Frontiers in Plant Science最新文献

{"title":"Potentially suitable geographical area for <i>Pulsatilla chinensis</i> Regel under current and future climatic scenarios based on the MaxEnt model.","authors":"Yanan Wu, Lanmeng Yan, Hongjian Shen, Rui Guan, Qianqian Ge, Ling Huang, Emelda Rosseleena Rohani, Jinmei Ou, Rongchun Han, Xiaohui Tong","doi":"10.3389/fpls.2025.1538566","DOIUrl":"https://doi.org/10.3389/fpls.2025.1538566","url":null,"abstract":"<p><p>Climate change has significantly impacted the distribution patterns of medicinal plants, highlighting the need for accurate models to predict future habitat shifts. In this study, the Maximum Entropy model to analyze the habitat distribution of <i>Pulsatilla chinensis</i> (Bunge) Regel under current conditions and two future climate scenarios (SSP245 and SSP585). Based on 105 occurrence records and 12 environmental variables, precipitation of the wettest quarter, isothermality, average November temperature, and the standard deviation of temperature seasonality were identified as key factors influencing the habitat suitability for <i>P. chinensis</i>. The reliability of the model was supported by a mean area under the curve (AUC) value of 0.916 and a True Skill Statistic (TSS) value of 0.608. The results indicated that although the total suitable habitat for <i>P. chinensis</i> expanded under both scenarios, the highly suitable area contracted significantly under SSP585 compared to SSP245. This suggests the importance of incorporating climate change considerations into <i>P. chinensis</i> management strategies to address potential challenges arising from future ecosystem dynamics.</p>","PeriodicalId":12632,"journal":{"name":"Frontiers in Plant Science","volume":"16 ","pages":"1538566"},"PeriodicalIF":4.1,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12116669/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144173529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genetic and wind field analysis of wheat leaf rust (<i>Puccinia triticina</i>) dispersal: from winter sources in Gansu and Shaanxi to summer epidemics in China.","authors":"Hongfu Li, Na Zhao, Qinqin Zhang, Liang Huang, Hao Zhang, Li Gao, Wanquan Chen, Taiguo Liu","doi":"10.3389/fpls.2025.1558898","DOIUrl":"https://doi.org/10.3389/fpls.2025.1558898","url":null,"abstract":"<p><p>Wheat leaf rust caused by <i>Puccinia triticina</i> (<i>Pt</i>) is one of the most serious diseases affecting wheat worldwide. Given that China is the world's largest wheat-producing country, there is a lack of comprehensive understanding regarding the temporal and spatial dynamics of wheat leaf rust epidemics. This study investigated the population structure of <i>Pt</i> across different wheat leaf rust epidemic seasons in Gansu and Shaanxi provinces. Samples were collected in the 2020 winter from Gansu and Shaanxi, and during the 2021 spring and summer from eight additional provinces: Shandong, Henan, Gansu, Shaanxi, Hubei, Yunnan, Guizhou, and Xinjiang. Population genetics analysis was conducted using 12 pairs of Simple sequence repeat (SSR) markers. The results indicated that the genetic diversity of the samples was highest in Shandong (SD) and Shaanxi in the 2020 winter (SN_20w), and Shaanxi in the 2021 summer (SN_21s), while Xinjiang (XJ) exhibited the lowest genetic diversity. Population structure analysis revealed six distinct genetic backgrounds across the 10 populations, with SN_20w and XJ showing greater genetic distances compared to other populations. There was less genetic differentiation and strong gene flow between pairwise populations of Henan (HA) and SN_21s, SD and Gansu in the winter of 2020 (GS_20w), Gansu in the summer of 2021 (GS_21s) and Hubei (HB), and GS_20w and HB, with six shared multi-locus genotypes detected among four pairwise populations. Integrating population genetic analysis, horizontal wind field analysis, topographic analysis, and the sampling timeline, this study concluded that the winter populations of <i>Pt</i> in the Guanzhong region experienced dominance shifts, with limited impact on the wheat leaf rust epidemic of 2021. In the 2021 epidemic season, two pathways of pathogen dispersal were proposed: (1) from the Guanzhong basin to Henan province; (2) through the Hanshui River Valley to Hubei province. These findings provide valuable insights into the spatial dynamics of wheat leaf rust and inform targeted prevention and control strategies.