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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
Article

Abstract

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.

Abstract

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.

Keywords

Sunflower Helianthus annuus LDrought Transcriptomics Metabolomics Data integration 

Notes

Acknowledgements

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)

References

  1. Aguirrezábal L, Orioli G, Hernández LF, Pereyra V, Miravé J (1996) Girasol: Aspectos fisiológicos que determinan el rendimiento. Balcarce, ArgentinaGoogle Scholar
  2. Allison LA (2000) The role of sigma factors in plastid transcription. Biochimie 82:537–548CrossRefPubMedGoogle Scholar
  3. Alonso R, Salavert F, Garcia-Garcia F, Carbonell-Caballero J, Bleda M, Garcia-Alonso L, Sanchis-Juan A, Perez-Gil D, Marin-Garcia P, Sanchez R, Cubuk C, Hidalgo MR, Amadoz A, Hernansaiz-Ballesteros RD, Alemán A, Tarraga J, Montaner D, Medina I, Dopazo J (2015) Babelomics 5.0: functional interpretation for new generations of genomic data. Nucleic Acids Res 43:W117–W121. doi: 10.1093/nar/gkv384 CrossRefPubMedPubMedCentralGoogle Scholar
  4. Alpert P, Simms EL (2002) The relative advantages of plasticity and fixity in different environments: when is it good for a plant to adjust? Evol Ecol 16:285–297. doi: 10.1023/A:1019684612767 CrossRefGoogle Scholar
  5. Amtmann A, Blatt MR (2009) Regulation of macronutrient transport. New Phytol 181:35–52. doi: 10.1111/j.1469-8137.2008.02666.x CrossRefPubMedGoogle Scholar
  6. Andrade FH, Gardiol JM (1994) Sequía y producción de los cultivos de maíz, girasol y soja. Boletín técnico 132. EEA INTA BalcarceGoogle Scholar
  7. Andrianasolo F, 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. Funct Plant Biol 43:797. doi: 10.1071/FP15235 CrossRefGoogle Scholar
  8. Ariel FD, Manavella P a, Dezar C a, Chan RL (2007) The true story of the HD-Zip family. Trends Plant Sci 12:419–426. doi: 10.1016/j.tplants.2007.08.003 CrossRefPubMedGoogle Scholar
  9. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25:25–29. doi: 10.1038/75556 CrossRefPubMedPubMedCentralGoogle Scholar
  10. Ashraf M, Foolad MR (2007) Roles of glycine betaine and proline in improving plant abiotic stress resistance. Environ Exp Bot 59:206–216. doi: 10.1016/j.envexpbot.2005.12.006 CrossRefGoogle Scholar
  11. Ben Rejeb K, Lefebvre-De Vos D, Le Disquet I, Leprince A-S, Bordenave M, Maldiney R, Jdey A, Abdelly C, Savouré A (2015) Hydrogen peroxide produced by NADPH oxidases increases proline accumulation during salt or mannitol stress in Arabidopsis thaliana. New Phytol 208:1138–1148. doi: 10.1111/nph.13550 CrossRefPubMedGoogle Scholar
  12. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B 57:289–300. doi: 10.2307/2346101 Google Scholar
  13. Borsani O, Díaz P, Monza J (1999) Proline is involved in water stress responses of lotus corniculatus nitrogen fixing and nitrate fed plants. J Plant Physiol 155:269–273. doi: 10.1016/S0176-1617(99)80018-2 CrossRefGoogle Scholar
  14. Cabello JV, Arce AL, Chan RL (2012) The homologous HD-Zip I transcription factors HaHB1 and AtHB13 confer cold tolerance via the induction of pathogenesis-related and glucanase proteins. Plant J 69:141–153. doi: 10.1111/j.1365-313X.2011.04778.x CrossRefPubMedGoogle Scholar
