Identifying gene regulatory networks common to multiple plant stress responses

I started my PhD, supervised by Dr. Katherine Denby at Warwick HRI (as part of the PRESTA project), in October 2009.

My PhD aims to identify gene regulatory networks that are operating in response to different stresses perceived by plants. This involves incorporating datasets produced by the PRESTA group investigate gene expression changes over time in response to different plant stresses. These stresses included pathogen infection (Botrytis cinerea and Pseudomonas syringae), senescence, drought and high light. Identifying gene regulatory networks will involve discovering ‘hub’ genes that will prove to be components of multiple plant stress responses, and determining the downstream target genes of these hubs in different stress responses.

I intend to use a systems biology approach to undertake this investigation: this will include generating new tools for modelling and clustering to discover candidate genes for further experimental validation.

Research interests:

Experimental

Phenotype screening:

1. Botrytis cinerea
2. Hyaloperonospora arabidopsidis
3. Senescence
4. Drought
5. High Light
6. Pseudomonas syringae

Generating crosses to:

• Confirm gene regulatory networks and
  
• Elucidate gene pathways in an epistatic manner

Discovering gene regulatory pathways and networks using:

• Microarrays for downstream targets of a gene of interest
• Yeast-1-Hybrid for upstream regulators of a gene of interest

Theoretical

Discovering gene modules that are co-expressed across multiple stresses using biclustering.

Network inference

Bioinformatics

Method development:

A collection of co-regulated genes are termed a ‘regulon’ (Tavazoie et al. 1999). Therefore, regulons can be under the control of the same transcription factor in multiple conditions. This differs from co-expressed genes, which merely have correlated expression. Co-expression, therefore, does not infer a shared regulatory mechanism, and a shared regulatory mechanism does not infer co-expression.

Functionally similar genes are usually co-expressed (Jiang et al. 2004). Therefore, identifying genes that are coexpressed across multiple stress responses is an important task in the discovery of a core regulatory network in A. thaliana. Multi-clustering is capable of discovering co-expressed genes across multiple gene expression time series, however, current multi-clustering algorithms can be limited by the large size of gene expression datasets. Also, the nature by which standard methods cluster usually involves the partitioning of data across all conditions. Therefore, the output from such methods requires genes to be significantly co-expressed in all conditions. The main challenge, however, with existing multi-clustering methods is that co-expression across multiple conditions does not infer a common regulatory mechanism: the genes may be co-expressed in several conditions, but the regulatory mechanisms by which they are controlled may be independent. Therefore, the genes are co-expressed, not co-regulated. In light of these issues, a new tool, Wigwams, was developed to discover potential regulons which may have a common regulatory mechanism in multiple stresses. Wigwams takes into consideration multiple gene expression time series datasets, and searches for regulons working over subsets of these conditions, in order to provide evidence for a possible shared regulatory mechanism for these sets of co-expressed genes. The aim of Wigwams is to detect co-expressed genes, or ‘regulons’, that are working over subsets of stress responses using gene expression time series data. These regulons may have a shared regulatory mechanism.