Additive and heterozygous (dis)advantage GWAS models reveal candidate genes involved in the genotypic variation of maize hybrids to Azospirillum brasilense


Autoři: Miriam Suzane Vidotti aff001;  Danilo Hottis Lyra aff002;  Júlia Silva Morosini aff001;  Ítalo Stefanine Correia Granato aff003;  Maria Carolina Quecine aff001;  João Lúcio de Azevedo aff001;  Roberto Fritsche-Neto aff001
Působiště autorů: Department of Genetics, “Luiz de Queiroz” College of Agriculture, University of São Paulo, Piracicaba, São Paulo, Brazil aff001;  Rothamsted Research, Harpenden, Hertfordshire, England, United Kingdom aff002;  French National Institute for Agricultural Research, Montpellier, France aff003
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0222788

Souhrn

Maize genotypes can show different responsiveness to inoculation with Azospirillum brasilense and an intriguing issue is which genes of the plant are involved in the recognition and growth promotion by these Plant Growth-Promoting Bacteria (PGPB). We conducted Genome-Wide Association Studies (GWAS) using additive and heterozygous (dis)advantage models to find candidate genes for root and shoot traits under nitrogen (N) stress and N stress plus A. brasilense. A total of 52,215 Single Nucleotide Polymorphism (SNP) markers were used for GWAS analyses. For the six root traits with significant inoculation effect, the GWAS analyses revealed 25 significant SNPs for the N stress plus A. brasilense treatment, in which only two were overlapped with the 22 found for N stress only. Most were found by the heterozygous (dis)advantage model and were more related to exclusive gene ontology terms. Interestingly, the candidate genes around the significant SNPs found for the maize–A. brasilense association were involved in different functions previously described for PGPB in plants (e.g. signaling pathways of the plant's defense system and phytohormone biosynthesis). Our findings are a benchmark in the understanding of the genetic variation among maize hybrids for the association with A. brasilense and reveal the potential for further enhancement of maize through this association.

Klíčová slova:

Biology and life sciences – Computational biology – Genome-wide association studies – Genetics – Genomics – Genome analysis – Human genetics – Gene expression – Gene regulation – Heredity – Genetic mapping – Variant genotypes – Molecular genetics – Phenotypes – Organisms – Eukaryota – Plants – Grasses – Maize – Biochemistry – Proteins – DNA-binding proteins – Transcription factors – Regulatory proteins – Hormones – Plant hormones – Plant science – Plant physiology – Plant defenses – Plant resistance to abiotic stress – Plant pathology – Plant ecology – Plant-environment interactions – Plant biochemistry – Ecology – Molecular biology – Research and analysis methods – Animal studies – Experimental organism systems – Model organisms – Plant and algal models – Ecology and environmental sciences


Zdroje

1. Fukami J, Ollero FJ, Megías M, Hungria M. Phytohormones and induction of plant-stress tolerance and defense genes by seed and foliar inoculation with Azospirillum brasilense cells and metabolites promote maize growth. AMB Express. 2017; 7: 153. doi: 10.1186/s13568-017-0453-7 28724262

2. Fukami J, Ollero FJ, de la Osa C, Valderrama-Fernández R, Nogueira MA, Megías M, et al. Antioxidant activity and induction of mechanisms of resistance to stresses related to the inoculation with Azospirillum brasilense. Arch Microbiol. 2018; 200: 1191–1203. doi: 10.1007/s00203-018-1535-x 29881875

3. Cassán F, Diaz-Zorita M. Azospirillum sp. in current agriculture: From the laboratory to the field. Soil Biol Biochem. 2016; 103: 117–130. doi: 10.1016/j.soilbio.2016.08.020

4. Carvalho TLG, Ballesteros HGF, Thiebaut F, Ferreira PCG, Hemerly AS. Nice to meet you: genetic, epigenetic and metabolic controls of plant perception of beneficial associative and endophytic diazotrophic bacteria in non-leguminous plants. Plant Mol Biol. 2016; 90: 561–574. doi: 10.1007/s11103-016-0435-1 26821805

