using the ondex system for exploring arabidopsis regulatory networks

Using the Ondex system for exploring Arabidopsis regulatory networks

Artem Lysenko

UK Plant Systems Biology Workshop 2011

artem.lysenko@bbsrc.ac.uk

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)