JP7084639B2 - Plant stress detection method and photoprotein detection method in plants - Google Patents

Description

The present invention relates to a method for detecting stress in a plant and a method for detecting a photoprotein in a plant.

Plants do not have the biological defense mechanism of the immune system such as antibody reaction and phagocytosis as seen in vertebrates, but they protect themselves from attacks by pathogens by a different mechanism. When a plant is infected with a pathogen in some tissues, it can send a signal to the whole body to induce an uninfected tissue to be defensive. It is known that there are multiple transmission pathways that transmit infection signals throughout the body, and typical ones include systemic acquired resistance signal transduction pathways, induced resistance signal transduction pathways, and induced systemic resistance signal transduction pathways. Exists. In such a signal transduction pathway, when some tissues of a plant are subjected to infection stress due to a pathogenic bacterium, some signal substance is released from the infected site, and it is still infected through the inside of the plant. It reaches a non-existent site, where it induces the expression of genes involved in resistance expression. Examples of the substance involved in this resistance include PR proteins having antibacterial activity by themselves, glucanase and chitinase that lyse the cell wall, and phytoalexin that is toxic to pathogens. Until now, it has been considered that such induction of resistance occurs only within the same plant individual. However, it was discovered that plant-derived volatile components such as methyl jasmonate, short-chain aldehydes, and isoprenoids have a function as an airborne signal that induces plant resistance, and are physically separated from each other. Alternatively, it has become known that different kinds of plants activate the biological defense mechanism (hearing effect) by recognizing it.
As a technique utilizing such an eavesdropping effect of a plant, it has been reported that a plant-derived volatile substance is used as a plant resistance inducer (Patent Document 1).
In addition, it is known that plants emit weak biological light in response to stress, and a method of observing light emission of a specific wavelength when a plant is subjected to stress such as pathogen attack (Patent Document 2) and immunostable. A method for evaluating the effect of a plant immunostabilizing material that inhibits the suppression of disease resistance reaction of plants by measuring biophotones generated by a material (absidic acid) and an inducer (salicylic acid, jasmonic acid) (Patent Documents). 3) has been reported.

Japanese Unexamined Patent Publication No. 2005-41782 Japanese Unexamined Patent Publication No. 2001-99830 Japanese Unexamined Patent Publication No. 2009-131187

Zarate et al., 2007 Plant Physiol. Vol. 143, 866-875 Reymond et al., 2004 Plant Cell 16: 2132-3147 Kishimoto et al., 2006 Phytochemistry 67: 1520-1529 Betsuyaku et al., Plant Cell Physiol. 59 (1): 8-16 (2018) Plant Biotechnology 35, 1-6 (2018) Takai et al., 2015 PNAS 112: 4352 Saito et al., 2012 Nature Communications 3: 1262

An object to be solved by the present invention is to detect damage caused by pests to a target plant such as a crop at an early stage.

As a result of diligent studies and experiments to solve the above problems, the inventors of the present application were operably linked to a stress-responsive promoter and downstream of the stress-responsive promoter in the vicinity of a target plant such as an agricultural crop. By arranging a monitor plant containing a photoprotein gene, we found a surprising finding that the photoprotein in the monitor plant is expressed and emits light at a stage where the damage to the target plant is slight. Based on this finding, we have succeeded in developing an image processing device, an image processing system, and an image processing program capable of automatically quantifying fluorescent signals in response to moving biological samples such as plants, and completed the present invention. I arrived.

