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ASTR

ABIOTIC STRESS TOLERANCE IN SUNFLOWER

 

The ASTR team aims to produce knowledge in genetics to adapt sunflower to the challenges of agro-ecological transition. 
Sunflower is the 4th most important oilseed in the world and the 2nd most important in France. Its low water and input requirements as well as its short cycle give it a very interesting ecological profile. It is an essential component of future agricultural systems (Debaeke et al. 2021), and its production has increased by 44% in the last 10 years (source USDA).


Mainly cultivated in environments subject to water stress, we are interested in
- the genetic control of yield plasticity in relation to water stress, cold following early sowing to avoid drought
- molecular mechanisms involved in responses to abiotic stresses in innovative cropping systems
- the impact of climate change on the attractiveness of sunflower for pollinators

To carry out our work, we are developing resources at three levels:
- at the genetic level with the CRB Tournesol
- at the genomic level with the ICSG
- at the phenomenal level, the high-throughput phenotyping platform Heliaphen which is part of the local infrastructure Phenotoul

 

MEMBERS

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Status

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Camille Tapy 2.JPG

Tapy Camille

Research technician

Nicolas Blanchet 1.JPG

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Olivier Catrice 1.JPG

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RESEARCH THEMES

Abiotic stress tolerance (Coordinator: Nicolas Langlade)

Identification of molecular and functional polymorphisms, in cultivated and wild sunflowers, in candidate genes involved in tolerance to abiotic stress and in development. Gene selection was performed mainly in the model species Arabidopsis thaliana.

Genetic analysis of (1) oil content under water stress, and (2) physiological and developmental responses to moderate and severe water stress in the field and under controlled conditions using association genetics and QTL mapping approaches . This work aims to integrate genetic modeling into the Sunflo agronomic model (Casadebaig et al. 2011)

A systems biology approach to transcriptomic regulatory pathways of hormonal signals and abiotic stresses. By using either variations in hormones and duration, or the genetic variability of the response to hormones, we reconstruct gene regulatory networks in relation to limiting factors in sunflower cultivation such as the regulation of perspiration (mediated by ABA), leaf senescence or flowering time. These models are then compared to observations of responses to abiotic stresses and to natural variability and in the cultivated pool to identify how the environment interacts with gene networks at short (responses of an organism) and long (evolution and domestication) scales. .

Resistance to Orobanche cumana, the sunflower broomrape (Coordinator: Stéphane Muños)

Mapping and cloning of total and quantitative resistance to the most virulent races of Orobanche.

Physiological characterization of the interaction between sunflower and O. cumana

 

Measurement of gene expression during the early stages of the interaction

New statistical strategies for Association Genetics and Genomics selection (coordinator: Brigitte Mangin)

New models for Association genetics and construction of confidence regions of detected QTLs.

Improving the accuracy of predictions of the value of complex characters in plant species of agronomic interest.

Resource development and maintenance

Public

Resources available under contract or in collaboration:

  • population of TILLING

  • Bioinformatics Resources (with strong support from the LIPM Bioinformatics team ):

    • Resequencing of the genome of INRA sunflower lines (including the INRA XRQ reference line)

    • Gene atlas (mRNA and sRNA transcriptome) of the INRA XRQ reference line

    • Database of SNP or indel polymorphisms developed from candidate genes and identified on cultivated and wild sunflowers

    • P.halstedii transcriptome (four races)

Collaborations

Sunflower genome sequencing project: University of British Columbia (Canada, L. Rieseberg): (Kane et al., 2010, 2011)

ACT: http://wwwagir.toulouse.inra.fr/agir/ group “Vasco” team: Ph.Debaeke

BIA: http://carlit.toulouse.inra.fr/wikiz/index.php/ Home group SaAB (Statistics and Algorithmics for Biology) B. Mangin and M. Vignes

URGV: S. Balzergues: http://www-urgv.versailles.inra.fr/

CNRGV: H.Berges, http://cnrgv.toulouse.inra.fr/en

Terres inovia: http://www.terresinovia.fr/ E.Mestries.

