Laminaria extracts and rhizobacteria (Paenibacillus alvei T22) elicit metabolic reprogramming of wheat seedlings: A metabolomics-guided biostimulants mode-of-action discovery for plant growth and defence priming

Laminaria extracts and rhizobacteria (Paenibacillus alvei T22) elicit metabolic reprogramming of wheat seedlings: A metabolomics-guided biostimulants mode-of-action discovery for plant growth and defence priming | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Laminaria extracts and rhizobacteria (Paenibacillus alvei T22) elicit metabolic reprogramming of wheat seedlings: A metabolomics-guided biostimulants mode-of-action discovery for plant growth and defence priming Manamele D. Mashabela, Lizelle A. Piater, Tarekegn Terefe, Pavel Kerchev, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7656371/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Jan, 2026 Read the published version in Plant Growth Regulation → Version 1 posted 10 You are reading this latest preprint version Abstract Plant biostimulants, including seaweed extracts (SWE) and plant growth-promoting rhizobacteria (PGPR), are known to enhance crop performance, while multi-component biostimulants, combining microbial and non-microbial agents, show promise for enhanced plant physiological responses and defence activation, yet their metabolic mechanisms remain enigmatic. This breakthrough study unveils the molecular mechanisms behind biostimulants action -PGPR ( Paenibacillus alvei T22), and seaweed extract laminarin (L-1)- in wheat seedlings ( Triticum aestivum L.) through comprehensive untargeted metabolomics using ultra-high-performance liquid chromatography coupled to high-definition mass spectrometry (UHPLC-HD-MS) and advanced pathway enrichment analysis. Three distinct metabolic phenotypes were identified: Laminarin (SWE) treatment triggers the modulation of the energy metabolism with maximum energy production, characterised by robust activation of the citric acid (TCA) cycle, and rapid activation of the secondary metabolism through the upregulation of aromatic amino acids (Phenylalanine, Tyrosine, Tryptophan), feeding into the phenylpropanoid pathway. PGPR treatment orchestrates precision defence priming with moderate and controlled activation of the energy metabolism, accompanied by a targeted modulation of secondary metabolism and the phenylpropanoid pathway. Remarkably, combined P. alvei (T22) and laminarin L-1 treatment achieved a metabolic optimisation, a harmonised activation and modulation of both the primary and secondary metabolism, transcending simple additive effects to create genuine metabolic enhancement. These biostimulants fundamentally reprogram plant metabolism through distinct pathway-level mechanisms revealed by metabolic network analysis, unlocking the molecular basis of superior plant performance. These discoveries provide the mechanistic framework for designing next generation biostimulants formulations tailored to specific crop requirements, environmental challenges, and performance targets in precision agriculture, for sustainable agricultural intensification through targeted metabolic reprogramming. Biostimulants Laminarin Metabolomics PGPR Plant Metabolism Seaweed Extracts Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Modern agriculture stands at an intersection of multiple global challenges, including climate variability, land degradation, depletion of finite and non-renewable resources and soil fertility loss, while under increasing pressure to sustainably meet the food demands of a rapidly growing population. These constraints are further exacerbated by the declining efficacy and ecological concerns associated with the overreliance on synthetic agrochemicals, which threaten both environmental and human health (Calicioglu et al., 2019 ; Rejeb et al., 2022). As a result, the agricultural sector is undergoing a paradigm shift towards the development of sustainable, eco-compatible alternatives that enhance crop productivity while minimising ecological footprint. One such alternative is the use of naturally sourced biostimulants, such as seaweed extracts (SWEs) and microbial-based plant growth-promoting rhizobacteria (PGPR), which have emerged as a promising frontier in enhancing plant growth, improving stress tolerance and nutrient efficiency at the forefront of climate-smart agriculture (Yakhin et al., 2017 ; Rouphael & Colla, 2020 ). These traits are particularly essential towards achieving key United Nations Sustainable Development Goals (UNSDGs) such as SDG 2 (Zero Hunger), SDG 13 (Climate Action) and SDG 15 (Life on Land). SWEs, notably from Laminaria digitata and L. hyperborea , have garnered considerable academic and industrial interest due to their rich biochemical composition, eliciting multifaceted effects on plant systems. These effects are largely attributed to key components such as laminarins, fucoidans, alginates, and phlorotannins, which act as signalling molecules, antioxidants, and elicitors of defence responses. For instance, laminarin is known to prime jasmonic acid and salicylic acid pathways, thereby strengthening plant innate immunity (Kahlon et al., 2023 ). Extracts from Laminaria have been shown to stimulate germination, promote early seedling vigour, enhance chlorophyll content, and induce tolerance to drought, salinity, and oxidative stress (Goñi et al., 2016 ; Shukla et al., 2019 ). Laminaria extracts also influence plant metabolism, modulating pathways related to osmolyte production, antioxidant enzyme activities, and hormone biosynthesis. Recent omics studies, including transcriptomics and metabolomics, have uncovered significant alterations in carbohydrate metabolism, amino acid biosynthesis, and secondary metabolite pathways in plants treated with Laminaria -based formulations (Ali et al., 2021 ; Sharma et al., 2023). These molecular-level responses indicate a sophisticated mechanism of metabolic reprogramming and physiological priming, positioning seaweed-based biostimulants as potent tools for sustainable crop enhancement. In parallel, PGPR have garnered increasing attention for their multifaceted contributions to plant health and productivity. These bacteria colonise the rhizosphere and engage in key functions such as biological nitrogen fixation, phosphate solubilisation, siderophore production, and the synthesis of phytohormones like indole-3-acetic acid (IAA) and gibberellins (Borriss, 2011 ; Wu et al., 2021). In addition to enhancing nutrient availability, PGPR, such a Paenibacillus alvei T22, are widely recognised for their capacity to trigger induced systemic resistance (ISR) in plants via the production of lipopeptides (surfactin, fengycin, iturin) and volatile organic compounds (VOCs), which serve as signalling cues in activating plant defence pathways (Mmotla et al., 2025 ). P. alvei were recently shown to increase wheat yields by approximately 10% individually and a significant increase of 30% to 60% in a consortium under reduced fertiliser conditions (Breedt et al., 2025 ), while Laminaria digitata L. extracts have been shown to improve tomato performance under drought stress by modulating photosynthesis, antioxidant response, and related metabolite profiles (Pereira et al., 2024 ). While the individual benefits of SWEs and PGPR are well established, recent trends suggest that their combined application may elicit synergistic or complementary effects, leading to enhanced biostimulants efficacy. However, the underlying molecular mechanisms and interactive dynamics remain underexplored. There is growing interest in multi-component biostimulants formulations that merge microbial and non-microbial agents, as these combinations may trigger broader physiological responses, enriched metabolic diversity, and robust defence activation (Rouphael et al., 2022 ). The advent of omics technologies, particularly metabolomics, offers a powerful platform to dissect the mode of action of biostimulants at a systems level. By profiling metabolic fluxes, omics tools can uncover regulatory networks, pathway crosstalk, and molecular biomarkers associated with biostimulants-induced growth promotion and defence priming (Weckwerth et al., 2020 ; Kumar et al., 2021 ). Integrating these data can provide a holistic view of how plants respond to complex biostimulants formulations, enabling evidence-based optimisation and refinement for formulations. This study aimed to apply comprehensive metabolomics analyses to investigate the individual and combined effects of Laminaria extracts (purified laminarin) and Paenibacillus alvei (T22) on wheat seedlings ( Triticum aestivum L.), with a specific focus on mode of action discovery, metabolic reprogramming, and priming-related modulation of the primary and secondary metabolism. By elucidating the signalling pathways and metabolites involved, this research seeks to advance our understanding of the Biostimulants mechanism of action, contributing toward the development of sustainable solutions for crop productivity under environmental stress. 2. Materials and Methods 2.1. Bacterial culture conditions and Preparation of Laminaria extracts Paenibacillus alvei (T22), obtained as glycerol stocks from Dr. Msizi Mhlongo, University of Johannesburg, South Africa was grown on Petri dishes with nutrient agar media overnight (O/N) at 28°C ± 2°C. Bacterial colonies were transferred into 50 mL Luria Broth (LB) culture medium for overnight incubation on a shaker-incubator at 140 rpm and 28°C ± 2°C. The optical density (OD) of overnight cultures was adjusted to 0.5 OD 600 for seed treatment. Bacterial cultures were centrifuged at 5000 rpm at 4°C for 15 min. The pellets were collected and gently washed in 5 mL sterile water, then reconstituted in 50 mL autoclaved water for seed treatment. Laminaria extracts (purified laminarin; code-L1) were sourced from BioAtlantis Ltd (Ireland) as a highly concentrated liquid SWE produced in accordance with Good Manufacturing Practices (GMP+) and certified by GMP + International B.V. Extracts were diluted to an optimal concentration of 1% v/v (2.5L/hectare in 250L) for foliar application. 2.2. Seed biopriming, plant growth conditions, and treatments Seeds from the Gariep cultivar of wheat ( Triticum aestivum L.) (obtained from the Agricultural Research Council- Small Grains, South Africa) were washed with autoclaved water, followed by surface sterilisation in 0.5% sodium hypochlorite (NaOCl) for 1 min. The seeds were then rinsed with autoclaved water and further washed in 70% ethanol to remove the NaOCl residues and allowed to dry. Dried seeds were introduced to the previously prepared suspensions of the bacterial cultures; the seeds were immersed in separate 50 mL of reconstituted P. alvei (OD 600 0.5) in centrifuge tubes and incubated for 3 h. Bacterial solutions were decanted, and seeds were dried at 28°C ± 2°C for 24 h on open Petri dishes in an incubator. Control seeds were treated with sterile autoclaved water for the same period and dried under similar conditions. For plant growth, a germination soil mixture (Culterra, Muldersdrift, South Africa) was soaked in 9 cm pots with Supafeed® 3:1:6 (46) (AECI Plant Health, Modderfontein, South Africa), a water soluble fertiliser consisting of nitrogen (N) -155 g/kg, phosphorus (P) − 46 g/kg, potassium (K) − 267 g/kg, sulphur (S) − 4.1 g/kg, magnesium (Mg) − 3.1 g/kg, zinc (Zn) − 711 mg/kg, boron (B) − 1073 mg/kg, molybdenum (Mo) − 67 mg/kg, iron (Fe) − 765 mg/kg, manganese (Mn) − 278 mg/kg and copper (Cu) − 77 mg/kg (Mashabela et al., 2023 ). Bioprimed and control seeds were sown and germinated under controlled greenhouse conditions (temperature = 22ºC to 23°C ± 2°C; relative humidity = 74%; min 32% and max 82%) with a light/dark cycle of 12 h/12 h and light intensity of 60 µmol/m2/s. Select seedlings were treated with laminarin extracts via foliar application, two days post germination; the open leaves of the seedlings were generously sprayed with 1% v/v reconstituted laminarin extract. The experimental set-up was as follows: Control (No PGPR/laminarin); T1 (PGPR); T2 (laminarin); T3 (PGPR_laminarin). Each condition of the experiment consisted of three biological replicates designed for a three-week temporal (time-dependent) metabolomic analysis experiment, for a total of 48 plants. The temporal approach provides a complete picture of how biostimulants treatments reshape plant metabolism over time, rather than just a snapshot at a single timepoint. Samples were collected weekly for three consecutive weeks, where three plants per sample were harvested as independent biological replicates and cryopreserved (quenched) in liquid nitrogen to prevent further metabolic and enzymatic activity. The samples were stored at -80ºC until metabolite extraction. 2.3. Metabolite extraction and UHPLC-ESI-Q-TOF-MS data acquisition Frozen leaf samples were crushed in liquid nitrogen; a 200 mg fine powder of pulverised leaf samples were resuspended in 2 ml of 80% ice-cold ( Romil-SpS™ Super Purity ) methanol and then sonicated for 10 min at 25ºC. The emulsified samples were centrifuged at 4000 rpm for 15 min, and the separated supernatants were dried at 55ºC in a dry bath. Samples were reconstituted in 350 µl of 50% ultra-LC grade ( Romil-SpS™ Ultra Purity ) methanol and filtered into LC-MS vials for analysis. Equal volumes of the sample aliquots were pooled together to prepare quality control (QC) samples used to assess the reliability and reproducibility of the analytical method. Data acquisition was performed on an ultra-high-performance liquid chromatography system coupled to high-definition mass spectrometry (UHPLC-HD-MS) (SYNAPT XS quadrupole time-of-flight Mass Spectrometer [QToF-MS], Waters Corporation, Milford, MA, USA) fitted with a Waters Acquity™ Premier HSS T3 C18 column (150 mm x 2.1 mm x 1.8 µm). The following parameters were observed: Solvent A was water containing 0.05% formic acid (FA) and 0.05% isopropyl alcohol (IPA), and solvent B was acetonitrile with IPA. The gradient elution protocol was as follows: 100% A to 0.0% B initially at 0 min, 100% A to 0.0% B from 0 to 1 min; 10% A to 90% B from 1 to 15 min; 1.0% A to 99.0% B from 15 to 15.10 min; 1.0% A to 99.0% B from 15.10 to 17 min; 100% A to 0.0% B from 17 to 17.10 min; and 100% A to 0.0% B from 17.10 to 20 min. The injection volume was 2 µl with a flow rate of 0.4 ml/min. The instrument scanned a mass range of 100–1500 Da. The source parameters were as follows: capillary voltage of + 2800 V, cone voltage of + 30 V, source temperature set at 120°C, desolvation temperature at 450°C, desolvation gas flow at 600 L/h, and cone gas flow at 50L/h. Data were collected in centroid mode at an approximate resolution of 10,000, with a scan time of 0.1 s to ensure over 10 data points per chromatographic peak. Internal mass calibration was achieved using the Lockspray interface (Waters Corporation), with a continuous infusion of leucine-enkephalin (500 ng/mL) at 15 µL/min. System operation and data acquisition were managed using MassLynx 4.1 software (Waters Corporation), producing raw (.raw) data files in both positive (ESI + ) and negative (ESI − ) ionisation modes. 2.4. Data processing, statistical analysis and biological interpretation Acquired data was processed on MzMine 4.7.8, an open-source and platform-independent software for mass spectrometry (MS) data processing and visualisation ( https://github.com/mzmine/mzmine/releases/tag/v4.7.8 ), with the following parameters: a retention time (rt) range of 0.00 to 30.00 min, max peaks in chromatograms of 15, with 4 minimum consecutive scans. Rt tolerance was set at 0.04 and 0.10 min for intra-sample and sample-to-sample, respectively. The QToF was set to detect absolute intensity for both MS 1 and MS 2 at 5% and 10% noise threshold. The mass ( m/z ) tolerance (scan-to-scan) was 0.0050 m/z , with 0.0015 m/z for intra-sample and 0.0040 m/z for sample-to-sample mass tolerance. MzMine actively imports [.raw] data files to convert to the compatible [.mzML] for processing, followed by mass detection and chromatogram building. The resulting data matrix was exported and modified for multivariate data analysis (MVDA) on MetaboAnalyst 6.0 ( https://www.metaboanalyst.ca/MetaboAnalyst/ModuleView.xhtml ). The data matrix (.csv) was modified through variable selection, sample filtering and data transposition and then uploaded onto MetaboAnalyst for sample normalisation by media, log transformation, and Pareto scaling for chemometrics and cluster analysis, utilising Principal Component Analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA) and heatmaps for exploratory and quantitative analysis. Similar parameters were specified for metabolite annotation in MzMine, with applied smoothing and stable ionisation across samples. Annotation followed Level 2 guidelines established by the Metabolomics Standards Initiative (MSI), ensuring annotations were based on similarities with published data, including spectral patterns and elemental compositions housed in the Global Natural Product Social Molecular Networking (GNPS) ecosystem. Annotation is performed via spectral matching, computing matched signals between MS2 spectra to MS Level 2 for similarity scores of 0.72 up to 0.94 for annotated metabolites, linked to databases such as the GNPS-Library Explorer-Version 0.1 ( https://library.gnps2.org/ ), MassBank Europe ( https://massbank.eu/MassBank/ ), MassBank of North America (MoNA- https://mona.fiehnlab.ucdavis.edu/ ), and Pubchem ( https://pubchem.ncbi.nlm.nih.gov/ ). Similar databases were explored for refined identifications of selected metabolites, using the m/z values of detected features and their empirical formulae. Subsequent pathway and enrichment analysis, and biological interpretation were performed on the MetPA tool within MetaboAnalyst 6.0, facilitating the analysis, identification, and visualisation of impacted pathways. MetPA utilises high-quality KEGG metabolic pathways as its underlying knowledge base. Employing MetPA for pathway analysis provided a framework for partially mapping the molecular landscape of the metabolome under study, enabling the biological interpretation of observed changes in the endo-metabolome. 