Skip to main content
SpringerLink
Log in

Systems Biology Approaches to Improve Drought Stress Tolerance in Plants: State of the Art and Future Challenges

  • Chapter
  • First Online:
Drought Stress Tolerance in Plants, Vol 2

Abstract

Drought stress induces a vast array of responses in plants that require the use of integrative and multidisciplinary approaches to understand the different levels of regulation. Holistic systems biology approaches still remain unexploited, which is especially important for plant and agricultural sciences. Given the increasing development of high-throughput genomic tools and concomitant progress on plant genome sequencing, it is now possible to gain quantitative information at a comprehensive scale and a quantitative overview on the gene-to-metabolite networks that are associated with a particular plant phenotype. Systems biology aims to find regulatory mechanisms controlling gene expression, to identify candidate genes and molecular markers to support promissory strategies to engineer and/or breed plants with desired traits such as enhanced quality. Most of the systems biology approaches rely upon three main axes representing the multiple layers of the regulation of gene expression: transcriptomics, proteomics, and metabolomics. Coupled with the study of the noncoding genome, new insights into the regulation of gene expression are being provided as well as their effects on the phenotypic changes in a specific biological context. Bioinformatics tools have been crucial in omics-based research to manage genome-wide datasets, extract valuable information, and facilitate knowledge exchange between model and crop species. The present chapter reviews the use of system biology approaches undertaken to understand drought stress response in plants, providing a critical discussion on the constraints and future prospects of using these approaches to address the current needs of agriculture in a context of climate change.

J.R. Parreira and D. Branco have equal contributions to this manuscript.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Boyer JS (1982) Plant productivity and environment. Science 218:443–448. doi:10.1126/science.218.4571.443

    Article  CAS  PubMed  Google Scholar 

  2. Chaves MM (1991) Effects of water deficits on carbon assimilation. J Exp Bot 42:1–16. doi:10.1093/jxb/42.1.1

    Article  CAS  Google Scholar 

  3. Liu Y-H, Offler CE, Ruan Y-L (2013) Regulation of fruit and seed response to heat and drought by sugars as nutrients and signals. Front Plant Sci 4:282. doi:10.3389/fpls.2013.00282

    PubMed  PubMed Central  Google Scholar 

  4. Araújo SS, Beebe S, Crespi M et al (2015) Abiotic stress responses in legumes: strategies used to cope with environmental challenges. Crit Rev Plant Sci 34:237–280. doi:10.1080/07352689.2014.898450

    Article  CAS  Google Scholar 

  5. Cramer GR, Urano K, Delrot S et al (2011) Effects of abiotic stress on plants: a systems biology perspective. BMC Plant Biol 11:163. doi:10.1186/1471-2229-11-163

    Article  PubMed  PubMed Central  Google Scholar 

  6. Jogaiah S, Govind SR, Tran L-SP (2013) Systems biology-based approaches toward understanding drought tolerance in food crops. Crit Rev Biotechnol 33:23–39. doi:10.3109/07388551.2012.659174

    Article  PubMed  Google Scholar 

  7. Duque AS, de Almeida AM, da Silva AB et al (2013) Abiotic stress responses in plants: unravelling the complexity of genes and networks to survive. In: Abiotic stress—plant responses and applications in agriculture. InTech, pp 1–54

    Google Scholar 

  8. Mochida K, Shinozaki K (2011) Advances in omics and bioinformatics tools for systems analyses of plant functions. Plant Cell Physiol 52:2017–2038. doi:10.1093/pcp/pcr153

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Gehan MA, Greenham K, Mockler TC, McClung CR (2015) Transcriptional networks—crops, clocks, and abiotic stress. Curr Opin Plant Biol 24:39–46. doi:10.1016/j.pbi.2015.01.004

    Article  CAS  PubMed  Google Scholar 

  10. Hazen SP, Wu Y, Kreps JA (2003) Gene expression profiling of plant responses to abiotic stress. Funct Integr Genomics 3:105–111. doi:10.1007/s10142-003-0088-4

    Article  CAS  PubMed  Google Scholar 

  11. Nakaminami K, Matsui A, Shinozaki K, Seki M (2012) RNA regulation in plant abiotic stress responses. Biochim Biophys Acta Gene Regul Mech 1819:149–153. doi:10.1016/j.bbagrm.2011.07.015

    Article  CAS  Google Scholar 

  12. Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10:57–63. doi:10.1038/nrg2484

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Zhu T, Wang X (2000) Large-scale profiling of the Arabidopsis transcriptome. Plant Physiol 124:1472–1476. doi:10.1104/pp.124.4.1472

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Agarwal P, Parida SK, Mahto A et al (2014) Expanding frontiers in plant transcriptomics in aid of functional genomics and molecular breeding. Biotechnol J 9:1480–1492. doi:10.1002/biot.201400063

    Article  CAS  PubMed  Google Scholar 

  15. Ma X, Sukiran N, Ma H, Su Z (2014) Moderate drought causes dramatic floral transcriptomic reprogramming to ensure successful reproductive development in Arabidopsis. BMC Plant Biol 14:164. doi:10.1186/1471-2229-14-164

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Dansana PK, Kothari KS, Vij S, Tyagi AK (2014) OsiSAP1 overexpression improves water-deficit stress tolerance in transgenic rice by affecting expression of endogenous stress-related genes. Plant Cell Rep 33:1425–1440. doi:10.1007/s00299-014-1626-3

    Article  CAS  PubMed  Google Scholar 

  17. Reddy SK, Liu S, Rudd JC et al (2014) Physiology and transcriptomics of water-deficit stress responses in wheat cultivars TAM 111 and TAM 112. J Plant Physiol 171:1289–1298. doi:10.1016/j.jplph.2014.05.005

    Article  CAS  PubMed  Google Scholar 

  18. Avramova V, AbdElgawad H, Zhang Z et al (2015) Drought induces distinct growth response, protection, and recovery mechanisms in the maize leaf growth zone. Plant Physiol 169:1382–1396. doi:10.1104/pp.15.00276

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Johnson SM, Lim F-L, Finkler A et al (2014) Transcriptomic analysis of Sorghum bicolor responding to combined heat and drought stress. BMC Genom 15:456. doi:10.1186/1471-2164-15-456

    Article  CAS  Google Scholar 

  20. Pasini L, Bergonti M, Fracasso A et al (2014) Microarray analysis of differentially expressed mRNAs and miRNAs in young leaves of sorghum under dry-down conditions. J Plant Physiol 171:537–548. doi:10.1016/j.jplph.2013.12.014

    Article  CAS  PubMed  Google Scholar 

  21. Candogan BN, Sincik M, Buyukcangaz H et al (2013) Yield, quality and crop water stress index relationships for deficit-irrigated soybean [Glycine max (L.) Merr.] in sub-humid climatic conditions. Agric Water Manag 118:113–121. doi:10.1016/j.agwat.2012.11.021

    Article  Google Scholar 

  22. Ha CV, Le DT, Nishiyama R et al (2013) Characterization of the newly developed soybean cultivar DT2008 in relation to the model variety W82 reveals a new genetic resource for comparative and functional genomics for improved drought tolerance. Biomed Res Int 2013:1–8. doi:10.1155/2013/759657

    Article  Google Scholar 

  23. Ha CV, Watanabe Y, Tran UT et al (2015) Comparative analysis of root transcriptomes from two contrasting drought-responsive Williams 82 and DT2008 soybean cultivars under normal and dehydration conditions. Front Plant Sci 6:1–12. doi:10.3389/fpls.2015.00551

    Article  Google Scholar 

  24. Ansorge WJ (2009) Next-generation DNA sequencing techniques. N Biotechnol 25:195–203. doi:10.1016/j.nbt.2008.12.009

    Article  CAS  PubMed  Google Scholar 

  25. Costa V, Angelini C, De Feis I, Ciccodicola A (2010) Uncovering the complexity of transcriptomes with RNA-Seq. J Biomed Biotechnol 2010:1–19. doi:10.1155/2010/853916

