JP2019058103A - Method of predicting incidence of white immature grains of rice, method of predicting whole grain rate of rice, maker gene, and method of cultivating rice - Google Patents

Abstract

To provide a method of predicting incidence of white immature grains of rice and a method of predicting whole grain rate of rice by which the predictive values with high reliability of the incidence of white immature grains of rice and whole grain rate of rice are obtained early in a ripening phase.SOLUTION: Measured is the expression level of at least one gene among Amy3A, Amy3E, L-APX1, L-APX2, L-APX4, α-glucosidase, Cellulose synthase, CRT/DRE1, Ras GTPase, XET, Cu/Zn SOD, Phosphoglucomutase, β-1,3-glucanase, NAM, Germin-like 8 in an unhulled rice harvested from a rice in a ripening phase.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a method for predicting the rate of white immature grain generation in rice, a method for predicting grain size regulation in rice, marker genes used in these prediction methods, and a method for cultivating rice that performs optimal cultivation management using these prediction methods It is.

  In recent years, abnormal weather that does not match the conventional climate pattern frequently occurs, and it is feared that it is due to the effects of global warming. Abnormal weather conditions such as summer extremely high temperature, extremely low temperature, heavy rain and light rain seriously affect the growth of crops, leading to a decrease in yield and quality.

  For example, in rice cultivation, the generation of white immature grains frequently occurs due to the abnormal high temperature during the ripening period, which causes a large decrease in the ratio of the first grade of harvested rice. This decline in the ratio of first-class rice brings down the price of rice and reduces the income of rice farmers, which is a big problem for people involved in rice production.

  In addition, the frequent occurrence of white immature grains has serious impacts not only on people involved in rice production but also on various industries that process and commercialize the rice.

  For example, in the sake brewing industry, it is difficult to ferment rice with a high temperature ripening problem with a broom, so there is a problem that adjustment of the degree of fermentation becomes difficult. Moreover, in the type | mold which handles cooked rice, since the state after rice cooking changes (it becomes easy to become hard when it cools), the example which blends taste etc. by blending other varieties etc. is also known. Problems caused by such raw material rice result in a decrease in production efficiency and an increase in cost.

  As described above, since white immature grains have a great influence on the deterioration of rice quality, it is important to grasp the risk of occurrence and to take appropriate measures early.

  In addition, white immature grains are classified into milk white grains, heart white grains, back white grains, belly white grains, base immature grains, etc. depending on their symptoms (the occurrence of white turbidity), and it is known that the generation factors differ (non-patent Literature 1). For example, back white grains and base immature grains are generated by the high temperature during the ripening stage, and milk white grains are mainly generated by lack of sunlight. Therefore, it is effective to predict the occurrence rate of the whole white immature grains to know the necessity of measures, and to predict the occurrence of specific white immature grains such as the base immature grains (specific ) Although it is extremely important to formulate countermeasures, a highly accurate early prediction method to meet such purpose has not been established at present.

  For example, Non-Patent Document 1 is a model that predicts white immature grain generation by inputting weather forecast information (such as air temperature and sunshine amount) during ripening stage and growth information (such as plant height, leaf color and number of stems) of rice. Is described. However, these methods are based on the uncertain information of weather forecast. Therefore, although the high temperature was predicted and the countermeasure was taken, when the temperature did not become high as expected, it is known that the problem which the taste of rice falls by the countermeasure taken by the countermeasure taken will arise, and it is reliable. There is a problem with sex.

  Further, Patent Document 1 and Non-patent Document 1 describe a method and apparatus for predicting white immature grains at harvest time by image analysis of a cross section of brown rice before harvest. However, since these methods based on image analysis require brown rice that has matured to a certain degree, although it can be used for quality prediction at harvest, it is an early stage used for cultivation management to promote normal ripening. Is not suitable as a white immature grain development prediction method in Japan.

  Thus, as a method for predicting white immature grain development at an early stage for incorporating it into a cultivation management system that suppresses the generation of white immature grains, use of uncertain environmental forecast data at an early stage before ripening progresses. Instead, methods based on direct monitoring of the physiological condition of rice are suitable.

  On the other hand, various studies have been conducted on the physiological response of rice to environmental changes. For example, Non-Patent Document 2 examines rice responses to low temperature stress and drought stress by examining metabolites, plant hormones and gene expression products, and that transcriptional regulation of CYP735A is related to these stress responses. Etc. are reported. In addition, Non-Patent Document 3 reports that differences in transcription of 35 genes were observed in transcriptome analysis comparing the changes that generability of nighttime temperatures at the early stage of ripening gives to high temperature resistant and sensitive lines of rice. doing. In addition, studies were conducted in which transcriptome analysis (Non-patent document 4) and quantitative trait locus (QTL) analysis (Non-patent document 5) were performed to identify high-temperature response genes during the ripening period, and further As a method of producing high temperature resistant rice, a method of suppressing the expression of α-amylase gene (patent document 2) and phospholipase D gene (patent document 3) has been invented.

  All of these previous studies are common in measuring gene expression changes in rice during environmental changes, but there is no study from the viewpoint of white immature grain development prediction. Also, it is needless to say that even if expression of the gene is measured, the occurrence of white immature grains can not be shorted even if expression is changed due to environmental changes.

Patent No. 5716234 gazette JP, 2013-208076, A Patent No. 5812386 Fiscal 2012 agricultural administration problem solution training text Global warming countermeasure training II "high temperature ripening failure countermeasure technology of paddy rice" July 3, 2012 Independent administrative institution National Agriculture and Food Industry Research Organization Kyushu Okinawa Agricultural Research Center Plant Physiol. Vol. 164, pp. 1759-1771, 2014 BMC Genomics, 16:18, 2015, DOI 10.1186 / s12864-015-1222-0 Plant Cell Physiol. 51, pp. 795-809, 2010 Breed. Sci. 58, pp. 337-343, 2008

  The present invention has been made in view of the above-mentioned current situation, and the incidence of white immature grains of rice at an early stage of the ripening period so that appropriate measures (cultivation management) with less risk can be implemented. It is an object of the present invention to provide a method for predicting the white immature grain generation rate of rice capable of accurately predicting the grain size adjustment rate, a grain size adjustment rate prediction method for rice, and a method for cultivating rice using these prediction methods. .

