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Fu J, McKinley B, James B, Chrisler W, Markillie LM, Gaffrey MJ, Mitchell HD, Riaz MR, Marcial B, Orr G, Swaminathan K, Mullet J, Marshall-Colon A. Cell-type-specific transcriptomics uncovers spatial regulatory networks in bioenergy sorghum stems. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2024; 118:1668-1688. [PMID: 38407828 DOI: 10.1111/tpj.16690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 12/17/2023] [Accepted: 02/07/2024] [Indexed: 02/27/2024]
Abstract
Bioenergy sorghum is a low-input, drought-resilient, deep-rooting annual crop that has high biomass yield potential enabling the sustainable production of biofuels, biopower, and bioproducts. Bioenergy sorghum's 4-5 m stems account for ~80% of the harvested biomass. Stems accumulate high levels of sucrose that could be used to synthesize bioethanol and useful biopolymers if information about cell-type gene expression and regulation in stems was available to enable engineering. To obtain this information, laser capture microdissection was used to isolate and collect transcriptome profiles from five major cell types that are present in stems of the sweet sorghum Wray. Transcriptome analysis identified genes with cell-type-specific and cell-preferred expression patterns that reflect the distinct metabolic, transport, and regulatory functions of each cell type. Analysis of cell-type-specific gene regulatory networks (GRNs) revealed that unique transcription factor families contribute to distinct regulatory landscapes, where regulation is organized through various modes and identifiable network motifs. Cell-specific transcriptome data was combined with known secondary cell wall (SCW) networks to identify the GRNs that differentially activate SCW formation in vascular sclerenchyma and epidermal cells. The spatial transcriptomic dataset provides a valuable source of information about the function of different sorghum cell types and GRNs that will enable the engineering of bioenergy sorghum stems, and an interactive web application developed during this project will allow easy access and exploration of the data (https://mc-lab.shinyapps.io/lcm-dataset/).
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Affiliation(s)
- Jie Fu
- Department of Plant Biology, University of Illinois Urbana-Champaign, Urbana, Illinois, 61801, USA
- DOE Center for Advanced Bioenergy and Bioproducts Innovation, Urbana, Illinois, 61801, USA
| | - Brian McKinley
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, Texas, 77843, USA
- DOE Great Lakes Bioenergy Resource Center, Madison, Wisconsin, 53726, USA
| | - Brandon James
- DOE Center for Advanced Bioenergy and Bioproducts Innovation, Urbana, Illinois, 61801, USA
- HudsonAlpha Institute for Biotechnology, Huntsville, Alabama, 35806, USA
| | - William Chrisler
- Pacific Northwest National Laboratory, Richland, Washington, 99354, USA
| | | | - Matthew J Gaffrey
- Pacific Northwest National Laboratory, Richland, Washington, 99354, USA
| | - Hugh D Mitchell
- Pacific Northwest National Laboratory, Richland, Washington, 99354, USA
| | - Muhammad Rizwan Riaz
- Department of Plant Biology, University of Illinois Urbana-Champaign, Urbana, Illinois, 61801, USA
| | - Brenda Marcial
- DOE Center for Advanced Bioenergy and Bioproducts Innovation, Urbana, Illinois, 61801, USA
- HudsonAlpha Institute for Biotechnology, Huntsville, Alabama, 35806, USA
| | - Galya Orr
- Pacific Northwest National Laboratory, Richland, Washington, 99354, USA
| | - Kankshita Swaminathan
- DOE Center for Advanced Bioenergy and Bioproducts Innovation, Urbana, Illinois, 61801, USA
- HudsonAlpha Institute for Biotechnology, Huntsville, Alabama, 35806, USA
| | - John Mullet
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, Texas, 77843, USA
- DOE Great Lakes Bioenergy Resource Center, Madison, Wisconsin, 53726, USA
| | - Amy Marshall-Colon
- Department of Plant Biology, University of Illinois Urbana-Champaign, Urbana, Illinois, 61801, USA
- DOE Center for Advanced Bioenergy and Bioproducts Innovation, Urbana, Illinois, 61801, USA
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2
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Tanaka R, Wu D, Li X, Tibbs-Cortes LE, Wood JC, Magallanes-Lundback M, Bornowski N, Hamilton JP, Vaillancourt B, Li X, Deason NT, Schoenbaum GR, Buell CR, DellaPenna D, Yu J, Gore MA. Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain. THE PLANT GENOME 2023; 16:e20276. [PMID: 36321716 DOI: 10.1002/tpg2.20276] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 09/21/2022] [Indexed: 06/16/2023]
Abstract
With an essential role in human health, tocochromanols are mostly obtained by consuming seed oils; however, the vitamin E content of the most abundant tocochromanols in maize (Zea mays L.) grain is low. Several large-effect genes with cis-acting variants affecting messenger RNA (mRNA) expression are mostly responsible for tocochromanol variation in maize grain, with other relevant associated quantitative trait loci (QTL) yet to be fully resolved. Leveraging existing genomic and transcriptomic information for maize inbreds could improve prediction when selecting for higher vitamin E content. Here, we first evaluated a multikernel genomic best linear unbiased prediction (MK-GBLUP) approach for modeling known QTL in the prediction of nine tocochromanol grain phenotypes (12-21 QTL per trait) within and between two panels of 1,462 and 242 maize inbred lines. On average, MK-GBLUP models improved predictive abilities by 7.0-13.6% when compared with GBLUP. In a second approach with a subset of 545 lines from the larger panel, the highest average improvement in predictive ability relative to GBLUP was achieved with a multi-trait GBLUP model (15.4%) that had a tocochromanol phenotype and transcript abundances in developing grain for a few large-effect candidate causal genes (1-3 genes per trait) as multiple response variables. Taken together, our study illustrates the enhancement of prediction models when informed by existing biological knowledge pertaining to QTL and candidate causal genes.
