Responses and adaptations of plants to abiotic stress: transcriptional regulation of secondary metabolic pathways, metabolomics, and nanobiological approaches.

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Learn more: PMC Disclaimer | PMC Copyright Notice Front Genet . 2026 May 19;17:1762633. doi: 10.3389/fgene.2026.1762633 Responses and adaptations of plants to abiotic stress: transcriptional regulation of secondary metabolic pathways, metabolomics, and nanobiological approaches Yashdeep Srivastava 1 , Neha Saini 2 , Nishu 2 , Sapna Yadav 2 , Sandhya Tripathi 3 , Akshay Kumar 2 , Neelam Yadav 2 , Monika Saini 2 , Neelam S Sangwan 2, * Author information Article notes Copyright and License information 1 Department of Biotechnology, Invertis University, Bareilly, Uttar Pradesh, India 2 Department of Biochemistry, Central University of Haryana, Haryana, India 3 Department of Biochemistry, ICAR - Indian Institute of Pulses Research, Kanpur, India * Correspondence: Neelam S. Sangwan, nsangwan@cuh.ac.in Roles Yashdeep Srivastava : Writing - original draft, Writing - review and editing Neha Saini : Writing - original draft, Writing - review and editing Nishu : Writing - original draft, Writing - review and editing Sapna Yadav : Writing - original draft, Writing - review and editing Sandhya Tripathi : Writing - original draft, Writing - review and editing Akshay Kumar : Writing - review and editing Neelam Yadav : Writing - review and editing Monika Saini : Writing - review and editing Neelam S Sangwan : Writing - original draft, Writing - review and editing, Conceptualization, Supervision Received 2025 Dec 7; Revised 2026 Jan 24; Accepted 2026 Jan 26; Collection date 2026. Copyright © 2026 Srivastava, Saini, Nishu, Yadav, Tripathi, Kumar, Yadav, Saini and Sangwan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. PMC Copyright notice PMCID: PMC13225781  PMID: 42232517 Abstract Plants are continuously exposed to diverse abiotic stresses such as drought, salinity, extreme temperatures, and heavy metal toxicity, each of which disrupts cellular homeostasis and reactive oxygen species (ROS) generation and impairs development. To survive under these adverse conditions, plants activate a complex network of transcriptional regulators that remodel primary and secondary metabolic pathways. These regulatory cascades, involving transcription factors such as AP2, WUSCHEL, MYB, bHLH, WRKY, and NAC, orchestrate the biosynthesis of key secondary metabolites, including phenolics, flavonoids, terpenoids, and alkaloids, that function as antioxidants, osmoprotectants, and signaling molecules. Advances in metabolomics have provided deeper insights into stress-induced metabolic reconfigurations, enabling high-resolution profiling of pathway metabolic fluxes and revealing novel metabolites associated with adaptive resilience and tolerance. Alongside engineering abiotic stress resistance, nanobiological approaches have emerged as innovative strategies to modulate and remediate transcriptional responses and secondary metabolite production. This review comprehends current understanding of transcriptional regulation of secondary metabolism under abiotic stress, integrates metabolomics-driven discoveries, and highlights the potential of metabolites and metabolomics-based tools to augment plant adaptive mechanisms. Together, these interconnected perspectives offer a comprehensive framework for developing stress-resilient crops in the era of climate change. Keywords: abiotic stress, crop resilience, nanobiology, omics, secondary metabolic pathways, stress signaling, transcription factor 1. Introduction The plants are immobile, so they are continuously exposed to a variety of abiotic stresses such as drought, salinity, extreme temperature, heavy metals, and nutrient deficiencies in their environment. These abiotic stresses severely restrict plant growth, metabolism, and reproduction ( Feng et al., 2025 ). Collectively, abiotic stresses account for the majority of worldwide crop losses in yield (a >50% reduction has been reported on major crops), which are further compounded by climate change-related issues ( Feller et al., 2014 ). Among the abiotic stresses, drought, salinity, heat, and cold stresses are the most deleterious; they modify plant morphology along with physiology and molecular metabolism ( Souza et al., 2025 ; Feng et al., 2025 ; Feller et al., 2014 ) ( Table 1 ). Such stresses typically induce a series of interconnected forms of cellular injury resulting from osmotic disturbances, oxidative damage, and metabolic stress ( Garcia-Caparros et al., 2021 ). To cope with such stress, plants activate molecular, cellular, and physiological processes, including stress sensors, signal transduction pathways, and transcriptional regulation of stress-responsive genes ( Martin et al., 2021 ; Qin et al., 2022 ; Saini et al., 2024 ). TABLE 1. Primary metabolite regulation in plants under abiotic stress. Abiotic stress Metabolite class Plant Affected pathway Metabolite change References Drought Lipids Arabidopsis thaliana ; wheat ( Triticum aestivum ) Phospholipid degradation and jasmonate biosynthesis; galactolipid biosynthesis Increased phosphatidic acid (PA) and jasmonates for signaling and stomatal regulation; reduced MGDG and DGDG affecting thylakoid membrane stability Bharath et al. (2021) ; Wang et al. (2020) Salinity Lipids Soybean ( Glycine max ) Sphingolipid biosynthesis Enhanced ceramide and sphingosine levels trigger programmed cell death and salt tolerance Henschel et al. (2024) Heat stress Lipids Tomato ( Solanum lycopersicum ) Phospholipid and oxylipin pathways Accumulation of oxylipins (e.g., hydroperoxyoctadecadienoic acids) for antioxidant protection Fitoussi et al. (2021) Cold stress Lipids Arabidopsis thaliana ; barley ( Hordeum vulgare ) Fatty acid desaturation and lipid remodeling; phosphatidylcholine and galactolipid metabolism Enhanced polyunsaturated fatty acid (PUFA) levels improve membrane fluidity; increased galactolipid synthesis stabilizes thylakoid membranes Coniglio et al. (2023) ; Zhao M. et al. (2024) Heavy metal stress Lipids Cucumber ( Cucumis sativus ) Lipid peroxidation and oxylipin biosynthesis Accumulation of malondialdehyde (MDA) and oxylipins for signaling and repair Gajewska et al. (2024) Heat stress Lipids (fatty acids) Sunflower ( Helianthus annuus ) Fatty acid metabolism Altered fatty acid composition maintains membrane stability under high temperature Rahnama et al. (2024) Cold stress Lipids Soybean ( Glycine max ) Lipid remodeling and fatty acid metabolism Altered lipid composition, enhancing membrane fluidity under cold stress Liu et al. (2024) Drought Carbohydrates Wheat ( Triticum aestivum ) Glycolysis and sucrose biosynthesis Increased sucrose, glucose, and fructose for osmotic balance and ROS detoxification Zurawski et al. (1975) Salinity Carbohydrates Tomato ( Solanum lycopersicum ) Starch degradation and sucrose biosynthesis Decreased starch and increased sucrose and glucose for osmotic adjustment Zhang et al. (2024a) Heat stress Carbohydrates Arabidopsis thaliana ; soybean ( Glycine max ) Glycolysis and starch degradation; trehalose biosynthesis Reduced starch synthesis; increased hexoses and trehalose, protecting cellular structures and mitigating ROS Korte et al. (2023) ; Jianing et al. (2022) Cold stress Carbohydrates Potato ( Solanum tuberosum ) Raffinose family oligosaccharides (RFO) pathway Accumulation of raffinose, sucrose, and galactinol for cryoprotection and membrane stabilization Folgado et al. (2014) Cold stress Carbohydrates (RFOs) Pea ( Pisum sativum ) Raffinose family oligosaccharides pathway Synthesis of raffinose family oligosaccharides protecting cellular structures under freezing Elango et al. (2022) Drought Carbohydrates (soluble sugars) Arabidopsis thaliana Glycolysis and sugar metabolism Accumulation of soluble sugars maintains osmotic balance and protects cellular structures Yang et al. (2021) Flooding Carbohydrates (starch) Rice ( Oryza sativa ) Starch degradation Reduced starch degradation, conserving energy under submergence Panda et al. (2021) Drought Organic acids Wheat ( Triticum aestivum ); asparagus ( A. officinalis ) TCA cycle; glycolysis; pyruvate metabolism Increased malate and citrate support osmotic adjustment, energy production, and metabolic reprogramming Marček et al. (2019) ; Zhang et al. (2024b) Salinity Organic acids Tomato ( Solanum lycopersicum ) GABA shunt Accumulation of GABA and succinate contributes to osmoprotection and signaling Guo et al. (2022) Heat stress Organic acids Soybean ( Glycine max ) Malate valve pathway Enhanced malate production maintaining redox balance during heat stress Dinakar et al. (2016) Cold stress Organic acids Potato ( Solanum tuberosum ); A. thaliana Glyoxylate cycle and TCA cycle; shikimate pathway Accumulation of malate, citrate, shikimate, and α-ketoglutarate supporting cryoprotection and secondary metabolism Lee et al. (2023) ; Xu et al. (2020) Heat stress Organic acids Maize ( Zea mays ) TCA cycle and organic acid metabolism Increased organic acid synthesis maintains metabolic flux and reduces heat damage Qu et al. (2018) Salinity Amino acids (proline) Wheat ( Triticum aestivum ) Proline biosynthesis Proline accumulation acts as an osmoprotectant and an ROS scavenger Renzetti et al. (2024) Salinity Polyamines Barley ( Hordeum vulgare ) Polyamine biosynthesis Enhanced polyamine synthesis stabilizes membranes and mitigates ionic toxicity Pottosin et al. (2014) UV radiation Antioxidants (ascorbic acid) Tomato ( Solanum lycopersicum ) Ascorbate–glutathione cycle Increased ascorbic acid reduces UV-induced oxidative stress Mishra et al. (2024) ; Mishra et al. (2017) Drought Osmoprotectants (glycine betaine) Spinach ( Spinacia oleracea ) Glycine betaine metabolism Accumulation of glycine betaine maintains water potential and protects macromolecules Yang et al. (2021) Open in a new tab Plants can dynamically reprogram their primary and secondary metabolism in response to abiotic stress such as drought, salinity, or extreme temperatures ( Ghasemi et al., 2023 ; Sangwan et al., 1994 ; Mishra et al., 2024 ). This reprogramming is a vital survival mechanism that involves redirecting resources toward the production of specific protective metabolites. Phenolics, terpenoids, and alkaloids accumulate in response to stresses and act as antioxidant agents with the ability to scavenge free radicals ( Sangwan et al., 1994 ). They also serve as osmoprotectants, as well as protectional for UV radiations and contributors to cell wall reinforcement. This metabolic adjustment increases plant tolerance, enabling acclimatization and survival under stress conditions ( Mishra et al., 2024 ). Finally, this mechanism ensures not only survival by several physiological modifications but also can enhance the medicinal and nutritional quality of the plants ( Mishra et al., 2024 ). Understanding these mechanisms is essential for molecular breeding and biotechnological approaches aimed at producing stress-resilient crop varieties, thereby ensuring sustainable agricultural yields under the changing environmental conditions ( Feng et al., 2025 ). Crop productivity is affected from seed germination to the reproductive stage through abiotic stresses like higher salinity, drought, temperature variations, and heavy metals ( Figure 1 ) ( Table 1 ). Heavy metals are extremely harmful and hazardous abiotic stressors for plants. Non-biodegradable metallic elements with a very high density are known as heavy metals. Trace amounts of many heavy metals, like nickel (Ni), arsenic (As), cadmium (Cd), zinc (Zn), lead (Pb), copper (Cu), and cobalt (Co), are important for plant growth and development ( Mishra et al., 2017 ; Mishra et al., 2024 ). However, excessive accumulation of these heavy metals within plants causes harmful effects. Heavy metal toxicity elevates the oxidative stress in plants by decreasing the antioxidant enzyme activity, which negatively affects plant development. Another harmful environmental factor for plant growth is high temperature. Plants have regulatory systems to protect them from extreme heat. Elevated environmental temperatures cause denaturation and aggregation of proteins, membrane-damaging lipid peroxidation, enzyme inactivation, protein synthesis suppression, and imbalances between the antioxidant system and reactive oxygen species (ROS) ( Saini et al., 2024 ). Cellular homeostasis is also disturbed by drought stress, which increases ROS generation and lowers antioxidant scavenging system activity. In response to ROS, plants modify their morphology, physiology, biochemistry, and molecular systems to protect themselves ( Figure 2 ; Saini et al., 2024 ). Salt stress, which results from factors like soil salinity, saline irrigation, and poor soil management, has a significant impact on agricultural production worldwide. It disturbs the ion balance, causes osmotic stress, and damages plants oxidatively, which limits growth, productivity, and reduces crop quality ( Saini et al., 2024 ). These abiotic factors disrupt the balance of agricultural ecosystems, significantly influencing crop productivity, quality, and sustainability. Increasing temperatures are changing the duration of growing seasons, thereby affecting crop yields and productivity, while changes in rainfall season lead to droughts or floods that disrupt soil moisture and nutrient availability ( Lesk et al., 2022 ). Increased soil salinity, driven by natural processes and human activities, has further degraded agricultural land, while severe weather events like heatwaves, storms, and cyclones cause extensive harm to crops ( Kumar et al., 2023 ). As climatic conditions become more unpredictable, these stress factors threaten the stability of agricultural methods and our ability to sustain a rising global population ( Shahzad et al., 2021 ). FIGURE 1. Open in a new tab Effects of abiotic stress on plant growth and productivity. FIGURE 2. Open in a new tab Omics-driven functional understanding of abiotic stress-induced ROS signaling for improving plant resilience. Agricultural practices are greatly sensitive to abiotic stress and face a high risk as these stresses intensify due to changing climatic conditions. This necessiate to develop plants which should be more potent, to grow more food, and to keep a safe food supply as the climate changes. It is crucial to recognize how abiotic stress impacts agricultural systems mainly through abiotic stressors to develop sustainable farming practices and improve crop resilience against stress-related challenges. Plant stress responses have been the subject of extensive research. Most existing reviews focus on transcriptional regulation, metabolomics, or stress physiology separately. This approach creates a limited view of how plants achieve stress tolerance. In this work, we provide a complex synthesis of existing knowledge that highlights metabolomics as a key framework connecting stress perception, acclimation, and crop resilience ( Wang H. et al., 2022 ). Environmental stress is interpreted as a disturbance of metabolic homeostasis. The tolerance to stress is enacted through well-organized metabolic reprogramming and signaling via metabolites. A significant breakthrough of this review article is the mechanism-based grouping of stress-responsive metabolites into the categories of protective compounds, stress-induced by-products, and signaling molecules, thus making a time-resolved view of acclimatization possible. The present review, through its comprehensive integration of metabolomics with transcription factor networks, redox and hormonal signaling, plant–microbe interactions, and novel nanobiological techniques, offers a systems-level and translational viewpoint that immediately supports predictive modeling and metabolomics-assisted breeding for climate-resilient crops. 2. Signaling pathways affected under stress conditions A number of genes and signaling pathways are affected under stress conditions in plants. Abscisic acid (ABA) plays a significant role in regulating the closing of stomata and gene expression during drought and salinity stress. Stress-responsive gene expression is caused by the activation of ABRE-binding factor (ABF) transcription factors and the mediation of ABA perception by the PYR/PYL/RCAR–PP2C–SnRK2 core signaling module ( Dong et al., 2022 ). In Arabidopsis thaliana , the SnRK2 gene plays an important role in seed germination, seedling growth, and increasing stress tolerance ( Amjad et al., 2024 ). Similarly, the salt overly sensitive (SOS) pathway helps to balance ion homeostasis in salinity stress. SOS3, a calcium-binding protein, detects salt stress and triggers SOS2, which phosphorylates the SOS1 Na + /H + antiporter, thereby decreasing the buildup of Na + ions in cells ( Hendricks, 2021 ). In A. thaliana , the SOS pathway plays an essential role in salt tolerance. Similarly, the maize SOS3/SOS2 complex phosphorylates, activating Na + -transporting function. Loss-of-function mutants exhibit phenotypes that are hypersensitive to salt ( Zhou et al., 2022 ). In heavy metal stress conditions, plants use phytochelatins (PCs) and metallothioneins (MTs). They chelate and sequester metals such as Cd 2+ and Pb 2+ into vacuoles through ABC transporters ( Leopold et al., 1999 ). Heat shock proteins (HSPs), which act as molecular chaperones to stop the denaturation and aggregation of proteins, are activated by heat stress-induced heat shock transcription factors (HSFs). The PIF4 protein, which increased stress tolerance by binding with the HSAF2 promoter and triggering the heat stress response to increase tolerance under heat stress, was found to accumulate in Arabidopsis and wheat ( Yang et al., 2022 ). In Aquilegia vulgaris , under salt stress, the metabolism of starch and sucrose, as well as the signal transduction of plant hormones, were significantly improved ( Chen L. et al., 2023 ). ROS behave as both stressors and signaling molecules. Oxidative stress triggers the activation of the MAP kinase (MAPK) cascade (MAPK3/MAPK6), which controls genes that counter stress and antioxidants like superoxide dismutase (SOD) and ascorbate peroxidase (APX) ( Figure 2 ). Under stress conditions, plant growth is adversely affected due to the inhibition of cell cycles. Stress causes activation of the signaling cascade that results in the S-phase’s prolonged and delayed entry into mitosis. In maize, downregulation of CDKA, CYCA, CDKB, and CYCB was found under drought and salinity stress, leading to a low cell proliferation rate ( Kamal et al., 2021 ). Similarly, it was found that CDKG-2s might be the primary regulators in cotton ( Gossypium spp.), while the CDKF4 gene was upregulated in Arabidopsis under salinity and drought stress ( Magwanga et al., 2018 ). Metabolomics can be used to analyze the metabolites in biological systems affected by abiotic stress ( Tinte et al., 2021 ), providing a valuable tool to understand the plant’s adaptation and responses against stress conditions, helping to expose the metabolic pathway network that influences plant growth and development. This helps identify key biomarkers and metabolites crucial for stress-resilient crops. This review aims to give an overview of how various abiotic stresses impact crop yield, productivity, and agricultural sustainability. It covers the impact of various types of abiotic stress on metabolites, pathways, regulation, and nanobiotechnological management. The review further emphasizes that a deeper understanding of stress-responsive metabolites can improve crop resilience, optimize resource use, and encourage sustainable farming practices, thereby providing a strategy toward global crop systems during this era of significant environmental change. 