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

Abstract

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.

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

“…However, to fully capture the maize metabolic diversity and enhance our understanding of the plant's mechanisms, the un-known motifs should be explored and annotated in the future. MS2LDA is thus limited by the vast amount of unannotated Mass2Motifs, that require expert knowledge for structural annotation, which makes this process laborious [17,19]. Furthermore, to support the MolNetEnhancer chemical compound class annotation (Figure 2), the Network Annotation Propagation (NAP) in silico annotation tool was applied (Figures 4 and 5).…”

“…These MN strategies enable a broad overview of the molecular information that can be inferred from the MS/MS data. This analysis of chemical relationships between every MS/MS spectrum, visualizing the entire metabolome detected in a sample using the MolNetEnhancer approach, reveals structurally related molecular families in maize plants [16][17][18][19]. Furthermore, the implementation of the Metabolomics Pathway Analysis (MetPA) tool, a MetaboAnalyst feature, aids in identifying the most relevant pathways within the metabolome [20,21].…”

Metabolic and Physiological Changes in the Roots of Two Oat Cultivars in Response to Complex Saline-Alkali Stress

“…Saline-alkali stress alters the physiological, biochemical, and molecular processes of plants ( El-Esawi et al, 2019 ). Metabolomics studies can reflect the biological and physiological processes in response to stressful conditions at the cellular and molecular levels by monitoring the changes in metabolite levels and fluxes ( Tinte et al, 2021 ). Therefore, metabolomics has become essential in studying the changes of metabolic pathways and tolerance mechanisms under environmental stress ( Shao et al, 2011 ).…”

Applications of Metabolomics for the Elucidation of Abiotic Stress Tolerance in Plants: A Special Focus on Osmotic Stress and Heavy Metal Toxicity

“…In the broader spectrum of metabolomics applications, NMR methods have been relegated in favour of MS-based analytical platforms due to their low sensitivity, low resolving power, and much lower dynamic range resulting in limited metabolic coverage [ 69 ]. In recent years, significant improvements have been made in NMR-base metabolomics studies; these include the development of cryogenic probes which can handle analytical conditions at −253 °C, and multidimensional NMR, which determines the 3D structures of organic and biomolecules, leading to increased sensitivity, resolution, and reduced acquisition times [ 61 , 69 ].…”