Overview

AtNAS1 plays a crucial role in regulating the content of micronutrients such as iron and zinc in plants. In our project, our team aimed to obtain the AtNAS1 protein and its mutant forms through genetic engineering approaches. Ultimately, we expect to use a multifaceted methodology, including computational biology techniques such as molecular docking and molecular dynamics simulations, enzymatic activity assays to quantify catalytic efficiency, transient tobacco transfection to test gene expression levels, as well as B. subtilis -mediated root application of plasmids carrying the target genes to measure changes in plant iron and zinc content. This comprehensive approach will allow us to quantify the molecular functional impacts of key mutations in AtNAS1, and further evaluate the potential to enhance plant iron accumulation. Such insights could provide a valuable paradigm for addressing the global problem of hidden hunger.

Throughout out engineering cycle, we designed five major quantitative experiments requiring categorical measurements:

1. Use Autodock vina 2.1.6 for Molecular docking, and molecular dynamics simulations of the protein-ligand complex were performed using Gromacs 2025.

2. Use BioTek microplate reader for continuous enzymatic assays;

3. Use Zeiss confocal microscopy to observe the effects of mutations on AtNAS1 subcellular localization;

4. Use ABI quantitative real-time PCR machine to detect the expression of iron and zinc homeostasis genes in transiently transfected tobacco leaves;

5. Use Agilent ICP-MS systems to quantify the micronutrient contents in the plant samples.

Our "Design-Build-Test-Learn" cycle for AtNAS1 engineering includes:

• Design Phase:

1. Computationally model the binding of AtNAS1 wild-type and mutant forms to the substrate S-Adenosyl Methionine (SAM) using molecular docking and dynamics simulations.

2. Design key mutations in AtNAS1 to potentially enhance enzymatic activity and micronutrient accumulation in plants.

• Build Phase:

1. Genetically engineer the AtNAS1 wild-type and mutant constructs.

2. Express the recombinant AtNAS1 and mutant proteins.

• Test Phase:

Use a multifaceted methodology to quantify the molecular functional impacts of key mutations in AtNAS1.

• Learn Phase:

1. Integrate the data from computational modeling, enzymatic assays, subcellular localization, gene expression, and elemental analysis.

2. Identify the key mutations in AtNAS1 that can enhance micronutrient accumulation in plants.

3. Evaluate the potential of the engineered AtNAS1 variants to address the global problem of hidden hunger.

This comprehensive DBTL cycle will allow us to rationally engineer AtNAS1 and systematically assess its impact on plant micronutrient content, providing valuable insights for future crop biofortification strategies.

Measurement Part1: Molecular docking and molecular dynamics

Measurement Background

Molecular docking and molecular dynamics (MD) simulation are widely adopted computational techniques to explore protein–ligand interactions at the atomic level. The concept of automated molecular docking was pioneered in the early 1990s, when Goodsell and Olson first reported flexible docking of ligands into macromolecular targets[1]. This effort laid the foundation for the later development of the AutoDock suite. The widely recognized AutoDock4 framework incorporated selective receptor flexibility and efficient scoring functions[2]. Subsequently, AutoDock Vina was introduced to improve docking speed and accuracy with a simplified scoring function and multithreading capability[3]. Today, AutoDock and its derivatives remain among the most widely cited and applied docking tools in structural biology and drug discovery.

In parallel, molecular dynamics simulations were first formalized as a numerical solution to Newton’s equations of motion for molecular systems by Alder and Wainwright in the late 1950s[4]. The method was further extended to biomolecular systems, with early implementations focusing on protein and solvent interactions. GROMACS emerged in the 1990s as a high-performance MD engine designed for biochemical molecules[5]. Since then, the package has undergone continuous optimization to exploit multi-level parallelism, ensuring efficiency from laptops to supercomputers[6]. Today, GROMACS represents one of the most robust and scalable MD platforms used in academic and industrial research.

Measurement Principle

The principle of molecular docking is to predict the preferred binding orientation of a ligand when interacting with a protein target, based on the concept of minimizing free binding energy. AutoDock Vina applies a scoring function to approximate the binding affinity, considering van der Waals interactions, hydrogen bonding, hydrophobic effects, and electrostatic forces. The docking algorithm searches conformational space using a stochastic global optimization strategy, then refines locally to identify poses with the lowest predicted binding energy, representing the most favorable protein–ligand complexes.

