Effects of ordered mutations on dynamics in signaling networks

BMC Med Genomics. 2020 Feb 20;13(Suppl 4):13. doi: 10.1186/s12920-019-0651-z.

Abstract

Background: Many previous clinical studies have found that accumulated sequential mutations are statistically related to tumorigenesis. However, they are limited in fully elucidating the significance of the ordered-mutation because they did not focus on the network dynamics. Therefore, there is a pressing need to investigate the dynamics characteristics induced by ordered-mutations.

Methods: To quantify the ordered-mutation-inducing dynamics, we defined the mutation-sensitivity and the order-specificity that represent if the network is sensitive against a double knockout mutation and if mutation-sensitivity is specific to the mutation order, respectively, using a Boolean network model.

Results: Through intensive investigations, we found that a signaling network is more sensitive when a double-mutation occurs in the direction order inducing a longer path and a smaller number of paths than in the reverse order. In addition, feedback loops involving a gene pair decreased both the mutation-sensitivity and the order-specificity. Next, we investigated relationships of functionally important genes with ordered-mutation-inducing dynamics. The network is more sensitive to mutations subject to drug-targets, whereas it is less specific to the mutation order. Both the sensitivity and specificity are increased when different-drug-targeted genes are mutated. Further, we found that tumor suppressors can efficiently suppress the amplification of oncogenes when the former are mutated earlier than the latter.

Conclusion: Taken together, our results help to understand the importance of the order of mutations with respect to the dynamical effects in complex biological systems.

Keywords: Boolean dynamics; Mutation-sensitivity; Order-specificity; Ordered-mutations; Signaling networks.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Computational Biology
  • Datasets as Topic
  • Drug Delivery Systems
  • Genes, Tumor Suppressor
  • Humans
  • Metabolic Networks and Pathways / genetics*
  • Models, Biological
  • Mutation*
  • Neoplasms / drug therapy
  • Neoplasms / genetics
  • Neoplasms / metabolism*
  • Oncogenes
  • Signal Transduction / genetics*