1
|
Luoni SAB, Ricci R, Corzo MA, Hoxha G, Melgani F, Fernandez P. Sunpheno: A Deep Neural Network for Phenological Classification of Sunflower Images. PLANTS (BASEL, SWITZERLAND) 2024; 13:1998. [PMID: 39065525 PMCID: PMC11280726 DOI: 10.3390/plants13141998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2024] [Revised: 07/16/2024] [Accepted: 07/17/2024] [Indexed: 07/28/2024]
Abstract
Leaf senescence is a complex trait which becomes crucial for grain filling because photoassimilates are translocated to the seeds. Therefore, a correct sync between leaf senescence and phenological stages is necessary to obtain increasing yields. In this study, we evaluated the performance of five deep machine-learning methods for the evaluation of the phenological stages of sunflowers using images taken with cell phones in the field. From the analysis, we found that the method based on the pre-trained network resnet50 outperformed the other methods, both in terms of accuracy and velocity. Finally, the model generated, Sunpheno, was used to evaluate the phenological stages of two contrasting lines, B481_6 and R453, during senescence. We observed clear differences in phenological stages, confirming the results obtained in previous studies. A database with 5000 images was generated and was classified by an expert. This is important to end the subjectivity involved in decision making regarding the progression of this trait in the field and could be correlated with performance and senescence parameters that are highly associated with yield increase.
Collapse
Affiliation(s)
- Sofia A. Bengoa Luoni
- Laboratory of Genetics, Wageningen University & Research, 6708 PB Wageningen, The Netherlands;
| | - Riccardo Ricci
- Department of Information Engineering and Computer Science, University of Trento, 38123 Trento, Italy; (R.R.); (F.M.)
| | | | - Genc Hoxha
- Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin, 10587 Berlin, Germany;
| | - Farid Melgani
- Department of Information Engineering and Computer Science, University of Trento, 38123 Trento, Italy; (R.R.); (F.M.)
| | | |
Collapse
|
2
|
Leconte JML, Marco M, Nicolas B, Gabriela B, Sébastien C, Olivier C, Alexis C, Marc L, Rémy M, Nicolas P, Camille T, Clémence P, Virginie MT, Langlade NB. Multi-scale characterisation of cold response reveals immediate and long-term impacts on cell physiology up to seed composition in sunflower. PLANT, CELL & ENVIRONMENT 2024. [PMID: 38828995 DOI: 10.1111/pce.14941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 03/27/2024] [Accepted: 04/28/2024] [Indexed: 06/05/2024]
Abstract
Early sowing can help summer crops escape drought and can mitigate the impacts of climate change on them. However, it exposes them to cold stress during initial developmental stages, which has both immediate and long-term effects on development and physiology. To understand how early night-chilling stress impacts plant development and yield, we studied the reference sunflower line XRQ under controlled, semi-controlled and field conditions. We performed high-throughput imaging of the whole plant parts and obtained physiological and transcriptomic data from leaves, hypocotyls and roots. We observed morphological reductions in early stages under field and controlled conditions, with a decrease in root development, an increase in reactive oxygen species content in leaves and changes in lipid composition in hypocotyls. A long-term increase in leaf chlorophyll suggests a stress memory mechanism that was supported by transcriptomic induction of histone coding genes. We highlighted DEGs related to cold acclimation such as chaperone, heat shock and late embryogenesis abundant proteins. We identified genes in hypocotyls involved in lipid, cutin, suberin and phenylalanine ammonia lyase biosynthesis and ROS scavenging. This comprehensive study describes new phenotyping methods and candidate genes to understand phenotypic plasticity better in response to chilling and study stress memory in sunflower.
