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HMG Advance Access published September 8, 2005 Human Genetics of the NMDA receptor complex

Synapse proteomics of multiprotein complexes: en route from genes to nervous system diseases.

Seth G.N. Grant 1, Michael C. Marshall1, Keri-Lee Page1, Mark A. Cumiskey1,2, J. Douglas Armstrong2.

1

Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire CB10 1SA. UK; 2School of Downloaded from http://hmg.oxfordjournals.org/ by guest on June 8, 2013

Informatics, Edinburgh University, UK.

Address for Correspondence: Prof. S.G.N. Grant Wellcome Trust Sanger Institute Hinxton, Cambridgeshire CB10 1SA. UK. Email: [email protected] Phone: 44-(0)1223-494908

Acknowledgements. SG, MM, KP, MC, JDA were supported by the Genes to Cognition project funded by the Wellcome Trust. See www.genes2cognition.org for details of authors’ contributions. We thank Jane Turner for administrative support and Peter Visscher for comments and Mark Collins for Figure 1.

© The Author 2005. Published by Oxford University Press. All rights reserved

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Human Genetics of the NMDA receptor complex

Abstract Proteomic experiments have produced a draft profile of the overall molecular composition of the mammalian neuronal synapse. It appears that synapses have over 1000 protein components and the mapping of their interactions, organisation and functions will lead to a global view of the role of synapses in physiology and disease.

A major functional

subcomponent of the synaptic machinery are multiprotein complexes of glutamate receptors and adhesion proteins with associated adaptor and signalling enzymes totally 185 proteins known as the N-methyl-D-aspartate receptor complex/MAGUK Associated Signalling Complex (NRC or MASC). Here we review the proteomic studies and functions diseases. Using a systematic literature search protocol we identified reports of mutations or polymorphisms in 47 genes associated with 183 disorders, of which 54 were nervous system disorders. A similar number of genes are important in mouse synaptic plasticity and behaviour where the NRC/MASC acts as a signalling complex with multiple functions provided by its individual protein components and their interactions. The individual gene mutations suggest not only an important role for the NRC/MASC in human diseases but that these diseases may be functionally connected by their common link to the NRC/MASC. The NRC/MASC is a rich source of genetic variation and provides a platform for understanding relationships of disease phenotype amenable to systematic studies such as the Genes to Cognition research consortium (www.genes2cognition.org) that links human and mouse genetics with proteomic studies.

Introduction Finding the genetic basis of human nervous system diseases that are not readily explained by major single gene effects poses serious logistical and theoretical questions for clinical genetics. For example, we may ask; how can many genes (perhaps dozens) contribute to a disease yet maintain a similar phenotype that is clinically identifiable? Implicit is the need for approaches that utilise sets of genes that have some functional and phenotypic link. Moreover, a guiding theme emerging from reductionist single-gene studies in basic biology is that sets of genes, rather than single genes, are responsible for physiological functions. This principle is of practical significance as many new technical approaches are well suited to identifying multiple genes or proteins in parallel, such as gene expression arrays or -2-

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of NRC/MASC and specifically report on the role of its component genes in human

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proteomic profiling experiments (Choudhary and Grant, 2004; Dougherty and Geschwind, 2005). These methods generate lists or sets of proteins, with some common functional attributes, and these sets can be used in human genetic association studies. Although this approach of linking proteomic with human genetic studies is a new strategy, and requires prospective study, we can evaluate its potential using existing data from single gene studies. Below, we describe a systematic search of the literature for human mutations in components of the multiprotein complex associated with glutamate receptors.

The list of all proteins that comprise the synapse is referred to the synapse proteome. Classical ultrastructural studies of the synapse show the pre- and post-synaptic terminal containing synaptic vesicles and post-synaptic density (PSD) respectively. Biochemical fractionation of isolated synapses has been used to separate these visible components and further separation techniques have defined greater detail, such as neurotransmitter receptor complexes (Husi et al, 2000)(Figure 1a). The individual proteins within these fractions and complexes have been identified using mass spectrometry techniques that provide list of hundreds of proteins (for comparison and details of proteomic studies, see Collins et al, 2005, in press). Within the synapse proteome, that subset found in the postsynaptic terminal and adherent synaptic junctional proteins is of major interest as it contains many proteins of importance in behaviour, physiology and disease including glutamate receptor complexes. The glutamate neurotransmitter receptor families found at excitatory synapses are themselves components of multiprotein complexes (Kim and Sheng, 2004). The N-methylD-aspartate (NMDA) subtype of glutamate receptor (Mayer and Armstrong, 2004) is linked to the complex of metabotropic glutamate receptors and the AMPA (α-amino-3-hydroxy-5methylisoxazole-4-propionate) receptor is in separate complexes. The N-methyl-Daspartate (NMDA) subtype of glutamate receptor (Mayer and Armstrong, 2004) is the prototype for multimolecular ion channel complexes and is often referred to as the NMDA Receptor Complex (NRC) or MAGUK Associated Signalling Complex (MASC). The intracellular domains of the NMDA receptor interact with scaffold proteins and enzymes -3-

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The synapse proteome and glutamate receptor complexes.

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both for the purposes of anchoring and signal transduction. The NRC/MASC contains 185 proteins, the metabotropic receptor complex 76 proteins and the AMPA complex 9 proteins (Husi et al, 2000; Collins et al, 2005; Farr et al, 2004). Figure 1b shows a schematic representation of the postsynaptic proteome and subcomplexes of glutamate receptors and the postsynaptic density. This illustrates the overlap in composition of proteins and the potential for functional interactions. Details of the individual proteins and these sets is described in Collins et al, 2005.

