The Metabolic Interplay between Plants and Phytopathogens

Oomycete Metabolism Is Highly Dynamic and Reflects Lifestyle Adaptations

The selective pressure of pathogen-host symbiosis drives adaptations. How these interactions shape the metabolism of pathogens is largely unknown. Here, we use comparative genomics to systematically analyze the metabolic networks of oomycetes, a diverse group of eukaryotes that includes saprotrophs as well as animal and plant pathogens, with the latter causing devastating diseases with significant economic and/or ecological impacts. In our analyses of 44 oomycete species, we uncover considerable variation in metabolism that can be linked to lifestyle differences. Comparisons of metabolic gene content reveal that plant pathogenic oomycetes have a bipartite metabolism consisting of a conserved core and an accessory set. The accessory set can be associated with the degradation of defense compounds produced by plants when challenged by pathogens. Obligate biotrophic oomycetes have smaller metabolic networks, and taxonomically distantly related biotrophic lineages display convergent evolution by repeated gene losses in both the conserved as well as the accessory set of metabolisms. When investigating to what extent the metabolic networks in obligate biotrophs differ from those in hemibiotrophic plant pathogens, we observe that the losses of metabolic enzymes in obligate biotrophs are not random and that gene losses predominantly influence the terminal branches of the metabolic networks. Our analyses represent the first metabolism-focused comparison of oomycetes at this scale and will contribute to a better understanding of the evolution of oomycete metabolism in relation to lifestyle adaptation. Numerous oomycete species are devastating plant pathogens that cause major damage in crops and natural ecosystems. Their interactions with hosts are shaped by strong selection, but how selection affects adaptation of the primary metabolism to a pathogenic lifestyle is not yet well established. By pan-genome and metabolic network analyses of distantly related oomycete pathogens and their nonpathogenic relatives, we reveal considerable lifestyle- and lineage-specific adaptations. This study contributes to a better understanding of metabolic adaptations in pathogenic oomycetes in relation to lifestyle, host, and environment, and the findings will help in pinpointing potential targets for disease control. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Metabolomic Analysis of Wheat Grains after Tilletia laevis Kühn Infection by Using Ultrahigh-Performance Liquid Chromatography–Q-Exactive Mass Spectrometry

Tilletia laevis causes common bunt disease in wheat, with severe losses of production yield and seed quality. Metabolomics studies provide detailed information about the biochemical changes at the cell and tissue level of the plants. Ultrahigh-performance liquid chromatography–Q-exactive mass spectrometry (UPLC-QE-MS) was used to examine the changes in wheat grains after T. laevis infection. PCA analysis suggested that T. laevis-infected and non-infected samples were scattered separately during the interaction. In total, 224 organic acids and their derivatives, 170 organoheterocyclic compounds, 128 lipids and lipid-like molecules, 85 organic nitrogen compounds, 64 benzenoids, 31 phenylpropanoids and polyketides, 21 nucleosides, nucleotides, their analogues, and 10 alkaloids and derivatives were altered in hyphal-infected grains. According to The Kyoto Encyclopedia of Genes and genomes analysis, the protein digestion and absorption, biosynthesis of amino acids, arginine and proline metabolism, vitamin digestion and absorption, and glycine, serine, and threonine metabolism pathways were activated in wheat crops after T. laevis infection.

Plant-Microbe Interaction: Aboveground to Belowground, from the Good to the Bad

Soil health and fertility issues are constantly addressed in the agricultural industry. Through the continuous and prolonged use of chemical heavy agricultural systems, most agricultural lands have been impacted, resulting in plateaued or reduced productivity. As such, to invigorate the agricultural industry, we would have to resort to alternative practices that will restore soil health and fertility. Therefore, in recent decades, studies have been directed towards taking a Magellan voyage of the soil rhizosphere region, to identify the diversity, density, and microbial population structure of the soil, and predict possible ways to restore soil health. Microbes that inhabit this region possess niche functions, such as the stimulation or promotion of plant growth, disease suppression, management of toxicity, and the cycling and utilization of nutrients. Therefore, studies should be conducted to identify microbes or groups of organisms that have assigned niche functions. Based on the above, this article reviews the aboveground and below-ground microbiomes, their roles in plant immunity, physiological functions, and challenges and tools available in studying these organisms. The information collected over the years may contribute toward future applications, and in designing sustainable agriculture.