  The subject matter of the present invention will be described with reference to the accompanying drawings.

It is a method for predicting the incidence of white immature grains of rice before harvest, which is an expression amount of at least one of the following genes (a) to (o) of a rice bran collected from rice during the ripening stage: The present invention relates to a method for predicting the white immature grain incidence of rice, which comprises measuring and predicting the incidence of white immature grains of the rice from the expression level of this gene.
Record
(a) Amy3A (b) Amy3E (c) L-APX1
(d) L-APX2 (e) L-APX4 (f) α-glucosidase
(g) Cellulose synthase (h) CRT / DRE1 (i) Ras GTPase
(j) XET (k) Cu / Zn SOD (l) Phosphoglucomutase
(m) β-1,3-Glucanase (n) NAM (o) Germin-like 8

  Further, in the method for predicting the white immature grain incidence of rice according to claim 1, the expression amount of the gene is measured in the early stage after flowering of the rice, according to the white immature grain incidence prediction method of rice It is a thing.

  Further, in the method for predicting the white immature grain incidence of rice according to claim 1, the expression amount of the gene is measured within 10 days after the flowering of the rice, and the method for predicting the white immature grain incidence of rice is characterized. Pertaining to

  In the method for predicting the incidence of white immature grains of rice according to any one of claims 1 to 3, the expression amount of one of the genes (a) to (o) is used as an explanatory variable, The present invention relates to a method for predicting white immature grain generation rate of rice, characterized by using a white immature grain generation rate prediction formula obtained by single regression analysis using the incidence rate of immature grains as an objective variable.

  In the method for predicting the incidence of white immature grains of rice according to any one of claims 1 to 3, the expression amounts of two or more genes including at least one of the genes (a) to (o) are described. The present invention relates to a method for predicting the white immature grain generation rate of rice, which is characterized by using a white immature grain incidence rate prediction equation obtained by multiple regression analysis using the generation rate of white immature grains as a variable as a variable.

In addition, it is a method for predicting grain size regulation rate of rice before harvest, and measuring the expression amount of at least one gene of the following (a) to (o) genes of rice bran collected from rice of ripening stage The method of the present invention relates to a method of predicting the grain size adjustment rate of rice, which comprises predicting the grain size adjustment rate of the rice from the expression amount of this gene.
Record
(a) Amy3A (b) Amy3E (c) L-APX1
(d) L-APX2 (e) L-APX4 (f) α-glucosidase
(g) Cellulose synthase (h) CRT / DRE1 (i) Ras GTPase
(j) XET (k) Cu / Zn SOD (l) Phosphoglucomutase
(m) β-1,3-Glucanase (n) NAM (o) Germin-like 8

  Further, in the method of predicting grain size regulation rate of rice according to claim 6, the expression amount of the gene is measured in the early stage after flowering of rice, which is related to the grain size regulation rate prediction method of rice.

  In the method for predicting grain size regulation rate of rice according to claim 1, the expression amount of the gene is measured within 10 days from the flowering of rice, which relates to the grain size regulation rate predicting method of rice. is there.

  In the method for predicting grain size regulation rate of rice according to any one of claims 6 to 8, the expression amount of one of the genes (a) to (o) is used as an explanatory variable, and the grain size regulation rate is used. The present invention relates to a method of predicting grain size proportions for rice, which comprises using a grain size proportion prediction formula obtained from single regression analysis with the objective variable as the objective variable.

  In the method for predicting grain size regulation of rice according to any one of claims 6 to 8, the expression amount of two or more genes including at least one of the genes (a) to (o) is used as an explanatory variable. The present invention relates to a method for predicting the grain size proportion of rice which is characterized by using a grain size proportion prediction equation obtained from multiple regression analysis with the grain size classification as an objective variable.

The present invention also relates to a marker gene which is used to predict the white immature grain development rate of rice before harvest, and is selected from the following genes (a) to (o): .
Record
(a) Amy3A (b) Amy3E (c) L-APX1
(d) L-APX2 (e) L-APX4 (f) α-glucosidase
(g) Cellulose synthase (h) CRT / DRE1 (i) Ras GTPase
(j) XET (k) Cu / Zn SOD (l) Phosphoglucomutase
(m) β-1,3-Glucanase (n) NAM (o) Germin-like 8

Further, the present invention relates to a marker gene which is used to predict the grain size regulation rate of rice before harvesting, and is selected from the following genes (a) to (o).
Record
(a) Amy3A (b) Amy3E (c) L-APX1
(d) L-APX2 (e) L-APX4 (f) α-glucosidase
(g) Cellulose synthase (h) CRT / DRE1 (i) Ras GTPase
(j) XET (k) Cu / Zn SOD (l) Phosphoglucomutase
(m) β-1,3-Glucanase (n) NAM (o) Germin-like 8

Moreover, it is a cultivation method of rice, Comprising: The expression amount of at least one gene is measured among the following genes (a) to (o) of the rice bran collected from rice at the ripening stage, and the expression amount of this gene It is characterized in that the predicted value of the white immature grain incidence rate of the rice concerned is acquired, and the cultivation management such as water management, fertilization management, harvesting time etc of the rice concerned is determined based on the prediction value of the white imitation grain incidence rate. It relates to the cultivation method of rice.
Record
(a) Amy3A (b) Amy3E (c) L-APX1
(d) L-APX2 (e) L-APX4 (f) α-glucosidase
(g) Cellulose synthase (h) CRT / DRE1 (i) Ras GTPase
(j) XET (k) Cu / Zn SOD (l) Phosphoglucomutase
(m) β-1,3-Glucanase (n) NAM (o) Germin-like 8

Moreover, it is a cultivation method of rice, Comprising: The expression amount of at least one gene is measured among the following genes (a) to (o) of the rice bran collected from rice at the ripening stage, and the expression amount of this gene The cultivation value of rice is characterized by acquiring the predicted value of the grain size regulation rate of the rice, and determining the cultivation management such as water management, fertilization management, harvesting time, etc. of the rice based on the predicted value of the grain size classification rate. It relates to the method.
Record
(a) Amy3A (b) Amy3E (c) L-APX1
(d) L-APX2 (e) L-APX4 (f) α-glucosidase
(g) Cellulose synthase (h) CRT / DRE1 (i) Ras GTPase
(j) XET (k) Cu / Zn SOD (l) Phosphoglucomutase
(m) β-1,3-Glucanase (n) NAM (o) Germin-like 8

  In the present invention, the incidence of white immature grains of rice can be predicted with high accuracy at the stage of the ripening stage, for example, the early stage of flowering of rice, and based on this predicted value (predicted incidence). Appropriate cultivation management measures can be implemented, and this can reduce the generation of white immature grains.

