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Part of the book series: Advances in Experimental Medicine and Biology ((PMISB,volume 1118))

Abstract

High comorbidity and complexity have precluded reliable diagnostic assessment and treatment of psychiatric disorders. Impaired molecular interactions may be relevant for underlying mechanisms of psychiatric disorders but by and large remain unknown. With the help of a number of publicly available databases and various technological tools, recent research has filled the paucity of information by generating a novel dataset of psychiatric interactomes. Different technological platforms including yeast two-hybrid screen, co-immunoprecipitation-coupled with mass spectrometry-based proteomics, and transcriptomics have been widely used in combination with cellular and molecular techniques to interrogate the psychiatric interactome. Novel molecular interactions have been identified in association with different psychiatric disorders including autism spectrum disorders, schizophrenia, bipolar disorder, and major depressive disorder. However, more extensive and sophisticated interactome research needs to be conducted to overcome the current limitations such as incomplete interactome databases and a lack of functional information among components. Ultimately, integrated psychiatric interactome databases will contribute to the implementation of biomarkers and therapeutic intervention.

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References

  1. Ryan DP, Matthews JM (2005) Protein-protein interactions in human disease. Curr Opin Struct Biol 15(4):441–446

    Article  CAS  PubMed  Google Scholar 

  2. Stumpf MP, Thorne T, de Silva E, Stewart R, An HJ, Lappe M et al (2008) Estimating the size of the human interactome. Proc Natl Acad Sci U S A 105(19):6959–6964

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Geschwind DH, Flint J (2015) Genetics and genomics of psychiatric disease. Science 349(6255):1489–1494

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014) Biological insights from 108 schizophrenia-associated genetic loci. Nature 511(7510):421–427

    Article  PubMed Central  CAS  Google Scholar 

  5. Wray NR, Ripke S, Mattheisen M, Trzaskowski M, Byrne EM, Abdellaoui A et al (2018) Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat Genet 50(5):668–681

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Mullins N, Ingason A, Porter H, Euesden J, Gillett A, Ólafsson S et al (2017) Reproductive fitness and genetic risk of psychiatric disorders in the general population. Nat Commun 8:15833. https://doi.org/10.1038/ncomms15833

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Wang T, Zhang X, Li A, Zhu M, Liu S, Qin W et al (2017) Polygenic risk for five psychiatric disorders and cross-disorder and disorder-specific neural connectivity in two independent populations. Neuroimage Clin 14:441–449

    Article  PubMed  PubMed Central  Google Scholar 

  8. Buckley PF, Miller BJ, Lehrer DS, Castle DJ (2009) Psychiatric comorbidities and schizophrenia. Schizophr Bull 35(2):383–402

    Article  PubMed  Google Scholar 

  9. Maddox BB, White SW (2015) Comorbid social anxiety disorder in adults with autism spectrum disorder. J Autism Dev Disord 45(12):3949–3960

    Article  PubMed  Google Scholar 

  10. Magnuson KM, Constantino JN (2011) Characterization of depression in children with autism spectrum disorders. J Dev Behav Pediatr 32(4):332–340

    Article  PubMed  PubMed Central  Google Scholar 

  11. Zaboski BA, Storch EA (2018) Comorbid autism spectrum disorder and anxiety disorders: a brief review. Future Neurol 13(1):31–37

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Careaga M, Van de Water J, Ashwood P (2010) Immune dysfunction in autism: a pathway to treatment. Neurotherapeutics 7(3):283–292

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Malki K, Pain O, Tosto MG, Du Rietz E, Carboni L, Schalkwyk LC (2015) Identification of genes and gene pathways associated with major depressive disorder by integrative brain analysis of rat and human prefrontal cortex transcriptomes. Transl Psychiatry 5:e519. https://doi.org/10.1038/tp.2015.15

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Nascimento JM, Martins-de-Souza D (2015) The proteome of schizophrenia. NPJ Schizophr 1:14003. https://doi.org/10.1038/npjschz.2014.3

    Article  PubMed  PubMed Central  Google Scholar 

  15. Camargo LM, Collura V, Rain JC, Mizuguchi K, Hermjakob H, Kerrien S et al (2007) Disrupted in Schizophrenia 1 Interactome: evidence for the close connectivity of risk genes and a potential synaptic basis for schizophrenia. Mol Psychiatry 12(1):74–86

    Article  CAS  PubMed  Google Scholar 

  16. Hashimoto R, Numakawa T, Ohnishi T, Kumamaru E, Yagasaki Y, Ishimoto T et al (2006) Impact of the DISC1 Ser704Cys polymorphism on risk for major depression, brain morphology and ERK signaling. Hum Mol Genet 15(20):3024–3033

    Article  CAS  PubMed  Google Scholar 

  17. Kilpinen H, Ylisaukko-Oja T, Hennah W, Palo OM, Varilo T, Vanhala R et al (2008) Association of DISC1 with autism and Asperger syndrome. Mol Psychiatry 13(2):187–196

    Article  CAS  PubMed  Google Scholar 

  18. Thomson PA, Wray NR, Millar JK, Evans KL, Hellard SL, Condie A et al (2005) Association between the TRAX/DISC locus and both bipolar disorder and schizophrenia in the Scottish population. Mol Psychiatry 10(7):657–668, 616

    Article  CAS  PubMed  Google Scholar 

  19. Zhou Y, Dong F, Lanz TA, Reinhart V, Li M, Liu L et al (2018) Interactome analysis reveals ZNF804A, a schizophrenia risk gene, as a novel component of protein translational machinery critical for embryonic neurodevelopment. Mol Psychiatry 23(4):952–962

    Article  CAS  PubMed  Google Scholar 

  20. Sakai Y, Shaw CA, Dawson BC, Dugas DV, Al-Mohtaseb Z, Hill DE et al (2011) Protein interactome reveals converging molecular pathways among autism disorders. Sci Transl Med 3(86):86ra49. https://doi.org/10.1126/scitranslmed.3002166

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Vignoli A, La Briola F, Peron A, Turner K, Vannicola C, Saccani M et al (2015) Autism spectrum disorder in tuberous sclerosis complex: searching for risk markers. Orphanet J Rare Dis 10:154. https://doi.org/10.1186/s13023-015-0371-1

    Article  PubMed  PubMed Central  Google Scholar 

  22. Martins-de-Souza D, Cassoli JS, Nascimento JM, Hensley K, Guest PC, Pinzon-Velasco AM et al (2015) The protein interactome of collapsin response mediator protein-2 (CRMP2/DPYSL2) reveals novel partner proteins in brain tissue. Proteomics Clin Appl 9(9–10):817–831

    Article  CAS  PubMed  Google Scholar 

  23. Liu Y, Pham X, Zhang L, Chen PL, Burzynski G, McGaughey DM et al (2014) Functional variants in DPYSL2 sequence increase risk of schizophrenia and suggest a link to mTOR signaling. G3 (Bethesda) 5(1):61–72

    Article  CAS  Google Scholar 

  24. Nakata K, Ujike H, Sakai A, Takaki M, Imamura T, Tanaka Y et al (2003) The human dihydropyrimidinase-related protein 2 gene on chromosome 8p21 is associated with paranoid-type schizophrenia. Biol Psychiatry 53(7):571–576

    Article  CAS  PubMed  Google Scholar 

  25. Martins-de-Souza D, Gattaz WF, Schmitt A, Maccarrone G, Hunyadi-Gulyás E, Eberlin MN et al (2009) Proteomic analysis of dorsolateral prefrontal cortex indicates the involvement of cytoskeleton, oligodendrocyte, energy metabolism and new potential markers in schizophrenia. J Psychiatr Res 43(11):978–986

    Article  PubMed  Google Scholar 

  26. Martins-de-Souza D, Gattaz WF, Schmitt A, Novello JC, Marangoni S, Turck CW et al (2009) Proteome analysis of schizophrenia patients Wernicke’s area reveals an energy metabolism dysregulation. BMC Psychiatry 9:17. https://doi.org/10.1186/1471-244X-9-17

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Alfieri A, Sorokina O, Adrait A, Angelini C, Russo I, Morellato A et al (2017) Synaptic interactome mining reveals p140Cap as a new hub for PSD proteins involved in psychiatric and neurological disorders. Front Mol Neurosci 10:212. https://doi.org/10.3389/fnmol.2017.00212

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Repetto D, Camera P, Melani R, Morello N, Russo I, Calcagno E et al (2014) p140Cap regulates memory and synaptic plasticity through Src-mediated and citron-N-mediated actin reorganization. J Neurosci 34(4):1542–1553

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Hyde TM, Lipska BK, Ali T, Mathew SV, Law AJ, Metitiri OE et al (2011) Expression of GABA signaling molecules KCC2, NKCC1, and GAD1 in cortical development and schizophrenia. J Neurosci 31(30):11088–11095

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Tao R, Li C, Newburn EN, Ye T, Lipska BK, Herman MM et al (2012) Transcript-specific associations of SLC12A5 (KCC2) in human prefrontal cortex with development, schizophrenia, and affective disorders. J Neurosci 32(15):5216–5222

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Mahadevan V, Khademullah CS, Dargaei Z, Chevrier J, Uvarov P, Kwan J et al (2017) Native KCC2 interactome reveals PACSIN1 as a critical regulator of synaptic inhibition. Elife 6:e28270. https://doi.org/10.7554/eLife.28270

    Article  PubMed  PubMed Central  Google Scholar 

  32. Andersson F, Jakobsson J, Löw P, Shupliakov O, Brodin L (2008) Perturbation of syndapin/PACSIN impairs synaptic vesicle recycling evoked by intense stimulation. J Neurosci 28(15):3925–3933

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Anggono V, Smillie KJ, Graham ME, Valova VA, Cousin MA, Robinson PJ (2006) Syndapin I is the phosphorylation-regulated dynamin I partner in synaptic vesicle endocytosis. Nat Neurosci 9(6):752–760

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Anggono V, Koç-Schmitz Y, Widagdo J, Kormann J, Quan A, Chen CM et al (2013) PICK1 interacts with PACSIN to regulate AMPA receptor internalization and cerebellar long-term depression. Proc Natl Acad Sci U S A 110(34):13976–13981

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Del Pino I, Koch D, Schemm R, Qualmann B, Betz H, Paarmann I (2014) Proteomic analysis of glycine receptor beta subunit (GlyRbeta)-interacting proteins: evidence for syndapin I regulating synaptic glycine receptors. J Biol Chem 289(16):11396–11409

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Perez-Otano I, Luján R, Tavalin SJ, Plomann M, Modregger J, Liu XB et al (2006) Endocytosis and synaptic removal of NR3A-containing NMDA receptors by PACSIN1/syndapin1. Nat Neurosci 9(5):611–621

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Qiu S, Lu Z, Levitt P (2014) MET receptor tyrosine kinase controls dendritic complexity, spine morphogenesis, and glutamatergic synapse maturation in the hippocampus. J Neurosci 34(49):16166–16179

    Article  PubMed  PubMed Central  Google Scholar 

  38. Campbell DB, Li C, Sutcliffe JS, Persico AM, Levitt P (2008) Genetic evidence implicating multiple genes in the MET receptor tyrosine kinase pathway in autism spectrum disorder. Autism Res 1(3):159–168

    Article  PubMed  PubMed Central  Google Scholar 

  39. Campbell DB, Sutcliffe JS, Ebert PJ, Militerni R, Bravaccio C, Trillo S et al (2006) A genetic variant that disrupts MET transcription is associated with autism. Proc Natl Acad Sci U S A 103(45):16834–16839

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Lee Y, Kim SG, Lee B, Zhang Y, Kim Y, Kim S et al (2017) Striatal transcriptome and interactome analysis of Shank3-overexpressing mice reveals the connectivity between Shank3 and mTORC1 signaling. Front Mol Neurosci 10:201. https://doi.org/10.3389/fnmol.2017.00201

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Han K, Holder JL Jr, Schaaf CP, Lu H, Chen H, Kang H et al (2013) SHANK3 overexpression causes manic-like behaviour with unique pharmacogenetic properties. Nature 503(7474):72–77

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Bonaglia MC, Giorda R, Borgatti R, Felisari G, Gagliardi C, Selicorni A et al (2001) Disruption of the ProSAP2 gene in a t(12;22)(q24.1;q13.3) is associated with the 22q13.3 deletion syndrome. Am J Hum Genet 69(2):261–268

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Durand CM, Betancur C, Boeckers TM, Bockmann J, Chaste P, Fauchereau F et al (2007) Mutations in the gene encoding the synaptic scaffolding protein SHANK3 are associated with autism spectrum disorders. Nat Genet 39(1):25–27

    Article  CAS  PubMed  Google Scholar 

  44. Failla P, Romano C, Alberti A, Vasta A, Buono S, Castiglia L et al (2007) Schizophrenia in a patient with subtelomeric duplication of chromosome 22q. Clin Genet 71(6):599–601

    Article  CAS  PubMed  Google Scholar 

  45. Boeckers TM, Bockmann J, Kreutz MR, Gundelfinger ED (2002) ProSAP/Shank proteins - a family of higher order organizing molecules of the postsynaptic density with an emerging role in human neurological disease. J Neurochem 81(5):903–910

    Article  CAS  PubMed  Google Scholar 

  46. Naisbitt S, Kim E, Tu JC, Xiao B, Sala C, Valtschanoff J et al (1999) Shank, a novel family of postsynaptic density proteins that binds to the NMDA receptor/PSD-95/GKAP complex and cortactin. Neuron 23(3):569–582

    Article  CAS  PubMed  Google Scholar 

  47. Costa-Mattioli M, Monteggia LM (2013) mTOR complexes in neurodevelopmental and neuropsychiatric disorders. Nat Neurosci 16(11):1537–1543

    Article  CAS  PubMed  Google Scholar 

  48. Jernigan CS, Goswami DB, Austin MC, Iyo AH, Chandran A, Stockmeier CA et al (2011) The mTOR signaling pathway in the prefrontal cortex is compromised in major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry 35(7):1774–1779

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Li N, Lee B, Liu RJ, Banasr M, Dwyer JM, Iwata M et al (2010) mTOR-dependent synapse formation underlies the rapid antidepressant effects of NMDA antagonists. Science 329(5994):959–964

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Fryland T, Christensen JH, Pallesen J, Mattheisen M, Palmfeldt J, Bak M et al (2016) Identification of the BRD1 interaction network and its impact on mental disorder risk. Genome Med 8(1):53. https://doi.org/10.1186/s13073-016-0308-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Nyegaard M, Severinsen JE, Als TD, Hedemand A, Straarup S, Nordentoft M et al (2010) Support of association between BRD1 and both schizophrenia and bipolar affective disorder. Am J Med Genet B Neuropsychiatr Genet 153b(2):582–591

    Article  CAS  PubMed  Google Scholar 

  52. Severinsen JE, Bjarkam CR, Kiaer-Larsen S, Olsen IM, Nielsen MM, Blechingberg J et al (2006) Evidence implicating BRD1 with brain development and susceptibility to both schizophrenia and bipolar affective disorder. Mol Psychiatry 11(12):1126–1138

    Article  CAS  PubMed  Google Scholar 

  53. Mishima Y, Miyagi S, Saraya A, Negishi M, Endoh M, Endo TA et al (2011) The Hbo1-Brd1/Brpf2 complex is responsible for global acetylation of H3K14 and required for fetal liver erythropoiesis. Blood 118(9):2443–2453

    Article  CAS  PubMed  Google Scholar 

  54. Christensen JH, Elfving B, Müller HK, Fryland T, Nyegaard M, Corydon TJ et al (2012) The Schizophrenia and Bipolar Disorder associated BRD1 gene is regulated upon chronic restraint stress. Eur Neuropsychopharmacol 22(9):651–656

    Article  CAS  PubMed  Google Scholar 

  55. Grover D, Verma R, Goes FS, Mahon PL, Gershon ES, McMahon FJ et al (2009) Family-based association of YWHAH in psychotic bipolar disorder. Am J Med Genet B Neuropsychiatr Genet 150b(7):977–983

    Article  CAS  PubMed  Google Scholar 

  56. Williams HJ, Craddock N, Russo G, Hamshere ML, Moskvina V, Dwyer S et al (2011) Most genome-wide significant susceptibility loci for schizophrenia and bipolar disorder reported to date cross-traditional diagnostic boundaries. Hum Mol Genet 20(2):387–291

    Article  CAS  PubMed  Google Scholar 

  57. Wong AH, Likhodi O, Trakalo J, Yusuf M, Sinha A, Pato CN et al (2005) Genetic and post-mortem mRNA analysis of the 14-3-3 genes that encode phosphoserine/threonine-binding regulatory proteins in schizophrenia and bipolar disorder. Schizophr Res 78(2–3):137–146

    Article  PubMed  Google Scholar 

  58. Wong AH, Macciardi F, Klempan T, Kawczynski W, Barr CL, Lakatoo S et al (2003) Identification of candidate genes for psychosis in rat models, and possible association between schizophrenia and the 14-3-3eta gene. Mol Psychiatry 8(2):156–166

    Article  CAS  PubMed  Google Scholar 

  59. Moreno-Villanueva M, Morath J, Vanhooren V, Elbert T, Kolassa S, Libert C et al (2013) N-glycosylation profiling of plasma provides evidence for accelerated physiological aging in post-traumatic stress disorder. Transl Psychiatry 3:e320. https://doi.org/10.1038/tp.2013.93

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Correll CU, Solmi M, Veronese N, Bortolato B, Rosson S, Santonastaso P et al (2017) Prevalence, incidence and mortality from cardiovascular disease in patients with pooled and specific severe mental illness: a large-scale meta-analysis of 3,211,768 patients and 113,383,368 controls. World Psychiatry 16(2):163–180

    Article  PubMed  PubMed Central  Google Scholar 

  61. Cassidy F, Ahearn E, Carroll BJ (1999) Elevated frequency of diabetes mellitus in hospitalized manic-depressive patients. Am J Psychiatry 156(9):1417–1420

    CAS  PubMed  Google Scholar 

  62. McIntyre RS, Konarski JZ, Misener VL, Kennedy SH (2005) Bipolar disorder and diabetes mellitus: epidemiology, etiology, and treatment implications. Ann Clin Psychiatry 17(2):83–93

    Article  PubMed  Google Scholar 

  63. Hajek T, Slaney C, Garnham J, Ruzickova M, Passmore M, Alda M (2005) Clinical correlates of current level of functioning in primary care-treated bipolar patients. Bipolar Disord 7(3):286–291

    Article  PubMed  Google Scholar 

  64. McIntyre RS, Danilewitz M, Liauw SS, Kemp DE, Nguyen HT, Kahn LS et al (2010) Bipolar disorder and metabolic syndrome: an international perspective. J Affect Disord 126(3):366–387

    Article  PubMed  Google Scholar 

  65. Annamalai A, Kosir U, Tek C (2017) Prevalence of obesity and diabetes in patients with schizophrenia. World J Diabetes 8(8):390–396

    Article  PubMed  PubMed Central  Google Scholar 

  66. Mezuk B, Eaton WW, Albrecht S, Golden SH (2008) Depression and type 2 diabetes over the lifespan: a meta-analysis. Diabetes Care 31(12):2383–2390

    Article  PubMed  PubMed Central  Google Scholar 

  67. Piazza I, Kochanowski K, Cappelletti V, Fuhrer T, Noor E, Sauer U et al (2018) A map of protein-metabolite interactions reveals principles of chemical communication. Cell 172(1–2):358–372.e23

    Article  CAS  PubMed  Google Scholar 

  68. Huber KV, Olek KM, Müller AC, Tan CS, Bennett KL, Colinge J et al (2015) Proteome-wide drug and metabolite interaction mapping by thermal-stability profiling. Nat Methods 12(11):1055–1057

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Cassoli JS, Iwata K, Steiner J, Guest PC, Turck CW, Nascimento JM et al (2016) Effect of MK-801 and clozapine on the proteome of cultured human oligodendrocytes. Front Cell Neurosci 10:52. https://doi.org/10.3389/fncel.2016.00052

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Park DI, Dournes C, Sillaber I, Ising M, Asara JM, Webhofer C et al (2017) Delineation of molecular pathway activities of the chronic antidepressant treatment response suggests important roles for glutamatergic and ubiquitin-proteasome systems. Transl Psychiatry 7(4):e1078. https://doi.org/10.1038/tp.2017.39

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Park DI, Dournes C, Sillaber I, Uhr M, Asara JM, Gassen NC et al (2016) Purine and pyrimidine metabolism: convergent evidence on chronic antidepressant treatment response in mice and humans. Sci Rep 6:35317. https://doi.org/10.1038/srep35317

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Steiner J, Martins-de-Souza D, Schiltz K, Sarnyai Z, Westphal S, Isermann B et al (2014) Clozapine promotes glycolysis and myelin lipid synthesis in cultured oligodendrocytes. Front Cell Neurosci 8:384. https://doi.org/10.3389/fncel.2014.00384

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Weckmann K, Deery MJ, Howard JA, Feret R, Asara JM, Dethloff F (2017) Ketamine’s antidepressant effect is mediated by energy metabolism and antioxidant defense system. Sci Rep 7(1):15788. https://doi.org/10.1038/s41598-017-16183-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Park, D.I., Turck, C.W. (2019). Interactome Studies of Psychiatric Disorders. 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_8

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