5. Cunha FN, Silva NF da, Ribeiro Rodrigues C, Alves Morais W, Ferreira Gomes FH, Filho Lopes, Luiz C, et al. Performance of different genotypes of maize subjected to inoculation with Azospirillum brasilense. African J Agric Res. 2016; 11: 3853–3862. doi: 10.5897/AJAR2016.11496

6. Brusamarello-Santos LC, Gilard F, Brulé L, Quilleré I, Gourion B, Ratet P, et al. Metabolic profiling of two maize (Zea mays L.) inbred lines inoculated with the nitrogen fixing plant-interacting bacteria Herbaspirillum seropedicae and Azospirillum brasilense. Aroca R, editor. PLoS One. 2017; 12: e0174576. doi: 10.1371/journal.pone.0174576 28362815

7. Rosas JE, Martínez S, Blanco P, Pérez de Vida F, Bonnecarrère V, Mosquera G, et al. Resistance to Multiple Temperate and Tropical Stem and Sheath Diseases of Rice. Plant Genome. 2017; 11: 1–13. doi: 10.3835/plantgenome2017.03.0029 29505639

8. Genissel A, Confais J, Lebrun M-H, Gout L. Association Genetics in Plant Pathogens: Minding the Gap between the Natural Variation and the Molecular Function. Front Plant Sci. 2017; 8: 8–11. doi: 10.3389/fpls.2017.00008

9. Lehnert H, Serfling A, Enders M, Friedt W, Ordon F. Genetics of mycorrhizal symbiosis in winter wheat (Triticum aestivum). New Phytol. 2017; 215: 779–791. doi: 10.1111/nph.14595 28517039

10. De Vita P, Avio L, Sbrana C, Laidò G, Marone D, Mastrangelo AM, et al. Genetic markers associated to arbuscular mycorrhizal colonization in durum wheat. Sci Rep. 2018; 8: 10612. doi: 10.1038/s41598-018-29020-6 30006562

11. Wallace JG, Kremling KA, Kovar LL, Buckler ES. Maize Leaf Microbiome. Phyt. J. 2018; 2:208–224. https://doi.org/10.1094/PBIOMES-02-18-0008-R

12. Kamfwa K, Cichy KA, Kelly JD. Genome-wide association analysis of symbiotic nitrogen fixation in common bean. Theor Appl Genet. 2015; 128: 1999–2017. doi: 10.1007/s00122-015-2562-5 26133733

13. Wintermans PCA, Bakker PAHM, Pieterse CMJ. Natural genetic variation in Arabidopsis for responsiveness to plant growth-promoting rhizobacteria. Plant Mol Biol. 2016; 90: 623–634. doi: 10.1007/s11103-016-0442-2 26830772

14. Pieterse CMJ, de Jonge R, Berendsen RL. The Soil-Borne Supremacy. Trends Plant Sci. 2016;21: 171–173. doi: 10.1016/j.tplants.2016.01.018 26853594

15. Carvalhais LC, Dennis PG, Fan B, Fedoseyenko D, Kierul K, Becker A, et al. Linking Plant Nutritional Status to Plant-Microbe Interactions. PLoS One. 2013; 8: e68555. doi: 10.1371/journal.pone.0068555 23874669

16. Castrillo G, Teixeira PJPL, Paredes SH, Law TF, De Lorenzo L, Feltcher ME, et al. Root microbiota drive direct integration of phosphate stress and immunity. Nature. 2017; 543: 513–518. doi: 10.1038/nature21417 28297714

17. Gomes EA, Lana UGP, Quensen JF, Oliveira CA, Guo J, Guimar LJM, et al. Root-associated microbiome of maize genotypes with contrasting phosphorus use efficiency. Phyt. J. 2018; 2: 129–137. https://doi.org/10.1094/PBIOMES-03-18-0012-R

18. Yang J, Mezmouk S, Baumgarten A, Buckler ES, Guill KE, McMullen MD, et al. Incomplete dominance of deleterious alleles contributes substantially to trait variation and heterosis in maize. PLOS Genet. 2017; 13: e1007019. doi: 10.1371/journal.pgen.1007019 28953891

