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Proteomic Investigations of Autism Spectrum Disorder: Past Findings, Current Challenges, and Future Prospects

  • Joseph Abraham
  • Nicholas Szoko
  • Marvin R. NatowiczEmail author
Chapter
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 1118)

Abstract

Proteomics is a powerful tool to study biological systems and is potentially useful in identifying biomarkers for clinical screening and diagnosis, for monitoring treatment, and for exploring pathogenetic mechanisms in autism. Unlike numerous other experimental approaches employed in autism research, there have been few proteomic-based analyses. Herein, we discuss the findings of studies regarding autism that utilized a proteomic approach and review key considerations in sample acquisition, processing, and analysis. Most proteomic studies on autism used blood or other peripheral tissues. Few studies used brain tissue, the main site of biological difference between persons with autism and others. The findings have varied and are not yet replicated. Some showed abnormalities of synaptic proteins or proteins of mitochondrial bioenergetics. Various abnormalities of proteins relating to immune processes and lipid metabolism have also been noted. Whether any of the proteomic differences between autism and control cases are primary or secondary phenomena is currently unclear. Consequently, no definitive biomarkers for autism have been identified, and the pathophysiological insights provided by proteomic studies to date are uncertain in the absence of replication. Based on this body of work and the challenges in using proteomics to study autism, we suggest considerations for future study design. These include attention to subject and specimen inclusion/exclusion criteria, attention to the state of specimens prior to proteomic analysis, and use of a replicate set of specimens. We end by discussing especially promising applications of proteomics in the study of autism pathobiology.

Keywords

Autism ASD Proteomic Proteomics Mass spectrometry Neuroproteomics 

References

  1. 1.
    American Psychiatric Publishing (2013) Diagnostic and statistical manual of mental disorders: DSM-5, 5th edn. American Psychiatric Publishing, Washington DC, pp 50–59. isbn:8123923791Google Scholar
  2. 2.
    Lai MC, Lombardo MV, Baron-Cohen S (2014) Autism. Lancet 383(9920):896–910PubMedGoogle Scholar
  3. 3.
    Bauman ML (2010) Medical comorbidities in autism: challenges to diagnosis and treatment. Neurotherapeutics 7(3):320–327PubMedPubMedCentralGoogle Scholar
  4. 4.
    Muskens JB, Velders FP, Staal WG (2017) Medical comorbidities in children and adolescents with autism spectrum disorders and attention deficit hyperactivity disorders: a systematic review. Eur Child Adolesc Psychiatry 26(9):1093–1103PubMedPubMedCentralGoogle Scholar
  5. 5.
    Baio J, Wiggins L, Christensen DL, Maenner MJ, Daniels J, Warren Z et al (2018) Prevalence of autism spectrum disorder among children aged 8 years—autism and developmental disabilities monitoring network, 11 sites, United States, 2014. MMWR Surveill Summ 67(6):1–23PubMedPubMedCentralGoogle Scholar
  6. 6.
    Xu G, Strathearn L, Liu B, Bao W (2018) Corrected prevalence of autism spectrum disorder among US children and adolescents. JAMA 319(5):505. https://doi.org/10.1001/jama.2018.0001PubMedGoogle Scholar
  7. 7.
    Baxter AJ, Brugha TS, Erskine HE, Scheurer RW, Vos T, Scott JG (2015) The epidemiology and global burden of autism spectrum disorders. Psychol Med 45(3):601–613PubMedGoogle Scholar
  8. 8.
    Willsey AJ, State MW (2015) Autism spectrum disorders: from genes to neurobiology. Curr Opin Neurobiol 30:92–99PubMedGoogle Scholar
  9. 9.
    Geschwind DH, Levitt P (2007) Autism spectrum disorders: developmental disconnection syndromes. Curr Opin Neurobiol 17(1):103–111PubMedGoogle Scholar
  10. 10.
    Amaral DG (2017) Examining the causes of Autism Cerebrum 2017. pii: cer-01-17. eCollection 2017 Jan-FebGoogle Scholar
  11. 11.
    Kleijer KTE, Huguet G, Tastet J, Bourgeron T, Burbach JPH (2017) Anatomy and cell biology of autism spectrum disorder: lessons from human genetics. Adv Anat Embryol Cell Biol 224:1–25PubMedGoogle Scholar
  12. 12.