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Affiliation(s)
- Ryokei Tanaka
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | - Di Wu
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | - Xiaowei Li
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | | | - Joshua C Wood
- Institute for Plant Breeding, Genetics & Genomics, Center for Applied Genetic Technologies, Dep. of Crop & Soil Sciences, Univ. of Georgia, Athens, GA, 30602, USA
| | | | - Nolan Bornowski
- Dep. of Plant Biology, Michigan State Univ., East Lansing, MI, 48824, USA
| | - John P Hamilton
- Institute for Plant Breeding, Genetics & Genomics, Center for Applied Genetic Technologies, Dep. of Crop & Soil Sciences, Univ. of Georgia, Athens, GA, 30602, USA
| | - Brieanne Vaillancourt
- Institute for Plant Breeding, Genetics & Genomics, Center for Applied Genetic Technologies, Dep. of Crop & Soil Sciences, Univ. of Georgia, Athens, GA, 30602, USA
| | - Xianran Li
- USDA ARS, Wheat Health, Genetics, and Quality Research Unit, Pullman, WA, 99164, USA
| | - Nicholas T Deason
- Dep. of Biochemistry and Molecular Biology, Michigan State Univ., East Lansing, MI, 48824, USA
| | | | - C Robin Buell
- Institute for Plant Breeding, Genetics & Genomics, Center for Applied Genetic Technologies, Dep. of Crop & Soil Sciences, Univ. of Georgia, Athens, GA, 30602, USA
| | - Dean DellaPenna
- Dep. of Biochemistry and Molecular Biology, Michigan State Univ., East Lansing, MI, 48824, USA
| | - Jianming Yu
- Dep. of Agronomy, Iowa State Univ., Ames, IA, 50011, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
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3
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Kent MA, Fonseca JMO, Klein PE, Klein RR, Hayes CM, Rooney WL. Use of genomic prediction to screen sorghum B-lines in hybrid testcrosses. THE PLANT GENOME 2023; 16:e20369. [PMID: 37455349 DOI: 10.1002/tpg2.20369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 05/24/2023] [Accepted: 06/15/2023] [Indexed: 07/18/2023]
Abstract
Use of trifluoromethanesulfonamide (TFMSA), a male gametocide, increases the opportunities to identify promising B-lines because large quantities of F1 seed can be generated prior to the laborious task of B-line sterilization. Combining TFMSA technology with genomic selection could efficiently evaluate sorghum B-lines in hybrid combination to maximize the rates of genetic gain of the crop. This study used two recombinant inbred B-line populations, consisting of 217 lines, which were testcrossed to two R-lines to produce 434 hybrids. Each population of testcross hybrids were evaluated across five environments. Population-based genomic prediction models were assessed across environments using three different cross-validation (CV) schemes, each with 70% training and 30% validation sets. The validation schemes were as follows: CV1-hybrids chosen randomly for validation; CV2-B-lines were randomly chosen, and each chosen B-line had one of the two corresponding testcross hybrids randomly chosen for the validation; and CV3-B-lines were randomly chosen, and each chosen B-line had both corresponding testcross hybrids chosen for the validation. CV1 and CV2 presented the highest prediction accuracies; nonetheless, the prediction accuracies of the CV schemes were not statistically different in many environments. We determined that combining the B-line populations could improve prediction accuracies, and the genomic prediction models were able to effectively rank the poorest 70% of hybrids even when genomic prediction accuracies themselves were low. Results indicate that combining genomic prediction models and TFMSA technology can effectively aid breeders in predicting B-line hybrid performance in early generations prior to the laborious task of generating A/B-line pairs.
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Affiliation(s)
- Mitchell A Kent
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, USA
| | - Jales M O Fonseca
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, USA
| | - Patricia E Klein
- Department of Horticultural Sciences, Texas A&M University, College Station, TX, USA
| | - Robert R Klein
- USDA-ARS, Crop Germplasm Research Unit, Southern Plains Agricultural Research Center, College Station, TX, USA
| | - Chad M Hayes
- USDA-ARS, Plant Stress and Germplasm Development Research Unit, Lubbock, TX, USA
| | - William L Rooney
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, USA
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4
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Aniza R, Chen WH, Pétrissans A, Hoang AT, Ashokkumar V, Pétrissans M. A review of biowaste remediation and valorization for environmental sustainability: Artificial intelligence approach. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 324:121363. [PMID: 36863440 DOI: 10.1016/j.envpol.2023.121363] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 02/09/2023] [Accepted: 02/24/2023] [Indexed: 06/18/2023]
Abstract
Biowaste remediation and valorization for environmental sustainability focuses on prevention rather than cleanup of waste generation by applying the fundamental recovery concept through biowaste-to-bioenergy conversion systems - an appropriate approach in a circular bioeconomy. Biomass waste (biowaste) is discarded organic materials made of biomass (e.g., agriculture waste and algal residue). Biowaste is widely studied as one of the potential feedstocks in the biowaste valorization process due to its being abundantly available. In terms of practical implementations, feedstock variability from biowaste, conversion costs and supply chain stability prevent the widespread usage of bioenergy products. Biowaste remediation and valorization have used artificial intelligence (AI), a newly developed idea, to overcome these difficulties. This report analyzed 118 works that applied various AI algorithms to biowaste remediation and valorization-related research published between 2007 and 2022. Four common AI types are utilized in biowaste remediation and valorization: neural networks, Bayesian networks, decision tree, and multivariate regression. The neural network is the most frequent AI for prediction models, the Bayesian network is utilized for probabilistic graphical models, and the decision tree is trusted for providing tools to assist decision-making. Meanwhile, multivariate regression is employed to identify the relationship between experimental variables. AI is a remarkably effective tool in predicting data, which is reportedly better than the conventional approach owing to its characteristics of time-saving and high accuracy. The challenge and future work in biowaste remediation and valorization are briefly discussed to maximize the model's performance.