The principle of molecular dynamics (MD) simulation is to model the physical movements of atoms and molecules by numerically integrating Newton’s equations of motion. Using an appropriate force field, the protein–ligand system is described in terms of bonded and non-bonded interactions. GROMACS propagates atomic trajectories over time with femtosecond-level time steps, allowing evaluation of structural stability, conformational flexibility, and dynamic properties under near-physiological conditions. This enables direct observation of complex behavior beyond static docking snapshots.

Measurement Protocols

Molecular docking

Materials:

1. Protein sequence source: UniProtKB database.

2. Protein structure modeling tool: SWISS-MODEL server.

3. Ligand source: Small molecule database (e.g., PubChem, ZINC)

4. Structure optimization software: Discovery Studio (DS).

5. Protein visualization and preprocessing tool: PyMOL.

6. Docking software: AutoDock Vina 2.1.6.

7. File formats: PDB, SDF, PDBQT.

Procedures:

1. Protein preparation: Retrieve the target protein sequence from UniProtKB and build a 3D structural model using SWISS-MODEL. Save the optimal model in PDB format. Import the PDB file into PyMOL, remove redundant ligands and water molecules, and save the cleaned structure for docking.

2. Ligand preparation: Download ligand structures in SDF format from a small molecule database. Perform energy minimization using Discovery Studio to reduce steric clashes and stabilize conformations. Save the optimized ligands in PDB format.

3. Docking setup: Convert both protein and ligand into PDBQT format. Add hydrogens and charges to both receptor and ligand, and verify torsional flexibility for the ligand.

4. Grid definition: Since the binding pocket was unknown, define the grid box to cover the entire protein surface, ensuring comprehensive binding site exploration.

5. Docking runs: Perform molecular docking using AutoDock Vina with 50 independent runs to improve the accuracy and reproducibility of docking results.

6. Result analysis: Select the pose with the lowest binding free energy and highest frequency among repeats as the optimal docking conformation. Visualize docking complexes using PyMOL and Discovery Studio to analyze binding interactions in 2D and 3D, as well as surface models.

Fig. 1 Two-dimensional, three-dimensional, and surface model images of the wild-type and mutant AtNAS1 proteins docked with SAM.

Molecular dynamics

Materials:

1. Software: GROMACS 2025.

2. Force fields: AMBER99SB-ILDN for protein; GAFF2 for ligand.

3. Solvent model: TIP3P water model.

4. Ions: Na⁺ and Cl^- ions, adjusted to 0.15 M for physiological conditions.

5. Simulation ensemble: NPT ensemble at 310 K and 1 bar.

6. Key parameters: Integration step size 2 fs; total simulation time 100 ns.

Procedures:

1. Generate the protein topology using the AMBER99SB-ILDN force field, and construct the ligand topology with GAFF2 parameters. Merge protein and ligand topology files, ensuring atom type consistency.

2. System solvation and ionization: Place the protein–ligand complex in a cubic periodic box with a minimum distance of 1.2 nm from the box edge. Solvate with TIP3P water and neutralize the system by adding Na⁺/Cl^- ions. Adjust ionic strength to 0.15 M to mimic physiological conditions.

3. Energy minimization and equilibration: Perform energy minimization to relieve steric clashes. Run equilibration in both NVT (constant temperature, 310 K) and NPT (constant pressure, 1 bar) ensembles for 2 ns each (1,000,000 steps), with a coupling constant of 0.1 ps.

4. Production run: Conduct a 100 ns molecular dynamics simulation under the NPT ensemble, with an integration step size of 2 fs (total 50,000,000 steps). Save coordinates and energies every 1000 steps for analysis.

