Hostname: page-component-7479d7b7d-k7p5g Total loading time: 0 Render date: 2024-07-11T07:34:58.592Z Has data issue: false hasContentIssue false
  • English
  • Français

Gene Networks for the Integration and Understanding of Gene Expression Characteristics

Published online by Cambridge University Press:  20 January 2017

Shisong Ma  and
Hans J. Bohnert
Shisong Ma
Affiliation:
Department of Plant Biology, University of Illinois at Urbana–Champaign, 1201 W. Gregory Drive, Urbana, IL 61801
Hans J. Bohnert*
Affiliation:
Department of Plant Biology, University of Illinois at Urbana–Champaign, 1201 W. Gregory Drive, Urbana, IL 61801 Department of Crop Sciences, University of Illinois at Urbana–Champaign, 1201 W. Gregory Drive, Urbana, IL 61801
*
Corresponding author's E-mail: bohnerth@life.uiuc.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Genome sequences and genome-wide transcript profiles are becoming increasingly available, opening a way to use this information in analyzing how groups of genes are connected in pathways or “regulons” that might explain how organisms accomplish the integration on an organismal level. We have begun to explore the large datasets that are available for transcripts of the best characterized plant model, Arabidopsis thaliana, setting up a gene network using clustering methods. A network, based on the Graphical Gaussian Model (GGM), describes coregulation of genes under a variety of external factors: abiotic, biotic, and chemical treatments. In its present structure, the network reveals coregulation for more than 7,000 genes in the Arabidopsis genome. The network appears to be particularly suited to reveal the regulatory structure of biochemical pathways and environmental stress responses. Examples describe network predictions centered on a trehalose-6-phosphate phosphatase, an Arabidopsis response regulator and EPSPS. Results from the statistical analysis and bioinformatics of large data sets provide hypotheses that must be checked by additional studies. However, networks, which should be expanded from transcripts to also include proteins and metabolites, can be expected to explain not only how the Arabidopsis gene network is structured, but also provide insight in how similar networks in weed species might deviate or show correspondence and overlap.

Keywords

Arabidopsis, Arabidopsis thaliana (L.) HeynhArabidopsis thalianafuzzy k-means clusteringgene networkGraphical Gaussian Model (GGM)
Type
Symposium
Information
Weed Science , Volume 56 , Issue 2 , April 2008 , pp. 314 - 321
Copyright
Copyright © Weed Science Society of America 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Literature Cited