3. Metabolomics under abiotic stress In biological systems, metabolomics provides a comprehensive analysis of all metabolites under particular conditions ( Klassen et al., 2017 ). This includes primary and secondary metabolites, which are important for energy production, cell signaling, stress responses, and various metabolic pathways ( Saini et al., 2024 ; Tripathi et al., 2016 ). Metabolomics also helps to advance precision agriculture practices, promoting sustainable farming and boosting the plant breeding process ( Figure 3 ). This makes it sensitive to stress conditions, disease processes, and other physiological conditions ( Satrio et al., 2024 ). Metabolomics enhances crop quality and yield, and the combination of metabolomics with other “omics” techniques like proteomics, transcriptomics, and genomics helps us understand entire plant systems and their response against stress conditions ( Carrera et al., 2021 ; Yang et al., 2024a ). Its aim is to examine qualitative and quantitative forms of metabolites, as well as the metabolic state of samples. This approach provides a unique insight into the biochemical pathways that might not be identified through other molecular biology techniques ( Tyagi et al., 2022 ). FIGURE 3. Open in a new tab Schematic workflow representing the major steps involved in metabolomics studies, including sample preparation, metabolite extraction, analytical platforms, data processing, and biological interpretation. 3.1. Role of metabolomics in stress biology and crop resilience Metabolomics plays a pivotal role in stress biology as it expresses the functional biochemical state of plants in response to environmental stress. This directly links stress perception to physiological and adaptive outcomes ( Anzano et al., 2022 ). Environmental stresses alter metabolic homeostasis, which triggers a cascade of signaling events. Metabolic reprogramming is aimed at achieving a new equilibrium through acclimation in stress conditions ( Ghatak et al., 2018 ). As metabolites are the final products of gene expression and protein activity, metabolomic profiling offers the closest and most comprehensive method of understanding stress responses ( Singh et al., 2020 ). Stress resistance in plants is essentially a metabolic trait that is governed by the complex dynamics of primary and secondary metabolites. Metabolomics contributes to the identification and quantification of molecules participating in different types of stress. These molecules, viz., sugars, amino acids, organic acids, polyols, phenolics, terpenoids, alkaloids, and phytohormones, are involved in redox homeostasis, osmotic adjustment, membrane stabilization, detoxification, and signaling. They function together as osmoprotectants, antioxidants, metal chelators, and signal molecules that affect how plants develop and survive under stress ( Khan et al., 2017 ). An important role of metabolomics in relation to crop resilience is its ability to detect specific metabolic fingerprints for stress responses ( Głuchowska et al., 2025 ). This function enables the distinction of stress-tolerant crop varieties from stress-sensitive varieties. These metabolic fingerprints serve as ideal biomarkers for crop resistance to stress. They can be exploited for the identification and early detection of stress. In major food crops such as cereals, metabolomic analysis of biomarkers for crop performance concerning grain quality under stress conditions has been used to address food security ( Dawid and Hille, 2018 ). Metabolomics is an important platform for the interconnections between genotype and phenotype, which allows the interpretation of complex phenotypes for stress tolerance ( Satrio et al., 2024 ). Utilization of the metabolomics platform with genomics helped researchers to identify the locus between genomics and metabolomics, resulting in the identification of metabolomics quantitative trait loci (mQTL) and metabolome-wide association studies (metGWAS) ( Hamany Djande et al., 2020 ; Wei et al., 2018 ). This shows regions of the genome that control the concentration of metabolites produced by the plants during stress ( Wei et al., 2018 ). Beyond plant-intrinsic responses, metabolomics uniquely describes the chemical interactions between plants and beneficial microbes ( Olanrewaju et al., 2024 ). Metabolomics helps to decipher processes such as how root exudates and microbial metabolites modulate nutrient acquisition, hormone signaling, immune priming, and stress tolerance ( Anzano et al., 2022 ). This metabolic perspective allows for the rational design and creation of a microbiome resilient to fluctuations, successfully integrating the roles of plant metabolism with microbial functions for an improved agricultural yield under stress-prone settings ( Rosier et al., 2018 ). Moreover, metabolomics allows for the investigation of metabolic plasticity under combined and fluctuating stress conditions that are more relevant to field conditions than single-stress experiments ( Olanrewaju et al., 2024 ). By elucidating how metabolic networks are rewired to maintain homeostasis and produce protective compounds under complex stress regimes, metabolomics delivers essential information for the breeding of crops resilient to climate variability ( Wei et al., 2018 ; Yang et al., 2024b ). Combined with other omics techniques, metabolomics enables a system perspective on stress acclimation and lays the basis for the translation of molecular information into robust crop phenotypes ( Satrio et al., 2024 ). 3.2. Primary and secondary metabolites: response against abiotic stress Plants developed different types of adaptations, such as changes in metabolism, under unfavorable environmental conditions ( Ashapkin et al., 2020 ). Several plant metabolites may function as osmolytes and osmoprotectants, helping to reduce abiotic stress impacts. Primary metabolites are organic compounds that play an important role in an organism’s growth, development, and reproduction ( Sangwan et al., 1994 ). They are important for the basic metabolic processes necessary for life by providing a carbon source from photosynthate and energy pathways ( Sangwan et al., 1994 ). These primary metabolites also include proline amino acids and soluble and insoluble carbohydrates such as glucose, sucrose, starch, lipids, and organic acids ( Bosco and Butnariu, 2022 ). 3.2.1. Amino acids Amino acids are the building blocks of proteins and other organic molecules like nucleic acids, which play an important role in how a plant reacts to various stresses. Amino acids are important for the production of proteins, like indole alkaloids, phenylpropanoids, polyamines, auxins, and glucosinolates, as well as for nitrogen fixation into glutamine during cold stress ( Zandalinas et al., 2022 ). Signaling and molecular regulation depend on amino acids. Additionally, proline and other amino acids, well-known osmoprotectants, are produced when plants are subjected to different abiotic stresses ( Singh et al., 1993 ; Hosseinifard et al., 2022 ). Proline acts as a molecular chaperone, protecting the protein structure and function. It also serves as a singlet oxygen quencher, which helps to balance ROS levels ( Godoy et al., 2021 ). NADPH, ATP, and pyrroline-5-carboxylase synthetase (P5CS) are required for the reductive process of proline biosynthesis, as they catalyze the conversion of glutamate into pyrroline-5-carboxylate (P5C). Pyrroline-5-carboxylate reductases (P5CR) then reduce the P5C to proline ( Salam et al., 2023 ). Another mechanism of proline biosynthesis occurs via the ornithine-δ-aminotransferase (OAT) enzyme, which is able to convert the ornithine into P5C ( Mushtaq et al., 2025 ). A study showed that ornithine pathways were activated when the nitrogen supply was higher than the optimal during seedling growth. However, under a nitrogen deficiency, the glutamate (Glu) pathway is the dominant pathway for proline biosynthesis ( Hosseinifard et al., 2022 ). It was found that tolerant species show more proline content than the sensitive species. Proline oxidase enzyme activity was increased in wheat lines, whereas the P5CR and OAT enzyme activities were reduced. Proline plays an important part in ROS scavenging by either increasing the enzyme activity involved in osmotic stress or protecting the glutathione-ascorbate cycle. Depending on the species, accumulation of proline shows variation under stress conditions. Another study found that excessive proline accumulation can cause P5C to produce more ROS and malondialdehyde and inhibit ABA and ethylene biosynthesis, lowers stress tolerance. In addition to proline, phenylalanine, tryptophan, and histidine were observed in maize hybrids under drought stress ( Wang C. et al., 2025 ). It was found that the transgenic plants with proline metabolism genes enhanced plant tolerance against salinity and drought stress ( Renzetti et al., 2024 ). The proline content of Portulaca oleracea L. was increased under 300 mM salinity stress ( Hnilickova et al., 2021 ). 3.2.1.1. The proline accumulation in relation to the metabolic trade-offs Although proline accumulation is a sign of the plant’s resilience to abiotic stresses, studies have shown that its participation is very much dependent on the context of the situation and that it necessitates significant metabolic and oxidative trade-offs ( Zulfiqar and Ashraf, 2023 ; Tiwari, 2024 ; Khan, 2025 ). The conversion of glutamate to proline is an energy-consuming process that requires a large amount of NADPH. During long or harsh stress periods, the overuse of reducing power for proline production may limit the activity of the other antioxidant systems, such as the ascorbate–glutathione cycle ( Naliwajski and Skłodowska, 2021 ). This redirection may both deplete and disrupt the balance of overall redox homeostasis instead of strengthening it. The metabolism of proline via proline dehydrogenase in mitochondria produces pyrroline-5-carboxylate (P5C), which is an intermediate that can lead to the generation of ROS if it accumulates in large quantities ( Chalecka et al., 2021 ). Several investigations have indicated that the proline turnover during stress recovery or rehydration is not well controlled, and this results in increased levels of malondialdehyde and oxidative damage ( Hanif et al., 2021 ). Thus, proline is not simply a universally protective metabolite; it is also a double-edged sword. In certain plant types, the buildup of proline has been linked to the reduction of ABA and ethylene biosynthesis, which may interfere with stress signaling and the development of adaptive responses ( Wang et al., 2025a ). Another debate is the question of whether proline accumulation is an active tolerance mechanism or simply a metabolic symptom of stress injury. Even though tolerant genotypes usually have higher proline levels, the transgenic manipulation of proline biosynthesis does not always result in the consistent increase of growth or yield under field-relevant stress combinations ( Raja et al., 2019 ; Renzetti et al., 2024 ). When subjected to combined or sequential stresses, especially drought and heat or salinity, proline accumulation may be so high that it causes carbon and nitrogen depletion, which then leads to penalties in growth and decreased reproductive fitness ( Siddique et al., 2018 ). Moreover, the functional results of proline buildup vary according to the tissue and developmental stage ( Trovato et al., 2019 ). Proline might give a protective effect to the vegetative tissues by reinforcing the proteins and membranes, but in the reproductive organs, its excessive accumulation has been associated with decreased pollen germination and reduced seed formation in some plants ( Siddique et al., 2018 ). Such findings underscore the necessity of examining the proline changes in the context of a larger metabolic network instead of treating them as an isolated indicator of stress ( Renzetti et al., 2024 ). Taken together, these results showed that the buildup of proline is not advantageous by itself but is a sign of a well-managed equilibrium between osmoregulation, redox regulation, and metabolic cost. It will be necessary to bring together fluxomics, spatial metabolomics, and redox profiling conducted in single and combined stress situations to resolve these disputes and understand how proline metabolism can be best tuned to the stress-resilient breeding and engineering strategies. 3.2.2. Lipids The essential component of the plasma membrane is lipids, which function as the interface between the cell and its environment. On the basis of chemical structure and distinct hydrophilic and hydrophobic components, lipids can be classified into eight major types: glycerides, steroids, glycolipids, phosphoglycerides, fatty acids, polyketides, sphingolipids, and isoprenoids ( Shiade et al., 2024 ). All biotic and abiotic stressors trigger lipid-mediated signaling ( Table 1 ). Plants frequently use their plasma membrane, which is usually the source of lipid signaling, to detect these stimuli and convert the signal into biochemical metabolism ( Hou et al., 2016 ). Signaling lipids like lipid kinases, phosphatases, and phospholipases are all acclimating enzymes. The major contributing signaling lipids are fatty acids, triacylglycerol, sphingolipids, phosphatides, lysophospholipids, oxylipins, triacylglycerols, and inositol phosphate ( Table 1 ). It was found that the ability of signaling lipids to attract membrane molecular markers affects the shape and function of cellular proteins and metabolites. Phospholipase A (PLA) plays an essential role in the formation of lysophospholipids and fatty acids ( Yaginuma et al., 2022 ). Lysophospholipids were found in small amounts in plant tissues, but they can be found in greater quantities under stressful conditions. Phospholipase A2 (PLA2) has been shown to increase the formation of certain elicitors in poppies, whilst lysophosphatidylcholine and lysophosphatidylethanolamine function as signal transducers in potato arbuscular symbiosis ( Okazaki and Saito, 2014 ). Lipid remodeling is a crucial adaptive process allowing plants to survive under harsh conditions like drought, salinity, heat, cold, and heavy metals, keeping their membranes stable, signaling, and metabolism in balance ( Table 1 ). Similar to proline and other osmolytes, lipid changes are a way of giving stress protection, which is a costly process concerning metabolism and redox. The alterations in the structure of membranes caused by stress require quick adjustment of lipids to keep the membranes fluid, permeable, and able to function with proteins. A decisive factor of lipid remodeling is the control of fatty acids’ saturation degree. Heat stress in general increases the total of saturated fatty acids to limit the hyperfluidity of membranes. In contrast, cold and drought stresses boost unsaturated fatty acids that, to an extent, through the fatty acid desaturases, help in the regulation of the membrane’s flexibility ( Hou et al., 2016 ). In addition, phospholipid-based signaling substances such as phosphatidic acid and lysophospholipids are formed quickly in large amounts under stress, leading to the activation of calcium signaling, MAP kinase (MAPK) cascades, and ABA-dependent transcriptional responses ( Testerink and Munnik, 2011 ). The galactolipids of chloroplasts, especially monogalactosyldiacylglycerol (MGDG) and digalactosyldiacylglycerol (DGDG), are remodeled for the purpose of thylakoid membrane stabilization and photosynthesis support during drought and salinity stress ( Moellering and Benning, 2011 ). On the other hand, lipid peroxidation produces oxylipins and reactive aldehydes that account for oxidative damage and carbon loss. 3.2.3. Carbohydrates The main components that provide support and energy to the plant biomass are carbohydrates, produced during photosynthesis. In plants, soluble carbohydrates may be crucial for metabolic processes as a carbon and energy source in a cell, as shown by Sangwan et al. (1994) ( Table 1 ). Many factors can affect the amount of carbohydrates, and the process of photosynthesis is linked to the accumulation of carbohydrates. It has been found that by upregulating stress-related genes and downregulating growth-related genes, soluble sugars such as hexose and sucrose increase stress tolerance. However, in water stress conditions, the myoinositol content was reduced in barley roots ( Salam et al., 2023 ). Furthermore, soluble carbohydrates like polymers of fructose molecules (fructans), oligosaccharides (raffinose and stachyose), and disaccharides (sucrose and trehalose), along with their associated metabolic enzymes, are crucial compatible osmolytes linked to the ROS during their assortment in plant tissues. However, the accumulation of carbohydrates in plants varies according to the type of stress ( Godoy et al., 2021 ). In rice, overexpression of the trehalose biosynthesis gene can promote plant growth, reduce ROS damage, improve mineral balance, and boost trehalose buildup by increasing resistance to salinity and drought stress. 