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Affiliation(s)
- Ria Aniza
- Department of Aeronautics and Astronautics, National Cheng Kung University, Tainan, 701, Taiwan; International Doctoral Degree Program on Energy Engineering, National Cheng Kung University, Tainan, 701, Taiwan
| | - Wei-Hsin Chen
- Department of Aeronautics and Astronautics, National Cheng Kung University, Tainan, 701, Taiwan; Research Center for Smart Sustainable Circular Economy, Tunghai University, Taichung, 407, Taiwan; Department of Mechanical Engineering, National Chin-Yi University of Technology, Taichung, 411, Taiwan.
| | | | - Anh Tuan Hoang
- Institute of Engineering, HUTECH University, Ho Chi Minh City, Viet Nam
| | - Veeramuthu Ashokkumar
- Biorefineries for Biofuels & Bioproducts Laboratory, Center for Transdisciplinary Research, Department of Pharmacology, Saveetha Dental College, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, 600077, India
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5
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Rooney TE, Kunze KH, Sorrells ME. Genome-wide marker effect heterogeneity is associated with a large effect dormancy locus in winter malting barley. THE PLANT GENOME 2022; 15:e20247. [PMID: 35971877 DOI: 10.1002/tpg2.20247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 06/20/2022] [Indexed: 06/15/2023]
Abstract
Prediction of trait values in plant breeding populations typically relies on assumptions about marker effect homogeneity across populations. Evidence is presented for winter malting barley (Hordeum vulgare L.) germination traits that a single, causative, large-effect gene in the Seed dormancy 1 region on Chromosome 5H, HvAlaAT1 (Qsd1), leads to heterogeneous estimated marker effects genome wide between groups of otherwise related individuals carrying different Qsd1 alleles. This led to reduced prediction accuracy across alleles when a model was trained either on individuals carrying both alleles or one allele. Several genomic prediction models were tested to increase prediction accuracy within the Qsd1 allele groups. Small gains (5-12%) in prediction accuracy were realized using structured genomic best linear unbiased predictor models when information about the Qsd1 allele was used to stratify the population. We concluded that a single large-effect locus can lead to heterogeneous marker effects in the same breeding family. Variance partitioning based on large-effect loci can be used to inform best practices in designing genomic prediction models; however, there are likely few cases for which it may be practical to do this. For malting barley, if germination traits are highly associated with malting quality traits, then similar steps should be considered for malting quality trait prediction.
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Affiliation(s)
- Travis E Rooney
- Plant Breeding and Genetics Section, School of Integrative Plant Sciences, Cornell Univ., Ithaca, NY, 14853, USA
| | - Karl H Kunze
- Plant Breeding and Genetics Section, School of Integrative Plant Sciences, Cornell Univ., Ithaca, NY, 14853, USA
| | - Mark E Sorrells
- Plant Breeding and Genetics Section, School of Integrative Plant Sciences, Cornell Univ., Ithaca, NY, 14853, USA
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6
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Yu KMJ, Oliver J, McKinley B, Weers B, Fabich HT, Evetts N, Conradi MS, Altobelli SA, Marshall-Colon A, Mullet J. Bioenergy sorghum stem growth regulation: intercalary meristem localization, development, and gene regulatory network analysis. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2022; 112:476-492. [PMID: 36038985 DOI: 10.1111/tpj.15960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 08/18/2022] [Accepted: 08/20/2022] [Indexed: 06/15/2023]
Abstract
Bioenergy sorghum is a highly productive drought tolerant C4 grass that accumulates 80% of its harvestable biomass in approximately 4 m length stems. Stem internode growth is regulated by development, shading, and hormones that modulate cell proliferation in intercalary meristems (IMs). In this study, sorghum stem IMs were localized above the pulvinus at the base of elongating internodes using magnetic resonance imaging, microscopy, and transcriptome analysis. A change in cell morphology/organization occurred at the junction between the pulvinus and internode where LATERAL ORGAN BOUNDARIES (SbLOB), a boundary layer gene, was expressed. Inactivation of an AGCVIII kinase in DDYM (dw2) resulted in decreased SbLOB expression, disrupted IM localization, and reduced internode cell proliferation. Transcriptome analysis identified approximately 1000 genes involved in cell proliferation, hormone signaling, and other functions selectively upregulated in the IM compared with a non-meristematic stem tissue. This cohort of genes is expressed in apical dome stem tissues before localization of the IM at the base of elongating internodes. Gene regulatory network analysis identified connections between genes involved in hormone signaling and cell proliferation. The results indicate that gibberellic acid induces accumulation of growth regulatory factors (GRFs) known to interact with ANGUSTIFOLIA (SbAN3), a master regulator of cell proliferation. GRF:AN3 was predicted to induce SbARF3/ETT expression and regulate SbAN3 expression in an auxin-dependent manner. GRFs and ARFs regulate genes involved in cytokinin and brassinosteroid signaling and cell proliferation. The results provide a molecular framework for understanding how hormone signaling regulates the expression of genes involved in cell proliferation in the stem IM.
