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de la Fuente C, Grondin A, Sine B, Debieu M, Belin C, Hajjarpoor A, Atkinson JA, Passot S, Salson M, Orjuela J, Tranchant-Dubreuil C, Brossier JR, Steffen M, Morgado C, Dinh HN, Pandey BK, Darmau J, Champion A, Petitot AS, Barrachina C, Pratlong M, Mounier T, Nakombo-Gbassault P, Gantet P, Gangashetty P, Guedon Y, Vadez V, Reichheld JP, Bennett MJ, Kane NA, Guyomarc'h S, Wells DM, Vigouroux Y, Laplaze L. Glutaredoxin regulation of primary root growth is associated with early drought stress tolerance in pearl millet. eLife 2024; 12:RP86169. [PMID: 38294329 PMCID: PMC10945517 DOI: 10.7554/elife.86169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2024] Open
Abstract
Seedling root traits impact plant establishment under challenging environments. Pearl millet is one of the most heat and drought tolerant cereal crops that provides a vital food source across the sub-Saharan Sahel region. Pearl millet's early root system features a single fast-growing primary root which we hypothesize is an adaptation to the Sahelian climate. Using crop modeling, we demonstrate that early drought stress is an important constraint in agrosystems in the Sahel where pearl millet was domesticated. Furthermore, we show that increased pearl millet primary root growth is correlated with increased early water stress tolerance in field conditions. Genetics including genome-wide association study and quantitative trait loci (QTL) approaches identify genomic regions controlling this key root trait. Combining gene expression data, re-sequencing and re-annotation of one of these genomic regions identified a glutaredoxin-encoding gene PgGRXC9 as the candidate stress resilience root growth regulator. Functional characterization of its closest Arabidopsis homolog AtROXY19 revealed a novel role for this glutaredoxin (GRX) gene clade in regulating cell elongation. In summary, our study suggests a conserved function for GRX genes in conferring root cell elongation and enhancing resilience of pearl millet to its Sahelian environment.
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Affiliation(s)
| | - Alexandre Grondin
- DIADE, Université de Montpellier, IRD, CIRADMontpellierFrance
- LMI LAPSEDakarSenegal
- CERAAS, ISRAThiesSenegal
| | | | - Marilyne Debieu
- DIADE, Université de Montpellier, IRD, CIRADMontpellierFrance
| | | | - Amir Hajjarpoor
- DIADE, Université de Montpellier, IRD, CIRADMontpellierFrance
| | - Jonathan A Atkinson
- School of Biosciences, University of NottinghamSutton BoningtonUnited Kingdom
| | - Sixtine Passot
- DIADE, Université de Montpellier, IRD, CIRADMontpellierFrance
| | - Marine Salson
- DIADE, Université de Montpellier, IRD, CIRADMontpellierFrance
| | - Julie Orjuela
- DIADE, Université de Montpellier, IRD, CIRADMontpellierFrance
| | | | | | - Maxime Steffen
- DIADE, Université de Montpellier, IRD, CIRADMontpellierFrance
| | | | - Hang Ngan Dinh
- DIADE, Université de Montpellier, IRD, CIRADMontpellierFrance
| | - Bipin K Pandey
- School of Biosciences, University of NottinghamSutton BoningtonUnited Kingdom
| | - Julie Darmau
- DIADE, Université de Montpellier, IRD, CIRADMontpellierFrance
| | - Antony Champion
- DIADE, Université de Montpellier, IRD, CIRADMontpellierFrance
| | | | | | | | | | | | - Pascal Gantet
- DIADE, Université de Montpellier, IRD, CIRADMontpellierFrance
| | | | - Yann Guedon
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut AgroMontpellierFrance
| | - Vincent Vadez
- DIADE, Université de Montpellier, IRD, CIRADMontpellierFrance
- LMI LAPSEDakarSenegal
- CERAAS, ISRAThiesSenegal
| | | | - Malcolm J Bennett
- School of Biosciences, University of NottinghamSutton BoningtonUnited Kingdom
| | | | | | - Darren M Wells
- School of Biosciences, University of NottinghamSutton BoningtonUnited Kingdom
| | - Yves Vigouroux
- DIADE, Université de Montpellier, IRD, CIRADMontpellierFrance
| | - Laurent Laplaze
- DIADE, Université de Montpellier, IRD, CIRADMontpellierFrance
- LMI LAPSEDakarSenegal
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2
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Robson JK, Ferguson JN, McAusland L, Atkinson JA, Tranchant-Dubreuil C, Cubry P, Sabot F, Wells DM, Price AH, Wilson ZA, Murchie EH. Chlorophyll fluorescence-based high-throughput phenotyping facilitates the genetic dissection of photosynthetic heat tolerance in African (Oryza glaberrima) and Asian (Oryza sativa) rice. J Exp Bot 2023; 74:5181-5197. [PMID: 37347829 PMCID: PMC10498015 DOI: 10.1093/jxb/erad239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 06/20/2023] [Indexed: 06/24/2023]
Abstract
Rising temperatures and extreme heat events threaten rice production. Half of the global population relies on rice for basic nutrition, and therefore developing heat-tolerant rice is essential. During vegetative development, reduced photosynthetic rates can limit growth and the capacity to store soluble carbohydrates. The photosystem II (PSII) complex is a particularly heat-labile component of photosynthesis. We have developed a high-throughput chlorophyll fluorescence-based screen for photosynthetic heat tolerance capable of screening hundreds of plants daily. Through measuring the response of maximum PSII efficiency to increasing temperature, this platform generates data for modelling the PSII-temperature relationship in large populations in a small amount of time. Coefficients from these models (photosynthetic heat tolerance traits) demonstrated high heritabilities across African (Oryza glaberrima) and Asian (Oryza sativa, Bengal Assam Aus Panel) rice diversity sets, highlighting valuable genetic variation accessible for breeding. Genome-wide association studies were performed across both species for these traits, representing the first documented attempt to characterize the genetic basis of photosynthetic heat tolerance in any species to date. A total of 133 candidate genes were highlighted. These were significantly enriched with genes whose predicted roles suggested influence on PSII activity and the response to stress. We discuss the most promising candidates for improving photosynthetic heat tolerance in rice.
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Affiliation(s)
- Jordan K Robson
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, UK
| | - John N Ferguson
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, UK
- Department of Plant Sciences, University of Cambridge, Cambridge, UK
- School of Life Sciences, University of Essex, Colchester, UK
| | - Lorna McAusland
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, UK
| | - Jonathan A Atkinson
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, UK
| | | | - Phillipe Cubry
- Institut de Recherche pour le Developpement, 911 Av. Agropolis, 34394 Montpellier, France
| | - François Sabot
- Institut de Recherche pour le Developpement, 911 Av. Agropolis, 34394 Montpellier, France
| | - Darren M Wells
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, UK
| | - Adam H Price
- Institut de Recherche pour le Developpement, 911 Av. Agropolis, 34394 Montpellier, France
| | - Zoe A Wilson
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, UK
| | - Erik H Murchie
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, UK
- School of Biological Sciences, University of Aberdeen, Aberdeen, UK
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3
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Ware A, Jones DH, Flis P, Chrysanthou E, Smith KE, Kümpers BMC, Yant L, Atkinson JA, Wells DM, Bhosale R, Bishopp A. Loss of ancestral function in duckweed roots is accompanied by progressive anatomical reduction and a re-distribution of nutrient transporters. Curr Biol 2023; 33:1795-1802.e4. [PMID: 36990089 DOI: 10.1016/j.cub.2023.03.025] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 02/07/2023] [Accepted: 03/09/2023] [Indexed: 03/30/2023]
Abstract
Organ loss occurs frequently during plant and animal evolution. Sometimes, non-functional organs are retained through evolution. Vestigial organs are defined as genetically determined structures that have lost their ancestral (or salient) function.1,2,3 Duckweeds, an aquatic monocot family, exhibit both these characteristics. They possess a uniquely simple body plan, variably across five genera, two of which are rootless. Due to the existence of closely related species with a wide diversity in rooting strategies, duckweed roots represent a powerful system for investigating vestigiality. To explore this, we employed a panel of physiological, ionomic, and transcriptomic analyses, with the main goal of elucidating the extent of vestigiality in duckweed roots. We uncovered a progressive reduction in root anatomy as genera diverge and revealed that the root has lost its salient ancestral function as an organ required for supplying nutrients to the plant. Accompanying this, nutrient transporter expression patterns have lost the stereotypical root biased localization observed in other plant species. While other examples of organ loss such as limbs in reptiles4 or eyes in cavefish5 frequently display a binary of presence/absence, duckweeds provide a unique snapshot of an organ with varying degrees of vestigialization in closely related neighbors and thus provide a unique resource for exploration of how organs behave at different stages along the process of loss.
