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Takaragawa H, Wakayama M. Responses of leaf gas exchange and metabolites to drought stress in different organs of sugarcane and its closely related species Erianthus arundinaceus. PLANTA 2024; 260:90. [PMID: 39256219 PMCID: PMC11387454 DOI: 10.1007/s00425-024-04508-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Accepted: 08/09/2024] [Indexed: 09/12/2024]
Abstract
MAIN CONCLUSION The high intrinsic water-use efficiency of Erianthus may be due to the low abaxial stomatal density and the accumulation of leaf metabolites such as betaine and gamma-aminobutyric acid. Sugarcane is an important crop that is widely cultivated in tropical and subtropical regions of the world. Because drought is among the main impediments limiting sugarcane production in these regions, breeding of drought-tolerant sugarcane varieties is important for sustainable production. Erianthus arundinaceus, a species closely related to sugarcane, exhibits high intrinsic water-use efficiency (iWUE), the underlying mechanisms for which remain unknown. To improve the genetic base for conferring drought tolerance in sugarcane, in the present study, we performed a comprehensive comparative analysis of leaf gas exchange and metabolites in different organs of sugarcane and Erianthus under wet and dry soil-moisture conditions. Erianthus exhibited lower stomatal conductance under both conditions, which resulted in a higher iWUE than in sugarcane. Organ-specific metabolites showed gradations between continuous parts and organs, suggesting linkages between them. Cluster analysis of organ-specific metabolites revealed the effects of the species and treatments in the leaves. Principal component analysis of leaf metabolites confirmed a rough ordering of the factors affecting their accumulations. Compared to sugarcane leaf, Erianthus leaf accumulated more raffinose, betaine, glutamine, gamma-aminobutyric acid, and S-adenosylmethionine, which function as osmolytes and stress-response compounds, under both the conditions. Our extensive analyses reveal that the high iWUE of Erianthus may be due to the specific accumulation of such metabolites in the leaves, in addition to the low stomatal density on the abaxial side of leaves. The identification of drought-tolerance traits of Erianthus will benefit the generation of sugarcane varieties capable of withstanding drought stress.
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Affiliation(s)
- Hiroo Takaragawa
- Tropical Agriculture Research Front, Japan International Research Center for Agricultural Sciences, Maezato Kawarabaru, Ishigaki, Okinawa, 907-0002, Japan.
| | - Masataka Wakayama
- Integrated Medical and Agricultural School for Public Health, Ehime University, Tōon, Ehime, 791-0295, Japan
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, 997-0052, Japan
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2
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Zhang Y, Gu S, Du J, Huang G, Shi J, Lu X, Wang J, Yang W, Guo X, Zhao C. Plant microphenotype: from innovative imaging to computational analysis. PLANT BIOTECHNOLOGY JOURNAL 2024; 22:802-818. [PMID: 38217351 PMCID: PMC10955502 DOI: 10.1111/pbi.14244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 11/09/2023] [Accepted: 11/11/2023] [Indexed: 01/15/2024]
Abstract
The microphenotype plays a key role in bridging the gap between the genotype and the complex macro phenotype. In this article, we review the advances in data acquisition and the intelligent analysis of plant microphenotyping and present applications of microphenotyping in plant science over the past two decades. We then point out several challenges in this field and suggest that cross-scale image acquisition strategies, powerful artificial intelligence algorithms, advanced genetic analysis, and computational phenotyping need to be established and performed to better understand interactions among genotype, environment, and management. Microphenotyping has entered the era of Microphenotyping 3.0 and will largely advance functional genomics and plant science.
