Nutrient control enables metabolic reconstruction of L. rhamnosus GG and analysis of secretions

Abstract

Lacticaseibacillus rhamnosus GG (LGG) is an important gut commensal bacterial strain that has been extensively studied in both industrial and health settings. Despite its long history of study, a high-quality genome-scale metabolic network model (GEM) for LGG has yet to be reconstructed. Only automatically-generated draft models have been published, which have notoriously limited functional accuracy. Furthermore, comprehensive nutrient requirements have not been established for well-controlled in vitro study. Here we present the first curated GEM for LGG using a new approach for reconstruction and validation that leverages multiple automatically-generated draft models, applied study literature, and high-throughput defined media experiments. In addition, our results include a series of chemically defined media, extensive single-component nutrient dropout growth data, insights from in silico and in vitro experiments into major secretion products lactate and indole-3-carboxaldehyde, a minimal medium and in silico characterization of LGGs nutrient requirements. Our approach for developing interdisciplinary research tools for LGG metabolism comprises a new framework that could be applied to many understudied microorganisms, particularly useful in studying bacteria within the human microbiome.