The present invention is as follows.
[1] A method for detecting stress in a target plant.
A step of placing a stress-responsive promoter and a monitor plant containing a photoprotein gene operably linked downstream of the stress-responsive promoter in the vicinity of the target plant.
The process of visualizing the stress of the target plant,
Here, in the visualization of the stress of the target plant, when the monitor plant receives a volatile substance released by the target plant in response to stress, the stress-responsive promoter responds in the monitor plant. A method characterized by this being carried out by the expression of the photoprotein.
[2] The stress-responsive promoters are PR1 promoter, PR2 promoter, PR3 promoter, PR4 promoter, PR5 promoter, AOS promoter, VSP1 promoter, VSP2 promoter, HPL promoter, AtMYC2 promoter, CYP83B1 promoter, At2g24850 promoter, LOX2 promoter, IAR3. Promoter, GST5 promoter, OPR3 promoter, ERF promoter, THI2.1 promoter, JAZ gene group promoter, PDF1.2 promoter, WRKY promoter, PAD4 promoter, EDS1 promoter, SID1 promoter, EDS5 promoter, BGL2 promoter, or a combination thereof. 1. The method according to 1.
[3] The luminescent protein is a protein using FRET technology such as green fluorescent protein, yellow fluorescent protein, red fluorescent protein, cyanide fluorescent protein, blue fluorescent protein and other fluorescent proteins, bioluminescent protein, chemical luminescent protein and nanolantern. , Or the method according to 1 or 2, characterized in that it is a combination thereof.
[4] The volatile substances are methyl jasmonate, methyl salicylate, green leaf alcohol, aromatics, terpenes, myrcene, (E, E) -4,8,12-trimethyltrideca-1,3,7,11- The method according to 1, wherein the method is selected from tetraene (TMTT), esters, aldehydes, aromatic compounds, 2-pentanol, or a combination thereof.
[5] The method according to 1, wherein the monitor plant is Arabidopsis thaliana, moss, cat jarashi, or brachypodium.
[6] A method for detecting photoproteins in plants.
A step of specifying an image region including a wavelength band of the emission wavelength of the photoprotein and a wavelength band of self-emission of the plant from an image of a plant containing a photoprotein gene, and light emission existing in the specified image region. The process of detecting only the emission wavelength signal of a protein,
A method characterized by including.
[7] The photoprotein in the plant is
A detection unit that detects an image region of light emitted by the organism from an image of light in a wavelength band including the wavelength of light emitted by the organism to be imaged.
An image processing target area extraction unit that extracts an image processing target area of the organism from the captured image based on the detected image area, and an image processing target area extraction unit.
A specific signal extraction unit that extracts an image region of a specific signal from the captured image,
A specific signal selection unit that extracts an image region of a specific signal included in the extracted image processing target region from the extracted image region of the specific signal, and a specific signal selection unit.
Image processing device, equipped with
6. The method according to 6.
[8] The photoprotein in the plant is
Non-sampled organisms and sample organisms are mixed and arranged,
An imaging unit that captures light in a wavelength band including the wavelength of light emitted by the sample organism, and
A detection unit that detects an image region of light emitted by the sample organism from an image captured by the imaging unit.
An image processing target area extraction unit that extracts an image processing target area of the sample organism from the captured image based on the detected image area, and an image processing target area extraction unit.
A specific signal extraction unit that extracts an image region of a specific signal from the captured image,
A specific signal selection unit that extracts an image region of a specific signal included in the extracted image processing target region from the extracted image region of the specific signal, and a specific signal selection unit.
Image processing system,
6. The method according to 6.
[9] The image processing apparatus or image processing system
On the computer
A detection function that detects the image region of the light emitted by the organism from the captured image of the light in the wavelength band including the wavelength of the light emitted by the organism to be imaged.
An image processing target area extraction function that extracts an image processing target area of the organism from the captured image based on the detected image area, and an image processing target area extraction function.
A specific signal extraction function that extracts an image region of a specific signal from the captured image, and
A specific signal selection function that extracts an image region of a specific signal included in the extracted image processing target region from the extracted image region of the specific signal, and a specific signal selection function.
Image processing program to realize
7. The method according to 7 or 8, wherein the method comprises.
[10] The method for detecting stress in a target plant according to any one of 1 to 5, wherein the photoprotein in the monitor plant is detected by the method according to any one of 6 to 9.
[11] A detection unit that detects an image region of light emitted by the organism from an image of light in a wavelength band including the wavelength of light emitted by the organism to be imaged.
An image processing target area extraction unit that extracts an image processing target area of the organism from the captured image based on the detected image area, and an image processing target area extraction unit.
A specific signal extraction unit that extracts an image region of a specific signal from the captured image,
A specific signal selection unit that extracts an image region of a specific signal included in the extracted image processing target region from the extracted image region of the specific signal, and a specific signal selection unit.
An image processing device comprising.
[12] Non-sampled organisms and sampled organisms are mixed and arranged.
An imaging unit that captures light in a wavelength band including the wavelength of light emitted by the sample organism, and
A detection unit that detects an image region of light emitted by the sample organism from an image captured by the imaging unit.
An image processing target area extraction unit that extracts an image processing target area of the sample organism from the captured image based on the detected image area, and an image processing target area extraction unit.
A specific signal extraction unit that extracts an image region of a specific signal from the captured image,
A specific signal selection unit that extracts an image region of a specific signal included in the extracted image processing target region from the extracted image region of the specific signal, and a specific signal selection unit.
Image processing system with.
[13] On the computer
A detection function that detects the image region of the light emitted by the organism from the captured image of the light in the wavelength band including the wavelength of the light emitted by the organism to be imaged.
An image processing target area extraction function that extracts an image processing target area of the organism from the captured image based on the detected image area, and an image processing target area extraction function.
A specific signal extraction function that extracts an image region of a specific signal from the captured image, and
A specific signal selection function that extracts an image region of a specific signal included in the extracted image processing target region from the extracted image region of the specific signal, and a specific signal selection function.
Image processing program to realize.

According to the present invention, it is possible to detect pest damage of a crop at an initial stage without performing genetic manipulation of the crop itself, which in turn contributes to ensuring food safety and enhancing and reducing resistance to pests.

It is a figure which shows the situation of information communication between a damaged plant and an eavesdropper. It is a schematic diagram of the Arabidopsis thaliana transformant. It is a figure which shows the image of the plant which moves in a time lapse by autofluorescence. It is a figure which shows the state of the detection of a fluorescent signal. It is a figure which shows the situation which detects the activated state of the immune system by a fluorescent signal in Arabidopsis plant group. It is a graph which shows the time-dependent change of the signal intensity for each individual. It is a concept diagram of futuristic agriculture pertaining to one embodiment. It is a figure which shows an example of the image processing method which concerns on one Embodiment. It is a photograph of a monitor plant growing device. Left: A photograph of the monitor plant growing device seen from above, with a divider buried in the center. Five wild-type seedlings are growing on the left side. Right: A side view of the monitor plant growth device, with a 0.5 mm hole in the part protruding above the divider's medium to allow volatiles to diffuse but pests to pass through. Seedlings of two monitor plants are growing. FIG. 3 is a fluorescence photograph of a monitor plant 24 hours after exposure to methyl jasmonate (MeJA), methyl salicylate (MeSA), and solvent (dichloromethane). Upper row: Photograph (white light) of the wild-type Arabidopsis thaliana and diamondback moth. Bottom: A photograph (fluorescent signal) of a plot of monitor plants. Upper row: Photograph (white light) of the plot of Mythimna separati and Arabidopsis thaliana. Bottom: A photograph (fluorescent signal) of a plot of monitor plants. It is a photograph which shows the fluorescence signal in the concentration gradient of methyl jasmonate. It is a photograph which shows the fluorescent signal by injury stress. It is a photograph showing a fluorescence signal in response to a volatile substance derived from a yellowcresses (Rorippa indica) extract (ground solution) immediately after the start (left) and 3 hours later (right). It is a photograph showing the fluorescence signal of a monitor plant after exposure to ocimene, methyl jasmonate (MeJA), and solvent (dichloromethane).

