Uncovering the Role of Metabolism in Oomycete–Host Interactions Using Genome-Scale Metabolic Models

Abstract

Metabolism is the set of biochemical reactions of an organism that enables it to assimilate nutrients from its environment and to generate building blocks for growth and proliferation. It forms a complex network that is intertwined with the many molecular and cellular processes that take place within cells. Systems biology aims to capture the complexity of cells, organisms, or communities by reconstructing models based on information gathered by high-throughput analyses (omics data) and prior knowledge. One type of model is a genome-scale metabolic model (GEM) that allows studying the distributions of metabolic fluxes, i.e., the “mass-flow” through the network of biochemical reactions. GEMs are nowadays widely applied and have been reconstructed for various microbial pathogens, either in a free-living state or in interaction with their hosts, with the aim to gain insight into mechanisms of pathogenicity. In this review, we first introduce the principles of systems biology and GEMs. We then describe how metabolic modeling can contribute to unraveling microbial pathogenesis and host–pathogen interactions, with a specific focus on oomycete plant pathogens and in particular Phytophthora infestans. Subsequently, we review achievements obtained so far and identify and discuss potential pitfalls of current models. Finally, we propose a workflow for reconstructing high-quality GEMs and elaborate on the resources needed to advance a system biology approach aimed at untangling the intimate interactions between plants and pathogens.

Endophytic Bacillus subtilis H17‐16 effectively inhibits Phytophthora infestans, the pathogen of potato late blight, and its potential application

“…Metabolism is essential for cell survival and proliferation 56 . Metabolic disorders may cause cell dystrophy, which may lead to cell apoptosis 57 . Therefore, insufficient metabolism may be an important reason for H17‐16 to inhibit the growth of Phytophthora infestans .…”

“…56 Metabolic disorders may cause cell dystrophy, which may lead to cell apoptosis. 57 Therefore, insufficient metabolism may be an important reason for H17-16 to inhibit the growth of Phytophthora infestans. In addition, there were four of the top 20 pathways related to the genetic information processing of Phytophthora infestans: ribosome biogenesis, RNA polymerase, RNA degradation and protein processing in endoplasmic reticulum (Fig.…”

“…Cell membranes are a barrier that controls the flow of substances into and out of cells, and the transport of metabolites across membranes is critical for pathogens nutrition. 57 Phospholipid and phosphatidylinositol are the main components of cell membranes. 58 In GO category, the top three significant terms were phospholipid binding, phosphatidylinositol binding, and lipid binding, meaning that H17-16 may cause changes in the composition and function of cell membranes to hinder Phytophthora infestans growth.…”

Oxidoreduction potential controlling for increasing the fermentability of enzymatically hydrolyzed steam-exploded corn stover for butanol production

“…The genome-scale metabolic (GSM) model analysis is a powerful tool for understanding the metabolic capacities of an organism and developing metabolic engineering strategies for strain development. By integrating all of the experimentally determined metabolic reactions taking place in an organism of interest, it can generate accurate predictions and informative hypotheses for cellular metabolism (Cheng et al, 2021;Rodenburg et al, 2021). It has been widely used in molecular mechanism study (Zhu et al, 2020), exogenous pathway designment (Zheng et al, 2017) and so on.…”

Oxidoreduction potential controlling for increasing the fermentability of enzymatically hydrolyzed steam-exploded corn stover for butanol production

“…The genome-scale metabolic (GSM) model analysis is a powerful tool for understanding the metabolic capacities of an organism and developing metabolic engineering strategies for strain development. By integrating all of the experimentally determined metabolic reactions taking place in an organism of interest, it can generate accurate predictions and informative hypotheses for cellular metabolism [ 11 , 12 ]. It has been widely used in molecular mechanism study [ 13 ], exogenous pathway designed [ 14 ] and so on.…”