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This content will become publicly available on April 24, 2025

Title: Transcription of microRNAs is regulated by developmental signaling pathways and transcription factors
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Author(s) / Creator(s):
; ;
Ben-Tabou, Smadar
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Frontiers in cell and developmental biology
Medium: X
Sponsoring Org:
National Science Foundation
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  1. Central to the development and survival of all organisms is the regulation of gene expression, which begins with the process of transcription catalyzed by RNA polymerases. During transcription of protein-coding genes, the general transcription factors (GTFs) work alongside RNA polymerase II (Pol II) to assemble the preinitiation complex at the transcription start site, open the promoter DNA, initiate synthesis of the nascent messenger RNA, transition to productive elongation, and ultimately terminate transcription. Through these different stages of transcription, Pol II is dynamically phosphorylated at the C-terminal tail of its largest subunit, serving as a control mechanism for Pol II elongation and a signaling/binding platform for co-transcriptional factors. The large number of core protein factors participating in the fundamental steps of transcription add dense layers of regulation that contribute to the complexity of temporal and spatial control of gene expression within any given cell type. The Pol II transcription system is highly conserved across different levels of eukaryotes; however, most of the information here will focus on the human Pol II system. This review walks through various stages of transcription, from preinitiation complex assembly to termination, highlighting the functions and mechanisms of the core machinery that participates in each stage.

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  2. null (Ed.)
    Cells in the brain, liver and skin, as well as many other organs, all contain the same DNA, yet behave in very different ways. This is because before a gene can produce its corresponding protein, it must first be transcribed into messenger RNA. As an organism grows, the transcription of certain genes is switched on or off by regulatory molecules called transcription factors, which guide cells towards a specific ‘fate’. These molecules bind to specific locations within the regulatory regions of DNA, and for decades biologist have tried to use the arrangement of these sites to predict which proteins a cell will make. Theoretical models known as thermodynamic models have been able to successfully predict transcription in bacteria. However, this has proved more challenging to do in eukaryotes, such as yeast, fruit flies and humans. One of the key differences is that DNA in eukaryotes is typically tightly wound into bundles called nucleosomes, which must be disentangled in order for transcription factors to access the DNA. Previous thermodynamic models have suggested that DNA in eukaryotes randomly switches between being in a wound and unwound state. The models assume that once unwound, regulatory proteins stabilize the DNA in this form, making it easier for other transcription factors to bind to the DNA. Now, Eck, Liu et al. have tested some of these models by studying the transcription of a gene involved in the development of fruit flies. The experiments showed that no thermodynamic model could accurately mimic how this gene is regulated in the embryos of fruit flies. This led Eck, Liu et al. to identify a model that is better at predicting the activation pattern of this developmental gene. In this model, instead of just ‘locking’ DNA into an unwound shape, transcription factors can also actively speed up the unwinding of DNA. This improved understanding builds towards the goal of predicting gene regulation, where DNA sequences can be used to tell where and when cell decisions will be made. In the future, this could allow the development of new types of therapies that can regulate transcription in different diseases. 
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  3. Abstract

    Detecting changes in the activity of a transcription factor (TF) in response to a perturbation provides insights into the underlying cellular process. Transcription Factor Enrichment Analysis (TFEA) is a robust and reliable computational method that detects positional motif enrichment associated with changes in transcription observed in response to a perturbation. TFEA detects positional motif enrichment within a list of ranked regions of interest (ROIs), typically sites of RNA polymerase initiation inferred from regulatory data such as nascent transcription. Therefore, we also introducemuMerge, a statistically principled method of generating a consensus list of ROIs from multiple replicates and conditions. TFEA is broadly applicable to data that informs on transcriptional regulation including nascent transcription (eg. PRO-Seq), CAGE, histone ChIP-Seq, and accessibility data (e.g., ATAC-Seq). TFEA not only identifies the key regulators responding to a perturbation, but also temporally unravels regulatory networks with time series data. Consequently, TFEA serves as a hypothesis-generating tool that provides an easy, rigorous, and cost-effective means to broadly assess TF activity yielding new biological insights.

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