Abstract

BackgroundCellular senescence irreversibly arrests growth of human diploid cells. In addition, recent studies have 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 considering gene-level interactions and continuously perturbed genes. It is necessary to reveal the genotypic mechanism inferred by affected genes and their interaction underlying the senescence process.ResultsWe suggested a novel computational approach to identify an integrative network which profiles an underlying genotypic signature from time-series gene expression data. The relatively perturbed genes were selected for each time point based on the proposed scoring measure denominated as perturbation scores. Then, the selected genes were integrated with protein-protein interactions to construct time point specific network. From these constructed networks, the conserved edges across time point were extracted for the common network and statistical 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 points could explain the phenotypic alteration. We also performed functional enrichment on the common network and identified high association with phenotypic alteration. Remarkably, we observed that the identified cell cycle specific common network played an important role in replicative senescence as a key regulator.ConclusionsHeretofore, the network analysis from time series gene expression data has been focused on what topological structure was changed over time point. Conversely, we focused on the conserved structure but its context was changed in course of time and showed it was available to explain the phenotypic changes. We expect that the proposed method will help to elucidate the biological mechanism unrevealed by the existing approaches.

Highlights

  • Cellular senescence irreversibly arrests growth of human diploid cells

  • We assume ‘average differences of perturbation scores from our Results To demonstrate the effectiveness of the proposed method, we applied it to three time-series gene expression datasets: two senescence datasets and a cancer progression dataset

  • We focused on senescence, we included the cancer progression dataset to test the applicability of our method to other types of datasets

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Summary

Introduction

Cellular senescence irreversibly arrests growth of human diploid cells. Recent studies have 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 considering gene-level interactions and continuously perturbed genes. It is necessary to reveal the genotypic mechanism inferred by affected genes and their interaction underlying the senescence process. Cellular senescence is irreversible exit from the cell cycle resulting from the limited replicative capacity [1] caused by telomere shortening [2, 3], DNA damage, and the epigenetic derepression of several genes such as the IKK4a/ARF locus [4]. A recent study analyzed genome-wide gene expression at various time points during the establishment of replicative. Genome-wide time-series analysis can reveal the genotypic signature underlying senescence

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