MRCLinkdb

MRCLinkdb aggregates and organizes data related to non-protein metabolite-ligand-receptor interactions in cell-cell communication to enable prediction of intercellular communication based on metabolite-related ligand-receptor interactions.

Key Features:

• Metabolite-Ligand-Receptor Interaction Curation: Manually curated over 790 human and 670 mouse metabolite-ligand-receptor interactions from literature and known databases.
• Enzyme and Transporter Annotation: Compiled information on over 1900 enzymes and 260 transporter entries associated with metabolites in the curated interactions.
• Single-Cell Transcriptomics Integration: Provides expression landscapes of metabolite-associated gene sets based on single-cell transcriptomics sequencing covering 35 human and 26 mouse tissues, and 52 human and 44 mouse cell types.
• Bulk Transcriptomics Integration: Incorporates bulk RNA-seq and microarray data encompassing 62 human and 39 mouse tissues for expression analysis of metabolite-associated gene sets.
• Metabolite Information Display: Presents fundamental metabolite information alongside receptor, enzyme, and transporter protein expression data.
• Intercellular Communication Analysis: Web server functionality dedicated to analyzing intercellular communication based on metabolite-ligand-receptor interactions.

Scientific Applications:

• Intercellular Communication Prediction: Predicts intercellular communication based on metabolite-related ligand-receptor interactions beyond proteinaceous ligand-receptor pairs.
• Metabolite-Associated Gene Expression Analysis: Enables investigation of receptor, enzyme, and transporter expression patterns across human and mouse tissues and cell types.
• Cell-Cell Communication Decoding: Provides perspectives for decoding intercellular communication mechanisms involving non-protein metabolites.

Methodology:

Manual curation of metabolite-ligand-receptor interactions from literature and known databases; integration of single-cell transcriptomics sequencing and bulk RNA-seq/microarray data for expression landscape analysis of metabolite-associated gene sets.