Aminochelates as Dual Micronutrient Carriers and Biostimulants: Modulating Rhizosphere Enzyme Hotspots and Root–Microbe Interactions in Sunflower

Aminochelates as Dual Micronutrient Carriers and Biostimulants: Modulating Rhizosphere Enzyme Hotspots and Root–Microbe Interactions in Sunflower | 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 Aminochelates as Dual Micronutrient Carriers and Biostimulants: Modulating Rhizosphere Enzyme Hotspots and Root–Microbe Interactions in Sunflower Mina Alipourbabadi, Mojtaba Norouzi Masir, Abdol Amir Moezzi, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7455689/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The mechanisms by which organic chelates influence rhizosphere enzyme dynamics and microbial function are poorly understood due to a lack of spatial visualization. To address this gap, we evaluated the effects of iron (Fe) and zinc (Zn) aminochelates on the spatial distribution of β-glucosidase (βG) and leucine aminopeptidase (LAP) activities in the rhizosphere of sunflower ( Helianthus annuus L.) using a novel integration of in situ zymography and biochemical assays. Glycine- (Gly) and methionine-based (Met) Fe and Zn aminochelates were synthesized and applied in rhizobox experiments, with untreated soils serving as controls. [Fe(Gly)₂] and [Zn(Met)₂] significantly enhanced βG (7–21%) and LAP (72–120%) activities, while expanding enzymatic hotspot zones by 270–450% and 78–251%, respectively. Kinetic analyses showed that [Zn(Met)₂] achieved the highest catalytic efficiency (Ka) and maximum velocity (Vmax, p < 0.01), while also increasing basal respiration (+ 42.3%) and microbial biomass C (3-fold) relative to the control. Root length and surface area were strongly correlated with hotspot intensity (Pearson’s r = 0.75–0.94), reflecting tight root–microbe feedbacks. Network analysis further revealed that [Fe(Met)₂] and [Zn(Met)₂] promoted the highest system-wide coordination, linking microbial enzyme activity with root architecture (Mantel’s r = 0.56–0.68, p < 0.05). By enhancing microbial activity, expanding biologically active zones, and improving root foraging traits, aminochelates demonstrated a dual functionality as both micronutrient carriers and physiological stimulants. These results establish methionine-based aminochelates, in particular, as promising next-generation biostimulants that can improve soil fertility, optimize nutrient cycling, and support resilient crop production systems. Aminochelates Enzyme kinetics Microbial biomass Sustainable fertilization Zymography Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction The escalating global population has intensified the pressure on agricultural systems to produce more food sustainably (Ghosh et al., 2024 ). While conventional chemical fertilizers have contributed to yield improvements, their prolonged use has been shown to degrade soil health, reduce microbial diversity, and compromise long-term productivity (He et al., 2024 ). As a result, there is growing interest in eco-friendly alternatives, particularly organic-based strategies that maintain nutrient supply while enhancing soil functionality (Asghar et al., 2022 ). Micronutrient deficiencies, especially of Fe and Zn, are common in many agricultural soils. These elements are typically supplied using sulfate-based fertilizers or synthetic chelating agents such as EDTA and DTPA (Zhao et al., 2019 ). Although synthetic chelates increase metal solubility, they are poorly biodegradable and may adversely affect microbial activity and contribute to metal leaching (Doostikhah et al., 2020 ). In contrast, organic chelating agents, particularly those derived from amino acids, offer a more sustainable alternative (Alipour Babadi et al., 2025 ). These aminochelates form stable complexes with micronutrients, facilitating their uptake and mobility while supporting microbial activity and reducing environmental risks (Souri and Hatamian, 2018 ; Areche et al., 2023 ). Aminochelates are increasingly recognized for their multifunctional benefits. In addition to improving nutrient delivery, they provide nitrogen, stimulate enzymatic pathways, and enhance metabolic processes in plants (Souri, 2016 ; Abou-Sreea et al., 2021 ). Their application also fosters microbial growth and diversity in the rhizosphere, potentially increasing the enzymatic activity linked to nutrient turnover (Blagodatskaya et al., 2021 ; Areche et al., 2023 ). Microorganisms, competing with plants for amino acids, activate a range of extracellular enzymes that catalyze nutrient cycling and reflect the intensity of root-microbe interactions (Moormann et al., 2022 ). Recent research has highlighted the importance of rhizosphere “enzymatic hotspots” for nutrient mobilization. These microsites exhibit elevated microbial respiration and biochemical transformation rates compared to bulk soil, often driven by plant exudates and microbial dynamics (Liu et al., 2025 ). Such fine-scale spatial variability plays a pivotal role in regulating ecosystem processes, yet remains poorly understood in relation to fertilization practices. Enzyme activity within these hotspots provides critical insights into soil health and plant–microbe interactions, serving as a sensitive bioindicator of microbial community composition and function (Peng et al., 2023 ). These enzymatic processes are particularly responsive to plant nutrient acquisition strategies and fertilizer inputs, with Fe and Zn playing crucial roles as enzyme cofactors (Kuzyakov & Razavi, 2019 ; Philippot et al., 2023 ). Hydrolytic enzymes like β-glucosidase and leucine aminopeptidase are central to carbon and nitrogen cycling. Their activity is influenced by nutrient availability, root growth traits, and microbial community structure (Philippot et al., 2023 ). While enzyme production often declines under nutrient-rich conditions, complex interactions in the rhizosphere can sustain or even stimulate activity due to intensified plant–microbe competition (Jia et al., 2024 ). Moreover, enzyme hotspots may reflect responses of both rhizosphere and endosphere microbiota, whose roles in nutrient uptake and signaling are becoming increasingly evident (Hao et al., 2022 ; Liu et al., 2025 ). To fully understand these dynamic interactions, advanced tools like in situ zymography are essential. Unlike traditional bulk assays, this technique provides high-resolution visualization of enzyme distribution around roots, revealing spatial patterns of activity linked to nutrient acquisition and microbial behavior (Razavi et al., 2019 ). By using fluorescent substrates to track enzyme kinetics in real time, zymography offers a window into the biochemical hotspots that drive plant-soil feedbacks (Bilyera & Kuzyakov, 2024 ). Despite their proven nutritional benefits, the effects of aminochelates on rhizosphere enzyme activity, microbial biomass, and root architecture remain unclear. This study addresses these critical gaps by integrating in situ zymography with biochemical assays to investigate the interactions between aminochelate fertilization, soil microbiology, and plant performance. Specifically, the objectives were to (1) visualize the spatial distribution of β-glucosidase and leucine aminopeptidase activity in the rhizosphere under varying Fe and Zn aminochelate treatments, (2) assess enzyme kinetic parameters and microbial responses to these treatments, and (3) evaluate associated changes in root system architecture traits. Collectively, these approaches aim to uncover the synergistic roles of organic chelates in promoting nutrient cycling, microbial functioning, and plant development within sustainable agricultural systems. 2. Materials and methods 2.1. Experimental site The soil was collected in September 2023 from a wheat field at the Experimental Farm Hohenschulen, Faculty of Agricultural and Nutritional Sciences, Christian-Albrechts-University of Kiel (54°19′05″N, 9°58′38″E), located approximately 15 km west of Kiel in Schleswig-Holstein, northwestern Germany. Soil samples were taken from three different locations within the field at a depth of 0–30 cm and thoroughly mixed to create a composite sample. The soil was air-dried, sieved through a 2 mm mesh, and stored at room temperature. It was classified as a pseudogleyic sandy loam (Luvisol) with the following properties: 100 g kg⁻¹ clay, a pH of 6.7 (in CaCl₂), 82 mg kg⁻¹ phosphorus (P), 200 mg kg⁻¹ potassium (K), 215 mg kg⁻¹ magnesium (Mg), 13.8 g kg⁻¹ (3%) organic carbon (C), and 1.1 g kg⁻¹ total nitrogen (N). 2.2. Synthesis and characterization of Fe and Zn aminochelates Aminochelates were synthesized using glycine (Gly) and methionine (Met) as ligands, following a modified version of the methodology described by Ashmead et al. ( 2000 ). Glycine, as the smallest amino acid, was selected for its high solubility and strong chelating ability, while methionine was chosen for its additional functional groups and its role in plant metabolism, which may enhance micronutrient mobility and uptake. Briefly, an aqueous solution containing the respective amino acid, calcium carbonate, iron(II) sulfate heptahydrate (FeSO₄·7H₂O), and zinc sulfate heptahydrate (ZnSO₄·7H₂O) was prepared to form Fe- and Zn-amino acid chelate complexes. A 2:1 molar ligand-to-metal ratio was maintained. The reaction mixture was continuously stirred at 50°C for 3 hours to ensure complete ion complexation. As a result, Fe(II)/Zn-amino acid chelates, calcium sulfate, and water were formed with minimal interference from other ions. The Fe-amino acid chelates appeared as light-brown crystalline precipitates, while the Zn-amino acid chelates were white. Both products were oven-dried before characterization. Chelation was confirmed using FT-IR analysis over the range of 4000–400 cm⁻¹ with an FT-IR-8400 Shimadzu spectrophotometer, employing KBr discs (Fig. 1 ). Elemental composition was determined using a CHN elemental analyzer (Perkin-Elmer 2400). Structural characterization of the aminochelates was conducted via solution-state ¹H NMR spectroscopy at room temperature using a 400 MHz Bruker Avance DRX (Germany) instrument with D₂O as the solvent. The key characteristics of the synthesized aminochelates are listed in supplementary data. 2.3. Experimental set-up and treatment application This study evaluated four aminochelate fertilizer treatments: (1) Fe (glycine) 2 [Fe (Gly) 2 ]; (2) Fe (methionine) 2 [Fe (Met) 2 ]; (3) Zn (glycine) 2 [Zn (Gly) 2 ]; and (4) Zn (methionine) 2 [Zn (Met) 2 ] aminochelates, along with a control treatment without any amendments. Sunflower ( Helianthus annuus L.) plants were grown in a rhizobox system arranged in a completely randomized design (CRD) with three replications, resulting in a total of 15 rhizoboxes. Sunflower seeds were surface-sterilized using 10% H₂O₂ to prevent fungal or bacterial infections, followed by thorough rinsing with deionized water. The seeds were then germinated on moist filter paper in sterile culture dishes at 25°C for 5 days to initiate growth. Sieved soil was packed into transparent rhizoboxes (3 × 20 × 20 cm, H × W × L; Clickbox®, Germany), with each box containing 2 kg of soil. Seedlings were transplanted into the rhizoboxes at a depth of 1 cm. The boxes were positioned at a 45° angle to direct root growth toward the front wall, following the method described by Razavi et al. ( 2016b ). The experiment was conducted in a greenhouse at Kiel University under controlled conditions, maintaining a daily light period of 8 hours, a temperature range of 20 to 25°C, and a relative humidity of 65 to 75%. Soil water content was adjusted to 70% of field capacity and maintained by regularly adding distilled water using a syringe to compensate for evapotranspiration losses. Aminochelate fertilizers were applied as a solution using a syringe near the seedlings. The fertilizers were applied to the soil to supply 5 mg kg⁻¹ of Fe or Zn, with application rates calculated based on the metal content of each aminochelate compound. Each rhizobox received 10 mL of the solution in two split applications: half after 1 week and the remaining half 4 weeks post-emergence. 2.4. Soil and plant analyses Four weeks after seedling establishment, the rhizoboxes were transferred to the laboratory and opened from the root side. Soil samples were collected from two distinct areas: (1) the rhizosphere soil (RH), defined as soil tightly adhering to the roots within a distance of ≤ 5 mm, and (2) the bulk soil (BS), representing root-free, non-rhizosphere soil. These samples were used for planned analyses. For root analysis, all visible roots were carefully separated from the soil, washed slowly with distilled water to remove any adhering soil particles, and preserved in 30% ethanol for further measurements. The cleaned roots were scanned using a LiDE 220 scanner (Epson Perfection 2480 Photo, Epson, Japan) at a resolution of 600 dpi. The root images were processed using the SmartRoot plugin of ImageJ software (Lobet et al., 2011 ) to determine the following root growth traits: average root diameter (RD), root length (RL), and root surface area (RSA). Shoots were severed below the crown. Both shoots and roots were placed in paper bags, oven-dried at 65°C for 48 h, and their dry weights were determined. 2.5. Zymography Soil zymography was employed as a non-destructive technique to visualize and localize hotspots of β-glucosidase (βG) and leucine aminopeptidase (LAP) activity on the soil surface, following the protocol established by Razavi et al. ( 2019 ). Fluorogenic substrate solutions (12 mM) based on 4-methylumbelliferone (MUF) and 7-amino-4-methylcoumarin (AMC) were used: 4-methylumbelliferyl-β-D-glucoside (MUF-β) (Sigma Aldrich, Germany) for βG, and L-Leucine-7-amino-4-methylcoumarin hydrochloride (AMC-L) (Sigma Aldrich, Germany) for LAP. These substrates were prepared using MES buffer (C₆H₁₃NO₄SNa₀.₅, Sigma-Aldrich, Darmstadt, Germany) and TRIZMA buffer (C₄H₁₁NO₃·HCl, C₄H₁₁NO₃, Sigma-Aldrich, Darmstadt, Germany), respectively. Upon enzymatic hydrolysis, MUF and AMC substrates produce fluorescent signals, which can be visualized and quantified under ultraviolet (UV) light (Spohn and Kuzyakov, 2013 ). Polyamide membrane filters (Tao Yuan, China) with a diameter of 20 cm and a pore size of 0.45 µm were used to minimize enzyme diffusion. The membranes were saturated with the respective fluorogenic substrate solutions, attached directly to the rooted side of each rhizobox, and covered with aluminum foil to prevent light exposure and dehydration. After 1 h of incubation (Razavi et al., 2016a ; Liu et al., 2017 ), the membranes were carefully removed from the soil surface and gently cleaned using a soft brush. To visualize the enzyme activity, the membranes were placed in a light-proof box and exposed to UV light at a wavelength of 355 nm in a dark room. Fluorescent signals were captured using a digital camera (Canon EOS 6D, Canon Inc.) equipped with a Canon EF 24–105 mm 1:4L IS II USM lens, with camera settings at an aperture of f/5.6 and a shutter speed of 1/8 second. To calibrate the fluorescence intensity, which is proportional to enzyme activity, 2×2 cm membrane sections were soaked in 15 µL of MUF or AMC substrate solutions at varying concentrations. For MUF, the concentrations were 0, 0.2, 0.5, 1, 2, 4, 6, 8, and 10 mM, while for AMC, the concentrations were 0, 0.1, 0.2, 1, 2, 4, and 5 mM. This calibration ensured accurate quantification of enzyme activity in the zymography images (Razavi et al., 2016a ). 2.6. Image processing To convert zymogram images into quantitative spatial data, the open-source ImageJ software was used. The RGB images were transformed into 16-bit grayscale images, and background was adjusted uniformly across images using ImageJ’s ‘Brightness and Contrast’ tool to minimize non-enzymatic signal interference. Enzyme activity (nmol MUF or AMC cm⁻² h⁻¹) was quantified by converting the gray values from the zymograms using a standard calibration curve generated from known concentrations of MUF and AMC (Guber et al., 2018 ). The hotspot percentage, representing regions of high enzymatic activity, was calculated by determining the proportion of bright pixels to the total image area. A threshold of Mean + 2 SD was used to delineate high-activity zones, typically corresponding to the top ~ 25% of signal intensities. The grayscale histogram was separated into two normal distributions, distinguishing hotspot regions from bulk soil. The grayscale range corresponding to hotspots was then overlaid onto the original image for visualization (Bilyera et al., 2020 ). Rhizosphere extent was quantified to assess the spatial influence of roots on enzyme activity. The rhizosphere extent was defined as the distance from the root boundary where enzyme activity was at least 30% higher than the average activity in the bulk soil (Ma et al., 2018 ). Six transect lines were randomly drawn at approximately 90° angles to the root for each enzyme (βG and LAP) in each replicate zymogram image. This resulted in a total of 36 lines per image (6 lines × 2 enzymes × 3 replicates), and the gray values along these lines were extracted. The mean gray value from the set of transect lines within each rhizobox zymogram was calculated and treated as a single data point representing that biological replicate. Enzyme activities were calculated using the calibration curve. Finally, the total enzyme activity in the rhizosphere was determined by summing the pixel-wise enzyme activity values from the root surface (as the starting point) outward to the rhizosphere limit, defined as the soil area surrounding the root (Ma et al., 2018 ). 2.7. Enzyme kinetics The kinetics of two hydrolytic enzymes involved in the carbon and nitrogen cycles (βG and LAP) were assessed for rhizosphere and bulk soil using the same fluorogenic substrates as applied in the zymography analysis. Eight substrate concentrations ranging from 0 to 400 µM were used, following the method of Razavi et al. ( 2016a ). To obtain RH soil samples, the complete root system was placed on a sterile surface, and soil tightly adhering to the root surface was gently brushed off using a sterile toothbrush. BS samples, free of visible roots, were collected in conical tubes and stored at 5°C under the same conditions as RH soil samples. For enzyme assays, 0.5 g of soil (dry weight equivalent) was suspended in 50 mL of deionized water in a Falcon tube and shaken for 20 minutes. Subsequently, a 96-well black microplate (Thermo Fisher, Denmark) was prepared by adding 50 µL of soil suspension, 50 µL of MES/Trizma buffer (pH 6.5 for βG and 7.2 for LAP), and 100 µL of substrate solution to each well. Fluorescence was measured using a CLARIOstar Plus microplate reader (BMG LABTECH, Germany) at an excitation wavelength of 355 nm and an emission wavelength of 460 nm. Measurements were taken at four time points: 0, 30, 60, and 120 minutes after incubation at room temperature. Enzyme activities were determined across the substrate concentrations (0, 20, 40, 60, 80, 100, 200, and 400 µmol g⁻¹ soil) to ensure a sufficient range for enzyme saturation. A calibration curve was established using MUF or AMC substrates at concentrations of 0, 10, 20, 30, 40, 50, 100, and 200 µM. The linearity of fluorescence increase with increasing substrate concentration was carefully evaluated. The 120-minute data, which showed the best fit with the substrate concentration plot, were used for further calculations (German et al., 2011 ). Enzyme kinetic parameters were estimated using the Michaelis-Menten equation: $$\:V=\frac{{V}_{max}\left[S\right]}{{K}_{m+}\left[S\right]}$$ 1 where V max is the maximum reaction rate, K m is the substrate concentration at 1/2 V max and [S] is substrate concentration. V max and K m parameters were determined by the non-linear regression routine of GraphPad Prism software (v. 10.3.0). The catalytic efficiency of enzymes (K a ), reflecting functional changes in microbial communities, was calculated using the equation described by Hoang et al. ( 2016 ): $$\:Ka\:=\frac{{V}_{max}}{{K}_{m}}$$ 2 2.8. Microbial biomass C Microbial biomass C (MBC), basal respiration (BR), and substrate-induced respiration (SIR) were measured using the 96-well MicroResp™ system, which quantifies CO₂ production via colorimetric detection (Campbell et al., 2003 ). Approximately 0.45 g of fresh soil was added to each well, with glucose for SIR or distilled water for BR measurements. A carbon dioxide detection microplate containing 300 µL per well of cresol red indicator dye was used to capture and quantify CO₂ emissions. The color change of the indicator dye was then used to determine respiration rates, providing insights into microbial activity and biomass. Bulk and root-affected soils (20 g each) were used to prepare replicate samples, from which 0.45 g aliquots were loaded into microtiter wells using the MicroResp™ filling device (Thermo LifeSciences, Basingstoke, United Kingdom). Colorimetric gel detector plates were prepared with a cresol red indicator solution and analyzed using a CLARIOstar Plus microplate reader (BMG LABTECH, Germany) at a wavelength of 570 nm. Absorbance data were recorded at the start (0 hours) and after 6 hours of incubation at 25°C. Respiration rates were calculated by subtracting the absorbance values of the blank sample (mean initial colorimetric values for each plate) from the test sample readings. All absorbance data were normalized, log-transformed before analysis, and converted to CO₂ concentrations using a standard calibration curve. MBC was estimated using the equation proposed by Anderson and Domsch ( 1990 ): MBC (µg C g − 1 soil) = SIR (µL CO 2 g − 1 soil h − 1 ) × 40. 