  That is, although the fertilization measures after heading which have been performed as a countermeasure to suppress the generation of white immature grains until now are useful for suppressing the generation of white immature grains due to high temperature during the ripening period, the taste of rice is Although it was difficult to take measures positively because it would increase the protein content to be reduced, the present invention significantly improves the prediction accuracy of the incidence rate of white immature grains, and this accuracy Cultivation measures (cultivation management) can be appropriately performed based on the predicted value of the high incidence rate of white immature grains.

  In addition, it is known that the incidence of white immature grains (in particular, immature base grains) increases rapidly as the harvest is delayed from the appropriate time, but the white immature grains obtained by the present invention The appropriate harvest time (early harvest time) can be determined based on the predicted value of the incidence rate of grains, and the early harvest (harvest) allows the generation of white immature grains, particularly the base immature grains as much as possible. It becomes possible to suppress.

  Further, in the present invention, it is possible to predict the grain grading ratio of rice with high precision at the stage of the ripening stage, for example, the early stage of flowering of rice, and appropriate cultivation management measures It is possible to implement, which can reduce the occurrence of ripening disorders such as white immature grains.

  That is, in general, the grain size adjustment percentage can be calculated approximately as the ratio of rice grains obtained by subtracting immature grains such as white immature grains from the total amount of rice grains, so obtaining the grain size adjustment percentage can obtain immature grains (white grains The predicted value of the incidence rate of immature grains can be obtained, and as described above, appropriate cultivation measures can be appropriately taken based on the predicted value of the incidence rate of white immature grains with high accuracy. For example, it is possible to suppress the occurrence of white immature grains as much as possible.

It is a figure which shows the correlation of the Amy3A gene expression level ratio in the rice bran of the 5th day after flowering of Example 1, a white immature grain ratio, a base immature grain ratio, and a grain size adjustment ratio. It is a figure which shows the correlation with the Amy3E gene expression level ratio in the rice straw of the 5th day after the flowering of Example 1, a white immature grain ratio, a base immature grain ratio, and a grain size adjustment ratio. FIG. 6 is a view showing the correlation between the L-APX1 gene expression ratio in the rice bran on the fifth day after flowering of Example 1, the white immature grain ratio, the base immature grain ratio, and the grain size adjustment ratio. FIG. 6 is a view showing the correlation between the L-APX2 gene expression ratio in the rice bran on the fifth day after flowering of Example 1, the white immature grain percentage, the base immature grain percentage, and the grain size adjustment percentage. FIG. 6 is a graph showing the correlation between the L-APX4 gene expression ratio and the base immature grain percentage, base immature grain percentage, and size adjustment percentage of rice bran on the fifth day after flowering of Example 1. FIG. 6 is a view showing the correlation between the ratio of expression amount of α-glucosidase gene in the rice bran on the fifth day after flowering of Example 1 with the white immature grain ratio, the base immature grain ratio, and the grain size adjustment ratio. It is a figure which shows the correlation with a cellulose synthe gene gene expression ratio and the white immature grain ratio, the base immature grain ratio, and the grain size adjustment ratio in the rice cake of the 5th day after the flowering of Example 1. It is a figure which shows correlation with a white immature grain ratio, a base immature grain ratio, and a grain adjustment ratio in CRT / DRE1 gene expression ratio in the rice bran of the 5th day after flowering of Example 1. FIG. It is a figure which shows the correlation with Ras GTPase gene expression ratio in the rice bran of the 5th day after flowering of Example 1, a white immature grain ratio, a base immature grain ratio, and a grain adjustment ratio. It is a figure which shows the correlation with the XET gene expression ratio in the rice straw of the 5th day after flowering of Example 1, a white immature grain ratio, a base immature grain ratio, and a grain size adjustment ratio. It is a figure which shows the correlation with the ratio of Cu / Zu SOD gene expression in the rice bran of the 5th day after flowering of Example 1, white immature grain ratio, base immature grain ratio, and a grain adjustment ratio. It is a figure which shows the correlation of the Amy3A gene expression level ratio in the rice bran of the 7th day after the flowering of Example 1, a white immature grain ratio, a base immature grain ratio, and a grain size adjustment ratio. It is a figure which shows correlation with the Amy3E gene expression level ratio in the rice bran of the 7th day after the flowering of Example 1, a white immature grain ratio, a base immature grain ratio, and a grain size adjustment ratio. FIG. 6 is a graph showing the correlation between the L-APX1 gene expression ratio and the base immature grain percentage, base immature grain percentage, and size adjustment percentage in the rice bran on day 7 after flowering of Example 1. FIG. 7 is a view showing the correlation between the L-APX2 gene expression ratio and the percentage of white immature grain, the base immature grain percentage, and the size adjustment percentage in the rice bran on day 7 after flowering of Example 1. FIG. 6 is a view showing the correlation between L-APX4 gene expression ratio and white immature grain ratio, base immature grain ratio, and size adjustment ratio in the rice bran on day 7 after flowering of Example 1. FIG. 7 is a view showing the correlation between the ratio of expression amount of α-glucosidase gene and the percentage of white immature grains, the percentage of immature base grains, and the percentage of grains in the rice straw of the 7th day after flowering of Example 1. It is a figure which shows the correlation with a cellulose synthase gene expression ratio and the white immature grain ratio, the base immature grain ratio, and the grain adjustment ratio in the rice cake of the 7th day after the flowering of Example 1. It is a figure which shows correlation with a white immature-grain ratio, a base immature-grain ratio, and a regular-grained ratio in CRT / DRE1 gene expression ratio in the rice cake of the 7th day after the flowering of Example 1. It is a figure which shows the correlation with Ras GTPase gene expression ratio in the rice bran of the 7th day after flowering of Example 1, a white immature grain ratio, a base immature grain ratio, and a grain size adjustment ratio. It is a figure which shows the correlation with the XET gene expression ratio in the rice straw of the 7th day after the flowering of Example 1, a white immature grain ratio, a base immature grain ratio, and a grain size adjustment ratio. It is a figure which shows the correlation with the ratio of Cu / Zu SOD gene expression in the rice straw of the 7th day after the flowering of Example 1, a white immature grain ratio, a base immature grain ratio, and a grain size adjustment ratio. FIG. 7 is a view showing the correlation between the ratio of expression amount of α-glucosidase gene and the percentage of white immature grains, the percentage of base immature grains, and the rate of regulation of grain size in the rice bran on day 10 after flowering in Example 1. It is a figure which shows the correlation with Ras GTPase gene expression ratio in the rice bran of the 10th day after the flowering of Example 1, a white immature grain ratio, a base immature grain ratio, and a grain adjustment ratio. It is a figure which shows correlation with a white immature grain ratio, a base immature grain ratio, and a grain size adjustment ratio in the rice straw of the 10th day after the flowering of Example 1 flowering. FIG. 6 is a graph showing the correlation between the β-1,3-glucanase gene expression ratio and the white immature grain percentage, the base immature grain percentage, and the grain size adjustment percentage in the rice bran on day 10 after flowering of Example 1. It is a figure which shows the correlation with the NAM gene expression level ratio in the rice straw of the 10th day after the flowering of Example 1, a white immature grain ratio, a base immature grain ratio, and a grain adjustment ratio. It is a figure which shows the correlation with Germin-like 8 gene expression ratio and the white immature grain ratio, the base immature grain ratio, and the grain adjustment ratio in the rice cake of the 10th day after the flowering of Example 1.