[1. Various words]
When a plant is infected with a pathogen in some tissues, it can send a signal to the whole body to induce an uninfected tissue to be defensive. It is known that there are multiple transmission pathways that transmit infection signals throughout the body, and typical ones include systemic acquired resistance signal transduction pathways, induced resistance signal transduction pathways, and induced systemic resistance signal transduction pathways. Exists.
In the systemic acquired resistance signaling pathway, when infected with a pathogen that causes a hypersensitive response, the signal substance generated locally in the infection moves to the whole body via the sieve duct tissue, and as a result, the plant becomes various pathogens. On the other hand, it becomes resistant to the whole body. Salicylic acid, a phytohormonal component, plays an important role in this signaling pathway. Salicylic acid accumulates not only at the site of infection but also in the cells to which the signal is transmitted, and when salicylic acid accumulates, NPR1 (PR protein production regulator), WRKY (transcription factor), PR-1, PR-2, It is believed that genes such as PR-5 are sequentially induced and resistance is expressed. On the other hand, in the induced resistance signal transduction pathway, when damaged by pests, jasmonic acid and ethylene are accumulated, and genes such as PDF1.2 are accumulated. In addition, jasmonic acid and ethylene are involved in the induced systemic resistance signal transduction pathway, and the infection stimulus in the root is transmitted to the whole body.
Specifically, PAD4, EDS1, SID1, EDS5, PR1, PR5, BGL2 and the like are known as genes whose expression levels are significantly increased when the salicylic acid signaling pathway is activated by pathogen infection (Non-Patent Documents). 1). Known genes commonly expressed in insect pests with methyl jasmonate include PR2, AtMYC2, CYP83B1, At2g24850, LOX2, VSP2, IAR3, GST5, OPR3, AOS, HPL, PR4, PDF1.2 and the like (non-patent documents). 2). Further, as genes whose expression is increased by methyl jasmonate, methyl salicylate, or green leaf alcohol, PR1, PR2, AOS, VSS1, HPL, PR3 and the like are known (Non-Patent Document 3).

Until now, it has been considered that such induction of resistance occurs only within the same plant individual. However, it was discovered that plant-derived volatile components such as methyl jasmonate, short-chain aldehydes, and isoprenoids have a function as an airborne signal that induces plant resistance, and are physically separated from each other. Alternatively, it has become known that different kinds of plants activate the biological defense mechanism (hearing effect) by recognizing it. That is, it is considered that when a plant is damaged by a pest, it emits a volatile immunoactive substance (scent) and activates the immune system of surrounding plants (eaves) (see FIG. 1).

The present invention utilizes such communication between individual plants via volatile immunoactive substances to provide a stress-responsive promoter and a photoprotein operably linked to the stress-responsive promoter in the vicinity of the target plant. By placing a monitor plant containing a gene, when the monitor plant receives a volatile substance released by the crop in response to damage caused by pests, the stress-responsive promoter, which is a gene marker of the immune pathway, responds in the monitor plant. However, it is based on visualizing the feeding damage or disease stress of the target plant by expressing the photoprotein (see FIG. 2).

In the present invention, the "stress responsive promoter" transcribes an operably linked photoprotein gene downstream of a volatile substance released by a plant in response to stress such as damage from a pest. Has a function.

[2. How to detect stress in target plants]
According to the first aspect of the present invention, it is a method for detecting stress of a target plant, and is a method of operably linked to a stress-responsive promoter in the vicinity of the target plant and downstream of the stress-responsive promoter. It includes a step of arranging a monitor plant containing a protein gene and a step of visualizing the stress of the target plant, wherein the visualization of the stress of the target plant is a volatile substance released by the target plant in response to the stress. Is accepted by the monitor plant, a stress-responsive promoter responds in the monitor plant, thereby providing a method characterized by the expression of the photoprotein. The stress of the target plant detected in the present invention includes feeding damage caused by insects and the like, diseases caused by molds and bacteria, and breakage or damage of the plant body.

The stress-responsive promoter used in the present invention is particularly limited as long as it has the function of transcribing a luminescent protein gene located downstream when a peripheral plant receives a volatile substance released in response to stress such as damage caused by a pest. However, typically, promoters of genes whose expression is increased by plant stress, namely PR1 promoter, PR2 promoter, PR3 promoter, PR4 promoter, PR5 promoter, AOS promoter, VSP1 promoter, VSP2 promoter, HPL promoter, AtMYC2. Promoter, CYP83B1 promoter, At2g24850 promoter, LOX2 promoter, IAR3 promoter, GST5 promoter, OPR3 promoter, ERF promoter, THI2.1 promoter, JAZ gene group promoter, PDF1.2 promoter, WRKY promoter, PAD4 promoter, EDS1 promoter, SID1 promoter, EDS5 promoter or BGL2 promoter, preferably PR1 promoter, PR2 promoter, AOS promoter, VSP1 promoter, HPL promoter, ERF promoter, THI2.1 promoter, JAZ gene group promoter, PDF1.2 promoter, WRKY promoter, or PR3. It is a promoter, most preferably a VSP1 promoter, a PDF1.2 promoter, or a PR1 promoter.

