Corn drought-resistant water-saving variety leaf shape related microRNA probe, detection method and application thereof

Abstract

The application discloses a corn drought-resistant water-saving variety leaf shape related microRNA probe and a detection method and application thereof. The application solves the problem of predicting and regulating corn drought resistance. The application specifically discloses the following any one of the applications of miRNA or a substance for detecting the miRNA: A1) in detecting or assisting in detecting the width-length ratio of a leaf of a plant in the family Poaceae and / or in preparing a product for detecting or assisting in detecting the width-length ratio of a leaf of a plant in the family Poaceae; A2) in detecting or assisting in detecting the leaf area of a plant in the family Poaceae and / or in preparing a product for detecting or assisting in detecting the leaf area of a plant in the family Poaceae; and A3) in detecting or assisting in detecting the drought resistance of a plant in the family Poaceae and / or in preparing a product for detecting or assisting in detecting the drought resistance of a plant in the family Poaceae, wherein the miRNA sequence is shown in sequence 1. The expression amount of the miRNA is detected to identify the width-length ratio and the leaf area of a leaf of a corn variety to be detected, and the corn variety to be detected can be used in industrial production.

Description

TECHNICAL FIELD

[0001] The present application relates to a corn drought-tolerant water-saving variety, and in particular to a microRNA probe related to leaf morphology of the corn drought-tolerant water-saving variety and a detection method and application thereof. BACKGROUND

[0002] At present, global agricultural water accounts for 70% of total water consumption. The global population has increased from 5 billion in 1990 to more than 7.5 billion at present, and is expected to increase to 9.7 billion to 10 billion by 2050. The demand for food will increase by 60% by 2050, which requires more agricultural water (World Water Resources Assessment Program). Corn is a water-intensive crop. From 1983 to 2009, droughts caused a 7% (0.15 tons / ha) loss in corn production worldwide. China is the second largest corn producer in the world. Corn accounts for 41 million hectares of land each year, with a yield of 261 million tons of corn, accounting for 23.18% of the world's grain production (China National Bureau of Statistics, 2020). In China, about 60% of corn planting areas are under water stress, causing a 20-30% reduction in yield each year. Therefore, it is very important to select drought-tolerant corn varieties, but drought is a quantitative trait controlled by multiple genes, and few single genes significantly enhance the drought tolerance of plants. Conventional methods for determining whether corn is drought-tolerant mainly rely on phenotypes and yields after drought stress, which is time-consuming and labor-intensive and is greatly affected by the cultivation environment. miRNA is a highly conserved small molecule in terrestrial plants that can simultaneously target and regulate hundreds of genes. Therefore, finding an miRNA probe technology to identify drought sensitivity of crops is a new direction for corn drought-tolerant cultivation and breeding screening. SUMMARY

[0003] The present application solves the technical problem of how to provide a corn drought-tolerant related molecular marker and a method for regulating the drought tolerance, leaf width-to-length ratio, and leaf area of corn.

[0004] To solve the above technical problem, the present application provides the following applications.

[0005] The application of miRNA, a substance for regulating the expression of the coding gene of the miRNA, or a substance for regulating the activity or content of the miRNA in any of the following:

[0006] A1) for regulating the leaf width-to-length ratio of a plant in the family Poaceae and / or for preparing a product for regulating the leaf width-to-length ratio of a plant in the family Poaceae;

[0007] A2) for regulating the leaf area of a plant in the family Poaceae and / or for preparing a product for regulating the leaf area of a plant in the family Poaceae;

[0008] A3) for plant breeding and / or for preparing a plant breeding product;

[0009] The RNA is any one of the following:

[0010] B1) the nucleotide sequence is the RNA molecule shown in Sequence 1;

[0011] B2) an RNA molecule having 80% or more identity to the nucleotide shown in B1) and having the same function obtained by substitution and / or deletion and / or addition of nucleotide residues of the protein shown in B1);

[0012] B3) a fusion RNA molecule obtained by connecting a N-terminal or / and C-terminal tag of B1) or B2).

[0013] In the above, the regulation can be knock-out or inhibition or reduction or down-regulation, or up-regulation or enhancement or increase.

[0014] In the above, the substance that knocks out or inhibits or reduces or down-regulates the expression of the gene encoding the RNA or the substance that knocks out or inhibits or reduces or down-regulates the activity or content of the RNA can reduce the width-length ratio of the leaf of the plant.

[0015] The substance that knocks out or inhibits or reduces or down-regulates the expression of the gene encoding the RNA or the substance that knocks out or inhibits or reduces or down-regulates the activity or content of the RNA can reduce the leaf area of the plant.

[0016] The substance that up-regulates or enhances or increases the expression of the gene encoding the RNA or the substance that up-regulates or enhances or increases the activity or content of the RNA can increase the width-length ratio of the leaf of the plant.

[0017] The substance that up-regulates or enhances or increases the expression of the gene encoding the RNA or the substance that up-regulates or enhances or increases the activity or content of the RNA can increase the leaf area of the plant.

[0018] In the above, the leaf can be a mature leaf.

[0019] In the above, the leaf can be the 11th-20th leaf.

[0020] In the above RNA, the 80% or more identity can be at least 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 95%, 96%, 98%, 99% or 100% identity.

[0021] In the above RNA, Sequence 1 (SEQ ID No. 1) consists of 21 nucleotide residues. It is named zma-miR171f or miR171f. Its encoding gene is miR171f gene.

[0022] In the above, the index of the breeding can be the width-length ratio of the leaf or the leaf area.

[0023] In the above use, the protein is derived from corn.

[0024] In the above use, the substance that regulates the expression of the gene encoding the protein is a substance that regulates at least one of the following six types of regulation: 1) regulation at the transcription level of the gene; 2) regulation after the transcription of the gene (i.e., regulation of splicing or processing of the primary transcript of the gene); 3) regulation of RNA transport of the gene (i.e., regulation of mRNA transport from the nucleus to the cytoplasm of the gene); 4) regulation of translation of the gene; 5) regulation of mRNA degradation of the gene.

[0025] In the above use, the substance that regulates the expression of the gene encoding the protein is any one of the following:

[0026] D1) a nucleic acid molecule that regulates the expression of the gene encoding the above RNA;

[0027] D2) a gene that encodes the nucleic acid molecule of D1);

[0028] D3) an expression cassette that contains the gene of D2);

[0029] D4) a recombinant vector that contains the gene of D2), or a recombinant vector that contains the expression cassette of D3);

[0030] D5) a recombinant microorganism that contains the gene of D2), or a recombinant microorganism that contains the expression cassette of D3), or a recombinant microorganism that contains the recombinant vector of D4);

[0031] D6) a transgenic plant cell line that contains the gene of D2), or a transgenic plant cell line that contains the expression cassette of D3), or a transgenic plant cell line that contains the recombinant vector of D4);

[0032] D7) a transgenic plant tissue that contains the gene of D2), or a transgenic plant tissue that contains the expression cassette of D3), or a transgenic plant tissue that contains the recombinant vector of D4);

[0033] D8) a transgenic plant organ that contains the gene of D2), or a transgenic plant organ that contains the expression cassette of D3), or a transgenic plant organ that contains the recombinant vector of D4).

