European Journal of Neuroscience, Vol. 39, pp. 1059–1067, 2014

doi:10.1111/ejn.12489

The synapse in schizophrenia Andrew J. Pocklington, Michael O’Donovan and Michael J. Owen MRC Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Hadyn Ellis Building, Maindy Road, Cathays, Cardiff CF24 4HQ, UK Keywords: proteomics, psychiatric genetics, synaptic plasticity

Abstract It has been several decades since synaptic dysfunction was first suggested to play a role in schizophrenia, but only in the last few years has convincing evidence been obtained as progress has been made in elucidating the genetic underpinnings of the disorder. In the intervening years much has been learned concerning the complex macromolecular structure of the synapse itself, and genetic studies are now beginning to draw upon these advances. Here we outline our current understanding of the genetic architecture of schizophrenia and examine the evidence for synaptic involvement. A strong case can now be made that disruption of glutamatergic signalling pathways regulating synaptic plasticity contributes to the aetiology of schizophrenia.

Introduction Schizophrenia is a severely debilitating psychiatric disorder affecting ~1% of the population worldwide (Saha et al., 2005), with onset typically occurring in adolescence or early adulthood. Individuals suffering from the disorder manifest a range of behavioural characteristics, including positive (delusions and hallucinations) and negative symptoms (e.g. blunted affect, lack of volition), as well as cognitive, mood and motor symptoms (Tandon et al., 2009). While the positive symptoms account for the majority of acute episodes of illness requiring contact with psychiatric services, it is the negative and cognitive symptoms that are particularly socially disabling and correlate with higher chronic functional impairment. The presence and severity of individual symptoms varies widely between patients and may change throughout the course of illness. Pre-morbid cognitive, motor and social impairments, evident in a proportion of cases, are associated with earlier disease onset and more severe cognitive and negative symptoms. While existing medications have proven effective in treating positive symptoms in the majority of cases, there has been little success in ameliorating the cognitive and negative deficits associated with much of the reduction in quality of life. Drug response varies widely between patients (de Leon et al., 2005; Mauri et al., 2007; Stroup, 2007) and the optimisation of treatment (both choice of agent and dose) is largely a matter of trial and error. In addition to phenotypic heterogeneity, a considerable degree of heterogeneity occurs at the genetic level. Schizophrenia is a highly heritable disorder with genetic factors accounting for ~80% of the liability for developing the disease (Cardno & Gottesman, 2000; Sullivan et al., 2003). Genotyping and sequencing studies indicate that the number of DNA variants contributing to susceptibility is large, encompassing both common polymorphisms and rare mutations with a correspondingly wide range of effect sizes (discussed in detail below). Initial evidence for synaptic involvement in the aetiology of schizophrenia was indirect, being largely based upon the efficacy of

Correspondence: Dr M. J. Owen, as above. E-mail: [email protected] Received 16 October 2013, revised 19 December 2013, accepted 20 December 2013

pharmacological agents to either ameliorate or induce psychotic symptoms through their action upon neurotransmitter systems. Interest first focused upon dopamine following observation that all of the effective medications in current use block dopamine D2 receptors; efficacy is correlated with D2 receptor affinity (Creese et al., 1976; Seeman et al., 1976); and psychotic symptoms can be induced through repeated amphetamine use. Later, models based upon the perturbation of N-methyl-D-aspartate receptor (NMDAR) function were also developed (Kim et al., 1980; Javitt & Zukin, 1991; Olney & Farber, 1995) due to similarities between the symptoms induced by phencyclidine, an NMDAR antagonist, and schizophrenia (Lodge & Anis, 1982). Theories concerning the role of other neurotransmitters have proliferated, with c-aminobutyric acid (GABA)ergic, serotonergic, metabotropic glutamatergic, muscarinic and nicotinic signalling linked to various aspects of disease (Freedman et al., 2003; Lewis et al., 2005; Abi-Dargham, 2007; Raedler et al., 2007; Harrison et al., 2008). This diversity of models raises the question of whether schizophrenia involves a general degradation in synaptic function, or the perturbation of specific neurotransmitter systems or pathways. Ongoing efforts to unravel the molecular organisation of synaptic function will prove essential in discriminating between these possibilities.

