Innovative Training Network
The TARGET Consortium
Innovative Training Networks (Call: H2020-MSCA-ITN-2018)
Prof. Ann Morgan and Prof. Sir Alex Markham represent the Faculty of Medicine and Health, and LIDA respectively in this Marie Skłodowska-Curie Actions ITN grant (HELICAL). The HELICAL network is comprised of 26 leading Universities and non-academic partners drawn from nine European countries and, as such, represents a significant wealth of expertise. The network will provide 15 early-career stage researchers with state-of-the-art training in the analysis of large datasets from individuals with chronic inflammatory disease. It focuses on three complementary areas:
1. Application of informatics to datasets to gain new biological insights.
2. Translation of biological into practical clinical outputs
3. Identification of novel ethical constraints imposed on such studies and development of strategies to manage them.
The objectives will be achieved through 15 specific research projects, delivered across 9 academic institutions and 5 non-academic/NGO beneficiaries. The work packages, although each address a different aspect of the common theme, are inter-linked to leverage skill sharing.
Work package 2 in this submission focuses on the identification of key pathogenic pathways suitable for therapeutic targeting in GCA. Specifically, we will investigate genetic, genomic, epigenomic and tissue signatures associated with GCA to identify pathogenic pathways amenable to therapeutic targeting. Our primary aim is to assemble and mine international GCA molecular and clinical databases, in conjunction with public genetic, genomic and epigenomic databases, to investigate the top loci from the recent Immunochip and genome wide association studies in order to: identify genotype-phenotype correlations; detect new genetic associations and pathogenic mechanisms through polygenic risk score and pathway analysis; examine the functional effects of these genomic variants on different immunological and pathogenic processes by performing both in silico analyses and functional studies on disease tissue. This will be achieved through a cohesive programme of genetic, genomic, epigenomic and functional work linking existing molecular data with GCA phenotype, taking advantage of Bayesian statistical methods and network-based machine learning approaches, as well as validating key findings in ex-vivo model systems.
All participants in the HELICAL ITN have a strong commitment to the establishment of this programme and bring a range of specific skillsets and expertise to bear on a focused programme of research. Most have a significant track record of success in various FP/H2020 research projects of direct relevance to the current proposal and eleven projects that have already received EU-funded feed into this proposal.