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Related Concept Videos

Cis-regulatory Sequences02:02

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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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Regulation of Expression at Multiple Steps01:23

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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
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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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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Predicting Evolution Using Regulatory Architecture.

Philippe Nghe1, Marjon G J de Vos2, Enzo Kingma3

  • 1Laboratoire de Biochimie, UMR CBI 8231, ESPCI Paris, PSL Research University, 75005 Paris, France.

Annual Review of Biophysics
|February 11, 2020
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Summary
This summary is machine-generated.

Understanding evolutionary constraints is key. New methods combining systems biology and genetic tools reveal mechanistic causes of epistasis in regulatory networks, offering insights into predictable evolutionary limits.

Keywords:
epistasisevolutionary constraintgene regulationpleiotropypredictionregulation networks

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Area of Science:

  • Evolutionary biology
  • Systems biology
  • Genetics

Background:

  • The causes of evolutionary constraint have been poorly understood due to limited mechanistic insight into phenotypes.
  • Investigating evolutionary limits requires understanding the underlying molecular and cellular mechanisms.

Purpose of the Study:

  • To review recent innovative approaches that leverage mechanistic information to study evolutionary constraint.
  • To highlight how these methods reveal the causes of epistasis at various biological levels.

Main Methods:

  • Integration of systems biology models with population and single-cell quantification.
  • Application of novel genetic tools to study complex cellular functions and engineered networks.
  • Analysis of epistasis across molecular recognition, single regulatory networks, and inter-network interactions.

Main Results:

  • Recent developments provide a mechanistic understanding of evolutionary constraint.
  • These approaches reveal the causes of epistasis at molecular, network, and inter-network levels.
  • The findings suggest predictable features of evolutionary constraint are emerging.

Conclusions:

  • Innovative mechanistic approaches are crucial for understanding evolutionary limits.
  • The study of epistasis through systems biology and genetic tools offers new perspectives on evolutionary constraint.
  • Predictable patterns in evolutionary constraint are becoming apparent through these integrated methods.