The luminescent protein used in the present invention is not particularly limited as long as it is expressed in a monitor plant and emits light to the extent that it can be visualized, but is typically green fluorescent protein, yellow fluorescent protein, red fluorescent protein, cyanide fluorescent protein, and blue. Fluorescent proteins such as fluorescent proteins, bioluminescent proteins (eg, luciferase), chemically luminescent proteins, or proteins using FRET technology such as nanolanterns.

The monitor plant used in the present invention is a vector containing a stress-responsive promoter and a photoprotein gene operably linked downstream of the stress-responsive promoter, using a conventional transformation method, for example, an agroinfiltration method. , Or by incorporating into a host plant using the particle gun method.

In the agroinfiltration method, a culture solution of Agrobacterium transformed with a T-DNA vector into which a target gene is inserted is placed in a plant tissue by a physical method (method such as injection with a syringe or permeation by decompression). It is a method of inserting a gene of interest into the genome of a plant and expressing it by introducing it and infecting the plant. By inserting into the genome of plant germ cells by the floral dip method, a stable transformation line can be created. Agrobacterium is a general term for Rhizobium, which is a soil bacterium belonging to Gram-negative bacteria, and has pathogenicity to plants. As an example of Agrobacterium, it is related to root cancer disease. Agrobacterium tumefaciens can be mentioned. When a foreign gene is constantly inserted into the genome of all cells using a vector derived from agrobacterium, the vector is usually inserted by a physical method (injection with a syringe or permeation by decompression). It is introduced into plant germ cells and infects plants. Once inserted into the germ cell genome, the target gene is stably passaged. Foreign genes are inserted into the genome of all plant cells.

The particle gun (particle bombardment) method is a method for introducing a target DNA into a cell by injecting fine particles of a metal such as gold or tungsten coated with DNA or a vector at high speed.

In the present specification, the volatile substance means a volatile immunoactive substance derived from a plant, and is not particularly limited as long as it can induce the eavesdropping effect of surrounding plants and activate the immune system, but is typically not limited. There are methyl jasmonate, methyl salicylate, ethylene, green leaf alcohol, green leaf aldehyde, terpenes, esters, aldehydes, aromatic compounds and the like. Specifically, Milsen, (E, E) -4,8,12-trimethyltrideca-1,3,7,11-tetraene (TMTT), 2-pentanol, 1-octen-1-ol, acetophenone. , Ferrandren, Trans-2-hexenal, (1R, 2S, 5R) -Pinan, Terpinerol, Farnesen, Beta Accoradian, Visabolen, Nerolidol, (E) -4,8-Dimethyl-1,3,7-Nonatrien (E) -4,8-Dimethyl-1,3,7-Nonatrien DMNT), 2-pentanal, kalen, 4-methyl-3-penten-2-one, benzaldehyde, ethyl salicylate, ossimen, 2,4-hexadienal, beta sesquiferrandrene, terpineol, geranylacetone, hexanol, limonene, Linarol, nerol, indol, elemente, velvenon, 2-pentene-1-ol, (Z) -3-hexen-1-ol, decanal, caproic acid, cis-3-hexenal, 3-hexenyl acetate, (Z)- 2-Hexanal, (Z) -2-hexanol, (Z) -2-hexanyl acetate, (E) -2-hexanal, (E) -2-hexanol, (E) -2-hexanyl acetate, (n) ) -2-Hexanal, (n) -2-hexanol, (n) -2-hexanyl acetate, or a combination thereof.

In the preparation of the monitor plant, the plant transformed as a host plant is not particularly limited as long as it can express a photoprotein, but is typically Arabidopsis thaliana, moss, cat jarashi, or brachypodium. The monitor plant may be the same species as the target plant.

The species of the target plant is not particularly limited, but is typically an agricultural product, and examples thereof include grasses, legumes, abranas, kikus, eggplants, roses, uris, and morning glories. Preferred plants include, for example, alfalfa, barley, common bean, canola, sage, cotton, corn, clover, hass, lens bean, lupine, millet, oat, pea, peanut, rice, lime, sweet clover, sunflower, sweet pea, soybean. , Morokoshi, Raikomugi, Kuzuimo, Hasshomame, Soybean, Wheat, Fuji, Fruit plant, etc. Asa, Togarashi, Hiyokome, Kenoposi, Kikunigana, Kankitsu, Coffee tree, Juzudama, Cucumber, Pumpkin, Gyogishiba, Camogaya, Chosen Asagao, Digitalis, Yamanoimo, Abra palm, Oshiba, Fescue, Strawberry, Owl soybean Wheat, sunflower, sweet potato, lettuce, sunflower, lily, flax, ryegrass, hass, tomato, mayorana, apple, mango, corn, horsego palm, African unlan, tobacco, corn, millet, tenjikuaoi, bean, tsukubane asagao, green bean, awagaeri , Strawberry tuna, cherry, kinpouge, radish, sugi, corn, strawberry, sugar cane, salmendana, limegi, senesio, setaria, white pearl oyster, eggplant, sorghum, bean, cacao, jajiso, reiryoko, grape and the like.

