Method and system for diagnosing and predicting rice recovery capacity after drought and rehydration based on photosynthesis and molecular indicators

Abstract

This invention discloses a diagnostic and predictive method and system for rice post-drought rehydration recovery capacity based on photosynthetic and molecular indicators, belonging to the field of rice drought resistance diagnostic technology. This invention collects leaf samples from rice at the normal water supply period, the end of drought, and the rehydration recovery period, respectively, to obtain net photosynthetic rate, Rubisco enzyme activity, and expression levels of several characteristic genes, constructing normalized photosynthetic recovery indicators, biochemical recovery indicators, and molecular recovery indicators; and constructs a comprehensive diagnostic index by weighted coupling of these indicators. This method effectively reduces the fluctuation bias caused by drought environmental disturbances to single indicators; while being compatible with special physiological scenarios such as abnormally elevated enzyme activity under drought stress, it improves the robustness of diagnostic results by averaging the expression levels of multiple genes, achieving quantitative diagnosis and accurate prediction of rice rehydration compensation capacity; it solves the problem of existing technologies using single indicators and difficulty in quantifying the physiological homeostasis reconstruction process.

Description

Technical Field

[0001] This invention relates to the field of rice drought resistance diagnostic technology, and in particular to a method for diagnosing and predicting rice post-drought rehydration recovery capacity based on photosynthetic and molecular indicators. Background Technology

[0002] Rice is one of the world's most important food crops, and its growth and yield formation are highly sensitive to water conditions. Drought stress significantly inhibits the photosynthetic carbon assimilation capacity of rice leaves, disrupts the photosystem structure, and affects key metabolic processes such as carbon and nitrogen, thus adversely affecting crop growth and final yield. In agricultural production practices, due to factors such as unstable rainfall and limited irrigation conditions, rice often experiences alternating periods of drought and re-irrigation during its growth cycle. The rate of recovery of photosynthetic function after re-irrigation and whether photosynthetic compensation occurs are of great significance to its subsequent growth.

[0003] In existing technologies, the assessment of rice's response to drought and rehydration largely relies on single physiological indicators such as net photosynthetic rate, stomatal conductance, chlorophyll content, or chlorophyll fluorescence parameters. While these methods can reflect the physiological state of rice to some extent, they typically only characterize one aspect of the photosynthetic system and cannot comprehensively reveal the recovery of key carbon-fixing enzyme activity and related metabolic regulation processes during rehydration, thus limiting their ability to distinguish between different recovery stages. Especially during rehydration, the internal physiological and biochemical processes of rice often exhibit nonlinear fluctuations and "lag" or "lead" phenomena. For example, key metabolic enzymes may show stress-induced increases in activity at the end of drought, and conventional evaluation methods, lacking effective normalization references, struggle to accurately identify the true recovery vector under such special physiological conditions, resulting in limited ability to distinguish between different recovery stages. Furthermore, the aforementioned methods are mostly used for post-event evaluation and are insufficient for judging the subsequent recovery trend of rice in the early stages of rehydration.

[0004] On the other hand, molecular biology detection methods can reveal changes in gene expression levels during drought and rehydration, but related studies are mostly focused on drought resistance mechanism analysis or functional gene research. There is still a lack of mature technical solutions that combine molecular expression information with photosynthetic physiology and biochemical indicators for the diagnosis and prediction of rice's post-drought rehydration recovery ability.

[0005] Therefore, how to quickly and quantitatively assess the photosynthetic recovery status of rice during the drought-to-re-watering process without relying on a single indicator, and how to predict its subsequent photosynthetic compensation capacity, remains a problem that urgently needs to be solved in existing technologies. Summary of the Invention

[0006] To address the shortcomings of existing technologies, such as reliance on single physiological indicators, difficulty in accurately diagnosing the complex physiological states during rice rehydration after drought, and lack of early prediction capabilities, this invention provides a method and system for diagnosing and predicting the recovery capacity of rice after drought rehydration based on photosynthetic and molecular indicators. The method provided by this invention achieves accurate diagnosis and prediction of the recovery capacity of rice after drought rehydration through a normalized coupling of physiological, biochemical, and molecular three-dimensional indicators. Specifically, it is implemented through the following techniques.