04 + 0.37 A higher SIR response during the initial 0–6 h period suggested a greater microbial community abundance in the soil due to the added substrates (Degens & Harris, 1997 ). The metabolic quotient (qCO₂), used to evaluate the effects of environmental conditions on soil microbial biomass, was calculated as the ratio of BR to MBC and expressed in µg CO₂-C mg⁻¹ MBC h⁻¹ (Anderson & Domsch, 1990 ). 2.9. Statistical analysis All data were analyzed using one-way analysis of variance (ANOVA) using the "agricolae" package in R (v.12.1 + 402) software. Mean values were compared using Tukey's HSD test at a significance level of p < 0.05. The detailed ANOVA tables (Tables S1–S3) are provided in the Supplementary Material. Graphs were generated with OriginPro (v.10.2, 2025) software. Enzyme kinetic parameters (V max , K m , and K a ) were evaluated using GraphPad Prism (v.10.3.1, 2024) software. Additionally, Pearson’s correlation analysis was performed to explore the relationships among the measured traits. An interactive Mantel test correlation heat map was generated via the Chiplot website ( https://www.chiplot.online/ ) using Bray-Curtis and Euclidean distance matrices derived from enzyme activity, microbial activity, and root trait datasets with significance assessed by 999 permutations ( p < 0.05). 3. Results 3.1. Enzyme activity, rhizosphere extent, and hotspot percentage of β-glucosidase and leucine aminopeptidase Representative βG and LAP zymograms visualized the spatial distribution of enzyme activity in the sunflower rhizosphere (Fig. 2 ). The [Fe(Gly)₂] and [Zn(Met)₂] treatments showed the highest enzyme activities, with [Fe(Gly)₂] exhibiting a 21.1% increase in βG activity ( p < 0.05) and [Zn(Met)₂] demonstrating a 120.7% increase in LAP activity ( p 0.05). Zymography revealed treatment-dependent variation in rhizosphere extents of both βG (p < 0.05) and LAP (p < 0.01) (Fig. 3 B). The rhizosphere extent of βG under [Fe(Gly)₂] averaged 0.42 mm, which was 83% higher than the control (p < 0.05). Similarly, the rhizosphere extent of LAP increased under [Zn(Met)₂], averaging 0.66 mm, which was about two times higher than the control (p < 0.01). Hotspots of enzyme activity, particularly in aminochelate-treated soils, were predominantly located on the rhizoplane and extended along the roots (Fig. 2 ). For βG, the hotspot percentage increased more than 4-fold compared to the control (p < 0.01), while LAP hotspot percentage more than doubled under aminochelate treatments (p 0.05) was observed between [Fe(Gly)₂] and [Zn(Met)₂] for LAP hotspot percentages. 3.2. Enzyme kinetic parameters For βG in the rhizosphere, the [Zn(Met)₂] treatment exhibited the highest V max , with an increase of 144.6% compared to the control (p < 0.01; Fig. 4 A). This reflects a substantial increase in enzymatic activity under [Zn(Met)₂]. In bulk soil, the [Fe(Met)₂] treatment showed the highest V max , with an increase of 103.2% relative to the control (p < 0.01). The K m values increased by 50.1% under the [Zn(Met)₂] treatment in the rhizosphere, indicating lower enzyme-substrate affinity (p < 0.05; Fig. 4 C). In bulk soil, K m differences were not statistically different. Regarding catalytic efficiency (K a ), the highest values in the rhizosphere were recorded under [Zn(Met)₂], with a 62.4% increase compared to the control (p < 0.05; Fig. 4 E). In bulk soil, the [Fe(Met)₂] treatment exhibited the greatest enhancement in K a , with a 69.8% increase relative to the control (p < 0.01; Fig. 4 E). For LAP, the [Zn(Met)₂] treatment in the rhizosphere and [Fe(Gly)₂] in bulk soil resulted in the highest V max , increasing by 74.7% and 73.2% relative to the control, respectively (p < 0.01; Fig. 4 B). K m values increased by 34.8% in the rhizosphere and 30.1% in bulk soil under [Fe(Gly)₂], indicating lower enzyme-substrate affinity (p < 0.05; Fig. 4 D). The K a values reached their maximum under [Zn(Met)₂], with increases of 56.8% in the rhizosphere and 49.8% in bulk soil compared to the control (p < 0.01; Fig. 4 F). 3.3. Microbial growth responses to substrate addition Application of aminochelates led to clear differences in BR compared with the control (Fig. 5 A). The [Zn(Met)₂] treatment produced the strongest effect, with BR increasing by 42.4% relative to the control (p < 0.01). Regarding SIR (Fig. 5 B), the highest SIR was recorded under the [Zn(Met)₂] treatment, which increased by 238.2% relative to the control. The [Fe(Met)₂] treatment also enhanced SIR by 238.2% relative to the control (p < 0.01). Similarly, MBC values (Fig. 5 C) improved under the [Zn(Met)₂] treatment, rising by 225.5% compared to the control ( p < 0.01). In contrast, the qCO₂ decreased under all aminochelate applications (Fig. 5 D). The strongest reduction occurred with [Zn(Met)₂], where qCO₂ values dropped by 55.0% compared to the control (p < 0.01). 3.4. Root system architecture traits The application of aminochelates showed the greatest increase of shoot dry weight under the [Fe(Gly)₂] treatment, with a 70.1% increase compared to the control (p < 0.01; Fig. 6 A). A similar trend was observed in root dry weight, which increased by 212.5% under the [Fe(Gly)₂] treatment (p < 0.01). RL (Fig. 6 B) was 281.0% greater under the [Fe(Gly)₂] treatment compared to the control (p < 0.01). However, there was no difference between the [Fe(Gly)₂] and [Zn(Met)₂] treatments. RD (Fig. 6 C) was reduced under [Fe(Gly)₂] treatment, decreasing by 86.5% compared to the control (p < 0.01). Conversely, the highest RD values were observed in the control treatment. RSA (Fig. 6 D) also increased under the [Fe(Gly)₂] treatment, showing more than a 4-fold increases compared to the control ( p < 0.01). 3.5. Integrated correlation patterns among soil biochemical, microbial, and root parameters To explore the integrated responses of all measured variables to different Fe and Zn aminochelate treatments, a Pearson’s correlation heatmap was generated. In addition, a Mantel test-based network was constructed to visualize the relationships among variables (Fig. 7 A). Enzyme hotspots and activities exhibited strong positive correlations (r > 0.85, p < 0.01) with root architecture traits such as RL and RSA, indicating spatial co-localization and potential feedbacks between root foraging capacity and rhizosphere biochemical functioning. These enzyme parameters also showed positive associations with microbial activity indicators, particularly BR (r = 0.79–0.99, p < 0.05). Notably, enzyme kinetic parameters (V max , K a ) exhibited synchronous patterns between rhizosphere and bulk soil compartments (r = 0.91–0.99, p < 0.001), suggesting synchronized enzymatic adaptation across soil zones. These kinetic parameters were also positively correlated with MBC (r = 0.76–0.92), SIR (r = 0.72–0.92), and root biomass (RDW; r = 0.88–0.93), reinforcing the notion that aminochelate fertilization enhances microbial-driven nutrient cycling via enzyme production. Treatment-specific analysis revealed that [Fe(Met)₂] and [Zn(Met)₂] aminochelates showed the most extensive network connectivity (degree centrality > 0.65), with significant associations to both enzymatic (Mantel's r = 0.58–0.68, p < 0.01) and morphological responses (Mantel's r = 0.54–0.59, p < 0.05). The glycinate treatments ([Fe(Gly)₂], [Zn(Gly)₂]) displayed intermediate connectivity (degree centrality = 0.42–0.48), while control samples showed minimal network integration (degree centrality < 0.15), emphasizing the impact of micronutrient chelation on the rhizosphere-enzyme-root-microbe nexus. The radar plot (Fig. 7 B) offers a comprehensive visual comparison of the treatment groups across distinct parameters. To account for the different value ranges among parameters, data were min-max normalized, allowing an equitable visual representation across treatments. The application of [Zn(Met)₂] aminochelate resulted in notably higher normalized values for the hotspot percentage of βG and LAP enzyme activities, as well as microbial indicators such as MBC, BR, and SIR, indicating enhanced enzyme functioning and microbial activity in the rhizosphere. In contrast, the [Fe(Gly)₂] treatment was associated with elevated values in root system architecture traits, including RL and RSA, suggesting a stimulation of root system development under this condition. The [Zn(Gly)₂] treatment was characterized by elevated values of enzyme kinetic parameters (V max , K m , and K a ), particularly within the rhizosphere zone, reflecting enhanced catalytic potential of soil enzymes in this environment. Notably, the [Fe(Met)₂] treatment displayed a moderate but consistent increases across enzyme activities, microbial traits, and plant morphology, implying potential synergistic interactions among rhizosphere biochemical functioning, microbial dynamics, and plant development. In contrast, the control treatment showed limited enhancement, with a relative increase observed only in the metabolic quotient (qCO₂) parameter. 4. Discussion 4.1. Enzyme activity, rhizosphere extent, and hotspot percentage of β-glucosidase and leucine aminopeptidase The application of Fe and Zn aminochelates, particularly [Fe(Gly)₂] and [Zn(Met)₂], markedly enhanced β-glucosidase (βG) and leucine aminopeptidase (LAP) activities in the sunflower rhizosphere (Fig. 3 A). These hydrolytic enzymes are central to organic matter turnover and nutrient cycling: βG catalyzes the hydrolysis of cellulose-derived compounds, while LAP degrades proteins, jointly driving carbon and nitrogen mineralization essential for plant nutrition (Allison & Vitousek, 2005 ; Burns et al., 2013 ). In situ zymography visualizations (Figs. 2 & 3 ) confirmed not only elevated enzyme activities but also enlarged rhizosphere extents and more pronounced enzymatic hotspots under aminochelate treatments. Such spatial broadening reflects intensified root–soil biochemical interactions, often associated with increased root exudation, microbial colonization, and extracellular enzyme secretion (Spohn & Kuzyakov, 2014 ; Razavi et al., 2019 ; Guber et al., 2018 ). These effects likely arise through both direct and indirect mechanisms: as essential cofactors of metalloenzymes, Fe and Zn in chelated forms may enhance enzymatic efficiency, while their improved bioavailability alleviates micronutrient limitations that constrain microbial metabolism, thereby stimulating microbial proliferation and enzyme production (Burns et al., 2013 ; Marschner et al., 2011 ; Canarini et al., 2019 ). Enzymatic hotspots emerge where root- or microbe-derived enzymes accumulate in microsites enriched with carbon and nutrients (Kuzyakov & Blagodatskaya, 2015 ). Unlike bulk soil, the rhizosphere is highly heterogeneous, containing both hotspots and coldspots shaped by root proximity and substrate availability (Zhang et al., 2021 ). The expansion of βG and LAP activity zones observed here signals greater microbial investment in enzyme synthesis, an adaptive strategy to optimize nutrient acquisition in carbon-rich environments (Allison & Vitousek, 2005 ; Bonner et al., 2018 ). While labile carbon can sometimes reduce microbial demand for extracellular enzymes (Sinsabaugh & Follstad Shah, 2012 ; Liu et al., 2022 ), our results indicate that nutrient cycling demands remained high despite elevated carbon inputs, possibly reflecting persistent nutrient limitations, greater microbial biomass, or rapid enzymatic turnover (Blagodatskaya et al., 2016 ). Importantly, these findings support the concept of rhizosphere spatial stability, where the persistence of enzymatic hotspots is maintained by a balance between enzyme synthesis and degradation, enabling plants to secure a competitive advantage in nutrient uptake (Miralles et al., 2012 ; Nannipieri et al., 2018 ; Schimel et al., 2017 ). Zymographic analysis confirmed that Fe and Zn aminochelates significantly increased both the density and spatial extent of enzymatic hotspots (Fig. 3 C). These zones of intense microbial colonization and nutrient cycling highlight elevated biochemical activity at the root–soil interface (Spohn & Kuzyakov, 2014 ; Razavi et al., 2019 ; Guber et al., 2018 ). Their expansion suggests that aminochelates stimulated microbial enzyme secretion by enhancing root exudation and microbial access to organic substrates (Liu et al., 2017 ; Zhang et al., 2021 ). This functional broadening supports more effective organic matter decomposition and nutrient mobilization (Bonner et al., 2018 ; Wei et al., 2019 ). In chemically complex soils such as the Luvisol used in this study, characterized by high clay content and strong nutrient fixation, aminochelates may further activate microbial pathways that release immobilized elements, particularly phosphorus and micronutrients (Kuzyakov & Blagodatskaya, 2015 ). The observed increase in hotspot density aligns with microbial strategies for nutrient acquisition: either through intensified enzyme production under carbon limitation (Allison & Vitousek, 2005 ) or by enhancing enzymatic capacity under high carbon availability and biomass conditions (Blagodatskaya et al., 2016 ). Moreover, the spatial extension of enzyme activity beyond the immediate root zone implies that aminochelates facilitated the proliferation of microbial communities engaged in nutrient turnover across a wider rhizosphere area (Zhao et al., 2020 ; Bilyera & Kuzyakov, 2024 ). Such functional expansion is particularly important for improving nutrient acquisition in soils with physical or chemical constraints. 4.2. Enzyme Kinetic parameters The kinetic parameters of βG and LAP provide insights into microbial responses to aminochelate application. Substantial increases in V max under [Zn(Met)₂] in the rhizosphere and [Fe(Met)₂] in bulk soil indicate enhanced catalytic capacity, particularly for βG (Fig. 4 A). This reflects greater microbial enzyme production and turnover of organic matter, stimulated by root-derived labile carbon (Kuzyakov & Blagodatskaya, 2015 ; Sanaullah et al., 2016 ). Such upregulation is consistent with rhizosphere hotspots dominated by fast-growing r-strategists producing high-activity isoenzymes (Dorodnikov et al., 2009 ; Liu et al., 2025 ). As noted by Jia et al. ( 2024 ), shifts in microbial functional groups and their metabolic traits explain the rise in V max following micronutrient chelate inputs. The observed increase in K m under [Zn(Met)₂], despite higher βG activity, suggests reduced enzyme-substrate affinity, an adaptation to elevated substrate concentrations favoring broader but less specific isoenzymes (Fierer et al., 2007 ). The concurrent rise of V max and K m reflects strategies to optimize nutrient acquisition under high carbon and stoichiometric imbalance (Sinsabaugh & Follstad Shah, 2012 ), highlighting the dual role of aminochelates in stimulating microbial growth and modifying enzyme systems. For LAP, responses were weaker: [Fe(Gly)₂] and [Zn(Met)₂] raised V max in both soil compartments but to a lesser extent than βG. This agrees with the lower responsiveness of N-acquiring enzymes under carbon-rich but nitrogen-limited conditions, due to the low N:C ratio of root exudates (Kuzyakov & Xu, 2013 ; Badalucco & Nannipieri, 2007 ). Catalytic efficiency (K a ), combining V max and K m , was maximized under [Zn(Met)₂], showing that even with lower substrate affinity, turnover capacity was sustained (Nannipieri et al., 1998; Razavi et al., 2016b ). These patterns, observed in nutrient-rich but chemically constrained soil, suggest that Fe and Zn aminochelates improve micronutrient functionality by overcoming limited bioavailability. By supplying Fe and Zn in bioavailable forms (Alipour Babadi et al., 2025 ), they act not only as nutrient sources but also as biostimulants modulating microbial communities and enzyme systems (Marschner et al., 2011 ; Blagodatskaya et al., 2016 ). Overall, the enhanced kinetic parameters emphasize the multifaceted role of aminochelates in promoting soil biochemical activity and nutrient cycling. 4.3. Microbial growth responses to substrate addition The observed increases in BR, SIR, and MBC across aminochelate treatments demonstrate a clear stimulatory effect on soil microbial metabolic activity and biomass accumulation (Fig. 5 A-D). Among treatments, [Zn(Met)₂] most strongly enhanced BR, indicating robust microbial oxidative metabolism, likely stimulated by improved micronutrient bioavailability and enhanced rhizodeposition (Blagodatskaya et al., 2014 ; Wang et al., 2021 ). Similarly, [Fe(Met)₂] significantly increased both BR and SIR, as evidenced by the high SIR rate, suggesting that this treatment not only activated the existing microbial pool but also enhanced microbial responsiveness to labile carbon inputs. The results are consistent with the concept of microbial activation in fertile soils, where abundant organic matter supports a high baseline of microbial activity and reduces lag time for substrate utilization (Blagodatskaya et al., 2014 ). This is particularly true in our experimental soil, which is nutrient-rich and well-supplied with organic carbon. In such environments, microbial communities are more capable of rapidly exploiting external C sources due to reduced energy limitations (German et al., 2011 ), explaining the strong and immediate SIR responses observed. Moreover, the high MBC values under Fe and Zn aminochelate treatments confirm the proliferation of active microbial biomass. As MBC reflects the size of the living microbial pool, its increase suggests not only enhanced microbial growth but also the stimulation of r-strategist microbes in the rhizosphere (Fierer et al., 2007 ), which are known to dominate in nutrient-rich hotspots and respond quickly to labile nutrient inputs (Blagodatskaya & Kuzyakov, 2013 ). These findings align with previous research indicating that rhizosphere hotspots in fertile soils contain active microbial communities capable of immediate substrate assimilation (Cheng, 2009 ; Dippold & Kuzyakov, 2013 ). The reduced qCO₂ observed under [Fe(Met)₂] and especially [Zn(Met)₂] treatments indicates enhanced microbial carbon-use efficiency. This suggests that microbes allocated more carbon to biomass production rather than respiration, resulting in lower energy loss and improved microbial yield due to the availability of chelated micronutrients (Fischer et al., 2010 ; Fterich et al., 2012 ). The chelated forms of Fe and Zn may contribute to this effect by enhancing microbial respiratory enzyme systems, particularly Fe-S cluster-containing proteins involved in electron transport chains, thereby improving the overall metabolic performance of the microbial community (Read et al., 2021 ; Chen et al., 2023 ). This improved efficiency likely contributes to the increased enzyme activities observed, as larger and more metabolically active microbial populations typically secrete greater amounts of extracellular enzymes in response to nutrient availability and rhizodeposition (Nannipieri et al., 2012 ; Razavi et al., 2016b ). 