    Article  CAS  Google Scholar 

  26. Sims D, Sudbery I, Ilott NE et al (2014) Sequencing depth and coverage: key considerations in genomic analyses. Nat Rev Genet 15:121–132. doi:10.1038/nrg3642

    Article  CAS  PubMed  Google Scholar 

  27. Potenza E, Racchi ML, Sterck L et al (2015) Exploration of alternative splicing events in ten different grapevine cultivars. BMC Genom 16:706. doi:10.1186/s12864-015-1922-5

    Article  CAS  Google Scholar 

  28. Szittya G, Moxon S, Santos DM et al (2008) High-throughput sequencing of Medicago truncatula short RNAs identifies eight new miRNA families. BMC Genom 9:593. doi:10.1186/1471-2164-9-593

    Article  CAS  Google Scholar 

  29. Vidal RO, Nascimento LC, Mondego JMC et al (2012) Identification of SNPs in RNA-seq data of two cultivars of Glycine max (soybean) differing in drought resistance. Genet Mol Biol 35:331–334. doi:10.1590/S1415-47572012000200014

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Champigny MJ, Sung WW, Catana V et al (2013) RNA-Seq effectively monitors gene expression in Eutrema salsugineum plants growing in an extreme natural habitat and in controlled growth cabinet conditions. BMC Genom 14:578. doi:10.1186/1471-2164-14-578

    Article  CAS  Google Scholar 

  31. He G, Chen B, Wang X et al (2013) Conservation and divergence of transcriptomic and epigenomic variation in maize hybrids. Genome Biol 14:R57. doi:10.1186/gb-2013-14-6-r57

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Quail M, Smith ME, Coupland P et al (2012) A tale of three next generation sequencing platforms: comparison of Ion torrent, pacific biosciences and illumina MiSeq sequencers. BMC Genom 13:341. doi:10.1186/1471-2164-13-341

    Article  CAS  Google Scholar 

  33. Marguerat S, Bähler J (2010) RNA-seq: from technology to biology. Cell Mol Life Sci 67:569–579. doi:10.1007/s00018-009-0180-6

    Article  CAS  PubMed  Google Scholar 

  34. Vasquez-Robinet C, Mane SP, Ulanov AV et al (2008) Physiological and molecular adaptations to drought in Andean potato genotypes. J Exp Bot 59:2109–2123. doi:10.1093/jxb/ern073

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Gong L, Zhang H, Gan X et al (2015) Transcriptome profiling of the potato (Solanum tuberosum L.) plant under drought stress and water-stimulus conditions. PLoS ONE 10:e0128041. doi:10.1371/journal.pone.0128041

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Bowman MJ, Park W, Bauer PJ et al (2013) RNA-Seq transcriptome profiling of upland cotton (Gossypium hirsutum L.) root tissue under water-deficit stress. PLoS ONE 8:e82634. doi:10.1371/journal.pone.0082634

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Chen Y, Liu Z-H, Feng L et al (2013) Genome-wide functional analysis of cotton (Gossypium hirsutum) in response to drought. PLoS ONE 8:e80879. doi:10.1371/journal.pone.0080879

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Rodrigues FA, Fuganti-Pagliarini R, Marcolino-Gomes J et al (2015) Daytime soybean transcriptome fluctuations during water deficit stress. BMC Genom 16:505. doi:10.1186/s12864-015-1731-x

    Article  CAS  Google Scholar 

  39. Shin J, Vaughn JN, Abdel-Haleem H et al (2015) Transcriptomic changes due to water deficit define a general soybean response and accession-specific pathways for drought avoidance. BMC Plant Biol 15:26. doi:10.1186/s12870-015-0422-8

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Wu J, Wang L, Li L, Wang S (2014) De novo assembly of the common bean transcriptome using short reads for the discovery of drought-responsive genes. PLoS ONE 9:e109262. doi:10.1371/journal.pone.0109262

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Ings J, Mur LAJ, Robson PRH, Bosch M (2013) Physiological and growth responses to water deficit in the bioenergy crop Miscanthus x giganteus. Front Plant Sci 4:468. doi:10.3389/fpls.2013.00468

    Article  PubMed  PubMed Central  Google Scholar 

  42. Fan Y, Wang Q, Kang L et al (2015) Transcriptome-wide characterization of candidate genes for improving the water use efficiency of energy crops grown on semiarid land. J Exp Bot 66:6415–6429. doi:10.1093/jxb/erv353

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. O’Farrell PH (1975) High resolution two-dimensional electrophoresis of proteins. J Biol Chem 250:4007–4021

    PubMed  PubMed Central  Google Scholar 

  44. Abreu IA, Farinha AP, Negrão S et al (2013) Coping with abiotic stress: proteome changes for crop improvement. J Proteomics 93:145–168. doi:10.1016/j.jprot.2013.07.014

    Article  CAS  PubMed  Google Scholar 

  45. Barkla BJ, Vera-Estrella R, Pantoja O (2013) Progress and challenges for abiotic stress proteomics of crop plants. Proteomics 13:1801–1815. doi:10.1002/pmic.201200401

    Article  CAS  PubMed  Google Scholar 

  46. Ghosh D, Xu J (2014) Abiotic stress responses in plant roots: a proteomics perspective. Front Plant Sci 5:6. doi:10.3389/fpls.2014.00006

    Article  PubMed  PubMed Central  Google Scholar 

  47. Komatsu S, Hossain Z (2013) Organ-specific proteome analysis for identification of abiotic stress response mechanism in crop. Front Plant Sci 4:71. doi:10.3389/fpls.2013.00071

    PubMed  PubMed Central  Google Scholar 

  48. Kosová K, Vítámvás P, Prášil IT, Renaut J (2011) Plant proteome changes under abiotic stress–contribution of proteomics studies to understanding plant stress response. J Proteomics 74:1301–1322. doi:10.1016/j.jprot.2011.02.006

    Article  PubMed  CAS  Google Scholar 

  49. Wittmann-Liebold B, Graack H-R, Pohl T (2006) Two-dimensional gel electrophoresis as tool for proteomics studies in combination with protein identification by mass spectrometry. Proteomics 6:4688–4703. doi:10.1002/pmic.200500874

    Article  CAS  PubMed  Google Scholar 

  50. Abdallah C, Dumas-Gaudot E, Renaut J, Sergeant K (2012) Gel-based and gel-free quantitative proteomics approaches at a glance. Int J Plant Genomics 2012:1–17. doi:10.1155/2012/494572

    Article  CAS  Google Scholar 

  51. Echevarría-Zomeño S, Ariza D, Jorge I et al (2009) Changes in the protein profile of Quercus ilex leaves in response to drought stress and recovery. J Plant Physiol 166:233–245. doi:10.1016/j.jplph.2008.05.008

    Article  PubMed  CAS  Google Scholar 

  52. Gazanchian A, Hajheidari M, Sima NK, Salekdeh GH (2007) Proteome response of Elymus elongatum to severe water stress and recovery. J Exp Bot 58:291–300. doi:10.1093/jxb/erl226

    Article  CAS  PubMed  Google Scholar 

  53. He C-Y, Zhang J-G, Duan A-G et al (2007) Proteins responding to drought and high-temperature stress in Pinus armandii Franch. Can J Bot 85:994–1001. doi:10.1139/b07-085

    Article  CAS  Google Scholar 

  54. Jorge I, Navarro RM, Lenz C et al (2006) Variation in the holm oak leaf proteome at different plant developmental stages, between provenances and in response to drought stress. Proteomics 6(Suppl 1):S207–S214. doi:10.1002/pmic.200500364

