Combined multivariate analysis and machine learning reveals a predictive module of metabolic stress response in Arabidopsis thaliana

A review of artificial intelligence-assisted omics techniques in plant defense: current trends and future directions

Plants intricately deploy defense systems to counter diverse biotic and abiotic stresses. Omics technologies, spanning genomics, transcriptomics, proteomics, and metabolomics, have revolutionized the exploration of plant defense mechanisms, unraveling molecular intricacies in response to various stressors. However, the complexity and scale of omics data necessitate sophisticated analytical tools for meaningful insights. This review delves into the application of artificial intelligence algorithms, particularly machine learning and deep learning, as promising approaches for deciphering complex omics data in plant defense research. The overview encompasses key omics techniques and addresses the challenges and limitations inherent in current AI-assisted omics approaches. Moreover, it contemplates potential future directions in this dynamic field. In summary, AI-assisted omics techniques present a robust toolkit, enabling a profound understanding of the molecular foundations of plant defense and paving the way for more effective crop protection strategies amidst climate change and emerging diseases.

Plant response to heavy metal stress toxicity: the role of metabolomics and other omics tools

Metabolomic investigations offers a significant foundation for improved comprehension of the adaptability of plants to reconfigure the key metabolic pathways and their response to changing climatic conditions. Their application to ecophysiology and ecotoxicology help to assess potential risks caused by the contaminants, their modes of action and the elucidation of metabolic pathways associated with stress responses. Heavy metal stress is one of the most significant environmental hazards affecting the physiological and biochemical processes in plants. Metabolomic tools have been widely utilised in the massive characterisation of the molecular structure of plants at various stages for understanding the diverse aspects of the cellular functioning underlying heavy metal stress-responsive mechanisms. This review emphasises on the recent progressions in metabolomics in plants subjected to heavy metal stresses. Also, it discusses the possibility of facilitating effective management strategies concerning metabolites for mitigating the negative impacts of heavy metal contaminants on the growth and productivity of plants.

Quantifying the impact of dynamic plant-environment interactions on metabolic regulation

A plant's genome encodes enzymes, transporters and many other proteins which constitute metabolism. Interactions of plants with their environment shape their growth, development and resilience towards adverse conditions. Although genome sequencing technologies and applications have experienced triumphantly rapid development during the last decades, enabling nowadays a fast and cheap sequencing of full genomes, prediction of metabolic phenotypes from genotype × environment interactions remains, at best, very incomplete. The main reasons are a lack of understanding of how different levels of molecular organisation depend on each other, and how they are constituted and expressed within a setup of growth conditions. Phenotypic plasticity, e.g., of the genetic model plant Arabidopsis thaliana, has provided important insights into plant-environment interactions and the resulting genotype x phenotype relationships. Here, we summarize previous and current findings about plant development in a changing environment and how this might be shaped and reflected in metabolism and its regulation. We identify current challenges in the study of plant development and metabolic regulation and provide an outlook of how methodological workflows might support the application of findings made in model systems to crops and their cultivation.

Sensing, signalling, and regulatory mechanism of cold-stress tolerance in plants

Cold stress is a crucial environmental factor influencing growth and distribution and possessing yield penalties. To survive in the cold, plants have evolved to use a range of molecular mechanisms. The major regulatory pathway under low-temperature stress involves the conversion of external stimulus into an internal signal that triggers a defence mechanism through a transcriptional cascade to counter stress. Cold-receptive mechanism and cell signalling involve cold-related signalling molecules, sensors, calcium signals, MAPK cascade, and ICE-COR-CBF pathway that modulate signal transduction in plants. Of these, the ICE-CBF-COR signalling is considered to be an important regulator for cold-stress acclimation. ICE stimulates acclimation to cold and plays a pivotal role in regulating CBF-mediated cold-tolerance mechanism. Thus, CBFs regulate COR gene expression by binding to its promoter. Similarly, the C-repeat binding factor-dependent signalling cascade also stimulates osmotic stress-regulatory gene expression. This review elucidates the regulatory mechanism underlying cold stress, i.e., signal molecules, cold receptors, signal-transduction pathways, metabolic regulation under cold stress, and crosstalk of regulatory pathways with other abiotic stresses in plants. The results may pave the way for crop improvement in low-temperature environments.

