A genetic assay for transcription errors reveals multilayer control of RNA polymerase II fidelity

Structural basis of transcription: RNA Polymerase II substrate binding and metal coordination at 3.0 Å using a free-electron laser

Catalysis and translocation of multi-subunit DNA-directed RNA polymerases underlie all cellular mRNA synthesis. RNA polymerase II (Pol II) synthesizes eukaryotic pre-mRNAs from a DNA template strand buried in its active site. Structural details of catalysis at near atomic resolution and precise arrangement of key active site components have been elusive. Here we present the free electron laser (FEL) structure of a matched ATP-bound Pol II, revealing the full active site interaction network at the highest resolution to date, including the trigger loop (TL) in the closed conformation, bonafide occupancy of both site A and B Mg2+, and a putative third (site C) Mg2+ analogous to that described for some DNA polymerases but not observed previously for cellular RNA polymerases. Molecular dynamics (MD) simulations of the structure indicate that the third Mg2+ is coordinated and stabilized at its observed position. TL residues provide half of the substrate binding pocket while multiple TL/bridge helix (BH) interactions induce conformational changes that could propel translocation upon substrate hydrolysis. Consistent with TL/BH communication, a FEL structure and MD simulations of the hyperactive Rpb1 T834P bridge helix mutant reveals rearrangement of some active site interactions supporting potential plasticity in active site function and long-distance effects on both the width of the central channel and TL conformation, likely underlying its increased elongation rate at the expense of fidelity.

Evolutionary conservation of the fidelity of transcription

Accurate transcription is required for the faithful expression of genetic information. However, relatively little is known about the molecular mechanisms that control the fidelity of transcription, or the conservation of these mechanisms across the tree of life. To address these issues, we measured the error rate of transcription in five organisms of increasing complexity and found that the error rate of RNA polymerase II ranges from 2.9 × 10-6 ± 1.9 × 10-7/bp in yeast to 4.0 × 10-6 ± 5.2 × 10-7/bp in worms, 5.69 × 10-6 ± 8.2 × 10-7/bp in flies, 4.9 × 10-6 ± 3.6 × 10-7/bp in mouse cells and 4.7 × 10-6 ± 9.9 × 10-8/bp in human cells. These error rates were modified by various factors including aging, mutagen treatment and gene modifications. For example, the deletion or modification of several related genes increased the error rate substantially in both yeast and human cells. This research highlights the evolutionary conservation of factors that control the fidelity of transcription. Additionally, these experiments provide a reasonable estimate of the error rate of transcription in human cells and identify disease alleles in a subunit of RNA polymerase II that display error-prone transcription. Finally, we provide evidence suggesting that the error rate and spectrum of transcription co-evolved with our genetic code.

Characterization of RNA polymerase II trigger loop mutations using molecular dynamics simulations and machine learning

Catalysis and fidelity of multisubunit RNA polymerases rely on a highly conserved active site domain called the trigger loop (TL), which achieves roles in transcription through conformational changes and interaction with NTP substrates. The mutations of TL residues cause distinct effects on catalysis including hypo- and hyperactivity and altered fidelity. We applied molecular dynamics simulation (MD) and machine learning (ML) techniques to characterize TL mutations in the Saccharomyces cerevisiae RNA Polymerase II (Pol II) system. We did so to determine relationships between individual mutations and phenotypes and to associate phenotypes with MD simulated structural alterations. Using fitness values of mutants under various stress conditions, we modeled phenotypes along a spectrum of continual values. We found that ML could predict the phenotypes with 0.68 R2 correlation from amino acid sequences alone. It was more difficult to incorporate MD data to improve predictions from machine learning, presumably because MD data is too noisy and possibly incomplete to directly infer functional phenotypes. However, a variational auto-encoder model based on the MD data allowed the clustering of mutants with different phenotypes based on structural details. Overall, we found that a subset of loss-of-function (LOF) and lethal mutations tended to increase distances of TL residues to the NTP substrate, while another subset of LOF and lethal substitutions tended to confer an increase in distances between TL and bridge helix (BH). In contrast, some of the gain-of-function (GOF) mutants appear to cause disruption of hydrophobic contacts among TL and nearby helices.

