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Obstructive Sleep Apnea and Dementia-Common Gene Associations Through Network-Based Identification of Common Driver Genes

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Obstructive Sleep Apnea and Dementia-Common Gene Associations Through Network-Based Identification of Common Driver Genes G C A T T A C G G C A T genes Article Obstructive Sleep Apnea and Dementia-Common Gene Associations through Network-Based Identification of Common Driver Genes Hyun-Hwan Jeong 1,*,†,‡, Arvind Chandrakantan 2,3,*,† and Adam C. Adler 2,3 1 Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA 2 Department of Anesthesiology & Pediatrics, Texas Children’s Hospital, Houston, TX 77030, USA; [email protected] 3 Department of Anesthesiology, Baylor College of Medicine, Houston, TX 77030, USA * Correspondence: [email protected] (H.-H.J.); [email protected] (A.C.) † These two authors contributed equally as first authors. ‡ Present address: School of Biomedical Informatics, UT Health Science Center at Houston, Houston, TX 77030, USA. Abstract: Background: Obstructive Sleep Apnea (OSA) occurs in 7% of the adult population. The relationship between neurodegenerative diseases such as dementia and sleep disorders have long attracted clinical attention; however, no comprehensive data exists elucidating common gene expres- sion between the two diseases. The objective of this study was to (1) demonstrate the practicability and feasibility of utilizing a systems biology approach called network-based identification of common driver genes (NICD) to identify common genomic features between two associated diseases and (2) Citation: Jeong, H.-H.; utilize this approach to identify genes associated with both OSA and dementia. Methods: This study Chandrakantan, A.; Adler, A.C. utilized 2 public databases (PCNet, DisGeNET) and a permutation assay in order to identify common Obstructive Sleep Apnea and Dementia-Common Gene genes between two co-morbid but mutually exclusive diseases. These genes were then linked to Associations through Network-Based their mechanistic pathways through Enrichr, producing a list of genes that were common between Identification of Common Driver the two different diseases. Results: 42 common genes were identified between OSA and dementia Genes. Genes 2021, 12, 542. which were primarily linked to the G-coupled protein receptor (GPCR) and olfactory pathways. No https://doi.org/10.3390/genes single nucleotide polymorphisms (SNPs) were identified. Conclusions: This study demonstrates the 12040542 viability of using publicly available databases and permutation assays along with canonical pathway linkage to identify common gene drivers as potential mechanistic targets for comorbid diseases. Academic Editor: Mariarosa Anna Beatrice Melone Keywords: Obstructive Sleep Apnea; driver genes; Alzheimer’s disease Received: 23 March 2021 Accepted: 6 April 2021 Published: 9 April 2021 1. Introduction Publisher’s Note: MDPI stays neutral Obstructive Sleep Apnea (OSA) occurs in 7% of the adult population and up to 7.5% of with regard to jurisdictional claims in the pediatric population. Clinically, OSA is characterized by intermittent apnea/hypopnea, published maps and institutional affil- arousals, and sleep fragmentation; however, the clinical symptoms vary greatly between iations. patients (Figure1). OSA is a common heritable disorder, but a few genetic loci and risk genes are reported from the previous studies. For example, Chen et al. performed a multiethnic meta genome-wide association analysis and found a possible association between the apnea-hypopnea index and rs12936587, which is on chromosome 17 and Copyright: © 2021 by the authors. is overlapped with RAI1 (Retinoic Acid Induced 1) [1]. OSA has been correlated with Licensee MDPI, Basel, Switzerland. symptomatology and disease expression in adults with dementia and is associated with This article is an open access article several neurological diseases including stroke, as well as neurocognitive and behavioral distributed under the terms and symptoms. Untreated OSA in the adult population has been shown to accelerate dementia conditions of the Creative Commons progression while treatment with continuous positive airway pressure (CPAP) has been Attribution (CC BY) license (https:// shown to slow disease progression to varying degrees [2,3]. creativecommons.org/licenses/by/ 4.0/). Genes 2021, 12, 542. https://doi.org/10.3390/genes12040542 https://www.mdpi.com/journal/genes Genes 2021, 12, x FOR PEER REVIEW 2 of 12 toms. Untreated OSA in the adult population has been shown to accelerate dementia pro- Genes 2021, 12, 542 2 of 11 gression while treatment with continuous positive airway pressure (CPAP) has been shown to slow disease progression to varying degrees [2,3]. Figure 1. DescribesDescribes the the variety variety of of pathophysiological pathophysiological changes changes within within OSA OSA leading