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Plant Molecular Biology Reporter

, Volume 32, Issue 2, pp 438–451 | Cite as

Differentially Expressed Genes during Flowering and Grain Filling in Common Bean (Phaseolus vulgaris) Grown under Drought Stress Conditions

  • Bárbara Salomão de Faria Müller
  • Tetsu Sakamoto
  • Ricardo Diógenes Dias Silveira
  • Patricia Fernanda Zambussi-Carvalho
  • Maristela Pereira
  • Georgios Joanis PappasJr
  • Marcos Mota do Carmo Costa
  • Cleber Moraes Guimarães
  • Wendell Jacinto Pereira
  • Claudio Brondani
  • Rosana Pereira Vianello-Brondani
Original Paper

Abstract

Drought stress, particularly during the flowering and grain-filling stages of growth, contributes to serious yield loss in common bean (Phaseolus vulgaris L.). The aim of this study was to identify genes induced in response to drought stress using transcriptome analysis of contrasting genotypes. Using leaf tissues of tolerant (BAT 477) and susceptible common bean genotypes (Pérola), collected at the flowering and grain-filling stages, four complementary deoxyribonucleic acid representational difference analysis subtractive libraries were constructed and then sequenced. A total of 7,203 (77.6 %) sequences with an average sequence size of 570 bp were considered valid, for a combined 4 Mbp sequence. According to a differential display analysis, 802 expressed sequence tags, distributed across 67 contigs, were differentially expressed by the tolerant (37 contigs) and susceptible genotypes (30 contigs) after identification under drought conditions during the two investigated plant developmental stages. Of these differential contigs, the 13 most frequent genes were exclusive to the tolerant genotype. Based on BLAST2GO, 73 % of the gene sequences were annotated and 12 % showed mapping results, with the highest similarity rate corresponding to Glycine max (41 %). According to gene ontology functional analysis, 48 % of the sequences were attributed to cell metabolic processes. Overall, 8.3 % of the transcribed sequences exhibited similarity to transcription factors, predominantly those of the AP2-EREBP family (97.8 %). Of the target sequences validated by quantitative real-time polymerase chain reaction, most genes showed an expression level that agreed with that predicted by in silico analysis. Thus, the drought transcriptome dataset is a valuable resource on the variation in these gene sequences, offering the opportunity to identify robust molecular markers tightly linked to trait-controlling loci for use in marker-assisted breeding.

Keywords

Legume crop Abiotic stress ESTs sequencing Drought stress 

Notes

Acknowledgments

We thank the National Council for Scientific and Technological Development (CNPq) for the grants to MP, GJPJr, CB, and RPV-B; the Coordination of Improvement of Higher-Education Personnel/Ministry of Education (CAPES/MEC) for the grants to BSFM, RDDS, and PFZC; the Minas Gerais State Research Foundation (Fapemig) for the grant to TS; and the Brazilian Enterprise for Agricultural Research (Embrapa MP2) for financial support for this research.

Supplementary material

11105_2013_651_MOESM1_ESM.doc (32 kb)
Additional File 1 TaqMan assay of the differentially expressed genes of common beans exposed to drought conditions. The library in which the sequence originated is indicated, as are the contig identification, TaqMan assay, and number of cluster copies (redundancy). (DOC 32 kb)
11105_2013_651_MOESM2_ESM.doc (40 kb)
Additional File 2 Data for the cDNA samples used in qPCR to validate genes that were identified as differentially expressed through in silico analysis. (DOC 40 kb)
11105_2013_651_MOESM3_ESM.doc (140 kb)
Additional File 3 Identification of the 67 contigs that were differentially expressed by the BAT 477 (37 contigs) and Pérola genotypes (30), which are tolerant and susceptible to water deficit, respectively, during both investigated developmental stages. Data include contig identification, derived gene products from the GenBank database, species with the highest similarity estimates and corresponding E-values, and the number of times the EST was sampled. (DOC 140 kb)
11105_2013_651_MOESM4_ESM.doc (109 kb)
Additional File 4 Distribution of sequences according to GO term categorization. (DOC 109 kb)
11105_2013_651_MOESM5_ESM.doc (34 kb)
Additional file 5 Statistical analysis based on values of the relative quantification (RQ) of four genes comparing the contrasting genotypes BAT477 (tolerant) and Pérola (susceptive) under different times of exposure to drought. The treatments were the control (T0) and two dehydration condition (T25 and T150). Letters shared in common between or among the groups would indicate no significant difference (95% IC, p < 0.05). (DOC 33 kb)
11105_2013_651_MOESM6_ESM.doc (68 kb)
Supplementary Figure 1 Representation of the distributions of EST sequence and plant species similarities from BLAST2GO and the NCBI database. (DOC 68.5 KB)
11105_2013_651_MOESM7_ESM.doc (64 kb)
Supplementary Figure 2 Annotation graph of the total sequences derived from the four P. vulgaris cDNA–RDA subtraction libraries. (DOC 63.5 KB)

