Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Resource
  • Published:

Spatiotemporal profile of postsynaptic interactomes integrates components of complex brain disorders

Nature Neuroscience volume 20, pages 1150–1161 (2017)Cite this article

Subjects

Abstract

The postsynaptic density (PSD) contains a collection of scaffold proteins used for assembling synaptic signaling complexes. However, it is not known how the core-scaffold machinery associates in protein-interaction networks or how proteins encoded by genes involved in complex brain disorders are distributed through spatiotemporal protein complexes. Here using immunopurification, proteomics and bioinformatics, we isolated 2,876 proteins across 41 in vivo interactomes and determined their protein domain composition, correlation to gene expression levels and developmental integration to the PSD. We defined clusters for enrichment of schizophrenia, autism spectrum disorders, developmental delay and intellectual disability risk factors at embryonic day 14 and adult PSD in mice. Mutations in highly connected nodes alter protein–protein interactions modulating macromolecular complexes enriched in disease risk candidates. These results were integrated into a software platform, Synaptic Protein/Pathways Resource (SyPPRes), enabling the prioritization of disease risk factors and their placement within synaptic protein interaction networks.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Developmental PSD scaffold interactomes.
Figure 2: Human developmental transcriptome correlation in scaffold protein complexes.
Figure 3: Protein domain profiling of PSD scaffold interactomes.
Figure 4: Developmental PSD scaffold network.
Figure 5: Protein domain profiling of protein interactomes.
Figure 6: Analysis of synaptic interactomes in complex brain disorders.
Figure 7: Disruption of scaffold networks by mutations in highly connected nodes.

Similar content being viewed by others

References

  1. Bayés, A. et al. Characterization of the proteome, diseases and evolution of the human postsynaptic density. Nat. Neurosci. 14, 19–21 (2011).

    Article  CAS  PubMed  Google Scholar 

  2. Collins, M.O. et al. Molecular characterization and comparison of the components and multiprotein complexes in the postsynaptic proteome. J. Neurochem. 97 (Suppl. 1), 16–23 (2006).

    Article  CAS  PubMed  Google Scholar 

  3. Coba, M.P. et al. Neurotransmitters drive combinatorial multistate postsynaptic density networks. Sci. Signal. 2, ra19 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. Collins, M.O. et al. Proteomic analysis of in vivo phosphorylated synaptic proteins. J. Biol. Chem. 280, 5972–5982 (2005).

    Article  CAS  PubMed  Google Scholar 

  5. Husi, H., Ward, M.A., Choudhary, J.S., Blackstock, W.P. & Grant, S.G. Proteomic analysis of NMDA receptor-adhesion protein signaling complexes. Nat. Neurosci. 3, 661–669 (2000).

    Article  CAS  PubMed  Google Scholar 

  6. Pawson, T. & Scott, J.D. Signaling through scaffold, anchoring, and adaptor proteins. Science 278, 2075–2080 (1997).

    Article  CAS  PubMed  Google Scholar 

  7. Scott, J.D. & Pawson, T. Cell signaling in space and time: where proteins come together and when they're apart. Science 326, 1220–1224 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Sheng, M. & Kim, E. The postsynaptic organization of synapses. Cold Spring Harb. Perspect. Biol. 3, a005678 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Kornau, H.C., Schenker, L.T., Kennedy, M.B. & Seeburg, P.H. Domain interaction between NMDA receptor subunits and the postsynaptic density protein PSD-95. Science 269, 1737–1740 (1995).

    Article  CAS  PubMed  Google Scholar 

  10. Fernández, E. et al. Targeted tandem affinity purification of PSD-95 recovers core postsynaptic complexes and schizophrenia susceptibility proteins. Mol. Syst. Biol. 5, 269 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Kim, E. et al. GKAP, a novel synaptic protein that interacts with the guanylate kinase-like domain of the PSD-95/SAP90 family of channel clustering molecules. J. Cell Biol. 136, 669–678 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Naisbitt, S. et al. Shank, a novel family of postsynaptic density proteins that binds to the NMDA receptor/PSD-95/GKAP complex and cortactin. Neuron 23, 569–582 (1999).

    Article  CAS  PubMed  Google Scholar 

  13. Vinade, L. et al. Affinity purification of PSD-95-containing postsynaptic complexes. J. Neurochem. 87, 1255–1261 (2003).