</p>","PeriodicalId":12632,"journal":{"name":"Frontiers in Plant Science","volume":"16 ","pages":"1558898"},"PeriodicalIF":4.1,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12116510/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144173593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterization analyses of <i>MADS-box</i> genes highlighting their functions with seed development in <i>Ricinus communis</i>.","authors":"Jing Sun, Zekun Zhou, Fanqing Meng, Mengyun Wen, Aizhong Liu, Anmin Yu","doi":"10.3389/fpls.2025.1589915","DOIUrl":"https://doi.org/10.3389/fpls.2025.1589915","url":null,"abstract":"<p><p>The <i>MADS-box</i> gene family plays a pivotal role in regulating floral organ development and various aspects of plant growth. Despite its well-established importance in many species, the function and evolution of <i>MADS-box</i> genes in <i>Ricinus communis</i> (castor) remain unexplored. This study presents an extensive genome-wide analysis of the <i>MADS-box</i> gene family in castor, covering their physicochemical characteristics, phylogenetics, gene architecture, chromosomal distribution, evolutionary dynamics, expression profiles, and co-expression networks. In total, 56 <i>MADS-box</i> genes were categorized into two main phylogenetic groups: type-I and type-II, which were further subdivided into three and two subgroups, respectively. Segmental duplication was found to be the primary driver of <i>MADS-box</i> gene expansion in castor, while purifying selection was evident across the entire gene family, as indicated by the <i>Ka</i>/<i>Ks</i> ratio. In-depth analyses of gene expression, promoter motifs, co-expression networks, and experimental validation (Y1H assays and qRT-PCR) revealed that <i>RcMADS16</i> and <i>RcMADS4</i>1 are key regulators of castor seed development, with <i>RcMADS16</i> may involve in seed coat formation and <i>RcMADS41</i> in oil accumulation. This study not only provides the first detailed insight into the evolutionary and functional landscape of <i>MADS-box</i> genes in castor, but also establishes a foundation for future investigations into the role of these genes in seed and organ development, both in castor and other plant species.</p>","PeriodicalId":12632,"journal":{"name":"Frontiers in Plant Science","volume":"16 ","pages":"1589915"},"PeriodicalIF":4.1,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12116605/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144173548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based anomaly detection for precision field crop protection.","authors":"Cheng Wei, Yifeng Shan, MengZhe Zhen","doi":"10.3389/fpls.2025.1576756","DOIUrl":"https://doi.org/10.3389/fpls.2025.1576756","url":null,"abstract":"<p><strong>Introduction: </strong>Precision agriculture relies on advanced technologies to optimize crop protection and resource utilization, ensuring sustainable and efficient farming practices. Anomaly detection plays a critical role in identifying and addressing irregularities, such as pest outbreaks, disease spread, or nutrient deficiencies, that can negatively impact yield. Traditional methods struggle with the complexity and variability of agricultural data collected from diverse sources.</p><p><strong>Methods: </strong>To address these challenges, we propose a novel framework that integrates the Integrated Multi-Modal Smart Farming Network (IMSFNet) with the Adaptive Resource Optimization Strategy (AROS). IMSFNet employs multimodal data fusion and spatiotemporal modeling to provide accurate predictions of crop health and yield anomalies by leveraging data from UAVs, satellites, ground sensors, and weather stations. AROS dynamically optimizes resource allocation based on real-time environmental feedback and multi-objective optimization, balancing yield maximization, cost efficiency, and environmental sustainability.</p><p><strong>Results: </strong>Experimental evaluations demonstrate the effectiveness of our approach in detecting anomalies and improving decision-making in precision agriculture.</p><p><strong>Discussion: </strong>This framework sets a new standard for sustainable and data-driven crop protection strategies.