    Article  PubMed  Google Scholar 

  55. Kawasaki S, Miyake C, Kohchi T, Fujii S, Uchida M, Yokota A (2000) Responses of wild watermelon to drought stress: accumulation of an ArgE homologue and citrulline in leaves during water deficits. Plant Cell Physiol 41:864–873. doi:10.1093/pcp/pcd005

    Article  CAS  PubMed  Google Scholar 

  56. Plomion C, Lalanne C, Claverol S et al (2006) Mapping the proteome of poplar and application to the discovery of drought-stress responsive proteins. Proteomics 6:6509–6527. doi:10.1002/pmic.200600362

    Article  CAS  PubMed  Google Scholar 

  57. Roy A, Rushton PJ, Rohila JS (2011) The potential of proteomics technologies for crop improvement under drought conditions. Crit Rev Plant Sci 30:471–490. doi:10.1080/07352689.2011.605743

    Article  CAS  Google Scholar 

  58. Vítámvás P, Urban MO, Škodáček Z et al (2015) Quantitative analysis of proteome extracted from barley crowns grown under different drought conditions. Front Plant Sci 6:479. doi:10.3389/fpls.2015.00479

    Article  PubMed  PubMed Central  Google Scholar 

  59. Jedmowski C, Ashoub A, Beckhaus T et al (2014) Comparative analysis of Sorghum bicolor proteome in response to drought stress and following recovery. Int J Proteomics 2014:1–10. doi:10.1155/2014/395905

    Article  CAS  Google Scholar 

  60. Faghani E, Gharechahi J, Komatsu S et al (2015) Comparative physiology and proteomic analysis of two wheat genotypes contrasting in drought tolerance. J Proteomics 114:1–15. doi:10.1016/j.jprot.2014.10.018

    Article  CAS  PubMed  Google Scholar 

  61. Paul S, Gayen D, Datta SK, Datta K (2015) Dissecting root proteome of transgenic rice cultivars unravels metabolic alterations and accumulation of novel stress responsive proteins under drought stress. Plant Sci 234:133–143. doi:10.1016/j.plantsci.2015.02.006

    Article  CAS  PubMed  Google Scholar 

  62. Zadražnik T, Hollung K, Egge-Jacobsen W et al (2013) Differential proteomic analysis of drought stress response in leaves of common bean (Phaseolus vulgaris L.). J Proteomics 78:254–272. doi:10.1016/j.jprot.2012.09.021

    Article  PubMed  CAS  Google Scholar 

  63. Almeida AM, Bassols A, Bendixen E et al (2015) Animal board invited review: advances in proteomics for animal and food sciences. Anim An Int J Anim Biosci 9:1–17. doi:10.1017/S1751731114002602

    Article  CAS  Google Scholar 

  64. Jorrín-Novo JV, Pascual J, Sánchez-Lucas R et al (2015) Fourteen years of plant proteomics reflected in proteomics: moving from model species and 2DE-based approaches to orphan species and gel-free platforms. Proteomics 15:1089–1112. doi:10.1002/pmic.201400349

    Article  PubMed  CAS  Google Scholar 

  65. Mirzaei M, Soltani N, Sarhadi E et al (2014) Manipulating root water supply elicits major shifts in the shoot proteome. J Proteome Res 13:517–526. doi:10.1021/pr400696u

    Article  CAS  PubMed  Google Scholar 

  66. Oh M, Komatsu S (2015) Characterization of proteins in soybean roots under flooding and drought stresses. J Proteomics 114:161–181. doi:10.1016/j.jprot.2014.11.008

    Article  CAS  PubMed  Google Scholar 

  67. Kottapalli KR, Zabet-Moghaddam M, Rowland D et al (2013) Shotgun label-free quantitative proteomics of water-deficit-stressed midmature peanut (Arachis hypogaea L.) seed. J Proteome Res 12:5048–5057. doi:10.1021/pr400936d

    Article  CAS  PubMed  Google Scholar 

  68. Koh J, Chen G, Yoo M-J et al (2015) Comparative proteomic analysis of Brassica napus in response to drought stress. J Proteome Res 14:3068–3081. doi:10.1021/pr501323d

    Article  CAS  PubMed  Google Scholar 

  69. Zhou S, Li M, Guan Q et al (2015) Physiological and proteome analysis suggest critical roles for the photosynthetic system for high water-use efficiency under drought stress in Malus. Plant Sci 236:44–60. doi:10.1016/j.plantsci.2015.03.017

    Article  CAS  PubMed  Google Scholar 

  70. Soares R, Franco C, Pires E et al (2012) Mass spectrometry and animal science: protein identification strategies and particularities of farm animal species. J Proteomics 75:4190–4206. doi:10.1016/j.jprot.2012.04.009

    Article  CAS  PubMed  Google Scholar 

  71. Sawada Y, Nakabayashi R, Yamada Y et al (2012) RIKEN tandem mass spectral database (ReSpect) for phytochemicals: a plant-specific MS/MS-based data resource and database. Phytochemistry 82:38–45. doi:10.1016/j.phytochem.2012.07.007

    Article  CAS  PubMed  Google Scholar 

  72. Khakimov B, Bak S, Engelsen SB (2014) High-throughput cereal metabolomics: current analytical technologies, challenges and perspectives. J Cereal Sci 59:393–418. doi:10.1016/j.jcs.2013.10.002

    Article  CAS  Google Scholar 

  73. Rodziewicz P, Swarcewicz B, Chmielewska K et al (2013) Influence of abiotic stresses on plant proteome and metabolome changes. Acta Physiol Plant 36:1–19. doi:10.1007/s11738-013-1402-y

    Article  CAS  Google Scholar 

  74. Pál M, Szalai G, Janda T (2015) Speculation: polyamines are important in abiotic stress signaling. Plant Sci 237:16–23. doi:10.1016/j.plantsci.2015.05.003

    Article  PubMed  CAS  Google Scholar 

  75. Cvikrova M, Gemperlova L, Martincova O et al (2013) Effect of drought and combined drought and heat stress on polyamine metabolism in proline-over-producing tobacco plants. Plant Physiol Biochem 73:7–15. doi:10.1016/j.plaphy.2013.08.005

    Article  CAS  PubMed  Google Scholar 

  76. Huang X, Zhou G, Yang W et al (2014) Drought-inhibited ribulose-1,5-bisphosphate carboxylase activity is mediated through increased release of ethylene and changes in the ratio of polyamines in pakchoi. J Plant Physiol 171:1392–1400. doi:10.1016/j.jplph.2014.06.007

    Article  CAS  PubMed  Google Scholar 

  77. Padmavathi TAV, Rao DM (2013) Differential accumulation of osmolytes in 4 cultivars of peanut (Arachis hypogaea L.) under drought stress. J Crop Sci Biotechnol 16:151–159. doi:10.1007/s12892-012-0102-2

    Article  Google Scholar 

  78. Gupta N, Thind SK, Bains NS (2013) Glycine betaine application modifies biochemical attributes of osmotic adjustment in drought stressed wheat. Plant Growth Regul 72:221–228. doi:10.1007/s10725-013-9853-0

    Article  CAS  Google Scholar 

  79. Akitha Devi MK, Giridhar P (2013) Variations in physiological response, lipid peroxidation, antioxidant enzyme activities, proline and isoflavones content in soybean varieties subjected to drought stress. In: Proceedings of the National Academy of Sciences, India Section B: Biological Sciences, vol 85, pp 35–44. doi:10.1007/s40011-013-0244-0

    Google Scholar 

  80. Oufir M, Schulz N, Sha Vallikhan PS et al (2009) Simultaneous measurement of proline and related compounds in oak leaves by high-performance ligand-exchange chromatography and electrospray ionization mass spectrometry for environmental stress studies. J Chromatogr A 1216:1094–1099. doi:10.1016/j.chroma.2008.12.030

