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2000
Volume 21, Issue 5
  • ISSN: 1570-1646
  • E-ISSN: 1875-6247

Abstract

Background

Sleep apnea is a significant health impediment, and it presently lacks efficacious therapeutic interventions. Thus, it is imperative to discover novel therapeutic targets that could guide clinical treatment strategies.

Objective

This study aims to utilize an integrative analytic approach to unearth previously unappreciated protein-encoding genes implicated in sleep apnea susceptibility.

Methods

Through the Multi-Marker Analysis of Genomic Annotation (MAGMA), we aligned Single-Nucleotide Polymorphism (SNP) summary statistics from Genome-Wide Association Studies (GWAS) of gene bodies to discern potential risk genes. Following the MAGMA results, we conducted a round of Transcriptome-Wide Association Studies (TWAS) and Proteome-Wide Association Studies (PWAS) to expedite the conversion of genetic associations into probable protein targets. Mendelian Randomization (MR) and co-localization analysis were employed to ascertain the causal linkage between the candidate target genes and sleep apnea. Finally, a mediation analysis was undertaken to explore the possible intermediary role of 150 inflammatory metabolites and 1,124 proteins.

Results

The MAGNA analysis revealed 2,819 genes in association with sleep apnea. TWAS and PWAS analyses indicated that cis-regulation of nine particular genes could play a role in sleep apnea onset blood protein level alterations. MR and co-localization analyses also suggested a causal relationship with sleep apnea for three genes (ACADVL, CCDC134, UPP1). Consistent associations were found between genetically predicted biomarkers and Albumin, HCC-1, N-acetyl carnosine, and RELT, pointing towards their potential mediating roles in sleep apnea's etiological pathway.

Conclusion

Our findings indicate that ACADVL, CCDC134, and UPP1 genes are potentially significant targets for further functional investigation and therapeutic interventions for sleep apnea.

© 2024 Bentham Science Publishers
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2025-11-01

