Predicting asthma phenotypes: characterization of IL1RL1 in asthma
Dijk, Fokelina Nicole
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Dijk, F. N. (2018). Predicting asthma phenotypes: characterization of IL1RL1 in asthma. Rijksuniversiteit Groningen.
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General introduction
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General introduction
Asthma
Asthma is among the most frequent chronic diseases in children worldwide, with 100,0000 children be-ing affected in the Netherlands.1,2 There is an increase in asthma prevalence in the Western world, which is accompanied by the observation that more people are concomitantly affected by one or more other allergic disorders such as eczema, rhinitis and food allergy.1 Asthma is a multifactorial disease caused by a complex interaction between multiple genetic and environmental influences.1 The disease is character-ized by ongoing airway inflammation associated with variable airflow obstruction, bronchial hyperres-ponsiveness (BHR) and airway remodeling.3 Asthma symptoms include episodes of wheeze, cough, and shortness of breath and occur by exposure to various triggers, such as allergens, (viral) infections, specific weather conditions or exercise.
Compared to healthy peers, children with asthma experience a lower quality of life, and it is the leading cause of emergency room visits, hospitalizations, school absenteeism, and school underperformance.4,5 It also affects their psychological functioning, physical activity and their quality of life of that of their parents.6 Approximately 250,000 people die prematurely from asthma each year, with asthma mortality rates in children ranging from 0.0-0.7 per 100,000.1,4 Moreover, the disease has a considerable economic burden with high medical costs due to emergency room visits/hospitalizations and use of pharmaceuti-cal agents.1
Obtaining better insight in factors causing asthma will lead to better prevention strategies and improve-ment in therapeutic, personalized manageimprove-ment, which is greatly warranted.
Diagnosis of asthma
Thirty to 50% of preschool children experience symptoms suggestive of asthma, for instance they report recurrent wheeze or cough.7 However, only approximately 30% of these children with respiratory symp-toms at preschool age will develop asthma at a later age. Due to the non-specific sympsymp-toms of asthma, it is hard to diagnose asthma or to determine which child will develop the disease and which child will not.8 Furthermore, there is no specific diagnostic test in childhood for asthma and lung function tests are often unreliable in preschool children.
Specific phenotypes of asthma
Asthma has a large phenotypic heterogeneity. Different asthma phenotypes can be distinguished based on the presence, timing, and severity of symptoms, such as age of onset and nocturnal symptoms, in com-bination with the presence of atopy, responsiveness to triggers, and type of airway inflammation. In the past, multiple approaches have been used to group different asthma phenotypes, in which cluster analyses of mostly adults has led to the identification of 5 specific groups based on age, gender, lung function, the presence of atopy, health care utilization and body mass index.9,10
Pediatric asthma differs from adult asthma in multiple ways11, therefore it is important to determine spe-cific phenotypes in children and adults separately.
Data driven approaches and longitudinal latent class analyses in children from two different birth co-horts have led to the identification of longitudinal wheezing phenotypes.12,13 A study performed in two large birth cohorts identified 5 specific wheezing phenotypes, i.e. 1) never/infrequent wheeze, 2) tran-sient early wheeze, 3) intermediate onset wheeze, 4) late onset wheeze and 5) persistent wheeze.13 In one birth cohort, transient wheeze was split into transient early wheeze and prolonged early wheeze. All those phenotypes were differentially associated with asthma, atopy, BHR and lung function at the age of 8 years, with intermediate onset, late onset and persistent wheeze being associated with an increased risk for doctor’s diagnosis of asthma and sensitization against common allergens, both at age 8 (Figure 1, Savenije et al.).13
Figure 1. The five discovered wheezing phenotypes in PIAMA, with their estimated prevalence of wheeze at each time point from birth to age 8 years. Reprinted from Savenije et al., with permission.13
In asthma, there has traditionally been thought to be a shift, or predilection, toward a Th2-cytokine profile resulting in eosinophilic inflammation characteristic of asthma. Aeroallergens or other environ-mental insults activate airway epithelial cells that release activators such as IL-25, IL-33 or TSLP, which in turn activate immune cells such as dendritic cells, Th2 cells and Innate type 2 lymphoid cells. The latter two are responsible for the increased production of type 2 cytokines such as IL-4, IL-5 and IL-13, leading to downstream cascade of inflammatory events (Figure 2, Fahy JV).14–16 Type 2 cytokines are thought to be responsible for IgE overproduction by B-cells, chemo-attraction of effector cells (mast cells, eosino-phils and basoeosino-phils), development of BHR and remodeling of the airway epithelium, all characteristics of asthma.15,16 Type 2 associated asthma starts most frequently during childhood (childhood-onset asth-ma) and is usually well responsive to corticosteroids.16,17 However, nowadays there is increasing evidence that a large proportion of asthma patients has airway inflammation driven by other mechanisms. Cellu-lar research on gene expression in bronchial brushes has shown evidence for distinct Th2 high and Th2 low asthma phenotypes.17–19 In line with this, a classification based on eosinophilic and non-eosinophilic asthma phenotypes has also been described.9,20,21 Those phenotypes have been found to have different airway histopathology, airway structure, mechanisms of airway dysfunction, time of onset of symptoms and difference response to asthma treatment.