19. Li Z, Coffey L, Garfin J, Miller ND, White MR, Spalding EP, et al. Genotype-by-environment interactions affecting heterosis in maize. PLoS One. 2018; 13: e0191321. doi: 10.1371/journal.pone.0191321 29342221

20. Li H, Yang Q, Gao L, Zhang M, Ni Z, Zhang Y. Identification of Heterosis-Associated stable QTLs for ear-weight-related traits in an elite maize hybrid by Zhengdan 958 Design III. Front Plant Sci. 2017; 8: 1–10. doi: 10.3389/fpls.2017.00001

21. Bonnafous F, Fievet G, Blanchet N, Boniface MC, Carrère S, Gouzy J, et al. Comparison of GWAS models to identify non-additive genetic control of flowering time in sunflower hybrids. Theor Appl Genet. 2018; 131: 319–332. doi: 10.1007/s00122-017-3003-4 29098310

22. Monir MM, Zhu J. Dominance and Epistasis Interactions Revealed as Important Variants for Leaf Traits of Maize NAM Population. Front Plant Sci. 2018; 9: 1–10. doi: 10.3389/fpls.2018.00001

23. Picard C, Bosco M. Maize heterosis affects the structure and dynamics of indigenous rhizospheric auxins-producing Pseudomonas populations. FEMS Microbiol Ecol. 2005; 53: 349–357. doi: 10.1016/j.femsec.2005.01.007 16329954

24. Picard C, Bosco M. Heterozygosis drives maize hybrids to select elite 2,4-diacethylphloroglucinol-producing Pseudomonas strains among resident soil populations. FEMS Microbiol Ecol. 2006; 58: 193–204. doi: 10.1111/j.1574-6941.2006.00151.x 17064261

25. Groszmann M, Gonzalez-Bayon R, Lyons RL, Greaves IK, Kazan K, Peacock WJ, et al. Hormone-regulated defense and stress response networks contribute to heterosis in Arabidopsis F1 hybrids. Proc Natl Acad Sci. 2015; 112: E6397–E6406. doi: 10.1073/pnas.1519926112 26527659

26. Hu X, Wang H, Li K, Wu Y, Liu Z, Huang C. Genome-wide proteomic profiling reveals the role of dominance protein expression in heterosis in immature maize ears. Sci Rep. 2017; 7: 16130. doi: 10.1038/s41598-017-15985-3 29170427

27. Ko DK, Rohozinski D, Song Q, Taylor SH, Juenger TE, Harmon FG, et al. Temporal Shift of Circadian-Mediated Gene Expression and Carbon Fixation Contributes to Biomass Heterosis in Maize Hybrids. PLOS Genet. 2016; 12: e1006197. doi: 10.1371/journal.pgen.1006197 27467757

28. Paschold A, Marcon C, Hoecker N, Hochholdinger F. Molecular dissection of heterosis manifestation during early maize root development. Theor Appl Genet. 2010; 120: 383–388. doi: 10.1007/s00122-009-1082-6 19526205

29. Hardoim PR, de Carvalho TLG, Ballesteros HGF, Bellieny-Rabelo D, Rojas CA, Venancio TM, et al. Genome-wide transcriptome profiling provides insights into the responses of maize (Zea mays L.) to diazotrophic bacteria. Plant Soil. Plant and Soil; 2019; doi: 10.1007/s11104-019-03939-9

30. Planchamp C, Glauser G, Mauch-Mani B. Root inoculation with Pseudomonas putida KT2440 induces transcriptional and metabolic changes and systemic resistance in maize plants. Front Plant Sci. 2015; 5: 1–10. doi: 10.3389/fpls.2014.00719 25628626

31. Calzavara AK, Paiva PHG, Gabriel LC, de Oliveira ALM, Milani K, Oliveira HC, et al. Associative bacteria influence maize (Zea mays L.) growth, physiology and root anatomy under different nitrogen levels. Plant Biol. 2018; 20: 870–878. doi: 10.1111/plb.12841 29762883