Collapse
Affiliation(s)
- Jean Michel Louis Leconte
- Université de Toulouse, INRAE, UMR LIPME, Castanet-Tolosan, France
- SYNGENTA SEEDS, Saint Sauveur, France
| | - Moroldo Marco
- Université de Toulouse, INRAE, UMR LIPME, Castanet-Tolosan, France
| | - Blanchet Nicolas
- Université de Toulouse, INRAE, UMR LIPME, Castanet-Tolosan, France
- Université de Toulouse, INRAE, UE APC, Castanet-Tolosan, France
| | - Bindea Gabriela
- INSERM, Laboratory of Integrative Cancer Immunology, Paris, France
- Centre de Recherche des Cordeliers, Sorbonne Université, Université Paris Cité, Paris, France
- Equipe Labellisée Ligue Contre le Cancer, Paris, France
| | | | - Catrice Olivier
- Université de Toulouse, INRAE, UMR LIPME, Castanet-Tolosan, France
| | | | | | - Marandel Rémy
- Université de Toulouse, INRAE, UE APC, Castanet-Tolosan, France
| | - Pouilly Nicolas
- Université de Toulouse, INRAE, UMR LIPME, Castanet-Tolosan, France
| | - Tapy Camille
- Université de Toulouse, INRAE, UMR LIPME, Castanet-Tolosan, France
| | | | | | | |
Collapse
|
3
|
Duruflé H, Balliau T, Blanchet N, Chaubet A, Duhnen A, Pouilly N, Blein-Nicolas M, Mangin B, Maury P, Langlade NB, Zivy M. Sunflower Hybrids and Inbred Lines Adopt Different Physiological Strategies and Proteome Responses to Cope with Water Deficit. Biomolecules 2023; 13:1110. [PMID: 37509146 PMCID: PMC10377273 DOI: 10.3390/biom13071110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 07/03/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
Sunflower is a hybrid crop that is considered moderately drought-tolerant and adapted to new cropping systems required for the agro-ecological transition. Here, we studied the impact of hybridity status (hybrids vs. inbred lines) on the responses to drought at the molecular and eco-physiological level exploiting publicly available datasets. Eco-physiological traits and leaf proteomes were measured in eight inbred lines and their sixteen hybrids grown in the high-throughput phenotyping platform Phenotoul-Heliaphen. Hybrids and parental lines showed different growth strategies: hybrids grew faster in the absence of water constraint and arrested their growth more abruptly than inbred lines when subjected to water deficit. We identified 471 differentially accumulated proteins, of which 256 were regulated by drought. The amplitude of up- and downregulations was greater in hybrids than in inbred lines. Our results show that hybrids respond more strongly to water deficit at the molecular and eco-physiological levels. Because of presence/absence polymorphism, hybrids potentially contain more genes than their parental inbred lines. We propose that detrimental homozygous mutations and the lower number of genes in inbred lines lead to a constitutive defense mechanism that may explain the lower growth of inbred lines under well-watered conditions and their lower reactivity to water deficit.
Collapse
Affiliation(s)
- Harold Duruflé
- INRAE UMR441, CNRS UMR2594, LIPME, Université de Toulouse, 31077 Toulouse, France
- INRAE, ONF, BioForA, 45075 Orleans, France
| | - Thierry Balliau
- AgroParisTech, GQE-Le Moulon, PAPPSO, Université Paris-Saclay, INRAE, CNRS, 91190 Gif-sur-Yvette, France
| | - Nicolas Blanchet
- INRAE UMR441, CNRS UMR2594, LIPME, Université de Toulouse, 31077 Toulouse, France
| | - Adeline Chaubet
- INRAE UMR441, CNRS UMR2594, LIPME, Université de Toulouse, 31077 Toulouse, France
| | - Alexandra Duhnen
- INRAE UMR441, CNRS UMR2594, LIPME, Université de Toulouse, 31077 Toulouse, France
| | - Nicolas Pouilly
- INRAE UMR441, CNRS UMR2594, LIPME, Université de Toulouse, 31077 Toulouse, France
| | - Mélisande Blein-Nicolas
- AgroParisTech, GQE-Le Moulon, PAPPSO, Université Paris-Saclay, INRAE, CNRS, 91190 Gif-sur-Yvette, France
| | - Brigitte Mangin
- INRAE UMR441, CNRS UMR2594, LIPME, Université de Toulouse, 31077 Toulouse, France
| | - Pierre Maury
- INRAE, INP-ENSAT Toulouse, UMR AGIR, Université de Toulouse, 31000 Toulouse, France
| | | | - Michel Zivy
- AgroParisTech, GQE-Le Moulon, PAPPSO, Université Paris-Saclay, INRAE, CNRS, 91190 Gif-sur-Yvette, France
| |
Collapse
|
4
|
Casadebaig P, Gauffreteau A, Landré A, Langlade NB, Mestries E, Sarron J, Trépos R, Vincourt P, Debaeke P. Optimized cultivar deployment improves the efficiency and stability of sunflower crop production at national scale. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:4049-4063. [PMID: 35294575 DOI: 10.1007/s00122-022-04072-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 02/26/2022] [Indexed: 06/14/2023]
Abstract
Crop simulation helps to analyze environmental impacts on crops and provides year-independent context information. This information is of major importance when deciding which cultivar to choose at sowing time. Plant breeding programs design new crop cultivars which, while developed for distinct populations of environments, are nevertheless grown over large areas during their time in the market. Over its cultivation area, the crop is exposed to highly diverse stress patterns caused by climatic uncertainty and multiple management options, which often leads to decreased expected crop performance. In this study, we aim to assess how finer spatial management of genetic resources could reduce the yield variance explained by genotype × environment interactions in a set of cropping environments and ultimately improve the efficiency and stability of crop production. We used modeling and simulation to predict the crop performance resulting from the interaction between cultivar growth and development, climate and soil conditions, and management practices. We designed a computational experiment that evaluated the performance of a collection of commercial sunflower cultivars in a realistic population of cropping conditions in France, built from extensive agricultural surveys. Distinct farming locations sharing similar simulated abiotic stress patterns were clustered together to specify environment types. We then used optimization methods to search for cultivars × environments combinations leading to increased yield expectations. Results showed that a single cultivar choice adapted to the most frequent environment-type in the population is a robust strategy. However, the relevance of cultivar recommendations to specific locations was gradually increasing with the knowledge of pedo-climatic conditions. We argue that this approach while being operational on current genetic material could act synergistically with plant breeding as more diverse material could enable access to cultivars with distinctive traits, more adapted to specific conditions.