Novel phenotypic strategies have attempted to cluster asthma patients based on 1) association with environmental factors (air pollution, cigarette smoke), 2) specific symptoms or clinical characteristics (age of onset, obesity) and 3) the presence of certain biomarkers (eosinophilic, neutrophilic). One ex-ample is the definitions proposed by Hekking et al. (Figure 3)18, who suggested 13 distinct clinical phe-notypes of asthma.
Identification of specific (biological) phenotypes is helpful in better understanding of disease pathogen-esis and susceptibility as well as starting personalized treatment. Moreover, the identification of asthma phenotypes aids the interpretation of asthma associated genes and can lead to more targeted genetic and perhaps subsequent intervention studies.
Figure 2. Type 2 immune responses characteristic for asthma. Airway epithelial derived cytokines, such as interleu-kin-33 (IL-33) and thymic stromal lymphopoeitin (TSLP), induces the expression of OX40 ligand (OX40L) on dendrit-ic cells (DCs). This leads to their mobilization to nearby lymph nodes where they activate naive CD4+ T cells to an IL-4-competent state. These IL-4-competent T cells migrate to B cell zones and differentiate into T follicular helper (TFH) cells and move into the circulation to mature to Th2 cells. IL-4-secreting TFH cells in parafollicular B cell zones mediate IgE class-switching in B cells, whereas TH2 cells that migrate to the airway epithelium and to the subepithelial mucosa secrete IL-5 and IL-13 to mediate inflammatory and remodelling changes in the airway mucosa that predis-pose an individual to asthma and to asthma exacerbations. Reprinted from Fahy JV, with permission.16
Figure 3. Grouping of asthma phenotypes in adulthood, divided by environmental factors, symptoms, clinical character-istics and eosinophilic versus non- eosinophilic airway inflammation. Reprinted from Hekking et al., with permission.18
The (epi)genome
The genome is the complete set of a person’s nucleic acid sequences, encoded as DNA within 22 chro-mosome pairs and one pair of sex chrochro-mosomes. DNA is located in the nucleus of each cell and in small DNA molecules within mitochondria. Human DNA is composed of base pairs that form a double helix structure when joined together. There are four bases; the base adenosine (A) always binds with thymine (T), and the base cytosine (C) always binds with guanine (G). Somatic cells of humans consist of six billion base pairs. Amino acids are formed from translation of three base pairs and they are the cornerstones to build proteins. DNA consists of coding and non-coding DNA, in which only coding DNA, around 1.5% of the entire DNA, is responsible for the production of proteins. Proteins are formed by transcription from DNA to mRNA followed by translation from mRNA to protein. Transcription and translation of a single gene results in one protein or different protein isoforms. The human genome contains approximately 25,000 genes, but several biological processes (such as alternative mRNA splicing) can lead to the forma-tion of many more unique proteins than the number of genes.22,23
The DNA of individuals differs only on 0.1% of this genetic variation, leading to variation in about 12 mil-lion base pairs. A variation in one base pair between persons that is prevalent in the population is called a single nucleotide polymorphism (SNP) (Figure 4), whereas rare variants (allele frequency <1%) are called mutations. The frequency at which the second most common allele is present in a population is called the minor allele frequency (MAF).22,24
SNPs in close physical proximity are frequently inherited together which results in a correlation between specific variants. This correlation is called linkage disequilibrium (LD, expressed as r2 or D’) when the fre-quency of association of different SNPs is higher than what would be expected if the SNPs were inde-pendently inherited from each other.25
Figure 4. Overview of DNA located in a chromosome in the nucleus of a cell. DNA is composed of a double helix struc-ture with base pairs adenosine (A) and thymine (T), and cytosine and guanine (G). A single nucleotide polymorphism (SNP) is variation in one base pair between individuals.
Besides genetics, the study of genes, genetic variation, and heredity, there has become more and more attention to epigenetics, which is the study of heritable changes in gene function that do not involve changes in the DNA sequence.23,24 This can involve for example changes that affect gene expression and function. Mechanisms responsible for such changes are DNA methylation, histone modification and the presence of small or long non-coding mRNAs. DNA methylation is the process in which a methyl group (CH3) is added to carbon 5 in a cytosine base to create 5-methylcytosine, which affects the regulation of gene expression. Methylation usually occurs at the sequence CG (5′-C-phosphate-G-3′) (CpG) and regions with a high frequency of CpG sites (CpG islands) are highly regulatory units.
Genetic association studies are performed to identify SNPs that are associated with diseases or specific traits (Figure 5). Different variants of a SNP called alleles, are being compared between patients (cases) and non-affected individuals (controls) (Figure 6). Allelic variation can regulate levels of mRNA expres-sion (called expresexpres-sion Quantitative Trait Locus, eQTL), DNA methylation at CpG sites (methylation Quan-titative Trait Locus, meQTLs), protein levels (protein QuanQuan-titative Trait Locus, pQTLs) or protein function.24
Figure 5. Flow chart of genetic association studies that can be performed to identify single nucleotide polymorphisms (SNPs) that are associated with methylation (such as with CpG sites, meQTL), expression (such as with mRNA, eQTL), or protein (such as with receptors, pQTL). eQTM, expression quantitative trait methylations; pQTM, protein quantitative trait methylations.
SNPs in genes and CpG sites on genes associated with the disease can be discovered by performing genome wide association studies (GWAS) or epigenome wide association studies (EWAS).