References

  1. Abid G, Sassi K, Muhovski Y, Jacquemin J-M, Mingeot D, Tarchoun H, Baudoi J-P (2012) Identification and analysis of differentially expressed genes during seed development using suppression subtractive hybridization (SSH) in Phaseolus vulgaris. Plant Mol Biol Rep 30:719–730. doi: 10.1007/s11105-011-0381-7 CrossRefGoogle Scholar
  2. Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ (1997) Gapped BLAST and PSI-BLAST : a new generation of protein database search programs. Nucleic Acids Res 25:3389–3402PubMedCentralPubMedCrossRefGoogle Scholar
  3. Asfaw A (2011) Breeding for drought tolerance by integrative design: the case of common bean (Phaseolus vulgaris L.) in Ethiopia. Ph.D. thesis, Wageningen UniversityGoogle Scholar
  4. Athanasios ST, Konstantinos P, Panagiotis M, Anagnostis A (2012) Sequence characterization and expression analysis of three APETALA2-like genes from saffron crocus. Plant Mol Biol Rep 30:443–452CrossRefGoogle Scholar
  5. Atkinson NJ, Urwin PE (2012) The interaction of plant biotic and abiotic stresses: from genes to the field. J Exp Bot 63(10):3523–3543. doi: 10.1093/jxb/ers100 PubMedCrossRefGoogle Scholar
  6. Beebe SE, Rao IM, Blair MW, Acosta-Gallegos JA (2013) Phenotyping common beans for adaptation to drought. Front Physiol 4:35. doi: 10.3389/fphys.2013.00035 PubMedCentralPubMedCrossRefGoogle Scholar
  7. Benešová M, Holá D, Fischer L, Jedelský PL, Hnilička F, Wilhelmová N, Rothová O et al (2012) The physiology and proteomics of drought tolerance in maize: early stomatal closure as a cause of lower tolerance to short-term dehydration. PloS One 7(6):e38017PubMedCentralPubMedCrossRefGoogle Scholar
  8. Bhatnagar-Mathur P, Vadez V, Sharma KK (2008) Transgenic approaches for abiotic stress tolerance in plants: retrospect and prospects. Plant Cell Rep 27:411–424PubMedCrossRefGoogle Scholar
  9. Blair MW, Fernandez AC, Pedraza F, Muñoz-Torres MC, Kapu NS, Brown K, Lynch JP (2011a) Parallel sequencing of expressed sequence tags from two complementary DNA libraries for high and low phosphorus adaptation in common beans. Plant Genome 4(3):204–217CrossRefGoogle Scholar
  10. Blair MW, Fernandez AC, Ishitani M, Moreta D, Seki M, Ayling S (2011b) Construction and EST sequencing of full-length, drought stress cDNA libraries for common beans (Phaseolus vulgaris L.). BMC Plant Biol 11(1):171PubMedCentralPubMedCrossRefGoogle Scholar
  11. Bray EA (1993) Molecular responses to water deficit. Plant Physiol 103:1035–1040PubMedCentralPubMedGoogle Scholar
  12. Bray EA (2007) Plant response to water-deficit stress. University of Chicago, Chicago, IL. doi: 10.1002/9780470015902.a0001298.pub2
  13. Brondani RPV, Brondani C, Grattapaglia D (2007) Manual prático para o desenvolvimento de marcadores microssatélites em plantas. Embrapa Informação Tecnológica, BrasíliaGoogle Scholar
  14. Chen N, Yang QL, Su MW, Pan LJ, Chi XY, Chen MN, He YN, Yang Z, Wang T, Wang M, Yu SL (2012) Cloning of six ERF family transcription factor genes from peanut and analysis of their expression during abiotic stress. Plant Mol Biol Rep 30:1415–1425CrossRefGoogle Scholar