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Affiliation(s)
- Ka Man Jasmine Yu
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, Texas, 77843-2128, USA
| | - Joel Oliver
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, Texas, 77843-2128, USA
| | - Brian McKinley
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, Texas, 77843-2128, USA
| | - Brock Weers
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, Texas, 77843-2128, USA
| | - Hilary T Fabich
- ABQMR, Inc., 2301 Yale Blvd. SE, Suite C2, Albuquerque, New Mexico, 87106, USA
| | - Nathan Evetts
- ABQMR, Inc., 2301 Yale Blvd. SE, Suite C2, Albuquerque, New Mexico, 87106, USA
| | - Mark S Conradi
- ABQMR, Inc., 2301 Yale Blvd. SE, Suite C2, Albuquerque, New Mexico, 87106, USA
| | - Stephen A Altobelli
- ABQMR, Inc., 2301 Yale Blvd. SE, Suite C2, Albuquerque, New Mexico, 87106, USA
| | - Amy Marshall-Colon
- Department of Plant Biology, University of Illinois, Champaign-Urbana, Illinois, 61801, USA
| | - John Mullet
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, Texas, 77843-2128, USA
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7
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Wang L, Liu Y, Gao L, Yang X, Zhang X, Xie S, Chen M, Wang YH, Li J, Shen Y. Identification of Candidate Forage Yield Genes in Sorghum ( Sorghum bicolor L.) Using Integrated Genome-Wide Association Studies and RNA-Seq. FRONTIERS IN PLANT SCIENCE 2022; 12:788433. [PMID: 35087554 PMCID: PMC8787639 DOI: 10.3389/fpls.2021.788433] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 12/06/2021] [Indexed: 05/26/2023]
Abstract
Genetic dissection of forage yield traits is critical to the development of sorghum as a forage crop. In the present study, association mapping was performed with 85,585 SNP markers on four forage yield traits, namely plant height (PH), tiller number (TN), stem diameter (SD), and fresh weight per plant (FW) among 245 sorghum accessions evaluated in four environments. A total of 338 SNPs or quantitative trait nucleotides (QTNs) were associated with the four traits, and 21 of these QTNs were detected in at least two environments, including four QTNs for PH, ten for TN, six for SD, and one for FW. To identify candidate genes, dynamic transcriptome expression profiling was performed at four stages of sorghum development. One hundred and six differentially expressed genes (DEGs) that were enriched in hormone signal transduction pathways were found in all stages. Weighted gene correlation network analysis for PH and SD indicated that eight modules were significantly correlated with PH and that three modules were significantly correlated with SD. The blue module had the highest positive correlation with PH and SD, and the turquoise module had the highest negative correlation with PH and SD. Eight candidate genes were identified through the integration of genome-wide association studies (GWAS) and RNA sequencing. Sobic.004G143900, an indole-3-glycerol phosphate synthase gene that is involved in indoleacetic acid biosynthesis, was down-regulated as sorghum plants grew in height and was identified in the blue module, and Sobic.003G375100, an SD candidate gene, encoded a DNA repair RAD52-like protein 1 that plays a critical role in DNA repair-linked cell cycle progression. These findings demonstrate that the integrative analysis of omics data is a promising approach to identify candidate genes for complex traits.
