Circulating MicroRNAs associated with Bronchodilator Response in Childhood Asthma.

Rationale: Bronchodilator response (BDR) is a measure of improvement in airway smooth muscle tone, inhibition of liquid accumulation and mucus section into the lumen in response to short-acting beta-2 agonists that varies among asthmatic patients. MicroRNAs (miRNAs) are well-known post-translational regulators. Identifying miRNAs associated with BDR could lead to a better understanding of the underlying complex pathophysiology. Objective: The purpose of this study is to identify circulating miRNAs associated with bronchodilator response in asthma and decipher possible mechanism of bronchodilator response variation. Methods: We used available small RNA sequencing on blood serum from 1,134 asthmatic children aged 6 to 14 years who participated in the Genetics of Asthma in Costa Rica Study (GACRS). We filtered the participants into high and low bronchodilator response (BDR) quartiles and used DeSeq2 to identify miRNAs with differential expression (DE) in high (N= 277) vs low (N= 278) BDR group. Replication was carried out in the Leukotriene modifier Or Corticosteroids or Corticosteroid-Salmeterol trial (LOCCS), an adult asthma cohort. The putative target genes of DE miRNAs were identified, and pathway enrichment analysis was performed. Results: We identified 10 down-regulated miRNAs having odds ratios (OR) between 0.37 and 0.76 for a doubling of miRNA counts and one up-regulated miRNA (OR=2.26) between high and low BDR group. These were assessed for replication in the LOCCS cohort, where two miRNAs (miR-200b-3p and miR-1246) were associated. Further, functional annotation of 11 DE miRNAs were performed as well as of two replicated miRs. Target genes of these miRs were enriched in regulation of cholesterol biosynthesis by SREBPs, ESR-mediated signaling, G1/S transition, RHO GTPase cycle, and signaling by TGFB family pathways. Conclusion: MiRNAs miR-1246 and miR-200b-3p are associated with both childhood and adult asthma BDR. Our findings add to the growing body of evidence that miRNAs play a significant role in the difference of asthma treatment response among patients as it points to genomic regulatory machinery underlying difference in bronchodilator response among patients. Trial registration: LOCCS cohort [ClinicalTrials.gov number: NCT00156819], GACRS cohort [ClinicalTrials.gov number: NCT00021840].

Pharmacogenetics of childhood uncontrolled asthma.

INTRODUCTION: Asthma is a heterogeneous, multifactorial disease with multiple genetic and environmental risk factors playing a role in pathogenesis and therapeutic response. Understanding of pharmacogenetics can help with matching individualized treatments to specific genotypes of asthma to improve therapeutic outcomes especially in uncontrolled or severe asthma. AREAS COVERED: In this review, we outline novel information about biology, pathways, and mechanisms related to interindividual variability in drug response (corticosteroids, bronchodilators, leukotriene modifiers, and biologics) for childhood asthma. We discuss candidate gene, genome-wide association studies and newer omics studies including epigenomics, transcriptomics, proteomics, and metabolomics as well as integrative genomics and systems biology methods related to childhood asthma. The articles were obtained after a series of searches, last updated November 2022, using database PubMed/CINAHL DB. EXPERT OPINION: Implementation of pharmacogenetic algorithms can improve therapeutic targeting in children with asthma, particularly with severe or uncontrolled asthma who typically have challenges in clinical management and carry considerable financial burden. Future studies focusing on potential biomarkers both clinical and pharmacogenetic can help formulate a prognostic test for asthma treatment response that would represent true bench to bedside clinical implementation.

Using machine learning to improve our understanding of COVID-19 infection in children.

PURPOSE: Children are at elevated risk for COVID-19 (SARS-CoV-2) infection due to their social behaviors. The purpose of this study was to determine if usage of radiological chest X-rays impressions can help predict whether a young adult has COVID-19 infection or not. METHODS: A total of 2572 chest impressions from 721 individuals under the age of 18 years were considered for this study. An ensemble learning method, Random Forest Classifier (RFC), was used for classification of patients suffering from infection. RESULTS: Five RFC models were implemented with incremental features and the best model achieved an F1-score of 0.79 with Area Under the ROC curve as 0.85 using all input features. Hyper parameter tuning and cross validation was performed using grid search cross validation and SHAP model was used to determine feature importance. The radiological features such as pneumonia, small airways disease, and atelectasis (confounded with catheter) were found to be highly associated with predicting the status of COVID-19 infection. CONCLUSIONS: In this sample, radiological X-ray films can predict the status of COVID-19 infection with good accuracy. The multivariate model including symptoms presented around the time of COVID-19 test yielded good prediction score.