Natural variation in Arabidopsis shoot branching plasticity in response to nitrate supply affects fitness


Autoři: Maaike de Jong aff001;  Hugo Tavares aff001;  Raj K. Pasam aff001;  Rebecca Butler aff001;  Sally Ward aff001;  Gilu George aff002;  Charles W. Melnyk aff001;  Richard Challis aff002;  Paula X. Kover aff003;  Ottoline Leyser aff001
Působiště autorů: Sainsbury Laboratory, University of Cambridge, Cambridge, United Kingdom aff001;  Department of Biology, University of York, York, United Kingdom aff002;  Department of Biology and Biochemistry, University of Bath, Claverton Down, Bath, United Kingdom aff003
Vyšlo v časopise: Natural variation in Arabidopsis shoot branching plasticity in response to nitrate supply affects fitness. PLoS Genet 15(9): e32767. doi:10.1371/journal.pgen.1008366
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pgen.1008366

Souhrn

The capacity of organisms to tune their development in response to environmental cues is pervasive in nature. This phenotypic plasticity is particularly striking in plants, enabled by their modular and continuous development. A good example is the activation of lateral shoot branches in Arabidopsis, which develop from axillary meristems at the base of leaves. The activity and elongation of lateral shoots depends on the integration of many signals both external (e.g. light, nutrient supply) and internal (e.g. the phytohormones auxin, strigolactone and cytokinin). Here, we characterise natural variation in plasticity of shoot branching in response to nitrate supply using two diverse panels of Arabidopsis lines. We find extensive variation in nitrate sensitivity across these lines, suggesting a genetic basis for variation in branching plasticity. High plasticity is associated with extreme branching phenotypes such that lines with the most branches on high nitrate have the fewest under nitrate deficient conditions. Conversely, low plasticity is associated with a constitutively moderate level of branching. Furthermore, variation in plasticity is associated with alternative life histories with the low plasticity lines flowering significantly earlier than high plasticity lines. In Arabidopsis, branching is highly correlated with fruit yield, and thus low plasticity lines produce more fruit than high plasticity lines under nitrate deficient conditions, whereas highly plastic lines produce more fruit under high nitrate conditions. Low and high plasticity, associated with early and late flowering respectively, can therefore be interpreted alternative escape vs mitigate strategies to low N environments. The genetic architecture of these traits appears to be highly complex, with only a small proportion of the estimated genetic variance detected in association mapping.

Klíčová slova:

Physical sciences – Chemistry – Chemical compounds – Nitrates – Biology and life sciences – Genetics – Genetic loci – Quantitative trait loci – Genomics – Genome analysis – Human genetics – Heredity – Population genetics – Organisms – Eukaryota – Plants – Brassica – Arabidopsis thaliana – Flowering plants – Computational biology – Genome-wide association studies – Evolutionary biology – Genetic polymorphism – Population biology – Research and analysis methods – Animal studies – Experimental organism systems – Model organisms – Plant and algal models


Zdroje

1. de Jong M, Leyser O. Developmental plasticity in plants. Cold Spring Harb Symp Quant Biol. 2012;77: 63–73. doi: 10.1101/sqb.2012.77.014720 23250989

2. Gratani L. Plant Phenotypic Plasticity in Response to Environmental Factors. Advances in Botany. 2014;2014: 1–17. doi: 10.1155/2014/208747

3. Ghalambor CK, McKAY JK, Carroll SP, Reznick DN. Adaptive versus non-adaptive phenotypic plasticity and the potential for contemporary adaptation in new environments. Funct Ecol. 2007;21: 394–407. doi: 10.1111/j.1365-2435.2007.01283.x

4. Abley K, Locke JCW, Leyser HMO. Developmental mechanisms underlying variable, invariant and plastic phenotypes. Ann Bot. 2016;117: 733–748. doi: 10.1093/aob/mcw016 27072645

5. van Kleunen M, Fischer M. Constraints on the evolution of adaptive phenotypic plasticity in plants. New Phytol. 2005;166: 49–60. doi: 10.1111/j.1469-8137.2004.01296.x 15760350