3. Results and Discussion 3.1. Multi-variate data analysis reveals treatment-specific classifications of metabolic profiles in wheat seedlings This study used microbial and non-microbial biostimulants in the form of PGPR and seaweed extracts (purified laminarin) for an untargeted (qualitative and quantitative) metabolomics approach to investigate the combined and individual effects of biostimulants on the metabolome of wheat seedlings. An untargeted metabolomics strategy, based on UHPLC-HD-QTOF/MS, was applied to obtain metabolomics data. Resulting data matrices, acquired in both negative and positive ionisation modes (ESI − ; ESI + ), produced 7970 and 117726 features, respectively, displaying the method as effective for obtaining a comprehensive coverage of the plant’s metabolome. After quality filtering, peak alignment, and statistical analysis (one-way ANOVA, VIP > 1.0, p < 0.05), significant features were subjected to Level 2 metabolite annotation using MS/MS spectral matching, resulting in 89 confidently identified metabolites. Given the complexity and the massive amount of metabolomics data, chemometrics methods were applied to further deconvolute the data and explore the metabolic landscapes of the wheat seedlings under different conditions. Principal component analysis (PCA), a dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional space, and partial least squares-discriminant analysis (PLS-DA), an effective binary and multi-group classifier, was used for group separation based on varying metabolic profiles of seedlings, and to reveal underlying patterns and metabolic signatures. PLS-DA describes the most significant variance to differentiate between practical classes to decipher the metabolic features that are most significant to the observed classification (Mashabela et al., 2022 ). The generated PCA (data not shown here) and PLS-DA models (Fig. 1 A) display distinct and sample-specific grouping, and clear separation was observed between control, PGPR-treated, laminarin-treated and combined PGPR_laminarin-treated samples, with PC 1 and PC 2 explaining 15.6% and 25%, while Component 1 and 2 explaining 17.4% and 17.8% of the total variance for the PCA and PLS-DA respectively. The PLS-DA model was cross-validated to an accuracy measure of 1.0, R 2 = 0.98 and Q 2 of 0.897 for five components and a 5-fold cross validation (CV) method. These differential sample groupings evidently highlight the underlying metabolic perturbations induced by the microbial, non-microbial and combined biostimulants applications. Laminarin-treated samples showed a significant separation in a distinct region (positive Component 1), indicating the greatest effect on wheat seedling metabolome compared to their counterparts. The separation indicates a complete metabolome reprogramming with no overlapping metabolic features compared to the control. PGPR treatments formed an intermediate cluster, showing only partial metabolic reprogramming relative to the control. In contrast, PGPR_laminarin combinations occupied a transitional space between individual treatments, suggesting a balanced metabolic adjustment rather than a strictly additive or synergistic effect. This implies that laminarin-driven changes may be moderated by the presence of PGPR, while laminarin treatment in turn may enhance the individual influence of PGPR treatment. The distinct SWE separation indicates that the laminarin extract alone induces a unique metabolic profile, likely driven by its bioactive constituents, such as polysaccharides consisting of a β-(1–3)-linked glucan backbone with β-(1–6)-linked side chains (Pramanik et al., 2024 ), which may activate pathways not strongly triggered by PGPR, and to a lesser extent, the combined biostimulants application (El Jazouli et al., 2024 ). Similar patterns were observed in the quantitative heatmap of the 100 most impactful features (Fig. 1 B). Laminarin-treated samples exhibited the highest metabolic impact across multiple features, displaying a clear, significant metabolic shift. The combined biostimulants application of PGPR and laminarin displays slightly comparable effects to PGPR treatment but shows a reprogramming spanning a greater range of metabolites compared to PGPR treatment’s concentrated effects. Hence, PGPR alone shows moderate, consistent changes, while combination treatment seems to avoid metabolic extremes. This observation could indicate a broad activation of the metabolome (metabolic pathways) by laminarin treatment, which is not triggered by the PGPR (El Jazouli et al., 2024 ; Tran and Kim., 2023), while the combined treatment may take on a balanced effect, and integration of the two separate treatments. The heatmap in Fig. 1 C shows a time-dependent metabolic reconfiguration, revealing dynamic metabolic responses with distinct temporal patterns for each treatment. Laminarin treatment revealed a near-instant metabolic shift, with an evidently significant impact on the metabolome of wheat seedlings from the onset of the treatment in week one samples. The upregulation of the top 100 impactful metabolites follows a strong upward trend over the three-week period, as compared to other treatments, demonstrating an instant reaction and a sustained metabolic shift in response to laminarin application over time. This pattern mirrors the PLS-DA model and the quantitative heatmap in Figs. 1 A &B , which greatly distinguishes the impact of laminarin treatment on the metabolome of wheat seedlings, potentially through slowly metabolised bioactive compounds or persistent signalling cascades (Tran and Kim., 2023). In contrast, control seedlings portray a normal metabolic state with metabolite upregulation following a slow and steady profile. The combined biostimulants application showed a gradual metabolic shift, slightly urged by the PGPR treatment, with some changes (up-/downregulations) reversing or stabilising over time. PGPR effects may be rapid and adaptive, as seen in the PGPR-induced metabolic shift at week 1 of treatment (Fig. 1 C), consistent with microbial priming behaviour known to prepare plants for stress without maintaining prolonged metabolic burden (Calvo et al., 2014 ). Notably, the temporal analysis revealed that certain metabolic features showed biphasic responses, with early activation followed by regulatory modulation, particularly evident in the combination treatment. laminarin may induce an immediate and longer-lasting metabolic signature, whereas PGPR effects could be more dynamic and context-dependent. According to Ali et al. ( 2021 ), the method of application of the seaweed extracts plays an important role in their use and responses by plants. Most application types are either foliar, root application, or a combination of both. Comparative studies have shown foliar sprays of SWEs to be optimal for the crop and more effective (Ali et al., 2021 ). The better performance of foliar applications has been attributed to the immediate interaction with the plant tissues, as foliar absorption happens almost immediately (Mughunth et al., 2024 ). This is consistent with our findings, which show a robust metabolic response at the onset of SWE foliar application, and a sustained metabolic reprogramming (Fig. 1 C), for an overall greater impact on the metabolome of wheat seedlings (Fig. 1 B). The hierarchical clustering of metabolic features identified distinct co-regulated metabolite clusters, suggesting coordinated metabolic network responses rather than isolated pathway activation. This systems-level metabolic coordination indicates that treatments trigger comprehensive metabolic reprogramming rather than targeted pathway-specific effects. This pattern mirrors findings from Arabidopsis and maize, where seaweed extracts elicit substantial shifts in metabolomic profiles, including primary metabolites and secondary defence compounds (Ertani et al., 2018 ; Tinte et al., 2022 ). These observations suggest that laminarin provides a stable, robust reprogramming, perhaps favouring energy metabolism and baseline readiness for stress, whereas PGPR contributes rapid, flexible modulation. Their combination does not sum their effects additively; instead, PGPR may buffer or reshape the laminarin-triggered metabolic landscape, offering a balanced outcome that may favour both growth and resilience. 3.2. Enrichment analysis show activation, and subsequent modulation of primary and secondary metabolism The pronounced metabolic differentiation observed in the PLS-DA analysis and the identification of laminarin (SWE) as the most metabolically impactful treatment prompted us to investigate which specific biochemical pathways were driving these observed metabolic shifts. The clear temporal dynamics and treatment-specific metabolite accumulation patterns suggested coordinated activation of metabolic networks rather than random metabolic perturbations. To understand the biological significance of these metabolomic changes, we conducted targeted pathway enrichment analysis, correlation studies and pathway analysis using putatively annotated metabolites to identify the key metabolic pathways responsible for the observed treatment-specific signatures. From detected features, 89 metabolites were confidently annotated, and 15 key metabolic pathways were significantly enriched. The peak intensities of annotated metabolites, spanning classes of metabolites such as phenolics (flavonoids and HCAs), lipids, organic acids, amino acids and benzoxazinoids, were used to construct and interactive heatmap (Fig. 2 A), illustrating the differential accumulation and relative metabolite concentrations under varying treatments. The heatmap reveals treatment-associated metabolite up/down-regulations, thus giving insight into the effects of each type of treatment on the measured metabolome of the wheat seedlings. A correlation analysis ( Fig. S1 ) further confirmed that these changes were not random but were driven by treatment-specific biochemical reprogramming: some treatments consistently upregulated metabolites while downregulating others, whereas alternative treatments produced the opposite pattern (Fig. 2 A). Importantly, the combined PGPR_laminarin treatment did not necessarily act synergistically in amplifying changes; instead, it appeared to produce a more moderate and balanced metabolic profile compared to the more extreme responses elicited by PGPR or laminarin alone. This systems-level approach allowed us to connect the individual metabolite changes to broader metabolic network activation and elucidate the mechanistic basis for the distinct physicochemical responses observed across treatment groups. The integration of multivariate metabolomic analysis with pathway-level interpretation provided comprehensive insights into the molecular mechanisms underlying plant growth promotion and the synergistic versus balanced effects of combined biostimulants applications. Pathway enrichment analysis (Fig. 2 B & Fig. S2 ) revealed highly significant activation of core metabolic processes across all treatments, with the citrate cycle (TCA cycle) emerging as the most significantly enriched pathway ( p = 1.259 e − 6 , False Discovery Rate [FDR = 1.171E-4). Additional strongly enriched pathways included glyoxylate and dicarboxylate metabolism ( p = 9.095 e − 6 ), phenylpropanoid biosynthesis ( p = 3.564 e − 4 ), and aromatic amino acid biosynthesis pathways (phenylalanine, tyrosine, and tryptophan biosynthesis; p = 0.001569). This enrichment pattern indicates enhanced energy metabolism coupled with secondary metabolite production, consistent with growth promotion and defence priming responses. 3.2.1. Primary metabolism: Energy production and central carbon flow Analysis of the quantitative distribution of annotated metabolites revealed profound treatment-specific alterations of the primary metabolism and cellular energy systems (Fig. 2 A). Laminarin treatment induced a comprehensive activation of the central carbon metabolism (the TCA cycle), informed by the massive elevation of TCA intermediates, such as citrate, as an indicator of high TCA entry point. The sustained accumulation of succinate, aconitate, oxaloacetate and malate confirmed the increased functional capacity of the TCA cycle, creating a metabolic state of energy abundance and high metabolic activity. The strong TCA cycle enrichment and accumulation of TCA intermediates suggest enhanced glycolytic flux feeding into central carbon metabolism, evidenced by the co-enrichment of the glyoxylate and dicarboxylate metabolism, butanoate metabolism, glycolysis and pyruvate metabolism, creating a coordinated primary metabolic surge that fundamentally transforms the plant's energy landscape. Laminarin treatment further showed a pronounced effect on lipid-associated metabolites, particularly monogalactosyldiacylglycerol (MGDG) isomers such as MG (16:3/18:3) and MG (18:3/18:3), as well as linoleoyl-containing glycerolipids. In contrast, PGPR did not strongly induce lipid metabolites or an enhanced activation of the central energy metabolism, and, in some cases, showed lower levels of activity compared to the control, while the PGPR_laminarin treatment maintained lipid metabolite levels closer to laminaria’s profile but with reduced extremes. Seaweed extracts are complex biostimulants containing polysaccharides, oligosaccharides, phytohormones (auxins, cytokinins), betaines, polyamines and a range of trace lipids and sterols; these compounds are reported repeatedly to improve nutrient uptake, photosynthetic capacity and abiotic stress tolerance and to act as elicitors of metabolic reprogramming toward energy supply and membrane stabilisation (Ali et al., 2021 ). As previously alluded to, laminarin is a glucose-rich bioactive compound (Pramanik et al., 2024 ); as such, sugar perception is expected to trigger growth/energy metabolism. Plants perceive β-glucans as Microbe-Associated Molecular Patterns (MAMPs) and, following partial hydrolysis by plant β-1,3-glucanases, can release short glucan oligomers and hexoses. Elevated cytosolic hexose and glucose bias the plant toward the upregulation of primary metabolic routes to meet energetic and biosynthetic demand (Eom et al., 2024 ; de Oliveira et al., 2024; 29. Giesbrecht et al., 2025 ). Thus, SWE application commonly stimulates primary carbon metabolism, glycolysis, photosynthesis, osmolyte accumulation and membrane lipid remodelling (Mishra et al., 2025 ), consistent with our data’s perceived enrichment of the primary metabolism. Accordingly, the depolymerisation of laminarin can directly feed carbon into central metabolism. Recent biochemical work highlights laminarin as a favourable β-1,3-glucan substrate for such enzymes as endo-β-1,3-glucanases, which act on laminarin to generate oligomers and ultimately glucose that enters the central energy metabolism (glycolysis/TCA) (Caseiro et al., 2024 ; Kim et al., 2024 ). PGPR treatment demonstrated a markedly different primary metabolic signature, characterised by increased levels of amino acids such as tyrosine, tryptophan, phenylalanine, and valine, indicating the activation of the amino acid biosynthetic pathways. Additionally, amino acid derivatives/conjugates, including malonyltryptophan, tryptoline, N -acetylaspartyl glucoside, phenyl butyrate glucoside, and N -Fructosyl isoleucine, were elevated (Fig. 2 A). These shifts mirror observations in PGPR-treated wheat, where elevated aromatic amino acids feed into phenylpropanoid-dependent defence pathways, supporting enhanced priming and ISR-like responses (Mashabela et al., 2023 ). This phenomenon could result from the PGPR’s nitrogen-fixing capabilities, leading to improved nutrient availability and, consequently, enhanced amino acid biosynthesis. Our pathway analysis reinforces these observations, showing strong enrichment for aromatic-amino-acid biosynthesis (Phe, Tyr, Trp), valine, leucine, isoleucine biosynthesis, and alanine, aspartate, and glutamate metabolism (Fig. 2 B). A similar enrichment of aromatic amino acid pathways has been reported in chickpea plants treated with Bacillus spp (Mashabela et al., 2022 ) and tomato plants, consistently amplifying levels of amino acids, organic acids, hydroxycinnamic acids, flavonoids, and fatty acids (Mhlongo et al., 2020 ). Interestingly, PGPR treatment moderately activated the TCA cycle, increasing intermediates such as citrate, succinate, and malate. This upregulation supported biosynthesis while remaining tightly regulated, unlike the rapid, “explosive” response from laminarin. The combined application indicated a balanced regulation, tempering laminarin-driven surges while enhancing PGPR effects. 