  15. Chen J, Song Y, Zhang H, Zhang D (2013) Genome-wide analysis of gene expression in response to drought stress in Populus simonii. Plant Mol Biol Rep 31:946–962. doi: 10.1007/s11105-013-0563-6 CrossRefGoogle Scholar
  16. Chomczynski P, Sacchi N (1987) Single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction. Anal Biochem 162(1):156–159PubMedCrossRefGoogle Scholar
  17. Chung M-Y, Vrebalov J, Alba R, Lee J, McQuinn R, Chung J-D, Klein P, Giovannoni J (2010) A tomato (Solanum lycopersicum) APETALA2/ERF gene, SlAP2a, is a negative regulator of fruit ripening. Plant J 64:936–939PubMedCrossRefGoogle Scholar
  18. Conesa A, Götz S, García-Gómez JM, Terol J, Talón M, Robles M (2005) Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 21(18):3674–3676PubMedCrossRefGoogle Scholar
  19. Day DA, Tuite MF (1998) Post-transcriptional gene regulatory mechanisms in eukaryotes: an overview. J Endocrinol 157(3):361–371PubMedCrossRefGoogle Scholar
  20. Deokar AA, Kondawar V, Jain PK, Karuppayil SM, Raju NL, Vadez V, Varshney RK, Srinivasan R (2011) Comparative analysis of expressed sequence tags (ESTs) between drought-tolerant and -susceptible genotypes of chickpea under terminal drought stress. BMC Plant Biol 11(1):70PubMedCentralPubMedCrossRefGoogle Scholar
  21. Diatchenko L, Lau YF, Campbell AP, Chenchik A, Moqadam F, Huang B, Lukyanov S, Lukyanov K, Gurskaya N, Sverdlov ED, Siebert PD (1996) Suppression subtractive hybridization: a method for generating differentially regulated or tissue-specific cDNA probes and libraries. Proc Natl Acad Sci U S A 93:6025–6030PubMedCentralPubMedCrossRefGoogle Scholar
  22. Dignat G, Welcker C, Sawkins M, Jm R, Tardie F (2013) The growths of leaves, shoots, roots and reproductive organs partly share their genetic control in maize plants. Plant Cell Environ 36:1105–1111PubMedCrossRefGoogle Scholar
  23. Emam Y, Shekoofa A, Salehi F, Jalali AH (2010) Water stress effects on two common bean cultivars with contrasting growth habits. Am–Eur J Agric Environ Sci 9(5):495–499Google Scholar
  24. Ewing B, Hillier L, Wendl MC, Green P (1998) Base-calling of automated sequencer traces using phred: I. Accuracy assessment. Genome Res 8(3):175–185PubMedCrossRefGoogle Scholar
  25. Fageria NK, Baligar VC, Jones CA (1991) Common bean and cowpea. Growth and mineral nutrition of field crops. Marcel Dekker, New York, pp 280–318Google Scholar
  26. FAO (2012) World water day 2012: water and food security. FAO water. http://www.fao.org/nr/water/news/wwd12.html. Accessed 08 February 2013
  27. Galle A, Florez-Sarasa I, Thameur A, Paepe R, Flexas J, Ribas-Carbo M (2010) Effects of drought stress and subsequent rewatering on photosynthetic and respiratory pathways in Nicotiana sylvestris wild type and the mitochondrial complex I-deficient CMSII mutant. J Exp Bot 61(3):765–775PubMedCrossRefGoogle Scholar
  28. Gonçalves RJS, Abreu AFB, Ramalho MAP, Bruzi AT (2009) Strategies for recommendation of common bean lines tested for value of cultivation and use in different environments. Crop Breed Appl Biotechnol 9:132–139Google Scholar
  29. Graham MA, Blanco-Lo L, Silvente S, Medrano-Soto A, Blair MW, Herna G, Vance CP, Lara M (2005) Sequencing and analysis of common bean ESTs. Building a foundation for functional genomics. Plant Physiol 137(4):1211–1227PubMedCentralPubMedCrossRefGoogle Scholar