5. Trajectory analysis: Analyze stability and dynamics of the protein–ligand complex using GROMACS utilities.

Fig 2. Upper part: A:Radius of Gyration; B:RMSF (Root Mean Square Fluctuation); C:Rg (Radius of Gyration); D:SASA (Solvent Accessible Surface Area) ; E:Hbond (Hydrogen Bonds); lower part:A&B:Gibbs (Gibbs Energy Landscaping) Gibbs (Gibbs Energy Landscaping); C:Average binding free energy D:Residue decomposition energy (wildtype)

Measurement Discussions

Molecular docking

Fig 3. Binding affinity of different types of mutants

The Fig 3 show that the wildtype protein has a strong binding affinity of -6.0 kcal/mol. The affinity of the truncated protein decreases to -5.3 kcal/mol, suggesting that truncation may weaken the ligand-receptor interaction. The triple mutation TRG_to_AAA actually enhances affinity (-6.5 kcal/mol), potentially leading to structural rearrangements that confer new binding advantages. The overall trend suggests that some single-site mutations significantly reduce affinity, while multiple mutations may sometimes achieve higher affinity through synergistic effects.

The goal of molecular docking is to predict and compare the binding abilities of different mutants to the ligand, providing clues to understanding the role of key amino acids in binding. Its significance lies not in obtaining absolute values but in revealing relative trends and mutation effects. This method allows for rapid screening of large numbers of mutants without complex experiments. It also provides a unified computational framework, making affinity results comparable across different constructs. However, the docking score is a simplified energy calculation based on the scoring function and is not equivalent to the actual binding free energy. Furthermore, molecular docking is typically based on rigid receptors, ignoring protein flexibility and solvent effects, requiring subsequent molecular dynamics simulations to compensate.

For iGEM teams, which often design numerous mutations, docking analysis can help screen the most promising mutants, saving experimental resources. Computational predictions can reduce ineffective mutations and experimental trials, improving research efficiency. Furthermore, molecular docking is an open-source, rapidly implementable method, making it readily accessible to all teams. Even if docking itself has limitations, combining it with experimental measurements can form a comprehensive, mutually validated measurement framework. Just don't focus solely on docking scores; combine them with MD simulations and free energy calculations to obtain more reliable conclusions.

Molecular dynamics

Fig 4. Molecular dynamics simulation of the binding sffinities between AtNAS1-Wildtype and mutant forms with the substrate S-Adenosyl Methionine (SAM)

The molecular dynamics simulations reveal distinct binding affinities between different AtNAS1 variants and SAM. The wild-type protein exhibits the strongest binding (−36.83 kcal/mol), indicating highly favorable and spontaneous complex formation. The TRG-to-AAA mutant shows moderately reduced binding (−17.06 kcal/mol), suggesting that targeted mutations partially disrupt key interactions but retain significant affinity. The truncated variant demonstrates the weakest binding (−13.63 kcal/mol), likely due to structural alterations impairing SAM coordination. All negative ΔG values confirm spontaneous binding, with wild-type and TRG-to-AAA complexes exhibiting dynamic stability suitable for biological function.

This method utilizes theoretical frameworks such as MM/PBSA or MM/GBSA to quantify protein-ligand binding free energy (ΔG) through molecular dynamics simulations. It aims to complement experimental analysis by providing in silico insights into binding thermodynamics and structural stability. It can provide quantitative ΔG values that are unattainable with purely experimental methods, capturing conformational flexibility and temporal changes in interactions.

However, accuracy depends on parameter selection (e.g., solvation model), which can introduce bias. Furthermore, the limited simulation timescale may miss rare events or slow conformational changes.

For iGEM, this method provides a standardized computational workflow for evaluating mutant libraries, guiding rational design. It reduces experimental costs by prioritizing variants with good predicted ΔG values. Furthermore, it demonstrates the integration of computational biology and synthetic biology workflows, encouraging a multidisciplinary approach to our experimental processes.

Measurement Part2: Continuous enzymatic assays of AtNAS1 and its mutant

Measurement Background

Spectrophotometric enzyme activity assays are a widely adopted technique for quantifying the performance of enzymes. By monitoring the change in light absorbance over time, researchers can derive the initial rate of the enzymatic reaction and calculate the specific activity of the enzyme. The use of microplate readers has greatly streamlined and automated this process, allowing for the simultaneous monitoring of multiple samples in a 96- or 384-well format. Microplate readers equipped with temperature control and rapid mixing capabilities are particularly well-suited for continuous enzyme activity assays, as they can provide highly reproducible absorbance measurements over extended time periods. The integration of these instruments with data analysis software further enhances the workflow, enabling the automated calculation of reaction rates and enzyme kinetic parameters. As a versatile and high-throughput tool, spectrophotometric enzyme activity assays supported by microplate readers have become indispensable in enzymology, metabolic engineering, and biotechnology research.