INRA: F. Delmotte (Bordeaux), P.Mestre (Colmar)

Financial ressources

SUNRISE: Project selected within the framework of "Investments for the Future" (wave 2011), bringing together 10 public research teams, CETIOM and 6 industrial partners involved in the genetic improvement of sunflowers. Project coordination by the LIPM team. Global funding: € 7 million, of which € 2.6 million for the LIPM. Duration: 7.4 years (2012-2019)

OLEOSOL (labeled by AGRIMIP Innovation http://www.agrimipinnovation.com/en/ ) funded by the Midi-Pyrénées Region, the FEDER and the FUI. Partnership with BIOGEMMA, SYNGENTA Seeds, SOLTIS, R2N: “Tools and resources towards sunflower genetic improvement for oil yield production. »€ 1,296k

AIP INRA Bioressources 2009 and 2010, INRA GAP Department, 2010: Contribution to sunflower genome sequencing. € 248K

[2009-2011]: PROMOSOL: Genetic analysis of tolerance to Phoma macdonaldii, 30K €

[2013-2014]: Sunflower * Orobanche interaction,

[2013-2015]: HELIADIV: sequence polymorphism analysis for at least 200 candidate genes involved in the response to abiotic and biotic stresses, in a large collection of lines (1000 accessions) and within various genetic pools, the whole of these genetic resources forming part of the collection of genetic resources managed by the team.

CETIOM: Phenotypic assessment of quantitative resistance to Downy Mildew Plasmopara halstedii. 17K €

 

scientific output

 

Adiredjo, AL, Casadebaig, P., Langlade, N., Lamaze, T., Grieu, P., 2018. Genetic Analysis of the Transpiration Control in Sunflower (Helianthus Annuus L) Subjected to Drought. VEGETOS: An International Journal of Plant Research 2018. https://doi.org/10.4172/2229-4473.1000368

Adiredjo, AL, Navaud, O., Muños, S., Langlade, NB, Lamaze, T., Grieu, P., 2014. Genetic Control of Water Use Efficiency and Leaf Carbon Isotope Discrimination in Sunflower (Helianthus annuus L.) Subjected to Two Drought Scenarios. PLoS ONE 9, e101218.

Andrianasolo, FN, Casadebaig, P., Langlade, N., Debaeke, P., Maury, P., 2016. Effects of plant growth stage and leaf aging on the response of transpiration and photosynthesis to water deficit in sunflower. Functional Plant Biology 43, 797–805.

Badouin, H., Gouzy, J., Grassa, CJ, Murat, F., Staton, SE, Cottret, L., Lelandais-Brière, C., Owens, GL, Carrère, S., Mayjonade, B., Legrand , L., Gill, N., Kane, NC, Bowers, JE, Hubner, S., Bellec, A., Bérard, A., Bergès, H., Blanchet, N., Boniface, M.-C., Brunel, D., Catrice, O., Chaidir, N., Claudel, C., Donnadieu, C., Faraut, T., Fievet, G., Helmstetter, N., King, M., Knapp, SJ, Lai , Z., Le Paslier, M.-C., Lippi, Y., Lorenzon, L., Mandel, JR, Marage, G., Marchand, G., Marquand, E., Bret-Mestries, E., Morien , E., Nambeesan, S., Nguyen, T., Pegot-Espagnet, P., Pouilly, N., Raftis, F., Sallet, E., Schiex, T., Thomas, J., Vandecasteele, C. , Varès, D., Vear, F., Vautrin, S., Crespi, M., Mangin, B., Burke, JM, Salse, J., Muños, S., Vincourt, P., Rieseberg, LH, Langlade , NB, 2017. The sunflower genome provides insights into oil metabolism, flowering and Asterid evolution. Nature 546, 148–152. https://doi.org/10.1038/nature22380

Balliau, T., Duruflé, H., Blanchet, N., Blein-Nicolas, M., Langlade, NB, Zivy, M., 2021. Proteomic data from leaves of twenty-four sunflower genotypes under water deficit. OCL 28, 12. https://doi.org/10.1051/ocl/2020074

Berton, T., Bernillon, S., Fernandez, O., Duruflé, H., Flandin, A., Cassan, C., Jacob, D., Langlade, NB, Gibon, Y., Moing, A., 2021 Leaf metabolomic data of eight sunflower lines and their sixteen hybrids under water deficit. OCL 28, 42. https://doi.org/10.1051/ocl/2021029

