Skip to main content
Download PDF
  • Methodology article
  • Open access
  • Published:

Using Gene Ontology to describe the role of the neurexin-neuroligin-SHANK complex in human, mouse and rat and its relevance to autism

BMC Bioinformatics volume 16, Article number: 186 (2015) Cite this article

Abstract

Background

People with an autistic spectrum disorder (ASD) display a variety of characteristic behavioral traits, including impaired social interaction, communication difficulties and repetitive behavior. This complex neurodevelopment disorder is known to be associated with a combination of genetic and environmental factors. Neurexins and neuroligins play a key role in synaptogenesis and neurexin-neuroligin adhesion is one of several processes that have been implicated in autism spectrum disorders.

Results

In this report we describe the manual annotation of a selection of gene products known to be associated with autism and/or the neurexin-neuroligin-SHANK complex and demonstrate how a focused annotation approach leads to the creation of more descriptive Gene Ontology (GO) terms, as well as an increase in both the number of gene product annotations and their granularity, thus improving the data available in the GO database.

Conclusions

The manual annotations we describe will impact on the functional analysis of a variety of future autism-relevant datasets. Comprehensive gene annotation is an essential aspect of genomic and proteomic studies, as the quality of gene annotations incorporated into statistical analysis tools affects the effective interpretation of data obtained through genome wide association studies, next generation sequencing, proteomic and transcriptomic datasets.

Background

The Gene Ontology (GO; www.geneontology.org) contains controlled vocabulary terms (GO terms), which are connected through defined relationships within a hierarchical order (Fig. 1) [1]. The association of GO terms with gene products enables proteins to be classified (grouped) according to their shared normal molecular functions, the biological processes they contribute to, and their location with respect to subcellular compartments (cellular components). Summarising the known role of gene products from published papers to populate the GO database, a process known as annotation, allows researchers to have access to information on the role of individual proteins and protein families in the form of controlled vocabulary terms [2]. GO provides one of the major annotation resources used for the analysis of high-throughput datasets, such as those from transcriptomic and proteomic studies, to identify pathways, functions or cellular components over-represented within a dataset. For example, common GO domains found in an analysis of a brain transcriptomic dataset associated with aging in the prefrontal cortical regions were calcium signalling, protein tyrosine kinase signalling, electrical excitability and neuropeptide hormones [3]. In addition, GO is also being used in pathway-driven analysis tools to identify risk Single Nucleotide Polymorphisms (SNPs) associated with specific phenotypes, and to inform biomarker identification studies [4–6]. However, the interpretation of these datasets depends on the quality of the gene annotations available and the statistical analysis tools used, and we have previously demonstrated that cardiovascular-focused manual GO annotation significantly improves the interpretation of cardiovascular-relevant datasets [7].

Fig. 1
figure 1

Part of the synapse domain of the GO cellular component ontology. The QuickGO (http://www.ebi.ac.uk/QuickGO) view of the parent–child relationships between the GO terms ‘postsynaptic membrane’ and ‘cellular component’. The black arrows are used where a term has an ‘is_a’ relationship to its parent term, the blue arrows indicate a ‘part_of’ relationship, www.geneontology.org/GO.ontology.relations.shtml

Full size image

Autistic spectrum disorder (ASD) is a complex neurodevelopmental condition known to be associated with a combination of genetic [8] and environmental factors [9]. ASD is often diagnosed around 3 years of age and many of the characteristic traits continue to adulthood [10]. Patients with ASD (as classified by the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, DSM-5) [11] exhibit a variety of behavioral deficits, such as social interaction and communication difficulties and restrictive and repetitive behavior [12]. Limited social interaction by ASD children is often interpreted as a lack of social interest, but it is now recognised to be the inability of ASD children to engage with others [13–15]. ASD patients also have limited communication skills, such as gesture, facial expression and speech deficits. Repetitive stereotype behaviors are usually detailed and structure orientated, where the activity and/or behavior contains a number of steps in order to complete the task [13, 14].

