2025-07-14 カリフォルニア大学サンディエゴ校(UCSD)
A new tool allows researchers to probe the metabolic processes occurring within the leaves, stems, and roots of a key citrus crop, the clementine. Photo: iStock
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＜関連情報＞
- https://today.ucsd.edu/story/for-tastier-and-hardier-citrus-researchers-built-a-tool-for-probing-plant-metabolism
- https://www.pnas.org/doi/abs/10.1073/pnas.2503406122
極小の探針振動振幅を用いた１ナノメートル分解能の散乱型近接場光顕微鏡法 Scattering near-field optical microscopy at 1-nm resolution using ultralow tip oscillation amplitudes
Anurag Passi, Diego Tec-Campos, Manish Kumar, +6 , and Karsten Zengler
Proceedings of the National Academy of Sciences July 16, 2025
DOI:https://doi.org/10.1073/pnas.2503406122
Significance
iCitrus2616 is a high-resolution, multiorgan genome-scale metabolic model for Citrus clementina, comprising 2,616 genes, 8,653 metabolites, and 10,654 reactions. The model offers a highly detailed look into the metabolic landscape of C. clementina, spanning key organs like the leaf, stem, and root. iCitrus2616 provides a platform for exploring essential processes such as photosynthesis, respiration, and nutrient assimilation. The model enables precise simulations of growth patterns, carbon and nitrogen allocation, and secondary metabolite production under diverse environmental conditions. By contextualizing omics data, crucial pathways related to flavonoid synthesis, hormone regulation, and stress responses are identified. iCitrus2616 provides a foundational tool for hypothesis generation and for guiding future efforts in crop improvement through systems-level understanding of metabolism in Citrus clementina.
Abstract
Understanding plant response to environmental factors such as temperature, drought, diseases, and carbon-to-nitrogen (C:N) ratio is essential for crop resilience, quality, and adaptation to climate change. Here, we present iCitrus2616, a high-resolution organ-specific genome-scale metabolic model for Citrus clementina, comprising 2,616 genes, 8,653 metabolites, and 10,654 reactions. The model integrates organ-specific metabolomics data, i.e., leaf, stem, and root, and predicts plant responses to different conditions with high accuracy. Lower C:N ratios showed higher growth rates compared to higher C:N ratios, suggesting an inverse relationship between growth and C:N ratios. Simulations show that polymers such as starch and hemicellulose increased 4-fold under mixotrophic compared to phototrophic conditions, contributing to enhanced rigidity of cell walls, thus improving mechanical and drought stress. Furthermore, iCitrus2616 revealed higher production of specialized metabolites such as flavonoids in the presence of specific nutrients. Additionally, transcriptomics data from symptomatic and asymptomatic leaf and root tissues across four seasons (winter, spring, fall, and summer) during Huanglongbing infection (citrus greening) were integrated into the model. This integration revealed tissue-specific metabolic adaptations, including shifts in energy allocation, secondary metabolite production, and stress-response pathways under biotic stress. These findings underscore the utility of iCitrus2616 in elucidating the metabolic underpinnings of biotic and abiotic stress resilience and could aid in improving crop productivity and quality, thereby meeting escalating market demands.