[0034] D1) The nucleic acid molecule can be easily modified by a person skilled in the art using known methods, such as directed evolution or point mutation. Those nucleotides which are artificially modified, have 80% or more identity with the sequence of the nucleic acid molecule isolated from the application and have the function of controlling the expression of the gene encoding the RNA, are derived from the nucleotide sequence of the application and are equivalent to the sequence of the application.

[0035] The 80% or more identity can be 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identity.

[0036] In this context, identity refers to the identity of the amino acid sequence or the nucleotide sequence. The identity of the amino acid sequence can be determined using the homology search site on the Internet, such as the BLAST page of the NCBI homepage. For example, the value of identity (%) can be obtained by performing a search in Advanced BLAST 2.1 using blastp as the program, setting the Expect value to 10, setting all Filters to OFF, using BLOSUM62 as the Matrix, and setting Gap existence cost, Per residue gap cost and Lambda ratio to 11, 1 and 0.85 (default values), respectively, and then calculating the identity of the amino acid sequence.

[0037] To solve the above technical problem, the application also provides a method for breeding a Gramineae plant with low leaf width-length ratio or leaf area.

[0038] The method comprises knocking out or inhibiting or reducing or down-regulating the expression of the gene encoding the above-mentioned RNA in the target Gramineae plant, and / or the activity and / or content of the above-mentioned RNA, to obtain a Gramineae plant with low leaf width-length ratio or leaf area, wherein the leaf width-length ratio or leaf area of the Gramineae plant with low leaf width-length ratio or leaf area is lower than that of the target Gramineae plant.

[0039] The above-mentioned method, wherein the knocking out or inhibiting or reducing or down-regulating the expression of the gene encoding the above-mentioned RNA in the Gramineae plant comprises introducing a gene knockout vector targeting sequence 3 into the target plant.

[0040] To solve the above technical problem, the application also provides a method for breeding a Gramineae plant with high leaf width-length ratio or leaf area.

[0041] The method comprises up-regulating or enhancing or increasing the expression of the gene encoding the above-mentioned RNA in the purpose grass plant, and / or the activity and / or content of the above-mentioned RNA is obtained in the grass plant with low leaf width-length ratio or leaf area, wherein the leaf width-length ratio or leaf area of the grass plant with high leaf width-length ratio or leaf area is higher than that of the purpose grass plant.

[0042] The method, wherein the up-regulation or enhancement or increase of the expression of the gene encoding the above-mentioned RNA in the grass plant comprises introducing the above-mentioned nucleic acid molecule or the gene or the vector into the purpose plant.

[0043] To solve the above-mentioned technical problem, the application further provides the following application.

[0044] The application of the miRNA or the substance for detecting the miRNA is as follows:

[0045] A1) the application in detecting or assisting in detecting the drought resistance of the grass plant and / or in preparing a product for detecting or assisting in detecting the drought resistance of the grass plant;

[0046] A2) the application in detecting or assisting in detecting the leaf width-length ratio of the grass plant and / or in preparing a product for detecting or assisting in detecting the leaf width-length ratio of the grass plant;

[0047] A3) the application in detecting or assisting in detecting the leaf area of the grass plant and / or in preparing a product for detecting or assisting in detecting the leaf area of the grass plant;

[0048] The miRNA sequence is shown in SEQ ID NO. 1.

[0049] In the above, the index of the drought resistance can be the leaf width-length ratio, the leaf area, the ASI, the in-vitro water loss rate, the rehydration survival rate, the relative water content of the leaf, the net photosynthetic rate of the leaf, the transpiration rate of the leaf, and the intercellular CO2 concentration of the leaf.

[0050] In the above, the substance for detecting the miRNA can be a substance for detecting the expression amount of the miRNA.

[0051] To solve the above-mentioned technical problem, the application further provides a product.

[0052] The product contains the above-mentioned substance for detecting the expression amount of the miRNA, and can be any one of the following G1) to G3):

[0053] G1) the application in detecting or assisting in detecting the drought resistance of the grass plant and / or in preparing a product for detecting or assisting in detecting the drought resistance of the grass plant;

[0054] G2) detecting or assisting in detecting the width-length ratio of the leaf of the plant of the family Poaceae and / or preparing a product for detecting or assisting in detecting the width-length ratio of the leaf of the plant of the family Poaceae;

[0055] G3) use in detecting or assisting in detecting the leaf area of the plant of the family Poaceae and / or preparing a product for detecting or assisting in detecting the leaf area of the plant of the family Poaceae.

[0056] In order to solve the above technical problems, the present application further provides a method for identifying or assisting in identifying the drought tolerance of the plant of the family Poaceae.

[0057] The method comprises detecting the expression amount of the above-mentioned miRNA in the high plant of the family Poaceae to be detected, and identifying or assisting in identifying the drought tolerance of the plant of the family Poaceae according to the expression of the miRNA of the plant of the family Poaceae to be detected;

[0058] The drought tolerance of the plant of the family Poaceae with high expression amount of the miRNA is higher than or is likely to be higher than the drought tolerance of the plant of the family Poaceae with low expression amount of the miRNA.

[0059] In order to solve the above technical problems, the present application further provides a method for identifying or assisting in identifying the width-length ratio of the leaf of the plant of the family Poaceae.

[0060] The method comprises detecting the expression amount of the above-mentioned miRNA in the high plant of the family Poaceae to be detected, and identifying or assisting in identifying the width-length ratio of the leaf of the plant of the family Poaceae according to the expression of the miRNA of the plant of the family Poaceae to be detected;

[0061] The width-length ratio of the leaf of the plant of the family Poaceae with high expression amount of the miRNA is greater than or is likely to be greater than the width-length ratio of the leaf of the plant of the family Poaceae with low expression amount of the miRNA.

[0062] In order to solve the above technical problems, the present application further provides a method for identifying or assisting in identifying the leaf area of the plant of the family Poaceae.

[0063] The method comprises detecting the expression amount of the above-mentioned miRNA in the high plant of the family Poaceae to be detected, and identifying or assisting in identifying the leaf area of the plant of the family Poaceae according to the expression of the miRNA of the plant of the family Poaceae to be detected;

[0064] The leaf area of the plant of the family Poaceae with high expression amount of the miRNA is greater than or is likely to be greater than the leaf area of the plant of the family Poaceae with low expression amount of the miRNA.

[0065] In the above, the plant of the family Poaceae can be a plant of the genus Zea. The plant of the genus Zea can be a plant of the species Zea mays. The plant of the species Zea mays can be corn variety JN1205, corn variety FM985, corn variety HY189, corn variety TN9, corn variety ZY8911, corn variety DK159, corn variety ZD958, corn variety ND108, corn variety LY99, corn variety XY335, corn variety XD20, corn variety JK968, corn variety JD50, corn variety HD188, corn variety NH101, corn variety LP206, corn variety XY1225, corn variety DH710, corn variety DH3737, corn variety DK517, corn variety XY1466, corn variety ND372, corn variety JND935, or corn variety DH518.