The molecular organisation of the synapse Both presynaptic vesicle release and postsynaptic activation are governed by highly complex macromolecular machines embedded in the synaptic terminals of their respective neurons and precisely aligned via trans-synaptic cell adhesion proteins (Fig. 1). Proteomic analyses have isolated ~200 distinct proteins from the presynaptic active zone (AZ; Morciano et al., 2009), which regulates the docking and priming of synaptic vesicles. The AZ also serves to localise calcium channels responsible for coupling presynaptic excitation to membrane fusion of primed vesicles and neurotransmitter release. Synaptic vesicles are themselves complex structures composed from over 400 proteins (Takamori et al., 2006; Gronborg et al., 2010), of which only a limited number are presumed to be constitutive elements of all vesicles (Sudhof, 2004).

© 2014 Federation of European Neuroscience Societies and John Wiley & Sons Ltd

1060 A. J. Pocklington et al.

Fig. 1. Proteomic dissection of the synapse. The composition of many synaptic components has been studied, primarily in rodents. Presynaptic structures include: the presynaptic active zone (AZ; Morciano et al., 2009); synaptic vesicles (Morciano et al., 2005; Takamori et al., 2006; Gronborg et al., 2010); and Cav2 Ca-channel complexes (Muller et al., 2010) – although this last was not a purely presynaptic study. In addition to isolation of the postsynaptic density (PSD) as a whole (Walikonis et al., 2000; Yamauchi, 2002; Jordan et al., 2004; Li et al., 2004a; Peng et al., 2004; Yoshimura et al., 2004; Bayes et al., 2011), postsynaptic sub-structures include: a-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) (Collins et al., 2006), N-methyl-D-aspartate (NMDA) complex (NRC)/membrane-associated guanylate kinase (MAGUK)-associated signalling complex (MASC; Husi et al., 2000; Husi & Grant, 2001), metabotropic glutamate receptor mGluR5 (Farr et al., 2004), serotonin 5-HT-2C receptor (Becamel et al., 2002) and PSD-95 (Fernandez et al., 2009) complexes. Nicotinic-alpha 7 acetylcholine receptor complexes (Paulo et al., 2009) are found at both pre- and postsynaptic sites. The figure illustrates the molecular overlap between these complexes, but does not attempt to show their precise localisation or abundance.

Inhibitory and excitatory synapses display marked postsynaptic differences, with excitatory, glutamatergic synapses possessing a prominent thickening of the postsynaptic membrane known as the postsynaptic density (PSD). Over 1400 proteins have been isolated from human PSD (Bayes et al., 2011), while it has been estimated that the PSD of an individual synapse may be constructed out of approximately 50–100 component proteins (Sheng & Hoogenraad, 2007; Selimi et al., 2009). The molecules affiliated with the PSD include many central to the regulation of synaptic plasticity – the alteration of synaptic properties in response to patterns of activity that is widely considered to form the molecular basis for behavioural learning and memory. Mechanisms contributing to synaptic plasticity include alterations in vesicle release probability, structural remodelling of the synapse, protein synthesis and the trafficking of postsynaptic a-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs) (Citri & Malenka, 2008). Both pre- and postsynaptic processes mediate plasticity at glutamatergic synapses, whereas inhibitory, GABAergic synapses – which do not contain the extensive signalling machinery found in the PSD (Heller et al., 2012) – are more heavily reliant upon presynaptic mechanisms, primarily mediated by the AZ. Regulation of protein interactions and the formation of functional complexes are as central to the synapse as they are to other cellular signalling domains (Scott & Pawson, 2009). Consequently, the isolation and characterisation of complexes containing a given protein can provide much information concerning its functional role. This is well illustrated by the purification of key components of the glutamatergic signalling machinery. Depolarisation of the