There is strong evidence for multiple genetic factors determining the development of ASD. To date, around 40 genomic loci and over 100 genes have been associated with ASD based on rare variant approaches. These approaches have identified sequence and copy number variants as well as chromosomal rearrangements [16]. These genes and loci have been identified based on SNPs, de novo losses or gain mutations and linkage studies [17, 18]. Although many single genes have been implicated, a complete understanding of ASD is still to be achieved. A key obstacle for researchers is that these SNPs or mutations are not common in all patients with ASD, consequently the identification of low frequency SNPs associated with ASD is challenging.

Synaptogenesis, chromatin re-modeling, morphogenic developmental processes, calcium homeostasis and mitochondrial function have all been identified as potentially contributing to ASD [18]. In this study, we have focused on the manual annotation of the neurexin, neuroligin and SHANK protein families, as the interaction between the neurexin and neuroligin cell adhesion molecules is one of the processes that has been implicated in the development of ASD [18]. However, variants in the neurexin, neuroligin and SHANK gene families have only been associated with a few percent of ASD cases [18].

Neurexins (NRXNs) and neuroligins (NLGNs) are single-pass transmembrane proteins that play a key role in synaptogenesis, a process which occurs before birth and continues into adulthood [19]. In the process of synapse formation, the NLGN and NRXN cell surface adhesion molecules interact in a calcium-dependent manner to initiate the first stage of synapse formation and the assembly of protein components required for presynaptic and postsynaptic cell membrane organization [20, 21]. The intercellular interaction between NRXNs and NLGNs facilitates axo-dendritic contact [19, 21], and the synapse that is formed becomes stabilized or eliminated based on the synaptic activity, which is driven by the action potential between two or more neurons [22]. The scaffolding SHANK proteins are required for the proper formation and function of neuronal synapses and are connected to NLGN cell adhesion molecules via their interaction with DLG4 (PSD-95, postsynaptic density-95) to facilitate postsynaptic organization of cytoskeletal and signaling complexes [23].

ASD-associated variants have been identified in members of both the NLGN [24] and NRXN [25, 26] gene families. Although all five members of the NLGN gene family are associated directly with synapse assembly [27–29], mutations in only 3 members of this family, NLGN3, NLGN4X and NLGN4Y, have been identified in autistic patients [24, 30–32]. There are also five members of the NRXN gene family in humans: NRXN1, NRXN2, NRXN3 [33] and two NRXN4 genes known as CNTNAP1 and CNTNAP2 (Contactin-associated protein 1 and 2 respectively) [34]. NRXN1-3 encode alpha and beta isoforms which have identical C-terminal transmembrane regions and cytoplasmic tails. However, alternative promoter use leads to the isoforms having different N-terminal extracellular sequences, the α-isoforms having much longer extracellular domains than the β-isoforms [29]. UniProt recognises these differences by providing unique identifiers for each isoform, rather than treating the isoforms as splice variants and listing the variants within a single protein record. Both the α- and β-isoforms have been associated with synapse formation [35], whereas the proteins encoded by CNTNAP1 and CNTNAP2 are involved in non-synaptic neurogenic processes [36, 37]. Mutations associated with ASD are found in both alpha and beta NRXN1 [26, 38], NRXN2 [39], NRXN3 [40] and CNTNAP2 [41]. Furthermore, variants in all three SHANK family members have been associated with ASD [42–44]. Neuroligin, neurexin and SHANK mutations link ASD with the molecular components of synaptogenesis. Therefore investigating the functional role of the proteins associated with the neurexin-neuroligin-SHANK complex in model organisms may explain how the mutations in some of these proteins result in the behavioral traits seen in some patients with ASD. DLG4 was also included in this focused annotation project due to its role as a scaffold protein connecting the NLGN and SHANK proteins [45].