Collapse
Affiliation(s)
| | - Arnaud Gauffreteau
- Univ. Paris-Saclay, INRAE, AgroParisTech, UMR Agronomie, 78850, Thiverval-Grignon, France
| | - Amélia Landré
- Univ. Toulouse, INRAE, UMR AGIR, 31320, Castanet-Tolosan, France
| | | | | | - Julien Sarron
- Univ. Toulouse, INRAE, UMR AGIR, 31320, Castanet-Tolosan, France
- Univ. Montpellier, CIRAD, UPR HortSys, 34398, Montpellier, France
| | - Ronan Trépos
- Univ. Toulouse, INRAE, UR MIAT, 31320, Castanet-Tolosan, France
| | - Patrick Vincourt
- Univ. Toulouse, INRAE, UMR LIPM, 31320, Castanet-Tolosan, France
| | - Philippe Debaeke
- Univ. Toulouse, INRAE, UMR AGIR, 31320, Castanet-Tolosan, France
| |
Collapse
|
5
|
Pomiès L, Brouard C, Duruflé H, Maigné É, Carré C, Gody L, Trösser F, Katsirelos G, Mangin B, Langlade NB, de Givry S. Gene regulatory network inference methodology for genomic and transcriptomic data acquired in genetically related heterozygote individuals. Bioinformatics 2022; 38:4127-4134. [PMID: 35792837 DOI: 10.1093/bioinformatics/btac445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 06/17/2022] [Accepted: 07/05/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Inferring gene regulatory networks in non-independent genetically related panels is a methodological challenge. This hampers evolutionary and biological studies using heterozygote individuals such as in wild sunflower populations or cultivated hybrids. RESULTS First, we simulated 100 datasets of gene expressions and polymorphisms, displaying the same gene expression distributions, heterozygosities and heritabilities as in our dataset including 173 genes and 353 genotypes measured in sunflower hybrids. Secondly, we performed a meta-analysis based on six inference methods [least absolute shrinkage and selection operator (Lasso), Random Forests, Bayesian Networks, Markov Random Fields, Ordinary Least Square and fast inference of networks from directed regulation (Findr)] and selected the minimal density networks for better accuracy with 64 edges connecting 79 genes and 0.35 area under precision and recall (AUPR) score on average. We identified that triangles and mutual edges are prone to errors in the inferred networks. Applied on classical datasets without heterozygotes, our strategy produced a 0.65 AUPR score for one dataset of the DREAM5 Systems Genetics Challenge. Finally, we applied our method to an experimental dataset from sunflower hybrids. We successfully inferred a network composed of 105 genes connected by 106 putative regulations with a major connected component. AVAILABILITY AND IMPLEMENTATION Our inference methodology dedicated to genomic and transcriptomic data is available at https://forgemia.inra.fr/sunrise/inference_methods. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Lise Pomiès
- MIAT, Université Fédérale de Toulouse, INRAE, Castanet-Tolosan 31326, France
| | - Céline Brouard
- MIAT, Université Fédérale de Toulouse, INRAE, Castanet-Tolosan 31326, France
| | - Harold Duruflé
- LIPME, Université de Toulouse, INRAE, CNRS, Castanet-Tolosan 31326, France
| | - Élise Maigné
- MIAT, Université Fédérale de Toulouse, INRAE, Castanet-Tolosan 31326, France
| | - Clément Carré
- MIAT, Université Fédérale de Toulouse, INRAE, Castanet-Tolosan 31326, France
| | - Louise Gody
- LIPME, Université de Toulouse, INRAE, CNRS, Castanet-Tolosan 31326, France
| | - Fulya Trösser
- MIAT, Université Fédérale de Toulouse, INRAE, Castanet-Tolosan 31326, France
| | - George Katsirelos
- MIA-Paris, AgroParisTech, Université Paris-Saclay, INRAE, Paris 75231, France
| | - Brigitte Mangin
- LIPME, Université de Toulouse, INRAE, CNRS, Castanet-Tolosan 31326, France
| | - Nicolas B Langlade
- LIPME, Université de Toulouse, INRAE, CNRS, Castanet-Tolosan 31326, France
| | - Simon de Givry
- MIAT, Université Fédérale de Toulouse, INRAE, Castanet-Tolosan 31326, France
| |
Collapse
|
6
|
Gill T, Gill SK, Saini DK, Chopra Y, de Koff JP, Sandhu KS. A Comprehensive Review of High Throughput Phenotyping and Machine Learning for Plant Stress Phenotyping. PHENOMICS (CHAM, SWITZERLAND) 2022; 2:156-183. [PMID: 36939773 PMCID: PMC9590503 DOI: 10.1007/s43657-022-00048-z] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 01/29/2022] [Accepted: 02/11/2022] [Indexed: 02/04/2023]
Abstract
During the last decade, there has been rapid adoption of ground and aerial platforms with multiple sensors for phenotyping various biotic and abiotic stresses throughout the developmental stages of the crop plant. High throughput phenotyping (HTP) involves the application of these tools to phenotype the plants and can vary from ground-based imaging to aerial phenotyping to remote sensing. Adoption of these HTP tools has tried to reduce the phenotyping bottleneck in breeding programs and help to increase the pace of genetic gain. More specifically, several root phenotyping tools are discussed to study the plant's hidden half and an area long neglected. However, the use of these HTP technologies produces big data sets that impede the inference from those datasets. Machine learning and deep learning provide an alternative opportunity for the extraction of useful information for making conclusions. These are interdisciplinary approaches for data analysis using probability, statistics, classification, regression, decision theory, data visualization, and neural networks to relate information extracted with the phenotypes obtained. These techniques use feature extraction, identification, classification, and prediction criteria to identify pertinent data for use in plant breeding and pathology activities. This review focuses on the recent findings where machine learning and deep learning approaches have been used for plant stress phenotyping with data being collected using various HTP platforms. We have provided a comprehensive overview of different machine learning and deep learning tools available with their potential advantages and pitfalls. Overall, this review provides an avenue for studying various HTP platforms with particular emphasis on using the machine learning and deep learning tools for drawing legitimate conclusions. Finally, we propose the conceptual challenges being faced and provide insights on future perspectives for managing those issues.