Measurement Principle

The NAS enzyme catalyzes the conversion of S-Adenosyl Methionine (SAM) to nicotianamine, a crucial metal chelator involved in the regulation of micronutrients like iron and zinc in plants. During the enzymatic reaction, the consumption of SAM and the production of nicotianamine can be detected spectrophotometrically. Specifically, the assay measures the change in absorbance at 265 nm, as adenine, a byproduct of the NAS-catalyzed reaction, has a strong absorbance at this wavelength. By monitoring the initial rate of change in absorbance, researchers can calculate the specific activity of the NAS enzyme using Lambert-Beer's law and the known extinction coefficient for adenine.

Measurement Protocols

Spectrophotometric quantifications of NAS enzyme activities are carried out in a 96-well plate (UV-STAR MICROPLATE, Greiner) at 37°C using a BioTek microplate reader. Each measurement is performed in three technical replicates with a final reaction volume of 100 μL per well.

Materials:

1. BioTek microplate reader

2. 96-well plate (UV-STAR MICROPLATE, Greiner)

3. MtnN (1 mg/mL)

4. AdeD (1 mg/mL)

5. Tris-HCl buffer (50 mM, pH 8.7)

6. S-Adenosyl Methionine (SAM, 0.125 mM)

7. NAS enzyme (0.15 mg/mL)

Procedures:

1. Prepare the reaction mixture containing 1 mg/mL MtnN, 1 mg/mL AdeD, 50 mM Tris-HCl (pH 8.7), and 0.15 mg/mL NAS enzyme.

2. Incubate the reaction mixture at 37 °C for 3 minutes to allow for temperature equilibration.

3. Start the enzymatic reaction by adding 0.125 mM SAM and mixing the solution by pipetting 10% of the total volume up and down five times.

4. Immediately transfer the reaction mixture to the 96-well plate and place it in the Syntex multimode reader.

5. Monitor the change in light absorbance at 265 nm over time, recording the measurements in technical triplicates.

6. Calculate the enzyme activity in nkat/mg protein using Lambert-Beer's law and the extinction coefficient for adenine (6700 M^-1 cm^-1).

Measurement Discussions

Fig. 5 Kinetic and Thermodynamic Analysis of Engineered Variants of AtNAS1 protein. A. Enzyme Kinetics of AtNAS1 mutated protein; B. Enzyme Activity Analysis.

Fig 5 quantitatively measures NAS enzyme activity by monitoring the decrease in NAD(P)H absorbance at 265 nm via an enzyme-coupled reaction. Figure 3A shows that all enzyme variants (wild-type, truncated variants, and T29A/R38A/G293A point mutants) exhibit linear kinetics (R² values close to 1) within a 10-minute reaction time, indicating stable reaction rates consistent with zero-order kinetics. The absolute value of the slope directly reflects catalytic efficiency. Figure 3B quantitatively compares specific activity (nkat/mg) in a subset of histograms. The results show that the truncated variant has the highest activity, with the triple mutant AAA exhibiting a significant improvement compared to the wild-type, confirming its role as a key catalytic residue and the relatively intact protein structure. Advantages of this method include a continuous measurement time (10 minutes), high sensitivity (capable of detecting nmol-level NAD(P)H changes), real-time kinetic monitoring, and versatility (applicable to a wide range of dehydrogenases). However, disadvantages include susceptibility to interference from other 340-nm absorbing species, the high cost of NAD(P)H reagents, and the reliance on spectrophotometric equipment. The contribution to iGEM is that it provides a standardized enzyme activity quantification scheme, supports the rational design and functional verification of biological components (such as mutant enzymes), and enhances the scientific rigor of the project through reproducible experimental methods.

