Abstract
Studying biological mechanisms underlying neuropsychiatric disorders is highly challenging as many risk genes are associated with these disorders. This complexity requires research approaches to reliably dissect the cell biology of the risk genes involved. Here, we describe a combined cellomics–proteomics approach that allows (a) medium-throughput functional screening and unbiased selection of important risk genes, and (b) discovery of common functional pathways and interactome connections of selected risk genes. The overlay of pathway and proteome data from selected genes in a biological context can be used to formulate new testable hypothesis of both the genetics and the biology of the disorders.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sullivan PF, Daly MJ, O’Donovan M (2012) Genetic architectures of psychiatric disorders: the emerging picture and its implications. Nat Rev Genet 13(8):537–551
Gejman PV, Sanders AR, Duan J (2010) The role of genetics in the etiology of schizophrenia. Psychiatr Clin North Am 33(1):35–66
Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014) Biological insights from 108 schizophrenia-associated genetic loci. Nature 511(7510):421–427
Pardinas AF, Holmans P, Pocklington AJ, Escott-Price V, Ripke S, Carrera N, Legge SE, Bishop S, Cameron D, Hamshere ML et al (2018) Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat Genet 50(3):381–389
Failla P, Romano C, Alberti A, Vasta A, Buono S, Castiglia L, Luciano D, Di Benedetto D, Fichera M, Galesi O (2007) Schizophrenia in a patient with subtelomeric duplication of chromosome 22q. Clin Genet 71(6):599–601
Fryland T, Christensen JH, Pallesen J, Mattheisen M, Palmfeldt J, Bak M, Grove J, Demontis D, Blechingberg J, Ooi HS et al (2016) Identification of the BRD1 interaction network and its impact on mental disorder risk. Genome Med 8(1):53
Huang CC, Cheng MC, Tsai HM, Lai CH, Chen CH (2014) Genetic analysis of GABRB3 at 15q12 as a candidate gene of schizophrenia. Psychiatr Genet 24(4):151–157
Stoll G, Pietilainen OPH, Linder B, Suvisaari J, Brosi C, Hennah W, Leppa V, Torniainen M, Ripatti S, Ala-Mello S et al (2013) Deletion of TOP3beta, a component of FMRP-containing mRNPs, contributes to neurodevelopmental disorders. Nat Neurosci 16(9):1228–1237
Vacic V, McCarthy S, Malhotra D, Murray F, Chou HH, Peoples A, Makarov V, Yoon S, Bhandari A, Corominas R et al (2011) Duplications of the neuropeptide receptor gene VIPR2 confer significant risk for schizophrenia. Nature 471(7339):499–503
Harrill JA, Robinette BL, Mundy WR (2011) Use of high content image analysis to detect chemical-induced changes in synaptogenesis in vitro. Toxicol In Vitro 25(1):368–387
Jain S, van Kesteren RE, Heutink P (2012) High content screening in neurodegenerative diseases. J Vis Exp (59):e3452
Kozak K (2009) Data mining techniques in high content screening: a survey. J Comput Sci Syst Biol 02(04)
Linhoff MW, Lauren J, Cassidy RM, Dobie FA, Takahashi H, Nygaard HB, Airaksinen MS, Strittmatter SM, Craig AM (2009) An unbiased expression screen for synaptogenic proteins identifies the LRRTM protein family as synaptic organizers. Neuron 61(5):734–749
Nieland TJ, Logan DJ, Saulnier J, Lam D, Johnson C, Root DE, Carpenter AE, Sabatini BL (2014) High content image analysis identifies novel regulators of synaptogenesis in a high-throughput RNAi screen of primary neurons. PLoS One 9(3):e91744
Sharma K, Choi SY, Zhang Y, Nieland TJ, Long S, Li M, Huganir RL (2013) High-throughput genetic screen for synaptogenic factors: identification of LRP6 as critical for excitatory synapse development. Cell Rep 5(5):1330–1341
