Using the Ondex system for exploring Arabidopsis regulatory networks


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Using the Ondex system for exploring Arabidopsis regulatory networks

  1. 1. Using the Ondex system for exploring Arabidopsis regulatory networks<br />Artem Lysenko<br />AAB conference, 14-15 April 2011<br /><br />
  2. 2. Biological data in network representation<br />ontologies<br />protein interactions<br />metabolic pathways<br />
  3. 3. Ondex system overview<br />Data input& transformation<br />Data integration<br />Visualisation<br />Clients/Tools<br />Heterogeneous <br />data sources<br />ONDEX<br />Integration<br />Methods<br />ONDEX <br />Visualization <br />Tool Kit<br />UniProt<br />Generalized Object Data Model<br />Database Layer<br />Accession<br />Parser<br />Name based<br />Web Client<br />AraCyc<br />Parser<br />Transitive<br />Taverna<br />GO<br />Blast<br />Parser<br />ProteinFamily<br />Pfam<br />Data Exchange<br />Parser<br />Pfam2GO<br />OXL/RDF<br />PDB<br />Lucene<br />Parser<br />WebService<br />Source: Ondex SABR project<br />
  4. 4. Sparseness of plant data<br />
  5. 5. Motivation<br />Information about regulation in plants is limited<br />KEGG – two maps with 232 and 48 genes related to signalling<br />AtRegNet – currently only covers 69 transcription factors in Arabidopsis, however data fro 9375 regulated genes<br />Other types of data are more abundant<br />Functional annotation<br />Protein-protein interactions<br />Gene expression<br />Use the latter to compensate for the lack of the former<br />
  6. 6. More resources = better coverage<br />Proteins<br />Interactions<br />
  7. 7. Inference methods<br />Analysis of microarray data<br />Meta-coexpression networks from NASC, ArrayExpress and GEO data<br />Databases: ATTED-II, CoexpressDB<br />Inter species comparison<br />Ortholog detection methods: OrthoMCL, Inparanoid<br />Databases: resources supporting OrthoXML format<br />Prediction of interactions<br />“Interolog” and domain-domain approaches<br />Databases: AtPID, TAIR predicted interactome<br />Prediction of functional role<br />Experimentally-determined interaction<br />Species A<br />Orthology<br />Species B<br />Inferred interaction<br />
  8. 8. The datasets for these application cases<br />Functional annotation – Gene Ontology<br />GOA EBI<br />TAIR<br />UniProtKB<br />Interaction<br />Experimental – BioGrid, IntAct, TAIR<br />Predicted – interolog approach<br />Expression data – gene coexpression networks<br />Targeted subsets from NASC, ArrayExpress and GEO data<br />
  9. 9. Example 1: NAR2.1-knockout microarray<br />NAR2.1 is required to target the high-affinity nitrate transporter NRT2.1 to plasma membrane<br />NRT2.1 is required to take up nitrate at low internal concentrations<br />Possible involvement of NAR2.1 in nitrate sensing<br />Another nitrate transporter (NRT1.1) have now been demonstrated to also function as a sensor<br />Image source: Miller et. al. (2007)<br />
  10. 10. From clusters to regulatory relationships<br />Meta-coexpression network<br />~140 nitrogen-relevant arrays<br />Gene list – nitrogen uptake mutant, grown under low nitrogen<br />Mutant versus wild-type<br />
  11. 11. From clusters to regulatory relationships<br />Localisation: chloroplast<br />Component of ribosome<br />Regulation of transcription<br />Markov clustering<br />Functions at 50% coverage<br />
  12. 12. From clusters to regulatory relationships<br />AT1G11850.1<br />LBD38<br />AT2G15880.1<br />NARS2 <br />AT1G25550.1<br />ATBZIP3 <br />AT1G06040.1<br />TGA1<br />AT3G02790.1<br />ARR6<br />AT2G15880.1<br />ATERF13 <br />WRKY40<br />ORA47<br />ATERF-1 <br />AT5G51190.1<br />ERF104 <br />ERF-5 <br />Identify transcription factors in clusters<br />ATSZF2<br />AT1G06040.1<br />AT3G02790.1<br />ATMYB34<br />
  13. 13. Example 2: nitrogen-responsive gene list<br />Nitrogen-responsive gene list from Gutiérrezet. al. (2007)<br />Only N-responsive genes selected<br />
  14. 14. PPI-driven signalling/regulation<br /><ul><li>Integrated PPI network:
  15. 15. Experimental and predicted PPIs
  16. 16. Pull out the PPI links of regulatory significance using GO annotation</li></ul>GO: regulation<br />Gene list(s)<br />
  17. 17. PPI-driven signalling/regulation<br />Oxidative stress response<br />Cytokinin<br />Circadian rhythm<br />Auxin<br />Gibberellin<br />
  18. 18. Nitrogen and phytohormones<br /><ul><li>Cytokinin (CK) and auxin (AUX) are key signals of nitrogen status
  19. 19. Regulation of uptake
  20. 20. Different regulatory mechanisms in the shoot versus the root</li></ul>Image source: Kibaet. al. (2006) <br />
  21. 21. Cytokinin, nitrogen and oxidative stress<br /><ul><li>Nitrogen deficiency lead to lower biomass and oxidative stress
  22. 22. Cytokinin identified as important for these processes
  23. 23. Additional cytokinin in the transgenic plant reduced the effects</li></li></ul><li>Acknowledgements<br /><ul><li>The Ondex team
  24. 24. Senior colleagues and supervisors:
  25. 25. Chris Rawlings, MansoorSaqi, Michael Defoin-Platel, Tony Miller and Charlie Hodgman
  26. 26. Funding:
  27. 27. PhD studentship: BBSRC (BBS/S/E/2006/13205)
  28. 28. Ondex development:
  29. 29. Ondex SABR project: BBSRC (BB/F006039/1)</li>