Functional classification of synapse complex proteins. We have taken the view that before understanding the organisation of the overall synapse datasets. We have focussed on the NRC/MASC set of 185 proteins because there is a substantial volume of data indicating their physiological importance, and the remainder of this article will address this set (NRC/MASC proteins are listed in Supplementary Table 1). A cornerstone of this process is to annotate structural or functional information to each protein or gene. Genome- and proteome-wide databases provide general information such as the classification of protein family (see Table 1 for classification of NRC/MASC and PSD proteins), protein domains and other sequence derived information. More physiological data has been obtained by examining the effect of knocking out individual genes in mice or interfering with the proteins with pharmacological tools. For example, knockout and knockin mutations that disrupt the interactions between NMDA receptors and (Sprengel et al, 1998) and the MAGUK scaffold protein PSD-95 (Migaud et al, 1998) demonstrated that the NRC/MASC was involved in learning and synaptic plasticity. These observations have been extended, and of 185 proteins found in the NRC/MASC complexes, mutations or drugs that interfere with the function of 43 proteins were reported to be important in synaptic plasticity and 40 have been associated with rodent behaviour (Grant et al, 2003; Pocklington et al, in preparation). The fact that in excess of 40 of the proteins are important in a highly specific function or synaptic physiology, namely NMDA receptor dependent synaptic plasticity, strongly supports the conclusion that the proteins in the complex work together in mediating this physiological process. It is important to note that the effects of each mutation contain -4-

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proteome we will need to develop tools that are useful and can be tested on smaller

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subtle differences. For example, long term potentiation (LTP) of synaptic transmission can be induced with brief trains of action potentials varying in frequency (e.g. 5, 50, 100 Hz) and some mutations disrupt all of these frequencies and others a specific frequency (see for example Komiyama et al, 2002). These complexes of 185 proteins are not equal at all synapses, and it appears as though distinct combinations of proteins occur at different parts of the nervous system. For example, SynGAP is not found in the spinal cord and thus does not contribute to the synaptic plasticity of pain although it is found in the hippocampus where it is important in spatial learning (Komiyama et al, 2002; Porter et al, . In contrast, PSD-95 is found in both hippocampus and spinal cord and contributes to both spatial learning and pain plasticity (Migaud et al, 1998; Garry et al, 2003). Thus, the mouse shows that specific and overlapping functions and pleiotropic roles of individual genes can

In the original proteomic experiments of the NRC/MASC it was recognised that several of these proteins were encoded by genes that were mutated in humans with mental retardation (Husi et al, 2000). These data from mice suggest more detailed scrutiny of the NRC/MASC genes may uncover further roles in human brain function and disease. Given the results from the mice, the expectation is not that all the genes would have identical phenotypes, but might have some overlapping or common functions with specific and variable aspects distinguishing the genes. IN other words, it might be that some diseases would have evidence of multiple NRC/MASC genes involved and may be relevant to a model of multiple gene based diseases. Below we describe a review of the published literature on NRC/MASC genes in human disease focussing on reports of mutations or polymorphisms. We will specifically describe the search and curation methods involved as these approaches are generally useful for similar studies were a list of genes from proteomic or microarray data is a starting point.

The need for literature mining in moving from proteomic data to human genetic experiments Generating a list of genes or proteins typifies a contemporary output from experimental molecular biology and raises the difficult problem of analysing the list. The first step is to ask – what is known in the literature about these proteins? Mining the literature for information already known about sets of proteins is a valuable procedure through which existing data can guide the direction of future research. Accumulating and sorting already -5-

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be observed at physiological and behavioural levels.

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available data in a meaningful way can reveal large scale relationships, which would otherwise go unidentified. Our experience suggests that searching the literature for a list of molecules requires the methods of automated text mining (Hoffman et al, 2005) as the process is an overwhelming task if performed manually. To illustrate this point, imagine a list of 10 genes that one may want to find information on in human brain disease. Each gene can have several (or dozens of) synonyms, e.g. PSD-95, SAP90, DLG4; also, each synonym can be written in multiple ways (e.g PSD-95 or PSD95 or Post Synaptic Density 95). To use PubMed to search for information on this gene would require manually entering each synonym and variant. The next problem is the searching of the biological or medical term, such as synaptic plasticity, which would be broken down into multiple distinct descriptors (e.g. long-term potentiation, depotentiation, LTD, LTP and so on). Thus the order of 1000 separate searches. Members of the Genes to Cognition (G2C) team have utilised text mining to take lists of molecules, then automatically collect synonyms and name variants for batch searching against multiple terms or descriptors of interest with a return of the abstracts ranked according to number of hits. This facilitates searching and reduces search time by an order of magnitude or more. This will be outlined below; specifics of these tools will be described elsewhere and will also be made available on the web (Howell et al, in preparation; Cumiskey et al, in preparation).

Literature Search and curation methods Our purpose in this search was to survey the literature on 185 genes and identify mutations that were believed to be linked to diseases or disorders in humans. We utilised a two step approach: high-throughput text mining to identify relevant abstracts followed by manual curation of data into spreadsheets. For each gene, synonyms and name variants were generated and were batch-loaded into the search tool. Generic search terms were used for each protein in order to home in on the subjects of human diseases and mutations.

The generic search terms were: mutation, polymorphism, snp, single

nucleotide polymorphism, duplication, deletion, inversion, translocation, overexpression, splice, splicing, chromosome, linkage, cytogenetics and Human, Homo sapiens. These gene names and generic terms were searched on PubMed’s database, chosen for its size and breadth of content.

When completed, this program returned a webpage which

displayed a list of proteins. Clicking on each name brought up a list of abstracts (with links to the page on PubMed) for that protein, where each search term was colour highlighted. -6-

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to comprehensively search for just 10 genes in a typical area of biology could require in

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It was therefore possible for us to scroll down through the lists of abstracts; when we found one that seemed to be relevant, we could then immediately link through to the correct page on PubMed and download the full text of the paper. Our selection criteria for papers to be included in the spreadsheet were broad. Essentially, any paper demonstrating that a mutation in the gene in question was associated with a human disease would be included; papers showing a clear lack of an association were also included as having returned a null result. In the majority of cases we obtained all our information from the abstract; only in a small percentage of papers was it necessary to study the full text; this enabled us to include papers for which the full text was not available on-line. Where possible we included only original papers. A small number of reviews have PubMed. The results were accumulated into a master spreadsheet (Supplementary Table 2), which due to its size (7 columns, 506 rows), was compiled to a simplified version (Table 2).

