Enriched partial correlations in genome-wide gene expression profiles of hybrids (A. thaliana): a systems biological approach towards the molecular basis of heterosis

  • Sandra Andorf
  • Joachim Selbig
  • Thomas Altmann
  • Kathrin Poos
  • Hanna Witucka-Wall
  • Dirk Repsilber
Original Paper

Abstract

Heterosis is a well-known phenomenon but the underlying molecular mechanisms are not yet established. To contribute to the understanding of heterosis at the molecular level, we analyzed genome-wide gene expression profile data of Arabidopsis thaliana in a systems biological approach. We used partial correlations to estimate the global interaction structure of regulatory networks. Our hypothesis states that heterosis comes with an increased number of partial correlations which we interpret as increased numbers of regulatory interactions leading to enlarged adaptability of the hybrids. This hypothesis is true for mid-parent heterosis for our dataset of gene expression in two homozygous parental lines and their reciprocal crosses. For the case of best-parent heterosis just one hybrid is significant regarding our hypothesis based on a resampling analysis. Summarizing, both metabolome and gene expression level of our illustrative dataset support our proposal of a systems biological approach towards a molecular basis of heterosis.

Notes

Acknowledgments

This work was supported by the German Research Council (DFG) under Grants RE 1654/2-1 and SE 611/3-1. We want to thank Dirk Hincha (MPIMP-Golm) and his lab for supporting our gene expression experiments.