    Forsberg SL, Ilieva M, Maria Michel T (2018) Epigenetics and cerebral organoids: promising directions in autism spectrum disorders. Transl Psychiatry 8(1):14. https://doi.org/10.1038/s41398-017-0062-x
  13. 13.
    Andrews SV, Ellis SE, Bakulski KM, Sheppard B, Croen LA, Hertz-Picciotto I et al (2017) Cross-tissue integration of genetic and epigenetic data offers insight into autism spectrum disorder. Nat Commun 8(1):1011. https://doi.org/10.1038/s41467-017-00868-y
  14. 14.
    Courchesne E, Pramparo T, Gazestani VH, Lombardo MV, Pierce K, Lewis NE (2018) The ASD living biology: from cell proliferation to clinical phenotype. Mol Psychiatry. https://doi.org/10.1038/s41380-018-0056-y. [Epub ahead of print]PubMedPubMedCentralGoogle Scholar
  15. 15.
    Mullins C, Fishell G, Tsien RW (2016) Unifying views of autism spectrum disorders: a consideration of autoregulatory feedback loops. Neuron 89(6):1131–1156PubMedPubMedCentralGoogle Scholar
  16. 16.
    Murphy CM, Wilson CE, Robertson DM, Ecker C, Daly EM, Hammond N et al (2016) Autism spectrum disorder in adults: diagnosis, management, and health services development. Neuropsychiatr Dis Treat 12:1669–1686PubMedPubMedCentralGoogle Scholar
  17. 17.
    Durkin MS, Elsabbagh M, Barbaro J, Gladstone M, Happe F, Hoekstra RA et al (2015) Autism screening and diagnosis in low resource settings: challenges and opportunities to enhance research and services worldwide. Autism Res 8(5):473–476PubMedPubMedCentralGoogle Scholar
  18. 18.
    Masi A, DeMayo MM, Glozier N, Guastella AJ (2017) An overview of autism spectrum disorder, heterogeneity and treatment options. Neurosci Bull 33(2):183–193PubMedPubMedCentralGoogle Scholar
  19. 19.
    Vrana JA, Theis JD, Dasari S, Mereuta OM, Dispenzieri A, Zeldenrust SR et al (2014) Clinical diagnosis and typing of systemic amyloidosis in subcutaneous fat aspirates by mass spectrometry-based proteomics. Haematologica 99(7):1239–1247PubMedPubMedCentralGoogle Scholar
  20. 20.
    Belczacka I, Latosinska A, Metzger J, Marx D, Vlahou A, Mischak H et al (2018) Proteomics biomarkers for solid tumors: current status and future prospects. Mass Spectrom Rev. https://doi.org/10.1002/mas.21572. [Epub ahead of print]PubMedGoogle Scholar
  21. 21.
    Sabbagh B, Mindt S, Neumaier M, Findeisen P (2016) Clinical applications of MS-based protein quantification. Proteomics Clin Appl 10(4):323–345PubMedGoogle Scholar
  22. 22.
    Evans B (2013) How autism became autism: the radical transformation of a central concept of child development in Britain. Hist Human Sci 26(3):3–31PubMedPubMedCentralGoogle Scholar
  23. 23.
    Verhoeff B (2013) Autism in flux: a history of the concept from Leo Kanner to DSM-5. Hist Psychiatry 24(4):442–458PubMedGoogle Scholar
  24. 24.
    London EB (2014) Categorical diagnosis: a fatal flaw for autism research? Trends Neurosci 37(12):683–686PubMedGoogle Scholar
  25. 25.
    Müller RA, Amaral DG (2017) Editorial: time to give up on autism spectrum disorder? Autism Res 10(1):10–14PubMedGoogle Scholar
  26. 26.
    Battaglia A (2007) On the selection of patients with developmental delay/mental retardation and autism spectrum disorders for genetic studies. Am J Med Genet 143A(8):789–790PubMedGoogle Scholar
  27. 27.
    Betancur C (2011) Etiological heterogeneity in autism spectrum disorders: more than 100 genetic and genomic disorders and still counting. Brain Res 1380:42–77PubMedGoogle Scholar
  28. 28.