  The preferred embodiments of the present invention will be briefly described by showing the operation of the present invention based on the drawings.

  Among the starch biosynthesis related genes, stress response related genes, cell wall synthesis related genes, etc. expressed in the rice bran at the ripening stage, the present inventor has an expression amount correlated with the incidence of white immature grains. It was found that a certain gene was present. The expression level of this gene can be sufficiently measured even at the early stage when rice grain maturity is low, for example, in the early flowering stage of rice, so the expression level of these genes can be measured to generate white immature grains. We thought that it would be possible to predict the rate early, and as a result of intensive investigations, we have established a method for predicting the rate of white immature grain in rice, which can predict the rate of white immature grain with high accuracy from the gene expression level.

  In addition, similarly, it is found that there is a correlation between the expression amount of this gene and the grain-adjusting percentage of rice, and it is possible to highly accurately predict the grain-adjusting percentage of rice from the gene expression amount. We established a method of grain size prediction.

  In addition, a gene to be measured was also identified. The genes of rice to be measured are comprehensively analyzed for the effects of environmental stress on gene expression level and white immature grain development in rice grown in an artificial climatic chamber or field, and gene expression level and white immature grain The gene which has been found to be correlated with the occurrence of the gene, specifically 15 genes shown in the following (a) to (o).

Record
(a) Amy3A (b) Amy3E (c) L-APX1
(d) L-APX2 (e) L-APX4 (f) α-glucosidase
(g) Cellulose synthase (h) CRT / DRE1 (i) Ras GTPase
(j) XET (k) Cu / Zn SOD (l) Phosphoglucomutase
(m) β-1,3-Glucanase (n) NAM (o) Germin-like 8

  (a) The Amy3A gene encodes α-amylase isozyme 3A, which is defined by accession number AK063988 in the rice genome database.

  (b) The Amy3E gene encodes α-amylase isozyme 3E, which is defined by accession number AK064300 in the rice genome database.

  (c) The L-APX1 gene encodes L-ascorbate peroxidase 1, and is defined by accession number AK061841 in the rice genome database.

  (d) The L-APX2 gene encodes L-ascorbate peroxidase 2 and is defined by accession number AK068430 in the rice genome database.

  (e) The L-APX4 gene encodes L-ascorbate peroxidase 4 and is defined by accession number AK104490 in the rice genome database.

  (f) The α-glucosidase gene encodes α-glucosidase and is defined by accession number AK105449 in the rice genome database.

  (g) Cellulose synthase gene encodes Cellulose synthase family protein, and is defined by accession number AK100523 in rice genome database.

  (h) The CRT / DRE1 gene encodes CRT / DRE binding factor 1 and is defined by accession number AK060550 in the rice genome database.

  (i) The Ras GTPase gene encodes Ras GTPase family protein, and is defined in the rice genome database with accession number CI474676.

  (j) The XET gene encodes Xyloglcan endotransglucosylase, which is defined by accession number AK070734 in the rice genome database.

  (k) The Cu / Zn SOD gene encodes Cu / Zn Superoxide dismutase, and is defined by accession number AK059841 in the rice genome database.

  (l) The Phosphoglucomutase gene encodes a Phosphoglucomutase precursor, which is defined by accession number AK068502 in the rice genome database.

  (m) The β-1,3-glucanase gene encodes β-1,3-glucanase-like protein and is defined by accession number AK100683 in the rice genome database.

  (n) The NAM gene codes for No apical meristem (NAM) protein domain containing protein, and is defined in the rice genome database with accession number AK063703.

  (o) The Germin-like 8 gene encodes Oxalate oxidase-like protein / germin-like protein 8 and is defined by accession number CI411375 in the rice genome database.