Uncovering the mechanisms of transcription elongation by eukaryotic RNA polymerases I, II, and III

Eukaryotes express three nuclear RNA polymerases (Pols I, II, and III) that are essential for cell survival. Despite extensive investigation of the three Pols, significant knowledge gaps regarding their biochemical properties remain because each Pol has been evaluated independently under disparate experimental conditions and methodologies. To advance our understanding of the Pols, we employed identical in vitro transcription assays for direct comparison of their elongation rates, elongation complex (EC) stabilities, and fidelities. Pol I is the fastest, most likely to misincorporate, forms the least stable EC, and is most sensitive to alterations in reaction buffers. Pol II is the slowest of the Pols, forms the most stable EC, and negligibly misincorporated an incorrect nucleotide. The enzymatic properties of Pol III were intermediate between Pols I and II in all assays examined. These results reveal unique enzymatic characteristics of the Pols that provide new insights into their evolutionary divergence.

High-throughput replica-pinning approach to screen for yeast genes controlling low-frequency events

Saccharomyces cerevisiae is a leading model system for genome-wide screens, but low-frequency events (e.g., point mutations, recombination events) are difficult to detect with existing approaches. Here, we describe a high-throughput screening technique to detect low-frequency events using high-throughput replica pinning of high-density arrays of yeast colonies. This approach can be used to screen genes that control any process involving low-frequency events for which genetically selectable reporters are available, e.g., spontaneous mutations, recombination, and transcription errors. For complete details on the use and execution of this protocol, please refer to (Novarina et al., 2020a, 2020b).

Genome-wide imaging screen uncovers molecular determinants of arsenite-induced protein aggregation and toxicity

The toxic metalloid arsenic causes widespread misfolding and aggregation of cellular proteins. How these protein aggregates are formed in vivo, the mechanisms by which they affect cells and how cells prevent their accumulation is not fully understood. To find components involved in these processes, we performed a genome-wide imaging screen and identified Saccharomyces cerevisiae deletion mutants with either enhanced or reduced protein aggregation levels during arsenite exposure. We show that many of the identified factors are crucial to safeguard protein homeostasis (proteostasis) and to protect cells against arsenite toxicity. The hits were enriched for various functions including protein biosynthesis and transcription, and dedicated follow-up experiments highlight the importance of accurate transcriptional and translational control for mitigating protein aggregation and toxicity during arsenite stress. Some of the hits are associated with pathological conditions, suggesting that arsenite-induced protein aggregation may affect disease processes. The broad network of cellular systems that impinge on proteostasis during arsenic stress identified in this current study provides a valuable resource and a framework for further elucidation of the mechanistic details of metalloid toxicity and pathogenesis. This article has an associated First Person interview with the first authors of the paper.

High Transcriptional Error Rates Vary as a Function of Gene Expression Level

Errors in gene transcription can be costly, and organisms have evolved to prevent their occurrence or mitigate their costs. The simplest interpretation of the drift barrier hypothesis suggests that species with larger population sizes would have lower transcriptional error rates. However, Escherichia coli seems to have a higher transcriptional error rate than species with lower effective population sizes, for example Saccharomyces cerevisiae. This could be explained if selection in E. coli were strong enough to maintain adaptations that mitigate the consequences of transcriptional errors through robustness, on a gene by gene basis, obviating the need for low transcriptional error rates and associated costs of global proofreading. Here, we note that if selection is powerful enough to evolve local robustness, selection should also be powerful enough to locally reduce error rates. We therefore predict that transcriptional error rates will be lower in highly abundant proteins on which selection is strongest. However, we only expect this result when error rates are high enough to significantly impact fitness. As expected, we find such a relationship between expression and transcriptional error rate for non-C→U errors in E. coli (especially G→A), but not in S. cerevisiae. We do not find this pattern for C→U changes in E. coli, presumably because most deamination events occurred during sample preparation, but do for C→U changes in S. cerevisiae, supporting the interpretation that C→U error rates estimated with an improved protocol, and which occur at rates comparable with E. coli non-C→U errors, are biological.