</p>","PeriodicalId":12632,"journal":{"name":"Frontiers in Plant Science","volume":"16 ","pages":"1576756"},"PeriodicalIF":4.1,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12116526/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144173552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of <i>TaMFT-3A</i> and <i>TaMKK3-4A</i> alleles on wheat pre-harvest sprouting.","authors":"Bo-Wen Zhang, Bai-Song Yang, Xiao-Neng Wan, Xin Ma, Kai-Di Lyu, Han Wang, Shu-Ying Yang, Hui-Hui Zhang, Shu-Nv Hao, Jian Ma, Guo-Zhong Sun","doi":"10.3389/fpls.2025.1594385","DOIUrl":"https://doi.org/10.3389/fpls.2025.1594385","url":null,"abstract":"<p><p>Pre-harvest sprouting (PHS) is a significant challenge affecting global production of wheat (<i>Triticum aestivum</i> L.). Resistance to PHS is governed by both genetic and environmental factors, making reliable molecular markers essential for enhancing PHS resistance through molecular marker-assisted selection (MAS). Genes <i>TaMFT-3A</i> and <i>TaMKK3-4A</i> have been cloned in wheat and are known to regulate PHS resistance in white-grained varieties. In this study, we assessed the allelic variations in these genes and their combined effects on PHS resistance using two recombinant inbred line (RIL) populations, Wanxianbaimaizi/Zhongyou 9507 (WZ) and Wanxianbaimaizi/Jing 411 (WJ), under two distinct field environmental conditions. PHS resistance was assessed by measuring seed germination in physiologically mature stage, correlation and ANOVA were used to analyze PHS data. The germination percentage (GP) and germination index (GI) were significantly correlated across both RIL populations. Specific allelic variations at positions -222, +646, and +666 in the <i>TaMFT-3A</i> gene strongly correlated with PHS resistance. The CGA haplotype at these loci was linked to the highest resistance, while the TAA haplotype was associated with the lowest resistance levels. Additionally, haplotype variation at the +660 locus of <i>TaMKK3-4A</i> demonstrated a weak but environmentally modulated correlation with PHS resistance. This study provides a theoretical foundation for utilizing <i>TaMFT-3A</i> and <i>TaMKK3-4A</i> in molecular breeding strategies to enhance wheat resilience to PHS.</p>","PeriodicalId":12632,"journal":{"name":"Frontiers in Plant Science","volume":"16 ","pages":"1594385"},"PeriodicalIF":4.1,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12116531/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144173555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From text to traits: exploring the role of large language models in plant breeding.","authors":"Mohsen Yoosefzadeh-Najafabadi","doi":"10.3389/fpls.2025.1583344","DOIUrl":"https://doi.org/10.3389/fpls.2025.1583344","url":null,"abstract":"<p><p>Modern plant breeders regularly deal with the intricate patterns within biological data in order to better understand the biological background behind a trait of interest and speed up the breeding process. Recently, Large Language Models (LLMs) have gained widespread adoption in everyday contexts, showcasing remarkable capabilities in understanding and generating human-like text. By harnessing the capabilities of LLMs, foundational models can be repurposed to uncover intricate patterns within biological data, leading to the development of robust and flexible predictive tools that provide valuable insights into complex plant breeding systems. Despite the significant progress made in utilizing LLMs in various scientific domains, their adoption within plant breeding remains unexplored, presenting a significant opportunity for innovation. This review paper explores how LLMs, initially designed for natural language tasks, can be adapted to address specific challenges in plant breeding, such as identifying novel genetic interactions, predicting performance of a trait of interest, and well-integrating diverse datasets such as multi-omics, phenotypic, and environmental sources. Compared to conventional breeding methods, LLMs offer the potential to enhance the discovery of genetic relationships, improve trait prediction accuracy, and facilitate informed decision-making. This review aims to bridge this gap by highlighting current advancements, challenges, and future directions for integrating LLMs into plant breeding, ultimately contributing to sustainable agriculture and improved global food security.</p>","PeriodicalId":12632,"journal":{"name":"Frontiers in Plant Science","volume":"16 ","pages":"1583344"},"PeriodicalIF":4.1,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12116590/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144173569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The short-term effect of nitrogen and phosphorus fertilizers on cold resistance in <i>Urtica cannabina</i> based on transcriptomics and metabolomics analysis.","authors":"Siqi Liu, Xiaoxue Zhang, Guorui Zhang, Jinmei Zhao, Xiaoqing Zhang","doi":"10.3389/fpls.2025.1598628","DOIUrl":"https://doi.org/10.3389/fpls.2025.1598628","url":null,"abstract":"<p><strong>Introduction: </strong>Freezing injury in winter is a major abiotic stress that significantly limits plant growth and survival. While nitrogen and phosphorus fertilizers have been demonstrated to alleviate the impact of freezing injury in various plant species, their role of fertilizers in the cold tolerance of <i>Urtica</i> spp. is still unknown.