Thus, by combining the results of these association studies, more knowledge can be gained on how all processes influ-ence each other (e.g. expression and protein) and the biological pathways underlining specific disease associations. In addition, these studies allow us to assess effects on asthma treatment and asthma phenotypes.
Figure 6. The concept of genetic association testing in which the differences in allele frequency between cases and controls is being tested. Significant findings are identified as risk/protective alleles for the trait being tested.
(Epi)genetics of asthma
Genetic family based and twin studies have shown that the heritability of asthma is around 60%.26 Iden-tification of the genetic factors that are responsible for asthma is difficult, since the disease is highly poly-genic; disparate genes are leading to the same disease in different subjects. Publication of the HapMAP project27 and the 1000 genomes project28 let to a detailed catalogue of human genetic variation, with the presence of sequence variants of more than 2000 participants from a number of different ethnicities. With this data it has become more accessible with increase in power to perform large hypothesis free genome wide association studies (GWAS). Over more than 90 GWAS have been performed in asthma, with the identification of more than 850 associated variants in multiple susceptibility genes.29 Recently, two consor-tia performed large meta-analyses of published GWAS on asthma30 and allergic diseases (either asthma, hay fever or eczema)31, which led to doubling of the number of genetic risk factors that are associated with asthma. The TAGC consortium meta-analysis performed in 142,000 subjects from different ethnicities has identified 18 asthma associated loci. Moreover, 136 independent genetic asthma and allergy variants were discovered in the meta-analyses of the SHARE consortium performed in 360,000 subjects.
Besides the identification of genes that are associated with asthma, epigenome wide studies (EWAS) have additionally been performed, showing that epigenetic regulation, such as methylation, has a strong association with asthma development as well.32,33 Epigenetics changes with exposures and with age, moreover it can specifically occur in one or a few cell types, therefore it is important to consider methyla-tion stages of different cells at different stages in life.
The discovery of (epi)genetic associations with asthma is promising, but the step from association to the mechanisms leading to the development of the disease is complex. To make it more complex, some genes will be more specifically related to the time of onset of the disease34, while others are more associated with severity or response to treatment.35 In addition, gene-gene36,37, gene-environment36,38 and more recently gene-epigenetic39 interactions have been described.
IL1RL1, asthma associated gene
In 2008, Reijmerink et al. from our group were the first to describe the association of SNPs in the interleu-kin-1 receptor–like 1 (IL1RL1), and IL18R1 gene with asthma and associated phenotypes.40
In 2009, a strong conformation of IL1RL1 as a new asthma gene, was provided by a GWAS on peripheral blood eosinophilia in the Icelandic population, and subsequently association with asthma was found in ten populations.41 A year later this gene was reported to be associated with (childhood onset) asthma in one of the largest consortium based GWAS on asthma.34 This finding has been reproduced by multiple studies42, with (SNPs) in IL1RL1 being associated with asthma and other atopic traits.40,43–50 Because of a complex LD structure in the IL1RL1 region it has been difficult to identify the true causal variants related to asthma.42
IL1RL1 (also called ST2) is part of the Toll-like/IL-1 receptor superfamily with expression on
inflammato-ry cells present in the lung.51 The gene is located on 2q12, consisting of a distal and proximal promotor and 13 exons. Alternative splicing leads to four different isoforms: IL1RL1-a (soluble ST2, sST2), which can be measured in serum; a transmembrane receptor, IL1RL1-b (S2TL); and two less well-character-ized isoforms, isoform 3 and IL1RL1-c (ST2V)52,53 (Figure 7, Dijk et al. adjusted).54
Figure 7. The interleukin-1 receptor-like 1 (IL1RL1) gene (GRCh37/hg19; chr2:102,927,962–102,968,497) with transcript annotation of IL1RL1-a, the soluble variant (sST2) (ENST00000311734.6), IL1RL1-b, the transmembrane variant (ST2L) (ENST00000233954.5), and two less well known variants, IL1RL1-c (ST2V) (ENST00000427077.1) and isoform 3 (ENST00000404917.6). Adjusted from Dijk et al.54
IL1RL1 is the receptor for Interleukin-33 (IL-33), a cytokine that is important in the regulation of several
allergic disorders.55 Binding of IL-33 to the receptor complex of IL1RL1-b and IL-1 receptor accessory pro-tein (IL-1RAcP) present on Th2 cells, basophils and mast cells and type 2 innate lymphoid cells leads to an inflammatory signaling cascade with the release of pro-inflammatory Th2 cytokines that facilitate inflammation in the lung.56 In contrast IL1RL1-a is thought to serve as a decoy receptor, and sequestering IL-33 leading to blockade of its function (Figure 8, Grotenboer et al.).57
Figure 8. The IL33-IL1RL1 signaling pathway assocaited with airway inflammation. Airway epithelial damage caused by allergic or enviromental stimuli will lead to IL-33 release. IL-33 binds to a receptor complex of IL1RL1-b and IL1RAcP which results in activation of multiple adaptors and signaling proteins, such as Mal or MyD88 (see lower insertion). This signaling cascade will initiates the release of pro-inflammatory Th2 cytokines promoting airway inflammation. IL1RL1-b can in addition inhibit receptor signaling through TIR domain–dependent sequestration of the adaptor pro-teins Mal and MYD88 (see upper insertion). The neutralizing effect of IL-33 by IL1RL1-a is also shown. Reprinted from Grotenboer et al., with permission.42
In this thesis, we hypothesize that SNPs and methylation of IL1RL1 are associated with a disbalance in
IL1RL1 gene expression, resulting in increased IL1RL1-b transcription and upregulation of
pro-inflamma-tory Th2 responses in asthma. In addition, downregulation of IL1RL-a levels may possibly attenuate Th2 inflammation. Finally, in patients with asthma induced by IL1RL1 dysfunction particularly, the disease might be characterized by specific Th2 inflammatory (bio)markers such as eosinophilic inflammation and an increase in fractional exhaled nitric oxide (FeNO).