    Article  CAS  PubMed  Google Scholar 

  14. Grant, S.G. Synaptopathies: diseases of the synaptome. Curr. Opin. Neurobiol. 22, 522–529 (2012).

    Article  CAS  PubMed  Google Scholar 

  15. Stark, C. et al. BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 34, D535–D539 (2006).

    Article  CAS  PubMed  Google Scholar 

  16. Calderone, A., Castagnoli, L. & Cesareni, G. Mentha: a resource for browsing integrated protein-interaction networks. Nat. Methods 10, 690–691 (2013).

    Article  CAS  PubMed  Google Scholar 

  17. Börnigen, D. et al. Concordance of gene expression in human protein complexes reveals tissue specificity and pathology. Nucleic Acids Res. 41, e171 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Taylor, I.W. et al. Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nat. Biotechnol. 27, 199–204 (2009).

    Article  CAS  PubMed  Google Scholar 

  19. Bossi, A. & Lehner, B. Tissue specificity and the human protein interaction network. Mol. Syst. Biol. 5, 260 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Jin, J. et al. Eukaryotic protein domains as functional units of cellular evolution. Sci. Signal. 2, ra76 (2009).

    Article  PubMed  Google Scholar 

  21. Letunic, I., Doerks, T. & Bork, P. SMART: recent updates, new developments and status in 2015. Nucleic Acids Res. 43, D257–D260 (2015).

    Article  CAS  PubMed  Google Scholar 

  22. Finn, R.D. et al. Pfam: the protein families database. Nucleic Acids Res. 42, D222–D230 (2014).

    Article  CAS  PubMed  Google Scholar 

  23. Fromer, M. et al. De novo mutations in schizophrenia implicate synaptic networks. Nature 506, 179–184 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Georgieva, L. et al. De novo CNVs in bipolar affective disorder and schizophrenia. Hum. Mol. Genet. 23, 6677–6683 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Kirov, G. et al. Support for the involvement of large copy number variants in the pathogenesis of schizophrenia. Hum. Mol. Genet. 18, 1497–1503 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Kirov, G. et al. De novo CNV analysis implicates specific abnormalities of postsynaptic signalling complexes in the pathogenesis of schizophrenia. Mol. Psychiatry 17, 142–153 (2012).

    Article  CAS  PubMed  Google Scholar 

  27. Pinto, D. et al. Convergence of genes and cellular pathways dysregulated in autism spectrum disorders. Am. J. Hum. Genet. 94, 677–694 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. State, M.W. & Levitt, P. The conundrums of understanding genetic risks for autism spectrum disorders. Nat. Neurosci. 14, 1499–1506 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Pavlowsky, A., Chelly, J. & Billuart, P. Emerging major synaptic signaling pathways involved in intellectual disability. Mol. Psychiatry 17, 682–693 (2012).

    Article  CAS  PubMed  Google Scholar 

  30. Xie, Z. et al. Receptor tyrosine kinase MET interactome and neurodevelopmental disorder partners at the developing synapse. Biol. Psychiatry 80, 933–942 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Endele, S. et al. Mutations in GRIN2A and GRIN2B encoding regulatory subunits of NMDA receptors cause variable neurodevelopmental phenotypes. Nat. Genet. 42, 1021–1026 (2010).

    Article  CAS  PubMed  Google Scholar 

  32. Genovese, G. et al. Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia. Nat. Neurosci. 19, 1433–1441 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

  34. Zybailov, B. et al. Statistical analysis of membrane proteome expression changes in Saccharomyces cerevisiae. J. Proteome Res. 5, 2339–2347 (2006).

    Article  CAS  PubMed  Google Scholar 

  35. Yang, H., Robinson, P.N. & Wang, K. Phenolyzer: phenotype-based prioritization of candidate genes for human diseases. Nat. Methods 12, 841–843 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Purcell, S.M. et al. A polygenic burden of rare disruptive mutations in schizophrenia. Nature 506, 185–190 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Li, J. et al. Long-term potentiation modulates synaptic phosphorylation networks and reshapes the structure of the postsynaptic interactome. Sci. Signal. 9, rs8 (2016).

    PubMed  Google Scholar 

  38. O'Roak, B.J. et al. Exome sequencing in sporadic autism spectrum disorders identifies severe de novo mutations. Nat. Genet. 43, 585–589 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Peça, J. & Feng, G. Cellular and synaptic network defects in autism. Curr. Opin. Neurobiol. 22, 866–872 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Pinto, D. et al. Functional impact of global rare copy number variation in autism spectrum disorders. Nature 466, 368–372 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Mattheisen, M. et al. Genome-wide association study in obsessive-compulsive disorder: results from the OCGAS. Mol. Psychiatry 20, 337–344 (2015).