[3. How to detect photoproteins in plants]
According to the second aspect of the present invention, it is a method for detecting a photoprotein in a plant, and the wavelength band of the photoprotein emission wavelength and the self-emission of the plant are obtained from an image of a plant containing a photoprotein gene. Provided is a method comprising the steps of identifying an image region including a wavelength band and detecting only the emission wavelength signal of a photoprotein present in the identified image region.

The detection of a photoprotein in the plant can be specifically achieved by using the following image processing apparatus, image processing system, and image processing program.

Normally, biological samples such as plants move significantly on the macro scale, so they cannot be quantified by the ROI (Region of Interest), which is frequently used on the micro scale (see FIG. 3). FIG. 3 shows an image of a plant moving during a time-lapse due to autofluorescence. In this example, it can be seen that the movement is large in 12 hours.

There is still no technology for automatic spatiotemporal quantification for moving samples. Therefore, we will construct software that can automatically quantify the expression dynamics of specific genes in the region against the background of autologous (constant) or tissue-specific fluorescence signals. When macro-scale quantitative tools are developed, mathematical modeling becomes possible directly. Therefore, it is possible to perform precise analysis of specific gene expression at a higher level of individual / population. By making this possible, for example, it becomes possible to predict the pest response in the population and how the pest damage spreads in the population.

In terms of application, it is possible to detect pest damage at an early stage by using a plant that emits light by detecting the scent emitted from the damaged plant. Mathematical modeling with quantitative data can be used to predict the movement of damage within crop populations. Quantitative techniques are indispensable for the application to "preventive nano-agriculture of pest stress early detection type" based on such image / video analysis.

Therefore, as a technical basis for the construction of a stress detection system, we further aimed to provide software that automatically analyzes the quantification of fluorescent signals at the individual level with high accuracy in space-time.

Some features of this embodiment are shown below.
-The point is to visualize the communication between plants using scents in real time.
-The point is to analyze the information diffusion of scents not only between individuals but also within the population.
-In the conventional technique, although living cells / individuals are used as samples, the side effect of stress due to immobilization cannot be avoided. In this embodiment, by quantifying a moving sample, it is possible to perform highly accurate quantification with fewer side effects.
-Extraction of partially specific signals for which you want to measure signals, such as autofluorescence. Therefore, non-plant backgrounds related to specific signals are automatically excluded. This is a quantitative method with extremely low background noise (see FIGS. 8 (C) and 8 (D) described later).
-Conventional techniques (Velasques, et al. (2015)) use firefly-derived photoproteins instead of fluorescent proteins to monitor root growth. Although the root growth can be quantified by this quantification method, it is difficult to observe specific gene expression because it is monitored in a single color. Moreover, since the emission signal is weaker than fluorescence, the exposure time is long. This limits the experiments performed. In the conventional technique, the exposure time is 5 minutes, but in the system of the present embodiment, 5 seconds is sufficient.

Here, time-lapse imaging using an electric stage and a fully automated stereomicroscope will be described.
1. 1. Fluorescent signals in response to scents from damaged plants were detected at the individual level (see Fig. 4).
2. 2. Spatio-temporal detection of immune system activation responses in plant populations (see Figure 5).
This makes it possible to detect fluorescent signals over a wide range (individual level / population level) over time. FIG. 4 shows the status of detection of a fluorescent signal emitted in response to a fragrant substance from a plant damaged by a pest. FIG. 5 shows a situation in which the activated state of the immune system in the Arabidopsis thaliana plant group is detected by a fluorescent signal.

The stress signal in the plant population is then manually quantified frame by frame using general image processing software (eg ImageJ). Select the morphology of the plant within the frame and manually measure the brightness in that area. FIG. 6 shows a graph of the results of manual quantification of changes in the fluorescence signal in FIG. 5 for each individual.
One object of the present embodiment is to provide highly accurate quantification software that automatically performs such quantitative analysis for each frame.

Further, in the present embodiment, since the fragrance substance released from the plant exposed to the damage of the pest is visualized by the nanosensor, the following basic biological (a, b) or agricultural field application ( In c and d), (a) communication between individual plants, (b) effect of (a) in a population, (c) early detection of pest damage when used in the field, (d) quantitative results and mathematical modeling. By combining them, it is possible to predict how the damage of pests will spread within the population. Attempts at "preventive" farming using scent-based monitoring at farm sites have not yet been reported. This is in principle different from the conventional pest control by chemical pesticides and genetically modified crops that induce the emergence of "resistant pests". To recognize the morphology of a sample that "moves" over time using autofluorescence (or constitutively expressed) or tissue-specifically expressed fluorescence. It is also possible to mark tissues, organs, cells, etc. that are specific to time and time, such as the developmental stage, with a fluorescent dye and use this as the background. Furthermore, it is to extract the gene-specific fluorescent signal existing in the recognized region. Therefore, even if there is a background signal in the gene-specific fluorescence signal, it is possible to perform smooth and automatic analysis. By moving the sample, noise that could not be detected before the analysis can be automatically removed.

FIG. 7 shows a conceptual diagram of futuristic agriculture by early detection of pest damage in the field and preventive measures by stress markers (immune activity) by fluorescent signals from plants. In FIG. 7, the white plant is a damaged plant that emits a fluorescent signal. Arrows represent pests. Since the damage is communicated as a scent component, by co-planting crops and stress markers, the genetically modified plants need only be markers, and the crops do not need to be genetically modified. Fluorescence is an insect-type robot, and damage spread is predicted from mathematical modeling combined with other data.

