Unlocking the lemon's flavor secret: epigenetics of citric acid
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Increased CHH methylation contributes to citric acid biosynthesis of lemon fruit.
view moreCredit: Horticulture Research
Lemons owe their signature tang to citric acid, yet the molecular mechanisms governing its accumulation have long remained a mystery. Now, a pioneering study has shed light on the epigenetic regulation behind this process, revealing how DNA methylation influences citric acid biosynthesis. By mapping the genome and DNA methylome of the 'Xiangshui' lemon variety, researchers have uncovered dynamic methylation changes that shape fruit flavor—findings that could revolutionize lemon breeding for enhanced taste and market value.
As one of the world's most economically significant citrus fruits, lemons are prized for their distinctive acidity, which plays a crucial role in their commercial appeal. While extensive research has been conducted on citric acid’s role in fruit flavor, the molecular and epigenetic factors controlling its accumulation have remained largely elusive. This knowledge gap presents challenges for both breeding improved lemon varieties and understanding the fundamental biological processes driving fruit development. Addressing these gaps, researchers have turned to epigenetics to decipher how DNA modifications influence citric acid levels.
A research team from Nanjing Agricultural University and Guangxi University has made a breakthrough, publishing their findings (DOI: 10.1093/hr/uhae005) in Horticulture Research on January 5, 2024. Their study delves into the role of DNA methylation in regulating citric acid biosynthesis throughout lemon fruit development, offering unprecedented insights into the epigenetic factors that determine lemon flavor.
By constructing a high-quality chromosomal-level genome assembly of the 'Xiangshui' lemon—spanning 364.85 Mb with 27,945 genes and 51.37% repetitive sequences—the team conducted a comprehensive DNA methylome analysis at various fruit development stages. Their findings revealed striking shifts in DNA methylation: CG and CHG methylation levels declined, while CHH methylation surged as the fruit matured. Notably, this increase in CHH methylation was closely linked to the activation of genes critical for citric acid biosynthesis, including phosphoenolpyruvate carboxykinase (ClPEPCK). Additionally, researchers found that genes involved in the RNA-directed DNA methylation (RdDM) pathway became more active as the fruit developed, suggesting that this pathway plays a pivotal role in regulating CHH methylation. These findings highlight a direct connection between DNA methylation patterns and citric acid metabolism, unveiling a key mechanism that dictates lemon flavor.
Dr. Haifeng Wang, a co-author of the study, underscored the impact of these discoveries: "Our research reveals a dynamic epigenetic interplay that governs citric acid biosynthesis during lemon fruit development. Understanding this mechanism opens exciting possibilities for breeding lemons with enhanced flavor and provides new insights into the broader biology of fruit metabolism."
The implications of this research extend far beyond lemon cultivation. By uncovering the epigenetic blueprint behind citric acid accumulation, scientists can now target specific genes and pathways to develop superior lemon varieties with improved taste and market appeal. Moreover, these findings pave the way for broader applications in citrus breeding, potentially leading to the cultivation of more resilient, high-quality fruit varieties. As epigenetics continues to emerge as a powerful tool in agricultural science, this study marks a significant step toward unlocking the full genetic potential of fruit crops.
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References
DOI
Original Source URL
https://doi.org/10.1093/hr/uhae005
Funding information
This work was supported by the Guangxi Natural Science Foundation (No. 2023GXNSFDA026034), National Natural Science Foundation of China (No. 32160142), Sugarcane Research Foundation of Guangxi University (No. 2022GZA002), State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources (SKLCUSA-b202302) to H.W., and Science and Technology Major Project of Guangxi (Gui Ke AA22068092) to G.H.
