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Related Experiment Videos

Conservation and coevolution in the scale-free human gene coexpression network.

I King Jordan1, Leonardo Mariño-Ramírez, Yuri I Wolf

  • 1National Center for Biotechnology Information, National Institutes of Health Bethesda, Maryland, USA.

Molecular Biology and Evolution
|July 30, 2004
PubMed
Summary

Physical network self-organization and natural selection shape human gene regulation evolution. Gene coexpression network topology influences gene sequence evolution, but not expression divergence between species.

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Area of Science:

  • Evolutionary biology
  • Systems biology
  • Genomics

Background:

  • Natural selection is a key driver of biological evolution.
  • Physical principles of network self-organization are increasingly recognized in biological systems.

Purpose of the Study:

  • To investigate the interplay between natural selection and network self-organization in human gene regulation evolution.
  • To analyze genome-scale sequence and expression data to understand evolutionary processes.

Main Methods:

  • Examined human gene coexpression networks derived from tissue-specific expression profiles.
  • Analyzed network topology, specifically scale-free properties and preferential attachment.
  • Assessed evolutionary rates of genes based on their network connectivity and sequence/expression divergence.

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Main Results:

  • Human gene coexpression networks exhibit scale-free topology, suggesting evolutionary self-organization.
  • Genes with high connectivity (hubs) evolve slower, indicating network topology affects selective constraints.
  • Selective constraints on coding and 3' untranslated regions (UTRs) are linked to network topology, but 5' UTRs differ.
  • No correlation found between gene sequence divergence and expression profile divergence between human and mouse.

Conclusions:

  • Gene coexpression network structure significantly impacts the evolution of gene sequences.
  • Distinct evolutionary mechanisms may govern gene sequence versus gene expression divergence.
  • A model involving adaptation-driven divergence and convergent evolution is proposed for expression pattern evolution.