An eco-systems biology approach for modeling tritrophic networks reveals the influence of dietary amino acids on symbiont dynamics of Bemisia tabaci

Metabolic conversions allow organisms to produce essential metabolites from the available nutrients in an environment, frequently requiring metabolic exchanges among co-inhabiting organisms. Here, we applied genomic-based simulations for exploring tri-trophic interactions among the sap-feeding insect whitefly (Bemisia tabaci), its host-plants, and symbiotic bacteria. The simplicity of this ecosystem allows capturing the interacting organisms (based on genomic data) and the environmental content (based on metabolomics data). Simulations explored the metabolic capacities of insect-symbiont combinations under environments representing natural phloem. Predictions were correlated with experimental data on the dynamics of symbionts under different diets. Simulation outcomes depict a puzzle of three-layer origins (plant-insect-symbionts) for the source of essential metabolites across habitats and stratify interactions enabling the whitefly to feed on diverse hosts. In parallel to simulations, natural and artificial feeding experiments provide supporting evidence for an environment-based effect on symbiont dynamics. Based on simulations, a decrease in the relative abundance of a symbiont can be associated with a loss of fitness advantage due to an environmental excess in amino-acids whose production in a deprived environment used to depend on the symbiont. The study demonstrates that genomic-based predictions can bridge environment and community dynamics and guide the design of symbiont manipulation strategies.

A Newly Isolated Streptomyces sp. YYS-7 With a Broad-Spectrum Antifungal Activity Improves the Banana Plant Resistance to Fusarium oxysporum f. sp. cubense Tropical Race 4

Fusarium wilt of banana caused by Fusarium oxysporum f. sp. cubense (Foc) is one of the most destructive diseases, severely limiting the development of banana industry. Especially, Foc tropical race 4 (Foc TR4) can infect and destroy almost all banana cultivars. Until now, there is still a lack of an effective method for controlling fusarium wilt. A biocontrol strategy using Actinobacteria is considered as a promising method for management of disease and pest. In this study, 229 Actinobacteria were isolated from rhizosphere soil samples of a primitive ecological mountain. An actinobacterium strain marked with YYS-7 exhibited a high antifungal activity against Foc TR4. Combining the physiological and biochemical characteristics as well as alignment of the 16S rRNA sequence, the strain YYS-7 was assigned to Streptomyces sp. The crude extracts of Streptomyces sp. YYS-7 obviously inhibited the mycelial growth of Foc TR4. The cell integrity and ultrastructure were seriously destroyed. In addition, Streptomyces sp. YYS-7 and crude extracts also showed a broad-spectrum antifungal activity against the selected seven phytopathogenic fungi. A gas chromatography-mass spectrometry (GC-MS) was used to predict the antifungal metabolites. A total of eleven different compounds were identified, including phenolic compounds, hydrocarbons, esters and acids. In the pot experiment, the crude extracts can significantly improve the banana plant’s resistance to Foc TR4. Hence, Streptomyces sp. YYS-7 will be a potential biocontrol agent for the biofertilizer exploitation and the discovery of new bioactive substances.

Virulence Factors in the Phytopathogen-Host Interactions: An Overview

Phytopathogens are responsible for great losses in agriculture, once they are able to subvert or elude the host defense mechanisms through virulence factors secretion for their dissemination. Herein, it is reviewed phytotoxins that act as virulence factors and are produced by bacterial phytopathogens (Candidatus Liberibacter spp., Erwinia amylovora, Pseudomonas syringae pvs and Xanthomonas spp.) and fungi (Alternaria alternata, Botrytis cinerea, Cochliobolus spp., Fusarium spp., Magnaporthe spp., and Penicillium spp.), which were selected in accordance to their worldwide importance due to the biochemical and economical aspects. In the current review, it is sought to understand the role of virulence factors in the pathogen-host interactions that result in plant diseases.