[0007] This invention provides a method for diagnosing and predicting the post-drought rehydration recovery capacity of rice based on photosynthetic and molecular indicators, comprising the following steps:

[0008] Rice plants were subjected to continuous drought treatment and post-drought rehydration treatment, with a control group set up; leaf samples were collected at different treatment periods.

[0009] The net photosynthetic rate, Rubisco enzyme activity, and expression levels of characteristic genes for each leaf were measured.

[0010] The normalized photosynthetic recovery index X was calculated based on the net photosynthetic rate. Pn The normalized enzyme activity recovery index X was calculated based on the Rubisco enzyme activity. Rubisco The relative expression level of the corresponding characteristic gene is calculated based on the expression level of the characteristic gene.

[0011] Based on the normalized photosynthetic recovery index X Pn Normalized enzyme activity recovery index X Rubisco The relative expression level X of the characteristic gene qPCR To conduct a comprehensive diagnosis and prediction of the rice's ability to recover after drought and subsequent irrigation.

[0012] The diagnostic prediction method provided by this invention, by introducing a normalized difference algorithm with positive and negative synergistic characteristics, can effectively offset the interference caused by the stress-induced increase or inhibition decrease of key metabolic enzymes (such as Rubisco enzymes) activity due to drought, significantly improving the universality of physiological assessment. The molecular-level recovery index constructed based on multi-feature gene expression levels (such as LOC_Os10g41780) enhances the robustness of the diagnostic model through a multi-gene arithmetic averaging strategy, effectively reducing the impact of varietal differences and experimental random errors. This invention can not only evaluate the current level of physiological recovery but also capture molecular and biochemical signals in the early stages of rehydration, thereby effectively predicting the subsequent photosynthetic compensation potential.

[0013] The method provided by this invention can not only be used to monitor the recovery capacity of rice after drought, but can also be extended to the monitoring of the functional recovery of other crops (such as various grasses, legumes, solanaceae, etc.) after drought stress.

[0014] In the method provided by the present invention, the raw values ​​of net photosynthetic rate of rice leaves at each stage can be obtained using a portable photosynthesis measurement system.

[0015] Specifically, during the measurement, the light intensity can be fixed at 1000-1500 μmol·m using an artificial light source. -2 ·s -1 (Preferred value is approximately 1200 μmol·m) -2 ·s -1 The CO2 concentration can be fixed at 380-420 μmol·mol⁻¹ through a gas path control system. -1 (Preferred size: 400 μmol·mol) -1 ).

[0016] Rubisco enzymes catalyze the carboxylation of ribulose-1,5-bisphosphate (RuBP) with CO2 to produce 3-phosphoglycerate (3-PGA). In the coupled enzyme system, the generated 3-PGA is further metabolized by the sequential action of 3-phosphoglycerate kinase and glyceraldehyde-3-phosphate dehydrogenase, along with the conversion of NADH to NAD. + The oxidation process.

[0017] Therefore, since NADH has a characteristic absorption peak at 340 nm, in the method provided by the present invention, the Rubisco enzyme activity can be indirectly determined by monitoring the rate of decrease of absorbance at a wavelength of 340 nm.

[0018] In the above method of the present invention, the selected characteristic genes are derived from a set of functional genes related to photosynthesis and nitrogen metabolism. Their expression changes can reflect the metabolic recovery status of rice under drought and rehydration conditions at the molecular level, and are suitable as molecular characteristic indicators for diagnosing rehydration recovery ability.

[0019] Furthermore, the normalized photosynthetic recovery index X Pn The calculation formula is:

[0020] ;

[0021] Among them, P nCK P nCE and P nCD The net photosynthetic rates of rice leaves at the control group, the post-drought rehydration treatment, and the end of the continuous drought treatment are respectively.

[0022] Furthermore, the normalized enzyme activity recovery index X Rubisco The calculation formula is:

[0023] ;

[0024] Among them, Act CK Act CE and Act CD The values ​​represent the Rubisco enzyme activity in rice leaves at the control group, the post-drought rehydration treatment, and the end of the continuous drought treatment, respectively, and Act... CK - Act CD ≠0.