postsynaptic cell is a relatively simple process driven by the activation of AMPARs, isolation of which recovers small functional complexes composed of receptor subunits and a few cytoskeletal and trafficking proteins regulating their localisation (Collins et al., 2006). In contrast, signalling via the NMDAR regulates the induction of synaptic plasticity through the coordinated activation of multiple downstream pathways, with membrane-associated guanylate kinase (MAGUK)-family scaffold proteins coupling the receptor to downstream signalling proteins. The purification of NMDAR complex/MAGUK-associated signalling complexes (NRC/MASCs) yields large, 2–3-MDa complexes that draw together components of diverse effector pathways underlying plasticity (Husi et al., 2000; Husi & Grant, 2001). The MAGUK protein PSD-95 is a major structural component of the PSD, lying close to the postsynaptic membrane and interacting with a wide variety of membrane-spanning molecules, including NMDARs and AMPARs. Reflecting this, isolation of PSD-95 complexes recovers many channels and receptors, but relatively few downstream signalling molecules (Fernandez et al., 2009), providing a ‘horizontal’ cross-section of the PSD compared with the ‘vertical’ view provided by NRC/MASC (Fig. 1). This dissection of synaptic signalling provides a valuable resource for psychiatric genetics, allowing individual complexes to be tested for enrichment with risk variants and more refined models of synaptic dysfunction to be developed. While a number of neurotransmitter systems of interest to schizophrenia research have been characterised in this way (Fig. 1), limited information is available for others, most notably dopamine.

© 2014 Federation of European Neuroscience Societies and John Wiley & Sons Ltd European Journal of Neuroscience, 39, 1059–1067

The synapse in schizophrenia 1061

Schizophrenia genetics Early attempts to identify schizophrenia risk mutations through linkage analysis and candidate gene studies were hampered by small sample sizes with sufficient power to reliably detect only common risk variants of large effect. Reports of novel risk loci proliferated, none of which replicated with any degree of consistency, and the only clear conclusion to be drawn was that common variants of large effect do not contribute to schizophrenia. It is only relatively recently, with the advent of genome-wide association studies (GWAS) in large samples, that individual loci contributing to disease susceptibility have been convincingly identified.

Common variants GWAS seek to identify common (minor allele frequency > 1%), single-nucleotide polymorphisms (SNPs) associated with risk of disease by comparing their frequencies in affected individuals with those in population-matched controls and testing for statistically robust differences. A typical GWAS will genotype over 105 SNPs, with imputation based on local correlation between markers (linkage disequilibrium, LD), allowing information to be recovered on up to ~107 SNPs. With an estimate of 900 000 effectively independent SNPs in the human genome (International HapMap Consortium, 2005; Dudbridge & Gusnanto, 2008), the threshold for genome-wide significant association is generally set at about P = 5.5 9 10 8, corresponding to a multiple-testing corrected P-value of 0.05. Of the genome-wide significant associations found in schizophrenia to date [Stefansson et al., 2009; Schizophrenia Psychiatric Genome-Wide Association Study (GWAS) Consortium, 2011; Ripke et al., 2013], the strongest occur within the extended major histocompatibility complex on chromosome 6, a region with extremely high LD in which it has so far proven impossible to localise the causal variant(s) to within even a megabase. Of the genes that localise near to other genome-wide significant signals, some play a role in brain development (miR137, TCF4), others in calcium signalling (NRGN, CACNA1C, CACNB2), while the function of many is relatively unknown. The association of variants within L-type calcium channels does not appear to be unique to schizophrenia, as both CACNA1C and CACNB2 SNPS are found to confer risk across a range of psychiatric disorders (Smoller et al., 2013). Components of CaV2 calcium channel complexes (Muller et al., 2010; Fig. 1) have also been noted in genome-wide significant loci spanning multiple genes (Ripke et al., 2013), although firm conclusions regarding pathogenicity are obviously difficult to draw. In addition to the identification of specific genome-wide significant variants, analysis of GWAS data has revealed the existence of a large number of polymorphisms, currently at sub-threshold levels of statistical significance, which contribute to disease risk and remain to be uncovered in larger studies. It has been estimated that one-third of the genetic variation underlying susceptibility to schizophrenia is captured by genotyped and imputed SNPs on current GWAS chips and that this contribution is largely due to causal variants that are also fairly common, although a contribution from rare mutations with which they are weakly correlated is also likely (International Schizophrenia Consortium et al., 2009; Lee et al., 2012). With identified genome-wide significant SNPs typically associated with odds ratios around 1.1 or less, the functional consequences of individual common variants are presumably mild – most SNPs do not alter protein coding and are likely to confer risk by perturbing gene expression rather than protein function. This would suggest

