Abstract
Autism spectrum disorders (ASD) are neurodevelopmental disorders. Genetic factors, along with non-genetic triggers, have been shown to play a causative role. Despite the various causes, a triad of common symptoms defines individuals with ASD; pervasive social impairments, impaired social communication, and repeated sensory-motor behaviors. Therefore, it can be hypothesized that different genetic and environmental factors converge on a single hypothetical neurobiological process that determines these behaviors. However, the cellular and subcellular signature of this process is, so far, not well understood. Here, we performed a comparative study using “omics” approaches to identify altered proteins and, thereby, biological processes affected in ASD. In this study, we mined publicly available repositories for genetic mouse model data sets, identifying six that were suitable, and compared them with in-house derived proteomics data from prenatal zinc (Zn)-deficient mice, a non-genetic mouse model with ASD-like behavior. Findings derived from these comparisons were further validated using in vitro neuronal cell culture models for ASD. We could show that a protein network, centered on VAMP2, STX1A, RAB3A, CPLX2, and AKAP5, is a key convergence point mediating synaptic vesicle release and recycling, a process affected across all analyzed models. Moreover, we demonstrated that Zn availability has predictable functional effects on synaptic vesicle release in line with the alteration of proteins in this network. In addition, drugs that target kinases, reported to regulate key proteins in this network, similarly impacted the proteins’ levels and distribution. We conclude that altered synaptic stability and plasticity through abnormal synaptic vesicle dynamics and function may be the common neurobiological denominator of the shared behavioral abnormalities in ASD and, therefore, a prime drug target for developing therapeutic strategies.
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The data sets and analysis pipelines generated for this study are available from the corresponding author upon reasonable request.
References
American Psychiatric Association (2013), American Psychiatric Association. DSM-5 Task Force. Diagnostic and statistical manual of mental disorders: DSM-5. American Psychiatric Association. p 947
Nakanishi M, Anderson MP, Takumi T (2019) Recent genetic and functional insights in autism spectrum disorder. Curr Opin Neurol 32(4):627–634
Masini E, Loi E, Vega-Benedetti AF, Carta M, Doneddu G, Fadda R et al (2020) An overview of the main genetic, epigenetic and environmental factors involved in autism spectrum disorder focusing on synaptic activity. Int J Mol Sci 21(21):1–22
Grabrucker AM (2013) Environmental factors in autism. Front Psychiatry 18(3):118. https://doi.org/10.3389/fpsyt.2012.00118
Sierra-Arregui T, Llorente J, Giménez Minguez P, Tønnesen J, Peñagarikano O (2020) Neurobiological mechanisms of autism spectrum disorder and epilepsy, insights from animal models. Neuroscience 445:69–82
Möhrle D, Fernández M, Peñagarikano O, Frick A, Allman B, Schmid S (2020) What we can learn from a genetic rodent model about autism. Neurosci Biobehav Rev 109:29–53
Won H, Mah W, Kim E (2013) Autism spectrum disorder causes, mechanisms, and treatments: focus on neuronal synapses. Front Mol Neurosci 5(6):19. https://doi.org/10.3389/fnmol.2013.00019 (PMID: 23935565; PMCID: PMC3733014)
Brielmaier J, Matteson PG, Silverman JL, Senerth JM, Kelly S, Genestine M et al (2012) Autism-relevant social abnormalities and cognitive deficits in engrailed-2 knockout mice. PLoS One 7(7):e40914. https://doi.org/10.1371/journal.pone.0040914
