The team uncovered the role of phosphorylation at the 881st threonine residue of the plasma membrane proton pump in response to red and blue light.

This research opens possibilities for manipulating plant physiology in specific ways, benefiting agriculture and the environment. The researchers reported their findings in Nature Communications.

Lead researcher Toshinori Kinoshita said the previously unknown phosphorylation event activates the proton pump, which aids stromatal opening.

“The findings shed light on the intricate signaling pathways underlying plant responses to light and hold promise for future applications in plant engineering.”

Stomata are microscopic pores on the surface of plant leaves. They play a crucial role in gas exchange by regulating the uptake of carbon dioxide essential for photosynthesis. Understanding the molecular mechanisms that govern stomatal opening in response to environmental signals, such as light, is fundamental to plant physiology and cultivation. Recent advances in understanding stomatal opening have increased the growth and yields of important crop plants.

A key part of this process is phosphorylation of amino acids. Phosphorylation is adding or removing a phosphate group from an amino acid using an enzyme. It acts like an on/off switch, altering the structure and function of the protein depending on whether the phosphate is present. 

Nagoya University researchers collaborated to investigate the role of an amino acid, Thr881. They employed extensive analysis on protoplasts derived from cells of thale cress.

The phosphorylation of Thr881 was observed in response to both red and blue light conditions. The dual activation mechanism, which relies on both photosynthesis and the blue light receptor phototropin, emphasizes the complex interaction between light signaling and physiological responses in plants.

Further investigations using thale cress mutants confirmed the essential role of Thr881 phosphorylation in stomatal opening. When they made plants expressing a mutant proton pump lacking Thr881 phosphorylation, they found reduced stomatal aperture and transpiration rates, underscoring the significance of this regulatory mechanism.

Researchers concluded that Thr-881 of the membrane protein AHA1, together with Thr-948, was phosphorylated in response to blue light. Phosphorylation of both Thr-881 and Thr-948 is crucial for activation of the enzyme H+ -ATPase, which allows stomatal opening. 

The researchers also observed Thr-881 phosphorylation in leaves and shoots, indicating that it plays a broader role in plant physiology.

“The plasma membrane proton pump functions in all plant cells, playing a vital role not only in the opening of the stomatal gland but also in the uptake of nutrients in the roots, the transport of photosynthetic products, and pollen tube elongation,” Kinoshita said.

“This suggests that the manipulation of Thr-881 could contribute to promoting plant growth, increasing carbon dioxide absorption, and reducing the use of fertilizers such as nitrogen and phosphorus.”

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There are various species of anthocyanins in plants that are divided by their molecular shape. Simple and rapid analytical techniques that can distinguish among these species in crops are necessary for breeding and quality assessment.

A team of Nagoya University researchers in Japan has used a technique called probe electrospray ionization tandem mass spectrometry to analyze anthocyanins in crops. Their results were published in the journal Horticulture Research. 

Conventionally, liquid chromatography and mass spectrometry are used to analyze anthocyanins. However, these require considerable time and work in the pre-treatment and chromatography step.

The team led by associate professor Katsuhiro Shiratake turned to the new technique developed by another member of the team, professor Kei Zaitsu, who used it to analyze metabolites in living organs including the brains of mice. Shiratake’s team instead used it for agricultural applications.  

The analysis involves an ambient mass spectrometry approach that is simpler than the conventional method because sampling can be easily done by sticking a probe into the sample. The compounds that adhere to the tip of the probe are ionized using high-voltage electricity and analyzed.

The group analyzed 16 types of fruits and vegetables and successfully detected 81 types of anthocyanins in only three minutes. They also found their technique could detect anthocyanins in small areas of plants, such as the tiny pips found on the skin of strawberries. 

“This study suggests the applicability of (the technique) to the analysis of not only anthocyanins but also other plant metabolites,” said Shiratake.

“Plant metabolites contribute to the quality of crops and their processed products, including their taste, aroma, colour and functionality,” Shiratake added, noting the technique eliminates time-consuming steps and can simplify and accelerate analysis. 

In the future, the team hopes to incorporate one of Zaitsu’s other platforms, called PiTMaP, which combines the new technique with bioinformatics. They expect this platform will allow them to analyze targeted metabolites and quickly analyze data.

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Generally, grafts have been thought to be compatible only between the same or closely related species. However, scientists at Nagoya University and colleagues in Japan recently found that the tobacco plant Nicotiana benthamiana promotes adhesion of tissue and can maintain grafts between a broad range of species.

