Modelling Transcriptional Networksin Plant Senescence

1. Introduction❖ Senescence is a highly important process of cell

death and nutrient recycling.❖ It is far more complicated than localised processes

such as cell development.❖ It can be quantified phenotypically by measuring

RUBISCO or chlorophyll levels; but little is known about gene interactions.

4. Data Manipulation❖ Check that no slides show heavy bias between

dyes (possible misprinted slide).❖ Check that the intensity of spots arenʼt saturating

the scanning equipment.❖ Check that technical replicates are representative

of each other.❖ Remove unbalanced dye bias using lowess

transformations across pin-tips and slides.❖ Visually inspect the slides for anomalies.❖ Repeat slides where necessary.

5. Using ANOVA❖ Define an ANOVA model to fit to the data:

Exp. ~ Dye + Array + (Day * ToD) / BioRep

❖ MAANOVA groups data from the experiment, finding contribution levels of each term towards final gene expression.

❖ F-tests on the terms identify which genes change expression significantly across the levels.

❖ Accounting for biological replicate variability gives strength to any conclusions drawn about gene significance.

2. Aims❖ Find genes involved in plant

senescence.❖ Identify interactions between them.❖ Produce and verify accurate

network models of these genes.

3. Planning❖ Use MAANOVA to normalise data.❖ Use MAANOVA to identify significantly changing

genes.❖ Use SplineCluster to find similar expression

profiles between genes.❖ Use Variational Bayes State Space Modelling to

quantify interactions.

Stuart David James McHattie

Systems Biology PhD Students.d.j.mchattie@warwick.ac.uk

6. SplineCluster❖ Significantly changing genes are clustered using

SplineCluster.❖ Each gene is placed in its own cluster and these

are combined where significant similarity exists.❖ A wrapper for R in MAANOVA arranges the data

for SplineCluster input.❖ The same wrapper annotates the SplineCluster

output.

7. VBSSM❖ Genes of similar expression are selected for

Variational Bayes State Space Modelling.❖ These are combined with genes suspected of

being involved in senescence processes.❖ VBSSM looks for relationships between

neighbouring time points to find interactive genes.❖ The output can be visually displayed in a software

package called Cytoscape.

8. Future Work❖ By selecting genes of interest from my ANOVA

model, I intend to produce several network maps.❖ The hubs of these networks, I intend to test using

under and over expresser mutants.❖ Expression of dependent genes can be checked

using lab techniques such as qPCR.❖ This information will provide priors for further runs

of VBSSM.

References❖ Kerr, M., Martin, M., Churchill, G., Analysis of Variance for

Gene Expression Microarray Data, 2000, The Jackson Laboratory, Maine, USA.

❖ Cui, X., Churchill, G., Statistical Tests for Differential Expression in cDNA Microarray Experiments, 2002, The Jackson Laboratory, Maine, USA.

❖ Beal, M., Falciani, F., Ghahramani, Z., A Bayesian approach to reconstructing genetic regulatory networks with hidden factors, 2005, Bioinformatics, (21), pp. 349-356

AbstractDuring natural senescence of Arabidopsis thaliana, a number of phenotypical changes can be seen as the plant tries to reabsorb nutrients from ageing leaves.  Very little, however, is known about the gene interactions during senescence.  A large quantity of highly replicated microarray timecourse data has been collected from leaves of Arabidopsis which will make it possible to quantify these interactions.  MAANOVA makes it possible to analyse this microarray data and obtain a normalised data set over the timecourse which contains no undesirable variability.  Variational Bayes State Space Modelling can then identify likely between gene interactions from select lists of genes.  The result will be a network map within which some genes act as nodes. These genes can be verified by experimental testing, using over and under expresser mutants.

Day ToD

Day:ToD 20348

5

1086

19

7764

11

1074

29

The flow diagram above represents some of the stages from microarray data to a network model. The top image represents normalising the data, the centre represents selecting significant genes due to different terms of the ANOVA model and the bottom image is an example of a network map from Cytoscape after VBSSM.