  15. Cabello JV, Giacomelli JI, Piattoni CV, Iglesias AA, Chan RL (2016) The sunflower transcription factor HaHB11 improves yield, biomass and tolerance to flooding in transgenic Arabidopsis plants. J Biotechnol 222:73–83. doi: 10.1016/j.jbiotec.2016.02.015 CrossRefPubMedGoogle Scholar
  16. Cattivelli L, Rizza F, Badeck F-W, Mazzucotelli E, Mastrangelo AM, Francia E, Marè C, Tondelli A, Stanca AM (2008) Drought tolerance improvement in crop plants: an integrated view from breeding to genomics. Field Crop Res 105:1–14. doi: 10.1016/j.fcr.2007.07.004 CrossRefGoogle Scholar
  17. Cellier F, Conejero G, Breitler JC, Casse F (1998) Molecular and physiological responses to water deficit in drought-tolerant and drought-sensitive lines of sunflower. Accumulation of dehydrin transcripts correlates with tolerance. Plant Physiol 116:319–328CrossRefPubMedPubMedCentralGoogle Scholar
  18. Chaves MM, Flexas J, Pinheiro C (2009) Photosynthesis under drought and salt stress: regulation mechanisms from whole plant to cell. Ann Bot 103:551–560. doi: 10.1093/aob/mcn125 CrossRefPubMedGoogle Scholar
  19. Chi W, He B, Mao J, Jiang J, Zhang L (2015) Plastid sigma factors: their individual functions and regulation in transcription. Biochim Biophys Acta 1847:770–778CrossRefPubMedGoogle Scholar
  20. Chimenti CA, Marcantonio M, Hall AJ (2006) Divergent selection for osmotic adjustment results in improved drought tolerance in maize (Zea mays L.) in both early growth and flowering phases. F Crop Res 95:305–315. doi: 10.1016/j.fcr.2005.04.003 CrossRefGoogle Scholar
  21. Ciríaco da Silva E, Mansur Custódio Nogueira RJ, Almeida da Silva M, Bandeira de Albuquerque M (2011) Drought stress and plant nutrition. Plant Stress 5:32–41Google Scholar
  22. Connor DJ, Jones TR (1985) Response of sunflower to strategies of irrigation II. Morphological and physiological responses to water stress. Field Crop Res 12:91–103CrossRefGoogle Scholar
  23. Connor DJ, Palta JA, Jones TR (1985) Response of sunflower to strategies of irrigation. III. Crop photosynthesis and transpiration. Field Crop Res 12:281–283CrossRefGoogle Scholar
  24. Corti Monzón G, Pinedo M, Di Rienzo J, Novo-Uzal E, Pomar F, Lamattina L, de la Canal L (2014) Nitric oxide is required for determining root architecture and lignin composition in sunflower. Supporting evidence from microarray analyses. Nitric Oxide 39:20–28. doi: 10.1016/j.niox.2014.04.004 CrossRefPubMedGoogle Scholar
  25. Couso LL, Fernández RJ (2012) Phenotypic plasticity as an index of drought tolerance in three Patagonian steppe grasses. Ann Bot 110:849–857. doi: 10.1093/aob/mcs147 CrossRefPubMedPubMedCentralGoogle Scholar
  26. Cramer GR, Ergül A, Grimplet J, Tillett RL, Tattersall EAR, Bohlman MC, Vincent D, Sonderegger J, Evans J, Osborne C, Quilici D, Schlauch KA, Schooley DA, Cushman JC (2007) Water and salinity stress in grapevines: early and late changes in transcript and metabolite profiles. Funct Integr Genom 7:111–134. doi: 10.1007/s10142-006-0039-y CrossRefGoogle Scholar