32. Di Salvo LP, Ferrando L, Fernández-Scavino A, García de Salamone IE. Microorganisms reveal what plants do not: wheat growth and rhizosphere microbial communities after Azospirillum brasilense inoculation and nitrogen fertilization under field conditions. Plant Soil. Plant and Soil; 2018; 1–13. doi: 10.1007/s11104-017-3548-7

33. Tsepilov YA, Shin S-Y, Soranzo N, Spector TD, Prehn C, Adamski J, et al. Nonadditive Effects of Genes in Human Metabolomics. Genetics. 2015; 200: 707–718. doi: 10.1534/genetics.115.175760 25977471

34. Goyette P, Boucher G, Mallon D, Ellinghaus E, Jostins L, Huang H, et al. High-density mapping of the MHC identifies a shared role for HLA-DRB1*01:03 in inflammatory bowel diseases and heterozygous advantage in ulcerative colitis. Nat Genet. 2015; 47: 172–9. doi: 10.1038/ng.3176 25559196

35. Hungria M, Campo RJ, Souza EM, Pedrosa FO. Inoculation with selected strains of Azospirillum brasilense and A. lipoferum improves yields of maize and wheat in Brazil. Plant Soil. 2010; 331: 413–425. doi: 10.1007/s11104-009-0262-0

36. Rodrigues Neto J, Malavolta VA Jr, Victor O. Meio simples para o isolamento e cultivo de Xanthomonas campestris pv. citri Tipo B. Summa Phytopathol. 1986; 12: 32.

37. Mendonça LDF, Granato ÍSC, Alves FC, Morais PPP, Vidotti MS, Fritsche-Neto R. Accuracy and simultaneous selection gains for N-stress tolerance and N-use efficiency in maize tropical lines. Sci Agric. 2017; 74: 481–488. doi: 10.1590/1678-992x-2016-0313

38. Lanes ECM, Viana JMS, Paes GP, Paula MFB, Maia C, Caixeta ET, et al. Population structure and genetic diversity of maize inbreds derived from tropical hybrids. Genet Mol Res. 2014; 13: 7365–7376. doi: 10.4238/2014.September.12.2 25222235

39. Morosini JS, Mendonça LDF, Lyra DH, Galli G, Vidotti MS, Fritsche-Neto R. Association mapping for traits related to nitrogen use efficiency in tropical maize lines under field conditions. Plant and Soil; 2017; 421: 453–463. doi: 10.1007/s11104-017-3479-3

40. Kox MAR, Lüke C, Fritz C, van den Elzen E, van Alen T, Op den Camp HJM, et al. Effects of nitrogen fertilization on diazotrophic activity of microorganisms associated with Sphagnum magellanicum. Plant and Soil; 2016; 406: 83–100. doi: 10.1007/s11104-016-2851-z

41. Zeffa DM, Fantin LH, Santos OJAP dos, Oliveira ALM de, Canteri MG, Scapim CA, et al. The influence of topdressing nitrogen on Azospirillum spp. inoculation in maize crops through meta-analysis. Bragantia. 2018; 77: 493–500. doi: 10.1590/1678-4499.2017273

42. Vidotti MS, Matias FI, Alves FC, Pérez-Rodríguez P, Beltran GA, Burgueño J, et al. Maize responsiveness to Azospirillum brasilense: Insights into genetic control, heterosis and genomic prediction. PLoS One. 2019; 14: e0217571. doi: 10.1371/journal.pone.0217571 31173600

43. Trachsel S, Kaeppler SM, Brown KM, Lynch JP. Maize root growth angles become steeper under low N conditions. F Crop Res. 2013; 140: 18–31. doi: 10.1016/j.fcr.2012.09.010

44. Butler DG, Cullis BR, Gilmour AR, Gogel BJ. Analysis of Mixed Models for S–language Environments: ASReml-R Reference Manual. 2009; Available: https://vsn.klever.co.uk/downloads/asreml/release3/asreml-R.pdf

45. Brien C. asremlPlus: Augments the Use of “ASReml-R” in Fitting Mixed Models. R Packag Version 20–12. Available: https://cran.r-project.org/web/packages/asremlPlus/asremlPlus.pdf

