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Gene Evolution - Fast or Slow?02:05

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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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Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
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Functional Optimization in Distinct Tissues and Conditions Constrains the Rate of Protein Evolution.

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Area of Science:

  • Evolutionary biology
  • Molecular biology
  • Genomics

Background:

  • The variability in protein evolutionary rates is not well understood.
  • Mechanisms of protein functional optimization in multicellular species remain unclear.
  • The moderate efficiency of many proteins, like enzymes, is not fully explained.

Purpose of the Study:

  • To investigate the relationship between protein functional optimality and evolutionary rates.
  • To understand the role of cellular expression costs in protein evolution.
  • To identify factors affecting protein evolution and functional efficiency in multicellular organisms.

Main Methods:

  • Analysis of genomics and functional datasets across multiple organisms.
  • Examination of protein expression levels and functional efficiency.
  • Investigation of tissue-specific expression patterns in animals and plants.

Main Results:

  • A strong inverse relationship exists between protein functional optimality and evolutionary rate.
  • Highly expressed proteins are significantly more functionally optimized.
  • Cellular expression costs drive functional optimization in abundant proteins, slowing evolution.
  • Tissue-specific expression, particularly in neurons and young plant tissues, impacts protein evolution and efficiency.

Conclusions:

  • Purifying selection for functional optimality significantly decelerates protein evolution.
  • Cellular expression costs and tissue-specific expression are key determinants of protein evolution and functional adaptation.
  • Constraints at molecular, cellular, and species levels jointly shape protein evolution.