3.2.4. Organic acids In plants, organic acid metabolite changes take place under different abiotic stresses ( Table 1 ). Citrate synthase enzyme converts oxaloacetate and acetyl-CoA into citric acid (CA), the first intermediate of the CAC cycle. In addition to being an antioxidant, CA can alleviate metals like aluminum, copper, and lead. Furthermore, it was reported a significant accumulation of calcium (Ca) in tomatoes cultivated under irrigation on calcareous soils, where elevated calcium levels enhanced the assimilation and uptake of Mn, Mg, and P in fruits, as well as Ca, Zn, Na, and N in leaves. Under Cd stress, antioxidant responses were enhanced by the increase in CAT, SOD, and POX enzyme activity in mustard ( Brassica juncea ) leaves by the exogenous applications of CA ( Godoy et al., 2021 ). 4. Secondary metabolites and their response to stress Secondary metabolites are crucial for defense mechanisms against biotic and abiotic factors. In higher plants, various kinds of secondary metabolites are produced from primary metabolic pathways, like the shikimate pathway, the pentose phosphate pathway, the CAC cycle, amino acid pathways, and glycolysis. The three main categories of secondary metabolites that plants produce are terpenes (terpenoids or isoprenoids), phenolic compounds (lignins, flavonoids/isoflavonoids, and tannins), and compounds that contain sulfur or nitrogen, like glucosinolates and alkaloids, respectively ( Amiripour et al., 2021 ; Ali et al., 2024 ; Sabir et al., 2012 ; Yadav et al., 2014 ; Zandalinas et al., 2022 ). Aromatic amino acids such as tyrosine, phenylalanine, and tryptophan are precursors for the synthesis of secondary metabolites ( Table 2 ). A variety of secondary metabolites from plants are crucial to the production of high food quality (taste, color, and smell), flavors, and odors and are used to produce, dyes, and pesticides. Under stressful conditions, plants use secondary metabolites, such as different signal molecules, which collectively play an important role in alleviating plant stress ( Table 2 ). Abiotic stressors were found to cause phenyl amide formation and a significant buildup of polyamines in tobacco and beans, indicating a potential antioxidant role. The formation of anthocyanins is also triggered by a variety of environmental stressors, like UV, intense light, wounding, drought, blue light, pathogen attack, and deficiencies in sugar and nutrients ( Table 2 ). The amount of secondary metabolite products depends on the environmental conditions and other entities, like the transcriptome and proteome, which affect various metabolic pathways that regulate the other metabolite production. TABLE 2. Regulation of secondary metabolites under abiotic stress in plants. Abiotic stress Metabolite class Plant Pathway/compound type Metabolite change References Drought Terpenes Maize ( Zea mays ) Sesquiterpene (β-caryophyllene) Significant increase in volatile emissions under drought stress Vaughan et al. (2018) Water deficit Terpenes Grapevine ( Vitis vinifera ) Monoterpenes (linalool and geraniol) Increased terpene concentration in berries Brillante et al. (2018) Water deficit Terpenes Lavender ( Lavandula spp.) Monoterpenes (1,8-cineole and linalyl acetate) Increased essential oil content Zheljazkov et al. (2014) High light/temperature Terpenes Maritime pine ( Pinus pinaster ) Monoterpenes (α-pinene and β-pinene) Increased emission under combined stress Loreto and Schnitzler (2010) Heat stress Terpenes Tomato ( Solanum lycopersicum ) Hemiterpene (isoprene) Induced emission correlating with thermotolerance Velikova et al. (2011) Salinity Terpenes Ginkgo biloba Diterpenes (ginkgolides) Increased accumulation in leaves Shan and Jin (2025) UV-B radiation Terpenes Oregano ( Origanum vulgare ) Monoterpenes (carvacrol and thymol) Increased concentration as a photoprotective response Ali et al. (2024) Water stress Terpenes Rosemary ( Rosmarinus officinalis ) Monoterpenes (α-pinene and 1,8-cineole) Increased concentration in leaves Sarmoum et al. (2019) Drought Terpenes Sweet wormwood ( Artemisia annua ) Sesquiterpene lactone (artemisinin) Enhanced accumulation under drought Yadav et al. (2014) Heat stress Terpenes Peppermint ( Mentha × piperita ) Monoterpenes (menthol and menthone) Altered ratio and total yield Chrysargyris et al. (2016) Drought Terpenes Lemongrass ( Cymbopogon spp.) Monoterpenes (citral, geraniol, and geranyl acetate) Enhanced accumulation under drought conditions Sangwan et al. (1994) Cold stress Terpenes Tea ( Camellia sinensis ) Sesquiterpene ((E)-nerolidol) Increased volatile emission under cold Yang et al. (2018) Salinity Terpenes Sweet basil ( Ocimum basilicum ) Phenylpropene/monoterpene Increased essential oil yield Caliskan et al. (2017) Drought Terpenes Qinghasu ( Artemisia annua ) Mono- and sesquiterpenoids Enhanced terpenoid content Yadav et al. (2017) Ozone stress Terpenes Norway spruce ( Picea abies ) Hemiterpene (isoprene) Induced emission as an antioxidant response Kivimäenpää et al. (2022) Waterlogging Terpenes Lemon ( Citrus × limon ) Monoterpene (limonene) Accumulation in roots with a protective role González-Mendoza et al. (2018) Heavy metal (Cd) Terpenes Castor bean ( Ricinus communis ) Diterpene derivative (ricinoleic acid) Altered biosynthesis in seeds Dubey et al. (2020) Water deficit Terpenes Geranium ( Pelargonium graveolens ) Monoterpenes (citronellol and geraniol) Increased essential oil percentage Amiri et al. (2015) Drought Terpenes Turmeric ( Curcuma longa ) Sesquiterpene (ar-turmerone) Elevated concentration in rhizomes Bharadwaj et al. (2024) High temperature and CO 2 Terpenes Eucalyptus spp. Monoterpene (1,8-cineole) Interactive effects on emission rates Singh (2024) UV-C radiation Terpenes Rice ( Oryza sativa ) Diterpenes (phytocassanes and momilactones) De novo synthesis of phytoalexins Kariya et al. (2024) Ozone stress Terpenes Tobacco ( Nicotiana tabacum ) Sesquiterpenes (duvatrienes) Induced emission under oxidative stress Zeng et al. (2021) High light intensity Terpenes Sage ( Salvia officinalis ) Monoterpenes (thujone and camphor) Increased synthesis and accumulation Hazrati et al. (2021) Salinity Terpenes Coriander ( Coriandrum sativum ) Monoterpene (linalool) Increased content in leaves and seeds Amiripour et al. (2021) Cold stress Terpenes Patchouli ( Pogostemon cablin ) Sesquiterpene (patchouli alcohol) Reduced yield but altered enzyme activity Zhang et al. (2023) Mechanical wounding Terpenes Japanese cedar ( Cryptomeria japonica ) Sesquiterpenes (α-cedrene and cedrol) Rapid induced emission Kivimäenpää et al. (2024) Drought Terpenes Thyme ( Thymus vulgaris ) Monoterpenes (thymol and γ-terpinene) Increased proportion of thymol chemotype Hashemifar et al. (2025) Heat stress Terpenes Cannabis ( Cannabis sativa ) Mono- and sesquiterpenes Increased total terpene concentration Yeshi et al. (2022) Nutrient deficiency (P) Terpenes Sunflower ( Helianthus annuus ) Sesquiterpene lactones Increased concentration in leaves Kusvuran (2021) Mechanical damage Terpenes Giant fir ( Abies grandis ) Monoterpenes (α-pinene and β-phellandrene) Induced systemic emission López-Villamor et al. (2021) UV-B radiation Terpenes Lemon balm ( Melissa officinalis ) Monoterpenes (citronellal and geranial) Increased essential oil yield and antioxidant activity Akram et al. (2024) Salinity Terpenes Neem ( Azadirachta indica ) Triterpenoid (azadirachtin) Upregulation of biosynthetic genes Bhambhani et al. (2017) Boron deficiency Terpenes Black spruce ( Picea mariana ) Monoterpene (bornyl acetate) Altered terpene synthase activity Shelyakin et al. (2022) Waterlogging Terpenes Jasmine ( Jasminum spp.) Mono- and sesquiterpenes Altered floral volatile profile Ali et al. (2024) Heavy metal (Cu) Terpenes Chili pepper ( Capsicum annuum ) Sesquiterpene phytoalexin (capsidiol) Induced synthesis under copper stress Zeng et al. (2021) Water stress Terpenes Valerian ( Valeriana officinalis ) Sesquiterpene (valerenic acid) Altered biomass and essential oil composition Mustafavi et al. (2016) Heavy metal (Cd) Steroids Ashwagandha ( Withania somnifera ) Steroids (withanolides and sterols) Induced accumulation Mishra et al. (2017) , Mishra et al. (2024) Salinity Steroids Ashwagandha ( Withania somnifera ) Steroids (withanolides) Increased accumulation under in vitro salt stress Sabir et al. (2012) Cold stress Steroids Arabidopsis thaliana Triterpenes (sitosterol and campesterol) Altered membrane sterol composition under chilling Feng et al. (2024) Drought Saponins Alfalfa ( Medicago sativa ) Triterpenoid saponin pathway Enhanced root growth, water uptake, and ROS reduction Zhang and Shi (2018) Drought Anthocyanins Maize ( Zea mays ) Anthocyanin biosynthesis Reduced oxidative stress and improved osmotic adjustment Seleiman et al. (2021) Heat stress Carotenoids Tomato ( Lycopersicon esculentum ) Carotenoid biosynthesis Stabilization of membranes and photosynthetic protection Spicher et al. (2016) Drought Carotenoids Maize ( Zea mays ); tomato ( Solanum lycopersicum ) Carotenoid biosynthesis; ABA biosynthesis pathway Increased lutein and β-carotene for ROS scavenging and membrane stability; elevated ABA levels regulating stomatal closure and water conservation Xiang et al. (2019) , Rocha Júnior et al. (2023) Salinity Carotenoids Rice ( Oryza sativa ) Xanthophyll cycle activation Enhanced zeaxanthin and violaxanthin levels dissipate excess light energy and reduce photodamage Chauhan et al. (2023) Heat stress Carotenoids Chickpea ( Cicer arietinum ) Carotenoid biosynthesis Enhanced zeaxanthin accumulation mitigates thermal damage to photosynthetic machinery Kumar et al. (2020) Cold stress Carotenoids Arabidopsis thaliana ABA biosynthesis via NCED Increased ABA levels enhance cold stress tolerance and ROS scavenging Qian et al. (2024) Oxidative stress Carotenoids Tobacco ( Nicotiana tabacum ) Carotenoid biosynthesis Increased carotenoids (lycopene and lutein) contribute to ROS neutralization Rasmus and Kozłowska et al. (2023) Salinity Tannins Grape ( Vitis vinifera ) Phenylpropanoid pathway Improved water retention and reduced ion toxicity Siemińska-Kuczer et al. (2022) Heavy metals Alkaloids (Nicotine) Tobacco ( Nicotiana tabacum ) Alkaloid biosynthesis Chelation of heavy metals reduces bioavailability Selwal et al. (2023) Drought Alkaloids Periwinkle ( Catharanthus roseus ) Terpenoid indole alkaloid (TIA) pathway Enhanced vindoline and catharanthine production to counteract ROS and maintain cellular integrity Nishanth et al. (2018) Salinity Alkaloids Barley ( Hordeum vulgare ) Gramine biosynthesis Increased gramine accumulation conferring protection against oxidative stress Kotzamani et al. (2021) Cold stress Alkaloids Coffee ( Coffea arabica ) Purine alkaloid (caffeine) biosynthesis Increased caffeine accumulation stabilizes membranes and prevents oxidative damage Acidri et al. (2020) Waterlogging Alkaloids Rice ( Oryza sativa ) Indole alkaloid biosynthesis Increased serotonin levels mitigate hypoxia-induced oxidative stress Liu et al. (2023) Oxidative stress Alkaloids Potato ( Solanum tuberosum ) Glycoalkaloid biosynthesis Increased solanine and chaconine production to neutralize ROS and stabilize cellular components Rainio et al. (2020) Drought Phenolics Wheat ( Triticum aestivum ); olive ( Olea europaea ) Phenylpropanoid pathway; flavonoid biosynthesis Increased phenolic acids (ferulic acid and p -coumaric acid) for ROS scavenging and membrane stabilization; enhanced flavonoids (quercetin and luteolin derivatives) protect against oxidative damage Laddomada et al. (2021) , Rao et al. (2025) Salinity Phenolics Rice ( Oryza sativa ); tomato ( Solanum lycopersicum ) Lignin biosynthesis via the phenylpropanoid pathway; flavonoid biosynthesis and phenolic acid pathways Increased lignin deposition strengthens cell walls and reduces ion permeability; enhanced chlorogenic acid and rutin levels for ROS detoxification Jia et al. (2022) Heat stress Phenolics Pinellia ternata Flavonoid pathways Inhibition of chlorogenic acid, pelargonidin, cyanidin, and epigallocatechin biosynthesis under heat stress Guo et al. (2023) Oxidative stress Phenolics Soybean ( Glycine max ) Phenolic acid and flavonoid biosynthesis Accumulation of isoflavones (e.g., genistein) and phenolic acids for ROS scavenging Król-Grzymała and Amarowicz et al. (2020) Drought Lignin Wheat ( Triticum aestivum ) Phenylpropanoid–lignin pathway Strengthened cell walls prevent water loss Liu et al. (2018) Heavy metals Polyphenols Sunflower ( Helianthus annuus ) Polyphenol metabolism Chelation of heavy metals and mitigation of oxidative damage Saidi et al. (2021) Open in a new tab 4.1. Terpenoids 4.1.1. Regulation of terpenoid biosynthesis in response to abiotic stress in plants Terpenes are the largest and most structurally diverse group of natural plant products, consisting of more than 65,000 known compounds ( Xavier et al., 2023 ). They play key ecological roles: They shield plants from herbivores and lure pollinators. Terpenes are derived from the five-carbon isotopic precursors isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP), which are produced by two independent pathways, the mevalonate (MVA) pathway and the 2-C-methylerythritol-4-phosphate (MEP) pathway. Following the MVA pathway, farnesyl diphosphate (FPP, C15) is a hub metabolite in the formation of sesquiterpenes, triterpenes, and sterols. In contrast, the MEP pathway generates IPP and DMAPP from pyruvate and glyceraldehyde-3-phosphate, which are condensed in the final common step by a geranyl diphosphate synthase (GPS) or a geranylgeranyl diphosphate synthase (GGPPS) to form precursor monophosphates for mono-, di-, or tetraterpenes: GPP; C10 terpenes: GGPP; C20 terpenes ( Couillaud et al., 2021 ; Shang et al., 2024 ; Sun et al., 2023 ). Subsequently, terpene synthases (TPSs) catalyze the cyclization of these intermediates, providing a wide variety of terpenoid skeletons, determining the structural and functional diversity of terpenes ( Tholl et al., 2023 ). Terpenoids, including monoterpenes, sesquiterpenes, and diterpenes, are important plant secondary metabolites produced from isoprene units. They are synthesized in different plant secretory organs and are involved in diverse aspects of the relationship between plants and the environment, especially under abiotic stress ( Saini et al., 2025 ; Semmar, 2024 ). As natural antioxidants and signal molecules, terpenoids can attenuate oxidative stress through scavenging ROS and increasing the activity of antioxidant enzymes ( Sangwan et al., 2024 ). As a result of their natural properties, the emissions are highly dependent on environmental conditions such as temperature, humidity, and solar insolation. Apart from abiotic stress alleviation, volatile terpenoids are involved in ecological signaling and antifeedant/anti-invasive compounds that contribute to the general stress tolerance and surge capacity of plants ( Ninkuu et al., 2021 ). Terpenoids play crucial roles in plant acclimation to abiotic stress, acting as antioxidants and signaling molecules to maintain cellular homeostasis and synchronize stress responses ( Li et al., 2023 ). The compounds work by interfusing environmental perception, metabolic control, and cell protection to promote plant resistance to stresses such as heat, drought, and oxidative damage ( Li et al., 2023 ; Garcia-Caparros et al., 2021 ; Chen Z. et al., 2022 ). Future research efforts should focus on hormonal cross talk, combined stress interactions, and severe chemical diversity found within terpenoids to reveal new networks of regulation and prompts in a few or metabolically engineered/selectively bred stress-tolerant crop varieties to grow under expected desertification due to climate change ( Rosenkranz et al., 2021 ). 4.1.2. Regulatory factors influencing terpenoid biosynthesis The control of volatile terpenoid formation in plants is coordinated by interactions among key enzymes, transcription factors, and plant hormones ( Rosenkranz et al., 2021 ). HMGR (3-hydroxy-3-methylglutaryl-CoA reductase), DXS (1-deoxy-D-xylulose-5-phosphate synthase), and DXR (1-deoxy-D-xylulose-5-phosphate reductoisomerase) are important to regulate metabolic fluxes in terpenoid biosynthesis ( Jadaun et al., 2017 ; Mishra et al., 2017 ). Their action affects the rate of precursor generation, which ultimately dictates terpenoid productivity ( Noushahi et al., 2022 ). The MYB, bHLH, and WRKY transcription factors have been shown to regulate the biosynthetic genes in response to environmental signals ( Jadaun et al., 2020 ). In addition, plant hormones, including jasmonic acid (JA), ethylene (ET), and abscisic acid (ABA), participate in the signaling networks that modulate terpenoid biosynthesis ( Lv et al., 2021 ). Among these, JA is the most significant because it mediates the expression of biosynthetic genes as well as terpenoid accumulation under abiotic and biotic stress conditions to contribute to plant tolerance or response against the stresses ( Li Y. et al., 2024 ; Lv et al., 2021 ). The regulation of terpenoid biosynthesis in plants in response to abiotic stress is a complex network, which includes the early signaling stress events, the interplay with hormones, transcriptional/epigenetic control, and post-translational modifications of its metabolic enzymes ( Mishra et al., 2024 ) ( Figures 4 , 5 ). FIGURE 4. Open in a new tab Abiotic stress perception and signal transduction leading to stress-responsive gene activation in plants. ROS, reactive oxygen species; CDK, cyclin dependent kinase; CDPK, calcium-dependent protein kinase; COR, cold responsive; MAPK, mitogen-activated protein kinase; SOS, salt overlay stress; ICE, inducer of C-repeat binding factor (CBF) expression; PLD, phospholipase D; CDKR, cyclin-dependent kinase regulatory; CaM, calmodulin; CaCmpk, Ca calmodulin-dependent protein kinases; EREBPs, ethylene-responsive element-binding proteins; ABA, abscisic acid; AP2EREBP, AP2(APETALA 2); NAC, NAM, ATAF1/2, and CUC2 (No Apical Meristem, Arabidopsis Transcription Activation Factor, and Cup-Shaped Cotyledon); bZIP, basic leucine zipper domain; LEA, late embryogenesis abundant; PLC, phospholipase C. FIGURE 5. Open in a new tab (a) Overview of terpene biosynthetic pathways in plants. (b) Transcriptional regulation and signaling of terpene synthesis pathways under abiotic stress. 4.1.3. Biosynthetic pathways and key enzymes Most terpenes are biosynthesized via two independent metabolic routes: the mevalonate (MVA) pathway and the 2-C-methyl-D-erythritol-4-phosphate (MEP) pathway ( Noushahi et al., 2022 ; Singh et al., 2023 ). The MVA pathway produces precursors for sesquiterpenes and triterpenes in the cytosol, while the MEP pathway works in plastids and makes a precursor for monoterpenes, diterpenes, and tetraterpenes ( Singh et al., 2023 ). Key regulatory enzymes in this pathway are 1-deoxy-D-xylulose 5-phosphate synthase (DXS) and 1-deoxy-D-xylulose 5-phosphate reductoisomerase (DXR) of the MEP pathway and 3-hydroxy-3-methylglutaryl-CoA reductase (HMGR) of the MVA pathway ( El Amerany, 2024 ). Moreover, several isoforms of terpene synthase (TPS) are involved in the diversification of terpenoid skeleton ( Singh et al., 2023 ). The accumulation and activities of these cascaded products are tightly regulated during abiotic stress, typically increasing the production of defensive terpenoids, which contribute to stress tolerance and defense ( EI Amerany et al., 2024 ). 