3.2.2. Secondary metabolism: defence, structure, and specialised metabolite production Secondary metabolite analysis revealed dramatic treatment-specific differences in the production of protective and structurally relevant compounds as well as signalling molecules. Laminarin treatment prominently increased specific lignin precursors, glycosylated flavonoids, and coumarin derivatives. Compounds such as caffeic acids, syringic acid, and certain coumaroyl putrescine derivatives were upregulated (Fig. 2 A), for an enriched phenylpropanoid pathway ( Fig. S2 ). The treatment also favoured isovitexin, saponarin, and orientin derivatives, compounds known for osmoprotective roles and membrane stabilisation (Al-Khayri et al., 2023). Interestingly, some metabolites highly upregulated by PGPR (e.g., DIMBOA and HMBOA), known for defence signalling, were suppressed under laminarin treatment, suggesting divergent signalling cues by the two biostimulants. Laminarin lowered enrichment in reactive DIMBOA derivatives but moderate enrichment in DIBOA and storage-glycoside forms, reflecting a less costly, standby defence mode compared to the highly upregulated and targeted defence signalling activation from PGPR treatment. Laminarin treatment also favoured flavone C-glycosylation for a comprehensive secondary metabolite activation. PGPR treatment presented a more targeted secondary metabolic strategy, primarily affecting defence-related compounds and specific amino acid pathways with a reconfiguration in the metabolism of phenolic compounds, flavonoids, polyamines and benzoxazinoids. This approach showed selective enrichment of metabolites, where aromatic amino acid changes were selective rather than comprehensive, with tyrosine showing moderate increases sufficient for targeted defence compound production without the metabolic expense of maximum accumulation. Phenylalanine levels showed controlled elevation that provides adequate phenylpropanoid precursors for essential secondary metabolite production while maintaining metabolic efficiency. PGPR treatment tends to push secondary metabolism toward rapid-response defence metabolites, such as DIMBOA glycosides, ferulic/isoferulic acid, tricin and luteolin glycosides. This pattern is consistent with classic PGPR effects; many root-associated rhizobacteria induce systemic resistance (ISR) and prime the plant immune system for faster or stronger accumulation of defence metabolites upon challenge. Mechanistically, ISR often involves priming of transcriptional programs controlled by hormone and MYB/bHLH regulatory networks that elevate flux through the shikimate-to-phenylpropanoid axis and into specialised defence products rather than long-term storage forms. These ISR/priming effects are well documented for Pseudomonas, Bacillus and similar PGPR strains (Backer et al., 2018 ). Application thereof further boosted phenylpropanoid derivatives, flavonoids (kaempferol glycosides), and certain hydroxycinnamic acids. This indicates an activation of defence-related secondary metabolism. A strong accumulation of aglycone flavones and flavanols was observed, followed by low levels of glycosylated derivatives. Amine conjugates were also elevated under PGPR treatment, inclusive of coumaroyl putrescine and feruloyl agmatine. Similar metabolomic patterns have been reported in PGPR-treated wheat, tomato, and maize, where elevated phenolic acids, flavonoids, and amine conjugates contribute to pathogen resistance, antioxidant defence, and lignin biosynthesis (Zhang et al., 2020 ; Mishra et al., 2025 ). Beyond direct defence, flavonoids such as kaempferol and quercetin also act as signalling molecules in plant-PGPR interactions, facilitating root colonisation and enhancing stress tolerance (Sharma et al., 2023). From a biochemical perspective, activating the shikimate and phenylpropanoid pathways requires substantial carbon flux from glycolysis and the pentose phosphate pathway. The metabolite and enrichment maps show that when PGPR strongly biases plants toward these secondary pathways, central carbon intermediates (and their ratios) move accordingly, consistent with a reallocation of carbon to defence. This explains the negative correlations observed between PGPR clusters and some energy/storage metabolites, such as sugars and TCA intermediates, a trade-off where defence demand transiently competes with storage/energy pools (Cai & Aharoni, 2022 ). The growth-defence trade-off is a recognised, general phenomenon in plants and explains the PGPR treatment’s increase in defence chemistry at the cost of changes in primary metabolite pools. Benzoxazinoids (BXs) are especially relevant in grasses (wheat, maize, rye) (Mashabela et al., 2022 ). BXs are potent antiherbivore/antimicrobial defences produced from tryptophan/aromatic metabolism and are known to be dynamically regulated in response to root and foliar stimuli. Transient conversion between active aglycones and glycosylated, storage forms is common; the balance between these forms determines immediate toxicity versus storage/readiness (Hama et al., 2025). Our observation that PGPR increased active BX signatures (and some conjugates) mirrors reports that beneficial rhizobacteria can prime BX-based defences in cereals (Hama et al., 2025). Similar to its modulation of the primary metabolism, a combined treatment showed a more moderate, broad enrichment across defence-related secondary pathways. Levels of secondary metabolites were generally less extreme than with either PGPR or laminarin alone. Some phenolics that were strongly upregulated by PGPR and downregulated by laminarin treatment stabilised at intermediate values, while others shifted slightly toward either PGPR- or laminarin-like profiles depending on pathway dominance. For instance, DIMBOA levels were reduced compared to PGPR alone, but storage forms (DIMBOA-glucoside) increased, a potential indication of a ready, but not metabolically burdensome defence priming. A balanced presence of ferulic acid and hydroxycinnamic acid derivatives was observed, mirrored by moderate levels of p -coumaroyl agmatine and feruloyl putrescine, without the PGPR-induced spikes. Remarkably, the PGPR_laminarin combination did not demonstrate classical synergistic effects where additive responses might be expected, as demonstrated in Fig. 1 A from a PLS-DA model of metabolic features and the treatment-specific pathway regulation in Fig. 2 B, suggesting important regulatory interactions between the two bioactive agents. Growth–defence trade-offs are regulated by hormone crosstalk (JA, SA, auxin, cytokinin, ethylene, ABA) and by systemic signalling that tunes resource allocation. When both a PGPR (which primes ISR) and laminarin (which supplies growth/energy signals and hormones) are present, the plant’s regulatory networks can balance the competing demands and produce a state of moderate readiness (some secondary metabolites), while preserving metabolic capacity for growth and membrane maintenance (Song et al., 2024 ). 3.2.3. Overall metabolic pathway analysis and implications of treatment-specific metabolic perturbations The correlation heatmap ( Fig. S3 ) shows tight sample clusters by treatment and large blocks of positive/negative correlation between treatment groups. This pattern means the treatments are changing broad, concerted programs of metabolism, not single metabolites, and the pathway enrichment maps confirm those programs: central carbon metabolism (TCA, glyoxylate, pyruvate), amino-acid biosynthesis (shikimate: Phe, Tyr, Trp), and phenylpropanoid/flavonoid/benzoxazinoid branches are all involved. In practice, this presents multiple interconnected pathways as being up- or down-regulated in concert, with central metabolites (e.g., oxaloacetate-TCA) acting as hubs that re-partition carbon and energy into different sinks (growth, lipids, defence). The correlation structure and pathway enrichment together show that PGPR and laminarin treatments impose distinct but overlapping metabolic programs in wheat seedlings. PGPR principally directs carbon into the shikimate, phenylpropanoid and benzoxazinoid branches associated with rapid biotic-defence responses, whereas laminarin treatment redirects flux toward TCA/glyoxylate, sugar and lipid pathways that support energy supply, membrane stability and osmoprotection, with the effects of treatments split between survival (plant growth and development by laminarin) and defence by PGPR. It should be noted that metabolic reprogramming is not only a matter of up- or downregulation of metabolites, with the subsequent impact on specified metabolic pathways, and the ultimate induction of a physiological response. The process is rather an intricate dynamic of metabolite flux, and metabolite correlation (cause and effect), i.e., the downregulation of one metabolite for the utility in the production of another, a progressive metabolic feeding mechanism that ultimately impacts biochemical processes characteristic of specified metabolic pathways (Huot et al., 2014 ). Differential responses across the phenylpropanoid network (cinnamic acid → coumaric acid → caffeic acid → ferulic acid → sinapaldehyde) show that treatments modulate specific branches rather than uniformly increasing activity. Laminarin treatment induced sharp shifts, with malate depletion and oxaloacetate and citrate accumulation, suggesting rapid turnover via malate dehydrogenase. Moderate increases in aconitate and succinate further support active TCA cycle engagement. The oxaloacetate profile shows the highest variability among treatments, suggesting dynamic and high metabolic activity. This pattern indicates that laminarin treatment pushes primary metabolism toward maximum output, hence elevated levels of oxaloacetate, an essential component of energy metabolism, given its involvement in multiple pathways such as gluconeogenesis, the urea cycle, amino acid synthesis, fatty acid synthesis (Fig. 3 A ) , and the glyoxylate cycle (Fig. 3 B), serving as a key hub in this metabolic acceleration (Yang et al., 2022 ). Mechanistically, this laminarin-driven reprogramming can be linked to three interconnected processes: (i) enhanced photosynthetic performance and nutrient uptake after laminarin application increases carbohydrate influx into central metabolism, thereby sustaining higher TCA flux and elevating acetyl-CoA availability; (ii) increased acetyl-CoA and CoA-dependent metabolism fuels fatty-acid elongation and glycerolipid synthesis, crucial for maintaining membrane integrity and fluidity under stress; and (iii) bioactive compounds in seaweed extracts, including polysaccharides and oligosaccharide fragments, can act as mild elicitors that promote glycosylation and stable storage of secondary metabolites (e.g., glycosylated flavonoids and glycosylated benzoxazinoids), providing a low-cost, energetically efficient protective strategy compared to maintaining high levels of reactive aglycones. These coordinated shifts, supported by multiple reports in the seaweed biostimulants literature, illustrate that laminarin’s metabolic impact is not a uniform “push” but a strategically routed redistribution of metabolic fluxes to optimise both energy metabolism and stress preparedness (Craigie, 2011 ). As previously highlighted, laminarin treatment prompts a comprehensive metabolic perturbation and thus pathway activation. For instance, phenylalanine reaches maximum accumulation levels, providing abundant precursors for the entire phenylpropanoid network. Tyrosine and tryptophan similarly show peak accumulation, creating comprehensive aromatic amino acid availability (Fig. 3 C). Downstream phenolic acid production shows coordinated maximum activation; cinnamic acid, coumaric acid, caffeic acid, ferulic acid, and sinapaldehyde reach their highest levels under laminarin treatment. This represents complete phenylpropanoid pathway activation from initial aromatic amino acid precursors through to specialised secondary metabolites. In comparison, phenylpropanoid pathway activation is selective and targeted under PGPR treatment. Phenylalanine, tyrosine, and tryptophan show moderate increases, providing adequate precursor availability without metabolic excess, while downstream phenolic acids show strategic patterns; coumaric acid and caffeic acid demonstrate moderate but consistent elevation, focusing on essential defensive compounds. Ferulic acid shows controlled increases supporting cell wall strengthening without excessive investment. Cinnamic acid and sinapaldehyde remain relatively stable, indicating selective pathway activation rather than comprehensive stimulation, such as seen under laminarin treatment. The pathway analysis reveals that PGPR treatment prioritises defensive preparation over maximum production, creating plants with enhanced stress tolerance capacity while maintaining metabolic efficiency. With overall greater impact on the secondary as compared to the primary metabolism, PGPR treatment clearly favours defence priming over basic growth and development. Additionally, the sophisticated metabolic integration of the combined biostimulants application was observed. PGPR_laminarin co-treatment harmonises the comprehensive and maximum metabolic pathway activation of laminarin treatment with the moderated and targeted approach by PGPR on both the primary and secondary metabolism. The intermediate levels in combination treatment are not simple averages, but rather representations of active regulatory modulation that capture benefits while avoiding costs, particularly evident in the controlled malate levels and optimised phenolic acid production (Figs. 3 A &C ). Concerning the treatment’s modulation of the primary metabolism, malate shows intermediate regulation; succinate and aconitate demonstrate harmonised intermediate levels that suggest regulatory modulation preventing metabolic extremes. Oxaloacetate variability is controlled, indicating sophisticated regulatory mechanisms that maintain metabolic flexibility while preventing destabilising fluctuations, thus a representation of metabolic intelligence. This is further evidenced through the phenylpropanoid pathway, which comprehensively captures the combined treatment’s potential to carry both the plant growth and development capabilities of laminarin treatment, driven by the enhanced activation of the energy metabolism, and the defence and priming traits characterised by the targeted modulation of the secondary metabolism by PGPR treatment. This correlation analysis demonstrates that secondary metabolism responds to biostimulants treatments through coordinated network-level regulation, with the combination treatment achieving balanced activation that maintains optimal metabolic network connectivity while capturing the benefits of both individual treatments. 3.2.4. Not synergistic, nor additive, rather intelligent integration and regulatory sophistication In agricultural biotechnology, combining two biostimulants is often assumed to yield either additive effects (sum of individual impacts) or synergistic effects (combined impact greater than the sum). However, our results challenge this binary framework. The combined PGPR_laminarin treatment did not simply amplify the metabolic trends of either treatment alone; instead, it produced a balanced, harmonised metabolic state that moderated the extremes observed under PGPR or laminarin treatment individually. PGPR alone shifted metabolism strongly toward defence-biased secondary pathways (shikimate-derived phenylpropanoids, benzoxazinoids), often at the expense of energy-storage intermediates, and laminarin treatment alone promoted robust primary metabolism (TCA cycle, lipid biosynthesis, osmolyte accumulation) sometimes with reduced investment in rapid-response defence compounds. The combined treatment distributed metabolic resources across both domains. This balanced state was evident in intermediate metabolite levels, moderated enrichment scores, and reduced extremes in correlation heatmaps compared with single treatments. Rather than reflecting a weaker interaction, such moderation likely arises from regulatory cross-talk between pathways mediated by plant hormone signalling and metabolic sensing. PGPR primes induced systemic resistance (ISR) largely via jasmonic acid (JA) and ethylene signalling (Pieterse et al., 2014 ), whereas laminarin treatment introduces a suite of hormonal and osmoprotective cues (cytokinins, auxins, betaines) that sustain growth and energy supply (Huot et al., 2014 ). The co-occurrence of these cues may activate feedback mechanisms, such as SA-JA antagonism, TOR-SnRK1 signalling, and redox balancing (Zhao et al., 2025 ), that optimise resource allocation rather than pushing metabolism toward a single extreme. From a functional standpoint, this "intelligent integration" may be more desirable in variable field conditions than a purely additive or synergistic boost to one domain (defence or growth). In fluctuating environments, plants require both readiness to respond to biotic stress and the metabolic capacity to maintain photosynthesis, membrane stability, and osmotic balance. A harmonised metabolic profile reduces the risk of over-investment in one function at the expense of the other, a trade-off well documented in growth-defence theory. Recent studies of combined biostimulants applications echo this phenomenon: multi-input treatments often yield complementary or buffering effects, where the presence of one input mitigates the metabolic cost of the other, leading to sustained performance and resilience. This suggests that the goal in biostimulants integration should not always be “more of everything” but rather regulatory sophistication, the plant’s ability to integrate diverse cues into a stable, adaptive metabolic state. In this context, our findings emphasise that balanced metabolic integration can be a strategic advantage, enabling crops to navigate environmental uncertainty without incurring the penalties associated with extreme metabolic investment in either growth or defence. This reframes the expectations for combined biostimulants: instead of seeking synergy in magnitude, we may need to value synergy in stability and adaptability. 