- Award ID(s):
- 1807486
- PAR ID:
- 10138523
- Date Published:
- Journal Name:
- Chemical Communications
- Volume:
- 56
- Issue:
- 15
- ISSN:
- 1359-7345
- Page Range / eLocation ID:
- 2296 to 2299
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Polen, Tino (Ed.)ABSTRACT Regulation of gene expression is a vital component of cellular biology. Transcription factor proteins often bind regulatory DNA sequences upstream of transcription start sites to facilitate the activation or repression of RNA polymerase. Research laboratories have devoted many projects to understanding the transcription regulatory networks for transcription factors, as these regulated genes provide critical insight into the biology of the host organism. Various in vivo and in vitro assays have been developed to elucidate transcription regulatory networks. Several assays, including SELEX-seq and ChIP-seq, capture DNA-bound transcription factors to determine the preferred DNA-binding sequences, which can then be mapped to the host organism’s genome to identify candidate regulatory genes. In this protocol, we describe an alternative in vitro , iterative selection approach to ascertaining DNA-binding sequences of a transcription factor of interest using restriction endonuclease, protection, selection, and amplification (REPSA). Contrary to traditional antibody-based capture methods, REPSA selects for transcription factor-bound DNA sequences by challenging binding reactions with a type IIS restriction endonuclease. Cleavage-resistant DNA species are amplified by PCR and then used as inputs for the next round of REPSA. This process is repeated until a protected DNA species is observed by gel electrophoresis, which is an indication of a successful REPSA experiment. Subsequent high-throughput sequencing of REPSA-selected DNAs accompanied by motif discovery and scanning analyses can be used for determining transcription factor consensus binding sequences and potential regulated genes, providing critical first steps in determining organisms’ transcription regulatory networks. IMPORTANCE Transcription regulatory proteins are an essential class of proteins that help maintain cellular homeostasis by adapting the transcriptome based on environmental cues. Dysregulation of transcription factors can lead to diseases such as cancer, and many eukaryotic and prokaryotic transcription factors have become enticing therapeutic targets. Additionally, in many understudied organisms, the transcription regulatory networks for uncharacterized transcription factors remain unknown. As such, the need for experimental techniques to establish transcription regulatory networks is paramount. Here, we describe a step-by-step protocol for REPSA, an inexpensive, iterative selection technique to identify transcription factor-binding sequences without the need for antibody-based capture methods.more » « less
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Abstract Multiple RNA polymerases (RNAPs) transcribing a gene have been known to exhibit collective group behavior, causing the transcription elongation rate to increase with the rate of transcription initiation. Such behavior has long been believed to be driven by a physical interaction or ‘push’ between closely spaced RNAPs. However, recent studies have posited that RNAPs separated by longer distances may cooperate by modifying the DNA segment under transcription. Here, we present a theoretical model incorporating the mechanical coupling between RNAP translocation and the DNA torsional response. Using stochastic simulations, we demonstrate DNA supercoiling-mediated long-range cooperation between co-transcribing RNAPs. We find that inhibiting transcription initiation can slow down the already recruited RNAPs, in agreement with recent experimental observations, and predict that the average transcription elongation rate varies non-monotonically with the rate of transcription initiation. We further show that while RNAPs transcribing neighboring genes oriented in tandem can cooperate, those transcribing genes in divergent or convergent orientations can act antagonistically, and that such behavior holds over a large range of intergenic separations. Our model makes testable predictions, revealing how the mechanical interplay between RNAPs and the DNA they transcribe can govern transcriptional dynamics.