6. Richards CL, Bossdorf O, Muth NZ, Gurevitch J, Pigliucci M. Jack of all trades, master of some? On the role of phenotypic plasticity in plant invasions. Ecol Lett. 2006;9: 981–993. doi: 10.1111/j.1461-0248.2006.00950.x 16913942

7. Sultan SE. Phenotypic plasticity in plants: a case study in ecological development. Evol Dev. 2003;5: 25–33. doi: 10.1046/j.1525-142X.2003.03005.x 12492406

8. Murren CJ, Auld JR, Callahan H, Ghalambor CK, Handelsman CA, Heskel MA, et al. Constraints on the evolution of phenotypic plasticity: limits and costs of phenotype and plasticity. Heredity. 2015;115: 293–301. doi: 10.1038/hdy.2015.8 25690179

9. Funk JL. Differences in plasticity between invasive and native plants from a low resource environment. Journal of Ecology. 2008;96: 1162–1173. doi: 10.1111/j.1365-2745.2008.01435.x

10. Dostál P, Fischer M, Chytrý M, Prati D. No evidence for larger leaf trait plasticity in ecological generalists compared to specialists. J Biogeogr. 2017;44: 511–521. doi: 10.1111/jbi.12881

11. Reynolds HL, D’Antonio C. The ecological significance of plasticity in root weight ratio in response to nitrogen: Opinion. Plant Soil. 1996;185: 75–97. doi: 10.1007/BF02257566

12. Weigel D. Natural variation in Arabidopsis: from molecular genetics to ecological genomics. Plant Physiol. 2012;158: 2–22. doi: 10.1104/pp.111.189845 22147517

13. Kristensen TN, Sørensen AC, Sorensen D, Pedersen KS, Sørensen JG, Loeschcke V. A test of quantitative genetic theory using Drosophila- effects of inbreeding and rate of inbreeding on heritabilities and variance components. J Evol Biol. 2005;18: 763–770. doi: 10.1111/j.1420-9101.2005.00883.x 16033547

14. Whitlock MC, Fowler K. The changes in genetic and environmental variance with inbreeding in Drosophila melanogaster. Genetics. 1999;152: 345–353. 10224265

15. Pigliucci M, Whitton J, Schlichting CD. Reaction norms of Arabidopsis. I. Plasticity of characters and correlations across water, nutrient and light gradients. J Evol Biol. 1995;8: 421–438. doi: 10.1046/j.1420-9101.1995.8040421.x

16. El-Soda M, Malosetti M, Zwaan BJ, Koornneef M, Aarts MGM. Genotype×environment interaction QTL mapping in plants: lessons from Arabidopsis. Trends Plant Sci. 2014;19: 390–398. doi: 10.1016/j.tplants.2014.01.001 24491827

17. Westerman JM, Lawrence MJ. Genotype-environment interaction and developmental regulation in Arabidopsis thaliana I. Inbred lines; description. Heredity. 1970;25: 609–627. doi: 10.1038/hdy.1970.66

18. Pigliucci M, Byrd N. Genetics and evolution of phenotypic plasticity to nutrient stress in Arabidopsis: drift, constraints or selection? Biological Journal of the Linnean Society. 1998;64: 17–40. doi: 10.1111/j.1095-8312.1998.tb01531.x

19. Botto JF, Coluccio MP. Seasonal and plant-density dependency for quantitative trait loci affecting flowering time in multiple populations of Arabidopsis thaliana. Plant Cell Environ. 2007;30: 1465–1479. doi: 10.1111/j.1365-3040.2007.01722.x 17897416

20. Sasaki E, Zhang P, Atwell S, Meng D, Nordborg M. “Missing” G x E Variation Controls Flowering Time in Arabidopsis thaliana. PLoS Genet. 2015;11: e1005597. doi: 10.1371/journal.pgen.1005597 26473359

21. Fournier-Level A, Korte A, Cooper MD, Nordborg M, Schmitt J, Wilczek AM. A map of local adaptation in Arabidopsis thaliana. Science. 2011;334: 86–89. doi: 10.1126/science.1209271 21980109

22. Pigliucci M, Schlichting CD. Reaction Norms of Arabidopsis (Brassicaceae). III. Response to Nutrients in 26 Populations from a Worldwide Collection. Am J Bot. 1995;82: 1117. doi: 10.2307/2446064