3.2.5. Implications for agricultural applications The metabolomic analysis revealed three distinct strategic phenotypes that fundamentally reshape our understanding of biostimulants mechanisms (Fig. 4 ).The three distinct metabolic strategies uncovered in this study offer practical guidance for farmers and the agroeconomic industry for optimised biostimulants formulations and applications (Fig. 4 ). The laminarin treatment delivers an instant and sustained metabolic burst, pushing plants into an explosive activation of the energy metabolism and broad secondary metabolite synthesis, a strategy most useful during favourable growing conditions with rapid and vigorous growth as the main objective. In contrast, PGPR acts more like a "steady marathon runner," supporting efficient energy use while steadily strengthening the plant’s natural defences and stress tolerance/resistance mechanisms. This makes PGPR particularly valuable when crops face stresses such as drought, heat, or disease pressure. For realistic field conditions, i.e., farming situations, where conditions are variable and both productivity and resilience matter, the combination treatment emerges as the smart choice, functioning like an intelligent management system. The combined PGPR_laminarin treatment can harmonise accelerated growth while prioritising survival by adapting metabolic resources as conditions change (Fig. 4 ). Considering a crop’s different phases of development, this metabolic flexibility is essential, where young plants may benefit from the laminarin treatment’s growth print through a burst in carbon and energy metabolism, while maturing plants facing reproductive or environmental stress may require the PGPR's steady support. At critical stages like flowering, the combination treatment provides an optimised balance that supports both yield and resilience. This research contributes to an especially exciting move beyond the traditional "one-size-fits-all" approach to biostimulants and toward a more sophisticated understanding of the mechanistic nature of biostimulants' action at the cellular level. By understanding how different treatments reshape plant metabolism, we can tailor applications to specific crops, growth stages, and environmental conditions. Looking ahead, this opens fascinating possibilities for developing precision agriculture tools that could monitor plant metabolic status in real-time, adjust biostimulants applications based on current growing conditions, and even guide plant breeding programs toward varieties that can better harness these metabolic benefits, ultimately helping farmers achieve both higher yields, stronger stress resilience, and more sustainable production systems. 4. Conclusions and potential prospects This system-level metabolomics study demonstrates that biostimulants treatments fundamentally reprogram plant metabolism through distinct mechanisms, creating three unique metabolic phenotypes that present different strategic approaches to plant performance enhancement. Laminarin treatment creates a “metabolic sprint” phenotype characterised by maximum energy production and comprehensive secondary metabolite activation, with high metabolic cost and potential instability, while PGPR treatment generates a “metabolic marathon”, with efficient energy production and targeted defensive preparation with optimal metabolic stability. Most interestingly, a combination of biostimulants achieves a “metabolic optimisation” phenotype that captures the high-performance benefits while maintaining regulatory control and metabolic intelligence through sophisticated metabolic integration rather than simple synergistic or additive effects. The correlation analysis and pathway-specific data reveal that treatments influence metabolism through pathway-level regulation rather than individual metabolite control, utilising branch point regulation within secondary metabolic networks, flux control mechanisms that prevent metabolic bottlenecks while maintaining high production capacity and coordinated precursor-product relationships that maintain metabolic efficiency. The most groundbreaking finding is the emergence of metabolic intelligence, the ability to balance high energy production capacity with regulatory control, as a key determinant of superior plant performance, manifesting as reduced metabolic variability while maintaining high average production, optimal rather than maximum metabolite accumulation, enhanced stress preparedness without metabolic panic responses, and sustainable high-performance metabolism. These insights reveal that metabolic optimisation, rather than metabolic maximisation, represents the fundamental principle underlying superior plant performance and provide a mechanistic framework for developing more effective biostimulants combinations through strategic application matching to specific crop growth stages, environmental stress levels, and desired performance outcomes in precision agriculture applications. Declarations Conflict of interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding The research was funded by project 101086366 “CropPrime” HORIZON-MSCA-2021-SE-01. Author Contribution Conceptualization: M.D.M and M.I.M., Methodology: M.D.M, T.T, N.S and M.I.M., Investigation: M.D.M., T.T., and M.I.M., Visualization: M.D.M., Funding acquisition: P.K., and M.I.M., Project administration: M.I.M. Supervision: M.I.M., T.T., P.K. and L.A.P., Writing - original draft: M.D.M., Writing - review & editing: M.I.M., P.K., T.T. and L.A.P. All authors reviewed and approved the final manuscript Acknowledgement The University of Johannesburg and the Global Excellence and Stature (GES) scholarship are thanked for fellowship support to M.D.M. The Agricultural Research Council - Small Grains (ARC-SG), South Africa, is thanked for providing seeds of the wheat cultivar used in this study. T.T. and Ms. Joyce Mebalo are thanked for their assistance and for hosting MDM at ARC-SG for the duration of the primary experimental work. The authors would also like to acknowledge BioAtlantis Ltd. For their collaboration, and project 101086366 - CropPrime - HORIZON-MSCA-2021-SE-01, for funding the project. References Ali, O., Ramsubhag, A., & Jayaraman, J. (2021). Biostimulant Properties of Seaweed Extracts in Plants: Implications towards Sustainable Crop Production. 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The SnRK1-JMJ15-CRF6 module integrates energy and mitochondrial signaling to balance growth and the oxidative stress response in Arabidopsis. The New phytologist , 246(1), 158–175. https://doi.org/10.1111/nph.20425 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 13 Jan, 2026 Read the published version in Plant Growth Regulation → Version 1 posted Editorial decision: Revision requested 09 Nov, 2025 Reviews received at journal 08 Nov, 2025 Reviews received at journal 03 Nov, 2025 Reviewers agreed at journal 16 Oct, 2025 Reviewers agreed at journal 14 Oct, 2025 Reviewers agreed at journal 01 Oct, 2025 Reviewers invited by journal 29 Sep, 2025 Editor assigned by journal 19 Sep, 2025 Submission checks completed at journal 19 Sep, 2025 First submitted to journal 19 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7656371","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":527326169,"identity":"e6da5456-72f5-4b78-b87c-50d029815397","order_by":0,"name":"Manamele D. 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06:04:15","extension":"html","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":168505,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7656371/v1/2b25de71cf36b4325a3ce11c.html"},{"id":93459891,"identity":"f2887778-6075-4e5c-bc4c-62252e6bfa3b","added_by":"auto","created_at":"2025-10-14 06:04:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":336003,"visible":true,"origin":"","legend":"\u003cp\u003eChemometric analysis of methanolic extracts from treated wheat seedlings.\u003cstrong\u003e \u003c/strong\u003eThe PLS-DA model (\u003cstrong\u003e1A\u003c/strong\u003e) shows distinct separation due to treatment-specific perturbation of metabolic profiles, complemented by an interactive heatmap (\u003cstrong\u003e1B\u003c/strong\u003e) analysis of the top 100 most impactful metabolite features, with laminarin treatment showing the most significant metabolomic effect. The time-dependent analysis in \u003cstrong\u003e1C \u003c/strong\u003eshows the temporal and dynamic, as well as intact and sustained treatment-dependent metabolic reprogramming of wheat seedlings over time.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7656371/v1/7caf3db7861655c9209d3bdc.png"},{"id":93460611,"identity":"718502f2-223d-4ef0-b177-003a3ad1db35","added_by":"auto","created_at":"2025-10-14 06:12:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":372297,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMetabolomic responses of wheat seedlings to biostimulants treatments: \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Hierarchical clustering heatmap of significantly altered metabolites (VIP \u0026gt; 1.0, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) detected by UHPLC-HD-QTOF/MS across four treatments: Control (pink), PGPR (purple), PGPR_laminarin (orange), and SWE (green). Rows represent putatively identified metabolites (Level 2), grouped by abundance patterns; columns represent biological replicates. Colour scale shows Z-score-normalised abundance (blue = down, red = up). Laminarin treatment shows a distinct metabolic profile with elevated amino acids, organic acids, phenylpropanoid intermediates, and carbohydrate metabolites, while PGPR and PGPR_laminarin cluster more closely. (\u003cstrong\u003eB\u003c/strong\u003e) KEGG-based pathway regulation heatmap, showing log₂ fold-change in pathway activity relative to control. Colour scale: blue = down-regulated, red = up-regulated. SWE strongly up-regulates central carbon metabolism (TCA cycle, glycolysis, pyruvate metabolism) and amino acid biosynthesis. PGPR induces moderate, broad-spectrum up-regulation of amino acid and cofactor metabolism. Combined PGPR_laminarin shows tempered regulation, suggesting interaction effects. Arrows indicate direction of change (↑ up, ↓ down); “Balanced” denotes near-control levels.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7656371/v1/c1aaadc0d27bafd2a304fb9f.png"},{"id":93459894,"identity":"da23882d-6785-4f68-9193-219188d3bf61","added_by":"auto","created_at":"2025-10-14 06:04:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":422225,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntegrated pathway–metabolite enrichment maps showing treatment-specific effects of PGPR, laminarin TREATMENT, and combined PGPR_laminarin on primary and secondary metabolism in wheat seedlings\u003c/strong\u003e. (\u003cstrong\u003eA\u003c/strong\u003e) Citrate cycle (TCA cycle): Violin plots depict relative abundance changes (log₂-normalised intensity) of key TCA intermediates (citrate, succinate, malate, oxaloacetate, aconitate) across treatments. KEGG pathway maps are overlaid with detected intermediates and relevant enzymes (green boxes), highlighting differential regulation patterns. PGPR induced moderate increases in TCA intermediates, laminarin treatment triggered a rapid and pronounced accumulation, while PGPR_laminarin exhibited an intermediate, balanced profile. (\u003cstrong\u003eB\u003c/strong\u003e) Glyoxylate and dicarboxylate metabolism: Relative abundances of shared intermediates (citrate, succinate, malate, oxaloacetate, aconitate) mapped onto the glyoxylate cycle, revealing co-regulation with the TCA cycle and links to amino acid metabolism, fatty acid biosynthesis, and photorespiration. laminarin treatment notably elevated intermediates linked to rapid energy turnover, while PGPR drove gradual increases, and PGPR_laminarin maintained metabolic homeostasis. (\u003cstrong\u003eC\u003c/strong\u003e) Phenylpropanoid biosynthesis: Violin plots show treatment-specific changes in aromatic amino acids (phenylalanine, tyrosine, tryptophan) and downstream hydroxycinnamic acids (cinnamic acid, coumaric acid, caffeic acid, ferulic acid, sinapaldehyde). KEGG map overlays highlight activation of the phenylpropanoid pathway, with PGPR markedly boosting flavonoids, phenolic acids, and amine conjugates, laminarin treatment promoting broad phenolic upregulation, and PGPR_laminarin integrating both effects into a moderated yet multi-pathway activation. Together, these pathway visualisations demonstrate that the treatments impact multiple, interconnected metabolic pathways rather than acting on isolated metabolic routes, revealing distinct modes of metabolic regulation.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7656371/v1/be63ba6bc3f6a17a6b112521.png"},{"id":93460938,"identity":"eac4fd56-9e1e-4f63-9560-a8f8c9f0b394","added_by":"auto","created_at":"2025-10-14 06:20:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":310722,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSummary diagram\u003c/strong\u003e: Integrated metabolic phenotypes and mechanistic insights of biostimulants treatments in wheat seedlings. The diagram illustrates three distinct metabolic strategies: Laminarin treatment (red) promotes metabolic acceleration through maximum energy output and comprehensive pathway activation; PGPR treatment (green) enables metabolic endurance via efficient energy production and targeted defence responses; Combined treatment (orange) achieves metabolic intelligence by balancing resource allocation and regulatory sophistication. Five key mechanistic insights (bottom panel) reveal that treatments coordinate metabolic networks through pathway-level regulation rather than individual metabolite effects, with non-additive integration enabling dynamic growth-defence trade-offs across distinct temporal patterns, ultimately providing precision agricultural applications based on crop-specific needs. \u003cem\u003eCreated in BioRender. Mashabela, M. (2025) https://BioRender.com/txnimag.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7656371/v1/ae9e64651f9927c894c0ea8c.png"},{"id":100614763,"identity":"68ee2eb9-3f2e-4b1a-9437-fdb76b09c347","added_by":"auto","created_at":"2026-01-19 17:24:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2474943,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7656371/v1/6b748a7e-ff81-49a2-8423-72bb896785a4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Laminaria extracts and rhizobacteria (Paenibacillus alvei T22) elicit metabolic reprogramming of wheat seedlings: A metabolomics-guided biostimulants mode-of-action discovery for plant growth and defence priming","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eModern agriculture stands at an intersection of multiple global challenges, including climate variability, land degradation, depletion of finite and non-renewable resources and soil fertility loss, while under increasing pressure to sustainably meet the food demands of a rapidly growing population. These constraints are further exacerbated by the declining efficacy and ecological concerns associated with the overreliance on synthetic agrochemicals, which threaten both environmental and human health (Calicioglu et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rejeb et al., 2022). As a result, the agricultural sector is undergoing a paradigm shift towards the development of sustainable, eco-compatible alternatives that enhance crop productivity while minimising ecological footprint. One such alternative is the use of naturally sourced biostimulants, such as seaweed extracts (SWEs) and microbial-based plant growth-promoting rhizobacteria (PGPR), which have emerged as a promising frontier in enhancing plant growth, improving stress tolerance and nutrient efficiency at the forefront of climate-smart agriculture (Yakhin et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rouphael \u0026amp; Colla, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These traits are particularly essential towards achieving key United Nations Sustainable Development Goals (UNSDGs) such as SDG 2 (Zero Hunger), SDG 13 (Climate Action) and SDG 15 (Life on Land).