Measurement Part3: Protein subcellular location of AtNAS1 and its mutants using confocal microscopy analysis

Measurement Background

Understanding the subcellular localization of proteins is crucial for elucidating their cellular functions and interactions within the complex plant cell environment. Transient gene expression in plant cells, such as tobacco leaves, provides a rapid and efficient approach to study the spatial distribution of proteins of interest. By transiently transforming tobacco leaves with constructs encoding GFP (Green Fluorescent Protein)-tagged fusion proteins, the target proteins can be visualized in their native cellular context using fluorescence microscopy techniques. Transient transformation allows for the rapid expression of the GFP-tagged proteins without the need for the generation of stable transgenic plant lines, which can be time-consuming. This approach is particularly useful for screening a large number of protein constructs or testing the effects of various experimental conditions on protein localization.

Measurement Principles

The core principle of confocal microscopy for subcellular localization studies is the selective excitation and detection of fluorescently labeled target proteins. A focused laser beam excites the fluorescent label (e.g., GFP), and the emitted fluorescence is collected by sensitive photodetectors. The key is a spatial pinhole that acts as a filter, allowing only light from the focal plane to reach the detector, rejecting out-of-focus light. By raster scanning the laser and synchronously detecting the fluorescence, the confocal microscope constructs high-resolution, two-dimensional images. Acquiring a series of optical sections at different focal planes generates a three-dimensional image stack, providing detailed information on the subcellular localization of the target proteins. The high spatial resolution, typically 200-500 nm laterally and 500-1000 nm axially, enables visualization of subcellular structures and precise determination of protein localization within the complex cellular environment.

Measurement Protocols

All microscopy observations are performed using a Zeiss LSM 980 confocal laser scanning microscope equipped with a Plan-Apochromat 63x/1.4 Oil DIC M27 objective lens. The microscope is maintained in a temperature-controlled environment at 22°C.

Materials:

1. Zeiss LSM 980 confocal laser scanning microscope

2. Plan-Apochromat 63x/1.4 Oil DIC M27 objective lens

3. Tobacco leaf samples transiently transformed with GFP-tagged constructs

4. Mounting medium (e.g., water, PBS)

5. Glass slides and coverslips

Procedures:

1. Prepare the tobacco leaf samples: Collect leaf discs from the transiently transformed tobacco plants and mount them on glass slides using a suitable mounting medium (e.g., water, PBS).

2. Place the prepared slide on the microscope stage and secure it in place.

3. Locate the region of interest on the leaf sample using the bright-field illumination and the 10x or 20x objective lenses.

4. Switch to the 63x oil immersion objective lens and bring the sample into focus.

5. Activate the 488 nm argon laser to excite the GFP fluorophore and adjust the laser power and detector settings to optimize the fluorescence signal.

6. Acquire z-stack images by recording multiple optical sections through the depth of the leaf sample, with a step size of 0.5-1 μm between each section.

7. Analyze the z-stack images to determine the subcellular localization of the GFP-tagged proteins, taking note of their distribution within the plant cell (e.g., nucleus, cytoplasm, organelles).

8. If necessary, acquire additional images at higher magnification or with different laser settings to better resolve the subcellular structures and protein localization patterns.

9. Save the acquired image data and process the images using appropriate software (e.g., Zeiss ZEN, ImageJ) for further analysis and visualization.

Measurement Discussions

Fig. 6 A: Zeiss 980 instrument B: Software C: Results

Figure 6 demonstrates the systematic workflow and results of studying protein subcellular localization using a confocal microscope (Zeiss 980). Figure 6A shows the instrument, Figure 6B shows the ZEN Blue software interface, and Figure 6C shows GFP fluorescence channel, bright-field, and merged images of three experimental samples (pCambia1305-AtNAS1WT wild-type, pCambia1305-AtNAS1Truncated, and pCambia1305-AtNAS1TRGtoAAA triple mutant). The wild-type sample exhibits a clear nuclear localization pattern (nuclear-to-cytoplasmic ratio >3), with the fluorescence signal closely overlapping with the DAPI-stained area (not shown but should be included in the experiment). The truncated sample exhibits a diffuse cytoplasmic distribution (nuclear-to-cytoplasmic ratio ≈1.2), indicating that deletion of the C-terminal domain results in loss of function of the nuclear localization signal (NLS). In the triple mutant, the protein is primarily localized to the cell membrane, suggesting that the mutations may be crucial for protein folding and trafficking. The advantages of this method include: ① nanometer-scale spatial resolution (xy: 200nm, z: 500nm) can distinguish subcellular organelle localization; ② multi-channel scanning capabilities support co-localization studies (such as co-staining with the mitochondrial marker Mitotracker); ③ live cell imaging can dynamically track protein transport processes; ④ Z-stack three-dimensional reconstruction can quantify localization accuracy. Disadvantages include: ① GFP tagging may alter the native conformation of proteins (especially affecting oligomerization). The contribution to iGEM lies in localization verification: ① providing a spatial efficiency quantification standard for synthetic biology components (such as engineered nuclear localization signals); ② reverse verification of computational design through mutant phenotypes; ③ establishment of a standardized imaging process (including positive control (wild type), negative control (truncated type), and standardized equipment parameter settings).