23. Pigliucci M, Schlichting CD. Reaction norms ofArabidopsis. V. Flowering time controls phenotypic architecture in response to nutrient stress. J Evol Biol. 1998;11: 285–301. doi: 10.1046/j.1420-9101.1998.11030285.x

24. Domagalska MA, Leyser O. Signal integration in the control of shoot branching. Nat Rev Mol Cell Biol. 2011;12: 211–221. doi: 10.1038/nrm3088 21427763

25. de Jong M, George G, Ongaro V, Williamson L, Willetts B, Ljung K, et al. Auxin and strigolactone signaling are required for modulation of Arabidopsis shoot branching by nitrogen supply. Plant Physiol. 2014;166: 384–395. doi: 10.1104/pp.114.242388 25059707

26. Müller D, Waldie T, Miyawaki K, To JPC, Melnyk CW, Kieber JJ, et al. Cytokinin is required for escape but not release from auxin mediated apical dominance. Plant J. 2015;82: 874–886. doi: 10.1111/tpj.12862 25904120

27. Hermans C, Hammond JP, White PJ, Verbruggen N. How do plants respond to nutrient shortage by biomass allocation? Trends Plant Sci. 2006;11: 610–617. doi: 10.1016/j.tplants.2006.10.007 17092760

28. Kover PX, Valdar W, Trakalo J, Scarcelli N, Ehrenreich IM, Purugganan MD, et al. A Multiparent Advanced Generation Inter-Cross to fine-map quantitative traits in Arabidopsis thaliana. PLoS Genet. 2009;5: e1000551. doi: 10.1371/journal.pgen.1000551 19593375

29. Cao J, Schneeberger K, Ossowski S, Günther T, Bender S, Fitz J, et al. Whole-genome sequencing of multiple Arabidopsis thaliana populations. Nat Genet. 2011;43: 956–963. doi: 10.1038/ng.911 21874002

30. Nordborg M, Hu TT, Ishino Y, Jhaveri J, Toomajian C, Zheng H, et al. The pattern of polymorphism in Arabidopsis thaliana. PLoS Biol. 2005;3: e196. doi: 10.1371/journal.pbio.0030196 15907155

31. Li Y, Huang Y, Bergelson J, Nordborg M, Borevitz JO. Association mapping of local climate-sensitive quantitative trait loci in Arabidopsis thaliana. Proc Natl Acad Sci USA. 2010;107: 21199–21204. doi: 10.1073/pnas.1007431107 21078970

32. Mauricio R, Rausher MD. Experimental manipulation of putative selective agents provides evidence for the role of natural enemies in the evolution of plant defense. Evolution. 1997;51: 1435–1444. doi: 10.1111/j.1558-5646.1997.tb01467.x 28568624

33. Scarcelli N, Cheverud JM, Schaal BA, Kover PX. Antagonistic pleiotropic effects reduce the potential adaptive value of the FRIGIDA locus. Proc Natl Acad Sci USA. 2007;104: 16986–16991. doi: 10.1073/pnas.0708209104 17940010

34. Pigliucci M, Kolodynska A. Phenotypic plasticity and integration in response to flooded conditions in natural accessions of Arabidopsis thaliana (L.) Heynh (Brassicaceae). Ann Bot. 2002;90: 199–207. doi: 10.1093/aob/mcf164 12197517

35. Donohue K, Polisetty CR, Wender NJ. Genetic basis and consequences of niche construction: plasticity-induced genetic constraints on the evolution of seed dispersal in Arabidopsis thaliana. Am Nat. 2005;165: 537–550. doi: 10.1086/429162 15795851

36. Kerwin R, Feusier J, Corwin J, Rubin M, Lin C, Muok A, et al. Natural genetic variation in Arabidopsis thaliana defense metabolism genes modulates field fitness. elife. 2015;4. doi: 10.7554/eLife.05604 25867014

37. Roux F, Gasquez J, Reboud X. The dominance of the herbicide resistance cost in several Arabidopsis thaliana mutant lines. Genetics. 2004;166: 449–460. 15020435