that their contribution to susceptibility arises through the mass action of large numbers of variants in co-expressed genes and proteins. With this in mind, efforts have been made to combine evidence across multiple loci and test for association in functionally relevant gene sets. A number of potential confounds must be considered when performing such analyses, including gene size, variable SNP density and LD, genomic inflation, and local clustering/overlap of genes (Wang et al., 2011). The variability seen across enrichment analyses to date in part reflects the ability of different methods to account for such factors, while differing methodological assumptions (e.g. whether association is likely to be concentrated within single or multiple semi-independent SNPs per gene) and the small size of individual GWAS studies used are also likely to exert a considerable influence. With a more powerful mega-analysis of multiple GWAS studies now available [Schizophrenia Psychiatric Genome-Wide Association Study (GWAS) Consortium, 2011], it remains to be seen whether properly controlled analyses produce more consistent results. Given that the number of genes harbouring schizophreniarisk SNPs runs into the hundreds, and possibly thousands (Ripke et al., 2013), it is clear that individual, small synaptic complexes cannot be the sole target for these mutations in schizophrenia. Although it is possible that common variants converge on larger synaptic structures, such as the PSD, clear evidence for this has yet to emerge from robustly controlled gene set enrichment analyses. While the discovery of genome-wide significant loci is gathering pace, the process of identifying the true causal risk variants is hampered by extensive LD, which can spread an association signal over several megabases of the genome. Even if causal variants could easily be isolated, they may still lie in intergenic regions at some distance from the gene(s) they influence (Bernstein et al., 2012), and only a fraction of such regulatory relationships have yet been mapped (Thurman et al., 2012). Given these caveats, the evidence to date suggests that common variants affecting synaptic function do indeed contribute to schizophrenia, although the synapse is unlikely to be the sole focus of disruption.

Copy number variants (CNVs) The strongest support for synaptic involvement in schizophrenia comes from the study of CNVs – duplications and deletions of extended sequences of DNA. Although originally designed for genotyping SNPs, probe intensity data from GWAS chips can be used to reliably detect CNVs over ~10 kb in size and covered by a minimum of ~10 probes. Analysis of such data has revealed good evidence for an increased burden of large, rare CNVs (> 100 kb, < 1%) in individuals with schizophrenia compared with matched controls (International Schizophrenia Consortium, 2008; Walsh et al., 2008; Rees et al., 2014), although this finding is not universal (Levinson et al., 2011). This excess burden is probably distributed across a large number of individual loci, but due to their rarity only a modest number, about 11, of individual CNV loci have been strongly implicated in schizophrenia (Table 1), with an estimated 2.5% of patients and 0.9% of unaffected controls carrying a CNV at one of these loci (Rees et al., 2014). These CNVs are typically large in both size and effect, with estimated odds ratios ranging from 2 to > 50 (Rees et al., 2014). The pathogenic effects of these CNVs are not confined to schizophrenia, most having been shown to increase risk for other disorders as well (Girirajan et al., 2012; Malhotra & Sebat, 2012). The phenotypic heterogeneity and rarity of schizophrenia CNVs, combined with the fact that many of them span multiple genes (Table 1), makes it difficult to infer the biological mechanism(s) through which they contribute to disease aetiology. The only single-