Identifying Transcription Error-Enriched Genomic Loci Using Nuclear Run-on Circular-Sequencing Coupled with Background Error Modeling

RNA polymerase transcribes certain genomic loci with higher errors rates. These transcription error-enriched genomic loci (TEELs) have implications in disease. Current deep-sequencing methods cannot distinguish TEELs from post-transcriptional modifications, stochastic transcription errors, and technical noise, impeding efforts to elucidate the mechanisms linking TEELs to disease. Here, we describe background error model-coupled precision nuclear run-on circular-sequencing (EmPC-seq) to discern genomic regions enriched for transcription misincorporations. EmPC-seq innovatively combines a nuclear run-on assay for capturing nascent RNA before post-transcriptional modifications, a circular-sequencing step that sequences the same nascent RNA molecules multiple times to improve accuracy, and a statistical model for distinguishing error-enriched regions among stochastic polymerase errors. Applying EmPC-seq to the ribosomal RNA transcriptome, we show that TEELs of RNA polymerase I are not randomly distributed but clustered together, with higher error frequencies at nascent transcript 3' ends. Our study establishes a reliable method of identifying TEELs with nucleotide precision, which can help elucidate their molecular origins.

The hunt for RNA polymerase II elongation factors: a historical perspective

The discovery of the three eukaryotic nuclear RNA polymerases paved the way for serious biochemical investigations of eukaryotic transcription and the identification of eukaryotic transcription factors. Here we describe this adventure from our vantage point, with a focus on the hunt for factors that regulate elongation by RNA polymerase II.

RNA-DNA and DNA-DNA base-pairing at the upstream edge of the transcription bubble regulate translocation of RNA polymerase and transcription rate

Translocation of RNA polymerase (RNAP) along DNA may be rate-limiting for transcription elongation. The Brownian ratchet model posits that RNAP rapidly translocates back and forth until the post-translocated state is stabilized by NTP binding. An alternative model suggests that RNAP translocation is slow and poorly reversible. To distinguish between these two models, we take advantage of an observation that pyrophosphorolysis rates directly correlate with the abundance of the pre-translocated fraction. Pyrophosphorolysis by RNAP stabilized in the pre-translocated state by bacteriophage HK022 protein Nun was used as a reference point to determine the pre-translocated fraction in the absence of Nun. The stalled RNAP preferentially occupies the post-translocated state. The forward translocation rate depends, among other factors, on melting of the RNA–DNA base pair at the upstream edge of the transcription bubble. DNA–DNA base pairing immediately upstream from the RNA–DNA hybrid stabilizes the post-translocated state. This mechanism is conserved between E. coli RNAP and S. cerevisiae RNA polymerase II and is partially dependent on the lid domain of the catalytic subunit. Thus, the RNA–DNA hybrid and DNA reannealing at the upstream edge of the transcription bubble emerge as targets for regulation of the transcription elongation rate.

Functional assays for transcription mechanisms in high-throughput

Dramatic increases in the scale of programmed synthesis of nucleic acid libraries coupled with deep sequencing have powered advances in understanding nucleic acid and protein biology. Biological systems centering on nucleic acids or encoded proteins greatly benefit from such high-throughput studies, given that large DNA variant pools can be synthesized and DNA, or RNA products of transcription, can be easily analyzed by deep sequencing. Here we review the scope of various high-throughput functional assays for studies of nucleic acids and proteins in general, followed by discussion of how these types of study have yielded insights into the RNA Polymerase II (Pol II) active site as an example. We discuss methodological considerations in the design and execution of these experiments that should be valuable to studies in any system.

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

Accurate transcription is required for the faithful expression of genetic information. Surprisingly though, little is known about the mechanisms that control the fidelity of transcription. To fill this gap in scientific knowledge, we recently optimized the circle-sequencing assay to detect transcription errors throughout the transcriptome of Saccharomyces cerevisiae, Drosophila melanogaster, and Caenorhabditis elegans. This protocol will provide researchers with a powerful new tool to map the landscape of transcription errors in eukaryotic cells so that the mechanisms that control the fidelity of transcription can be elucidated in unprecedented detail.

Transcription fidelity and its roles in the cell

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The Trigger Loop is one of the major determinants of transcription fidelity.

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Intrinsic proofreading occurs via transcript-assisted cleavage.

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Factor-assisted proofreading takes place via exchange of RNAP active centres.

