Вавиловский журнал генетики и селекции (Mar 2021)

Phylostratigraphic analysis of gene networks of human diseases

  • Z. S. Mustafin,
  • S. A. Lashin,
  • Yu. G. Matushkin

DOI
https://doi.org/10.18699/VJ21.006
Journal volume & issue
Vol. 25, no. 1
pp. 46 – 56

Abstract

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Phylostratigraphic analysis is an approach to the study of gene evolution that makes it possible to determine the time of the origin of genes by analyzing their orthologous groups. The age of a gene belonging to an orthologous group is def ined as the age of the most recent ancestor of all species represented in that group. Such an analysis can reveal important stages in the evolution of both the organism as a whole and groups of functionally related genes, in particular gene networks. In addition to investigating the time of origin of a gene, the level of its genetic variability and what type of selection the gene is subject to in relation to the most closely related organisms is studied. Using the Orthoscape application, gene networks from the KEGG Pathway, Human Diseases database describing various human diseases were analyzed. It was shown that the majority of genes described in gene networks are under stabilizing selection and a high reliable correlation was found between the time of gene origin and the level of genetic variability: the younger the gene, the higher the level of its variability is. It was also shown that among the gene networks analyzed, the highest proportion of evolutionarily young genes was found in the networks associated with diseases of the immune system (65 %), and the highest proportion of evolutionarily ancient genes was found in the networks responsible for the formation of human dependence on substances that cause addiction to chemical compounds (88 %); gene networks responsible for the development of infectious diseases caused by parasites are signif icantly enriched for evolutionarily young genes, and gene networks responsible for the development of specif ic types of cancer are signif icantly enriched for evolutionarily ancient genes.

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