Plant Molecular Biology

, Volume 53, Issue 4, pp 467–478 | Cite as

Evaluation of light regulatory potential of Calvin cycle steps based on large-scale gene expression profiling data

  • Ning Sun
  • Ligeng Ma
  • Deyun Pan
  • Hongyu Zhao
  • Xing Wang Deng


Although large-scale gene expression data have been studied from many perspectives, they have not been systematically integrated to infer the regulatory potentials of individual genes in specific pathways. Here we report the analysis of expression patterns of genes in the Calvin cycle from 95 Arabidopsis microarray experiments, which revealed a consistent gene regulation pattern in most experiments. This identified pattern, likely due to gene regulation by light rather than feedback regulations of the metabolite fluxes in the Calvin cycle, is remarkably consistent with the rate-limiting roles of the enzymes encoded by these genes reported from both experimental and modeling approaches. Therefore, the regulatory potential of the genes in a pathway may be inferred from their expression patterns. Furthermore, gene expression analysis in the context of a known pathway helps to categorize various biological perturbations that would not be recognized with the prevailing methods.

Calvin cycle gene expression gene regulation microarray regulatory potentials 


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  1. Buchanan, B.B., Gruissem, W. and Jones, R.L. 2000. Photosynthesis: Biochemistry & Molecular Biology of Plants. American Society of Plant Physiology, Rockville, MD.Google Scholar
  2. Den-Dor, A. and Yakhini, Z. 1999. Clustering gene expression patterns. In: S. Istrail, P. Pevzner and M.S. Waterman(Eds.) Recomb 99, ACM Press, Washington, DC, p. 188.Google Scholar
  3. Donald, R.G. and Cashmore, A.R. 1990. Mutation of either G box or I box sequences profoundly affects expression from the Arabidopsis rbcS-1A promoter. EMBO J. 9: 1717–1726.PubMedPubMedCentralGoogle Scholar
  4. Dudoit, S., Yang, Y.H., Speed, T.P. and Callow, M.J. 2002. Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Stat. Sin. 12: 111.Google Scholar
  5. Efron, B., Tibshirani, R., Storey, J.D. and Tusher, V. 2001. Empirical Bayes analysis of a microarray experiment. J. Am. Stat. Ass. 96: 1151.CrossRefGoogle Scholar
  6. Eisen, M.B., Spellman, P.T., Brown, P.O. and Botstein, D. 1998. Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA 95: 14863.CrossRefPubMedPubMedCentralGoogle Scholar
  7. Fichtner, K., Quick, W.P., Schulze, E-D, Mooney, H.A., Rodermel, S.R., Bogorad, L. and Stitt, M. 1993. Decreased ribulose-1,5-bisphosphate carboxylase-oxygenase in transgenic tobacco transformed with 'antisense' rbcS. V. Relationship between photosynthetic rate, storage strategy, biomass allocation and vegetative plant growth at three different nitrogen supplies. Planta 190: 1.CrossRefGoogle Scholar
  8. Fridlyand, L.E., Backhausen, J.E. and Scheibe, R. 1999. Homeostatic regulation upon changes of enzyme activities in the Calvin cycle as an example for general mechanisms of flux control. What can we expect from transgenic plants? Photosynth. Res. 61: 227.CrossRefGoogle Scholar
  9. Haake, V., Zrenner, R., Sonnewald, U. and Stitt, M. 1998. A moderate decrease of plastid aldolase activity inhibits photosynthesis, alters the levels of sugars and starch and inhibits growth of potato plants. Plant J. 14: 147.CrossRefPubMedGoogle Scholar
  10. Harrison, E.P., Willingham, N.M., Lloyd, J.C. and Raines, C.A. 1998. Reduced sedoheptulose-1,7-bisphosphatase levels in transgenic tobacco lead to decreased photosynthetic capacity and altered carbohydrate accumulation. Planta 204: 27.CrossRefGoogle Scholar
  11. Hastie, T., Tibshirani, R., Eisen, M.B., Alizadeh, A., Levy, R., Staudt, L., Chan, W.C., Botstein, D. and Brown, P. 2000. 'Gene shaving' as a method for identifying distinct sets of genes with similar expression patterns. Genome Biol. 1, research0003.1-0003.21.Google Scholar
  12. Henkes, S., Sonnewald, U., Flachmann, R., Badur, R. and Stitt, M. 2001. A small decrease of plastid transketolase activity in antisense tobacco transformants has dramatic effects on photosynthesis and phenylpropanoid metabolism. Plant Cell 13: 535.CrossRefPubMedPubMedCentralGoogle Scholar
  13. Hughes, J.D., Estep, P.W., Tavazoie, S. and Church, G.M. 2000. Computational identification of cis-regulatory elements associated with groups of functionally related genes in Saccharomyces cerevisiae. J. Mol. Biol. 296: 1205.CrossRefPubMedGoogle Scholar
  14. Kerr, M.K., Martin, M. and Churchill, G. 2000. Analysis of variance for gene expression microarray data. J. Comp. Biol. 7: 819.CrossRefGoogle Scholar
  15. Kossmann, J., Sonnewald, U. and Willmitzer, L. 1994. Reduction of the chloroplastic fructose-1,6-bisphosphatase in transgenic potato plants impairs photosynthesis and plant growth. Plant J. 6: 637.CrossRefGoogle Scholar
