HORTICULTURE
Keys to aging hidden in the leaves
Discovery brings nearly dead plants back to life
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UC RIVERSIDE RESEARCHERS HEESEUNG CHOI AND KATIE DEHESH HOLDING YOUNG, GREEN AND OLD, YELLOW ARABIDOPSIS PLANTS IN THE LABORATORY.
view moreCREDIT: KATIE DEHESH/UCR
Scientists have known about a particular organelle in plant cells for over a century. However, UC Riverside scientists have only now discovered that organelle’s key role in aging.
The researchers initially set out to understand more generally which parts of plant cells control plant responses to stress from things like infections, too much salt, or too little light. Serendipitously, they found this organelle, and a protein responsible for maintaining the organelle, control whether plants survive being left too often in the dark.
Because they had not expected this discovery, which is described in a Nature Plants journal article, the research team was thrilled.
“For us, this finding is a big deal. For the first time, we have defined the profound importance of an organelle in the cell that was not previously implicated in the process of aging,” said Katie Dehesh, distinguished professor of molecular biochemistry at UCR and co-author of the new article.
Sometimes described as appearing like a stack of deflated balloons or some dropped lasagna, the organelle called the Golgi body is composed of a series of cup-shaped membrane-covered sacs. It sorts various molecules in the cell and ensures they get to the right places.
“Golgi are like the post office of the cell. They package and send out proteins and lipids to where they’re needed,” said Heeseung Choi, a researcher in UCR’s Botany and Plant Sciences Department and co-author of the new study. “A damaged Golgi can create confusion and trouble in the cell’s activities, affecting how the cell works and stays healthy.”
If the Golgi is the post office, then the COG protein is the postal worker. This protein controls and coordinates the movement of small sac “envelopes” that transport other molecules around the cell.
Additionally, COG helps Golgi bodies attach sugars to other proteins or lipids before they are sent elsewhere in the cell. This sugar modification, called glycosylation, is crucial for many biological processes, including immune response.
To learn more about how COG affects plant cells, the research team modified some plants so that they could not produce it. Under normal growing conditions, the modified plants grew just fine, and were indistinguishable from unmodified plants.
However, depriving plants of light means plants are unable to make sugar from sunlight to fuel growth. When exposed to excessive darkness the leaves of the mutant, COG-free plants began to turn yellow, wrinkled, and thin — signs the plants were dying.
“In the dark, the COG mutants showed signs of aging that typically appear in wild, unmodified plants around day nine. But in the mutants, these signs manifested in just three days,” Choi said.
Reversing the mutation and returning the COG protein back into the plants rapidly brought them back to life. “It’s like nothing happened to them once we reversed the mutation,” Dehesh said. “These responses highlight the critical importance of the COG protein and normal Golgi function in stress management,” Choi added.
Part of the excitement surrounding this discovery is that humans, plants, and all eukaryotic organisms have Golgi bodies in their cells. Now, plants can serve as a platform to explore the intricacies of the Golgi's role in human aging. For this reason, the research team is planning further studies of the molecular mechanisms behind the results from this study.
“Not only does our research advance our knowledge about how plants age, but it could also provide crucial clues about aging in humans,” Dehesh said. “When the COG protein complex doesn't work properly, it might make our cells age faster, just like what we saw in plants when they lacked light. This breakthrough could have far-reaching implications for the study of aging and age-related diseases.”
JOURNAL
Nature Plants
ARTICLE TITLE
COG-imposed Golgi functional integrity determines the onset of dark-induced senescence
Revolutionizing grapevine phenotyping: harnessing LiDAR for enhanced growth assessment and genetic insights
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OVERVIEW OF THE EXPERIMENTAL AND TECHNICAL DESIGN OF THE STUDY.
view moreCREDIT: PLANT PHENOMICS
In response to the pressing need to reduce pesticide usage and adapt grapevine varieties to climate change, there's an unprecedented effort to phenotype new genotypes using high-throughput methods. Teams globally are developing advanced systems, employing technologies like multispectral cameras and LiDAR, to assess growth traits, photosynthetic capability, and other architectural parameters. However, traditional methods remain time-consuming and less efficient for large-scale studies. The current research gap lies in effectively employing LiDAR technology to explore genetic factors affecting grapevine vigor for sustainable viticulture.
In November 2023, Plant Phenomics published a research article entitled by “LiDAR Is Effective in Characterizing Vine Growth and Detecting Associated Genetic Loci ”.
The study assessed growth traits in 209 grapevine genotypes using methods such as fresh pruning wood weight, exposed leaf area from digital images, leaf chlorophyll concentration, and LiDAR-derived volumes. It found 6 genomic regions associated with trait variations, validating LiDAR as an effective tool for characterizing grapevine growth. LiDAR-derived canopy volumes showed strong correlations with traditional methods, and pruning wood volume from LiDAR positively correlated with actual pruning weight. However, some relationships varied between seasons, indicating that LiDAR provided more consistent measurements overall. Traits except exposed leaf area (ELA) in certain years met normality criteria, and parents displayed significant differences for most traits. LiDAR-derived traits exhibited high, stable heritability, outperforming traditional methods. These traits also led to effective genetic models explaining substantial phenotypic variance. The study generated high-density genetic maps and identified quantitative trait loci (QTLs) associated with growth traits. It found stable QTLs across seasons and validated the genetic determinism of grapevine vigor using LiDAR. The study also noted that LiDAR-derived volumes at véraison and winter were more reliable and heritable than traditional methods, and powerful QTL detection confirmed their efficacy.