[0025] Furthermore, the characteristic genes selected for determining the expression level of the characteristic genes are one or more of the following: LOC_Os05g42350, LOC_Os10g41780, LOC_Os04g59440, LOC_Os08g44340, LOC_Os01g11054, and LOC_Os01g64120.

[0026] Furthermore, when at least two characteristic genes are selected, the relative expression level X of the characteristic genes is... qPCR The arithmetic mean, geometric mean, or weighted mean of the relative expression levels of all the described characteristic genes.

[0027] Furthermore, the relative expression level X of the characteristic gene qPCR The calculation method is as follows: using leaf samples at the end of the continuous drought treatment as the control baseline, the relative expression level of rice leaves after the drought-induced re-watering treatment was calculated using the ΔΔCt method. -ΔΔCt , that is, X qPCR .

[0028] Furthermore, the method for comprehensive diagnosis and prediction of rice's post-drought water recovery capacity is as follows: calculate the Comprehensive Diagnostic Index (PCI). * The calculation formula is:

[0029] ;

[0030] Where ω1, ω2 and ω3 represent weight coefficients, and ω1+ω2+ω3=1.

[0031] Furthermore, ω1=0.3-0.5, ω2=0.2-0.4, ω3=0.2-0.4.

[0032] Specifically, ω1=0.4, ω2=0.3, ω3=0.3.

[0033] In the method of this invention, net photosynthetic rate directly reflects the carbon assimilation capacity of leaves and is the main phenotypic characteristic of photosynthetic compensation level after rehydration; therefore, it is given a relatively high weight in comprehensive diagnosis. Rubisco enzyme activity is closely related to photosynthetic carbon fixation potential, but its measurement results are easily affected by sample water content and environmental stress; therefore, it is given a moderate weight. Molecular-level indicators can reflect the regulatory status of related metabolic pathways during rehydration, but their expression levels exhibit certain instantaneous fluctuations; therefore, they are also given a moderate weight. By setting the above weighting intervals, biochemical and molecular-level information can be introduced while highlighting the main physiological phenotypic information to reduce the impact of fluctuations in a single indicator on the comprehensive diagnostic results.

[0034] Furthermore, based on the comprehensive diagnostic index PCI * When conducting a comprehensive diagnostic prediction of rice's post-drought re-irrigation recovery capacity, the obtained PCI (Potential Indicator) will be used. * Compare with the standard threshold.

[0035] Optionally, PCI * ≥1.0 indicates a strongly compensated type; 0.75≤PCI * <1.0 indicates a fully recoverable PCI. * <0.75 indicates a recovery from damage.

[0036] The present invention also provides a diagnostic and prediction system for the recovery capacity of rice after drought and water refill based on photosynthesis and molecular indicators, including a data acquisition module, a data processing module and an output module;

[0037] The data acquisition module is used to collect the net photosynthetic rate, Rubisco enzyme activity, and characteristic gene expression levels of each rice leaf.

[0038] The data processing module is used to calculate and obtain the normalized photosynthetic recovery index X. Pn Normalized enzyme activity recovery index X Rubisco The relative expression levels of characteristic genes; also used based on the normalized photosynthetic recovery index X. Pn Normalized enzyme activity recovery index X Rubisco The relative expression level X of the characteristic gene qPCR To conduct a comprehensive diagnosis and prediction of the rice's ability to recover after drought and subsequent irrigation;

[0039] The output module is used to output a comprehensive diagnostic prediction result of the rice's ability to recover after drought and subsequent irrigation.

[0040] Compared with the prior art, the advantages of the present invention are:

[0041] 1. A cross-scale collaborative diagnostic system was established, significantly improving the accuracy of assessing the recovery capacity of rice after drought: This invention integrates macroscopic photosynthetic physiology (P... nThis approach employs a three-dimensional weighted coupling of meso-level biochemical dynamics (Rubisco) and micro-level molecular instructions (qPCR). This comprehensive characterization, encompassing multiple levels from functional output to molecular regulation, effectively overcomes the drawbacks of single indicators being susceptible to transient environmental interference. It ensures that diagnostic results not only possess physiological significance but also have molecular logical support, significantly enhancing stability.