\u003c/p\u003e\u003cp\u003eSWEs, notably from \u003cem\u003eLaminaria digitata\u003c/em\u003e and \u003cem\u003eL. hyperborea\u003c/em\u003e, have garnered considerable academic and industrial interest due to their rich biochemical composition, eliciting multifaceted effects on plant systems. These effects are largely attributed to key components such as laminarins, fucoidans, alginates, and phlorotannins, which act as signalling molecules, antioxidants, and elicitors of defence responses. For instance, laminarin is known to prime jasmonic acid and salicylic acid pathways, thereby strengthening plant innate immunity (Kahlon et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Extracts from \u003cem\u003eLaminaria\u003c/em\u003e have been shown to stimulate germination, promote early seedling vigour, enhance chlorophyll content, and induce tolerance to drought, salinity, and oxidative stress (Go\u0026ntilde;i et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Shukla et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). \u003cem\u003eLaminaria\u003c/em\u003e extracts also influence plant metabolism, modulating pathways related to osmolyte production, antioxidant enzyme activities, and hormone biosynthesis. Recent omics studies, including transcriptomics and metabolomics, have uncovered significant alterations in carbohydrate metabolism, amino acid biosynthesis, and secondary metabolite pathways in plants treated with \u003cem\u003eLaminaria\u003c/em\u003e-based formulations (Ali et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sharma et al., 2023). These molecular-level responses indicate a sophisticated mechanism of metabolic reprogramming and physiological priming, positioning seaweed-based biostimulants as potent tools for sustainable crop enhancement.\u003c/p\u003e\u003cp\u003eIn parallel, PGPR have garnered increasing attention for their multifaceted contributions to plant health and productivity. These bacteria colonise the rhizosphere and engage in key functions such as biological nitrogen fixation, phosphate solubilisation, siderophore production, and the synthesis of phytohormones like indole-3-acetic acid (IAA) and gibberellins (Borriss, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Wu et al., 2021). In addition to enhancing nutrient availability, PGPR, such a \u003cem\u003ePaenibacillus alvei\u003c/em\u003e T22, are widely recognised for their capacity to trigger induced systemic resistance (ISR) in plants via the production of lipopeptides (surfactin, fengycin, iturin) and volatile organic compounds (VOCs), which serve as signalling cues in activating plant defence pathways (Mmotla et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). \u003cem\u003eP. alvei\u003c/em\u003e were recently shown to increase wheat yields by approximately 10% individually and a significant increase of 30% to 60% in a consortium under reduced fertiliser conditions (Breedt et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), while \u003cem\u003eLaminaria digitata\u003c/em\u003e L. extracts have been shown to improve tomato performance under drought stress by modulating photosynthesis, antioxidant response, and related metabolite profiles (Pereira et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile the individual benefits of SWEs and PGPR are well established, recent trends suggest that their combined application may elicit synergistic or complementary effects, leading to enhanced biostimulants efficacy. However, the underlying molecular mechanisms and interactive dynamics remain underexplored. There is growing interest in multi-component biostimulants formulations that merge microbial and non-microbial agents, as these combinations may trigger broader physiological responses, enriched metabolic diversity, and robust defence activation (Rouphael et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe advent of omics technologies, particularly metabolomics, offers a powerful platform to dissect the mode of action of biostimulants at a systems level. By profiling metabolic fluxes, omics tools can uncover regulatory networks, pathway crosstalk, and molecular biomarkers associated with biostimulants-induced growth promotion and defence priming (Weckwerth et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kumar et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Integrating these data can provide a holistic view of how plants respond to complex biostimulants formulations, enabling evidence-based optimisation and refinement for formulations.\u003c/p\u003e\u003cp\u003eThis study aimed to apply comprehensive metabolomics analyses to investigate the individual and combined effects of \u003cem\u003eLaminaria\u003c/em\u003e extracts (purified laminarin) and \u003cem\u003ePaenibacillus alvei\u003c/em\u003e (T22) on wheat seedlings (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.), with a specific focus on mode of action discovery, metabolic reprogramming, and priming-related modulation of the primary and secondary metabolism. By elucidating the signalling pathways and metabolites involved, this research seeks to advance our understanding of the Biostimulants mechanism of action, contributing toward the development of sustainable solutions for crop productivity under environmental stress.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Bacterial culture conditions and Preparation of \u003cem\u003eLaminaria\u003c/em\u003e extracts\u003c/h2\u003e\u003cp\u003e\u003cem\u003ePaenibacillus alvei\u003c/em\u003e (T22), obtained as glycerol stocks from Dr. Msizi Mhlongo, University of Johannesburg, South Africa was grown on Petri dishes with nutrient agar media overnight (O/N) at 28\u0026deg;C\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C. Bacterial colonies were transferred into 50 mL Luria Broth (LB) culture medium for overnight incubation on a shaker-incubator at 140 rpm and 28\u0026deg;C\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C. The optical density (OD) of overnight cultures was adjusted to 0.5 OD\u003csub\u003e600\u003c/sub\u003e for seed treatment. Bacterial cultures were centrifuged at 5000 rpm at 4\u0026deg;C for 15 min. The pellets were collected and gently washed in 5 mL sterile water, then reconstituted in 50 mL autoclaved water for seed treatment. \u003cem\u003eLaminaria\u003c/em\u003e extracts (purified laminarin; code-L1) were sourced from BioAtlantis Ltd (Ireland) as a highly concentrated liquid SWE produced in accordance with Good Manufacturing Practices (GMP+) and certified by GMP\u0026thinsp;+\u0026thinsp;International B.V. Extracts were diluted to an optimal concentration of 1% v/v (2.5L/hectare in 250L) for foliar application.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Seed biopriming, plant growth conditions, and treatments\u003c/h2\u003e\u003cp\u003eSeeds from the Gariep cultivar of wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.) (obtained from the Agricultural Research Council- Small Grains, South Africa) were washed with autoclaved water, followed by surface sterilisation in 0.5% sodium hypochlorite (NaOCl) for 1 min. The seeds were then rinsed with autoclaved water and further washed in 70% ethanol to remove the NaOCl residues and allowed to dry. Dried seeds were introduced to the previously prepared suspensions of the bacterial cultures; the seeds were immersed in separate 50 mL of reconstituted \u003cem\u003eP. alvei\u003c/em\u003e (OD\u003csub\u003e600\u003c/sub\u003e 0.5) in centrifuge tubes and incubated for 3 h. Bacterial solutions were decanted, and seeds were dried at 28\u0026deg;C\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C for 24 h on open Petri dishes in an incubator. Control seeds were treated with sterile autoclaved water for the same period and dried under similar conditions.\u003c/p\u003e\u003cp\u003eFor plant growth, a germination soil mixture (Culterra, Muldersdrift, South Africa) was soaked in 9 cm pots with Supafeed\u0026reg; 3:1:6 (46) (AECI Plant Health, Modderfontein, South Africa), a water soluble fertiliser consisting of nitrogen (N) -155 g/kg, phosphorus (P) \u0026minus;\u0026thinsp;46 g/kg, potassium (K) \u0026minus;\u0026thinsp;267 g/kg, sulphur (S) \u0026minus;\u0026thinsp;4.1 g/kg, magnesium (Mg) \u0026minus;\u0026thinsp;3.1 g/kg, zinc (Zn) \u0026minus;\u0026thinsp;711 mg/kg, boron (B) \u0026minus;\u0026thinsp;1073 mg/kg, molybdenum (Mo) \u0026minus;\u0026thinsp;67 mg/kg, iron (Fe) \u0026minus;\u0026thinsp;765 mg/kg, manganese (Mn) \u0026minus;\u0026thinsp;278 mg/kg and copper (Cu) \u0026minus;\u0026thinsp;77 mg/kg (Mashabela et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Bioprimed and control seeds were sown and germinated under controlled greenhouse conditions (temperature\u0026thinsp;=\u0026thinsp;22\u0026ordm;C to 23\u0026deg;C\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C; relative humidity\u0026thinsp;=\u0026thinsp;74%; min 32% and max 82%) with a light/dark cycle of 12 h/12 h and light intensity of 60 \u0026micro;mol/m2/s. Select seedlings were treated with laminarin extracts via foliar application, two days post germination; the open leaves of the seedlings were generously sprayed with 1% v/v reconstituted laminarin extract. The experimental set-up was as follows: Control (No PGPR/laminarin); T1 (PGPR); T2 (laminarin); T3 (PGPR_laminarin).\u003c/p\u003e\u003cp\u003eEach condition of the experiment consisted of three biological replicates designed for a three-week temporal (time-dependent) metabolomic analysis experiment, for a total of 48 plants. The temporal approach provides a complete picture of how biostimulants treatments reshape plant metabolism over time, rather than just a snapshot at a single timepoint. Samples were collected weekly for three consecutive weeks, where three plants per sample were harvested as independent biological replicates and cryopreserved (quenched) in liquid nitrogen to prevent further metabolic and enzymatic activity. The samples were stored at -80\u0026ordm;C until metabolite extraction.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Metabolite extraction and UHPLC-ESI-Q-TOF-MS data acquisition\u003c/h2\u003e\u003cp\u003eFrozen leaf samples were crushed in liquid nitrogen; a 200 mg fine powder of pulverised leaf samples were resuspended in 2 ml of 80% ice-cold (\u003cem\u003eRomil-SpS\u0026trade; Super Purity\u003c/em\u003e) methanol and then sonicated for 10 min at 25\u0026ordm;C. The emulsified samples were centrifuged at 4000 rpm for 15 min, and the separated supernatants were dried at 55\u0026ordm;C in a dry bath. Samples were reconstituted in 350 \u0026micro;l of 50% ultra-LC grade (\u003cem\u003eRomil-SpS\u0026trade; Ultra Purity\u003c/em\u003e) methanol and filtered into LC-MS vials for analysis. Equal volumes of the sample aliquots were pooled together to prepare quality control (QC) samples used to assess the reliability and reproducibility of the analytical method.\u003c/p\u003e\u003cp\u003eData acquisition was performed on an ultra-high-performance liquid chromatography system coupled to high-definition mass spectrometry (UHPLC-HD-MS) (SYNAPT XS quadrupole time-of-flight Mass Spectrometer [QToF-MS], Waters Corporation, Milford, MA, USA) fitted with a Waters Acquity\u0026trade; Premier HSS T3 C18 column (150 mm x 2.1 mm x 1.8 \u0026micro;m). The following parameters were observed: Solvent A was water containing 0.05% formic acid (FA) and 0.05% isopropyl alcohol (IPA), and solvent B was acetonitrile with IPA. The gradient elution protocol was as follows: 100% A to 0.0% B initially at 0 min, 100% A to 0.0% B from 0 to 1 min; 10% A to 90% B from 1 to 15 min; 1.0% A to 99.0% B from 15 to 15.10 min; 1.0% A to 99.0% B from 15.10 to 17 min; 100% A to 0.0% B from 17 to 17.10 min; and 100% A to 0.0% B from 17.10 to 20 min. The injection volume was 2 \u0026micro;l with a flow rate of 0.4 ml/min. The instrument scanned a mass range of 100\u0026ndash;1500 Da. The source parameters were as follows: capillary voltage of +\u0026thinsp;2800 V, cone voltage of +\u0026thinsp;30 V, source temperature set at 120\u0026deg;C, desolvation temperature at 450\u0026deg;C, desolvation gas flow at 600 L/h, and cone gas flow at 50L/h. Data were collected in centroid mode at an approximate resolution of 10,000, with a scan time of 0.1 s to ensure over 10 data points per chromatographic peak. Internal mass calibration was achieved using the Lockspray interface (Waters Corporation), with a continuous infusion of leucine-enkephalin (500 ng/mL) at 15 \u0026micro;L/min. System operation and data acquisition were managed using MassLynx 4.1 software (Waters Corporation), producing raw (.raw) data files in both positive (ESI\u003csup\u003e+\u003c/sup\u003e) and negative (ESI\u003csup\u003e\u0026minus;\u003c/sup\u003e) ionisation modes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Data processing, statistical analysis and biological interpretation\u003c/h2\u003e\u003cp\u003eAcquired data was processed on MzMine 4.7.8, an open-source and platform-independent software for mass spectrometry (MS) data processing and visualisation (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/mzmine/mzmine/releases/tag/v4.7.8\u003c/span\u003e\u003cspan address=\"https://github.com/mzmine/mzmine/releases/tag/v4.7.8\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), with the following parameters: a retention time (rt) range of 0.00 to 30.00 min, max peaks in chromatograms of 15, with 4 minimum consecutive scans. Rt tolerance was set at 0.04 and 0.10 min for intra-sample and sample-to-sample, respectively. The QToF was set to detect absolute intensity for both MS\u003csup\u003e1\u003c/sup\u003e and MS\u003csup\u003e2\u003c/sup\u003e at 5% and 10% noise threshold. The mass (\u003cem\u003em/z\u003c/em\u003e) tolerance (scan-to-scan) was 0.0050 \u003cem\u003em/z\u003c/em\u003e, with 0.0015 \u003cem\u003em/z\u003c/em\u003e for intra-sample and 0.0040 \u003cem\u003em/z\u003c/em\u003e for sample-to-sample mass tolerance. MzMine actively imports [.raw] data files to convert to the compatible [.mzML] for processing, followed by mass detection and chromatogram building. The resulting data matrix was exported and modified for multivariate data analysis (MVDA) on MetaboAnalyst 6.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.metaboanalyst.ca/MetaboAnalyst/ModuleView.xhtml\u003c/span\u003e\u003cspan address=\"https://www.metaboanalyst.ca/MetaboAnalyst/ModuleView.xhtml\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The data matrix (.csv) was modified through variable selection, sample filtering and data transposition and then uploaded onto MetaboAnalyst for sample normalisation by media, log transformation, and \u003cem\u003ePareto\u003c/em\u003e scaling for chemometrics and cluster analysis, utilising Principal Component Analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA) and heatmaps for exploratory and quantitative analysis.\u003c/p\u003e\u003cp\u003eSimilar parameters were specified for metabolite annotation in MzMine, with applied smoothing and stable ionisation across samples. Annotation followed Level 2 guidelines established by the Metabolomics Standards Initiative (MSI), ensuring annotations were based on similarities with published data, including spectral patterns and elemental compositions housed in the Global Natural Product Social Molecular Networking (GNPS) ecosystem. Annotation is performed via spectral matching, computing matched signals between MS2 spectra to MS Level 2 for similarity scores of 0.72 up to 0.94 for annotated metabolites, linked to databases such as the GNPS-Library Explorer-Version 0.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://library.gnps2.org/\u003c/span\u003e\u003cspan address=\"https://library.gnps2.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), MassBank Europe (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://massbank.eu/MassBank/\u003c/span\u003e\u003cspan address=\"https://massbank.eu/MassBank/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), MassBank of North America (MoNA- \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mona.fiehnlab.ucdavis.edu/\u003c/span\u003e\u003cspan address=\"https://mona.fiehnlab.ucdavis.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and Pubchem (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Similar databases were explored for refined identifications of selected metabolites, using the \u003cem\u003em/z\u003c/em\u003e values of detected features and their empirical formulae. Subsequent pathway and enrichment analysis, and biological interpretation were performed on the MetPA tool within MetaboAnalyst 6.0, facilitating the analysis, identification, and visualisation of impacted pathways. MetPA utilises high-quality KEGG metabolic pathways as its underlying knowledge base. Employing MetPA for pathway analysis provided a framework for partially mapping the molecular landscape of the metabolome under study, enabling the biological interpretation of observed changes in the endo-metabolome.