of a robust assay. Using this format, we identified JG40, an inhibitor of Hsp70, as an inhibitor of EBOV replication, highlighting the potential for this system as a tool for antiviral drug screening. In summary, this updated EBOV minigenome system provides a convenient and effective means of advancing the field of EBOV research.

Strand-specific, high-resolution mapping of modified RNA polymerase II.

Reversible modification of the RNAPII C-terminal domain links transcription with RNA processing and surveillance activities. To better understand this, we mapped the location of RNAPII carrying the five types of CTD phosphorylation on the RNA transcript, providing strand-specific, nucleotide-resolution information, and we used a machine learning-based approach to define RNAPII states. This revealed enrichment of Ser5P, and depletion of Tyr1P, Ser2P, Thr4P, and Ser7P in the transcription start site (TSS) proximal ~150 nt of most genes, with depletion of all modifications close to the poly(A) site. The TSS region also showed elevated RNAPII relative to regions further 3', with high recruitment of RNA surveillance and termination factors, and correlated with the previously mapped 3' ends of short, unstable ncRNA transcripts. A hidden Markov model identified distinct modification states associated with initiating, early elongating and later elongating RNAPII. The initiation state was enriched near the TSS of protein-coding genes and persisted throughout exon 1 of intron-containing genes. Notably, unstable ncRNAs apparently failed to transition into the elongation states seen on protein-coding genes.

Annotation of genomics data using bidirectional hidden Markov models unveils variations in Pol II transcription cycle.

DNA replication, transcription and repair involve the recruitment of protein complexes that change their composition as they progress along the genome in a directed or strand-specific manner. Chromatin immunoprecipitation in conjunction with hidden Markov models (HMMs) has been instrumental in understanding these processes, as they segment the genome into discrete states that can be related to DNA-associated protein complexes. However, current HMM-based approaches are not able to assign forward or reverse direction to states or properly integrate strand-specific (e.g., RNA expression) with non-strand-specific (e.g., ChIP) data, which is indispensable to accurately characterize directed processes. To overcome these limitations, we introduce bidirectional HMMs which infer directed genomic states from occupancy profiles de novo. Application to RNA polymerase II-associated factors in yeast and chromatin modifications in human T cells recovers the majority of transcribed loci, reveals gene-specific variations in the yeast transcription cycle and indicates the existence of directed chromatin state patterns at transcribed, but not at repressed, regions in the human genome. In yeast, we identify 32 new transcribed loci, a regulated initiation-elongation transition, the absence of elongation factors Ctk1 and Paf1 from a class of genes, a distinct transcription mechanism for highly expressed genes and novel DNA sequence motifs associated with transcription termination. We anticipate bidirectional HMMs to significantly improve the analyses of genome-associated directed processes.

Millisecond dynamics of RNA polymerase II translocation at atomic resolution.

Transcription is a central step in gene expression, in which the DNA template is processively read by RNA polymerase II (Pol II), synthesizing a complementary messenger RNA transcript. At each cycle, Pol II moves exactly one register along the DNA, a process known as translocation. Although X-ray crystal structures have greatly enhanced our understanding of the transcription process, the underlying molecular mechanisms of translocation remain unclear. Here we use sophisticated simulation techniques to observe Pol II translocation on a millisecond timescale and at atomistic resolution. We observe multiple cycles of forward and backward translocation and identify two previously unidentified intermediate states. We show that the bridge helix (BH) plays a key role accelerating the translocation of both the RNA:DNA hybrid and transition nucleotide by directly interacting with them. The conserved BH residues, Thr831 and Tyr836, mediate these interactions. To date, this study delivers the most detailed picture of the mechanism of Pol II translocation at atomic level.

Transcriptional regulation: effects of promoter proximal pausing on speed, synchrony and reliability.

Recent whole genome polymerase binding assays in the Drosophila embryo have shown that a substantial proportion of uninduced genes have pre-assembled RNA polymerase-II transcription initiation complex (PIC) bound to their promoters. These constitute a subset of promoter proximally paused genes for which mRNA elongation instead of promoter access is regulated. This difference can be described as a rearrangement of the regulatory topology to control the downstream transcriptional process of elongation rather than the upstream transcriptional initiation event. It has been shown experimentally that genes with the former mode of regulation tend to induce faster and more synchronously, and that promoter-proximal pausing is observed mainly in metazoans, in accord with a posited impact on synchrony. However, it has not been shown whether or not it is the change in the regulated step per se that is causal. We investigate this question by proposing and analyzing a continuous-time Markov chain model of PIC assembly regulated at one of two steps: initial polymerase association with DNA, or release from a paused, transcribing state. Our analysis demonstrates that, over a wide range of physical parameters, increased speed and synchrony are functional consequences of elongation control. Further, we make new predictions about the effect of elongation regulation on the consistent control of total transcript number between cells. We also identify which elements in the transcription induction pathway are most sensitive to molecular noise and thus possibly the most evolutionarily constrained. Our methods produce symbolic expressions for quantities of interest with reasonable computational effort and they can be used to explore the interplay between interaction topology and molecular noise in a broader class of biochemical networks. We provide general-purpose code implementing these methods.

Gene clustering via integrated Markov models combining individual and pairwise features.

UNLABELLED: Clustering of genes into groups sharing common characteristics is a useful exploratory technique for a number of subsequent computational analysis. A wide range of clustering algorithms have been proposed in particular to analyze gene expression data, but most of them consider genes as independent entities or include relevant information on gene interactions in a suboptimal way. We propose a probabilistic model that has the advantage to account for individual data (e.g., expression) and pairwise data (e.g., interaction information coming from biological networks) simultaneously. Our model is based on hidden Markov random field models in which parametric probability distributions account for the distribution of individual data. Data on pairs, possibly reflecting distance or similarity measures between genes, are then included through a graph, where the nodes represent the genes, and the edges are weighted according to the available interaction information. As a probabilistic model, this model has many interesting theoretical features. In addition, preliminary experiments on simulated and real data show promising results and points out the gain in using such an approach. AVAILABILITY: The software used in this work is written in C++ and is available with other supplementary material at http://mistis.inrialpes.fr/people/forbes/transparentia/supplementary.html.

Preservation of a kinetically originated folding of the cis antirepressor sequence for transport of HIV-1 viral RNA.

We compute a metastable secondary structure for the cis antirepressor sequence (CAR) in the viral RNA of human immunodeficiency virus 1 (HIV-1) whose lifetime is long enough to allow for further stabilization by interaction with the ribosomal machinery. The structure emerges as the viral genome RNA is being synthesized by RNA polymerase II and corresponds to the biologically active structure sustained between units 7364 and in env RNA. It is the most probable among the fast-formed structures which emerge during transcription. No tertiary interactions appear to influence the statistical weight of this metastable state. The structure is predicted by means of a Monte Carlo simulation which computes refolding events occurring as the CAR portion of viral RNA is being assembled. The final emerging structure is preserved for transportation of viral RNA and spliced env RNA from the nucleus to the cytoplasm of the host cell.