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Affiliation(s)
- Alexander Ware
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough LE12 5RD, UK.
| | - Dylan H Jones
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough LE12 5RD, UK
| | - Paulina Flis
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough LE12 5RD, UK; Future Food Beacon, University of Nottingham, Sutton Bonington Campus, Loughborough LE12 5RD, UK
| | - Elina Chrysanthou
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough LE12 5RD, UK
| | - Kellie E Smith
- Future Food Beacon, University of Nottingham, Sutton Bonington Campus, Loughborough LE12 5RD, UK; School of Life Sciences, University of Nottingham, University Park Campus, Nottingham NG7 2RD, UK
| | - Britta M C Kümpers
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough LE12 5RD, UK
| | - Levi Yant
- Future Food Beacon, University of Nottingham, Sutton Bonington Campus, Loughborough LE12 5RD, UK; School of Life Sciences, University of Nottingham, University Park Campus, Nottingham NG7 2RD, UK
| | - Jonathan A Atkinson
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough LE12 5RD, UK
| | - Darren M Wells
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough LE12 5RD, UK
| | - Rahul Bhosale
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough LE12 5RD, UK; Future Food Beacon, University of Nottingham, Sutton Bonington Campus, Loughborough LE12 5RD, UK
| | - Anthony Bishopp
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough LE12 5RD, UK.
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4
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Kümpers BMC, Han J, Vaughan-Hirsch J, Redman N, Ware A, Atkinson JA, Leftley N, Janes G, Castiglione G, Tarr PT, Pyke K, Voß U, Wells DM, Bishopp A. Dual expression and anatomy lines allow simultaneous visualization of gene expression and anatomy. Plant Physiol 2022; 188:56-69. [PMID: 34718789 PMCID: PMC8774739 DOI: 10.1093/plphys/kiab503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 10/04/2021] [Indexed: 06/13/2023]
Abstract
Studying the developmental genetics of plant organs requires following gene expression in specific tissues. To facilitate this, we have developed dual expression anatomy lines, which incorporate a red plasma membrane marker alongside a fluorescent reporter for a gene of interest in the same vector. Here, we adapted the GreenGate cloning vectors to create two destination vectors showing strong marking of cell membranes in either the whole root or specifically in the lateral roots. This system can also be used in both embryos and whole seedlings. As proof of concept, we follow both gene expression and anatomy in Arabidopsis (Arabidopsis thaliana) during lateral root organogenesis for a period of over 24 h. Coupled with the development of a flow cell and perfusion system, we follow changes in activity of the DII auxin sensor following application of auxin.
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Affiliation(s)
- Britta M C Kümpers
- School of Biosciences, University of Nottingham, Loughborough, LE12 5RD, UK
| | - Jingyi Han
- School of Biosciences, University of Nottingham, Loughborough, LE12 5RD, UK
| | | | - Nicholas Redman
- School of Biosciences, University of Nottingham, Loughborough, LE12 5RD, UK
| | - Alexander Ware
- School of Biosciences, University of Nottingham, Loughborough, LE12 5RD, UK
| | | | - Nicola Leftley
- School of Biosciences, University of Nottingham, Loughborough, LE12 5RD, UK
| | - George Janes
- School of Biosciences, University of Nottingham, Loughborough, LE12 5RD, UK
| | | | - Paul T Tarr
- Howard Hughes Medical Institute, California Institute of Technology, 1200 East California Boulevard, Pasadena, California 91125, USA
- Division of Biology and Biological Engineering 156-29, California Institute of Technology, 1200 East California Boulevard, Pasadena, California 91125, USA
| | - Kevin Pyke
- School of Biosciences, University of Nottingham, Loughborough, LE12 5RD, UK
| | - Ute Voß
- School of Biosciences, University of Nottingham, Loughborough, LE12 5RD, UK
| | - Darren M Wells
- School of Biosciences, University of Nottingham, Loughborough, LE12 5RD, UK
| | - Anthony Bishopp
- School of Biosciences, University of Nottingham, Loughborough, LE12 5RD, UK
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5
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Pandey BK, Atkinson JA, Sturrock CJ. Non-invasive Imaging of Rice Roots in Non-compacted and Compacted Soil. Bio Protoc 2021; 11:e4252. [PMID: 35087914 DOI: 10.21769/bioprotoc.4252] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 09/09/2021] [Accepted: 10/06/2021] [Indexed: 11/02/2022] Open
Abstract
Roots are the prime organ for nutrient and water uptake and are therefore fundamental to the growth and development of plants. However, physical challenges of a heterogeneous environment and diverse edaphic stresses affect root growth in soil. Compacted soil is a serious global problem, causing inhibition of root elongation, which reduces surface area and impacts resource foraging. Visualisation and quantification of roots in soil is difficult due to this growth substrate's opaque nature; however, non-destructive imaging technologies are now becoming more widely available to plant and soil scientists working to address this challenge. We have recently developed an integrated approach, combining X-ray Computed Tomography (X-ray CT) and confocal microscopy to image roots grown in compacted soil conditions from a plant to a cellular scale. The method is suited to visualize cellular responses of root tips grown in both non-compacted and compacted soils. This protocol presents a fully integrated workflow, including soil column preparation, creation of compaction conditions, plant growth, imaging, and quantification of root adaptive responses at a cellular scale.
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Affiliation(s)
- Bipin K Pandey
- Division of Plant and Crop Science, School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, UK
| | - Jonathan A Atkinson
- Division of Plant and Crop Science, School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, UK
| | - Craig J Sturrock
- The Hounsfield Facility, Division of Agricultural and Environmental Sciences, School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, UK
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6
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George PBL, Fidler DB, Van Nostrand JD, Atkinson JA, Mooney SJ, Creer S, Griffiths RI, McDonald JE, Robinson DA, Jones DL. Shifts in Soil Structure, Biological, and Functional Diversity Under Long-Term Carbon Deprivation. Front Microbiol 2021; 12:735022. [PMID: 34594317 PMCID: PMC8477002 DOI: 10.3389/fmicb.2021.735022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 08/09/2021] [Indexed: 01/16/2023] Open
Abstract
Soil organic matter is composed of a variety of carbon (C) forms. However, not all forms are equally accessible to soil microorganisms. Deprivation of C inputs will cause changes in the physical and microbial community structures of soils; yet the trajectories of such changes are not clear. We assessed microbial communities using phospholipid fatty acid profiling, metabarcoding, CO2 emissions, and functional gene microarrays in a decade-long C deprivation field experiment. We also assessed changes in a range of soil physicochemical properties, including using X-ray Computed Tomography imaging to assess differences in soil structure. Two sets of soils were deprived of C inputs by removing plant inputs for 10 years and 1 year, respectively. We found a reduction in diversity measures, after 10 years of C deprivation, which was unexpected based on previous research. Fungi appeared to be most impacted, likely due to competition for scarce resources after exhausting the available plant material. This suggestion was supported by evidence of bioindicator taxa in non-vegetated soils that may directly compete with or consume fungi. There was also a reduction in copies of most functional genes after 10 years of C deprivation, though gene copies increased for phytase and some genes involved in decomposing recalcitrant C and methanogenesis. Additionally, soils under C deprivation displayed expected reductions in pH, organic C, nitrogen, and biomass as well as reduced mean pore size, especially in larger pores. However, pore connectivity increased after 10 years of C deprivation contrary to expectations. Our results highlight concurrent collapse of soil structure and biodiversity following long-term C deprivation. Overall, this study shows the negative trajectory of continuous C deprivation and loss of organic matter on a wide range of soil quality indicators and microorganisms.