Pharmacogenetics
Asthma treatment is based on a stepwise protocol, yet the different phenotypes as mentioned above are currently treated very similar. Most asthma medications (ICS, short- and long-acting b2-agonists, and leukotriene receptor antagonists) are effective in most patients. Notwithstanding this, there is still a considerable proportion of asthma patients that responds less to these treatments or exhibits side- effects.58 It is thought that this heterogeneous response to medication might be genetically determined. Pharmacogenetics is the study of how genetic variations influence drug response, with recent candidate and GWAS studies discovering multiple genes that exert an effect on asthma treatment response.59 Recently, molecular insight into asthma development has led to the identification of new therapeutic regimens focused on for example cytokine inhibition, such as anti-IgE, anti-IL-5 or anti-IL-13, all with the aim to downregulate the inflammatory response.60
Eosinophilic, Th2 associated airway inflammation has been associated with responsiveness to ICS in asth-ma.17,61 Since IL1RL1 is thought to be important in type 2 inflammatory responses, it might be that patients with specific IL1RL1 risk variants represent responders to ICS therapy. Identification of such genetic variants could lead to better, personalized treatment.
Prediction of asthma
Accurately predicting which child will develop asthma is important for primary prevention and for improve-ments in asthma diagnosis. Yet it is extremely difficult at childhood to diagnose asthma, this may lead to over-treatment with steroids in children that do effectively not have asthma, but also to a late diagnosis and possibly under-treatment, which is associated with reduced lung growth, a high personal burden due to recur-rent symptoms, school absence and consequently a low quality of life.62,63
Several prediction models based on personal and environmental factors have been developed to improve the early diagnosis of asthma64–66 All these predictions are based on preschool children with respiratory symptoms, a subjective measure. Next to symptoms, asthma is characterized by variable airflow obstruc-tion, an objective measure. We know that the impairment of lung function at one month after birth is a risk factor for asthma at age 8.67 Further, approximately 75% of all children with mild-to-moderate se-vere asthma have abnormal patterns of lung growth and early decline of lung function.68 Up to now there is no risk model that can predict the development of asthma directly after birth when no symptoms have yet occurred. Attempts have been made to develop genetic risks scores for asthma.69,70 However, predic-tion models made thus far have no sufficiently sensitive or specific clinical value. Since the multifactorial origin of asthma, with personal- and environmental factors as well as genetic factors being involved, it might be that better prediction can be achieved by combining all these factors into one predictive score. Study groups
To identify (epi)genetic associations and link those to phenotypic traits or search for interactions with environmental factors large cohorts and studies are needed. For the purpose of this study we were able to use data from different studies, like birth cohorts, family studies and case-control studies. These studies originated from all over the world and multiple ethnicities were included. Information present in the cohorts included complete phenotypic, perinatal, familial, (epi) genetic and protein data.
For the investigation in heterogeneous diseases such as asthma there is the need to use large study groups. By combining data from multiple cohorts, when for example performing meta-analyses or repli-cation studies, we were able to increase power in our research. Harmonization of definitions, serum mea-surements or genetic data is important in achieving a well powered and reliable outcome. The included cohorts in this thesis are summarized in Table 1.
In almost all the studies performed for this thesis we used data from the Prevention and Incidence of Asthma and Mite Allergy (PIAMA) study.71 This is a multicenter Dutch prospective birth cohort, which was initiated in 1996. Children were recruited from the north (Groningen and surrounding), middle (Utrecht and surrounding) and west (Rotterdam and surrounding) in the Netherlands. 7862 pregnant women (2779 with allergy and 5083 without allergy) were invited to participate in the study. At the end 3963 live-born children participated in the study (1327 with a mother with allergy were defined as high-risk, and 2726 children with a mother without allergy were defined as low-risk). Questionnaires for parental com-pletion, partly based on the International Study of Asthma and Allergies in Childhood core question-naires, were sent to the parents during pregnancy, when the infant was 3 months old, yearly from 1 until 8 years, and at ages 11, 14 and 17 years. Subgroups of high-risk children and low-risk children were selected for an extensive medical examination at age 4, 8, 12 and 16 years. Blood or a buccal brush was used to obtain DNA data.