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Affiliation(s)
- Lihua Wang
- College of Agro-Grassland Science, Nanjing Agricultural University, Nanjing, China
- College of Agriculture, Anhui Science and Technology University, Fengyang, China
| | - Yanlong Liu
- College of Agriculture, Anhui Science and Technology University, Fengyang, China
| | - Li Gao
- College of Agriculture, Anhui Science and Technology University, Fengyang, China
| | - Xiaocui Yang
- College of Agriculture, Anhui Science and Technology University, Fengyang, China
| | - Xu Zhang
- College of Agriculture, Anhui Science and Technology University, Fengyang, China
| | - Shaoping Xie
- College of Agriculture, Anhui Science and Technology University, Fengyang, China
| | - Meng Chen
- College of Agriculture, Anhui Science and Technology University, Fengyang, China
| | - Yi-Hong Wang
- Department of Biology, University of Louisiana at Lafayette, Lafayette, LA, United States
| | - Jieqin Li
- College of Agriculture, Anhui Science and Technology University, Fengyang, China
| | - Yixin Shen
- College of Agro-Grassland Science, Nanjing Agricultural University, Nanjing, China
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8
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Pignon CP, Fernandes SB, Valluru R, Bandillo N, Lozano R, Buckler E, Gore MA, Long SP, Brown PJ, Leakey ADB. Phenotyping stomatal closure by thermal imaging for GWAS and TWAS of water use efficiency-related genes. PLANT PHYSIOLOGY 2021; 187:2544-2562. [PMID: 34618072 PMCID: PMC8644692 DOI: 10.1093/plphys/kiab395] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 07/26/2021] [Indexed: 05/07/2023]
Abstract
Stomata allow CO2 uptake by leaves for photosynthetic assimilation at the cost of water vapor loss to the atmosphere. The opening and closing of stomata in response to fluctuations in light intensity regulate CO2 and water fluxes and are essential for maintaining water-use efficiency (WUE). However, a little is known about the genetic basis for natural variation in stomatal movement, especially in C4 crops. This is partly because the stomatal response to a change in light intensity is difficult to measure at the scale required for association studies. Here, we used high-throughput thermal imaging to bypass the phenotyping bottleneck and assess 10 traits describing stomatal conductance (gs) before, during and after a stepwise decrease in light intensity for a diversity panel of 659 sorghum (Sorghum bicolor) accessions. Results from thermal imaging significantly correlated with photosynthetic gas exchange measurements. gs traits varied substantially across the population and were moderately heritable (h2 up to 0.72). An integrated genome-wide and transcriptome-wide association study identified candidate genes putatively driving variation in stomatal conductance traits. Of the 239 unique candidate genes identified with the greatest confidence, 77 were putative orthologs of Arabidopsis (Arabidopsis thaliana) genes related to functions implicated in WUE, including stomatal opening/closing (24 genes), stomatal/epidermal cell development (35 genes), leaf/vasculature development (12 genes), or chlorophyll metabolism/photosynthesis (8 genes). These findings demonstrate an approach to finding genotype-to-phenotype relationships for a challenging trait as well as candidate genes for further investigation of the genetic basis of WUE in a model C4 grass for bioenergy, food, and forage production.
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Affiliation(s)
- Charles P Pignon
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Samuel B Fernandes
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Ravi Valluru
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853, USA
- Lincoln Institute for Agri-Food Technology, University of Lincoln, Lincoln LN1 3QE, UK
| | - Nonoy Bandillo
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853, USA
- Department of Plant Sciences, North Dakota State University, Fargo, North Dakota 58105, USA
| | - Roberto Lozano
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853, USA
| | - Edward Buckler
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853, USA
- United States Department of Agriculture, Agricultural Research Service (USDA-ARS) R.W. Holley Center for Agriculture and Health, Ithaca, New York 14853, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853, USA
| | - Stephen P Long
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Lancaster Environment Centre, University of Lancaster, Lancaster LA1 1YX, UK
| | - Patrick J Brown
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Andrew D B Leakey
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853, USA
- Author for communication:
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9
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Ferguson JN, Fernandes SB, Monier B, Miller ND, Allen D, Dmitrieva A, Schmuker P, Lozano R, Valluru R, Buckler ES, Gore MA, Brown PJ, Spalding EP, Leakey ADB. Machine learning-enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions. PLANT PHYSIOLOGY 2021; 187:1481-1500. [PMID: 34618065 PMCID: PMC9040483 DOI: 10.1093/plphys/kiab346] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 06/29/2021] [Indexed: 05/04/2023]
Abstract
Sorghum (Sorghum bicolor) is a model C4 crop made experimentally tractable by extensive genomic and genetic resources. Biomass sorghum is studied as a feedstock for biofuel and forage. Mechanistic modeling suggests that reducing stomatal conductance (gs) could improve sorghum intrinsic water use efficiency (iWUE) and biomass production. Phenotyping to discover genotype-to-phenotype associations remains a bottleneck in understanding the mechanistic basis for natural variation in gs and iWUE. This study addressed multiple methodological limitations. Optical tomography and a machine learning tool were combined to measure stomatal density (SD). This was combined with rapid measurements of leaf photosynthetic gas exchange and specific leaf area (SLA). These traits were the subject of genome-wide association study and transcriptome-wide association study across 869 field-grown biomass sorghum accessions. The ratio of intracellular to ambient CO2 was genetically correlated with SD, SLA, gs, and biomass production. Plasticity in SD and SLA was interrelated with each other and with productivity across wet and dry growing seasons. Moderate-to-high heritability of traits studied across the large mapping population validated associations between DNA sequence variation or RNA transcript abundance and trait variation. A total of 394 unique genes underpinning variation in WUE-related traits are described with higher confidence because they were identified in multiple independent tests. This list was enriched in genes whose Arabidopsis (Arabidopsis thaliana) putative orthologs have functions related to stomatal or leaf development and leaf gas exchange, as well as genes with nonsynonymous/missense variants. These advances in methodology and knowledge will facilitate improving C4 crop WUE.