  Depending on the variety of rice to be evaluated, there may be a mutation in the sequence of the gene defined by each accession number, but if the sequence homology is 90% or more, it is determined by each accession number It can be regarded as a gene that

  Moreover, in the present invention, the expression level of the above-mentioned gene in the persimmon can be measured at any time of the ripening stage, but, for example, effective cultivation control is performed using the present invention to In order to reduce the occurrence, it is desirable to measure as early as possible, for example, about 10 days after flowering of rice.

  The method of measuring the expression level of the gene in the present invention is not particularly limited as long as the purpose is to obtain the expression level of the gene. For example, the gene expression may be measured at the transcription level, or transcription It is also possible to measure at the protein level translated from the product.

  For example, for measurement of transcription level, real-time PCR based on PCR, digital PCR, etc. can be applied, or microarray analysis, isothermal nucleic acid amplification such as LAMP, RNA dot blot, etc. may be used. On the other hand, in the measurement at the protein level, immunological detection methods such as ELISA and Western blotting can be applied, and if the expression product is an enzyme, measurement of enzyme activity, quantification of an enzyme reaction product, and the like can be applied.

  Although various measurement methods can be used to measure the expression level of the gene in the present invention, in order to measure the expression level of the gene with high sensitivity and high accuracy, real-time PCR, digital PCR, etc. can be used. It is desirable to use the PCR method.

  Furthermore, the present invention can be used, for example, to predict the incidence of white immature grains or only the base immature grains of the white immature grains from the expression level of the obtained gene, or to predict grain size regulation (to obtain a predicted value ) The occurrence rate (occurrence rate) can be predicted more easily and precisely (predicted value is obtained) by creating a prediction equation in advance and using the prediction equation of this prediction equation. The expression rate of one gene selected from the genes shown in the above (a) to (o) is used as an explanatory variable, and the incidence of white immature grains, or the incidence of base immature grains, or the regulation rate is used as an objective variable It is possible to create from single regression analysis, and also to calculate the average value of the incidence of white immature grain and base immature grain from single regression analysis by the expression level of two or more genes, in order to improve prediction accuracy Is valid.

  Furthermore, the expression amounts of two or more genes including at least one of the genes shown in (a) to (o) above, for example, two or more of the genes shown in (a) to (o) above It can also be created by multiple regression analysis with the incidence rate of white immature grains or base immature grains as the explanatory variable, or the grain size adjustment rate as the objective variable. By using these single regression equations or multiple regression equations, it is possible to predict the incidence rate of white immature grains, the incidence rate of base immature grains, and the grading ratio with high accuracy.

  In addition, although prediction accuracy is inferior, it is possible to obtain a prediction value by simple evaluation without using the above prediction equation, for example, a reference value of expression level of gene is set in advance, and gene expression relative to the reference value From the fluctuation amount, it is possible to know the incidence of white immature grains and base immature grains, or the tendency of the grain size proportion.

  The present invention can predict the incidence of white immature grains, or the incidence of only immature base grains of white immature grains, and further, the grain size adjustment rate easily and with high accuracy by such a series of methods. It becomes a thing.

  In addition, as described above, white immature grains include types such as milk white grains, heart white grains, back white grains, belly white grains, and base immature grains, and the cause of generation varies depending on this type, so the present invention is used For example, if many of the white immature grains are base immature grains, it is determined that the cause is high temperature failure, or if the base immature grains alone can not explain the incidence rate of the whole white immature grains due to other factors such as lack of sunlight. Appropriate timing, such as appropriate water management or fertilization management as a measure to suppress white immature grain generation based on such a judgment, or early cutting time to prevent an increase in the white immature grain generation number, etc. Appropriate measures can be taken, and the generation of white immature grains can be suppressed in advance.

  Specific Example 1 of the present invention will be described based on the drawings.

  In this example, rice (Oryza sativa subsp. Japonica) prior to harvest, specifically, rice at the ripening stage, more specifically, rice (a) Amy3A, which is obtained from rice grown at the early stage after flowering (a) b) Amy3E, (c) L-APX1, (d) L-APX2, (e) L-APX4, (f) α-glucosidase, (g) Cellulose synthase, (h) CRT / DRE1, (i) Ras GTPase (J) XET, (k) Cu / Zn SOD, (l) Phosphoglucomutase, (m) β-1,3-gluconanase, (n) NAM, (o) at least 15 genes of Germin-like 8 The expression level of one gene is measured, and from the expression level of this gene, the incidence of white immature grains of the rice, or the incidence of base immature grains of this immature white grains, or the regulation rate of the rice concerned From the predicted value of the white immature grain incidence rate, or the predicted value of the base immature grain incidence rate, or the predicted value of the grain size percentage, the high temperature ripening failure of the rice concerned at the early stage before harvest It is a diagnostic method of rice that diagnoses the quality deterioration risk of rice caused by

  Specifically, in this example, the expression amount of one of the genes (a) to (o) is used as an explanatory variable, and the incidence rate of white immature grains, the incidence rate of proximal immature grains, or adjustment The prediction equation obtained from single regression analysis with grain ratio as the objective variable is prepared in advance, and this prediction equation and the early stage before harvest, specifically, the early stage after flowering (for example, 5 to 10 days after flowering) The incidence of white immature grains of the rice, or the incidence of base immature grains of the immature white grains, or the regulation rate of the grain of the rice, according to the expression level of the gene obtained from the rice straw of It is a diagnostic method of rice which acquires a predicted value and diagnoses the quality deterioration risk of rice due to the high temperature ripening failure of the said rice from this predicted value.

  Hereinafter, the present embodiment will be described in detail.

[Create prediction formula]
In this example, in this example, the Niigata Prefectural Agricultural Comprehensive Research Project is used to create the prediction values used to obtain the predicted values of the incidence rate of white immature grains and base immature grains and the predicted value of grain adjustment percentage. Rice varieties "Koshihikari BL" grown in about 60 test districts with different temperature and water management conditions and fertilization conditions in the field of a crop research center (Nagaoka City, Niigata Prefecture) are considered as the target rice and about 10 strains of rice per test district 10 to 20 drops of persimmon were sampled on the 5th, 7th, 10th day after flowering, frozen with liquid nitrogen, then stored at -80 ° C, and used as a sample .

