Beyond Pathway Analysis: Identification of Active Subnetworks in Rett Syndrome

Pathway and network approaches are valuable tools in analysis and interpretation of large complex omics data. Even in the field of rare diseases, like Rett syndrome, omics data are available, and the maximum use of such data requires sophisticated tools for comprehensive analysis and visualization of the results. Pathway analysis with differential gene expression data has proven to be extremely successful in identifying affected processes in disease conditions. In this type of analysis, pathways from different databases like WikiPathways and Reactome are used as separate, independent entities. Here, we show for the first time how these pathway models can be used and integrated into one large network using the WikiPathways RDF containing all human WikiPathways and Reactome pathways, to perform network analysis on transcriptomics data. This network was imported into the network analysis tool Cytoscape to perform active submodule analysis. Using a publicly available Rett syndrome gene expression dataset from frontal and temporal cortex, classical enrichment analysis, including pathway and Gene Ontology analysis, revealed mainly immune response, neuron specific and extracellular matrix processes. Our active module analysis provided a valuable extension of the analysis prominently showing the regulatory mechanism of MECP2, especially on DNA maintenance, cell cycle, transcription, and translation. In conclusion, using pathway models for classical enrichment and more advanced network analysis enables a more comprehensive analysis of gene expression data and provides novel results

Advancing food, nutrition, and health research in Europe by connecting and building research infrastructures in a DISH-RI: Results of the EuroDISH project

Background: Research infrastructures (RIs) are essential to advance research on the relationship between food, nutrition, and health. RIs will facilitate innovation and allow insights at the systems level which are required to design (public health) strategies that will address societal challenges more effectively. Approach: In the EuroDISH project we mapped existing RIs in the food and health area in Europe, identified outstanding needs, and synthesised this into a conceptual design of a pan-European DISH-RI. The DISH model was used to describe and structure the research area: Determinants of food choice, Intake of foods and nutrients, Status and functional markers of nutritional health, and Health and disease risk. Key findings: The need to develop RIs in the food and health domain clearly emerged from the EuroDISH project. It showed the necessity for a unique interdisciplinary and multi-stakeholder RI that overarches the research domains. A DISH-RI should bring services to the research community that facilitate network and community building and provide access to standardised, interoperable, and innovative data and tools. It should fulfil the scientific needs to connect within and between research domains and make use of current initiatives. Added value can also be created by providing services to policy makers and industry, unlocking data and enabling valorisation of research insights in practice through public-private partnerships. The governance of these services (e.g. ownership) and the centralised and distributed activities of the RI itself (e.g. flexibility, innovation) needs to be organised and aligned with the different interests of public and private partners

A user-friendly workflow for analysis of Illumina gene expression bead array data available at the arrayanalysis.org portal

BACKGROUND: Illumina whole-genome expression bead arrays are a widely used platform for transcriptomics. Most of the tools available for the analysis of the resulting data are not easily applicable by less experienced users. ArrayAnalysis.org provides researchers with an easy-to-use and comprehensive interface to the functionality of R and Bioconductor packages for microarray data analysis. As a modular open source project, it allows developers to contribute modules that provide support for additional types of data or extend workflows. RESULTS: To enable data analysis of Illumina bead arrays for a broad user community, we have developed a module for ArrayAnalysis.org that provides a free and user-friendly web interface for quality control and pre-processing for these arrays. This module can be used together with existing modules for statistical and pathway analysis to provide a full workflow for Illumina gene expression data analysis. The module accepts data exported from Illumina's GenomeStudio, and provides the user with quality control plots and normalized data. The outputs are directly linked to the existing statistics module of ArrayAnalysis.org, but can also be downloaded for further downstream analysis in third-party tools. CONCLUSIONS: The Illumina bead arrays analysis module is available at http://www.arrayanalysis.org . A user guide, a tutorial demonstrating the analysis of an example dataset, and R scripts are available. The module can be used as a starting point for statistical evaluation and pathway analysis provided on the website or to generate processed input data for a broad range of applications in life sciences research