  27. DaMatta FM, Loos RA, Silva EA, Loureiro ME, Ducatti C (2002) Effects of soil water deficit and nitrogen nutrition on water relations and photosynthesis of pot-grown Coffea canephora Pierre. Trees 16:555–558. doi: 10.1007/s00468-002-0205-3 CrossRefGoogle Scholar
  28. De Witt T, Sih A, Wilson D (1998) Costs and limits of phenotypic plasticity. Trends Ecol Evol 13:77–81CrossRefGoogle Scholar
  29. Dezar CA, Fedrigo GV, Chan RL (2005a) The promoter of the sunflower HD-Zip protein gene Hahb4 directs tissue-specific expression and is inducible by water stress, high salt concentrations and ABA. Plant Sci 169:447–456CrossRefGoogle Scholar
  30. Dezar CA, Gago GM, Gonzalez DH, Chan RL (2005b) Hahb-4, a sunflower homeobox-leucine zipper gene, is a developmental regulator and confers drought tolerance to Arabidopsis thaliana plants. Transgenic Res 14:429–440CrossRefPubMedGoogle Scholar
  31. Díaz P, Betti M, García-Calderón M, Pérez-Delgado CM, Signorelli S, Borsani O, Márquez AJ, Monza J (2014) Amino acids and drought stress in lotus: use of transcriptomics and plastidic glutamine synthetase mutants for new insights in proline metabolism. In: Anjum NA, Gill SS, Gill R (eds) Plant adaptation to environmental change: significance of amino acids and their derivatives. CABI International, BostonGoogle Scholar
  32. Dubois M, Gilles K, Hamilton J, Rebus P, Smith F (1956) Colorimetric method for the determination of sugars and related substances. Anal Chem 28:350–356CrossRefGoogle Scholar
  33. Dumas A (1826) Annales de chimie 33:342Google Scholar
  34. El-Maarouf-Bouteau H, Sajjad Y, Bazin J, Langlade N, Cristescu SM, Balzergue S, Baudouin E, Bailly C (2015) Reactive oxygen species, abscisic acid and ethylene interact to regulate sunflower seed germination. Plant Cell Environ 38:364–374. doi: 10.1111/pce.12371 CrossRefPubMedGoogle Scholar
  35. Farooq M, Hussain M, Wahid A, Siddique KHM (2012) Drought Stress in plants: an overview. In: Aroca R (ed) Plant responses to drought stress—from morphological to molecular features. Springer, Berlin, pp 1–33Google Scholar
  36. Fernandez P, Rienzo J Di, Fernandez L, Hopp HE, Paniego N, Heinz RA (2008) Transcriptomic identification of candidate genes involved in sunflower responses to chilling and salt stresses based on cDNA microarray analysis. BMC Plant Biol 8:1–18. doi: 10.1186/1471-2229-8-11 CrossRefGoogle Scholar
  37. Fernandez P, Di Rienzo JA, Moschen S, Dosio GA, Aguirrezabal LA, Hopp HE, Paniego N, Heinz RA (2011) Comparison of predictive methods and biological validation for qPCR reference genes in sunflower leaf senescence transcript analysis. Plant Cell Rep 30:63–74. doi: 10.1007/s00299-010-0944-3 CrossRefPubMedGoogle Scholar
  38. Fernandez P, Soria M, Blesa D, DiRienzo J, Moschen S, Rivarola M, Clavijo BJ, Gonzalez S, Peluffo L, Príncipi D, Dosio G, Aguirrezabal L, García-García F, Conesa A, Hopp E, Dopazo J, Heinz RA, Paniego N (2012) Development, characterization and experimental validation of a cultivated sunflower (Helianthus annuus L.) gene expression oligonucleotide microarray. PLoS ONE 7:1–11. doi: 10.1371/journal.pone.0045899 CrossRefGoogle Scholar
  39. Gago GM, Almoguera C, Jordano J, Gonzalez DH, Chan RL (2002) Hahb-4, a homeobox-leucine zipper gene potentially involved in abscisic acid-dependent responses to water stress in sunflower. Plant Cell Environ 25:633–640. doi: 10.1046/j.1365-3040.2002.00853.x CrossRefGoogle Scholar