38. Ibañez C, Poeschl Y, Peterson T, Bellstädt J, Denk K, Gogol-Döring A, et al. Ambient temperature and genotype differentially affect developmental and phenotypic plasticity in Arabidopsis thaliana. BMC Plant Biol. 2017;17: 114. doi: 10.1186/s12870-017-1068-5 28683779

39. Wang R, Okamoto M, Xing X, Crawford NM. Microarray analysis of the nitrate response in Arabidopsis roots and shoots reveals over 1,000 rapidly responding genes and new linkages to glucose, trehalose-6-phosphate, iron, and sulfate metabolism. Plant Physiol. 2003;132: 556–567. doi: 10.1104/pp.103.021253 12805587

40. Zhang H, Forde BG. An Arabidopsis MADS box gene that controls nutrient-induced changes in root architecture. Science. 1998;279: 407–409. doi: 10.1126/science.279.5349.407 9430595

41. Ruffel S, Krouk G, Ristova D, Shasha D, Birnbaum KD, Coruzzi GM. Nitrogen economics of root foraging: transitive closure of the nitrate-cytokinin relay and distinct systemic signaling for N supply vs. demand. Proc Natl Acad Sci USA. 2011;108: 18524–18529. doi: 10.1073/pnas.1108684108 22025711

42. Guan P, Wang R, Nacry P, Breton G, Kay SA, Pruneda-Paz JL, et al. Nitrate foraging by Arabidopsis roots is mediated by the transcription factor TCP20 through the systemic signaling pathway. Proc Natl Acad Sci USA. 2014;111: 15267–15272. doi: 10.1073/pnas.1411375111 25288754

43. Broman KW, Gatti DM, Simecek P, Furlotte NA, Prins P, Sen Ś, et al. R/qtl2: Software for Mapping Quantitative Trait Loci with High-Dimensional Data and Multiparent Populations. Genetics. 2019;211: 495–502. doi: 10.1534/genetics.118.301595 30591514

44. Kearsey MJ, Pooni HS, Syed NH. Genetics of quantitative traits in Arabidopsis thaliana. Heredity. 2003;91: 456–464. doi: 10.1038/sj.hdy.6800306 14576738

45. Torii KU, Mitsukawa N, Oosumi T, Matsuura Y, Yokoyama R, Whittier RF, et al. The Arabidopsis ERECTA gene encodes a putative receptor protein kinase with extracellular leucine-rich repeats. Plant Cell. 1996;8: 735–746. doi: 10.1105/tpc.8.4.735 8624444

46. Oh S, Zhang H, Ludwig P, van Nocker S. A mechanism related to the yeast transcriptional regulator Paf1c is required for expression of the Arabidopsis FLC/MAF MADS box gene family. Plant Cell. 2004;16: 2940–2953. doi: 10.1105/tpc.104.026062 15472079

47. Roux F, Touzet P, Cuguen J, Le Corre V. How to be early flowering: an evolutionary perspective. Trends Plant Sci. 2006;11: 375–381. doi: 10.1016/j.tplants.2006.06.006 16843035

48. Zhao K, Aranzana MJ, Kim S, Lister C, Shindo C, Tang C, et al. An Arabidopsis example of association mapping in structured samples. PLoS Genet. 2007;3: e4. doi: 10.1371/journal.pgen.0030004 17238287

49. Malosetti M, Ribaut J-M, van Eeuwijk FA. The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis. Front Physiol. 2013;4: 44. doi: 10.3389/fphys.2013.00044 23487515

50. van Eeuwijk FA, Bink MCAM, Chenu K, Chapman SC. Detection and use of QTL for complex traits in multiple environments. Curr Opin Plant Biol. 2010;13: 193–205. doi: 10.1016/j.pbi.2010.01.001 20137999

51. Atwell S, Huang YS, Vilhjálmsson BJ, Willems G, Horton M, Li Y, et al. Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature. 2010;465: 627–631. doi: 10.1038/nature08800 20336072

52. Ehrenreich IM, Hanzawa Y, Chou L, Roe JL, Kover PX, Purugganan MD. Candidate gene association mapping of Arabidopsis flowering time. Genetics. 2009;183: 325–335. doi: 10.1534/genetics.109.105189 19581446

