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Tortajada, M.

Publications and source records attributed to Tortajada, M..

2 recordsLinked to original sources

A metabolic model based on a pangenome core unveils new biochemical features of the phytopathogen Xylella fastidiosa

Xylella fastidiosa is a xylem-limited phytopathogenic bacterium responsible for severe diseases in many economically important crops. Despite its impact, its metabolism remains poorly characterized due to fastidious growth and the limited availability of defined culture media. Here, we reconstruct the first pangenome-based genome-scale metabolic model for X. fastidiosa, integrating conserved metabolic functions from 18 strains across five subspecies. The resulting core model, iXfcore, is manually curated and used to explore the species metabolic capabilities. Model simulations predict minimal nutritional requirements that guided us in the formulation of defined media supporting biofilm formation in vitro, providing validation of the models predictive capacity. Network analysis also identifies a previously undescribed pathway enabling growth on acetate as a sole carbon source. In addition, the model predicts the overproduction of polyamines, compounds linked to virulence in other phytopathogens. Experimental analyses confirm the production and secretion of polyamines in multiple X. fastidiosa strains, providing the first evidence of this capability. These results suggest that polyamine biosynthesis may represent an uncharacterized virulence factor for X. fastidiosa, potentially contributing to protection against host-induced oxidative stress. Overall, iXfcore provides a systems-level framework to investigate X. fastidiosa metabolism, generate testable hypotheses on its physiology and virulence, and support future strain-specific models and studies of host-pathogen metabolic interactions.

systems biology↗

Flux modelling analysis reveals the metabolic impact of cryptic plasmids and environmental conditions in probiotic Escherichia coli Nissle 1917

Escherichia coli Nissle 1917 (EcN) is a well-characterized Gram-negative probiotic distinguished by its unique, strain-specific physiology. Genome-scale metabolic models (GEMs) are powerful tools for elucidating metabolic traits and predicting genotype-phenotype relationships. Although several EcN GEMs have been published, none have explicitly represented its probiotic physiology. Here, we present a manually curated GEM of EcN that, for the first time, incorporates the energetic costs associated with its cryptic plasmids. Inclusion of a plasmid-specific module improved biomass yield predictions and overall model accuracy, providing a more physiologically realistic representation of EcN metabolism. Using COBRA methodologies and possibilistic metabolic flux analysis, this model and previous EcN reconstructions were systematically compared to evaluate the trade-off between model complexity and predictive performance. The analysis revealed that increased structural detail does not necessarily enhance quantitative accuracy and that predictive reliability depends on both computational methodology and model context. Metabolomic profiling under gut-like anaerobic conditions further showed that EcN exhibits a distinctive metabolic phenotype, characterized by elevated amino acid consumption and enhanced short-chain fatty acid production. These findings highlight the unique probiotic physiology of EcN and demonstrate the utility of metabolic modeling for reproducing and exploring such traits. Overall, this study provides a quantitatively reliable and physiologically relevant framework for modeling E. coli Nissle 1917 and related commensal bacteria, supporting advances in probiotic engineering, synthetic biology, and bioprocess design. Graphical Abstract SummaryThis study presents a manually curated genome-scale model of Escherichia coli Nissle 1917 that accounts for the metabolic cost of its cryptic plasmids. Through systematic comparison with previous reconstructions and validation against fluxomics datasets, the models improved accuracy in predicting growth and fluxes. Simulations and experiments under gut-like conditions provide new insights into EcNs unique probiotic traits. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=120 SRC="FIGDIR/small/688048v2_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@6c8c98org.highwire.dtl.DTLVardef@8263e3org.highwire.dtl.DTLVardef@6c04c3org.highwire.dtl.DTLVardef@1acb811_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