Pediatric sleep disorders, such as obstructive sleep apnea (OSA), impact 5-10% of children. Children are diagnosed with sleep-disordered breathing through polysomnography (PSG), which requires hours of sleep and physiology data including EEG tracings, cardiograms, pulse-oximetry, and airflow monitoring. PSG data can be used to create individualized therapy and advance the care of children with sleep disorders. To facilitate novel diagnostic and validation studies using data collected on PSG and patient questionnaires, we created a data bank of PSG and patient data. Through creating a patient database in R, we can analyze sleep behaviors and medical diagnosis of pediatric patients and support future investigations. We aimed to create a pediatric sleep disorder research database to analyze sleep behaviors of pediatric patients, hypothesizing that chronotype classification would differ with age. We created a custom R script to analyze the raw data bank for medical diagnosis, age, sex, PSG diagnosis, chronotype, patient symptom scales and reported summary statistics including count, range, and standard deviation. Dplyr and tidyverse packages were used to create data summaries and ggplot2 for graphical presentation. An initial cohort of 111 participants were analyzed for correlation of chronotype and age range (>11, <=11). Initial analysis revealed a cohort of 111 participants with an age range of 6 months to 18 years (median: 7), medical history of 15 prematurity, 16 allergic rhinitis, and 3 congenital heart disease patients, PSG diagnosis of 44 normal and 8 severe, chronotype scoring of 8 evening to 34 morning patients, OSA-18 scores ranging from 34 to 102 (median: 61). Correlation analysis revealed that chronotype distribution is statistically different between age groups. We have created a custom analysis tool to create a summary report of a new sleep data bank repository. Future studies will use the tool to inform preliminary summaries of available demographic and data. 

Obstructive sleep apnea (OSA) in children is linked to early life viral infections and increased severity of viral respiratory illnesses. As respiratory viral infections occur in airway epithelial cells, we investigated differences in viral responses using an organotypic epithelial cell model in children with OSA as compared to children without. We hypothesized that gene expression in response to rhinovirus (RV16) infection would differ between healthy children and children with OSA. Primary airway epithelial cells (AECS), from both healthy pediatric donors and children diagnosed with OSA by polysomnography, cultured at an air-liquid interface, were infected with RV16 on the apical surface at a multiplicity of infection of 0.5. RNA-sequencing quantified gene expression at baseline and after RV16 infection. Limma was used to identify genes with differential expression post-infection in healthy AECs as compared to AECs from donors with OSA. Weighted gene co-expression network analysis (WGCNA) was able to organize the identified genes into groups of interest. Using Enrichr, the primary biological functions of the gene groupings were analyzed. Following infection, 122 genes were found to have differing gene expression responses to RV16 in OSA when compared to healthy cell lines. WGCNA identified two modules of gene expression with opposite expression patterns following infection in OSA compared to healthy. One module consists of 43 genes enriched for glycogen metabolism which are downregulated in healthy but upregulated in OSA following infection. A second module consists of 23 genes enriched for DNA repair and replication which are upregulated in healthy but downregulated in OSA after infection. Epithelial cell gene expression differs in response to RV16 in healthy AECs as compared to AECs from children with OSA. Given the small sample size, further studies are needed to investigate the relationship of OSA severity and clinical phenotypes of OSA with epithelial responses to viral infection.