    Article  CAS  PubMed  Google Scholar 

  42. Elia, J. et al. Genome-wide copy number variation study associates metabotropic glutamate receptor gene networks with attention deficit hyperactivity disorder. Nat. Genet. 44, 78–84 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Walsh, T. et al. Rare structural variants disrupt multiple genes in neurodevelopmental pathways in schizophrenia. Science 320, 539–543 (2008).

    Article  CAS  PubMed  Google Scholar 

  44. Basu, M.K., Carmel, L., Rogozin, I.B. & Koonin, E.V. Evolution of protein domain promiscuity in eukaryotes. Genome Res. 18, 449–461 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Carter, H., Hofree, M. & Ideker, T. Genotype to phenotype via network analysis. Curr. Opin. Genet. Dev. 23, 611–621 (2013).

    Article  CAS  PubMed  Google Scholar 

  46. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Arbuckle, M.I. et al. The SH3 domain of postsynaptic density 95 mediates inflammatory pain through phosphatidylinositol-3-kinase recruitment. EMBO Rep. 11, 473–478 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. De Rubeis, S. et al. CYFIP1 coordinates mRNA translation and cytoskeleton remodeling to ensure proper dendritic spine formation. Neuron 79, 1169–1182 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. McLaren, W. et al. Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics 26, 2069–2070 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Kosmicki, J.A. et al. Refining the role of de novo protein-truncating variants in neurodevelopmental disorders by using population reference samples. Nat. Genet. 49, 504–510 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Samocha, K.E. et al. A framework for the interpretation of de novo mutation in human disease. Nat. Genet. 46, 944–950 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Coba, M.P. et al. TNiK is required for postsynaptic and nuclear signaling pathways and cognitive function. J. Neurosci. 32, 13987–13999 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Cuthbert, P.C. et al. Synapse-associated protein 102/dlgh3 couples the NMDA receptor to specific plasticity pathways and learning strategies. J. Neurosci. 27, 2673–2682 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by grants from the National Institute of Child Health and Human Development (MH104603-01A1 to M.P.C.), NIH grant MH108728 (to K.W. and H.Y.), Simons Foundation Autism Research Initiative (SFARI) grants 248429 and 345034, and DOD CDMRP AR110189 (to T.A.). B.N. acknowledges support from the Stanley Center for Psychiatric Research.

Author information

Authors and Affiliations

  1. Zilkha Neurogenetic Institute, University of Southern California, Los Angeles, California, USA

    Jing Li, Hui Yang, Brent Wilkinson, Tade Souaiaia, Oleg V Evgrafov, Veronica A Clementel, James A Knowles, Kai Wang & Marcelo P Coba

  2. Molecular and Computational Biology Program, University of Southern California, Los Angeles, California, USA

    Wangshu Zhang & Fengzhu Sun

  3. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA

    Daniel P Howrigan & Benjamin M Neale

  4. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA

    Daniel P Howrigan, Giulio Genovese & Benjamin M Neale

  5. Department of Psychiatry and Behavioral Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA

    Oleg V Evgrafov, James A Knowles, Kai Wang & Marcelo P Coba

  6. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA

    Giulio Genovese & Benjamin M Neale

  7. Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA

    Giulio Genovese

  8. Department of Biology, Saint Joseph's University, Philadelphia, Pennsylvania, USA

    Jennifer C Tudor

  9. Department of Molecular Physiology and Biophysics, Iowa Neuroscience Institute, University of Iowa, Iowa City, Iowa, USA

    Ted Abel

  10. Institute for Genomic Medicine, Columbia University Medical Center, New York, New York, USA

    Kai Wang

Authors
  1. Jing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wangshu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hui Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Daniel P Howrigan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Brent Wilkinson
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tade Souaiaia
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Oleg V Evgrafov
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Giulio Genovese
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Veronica A Clementel
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Jennifer C Tudor
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Ted Abel
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. James A Knowles
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Benjamin M Neale
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Kai Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Fengzhu Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Marcelo P Coba
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

J.L., W.Z., B.W., V.A.C., J.C.T. and M.P.C. performed experiments; J.L., W.Z., B.W., H.Y., T.S., O.V.E., D.P.H. and G.G. performed analysis; T.A., J.A.K., K.W., B.M.N., F.S. and M.P.C. supervised analysis; F.S., K.W. and M.P.C. designed experiments and analysis; J.L., B.W., F.S. and M.P.C. and wrote the manuscript; M.P.C. supervised the project.