  Then, this cryopreserved sample is crushed in a mortar, and the rat tissue is suspended by adding cetyltrimethylammonium bromide (CTAB), 100 mM Tris-HCl (pH 8.0), 20 mM EDTA, 1.4 M NaCl, and RNeasy Plant. Total RNA was purified according to a conventional method using Mini Kit (QIAGEN). After cDNA was synthesized from 5 μg of total RNA using RNA PCR kit Ver. 3.0 (Takara Bio Inc.), quantitative PCR analysis was performed using the primer set shown in Table 1.

  The primer set was designed based on gene sequence information obtained from rice gene expression database RiceXPro (National Institute of Bioresources; http: // ricepro. Dan. Affrc. Go. Jp). In addition, SsoFast Eva Green Supermix (Bio-Rad) was used for quantitative PCR, and the reaction was performed under the conditions shown in Table 2. In addition, the expression level of each gene is shown as a common logarithm as a relative expression level to 18S rRNA.

  In addition, on the other hand, for brown rice harvested at a harvest period of an integrated temperature of about 1000 ° C. from each test section in which the gene expression level was measured, the measurement result (appearance quality survey result) with a grain classifier (RGQI 20A manufactured by Satake Co., Ltd.) Based on these, the incidences of white immature grains and base immature grains and grain size adjustment rates are calculated, and the incidence of white immature grains, incidence rate of base immature grains, grain size adjustment rate and gene expression amount calculated by this appearance quality survey Correlation was determined. FIGS. 1 to 28 show the correlation between the expression level of each gene (specifically, the relative expression level to 18S rRNA), the incidence of white immature grains and base immature grains, and the grain size adjustment rate.

  In this embodiment, this correlation is analyzed by polynomial approximation, and a prediction formula for calculating the occurrence rate of white immature grains, the incidence rate of base immature grains, and the predicted grain size adjustment rate from the expression level of the target gene ( Single regression equation was created as shown in Tables 3-11. Tables 3 to 5 are prediction equations used when calculating gene expression levels on day 5 after flowering to calculate predicted values of white immature grain incidence, base immature grain incidence, and grain size regulation percentage. 6-8 are prediction equations used when calculating the prediction value of the white immature grain generation | occurrence | production rate, the base immature grain generation | occurrence | production rate, and the grain size adjustment percentage by measuring the gene expression level 7 days after flowering. Tables 9 to 11 are prediction formulas used when calculating the predicted values of white immature grain incidence, base immature grain incidence, and size regulation percentage by measuring the gene expression level on day 10 after flowering. . In the prediction formulas shown in the table, y represents the gene expression level, and x represents each predicted value of white immature grain generation rate, base immature grain generation rate, and grain size adjustment percentage.

  In this example, α-glucosidase, Amy3A, Amy3E, Cellulose synthetase, CRT / DRE1, Cu / Zn SOD, L-APX1, L-APX2, L as explanatory variables on day 5 and day 7 after flowering. -Adopt genes of APX4, Ras GTPase and XET, and adopt genes of α-glucosidase, Ras GTPase, Phosphoglucocomutase, β-1,3-Glucanase, NAM and Germin-like 8 as an explanatory variable on day 10 after flowering did. The genes employed as explanatory variables are not limited to the above.

[Verification of accuracy (reliability) of prediction formula]
The accuracy (reliability) of each prediction formula created as described above was verified.

  Specifically, in 2016, the cultivar "Koshihikari BL" is cultivated in three fields (fields A, B, C) different from the fields where the rice sampled when creating the above-mentioned prediction formula is grown. The sample is sampled using the same procedure as for the prediction equation to make a sample, the gene expression amount in rice of each of the fields A to C is measured, and the measured gene expression amount is substituted into the prediction equation. Then, the predicted value of the incidence rate of white immature grains of rice, the predicted value of the incidence rate of base immature grains, and the prediction value of the grain size percentage in each of the fields A to C were calculated.

  After that, the rice in each of the fields A to C is harvested, and the appearance quality of the harvested brown rice is investigated, and the actual incidence rate of white immature grains, the actual incidence rate of immature base grains and the actual grain size adjustment ratio Were calculated, and these values (measured values) were compared with predicted values calculated using a prediction formula. Tables 12 to 20 show the comparison results.

  As shown in each table, in the predicted value, although there is a slight difference depending on the measurement time of the gene expression amount and the target gene, the result showing the value almost close to the actual value was obtained. In addition, when the measured value of white immature grains (base immature grains) exceeds 10%, the prediction error is smaller than the case of less than that, so the higher the incidence rate of white immature grains (base immature grains), the predicted value The result was obtained that it was judged that the accuracy of That is, these results indicate that gene diagnosis at the ripening stage (measurement of the gene expression level) is effective in predicting the occurrence of white immature grains (base immature grains).

  As described above, in this example, a prediction equation (single regression equation) is created from the correlation between white immature grain, base immature grain, and grain size regulation rate and gene expression amount, and using this prediction equation, in the early stage of the ripening period Based on the gene expression level, the incidence of white immature grains and base immature grains of harvested rice, and the grain size adjustment rate can be predicted with high accuracy with high accuracy, and based on this prediction result, appropriate cultivation management ( Water management, fertilization management, management of harvesting time and the like can be appropriately performed at appropriate timing, and it becomes possible to suppress the generation of white immature grains and to realize a high grading ratio.

  Below, the example is shown.

[Verification of effectiveness of cultivation management (cultivation measures) of rice based on diagnosis of rice at the early stage of ripening period]
Using two test plots, one test plot is a fertilization-matched test plot, and another test plot is an early harvest-matched test plot, and the amount of gene expression after 5 days of flowering in rice of each test plot (in this example) , Amy3A, Amy3E, L-APX1, L-APX4, α-glucosidase, XET, and Cu / Zn SOD were adopted, and these were substituted into the prediction formula shown in Table 4 to obtain base immature grains Predicted incidence rate was obtained. In the present example, verification was performed using the variety "Koshihikari BL".