    Sztainberg Y, Zoghbi HY (2016) Lessons learned from studying syndromic autism spectrum disorders. Nat Neurosci 19(11):1408–1417PubMedPubMedCentralGoogle Scholar
  29. 29.
    Jones RM, Lord C (2013) Diagnosing autism in neurobiological research studies. Behav Brain Res 251:113–124PubMedGoogle Scholar
  30. 30.
    Sarkis GA, Mangaonkar MD, Moghieb A, Lelling B, Guertin M, Yadikar H et al (2017) The application of proteomics to traumatic brain and spinal cord injuries. Curr Neurol Neurosci Rep 17(3):23. https://doi.org/10.1007/s11910-017-0736-z
  31. 31.
    Shao G, Wang Y, Guan S, Burlingame AL, Lu F, Knox R et al (2017) Proteomic analysis of mouse cortex postsynaptic density following neonatal brain hypoxia-ischemia. Dev Neurosci 39(1–4):66–81PubMedPubMedCentralGoogle Scholar
  32. 32.
    Kitchen RR, Rozowsky JS, Gerstein MB, Nairn AC (2014) Decoding neuroproteomics: integrating the genome, translatome and functional anatomy. Nat Neurosci 17(11):1491–1499PubMedPubMedCentralGoogle Scholar
  33. 33.
    Hosp F, Mann M (2017) A primer on concepts and applications of proteomics in neuroscience. Neuron 96(3):558–571PubMedGoogle Scholar
  34. 34.
    Ramadan N, Ghazale H, El-Sayyad M, El-Haress M, Kobeissy FH (2017) Neuroproteomics studies: challenges and updates. Methods Mol Biol 1598:3–19PubMedGoogle Scholar
  35. 35.
    Fountoulakis M, Hardmeier R, Höger H, Lubec G (2001) Postmortem changes in the level of brain proteins. Exp Neurol 167(1):86–94PubMedGoogle Scholar
  36. 36.
    Crecelius A, Götz A, Arzberger T, Fröhlich T, Arnold GJ, Ferrer I et al (2008) Assessing quantitative post-mortem changes in the gray matter of the human frontal cortex proteome by 2-D DIGE. Proteomics 8(6):1276–1291PubMedGoogle Scholar
  37. 37.
    ElHajj Z, Cachot A, Müller T, Riederer IM, Riederer BM (2016) Effects of postmortem delays on protein composition and oxidation. Brain Res Bull 121:98–104PubMedGoogle Scholar
  38. 38.
    Banks RE (2008) Preanalytical influences in clinical proteomic studies: raising awareness of fundamental issues in sample banking. Clin Chem 54(1):6–7PubMedGoogle Scholar
  39. 39.
    Becker KF (2015) Using tissue samples for proteomic studies-critical considerations. Proteomics Clin Appl 9(3–4):257–267PubMedGoogle Scholar
  40. 40.
    Oberg AL, Vitek O (2009) Statistical design of quantitative mass spectrometry-based proteomic experiments. J Proteome Res 8(5):2144–2156PubMedGoogle Scholar
  41. 41.
    Schmidt A, Forne I, Imhof A (2014) Bioinformatic analysis of proteomics data. BMC Syst Biol 8(Suppl 2):S3. https://doi.org/10.1186/1752-0509-8-S2-S3PubMedPubMedCentralGoogle Scholar
  42. 42.
    Aebersold R, Mann M (2016) Mass-spectrometric exploration of proteome structure and function. Nature 537(7620):347–355PubMedGoogle Scholar
  43. 43.
    Hu A, Noble WS, Wolf-Yadlin A (2016) Technical advances in proteomics: new developments in data-independent acquisition. F1000Res 5. pii: F1000 Faculty Rev-419. https://doi.org/10.12688/f1000research.7042.1Google Scholar
  44. 44.
    Ruderman D (2017) Designing successful proteomics experiments. Methods Mol Biol 1550:271–288PubMedGoogle Scholar
  45. 45.
    Listgarten J, Emili A (2005) Statistical and computational methods for comparative proteomic profiling using liquid chromatography-tandem mass spectrometry. Mol Cell Proteomics 4(4):419–434PubMedGoogle Scholar
  46. 46.