4.4. Root system architecture traits The application of Fe and Zn aminochelates significantly enhanced shoot and root biomass, along with key root architecture traits such as length and surface area (Fig. 6 A–D). These improvements likely result from enhanced micronutrient availability in the rhizosphere, which supports better plant growth and root development. Chelating ligands such as glycine and methionine protect micronutrients (especially Fe and Zn) from precipitation and immobilization in soils with unfavorable chemical properties, enhancing their uptake efficiency (Chen et al., 2004 ; Lucena, 2006 ). Moreover, amino acid ligands can be absorbed by plants and have been shown to promote physiological improvements, including stress mitigation, enzyme activation, and hormonal regulation (Souri & Hatamian, 2018 ). These effects are consistent with the biostimulant properties of amino acid-based chelates, which not only act as carriers of essential micronutrients but also positively influence metabolic processes involved in chlorophyll biosynthesis, energy production, and growth regulation (Souri & Hatamian, 2018 ). Moreover, the distribution patterns of biomass in aminochelate-treated plants support the optimal partitioning hypothesis (McConnaughay & Coleman, 1999 ), which proposes that under nutrient-limited conditions, plants prioritize root growth to improve nutrient foraging. This shift likely enhanced rhizodeposition, which stimulates microbial activity and accelerates nutrient turnover in the rhizosphere (Dakora & Phillips, 2002 ). Notably, [Fe(Gly)₂] application promoted greater root elongation and finer root structures, reflected by increased root length and reduced root diameter (Fig. 6 B & C ). Such changes facilitate efficient uptake of water and nutrients by expanding the root-soil contact surface (Kuzyakov & Razavi, 2019 ; Rahnama et al., 2024 ). The enlarged root surface area (RSA; Fig. 6 C) also promotes intensified rhizosphere interactions, where increased exudation stimulates microbial proliferation and enzymatic activity (Canarini et al., 2019 ). A more extensive and finely branched root system promotes higher rhizodeposition rates, which in turn stimulate microbial biomass and extracellular enzyme production, ultimately improving nutrient availability and turnover (Canarini et al., 2019 ). Moreover, enhanced initiation and elongation of lateral roots contribute to greater total root length, improving the plant’s ability to explore the soil and access water and nutrients more efficiently (Rahnama et al., 2019 ). These processes may underlie the enhanced plant-soil interactions observed under aminochelate treatments. Such improvements in root architecture and rhizosphere functioning likely create a reinforcing cycle of nutrient acquisition and plant growth, resulting in a root system with greater functional capacity and increased aboveground productivity. Conclusion This study demonstrated that Fe and Zn aminochelates, particularly methionine-derived forms, substantially enhanced rhizosphere functioning in sunflower by stimulating microbial activity and enzyme kinetics, expanding enzymatic hotspot distribution, and improving root growth and architecture. These synergistic effects supported more efficient nutrient cycling and improved plant performance, underscoring the role of aminochelates not only as micronutrient carriers but also as physiological stimulants that optimize plant–microbe interactions. Among treatments, [Fe(Met)₂] and [Zn(Met)₂] were most effective in synchronizing microbial processes with root system traits, fostering close linkages between enzyme activity and root architecture. In contrast, [Fe(Gly)₂] showed more localized effects with limited systemic integration, suggesting that chelate structure strongly influences the scale and coherence of biological responses. These findings highlight the critical importance of ligand design in shaping rhizosphere processes and plant performance. Tailoring chelate formulations to align molecular efficacy with ecosystem-level functionality will be key to advancing sustainable nutrient management. Ultimately, rational design of next-generation aminochelates offers a promising pathway to improve soil fertility, enhance crop productivity, and support resilient agroecosystems. Declarations Declaration of competing 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. Supplementary data Supplementary data related to this article can be found in the supplementary material file available online. ACKNOWLEDGMENTS The authors extend their gratitude to the Ministry of Science, Research, and Technology of Iran (MSRT) for its financial support. DATA AVAILABILITY STATEMENT The data that support the findings of this study are available from the corresponding author upon reasonable request. References Abou-Sreea AI, Rady MM, Roby MH, Ahmed SM, Majrashi A, Ali EF (2021) Cattle manure and bio-nourishing royal jelly as alternatives to chemical fertilizers: potential for sustainable production of organic Hibiscus sabdariffa L. J Appl Res Med Aromat Plants 25:100334. https://doi.org/10.1016/j.jarmap.2021.100334 Alipour Babadi M, Norouzi Masir M, Moezzi AA, Rahnama A, Taghavi M (2025) Iron (Fe) biofortification using Fe-aminochelates in sunflower growing in calcareous soils. J Plant Nutr Soil Sci 188:519–535. https://doi.org/10.1002/jpln.12004 Allison SD, Vitousek PM (2005) Responses of extracellular enzymes to simple and complex nutrient inputs. Soil Biol Biochem 37:937–944. https://doi.org/10.1016/j.soilbio.2004.09.014 Anderson TH, Domsch KH (1990) Application of eco-physiological quotients (qCO 2 and qD) on microbial biomasses from soils of different cropping histories. Soil Biol Biochem 22:251–255. https://doi.org/10.1016/0038-0717(90)90094-G Areche FO, Aguilar SV, More López JM, Castañeda Chirre ET, Sumarriva-Bustinza LA, Pacovilca-Alejo OV, Camposano Córdova YF, Montesinos CCZ, Quincho Astete JA, Quispe-Vidalon D, Brito Mallqui CH, Camayo-Lapa BF, Malpartida Yapias RJ, Corilla Flores DD, Salas-Contreras WH (2023) Recent and historical developments in chelated fertilizers as plant nutritional sources, their usage efficiency, and application methods. Braz J Biol 83:e271055. https://doi.org/10.1590/1519-6984.271055 Asghar W, Akça MO, Akça H, Tarf OJ, Kataoka R, Turgay OC (2022) Alternative strategies to synthetic chemical fertilizers: revitalization of soil quality for sustainable agriculture using organic-based approaches. In: New and Future Developments in Microbial Biotechnology and Bioengineering. Elsevier , pp 1–30. https://doi.org/10.1016/B978-0-323-85581-5.00003-3 Ashmead SD, Wheelwright DC, Ericson C, Pedersen M (2000) A Composition and Method for Preparing Amino Acid Chelates Free of Interfering Ions. Google Patents. Patent no. WO 2002/030948 A3. https://patents.google.com/patent/WO2002030948A3 Badalucco L, Nannipieri P (2007) Nutrient transformation in the rhizosphere. In: Pinton R, Varanini Z, Nannipieri P (eds) The Rhizosphere: Biochemistry and Organic Substrates at the Soil-Plant Interface. CRC, Boca Raton, FL, pp 111–133. https://doi.org/10.1201/9781420005585-9 Bilyera N, Kuzyakov Y (2024) Soil zymography: a decade of rapid development in microbial hotspot imaging. Soil Biol Biochem 189:109264. https://doi.org/10.1016/j.soilbio.2023.109264 Bilyera N, Kuzyakova I, Guber A, Razavi BS, Kuzyakov Y (2020) How hot are hotspots: statistically localizing the high-activity areas on soil and rhizosphere images. Rhizosphere 16:100259. https://doi.org/10.1016/j.rhisph.2020.100259 Blagodatskaya E, Blagodatsky S, Anderson TH, Kuzyakov Y (2014) Microbial growth and carbon use efficiency in the rhizosphere and root-free soil. PLoS ONE 9(4):e93282. https://doi.org/10.1371/journal.pone.0093282 Blagodatskaya E, Kuzyakov Y (2013) Active microorganisms in soil: Critical review of estimation criteria and approaches. Soil Biol Biochem 67:192–211. https://doi.org/10.1016/j.soilbio.2013.08.024 Blagodatskaya E, Tarkka M, Knief C, Koller R, Peth S, Schmidt V, Spielvogel S, Uteau D, Weber M, Razavi BS (2021) Bridging Microbial Functional Traits with Localized Process Rates at Soil Interfaces. Front Microbiol 12:625697. https://doi.org/10.3389/fmicb.2021.625697 Blagodatskaya Е, Blagodatsky S, Khomyakov N, Myachina O, Kuzyakov Y (2016) Temperature sensitivity and enzymatic mechanisms of soil organic matter decomposition along an altitudinal gradient on Mount Kilimanjaro. Sci Rep 6:22240. https://doi.org/10.1038/srep22240 Bonner MTL, Shoo LP, Brackin R, Schmidt S (2018) Relationship between microbial composition and substrate use efficiency in a tropical soil. Geoderma 315:96–103. https://doi.org/10.1016/j.geoderma.2017.11.026 Burns RG, DeForest JL, Marxsen J, Sinsabaugh RL, Stromberger ME, Wallenstein MD, Weintraub MN, Zoppini A (2013) Soil enzymes in a changing environment: current knowledge and future directions. Soil Biol Biochem 58:216–234. https://doi.org/10.1016/j.soilbio.2012.11.009 Campbell CD, Chapman SJ, Cameron CM, Davidson MS, Potts JM (2003) A rapid microtiter plate method to measure carbon dioxide evolved from carbon substrate amendments so as to determine the physiological profiles of soil microbial communities by using whole soil. Appl Environ Microbiol 69:3593–3599. https://doi.org/10.1128/AEM.69.6.3593-3599.2003 Canarini A, Kaiser C, Merchant A, Richter A, Wanek W (2019) Root Exudation of Primary Metabolites: Mechanisms and Their Roles in Plant Responses to Environmental Stimuli. Front Plant Sci 10:157. https://doi.org/10.3389/fpls.2019.00157 Chen J, Calderone LA, Pan L, Quist T, Pandelia ME (2023) The Fe and Zn cofactor dilemma. Biochim Biophys Acta Proteins Proteom 1871:140931. https://doi.org/10.1016/j.bbapap.2023.140931 Chen Y, Clapp CE, Magen H (2004) Mechanisms of plant growth stimulation by humic substances: The role of organo-iron complexes. Soil Sci Plant Nutr 50:1089–1095. https://doi.org/10.1080/00380768.2004.10408579 Cheng WX (2009) Rhizosphere priming effect: its functional relationships with microbial turnover, evapotranspiration, and C-N budgets. Soil Biol Biochem 41:1795–1801. https://doi.org/10.1016/j.soilbio.2008.04.018 Dakora FD, Phillips DA (2002) Root exudates as mediators of mineral acquisition in low-nutrient environments. Plant Soil 245:35–47. https://doi.org/10.1023/A:1020809400075 Degens BP, Harris JA (1997) Development of a physiological approach to measuring the catabolic diversity of soil microbial communities. Soil Biol Biochem 29:1309–1320. https://doi.org/10.1016/S0038-0717(97)00076-5 Dippold MA, Kuzyakov Y (2013) Biogeochemical transformations of amino acids in soil assessed by position-specific labelling. Plant Soil 373:385–401. https://doi.org/10.1007/s11104-013-1764-3 Doostikhah N, Panahpour E, Nadian H, Gholami A (2020) Tomato (Lycopersicon esculentum L.) nutrient and lead uptake affected by zeolite and DTPA in a lead polluted soil. Plant Biol 22:317–322. https://doi.org/10.1111/plb.13059 Dorodnikov M, Blagodatskaya E, Blagodatsky S, Marhan S, Fangmeier A, Kuzyakov Y (2009) Stimulation of microbial extracellular enzyme activities by elevated CO 2 depends on aggregate size. Glob Change Biol 15:1603–1614. https://doi.org/10.1111/j.1365-2486.2009.01844.x Fierer N, Bradford MA, Jackson RB (2007) Toward an ecological classification of soil bacteria. Ecology 88:1354–1364. http://www.jstor.org/stable/27651243 Fischer H, Ingwersen J, Kuzyakov Y (2010) Microbial uptake of low-molecular-weight organic substances in soil: Review of mechanisms and methods. Soil Biol Biochem 42:1562–1571. https://doi.org/10.1111/j.1365-2389.2010.01244.x Fterich A, Mosbah M, Mars M (2012) Impact of grazing on soil microbial communities along a chronosequence of Acacia tortilis subsp. raddiana in arid soils in Tunisia. Eur J Soil Biol 50:56–63. https://doi.org/10.1016/j.ejsobi.2011.12.002 German DP, Chacon SS, Allison SD (2011) Substrate concentration and enzyme allocation can affect rates of microbial decomposition. Ecology 92:1471–1480. https://doi.org/10.1890/10-2028.1 Ghosh A, Kumar A, Biswas G (2024) Exponential population growth and global food security: Challenges and alternatives. Bioremediation of Emerging Contaminants from Soils. Elsevier, Amsterdam, pp 1–20. https://doi.org/10.1016/B978-0-443-13993-2.00001-3 Guber A, Kravchenko A, Razavi BS, Uteau D, Peth S, Blagodatskaya E, Kuzyakov Y (2018) Quantitative soil zymography: mechanisms, processes of substrate and enzyme diffusion in porous media. Soil Biol Biochem 127:156–167. https://doi.org/10.1016/j.soilbio.2018.09.030 Hao C, Dungait JA, Wei X, Ge T, Kuzyakov Y, Cui Z, Tian J, Zhang F (2022) Maize root exudate composition alters rhizosphere bacterial community to control hotspots of hydrolase activity in response to nitrogen supply. Soil Biol Biochem 170:108717. https://doi.org/10.1016/j.soilbio.2022.108717 He H, Peng M, Hou Z, Li J (2024) Organic substitution contrasting direct fertilizer reduction increases wheat productivity, soil quality, microbial diversity and network complexity. Environ Technol Innov 36:103784. https://doi.org/10.1016/j.eti.2024.103784 Hoang DTT, Pausch J, Razavi BS, Kuzyakova I, Banfield CC, Kuzyakov Y (2016) Hotspots of microbial activity induced by earthworm burrows, old root channels, and their combination in subsoil. Biol Fertil Soils 52:1105–1119. https://doi.org/10.1007/s00374-016-1148-y Jia R, Zhou J, Yang L, Blagodatskaya E, Jones DL, Razavi BS, Yang Y, Kuzyakov Y, Zeng Z, Zang H (2024) Trade-off between soil enzyme activities and hotspots area depends on long-term fertilization: In situ field zymography. Sci Total Environ 954:176386. https://doi.org/10.1016/j.scitotenv.2024.176386 Kuzyakov Y, Blagodatskaya E (2015) Microbial hotspots and hot moments in soil: Concept & review. Soil Biol Biochem 83:184–199. https://doi.org/10.1016/j.soilbio.2015.01.025 Kuzyakov Y, Razavi BS (2019) Rhizosphere size and shape: temporal dynamics and spatial stationarity. Soil Biol Biochem 135:343–360. https://doi.org/10.1016/j.soilbio.2019.05.011 Kuzyakov Y, Xu X (2013) Competition between roots and microorganisms for nitrogen: mechanisms and ecological relevance. New Phytol 198:656–669. https://doi.org/10.1111/nph.12235 Liu S, He F, Kuzyakov Y, Xiao H, Hoang DTT, Pu S, Razavi BS (2022) Nutrients in the rhizosphere: A meta-analysis of content, availability, and influencing factors. Sci Total Environ 826:153908. https://doi.org/10.1016/j.scitotenv.2022.153908 Liu S, Razavi BS, Su X, Maharjan M, Zarebanadkouki M, Blagodatskaya E, Kuzyakov Y (2017) Spatio-temporal patterns of enzyme activities after manure application reflect mechanisms of niche differentiation between plants and microorganisms. Soil Biol Biochem 112:100–109. https://doi.org/10.1016/j.soilbio.2017.05.006 Liu Z, Kuzyakov Y, Fang J, Li Y, Xiao H, Li Z, Zhou J, Razavi BS (2025) Hotspots of enzyme activities reflect micro-scale heterogeneity in nutrient mobilization in paddy soils. Plant Soil. https://doi.org/10.1007/s11104-025-07265-1 Lobet G, Pagès L, Draye X (2011) A novel image-analysis toolbox enabling quantitative analysis of root system Architecture. Plant Physiol 157:29–39. https://doi.org/10.1104/pp.111.179895 Lucena JJ (2006) Synthetic Iron Chelates to Correct Iron Deficiency in Plants. In: Barton LL, Abadia J (eds) Iron Nutrition in Plants and Rhizospheric Microorganisms. Springer, Dordrecht, pp 85–109. https://doi.org/10.1007/1-4020-4743-6_5 Ma X, Liu Y, Zarebanadkouki M, Razavi BS, Blagodatskaya E, Kuzyakov Y (2018) Spatiotemporal patterns of enzyme activities in the rhizosphere: effects of plant growth and root morphology. Biol Fertil Soils 54:819–828. https://doi.org/10.1007/s00374-018-1305-6 Marschner P, Crowley D, Rengel Z (2011) Rhizosphere Interactions between Microorganisms and Plants Govern Iron and Phosphorus Acquisition along the Root Axis-Model and Research Methods. Soil Biol Biochem 43:883–894. https://doi.org/10.1016/j.soilbio.2011.01.005 McConnaughay KDM, Coleman JS (1999) Biomass allocation in plants: ontogeny or optimality? A test along three resource gradients. Ecology 80:2581–2593. https://doi.org/10.2307/177242 Miralles I, Domingo F, Cantón Y, Trasar-Cepeda C, Leirós MC, Gil-Sotres F (2012) Hydrolase enzyme activities in a successional gradient of biological soil crusts in arid and semi-arid zones. Soil Biol Biochem 53:124–132. https://doi.org/10.1016/j.soilbio.2012.05.016 Moormann J, Heinemann B, Hildebrandt TM (2022) News about amino acid metabolism in plant-microbe interactions. Trends Biochem Sci 47:849–861. https://doi.org/10.1016/j.tibs.2022.07.001 Nannipieri P, Giagnoni L, Renella G, Puglisi E, Ceccanti B, Masciandaro G, Fornasier F, Moscatelli MC, Marinari S (2012) Soil enzymology: classical and molecular approaches. Biol Fertil Soils 48:743–762. https://doi.org/10.1007/s00374-012-0723-0 Nannipieri P, Gianfreda L (1998) Kinetics of enzyme reactions in soil environments. In: Huang PM, Senesi N, Buffle J (eds) Structure and Surface Reactions. Wiley, New York, pp 449–479 Nannipieri P, Trasar-Cepeda C, Dick RP (2018) Soil enzyme activity: a brief history and biochemistry as a basis for appropriate interpretations and meta-analysis. Biol Fertil Soils 54:11–19. https://doi.org/10.1007/s00374-017-1245-6 Peng M, He H, Jiang M, Wang Z, Li G, Zhuang L (2023) Morphological, physiological and metabolomic analysis to unravel the adaptive relationship between root growth of ephemeral plants and different soil habitats. Plant Physiol Biochem 202:107986. https://doi.org/10.1016/j.plaphy.2023.107986 Philippot L, Chenu C, Kappler A, Rillig MC, Fierer N (2023) The interplay between microbial communities and soil properties. Nat Rev Microbiol 22:1–14. https://doi.org/10.1038/s41579-023-00980-5 Rahnama A, Fakhri S, Meskarbashee M (2019) Root growth and architecture responses of bread wheat cultivars to salinity stress. Agron J 111:2991–2998. https://doi.org/10.2134/agronj2018.12.0795 Rahnama A, Hosseinalipour B, Farrokhian Firouzi A, Harrison MT, Ghorbanpour M (2024) Root architecture traits and genotypic responses of wheat at seedling stage to water-deficit stress. Cereal Res Commun 52:1499–1510. https://doi.org/10.1007/s42976-023-00481-4 Razavi BS, Blagodatskaya E, Kuzyakov Y (2016a) Temperature selects for static soil enzyme systems to maintain high catalytic efficiency. Soil Biol Biochem 97:15–22. https://doi.org/10.1016/j.soilbio.2016.02.018 Razavi BS, Zarebanadkouki M, Blagodatskaya E, Kuzyakov Y (2016b) Rhizosphere shape of lentil and maize: spatial distribution of enzyme