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Affiliation(s)
- Paul B L George
- School of Natural Sciences, Bangor University, Bangor, United Kingdom.,UK Centre for Ecology & Hydrology, Bangor, United Kingdom.,Département de Médecine Moléculaire, Université Laval, Quebec City, QC, Canada
| | - David B Fidler
- School of Natural Sciences, Bangor University, Bangor, United Kingdom
| | - Joy D Van Nostrand
- Institute for Environmental Genomics, The University of Oklahoma, Norman, OK, United States
| | - Jonathan A Atkinson
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Sutton Bonington, United Kingdom
| | - Sacha J Mooney
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Sutton Bonington, United Kingdom
| | - Simon Creer
- School of Natural Sciences, Bangor University, Bangor, United Kingdom
| | | | - James E McDonald
- School of Natural Sciences, Bangor University, Bangor, United Kingdom
| | | | - Davey L Jones
- School of Natural Sciences, Bangor University, Bangor, United Kingdom.,SoilsWest, UWA School of Agriculture and Environment, The University of Western Australia, Perth, WA, Australia
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7
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Zhou H, Whalley WR, Hawkesford MJ, Ashton RW, Atkinson B, Atkinson JA, Sturrock CJ, Bennett MJ, Mooney SJ. The interaction between wheat roots and soil pores in structured field soil. J Exp Bot 2021; 72:747-756. [PMID: 33064808 PMCID: PMC7853603 DOI: 10.1093/jxb/eraa475] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 10/16/2020] [Indexed: 05/11/2023]
Abstract
Wheat (Triticum aestivum L.) root growth in the subsoil is usually constrained by soil strength, although roots can use macropores to elongate to deeper layers. The quantitative relationship between the elongation of wheat roots and the soil pore system, however, is still to be determined. We studied the depth distribution of roots of six wheat varieties and explored their relationship with soil macroporosity from samples with the field structure preserved. Undisturbed soil cores (to a depth of 100 cm) were collected from the field and then non-destructively imaged using X-ray computed tomography (at a spatial resolution of 90 µm) to quantify soil macropore structure and root number density (the number of roots cm-2 within a horizontal cross-section of a soil core). Soil macroporosity changed significantly with depth but not between the different wheat lines. There was no significant difference in root number density between wheat varieties. In the subsoil, wheat roots used macropores, especially biopores (i.e. former root or earthworm channels) to grow into deeper layers. Soil macroporosity explained 59% of the variance in root number density. Our data suggested that the development of the wheat root system in the field was more affected by the soil macropore system than by genotype. On this basis, management practices which enhance the porosity of the subsoil may therefore be an effective strategy to improve deep rooting of wheat.
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Affiliation(s)
- Hu Zhou
- School of Biosciences, University of Nottingham, Loughborough, Leicestershire, UK
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Sciences, Chinese Academy of Sciences, Nanjing, PR China
- Correspondence:
| | | | | | | | - Brian Atkinson
- School of Biosciences, University of Nottingham, Loughborough, Leicestershire, UK
| | - Jonathan A Atkinson
- School of Biosciences, University of Nottingham, Loughborough, Leicestershire, UK
| | - Craig J Sturrock
- School of Biosciences, University of Nottingham, Loughborough, Leicestershire, UK
| | - Malcolm J Bennett
- School of Biosciences, University of Nottingham, Loughborough, Leicestershire, UK
| | - Sacha J Mooney
- School of Biosciences, University of Nottingham, Loughborough, Leicestershire, UK
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8
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Bagley SA, Atkinson JA, Hunt H, Wilson MH, Pridmore TP, Wells DM. Low-Cost Automated Vectors and Modular Environmental Sensors for Plant Phenotyping. Sensors (Basel) 2020; 20:E3319. [PMID: 32545168 PMCID: PMC7309146 DOI: 10.3390/s20113319] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Revised: 06/05/2020] [Accepted: 06/09/2020] [Indexed: 11/17/2022]
Abstract
High-throughput plant phenotyping in controlled environments (growth chambers and glasshouses) is often delivered via large, expensive installations, leading to limited access and the increased relevance of "affordable phenotyping" solutions. We present two robot vectors for automated plant phenotyping under controlled conditions. Using 3D-printed components and readily-available hardware and electronic components, these designs are inexpensive, flexible and easily modified to multiple tasks. We present a design for a thermal imaging robot for high-precision time-lapse imaging of canopies and a Plate Imager for high-throughput phenotyping of roots and shoots of plants grown on media plates. Phenotyping in controlled conditions requires multi-position spatial and temporal monitoring of environmental conditions. We also present a low-cost sensor platform for environmental monitoring based on inexpensive sensors, microcontrollers and internet-of-things (IoT) protocols.
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Affiliation(s)
- Stuart A. Bagley
- Integrated Phenomics Group, School of Biosciences, University of Nottingham, Sutton Bonington Campus, Sutton Bonington LE12 5RD, UK;
| | - Jonathan A. Atkinson
- Future Food Beacon, School of Biosciences, University of Nottingham, Sutton Bonington Campus, Sutton Bonington LE12 5RD, UK; (J.A.A.); (M.H.W.)
| | - Henry Hunt
- School of Computer Science, University of Nottingham, Nottingham NG8 1BB, UK; (H.H.); (T.P.P.)
| | - Michael H. Wilson
- Future Food Beacon, School of Biosciences, University of Nottingham, Sutton Bonington Campus, Sutton Bonington LE12 5RD, UK; (J.A.A.); (M.H.W.)
| | - Tony P. Pridmore
- School of Computer Science, University of Nottingham, Nottingham NG8 1BB, UK; (H.H.); (T.P.P.)
| | - Darren M. Wells
- Integrated Phenomics Group, School of Biosciences, University of Nottingham, Sutton Bonington Campus, Sutton Bonington LE12 5RD, UK;
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9
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Atkinson JA, Hawkesford MJ, Whalley WR, Zhou H, Mooney SJ. Soil strength influences wheat root interactions with soil macropores. Plant Cell Environ 2020. [PMID: 31600410 DOI: 10.1111/pce:13659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Deep rooting is critical for access to water and nutrients found in subsoil. However, damage to soil structure and the natural increase in soil strength with depth, often impedes root penetration. Evidence suggests that roots use macropores (soil cavities greater than 75 μm) to bypass strong soil layers. If roots have to exploit structures, a key trait conferring deep rooting will be the ability to locate existing pore networks; a trait called trematotropism. In this study, artificial macropores were created in repacked soil columns at bulk densities of 1.6 g cm-3 and 1.2 g cm-3 , representing compact and loose soil. Near isogenic lines of wheat, Rht-B1a and Rht-B1c, were planted and root-macropore interactions were visualized and quantified using X-ray computed tomography. In compact soil, 68.8% of root-macropore interactions resulted in pore colonization, compared with 12.5% in loose soil. Changes in root growth trajectory following pore interaction were also quantified, with 21.0% of roots changing direction (±3°) in loose soil compared with 76.0% in compact soil. These results indicate that colonization of macropores is an important strategy of wheat roots in compacted subsoil. Management practices to reduce subsoil compaction and encourage macropore formation could offer significant advantage in helping wheat roots penetrate deeper into subsoil.