53. Bakshi A, Zhu Z, Vinkhuyzen AAE, Hill WD, McRae AF, Visscher PM, et al. Fast set-based association analysis using summary data from GWAS identifies novel gene loci for human complex traits. Sci Rep. 2016;6: 32894. doi: 10.1038/srep32894 27604177

54. Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, et al. Common SNPs explain a large proportion of the heritability for human height. Nat Genet. 2010;42: 565–569. doi: 10.1038/ng.608 20562875

55. The 1001 Genomes Consortium. 1,135 Genomes reveal the global pattern of polymorphism in Arabidopsis thaliana. Cell. 2016;166: 481–491. doi: 10.1016/j.cell.2016.05.063 27293186

56. Kooke R, Kruijer W, Bours R, Becker F, Kuhn A, van de Geest H, et al. Genome-Wide Association Mapping and Genomic Prediction Elucidate the Genetic Architecture of Morphological Traits in Arabidopsis. Plant Physiol. 2016;170: 2187–2203. doi: 10.1104/pp.15.00997 26869705

57. Gao X, Becker LC, Becker DM, Starmer JD, Province MA. Avoiding the high Bonferroni penalty in genome-wide association studies. Genet Epidemiol. 2010;34: 100–105. doi: 10.1002/gepi.20430 19434714

58. Abney M. Permutation testing in the presence of polygenic variation. Genet Epidemiol. 2015;39: 249–258. doi: 10.1002/gepi.21893 25758362

59. Zan Y, Carlborg Ö. A Polygenic Genetic Architecture of Flowering Time in the Worldwide Arabidopsis thaliana Population. Mol Biol Evol. 2019;36: 141–154. doi: 10.1093/molbev/msy203 30388255

60. Kerdaffrec E, Filiault DL, Korte A, Sasaki E, Nizhynska V, Seren Ü, et al. Multiple alleles at a single locus control seed dormancy in Swedish Arabidopsis. elife. 2016;5. doi: 10.7554/eLife.22502 27966430

61. Toomajian C, Hu TT, Aranzana MJ, Lister C, Tang C, Zheng H, et al. A nonparametric test reveals selection for rapid flowering in the Arabidopsis genome. PLoS Biol. 2006;4: e137. doi: 10.1371/journal.pbio.0040137 16623598

62. Valladares F, Gianoli E, Gómez JM. Ecological limits to plant phenotypic plasticity. New Phytol. 2007;176: 749–763. doi: 10.1111/j.1469-8137.2007.02275.x 17997761

63. Březina S, Jandová K, Pecháčková S, Hadincová V, Skálová H, Krahulec F, et al. Nutrient patches are transient and unpredictable in an unproductive mountain grassland. Plant Ecol. 2019;220: 111–123. doi: 10.1007/s11258-019-00906-3

64. Farley RA, Fitter AH. Temporal and spatial variation in soil resources in a deciduous woodland. Journal of Ecology. 1999;87: 688–696. doi: 10.1046/j.1365-2745.1999.00390.x

65. Hodge A. The plastic plant: root responses to heterogeneous supplies of nutrients. New Phytol. 2004;162: 9–24. doi: 10.1111/j.1469-8137.2004.01015.x

66. Dener E, Kacelnik A, Shemesh H. Pea plants show risk sensitivity. Curr Biol. 2016;26: 1763–1767. doi: 10.1016/j.cub.2016.05.008 27374342

67. Pigliucci M, Schlichting CD. Reaction norms of Arabidopsis IV. Relationships between plasticity and fitness. Heredity. 1996;76 (Pt 5): 427–436. doi: 10.1038/hdy.1996.65 8666543

68. De Kroon H, Visser EJW, Huber H, Mommer L, Hutchings MJ. A modular concept of plant foraging behaviour: the interplay between local responses and systemic control. Plant Cell Environ. 2009;32: 704–712. doi: 10.1111/j.1365-3040.2009.01936.x 19183298

69. Nord EA, Lynch JP. Delayed reproduction in Arabidopsis thaliana improves fitness in soil with suboptimal phosphorus availability. Plant Cell Environ. 2008;31: 1432–1441. doi: 10.1111/j.1365-3040.2008.01857.x 18643901