© 2014 Federation of European Neuroscience Societies and John Wiley & Sons Ltd European Journal of Neuroscience, 39, 1059–1067

1062 A. J. Pocklington et al. Table 1. CNV loci conferring risk of schizophrenia

Locus

Del/Dup

Position (kb)

N gene

Synaptic genes

1q21.1

Del & dup

chr1:146570147390

7

NRXN1

Del

chr2:5015051260

1

3q29

Del

chr3:195730197340

21

DLG1

Williams Beuren syndrome 15q11.2

Dup

chr7:7274074140

24

STX1A,CLIP2

Del

chr15:2280023090

4

Angelman/ Prader-Willi

Dup

chr15:2482028430

10

15q13.3

Del

chr15:3113032480

7

CHRNA7

16p13.11

Dup

chr16:1551016300

7

MYH11,C16orf45

16p11.2

Dup

chr16:2964030200

27

ALDOA,TAOK2, MAPK3,CORO1A, MVP

22q11.2

Del

chr22:1902020260

27

SLC25A1,SEPT5

NRXN1

CYFIP1 GABRA5,GABRB3

Risk loci from Rees et al. (2014) surpassing their suggested genome-wide significance threshold (P < 4.1 9 10 4). Genomic positions in Build 37.3 are given, along with the number of protein-coding genes spanned by these loci. Synaptic genes intersecting CNV regions were identified based upon proteomic studies mentioned in the main text and channel/receptor complexes shown in Fig. 1. Italics indicate genes whose expression was found to be dysregulated by these CNVs in Luo et al. (2012).

gene CNV locus to have been consistently associated with schizophrenia to date lies within NRXN1 (Kirov et al., 2008; Rees et al., 2014), which codes for the presynaptic cell adhesion protein neurexin 1. While most well-supported CNV loci contain at least one synaptic gene (Table 1), it is unclear to what extent these contribute to CNV pathogenicity. Initial estimates indicate that large (> 100 kb) CNVs account for ~20% of the heritable variation in gene expression (Stranger et al., 2007), influencing genes both inside and outside their boundaries (Stranger et al., 2007; Henrichsen et al., 2009; Luo et al., 2012), with deletions typically exerting a greater effect on gene expression than duplications (Stranger et al., 2007; Ye et al., 2012). Effects on expression are generally positively correlated with dosage for genes within CNVs, but are more complex amongst those in flanking regions (Stranger et al., 2007; Henrichsen et al., 2009; Luo et al., 2012). Influence over flanking genes, presumably via perturbation of regulatory elements, extends up to 500 kb (Stranger et al., 2007; Henrichsen et al., 2009), although a few interactions may occur over much larger distances (Stranger et al., 2007). To complicate matters, not all genes inside CNV regions exhibit altered expression. Work in mouse indicates that the effect of CNVs on brain gene expression is more tightly regulated than in other tissues (Henrichsen et al., 2009). The extent to which expression is buffered has been found to vary between brain regions and throughout development, with CNVs exerting their greatest influence over gene expression during the period of neuronal outgrowth, differentiation and synaptogenesis (Chaignat et al., 2011). To identify genes dysregulated in CNV

