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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
American Psychiatric Publishing (2013) Diagnostic and statistical manual of mental disorders: DSM-5, 5th edn. American Psychiatric Publishing, Washington DC, pp 50–59. isbn:8123923791
Lai MC, Lombardo MV, Baron-Cohen S (2014) Autism. Lancet 383(9920):896–910
Bauman ML (2010) Medical comorbidities in autism: challenges to diagnosis and treatment. Neurotherapeutics 7(3):320–327
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):1093–1103
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):1–23
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.2018.0001
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):601–613
Willsey AJ, State MW (2015) Autism spectrum disorders: from genes to neurobiology. Curr Opin Neurobiol 30:92–99
Geschwind DH, Levitt P (2007) Autism spectrum disorders: developmental disconnection syndromes. Curr Opin Neurobiol 17(1):103–111
Amaral DG (2017) Examining the causes of Autism Cerebrum 2017. pii: cer-01-17. eCollection 2017 Jan-Feb
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 224:1–25
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
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
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]
Mullins C, Fishell G, Tsien RW (2016) Unifying views of autism spectrum disorders: a consideration of autoregulatory feedback loops. Neuron 89(6):1131–1156
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 12:1669–1686
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):473–476
Masi A, DeMayo MM, Glozier N, Guastella AJ (2017) An overview of autism spectrum disorder, heterogeneity and treatment options. Neurosci Bull 33(2):183–193
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):1239–1247
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]
Sabbagh B, Mindt S, Neumaier M, Findeisen P (2016) Clinical applications of MS-based protein quantification. Proteomics Clin Appl 10(4):323–345
Evans B (2013) How autism became autism: the radical transformation of a central concept of child development in Britain. Hist Human Sci 26(3):3–31
Verhoeff B (2013) Autism in flux: a history of the concept from Leo Kanner to DSM-5. Hist Psychiatry 24(4):442–458
London EB (2014) Categorical diagnosis: a fatal flaw for autism research? Trends Neurosci 37(12):683–686
Müller RA, Amaral DG (2017) Editorial: time to give up on autism spectrum disorder? Autism Res 10(1):10–14
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):789–790
Betancur C (2011) Etiological heterogeneity in autism spectrum disorders: more than 100 genetic and genomic disorders and still counting. Brain Res 1380:42–77
Sztainberg Y, Zoghbi HY (2016) Lessons learned from studying syndromic autism spectrum disorders. Nat Neurosci 19(11):1408–1417
Jones RM, Lord C (2013) Diagnosing autism in neurobiological research studies. Behav Brain Res 251:113–124
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
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):66–81
Kitchen RR, Rozowsky JS, Gerstein MB, Nairn AC (2014) Decoding neuroproteomics: integrating the genome, translatome and functional anatomy. Nat Neurosci 17(11):1491–1499
Hosp F, Mann M (2017) A primer on concepts and applications of proteomics in neuroscience. Neuron 96(3):558–571
Ramadan N, Ghazale H, El-Sayyad M, El-Haress M, Kobeissy FH (2017) Neuroproteomics studies: challenges and updates. Methods Mol Biol 1598:3–19
Fountoulakis M, Hardmeier R, Höger H, Lubec G (2001) Postmortem changes in the level of brain proteins. Exp Neurol 167(1):86–94
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):1276–1291
ElHajj Z, Cachot A, Müller T, Riederer IM, Riederer BM (2016) Effects of postmortem delays on protein composition and oxidation. Brain Res Bull 121:98–104
Banks RE (2008) Preanalytical influences in clinical proteomic studies: raising awareness of fundamental issues in sample banking. Clin Chem 54(1):6–7
Becker KF (2015) Using tissue samples for proteomic studies-critical considerations. Proteomics Clin Appl 9(3–4):257–267
Oberg AL, Vitek O (2009) Statistical design of quantitative mass spectrometry-based proteomic experiments. J Proteome Res 8(5):2144–2156
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-S3
Aebersold R, Mann M (2016) Mass-spectrometric exploration of proteome structure and function. Nature 537(7620):347–355
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.7042.1
Ruderman D (2017) Designing successful proteomics experiments. Methods Mol Biol 1550:271–288
Listgarten J, Emili A (2005) Statistical and computational methods for comparative proteomic profiling using liquid chromatography-tandem mass spectrometry. Mol Cell Proteomics 4(4):419–434
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-S6
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/gkv007
Szoko N, McShane AJ, Natowicz MR (2017) Proteomic explorations of autism spectrum disorder. Autism Res 10(9):1460–1469
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):292–306
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):5327–5332
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):249–255
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):1213–1220
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):443–452
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.2012.19
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):338–350
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):2664–2678
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):2046–2054
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/10.1186/2040-2392-5-4
Suganya V, Geetha A, Sujatha S (2015) Urine proteome analysis to evaluate protein biomarkers in children with autism. Clin Chim Acta 450:210–219
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):1103–1112
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
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/srep46041
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.201700085
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]
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):233–237
Stephan AH, Barres BA, Stevens B (2012) The complement system: an unexpected role in synaptic pruning during development and disease. Annu Rev Neurosci 35:369–389
Presumey J, Bialas AR, Carroll MC (2017) Complement system in neural synapse elimination in development and disease. Adv Immunol 135:53–79
Mead J, Ashwood P (2015) Evidence supporting an altered immune response in ASD. Immunol Lett 163(1):49–55
Meltzer A, Van de Water J (2017) The role of the immune system in autism spectrum disorder. Neuropsychopharmacology 42(1):284–298
Tamiji J, Crawford DA (2010) The neurobiology of lipid metabolism in autism spectrum disorders. Neurosignals 18(2):98–112
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/nu9020155
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):11–17
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/10.1186/2040-2392-5-41
Aebersold R, Burlingame AL, Bradshaw RA (2013) Western blots versus selected reaction monitoring assays: time to turn the tables? Mol Cell Proteomics 12(9):2381–2382
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–R189
Wang K, Huang C, Nice E (2014) Recent advances in proteomics: towards the human proteome. Biomed Chromatogr 28(6):848–857
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):1747–1758
Rigbolt KTG, Blagoev B (2012) Quantitative phosphoproteomics to characterize signaling networks. Semin Cell Dev Biol 23(8):863–8671
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):558–568
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
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
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 312:26–34
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):1199–1203
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):426–444
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):15281–15286
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
Sabidó E, Selevsek N, Aebersold R (2012) Mass spectrometry-based proteomics for systems biology. Curr Opin Biotechnol 23(4):591–597
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Abraham, J., Szoko, N., Natowicz, M.R. (2019). Proteomic Investigations of Autism Spectrum Disorder: Past Findings, Current Challenges, and Future Prospects. In: Guest, P. (eds) Reviews on Biomarker Studies in Psychiatric and Neurodegenerative Disorders. Advances in Experimental Medicine and Biology(), vol 1118. Springer, Cham. https://doi.org/10.1007/978-3-030-05542-4_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-05542-4_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05541-7
Online ISBN: 978-3-030-05542-4
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)