In summary, this research underscores the potential of LiDAR technology for high-throughput phenotyping and genetic studies of grapevine growth, providing a more efficient alternative to conventional methods. It opens avenues for understanding environmental effects, management techniques, and training systems on grapevine growth, moving towards more detailed genetic insights into grapevine vigor and architecture.
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References
Authors
Elsa Chedid1, Komlan Avia1, Vincent Dumas1, Lionel Ley2, Nicolas Reibel2, Gisèle Butterlin1, Maxime Soma3, Raul Lopez-Lozano4, Frédéric Baret4, Didier Merdinoglu1, and Éric Duchêne1*
Affiliations
1INRAE, University of Strasbourg, UMR SVQV, 28, rue de Herrlisheim, 68000 Colmar, France.
2INRAE, UEAV, 28, rue de Herrlisheim, 68000 Colmar, France.
3INRAE, Aix-Marseille Université, UMR RECOVER, 3275 Route de Cézanne, 13182 Aix-en-Provence, France.
4INRAE, Avignon Université, UMR EMMAH, UMT CAPTE, 228, route de l’aérodrome, 84914 Avignon, France.
About Éric Duchêne
He is currently the Deputy Director of the Joint INRAE-UNISTRA Research Unit on Vine Health and Wine Quality (SVQV). His research revolves around two main lines: on the one hand, plant-(vector)-pathogen interactions to reduce the use of pesticides and the impact of diseases; on the other hand, to maintain the productivity of vineyards and the quality of wines in the context of vine decline and climate change.
JOURNAL
Plant Phenomics
METHOD OF RESEARCH
Experimental study
SUBJECT OF RESEARCH
Not applicable
ARTICLE TITLE
LiDAR Is Effective in Characterizing Vine Growth and Detecting Associated Genetic Loci
Revolutionizing grape yield predictions: the rise of semi-supervised berry counting with CDMENet
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(A AND B) EXAMPLES OF GRAPE SPECIES AND MANUALLY LABELED IMAGES IN THE FIELD GRAPE BERRY DATASET.
view moreCREDIT: PLANT PHENOMICS
To improve grape yield predictions, automated berry counting has emerged as a crucial yet challenging task due to the dense distribution and occlusion of berries. While grape cultivation is a significant global economic activity, traditional manual counting methods are inaccurate and inefficient. Recent research has shifted towards deep learning and computer vision, employing detection and density estimation techniques for more precise counts. However, these methods grapple with the variability of farmland and high occlusion rates, leading to significant counting errors. Additionally, creating high-performance algorithms demands expensive data labeling, making it a significant hurdle for widespread use and adaptability in the field. The need for a cost-effective, accurate automated counting method remains a pressing research problem.
In November 2023, Plant Phenomics published a research article entitled by “Semi-supervised Counting of Grape Berries in the Field Based on Density Mutual Exclusion”.
To effectively and cost-efficiently count grape berries, the research presents CDMENet, a semi-supervised method that uses VGG16 for image feature extraction and density mutual exclusion to understand spatial patterns and unlabeled data. The method also employs a density difference loss to amplify feature differences between varying density levels. Conducted on an Ubuntu system with Python and deep learning frameworks, the algorithm's performance was tested using a specified hardware setup, preprocessing techniques, and training details such as learning rates and optimization methods. The results were promising, with CDMENet outperformed both fully and semi-supervised counterparts in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R2). Particularly notable was its performance with limited labeled data, showcasing superior accuracy and reduced errors compared to other models. Additionally, the method's robustness was further evidenced in various ablation studies, demonstrating the significant roles of unlabeled data, density difference loss, and the number of auxiliary task predictors. Despite minor influences from prediction confidence thresholds, CDMENet maintained stable and robust performance.
In conclusion, CDMENet presents a viable solution for grape berry counting in fields, reducing the need for extensive manual labeling while improving accuracy and reducing errors. Its efficient use of unlabeled data, enhanced feature representation through density difference loss, and overall robust performance against a backdrop of varying conditions underscore its potential as a cost-effective tool in agricultural yield estimation. Future work might explore optimizing loss functions further and deploying the algorithm in field robots or other practical applications.
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References
Authors
Yanan Li1,2†, Yuling Tang1,2*†, Yifei Liu1,2, and Dingrun Zheng1,2
†These authors contributed equally to this work.
Affiliations
1School of Computer Science and Engineering, School of Artificial Intelligence, Wuhan Institute of Technology, Wuhan 430205, China.
2Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan 430073, China.
About Yanan Li
She is currently a Lecturer with the School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan. Her research interests include computer vision and machine learning, with particular emphasis on image segmentation, domain adaptation, and various computer vision applications in agriculture.
JOURNAL
Plant Phenomics
METHOD OF RESEARCH
Experimental study
SUBJECT OF RESEARCH
Not applicable
ARTICLE TITLE
Semi-supervised Counting of Grape Berries in the Field Based on Density Mutual Exclusion
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