NetMet: A Network-Based Tool for Predicting Metabolic Capacities of Microbial Species and their Interactions

Metabolic conversions allow organisms to produce a set of essential metabolites from the available nutrients in an environment, frequently requiring metabolic exchanges among co-inhabiting organisms. Genomic-based metabolic simulations are being increasingly applied for exploring metabolic capacities, considering different environments and different combinations of microorganisms. NetMet is a web-based tool and a software package for predicting the metabolic performances of microorganisms and their corresponding combinations in user-defined environments. The algorithm takes, as input, lists of (i) species-specific enzymatic reactions (EC numbers), and (ii) relevant metabolic environments. The algorithm generates, as output, lists of (i) compounds that individual species can produce in each given environment, and (ii) compounds that are predicted to be produced through complementary interactions. The tool is demonstrated in two case studies. First, we compared the metabolic capacities of different haplotypes of the obligatory fruit and vegetable pathogen Candidatus Liberibacter solanacearum to those of their culturable taxonomic relative Liberibacter crescens. Second, we demonstrated the potential production of complementary metabolites by pairwise combinations of co-occurring endosymbionts of the plant phloem-feeding whitefly Bemisia tabaci.

Metabolic route computation in organism communities

BACKGROUND

Microbiomes are complex aggregates of organisms, each of which has its own extensive metabolic network. A variety of metabolites are exchanged between the microbes. The challenge we address is understanding the overall metabolic capabilities of a microbiome: through what series of metabolic transformations can a microbiome convert a starting compound to an ending compound?

RESULTS

We developed an efficient software tool to search for metabolic routes that include metabolic reactions from multiple organisms. The metabolic network for each organism is obtained from BioCyc, where the network was inferred from the annotated genome. The tool searches for optimal metabolic routes that minimize the number of reactions in each route, maximize the number of atoms conserved between the starting and ending compounds, and minimize the number of organism switches. The tool pre-computes the reaction sets found in each organism from BioCyc to facilitate fast computation of the reactions defined in a researcher-specified organism set. The generated routes are depicted graphically, and for each reaction in a route, the tool lists the organisms that can catalyze that reaction. We present solutions for three route-finding problems in the human gut microbiome: (1) production of indoxyl sulfate, (2) production of trimethylamine N-oxide (TMAO), and (3) synthesis and degradation of autoinducers. The optimal routes computed by our multi-organism route-search (MORS) tool for indoxyl sulfate and TMAO were the same as routes reported in the literature.

CONCLUSIONS

Our tool quickly found plausible routes for the discussed multi-organism route-finding problems. The routes shed light on how diverse organisms cooperate to perform multi-step metabolic transformations. Our tool enables scientists to consider multiple alternative routes and identifies the organisms responsible for each reaction.

Metabolic Modeling of Pectobacterium parmentieri SCC3193 Provides Insights into Metabolic Pathways of Plant Pathogenic Bacteria

Understanding plant⁻microbe interactions is crucial for improving plants' productivity and protection. Constraint-based metabolic modeling is one of the possible ways to investigate the bacterial adaptation to different ecological niches and may give insights into the metabolic versatility of plant pathogenic bacteria. We reconstructed a raw metabolic model of the emerging plant pathogenic bacterium Pectobacterium parmentieri SCC3193 with the use of KBase. The model was curated by using inParanoind and phenotypic data generated with the use of the OmniLog system. Metabolic modeling was performed through COBRApy Toolbox v. 0.10.1. The curated metabolic model of P. parmentieri SCC3193 is highly reliable, as in silico obtained results overlapped up to 91% with experimental data on carbon utilization phenotypes. By mean of flux balance analysis (FBA), we predicted the metabolic adaptation of P. parmentieri SCC3193 to two different ecological niches, relevant for the persistence and plant colonization by this bacterium: soil and the rhizosphere. We performed in silico gene deletions to predict the set of essential core genes for this bacterium to grow in such environments. We anticipate that our metabolic model will be a valuable element for defining a set of metabolic targets to control infection and spreading of this plant pathogen.

