Time-series clustering of gene expression in irradiated and bystander fibroblasts: an application of FBPA clustering

<p>Abstract</p> <p>Background</p> <p>The radiation bystander effect is an important component of the overall biological response of tissues and organisms to ionizing radiation, but the signaling mechanisms between irradiated and non-irradiated bystander cells are not fully understood. In this study, we measured a time-series of gene expression after α-particle irradiation and applied the Feature Based Partitioning around medoids Algorithm (FBPA), a new clustering method suitable for sparse time series, to identify signaling modules that act in concert in the response to direct irradiation and bystander signaling. We compared our results with those of an alternate clustering method, Short Time series Expression Miner (STEM).</p> <p>Results</p> <p>While computational evaluations of both clustering results were similar, FBPA provided more biological insight. After irradiation, gene clusters were enriched for signal transduction, cell cycle/cell death and inflammation/immunity processes; but only FBPA separated clusters by function. In bystanders, gene clusters were enriched for cell communication/motility, signal transduction and inflammation processes; but biological functions did not separate as clearly with either clustering method as they did in irradiated samples. Network analysis confirmed p53 and NF-κB transcription factor-regulated gene clusters in irradiated and bystander cells and suggested novel regulators, such as KDM5B/JARID1B (lysine (K)-specific demethylase 5B) and HDACs (histone deacetylases), which could epigenetically coordinate gene expression after irradiation.</p> <p>Conclusions</p> <p>In this study, we have shown that a new time series clustering method, FBPA, can provide new leads to the mechanisms regulating the dynamic cellular response to radiation. The findings implicate epigenetic control of gene expression in addition to transcription factor networks.</p

Influenza Virus Infection Induces Metallothionein Gene Expression in the Mouse Liver and Lung by Overlapping but Distinct Molecular Mechanisms

Metallothionein I (MT-I) and MT-II have been implicated in the protection of cells against reactive oxygen species (ROS), heavy metals, and a variety of pathological and environmental stressors. Here, we show a robust increase in MT-I/MT-II mRNA level and MT proteins in the livers and lungs of C57BL/6 mice exposed to the influenza A/PR8 virus that infects the upper respiratory tract and lungs. Interleukin-6 (IL-6) had a pronounced effect on the induction of these genes in the liver but not the lung. Treatment of the animals with RU-486, a glucocorticoid receptor antagonist, inhibited induction of MT-I/MT-II in both liver and lung, revealing a direct role of glucocorticoid that is increased upon infection in this induction process. In vivo genomic footprinting (IVGF) analysis demonstrated involvement of almost all metal response elements, major late transcription factor/antioxidant response element (MLTF/ARE), the STAT3 binding site on the MT-I upstream promoter, and the glucocorticoid responsive element (GRE1), located upstream of the MT-II gene, in the induction process in the liver and lung. In the lung, inducible footprinting was also identified at a unique gamma interferon (IFN-γ) response element (γ-IRE) and at Sp1 sites. The mobility shift analysis showed activation of STAT3 and the glucocorticoid receptor in the liver and lung nuclear extracts, which was consistent with the IVGF data. Analysis of the newly synthesized mRNA for cytokines in the infected lung by real-time PCR showed a robust increase in the levels of IL-10 and IFN-γ mRNA that can activate STAT3 and STAT1, respectively. A STAT1-containing complex that binds to the γ-IRE in vitro was activated in the infected lung. No major change in MLTF/ARE DNA binding activity in the liver and lung occurred after infection. These results have demonstrated that MT-I and MT-II can be induced robustly in the liver and lung following experimental influenza virus infection by overlapping but distinct molecular mechanisms