4.1.4. Defensive functions of terpenoids against abiotic stress Terpenoids serve as a dual dynamic modulator of plant defense in response to abiotic stress, both protecting plants from oxidative damage and strengthening systemic stress resistance ( Holopainen et al., 2025 ). In addition, volatile terpenoids, including isoprene, monoterpenes, and sesquiterpenes, can efficiently scavenge highly reactive oxygen species (ROS) such as hydroxyl radicals (OH) and singlet oxygen, which interfere with lipid peroxidation, protein oxidation, and chloroplast membrane destruction. This antioxidative role keeps the efficiency of photosynthesis, especially under heat and oxidative stress ( Holopainen et al., 2025 ; He et al., 2023 ). In addition, isoprene reacts with O 3 in the atmosphere, depleting it within mesophyll tissues and preventing harmful reactive nitrogen species, which may lower oxidative load ( He et al., 2023 ). In addition, isoprene-emitting plants often have high reduced ascorbate levels, suggesting that, because of its direct scavenging ability, they rely less on enzymatic antioxidants ( Srikanth et al., 2024 ). Isoprene oxidation products serve as secondary signals that induce the expression of antioxidant defense genes and systemic protectives ( Ahmad et al., 2023 ). Volatiles are airborne chemical signals that prepare a defense at the local and systemic (neighboring plants or tissues) levels, achieving systemic stress tolerance. Increased emission of these substances under moderate drought or heat treatment also leads to better physiological resistance, such as rooting and drought tolerance, in plant species such as Salvia nemorosa and Torreya grandis ( Papafotiou et al., 2021 ; Zhang B. et al., 2025 ). Together, these mechanisms highlight the multiple protective and signaling roles of terpenoids as biochemical weapons and chemical mediators in plant response to abiotic stress. 4.1.5. Terpene emission and plant responses Strong relationships between environmental conditions and isoprene emission in plants have been demonstrated by recent study ( Malone et al., 2023 ). Isoprene emission exhibits strong sensitivity to water stress, temperature, and light intensity; temperature and light are the near-determining factors for the rate and the diurnal pattern of emission via their impact on the photosynthetic performance during active metabolism and precursor availability ( Zhang et al., 2022a ). Temperature has a large impact on post-illumination isoprene emissions of oak and poplar, suggesting that the synthesis and the emission of isoprene are controlled by thermal conditions upon light exposure ( Furnell et al., 2024 ). A thorough mechanistic investigation of the temperature response of isoprene emission explained its variation by substrate (e.g., DMAPP) availability and enzyme activity within the MEP pathway was reported by Souza et al. (2025) . Together, these studies demonstrate that isoprene release is a tightly regulated physiological response driven by numerous environmental and metabolic cues contributing to overall plant stress adaptation and thermal profile. 4.1.6. Early stress signaling and cascade activation Abiotic stress induces prompt production of ROS such as superoxide anions, hydrogen peroxide, and hydroxyl radicals mainly due to photosynthetic and mitochondrial perturbations ( Sachdev et al., 2021 ). Excess ROS lead to oxidative damage, but the same species also serve as signaling molecules. In parallel, stress generates transient cytosolic calcium influxes (Ca 2+ flux), known as calcium bursts. ROS and Ca 2+ signals construct a positive (self-sustaining) loop: ROS activates inward-rectifying Ca 2+ -permeable channels (IMCC) to promote Ca 2+ influx, while Ca 2+ triggers NADPH oxidases that produce ROS. This primary chain of events involves a quick transmission of signals that are transmitted within and among the plant cells, evoking secondary hormonal and gene expression responses ( Hendricks et al., 2021 ). 4.1.7. The role of monoterpenes and sesquiterpenes in plant defense Monoterpenes and sesquiterpenes play a critical role in improving the antioxidant capacity of plants under different abiotic stress conditions. Monoterpenes (including α-pinene and β-pinene) boost the thermal stability of the photosynthetic apparatus, which can help to sustain efficient photosynthesis under heat stress ( El Amerany, 2024 ). In addition, these volatile compounds could react directly with ozone (O 3 ), thus decreasing mesophyll oxidative damage and secondary reactive species production ( Singh et al., 2023 ). Because of their greater hydrophobicity, sesquiterpenes intercalate into cellular lipid bilayers and help to maintain membrane integrity by reducing the lipid peroxidation. Both monoterpenes and sesquiterpenes have potent antioxidant effects and cross talk with stress signaling pathways, leading to redox homeostasis and better physiological status during environmental stresses ( Shan and Jin, 2025 ) ( Figure 4 ). As substantiated by several reports, volatile terpenoids form a first line of defense and protect plants from stress. Research showed that isoprene emissions in Platanus orientalis have a protective role against high temperature stress by preventing cell membrane damage and photochemical efficiency ( Bertamini et al., 2019 ). Similarly, monoterpene emissions in Cinnamomum camphora play a crucial role in thermotolerance, implying the photoprotective properties of monoterpenes against heat stress ( Zuo et al., 2017 ). These findings in combination highlight the complexity of volatile terpenoids for improved plant resilience. 4.2. Phenolic compounds The phenol group is a secondary product produced by plants, a chemically heterogeneous hydroxyl functional group on an aromatic ring ( Lattanzio, 2013 ). It is an important parameter regulated in plant defense mechanisms against stress conditions ( Table 2 ). Phenolics mitigate oxidative stress due to their antioxidant properties. Phenolic compounds are synthesized through the phenylpropanoid and shikimic acid pathway. Different stress conditions change the phenolic metabolites and affect metabolic pathways, as shown in Table 2 . Naringenin is a flavanone intermediate that is widely found in plants and is the source of isoflavonoids ( Shen et al., 2025 ). It is vital for plant growth and defensive response. Production of these flavonoids appears to be an effective defense against ROS. This may be because the phenolic compounds, which may serve as substrates for other peroxidases, are the initial line of defense against environmental stress. The biosynthesis of flavonoids is a well-established pathway for the production of natural dietary antioxidants ( Muthusamy and Lee, 2024 ). Phenylalanine is involved in the generation of flavonoids via the shikimate pathway. 4.3. Alkaloids Approximately 20% of vascular plant species have a large family of nitrogen-containing secondary metabolites, which are most commonly found in herbaceous dicots and less commonly found in a few gymnosperms and monocots ( Muthusamy and Lee, 2024 ; Chen D. et al., 2022 ). Alkaloids are synthesized from amino acids, such as aspartic acid, tyrosine, lysine, and tryptophan. Alkaloids defend plants against heavy metal toxicity and salinity by changing ion channels, which aids them to tolerate the stress ( Table 2 ). Subgroups of alkaloids, which include isoquinoline, indole, purine, tropane, imidazole, phenylethylamine, and terpenoids, can be distinguished by their basic heterocyclic nucleus ( Jadaun et al., 2017 ; Muthusamy and Lee, 2024 ). 4.4. Carotenoids The development and defense of a variety of biological processes in plants, like the formation of photosystems, the light-harvesting antenna complexes for photosynthesis and photoprotection, and the regulation of growth and development, depend on carotenoids and on apocarotenoids ( Muthusamy and Lee, 2024 ). According to Sun et al. (2022) , carotenoids and their products are necessary for the generation of phytohormones like strigolactones (SLs) and abscisic acid (ABA), which act as signaling molecules mediating developmental and environmental responses. Carotenoids are classified into two categories: unsaturated carotenes (C40 hydrocarbons) and their oxygenated derivatives known as xanthophylls. Apocarotenoids mediate signaling and the connections between the environment and the plant under stress conditions. Exposure to environmental stress changes the plant carotenoid composition, similar to other metabolites ( Table 2 ). 5. Impact of abiotic stress on secondary metabolites Plant secondary metabolites (PSMs) are synthesized and accumulated in response to environmental factors and have important roles in plant adaptation to abiotic stresses ( Khare et al., 2020 ). Alterations in growth conditions and imposition of stressful environments like drought, salinity, temperature extremes, and heavy metals are known to induce changes in the metabolic pathways and subsequently increase production of different PSMs ( Yeshi et al., 2022 ). These molecules, including phenolics, flavonoids, alkaloids, and terpenes, act as antioxidants ( Table 2 ), osmoprotectants, or signaling molecules in plant systems, where they protect plants from oxidative stress and maintain cellular homeostasis. Therefore, the formation of PSM is frequently induced by abiotic stress as an important adaptive and defensive reaction ( Yeshi et al., 2022 ). 5.1. Effect of heavy metals on the production of secondary metabolites Plants have been observed to be impacted by the heavy metal stress. It has been reported that heavy metal exposure alters the biosynthesis of photosynthetic pigments, sugars, proteins, and nonprotein thiols. These metals can alter secondary metabolism pathways and, consequently, the production of bioactive substances. Silver, europium, lanthanum, cadmium, and oxalates are some of the metal ions that regulate secondary metabolite biosynthesis. Nickel, an essential element for the urease enzyme, plays an important role in plant growth and metabolism ( Gupta and Pathak, 2025 ). Additionally, copper (Cu 2+ ) and cadmium (Cd 2+ ) have been shown to increase the accumulation of shikonin-like metabolites ( Ghazagh et al., 2023 ). The analysis of Hypericum perforatum under cadmium stress showed an increase in phenolic acids such as ferulic acid, but flavonoids such as epicatechin and procyanidin were found to be decreased in roots and shoots ( Giebułtowicz et al., 2024 ). The level of activity of phenylalanine ammonia-lyase (PAL), the first enzyme in the phenylpropanoid pathway, was positively related to heavy metal accumulation, indicating that genes encoding PAL and related enzymes are upregulated by metal stress ( González-Mendoza et al., 2018 ). To protect themselves under heavy metal stress, plants produce phenolic acids, which work as antioxidants and chelating compounds without moving to the synthesis of flavonoid or anthocyanin. For instance, Matricaria chamomilla roots overproduce phenolics to chelate cadmium and decrease its toxicity ( Kolodziejczyk-Czepas et al., 2015 ). There is a fine balance between oxidants and antioxidants in plants that prevents the overaccumulation of ROS. Phenolic and terpenoid secondary metabolites represent important antioxidants for the scavenging of ROS to prevent oxidative damage ( Saleem et al., 2022 ). The radicals are stabilized by the antioxidant activity of flavonoids due to their hydroxyl (–OH) groups. Soluble phenolic content was stimulated by cadmium in Populus × canescens , with particular accumulation recorded in bark tissues, suggesting organ-specific metabolic response of the proanthocyanidin pathway to heavy metal stress ( Kebert et al., 2022 ). These results support the adaptive significance of secondary metabolites against heavy metal detoxification and oxidative stress ( Sharma et al., 2025 ). 5.2. Effect of temperature stress on secondary metabolite production Temperature is an important abiotic factor that affects plant growth, metabolism, and the synthesis of secondary metabolites. Extreme heat and cold both induce stress, which can change physiological, biochemical, and molecular mechanisms in plants ( Qaderi et al., 2023 ). Heat stress-induced leaf senescence and decreased photosynthesis efficiency, which can result in limited CO 2 assimilation, resulting in a negative impact on the growth and yield production. It also involves secondary metabolism: some plants produce more secondary metabolites (SM) in response to heat stress, while others produce fewer. For example , Panax quinquefolius displays greater amounts of ginsenosides in increased temperature, while other species demonstrate reduced SM content ( Rehman et al., 2024 ). Elevated temperatures favor the production of osmolytes, phenolic compounds, flavonoids, and sugars, which are involved in osmoregulation, membrane stabilization, and ROS scavenging ( Upadhyay and Srivastava, 2019 ; Rao et al., 2025 ). Heat shock proteins (HSPs) and compatible solutes, such as glycine betaine and γ-aminobutyric acid (GABA), are important in protecting cellular components and conferring thermotolerance (Zhang et al., 2022c). Cold stress is harmful, particularly in temperate plants, suppressing metabolism, impeding water exchange, and causing desiccation ( Manasa et al., 2022 ). To survive freezing stress, plants encounter cold acclimation, which results in the accumulation of cryoprotective molecules such as sugars, sugar alcohols, and nitrogenous compounds ( Thakur and Nayyar, 2012 ). These agents protect membranes and proteins, thus minimizing freezing damage ( Feng et al., 2025 ). Cold stress contributes to phenolic metabolism and the accumulation of phenolic acids and flavonoids, which can serve as antioxidants and strengthen cell walls through lignin synthesis and suberin deposition ( Hassan et al., 2021 ). The phenolic acid and isoflavonoid levels were increased by cold treatment in soybean roots, while Capsicum annuum decreased photosynthesis and growth under freezing temperatures ( Qian et al., 2024 ). Polyamines such as putrescine and spermidine are crucial in cold resistance ( Wang Y. et al., 2024 ). Melatonin attenuates cold-induced apoptosis by modulating polyamine synthesis. Cold stress, even in the absence of freezing, disturbs photosystems and increases ROS concentration ( Rahman et al., 2024 ). Plants acclimate by adjusting hormones such as abscisic acid (ABA) and increasing cold-regulated genes to improve resistance ( Raza et al., 2023 ). Both high and low temperature extremes greatly influence secondary metabolism by inducing the production of stress protective metabolites that help plants to survive and acclimatize in temperature-intensive environments ( Table 2 ). 5.3. Effect of salt stress on secondary metabolite production Salt stress is an important abiotic factor in restricting plant growth, yield, and formation of bioactive compounds ( Toscano et al., 2019 ). This disrupted cell homeostasis results in lowered photosynthetic rate, compromised CO 2 fixation, and reduced cell enlargement ( Noman et al., 2018 ). Plants respond to these stresses by synthesizing secondary metabolites, including phenolics, flavonoids, and polyamines, as antioxidants and osmoprotectants ( Saini et al., 2024 ; Ahlawat et al., 2024 ). The aggregations of phenolics when the plant is subjected to salt stress; the aggregations contribute to ROS scavenging and redox balance at the same time ( Saleem et al., 2022 ). Changes in polyamine metabolism due to salinity stress have been reported as part of plant involvement in stress adaptation ( Choudhary et al., 2023 ). The effects of various salts differ: potassium chloride (KCl) treatments led to increased phenolic and flavonoid contents in the leaves of Cynara cardunculus more than sodium or calcium chloride treatments ( Lucini et al., 2016 ). Worldwide, saline soils are a threat to almost 20% of the total irrigated land, which is indeed a major constraint to food production. Plants produce compatible solutes like glycine betaine, trehalose, mannitol, and sucrose to counter the osmotic imbalance, while enzymes such as superoxide dismutase (SOD), catalase (CAT), and ascorbate peroxidase (APX) together with antioxidants like ascorbate (ASC), glutathione (GSH), and carotenoids are activated to protect the cellular components defending against salinity ( Yang et al., 2021 ). 5.4. Effect of drought stress on secondary metabolite biosynthesis Drought stress is one of the most important abiotic factors limiting plant growth, productivity, and survival. Proteins, lipids, carbohydrates, and nucleic acids are irreversibly damaged by it, and the plant height, root system, and leaf area are disturbed ( Ghasemi et al., 2023 ). Physiologically, drought reduces osmotic potential, stomatal conductance, photosynthesis, and transpiration rate, resulting in a reduction of yield that is a major threat to global food security. As a means of coping, plants use morphological, biochemical, and physiological strategies that contribute to drought tolerance ( Ahmad et al., 2023 ). Plants under drought commonly synthesize secondary metabolites (SMs), including flavonoids, phenolic acids, and essential oils, to protect themselves from oxidative stress ( Ahmad et al., 2023 ). Examples include the higher content of flavonoids and phenolics in leaves and increased total phenolics (TP) in Salix (willow) under drought and increased total flavonoids in Glechoma longituba . In the same context, under drought stress, A. annua was reported to produce the most effective SMs under drought conditions ( Jia et al., 2020 ; Yadav et al., 2014 ; Wang R. et al., 2024 ). In contrast, some metabolites, such as saponins in Chenopodium quinoa , decreased under severe water deficit ( Huan et al., 2022 ). To alleviate oxidative damage induced by drought, plants accumulate osmoprotectants (such as proline, glycine betaine, sorbitol, and sucrose) to ameliorate osmotic effects and stabilize proteins and membranes. Enzyme antioxidants, including SOD, CAT, and peroxidases, scavenge superoxide, hydrogen peroxide, and other reactive oxygen species ( Ighodaro and Akinloye, 2018 ). Plant hormones such as ABA, salicylic acid, cytokinins, and even GA play a role in stress response that allows the plant to survive reduced water availability while it reroutes its metabolites ( Singh and Roychoudhury, 2023 ). 