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Affiliation(s)
- Ying Zhang
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Shenghao Gu
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Jianjun Du
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Guanmin Huang
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Jiawei Shi
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Xianju Lu
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Jinglu Wang
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Wanneng Yang
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Xinyu Guo
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Chunjiang Zhao
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
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3
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McLaughlin CM, Li M, Perryman M, Heymans A, Schneider H, Lasky JR, Sawers RJH. Evidence that variation in root anatomy contributes to local adaptation in Mexican native maize. Evol Appl 2024; 17:e13673. [PMID: 38468714 PMCID: PMC10925829 DOI: 10.1111/eva.13673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 02/07/2024] [Accepted: 02/22/2024] [Indexed: 03/13/2024] Open
Abstract
Mexican native maize (Zea mays ssp. mays) is adapted to a wide range of climatic and edaphic conditions. Here, we focus specifically on the potential role of root anatomical variation in this adaptation. Given the investment required to characterize root anatomy, we present a machine-learning approach using environmental descriptors to project trait variation from a relatively small training panel onto a larger panel of genotyped and georeferenced Mexican maize accessions. The resulting models defined potential biologically relevant clines across a complex environment that we used subsequently for genotype-environment association. We found evidence of systematic variation in maize root anatomy across Mexico, notably a prevalence of trait combinations favoring a reduction in axial hydraulic conductance in varieties sourced from cooler, drier highland areas. We discuss our results in the context of previously described water-banking strategies and present candidate genes that are associated with both root anatomical and environmental variation. Our strategy is a refinement of standard environmental genome-wide association analysis that is applicable whenever a training set of georeferenced phenotypic data is available.
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Affiliation(s)
- Chloee M. McLaughlin
- Intercollege Graduate Degree Program in Plant BiologyThe Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA
| | - Meng Li
- Department of Plant ScienceThe Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA
| | - Melanie Perryman
- Department of Plant ScienceThe Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA
| | - Adrien Heymans
- Umeå Plant Science Centre, Department of Forest Genetics and Plant PhysiologySwedish University of Agricultural SciencesUmeåSweden
- Earth and Life InstituteUC LouvainLouvain‐la‐NeuveBelgium
| | - Hannah Schneider
- Department of Physiology and Cell BiologyLeibniz Institute for Plant Genetics and Crop Plant Research (IPK)SeelandGermany
| | - Jesse R. Lasky
- Department of BiologyThe Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA
| | - Ruairidh J. H. Sawers
- Department of Plant ScienceThe Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA
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Kimotho RN, Maina S. Unraveling plant-microbe interactions: can integrated omics approaches offer concrete answers? JOURNAL OF EXPERIMENTAL BOTANY 2024; 75:1289-1313. [PMID: 37950741 PMCID: PMC10901211 DOI: 10.1093/jxb/erad448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 11/08/2023] [Indexed: 11/13/2023]
Abstract
Advances in high throughput omics techniques provide avenues to decipher plant microbiomes. However, there is limited information on how integrated informatics can help provide deeper insights into plant-microbe interactions in a concerted way. Integrating multi-omics datasets can transform our understanding of the plant microbiome from unspecified genetic influences on interacting species to specific gene-by-gene interactions. Here, we highlight recent progress and emerging strategies in crop microbiome omics research and review key aspects of how the integration of host and microbial omics-based datasets can be used to provide a comprehensive outline of complex crop-microbe interactions. We describe how these technological advances have helped unravel crucial plant and microbial genes and pathways that control beneficial, pathogenic, and commensal plant-microbe interactions. We identify crucial knowledge gaps and synthesize current limitations in our understanding of crop microbiome omics approaches. We highlight recent studies in which multi-omics-based approaches have led to improved models of crop microbial community structure and function. Finally, we recommend holistic approaches in integrating host and microbial omics datasets to achieve precision and efficiency in data analysis, which is crucial for biotic and abiotic stress control and in understanding the contribution of the microbiota in shaping plant fitness.
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Affiliation(s)
- Roy Njoroge Kimotho
- Hebei Key Laboratory of Soil Ecology, Key Laboratory of Agricultural Water Resources, Centre for Agricultural Resources Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Shijiazhuang 050021, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Solomon Maina
- Elizabeth Macarthur Agricultural Institute, NSW Department of Primary Industries, Menangle, New South Wales 2568, Australia
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5
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Sears RG, Lenaghan SC, Stewart CN. AI to enable plant cell metabolic engineering. TRENDS IN PLANT SCIENCE 2024; 29:126-129. [PMID: 37778886 DOI: 10.1016/j.tplants.2023.09.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 08/30/2023] [Accepted: 09/08/2023] [Indexed: 10/03/2023]
Abstract
Plant metabolic engineering must take into consideration the heterogeneous cell types that play a role in metabolite production; cells do not participate equally. We posit that artificial intelligence (AI) developed for biomedical purposes can be applied to plant cell characterization to accelerate the development of metabolic engineering strategies in plants.