[0066] Beneficial effects

[0067] The application discloses a corn drought-resistant water-saving variety midrib shape related microRNA probe and a detection method and application thereof. The application solves the problem of predicting and regulating corn drought resistance. The application specifically discloses the following any one of the applications of miRNA or a substance for detecting the miRNA: A1) in detecting or assisting in detecting the width-length ratio of a leaf of a plant of the family Poaceae and / or in preparing a product for detecting or assisting in detecting the width-length ratio of a leaf of a plant of the family Poaceae; A2) in detecting or assisting in detecting the leaf area of a plant of the family Poaceae and / or in preparing a product for detecting or assisting in detecting the leaf area of a plant of the family Poaceae; and A3) in detecting or assisting in detecting the drought resistance of a plant of the family Poaceae and / or in preparing a product for detecting or assisting in detecting the drought resistance of a plant of the family Poaceae, wherein the miRNA sequence is shown in sequence 1. The width-length ratio and the leaf area of a leaf of a corn variety to be detected are identified by detecting the expression amount of the miRNA, and the corn variety to be detected can be used for industrial production. BRIEF DESCRIPTION OF DRAWINGS

[0068] Figure 1Phenotypic and physiological responses of different drought-resistant maize varieties under ample water and drought conditions are shown in the diagrams. A: Plants were fully watered within 10 days of germination, then watering was stopped for 7 days after drought, during which time photographs were taken and samples were collected. B: Plants in the drought group were not watered for 15 days, then re-watered for 3 days, during which time photographs were taken. C: Relative leaf water content (RWCs) of three different maize varieties under drought and ample water conditions. D: Comparison of survival rates among different maize varieties 7 days after re-watering. E: Average leaf area of ​​the first three leaves of maize seedlings. F: Water loss rate of the third leaf of maize seedlings. The V value is Mean ± SD (n ≥ 3). For each treatment, the differences in the bar graphs of different letters were significant (P ≤ 0.05). This section mainly focuses on the initial screening of more than 50 widely cultivated maize varieties nationwide, followed by the identification of three different drought-resistant maize varieties, and seedling screening. During the three-leaf-one-heart stage, the seeds are grown in flowerpots in a greenhouse, with daytime temperatures between 28℃ and 30℃, nighttime temperatures between 20℃ and 22℃, and relative humidity between 50% and 80%.

[0069] Figure 2 A: Phylogenetic clustering diagram showing the drought-significant miRNAs in three maize varieties for whole-miRNA analysis; B: Principal component analysis of UMI for the three maize varieties; C: Trends in the expression of marker miRNAs from the miR399, miR166, miR169, and miR171 families; D: Heatmap showing the expression levels of differentially expressed miRNAs in the three maize genotypes (n=3), with color bars indicating log2 (UMI reads) between different genotypes, and upper color bars representing relative gene expression levels, where red, black, and green represent high, medium, and low gene expression levels, respectively; E: Number and possible functions of each type of miRNA, including known miRNAs (…). Figure 1 (F) and new miRNAs ( Figure 1 V-ENN plot analysis was performed on the upregulation and downregulation of G in three maize genotypes. The three drought-resistant or non-drought-resistant materials were selected. Before sowing, seeds were surface-sterilized in 10% H2O2 for 20 min, then rinsed five times with distilled water for 2 min each time. They were then soaked in distilled water for 8 hours. Seeds were grown in pots in a greenhouse with daytime temperatures between 28℃ and 30℃, nighttime temperatures between 20℃ and 22℃, and relative humidity between 50% and 80%. At the fully expanded third leaf stage, after 7 days of normal watering and 7 days of drought treatment, aboveground parts were harvested for UMI sequencing.

[0070] Figure 3Figure 1. Heatmap of drought-specific miRNAs involved in leaf development; A: The expression levels of up- or down-regulated miRNAs in three different types of maize under drought stress, the histogram shows the miRNA levels, and the line graph shows the predicted target gene expression levels, abbreviations are SV: sensitive variety; SV_D: sensitive variety under drought; MV: moderate variety; MV_D: moderate variety under drought; RV: drought-resistant variety; RV_D: drought-resistant variety under drought. B: 14 groups of published data show that these significantly different miRNAs are mainly involved in leaf morphogenesis, and miRNAs are highly conserved in maize, soybean, cotton, Arabidopsis, wheat and tomato. The key miRNAs with significant changes in UMI sequencing were analyzed.

[0071] Figure 4 Figure 2. Leaf morphology of wild type and mutant maize plants regulated by miR171f; A: ear leaf phenotype, B: relative expression levels of wild type and mutant maize plants, miR171f regulates the length / width ratio (Figure C) and leaf area (Figure D) of maize leaves, the values are Mean±SD, ***P<0.001, **P<0.01, *P<0.05.

[0072] Figure 5 Figure 3. Leaf water loss rate, flowering-silking interval (ASI) and grain yield of different maize varieties; A: 3 leaves of each type were measured continuously for 24h (n=3), and maize varieties were divided into drought-resistant (red), moderate (black) and drought-sensitive (green) types according to 24h water loss rate, vertical lines are Mean±SD, B: There is a significant negative correlation between three-leaf water loss rate and membership function value, (C) Data represent ASI of each maize variety, the values are Mean±SE (n≥22), C: There is a significant correlation between ASI and MFV. ****P<0.001, **P<0.01. The indicators of 24 varieties under normal conditions were screened at the mature stage.

[0073] Figure 6 Figure 4. Significant correlation between leaf width / length ratio and membership function value; A: Leaf length and width were measured for the 4th to 19th leaves to calculate the leaf width / length ratio, B: Relationship between membership function value and leaf width / length ratio, the data used for regression analysis are the average values of membership function value and leaf width / length ratio of 24 genotypes, the values are Mean±SD (n≥5), (*P<0.05, **P<0.01, ***P<0.001).

[0074] Figure 7Phenotypic diagrams of drought and rehydration processes for three maize varieties were generated. Under adequate moisture conditions (maize seedlings were raised to the three-leaf stage under normal moisture conditions, and then watering was stopped), a 15-day drought treatment was applied. Phenotypic data were recorded at 1, 3, 5, 7, 9, 11, 13, and 15 days. Recovery growth was then recorded three days after rehydration. The phenotypes of the drought process during variety selection were also presented.

[0075] Figure 8 The graphs show leaf length and width at different leaf positions under normal moisture conditions; the left bar chart shows the leaf length of the first three leaves of the corn seedling, and the right bar chart shows the leaf width of the first three leaves. The V value is Mean ± SD (n = 3). For each treatment, the differences between the different letters were significant (P ≤ 0.05). When selecting the three varieties at the three-leaf stage, the length and width of the three leaves were measured respectively.

[0076] Figure 9 The distribution characteristics of miRNA sequences are shown below; (SV, MV, and RV represent drought-sensitive, intermediate, and drought-resistant varieties under normal irrigation conditions, respectively; SV_D, MV_D, and RV_D represent drought-sensitive, intermediate, and drought-resistant varieties under drought conditions, respectively.)

