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  • A computational model of Pseudomonas syringae metabolism unveils a role for branched-chain amino acids in Arabidopsis leaf colonization
Title: A computational model of Pseudomonas syringae metabolism unveils a role for branched-chain amino acids in Arabidopsis leaf 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 ofArabidopsis thalianaleaves byPseudomonas syringaepvtomatoDC3000 (PstDC3000), we created iPst19, an ensemble of 100 genome-scale network reconstructions ofPstDC3000 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 withPstDC3000 gene expression data obtained from naïve-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-expression-contextualized iPst19 simulation. These predictions were tested and confirmed experimentally.PstDC3000 growth and gene expression analysis showed that BCAAs suppress virulence gene expressionin vitrowithout 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 catabolism is necessary to express virulence and colonize the host. Overall, this study provides valuable insights into how plant immunity impactsPstDC3000 metabolism, and how bacterial metabolism impacts the expression of virulence.  more » « less
Award ID(s):
1943120
PAR ID:
10506971
Author(s) / Creator(s):
; ; ; ; ;
Editor(s):
Wallqvist, Anders
Publisher / Repository:
PLOS
Date Published:
Journal Name:
PLOS Computational Biology
Volume:
19
Issue:
12
ISSN:
1553-7358
Page Range / eLocation ID:
e1011651
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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