activities. Soil Biol Biochem 96:229–237. https://doi.org/10.1016/j.soilbio.2016.02.020 Razavi BS, Zhang X, Bilyera N, Guber A, Zarebanadkouki M (2019) Soil zymography: Simple and reliable? Review of current knowledge and optimization of the method. Rhizosphere 11:100161. https://doi.org/10.1016/j.rhisph.2019.100161 Read AD, Bentley RE, Archer SL, Dunham-Snary KJ (2021) Mitochondrial iron-sulfur clusters: Structure, function, and an emerging role in vascular biology. Redox Biol 47:102164. https://doi.org/10.1016/j.redox.2021.102164 Sanaullah M, Razavi BS, Blagodatskaya E, Kuzyakov Y (2016) Spatial distribution and catalytic mechanisms of b-glucosidase activity at the root-soil interface. Biol Fertil Soils 52:505–514. https://doi.org/10.1007/s00374-016-1094-8 Schimel J, Becerra CA, Blankinship J (2017) Estimating decay dynamics for enzyme activities in soils from different ecosystems. Soil Biol Biochem 114:5–11. https://doi.org/10.1016/j.soilbio.2017.06.023 Sinsabaugh RL, Follstad Shah JJ (2012) Ecoenzymatic stoichiometry and ecological theory. Annu Rev Ecol Evol Syst 43:313–343. https://doi.org/10.1038/nature08632 Souri MK (2016) Aminochelate fertilizers: the new approach to the old problem; a review. Open Agric 1:118–123. https://doi.org/10.1515/opag-2016-0016 Souri MK, Hatamian M (2018) Aminochelates in plant nutrition: a review. J Plant Nutr 42:67–78. https://doi.org/10.1080/01904167.2018.1549671 Spohn M, Kuzyakov Y (2013) Distribution of microbial- and root-derived phosphatase activities in the rhizosphere depending on P availability and C allocation Coupling soil zymography with 14C imaging. Soil Biol Biochem 67:106. https://doi.org/10.1016/j.soilbio.2013.08.015 Spohn M, Kuzyakov Y (2014) Spatial and temporal dynamics of hotspots of enzyme activity in soil as affected by living and dead roots-a soil zymography analysis. Plant Soil 379:67–77. https://doi.org/10.1007/s11104-014-2041-9 Wang R, Bicharanloo B, Shirvan MB, Cavagnaro TR, Jiang Y, Keitel C, Dijkstra FA (2021) A novel 13C pulse-labelling method to quantify the contribution of rhizodeposits to soil respiration in a grassland exposed to drought and nitrogen addition. New Phytol 230:857–866. https://doi.org/10.1111/nph.17118 Wei X, Ge T, Zhu Z, Hu Y, Liu S, Li Y, Wu J, Razavi BS (2019) Expansion of rice enzymatic rhizosphere: temporal dynamics in response to phosphorus and cellulose application. Plant Soil 445:169–181. https://doi.org/10.1007/s11104-018-03902-0 Zhang X, Myrold DD, Shi L, Kuzyakov Y, Dai H, Thu Hoang DT, Dippold MA, Meng X, Song X, Li Z, Zhou J, Razavi BS (2021) Resistance of microbial community and its functional sensitivity in the rhizosphere hotspots to drought. Soil Biol Biochem 161:108360. https://doi.org/10.1016/j.soilbio.2021.108360 Zhao A, Yang S, Wang B, Tian X (2019) Effects of ZnSO₄ and Zn-EDTA applied by broadcasting or by banding on soil Zn fractions and Zn uptake by wheat (Triticum aestivum L.) under greenhouse conditions. J Plant Nutr Soil Sci 182:307–317. https://doi.org/10.1002/jpln.201800341 Zhao ZB, He JZ, Quan Z, Wu CF, Sheng R, Zhang LM, Geisen S (2020) Fertilization changes soil microbiome functioning, especially phagotrophic protists. Soil Biol Biochem 148:107863. https://doi.org/10.1016/j.soilbio.2020.107863 Supplementary Files ConflictofInterest.doc Highlights.docx Supplementarydata.docx Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-7455689","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":510495585,"identity":"5223d474-e755-4ac0-b7c0-02a4e61a9309","order_by":0,"name":"Mina Alipourbabadi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYDACZhBRIAEi2RgYGxgY29gbIIL4tRgga+E5QEALGBgwILQ0SCTg16Lbznvwww8Dizx+idxnD37usJPtk3xj+LmgwoaBv707AZsWs8N8yZI9BhLFkjPSzQ17zyQbt0nnGEvPOJPGIHHm7AbsWngMJIAoccONNDYJ3jbmRKAWA2netsNAD+bi0mL88w9Ui+TftvrENskzxr8JaDGThtkCMjyxTQIoQkiLtQzILz3P2I1lzxw3buNJK7PmOZPGg9Mv588Y33xTUZfHz57G9vDtjmrZ+e2HN9/mqbCR42/vxaoFBhKQ2BzgaOLBpxxdC/sDQqpHwSgYBaNgZAEAs1NcIvBrBHcAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-8393-6354","institution":"Shahid Chamran University of Ahvaz Faculty of Agriculture","correspondingAuthor":true,"prefix":"","firstName":"Mina","middleName":"","lastName":"Alipourbabadi","suffix":""},{"id":510495586,"identity":"d049e418-6e56-48df-b8a2-71497693bc66","order_by":1,"name":"Mojtaba Norouzi Masir","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Mojtaba","middleName":"Norouzi","lastName":"Masir","suffix":""},{"id":510495587,"identity":"bc48455e-588d-433b-81b1-ae3ebfc27a1f","order_by":2,"name":"Abdol Amir Moezzi","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Abdol","middleName":"Amir","lastName":"Moezzi","suffix":""},{"id":510495588,"identity":"6cff6e16-6d47-4852-a604-c1d3c31a98e6","order_by":3,"name":"Afrasyab Rahnama","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Afrasyab","middleName":"","lastName":"Rahnama","suffix":""},{"id":510495589,"identity":"fd992b00-9278-4332-964b-00ae7bcba003","order_by":4,"name":"Mehdi Taghavi","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Mehdi","middleName":"","lastName":"Taghavi","suffix":""},{"id":510495590,"identity":"e35fdce3-fced-447e-8685-4a3acc1976b1","order_by":5,"name":"Mehdi Rashtbari","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Mehdi","middleName":"","lastName":"Rashtbari","suffix":""},{"id":510495591,"identity":"c2839e52-6a7d-4dae-a50f-2c492075da3b","order_by":6,"name":"Bahar S. Razavi","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Bahar","middleName":"S.","lastName":"Razavi","suffix":""}],"badges":[],"createdAt":"2025-08-25 16:39:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7455689/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7455689/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91081833,"identity":"c44f4568-9612-4b4f-8ba7-635fd141c6e4","added_by":"auto","created_at":"2025-09-11 11:46:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":43603,"visible":true,"origin":"","legend":"\u003cp\u003eThe FT-IR spectrum of [Fe(Gly)\u003csub\u003e2\u003c/sub\u003e] (A), [Fe(Met)\u003csub\u003e2\u003c/sub\u003e] (B), [Zn(Gly)\u003csub\u003e2\u003c/sub\u003e] (C),\u0026nbsp; and [Zn(Met)\u003csub\u003e2\u003c/sub\u003e] (D) aminochelates.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7455689/v1/51914a3b51742e0523489748.png"},{"id":91081836,"identity":"e4bcebae-00e9-4347-a1f9-e72a4533eac5","added_by":"auto","created_at":"2025-09-11 11:46:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1538168,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative root images and zymograms showing β-glucosidase (βG) and leucine aminopeptidase (LAP) activities under different aminochelate treatments. Hotspots (red) indicate regions of elevated enzyme activity; low activity is shown in blue. Color intensity corresponds to enzyme activity, scaled in pmol MUF (for βG) or AMC (for LAP) mm⁻² h⁻¹.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7455689/v1/dc870f811d3e531097ee3873.png"},{"id":91081834,"identity":"a520d68c-3b60-49e8-b0cf-d6ec1952841e","added_by":"auto","created_at":"2025-09-11 11:46:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":67666,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of aminochelate treatments on (A) total enzyme activity in the rhizosphere, (B) rhizosphere extent, and (C) hotspot percentage for β-glucosidase and leucine aminopeptidase. Bars represent mean ± standard deviation (n = 3). Different letters above bars indicate statistically significant differences between treatments (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, Tukey’s HSD test).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7455689/v1/cd9efbf15901a319c6821009.png"},{"id":91082853,"identity":"6a3224e2-3fbf-4ea4-bda3-b295a542090c","added_by":"auto","created_at":"2025-09-11 11:54:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":273180,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of different aminochelate treatments on maximum velocity (V\u003csub\u003emax\u003c/sub\u003e; A and B), enzyme affinity (K\u003csub\u003em\u003c/sub\u003e; C and D), and catalytic efficiency (K\u003csub\u003ea\u003c/sub\u003e; E and F) of β-glucosidase and leucine aminopeptidase enzymes in rhizosphere and bulk soil. Boxes represent mean ± standard deviation (n = 3). Different letters above boxes indicate statistically significant differences between treatments (\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05, Tukey’s HSD test).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7455689/v1/db8a2162f9faf145e05eb55f.png"},{"id":91081835,"identity":"f8886df6-0d1b-4f48-977b-eb95ca8c660f","added_by":"auto","created_at":"2025-09-11 11:46:27","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":106914,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of different aminochelate treatments on basal respiration (BR; A), substrate-induced respiration (SIR; B), microbial biomass carbon (MBC; C), and metabolic quotient (qCO₂; D) in the rhizosphere of sunflower. Bars represent mean ± standard deviation (n = 3). Different letters above bars indicate statistically significant differences between treatments (p \u0026lt; 0.05, Tukey’s HSD test).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7455689/v1/275f277622207443ce9f37a1.png"},{"id":91081838,"identity":"5116dd46-e429-49a5-9314-8c05826a64d0","added_by":"auto","created_at":"2025-09-11 11:46:28","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":103019,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of aminochelate treatments on shoot and root dry weight (A), root length (RL; B), root diameter (RD; C), and root surface area (RSA; D) in sunflower plants. Bars represent mean ± standard deviation (n = 3). Different letters above bars indicate statistically significant differences between treatments (p \u0026lt; 0.05, Tukey’s HSD test).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7455689/v1/98d364079984a97553f3b2eb.png"},{"id":91082858,"identity":"5703e237-a3e7-46b8-a6fc-e8d060360930","added_by":"auto","created_at":"2025-09-11 11:54:28","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":615100,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Mantel test correlation heatmap comparing pairwise relationships among enzyme activities, microbial respiration, and root traits across treatments. Cells represent Pearson’s r values between distance matrices; significance was determined by 999 permutations (p \u0026lt; 0.05). Color gradients indicate strength and direction of correlations. (B) Radar plot illustrating normalized treatment responses across multiple parameters. Values were min-max normalized for comparability. Abbreviations: βG – β-glucosidase; BR – basal respiration; BS – bulk soil; LAP – leucine aminopeptidase; MBC – microbial biomass C; qCO₂ – metabolic quotient; RD – root diameter; RDW – root dry weight; RH – rhizosphere; RL – root length; RSA – root surface area; SDW – shoot dry weight; SIR – substrate-induced respiration.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7455689/v1/809cf9c6104d52074eaef1ec.png"},{"id":99789686,"identity":"bda0c591-9ef8-4e3d-860f-997dc75b26c3","added_by":"auto","created_at":"2026-01-08 12:50:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3680857,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7455689/v1/04abe2ca-1c36-42b1-b4da-984b2ab22af3.pdf"},{"id":91082857,"identity":"3ca1fd66-c90f-4fc3-9321-50048d6b2203","added_by":"auto","created_at":"2025-09-11 11:54:28","extension":"doc","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":34816,"visible":true,"origin":"","legend":"","description":"","filename":"ConflictofInterest.doc","url":"https://assets-eu.researchsquare.com/files/rs-7455689/v1/263ae81b8e458b6e5c8151bf.doc"},{"id":91081843,"identity":"b5f827d1-9ad1-4cc1-89e4-4e09b1807d20","added_by":"auto","created_at":"2025-09-11 11:46:28","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":15194,"visible":true,"origin":"","legend":"","description":"","filename":"Highlights.docx","url":"https://assets-eu.researchsquare.com/files/rs-7455689/v1/ab1d4082759f2cc4e5342b35.docx"},{"id":91081845,"identity":"9672d904-f7be-42ee-a7db-6e82a2847990","added_by":"auto","created_at":"2025-09-11 11:46:28","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":21478,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata.docx","url":"https://assets-eu.researchsquare.com/files/rs-7455689/v1/0cddaaff19341ec24a8b5d8a.docx"}],"financialInterests":"","formattedTitle":"Aminochelates as Dual Micronutrient Carriers and Biostimulants: Modulating Rhizosphere Enzyme Hotspots and Root–Microbe Interactions in Sunflower","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe escalating global population has intensified the pressure on agricultural systems to produce more food sustainably (Ghosh et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). While conventional chemical fertilizers have contributed to yield improvements, their prolonged use has been shown to degrade soil health, reduce microbial diversity, and compromise long-term productivity (He et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). As a result, there is growing interest in eco-friendly alternatives, particularly organic-based strategies that maintain nutrient supply while enhancing soil functionality (Asghar et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMicronutrient deficiencies, especially of Fe and Zn, are common in many agricultural soils. These elements are typically supplied using sulfate-based fertilizers or synthetic chelating agents such as EDTA and DTPA (Zhao et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Although synthetic chelates increase metal solubility, they are poorly biodegradable and may adversely affect microbial activity and contribute to metal leaching (Doostikhah et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In contrast, organic chelating agents, particularly those derived from amino acids, offer a more sustainable alternative (Alipour Babadi et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These aminochelates form stable complexes with micronutrients, facilitating their uptake and mobility while supporting microbial activity and reducing environmental risks (Souri and Hatamian, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Areche et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Aminochelates are increasingly recognized for their multifunctional benefits. In addition to improving nutrient delivery, they provide nitrogen, stimulate enzymatic pathways, and enhance metabolic processes in plants (Souri, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Abou-Sreea et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Their application also fosters microbial growth and diversity in the rhizosphere, potentially increasing the enzymatic activity linked to nutrient turnover (Blagodatskaya et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Areche et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Microorganisms, competing with plants for amino acids, activate a range of extracellular enzymes that catalyze nutrient cycling and reflect the intensity of root-microbe interactions (Moormann et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRecent research has highlighted the importance of rhizosphere \u0026ldquo;enzymatic hotspots\u0026rdquo; for nutrient mobilization. These microsites exhibit elevated microbial respiration and biochemical transformation rates compared to bulk soil, often driven by plant exudates and microbial dynamics (Liu et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Such fine-scale spatial variability plays a pivotal role in regulating ecosystem processes, yet remains poorly understood in relation to fertilization practices. Enzyme activity within these hotspots provides critical insights into soil health and plant\u0026ndash;microbe interactions, serving as a sensitive bioindicator of microbial community composition and function (Peng et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These enzymatic processes are particularly responsive to plant nutrient acquisition strategies and fertilizer inputs, with Fe and Zn playing crucial roles as enzyme cofactors (Kuzyakov \u0026amp; Razavi, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Philippot et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHydrolytic enzymes like β-glucosidase and leucine aminopeptidase are central to carbon and nitrogen cycling. Their activity is influenced by nutrient availability, root growth traits, and microbial community structure (Philippot et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While enzyme production often declines under nutrient-rich conditions, complex interactions in the rhizosphere can sustain or even stimulate activity due to intensified plant\u0026ndash;microbe competition (Jia et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Moreover, enzyme hotspots may reflect responses of both rhizosphere and endosphere microbiota, whose roles in nutrient uptake and signaling are becoming increasingly evident (Hao et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo fully understand these dynamic interactions, advanced tools like \u003cem\u003ein situ\u003c/em\u003e zymography are essential. Unlike traditional bulk assays, this technique provides high-resolution visualization of enzyme distribution around roots, revealing spatial patterns of activity linked to nutrient acquisition and microbial behavior (Razavi et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). By using fluorescent substrates to track enzyme kinetics in real time, zymography offers a window into the biochemical hotspots that drive plant-soil feedbacks (Bilyera \u0026amp; Kuzyakov, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite their proven nutritional benefits, the effects of aminochelates on rhizosphere enzyme activity, microbial biomass, and root architecture remain unclear. This study addresses these critical gaps by integrating in situ zymography with biochemical assays to investigate the interactions between aminochelate fertilization, soil microbiology, and plant performance. Specifically, the objectives were to (1) visualize the spatial distribution of β-glucosidase and leucine aminopeptidase activity in the rhizosphere under varying Fe and Zn aminochelate treatments, (2) assess enzyme kinetic parameters and microbial responses to these treatments, and (3) evaluate associated changes in root system architecture traits. Collectively, these approaches aim to uncover the synergistic roles of organic chelates in promoting nutrient cycling, microbial functioning, and plant development within sustainable agricultural systems.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Experimental site\u003c/h2\u003e\u003cp\u003eThe soil was collected in September 2023 from a wheat field at the Experimental Farm Hohenschulen, Faculty of Agricultural and Nutritional Sciences, Christian-Albrechts-University of Kiel (54\u0026deg;19\u0026prime;05\u0026Prime;N, 9\u0026deg;58\u0026prime;38\u0026Prime;E), located approximately 15 km west of Kiel in Schleswig-Holstein, northwestern Germany. Soil samples were taken from three different locations within the field at a depth of 0\u0026ndash;30 cm and thoroughly mixed to create a composite sample.\u003c/p\u003e\u003cp\u003eThe soil was air-dried, sieved through a 2 mm mesh, and stored at room temperature. It was classified as a pseudogleyic sandy loam (Luvisol) with the following properties: 100 g kg⁻\u0026sup1; clay, a pH of 6.7 (in CaCl₂), 82 mg kg⁻\u0026sup1; phosphorus (P), 200 mg kg⁻\u0026sup1; potassium (K), 215 mg kg⁻\u0026sup1; magnesium (Mg), 13.8 g kg⁻\u0026sup1; (3%) organic carbon (C), and 1.1 g kg⁻\u0026sup1; total nitrogen (N).