Alterations in plant sugar metabolism: signatory of pathogen attack

This review summarizes the current understanding, future challenges and ongoing quest on sugar metabolic alterations that influence the outcome of plant-pathogen interactions. Intricate cellular and molecular events occur during plant-pathogen interactions. They cause major metabolic perturbations in the host and alterations in sugar metabolism play a pivotal role in governing the outcome of various kinds of plant-pathogen interactions. Sugar metabolizing enzymes and transporters of both host and pathogen origin get differentially regulated during the interactions. Both plant and pathogen compete for utilizing the host sugar metabolic machinery and in turn promote resistant or susceptible responses. However, the kind of sugar metabolism alteration that is beneficial for the host or pathogen is yet to be properly understood. Recently developed tools and methodologies are facilitating research to understand the intricate dynamics of sugar metabolism during the interactions. The present review elaborates current understanding, future challenges and ongoing quest on sugar metabolism, mobilization and regulation during various plant-pathogen interactions.

A Genome-Scale Metabolic Reconstruction of Phytophthora infestans With the Integration of Transcriptional Data Reveals the Key Metabolic Patterns Involved in the Interaction of Its Host

Phytophthora infestans, the causal agent of late blight disease, affects potatoes and tomatoes worldwide. This plant pathogen has a hemibiotrophic lifestyle, having an initial biotrophic infection phase during which the pathogen spreads within the host tissue, followed by a necrotrophic phase in which host cell death is induced. Although increasing information is available on the molecular mechanisms, underlying the distinct phases of the hemibiotrophic lifestyle, studies that consider the entire metabolic processes in the pathogen while undergoing the biotrophic, transition to necrotrophic, and necrotrophic phases have not been conducted. In this study, the genome-scale metabolic reconstruction of P. infestans was achieved. Subsequently, transcriptional data (microarrays, RNA-seq) was integrated into the metabolic reconstruction to obtain context-specific (metabolic) models (CSMs) of the infection process, using constraint-based reconstruction and analysis. The goal was to identify specific metabolic markers for distinct stages of the pathogen's life cycle. Results indicate that the overall metabolism show significant changes during infection. The most significant changes in metabolism were observed at the latest time points of infection. Metabolic activity associated with purine, pyrimidine, fatty acid, fructose and mannose, arginine, glycine, serine, and threonine amino acids appeared to be the most important metabolisms of the pathogen during the course of the infection, showing high number of reactions associated with them and expression switches at important stages of the life cycle. This study provides a framework for future throughput studies of the metabolic changes during the hemibiotrophic life cycle of this important plant pathogen.

Genome-wide characterization of Phytophthora infestans metabolism: a systems biology approach

Genome-scale metabolic models (GEMs) provide a functional view of the complex network of biochemical reactions in the living cell. Initially mainly applied to reconstruct the metabolism of model organisms, the availability of increasingly sophisticated reconstruction methods and more extensive biochemical databases now make it possible to reconstruct GEMs for less well-characterized organisms, and have the potential to unravel the metabolism in pathogen-host systems. Here, we present a GEM for the oomycete plant pathogen Phytophthora infestans as a first step towards an integrative model with its host. We predict the biochemical reactions in different cellular compartments and investigate the gene-protein-reaction associations in this model to obtain an impression of the biochemical capabilities of P. infestans. Furthermore, we generate life stage-specific models to place the transcriptomic changes of the genes encoding metabolic enzymes into a functional context. In sporangia and zoospores, there is an overall down-regulation, most strikingly reflected in the fatty acid biosynthesis pathway. To investigate the robustness of the GEM, we simulate gene deletions to predict which enzymes are essential for in vitro growth. This model is an essential first step towards an understanding of P. infestans and its interactions with plants as a system, which will help to formulate new hypotheses on infection mechanisms and disease prevention.

