Abstract
Background: Merging multiple 'omics datasets on the basis of mechanistic pathway information is a continuously evolving approach for uncovering genotype-phenotype relationships (Ritchie et al, Nat Rev Genet, 2015).
Objectives: 1. Develop comprehensive reconstruction of the eicosanoid metabolism. 2. Map multi-omics signature (handprint) from asthma patients. 3. Provide an integrative interpretation and derive hypotheses.
Methods: The eicosanoid pathway is built by mining literature and well-curated databases (e.g. MetaCore), presented in the SBGN standards (www.sbgn.org) and further edited by domain experts. Multi-omics signature are derived from the U-BIOPRED blood handprint analysis (De Meulder et al, ERS International Congress 2015) and visualised through Google Maps API using MINERVA platform (http://asthma.uni.lu/).
Results: Differences in gene expression and eicosanoids levels between two phenotypic clusters are mapped. Those clusters contain the most severe asthmatic patients in the U-BIOPRED data. Different profiles of PGD2 derivatives and isoprostanes are shown, suggesting different routes of treatment for those patients. Moreover, the regulation of the leukotriene metabolism is highlighted, suggesting 5-lipoxygenase and arachidonate 5-lipoxygenase-activating protein or downstream LTC4 synthase and LTA4 hydrolase as drug targets.
Conclusion: Integration of multiple sources of evidence pointing to the same disease-relevant molecular processes is a powerful tool for data analysis and interpretation. This is starting to provide novel biological insights and hypotheses for the treatment of specific phenotypes of asthma.
Acknowledgements: Funded by the Innovative Medicines Initiative (U-BIOPRED n°115010, eTRIKS n°115446).
Eicosanoid production from arachidonic acid with multi-omics data mapped.
- Copyright ©the authors 2016