  40. Gershenzon J, Dudareva N (2007) The function of terpene natural products in the natural world. Nat Chem Biol 3:408–414. doi: 10.1038/nchembio.2007.5 CrossRefPubMedGoogle Scholar
  41. Giordani T, Natali L, D’Ercole A, Pugliesi C, Fambrini M, Vernieri P, Vitagliano C, Cavallini A (1999) Expression of a dehydrin gene during embryo development and drought stress in ABA-deficient mutants of sunflower (Helianthus annuus L.). Plant Mol Biol 39:739–748CrossRefPubMedGoogle Scholar
  42. Hanson AD, Scott NA (1980) Betaine synthesis from radioactive precursors in attached, water-stressed barley leaves. Plant Physiol 66:342–348CrossRefPubMedPubMedCentralGoogle Scholar
  43. Huang L, Ye Z, Bell RW, Dell B (2005) Boron nutrition and chilling tolerance of warm climate crop species. Ann Bot 96:755–767CrossRefPubMedPubMedCentralGoogle Scholar
  44. Inskeep WP, Bloom PR (1985) Extinction coefficients of chlorophyll a and b in N, N-dimethylformamide and 80% acetone. Plant Physiol 77:483–485CrossRefPubMedPubMedCentralGoogle Scholar
  45. Jahantigh O, Najafi F, Badi HN, Khavari-Nejad RA, Sanjarian F (2016) Changes in antioxidant enzymes activities and proline, total phenol and anthocyanine contents in Hyssopus officinalis L. plants under salt stress. Acta Biol Hung 67:195–204. doi: 10.1556/018.67.2016.2.7 CrossRefPubMedGoogle Scholar
  46. Kiani SP, Talia P, Maury P, Grieu P, Heinz R, Perrault a., Nishinakamasu V, Hopp E, Gentzbittel L, Paniego N, Sarrafi a (2007a) Genetic analysis of plant water status and osmotic adjustment in recombinant inbred lines of sunflower under two water treatments. Plant Sci 172:773–787. doi: 10.1016/j.plantsci.2006.12.007 CrossRefGoogle Scholar
  47. Kiani SP, Grieu P, Maury P, Hewezi T, Gentzbittel L, Sarrafi A, Kiani PS (2007b) Genetic variability for physiological traits under drought conditions and differential expression of water stress-associated genes in sunflower (Helianthus annuus L.). Theor Appl Genet 114:193–207. doi: 10.1007/s00122-006-0419-7 CrossRefGoogle Scholar
  48. Kiniry JR, Blanchet R, Williams JR, Texier V, Jones K, Cabelguenne M (1992) Sunflower simulation using the EPIC and ALMANAC models. Field Crop Res 30:403–423CrossRefGoogle Scholar
  49. Kratsch HA, Wise RR (2000) The ultrastructure of chilling stress. Plant Cell Environ 23:337–350CrossRefGoogle Scholar
  50. Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9:559. doi: 10.1186/1471-2105-9-559 CrossRefPubMedPubMedCentralGoogle Scholar
  51. Lohse M, Nagel A, Herter T, May P, Schroda M, Zrenner R, Tohge T, Fernie AR, Stitt M, Usadel B (2014) Mercator: a fast and simple web server for genome scale functional annotation of plant sequence data. Plant Cell Environ 37:1250–1258. doi: 10.1111/pce.12231 CrossRefPubMedGoogle Scholar
  52. Luedemann A, Strassburg K, Erban A, Kopka J (2008) TagFinder for the quantitative analysis of gas chromatography–mass spectrometry (GC-MS)-based metabolite profiling experiments. Bioinformatics 24:732–737. doi: 10.1093/bioinformatics/btn023 CrossRefPubMedGoogle Scholar