[0077] Figure 10 A graph showing leaf photosynthesis, biomass, and economic yield reflecting drought sensitivity; A: 24 different maize varieties were grown in an electric greenhouse. Drought stress treatments were administered 15 days after sowing. Plant height and ear height were measured during R1. Chlorophyll content (SPAD) was measured during R2. Photosynthetic parameters were measured during R3. Dry matter weight, yield, and yield components were measured during R6. Photosynthetic parameters under water-sufficient and drought stress conditions (…) Figure 10 The area represents the difference between different varieties under conditions of sufficient water and drought stress (BD), and the V value is Mean±SD (n=5). E: drought resistance coefficient of net photosynthetic rate. Figure 10 Drought resistance coefficient (SPAD) of different maize varieties (FH). This dot plot represents the drought resistance coefficient of the 12th, 13th, and 14th leaves, and the bar chart represents the chlorophyll content under drought conditions. The average drought resistance coefficient of the 12th, 13th, and 14th leaves (FH) Figure 10 (I). Different maize varieties are arranged from largest to smallest according to their drought resistance coefficient. Figure 10 (J). DRC, drought resistance coefficient; PH, plant height; EH, ear height; DW, dry weight; ED, ear diameter; EL, ear length; KN, number of grains per row; TKW, thousand-grain weight. Physiological indicators, photosynthesis, biomass, and economic yield, measured under normal and drought conditions at maturity.

[0078] Figure 11Figure A: Classification of drought resistance of different maize varieties by DRC; Figure B: Cluster analysis of different maize varieties according to drought resistance coefficients of 12 traits using Euclidean distance as the clustering method.

[0079] Figure 12 Figure A: Classification of drought resistance of different maize varieties by DRC; Figure B: Cluster analysis of different maize varieties according to drought resistance coefficients of 12 traits using Euclidean distance as the clustering method. DETAILED DESCRIPTION

[0080] The present application is further described in detail by the specific embodiments below, and the examples given are only to illustrate the present application, not to limit the scope of the present application. The examples provided below can serve as a guide for further improvement by those skilled in the art, and do not constitute any limitation on the present application in any way.

[0081] The experimental methods in the following examples are all routine methods, unless otherwise specified, which are performed according to the techniques or conditions described in the literature in the art or according to the product instructions. The materials, reagents, etc. used in the following examples, unless otherwise specified, can be obtained commercially.

[0082] The IBM SPSS Statistics 26 statistical software is used to process the data in the following examples, and the experimental results are expressed as mean ± standard deviation. One-way ANOVA test is used, P<0.05 (*) indicates significant difference, P<0.01 (**) indicates extremely significant difference, and P<0.001 (***) indicates extremely significant difference.

[0083] Maize variety JN1205 is also known as Jiliu 1205. In the present application, JN1205 is a drought-sensitive variety.

[0084] Maize variety FM985 is also known as Fumin 985. In the present application, FM985 is a moderately drought-sensitive variety.

[0085] Maize variety HY189 is also known as Heyu 189. In the present application, HY189 is a drought-resistant variety.

[0086] Example 1: Screening of key miRNAs involved in drought by UMI method

[0087] Before sowing, corn varieties JN1205, FM985 and HY189 seeds were surface sterilized in 10% H2O2 for 20 min, then rinsed with distilled water for 5 times, 2 min each time. Then they were soaked in distilled water for 8 hours, and the seeds were grown in pots in a greenhouse, with the temperature between 28-30°C during the day and 20-22°C at night, and the relative humidity between 50-80%. At the third leaf fully expanded stage, corn plants were randomly divided into two groups, normal treatment group and drought treatment group. The aboveground parts were taken for UMI sequencing after 7 days of normal watering in the normal treatment group and 7 days of drought treatment (no additional watering) in the drought treatment group.

[0088] We performed UMI transcriptome sequencing on the seedlings (at the third leaf fully expanded stage) of the three typical corn genotypes (JN1205, FM985 and HY189) (Table 4, SV_1, SV_2, SV_3, SV_D1, SV_D2, SV_D3, MV_1, MV_2, MV_3, MV_D1, MV_D2, MV_D3, RV_1, RV_2, RV_3, RV_D1, RV_D2 and RV_D3 in Table 4 represent sensitive variety-1, -2, -3, moderately drought-resistant variety-1, -2, -3, drought-resistant variety-1, -2, -3, sensitive variety under drought-1, -2, -3, moderately drought-resistant variety under drought-1, -2, -3, drought-resistant variety under drought-1, -2, -3, respectively; Figure 9 ), and a total of 201 mature and hairpin forms of corn miRNAs were identified. There were 31 new miRNAs and 170 known miRNAs (compared with the miRBase database) (as shown in Table 3, SV_1, SV_2, SV_3, SV_D1, SV_D2, SV_D3, MV_1, MV_2, MV_3, MV_D1, MV_D2, MV_D3, RV_1, RV_2, RV_3, RV_D1, RV_D2 and RV_D3 in Table 3 represent the same as above;). Systematic cluster analysis (HCA) found that the level and type of miRNA were highly related to corn genotype Figure 2 (A). Principal component analysis (PCA) showed a clear distinction between drought and water-sufficient plants Figure 2 (B). In this study, miR399j and miR166g were also up-regulated under drought conditions, with the greatest up-regulation in drought-resistant varieties.

[0089] The miRNAs under different treatment conditions of three varieties were hierarchically clustered using the HCA algorithm in MeV (V4.9) software (https: / / sourceforge.net / projects / mev-tm4 / files / ). Principal component analysis (PCA) was performed using the website (https: / / biit.cs.ut.ee / clustvis / ). The heat map of miRNA expression patterns was analyzed and made using Morpheus (https: / / software.broadinstitute.org / morpheus / ). The miRNA expression was normalized using log2(UMI read length).

[0090] MiR171f was down-regulated under drought conditions Figure 2 The Log2(SV_D / SV) was -0.26, the Log2(MV_D / MV) was -0.99, and the Log2(RV_D / RV) was -1.32, where the SV_D was the UMI read length of miR171f in the drought treatment group of corn JN1205, the SV was the UMI read length of miR171f in the normal treatment group of corn, the MV_D was the UMI read length of miR171f in the drought treatment group of corn FM985, the MV was the UMI read length of miR171f in the normal treatment group of corn FM985, the RV_D was the UMI read length of miR171f in the drought treatment group of corn HY189, and the RV was the UMI read length of miR171f in the normal treatment group of corn HY189; the sequence of zma-miR171f was 5'-UUGAGCCGUGCCAAUAUCACA-3' (Sequence 1). In the sequence table, Sequence 1 was replaced by T instead of U.

[0091] As described above, a drought resistance-related molecular marker, Log2(expression of miR171f under drought conditions / expression of miR171f under normal conditions), was found. The greater the absolute value of Log2(expression of miR171f under drought conditions / expression of miR171f under normal conditions), the stronger the drought resistance.

[0092] Cluster analysis showed that there were significant differences between different drought-sensitive varieties. The 201 miRNAs were divided into six categories according to different expression patterns Figure 2Class I (I) showed a decrease in miRNA expression levels in 3 maize varieties under drought conditions. Compared with the first class, the second class (II) showed an opposite trend, and the miRNAs could control stomatal number and leaf development. The third class (III) was HY189 and JN1205 with decreased miRNA expression levels, and the fourth class (IV) was the two genes with increased miRNA expression levels. The miRNAs in these two classes could also regulate leaf and stomatal development. In the fifth class (V), the miRNAs were usually expressed in one or two genotypes, but not in all three genotypes. This class of miRNAs may be involved in reproductive organ development and nutrient uptake. Finally, the irregular miRNAs were classified as the sixth class (VI). In summary, most of the different miRNAs were involved in leaf development.

