using the ondex system for exploring arabidopsis regulatory networks
TRANSCRIPT
Using the Ondex system for exploring Arabidopsis regulatory networks
Artem Lysenko
UK Plant Systems Biology Workshop 2011
Biological data in network representation
protein interactions metabolic pathways ontologies
Ondex system overview
Source: Ondex SABR project
Clients/ToolsHeterogeneous data sources
UniProt
AraCyc
GO
Pfam
Parser
Parser
Parser
Parser
ONDEX
Generalized O
bject Data M
odel
Database Layer
IntegrationMethods
Accession
Name based
Blast
ProteinFamily
Transitive
Data Exchange
Taverna
Web Client
ONDEX Visualization
Tool Kit
LucenePDB Parser
OXL/RDF
WebService
Pfam2GO
Data input& transformation Data integration Visualisation
Sparseness of plant data
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
More resources = better coverage
Proteins Interactions
Uniprot GOA-EBI TAIR Combined0
5000
10000
15000
20000
25000
30000
35000
24617 2509523044
30263
Nubver
of
GO
annota
tions
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 roleOrthology
Experimentally-determined interaction
Inferred interaction
Species A
Species B
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
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)
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
From clusters to regulatory relationships
Markov clustering Functions at 50% coverage
Component of ribosome
Localisation: chloroplast
Regulation of transcription
From clusters to regulatory relationships
LBD38 AT1G25550.1
ATBZIP3 TGA1
ARR6
NARS2
AT1G11850.1
AT2G15880.1
AT1G06040.1
AT3G02790.1
ATMYB34
ATERF13
WRKY40
ORA47 ERF104
ATERF-1
ATSZF2
AT5G51190.1
ERF-5 Identify transcription factors in clusters
AT2G15880.1
AT3G02790.1
AT1G06040.1
Example 2: nitrogen-responsive gene list
Nitrogen-responsive gene list from Gutiérrez et. al. (2007) Only N-responsive genes selected
PPI-driven signalling/regulation
Integrated PPI network:• Experimental and predicted PPIs
Pull out the PPI links of regulatory significance using GO annotation
GO: regulation Gene list(s)
PPI-driven signalling/regulation
Oxidative stress response
Cytokinin
Circadian rhythm
Auxin
Gibberellin
Nitrogen and phytohormones
Image source: Kiba et. al. (2006)
Cytokinin (CK) and auxin (AUX) are key signals of nitrogen status
Regulation of uptake
Different regulatory mechanisms in the shoot versus the root
Cytokinin, nitrogen and oxidative stress
Nitrogen deficiency lead to lower biomass and oxidative stress Cytokinin identified as important for these processes Additional cytokinin in the transgenic plant reduced the effects
Acknowledgements
o The Ondex teamo Senior colleagues and supervisors:
• Chris Rawlings, Mansoor Saqi, Michael Defoin-Platel, Tony Miller and Charlie Hodgman
o Funding:• PhD studentship: BBSRC (BBS/S/E/2006/13205)
o Ondex development:• Ondex SABR project: BBSRC (BB/F006039/1)