Project: Iterative modelling of gene regulatory interactions underlying stress, disease and ageing in C. elegans
The genetic predispositions of many complex human diseases, stress responses and ageing are proving difficult to uncover due to involvement of many different genes and because these gene groups interact with the environment. Our approach to resolving this problem is to identify gene networks by mapping the properties of genetic stress-responses onto graphical representations of the underlying network. This approach will be powered by taking advantage of natural genetic variation among individuals in respect of disease susceptibility and ageing. Compared to other network approaches, this focuses attention more specifically on functionally important gene-gene interactions and the gene regulatory networks. For this we shall take advantage of a powerful model genetic system presented by the nematode worm, Caenorhabditis elegans. Approximately 200 genetic mosaic lines have been created by crossing genetically-divergent parental strains. These present a wide spectrum of responses to stress exposure, disease susceptibility and longevity. The gene regulatory properties of each line will be determined in response to stress treatment using DNA microarrays, thereby providing a detailed response profile for all lines across the 17,000 known genes. Detailed genetic mapping of these gene expression traits allows the identification of the regulatory genetic locus and ultimately the gene regulating the trait and associated genes. The gene regulatory interactions that affect the relevant biomedical phenotypes will be mapped onto existing depictions of gene interactions. New network models will be developed which will suggest an additional set of gene perturbation tests, the outcome of which will further refine the network model. This iterative loop of directed gene perturbation experiments, network refinement and model prediction is a key means of leveraging our understanding of such complex systems. This functional, genetic approach contrasts with previous protein-protein interaction or gene co-expression interaction maps used to date. In particular, we seek to identify large-scale connectivity patterns between genes that re-occur at multiple sites across the network.
| Acronym | GRAPPLE |
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Project Results (after finalisation) |
Most human disease is genetically complex arising both from the interaction between mutations in many different genes and the interactions of these mutations with the environment. Whilst progress has been made in identifying genes involved in rare monogenic diseases the analysis of other common diseases and its relationship to lifespan has been far more complex and challenging. Indeed, despite the abundance of genome-wide association studies, most of the defined hereditary of most diseases remains unexplained. Graph theory and network-based analytical descriptions of complex systems offers insights into the structure of relationships between interacting elements of a complex system. Networks display scale-free properties, with very few nodes with many connections and vice versa. Biological networks follow this distribution and particular progress has been made in using gene-gene or protein-protein interactions between the component genes and/or proteins of a given system to describe these network structures. A rational understanding of preferred network topologies might provide new approaches to the high throughput detection of novel regulatory genes affecting important biomedical phenotypes. The GRAPPLE programme was designed to verify this prediction using the powerful eQTL (expression quantitative trait locus analysis) method for detecting gene regulatory interactions, using natural genetic variation between genetic lines rather than mutated lines to determine effect. Integrating eQTL data with genome-scale interaction data significantly improves the statistical power in defining the gene-gene relationships and for informing on the underpinning molecular mechanisms involved. Our approach, using the simplest animal system (C. elegans), of iterative experimentation followed by model building, and of directly linking eQTLs to phenotypic outcomes, provides an unrivalled opportunity to generate a model describing regulatory interactions en mass. This is leveraged by the availability of (i) cheap whole genome sequencing, (ii) advanced models for network analysis and construction, (iii) rapid methods for directed perturbation and validation experiments, and the (iv) novel mathematical approaches to matching network structures to seek common and differentiated features. Our programme consisted of 6 work packages (WPs), combining 5 laboratories from across Europe. WPs 1 and 2 led to the generation of eQTL data in ~200 recombinant inbred lines of worms subjected to a standard stressor. The chromosomal definition of genes within eQTL was substantially improved by the determination of the genomes sequences of all lines used in this programme. This work identified ~16,000 eQTLs, some of which were cis-QTLs representing local regulatory areas, whilst many others were trans-eQTL, representing regulator-target interactions, which were differentiated between 3 conditioning experiences. Data relating to regulatory-target gene interactions in C. elegans was systematically collated to provide a repository that builds on WormNet. The network properties of these data were reassessed and characterised in detail. The genes within eQTL regions were then prioritised for possessing regulatory properties or functions using new network matching protocols developed specifically for this purpose to separate regulatory from target genes. We then tested the top 40 genes by mutational ablation, followed by assessment of expression profiles using arrays. Those mutant lines whose target genes were most significantly affected by putative regulatory genes were then tested for longevity, and 4 were shown to have lifespan extending properties. This programme offers a new, high throughput approach to the discovery of regulatory processes in biomedically-important phenotypes. |
| Network | ERASysBio+ |
| Call | ERASysBio+-2008-01 |
Project partner
| Number | Name | Role | Country |
|---|---|---|---|
| 1 | University of Liverpool | Coordinator | United Kingdom |
| 2 | European Molecular Biology Laboratory | Partner | Spain |
| 3 | Wageningen University | Partner | Netherlands |
| 4 | MRC Laboratory of Molecular Biology | Partner | United Kingdom |
| 5 | Universite Paris Sud | Partner | France |