The PIAMA cohort is a great example of how birth cohorts can help in the investigation of risk factors related to disease such as house dust mites for allergy72 or sugar-containing beverages for asthma.73 The fact that the cohort has participants from rural and more urban areas provides the ability for example to investigate the effect of air pollution differences on asthma development throughout childhood and adolescence.74 The identification of the aforementioned longitudinal phenotypes13, which was possible because of the intensive follow-up present, is furthermore an example of the power of this study. The effectiveness of collaboration between cohorts is proven by the studies we performed in cohorts as part of the Mechanisms of the Development of Allergy (MeDALL) project75 and the Pharmacogenomics in Childhood Asthma (PiCA) consortium.76 The MeDALL project consists of 14 European birth cohorts (with PIAMA being one of them), including 44,010 participants, which were followed up between pregnancy and age 20 years. With usage of a standardized MeDALL Core Questionnaire and a strictly defined defi-nition for asthma, there is a high improvement in linking epidemiologic, clinical, and functional research among studies, needed to understand the complex mechanisms behind multifactorial diseases. For the pharmacogenetic study described in this thesis we used data from 5 cohorts of the PiCA consor-tium76, an international collaboration cohort, integrating a total of 21 studies worldwide, with the inclu-sion of 14,227 children/adolescences from 12 different countries. The consortium contains 619 users of ICS who have data on asthma symptoms, exacerbations and treatment response. This research project provides a unique change to discover (pharmaco)genetic differences between multiple ethnicities which could lead to novel precision based asthma medicine.
Table 1. Description of the cohorts and data used for this thesis.
*The PIAMA, BAMSE and INMA cohort are part of the Mechanisms of the Development of Allergy (MeDALL) project. #The PACMAN, GALAII, SAGE, SLOVENIA and ESTATe cohort are all part of the Pharmacogenomics in Childhood Asthma (PiCA) consortium.
Aims of this thesis
The first aim of this thesis is to improve our understanding of the role of (epi)genetics in specific asthma phe-notypes, with a special focus on the IL1RL1 gene. The second aim of this study is to gain more insight on the role of IL1RL1 in the effect of ICS treatment in asthma. And finally, the third aim is to investigate the added value of genetics in asthma prediction.
Scope of the thesis
Chapter 2 gives an overview of the progress in defining early wheezing phenotypes and describes genetic factors associated with the age of onset of asthma. Furthermore a comparison between asthma and ato-py associated genes is made.
In Chapter 3 we show the results of a candidate gene study performed in two birth cohorts in whom we investigated the association of the IL33-IL1RL1 pathway with asthma and longitudinal wheezing pheno-types. The phenotypes used have been identified and studied previously by our group as described above, with the important finding that specific phenotypes show different associations with lung function, sensitization and asthma. In this chapter we also searched for the association of gene-gene interactions within the IL33-IL1RL1 pathway with asthma and the asthma associated wheezing phenotypes.
With the use of three independent asthma cohorts and re-sequencing data we investigated in Chapter 4 the association of independent IL1RL1 asthma related genetic variants to specific features of asthma as defined by clinical and immunological measures. Moreover, with the use resequencing data and in vitro studies we tried to identify SNP driven mechanisms that may contribute to the identified genetic association signals in lung and airway structural cells.
Since the (epi)genetic regulation of IL1RL1 protein expression has not been established we focus in Chap-ter 5 on the association between IL1RL1 genetic variants, IL1RL1 blood DNA methylation and serum IL-1RL1-a protein levels. With this study, performed in one adult cohort and three birth cohorts, we aimed to identify causal pathways in asthma relating to genetic variation in IL1RL1, white blood cell IL1RL1 methyl-ation and IL1RL1-a protein expression.
The translation of genetic and functional data related to a disease back to the patients is of great impor-tance. In Chapter 6 we therefore investigated the role of IL1RL1 genetic variants on exacerbations, asthma control, FeNO levels and FEV1% predicted in multi-ancestry asthma patients during ICS treatment. Fur-thermore we aimed to identify whether there is a pharmacogenetic effect of IL1RL1 SNPs on ICS treat-ment response in asthma patients.
In Chapter 7 we tried to replicate experimental data in animals in human studies. In this chapter we focus on the transient receptor potential ankyrin-1 (TRPA1) gene, which has been thought to play a key role in pro-moting airway inflammation in asthma and may mediate effects of paracetamol on asthma. We inves-tigated in two large birth cohorts the association between TRPA1 gene variants with childhood asthma and total IgE concentration. Especially, we searched for interactions between TRPA1 and prenatal parac-etamol exposure on these outcomes.
Asthma prediction might be improved with the identification of novel biomarkers, more specific defi-nitions of asthma subphenotypes and better understanding of the genetic and biological pathways in-volved in asthma. In Chapter 8 we investigated in preschool wheezers whether serum IL1RL1-a levels can be used to predict asthma, with a special focus on asthma with elevated FeNO, as a marker of eosinophilic asthma. We compared this to the commonly used Asthma Prediction Index (API).
In Chapter 9 we aimed to generate a prediction model for asthma in the first 8 years of life based on the combination of family, perinatal, environmental and genetic risk factors. Several prediction models based on personal and environmental factors have been developed to improve early diagnosis of asthma. With this study set out to investigate the added value of genetics at predicting asthma at birth.
References
1. Masoli M, Fabian D, Holt S, Beasley R, Program GI for A (GINA). The global burden of asthma: executive summary of the GINA Dissemination Committee report. Allergy. 2004;59:469–78.
2. Volksgezondheidenzorg.info (2018): https://www. volksgezondheidenzorg.info/onderwerp/astma, RIVM: Bilthoven, juli 2018.
3. Global Initiative for Asthma. Global Strategy for Asthma Management and Prevention, 2018. Available from: www.ginasthma.org.