Agarwal, P. K., Agarwal, P., Reddy, M. K., and Sopory, S. K. 2006. Role of DREB transcription factors in abiotic and biotic stress tolerance in plants. Plant Cell Rep. 25:1263–1274.CrossRefGoogle ScholarPubMed
Arabidopsis Genome Initiative 2000. Analysis of the genome sequence of the flowering plant Arabidopsis thaliana . Nature. 408:796–815.Google Scholar
Barabasi, A. L. and Oltvai, Z. N. 2004. Network biology: understanding the cell's functional organization. Nat. Rev. Genet. 5:101–113.Google Scholar
Breitling, R., Armengaud, P., Amtmann, A., and Herzyk, P. 2004. Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. FEBS Lett. 573:83–92.CrossRefGoogle ScholarPubMed
Buck, M. J. and Lieb, J. D. 2004. ChIP-chip: considerations for the design, analysis, and application of genome-wide chromatin immunoprecipitation experiments. Genomics. 83:349–360.Google Scholar
Carey, V. J., Gentry, J., Whalen, E., and Gentleman, R. 2005. Network structures and algorithms in Bioconductor. Bioinformatics. 21:135–136.Google Scholar
Carey, V. J. and Long, L. 2006. RBGL: interface to boost C++ graph library. R package vers. 1.10.0. http://www.bioconductor.org. Accessed: August 12, 2006.Google Scholar
Craigon, D. J., James, N., Okyere, J., Higgins, J., Jotham, J., and May, S. 2004. NASCArrays: a repository for microarray data generated by NASC's transcriptomics service. Nucleic Acids Res. 32:D575–D577.Google Scholar
de la Fuente, A., Brazhnik, P., and Mendes, P. 2002. Linking the genes: inferring quantitative gene networks from microarray data. Trends Genet. 18:395–398.Google Scholar
Gasch, A. P. and Eisen, M. B. 2002. Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering. Genome Biol. 3:R0059.CrossRefGoogle ScholarPubMed
Gavin, A. C., Bosche, M., Krause, R., Grandi, P., Marzioch, M., Bauer, A., Schultz, J., Rick, J. M., Michon, A. M., Cruciat, C. M., Remor, M., Hofert, C., Schelder, M., Brajenovic, M., Ruffner, H., Merino, A., Klein, K., Hudak, M., Dickson, D., Rudi, T., Gnau, V., Bauch, A., Bastuck, S., Huhse, B., Leutwein, C., Heurtier, M. A., Copley, R. R., Edelmann, A., Querfurth, E., Rybin, V., Drewes, G., Raida, M., Bouwmeester, T., Bork, P., Seraphin, B., Kuster, B., Neubauer, G., and Superti-Furga, G. 2002. Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature. 415:141–147.Google Scholar
Gentleman, R. C., Carey, V. J., Bates, D. M., 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, A. J., Sawitzki, G., Smith, C., Smyth, G., Tierney, L., Yang, J. Y., and Zhang, J. 2004. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5:R80.CrossRefGoogle ScholarPubMed
Gomez, L. D., Baud, S., Gilday, A., Li, Y., and Graham, I. A. 2006. Delayed embryo development in the ARABIDOPSIS TREHALOSE-6-PHOSPHATE SYNTHASE 1 mutant is associated with altered cell wall structure, decreased cell division and starch accumulation. Plant J. 46:69–84.Google Scholar
Gong, Q., Li, P., Ma, S., Rupassara, S. I., and Bohnert, H. J. 2005. Salinity stress adaptation competence in the extremophile Thellungiella halophila in comparison with its relative Arabidopsis thaliana . Plant J. 44:826–839.CrossRefGoogle ScholarPubMed
Gordon, A. D. 1999. Classification. 2nd ed. Boca Raton, FL Chapman and Hall/CRC. 256. p.Google Scholar
Gutierrez, R. A., Lejay, L. V., Dean, A., Chiaromonte, F., Shasha, D. E., and Coruzzi, G. M. 2007. Qualitative network models and genome-wide expression data define carbon/nitrogen-responsive molecular machines in arabidopsis. Genome Biol. 8:R7.Google Scholar
Halford, N. G., Hey, S., Jhurreea, D., Laurie, S., McKibbin, R. S., Paul, M., and Zhang, Y. 2003. Metabolic signalling and carbon partitioning: role of Snf1-related (SnRK1) protein kinase. J. Exp. Bot. 54:467–475.Google Scholar
Horvath, D. P., Gulden, R., and Clay, S. 2006. Microarray analysis of velvetleaf (Abutilon theoprasti) impact on maize. Weed Sci. 54:983–994.CrossRefGoogle Scholar
Ito, T., Chiba, T., Ozawa, R., Yoshida, M., Hattori, M., and Sakaki, Y. 2001. A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc. Natl. Acad. Sci. USA. 98:4569–4574.CrossRefGoogle ScholarPubMed
Karim, S., Aronsson, H., Ericson, H., Pirhonen, M., Leyman, B., Welin, B., Mantyla, E., Palva, E. T., Van Dijck, P., and Holmstrom, K. O. 2007. Improved drought tolerance without undesired side effects in transgenic plants producing trehalose. Plant Mol. Biol. 64:371–386.Google Scholar
Klee, H. J., Muskopf, Y. M., and Gasser, C. S. 1987. Cloning of an Arabidopsis thaliana gene encoding 5-enolpyruvylshikimate-3-phosphate synthase: sequence analysis and manipulation to obtain glyphosate-tolerant plants. Mol. Gen. Genet. 210:437–442.Google Scholar
Kolbe, A., Tiessen, A., Schluepmann, H., Paul, M., Ulrich, S., and Geigenberger, P. 2005. Trehalose 6-phosphate regulates starch synthesis via posttranslational redox activation of ADP-glucose pyrophosphorylase. Proc. Natl. Acad. Sci. USA. 102:11118–11123.Google Scholar
Kreps, J. A. and Simon, A. E. 1997. Environmental and genetic effects on circadian clock-regulated gene expression in Arabidopsis. Plant Cell. 9:297–304.Google Scholar