Collapse
Affiliation(s)
- Taqdeer Gill
- grid.280741.80000 0001 2284 9820Department of Agricultural and Environmental Sciences, Tennessee State University, Nashville, TN 37209 USA
| | - Simranveer K. Gill
- grid.412577.20000 0001 2176 2352College of Agriculture, Punjab Agricultural University, Ludhiana, Punjab 141004 India
| | - Dinesh K. Saini
- grid.412577.20000 0001 2176 2352Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab 141004 India
| | - Yuvraj Chopra
- grid.412577.20000 0001 2176 2352College of Agriculture, Punjab Agricultural University, Ludhiana, Punjab 141004 India
| | - Jason P. de Koff
- grid.280741.80000 0001 2284 9820Department of Agricultural and Environmental Sciences, Tennessee State University, Nashville, TN 37209 USA
| | - Karansher S. Sandhu
- grid.30064.310000 0001 2157 6568Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99163 USA
| |
Collapse
|
7
|
Saini DK, Chopra Y, Singh J, Sandhu KS, Kumar A, Bazzer S, Srivastava P. Comprehensive evaluation of mapping complex traits in wheat using genome-wide association studies. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2022; 42:1. [PMID: 37309486 PMCID: PMC10248672 DOI: 10.1007/s11032-021-01272-7] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 12/10/2021] [Indexed: 06/14/2023]
Abstract
Genome-wide association studies (GWAS) are effectively applied to detect the marker trait associations (MTAs) using whole genome-wide variants for complex quantitative traits in different crop species. GWAS has been applied in wheat for different quality, biotic and abiotic stresses, and agronomic and yield-related traits. Predictions for marker-trait associations are controlled with the development of better statistical models taking population structure and familial relatedness into account. In this review, we have provided a detailed overview of the importance of association mapping, population design, high-throughput genotyping and phenotyping platforms, advancements in statistical models and multiple threshold comparisons, and recent GWA studies conducted in wheat. The information about MTAs utilized for gene characterization and adopted in breeding programs is also provided. In the literature that we surveyed, as many as 86,122 wheat lines have been studied under various GWA studies reporting 46,940 loci. However, further utilization of these is largely limited. The future breakthroughs in area of genomic selection, multi-omics-based approaches, machine, and deep learning models in wheat breeding after exploring the complex genetic structure with the GWAS are also discussed. This is a most comprehensive study of a large number of reports on wheat GWAS and gives a comparison and timeline of technological developments in this area. This will be useful to new researchers or groups who wish to invest in GWAS.
Collapse
Affiliation(s)
- Dinesh K. Saini
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, 141004 India
| | - Yuvraj Chopra
- College of Agriculture, Punjab Agricultural University, Ludhiana, 141004 India
| | - Jagmohan Singh
- Division of Plant Pathology, Indian Agricultural Research Institute, New Delhi, 110012 India
| | - Karansher S. Sandhu
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99163 USA
| | - Anand Kumar
- Department of Genetics and Plant Breeding, Chandra Shekhar Azad University of Agriculture and Technology, Kanpur, 202002 India
| | - Sumandeep Bazzer
- Division of Plant Sciences, University of Missouri, Columbia, MO 65211 USA
| | - Puja Srivastava
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, 141004 India
| |
Collapse
|
8
|
Korwin Krukowski P, Ellenberger J, Röhlen-Schmittgen S, Schubert A, Cardinale F. Phenotyping in Arabidopsis and Crops-Are We Addressing the Same Traits? A Case Study in Tomato. Genes (Basel) 2020; 11:E1011. [PMID: 32867311 PMCID: PMC7564427 DOI: 10.3390/genes11091011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 08/21/2020] [Accepted: 08/24/2020] [Indexed: 11/18/2022] Open
Abstract
The convenient model Arabidopsis thaliana has allowed tremendous advances in plant genetics and physiology, in spite of only being a weed. It has also unveiled the main molecular networks governing, among others, abiotic stress responses. Through the use of the latest genomic tools, Arabidopsis research is nowadays being translated to agronomically interesting crop models such as tomato, but at a lagging pace. Knowledge transfer has been hindered by invariable differences in plant architecture and behaviour, as well as the divergent direct objectives of research in Arabidopsis versus crops compromise transferability. In this sense, phenotype translation is still a very complex matter. Here, we point out the challenges of "translational phenotyping" in the case study of drought stress phenotyping in Arabidopsis and tomato. After briefly defining and describing drought stress and survival strategies, we compare drought stress protocols and phenotyping techniques most commonly used in the two species, and discuss their potential to gain insights, which are truly transferable between species. This review is intended to be a starting point for discussion about translational phenotyping approaches among plant scientists, and provides a useful compendium of methods and techniques used in modern phenotyping for this specific plant pair as a case study.