46. Unterseer S, Bauer E, Haberer G, Seidel M, Knaak C, Ouzunova M, et al. A powerful tool for genome analysis in maize: development and evaluation of the high density 600 k SNP genotyping array. BMC Genomics. 2014; 15: 823. doi: 10.1186/1471-2164-15-823 25266061

47. Browning BL, Browning SR. Genotype Imputation with Millions of Reference Samples. Am J Hum Genet. 2016; 98: 116–126. doi: 10.1016/j.ajhg.2015.11.020 26748515

48. Zheng X, Levine D, Shen J, Gogarten SM, Laurie C, Weir BS. A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics. 2012; 28: 3326–3328. doi: 10.1093/bioinformatics/bts606 23060615

49. Liu X, Huang M, Fan B, Buckler ES, Zhang Z. Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association Studies. PLoS Genet. 2016; 12: 100. doi: 10.1371/journal.pgen.1005767 26828793

50. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, et al. PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses. Am J Hum Genet. 2007; 81: 559–575. doi: 10.1086/519795 17701901

51. Chen H, Boutros PC. VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinformatics. 2011; 12: 35. doi: 10.1186/1471-2105-12-35 21269502

52. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K D, t SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM SG. Gene Ontology: tool for the unification of biology. Nat Genet. 2000; 25: 25–29. https://doi.org/10.1038/75556 10802651

53. Conesa A, Gotz S, Garcia-Gomez JM, Terol J, Talon M, Robles M. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 2005;21: 3674–3676. doi: 10.1093/bioinformatics/bti610 16081474

54. Rozier C, Erban A, Hamzaoui J, Prigent-Combaret C, Comte G, Kopka J, et al. Xylem Sap Metabolite Profile Changes During Phytostimulation of Maize by the Plant Growth-Promoting Rhizobacterium, Azospirillum lipoferum CRT1. J Postgenomics Drug Biomark Dev. 2016; 6: 1–10. doi: 10.4172/2153-0769.1000182

55. Zeffa DM, Perini LJ, Silva MB, de Sousa NV, Scapim CA, Oliveira ALM de, et al. Azospirillum brasilense promotes increases in growth and nitrogen use efficiency of maize genotypes. PLoS One. 2019; 14: e0215332. doi: 10.1371/journal.pone.0215332 30998695

56. Cohen AC, Travaglia CN, Bottini R, Piccoli PN. Participation of abscisic acid and gibberellins produced by endophytic Azospirillum in the alleviation of drought effects in maize. Botany. 2009; 87: 455–462. doi: 10.1139/B09-023

57. Walker V, Bertrand C, Bellvert F, Moënne-Loccoz Y, Bally R, Comte G. Host plant secondary metabolite profiling shows a complex, strain-dependent response of maize to plant growth-promoting rhizobacteria of the genus Azospirillum. New Phytol. 2011; 189: 494–506. doi: 10.1111/j.1469-8137.2010.03484.x 20946131

58. Rozier C, Hamzaoui J, Lemoine D, Czarnes S, Legendre L. Field-based assessment of the mechanism of maize yield enhancement by Azospirillum lipoferum CRT1. Sci Rep. 2017; 7: 1–12. doi: 10.1038/s41598-016-0028-x

59. Martins MR, Jantalia CP, Reis VM, Döwich I, Polidoro JC, Alves BJR, et al. Impact of plant growth-promoting bacteria on grain yield, protein content, and urea-15 N recovery by maize in a Cerrado Oxisol. Plant and Soil; 2018; 422: 239–250. doi: 10.1007/s11104-017-3193-1

60. Curá JA, Franz DR, Filosofía JE, Balestrasse KB, Burgueño LE. Inoculation with Azospirillum sp. and Herbaspirillum sp. bacteria increases the tolerance of maize to drought stress. Microorganisms. 2017; 5: 41. doi: 10.3390/microorganisms5030041 28933739

61. Spaepen S, Bossuyt S, Engelen K, Marchal K, Vanderleyden J. Phenotypical and molecular responses of Arabidopsis thaliana roots as a result of inoculation with the auxin-producing bacterium Azospirillum brasilense. New Phytol. 2014; 201: 850–861. doi: 10.1111/nph.12590 24219779