4. Asher I, Pearce N. Global burden of asthma among children. Int J Tuberc Lung Dis. 2014;18:1269–78.
5. Stridsman C, Dahlberg E, Zandrén K, Hedman L. Asthma in adolescence affects daily life and school attendance - Two cross-sectional population-based studies 10 years apart. Nurs Open. 2017;4:143–8.
6. Cerdan NS, Alpert PT, Moonie S, Cyrkiel D, Rue S. Asthma severity in children and the quality of life of their parents. Appl Nurs Res. 2012;25:131–7.
7. Sears MR, Greene JM, Willan AR, Wiecek EM, Taylor DR, Flannery EM, et al. A longitudinal, population-based, cohort study of childhood asthma followed to adulthood. N Engl J Med. 2003;349:1414–22.
8. Savenije OE, Kerkhof M, Koppelman GH, Postma DS. Predicting who will have asthma at school age among preschool children. J Allergy Clin Immunol. 2012;130:325–31.
9. Haldar P, Pavord ID, Shaw DE, Berry MA, Thomas M, Brightling CE, et al. Cluster analysis and clinical asthma phenotypes. Am J Respir Crit Care Med. 2008;178:218–24.
10. Moore WC, Meyers DA, Wenzel SE, Teague WG, Li H, Li X, et al. Identification of asthma phenotypes using cluster analysis in the severe asthma research program. Am J Respir Crit Care Med. 2010;181:315–23.
11. Gelfand EW. Pediatric Asthma: A Different Disease. Proc Am Thorac Soc. 2009;6:278–82.
12. Martinez FD, Wright AL, Taussig LM, Holberg CJ, Halonen M, Morgan WJ. Asthma and wheezing in the first six years of life. The Group Health Medical Associates. N Engl J Med. 1995;332:133–8.
13. Savenije OE, Granell R, Caudri D, Koppelman GH, Smit HA, Wijga A, et al. Comparison of childhood wheezing phenotypes in 2 birth cohorts: ALSPAC and PIAMA. J Allergy Clin Immunol. 2011;127:1505–12.e14.
14. Barnes PJ. Review series The cytokine network in asthma and chronic obstructive pulmonary disease. J Clin Invest. 2008;118:3546–56.
15. Schatz M, Rosenwasser L. The Allergic Asthma Phenotype. J Allergy Clin Immunol Pract. 2014;2:645–8.
16. Fahy J V. Type 2 inflammation in asthma — present in most, absent in many. Nat Rev Immunol. 2014;15:57–65.
17. Woodruff PG, Modrek B, Choy DF, Jia G, Abbas AR, Ellwanger A, et al. T-helper Type 2 – driven Inflammation Defines Major Subphenotypes of Asthma. Am J Respir Crit Care Med. 2009;180:388-95.
18. Hekking PPW, Bel EH. Developing and emerging clinical asthma phenotypes. J Allergy Clin Immunol Pract. 2014;2:671–80.
19. Zhang XY, Simpson JL, Powell H, Yang IA, Upham JW, Reynolds PN, et al. Full blood count parameters for the detection of asthma inflammatory phenotypes. Clin Exp Allergy. 2014;44:1137–45.
20. Haldar P, Pavord ID. Noneosinophilic asthma: A distinct clinical and pathologic phenotype. J Allergy Clin Immunol. 2007;119:1043–52.
21. Brooks CR, Van Dalen CJ, Zacharasiewicz A, Simpson JL, Harper JL, Le Gros G, et al. Absence of airway inflammation in a large proportion of adolescents with asthma. Respirology. 2016;21:460–6.
22. E. S. Lander et al. Initial sequencing and analysis of the human genome. 2001;409.
23. Dunham I, Kundaje A, Aldred SF, Collins PJ, Davis CA, Doyle F, et al. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489:57–74.
24. McCarthy MI, Abecasis GR, Cardon LR, Goldstein DB, Little J, Ioannidis JP, et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev. 2008;9:356–69.
25. Slatkin M. Linkage disequilibrium - Understanding the evolutionary past and mapping the medical future. Nat Rev Genet. 2008;9:477–85.
26. Koppelman GH, Los H, Postma DS. Genetic and environment in asthma: the answer of twin studies. Eur Respir J. 1999;13:2–4.
27. Altshuler DM, Gibbs RA, Peltonen L, Altshuler DM, Gibbs RA, Peltonen L, et al. Integrating common and rare genetic variation in diverse human populations. Nature. 2010;467:52–8.
28. Auton A, Abecasis GR, Altshuler DM, Durbin RM, Abecasis GR, Bentley DR, et al. A global reference for human genetic variation. Nature. 2015;526:68–74.
29. MacArthur J, Bowler E, Cerezo M, Gil L, Hall P, Hastings E, et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 2017;45:896–901.
30. Demenais F, Margaritte-Jeannin P, Barnes KC, Cookson WOC, Altmüller J, Ang W, et al. Multiancestry association study identifies new asthma risk loci that colocalize with immune-cell enhancer marks. Nat Genet. 2018;50:42–50.
31. Ferreira MA, Vonk JM, Baurecht H, Marenholz I, Tian C, Hoffman JD, et al. Shared genetic origin of asthma, hay fever and eczema elucidates allergic disease biology. Nat Genet. 2017;49:1752–7.
32. Davidson EJ, Yang I V. Role of epigenetics in the development of childhood asthma. Curr Opin Allergy Clin Immunol. 2018;18:132-38.
