Genome-Scale Metabolic Model of Xanthomonas phaseoli pv. manihotis: An Approach to Elucidate Pathogenicity at the Metabolic Level

Abstract

Xanthomonas phaseoli pv. manihotis (Xpm) is the causal agent of cassava bacterial blight, the most important bacterial disease in this crop. There is a paucity of knowledge about the metabolism of Xanthomonas and its relevance in the pathogenic process, with the exception of the elucidation of the xanthan biosynthesis route. Here we report the reconstruction of the genome-scale model of Xpm metabolism and the insights it provides into plant-pathogen interactions. The model, iXpm1556, displayed 1,556 reactions, 1,527 compounds, and 890 genes. Metabolic maps of central amino acid and carbohydrate metabolism, as well as xanthan biosynthesis of Xpm, were reconstructed using Escher (https://escher.github.io/) to guide the curation process and for further analyses. The model was constrained using the RNA-seq data of a mutant of Xpm for quorum sensing (QS), and these data were used to construct context-specific models (CSMs) of the metabolism of the two strains (wild type and QS mutant). The CSMs and flux balance analysis were used to get insights into pathogenicity, xanthan biosynthesis, and QS mechanisms. Between the CSMs, 653 reactions were shared; unique reactions belong to purine, pyrimidine, and amino acid metabolism. Alternative objective functions were used to demonstrate a trade-off between xanthan biosynthesis and growth and the reallocation of resources in the process of biosynthesis. Important features altered by QS included carbohydrate metabolism, NAD(P) + balance, and fatty acid elongation. In this work, we modeled the xanthan biosynthesis and the QS process and their impact on the metabolism of the bacterium. This model will be useful for researchers studying host-pathogen interactions and will provide insights into the mechanisms of infection used by this and other Xanthomonas species.

Climate change impacts on plant pathogens, food security and paths forward

“…For example, 'omics' combined with robust physiological/morphological/symptom training data sets can be used for predicting different aspects of plant-pathogen interactions including gene regulatory networks, pathogen effector proteins, pathogen adaptive strategies and genes involved in plantpathogen interactions under different climate change scenarios. Genome-scale network reconstructions can model intracellular metabolism to predict virulence and pathogen-host interactions under a range of environmental and physiological conditions [162][163][164] . Such models are now being used to provide detailed insights into the interactions between the invading pathogen and the host-associated microbiome to predict disease incidence and interventions 165 .…”

Cassava diseases caused byXanthomonas phaseolipv.manihotisandXanthomonas cassavae

“…Recently, an RNA‐seq study of a quorum‐sensing (QS)‐insensitive mutant of Xpm versus wild type grown in vitro was reported (Botero et al, 2020 ). The authors concluded that the QS system controls several subsequent signalling routes, including a number of phosphorylation sensor and transduction pathways, some of which share a c ‐di‐GMP phosphodiesterase activity (HD‐GYP domain).…”

Evaluating and deploying genome-scale metabolic models for microbial cell factories

“…However, BiGG is a good candidate as a standard namespace for GEMs in the future since BiGG identifiers are human-readable and the database has been specially curated for GEMs [330,338]. Many high quality GEMs and newly published GEMs have been converting their models to this namespace [225,362,363,349,364,365].…”