Unlocking the tea leaf's secret: decoding the amino acid blueprint of tea plant
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A WORKING MODEL FOR N ASSIMILATION, AA SYNTHESIS, TRANSPORT, AND DEGRADATION/RECYCLING IN TEA PLANTS.
view moreCREDIT: HORTICULTURE RESEARCH
A recent study has unlocked the secrets of amino acid metabolism in tea plants, revealing the pivotal role of nitrogen assimilation in root tissues and the long-distance transport of key amino acids to leaves. This discovery offers a pathway to enhance tea's flavor and health benefits, providing a foundation for improving tea cultivation practices and potentially elevating the quality of tea beverages.
Amino acids are vital for plant growth and significantly influence tea flavor and health benefits. Tea plants, particularly Camellia sinensis, exhibit unique amino acid profiles that contribute to their distinctive taste and nutritional value. Despite the known importance of amino acids like theanine and glutamine (Gln), the detailed dynamics of their synthesis, transport, and degradation in tea plants remain unclear. Due to these challenges, there is a need to conduct in-depth research to understand the complex metabolic pathways and spatial distribution of amino acids within tea plants.
Researchers from Hunan Agricultural University, in a study (DOI: 10.1093/hr/uhae060) published on February 28, 2024, in Horticulture Research, dissected the spatial dynamics of amino acid biosynthesis, transport, and turnover in tea plants. The study provides a detailed analysis of the metabolic pathways and gene expressions that govern these processes. By understanding these mechanisms, the researchers aim to improve tea cultivation and enhance the quality of tea beverages.
The study revealed that nitrogen assimilation primarily occurs in the roots, where Gln, theanine, and arginine (Arg) are actively synthesized. These amino acids are then transported through the plant’s vascular system. Transcriptome analyses identified that genes involved in Arg synthesis are highly expressed in roots, while genes responsible for Arg transport and degradation are expressed in stems and young leaves. This indicates a sophisticated system of amino acid management within the plant. One key finding is the role of the CsGSIa gene, which is crucial for amino acid synthesis, transport, and recycling. Overexpression and knockdown experiments of CsGSIa in transgenic tea plants demonstrated its significant impact on Gln and theanine levels. The study also highlighted that Arg, Gln, glutamate (Glu), and theanine are the major amino acids transported through the xylem sap, facilitating long-distance nitrogen transport from roots to leaves.
Dr. Jian Zhao, the lead researcher, stated, "Our findings offer a detailed map of amino acid metabolism in tea plants, which is crucial for both basic science and applied agricultural practices. Understanding these metabolic pathways opens up new possibilities for breeding tea varieties with enhanced flavors and health benefits."
The study’s findings have significant implications for the tea industry. By elucidating the pathways of amino acid metabolism, this research paves the way for developing tea plants with higher levels of beneficial amino acids, enhancing both flavor and nutritional value. These insights can be applied in breeding programs and cultivation practices to produce superior tea varieties. Additionally, understanding these metabolic processes can help in developing strategies to improve nitrogen utilization efficiency, contributing to more sustainable and productive tea farming.
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References
DOI
Original Source URL
https://doi.org/10.1093/hr/uhae060
Funding information
This work was supported by Natural Science Foundation of China (U23A20214), the funds from Hunan 'Three Top' Innovative Talents Project (2022RC1142).
About Horticulture Research
Horticulture Research is an open access journal of Nanjing Agricultural University and ranked number one in the Horticulture category of the Journal Citation Reports ™ from Clarivate, 2022. The journal is committed to publishing original research articles, reviews, perspectives, comments, correspondence articles and letters to the editor related to all major horticultural plants and disciplines, including biotechnology, breeding, cellular and molecular biology, evolution, genetics, inter-species interactions, physiology, and the origination and domestication of crops.
JOURNAL
Horticulture Research
SUBJECT OF RESEARCH
Not applicable
ARTICLE TITLE
Dissection of the spatial dynamics of biosynthesis, transport, and turnover of major amino acids in tea plants (Camellia sinensis)
Decoding the leafy head: transcriptomic insights into Chinese cabbage's unique morphology
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LASER MICRODISSECTION OF LEAF PRIMORDIAL DOMAINS.
view moreCREDIT: HORTICULTURE RESEARCH
A pivotal study illuminates the genetic blueprint of Chinese cabbage, focusing on the critical phase of leaf initiation. The research uncovers the molecular choreography of gene expression that directs the development of the plant's characteristic leafy head, offering new insights into the formation of this vital agricultural crop.
