1. Using the Ondex system for exploring Arabidopsis regulatory networks Artem Lysenko UK Plant Systems Biology Workshop 2011 artem.lysenko@bbsrc.ac.uk

2. Biological data in network representation ontologies protein interactions metabolic pathways

3. Ondex system overview Data input& transformation Data integration Visualisation Clients/Tools Heterogeneous data sources ONDEX Integration Methods ONDEX Visualization Tool Kit UniProt Generalized Object Data Model Database Layer Accession Parser Name based Web Client AraCyc Parser Transitive Taverna GO Blast Parser ProteinFamily Pfam Data Exchange Parser Pfam2GO OXL/RDF PDB Lucene Parser WebService Source: Ondex SABR project

4. Sparseness of plant data

5. Motivation Information about regulation in plants is limited KEGG – two maps with 232 and 48 genes related to signalling AtRegNet – currently only covers 69 transcription factors in Arabidopsis, however data fro 9375 regulated genes Other types of data are more abundant Functional annotation Protein-protein interactions Gene expression Use the latter to compensate for the lack of the former

6. More resources = better coverage Proteins Interactions

7. Inference methods Analysis of microarray data Meta-coexpression networks from NASC, ArrayExpress and GEO data Databases: ATTED-II, CoexpressDB Inter species comparison Ortholog detection methods: OrthoMCL, Inparanoid Databases: resources supporting OrthoXML format Prediction of interactions “Interolog” and domain-domain approaches Databases: AtPID, TAIR predicted interactome Prediction of functional role Experimentally-determined interaction Species A Orthology Species B Inferred interaction

8. The datasets for these application cases Functional annotation – Gene Ontology GOA EBI TAIR UniProtKB Interaction Experimental – BioGrid, IntAct, TAIR Predicted – interolog approach Expression data – gene coexpression networks Targeted subsets from NASC, ArrayExpress and GEO data

9. Example 1: NAR2.1-knockout microarray NAR2.1 is required to target the high-affinity nitrate transporter NRT2.1 to plasma membrane NRT2.1 is required to take up nitrate at low internal concentrations Possible involvement of NAR2.1 in nitrate sensing Another nitrate transporter (NRT1.1) have now been demonstrated to also function as a sensor Image source: Miller et. al. (2007)

10. From clusters to regulatory relationships Meta-coexpression network ~140 nitrogen-relevant arrays Gene list – nitrogen uptake mutant, grown under low nitrogen Mutant versus wild-type

11. From clusters to regulatory relationships Localisation: chloroplast Component of ribosome Regulation of transcription Markov clustering Functions at 50% coverage

12. From clusters to regulatory relationships AT1G11850.1 LBD38 AT2G15880.1 NARS2 AT1G25550.1 ATBZIP3 AT1G06040.1 TGA1 AT3G02790.1 ARR6 AT2G15880.1 ATERF13 WRKY40 ORA47 ATERF-1 AT5G51190.1 ERF104 ERF-5 Identify transcription factors in clusters ATSZF2 AT1G06040.1 AT3G02790.1 ATMYB34

13. Example 2: nitrogen-responsive gene list Nitrogen-responsive gene list from Gutiérrezet. al. (2007) Only N-responsive genes selected

15. Experimental and predicted PPIs

16. Pull out the PPI links of regulatory significance using GO annotationGO: regulation Gene list(s)

17. PPI-driven signalling/regulation Oxidative stress response Cytokinin Circadian rhythm Auxin Gibberellin

19. Regulation of uptake

20. Different regulatory mechanisms in the shoot versus the rootImage source: Kibaet. al. (2006)

22. Cytokinin identified as important for these processes

24. Senior colleagues and supervisors:

25. Chris Rawlings, MansoorSaqi, Michael Defoin-Platel, Tony Miller and Charlie Hodgman

26. Funding:

27. PhD studentship: BBSRC (BBS/S/E/2006/13205)

28. Ondex development:

29. Ondex SABR project: BBSRC (BB/F006039/1)