carriers (Luo et al., 2012) analysed gene expression in lymphoblasts derived from autism probands and unaffected family members. Amongst these individuals were carriers of know schizophrenia loci, and a number of synaptic genes within these regions were found to have dysregulated expression (Table 1). While supportive, these data are clearly limited by differences between lymphoblasts and neuronal cell lines in terms of genes actively expressed and possibly the extent to which expression levels are regulated. Set-based CNV analyses that test whether functionally related sets of genes are enriched in case CNVs have been applied to the wider case–control data. Initial reports suggested enrichment for neurodevelopmental and synaptic gene sets (Walsh et al., 2008; Glessner et al., 2010), although it has since been shown that these studies did not completely control for confounders such as CNV and gene size (Raychaudhuri et al., 2010). The quality of gene-set annotation is also a factor in the reliability and sensitivity of such studies, which typically rely upon large annotation databases that provide a far from comprehensive survey of the literature. Schizophrenia is associated with a marked reduction in fecundity (Laursen & Munk-Olsen, 2010; Bundy et al., 2011), from which it has been predicted that pathogenic variants of large effect present in multiple cases are likely to have occurred via independent de novo mutation events. In contrast to the modest elevation seen in case– control studies, the rate of de novo CNVs in individuals with schizophrenia is over twice that found in controls, indicating that over 50% of case de novo CNVs may be disease relevant (Kirov et al., 2012). While highly enriched for pathogenic CNVs, the collection of complete parent-proband trios necessary to identify de novos has proven difficult and sample sizes are typically small. The largest study of de novo schizophrenia CNVs to date performed a systematic analysis of synaptic complexes, applying carefully controlled enrichment analyses to gene-sets derived from many of the proteomic studies highlighted in Fig. 1. This revealed significant enrichment for genes encoding PSD proteins within case de novos compared with control de novos. The PSD enrichment signal was largely concentrated within the subset of NRC/MASC connected to the NMDAR by known protein–protein interactions (referred to below as the NMDAR network), and neuronal activity-regulated cytoskeleton-associated (ARC) protein complexes (Kirov et al., 2012). Re-analysing CNV data from large case–control studies, modest enrichment of case CNVs was found for the NMDAR network but not ARC. The convergence of de novo CNVs on protein complexes intimately linked to synaptic plasticity is striking: NMDAR signalling regulates the induction of multiple forms of synaptic plasticity (Malenka & Nicoll, 1993), while transcription of the immediate-early gene ARC is required for the maintenance of synaptic changes (Bramham et al., 2010). In the same study, additional enrichment signals in the nucleus and presynaptic AZ were also shown to be attributable to their overlap with NMDAR network and ARC gene-sets (Kirov et al., 2012), although a pleiotropic (pre- and postsynaptic) contribution to disease risk from some of the variants should not be ruled out. Overall there is strong support for the convergence of pathogenic CNVs on synaptic complexes regulating plasticity. Pre- and postsynaptic specialisations are closely interlinked, and while the evidence primarily suggests postsynaptic disruption, a presynaptic component is indicated by the consistent enrichment of NRXN1 micro-deletions in patients (Rees et al., 2014). Given the association between synaptic plasticity and behavioural learning, rare CNV burden may be particularly relevant to the cognitive symptoms of schizophrenia. However, investigations into the effects of NMDAR antagonists on cognitive deficits have yet to demonstrate clear benefits.

© 2014 Federation of European Neuroscience Societies and John Wiley & Sons Ltd European Journal of Neuroscience, 39, 1059–1067

The synapse in schizophrenia 1063 The comparatively modest level of enrichment found in case–control data is unsurprising, given that CNVs retained in the population are likely to be less pathogenic than de novos. Even assuming that de novo CNVs account for the modest increase in CNV rate evident in large case–control population studies, it is still possible that the genomic distribution of case CNVs systematically differs from that of control CNVs. Whether CNVs retained in the population contribute to schizophrenia through the (presumably milder) perturbation of synaptic processes where the major disease-relevant effects are due to high impact de novos remains to be seen.

Rare sequence variants Exome sequencing allows differences in genetic sequence to be identified at a single-base resolution, extending the range of variants that can be studied to include rare point mutations and short insertion and deletion events (indels, < 1 kb) that could not be detected by GWAS genotyping chips. Exome chips lie intermediate between typical GWAS genotyping arrays and exome sequencing, using the same basic technology as GWAS chips but covering both common and rare coding variants drawn from existing sequence databases. Initial rare variant studies have met with limited success in identifying novel schizophrenia risk factors, having been based on small samples or restricted coverage of a limited number of candidate genes (Need et al., 2012; Crowley et al., 2013; Takata et al., 2013; Timms et al., 2013). Studies of de novo rare variants have been similarly inconclusive, sample size again being a major limitation (Girard et al., 2011; Xu et al., 2012; Gulsuner et al., 2013). While the evidence from these studies is inconsistent, the picture emerging from larger autism studies suggests that the de novo mutation rate is unlikely to be strikingly elevated in schizophrenia. With no clear excess of pathogenic mutations, the support for individual published variants is low and is likely to remain so until much larger studies become available. In the short to medium term, set-based tests are likely to prove much more powerful in uncovering relevant associations, as they have in the analysis of de novo CNVs.