Comprehensive manual annotation of the NRXN, NLGN, SHANK and DLG4 proteins identified several GO domains that are associated with the majority of these proteins, including behavior, synaptogenesis and neurogenesis. During the annotation process we identified a lack of GO terms which could describe the role of NRXNs and NLGNs in the process of synaptogenesis; to fill this void, new synapse assembly GO terms were created. These new GO terms were then associated with the NRXN, NLGN, SHANK and DLG4 proteins, when there was supporting experimental evidence. Furthermore, confirmed orthology predictions between the human, rat and mouse ASD-associated proteins supported the propagation of GO annotations from each protein to the orthologous proteins in these species (http://www.geneontology.org/cgi-bin/references.cgi#GO_REF:0000024) [2]. These new GO annotations and terms are now included in the GO database, enriching both the annotation data and the ontology structure.

Methods

Selection of experimental data to annotate

The PubMed database (http://www.ncbi.nlm.nih.gov/pubmed) was used to locate papers that contained experimental data describing each member of the NRXN, NLGN, SHANK or DLG4 protein families. The searches were performed, during April to November 2011, using the following gene names and symbols: neurexin, neuroligin, NRXN*, NLGN*, SHANK1, SHANK2, SHANK3, CASPR, DLG4, DLG-4, PSD95, PSD-95. This search retrieved over 2000 papers. Consequently, we undertook a PubMed search with each individual gene symbol or name and additional filters, in order to provide a comprehensive coverage of the role of these proteins with respect to autism. The following filters were applied to each of the symbol and name searches: ‘AND autism/ASD’, ‘AND synaptogenesis/synapse assembly’, ‘AND autism/ASD AND synaptogenesis/synapse assembly’. The number of papers available that described each gene influenced the number of filters applied. The decision about which paper to annotate was then based on whether: 1) new information would be added to the current GO annotation data associated with the protein; 2) it was possible to identify the species the protein or transfected cDNA construct was derived from. Only papers that met both criteria were annotated. The choice of papers annotated was therefore influenced by the information captured in previously annotated papers. In total, 66 papers with experimental data that were relevant to ASD or synaptogenesis were originally selected for annotation. However, following the identification of an association of NLGN4Y, NRXN2 and NRXN3 alpha and beta variants with ASD [39, 40], these two additional papers were also annotated (February 2014) bringing the total number of papers annotated to 68 (see Additional file 1).

Identification of orthologous proteins

Orthologous proteins were identified for the NRXN, NLGN, SHANK and DLG4 protein families between the human, mouse and rat species, using the HUGO Gene Nomenclature Committee (http://www.genenames.org/) ortholog prediction tool (HCOP). HCOP compiles data from 11 different orthology prediction tools, including EnsemblCompara, Homologene and Inparanoid [46]. The predicted ortholog amino acid sequences were also aligned, using the default settings on the Basic Local Alignment Search Tool – BLASTP (http://blast.ncbi.nlm.nih.gov/Blast.cgi), to confirm high homology. In all cases there was greater than 89 % amino acid sequence identity between these aligned mammalian orthologs.

Gene Ontology annotation - manual curation process

Manual GO annotation requires a GO curator to read publications and convert the data presented into an annotation. Each annotation includes the protein identifier, a GO term, an evidence code, and a reference [2]. During this focused annotation project GO terms were associated with protein records based on experimental data describing the human, rat and mouse NRXN, NLGN, SHANK and DLG4 proteins. The QuickGO browser (http://www.ebi.ac.uk/QuickGO) was used to identify the most specific GO terms to ‘capture’ the experimental data presented in each paper, and a consistent annotation approach was undertaken [2]. Evidence codes were associated to each annotation based on the type of experimental data presented in the paper (www.geneontology.org/GO.evidence.shtml) [2]. To complete the manual annotation process, GO annotations with experimental evidence codes were transferred to orthologous human, mouse and rat proteins. These annotations include the ISS (Inferred from Sequence Similarity) evidence code. New, more descriptive GO terms were created, along with improvements to the ontology structure, using the GO editorial tool, OBO-Edit [47].