[0042] 2. A unique normalization algorithm addresses diagnostic distortions caused by complex physiological changes: To address nonlinear fluctuations such as increased or decreased Rubisco enzyme activity at the end of drought, this invention introduces a normalized difference algorithm. This algorithm automatically corrects for physiological background noise by calculating the ratio of the difference vector between the post-drought rehydration treatment (CE) and the end-of-drought treatment (CD) to the difference between the control group (CK) and the end-of-drought treatment (CD). Compared to conventional absolute or ratio methods, this method solves the problem of model collapse caused by drought stress data, ensuring PCI accuracy. * The universality of the index under extreme physiological conditions.

[0043] 3. The multi-gene coupling strategy provides extremely high diagnostic robustness: This invention accurately screens six core characteristic gene groups highly related to carbon and nitrogen metabolism, and innovatively uses the arithmetic mean of multi-gene expression levels to construct molecular-level indicators. This strategy effectively eliminates the randomness error of single-gene expression across different varieties or individuals, making molecular-level diagnosis no longer dependent on a single indicator, significantly reducing the misjudgment rate, and giving the model stronger transferability across different rice varieties.

[0044] 4. This invention can be used for evaluating the drought resistance and recovery capacity of rice, screening drought-resistant breeding materials, and making water management decisions under water-saving irrigation conditions. It can also provide a quantitative analysis method for studying the photosynthetic recovery mechanism during drought-rehydration processes, and has good application prospects. Attached Figure Description

[0045] Figure 1 This is a flowchart illustrating the diagnostic and prediction method for rice post-drought rehydration recovery capacity based on photosynthetic and molecular indicators provided by the present invention.

[0046] Figure 2 This figure shows the changes in physiological, biochemical, and molecular indicators of rice under different treatments during drought and re-watering. Figure (a) shows the physiological indicator P. n (a) Changes during treatment; (b) Changes in the biochemical indicator Rubisco activity during treatment; (c) Changes in the molecular-level recovery indicator X. qPCR Changes during the processing.

[0047] Figure 3 PCI * A diagram illustrating the calculation formula. Detailed Implementation

[0048] The technical solution of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] Example

[0050] like Figure 1 As shown in the figure, the specific steps of the method for diagnosing and predicting the recovery capacity of rice after drought based on photosynthesis and molecular indicators provided in this embodiment are as follows.

[0051] Step 1: Rice Cultivation and Water Stress

[0052] 1. Cultivating rice plants

[0053] This example uses the rice variety Nipponbare as the material. After the seeds germinate, they are transplanted into a hydroponic container and cultured using Yoshida complete nutrient solution. The environment is set to alternate between 14 hours of light and 10 hours of darkness, with a temperature of 28℃.

[0054] 2. Design of experimental groups

[0055] In the pre-cultivation stage, rice seeds are disinfected and germinated, then cultured in standard Yoshida nutrient solution until a preset seedling age is reached before transplanting and grouping. The preset seedling age can be 10-18 days after sowing, preferably 12-14 days; or it can be characterized by leaf age as 2.5-4.0 leaf stage, preferably 3.0 leaf stage.

[0056] When transplanting, select seedlings with uniform growth, gently wash away any attached material from the root surface while keeping the root system intact, and fix each seedling in the planting hole so that the roots are completely submerged in the nutrient solution; after transplanting, acclimatize in standard Yoshida nutrient solution for 12-24 hours, and then begin drought simulation and rehydration treatment.

[0057] Control group (CK): Cultured in standard Yoshida nutrient solution throughout. Sampling was performed on days 3, 4, 5, and 7 after treatment.

[0058] Continuous drought group (CD): Some acclimatized plants from the control group were transplanted into a Yoshida nutrient solution containing 15-25% PEG-6000 (preferably 20%) to simulate water stress. Sampling was conducted on days 3, 4, 5, and 7 after the simulated water stress treatment.

[0059] Rehydration recovery group (CE): Following the method of the continuous drought group (CD), the cells were cultured in a nutrient solution containing PEG-6000 to simulate water stress for 3 days, and then transferred to normal standard Yoshida nutrient solution for rehydration treatment. Sampling was performed on days 1, 2, and 4 after rehydration (corresponding to days 4, 5, and 7 of the entire experiment).

