A computational model of < i > Pseudomonas syringae < /i > metabolism unveils a role for branched-chain amino acids in Arabidopsis leaf colonization

by Philip J. Tubergen, Greg Medlock, Anni Moore, Xiaomu Zhang, Jason A. Papin, Cristian H. Danna 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 ofArabidopsis thaliana leaves byPseudomonas syringae pvtomato DC3000 (Pst DC3000), we created iPst19, an ensemble of 100 genome-scale network reconstructions ofPst DC3000 metabolism. We developed a novel approach for gene essentiality screens, leveraging the predictive power of iPst19 to identify core and ancillary condition-specific essential genes. Constraining the metabolic flux of iPst19 withPst DC3000 gene expression data obtained from na ïve-infected or pre-immunized-infected plants, revealed changes in bacterial metabolism imposed by plant immunity. Machine learning analysis revealed that among other amino acids, branched-chain amino acids (BCAAs) metabolism significantly contributed to the overall metabolic status of each gene-ex pression-contextualized iPst19 simulation. These predictions were tested and confirmed experimentally.Pst DC3000 growth and gene expression analysis showed that BCAAs suppress virulence gene expressionin vitro without affecting bacterial growth.In planta, however, an excess of BCAAs suppress the expression of virulence genes at the early stages of infection and significantly impair the colonization of Arabidopsis leaves. Our findings suggesting that BCAAs ca...
Source: PLoS Computational Biology - Category: Biology Authors: Source Type: research