5.5. Effect of light stress on synthesis of secondary metabolites Light is one of the most important environmental parameters controlling plant growth, development, and secondary metabolite (SM) biosynthesis. Diverse light intensities, wavelengths, and photoperiods greatly affect or stimulate metabolic processes and the synthesis of bioactive compounds ( Wu et al., 2025 ). Zingiber officinale cell cultures showed that synthesis of gingerol and zingerone is increased by light, as are those of zingiberene, with an increase in phenolic accumulation at higher light intensity. In the same way, long light periods increase coumarin content, which decreases during short photoperiods ( Gong et al., 2023 ). In P. quinquefolius (American ginseng), ginsenoside concentration in roots is higher when the plant has been exposed to sunlight for a longer period of time ( Liu et al., 2022 ). Metabolite production is influenced by the quality of light to which plants are exposed. Blue light has been reported as being most effective in promoting the production of SMs in Scutellaria lateriflora , and synergistic effects of red/blue LED have been observed for metabolite synthesis in Peucedanum japonicum ( Costine et al., 2022 ). Biomass and SM accumulation changes with light versus dark incubation in Artemisia absinthium cultures ( Sánchez-Ramos et al., 2022 ). Ultraviolet (UV) light also affects metabolism; UV-B irradiation decreases chlorophyll, but increases flavonoids and phenylalanine ammonia-lyase (PAL) activity. For example, UV light increases root flavonoids in Pisum sativum and rutin and quercetin content in Fagopyrum esculentum ( Innes et al., 2019 ). In addition, photo-periodic changes modulate the endogenous indoleamines, including serotonin and melatonin, in algae. Light, in general, is an energy source and signal regulator affecting primary and secondary metabolism, such as flavonoids, anthocyanins, and artemisinin ( Innes et al., 2019 ; Thakur and Sharma, 2024 ). 6. Transcriptional regulation of major secondary metabolic pathways Transcription factors (TFs) are regarded as the master regulators of plant metabolism. They are attached to the enhancer regions (cis-regulatory elements) in the promoters of their target genes. Depending on this, they either turn on or silence the expression of the specific gene ( Shrestha et al., 2018 ). Among various TF families, the MYB, bHLH, WRKY, AP2/ERF, NAC, bZIP, TCP, and BBX families have been considered as main players in the regulation of secondary metabolism ( Jan et al., 2021 ; Rabeh et al., 2025 ). Recent research in genomics, transcriptomics, and functional genomics has dramatically expanded our knowledge of transcriptional regulation in secondary metabolism ( Zhou et al., 2024 ). Together, genome assemblies with multi-omics analyses allowed the identification of regulatory networks that are involved in the control of the tissue-specific and stress-induced accumulation of metabolites. Transcriptomics and metabolomics in integration decoded the complex regulatory hierarchies and feedback mechanisms of plant metabolism. Several TF families are involved in pathway-specific regulation. R2R3-MYB proteins act as important regulators in controlling the biosynthesis of flavonoid and phenylpropanoid pathways ( Ma et al., 2018 ). In the biosynthesis of anthocyanins, MYB factors like PAP1, PAP2/MYB113, and MYB114 have been reported as strong inducers, while MYBL2 and CPC are repressors, thus allowing fine-tuning of the pathway flux. A comparable specificity is found in the biogenesis of glucosinolates; MYBs regulate genes that encode central enzymes for precursor generation ( Xiao et al., 2021 ). The bHLH family, including the MYC-type regulators, is another major regulatory network. MYC2 and its bHLH relatives are involved in combining jasmonate (JA) signals to induce the expression of terpenoid, sesquiterpene, nicotine, and alkaloid biosynthesis genes ( Martin et al., 2021 ). In Catharanthus roseus , MYC2 acts as a regulator of AP2/ERF TFs in the JA signaling pathway that connects the JA perception with the activation of the terpenoid indole alkaloid (TIA) pathway ( Table 3 ) ( Pan et al., 2019 ). TABLE 3. Major transcription factor families regulating the terpenoid pathway. TF family Reported size/abundance Representative target genes/pathway nodes Reported roles in abiotic stress Key experimental evidence AP2/ERF (DREB/ERF) 187 AP2/ERF-related transcripts identified in W. somnifera HMGR, DXS, DXR, GGPPS, CAS (terpenoid backbone/sterol) Induced by wounding, SA, MeJA, heat, and cold; links stress signals to terpenoid flux WsAP2 overexpression upregulates terpenoid pathway genes; transcriptome mining and functional assays ( Tripathi et al., 2020b ) WDR (WD40-repeat) Most abundant TF family repertoire in leaf/root Developmental regulators interfacing with secondary metabolism Developmental integration: potential modulation of metabolite biosynthesis under stress Overexpression of WsLWD1/WUSCHEL increased withanolide content ( Tripathi et al., 2017 ) WRKY High abundance in the TF family repertoire in leaf/root General phenylpropanoid/terpenoid regulation via JA/SA signaling Canonical stress-responsive TFs; cross talk with JA/SA pathways Transcriptomic prevalence; functional roles in regulation of withanolides ( Tripathi et al., 2017 ) MYB Substantial representation across tissues Phenylpropanoid/flavonoid; interaction with terpenoid genes Light and stress-responsive regulation of secondary metabolism Tissue-specific enrichment in berry transcriptome ( Tripathi et al., 2020a ) bHLH Present in TF repertoire Often partners with MYB; affects terpenoid/phenylpropanoid branches Integration of hormonal and stress cues Transcriptomic presence; inferred roles from related species ( Tripathi et al., 2017 ) MADS/ELI (light-responsive) Enriched in berry vs. leaf/root Developmental and photomorphogenic control impacting downstream metabolism Light/photoperiod coupling with stress responses; potential modulation of terpenoid flux Differential TF distribution in berry transcriptome aligned with secondary metabolism ( Tripathi et al., 2020a ) Open in a new tab AP2/ERF family proteins are essential for JAs-responsive alkaloid biosynthesis ( Tripathi et al., 2020a ). ORCA1, ORCA2, and ORCA3 in C. roseus are rapidly upregulated by elicitors, and they induce early and late TIA pathway genes such as TDC and STR. Likewise, NIC2 locus ERFs are involved in tobacco nicotine synthesis together with bHLH proteins ( Padmanabhan et al., 2025 ). WRKY transcription factors mainly function in defense-related secondary metabolism. They function downstream of MAPK signaling cascades to induce biosynthesis of phytoalexins, such as camalexin and the specialized terpenoids, gossypol, and artemisinin. The ability of these sensors to couple stress sensing with metabolic productivity highlights their importance in inducible secondary metabolite pathways ( Javed and Gao, 2023 ). Other TF families, including NAC, DOF, SQUAMOSA promoter binding-like proteins (SPL), and bZIP proteins, participate in fine-tuning the pathway ( Wang et al., 2025a ; Bu et al., 2025 ; Xiong et al., 2025 ). ANAC042 controls camalexin production, OBP2 (DOF family) modulates the biosynthesis of glucosinolates, and SPL9 represses anthocyanin synthesis through its ability to disrupt complex formation by MYB–bHLH–WD40 ( Qin et al., 2022 ). Alkaloid pathways are also regulated by bZIP proteins via transcriptional repression ( Yamada and Sato, 2021 ). 6.1. Phenylpropanoid and flavonoid pathways The phenylpropanoid pathway and its branch pathways involved in the production of flavonoids, anthocyanins, proanthocyanidins, and lignin are among the most well-known examples in which transcription regulation has been characterized in detail. Ma et al. (2018) established a full understanding of MYB regulation in the metabolism of phenylpropanoids, showing highly specific regulation of particular groups of MYB proteins to specific branches of the pathways ( Ma et al., 2018 ). Flavonoid biosynthesis is regulated through control of the MBW complex, which is made up of R2R3-MYB, bHLH, and WD40 proteins ( Xu et al., 2021 ; Zhang et al., 2019 ). The complex interacts and binds to the promoters of structural genes that encode enzymes like chalcone synthase (CHS), chalcone isomerase (CHI), flavanone 3-hydroxylase (F3H), and dihydroflavonol 4-reductase (DFR) ( Kaur et al., 2020 ). The regulation of this MBW complex is variable and depends on developmental and environmental contexts ( Xu et al., 2021 ). Specific combinations of MYB and bHLH regulate the late biosynthesis genes (LBGs) of the anthocyanin pathway, including dihydroflavonol 4-reductase (DFR), anthocyanidin synthase (ANS), and UDP-glucose:flavonoid 3-O-glucosyltransferase (UGFT) ( Sharma et al., 2024 ). Reports showed anthocyanin pigment regulation in plants and introduced factors, such as light and temperature, regulate biosynthesis through transcription factors such as HY5 ( Zhao et al., 2021 ). In a recent study, the regulation of flavonoid biosynthesis has been viewed as a crucial means of unraveling plant adaptive mechanisms, pointing out that the patterns of flavonoid accumulation are a reflection of genetic programming and/or a response to the environment ( Wang et al., 2025a ). By integrating developmental and environmental information at the level of gene expression, it becomes possible for plants to optimize their flavonoid production ( Wang et al., 2025a ). Recent reports suggested the relevance of tissue-specific control of metabolic pathways by TFs. In Quercus variabilis , the activation of tannin production was reported by the activation of TCP3 TFs ( Wang R. et al., 2024 ). This suggested that TFs can control metabolic pathways in a tissue-specific manner. Similarly, tissue-specific control of phenylphenalenone production by WRKY TFs was reported in Musella lasiocarpa , which showed that TFs have specialized control over metabolic pathways according to plant tissues ( Huang et al., 2025 ). 6.2. Terpenoid biosynthesis Terpenoids, also known as isoprenoids, are the largest class of plant secondary compounds. They have various roles in plants, ranging from stress regulation to defense ( Xavier et al., 2023 ). The regulation of terpenoid biosynthesis by transcription has been well investigated in medicinal plants, where terpenoids are considered the major active compounds ( Huang et al., 2025 ; Tholl et al., 2023 ). Chen et al. (2021) reported that the ERF TF PjERF1 directly binds to the promoters of central genes involved in the biosynthesis of triterpenoid saponins in Panax japonicus and controls the production of these compounds in the plant ( Chen et al., 2021 ). This kind of regulation at multiple points ensures the simultaneous regulation of the expression of genes involved in the biosynthesis of saponins, such as β-amyrin synthase (βAS), cycloartenol synthase (CAS), and squalene epoxidase (SE), which stimulates the production of ginsenosides and saponins, and is an effective strategy in the regulation of a complex biosynthetic pathway ( da Silva Magedans et al., 2021 ). A recent study emphasized the range of regulatory processes present among different terpenoids. The regulation of monoterpene biosynthesis pathways in grape plants, for example, is mediated by bHLH-type TFs to directly induce terpene synthase genes ( Sun et al., 2025 ). Certain bHLH genes were later identified to express proportionally with monoterpene content in grape berries ( Sun et al., 2025 ). Salvia miltiorrhiza , a medicinal herb known for tanshinone (diterpenoid quinone) production, revealed a complex regulation network orchestrated by several families of TF proteins, namely, MYB, bHLH, and WRKY, to control the expression levels of terpene synthases and cytochrome P450s, which participate in tanshinone biosynthesis pathways ( Shao et al., 2025 ). Reviewing complex regulation patterns in secondary metabolism in medicinal plants, researchers explained the complex patterns in terpenoid metabolism regulation in medicinal plants ( Wang et al., 2025a ). Paclitaxel biosynthesis in Taxus chinensis is an example of extremely complex transcription regulation ( Ren et al., 2025 ). A recent study reported that TFs are involved in paclitaxel biosynthesis, in which jasmonic acid-elicited responses trigger massive transcriptional reprogramming. This complexity reflects the nature of taxane biosynthesis, which involves enzymatic steps and cellular compartments ( Song C. et al., 2022 ). 6.3. Alkaloid biosynthesis Alkaloids are nitrogen-containing plant secondary metabolites. These compounds have various biological activities, ranging from drugs to plant defense compounds. Alkaloid biosynthesis is under transcriptional regulation and varies among plant groups ( Faisal et al., 2024 ). Researchers studied the regulatory role of plant TFs in alkaloid biosynthesis ( Godbole et al., 2022 ). The report emphasized the complexity of TF families involved in the process, which include MYB, bHLH, WRKY, AP2/ERF, and bZIP. Various alkaloid biosynthetic pathways follow different modes of regulation based on the individual evolution history and the resulting chemical properties ( Yamada and Sato, 2021 ). Tropane alkaloid biosynthesis, which has received intense scrutiny in Solanaceae species, is controlled by TFs and microRNAs. A recent study discussed TF and microRNA-mediated regulation of tropane alkaloid biosynthesis. This report makes it clear that both transcriptional and post-transcriptional regulation of tropane biosynthesis occur ( Rabeh et al., 2025 ). TF-mediated regulation of biosynthesis is thought to play an important role in controlling tropane alkaloid biosynthesis. Huperzia serrata is a lycophyte that produces the acetylcholinesterase inhibitor huperzine A. A study suggested that 13 coordinately expressed TFs were key regulators in huperzine A biosynthesis. This case study demonstrates that, even though these organisms are non-flowering, there can be a complex transcriptional regulation of alkaloid biosynthesis in such organisms. These key regulators can be used to increase huperzine A production using a metabolic engineering strategy. The biosynthesis of isoflavones in Trifolium pratense (red clover) is controlled by transcriptional regulation that involves isoflavone synthase and other enzymes specific to the biosynthetic pathway ( Cao et al., 2025 ). 6.4. TF complexes of higher orders and network combinations Combinatorial interactions usually amplify the contribution of individual TFs. The MYB–bHLH–WD40 (MBW) complex is a typical case that was identified as a key regulatory module in the biosynthesis of flavonoid and anthocyanin ( Qin et al., 2022 ). Its constitution defines tissue specificity of the pigmentation and developmental control, allowing for plastic pigmentation patterns to be set in motion under the influence of light, sucrose, or hormonal signals. In addition to transcriptional regulation, MBWs are also modulated by miRNAs, siRNA, and protein stability mediated through the ubiquitin–proteasome system ( Liu et al., 2022 ). Family-to-family interactions are also the basis of the alkaloid and terpenoid pathways. The bHLH-mediated cooperation with AP2/ERF TFs enhances the JA-responsive induction of TIA pathways, and the coordinated biosynthesis of phytoalexin is coordinately modulated by WRKY–MAPK modules during pathogen responses ( Tripathi et al., 2017 ). Secondary metabolites, usually induced under biotic or abiotic stress conditions, and TFs are key factors mediating these inducible metabolic responses ( Tripathi et al., 2017 ). Stress-induced TFs induce defense pathways involved in the biosynthesis of secondary metabolites, including nicotine, camalexin, diterpenoid phytoalexins, and artemisinin. TF networks provide an adaptive interface that redirects metabolic flux toward defensive compounds by sensing environmental stress signals and transducing them into biochemical defense outputs. 6.5. Hormonal cross talk and transcription factor activation Hormones such as abscisic acid (ABA), jasmonic acid (JA), salicylic acid (SA), ethylene (ET), auxin, and gibberellins are pivotal in transmitting the stress-induced responses by integrating various stress signals through controlling the expression of specific transcription factors ( Singh and Roychoudhury, 2023 ). Several TF families are specifically engaged in this regulation: MYB regulates sesquiterpene biosynthesis and drought response; WRKY controls defense-related and secondary metabolism genes; bZIP participates in activation of ABA-responsive genes, ABRE-binding factors (ABFs); ERF/AP2 is a player of ethylene signaling for stress acclimation; NAC coordinates JA- and SA-mediated pathway often induced by drought. TFs bind to the promoter regions of terpenoid biosynthesis genes, including terpene synthases (TPSs), geranyl diphosphate synthase (GPPS), farnesyl pyrophosphate synthase (FPPS), and geranylgeranyl diphosphate synthase (GGPPS), affecting the transcription process and modulating metabolites in stress response as well as metabolism ( Srivastava et al., 2022 ). 7. Nanotechnology interfaces with metabolomics: an integrated framework 7.1. Nanomaterials as enablers of advanced metabolomics Nanotechnology is changing metabolomics by detecting metabolites in a very sensitive, spatially resolved, and real-time manner, thus changing our knowledge about cellular metabolism under normal and stressful conditions ( Lin et al., 2025 ; Majumdar and Keller, 2021 ). The center of this collaboration is the application of engineered nanomaterials like gold and silver NPs, quantum dots, carbon nanotubes, graphene, and metal–organic frameworks (MOFs), in metabolomics platforms ( Lin et al., 2025 ). Metabolites have high surface-area-to-volume ratios and tunable surface chemistry; their plasmonic effects and excellent electrical conductivity characteristics enhance their capture, separation, and signal transduction ( Cho et al., 2025 ). Thus, the existing workflows in metabolomics gain much in terms of analytical sensitivity, selectivity, and throughput. 