High-throughput Informatics

High-throughput experiments performed at the John Innes Centre are used to address questions of increasing scientific complexity. As a result, the demand for statistical assessment of the conclusions from the experimental data increases. The benefits of employing optimal experimental designs together with high-throughput technologies are two-fold. Firstly, it ensures that the data obtained are amenable to statistical analysis. Secondly, high-throughput experimentation can be resource intensive, so optimised designs economise on materials without compromising the accuracy of the experiment.

The aim of the Group is to accelerate and further JIC science by enabling scientific investigations of greater complexity. The Group will develop algorithms and statistical methods to originate hypotheses explaining observations in high-throughput experimental data. Such hypotheses are subsequently tested via experiments which are optimally designed with respect to information content and economic restraints. Apart from contributions to the field of Computational Systems Biology; the activities of the Group is expected to contribute significantly to JIC research programmes and scientific clusters.

Presently, the Group is collborating on the following exciting research projects

Ambient temperature perception in Arabidopsis thaliana

Led by Phil Wigge, this project seeks to understand the mechanisms by which plants perceive and respond to relatively mild changes in ambient temperature. The project involves determination of the ambient temperature transcriptome, that is, the complete set of genes that undergo a significant expression change within hours of shifting between 22 ºC and 16 ºC. Additionally, the creation of transgenic lines containing reporters for positive (LUCIFERASE) as well as negative (tms2) selection is a crucial part of this work. These will be used in forward genetic screens to determine components of the signalling pathway required to detect and respond to changes in ambient temperature.

Regulation of sulphur partitioning between primary and secondary metabolism

Led by Stanislav Kopriva. The aim of this project is to decipher mechanisms controlling the partitioning of sulphur between primary and secondary metabolism. In the model plant Arabidopsis thaliana the APS kinase gene family will be studied using
• biochemical characterisation of recombinant proteins
• detailed expression analysis
• metabolite analysis of transgenic plants in which APS kinase activity will be either increased by overexpression or decreased by RNAi and gene knock-outs.

Identification of transcription factors regulating plant secondary metabolism through the integration of functional genomics and metabolomics

Led by Cathie Martin, this project aims to identify new genes encoding transcription factors regulating secondary metabolism in plants. The work will focus on identification of the function of Arabidopsis. The identification of new regulatory activities will provide key tools for the improvement of production of important secondary metabolites in plants, and lead to the identification of new regulatory activities in species associated with the production of important specialised metabolites. The key anticipated deliverables of this project will are
• The identification of new transcriptional regulators of secondary metabolism in Arabidopsis.
• Characterisation of the metabolic consequences of over- expressing specific transcription factors.
• New methods for relatively holistic profiling of plant metabolism using NMR.
• New methods for metabolite identification in LC/MS and NMR (and possibly GC/MS) through the concerted use of LC/SPE/NMR/MS.

Roles of plant hormone signalling components in plant defence and susceptibility

Led by Jonathan Jones, this project investigates whether certain plant hormones promote disease caused by biotrophic and necrotrophic pathogens; what the mechanisms are by which these hormones promote bacterial disease sensitivity; and whether a system can be established to examine the effects of various hormones and of infection specifically in one leaf cell type, namely the L2 photosynthetic cells of the mature leaf.

Data analysis support for existing projects in SP2 with emphasis on integrating results across gene expression and QTL mapping experiments

Led by Guy Davenport, this project is intended to facilitate a greater understanding of the mechanisms and factors relating to abiotic and biotic stress response across multiple crops. This will be achieved via the consolidation and interpretation of GCP SP2 cross-project comparative crop datasets.