Probing Genome-Scale Model Reveals Metabolic Capability and Essential Nutrients for Growth of Probiotic Limosilactobacillus reuteri KUB-AC5

Abstract

Limosilactobacillus reuteri KUB-AC5 displays the hallmark features of probiotic properties for food and feed industries. Optimization of cultivation condition for the industrial production is important to reach cell concentration and cost reduction. Considering the strain-specific growth physiology, metabolic capability, and essential nutrients of L. reuteri KUB-AC5, the genome-scale metabolic model (GSMM) of L. reuteri KUB-AC5 was developed. Hereby, the GSMM of iTN656 was successfully constructed which contained 656 genes, 831 metabolites, and 953 metabolic reactions. The iTN656 model could show a metabolic capability under various carbon sources and guide potentially 14 essential single nutrients (e.g., vitamin B complex and amino acids) and 2 essential double nutrients (pairwise glutamine-glutamate and asparagine-aspartate) for L. reuteri KUB-AC5 growth through single and double omission analysis. Promisingly, the iTN656 model was further integrated with transcriptome data suggesting that putative metabolic routes as preferable paths e.g., sucrose uptake, nucleotide biosynthesis, urea cycle, and glutamine transporter for L. reuteri KUB-AC5 growth. The developed GSMM offers a powerful tool for multi-level omics analysis, enabling probiotic strain optimization for biomass overproduction on an industrial scale.

In Silico Genomic and Metabolic Atlas of Limosilactobacillus reuteri DSM 20016: An Insight into Human Health

“…L. reuteri MM4-1A is a currently used probiotic that is used to prevent bone loss and to promote immunomodulation [ 36 ]. Genomic-scale models of another strain, L. reuteri KUB-AC5, highlighted its preferable growth environment, highlighting the use of transcriptome analysis to analyze metabolic routes and optimize cell growth [ 37 ]. This strain is highly competitive against Salmonella in poultry [ 38 ].…”

A semantics, energy-based approach to automate biomodel composition

“…In cases where the selected reference models do not describe this part of the biology, users can apply their knowledge or search the literature to add any missing steps or subsystems to the composed bond graph model manually. To enhance this procedure in future, we could employ genome-scale metabolic models (GSMMs) as scaffolds to identify the missing entities or reactions [ 63 , 64 ].…”

SBML to bond graphs: from conversion to composition

“…However, the final case could potentially be automated using genome-scale metabolic models (GSMMs). GSMMs are comprehensive stoichiometric networks of metabolic reactions and biochemical data, which help to comprehend the metabolism and physiology of the organisms [53, 54]. These can be employed as scaffolds to automatically detect and locate the missing energy sources and avoiding the need for auxiliary species.…”