Cheh MA, Millonig JH, Roselli LM, Ming X, Jacobsen E, Kamdar S et al (2006) En2 knockout mice display neurobehavioral and neurochemical alterations relevant to autism spectrum disorder. Brain Res 1116(1):166–176. https://doi.org/10.1016/j.brainres.2006.07.086
Horev G, Ellegood J, Lerch JP, Son YEE, Muthuswamy L, Vogel H et al (2011) Dosage-dependent phenotypes in models of 16p11.2 lesions found in autism. Proc Natl Acad Sci USA. https://doi.org/10.1073/pnas.1114042108
Portmann T, Yang M, Mao R, Panagiotakos G, Ellegood J, Dolen G et al (2014) Behavioral abnormalities and circuit defects in the basal ganglia of a mouse model of 16p11.2 deletion syndrome. Cell Rep 7(4):1079–92. https://doi.org/10.1016/j.celrep.2014.03.036
Jiang Y, Ehlers M (2013) Modeling Autism by SHANK gene mutations in mice. Neuron 78(1):8–27. https://doi.org/10.1016/j.neuron.2013.03.016
Schmeisser MJ, Ey E, Wegener S, Bockmann J, Stempel AV, Kuebler A et al (2012) Autistic-like behaviours and hyperactivity in mice lacking ProSAP1/Shank2. Nature 486(7402):256–260. https://doi.org/10.1038/nature11015
Kazdoba TM, Leach PT, Silverman JL, Crawley JN (2014) Modeling fragile X syndrome in the Fmr1 knockout mouse. Intractable Rare Dis Res 3(4):118–133. https://doi.org/10.5582/irdr.2014.01024
Liu J, Nyholt DR, Magnussen P, Parano E, Pavone P, Geschwind D et al (2001) A genomewide screen for autism susceptibility loci the autism genetic resource exchange consortium. Am J Hum Genet 69:327–340. https://doi.org/10.1086/321980
Loke YJ, Hannan AJ, Craig JM (2015) The role of epigenetic change in autism spectrum disorders. Front Neurol 6:107. https://doi.org/10.3389/fneur.2015.00107
Rein B, Yan Z (2020) 16p112 Copy number variations and neurodevelopmental disorders. Trends Neurosci 43(11):886–901. https://doi.org/10.1016/j.tins.2020.09.001
Leblond CS, Nava C, Polge A, Gauthier J, Huguet G, Lumbroso S et al (2014) Meta-analysis of SHANK mutations in autism spectrum disorders: a gradient of severity in cognitive impairments. PLoS Genet. https://doi.org/10.1371/journal.pgen.1004580
Grabrucker AM, Schmeisser MJ, Schoen M, Boeckers TM (2011) Postsynaptic ProSAP/Shank scaffolds in the cross-hair of synaptopathies. Trends Cell Biol 21(10):594–603. https://doi.org/10.1016/j.tcb.2011.07.003
Schmeisser MJ (2015) Translational neurobiology in Shank mutant mice—model systems for neuropsychiatric disorders. Ann Anat 200:115–117. https://doi.org/10.1016/j.aanat.2015.03.006
Kaufmann WE, Kidd SA, Andrews HF, Budimirovic DB, Esler A, Haas-Givler B, Stackhouse T, Riley C, Peacock G, Sherman SL, Brown WT, Berry-Kravis E (2017) Autism spectrum disorder in fragile X syndrome: cooccurring conditions and current treatment. Pediatrics 139(Suppl 3):S194–S206. https://doi.org/10.1542/peds.2016-1159F
Bernardet M, Crusio WE (2006) Fmr1 KO mice as a possible model of autistic features. Sci World J 6:1164–1176. https://doi.org/10.1100/tsw.2006.220
Chaliha D, Albrecht M, Vaccarezza M, Takechi R, Lam V, Al-Salami H et al (2020) A systematic review of the valproic-acid-induced rodent model of autism. Dev Neurosci 42(1):12–48. https://doi.org/10.1159/000509109
Careaga M, Murai T, Bauman MD (2017) Maternal immune activation and autism spectrum disorder: from rodents to nonhuman and human primates. Biol Psychiatry 81(5):391–401. https://doi.org/10.1016/j.biopsych.2016.10.020
Grabrucker S, Boeckers TM, Grabrucker AM (2016) Gender dependent evaluation of autism like behavior in mice exposed to prenatal zinc deficiency. Front Behav Neurosci 10:37. https://doi.org/10.3389/fnbeh.2016.00037
Grabrucker S, Jannetti L, Eckert M, Gaub S, Chhabra R, Pfaender S et al (2014) Zinc deficiency dysregulates the synaptic ProSAP/Shank scaffold and might contribute to autism spectrum disorders. Brain 137(1):137–152. https://doi.org/10.1093/brain/awt303