Advances in Plant Metabolomics and Its Applications in Stress and Single-Cell Biology

In the past two decades, the post-genomic era envisaged high-throughput technologies, resulting in more species with available genome sequences. In-depth multi-omics approaches have evolved to integrate cellular processes at various levels into a systems biology knowledge base. Metabolomics plays a crucial role in molecular networking to bridge the gaps between genotypes and phenotypes. However, the greater complexity of metabolites with diverse chemical and physical properties has limited the advances in plant metabolomics. For several years, applications of liquid/gas chromatography (LC/GC)-mass spectrometry (MS) and nuclear magnetic resonance (NMR) have been constantly developed. Recently, ion mobility spectrometry (IMS)-MS has shown utility in resolving isomeric and isobaric metabolites. Both MS and NMR combined metabolomics significantly increased the identification and quantification of metabolites in an untargeted and targeted manner. Thus, hyphenated metabolomics tools will narrow the gap between the number of metabolite features and the identified metabolites. Metabolites change in response to environmental conditions, including biotic and abiotic stress factors. The spatial distribution of metabolites across different organs, tissues, cells and cellular compartments is a trending research area in metabolomics. Herein, we review recent technological advancements in metabolomics and their applications in understanding plant stress biology and different levels of spatial organization. In addition, we discuss the opportunities and challenges in multiple stress interactions, multi-omics, and single-cell metabolomics.

Machine Learning for Plant Stress Modeling: A Perspective towards Hormesis Management

Plant stress is one of the most significant factors affecting plant fitness and, consequently, food production. However, plant stress may also be profitable since it behaves hormetically; at low doses, it stimulates positive traits in crops, such as the synthesis of specialized metabolites and additional stress tolerance. The controlled exposure of crops to low doses of stressors is therefore called hormesis management, and it is a promising method to increase crop productivity and quality. Nevertheless, hormesis management has severe limitations derived from the complexity of plant physiological responses to stress. Many technological advances assist plant stress science in overcoming such limitations, which results in extensive datasets originating from the multiple layers of the plant defensive response. For that reason, artificial intelligence tools, particularly Machine Learning (ML) and Deep Learning (DL), have become crucial for processing and interpreting data to accurately model plant stress responses such as genomic variation, gene and protein expression, and metabolite biosynthesis. In this review, we discuss the most recent ML and DL applications in plant stress science, focusing on their potential for improving the development of hormesis management protocols.

Uncovering Pathways Highly Correlated to NUE through a Combined Metabolomics and Transcriptomics Approach in Eggplant

Nitrogen (N) fertilization is one of the main inputs to increase crop yield and food production. However, crops utilize only 30–40% of N applied; the remainder is leached into the soil, causing environmental and health damage. In this scenario, the improvement of nitrogen-use efficiency (NUE) will be an essential strategy for sustainable agriculture. Here, we compared two pairs of NUE-contrasting eggplant (Solanum melongena L.) genotypes, employing GC-MS and UPLC-qTOF-MS-based technologies to determine the differential profiles of primary and secondary metabolites in root and shoot tissues, under N starvation as well as at short- and long-term N-limiting resupply. Firstly, differences in the primary metabolism pathways of shoots related to alanine, aspartate and glutamate; starch, sucrose and glycine; serine and threonine; and in secondary metabolites biosynthesis were detected. An integrated analysis between differentially accumulated metabolites and expressed transcripts highlighted a key role of glycine accumulation and the related glyA transcript in the N-use-efficient genotypes to cope with N-limiting stress. Interestingly, a correlation between both sucrose synthase (SUS)- and fructokinase (scrK)-transcript abundances, as well as D-glucose and D-fructose accumulation, appeared useful to distinguish the N-use-efficient genotypes. Furthermore, increased levels of L-aspartate and L-asparagine in the N-use-efficient genotypes at short-term low-N exposure were detected. Granule-bound starch synthase (WAXY) and endoglucanase (E3.2.1.4) downregulation at long-term N stress was observed. Therefore, genes and metabolites related to these pathways could be exploited to improve NUE in eggplant.