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Affiliation(s)
- John N Ferguson
- Institute for Genomic Biology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61901, USA
| | - Samuel B Fernandes
- Institute for Genomic Biology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61901, USA
| | - Brandon Monier
- Institute for Genomic Diversity, Cornell University, Ithaca, New
York 14853, USA
| | - Nathan D Miller
- Department of Botany, University of Wisconsin, Madison, Wisconsin
53706, USA
| | - Dylan Allen
- Institute for Genomic Biology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61901, USA
| | - Anna Dmitrieva
- Institute for Genomic Biology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61901, USA
| | - Peter Schmuker
- Institute for Genomic Biology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61901, USA
| | - Roberto Lozano
- Plant Breeding and Genetics Section, School of Integrative Plant Science,
Cornell University, Ithaca, New York 14853, USA
| | - Ravi Valluru
- Institute for Genomic Diversity, Cornell University, Ithaca, New
York 14853, USA
- Present address: Lincoln Institute for Agri-Food Technology,
University of Lincoln, Lincoln LN2 2LG, UK
| | - Edward S Buckler
- Institute for Genomic Diversity, Cornell University, Ithaca, New
York 14853, USA
- Plant Breeding and Genetics Section, School of Integrative Plant Science,
Cornell University, Ithaca, New York 14853, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science,
Cornell University, Ithaca, New York 14853, USA
| | - Patrick J Brown
- Institute for Genomic Biology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61901, USA
- Present address: Section of Agricultural Plant Biology,
Department of Plant Sciences, University of California Davis, California 95616,
USA
| | - Edgar P Spalding
- Department of Botany, University of Wisconsin, Madison, Wisconsin
53706, USA
| | - Andrew D B Leakey
- Institute for Genomic Biology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61901, USA
- Department of Crop Sciences, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61901, USA
- Department of Plant Biology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61901, USA
- Author for communication: ,
Present address: Department of Plant Sciences, University of Cambridge, Cambridge CB2 3EA,
UK
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10
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Ferguson JN, Fernandes SB, Monier B, Miller ND, Allen D, Dmitrieva A, Schmuker P, Lozano R, Valluru R, Buckler ES, Gore MA, Brown PJ, Spalding EP, Leakey ADB. Machine learning-enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions. PLANT PHYSIOLOGY 2021; 187:1481-1500. [PMID: 34618065 DOI: 10.1093/plphys/kiab34] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 06/29/2021] [Indexed: 05/27/2023]
Abstract
Sorghum (Sorghum bicolor) is a model C4 crop made experimentally tractable by extensive genomic and genetic resources. Biomass sorghum is studied as a feedstock for biofuel and forage. Mechanistic modeling suggests that reducing stomatal conductance (gs) could improve sorghum intrinsic water use efficiency (iWUE) and biomass production. Phenotyping to discover genotype-to-phenotype associations remains a bottleneck in understanding the mechanistic basis for natural variation in gs and iWUE. This study addressed multiple methodological limitations. Optical tomography and a machine learning tool were combined to measure stomatal density (SD). This was combined with rapid measurements of leaf photosynthetic gas exchange and specific leaf area (SLA). These traits were the subject of genome-wide association study and transcriptome-wide association study across 869 field-grown biomass sorghum accessions. The ratio of intracellular to ambient CO2 was genetically correlated with SD, SLA, gs, and biomass production. Plasticity in SD and SLA was interrelated with each other and with productivity across wet and dry growing seasons. Moderate-to-high heritability of traits studied across the large mapping population validated associations between DNA sequence variation or RNA transcript abundance and trait variation. A total of 394 unique genes underpinning variation in WUE-related traits are described with higher confidence because they were identified in multiple independent tests. This list was enriched in genes whose Arabidopsis (Arabidopsis thaliana) putative orthologs have functions related to stomatal or leaf development and leaf gas exchange, as well as genes with nonsynonymous/missense variants. These advances in methodology and knowledge will facilitate improving C4 crop WUE.
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Affiliation(s)
- John N Ferguson
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61901, USA
| | - Samuel B Fernandes
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61901, USA
| | - Brandon Monier
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853, USA
| | - Nathan D Miller
- Department of Botany, University of Wisconsin, Madison, Wisconsin 53706, USA
| | - Dylan Allen
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61901, USA
| | - Anna Dmitrieva
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61901, USA
| | - Peter Schmuker
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61901, USA
| | - Roberto Lozano
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853, USA
| | - Ravi Valluru
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853, USA
| | - Edward S Buckler
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853, USA
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853, USA
| | - Patrick J Brown
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61901, USA
| | - Edgar P Spalding
- Department of Botany, University of Wisconsin, Madison, Wisconsin 53706, USA
| | - Andrew D B Leakey
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61901, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois 61901, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61901, USA
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Fonseca JMO, Klein PE, Crossa J, Pacheco A, Perez-Rodriguez P, Ramasamy P, Klein R, Rooney WL. Assessing combining abilities, genomic data, and genotype × environment interactions to predict hybrid grain sorghum performance. THE PLANT GENOME 2021; 14:e20127. [PMID: 34370387 DOI: 10.1002/tpg2.20127] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 06/08/2021] [Indexed: 05/02/2023]
Abstract
Genomic selection in maize (Zea mays L.) has been one factor that has increased the rate of genetic gain when compared with other cereals. However, the technological foundations in maize also exist in other cereal crops that would allow prediction of hybrid performance based on general (GCA) and specific (SCA) combining abilities applied through genomic-enabled prediction models. Further, the incorporation of genotype × environment (G × E) interaction effects present an opportunity to deploy hybrids to targeted environments. To test these concepts, a factorial mating design of elite yet divergent grain sorghum lines generated hybrids for evaluation. Inbred parents were genotyped, and markers were used to assess population structure and develop the genomic relationship matrix (GRM). Grain yield, height, and days to anthesis were collected for hybrids in replicated trials, and best linear unbiased estimates were used to train classical GCA-SCA-based and genomic (GB) models under a hierarchical Bayesian framework. To incorporate population structure, GB was fitted using the GRM of both parents and hybrids. For GB models, G × E interaction effects were included by the Hadamard product between GRM and environments. A leave-one-out cross-validation scheme was used to study the prediction capacity of models. Classical and genomic models effectively predicted hybrid performance and prediction accuracy increased by including genomic data. Genomic models effectively partitioned the variation due to GCA, SCA, and their interaction with the environment. A strategy to implement genomic selection for hybrid sorghum [Sorghum bicolor (L.) Moench] breeding is presented herein.