[0093] Three typical maize varieties, HY189 (drought resistance), FM985 (moderate sensitivity), and JN1205 (drought sensitivity), were selected to screen key miRNAs involved in drought sensitivity. The seeds of maize varieties HY189 (drought resistance), FM985 (moderate sensitivity), and JN1205 (drought sensitivity) were germinated at 25°C, and the temperature was between 28°C and 30°C during the day and between 20°C and 22°C at night. The plants were grown under sufficient watering conditions (relative humidity between 50% and 80%) to the two-leaf stage, and then randomly divided into two groups, namely, the normal treatment group and the drought treatment group.

[0094] Drought treatment group:

[0095] After 7 days of drought treatment (maintaining 40% of the field water holding capacity), the plants were grown under conditions of temperature between 28°C and 30°C during the day and between 20°C and 22°C at night. The phenotypes were recorded after 7 days of drought stress treatment Figure 1 Class A; Figure 1 Normal: normal treatment group; Drought: drought treatment group; JN1205: maize variety JN1205; FM985: maize variety FM985; HY189: maize variety HY189), and a large number of drought-sensitive plants died after 15 days of drought stress. Then, the plants were watered again, and grown for 3 days under conditions of temperature between 28°C and 30°C during the day and between 20°C and 22°C at night (and the phenotypes after 1d, 3d, 5d, 7d, 9d, 11d, 13d, 15d of drought treatment and 3 days of rehydration were recorded, as shown in Figure 7 Survival rate was calculated, and the surviving plants gradually recovered to normal growth Figure 1 Class B, Figure 1Normal: normal treatment group; Drought: drought treatment group; JN1205: corn variety JN1205; FM985: corn variety FM985; HY189: corn variety HY189), the drought-resistant variety HY189 had the highest survival rate after rehydration, reaching 58% Figure 1 D), compared with the drought-sensitive variety JN1205, the relative water content of the leaves of HY189 was higher Figure 1 C).

[0096] Relative water content of leaves: the relative water content of leaves was detected after seven days of drought treatment. Fresh leaves were first cut and weighed (FW), then soaked in distilled water overnight to obtain saturated fresh weight (SW), and finally dried in an oven at 85°C until constant weight (DW). The calculation formula is: RLWC (%) = (FW-DW / SW-DW) x 100%.

[0097] Normal treatment group:

[0098] The normal treatment group was only different from the drought treatment group in that it did not undergo drought treatment, and other operations and detections were the same as those of the drought treatment group.

[0099] When the corn in the drought treatment group and the normal treatment group grew to three leaves and one heart and the leaf area of the third leaf was detected on the same growth date, the leaf area of HY189 was 30.4% smaller than that of JN1205. The third leaf areas of JN1205, FM985 and HY189 were 47.2 cm -2 , 44.2 cm -2 and 32.9 cm -2 ( Figure 1 E), respectively. The leaf length of the three varieties did not change significantly, but the leaf width of the drought-resistant varieties narrowed Figure 8 , and the water loss rate of HY189 was significantly lower than that of JN1205, which was mainly due to its narrow leaf shape Figure 1 F). These results show that HY189 is a drought-resistant variety, FM985 is a moderately drought-resistant variety, and JN1205 is a drought-sensitive variety.

[0100] The third leaf is the third corn leaf from the bottom up.

[0101] In vitro water loss rate: under sufficient watering conditions, the same leaf position leaves of different corn varieties were cut along the leaf base when they were cultured to two leaves and one heart, then weighed every 2 hours for 24 hours on a precision balance, and each treatment had five replicates. The calculation formula is: WL (%) = (Ti-Tn / Ti) x 100%, where Ti is the initial fresh weight and Tn is the leaf weight at the nth hour after the leaf is cut off.

[0102] Table 3 New and known miRNA counts in the library

[0103] Name Known miRNAs New miRNAs New siRNAs SV_1 96 19 309 SV_2 95 19 408 SV_3 91 21 288 SV_D1 91 20 322 SV_D2 88 21 296 SV_D3 79 19 223 MV_1 101 23 838 MV_2 89 20 676 MV_3 102 24 773 MV_D1 89 14 333 MV_D2 94 20 552 MV_D2 107 21 683 RV_1 93 22 368 RV_2 76 19 267 RV_3 78 21 271 RV_D1 67 20 220 RV_D2 70 19 160 RV_D3 83 19 274

[0104] Table 4 Summary of high-throughput sequencing results for small RNA libraries

[0105]

[0106] Example 2 demonstrates that the function of key miRNAs indicates that leaf morphology plays a crucial role in drought sensitivity.

[0107] We conducted further analysis to identify the miRNAs that changed significantly in the three varieties (JN1205, FM985, and HY189) under drought conditions (|log2FC|≥1). Figure 3 (A). Of the 6 known miRNAs, 9 were upregulated under drought conditions, and 8 were downregulated under drought conditions. Figure 2 (See Table 5). The newly detected miRNAs were then analyzed. In the three maize varieties, no new miRNAs were upregulated, but four miRNAs were downregulated: new_miR39, new_miR42, new_miR55, and new_miR57. Figure 2 (See Table 6). Finally, we identified 21 key miRNAs, including 9 drought-upregulated miRNAs with high expression levels in drought-resistant genes and 12 drought-downregulated miRNAs with low expression levels in drought-sensitive genes (Table 6).

[0108] In addition, data from 21 published miRNA datasets indicate that 7 key miRNAs are primarily involved in leaf morphogenesis. Figure 3 (Middle B). We chose to investigate the drought sensitivity of different maize genotypes by detecting the upregulation of miR171 expression.

[0109] Table 5. Upregulated and downregulated miRNAs in three maize varieties

[0110]

[0111]

[0112] Table 6. Genetic information of four new miRNAs

[0113]

[0114]

[0115] Example 3: MiR171 regulates maize leaf morphology

[0116] 1. The precursor sequence of zma-miR171f (SEQ 2) is constructed into the pCUN-mGFP vector to obtain a recombinant overexpression transgene vector (recombinant plasmid pCUN-zma-miR171f-mGFP);

[0117] SEQ 2 is as follows:

[0118] TTGGTTGTTGGCTGAGAGAGTGCGATGTTGGCATGGCTCAATCAACTCGCCGGCCGCGGGTGGCTTATAGCTT AATTCTGCGCATTCGATCGAGGTGCGGGCGCAGTGTTTAATTGATTGAGCCGTGCCAATATCACAACCTTCTCTAGC CTATA.

[0119] The recombinant plasmid pCUN-zma-miR171f-mGFP is obtained by replacing the fragment between the restriction endonuclease Spe I and Kpn I enzyme cutting sites of the pCUN-mGFP vector with SEQ 2, while keeping other nucleotide sequences of the pCUN-mGFP vector unchanged.