This simplification involved; i) removing genes for which there were no reported

mutations, ii) removing mutations which had not been shown to be associated with a disorder, iii) where more than one report had been included showing that a gene was associated with a particular disease, these reports were amalgamated into one. Further details are provided in the legend to Table 2. We found that text-mining was highly effective for obtaining a large volume of relevant papers as it is designed to be all-encompassing. As a result, of the lists of abstracts returned, only a small percentage was relevant, and this made the task of studying them time-consuming. However, any attempt to narrow the search would be likely to overlook important papers. The major difficulty inherent to this method is that of the protein names. Some protein names contain a large number of generic words, e.g. a search for ‘guanine nucleotide-binding protein’ will select abstracts that contain the word ‘protein’ even if they have no other relevance. Consequently we are likely to have missed important results on proteins whose names contain generic words. Whenever possible, we downloaded the full text of the paper, typically in PDF format. In some cases, papers had not been archived on-line. In others, we did not have access to the journal in question; this was particularly problematic when papers had been published in specialist or foreign journals. Of the 395 abstracts we examined on-line, we were able to download 243 papers (62%).

More

complete access to journals, perhaps with new public access policies will increase this

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also been included, where it was not possible to locate the original publications on

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percentage in future. The complete list of references is presented in Supplementary Table 3.

Human mutations and diseases in NMDA receptor complex genes Of the 185 proteins in the NRC/MASC our search returned abstracts for 135 of them (73%). Of these 135, there were 47 (25%) in which a mutation had been identified in humans. In total, we found 395 reports of mutations. While some of these reports duplicate one another, it is nevertheless clear that many of these 47 genes exhibit a range of different mutations. However, this should not be taken as strong evidence that these genes are not been studied as intensely, a possibility supported by our search’s failure to identify any abstracts for 50 (27%) of the MASC proteins. The nature of the mutation was typically taken down verbatim from the text of the abstract, with little or no attempt to re-classify. Consequently the range of different mutation types described is wide, and a full key has been provided in the table legends. It is very likely that a more in-depth examination of the genetic information would enable a simplification; for example, many nonsense mutations are likely to be SNPs or insertions. We constructed a list of genes reported to exhibit a pathogenic mutation. In tandem, a list was assembled of genes reported to exhibit a non-pathogenic mutation (for instance, several SNPs in NR1 were suggested to be associated with schizophrenia, but an association was not found and thus it was not considered pathogenic). These lists are shown in Table 3. In total, 40 genes were reported to exhibit a pathogenic mutation, while 27 exhibited non-pathogenic mutations; 20 genes showed both. Of the genes with nonpathogenic mutations, 74% also exhibit pathogenic mutations. This result indicates that we should be cautious about any suggestion that the 40 genes exhibiting pathogenic mutations are more ‘critical’ than the other 145 members of the NRC/MASC. It is very probable that our finding of a small group of proteins within the NRC/MASC exhibiting these mutations reflects previous research emphases rather than a true property of the NRC/MASC. To examine the diversity of diseases involving NRC/MASC genes we have tabulated the disorders (Supplementary Table 4). 183 disorders were reported of which 54 were classed -8-

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more ‘prone to mutations’ than the others; it is equally plausible that the other genes have

Human Genetics of the NMDA receptor complex

as nervous system diseases (30%); these are listed in Table 4. The remainder are highly varied, affecting a wide range of physiological systems and anatomical regions.

A

significant proportion (68: 37%) of the disorders were tumours and cancers. Any human disorder was eligible for inclusion and if multiple papers showed a link between a protein and the same disease, we included all of them, even if some of them were apparently considering the same mutation.

We included papers which claimed that a particular

gene/mutation was definitely not linked to a disease; an extra column was introduced to state whether or not the mutation was thought to be involved in the disease. This was a crucial decision, as in some cases a variety of studies had been carried out into the possibility of a particular gene being involved in a particular disorder, with contradictory results; had we only included the studies which claimed a link, the spreadsheet would looked for a mutation in a gene that could be involved in a disorder but could not find one, for similar reasons. Supplementary Table 2 lists all the disorders included in the spreadsheet and further details on the curation process are included in the legend.

Overview of NRC in human disease Proteomic studies show the NRC/MASC complex has 185 proteins and many of these important in the physiology of learning and memory and other forms of plasticity in rodents. Here we describe the systematic text mining and curation of a set of NRC/MASC genes encoding proteins found in synaptic signalling complexes in mammalian nervous system. Mutations were reported in 47 genes and associated with 183 disorders including 54 affecting the nervous system. Proteomic data from functionally important entities in nerve cells, such as other complexes, the postsynaptic density can also be mined in a similar manner and provide the basis for linking physiology and disease with proteomics and genetics. The finding that over one-third of genes encoding the NRC/MASC are important in human disease is a figure that may be more likely an underestimate for several reasons. First, the rate of discovery of mutations and their associations has not reached a plateau or decreased (data not shown). Second, although 47 genes implicated in humans appears high, data from rodent studies show interference with 43 genes by mutation or drugs impairs synaptic plasticity (Grant et al, 2003). Moreover, there are many genes that have -9-

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have presented a misleading picture of the literature. We also included papers which

