Genetic Variation in the Nuclear and Organellar Genomes Modulates Stochastic Variation in the Metabolome, Growth, and Defense
Systems biology is largely based on the principal that the link between genotype and phenotype is deterministic, and, if we know enough, can be predicted with high accuracy. In contrast, recent work studying transcription within single celled organisms has shown that the genotype to phenotype link is stochastic, i.e. a single genotype actually makes a range of phenotypes even in a single environment. Further, natural variation within genes can lead to each allele displaying a different phenotypic distribution. To test if multi-cellular organisms also display natural genetic variation in the stochastic link between genotype and phenotype, we measured the metabolome, growth and defense metabolism within an Arabidopsis RIL population and mapped quantitative trait loci. We show that genetic variation in the nuclear and organeller genomes influence the stochastic variation in all measured traits. Further, each trait class has distinct genetics underlying the stochastic variance, showing that there are different mechanisms controlling the stochastic genotype to phenotype link for each trait. Further work is necessary to identify the mechanisms underpinning the stochastic nature of the genotype to phenotype link.
Vyšlo v časopise:
Genetic Variation in the Nuclear and Organellar Genomes Modulates Stochastic Variation in the Metabolome, Growth, and Defense. PLoS Genet 11(1): e32767. doi:10.1371/journal.pgen.1004779
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pgen.1004779
Souhrn
Systems biology is largely based on the principal that the link between genotype and phenotype is deterministic, and, if we know enough, can be predicted with high accuracy. In contrast, recent work studying transcription within single celled organisms has shown that the genotype to phenotype link is stochastic, i.e. a single genotype actually makes a range of phenotypes even in a single environment. Further, natural variation within genes can lead to each allele displaying a different phenotypic distribution. To test if multi-cellular organisms also display natural genetic variation in the stochastic link between genotype and phenotype, we measured the metabolome, growth and defense metabolism within an Arabidopsis RIL population and mapped quantitative trait loci. We show that genetic variation in the nuclear and organeller genomes influence the stochastic variation in all measured traits. Further, each trait class has distinct genetics underlying the stochastic variance, showing that there are different mechanisms controlling the stochastic genotype to phenotype link for each trait. Further work is necessary to identify the mechanisms underpinning the stochastic nature of the genotype to phenotype link.
Zdroje
1. AlbertR, BarabasiAL (2002) Statistical mechanics of complex networks. Reviews of Modern Physics 74: 47–97.
2. AlbertR, JeongH, BarabasiAL (2000) Error and attack tolerance of complex networks. Nature 406: 378–382.
3. BarkaiN, LeiblerS (1997) Robustness in simple biochemical networks. Nature 387: 913–917.
4. AustinDW, AllenMS, McCollumJM, DarRD, WilgusJR, et al. (2006) Gene network shaping of inherent noise spectra. Nature 439: 608–611.
5. KitanoH (2007) Towards a theory of biological robustness. Molecular Systems Biology 3: 137.
6. KitanoH (2004) Biological robustness. Nature Reviews Genetics 5: 826–837.
7. WaddingtonCH (1942) Canalization of development and the inheritance of acquired characters. Nature 150: 563–565.
8. Schmalhausen I (1949) Factors of Evolution: The theory of stabilizing selection. Philadelphia, PA: Blakiston.
9. LehnerB (2010) Genes Confer Similar Robustness to Environmental, Stochastic, and Genetic Perturbations in Yeast. PLos ONE 5: e9035.
10. ConteM, de SimoneS, SimmonsSJ, BallareCL, StapletonAE (2010) Chromosomal important for cotyledon opening under UV-B in Arabidopsis thaliana. BMC Plant Biology 10: 112.
11. HallMC, DworkinI, UngererMC, PuruggananM (2007) Genetics of microenvironmental canalization in Arabidopsis thaliana. Proceedings of the National Academy of Sciences of the United States of America 104: 13717–13722.
12. JaroszDF, LindquistS (2010) Hsp90 and Environmental Stress Transform the Adaptive Value of Natural Genetic Variation. Science 330: 1820–1824.
13. SangsterTA, SalathiaN, LeeHN, WatanabeE, SchellenbergK, et al. (2008) HSP90-buffered genetic variation is common in Arabidopsis thaliana. Proceedings of the National Academy of Sciences of the United States of America 105: 2969–2974.
