A computational model of Pseudomonas syringae metabolism unveils a role for branched-chain amino acids in Arabidopsis leaves colonization.

Bacterial pathogens adapt their metabolism to the plant environment to successfully colonize their hosts. In our efforts to uncover the metabolic pathways that contribute to the colonization of Arabidopsis thaliana by Pseudomonas syringae pv tomato DC3000 (Pst DC3000), we created iPst19, an ensemble of 100 genome-scale network reconstructions of Pst DC3000 metabolism. We developed a novel approach for gene essentiality screens, leveraging the predictive power of the iPst19 to identify core and ancillary bacterial metabolic genes that are condition specific. Constraining the metabolic flux of iPst19 with Pst DC3000 gene expression data obtained from naïve-infected or pre-immunized-infected plants, revealed crucial changes in bacterial metabolism imposed by plant immunity.

This repository contains the relevant code for building and testing iPst19, the base reconstructions for the gap filling process, relevant in vitro growth data on single carbon sources, and datasets from previously publish manuscripts which informed this reconstruction.