</p><p><strong>Methods: </strong>This study investigated the effects of fertilizers on the cold resistance of <i>U. cannabina</i> by comprehensively analyzing the physiological and biochemical indices, transcriptome, and metabolome of the <i>U. cannabina</i> under applications of 150 kg nitrogen ha-1 (N) and 90 kg phosphorus ha-1 (P), using \"no fertilizer\" (CK) as the control.</p><p><strong>Results: </strong>The results showed that applying nitrogen and phosphorus fertilizers reduced the malondialdehyde concentration and had much higher superoxide dismutase activity and soluble sugar and proline concentrations. Transcriptomics and metabolomics analysis revealed that applying nitrogen and phosphorus fertilizers tended to involve several critical regulatory pathways in the biosynthesis of secondary metabolites, flavonoid biosynthesis, and phenylpropanoid biosynthesis pathways. Concretely speaking, these fertilizers can affect the biosynthesis of naringenin, pinobanksin 3-acetate, galangin, and p-Coumaroyl shikimic acid and the expression of related genes to regulate the cold tolerance of <i>U. cannabina</i>. Moreover, through using weighted correlation network analysis (WGCNA), 4210 genes in response to nitrogen fertilizer and 5975 genes in response to phosphorus fertilizer, positively correlating with key metabolites, were identified. Several genes encoding enzymes including glucan endo-1,3-beta-glucosidase, pectinesterase, trehalase, hydroquinone glucosyltransferase, monodehydroascorbate reductase, tyrosine aminotransferase, and peroxidase were verified to be hub genes involved in the cold-stress response of <i>U. cannabina</i>.</p><p><strong>Discussion: </strong>Overall, these findings have laid a theoretical foundation for the highly efficient utilization of nitrogen and phosphorus in <i>U. cannabina</i> and provide novel insights into the regulatory network of <i>U. cannabina</i> in response to cold-temperature stress.</p>","PeriodicalId":12632,"journal":{"name":"Frontiers in Plant Science","volume":"16 ","pages":"1598628"},"PeriodicalIF":4.1,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12116482/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144173096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integration of smart sensors and IOT in precision agriculture: trends, challenges and future prospectives.","authors":"Sheikh Mansoor, Shahzad Iqbal, Simona M Popescu, Song Lim Kim, Yong Suk Chung, Jeong-Ho Baek","doi":"10.3389/fpls.2025.1587869","DOIUrl":"https://doi.org/10.3389/fpls.2025.1587869","url":null,"abstract":"<p><p>Traditional farming methods, effective for generations, struggle to meet rising global food demands due to limitations in productivity, efficiency, and sustainability amid climate change and resource scarcity. Precision agriculture presents a viable solution by optimizing resource use, enhancing efficiency, and fostering sustainable practices through data-driven decision-making supported by advanced sensors and Internet of Things (IoT) technologies. This review examines various smart sensors used in precision agriculture, including soil sensors for moisture, pH, and plant stress sensors etc. These sensors deliver real-time data that enables informed decision-making, facilitating targeted interventions like optimized irrigation, fertilization, and pest management. Additionally, the review highlights the transformative role of IoT in precision agriculture. The integration of sensor networks with IoT platforms allows for remote monitoring, data analysis via artificial intelligence (AI) and machine learning (ML), and automated control systems, enabling predictive analytics to address challenges such as disease outbreaks and yield forecasting. However, while precision agriculture offers significant benefits, it faces challenges including high initial investment costs, complexities in data management, needs for technical expertise, data security and privacy concerns, and issues with connectivity in remote agricultural areas. Addressing these technological and economic challenges is essential for maximizing the potential of precision agriculture in enhancing global food security and sustainability. Therefore, in this review we explore the latest trends, challenges, and opportunities associated with IoT enabled smart sensors in precision agriculture.