Non-destructive Determination of Shikimic Acid Concentration in Transgenic Maize Exhibiting Glyphosate Tolerance Using Chlorophyll Fluorescence and Hyperspectral Imaging

The development of transgenic glyphosate-tolerant crops has revolutionized weed control in crops in many regions of the world. The early, non-destructive identification of superior plant phenotypes is an important stage in plant breeding programs. Here, glyphosate-tolerant transgenic maize and its parental wild-type control were studied at 2, 4, 6, and 8 days after glyphosate treatment. Visible and near-infrared hyperspectral imaging and chlorophyll fluorescence imaging techniques were applied to monitor the performance of plants. In our research, transgenic maize, which was highly tolerant to glyphosate, was phenotyped using these high-throughput non-destructive methods to validate low levels of shikimic acid accumulation and high photochemical efficiency of photosystem II as reflected by maximum quantum yield and non-photochemical quenching in response to glyphosate. For hyperspectral imaging analysis, the combination of spectroscopy and chemometric methods was used to predict shikimic acid concentration. Our results indicated that a partial least-squares regression model, built on optimal wavelengths, effectively predicted shikimic acid concentrations, with a coefficient of determination value of 0.79 for the calibration set, and 0.82 for the prediction set. Moreover, shikimic acid concentration estimates from hyperspectral images were visualized on the prediction maps by spectral features, which could help in developing a simple multispectral imaging instrument for non-destructive phenotyping. Specific physiological effects of glyphosate affected the photochemical processes of maize, which induced substantial changes in chlorophyll fluorescence characteristics. A new data-driven method, combining mean fluorescence parameters and featuring a screening approach, provided a satisfactory relationship between fluorescence parameters and shikimic acid content. The glyphosate-tolerant transgenic plants can be identified with the developed discrimination model established on important wavelengths or sensitive fluorescence parameters 6 days after glyphosate treatment. The overall results indicated that both hyperspectral imaging and chlorophyll fluorescence imaging techniques could provide useful tools for stress phenotyping in maize breeding programs and could enable the detection and evaluation of superior genotypes, such as glyphosate tolerance, with a non-destructive high-throughput technique.

[1]  R. Sammons,et al.  Glyphosate resistance: state of knowledge , 2014, Pest management science.

[2]  A. Messéan,et al.  Simulating changes in cropping practises in conventional and glyphosate-tolerant maize. I. Effects on weeds , 2017, Environmental Science and Pollution Research.

[3]  Martin Trtílek,et al.  High-Throughput Non-destructive Phenotyping of Traits that Contribute to Salinity Tolerance in Arabidopsis thaliana , 2016, Front. Plant Sci..

[4]  Magdalena D. Cetner,et al.  Identification of nutrient deficiency in maize and tomato plants by in vivo chlorophyll a fluorescence measurements. , 2014, Plant physiology and biochemistry : PPB.

[5]  Weiwei Sun,et al.  Manifold Coordinates Repairing of Boundary Points with PLS for Isomap Nonlinear Dimensionality Reduction of Hyperspectral Image , 2011, 2011 International Workshop on Multi-Platform/Multi-Sensor Remote Sensing and Mapping.

[6]  Weiwei Sun,et al.  Band Selection Using Improved Sparse Subspace Clustering for Hyperspectral Imagery Classification , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[7]  E. Mateos-Naranjo,et al.  Effects of sub-lethal glyphosate concentrations on growth and photosynthetic performance of non-target species Bolboschoenus maritimus. , 2013, Chemosphere.

[8]  P. Mohanty,et al.  Chlorophyll a Fluorescence as a Probe of Heavy Metal Ion Toxicity in Plants , 2004 .

[9]  S. Duke,et al.  Glyphosate: a once-in-a-century herbicide. , 2008, Pest management science.

[10]  J. Maul,et al.  Lack of transgene and glyphosate effects on yield, and mineral and amino acid content of glyphosate-resistant soybean. , 2018, Pest management science.

[11]  Wei Chen,et al.  Nondestructive and intuitive determination of circadian chlorophyll rhythms in soybean leaves using multispectral imaging , 2015, Scientific Reports.

