Expression quantitative trait loci for ETV4 and MEOX1 are associated with adult asthma in Japanese populations

Epigenetic patient stratification via contrastive machine learning refines hallmark biomarkers in minoritized children with asthma

Identifying and refining clinically significant patient stratification is a critical step toward realizing the promise of precision medicine in asthma. Several peripheral blood hallmarks, including total peripheral blood eosinophil count (BEC) and immunoglobulin E (IgE) levels, are routinely used in asthma clinical practice for endotype classification and predicting response to state-of-the-art targeted biologic drugs. However, these biomarkers appear ineffective in predicting treatment outcomes in some patients, and they differ in distribution between racially and ethnically diverse populations, potentially compromising medical care and hindering health equity due to biases in drug eligibility. Here, we propose constructing an unbiased patient stratification score based on DNA methylation (DNAm) and utilizing it to refine the efficacy of hallmark biomarkers for predicting drug response. We developed Phenotype Aware Component Analysis (PACA), a novel contrastive machine-learning method for learning combinations of DNAm sites reflecting biomedically meaningful patient stratifications. Leveraging whole-blood DNAm from Latino (discovery; n=1,016) and African American (replication; n=756) pediatric asthma case-control cohorts, we applied PACA to refine the prediction of bronchodilator response (BDR) to the short-acting β2-agonist albuterol, the most used drug to treat acute bronchospasm worldwide. While BEC and IgE correlate with BDR in the general patient population, our PACA-derived DNAm score renders these biomarkers predictive of drug response only in patients with high DNAm scores. BEC correlates with BDR in patients with upper-quartile DNAm scores (OR 1.12; 95% CI [1.04, 1.22]; P=7.9 e-4) but not in patients with lower-quartile scores (OR 1.05; 95% CI [0.95, 1.17]; P=0.21); and IgE correlates with BDR in above-median (OR for response 1.42; 95% CI [1.24, 1.63]; P=3.9e-7) but not in below-median patients (OR 1.05; 95% CI [0.92, 1.2]; P=0.57). These results hold within the commonly recognized type 2 (T2)-high asthma endotype but not in T2-low patients, suggesting that our DNAm score primarily represents an unknown variation of T2 asthma. Among T2-high patients with high DNAm scores, elevated BEC or IgE also corresponds to baseline clinical presentation that is known to benefit more from biologic treatment, including higher exacerbation scores, higher allergen sensitization, lower BMI, more recent oral corticosteroids prescription, and lower lung function. Our findings suggest that BEC and IgE, the traditional asthma biomarkers of T2-high asthma, are poor biomarkers for millions worldwide. Revisiting existing drug eligibility criteria relying on these biomarkers in asthma medical care may enhance precision and equity in treatment.

The primary ciliary dyskinesia-related genetic risk score is associated with susceptibility to adult-onset asthma

BACKGROUND

Disturbance of mucociliary clearance is an important factor in the pathogenesis of asthma. We hypothesized that common variants in genes responsible for ciliary function may contribute to the development of asthma with certain phenotypes.

METHODS

Three independent adult Japanese populations (including a total of 1,158 patients with asthma and 2,203 non-asthmatic healthy participants) were studied. First, based on the ClinVar database (https://www.ncbi.nlm.nih.gov/clinvar/), we selected 12 common single-nucleotide polymorphisms (SNPs) with molecular consequences (missense, nonsense, and 3'-untranslated region mutation) in 5 primary ciliary dyskinesia (PCD)-related genes and calculated a PCD-genetic risk score (GRS) as a cumulative effect of these PCD-related genes. Second, we performed a two-step cluster analysis using 3 variables, including PCD-GRS, forced expiratory volume in 1 second (%predicted FEV1), and age of asthma onset.

RESULTS

Compared to adult asthma clusters with an average PCD-GRS, clusters with high and low PCD-GRS had similar overall characteristics: adult-onset, female predominance, preserved lung function, and fewer features of type 2 immunity as determined by IgE reactivity and blood eosinophil counts. The allele frequency of rs1530496, a SNP representing an expression quantitative trait locus (eQTL) of DNAH5 in the lung, showed the largest statistically significant difference between the PCD-GRS-High and PCD-GRS-Low asthma clusters (p = 1.4 x 10-15).

CONCLUSION

Genes associated with PCD, particularly the common SNPs associated with abnormal expression of DNAH5, may have a certain influence on the development of adult-onset asthma, perhaps through impaired mucociliary clearance.

The understanding of asthma pathogenesis in the era of precision medicine

Asthma is a syndrome with extremely diverse clinical phenotypes in which the onset, severity, and response to treatment are defined by the complex interplay of many genetic and environmental factors. Environmental factors epigenetically affect gene expression, and the disease is driven by a multidimensional dynamic network involving RNA and protein molecules derived from gene expression, as well as various metabolic products. In other words, specific pathophysiological mechanisms or endotypes are dynamic networks that arise in response to individual genotypes and the various environmental factors to which individuals have been exposed since before birth, such as diet, infection, air pollution, smoking, antibiotic use, and the bacterial flora of the intestinal tract, skin, and lungs. A key feature of asthma genome scans is their potential to reveal the molecular pathways that lead to pathogenesis. Endotypes that drive the disease have a significant impact on the phenotypes of asthma patients, including their drug responsiveness. Understanding endotypes will lead to not only the implementation of therapies that are tailored to the specific molecular network(s) underlying the patient's condition, but also to the development of therapeutic strategies that target individual endotypes, as well as to precision health, which will enable the prediction of disease onset with high accuracy from an early stage and the implementation of preventive strategies based on endotypes. Understanding of endotypes will pave the way for the practice of precision medicine in asthma care, moving away from 'one-size-fits-all' medicine and population-based prevention approaches that do not take individuals' susceptibility into account.