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What is Gene Expression?01:36

What is Gene Expression?

A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is comprised  of nucleotides and proteins are comprised of amino acids, a mediator is required to convert the information encoded in DNA into proteins. This mediator is the messenger RNA (mRNA). mRNA copies the blueprint from DNA by a process called transcription. In eukaryotes, transcription occurs in the nucleus by complementary base-pairing with the DNA template. The mRNA is then processed and...
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Expression level, evolutionary rate, and the cost of expression.

Joshua L Cherry1

  • 1National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA. jcherry@ncbi.nlm.nih.gov

Genome Biology and Evolution
|October 2, 2010
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Summary

Protein evolutionary rates vary, with highly expressed proteins evolving slowly. A new model suggests selection for optimal protein function, balancing benefits against expression costs, explains this pattern, not just mistranslation effects.

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Area of Science:

  • Molecular Biology
  • Evolutionary Biology
  • Genomics

Background:

  • Protein sequence evolution rates differ significantly within genomes.
  • Highly expressed proteins exhibit slower evolutionary rates, a phenomenon linked to the mistranslation-induced misfolding (MIM) hypothesis.
  • The MIM hypothesis posits that toxic misfolded proteins from expression errors drive slow evolution in highly expressed proteins.

Purpose of the Study:

  • To propose and model an alternative explanation for the correlation between protein expression level and evolutionary rate.
  • To investigate the role of selection for protein function in shaping evolutionary rates.
  • To evaluate if a function-driven model can explain observed empirical relationships.

Main Methods:

  • Development of a theoretical model based on selection for protein function.
  • Simulations to test the model's predictions against empirical data.
  • Comparison of model predictions with the MIM hypothesis and gene knockout fitness data.

Main Results:

  • The proposed model successfully explains the inverse correlation between protein expression level and evolutionary rate.
  • Simulations confirmed the model's predictions.
  • The model accounts for empirical observations, including weak correlations between evolutionary rate and fitness measured by gene knockout.

Conclusions:

  • Selection for optimal protein function, balancing functional benefits against expression costs, provides a robust explanation for varying protein evolutionary rates.
  • This function-driven model is consistent with empirical data and offers an alternative to the MIM hypothesis.
  • The rate of protein evolution is primarily shaped by functional selection rather than solely by the costs of mistranslation.