Blanchet, N., Casadebaig, P., Debaeke, P., Duruflé, H., Gody, L., Gosseau, F., Langlade, NB, Maury, P., 2018. Data describing the eco-physiological responses of twenty -four sunflower genotypes to water deficit. Data Brief 21, 1296–1301. https://doi.org/10.1016/j.dib.2018.10.045

Bonnafous, F., Fievet, G., Blanchet, N., Boniface, M.-C., Carrère, S., Gouzy, J., Legrand, L., Marage, G., Bret-Mestries, E., Munos, S., Pouilly, N., Vincourt, P., Langlade, N., Mangin, B., 2018. Comparison of GWAS models to identify non-additive genetic control of flowering time in sunflower hybrids. Theor. Appl. Broom. 131, 319–332. https://doi.org/10.1007/s00122-017-3003-4

Bordat, A., Marchand, G., Langlade, NB, Pouilly, N., Muños, S., Dechamp-Guillaume, G., Vincourt, P., Bret-Mestries, E., 2017. Different genetic architectures underlie crop responses to the same pathogen: the {Helianthus annuus * Phoma macdonaldii} interaction case for black stem disease and premature ripening. BMC Plant Biology 17, 167. https://doi.org/10.1186/s12870-017-1116-1

Debaeke, P., Casadebaig, P., Flenet, F., Langlade, N., 2017. Sunflower crop and climate change: vulnerability, adaptation, and mitigation potential from case-studies in Europe. OCL 24, D102. https://doi.org/10.1051/ocl/2016052

Debaeke, P., Casadebaig, P., Langlade, NB, 2021. New challenges for sunflower ideotyping in changing environments and more ecological cropping systems. OCL 28, 29. https://doi.org/10.1051/ocl/2021016

Fernandez, O., Urrutia, M., Bernillon, S., Giauffret, C., Tardieu, F., Le Gouis, J., Langlade, N., Charcosset, A., Moing, A., Gibon, Y. , 2016. Fortune telling: metabolic markers of plant performance. Metabolomics 12, 158.

Fernandez, O., Urrutia, M., Berton, T., Bernillon, S., Deborde, C., Jacob, D., Maucourt, M., Maury, P., Duruflé, H., Gibon, Y., Langlade, NB, Moing, A., 2019. Metabolomic characterization of sunflower leaf allows discriminating genotype groups or stress levels with a minimal set of metabolic markers. Metabolomics 15, 56. https://doi.org/10.1007/s11306-019-1515-4

Gascuel, Q., Bordat, A., Sallet, E., Pouilly, N., Carrere, S., Roux, F., Vincourt, P., Godiard, L., 2016a. Effector Polymorphisms of the Sunflower Downy Mildew Pathogen Plasmopara halstedii and Their Use to Identify Pathotypes from Field Isolates. PLoS ONE 11, e0148513. https://doi.org/10.1371/journal.pone.0148513

Gascuel, Q., Buendia, L., Pecrix, Y., Blanchet, N., Muños, S., Vear, F., Godiard, L., 2016b. RXLR and CRN effectors from the sunflower downy mildew pathogen Plasmopara halstedii induce hypersensitive-like responses in resistant sunflower lines. Forehead. Plant Sci. 7. https://doi.org/10.3389/fpls.2016.01887

Gody, L., Duruflé, H., Blanchet, N., Carré, C., Legrand, L., Mayjonade, B., Muños, S., Pomiès, L., Givry, S. de, Langlade, NB, Mangin, B., 2020a. Transcriptomic data of leaves from eight sunflower lines and their sixteen hybrids under water deficit. OCL 27, 48. https://doi.org/10.1051/ocl/2020044

Gosseau, F., Blanchet, N., Varès, D., Burger, P., Campergue, D., Colombet, C., Gody, L., Liévin, J.-F., Mangin, B., Tison, G., Vincourt, P., Casadebaig, P., Langlade, N., 2019. Heliaphen, an Outdoor High-Throughput Phenotyping Platform for Genetic Studies and Crop Modeling. Forehead. Plant Sci. 9. https://doi.org/10.3389/fpls.2018.01908