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Affiliation(s)
- Jonathan A Atkinson
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, LE12 5RD, UK
| | | | | | - Hu Zhou
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, LE12 5RD, UK
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, 210008, P.R. China
| | - Sacha J Mooney
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, LE12 5RD, UK
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10
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Atkinson JA, Hawkesford MJ, Whalley WR, Zhou H, Mooney SJ. Soil strength influences wheat root interactions with soil macropores. Plant Cell Environ 2020; 43:235-245. [PMID: 31600410 PMCID: PMC7027857 DOI: 10.1111/pce.13659] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 08/19/2019] [Accepted: 09/18/2019] [Indexed: 05/24/2023]
Abstract
Deep rooting is critical for access to water and nutrients found in subsoil. However, damage to soil structure and the natural increase in soil strength with depth, often impedes root penetration. Evidence suggests that roots use macropores (soil cavities greater than 75 μm) to bypass strong soil layers. If roots have to exploit structures, a key trait conferring deep rooting will be the ability to locate existing pore networks; a trait called trematotropism. In this study, artificial macropores were created in repacked soil columns at bulk densities of 1.6 g cm-3 and 1.2 g cm-3 , representing compact and loose soil. Near isogenic lines of wheat, Rht-B1a and Rht-B1c, were planted and root-macropore interactions were visualized and quantified using X-ray computed tomography. In compact soil, 68.8% of root-macropore interactions resulted in pore colonization, compared with 12.5% in loose soil. Changes in root growth trajectory following pore interaction were also quantified, with 21.0% of roots changing direction (±3°) in loose soil compared with 76.0% in compact soil. These results indicate that colonization of macropores is an important strategy of wheat roots in compacted subsoil. Management practices to reduce subsoil compaction and encourage macropore formation could offer significant advantage in helping wheat roots penetrate deeper into subsoil.
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Affiliation(s)
- Jonathan A. Atkinson
- School of BiosciencesUniversity of Nottingham, Sutton Bonington CampusLoughboroughLE12 5RDUK
| | | | | | - Hu Zhou
- School of BiosciencesUniversity of Nottingham, Sutton Bonington CampusLoughboroughLE12 5RDUK
- State Key Laboratory of Soil and Sustainable AgricultureInstitute of Soil Science, Chinese Academy of SciencesNanjing210008P.R. China
| | - Sacha J. Mooney
- School of BiosciencesUniversity of Nottingham, Sutton Bonington CampusLoughboroughLE12 5RDUK
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11
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Yasrab R, Atkinson JA, Wells DM, French AP, Pridmore TP, Pound MP. RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures. Gigascience 2019; 8:giz123. [PMID: 31702012 PMCID: PMC6839032 DOI: 10.1093/gigascience/giz123] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 08/23/2019] [Accepted: 09/22/2019] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND In recent years quantitative analysis of root growth has become increasingly important as a way to explore the influence of abiotic stress such as high temperature and drought on a plant's ability to take up water and nutrients. Segmentation and feature extraction of plant roots from images presents a significant computer vision challenge. Root images contain complicated structures, variations in size, background, occlusion, clutter and variation in lighting conditions. We present a new image analysis approach that provides fully automatic extraction of complex root system architectures from a range of plant species in varied imaging set-ups. Driven by modern deep-learning approaches, RootNav 2.0 replaces previously manual and semi-automatic feature extraction with an extremely deep multi-task convolutional neural network architecture. The network also locates seeds, first order and second order root tips to drive a search algorithm seeking optimal paths throughout the image, extracting accurate architectures without user interaction. RESULTS We develop and train a novel deep network architecture to explicitly combine local pixel information with global scene information in order to accurately segment small root features across high-resolution images. The proposed method was evaluated on images of wheat (Triticum aestivum L.) from a seedling assay. Compared with semi-automatic analysis via the original RootNav tool, the proposed method demonstrated comparable accuracy, with a 10-fold increase in speed. The network was able to adapt to different plant species via transfer learning, offering similar accuracy when transferred to an Arabidopsis thaliana plate assay. A final instance of transfer learning, to images of Brassica napus from a hydroponic assay, still demonstrated good accuracy despite many fewer training images. CONCLUSIONS We present RootNav 2.0, a new approach to root image analysis driven by a deep neural network. The tool can be adapted to new image domains with a reduced number of images, and offers substantial speed improvements over semi-automatic and manual approaches. The tool outputs root architectures in the widely accepted RSML standard, for which numerous analysis packages exist (http://rootsystemml.github.io/), as well as segmentation masks compatible with other automated measurement tools. The tool will provide researchers with the ability to analyse root systems at larget scales than ever before, at a time when large scale genomic studies have made this more important than ever.
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Affiliation(s)
- Robail Yasrab
- School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham NG8 1BB, UK
| | - Jonathan A Atkinson
- School of Biosciences, Sutton Bonington Campus, University of Nottingham, Nottingham LE12 5RD, UK
| | - Darren M Wells
- School of Biosciences, Sutton Bonington Campus, University of Nottingham, Nottingham LE12 5RD, UK
| | - Andrew P French
- School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham NG8 1BB, UK
- School of Biosciences, Sutton Bonington Campus, University of Nottingham, Nottingham LE12 5RD, UK
| | - Tony P Pridmore
- School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham NG8 1BB, UK
| | - Michael P Pound
- School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham NG8 1BB, UK
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12
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Yasrab R, Atkinson JA, Wells DM, French AP, Pridmore TP, Pound MP. RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures. Gigascience 2019; 8:5614712. [PMID: 31702012 DOI: 10.1101/709147] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 08/23/2019] [Accepted: 09/22/2019] [Indexed: 05/21/2023] Open
Abstract
BACKGROUND In recent years quantitative analysis of root growth has become increasingly important as a way to explore the influence of abiotic stress such as high temperature and drought on a plant's ability to take up water and nutrients. Segmentation and feature extraction of plant roots from images presents a significant computer vision challenge. Root images contain complicated structures, variations in size, background, occlusion, clutter and variation in lighting conditions. We present a new image analysis approach that provides fully automatic extraction of complex root system architectures from a range of plant species in varied imaging set-ups. Driven by modern deep-learning approaches, RootNav 2.0 replaces previously manual and semi-automatic feature extraction with an extremely deep multi-task convolutional neural network architecture. The network also locates seeds, first order and second order root tips to drive a search algorithm seeking optimal paths throughout the image, extracting accurate architectures without user interaction. RESULTS We develop and train a novel deep network architecture to explicitly combine local pixel information with global scene information in order to accurately segment small root features across high-resolution images. The proposed method was evaluated on images of wheat (Triticum aestivum L.) from a seedling assay. Compared with semi-automatic analysis via the original RootNav tool, the proposed method demonstrated comparable accuracy, with a 10-fold increase in speed. The network was able to adapt to different plant species via transfer learning, offering similar accuracy when transferred to an Arabidopsis thaliana plate assay. A final instance of transfer learning, to images of Brassica napus from a hydroponic assay, still demonstrated good accuracy despite many fewer training images. CONCLUSIONS We present RootNav 2.0, a new approach to root image analysis driven by a deep neural network. The tool can be adapted to new image domains with a reduced number of images, and offers substantial speed improvements over semi-automatic and manual approaches. The tool outputs root architectures in the widely accepted RSML standard, for which numerous analysis packages exist (http://rootsystemml.github.io/), as well as segmentation masks compatible with other automated measurement tools. The tool will provide researchers with the ability to analyse root systems at larget scales than ever before, at a time when large scale genomic studies have made this more important than ever.
