Systems Biology Analyses of Gene Expression and Genome Wide Association Study Data in Obstructive Sleep Apnea

Abstract

The precise molecular etiology of obstructive sleep apnea (OSA) is unknown; however recent research indicates that several interconnected aberrant pathways and molecular abnormalities are contributors to OSA. Identifying the genes and pathways associated with OSA can help to expand our understanding of the risk factors for the disease as well as provide new avenues for potential treatment. Towards these goals, we have integrated relevant high dimensional data from various sources, such as genome-wide expression data (microarray), protein-protein interaction (PPI) data and results from genome-wide association studies (GWAS) in order to define sub-network elements that connect some of the known pathways related to the disease as well as define novel regulatory modules related to OSA. Two distinct approaches are applied to identify sub-networks significantly associated with OSA. In the first case we used a biased approach based on sixty genes/proteins with known associations with sleep disorders and/or metabolic disease to seed a search using commercial software to discover networks associated with disease followed by information theoretic (mutual information) scoring of the sub-networks. In the second case we used an unbiased approach and generated an interactome constructed from publicly available gene expression profiles and PPI databases, followed by scoring of the network with p-values from GWAS data derived from OSA patients to uncover sub-networks significant for the disease phenotype. A comparison of the approaches reveals a number of proteins that have been previously known to be associated with OSA or sleep. In addition, our results indicate a novel association of Phosphoinositide 3-kinase, the STAT family of proteins and its related pathways with OSA.

Personalised medicine in sleep respiratory disorders: focus on obstructive sleep apnoea diagnosis and treatment

“…Research on "-omics" (figure 1) continues, and may ultimately provide clinically useful biomarkers for adult [97] and paediatric [29,98,99] OSA. Examples are studies in adults and children with OSA using proteomics [29,100], studies on miRNA in patients with OSA and resistant hypertension [101], the application of system biology to genetic studies of OSA [102] and studies on visceral fat transcriptome to assess the molecular basis of the interaction between OSA and obesity [103,104]. A roadmap for a personalised approach to OSA has been proposed according to P4 medicine, ( personalisation, prediction, prevention and patient participation in healthcare) [27,28].…”

Sleep Apnea and Glaucoma – Greater Risk for Blacks?

“…Along with further epidemiological investigation, genetic research could also be explored. Genome scanning has already suggested genetic factors linking sleep apnea to obesity, diabetes, and insulin resistance [26,41,42]. More recent analysis of mRNA expression, protein interaction, and results from genome-wide association studies has made an attempt to connect some of the known pathways between these known co-morbidities [16].…”

“…[18][19][20]. Sleep apnea is also associated with a number of metabolic diseases [21][22][23][24][25][26][27][28], cardiac arrhythmias [18,19], cardiovascular disease [18,20], decreased quality of life [17] and early mortality [18,28]. It causes significant sleep disturbances and excessive daytime sleepiness [18,29], which often lead to road traffic and industrial accidents [18,27,30] as well as cognitive deficits and poor performance [31].…”

Protein interaction and microRNA network analysis in osteoarthritis meniscal cells

“…Network modeling of protein-protein interactions (PPIs) and miRNA-gene interactions provides a relatively new context to study disease and identify disease-related targets (Liu et al, 2011). The miRNAs represent a class of small (typically 22 nucleotides in length) non-coding RNAs that can degrade their target mRNAs or block their translation (Wu and Song, 2011).…”