  30. Guimarães CM, Zimmermann MJ, Rocha M, Yamada T (1988) Efeitos fisiológicos do estresse hídrico. Cultura do feijoeiro: fatores que afetam a produtividade. Associação Brasileira para Pesquisa da Potassa e do Fosfato, Piracicaba, pp 157–174Google Scholar
  31. Guimarães CM, Stone LF, Moreira JAA (2002) Compactação do solo na cultura do feijoeiro: II. efeito sobre o desenvolvimento radicular e da parte aérea. R Bras Eng Agric Ambiental 6(2):213–218CrossRefGoogle Scholar
  32. Harb A, Krishnan A, Ambavaram MMR, Pereira A (2010) Molecular and physiological analysis of drought stress in Arabidopsis reveals early responses leading to acclimation in plant growth. Plant Physiol 154(3):1254–1271PubMedCentralPubMedCrossRefGoogle Scholar
  33. Hellemans J, Mortier G, Paepe A, Speleman F, Vandesompele J (2007) qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data. Genome Biol 8(2):R19PubMedCentralPubMedCrossRefGoogle Scholar
  34. Jiang T, Fountain J, Davis G, Kemerait R, Scully B, Lee RD, Guo B (2012) Root morphology and gene expression analysis in response to drought stress in maize (Zea mays). Plant Mol Biol Rep 30:360–369CrossRefGoogle Scholar
  35. Jones SI, Vodkin LO (2013) Using RNA-Seq to profile soybean seed development from fertilization to maturity. PLoS One 8(3):e59270. doi: 10.1371/journal.pone.0059270 PubMedCentralPubMedCrossRefGoogle Scholar
  36. Jung HW, Lim CW, Lee SC, Choi HW, Hwang CH, Hwang BK (2008) Distinct roles of the pepper hypersensitive induced reaction protein gene CaHIR1 in disease and osmotic stress, as determined by comparative transcriptome and proteome analyses. Planta 227(2):409–425PubMedCrossRefGoogle Scholar
  37. Jung C-H, Wong CR, Singh MB, Bhalla PL (2013) Comparative genomic analysis of soybean flowering genes. PLoS One 7:e38250CrossRefGoogle Scholar
  38. Kakumanu A, Ambavaram MMR, Klumas C, Krishnan A, Batlang U, Myers E, Grene R, Pereira A (2012) Effects of drought on gene expression in maize reproductive and leaf meristem tissue revealed by RNA-Seq. Plant Physiol 160(2):846–867PubMedCentralPubMedCrossRefGoogle Scholar
  39. Kalavacharla V, Liu Z, Meyers BC, Thimmapuram J, Melmaiee K (2011) Identification and analysis of common bean (Phaseolus vulgaris L.) transcriptomes by massively parallel pyrosequencing. BMC Plant Biol 11(1):135PubMedCentralPubMedCrossRefGoogle Scholar
  40. Kam J, Gresshoff PM, Shorter R, Xue GP (2008) The Q-type C2H2 zinc finger subfamily of transcription factors in Triticum aestivum is predominantly expressed in roots and enriched with members containing an EAR repressor motif and responsive to drought stress. Plant Mol Biol 67(3):305–322PubMedCrossRefGoogle Scholar
  41. Kavar T, Maras M, Kidric M, Šuštar-Vozlic J, Meglic V (2008) Identification of genes involved in the response of leaves of Phaseolus vulgaris to drought stress. Mol Breed 21:159–172CrossRefGoogle Scholar
  42. Kim SH, Hong JK, Lee SC, Sohn KH, Jung HW (2004) CAZFP1, Cys 2/His 2-type zinc-finger transcription factor gene functionsas a pathogen-induced early-defense gene in Capsicum annuum. Plant Mol Biol 55:883–904PubMedCrossRefGoogle Scholar