    Article  CAS  PubMed  Google Scholar 

  81. Giri J (2011) Glycinebetaine and abiotic stress tolerance in plants. Plant Signal Behav 6:1746–1751. doi:10.4161/psb.6.11.17801

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Merchant A, Richter AA (2011) Polyols as biomarkers and bioindicators for 21st century plant breeding. Funct Plant Biol 38:934–940. doi:10.1071/FP11105

    Article  CAS  Google Scholar 

  83. de Carvalho K, Petkowicz CL, Nagashima GT et al (2014) Homeologous genes involved in mannitol synthesis reveal unequal contributions in response to abiotic stress in Coffea arabica. Mol Genet Genomics 289:951–963. doi:10.1007/s00438-014-0864-y

    Article  PubMed  CAS  Google Scholar 

  84. Griesser M, Weingart G, Schoedl-Hummel K et al (2015) Severe drought stress is affecting selected primary metabolites, polyphenols, and volatile metabolites in grapevine leaves (Vitis vinifera cv. Pinot noir). Plant Physiol Biochem 88:17–26. doi:10.1016/j.plaphy.2015.01.004

    Article  CAS  PubMed  Google Scholar 

  85. Kleinwächter M, Paulsen J, Bloem E et al (2015) Moderate drought and signal transducer induced biosynthesis of relevant secondary metabolites in thyme (Thymus vulgaris), greater celandine (Chelidonium majus) and parsley (Petroselinum crispum). Ind Crops Prod 64:158–166. doi:10.1016/j.indcrop.2014.10.062

    Article  CAS  Google Scholar 

  86. Selmar D, Kleinwächter M (2013) Influencing the product quality by deliberately applying drought stress during the cultivation of medicinal plants. Ind Crops Prod 42:558–566. doi:10.1016/j.indcrop.2012.06.020

    Article  CAS  Google Scholar 

  87. Ahmed IM, Nadira UA, Bibi N et al (2015) Secondary metabolism and antioxidants are involved in the tolerance to drought and salinity, separately and combined, in Tibetan wild barley. Environ Exp Bot 111:1–12. doi:10.1016/j.envexpbot.2014.10.003

    Article  CAS  Google Scholar 

  88. Baroowa B, Gogoi N (2013) Biochemical changes in two Vigna spp. during drought and subsequent recovery. Indian J Plant Physiol 18:319–325. doi:10.1007/s40502-013-0048-5

    Article  Google Scholar 

  89. Li Z, Peng Y, Ma X (2012) Different response on drought tolerance and post-drought recovery between the small-leafed and the large-leafed white clover (Trifolium repens L.) associated with antioxidative enzyme protection and lignin metabolism. Acta Phys Plant 35:213–222. doi:10.1007/s11738-012-1066-z

    Article  CAS  Google Scholar 

  90. Dunn WB, Erban A, Weber RJM et al (2012) Mass appeal: metabolite identification in mass spectrometry-focused untargeted metabolomics. Metabolomics 9(S1):44–66. doi:10.1007/s11306-012-0434-4

    Article  CAS  Google Scholar 

  91. Hirayama A, Wakayama M, Soga T (2014) Metabolome analysis based on capillary electrophoresis-mass spectrometry. Trends Analyt Chem 61:215–222. doi:10.1016/j.trac.2014.05.005

    Article  CAS  Google Scholar 

  92. Saito K (2013) Phytochemical genomics–a new trend. Curr Opin Plant Biol 16:373–380. doi:10.1016/j.pbi.2013.04.001

    Article  CAS  PubMed  Google Scholar 

  93. Sangwan NS, Tiwari P, Mishra SK et al (2015) Plant metabolomics: an overview of technology platforms for applications in metabolism. PlantOmics: the omics of plant science. Springer India, New Delhi, pp 257–298

    Google Scholar 

  94. Cox DG, Oh J, Keasling A et al (2014) The utility of metabolomics in natural product and biomarker characterization. Biochim Biophys Acta 1840:3460–3474. doi:10.1016/j.bbagen.2014.08.007

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Zhang A, Sun H, Wang P, Han Y, Wang X (2012) Modern analytical techniques in metabolomics analysis. The Analyst 137:293–300. doi:10.1039/c1an15605e

    Article  CAS  PubMed  Google Scholar 

  96. Wolfender JL, Marti G, Thomas A et al (2015) Current approaches and challenges for the metabolite profiling of complex natural extracts. J Chromatogr A 1382:136–164. doi:10.1016/j.chroma.2014.10.091

    Article  CAS  PubMed  Google Scholar 

  97. Barnaby JY, Kim M, Bauchan G et al (2013) Drought responses of foliar metabolites in three maize hybrids differing in water stress tolerance. PLoS ONE 8:e77145. doi:10.1371/journal.pone.0077145

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Silvente S, Sobolev AP, Lara M (2012) Metabolite adjustments in drought tolerant and sensitive soybean genotypes in response to water stress. PLoS ONE 7:e38554. doi:10.1371/journal.pone.0038554

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Vogel A, Fester T, Eisenhauer N et al (2013) Separating drought effects from roof artifacts on ecosystem processes in a grassland drought experiment. PLoS ONE 8:e70997. doi:10.1371/journal.pone.0070997

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. McKiernan AB, Hovenden MJ, Brodribb TJ et al (2014) Effect of limited water availability on foliar plant secondary metabolites of two Eucalyptus species. Environ Exp Bot 105:55–64. doi:10.1016/j.envexpbot.2014.04.008

    Article  CAS  Google Scholar 

  101. Budak H, Akpinar BA (2015) Plant miRNAs: biogenesis, organization and origins. Funct Integr Genomics 15:523–531. doi:10.1007/s10142-015-0451-2

    Article  CAS  PubMed  Google Scholar 

  102. Ding Y, Tao Y, Zhu C (2013) Emerging roles of microRNAs in the mediation of drought stress response in plants. J Exp Bot 64:3077–3086

    Article  CAS  PubMed  Google Scholar 

  103. Rogers K, Chen X (2013) Biogenesis, turnover, and mode of action of plant microRNAs. Plant Cell 25:2383–2399. doi:10.1105/tpc.113.113159

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Bartel DP (2004) MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116:281–297. doi:10.1016/S0092-8674(04)00045-5

    Article  CAS  PubMed  Google Scholar 

  105. Jones-Rhoades MW, Bartel DP, Bartel B (2006) MicroRNAS and their regulatory roles in plants. Annu Rev Plant Biol 57:19–53. doi:10.1146/annurev.arplant.57.032905.105218

    Article  CAS  PubMed  Google Scholar 

  106. Xie Z, Allen E, Fahlgren N et al (2005) Expression of Arabidopsis MIRNA genes. Plant Physiol 138:2145–2154. doi:10.1104/pp.105.062943

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Axtell MJ, Westholm JO, Lai EC (2011) Vive la différence: biogenesis and evolution of microRNAs in plants and animals. Genome Biol 12:221. doi:10.1186/gb-2011-12-4-221

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Piriyapongsa J, Jordan IK (2008) Dual coding of siRNAs and miRNAs by plant transposable elements. RNA 14:814–821. doi:10.1261/rna.916708

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Budak H, Kantar M, Bulut R, Akpinar BA (2015) Stress responsive miRNAs and isomiRs in cereals. Plant Sci 235:1–13. doi:10.1016/j.plantsci.2015.02.008

    Article  CAS  PubMed  Google Scholar 

  110. Ferdous J, Hussain SS, Shi B-J (2015a) Role of microRNAs in plant drought tolerance. Plant Biotechnol J 1–13. doi:10.1111/pbi.12318

