To create an interactive web database to access unique data resource comprising integrated molecular network data linking human gastrointestinal tissues, gut immune system and the gut microbiome. This will enable to identify glycosylation relevance in gut barrier in response to commensal and pathogenic bacteria for modulating the gut immune tolerance. Additionally, by leveraging diverse omics data focusing on glycome biology, under diverse gastrointestinal immune dysfunction background, we aim to identify core mechanisms underlying these diseases that will aid novel therapies, diagnostic markers which will significantly address the large set of unmet needs in the treatment of these global diseases that are assuming epidemic proportions.Current Status :
The project is aimed to be completed in three different versions. In the current version 1 of the database, we provided a user friendly web interface to systematically analyze the unique list of glycosylation process related genes (glycogenes) for identifying gene expression changes under gastrointestinal immune dysfunction background. Currently the database comprises of 548 well characterized genes belonging to glycogenes and lectins along with selected gene expression data obtained from human biopsy samples under both H. pylori infection and inflammatory bowel disease (IBD) condition available in the public gene expression database (GEO). In version 2 and 3 we aim to create an integrated molecular network data linking human gastrointestinal tissues, gut immune system and the gut microbiome providing a unique source to identify glycosylation relevance in gut barrier in response to commensal and pathogenic bacteria for modulating the gut immune tolerance.Features :
The Gene Expression Analysis button guides you to perform the gene expression analysis. This section comprises of two hierarchical treeview structures:
Glycosylation gene pool: User can select single or multiple genes by checking the category list for subsequent gene expression analysis.
Gene expression datasets: The expression datasets are categorized based on the disease background and inflammatory condition. Mouseover on the datapoints provides a description of the sample and controls used for that comparison. User can browse to select one or multiple datapoints from the lists to fetch the fold change values from the GEO2R result matrix.
Results are displayed in the form of a heatmap wherein overexpressed and underexpressed genes are represented as red and green respectively. The data points used for the analysis are also displayed in two font colours to differentiate if they are selected from two different phenotypes.
An option to filter genes based on both p-value and adjusted p-value is available.
Annotation for the differentially expressed genes from HGNC and CFG are provided as hyperlinks on the gene symbols in the heatmap.
The data points in the heatmap are hyperlinked to their respective GEO experiments.
Mouseover on the heatmap displays log 2 transformed fold change values generated using auto-detect option in GEO2R tool for selected control to sample ratio in individual data points.
An inbuilt algorithm, to quickly identify similarly regulated genes across different phenotype is provided in the user interface.