Understanding the genetic mechanisms behind leaf development is crucial for improving crop yields and resilience. In Chinese cabbage, the formation of leafy heads involves complex gene interactions that determine leaf shape and orientation. Despite progress in model plants like Arabidopsis, the molecular basis of these processes in Brassica species remains less clear. Based on these challenges, it is necessary to conduct in-depth research to uncover the genetic regulation of leaf development in crops like Chinese cabbage.
Researchers from Hebei Agricultural University, in collaboration with Wageningen University & Research, have published a seminal article (DOI: 10.1093/hr/uhae059) in Horticulture Research on February 28, 2024, detailing the transcriptomic analysis of Chinese cabbage. The study focuses on the gene expression patterns during leaf initiation, a key phase in the plant's development.
The study delves into the molecular mechanisms driving leaf development in Chinese cabbage, particularly the initiation of leaf primordia and the establishment of adaxial-abaxial polarity. Researchers utilized laser microdissection to isolate cells from the shoot apical meristem (SAM) and leaf primordia, followed by RNA sequencing to profile gene expression. Key findings include the identification of genes involved in hormone signaling pathways, such as auxin, cytokinin, and gibberellin, which play critical roles in leaf formation. The study also highlighted the expression of transcription factors like HD-ZIP III, KANADI, and YABBY, essential for adaxial-abaxial polarity establishment. Notably, the gene expression patterns in Chinese cabbage showed similarities and differences compared to model plants like Arabidopsis, reflecting the unique evolutionary path of Brassica species. The analysis revealed that gene expression in the SAM clustered with the adaxial side of leaf primordia, while the abaxial side aligned with the expanding rosette leaves. This co-expression pattern underscores the intricate regulatory networks governing leaf development. The study provides a comprehensive catalog of differentially expressed genes, offering a foundation for future research on leafy head formation and crop improvement.
Dr. Guusje Bonnema, a co-author from Wageningen University, stated, "This study advances our understanding of the genetic basis of leaf development in Brassica crops. By elucidating the gene expression patterns associated with leaf primordia and polarity, we can better understand the formation of leafy heads in Chinese cabbage, paving the way for targeted breeding strategies to enhance crop performance."
The insights from this study have significant implications for agriculture and crop breeding. Understanding the genetic regulation of leaf development enables breeders to develop Chinese cabbage varieties with optimized leaf shapes and sizes, enhancing yield and quality. The knowledge of gene expression patterns in leaf development is a valuable resource for future genetic engineering and breeding programs, addressing global food security challenges.
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References
DOI
Original Source URL
https://doi.org/10.1093/hr/uhae059
Funding information
This study was supported by the National Natural Science Foundation of China (grant 32330096, 32002054, 3217180965), the Dutch Royal Academy of Sciences China Exchange Program (grant 530-4CDP08), Project of Hebei Provincial Department of Human Resources and Social Security (C20210363).
About Horticulture Research
Horticulture Research is an open access journal of Nanjing Agricultural University and ranked number one in the Horticulture category of the Journal Citation Reports ™ from Clarivate, 2022. The journal is committed to publishing original research articles, reviews, perspectives, comments, correspondence articles and letters to the editor related to all major horticultural plants and disciplines, including biotechnology, breeding, cellular and molecular biology, evolution, genetics, inter-species interactions, physiology, and the origination and domestication of crops.
JOURNAL
Horticulture Research
SUBJECT OF RESEARCH
Not applicable
ARTICLE TITLE
Transcriptomic analyses to summarize gene expression patterns that occur during leaf initiation of Chinese cabbage
Berkeley Lab researchers advance AI-driven plant root analysis
Enhancing biomass assessment and plant root growth monitoring in hydroponic systems
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DEVELOPED BY BERKELEY LAB RESEARCHERS, RHIZONET IS A NEW COMPUTATIONAL TOOL THAT HARNESSES THE POWER OF AI TO TRANSFORM HOW WE STUDY PLANT ROOTS, OFFERING NEW INSIGHTS INTO ROOT BEHAVIOR UNDER VARIOUS ENVIRONMENTAL CONDITIONS. IT WORKS IN CONJUNCTION WITH ECOFAB, A NOVEL HYDROPONIC DEVICE THAT FACILITATES IN-SITU PLANT IMAGING BY OFFERING A DETAILED VIEW OF PLANT ROOT SYSTEMS.