Results and discussion

GO annotation of the neurexin, neuroligin, SHANK and DLG4 protein families

There are several thousand publications describing the mammalian members of the NRXN, NLGN, SHANK and DLG4 protein families. With limited resources available, we restricted our manual annotation focus to experimental data describing the functional role and cellular location of these proteins, where there was a clear relevance to synaptogenesis and autism. Consequently, only a fraction of the available experimental data describing the NRXN, NLGN, SHANK and DLG4 proteins has been captured. Synaptogenesis and other pathways that impact on behavior are complex processes, involving numerous proteins, and full annotation of these processes is not attempted in this report. GO terms were associated with the mammalian members of the NRXN, NLGN, SHANK and DLG4 protein families based on the experimental data present in 68 published experimental papers (see Additional files 1, 2 and 3), increasing the number of papers contributing to the manual annotations of these proteins from 172 to 240. Whenever possible, annotations were created that capture the molecular function of each protein, the biological processes these proteins contribute to, and their intracellular location (cellular component, see Additional file 4). This approach has created over 500 publication-supported manual annotations (see Additional files 3, 4, 5 and 6), doubling the previous number of these annotations to over 1000. In addition, to maximise the utility of this annotation project, almost 700 GO annotations were propagated to orthologs in the three species annotated (human, mouse and rat). These annotations are identified by the associated ISS (Inferred from Sequence Similarity) evidence code and were only created when orthology was confirmed and when the GO term was not already ‘manually’ associated with the protein record [2]. The propagation of annotations across these mammalian orthologs increased the number of manual annotations to these 47 proteins to over 1800 (see Additional file 3). There are now molecular function, biological process and cellular component annotations associated with almost all members of these families supported by confirmed experimental data. No experimental data was identified to support cellular component annotations for the human, mouse or rat NLGN4Y, NRXN2 and NRXN3 proteins, or to support molecular function annotations for the mouse or rat Nlgn4l proteins. This annotation approach has meant that over 400 unique GO manual annotations, directly supported by experimental evidence, are now associated with the human NRXN, NLGN, SHANK and DLG4 protein families. The BHF-UCL and UniProt-GOA teams have created the majority of manual annotations associated with the human NRXNs, NLGNs, SHANKs and DLG4 protein families (see Additional file 7).

The full definition and ontology placement for each GO term listed below is available in the QuickGO term page using the hyperlinks provided.

Cellular component GO terms

The manual annotation of 87 papers confirms the cellular location of many of the human, rat and mouse NRXN, NLGN, SHANK and DLG4 proteins (see Additional files 3 and 4). These cellular component GO annotations are primarily associated with either the evidence code IDA (Inferred from Direct Assay) or ISS. Furthermore, many of these cellular component manual annotations describe the neuron-relevant localisation of these proteins, such as ‘excitatory synapse’, ‘postsynaptic membrane’, and ‘dendrite’ (Fig. 2).

Fig. 2
figure 2

Cellular component GO terms associated manually to the ASD-relevant human proteins. A subsection of the cellular component domain, with the human protein families (blue ovals) and their associated GO terms (white boxes), linked with red dotted line (see Additional file 4). All associations (except ‘Inferred from Electronic Annotation’ (IEA) supported data) are included. If one or several member(s) of a protein family is associated with a specific term then the family name, or the protein name DLG4, is listed below the term. ‘ALL’ is used when a member of the CNTNAP, NLGN, NRXN, SHANK families as well as DLG4 have been associated with the GO term. The numbered circle indicates the minimum number of GO terms in the ancestor chart between two of the listed GO terms. For example there are 2 GO terms (‘synaptic membrane’ and ‘synapse part’) between the GO terms ‘synapse’ and ‘postsynaptic membrane’ (Fig. 1)

Full size image

NRXNs and NLGNs contain a single transmembrane region, and their involvement in cell adhesion suggests they are located in the plasma membrane. Experimental evidence supports the association of the GO terms ‘plasma membrane’ and ‘cell surface’ with many of these proteins. However, in order to associate the GO term ‘plasma membrane’ with human NRXN1-α the evidence code IC (Inferred by Curator) is applied. This inference is based on the immunofluorescence analysis of NRXN1-α transfected COS cells [39], which supports the NRXN1-α ‘cell surface’ annotation, along with the knowledge that this protein contains a transmembrane domain and is therefore likely to be located in the plasma membrane.

