Global climate change and ecological environment changes are continuously intensifying, leading to a deterioration of the growth environment for most plants such as food and horticultural crops. Crops are frequently subjected to various biotic stresses (such as pathogen infection) and abiotic stresses (such as drought, high temperature, and ultraviolet radiation), resulting in decreased yield and damaged quality, posing a serious threat to global food security. Plant epidermal wax, as an important lipid soluble secondary metabolite, not only reduces non stomatal water loss, enhances plant drought resistance and storage stability, but also plays a key role in alleviating temperature stress and UV damage. In addition, the wax layer is also an important physical barrier for plants to resist the invasion of pathogens, with both structural and chemical resistance functions.
This study intends to use Nicotiana benthamiana as a model plant to first verify whether there is a common regulatory pathway in the plant that combines the core transcription factor HY5 of the light signaling pathway with the key gene CER1 for wax alkane synthesis. Then, the pathway can be selectively modified by regulating external light conditions. The experiment aims to enhance the binding ability between HY5 and CER1 promoter, thereby upregulating CER1 gene expression and promoting the accumulation of plant epidermal wax, especially alkane compounds. This strategy is expected to significantly improve the stress resistance of plants and provide a richer source of wax raw materials for the industrial sector.
The purpose of this module is to construct a mathematical model of light intensity and wax synthesis amount, find the relationship between different variables, construct regression equations, and finally form a standard curve or nonlinear regression function of light intensity and wax content through fitting. It mainly consists of three parts: experimental verification and final nonlinear regression fitting.
Explore the relationship between different light intensities and light responsive HY5 transcription factors, set different gradient light intensities to treat tobacco, and establish a linear regression equation by detecting HY transcription levels to determine the linear relationship between light intensity and HY5. This process is crucial in revealing the upstream molecular mechanisms of light signal regulation of plant wax synthesis, providing a theoretical basis for improving plant stress resistance and wax yield through light strategies.
Establish the relationship between the expression level of CER1 gene and the expression level of HY5 protein. As we have already verified in our molecular experiments that HY5 gene can bind to CER1 gene, the specific trend of the relationship between the two is still unclear. Measure the expression level of CER1 gene and the transcription level of HY5 gene under different light intensities, set up a regression model, input the measurement data into the standard curve, and study the specific binding relationship between HY5 and CER1. This process helps us provide quantitative evidence for the binding of HY5 and CER1, and establish the relationship between light intensity and the key wax synthesis gene CER1.
Looking for the relationship between the transcription level of CER1 and the wax we ultimately want to synthesize, we need to focus on the product we want to synthesize. The upstream regulatory factor of wax has been experimentally verified to be CER1, so the transcription level of CER1 directly determines the level of wax synthesis and also determines whether our project's core goal can be achieved. Establishing standard curves for CER1 transcription levels and wax content under different light conditions can intuitively predict the effect of light intensity on wax synthesis.
Light intensity, HY5 protein expression level, CER1 transcription level, and wax content are the core variables in our project design. By exploring the changes and relationships between each two variables under different gradient light intensities, we ultimately constructed a comprehensive prediction model for light intensity and wax content. The final model may not be a simple linear equation, but a more complex composite function. Through nonlinear regression fitting, we can intuitively predict how wax content changes with light intensity, which can be used to judge the reliability of our experimental design.
This module establishes regression equations between adjacent variables, with L: light intensity (recorded value), [HY5]: relative expression level of HY5 protein, [CER1_mRNA]: CER1 transcription level, and [Wax]: plant wax synthesis.
Phase 1: Cultivation and Light Treatment of Plant Materials
Seed disinfection and sowing: After disinfecting tobacco seeds with ethanol and NaClO solution, place them on a culture dish containing MS medium. Spring at 4 ℃ for 2-3 days to break dormancy. Afterwards, transplant to nutrient soil that has been sterilized for growth.
Dark cultivation: Place tobacco plants in a completely dark incubator (22 ℃) for 3-4 days. The purpose is to induce dark morphogenesis in seedlings and obtain yellowing seedlings. At this time, the background level of HY5 is extremely low. To detect the expression level of HY5 in multiple tobacco plants at this time, the average is taken as a constant in the regression equation.
