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Related Concept Videos

Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

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Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form...
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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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Prokaryotic Transcriptional Activators and Repressors01:58

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The organization of prokaryotic genes in their genome is notably different from that of eukaryotes. Prokaryotic genes are organized, such that the genes for proteins involved in the same biochemical process or function are located together in groups. This group of genes, along with their regulatory elements, are collectively known as an operon. The functional genes in an operon are transcribed together to give a single strand of mRNA known as polycistronic mRNA.
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Operons02:09

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Prokaryotes can control gene expression through operons—DNA sequences consisting of regulatory elements and clustered, functionally related protein-coding genes. Operons use a single promoter sequence to initiate transcription of a gene cluster (i.e., a group of structural genes) into a single mRNA molecule. The terminator sequence ends transcription. An operator sequence, located between the promoter and structural genes, prohibits the operon’s transcriptional activity if bound by...
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Gene transcription is regulated by the synergistic action of several proteins that form a complex at a gene regulatory site. This is observed in eukaryotes, where the regulation of gene expression is a complex process. Regulatory proteins in eukaryotes can broadly be classified into two types – regulators that bind directly to specific DNA sequences and co-regulators that associate with regulatory proteins but cannot directly bind to the DNA. These co-regulators are further divided into...
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Proteins that regulate transcription can do so either via direct contact with RNA Polymerase or through indirect interactions facilitated by adaptors, mediators, histone-modifying proteins, and nucleosome remodelers. Direct interactions to activate transcription is seen in bacteria as well as in some eukaryotic genes. In these cases, upstream activation sequences are adjacent to the promoters, and the activator proteins interact directly with the transcriptional machinery. For example, in...
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Related Experiment Video

Updated: Jul 5, 2025

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Inferred regulons are consistent with regulator binding sequences in E. coli.

Sizhe Qiu1, Xinlong Wan1, Yueshan Liang1

  • 1Department of Bioengineering, University of California San Diego, La Jolla, CA, United States of America.

Plos Computational Biology
|January 22, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning models confirm that inferred bacterial regulons from RNA-seq data have a strong biochemical basis in promoter DNA sequences. Promoter sequence features, including motifs and DNA shape, successfully predict regulatory activity, validating top-down inference methods for discovering transcriptional regulatory networks.

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Computational Biology

Background:

  • The transcriptional regulatory network (TRN) in E. coli involves complex interactions between regulators and DNA sequences.
  • Regulons are identified through experimental binding site measurement or inferred from gene expression data.
  • Independent component analysis (ICA) of RNA-seq data is a powerful tool for inferring bacterial regulons.

Purpose of the Study:

  • To investigate the biochemical basis of regulon structures inferred by ICA using promoter sequence features.
  • To develop and validate machine learning models for predicting E. coli regulon structures based on promoter sequences.
  • To assess the extent to which promoter sequence characteristics explain ICA-inferred regulon organization.

Main Methods:

  • Development of machine learning models to predict E. coli regulon structures.
  • Utilizing promoter sequence features, including regulator motifs, DNA shape, and extended motifs for multimeric binding.
  • Cross-validation using AUROC (Area Under the Receiver Operating Characteristic curve) for model performance evaluation.
  • Analysis of regulons where initial models failed to identify novel sequence features.

Main Results:

  • Machine learning models successfully predicted regulon structures for 85% of ICA-inferred E. coli regulons (AUROC >= 0.8).
  • Promoter motifs alone predicted regulatory activity in 40% of regulons.
  • Additional features like DNA shape and extended motifs improved predictions for the remaining 60% of regulons.
  • Investigation of model failures revealed new regulator-specific features that enhanced accuracy.

Conclusions:

  • The structure of ICA-inferred regulons is largely explained by the strength of regulator binding sites in promoter regions.
  • Promoter sequence features provide a biochemical foundation for top-down regulon inference.
  • This study reinforces the utility of ICA and machine learning for discovering bacterial transcriptional regulatory networks.