14. QueitschC, SangsterTA, LindquistS (2002) Hsp90 as a capacitor of phenotypic variation. Nature 417: 618–624.
15. L'hommeJP, WinkelT (2002) Diversity-stability relationships in community ecology: Re-examination of the portfolio effect. Theoretical Population Biology 62: 271–279.
16. ElowitzMB, LevineAJ, SiggiaED, SwainPS (2002) Stochastic gene expression in a single cell. Science 297: 1183–1186.
17. ToTL, MaheshriN (2010) Noise Can Induce Bimodality in Positive Transcriptional Feedback Loops Without Bistability. Science 327: 1142–1145.
18. ZhangZH, QianWF, ZhangJZ (2009) Positive selection for elevated gene expression noise in yeast. Molecular Systems Biology 5: 299.
19. FraserD, KaernM (2009) A chance at survival: gene expression noise and phenotypic diversification strategies. Molecular Microbiology 71: 1333–1340.
20. RajA, van OudenaardenA (2008) Nature, Nurture, or Chance: Stochastic Gene Expression and Its Consequences. Cell 135: 216–226.
21. RaserJM, O'SheaEK (2004) Control of stochasticity in eukaryotic gene expression. Science 304: 1811–1814.
22. Fraser HB, Schadt EE (2010) The Quantitative Genetics of Phenotypic Robustness. PLos ONE 5.
23. AnselJ, BottinH, Rodriguez-BeltranC, DamonC, NagarajanM, et al. (2008) Cell-to-cell Stochastic variation in gene expression is a complex genetic trait. PLOS Genetics 4: e1000049.
24. Jimenez-GomezJM, CorwinJA, JosephB, MaloofJN, KliebensteinDJ (2011) Genomic Analysis of QTLs and Genes Altering Natural Variation in Stochastic Noise. PLOS Genetics 7: e1002295.
25. Wang G, Yang E, Brinkmeyer-Langford CL, Cai JJ (2013) Additive, epistatic, and environmental effects through the lens of expression variability QTLs in a twin cohort. Genetics: genetics. 113.157503.
26. ZüstT, HeichingerC, GrossniklausU, HarringtonR, KliebensteinDJ, et al. (2012) Natural enemies drive geographic variation in plant defenses. Science 338: 116–119.
27. Bidart-BouzatMG, KliebensteinDJ (2008) Differential levels of insect herbivory in the field associated with genotypic variation in glucosinolates in Arabidopsis thaliana. Journal of Chemical Ecology 34: 1026–1037.
28. SheltonAL (2005) Within-plant variation in glucosinolate concentrations of Raphanus sativus across multiple scales. Journal of Chemical Ecology 31: 1711–1732.
29. SheltonAL (2004) Variation in chemical defences of plants may improve the effectiveness of defence. Evolutionary Ecology Research 6: 709–726.
30. Fell D Understanding the Control of Metabolism (1997) Portland, London.
31. SegreD, DeLunaA, ChurchGM, KishonyR (2005) Modular epistasis in yeast metabolism. Nature Genetics 37: 77–83.
32. LabhsetwarP, ColeJA, RobertsE, PriceND, Luthey-SchultenZA (2013) Heterogeneity in protein expression induces metabolic variability in a modeled Escherichia coli population. Proceedings of the National Academy of Sciences of the United States of America 110: 14006–14011.
33. LevineE, HwaT (2007) Stochastic fluctuations in metabolic pathways. Proceedings of the National Academy of Sciences of the United States of America 104: 9224–9229.
34. AtwellS, HuangY, VilhjalmssonBJ, WillemsG, HortonM, et al. (2010) Genome-wide association study of 107 phenotypes in a common set of Arabidopsis thaliana in-bred lines. Nature 465: 627–631.
35. SulpiceR, PylET, IshiharaH, TrenkampS, SteinfathM, et al. (2009) Starch as a major integrator in the regulation of plant growth. Proceedings of the National Academy of Sciences of the United States of America 106: 10348–10353.
36. Keurentjes JJB (2009) Genetical metabolomics: closing in on phenotypes. Current Opinion in Plant Biology In Press.
37. KliebensteinDJ (2010) Systems biology uncovers the foundation of natural genetic diversity. Plant Physiol 152: 480–486.
