Plant Molecular Biology

, Volume 94, Issue 4–5, pp 549–564 | Cite as

Integration of transcriptomic and metabolic data reveals hub transcription factors involved in drought stress response in sunflower (Helianthus annuus L.)

  • Sebastián Moschen
  • Julio A. Di Rienzo
  • Janet Higgins
  • Takayuki Tohge
  • Mutsumi Watanabe
  • Sergio González
  • Máximo Rivarola
  • Francisco García-García
  • Joaquin Dopazo
  • H. Esteban Hopp
  • Rainer Hoefgen
  • Alisdair R. Fernie
  • Norma Paniego
  • Paula Fernández
  • Ruth A. Heinz


Key message

By integration of transcriptional and metabolic profiles we identified pathways and hubs transcription factors regulated during drought conditions in sunflower, useful for applications in molecular and/or biotechnological breeding.


Drought is one of the most important environmental stresses that effects crop productivity in many agricultural regions. Sunflower is tolerant to drought conditions but the mechanisms involved in this tolerance remain unclear at the molecular level. The aim of this study was to characterize and integrate transcriptional and metabolic pathways related to drought stress in sunflower plants, by using a system biology approach. Our results showed a delay in plant senescence with an increase in the expression level of photosynthesis related genes as well as higher levels of sugars, osmoprotectant amino acids and ionic nutrients under drought conditions. In addition, we identified transcription factors that were upregulated during drought conditions and that may act as hubs in the transcriptional network. Many of these transcription factors belong to families implicated in the drought response in model species. The integration of transcriptomic and metabolomic data in this study, together with physiological measurements, has improved our understanding of the biological responses during droughts and contributes to elucidate the molecular mechanisms involved under this environmental condition. These findings will provide useful biotechnological tools to improve stress tolerance while maintaining crop yield under restricted water availability.


Sunflower Helianthus annuus LDrought Transcriptomics Metabolomics Data integration 



We thank Guillermo Dosio and Luis Aguirrezabal for scientific advice and Luis Mendez, Carlos Antonelli, Silvio Giuliano, for support in field experiments at INTA Balcarce and Claudio Villan for technical support. Dr. Julia Sabio y Garcia is gratefully acknowledged for critical reading of this manuscript. This study was funded by INTA PE 1131022, 1131043; ANPCyT Préstamo BID PICT 2012 0390, PICT 2011 1365, PICT 2014 0701 and PIP CONICET 11220120100262CO; Agencia Española de Cooperación Internacional y Desarrollo (D/031348/10;A1/041041/11); Marie Curie IRSES Project DEANN (PIRSES-GA-2013-612583).

Author contributions

SM, HEH, NP, PF, RAH conceived and designed the experiments. JADR performed statistical analysis. JH, SM analyzed data integration by WGCNA. SM, TT, MW, RH, ARF designed and performed metabolic analysis. SG, MR carry out bioinformatics analysis of microarrays. FGG, JD execute functional analysis of data. All authors contributed to the work by the interpretation, discussion of the data and critically revised the manuscript. All authors read and approved the final manuscript.

Supplementary material

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Fig. S1: Microarray validation by qPCR (PNG 23 KB)
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Fig. S2: Enriched GO categories under drought condition. a Biological Process downregulated at T1; b Biological Process downregulated at T2; c Biological Process downregulated at T3; d Biological Process upregulated at T1; e Biological Process upregulated at T2; f Biological Process upregulated at T3 (PNG 1172 KB)
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Supplementary material 3 (PNG 1311 KB)
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Supplementary material 4 (PNG 1439 KB)
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Supplementary material 5 (PNG 437 KB)
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Supplementary material 6 (PNG 678 KB)
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Supplementary material 7 (PNG 504 KB)
11103_2017_625_MOESM8_ESM.pdf (81 kb)
Fig. S3: WGCNA gene module correlated with metabolite levels (PDF 80 KB)
11103_2017_625_MOESM9_ESM.xlsx (38 kb)
Table S1: List of genes up- and downregulated (41 and 101 genes respectively) at the three sampling points showed in the Venn diagram (Fig. 3) (XLSX 38 KB)
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Table S2: Top list of up- and downregulated genes with a fold change higher or lower than 4 in at least one of the evaluated conditions (XLSX 167 KB)
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Table S3: Transcription factors differentially expressed under drought conditions with a fold change higher or lower than 4 in at least one of the three sampling times (XLSX 12 KB)
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Table S4: Number of genes per module and distribution of the 93 upregulated and 95 downregulated TFs under drought in these modules (XLSX 42 KB)
11103_2017_625_MOESM13_ESM.xlsx (16 kb)
Table S5: Expression profiles of TF families associated to leaf senescence under natural and drought condition (XLSX 16 KB)


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Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  • Sebastián Moschen
    • 1
    • 2
  • Julio A. Di Rienzo
    • 3
  • Janet Higgins
    • 4
  • Takayuki Tohge
    • 5
  • Mutsumi Watanabe
    • 5
  • Sergio González
    • 1
    • 2
  • Máximo Rivarola
    • 1
    • 2
  • Francisco García-García
    • 6
  • Joaquin Dopazo
    • 6
  • H. Esteban Hopp
    • 1
    • 7
  • Rainer Hoefgen
    • 5
  • Alisdair R. Fernie
    • 5
  • Norma Paniego
    • 1
    • 2
  • Paula Fernández
    • 1
    • 2
    • 8
  • Ruth A. Heinz
    • 1
    • 2
    • 7
  1. 1.Instituto de Biotecnología, Centro de Investigaciones en Ciencias Agronómicas y VeterinariasInstituto Nacional de Tecnología AgropecuariaHurlinghamArgentina
  2. 2.Consejo Nacional de Investigaciones Científicas y TécnicasCiudad Autónoma de Buenos AiresArgentina
  3. 3.Facultad de Ciencias AgropecuariasUniversidad Nacional de CórdobaCórdobaArgentina
  4. 4.Earlham InstituteNorwich Research ParkNorwichUK
  5. 5.Max-Planck-Institut für Molekulare PflanzenphysiologiePotsdam-GolmGermany
  6. 6.Computational Genomics DepartmentCentro de Investigación Príncipe Felipe. Functional Genomics Node (INB-ELIXIR-es). Bioinformatics in Rare Diseases (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER)ValenciaSpain
  7. 7.Facultad de Ciencias Exactas y NaturalesUniversidad de Buenos AiresCiudad Autónoma de Buenos AiresArgentina
  8. 8.Escuela de Ciencia y TecnologíaUniversidad Nacional de San MartínSan MartínArgentina

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