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Affiliation(s)
- Robert G Sears
- Department of Plant Sciences, The University of Tennessee, Knoxville, Knoxville, TN, USA; Center for Agricultural Synthetic Biology, The University of Tennessee, Knoxville, Knoxville, TN, USA
| | - Scott C Lenaghan
- Center for Agricultural Synthetic Biology, The University of Tennessee, Knoxville, Knoxville, TN, USA; Department of Food Science, The University of Tennessee, Knoxville, Knoxville, TN, USA
| | - C Neal Stewart
- Department of Plant Sciences, The University of Tennessee, Knoxville, Knoxville, TN, USA; Center for Agricultural Synthetic Biology, The University of Tennessee, Knoxville, Knoxville, TN, USA.
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Cunha Neto IL, Hall BT, Lanba AR, Blosenski JD, Onyenedum JG. Laser ablation tomography (LATscan) as a new tool for anatomical studies of woody plants. THE NEW PHYTOLOGIST 2023; 239:429-444. [PMID: 36811411 DOI: 10.1111/nph.18831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 02/09/2023] [Indexed: 06/02/2023]
Abstract
Traditionally, botanists study plant anatomy by carefully sectioning samples, histological staining to highlight tissues of interests, then imaging slides under light microscopy. This approach generates significant details; however, this workflow is laborious, particularly in woody vines (lianas) with heterogeneous anatomies, and ultimately yields two-dimensional (2D) images. Laser ablation tomography (LATscan) is a high-throughput imaging system that yields hundreds of images per minute. This method has proven useful for studying the structure of delicate plant tissues; however, its utility in understanding the structure of woody tissues is underexplored. We report LATscan-derived anatomical data from several stems of lianas (c. 20 mm) of seven species and compare these results with those obtained through traditional anatomical techniques. LATscan successfully allows the description of tissue composition by differentiating cell type, size, and shape, but also permits the recognition of distinct cell wall composition (e.g. lignin, suberin, cellulose) based on differential fluorescent signals on unstained samples. LATscan generate high-quality 2D images and 3D reconstructions of woody plant samples; therefore, this new technology is useful for both qualitative and quantitative analyses. This high-throughput imaging technology has the potential to bolster phenotyping of vegetative and reproductive anatomy, wood anatomy, and other biological systems.
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Affiliation(s)
- Israel L Cunha Neto
- School of Integrative Plant Sciences and L. H. Bailey Hortorium, Cornell University, NY, 14853, Ithaca, USA
| | - Benjamin T Hall
- Laser for Innovative Solutions (L4iS), Suite 261, 200 Innovation Boulevard, State College, PA, 16803, USA
| | - Asheesh R Lanba
- Laser for Innovative Solutions (L4iS), Suite 261, 200 Innovation Boulevard, State College, PA, 16803, USA
- Department of Engineering, University of Southern Maine, 37 College Ave., Gorham, ME, 04038, USA
| | - Joshua D Blosenski
- Laser for Innovative Solutions (L4iS), Suite 261, 200 Innovation Boulevard, State College, PA, 16803, USA
| | - Joyce G Onyenedum
- School of Integrative Plant Sciences and L. H. Bailey Hortorium, Cornell University, NY, 14853, Ithaca, USA
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Singh B, Kumar S, Elangovan A, Vasht D, Arya S, Duc NT, Swami P, Pawar GS, Raju D, Krishna H, Sathee L, Dalal M, Sahoo RN, Chinnusamy V. Phenomics based prediction of plant biomass and leaf area in wheat using machine learning approaches. FRONTIERS IN PLANT SCIENCE 2023; 14:1214801. [PMID: 37448870 PMCID: PMC10337996 DOI: 10.3389/fpls.2023.1214801] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 06/07/2023] [Indexed: 07/15/2023]
Abstract
Introduction Phenomics has emerged as important tool to bridge the genotype-phenotype gap. To dissect complex traits such as highly dynamic plant growth, and quantification of its component traits over a different growth phase of plant will immensely help dissect genetic basis of biomass production. Based on RGB images, models have been developed to predict biomass recently. However, it is very challenging to find a model performing stable across experiments. In this study, we recorded RGB and NIR images of wheat germplasm and Recombinant Inbred Lines (RILs) of Raj3765xHD2329, and examined the use of multimodal images from RGB, NIR sensors and machine learning models to predict biomass and leaf area non-invasively. Results The image-based traits (i-Traits) containing geometric features, RGB based indices, RGB colour classes and NIR features were categorized into architectural traits and