Computational Metabolomics Tools Reveal Metabolic Reconfigurations Underlying the Effects of Biostimulant Seaweed Extracts on Maize Plants under Drought Stress Conditions.

Drought is one of the major abiotic stresses causing severe damage and losses in economically important crops worldwide. Drought decreases the plant water status, leading to a disruptive metabolic reprogramming that negatively affects plant growth and yield. Seaweed extract-based biostimulants show potential as a sustainable strategy for improved crop health and stress resilience. However, cellular, biochemical, and molecular mechanisms governing the agronomically observed benefits of the seaweed extracts on plants are still poorly understood. In this study, a liquid chromatography-mass spectrometry-based untargeted metabolomics approach combined with computational metabolomics strategies was applied to unravel the molecular 'stamps' that define the effects of seaweed extracts on greenhouse-grown maize (Zea mays) under drought conditions. We applied mass spectral networking, substructure discovery, chemometrics, and metabolic pathway analyses to mine and interpret the generated mass spectral data. The results showed that the application of seaweed extracts induced alterations in the different pathways of primary and secondary metabolism, such as phenylpropanoid, flavonoid biosynthesis, fatty acid metabolism, and amino acids pathways. These metabolic changes involved increasing levels of phenylalanine, tryptophan, coumaroylquinic acid, and linolenic acid metabolites. These metabolic alterations are known to define some of the various biochemical and physiological events that lead to enhanced drought resistance traits. The latter include root growth, alleviation of oxidative stress, improved water, and nutrient uptake. Moreover, this study demonstrates the use of molecular networking in annotating maize metabolome. Furthermore, the results reveal that seaweed extract-based biostimulants induced a remodeling of maize metabolism, subsequently readjusting the plant towards stress alleviation, for example, by increasing the plant height and diameter through foliar application. Such insights add to ongoing efforts in elucidating the modes of action of biostimulants, such as seaweed extracts. Altogether, our study contributes to the fundamental scientific knowledge that is necessary for the development of a biostimulants industry aiming for a sustainable food security.

Soil Salinity, a Serious Environmental Issue and Plant Responses: A Metabolomics Perspective.

The effects of global warming have increasingly led to devastating environmental stresses, such as heat, salinity, and drought. Soil salinization is a serious environmental issue and results in detrimental abiotic stress, affecting 7% of land area and 33% of irrigated lands worldwide. The proportion of arable land facing salinity is expected to rise due to increasing climate change fuelled by anthropogenic activities, exacerbating the threat to global food security for the exponentially growing populace. As sessile organisms, plants have evolutionarily developed mechanisms that allow ad hoc responses to salinity stress. The orchestrated mechanisms include signalling cascades involving phytohormones, kinases, reactive oxygen species (ROS), and calcium regulatory networks. As a pillar in a systems biology approach, metabolomics allows for comprehensive interrogation of the biochemistry and a deconvolution of molecular mechanisms involved in plant responses to salinity. Thus, this review highlights soil salinization as a serious environmental issue and points to the negative impacts of salinity on plants. Furthermore, the review summarises mechanisms regulating salinity tolerance on molecular, cellular, and biochemical levels with a focus on metabolomics perspectives. This critical synthesis of current literature is an opportunity to revisit the current models regarding plant responses to salinity, with an invitation to further fundamental research for novel and actionable insights.

Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview.

Plants are constantly challenged by changing environmental conditions that include abiotic stresses. These are limiting their development and productivity and are subsequently threatening our food security, especially when considering the pressure of the increasing global population. Thus, there is an urgent need for the next generation of crops with high productivity and resilience to climate change. The dawn of a new era characterized by the emergence of fourth industrial revolution (4IR) technologies has redefined the ideological boundaries of research and applications in plant sciences. Recent technological advances and machine learning (ML)-based computational tools and omics data analysis approaches are allowing scientists to derive comprehensive metabolic descriptions and models for the target plant species under specific conditions. Such accurate metabolic descriptions are imperatively essential for devising a roadmap for the next generation of crops that are resilient to environmental deterioration. By synthesizing the recent literature and collating data on metabolomics studies on plant responses to abiotic stresses, in the context of the 4IR era, we point out the opportunities and challenges offered by omics science, analytical intelligence, computational tools and big data analytics. Specifically, we highlight technological advancements in (plant) metabolomics workflows and the use of machine learning and computational tools to decipher the dynamics in the chemical space that define plant responses to abiotic stress conditions.

Lipopolysaccharide perception in Arabidopsis thaliana: Diverse LPS chemotypes from Burkholderia cepacia, Pseudomonas syringae and Xanthomonas campestris trigger differential defence-related perturbations in the metabolome.

Lipopolysaccharides (LPSs) are microbe-associated molecular pattern molecules (MAMPs) from Gram-negative bacterial pathogens that potentially contain three different MAMPs (the O-polysaccharide chain, the oligosaccharide core and lipid A). LPSs was purified from Burkholderia cepacia, Pseudomonas syringae and Xanthomonas campestris and electrophoretically profiled. Outcomes of the interactions of the three different LPS chemotypes with Arabidopsis thaliana, as reflected in the induced defence metabolites, profiled at 12 h and 24 h post elicitation, were investigated. Plants were pressure-infiltrated with LPS solutions and methanol-based extractions at different time points were performed for untargeted metabolomics using ultra-high performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry. Multivariate data modelling and chemometric analysis were applied to generate interpretable biochemical information from the multidimensional data sets. The three LPSs triggered differential metabolome changes in the plants as apparent from chromatographically distinct MS chromatograms. Unsupervised and supervised multivariate data models exhibited time- and treatment-related variations, and revealed discriminating metabolite variables. Heat map models comparatively displayed the up-regulated pathways affecting the metabolomes and Venn diagrams indicated up-regulated and shared metabolites among the three LPS treatments. The altered metabolomes reflect the up-regulation of metabolites from not only the glucosinolate pathway, but also from the shikimate-phenylpropanoid-flavonoid -, terpenoid - and indolic/alkaloid pathways, as well as oxygenated fatty acids. Distinct phytochemical profiles, especially at the earlier time point, suggest differences in the perception of the three LPS chemotypes, associated with the molecular patterns within the tripartite lipoglycans.