  43. Lacerda CF, Carvalho CM, Vieira MR, Nobre JGA, Neves ALR, Rodrigues CF (2010) Análise de crescimento de milho e feijão sob diferentes condições de sombreamento. R Bras Ciências Agrár 5(1):18–24Google Scholar
  44. Le DT, Nishiyama R, Watanabe Y, Tanaka M, Seki M, Ham LH, Yamaguchi-Shinozaki K, Shinozaki K, Tran L-SP (2012) Differential gene expression in soybean leaf tissues at late developmental stages under drought stress revealed by genome-wide transcriptome analysis. PLoS One 7:e49522PubMedCentralPubMedCrossRefGoogle Scholar
  45. Lee J, Han CT, Hur Y (2013) Molecular characterization of the Brassica rapa auxin-repressed, superfamily genes, BrARP1 and BrDRM1. Mol Biol Rep 40(1):197–209PubMedCrossRefGoogle Scholar
  46. Li G, Tai FJ, Zheng Y, Luo J, Gong SY, Zhang ZT, Li XB (2010) Two cotton Cys2/His2-type zinc-finger proteins, GhDi19-1 and GhDi19-2, are involved in plant response to salt/drought stress and abscisic acid signaling. Plant Mol Biol 74(4–5):437–452PubMedCrossRefGoogle Scholar
  47. Li M-Y, Wang F, Jiang Q, Li R, Ma J, Xiong A-S (2013) Genome-wide analysis of the distribution of AP2/ERF transcription factors reveals duplication and elucidates their potential function in Chinese cabbage (Brassica rapa ssp. pekinensis). Plant Mol Biol Rep 31:1002–1011. doi: 10.1007/s11105-013-0570-7 CrossRefGoogle Scholar
  48. Liebrand TWH, Smit P, Abd-El-Haliem A, Jonge R, Cordewener JHG, America AHP, Sklenar J, Jones AME, Robatzek S, Thomma BPHJ, Tameling WIL, Joosten MHAJ (2012) Endoplasmic reticulum-quality control chaperones facilitate the biogenesis of Cf receptor-like proteins involved in pathogen resistance of tomato. Plant Physiol 159:1819–1833PubMedCentralPubMedCrossRefGoogle Scholar
  49. Lisitsyn N, Lisitsyn N, Wigler M (1993) Cloning the differences between two complex genomes. Science 259:946–951PubMedCrossRefGoogle Scholar
  50. Liu M, Shi J, Lu C (2013) Identification of stress-responsive genes in Ammopiptanthus mongolicus using EST generated from cold- and drought-stressed seedlings. BMC Plant Biol 13:88PubMedCentralPubMedCrossRefGoogle Scholar
  51. Lizana C, Wentworth M, Martinez JP, Villegas D, Meneses R, Murchie EH, Pastenes C et al (2006) Differential adaptation of two varieties of common bean to abiotic stress: I. Effects of drought on yield and photosynthesis. J Exp Bot 57(3):685–697PubMedCrossRefGoogle Scholar
  52. Mantri NL, Ford R, Coram TE, Pang ECK (2007) Trascriptional profiling of chickpea genes differentially regulated in response to high-salinity, cold and drought. BMC Genomics 8:303PubMedCentralPubMedCrossRefGoogle Scholar
  53. Martins PK, Jordão BQ, Yamanaka N, Farias JRB, Beneventi MA, Binneck E, Fuganti R, Stolf R, Nepomuceno AL (2008) Differential gene expression and mitotic cell analysis of the drought tolerant soybean (Glycine max L. Merrill Fabales, Fabaceae) Cultivar MG/BR46 (Conquista) under two water deficit induction systems. Genet Mol Biol 31(2):512–521CrossRefGoogle Scholar
  54. McClean PE, Mamidi S, McConnell M, Chikara S, Lee R (2010) Synteny mapping between common bean and soybean reveals extensive blocks of shared loci. BMC Genomics 18(11):184CrossRefGoogle Scholar
  55. Miao GH, Hong Z, Verma DP (1992) Topology and phosphorylation of soybean Nodulin-26, an intrinsic protein of the peribacteroid membrane. J Cell Biol 118(2):481–490PubMedCrossRefGoogle Scholar