Modeling trophic dependencies and exchanges among insects' bacterial symbionts in a host-simulated environment

BACKGROUND

Individual organisms are linked to their communities and ecosystems via metabolic activities. Metabolic exchanges and co-dependencies have long been suggested to have a pivotal role in determining community structure. In phloem-feeding insects such metabolic interactions with bacteria enable complementation of their deprived nutrition. The phloem-feeding whitefly Bemisia tabaci (Hemiptera: Aleyrodidae) harbors an obligatory symbiotic bacterium, as well as varying combinations of facultative symbionts. This well-defined bacterial community in B. tabaci serves here as a case study for a comprehensive and systematic survey of metabolic interactions within the bacterial community and their associations with documented occurrences of bacterial combinations. We first reconstructed the metabolic networks of five common B. tabaci symbionts genera (Portiera, Rickettsia, Hamiltonella, Cardinium and Wolbachia), and then used network analysis approaches to predict: (1) species-specific metabolic capacities in a simulated bacteriocyte-like environment; (2) metabolic capacities of the corresponding species' combinations, and (3) dependencies of each species on different media components.

RESULTS

The predictions for metabolic capacities of the symbionts in the host environment were in general agreement with previously reported genome analyses, each focused on the single-species level. The analysis suggests several previously un-reported routes for complementary interactions and estimated the dependency of each symbiont in specific host metabolites. No clear association was detected between metabolic co-dependencies and co-occurrence patterns.

CONCLUSIONS

The analysis generated predictions for testable hypotheses of metabolic exchanges and co-dependencies in bacterial communities and by crossing them with co-occurrence profiles, contextualized interaction patterns into a wider ecological perspective.

Production of a Mixture of Fengycins with Surfactant and Antifungal Activities by Bacillus sp. MA04, a Versatile PGPR

Bacillus sp. strain MA04 a plant growth-promoting rhizobacteria (PGPR) showed hemolytic activity on blood agar plates, and the supernatant from liquid culture in nutrient broth at 24 h exhibited emulsification activity, suggesting the production of biosurfactants. In antagonist assays, the supernatant showed antifungal activity against phytopathogenic fungi such as Penicillium expansum, Fusarium stilboides, Sclerotium rolfsii y Rhizoctonia solani, finding a reduction of mycelial growth of all fungi tested, ranging from 35 to 69%, this activity was increased with time of culture, accomplishing percentages of inhibition up to 85% with supernatants obtained at 72 h. Then, the crude biorsurfactant (CB) was isolated from the supernatant in order to assay its antagonistic effect on the phytopathogens previously tested, finding an increase in the inhibition up to 97% at 500 mg/L of CB. The composition of CB was determined by infrared spectroscopy, identifying various functional groups related to lipopeptides, which were purified by high-performance liquid chromatography and analyzed by MALDI-TOF/TOF-MS, revealing a mixture of fengycins A and B whose high antifungal activity is been widely recognized. These results show that PGPR Bacillus sp. MA04 could also contribute to plant health status through the production of metabolites with antimicrobial activity.

Network Analyses in Plant Pathogens

Even in the age of big data in Biology, studying the connections between the biological processes and the molecular mechanisms behind them is a challenging task. Systems biology arose as a transversal discipline between biology, chemistry, computer science, mathematics, and physics to facilitate the elucidation of such connections. A scenario, where the application of systems biology constitutes a very powerful tool, is the study of interactions between hosts and pathogens using network approaches. Interactions between pathogenic bacteria and their hosts, both in agricultural and human health contexts are of great interest to researchers worldwide. Large amounts of data have been generated in the last few years within this area of research. However, studies have been relatively limited to simple interactions. This has left great amounts of data that remain to be utilized. Here, we review the main techniques in network analysis and their complementary experimental assays used to investigate bacterial-plant interactions. Other host-pathogen interactions are presented in those cases where few or no examples of plant pathogens exist. Furthermore, we present key results that have been obtained with these techniques and how these can help in the design of new strategies to control bacterial pathogens. The review comprises metabolic simulation, protein-protein interactions, regulatory control of gene expression, host-pathogen modeling, and genome evolution in bacteria. The aim of this review is to offer scientists working on plant-pathogen interactions basic concepts around network biology, as well as an array of techniques that will be useful for a better and more complete interpretation of their data.