Collapse
Affiliation(s)
- Paolo Korwin Krukowski
- Plant Stress Lab, Department of Agriculture, Forestry and Food Sciences DISAFA-Turin University, 10095 Grugliasco, Italy; (A.S.); (F.C.)
| | - Jan Ellenberger
- INRES Horticultural Sciences, University of Bonn, 53121 Bonn, Germany;
| | | | - Andrea Schubert
- Plant Stress Lab, Department of Agriculture, Forestry and Food Sciences DISAFA-Turin University, 10095 Grugliasco, Italy; (A.S.); (F.C.)
| | - Francesca Cardinale
- Plant Stress Lab, Department of Agriculture, Forestry and Food Sciences DISAFA-Turin University, 10095 Grugliasco, Italy; (A.S.); (F.C.)
| |
Collapse
|
9
|
Martignago D, Rico-Medina A, Blasco-Escámez D, Fontanet-Manzaneque JB, Caño-Delgado AI. Drought Resistance by Engineering Plant Tissue-Specific Responses. FRONTIERS IN PLANT SCIENCE 2020; 10:1676. [PMID: 32038670 PMCID: PMC6987726 DOI: 10.3389/fpls.2019.01676] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Accepted: 11/28/2019] [Indexed: 05/18/2023]
Abstract
Drought is the primary cause of agricultural loss globally, and represents a major threat to food security. Currently, plant biotechnology stands as one of the most promising fields when it comes to developing crops that are able to produce high yields in water-limited conditions. From studies of Arabidopsis thaliana whole plants, the main response mechanisms to drought stress have been uncovered, and multiple drought resistance genes have already been engineered into crops. So far, most plants with enhanced drought resistance have displayed reduced crop yield, meaning that there is still a need to search for novel approaches that can uncouple drought resistance from plant growth. Our laboratory has recently shown that the receptors of brassinosteroid (BR) hormones use tissue-specific pathways to mediate different developmental responses during root growth. In Arabidopsis, we found that increasing BR receptors in the vascular plant tissues confers resistance to drought without penalizing growth, opening up an exceptional opportunity to investigate the mechanisms that confer drought resistance with cellular specificity in plants. In this review, we provide an overview of the most promising phenotypical drought traits that could be improved biotechnologically to obtain drought-tolerant cereals. In addition, we discuss how current genome editing technologies could help to identify and manipulate novel genes that might grant resistance to drought stress. In the upcoming years, we expect that sustainable solutions for enhancing crop production in water-limited environments will be identified through joint efforts.
Collapse
Affiliation(s)
| | | | | | | | - Ana I. Caño-Delgado
- Department of Molecular Genetics, Centre for Research in Agricultural Genomics (CRAG) CSIC-IRTA-UAB-UB, Barcelona, Spain
| |
Collapse
|
10
|
Moschen S, Marino J, Nicosia S, Higgins J, Alseekh S, Astigueta F, Bengoa Luoni S, Rivarola M, Fernie AR, Blanchet N, Langlade NB, Paniego N, Fernández P, Heinz RA. Exploring gene networks in two sunflower lines with contrasting leaf senescence phenotype using a system biology approach. BMC PLANT BIOLOGY 2019; 19:446. [PMID: 31651254 PMCID: PMC6813990 DOI: 10.1186/s12870-019-2021-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 09/06/2019] [Indexed: 05/05/2023]
Abstract
BACKGROUND Leaf senescence is a complex process, controlled by multiple genetic and environmental variables. In sunflower, leaf senescence is triggered abruptly following anthesis thereby limiting the capacity of plants to keep their green leaf area during grain filling, which subsequently has a strong impact on crop yield. Recently, we performed a selection of contrasting sunflower inbred lines for the progress of leaf senescence through a physiological, cytological and molecular approach. Here we present a large scale transcriptomic analysis using RNA-seq and its integration with metabolic profiles for two contrasting sunflower inbred lines, R453 and B481-6 (early and delayed senescence respectively), with the aim of identifying metabolic pathways associated to leaf senescence. RESULTS Gene expression profiles revealed a higher number of differentially expressed genes, as well as, higher expression levels in R453, providing evidence for early activation of the senescence program in this line. Metabolic pathways associated with sugars and nutrient recycling were differentially regulated between the lines. Additionally, we identified transcription factors acting as hubs in the co-expression networks; some previously reported as senescence-associated genes in model species but many are novel candidate genes. CONCLUSIONS Understanding the onset and the progress of the senescence process in crops and the identification of these new candidate genes will likely prove highly useful for different management strategies to mitigate the impact of senescence on crop yield. Functional characterization of candidate genes will help to develop molecular tools for biotechnological applications in breeding crop yield.