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Affiliation(s)
- Robail Yasrab
- School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham NG8 1BB, UK
| | - Jonathan A Atkinson
- School of Biosciences, Sutton Bonington Campus, University of Nottingham, Nottingham LE12 5RD, UK
| | - Darren M Wells
- School of Biosciences, Sutton Bonington Campus, University of Nottingham, Nottingham LE12 5RD, UK
| | - Andrew P French
- School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham NG8 1BB, UK
- School of Biosciences, Sutton Bonington Campus, University of Nottingham, Nottingham LE12 5RD, UK
| | - Tony P Pridmore
- School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham NG8 1BB, UK
| | - Michael P Pound
- School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham NG8 1BB, UK
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Mohammed U, Caine RS, Atkinson JA, Harrison EL, Wells D, Chater CC, Gray JE, Swarup R, Murchie EH. Author Correction: Rice plants overexpressing OsEPF1 show reduced stomatal density and increased root cortical aerenchyma formation. Sci Rep 2019; 9:14827. [PMID: 31597936 PMCID: PMC6785534 DOI: 10.1038/s41598-019-51402-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Affiliation(s)
- U Mohammed
- Division of Plant and Crop Science, School of Biosciences, University of Nottingham, Sutton Bonington campus, LE12 5RD, Nottingham, UK
| | - R S Caine
- Department of Molecular Biology and Biotechnology, University of Sheffield, Western Bank, S10 2TN, Sheffield, UK
| | - J A Atkinson
- Division of Plant and Crop Science, School of Biosciences, University of Nottingham, Sutton Bonington campus, LE12 5RD, Nottingham, UK
| | - E L Harrison
- Department of Molecular Biology and Biotechnology, University of Sheffield, Western Bank, S10 2TN, Sheffield, UK
| | - D Wells
- Division of Plant and Crop Science, School of Biosciences, University of Nottingham, Sutton Bonington campus, LE12 5RD, Nottingham, UK
| | - C C Chater
- Department of Molecular Biology and Biotechnology, University of Sheffield, Western Bank, S10 2TN, Sheffield, UK
| | - J E Gray
- Department of Molecular Biology and Biotechnology, University of Sheffield, Western Bank, S10 2TN, Sheffield, UK
| | - R Swarup
- Division of Plant and Crop Science, School of Biosciences, University of Nottingham, Sutton Bonington campus, LE12 5RD, Nottingham, UK
| | - E H Murchie
- Division of Plant and Crop Science, School of Biosciences, University of Nottingham, Sutton Bonington campus, LE12 5RD, Nottingham, UK.
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14
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McAusland L, Atkinson JA, Lawson T, Murchie EH. High throughput procedure utilising chlorophyll fluorescence imaging to phenotype dynamic photosynthesis and photoprotection in leaves under controlled gaseous conditions. Plant Methods 2019; 15:109. [PMID: 31548849 PMCID: PMC6749646 DOI: 10.1186/s13007-019-0485-x] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Accepted: 08/14/2019] [Indexed: 05/18/2023]
Abstract
BACKGROUND As yields of major crops such as wheat (T. aestivum) have begun to plateau in recent years, there is growing pressure to efficiently phenotype large populations for traits associated with genetic advancement in yield. Photosynthesis encompasses a range of steady state and dynamic traits that are key targets for raising Radiation Use Efficiency (RUE), biomass production and grain yield in crops. Traditional methodologies to assess the full range of responses of photosynthesis, such a leaf gas exchange, are slow and limited to one leaf (or part of a leaf) per instrument. Due to constraints imposed by time, equipment and plant size, photosynthetic data is often collected at one or two phenological stages and in response to limited environmental conditions. RESULTS Here we describe a high throughput procedure utilising chlorophyll fluorescence imaging to phenotype dynamic photosynthesis and photoprotection in excised leaves under controlled gaseous conditions. When measured throughout the day, no significant differences (P > 0.081) were observed between the responses of excised and intact leaves. Using excised leaves, the response of three cultivars of T. aestivum to a user-defined dynamic lighting regime was examined. Cultivar specific differences were observed for maximum PSII efficiency (F v'/F m'-P < 0.01) and PSII operating efficiency (F q'/F m'-P = 0.04) under both low and high light. In addition, the rate of induction and relaxation of non-photochemical quenching (NPQ) was also cultivar specific. A specialised imaging chamber was designed and built in-house to maintain gaseous conditions around excised leaf sections. The purpose of this is to manipulate electron sinks such as photorespiration. The stability of carbon dioxide (CO2) and oxygen (O2) was monitored inside the chambers and found to be within ± 4.5% and ± 1% of the mean respectively. To test the chamber, T. aestivum 'Pavon76' leaf sections were measured under at 20 and 200 mmol mol-1 O2 and ambient [CO2] during a light response curve. The F v'/F m'was significantly higher (P < 0.05) under low [O2] for the majority of light intensities while values of NPQ and the proportion of open PSII reaction centers (qP) were significantly lower under > 130 μmol m-2 s-1 photosynthetic photon flux density (PPFD). CONCLUSIONS Here we demonstrate the development of a high-throughput (> 500 samples day-1) method for phenotyping photosynthetic and photo-protective parameters in a dynamic light environment. The technique exploits chlorophyll fluorescence imaging in a specifically designed chamber, enabling controlled gaseous environment around leaf sections. In addition, we have demonstrated that leaf sections do not different from intact plant material even > 3 h after sampling, thus enabling transportation of material of interest from the field to this laboratory based platform. The methodologies described here allow rapid, custom screening of field material for variation in photosynthetic processes.
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Affiliation(s)
- Lorna McAusland
- Division of Plant and Crop Science, School of Biosciences, University of Nottingham, Sutton Bonington Campus, Sutton Bonington, Leicestershire LE12 5RD UK
| | - Jonathan A. Atkinson
- Division of Plant and Crop Science, School of Biosciences, University of Nottingham, Sutton Bonington Campus, Sutton Bonington, Leicestershire LE12 5RD UK
| | - Tracy Lawson
- School of Life Sciences, University of Essex, Wivenhoe Park, Colchester, Essex CO4 3SQ UK
| | - Erik H. Murchie
- Division of Plant and Crop Science, School of Biosciences, University of Nottingham, Sutton Bonington Campus, Sutton Bonington, Leicestershire LE12 5RD UK
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15
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Lobet G, Paez-Garcia A, Schneider H, Junker A, Atkinson JA, Tracy S. Demystifying roots: A need for clarification and extended concepts in root phenotyping. Plant Sci 2019; 282:11-13. [PMID: 31003606 DOI: 10.1016/j.plantsci.2018.09.015] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Revised: 09/05/2018] [Accepted: 09/21/2018] [Indexed: 05/13/2023]
Abstract
Plant roots have major roles in plant anchorage, resource acquisition and offer environmental benefits including carbon sequestration and soil erosion mitigation. As such, the study of root system architecture, anatomy and functional properties is of crucial interest to plant breeding, with the aim of sustainable yield production and environmental stewardship. Due to the importance of the root system studies, there is a need for clarification of terms and concepts in the root phenotyping community. In particular in this contribution, we advocate for the use of a reference naming system (ontologies) for roots and root phenes. Such uniformity would not only allow better understanding of research results, but would also enable a better sharing of data. In addition, we highlight the need to incorporate the concept of plasticity in breeding programs, as it is an essential component of root system development in heterogeneous environments.
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Affiliation(s)
- Guillaume Lobet
- Agrosphere, IBG3, Forschungszentrum Jülich, Jülich, Germany; Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium.
| | | | - Hannah Schneider
- Department of Plant Science, The Pennslyvania State University, University Park, USA.
| | - Astrid Junker
- Acclimation Dynamics & Phenotyping Group, Dept. of Molecular Genetics at Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Germany.
| | - Jonathan A Atkinson
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, UK.
| | - Saoirse Tracy
- School of Agriculture and Food Science, University College Dublin, Ireland.