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Misincorporation is a major source of transcription pausing.

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Another role of fidelity is the prevention of conflicts with other cellular processes.

Accuracy of transcription is essential for productive gene expression, and the past decade has brought new understanding of the mechanisms ensuring transcription fidelity. The discovery of a new catalytic domain, the Trigger Loop, revealed that RNA polymerase can actively choose the correct substrates. Also, the intrinsic proofreading activity was found to proceed via a ribozyme-like mechanism, whereby the erroneous nucleoside triphosphate (NTP) helps its own excision. Factor-assisted proofreading was shown to proceed through an exchange of active centres, a unique phenomenon among proteinaceous enzymes. Furthermore, most recent in vivo studies have revised the roles of transcription accuracy and proofreading factors, as not only required for production of errorless RNAs, but also for prevention of frequent misincorporation-induced pausing that may cause conflicts with fellow RNA polymerases and the replication machinery.

A Cre Transcription Fidelity Reporter Identifies GreA as a Major RNA Proofreading Factor in Escherichia coli

We made a coupled genetic reporter that detects rare transcription misincorporation errors to measure RNA polymerase transcription fidelity in Escherichia coli. Using this reporter, we demonstrated in vivo that the transcript cleavage factor GreA, but not GreB, is essential for proofreading of a transcription error where a riboA has been misincorporated instead of a riboG. A greA mutant strain had more than a 100-fold increase in transcription errors relative to wild-type or a greB mutant. However, overexpression of GreB in ΔgreA cells reduced the misincorporation errors to wild-type levels, demonstrating that GreB at high concentration could substitute for GreA in RNA proofreading activity in vivo.

High-Resolution Phenotypic Landscape of the RNA Polymerase II Trigger Loop

The active sites of multisubunit RNA polymerases have a “trigger loop” (TL) that multitasks in substrate selection, catalysis, and translocation. To dissect the Saccharomyces cerevisiae RNA polymerase II TL at individual-residue resolution, we quantitatively phenotyped nearly all TL single variants en masse. Three mutant classes, revealed by phenotypes linked to transcription defects or various stresses, have distinct distributions among TL residues. We find that mutations disrupting an intra-TL hydrophobic pocket, proposed to provide a mechanism for substrate-triggered TL folding through destabilization of a catalytically inactive TL state, confer phenotypes consistent with pocket disruption and increased catalysis. Furthermore, allele-specific genetic interactions among TL and TL-proximal domain residues support the contribution of the funnel and bridge helices (BH) to TL dynamics. Our structural genetics approach incorporates structural and phenotypic data for high-resolution dissection of transcription mechanisms and their evolution, and is readily applicable to other essential yeast proteins.

Proper regulation of Pol II transcription, the first step of gene expression, is essential for life. Extensive evidence has revealed a widely conserved and dynamic polymerase active site component, termed the Trigger Loop (TL), in balancing transcription rate and fidelity while possibly allowing control of transcription elongation. Coupling high-throughput sequencing with our previously established genetic system, we are able to assess the in vivo phenotypes for almost all possible single substitution Pol II TL mutants in the budding yeast Saccharomyces cerevisiae. We show that mutants in the TL nucleotide interacting and linker regions widely confer dominant and severe growth defects. Clustering of TL mutants’ transcription-related and general stress phenotypes reveals three main classes of TL mutants, including previously identified fast and slow elongating mutants. Comprehensive analyses of the distribution of fast and slow elongation mutants in light of existing Pol II crystal structures reveal critical regions contributing to proper TL dynamics and function. Evidence is presented linking a previously observed hydrophobic pocket to NTP substrate-induced TL closing, the mechanism critical for correct substrates selection and transcription fidelity. Finally, we assess the functional interplay between TL and its proximal domains, and their presumptive roles in the function and evolution of the TL. Utilizing the Pol II TL as a case study, we present a structural genetics approach that reveals insights into a complex, multi-functional, and essential domain in yeast.

