Temporal clustering analysis of endothelial cell gene expression following exposure to a conventional radiotherapy dose fraction using Gaussian process clustering.

The vascular endothelium is considered as a key cell compartment for the response to ionizing radiation of normal tissues and tumors, and as a promising target to improve the differential effect of radiotherapy in the future. Following radiation exposure, the global endothelial cell response covers a wide range of gene, miRNA, protein and metabolite expression modifications. Changes occur at the transcriptional, translational and post-translational levels and impact cell phenotype as well as the microenvironment by the production and secretion of soluble factors such as reactive oxygen species, chemokines, cytokines and growth factors. These radiation-induced dynamic modifications of molecular networks may control the endothelial cell phenotype and govern recruitment of immune cells, stressing the importance of clearly understanding the mechanisms which underlie these temporal processes. A wide variety of time series data is commonly used in bioinformatics studies, including gene expression, protein concentrations and metabolomics data. The use of clustering of these data is still an unclear problem. Here, we introduce kernels between Gaussian processes modeling time series, and subsequently introduce a spectral clustering algorithm. We apply the methods to the study of human primary endothelial cells (HUVECs) exposed to a radiotherapy dose fraction (2 Gy). Time windows of differential expressions of 301 genes involved in key cellular processes such as angiogenesis, inflammation, apoptosis, immune response and protein kinase were determined from 12 hours to 3 weeks post-irradiation. Then, 43 temporal clusters corresponding to profiles of similar expressions, including 49 genes out of 301 initially measured, were generated according to the proposed method. Forty-seven transcription factors (TFs) responsible for the expression of clusters of genes were predicted from sequence regulatory elements using the MotifMap system. Their temporal profiles of occurrences were established and clustered. Dynamic network interactions and molecular pathways of TFs and differential genes were finally explored, revealing key node genes and putative important cellular processes involved in tissue infiltration by immune cells following exposure to a radiotherapy dose fraction.

Parameter estimation of perfusion models in dynamic contrast-enhanced imaging: a unified framework for model comparison.

Patients follow-up in oncology is generally performed through the acquisition of dynamic sequences of contrast-enhanced images. Estimating parameters of appropriate models of contrast intake diffusion through tissues should help characterizing the tumour physiology. However, several models have been developed and no consensus exists on their clinical use. In this paper, we propose a unified framework to analyse models of perfusion and estimate their parameters in order to obtain reliable and relevant parametric images. After defining the biological context and the general form of perfusion models, we propose a methodological framework for model assessment in the context of parameter estimation from dynamic imaging data: global sensitivity analysis, structural and practical identifiability analysis, parameter estimation and model comparison. Then, we apply our methodology to five of the most widely used compartment models (Tofts model, extended Tofts model, two-compartment model, tissue-homogeneity model and distributed-parameters model) and illustrate the results by analysing the behaviour of these models when applied to data acquired on five patients with abdominal tumours.

RAR/RXR binding dynamics distinguish pluripotency from differentiation associated cis-regulatory elements.

In mouse embryonic cells, ligand-activated retinoic acid receptors (RARs) play a key role in inhibiting pluripotency-maintaining genes and activating some major actors of cell differentiation. To investigate the mechanism underlying this dual regulation, we performed joint RAR/RXR ChIP-seq and mRNA-seq time series during the first 48 h of the RA-induced Primitive Endoderm (PrE) differentiation process in F9 embryonal carcinoma (EC) cells. We show here that this dual regulation is associated with RAR/RXR genomic redistribution during the differentiation process. In-depth analysis of RAR/RXR binding sites occupancy dynamics and composition show that in undifferentiated cells, RAR/RXR interact with genomic regions characterized by binding of pluripotency-associated factors and high prevalence of the non-canonical DR0-containing RA response element. By contrast, in differentiated cells, RAR/RXR bound regions are enriched in functional Sox17 binding sites and are characterized with a higher frequency of the canonical DR5 motif. Our data offer an unprecedentedly detailed view on the action of RA in triggering pluripotent cell differentiation and demonstrate that RAR/RXR action is mediated via two different sets of regulatory regions tightly associated with cell differentiation status.

Detecting time periods of differential gene expression using Gaussian processes: an application to endothelial cells exposed to radiotherapy dose fraction.

MOTIVATION: Identifying the set of genes differentially expressed along time is an important task in two-sample time course experiments. Furthermore, estimating at which time periods the differential expression is present can provide additional insight into temporal gene functions. The current differential detection methods are designed to detect difference along observation time intervals or on single measurement points, warranting dense measurements along time to characterize the full temporal differential expression patterns. RESULTS: We propose a novel Bayesian likelihood ratio test to estimate the differential expression time periods. Applying the ratio test to systems of genes provides the temporal response timings and durations of gene expression to a biological condition. We introduce a novel non-stationary Gaussian process as the underlying expression model, with major improvements on model fitness on perturbation and stress experiments. The method is robust to uneven or sparse measurements along time. We assess the performance of the method on realistically simulated dataset and compare against state-of-the-art methods. We additionally apply the method to the analysis of primary human endothelial cells under an ionizing radiation stress to study the transcriptional perturbations over 283 measured genes in an attempt to better understand the role of endothelium in both normal and cancer tissues during radiotherapy. As a result, using the cascade of differential expression periods, domain literature and gene enrichment analysis, we gain insights into the dynamic response of endothelial cells to irradiation. AVAILABILITY AND IMPLEMENTATION: R package 'nsgp' is available at www.ibisc.fr/en/logiciels_arobas.