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not been tested using the mouse knockouts, and it seems very likely that the number of mutations with phenotypes will increase. On the other hand, the over-reporting of associations which do not hold up to replication may reduce this number. Systematic testing of these genes in multiple clinical centres and cohorts will be required to refine these figures. The Genes to Cognition program (www.genes2cognition.org) aims to facilitate these activities by providing information and tools to collaborators and a central repository of data from human, mouse and other studies on this NRC/MASC proteins. The wide range of medical disorders involving NRC/MASC genes raises several interesting issues. First, the fact that 129 of 183 disorders are not primarily classified as nervous system disorders could be most easily explained by knowledge from gene cells (data not shown). Second, the disorders vary in aspects of their cellular pathology; for example some genes are involved in cancers and others degenerative disorders and this may be because of common signalling pathways. Third, the complexes contain proteins that are responsible for regulating multiple cell biological processes such as receptor trafficking, nuclear signalling and cytoskeletal rearrangement (Husi et al, 2000; Husi and Grant 2001). Together this provides an explanation for the pleiotropic role of mutations affecting the NRC/MASC. The NRC/MASC appears to be involved with both psychiatric and neurological conditions (see Supplementary Table 4). A considerable number of these disorders have cognitive components (autism, schizophrenia, mental retardation) consistent with mouse genetic studies showing specific impairments in cognitive function. It is also clear that not all mouse mutations in the NRC/MASC produce similar cognitive impairments. For example, Dlg4 (PSD-95) and Dlg3 (SAP-102) are homologues in the MAGUK family and bind directly to NMDA receptor subunits, yet mouse knockouts for these two genes have clearly distinct phenotypes in assays of working memory (unpublished results). In addition, several knockouts in NRC proteins including NR2B, SynGAP are perinatal lethals (Komiyama et al, 2003), whereas NR2A (Sakimura et al, 1995), PSD-95 (Migaud et al, 1998) and SAP102 are viable. Again this illustrates similar general patterns of pleiotropic function in mouse and humans. The NRC/MASC set will likely be a rich set of genes to investigate for human studies in the future.

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expression studies that at least 40% of NRC/MASC genes are expressed in non-neural

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Systematic studies of NRC/MASC genes, integrating mouse and human genetics, are underway. The Genes to Cognition Programme (G2C) was recently established in the UK to bring together an integrated research program linking basic and clinical neuroscience around the study of the NRC (www.genes2cognition.org). In addition to systematically analysing human mutations in disease cohorts, and creating and characterising mouse mutants, tools are under development to understand the diversity of molecules and their roles in different phenotypes. Large scale integrative approaches (human, mouse, physiology, behaviour etc) using scalable methods for analysing hundreds and thousands of genes will be essential for dissecting the subtly of functions and their mapping onto human diseases. These strategies are not only essential for studying the basic biology of disease, but will provide new approaches to identification of drug targets, which will be the number of druggable targets from simply the mutant genes directly involved with the aetiology of disease.

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other molecules in the complexes and networks. This will be important as it will increase

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References Collins, M.O., Husi , H., Yu, L., Brandon , J.M., Anderson, C.N.G., Blackstock, W.P., Choudhary, J.S., Grant, S.G.N. Molecular characterization and comparison of the components and multi-protein complexes in the postsynaptic proteome. J. Neurochemistry, (2005 in press) Choudhary, J. & Grant, S. G. Proteomics in postgenomic neuroscience: the end of the beginning. Nature Neuroscience 7:440-5 (2004). Dougherty, J.D., Geschwind, D.H. Progress in realizing the promise of microarrays in systems neurobiology. Neuron. 20:45(2):183-5. (2005) Farr C. D., Gafken P. R., Norbeck A. D., Doneanu C. E., Stapels M. D., Barofsky D.F., Minami M. and Saugstad J. A. Proteomic analysis of native metabotropic glutamate receptor 5 protein complexes reveals novel molecular constituents. J Neurochemistry 91, 438-450. (2004)

Grant, S. G. N., Husi, H., Choudhary, J., Cumiskey, M., Blackstock, W., Armstrong, J.D. The organisation and integrative function of the postsynaptic proteome. In Excitatory-Inhibitory Balance: Synapses, Circuits, Systems (ed. Hensch, T. K. F., M.) 13-44 (Kluwer Academic Publishers / Plenum Publishers, New York, 2003). Hoffmann, R., Krallinger, M., Andres, E., Tamames, J., Blaschke, C., Valencia, A. Text mining for metabolic pathways, signaling cascades, and protein networks. Science STKE. (2005) May 10;2005(283):pe21. Husi, H. & Grant, S. G. Proteomics of the nervous system. Trends Neuroscience 24:259-66 (2001). Husi, H., Ward, M. A., Choudhary, J. S., Blackstock, W. P. & Grant, S. G. Proteomic analysis of NMDA receptor-adhesion protein signaling complexes. Nature Neuroscience 3:661-9 (2000). Kim, E., Sheng, M. PDZ domain proteins of synapses. Nature Reviews Neuroscience. 5(10):771 (2004) 81. Komiyama, N. H., Watabe, A.M., Carlisle, H.J., Porter, K., Charlesworth, P., Monti, J., Strathdee, D.J.C., O’Carroll, C.M., Martin, S.J., Morris, R.G.M., O’Dell, T.J. & Grant, S.G.N. SynGAP regulates ERK/MAPK signaling, synaptic plasticity, and learning in the complex with postsynaptic density 95 and NMDA receptor. Journal of Neuroscience 22:9721-32 (2002). Mayer ML, Armstrong N. Structure and function of glutamate receptor ion channels. Annual Review of Physiology.66:161-81 (2004). Migaud, M., Charlesworth, P. Dempster, M., Webster, L.C., Watabe, A.M., Makhinson, M., He, Y., Ramsay, M.F., Morris, R.G.M., Morrison, J.H., O’Dell, T.J. & Grant, S.G.N. Enhanced long-term potentiation and impaired learning in mice with mutant postsynaptic density-95 protein. Nature 396: 433-9 (1998). Porter, K., Komiyama, N.H., Vitalis, T., Kind, P.C., Grant, S.G.N. Differential expression of two NMDA receptor interacting proteins, PSD-95 and SynGAP, during mouse development. European Journal of Neuroscience 21:351-362 (2005). Sakimura, K., Kutsuwada, T., Ito, I., Manabe, T., Takayama, C., Kushiya, E., Yagi, T., Aizawa, S., Inoue, Y., Sugiyama, H. & Mishina, M. Reduced hippocampal LTP and spatial learning in mice lacking NMDA receptor ε1 subunit. Nature 373:151-155 (1995). Sprengel, R., Suchanek, B., Amico, C., Brusa, R., Burnashev, N., Rozov, A., Hvalby, Ø., Jenson, V., Paulsen, O., Andersen, P., Kim, J.J., Thompson, R.F., Sun, W., Webster, L.C., Grant, S.G.N., Eilers, J., Konnerth, A., Li, J., McNamara, J.O., Seeburg, P.H. Importance of the intracellular domain of NR2 subunits for NMDA receptor function in vivo. Cell 92:279-89 (1998).