[0060] Step 2: Measuring and calculating photosynthetic indices

[0061] At each sampling time point, the photosynthetic rate of the target leaves in the three experimental treatment groups was measured. The photosynthetic rate measurement was conducted without detaching the leaves from the plant to avoid interference with the photosynthetic state during sampling. The photosynthetic indices used in this embodiment include net photosynthetic rate and photosynthetic recovery index X. Pn .

[0062] 1. Obtaining the net photosynthetic rate

[0063] Raw data of net photosynthetic rate of leaves in three experimental groups at different time points were obtained using a portable photosynthesis measurement system. During measurement, a fixed light intensity of 1200 μmol·m⁻¹ was used under an artificial light source. -2 ·s -1 The CO2 concentration was fixed at 400 μmol·mol⁻¹ using a gas path control system. -1 .

[0064] The raw data of the net photosynthetic rate of leaves in the three experimental groups are as follows: Figure 2 As shown in (a), it can be seen that the photosynthetic rate after rehydration does not fluctuate steadily over time, and it drops on the fifth day.

[0065] 2. Constructing the photosynthetic recovery index X Pn

[0066] Based on the raw data of leaf net photosynthetic rate obtained in step 1 for the three experimental groups, the photosynthetic recovery index was calculated for the control group (CK), the end of the prolonged drought (CD), and the recovery period after rehydration (CE). The normalized photosynthetic recovery index X was constructed according to the following formula. Pn :

[0067] ;

[0068] Where P nCK P nCE and P nCD The figures represent the net photosynthetic rates of rice leaves at the same sampling time point: control group (CK), rehydration and recovery group (CE), and end of drought (CD). The end of drought (CD) refers to the time point before the start of rehydration treatment, i.e., day 3 of the drought treatment.

[0069] 3. Quick-freeze the leaves with liquid nitrogen and preserve them.

[0070] After the photosynthesis measurement was completed, the corresponding leaf tissue was immediately cut and quick-frozen in liquid nitrogen. Then, it was transferred to a -80°C freezer for storage.

[0071] Step 3: Obtain Rubisco enzyme activity and construct enzyme activity recovery index X. Rubisco

[0072] 1. Obtain Rubisco enzyme activity

[0073] (1) Take out the detached leaf sample that has been frozen by liquid nitrogen from the -80℃ freezer, grind it into powder by liquid nitrogen, weigh 0.1 g of sample, add 0.25 mL of extraction medium, grind it into a homogenate on an ice bath, transfer it to a centrifuge tube, centrifuge at 10000 g for 10 min at 4℃, and the supernatant is the crude enzyme solution.

[0074] Take 20 μL of crude enzyme solution, add 7 μL of phosphoglycerate kinase solution and 7 μL of glyceraldehyde-3-phosphate dehydrogenase solution, then add 180 μL of working reaction solution, mix well, and record the absorbance at 340 nm at 20 s and 5 min 20 s, denoted as A. 空白20s A 空白5min20s Calculate A 测定 =A 空白20s - A 空白5min20s .

[0075] The blank tube was treated with distilled water instead of the supernatant, and the same procedure was performed on both tubes simultaneously. The absorbance values ​​were recorded as A. 空白20s A 空白5min20s ΔA 空白 =A 空白20s - A 空白5min20s ΔA 测定 =A 测定 - ΔA 空白 .

[0076] (2) The total activity of Rubisco is calculated according to the following formula.

[0077] Rubisco (U / g) = [ΔA] 测定 ×V 反总 / (ε×d)×10 9 ] / (V 样 / V 样总 ×M) / T;

[0078] The Rubisco unit U / g above means: one unit of enzyme activity is the oxidation of 1 nmol NADH per minute per gram of tissue at 25°C.

[0079] In the above formula, V 反总 This represents the total volume of the reaction system, 0.214 × 10⁻⁶.-3 L; ε represents the NADH molar extinction coefficient, 6220 L / (mol·cm); d represents the optical path length of the 96-well plate, 0.6 cm; V 样 This indicates the volume of sample added, 0.02 mL; V 样总 The volume of extract added is 0.25 mL; T represents the reaction time, 5 min; M represents the sample mass, g; 10 9 This indicates unit conversion: 1 mol = 10 9 nmol.