The Molecular Biology of Susceptibility to Post-Traumatic Stress Disorder: Highlights of Epigenetics and Epigenomics

Exposure to trauma is one of the most important and prevalent risk factors for mental and physical ill-health. Excessive or prolonged stress exposure increases the risk of a wide variety of mental and physical symptoms. However, people differ strikingly in their susceptibility to develop signs and symptoms of mental illness after traumatic stress. Post-traumatic stress disorder (PTSD) is a debilitating disorder affecting approximately 8% of the world’s population during their lifetime, and typically develops after exposure to a traumatic event. Despite that exposure to potentially traumatizing events occurs in a large proportion of the general population, about 80–90% of trauma-exposed individuals do not develop PTSD, suggesting an inter-individual difference in vulnerability to PTSD. While the biological mechanisms underlying this differential susceptibility are unknown, epigenetic changes have been proposed to underlie the relationship between exposure to traumatic stress and the susceptibility to develop PTSD. Epigenetic mechanisms refer to environmentally sensitive modifications to DNA and RNA molecules that regulate gene transcription without altering the genetic sequence itself. In this review, we provide an overview of various molecular biological, biochemical and physiological alterations in PTSD, focusing on changes at the genomic and epigenomic level. Finally, we will discuss how current knowledge may aid us in early detection and improved management of PTSD patients

Characteristics of the selected datasets.

<p>The number of excluded arrays for each dataset using the standardized QC procedure is indicated in brackets. Abbreviations: NP: normal prostate, pPC: primary prostate cancer, mPC: metastatic prostate cancer, AI: androgen independent, AD: androgen dependent.</p

Overview of the QC results of two selected datasets.

<p>Several QC results of the dataset by Varambally <i>et al.</i> comparing arrays of benign prostate tissue (maroon), primary prostate cancer (blue) and metastatic prostate cancer samples (green) are shown in panel a-d. Several QC results of the dataset by Gregg <i>et al.</i> comparing arrays of prostatic epithelial (maroon) with interstitial stromal cell samples (teal) are depicted in panel e-h. a) Boxplot of raw intensities; b) density histogram of log intensities before normalization; c) density histogram of log intensities after normalization; d) MA-plot before (upper panel) and after normalization (lower panel) of one example array of the dataset by Varambally <i>et al.</i>; e) boxplot of raw intensities; f) density histogram of log intensities before normalization; g) density histogram of log intensities after normalization; h) MA-plot before (upper panel) and after normalization (lower panel) of one example array of the dataset by Gregg <i>et al.</i></p

PathVisio results of significant pathways found in the dataset by Sun <i>et al.</i> comparing processed data provided by ArrayExpress with the reprocessed data after quality control.

<p>Pathway analysis is based on a comparison between recurrent and non-recurrent prostate cancer. Only significant pathways with a Z-score >1.9 in at least one of the two analyses are included. Significant Z-scores are depicted in bold. A NaN value commonly occurs when none of the genes in the pathway is present in the dataset.</p

PathVisio results of significant pathways found in the dataset by Best <i>et al.</i> comparing processed data provided by ArrayExpress with the reprocessed data after quality control.

<p>Pathway analysis is based on a comparison between androgen-dependent and androgen-independent prostate cancer. Only significant pathways with a Z-score >1.9 in at least one of the two analyses are included. Significant Z-scores are depicted in bold; matches between the analyses are in italics.</p

PathVisio results of significant pathways found in the dataset by Varambally <i>et al.</i> comparing processed data provided by ArrayExpress with the reprocessed data.

<p>Pathway analysis is based on a comparison between primary prostate cancer and metastatic prostate cancer. Only significant pathways with a Z-score >1.9 in at least one of the two analyses are included. Significant Z-scores are depicted in bold; matches in pathways between the analyses are in italics.</p