Plasticity Regulators Modulate Specific Root Traits in Discrete Nitrogen Environments

English version České info

Plant development is remarkably plastic but how precisely can the plant customize its form to specific environments? When the plant adjusts its development to different environments, related traits can change in a coordinated fashion, such that two traits co-vary across many genotypes. Alternatively, traits can vary independently, such that a change in one trait has little predictive value for the change in a second trait. To characterize such “tunability” in developmental plasticity, we carried out a detailed phenotypic characterization of complex root traits among 96 accessions of the model Arabidopsis thaliana in two nitrogen environments. The results revealed a surprising level of independence in the control of traits to environment –⁠ a highly tunable form of plasticity. We mapped genetic architecture of plasticity using genome-wide association studies and further used gene expression analysis to narrow down gene candidates in mapped regions. Mutants in genes implicated by association and expression analysis showed precise defects in the predicted traits in the predicted environment, corroborating the independent control of plasticity traits. The overall results suggest that there is a pool of genetic variability in plants that controls traits in specific environments, with opportunity to tune crop plants to a given environment.

Vyšlo v časopise: Plasticity Regulators Modulate Specific Root Traits in Discrete Nitrogen Environments. PLoS Genet 9(9): e32767. doi:10.1371/journal.pgen.1003760
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1003760

Souhrn

Zdroje

1. Epstein E, Bloom AJ (2005) Mineral Nutrition of Plants: Principles and Perspectives. Sunderland, MA: Sinauer.

2. MalamyJE (2005) Intrinsic and environmental response pathways that regulate root system architecture. Plant Cell Environ 28 : 67–77.

3. OsmontKS, SiboutR, HardtkeCS (2007) Hidden branches: developments in root system architecture. Annu Rev Plant Biol 58 : 93–113.

4. De SmetI, WhitePJ, BengoughAG, DupuyL, ParizotB, et al. (2012) Analyzing lateral root development: how to move forward. Plant Cell 24 : 15–20.

5. ChavesMM, PereiraJS, MarocoJ, RodriguesML, RicardoCPP, et al. (2002) How plants cope with water stress in the field. Photosynthesis and growth. Annals of Botany 89 : 907–916.

6. PriceAH, SteeleKA, GorhamJ, BridgesJM, MooreBJ, et al. (2002) Upland rice grown in soil-filled chambers and exposed to contrasting water-deficit regimes I. Root distribution, water use and plant water status. Field Crops Research 76 : 11–24.

7. HodgeA, RobinsonD, GriffithsBS, FitterAH (1999) Why plants bother: root proliferation results in increased nitrogen capture from an organic patch when two grasses compete. Plant Cell and Environment 22 : 811–820.

8. RobinsonD, HodgeA, GriffithsBS, FitterAH (1999) Plant root proliferation in nitrogen-rich patches confers competitive advantage. Proceedings of the Royal Society B-Biological Sciences 266 : 431–435.

9. GiffordML, DeanA, GutierrezRA, CoruzziGM, BirnbaumKD (2008) Cell-specific nitrogen responses mediate developmental plasticity. Proc Natl Acad Sci U S A 105 : 803–808.

10. MalamyJE, RyanKS (2001) Environmental regulation of lateral root initiation in Arabidopsis. Plant Physiol 127 : 899–909.

11. ZhangH, FordeBG (1998) An Arabidopsis MADS box gene that controls nutrient-induced changes in root architecture. Science 279 : 407–409.

12. VidalEA, ArausV, LuC, ParryG, GreenPJ, et al. (2010) Nitrate-responsive miR393/AFB3 regulatory module controls root system architecture in Arabidopsis thaliana. Proc Natl Acad Sci U S A 107 : 4477–4482.

13. PigliucciM (2003) Phenotypic integration: studying the ecology and evolution of complex phenotypes. Ecology Letters 6 : 265–272.

14. Dewitt TJ, Scheiner SM (2004) Phenotypic variation from single genotypes: a primer. In: Dewitt TJ, Scheiner SM, editors. Phenotypic plasticity: functional and conceptual approaches. Oxford: Oxford University Press.

15. BergRL (1960) The ecological significance of correlation pleiades. Evolution 14 : 171–180.

16. BayeTM, AbebeT, WilkeRA (2011) Genotype-environment interactions and their translational implications. Per Med 8 : 59–70.

17. Anicchiarico P (2002) Genotype x environment interactions: Challenges and opportunities for plant breeding and cultivar recommendations.: FAO plant production and protection paper : Food and Agricultural Organization of the United Nations.

18. NordborgM, HuTT, IshinoY, JhaveriJ, ToomajianC, et al. (2005) The pattern of polymorphism in Arabidopsis thaliana. PLOS Biol 3: e196.

19. AtwellS, HuangYS, VilhjalmssonBJ, WillemsG, HortonM, et al. (2010) Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature 465 : 627–631.

20. Raya-GonzalezJ, Pelagio-FloresR, Lopez-BucioJ (2012) The jasmonate receptor COI1 plays a role in jasmonate-induced lateral root formation and lateral root positioning in Arabidopsis thaliana. J Plant Physiol 169 : 1348–1358.

21. Garcia-HernandezM, BerardiniTZ, ChenG, CristD, DoyleA, et al. (2002) TAIR: a resource for integrated Arabidopsis data. Funct Integr Genomics 2 : 239–253.

22. SchollRL, MayST, WareDH (2000) Seed and molecular resources for Arabidopsis. Plant Physiol 124 : 1477–1480.

23. GutierrezRA, LejayLV, DeanA, ChiaromonteF, ShashaDE, et al. (2007) Qualitative network models and genome-wide expression data define carbon/nitrogen-responsive molecular machines in Arabidopsis. Genome Biol 8: R7.

24. DubrovskyJG, FordeBG (2012) Quantitative analysis of lateral root development: pitfalls and how to avoid them. Plant Cell 24 : 4–14.

25. Bates D, Maechler M, Bolker (2013) lme4: Linear mixed-effects models using S4 classes. R package version 0.999999-2.

26. R Development Core Team (2012) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

27. CorbeilRR, SearleSR (1976) Restricted maximum likelihood (REML) estimation of variance components in the mixed model. Technometrics 18 : 31–38.

28. ScheinerSM, LymanRF (1989) The genetics of phenotypic plasticity. I. Heritability. Journal of Evolutionary Biology 2 : 95–107.

29. MoranMD (2003) Arguments for rejecting the sequential Bonferroni in ecological studies. OIKOS 100 : 403–405.

30. StoreyJD, TibshiraniR (2003) Statistical significance for genomewide studies. Proc Natl Acad Sci U S A 100 : 9440–9445.

31. Dabney A, Storey JD (2010) qvalue: Q-value estimation for false discovery rate control. R package version 1.22.0.

32. McCallMN, MurakamiPN, LukkM, HuberW, IrizarryRA (2011) Assessing Affymetrix GeneChip microarray quality. BMC Bioinformatics 12 : 137.

33. YuJ, PressoirG, BriggsWH, Vroh BiI, YamasakiM, et al. (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet 38 : 203–208.

34. BenjaminiY, HochbergY (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B 57 : 289–300.

35. KatariM, NowickiS, AceitunoF, NeroD, KelferJ, et al. (2010) VirtualPlant: a software platform to support systems biology research. Plant Physiol 152 : 500–515.

36. Sokal RR, Rohlf FJ (1995) Biometry: the principles and practice of statistics in biological research. 2nd edition. New York: W.H. Freeman and Company.