References

  1. Agilent Technologies Inc. (2008) Agilent feature extraction software: reference guide, 6th edn. USA, G4460-90020Google Scholar
  2. Andorf S, Gärtner T, Steinfath M, Witucka-Wall H, Altmann T, Repsilber D (2009) Towards systems biology of heterosis: a hypothesis about molecular network structure applied for the arabidopsis metabolome. EURASIP J Bioinform Syst Biol 147157Google Scholar
  3. Backes C, Keller A, Kuentzer J, Kneissl B, Comtesse N, Elnakady YA, Müller R, Meese E, Lenhof HP (2007) Genetrail—advanced gene set enrichment analysis. Nucleic Acids Res 35(Web Server issue):W186–W192CrossRefPubMedGoogle Scholar
  4. Barabási AL, Oltvai ZN (2004) Network biology: understanding the cell’s functional organization. Nat Rev Genet 5(2):101–113CrossRefPubMedGoogle Scholar
  5. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol 57(1):289–300Google Scholar
  6. Birchler JA, Auger DL, Riddle NC (2003) In search of the molecular basis of heterosis. Plant Cell 15:2236–2239CrossRefPubMedGoogle Scholar
  7. Bruce AB (1910) The mendelian theory of heredity and the augmentation of vigor. Science 32(827):627–628CrossRefPubMedGoogle Scholar
  8. Butte AJ, Tamayo P, Slonim D, Golub TR, Kohane IS (2000) Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks. Proc Natl Acad Sci USA 97(22):12182–12186CrossRefPubMedGoogle Scholar
  9. Crow JF (1952) Heterosis. In: Dominance and overdominance. Iowa State College Press, Ames, pp 282–297Google Scholar
  10. Draghici S, Khatri P, Martins RP, Ostermeier GC, Krawetz SA (2003) Global functional profiling of gene expression. Genomics 81(2):98–104CrossRefPubMedGoogle Scholar
  11. East EM (1936) Heterosis. Genetics 21(4):375–397PubMedGoogle Scholar
  12. Frisch M, Thiemann A, Fu J, Schrag TA, Scholten S, Melchinger AE (2010) Transcriptome-based distance measures for grouping of germplasm and prediction of hybrid performance in maize. Theor Appl Genet (accepted)Google Scholar
  13. Gärtner T, Steinfath M, Andorf S, Lisec J, Meyer RC, Altmann T, Willmitzer L, Selbig J (2009) Improved heterosis prediction by combining information on DNA- and metabolic markers. PLoS One 4(4):e5220CrossRefPubMedGoogle Scholar
  14. Genoud T, Métraux JP (1999) Crosstalk in plant cell signaling: structure and function of the genetic network. Trends Plant Sci 4(12):503–507CrossRefPubMedGoogle Scholar
  15. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JY, Zhang J (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5:R80CrossRefPubMedGoogle Scholar
  16. Guo M, Rupe MA, Yang X, Crasta O, Zinselmeier C, Smith OS, Bowen B (2006) Genome-wide transcript analysis of maize hybrids: allelic additive gene expression and yield heterosis. Theor Appl Genet 113(5):831–845CrossRefPubMedGoogle Scholar
  17. Harrison GA (1962) Heterosis and adaptability in the heat tolerance of mice. Genetics 47(4):427–434PubMedGoogle Scholar
  18. Hartwell LH, Hopfield JJ, Leibler S, Murray AW (1999) From molecular to modular cell biology. Nature 402(SUPP):C47–C52CrossRefPubMedGoogle Scholar
  19. Kerr MK, Churchill GA (2001) Experimental design for gene expression microarrays. Biostatistics 2(2):183–201CrossRefPubMedGoogle Scholar
  20. Kerr MK, Martin M, Churchill GA (2000) Analysis of variance for gene expression microarray data. J Comput Biol 7(6):819–837CrossRefPubMedGoogle Scholar
  21. Lamkey KR, Edwards JW (1999) The quantitative genetics of heterosis. In: Coors JG, Pandey S (eds) The genetics and exploitation of heterosis in crops. ASA, CSSA, and SSSA, Madison, pp 31–48Google Scholar
  22. Lee TI, Rinaldi NJ, Robert F, Odom DT, Bar-Joseph Z, Gerber GK, Hannett NM, Harbison CT, Thompson CM, Simon I, Zeitlinger J, Jennings EG, Murray HL, Gordon DB, Ren B, Wyrick JJ, Tagne JB, Volkert TL, Fraenkel E, Gifford DK, Young RA (2002) Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 298(5594):799–804CrossRefPubMedGoogle Scholar
  23. Li ZK, Luo LJ, Mei HW, Wang DL, Shu QY, Tabien R, Zhong DB, Ying CS, Stansel JW, Khush GS, Paterson AH (2001) Overdominant epistatic loci are the primary genetic basis of inbreeding depression and heterosis in rice. I. Biomass and grain yield. Genetics 158:1737–1753PubMedGoogle Scholar
  24. Luo LJ, Li ZK, Mei HW, Shu QY, Tabien R, Zhong DB, Ying CS, Stansel JW, Khush GS, Paterson AH (2001) Overdominant epistatic loci are the primary genetic basis of inbreeding depression and heterosis in rice. II. Grain yield components. Genetics 158:1755–1771PubMedGoogle Scholar
  25. Ma L, Sun N (2005) Organ-specific expression of Arabidopsis genome during development. Plant Physiol 138:80–91CrossRefPubMedGoogle Scholar
  26. Maynard Smith J (1956) Acclimatization to high temperatures in inbred and outbred Drosophila subobscura. J Genet 54(1):497–505CrossRefGoogle Scholar
  27. Melchinger AE, Utz HF, Piepho HP, Zeng ZB, Schön CC (2007) The role of epistasis in the manifestation of heterosis: a systems-orientated approach. Genetics 177:1815–1825CrossRefPubMedGoogle Scholar
  28. Meyer RC, Törjék O, Becher M, Altmann T (2004) Heterosis of biomass production in arabidopsis. Establsiment during early development. Plant Physiol 134:1813–1823CrossRefPubMedGoogle Scholar
  29. Milo R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovskii D, Alon U (2002) Network motifs: simple building blocks of complex networks. Science 298(5594):824–827CrossRefPubMedGoogle Scholar
  30. Opgen-Rhein R, Strimmer K (2007) From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data. BMC Syst Biol 1:37CrossRefPubMedGoogle Scholar
  31. Opgen-Rhein R, Schäfer J, Strimmer K (2007) GeneNet: Modeling and Inferring Gene Networks. http://strimmerlab.org/software/genenet/