70. Davidson AM, Jennions M, Nicotra AB. Do invasive species show higher phenotypic plasticity than native species and, if so, is it adaptive? A meta-analysis. Ecol Lett. 2011;14: 419–431. doi: 10.1111/j.1461-0248.2011.01596.x 21314880

71. Valladares F, Niinemets Ü. Shade Tolerance, a Key Plant Feature of Complex Nature and Consequences. Annu Rev Ecol Evol Syst. 2008;39: 237–257. doi: 10.1146/annurev.ecolsys.39.110707.173506

72. van Tienderen PH. Generalists, specialists, and the evolution of phenotypic plasticity in sympatric populations of distinct species. Evolution. 1997;51: 1372–1380. doi: 10.1111/j.1558-5646.1997.tb01460.x 28568610

73. North KA, Ehlting B, Koprivova A, Rennenberg H, Kopriva S. Natural variation in Arabidopsis adaptation to growth at low nitrogen conditions. Plant Physiol Biochem. 2009;47: 912–918. doi: 10.1016/j.plaphy.2009.06.009 19628403

74. Bi Y-M, Wang R-L, Zhu T, Rothstein SJ. Global transcription profiling reveals differential responses to chronic nitrogen stress and putative nitrogen regulatory components in Arabidopsis. BMC Genomics. 2007;8: 281. doi: 10.1186/1471-2164-8-281 17705847

75. Booker J, Auldridge M, Wills S, McCarty D, Klee H, Leyser O. MAX3/CCD7 is a carotenoid cleavage dioxygenase required for the synthesis of a novel plant signaling molecule. Curr Biol. 2004;14: 1232–1238. doi: 10.1016/j.cub.2004.06.061 15268852

76. Booker J, Sieberer T, Wright W, Williamson L, Willett B, Stirnberg P, et al. MAX1 encodes a cytochrome P450 family member that acts downstream of MAX3/4 to produce a carotenoid-derived branch-inhibiting hormone. Dev Cell. 2005;8: 443–449. doi: 10.1016/j.devcel.2005.01.009 15737939

77. Kiba T, Kudo T, Kojima M, Sakakibara H. Hormonal control of nitrogen acquisition: roles of auxin, abscisic acid, and cytokinin. J Exp Bot. 2011;62: 1399–1409. doi: 10.1093/jxb/erq410 21196475

78. Takei K, Ueda N, Aoki K, Kuromori T, Hirayama T, Shinozaki K, et al. AtIPT3 is a key determinant of nitrate-dependent cytokinin biosynthesis in Arabidopsis. Plant Cell Physiol. 2004;45: 1053–1062. doi: 10.1093/pcp/pch119 15356331

79. Wu S, Alseekh S, Cuadros-Inostroza Á, Fusari CM, Mutwil M, Kooke R, et al. Combined Use of Genome-Wide Association Data and Correlation Networks Unravels Key Regulators of Primary Metabolism in Arabidopsis thaliana. PLoS Genet. 2016;12: e1006363. doi: 10.1371/journal.pgen.1006363 27760136

80. Bazakos C, Hanemian M, Trontin C, Jiménez-Gómez JM, Loudet O. New Strategies and Tools in Quantitative Genetics: How to Go from the Phenotype to the Genotype. Annu Rev Plant Biol. 2017;68: 435–455. doi: 10.1146/annurev-arplant-042916-040820 28226236

81. Klasen JR, Barbez E, Meier L, Meinshausen N, Bühlmann P, Koornneef M, et al. A multi-marker association method for genome-wide association studies without the need for population structure correction. Nat Commun. 2016;7: 13299. doi: 10.1038/ncomms13299 27830750

82. Loudet O, Chaillou S, Krapp A, Daniel-Vedele F. Quantitative trait loci analysis of water and anion contents in interaction with nitrogen availability in Arabidopsis thaliana. Genetics. 2003;163: 711–722. 12618408

83. Loudet O, Chaillou S, Merigout P, Talbotec J, Daniel-Vedele F. Quantitative trait loci analysis of nitrogen use efficiency in Arabidopsis. Plant Physiol. 2003;131: 345–358. doi: 10.1104/pp.102.010785 12529542