Collapse
Affiliation(s)
- Sebastián Moschen
- Estación Experimental Agropecuaria Famaillá, Instituto Nacional de Tecnología Agropecuaria, Famaillá, Tucumán Argentina
- Instituto de Agrobiotecnología y Biología Molecular – IABiMo – INTA-CONICET, Instituto de Biotecnología, Centro de Investigaciones en Ciencias Veterinarias y Agronómicas, Instituto Nacional de Tecnología Agropecuaria, Hurlingham, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas, Ciudad Autónoma de Buenos Aires, Argentina
| | - Johanna Marino
- Escuela de Ciencia y Tecnología, Universidad Nacional de San Martín, San Martín, Argentina
| | - Salvador Nicosia
- Instituto de Agrobiotecnología y Biología Molecular – IABiMo – INTA-CONICET, Instituto de Biotecnología, Centro de Investigaciones en Ciencias Veterinarias y Agronómicas, Instituto Nacional de Tecnología Agropecuaria, Hurlingham, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas, Ciudad Autónoma de Buenos Aires, Argentina
| | - Janet Higgins
- Earlham Institute, Norwich Research Park, Norwich, NR4 7UZ UK
| | - Saleh Alseekh
- Max-Planck-Institut für Molekulare Pflanzenphysiologie, Potsdam-Golm, Germany
| | - Francisco Astigueta
- Instituto de Agrobiotecnología y Biología Molecular – IABiMo – INTA-CONICET, Instituto de Biotecnología, Centro de Investigaciones en Ciencias Veterinarias y Agronómicas, Instituto Nacional de Tecnología Agropecuaria, Hurlingham, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas, Ciudad Autónoma de Buenos Aires, Argentina
| | - Sofia Bengoa Luoni
- Instituto Tecnológico Chascomús (INTECh), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)-Universidad Nacional de General San Martín (UNSAM), Chascomús, Argentina
| | - Máximo Rivarola
- Instituto de Agrobiotecnología y Biología Molecular – IABiMo – INTA-CONICET, Instituto de Biotecnología, Centro de Investigaciones en Ciencias Veterinarias y Agronómicas, Instituto Nacional de Tecnología Agropecuaria, Hurlingham, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas, Ciudad Autónoma de Buenos Aires, Argentina
| | - Alisdair R. Fernie
- Max-Planck-Institut für Molekulare Pflanzenphysiologie, Potsdam-Golm, Germany
| | - Nicolas Blanchet
- LIPM, INRA, CNRS, Université de Toulouse, Castanet-Tolosan, France
| | | | - Norma Paniego
- Instituto de Agrobiotecnología y Biología Molecular – IABiMo – INTA-CONICET, Instituto de Biotecnología, Centro de Investigaciones en Ciencias Veterinarias y Agronómicas, Instituto Nacional de Tecnología Agropecuaria, Hurlingham, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas, Ciudad Autónoma de Buenos Aires, Argentina
| | - Paula Fernández
- Instituto de Agrobiotecnología y Biología Molecular – IABiMo – INTA-CONICET, Instituto de Biotecnología, Centro de Investigaciones en Ciencias Veterinarias y Agronómicas, Instituto Nacional de Tecnología Agropecuaria, Hurlingham, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas, Ciudad Autónoma de Buenos Aires, Argentina
- Escuela de Ciencia y Tecnología, Universidad Nacional de San Martín, San Martín, Argentina
| | - Ruth A. Heinz
- Instituto de Agrobiotecnología y Biología Molecular – IABiMo – INTA-CONICET, Instituto de Biotecnología, Centro de Investigaciones en Ciencias Veterinarias y Agronómicas, Instituto Nacional de Tecnología Agropecuaria, Hurlingham, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas, Ciudad Autónoma de Buenos Aires, Argentina
| |
Collapse
|
11
|
Fernandez O, Urrutia M, Berton T, Bernillon S, Deborde C, Jacob D, Maucourt M, Maury P, Duruflé H, Gibon Y, Langlade NB, Moing A. Metabolomic characterization of sunflower leaf allows discriminating genotype groups or stress levels with a minimal set of metabolic markers. Metabolomics 2019; 15:56. [PMID: 30929085 PMCID: PMC6441456 DOI: 10.1007/s11306-019-1515-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Accepted: 03/18/2019] [Indexed: 12/18/2022]
Abstract
INTRODUCTION Plant and crop metabolomic analyses may be used to study metabolism across genetic and environmental diversity. Complementary analytical strategies are useful for investigating metabolic changes and searching for biomarkers of response or performance. METHODS AND OBJECTIVES The experimental material consisted in eight sunflower lines with two line status, four restorers (R, used as males) and four maintainers (B, corresponding to females) routinely used for sunflower hybrid varietal production, respectively to complement or maintain the cytoplasmic male sterility PET1. These lines were either irrigated at full soil capacity (WW) or submitted to drought stress (DS). Our aim was to combine targeted and non-targeted metabolomics to characterize sunflower leaf composition in order to investigate the effect of line status genotypes and environmental conditions and to find the best and smallest set of biomarkers for line status and stress response using a custom-made process of variables selection. RESULTS Five hundred and eighty-eight metabolic variables were measured by using complementary analytical methods such as 1H-NMR, MS-based profiles and targeted analyses of major metabolites. Based on statistical analyses, a limited number of markers were able to separate WW and DS samples in a more discriminant manner than previously published physiological data. Another metabolic marker set was able to discriminate line status. CONCLUSION This study underlines the potential of metabolic markers for discriminating genotype groups and environmental conditions. Their potential use for prediction is discussed.