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16
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Atkinson JA, Pound MP, Bennett MJ, Wells DM. Uncovering the hidden half of plants using new advances in root phenotyping. Curr Opin Biotechnol 2019; 55:1-8. [PMID: 30031961 PMCID: PMC6378649 DOI: 10.1016/j.copbio.2018.06.002] [Citation(s) in RCA: 142] [Impact Index Per Article: 28.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Revised: 06/06/2018] [Accepted: 06/15/2018] [Indexed: 11/08/2022]
Abstract
Major increases in crop yield are required to keep pace with population growth and climate change. Improvements to the architecture of crop roots promise to deliver increases in water and nutrient use efficiency but profiling the root phenome (i.e. its structure and function) represents a major bottleneck. We describe how advances in imaging and sensor technologies are making root phenomic studies possible. However, methodological advances in acquisition, handling and processing of the resulting 'big-data' is becoming increasingly important. Advances in automated image analysis approaches such as Deep Learning promise to transform the root phenotyping landscape. Collectively, these innovations are helping drive the selection of the next-generation of crops to deliver real world impact for ongoing global food security efforts.
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Affiliation(s)
| | - Michael P Pound
- School of Biosciences, University of Nottingham, Sutton Bonington, UK; School of Computer Science, University of Nottingham, Nottingham, UK
| | - Malcolm J Bennett
- School of Biosciences, University of Nottingham, Sutton Bonington, UK.
| | - Darren M Wells
- School of Biosciences, University of Nottingham, Sutton Bonington, UK.
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17
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Pound MP, Atkinson JA, Townsend AJ, Wilson MH, Griffiths M, Jackson AS, Bulat A, Tzimiropoulos G, Wells DM, Murchie EH, Pridmore TP, French AP. Erratum to: Deep machine learning provides state-of-the-art performance in image-based plant phenotyping. Gigascience 2018; 7:5057043. [PMID: 30053289 PMCID: PMC6055546 DOI: 10.1093/gigascience/giy042] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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18
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Atkinson JA, Lobet G, Noll M, Meyer PE, Griffiths M, Wells DM. Erratum to: Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large-scale genetic studies. Gigascience 2018; 7:5057044. [PMID: 30053290 PMCID: PMC6055540 DOI: 10.1093/gigascience/giy043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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19
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Kenobi K, Atkinson JA, Wells DM, Gaju O, De Silva JG, Foulkes MJ, Dryden IL, Wood ATA, Bennett MJ. Linear discriminant analysis reveals differences in root architecture in wheat seedlings related to nitrogen uptake efficiency. J Exp Bot 2017; 68:4969-4981. [PMID: 29048563 PMCID: PMC5853436 DOI: 10.1093/jxb/erx300] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2017] [Accepted: 08/02/2017] [Indexed: 05/23/2023]
Abstract
Root architecture impacts water and nutrient uptake efficiency. Identifying exactly which root architectural properties influence these agronomic traits can prove challenging. In this paper, approximately 300 wheat (Triticum aestivum) plants were divided into four groups using two binary classifications, high versus low nitrogen uptake efficiency (NUpE), and high versus low nitrate in the growth medium. The root system architecture for each wheat plant was captured using 16 quantitative variables. The multivariate analysis tool, linear discriminant analysis, was used to construct composite variables, each a linear combination of the original variables, such that the score of the plants on the new variables showed the maximum between-group variability. The results show that the distribution of root-system architecture traits differs between low- and high-NUpE plants and, less strongly, between low-NUpE plants grown on low versus high nitrate media.
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Affiliation(s)
- Kim Kenobi
- Department of Mathematics, Aberystwyth University, Penglais, Aberystwyth, Ceredigion
| | - Jonathan A Atkinson
- Centre for Plant Integrative Biology, School of Biosciences, Sutton Bonington Campus, University of Nottingham, UK
| | - Darren M Wells
- Centre for Plant Integrative Biology, School of Biosciences, Sutton Bonington Campus, University of Nottingham, UK
| | - Oorbessy Gaju
- Division of Plant and Crop Sciences, Sutton Bonington Campus, University of Nottingham, UK
| | - Jayalath G De Silva
- Division of Plant and Crop Sciences, Sutton Bonington Campus, University of Nottingham, UK
| | - M John Foulkes
- Division of Plant and Crop Sciences, Sutton Bonington Campus, University of Nottingham, UK
| | - Ian L Dryden
- Centre for Plant Integrative Biology, School of Biosciences, Sutton Bonington Campus, University of Nottingham, UK
- School of Mathematical Sciences, University of Nottingham, University Park, UK
| | - Andrew T A Wood
- Centre for Plant Integrative Biology, School of Biosciences, Sutton Bonington Campus, University of Nottingham, UK
- School of Mathematical Sciences, University of Nottingham, University Park, UK
| | - Malcolm J Bennett
- Centre for Plant Integrative Biology, School of Biosciences, Sutton Bonington Campus, University of Nottingham, UK
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20
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Pound MP, Atkinson JA, Townsend AJ, Wilson MH, Griffiths M, Jackson AS, Bulat A, Tzimiropoulos G, Wells DM, Murchie EH, Pridmore TP, French AP. Deep machine learning provides state-of-the-art performance in image-based plant phenotyping. Gigascience 2017; 6:1-10. [PMID: 29020747 PMCID: PMC5632296 DOI: 10.1093/gigascience/gix083] [Citation(s) in RCA: 122] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Revised: 07/27/2017] [Accepted: 08/16/2017] [Indexed: 11/12/2022] Open
Abstract
In plant phenotyping, it has become important to be able to measure many features on large image sets in order to aid genetic discovery. The size of the datasets, now often captured robotically, often precludes manual inspection, hence the motivation for finding a fully automated approach. Deep learning is an emerging field that promises unparalleled results on many data analysis problems. Building on artificial neural networks, deep approaches have many more hidden layers in the network, and hence have greater discriminative and predictive power. We demonstrate the use of such approaches as part of a plant phenotyping pipeline. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping and demonstrate state-of-the-art results (>97% accuracy) for root and shoot feature identification and localization. We use fully automated trait identification using deep learning to identify quantitative trait loci in root architecture datasets. The majority (12 out of 14) of manually identified quantitative trait loci were also discovered using our automated approach based on deep learning detection to locate plant features. We have shown deep learning-based phenotyping to have very good detection and localization accuracy in validation and testing image sets. We have shown that such features can be used to derive meaningful biological traits, which in turn can be used in quantitative trait loci discovery pipelines. This process can be completely automated. We predict a paradigm shift in image-based phenotyping bought about by such deep learning approaches, given sufficient training sets.
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Affiliation(s)
- Michael P. Pound
- School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, NG8 1BB, UK
| | - Jonathan A. Atkinson
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Nr Loughborough, LE12 5RD, UK
| | - Alexandra J. Townsend
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Nr Loughborough, LE12 5RD, UK
| | - Michael H. Wilson
- Centre for Plant Sciences, Faculty of Biological Sciences, University of Leeds, Leeds, LS2 9JT, UK
| | - Marcus Griffiths
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Nr Loughborough, LE12 5RD, UK
| | - Aaron S. Jackson
- School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, NG8 1BB, UK
| | - Adrian Bulat
- School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, NG8 1BB, UK
| | - Georgios Tzimiropoulos
- School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, NG8 1BB, UK
| | - Darren M. Wells
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Nr Loughborough, LE12 5RD, UK
| | - Erik H. Murchie
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Nr Loughborough, LE12 5RD, UK
| | - Tony P. Pridmore
- School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, NG8 1BB, UK
| | - Andrew P. French
- School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, NG8 1BB, UK
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Nr Loughborough, LE12 5RD, UK
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21
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Atkinson JA, Lobet G, Noll M, Meyer PE, Griffiths M, Wells DM. Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large-scale genetic studies. Gigascience 2017; 6:1-7. [PMID: 29020748 PMCID: PMC5632292 DOI: 10.1093/gigascience/gix084] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2017] [Revised: 08/09/2017] [Accepted: 08/16/2017] [Indexed: 12/22/2022] Open
Abstract
Genetic analyses of plant root systems require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a limited number of architectural traits) or semi-automated ones (that are highly time consuming). We trained a Random Forest algorithm to infer architectural traits from automatically extracted image descriptors. The training was performed on a subset of the dataset, then applied to its entirety. This strategy allowed us to (i) decrease the image analysis time by 73% and (ii) extract meaningful architectural traits based on image descriptors. We also show that these traits are sufficient to identify the quantitative trait loci that had previously been discovered using a semi-automated method. We have shown that combining semi-automated image analysis with machine learning algorithms has the power to increase the throughput of large-scale root studies. We expect that such an approach will enable the quantification of more complex root systems for genetic studies. We also believe that our approach could be extended to other areas of plant phenotyping.
