Award details

Systems approach to gene regulation biology through nuclear receptors

Reference	BB/I004769/2
Principal Investigator / Supervisor	Professor Neil Lawrence
Co-Investigators / Co-Supervisors	Professor Magnus Rattray
Institution	University of Sheffield
Department	Computer Science
Funding type	Research
Value (£)	323,043
Status	Completed
Type	Research Grant
Start date	01/01/2011
End date	31/08/2013
Duration	32 months

Abstract

In WP3 we will contribute to the experimental design in WP1 and WP2 and apply statistical methods so that the data are most useful for the advanced modelling in WP4 and WP5. Sequence analysis will be used to suggest interacting TFs that most likely have impact to the NR-mediated gene regulation. We will implement a relational database for sharing and disseminating annotated raw and normalized data produced within SYNERGY to the partners and later to the research community. After the completion of WP3, we will have a prototype network linking NRs and related TFs to enhancers and their associated target genes. In WP4 we will integrate this network of putative interactions with time-series data produced in WP1 and 2 describing changes in target gene expression, NR/RNAPII binding occupancy, chromatin features and DNA methylation state to derive quantitative and predictive differential equation models of transcriptional regulation for all identified target genes of each NR in each cell type studied. The models developed under WP4 and 5 will be used to construct testable predictions for validation and further iterative model refinement in WP6. For example, models from WP4 will predict the effect of changes in nuclear NR concentration on RNAPII occupancy at promoters (initiation) and transcribed part (elongation) of the genes, and corresponding changes in the transcription rate of target genes. These models of direct transcriptional regulation will also provide a starting point for the development of downstream gene regulatory network models in WP5, which will provide predictions of effects further downstream, such as the activation of particular pathways and feedback control mechanisms.

Summary

The overall goal of this multidisciplinary project (SYNERGY) is to gain a comprehensive, quantitative and predictive understanding of nuclear receptor (NR) regulated gene regulatory networks through allying state-of-the-art experimental technologies with cutting edge bioinformatics and mathematical modeling. Transcription of NR-regulated genes is a complex, tightly regulated process where distinct NRs, in conjunction with other transcription factors (TFs), the basal transcription machinery and covalent modifications to chromatin, regulate gene expression. Computational methods, combined with biomedical knowledge and leading technology to describe genome-wide gene regulation at a molecular and mechanistic level will be used to describe and predict gene networks regulated by NRs. We will derive experimentally validated, dynamic computational models that (i) describe the synergy of NRs and other TFs with modifications to chromatin, (ii) predict gene regulation and (iii) characterize downstream effects of the NR-regulated genes. Close, iterative collaborations between modeling and experimental focused partners are the driving force of SYNERGY. We will conduct two large-scale cycles that include experimental design, large-scale data production, bioinformatics, mathematical modeling and experimental validation. Both series involve several smaller iteration loops, where predictions and observations from computational work packages are validated or used in experimental design. In the first large-scale cycle, we will characterize comprehensively estrogen receptor alpha (ERalpha) induced gene regulation in MCF7 (breast cancer) and MCF10 cells ('normal' breast epithelial). We will describe transcriptional networks at a number of time points for ERalpha, the NR-interacting protein p65 and other TFs (identified within SYNERGY) using ChIP-seq to define dynamic histone, DNA methylation and covalent chromatin marks in conjunction with RNA-seq to report the transcriptome. This unique and comprehensive data set on ERalpha-induced gene regulation will be complemented with positional definition of the association of distal enhancer elements with NR regulated promoters. Two mathematical approaches will be used to translate these data to predictions. Firstly, a Gaussian process approach with differential equations will be used to model the dynamic transcriptional response resulting from dynamic TF binding and chromatin states. Secondly, continuous-time state-space models will be used to identify NR-activated pathways and gene regulatory networks. These models will facilitate quantitative predictions of effects of perturbations to NRs and related regulators on direct targets as well as predictions of further downstream effects, thus enabling in silico simulations. The models and predictions from our analyses will be extensively validated with relevant perturbation experiments, such as siRNA and chemigenetics approaches. Based on our experience acquired within the large-scale ERalpha cycle, we will design experiments and conditions that define the action of the glucocorticoid receptor (GR) and the androgen receptor (AR) in LNCaP-1F5 prostate cancer cells. Collectively, both experimental/modeling cycles will assess the robustness and scalability of the methodologies developed, as well as provide guidelines for future experimental design.

Committee	Research Committee C (Genes, development and STEM approaches to biology)
Research Topics	Systems Biology
Research Priority	Systems Approach to Biological research
Research Initiative	ERASysBio plus (ERASysBioPlus) [2010]
Funding Scheme	X – not Funded via a specific Funding Scheme

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