PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 39241983

Pause Patrol: Negative Elongation Factor's Role in Promoter-Proximal Pausing and Beyond.

Annette J Diao·Bonnie G Su·Seychelle M Vos
View original source ↗Export metadata ↓

Abstract

RNA polymerase (Pol) II is highly regulated to ensure appropriate gene expression. Early transcription elongation is associated with transient pausing of RNA Pol II in the promoter-proximal region. In multicellular organisms, this pausing is stabilized by the association of transcription elongation factors DRB-sensitivity inducing factor (DSIF) and Negative Elongation Factor (NELF). DSIF is a broadly conserved transcription elongation factor whereas NELF is mostly restricted to the metazoan lineage. Mounting evidence suggests that NELF association with RNA Pol II serves as checkpoint for either release into rapid and productive transcription elongation or premature termination at promoter-proximal pause sites. Here we summarize NELF's roles in promoter-proximal pausing, transcription termination, DNA repair, and signaling based on decades of cell biological, biochemical, and structural work and describe areas for future research.

Explore related subjects

Promoter Regions, GeneticRNA Polymerase IIHumansAnimalsTranscription FactorsDNA RepairTranscriptional Elongation FactorsTranscription, GeneticGene Expression RegulationSignal TransductionRNA polymerase IIgene expressionnegative elongation factorpromoter proximal pausingtranscriptionJournal ArticleReviewResearch Support, Non-U.S. Gov'tResearch Support, N.I.H., Extramural

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Annette J Diao, Bonnie G Su, Seychelle M Vos. 2024-09-04. Pause Patrol: Negative Elongation Factor's Role in Promoter-Proximal Pausing and Beyond.. https://doi.org/10.1016/j.jmb.2024.168779

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

Modular synthetic cross-kingdom promoters enable coordinated expression in Escherichia coli and Saccharomyces cerevisiae.

Synthetic biology and metabolic engineering increasingly demand predictable and interoperable gene expression across phylogenetically distant organisms, as the need for portable genetic systems and transferable metabolic pathways continues to grow. However, fundamental differences in promoter architecture and transcriptional logic across kingdoms remain a key bottleneck in developing universal expression platforms. Here, we designed a set of modular hybrid promoters that enable tunable and quantitatively consistent gene expression in both Escherichia coli and Saccharomyces cerevisiae. These promoters integrate bacterial -10/-35 motifs and Shine-Dalgarno sequences with minimal yeast TATA boxes and Kozak sequences to ensure transcriptional and translational compatibility. The promoter set supported weak, moderate, and strong expression with high relative consistency across species. Applied to the biosynthetic pathway for the valuable pigment prodeoxyviolacein, the hybrid promoters enabled coordinated production in both hosts. This work establishes a broadly compatible promoter architecture and provides a foundational toolkit for cross-kingdom, multi-host synthetic biology.

Promoter Regions, Genetic↗
PubMed2026

Structural Features of DNA in TATA-Containing and TATA-Less Core Promoters of RNA Polymerase II Differ.

Nucleotide motifs in the core promoters of eukaryotic protein-coding genes transcribed by RNA polymerase II (Pol II) play an important role in the transcription process. We analyzed the role of an octanucleotide located in the TATA box position. Depending on whether this octanucleotide can form a complex with the TATA-binding protein (TBP), the promoter is classified as either TATA-containing or TATA-less. We analyzed the differences in the primary and spatial structures, as well as their dynamics, in TATA-containing and TATA-less promoters of mammals and plants. We divided the complete promoter sets of six organisms (H. sapiens, M. musculus, C. familiaris, A. thaliana, Z. mays, and H. vulgare) from the EPDnew database into TATA-containing and TATA-less fractions. The sizes of the TATA-containing promoter fractions are significantly smaller than those of the TATA-less fractions in all studied organisms, except in A. thaliana, where the sizes of both fractions are approximately equal. We characterized promoter architecture using variation profiles of various base-pair step parameters, minor-groove width, and the conformational dynamics of native DNA. The architectures of TATA-containing and TATA-less promoters differ significantly. The possible mechanistic influence of DNA structural features on the formation of the pre-initiation complex (PIC) in both types of promoters is discussed.

Promoter Regions, Genetic↗
PubMed2026

EvoSNR-Prom: Predicting promoters at single-nucleotide resolution with label-aware transfer learning of the pretrained EVO model.

The precise identification of promoters is crucial for understanding gene regulation. Deep learning methods have achieved considerable success in promoter prediction, yet most operate at the sequence level with coarse-grained labels. This means they label an entire DNA segment as either a "promoter" or "non-promoter," which results in a lack of the nucleotide-level resolution in prediction. In this study, we propose EvoSNR-Prom, a model designed for promoter prediction at single-nucleotide resolution. EvoSNR-Prom is built on the Evo foundation model and formulates promoter identification as a token-level sequence labeling problem, analogous to named entity recognition in natural language processing. To address the limited contextual information available in single-nucleotide tokenization, we introduce a lexicon-enhanced embedding strategy that incorporates biologically meaningful DNA lexicons, enriching contextual representations and improving the model's ability to capture complex sequence motifs. Furthermore, to enhance predictive performance on small size datasets, we integrate a label-aware transfer learning framework to leverage knowledge from well-annotated source species to a target organism. The results across various prokaryotic datasets show that EvoSNR-Prom achieves excellent performance. This work provides a valuable computational framework for the high-precision analysis of gene regulatory elements, contributing to the advancement of promoter prediction at single-nucleotide resolution.

Promoter Regions, Genetic↗