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References

  1. BenjafieldA.V. AyasN.T. EastwoodP.R. HeinzerR. IpM.S.M. MorrellM.J. NunezC.M. PatelS.R. PenzelT. PépinJ.L. PeppardP.E. SinhaS. TufikS. ValentineK. MalhotraA. Estimation of the global prevalence and burden of obstructive sleep apnoea: A literature-based analysis.Lancet Respir. Med.20197868769810.1016/S2213‑2600(19)30198‑531300334
     [Google Scholar]
  2. The AASM International Classification of Sleep Disorders – Third Edition, Text Revision (ICSD-3-TR).2005Available from: https://aasm.org/clinical-resources/international-classification-sleep-disorders/
  3. MihaicutaS. UdrescuL. UdrescuM. TothI.A. TopîrceanuA PleavăR. ArdeleanC. Analyzing Neck Circumference as an Indicator of CPAP Treatment Response in Obstructive Sleep Apnea with Network Medicine.Diagnostics (Basel)20211118610.3390/diagnostics11010086.
     [Google Scholar]
  4. ParatiG. LombardiC. NarkiewiczK. Sleep apnea: Epidemiology, pathophysiology, and relation to cardiovascular risk.Am. J. Physiol. Regul. Integr. Comp. Physiol.20072934R1671R168310.1152/ajpregu.00400.200717652356
     [Google Scholar]
  5. PunjabiN.M. CaffoB.S. GoodwinJ.L. GottliebD.J. NewmanA.B. O’ConnorG.T. RapoportD.M. RedlineS. ResnickH.E. RobbinsJ.A. ShaharE. UnruhM.L. SametJ.M. Sleep-disordered breathing and mortality: A prospective cohort study.PLoS Med.200968e100013210.1371/journal.pmed.100013219688045
     [Google Scholar]
  6. SharmaK. SchmittS. BergnerC.G. TyanovaS. KannaiyanN. Manrique-HoyosN. KongiK. CantutiL. HanischU.K. PhilipsM.A. RossnerM.J. MannM. SimonsM. Cell type– and brain region–resolved mouse brain proteome.Nat. Neurosci.201518121819183110.1038/nn.416026523646
     [Google Scholar]
  7. VogelC. MarcotteE.M. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses.Nat. Rev. Genet.201213422723210.1038/nrg318522411467
     [Google Scholar]
  8. RollandD.C.M. BasrurV. JeonY.K. McNeil-SchwalmC. FerminD. ConlonK.P. ZhouY. NgS.Y. TsouC.C. BrownN.A. ThomasD.G. BaileyN.G. OmennG.S. NesvizhskiiA.I. RootD.E. WeinstockD.M. FaryabiR.B. LimM.S. Elenitoba-JohnsonK.S.J. Functional proteogenomics reveals biomarkers and therapeutic targets in lymphomas.Proc. Natl. Acad. Sci. USA2017114256581658610.1073/pnas.170126311428607076
     [Google Scholar]
  9. PatelS.R. GoodloeR. DeG. KowgierM. WengJ. BuxbaumS.G. CadeB. FulopT. GharibS.A. GottliebD.J. HillmanD. LarkinE.K. LauderdaleD.S. LiL. MukherjeeS. PalmerL. ZeeP. ZhuX. RedlineS. Association of genetic loci with sleep apnea in European Americans and African-Americans: The Candidate Gene Association Resource (CARe).PLoS One2012711e4883610.1371/journal.pone.004883623155414
     [Google Scholar]
  10. WingoA.P. DammerE.B. BreenM.S. LogsdonB.A. DuongD.M. TroncoscoJ.C. ThambisettyM. BeachT.G. SerranoG.E. ReimanE.M. CaselliR.J. LahJ.J. SeyfriedN.T. LeveyA.I. WingoT.S. Large-scale proteomic analysis of human brain identifies proteins associated with cognitive trajectory in advanced age.Nat. Commun.2019101161910.1038/s41467‑019‑09613‑z30962425
     [Google Scholar]
  11. OuY.N. YangY.X. DengY.T. ZhangC. HuH. WuB.S. LiuY. WangY.J. ZhuY. SucklingJ. TanL. YuJ.T. Identification of novel drug targets for Alzheimer’s disease by integrating genetics and proteomes from brain and blood.Mol. Psychiatry202126106065607310.1038/s41380‑021‑01251‑634381170
     [Google Scholar]
  12. ZhangC. QinF. LiX. DuX. LiT. Identification of novel proteins for lacunar stroke by integrating genome-wide association data and human brain proteomes.BMC Med.202220121110.1186/s12916‑022‑02408‑y35733147
     [Google Scholar]
  13. HumerE. PiehC. BrandmayrG. Metabolomics in Sleep, Insomnia and Sleep Apnea.Int. J. Mol. Sci.20202119724410.3390/ijms2119724433008070
     [Google Scholar]
  14. XiaY. FuY. XuH. GuanJ. YiH. YinS. Changes in cerebral metabolites in obstructive sleep apnea: A systemic review and meta-analysis.Sci. Rep.2016612871210.1038/srep2871227349417
     [Google Scholar]
  15. BolboacăS.D. RoşcaD.D. JäntschiL. Structure-activity relationships from natural evolution.Match (Mulh.)201471149172
     [Google Scholar]
  16. FinnGen: An expedition into genomics and medicine.Available from: https://www.finngen.fi/en