62. Chamam A, Wisniewski-Dyé F, Comte G, Bertrand C, Prigent-Combaret C. Differential responses of Oryza sativa secondary metabolism to biotic interactions with cooperative, commensal and phytopathogenic bacteria. Planta. 2015; 242: 1439–1452. doi: 10.1007/s00425-015-2382-5 26303982

63. Gopal M, Gupta A. Microbiome selection could spur next-generation plant breeding strategies. Front Microbiol. 2016; 7: 1–10. doi: 10.3389/fmicb.2016.00001

64. Hohmann P, Messmer MM. Breeding for mycorrhizal symbiosis: focus on disease resistance. Euphytica. 2017;213: 1–11. doi: 10.1007/s10681-017-1900-x

65. Nogales A, Nobre T, Valadas V, Ragonezi C, Döring M, Polidoros A, et al. Can functional hologenomics aid tackling current challenges in plant breeding? Brief Funct Genomics. 2016;1 5: 288–297. doi: 10.1093/bfgp/elv030 26293603

66. Fukami J, Nogueira MA, Araujo RS, Hungria M. Accessing inoculation methods of maize and wheat with Azospirillum brasilense. AMB Express. 2016; 6: 3. doi: 10.1186/s13568-015-0171-y 26759120

67. Rosier A, Bishnoi U, Lakshmanan V, Sherrier DJ, Bais HP. A perspective on inter-kingdom signaling in plant–beneficial microbe interactions. Plant Mol Biol. 2016; 90: 537–548. doi: 10.1007/s11103-016-0433-3 26792782

68. Fesel PH, Zuccaro A. Dissecting endophytic lifestyle along the parasitism/mutualism continuum in Arabidopsis. Curr Opin Microbiol. 2016; 32: 103–112. doi: 10.1016/j.mib.2016.05.008 27280851

69. Schulz B, Boyle C. The endophytic continuum. Mycol Res. 2005; 109: 661–686. doi: 10.1017/S095375620500273X 16080390

70. German MA, Burdman S, Okon Y, Kigel J. Effects of Azospirillum brasilense on root morphology of common bean (Phaseolus vulgaris L.) under different water regimes. Biol Fertil Soils. 2000; 32: 259–264. doi: 10.1007/s003740000245

71. Duca DR, Rose DR, Glick BR. Indole acetic acid overproduction transformants of the rhizobacterium Pseudomonas sp. UW4. Antonie Van Leeuwenhoek. Springer International Publishing; 2018; 111: 1645–1660. doi: 10.1007/s10482-018-1051-7 29492769

72. Wang C, Liu W, Li Q, Ma D, Lu H, Feng W, et al. Effects of different irrigation and nitrogen regimes on root growth and its correlation with above-ground plant parts in high-yielding wheat under field conditions. F Crop Res. 2014; 165: 138–149. doi: 10.1016/j.fcr.2014.04.011

73. Xu W, Cui K, Xu A, Nie L, Huang J, Peng S. Drought stress condition increases root to shoot ratio via alteration of carbohydrate partitioning and enzymatic activity in rice seedlings. Acta Physiol Plant. 2015; 37. doi: 10.1007/s11738-014-1760-0

74. Yan J, Warburton M, Crouch J. Association Mapping for Enhancing Maize (L.) Genetic Improvement. Crop Sci. 2011; 51: 433. doi: 10.2135/cropsci2010.04.0233

75. Korte A, Farlow A. The advantages and limitations of trait analysis with GWAS: a review. Plant Methods. 2013; 9: 29. doi: 10.1186/1746-4811-9-29 23876160

76. Wang H, Xu C, Liu X, Guo Z, Xu X, Wang S, et al. Development of a multiple-hybrid population for genome-wide association studies: theoretical consideration and genetic mapping of flowering traits in maize. Sci Rep. 2017; 7: 40239. doi: 10.1038/srep40239 28071695