  53. Maas EV, Hoffman GJ (1977) Crop salt tolerance, current assessment. J Irrig Drain Div ASCE 103:115–134Google Scholar
  54. Mahouachi J, Socorro AR, Talon M (2006) Responses of papaya seedlings (Carica papaya L.) to water stress and re-hydration: growth, photosynthesis and mineral nutrient imbalance. Plant Soil 281:137–146. doi: 10.1007/s11104-005-3935-3 CrossRefGoogle Scholar
  55. Manavella PA, Arce AL, Dezar CA, Bitton F, Renou J-PP, Crespi M, Chan RL (2006) Cross-talk between ethylene and drought signalling pathways is mediated by the sunflower Hahb-4 transcription factor. Plant J 48:125–137CrossRefPubMedGoogle Scholar
  56. Manavella PA, Dezar CA, Bonaventure G, Baldwin IT, Chan RL (2008a) HAHB4, a sunflower HD-Zip protein, integrates signals from the jasmonic acid and ethylene pathways during wounding and biotic stress responses. Plant J 56:376–388. doi: 10.1111/j.1365-313X.2008.03604.x CrossRefPubMedGoogle Scholar
  57. Manavella PA, Dezar C, Ariel FD, Drincovich MF, Chan RL (2008b) The sunflower HD-Zip transcription factor HAHB4 is up-regulated in darkness, reducing the transcription of photosynthesis-related genes. J Exp Bot 59:3143–3155CrossRefPubMedGoogle Scholar
  58. Masclaux-Daubresse C, Valadier M-H, Carrayol E, Reisdorf-Cren M, Hirel B (2002) Diurnal changes in the expressionof glutamate dehydrogenase and nitrate reductase are involved in the C/N balance of tobacco source leaves. Plant Cell Environ 25:1451–1462. doi: 10.1046/j.1365-3040.2002.00925.x CrossRefGoogle Scholar
  59. Mir RR, Zaman-Allah M, Sreenivasulu N, Trethowan R, Varshney RK (2012) Integrated genomics, physiology and breeding approaches for improving drought tolerance in crops. Theor Appl Genet 125:625–645. doi: 10.1007/s00122-012-1904-9 CrossRefPubMedPubMedCentralGoogle Scholar
  60. Montaner D, Dopazo J (2010) Multidimensional gene set analysis of genomic data. PLoS ONE 5:e10348. doi: 10.1371/journal.pone.0010348 CrossRefPubMedPubMedCentralGoogle Scholar
  61. Moschen S, Bengoa Luoni S, Paniego NB, Hopp HE, Dosio GAA, Fernandez P, Heinz RA (2014a) Identification of candidate genes associated with leaf senescence in cultivated sunflower (Helianthus annuus L.). PLoS ONE 9:e104379. doi: 10.1371/journal.pone.0104379 CrossRefPubMedPubMedCentralGoogle Scholar
  62. Moschen S, Radonic LM, Ehrenbolger GF, Fernández P, Lía V, Paniego NB, López Bilbao M, Heinz RA, Hopp HE (2014b) Functional genomics and transgenesis applied to sunflower breeding. In: Arribas JI (ed) Sunflowers: growth and development, environmental influences and pests/diseases. Nova Science Publishers, Hauppauge, pp 131–164Google Scholar
  63. Moschen S, Bengoa Luoni S, Di Rienzo J, Caro M, Tohge T, Watanabe M, Hollmann J, González S, Rivarola M, García-García F, Dopazo J, Hopp HE, Hoefgen R, Fernie A, Paniego N, Fernández P, Heinz R (2016a) Integrating transcriptomic and metabolomic analysis to understand natural leaf senescence in sunflower. Plant Biotechnol J 14:719–734. doi: 10.1111/pbi.12422 CrossRefPubMedGoogle Scholar
  64. Moschen S, Higgins J, Di Rienzo JA, Heinz RA, Paniego N, Fernandez P (2016b) Network and biosignature analysis for the integration of transcriptomic and metabolomic data to characterize leaf senescence process in sunflower. BMC Bioinformatics 17:174. doi: 10.1186/s12859-016-1045-2 CrossRefPubMedPubMedCentralGoogle Scholar