RNA polymerase II senses obstruction in the DNA minor groove via a conserved sensor motif

RNA polymerase II (pol II) encounters numerous barriers during transcription elongation, including DNA strand breaks, DNA lesions, and nucleosomes. Pyrrole-imidazole (Py-Im) polyamides bind to the minor groove of DNA with programmable sequence specificity and high affinity. Previous studies suggest that Py-Im polyamides can prevent transcription factor binding, as well as interfere with pol II transcription elongation. However, the mechanism of pol II inhibition by Py-Im polyamides is unclear. Here we investigate the mechanism of how these minor-groove binders affect pol II transcription elongation. In the presence of site-specifically bound Py-Im polyamides, we find that the pol II elongation complex becomes arrested immediately upstream of the targeted DNA sequence, and is not rescued by transcription factor IIS, which is in contrast to pol II blockage by a nucleosome barrier. Further analysis reveals that two conserved pol II residues in the Switch 1 region contribute to pol II stalling. Our study suggests this motif in pol II can sense the structural changes of the DNA minor groove and can be considered a "minor groove sensor." Prolonged interference of transcription elongation by sequence-specific minor groove binders may present opportunities to target transcription addiction for cancer therapy.

Lineage-specific variations in the trigger loop modulate RNA proofreading by bacterial RNA polymerases

RNA cleavage by bacterial RNA polymerase (RNAP) has been implicated in transcriptional proofreading and reactivation of arrested transcription elongation complexes but its molecular mechanism is less understood than the mechanism of nucleotide addition, despite both reactions taking place in the same active site. RNAP from the radioresistant bacterium Deinococcus radiodurans is characterized by highly efficient intrinsic RNA cleavage in comparison with Escherichia coli RNAP. We find that the enhanced RNA cleavage activity largely derives from amino acid substitutions in the trigger loop (TL), a mobile element of the active site involved in various RNAP activities. The differences in RNA cleavage between these RNAPs disappear when the TL is deleted, or in the presence of GreA cleavage factors, which replace the TL in the active site. We propose that the TL substitutions modulate the RNA cleavage activity by altering the TL folding and its contacts with substrate RNA and that the resulting differences in transcriptional proofreading may play a role in bacterial stress adaptation.

RNA polymerase errors cause splicing defects and can be regulated by differential expression of RNA polymerase subunits

Errors during transcription may play an important role in determining cellular phenotypes: the RNA polymerase error rate is >4 orders of magnitude higher than that of DNA polymerase and errors are amplified >1000-fold due to translation. However, current methods to measure RNA polymerase fidelity are low-throughout, technically challenging, and organism specific. Here I show that changes in RNA polymerase fidelity can be measured using standard RNA sequencing protocols. I find that RNA polymerase is error-prone, and these errors can result in splicing defects. Furthermore, I find that differential expression of RNA polymerase subunits causes changes in RNA polymerase fidelity, and that coding sequences may have evolved to minimize the effect of these errors. These results suggest that errors caused by RNA polymerase may be a major source of stochastic variability at the level of single cells.

RNA polymerase II transcriptional fidelity control and its functional interplay with DNA modifications

Accurate genetic information transfer is essential for life. As a key enzyme involved in the first step of gene expression, RNA polymerase II (Pol II) must maintain high transcriptional fidelity while it reads along DNA template and synthesizes RNA transcript in a stepwise manner during transcription elongation. DNA lesions or modifications may lead to significant changes in transcriptional fidelity or transcription elongation dynamics. In this review, we will summarize recent progress toward understanding the molecular basis of RNA Pol II transcriptional fidelity control and impacts of DNA lesions and modifications on Pol II transcription elongation.

Transcription errors induce proteotoxic stress and shorten cellular lifespan

Transcription errors occur in all living cells; however, it is unknown how these errors affect cellular health. To answer this question, we monitor yeast cells that are genetically engineered to display error-prone transcription. We discover that these cells suffer from a profound loss in proteostasis, which sensitizes them to the expression of genes that are associated with protein-folding diseases in humans; thus, transcription errors represent a new molecular mechanism by which cells can acquire disease phenotypes. We further find that the error rate of transcription increases as cells age, suggesting that transcription errors affect proteostasis particularly in aging cells. Accordingly, transcription errors accelerate the aggregation of a peptide that is implicated in Alzheimer's disease, and shorten the lifespan of cells. These experiments reveal a previously unappreciated role for transcriptional fidelity in cellular health and aging.