Collapse
Affiliation(s)
- Olivier Fernandez
- UMR1332 Biologie du Fruit et Pathologie, INRA, Centre INRA de Bordeaux, 71 av Edouard Bourlaux, 33140 Villenave d’Ornon, France
- Present Address: Laboratoire RIBP, Université de Reims Champagne Ardenne, Moulin de la Housse Chemin des Rouliers, 51100 Reims, France
| | - Maria Urrutia
- UMR1332 Biologie du Fruit et Pathologie, INRA, Centre INRA de Bordeaux, 71 av Edouard Bourlaux, 33140 Villenave d’Ornon, France
- UMR AgroImpact, INRA, Estrées-Mons, 80203 Péronne, France
- Present Address: Enza Zaden Centro de Investigacion S.L., Santa Maria del Aguila, 04710 Almeria, Spain
| | - Thierry Berton
- UMR1332 Biologie du Fruit et Pathologie, INRA, Centre INRA de Bordeaux, 71 av Edouard Bourlaux, 33140 Villenave d’Ornon, France
- Present Address: Centre for CardioVascular and Nutrition, UMR INRA-INSERM, Aix-Marseille Univ, INSERM, 13005 Marseilles, France
| | - Stéphane Bernillon
- UMR1332 Biologie du Fruit et Pathologie, INRA, Centre INRA de Bordeaux, 71 av Edouard Bourlaux, 33140 Villenave d’Ornon, France
- Plateforme Métabolome Bordeaux, CGFB, MetaboHUB-PHENOME, 33140 Villenave d’Ornon, France
| | - Catherine Deborde
- UMR1332 Biologie du Fruit et Pathologie, INRA, Centre INRA de Bordeaux, 71 av Edouard Bourlaux, 33140 Villenave d’Ornon, France
- Plateforme Métabolome Bordeaux, CGFB, MetaboHUB-PHENOME, 33140 Villenave d’Ornon, France
| | - Daniel Jacob
- UMR1332 Biologie du Fruit et Pathologie, INRA, Centre INRA de Bordeaux, 71 av Edouard Bourlaux, 33140 Villenave d’Ornon, France
- Plateforme Métabolome Bordeaux, CGFB, MetaboHUB-PHENOME, 33140 Villenave d’Ornon, France
| | - Mickaël Maucourt
- UMR1332 Biologie du Fruit et Pathologie, INRA, Centre INRA de Bordeaux, 71 av Edouard Bourlaux, 33140 Villenave d’Ornon, France
- Plateforme Métabolome Bordeaux, CGFB, MetaboHUB-PHENOME, 33140 Villenave d’Ornon, France
- Present Address: Enza Zaden Centro de Investigacion S.L., Santa Maria del Aguila, 04710 Almeria, Spain
| | - Pierre Maury
- UMR LIPM, INRA, CNRS, Université de Toulouse, 31326 Castanet-Tolosan, France
| | - Harold Duruflé
- UMR LIPM, INRA, CNRS, Université de Toulouse, 31326 Castanet-Tolosan, France
| | - Yves Gibon
- UMR1332 Biologie du Fruit et Pathologie, INRA, Centre INRA de Bordeaux, 71 av Edouard Bourlaux, 33140 Villenave d’Ornon, France
- Plateforme Métabolome Bordeaux, CGFB, MetaboHUB-PHENOME, 33140 Villenave d’Ornon, France
| | - Nicolas B. Langlade
- UMR LIPM, INRA, CNRS, Université de Toulouse, 31326 Castanet-Tolosan, France
| | - Annick Moing
- UMR1332 Biologie du Fruit et Pathologie, INRA, Centre INRA de Bordeaux, 71 av Edouard Bourlaux, 33140 Villenave d’Ornon, France
- Plateforme Métabolome Bordeaux, CGFB, MetaboHUB-PHENOME, 33140 Villenave d’Ornon, France
| |
Collapse
|
12
|
Schoving C, Stöckle CO, Colombet C, Champolivier L, Debaeke P, Maury P. Combining Simple Phenotyping and Photothermal Algorithm for the Prediction of Soybean Phenology: Application to a Range of Common Cultivars Grown in Europe. FRONTIERS IN PLANT SCIENCE 2019; 10:1755. [PMID: 32063913 PMCID: PMC7000526 DOI: 10.3389/fpls.2019.01755] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Accepted: 12/16/2019] [Indexed: 05/15/2023]
Abstract
Developing new cropping strategies (very early sowing, crop expansion at higher latitudes, double cropping) to improve soybean production in Europe under climate change needs a good prediction of phenology under different temperature and photoperiod conditions. For that purpose, a simple phenology algorithm (SPA) was developed and parameterized for 10 contrasting soybean cultivars (maturity group 000 to II). Two experiments were carried out at INRA Toulouse (France) for parameterization: 1) Phenological monitoring of plants in pots on an outdoor platform with 6 planting dates. 