77. Huh SU, Paek K-H. Plant RNA binding proteins for control of RNA virus infection. Front Physiol. 2013; 4: 1–5. doi: 10.3389/fphys.2013.00001

78. Cheng P, Gedling CR, Patil G, Vuong TD, Shannon JG, Dorrance AE, et al. Genetic mapping and haplotype analysis of a locus for quantitative resistance to Fusarium graminearum in soybean accession PI 567516C. Theor Appl Genet. 2017; 130: 999–1010. doi: 10.1007/s00122-017-2866-8 28275816

79. Klay I, Gouia S, Liu M, Mila I, Khoudi H, Bernadac A, et al. Ethylene Response Factors (ERF) are differentially regulated by different abiotic stress types in tomato plants. Plant Sci. 2018; 274: 137–145. doi: 10.1016/j.plantsci.2018.05.023 30080597

80. Sun X, Yu G, Li J, Liu J, Wang X, Zhu G, et al. AcERF2, an ethylene-responsive factor of Atriplex canescens, positively modulates osmotic and disease resistance in Arabidopsis thaliana. Plant Sci. 2018; 274: 32–43. doi: 10.1016/j.plantsci.2018.05.004 30080618

81. Rai A, Kumar S, Bauddh K, Singh N, Singh RP. Improvement in growth and alkaloid content of Rauwolfia serpentina on application of organic matrix entrapped biofertilizers (Azotobacter chroococcum, Azospirillum brasilense and Pseudomonas putida). J Plant Nutr. 2017; 40: 2237–2247. doi: 10.1080/01904167.2016.1222419

82. Tivendale ND, Ross JJ, Cohen JD. The shifting paradigms of auxin biosynthesis. Trends Plant Sci. 2014; 19: 44–51. doi: 10.1016/j.tplants.2013.09.012 24524164

83. Wang Y, Yuan G, Yuan S, Duan W, Wang P, Bai J, et al. TaOPR2 encodes a 12-oxo-phytodienoic acid reductase involved in the biosynthesis of jasmonic acid in wheat (Triticum aestivum L.). Biochem Biophys Res Commun. 2016; 470: 233–238. doi: 10.1016/j.bbrc.2016.01.043 26778003

84. Koo AJ. Metabolism of the plant hormone jasmonate: a sentinel for tissue damage and master regulator of stress response. Phytochem Rev. 2018; 17: 51–80. doi: 10.1007/s11101-017-9510-8

85. Dicke M. Plant phenotypic plasticity in the phytobiome: a volatile issue. Curr Opin Plant Biol. 2016; 32: 17–23. doi: 10.1016/j.pbi.2016.05.004 27267277

86. Sharifi R, Lee S-M, Ryu C-M. Microbe-induced plant volatiles. New Phytol. 2017; 220: 684–691. doi: 10.1111/nph.14955 29266296

87. Disi JO, Zebelo S, Kloepper JW, Fadamiro H. Seed inoculation with beneficial rhizobacteria affects European corn borer (Lepidoptera: Pyralidae) oviposition on maize plants. Entomol Sci. 2018; 21: 48–58. doi: 10.1111/ens.12280

88. Ding Y, Huffaker A, Köllner TG, Weckwerth P, Robert CAM, Spencer JL, et al. Selinene Volatiles Are Essential Precursors for Maize Defense Promoting Fungal Pathogen Resistance. Plant Physiol. 2017; 175: 1455–1468. doi: 10.1104/pp.17.00879 28931629

89. D’Auria JC, Pichersky E, Schaub A, Hansel A, Gershenzon J. Characterization of a BAHD acyltransferase responsible for producing the green leaf volatile (Z)-3-hexen-1-yl acetate in Arabidopsis thaliana. Plant J. 2007; 49: 194–207. doi: 10.1111/j.1365-313X.2006.02946.x 17163881

90. Liu L-H, Ludewig U, Gassert B, Frommer WBF, Wirén N Von. Urea Transport by Nitrogen-Regulated Tonoplast Intrinsic Proteins in Arabidopsis. Plant Physiol. 2003; 133: 1220–1228. doi: 10.1104/pp.103.027409 14576283