  56. Miklas PN, Kelly JD, Beebe SD, Blair MW (2006) Common bean breeding for resistance against biotic and abiotic stresses: from classical to MAS breeding. Euphytica 147:105–131CrossRefGoogle Scholar
  57. Morais Júnior OP, Menezes J, Silva SI, Silva ACL, Stone LF, Guimarães CM (2008) Ajustamento hídrico do feijoeiro em condições de seca. Documentos, IAC, Campinas 85(62):1129–1132Google Scholar
  58. Mullet JE, Whitsitt MS (1996) Plant cellular responses to water deficit. Plant Growth Regul 20(2): 119–124 http://link.springer.com/journal/10725
  59. Nadimpalli R, Yalpani N, Johal GS, Simmons CR (2000) Prohibitins, stomatins, and plant disease response genes compose a protein superfamily that controls cell proliferation, ion channel regulation, and death. J Biol Chem 275(38):29579–29586PubMedCrossRefGoogle Scholar
  60. Nagalakshmi U, Waern K, Snyder M (2010) RNA-Seq: a method for comprehensive transcriptome analysis, chap 4. Curr Protoc Mol Biol. Unit 4.11.1–13Google Scholar
  61. Nunez-Barrios A (1991) Effects of soil water deficits on dry beans (Phaseolus vulgaris L.) at different growing stages. Ph.D. thesis, Michigan State UniversityGoogle Scholar
  62. Oh SJ, Song SI, Kim YS, Jang HJ, Kim SY, Kim M, Kim YK, Nahm BH, Kim JK (2005) Arabidopsis CBF3/DREB1A and ABF3 in transgenic rice increased tolerance to abiotic stress without stunting growth. Plant Physiol 138(1):341–351PubMedCentralPubMedCrossRefGoogle Scholar
  63. Pappas GJ Jr, Miranda RP, Martins NF, Togawa RC, Costa MMC (2008) SisGen: a CORBA-based data management program for DNA sequencing projects. Lect Notes Comput Sci 5109:116–123CrossRefGoogle Scholar
  64. Pastorian K, Hawel L, Byus CV (2000) Optimization of cDNA representational difference analysis for the identification of differentially expressed mrnas. Anal Biochem 283(1):89–98, July 15PubMedCrossRefGoogle Scholar
  65. Pérez-Rodríguez P, Riaño-Pachón DM, Corrêa LG, Rensing SA, Kersten B, Mueller-Roeber B (2010) PlnTFDB: updated content and new features of the plant transcription factor database. Nucleic Acids Res 38(Database issue):D822–D827PubMedCentralPubMedCrossRefGoogle Scholar
  66. Pertea G, Huang X, Liang F, Antonescu V, Sultana R, Karamycheva S, Lee Y, White J, Cheung F, Parvizi B, Tsai J, Quackenbush J (2003) TIGR gene indices clustering tools (TGICL): a software system for fast clustering of large EST datasets. Bioinformatics 19(5):651–652PubMedCrossRefGoogle Scholar
  67. Pieters A, Souki SE (2005) Effects of drought during grain filling on PSII activity in rice. J Plant Physiol 162:903–911PubMedCrossRefGoogle Scholar
  68. Prabu G, Kawar PG, Pagariya MC, Prasad DT (2011) Identification of water deficit stress upregulated genes in sugarcane. Plant Mol Biol Rep 29:291–304CrossRefGoogle Scholar
  69. Rabello AR, Guimarães CM, Rangel PH, da Silva FR, Seixas D, de Souza E, Brasileiro AC, Spehar CR, Ferreira ME, Mehta A (2008) Identification of drought-responsive genes in roots of upland rice (Oryza sativa L.). BMC Genomics 9:485PubMedCentralPubMedCrossRefGoogle Scholar
