Transcriptomic analyses of the radiation response in head and neck squamous cell carcinoma subclones with different radiation sensitivity: time-course gene expression profiles and gene association networks

Background: Acquired and inherent radioresistance of tumor cells is related to tumor relapse and poor prognosis not only in head and neck squamous cell carcinoma (HNSCC). The underlying molecular mechanisms are largely unknown. Therefore, systemic in-depth analyses are needed to identify key regulators of radioresistance. In the present study, subclones of the CAL-33 HNSCC cell line with different radiosensitivity were analyzed to identify signaling pathways related to the different phenotypes. Methods: Subclones with altered radiosensitivity were generated by fractionated irradiation of the parental CAL-33 cells. Differences in radiosensitivity were confirmed in colony formation assays. Selected subclones were characterized at the genomic and transcriptomic level by SKY, array CGH, and mRNA-microarray analyses. Time-course gene expression analyses upon irradiation using a natural cubic spline regression model identified temporally differentially expressed genes. Moreover, early and late responding genes were identified. Gene association networks were reconstructed using partial correlation. The Reactome pathway database was employed to conduct pathway enrichment analyses. Results: The characterization of two subclones with enhanced radiation resistance (RP) and enhanced radiosensitivity (SP) revealed distinct genomic and transcriptomic changes compared to the parental cells. Differentially expressed genes after irradiation shared by both subclones pointed to important pathways of the early and late radiation response, including senescence, apoptosis, DNA repair, Wnt, PI3K/AKT, and Rho GTPase signaling. The analysis of the most important nodes of the gene association networks revealed pathways specific to the radiation response in different phenotypes of radiosensitivity. Exemplarily, for the RP subclone the senescence-associated secretory phenotype (SASP) together with GPCR ligand binding were considered as crucial. Also, the expression of endogenous retrovirus ERV3-1in response to irradiation has been observed, and the related gene association networks have been identified. Conclusions: Our study presents comprehensive gene expression data of CAL-33 subclones with different radiation sensitivity. The resulting networks and pathways associated with the resistant phenotype are of special interest and include the SASP. The radiation-associated expression of ERV3-1 also appears highly attractive for further studies of the molecular mechanisms underlying acquired radioresistance. The identified pathways may represent key players of radioresistance, which could serve as potential targets for molecularly designed, therapeutical intervention

IGF-1 GENE EXPRESSION IN RAT COLONIC MUCOSA AFTER DIFFERENT EXERCISE VOLUMES

The evidence is increasing for a close link between the insulin/insulin-like growth factor (IGF) system and colon cancer prevention by physical exercise. To reveal exercise-induced alterations in colon mucosa, gene expression of IGF-1 and related genes and serum IGF-1 were investigated. Twenty male Wistar rats performed a 12 week voluntary exercise program. Nine rats served as the control group. Gene expression of IGF-1, IGF-1 receptor (IGF-1R) and IGF-binding protein 3 (IGF-BP3) were quantified by real-time RT-PCR. Circulating IGF-1 was analyzed exercise volume-dependent. Based on 3 distinguished groups with low (L-EX, 8314 m·night-1), we observed lower serum IGF-1 levels (P < 0.05) in all exercise groups as compared to the control group and IGF-1 levels declined proportional to the increase in exercise volume. A significant (p < 0.05) positive correlation was found between IGF-1 concentration and body mass (r = 0.50) and a significant negative correlation exists between body mass and exercise volume (r = -0.50). Significant differences in colonic mRNA levels of IGF-1, IGF-1R and IGF-BP3 could not be observed. Based on our data we propose that the exercise as well as the body mass reduction leads to a decrease in circulating IGF-1 and this might represent a prime link to colon cancer preventio

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

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

Example of fitted spline regression models.

<p>The plot shows spline regression models fitted to the measured time-course expression data of an arbitrary chosen gene (BBC3). The blue line represents the fitted model for the control (0 Gy) and read line that for the irradiated group (1 Gy). Blue and red dots represent the measured expression levels of the biological replicates. Vertical lines represent the endpoints and interior knots correspond to the 0.33- and 0.66-quantiles.</p

Schematic workflow of the analysis of gene expression time-course data.

<p>Samples were collected 0.25, 0.5, 1, 2, 4, 8 and 24 hours after sham or actual irradiation. Transcriptional profiling was performed using Agilent gene expression microarrays and comprises three major steps: the identification of differentially expressed genes from time-course expression data by employing a natural cubic spline regression model; the use of regularized dynamic partial correlation method to infer gene associations networks from differentially expressed genes and the topological identification and functional characterization of the key nodes in the reconstructed networks.</p

Number of detected and differentially expressed genes for each dose and cell lines for NCSRM and BETR methods.

<p>Number of detected and differentially expressed genes for each dose and cell lines for NCSRM and BETR methods.</p

Number of genes subjected to GAN reconstruction and properties of resulted GANs.

<p>Number of genes subjected to GAN reconstruction and properties of resulted GANs.</p

Comparison of hub genes in networks resulting from different methods.

<p>Comparison of hub genes in networks resulting from different methods.</p