38. KliebensteinDJ (2009) Advancing genetic theory and application by metabolic quantitative trait loci analysis. Plant Cell 21: 1637–1646.
39. KliebensteinD (2009) Quantitative Genomics: Analyzing Intraspecific Variation Using Global Gene Expression Polymorphisms or eQTLs. Annual Review of Plant Biology 60: 93–114.
40. KeurentjesJJB, FuJY, TerpstraIR, GarciaJM, van den AckervekenG, et al. (2007) Regulatory network construction in Arabidopsis by using genome-wide gene expression quantitative trait loci. Proceedings of the National Academy of Sciences of the United States of America 104: 1708–1713.
41. WestMAL, KimK, KliebensteinDJ, van LeeuwenH, MichelmoreRW, et al. (2007) Global eQTL mapping reveals the complex genetic architecture of transcript level variation in Arabidopsis. Genetics 175: 1441–1450.
42. KeurentjesJJB, FuJY, de VosCHR, LommenA, HallRD, et al. (2006) The genetics of plant metabolism. Nature Genetics 38: 842–849.
43. ChanEK, RoweHC, HansenBG, KliebensteinDJ (2010) The complex genetic architecture of the metabolome. PLoS Genet 6: e1001198.
44. RoweHC, HansenBG, HalkierBA, KliebensteinDJ (2008) Biochemical networks and epistasis shape the Arabidopsis thaliana metabolome. Plant Cell 20: 1199–1216.
45. ClarkRM, SchweikertG, ToomajianC, OssowskiS, ZellerG, et al. (2007) Common sequence polymorphisms shaping genetic diversity in Arabidopsis thaliana. Science 317: 338–342.
46. LoudetO, ChaillouS, CamilleriC, BouchezD, Daniel-VedeleF (2002) Bay-0 x Shahdara recombinant inbred line population: a powerful tool for the genetic dissection of complex traits in Arabidopsis. Theoretical And Applied Genetics 104: 1173–1184.
47. Alonso-BlancoC, PeetersAJM, KoornneefM, ListerC, DeanC, et al. (1998) Development of an AFLP based linkage map of Ler, Col and Cvi Arabidopsis thaliana ecotypes and construction of a Ler/Cvi recombinant inbred line population. Plant Journal 14: 259–271.
48. ListerC, DeanD (1993) Recombinant inbred lines for mapping RFLP and phenotypic markers in Arabidopsis thaliana. Plant Journal 4: 745–750.
49. ChanEK, RoweHC, CorwinJA, JosephB, KliebensteinDJ (2011) Combining genome-wide association mapping and transcriptional networks to identify novel genes controlling glucosinolates in Arabidopsis thaliana. PLoS Biol 9: e1001125.
50. MaloofJN (2003) Genomic approaches to analyzing natural variation in Arabidopsis thaliana. Current Opinion in Genetics & Development 13: 576–582.
51. JosephB, CorwinJA, LiB, AtwellS, KliebensteinDJ (2013) Cytoplasmic genetic variation and extensive cytonuclear interactions influence natural variation in the metabolome. eLife 2: e00776.
52. RonnegardL, ValdarW (2011) Detecting Major Genetic Loci Controlling Phenotypic Variability in Experimental Crosses. Genetics 188: 435–U338.
53. Ronnegard L, Valdar W (2012) Recent developments in statistical methods for detecting genetic loci affecting phenotypic variability. Bmc Genetics 13.
54. Shen X, Pettersson M, Ronnegard L, Carlborg O (2012) Inheritance Beyond Plain Heritability: Variance-Controlling Genes in Arabidopsis thaliana. Plos Genetics 8.
55. Yang J, Loos RJF, Powell JE, Medland SE, Speliotes EK, et al. (2012) FTO genotype is associated with phenotypic variability of body mass index. Nature 490: 267-+.
56. JosephB, CorwinJA, ZuestT, LiB, IravaniM, et al. (2013) Hierarchical nuclear and cytoplasmic genetic architectures for plant growth and defense within Arabidopsis. Plant Cell 25: 1929–1945.
57. McKayJK, RichardsJH, NemaliKS, SenS, Mitchell-OldsT, et al. (2008) Genetics of drought adaptation in Arabidopsis thaliana II. QTL analysis of a new mapping population Kas-1 x Tsu-1. Evolution 62: 3014–3026.