  65. Nagashima A, Hanaoka M, Shikanai T, Fujiwara M, Kanamaru K, Takahashi H, Tanaka K (2004) The multiple-stress responsive plastid sigma factor, SIG5, directs activation of the psbD blue light-responsive promoter (BLRP) in Arabidopsis thaliana. Plant Cell Physiol 45:357–368. doi: 10.1093/PCP/PCH050 CrossRefPubMedGoogle Scholar
  66. Nakabayashi R, Mori T, Saito K (2014a) Alternation of flavonoid accumulation under drought stress in Arabidopsis thaliana. Plant Signal Behav 9:e29518CrossRefPubMedCentralGoogle Scholar
  67. Nakabayashi R, Yonekura-Sakakibara K, Urano K, Suzuki M, Yamada Y, Nishizawa T, Matsuda F, Kojima M, Sakakibara H, Shinozaki K, Michael AJ, Tohge T, Yamazaki M, Saito K (2014b) Enhancement of oxidative and drought tolerance in Arabidopsis by overaccumulation of antioxidant flavonoids. Plant J 77:367–379. doi: 10.1111/tpj.12388 CrossRefPubMedGoogle Scholar
  68. Ouvrard O, Cellier F, Ferrare K, Tousch D, Lamaze T, Dupuis JM, Casse-Delbart F (1996) Identification and expression of water stress- and abscisic acid-regulated genes in a drought-tolerant sunflower genotype. Plant Mol Biol 31:819–829CrossRefPubMedGoogle Scholar
  69. Palmer-Young EC, Veit D, Gershenzon J, Schuman MC (2015) The sesquiterpenes(E)-ß-farnesene and (E)-α-bergamotene quench ozone but fail to protect the wild tobacco Nicotiana attenuata from ozone, UVB, and drought stresses. PLoS ONE 10:e0127296. doi: 10.1371/journal.pone.0127296 CrossRefPubMedPubMedCentralGoogle Scholar
  70. Peluffo L, Lia V, Troglia C, Maringolo C, Norma P, Escande A, Esteban Hopp H, Lytovchenko A, Fernie AR, Heinz R, Carrari F (2010) Metabolic profiles of sunflower genotypes with contrasting response to Sclerotinia sclerotiorum infection. Phytochemistry 71:70–80. doi: 10.1016/j.phytochem.2009.09.018 CrossRefPubMedGoogle Scholar
  71. Pereyra-Irujo GA, Velázquez L, Granier C, Aguirrezábal LAN (2007) A method for drought tolerance screening in sunflower. Plant Breed 126:445–448. doi: 10.1111/j.1439-0523.2007.01375.x CrossRefGoogle Scholar
  72. Pérez-Rodríguez P, Riaño-Pachón DM, Corrêa LGG, Rensing SA, Kersten B, Mueller-Roeber B (2010) PlnTFDB: updated content and new features of the plant transcription factor database. Nucleic Acids Res 38:D822–D827. doi: 10.1093/nar/gkp805 CrossRefPubMedGoogle Scholar
  73. Pinheiro J, Bates D, DebRoy S, Sarkar D (2012) nlme: linear and nonlinear mixed effects models. R packageGoogle Scholar
  74. Planchet E, Verdu I, Delahaie J, Cukier C, Girard C, Morère-Le Paven M-C, Limami AM (2014) Abscisic acid-induced nitric oxide and proline accumulation in independent pathways under water-deficit stress during seedling establishment in Medicago truncatula. J Exp Bot 65:2161–2170. doi: 10.1093/jxb/eru088 CrossRefPubMedGoogle Scholar
  75. R Core Team (2012) R: a language and environment for statistical computing. R Foundation for Statistical Computing. ISBN 3-900051-07-0Google Scholar
  76. Raineri J, Ribichich KF, Chan RL (2015) The sunflower transcription factor HaWRKY76 confers drought and flood tolerance to Arabidopsis thaliana plants without yield penalty. Plant Cell Rep 34:2065–2080. doi: 10.1007/s00299-015-1852-3 CrossRefPubMedGoogle Scholar
