Asthma is a chronic respiratory disease that affects approximately 300 million people worldwide. It is characterised by recurrent respiratory symptoms and variable airflow limitation. Due to its significant health impact and continuously high prevalence (~6 – 8 %) both in (early) childhood as well as adulthood, asthma significantly impairs healthy ageing throughout the lifespan. Despite progress in understanding this chronic disease, there has been no success in developing new drugs that target the underlying mechanisms rather than suppressing symptoms. Genomics-directed drug repositioning may provide a solution to this problem. Based on our increasing understanding of asthma genetics, druggable targets may be identified that could allow for improved treatment of asthma by existing drugs or drugs under development for unrelated diseases.
In 2017, we anticipate our publication of the largest genetic consortium on allergic disease to date, the SHARE consortium (Fereira et al, Nature Genetics, accepted for publication). This analysis was performed in 360,838 subjects worldwide, including the Dutch LifeLines cohort (Koppelman and Vonk, co-PIs). We identified 136 genetic risk variants in 99 loci contributing to allergic disease (asthma, rhinitis, eczema). Functional analysis of these 136 risk variants revealed that they regulate expression of 244 plausible target genes in for allergy relevant cells and tissues. Following the approach as described by Sanseu et al. [2], we identified that 61 of these target genes are being considered as drug targets in human disease; 15 of which for allergic disease (for example JAK2, STAT6, IL33, IL1RL1, TSLP), 11 for autoimmune diseases and 35 for other diseases . This opens up a great opportunity to reposition drugs developed or in clinical trials for other diseases for the treatment of asthma and allergies.
We will accomplish this by taking the following stepwise approach:
Step 1:
We will first validate the risk SNPs associated to the 61 druggable genes for asthma, lung function and blood eosinophils (LifeLines cohort) and then determine which specific asthma subtype is regulated by these SNPs, by relating risk SNPs to asthma and its related subtypes (total IgE, IgE sensitisation, blood eosinophils, airway hyperresponsiveness (AHR), lung function, asthma onset) in three cohorts with rich asthma phenotypes. (Dutch Asthma GWAS study, PIAMA). We will use this information to understand which asthma related trait is specifically regulated by this SNP. This may help to define readouts in any further drug study. For example, a SNP may regulate a transcript involved in AHR. A drug developed to counteract this transcript may then first be studied for any effect on AHR.
Step 2:
In order to select the targets to be considered for drug repositioning, we will employ a staged approach: We will first validate if the gene transcripts display the expected differential expression in RNA Seq datasets from lung tissue or bronchial epithelial cells obtained from asthma patients and controls (biopsy and single-cell RNA sequencing datasets that are already available). Next, we will interrogate the expression of these genes in human primary cell culture models such as lung epithelial organoid cultures, air-liquid interface cultured human airway epithelial cells and human airway smooth muscle cells to evaluate the suitability of these models for step (3). We will compare expression levels between cells obtained from asthma patients and controls, as well as generate an asthma-like phenotype in these primary cells by exposing them to IL-13 and/or TGF-β. These experiments will not only provide additional support for the gene transcripts to be associated with asthma, but also will help prioritise the validation studies under (4). We will prioritise druggable genes, that (1) show consistent association with asthma (related traits); (2) and show differential expression in human asthma versus controls datasets.
Step 3:
Drugs that have been developed to target validated genes will then be prioritised based on type (where we will select small molecules over biologicals), availability and ability to manufacture. These drugs will then be tested in proof of concept studies in vitro, for which the selection of the models will be based on the validation studies under 2. The most promising drugs will then be evaluated in vivo. To direct the choice of in vivo model, we will first test which genes display similar expression changes in lung tissue in our in vivo models of asthma, including house dust mite induced asthma as well as ovalbumin induced asthma in the mouse and guinea pig by analysing existing gene expression datasets (whole lung RNA seq, airway epithelial RNA seq). We anticipate to test at least 3 drugs in the in vivo models that have been shown relevant for the specific target in the validation studies.
Asthma genetics has been a key strength of GRIAC since the mid-90s, when the chromosome 5q locus was first identified as harbouring asthma and hyperresponsiveness genes. Since then, technological advances have allowed for more rapid, cost-effective and unbiased screening for asthma susceptibility genes.
Despite of the increasing understanding of asthma genetics and mechanisms, these successes have not yet lead to new drugs that target the underpinnings of asthma rather than its symptoms. We will use a genomics-directed drug repositioning strategy to identify potential new drug candidates for the treatment of asthma based on drugs on the market or under development for unrelated diseases. This drug repositioning strategy has proven efficacy and takes full advantage of the opportunities that the diversity in expertise and the existing interrelationships between GRIP and UMCG have to offer. Functional genomics and genomics-directed drug discovery will likely take center stage in (personalised) medicine.
Obesity, diabetes, and asthma have attained global epidemic proportions. Multiple studies have shown a strong epidemiological and experimental link between obesity and asthma that further relates to a diverse set of etiologic factors including altered lung mechanics, adipose hormones, and inflammatory cytokines. There is strong evidence that overt diabetes and insulin resistance are strongly associated with reduced lung function characterized by low forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC). Given these epidemiological findings, common mechanistic links between asthma and diabetes may exist, which positions this proposal in an unconventional yet unique place in the PROMINENT program, by developing disease-overarching ideas and personalised medicine approaches.