physiological traits. Total 77 i-Traits were selected for prediction of biomass and leaf area consisting of 35 architectural and 42 physiological traits. We have shown that different biomass related traits such as fresh weight, dry weight and shoot area can be predicted accurately from RGB and NIR images using 16 machine learning models. We applied the models on two consecutive years of experiments and found that measurement accuracies were similar suggesting the generalized nature of models. Results showed that all biomass-related traits could be estimated with about 90% accuracy but the performance of model BLASSO was relatively stable and high in all the traits and experiments. The R2 of BLASSO for fresh weight prediction was 0.96 (both year experiments), for dry weight prediction was 0.90 (Experiment 1) and 0.93 (Experiment 2) and for shoot area prediction 0.96 (Experiment 1) and 0.93 (Experiment 2). Also, the RMSRE of BLASSO for fresh weight prediction was 0.53 (Experiment 1) and 0.24 (Experiment 2), for dry weight prediction was 0.85 (Experiment 1) and 0.25 (Experiment 2) and for shoot area prediction 0.59 (Experiment 1) and 0.53 (Experiment 2). Discussion Based on the quantification power analysis of i-Traits, the determinants of biomass accumulation were found which contains both architectural and physiological traits. The best predictor i-Trait for fresh weight and dry weight prediction was Area_SV and for shoot area prediction was projected shoot area. These results will be helpful for identification and genetic basis dissection of major determinants of biomass accumulation and also non-invasive high throughput estimation of plant growth during different phenological stages can identify hitherto uncovered genes for biomass production and its deployment in crop improvement for breaking the yield plateau.
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Affiliation(s)
- Biswabiplab Singh
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Sudhir Kumar
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Allimuthu Elangovan
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Devendra Vasht
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Sunny Arya
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Nguyen Trung Duc
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
- Vietnam National University of Agriculture, Hanoi, Vietnam
| | - Pooja Swami
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Godawari Shivaji Pawar
- Division of Agricultural Botany, Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani, India
| | - Dhandapani Raju
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Hari Krishna
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Lekshmy Sathee
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Monika Dalal
- ICAR-National Institute for Plant Biotechnology, New Delhi, India
| | - Rabi Narayan Sahoo
- Division of Agricultural Physics, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Viswanathan Chinnusamy
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
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Sidhu JS, Ajmera I, Arya S, Lynch JP. RootSlice-A novel functional-structural model for root anatomical phenotypes. PLANT, CELL & ENVIRONMENT 2023; 46:1671-1690. [PMID: 36708192 DOI: 10.1111/pce.14552] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 01/18/2023] [Accepted: 01/23/2023] [Indexed: 06/18/2023]
Abstract
Root anatomy is an important determinant of root metabolic costs, soil exploration, and soil resource capture. Root anatomy varies substantially within and among plant species. RootSlice is a multicellular functional-structural model of root anatomy developed to facilitate the analysis and understanding of root anatomical phenotypes. RootSlice can capture phenotypically accurate root anatomy in three dimensions of different root classes and developmental zones, of both monocotyledonous and dicotyledonous species. Several case studies are presented illustrating the capabilities of the model. For maize nodal roots, the model illustrated the role of vacuole expansion in cell elongation; and confirmed the individual and synergistic role of increasing root cortical aerenchyma and reducing the number of cortical cell files in reducing root metabolic costs. Integration of RootSlice for different root zones as the temporal properties of the nodal roots in the whole-plant and soil model OpenSimRoot/maize enabled the multiscale evaluation of root anatomical phenotypes, highlighting the role of aerenchyma formation in enhancing the utility of cortical cell files for improving plant performance over varying soil nitrogen supply. Such integrative in silico approaches present avenues for exploring the fitness landscape of root anatomical phenotypes.