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Affiliation(s)
- Jonathan A. Atkinson
- Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, Sutton Bonington, LE12 5RD, United Kingdom
| | - Guillaume Lobet
- Agrosphere, IBG3, Forschungszentrum Jülich, Jülich 52425, Germany
- Earth and Life Institute, Université Catholique de Louvain, B-1348 Louvain-la-Neuve, Belgium
| | - Manuel Noll
- InBios, Université de Liège, 4000 Liège, Belgium
| | | | - Marcus Griffiths
- Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, Sutton Bonington, LE12 5RD, United Kingdom
| | - Darren M. Wells
- Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, Sutton Bonington, LE12 5RD, United Kingdom
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22
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Atkinson JA, Wells DM. An Updated Protocol for High Throughput Plant Tissue Sectioning. Front Plant Sci 2017; 8:1721. [PMID: 29046689 PMCID: PMC5632646 DOI: 10.3389/fpls.2017.01721] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2017] [Accepted: 09/20/2017] [Indexed: 05/09/2023]
Abstract
Quantification of the tissue and cellular structure of plant material is essential for the study of a variety of plant sciences applications. Currently, many methods for sectioning plant material are either low throughput or involve free-hand sectioning which requires a significant amount of practice. Here, we present an updated method to provide rapid and high-quality cross sections, primarily of root tissue but which can also be readily applied to other tissues such as leaves or stems. To increase the throughput of traditional agarose embedding and sectioning, custom designed 3D printed molds were utilized to embed 5-15 roots in a block for sectioning in a single cut. A single fluorescent stain in combination with laser scanning confocal microscopy was used to obtain high quality images of thick sections. The provided CAD files allow production of the embedding molds described here from a number of online 3D printing services. Although originally developed for roots, this method provides rapid, high quality cross sections of many plant tissue types, making it suitable for use in forward genetic screens for differences in specific cell structures or developmental changes. To demonstrate the utility of the technique, the two parent lines of the wheat (Triticum aestivum) Chinese Spring × Paragon doubled haploid mapping population were phenotyped for root anatomical differences. Significant differences in adventitious cross section area, stele area, xylem, phloem, metaxylem, and cortical cell file count were found.
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Affiliation(s)
- Jonathan A. Atkinson
- The Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, Nottingham, United Kingdom
- BBSRC/Nottingham Wheat Research Centre, University of Nottingham, Nottingham, United Kingdom
- *Correspondence: Jonathan A. Atkinson,
| | - Darren M. Wells
- The Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, Nottingham, United Kingdom
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Passot S, Gnacko F, Moukouanga D, Lucas M, Guyomarc’h S, Ortega BM, Atkinson JA, Belko MN, Bennett MJ, Gantet P, Wells DM, Guédon Y, Vigouroux Y, Verdeil JL, Muller B, Laplaze L. Characterization of Pearl Millet Root Architecture and Anatomy Reveals Three Types of Lateral Roots. Front Plant Sci 2016; 7:829. [PMID: 27379124 PMCID: PMC4904005 DOI: 10.3389/fpls.2016.00829] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2016] [Accepted: 05/26/2016] [Indexed: 05/04/2023]
Abstract
Pearl millet plays an important role for food security in arid regions of Africa and India. Nevertheless, it is considered an orphan crop as it lags far behind other cereals in terms of genetic improvement efforts. Breeding pearl millet varieties with improved root traits promises to deliver benefits in water and nutrient acquisition. Here, we characterize early pearl millet root system development using several different root phenotyping approaches that include rhizotrons and microCT. We report that early stage pearl millet root system development is characterized by a fast growing primary root that quickly colonizes deeper soil horizons. We also describe root anatomical studies that revealed three distinct types of lateral roots that form on both primary roots and crown roots. Finally, we detected significant variation for two root architectural traits, primary root lenght and lateral root density, in pearl millet inbred lines. This study provides the basis for subsequent genetic experiments to identify loci associated with interesting early root development traits in this important cereal.
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Affiliation(s)
- Sixtine Passot
- UMR DIADE, Institut de Recherche pour le Développement, MontpellierFrance
- UMR AGAP, Centre International de Recherche Agronomique pour le Développement–Virtual Plants, Institut National de Recherche en Informatique et en Automatique, MontpellierFrance
| | - Fatoumata Gnacko
- UMR DIADE, Institut de Recherche pour le Développement, MontpellierFrance
| | - Daniel Moukouanga
- UMR DIADE, Institut de Recherche pour le Développement, MontpellierFrance
- Laboratoire Mixte International Adaptation des Plantes et Microorganismes Associés aux Stress Environnementaux, DakarSénégal
| | - Mikaël Lucas
- UMR DIADE, Institut de Recherche pour le Développement, MontpellierFrance
- Laboratoire Mixte International Adaptation des Plantes et Microorganismes Associés aux Stress Environnementaux, DakarSénégal
- Laboratoire Commun de Microbiologie IRD/ISRA/UCAD, DakarSénégal
| | | | - Beatriz Moreno Ortega
- Laboratoire d’Ecophysiologie des Plantes sous Stress Environnementaux (UMR LEPSE, INRA-Supagro), MontpellierFrance
| | - Jonathan A. Atkinson
- Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, Sutton BoningtonUK
| | - Marème N. Belko
- Laboratoire Mixte International Adaptation des Plantes et Microorganismes Associés aux Stress Environnementaux, DakarSénégal
- Centre d’Etude Régional pour l’Amélioration de l’Adaptation à la Sécheresse, Institut Sénégalais des Recherches Agricoles, ThièsSénégal
| | - Malcolm J. Bennett
- Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, Sutton BoningtonUK
| | - Pascal Gantet
- UMR DIADE, Université de Montpellier, MontpellierFrance
| | - Darren M. Wells
- Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, Sutton BoningtonUK
| | - Yann Guédon
- UMR AGAP, Centre International de Recherche Agronomique pour le Développement–Virtual Plants, Institut National de Recherche en Informatique et en Automatique, MontpellierFrance
| | - Yves Vigouroux
- UMR DIADE, Institut de Recherche pour le Développement, MontpellierFrance
- Laboratoire Mixte International Adaptation des Plantes et Microorganismes Associés aux Stress Environnementaux, DakarSénégal
| | - Jean-Luc Verdeil
- Plateforme PHIV, UMR AGAP, Centre International de Recherche Agricole pour le Développement, MontpellierFrance
| | - Bertrand Muller
- Laboratoire d’Ecophysiologie des Plantes sous Stress Environnementaux (UMR LEPSE, INRA-Supagro), MontpellierFrance
| | - Laurent Laplaze
- UMR DIADE, Institut de Recherche pour le Développement, MontpellierFrance
- Laboratoire Mixte International Adaptation des Plantes et Microorganismes Associés aux Stress Environnementaux, DakarSénégal
- Laboratoire Commun de Microbiologie IRD/ISRA/UCAD, DakarSénégal
- *Correspondence: Laurent Laplaze,
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Atkinson JA, Wingen LU, Griffiths M, Pound MP, Gaju O, Foulkes MJ, Le Gouis J, Griffiths S, Bennett MJ, King J, Wells DM. Phenotyping pipeline reveals major seedling root growth QTL in hexaploid wheat. J Exp Bot 2015; 66:2283-92. [PMID: 25740921 PMCID: PMC4407652 DOI: 10.1093/jxb/erv006] [Citation(s) in RCA: 57] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2014] [Revised: 12/17/2014] [Accepted: 12/19/2014] [Indexed: 05/18/2023]
Abstract
Seedling root traits of wheat (Triticum aestivum L.) have been shown to be important for efficient establishment and linked to mature plant traits such as height and yield. A root phenotyping pipeline, consisting of a germination paper-based screen combined with image segmentation and analysis software, was developed and used to characterize seedling traits in 94 doubled haploid progeny derived from a cross between the winter wheat cultivars Rialto and Savannah. Field experiments were conducted to measure mature plant height, grain yield, and nitrogen (N) uptake in three sites over 2 years. In total, 29 quantitative trait loci (QTLs) for seedling root traits were identified. Two QTLs for grain yield and N uptake co-localize with root QTLs on chromosomes 2B and 7D, respectively. Of the 29 root QTLs identified, 11 were found to co-localize on 6D, with four of these achieving highly significant logarithm of odds scores (>20). These results suggest the presence of a major-effect gene regulating seedling root vigour/growth on chromosome 6D.