  16. Lazzeroni, L. and Owen, A. 2002. Plaid models for gene expression data. Stat. Sin. 12: 61.Google Scholar
  17. Li, C. and Wong, W.H. 2001. Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proc Natl. Acad. Sci. USA 98: 31.CrossRefPubMedGoogle Scholar
  18. Ma, L., Li, J., Qu, L., Hager, J., Chen, Z., Zhao, H. and Deng, X.W 2001. Light control of Arabidopsis development entails coordinated regulation of genome expression and cellular pathways. Plant Cell 13: 2589–2607.CrossRefPubMedPubMedCentralGoogle Scholar
  19. Ma, L., Gao, Y., Qu, L., Chen, Z., Li, J., Zhao, H. and Deng, X.W. 2002. Genomic evidence for COP1 as a repressor of lightregulated gene expression and development in Arabidopsis. Plant Cell 14: 2383–2398.CrossRefPubMedPubMedCentralGoogle Scholar
  20. Ma, L., Zhao, H. and Deng, X.W. 2003. Analysis of the mutational effects of the COP/DET/FUS loci on genome expression pro-files reveals their overlapping yet not identical roles in regulating Arabidopsis seedling development. Development 130: 969–981.CrossRefPubMedGoogle Scholar
  21. Miller, A., Schlagenhaufer, C., Spalding, M. and Rodermel, S.R. 2000. Carbohydrate regulation of leaf development: prolongation of leaf senescence in Rubisco antisense mutants of tobacco. Photosynth. Res. 63: 1.CrossRefPubMedGoogle Scholar
  22. Muschak, M., Hoffmann-Benning, S., Fuss, H., Kossmann, J., Willmitzer, L. and Fisahn, J. 1997. Gas exchange and ultrastructural analysis of transgenic potato plants expressing mRNA antisense construct targeted to the cp-fructose-1,6-bisphosphate phosphatase. Photosynthetica 33: 455.Google Scholar
  23. Panda, S., Antoch, M.P., Miller, B.H., Su, A.I., Schook, A.B., Straume, M., Schultz, P.G., Kay, S.A., Takahashi, J.S. and Hogenesch, J.B. 2002. Coordinated transcription of key pathways in the mouse by the circadian clock. Cell 109: 307–320.CrossRefPubMedGoogle Scholar
  24. Paul, M.J., Knight, J.S., Habash, D., Parry, M.A.J., Lawlor, D.W., Barnes, A.A., Loynes, A. and Gray, J.C. 1995. Reduction in phosphoribulokinase activity by antisense RNA in transgenic tobacco: effect on CO2 assimilation and growth in low irradiance. Plant J. 7: 535.CrossRefGoogle Scholar
  25. Pettersson, G. and Ryde-Pettersson, U. 1988. A mathematical model of the Calvin photosynthesis cycle. Eur. J. Biochem.175: 661.Google Scholar
  26. Poolman, M., Fell, D. and Thomas, S. 2000. Modeling photosynthesis and its control. J. Exp. Bot.51: 319.CrossRefPubMedGoogle Scholar
  27. Poolman, M.G., Ölcer, H., Lloyd, J.C., Raines, C.A. and Fell, D.A. 2001. Computer modelling and experimental evidence for two steady states in the photosynthetic Calvin cycle. Eur. J. Biochem. 268: 2810.CrossRefPubMedGoogle Scholar
  28. Puente, P., Wei, N. and Deng, X.W. 1996. Combinatorial interplay of promoter elements constitutes the minimal determinants for light and developmental control of gene expression in Arabidopsis. EMBO J. 15: 3732–3743.PubMedPubMedCentralGoogle Scholar
  29. Price, G.D., Evans, J.R., Von Caemmerer, S., Yu, J.-W and Badger, M.R. 1995. Specific reduction of chloroplast glyceraldehyde-3-phosphate dehydrogenase activity by antisense RNA reduces CO2 assimilation via a reduction in ribulose bisphosphate regeneration in transgenic tobacco plants. Planta 195: 369.CrossRefPubMedGoogle Scholar
  30. Quick, W.P., Schurr, U., Scheibe, R., Schulze, E-D, Rodermel, S.R., Bogorad, L. and Stitt, M. 1991. Decreased ribulose-1,5-bisphosphate carboxylase-oxygenase in transgenic tobacco transformed with 'antisense' rbcS. I. Impact on photosynthesis in ambient growth conditions. Planta 183: 542.CrossRefPubMedGoogle Scholar
  31. Roth, F.R., Hughes, J.D., Estep, P.E. and Church, G.M. 1998. Finding DNA regulatory motifs within unaligned non-coding sequences clustered by whole-genome mRNA quantitation. Nature Biotech. 16: 939.CrossRefGoogle Scholar
  32. Stitt, M., Quick, W.P., Schurr, U., Schulze, E-D, Rodermel, S.R. and Bogorad, L. 1991. Decreased ribulose-1,5-bisphosphate carboxylase-oxygenase in transgenic tobacco transformed with ‘antisense’ rbcS. II. Flux-control coefficients for photosynthesis in varying light, CO2, and air humidity. Planta 183: 555.PubMedGoogle Scholar
  33. The supplemental materials of this paper are also available at Cycle.Google Scholar

Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • Ning Sun
    • 1
  • Ligeng Ma
    • 2
    • 3
  • Deyun Pan
    • 1
  • Hongyu Zhao
    • 1
    • 3
  • Xing Wang Deng
    • 2
    • 3
  1. 1.Department of Epidemiology and Public HealthYale University School of MedicineNew HavenUSA
  2. 2.Peking-Yale Joint Center of Plant Molecular Genetics and Agrobiotechnology, College of Life SciencesPeking UniversityBeijingChina
  3. 3.Department of Molecular, Cellular, and Developmental BiologyYale UniversityNew HavenUSA

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