Temperature-induced dynamics of plant carbohydrate metabolism

Carbohydrates are direct products of photosynthetic CO₂ assimilation. Within a changing temperature regime, both photosynthesis and carbohydrate metabolism need tight regulation to prevent irreversible damage of plant tissue and to sustain energy metabolism, growth and development. Due to climate change, plants are and will be exposed to both long-term and short-term temperature changes with increasing amplitude. Particularly sudden fluctuations, which might comprise a large temperature amplitude from low to high temperature, pose a challenge for plants from the cellular to the ecosystem level. A detailed understanding of fundamental regulatory processes, which link photosynthesis and carbohydrate metabolism under such fluctuating environmental conditions, is essential for an estimate of climate change consequences. Further, understanding these processes is important for biotechnological application, breeding and engineering. Environmental light and temperature regimes are sensed by a molecular network that comprises photoreceptors and molecular components of the circadian clock. Photosynthetic efficiency and plant productivity then critically depend on enzymatic regulation and regulatory circuits connecting plant cells with their environment and re-stabilising photosynthetic efficiency and carbohydrate metabolism after temperature-induced deflection. This review summarises and integrates current knowledge about re-stabilisation of photosynthesis and carbohydrate metabolism after perturbation by changing temperature (heat and cold).

A Global Metabolic Map Defines the Effects of a Si-Based Biostimulant on Tomato Plants under Normal and Saline Conditions

The ongoing unpredictability of climate changes is exponentially exerting a negative impact on crop production, further aggravating detrimental abiotic stress effects. Several research studies have been focused on the genetic modification of crop plants to achieve more crop resilience against such stress factors; however, there has been a paradigm shift in modern agriculture focusing on more organic, eco-friendly and long-lasting systems to improve crop yield. As such, extensive research into the use of microbial and nonmicrobial biostimulants has been at the core of agricultural studies to improve crop growth and development, as well as to attain tolerance against several biotic and abiotic stresses. However, the molecular mechanisms underlying the biostimulant activity remain enigmatic. Thus, this study is a liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics approach to unravel the hypothetical biochemical framework underlying effects of a nonmicrobial biostimulant (a silicon-based formulation) on tomato plants (Solanum lycopersium) under salinity stress conditions. This metabolomics study postulates that Si-based biostimulants could alleviate salinity stress in tomato plants through modulation of the primary metabolism involving changes in the tricarboxylic acid cycle, fatty acid and numerous amino acid biosynthesis pathways, with further reprogramming of several secondary metabolism pathways such as the phenylpropanoid pathway, flavonoid biosynthesis pathways including flavone and flavanol biosynthesis. Thus, the postulated hypothetical framework, describing biostimulant-induced metabolic events in tomato plants, provides actionable knowledge necessary for industries and farmers to, confidently and innovatively, explore, design, and fully implement Si-based formulations and strategies into agronomic practices for sustainable agriculture and food production.

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.

Predicting nanotoxicity by an integrated machine learning and metabolomics approach

Predicting the biological responses to engineered nanoparticles (ENPs) is critical to their environmental health assessment. The disturbances of metabolic pathways reflect the global profile of biological responses to ENPs but are difficult to predict due to the highly heterogeneous data from complicated biological systems and various ENP properties. Herein, integrating multiple machine learning models and metabolomics enabled accurate prediction of the disturbance of metabolic pathways induced by 33 ENPs. Screening nine typical properties of ENPs identified type and size as the top features determining the effects on metabolic pathways. Similarity network analysis and decision tree models overcame the highly heterogeneous data sources to visualize and judge the occurrence of metabolic pathways depending on the sorting priority features. The model accuracy was verified by animal experiments and reached 75%-100%, even for the prediction of ENPs outside of databases. The models also predicted metabolic pathway-related histopathology. This work provides an approach for the quick assessment of environmental health risks induced by known and unknown ENPs.

Spirulina-in Silico-Mutations and Their Comparative Analyses in the Metabolomics Scale by Using Proteome-Based Flux Balance Analysis

This study used an in silico metabolic engineering strategy for modifying the metabolic capabilities of Spirulina under specific conditions as an approach to modifying culture conditions in order to generate the intended outputs. In metabolic models, the basic metabolic fluxes in steady-state metabolic networks have generally been controlled by stoichiometric reactions; however, this approach does not consider the regulatory mechanism of the proteins responsible for the metabolic reactions. The protein regulatory network plays a critical role in the response to stresses, including environmental stress, encountered by an organism. Thus, the integration of the response mechanism of Spirulina to growth temperature stresses was investigated via simulation of a proteome-based GSMM, in which the boundaries were established by using protein expression levels obtained from quantitative proteomic analysis. The proteome-based flux balance analysis (FBA) under an optimal growth temperature (35 °C), a low growth temperature (22 °C) and a high growth temperature (40 °C) showed biomass yields that closely fit the experimental data obtained in previous research. Moreover, the response mechanism was analyzed by the integration of the proteome and protein-protein interaction (PPI) network, and those data were used to support in silico knockout/overexpression of selected proteins involved in the PPI network. The Spirulina, wild-type, proteome fluxes under different growth temperatures and those of mutants were compared, and the proteins/enzymes catalyzing the different flux levels were mapped onto their designated pathways for biological interpretation.