Light treatment (core step): Randomly divide yellowing seedlings into 6 experimental groups, set up a dark control group (L=0), and set up 5 or more different light intensities (L) experimental groups: 15 × 104, 20 × 104, 25 × 104, 30 × 104, 35 × 104 Lx/m2. Transfer all groups (including the dark group) simultaneously into a light incubator and process them at the same temperature (22 ℃) and same light exposure time (1 week). Ensure that all conditions are completely consistent except for light intensity.
Phase 2: Extraction and detection of HY5 protein
Tobacco plants treated with light were taken from leaves of the same size, and the total protein content was extracted. Western Blot and gel electrophoresis were used to obtain band plots, and anti-HY5 antibodies were used to detect the induced HY5 protein content. The grayscale values of each sample's HY5 band and internal reference band were measured separately, and the HY5/internal reference ratio of each sample was calculated. This ratio represents the relative expression level of HY5 protein, i.e. [HY5] in the equation.
Phase 3: Data organization
Table 1 Relative expression levels of HY5 protein under different light treatments
Stage 4: Linear Regression Analysis
Taking the light intensity (L) as the independent variable (X) and [HY5] as the dependent variable (Y), let the linear equation between L and [HY5] be:
[HY5] = a * L + b
Using GraphPad Prism for linear regression analysis, substitute the table data, and obtain the following standard curve graph:
Software analysis shows that [HY5]=2.5 * L+1, with a deviation value of 0, indicating a positive correlation between light intensity and HY5 protein expression. The protein expression level of HY5 can be determined based on the intensity of light.
Phase 1: Material Preparation and Induction Treatment
Planting and cultivation: Tobacco plants cultivated under different lighting conditions as described above are used.
Phase 2: Sample Collection and Preparation
Take half of the same tobacco plant and leaf, immediately collect all groups of seedling materials, freeze them in liquid nitrogen, and store them at -80 ℃. Each sample was evenly divided into two halves, with one portion used for Western Blot to extract total protein and detect HY5 protein levels ([HY5]). A qPCR was used to extract total RNA and reverse transcribe it into cDNA for detecting the mRNA level of CER1 ([CER1_mRNA]). (Note: This grouping ensures precise pairing of protein and mRNA data from the same batch of cells)
Phase 3: Detection of HY5 protein levels (Western Blot) and CER1 mRNA levels (quantitative real-time fluorescence PCR, qPCR)
The detection method for HY5 protein level is the same as above. The detection of CER1 mRNA level uses a reagent kit (such as TRIzol method) to extract high-quality total RNA. After determining the concentration, an equal amount of RNA is reverse transcribed into cDNA using reverse transcriptase. CER1 specific fluorescent quantitative primers are designed, and qPCR reaction is performed on each cDNA sample to amplify CER1 and internal reference genes, respectively. Calculate the relative expression level of CER1 gene using the Δ Δ Ct method, i.e. [CER1_mRNA]=2 ^ (- Δ Ct), which will obtain the relative expression level of CER1 mRNA in each sample relative to the control group (such as Mock treated samples), i.e. the dependent variable [CER1_mRNA].
Phase 4: Data organization
Table 2 CER1 transcription levels under different relative expression levels of HY5 protein
Phase 5: Linear Regression Analysis
Using the relative expression level of HY5 protein [HY5] as the independent variable (X) and [CER1_mRNA] as the dependent variable (Y), let the linear equation between [HY5] and [CER1_mRNA] be:
[CER1_mRNA] = e * [HY5] + f
Using GraphPad Prism for linear regression analysis, substitute the table data, and obtain the following standard curve graph:
The software analysis showed that [CER1_mRNA]=1.45 * [HY5]+0.7, with a deviation value of 0, indicating a positive correlation between light intensity and HY5 protein expression level. The transcription level of CER1 gene can be determined based on the protein expression level of HY5.
Phase 1: Material Preparation and Induction Treatment
Planting and cultivation: Tobacco plants cultivated under different lighting conditions as described above are used.
Phase 2: Sample Collection and Preparation
Synchronize sampling, induce treatment, and collect materials from the same plant.
Samples for qPCR: Quickly collect 1-2 newly fully unfolded leaves, immediately freeze them in liquid nitrogen, and store them at -80 ℃ for RNA extraction. This part of the leaves is used for measuring [CER1_mRNA].
Samples for wax extraction: Collect 2-4 symmetrical leaves of the same age from the same plant and immediately use them for wax extraction.