  17. ZhangJ. DuttaD. KöttgenA. TinA. SchlosserP. GramsM.E. HarveyB. YuB. BoerwinkleE. CoreshJ. ChatterjeeN. Plasma proteome analyses in individuals of European and African ancestry identify cis-pQTLs and models for proteome-wide association studies.Nat. Genet.202254559360210.1038/s41588‑022‑01051‑w35501419
     [Google Scholar]
  18. VõsaU. ClaringbouldA. WestraH.J. BonderM.J. DeelenP. ZengB. KirstenH. SahaA. KreuzhuberR. YazarS. BruggeH. OelenR. de VriesD.H. van der WijstM.G.P. KaselaS. PervjakovaN. AlvesI. FavéM.J. AgbessiM. ChristiansenM.W. JansenR. SeppäläI. TongL. TeumerA. SchrammK. HemaniG. VerlouwJ. YaghootkarH. Sönmez FlitmanR. BrownA. KukushkinaV. KalnapenkisA. RüegerS. PorcuE. KronbergJ. KettunenJ. LeeB. ZhangF. QiT. HernandezJ.A. ArindrartoW. BeutnerF. ’t HoenP.A.C. van MeursJ. van DongenJ. van ItersonM. SwertzM.A. Jan BonderM. DmitrievaJ. ElansaryM. FairfaxB.P. GeorgesM. HeijmansB.T. HewittA.W. KähönenM. KimY. KnightJ.C. KovacsP. KrohnK. LiS. LoefflerM. MarigortaU.M. MeiH. MomozawaY. Müller-NurasyidM. NauckM. NivardM.G. PenninxB.W.J.H. PritchardJ.K. RaitakariO.T. RotzschkeO. SlagboomE.P. StehouwerC.D.A. StumvollM. SullivanP. ’t HoenP.A.C. ThieryJ. TönjesA. van DongenJ. van ItersonM. VeldinkJ.H. VölkerU. WarmerdamR. WijmengaC. SwertzM. AndiappanA. MontgomeryG.W. RipattiS. PerolaM. KutalikZ. DermitzakisE. BergmannS. FraylingT. van MeursJ. ProkischH. AhsanH. PierceB.L. LehtimäkiT. BoomsmaD.I. PsatyB.M. GharibS.A. AwadallaP. MilaniL. OuwehandW.H. DownesK. StegleO. BattleA. VisscherP.M. YangJ. ScholzM. PowellJ. GibsonG. EskoT. FrankeL. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression.Nat. Genet.20215391300131010.1038/s41588‑021‑00913‑z34475573
     [Google Scholar]
  19. SunB.B. MaranvilleJ.C. PetersJ.E. StaceyD. StaleyJ.R. BlackshawJ. BurgessS. JiangT. PaigeE. SurendranP. Oliver-WilliamsC. KamatM.A. PrinsB.P. WilcoxS.K. ZimmermanE.S. ChiA. BansalN. SpainS.L. WoodA.M. MorrellN.W. BradleyJ.R. JanjicN. RobertsD.J. OuwehandW.H. ToddJ.A. SoranzoN. SuhreK. PaulD.S. FoxC.S. PlengeR.M. DaneshJ. RunzH. ButterworthA.S. Genomic atlas of the human plasma proteome.Nature20185587708737910.1038/s41586‑018‑0175‑229875488
     [Google Scholar]
  20. SuhreK. ArnoldM. BhagwatA.M. CottonR.J. EngelkeR. RafflerJ. SarwathH. TharejaG. WahlA. DeLisleR.K. GoldL. PezerM. LaucG. El-Din SelimM.A. Mook-KanamoriD.O. Al-DousE.K. MohamoudY.A. MalekJ. StrauchK. GrallertH. PetersA. KastenmüllerG. GiegerC. GraumannJ. Connecting genetic risk to disease end points through the human blood plasma proteome.Nat. Commun.2017811435710.1038/ncomms1435728240269
     [Google Scholar]
  21. FolkersenL. FaumanE. Sabater-LlealM. StrawbridgeR.J. FrånbergM. SennbladB. BaldassarreD. VegliaF. HumphriesS.E. RauramaaR. de FaireU. SmitA.J. GiralP. KurlS. MannarinoE. EnrothS. JohanssonÅ. EnrothS.B. GustafssonS. LindL. LindgrenC. MorrisA.P. GiedraitisV. SilveiraA. Franco-CerecedaA. TremoliE. GyllenstenU. IngelssonE. BrunakS. ErikssonP. ZiemekD. HamstenA. MälarstigA. Mapping of 79 loci for 83 plasma protein biomarkers in cardiovascular disease.PLoS Genet.2017134e100670610.1371/journal.pgen.100670628369058
     [Google Scholar]
  22. ShinS.Y. FaumanE.B. PetersenA.K. KrumsiekJ. SantosR. HuangJ. ArnoldM. ErteI. ForgettaV. YangT.P. WalterK. MenniC. ChenL. VasquezL. ValdesA.M. HydeC.L. WangV. ZiemekD. RobertsP. XiL. GrundbergE. WaldenbergerM. RichardsJ.B. MohneyR.P. MilburnM.V. JohnS.L. TrimmerJ. TheisF.J. OveringtonJ.P. SuhreK. BrosnanM.J. GiegerC. KastenmüllerG. SpectorT.D. SoranzoN. An atlas of genetic influences on human blood metabolites.Nat. Genet.201446654355010.1038/ng.298224816252
     [Google Scholar]
  23. RoedererM. QuayeL. ManginoM. BeddallM.H. MahnkeY. ChattopadhyayP. TosiI. NapolitanoL. Terranova BarberioM. MenniC. VillanovaF. Di MeglioP. SpectorT.D. NestleF.O. The genetic architecture of the human immune system: A bioresource for autoimmunity and disease pathogenesis.Cell2015161238740310.1016/j.cell.2015.02.04625772697
     [Google Scholar]