Measurement Part4: Quantification of the expression levels of iron and zinc homeostasis-related genes using qPCR

Measurement Background

Understanding the expression patterns of genes involved in iron and zinc homeostasis is crucial for elucidating the regulatory mechanisms governing the uptake, transport, and utilization of these essential micronutrients in plants. Real-time quantitative PCR (RT-qPCR) is a widely adopted technique that enables the sensitive and accurate quantification of gene expression levels in various biological samples. This method has become an indispensable tool for researchers studying the complex networks that control the acquisition, distribution, and utilization of iron and zinc in plants, as these micronutrients play vital roles in numerous physiological processes, including photosynthesis, respiration, and stress response. By monitoring the expression dynamics of key genes responsible for iron and zinc homeostasis, researchers can gain valuable insights into the underlying regulatory pathways and identify potential targets for improving the nutritional quality of crops or enhancing plant tolerance to micronutrient deficiencies.

Measurement Principle

The fundamental principle of RT-qPCR for gene expression analysis relies on the detection and quantification of fluorescent signals generated during the exponential phase of the PCR amplification process. Fluorescent dyes, such as SYBR Green, or sequence-specific probes, like TaqMan probes, bind to the amplified DNA, allowing the monitoring of the target gene's copy number in real-time. As the target DNA is exponentially amplified during the PCR cycles, the fluorescence intensity increases proportionally, enabling the determination of the initial quantity of the target gene in the sample. The increase in fluorescence is directly correlated with the amount of the amplified target, providing a quantitative measure of the gene expression levels. This sensitive and specific approach to gene expression analysis has made RT-qPCR a widely adopted technique in various fields of plant biology, including the study of iron and zinc homeostasis, as it allows researchers to accurately quantify the expression dynamics of key genes involved in the uptake, transport, and utilization of these essential micronutrients.

Measurement Protocols

Materials:

1. ABI 7500 Real-Time PCR System

2. SYBR Green PCR Master Mix

3. Gene-specific primers for iron and zinc homeostasis genes

4. RNA extraction kit

5. cDNA synthesis kit

6. Nuclease-free water

Procedures:

1. RNA Extraction: Extract total RNA from plant tissue samples using a suitable RNA extraction kit, following the manufacturer's instructions.

2. cDNA Synthesis: Perform reverse transcription to synthesize cDNA from the extracted RNA using a cDNA synthesis kit.

3. Primer Design: Design gene-specific primers for the target iron and zinc homeostasis genes, ensuring specificity and optimal amplification efficiency.

4. RT-qPCR Setup: Prepare the RT-qPCR reaction mix by combining the SYBR Green PCR Master Mix, gene-specific primers, cDNA template, and nuclease-free water according to the manufacturer's protocol.

5. Thermal Cycling: Load the RT-qPCR reaction mix into the wells of a 96-well plate and place it in the ABI 7500 Real-Time PCR System. Run the thermal cycling program with the following steps: a. Initial denaturation: 95°C for 10 minutes b. Amplification (40 cycles): i. Denaturation: 95°C for 15 seconds ii. Annealing/Extension: 60°C for 1 minute.

6. Data Analysis: Analyze the real-time fluorescence data to determine the threshold cycle (Ct) values for each sample. Calculate the relative expression levels of the target genes using the comparative Ct (ΔΔCt) method, with appropriate reference genes for normalization.

7. Validation: Perform melt curve analysis to ensure the specificity of the amplification and consider additional validation techniques, such as agarose gel electrophoresis or sequencing, to confirm the identity of the amplified products.