58. MackayTFC (2001) The genetic architecture of quantitative traits. Annual Review Of Genetics 35: 303–339.
59. ManolioTA, CollinsFS, CoxNJ, GoldsteinDB, HindorffLA, et al. (2009) Finding the missing heritability of complex diseases. Nature 461: 747–753.
60. KliebensteinD, LambrixV, ReicheltM, GershenzonJ, Mitchell-OldsT (2001) Gene duplication and the diversification of secondary metabolism: side chain modification of glucosinolates in Arabidopsis thaliana. Plant Cell 13: 681–693.
61. KerwinRE, Jiménez-GómezJM, FulopD, HarmerSL, MaloofJN, et al. (2011) Network quantitative trait loci mapping of circadian clock outputs identifies metabolic pathway-to-clock linkages in Arabidopsis. Plant Cell 23: 471–485.
62. KroymannJ, DonnerhackeS, SchnabelrauchD, Mitchell-OldsT (2003) Evolutionary dynamics of an Arabidopsis insect resistance quantitative trait locus. Proceedings Of The National Academy Of Sciences Of The United States Of America 100: 14587–14592.
63. WentzellAM, RoweHC, HansenBG, TicconiC, HalkierBA, et al. (2007) Linking metabolic QTLs with network and cis-eQTLs controlling biosynthetic pathways. PLoS Genet 3: 1687–1701.
64. SønderbyIE, BurowM, RoweHC, KliebensteinDJ, HalkierBA (2010) A complex interplay of three R2R3 MYB transcription factors determines the profile of aliphatic glucosinolates in Arabidopsis. Plant Physiol 153: 348–363.
65. SønderbyIE, HansenBG, BjarnholtN, TicconiC, HalkierBA, et al. (2007) A systems biology approach identifies a R2R3 MYB gene subfamily with distinct and overlapping functions in regulation of aliphatic glucosinolates. PLos ONE 2: e1322.
66. GigolashviliT, YatusevichR, BergerB, MüllerC, FlüggeUI (2007) The R2R3-MYB transcription factor HAG1/MYB28 is a regulator of methionine-derived glucosinolate biosynthesis in Arabidopsis thaliana. The Plant Journal 51: 247–261.
67. HiraiM, SugiyamaK, SawadaY, TohgeT, ObayashiT, et al. (2007) Omics-based identification of Arabidopsis Myb transcription factors regulating aliphatic glucosinolate biosynthesis Proc Natl Acad Sci U S A. 104: 6478–6483.
68. MuellerLA, ZhangPF, RheeSY (2003) AraCyc: A biochemical pathway database for Arabidopsis. Plant Physiology 132: 453–460.
69. ZhangPF, FoersterH, TissierCP, MuellerL, PaleyS, et al. (2005) MetaCyc and AraCyc. Metabolic pathway databases for plant research. Plant Physiology 138: 27–37.
70. Falconer DS, Mackay TFC (1996) Introduction to Quantitative Genetics. Essex: Longman, Harlow.
71. FukushimaA, KusanoM, NakamichiN, KobayashiM, HayashiN, et al. (2009) Impact of clock-associated Arabidopsis pseudo-response regulators in metabolic coordination. Proc Natl Acad Sci U S A 106: 7251–7256.
72. DimitrovLN, BremRB, KruglyakL, GottschlingDE (2009) Polymorphisms in Multiple Genes Contribute to the Spontaneous Mitochondrial Genome Instability of Saccharomyces cerevisiae S288C Strains. Genetics 183: 365–383.
73. Angel A, Song J, Dean C, Howard M (2011) A Polycomb-based switch underlying quantitative epigenetic memory. Nature 476: 105-+.
74. SongJ, AngelA, HowardM, DeanC (2012) Vernalization - a cold-induced epigenetic switch. Journal of Cell Science 125: 3723–3731.
75. Lempe J, Lachowiec J, Sullivan AM, Queitsch C (2013) Molecular mechanisms of robustness in plants. Curr Opin Plant Biol 16: online.
76. FrancisTR, KannenbergLW (1978) Yield stability studies in short-season maize.1. Descriptive method for grouping genotypes. Canadian Journal of Plant Science 58: 1029–1034.