    Clough T, Thaminy S, Ragg S, Aebersold R, Vitek O (2012) Statistical protein quantification and significance analysis in label-free LC-MS experiments with complex designs. BMC Bioinformatics 13(Suppl 16):S6. https://doi.org/10.1186/1471-2105-13-S16-S6PubMedPubMedCentralGoogle Scholar
  47. 47.
    Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W et al (2015) Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43(7):e47. https://doi.org/10.1093/nar/gkv007PubMedPubMedCentralGoogle Scholar
  48. 48.
    Szoko N, McShane AJ, Natowicz MR (2017) Proteomic explorations of autism spectrum disorder. Autism Res 10(9):1460–1469PubMedGoogle Scholar
  49. 49.
    Corbett BA, Kantor AB, Schulman H, Walker WL, Lit L, Ashwood P et al (2007) A proteomic study of serum from children with autism showing differential expression of apolipoproteins and complement proteins. Mol Psychiatry 12(3):292–306PubMedGoogle Scholar
  50. 50.
    Castagnola M, Messana I, Inzitari R, Fanali C, Cabras T, Morelli A et al (2008) Hypo-phosphorylation of salivary peptidome as a clue to the molecular pathogenesis of autism spectrum disorders. J Proteome Res 7(12):5327–5332PubMedGoogle Scholar
  51. 51.
    Taurines R, Dudley E, Conner AC, Grassl J, Jans T, Guderian F et al (2010) Serum protein profiling and proteomics in autistic spectrum disorder using magnetic bead-assisted mass spectrometry. Eur Arch Psychiatry Clin Neurosci 260(3):249–255PubMedGoogle Scholar
  52. 52.
    Schwarz E, Guest PC, Rahmoune H, Wang L, Levin Y, Ingudomnukul E et al (2011) Sex-specific serum biomarker patterns in adults with Asperger’s syndrome. Mol Psychiatry 16(12):1213–1220PubMedGoogle Scholar
  53. 53.
    Shen C, Zhao Xl JW, Zou XB, Huo LR, Yan W et al (2011) A proteomic investigation of B lymphocytes in an autistic family: a pilot study of exposure to natural rubber latex (NRL) may lead to autism. J Mol Neurosci 43(3):443–452PubMedGoogle Scholar
  54. 54.
    Momeni N, Bergquist J, Brudin L, Behnia F, Sivberg B, Joghataei MT et al (2012) A novel blood-based biomarker for detection of autism spectrum disorders. Transl Psychiatry 2:e91. https://doi.org/10.1038/tp.2012.19PubMedPubMedCentralGoogle Scholar
  55. 55.
    Ngounou Wetie AG, Wormwood KL, Russell S, Ryan JP, Darie CC, Woods AG (2015) A pilot proteomic analysis of salivary biomarkers in autism spectrum disorder. Autism Res 8(3):338–350PubMedGoogle Scholar
  56. 56.
    Ngounou Wetie AG, Wormwood KL, Charette L, Ryan JP, Woods AG, Darie CC (2015) Comparative two-dimensional polyacrylamide gel electrophoresis of the salivary proteome of children with autism spectrum disorder. J Cell Mol Med 19(11):2664–2678PubMedPubMedCentralGoogle Scholar
  57. 57.
    Ngounou Wetie AG, Wormwood K, Thome J, Dudley E, Taurines R, Gerlach M et al (2014) A pilot proteomic study of protein markers in autism spectrum disorder. Electrophoresis 35(14):2046–2054PubMedGoogle Scholar
  58. 58.
    Steeb H, Ramsey JM, Guest PC, Stocki P, Cooper JD, Rahmoune H et al (2014) Serum proteomic analysis identifies sex-specific differences in lipid metabolism and inflammation profiles in adults diagnosed with Asperger syndrome. Mol Autism 5(1):4. https://doi.org/10.1186/2040-2392-5-4PubMedPubMedCentralGoogle Scholar
  59. 59.
    Suganya V, Geetha A, Sujatha S (2015) Urine proteome analysis to evaluate protein biomarkers in children with autism. Clin Chim Acta 450:210–219PubMedGoogle Scholar
  60. 60.
