Original ArticleGenome-wide gene expression profiling in children with non-obese obstructive sleep apnea☆
Introduction
Obstructive sleep apnea (OSA) is a relatively prevalent disorder across the lifespan, in which both genetic and environmental factors may be involved [1]. OSA is characterized by episodic partial or complete upper airway obstruction during sleep in association with loud snoring, altered gas exchange, and sleep fragmentation. This condition may affect up to 3% of otherwise healthy school-aged children [2], and has been associated with substantial cardiovascular, metabolic, and neurocognitive morbidities [3], [4], [5], [6]. Indeed, schooling and behavioral problems such as restlessness, inattention and impulsiveness, aggressive behavior, excessive daytime sleepiness, and poor test performances have been repeatedly reported in children with OSA [6], [7], [8], [9], [10]. In addition, systemic and pulmonary hypertension, reduced somatic growth, mood disturbances, and decreased quality of life may develop in pediatric OSA patients [11], [12]. In children, hypertrophy of the tonsils and adenoids in the upper airway is the most frequent and prominent abnormality associated with OSA, such that surgical extirpation of the enlarged upper airway lymphoid tissue is usually the initial management approach [13], [14]. The definitive diagnosis of OSA currently requires an overnight polysomnographic evaluation in a sleep laboratory, and is therefore an onerous and labor intensive procedure, such that delays in the timely diagnosis and treatment are frequent occurrences. Although much has been learned on the pathophysiology and consequences of pediatric OSA in the last 30 years, the mechanisms and specific genes associated with such processes remain poorly defined.
In the last decade, development of high-throughput technologies, such as gene expression profiling using microarrays, has become a fundamental approach for identifying potential diagnostic and therapeutic targets for many diseases. The information gained by such a non a priori approach offers an unprecedented opportunity to fully characterize biological processes, since DNA microarrays yield a simultaneous measurement of gene expression levels for thousands of genes or for the whole genome, thereby allowing for analysis of differential gene expression patterns [15], [16]. Several microarray platforms are currently available [15], [16], [17], [18] and use different approaches to the construction, layout, optimization, hybridization, image acquisition and data extraction methods. Several reviews using different microarray platforms have been reported and summarize the major and most salient points related to their inherent advantages and limitations [19], [20]. Although microarrays have been used to identify molecular signatures for many diseases, such as asthma [21], and heart disease [22], there have been only a few reports on the use of this technology in sleep-related issues [23], [24]. Furthermore, we are aware of only one published study in four adult patients with OSA in whom microarrays were employed. In this report, differential expression of genes mediating oxidative stress was found and postulated as playing an important role in end-organ injury associated with OSA [25].
Clearly, the characterization of specific genes and particular biological pathways in children with OSA using high-throughput gene expression may further enhance our knowledge of this condition in the pediatric age range and allow for reliable and convenient clinical approaches for diagnosis and treatment of these children in the future. Royce et al. [26] reported that the applications of microarrays span from the bench to the bedside, thereby providing tools that require less effort, expense, and sample amount than any other technology. Based on aforementioned considerations, we hypothesized that specific changes in gene expression would occur in children with OSA. Therefore, the aims of the present study were to examine global changes in gene expression profiles in children with OSA, identify differentially expressed genes, and validate some of the latter using other molecular techniques such as QRT-PCR.
Section snippets
Subjects
Approval for the study was obtained from the institutional review board of the University of Louisville School of Medicine. Parental informed consent and child assent, in the presence of a parent, were obtained. Consecutive pre-pubertal non-obese children between the ages of four and nine years of age with a polysomnographic diagnosis of OSA (see below) were identified and recruited to the study. Control subjects were recruited from an ongoing large-scale population study and were initially
Demographic and polysomnographic characteristics
The demographic characteristics of children with non-obese OSA and their matched control groups are shown in Table 1 and their polysomnographic findings are presented in Table 2. In this cohort of non-obese children with OSA, evidence for enlarged adenotonsillar tissues in the upper airway was present in all children and considered as the major pathophysiological mechanism contributing to the development of OSA. However, we can not exclude that subtle changes in craniofacial anthropometrics or
Discussion
To the best of our knowledge, this study represents the first attempt to characterize genome-wide gene expression profiling analysis of circulating leukocytes among non-obese children with OSA. The major findings support the activation and regulation of several functionally-related pathways in children with OSA, a significance that, while completely unexplored, is clearly of great potential interest for future discovery. Among such pathways, up-regulation and modulation of inflammation was
References (64)
- et al.