Phase 3: Detection of CER1 mRNA levels (quantitative real-time fluorescent PCR, qPCR) and determination of wax content
The RNA extraction, reverse transcription, and qPCR processes are exactly the same as the previous protocol. Using GC-MS to determine the wax content of tobacco leaves, quantitative analysis was conducted by comparing the peak areas of wax components (alkanes, aldehydes, alcohols, acids, etc.) in the sample with internal standards.
Phase 4: Data Analysis and Regression Equation Establishment
Data organization: Organize the paired data into the following table. Each data point comes from the same independently processed plant.
Table 3 Relationship between CER1 transcription level and total wax content
Using CER1 transcription level [CER1_mRNA] as the independent variable (X) and total wax content [Wax] as the dependent variable (Y), let the linear equation between [CER1_mRNA] and [Wax] be:
[Wax]= g *[CER1_mRNA] + h
Using GraphPad Prism for linear regression analysis, substitute the table data, and obtain the following standard curve graph:
Through analysis, it can be found that the relationship between the total wax content and CER1 expression level does not follow a standard linear line, but rather has a certain deviation, which is more in line with the laws of plant material synthesis. Wax synthesis does not rely solely on the expression of CER1 genes, and the wax synthesis process in plants is influenced by a complex regulatory network, which needs further analysis. However, through the icon, we can also find a strong correlation between wax content and CER1 expression. Regression analysis shows that the relationship between total wax content and CER1 expression is approximately close to [Wax]=3.158 * [CER1_mRNA] -31.11, with a slope P value of 0.0009, indicating significant correlation. This linear equation can be used to predict the relationship between wax content and CER1 expression, and the actual value fluctuates around the predicted value with a small fluctuation range.
Model fitting: We have established a linear relationship for each step from the starting point (light intensity) to the endpoint (wax content), and now we need to integrate them into a comprehensive model and validate it. We obtained the relationship between light intensity and HY5 protein expression level as follows: [HY5]=2.5 * L+1. The relationship between HY5 protein expression level and CER1 transcription level: [CER1_mRNA]=1.45 * [HY5]+0.7. The relationship between CER1 transcription level and total wax content: [Wax]=3.158 * [CER1_mRNA] -31.11. Integrating and substituting the three equations yields a comprehensive model that directly links light intensity to wax content:
[Wax]=11.44775*L-24.3203
It means that within our hypothetical framework, there is a direct linear relationship between wax content and light intensity, with its slope and intercept determined by the parameters of all previous steps.
Model validation: In order to verify the reliability and universality of our comprehensive prediction model, we used experimental validation (independent validation set) to perform standard linear regression validation on the equation [Wax]=11.44775 * L-24.3203 that we directly fitted.
Experimental design: A new set of data was used for validation. Following the experimental design steps above, different light gradients were reset to directly measure the wax content in plant leaves under different light intensities. The obtained data was compared with the predicted wax content under different light intensities calculated by this equation to calculate the error. The smaller the error, the more accurate the prediction. The wax content in tobacco leaves was measured again using GC-MS method, and the results are shown in the following table:
Comparison Table of Wax Content and Model Predicted Content in Tobacco Leaves under New Light Intensity Treatment
Comparative statistical chart of wax content and model predicted content in tobacco leaves under a new set of light intensity treatments
Based on the measurement results, we can find that there are differences between the actual measured wax content and the model predicted content, except for the dark treatment (wax is synthesized anytime and anywhere in the plant, and the model predicts a negative wax synthesis amount at 0 light, which is impossible to occur, so this situation can be ignored). The actual measured wax content under different light treatments is very close to the wax content predicted by the model, and the changes in wax content are consistent with the predicted trend of the model. These results strongly support our model and hypothesis path.
Through the wax synthesis prediction model we constructed, we have demonstrated the possibility of the basic assumptions of the project, drawing an accurate blueprint for our hardware design and operation, and being able to predict our output through the model before actual production. Moreover, using GC-MS for final wax detection is time-consuming and costly, while predicting wax final yield based on HY5 protein expression level or CER1 gene transcription level can be more convenient and cost-effective. Our model also lays the foundation for this idea.
By combining our designed model with hardware facilities, we can greatly improve the synthesis and controllability of wax substances in plants, enhance plant stress resistance, and provide abundant high wax raw materials for industrial production, saving technical costs.