  32. Parrish RS, Spencer III HJ, Xu P (2009) Distribution modeling and simulation of gene expression data. Comput Stat Data Anal 53(5):1650–1660CrossRefGoogle Scholar
  33. R Development Core Team (2008) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. ISBN 3-900051-07-0. http://www.R-project.org
  34. Ritchie ME, Silver J, Oshlack A, Holmes M, Diyagama D, Holloway A, Smyth GK (2007) A comparison of background correction methods for two-colour microarrays. Bioinformatics 23:2700–2707CrossRefPubMedGoogle Scholar
  35. Robertson FW, Reeve EC (1952) Heterozygosity, environmental variation and heterosis. Nature 170(4320):286CrossRefPubMedGoogle Scholar
  36. Schäfer J, Strimmer K (2005a) An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics 21(6):754–764CrossRefPubMedGoogle Scholar
  37. Schäfer J, Strimmer K (2005b) A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics. Stat Appl Genet Mol Biol 4:32Google Scholar
  38. Schnell FW, Cockerham CC (1992) Multiplicative vs. arbitrary gene action in heterosis. Genetics 131(2):461–469PubMedGoogle Scholar
  39. Shubik M (1996) Simulations, models and simplicity. Complexity 2(1):60CrossRefGoogle Scholar
  40. Shull GH (1908) The composition of a field of maize. Am Breeders Assoc Rep 4:296–301Google Scholar
  41. Shull GH (1952) Beginnings of the heterosis concept. In: Gowen JW (ed) Heterosis: a record of researches directed toward explaining and utilizing the vigor of hybrids. Iowa State College Press, Ames, pp 14–48Google Scholar
  42. Smyth GK (2005) Limma: linear models for microarray data. In: Gentleman R, Carey V, Dudoit S, Irizarry R, Huber W (eds) Bioinformatics and computational biology solutions using R and bioconductor. Springer, New York, pp 397–420CrossRefGoogle Scholar
  43. Smyth GK, Speed T (2003) Normalization of cDNA microarray data. Methods 31:265–273CrossRefPubMedGoogle Scholar
  44. Somogyi R, Sniegoski CA (1996) Modeling the complexity of genetic networks: understanding multigenic and pleiotropic regulation. Complexity 1:45–63Google Scholar
  45. Song R, Messing J (2003) Gene expression of a gene family in maize based on noncollinear haplotypes. Proc Natl Acad Sci USA 100:9055–9060CrossRefPubMedGoogle Scholar
  46. Steinfath M, Gärtner T, Lisec J, Meyer RC, Altmann T, Willmitzer L, Selbig J (2010) Prediction of hybrid biomass in Arabidopsis thaliana by selected parental SNP and metabolic markers. Theor Appl Genet (accepted)Google Scholar
  47. Strimmer K (2008) A unified approach to false discovery rate estimation. BMC Bioinformatics 9:303CrossRefPubMedGoogle Scholar
  48. Swanson-Wagner RA, Jia Y, DeCook R, Borsuk LA, Nettleton D, Schnable PS (2006) All possible modes of gene action are observed in a global comparison of gene expression in a maize F1 hybrid and its inbred parents. Proc Natl Acad Sci USA 103(18):6805–6810CrossRefPubMedGoogle Scholar
  49. Swarbreck D, Wilks C, Lamesch P, Berardini TZ, Garcia-Hernandez M, Foerster H, Li D, Meyer T, Muller R, Ploetz L, Radenbaugh A, Singh S, Swing V, Tissier C, Zhang P, Huala E (2008) The Arabidopsis information resource (TAIR): gene structure and function annotation. Nucleic Acids Res 36(Database issue):D1009–D1014. http://www.arabidopsis.org Google Scholar
  50. The Plant Ontology Consortium (2002) The plant ontology consortium and plant ontologies. Comp Funct Genomics 3:137–142. http://www.plantontology.org
  51. Thiemann A, Fu J, Schrag TA, Melchinger AE, Frisch M, Scholten S (2010) Correlation between parental transcriptome and field data for the characterization of heterosis in Zea mays L. Theor Appl Genet (accepted)Google Scholar
  52. Thimm O, Bläsing O, Gibon Y, Nagel A, Meyer S, Krüger P, Selbig J, Müller LA, Rhee SY, Stitt M (2004) MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J 37(6):914–939CrossRefPubMedGoogle Scholar
  53. Usadel B, Poree F, Nagel A, Lohse M, Czedik-Eysenberg A, Stitt M (2009) A guide to using MapMan to visualize and compare omics data in plants: a case study in the crop species, Maize. Plant Cell Environ 32(9):1211–1229CrossRefPubMedGoogle Scholar
  54. Vuylsteke M, van Eeuwijk F, Van Hummelen P, Kuiper M, Zabeau M (2005) Genetic analysis of variation in gene expression in Arabidopsis thaliana. Genetics 171(3):1267–1275CrossRefPubMedGoogle Scholar
  55. Wei G, Tao Y, Liu G, Chen C, Luo R, Xia H, Gan Q, Zeng H, Lu Z, Han Y, Li X, Song G, Zhai H, Peng Y, Li D, Xu H, Wei X, Cao M, Deng H, Xin Y, Fu X, Yuan L, Yu J, Zhu Z, Zhu L (2009) A transcriptomic analysis of superhybrid rice LYP9 and its parents. Proc Natl Acad Sci USA 106(19):7695–7701CrossRefPubMedGoogle Scholar
  56. Werhli AV, Grzegorczyk M, Husmeier D (2006) Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and bayesian networks. Bioinformatics 22(20):2523–2531CrossRefPubMedGoogle Scholar
  57. Xiao J, Li J, Yuan L, Tanksley SD (1995) Dominance is the major genetic basis of heterosis in rice as revealed by QTL analysis using molecular markers. Genetics 140:745–754PubMedGoogle Scholar
  58. Yang YH, Dudoit S, Luu P, Lin DM, Peng V, Ngai J, Speed TP (2002) Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res 30(4):e15CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Sandra Andorf
    • 1
  • Joachim Selbig
    • 2
  • Thomas Altmann
    • 3
  • Kathrin Poos
    • 4
  • Hanna Witucka-Wall
    • 2
  • Dirk Repsilber
    • 1
  1. 1.Research Institute for the Biology of Farm Animals (FBN)DummerstorfGermany
  2. 2.University of PotsdamPotsdam-GolmGermany
  3. 3.Leibniz Institute of Plant Genetics and Crop Plant Research (IPK)GaterslebenGermany
  4. 4.University of Applied Sciences GelsenkirchenRecklinghausenGermany

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