[0120] 2. The above recombinant plasmid pCUN-zma-miR171f-mGFP is transformed into Agrobacterium tumefaciens EHA105 to obtain a recombinant Agrobacterium, which is then used to infect the immature embryos of corn variety ND101 (corn variety ND101 is recorded in the literature “Amaize epimerase modulates cell wall synthesis and glycosylation during stomatal morphogenesis”, and the name in the literature is B73-329) to obtain T0 generation transgenic plants; the T0 generation transgenic plants are selfed to obtain two independent overexpression homozygous plants miR171f ND101 OE1 and miR171f ND101 OE2 (this kind of germplasm is created by the Crop Functional Genomics and Molecular Breeding Research Center of China Agricultural University), and the pure T2 generation germplasm resources are obtained from the center, and are propagated at the Shangzhuang Experimental Station of China Agricultural University in the same year, and the overexpression of miR171 is detected at the DNA and RNA levels at the three-leaf stage of the seedlings, and strict self-pollination is performed, and the harvested seeds are used for subsequent experiments.

[0121] 3. Construction of miR171f editing vector

[0122] To generate miR171f CRISPR-Cas9 mutants, sgRNAs of miR171f were designed and evaluated using CRISPR online server (http: / / crispor.tefor.net / ). The sgrna was inserted into pBCXUN vector and verified by Sanger sequencing. The construct of pBCXUN-miR171f-sgRNA1 was transformed into selfing line ND101 as previously described by Agrobacterium (strain EHA105). The positive transgenic lines were confirmed by PCR and sequencing. The stably inherited lines were selected for further phenotype study. The miR171f CRISPR-Cas9 mutants were created by the Center of Crop Functional Genome and Molecular Breeding, China Agricultural University.

[0123] The miR171f sequence (sequence 3) was provided to the Crop Center of China Agricultural University for miR171f editing vector construction. The miR171f knockout vector was obtained. The miR171f knockout vector was transformed into Agrobacterium EHA105 competent cells. Agrobacterium EHA105 / miR171f knockout vector was obtained. The Agrobacterium EHA105 / miR171f knockout vector is Agrobacterium EHA105 containing the miR171f knockout vector.

[0124] Genetic transformation of maize

[0125] The Agrobacterium EHA105 / miR171f knockout vector prepared above was used to infect maize variety ND101 to obtain T0 generation transgenic plants; the T0 generation transgenic plants were selfed to obtain an independent gene editing homozygous plant miR171f ND101Crispr .

[0126] The miR171f ND101 The genomic DNA of the Crispr seedling was used as a template, and target PCR primer F (5'-TGGTTGTTGGCTGAGAGAG-3') and target PCR primer R (5'-CATAGCAGGACGACAAAGAAC-3') were used for amplification sequencing, respectively.

[0127] The sequencing results showed that:

[0128] The miR171f ND101 Compared with the wild type maize variety ND101, the miR171f in the maize genome was mutated: in the sequence table, there were 5 deoxynucleotide residue deletion mutations between the 128th and 134th positions of sequence 3 in the two homologous chromosomes, i.e. the deletion of (sequence 5'-CAATA-3') between the 12th and 16th positions of sequence 4 (the mature sequence of miR171f), the deletion of the mature region of miR171f, thereby knocking out miR171f.

[0129] Sequence 3 is specified as follows:

[0130] TCTTCCTCGGCTTACGTACGCCTACGCCCATTCGTACCGTCGCCGCGCGCCTATTAATTCGGCTGCTTCGCCTCGCCTGCTTGCTTGCTCCGTTGCCAGCACAGCAGCTCAAGTTCTCCGTCTCCCTCCAGTTTAGGGTTGCGTGGCTGGGAGCTCGGGTAGCTAGTCGTCCATGGCGATCGATCGAGAAGGGGTGGTGGCGTTTGGTTGTTGGCTGAGAGAGTGCGATGTTGGCATGGCTCAATCAACTCGCCGGCCGCGGGTGGCTTATAGCTTAATTCTGCGCATTCGATCGAGGTGCGGGCGCAGTGTTTAATTGATTGAGCCGTGCCAATATCACAACCTTCTCTAGCCTATATATGTGGAGTGGACTACAGGTACGGGGGTTTTGTGTTCTTTGTCGTCCTGCTATGCGCCGTGAAATTTAATTAAGATTCGGTTTTCCATCTATATATCGTGTATATACCATGGATGTGTGTACTGTTTGTGGCATTATTATCAGGTCATGTTTACAAGTATAGTAGGTGTGGCAGCAGTTGA.

[0131] Sequence 4 is specified as follows:

[0132] UUGAGCCGUGCCAAUAUCACA.

[0133] 4. Corn variety ND101, miR171f ND101 OE1 and miR171f ND101 OE2 and miR171f ND101 Seeds of Crispr were planted at 25°C, and the 11th-20th leaf width-length ratio and leaf area were detected when they grew to the flowering stage, and the phenotypes were recorded.

[0134] Leaf width-length ratio: leaf width / leaf length.

[0135] Leaf area: leaf width*leaf length*0.75.

[0136] The 11th-20th leaf is the 11th-20th corn leaf from bottom to top.

[0137] ResultsFigure 4 MiR171f ND101OE1 and miR171f ND101OE2 The leaf width / length ratio of the 11th-20th leaf was significantly increased in the miR171f Figure 4 C). The leaf area of the 12th leaf, 13th leaf (ear leaf) and 14th leaf of the miR171f Figure 4 D) was also significantly increased. In contrast to the overexpression transgenic lines, the leaf width / length ratio and leaf area of the miR171f ND101Crispr lines were significantly lower than that of ND101, indicating that miR171f plays a key role in regulating leaf width (A). Figure 4

[0138] 5. The miR171f expression levels of the corn variety ND101 (background material) and miR171f ND101OE1 and, miR171f ND101OE2 and miR171f ND101Crispr were detected according to the following methods, respectively: total RNA was extracted from the leaves of the plants and cDNA was obtained by reverse transcription, ZmU6 gene was used as an internal reference gene, and the relative expression level of miR171f was detected by Real time qPCR. The results showed that, compared with the transgenic receptor corn variety ND101, the expression levels of miR171f ND101OE1 and miR171f ND101OE2 in the two independent miR171f Figure 4 B, miR171f ND101OE1 and miR171f ND101OE2 transgenic lines were significantly increased, while the 21-NT mature 171f region had 5 base deletions (A and B). ND101OE1 ND101OE2 The miR171f ND101Crispr was an independent gene-edited homozygous plant. ND101Crispr The expression levels of miRNA171f in the two independent miR171f ND101OE1 and miR171f ND101OE2 transgenic lines were significantly increased (A and B). Figure 4

[0139] The primers used for miR171f were as follows:

[0140] F: 5'-GAATGCTTGAGCCGTGCCAATATCAC-3'

[0141] R: 5'-GTGCAGGGTCCGAGGT-3'

[0142] ​​​The primers for internal reference U6 are:

[0143] F: 5'-GGAACGATACAGAGAAGATTAGCA-3'

[0144] R: 5'-GTGCAGGGTCCGAGGT-3

[0145] Classification of different maize varieties on drought sensitivity

[0146] Further, the main varieties selected subsequently covering the maize planting zone in China were planted in the electric rainproof shed, and the schematic diagram shows the growth state and measurement time Figure 10 A).