  70. Rosales MA, Cuellar-Ortiz SM, de la Paz A-MM, Acosta-Gallegos J, Covarrubias AA (2012a) Physiological traits related to terminal drought resistance in common bean (Phaseolus vulgaris L.). J Sci Food Agric 93(2):324–331PubMedCrossRefGoogle Scholar
  71. Rosales MA, Ocampo E, Rodríguez-Valentín R, Olvera-Carrillo Y, Acosta-Gallegos J, Covarrubias AA (2012b) Physiological analysis of common bean (Phaseolus vulgaris L.) cultivars uncovers characteristics related to terminal drought resistance. Plant Physiol Biochem 56:24–34PubMedCrossRefGoogle Scholar
  72. Rosales-Serna R, Kohashi-Shibata J, Acosta-Gallegos JA, Trejo-López C, Ortiz-Cereceres J, Kelly JD (2004) Biomass distribution, maturity acceleration and yield in drought-stressed common bean cultivars. Field Crops Res 85(2–3):203–211CrossRefGoogle Scholar
  73. Schena M, Shalon D, Davis RW, Brown PO (1995) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270:467–470PubMedCrossRefGoogle Scholar
  74. Schmutz J, Cannon SB, Schlueter J, Ma J, Mitros T, Nelson W, Hyten DL et al (2010) Genome sequence of the palaeopolyploid soybean. Nature 463(7278):178–183PubMedCrossRefGoogle Scholar
  75. Seki M, Narusaka M, Ishida J, Nanjo T, Fujita M, Oono Y, Kamiya A et al (2002) Monitoring the expression profiles of 7000 Arabidopsis genes under drought, cold and high-salinity stresses using a full-length cDNA microarray. Plant J 31(3):279–292PubMedCrossRefGoogle Scholar
  76. Serraj R, Krishnamurthy L, Kashiwagi J, Kumar J, Chandra S, Crouch JH (2004) Variation in root traits of chick pea (Cicer arietinum L.) grown under terminal drought. Field Crops Res 88:115–127CrossRefGoogle Scholar
  77. Szilagyi L (2003) Influence of drought on seed components in common bean. Bulg J Plant Physiol Special Issue:320–330Google Scholar
  78. Teaster ND, Keereetaweep J, Kilaru A, Wang Y-S, Tang Y, Tran CN-Q, Ayre BG, Chapman KD, Blancaflor EB (2012) Overexpression of fatty acid amide hydrolase induces early flowering in Arabidopsis thaliana. Front Plant Sci 3:32. doi: 10.3389/fpls.2012.00032 PubMedCentralPubMedCrossRefGoogle Scholar
  79. Terán H, Singh SP (2002) Comparison of sources and lines selected for drought resistance in common bean. Crop Sci 42:64–70PubMedCrossRefGoogle Scholar
  80. Tezara W, Mitchell VJ, Driscoll SD, Lawlor DW (1999) Water stress inhibits plant photosynthesis by decreasing coupling factor and ATP. Nature 401:914–917CrossRefGoogle Scholar
  81. Thompson JD, Higgins DG, Gibson TJ (1994) CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res 22:4673–4680PubMedCentralPubMedCrossRefGoogle Scholar
  82. Tian J, Venkatachalam P, Liao H, Yan X, Raghothama K (2007) Molecular cloning and characterization of phosphorus starvation responsive genes in common bean (Phaseolus vulgaris L.). Planta 227(1):151–165PubMedCrossRefGoogle Scholar
  83. Torres MA, Dangl JL (2005) Functions of the respiratory burst oxidase in biotic interactions, abiotic stress and development. Curr Opin Plant Biol 8(4):397–403PubMedCrossRefGoogle Scholar
  84. van der Luit AH, Olivari C, Haley A, Knight MR, Trewavas AJ (1999) Distinct Calcium Signaling Pathways Regulate Calmodulin Gene Expression in Tobacco. Plant Physiol 121:705–714PubMedCentralCrossRefGoogle Scholar