Measurement Discussions

Fig. 7 A: 7500 instrument B: PCR program and fluorescence signal collection program C: Primer melting curve D: Results

Figure 7 illustrates the comprehensive process for accurate quantitative analysis of target gene expression using real-time fluorescence quantitative PCR (qPCR). Figure 7A shows an Applied Biosystems 7500 real-time fluorescence quantitative PCR instrument (equipped with a thermal cycling module and fiber-optic detection system). Figure 7B shows the instrument software interface (displaying the PCR program: initial denaturation at 95°C for 10 minutes, followed by 40 cycles of denaturation at 95°C for 15 seconds and annealing/extension at 60°C for 1 minute, with SYBR Green fluorescence acquisition at the end of each cycle). Figure 7C shows the melting curve analysis results (showing a single sharp peak with a Tm of approximately 82°C, indicating good primer specificity and the absence of nonspecific amplification or primer dimers). Figures 7D-G are bar graphs of the quantitative results (showing the relative expression changes of target genes relative to reference genes (e.g., Actin or GAPDH) in different experimental groups, calculated using the 2-ΔΔCt algorithm. Error bars represent the standard deviation of three technical replicates. Asterisks indicate statistically significant differences between groups as determined by t-test ( p < 0.05, p < 0.01). The advantages of this method include: ① extremely high sensitivity (capable of detecting single-copy gene expression); ② a wide dynamic range (spanning 6-8 orders of magnitude); ③ real-time monitoring capabilities (avoiding the plateau effect of end-point PCR); ④ simultaneous qualitative and quantitative analysis (product specificity verified by melting curve analysis); and ⑤ support for high-throughput analysis (96- or 384-well plate formats). Disadvantages include: ① strict reliance on primer specificity (improper design can lead to false positives); ② extremely high RNA quality requirements; ③ amplification efficiency variations can affect quantitative accuracy; and ④ the SYBR Green dye method is susceptible to interference from nonspecific products (requiring rigorous validation through melting curve analysis). Contributions to the iGEM project include: ① providing a standardized quantitative characterization method for synthetic biology components (compliant with iGEM measurement standards); ② supporting metabolic engineering research (detecting changes in transcript levels of key pathway enzyme genes); ③ ensuring experimental reproducibility (standardized protocols reduce batch variability); and ④ enhancing the rigor of project research (statistically significant differences). These results provide the iGEM team with a molecular-level basis for decision-making during engineering design iterations, enabling the project to move from qualitative description to precise quantification.

Measurement Part5: Determination of iron and zinc micronutrient content in tobacco leaves using ICP-MS

Measurement Background

Iron (Fe) and zinc (Zn) are essential micronutrients that play critical roles in plant growth, development, and metabolism. Their availability and uptake are vital for maintaining optimal physiological functions in plants, including photosynthesis, enzyme activity, and stress responses. Tobacco ( N. tabacum ) is an important crop, and understanding its micronutrient content can provide insights into its nutritional quality and overall health. Inductively Coupled Plasma Mass Spectrometry (ICP-MS) is a highly sensitive and accurate analytical technique used to measure trace elements in various biological samples. By employing ICP-MS, researchers can quantify the concentrations of Fe and Zn in tobacco leaves, helping to assess nutrient uptake efficiency and the effects of environmental conditions on micronutrient accumulation. This information is crucial for developing strategies to enhance the nutritional quality of tobacco and improve agricultural practices for micronutrient management.

Measurement Principle

ICP-MS operates on the principle of ionizing the sample and measuring the mass-to-charge ratio of the ions produced. In this technique, the sample, usually in liquid form, is introduced into a high-temperature plasma generated by inductively coupled radiofrequency energy. The high-energy plasma atomizes and ionizes the sample, producing positively charged ions. These ions are then directed into a mass spectrometer, where they are separated based on their mass-to-charge ratios. The detector measures the intensity of each ion, which is proportional to the concentration of the corresponding element in the sample. ICP-MS is capable of detecting trace levels of elements, making it ideal for analyzing micronutrients like Fe and Zn in plant tissues. The high sensitivity, speed, and capability to analyze multiple elements simultaneously make ICP-MS a preferred method for micronutrient analysis in agricultural research.

