Nutrient control enables metabolic reconstruction of Lacticaseibacillus rhamnosus GG and analysis of secretions

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 studies. 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 secretion products, lactate, and indole-3-carboxaldehyde, a minimal medium, and in silico characterization of LGG’s 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.

IMPORTANCE

This study provides an important foundation for understanding how the beneficial gut bacterium Lacticaseibacillus rhamnosus GG (LGG) grows and interacts with its environment. Although LGG is one of the most widely studied probiotic bacteria, used in both health and industrial applications, a reliable tool for simulating its metabolic behavior and a clear understanding of its nutritional needs is lacking. By developing the first manually curated metabolic model of LGG and validating it through extensive laboratory experiments, this study provides new understanding of LGG’s requirements and capabilities for utilizing nutrients and resources to investigate its metabolism. Completely chemically defined growth media and associated single-component dropout growth effects are also presented, and pathways for production of important compounds influencing human health are explored. The methods developed in this work also provide a framework for studying other poorly characterized microbes, helping advance research on the human microbiome and its role in health and disease.