Investigating transcription reinitiation through in vitro approaches

Transcription recycling assays identify PAF1 as a driver for RNA Pol II recycling

RNA Polymerase II (Pol II) transcriptional recycling is a mechanism for which the required factors and contributions to overall gene expression levels are poorly understood. We describe an in vitro methodology facilitating unbiased identification of putative RNA Pol II transcriptional recycling factors and quantitative measurement of transcriptional output from recycled transcriptional components. Proof-of-principle experiments identified PAF1 complex components among recycling factors and detected defective transcriptional output from Pol II recycling following PAF1 depletion. Dynamic ChIP-seq confirmed PAF1 silencing triggered defective Pol II recycling in human cells. Prostate tumors exhibited enhanced transcriptional recycling, which was attenuated by antibody-based PAF1 depletion. These findings identify Pol II recycling as a potential target in cancer and demonstrate the applicability of in vitro and cellular transcription assays to characterize Pol II recycling in other disease states.

RNA Polymerase II (Pol II) recycling can influence transcription efficiency. Here the authors describe an approach aimed at facilitating the identification of factors involved in Pol II recycling and identify PAF1 complex components as mediators of recycling.

RNA Polymerase III Subunit Mutations in Genetic Diseases

RNA polymerase (Pol) III transcribes small untranslated RNAs such as 5S ribosomal RNA, transfer RNAs, and U6 small nuclear RNA. Because of the functions of these RNAs, Pol III transcription is best known for its essential contribution to RNA maturation and translation. Surprisingly, it was discovered in the last decade that various inherited mutations in genes encoding nine distinct subunits of Pol III cause tissue-specific diseases rather than a general failure of all vital functions. Mutations in the POLR3A, POLR3C, POLR3E and POLR3F subunits are associated with susceptibility to varicella zoster virus-induced encephalitis and pneumonitis. In addition, an ever-increasing number of distinct mutations in the POLR3A, POLR3B, POLR1C and POLR3K subunits cause a spectrum of neurodegenerative diseases, which includes most notably hypomyelinating leukodystrophy. Furthermore, other rare diseases are also associated with mutations in genes encoding subunits of Pol III (POLR3H, POLR3GL) and the BRF1 component of the TFIIIB transcription initiation factor. Although the causal relationship between these mutations and disease development is widely accepted, the exact molecular mechanisms underlying disease pathogenesis remain enigmatic. Here, we review the current knowledge on the functional impact of specific mutations, possible Pol III-related disease-causing mechanisms, and animal models that may help to better understand the links between Pol III mutations and disease.

Added value of autoregulation and multi-step kinetics of transcription initiation

Bacterial gene expression regulation occurs mostly during transcription, which has two main rate-limiting steps: the close complex formation, when the RNA polymerase binds to an active promoter, and the subsequent open complex formation, after which it follows elongation. Tuning these steps' kinetics by the action of e.g. transcription factors, allows for a wide diversity of dynamics. For example, adding autoregulation generates single-gene circuits able to perform more complex tasks. Using stochastic models of transcription kinetics with empirically validated parameter values, we investigate how autoregulation and the multi-step transcription initiation kinetics of single-gene autoregulated circuits can be combined to fine-tune steady state mean and cell-to-cell variability in protein expression levels, as well as response times. Next, we investigate how they can be jointly tuned to control complex behaviours, namely, time counting, switching dynamics and memory storage. Overall, our finding suggests that, in bacteria, jointly regulating a single-gene circuit's topology and the transcription initiation multi-step dynamics allows enhancing complex task performance.

Application of the SSB biosensor to study in vitro transcription

Gene expression, catalysed by RNA polymerases (RNAP), is one of the most fundamental processes in living cells. The majority of methods to quantify mRNA are based upon purification of the nucleic acid which leads to experimental inaccuracies and loss of product, or use of high cost dyes and sensitive spectrophotometers. Here, we describe the use of a fluorescent biosensor based upon the single stranded binding (SSB) protein. In this study, the SSB biosensor showed similar binding properties to mRNA, to that of its native substrate, single-stranded DNA (ssDNA). We found the biosensor to be reproducible with no associated loss of product through purification, or the requirement for expensive dyes. Therefore, we propose that the SSB biosensor is a useful tool for comparative measurement of mRNA yield following in vitro transcription.

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Single-stranded binding protein can bind mRNA similar to single-stranded DNA.

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The biosensor MDCC-SSB can be used to quantify mRNA yield from in vitro transcription.

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Myosin VI motor activity is required for in vitro and in vivo transcription.