  85. Velculescu VE, Zhang L, Vogelstein B, Kinzler KW (1995) Serial analysis of gene expression. Science 270(5235):484–487PubMedCrossRefGoogle Scholar
  86. Vicuna D, Malone RP, Dix PJ (2011) Increased tolerance to abiotic stresses in tobacco plants expressing a barley cell wall peroxidase. J Plant Sci 6:1–13CrossRefGoogle Scholar
  87. Wallace IS, Choi WG, Roberts DM (2006) The structure, function and regulation of the nodulin 26-like intrinsic protein family of plant aquaglyceroporins. Biochim Biophys Acta 1758(8):1165–1175PubMedCrossRefGoogle Scholar
  88. Weber H, Borisjuk L, Wobus U (2005) Molecular physiology of legumes seed development. Annu Rev Plant Biol 56:253–279PubMedCrossRefGoogle Scholar
  89. Xiao F, Tang X, Zhou JM (2001) Expression of 35S:Pto globally activates defense-related genes in tomato plants. Plant Physiol 126(4):1637–1645PubMedCentralPubMedCrossRefGoogle Scholar
  90. Xiong L, Schumaker K, Zhu J (2002) Cell signaling during cold, drought, and salt stress. Plant Cell Online 14:165–183CrossRefGoogle Scholar
  91. Yang L, Ji W, Zhu Y, Gao P, Li Y, Cai H, Bai X, Guo D (2010) GsCBRLK, a calcium/calmodulin-binding receptor-like kinase, is a positive regulator of plant tolerance to salt and ABA stress. J Exp Bot 61(9):2519–2533PubMedCrossRefGoogle Scholar
  92. Yoshioka K, Fukushima S, Yamazaki T, Yoshida M, Takatsuji H (2001) The plant zinc finger protein ZPT2-2 has a unique mode of DNA interaction. J Biol Chem 276(38):35802–35807PubMedCrossRefGoogle Scholar
  93. Yu J, Farjo R, MacNee SP, Baehr W, Stambolian DE, Swaroop A (2003) Annotation and analysis of 10,000 expressed sequence tags from developing mouse eye and adult retina. Genome Biol 4(10):R65PubMedCentralPubMedCrossRefGoogle Scholar
  94. Zhou MB, Yang P, Gao PJ, Tang D (2011) Identification of differentially expressed sequence taqs in rapidly elongating Phyllostachys pubescens internodes by suppressive subtractive hybridization. Plant Mol Biol Rep 29:224–231CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Bárbara Salomão de Faria Müller
    • 1
    • 3
  • Tetsu Sakamoto
    • 2
  • Ricardo Diógenes Dias Silveira
    • 3
  • Patricia Fernanda Zambussi-Carvalho
    • 4
  • Maristela Pereira
    • 4
  • Georgios Joanis PappasJr
    • 5
  • Marcos Mota do Carmo Costa
    • 6
  • Cleber Moraes Guimarães
    • 7
  • Wendell Jacinto Pereira
    • 3
  • Claudio Brondani
    • 3
  • Rosana Pereira Vianello-Brondani
    • 3
  1. 1.Plant Molecular Genetics Laboratory, Institute of Biotechnology Applied to Agriculture and Animal Science (BIOAGRO)Federal University of ViçosaViçosaBrazil
  2. 2.BioData Laboratory, Institute of Biological ScienceFederal University of Minas GeraisBelo HorizonteBrazil
  3. 3.Biotechnology LaboratoryEmbrapa Rice and BeansSanto Antônio de GoiásBrazil
  4. 4.Molecular Biology Laboratory, Department of Biochemistry and Molecular Biology, Institute of Biological ScienceFederal University of GoiásGoiâniaBrazil
  5. 5.Laboratory of Molecular Biology, Department of Cellular BiologyUniversity of BrasíliaBrasíliaBrazil
  6. 6.Bioinformatics LaboratoryEmbrapa Genetic Resources and BiotechnologyBrasíliaBrazil
  7. 7.Agrophysiology LaboratoryEmbrapa Rice and BeansSanto Antônio de GoiásBrazil

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