Epigenetics of RSV infection and allergic diseases
Collaboration Partners: Prof. Louis Bont (UMC Utrecht, Netherlands) and Prof. Gerard Koppelman (UMC Groningen, Netherlands)
Epigenetic signatures may provide insights into mechanisms of allergic disease and infection. Recently, our large epigenetics cohort’s study shows the link between asthma inceptions and early life antiviral immunity. Respiratory Syncytial Virus (RSV) infection in early life is associated with an increased prevalence of early childhood wheezing and may also affect long-term asthma and allergy development. How RSV infection in infancy, when the immune system is immature, would have a long-term effect on the host immune system and how this would relate to the development of asthma and allergic diseases remains unknown. We hypothesized that RSV infection in early life causes changes in DNA methylation in the nasal airway epithelium during childhood. To test this hypothesis, we investigated the randomized clinical trial MAKI to find the direct link between RSV prophylaxis during infancy and DNA methylation changes in the nasal epithelium at age 6
Allergy risk prediction through deep learning of cross-omics
Collaboration Partners: Prof. Gerard Koppelman (UMC Groningen, Netherlands) and Dr. Marnix Bügel (Micompany, Netherlands)
Although many genes and environmental factors have been identified to associate with allergy risk, it is not yet possible to cure allergy diseases. In this project, the team uses an enormous growing amount of cross-omics data (genomics, epigenomics and transcriptomics etc) and latest artificial intelligence (AI) techniques to predict the risk of disease, such as allergy. By integrating cross-omics data, using a Bayesian causal inference method, a more comprehensive network can be constructed to paint a more complete picture of the molecular process underlying the physiological state. The prediction model from AI makes it easier to identify the most important factors that contribute to allergy. It can also lead to the development of new clinical applications to diagnose high-risk patients, a key step for personalized medicine.