Active Center Control of Termination by RNA Polymerase III and tRNA Gene Transcription Levels In Vivo

The ability of RNA polymerase (RNAP) III to efficiently recycle from termination to reinitiation is critical for abundant tRNA production during cellular proliferation, development and cancer. Yet understanding of the unique termination mechanisms used by RNAP III is incomplete, as is its link to high transcription output. We used two tRNA-mediated suppression systems to screen for Rpc1 mutants with gain- and loss- of termination phenotypes in S. pombe. 122 point mutation mutants were mapped to a recently solved 3.9 Å structure of yeast RNAP III elongation complex (EC); they cluster in the active center bridge helix and trigger loop, as well as the pore and funnel, the latter of which indicate involvement of the RNA cleavage domain of the C11 subunit in termination. Purified RNAP III from a readthrough (RT) mutant exhibits increased elongation rate. The data strongly support a kinetic coupling model in which elongation rate is inversely related to termination efficiency. The mutants exhibit good correlations of terminator RT in vitro and in vivo, and surprisingly, amounts of transcription in vivo. Because assessing in vivo transcription can be confounded by various parameters, we used a tRNA reporter with a processing defect and a strong terminator. By ruling out differences in RNA decay rates, the data indicate that mutants with the RT phenotype synthesize more RNA than wild type cells, and than can be accounted for by their increased elongation rate. Finally, increased activity by the mutants appears unrelated to the RNAP III repressor, Maf1. The results show that the mobile elements of the RNAP III active center, including C11, are key determinants of termination, and that some of the mutations activate RNAP III for overall transcription. Similar mutations in spontaneous cancer suggest this as an unforeseen mechanism of RNAP III activation in disease.

Reinitiation enhances reliable transcriptional responses in eukaryotes

Gene transcription is a noisy process carried out by the transcription machinery recruited to the promoter. Noise reduction is a fundamental requirement for reliable transcriptional responses which in turn are crucial for signal transduction. Compared with the relatively simple transcription initiation in prokaryotes, eukaryotic transcription is more complex partially owing to its additional reinitiation mechanism. By theoretical analysis, we showed that reinitiation reduces noise in eukaryotic transcription independent of the transcription level. Besides, a higher reinitiation rate enables a stable scaffold complex an advantage in noise reduction. Finally, we showed that the coupling between scaffold formation and transcription can further reduce transcription noise independent of the transcription level. Furthermore, compared with the reinitiation mechanism, the noise reduction effect of the coupling can be of more significance in the case that the transcription level is low and the intrinsic noise dominates. Our results uncover a mechanistic route which eukaryotes may use to facilitate a more reliable response in the noisy transcription process.

Coordinated gene expression of neuroinflammatory and cell signaling markers in dorsolateral prefrontal cortex during human brain development and aging

BACKGROUND

Age changes in expression of inflammatory, synaptic, and neurotrophic genes are not well characterized during human brain development and senescence. Knowing these changes may elucidate structural, metabolic, and functional brain processes over the lifespan, as well vulnerability to neurodevelopmental or neurodegenerative diseases.

HYPOTHESIS

Expression levels of inflammatory, synaptic, and neurotrophic genes in the human brain are coordinated over the lifespan and underlie changes in phenotypic networks or cascades.

METHODS

We used a large-scale microarray dataset from human prefrontal cortex, BrainCloud, to quantify age changes over the lifespan, divided into Development (0 to 21 years, 87 brains) and Aging (22 to 78 years, 144 brains) intervals, in transcription levels of 39 genes.

RESULTS

Gene expression levels followed different trajectories over the lifespan. Many changes were intercorrelated within three similar groups or clusters of genes during both Development and Aging, despite different roles of the gene products in the two intervals. During Development, changes were related to reported neuronal loss, dendritic growth and pruning, and microglial events; TLR4, IL1R1, NFKB1, MOBP, PLA2G4A, and PTGS2 expression increased in the first years of life, while expression of synaptic genes GAP43 and DBN1 decreased, before reaching plateaus. During Aging, expression was upregulated for potentially pro-inflammatory genes such as NFKB1, TRAF6, TLR4, IL1R1, TSPO, and GFAP, but downregulated for neurotrophic and synaptic integrity genes such as BDNF, NGF, PDGFA, SYN, and DBN1.

CONCLUSIONS

Coordinated changes in gene transcription cascades underlie changes in synaptic, neurotrophic, and inflammatory phenotypic networks during brain Development and Aging. Early postnatal expression changes relate to neuronal, glial, and myelin growth and synaptic pruning events, while late Aging is associated with pro-inflammatory and synaptic loss changes. Thus, comparable transcriptional regulatory networks that operate throughout the lifespan underlie different phenotypic processes during Aging compared to Development.