2) Response of seed germination to temperature in controlled conditions. Multi-location field trials including 5 sites, 4 years, 2 sowing dates, and 10 cultivars were used to evaluate the SPA phenology predictions. Mean cardinal temperatures (minimum, optimum, and maximum) for germination were ca. 2, 30, and 40°C, respectively with significant differences among cultivars. The photoperiod sensitivity coefficient varied among cultivars when fixing Popt and Pcrt, optimal and critical photoperiods respectively, by maturity group. The parameterized algorithm showed an RMSE of less than 6 days for the prediction of crop cycle duration (i.e. cotyledons stage to physiological maturity) in the field trials including 75 data points. Flowering (R1 stage), and beginning of grain filling (R5 stage) dates were satisfactorily predicted with RMSEs of 8.2 and 9.4 days respectively. Because SPA can be also parameterized using data from field experiments, it can be useful as a plant selection tool across environments. The algorithm can be readily applied to species other than soybean, and its incorporation into cropping systems models would enhance the assessment of the performance of crop cultivars under climate change scenarios.
Collapse
Affiliation(s)
- Céline Schoving
- Université de Toulouse, INRAE, UMR AGIR, Castanet-Tolosan, France
- *Correspondence: Céline Schoving,
| | | | - Céline Colombet
- Université de Toulouse, INRAE, UMR AGIR, Castanet-Tolosan, France
| | - Luc Champolivier
- Terres Inovia–Institut technique des oléagineux, des protéagineux et du chanvre, Paris, France
| | - Philippe Debaeke
- Université de Toulouse, INRAE, UMR AGIR, Castanet-Tolosan, France
| | - Pierre Maury
- Université de Toulouse, INRAE, INP-ENSAT Toulouse, UMR AGIR, Castanet-Tolosan, France
| |
Collapse
|
13
|
Blanchet N, Casadebaig P, Debaeke P, Duruflé H, Gody L, Gosseau F, Langlade NB, Maury P. Data describing the eco-physiological responses of twenty-four sunflower genotypes to water deficit. Data Brief 2018; 21:1296-1301. [PMID: 30456247 PMCID: PMC6231244 DOI: 10.1016/j.dib.2018.10.045] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Revised: 10/10/2018] [Accepted: 10/15/2018] [Indexed: 11/17/2022] Open
Abstract
This article presents experimental data describing the physiology and morphology of sunflower plants subjected to water deficit. Twenty-four sunflower genotypes were selected to represent genetic diversity within cultivated sunflower and included both inbred lines and their hybrids. Drought stress was applied to plants in pots at the vegetative stage using the high-throughput phenotyping platform Heliaphen at INRA Toulouse (France). Here, we provide data including specific leaf area, osmotic potential and adjustment, carbon isotope discrimination, leaf transpiration, plant architecture: plant height, leaf number, stem diameter. We also provide leaf areas of individual organs through time and growth rate during the stress period, environmental data such as temperatures, wind and radiation during the experiment. These data differentiate both treatment and the different genotypes and constitute a valuable resource to the community to study adaptation of crops to drought and the physiological basis of heterosis. It is available on the following repository: https://doi.org/10.25794/phenotype/er6lPW7V.
Collapse
Affiliation(s)
- Nicolas Blanchet
- LIPM, Université de Toulouse, INRA, CNRS, Castanet-Tolosan, France
| | | | | | - Harold Duruflé
- LIPM, Université de Toulouse, INRA, CNRS, Castanet-Tolosan, France
| | - Louise Gody
- LIPM, Université de Toulouse, INRA, CNRS, Castanet-Tolosan, France
| | - Florie Gosseau
- LIPM, Université de Toulouse, INRA, CNRS, Castanet-Tolosan, France
| | | | - Pierre Maury
- AGIR, Université de Toulouse, INP, ENSAT, Castanet-Tolosan, France
| |
Collapse
|