Transcriptional Dynamics Reveal Critical Roles for Non-coding RNAs in the Immediate-Early Response

<div><p>The immediate-early response mediates cell fate in response to a variety of extracellular stimuli and is dysregulated in many cancers. However, the specificity of the response across stimuli and cell types, and the roles of non-coding RNAs are not well understood. Using a large collection of densely-sampled time series expression data we have examined the induction of the immediate-early response in unparalleled detail, across cell types and stimuli. We exploit cap analysis of gene expression (CAGE) time series datasets to directly measure promoter activities over time. Using a novel analysis method for time series data we identify transcripts with expression patterns that closely resemble the dynamics of known immediate-early genes (IEGs) and this enables a comprehensive comparative study of these genes and their chromatin state. Surprisingly, these data suggest that the earliest transcriptional responses often involve promoters generating non-coding RNAs, many of which are produced in advance of canonical protein-coding IEGs. IEGs are known to be capable of induction without de novo protein synthesis. Consistent with this, we find that the response of both protein-coding and non-coding RNA IEGs can be explained by their transcriptionally poised, permissive chromatin state prior to stimulation. We also explore the function of non-coding RNAs in the attenuation of the immediate early response in a small RNA sequencing dataset matched to the CAGE data: We identify a novel set of microRNAs responsible for the attenuation of the IEG response in an estrogen receptor positive cancer cell line. Our computational statistical method is well suited to meta-analyses as there is no requirement for transcripts to pass thresholds for significant differential expression between time points, and it is agnostic to the number of time points per dataset.</p></div

Chemical genomic-based pathway analyses for epidermal growth factor-mediated signaling in migrating cancer cells.

To explore the diversity and consistency of the signaling pathways that regulate tumor cell migration, we chose three human tumor cell lines that migrated after treatment with EGF. We then quantified the effect of fifteen inhibitors on the levels of expression or the phosphorylation levels of nine proteins that were induced by EGF stimulation in each of these cell lines. Based on the data obtained in this study and chemical-biological assumptions, we deduced cell migration pathways in each tumor cell line, and then compared them. As a result, we found that both the MEK/ERK and JNK/c-Jun pathways were activated in all three migrating cell lines. Moreover, GSK-3 and p38 were found to regulate PI3K/Akt pathway in only EC109 cells, and JNK was found to crosstalk with p38 and Fos related pathway in only TT cells. Taken together, our analytical system could easily distinguish between the common and cell type-specific pathways responsible for tumor cell migration

IκBα is required for full transcriptional induction of some NFκB-regulated genes in response to TNF in MCF-7 cells

Inflammatory stimuli triggers the degradation of three inhibitory κB (IκB) proteins, allowing for nuclear translocation of nuclear factor-κB (NFκB) for transcriptional induction of its target genes. Of these three, IκBα is a well-known negative feedback regulator that limits the duration of NFκB activity. We sought to determine whether IκBα's role in enabling or limiting NFκB activation is important for tumor necrosis factor (TNF)-induced gene expression in human breast cancer cells (MCF-7). Contrary to our expectations, many more TNF-response genes showed reduced induction than enhanced induction in IκBα knockdown cells. Mathematical modeling was used to investigate the underlying mechanism. We found that the reduced activation of some NFκB target genes in IκBα-deficient cells could be explained by the incoherent feedforward loop (IFFL) model. In addition, for a subset of genes, prolonged NFκB activity due to loss of negative feedback control did not prolong their transient activation; this implied a multi-state transcription cycle control of gene induction. Genes encoding key inflammation-related transcription factors, such as JUNB and KLF10, were found to be best represented by a model that contained both the IFFL and the transcription cycle motif. Our analysis sheds light on the regulatory strategies that safeguard inflammatory gene expression from overproduction and repositions the function of IκBα not only as a negative feedback regulator of NFκB but also as an enabler of NFκB-regulated stimulus-responsive inflammatory gene expression. This study indicates the complex involvement of IκBα in the inflammatory response to TNF that is induced by radiation therapy in breast cancer

The concept for deduction of signaling pathway.

<p>Detailed description is presented in the Results section and Experimental Procedures section.</p

EGF-induced time course of protein phosphorylation and expression in three cancer cell lines.

<p>A431 cells, EC109 cells, and TT cells were stimulated by EGF (30 ng ml⁻¹) for the indicated time and total cell lysates were subjected to western blotting. A.U., arbitrary unit normalized by the intensity of actin. The means and SDs of three independent experiments are shown. The graph colors represent each cell line data: A431 cells (red), EC109 cells (green), and TT cells (blue). The dashed line indicates that EGF stimulation did not cause a significant change of A.U. of each molecule (One-way ANOVA). Original immunoblot images are shown in <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0096776#pone.0096776.s001" target="_blank">Figure S1</a></b>.</p

Comparison of signaling network between EC109 cells and TT cells.

<p>(<b>a</b>) Common signaling network between EC109 cells and TT cells. (<b>b</b>) Specific EGF-induced signaling network in EC109 cells. (<b>c</b>) Specific EGF-induced signaling network in TT cells. The edge color is decided based on the sign of edges; red indicates positive signaling, and blue indicates negative signaling.</p

Robustness analysis of the detailed kinetic model of an ErbB signaling network by using dynamic sensitivity

<div><p>The ErbB receptor signaling pathway plays an important role in the regulation of cellular proliferation, survival and differentiation, and dysregulation of the pathway is linked to various types of human cancer. Mathematical models have been developed as a practical complementary approach to deciphering the complexity of ErbB receptor signaling and elucidating how the pathways discriminate between ligands to induce different cell fates. In this study, we developed a simulator to accurately calculate the dynamic sensitivity of extracellular-signal-regulated kinase (ERK) activity (ERK*) and Akt activity (Akt*), downstream of the ErbB receptors stimulated with epidermal growth factor (EGF) and heregulin (HRG). To demonstrate the feasibility of this simulator, we estimated how the reactions critically responsible for ERK* and Akt* change with time and in response to different doses of EGF and HRG, and predicted that only a small number of reactions determine ERK* and Akt*. ERK* increased steeply with increasing HRG dose until saturation, while showing a gently rising response to EGF. Akt* had a gradual wide-range response to HRG and a blunt response to EGF. Akt* was sensitive to perturbations of intracellular kinetics, while ERK* was more robust due to multiple, negative feedback loops. Overall, the simulator predicted reactions that were critically responsible for ERK* and Akt* in response to the dose of EGF and HRG, illustrated the response characteristics of ERK* and Akt*, and estimated mechanisms for generating robustness in the ErbB signaling network.</p></div

Time course simulation of ERK* and Akt*.

<p>(A) Simulated and experimental time course of ERK*. (B) Simulated and experimental time course of Akt*. The HRG concentrations were 0.1, 0.5 and 10 nM, while the EGF concentration was set to 0.5 nM. The solid, dashed and dot-dash lines indicate the simulated results at 0, 0.5 and 10 nM HRG, respectively. The cross, circle and triangle indicate the experimental activity at 0.1, 0.5 and 10 nM HRG, respectively. The initial activities are set to zero by subtracting the background intensity from the measured activities and then the resultant activities are normalized so that the maximum intensity during time course is 1. The error bars denote the standard deviations of signal intensities in quadruplicate independent experiments.</p

Frequency distributions of the DSs of ERK* and Akt*.

<p>(A) Frequency distribution of the DSs of ERK*. (B) Frequency distribution of the DSs of Akt*. DSs (n = 237) were simulated at 100 s and 300 s with (HRG, EGF) = (0.5 nM, 0.5 nM). The black and white bars indicate the distributions at 100 s and 300 s.</p