Plant Microbe Interactions in Post Genomic Era: Perspectives and Applications

Deciphering plant-microbe interactions is a promising aspect to understand the benefits and the pathogenic effect of microbes and crop improvement. The advancement in sequencing technologies and various 'omics' tool has impressively accelerated the research in biological sciences in this area. The recent and ongoing developments provide a unique approach to describing these intricate interactions and test hypotheses. In the present review, we discuss the role of plant-pathogen interaction in crop improvement. The plant innate immunity has always been an important aspect of research and leads to some interesting information like the adaptation of unique immune mechanisms of plants against pathogens. The development of new techniques in the post - genomic era has greatly enhanced our understanding of the regulation of plant defense mechanisms against pathogens. The present review also provides an overview of beneficial plant-microbe interactions with special reference to Agrobacterium tumefaciens-plant interactions where plant derived signal molecules and plant immune responses are important in pathogenicity and transformation efficiency. The construction of various Genome-scale metabolic models of microorganisms and plants presented a better understanding of all metabolic interactions activated during the interactions. This review also lists the emerging repertoire of phytopathogens and its impact on plant disease resistance. Outline of different aspects of plant-pathogen interactions is presented in this review to bridge the gap between plant microbial ecology and their immune responses.

Transcriptional modulation of squalene synthase genes in barley treated with PGPR

Phytosterol contents and food quality of plant produce is directly associated with transcription of gene squalene synthase (SS). In current study, barley plants were treated with different rhizobacterial strains under semi controlled (27 ± 3°C) greenhouse conditions in order to modulate expression of SS gene. Plant samples were analyzed through semi-quantitative PCR to evaluate effect of rhizobacterial application on transcriptional status of SS. Results revealed that among four SS genes (i.e., SSA, SS1, SS2, and SS3), the most expressive gene was SSA; while, SS2 was screened out as the second best induced gene due to Acetobacter aceti. The most efficient bacterial strain which recorded maximum gene expression was A. aceti AC8. Moreover, AC7 was reported as the least efficient bacterial species for inducing SS gene expression. AC8 enhanced the share of SSA and SS2 up to 43 and 31%, respectively. The study also described ribosomal sequence of the most efficient bacterial strain AC8, which was used to determine its phylogenetic relationships with other microbial strains. The study would be helpful to improve quality of plant produce by modulating transcription of SS genes.

Plant Innate Immunity Multicomponent Model

Our understanding of plant-pathogen interactions is making rapid advances in order to address issues of global importance such as improving agricultural productivity and sustainable food security. Innate immunity has evolved in plants, resulting in a wide diversity of defense mechanisms adapted to specific threats. The postulated PTI/ETI model describes two perception layers of plant innate immune system, which belong to a first immunity component of defense response activation. To better describe the sophisticated defense system of plants, we propose a new model of plant immunity. This model considers the plant's ability to distinguish the feeding behavior of their many foes, such as a second component that modulates innate immunity. This hypothesis provides a new viewpoint highlighting the relevance of hormone crosstalk and primary metabolism in regulating plant defense against the different behaviors of pathogens with the intention to stimulate further interest in this research area.

Basics of mass spectrometry based metabolomics

The emerging field of metabolomics, aiming to characterize small molecule metabolites present in biological systems, promises immense potential for different areas such as medicine, environmental sciences, agronomy, etc. The purpose of this article is to guide the reader through the history of the field, then through the main steps of the metabolomics workflow, from study design to structure elucidation, and help the reader to understand the key phases of a metabolomics investigation and the rationale underlying the protocols and techniques used. This article is not intended to give standard operating procedures as several papers related to this topic were already provided, but is designed as a tutorial aiming to help beginners understand the concept and challenges of MS-based metabolomics. A real case example is taken from the literature to illustrate the application of the metabolomics approach in the field of doping analysis. Challenges and limitations of the approach are then discussed along with future directions in research to cope with these limitations. This tutorial is part of the International Proteomics Tutorial Programme (IPTP18).