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Garry, E.M., Moss, A., Delaney, A., O'Neill, F., Blakemore, J., Bowen, J., Husi, H., Mitchell R., Grant, S.G.N., Fleetwood-Walker, S.M. Neuropathic sensitization of behavioral reflexes and spinal NMDA receptor/CaM kinase II interactions are disrupted in PSD-95 mutant mice. Current Biology 13(4):321-8. (2003).

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Tables and Figures Figure 1. Synapse proteome sets. 1a. The synapse is comprised of presynaptic terminal and postsynaptic terminals. Within the postsynaptic terminal reside glutamate receptor complexes (N, NRC/MASC; m, metabotropic; A, AMPA complexes), which are assembled into the postsynaptic density. 1b. Venn diagram illustrating the overlap of three glutamate receptor complexes with the PSD datasets. It can be seen that the majority of overlap between components of these complexes (NRC/MASC, AMPA, mGLuR5 and the PSD occurs in a subset of the PSD known as the cPSD (consensus PSD). Proteins detected in these multi-protein complexes which were not found in any of the PSD datasets are generally of low abundance that are enriched in immuno-purifications of complexes compared to whole

Table 1 Molecular classification of NRC/MASC and postsynaptic density proteins. Proteins found in the PSD and NRC/MASC datasets were functionally classified to show protein class enrichment. A diversity of structural classes illustrates the functional diversity and the need to identify pathways and processes for integration of signals. Details for individual proteins and further sub-classifications are found in Collins et al, 2005.

Table 2 Curated literature reporting mutations in NRC/MASC genes This is a summary of the master list (Supplementary Table 2) and was created to reduce the size of the original version (7 columns, 506 rows). The simplification processes were: i) removal of genes for which there were no reported mutations (this also enabled the removal of the ‘Mutation?’ column); ii) removal of mutations which had not been shown to be associated with a disorder; iii) where more than one report had been included showing that a gene was associated with a particular disease, these reports were amalgamated into one; iv) within each gene, the diseases were separated into nervous system disorders (black) and non-nervous system disorders (grey). Within each group, the diseases were alphabeticised; v) the ‘PPID’ and ‘PMID’ columns were removed, in order to reduce the physical size of the spreadsheet further. Columns: name, common protein name; mutation type, see mutation type key below; disease(s), diseases suggested to be associated with mutations in the gene; associated?, - 14 -

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PSD analyses. This figure adapted and further described in Collins et al, 2005.

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total numbers of reports indicating that the disease is, or is not, linked to mutations in the gene, e.g. ‘6Y, 2N’ indicates that 6 papers stated that the gene was associated with the disease and that 2 stated that it was not. Mutation type key: D, deletion; DNP, dinucleotide polymorphism; Du, duplication; FS, frameshift

mutation;

I,

insertion;

I/D,

insertion/deletion;

Mi,

missense;

MD/I,

microdeletion/insertion; MI/D, microinsertion/deletion; MSP, microsatellite polymorphism; MSRP, microsatellite repeat polymorphism; MSV, microsatellite variation; N, no mutation found; No, nonsense; Nu, null; P, polymorphism; RP, repeat polymorphism; SND, single nucleotide deletion; SNI, single nucleotide insertion; SNP, single nucleotide polymorphism; SpS, splice site mutation; T, translocation; TaP, TaqI polymorphism; TF, translocation fusion; TriNS; trinucleotide substitution.

Pathogenic and non-pathogenic mutations This lists those genes for which our search returned abstracts, and provides a summary of the mutations identified in them. Columns: name, common protein name; pathogenic mutation?, is there a report of a mutation that was associated with a disease; non-pathogenic mutation?, is there a report of a mutation that was not associated with a disease.

Table 4 Nervous system diseases This lists all the nervous system disorders reported in our search.

It is derived from

Supplementary Table 3, the legend of which provides additional information on selection criteria.

Supplementary Table 1: Proteins in the human NRC/MASC. All proteins are identified by their name, PPID number (which is a numbering scheme for the protein interaction database, www.PPID.org) and the majority are also identified by their Ensembl ID. We also provide information about the types of protein found; these are identical to those used by Collins et al (submitted). The table includes expansions of protein families such as Guanine Nucleotide Binding Proteins. - 15 -

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Table 3

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Columns: PPID, protein interaction database identifier; name, common protein name; ensemble ID, gene identifier in www.ensembl.org; chromosome number, specific human chromosome; category, class of protein.