The NRXN, NLGN, SHANK and DLG4 proteins play a key role in synapse assembly and there is considerable experimental evidence to support GO annotations describing the localization of these proteins to neuronal-specific components, such as ‘excitatory synapse’ [23, 48–52] and ‘dendrite’ [28, 51, 53, 54]. In addition, while the majority of the NRXN family are associated with the synapse, the CNTNAP proteins are located in the ‘juxtaparanode region of axon’ and ‘paranode region of axon’ [36, 37] (Fig. 2, see Additional files 3 and 4).

Biological process GO terms

Several neurological processes are associated with the NRXN, NLGN, SHANK and DLG4 proteins families, such as neurogenesis, synaptic organisation, synaptic transmission, and behavior (Fig. 3). In contrast, CNTNAP1 and CNTNAP2 are involved in axon assembly, with CNTNAP1 required for neuronal action potential propagation [37, 55]. The cellular component and biological process annotations associated with the CNTNAPs reflect the lack of evidence for a role of these proteins in synaptic processes and identifies these proteins as functionally distinct from the other members of the NRXN family.

Fig. 3
figure 3

Distribution of biological process GO terms associated to each of the selected human protein records. The GO terms ‘directly’ associated with a human protein record have been grouped into broader biological process parent GO term categories (see Additional file 5). Due to the structure of the ontology there are some GO terms that are child terms of more than one of these broader GO parent terms. In these instances the annotation is then represented more than once in the histogram. No IEA annotations are included in the histogram

Full size image

Synapse assembly

A synapse includes cellular components contributed by two adjacent cells. Consequently, synapse assembly covers a wide variety of processes, including presynaptic membrane assembly, postsynaptic membrane assembly, postsynaptic density assembly and the clustering of the various adhesion molecules, receptors, channels and scaffold proteins.

The calcium-dependent interaction between an NLGN and NRXN protein, located in different cells, initiates the first stage of synapse formation [20, 21, 49, 56]. Therefore, NRXNs and NLGNs are annotated with ‘calcium-dependent cell-cell adhesion via plasma membrane cell adhesion molecules’ [35], ‘heterophilic cell-cell adhesion via plasma membrane cell adhesion molecule’ [35, 57]’ and ‘neuron cell-cell adhesion’ [29, 35, 56]. In addition, the NRXNs and NLGNs play an essential role in the clustering of proteins at the presynaptic membrane and postsynaptic membrane during synapse assembly [21, 35, 48]. In order to capture the specific detail of synapse assembly new, highly descriptive GO terms have been created (Table 1, Fig. 4). The assembly process involves organization of the synaptic membrane on either side of the synapse [35], consequently two new GO terms were created, ‘postsynaptic membrane assembly’ and ‘presynaptic membrane assembly’ with ‘is_a’ child relationships to the biological process GO terms ‘postsynaptic membrane organization’ or ‘presynaptic membrane organization’, respectively. These new GO terms enabled the mouse Nrxn1-α, Nrxn1-β, Nrxn2-α and Nlgn1-3 [35, 39, 48, 58] to be annotated with ‘postsynaptic membrane assembly’ based on experimental data. Similarly, the synaptic impact of murine Nlgn1 or Nlgn2 ectopic expression, in mixed culture assays, supports the association of ‘presynaptic membrane assembly’ [21, 35, 48]. Furthermore, to capture the involvement of NLGNs and NRXNs in initiating the clustering of specific proteins and organelles to synaptic locations, additional new GO terms have been created (Table 1, Fig. 4). Clustering of scaffold and receptor proteins is part of the process of postsynaptic and presynaptic membrane organization. Therefore, new GO terms, describing the clustering of these specific proteins, were created as ‘part_of’ child terms to either ‘postsynaptic membrane organization’ or ‘presynaptic membrane organization’ terms, with ‘is_a’ child relationships to ‘protein localization to membrane’, or part_of child terms to ‘postsynaptic density assembly’, with ‘is_a’ child relationships to ‘protein localization to synapse’. There is considerable experimental evidence describing the clustering of specific proteins at the synaptic membrane area. For example Gauthier et al., [39] showed that when Nrxn2-α is mutated there is impairment in GABAergic postsynaptic components and gephyrin at the dendrite contact sites of postsynaptic neurons [48]. Experimental data such as this has been used to support the association of the new GO term ‘gephyrin clustering involved in postsynaptic density assembly’ with Nrxn1-α, Nrxn1-β and Nrxn2-α and the new GO term ‘postsynaptic density protein 95 clustering’ with Nrxn1-α, Nrxn1-β, Nrxn2-α, Ngln1 and Nlgn2 [35, 39, 48, 59] (Fig. 4).

Table 1 New GO terms created to support detailed ASD-relevant annotations. The ancestral relationship of these new GO terms is described in Figs. 4 and 5 (with the exception of the new protein binding terms and ‘receptor localization to synapse’). The term records in the QuickGO browser (http://www.ebi.ac.uk/QuickGO) provide the full definition and ontology placement for each GO term provide the full definition and ontology placement for each GO term. The reference column lists the references used as a source of information when creating each term
Full size table
Fig. 4
figure 4

Ancestral relationships of the new GO terms created to support detailed ASD-relevant annotations in the domain of synapse organization. A subsection of the ‘synapse organization’ domain ontology, human protein families (blue ovals) and their associated GO terms (green boxes), linked with red dotted line (see Additional files 5 and 8); all associations (except IEA-supported data) are included. If one or several member(s) of a protein family is associated with a specific term then the family name, or the protein name DLG4, is listed below the term. * indicates new GO terms (Table 1). Black arrows indicate an ‘is_a’ relationship between a term and its parent; blue arrows indicate a ‘part_of’ relationship

Full size image

Following common practice when creating GO terms, the cellular component ontology was used to guide the biological process ontology. For example, the cellular component term ‘postsynaptic density’ ‘is_a’ child term of ‘cytoskeletal part’ and, therefore, in the biological process ontology ‘postsynaptic density organization’ has an ‘is_a’ relationship link with ‘cytoskeleton organization’. Biochemical analysis of postsynaptic densities purified from the striatum of wild type and Shank3B −/− mice demonstrates that the Shank3 protein is required for correct postsynaptic density assembly [23]. Consequently, the new GO term ‘postsynaptic density organization’ is associated with murine Shank3.

Synaptic vesicle clustering occurs at the presynaptic membrane, as well as below this membrane. Therefore, the new GO term ‘synaptic vesicle clustering’ is placed as part_of ‘presynaptic active zone assembly’ rather than as a child term of ‘presynaptic membrane organization’. This ‘synaptic vesicle clustering’ GO term is associated with both murine Nlgn1 and Nrxn1-β, based on Nlgn1 ectopic expression data, or the recruitment of synaptic vesicle markers in cultured hippocampal neurons, following oligomerization of overexpressed murine Nrxn1-β, respectively [21, 35, 60].

The two closely related paralogs CNTNAP1 and CNTNAP2 are both associated with neurogenesis, but only CNTNAP2 has been found to be associated with ASD [61]. In contrast to the other members of the NRXN family, CNTNAP1 is found on paranode region of axons [36, 37], whereas CNTNAP2 is located on the juxtaparanode region of axons [36, 62]. Furthermore, CNTNAPs appear to play a role in axon assembly, rather than synapse assembly. For example, Garcia-Fresco et al. [37], show impaired localization of mitochondria and neurofilament to the paranodal region of the axon in mice deficient in Cntnap1. Based on this experimental evidence the GO term ‘protein localization to paranode region of axon’ is associated with murine Cntnap1 protein record (see Additional file 5) [55]. Furthermore, deletion of Cntnap2 in mice demonstrates that Cntnap2 is required for clustering of voltage-gated potassium channels to the juxtaparanodal region of myelinated axons, similar to the role of NRXNs in neurotransmitter-gated ion channel clustering at the synapse [63]. This data was captured using the GO terms: ‘clustering of voltage-gated potassium channels’ and ‘protein localization to juxtaparanode region of axon’.