    Cortelazzo A, De Felice C, Guerranti R, Signorini C, Leoncini S, Zollo G et al (2016) Expression and oxidative modifications of plasma proteins in autism spectrum disorders: interplay between inflammatory response and lipid peroxidation. Proteomics Clin Appl 10(11):1103–1112PubMedGoogle Scholar
  61. 61.
    Feng C, Chen Y, Pan J, Yang A, Niu L, Min J et al (2017) Redox proteomic identification of carbonylated proteins in autism plasma: insight into oxidative stress and its related biomarkers in autism. Clin Proteomics 14:2. https://doi.org/10.1186/s12014-017-9138-0
  62. 62.
    Qin Y, Chen Y, Yang J, Wu F, Zhao L, Yang F et al (2017) Serum glycopattern and Maackia amurensis lectin-II binding glycoproteins in autism spectrum disorder. Sci Rep 7:46041. https://doi.org/10.1038/srep46041PubMedPubMedCentralGoogle Scholar
  63. 63.
    Shen L, Zhang K, Feng C, Chen Y, Li S, Iqbal J et al (2018) Itraq-based proteomic analysis reveals protein profile in plasma from children with autism. Proteomics Clin 12(3):e1700085. https://doi.org/10.1002/prca.201700085Google Scholar
  64. 64.
    Yang J, Chen Y, Xiong X, Zhou X, Han L, Ni L et al (2018) Peptidome analysis reveals novel serum biomarkers for children with autism spectrum disorder in China. Proteomics Clin Appl 13:e1700164. https://doi.org/10.1002/prca.201700164. [Epub ahead of print]Google Scholar
  65. 65.
    Chen YN, Du HY, Shi ZY, He L, He YY, Wang D (2018) Serum proteomic profiling for autism using magnetic bead-assisted matrix-assisted laser desorption ionization time-of-flight mass spectrometry: a pilot study. World J Pediatr 14(3):233–237PubMedGoogle Scholar
  66. 66.
    Stephan AH, Barres BA, Stevens B (2012) The complement system: an unexpected role in synaptic pruning during development and disease. Annu Rev Neurosci 35:369–389PubMedGoogle Scholar
  67. 67.
    Presumey J, Bialas AR, Carroll MC (2017) Complement system in neural synapse elimination in development and disease. Adv Immunol 135:53–79PubMedGoogle Scholar
  68. 68.
    Mead J, Ashwood P (2015) Evidence supporting an altered immune response in ASD. Immunol Lett 163(1):49–55PubMedGoogle Scholar
  69. 69.
    Meltzer A, Van de Water J (2017) The role of the immune system in autism spectrum disorder. Neuropsychopharmacology 42(1):284–298PubMedGoogle Scholar
  70. 70.
    Tamiji J, Crawford DA (2010) The neurobiology of lipid metabolism in autism spectrum disorders. Neurosignals 18(2):98–112PubMedGoogle Scholar
  71. 71.
    Mazahery H, Stonehouse W, Delshad M, Kruger MC, Conlon CA, Beck KL et al (2017) Relationship between long chain n-3 polyunsaturated fatty acids and autism spectrum disorder: systematic review and meta-analysis of case-control and randomised controlled trials. Nutrients 9(2). pii: E155. https://doi.org/10.3390/nu9020155PubMedCentralGoogle Scholar
  72. 72.
    Junaid MA, Kowal D, Barua M, Pullarkat PS, Sklower Brooks S, Pullarkat RK (2004) Proteomic studies identified a single nucleotide polymorphism in glyoxalase I as autism susceptibility factor. Am J Med Genet A 131(1):11–17PubMedPubMedCentralGoogle Scholar
  73. 73.
    Broek JA, Guest PC, Rahmoune H, Bahn S (2014) Proteomic analysis of post mortem brain tissue from autism patients: evidence for opposite changes in prefrontal cortex and cerebellum in synaptic connectivity-related proteins. Mol Autism 5:41. https://doi.org/10.1186/2040-2392-5-41PubMedPubMedCentralGoogle Scholar
  74. 74.
    Aebersold R, Burlingame AL, Bradshaw RA (2013) Western blots versus selected reaction monitoring assays: time to turn the tables? Mol Cell Proteomics 12(9):2381–2382PubMedPubMedCentralGoogle Scholar
  75. 75.
