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Related Concept Videos

Global Regulatory Systems01:28

Global Regulatory Systems

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Global regulatory systems in bacteria enable rapid and coordinated responses to environmental changes by integrating sensory inputs with gene expression, ensuring efficient adaptation to fluctuating conditions. Key global regulatory mechanisms include regulons, two-component systems, sigma factors, and secondary messengers.Regulons and Global RegulatorsA regulon is a collection of genes and operons controlled by a common global regulator. These regulators enable bacteria to prioritize resource...
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Constitutive and Regulated Gene Expression01:27

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Gene expression in prokaryotes is governed by constitutive and regulated systems, allowing cells to balance the production of essential proteins with adaptive responses to environmental changes.Constitutive Gene ExpressionConstitutive, or housekeeping, genes are continuously expressed as they encode proteins vital for fundamental cellular processes. These include enzymes for glycolysis, ribosomal components for protein synthesis, and proteins involved in DNA replication. Their constant...
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Operons02:09

Operons

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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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Regulation of Expression Occurs at Multiple Steps02:24

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Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
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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.
Transcription of prokaryotic...
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Computing and Applying Atomic Regulons to Understand Gene Expression and Regulation.

José P Faria1, James J Davis2, Janaka N Edirisinghe2

  • 1Computation Institute, University of ChicagoChicago, IL, USA; Computing, Environment and Life Sciences, Argonne National LaboratoryArgonne, IL, USA; Centre of Biological Engineering, University of Minho, Campus de GualtarBraga, Portugal; Mathematics and Computer Science Division, Argonne National LaboratoryArgonne, IL, USA.

Frontiers in Microbiology
|December 10, 2016
PubMed
Summary

Identifying Atomic Regulons (ARs) helps understand gene function and regulation. This new method infers ARs from expression data, outperforming clustering methods and revealing conserved gene sets across bacteria.

Keywords:
CLREscherichia coliatomic regulonclusteringgene expression analysishierarchical clusteringk-means clusteringtranscriptomic data

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Area of Science:

  • Systems Biology
  • Genomics
  • Bioinformatics

Background:

  • Understanding gene function and regulation is crucial for predicting cellular responses.
  • Identifying co-expressed gene sets, or Atomic Regulons (ARs), is key to understanding fundamental cellular units.
  • ARs can link genes of unknown function to cellular processes and aid genetic engineering.

Purpose of the Study:

  • To develop and validate a novel approach for inferring Atomic Regulons (ARs) using large-scale gene expression data.
  • To compare the performance of AR inference against traditional data-driven clustering methods.
  • To investigate the conservation of ARs across different bacterial species.

Main Methods:

  • Inferred ARs for *Escherichia coli* using 907 gene expression experiments, gene context, and functional relationships.
  • Compared ARs with Hierarchical clustering and k-means clustering results against curated regulatory interactions (RegulonDB).
  • Assessed ARs and clusters against Context Likelihood of Relatedness (CLR) predicted interactions and evaluated data quantity impact.

Main Results:

  • ARs demonstrated higher consistency with gold-standard regulons and CLR-predicted interactions than data-driven clusters.
  • High-quality ARs can be produced without using the entire available gene expression dataset for *E. coli*.
  • AR gene membership consistency correlates with phylogenetic distance across *E. coli*, *Shewanella oneidensis*, *Pseudomonas aeruginosa*, *Thermus thermophilus*, and *Staphylococcus aureus*.

Conclusions:

  • The novel AR inference approach provides a more accurate representation of gene regulatory units compared to clustering methods.
  • Comparative analysis of ARs across species offers insights into conserved regulatory modules and evolutionary divergence.
  • This method facilitates a deeper understanding of gene regulation and enables the design of genetic engineering strategies.