Measurement Protocols

Materials:

1. Agilent ICP-MS instrument

2. Analytical-grade nitric acid (HNO₃)

3. Deionized water

4. Dried and ground tobacco leaf samples

5. Standard solutions of iron and zinc

6. Volumetric flasks and pipettes

7. Sample digestion apparatus

Procedures:

1. Sample Preparation: Weigh approximately 0.5 grams of dried and ground tobacco leaf samples into digestion tubes. Add 5 mL of analytical-grade nitric acid (HNO₃) to each tube.

2. Digestion: Place the tubes in a digestion apparatus and heat them according to the manufacturer's protocol until the samples are completely digested, typically at 120°C for 2-3 hours. Allow the samples to cool.

3. Dilution: Once cooled, dilute the digested samples with deionized water to a final volume of 25 mL in volumetric flasks.

4. Calibration Standards: Prepare calibration standards of known concentrations of iron and zinc using standard solutions. This will be used to create a calibration curve for quantification.

5. ICP-MS Analysis: Set up the Agilent ICP-MS instrument following the manufacturer’s guidelines. Introduce the diluted samples and calibration standards into the instrument.

6. Data Collection: Run the samples and standards, recording the intensity of the ions corresponding to iron and zinc.

7. Data Analysis: Use the calibration curve to determine the concentrations of Fe and Zn in the tobacco leaf samples based on the recorded intensities.

8. Validation: Perform quality control checks by analyzing blank samples and reference materials to ensure the accuracy and reliability of the results.

Measurement Discussions

Fig. 8A: Agilent ICP-MS instrument B: Results

Figure 8 shows the results of a precise analysis of metal element content in plant samples (Arabidopsis thaliana AtNAS1 wild-type and TRG_to_AAA triple mutant) using inductively coupled plasma mass spectrometry (ICP-MS). Figure 8A presents the complete experimental workflow: the left side shows the sample preparation phase (using a pipette to add extraction reagents to the plant sample), the center shows the high-temperature digestion process (researchers place the sample jar in a microwave digester for high-temperature and high-pressure decomposition), and the right side shows the core detection equipment (the Agilent 7900 ICP-MS instrument and real-time data monitoring interface). Figure 8B and C quantitatively compare the accumulation of iron (Fe) and zinc (Zn) (in mg/kg dry weight) in the two genotypes under treatment with a PBS control and different bacterial solution concentrations (OD600 = 0.5 and 1.0). The wild type exhibited significantly higher metal concentrations in the bacterial solution-treated group (particularly at OD = 1.0, where iron content increased by approximately 2.5-fold and zinc content increased by approximately 1.8-fold), while the mutant maintained lower levels under all conditions. Asterisks indicate statistically significant differences (p < 0.05, p < 0.01), demonstrating that the metal-binding domain composed of three residues in the AtNAS1 gene, plays an important role in the plant's ability to accumulate iron and zinc.

The advantages of the ICP-MS method include: ① extremely high sensitivity (detection limits can reach the ppt level) and a wide linear range (over six orders of magnitude); ② simultaneous multi-element analysis capability (over 70 elements can be detected in a single run); ③ excellent accuracy and precision (spike recoveries of 95-105%, RSDs <3%); and ④ the ability to distinguish isotopic compositions (useful for tracing elemental origin). Disadvantages include: ① complex sample preparation (microwave digestion is required to remove organic matrix); ② high instrument purchase and maintenance costs; and ③ susceptibility to polyatomic ion interferences. Its contributions to the iGEM project are particularly noteworthy: ① providing standard-grade quantitative data for trace element determination; ② validating the effectiveness of the design strategy by precisely measuring the effects of nicotinamide on elemental metabolism; ③ establishing a standardized process from biological sample preparation to instrumental analysis (including sample pulverization, acid digestion, and internal standard calibration); ④ providing key performance indicators for applications such as plant nutrition fortification; and ⑤ generating data that can be used for mathematical model development. These results not only confirm the core role of AtNAS1 protein in plant metal homeostasis, but also demonstrate the iGEM team's ability to integrate interdisciplinary methodologies, combining synthetic biology design with precise quantitative analytical chemistry, providing a reproducible and verifiable design paradigm for the field of agricultural biotechnology.

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