iNAP 2.0: Harnessing metabolic complementarity in microbial network analysis.

Area of Science:

• Microbial Ecology
• Metagenomics
• Systems Biology
• Bioinformatics

Background:

• Metagenomic sequencing enables studying microbial ecological networks.
• Traditional co-occurrence networks cannot infer metabolic interspecies interactions.
• Novel methods are needed to analyze metabolic complementarity from metagenomic data.

Purpose of the Study:

• Introduce the integrated Network Analysis Pipeline 2.0 (iNAP 2.0) for microbial metabolic interaction analysis.
• Provide a comprehensive protocol for constructing and analyzing metabolic complementarity networks from metagenomic data.
• Enable the discovery of interspecies metabolic interactions and transferable metabolites.

Main Methods:

• iNAP 2.0 employs a four-module process: model preparation, interaction inference, network construction, and network analysis.
• Methods include PhyloMint, SMETANA, and parsimonious flux balance analysis (pFBA) for quantifying metabolic complementarity.
• Random matrix theory (RMT) is integrated for determining appropriate thresholds in network construction.

Main Results:

• iNAP 2.0 facilitates the construction of metabolic complementarity networks from metagenomic data.
• The pipeline identifies potentially transferable metabolites, visualized as intermediate nodes connecting microbial species.
• Topological feature analysis, including hub node determination, can be performed on the constructed networks.

Conclusions:

• iNAP 2.0 offers an innovative approach to inferring microbial metabolic interactions from metagenomics.
• The pipeline enhances understanding of microbial ecological networks by revealing metabolic cross-feeding and complementarity.
• iNAP 2.0 provides a valuable, free resource for researchers studying microbial communities.