  24. KettunenJ. DemirkanA. WürtzP. DraismaH.H.M. HallerT. RawalR. VaarhorstA. KangasA.J. LyytikäinenL.P. PirinenM. PoolR. SarinA.P. SoininenP. TukiainenT. WangQ. TiainenM. TynkkynenT. AminN. ZellerT. BeekmanM. DeelenJ. van DijkK.W. EskoT. HottengaJ.J. van LeeuwenE.M. LehtimäkiT. MihailovE. RoseR.J. de CraenA.J.M. GiegerC. KähönenM. PerolaM. BlankenbergS. SavolainenM.J. VerhoevenA. ViikariJ. WillemsenG. BoomsmaD.I. van DuijnC.M. ErikssonJ. JulaA. JärvelinM.R. KaprioJ. MetspaluA. RaitakariO. SalomaaV. SlagboomP.E. WaldenbergerM. RipattiS. Ala-KorpelaM. Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA.Nat. Commun.2016711112210.1038/ncomms1112227005778
     [Google Scholar]
  25. GroenewoudD. ShyeA. ElkonR. Incorporating regulatory interactions into gene-set analyses for GWAS data: A controlled analysis with the MAGMA tool.PLOS Comput. Biol.2022183e100990810.1371/journal.pcbi.100990835316269
     [Google Scholar]
  26. GusevA. KoA. ShiH. BhatiaG. ChungW. PenninxB.W.J.H. JansenR. de GeusE.J.C. BoomsmaD.I. WrightF.A. SullivanP.F. NikkolaE. AlvarezM. CivelekM. LusisA.J. LehtimäkiT. RaitoharjuE. KähönenM. SeppäläI. RaitakariO.T. KuusistoJ. LaaksoM. PriceA.L. PajukantaP. PasaniucB. Integrative approaches for large-scale transcriptome-wide association studies.Nat. Genet.201648324525210.1038/ng.350626854917
     [Google Scholar]
  27. ZhuZ. ZhangF. HuH. BakshiA. RobinsonM.R. PowellJ.E. MontgomeryG.W. GoddardM.E. WrayN.R. VisscherP.M. YangJ. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets.Nat. Genet.201648548148710.1038/ng.353827019110
     [Google Scholar]
  28. DaviesN.M. HolmesM.V. Davey SmithG. Reading Mendelian randomisation studies: A guide, glossary, and checklist for clinicians.BMJ2018362k60110.1136/bmj.k60130002074
     [Google Scholar]
  29. YangJ. FerreiraT. MorrisA.P. MedlandS.E. MaddenP.A. HeathA.C. MarinN.G. MontgomeryG.W. WeedonM.N. LoosR.J. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits.Nat Genet201244436975S1310.1038/ng.2213
     [Google Scholar]
  30. WandersR.J.A. KomenJ. KempS. Fatty acid omega-oxidation as a rescue pathway for fatty acid oxidation disorders in humans.FEBS J.2011278218219410.1111/j.1742‑4658.2010.07947.x21156023
     [Google Scholar]
  31. CalderP.C. Mechanisms of action of (n-3) fatty acids.J. Nutr.20121423592S599S10.3945/jn.111.15525922279140
     [Google Scholar]
  32. FerrucciL. CherubiniA. BandinelliS. BartaliB. CorsiA. LauretaniF. MartinA. Andres-LacuevaC. SeninU. GuralnikJ.M. Relationship of plasma polyunsaturated fatty acids to circulating inflammatory markers.J. Clin. Endocrinol. Metab.200691243944610.1210/jc.2005‑130316234304
     [Google Scholar]
  33. FanJ. ShiS. QiuY. LiuM. ShuQ. Analysis of signature genes and association with immune cells infiltration in pediatric septic shock.Front. Immunol.202213105675010.3389/fimmu.2022.105675036439140
     [Google Scholar]
  34. ZhouR. DangX. SpragueR.S. MustafaS.J. ZhouZ. Alteration of purinergic signaling in diabetes: Focus on vascular function.J. Mol. Cell. Cardiol.20201401910.1016/j.yjmcc.2020.02.00432057736
     [Google Scholar]
  35. HuangJ. XiaoL. GongX. ShaoW. YinY. LiaoQ. MengY. ZhangY. MaD. QiuX. Cytokine-like molecule CCDC134 contributes to CD8⁺ T-cell effector functions in cancer immunotherapy.Cancer Res.201474205734574510.1158/0008‑5472.CAN‑13‑313225125657
     [Google Scholar]
  36. Schulz-KnappeP. MägertH.J. DewaldB. MeyerM. CetinY. KubbiesM. TomeczkowskiJ. KirchhoffK. RaidaM. AdermannK. KistA. ReineckeM. SillardR. PardigolA. UguccioniM. BaggioliniM. ForssmannW.G. HCC-1, a novel chemokine from human plasma.J. Exp. Med.1996183129529910.1084/jem.183.1.2958551235
     [Google Scholar]
  37. LeawC.L. RenE.C. ChoongM.L. Hcc-1 is a novel component of the nuclear matrix with growth inhibitory function.Cell. Mol. Life Sci.200461172264227310.1007/s00018‑004‑4205‑x15338056
     [Google Scholar]
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Identification of Potential Drug Targets for Sleep Apnea Through Mendelian Randomization
Curr. Proteomics 21, 476 (2024); https://doi.org/10.2174/0115701646330324241001102706
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  • Article Type:     Research Article
Keyword(s): mediation analysis; mendelian randomization; multi-marker analysis of genomic annotation analysis; proteome-wide association studies; Sleep apnea; transcriptome-wide association study

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