    Google Scholar 

  111. Gentile A, Dias LI, Mattos RS et al (2015) MicroRNAs and drought responses in sugarcane. Front Plant Sci 6:58. doi:10.3389/fpls.2015.00058

    Article  PubMed  PubMed Central  Google Scholar 

  112. Jeong D-H, Green PJ (2013) The role of rice microRNAs in abiotic stress responses. J Plant Biol 56:187–197. doi:10.1007/s12374-013-0213-4

    Article  CAS  Google Scholar 

  113. Zhang B (2015) MicroRNA: a new target for improving plant tolerance to abiotic stress. J Exp Bot 66:1749–1761. doi:10.1093/jxb/erv013

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Jiang Q, Wang F, Li M-Y et al (2014) High-throughput analysis of small RNAs and characterization of novel microRNAs affected by abiotic stress in a local celery cultivar. Sci Hortic (Amsterdam) 169:36–43. doi:10.1016/j.scienta.2014.02.007

    Article  CAS  Google Scholar 

  115. Bertolini E, Verelst W, Horner DS et al (2013) Addressing the role of microRNAs in reprogramming leaf growth during drought stress in Brachypodium distachyon. Mol Plant 6:423–443. doi:10.1093/mp/sss160

    Article  CAS  PubMed  Google Scholar 

  116. Bhardwaj AR, Joshi G, Pandey R et al (2014) A genome-wide werspective of miRNAome in response to high temperature, salinity and drought stresses in Brassica juncea (Czern) L. PLoS ONE 9:e92456. doi:10.1371/journal.pone.0092456

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  117. Hajyzadeh M, Turktas M, Khawar KM et al (2015) miR408 overexpression causes increased drought tolerance in chickpea. Gene 555:186–193. doi:10.1016/j.gene.2014.11.002

    Article  CAS  PubMed  Google Scholar 

  118. Wang M, Wang Q, Zhang B (2013) Response of miRNAs and their targets to salt and drought stresses in cotton (Gossypium hirsutum L.). Gene 530:26–32. doi:10.1016/j.gene.2013.08.009

    Article  CAS  PubMed  Google Scholar 

  119. Xie F, Wang Q, Sun R, Zhang B (2015) Deep sequencing reveals important roles of microRNAs in response to drought and salinity stress in cotton. J Exp Bot 66:789–804. doi:10.1093/jxb/eru437

    Article  CAS  PubMed  Google Scholar 

  120. Ferdous J, Li Y, Reid N et al (2015) Identification of reference genes for quantitative expression analysis of microRNAs and mRNAs in barley under various stress conditions. PLoS ONE 10:e0118503. doi:10.1371/journal.pone.0118503

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  121. Hackenberg M, Gustafson P, Langridge P, Shi B-J (2015) Differential expression of microRNAs and other small RNAs in barley between water and drought conditions. Plant Biotechnol J 13:2–13. doi:10.1111/pbi.12220

    Article  CAS  PubMed  Google Scholar 

  122. Zhai J, Dong Y, Sun Y et al (2014) Discovery and analysis of microRNAs in Leymus chinensis under saline-alkali and drought stress using high-throughput sequencing. PLoS ONE 9:e105417. doi:10.1371/journal.pone.0105417

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  123. Ballén-Taborda C, Plata G, Ayling S et al (2013) Identification of cassava microRNAs under abiotic stress. Int J Genomics 2013:1–10. doi:10.1155/2013/857986

    Article  CAS  Google Scholar 

  124. Yin F, Qin C, Gao J et al (2015) Genome-wide identification and analysis of drought-responsive genes and microRNAs in tobacco. Int J Mol Sci 16:5714–5740. doi:10.3390/ijms16035714

    Article  CAS  PubMed  Google Scholar 

  125. Yin F, Gao J, Liu M et al (2014) Genome-wide analysis of water-stress-responsive microRNA expression profile in tobacco roots. Funct Integr Genomics 14:319–332. doi:10.1007/s10142-014-0365-4

    Article  CAS  PubMed  Google Scholar 

  126. Jiang Q, Wang F, Tan H-W et al (2015) De novo transcriptome assembly, gene annotation, marker development, and miRNA potential target genes validation under abiotic stresses in Oenanthe javanica. Mol Genet Genomics 290:671–683. doi:10.1007/s00438-014-0953-y

    Article  CAS  PubMed  Google Scholar 

  127. Kansal S, Devi RM, Balyan SC et al (2015) Unique miRNome during anthesis in drought-tolerant indica rice var. Nagina 22. Planta 241:1543–1559. doi:10.1007/s00425-015-2279-3

    Article  CAS  PubMed  Google Scholar 

  128. Zheng L-L, Qu L-H (2015) Application of microRNA gene resources in the improvement of agronomic traits in rice. Plant Biotechnol J 13:329–336. doi:10.1111/pbi.12321

    Article  CAS  PubMed  Google Scholar 

  129. Xie F, Stewart CN, Taki FA et al (2014) High-throughput deep sequencing shows that microRNAs play important roles in switchgrass responses to drought and salinity stress. Plant Biotechnol J 12:354–366. doi:10.1111/pbi.12142

    Article  CAS  PubMed  Google Scholar 

  130. Gentile A, Ferreira TH, Mattos RS et al (2013) Effects of drought on the microtranscriptome of field-grown sugarcane plants. Planta 237:783–798. doi:10.1007/s00425-012-1795-7

    Article  CAS  PubMed  Google Scholar 

  131. Thiebaut F, Grativol C, Tanurdzic M et al (2014) Differential sRNA regulation in leaves and roots of sugarcane under water depletion. PLoS ONE 9:e93822. doi:10.1371/journal.pone.0093822

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  132. Yang J, Zhang N, Ma C et al (2013) Prediction and verification of microRNAs related to proline accumulation under drought stress in potato. Comput Biol Chem 46:48–54. doi:10.1016/j.compbiolchem.2013.04.006

    Article  CAS  PubMed  Google Scholar 

  133. Yang J, Zhang N, Mi X et al (2014) Identification of miR159 s and their target genes and expression analysis under drought stress in potato. Comput Biol Chem 53:204–213. doi:10.1016/j.compbiolchem.2014.09.009

    Article  CAS  Google Scholar 

  134. Zhang N, Yang J, Wang Z et al (2014) Identification of novel and conserved microRNAs related to drought stress in potato by deep sequencing. PLoS ONE 9:e95489. doi:10.1371/journal.pone.0095489

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  135. Ma X, Xin Z, Wang Z et al (2015) Identification and comparative analysis of differentially expressed miRNAs in leaves of two wheat (Triticum aestivum L.) genotypes during dehydration stress. BMC Plant Biol 15:21. doi:10.1186/s12870-015-0413-9

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  136. Zhao YY, Guo CJ, Li XJ et al (2015) Characterization and expression pattern analysis of microRNAs in wheat under drought stress. Biol Plant 59:37–46. doi:10.1007/s10535-014-0463-0

    Article  CAS  Google Scholar 

  137. Shui XR, Chen ZW, Li JX (2013) MicroRNA prediction and its function in regulating drought-related genes in cowpea. Plant Sci 210:25–35. doi:10.1016/j.plantsci.2013.05.002

    Article  CAS  PubMed  Google Scholar 

  138. Li JS, Fu FL, Ming AN et al (2013) Differential expression of microRNAs in response to drought stress in maize. J Integr Agric 12:1414–1422. doi:10.1016/S2095-3119(13)60311-1

    Article  Google Scholar 

  139. Luan M, Xu M, Lu Y et al (2015) Expression of zma-miR169 miRNAs and their target ZmNF-YA genes in response to abiotic stress in maize leaves. Gene 555:178–185. doi:10.1016/j.gene.2014.11.001