[12]  Ganesh M. Kishore,et al.  Development, identification, and characterization of a glyphosate-tolerant soybean line , 1995 .

[13]  N. Baker,et al.  Rapid, Noninvasive Screening for Perturbations of Metabolism and Plant Growth Using Chlorophyll Fluorescence Imaging1 , 2003, Plant Physiology.

[14]  Xueguang Shao,et al.  Determination of Chlorogenic Acid in Plant Samples by Using Near-Infrared Spectrum with Wavelet Transform Preprocessing , 2004, Analytical sciences : the international journal of the Japan Society for Analytical Chemistry.

[15]  Roberto Kawakami Harrop Galvão,et al.  A method for calibration and validation subset partitioning. , 2005, Talanta.

[16]  M. E. Figueroa,et al.  Effectiveness of glyphosate and imazamox on the control of the invasive cordgrass Spartina densiflora. , 2009, Ecotoxicology and environmental safety.

[17]  Xiaoli Li,et al.  Hyperspectral Imaging for Determining Pigment Contents in Cucumber Leaves in Response to Angular Leaf Spot Disease , 2016, Scientific Reports.

[18]  Andrew M Mutka,et al.  Image-based phenotyping of plant disease symptoms , 2015, Front. Plant Sci..

[19]  S. Morin,et al.  Short term recovery of periphyton photosynthesis after pulse exposition to the photosystem II inhibitors atrazine and isoproturon. , 2011, Chemosphere.

[20]  Y. Seo,et al.  Effects of the phenylurea herbicide diuron on the physiology ofSaccharina japonica aresch , 2010, Toxicology and Environmental Health Sciences.

[21]  D. Arnon COPPER ENZYMES IN ISOLATED CHLOROPLASTS. POLYPHENOLOXIDASE IN BETA VULGARIS. , 1949, Plant physiology.

[22]  Andrew P French,et al.  Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress , 2017, Plant Methods.

[23]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[24]  A. C. Costa,et al.  Chlorophyll Fluorescence as an Indicator of Cellular Damage by Glyphosate Herbicide in Raphanus sativus L. Plants , 2014 .

[25]  Chu Zhang,et al.  Rapid and non-destructive measurement of spinach pigments content during storage using hyperspectral imaging with chemometrics , 2017 .

[26]  Davoud Ashourloo,et al.  Developing Two Spectral Disease Indices for Detection of Wheat Leaf Rust (Pucciniatriticina) , 2014, Remote. Sens..

[27]  A. Walter,et al.  Plant phenotyping: from bean weighing to image analysis , 2015, Plant Methods.

[28]  Chu Zhang,et al.  Discrimination of Transgenic Maize Kernel Using NIR Hyperspectral Imaging and Multivariate Data Analysis , 2017, Sensors.

[29]  Qing-Song Xu,et al.  Random frog: an efficient reversible jump Markov Chain Monte Carlo-like approach for variable selection with applications to gene selection and disease classification. , 2012, Analytica chimica acta.

[30]  M. Fromm,et al.  Glyphosate as a selective agent for the production of fertile transgenic maize (Zea mays L.) plants , 2002, Molecular Breeding.

[31]  Chu Zhang,et al.  Application of Near-Infrared Hyperspectral Imaging with Variable Selection Methods to Determine and Visualize Caffeine Content of Coffee Beans , 2016, Food and Bioprocess Technology.

[32]  Hailong Wang,et al.  Fruit Quality Evaluation Using Spectroscopy Technology: A Review , 2015, Sensors.

[33]  A. Rutherford,et al.  Herbicide-induced oxidative stress in photosystem II. , 2001, Trends in biochemical sciences.

[34]  C. Frankenberg,et al.  Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges. , 2014, Journal of experimental botany.

[35]  Fei Liu,et al.  Application of successive projections algorithm for variable selection to determine organic acids of plum vinegar. , 2009 .

[36]  Marc Lucotte,et al.  Differential effects of glyphosate and aminomethylphosphonic acid (AMPA) on photosynthesis and chlorophyll metabolism in willow plants. , 2016, Pesticide biochemistry and physiology.