33. Xu CJ, Söderhäll C, Bustamante M, Baïz N, Gruzieva O, Gehring U, et al. DNA methylation in childhood asthma: an epigenome-wide meta-analysis. Lancet Respir Med. 2018;6:379–88.
34. Moffatt MF, Gut IG, Demenais F, Strachan DP, Bouzigon E, Heath S, et al. A large-scale, consortium-based genomewide association study of asthma. N Engl J Med. 2010;363:1211–21.
35. Perin P, Potočnik U. Polymorphisms in recent GWA identified asthma genes CA10, SGK493, and CTNNA3 are associated with disease severity and treatment response in childhood asthma. Immunogenetics. 2014;66:143–51.
36. Su MW, Tung KY, Liang PH, Tsai CH, Kuo NW, Lee YL. Gene-gene and gene-environmental interactions of childhood asthma: A multifactor dimension reduction approach. PLoS One. 2012;7:1–9.
37. Murk W, DeWan AT. Genome-wide search identifies a gene-gene interaction between 20p13 and 2q14 in asthma. BMC Genet. 2016;17:1–9.
38. Lee J-U, Kim JD, Park C-S. Gene-Environment Interactions in Asthma: Genetic and Epigenetic Effects. Yonsei Med J. 2015;56:877–86.
39. Berlivet S, Moussette S, Ouimet M, Verlaan DJ, Koka V, Al Tuwaijri A, et al. Interaction between genetic and epigenetic variation defines gene expression patterns at the asthma-associated locus 17q12-q21 in lymphoblastoid cell lines. Hum Genet. 2012;131:1161–71.
40. Reijmerink NE, Postma DS, Bruinenberg M, Nolte IM, Meyers DA, Bleecker ER, et al. Association of IL1RL1, IL18R1, and IL18RAP gene cluster polymorphisms with asthma and atopy. J Allergy Clin Immunol. 2008;122:651–4.e8.
41. Gudbjartsson DF, Bjornsdottir US, Halapi E, Helgadottir A, Sulem P, Jonsdottir GM, et al. Sequence variants affecting eosinophil numbers associate with asthma and myocardial infarction. Nat Genet. 2009;41:342–7.
42. Grotenboer NS, Ketelaar ME, Koppelman GH, Nawijn MC. Decoding asthma: translating genetic variation in IL33 and IL1RL1 into disease pathophysiology. J Allergy Clin Immunol. 2013;131:856–65.
43. Ferreira MAR, McRae AF, Medland SE, Nyholt DR, Gordon SD, Wright MJ, et al. Association between ORMDL3, IL1RL1 and a deletion on chromosome 17q21 with asthma risk in Australia. Eur J Hum Genet. 2011;19:458–64.
44. Torgerson DG, Ampleford EJ, Chiu GY, Gauderman WJ, Gignoux CR, Graves PE, et al. Meta-analysis of genome-wide association studies of asthma in ethnically diverse North American populations. Nat Genet. 2011;43:887–92.
45. Castano R, Bosse Y, Endam LM, Desrosiers M. Evidence of association of interleukin-1 receptor-like 1 gene polymorphisms with chronic rhinosinusitis. Am J Rhinol Allergy. 2009;23:377–84.
46. Shimizu M, Matsuda A, Yanagisawa K, Hirota T, Akahoshi M, Inomata N, et al. Functional SNPs in the distal promoter of the ST2 gene are associated with atopic dermatitis. Hum Mol Genet. 2005;14:2919–27.
47. Bonnelykke K, Matheson MC, Pers TH, Granell R, Strachan DP, Alves AC, et al. Meta-analysis of genome-wide association studies identifies ten loci influencing allergic sensitization. Nat Genet. 2013;45:902–6.
48. Ferreira MAR, Matheson MC, Tang CS, Granell R, Ang W, Hui J, et al. Genome-wide association analysis identifies 11 risk variants associated with the asthma with hay fever phenotype. J Allergy Clin Immunol. 2014;133:1564–71.
49. Gordon ED, Palandra J, Wesolowska-Andersen A, Ringel L, Rios CL, Lachowicz-Scroggins ME, et al. IL1RL1 asthma risk variants regulate airway type 2 inflammation. JCI Insight. 2016;1:e87871.
50. Sarnowski C, Sugier PE, Granell R, et al. Identification of a new locus at 16q12 associated with time to asthma onset. J Allergy Clin Immunol. 2016; 138: 1071–80.
51. Coyle AJ, Lloyd C, Tian J, Nguyen T, Erikkson C, Wang L, et al. Crucial role of the interleukin 1 receptor family member T1/ST2 in T helper cell type 2-mediated lung mucosal immune responses. J Exp Med. 1999;190:895– 902.
52. Tominaga S, Kuroiwa K, Tago K, Iwahana H, Yanagisawa K, Komatsu N. Presence and expression of a novel variant form of ST2 gene product in human leukemic cell line UT-7/GM. Biochem Biophys Res Commun. 1999;264:14–8.
53. Tago K, Noda T, Hayakawa M, Iwahana H, Yanagisawa K, Yashiro T, et al. Tissue distribution and subcellular localization of a variant form of the human ST2 gene product, ST2V. Biochem Biophys Res Commun. 2001;285:1377–83.