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Affiliation(s)
- Jales M O Fonseca
- Dep. of Soil and Crop Sciences, Texas A&M Univ., College Station, TX, 77843, USA
| | - Patricia E Klein
- Dep. of Horticultural Sciences, Texas A&M Univ., College Station, TX, 77843, USA
| | - Jose Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Él Batán, Mexico
| | - Angela Pacheco
- International Maize and Wheat Improvement Center (CIMMYT), Él Batán, Mexico
| | | | - Perumal Ramasamy
- Agriculture Research Center, Kansas State Univ., Hays, KS, 67601, USA
| | - Robert Klein
- Southern Plains Agricultural Research Center, USDA-ARS, College Station, TX, 77845, USA
| | - William L Rooney
- Dep. of Soil and Crop Sciences, Texas A&M Univ., College Station, TX, 77843, USA
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12
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Hao H, Li Z, Leng C, Lu C, Luo H, Liu Y, Wu X, Liu Z, Shang L, Jing HC. Sorghum breeding in the genomic era: opportunities and challenges. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1899-1924. [PMID: 33655424 PMCID: PMC7924314 DOI: 10.1007/s00122-021-03789-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 02/05/2021] [Indexed: 05/04/2023]
Abstract
The importance and potential of the multi-purpose crop sorghum in global food security have not yet been fully exploited, and the integration of the state-of-art genomics and high-throughput technologies into breeding practice is required. Sorghum, a historically vital staple food source and currently the fifth most important major cereal, is emerging as a crop with diverse end-uses as food, feed, fuel and forage and a model for functional genetics and genomics of tropical grasses. Rapid development in high-throughput experimental and data processing technologies has significantly speeded up sorghum genomic researches in the past few years. The genomes of three sorghum lines are available, thousands of genetic stocks accessible and various genetic populations, including NAM, MAGIC, and mutagenised populations released. Functional and comparative genomics have elucidated key genetic loci and genes controlling agronomical and adaptive traits. However, the knowledge gained has far away from being translated into real breeding practices. We argue that the way forward is to take a genome-based approach for tailored designing of sorghum as a multi-functional crop combining excellent agricultural traits for various end uses. In this review, we update the new concepts and innovation systems in crop breeding and summarise recent advances in sorghum genomic researches, especially the genome-wide dissection of variations in genes and alleles for agronomically important traits. Future directions and opportunities for sorghum breeding are highlighted to stimulate discussion amongst sorghum academic and industrial communities.
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Affiliation(s)
- Huaiqing Hao
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China.
| | - Zhigang Li
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
| | - Chuanyuan Leng
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
| | - Cheng Lu
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Hong Luo
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
| | - Yuanming Liu
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xiaoyuan Wu
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
| | - Zhiquan Liu
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
| | - Li Shang
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
| | - Hai-Chun Jing
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China.
- Engineering Laboratory for Grass-based Livestock Husbandry, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
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Jaikumar NS, Stutz SS, Fernandes SB, Leakey ADB, Bernacchi CJ, Brown PJ, Long SP. Can improved canopy light transmission ameliorate loss of photosynthetic efficiency in the shade? An investigation of natural variation in Sorghum bicolor. JOURNAL OF EXPERIMENTAL BOTANY 2021; 72:4965-4980. [PMID: 33914063 PMCID: PMC8219039 DOI: 10.1093/jxb/erab176] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Accepted: 04/28/2021] [Indexed: 05/29/2023]
Abstract
Previous studies have found that maximum quantum yield of CO2 assimilation (Φ CO2,max,app) declines in lower canopies of maize and miscanthus, a maladaptive response to self-shading. These observations were limited to single genotypes, leaving it unclear whether the maladaptive shade response is a general property of this C4 grass tribe, the Andropogoneae. We explored the generality of this maladaptation by testing the hypothesis that erect leaf forms (erectophiles), which allow more light into the lower canopy, suffer less of a decline in photosynthetic efficiency than drooping leaf (planophile) forms. On average, Φ CO2,max,app declined 27% in lower canopy leaves across 35 accessions, but the decline was over twice as great in planophiles than in erectophiles. The loss of photosynthetic efficiency involved a decoupling between electron transport and assimilation. This was not associated with increased bundle sheath leakage, based on 13C measurements. In both planophiles and erectophiles, shaded leaves had greater leaf absorptivity and lower activities of key C4 enzymes than sun leaves. The erectophile form is considered more productive because it allows a more effective distribution of light through the canopy to support photosynthesis. We show that in sorghum, it provides a second benefit, maintenance of higher Φ CO2,max,app to support efficient use of that light resource.