[0147] Seeds of 24 maize varieties (specifically as shown in Table 1) were planted in the electric rainproof shed, with row length of 3 m, row spacing of 0.5 m, and plant spacing of 0.25 m, 12 repetitions for each variety, a total of 24 plots, irrigation was carried out every two weeks, irrigation amount in normal watering area was more than 80% of field water holding capacity, and irrigation amount in drought area was 40% of field water holding capacity 3 / hm 2 When grown to the flowering stage, the net photosynthetic rate (Pn), transpiration rate (Tr), intercellular CO2 concentration (Ci) of the ear leaves of the 24 maize varieties (specifically as shown in Table 1) were sorted Figure 10 B-D, where D-* is the drought area, and W-* is the normal watering area).

[0148] When grown to the maturity stage, plant height (PH), ear height (EH), dry weight (DW), single ear yield, and yield component factors (ear diameter ED; ear length EL; kernel number KN; thousand kernel weight TKW) were detected.

[0149] The results showed that varieties such as DH710 and HY189 could still maintain good photosynthetic performance under drought conditions, while varieties such as JN1205 and LY99 showed strong photosynthetic performance under drought conditions Figure 10 E and I). Eight traits such as plant height (PH), ear height (EH), dry weight (DW), single ear yield, and yield component factors (ear diameter ED; ear length EL; kernel number KN; thousand kernel weight TKW) were studied. Different maize genotypes were sorted by DRCS Figure 10 J, Table 7). The drought resistance coefficients (DRC) of different traits showed a very high consistency in the genetic order of different varieties, such as DH710 and HY189 still ranked first, and JN1205 and LY99 ranked last.

[0150] To investigate the crucial role of leaf morphology in drought sensitivity during the growth and flowering stages, the leaf length and width of leaves 4-19 of 24 major varieties were measured to calculate the leaf width / length ratio. Figure 6 (A). Among all maize varieties studied, MFV showed a high correlation with leaf length ratio. Figure 6 (Middle B). The results showed that all 16 leaves were significantly negatively correlated (P<0.05). The correlation coefficients R ranged from -0.517 to 0.809. The 10 correlation coefficients for leaves 12, 13 (spike leaves), and 14 were -0.672, -0.681, and -0.760, respectively, indicating that they were closely related to drought resistance. Drought-resistant varieties have slender leaves.

[0151] Net photosynthetic rate (Pn) of panicle leaves was measured using a Li6400 photosynthesis system.

[0152] Transpiration rate (Tr) of panicle leaves was detected using a Li6400 photosynthesis system.

[0153] Intercellular CO2 concentration (Ci) in spikelet leaves was detected using a Li6400 photosynthesis system.

[0154] Detection of chlorophyll content in the 12th, 13th (spike leaves) and 14th unfolded leaves of the spike: The SPAD value of the plant was measured once at the top, middle and bottom of the leaf using a handheld SPAD meter.

[0155] The length and width of leaves 4-19 were measured: leaf length and width were measured with a ruler.

[0156] Plant height (PH): The distance from the ground to the highest point of the tassel.

[0157] Ear height (EH): The distance from the ground to the base of the female ear.

[0158] Dry weight (DW): Bake at 80 degrees Celsius until the weight no longer decreases.

[0159] Yield per ear: weight of grains per ear.

[0160] DRC was significant in all cases based on 12 traits. Figure 11 (A) All traits showed highly significant positive correlations, indicating that the drought resistance coefficients of the 12 traits can be used as indicators for drought resistance selection. The drought resistance coefficient (DRC) is an important indicator for evaluating the drought resistance of different maize genotypes. It refers to the ratio of the value of a certain agronomic or physiological trait under drought conditions to the value under a fully irrigated control condition.

[0161] The formula for calculating DRC is:

[0162] DRCmn=Tmnd / Tmnww (Formula 2-7)

[0163] where DCmn is the drought resistance coefficient of the mth trait of the nth maize genotype; Tmnd and Tmnww are the values of the mth trait of the nth maize genotype under drought and well-watered treatments, respectively

[0164] The DRCs of the 12 traits were subjected to cluster analysis, and the results showed that the 24 maize varieties were clustered into 3 groups according to the DRCs of the 12 traits Figure 11 Group I consisted of 9 drought-tolerant maize varieties (DH3737, HY189, DK157, DH710, ND372, XY1466, XY1225, JND935, and DH518). Group III consisted of 7 drought-sensitive maize varieties (Tn9, JN1205, XY335, ZD958, DK159, ZY8911, and ND108). The remaining varieties were classified as moderately drought-sensitive. The membership function value (MFV) was used as a comprehensive evaluation index for drought resistance of maize varieties. The values estimated from the 12 indicators are shown in Table 1. The minimum and maximum MFVs were 0.08 (JN1205) and 0.94 (DH518), respectively. According to the size of the MFV, the maize varieties were divided into 3 levels (I: 0-0.333; II: 0.333-0.666; III: 0.666-1). Among the 24 maize varieties, 4 (JN1205, Tn9, ZY8911, and DK159) had low MFV values (0-0.333) and were classified as drought-sensitive. Ten maize varieties (LP206, XY1225, DH710, DH3737, DK517, XY1466, HY189, ND372, JND935, and DH518) had high MFV values (0.666-1), indicating that they were drought-tolerant. The remaining varieties were classified as moderately drought-tolerant.

[0165] The classification of drought sensitivity using these two methods was consistent. For example, JN1205, Tn9, ZY8911, and DK159 were drought-sensitive varieties depending on the MFV (Table 1), and these maize varieties also appeared in the sensitive interval depending on the DRC value Figure 11 Therefore, the classification of drought sensitivity of different maize varieties is reliable.

[0166] The seeds of 24 maize varieties (as shown in Table 1) were cultured at 25°C in a laboratory at the seedling stage until they grew to five leaves with a central leaf. The leaf water loss rate of the 3rd, 4th, and 5th leaves was detected, which is an important indicator of drought resistance of different maize varieties. In all varieties, the leaf water loss rates of the 3rd, 4th, and 5th leaves showed similar trends Figure 5The 24-hour water loss rates of TN9, JN1205 and ZY8911 were 77.24%, 73.49% and 72.76%, respectively, which were similar to the results of DRC and MFV, belonging to drought-sensitive maize varieties. The water loss rates of DH518, DK517 and HY189 were 42.15%, 57.44% and 58.22%, respectively, belonging to drought-resistant maize varieties. Regression analysis showed that the membership function value was significantly negatively correlated with the water loss rate of the third leaf (R2=0. 657; P<0.001). Figure 5

[0167] The interval between anthesis and silking (ASI) is a key indicator of maize growth and development under drought stress when growing to the flowering stage. Drought stress can cause different steps of ASI, ultimately leading to maize yield reduction. Here, we used it to further test the classification accuracy of drought sensitivity.