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
The lemon genome and DNA methylome unveil epigenetic regulation of citric acid biosynthesis during fruit development
Smart orchards get smarter: new AI method for fruit labeling
Nanjing Agricultural University The Academy of Science
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Overall architecture of the EasyDAM_V4 method. The overall architecture of the proposed method involves seven main parts. The source domain foreground fruit image represented by (1) and the labeled target domain fruit dataset represented by (7) are the input and output of the method, respectively. Boxes (2) and (3) illustrate the main innovation points of this paper. (2) The shape texture feature map obtained by using the latent space-based multi-dimensional phenotype feature extraction method. The fruit translation model is trained together with the original RGB image after concatenation. (3) The multi-dimensional loss function module based on the entropy weight method, which is used to accurately describe the phenotypic features of fruits during the training process. Rectangles of different colors indicate the fruit image translation results controlled by different categories of loss functions.
view moreCredit: Horticulture Research
A cutting-edge AI method, EasyDAM_V4, is set to transform fruit detection in agriculture by enabling automatic labeling of fruit datasets with unprecedented adaptability across species. This breakthrough is crucial for the development of smart orchards, where high-precision fruit detection is the backbone of applications like yield prediction, automated harvesting, and phenotypic analysis. By harnessing advanced image translation techniques and a novel Guided-GAN model, EasyDAM_V4 dramatically reduces manual labeling costs while boosting detection accuracy, marking a significant step toward automated and sustainable agriculture.
Traditional fruit detection models rely on extensive manually labeled datasets, a labor-intensive and time-consuming process that becomes particularly challenging for fruits with diverse shapes, sizes, and textures. Real-world orchard environments introduce further complexity, as fruits are often densely packed, occluded by leaves, or displayed under varying lighting conditions. These challenges underscore the need for an automated, intelligent labeling system that can handle substantial shape variations while improving detection generalization.
In response to these challenges, a research team from Beijing University of Technology and The University of Tokyo has developed EasyDAM_V4, a pioneering AI-driven approach, published (DOI: 10.1093/hr/uhae007) in Horticulture Research on January 10, 2024. Leveraging a Guided-GAN model, this method enables cross-species fruit image translation, significantly improving labeling accuracy even for fruits with large phenotypic variations. The study’s results highlight substantial improvements in automated labeling, offering a powerful new tool for agricultural image processing.
At the core of EasyDAM_V4 lies a multi-dimensional phenotypic feature extraction technique, which integrates deep learning and latent space modeling to enhance fruit image translation. The method employs a pre-trained VGG16 network to extract shape and texture features from fruit images, which are then fused with original RGB images to enhance the input for the GAN model. A novel multi-dimensional loss function is introduced, with separate loss components for shape, texture, and color features, dynamically weighted using an entropy-based adjustment strategy. This sophisticated approach enables precise control over the generation of fruit features, overcoming previous limitations in handling large shape and texture variations across fruit species.
The effectiveness of EasyDAM_V4 was demonstrated using pear images as the source domain and pitaya, eggplant, and cucumber images as target domains. The model achieved impressive labeling accuracies of 87.8% for pitaya, 87.0% for eggplant, and 80.7% for cucumber, significantly outperforming existing methods. The Guided-GAN model’s ability to generate realistic fruit images with large phenotypic differences represents a major leap forward in automated dataset generation for agricultural AI.
Dr. Wenli Zhang from Beijing University of Technology, one of the lead researchers behind the study, emphasized the impact of this breakthrough:"EasyDAM_V4 represents a significant step toward full automation in fruit detection. By effectively addressing the challenges of shape variance and domain adaptation, this technology not only enhances labeling accuracy but also lays the groundwork for the next generation of agricultural AI and smart orchards."
The potential applications of EasyDAM_V4 extend far beyond fruit labeling. By streamlining dataset preparation, this innovation could accelerate the development of smart agriculture, enabling more accurate yield predictions, efficient robotic harvesting, and advanced phenotypic studies. Additionally, high-quality labeled datasets will support plant phenomics and breeding strategies, ultimately contributing to more sustainable and resilient agricultural systems.
As AI continues to reshape modern farming, EasyDAM_V4 stands at the forefront, revolutionizing how fruit detection models are built, trained, and deployed—one pixel at a time.
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References
DOI
Original Source URL
https://doi.org/10.1093/hr/uhae007
Funding information
This study was partially supported by the National Natural Science Foundation of China (NSFC) Program 62276009 and the Japan Science and Technology Agency (JST) AIP Acceleration Research JPMJCR21U3.
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
EasyDAM_V4: Guided-GAN-based cross-species data labeling for fruit detection with significant shape difference
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