[0080] The results are as follows Figure 2 As shown in (b), the Rubisco enzyme activity varies under different treatments. Among them, the samples under drought treatment (continuous drought group CD) showed higher enzyme activity levels per unit fresh weight, which may be related to the concentration mechanism after drought dehydration.

[0081] 2. Constructing the enzyme activity recovery index X Rubisco

[0082] Based on the Rubisco enzyme activity data obtained in step 1, the enzyme activity index values ​​of the control group (CK), the continuous drought group (CD), and the rehydration recovery group (CE) on day 3 were calculated, and the normalized enzyme activity recovery index X was constructed according to the following formula. Rubisco :

[0083] ;

[0084] Among them, Act CK Act CE and Act CD The values ​​represent the Rubisco enzyme activity in rice leaves of the control group (CK) and the rehydration and recovery group (CE) at the same sampling time point, and the Rubisco enzyme activity in rice leaves of the continuous drought group (CD) at the end of the drought treatment. The end of the drought treatment refers to the time point before the start of the rehydration treatment, i.e., the third day of continuous drought.

[0085] Step 4: Obtain the expression levels of characteristic genes and construct the molecular recovery index X. qPCR

[0086] 1. Obtaining the expression levels of characteristic genes:

[0087] Take an appropriate amount of quick-frozen leaf samples, grind them thoroughly into a fine powder under liquid nitrogen conditions, add TRIzol reagent for lysis, and complete the phase separation and RNA precipitation steps according to the standard operating procedure of TRIzol reagent to obtain total RNA. The obtained RNA is dissolved in RNase-free water for later use.

[0088] The concentration and purity of extracted total RNA were determined using a NanoDrop 2000 micro spectrophotometer, and RNA integrity was assessed by agarose gel electrophoresis and an automated nucleic acid analysis system based on an Agilent 5300 bioanalyzer to obtain RNA integrity numbers (RQN). RNA samples meeting the requirements for subsequent molecular assays were used for reverse transcription and real-time quantitative PCR analysis. Using qualified total RNA as a template, a commercially available reverse transcription kit was used to synthesize cDNA under suitable temperature conditions. The resulting cDNA was used as a template for subsequent real-time quantitative PCR amplification.

[0089] The characteristic genes selected were LOC_Os05g42350, LOC_Os10g41780, LOC_Os04g59440, LOC_Os08g44340, LOC_Os01g11054, and LOC_Os01g64120. Specific primers were designed for these characteristic genes, as shown in Table 1. After verification of specificity and amplification efficiency, the primers were used for real-time quantitative PCR detection.

[0090] Table 1 Amplification Primers

[0091]

[0092] 2. Molecular recovery index X qPCR Construction

[0093] Using samples from the end of the continuous drought treatment (CD) as a control, the relative expression levels of each characteristic gene in samples from the rehydration recovery period (CE) were calculated using the ΔΔCt method. The obtained relative expression levels were used to characterize the degree of molecular-level recovery after rehydration treatment.

[0094] When a single characteristic gene is selected, its relative expression level is used as the molecular-level recovery index X. qPCR When multiple characteristic genes are selected, X qPCR By taking the arithmetic mean of the relative expression levels of multiple characteristic genes, the uncertainty caused by environmental fluctuations affecting a single gene is reduced, thereby improving the stability and robustness of molecular indicators.

[0095] The arithmetic mean of the relative expression levels of the six characteristic genes was taken as X. qPCR The results are shown in Table 2 and Figure 2 As shown in (c).

[0096] Table 2. Relative expression levels of core characteristic genes and X during the rice rehydration process. qPCR

[0097]

[0098] Step 5: Based on the normalized indicators of the above physiological, biochemical, and molecular dimensions, namely the photosynthetic recovery index X. Pn Enzyme activity recovery index X Rubisco and molecular recovery index X qPCR Construct a comprehensive diagnostic index PCI * .

[0099] 1. Assigning indicator weights

[0100] Configure PCI * The calculation formula is:

[0101] ;

[0102] Where ω1=0.4, ω2=0.3, ω3=0.3, the result is as follows Figure 3 As shown in Table 2.