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Synthesis and characterization of Fe and Zn aminochelates\u003c/h2\u003e\u003cp\u003eAminochelates were synthesized using glycine (Gly) and methionine (Met) as ligands, following a modified version of the methodology described by Ashmead et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Glycine, as the smallest amino acid, was selected for its high solubility and strong chelating ability, while methionine was chosen for its additional functional groups and its role in plant metabolism, which may enhance micronutrient mobility and uptake. Briefly, an aqueous solution containing the respective amino acid, calcium carbonate, iron(II) sulfate heptahydrate (FeSO₄\u0026middot;7H₂O), and zinc sulfate heptahydrate (ZnSO₄\u0026middot;7H₂O) was prepared to form Fe- and Zn-amino acid chelate complexes. A 2:1 molar ligand-to-metal ratio was maintained. The reaction mixture was continuously stirred at 50\u0026deg;C for 3 hours to ensure complete ion complexation. As a result, Fe(II)/Zn-amino acid chelates, calcium sulfate, and water were formed with minimal interference from other ions. The Fe-amino acid chelates appeared as light-brown crystalline precipitates, while the Zn-amino acid chelates were white. Both products were oven-dried before characterization.\u003c/p\u003e\u003cp\u003eChelation was confirmed using FT-IR analysis over the range of 4000\u0026ndash;400 cm⁻\u0026sup1; with an FT-IR-8400 Shimadzu spectrophotometer, employing KBr discs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Elemental composition was determined using a CHN elemental analyzer (Perkin-Elmer 2400). Structural characterization of the aminochelates was conducted via solution-state \u0026sup1;H NMR spectroscopy at room temperature using a 400 MHz Bruker Avance DRX (Germany) instrument with D₂O as the solvent. The key characteristics of the synthesized aminochelates are listed in supplementary data.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Experimental set-up and treatment application\u003c/h2\u003e\u003cp\u003eThis study evaluated four aminochelate fertilizer treatments: (1) Fe (glycine)\u003csub\u003e2\u003c/sub\u003e [Fe (Gly)\u003csub\u003e2\u003c/sub\u003e]; (2) Fe (methionine)\u003csub\u003e2\u003c/sub\u003e [Fe (Met)\u003csub\u003e2\u003c/sub\u003e]; (3) Zn (glycine)\u003csub\u003e2\u003c/sub\u003e [Zn (Gly)\u003csub\u003e2\u003c/sub\u003e]; and (4) Zn (methionine)\u003csub\u003e2\u003c/sub\u003e [Zn (Met)\u003csub\u003e2\u003c/sub\u003e] aminochelates, along with a control treatment without any amendments. Sunflower (\u003cem\u003eHelianthus annuus\u003c/em\u003e L.) plants were grown in a rhizobox system arranged in a completely randomized design (CRD) with three replications, resulting in a total of 15 rhizoboxes.\u003c/p\u003e\u003cp\u003eSunflower seeds were surface-sterilized using 10% H₂O₂ to prevent fungal or bacterial infections, followed by thorough rinsing with deionized water. The seeds were then germinated on moist filter paper in sterile culture dishes at 25\u0026deg;C for 5 days to initiate growth. Sieved soil was packed into transparent rhizoboxes (3 \u0026times; 20 \u0026times; 20 cm, H \u0026times; W \u0026times; L; Clickbox\u0026reg;, Germany), with each box containing 2 kg of soil. Seedlings were transplanted into the rhizoboxes at a depth of 1 cm. The boxes were positioned at a 45\u0026deg; angle to direct root growth toward the front wall, following the method described by Razavi et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016b\u003c/span\u003e). The experiment was conducted in a greenhouse at Kiel University under controlled conditions, maintaining a daily light period of 8 hours, a temperature range of 20 to 25\u0026deg;C, and a relative humidity of 65 to 75%. Soil water content was adjusted to 70% of field capacity and maintained by regularly adding distilled water using a syringe to compensate for evapotranspiration losses.\u003c/p\u003e\u003cp\u003eAminochelate fertilizers were applied as a solution using a syringe near the seedlings. The fertilizers were applied to the soil to supply 5 mg kg⁻\u0026sup1; of Fe or Zn, with application rates calculated based on the metal content of each aminochelate compound. Each rhizobox received 10 mL of the solution in two split applications: half after 1 week and the remaining half 4 weeks post-emergence.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Soil and plant analyses\u003c/h2\u003e\u003cp\u003eFour weeks after seedling establishment, the rhizoboxes were transferred to the laboratory and opened from the root side. Soil samples were collected from two distinct areas: (1) the rhizosphere soil (RH), defined as soil tightly adhering to the roots within a distance of \u0026le;\u0026thinsp;5 mm, and (2) the bulk soil (BS), representing root-free, non-rhizosphere soil. These samples were used for planned analyses.\u003c/p\u003e\u003cp\u003eFor root analysis, all visible roots were carefully separated from the soil, washed slowly with distilled water to remove any adhering soil particles, and preserved in 30% ethanol for further measurements. The cleaned roots were scanned using a LiDE 220 scanner (Epson Perfection 2480 Photo, Epson, Japan) at a resolution of 600 dpi. The root images were processed using the SmartRoot plugin of ImageJ software (Lobet et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) to determine the following root growth traits: average root diameter (RD), root length (RL), and root surface area (RSA). Shoots were severed below the crown. Both shoots and roots were placed in paper bags, oven-dried at 65\u0026deg;C for 48 h, and their dry weights were determined.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Zymography\u003c/h2\u003e\u003cp\u003eSoil zymography was employed as a non-destructive technique to visualize and localize hotspots of β-glucosidase (βG) and leucine aminopeptidase (LAP) activity on the soil surface, following the protocol established by Razavi et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Fluorogenic substrate solutions (12 mM) based on 4-methylumbelliferone (MUF) and 7-amino-4-methylcoumarin (AMC) were used: 4-methylumbelliferyl-β-D-glucoside (MUF-β) (Sigma Aldrich, Germany) for βG, and L-Leucine-7-amino-4-methylcoumarin hydrochloride (AMC-L) (Sigma Aldrich, Germany) for LAP. These substrates were prepared using MES buffer (C₆H₁₃NO₄SNa₀.₅, Sigma-Aldrich, Darmstadt, Germany) and TRIZMA buffer (C₄H₁₁NO₃\u0026middot;HCl, C₄H₁₁NO₃, Sigma-Aldrich, Darmstadt, Germany), respectively. Upon enzymatic hydrolysis, MUF and AMC substrates produce fluorescent signals, which can be visualized and quantified under ultraviolet (UV) light (Spohn and Kuzyakov, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePolyamide membrane filters (Tao Yuan, China) with a diameter of 20 cm and a pore size of 0.45 \u0026micro;m were used to minimize enzyme diffusion. The membranes were saturated with the respective fluorogenic substrate solutions, attached directly to the rooted side of each rhizobox, and covered with aluminum foil to prevent light exposure and dehydration. After 1 h of incubation (Razavi et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016a\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), the membranes were carefully removed from the soil surface and gently cleaned using a soft brush. To visualize the enzyme activity, the membranes were placed in a light-proof box and exposed to UV light at a wavelength of 355 nm in a dark room. Fluorescent signals were captured using a digital camera (Canon EOS 6D, Canon Inc.) equipped with a Canon EF 24\u0026ndash;105 mm 1:4L IS II USM lens, with camera settings at an aperture of f/5.6 and a shutter speed of 1/8 second. To calibrate the fluorescence intensity, which is proportional to enzyme activity, 2\u0026times;2 cm membrane sections were soaked in 15 \u0026micro;L of MUF or AMC substrate solutions at varying concentrations. For MUF, the concentrations were 0, 0.2, 0.5, 1, 2, 4, 6, 8, and 10 mM, while for AMC, the concentrations were 0, 0.1, 0.2, 1, 2, 4, and 5 mM. This calibration ensured accurate quantification of enzyme activity in the zymography images (Razavi et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016a\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6. Image processing\u003c/h2\u003e\u003cp\u003eTo convert zymogram images into quantitative spatial data, the open-source ImageJ software was used. The RGB images were transformed into 16-bit grayscale images, and background was adjusted uniformly across images using ImageJ\u0026rsquo;s \u0026lsquo;Brightness and Contrast\u0026rsquo; tool to minimize non-enzymatic signal interference. Enzyme activity (nmol MUF or AMC cm⁻\u0026sup2; h⁻\u0026sup1;) was quantified by converting the gray values from the zymograms using a standard calibration curve generated from known concentrations of MUF and AMC (Guber et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe hotspot percentage, representing regions of high enzymatic activity, was calculated by determining the proportion of bright pixels to the total image area. A threshold of Mean\u0026thinsp;+\u0026thinsp;2 SD was used to delineate high-activity zones, typically corresponding to the top\u0026thinsp;~\u0026thinsp;25% of signal intensities. The grayscale histogram was separated into two normal distributions, distinguishing hotspot regions from bulk soil. The grayscale range corresponding to hotspots was then overlaid onto the original image for visualization (Bilyera et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRhizosphere extent was quantified to assess the spatial influence of roots on enzyme activity. The rhizosphere extent was defined as the distance from the root boundary where enzyme activity was at least 30% higher than the average activity in the bulk soil (Ma et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSix transect lines were randomly drawn at approximately 90\u0026deg; angles to the root for each enzyme (βG and LAP) in each replicate zymogram image. This resulted in a total of 36 lines per image (6 lines \u0026times; 2 enzymes \u0026times; 3 replicates), and the gray values along these lines were extracted. The mean gray value from the set of transect lines within each rhizobox zymogram was calculated and treated as a single data point representing that biological replicate. Enzyme activities were calculated using the calibration curve. Finally, the total enzyme activity in the rhizosphere was determined by summing the pixel-wise enzyme activity values from the root surface (as the starting point) outward to the rhizosphere limit, defined as the soil area surrounding the root (Ma et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7. Enzyme kinetics\u003c/h2\u003e\u003cp\u003eThe kinetics of two hydrolytic enzymes involved in the carbon and nitrogen cycles (βG and LAP) were assessed for rhizosphere and bulk soil using the same fluorogenic substrates as applied in the zymography analysis. Eight substrate concentrations ranging from 0 to 400 \u0026micro;M were used, following the method of Razavi et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016a\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo obtain RH soil samples, the complete root system was placed on a sterile surface, and soil tightly adhering to the root surface was gently brushed off using a sterile toothbrush. BS samples, free of visible roots, were collected in conical tubes and stored at 5\u0026deg;C under the same conditions as RH soil samples.\u003c/p\u003e\u003cp\u003eFor enzyme assays, 0.5 g of soil (dry weight equivalent) was suspended in 50 mL of deionized water in a Falcon tube and shaken for 20 minutes. Subsequently, a 96-well black microplate (Thermo Fisher, Denmark) was prepared by adding 50 \u0026micro;L of soil suspension, 50 \u0026micro;L of MES/Trizma buffer (pH 6.5 for βG and 7.2 for LAP), and 100 \u0026micro;L of substrate solution to each well. Fluorescence was measured using a CLARIOstar Plus microplate reader (BMG LABTECH, Germany) at an excitation wavelength of 355 nm and an emission wavelength of 460 nm. Measurements were taken at four time points: 0, 30, 60, and 120 minutes after incubation at room temperature.\u003c/p\u003e\u003cp\u003eEnzyme activities were determined across the substrate concentrations (0, 20, 40, 60, 80, 100, 200, and 400 \u0026micro;mol g⁻\u0026sup1; soil) to ensure a sufficient range for enzyme saturation. A calibration curve was established using MUF or AMC substrates at concentrations of 0, 10, 20, 30, 40, 50, 100, and 200 \u0026micro;M. The linearity of fluorescence increase with increasing substrate concentration was carefully evaluated. The 120-minute data, which showed the best fit with the substrate concentration plot, were used for further calculations (German et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEnzyme kinetic parameters were estimated using the Michaelis-Menten equation:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:V=\\frac{{V}_{max}\\left[S\\right]}{{K}_{m+}\\left[S\\right]}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere V\u003csub\u003emax\u003c/sub\u003e is the maximum reaction rate, K\u003csub\u003em\u003c/sub\u003e is the substrate concentration at 1/2 V\u003csub\u003emax\u003c/sub\u003e and [S] is substrate concentration. V\u003csub\u003emax\u003c/sub\u003e and K\u003csub\u003em\u003c/sub\u003e parameters were determined by the non-linear regression routine of GraphPad Prism software (v. 10.3.0).\u003c/p\u003e\u003cp\u003eThe catalytic efficiency of enzymes (K\u003csub\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sub\u003e), reflecting functional changes in microbial communities, was calculated using the equation described by Hoang et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e):\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:Ka\\:=\\frac{{V}_{max}}{{K}_{m}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8. Microbial biomass C\u003c/h2\u003e\u003cp\u003eMicrobial biomass C (MBC), basal respiration (BR), and substrate-induced respiration (SIR) were measured using the 96-well MicroResp\u0026trade; system, which quantifies CO₂ production via colorimetric detection (Campbell et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eApproximately 0.45 g of fresh soil was added to each well, with glucose for SIR or distilled water for BR measurements. A carbon dioxide detection microplate containing 300 \u0026micro;L per well of cresol red indicator dye was used to capture and quantify CO₂ emissions. The color change of the indicator dye was then used to determine respiration rates, providing insights into microbial activity and biomass. Bulk and root-affected soils (20 g each) were used to prepare replicate samples, from which 0.45 g aliquots were loaded into microtiter wells using the MicroResp\u0026trade; filling device (Thermo LifeSciences, Basingstoke, United Kingdom). Colorimetric gel detector plates were prepared with a cresol red indicator solution and analyzed using a CLARIOstar Plus microplate reader (BMG LABTECH, Germany) at a wavelength of 570 nm. Absorbance data were recorded at the start (0 hours) and after 6 hours of incubation at 25\u0026deg;C. Respiration rates were calculated by subtracting the absorbance values of the blank sample (mean initial colorimetric values for each plate) from the test sample readings. All absorbance data were normalized, log-transformed before analysis, and converted to CO₂ concentrations using a standard calibration curve.\u003c/p\u003e\u003cp\u003eMBC was estimated using the equation proposed by Anderson and Domsch (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1990\u003c/span\u003e):\u003c/p\u003e\u003cp\u003eMBC (\u0026micro;g C g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e soil)\u0026thinsp;=\u0026thinsp;SIR (\u0026micro;L CO\u003csub\u003e2\u003c/sub\u003e g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e soil h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) \u0026times; 40. 04\u0026thinsp;+\u0026thinsp;0.37\u003c/p\u003e\u003cp\u003eA higher SIR response during the initial 0\u0026ndash;6 h period suggested a greater microbial community abundance in the soil due to the added substrates (Degens \u0026amp; Harris, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe metabolic quotient (qCO₂), used to evaluate the effects of environmental conditions on soil microbial biomass, was calculated as the ratio of BR to MBC and expressed in \u0026micro;g CO₂-C mg⁻\u0026sup1; MBC h⁻\u0026sup1; (Anderson \u0026amp; Domsch, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1990\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9. Statistical analysis\u003c/h2\u003e\u003cp\u003eAll data were analyzed using one-way analysis of variance (ANOVA) using the \"agricolae\" package in R (v.12.1\u0026thinsp;+\u0026thinsp;402) software. Mean values were compared using Tukey's HSD test at a significance level of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The detailed ANOVA tables (Tables S1\u0026ndash;S3) are provided in the Supplementary Material. Graphs were generated with OriginPro (v.10.2, 2025) software. Enzyme kinetic parameters (V\u003csub\u003emax\u003c/sub\u003e, K\u003csub\u003em\u003c/sub\u003e, and K\u003csub\u003ea\u003c/sub\u003e) were evaluated using GraphPad Prism (v.10.3.1, 2024) software. Additionally, Pearson\u0026rsquo;s correlation analysis was performed to explore the relationships among the measured traits. An interactive Mantel test correlation heat map was generated via the Chiplot website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.chiplot.online/\u003c/span\u003e\u003cspan address=\"https://www.chiplot.online/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using Bray-Curtis and Euclidean distance matrices derived from enzyme activity, microbial activity, and root trait datasets with significance assessed by 999 permutations (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1. Enzyme activity, rhizosphere extent, and hotspot percentage of \u0026beta;-glucosidase and leucine aminopeptidase\u003c/h2\u003e\n\u003cp\u003eRepresentative \u0026beta;G and LAP zymograms visualized the spatial distribution of enzyme activity in the sunflower rhizosphere (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe [Fe(Gly)₂] and [Zn(Met)₂] treatments showed the highest enzyme activities, with [Fe(Gly)₂] exhibiting a 21.1% increase in \u0026beta;G activity (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and [Zn(Met)₂] demonstrating a 120.7% increase in LAP activity (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), compared to the control (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA). However, the increase in \u0026beta;G activity was comparable between the [Fe(Gly)₂] and [Zn(Met)₂] treatments (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003cp\u003eZymography revealed treatment-dependent variation in rhizosphere extents of both \u0026beta;G (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and LAP (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB). The rhizosphere extent of \u0026beta;G under [Fe(Gly)₂] averaged 0.42 mm, which was 83% higher than the control (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Similarly, the rhizosphere extent of LAP increased under [Zn(Met)₂], averaging 0.66 mm, which was about two times higher than the control (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Hotspots of enzyme activity, particularly in aminochelate-treated soils, were predominantly located on the rhizoplane and extended along the roots (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). For \u0026beta;G, the hotspot percentage increased more than 4-fold compared to the control (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while LAP hotspot percentage more than doubled under aminochelate treatments (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC). The highest hotspot percentages were recorded under [Zn(Met)₂], indicating strongly localized zones of enzyme activity. By contrast, no detectable difference (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) was observed between [Fe(Gly)₂] and [Zn(Met)₂] for LAP hotspot percentages.