genes linked to neuronal signalling (Perez-Santiago et al., 2012; Mistry et al., 2013). Despite drawing upon highly overlapping data [out of eight separate studies, six were used in Perez-Santiago et al. (2012) and seven in Mistry et al. (2013)], the concordance between the two studies in terms of differentially expressed genes was poor (Perez-Santiago et al., 2012). Neither study was able to properly control for medication or smoking, although Mistry et al. (2013) did correct for age, gender and brain pH. In addition, neither analysis corrected for the number of expression probes assigned to each gene, which may also bias results. The study of gene or protein expression cannot by itself identify causal factors, as it is unable (even in principle) to distinguish causation from compensatory mechanisms. It is also extremely difficult to disentangle aetiological significance from effects due to treatment or environmental factors correlated with disease status. Even within these constraints, the analysis of post-mortem expression to date raises more questions than it answers, and is at best suggestive of synaptic involvement.

Animal models Our understanding of the genetic basis of schizophrenia has rapidly evolved over the last decade, and many of the early candidate genes on which animal models have been based fail to withstand closer scrutiny (Sullivan, 2013). Point mutations of large effect have not been found, and single gene models based on association through common polymorphisms of small effect are clearly inadequate (Nestler & Hyman, 2010). CNVs and environmental factors that substantially increase risk provide a better basis for valid animal models, although such factors almost invariably confer risk of multiple disorders and exhibit pleiotropic functional effects. The most widely studied models of potential relevance to schizophrenia are those of maternal infection (Meyer et al., 2009; Boksa, 2010), which finds reasonable support as an environmental risk factor (Brown & Derkits, 2010). Rodent models of maternal infection display effects on dopamine, serotonin and GABA neurotransmitter systems, with the most consistent evidence supporting altered glutamatergic neurotransmission (Boksa, 2010).

Post-mortem expression studies Many studies have now compared gene or protein expression in post-mortem brain tissue from schizophrenia cases with that from healthy controls, mostly in small samples. In addition to sample size, the reliability of these studies is influenced by a number of other factors. Age, gender, brain pH and post-mortem interval all alter gene expression, including that of synapse components (Mistry & Pavlidis, 2010). More difficult to account for are the effects of smoking and medication, for which information is often limited. Smoking has consistently been found to have greater prevalence amongst patients with schizophrenia than the general population (de Leon & Diaz, 2005), while rodent studies indicate that smoking may alter synaptic gene expression (Li et al., 2004b; Wang et al., 2008). Antipsychotic medication has also been shown to alter the expression of synaptic proteins in rodents and primates, with effects varying between drugs and brain regions (Damask et al., 1996; Healy & Meador-Woodruff, 1997; Brene et al., 1998; Schmitt et al., 2003; O’Connor et al., 2007). While expression changes due to medication (and smoking has also been interpreted by some as a form of self-medication) may reflect correction of an imbalance contributing to disease, they may instead represent compensatory mechanisms unrelated to aetiology. Two recent analyses have drawn together data from several earlier studies of post-mortem gene expression, and both report changes in