7.2. Nanostructured enhancements in mass spectrometry-based metabolomics In mass spectrometry-based metabolomics, nanostructured matrices, and particularly the ones used in nanoparticle-assisted laser desorption/ionization (nano-LDI), have played an important role in improving analysis performance ( Wang D. et al., 2024 ). The nano-LDI matrices not only provide a significant reduction of chemical background noise compared to traditional organic matrices but also yield better ionization efficiency. They make the sensitive and reproducible detection of low-molecular-weight and low-abundance metabolites, which include sugars, organic acids, amino acids, lipids, and phytohormones, possible ( Jorge et al., 2016 ). These metabolites are often difficult to study using the conventional MALDI methods ( Ponce-Rodríguez et al., 2020 ). In addition, the use of nanostructured coatings and nanoporous materials for the chromatographic columns and solid-phase extraction systems has resulted in enhancing the metabolite enrichment, decreasing the sample complexity, and elevating the analytical depth and reproducibility across the whole metabolomic datasets ( Souza and Fernie, 2024 ). 7.3. Nanoengineered microfluidics and single-cell metabolomics Nanotechnology has led to revolutionary changes in microfluidics and lab-on-a-chip-based metabolomics. Nanofabricated microfluidic platforms conduct rapid, fully automated sample processing with hardly any or unnoticeable metabolite degradation or loss, all the while working with volumes as small as nanoliters and picoliters. These devices provide high precision in fluidic control, low reagent consumption, and fast processing; thus, they are especially appropriate for time-resolved and large-scale metabolomic studies. If connected with mass spectrometry or optical detection systems, microfluidic units can carry out metabolic profiling on extremely small biological samples such as rare cell types, chosen areas within tissues, and even individual cells ( Kumar and Katiyar, 2025 ). This fusion has played a key role in revealing the metabolic diversity of cells and the metabolism-related reactions that are lost in large-scale and bulk metabolomics techniques. 7.4. Nanobiosensors for real-time and in vivo metabolite monitoring Nanobiosensors connect the fields of nanotechnology and metabolomics, facilitating the instant and continuous observation of metabolic processes not only during the normal functioning of organisms but also under stress conditions. The working of these sensors is based on the application of functionalized NPs like gold and silver NPs, quantum dots, carbon nanotubes, graphene-based materials, and metal oxide NPs, which are specially designed to selectively find, join, and capture certain metabolites ( Silva et al., 2023 ). By changing the surface with enzymes, antibodies, aptamers, or molecularly imprinted polymers, the nanobiosensors attain outstanding specificity and sensitivity for the detection of key metabolic compounds, including sugars, amino acids, phytohormones, reactive oxygen species (ROS), and a variety of secondary metabolites ( Sharma and Dhadly, 2023 ). A key advantage of biosensors that utilize NPs is their capability to manufacture and change their optical, electrochemical, and physicochemical properties very finely. For example, the binding of a metabolite leads to changes in fluorescence, surface plasmon resonance, electrical conductivity, or redox signals, which result in rapid signal transduction with a low level of interference from the biological matrices ( Naresh and Lee, 2021 ). The whole process results in very fast response times and high temporal resolution, which means that the transient metabolic fluctuations and dynamic metabolic fluxes that are usually missed by the conventional end-point metabolomics approaches can now be captured. Crucially, the aforementioned nanosensors can be utilized in vivo , allowing for the monitoring of metabolic processes in plants, microbes, and animal systems to be done non-destructively and be conducted with high spatial resolution ( Wurtzel and Kutchan, 2016 ). To illustrate, in the field of plant biology, nanobiosensors have been employed to track the occurrences of ROS signaling, sugar movement, and hormone distribution changes in real time in various conditions such as development and abiotic stress ( Butnariu and Butu, 2019 ). Likewise, in both microbial and animal systems, sensors based on NPs grant knowledge of metabolic control, cell communication, and stress resistance, among other things. All in all, nanobiosensors link the static metabolite profiling to the dynamic metabolic regulation, thereby making a considerable contribution to the functional interpretation of the underlying data from the metabolomics process. 8. Abiotic stress mitigation through the application of nanoparticles Nanoparticles (NPs) have become key players in protecting agricultural production from various abiotic stresses, including salinity, drought, heat, and heavy metals. They exert their influence mainly through modulating photosynthetic efficiency, redox homeostasis, and nutrient acquisition processes. Contemporary reviews and empirical studies vouch for the ability of the metal and metal oxide NPs, and NP–hormone synergies, to significantly increase the tolerance of stress in a wide range of crops ( Singh et al., 2021 ) ( Table 4 ). TABLE 4. Abiotic stress mitigation by different nanoparticle strategies. Abiotic stress Monometallic NP Bimetallic/advanced NP NP + phytohormone References Drought ZnO, TiO 2 , Fe 3 O 4 , and SiO 2 NPs improve germination, root growth, RWC, photosynthesis, antioxidants, and osmolytes in cereals and legumes Bimetallic/engineered NPs proposed to enhance ROS control and water-use efficiency NPs + ABA/melatonin/SA/NO donors strongly improve water status, antioxidant system, and drought-responsive genes vs. single treatments Khan et al. (2024) ; Dilnawaz et al. (2023) ; Al-Khayri et al. (2023) ; Alabdallah et al. (2021) Salinity ZnO, SiO 2 , CuO, and Se NPs reduce Na + uptake, improve K + /Na + ratio, chlorophyll, antioxidants, and SOS/ABA responses Bimetallic NPs are suggested to boost ion homeostasis and redox buffering under salt stress Co-application of NPs with cytokinins, GA, SA, and PGPR-derived signals enhances ion balance, osmolytes, and photosynthesis under salinity Khan et al. (2024) , Rajput et al. (2021) ; Dilnawaz et al. (2023) Heavy metals (Cd, Pb, etc.) SiO 2 , Fe 3 O 4 , ZnO, and Cu-NPs limit metal uptake/translocation, reduce ROS/MDA and enhance antioxidants and phytohormone balance Bimetallic NPs proposed for stronger metal immobilization and ROS quenching Melatonin + SiO 2 NPs in Cd-stressed rice: higher growth, photosynthesis, antioxidant enzymes, lower ROS and lipid peroxidation; similar concepts with SA/JA/ethylene modulators Khalid et al. (2025) , Kaleem et al. (2024) , Faisal et al. (2024) , Rajput et al. (2021) , Dilnawaz et al. (2023) , El-Nasr et al. (2025) Temperature extremes ZnO, Ag, and Si-based NPs stabilize membranes, pigments, and antioxidants under heat/cold stress Not well explored for temperature stress yet NPs interacting with ABA, JA, and SA help regulate heat/cold-responsive signaling and ROS–hormone cross talk Khalid et al. (2025) , Rajput et al. (2021) , Thabet and Alqudah (2024) , El-Saadony et al. (2022) , Al-Khayri et al. (2023) Open in a new tab 8.1. Monometallic NPs and abiotic stress tolerance Notably, the NPs of ZnO, TiO 2 , Fe 3 O 4 /Fe 2 O 3 , SiO 2 , Ag, CuO, and Au have been empirically shown to improve vegetative growth, biomass accumulation, chlorophyll concentration, photosynthetic performance, and activity of enzymes such as superoxide dismutase (SOD), catalase (CAT), ascorbate peroxidase (APX), and peroxidase (POD), which are important in antioxidant defense. These benefits have been attributed to the mitigation of reactive oxygen species (ROS), a reduction of H 2 O 2 and malondialdehyde (MDA) levels, improved K + /Na + ratios, and an enhancement of water status ( El-Saadony et al., 2022 ; Sen et al., 2025 ; Ghani et al., 2021 ; Wang L. et al., 2022 ). While low to moderate concentrations of NPs offer beneficial results, increased dosages, especially of ZnO and CuO, can induce oxidative stress and inhibit plant growth ( Shaghaleh et al., 2025 ; Alabdallah et al., 2021 ; Al-Khayri et al., 2023 ) ( Table 4 ). 8.2. Bimetallic nanoparticles and increased mitigation Bimetallic formulations like Ag/ZnO or Ni-Au tend to have better surface reactivity and catalytic proficiency than single-metal systems, leading to better redox regulation and electron transfer processes. Consequently, these tend to be superior to their monometallic counterparts in antioxidant activity, photocatalytic activity, and antimicrobial activity, highlighting the significance of their possible applicability in multi-stress plant situations ( Dubey et al., 2024 ; Pavel et al., 2025 ; Zhang L. et al., 2025 ; Afzal et al., 2023 ). 8.3. Nanoparticles as an additive to phytohormones Co-application of NPs with phytohormones (salicylic acid (SA), abscisic acid (ABA), jasmonic acid (JA), gibberellins (GA), and cytokinins) or alternative mitigating agents (plant growth-promoting rhizobacteria (PGPR), fungus, metal salts, and biochar) exhibits synergistic effects. These include increased antioxidant capacity, accumulation of osmolytes, better nutrient uptake, water-use efficiency, and the overexpression of stress-responsive genes, which outperform the individual responses to either intervention ( Khan et al., 2024 ; Rehman et al., 2024 ). 8.3.1. Genomic insights into terpenoid (withanolide) biosynthesis under abiotic stress Plants respond to environmental extremities comprising abiotic stresses (e.g., drought, salinity, and temperature extremes) by controlling primary and secondary metabolism, usually by redirecting fluxes via terpenoid, phenylpropanoid, and flavonoid pathways ( Sangwan et al., 2013 ). In medicinal/stress-resilient plants such as Withania somnifera , the steroidal lactones (withanolides) are synthesized through the terpenoid backbone synthesis ( via the mevalonate [MVA] and 2-C-methyl-D-erythritol-4-phosphate [MEP] pathways) and downstream cyclization and functionalization steps ( Sangwan et al., 2008 ; Srivastava and Sangwan, 2020 ). The genomic and transcriptomic surveys could provide key insights into how this biosynthesis is adapted under stress. In a berry transcriptome study of W. somnifera, we studied a high-depth dataset of berry tissue (alongside leaf and root) and found almost all genes associated with withanolide biosynthesis. It was suggested that while the terpenoid/sterol backbone genes had broadly similar transcript abundances across tissues, the berry transcriptome was enriched in transcripts linked to steroid, phenylpropanoid, and flavonoid metabolism, as well as in cytochrome P450s, methyltransferases, and glycosyltransferases, which likely drive chemical diversification ( Tripathi et al., 2020a ). Importantly, the investigation identified distinct tissue-specific transcription factor (TF) distributions (e.g., MYB, ELI (early light-inducible protein), MADS, and WRKY) associated with developmental and secondary metabolism regulation ( Tripathi et al., 2020a ). In the light of abiotic stress, the conjunction of developmental signals, light/photoperiod responses, and stress signaling can affect terpenoid metabolism. For example, the alignment of ELI (light-responsive) and related TF families against secondary metabolism-associated genes in berry speculates a coupling of developmental/photomorphogenic control with metabolite synthesis. During stress, plants often induce isoprenoid flux to strengthen defense, membrane integrity, or antioxidant capacity; therefore, the observed TF metabolite gene network in W. somnifera provides a genomic basis for stress adaptation via terpenoid metabolism. Our study, comprising in silico mining and functional characterization of the AP2/ERF TF super-family in W. somnifera , provides a direct link between transcriptional regulation, terpenoid biosynthesis, and stress responses ( Tripathi et al., 2020b ). In this study, we identified 187 AP2/ERF gene-related transcripts (GRTs) in the transcriptome of W. somnifera , classified them into sub-families (AP2, ERF, DREB, etc.), and conducted cloning experiments followed by transient overexpression of one representative member ( WsAP2/ERF ) in leaf explants. The overexpression of WsAP2 led to enhanced expression of key terpenoid pathway genes such as HMGR (3-hydroxy-3-methylglutaryl-CoA reductase), DXS (1-deoxy-D-xylulose-5-phosphate synthase), DXR (1-deoxy-D-xylulose-5-phosphate reductoisomerase), CAS (cycloartenol synthase), and GGPPS (geranylgeranyl diphosphate synthase) ( Tripathi et al., 2020b ). We further proposed that WsAP2 may directly or indirectly regulate the GGPPS gene, thereby channeling the flux toward the terpenoid backbone, probably increasing the accumulation of withanolides during stress conditions. Taken together, these transcriptomic studies of W. somnifera presented important lines of evidence: first, for the terpenoid/withanolide pathway components that are broadly expressed but show tissue-specific modulation; second, TF families (WRKY, MYB, AP2/ERF, and WDR) are abundantly present and likely serve as nodes linking developmental/stress signals to metabolic gene expression, and lastly, the overexpression of specific TFs (such as WsAP2) can upregulate pathway genes, predicting a regulatory route that could be potentially exploited to enhance metabolite accumulation or stress resilience. In the view of abiotic stress adaptation, the increased flux through terpenoid/sterol pathways may provide protective functions in the form of membrane stability, antioxidation, and signaling. The TF metabolite gene analysis offers a mechanistic basis for such adaptation. 8.3.2. Terpenoid pathway gene regulation As demonstrated in the AP2/ERF study, the overexpression of WsAP2 visibly upregulated genes such as HMGR, DXS, DXR, CAS, and GGPPS. These are the expected key nodes in MVA/MEP/sterol pathways, which channelize toward withanolide biosynthesis. Therefore, TFs might shift the metabolic flux toward terpenoid skeleton production, possibly enhancing specialized metabolite accumulation ( Tripathi et al., 2020b ). 8.3.3. Tissue and developmental specificity The study related to the berry transcriptome reported that TFs such as ELI, MYB, MADS, and WRKY are expressed differentially across various tissue transcriptomes, such as berry, leaf, and root, and likely are under developmental regulation ( Tripathi et al., 2020a ). Because terpenoid metabolites usually accumulate in specific tissues or different developmental stages, e.g., fruit, berry, root, etc., the combinatorial effect of TF networks suggests spatiotemporal fine-tuning of pathway gene expression ( Tripathi et al., 2020a ). 8.3.3.1. Cross talk with stress signaling The stress-oriented accumulation of terpenoids and other secondary metabolites is well recognized. TFs such as AP2/ERF and WRKY relate stress signaling (e.g., via JA/SA/ethylene) to the activation of biosynthetic pathways. As in the case of W. somnifera , WsAP2 is induced by abiotic stress, and its overexpression enhances terpenoid pathway gene expression, suggesting that plants may direct such TF metabolite modules as part of the adaptive response ( Tripathi et al., 2020b ). From a genomics perspective, this implies that the regulatory pattern for terpenoid metabolism is expected to possess both constitutive developmental TFs (WDR, MYB, bHLH, and MADS) and stress-responsive TFs (AP2/ERF and WRKY). In combination, these facilitate plastic adaptation, that is, under normal growth, they maintain baseline terpenoid synthesis, whereas under abiotic stress, they shoot up flux via induced TFs, thus reinforcing secondary metabolite accumulation and thereby contributing to stress adaptation ( Tripathi et al., 2020a ; b ). 9. Tools and techniques of metabolomics 9.1. Preparation of samples In metabolomics, plant sample preparation involves collecting the sample. After that, metabolites are extracted and measured. Various steps are involved in sample preparation, like harvesting, quenching, extraction, and derivatization, as shown in Figure 3 . 