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003e\u003cstrong\u003e3.2. Enzyme kinetic parameters\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eFor \u0026beta;G in the rhizosphere, the [Zn(Met)₂] treatment exhibited the highest V\u003csub\u003emax\u003c/sub\u003e, with an increase of 144.6% compared to the control (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). This reflects a substantial increase in enzymatic activity under [Zn(Met)₂]. In bulk soil, the [Fe(Met)₂] treatment showed the highest V\u003csub\u003emax\u003c/sub\u003e, with an increase of 103.2% relative to the control (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The K\u003csub\u003em\u003c/sub\u003e values increased by 50.1% under the [Zn(Met)₂] treatment in the rhizosphere, indicating lower enzyme-substrate affinity (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC). In bulk soil, K\u003csub\u003em\u003c/sub\u003e differences were not statistically different. Regarding catalytic efficiency (K\u003csub\u003ea\u003c/sub\u003e), the highest values in the rhizosphere were recorded under [Zn(Met)₂], with a 62.4% increase compared to the control (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eE). In bulk soil, the [Fe(Met)₂] treatment exhibited the greatest enhancement in K\u003csub\u003ea\u003c/sub\u003e, with a 69.8% increase relative to the control (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eE).\u003c/p\u003e\n\u003cp\u003eFor LAP, the [Zn(Met)₂] treatment in the rhizosphere and [Fe(Gly)₂] in bulk soil resulted in the highest V\u003csub\u003emax\u003c/sub\u003e, increasing by 74.7% and 73.2% relative to the control, respectively (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB). K\u003csub\u003em\u003c/sub\u003e values increased by 34.8% in the rhizosphere and 30.1% in bulk soil under [Fe(Gly)₂], indicating lower enzyme-substrate affinity (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD). The K\u003csub\u003ea\u003c/sub\u003e values reached their maximum under [Zn(Met)₂], with increases of 56.8% in the rhizosphere and 49.8% in bulk soil compared to the control (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eF).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003e3.3. Microbial growth responses to substrate addition\u003c/h2\u003e\n\u003cp\u003eApplication of aminochelates led to clear differences in BR compared with the control (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA). The [Zn(Met)₂] treatment produced the strongest effect, with BR increasing by 42.4% relative to the control (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\n\u003cp\u003eRegarding SIR (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB), the highest SIR was recorded under the [Zn(Met)₂] treatment, which increased by 238.2% relative to the control. The [Fe(Met)₂] treatment also enhanced SIR by 238.2% relative to the control (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Similarly, MBC values (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC) improved under the [Zn(Met)₂] treatment, rising by 225.5% compared to the control (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In contrast, the qCO₂ decreased under all aminochelate applications (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eD). The strongest reduction occurred with [Zn(Met)₂], where qCO₂ values dropped by 55.0% compared to the control (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n\u003ch2\u003e3.4. Root system architecture traits\u003c/h2\u003e\n\u003cp\u003eThe application of aminochelates showed the greatest increase of shoot dry weight under the [Fe(Gly)₂] treatment, with a 70.1% increase compared to the control (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA). A similar trend was observed in root dry weight, which increased by 212.5% under the [Fe(Gly)₂] treatment (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\n\u003cp\u003eRL (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB) was 281.0% greater under the [Fe(Gly)₂] treatment compared to the control (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). However, there was no difference between the [Fe(Gly)₂] and [Zn(Met)₂] treatments. RD (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC) was reduced under [Fe(Gly)₂] treatment, decreasing by 86.5% compared to the control (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Conversely, the highest RD values were observed in the control treatment. RSA (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eD) also increased under the [Fe(Gly)₂] treatment, showing more than a 4-fold increases compared to the control (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n\u003ch2\u003e3.5. Integrated correlation patterns among soil biochemical, microbial, and root parameters\u003c/h2\u003e\n\u003cp\u003eTo explore the integrated responses of all measured variables to different Fe and Zn aminochelate treatments, a Pearson\u0026rsquo;s correlation heatmap was generated. In addition, a Mantel test-based network was constructed to visualize the relationships among variables (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA). Enzyme hotspots and activities exhibited strong positive correlations (r\u0026thinsp;\u0026gt;\u0026thinsp;0.85, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) with root architecture traits such as RL and RSA, indicating spatial co-localization and potential feedbacks between root foraging capacity and rhizosphere biochemical functioning. These enzyme parameters also showed positive associations with microbial activity indicators, particularly BR (r\u0026thinsp;=\u0026thinsp;0.79\u0026ndash;0.99, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Notably, enzyme kinetic parameters (V\u003csub\u003emax\u003c/sub\u003e, K\u003csub\u003ea\u003c/sub\u003e) exhibited synchronous patterns between rhizosphere and bulk soil compartments (r\u0026thinsp;=\u0026thinsp;0.91\u0026ndash;0.99, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting synchronized enzymatic adaptation across soil zones. These kinetic parameters were also positively correlated with MBC (r\u0026thinsp;=\u0026thinsp;0.76\u0026ndash;0.92), SIR (r\u0026thinsp;=\u0026thinsp;0.72\u0026ndash;0.92), and root biomass (RDW; r\u0026thinsp;=\u0026thinsp;0.88\u0026ndash;0.93), reinforcing the notion that aminochelate fertilization enhances microbial-driven nutrient cycling via enzyme production.\u003c/p\u003e\n\u003cp\u003eTreatment-specific analysis revealed that [Fe(Met)₂] and [Zn(Met)₂] aminochelates showed the most extensive network connectivity (degree centrality\u0026thinsp;\u0026gt;\u0026thinsp;0.65), with significant associations to both enzymatic (Mantel's r\u0026thinsp;=\u0026thinsp;0.58\u0026ndash;0.68, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and morphological responses (Mantel's r\u0026thinsp;=\u0026thinsp;0.54\u0026ndash;0.59, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The glycinate treatments ([Fe(Gly)₂], [Zn(Gly)₂]) displayed intermediate connectivity (degree centrality\u0026thinsp;=\u0026thinsp;0.42\u0026ndash;0.48), while control samples showed minimal network integration (degree centrality\u0026thinsp;\u0026lt;\u0026thinsp;0.15), emphasizing the impact of micronutrient chelation on the rhizosphere-enzyme-root-microbe nexus.\u003c/p\u003e\n\u003cp\u003eThe radar plot (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eB) offers a comprehensive visual comparison of the treatment groups across distinct parameters. To account for the different value ranges among parameters, data were min-max normalized, allowing an equitable visual representation across treatments. The application of [Zn(Met)₂] aminochelate resulted in notably higher normalized values for the hotspot percentage of \u0026beta;G and LAP enzyme activities, as well as microbial indicators such as MBC, BR, and SIR, indicating enhanced enzyme functioning and microbial activity in the rhizosphere. In contrast, the [Fe(Gly)₂] treatment was associated with elevated values in root system architecture traits, including RL and RSA, suggesting a stimulation of root system development under this condition. The [Zn(Gly)₂] treatment was characterized by elevated values of enzyme kinetic parameters (V\u003csub\u003emax\u003c/sub\u003e, K\u003csub\u003em\u003c/sub\u003e, and K\u003csub\u003ea\u003c/sub\u003e), particularly within the rhizosphere zone, reflecting enhanced catalytic potential of soil enzymes in this environment. Notably, the [Fe(Met)₂] treatment displayed a moderate but consistent increases across enzyme activities, microbial traits, and plant morphology, implying potential synergistic interactions among rhizosphere biochemical functioning, microbial dynamics, and plant development. In contrast, the control treatment showed limited enhancement, with a relative increase observed only in the metabolic quotient (qCO₂) parameter.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Enzyme activity, rhizosphere extent, and hotspot percentage of β-glucosidase and leucine aminopeptidase\u003c/h2\u003e\u003cp\u003eThe application of Fe and Zn aminochelates, particularly [Fe(Gly)₂] and [Zn(Met)₂], markedly enhanced β-glucosidase (βG) and leucine aminopeptidase (LAP) activities in the sunflower rhizosphere (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). These hydrolytic enzymes are central to organic matter turnover and nutrient cycling: βG catalyzes the hydrolysis of cellulose-derived compounds, while LAP degrades proteins, jointly driving carbon and nitrogen mineralization essential for plant nutrition (Allison \u0026amp; Vitousek, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Burns et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). \u003cem\u003eIn situ\u003c/em\u003e zymography visualizations (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u0026amp; \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) confirmed not only elevated enzyme activities but also enlarged rhizosphere extents and more pronounced enzymatic hotspots under aminochelate treatments. Such spatial broadening reflects intensified root\u0026ndash;soil biochemical interactions, often associated with increased root exudation, microbial colonization, and extracellular enzyme secretion (Spohn \u0026amp; Kuzyakov, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Razavi et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Guber et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These effects likely arise through both direct and indirect mechanisms: as essential cofactors of metalloenzymes, Fe and Zn in chelated forms may enhance enzymatic efficiency, while their improved bioavailability alleviates micronutrient limitations that constrain microbial metabolism, thereby stimulating microbial proliferation and enzyme production (Burns et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Marschner et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Canarini et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEnzymatic hotspots emerge where root- or microbe-derived enzymes accumulate in microsites enriched with carbon and nutrients (Kuzyakov \u0026amp; Blagodatskaya, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Unlike bulk soil, the rhizosphere is highly heterogeneous, containing both hotspots and coldspots shaped by root proximity and substrate availability (Zhang et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The expansion of βG and LAP activity zones observed here signals greater microbial investment in enzyme synthesis, an adaptive strategy to optimize nutrient acquisition in carbon-rich environments (Allison \u0026amp; Vitousek, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Bonner et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). While labile carbon can sometimes reduce microbial demand for extracellular enzymes (Sinsabaugh \u0026amp; Follstad Shah, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), our results indicate that nutrient cycling demands remained high despite elevated carbon inputs, possibly reflecting persistent nutrient limitations, greater microbial biomass, or rapid enzymatic turnover (Blagodatskaya et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Importantly, these findings support the concept of rhizosphere spatial stability, where the persistence of enzymatic hotspots is maintained by a balance between enzyme synthesis and degradation, enabling plants to secure a competitive advantage in nutrient uptake (Miralles et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Nannipieri et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Schimel et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Zymographic analysis confirmed that Fe and Zn aminochelates significantly increased both the density and spatial extent of enzymatic hotspots (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). These zones of intense microbial colonization and nutrient cycling highlight elevated biochemical activity at the root\u0026ndash;soil interface (Spohn \u0026amp; Kuzyakov, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Razavi et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Guber et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Their expansion suggests that aminochelates stimulated microbial enzyme secretion by enhancing root exudation and microbial access to organic substrates (Liu et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This functional broadening supports more effective organic matter decomposition and nutrient mobilization (Bonner et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wei et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In chemically complex soils such as the Luvisol used in this study, characterized by high clay content and strong nutrient fixation, aminochelates may further activate microbial pathways that release immobilized elements, particularly phosphorus and micronutrients (Kuzyakov \u0026amp; Blagodatskaya, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The observed increase in hotspot density aligns with microbial strategies for nutrient acquisition: either through intensified enzyme production under carbon limitation (Allison \u0026amp; Vitousek, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) or by enhancing enzymatic capacity under high carbon availability and biomass conditions (Blagodatskaya et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Moreover, the spatial extension of enzyme activity beyond the immediate root zone implies that aminochelates facilitated the proliferation of microbial communities engaged in nutrient turnover across a wider rhizosphere area (Zhao et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Bilyera \u0026amp; Kuzyakov, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Such functional expansion is particularly important for improving nutrient acquisition in soils with physical or chemical constraints.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Enzyme Kinetic parameters\u003c/h2\u003e\u003cp\u003eThe kinetic parameters of βG and LAP provide insights into microbial responses to aminochelate application. Substantial increases in V\u003csub\u003emax\u003c/sub\u003e under [Zn(Met)₂] in the rhizosphere and [Fe(Met)₂] in bulk soil indicate enhanced catalytic capacity, particularly for βG (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). This reflects greater microbial enzyme production and turnover of organic matter, stimulated by root-derived labile carbon (Kuzyakov \u0026amp; Blagodatskaya, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Sanaullah et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Such upregulation is consistent with rhizosphere hotspots dominated by fast-growing r-strategists producing high-activity isoenzymes (Dorodnikov et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). As noted by Jia et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), shifts in microbial functional groups and their metabolic traits explain the rise in V\u003csub\u003emax\u003c/sub\u003e following micronutrient chelate inputs.\u003c/p\u003e\u003cp\u003eThe observed increase in K\u003csub\u003em\u003c/sub\u003e under [Zn(Met)₂], despite higher βG activity, suggests reduced enzyme-substrate affinity, an adaptation to elevated substrate concentrations favoring broader but less specific isoenzymes (Fierer et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The concurrent rise of V\u003csub\u003emax\u003c/sub\u003e and K\u003csub\u003em\u003c/sub\u003e reflects strategies to optimize nutrient acquisition under high carbon and stoichiometric imbalance (Sinsabaugh \u0026amp; Follstad Shah, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), highlighting the dual role of aminochelates in stimulating microbial growth and modifying enzyme systems. For LAP, responses were weaker: [Fe(Gly)₂] and [Zn(Met)₂] raised V\u003csub\u003emax\u003c/sub\u003e in both soil compartments but to a lesser extent than βG. This agrees with the lower responsiveness of N-acquiring enzymes under carbon-rich but nitrogen-limited conditions, due to the low N:C ratio of root exudates (Kuzyakov \u0026amp; Xu, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Badalucco \u0026amp; Nannipieri, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Catalytic efficiency (K\u003csub\u003ea\u003c/sub\u003e), combining V\u003csub\u003emax\u003c/sub\u003e and K\u003csub\u003em\u003c/sub\u003e, was maximized under [Zn(Met)₂], showing that even with lower substrate affinity, turnover capacity was sustained (Nannipieri et al., 1998; Razavi et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016b\u003c/span\u003e). These patterns, observed in nutrient-rich but chemically constrained soil, suggest that Fe and Zn aminochelates improve micronutrient functionality by overcoming limited bioavailability. By supplying Fe and Zn in bioavailable forms (Alipour Babadi et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), they act not only as nutrient sources but also as biostimulants modulating microbial communities and enzyme systems (Marschner et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Blagodatskaya et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Overall, the enhanced kinetic parameters emphasize the multifaceted role of aminochelates in promoting soil biochemical activity and nutrient cycling.