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Affiliation(s)
- Jagdeep Singh Sidhu
- Department of Plant Science, The Pennsylvania State University, University Park, State College, Pennsylvania, USA
| | - Ishan Ajmera
- Department of Plant Science, The Pennsylvania State University, University Park, State College, Pennsylvania, USA
| | - Sankalp Arya
- Department of Plant Science, The Pennsylvania State University, University Park, State College, Pennsylvania, USA
| | - Jonathan P Lynch
- Department of Plant Science, The Pennsylvania State University, University Park, State College, Pennsylvania, USA
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Lin PA, Kansman J, Chuang WP, Robert C, Erb M, Felton GW. Water availability and plant-herbivore interactions. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:2811-2828. [PMID: 36477789 DOI: 10.1093/jxb/erac481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 12/04/2022] [Indexed: 06/06/2023]
Abstract
Water is essential to plant growth and drives plant evolution and interactions with other organisms such as herbivores. However, water availability fluctuates, and these fluctuations are intensified by climate change. How plant water availability influences plant-herbivore interactions in the future is an important question in basic and applied ecology. Here we summarize and synthesize the recent discoveries on the impact of water availability on plant antiherbivore defense ecology and the underlying physiological processes. Water deficit tends to enhance plant resistance and escape traits (i.e. early phenology) against herbivory but negatively affects other defense strategies, including indirect defense and tolerance. However, exceptions are sometimes observed in specific plant-herbivore species pairs. We discuss the effect of water availability on species interactions associated with plants and herbivores from individual to community levels and how these interactions drive plant evolution. Although water stress and many other abiotic stresses are predicted to increase in intensity and frequency due to climate change, we identify a significant lack of study on the interactive impact of additional abiotic stressors on water-plant-herbivore interactions. This review summarizes critical knowledge gaps and informs possible future research directions in water-plant-herbivore interactions.
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Affiliation(s)
- Po-An Lin
- Department of Entomology, National Taiwan University, Taipei, Taiwan
| | - Jessica Kansman
- Department of Entomology, the Pennsylvania State University, University Park, PA, USA
| | - Wen-Po Chuang
- Department of Agronomy, National Taiwan University, Taipei, Taiwan
| | | | - Matthias Erb
- Institute of Plant Science, University of Bern, Bern, Switzerland
| | - Gary W Felton
- Department of Entomology, the Pennsylvania State University, University Park, PA, USA
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Thomas DJ, Rainbow J, Bartley LE. The rapid-tome, a 3D-printed microtome, and an updated hand-sectioning method for high-quality plant sectioning. PLANT METHODS 2023; 19:12. [PMID: 36739429 PMCID: PMC9898918 DOI: 10.1186/s13007-023-00986-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 01/21/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Microscopic analysis of plant anatomy is a common procedure in biology to study structure and function that requires high-quality sections for accurate measurements. Hand sectioning of specimens is typically limited to moderately soft tissue while harder samples prohibit sectioning by hand and/or result in inconsistent thicknesses. RESULTS Here we present both a clearly described hand-sectioning method and a novel microtome design that together provide the means to section a variety of plant sample types. The described hand-sectioning method for herbaceous stems works well for softer subjects but is less suitable for samples with secondary growth (e.g., wood production). Instead, the "Rapid-Tome" is a novel tool for sectioning both soft and tougher high-aspect-ratio samples, such as stems and roots, with excellent sample control. The Rapid-Tome can be 3D-printed in approximately 18 h on a mid-quality printer common at university maker spaces. After printing and trimming, Rapid-Tome assembly takes a few minutes with five metal parts common at hardware stores. Users sectioned a variety of plant samples including the hollow internodes of switchgrass (Panicum virgatum), fibrous switchgrass roots containing aerenchyma, and woody branches of eastern red cedar (Juniperus virginiana) and American sycamore (Platanus occidentalis). A comparative analyses with Rapid-Tome-produced sections readily revealed a significant difference in seasonal growth of sycamore xylem vessel area in spring (49%) vs. summer (23%). Additionally, high school students with no prior experience produced sections with the Rapid-Tome adequate for comparative analyses of various plant samples in less than an hour. CONCLUSIONS The described hand-sectioning method is suitable for softer tissues, including hollow-stemmed grasses and similar samples. In addition, the Rapid-Tome provides capacity to safely produce high-quality sections of tougher plant materials at a fraction of the cost of traditional microtomes combined with excellent sample control. The Rapid-Tome features rapid sectioning, sample advancement, blade changes, and sample changes; it is highly portable and can be used easily with minimal training making production of thin sections accessible for classroom and outreach use, in addition to research.