54. Dijk FN, Xu C, Melén E, Carsin A-E, Kumar A, Nolte IM, et al. Genetic regulation of IL1RL1 methylation and IL1RL1-a protein levels in asthma. Eur Respir J. 2018;51:1701377.
55. Saluja R, Ketelaar ME, Hawro T, Church MK, Maurer M, Nawijn MC. The role of the IL-33/IL-1RL1 axis in mast cell and basophil activation in allergic disorders. Mol Immunol. 2015;63:80–5.
56. Traister RS, Uvalle CE, Hawkins GA, Meyers DA, Bleecker ER, Wenzel SE. Phenotypic and genotypic association of epithelial IL1RL1 to human TH2-like asthma. J Allergy Clin Immunol. 2015;135:92–9.
57. Lloyd CM. IL-33 family members and asthma - bridging innate and adaptive immune responses. Curr Opin Immunol. 2010;22:800–6.
58. Drazen JM, Silverman EK, Lee TH. Heterogeneity of therapeutic responses in asthma. Br Med Bull. 2000;56:1054–70.
59. Kersten ETG, Koppelman GH. Pharmacogenetics of asthma : toward precision medicine. Curr Opin Pulm Med. 2017;23:12–20.
60. Chung KF. Asthma phenotyping: A necessity for improved therapeutic precision and new targeted therapies. J Intern Med. 2016;279:192–204.
61. Wadsworth SJ, Sandford AJ. Personalised medicine and asthma diagnostics/management. Current Allergy and Asthma Reports. 2013;13:118–29.
62. Caudri D, Wijga AH, Smit HA, Koppelman GH, Kerkhof M, Hoekstra MO, et al. Asthma symptoms and medication in the PIAMA birth cohort: Evidence for under and overtreatment. Pediatr Allergy Immunol. 2011;22:652–9.
63. Halterman JS, Aligne A, Auinger P, McBride JT, Szilagyi PG. Inadequate therapy for asthma among children in the United States. Psychol Heal. 2000;105:25–50.
64. Caudri D, Wijga A, A. Schipper CM, Hoekstra M, Postma DS, Koppelman GH, et al. Predicting the long-term prognosis of children with symptoms suggestive of asthma at preschool age. J Allergy Clin Immunol. 2009;124:903–910.e7.
65. Hafkamp-De Groen E, Lingsma HF, Caudri D, Levie D, Wijga A, Koppelman GH, et al. Predicting asthma in preschool children with asthma-like symptoms: Validating and updating the PIAMA risk score. J Allergy Clin Immunol. 2013;132:1303-10.
66. Castro-Rodriguez JA. Another Predictive Score for Childhood Asthma: The Search Remains. J Allergy Clin Immunol Pract. 2014;2:716–8.
67. Bisgaard H, Jensen SM, Bonnelykke K. Interaction between asthma and lung function growth in early life. Am J Respir Crit Care Med. 2012;185:1183–9.
68. Wise RA, Szefler SJ, Sharma S, Kho AT, Cho MH, Chonka DCC. Patterns of Growth and Decline in Lung Function in Persistent Childhood Asthma. 2016;19:1842-1852
69. Spycher BD, Henderson J, Granell R, Evans DM, Smith GD, Timpson NJ, et al. Genome-wide prediction of childhood asthma and related phenotypes in a longitudinal birth cohort. J Allergy Clin Immunol. 2012;130:503–9.e7.
70. Belsky DW, Sears MR, Hancox RJ, Harrington H, Houts R, Moffitt TE, et al. Polygenic risk and the development and course of asthma: An analysis of data from a four-decade longitudinal study. Lancet Respir Med. 2013;1:453–61.
71. Wijga AH, Kerkhof M, Gehring U, de Jongste JC, Postma DS, Aalberse RC, et al. Cohort profile: the prevention and incidence of asthma and mite allergy (PIAMA) birth cohort. Int J Epidemiol. 2014;43:527–35.
72. Antens CJM, Oldenwening M, Wolse A, Gehring U, Smit HA, Aalberse RC, et al. Repeated measurements of mite and pet allergen levels in house dust over a time period of 8 years. Clin Exp Allergy. 2006;36:1525–31.
73. Berentzen NE, Stokkom VL Van, Gehring U, Koppelman GH, Schaap LA, Smit HA, et al. Associations of sugar-containing beverages with asthma prevalence in 11-year-old children : the PIAMA birth cohort. Eur J Clin Nutr. 2015;69:303-8.
74. Gehring U, Wijga AH, Hoek G, Bellander T, Berdel D, Brüske I, et al. Exposure to air pollution and development of asthma and rhinoconjunctivitis throughout childhood and adolescence: A population-based birth cohort study. Lancet Respir Med. 2015;3:933–42.
75. Bousquet J, Anto J, Auffray C, Akdis M, Cambon-Thomsen A, Keil T, et al. MeDALL (Mechanisms of the Development of ALLergy): an integrated approach from phenotypes to systems medicine. Allergy. 2011;66:596–604.
76. Farzan N, Vijverberg SJ, Andiappan AK, Arianto L, Berce V, Blanca-López N, et al. Rationale and design of the multiethnic Pharmacogenomics in Childhood Asthma consortium. Pharmacogenomics. 2017;18:931–43.
F. Nicole Dijk, Johan C. de Jongste, Dirkje S. Postma, and Gerard H. Koppelman