Their findings, published recently in the journal Science, have also shown that using tobacco as an intermediary, the upper part (scion) of a tomato plant grafted onto the lower part (rootstock) of a Chrysanthemum morifolium (widely known as Florist’s daisy) successfully bore fruit.

Grafting has been conducted for thousands of years for the propagation of fruits and vegetables, in which a productive scion is attached onto a rootstock that is resistant to diseases and environmental stresses. However, exactly how grafts are established has been unclear, and grafting is considered difficult between different family species.

A team of scientists from Nagoya University, Teikyo University, Riken, Chubu University, and Gra&Green Inc. (a start-up venture company from Nagoya University) recently conducted a study on grafting between different family species.

The team focused on Nicotiana in the Solanaceae family, because a previous study had shown that its scion can be grafted onto the rootstock of Arabidopsis thaliana in the mustard family. The team conducted grafting experiments using plants of seven Nicotiana species and their partners from 84 species in 42 families. The results showed that Nicotiana, used as either scion or rootstock, succeeded in maintaining grafts for more than a month with 73 species in 38 families.

Next, the scientists examined the cellular mechanisms that enable Nicotiana to form grafts with plants from a wide range of families. They analyzed transcriptomes at graft junctions between Nicotiana and Arabidopsis and hypothesized that the expression of β-1,4 glucanases secreted into the extracellular region is involved in cell wall digestion. In further experiments, when β-1,4 glucanases were overexpressed in Arabidopsis, the adhesion property of the grafts was enhanced. Thus, they concluded that the expression of β-1,4 glucanases is a key in facilitating tissue adhesion of the grafts.

In addition, they conducted experiments to see whether Nicotiana can act as an intermediary in the grafting of different family species, by using a tomato scion and the rootstock of Florist’s daisy, a garden plant resistant to environmental stress. About three months later, the tomato plant successfully produced a small fruit.

“Using Nicotiana as an intermediate, we also achieved other grafts in which the scion, interscion, and rootstock all belonged to different plant families,” says Nagoya University bioscientist Michitaka Notaguchi, the corresponding author of this study.

“Our latest results regarding the key molecules involved, not just interfamily grafting itself, could help improve plant grafting techniques so that the variety of root systems available to aid crop production can be increased with minimal destruction of ecosystems.”

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A team of scientists led by Yosuke Toda, designated assistant professor at the Institute of Transformative Bio-Molecules (WPI-ITbM), Nagoya University, and Fumio Okura, assistant professor at the Institute of Scientific and Industrial Research, Osaka University, have developed a system which uses image analysis and artificial intelligence (AI) to analyze the shape of large numbers of seeds from a single image.

Dr. Toda’s research team generated a training dataset to be used for machine learning (deep learning) by synthesizing randomized barley seed images on a virtual canvas. The trained model, using only the synthesized data, was able to detect and segment the individual seeds from images of various barley cultivars as accurately as when done manually, as well as being able to analyze seeds of other crops.

Training data is required to make use of deep learning. Usually, training data is prepared by hand, for example by labeling every object in the images with different colours. However, for objects such as seeds, whose numbers are vast, creating the training data is very time consuming (for example, having to individually colour hundreds of seeds for 10s or 100s of images for each seed variety). Thus, it has been considered difficult to generate a machine learning model that can quickly and simply analyze the seed shapes of different varieties or species. Dr. Toda’s research group succeeded in creating a large volume of training data from only a small number of seeds to effectively train the machine learning model.

The study showed that the same method can readily be employed to measure the seeds of a variety of different crops, such as rice, wheat, oats and lettuce.

Beyond just a variety evaluation, this study is expected to contribute to the plant science domain by revealing characteristics of seeds not formerly observed by the human eye.

The majority of research into instance segmentation-based image analysis is conducted using existing datasets including things such as people and cars. On the other hand, plant image analysis has a variety of its own characteristics.

Since there is great variation in plants’ species, location and individual appearance, different training data is needed for respective applications. While this is also the case for others with multiple applications, the creation of new training data for plants is particularly difficult.

The method of generating synthetic training data employed in this study can be used in a variety of applications. Based on the initiative of this research, it is expected that it will be possible to go beyond the analysis of seeds, and accelerate the development of a machine learning model for the measurement of various plant phenotypes.

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