Metabolic correlates with obstructive sleep apnea in obese subjects
J Pediatr
(2002) - et al.
Oxidant stress and inflammation in the snoring child: confluent pathways to upper airway pathogenesis and end-organ morbidity
Sleep Med Rev
(2006) - et al.
Disorders of breathing during sleep
- et al.
Treatment of obstructive sleep apnea in children: do we really know how?
Sleep Med Rev
(2003) - et al.
Microarray-based identification of novel biomarkers in asthma
Allergol Int
(2006) - et al.
Differential gene expression profiling in genetic and multifactorial cardiovascular diseases
J Mol Cell Cardiol
(2006) - et al.
Extrapolating traditional DNA microarray statistics to tiling and protein microarray technologies
Methods Enzymol
(2006) - et al.
Analysis of relative gene expression data using real-time quantitative PCR and the 2(−Delta Delta C(T)) method
Methods
(2001) - et al.
Human polyserase-2, a novel enzyme with three tandem serine protease domains in a single polypeptide chain
J Biol Chem
(2005) - et al.
Systemic inflammation in non-obese children with obstructive sleep apnea
Sleep Med
(2008)
Gene expression profiles at diagnosis in de novo childhood AML patients identify FLT3 mutations with good clinical outcomes
Blood
The peripheral blood transcriptome dynamically reflects system wide biology: a potential diagnostic tool
J Lab Clin Med
Plasma adhesion molecules in children with sleep-disordered breathing
Chest
Obstructive sleep apnoea syndrome and genes
Neth J Med
Snoring, sleep disturbance, and behaviour in 4–5 year olds
Arch Dis Child
Left ventricular hypertrophy and abnormal ventricular geometry in children and adolescents with obstructive sleep apnea
Am J Respir Crit Care Med
Neurocognitive dysfunction in children with sleep disorders
Dev Sci
Congenital central hypoventilation syndrome: an update
Pediatr Pulmonol
Snoring during early childhood and academic performance at ages thirteen to fourteen years
Pediatrics
Sleep disturbance in preschool-aged hyperactive and nonhyperactive children
Pediatrics
Inattention, hyperactivity, and symptoms of sleep-disordered breathing
Pediatrics
Actigraphy and parental ratings of sleep in children with attention-deficit/hyperactivity disorder (ADHD)
Sleep
Indications for tonsillectomy and adenoidectomy
Laryngoscope
Genomics, gene expression and DNA arrays
Nature
Quantitative monitoring of gene expression patterns with a complementary DNA microarray
Science
High density synthetic oligonucleotide arrays
Nat Genet
Microarray platforms – comparisons and contrasts
Pharmacogenomics
Applications of DNA microarrays in biology
Annu Rev Biochem
Changes in brain gene expression after long-term sleep deprivation
J Neurochem
Sleep deprivation and activation of morning levels of cellular and genomic markers of inflammation
Arch Intern Med
Microarray studies of genomic oxidative stress and cell cycle responses in obstructive sleep apnea
Antioxid Redox Signal
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Funding sources: A.K. is supported by University of Louisville Institutional Research Grant E0606. D.G. is supported by NIH Grants HL-65270 and HL-83075, The Children’s Foundation Endowment for Sleep Research, and by the Commonwealth of Kentucky Challenge for Excellence Trust Fund. L.K.G. is supported by a grant from the National Space Agency (NNJ05HF 06G).