Corresponding author

Correspondence to Marcelo P Coba.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Reported (nonredundant) protein interactions in PSD scaffold complexes

Comparison of Dlg4, Dlgap1 and Shank3 protein interactions isolated by HPLC-MS/MS compared to interactions reported in the BioGRID15 and Mentha16 protein-protein interaction databases. Heatmap shows comparison to e14/p7/p14/adult redundant interactions without discrimination between developmental stage

Supplementary Figure 2 Assay classification of reported protein–protein interactions.

Figure illustrates the distribution of the methods used to characterize previously reported protein-protein interactions that are also reported in the current study. The methods include those that show direct protein-protein interactions (yeast two-hybrid and co-crystallization) and indirect protein-protein interactions (co-fractionation, affinity capture followed by mass spectrometry, affinity capture followed by western blot, reconstituted complex, co-localization, co-purification, and far-western blotting). Each previously reported interaction listed in supplementary table 2 was annotated as being shown by direct methods, indirect methods, or both where applicable. Detection methods are separated by the corresponding species in which the protein-protein interaction was shown as in A) All species combined, B) Homo sapiens, C) Mus musculus, and D) Rattus Norvegicus.

Supplementary Figure 3 Reported (nonredundant) protein interactions in PSD scaffold interactors complexes

Comparison of scaffold interactomes isolated by HPLC-MS/MS compared to interactions reported in the BioGRID15 and Mentha16 protein-protein interaction databases for interactions at different developmental stages and spatial localization (top, redundant) and interactions without any discrimination between developmental stage or cellular localization (bottom, non-redundant).

Supplementary Figure 4 Protein content analysis

Protein content in Tnik−/− and Shank3ΔC−/+ mice.

Quantitation of total protein content for protein interactions determined by MS (Figure 7) in mouse PFC. Representative cropped images of WB from four independent experiments which were analyzed using the unpaired t-test. All data represented as mean ± s.e.m.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–4 and Supplementary Datasets 1 and 2 (PDF 9122 kb)

Supplementary Methods Checklist (PDF 505 kb)

Supplementary Table 1

Interactomes dataset Tab 1 (PSD Scaffolds) Protein interactions identified in MS analysis of protein complexes from PSD scaffolds (Dlg4, Dlgap1, Shank3) All protein interactors have no less than 2 peptides identified together in at least triplicate samples by MASCOT and SEQUEST, and are not present in negative control purifications. Column A: Developmental stage/Cellular localization/Cell type Column B: Target Protein Column C Protein interaction (Gene name) Supplementary table contains interactomes of the PSD scaffolding proteins (sheet 1) and PSD scaffold-interactors (sheet 2) described in the main text. Developmental stage/localization column describes at what developmental stage the immunopurification was performed and whether it was with PSD or non-PSD fractions. Tab 2 (Scaffold interactors) Protein interactions identified in MS analysis of protein complexes from PSD proteins that are scaffold interactors All protein interactors have no less than 2 peptides identified together in at least triplicate samples by MASCOT and SEQUEST, and are not present in negative control purifications. Column A: Developmental stage/Cellular localization/Cell type Column B: Target Protein Column C Protein interaction (Gene name) The protein column contains the target protein used for immunopurification while the Interactor columns contain the interactors of that particular protein identified via mass spectrometry with the corresponding mouse, human, or rat annotation corresponding gene symbol according to the Entrez Gene Database.The Mentha and Biogrid columns contain a yes if that particular interaction was reported in the protein-protein interaction databases for the corresponding species Tab 3 Annotations for all protein interactions identified in MS assays A: Interactor gene name B: Cytogenetic location C: Genomic coordinates (GRCh37)D: HUGO Gene Nomenclature Committee (HGNC) id E: Mouse Genome Informatics (MGI) id F-H: Online Mendelian Inheritance in Man (OMIM) annotation (XLSX 375 kb)

Supplementary Table 2

Reported interactions dataset Supplementary table contains previously reported protein-protein interactions overlapping with the current dataset. With a majority of previously reported interactions being derived from BioGRID (release 3.4.142) the table contains the BioGRID-specifc identifiers such as BioGRID interaction and organism ID. Organism ID corresponds to: Homo sapiens = 9606, Mus Musculus = 10090, Rattus norvegicus = 10116. Table also contains Entrez Gene Database identifiers, official gene symbols, reference pubmed ID, and the experimental system for each previously reported interaction. (XLSX 58 kb)