77. TollenaarM, LeeEA (2002) Yield potential, yield stability and stress tolerance in maize. Field Crops Research 75: 161–169.
78. JanderG, NorrisSR, RounsleySD, BushDF, LevinIM, et al. (2002) Arabidopsis map-based cloning in the post-genome era. Plant Physiology 129: 440–450.
79. AlonsoJM, StepanovaAN, LeisseTJ, KimCJ, ChenHM, et al. (2003) Genome-wide Insertional mutagenesis of Arabidopsis thaliana. Science 301: 653–657.
80. AjjawiI, LuY, SavageLJ, BellSM, LastRL (2010) Large-Scale Reverse Genetics in Arabidopsis: Case Studies from the Chloroplast 2010 Project. Plant Physiology 152: 529–540.
81. BremRB, YvertG, ClintonR, KruglyakL (2002) Genetic dissection of transcriptional regulation in budding yeast. Science 296: 752–755.
82. TongAHY, LesageG, BaderGD, DingHM, XuH, et al. (2004) Global mapping of the yeast genetic interaction network. Science 303: 808–813.
83. KliebensteinDJ (2008) A role for gene duplication and natural variation of gene expression in the evolution of metabolism. PLos ONE 3: e1838.
84. WestMA, van LeeuwenH, KozikA, KliebensteinDJ, DoergeRW, et al. (2006) High-density haplotyping with microarray-based expression and single feature polymorphism markers in Arabidopsis. Genome Res 16: 787–795.
85. FernieAR, AharoniA, WillmitzerL, StittM, TohgeT, et al. (2011) Recommendations for Reporting Metabolite Data. Plant Cell 23: 2477–2482.
86. FiehnO, WohlgemuthG, ScholzM, KindT, LeeDY, et al. (2008) Quality control for plant metabolomics: reporting MSI-compliant studies. Plant Journal 53: 691–704.
87. Fiehn O, Wohlgemuth G, Scholz M (2005) Setup and annotation of metabolomic experiments by integrating biological and mass spectrometric metadata. Data Integration In The Life Sciences, Proceedings. pp. 224–239.
88. Liu B-H (1998) Statistical Genomics: Linkage, Mapping and QTL Analysis. Boca Raton, Florida: CRC Press.
89. ChurchillGA, DoergeRW (1994) Empirical Threshold Values For Quantitative Trait Mapping. Genetics 138: 963–971.
90. DoergeRW, ChurchillGA (1996) Permutation tests for multiple loci affecting a quantitative character. Genetics 142: 285–294.
91. RebaiA (1997) Comparison of methods for regression interval mapping in QTL analysis with non-nomral traits. Genet Rec 69: 69–74.
92. Wang S, Basten CJ, Zeng Z-B (2006) Windows QTL Cartographer 2.5. Department of Statistics, North Carolina State University, Raleigh, NC.
93. R Development Core Team (2014) R: A Language and Environment for Statistical Computing. In: Computing RFfs, editor. Vienna.
94. KliebensteinDJ, WestMA, van LeeuwenH, LoudetO, DoergeRW, et al. (2006) Identification of QTLs controlling gene expression networks defined a priori. Bmc Bioinformatics 7: 308.
95. Fox J, Weisberg S (2011) An R companion to applied regression. Thousand Oaks, CA, USA: SAGE.
96. SmootM, OnoK, RuscheinskiJ, WangP-L, IdekerT (2011) Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27: 431–432.
Štítky
Genetika Reprodukčná medicínaČlánok vyšiel v časopise
PLOS Genetics
2015 Číslo 1
- Je „freeze-all“ pro všechny? Odborníci na fertilitu diskutovali na virtuálním summitu
- Gynekologové a odborníci na reprodukční medicínu se sejdou na prvním virtuálním summitu
Najčítanejšie v tomto čísle
- The Global Regulatory Architecture of Transcription during the Cell Cycle
- A Truncated NLR Protein, TIR-NBS2, Is Required for Activated Defense Responses in the Mutant
- Proteasomes, Sir2, and Hxk2 Form an Interconnected Aging Network That Impinges on the AMPK/Snf1-Regulated Transcriptional Repressor Mig1
- Regulating Maf1 Expression and Its Expanding Biological Functions