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Science China Life Sciences

, Volume 62, Issue 4, pp 594–608 | Cite as

Topological evolution of coexpression networks by new gene integration maintains the hierarchical and modular structures in human ancestors

  • Jian ZuEmail author
  • Yuexi Gu
  • Yu Li
  • Chentong Li
  • Wenyu Zhang
  • Yong E. Zhang
  • UnJin Lee
  • Li Zhang
  • Manyuan LongEmail author
Research Paper
  • 81 Downloads

Abstract

We analyze the global structure and evolution of human gene coexpression networks driven by new gene integration. When the Pearson correlation coefficient is greater than or equal to 0.5, we find that the coexpression network consists of 334 small components and one “giant” connected subnet comprising of 6317 interacting genes. This network shows the properties of power-law degree distribution and small-world. The average clustering coefficient of younger genes is larger than that of the elderly genes (0.6685 vs. 0.5762). Particularly, we find that the younger genes with a larger degree also show a property of hierarchical architecture. The younger genes play an important role in the overall pivotability of the network and this network contains few redundant duplicate genes. Moreover, we find that gene duplication and orphan genes are two dominant evolutionary forces in shaping this network. Both the duplicate genes and orphan genes develop new links through a “rich-gets-richer” mechanism. With the gradual integration of new genes into the ancestral network, most of the topological structure features of the network would gradually increase. However, the exponent of degree distribution and modularity coefficient of the whole network do not change significantly, which implies that the evolution of coexpression networks maintains the hierarchical and modular structures in human ancestors.

Keywords

Network biology gene network evolution scale-free network natural selection gene expression self-organization gene duplication 

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Notes

Acknowledgements

We thank Profs. Yicang Zhou and Yanni Xiao for their valuable discussion. This work was supported by grants from the National Natural Science Foundation of China (11571272, 11201368 and 11631012), the National Science and Technology Major Project of China (2012ZX10002001), the Natural Science Foundation of Shaanxi Province (2015JQ1011) and the China Postdoctoral Science Foundation (2014M560755).

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Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Jian Zu
    • 1
    • 2
    Email author
  • Yuexi Gu
    • 1
  • Yu Li
    • 1
  • Chentong Li
    • 1
  • Wenyu Zhang
    • 3
  • Yong E. Zhang
    • 4
  • UnJin Lee
    • 2
  • Li Zhang
    • 2
  • Manyuan Long
    • 2
    Email author
  1. 1.School of Mathematics and StatisticsXi’an Jiaotong UniversityXi’anChina
  2. 2.Department of Ecology and EvolutionThe University of ChicagoChicagoUSA
  3. 3.Center for Systems BiologySoochow UniversitySuzhouChina
  4. 4.State Key Laboratory of Integrated Management of Pest Insects and Rodents & Key Laboratory of the Zoological Systematics and Evolution, Institute of ZoologyChinese Academy of SciencesBeijingChina

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