Supplementary Table 2 Master table of curated literature This includes all the genes which returned abstracts. Each paper included is given a row to itself and relevant information reported in the columns. Columns: name, common protein name; ensemble ID, gene identifier in www.ensembl.org; mutation?, has a mutation been found in the gene; mutation type, see mutation type key below; disease, diseases suggested to be associated with mutations in the gene; for the paper on www.pubmed.org. Mutation type key: D, deletion; DNP, dinucleotide polymorphism; Du, duplication; FS, frameshift

mutation;

I,

insertion;

I/D,

insertion/deletion;

Mi,

missense;

MD/I,

microdeletion/insertion; MI/D, microinsertion/deletion; MSP, microsatellite polymorphism; MSRP, microsatellite repeat polymorphism; MSV, microsatellite variation; N, no mutation found; No, nonsense; Nu, null; P, polymorphism; RP, repeat polymorphism; SND, single nucleotide deletion; SNI, single nucleotide insertion; SNP, single nucleotide polymorphism; SpS, splice site mutation; T, translocation; TaP, TaqI polymorphism; TF, translocation fusion; TriNS; trinucleotide substitution.

Supplementary Table 3 Complete reference list All papers included in the spreadsheet are listed by their PubMed ID. Full references are given for each. Columns: PMID, identifier for the paper on www.pubmed.org ; Date, original publication date; Journal; Authors; Title; Volume; Issue; Pages.

Supplementary Table 4 Diseases associated with NRC/MASC genes. Summary table listing the diseases reported involving NRC/MASC genes. This includes any disorders which were ultimately claimed not to be linked to any of the NRC/MASC - 16 -

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associated?, is the mutation indicated to be associated with the disease; PMID, identifier

Human Genetics of the NMDA receptor complex

genes; for instance, if a paper sought to identify a link between a mutation in gene X and disorder Y, and found no evidence, disorder Y is still included in our list. The following procedures were followed to compile the list: 1. Certain disease terms proved to be synonymous; only one of the synonyms was retained in each case; 2. Certain

disease

terms

were

very

closely

related

to

each

other,

e.g.

pseudhypoparathyroidism types 1a and 1b. In these cases, all the variants were grouped together into one listing; 3. In some cases, a disease was only reported in conjunction with another. These diseases were separated into individual entries; 4. In some cases, a modification of a disease was reported, e.g. ‘autism with Rett-like Once completed, the disease list was annotated. Each disease was classified as being a nervous system or non-nervous system disease: 1. The nervous system was defined as being both the central and peripheral nervous systems. Hence, a disease confined to a distal nerve ending was still classed as a nervous system disease; 2. Many diseases have a range of major symptoms, observed in different regions of the body. If only one of these cardinal symptoms was a nervous system effect, the disease was classed as being a nervous system disease; 3. Certain forms of cancer may invade the nervous system, but are not defined as doing so. These were classified as non-nervous system diseases; 4. Any disease producing mental retardation or other psychological effect was classed as being a nervous system disease. 183 disorders were reported, of which 54 were classed as nervous system diseases (29.5%).

- 17 -

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syndromes’. These were given separate entries to their parent diseases.

1a Presynaptic terminal

Postsynaptic terminal

N

m A

Postsynaptic proteome

dendrite

1b 34

5

NRC/ MASC

122 3 951

1

19

1

30 mGluR5-R 14 Total PSD

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AMPA-R

Table 1

Channels and Receptors MAGUKs / Adaptors / Scaffolders Ser/Thr Kinases Tyr Kinase Protein Phosphatases G-proteins and Modulators Signalling molecules and Enzymes Transcription and Translation Cytoskeletal and Cell Adhesion Molecules Synaptic Vesicles and Protein Transport Novel Other Summary

Total PSD 80 54 46 3 18 77 278 119 153 159 107 30 1124

MASC 12 20 21 2 7 19 40 5 35 22 3 0 186

Grant et al, Table 2

Gene NR1

NR2A NR2B CALM SPNB

DLG1 DLG3 Src

PTP1D

GRIK2

STX

GNAS1

GNAZ GNAT2 GNB3 ACT

cPLA2

b-catenin

CLTC MTAP2

1

Mutation type(s) SNP SNP, D, MI/D SNP SNP RP SNP SNP T SNP SNP I D SNP, D, RFLP D, No, Nu, SNP SNP? SNP SNP SNI, SNP SNP SNP D Mi Mi, SNP SNP, Mi SNP Mi, SNP SNP, Mi, TriNS Mi SNP RP SNP SNP SNP SNP D SNP Mi, I, D, SpS, SNP, SNI, SND, No Mi I, SNP SNP ? Mi Mi D Mi No, Mi D, Mi, Mi Mi, D, I Mi, D? SNP D ? SNP I SNP Mi SNP N Mi SNP, RP RP SNP Mi ? D, SNP, Mi SNP, D Mi RP SNP SNP, D SNP, D SNP, D SNP Mi, D Mi ? Mi Mi ? SNP SNP SNP, Mi SNP Mi Mi, D TF TF D