    Article  CAS  PubMed  Google Scholar 

  140. Sheng L, Chai W, Gong X et al (2015) Identification and characterization of novel maize miRNAsinvolved in different genetic background. Int J Biol Sci 11:781–793. doi:10.7150/ijbs.11619

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  141. Wang Y-G, An M, Zhou S-F et al (2014) Expression profile of maize microRNAs corresponding to their target genes under drought stress. Biochem Genet 52:474–493. doi:10.1007/s10528-014-9661-x

    Article  CAS  PubMed  Google Scholar 

  142. Llave C, Kasschau KD, Rector MA, Carrington JC (2002) Endogenous and silencing-associated small RNAs in plants. Plant Cell 14:1605–1619. doi:10.1105/tpc.003210

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. Park W, Li J, Song R et al (2002) CARPEL FACTORY, a Dicer homolog, and HEN1, a novel protein, act in microRNA metabolism in Arabidopsis thaliana. Curr Biol 12:1484–1495. doi:10.1016/S0960-9822(02)01017-5

    Article  CAS  PubMed  Google Scholar 

  144. Reinhart BJ, Weinstein EG, Rhoades MW et al (2002) MicroRNAs in plants. Genes Dev 16:1616–1626. doi:10.1101/gad.1004402

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  145. Liu B, Li J, Cairns MJ (2014) Identifying miRNAs, targets and functions. Brief Bioinform 15:1–19. doi:10.1093/bib/bbs075

    Article  PubMed  CAS  Google Scholar 

  146. Kang W, Friedländer MR (2015) Computational prediction of miRNA genes from small RNA sequencing data. Front Bioeng Biotechnol 3:7. doi:10.3389/fbioe.2015.00007

    Article  PubMed  PubMed Central  Google Scholar 

  147. Sun X, Zhang Y, Zhu X et al (2014) Advances in identification and validation of plant microRNAs and their target genes. Physiol Plant 152:203–218. doi:10.1111/ppl.12191

    Article  CAS  PubMed  Google Scholar 

  148. Meyers BC, Axtell MJ, Bartel B et al (2008) Criteria for annotation of plant MicroRNAs. Plant Cell 20:3186–3190. doi:10.1105/tpc.108.064311

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  149. Yang X, Li L (2011) miRDeep-P: a computational tool for analyzing the microRNA transcriptome in plants. Bioinformatics 27:2614–2615. doi:10.1093/bioinformatics/btr430

    CAS  PubMed  Google Scholar 

  150. Xie F, Xiao P, Chen D et al (2012) miRDeepFinder: a miRNA analysis tool for deep sequencing of plant small RNAs. Plant Mol Biol 80:75–84. doi:10.1007/s11103-012-9885-2

    Article  CAS  Google Scholar 

  151. An J, Lai J, Sajjanhar A et al (2014) miRPlant: an integrated tool for identification of plant miRNA from RNA sequencing data. BMC Bioinformatics 15:275. doi:10.1186/1471-2105-15-275

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  152. Baumberger N, Baulcombe DC (2005) Arabidopsis ARGONAUTE1 is an RNA slicer that selectively recruits microRNAs and short interfering RNAs. Proc Natl Acad Sci USA 102:11928–11933. doi:10.1073/pnas.0505461102

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  153. Rhoades MW, Reinhart BJ, Lim LP et al (2002) Prediction of plant microRNA targets. Cell 110:513–520. doi:10.1016/S0092-8674(02)00863-2

    Article  CAS  PubMed  Google Scholar 

  154. Dai X, Zhao PX (2011) PsRNATarget: a plant small RNA target analysis server. Nucleic Acids Res 39:155–159. doi:10.1093/nar/gkr319

    Article  CAS  Google Scholar 

  155. Fahlgren N, Carrington JC (2010) miRNA target prediction in plants. Methods Mol Biol 592:51–57. doi:10.1007/978-1-60327-005-2_4

    Article  CAS  PubMed  Google Scholar 

  156. Fahlgren N, Howell MD, Kasschau KD et al (2007) High-throughput sequencing of Arabidopsis microRNAs: evidence for frequent birth and death of MIRNA genes. PLoS ONE 2:e219. doi:10.1371/journal.pone.0000219

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  157. Xie FL, Huang SQ, Guo K et al (2007) Computational identification of novel microRNAs and targets in Brassica napus. FEBS Lett 581:1464–1474. doi:10.1016/j.febslet.2007.02.074

    Article  CAS  PubMed  Google Scholar 

  158. Ossowski S, Schwab R, Weigel D (2008) Gene silencing in plants using artificial microRNAs and other small RNAs. Plant J 53:674–690. doi:10.1111/j.1365-313X.2007.03328.x

    Article  CAS  PubMed  Google Scholar 

  159. Schwab R, Ossowski S, Riester M et al (2006) Highly specific gene silencing by artificial microRNAs in Arabidopsis. Plant Cell 18:1121–1133. doi:10.1105/tpc.105.039834

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  160. Llave C, Franco-Zorrilla JM, Solano R, Barajas D (2011) Target validation of plant microRNAs. Methods Mol Biol 732:187–208. doi:10.1007/978-1-61779-083-6_14

    Article  CAS  PubMed  Google Scholar 

  161. Chen C, Ridzon DA, Broomer AJ et al (2005) Real-time quantification of microRNAs by stem-loop RT-PCR. Nucleic Acids Res 33:e179. doi:10.1093/nar/gni178

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  162. Varkonyi-Gasic E, Gould N, Sandanayaka M et al (2010) Characterisation of microRNAs from apple (Malus domestica “Royal Gala”) vascular tissue and phloem sap. BMC Plant Biol 10:159. doi:10.1186/1471-2229-10-159

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  163. Trumbo JL, Zhang B, Stewart CN (2015) Manipulating microRNAs for improved biomass and biofuels from plant feedstocks. Plant Biotechnol J 13:337–354. doi:10.1111/pbi.12319

    Article  CAS  PubMed  Google Scholar 

  164. Xie M, Zhang S, Yu B (2015) microRNA biogenesis, degradation and activity in plants. Cell Mol Life Sci 72:87–99. doi:10.1007/s00018-014-1728-7

    Article  CAS  PubMed  Google Scholar 

  165. Lauressergues D, Couzigou J-M, Clemente HS et al (2015) Primary transcripts of microRNAs encode regulatory peptides. Nature 520:U90–U205. doi:10.1038/nature14346

    Article  CAS  Google Scholar 

  166. Sheth BP, Thaker VS (2014) Plant systems biology: insights, advances and challenges. Planta 240:33–54. doi:10.1007/s00425-014-2059-5

    Article  CAS  PubMed  Google Scholar 

  167. Schneider MV, Orchard S (2011) Omics technologies, data and bioinformatics principles. Methods Mol Biol 719:3–30. doi:10.1007/978-1-61779-027-0_1

    Article  CAS  PubMed  Google Scholar 

  168. Cochrane G, Karsch-Mizrachi I, Nakamura Y (2011) The international nucleotide sequence database collaboration. Nucleic Acids Res 39:D15–D18. doi:10.1093/nar/gkq1150

    Article  CAS  PubMed  Google Scholar 

  169. Karsch-Mizrachi I, Nakamura Y, Cochrane G (2012) The international nucleotide sequence database collaboration. Nucleic Acids Res 40:D33–D37. doi:10.1093/nar/gkr1006

    Article  CAS  PubMed  Google Scholar 

  170. Lamesch P, Berardini TZ, Li D et al (2012) The Arabidopsis information resource (TAIR): improved gene annotation and new tools. Nucleic Acids Res 40:D1202–D1210. doi:10.1093/nar/gkr1090

    Article  CAS  PubMed  Google Scholar 

  171. Youens-Clark K, Buckler E, Casstevens T et al (2011) Gramene database in 2010: updates and extensions. Nucleic Acids Res 39:D1085–D1094. doi:10.1093/nar/gkq1148