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Multi-variate data analysis reveals treatment-specific classifications of metabolic profiles in wheat seedlings\u003c/h2\u003e\u003cp\u003eThis study used microbial and non-microbial biostimulants in the form of PGPR and seaweed extracts (purified laminarin) for an untargeted (qualitative and quantitative) metabolomics approach to investigate the combined and individual effects of biostimulants on the metabolome of wheat seedlings.\u003c/p\u003e\u003cp\u003eAn untargeted metabolomics strategy, based on UHPLC-HD-QTOF/MS, was applied to obtain metabolomics data. Resulting data matrices, acquired in both negative and positive ionisation modes (ESI\u003csup\u003e\u0026minus;\u003c/sup\u003e; ESI\u003csup\u003e+\u003c/sup\u003e), produced 7970 and 117726 features, respectively, displaying the method as effective for obtaining a comprehensive coverage of the plant\u0026rsquo;s metabolome. After quality filtering, peak alignment, and statistical analysis (one-way ANOVA, VIP\u0026thinsp;\u0026gt;\u0026thinsp;1.0, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), significant features were subjected to Level 2 metabolite annotation using MS/MS spectral matching, resulting in 89 confidently identified metabolites. Given the complexity and the massive amount of metabolomics data, chemometrics methods were applied to further deconvolute the data and explore the metabolic landscapes of the wheat seedlings under different conditions. Principal component analysis (PCA), a dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional space, and partial least squares-discriminant analysis (PLS-DA), an effective binary and multi-group classifier, was used for group separation based on varying metabolic profiles of seedlings, and to reveal underlying patterns and metabolic signatures. PLS-DA describes the most significant variance to differentiate between practical classes to decipher the metabolic features that are most significant to the observed classification (Mashabela et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The generated PCA (data not shown here) and PLS-DA models (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) display distinct and sample-specific grouping, and clear separation was observed between control, PGPR-treated, laminarin-treated and combined PGPR_laminarin-treated samples, with PC 1 and PC 2 explaining 15.6% and 25%, while Component 1 and 2 explaining 17.4% and 17.8% of the total variance for the PCA and PLS-DA respectively. The PLS-DA model was cross-validated to an accuracy measure of 1.0, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.98 and Q\u003csup\u003e2\u003c/sup\u003e of 0.897 for five components and a 5-fold cross validation (CV) method.\u003c/p\u003e\u003cp\u003eThese differential sample groupings evidently highlight the underlying metabolic perturbations induced by the microbial, non-microbial and combined biostimulants applications. Laminarin-treated samples showed a significant separation in a distinct region (positive Component 1), indicating the greatest effect on wheat seedling metabolome compared to their counterparts. The separation indicates a complete metabolome reprogramming with no overlapping metabolic features compared to the control. PGPR treatments formed an intermediate cluster, showing only partial metabolic reprogramming relative to the control. In contrast, PGPR_laminarin combinations occupied a transitional space between individual treatments, suggesting a balanced metabolic adjustment rather than a strictly additive or synergistic effect. This implies that laminarin-driven changes may be moderated by the presence of PGPR, while laminarin treatment in turn may enhance the individual influence of PGPR treatment. The distinct SWE separation indicates that the laminarin extract alone induces a unique metabolic profile, likely driven by its bioactive constituents, such as polysaccharides consisting of a β-(1\u0026ndash;3)-linked glucan backbone with β-(1\u0026ndash;6)-linked side chains (Pramanik et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), which may activate pathways not strongly triggered by PGPR, and to a lesser extent, the combined biostimulants application (El Jazouli et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSimilar patterns were observed in the quantitative heatmap of the 100 most impactful features (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Laminarin-treated samples exhibited the highest metabolic impact across multiple features, displaying a clear, significant metabolic shift. The combined biostimulants application of PGPR and laminarin displays slightly comparable effects to PGPR treatment but shows a reprogramming spanning a greater range of metabolites compared to PGPR treatment\u0026rsquo;s concentrated effects. Hence, PGPR alone shows moderate, consistent changes, while combination treatment seems to avoid metabolic extremes. This observation could indicate a broad activation of the metabolome (metabolic pathways) by laminarin treatment, which is not triggered by the PGPR (El Jazouli et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tran and Kim., 2023), while the combined treatment may take on a balanced effect, and integration of the two separate treatments.\u003c/p\u003e\u003cp\u003eThe heatmap in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC shows a time-dependent metabolic reconfiguration, revealing dynamic metabolic responses with distinct temporal patterns for each treatment. Laminarin treatment revealed a near-instant metabolic shift, with an evidently significant impact on the metabolome of wheat seedlings from the onset of the treatment in week one samples. The upregulation of the top 100 impactful metabolites follows a strong upward trend over the three-week period, as compared to other treatments, demonstrating an instant reaction and a sustained metabolic shift in response to laminarin application over time. This pattern mirrors the PLS-DA model and the quantitative heatmap in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA\u003cb\u003e\u0026amp;B\u003c/b\u003e, which greatly distinguishes the impact of laminarin treatment on the metabolome of wheat seedlings, potentially through slowly metabolised bioactive compounds or persistent signalling cascades (Tran and Kim., 2023).\u003c/p\u003e\u003cp\u003eIn contrast, control seedlings portray a normal metabolic state with metabolite upregulation following a slow and steady profile. The combined biostimulants application showed a gradual metabolic shift, slightly urged by the PGPR treatment, with some changes (up-/downregulations) reversing or stabilising over time. PGPR effects may be rapid and adaptive, as seen in the PGPR-induced metabolic shift at week 1 of treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), consistent with microbial priming behaviour known to prepare plants for stress without maintaining prolonged metabolic burden (Calvo et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Notably, the temporal analysis revealed that certain metabolic features showed biphasic responses, with early activation followed by regulatory modulation, particularly evident in the combination treatment. laminarin may induce an immediate and longer-lasting metabolic signature, whereas PGPR effects could be more dynamic and context-dependent. According to Ali et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the method of application of the seaweed extracts plays an important role in their use and responses by plants. Most application types are either foliar, root application, or a combination of both. Comparative studies have shown foliar sprays of SWEs to be optimal for the crop and more effective (Ali et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The better performance of foliar applications has been attributed to the immediate interaction with the plant tissues, as foliar absorption happens almost immediately (Mughunth et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This is consistent with our findings, which show a robust metabolic response at the onset of SWE foliar application, and a sustained metabolic reprogramming (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), for an overall greater impact on the metabolome of wheat seedlings (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eThe hierarchical clustering of metabolic features identified distinct co-regulated metabolite clusters, suggesting coordinated metabolic network responses rather than isolated pathway activation. This systems-level metabolic coordination indicates that treatments trigger comprehensive metabolic reprogramming rather than targeted pathway-specific effects. This pattern mirrors findings from \u003cem\u003eArabidopsis\u003c/em\u003e and maize, where seaweed extracts elicit substantial shifts in metabolomic profiles, including primary metabolites and secondary defence compounds (Ertani et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tinte et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These observations suggest that laminarin provides a stable, robust reprogramming, perhaps favouring energy metabolism and baseline readiness for stress, whereas PGPR contributes rapid, flexible modulation. Their combination does not sum their effects additively; instead, PGPR may buffer or reshape the laminarin-triggered metabolic landscape, offering a balanced outcome that may favour both growth and resilience.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Enrichment analysis show activation, and subsequent modulation of primary and secondary metabolism\u003c/h2\u003e\u003cp\u003eThe pronounced metabolic differentiation observed in the PLS-DA analysis and the identification of laminarin (SWE) as the most metabolically impactful treatment prompted us to investigate which specific biochemical pathways were driving these observed metabolic shifts. The clear temporal dynamics and treatment-specific metabolite accumulation patterns suggested coordinated activation of metabolic networks rather than random metabolic perturbations. To understand the biological significance of these metabolomic changes, we conducted targeted pathway enrichment analysis, correlation studies and pathway analysis using putatively annotated metabolites to identify the key metabolic pathways responsible for the observed treatment-specific signatures. From detected features, 89 metabolites were confidently annotated, and 15 key metabolic pathways were significantly enriched. The peak intensities of annotated metabolites, spanning classes of metabolites such as phenolics (flavonoids and HCAs), lipids, organic acids, amino acids and benzoxazinoids, were used to construct and interactive heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), illustrating the differential accumulation and relative metabolite concentrations under varying treatments. The heatmap reveals treatment-associated metabolite up/down-regulations, thus giving insight into the effects of each type of treatment on the measured metabolome of the wheat seedlings.\u003c/p\u003e\u003cp\u003eA correlation analysis (\u003cb\u003eFig. S1\u003c/b\u003e) further confirmed that these changes were not random but were driven by treatment-specific biochemical reprogramming: some treatments consistently upregulated metabolites while downregulating others, whereas alternative treatments produced the opposite pattern (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Importantly, the combined PGPR_laminarin treatment did not necessarily act synergistically in amplifying changes; instead, it appeared to produce a more moderate and balanced metabolic profile compared to the more extreme responses elicited by PGPR or laminarin alone. This systems-level approach allowed us to connect the individual metabolite changes to broader metabolic network activation and elucidate the mechanistic basis for the distinct physicochemical responses observed across treatment groups. The integration of multivariate metabolomic analysis with pathway-level interpretation provided comprehensive insights into the molecular mechanisms underlying plant growth promotion and the synergistic versus balanced effects of combined biostimulants applications.\u003c/p\u003e\u003cp\u003ePathway enrichment analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB \u003cb\u003e\u0026amp; Fig. S2\u003c/b\u003e) revealed highly significant activation of core metabolic processes across all treatments, with the citrate cycle (TCA cycle) emerging as the most significantly enriched pathway (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.259\u003csup\u003ee\u0026thinsp;\u0026minus;\u0026thinsp;6\u003c/sup\u003e, False Discovery Rate [FDR\u0026thinsp;=\u0026thinsp;1.171E-4). Additional strongly enriched pathways included glyoxylate and dicarboxylate metabolism (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.095\u003csup\u003ee\u0026thinsp;\u0026minus;\u0026thinsp;6\u003c/sup\u003e), phenylpropanoid biosynthesis (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.564\u003csup\u003ee\u0026thinsp;\u0026minus;\u0026thinsp;4\u003c/sup\u003e), and aromatic amino acid biosynthesis pathways (phenylalanine, tyrosine, and tryptophan biosynthesis; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001569). This enrichment pattern indicates enhanced energy metabolism coupled with secondary metabolite production, consistent with growth promotion and defence priming responses.\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1. Primary metabolism: Energy production and central carbon flow\u003c/h2\u003e\u003cp\u003eAnalysis of the quantitative distribution of annotated metabolites revealed profound treatment-specific alterations of the primary metabolism and cellular energy systems (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Laminarin treatment induced a comprehensive activation of the central carbon metabolism (the TCA cycle), informed by the massive elevation of TCA intermediates, such as citrate, as an indicator of high TCA entry point. The sustained accumulation of succinate, aconitate, oxaloacetate and malate confirmed the increased functional capacity of the TCA cycle, creating a metabolic state of energy abundance and high metabolic activity.\u003c/p\u003e\u003cp\u003eThe strong TCA cycle enrichment and accumulation of TCA intermediates suggest enhanced glycolytic flux feeding into central carbon metabolism, evidenced by the co-enrichment of the glyoxylate and dicarboxylate metabolism, butanoate metabolism, glycolysis and pyruvate metabolism, creating a coordinated primary metabolic surge that fundamentally transforms the plant's energy landscape. Laminarin treatment further showed a pronounced effect on lipid-associated metabolites, particularly monogalactosyldiacylglycerol (MGDG) isomers such as MG (16:3/18:3) and MG (18:3/18:3), as well as linoleoyl-containing glycerolipids. In contrast, PGPR did not strongly induce lipid metabolites or an enhanced activation of the central energy metabolism, and, in some cases, showed lower levels of activity compared to the control, while the PGPR_laminarin treatment maintained lipid metabolite levels closer to laminaria\u0026rsquo;s profile but with reduced extremes.\u003c/p\u003e\u003cp\u003eSeaweed extracts are complex biostimulants containing polysaccharides, oligosaccharides, phytohormones (auxins, cytokinins), betaines, polyamines and a range of trace lipids and sterols; these compounds are reported repeatedly to improve nutrient uptake, photosynthetic capacity and abiotic stress tolerance and to act as elicitors of metabolic reprogramming toward energy supply and membrane stabilisation (Ali et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As previously alluded to, laminarin is a glucose-rich bioactive compound (Pramanik et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); as such, sugar perception is expected to trigger growth/energy metabolism. Plants perceive β-glucans as Microbe-Associated Molecular Patterns (MAMPs) and, following partial hydrolysis by plant β-1,3-glucanases, can release short glucan oligomers and hexoses.