Hübner, S., Bercovich, N., Todesco, M., Mandel, JR, Odenheimer, J., Ziegler, E., Lee, JS, Baute, GJ, Owens, GL, Grassa, CJ, Ebert, DP, Ostevik , KL, Moyers, BT, Yakimowski, S., Masalia, RR, Gao, L., Ćalić, I., Bowers, JE, Kane, NC, Swanevelder, DZH, Kubach, T., Muños, S., Langlade, NB, Burke, JM, Rieseberg, LH, 2019. Sunflower pan-genome analysis shows that hybridization altered gene content and disease resistance. Nature Plants 5, 54–62. https://doi.org/10.1038/s41477-018-0329-0

Layat, E., Leymarie, J., El-Maarouf-Bouteau, H., Caius, J., Langlade, N., Bailly, C., 2014. Translatome profiling in dormant and nondormant sunflower (Helianthus annuus) seeds highlights post - transcriptional regulation of germination. New Phytologist 204, 864–872.

Leroux, D., Rahmani, A., Jasson, S., Ventelon, M., Louis, F., Moreau, L., Mangin, B., 2014. Clusthaplo: a plug-in for MCQTL to enhance QTL detection using ancestral alleles in multi-cross design. Theoretical and applied genetics 127, 921–933.

Louarn, J., Boniface, M.-C., Pouilly, N., Velasco, L., Pérez-Vich, B., Vincourt, P., Muños, S., 2016. Sunflower Resistance to Broomrape (Orobanche cumana) Is Controlled by Specific QTLs for Different Parasitism Stages. Forehead. Plant Sci. 7. https://doi.org/10.3389/fpls.2016.00590

Luoni, SAB, Cenci, A., Moschen, S., Nicosia, S., Radonic, LM, Garcia, JS y, Langlade, NB, Vile, D., Rovere, CV, Fernandez, P., 2021. Genome- Wide Analysis of NAC Transcription Factors in Sunflower (Helianthus Annuus), Their Comparative Phylogenetic Analysis and Association With Leaf Senescence. BMC Plant Biology. https://doi.org/10.21203/rs.3.rs-860249/v1

Mangin, B., Bonnafous, F., Blanchet, N., Boniface, M.-C., Bret-Mestries, E., Carrère, S., Cottret, L., Legrand, L., Marage, G., Pegot-Espagnet, P., Munos, S., Pouilly, N., Vear, F., Vincourt, P., Langlade, NB, 2017a. Genomic Prediction of Sunflower Hybrids Oil Content. Forehead. Plant Sci. 8. https://doi.org/10.3389/fpls.2017.01633

Mangin, B., Casadebaig, P., Cadic, E., Blanchet, N., Boniface, M.-C., Carrère, S., Gouzy, J., Legrand, L., Mayjonade, B., Pouilly, N., André, T., Coque, M., Piquemal, J., Laporte, M., Vincourt, P., Muños, S., Langlade, NB, 2017b. Genetic control of plasticity of oil yield for combined abiotic stresses using a joint approach of crop modeling and genome-wide association. Plant, Cell & Environment 40, 2276–2291. https://doi.org/10.1111/pce.12961

Mangin, B., Casadebaig, P., Cadic, E., Blanchet, N., Boniface, M.-C., Carrère, S., Gouzy, J., Legrand, L., Mayjonade, B., Pouilly, N., André, T., Coque, M., Piquemal, J., Laporte, M., Vincourt, P., Muños, S., Langlade, NB, 2017c. Genetic control of oil yield plasticity to combined abiotic stresses using a joint approach of crop modeling and genome-wide association. Plant, Cell & Environment 40, 2276–2291.

Mangin, B., Pouilly, N., Boniface, M.-C., Langlade, NB, Vincourt, P., Vear, F., Muños, S., 2017d. Molecular diversity of sunflower populations maintained as genetic resources is affected by multiplication processes and breeding for major traits. Theor Appl Genet 130, 1099–1112. https://doi.org/10.1007/s00122-017-2872-x

Mangin, B., Rincent, R., Rabier, C.-E., Moreau, L., Goudemand-Dugue, E., 2019. Training set optimization of genomic prediction by means of EthAcc. PloS one 14, e0205629.