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Affiliation(s)
- Jonathan A Atkinson
- Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, UK
| | - Luzie U Wingen
- Department of Crop Genetics, John Innes Centre, Norwich Research Park, Norwich NR4 7UH, UK
| | - Marcus Griffiths
- Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, UK
| | - Michael P Pound
- Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, UK
| | - Oorbessy Gaju
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, UK
| | - M John Foulkes
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, UK
| | - Jacques Le Gouis
- INRA, UMR 1095 Génétique, Diversité et Ecophysiologie des Céréales, 63100 Clermont-Ferrand, France
| | - Simon Griffiths
- Department of Crop Genetics, John Innes Centre, Norwich Research Park, Norwich NR4 7UH, UK
| | - Malcolm J Bennett
- Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, UK
| | - Julie King
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, UK
| | - Darren M Wells
- Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, UK
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Atkinson JA, Rasmussen A, Traini R, Voß U, Sturrock C, Mooney SJ, Wells DM, Bennett MJ. Branching out in roots: uncovering form, function, and regulation. Plant Physiol 2014; 166:538-50. [PMID: 25136060 PMCID: PMC4213086 DOI: 10.1104/pp.114.245423] [Citation(s) in RCA: 150] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2014] [Accepted: 08/12/2013] [Indexed: 05/18/2023]
Abstract
Root branching is critical for plants to secure anchorage and ensure the supply of water, minerals, and nutrients. To date, research on root branching has focused on lateral root development in young seedlings. However, many other programs of postembryonic root organogenesis exist in angiosperms. In cereal crops, the majority of the mature root system is composed of several classes of adventitious roots that include crown roots and brace roots. In this Update, we initially describe the diversity of postembryonic root forms. Next, we review recent advances in our understanding of the genes, signals, and mechanisms regulating lateral root and adventitious root branching in the plant models Arabidopsis (Arabidopsis thaliana), maize (Zea mays), and rice (Oryza sativa). While many common signals, regulatory components, and mechanisms have been identified that control the initiation, morphogenesis, and emergence of new lateral and adventitious root organs, much more remains to be done. We conclude by discussing the challenges and opportunities facing root branching research.
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Affiliation(s)
- Jonathan A Atkinson
- Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, United Kingdom (J.A.A., A.R., R.T., U.V., C.S., S.J.M., D.M.W., M.J.B.); andCollege of Science, King Saud University, Riyadh 11451, Kingdom of Saudi Arabia (M.J.B.)
| | - Amanda Rasmussen
- Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, United Kingdom (J.A.A., A.R., R.T., U.V., C.S., S.J.M., D.M.W., M.J.B.); andCollege of Science, King Saud University, Riyadh 11451, Kingdom of Saudi Arabia (M.J.B.)
| | - Richard Traini
- Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, United Kingdom (J.A.A., A.R., R.T., U.V., C.S., S.J.M., D.M.W., M.J.B.); andCollege of Science, King Saud University, Riyadh 11451, Kingdom of Saudi Arabia (M.J.B.)
| | - Ute Voß
- Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, United Kingdom (J.A.A., A.R., R.T., U.V., C.S., S.J.M., D.M.W., M.J.B.); andCollege of Science, King Saud University, Riyadh 11451, Kingdom of Saudi Arabia (M.J.B.)
| | - Craig Sturrock
- Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, United Kingdom (J.A.A., A.R., R.T., U.V., C.S., S.J.M., D.M.W., M.J.B.); andCollege of Science, King Saud University, Riyadh 11451, Kingdom of Saudi Arabia (M.J.B.)
| | - Sacha J Mooney
- Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, United Kingdom (J.A.A., A.R., R.T., U.V., C.S., S.J.M., D.M.W., M.J.B.); andCollege of Science, King Saud University, Riyadh 11451, Kingdom of Saudi Arabia (M.J.B.)
| | - Darren M Wells
- Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, United Kingdom (J.A.A., A.R., R.T., U.V., C.S., S.J.M., D.M.W., M.J.B.); andCollege of Science, King Saud University, Riyadh 11451, Kingdom of Saudi Arabia (M.J.B.)
| | - Malcolm J Bennett
- Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, United Kingdom (J.A.A., A.R., R.T., U.V., C.S., S.J.M., D.M.W., M.J.B.); andCollege of Science, King Saud University, Riyadh 11451, Kingdom of Saudi Arabia (M.J.B.)
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Pound MP, French AP, Atkinson JA, Wells DM, Bennett MJ, Pridmore T. RootNav: navigating images of complex root architectures. Plant Physiol 2013; 162:1802-14. [PMID: 23766367 PMCID: PMC3729762 DOI: 10.1104/pp.113.221531] [Citation(s) in RCA: 123] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2013] [Accepted: 06/02/2013] [Indexed: 05/18/2023]
Abstract
We present a novel image analysis tool that allows the semiautomated quantification of complex root system architectures in a range of plant species grown and imaged in a variety of ways. The automatic component of RootNav takes a top-down approach, utilizing the powerful expectation maximization classification algorithm to examine regions of the input image, calculating the likelihood that given pixels correspond to roots. This information is used as the basis for an optimization approach to root detection and quantification, which effectively fits a root model to the image data. The resulting user experience is akin to defining routes on a motorist's satellite navigation system: RootNav makes an initial optimized estimate of paths from the seed point to root apices, and the user is able to easily and intuitively refine the results using a visual approach. The proposed method is evaluated on winter wheat (Triticum aestivum) images (and demonstrated on Arabidopsis [Arabidopsis thaliana], Brassica napus, and rice [Oryza sativa]), and results are compared with manual analysis. Four exemplar traits are calculated and show clear illustrative differences between some of the wheat accessions. RootNav, however, provides the structural information needed to support extraction of a wider variety of biologically relevant measures. A separate viewer tool is provided to recover a rich set of architectural traits from RootNav's core representation.
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27
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Atkinson JA. The president speaking: "Vigilance is the price of liberty". J Miss State Med Assoc 1975; 16:378. [PMID: 1195364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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Atkinson JA. "Malpractice insurance". J Miss State Med Assoc 1975; 16:216. [PMID: 1206696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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29
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Atkinson JA. Report of the Council on Medical Service: "Medicare/Medicaid amendments. J Miss State Med Assoc 1973; 14:538-43. [PMID: 4617775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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