[37]  Marek Zivcak,et al.  Chlorophyll a fluorescence as a tool to monitor physiological status of plants under abiotic stress conditions , 2016, Acta Physiologiae Plantarum.

[38]  Dale L. Shaner,et al.  Rapid Determination of Glyphosate Injury to Plants and Identification of Glyphosate-Resistant Plants , 1998, Weed Technology.

[39]  Qin Zhang,et al.  A Review of Imaging Techniques for Plant Phenotyping , 2014, Sensors.

[40]  Weiwei Sun,et al.  A Dissimilarity-Weighted Sparse Self-Representation Method for Band Selection in Hyperspectral Imagery Classification , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[41]  Georg Noga,et al.  Spectral and time-resolved fluorescence signature of four weed species as affected by selected herbicides , 2011 .

[42]  S. Đurović,et al.  Chlorophyll as a measure of plant health: agroecological aspects. , 2014 .

[43]  Giovanni Attolico,et al.  Non-destructive grading of peaches by near-infrared spectrometry , 2004 .

[44]  Caiyun Yang,et al.  Use of chlorophyll fluorescence and P700 absorbance to rapidly detect glyphosate resistance in goosegrass (Eleusine indica) , 2015 .

[45]  C. N. Stewart,et al.  Shikimate accumulates in both glyphosate-sensitive and glyphosate-resistant horseweed (Conyza canadensis L. Cronq.). , 2003, Journal of agricultural and food chemistry.

[46]  Govindjee,et al.  Chlorophyll a Fluorescence , 2004, Advances in Photosynthesis and Respiration.

[47]  S. Utami,et al.  Increasing P-Availability and P-Uptake Using Sugarcane Filter Cake and Rice Husk Ash to Improve Chinesse Cabbage (Brassica Sp) Growth in Andisol, East Java , 2012 .

[48]  Dale L. Shaner,et al.  The impact of glyphosate‐tolerant crops on the use of other herbicides and on resistance management , 2000 .

[49]  Steven J. Thomson,et al.  Early Detection of Soybean Plant Injury from Glyphosate by Measuring Chlorophyll Reflectance and Fluorescence , 2012 .

[50]  K Maxwell,et al.  Chlorophyll fluorescence--a practical guide. , 2000, Journal of experimental botany.

[51]  Joelle Prange,et al.  The Impact of the Herbicide Diuron on Photosynthesis in Three Species of Tropical Seagrass , 2000 .

[52]  S. Duke,et al.  Tolerance and accumulation of shikimic acid in response to glyphosate applications in glyphosate-resistant and nonglyphosate-resistant cotton (Gossypium hirsutum L.). , 2002, Journal of agricultural and food chemistry.

[53]  L. Lepage,et al.  Alteration of plant physiology by glyphosate and its by-product aminomethylphosphonic acid: an overview. , 2014, Journal of experimental botany.

[54]  P. Rathod,et al.  Proximal Spectral Sensing to Monitor Phytoremediation of Metal-Contaminated Soils , 2013, International journal of phytoremediation.

[55]  Da-Wen Sun,et al.  Recent Advances in Wavelength Selection Techniques for Hyperspectral Image Processing in the Food Industry , 2014, Food and Bioprocess Technology.

[56]  C. Granier,et al.  Quantifying spatial heterogeneity of chlorophyll fluorescence during plant growth and in response to water stress , 2015, Plant Methods.

[57]  Haiyan Cen,et al.  Chlorophyll Fluorescence Imaging Uncovers Photosynthetic Fingerprint of Citrus Huanglongbing , 2017, Front. Plant Sci..

[58]  Hongbo Shao,et al.  Applying hyperspectral imaging to explore natural plant diversity towards improving salt stress tolerance. , 2017, The Science of the total environment.

[59]  Gustavo A. Lobos,et al.  Fluorescence phenotyping in blueberry breeding for genotype selection under drought conditions, with or without heat stress , 2015 .

[60]  Jan F. Humplík,et al.  Automated phenotyping of plant shoots using imaging methods for analysis of plant stress responses – a review , 2015, Plant Methods.

[61]  Hartmut K. Lichtenthaler,et al.  Fluorescence imaging as a diagnostic tool for plant stress , 1997 .