\u003c/p\u003e\u003cp\u003eElevated cytosolic hexose and glucose bias the plant toward the upregulation of primary metabolic routes to meet energetic and biosynthetic demand (Eom et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; de Oliveira et al., 2024; 29. Giesbrecht et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Thus, SWE application commonly stimulates primary carbon metabolism, glycolysis, photosynthesis, osmolyte accumulation and membrane lipid remodelling (Mishra et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), consistent with our data\u0026rsquo;s perceived enrichment of the primary metabolism. Accordingly, the depolymerisation of laminarin can directly feed carbon into central metabolism. Recent biochemical work highlights laminarin as a favourable β-1,3-glucan substrate for such enzymes as endo-β-1,3-glucanases, which act on laminarin to generate oligomers and ultimately glucose that enters the central energy metabolism (glycolysis/TCA) (Caseiro et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kim et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePGPR treatment demonstrated a markedly different primary metabolic signature, characterised by increased levels of amino acids such as tyrosine, tryptophan, phenylalanine, and valine, indicating the activation of the amino acid biosynthetic pathways. Additionally, amino acid derivatives/conjugates, including malonyltryptophan, tryptoline, \u003cem\u003eN\u003c/em\u003e-acetylaspartyl glucoside, phenyl butyrate glucoside, and \u003cem\u003eN\u003c/em\u003e-Fructosyl isoleucine, were elevated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). These shifts mirror observations in PGPR-treated wheat, where elevated aromatic amino acids feed into phenylpropanoid-dependent defence pathways, supporting enhanced priming and ISR-like responses (Mashabela et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This phenomenon could result from the PGPR\u0026rsquo;s nitrogen-fixing capabilities, leading to improved nutrient availability and, consequently, enhanced amino acid biosynthesis. Our pathway analysis reinforces these observations, showing strong enrichment for aromatic-amino-acid biosynthesis (Phe, Tyr, Trp), valine, leucine, isoleucine biosynthesis, and alanine, aspartate, and glutamate metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). A similar enrichment of aromatic amino acid pathways has been reported in chickpea plants treated with \u003cem\u003eBacillus\u003c/em\u003e spp (Mashabela et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and tomato plants, consistently amplifying levels of amino acids, organic acids, hydroxycinnamic acids, flavonoids, and fatty acids (Mhlongo et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eInterestingly, PGPR treatment moderately activated the TCA cycle, increasing intermediates such as citrate, succinate, and malate. This upregulation supported biosynthesis while remaining tightly regulated, unlike the rapid, \u0026ldquo;explosive\u0026rdquo; response from laminarin. The combined application indicated a balanced regulation, tempering laminarin-driven surges while enhancing PGPR effects.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2. Secondary metabolism: defence, structure, and specialised metabolite production\u003c/h2\u003e\u003cp\u003eSecondary metabolite analysis revealed dramatic treatment-specific differences in the production of protective and structurally relevant compounds as well as signalling molecules. Laminarin treatment prominently increased specific lignin precursors, glycosylated flavonoids, and coumarin derivatives. Compounds such as caffeic acids, syringic acid, and certain coumaroyl putrescine derivatives were upregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), for an enriched phenylpropanoid pathway (\u003cb\u003eFig. S2\u003c/b\u003e). The treatment also favoured isovitexin, saponarin, and orientin derivatives, compounds known for osmoprotective roles and membrane stabilisation (Al-Khayri et al., 2023). Interestingly, some metabolites highly upregulated by PGPR (e.g., DIMBOA and HMBOA), known for defence signalling, were suppressed under laminarin treatment, suggesting divergent signalling cues by the two biostimulants. Laminarin lowered enrichment in reactive DIMBOA derivatives but moderate enrichment in DIBOA and storage-glycoside forms, reflecting a less costly, standby defence mode compared to the highly upregulated and targeted defence signalling activation from PGPR treatment. Laminarin treatment also favoured flavone C-glycosylation for a comprehensive secondary metabolite activation.\u003c/p\u003e\u003cp\u003ePGPR treatment presented a more targeted secondary metabolic strategy, primarily affecting defence-related compounds and specific amino acid pathways with a reconfiguration in the metabolism of phenolic compounds, flavonoids, polyamines and benzoxazinoids. This approach showed selective enrichment of metabolites, where aromatic amino acid changes were selective rather than comprehensive, with tyrosine showing moderate increases sufficient for targeted defence compound production without the metabolic expense of maximum accumulation. Phenylalanine levels showed controlled elevation that provides adequate phenylpropanoid precursors for essential secondary metabolite production while maintaining metabolic efficiency. PGPR treatment tends to push secondary metabolism toward rapid-response defence metabolites, such as DIMBOA glycosides, ferulic/isoferulic acid, tricin and luteolin glycosides.\u003c/p\u003e\u003cp\u003eThis pattern is consistent with classic PGPR effects; many root-associated rhizobacteria induce systemic resistance (ISR) and prime the plant immune system for faster or stronger accumulation of defence metabolites upon challenge. Mechanistically, ISR often involves priming of transcriptional programs controlled by hormone and MYB/bHLH regulatory networks that elevate flux through the shikimate-to-phenylpropanoid axis and into specialised defence products rather than long-term storage forms. These ISR/priming effects are well documented for \u003cem\u003ePseudomonas, Bacillus\u003c/em\u003e and similar PGPR strains (Backer et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Application thereof further boosted phenylpropanoid derivatives, flavonoids (kaempferol glycosides), and certain hydroxycinnamic acids. This indicates an activation of defence-related secondary metabolism. A strong accumulation of aglycone flavones and flavanols was observed, followed by low levels of glycosylated derivatives. Amine conjugates were also elevated under PGPR treatment, inclusive of coumaroyl putrescine and feruloyl agmatine. Similar metabolomic patterns have been reported in PGPR-treated wheat, tomato, and maize, where elevated phenolic acids, flavonoids, and amine conjugates contribute to pathogen resistance, antioxidant defence, and lignin biosynthesis (Zhang et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mishra et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Beyond direct defence, flavonoids such as kaempferol and quercetin also act as signalling molecules in plant-PGPR interactions, facilitating root colonisation and enhancing stress tolerance (Sharma et al., 2023).\u003c/p\u003e\u003cp\u003eFrom a biochemical perspective, activating the shikimate and phenylpropanoid pathways requires substantial carbon flux from glycolysis and the pentose phosphate pathway. The metabolite and enrichment maps show that when PGPR strongly biases plants toward these secondary pathways, central carbon intermediates (and their ratios) move accordingly, consistent with a reallocation of carbon to defence. This explains the negative correlations observed between PGPR clusters and some energy/storage metabolites, such as sugars and TCA intermediates, a trade-off where defence demand transiently competes with storage/energy pools (Cai \u0026amp; Aharoni, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The growth-defence trade-off is a recognised, general phenomenon in plants and explains the PGPR treatment\u0026rsquo;s increase in defence chemistry at the cost of changes in primary metabolite pools. Benzoxazinoids (BXs) are especially relevant in grasses (wheat, maize, rye) (Mashabela et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). BXs are potent antiherbivore/antimicrobial defences produced from tryptophan/aromatic metabolism and are known to be dynamically regulated in response to root and foliar stimuli. Transient conversion between active aglycones and glycosylated, storage forms is common; the balance between these forms determines immediate toxicity versus storage/readiness (Hama et al., 2025). Our observation that PGPR increased active BX signatures (and some conjugates) mirrors reports that beneficial rhizobacteria can prime BX-based defences in cereals (Hama et al., 2025).\u003c/p\u003e\u003cp\u003eSimilar to its modulation of the primary metabolism, a combined treatment showed a more moderate, broad enrichment across defence-related secondary pathways. Levels of secondary metabolites were generally less extreme than with either PGPR or laminarin alone. Some phenolics that were strongly upregulated by PGPR and downregulated by laminarin treatment stabilised at intermediate values, while others shifted slightly toward either PGPR- or laminarin-like profiles depending on pathway dominance. For instance, DIMBOA levels were reduced compared to PGPR alone, but storage forms (DIMBOA-glucoside) increased, a potential indication of a ready, but not metabolically burdensome defence priming. A balanced presence of ferulic acid and hydroxycinnamic acid derivatives was observed, mirrored by moderate levels of \u003cem\u003ep\u003c/em\u003e-coumaroyl agmatine and feruloyl putrescine, without the PGPR-induced spikes.\u003c/p\u003e\u003cp\u003eRemarkably, the PGPR_laminarin combination did not demonstrate classical synergistic effects where additive responses might be expected, as demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA from a PLS-DA model of metabolic features and the treatment-specific pathway regulation in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, suggesting important regulatory interactions between the two bioactive agents. Growth\u0026ndash;defence trade-offs are regulated by hormone crosstalk (JA, SA, auxin, cytokinin, ethylene, ABA) and by systemic signalling that tunes resource allocation. When both a PGPR (which primes ISR) and laminarin (which supplies growth/energy signals and hormones) are present, the plant\u0026rsquo;s regulatory networks can balance the competing demands and produce a state of moderate readiness (some secondary metabolites), while preserving metabolic capacity for growth and membrane maintenance (Song et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e3.2.3. Overall metabolic pathway analysis and implications of treatment-specific metabolic perturbations\u003c/h2\u003e\u003cp\u003eThe correlation heatmap (\u003cb\u003eFig. S3\u003c/b\u003e) shows tight sample clusters by treatment and large blocks of positive/negative correlation between treatment groups. This pattern means the treatments are changing broad, concerted programs of metabolism, not single metabolites, and the pathway enrichment maps confirm those programs: central carbon metabolism (TCA, glyoxylate, pyruvate), amino-acid biosynthesis (shikimate: Phe, Tyr, Trp), and phenylpropanoid/flavonoid/benzoxazinoid branches are all involved. In practice, this presents multiple interconnected pathways as being up- or down-regulated in concert, with central metabolites (e.g., oxaloacetate-TCA) acting as hubs that re-partition carbon and energy into different sinks (growth, lipids, defence). The correlation structure and pathway enrichment together show that PGPR and laminarin treatments impose distinct but overlapping metabolic programs in wheat seedlings. PGPR principally directs carbon into the shikimate, phenylpropanoid and benzoxazinoid branches associated with rapid biotic-defence responses, whereas laminarin treatment redirects flux toward TCA/glyoxylate, sugar and lipid pathways that support energy supply, membrane stability and osmoprotection, with the effects of treatments split between survival (plant growth and development by laminarin) and defence by PGPR.\u003c/p\u003e\u003cp\u003eIt should be noted that metabolic reprogramming is not only a matter of up- or downregulation of metabolites, with the subsequent impact on specified metabolic pathways, and the ultimate induction of a physiological response. The process is rather an intricate dynamic of metabolite flux, and metabolite correlation (cause and effect), i.e., the downregulation of one metabolite for the utility in the production of another, a progressive metabolic feeding mechanism that ultimately impacts biochemical processes characteristic of specified metabolic pathways (Huot et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Differential responses across the phenylpropanoid network (cinnamic acid \u0026rarr; coumaric acid \u0026rarr; caffeic acid \u0026rarr; ferulic acid \u0026rarr; sinapaldehyde) show that treatments modulate specific branches rather than uniformly increasing activity. Laminarin treatment induced sharp shifts, with malate depletion and oxaloacetate and citrate accumulation, suggesting rapid turnover via malate dehydrogenase. Moderate increases in aconitate and succinate further support active TCA cycle engagement. The oxaloacetate profile shows the highest variability among treatments, suggesting dynamic and high metabolic activity. This pattern indicates that laminarin treatment pushes primary metabolism toward maximum output, hence elevated levels of oxaloacetate, an essential component of energy metabolism, given its involvement in multiple pathways such as gluconeogenesis, the urea cycle, amino acid synthesis, fatty acid synthesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e, and the glyoxylate cycle (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), serving as a key hub in this metabolic acceleration (Yang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMechanistically, this laminarin-driven reprogramming can be linked to three interconnected processes: (i) enhanced photosynthetic performance and nutrient uptake after laminarin application increases carbohydrate influx into central metabolism, thereby sustaining higher TCA flux and elevating acetyl-CoA availability; (ii) increased acetyl-CoA and CoA-dependent metabolism fuels fatty-acid elongation and glycerolipid synthesis, crucial for maintaining membrane integrity and fluidity under stress; and (iii) bioactive compounds in seaweed extracts, including polysaccharides and oligosaccharide fragments, can act as mild elicitors that promote glycosylation and stable storage of secondary metabolites (e.g., glycosylated flavonoids and glycosylated benzoxazinoids), providing a low-cost, energetically efficient protective strategy compared to maintaining high levels of reactive aglycones. These coordinated shifts, supported by multiple reports in the seaweed biostimulants literature, illustrate that laminarin\u0026rsquo;s metabolic impact is not a uniform \u0026ldquo;push\u0026rdquo; but a strategically routed redistribution of metabolic fluxes to optimise both energy metabolism and stress preparedness (Craigie, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAs previously highlighted, laminarin treatment prompts a comprehensive metabolic perturbation and thus pathway activation. For instance, phenylalanine reaches maximum accumulation levels, providing abundant precursors for the entire phenylpropanoid network. Tyrosine and tryptophan similarly show peak accumulation, creating comprehensive aromatic amino acid availability (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Downstream phenolic acid production shows coordinated maximum activation; cinnamic acid, coumaric acid, caffeic acid, ferulic acid, and sinapaldehyde reach their highest levels under laminarin treatment. This represents complete phenylpropanoid pathway activation from initial aromatic amino acid precursors through to specialised secondary metabolites. In comparison, phenylpropanoid pathway activation is selective and targeted under PGPR treatment. Phenylalanine, tyrosine, and tryptophan show moderate increases, providing adequate precursor availability without metabolic excess, while downstream phenolic acids show strategic patterns; coumaric acid and caffeic acid demonstrate moderate but consistent elevation, focusing on essential defensive compounds. Ferulic acid shows controlled increases supporting cell wall strengthening without excessive investment. Cinnamic acid and sinapaldehyde remain relatively stable, indicating selective pathway activation rather than comprehensive stimulation, such as seen under laminarin treatment. The pathway analysis reveals that PGPR treatment prioritises defensive preparation over maximum production, creating plants with enhanced stress tolerance capacity while maintaining metabolic efficiency. With overall greater impact on the secondary as compared to the primary metabolism, PGPR treatment clearly favours defence priming over basic growth and development.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAdditionally, the sophisticated metabolic integration of the combined biostimulants application was observed. PGPR_laminarin co-treatment harmonises the comprehensive and maximum metabolic pathway activation of laminarin treatment with the moderated and targeted approach by PGPR on both the primary and secondary metabolism. The intermediate levels in combination treatment are not simple averages, but rather representations of active regulatory modulation that capture benefits while avoiding costs, particularly evident in the controlled malate levels and optimised phenolic acid production (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u003cb\u003e\u0026amp;C\u003c/b\u003e). Concerning the treatment\u0026rsquo;s modulation of the primary metabolism, malate shows intermediate regulation; succinate and aconitate demonstrate harmonised intermediate levels that suggest regulatory modulation preventing metabolic extremes. Oxaloacetate variability is controlled, indicating sophisticated regulatory mechanisms that maintain metabolic flexibility while preventing destabilising fluctuations, thus a representation of metabolic intelligence.