leading to dementia. to dementia. Given the number of overlapping processes,processes, therethere areare aa varietyvariety ofof contributorycontributory mechanisms mechanisms leading lead- toing or to aggravating or aggravating underlying underlying dementia. dementia. TIA: TIA: Transient Transient ischemic ischemic attack. attack. The figureThe figure adapted adapted from [4], withfrom permission[4], with permission from Elsevier, from 2016.Elsevier, 2016. However, demonstration of causality is difficult,difficult, as dementia in the absence of OSA is knownknown toto causecause abnormalitiesabnormalities inin sleepsleep structure.structure. Furthermore,Furthermore, sleepsleep fragmentationfragmentation isis known to be an early feature of Alzheimer’s disease and is independently associated with disease progression progression and and intensity intensity [5 []5. ].There Therefore,fore, as astreatment treatment of OSA of OSA may may impact impact the pro- the progressiongression of dementia, of dementia, it is it isimportant important to to delineate delineate common common gene gene drivers drivers between these seemingly interrelated disease processes. These would provide clinical biomarkers whichwhich could be potential therapeutic targets. To date,date, characterizingcharacterizing commoncommon genomicgenomic featuresfeatures between the two, despite several studies independently identifying lociloci ofof disease in both groups, has not been elucidated [[1,1,6].]. Therefore, we designed a systems biology approach to identify common genes associatedassociated with both dementia and OSA.OSA. ThisThis approachapproach usesuses network-basednetwork-based identification identification of of common common driver driver (NICD) (NICD) genes genes of ofmultiple multiple phenotypes phenotypes or ordisease disease-associated-associated phenotypes phenotypes utilizing utilizing a sequence a sequence of techniques of techniques involving involving bioinformat- bioinfor- matics,ics, public public biomedical biomedical databa databases,ses, and andcomputational computational biology. biology. Specifically, Specifically, NICD NICD uses uses the theParsimonious Parsimonious Composite Composite Network Network (PCNet), (PCNet), which which is a iscomposite a composite network network created created by by combining 21 gene–gene interaction networks such as STRING [7], GIANT [8], and ConsensusPathDB [9], and is focused on reducing the false positives of novel disease gene identification [10] and DisGeNET, which is a database which identifies associations between diseases utilizing computational mining techniques classified by evidence [11] to identify the common genes that are associated with multiple diseases. Genes 2021, 12, x FOR PEER REVIEW 3 of 12 combining 21 gene–gene interaction networks such as STRING [7], GIANT [8], and Con- sensusPathDB [9], and is focused on reducing the false positives of novel disease gene Genes 2021, 12, 542 identification [10] and DisGeNET, which is a database which identifies associations be-3 of 11 tween diseases utilizing computational mining techniques classified by evidence [11] to identify the common genes that are associated with multiple diseases. TheThe aim aim of ofthis this study study was was to to identify identify driver driver genes genes that contribute toto diseasedisease pathogen-patho- genesisesis for for dementia dementia and and OSA. OSA. 2. Methods2. Methods TheThe preparation preparation and and validation validation of ofNICD NICD was was a multistep a multistep process process (Figure (Figure 2).2 ). Figure 2. This figure demonstrates the network-based identification of common driver genes (NICD) workflow, with the Figurespecific 2. This data figure sets and demonstrates programs usedthe network construct-based the algorithm. identification In the of topmostcommon panel,driver thegenes diseases (NICD and) workflow, genes are with identified. the specific data sets and programs used construct the algorithm. In the topmost panel, the diseases and genes are identified. These are then placed into PCNet, which is a composite gene network focused on common gene identification for query. In These are then placed into PCNet, which is a composite gene network focused on common gene identification for query. order to remove those genes which maybe randomly associated, a permutation assay with Benjamini–Hoichberg correction In order to remove those genes which maybe randomly associated, a permutation assay with Benjamini–Hoichberg cor- rectionis done is done to ensure to ensure that that proper proper candidates candidates demonstrate demonstrate strength strength of association. of association. In theIn the bottom-most bottom-most panel, panel, an an enrichment enrich- mentassay assay is done is done on theon the candidate candidate genes genes which which tests tests their their association association
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