GCgx: transcriptome-wide exploration of the response to glucocorticoids

Abstract

Glucocorticoids are the cornerstone of immunosuppressive and anti-inflammatory therapy in humans, yet the mechanisms of glucocorticoid immunoregulation and toxicity remain unclear. The response to glucocorticoids is highly cell type-dependent, so translating results from different experimental systems into a better understanding of glucocorticoid effects in humans would benefit from rapid access to high-quality data on the response to glucocorticoids by different cell types. We introduce GCgx, a web application that allows investigators to quickly visualize changes in transcript abundance in response to glucocorticoids in a variety of cells and species. The tool is designed to grow by the addition of datasets based on input from the user community. GCgx is implemented in R and HTML and packaged as a Docker image. The tool and its source code are publicly available.

Keywords: gene expression; glucocorticoids; mobile applications; software tools.

Figure 1.. Visualization of changes in transcript abundance in response to glucocorticoids with GCgx.

(a) A search tool allows users to identify glucocorticoid-responsive genes. User-defined parameters include the direction of change (up- or down-regulation), the threshold to use for log2 fold-change and adjusted p-value for differential expression, and the specific cell types. In the example provided, these parameters were set to search for glucocorticoid-responsive genes that are upregulated at 6 hours in hematopoietic cells, but not in non-hematopoietic cells, with an adjusted p-value value lower than 0.05 and log2 fold-change greater than 0. A table displays all the genes that meet the search criteria. Search results can be saved or can be used as input for the GCgx visualization tools. A heatmap tool allows users to visualize the expression of sets of genes across multiple cell types, either at baseline (b) or in response to glucocorticoids (c). In the example provided, the results of the search for genes whose expression is induced by glucocorticoids in hematopoietic cells only (a) were used as input for heatmap visualization. A dot plot tool allows users to visualize the expression of individual genes and to assess the level of variation across biological replicates. Users can specify the RNA-seq read count normalization method (FPKM, TPM, or median-of-ratios), the specific cell types, and whether to display the data by time point (d) or by cell type (e). In the example provided, one of the genes identified as glucocorticoid-induced in hematopoietic cells only, DYNLT1, is shown. In (d), the visualization parameters were set to display the expression of this gene across the 9 cell types at baseline, with FPKM as the normalization method. In (e), the visualization parameters were set to display the transcript-level response of DYNLT1 to glucocorticoid treatment at 2 and 6 hours, with FPKM as the normalization method, in three hematopoietic cells (CD4+ T cells, monocytes, and neutrophils) and three non-hematopoietic cells (endothelial cells, fibroblasts, and osteoblasts).

Figure 2.. Topology diagram of GCgx.

The source code is maintained in GitHub Enterprise and it is built and deployed via the NIAID Monarch platform. Users can freely access GCgx through the Internet, and the application container runs behind an ALB that distributes incoming web requests. The application is hosted in AWS, on an externally managed EC2 cluster. The computational infrastructure behind GCgx is designed to maximize security, apply standardized and automated methods for quality assurance, maintenance and update, and facilitate the incorporation over time of additional datasets for query and visualization. ALB: Application Load Balancer. AWS: Amazon Web Services. ECS: Elastic Container Service. VPC: Virtual Private Cloud.