Krizek, B. A. and Fletcher, J. C. 2005. Molecular mechanisms of flower development: an armchair guide. Nat. Rev. Genet. 6:688–698.CrossRefGoogle ScholarPubMed
Ma, S. and Bohnert, H. J. 2007. Integration of Arabidopsis thaliana stress-related transcript profiles, promoter structures, and cell-specific expression. Genome Biol. 8:R49.Google Scholar
Ma, S., Gong, Q., and Bohnert, H. J. 2006. Dissecting salt stress pathways. J. Exp. Bot. 57:1097–1107.Google Scholar
Ma, S., Gong, Q., and Bohnert, H. J. 2007. An arabidopsis gene network based on the Graphical Gaussian Model. Genome Res. 17: epub October 5, 2007.Google Scholar
Marquez, L. M., Redman, R. S., Rodriguez, R. J., and Roossinck, M. J. 2007. A virus in a fungus in a plant: three-way symbiosis required for thermal tolerance. Science. 315:513–515.Google Scholar
Nikiforova, V. J., Daub, C. O., Hesse, H., Willmitzer, L., and Hoefgen, R. 2005. Integrative gene-metabolite network with implemented causality deciphers informational fluxes of sulphur stress response. J. Exp. Bot. 56:1887–1896.Google Scholar
Obayashi, T., Kinoshita, K., Nakai, K., Shibaoka, M., Hayashi, S., Saeki, M., Shibata, D., Saito, K., and Ohta, H. 2007. ATTED-II: a database of co-expressed genes and cis elements for identifying co-regulated gene groups in Arabidopsis. Nucleic Acids Res. 35:D863–D869.Google Scholar
Opgen-Rhein, R., Schafer, J., and Strimmer, K. 2006. GeneNet: Modeling and Inferring Gene Networks. R package vers. 1.0.1. http://www.strimmerlab.org/software/genenet/. Accessed: August 4, 2006.Google Scholar
Ostergaard, L. and Yanofsky, M. F. 2004. Establishing gene function by mutagenesis in Arabidopsis thaliana . Plant J. 39:682–696.Google Scholar
Pan, X., Ye, P., Yuan, D. S., Wang, X., Bader, J. S., and Boeke, J. D. 2006. A DNA integrity network in the yeast Saccharomyces cerevisiae . Cell. 124:1069–1081.CrossRefGoogle ScholarPubMed
Persson, S., Wei, H., Milne, J., Page, G. P., and Somerville, C. R. 2005. Identification of genes required for cellulose synthesis by regression analysis of public microarray data sets. Proc. Natl. Acad. Sci. USA. 102:8633–8638.Google Scholar
Ramon, M. and Rolland, F. 2007. Plant development: introducing trehalose metabolism. Trends Plant Sci. 12:185–188.Google Scholar
Salome, P. A. and McClung, C. R. 2004. The Arabidopsis thaliana clock. J. Biol. Rhythms. 19:425–435.Google Scholar
Satoh-Nagasawa, N., Nagasawa, N., Malcomber, S., Sakai, H., and Jackson, D. 2006. A trehalose metabolic enzyme controls inflorescence architecture in maize. Nature. 441:227–230.Google Scholar
Schäfer, J. and Strimmer, K. 2005a. An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics. 21:754–764.Google Scholar
Schäfer, J. and Strimmer, K. 2005b. A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics. Stat. Appl. Genet. Mol. Biol. 4. Article32.Google Scholar
Schmid, M., Davison, T. S., Henz, S. R., Pape, U. J., Demar, M., Vingron, M., Scholkopf, B., Weigel, D., and Lohmann, J. U. 2005. A gene expression map of Arabidopsis thaliana development. Nat. Genet. 37:501–506.Google Scholar
Schoch, G., Goepfert, S., Morant, M., Hehn, A., Meyer, D., Ullmann, P., and Werck-Reichhart, D. 2001. CYP98A3 from Arabidopsis thaliana is a 3′-hydroxylase of phenolic esters, a missing link in the phenylpropanoid pathway. J. Biol. Chem. 276:36566–36574.Google Scholar
Sokolov, L. N., Dominguez-Solis, J. R., Allary, A. L., Buchanan, B. B., and Luan, S. 2006. A redox-regulated chloroplast protein phosphatase binds to starch diurnally and functions in its accumulation. Proc. Natl. Acad. Sci. USA. 103:9732–9737.Google Scholar
Thibaud-Nissen, F., Shealy, R. T., Khanna, A., and Vodkin, L. O. 2003. Clustering of microarray data reveals transcript patterns associated with somatic embryogenesis in soybean. Plant Physiol. 132:118–136.Google Scholar
Usadel, B., Nagel, A., Thimm, O., Redestig, H., Blaesing, O. E., Palacios-Rojas, N., Selbig, J., Hannemann, J., Piques, M. C., Steinhauser, D., Scheible, W. R., Gibon, Y., Morcuende, R., Weicht, D., Meyer, S., and Stitt, M. 2005. Extension of the visualization tool MapMan to allow statistical analysis of arrays, display of corresponding genes, and comparison with known responses. Plant Physiol. 138:1195–1204.Google Scholar
Wille, A., Zimmermann, P., Vranova, E., Furholz, A., Laule, O., Bleuler, S., Hennig, L., Prelic, A., von Rohr, P., Thiele, L., Zitzler, E., Gruissem, W., and Buhlmann, P. 2004. Sparse graphical Gaussian modeling of the isoprenoid gene network in Arabidopsis thaliana . Genome Biol. 5:R92.Google Scholar
Zeng, H., Luo, L., Zhang, W., Zhou, J., Li, Z., Liu, H., Zhu, T., Feng, X., and Zhong, Y. 2007. PlantQTL-GE: a database system for identifying candidate genes in rice and arabidopsis by gene expression and QTL information. Nucleic Acids Res. 35:879–882.Google Scholar
Zimmermann, P., Hirsch-Hoffmann, M., Hennig, L., and Gruissem, W. 2004. GENEVESTIGATOR. Arabidopsis microarray database and analysis toolbox. Plant Physiol. 136:2621–2632.Google Scholar