Nocturnal asthma is affected by genetic interactions between RORA and NPSR1

Parent-of-Origin Effects in Childhood Asthma at Seven Years of Age

Childhood asthma is more common among children whose mothers have asthma than among those whose fathers have asthma. The reasons for this are unknown, and we hypothesize that genomic imprinting may partly explain this observation. Our aim is to assess parent-of-origin (PoO) effects on childhood asthma by analyzing SNP array genotype data from a large population-based cohort. To estimate PoO effects in parent-reported childhood asthma at 7 years of age, we fit a log-linear model implemented in the HAPLIN R package to SNP array genotype data from 915 mother-father-child case triads, 603 mother-child case dyads, and 113 father-child case dyads participating in the Norwegian Mother, Father, and Child Cohort Study (MoBa). We found that alleles at two SNPs-rs3003214 and rs3003211-near the adenylosuccinate synthase 2 gene (ADSS2 on chromosome 1q44) showed significant PoO effects at a false positive rate ≤ 0.05. The ratio of the effect of the maternally and paternally inherited G-allele at rs3003214 was 1.68 (95% CI: 1.41-2.03, p value = 1.13E-08). Our results suggest PoO effects at the ADSS2 gene, particularly the maternally inherited G-allele at rs3003214, may contribute to the maternal effect in childhood asthma.

Machine Learning Prediction of Treatment Response to Inhaled Corticosteroids in Asthma

BACKGROUND

Although inhaled corticosteroids (ICS) are the first-line therapy for patients with persistent asthma, many patients continue to have exacerbations. We developed machine learning models to predict the ICS response in patients with asthma.

METHODS

The subjects included asthma patients of European ancestry (n = 1371; 448 children; 916 adults). A genome-wide association study was performed to identify the SNPs associated with ICS response. Using the SNPs identified, two machine learning models were developed to predict ICS response: (1) least absolute shrinkage and selection operator (LASSO) regression and (2) random forest.

RESULTS

The LASSO regression model achieved an AUC of 0.71 (95% CI 0.67-0.76; sensitivity: 0.57; specificity: 0.75) in an independent test cohort, and the random forest model achieved an AUC of 0.74 (95% CI 0.70-0.78; sensitivity: 0.70; specificity: 0.68). The genes contributing to the prediction of ICS response included those associated with ICS responses in asthma (TPSAB1, FBXL16), asthma symptoms and severity (ABCA7, CNN2, PTRN3, and BSG/CD147), airway remodeling (ELANE, FSTL3), mucin production (GAL3ST), leukotriene synthesis (GPX4), allergic asthma (ZFPM1, SBNO2), and others.

CONCLUSIONS

An accurate risk prediction of ICS response can be obtained using machine learning methods, with the potential to inform personalized treatment decisions. Further studies are needed to examine if the integration of richer phenotype data could improve risk prediction.

Pre-asthma: a useful concept for prevention and disease-modification? A EUFOREA paper. Part 1-allergic asthma

Asthma, which affects some 300 million people worldwide and caused 455,000 deaths in 2019, is a significant burden to suffers and to society. It is the most common chronic disease in children and represents one of the major causes for years lived with disability. Significant efforts are made by organizations such as WHO in improving the diagnosis, treatment and monitoring of asthma. However asthma prevention has been less studied. Currently there is a concept of pre- diabetes which allows a reduction in full blown diabetes if diet and exercise are undertaken. Similar predictive states are found in Alzheimer's and Parkinson's diseases. In this paper we explore the possibilities for asthma prevention, both at population level and also investigate the possibility of defining a state of pre-asthma, in which intensive treatment could reduce progression to asthma. Since asthma is a heterogeneous condition, this paper is concerned with allergic asthma. A subsequent one will deal with late onset eosinophilic asthma.

Co-regulation of circadian clock genes and microRNAs in bone metabolism

Mammalian bone is constantly metabolized from the embryonic stage, and the maintenance of bone health depends on the dynamic balance between bone resorption and bone formation, mediated by osteoclasts and osteoblasts. It is widely recognized that circadian clock genes can regulate bone metabolism. In recent years, the regulation of bone metabolism by non-coding RNAs has become a hotspot of research. MicroRNAs can participate in bone catabolism and anabolism by targeting key factors related to bone metabolism, including circadian clock genes. However, research in this field has been conducted only in recent years and the mechanisms involved are not yet well established. Recent studies have focused on how to target circadian clock genes to treat some diseases, such as autoimmune diseases, but few have focused on the co-regulation of circadian clock genes and microRNAs in bone metabolic diseases. Therefore, in this paper we review the progress of research on the co-regulation of bone metabolism by circadian clock genes and microRNAs, aiming to provide new ideas for the prevention and treatment of bone metabolic diseases such as osteoporosis.

Maresin-1 and Its Receptors RORα/LGR6 as Potential Therapeutic Target for Respiratory Diseases

Maresin-1 is one of the representative specialized pro-resolving mediators that has shown beneficial effects in inflammatory disease models. Recently, two distinct types of receptor molecules were discovered as the targets of maresin-1, further revealing the pro-resolution mechanism of maresin-1. One is retinoic acid-related orphan receptor α (RORα) and the another one is leucine-rich repeat domain-containing G protein-coupled receptor 6 (LGR6). In this review, we summarized the detailed role of maresin-1 and its two different receptors in respiratory diseases. RORα and LGR6 are potential targets for the treatment of respiratory diseases. Future basic research and clinical trials on MaR1 and its receptors should provide useful information for the treatment of respiratory diseases.