\u003c/p\u003e\u003cp\u003eThis is further evidenced through the phenylpropanoid pathway, which comprehensively captures the combined treatment\u0026rsquo;s potential to carry both the plant growth and development capabilities of laminarin treatment, driven by the enhanced activation of the energy metabolism, and the defence and priming traits characterised by the targeted modulation of the secondary metabolism by PGPR treatment. This correlation analysis demonstrates that secondary metabolism responds to biostimulants treatments through coordinated network-level regulation, with the combination treatment achieving balanced activation that maintains optimal metabolic network connectivity while capturing the benefits of both individual treatments.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e3.2.4. Not synergistic, nor additive, rather intelligent integration and regulatory sophistication\u003c/h2\u003e\u003cp\u003eIn agricultural biotechnology, combining two biostimulants is often assumed to yield either additive effects (sum of individual impacts) or synergistic effects (combined impact greater than the sum). However, our results challenge this binary framework. The combined PGPR_laminarin treatment did not simply amplify the metabolic trends of either treatment alone; instead, it produced a balanced, harmonised metabolic state that moderated the extremes observed under PGPR or laminarin treatment individually. PGPR alone shifted metabolism strongly toward defence-biased secondary pathways (shikimate-derived phenylpropanoids, benzoxazinoids), often at the expense of energy-storage intermediates, and laminarin treatment alone promoted robust primary metabolism (TCA cycle, lipid biosynthesis, osmolyte accumulation) sometimes with reduced investment in rapid-response defence compounds. The combined treatment distributed metabolic resources across both domains. This balanced state was evident in intermediate metabolite levels, moderated enrichment scores, and reduced extremes in correlation heatmaps compared with single treatments.\u003c/p\u003e\u003cp\u003eRather than reflecting a weaker interaction, such moderation likely arises from regulatory cross-talk between pathways mediated by plant hormone signalling and metabolic sensing. PGPR primes induced systemic resistance (ISR) largely via jasmonic acid (JA) and ethylene signalling (Pieterse et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), whereas laminarin treatment introduces a suite of hormonal and osmoprotective cues (cytokinins, auxins, betaines) that sustain growth and energy supply (Huot et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The co-occurrence of these cues may activate feedback mechanisms, such as SA-JA antagonism, TOR-SnRK1 signalling, and redox balancing (Zhao et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), that optimise resource allocation rather than pushing metabolism toward a single extreme.\u003c/p\u003e\u003cp\u003eFrom a functional standpoint, this \"intelligent integration\" may be more desirable in variable field conditions than a purely additive or synergistic boost to one domain (defence or growth). In fluctuating environments, plants require both readiness to respond to biotic stress and the metabolic capacity to maintain photosynthesis, membrane stability, and osmotic balance. A harmonised metabolic profile reduces the risk of over-investment in one function at the expense of the other, a trade-off well documented in growth-defence theory. Recent studies of combined biostimulants applications echo this phenomenon: multi-input treatments often yield complementary or buffering effects, where the presence of one input mitigates the metabolic cost of the other, leading to sustained performance and resilience. This suggests that the goal in biostimulants integration should not always be \u0026ldquo;more of everything\u0026rdquo; but rather regulatory sophistication, the plant\u0026rsquo;s ability to integrate diverse cues into a stable, adaptive metabolic state.\u003c/p\u003e\u003cp\u003eIn this context, our findings emphasise that balanced metabolic integration can be a strategic advantage, enabling crops to navigate environmental uncertainty without incurring the penalties associated with extreme metabolic investment in either growth or defence. This reframes the expectations for combined biostimulants: instead of seeking synergy in magnitude, we may need to value synergy in stability and adaptability.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e3.2.5. Implications for agricultural applications\u003c/h2\u003e\u003cp\u003eThe metabolomic analysis revealed three distinct strategic phenotypes that fundamentally reshape our understanding of biostimulants mechanisms (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).The three distinct metabolic strategies uncovered in this study offer practical guidance for farmers and the agroeconomic industry for optimised biostimulants formulations and applications (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The laminarin treatment delivers an instant and sustained metabolic burst, pushing plants into an explosive activation of the energy metabolism and broad secondary metabolite synthesis, a strategy most useful during favourable growing conditions with rapid and vigorous growth as the main objective. In contrast, PGPR acts more like a \"steady marathon runner,\" supporting efficient energy use while steadily strengthening the plant\u0026rsquo;s natural defences and stress tolerance/resistance mechanisms. This makes PGPR particularly valuable when crops face stresses such as drought, heat, or disease pressure. For realistic field conditions, i.e., farming situations, where conditions are variable and both productivity and resilience matter, the combination treatment emerges as the smart choice, functioning like an intelligent management system. The combined PGPR_laminarin treatment can harmonise accelerated growth while prioritising survival by adapting metabolic resources as conditions change (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Considering a crop\u0026rsquo;s different phases of development, this metabolic flexibility is essential, where young plants may benefit from the laminarin treatment\u0026rsquo;s growth print through a burst in carbon and energy metabolism, while maturing plants facing reproductive or environmental stress may require the PGPR's steady support. At critical stages like flowering, the combination treatment provides an optimised balance that supports both yield and resilience.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThis research contributes to an especially exciting move beyond the traditional \"one-size-fits-all\" approach to biostimulants and toward a more sophisticated understanding of the mechanistic nature of biostimulants' action at the cellular level. By understanding how different treatments reshape plant metabolism, we can tailor applications to specific crops, growth stages, and environmental conditions. Looking ahead, this opens fascinating possibilities for developing precision agriculture tools that could monitor plant metabolic status in real-time, adjust biostimulants applications based on current growing conditions, and even guide plant breeding programs toward varieties that can better harness these metabolic benefits, ultimately helping farmers achieve both higher yields, stronger stress resilience, and more sustainable production systems.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"4. Conclusions and potential prospects","content":"\u003cp\u003eThis system-level metabolomics study demonstrates that biostimulants treatments fundamentally reprogram plant metabolism through distinct mechanisms, creating three unique metabolic phenotypes that present different strategic approaches to plant performance enhancement. Laminarin treatment creates a \u0026ldquo;metabolic sprint\u0026rdquo; phenotype characterised by maximum energy production and comprehensive secondary metabolite activation, with high metabolic cost and potential instability, while PGPR treatment generates a \u0026ldquo;metabolic marathon\u0026rdquo;, with efficient energy production and targeted defensive preparation with optimal metabolic stability. Most interestingly, a combination of biostimulants achieves a \u0026ldquo;metabolic optimisation\u0026rdquo; phenotype that captures the high-performance benefits while maintaining regulatory control and metabolic intelligence through sophisticated metabolic integration rather than simple synergistic or additive effects. The correlation analysis and pathway-specific data reveal that treatments influence metabolism through pathway-level regulation rather than individual metabolite control, utilising branch point regulation within secondary metabolic networks, flux control mechanisms that prevent metabolic bottlenecks while maintaining high production capacity and coordinated precursor-product relationships that maintain metabolic efficiency.\u003c/p\u003e\u003cp\u003eThe most groundbreaking finding is the emergence of metabolic intelligence, the ability to balance high energy production capacity with regulatory control, as a key determinant of superior plant performance, manifesting as reduced metabolic variability while maintaining high average production, optimal rather than maximum metabolite accumulation, enhanced stress preparedness without metabolic panic responses, and sustainable high-performance metabolism. These insights reveal that metabolic optimisation, rather than metabolic maximisation, represents the fundamental principle underlying superior plant performance and provide a mechanistic framework for developing more effective biostimulants combinations through strategic application matching to specific crop growth stages, environmental stress levels, and desired performance outcomes in precision agriculture applications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflict of interest\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThe research was funded by project 101086366 \u0026ldquo;CropPrime\u0026rdquo; HORIZON-MSCA-2021-SE-01.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: M.D.M and M.I.M., Methodology: M.D.M, T.T, N.S and M.I.M., Investigation: M.D.M., T.T., and M.I.M., Visualization: M.D.M., Funding acquisition: P.K., and M.I.M., Project administration: M.I.M. Supervision: M.I.M., T.T., P.K. and L.A.P., Writing - original draft: M.D.M., Writing - review \u0026amp; editing: M.I.M., P.K., T.T. and L.A.P. All authors reviewed and approved the final manuscript\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe University of Johannesburg and the Global Excellence and Stature (GES) scholarship are thanked for fellowship support to M.D.M. The Agricultural Research Council - Small Grains (ARC-SG), South Africa, is thanked for providing seeds of the wheat cultivar used in this study. T.T. and Ms. Joyce Mebalo are thanked for their assistance and for hosting MDM at ARC-SG for the duration of the primary experimental work. The authors would also like to acknowledge BioAtlantis Ltd. For their collaboration, and project 101086366 - CropPrime - HORIZON-MSCA-2021-SE-01, for funding the project.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAli, O., Ramsubhag, A., \u0026amp; Jayaraman, J. (2021). 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The SnRK1-JMJ15-CRF6 module integrates energy and mitochondrial signaling to balance growth and the oxidative stress response in Arabidopsis. \u003cem\u003eThe New phytologist\u003c/em\u003e, 246(1), 158\u0026ndash;175. https://doi.org/10.1111/nph.20425\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"plant-growth-regulation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"grow","sideBox":"Learn more about [Plant Growth Regulation](https://www.springer.com/journal/10725)","snPcode":"10725","submissionUrl":"https://submission.nature.com/new-submission/10725/3","title":"Plant Growth Regulation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Biostimulants, Laminarin, Metabolomics, PGPR, Plant Metabolism, Seaweed Extracts","lastPublishedDoi":"10.21203/rs.3.rs-7656371/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7656371/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePlant biostimulants, including seaweed extracts (SWE) and plant growth-promoting rhizobacteria (PGPR), are known to enhance crop performance, while multi-component biostimulants, combining microbial and non-microbial agents, show promise for enhanced plant physiological responses and defence activation, yet their metabolic mechanisms remain enigmatic. This breakthrough study unveils the molecular mechanisms behind biostimulants action -PGPR (\u003cem\u003ePaenibacillus alvei\u003c/em\u003e T22), and seaweed extract laminarin (L-1)- in wheat seedlings (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.) through comprehensive untargeted metabolomics using ultra-high-performance liquid chromatography coupled to high-definition mass spectrometry (UHPLC-HD-MS) and advanced pathway enrichment analysis. Three distinct metabolic phenotypes were identified: Laminarin (SWE) treatment triggers the modulation of the energy metabolism with maximum energy production, characterised by robust activation of the citric acid (TCA) cycle, and rapid activation of the secondary metabolism through the upregulation of aromatic amino acids (Phenylalanine, Tyrosine, Tryptophan), feeding into the phenylpropanoid pathway. PGPR treatment orchestrates precision defence priming with moderate and controlled activation of the energy metabolism, accompanied by a targeted modulation of secondary metabolism and the phenylpropanoid pathway. Remarkably, combined \u003cem\u003eP. alvei\u003c/em\u003e (T22) and laminarin L-1 treatment achieved a metabolic optimisation, a harmonised activation and modulation of both the primary and secondary metabolism, transcending simple additive effects to create genuine metabolic enhancement. These biostimulants fundamentally reprogram plant metabolism through distinct pathway-level mechanisms revealed by metabolic network analysis, unlocking the molecular basis of superior plant performance. These discoveries provide the mechanistic framework for designing next generation biostimulants formulations tailored to specific crop requirements, environmental challenges, and performance targets in precision agriculture, for sustainable agricultural intensification through targeted metabolic reprogramming.\u003c/p\u003e","manuscriptTitle":"Laminaria extracts and rhizobacteria (Paenibacillus alvei T22) elicit metabolic reprogramming of wheat seedlings: A metabolomics-guided biostimulants mode-of-action discovery for plant growth and defence priming","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-14 06:04:10","doi":"10.21203/rs.3.rs-7656371/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-09T07:01:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-08T12:29:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-03T08:17:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"252351530725040523130240835182921947468","date":"2025-10-16T07:24:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"274160662964986751973778610864997039017","date":"2025-10-14T08:03:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"186057265570571945029682540638731036516","date":"2025-10-02T03:55:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-29T22:21:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-19T12:31:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-19T12:29:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant Growth Regulation","date":"2025-09-19T08:36:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"plant-growth-regulation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"grow","sideBox":"Learn more about [Plant Growth Regulation](https://www.springer.com/journal/10725)","snPcode":"10725","submissionUrl":"https://submission.nature.com/new-submission/10725/3","title":"Plant Growth Regulation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d6d176d6-6ede-47ab-8fe1-fb644fb880b6","owner":[],"postedDate":"October 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-19T16:50:03+00:00","versionOfRecord":{"articleIdentity":"rs-7656371","link":"https://doi.org/10.1007/s10725-025-01411-6","journal":{"identity":"plant-growth-regulation","isVorOnly":false,"title":"Plant Growth Regulation"},"publishedOn":"2026-01-13 16:30:54","publishedOnDateReadable":"January 13th, 2026"},"versionCreatedAt":"2025-10-14 06:04:10","video":"","vorDoi":"10.1007/s10725-025-01411-6","vorDoiUrl":"https://doi.org/10.1007/s10725-025-01411-6","workflowStages":[]},"version":"v1","identity":"rs-7656371","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7656371","identity":"rs-7656371","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}