Gene Expression Time Series Analysis Research Articles

S100A6 Regulates Endothelial Cell Cycle Progression by Attenuating Antiproliferative Signal Transducers and Activators of Transcription 1 Signaling.

S100A6, a member of the S100 protein family, has been described as relevant for cell cycle entry and progression in endothelial cells. The molecular mechanism conferring S100A6's proliferative actions, however, remained elusive. Originating from the clinically relevant observation of enhanced S100A6 protein expression in proliferating endothelial cells in remodeling coronary and carotid arteries, our study unveiled S100A6 as a suppressor of antiproliferative signal transducers and activators of transcription 1 signaling. Discovery of the molecular liaison was enabled by combining gene expression time series analysis with bioinformatic pathway modeling in S100A6-silenced human endothelial cells stimulated with vascular endothelial growth factor A. This unbiased approach led to successful identification and experimental validation of interferon-inducible transmembrane protein 1 and protein inhibitors of activated signal transducers and activators of transcription as key components of the link between S100A6 and signal transducers and activators of transcription 1. Given the important role of coordinated endothelial cell cycle activity for integrity and reconstitution of the inner lining of arterial blood vessels in health and disease, signal transducers and activators of transcription 1 suppression by S100A6 may represent a promising therapeutic target to facilitate reendothelialization in damaged vessels.

A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression

BackgroundThe analysis of gene expression from time series underpins many biological studies. Two basic forms of analysis recur for data of this type: removing inactive (quiet) genes from the study and determining which genes are differentially expressed. Often these analysis stages are applied disregarding the fact that the data is drawn from a time series. In this paper we propose a simple model for accounting for the underlying temporal nature of the data based on a Gaussian process.ResultsWe review Gaussian process (GP) regression for estimating the continuous trajectories underlying in gene expression time-series. We present a simple approach which can be used to filter quiet genes, or for the case of time series in the form of expression ratios, quantify differential expression. We assess via ROC curves the rankings produced by our regression framework and compare them to a recently proposed hierarchical Bayesian model for the analysis of gene expression time-series (BATS). We compare on both simulated and experimental data showing that the proposed approach considerably outperforms the current state of the art.ConclusionsGaussian processes offer an attractive trade-off between efficiency and usability for the analysis of microarray time series. The Gaussian process framework offers a natural way of handling biological replicates and missing values and provides confidence intervals along the estimated curves of gene expression. Therefore, we believe Gaussian processes should be a standard tool in the analysis of gene expression time series.