  And in fertilization correspondence examination ward, we divide into actual fertilization measures ward which performs fertilization measures based on prediction value of base immature grain incidence rate that we acquired, and normal cultivation ward which does not take measures in actual fertilization measures ward A nitrogen content of 1 kg / 10 air of real fertilizer was applied in the same period of earing, and thereafter, the incidence rate of base immature grains was measured in each of the brown rice harvested in the harvesting period of about 1000 ° C. in both categories.

  In addition, in the early harvest response test zone, it is divided into an early harvest zone in which rice is harvested and harvested early based on the acquired predicted value of the base immature grain incidence rate, and a normal harvest zone in which no measures are taken. In harvest, harvest at about 950 ° C, about 50 ° C faster than about 1000 ° C of the total integrated temperature of the daily average temperature, which is a measure of harvest season, and harvest the harvest area at about 1000 ° C as usual, The incidence of immature grains was measured.

  The predicted value of the incidence rate of the base immature grain based on the gene expression in each test section and the measurement result of the incidence rate of the actual base imitation grain according to the presence or absence of cultivation measures are shown in Table 21.

  As shown in Table 21, in the fertilization correspondence test area, the predicted value of the incidence rate of the base immature grain is 24.2% (the average value of the predicted values respectively obtained from the seven genes). The incidence rate (measured value) of the base immature grain of the normal cultivation area which does not take measures was 18.0%, but the actual base immature grain of the actual fertilization area of the fertilization measures area which applied the fertilization measures based on the predicted value The incidence was 4.3%.

  In addition, in the early harvest response test zone, the predicted value of the incidence rate of the base immature grain is 11.5% (average value of predicted values obtained from each of the 7 genes), and no countermeasure is taken against this. Although the actual incidence rate (measured value) of the base immature grain in the harvest area was 14.5%, the actual incidence rate of the immature base imitation grain in the early harvest area, in which the harvest time was advanced based on the predicted value, It was 7.6%.

  From the above results, using the method for diagnosing rice of this example, white immature white at the early stage before harvesting (specifically, the stage after flowering of rice (about 5 to 10 days after flowering)) By predicting the incidence rate of grain or base immature grains or grain size regulation and performing appropriate cultivation measures (water management, fertilization management, early harvest (early harvest)) based on this prediction result, white due to abnormal weather By suppressing the generation of immature grains in advance, it becomes possible to realize the cultivation of rice with a high proportion of grain size regulation.

  In the present example, the cultivar "Koshihikari BL" was used to prepare the sample and verify the prediction formula, but it has been confirmed that the same result can be obtained with the conventional cultivar "Koshihikari".

  A specific embodiment 2 of the present invention will be described based on the drawings.

  The present example is a case where a prediction formula was created by a method different from Example 1. Specifically, in Example 1, the white immature grain incidence obtained from the gene expression data and the appearance quality survey result, the base Single regression analysis was performed using the immature grain generation rate and the grain size adjustment ratio, and each prediction equation was created. In this example, multiple regression analysis is performed to create a prediction equation.

  Hereinafter, the present embodiment will be described in detail.

[Create prediction formula]
In the multiple regression analysis in this example, white immature grains were obtained using the gene expression levels on days 5, 7, and 10 after flowering of the genes of (a) to (o) mentioned in Example 1 as an explanatory variable. The analysis was performed using statistical analysis software (EXCEL prediction Ver. 3.0 manufactured by Esumi Co., Ltd.) with the incidence rate of stem area, the incidence rate of base immature grains, or the regulation rate as an objective variable.

  Specifically, in the present example, the combination of Amy3E, Cellulose synthase, CRT / DRE1, L-APX1, L-APX4 and XET genes at the 5th day after flowering is the incidence of white immature grains, Amy3E, The combination of Cellulose synthetase, CRT / DRE1 and XET is the incidence rate of the base immature grain, and the combination of Amy3E, Cellulose synthase, CRT / DRE1 and L-APX4 is the explanatory variable of the grain size adjustment rate, and on day 7 after flowering Describes the combination ratio of α-glucosidase, Cu / Zn SOD, L-APX2, Ras GTPase and the proportion of white immature grain and base immature grain, Amy3E, Cu / Zn SOD, Ras GTPase, XET In addition, at 10 days after flowering, the combination of α-glucosidase, β-1,3-glucanase, Ras GTPase, and NAM is the incidence of white immature grain and base immature grain, and explanatory variable of grading frequency Then, each prediction formula shown in Table 22 was created.

[Verification of accuracy (reliability) of prediction formula]
The accuracy (reliability) of each prediction formula created as described above was verified.

  Specifically, in the present example, in the field of the Niigata Prefectural Agricultural Research Institute Crop Research Center, in the three test zones (test zones X, Y, Z) different from the first embodiment, the varieties “2016” "Koshihikari BL" is grown, the sample is sampled by the same procedure as in Example 1 to prepare a sample, the gene expression amount in rice of each test zone of X to Z is measured, and the measured gene expression amount is The prediction value of the incidence rate of white immature grains of rice, the prediction value of the incidence rate of base immature grains, and the prediction value of grain size regulation percentage in each of the test sections X to Z were calculated by substituting the above prediction equation.

  After that, the rice in each of the test zones X to Z is harvested, and the appearance quality of the harvested brown rice is examined to determine the actual incidence rate of white immature grains, the actual incidence rate of immature base grains, and the actual grain size adjustment The commissions were calculated, and these values (measured values) were compared with predicted values calculated using a prediction formula. Tables 23 to 25 show the comparison results.

  Compared with the prediction accuracy of the predicted value obtained by the prediction formula prepared by performing the single regression analysis shown in Tables 12 to 20 of Example 1, it is obtained by the prediction formula prepared by performing the multiple regression analysis of this example. The prediction accuracy of the predicted value tends to be higher. From this result, by using a prediction equation created by performing multiple regression analysis using the expression levels of a plurality of genes as explanatory variables, prediction accuracy is improved, and in addition to single regression analysis, multiple regression analysis is also available. It has been confirmed that this method is effective for the early prediction of the incidence of white immature grains and immature base grains and the adjustment ratio.

