Natural Cubic Spline Regression Modeling Followed by Dynamic Network Reconstruction for the Identification of Radiation-Sensitivity Gene Association Networks from Time-Course Transcriptome Data

Agata Michna; Herbert Braselmann; Martin Selmansberger; Anne Dietz; Julia Hess; Maria Gomolka; Sabine Hornhardt; Nils Blüthgen; Horst Zitzelsberger; Kristian Unger

doi:10.1371/journal.pone.0160791

Natural Cubic Spline Regression Modeling Followed by Dynamic Network Reconstruction for the Identification of Radiation-Sensitivity Gene Association Networks from Time-Course Transcriptome Data

PLoS One. 2016 Aug 9;11(8):e0160791. doi: 10.1371/journal.pone.0160791. eCollection 2016.

Authors

Agata Michna¹, Herbert Braselmann^{1

2}, Martin Selmansberger¹, Anne Dietz³, Julia Hess^{1

2}, Maria Gomolka³, Sabine Hornhardt³, Nils Blüthgen⁴, Horst Zitzelsberger^{1

2}, Kristian Unger^{1

2}

Affiliations

¹ Research Unit Radiation Cytogenetics, Helmholtz Zentrum München, German Research Center for Environmental Health GmbH, Neuherberg, Germany.
² Clinical Cooperation Group "Personalized Radiotherapy in Head and Neck Cancer", Helmholtz Zentrum München, Neuherberg, Germany.
³ Department of Radiation Protection and Health, Federal Office for Radiation Protection, Neuherberg, Germany.
⁴ Institute of Pathology, Charité-Universitätsmedizin Berlin, Berlin, Germany.

Abstract

Gene expression time-course experiments allow to study the dynamics of transcriptomic changes in cells exposed to different stimuli. However, most approaches for the reconstruction of gene association networks (GANs) do not propose prior-selection approaches tailored to time-course transcriptome data. Here, we present a workflow for the identification of GANs from time-course data using prior selection of genes differentially expressed over time identified by natural cubic spline regression modeling (NCSRM). The workflow comprises three major steps: 1) the identification of differentially expressed genes from time-course expression data by employing NCSRM, 2) the use of regularized dynamic partial correlation as implemented in GeneNet to infer GANs from differentially expressed genes and 3) the identification and functional characterization of the key nodes in the reconstructed networks. The approach was applied on a time-resolved transcriptome data set of radiation-perturbed cell culture models of non-tumor cells with normal and increased radiation sensitivity. NCSRM detected significantly more genes than another commonly used method for time-course transcriptome analysis (BETR). While most genes detected with BETR were also detected with NCSRM the false-detection rate of NCSRM was low (3%). The GANs reconstructed from genes detected with NCSRM showed a better overlap with the interactome network Reactome compared to GANs derived from BETR detected genes. After exposure to 1 Gy the normal sensitive cells showed only sparse response compared to cells with increased sensitivity, which exhibited a strong response mainly of genes related to the senescence pathway. After exposure to 10 Gy the response of the normal sensitive cells was mainly associated with senescence and that of cells with increased sensitivity with apoptosis. We discuss these results in a clinical context and underline the impact of senescence-associated pathways in acute radiation response of normal cells. The workflow of this novel approach is implemented in the open-source Bioconductor R-package splineTimeR.

MeSH terms

Gene Expression Profiling*
Gene Regulatory Networks / radiation effects*
Kinetics
Models, Genetic*
Radiation Tolerance / genetics*
Regression Analysis

Grants and funding

ZiSS project, 02NUK024B, https://www.bmbf.de/en/index.html, Federal Ministry of Education and Research, KU JH HZ. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.