Grabrucker S, Haderspeck JC, Sauer AK, Kittelberger N, Asoglu H, Abaei A et al (2018) Brain lateralization in mice is associated with zinc signaling and altered in prenatal zinc deficient mice that display features of autism spectrum disorder. Front Mol Neurosci 10:450. https://doi.org/10.3389/fnmol.2017.00450
Cezar LC, Kirsten TB, da Fonseca CCN, de Lima APN, Bernardi MM, Felicio LF (2018) Zinc as a therapy in a rat model of autism prenatally induced by valproic acid. Prog Neuro-Psychopharmacology Biol Psychiatry 84:173–180. https://doi.org/10.1016/j.pnpbp.2018.02.008
Grabrucker AM (2020) Biometals in Autism Spectrum. Academic Press/Elsevier, Amsterdam. https://doi.org/10.1016/C2019-0-01808-0
Sgadò P, Provenzano G, Dassi E, Adami V, Zunino G, Genovesi S et al (2013) Transcriptome profiling in engrailed-2 mutant mice reveals common molecular pathways associated with autism spectrum disorders. Mol Autism 4(1):51. https://doi.org/10.1186/2040-2392-4-51
Lim CS, Kim H, Yu NK, Kang SJ, Kim TH, Ko HG et al (2017) Enhancing inhibitory synaptic function reverses spatial memory deficits in Shank2 mutant mice. Neuropharmacology 112:104–112. https://doi.org/10.1016/j.neuropharm.2016.08.016
Reim D, Distler U, Halbedl S, Verpelli C, Sala C, Bockmann J et al (2017) Proteomic analysis of post-synaptic density fractions from Shank3 mutant mice reveals brain region specific changes relevant to autism spectrum disorder. Front Mol Neurosci 10:26. https://doi.org/10.3389/fnmol.2017.00026
Prilutsky D, Kho AT, Palmer NP, Bhakar AL, Smedemark-Margulies N, Kong SW et al (2015) Gene expression analysis in Fmr1KO mice identifies an immunological signature in brain tissue and mGluR5-related signaling in primary neuronal cultures. Mol Autism 6:66
Lanz TA, Guilmette E, Gosink MM, Fischer JE, Fitzgerald LW, Stephenson DT et al (2013) Transcriptomic analysis of genetically defined autism candidate genes reveals common mechanisms of action. Mol Autism 4(1):45. https://doi.org/10.1186/2040-2392-4-45
Tyanova S, Temu T, Sinitcyn P, Carlson A, Hein MY, Geiger T et al (2016) The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat Methods 13(9):731–740. https://doi.org/10.1038/nmeth.3901
Eisenberg E, Levanon EY (2013) Human housekeeping genes, revisited. Trends Genet 29(10):569–574. https://doi.org/10.1016/j.tig.2013.05.010
Smith TC, Frank E (2016) Introducing machine learning concepts with WEKA. In: Mathé E, Davis S (eds) Statistical genomics methods in molecular biology, vol 1418. Humana Press, New York. https://doi.org/10.1007/978-1-4939-3578-9_17
Kotthoff L, Thornton C, Hoos HH, Hutter F, Leyton-Brown K (2016) Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA. J Machine Learn Res 17:1–5 Available from: http://automl.org/autoweka
Maere S, Heymans K, Kuiper M (2005) BiNGO: A cytoscape plugin to assess overrepresentation of gene ontology categories in biological Networks. Bioinformatics 21(16):3448–3449. https://doi.org/10.1093/bioinformatics/bti551
Mi H, Muruganujan A, Casagrande JT, Thomas PD (2013) Large-scale gene function analysis with the panther classification system. Nat Protoc 8(8):1551–1566. https://doi.org/10.1038/nprot.2013.092
Merico D, Isserlin R, Stueker O, Emili A, Bader GD (2010) Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS One 5(11):e13984. https://doi.org/10.1371/journal.pone.0013984
Frederickson CJ, Suh SW, Koh JY, Cha YK, Thompson RB, LaBuda CJ, Balaji RV, Cuajungco MP (2002) Depletion of intracellular zinc from neurons by use of an extracellular chelator in vivo and in vitro. J Histochem Cytochem 50(12):1659–1662. https://doi.org/10.1177/002215540205001210