84. Rauh L, Basten C, Buckler S. Quantitative trait loci analysis of growth response to varying nitrogen sources in Arabidopsis thaliana. Theor Appl Genet. 2002;104: 743–750. doi: 10.1007/s00122-001-0815-y 12582633

85. Marchadier E, Hanemian M, Tisne S, Bach L, Bazakos C, Gilbault E, et al. The complex genetic architecture of shoot growth natural variation in Arabidopsis thaliana. BioRxiv. 2018; doi: 10.1101/354738

86. Mott R, Talbot CJ, Turri MG, Collins AC, Flint J. A method for fine mapping quantitative trait loci in outbred animal stocks. Proc Natl Acad Sci USA. 2000;97: 12649–12654. doi: 10.1073/pnas.230304397 11050180

87. Aguilar-Martínez JA, Poza-Carrión C, Cubas P. Arabidopsis BRANCHED1 acts as an integrator of branching signals within axillary buds. Plant Cell. 2007;19: 458–472. doi: 10.1105/tpc.106.048934 17307924

88. Finlayson SA, Krishnareddy SR, Kebrom TH, Casal JJ. Phytochrome regulation of branching in Arabidopsis. Plant Physiol. 2010;152: 1914–1927. doi: 10.1104/pp.109.148833 20154098

89. Seale M, Bennett T, Leyser O. BRC1 expression regulates bud activation potential but is not necessary or sufficient for bud growth inhibition in Arabidopsis. Development. 2017;144: 1661–1673. doi: 10.1242/dev.145649 28289131

90. Andrés F, Coupland G. The genetic basis of flowering responses to seasonal cues. Nat Rev Genet. 2012;13: 627–639. doi: 10.1038/nrg3291 22898651

91. Shindo C, Aranzana MJ, Lister C, Baxter C, Nicholls C, Nordborg M, et al. Role of FRIGIDA and FLOWERING LOCUS C in determining variation in flowering time of Arabidopsis. Plant Physiol. 2005;138: 1163–1173. doi: 10.1104/pp.105.061309 15908596

92. Springate DA, Kover PX. Plant responses to elevated temperatures: a field study on phenological sensitivity and fitness responses to simulated climate warming. Glob Chang Biol. 2014;20: 456–465. doi: 10.1111/gcb.12430 24130095

93. Hirel B, Le Gouis J, Ney B, Gallais A. The challenge of improving nitrogen use efficiency in crop plants: towards a more central role for genetic variability and quantitative genetics within integrated approaches. J Exp Bot. 2007;58: 2369–2387. doi: 10.1093/jxb/erm097 17556767

94. Senthilvel S, Vinod KK, Malarvizhi P, Maheswaran M. QTL and QTL x environment effects on agronomic and nitrogen acquisition traits in rice. J Integr Plant Biol. 2008;50: 1108–1117. doi: 10.1111/j.1744-7909.2008.00713.x 18844779

95. Jiang H, Jiang L, Guo L, Gao Z, Zeng D, Zhu L, et al. Conditional and unconditional mapping of quantitative trait loci underlying plant height and tiller number in rice (Oryza sativa L.) grown at two nitrogen levels. Progress in Natural Science. 2008;18: 1539–1547. doi: 10.1016/j.pnsc.2008.05.025

96. Mahjourimajd S, Taylor J, Sznajder B, Timmins A, Shahinnia F, Rengel Z, et al. Genetic basis for variation in wheat grain yield in response to varying nitrogen application. PLoS ONE. 2016;11: e0159374. doi: 10.1371/journal.pone.0159374 27459317

97. Laperche A, Brancourt-Hulmel M, Heumez E, Gardet O, Hanocq E, Devienne-Barret F, et al. Using genotype x nitrogen interaction variables to evaluate the QTL involved in wheat tolerance to nitrogen constraints. Theor Appl Genet. 2007;115: 399–415. doi: 10.1007/s00122-007-0575-4 17569029

98. Han M, Wong J, Su T, Beatty PH, Good AG. Identification of nitrogen use efficiency genes in barley: searching for qtls controlling complex physiological traits. Front Plant Sci. 2016;7: 1587. doi: 10.3389/fpls.2016.01587 27818673