Disease(s) Associated? Bipolar disorder Y Schizophrenia Y, 6N Alcoholism Y ADHD Y Y Schizophrenia Schizophrenia Y, N Alcoholism Y Leukaemia Y Dominant hereditary spherocytosis 2Y Dominant hereditary elliptocytosis Y Elliptocytosis Y Elliptocytosis, Hemolysis Y Hereditary elliptocytosis 9Y Hereditary spherocytosis 4Y Hydrops fetalis 2Y Y Spherocytic elliptocytosis Mammary ductal carcinoma Y X-linked mental retardation Y Colon cancer Y Endometrial carcinoma Y Myeloid leukaemia 2Y Childhood acute lymphoblastic leukaemia, acute myeloid leukemia Y Juvenile myelomonocytic leukaemia 2Y Leopard syndrome 4Y Leopard syndrome, hypertrophic cardiomyopathy Y Noonan syndrome 5Y Noonan syndrome, juvenile myelomonocytic leukaemia, myelodysplastic syndrome, acute myeloid leukemY 2Y Noonan syndrome, pulmonary valve stenosis Autism Y Huntington's disease 3Y Obsessive-compulsive disorder Y Acute lymphocytic leukaemia Y Oculodentodigital dysplasia ? Schizophrenia Y Williams syndrome Y Y Type II diabetes Albright hereditary osteodystrophy 7Y Cushing's syndrome secondary to ACTH-independent macronodular adrenal hyperplasia Y Enhanced trauma-related bleeding tendency Y Essential hypertension Y Fibrous dysplasia Y McCune-Albright syndrome 2Y McCune-Albright syndrome with precocious puberty Y Platelike osteoma cutis Y Premature thelarche Y Progressive osseous heteroplasia 3Y Pseudohypoparathyroidism Y Pseudohypoparathyroidism type Ia 3Y Pseudohypoparathyroidism type Ia + Ic Y Pseudohypoparathyroidism type Ia, testotoxicosis Y Pseudohypoparathyroidism type Ib Y Y Thyroid carcinoma in McCune-Albright syndrome Bipolar disorder Y Achromatopsia Y Seasonal affective disorder Y Age-related hearing loss Y Autosomal dominant hearing loss Y Hereditary progressive hearing loss ? Y Dilated cardiomyopathy Schizophrenia 3Y, 2N Major depressive disorder, bipolar disorder Y, N Y Diabetes mellitus Adenocarcinoma Y Colorectal adenoma ? Colorectal cancer 3Y Colorectal cancer, endometrial carcinoma Y Endometrial cancer Y Endometrial carcinoma Y Hepatoblastoma Y Hepatocellular carcinoma Y Lung cancer, malignant mesothelioma Y Malignant melanoma 2Y Medulloblastoma ? Melanoma Y, N Ovarian endometrioid adenocarcinoma Y Ovarian endometrioid carcinoma Y Ovarian endometrioid tumour Y Ovarian epithelial tumour Y Papillary thyroid carcinoma Y Pilomatricoma 2Y Primitive neuroectodermal tumour Y Prostate cancer 2Y Sporadic desmoid tumour Y Thyroid carcinoma Y Y Uterine endometrioid carcinoma Inflammatory myofibroblastic tumour ? Paediatric renal adenocarcinoma ? Autism, Rett-like symptoms ?

Grant et al, Table 2

RAF1 Myosin (V)

D TF Mi, No, D Mi Mi, RP, No, D, SNP, I/D SNP, Mi, D, I Mi Jip-1 SNP Mi PKCbeta SNP Rac1 SNP NF-1 Mi MD ? SNP, D, SpS, No, I, Mi, T, RP, Du I D, I Mi, SpS D? D ? D D H-Ras RP ? D TaP D D ? Mi SNP, Mi Filamin ? Mi, D, No SNP FUS TF TF TF TF TF TF TF TF TF PKCgamma SNP Mi SNP, Mi nNOS DNP SNP, RP SNP, MSV, DNP RP MSV SNP ? Synaptogyrin No L1CAM ? SNP Du Mi, No, D, SpS D, SNP SNP ? No, FS, SpS, MI, D, SNP, I SpS, No SpS, Mi SpS D, Mi DSG SNP No SNI, SND, SNP PPP2R1A SNP, SNI MIRP mGluR5 SNAP25 RP D DNP ATP1A1 Mi RFLP ? D MBP D RP, PM, FLP, D, MSV PM and Du PLP1 PM Mi PM (multiple different) I, D, FS, PM, Mi, Du, SNP PM

2

Urinary bladder cancer Acute myeloid leukaemia Autosomal dominant macrothrombocytopenia Autosomal dominant myopathy Familial hypertrophic cardiomyopathy Griscelli syndrome Idiopathic dilated cardiomyopathy Alzheimer's Type 2 diabetes Diabetic nephropathy Brain tumours (various) Anaplastic astrocytoma, colon adenocarcinoma, myelodysplastic syndrome Malignant peripheral nerve sheath tumours Neuroblastoma Neurofibromatosis type I Neurofibromatosis type I, Watson syndrome, Noonan syndrome Neurofibromatosis-Noonan syndrome Spinal neurofibromatosis Desmoplastic neurotropic melanoma Juvenile chronic myelogenous leukaemia Juvenile myelomonocytic leukaemia Malignant melanoma Uveal melanoma Autism Lung cancer, prostate cancer, non-Hodgkin's lymphoma Malignant lymphoma Malignant melanoma Multiple endocrine neoplasia type I Nonmucinous epithelial ovarian carcinoma Ovarian cancer Spitz nevi Otopalatodigital syndrome types 1 and 2 ), frontometaphyseal dysplasia, Melnick-Needles syndrome Periventricular heterotopia Periventricular nodular heterotopia Periventricular nodular heterotopia, frontometaphyseal dysplasia Acute myeloid leukaemia Angiomatoid fibrous histiocytoma Low grade fibromyxoid sarcoma Malignant liposarcoma Myxoid liposarcoma Myxoid liposarcoma, acute myeloid leukaemia Myxoid liposarcoma, malignant fibrous histiocytoma Myxoid liposarcoma, round cell liposarcoma Myxoid liposarcoma, round cell liposarcoma, well-differentiated liposarcoma, pleomorphic liposarcoma Autosomal dominant cerebellar ataxia Dominant non-episodic cerebellar ataxia Spinocerebellar ataxia type 14 Parkinson's Schizophrenia Asthma Cystic fibrosis End-stage renal disease Immunoglobulin E-mediated allergic diseases Infantile pyloric stenosis Schizophrenia CRASH syndrome Hydrocephalus, Hirschsprung's disease Hydrocephalus-stenosis of the aqueduct of Sylvius Hydrocephalus-stenosis of the aqueduct of Sylvius, MASA syndrome MASA syndrome Schizophrenia X-linked complicated spastic paraplegia, MASA syndrome, HSAS X-linked hydrocephalus X-linked hydrocephalus, Hirschsprung's disease X-linked hydrocephalus, MASA syndrome X-linked hydrocephalus, spastic paraplegia X-linked spastic paraplegia, MASA syndrome Pemphigus foliaceus Striate keratoderma Striate palmoplantar keratoderma Breast carcinoma, lung carcinoma, melanoma Schizophrenia Schizophrenia Alagille syndrome Bipolar disorder rapid-onset dystonia parkinsonism. Diabetes (diabetic neuropathy) Type 1 or type 2 diabetes 18q-syndrome Intractable epilepsy Multiple sclerosis Charcot-Marie-Tooth disease type 1A Hereditary spastic paraplegia Late-onset spastic paraplegia type 2 Pelizaeus-Merzbacher disease / X-linked spastic paraplegia Pelizaeus-Merzbacher disease. X-linked spastic paraplegia