9.2. Key analytical tools in agricultural metabolomics There are two types of metabolomics analysis, targeted and non-targeted. Non-targeted analysis involves the separation of many metabolites on pattern-based global and unbiased screening ( Saini et al., 2024 ; Naz et al., 2014 ). It is used more frequently to answer the discovery-based queries, such as characterization of the most significant metabolic alterations under stress conditions. The advantages of non-targeted metabolomics include high unbiased coverage; however, it has difficulties with relative quantification, complex data processing, and unknown identification. In contrast, the aim of targeted metabolomics is the identification, quantification, and interpretation of specific responses. Targeted metabolomics focuses only on known metabolites and can provide absolute quantification. Metabolite analysis could be carried out based on mass spectrometry (MS), which entails the identification and measurement of plant secondary compounds in a sample. Time-of-flight (TOF) and quadruple or ion trap analyzers may be used in a precise manner to obtain information on the compound nature formed via ionization using positive and negative ion modes ( Isah, 2019 ). Nuclear magnetic resonance (NMR) is commonly used for the identification of metabolites. One benefit of NMR is that it does not require sample separation, allowing samples to be retrieved for further study ( Powers et al., 2024 ). Most MS methods are, however, intrinsically more sensitive than NMR; it can be challenging to analyze a complex mixture of NMR spectra. Gas chromatography-mass spectrometry (GC-MS) allows the identification of metabolites without the need for standard compounds. While GC-MS is recognized for its high capacity, excellent retention time repeatability, and readily available compound libraries, it does necessitate that the samples be heated until they become gaseous. As a result, GC-MS is often employed to identify primary metabolites and volatile compounds. In contrast, liquid chromatography-mass spectrometry (LC-MS) can detect a wide range of metabolites. In LC-MS, the chemical derivatization of nonvolatile metabolites is not required ( Aszyk et al., 2018 ). The development of ultra-performance liquid chromatography (UPLC) significantly boosted the speed and sensitivity of detection. Therefore, techniques like GC-MS, UPLC, and LC-MS are widely utilized in metabolomics studies. Additionally, metabolomics studies frequently involve the use of matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-MS), direct infusion-mass spectrometry (DI-MS), and capillary electrophoresis-mass spectrometry (CS-MS) due to their different functions ( Ren et al., 2018 ) ( Table 5 ). Due to the complex nature of metabolites, it is essential to select the most suitable detection techniques based on cost, sensitivity, throughput, and resolution ( Feng et al., 2020 ). TABLE 5. Different techniques in plant metabolite identification under stress conditions. S. No. Technique Principle Plant Application Advantage Limitation References 1 Gas chromatography-mass spectrometry (GC-MS) Separates metabolites based on volatility and mass-to-charge ratio (m/z) Arabidopsis thaliana Triticum aestivum (wheat) Hordeum vulgare Cucumis sativus Helianthus annuus Glycine max (soybean) Primary metabolites like sugars, amino acids, and organic acids are affected by drought, salt, and temperature stress High sensitivity, well-established libraries (NIST and FiehnLib) Requires derivatization for nonvolatile metabolites and time-consuming Baek et al. (2021) ; Ammar et al. (2023) ; Derakhshani et al. (2020) ; Andrade et al. (2021) ; Nam et al. (2019) 2 Liquid chromatography-mass spectrometry (LC-MS) Separates metabolites in the liquid phase, coupled with MS for identification Oryza sativa (rice) Solanum lycopersicum (tomato) Used for secondary metabolites (flavonoids, phenolics, and alkaloids) in response to heavy metal, drought, and UV stress High sensitivity, covers a broad range of metabolites Complex data analysis and variable ionization efficiency Zhao Y. et al. (2024) ; Sudiro et al. (2022) 3 Nuclear magnetic resonance (NMR) spectroscopy Identifies metabolites based on the magnetic properties of nuclei Glycine max (soybean) Brassica napus (rapeseed) Medicago sativa (alfalfa) Used for metabolic fingerprinting of plants under salinity, drought, and oxidative stress Non-destructive, quantitative, and requires minimal sample preparation Lower sensitivity than MS-based methods and expensive equipment Msomi et al. (2024) ; Raza et al. (2023) 4 Fourier transform infrared spectroscopy (FTIR) Measures infrared absorption to identify functional groups of metabolites Sesuvium portulacastrum (sea purslane) Used for analyzing lipid and carbohydrate composition under drought and salinity stress Rapid, non-destructive, and minimal sample preparation Limited metabolite specificity and overlapping peaks Nikalje et al. (2019) 5 Capillary electrophoresis-mass spectrometry (CE-MS) Identifies metabolites based on the charge-to-size ratio; metabolites are separated under an electric field, detected via mass spectrometry Arabidopsis thaliana ( Arabidopsis ) Analysis of amino acids and organic acids under stress High sensitivity and suitable for ionic and polar metabolites Limited to polar metabolites and complex sample preparation Ohnishi et al. (2018) 6 Gas chromatography-time-of-flight mass spectrometry (GC-TOF-MS) Measures the time it takes for ions to reach the detector, allowing for high-throughput metabolite identification Hordeum vulgare (barley) Identifies unknown metabolites and secondary metabolites under stress High mass accuracy, broad metabolite coverage, and detection of unknown compounds Expensive instrumentation and requires advanced data analysis Chmielewska et al. (2016) Open in a new tab 9.3. Data analysis and interpretation in metabolomics Analyzing and interpreting the huge amounts of information produced by metabolomics data is complex and necessitates the use of advanced statistical methods and computational tools. The data analysis procedure consists of several essential steps. 9.3.1. Data pre-processing Data received from instruments, such as NMR and MS, undergo processing to remove noise and correct the differences between samples. The step involves peak detection, alignment, and normalization to ensure accurate comparison ( Amberg et al., 2017 ). NMR-based metabolomics analysis is a non-destructive method that effectively reveals the structural information of metabolites ( Deborde et al., 2017 ). MS is often paired with chromatographic separation techniques, like LC-MS and GC-MS. In order to quantify metabolites directly without requiring prior chromatographic separation, the direct injection mass spectrometry (DI-MS) technique has been upgraded to involve rapid, high-throughput fingerprinting techniques with the use of high-resolution mass spectrometers like Fourier transform ion cyclotron resonance (FT-ICR). This method enables the separation of complex biological samples comprising a mixture of metabolites before ion detection, which aids in differentiating isobaric compounds with similar masses. 9.3.2. Multivariate analysis Univariate, multivariate, supervised, and unsupervised analyses can be employed to perform various statistical techniques ( Blaise et al., 2021 ). Univariate statistics are used in the targeted metabolomics to evaluate hypotheses related to each metabolite of interest and determine their significance ( Xu et al., 2024 ). In contrast, multivariate statistics are often used in the untargeted metabolomics to analyze a global matrix dataset ( Alonso et al., 2015 ). This includes unsupervised techniques like principal component analysis (PCA), along with supervised multivariate techniques like partial least squares-discriminant analysis (PLS-DA), and orthogonal projections to latent structures discriminant analysis (OPLS-DA). Unsupervised methods are useful for sample clustering, which helps to identify trends, outliers, and groups of observations. The identification of potential biomarkers in OPLS-DA is often carried out using variable importance in projection (VIP) scores ( Table 5 ). After processing, the spectrum databases can be employed to identify interesting substances, which can then be confirmed using reference standards. 9.3.3. Targeted metabolomics In wheat, fatty acid abundance plays a significant role in drought stress tolerance, which was identified through targeted metabolomics ( Ullah et al., 2022 ). Similarly, a number of metabolites and the differences among them were analyzed by using target metabolomics analysis in licorice ( Zhang et al., 2023 ). Zhang et al. (2021) observed that some metabolic changes take place in two cotton varieties during drought stress by using a targeted metabolomics approach. In broomcorn millet grains, a widely targeted metabolomics analysis revealed the impact of alkaline stress on metabolites ( Ma et al., 2023 ). A study of the metabolic profile of chestnut ( Castanea mollissima ) using targeted metabolomics provided a new understanding of the chestnut calcification process ( Xiao et al., 2021 ). Targeted metabolomics was used to determine the mechanism of flue-cured tobacco quality improvement ( Meng et al., 2022 ). In maize, tryptophan enhances drought stress tolerance through IAA signaling, and SA and nicotinamide adenine dinucleotide (NAD) subsequently scavenge ROS. Three key genes involved in the production of tryptophan are flavin-containing monooxygenase 6 (ZmYUC6), catalase 1 (ZmCAT1), and indole-3-acetaldehyde oxidase 1 (ZmAO1). The high expression of these genes has a major impact on the regulation of drought tolerance ( Li S. et al., 2024 ). In Pinus taeda seedling roots, the amino acids glycine and asparagine and the phytohormone gibberelline (GA4) were found, while in needles, the phytohormones abscisic acid and salicylic acid were significantly increased ( Wu et al., 2023 ). Similarly, under molybdenum (MO), the hormone salicylic acids, jasmonic acids, and methyl jasmonate, amino acids like 2-oxoarginine and l-phenylalanine, and lipids like lysophosphatidylcholine, monoglyceride, phosphatidyl glycerol, and diacylglycerol were increased ( Feng et al., 2020 ). 9.3.4. Untargeted metabolomics Untargeted metabolomics was used to analyze the variations in the metabolites of two genotypes of Glycyrrhiza uralensis under drought stress ( Zhang et al., 2022a ). In P. taeda seedlings, untargeted metabolomics found that sugars, flavonoids, long-chain lipids, and terpenoids are increased under drought stress ( Wu et al., 2023 ). Under salinity stress conditions, metabolic pathways and salt-responsive metabolites in Haloxylon salicornicum were analyzed by a non-targeted metabolomics approach ( Panda et al., 2021 ). Under drought stress, 523, 406, 301, and 272 metabolites were recognized using untargeted metabolomics analysis in two genotypes of quinoa ( Zhu et al., 2023 ). Similarly, in Quercus ilex L. leaf seedlings, a number of putative compounds, which are related to plant tolerance under drought stress, were identified through the use of untargeted MS-based metabolomics analysis ( Tienda-Parrilla et al., 2022 ). In Aphanamixis polystachya seedlings, the regulatory pathways were determined by using untargeted metabolomics in response to salicylic acid-mediated stress conditions. A study using LC-MS-based untargeted metabolomics analysis found that bioactive compounds of Punica granatum juice increased in drought stress ( Gómez-Bellot et al., 2023 ). In cotyledons and roots of Ricinus communis seedlings, untargeted LC-MS-based metabolomics analysis identified metabolic changes under salinity stress ( Wang et al., 2021 ). In the edible and medicinal plant Saliva miltiorrhiza , untargeted LC-MS/MS-based metabolomics profiling was done to analyze a number of metabolites under cadmium stress ( Yuan et al., 2022 ). In Pinus pinaster , the essential biochemical rearrangement was identified by using untargeted metabolomics under heat and drought stress ( López-Hidalgo et al., 2023 ). The adaptation response of Gliricidia sepium was shown under salinity stress by using untargeted metabolomics analysis ( Braga et al., 2022 ). 9.3.4.1. Single-cell and spatial metabolomics for tissue-level resolution of stress responses The research on the effect of abiotic stress on plants has been significantly and positively impacted by single-cell and spatial metabolomics, which have affected the metabolic reprogramming of the plant tissues to the specific resolution of the cell types, thus performing bulk metabolomics averaging. The single-cell metabolomics studies that have been done using A. thaliana have shown the specific accumulation of proline, malate, and soluble sugars in the guard cells under drought stress conditions, thereby establishing a direct link between the dynamics of osmolytes and stomatal regulation as well as water-use efficiency ( Katam et al., 2022 ). In the same way, cell-resolved metabolomics in rice and maize roots has shown spatially distinct accumulation of amino acids, organic acids, and compatible solutes between epidermal, cortical, and vascular tissues under salinity stress, thus pointing out coordinated and at the same time heterogeneous metabolic adaptation ( Deng et al., 2020 ). NPs such as fluorescent quantum dots, plasmonic gold nanoparticles, and carbon-based materials, have been used successfully in plant metabolomics to visualize the stress-induced changes in redox metabolites, gradients of hormones (like abscisic acid and auxins), and ROS signaling at the tissue and cellular levels ( Song Y. et al., 2022 ). These methods reveal the metabolic reprogramming during abiotic stress conditions, such as drought, salinity, temperature extremes, and heavy metal exposure, spatially resolved in a way that has never been seen before. Nanotechnology-enabled metabolomics brings together the physiological outcomes with the nanoscale metabolic signals, thus bridging the gap between molecular metabolism, stress adaptation, and phenotype expression ( Zainal-Abidin et al., 2024 ). Mass spectrometry imaging techniques like MALDI-MSI and DESI-MSI have boosted the power of spatial metabolomics and provided an opportunity to see the gradients of metabolites caused by stress throughout the tissue ( Ma and Fernández, 2024 ). For instance, in drought and heat stress studies, it was confirmed that the epidermis of the leaves was the main site for the accumulation of flavonoids and phenolic compounds, which in turn, led to ROS scavenging and photoprotection ( Zhang et al., 2022b ). In a significant development, the technique of spatial lipidomics has uncovered the localized transformation of chloroplast galactolipids, that is, MGDG and DGDG, in the mesophyll tissues, which played the role of stabilizing the thylakoid membranes and maintaining the photosynthetic efficiency during drought and salinity stress ( Hölzl and Dörmann, 2019 ). When considering this from the applied point of view, the merging of single-cell and spatial metabolomics with transcriptomics and phenotyping provides a very high opportunity for spotting tissue-specific metabolic hubs, hence speeding precision breeding as well as the targeting of metabolic engineering for resistance against stress in crops ( Feussner and Polle, 2015 ; Zhang S. et al., 2025 ). 9.3.4.2. Stable isotope tracing and fluxomics for resolving pathway directionality under abiotic stress Stable isotope tracing together with metabolic flux analysis (fluxomics) is an effective method of identifying the direction of the pathway and measuring metabolic reprogramming in plants under abiotic stress ( Koley et al., 2025 ). In contrast, steady-state metabolomics, which shows the sizes of metabolite pools by labeling isotopes with 13 C, 15 N, or 2 H tracers, monitors the flow of carbon and nitrogen through interconnected metabolic networks and reveals the exact conditions under which pathways are activated, suppressed, or rerouted under stress (Srivastava et al., 2016). For instance, research using 13 CO 2 and 13 C-glucose labeling in Arabidopsis and some crops verified the rerouting of carbon from the Calvin–Benson cycle to glycolysis, the oxidative pentose phosphate pathway (OPPP), and amino acid production during drought and salt stress thus, leading to increased NADPH generation for redox equilibrium ( Jang et al., 2018 ; Alseekh et al., 2018 ). Likewise, the use of 15 N-labeling has determined the path of nitrogen movement toward proline, glutamate, and polyamine production during osmotic stress, putting an end to the argument of whether proline increase means active synthesis or reduces respiration ( Limami et al., 2023 ). Flow studies have additionally brought to light the stress-related alterations at the TCA cycle branch points, among which the increased flow into the organic acid pools that are connected with ion chelation and pH regulation during the stress of heavy metals and salt. Using isotope tracing in combination with spatial or cell-type-specific sampling has made it possible to analyze tissue-resolved fluxes, thereby exposing different metabolic patterns in roots and shoots or in mesophyll and vascular tissues ( Jang et al., 2018 ). The detection of stress-adaptive metabolic bottlenecks through fluxomics is highly advantageous as it gives a logical foundation for the usage of metabolic engineering and breeding. These kinds of approaches will both increase the factor of a plant’s stress resistance and maintain the metabolic costs at a low level ( Koley et al., 2024 ). 9.3.4.3. AI-driven multi-omics integration for predictive modeling of abiotic stress responses The rapid accumulation of vast omics datasets in multiple dimensions has considerably changed the area of plant stress biology; nevertheless, obtaining predictive and mechanistic insight from these layers of complicated data is still a very difficult challenge ( Roychowdhury et al., 2023 ). By identifying nonlinear, hierarchical, and context-dependent interactions between the various omics categories, including genomics, transcriptomics, proteomics, metabolomics, lipidomics, and fluxomics, the application of artificial intelligence (AI) in multi-omics integration has been a groundbreaking technique in the modeling of plant responses to abiotic stress ( Wu and Xie, 2025 ). Unlike traditional statistical techniques, AI frameworks may simultaneously incorporate many data types and environmental elements, allowing for the prediction of phenotypic outcomes in dynamic stress settings ( Walsh et al., 2024 ). Supervised machine learning algorithms, including random forests, support vector machines, and gradient boosting, have been extensively utilized for stress-responsive biomarker identification and for predicting tolerance traits ( Gou et al., 2024 ). One remarkable example is the integrative models that combined genomic markers with transcriptomic and metabolomic features, which led to a substantial increase in the accuracy of drought and salinity tolerance predictions in rice, wheat, and chickpea when compared to single-omics genomic prediction models ( Washburn et al., 2019 ; Westhues et al., 2021 ; Souza and Fernie, 2024 ). These methods have recognized the existence of key metabolic signatures like osmolytes, antioxidants, and lipid species and proved them to be secure predictors of stress performance. In the past few years, deep learning architectures have made it possible to perform network-level inference and mechanistic modeling simultaneously. Dimensionality reduction and extraction of features from large-scale multi-omics datasets have been accomplished via autoencoders and variational autoencoders, leading to the discovery of underlying stress-responsive modules. In addition, the use of graph neural networks (GNNs) and attention-based models has further contributed to the reconstruction of regulatory and metabolic interaction networks by identifying gene–protein–metabolite relationships explicitly ( Vrahatis et al., 2024 ). A case in point, GNN-based multi-omics integration has been successfully utilized to forecast the plant’s response to abiotic stresses and the corresponding regulatory modules and miRNAs, marking the crucial points of control in signaling networks during stress ( Subramanian et al., 2020 ). In the pursuit of more precise forecasts, it is noteworthy that AI-driven methods are increasingly being combined with spatial metabolomics and fluxomics. Models can infer the directionality of pathways, the presence of metabolic bottlenecks, and the trade-offs between growth and stress defense by combining isotope-based flux data with the layers of metabolomics and transcriptomics. Compared to using metabolite abundance alone, this type of modeling has made it possible to predict tolerance outcomes more accurately by revealing the diversion of carbon and nitrogen fluxes caused by stress toward redox maintenance and osmoprotection ( Arrivault et al., 2019 ; Pérez-de-Souza et al., 2020 ). In practical terms, the AI-powered integration of all omics offers a robust and comprehensive platform for mapping genotype to phenotype, conducting in silico stress studies, and choosing the optimal genes or pathways for genome editing and breeding ( Gou et al., 2024 ). Predictive models can, in fact, lead to precise gene editing, climate-resilient crop design, metabolomics-assisted breeding, and more by integrating omics features with phenotypic and environmental data ( Zenda et al., 2021 ). The strength of AI in predictive modeling will likely be the primary driver behind a new era of plant stress biology and the sustainable agriculture industry, with the development of explainable AI techniques and the standardization of multi-omics pipelines ( Harfouche et al., 2019 ). 