Hot drought reduces the effects of elevated CO2 on tree water-use efficiency and carbon metabolism

Trees are increasingly exposed to hot droughts due to CO₂ -induced climate change. However, the direct role of [CO₂ ] in altering tree physiological responses to drought and heat stress remains ambiguous. Pinus halepensis (Aleppo pine) trees were grown from seed under ambient (421 ppm) or elevated (867 ppm) [CO₂ ]. The 1.5-yr-old trees, either well watered or drought treated for 1 month, were transferred to separate gas-exchange chambers and the temperature gradually increased from 25°C to 40°C over a 10 d period. Continuous whole-tree shoot and root gas-exchange measurements were supplemented by primary metabolite analysis. Elevated [CO₂ ] reduced tree water loss, reflected in lower stomatal conductance, resulting in a higher water-use efficiency throughout amplifying heat stress. Net carbon uptake declined strongly, driven by increases in respiration peaking earlier in the well-watered (31-32°C) than drought (33-34°C) treatments unaffected by growth [CO₂ ]. Further, drought altered the primary metabolome, whereas the metabolic response to [CO₂ ] was subtle and mainly reflected in enhanced root protein stability. The impact of elevated [CO₂ ] on tree stress responses was modest and largely vanished with progressing heat and drought. We therefore conclude that increases in atmospheric [CO₂ ] cannot counterbalance the impacts of hot drought extremes in Aleppo pine.

Dynamics of Plant Metabolism during Cold Acclimation

Plants have evolved strategies to tightly regulate metabolism during acclimation to a changing environment. Low temperature significantly constrains distribution, growth and yield of many temperate plant species. Exposing plants to low but non-freezing temperature induces a multigenic processes termed cold acclimation, which eventually results in an increased freezing tolerance. Cold acclimation comprises reprogramming of the transcriptome, proteome and metabolome and affects communication and signaling between subcellular organelles. Carbohydrates play a central role in this metabolic reprogramming. This review summarizes current knowledge about the role of carbohydrate metabolism in plant cold acclimation with a focus on subcellular metabolic reprogramming, its thermodynamic constraints under low temperature and mathematical modelling of metabolism.

The Power of LC-MS Based Multiomics: Exploring Adipogenic Differentiation of Human Mesenchymal Stem/Stromal Cells

The molecular study of fat cell development in the human body is essential for our understanding of obesity and related diseases. Mesenchymal stem/stromal cells (MSC) are the ideal source to study fat formation as they are the progenitors of adipocytes. In this work, we used human MSCs, received from surgery waste, and differentiated them into fat adipocytes. The combination of several layers of information coming from lipidomics, metabolomics and proteomics enabled network analysis of the biochemical pathways in adipogenesis. Simultaneous analysis of metabolites, lipids, and proteins in cell culture is challenging due to the compound’s chemical difference, so most studies involve separate analysis with unimolecular strategies. In this study, we employed a multimolecular approach using a two–phase extraction to monitor the crosstalk between lipid metabolism and protein-based signaling in a single sample (~10⁵ cells). We developed an innovative analytical workflow including standardization with in-house produced ¹³C isotopically labeled compounds, hyphenated high-end mass spectrometry (high-resolution Orbitrap MS), and chromatography (HILIC, RP) for simultaneous untargeted screening and targeted quantification. Metabolite and lipid concentrations ranged over three to four orders of magnitude and were detected down to the low fmol (absolute on column) level. Biological validation and data interpretation of the multiomics workflow was performed based on proteomics network reconstruction, metabolic modelling (MetaboAnalyst 4.0), and pathway analysis (OmicsNet). Comparing MSCs and adipocytes, we observed significant regulation of different metabolites and lipids such as triglycerides, gangliosides, and carnitine with 113 fully reprogrammed pathways. The observed changes are in accordance with literature findings dealing with adipogenic differentiation of MSC. These results are a proof of principle for the power of multimolecular extraction combined with orthogonal LC-MS assays and network construction. Considering the analytical and biological validation performed in this study, we conclude that the proposed multiomics workflow is ideally suited for comprehensive follow-up studies on adipogenesis and is fit for purpose for different applications with a high potential to understand the complex pathophysiology of diseases.