Predicting strain-specific metabolic capabilities in the Genus Pseudomonas using a Flux-to-AI approach unravels hidden cell envelope properties.

Bacteria from the Pseudomonas genus are omnipresent in air, soil, and water. They have been widely studied for their broad metabolic versatility and presence in the epidemiological chain, bioproduction, bioremediation, and disease processes. Each year, more genomic sequences are reported in databases and repositories. However, the relationship between the genomic variability of Pseudomonas strains and the diversity in their metabolic capabilities under environmentally relevant phenotypes remains unknown. Additionally, predictive tools for the analysis of different strains in a systematic framework are limited. Here, we reconstructed genome-scale metabolic models (GEMs) of 44 Pseudomonas strains from various environments and investigated their capabilities to metabolize different carbon sources and metabolic intermediaries. By systematically testing the substrate utilization of the models, we demonstrate how GEM-predicted capabilities can differentiate between strains and that high metabolic versatility is associated with the ability of the strains to remove toxic compounds while maintaining core functionalities. Hundreds of model simulations were used as input for a classification schema that uses machine learning algorithms, resulting in the identification of metabolic capabilities that better differentiate between species. Interestingly, transcription and expression models validated these findings by showing how pathways in which those metabolites are members change their proteome allocation across strains (e.g. phenylalanine metabolism as well as in the carbohydrate metabolism).