Y Y Y Y 13Y, 2N 4Y, 2N Y Y Y Y Y Y Y Y 23Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y ? Y Y 3Y Y 2Y Y 2Y Y 3Y 2Y Y 2Y Y Y Y 2Y 2Y Y, N 5Y Y ? ? Y Y Y ? Y Y Y Y Y 15Y Y 3Y Y Y Y Y Y ? Y Y ? Y 2Y Y ? 5Y, N Y 6Y, 6N Y Y? Y Y 34Y Y

Grant et al, Table 2

Code D DNP Du FS I I/D Mi MD/I MI/D MSP MSRP MSV N No Nu P RP SND SNI SNP SpS T TaP TF TriNS

3

Mutation type Deletion Dinucleotide polymorphism Duplication Frameshift mutation Insertion Insertion / deletion Missense Microdeletion/insertion Microinsertion/deletion Microsatellite polymorphism Microsatellite repeat polymorphism Microsatellite variation No mutation found Nonsense Null Polymorphism Repeat polymorphism Single nucleotide deletion Single nucleotide insertion Single nucleotide polymorphism Splice site mutation Translocation TaqI polymorphism Translocation fusion (with another gene) Trinucleotide substitution

Grant et al, Table 3 Pathogenic mutation?

Name

14-3-3gamma 14-3-3zeta/delta ACT ACTN AKAP5 APPL Arg3.1 ARPC2 ARPC4 ATP1A1 ATP1A3 ATP2B4 Bassoon b-catenin CALM CAMK2A CIT CLTC CortBP-1 cPLA2 CTTN DLG1 DLG2 DLG3 DLG4 DLGAP1 DNM1 DSG Erk1 Erk2 FAK2 Filamin FUS GAP43 GAPDH GCFC GNA11 GNA12 GNA13 GNA14 GNAI1 GNAI2 GNAI3 GNAL GNAO1 GNAQ GNAS1 GNAT1 GNAT2 GNAT3 GNAZ GNB1 GNB2 GNB3 GNB5 GNG1 GNG10

1

Non-pathogenic mutation?

Y

Y

Y Y

Y

Y Y

Y Y

Y

Y Y Y Y

Y Y

Y Y

Y

Y

Y

Y Y

Y

Y

Name

GNG11 GNG12 GNG2 GNG3 GNG4 GNG5 GNG7 GNG8 GNGT2 GRB2 GRIK2 GSK3 beta HAPIP HOMER1 HPK1 H-Ras INA Jip-1 L1CAM MBP MEK1 MEK2 mGluR1a mGluR5 MKK7 MKP2 MOG MTAP2 Myosin (V) N-cadherin NF-1 nNOS NR1 NR2A NR2B NSF PDK-1 PI3-K PKA-R1a/b PKA-R1a-a PKA-R2a PKA-R2b PKCbeta PKCepsilon PKCgamma PLCb PLCg-1 PLP1 PP2B PP5 PPP2CA PPP2R1A PRKA9 PRKACA PTP1D PTPN5 Rab2

Pathogenic mutation?

Y

Non-pathogenic mutation?

Y Y Y

Y

Y

Y Y Y

Y

Y

Y Y Y Y Y Y Y Y

Y Y Y Y Y

Y Y

Y Y

Y

Y

Y

Y

Name

Rab3 Rac1 RACK-1 RAF1 RalA RAN Rap2 Rsk-2 SLMAP SNAP25 SPANK1 SPNB Src STX STXBP1 Synaptogyrin SynGAP SYT1 Tubulin YWHAE ZO-1 Total

Pathogenic mutation?

Y Y

Y Y Y Y Y

40

Grant et al, Table 3 Non pathogenic mutation?

Y

Y

Y

27

2

Grant et al, Table 4

Nervous system disorders 18q-syndrome Achromatopsia Age-related hearing loss Albright hereditary osteodystrophy Alcoholism Alzheimer's disease Anaplastic astrocytoma Attention deficit hyperactivity disorder (ADHD) Autism Autism, Rett-like symptoms Autosomal dominant cerebellar ataxia Autosomal dominant hearing loss Bipolar disorder Brain tumours (various) Charcot-Marie-Tooth disease type 1A CRASH syndrome Distal hereditary motor neuropathy type II Dominant non-episodic cerebellar ataxia Familial periventricular heterotopia Gilles de la Tourette syndrome Hereditary progressive hearing loss Hereditary spastic paraplegia Huntington's disease Hydrocephalus Hydrocephalus-stenosis of the aqueduct of Sylvius (HSAS) Intractable epilepsy Late-onset spastic paraplegia type 2 Major depressive disorder Malignant peripheral nerve sheath tumours MASA syndrome Monosymptomatic idiopathic optic neuritis Multiple sclerosis Neuroblastoma Neurofibromatosis type I Neurofibromatosis-Noonan syndrome Obsessive-compulsive disorder Otopalatodigital syndrome types 1 and 2 Parkinson's disease Pelizaeus-Merzbacher disease Periventricular heterotopia Periventricular nodular heterotopia

Rapid-onset dystonia parkinsonism Schizoaffective disorder Schizophrenia Seasonal affective disorder Spinal neurofibromatosis Spinocerebellar ataxia type 14 Unipolar affective disorder Watson syndrome Williams syndrome X-linked complicated spastic paraplegia X-linked hydrocephalus X-linked mental retardation X-linked spastic paraplegia