[0168] The results showed that under drought conditions, different varieties had great differences in ASI from 1d to 8d Figure 5 The ASI of drought-resistant genes DK517, HY189 and DH518 were 2.04d, 2.33d and 2.54d, respectively. The ASI values of LY99, XD20 and JN1205 were 5.22d, 4.96d and 4.83d, respectively, belonging to drought-sensitive maize varieties. In addition, there was a highly significant negative correlation between ASI and MFV (R2=0.517; P<0.001), further verifying the accuracy of the previous classification Figure 5

[0169] Water loss rate of the 3rd, 4th and 5th leaves: First, cut the 3rd, 4th and 5th leaves of each maize along the leaf base for leaf isolation, then weigh every 2 hours for 24 hours on a precision balance, with five replicates for each treatment. The formula is:

[0170] WL (%) = (Ti-Tn) / Ti; where Ti is the initial fresh weight, and Tn is the leaf weight at the n hour after leaf isolation.

[0171] Interval between anthesis and silking (ASI): The time difference between the pollen shedding (male ear maturity) and the silking (female ear maturity) periods.

[0172] Table 7 Drought resistance coefficients (DRC) of 12 traits

[0173]

[0174]

[0175] Genetic correlation analysis showed that the DRC of 12 traits reached a significant level in all cases Figure 5 ​​Drought resistance coefficients of 12 traits were significantly positively correlated with each other, indicating that the 12 traits could be used as selection indexes for drought resistance. The cluster analysis showed that 24 maize genotypes were classified into 3 groups based on the DRC of 12 traits Figure 5 Drought resistance coefficients of 12 traits were significantly positively correlated with each other, indicating that the 12 traits could be used as selection indexes for drought resistance. The cluster analysis showed that 24 maize genotypes were classified into 3 groups based on the DRC of 12 traits

[0176] The results of the two methods were consistent. For example, JN1205, Tn9, ZY8911 and DK159 were drought-sensitive genotypes based on MFV (Table 1), and these maize genotypes also appeared in the sensitive zone based on DRC values (Fig. 1B). Figure 11

[0177] We focused on the classification of drought sensitivity during the reproductive stage. Is it suitable for the vegetative stage? Leaf water loss rate is an important index for drought resistance of maize genotypes during the vegetative stage. In all genotypes, the water loss rates of the 3rd, 4th and 5th leaves showed similar trends (Fig. 2B). Figure 5 ​The 24-hour water loss rates of TN9, JN1205 and ZY8911 were 77.24%, 73.49% and 72.76%, respectively, which were similar to the results of DRC and MFV, belonging to drought-sensitive maize varieties. The water loss rates of DH518, DK517 and HY189 were 42.15%, 57.44% and 58.22%, respectively, belonging to drought-resistant maize varieties. Regression analysis showed that the membership function value was significantly negatively correlated with the three-leaf water loss rate Figure 5

[0178] ASI is a key indicator of maize growth and development under drought stress. Drought stress can cause different steps of ASI, eventually leading to maize yield reduction. Here, we used it to further test the classification accuracy of drought sensitivity. The results showed that under drought conditions, different varieties had a large difference in ASI from 1d to 8d Figure 5 Figure 5

[0179] The membership function method (MFV) was used to quantify the drought resistance of different genotypes of maize through multiple agronomic and physiological traits. The formula is as follows:

[0180]

[0181] In the formula, DRCmn is the drought resistance coefficient of the mth trait of the nth variety (n=24 varieties); Tmnd is the value of the mth trait of the nth variety under drought treatment; Tmw is the value of the mth trait under sufficient irrigation treatment. DRCmmax and DRCmmin are the maximum and minimum values of the drought resistance coefficient of the mth trait, respectively. MFVn is the average membership function value of the nth variety, which represents the drought resistance of the variety.

[0182] Table 124 Membership function analysis (MFV) and drought resistance classification of 24 maize varieties

[0183]

[0184]

[0185] ​​​The membership function value and category of the drought resistance of the genotypes. The genotypes were divided into three categories according to the membership function value. I: 0-0.333; II: 0.333-0.666; III: 0.666-1. MFV, membership function value; DRL, drought resistance level.

[0186] The above corn varieties were grown under normal watering conditions to the three-leaf-one-heart stage (12 days after sowing, i.e. the three-leaf-one-heart stage), and the expression of the miR171f mature sequence in the third leaf of the seedlings of DH710, NH101, HD188, XD20, ZY8911, LY99, ND108, JK968, DK159 and TN9 was detected, according to the following method:

[0187] Total RNA was extracted from the leaves using the Trizol method, and reverse transcription was performed using the specific primer 5'-GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACCAGATC-3', with U6 as the internal reference. The U6 reverse transcription sequence was 5'-GTGCAGGGTCCGAGGTTTTGGACCATTTCTCGAT-3', and the reverse transcription product was subjected to RT-PCR detection.

[0188] The primers used for miR171f were as follows:

[0189] F: 5'-GAATGCTTGAGCCGTGCCAATATCAC-3'

[0190] R: 5'-GTGCAGGGTCCGAGGT-3'

[0191] The primers used for the internal reference U6 were as follows:

[0192] F: 5'-GGAACGATACAGAGAAGATTAGCA-3'

[0193] R: 5'-GTGCAGGGTCCGAGGT-3'

[0194] The results, as shown in Table 1, showed that the drought resistance of the corn varieties was positively correlated with the expression amount of miR171f. Figure 12

[0195] ​The application has been described in detail. For those skilled in the art, the application can be implemented in a wider range under the same parameters, concentrations and conditions without departing from the spirit and scope of the application and without unnecessary experiments. Although the application gives a special example, it should be understood that the application can be further improved. In summary, according to the principle of the application, the application intends to include any change, use or improvement of the application, including changes made by conventional techniques known in the art, which deviates from the range disclosed in the application. Some basic features can be applied within the scope of the following attached claims.

Claims

1. The use of a substance for detecting the miRNA in any one of the following: A1) detecting or assisting in detecting the width-length ratio of the leaf of a plant of the family Poaceae and / or preparing a product for detecting or assisting in detecting the width-length ratio of the leaf of a plant of the family Poaceae; A2) detecting or assisting in detecting the leaf area of a plant of the family Poaceae and / or preparing a product for detecting or assisting in detecting the leaf area of a plant of the family Poaceae; the sequence of the miRNA is shown in SEQ ID NO: 1; the plant of the family Poaceae is corn.

2. A method of identifying or assisting in the identification of the leaf width to length ratio of a grass plant, characterised in that, The method comprises detecting the expression amount of the miRNA in claim 1 in a test plant of the family Poaceae, and identifying or assisting in identifying the width-length ratio of the leaf of the plant of the family Poaceae according to the expression of the miRNA in the test plant of the family Poaceae; the leaf of the plant of the family Poaceae with a high expression amount of the miRNA has a width-length ratio of the leaf greater than or candidate greater than that of the plant of the family Poaceae with a low expression amount of the miRNA; the plant of the family Poaceae is corn.

3. A method of identifying or aiding in the identification of leaf area of a plant of the family Poaceae, characterized in that, The method comprises detecting the expression amount of the miRNA in claim 1 in a test plant of the family Poaceae, and identifying or assisting in identifying the leaf area of the plant of the family Poaceae according to the expression of the miRNA in the test plant of the family Poaceae; the leaf of the plant of the family Poaceae with a high expression amount of the miRNA has a width-length ratio of the leaf greater than or candidate greater than that of the plant of the family Poaceae with a low expression amount of the miRNA; the plant of the family Poaceae is corn.

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