Zukin S, Jover T, Yokota H, Calderone A, Simionescu M, Lau G (2004) Chapter 42—molecular and cellular mechanisms of ischemia-induced neuronal death. In: Mohr JP, Choi DW, Grotta JC, Weir B, Wolf PA (eds) Stroke, 4th edn. Churchill Livingstone, London, pp 829–854
Takeda A, Minami A, Seki Y, Oku N (2004) Differential effects of zinc on glutamatergic and GABAergic neurotransmitter systems in the hippocampus. J Neurosci Res 75(2):225–229. https://doi.org/10.1002/jnr.10846
Frederickson CJ, Giblin LJ, Balaji RV, Rengarajan B, Masalha R, Frederickson CJ, Zeng Y, Lopez EV, Koh JY, Chorin U, Besser L, Hershfinkel M, Li Y, Thompson RB, Krezel A (2006) Synaptic release of zinc from brain slices: factors governing release, imaging, and accurate calculation of concentration. J Neurosci Methods 154(1–2):19–29. https://doi.org/10.1016/j.jneumeth.2005.11.014
Bozym RA, Chimienti F, Giblin LJ, Gross GW, Korichneva I, Li Y, Libert S, Maret W, Parviz M, Frederickson CJ, Thompson RB (2010) Free zinc ions outside a narrow concentration range are toxic to a variety of cells in vitro. Exp Biol Med (Maywood) 235(6):741–750. https://doi.org/10.1258/ebm.2010.009258
Vogt K, Mellor J, Tong G, Nicoll R (2000) The actions of synaptically released zinc at hippocampal mossy fiber synapses. Neuron 26(1):187–196. https://doi.org/10.1016/s0896-6273(00)81149-6
Kamentsky L, Jones TR, Fraser A, Bray MA, Logan DJ, Madden KL et al (2011) Improved structure, function and compatibility for cellprofiler: Modular high-throughput image analysis software. Bioinformatics 27(8):1179–1180. https://doi.org/10.1093/bioinformatics/btr095
Nieland TJF, Logan DJ, Saulnier J, Lam D, Johnson C, Root DE et al (2014) High content image analysis identifies novel regulators of synaptogenesis in a high-throughput RNAi screen of primary neurons. PLoS ONE 9(3):e91744. https://doi.org/10.1371/journal.pone.0091744
Augustin H, McGourty K, Steinert JR, Cochemé HM, Adcott J, Cabecinha M et al (2017) Myostatin-like proteins regulate synaptic function and neuronal morphology. Dev 144(13):2445–2455. https://doi.org/10.1242/dev.152975
Szatmari P, Paterson AD, Zwaigenbaum L, Roberts W, Brian J, Liu XQ et al (2007) Mapping autism risk loci using genetic linkage and chromosomal rearrangements. Nat Genet 39(3):319–328. https://doi.org/10.1038/ng1985
Bölte S, Girdler S, Marschik PB (2019) The contribution of environmental exposure to the etiology of autism spectrum disorder. Cell Mol Life Sci 76(7):1275–1297. https://doi.org/10.1007/s00018-018-2988-4
Geschwind DH (2011) Genetics of autism spectrum disorders. Trends Cogn Sci 15(9):409–416. https://doi.org/10.1016/j.tics.2011.07.003
Schaaf CP, Betancur C, Yuen RKC, Parr JR, Skuse DH, Gallagher L et al (2020) A framework for an evidence-based gene list relevant to autism spectrum disorder. Nat Rev Genet 21(6):367–376. https://doi.org/10.1038/s41576-020-0231-2
Yoo H (2015) Genetics of autism spectrum disorder: current status and possible clinical applications. Exp Neurobiol 24(4):257–272. https://doi.org/10.5607/en.2015.24.4.257
Jęśko H, Cieślik M, Gromadzka G, Adamczyk A (2020) Dysfunctional proteins in neuropsychiatric disorders: from neurodegeneration to autism spectrum disorders. Neurochem Int 141:104853. https://doi.org/10.1016/j.neuint.2020.104853
Kern JK, Geier DA, Sykes LK, Geier MR (2013) Evidence of neurodegeneration in autism spectrum disorder. Transl Neurodegener. https://doi.org/10.1186/2047-9158-2-17
Kern JK, Geier DA, King PG, Sykes LK, Mehta JA, Geier MR (2015) Shared brain connectivity issues, symptoms, and comorbidities in autism spectrum disorder, attention deficit/hyperactivity disorder, and tourette syndrome. Brain Connect 5(6):321–335. https://doi.org/10.1089/brain.2014.0324
Richter S, Gorny X, Marco-Pallares J, Krämer UM, Machts J, Barman A et al (2011) A potential role for a genetic variation of AKAP5 in human aggression and anger control. Front Hum Neurosci 5:175. https://doi.org/10.3389/fnhum.2011.00175
Antonucci F, Corradini I, Fossati G, Tomasoni R, Menna E, Matteoli M (2016) SNAP-25, a known presynaptic protein with emerging postsynaptic functions. Front Synaptic Neurosci 8:7. https://doi.org/10.3389/fnsyn.2016.00007
Weisenhaus M, Allen ML, Yang L, Lu Y, Nichols CB, Su T et al (2017) Mutations in AKAP5 disrupt dendritic signaling complexes and lead to electrophysiological and behavioral phenotypes in mice. PLoS One 5(4):e10325. https://doi.org/10.1371/journal.pone.0010325
Ribrault C, Reingruber J, Petković M, Galli T, Ziv NE, Holcman D, Triller A (2011) Syntaxin1A lateral diffusion reveals transient and local SNARE interactions. J Neurosci 31(48):17590–17602. https://doi.org/10.1523/JNEUROSCI.4065-11.2011
Grabrucker AM, Knight MJ, Proepper C, Bockmann J, Joubert M, Rowan M, Nienhaus GU, Garner CC, Bowie JU, Kreutz MR, Gundelfinger ED, Boeckers TM (2011) Concerted action of zinc and ProSAP/Shank in synaptogenesis and synapse maturation. EMBO J 30(3):569–581. https://doi.org/10.1038/emboj.2010.336
Park AJ, Havekes R, Choi JHK, Luczak V, Nie T, Huang T et al (2014) A presynaptic role for PKA in synaptic tagging and memory. Neurobiol Learn Mem 114:101–112. https://doi.org/10.1016/j.nlm.2014.05.005
Horev G, Ellegood J, Lerch JP, Son YEE, Muthuswamy L, Vogel H et al (2011) Dosage-dependent phenotypes in models of 16p11.2 lesions found in autism. Proc Natl Acad Sci USA 108(41):17076–81. https://doi.org/10.1073/pnas.1114042108
Nakaya HI (2021) Bioinformatics. Exon Publications, Brisbane. https://doi.org/10.36255/exonpublications.bioinformatics.2021
Misra BB, Langefeld C, Olivier M, Cox LA (2019) Integrated omics: tools, advances and future approaches. J Mol Endocrinol 62(1):R21-45. https://doi.org/10.1530/JME-18-0055
Parmeggiani A (2014) Gastrointestinal disorders and autism. In: Patel V, Preedy V, Martin C (eds) Comprehensive Guide to Autism. Springer, New York. https://doi.org/10.1007/978-1-4614-4788-7_122
Lee M, Krishnamurthy J, Susi A, Sullivan C, Gorman GH, Hisle-Gorman E et al (2018) Association of autism spectrum disorders and inflammatory bowel disease. J Autism Dev Disord 48(5):1523–1529. https://doi.org/10.1007/s10803-017-3409-5
De Theije CGM, Koelink PJ, Korte-Bouws GAH, Lopes da Silva S, Korte SM, Olivier B et al (2014) Intestinal inflammation in a murine model of autism spectrum disorders. Brain Behav Immun 37:240–247. https://doi.org/10.1016/j.bbi.2013.12.004
White JF (2003) Intestinal pathophysiology in autism. Exp Biol Med 228(6):639–649. https://doi.org/10.1177/153537020322800601
Poelmans G, Franke B, Pauls DL, Glennon JC, Buitelaar JK (2013) AKAPs integrate genetic findings for autism spectrum disorders. Transl Psychiatry 3(6):e270. https://doi.org/10.1038/tp.2013.48
Grabrucker A, Vaida B, Bockmann J, Boeckers TM (2009) Synaptogenesis of hippocampal neurons in primary cell culture. Cell Tissue Res 338(3):333–341. https://doi.org/10.1007/s00441-009-0881-z
Grabrucker AM, Rowan M, Garner CC (2011) Brain-delivery of zinc-ions as potential treatment for neurological diseases: mini review. Drug Deliv Lett 1(1):13–23. https://doi.org/10.2174/2210303111101010013
Acknowledgements
The Autism Research Institute funds AMG. The authors would like to acknowledge networking support by the COST Action TD1304. SM and SEH are funded by the Irish Research Council (IRC) postgraduate grants; GOIPG/2019/3693 and ESPG/2020/88, respectively.