Supplementary Table 3

Protein domain analysis of interactomes Proteins isolated by HPLC-MS/MS from mouse PFC Column A: Gene ID Column B: MGI ID Column C: Description Protein domain composition of PSD Proteins (SMART/Pfam databases) Column A: Database Column B: Domain ID Column C: Number of PSD proteins with domain Column D: p-value Column E: Gene IDs with domains Column F: Total number of domains Column G: Total-Database Column H: Enrichment Column I: Correction (Bonferroni) Column J: False Discovery Rate (XLSX 126 kb)

Supplementary Table 4

Protein domain annotation and enrichment for developmental scaffold protein complexes Individual protein complexes are shown in separate spreadsheets Column A: Domain ID (SMART/Pfam) Column B: Domain name Column C: Number of proteins containing the domain in the protein complex Column D: p-value Column E: Corrected p-value (Bonferroni) (XLSX 48 kb)

Supplementary Table 5

Clustering of developmental Dlg4/Dlgap1/Shank3 protein complexes Node name and network parameters for Dlg4, Dlgap1 and Shank3 (e14, p7, p14 adult) protein complexes. Network parameters include: number of nodes, average degree (average connectivity of a node in the network), network density (normalize average connectivity of each node), clustering coefficient (ratio of N / M, where N is the number of edges between the neighbors of this node, and M is the maximum number of edges that could possibly exist between the neighbors of the node), clustering coefficient of a node is always a number between 0 and 1.The network clustering coefficient is the average of the clustering coefficients for all nodes in the network. Here, nodes with less than two neighbors are assumed to have a clustering coefficient of 0. (XLSX 94 kb)

Supplementary Table 6

Protein domain annotation and enrichment for scaffold interactors protein complexes Individual protein complexes are shown in separate spreadsheets Column A: Domain ID (SMART/Pfam) Column B: Domain name Column C: Number of proteins containing the domain in the protein complex Column D: p-value Column E: Corrected p-value (Bonferroni) (XLSX 80 kb)

Supplementary Table 7

Testing for gene set enrichment among de novo mutations in brain disorders Individual spreadsheets indicate datasets, description and analysis: SCZ, ASD, DD, ID, SIB_CONTROL, CHD-NS, CHD-S. (XLSX 132 kb)

Supplementary Table 8

Clustering of Adult PSD protein complexes Dataset includes: Tnik, Dlg4, Dlgap1, Shank3, Tsc1, Homer1, Nckap1, Cyfip1, Cyfip2, Syngap1, Fmr1, Cnksr2 nodes Columns A-N: Node name and network parameters Columns O-Q: Disrupted interactions in MAGUK protein complexes with Tnik−/− mutation. Columns R-T: Disrupted interactions in Dlgap1 protein complexes with Tnik−/− mutation. Columns U-W: Disrupted interactions in Shank3 protein complexes with Tnik−/− mutation. Columns X-Z: Disrupted interactions in Dlgap1 protein complexes with Shank3ΔC−/+ mutation Columns AA-AC: Disrupted interactions in Shank3 protein complexes with Shank3ΔC−/+ mutation All protein interactions were determined in triplicate samples. Normalized spectral abundance factor (NSAF) was calculated for each protein34, 54 and used for comparison between wt/mutant samples. Table shows disruption of highly connected nodes. Tnik and Shank3 spreadsheets, shows quantitation of total protein levels by WB analysis of PSD proteins with impaired associations to PSD protein complexes in wt and Tnik−/−, Shank3ΔC/+ mice. Columns D and E shows ratios wt/mutant mice rations and standard deviation for triplicate assays. Protein ratios did not present statistical significance (0.66/1.5 p<0.05). Bottom tables shows exact p values for quantitation of protein interactions in scaffold complexes by western blot assays (XLSX 95 kb)

Supplementary Table 9

Non-specific proteins determined by MS. Column A: Uniprot id, Column B: Gene name (XLSX 10 kb)

Supplementary Table 10

Non-specific RNA binding proteins determined by MS. Table shows RNA binding proteins determined by MS in samples treated with RNAse T1/A. Column A: Uniprot id, Column B: Gene name (XLSX 41 kb)

Supplementary Table 11

Antibodies used in IP, and WB assays (XLSX 51 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, J., Zhang, W., Yang, H. et al. Spatiotemporal profile of postsynaptic interactomes integrates components of complex brain disorders. Nat Neurosci 20, 1150–1161 (2017). https://doi.org/10.1038/nn.4594

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nn.4594

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing