MACC

MACC catalogs metabolite-associated cell communications by providing a curated repository of experimentally supported metabolite–cell interaction pairs, associated enzymes and transporters, pathway annotations, and expression landscapes for analysis.

Key Features:

• Curated experimental evidence: Data are curated from experimental literature and comprise experimentally supported metabolite–cell communication entries.
• Comprehensive interaction set: Contains 4,206 metabolite-associated cell communication pairs involving 244 human endogenous metabolites with in vivo and in vitro evidence.
• Biological contexts: Covers 226 cell subtypes and 296 disease states, including cancers, autoimmune diseases, and pathogenic infections.
• Enzymes and transporters: Includes 4,508 metabolite-related enzymes and transporters annotated across 542 pathways.
• Network visualization: Provides network visualization of multiple metabolite–cell interactions for exploration of interaction topologies.
• Expression landscape analysis: Derives overall expression landscapes of metabolite-associated gene sets from over 1,500 single-cell expression profiles to infer metabolite variation across cell types.
• Cross-references: Cross-links entries to HMDB, DrugBank, TTD, and PubMed for integration with metabolomics and drug-target resources.

Scientific Applications:

• Study of metabolite-mediated communication: Enables characterization of metabolite secretion and adsorption as alternative cellular communication mechanisms.
• Cancer biology: Supports analysis of metabolite–cell interactions relevant to tumor biology and cancer-associated states.
• Immunology: Facilitates investigation of metabolite-associated communication in immune cells and autoimmune diseases.
• Infectious disease research: Enables examination of metabolite interactions in pathogenic infections.
• Integration with metabolomics and drug-target studies: Cross-links to HMDB, DrugBank, and TTD to support integration with metabolite and therapeutic target data.

Methodology:

Data were collected by curation of experimental literature, and expression landscapes were derived from analysis of over 1,500 single-cell expression profiles.