Cohen E, Ivenshitz M, Amor-Baroukh V, Greenberger V, Segal M (2008) Determinants of spontaneous activity in networks of cultured hippocampus. Brain Res 1235:21–30
Chen N, Koopmans F, Gordon A, Paliukhovich I, Klaassen RV, van der Schors RC, Peles E, Verhage M, Smit AB, Li KW (2015) Interaction proteomics of canonical Caspr2 (CNTNAP2) reveals the presence of two Caspr2 isoforms with overlapping interactomes. Biochim Biophys Acta 1854(7):827–833
Pandya NJ, Klaassen RV, van der Schors RC, Slotman JA, Houtsmuller A, Smit AB, Li KW (2016) Group 1 metabotropic glutamate receptors 1 and 5 form a protein complex in mouse hippocampus and cortex. Proteomics 16(20):2698–2705
Pandya NJ, Koopmans F, Slotman JA, Paliukhovich I, Houtsmuller AB, Smit AB, Li KW (2017) Correlation profiling of brain sub-cellular proteomes reveals co-assembly of synaptic proteins and subcellular distribution. Sci Rep 7(1):12107
Daub A, Sharma P, Finkbeiner S (2009) High-content screening of primary neurons: ready for prime time. Curr Opin Neurobiol 19(5):537–543
Twarog NR, Low JA, Currier DG, Miller G, Chen T, Shelat AA (2016) Robust classification of small-molecule mechanism of action using a minimalist high-content microscopy screen and multidimensional phenotypic trajectory analysis. PLoS One 11(2):e0149439
Bates D, Mächler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67(1)
Lundby A, Rossin EJ, Steffensen AB, Acha MR, Newton-Cheh C, Pfeufer A, Lynch SN, Consortium QTIIG, Olesen SP, Brunak S et al (2014) Annotation of loci from genome-wide association studies using tissue-specific quantitative interaction proteomics. Nat Methods 11(8):868–874
Kamburov A, Lawrence MS, Polak P, Leshchiner I, Lage K, Golub TR, Lander ES, Getz G (2015) Comprehensive assessment of cancer missense mutation clustering in protein structures. Proc Natl Acad Sci U S A 112(40):E5486–E5495
Rosenbluh J, Mercer J, Shrestha Y, Oliver R, Tamayo P, Doench JG, Tirosh I, Piccioni F, Hartenian E, Horn H et al (2016) Genetic and proteomic interrogation of lower confidence candidate genes reveals signaling networks in beta-catenin-active cancers. Cell Syst 3(3):302–316.e4
Li T, Wernersson R, Hansen RB, Horn H, Mercer J, Slodkowicz G, Workman CT, Rigina O, Rapacki K, Staerfeldt HH et al (2017) A scored human protein-protein interaction network to catalyze genomic interpretation. Nat Methods 14(1):61–64
Horn H, Lawrence MS, Chouinard CR, Shrestha Y, Hu JX, Worstell E, Shea E, Ilic N, Kim E, Kamburov A et al (2018) NetSig: network-based discovery from cancer genomes. Nat Methods 15(1):61–66
Li T, Kim A, Rosenbluh J, Horn H, Greenfeld L, An D, Zimmer A, Liberzon A, Bistline J, Natoli T et al (2018) GeNets: a unified web platform for network-based genomic analyses. Nat Methods 15(7):543–546
Acknowledgments
MR was supported by the European grant U-FP7 MC-ITN IN-SENS (#607616) and the Schizophrenia United Network (SUN) project. We gratefully acknowledge support from the Swedish Research Council (VetenskapsrĂĄdet, award D0886501).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Rosato, M. et al. (2019). A Combined Cellomics and Proteomics Approach to Uncover Neuronal Pathways to Psychiatric Disorder. In: Li, K. (eds) Neuroproteomics. Neuromethods, vol 146. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9662-9_16
Download citation
DOI: https://doi.org/10.1007/978-1-4939-9662-9_16
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-4939-9661-2
Online ISBN: 978-1-4939-9662-9
eBook Packages: Springer Protocols