    Cayer DM, Nazor KL, Schork NJ (2016) Mission critical: the need for proteomics in the era of next-generation sequencing and precision medicine. Hum Mol Genet 25(R2):R182–R189PubMedGoogle Scholar
  76. 76.
    Wang K, Huang C, Nice E (2014) Recent advances in proteomics: towards the human proteome. Biomed Chromatogr 28(6):848–857PubMedGoogle Scholar
  77. 77.
    Schubert KO, Weiland F, Baune BT, Hoffmann P (2016) The use of MALDI-MSI in the investigation of psychiatric and neurodegenerative disorders: a review. Proteomics 16(11–12):1747–1758PubMedGoogle Scholar
  78. 78.
    Rigbolt KTG, Blagoev B (2012) Quantitative phosphoproteomics to characterize signaling networks. Semin Cell Dev Biol 23(8):863–8671PubMedGoogle Scholar
  79. 79.
    Ilieva M, Fex Svenningsen Å, Thorsen M, Michel TM (2018) Psychiatry in a dish: stem cells and brain organoids modeling autism spectrum disorders. Biol Psychiatry 83(7):558–568PubMedGoogle Scholar
  80. 80.
    Wang P, Mokhtari R, Pedrosa E, Kirschenbaum M, Bayrak C, Zheng D et al (2017) Crispr/cas9-mediated heterozygous knockout of the autism gene CHD8 and characterization of its transcriptional networks in cerebral organoids derived from IPS cells. Mol Autism 8:11. https://doi.org/10.1186/s13229-017-0124-1
  81. 81.
    Daimon CM, Jasien JM, Wood WH, Zhang Y, Becker KG, Silverman JL et al (2015) Hippocampal transcriptomic and proteomic alterations in the BTBR mouse model of autism spectrum disorder. Front Physiol 6:324. https://doi.org/10.3389/fphys.2015.00324
  82. 82.
    Wei H, Ma Y, Liu J, Ding C, Hu F, Yu L (2016) Proteomic analysis of cortical brain tissue from the BTBRmouse model of autism: evidence for changes in STOP and myelin-related proteins. Neuroscience 312:26–34PubMedGoogle Scholar
  83. 83.
    Bidinosti M, Botta P, Krüttner S, Proenca CC, Stoehr N, Bernhard M et al (2016) Clk2 inhibition ameliorates autistic features associated with shank3 deficiency. Science 351(6278):1199–1203PubMedGoogle Scholar
  84. 84.
    Niere F, Namjoshi S, Song E, Dilly GA, Schoenhard G, Zemelman BV et al (2016) Analysis of proteins that rapidly change upon mechanistic/mammalian target of rapamycin complex 1 (mTORC1) repression identifies parkinson protein 7 (PARK7) as a novel protein aberrantly expressed in tuberous sclerosis complex (TSC). Mol Cell Proteomics 15(2):426–444PubMedGoogle Scholar
  85. 85.
    Liao L, Park SK, Xu T, Vanderklish P, Yates JR (2008) Quantitative proteomic analysis of primary neurons reveals diverse changes in synaptic protein content in fmr1 knockout mice. Proc Natl Acad Sci U S A 105(40):15281–15286PubMedPubMedCentralGoogle Scholar
  86. 86.
    Pacheco NL, Heaven MR, Holt LM, Crossman DK, Boggio KJ, Shaffer SA et al (2017) RNA sequencing and proteomics approaches reveal novel deficits in the cortex of MECP2-deficient mice, a model for RETT syndrome. Mol Autism 8:56. https://doi.org/10.1186/s13229-017-0174-4
  87. 87.
    Sabidó E, Selevsek N, Aebersold R (2012) Mass spectrometry-based proteomics for systems biology. Curr Opin Biotechnol 23(4):591–597PubMedGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Joseph Abraham
    • 1
  • Nicholas Szoko
    • 1
  • Marvin R. Natowicz
    • 1
    • 2
    Email author
  1. 1.Cleveland Clinic Lerner College of Medicine of Case Western Reserve UniversityClevelandUSA
  2. 2.Pathology & Laboratory Medicine, Genomic Medicine, Neurology and Pediatrics InstitutesCleveland ClinicClevelandUSA

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