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Microbial growth responses to substrate addition\u003c/h2\u003e\u003cp\u003eThe observed increases in BR, SIR, and MBC across aminochelate treatments demonstrate a clear stimulatory effect on soil microbial metabolic activity and biomass accumulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-D). Among treatments, [Zn(Met)₂] most strongly enhanced BR, indicating robust microbial oxidative metabolism, likely stimulated by improved micronutrient bioavailability and enhanced rhizodeposition (Blagodatskaya et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Similarly, [Fe(Met)₂] significantly increased both BR and SIR, as evidenced by the high SIR rate, suggesting that this treatment not only activated the existing microbial pool but also enhanced microbial responsiveness to labile carbon inputs. The results are consistent with the concept of microbial activation in fertile soils, where abundant organic matter supports a high baseline of microbial activity and reduces lag time for substrate utilization (Blagodatskaya et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This is particularly true in our experimental soil, which is nutrient-rich and well-supplied with organic carbon. In such environments, microbial communities are more capable of rapidly exploiting external C sources due to reduced energy limitations (German et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), explaining the strong and immediate SIR responses observed. Moreover, the high MBC values under Fe and Zn aminochelate treatments confirm the proliferation of active microbial biomass. As MBC reflects the size of the living microbial pool, its increase suggests not only enhanced microbial growth but also the stimulation of r-strategist microbes in the rhizosphere (Fierer et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), which are known to dominate in nutrient-rich hotspots and respond quickly to labile nutrient inputs (Blagodatskaya \u0026amp; Kuzyakov, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). These findings align with previous research indicating that rhizosphere hotspots in fertile soils contain active microbial communities capable of immediate substrate assimilation (Cheng, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Dippold \u0026amp; Kuzyakov, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The reduced qCO₂ observed under [Fe(Met)₂] and especially [Zn(Met)₂] treatments indicates enhanced microbial carbon-use efficiency. This suggests that microbes allocated more carbon to biomass production rather than respiration, resulting in lower energy loss and improved microbial yield due to the availability of chelated micronutrients (Fischer et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Fterich et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe chelated forms of Fe and Zn may contribute to this effect by enhancing microbial respiratory enzyme systems, particularly Fe-S cluster-containing proteins involved in electron transport chains, thereby improving the overall metabolic performance of the microbial community (Read et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This improved efficiency likely contributes to the increased enzyme activities observed, as larger and more metabolically active microbial populations typically secrete greater amounts of extracellular enzymes in response to nutrient availability and rhizodeposition (Nannipieri et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Razavi et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016b\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e4.4. Root system architecture traits\u003c/h2\u003e\u003cp\u003eThe application of Fe and Zn aminochelates significantly enhanced shoot and root biomass, along with key root architecture traits such as length and surface area (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA\u0026ndash;D). These improvements likely result from enhanced micronutrient availability in the rhizosphere, which supports better plant growth and root development. Chelating ligands such as glycine and methionine protect micronutrients (especially Fe and Zn) from precipitation and immobilization in soils with unfavorable chemical properties, enhancing their uptake efficiency (Chen et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Lucena, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Moreover, amino acid ligands can be absorbed by plants and have been shown to promote physiological improvements, including stress mitigation, enzyme activation, and hormonal regulation (Souri \u0026amp; Hatamian, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These effects are consistent with the biostimulant properties of amino acid-based chelates, which not only act as carriers of essential micronutrients but also positively influence metabolic processes involved in chlorophyll biosynthesis, energy production, and growth regulation (Souri \u0026amp; Hatamian, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Moreover, the distribution patterns of biomass in aminochelate-treated plants support the optimal partitioning hypothesis (McConnaughay \u0026amp; Coleman, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), which proposes that under nutrient-limited conditions, plants prioritize root growth to improve nutrient foraging. This shift likely enhanced rhizodeposition, which stimulates microbial activity and accelerates nutrient turnover in the rhizosphere (Dakora \u0026amp; Phillips, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Notably, [Fe(Gly)₂] application promoted greater root elongation and finer root structures, reflected by increased root length and reduced root diameter (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB \u003cb\u003e\u0026amp; C\u003c/b\u003e). Such changes facilitate efficient uptake of water and nutrients by expanding the root-soil contact surface (Kuzyakov \u0026amp; Razavi, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rahnama et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The enlarged root surface area (RSA; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC) also promotes intensified rhizosphere interactions, where increased exudation stimulates microbial proliferation and enzymatic activity (Canarini et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). A more extensive and finely branched root system promotes higher rhizodeposition rates, which in turn stimulate microbial biomass and extracellular enzyme production, ultimately improving nutrient availability and turnover (Canarini et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Moreover, enhanced initiation and elongation of lateral roots contribute to greater total root length, improving the plant\u0026rsquo;s ability to explore the soil and access water and nutrients more efficiently (Rahnama et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These processes may underlie the enhanced plant-soil interactions observed under aminochelate treatments. Such improvements in root architecture and rhizosphere functioning likely create a reinforcing cycle of nutrient acquisition and plant growth, resulting in a root system with greater functional capacity and increased aboveground productivity.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrated that Fe and Zn aminochelates, particularly methionine-derived forms, substantially enhanced rhizosphere functioning in sunflower by stimulating microbial activity and enzyme kinetics, expanding enzymatic hotspot distribution, and improving root growth and architecture. These synergistic effects supported more efficient nutrient cycling and improved plant performance, underscoring the role of aminochelates not only as micronutrient carriers but also as physiological stimulants that optimize plant\u0026ndash;microbe interactions.\u003c/p\u003e\u003cp\u003eAmong treatments, [Fe(Met)₂] and [Zn(Met)₂] were most effective in synchronizing microbial processes with root system traits, fostering close linkages between enzyme activity and root architecture. In contrast, [Fe(Gly)₂] showed more localized effects with limited systemic integration, suggesting that chelate structure strongly influences the scale and coherence of biological responses.\u003c/p\u003e\u003cp\u003eThese findings highlight the critical importance of ligand design in shaping rhizosphere processes and plant performance. Tailoring chelate formulations to align molecular efficacy with ecosystem-level functionality will be key to advancing sustainable nutrient management. Ultimately, rational design of next-generation aminochelates offers a promising pathway to improve soil fertility, enhance crop productivity, and support resilient agroecosystems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eDeclaration of competing 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\u003cp\u003e\u003ch2\u003eSupplementary data\u003c/h2\u003e\u003cp\u003eSupplementary data related to this article can be found in the supplementary material file available online.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eACKNOWLEDGMENTS\u003c/h2\u003e\u003cp\u003eThe authors extend their gratitude to the Ministry of Science, Research, and Technology of Iran (MSRT) for its financial support.\u003c/p\u003e\u003ch2\u003eDATA AVAILABILITY STATEMENT\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbou-Sreea AI, Rady MM, Roby MH, Ahmed SM, Majrashi A, Ali EF (2021) Cattle manure and bio-nourishing royal jelly as alternatives to chemical fertilizers: potential for sustainable production of organic Hibiscus sabdariffa L. J Appl Res Med Aromat Plants 25:100334. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jarmap.2021.100334\u003c/span\u003e\u003cspan address=\"10.1016/j.jarmap.2021.100334\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlipour Babadi M, Norouzi Masir M, Moezzi AA, Rahnama A, Taghavi M (2025) Iron (Fe) biofortification using Fe-aminochelates in sunflower growing in calcareous soils. J Plant Nutr Soil Sci 188:519\u0026ndash;535. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/jpln.12004\u003c/span\u003e\u003cspan address=\"10.1002/jpln.12004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAllison SD, Vitousek PM (2005) Responses of extracellular enzymes to simple and complex nutrient inputs. Soil Biol Biochem 37:937\u0026ndash;944. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.soilbio.2004.09.014\u003c/span\u003e\u003cspan address=\"10.1016/j.soilbio.2004.09.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnderson TH, Domsch KH (1990) Application of eco-physiological quotients (qCO\u003csub\u003e2\u003c/sub\u003e and qD) on microbial biomasses from soils of different cropping histories. Soil Biol Biochem 22:251\u0026ndash;255. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0038-0717(90)90094-G\u003c/span\u003e\u003cspan address=\"10.1016/0038-0717(90)90094-G\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAreche FO, Aguilar SV, More L\u0026oacute;pez JM, Casta\u0026ntilde;eda Chirre ET, Sumarriva-Bustinza LA, Pacovilca-Alejo OV, Camposano C\u0026oacute;rdova YF, Montesinos CCZ, Quincho Astete JA, Quispe-Vidalon D, Brito Mallqui CH, Camayo-Lapa BF, Malpartida Yapias RJ, Corilla Flores DD, Salas-Contreras WH (2023) Recent and historical developments in chelated fertilizers as plant nutritional sources, their usage efficiency, and application methods. Braz J Biol 83:e271055. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1590/1519-6984.271055\u003c/span\u003e\u003cspan address=\"10.1590/1519-6984.271055\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAsghar W, Ak\u0026ccedil;a MO, Ak\u0026ccedil;a H, Tarf OJ, Kataoka R, Turgay OC (2022) Alternative strategies to synthetic chemical fertilizers: revitalization of soil quality for sustainable agriculture using organic-based approaches. In: New and Future Developments in Microbial Biotechnology and Bioengineering. \u003cem\u003eElsevier\u003c/em\u003e, pp 1\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/B978-0-323-85581-5.00003-3\u003c/span\u003e\u003cspan address=\"10.1016/B978-0-323-85581-5.00003-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAshmead SD, Wheelwright DC, Ericson C, Pedersen M (2000) A Composition and Method for Preparing Amino Acid Chelates Free of Interfering Ions. Google Patents. Patent no. WO 2002/030948 A3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://patents.google.com/patent/WO2002030948A3\u003c/span\u003e\u003cspan address=\"https://patents.google.com/patent/WO2002030948A3\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBadalucco L, Nannipieri P (2007) Nutrient transformation in the rhizosphere. In: Pinton R, Varanini Z, Nannipieri P (eds) The Rhizosphere: Biochemistry and Organic Substrates at the Soil-Plant Interface. CRC, Boca Raton, FL, pp 111\u0026ndash;133. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1201/9781420005585-9\u003c/span\u003e\u003cspan address=\"10.1201/9781420005585-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBilyera N, Kuzyakov Y (2024) Soil zymography: a decade of rapid development in microbial hotspot imaging. Soil Biol Biochem 189:109264. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.soilbio.2023.109264\u003c/span\u003e\u003cspan address=\"10.1016/j.soilbio.2023.109264\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBilyera N, Kuzyakova I, Guber A, Razavi BS, Kuzyakov Y (2020) How hot are hotspots: statistically localizing the high-activity areas on soil and rhizosphere images. Rhizosphere 16:100259. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rhisph.2020.100259\u003c/span\u003e\u003cspan address=\"10.1016/j.rhisph.2020.100259\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBlagodatskaya E, Blagodatsky S, Anderson TH, Kuzyakov Y (2014) Microbial growth and carbon use efficiency in the rhizosphere and root-free soil. PLoS ONE 9(4):e93282. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0093282\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0093282\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBlagodatskaya E, Kuzyakov Y (2013) Active microorganisms in soil: Critical review of estimation criteria and approaches. Soil Biol Biochem 67:192\u0026ndash;211. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.soilbio.2013.08.024\u003c/span\u003e\u003cspan address=\"10.1016/j.soilbio.2013.08.024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBlagodatskaya E, Tarkka M, Knief C, Koller R, Peth S, Schmidt V, Spielvogel S, Uteau D, Weber M, Razavi BS (2021) Bridging Microbial Functional Traits with Localized Process Rates at Soil Interfaces. Front Microbiol 12:625697. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fmicb.2021.625697\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2021.625697\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBlagodatskaya Е, Blagodatsky S, Khomyakov N, Myachina O, Kuzyakov Y (2016) Temperature sensitivity and enzymatic mechanisms of soil organic matter decomposition along an altitudinal gradient on Mount Kilimanjaro. Sci Rep 6:22240. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/srep22240\u003c/span\u003e\u003cspan address=\"10.1038/srep22240\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBonner MTL, Shoo LP, Brackin R, Schmidt S (2018) Relationship between microbial composition and substrate use efficiency in a tropical soil. Geoderma 315:96\u0026ndash;103. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.geoderma.2017.11.026\u003c/span\u003e\u003cspan address=\"10.1016/j.geoderma.2017.11.026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBurns RG, DeForest JL, Marxsen J, Sinsabaugh RL, Stromberger ME, Wallenstein MD, Weintraub MN, Zoppini A (2013) Soil enzymes in a changing environment: current knowledge and future directions. Soil Biol Biochem 58:216\u0026ndash;234. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.soilbio.2012.11.009\u003c/span\u003e\u003cspan address=\"10.1016/j.soilbio.2012.11.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCampbell CD, Chapman SJ, Cameron CM, Davidson MS, Potts JM (2003) A rapid microtiter plate method to measure carbon dioxide evolved from carbon substrate amendments so as to determine the physiological profiles of soil microbial communities by using whole soil. Appl Environ Microbiol 69:3593\u0026ndash;3599. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1128/AEM.69.6.3593-3599.2003\u003c/span\u003e\u003cspan address=\"10.1128/AEM.69.6.3593-3599.2003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCanarini A, Kaiser C, Merchant A, Richter A, Wanek W (2019) Root Exudation of Primary Metabolites: Mechanisms and Their Roles in Plant Responses to Environmental Stimuli. Front Plant Sci 10:157. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2019.00157\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2019.00157\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen J, Calderone LA, Pan L, Quist T, Pandelia ME (2023) The Fe and Zn cofactor dilemma. Biochim Biophys Acta Proteins Proteom 1871:140931. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.bbapap.2023.140931\u003c/span\u003e\u003cspan address=\"10.1016/j.bbapap.2023.140931\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen Y, Clapp CE, Magen H (2004) Mechanisms of plant growth stimulation by humic substances: The role of organo-iron complexes. Soil Sci Plant Nutr 50:1089\u0026ndash;1095. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/00380768.2004.10408579\u003c/span\u003e\u003cspan address=\"10.1080/00380768.2004.10408579\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCheng WX (2009) Rhizosphere priming effect: its functional relationships with microbial turnover, evapotranspiration, and C-N budgets. Soil Biol Biochem 41:1795\u0026ndash;1801. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.soilbio.2008.04.018\u003c/span\u003e\u003cspan address=\"10.1016/j.soilbio.2008.04.018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDakora FD, Phillips DA (2002) Root exudates as mediators of mineral acquisition in low-nutrient environments. Plant Soil 245:35\u0026ndash;47. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1023/A:1020809400075\u003c/span\u003e\u003cspan address=\"10.1023/A:1020809400075\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDegens BP, Harris JA (1997) Development of a physiological approach to measuring the catabolic diversity of soil microbial communities. Soil Biol Biochem 29:1309\u0026ndash;1320. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0038-0717(97)00076-5\u003c/span\u003e\u003cspan address=\"10.1016/S0038-0717(97)00076-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDippold MA, Kuzyakov Y (2013) Biogeochemical transformations of amino acids in soil assessed by position-specific labelling. Plant Soil 373:385\u0026ndash;401. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11104-013-1764-3\u003c/span\u003e\u003cspan address=\"10.1007/s11104-013-1764-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDoostikhah N, Panahpour E, Nadian H, Gholami A (2020) Tomato (Lycopersicon esculentum L.) nutrient and lead uptake affected by zeolite and DTPA in a lead polluted soil. Plant Biol 22:317\u0026ndash;322. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/plb.13059\u003c/span\u003e\u003cspan address=\"10.1111/plb.13059\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDorodnikov M, Blagodatskaya E, Blagodatsky S, Marhan S, Fangmeier A, Kuzyakov Y (2009) Stimulation of microbial extracellular enzyme activities by elevated CO\u003csub\u003e2\u003c/sub\u003e depends on aggregate size. Glob Change Biol 15:1603\u0026ndash;1614. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-2486.2009.01844.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2486.2009.01844.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFierer N, Bradford MA, Jackson RB (2007) Toward an ecological classification of soil bacteria. Ecology 88:1354\u0026ndash;1364. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.jstor.org/stable/27651243\u003c/span\u003e\u003cspan address=\"http://www.jstor.org/stable/27651243\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFischer H, Ingwersen J, Kuzyakov Y (2010) Microbial uptake of low-molecular-weight organic substances in soil: Review of mechanisms and methods. Soil Biol Biochem 42:1562\u0026ndash;1571. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-2389.2010.01244.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2389.2010.01244.