Metabolic Model of the Phytophthora infestans-Tomato Interaction Reveals Metabolic Switches during Host Colonization

Abstract

The oomycete pathogen Phytophthora infestans causes potato and tomato late blight, a disease that is a serious threat to agriculture. P. infestans is a hemibiotrophic pathogen, and during infection, it scavenges nutrients from living host cells for its own proliferation. To date, the nutrient flux from host to pathogen during infection has hardly been studied, and the interlinked metabolisms of the pathogen and host remain poorly understood. Here, we reconstructed an integrated metabolic model of P. infestans and tomato (Solanum lycopersicum) by integrating two previously published models for both species. We used this integrated model to simulate metabolic fluxes from host to pathogen and explored the topology of the model to study the dependencies of the metabolism of P. infestans on that of tomato. This showed, for example, that P. infestans, a thiamine auxotroph, depends on certain metabolic reactions of the tomato thiamine biosynthesis. We also exploited dual-transcriptome data of a time course of a full late blight infection cycle on tomato leaves and integrated the expression of metabolic enzymes in the model. This revealed profound changes in pathogen-host metabolism during infection. As infection progresses, P. infestans performs less de novo synthesis of metabolites and scavenges more metabolites from tomato. This integrated metabolic model for the P. infestans-tomato interaction provides a framework to integrate data and generate hypotheses about in planta nutrition of P. infestans throughout its infection cycle.IMPORTANCE Late blight disease caused by the oomycete pathogen Phytophthora infestans leads to extensive yield losses in tomato and potato cultivation worldwide. To effectively control this pathogen, a thorough understanding of the mechanisms shaping the interaction with its hosts is paramount. While considerable work has focused on exploring host defense mechanisms and identifying P. infestans proteins contributing to virulence and pathogenicity, the nutritional strategies of the pathogen are mostly unresolved. Genome-scale metabolic models (GEMs) can be used to simulate metabolic fluxes and help in unravelling the complex nature of metabolism. We integrated a GEM of tomato with a GEM of P. infestans to simulate the metabolic fluxes that occur during infection. This yields insights into the nutrients that P. infestans obtains during different phases of the infection cycle and helps in generating hypotheses about nutrition in planta.

Keywords: Phytophthora infestans; metabolic modeling; metabolism; oomycetes; tomato.

FIG 1

Integrated P. infestans-tomato model. (A) Schematic illustration. Dots are metabolites, arrows are reactions, and dotted lines represent the host-pathogen transport reactions. (B) Numbers of unique and shared cytosol metabolites.

FIG 2

Flux coupling between reactions in the P. infestans-tomato model. (A) Graph showing the coupled reactions in the model. Nodes represent reactions in tomato (red) or P. infestans (blue) and host-pathogen transport (green), and edges represent coupling between those reactions. Node size reflects essentiality for P. infestans biomass production. Stars represent transport reactions listed in panel B. Highly connected nodes (1 to 5) and clusters (I to V) are indicated and listed in the boxes on the right (see also main text). (B) Nutrients associated with the 12 most frequently coupled host-pathogen transport reactions. The bars are stacked and indicate the number of coupled reactions per species.

FIG 3

Submodels based on dual-transcriptome data from a time course covering a full infection cycle of P. infestans on tomato leaf. Submodels are representative for the sampling time postinoculation shown in days (d) and hours on the y axis (middle) and x axis (left). Left, stacked bar graph indicating the number of reactions per species and number of host-pathogen transport reactions (not part of either species). Right, Jaccard similarity (intersection divided by union) between the reaction content of the submodels.

FIG 4

Enriched KEGG pathways in transcriptome-based submodels from a time course covering a full infection cycle of P. infestans on tomato. Submodels are representative for the sampling time postinoculation shown on the x axis in days (d) and hours. Enrichment is calculated based on the reaction content of each of the submodels compared to the full model. Colors scale to the adjusted P value. metab, metabolism; biosyn, biosynthesis; syn, synthesis; deg, degradation.

FIG 5

Robustness of transcriptome-based submodels from a time course covering a full infection cycle of P. infestans on tomato. The y axis shows the robustness as the fraction of single reaction deletions that do not disable the biomass flux for P. infestans. Submodels are representative of the sampling time postinoculation shown on the x axis in days and hours.

FIG 6

Overlap of reaction and metabolite content of submodels, based on transcriptome data only (T) or transcriptome data and metabolome data (T+M). Data used to generate these submodels were obtained from P. infestans-infected tomato leaves at 2d/12h and 5d/12h postinoculation.