  77. Roche J, Hewezi T, Bouniols A, Gentzbittel L (2007) Transcriptional profiles of primary metabolism and signal transduction-related genes in response to water stress in field-grown sunflower genotypes using a thematic cDNA microarray. Planta 226:601–617. doi: 10.1007/s00425-007-0508-0 CrossRefPubMedGoogle Scholar
  78. Roessner-Tunali U, Hegemann B, Lytovchenko A, Carrari F, Bruedigam C, Granot D, Fernie AR (2003) Metabolic profiling of transgenic tomato plants overexpressing hexokinase reveals that the influence of hexose phosphorylation diminishes during fruit development. Plant Physiol 133:84–99. doi: 10.1104/pp.103.023572 CrossRefPubMedPubMedCentralGoogle Scholar
  79. Rousseaux MC. C, Hall AJ, Sanchez RA (1996) Far-red enrichment and photosynthetically active radiation level influence leaf senescence in field-grown sunflower. Physiol Plant 96:217–224CrossRefGoogle Scholar
  80. Rozen S, Skaletsky HJ (2000) Primer3 on the WWW for general users and for biologist programmers. Bioinform Methods Protoc 132:365–386CrossRefGoogle Scholar
  81. Ruiz-Lozano JM, Azcón R (1996) Mycorrhizal colonization and drought stress as factors affecting nitrate reductase activity in lettuce plants. Agric Ecosyst Environ 60:175–181. doi: 10.1016/S0167-8809(96)01074-2 CrossRefGoogle Scholar
  82. Sadras VO, Whitfi eld DM, Connor DJ (1991) Regulation of evapotranspiration and its partitioning between transpiration and soil evaporation by sunflower crops. A comparison between hybrids of different stature. Field Crop Res 28:17–37CrossRefGoogle Scholar
  83. Saito K, Yonekura-Sakakibara K, Nakabayashi R, Higashi Y, Yamazaki M, Tohge T, Fernie AR (2013) The flavonoid biosynthetic pathway in Arabidopsis: structural and genetic diversity. Plant Physiol Biochem 72:21–34. doi: 10.1016/j.plaphy.2013.02.001 CrossRefPubMedGoogle Scholar
  84. Sartor MA, Leikauf GD, Medvedovic M (2009) LRpath: a logistic regression approach for identifying enriched biological groups in gene expression data. Bioinformatics 25:211–217. doi: 10.1093/bioinformatics/btn592 CrossRefPubMedGoogle Scholar
  85. Schaefer RJ, Michno J-M, Myers CL (2016) Unraveling gene function in agricultural species using gene co-expression networks. Biochim Biophys Acta-Gene Regul Mech. doi: 10.1016/j.bbagrm.2016.07.016 Google Scholar
  86. Schmidhuber J, Tubiello FN (2007) Global food security under climate change. Proc Natl Acad Sci USA 104:19703–19708. doi: 10.1073/pnas.0701976104 CrossRefPubMedPubMedCentralGoogle Scholar
  87. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504. doi: 10.1101/gr.1239303 CrossRefPubMedPubMedCentralGoogle Scholar
  88. Sharkey TD (2001) Isoprene increases thermotolerance of fosmidomycin-fed leaves. Plant Physiol 125:2001–2006. doi: 10.1104/pp.125.4.2001 CrossRefPubMedPubMedCentralGoogle Scholar
  89. Sharkey TD, Wiberley AE, Donohue AR (2008) Isoprene emission from plants: why and how. Ann Bot 101:5–18. doi: 10.1093/aob/mcm240 CrossRefPubMedGoogle Scholar
  90. Skopelitis DS, Paranychianakis NV, Paschalidis KA, Pliakonis ED, Delis ID, Yakoumakis DI, Kouvarakis A, Papadakis AK, Stephanou EG, Roubelakis-Angelakis KA (2006) Abiotic stress generates ROS that signal expression of anionic glutamate dehydrogenases to form glutamate for proline synthesis in tobacco and grapevine. Plant Cell 18:2767–2781. doi: 10.1105/tpc.105.038323
