Gempipe: a tool for drafting, curating, and analyzing pan and multi-strain genome-scale metabolic models

Area of Science:

• Microbial Metabolism
• Systems Biology
• Bioinformatics

Background:

• Genome-scale metabolic models (GSMMs) explain bacterial strain phenotypic differences.
• Current multi-strain GSMM reconstruction methods have limitations: reference-based methods miss new data, while universe-based methods ignore curated information.
• Reconstructing comprehensive pan-GSMMs for species is resource-intensive and rare.

Purpose of the Study:

• Introduce Gempipe, a computational tool for streamlined multi-strain GSMM reconstruction and analysis.
• Develop a hybrid reconstruction method combining reference-based and reference-free approaches for improved accuracy.
• Facilitate large-scale exploration and bioprospecting of bacterial metabolic diversity at the strain level.

Main Methods:

• Gempipe employs a hybrid reconstruction strategy, optionally expanding GSMMs with reactions from reference-free reconstructions.
• The tool automates genome downloading, filtering, annotation, gene recovery, and model content annotation.
• It includes a programming interface for GSMM curation and multi-strain analysis.

Main Results:

• Gempipe demonstrated improved accuracy in multi-strain GSMM reconstruction compared to existing tools.
• The tool successfully produced pan-GSMMs even with a single-strain curated model as reference.
• Analysis of *Limosilactobacillus reuteri* revealed metabolic diversities and predicted health-related metabolite biosynthesis.

Conclusions:

• Gempipe overcomes limitations of existing tools, offering a high-throughput solution for multi-strain GSMM reconstruction and analysis.
• The hybrid approach enhances accuracy and enables pan-GSMM generation in common scenarios.
• Gempipe will accelerate strain-level metabolic diversity exploration, bioprospecting, and rational strain selection.