  The present invention is not limited to the first and second embodiments, and the specific configuration of each component can be designed as appropriate.

Claims (14)

It is a method for predicting the incidence of white immature grains of rice before harvest, which is an expression amount of at least one of the following genes (a) to (o) of a rice bran collected from rice during the ripening stage: A method for predicting the white immature grain incidence of rice, which comprises measuring and predicting the incidence of white immature grains of said rice from the expression level of this gene.
Record
(a) Amy3A (b) Amy3E (c) L-APX1
(d) L-APX2 (e) L-APX4 (f) α-glucosidase
(g) Cellulose synthase (h) CRT / DRE1 (i) Ras GTPase
(j) XET (k) Cu / Zn SOD (l) Phosphoglucomutase
(m) β-1,3-Glucanase (n) NAM (o) Germin-like 8   The method for predicting the white immature grain incidence of rice according to claim 1, wherein the expression amount of the gene is measured at an early stage after flowering of the rice.   The method for predicting the white immature grain incidence of rice according to claim 1, wherein the expression amount of the gene is measured within 10 days from the flowering of the rice.   In the white immature grain incidence prediction method of rice according to any one of claims 1 to 3, the expression amount of one of the genes (a) to (o) is used as an explanatory variable, and the white immature grain A method for predicting the white immature grain incidence of rice, which comprises using the white immature grain incidence prediction equation obtained from single regression analysis with the incidence rate of the target variable as an objective variable.   The method according to any one of claims 1 to 3, wherein the expression amount of two or more genes including at least one of the genes (a) to (o) is used as an explanatory variable. A method for predicting the white immature grain incidence rate of rice, which comprises using a white immature grain incidence rate prediction equation obtained from multiple regression analysis using the incidence rate of white immature grains as an objective variable. It is a method for predicting grain size regulation of rice before harvesting, and measuring the expression level of at least one gene among the following genes (a) to (o) of the rice bran collected from rice in the ripening stage, A method for predicting grain size regulation rate of rice, which comprises predicting the grain size regulation rate of said rice from the expression amount of this gene.
Record
(a) Amy3A (b) Amy3E (c) L-APX1
(d) L-APX2 (e) L-APX4 (f) α-glucosidase
(g) Cellulose synthase (h) CRT / DRE1 (i) Ras GTPase
(j) XET (k) Cu / Zn SOD (l) Phosphoglucomutase
(m) β-1,3-Glucanase (n) NAM (o) Germin-like 8   The method of predicting grain size regulation rate of rice according to claim 6, wherein the expression amount of the gene is measured at an early stage after flowering of rice.   The method according to claim 1, wherein the expression level of the gene is measured within 10 days from the flowering of the rice.   The method for predicting grain size regulation rate in rice according to any one of claims 6 to 8, wherein the expression amount of one of the genes (a) to (o) is used as an explanatory variable and the grain size regulation is aimed. A method for predicting the grain size adjustment rate of rice, which comprises using a grain size adjustment rate prediction formula obtained from single regression analysis as a variable.   The method for predicting grain size regulation rate of rice according to any one of claims 6 to 8, wherein the expression amount of two or more genes including at least one of the genes (a) to (o) is used as an explanatory variable, A method for predicting the grain proportion of rice, which comprises using a grain size proportion prediction equation obtained by multiple regression analysis with grain proportion as an objective variable. A marker gene which is used when predicting the white immature grain development rate of rice before harvesting, and is selected from the following genes (a) to (o):
Record
(a) Amy3A (b) Amy3E (c) L-APX1
(d) L-APX2 (e) L-APX4 (f) α-glucosidase
(g) Cellulose synthase (h) CRT / DRE1 (i) Ras GTPase
(j) XET (k) Cu / Zn SOD (l) Phosphoglucomutase
(m) β-1,3-Glucanase (n) NAM (o) Germin-like 8 A marker gene, which is used to predict the grain size regulation rate of rice before harvesting, and is selected from the following genes (a) to (o):
Record
(a) Amy3A (b) Amy3E (c) L-APX1
(d) L-APX2 (e) L-APX4 (f) α-glucosidase
(g) Cellulose synthase (h) CRT / DRE1 (i) Ras GTPase
(j) XET (k) Cu / Zn SOD (l) Phosphoglucomutase
(m) β-1,3-Glucanase (n) NAM (o) Germin-like 8 In the method of cultivating rice, the expression amount of at least one gene of the following (a) to (o) genes of the potato collected from rice at the ripening stage is measured, and the expression amount of this gene is measured from the said rice The rice is characterized by acquiring the predicted value of the white immature grain incidence rate of rice and determining the cultivation management such as water management, fertilization management, harvesting time, etc. of the rice based on the prediction value of the white immature grain incidence rate. How to grow.
Record
(a) Amy3A (b) Amy3E (c) L-APX1
(d) L-APX2 (e) L-APX4 (f) α-glucosidase
(g) Cellulose synthase (h) CRT / DRE1 (i) Ras GTPase
(j) XET (k) Cu / Zn SOD (l) Phosphoglucomutase
(m) β-1,3-Glucanase (n) NAM (o) Germin-like 8 In the method of cultivating rice, the expression amount of at least one gene of the following (a) to (o) genes of the potato collected from rice at the ripening stage is measured, and the expression amount of this gene is measured from the said rice A method for cultivating rice, comprising acquiring a predicted value of a grain size regulation percentage and determining cultivation management such as water management, fertilization management, harvesting time, etc. of the rice based on the predicted value of the grain size classification percentage.
Record
(a) Amy3A (b) Amy3E (c) L-APX1
(d) L-APX2 (e) L-APX4 (f) α-glucosidase
(g) Cellulose synthase (h) CRT / DRE1 (i) Ras GTPase
(j) XET (k) Cu / Zn SOD (l) Phosphoglucomutase
(m) β-1,3-Glucanase (n) NAM (o) Germin-like 8

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