  91. Smyth G (2005) Limma: linear models for microarray data. In: Gentleman R, Carey V, Dudoit S, Irizarry R, Huber W (eds) Bioinformatics and computational biology solutions using R and bioconductor. Springer, New York, pp 397–420CrossRefGoogle Scholar
  92. Sperdouli I, Moustakas M (2012) Interaction of proline, sugars, and anthocyanins during photosynthetic acclimation of Arabidopsis thaliana to drought stress. J Plant Physiol 169:577–585. doi: 10.1016/j.jplph.2011.12.015 CrossRefPubMedGoogle Scholar
  93. Tardieu F, Tuberosa R (2010) Dissection and modelling of abiotic stress tolerance in plants. Curr Opin Plant Biol 13:206–212. doi: 10.1016/j.pbi.2009.12.012 CrossRefPubMedGoogle Scholar
  94. Thimm O, Blasing O, Gibon Y, Nagel A, Meyer S, Kruger P, Selbig J, Muller LA, Rhee SY, Stitt M, Bläsing O, Krüger P, Müller L a (2004) MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J 37:914–939. doi: 10.1111/j.1365-313X.2004.02016.x CrossRefPubMedGoogle Scholar
  95. Thomas WTB (2015) Drought-resistant cereals: impact on water sustainability and nutritional quality. Proc Nutr Soc 74:191–197. doi: 10.1017/S0029665115000026 CrossRefPubMedGoogle Scholar
  96. Tran L-SP, Nakashima K, Sakuma Y, Osakabe Y, Qin F, Simpson SD, Maruyama K, Fujita Y, Shinozaki K, Yamaguchi-Shinozaki K (2006) Co-expression of the stress-inducible zinc finger homeodomain ZFHD1 and NAC transcription factors enhances expression of the ERD1 gene in Arabidopsis. Plant J 49:46–63. doi: 10.1111/j.1365-313X.2006.02932.x CrossRefGoogle Scholar
  97. Utrillas MJ, Alegre L, Simon E (1995) Seasonal changes in production and nutrient content of Cynodon dactylon (L.) Pers. subjected to water deficits. Plant Soil 175:153–157. doi: 10.1007/BF02413021 CrossRefGoogle Scholar
  98. van Kleunen M, Fischer M (2005) Constraints on the evolution of adaptive phenotypic plasticity in plants. New Phytol 166:49–60. doi: 10.1111/j.1469-8137.2004.01296.x CrossRefPubMedGoogle Scholar
  99. Vickers CE, Gershenzon J, Lerdau MT, Loreto F (2009) A unified mechanism of action for volatile isoprenoids in plant abiotic stress. Nat Chem Biol 5:283–291. doi: 10.1038/nchembio.158 CrossRefPubMedGoogle Scholar
  100. Wang W, Wu P, Li Y, Hou X (2016) Genome-wide analysis and expression patterns of ZF-HD transcription factors under different developmental tissues and abiotic stresses in Chinese cabbage. Mol Genet Genom 291:1451–1464. doi: 10.1007/s00438-015-1136-1 CrossRefGoogle Scholar
  101. Yaish MW (2015) Proline accumulation is a general response to abiotic stress in the date palm tree (Phoenix dactylifera L.). Genet Mol Res 14:9943–9950. doi: 10.4238/2015.August.19.30 CrossRefPubMedGoogle Scholar
  102. Yamada M, Morishita H, Urano K, Shiozaki N, Yamaguchi-Shinozaki K, Shinozaki K, Yoshiba Y (2005) Effects of free proline accumulation in petunias under drought stress. J Exp Bot 56:1975–1981. doi: 10.1093/jxb/eri195 CrossRefPubMedGoogle Scholar

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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