Mangin, B., Sandron, F., Henry, K., Devaux, B., Willems, G., Devaux, P., Goudemand, E., 2015. Breeding patterns and cultivated beets origins by genetic diversity and linkage disequilibrium analyzes . Theor. Appl. Broom. 128, 2255–2271. https://doi.org/10.1007/s00122-015-2582-1

Marchand, G., Huynh‐Thu, VA, Kane, NC, Arribat, S., Varès, D., Rengel, D., Balzergue, S., Rieseberg, LH, Vincourt, P., Geurts, P., Vignes , M., Langlade, NB, 2014. Bridging physiological and evolutionary time-scales in a gene regulatory network. New Phytologist 203, 685–696. https://doi.org/10.1111/nph.12818

Mayjonade, B., Gouzy, J., Donnadieu, C., Pouilly, N., Marande, W., Callot, C., Langlade, N., Muños, S., 2016. Extraction of high-molecular-weight genomics DNA for long-read sequencing of single molecules. BioTechniques 61, 203–205. https://doi.org/10.2144/000114460

Meimoun, P., Mordret, E., Langlade, NB, Balzergue, S., Arribat, S., Bailly, C., El-Maarouf-Bouteau, H., 2014. Is Gene Transcription Involved in Seed Dry After-Ripening ? PLoS ONE 9, e86442. https://doi.org/10.1371/journal.pone.0086442

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, 2019. Exploring gene networks in two sunflower lines with contrasting leaf senescence phenotype using a system biology approach. BMC Plant Biology 19, 446. https://doi.org/10.1186/s12870-019-2021-6

Pecrix, Y., Buendia, L., Penouilh-Suzette, C., Maréchaux, M., Legrand, L., Bouchez, O., Rengel, D., Gouzy, J., Cottret, L., Vear, F ., Godiard, L., 2019. Sunflower resistance to multiple downy mildew pathotypes revealed by recognition of conserved effectors of the oomycete Plasmopara halstedii. Plant J. 97, 730–748. https://doi.org/10.1111/tpj.14157

Penouilh-Suzette, C., Pomiès, L., Duruflé, H., Blanchet, N., Bonnafous, F., Dinis, R., Brouard, C., Gody, L., Grassa, C., Heudelot, X ., Laporte, M., Larroque, M., Marage, G., Mayjonade, B., Mangin, B., Givry, S. de, Langlade, NB, 2020. RNA expression dataset of 384 sunflower hybrids in field condition. OCL 27, 36. https://doi.org/10.1051/ocl/2020027

Rabier, C.-E., Barre, P., Asp, T., Charmet, G., Mangin, B., 2016. On the Accuracy of Genomic Selection. PLOS ONE 11, e0156086. https://doi.org/10.1371/journal.pone.0156086

Rabier, C.-E., Mangin, B., Grusea, S., 2019. On the accuracy in high-dimensional linear models and its application to genomic selection. Scandinavian Journal of Statistics 46, 289–313. https://doi.org/10.1111/sjos.12352

Sahari, K., Nicolas, P., Stéphane, M., Aurélie, B., Fayçal, BJ, Patrick, V., Dominique, B., 2016. Genetic Diversity and Core Collection Constitution for Subsequent Creation of New Sunflower Varieties in Tunisia. Helia. https://doi.org/10.1515/helia-2016-0002

Saux, M., Ponnaiah, M., Langlade, N., Zanchetta, C., Balliau, T., El‐Maarouf‐Bouteau, H., Bailly, C., 2020. A multiscale approach reveals regulatory players of water stress responses in seeds during germination. Plant, Cell & Environment 43, 1300–1313. https://doi.org/10.1111/pce.13731

Terzić, S., Boniface, M.-C., Marek, L., Alvarez, D., Baumann, K., Gavrilova, V., Joita-Pacureanu, M., Sujatha, M., Valkova, D., Velasco, L., Hulke, BS, Jocić, S., Langlade, N., Muños, S., Rieseberg, L., Seiler, G., Vear, F., 2020. Gene banks for wild and cultivated sunflower genetic resources . OCL 27, 9. https://doi.org/10.1051/ocl/2020004

Xia, Q., Saux, M., Ponnaiah, M., Gilard, F., Perreau, F., Huguet, S., Balzergue, S., Langlade, N., Bailly, C., Meimoun, P., Corbineau, F., El-Maarouf-Bouteau, H., 2018. One Way to Achieve Germination: Common Molecular Mechanism Induced by Ethylene and After-Ripening in Sunflower Seeds. International Journal of Molecular Sciences 19, 2464. https://doi.org/10.3390/ijms19082464