Some features of this embodiment are shown below.
-It is a highly versatile method that can be used immediately not only for plants but also for animals and insects.
-By creating an add-on macro to general image processing software that is open source (for example, ImageJ or Fiji), it is possible to contribute to the development of higher-level software based on it.
-Pesticide saving is not only a sustainable agriculture aspect, but also a safe food production method for consumers and producers. Since it is labor-saving, it can contribute to ensuring food security in an aging society. It is a technology that can respond to difficult-to-predict situations such as global warming and abnormal weather by processing data. It is possible to create a next-generation primary industry that uses AI to analyze not only experience but also data.
-In the future, there will be digitization of agricultural ecosystems that monitor weather conditions, soil components, fertilizer input, and even growth conditions. It is possible to detect damage symptoms at an early stage from image data processing, and it is possible to prevent the spread of damage. This technology enables labor saving, pesticide saving, and prevention of damage spread, and sustainable precision agriculture can be achieved. Preventive precision agriculture that is safe for consumers and production workers.

Next, an example of the image processing method used in the present invention will be described with reference to FIG. FIG. 8 shows an image corresponding to each step of the automatic quantification software for macro time-lapse video.
Step A: Recognition of autofluorescence (FIG. 8 (A)).
Step B: Extraction to the quantitative stage (FIG. 8 (B)).
In steps A and B, autofluorescence is extracted and used for recognizing the shape of the plant. The measurement target area is set by tracking this over time.

Step C: Extraction of specific signals (FIG. 8 (C)). In step C, the target gene-specific signal is extracted.

Step D: Overlay the specific signal (FIG. 8 (D)). In step D, the specific signal at the individual level can be extracted and quantified by superimposing the measurement target region of step B and the specific signal of step D.

According to the image processing method used in the present invention shown in FIG. 8, the specific signal S of only the portion having the autofluorescence F can be specifically measured by the overlay in which the autofluorescence and the specific signal are superimposed. In addition, the background signal (noise) can be automatically removed (see FIGS. 8 (C) and 8 (D)).

The image processing used in the present invention may be programmed by an add-in or macro of general image processing software (for example, ImageJ or Fiji) which is an open source.

The image processing apparatus used in the present invention is
A detection unit that detects an image region of light emitted by the organism from an image of light in a wavelength band including the wavelength of light emitted by the organism to be imaged.
An image processing target area extraction unit that extracts an image processing target area of the organism from the captured image based on the detected image area, and an image processing target area extraction unit.
A specific signal extraction unit that extracts an image region of a specific signal from the captured image,
A specific signal selection unit that extracts an image region of a specific signal included in the extracted image processing target region from the extracted image region of the specific signal, and a specific signal selection unit.
It may be an image processing apparatus provided with.

The image processing device used in the present invention may be realized by dedicated hardware, or may be configured by a computer system such as a personal computer, and functions of each part of the image processing device used in the present invention. The function may be realized by executing a program for realizing the above.

Further, an input device, a display device, or the like may be connected to the image processing device as peripheral devices. Here, the input device means an input device such as a keyboard and a mouse. The display device refers to a CRT (Cathode Ray Tube), a liquid crystal display device, or the like.
Further, the peripheral device may be directly connected to the image processing device, or may be connected via a communication line.

The image processing system used in the present invention is
Non-sampled organisms and sample organisms are mixed and arranged,
An imaging unit that captures light in a wavelength band including the wavelength of light emitted by the sample organism, and
A detection unit that detects an image region of light emitted by the sample organism from an image captured by the imaging unit.
An image processing target area extraction unit that extracts an image processing target area of the sample organism from the captured image based on the detected image area, and an image processing target area extraction unit.
A specific signal extraction unit that extracts an image region of a specific signal from the captured image,
A specific signal selection unit that extracts an image region of a specific signal included in the extracted image processing target region from the extracted image region of the specific signal, and a specific signal selection unit.
It may be an image processing system including.

The image processing program used in the present invention is
On the computer
A detection function that detects the image region of the light emitted by the organism from the captured image of the light in the wavelength band including the wavelength of the light emitted by the organism to be imaged.
An image processing target area extraction function that extracts an image processing target area of the organism from the captured image based on the detected image area, and an image processing target area extraction function.
A specific signal extraction function that extracts an image region of a specific signal from the captured image, and
A specific signal selection function that extracts an image region of a specific signal included in the extracted image processing target region from the extracted image region of the specific signal, and a specific signal selection function.
It may be an image processing program for realizing the above.

Although the embodiments relating to the image processing apparatus, the image processing system, or the image processing program used in the present invention have been described in detail with reference to the drawings, the specific configuration is not limited to this embodiment. It also includes design changes within the scope of the gist of the invention.

Further, a computer program for realizing the functions of the above-mentioned device may be recorded on a computer-readable recording medium, and the program recorded on the recording medium may be read by the computer system and executed. The "computer system" here may include hardware such as an OS and peripheral devices.
The "computer-readable recording medium" is a flexible disk, a magneto-optical disk, a ROM, a writable non-volatile memory such as a flash memory, a portable medium such as a DVD (Digital Versatile Disc), and a built-in computer system. A storage device such as a hard disk.