view moreCREDIT: THOR SWIFT, LAWRENCE BERKELEY NATIONAL LABORATORY
In a world striving for sustainability, understanding the hidden half of a living plant – the roots – is crucial. Roots are not just an anchor; they are a dynamic interface between the plant and soil, critical for water uptake, nutrient absorption, and, ultimately, the survival of the plant. In an investigation to boost agricultural yields and develop crops resilient to climate change, scientists from Lawrence Berkeley National Laboratory’s (Berkeley Lab’s) Applied Mathematics and Computational Research (AMCR) and Environmental Genomics and Systems Biology (EGSB) Divisions have made a significant leap. Their latest innovation, RhizoNet, harnesses the power of artificial intelligence (AI) to transform how we study plant roots, offering new insights into root behavior under various environmental conditions.
This pioneering tool, detailed in a study published on June 5 in Scientific Reports, revolutionizes root image analysis by automating the process with exceptional accuracy. Traditional methods, which are labor-intensive and prone to errors, fall short when faced with the complex and tangled nature of root systems. RhizoNet steps in with a state-of-the-art deep learning approach, enabling researchers to track root growth and biomass with precision. Using an advanced deep learning-based backbone based on a convolutional neural network, this new computational tool semantically segments plant roots for comprehensive biomass and growth assessment, changing the way laboratories can analyze plant roots and propelling efforts toward self-driving labs.
As Berkeley Lab’s Daniela Ushizima, lead investigator of the AI-driven software, explained, “The capability of RhizoNet to standardize root segmentation and phenotyping represents a substantial advancement in the systematic and accelerated analysis of thousands of images. This innovation is instrumental in our ongoing efforts to enhance the precision in capturing root growth dynamics under diverse plant conditions.”
Getting to the Roots
Root analysis has traditionally relied on flatbed scanners and manual segmentation methods, which are not only time-consuming but also susceptible to errors, particularly in extensive multi-plant studies. Root image segmentation also presents significant challenges due to natural phenomena like bubbles, droplets, reflections, and shadows. The intricate nature of root structures and the presence of noisy backgrounds further complicate the automated analysis process. These complications are particularly acute at smaller spatial scales, where fine structures are sometimes only as wide as a pixel, making manual annotation extremely challenging even for expert human annotators.
EGSB recently introduced the latest version (2.0) of EcoFAB, a novel hydroponic device that facilitates in-situ plant imaging by offering a detailed view of plant root systems. EcoFAB – developed via a collaboration between EGSB, the DOE Joint Genome Institute (JGI), and the Climate & Ecosystem Sciences division at Berkeley Lab – is part of an automated experimental system designed to perform fabricated ecosystem experiments that enhance data reproducibility. RhizoNet, which processes color scans of plants grown in EcoFAB that are subjected to specific nutritional treatments, addresses the scientific challenges of plant root analysis. It employs a sophisticated Residual U-Net architecture (an architecture used in semantic segmentation that improves upon the original U-Net by adding residual connections between input and output blocks within the same level, i.e. resolution, in both the encoder and decoder pathways) to deliver root segmentation specifically adapted for EcoFAB conditions, significantly enhancing prediction accuracy. The system also integrates a convexification procedure that serves to encapsulate identified roots from time series and helps quickly delineate the primary root components from complex backgrounds. This integration is key for accurately monitoring root biomass and growth over time, especially in plants grown under varied nutritional treatments in EcoFABs.
To illustrate this, the new Scientific Reports paper details how the researchers used EcoFAB and RhizoNet to process root scans of Brachypodium distachyon (a small grass species) plants subjected to different nutrient deprivation conditions over approximately five weeks. These images, taken every three to seven days, provide vital data that help scientists understand how roots adapt to varying environments. The high-throughput nature of EcoBOT, the new image acquisition system for EcoFABs, offers research teams the potential for systematic experimental monitoring – as long as data is analyzed promptly.