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The Autism Research Institute funds AMG. SM and SEH are funded by the Irish Research Council (IRC) postgraduate grants; GOIPG/2019/3693 and ESPG/2020/88, respectively.
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SM, AKS, AMG, and KMcG contributed to the study conception and design. Material preparation, data collection, and analysis were performed by SM, AKS, MF, AMG, and KMcG. AMG and KMcG wrote the first draft of the manuscript, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Supplementary file1 Immunoblot quantification of AKAP5 A AKAP5 protein immunoblot analysis using Control and PZD mouse hippocampal brain lysates. Data are normalized to the housekeeping protein ACTIN and shown as mean ± SEM. A significantly higher AKAP5 protein expression was found in PZD brain lysates (t test; p ≤ 0.001). B Exemplary WB bands. (TIF 17729 KB)
18_2022_4617_MOESM2_ESM.tif
Supplementary file2 Detection and quantification of neuronal synapses and pre-synaptic assemblies Rat hippocampal neurons were grown either in full growth media (CTL) for 14 DIV or changed into growth media additionally supplemented with 50 µM CaEDTA for 14 DIV (CaEDTA) prior to measurement by fluorescent microscopy and downstream image analysis. A (i) Microscopy image quantification of the median number of synapses (marked by co-localizing homer and synaptophysin punctae) or pre-synaptic assemblies (marked by AKAP5 and SNAP25 punctae) per neuronal cell in CaEDTA conditions normalized to control conditions. (ii) Microscopy image quantification of the median number of punctae of homer, synatophysin, AKAP5 or SNAP25 per neuronal cell in CaEDTA conditions normalized to control conditions. B Proportional fluorescence intensity density plot analysis in synapses (Homer&Synatophysin) and pre-synaptic assemblies (AKAP5&SNAP25) in neurons cultured under control conditions or supplemented with CaEDTA C) Quantification of indicated proteins through fluorescence intensity analysis in synapses (Homer&Synatophysin) and pre-synaptic assemblies (AKAP5&SNAP25) in neurons cultured in control or CaEDTA conditions. A minimum of 80,000 synapse/pre-synaptic assemblies per condition per experiment were detected in control conditions and a minimum of 50,000 in CaEDTA conditions. Data were aggregated across experimental repeats (n = 3) and represent the geometric mean of each marker within a synapse (Homer&Synatophysin) /pre-synaptic (AKAP5&SNAP25) assembly identified through image analysis. Data were analysed by student’s t tests comparisons of two independent groups: *P<0.05; **P<0.01; ***P<0.001; n.s. not significant (TIF 54577 KB)
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Malijauskaite, S., Sauer, A.K., Hickey, S.E. et al. Identification of the common neurobiological process disturbed in genetic and non-genetic models for autism spectrum disorders. Cell. Mol. Life Sci. 79, 589 (2022). https://doi.org/10.1007/s00018-022-04617-3
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DOI: https://doi.org/10.1007/s00018-022-04617-3