[0103] 2. Capacity Prediction and Classification

[0104] Calculated PCI * The original indicators were converted into standardized quantitative values, and rice at different rehydration times was graded and diagnosed. The results are shown in Table 3 below.

[0105] Table 3. Comprehensive Data Table for Diagnostic of Rice Seedling Stage Hydroponic Rehydration and Recovery Capacity

[0106]

[0107] Note: P n The unit is µmol·m -2 ·s -1 Rubisco activity units are (based on fresh sample, U / g); 2 -ΔΔCt It is dimensionless.

[0108] In this embodiment, PCI on day 1 of rehydration * The value is 0.79, at which point the biochemical indicator X... Rubisco Although it is still in a negative value (-0.55), due to X Pn It exhibits extremely strong overcompensation (1.67), causing the composite index to climb rapidly to a high level.

[0109] This diagnostic result occurred significantly earlier than the point at which plant biomass showed a clear difference, allowing for an accurate prediction at least 72 hours in advance of the rice's potential for excess compensation during the later stages of rehydration.

[0110] 3. Model Validation

[0111] (1) The comprehensive diagnostic index PCI calculated during the rehydration period *The results showed that the rehydration treatment had a significantly higher compensation potential than the continuous drought treatment and the control treatment.

[0112] (2) The dry weight of individual plants on the 12th day after treatment was used as a phenotypic indicator of later growth to characterize the growth recovery effect of rice after rehydration. The measured dry weights of individual plants were: 0.22 g for the control group (CK), 0.17 g for the drought-prone group (CD), and 0.23 g for the rehydration group (CE). The dry weight results showed that the dry weight of individual rice plants after rehydration treatment was higher than that of the drought-prone group and not lower than that of the control group with normal water supply, which reflects the promoting effect of rehydration treatment on later growth.

[0113] This indicates that PCI * Higher treatments corresponded to higher single-plant dry weight on day 12, and the two showed a consistent trend.

[0114] The above test results demonstrate the main features and advantages of this invention. Compared with traditional evaluation methods that rely on a single physiological indicator or require waiting until the end of crop growth to measure biomass, this invention can utilize only the net photosynthetic rate in the early rehydration stage, the activity of the core metabolic enzyme Rubisco, and the molecular-level recovery indicator X. qPCR The recovery potential of plants can be accurately diagnosed through a three-dimensional coupling model.

[0115] On day 1 of rehydration (day 4 of the experiment), the comprehensive diagnostic index PCI was... * When the dry weight ratio reaches 0.79, it can be accurately predicted that the variety has the potential for supercompensatory growth. Actual dry weight data (CE group dry weight 0.23 g, significantly higher than CK group 0.22 g and CD group 0.17 g) also verify this. This advances the diagnostic time by more than 5 days compared to the traditional dry weight measurement method.

[0116] This invention effectively avoids the dependence of traditional models on complex environmental parameters, eliminates the interference of physiological fluctuations in the control group on the evaluation system by using a normalization algorithm, and effectively offsets the random noise of single gene transcription level by combining the arithmetic mean of the expression levels of six characteristic genes. It fills the gap in the early multidimensional evaluation method of rehydration under stress. While ensuring that the model has strong robustness and accuracy, it also has clear biological mechanism interpretability, providing a scientific and efficient digital decision-making tool for rice drought-resistant breeding and cultivation management.

[0117] The above detailed embodiments describe the implementation of the present invention; however, the present invention is not limited to the specific details described in the above embodiments. Within the scope of the claims and technical concept of the present invention, various simple modifications and changes can be made to the technical solution of the present invention, and these simple modifications all fall within the protection scope of the present invention.

Claims

1. A method for diagnosing and predicting the post-drought rehydration recovery capacity of rice based on photosynthetic and molecular indicators, characterized in that, Includes the following steps: Rice plants were subjected to continuous drought treatment and post-drought rehydration treatment, with a control group set up; leaf samples were collected at different treatment periods. The net photosynthetic rate, Rubisco enzyme activity, and expression levels of characteristic genes for each leaf were measured. The normalized photosynthetic recovery index X was calculated based on the net photosynthetic rate. Pn The normalized enzyme activity recovery index X was calculated based on the Rubisco enzyme activity. Rubisco The relative expression level of the corresponding characteristic gene is calculated based on the expression level of the characteristic gene. Based on the normalized photosynthetic recovery index X Pn Normalized enzyme activity recovery index X Rubisco The relative expression level X of the characteristic gene qPCR To conduct a comprehensive diagnosis and prediction of the rice's ability to recover after drought and subsequent irrigation.

