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Dynamic response of EGF stimulation in lung cancer cells [EGF]

ABSTRACT: The analysis of microarray time series promises a deeper insight into the dynamics of the cellular response following stimulation. A common observation in this type of data is that some genes respond with quick, transient dynamics, while other genes change their expression slowly over time. The existing methods for the detection of significant expression dynamics often fail when the expression dynamics show a large heterogeneity, and often cannot cope with irregular and sparse measurements.

ORGANISM(S): Homo sapiens

PROVIDER: GSE84095 | GEO | 2016/11/18

SECONDARY ACCESSION(S): PRJNA328017

REPOSITORIES: GEO

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Dynamic response of EGF stimulation in lung cancer cells [controls]

Project description:The analysis of microarray time series promises a deeper insight into the dynamics of the cellular response following stimulation. A common observation in this type of data is that some genes respond with quick, transient dynamics, while other genes change their expression slowly over time. The existing methods for the detection of significant expression dynamics often fail when the expression dynamics show a large heterogeneity, and often cannot cope with irregular and sparse measurements.

2016-11-18 | GSE84094 | GEO

6KR-EGFR expressing MCF7 following EGF or HRG stimulation

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2014-12-01 | GSE57547 | GEO

TimeChange: Topology inspired methods for the detection of differential dynamics in time-series data

Project description:The circadian clock drives the oscillatory expression of thousands of genes across all tissues and bears significant implications for human health. RNA-seq time-series experiments interrogate the mechanistic links between transcriptional rhythms and phenotypic outcomes. Analysis methods must overcome the challenges of sparse temporal sampling, noisy data, and non-strictly periodic dynamics. Moreover, there remains a need for differential cycling analysis methods that can identify complex changes in rhythmicity for 2-sample comparisons across experimental conditions. We present TimeChange -- a non-parametric and model-free method for the quantification of differential dynamics across experimental conditions. The method leverages a data transformation technique known as time-delay embedding to reconstruct the underlying state space for each gene-of-interest. Takens’ embedding theorem implies that rhythmic dynamics will exhibit circular patterns in the embedded space. TimeChange non-parametrically compares the distributions of points in the embedded space via the Fasano-Franceschini test to assess whether the topological structures differ significantly between phenotypes, thereby quantifying differences in transcriptional dynamics without requiring knowledge of the underlying model. Application of TimeChange to synthetic data shows that it accurately identifies changes in transcriptomic dynamics, including differences in amplitude/peaked-ness; changes in sawtooth asymmetries; and trending oscillatory drifts (e.g. linear, damped, and contractile). We further show that the method has potential utility beyond circadian dynamics. For instance, initial tests of TimeChange using erg rowing-machine data shows accurate classification of differential stroke dynamics in real time, suggesting potential uses of TimeChange in the field of sports science and medicine.

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Time-course gene expression profiles of gefitinib-sensitive or -resistant lung adenocarcinoma cells stimulated with EGF, in the presence or absence of gefitinib, EGF receptor tyrosine kinase-specific inhibitor

Project description:Gefitinib, an epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI), induces substantial clinical responses for non-small cell lung cancer (NSCLC) cells harboring EGFR activating mutations, but most of them invariably develop resistance. By generating a gefitinib resistance (PC9GR) from a human NSCLC-derived drug sensitive cell line (PC9), we studied differences of transcription dynamics between them by the aid of a computational decoupling of hidden regulatory signals from time course gene expression profiles. Given a collection of transcription factors (TFs) and their regulatory targets, the method captured temporally-synchronized shifts in evolving expression of target genes sharing each TF regulatory unit, and drew underlying regulatory signals. The analysis identified sterol regulatory element binding protein 1 (SREBP-1) as a key regulatory agent that facilitates the maintenance of drug tolerance, involving transcription controls of a G1-specific cyclin dependent kinase inhibitor whose expression was specifically elevated in PC9, but in turn, reduced in PC9GR Gefitinib-resistance cell line (PC9GR) was established derived from lung adenocarcinoma cell line PC9. PC9 cells and PC9GR cells were treated with the four different conditions, control (No treatment), EGF-treatment, gefitinib-treatment, and both gefitinib and EGF-treatment. In each condition, the gene expression was measured at 26 time points during 24 hrs.

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