“We’ve made a lot of progress in reducing the manual work involved in plant cultivation experiments with the EcoBOT, and now RhizoNet is reducing the manual work involved in analyzing the data generated,” noted Peter Andeer, a research scientist in EGSB and a lead developer of EcoBOT, who collaborated with Ushizima on this work. “This increases our throughput and moves us toward the goal of self-driving labs.” Resources at the National Energy Research Scientific Computing Center (NERSC) – a U.S. Department of Energy (DOE) user facility located at Berkeley Lab – were used to train RhizoNet and perform inference, bringing this capability of computer vision to the EcoBOT, Ushizima noted.
“EcoBOT is capable of collecting images automatically, but it was unable to determine if how athe plant responds to different environmental changes alive or not or growing or not,” Ushizima explained. “By measuring the roots with RhizoNet, we capture detailed data on root biomass and growth not solely to determine plant vitality but to provide comprehensive, quantitative insights that are not readily observable through conventional means. After training the model, it can be reused for multiple experiments (unseen plants).”
“In order to analyze the complex plant images from the EcoBOT, we created a new convolutional neural network for semantic segmentation," added Zineb Sordo, a computer systems engineer in AMCR working as a data scientist on the project. "Our goal was to design an optimized pipeline that uses prior information about the time series to improve the model's accuracy beyond manual annotations done on a single frame. RhizoNet handles noisy images, detecting plant roots from images so biomass and growth can be calculated.”
One Patch at a Time
During model tuning, the findings indicated that using smaller image patches significantly enhances the model's performance. In these patches, each neuron in the early layers of the artificial neural network has a smaller receptive field. This allows the model to capture fine details more effectively, enriching the latent space with diverse feature vectors. This approach not only improves the model's ability to generalize to unseen EcoFAB images but also increases its robustness, enabling it to focus on thin objects and capture intricate patterns despite various visual artifacts.
Smaller patches also help prevent class imbalance by excluding sparsely labeled patches – those with less than 20% of annotated pixels, predominantly background. The team’s results show high accuracy, precision, recall, and Intersection over Union (IoU) for smaller patch sizes, demonstrating the model's improved ability to distinguish roots from other objects or artifacts.
To validate the performance of root predictions, the paper compares predicted root biomass to actual measurements. Linear regression analysis revealed a significant correlation, underscoring the precision of automated segmentation over manual annotations, which often struggle to distinguish thin root pixels from similar-looking noise. This comparison highlights the challenge human annotators face and showcases the advanced capabilities of the RhizoNet models, particularly when trained on smaller patch sizes.
This study demonstrates the practical applications of RhizoNet in current research settings, the authors noted, and lays the groundwork for future innovations in sustainable energy solutions as well as carbon-sequestration technology using plants and microbes. The research team is optimistic about the implications of their findings.
“Our next steps involve refining RhizoNet’s capabilities to further improve the detection and branching patterns of plant roots,” said Ushizima. "We also see potential in adapting and applying these deep-learning algorithms for roots in soil as well as new materials science investigations. We're exploring iterative training protocols, hyperparameter optimization, and leveraging multiple GPUs. These computational tools are designed to assist science teams in analyzing diverse experiments captured as images, and have applicability in multiple areas.”
Further research work in plant root growth dynamics is described in a pioneering book on autonomous experimentation edited by Ushizima and Berkeley Lab colleague Marcus Noack that was released in 2023. Other team members from Berkeley Lab include Peter Andeer, Trent Northen, Camille Catoulos, and James Sethian. This multidisciplinary group of scientists is part of Twin Ecosystems, a DOE Office of Science Genomic Science Program project that integrates computer vision software and autonomous experimental design software developed at Berkeley Lab (gpCAM) with an automated experimental system (EcoFAB and EcoBOT) to perform fabricated ecosystem experiments and enhance data reproducibility. The work of analyzing plant roots under different kinds of nutrition and environmental conditions is also part of the DOE’s Carbon Negative Earthshot initiative (see sidebar).
JOURNAL
Scientific Reports
METHOD OF RESEARCH
Computational simulation/modeling
SUBJECT OF RESEARCH
Not applicable
ARTICLE TITLE
Berkeley Lab Researchers Advance AI-Driven Plant Root Analysis
ARTICLE PUBLICATION DATE
20-Jun-2024