    Article  CAS  PubMed  Google Scholar 

  172. Fernandez-Pozo N, Menda N, Edwards JD et al (2015) The sol genomics network (SGN)–from genotype to phenotype to breeding. Nucleic Acids Res 43:D1036–D1041. doi:10.1093/nar/gku1195

    Article  PubMed  Google Scholar 

  173. Sato S, Hirakawa H, Isobe S et al (2010) Sequence analysis of the genome of an oil-bearing tree, Jatropha curcas L. DNA Res 18:65–76. doi:10.1093/dnares/dsq030

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  174. Jung S, Ficklin SP, Lee T et al (2014) The genome database for rosaceae (GDR): year 10 update. Nucleic Acids Res 42:D1237–D1244. doi:10.1093/nar/gkt1012

    Article  CAS  PubMed  Google Scholar 

  175. Gonzales MD, Archuleta E, Farmer A et al (2005) The legume information system (LIS): an integrated information resource for comparative legume biology. Nucleic Acids Res 33:D660–D665. doi:10.1093/nar/gki128

    Article  CAS  PubMed  Google Scholar 

  176. Li J, Dai X, Liu T, Zhao PX (2012) LegumeIP: an integrative database for comparative genomics and transcriptomics of model legumes. Nucleic Acids Res 40:D1221–D1229. doi:10.1093/nar/gkr939

    Article  CAS  PubMed  Google Scholar 

  177. Goodstein DM, Shu S, Howson R et al (2012) Phytozome: a comparative platform for green plant genomics. Nucleic Acids Res 40:D1178–D1186. doi:10.1093/nar/gkr944

    Article  CAS  PubMed  Google Scholar 

  178. Kersey PJ, Allen JE, Christensen M et al (2014) Ensembl genomes 2013: scaling up access to genome-wide data. Nucleic Acids Res 42:D546–D552. doi:10.1093/nar/gkt979

    Article  CAS  PubMed  Google Scholar 

  179. Smedley D, Haider S, Durinck S et al (2015) The BioMart community portal: an innovative alternative to large, centralized data repositories. Nucleic Acids Res 43:W589–W598. doi:10.1093/nar/gkv350

    Article  PubMed  PubMed Central  Google Scholar 

  180. Wheeler DL, Barrett T, Benson DA et al (2008) Database resources of the national center for biotechnology information. Nucleic Acids Res 36:D13–D21. doi:10.1093/nar/gkm1000

    Article  CAS  PubMed  Google Scholar 

  181. Kozomara A, Griffiths-Jones S (2011) miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res 39:D152–D157. doi:10.1093/nar/gkq1027

    Article  CAS  PubMed  Google Scholar 

  182. Kozomara A, Griffiths-Jones S (2014) miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res 42:D68–D73. doi:10.1093/nar/gkt1181

    Article  CAS  PubMed  Google Scholar 

  183. Hsu S-D, Tseng Y-T, Shrestha S et al (2014) miRTarBase update 2014: an information resource for experimentally validated miRNA-target interactions. Nucleic Acids Res 42:D78–D85. doi:10.1093/nar/gkt1266

    Article  CAS  PubMed  Google Scholar 

  184. Vlachos IS, Paraskevopoulou MD, Karagkouni D et al (2015) DIANA-TarBase v7.0: indexing more than half a million experimentally supported miRNA:mRNA interactions. Nucleic Acids Res 43:D153–D159. doi:10.1093/nar/gku1215

    Article  PubMed  Google Scholar 

  185. Zhang S, Yue Y, Sheng L et al (2013) PASmiR: a literature-curated database for miRNA molecular regulation in plant response to abiotic stress. BMC Plant Biol 13:33. doi:10.1186/1471-2229-13-33

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  186. The UniProt Consortium (2015) UniProt: a hub for protein information. Nucleic Acids Res 43:D204–D212. doi:10.1093/nar/gku989

    Article  Google Scholar 

  187. Griss J, Foster JM, Hermjakob H, Vizcaíno JA (2013) PRIDE cluster: building a consensus of proteomics data. Nat Methods 10:95–96. doi:10.1038/nmeth.2343

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  188. Haug K, Salek RM, Conesa P et al (2013) MetaboLights–an open-access general-purpose repository for metabolomics studies and associated meta-data. Nucleic Acids Res 41:D781–D786. doi:10.1093/nar/gks1004

    Article  CAS  PubMed  Google Scholar 

  189. Wicker N (2002) Density of points clustering, application to transcriptomic data analysis. Nucleic Acids Res 30:3992–4000. doi:10.1093/nar/gkf511

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  190. Saeed AI, Sharov V, White J et al (2003) TM4: a free, open-source system for microarray data management and analysis. Biotechniques 34:374–378

    CAS  PubMed  Google Scholar 

  191. Ulitsky I, Maron-Katz A, Shavit S et al (2010) Expander: from expression microarrays to networks and functions. Nat Protoc 5:303–322. doi:10.1038/nprot.2009.230

    Article  CAS  PubMed  Google Scholar 

  192. Gentleman RC, Carey VJ, Bates DM et al (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5:R80. doi:10.1186/gb-2004-5-10-r80

    Article  PubMed  PubMed Central  Google Scholar 

  193. Ashburner M, Ball CA, Blake JA et al (2000) Gene ontology: tool for the unification of biology. Gene Ontol Consortium Nat Genet 25:25–29. doi:10.1038/75556

    CAS  Google Scholar 

  194. Zeeberg B, Feng W, Wang G et al (2003) GoMiner: a resource for biological interpretation of genomic and proteomic data. Genome Biol 4:R28. doi:10.1186/gb-2003-4-4-r28

    Article  PubMed  PubMed Central  Google Scholar 

  195. Du Z, Zhou X, Ling Y et al (2010) agriGO: a GO analysis toolkit for the agricultural community. Nucleic Acids Res 38:W64–W70. doi:10.1093/nar/gkq310

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  196. Lohse M, Nagel A, Herter T et al (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

    Article  CAS  Google Scholar 

  197. Yi X, Du Z, Su Z (2013) PlantGSEA: a gene set enrichment analysis toolkit for plant community. Nucleic Acids Res 41:W98–W103. doi:10.1093/nar/gkt281

    Article  PubMed  PubMed Central  Google Scholar 

  198. Blake JA (2013) Ten quick tips for using the gene ontology. PLoS Comput Biol 9:e1003343. doi:10.1371/journal.pcbi.1003343

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  199. Rohn H, Junker A, Hartmann A et al (2012) VANTED v2: a framework for systems biology applications. BMC Syst Biol 6:139. doi:10.1186/1752-0509-6-139

    Article  PubMed  PubMed Central  Google Scholar 

  200. Long TA, Brady SM, Benfey PN (2008) Systems approaches to identifying gene regulatory networks in plants. Annu Rev Cell Dev Biol 24:81–103. doi:10.1146/annurev.cellbio.24.110707.175408

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  201. Moreno-Risueno MA, Busch W, Benfey PN (2010) Omics meet networks—using systems approaches to infer regulatory networks in plants. Curr Opin Plant Biol 13:126–131. doi:10.1016/j.pbi.2009.11.005

    Article  PubMed  Google Scholar 

  202. Sussman MR, Huttlin EL, Wohlbach DJ (2009) Plant systems biology. Humana Press, Totowa

    Google Scholar 

  203. Weckwerth W (2011) Green systems biology—from single genomes, proteomes and metabolomes to ecosystems research and biotechnology. J Proteomics 75:284–305. doi:10.1016/j.jprot.2011.07.010

    Article  CAS  PubMed  Google Scholar 

  204. Gehlenborg N, O’Donoghue SI, Baliga NS et al (2010) Visualization of omics data for systems biology. Nat Methods 7:S56–S68. doi:10.1038/nmeth.1436

