Yuanyuan Cao1, Qing Zhu2, Xintian Cai2, Ting Wu2, Xiayire Aierken2, Ayguzal Ahmat2, Shasha Liu2, Nanfang Li1,2. 1. Xinjiang Medical University, Urumqi, 830001 Xinjiang, China. 2. Hypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Hypertension Institute, National Health Committee Key Laboratory of Hypertension Clinical Research, Urumqi, 830001 Xinjiang, China.
Abstract
BACKGROUND: Obstructive sleep apnea (OSA) is a prevalent chronic disease that increases the risk of cardiovascular disease and metabolic and neuropsychiatric disorders, resulting in a considerable socioeconomic burden. The present study was aimed at identifying potential key genes influencing the mechanisms and consequences of OSA. METHODS: Gene expression profiles associated with OSA were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in subcutaneous adipose tissues from patients with OSA and normal tissues were screened using R software, followed by gene ontology and pathway enrichment analyses. Subsequently, a protein-protein interaction (PPI) network was constructed and hub genes were extracted using Cytoscape plugins. The intersected core genes derived from different topological algorithms were considered hub genes, and the potential candidate gene was selected from them for further analyses of expression variations using another GEO dataset and targeted capture sequencing in 100 subjects (50 with severe OSA and 50 without OSA). RESULTS: A total of 373 DEGs were identified in OSA samples relative to normal controls, which were primarily associated with olfactory transduction and neuroactive ligand-receptor interaction. Upon analyses of nine topological algorithms and available literature, we finally focused on glial cell-derived neurotrophic factor (GDNF) as the candidate gene and validated its low expression in OSA samples. Two rare nonsynonymous variants (p.D56N and p.R93Q) were identified among the 100 cases through targeted sequencing of GDNF, which could be potentially deleterious based on pathogenicity prediction programs; however, no significant association was detected in single nucleotide polymorphisms. CONCLUSION: The present study identified GDNF as a promising candidate gene for OSA and its two rare and potentially deleterious mutations through a combination of bioinformatics and targeted capture sequencing analyses.
BACKGROUND: Obstructive sleep apnea (OSA) is a prevalent chronic disease that increases the risk of cardiovascular disease and metabolic and neuropsychiatric disorders, resulting in a considerable socioeconomic burden. The present study was aimed at identifying potential key genes influencing the mechanisms and consequences of OSA. METHODS: Gene expression profiles associated with OSA were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in subcutaneous adipose tissues from patients with OSA and normal tissues were screened using R software, followed by gene ontology and pathway enrichment analyses. Subsequently, a protein-protein interaction (PPI) network was constructed and hub genes were extracted using Cytoscape plugins. The intersected core genes derived from different topological algorithms were considered hub genes, and the potential candidate gene was selected from them for further analyses of expression variations using another GEO dataset and targeted capture sequencing in 100 subjects (50 with severe OSA and 50 without OSA). RESULTS: A total of 373 DEGs were identified in OSA samples relative to normal controls, which were primarily associated with olfactory transduction and neuroactive ligand-receptor interaction. Upon analyses of nine topological algorithms and available literature, we finally focused on glial cell-derived neurotrophic factor (GDNF) as the candidate gene and validated its low expression in OSA samples. Two rare nonsynonymous variants (p.D56N and p.R93Q) were identified among the 100 cases through targeted sequencing of GDNF, which could be potentially deleterious based on pathogenicity prediction programs; however, no significant association was detected in single nucleotide polymorphisms. CONCLUSION: The present study identified GDNF as a promising candidate gene for OSA and its two rare and potentially deleterious mutations through a combination of bioinformatics and targeted capture sequencing analyses.
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