</p>","PeriodicalId":12632,"journal":{"name":"Frontiers in Plant Science","volume":"16 ","pages":"1587869"},"PeriodicalIF":4.1,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12116683/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144173604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reducing annotation effort in agricultural data: simple and fast unsupervised coreset selection with DINOv2 and K-means.","authors":"Laura Gómez-Zamanillo, Nagore Portilla, Artzai Picón, Itziar Egusquiza, Ramón Navarra-Mestre, Andoni Elola, Arantza Bereciartua-Perez","doi":"10.3389/fpls.2025.1546756","DOIUrl":"https://doi.org/10.3389/fpls.2025.1546756","url":null,"abstract":"<p><p>The need for large amounts of annotated data is a major obstacle to adopting deep learning in agricultural applications, where annotation is typically time-consuming and requires expert knowledge. To address this issue, methods have been developed to select data for manual annotation that represents the existing variability in the dataset, thereby avoiding redundant information. Coreset selection methods aim to choose a small subset of data samples that best represents the entire dataset. These methods can therefore be used to select a reduced set of samples for annotation, optimizing the training of a deep learning model for the best possible performance. In this work, we propose a simple yet effective coreset selection method that combines the recent foundation model DINOv2 as a powerful feature selector with the well-known K-Means clustering method. Samples are selected from each calculated cluster to form the final coreset. The proposed method is validated by comparing the performance metrics of a multiclass classification model trained on datasets reduced randomly and using the proposed method. This validation is conducted on two different datasets, and in both cases, the proposed method achieves better results, with improvements of up to 0.15 in the F1 score for significant reductions in the training datasets. Additionally, the importance of using DINOv2 as a feature extractor to achieve these good results is studied.</p>","PeriodicalId":12632,"journal":{"name":"Frontiers in Plant Science","volume":"16 ","pages":"1546756"},"PeriodicalIF":4.1,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12116677/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144172865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The molecular architecture distinctions between compression, opposite and normal wood of <i>Pinus radiata</i>.","authors":"Rosalie Cresswell, Alan Dickson, Michael Robertson, Suzanne Gallagher, Regis Risani, Marie Joo Le Guen, Henry Temple, Aleksandra Liszka, Lloyd Donaldson, Nigel Kirby, John Ralph, Stefan Hill, Paul Dupree, Ray Dupree, Mathias Sorieul","doi":"10.3389/fpls.2025.1576928","DOIUrl":"https://doi.org/10.3389/fpls.2025.1576928","url":null,"abstract":"<p><p>In gymnosperms compression wood is a specialised type of structural cell wall formed in response to biomechanical stresses. The differences in terms of gross structure, ultrastructure and chemistry are well-known. However, the differences between compression wood, normal wood, and opposite wood regarding the arrangements and interactions of the various polymers and water within their cell walls still needs to be established. The analysis of ¹³C-labelled <i>Pinus radiata</i> by solid-state NMR spectroscopy and other complementary techniques revealed several new aspects of compression and opposite wood molecular architecture. Compared to normal wood, compression wood has a lower water content, its overall nanoporosity is reduced, and the water and matrix polymers have a lower molecular mobility. Galactan, which is a specific marker of compression wood, is broadly distributed within the cell wall, disordered, and not aligned with cellulose, and is found to be in close proximity to xylan. Dehydroabietic acid (a resin acid) is immobilised and close to the H-lignin only in compression wood. Although the overall molecular mobility of normal wood and opposite wood are similar, opposite wood has different arabinose conformations, a large increase in the amount of chain ends, contains significantly more galactan and has additional unassigned mobile components highlighting the different molecular arrangement of cell wall polymers in opposite and normal wood.</p>","PeriodicalId":12632,"journal":{"name":"Frontiers in Plant Science","volume":"16 ","pages":"1576928"},"PeriodicalIF":4.1,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12106396/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144158012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}