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Affiliation(s)
- Nikhil S Jaikumar
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Samantha S Stutz
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Samuel B Fernandes
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Andrew D B Leakey
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Carl J Bernacchi
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- USDA ARS Global Change and Photosynthesis Research Unit, Urbana, IL 61801, USA
| | - Patrick J Brown
- Department of Plant Sciences, University of California at Davis, Davis, CA 95616, USA
| | - Stephen P Long
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Lancaster Environment Centre, University of Lancaster, Lancaster LA1 4YQ, UK
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14
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Understanding Growth Dynamics and Yield Prediction of Sorghum Using High Temporal Resolution UAV Imagery Time Series and Machine Learning. REMOTE SENSING 2021. [DOI: 10.3390/rs13091763] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Unmanned aerial vehicles (UAV) carrying multispectral cameras are increasingly being used for high-throughput phenotyping (HTP) of above-ground traits of crops to study genetic diversity, resource use efficiency and responses to abiotic or biotic stresses. There is significant unexplored potential for repeated data collection through a field season to reveal information on the rates of growth and provide predictions of the final yield. Generating such information early in the season would create opportunities for more efficient in-depth phenotyping and germplasm selection. This study tested the use of high-resolution time-series imagery (5 or 10 sampling dates) to understand the relationships between growth dynamics, temporal resolution and end-of-season above-ground biomass (AGB) in 869 diverse accessions of highly productive (mean AGB = 23.4 Mg/Ha), photoperiod sensitive sorghum. Canopy surface height (CSM), ground cover (GC), and five common spectral indices were considered as features of the crop phenotype. Spline curve fitting was used to integrate data from single flights into continuous time courses. Random Forest was used to predict end-of-season AGB from aerial imagery, and to identify the most informative variables driving predictions. Improved prediction of end-of-season AGB (RMSE reduction of 0.24 Mg/Ha) was achieved earlier in the growing season (10 to 20 days) by leveraging early- and mid-season measurement of the rate of change of geometric and spectral features. Early in the season, dynamic traits describing the rates of change of CSM and GC predicted end-of-season AGB best. Late in the season, CSM on a given date was the most influential predictor of end-of-season AGB. The power to predict end-of-season AGB was greatest at 50 days after planting, accounting for 63% of variance across this very diverse germplasm collection with modest error (RMSE 1.8 Mg/ha). End-of-season AGB could be predicted equally well when spline fitting was performed on data collected from five flights versus 10 flights over the growing season. This demonstrates a more valuable and efficient approach to using UAVs for HTP, while also proposing strategies to add further value.
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Chang S, Lee U, Hong MJ, Jo YD, Kim JB. High-Throughput Phenotyping (HTP) Data Reveal Dosage Effect at Growth Stages in Arabidopsis thaliana Irradiated by Gamma Rays. PLANTS (BASEL, SWITZERLAND) 2020; 9:E557. [PMID: 32349236 PMCID: PMC7284948 DOI: 10.3390/plants9050557] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 04/22/2020] [Accepted: 04/24/2020] [Indexed: 01/25/2023]
Abstract
The effects of radiation dosages on plant species are quantitatively presented as the lethal dose or the dose required for growth reduction in mutation breeding. However, lethal dose and growth reduction fail to provide dynamic growth behavior information such as growth rate after irradiation. Irradiated seeds of Arabidopsis were grown in an environmentally controlled high-throughput phenotyping (HTP) platform to capture growth images that were analyzed with machine learning algorithms. Analysis of digital phenotyping data revealed unique growth patterns following treatments below LD50 value at 641 Gy. Plants treated with 100-Gy gamma irradiation showed almost identical growth pattern compared with wild type; the hormesis effect was observed >21 days after sowing. In 200 Gy-treated plants, a uniform growth pattern but smaller rosette areas than the wild type were seen (p < 0.05). The shift between vegetative and reproductive stages was not retarded by irradiation at 200 and 300 Gy although growth inhibition was detected under the same irradiation dose. Results were validated using 200 and 300 Gy doses with HTP in a separate study. To our knowledge, this is the first study to apply a HTP platform to measure and analyze the dosage effect of radiation in plants. The method enabled an in-depth analysis of growth patterns, which could not be detected previously due to a lack of time-series data. This information will improve our knowledge about the effects of radiation in model plant species and crops.
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Affiliation(s)
- Sungyul Chang
- Radiation Breeding Research Team, Advanced Radiation Technology Institute (ARTI), Korea Atomic Energy Research Institute (KAERI), 29 Geumgu-gil, Jeongeup-si, Jeollabuk-do 56212, Korea; (S.C.); (M.J.H.)
| | - Unseok Lee
- Smart Farm Research Center, Korea Institute of Science and Technology (KIST), 679 Saimdang-ro, Gangneung, Gangwon-do 210-340, Korea;
| | - Min Jeong Hong
- Radiation Breeding Research Team, Advanced Radiation Technology Institute (ARTI), Korea Atomic Energy Research Institute (KAERI), 29 Geumgu-gil, Jeongeup-si, Jeollabuk-do 56212, Korea; (S.C.); (M.J.H.)
| | - Yeong Deuk Jo
- Radiation Breeding Research Team, Advanced Radiation Technology Institute (ARTI), Korea Atomic Energy Research Institute (KAERI), 29 Geumgu-gil, Jeongeup-si, Jeollabuk-do 56212, Korea; (S.C.); (M.J.H.)
| | - Jin-Baek Kim
- Radiation Breeding Research Team, Advanced Radiation Technology Institute (ARTI), Korea Atomic Energy Research Institute (KAERI), 29 Geumgu-gil, Jeongeup-si, Jeollabuk-do 56212, Korea; (S.C.); (M.J.H.)
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