MIMOSA2

MIMOSA2 integrates paired microbiome and metabolomics datasets to construct community metabolic models from microbiome data and predict metabolite-level variation using genomic and metabolic reference databases.

Key Features:

• Community Metabolic Modeling: Constructs community metabolic models to represent the collective metabolic capabilities of microbial communities within samples.
• Data Integration and Prediction: Uses genomic and metabolic reference databases to predict metabolite levels across samples from constructed community models and compares predictions to measured metabolomics data.
• Identification of Microbiome-Metabolite Links: Identifies taxa and metabolic reactions associated with observed variations in metabolite concentrations.
• Customization and Flexibility: Supports various input data types and incorporation of user-defined metabolic pathways.
• Validation and Application: Validated on simulation datasets and applied to real datasets including honeybee microbiota and human inflammatory bowel disease.
• Implementation: Implemented as an R package and distributed under the GNU General Public License v3.0.

Scientific Applications:

• Microbiome–metabolome integration studies: Linking microbial community composition and metabolic potential to metabolite variation in samples.
• Mechanistic inference of microbial contributions: Identifying specific taxa, genes, or reactions that potentially explain changes in metabolite levels.
• Disease and host-microbiota research: Investigating host–microbe metabolic interactions in contexts such as human inflammatory bowel disease and honeybee microbiota.
• Simulation-based method evaluation: Using simulation datasets to evaluate the ability to recover true microbial mechanisms underlying metabolite variation.

Methodology:

Constructs community metabolic models from microbiome data using genomic and metabolic reference databases, predicts metabolite levels across samples from those models, and compares predictions to measured metabolomics data to attribute metabolite variation to taxa and reactions.