2. The method for diagnosing and predicting the post-drought rehydration capacity of rice based on photosynthetic and molecular indicators according to claim 1, characterized in that, The normalized photosynthetic recovery index X Pn The calculation formula is: ； Among them, P nCK P nCE and P nCD The net photosynthetic rates of rice leaves at the control group, the post-drought rehydration treatment, and the end of the continuous drought treatment are respectively.

3. The method for diagnosing and predicting the post-drought rehydration capacity of rice based on photosynthetic and molecular indicators according to claim 1, characterized in that, The normalized enzyme activity recovery index X Rubisco The calculation formula is: ； Among them, Act CK Act CE and Act CD The values ​​represent the Rubisco enzyme activity in rice leaves at the control group, the post-drought rehydration treatment, and the end of the continuous drought treatment, respectively, and Act... CK - Act CD ≠0.

4. The method for diagnosing and predicting the post-drought rehydration recovery capacity of rice based on photosynthetic and molecular indicators according to claim 1, characterized in that, The characteristic genes selected for determining the expression level of the characteristic genes are one or more of the following: LOC_Os05g42350, LOC_Os10g41780, LOC_Os04g59440, LOC_Os08g44340, LOC_Os01g11054, and LOC_Os01g64120.

5. The method for diagnosing and predicting the post-drought rehydration capacity of rice based on photosynthetic and molecular indicators according to claim 4, characterized in that, When at least two characteristic genes are selected, the relative expression level X of the characteristic genes is... qPCR The arithmetic mean, geometric mean, or weighted mean of the relative expression levels of all the described characteristic genes.

6. The method for diagnosing and predicting the post-drought rehydration recovery capacity of rice based on photosynthetic and molecular indicators according to claim 1, characterized in that, The relative expression level X of the characteristic gene qPCR The calculation method is as follows: using leaf samples at the end of the continuous drought treatment as the control baseline, the relative expression level of rice leaves after the drought-induced re-watering treatment was calculated using the ΔΔCt method. -ΔΔCt , that is, X qPCR .

7. The method for diagnosing and predicting the post-drought rehydration recovery capacity of rice based on photosynthetic and molecular indicators according to claim 1, characterized in that, The method for comprehensive diagnosis and prediction of rice's post-drought re-irrigation recovery capacity is as follows: Calculate the Comprehensive Diagnostic Index (PCI). * The calculation formula is: ； Where ω1, ω2 and ω3 represent weight coefficients, and ω1+ω2+ω3=1.

8. The method for diagnosing and predicting the post-drought rehydration recovery capacity of rice based on photosynthetic and molecular indicators according to claim 7, characterized in that, ω1=0.3-0.5, ω2=0.2-0.4, ω3=0.2-0.

4.

9. The method for diagnosing and predicting the post-drought rehydration capacity of rice based on photosynthetic and molecular indicators according to claim 8, characterized in that, ω1=0.4, ω2=0.3, ω3=0.

3.

10. A diagnostic and predictive system for rice post-drought rehydration capacity based on photosynthetic and molecular indicators, characterized in that, It includes a data acquisition module, a data processing module, and an output module; The data acquisition module is used to collect the net photosynthetic rate, Rubisco enzyme activity, and characteristic gene expression levels of each rice leaf. The data processing module is used to calculate and obtain the normalized photosynthetic recovery index X. Pn Normalized enzyme activity recovery index X Rubisco The relative expression levels of characteristic genes; also used based on the normalized photosynthetic recovery index X. Pn Normalized enzyme activity recovery index X Rubisco The relative expression level X of the characteristic gene qPCR To conduct a comprehensive diagnosis and prediction of the rice's ability to recover after drought and subsequent irrigation; The output module is used to output a comprehensive diagnostic prediction result of the rice's ability to recover after drought and subsequent irrigation.

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