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Affiliation(s)
- David J Thomas
- Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK, 73019, USA
- Institute of Biological Chemistry, Washington State University, Pullman, WA, 99164, USA
| | - Jordan Rainbow
- Institute of Biological Chemistry, Washington State University, Pullman, WA, 99164, USA
| | - Laura E Bartley
- Institute of Biological Chemistry, Washington State University, Pullman, WA, 99164, USA.
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11
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Schneider HM. Functional implications of multiseriate cortical sclerenchyma for soil resource capture and crop improvement. AOB PLANTS 2022; 14:plac050. [PMID: 36545297 PMCID: PMC9762723 DOI: 10.1093/aobpla/plac050] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 10/17/2022] [Indexed: 06/09/2023]
Abstract
Suboptimal nutrient and water availability are primary constraints to crop growth. Global agriculture requires crops with greater nutrient and water efficiency. Multiseriate cortical sclerenchyma (MCS), a root anatomical trait characterized by small cells with thick cell walls encrusted with lignin in the outer cortex, has been shown to be an important trait for adaptation in maize and wheat in mechanically impeded soils. However, MCS has the potential to improve edaphic stress tolerance in a number of different crop taxa and in a number of different environments. This review explores the functional implications of MCS as an adaptive trait for water and nutrient acquisition and discusses future research perspectives on this trait for incorporation into crop breeding programs. For example, MCS may influence water and nutrient uptake, resistance to pests, symbiotic interactions, microbial interactions in the rhizosphere and soil carbon deposition. Root anatomical phenotypes are underutilized; however, important breeding targets for the development of efficient, productive and resilient crops urgently needed in global agriculture.
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12
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Amitrano C, Rouphael Y, De Pascale S, De Micco V. Vapour Pressure Deficit (VPD) Drives the Balance of Hydraulic-Related Anatomical Traits in Lettuce Leaves. PLANTS (BASEL, SWITZERLAND) 2022; 11:2369. [PMID: 36145772 PMCID: PMC9502365 DOI: 10.3390/plants11182369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 09/03/2022] [Accepted: 09/08/2022] [Indexed: 06/16/2023]
Abstract
The coordination of leaf hydraulic-related traits with leaf size is influenced by environmental conditions and especially by VPD. Water and gas flows are guided by leaf anatomical and physiological traits, whose plasticity is crucial for plants to face environmental changes. Only a few studies have analysed how variations in VPD levels influence stomatal and vein development and their correlation with leaf size, reporting contrasting results. Thus, we applied microscopy techniques to evaluate the effect of low and high VPDs on the development of stomata and veins, also analysing leaf functional traits. We hypothesized that leaves under high VPD with a modified balance between veins and stomata face higher transpiration. We also explored the variability of stomata and vein density across the leaf lamina. From the results, it was evident that under both VPDs, plants maintained a coordinated development of stomata and veins, with a higher density at low VPD. Moreover, more stomata but fewer veins developed in the parts of the lettuce head exposed to light, suggesting that their differentiation during leaf expansion is strictly dependent on the microclimatic conditions. Knowing the plasticity of hydraulic-related morpho-functional traits and its intra-leaf variability is timely for their impact on water and gas fluxes, thus helping to evaluate the impact of environmental-driven anatomical variations on productivity of natural ecosystems and crops, in a climate change scenario.
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Affiliation(s)
- Chiara Amitrano
- Correspondence: (C.A.); (V.D.M.); Tel.: +39-081-2532026 (C.A. & V.D.M.)
| | | | | | - Veronica De Micco
- Correspondence: (C.A.); (V.D.M.); Tel.: +39-081-2532026 (C.A. & V.D.M.)
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