Expression Atlas: gene and protein expression across multiple studies and organisms

Abstract

Expression Atlas (http://www.ebi.ac.uk/gxa) is an added value database that provides information about gene and protein expression in different species and contexts, such as tissue, developmental stage, disease or cell type. The available public and controlled access data sets from different sources are curated and re-analysed using standardized, open source pipelines and made available for queries, download and visualization. As of August 2017, Expression Atlas holds data from 3,126 studies across 33 different species, including 731 from plants. Data from large-scale RNA sequencing studies including Blueprint, PCAWG, ENCODE, GTEx and HipSci can be visualized next to each other. In Expression Atlas, users can query genes or gene-sets of interest and explore their expression across or within species, tissues, developmental stages in a constitutive or differential context, representing the effects of diseases, conditions or experimental interventions. All processed data matrices are available for direct download in tab-delimited format or as R-data. In addition to the web interface, data sets can now be searched and downloaded through the Expression Atlas R package. Novel features and visualizations include the on-the-fly analysis of gene set overlaps and the option to view gene co-expression in experiments investigating constitutive gene expression across tissues or other conditions.

The protein interaction networks of mucolipins and two-pore channels

“…IL7 signalling is recognised for regulating CD4+ T-cell numbers (increasing numbers upon direct T-cell stimulation, decreasing numbers indirectly through dendritic stimulation) [93], and recently, for regulating tissue resident macrophage development [94]. Accordingly, TRPML3 appears highly expressed in the thymus [3,95], being transcribed in macrophages, CD4+ T-cells, and dendritic cells [95]. The TRPML3-interacting non-RTKs FYN and LCK are recruited and activated by IL7R upon IL7 signalling, yet their downstream pathways following IL7 stimulation remain uncharacterised [96].…”

“…FYN and LCK are better described in the context of T-cell receptor (TCR) signalling, and could in theory serve similar functions upon IL7 signalling: TCR-activated LCK recruits intracellular FYN and lipid rafts, culminating in outcomes such as thymocyte development, T-cell survival, or antigen recognition [97]. TRPML3 appears upregulated in squamous cell carcinoma and hepatocellular carcinoma, and downregulated in erythroleukemia and platelets of cancer patients [95]. The potential implications of TRPML3 in immunological and oncogenic signalling pathways may be of particular relevance here.…”

MEK2 is a critical modulating mechanism to down‐regulate GCIP stability and function in cancer cells

“…First, we confirmed the tumor suppressor role of GCIP using The Cancer Genome Atlas (https ://www.fireb rowse.org/) and EMBL-EBI (http://www.ebi.ac.uk/gxa) 28 databases and found that the levels of GCIP expression are lower among several tumor types when compared to their normal counterpart tissues, such as osteosarcoma, esophageal adenocarcinoma, stomach adenocarcinoma, lung adenocarcinoma, glioblastoma, glioma and prostate carcinoma. Using the Kaplan-Meier Plotter tool (http://kmplot.com/analy sis/) to analyze the association of GCIP expression with the survival rate in cancer patients, the results also revealed that higher GCIP gene expression was correlated with higher overall survival rates and post-progression survival among patients with gastric, lung, breast, ovarian, and liver cancers (data not shown).…”

Community-driven roadmap for integrated disease maps

“…Replication of hallmark findings in a given domain is often tangible, as many appropriate datasets are now publicly available, either via general repositories of molecular phenotypes, such as Gene Expression Omnibus [34] and the Expression Atlas [35] or disease-specific resources such as the Genomic Data Commons [36] and the Human Protein Atlas [37] in the case of cancer. Identification of differentially expressed molecules and their visualization on the map will help to refine the map's content, but also will be a demonstration of its utility.…”