MicrobiomeNet: exploring microbial associations and metabolic profiles for mechanistic insights

Area of Science:

• Microbiology
• Systems Biology
• Bioinformatics

Background:

• Microbiome research has expanded significantly, revealing numerous microbial associations.
• Microbial small molecules are crucial for inter-microbe communication and environmental modulation.
• Understanding microbial metabolic profiles is key to deciphering association patterns for translational research.

Purpose of the Study:

• To develop MicrobiomeNet, a comprehensive database integrating microbial associations with metabolic profiles.
• To provide mechanistic insights into microbial interactions and community dynamics.
• To facilitate intuitive exploration and comparison of microbial associations and their metabolic data.

Main Methods:

• Integration of approximately 5.8 million known microbial associations.
• Inclusion of over 12,400 genome-scale metabolic models (GEMs).
• Coverage of approximately 6,000 microbial species.

Main Results:

• MicrobiomeNet provides a unified platform for exploring microbial associations and metabolic data.
• The database facilitates comparative analysis of metabolic profiles across different microbial species and associations.
• Case studies demonstrate MicrobiomeNet's ability to yield literature-consistent mechanistic insights.

Conclusions:

• MicrobiomeNet serves as a valuable resource for researchers studying microbial communities.
• The database enhances understanding of microbial interactions through integrated association and metabolic data.
• MicrobiomeNet supports translational applications by providing mechanistic insights into microbiome functions.