    Article  CAS  PubMed  Google Scholar 

  205. Schreiber F, Colmsee C, Czauderna T et al (2012) MetaCrop 2.0: managing and exploring information about crop plant metabolism. Nucleic Acids Res 40:D1173–D1177. doi:10.1093/nar/gkr1004

    Article  CAS  PubMed  Google Scholar 

  206. Shannon P, Markiel A, Ozier O et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504. doi:10.1101/gr.1239303

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  207. Verdier J, Dessaint F, Schneider C, Abirached-Darmency M (2013) A combined histology and transcriptome analysis unravels novel questions on Medicago truncatula seed coat. J Exp Bot 64:459–470. doi:10.1093/jxb/ers304

    Article  CAS  PubMed  Google Scholar 

  208. Usadel B, Poree F, Nagel A et al (2009) A guide to using MapMan to visualize and compare Omics data in plants: a case study in the crop species, Maize. Plant, Cell Environ 32:1211–1229. doi:10.1111/j.1365-3040.2009.01978.x

    Article  Google Scholar 

  209. Kelder T, van Iersel MP, Hanspers K et al (2012) WikiPathways: building research communities on biological pathways. Nucleic Acids Res 40:D1301–D1307. doi:10.1093/nar/gkr1074

    Article  CAS  PubMed  Google Scholar 

  210. Salomonis N, Hanspers K, Zambon AC et al (2007) GenMAPP 2: new features and resources for pathway analysis. BMC Bioinformatics 8:217. doi:10.1186/1471-2105-8-217

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  211. Wilmes A, Limonciel A, Aschauer L et al (2013) Application of integrated transcriptomic, proteomic and metabolomic profiling for the delineation of mechanisms of drug induced cell stress. J Proteomics 79:180–194. doi:10.1016/j.jprot.2012.11.022

    Article  CAS  PubMed  Google Scholar 

  212. Villar M, Ayllon N, Alberdi P et al (2015) Integrated metabolomics, transcriptomics and proteomics identifies metabolic pathways affected by Anaplasma phagocytophilum infection in tick cells. Mol Cell Proteomics mcp.M115.051938. doi:10.1074/mcp.M115.051938

    Google Scholar 

  213. Rabara RC, Tripathi P, Reese RN et al (2015) Tobacco drought stress responses reveal new targets for Solanaceae crop improvement. BMC Genom 16:484. doi:10.1186/s12864-015-1575-4

    Article  CAS  Google Scholar 

  214. Zhang JY, Cruz de Carvalho MH, Torres-Jerez I et al (2014) Global reprogramming of transcription and metabolism in Medicago truncatula during progressive drought and after rewatering. Plant, Cell Environ 37:2553–2576. doi:10.1111/pce.12328

    Article  Google Scholar 

  215. Li X, Lawas LMF, Malo R et al (2015) Metabolic and transcriptomic signatures of rice floral organs reveal sugar starvation as a factor in reproductive failure under heat and drought stress. Plant, Cell Environ 38:2171–2192. doi:10.1111/pce.12545

    Article  CAS  Google Scholar 

  216. Srivastava V, Obudulu O, Bygdell J et al (2013) OnPLS integration of transcriptomic, proteomic and metabolomic data shows multi-level oxidative stress responses in the cambium of transgenic hipI- superoxide dismutase Populus plants. BMC Genom 14:893. doi:10.1186/1471-2164-14-893

    Article  CAS  Google Scholar 

  217. IPCC (2012) Intergovernmental panel on climate change—managing the risks of extreme events and disasters to advance climate change adaptation. Cambridge University Press, Cambridge

    Google Scholar 

  218. Chaves MM, Oliveira MM (2004) Mechanisms underlying plant resilience to water deficits: prospects for water-saving agriculture. J Exp Bot 55:2365–2384. doi:10.1093/jxb/erh269

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

The financial support from Fundação para a Ciência e a Tecnologia (Lisbon, Portugal) is acknowledged through research projects (PTDC/AGR-TEC/3555/2012 and PTDC/AGR-GPL/110244/2009), research unit GREEN-it “Bioresources for Sustainability” (UID/Multi/04551/2013), JRP Plants for Life PhD grant (PD/BD/113474/2015), DB PhD grant (SFRH/BD/82283/2011) and SSA Post-Doctoral Grant (SFRH/BPD/108032/2015).

JAPP acknowledges the research contract in the framework of the EU BIO-TALENT (The Creation of the Department of Integrative Plant Biology) project submitted under FP7-ERAChairs-Pilot Call-2013 (Grant agreement n°621321).

SSA acknowledges the research contract in frame of the project “Advanced Priming Technologies for the Lombardy Agro-Seed Industry-PRIMTECH” (Action 3, Code 2013-1727) sponsored by CARIPLO foundation and Regione Lombardia, Italy.

The authors JRP, DB, JAPP, and SSA are members of COST action FA 1306—“The quest for tolerant varieties—Phenotyping at plant and cellular level” to which networking support is acknowledged.

Author information

Authors and Affiliations

  1. Plant Cell Biotechnology Laboratory, Instituto de Tecnologia Química e Biológica (ITQB NOVA) - Universidade Nova de Lisboa, Av. da República, Estação Agronómica Nacional, 2780-157, Oeiras, Portugal

    José Ricardo Parreira, Diana Branco & Susana de Sousa Araújo

  2. Ross University School of Veterinary Medicine, PO box 334, Basseterre, Saint Kitts and Nevis

    André M. Almeida

  3. Department of Plant Breeding and Biotechnology, Institute of Soil Science and Plant Cultivation—State Research Institute, 8 Czartoryskich Street, 24-100, Puławy, Poland

    Anna Czubacka & Monika Agacka-Mołdoch

  4. Department of Integrative Plant Biology, Institute of Plant Genetics, Polish Academy of Sciences, Ul. Strzeszynska 34, 60-479, Poznan, Poland

    Jorge A. P. Paiva

  5. Okinawa Institute of Science and Technology Graduate University, Onna, Okinawa, 904-0495, Japan

    Filipe Tavares-Cadete

  6. Plant Biotechnology Laboratory, Department of Biology and Biotechnology ‘L. Spallanzani’, Università Degli Studi Di Pavia, Via Ferrata 1, 27100, Pavia, Italy

    Susana de Sousa Araújo

Authors
  1. José Ricardo Parreira
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Diana Branco
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. André M. Almeida
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Anna Czubacka
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Monika Agacka-Mołdoch
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jorge A. P. Paiva
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Filipe Tavares-Cadete
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Susana de Sousa Araújo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Susana de Sousa Araújo .

Editor information

Editors and Affiliations

  1. Genetics & Plant Breeding, Bangladesh Agricultural University, Mymensingh, Bangladesh

    Mohammad Anwar Hossain

  2. Plant Breeding & Genetics, SKUAST-Kashmir, Srinagar, Jammu and Kashmir, India

    Shabir Hussain Wani

  3. Botany, University of Burdwan, Burdwan, West Bengal, India

    Soumen Bhattacharjee

  4. Botany, University of Otago, Dunedin, Otago, New Zealand

    David J Burritt

  5. Signaling Pathway Research Unit, RIKEN Center for Sustainable Resource Sc, Yokohama, Japan

    Lam-Son Phan Tran

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Parreira, J.R. et al. (2016). Systems Biology Approaches to Improve Drought Stress Tolerance in Plants: State of the Art and Future Challenges. In: Hossain, M., Wani, S., Bhattacharjee, S., Burritt, D., Tran, LS. (eds) Drought Stress Tolerance in Plants, Vol 2. Springer, Cham. https://doi.org/10.1007/978-3-319-32423-4_16

Download citation

Publish with us

Policies and ethics

Search

Navigation