Systematic identification of an integrative network module during senescence from time-series gene expression

Abstract

Background: Cellular senescence irreversibly arrests growth of human diploid cells. In addition, recent studieshave indicated that senescence is a multi-step evolving process related to important complex biological processes.Most studies analyzed only the genes and their functions representing each senescence phase without consideringgene-level interactions and continuously perturbed genes. It is necessary to reveal the genotypic mechanisminferred by affected genes and their interaction underlying the senescence process.Results: We suggested a novel computational approach to identify an integrative network which profiles anunderlying genotypic signature from time-series gene expression data. The relatively perturbed genes were selectedfor each time point based on the proposed scoring measure denominated as perturbation scores. Then, theselected genes were integrated with protein-protein interactions to construct time point specific network. Fromthese constructed networks, the conserved edges across time point were extracted for the common network andstatistical test was performed to demonstrate that the network could explain the phenotypic alteration. As a result,it was confirmed that the difference of average perturbation scores of common networks at both two time pointscould explain the phenotypic alteration. We also performed functional enrichment on the common network andidentified high association with phenotypic alteration. Remarkably, we observed that the identified cell cyclespecific common network played an important role in replicative senescence as a key regulator.Conclusions: Heretofore, the network analysis from time series gene expression data has been focused on whattopological structure was changed over time point. Conversely, we focused on the conserved structure but itscontext was changed in course of time and showed it was available to explain the phenotypic changes. We expectthat the proposed method will help to elucidate the biological mechanism unrevealed by the existing approaches