Summary Schizophrenia is a highly complex disorder in terms of both phenotype and genotype. A wide array of genetic variants (common and rare, point mutations and CNVs) contribute to disease susceptibility by altering the expression and function of potentially thousands of genes. Although almost certainly not the only pathogenic process contributing to schizophrenia, perturbed signalling at glutamatergic synapses is the factor most strongly supported by current genetic data. De novo CNVs present in schizophrenia cases converge on postsynaptic complexes regulating synaptic plasticity, while an elevated rate of deletions in presynaptic cell adhesion molecule NRXN1 has been consistently reported in patients. Calcium signalling is integral to the regulation of presynaptic vesicle release and the postsynaptic induction of plasticity. The identification of genome-wide significant polymorphisms in L-type calcium channels contributing to risk in multiple psychiatric disorders provides further support, while schizophrenia-specific risk SNPs in the vicinity of miR137 and TCF4 indicate that additional processes (e.g. neuronal proliferation, differentiation and maturation) are also likely to play an important role (Blake et al., 2010; Smrt et al., 2010). Given their rarity, size and clearly elevated rate, the mutations found to disrupt synaptic signalling are likely to be amongst the most pathogenic contributing to disease. It is interesting to note that none

© 2014 Federation of European Neuroscience Societies and John Wiley & Sons Ltd European Journal of Neuroscience, 39, 1059–1067

1064 A. J. Pocklington et al. of these highly pathogenic CNVs affects the central components of NRC/MASC and ARC complexes – glutamate receptor subunits, or ARC itself – but instead target associated structural and signalling components. Even here, key proteins such as CaMKII appear to be spared. Of the MAGUK scaffolding proteins, it is DLG1 and DLG2 that are known to be disrupted by de novo deletions, while no such mutation has yet been identified in DLG4 (PSD-95), which exhibits the most severe behavioural phenotypes in knockout mice (Nithianantharajah & Grant, 2013). This may reflect the fact that profound perturbation of such genes would lead to more severe neuropsychiatric phenotypes (e.g. intellectual disability) or seriously compromise viability. If so, these genes may be found to harbour risk variants with milder functional consequences (e.g. point mutations or inframe indels). Outside core functional complexes the efficiency and precision of synaptic signalling is dependent upon a host of additional processes: modulatory neurotransmitter systems, protein turnover, cytoskeletal dynamics and energy metabolism amongst others. While each may be tolerant to minor disruption, a coherent pattern of perturbation across multiple processes could have a significant impact on synapse physiology. This may provide an additional mechanism through which the large numbers of risk variants circulating in the general population contribute to schizophrenia. The characterisation of synaptic protein complexes has contributed much to our understanding of schizophrenia biology, and further molecular dissection of synaptic signalling may yet allow more refined models to be developed. Neurotransmitter receptor complexes are of particular interest in modelling disease aetiology and treatment. A detailed study of dopaminergic signalling would be an invaluable complement to existing glutamate-centric analyses, while the correlation between genetic disruption of specific neurotransmitter systems and phenotypic presentation (including treatment response) is an as yet unexplored avenue of research that may well yield important insights. At a more detailed level, protein interaction networks may be used to examine functional organisation within complexes (Pocklington et al., 2006). The large-scale mapping of protein–protein interactions between synapse components promises to be a valuable resource for future analyses (von Eichborn et al., 2013). While the evidence for synaptic involvement is strong, whether the disruption of plasticity is unique to schizophrenia or a shared feature of several disorders has yet to be resolved. Although unlikely, it is entirely possible that exactly the same set of synaptic genes is disrupted in intellectual disability, autism, schizophrenia and bipolar disorder, with differences in the density and severity of mutations in each individual case determining disease presentation. Comparative analyses of mutation burden may yet uncover novel aspects of synaptic dysfunction unique to schizophrenia.

Acknowledgements This work was supported by Medical Research Council (MRC) Centre (G0800509) and Program Grants (G0801418). The authors have no conflicting interests to declare.

Abbreviations AMPAR, a-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor; ARC, activity-regulated cytoskeleton-associated; AZ, active zone; CNV, copy number variant; GABA, c-aminobutyric acid; GWAS, genome-wide association studies; LD, linkage disequilibrium; MAGUK, membrane-associated guanylate kinase; MASC, MAGUK-associated signalling complex; NMDAR, N-methyl-D-aspartate receptor; NRC, NMDAR complex; PSD, postsynaptic density; SNP, single-nucleotide polymorphism.

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