Furthermore, the "computer-readable recording medium" is a volatile memory inside a computer system that serves as a server or client when a program is transmitted via a network such as the Internet or a communication line such as a telephone line (for example, DRAM (Dynamic)). It also includes those that hold the program for a certain period of time, such as Random Access Memory)).
Further, the program may be transmitted from a computer system in which this program is stored in a storage device or the like to another computer system via a transmission medium or by a transmission wave in the transmission medium. Here, the "transmission medium" for transmitting a program refers to a medium having a function of transmitting information, such as a network (communication network) such as the Internet or a communication line (communication line) such as a telephone line.
Further, the above program may be for realizing a part of the above-mentioned functions. Further, a so-called difference file (difference program) may be used, which can realize the above-mentioned function in combination with a program already recorded in the computer system.

As described above, by detecting the stress of the target plant that detects the photoprotein in the plant, it becomes possible to detect the stress of the target plant such as an agricultural product.

The present invention will be described in more detail with reference to the following examples. The present invention is not limited to this.

[Example 1. Transformed Arabidopsis thaliana into which the yellow fluorescent protein gene has been introduced]
1. 1. Material A white fluorescent protein-responsive yellow fluorescent protein-responsive yellow fluorescent protein-introduced strain was used as a monitor plant (VSP1 promoter + yellow fluorescent protein gene). This monitor plant is the same as that described in Betsuyaku et al., Plant Cell Physiol. 59 (1): 8-16 (2018), and was provided by Dr. Betsuyaku, the author of this paper. ..
On the crop side, wild-type Arabidopsis thaliana was used.

2. 2. Growth After sterilization of seeds, Arabidopsis transformants and wild type (crop side) were sown in a 9 cm petri dish. The medium in the petri dish is 1/2 × Murashige and Skoog medium, 1% sucrose, 0.3% Fitagel®.
The medium was prepared in a petri dish with the attached partition installed in advance. Five wild-type plants were sown on one of the partitions, and two transformants were sown on the other (Fig. 9).
Cultivation was carried out at 22 ° C., 16 hours in the light period / 8 hours in the dark period. The plant was used 3 weeks after sowing. The petri dish had no root contact and the transformed plant and the crop-side plant were physically separated, but a small hole was made in the above-ground part to allow volatile substances to pass through. The part buried in the medium is not provided with such a vent.
Diamondback moth (Plutella xylostella) was grown at 25 ± 10 ° C., 60 ± 10% RH, 16 hours in the light period and 8 hours in the dark period. 3-4th instar larvae were used and fasted from the day before the experiment. The growth of Mythimna separata was carried out in the same manner. After fasting, pests were released into wild-type petri dishes. 3-4 diamondback moths and 2 mythimna separata were used.

3. 3. Shooting time-lapse image An M205FA autostereoscopic microscope equipped with an electric stage was used to acquire a live image. The image was taken with a DFC7000T color CCD camera (Leica Microsystems). This device was controlled by LasX software (Leica Microsystems). A metal halide bulb (Leica EL6000) was used as an excitation light source. Chlorophyll autofluorescence (Komis et al., 2015) and YFP signals were detected by Texas Red Filter and YFP Filter, respectively (see Leica Microsystems; Plant Biotechnology 35, 1-6 (2018)).
The excitation emission wavelengths of the YFP and Texas Red Filter were 510 / 20-560 / 40 nm and 560 / 40-610 LP nm, respectively. The Texas Red Filter reduced almost all non-specific autofluorescent signals from dead plant cells (Betsuyaku et al., 2018).
Brightfield, YFP and Texas Red images were captured every 20 minutes. Plant specimens were exposed to white light during these times using LasX software (Leica Microsystems).

4. Results 4-1 Detection of volatile substances Monitor plants were exposed to 0.1 M methyl jasmonate (MeJA), methyl salicylate (MeSA), solvent (dichloromethane), and photographed 24 hours later.
As shown in FIG. 10, it reacted specifically to MeJA.

4-2 Detection of diamondback moth feeding damage As shown in FIG. 11, it was possible to detect diamondback moth feeding damage at a minute stage.

4-3 Detection of Mythimna separata's feeding damage As shown in FIG. 12, it was possible to detect the feeding damage of Mythimna separata at a minute stage.

4-4 Prediction of the source of volatile substances by the signal according to the concentration gradient As shown in FIG. 13, the gradient of the fluorescent signal was created by the concentration gradient of the source substance of the volatile substance (meJA). This makes it possible to predict where the volatile substances are diffusing.

4-5 Fluorescent signal due to injury stress As shown in FIG. 14, a fluorescent signal (circled part) was detected in the monitor plant only by injury stress. The injured stress was in a wild-type Arabidopsis, physically isolated plot (outside the photo).

4-6 Response to injury stress in other plants Response to injury stress of monitor plants by crushing 50 mg of raw weight Rorippa indica with a milk stick and a milk stick and impregnating the extract with filter paper. Was observed.
As shown in FIG. 15, the monitor plant sensed the volatile substances emitted from the extract, and the fluorescent signal was detected (circled part).

4-7 Reaction to other volatile components 0.01 M of ocimene or methyl jasmonate was diffused in the petri dish and the fluorescent signal of the monitor plant was detected.
As shown in FIG. 16, monitor plants were found to signal in response to ocimene as well as methyl jasmonate.

