Pangenome reconstruction of Lactobacillaceae metabolism predicts species-specific metabolic traits

• Published in: mSystems Vol. 9; no. 7; p. e0015624
• Main Authors: Ardalani, O, Phaneuf, P V, Mohite, O S, Nielsen, L K, Palsson, B O
• Format: Journal Article
• Language: English
• Published: United States American Society for Microbiology 23.07.2024
• Subjects: Accuracy; Amino acids; Annotations; Carbon; Fermentation; Fermented food; Food industry; Food selection; Genes; Genetic engineering; genome-scale metabolic model; genome-scale reconstruction; Genomes; Genomic analysis; Industrial applications; Lactobacillaceae; Metabolic networks; Metabolism; Metabolites; pangenome; Proteins; Research Article; Scale models; Statistical analysis; Systems Biology; Lactobacillaceae; pangenome; genome-scale reconstruction; genome-scale metabolic model
• ISSN: 2379-5077; 2379-5077
• DOI: 10.1128/msystems.00156-24

Strains across the family form the basis for a trillion-dollar industry. Our understanding of the genomic basis for their key traits is fragmented, however, including the metabolism that is foundational to their industrial uses. Pangenome analysis of publicly available genomes allowed us to generate genome-scale metabolic network reconstructions for 26 species of industrial importance. Their manual curation led to more than 75,000 gene-protein-reaction associations that were deployed to generate 2,446 genome-scale metabolic models. Cross-referencing genomes and known metabolic traits allowed for manual metabolic network curation and validation of the metabolic models. As a result, we provide the first pangenomic basis for metabolism in the family and a collection of predictive computational metabolic models that enable a variety of practical uses.IMPORTANCE , a bacterial family foundational to a trillion-dollar industry, is increasingly relevant to biosustainability initiatives. Our study, leveraging approximately 2,400 genome sequences, provides a pangenomic analysis of metabolism, creating over 2,400 curated and validated genome-scale models (GEMs). These GEMs successfully predict (i) unique, species-specific metabolic reactions; (ii) niche-enriched reactions that increase organism fitness; (iii) essential media components, offering insights into the global amino acid essentiality of ; and (iv) fermentation capabilities across the family, shedding light on the metabolic basis of -based commercial products. This quantitative understanding of metabolic properties and their genomic basis will have profound implications for the food industry and biosustainability, offering new insights and tools for strain selection and manipulation.