99. Mickelson S, See D, Meyer FD, Garner JP, Foster CR, Blake TK, et al. Mapping of QTL associated with nitrogen storage and remobilization in barley (Hordeum vulgare L.) leaves. J Exp Bot. 2003;54: 801–812. doi: 10.1093/jxb/erg084 12554723

100. Gelli M, Konda AR, Liu K, Zhang C, Clemente TE, Holding DR, et al. Validation of QTL mapping and transcriptome profiling for identification of candidate genes associated with nitrogen stress tolerance in sorghum. BMC Plant Biol. 2017;17: 123. doi: 10.1186/s12870-017-1064-9 28697783

101. Gallais A, Hirel B. An approach to the genetics of nitrogen use efficiency in maize. J Exp Bot. 2004;55: 295–306. doi: 10.1093/jxb/erh006 14739258

102. Presterl T, Seitz G, Landbeck M, Thiemt EM, Schmidt W, Geiger HH. Improving Nitrogen-Use Efficiency in European Maize. Crop Sci. 2003;43: 1259. doi: 10.2135/cropsci2003.1259

103. Wilson AK, Pickett FB, Turner JC, Estelle M. A dominant mutation in Arabidopsis confers resistance to auxin, ethylene and abscisic acid. Mol Gen Genet. 1990;222: 377–383. doi: 10.1007/bf00633843 2148800

104. Hempel F, Feldman L. Bi-directional inflorescence development in Arabidopsis thaliana: Acropetal initiation of flowers and basipetal initiation of paraclades. Planta. 1994;192. doi: 10.1007/BF00194463

105. Valladares F, Sanchez-Gomez D, Zavala MA. Quantitative estimation of phenotypic plasticity: bridging the gap between the evolutionary concept and its ecological applications. Journal of Ecology. 2006;94: 1103–1116. doi: 10.1111/j.1365-2745.2006.01176.x

106. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta C(T)) Method. Methods. 2001;25: 402–408. doi: 10.1006/meth.2001.1262 11846609

107. Team RC. R: A Language and Environment for Statistical Computing. 2017;

108. Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. J Stat Softw. 2015;67: 1–48. doi: 10.18637/jss.v067.i01

109. Fox J, Weisberg S. An R Companion to Applied Regression. Sage; 2011.

110. Falconer DS, Mackay TFC. Introduction to Quantitative Genetics. 4th ed. Longman; 1996.

111. Kluen E, Brommer JE. Context-specific repeatability of personality traits in a wild bird: a reaction-norm perspective. Behavioral Ecology. 2013;24: 650–658. doi: 10.1093/beheco/ars221

112. Brommer JE. Variation in plasticity of personality traits implies that the ranking of personality measures changes between environmental contexts: calculating the cross-environmental correlation. Behav Ecol Sociobiol. 2013;67: 1709–1718. doi: 10.1007/s00265-013-1603-9

113. Cogni R, Cao C, Day JP, Bridson C, Jiggins FM. The genetic architecture of resistance to virus infection in Drosophila. Mol Ecol. 2016;25: 5228–5241. doi: 10.1111/mec.13769 27460507

114. Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet. 2011;88: 76–82. doi: 10.1016/j.ajhg.2010.11.011 21167468

115. Lippert C, Casale FP, Rakitsch B, Stegle O. LIMIX: genetic analysis of multiple traits. BioRxiv. 2014; doi: 10.1101/003905

116. Wickham H. tidyverse: Easily Install and Load the “Tidyverse.” 2017;

117. Krapp A, Berthomé R, Orsel M, Mercey-Boutet S, Yu A, Castaings L, et al. Arabidopsis roots and shoots show distinct temporal adaptation patterns toward nitrogen starvation. Plant Physiol. 2011;157: 1255–1282. doi: 10.1104/pp.111.179838 21900481

118. Diaz C, Lemaître T, Christ A, Azzopardi M, Kato Y, Sato F, et al. Nitrogen recycling and remobilization are differentially controlled by leaf senescence and development stage in Arabidopsis under low nitrogen nutrition. Plant Physiol. 2008;147: 1437–1449. doi: 10.1104/pp.108.119040 18467460

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Genetika Reprodukční medicína

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PLOS Genetics


2019 Číslo 9

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