9.4. Analysis of metabolic pathways Advanced bioinformatics tools are used to align metabolite data with metabolic pathways, offering a functional understanding of the metabolic changes observed ( Cambiaghi et al., 2017 ). This analysis can reveal pathways that are either upregulated or disrupted, which are linked to particular conditions such as environmental stresses. Therefore, molecular networking, pathway analysis, biological modeling, and correction-based analysis can be used to link the biological interruption of metabolites identified by a metabolic network. In recent years, metabolomics and its integration with other omics techniques, such as viewers, route database, and molecular networking tools like BioCyc, GNP, KEGG, and Reactome, have been significantly developed. 10. Integration of metabolomics with genomics, transcriptomics, and proteomics Metabolomics enhances genomic studies by linking genetic variations to specific metabolic traits. Genome-wide association studies (GWAS), along with metabolomics, have pinpointed specific loci that regulate metabolite accumulation in crops like rice and maize ( Chen et al., 2016 ). In maize, a combination of GWAS and metabolomics has pinpointed loci that regulate the levels of essential amino acids and secondary metabolites, which is beneficial for increasing crop nutrition. Genomic information can forecast genes that code for enzymes, while metabolomics confirms the functionality of these enzymes by identifying intermediates and end products in metabolic pathways. In rice, the merging of genomic and metabolomics data has revealed novel pathways that show an important role in drought stress tolerance. Combining metabolomics with transcriptomics aids in understanding the regulatory networks that govern metabolic pathways ( Sanches et al., 2024 ). For instance, combining these methods in Arabidopsis showed how transcriptional regulation affects the secondary metabolites production under stress conditions. Studies examining gene expression and metabolite profiles during stress conditions highlight the timing and coordination of metabolic pathways. In tomatoes, a combined analysis of transcriptomics and metabolomics clarified how secondary metabolites are regulated throughout the fruit ripening process ( Liu et al., 2023 ). Proteomics helps identify the enzymes that play a role in metabolic pathways, whereas metabolomics measures the intermediates in these pathways to confirm enzyme activity. For instance, combining proteomics and metabolomics in barley has uncovered enzymes that are involved in the metabolic changes triggered by drought ( Chmielewska et al., 2016 ). Proteomics identifies post-translational modifications (PTMs) like phosphorylation and acetylation that influence enzyme activity, whereas metabolomics uncovers the subsequent metabolic effects of these changes. By combining proteomics and metabolomics, researchers can build metabolic networks that aid in forecasting how systems respond to environmental shifts. By combining metabolomics with other technologies like genomics and proteomics, researchers can develop a comprehensive understanding of entire plant systems and their responses to shifting environmental conditions ( Yang et al., 2021 ). In tomato, various omics-based techniques, like metabolomics, proteomics, transcriptomics, etc., have been used to understand the plant’s response against abiotic stress ( Naik et al., 2023 ). Under Cd stress in Pistia stratiotes , eight metabolic pathways and 3,107 differently expressed genes involved in various metabolic pathways were identified ( Wei et al., 2023 ). In soybean, transcriptomics analysis shows significant changes in transcription factors and in some signaling pathways. In maize, the leaf responses during drought, salinity, and heat stress were analyzed at the metabolome and transcriptome levels. Reduction of essential glycolysis and tricarboxylic acid cycle intermediates, along with a noticeable buildup of amino acids like proline, arginine, and γ-amino butyrate, suggested a change in the equilibrium of carbon and nitrogen metabolism in leaves ( Joshi et al., 2021 ). According to proteomic data, phosphopyruvate hydrase was enriched after salt stress and exhibited salt-induced DNA methylation. Additionally, it interacts with various metabolic pathway enzymes. The production of several metabolites was revealed using metabolomics data ( Fu et al., 2024 ). 10.1. Integration of omics in stress-tolerant crop breeding Abiotic stress induces drastic metabolic alterations in plants, which can lead to reduced yields and quality. To overcome this stress, the integration of omics into the crop breeding process has emerged as a viable strategy for the development of stress-tolerant crop varieties ( Figure 4 ). Metabolomics provides information on the biochemical changes triggered by stress and is useful for identifying metabolic traits related to resilience ( Satrio et al., 2024 ). It also connects phenotype and genotype by identifying the end products of cellular processes ( Isah, 2019 ). 11. Metabolomics in crop improvement Metabolomics is a comprehensive understanding of the biochemical conditions of the plant, with the ability of researchers to identify metabolic pathways and metabolites showing a response toward stress that can be utilized for an improvement in the crop quality. It provides high-throughput phenotyping through which genotypes tolerant to the stress can be identified by targeting metabolites responsible for stress tolerance ( Raza et al., 2023 ). In rice, the metabolic profiling has detected important biomarkers, including sugars, flavonoids, and polyamines associated with drought tolerance ( Ghorbanzadeh et al., 2023 ). Similarly, under drought stress in maize, metabolomics revealed the accumulation of amino acids, organic acids, and sugars, indicating their function in maintaining ROS detoxification and osmotic balance ( Wang R. et al., 2024 ). Metabolomics has a vital role in the development of nutritionally enhanced crops by identifying the important metabolic pathways that contribute to the production of vitamins, amino acids, and bioactive compounds ( Gandhi and Singh, 2024 ). 12. Conclusion This review highlights the emerging evidence that plants’ resistance to abiotic stresses is the result of a well-organized metabolic reprogramming, which includes the production of osmolytes, the strengthening of antioxidant defense systems, and the changing of lipids on a large scale. The storage of compatible solutes such as soluble sugars, proline, glycine betaine, and raffinose family oligosaccharides is a conserved adaptive strategy across drought, salinity, cold, and heat stresses, helping with osmotic adjustment, cellular protection, and the preservation of metabolic homeostasis. The increased production of antioxidants like ascorbic acid, flavonoids, carotenoids, and polyphenols is of paramount importance in the prevention of oxidative damage and maintaining photosynthetic process efficiency, which are both caused by stress. Lipid remodeling, mainly the changes in fatty acid saturation and membrane lipid composition, also supports the plants to stay intact and functional in terms of membrane under severe heat and salt stress. In rice, for example, elevated levels of proline, soluble sugars, and anaerobic metabolism-associated organic acids correlate with enhanced submergence and drought tolerance, while in wheat, accumulation of osmoprotectants and phenylpropanoid-derived antioxidants has been linked to improved performance under water deficit and temperature extremes. Similarly, maize genotypes exhibiting increased organic acid flux and secondary metabolite accumulation display superior tolerance to heat and drought stress. In addition to improving mechanistic knowledge, the responses centered on metabolites compiled in the current review provide obvious and practical chances for the enhancement of crops in the case of important cereals and staple crops. Amongst the metabolites produced due to stress that comprise proline, glycine betaine, raffinose family oligosaccharides, malate, GABA, ascorbate, flavonoids, and unsaturated fatty acids, rice ( Oryza sativa ), wheat ( T. aestivum ), and maize ( Zea mays ) have been repeatedly linked with their stress tolerance, indicating their potential to serve as reliable metabolic biomarkers. Metabolomics-assisted breeding programs can make use of targeted metabolomics platform integration for fast screening of different germplasm panels and breeding populations, which eventually leads to early stress-resilient genotype identification under drought, salinity, heat, and waterlogging conditions. For instance, in rice, high amounts of proline, soluble sugars, and organic acids are related to anaerobic metabolism and are directly related to better tolerance to flooding and drought. Alternatively, in wheat, the accumulation of osmoprotectants and phenolic antioxidants has been associated with better performance under water deficit and heat extremes. Likewise, corn varieties that show increased organic acid flow and secondary metabolite buildup have a higher tolerance to heat and drought stress. Genetic engineering has made significant progress, which allows for direct interventions at crucial metabolic and regulatory sites that cause this manipulation of various adaptive traits. In a case study of rice, the specific adjustment of osmolyte biosynthesis enzymes like pyrroline-5-carboxylate synthase and betaine aldehyde dehydrogenase has led to the improvement of the plant’s resistance to drought and salinity, while the alteration of fatty acid desaturases results in the enhancement of membrane stability at high temperatures. The engineering of antioxidant enzyme genes and transcription factors from the NAC and WRKY families in wheat has led to better redox balance and yield stability under adverse environmental conditions. In the case of maize, the regulation of enzymes involved in the metabolism of organic acids and the production of secondary metabolites, including those associated with the TCA cycle and the phenylpropanoid pathway, is showing great promise for the cultivation of multi-stress-tolerant maize varieties. It is critical to target the upstream stress-responsive transcription factors such as NAC, MYB, WRKY, and bHLH proteins, as they provide a synchronized approach to reprogramming the whole metabolic networks rather than only the isolated pathways. Such pathway-level interventions, when accurately adjusted to minimize the trade-offs between growth and stress tolerance, have very good potential in the production of high-yield and multi-stress resistant crop varieties that will be able to grow in different and changing agro-climatic environments. The integration of high-resolution metabolomics, CRISPR/Cas-mediated genome editing, and AI-powered predictive modeling will change how we design stress-resilient crops. It is possible to establish systematic connections between pathway-level metabolic states, the underlying genetic variation, and the environmental complexity. Changes in mass spectrometry- and NMR-based metabolomics have now made it possible to do a thorough, spatially resolved, and time-dependent analysis of metabolites that are stress-responsive, thus helping to uncover the metabolic nodes and steps that are controlling the fluxes and that are associated with tolerance phenotypes. The metabolomic signatures, when correlated with the genome-wide association studies (mGWAS) and quantitative trait locus (QTL) mapping, can be linked to stress-related genes and regulatory elements directly. CRISPR/Cas genome editing offers an accurate and large-scale platform for target validation and engineering that enables the slight alteration of the activities of enzymes, transcription factors, and the points of metabolic branching within pathways like the TCA cycle, GABA shunt, and shikimate pathway. Simultaneously, processes that are based on AI and machine learning can be incorporated with multi-omics datasets along with environmental and phenotypic data to represent the nonlinear stress reactions of complexity, anticipating the interplay between the genotype and the environment, and giving preference to the best metabolic configurations for the particular agro-climatic conditions. This integrative framework makes it possible to transition from empirical selection to predictive selection based on pathways, thus speeding the development of climate-resilient varieties that can maintain their productivity even under the highly variable and multifactorial stress environments of the future ( Yang X. et al., 2024 ). 13. Future perspectives and emerging directions A crucial aspect of future research on plant stress biology is to study the metabolism of plants facing combined and sequential stress conditions ( Afzal et al., 2021 ; Ahmed et al., 2022 ; Saini et al., 2024 ). In nature, plants face combined and/or sequential stresses, instead of a single stress. They must cope with combined stress interactions such as drought-heat, salt-nutrient deficit, or heavy metals-oxidative conditions ( Amjad et al., 2024 ). There is growing evidence that metabolism under stress interactions is often non-additive and cannot be predicted by studying single-stress responses ( Bharath et al., 2021 ; Bueno and Lopes, 2020 ). The specifics of each stress interaction must be investigated in view of metabolic conditions and signaling interactions ( Chmielewska et al., 2016 ; Carrera et al., 2021 ; Chen Y. et al., 2023 ). Further research on plant stress biology along these lines, especially relating to time-course analyses on plant metabolism, would be most critical for understanding universal versus specific plant stress signatures. Another emerging and important investigation is the fusion of metabolomics with predictive and computational modeling approaches ( Meng et al., 2022 ; Chong and Xia, 2017 ; Kar and Leszczynski, 2017 ; Ghosh et al., 2020 ). As metabolomics provides high-dimensional datasets characterizing stress-induced biochemistries, the predictability and potential within these data have been largely untapped. Utilization of metabolomics data for predictive modeling with genome-resolved metabolic models, flux balance analysis, and isotope tracing may offer a way for making quantitative predictions regarding pathway orientation, metabolic bottlenecks, and resource allocation under stress. Collectively, artificial intelligence (AI) and machine learning-based multi-omics fusion analysis are being increasingly employed for the elucidation of nonlinear correlations between metabolite data, gene expression data, environmental parameters, and phenotypes for the diagnosis and prediction of stress and yield ( Baishya et al., 2024 ). Major translational opportunities exist within the context of constructing metabolomics-assisted breeding platforms. Stress traits for plants tend to be polygenic and show large genotype × environment interactions; consequently, traditional plant breeding systems tend to be less efficient ( Mahmood et al., 2022 ). Metabolomics bridges this gap by offering an approach that helps detect metabolic biomarkers, mQTL, and genome-wide association signals related to stress traits and plant quality ( Wang et al., 2025b ). The integration of metabolite markers with genome selection platforms may improve plant selection efficiency during earlier stages of plant development or when faced with varied environmental factors. In future breeding programs, attention could be given to tissue and development stage-specific metabolism responses that are usually overlooked in whole plant metabolism calculations and might obscure localized stress responses associated with tissue-level metabolism responses ( Taylor et al., 2021 ). Recent advances in spatial and single-cell metabolomics are foreseen to bring a paradigm shift in understanding tissue-level metabolism differences and hence improve trait discovery and selection ( Taylor et al., 2021 ; Petrova and Guler, 2024 ). Taken together, “multi-stress metabolomics,” “predictive modeling,” and “metabolomics-assisted breeding” represent a major emerging direction in crop science. Embedding metabolomic data within breeding pipelines and decision-support systems will enable a shift from reactive stress mitigation toward “predictive, design-based crop resilience,” a condition under which agricultural productivity and food security can be sustained in the face of accelerating climate change. Funding Statement The author(s) declared that financial support was not received for this work and/or its publication. Footnotes Edited by: Pandiyan Muthuramalingam , Gyeongsang National University, Republic of Korea Reviewed by: Fahimeh Shahinnia , Agriculture and Agri-Food Canada (AAFC), Canada Xianbin Hou , Baise University, China Author contributions YS: Writing – original draft, Writing – review and editing. NhS: Writing – original draft, Writing – review and editing. Nishu: Writing – original draft, Writing – review and editing. SY: Writing – original draft, Writing – review and editing. ST: Writing – original draft, Writing – review and editing. AK: Writing – review and editing. NeY: Writing – review and editing. MS: Writing – review and editing. NS: Writing – original draft, Writing – review and editing, Conceptualization, Supervision. Conflict of interest The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Generative AI statement The author(s) declared that generative AI was used in the creation of this manuscript. AI was used by some co-authors for language editing. Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us. Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. References Acidri R., Sawai Y., Sugimoto Y., Handa T., Sasagawa D., Masunaga T., et al. (2020). 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