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFterich A, Mosbah M, Mars M (2012) Impact of grazing on soil microbial communities along a chronosequence of Acacia tortilis subsp. raddiana in arid soils in Tunisia. Eur J Soil Biol 50:56\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ejsobi.2011.12.002\u003c/span\u003e\u003cspan address=\"10.1016/j.ejsobi.2011.12.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGerman DP, Chacon SS, Allison SD (2011) Substrate concentration and enzyme allocation can affect rates of microbial decomposition. Ecology 92:1471\u0026ndash;1480. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1890/10-2028.1\u003c/span\u003e\u003cspan address=\"10.1890/10-2028.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGhosh A, Kumar A, Biswas G (2024) Exponential population growth and global food security: Challenges and alternatives. Bioremediation of Emerging Contaminants from Soils. Elsevier, Amsterdam, pp 1\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/B978-0-443-13993-2.00001-3\u003c/span\u003e\u003cspan address=\"10.1016/B978-0-443-13993-2.00001-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuber A, Kravchenko A, Razavi BS, Uteau D, Peth S, Blagodatskaya E, Kuzyakov Y (2018) Quantitative soil zymography: mechanisms, processes of substrate and enzyme diffusion in porous media. Soil Biol Biochem 127:156\u0026ndash;167. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.soilbio.2018.09.030\u003c/span\u003e\u003cspan address=\"10.1016/j.soilbio.2018.09.030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHao C, Dungait JA, Wei X, Ge T, Kuzyakov Y, Cui Z, Tian J, Zhang F (2022) Maize root exudate composition alters rhizosphere bacterial community to control hotspots of hydrolase activity in response to nitrogen supply. Soil Biol Biochem 170:108717. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.soilbio.2022.108717\u003c/span\u003e\u003cspan address=\"10.1016/j.soilbio.2022.108717\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHe H, Peng M, Hou Z, Li J (2024) Organic substitution contrasting direct fertilizer reduction increases wheat productivity, soil quality, microbial diversity and network complexity. Environ Technol Innov 36:103784. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.eti.2024.103784\u003c/span\u003e\u003cspan address=\"10.1016/j.eti.2024.103784\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHoang DTT, Pausch J, Razavi BS, Kuzyakova I, Banfield CC, Kuzyakov Y (2016) Hotspots of microbial activity induced by earthworm burrows, old root channels, and their combination in subsoil. Biol Fertil Soils 52:1105\u0026ndash;1119. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00374-016-1148-y\u003c/span\u003e\u003cspan address=\"10.1007/s00374-016-1148-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJia R, Zhou J, Yang L, Blagodatskaya E, Jones DL, Razavi BS, Yang Y, Kuzyakov Y, Zeng Z, Zang H (2024) Trade-off between soil enzyme activities and hotspots area depends on long-term fertilization: In situ field zymography. Sci Total Environ 954:176386. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2024.176386\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2024.176386\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKuzyakov Y, Blagodatskaya E (2015) Microbial hotspots and hot moments in soil: Concept \u0026amp; review. Soil Biol Biochem 83:184\u0026ndash;199. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.soilbio.2015.01.025\u003c/span\u003e\u003cspan address=\"10.1016/j.soilbio.2015.01.025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKuzyakov Y, Razavi BS (2019) Rhizosphere size and shape: temporal dynamics and spatial stationarity. Soil Biol Biochem 135:343\u0026ndash;360. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.soilbio.2019.05.011\u003c/span\u003e\u003cspan address=\"10.1016/j.soilbio.2019.05.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKuzyakov Y, Xu X (2013) Competition between roots and microorganisms for nitrogen: mechanisms and ecological relevance. New Phytol 198:656\u0026ndash;669. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/nph.12235\u003c/span\u003e\u003cspan address=\"10.1111/nph.12235\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu S, He F, Kuzyakov Y, Xiao H, Hoang DTT, Pu S, Razavi BS (2022) Nutrients in the rhizosphere: A meta-analysis of content, availability, and influencing factors. Sci Total Environ 826:153908. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2022.153908\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2022.153908\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu S, Razavi BS, Su X, Maharjan M, Zarebanadkouki M, Blagodatskaya E, Kuzyakov Y (2017) Spatio-temporal patterns of enzyme activities after manure application reflect mechanisms of niche differentiation between plants and microorganisms. Soil Biol Biochem 112:100\u0026ndash;109. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.soilbio.2017.05.006\u003c/span\u003e\u003cspan address=\"10.1016/j.soilbio.2017.05.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu Z, Kuzyakov Y, Fang J, Li Y, Xiao H, Li Z, Zhou J, Razavi BS (2025) Hotspots of enzyme activities reflect micro-scale heterogeneity in nutrient mobilization in paddy soils. Plant Soil. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11104-025-07265-1\u003c/span\u003e\u003cspan address=\"10.1007/s11104-025-07265-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLobet G, Pag\u0026egrave;s L, Draye X (2011) A novel image-analysis toolbox enabling quantitative analysis of root system Architecture. Plant Physiol 157:29\u0026ndash;39. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1104/pp.111.179895\u003c/span\u003e\u003cspan address=\"10.1104/pp.111.179895\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLucena JJ (2006) Synthetic Iron Chelates to Correct Iron Deficiency in Plants. In: Barton LL, Abadia J (eds) Iron Nutrition in Plants and Rhizospheric Microorganisms. Springer, Dordrecht, pp 85\u0026ndash;109. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/1-4020-4743-6_5\u003c/span\u003e\u003cspan address=\"10.1007/1-4020-4743-6_5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMa X, Liu Y, Zarebanadkouki M, Razavi BS, Blagodatskaya E, Kuzyakov Y (2018) Spatiotemporal patterns of enzyme activities in the rhizosphere: effects of plant growth and root morphology. Biol Fertil Soils 54:819\u0026ndash;828. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00374-018-1305-6\u003c/span\u003e\u003cspan address=\"10.1007/s00374-018-1305-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarschner P, Crowley D, Rengel Z (2011) Rhizosphere Interactions between Microorganisms and Plants Govern Iron and Phosphorus Acquisition along the Root Axis-Model and Research Methods. Soil Biol Biochem 43:883\u0026ndash;894. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.soilbio.2011.01.005\u003c/span\u003e\u003cspan address=\"10.1016/j.soilbio.2011.01.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcConnaughay KDM, Coleman JS (1999) Biomass allocation in plants: ontogeny or optimality? A test along three resource gradients. Ecology 80:2581\u0026ndash;2593. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/177242\u003c/span\u003e\u003cspan address=\"10.2307/177242\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMiralles I, Domingo F, Cant\u0026oacute;n Y, Trasar-Cepeda C, Leir\u0026oacute;s MC, Gil-Sotres F (2012) Hydrolase enzyme activities in a successional gradient of biological soil crusts in arid and semi-arid zones. Soil Biol Biochem 53:124\u0026ndash;132. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.soilbio.2012.05.016\u003c/span\u003e\u003cspan address=\"10.1016/j.soilbio.2012.05.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoormann J, Heinemann B, Hildebrandt TM (2022) News about amino acid metabolism in plant-microbe interactions. Trends Biochem Sci 47:849\u0026ndash;861. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tibs.2022.07.001\u003c/span\u003e\u003cspan address=\"10.1016/j.tibs.2022.07.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNannipieri P, Giagnoni L, Renella G, Puglisi E, Ceccanti B, Masciandaro G, Fornasier F, Moscatelli MC, Marinari S (2012) Soil enzymology: classical and molecular approaches. Biol Fertil Soils 48:743\u0026ndash;762. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00374-012-0723-0\u003c/span\u003e\u003cspan address=\"10.1007/s00374-012-0723-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNannipieri P, Gianfreda L (1998) Kinetics of enzyme reactions in soil environments. In: Huang PM, Senesi N, Buffle J (eds) Structure and Surface Reactions. Wiley, New York, pp 449\u0026ndash;479\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNannipieri P, Trasar-Cepeda C, Dick RP (2018) Soil enzyme activity: a brief history and biochemistry as a basis for appropriate interpretations and meta-analysis. Biol Fertil Soils 54:11\u0026ndash;19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00374-017-1245-6\u003c/span\u003e\u003cspan address=\"10.1007/s00374-017-1245-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePeng M, He H, Jiang M, Wang Z, Li G, Zhuang L (2023) Morphological, physiological and metabolomic analysis to unravel the adaptive relationship between root growth of ephemeral plants and different soil habitats. Plant Physiol Biochem 202:107986. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.plaphy.2023.107986\u003c/span\u003e\u003cspan address=\"10.1016/j.plaphy.2023.107986\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePhilippot L, Chenu C, Kappler A, Rillig MC, Fierer N (2023) The interplay between microbial communities and soil properties. Nat Rev Microbiol 22:1\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41579-023-00980-5\u003c/span\u003e\u003cspan address=\"10.1038/s41579-023-00980-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRahnama A, Fakhri S, Meskarbashee M (2019) Root growth and architecture responses of bread wheat cultivars to salinity stress. Agron J 111:2991\u0026ndash;2998. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2134/agronj2018.12.0795\u003c/span\u003e\u003cspan address=\"10.2134/agronj2018.12.0795\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRahnama A, Hosseinalipour B, Farrokhian Firouzi A, Harrison MT, Ghorbanpour M (2024) Root architecture traits and genotypic responses of wheat at seedling stage to water-deficit stress. Cereal Res Commun 52:1499\u0026ndash;1510. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s42976-023-00481-4\u003c/span\u003e\u003cspan address=\"10.1007/s42976-023-00481-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRazavi BS, Blagodatskaya E, Kuzyakov Y (2016a) Temperature selects for static soil enzyme systems to maintain high catalytic efficiency. Soil Biol Biochem 97:15\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.soilbio.2016.02.018\u003c/span\u003e\u003cspan address=\"10.1016/j.soilbio.2016.02.018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRazavi BS, Zarebanadkouki M, Blagodatskaya E, Kuzyakov Y (2016b) Rhizosphere shape of lentil and maize: spatial distribution of enzyme activities. Soil Biol Biochem 96:229\u0026ndash;237. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.soilbio.2016.02.020\u003c/span\u003e\u003cspan address=\"10.1016/j.soilbio.2016.02.020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRazavi BS, Zhang X, Bilyera N, Guber A, Zarebanadkouki M (2019) Soil zymography: Simple and reliable? Review of current knowledge and optimization of the method. Rhizosphere 11:100161. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rhisph.2019.100161\u003c/span\u003e\u003cspan address=\"10.1016/j.rhisph.2019.100161\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRead AD, Bentley RE, Archer SL, Dunham-Snary KJ (2021) Mitochondrial iron-sulfur clusters: Structure, function, and an emerging role in vascular biology. Redox Biol 47:102164. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.redox.2021.102164\u003c/span\u003e\u003cspan address=\"10.1016/j.redox.2021.102164\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSanaullah M, Razavi BS, Blagodatskaya E, Kuzyakov Y (2016) Spatial distribution and catalytic mechanisms of b-glucosidase activity at the root-soil interface. Biol Fertil Soils 52:505\u0026ndash;514. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00374-016-1094-8\u003c/span\u003e\u003cspan address=\"10.1007/s00374-016-1094-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchimel J, Becerra CA, Blankinship J (2017) Estimating decay dynamics for enzyme activities in soils from different ecosystems. Soil Biol Biochem 114:5\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.soilbio.2017.06.023\u003c/span\u003e\u003cspan address=\"10.1016/j.soilbio.2017.06.023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSinsabaugh RL, Follstad Shah JJ (2012) Ecoenzymatic stoichiometry and ecological theory. Annu Rev Ecol Evol Syst 43:313\u0026ndash;343. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nature08632\u003c/span\u003e\u003cspan address=\"10.1038/nature08632\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSouri MK (2016) Aminochelate fertilizers: the new approach to the old problem; a review. Open Agric 1:118\u0026ndash;123. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1515/opag-2016-0016\u003c/span\u003e\u003cspan address=\"10.1515/opag-2016-0016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSouri MK, Hatamian M (2018) Aminochelates in plant nutrition: a review. J Plant Nutr 42:67\u0026ndash;78. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/01904167.2018.1549671\u003c/span\u003e\u003cspan address=\"10.1080/01904167.2018.1549671\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSpohn M, Kuzyakov Y (2013) Distribution of microbial- and root-derived phosphatase activities in the rhizosphere depending on P availability and C allocation Coupling soil zymography with 14C imaging. Soil Biol Biochem 67:106. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.soilbio.2013.08.015\u003c/span\u003e\u003cspan address=\"10.1016/j.soilbio.2013.08.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSpohn M, Kuzyakov Y (2014) Spatial and temporal dynamics of hotspots of enzyme activity in soil as affected by living and dead roots-a soil zymography analysis. Plant Soil 379:67\u0026ndash;77. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11104-014-2041-9\u003c/span\u003e\u003cspan address=\"10.1007/s11104-014-2041-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang R, Bicharanloo B, Shirvan MB, Cavagnaro TR, Jiang Y, Keitel C, Dijkstra FA (2021) A novel 13C pulse-labelling method to quantify the contribution of rhizodeposits to soil respiration in a grassland exposed to drought and nitrogen addition. New Phytol 230:857\u0026ndash;866. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/nph.17118\u003c/span\u003e\u003cspan address=\"10.1111/nph.17118\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWei X, Ge T, Zhu Z, Hu Y, Liu S, Li Y, Wu J, Razavi BS (2019) Expansion of rice enzymatic rhizosphere: temporal dynamics in response to phosphorus and cellulose application. Plant Soil 445:169\u0026ndash;181. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11104-018-03902-0\u003c/span\u003e\u003cspan address=\"10.1007/s11104-018-03902-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang X, Myrold DD, Shi L, Kuzyakov Y, Dai H, Thu Hoang DT, Dippold MA, Meng X, Song X, Li Z, Zhou J, Razavi BS (2021) Resistance of microbial community and its functional sensitivity in the rhizosphere hotspots to drought. Soil Biol Biochem 161:108360. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.soilbio.2021.108360\u003c/span\u003e\u003cspan address=\"10.1016/j.soilbio.2021.108360\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao A, Yang S, Wang B, Tian X (2019) Effects of ZnSO₄ and Zn-EDTA applied by broadcasting or by banding on soil Zn fractions and Zn uptake by wheat (Triticum aestivum L.) under greenhouse conditions. J Plant Nutr Soil Sci 182:307\u0026ndash;317. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/jpln.201800341\u003c/span\u003e\u003cspan address=\"10.1002/jpln.201800341\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao ZB, He JZ, Quan Z, Wu CF, Sheng R, Zhang LM, Geisen S (2020) Fertilization changes soil microbiome functioning, especially phagotrophic protists. Soil Biol Biochem 148:107863. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.soilbio.2020.107863\u003c/span\u003e\u003cspan address=\"10.1016/j.soilbio.2020.107863\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Aminochelates, Enzyme kinetics, Microbial biomass, Sustainable fertilization, Zymography","lastPublishedDoi":"10.21203/rs.3.rs-7455689/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7455689/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe mechanisms by which organic chelates influence rhizosphere enzyme dynamics and microbial function are poorly understood due to a lack of spatial visualization. To address this gap, we evaluated the effects of iron (Fe) and zinc (Zn) aminochelates on the spatial distribution of β-glucosidase (βG) and leucine aminopeptidase (LAP) activities in the rhizosphere of sunflower (\u003cem\u003eHelianthus annuus\u003c/em\u003e L.) using a novel integration of in situ zymography and biochemical assays. Glycine- (Gly) and methionine-based (Met) Fe and Zn aminochelates were synthesized and applied in rhizobox experiments, with untreated soils serving as controls.\u003c/p\u003e\u003cp\u003e[Fe(Gly)₂] and [Zn(Met)₂] significantly enhanced βG (7\u0026ndash;21%) and LAP (72\u0026ndash;120%) activities, while expanding enzymatic hotspot zones by 270\u0026ndash;450% and 78\u0026ndash;251%, respectively. Kinetic analyses showed that [Zn(Met)₂] achieved the highest catalytic efficiency (Ka) and maximum velocity (Vmax, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while also increasing basal respiration (+\u0026thinsp;42.3%) and microbial biomass C (3-fold) relative to the control. Root length and surface area were strongly correlated with hotspot intensity (Pearson\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.75\u0026ndash;0.94), reflecting tight root\u0026ndash;microbe feedbacks. Network analysis further revealed that [Fe(Met)₂] and [Zn(Met)₂] promoted the highest system-wide coordination, linking microbial enzyme activity with root architecture (Mantel\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.56\u0026ndash;0.68, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). By enhancing microbial activity, expanding biologically active zones, and improving root foraging traits, aminochelates demonstrated a dual functionality as both micronutrient carriers and physiological stimulants. These results establish methionine-based aminochelates, in particular, as promising next-generation biostimulants that can improve soil fertility, optimize nutrient cycling, and support resilient crop production systems.\u003c/p\u003e","manuscriptTitle":"Aminochelates as Dual Micronutrient Carriers and Biostimulants: Modulating Rhizosphere Enzyme Hotspots and Root–Microbe Interactions in Sunflower","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-11 11:46:23","doi":"10.21203/rs.3.rs-7455689/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"39d16b28-b01a-46c5-8a4f-d2257cf20947","owner":[],"postedDate":"September 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-03T22:58:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-11 11:46:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7455689","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7455689","identity":"rs-7455689","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}