91. Ding L, Lu Z, Gao L, Guo S, Shen Q. Is Nitrogen a Key Determinant of Water Transport and Photosynthesis in Higher Plants Upon Drought Stress? Front Plant Sci. 2018; 9: 1–12. doi: 10.3389/fpls.2018.00001

92. Kavi Kishor PB. Role of proline in cell wall synthesis and plant development and its implications in plant ontogeny. Front Plant Sci. 2015; 6: 1–17. doi: 10.3389/fpls.2015.00001

93. Nguema-Ona E, Vicré-Gibouin M, Cannesan M-A, Driouich A. Arabinogalactan proteins in root–microbe interactions. Trends Plant Sci. 2013; 18: 440–449. doi: 10.1016/j.tplants.2013.03.006 23623239

94. Oda Y, Hasezawa S. Cytoskeletal organization during xylem cell differentiation. J Plant Res. 2006; 119: 167–177. doi: 10.1007/s10265-006-0260-8 16570127

95. Ding L, Wang KJ, Jiang GM, Biswas DK, Xu H, Li LF, et al. Effects of Nitrogen Deficiency on Photosynthetic Traits of Maize Hybrids Released in Different Years. Ann Bot. 2005; 96: 925–930. doi: 10.1093/aob/mci244 16103036

96. Timm CM, Pelletier DA, Jawdy SS, Gunter LE, Henning JA, Engle N, et al. Two Poplar-Associated Bacterial Isolates Induce Additive Favorable Responses in a Constructed Plant-Microbiome System. Plant Sci. 2016; 7: 10. doi: 10.3389/fpls.2016.00497 27200001

97. Luo B, Tang H, Liu H, Shunzong S, Zhang S, Wu L, et al. Mining for low-nitrogen tolerance genes by integrating meta-analysis and large-scale gene expression data from maize. Euphytica. 2015; 206: 117–131. doi: 10.1007/s10681-015-1481-5

98. Młodzińska E, Kłobus G, Christensen MD, Fuglsang AT. The plasma membrane H + -ATPase AHA2 contributes to the root architecture in response to different nitrogen supply. Physiol Plant. 2015; 154: 270–282. doi: 10.1111/ppl.12305 25382626

99. Velasquez SM, Ricardi MM, Dorosz JG, Fernandez P V., Nadra AD, Pol-Fachin L, et al. O-Glycosylated Cell Wall Proteins Are Essential in Root Hair Growth. Science. 2011;332: 1401–1403. doi: 10.1126/science.1206657 21680836

100. Khan MIR, Trivellini A, Fatma M, Masood A, Francini A, Iqbal N, et al. Role of ethylene in responses of plants to nitrogen availability. Front Plant Sci. 2015;6: 1–15. doi: 10.3389/fpls.2015.00001

101. Ristova D, Carré C, Pervent M, Medici A, Kim GJ, Scalia D, et al. Combinatorial interaction network of transcriptomic and phenotypic responses to nitrogen and hormones in the Arabidopsis thaliana root. Sci Signal. 2016; 9. doi: 10.1126/scisignal.aaf2768 27811143

102. Abri MA, Posbergh C, Palermo K, Sutter NB, Eberth J, Hoffman GE, et al. Genome-Wide Scans Reveal a Quantitative Trait Locus for Withers Height in Horses Near the ANKRD1 Gene. J Equine Vet Sci. 2018; 60: 67-73.e1. doi: 10.1016/j.jevs.2017.05.008

103. Lee Y-S, Shin D, Song K-D. Dominance effects of ion transport and ion transport regulator genes on the final weight and backfat thickness of Landrace pigs by dominance deviation analysis. Genes Genomics. 2018; 40: 1331–1338. doi: 10.1007/s13258-018-0728-7 30136073

104. Tsairidou S, Allen AR, Pong-Wong R, McBride SH, Wright DM, Matika O, et al. An analysis of effects of heterozygosity in dairy cattle for bovine tuberculosis resistance. Anim Genet. 2018; 49: 103–109. doi: 10.1111/age.12637 29368428


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2019 Číslo 9
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