[Example 2. Arabidopsis strain monitor plant into which the nanolantern gene has been introduced]
Arabidopsis transformation of nanolantern DNA (distributed from Nagai Laboratory, Osaka University; Takai et al., 2015 PNAS 112: 4352; Saito et al., 2012 Nature Communications 3: 1262) using In-fusion (Clontech). The vector (pBA002a) was cloned using EcoRV, MluI, and XhoI. Jasmonic acid-responsive VSS1 and salicylic acid-responsive PR1 were used as promoters.
This was transformed into Agrobacterium (GV3101) by an electroporation method. VSP1-nanolantan and PR1-nanolantan were each mass-cultured in LB medium and transformed into wild-type Arabidopsis thaliana by the Lauraldip method. Arabidopsis cultivated in pots covered with a net (nylon mesh, for swill) was turned upside down and soaked in an Agrobacterium suspension for 2 minutes. This was leveled, wrapped in paper towels and saran wrap, and recovered in the dark for 24 hours. After that, it was grown normally. The obtained seeds were selected in a medium containing bialaphosammonium (BASTA) and carbenicin to obtain transformed Arabidopsis thaliana.

F ... autofluorescence, S ... specific signal

Claims (9)

It is a method to detect the stress of the target plant.
A step of placing a stress-responsive promoter and a monitor plant containing a photoprotein gene operably linked downstream of the stress-responsive promoter in the vicinity of the target plant.
The process of visualizing the stress of the target plant,
Here, in the visualization of the stress of the target plant, when the monitor plant receives a volatile substance released by the target plant in response to stress, the stress-responsive promoter responds in the monitor plant. A method characterized by this being carried out by the expression of the photoprotein. The stress-responsive promoters are PR1 promoter, PR2 promoter, PR3 promoter, PR4 promoter, PR5 promoter, AOS promoter, VSP1 promoter, VSP2 promoter, HPL promoter, AtMYC2 promoter, CYP83B1 promoter, At2g24850 promoter, LOX2 promoter, IAR3 promoter, GST5. It is characterized by being a promoter, an OPR3 promoter, an ERF promoter, a THI2.1 promoter, a JAZ gene group promoter, a PDF1.2 promoter, a WRKY promoter, a PAD4 promoter, an EDS1 promoter, a SID1 promoter, an EDS5 promoter, a BGL2 promoter, or a combination thereof. The method according to claim 1. The luminescent protein is a fluorescent protein such as green fluorescent protein, yellow fluorescent protein, red fluorescent protein, cyanide fluorescent protein, blue fluorescent protein, bioluminescent protein, chemically luminescent protein, protein using FRET technology such as nanolantern, or a protein thereof. The method according to claim 1 or 2, characterized in that it is a combination of. The volatile substances are methyl jasmonate, methyl salicylate, green leaf alcohol, aromatics, terpenes, myrcene, (E, E) -4,8,12-trimethyltrideca-1,3,7,11-tetraene (TMTT). ), Esters, aldehydes, aromatic compounds, 2-pentanol, or a combination thereof, wherein the method according to any one of claims 1 to 3. The method according to any one of claims 1 to 4, wherein the monitor plant is Arabidopsis thaliana, moss, cat jarashi, or brachypodium. The photoprotein in the monitor plant is a method for detecting the photoprotein in the plant.
A step of specifying an image region including a wavelength band of the emission wavelength of the photoprotein and a wavelength band of self-emission of the plant from an image of a plant containing a photoprotein gene, and light emission existing in the specified image region. The process of detecting only the emission wavelength signal of a protein,
The method according to any one of claims 1 to 5, which is detected by a method for detecting a photoprotein in a plant, which comprises. The photoprotein in the plant
A detection unit that detects an image region of light emitted by the organism from an image of light in a wavelength band including the wavelength of light emitted by the organism to be imaged.
An image processing target area extraction unit that extracts an image processing target area of the organism from the captured image based on the detected image area, and an image processing target area extraction unit.
A specific signal extraction unit that extracts an image region of a specific signal from the captured image,
A specific signal selection unit that extracts an image region of a specific signal included in the extracted image processing target region from the extracted image region of the specific signal, and a specific signal selection unit.
Image processing device, equipped with
6. The method of claim 6. The photoprotein in the plant
Non-sampled organisms and sample organisms are mixed and arranged,
An imaging unit that captures light in a wavelength band including the wavelength of light emitted by the sample organism, and
A detection unit that detects an image region of light emitted by the sample organism from an image captured by the imaging unit.
An image processing target area extraction unit that extracts an image processing target area of the sample organism from the captured image based on the detected image area, and an image processing target area extraction unit.
A specific signal extraction unit that extracts an image region of a specific signal from the captured image,
A specific signal selection unit that extracts an image region of a specific signal included in the extracted image processing target region from the extracted image region of the specific signal, and a specific signal selection unit.
Image processing system,
6. The method of claim 6. The image processing device or image processing system
On the computer
A detection function that detects the image region of the light emitted by the organism from the captured image of the light in the wavelength band including the wavelength of the light emitted by the organism to be imaged.
An image processing target area extraction function that extracts an image processing target area of the organism from the captured image based on the detected image area, and an image processing target area extraction function.
A specific signal extraction function that extracts an image region of a specific signal from the captured image, and
A specific signal selection function that extracts an image region of a specific signal included in the extracted image processing target region from the extracted image region of the specific signal, and a specific signal selection function.
Image processing program to realize
7. The method according to claim 7 or 8, wherein the method is provided.

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