The synaptic scaffold SHANK family members and DLG4 are also key proteins in synapse assembly, providing essential structural support, and are involved in bringing necessary protein components to the synapse [48, 52]. Wang et al. [64] identifies that levels of Homer1b/c and GKAP in the postsynaptic density and GluA1 and NR2A in the synaptic plasma membrane are lower in Shank3 e4–9 mice, compared to wild type mice. GluA1 is a subunit of the AMPA receptor; therefore, the GO term ‘alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionate selective receptor clustering’ is associated with the mouse Shank3 protein record. This term is also applied to the rat Dlg4 protein, as RNA interference knockdown of rat Dlg4 in mixed culture assay leads to a reduction in the number of AMPA receptor type structures near the synapse compared to controls [65]. Further experimental evidence also supports the role of other NLGN, NRXN, SHANK and DLG4 proteins in the process of AMPA, GABA and NMDA receptor clustering in glutamatergic postsynaptic cells, and GO annotations capturing this information are now available [35, 48, 59, 64–67] (Fig. 4, see Additional files 5 and 8).

Regulation of postsynaptic membrane potential

Experimental evidence supports the association of the GO term ‘regulation of excitatory postsynaptic membrane potential’ (or child terms) with the NLGN, NRXN, SHANK and DLG4 proteins [28, 51] (Fig. 5, see Additional files 5 and 8). For example, cultured neurons transfected with rat Nlgn1(R473C), corresponding to a human variant associated with an autism disorder, showed a decrease in excitatory synaptic transmission for both AMPA receptor and NMDA receptor response, suggesting positive regulation of this process [51]. In contrast, neurons transfected with human NLGN4X selectively suppress the frequency of mEPSCs but not mIPSCs, suggesting that NLGN4X is involved in ‘negative regulation of excitatory postsynaptic membrane potential’ [28]. Transfection and transgenic data also supports the association of ‘regulation of inhibitory postsynaptic membrane potential’ (or child terms) with mouse and rat Nlgn2 and Nlgn3 proteins [48, 51, 68, 69] (Fig. 5, see Additional files 5 and 8). Neurons transfected with rat Nlgn3(R471C), corresponding to another ASD associated variant, have a decreased inhibitory postsynaptic membrane potential [69]. Although both Nlgn2 and Nlgn3 [50] are found within inhibitory and excitatory synapses, the expression of Nlgn2 and Nlgn3 is greater at the inhibitory synapse [48, 51, 69]. This difference of expression appears to be reflected in their role in the regulation of membrane potentials. The role of the NRXN and NLGN proteins in excitatory and inhibitory synaptic transmission suggests that a balance between these processes is necessary for normal brain development and that the dysregulation of these processes may be linked to the behavioral phenotypes seen in ASD individuals [19]. However, NRXN, NLGN, SHANK and DLG4 proteins also play a role in synaptic plasticity [70], which is also likely to contribute to the ASD phenotypes. 

Fig. 5
figure 5

Ancestral relationships of the new GO terms created to support detailed ASD-relevant annotations in the domain of cell communication. A subsection of the ‘cell communication’ domain ontology, human protein families (blue ovals) and their associated GO terms (green boxes), linked with red dotted line (see Additional files 5 and 8); all associations (except IEA-supported data) are included. If one or several member(s) of a protein family is associated with a specific term then the family name, or the protein name DLG4, is listed below the term. * indicates new GO terms (Table 1). Black arrows indicate an ‘is_a’ relationship between a term and its parent; blue arrows, a ‘part_of’ relationship; yellow arrows, a ‘regulation’ relationship; green arrows, a ‘positive_regulation’ relationship

Full size image

Social behavior

Impaired communication and social behavior are the key behavioral changes seen in ASD individuals, and variants in the NRXN [26, 38], NLGN [28, 71], SHANK [42–44] gene families as well as in the CNTNAP2 gene are associated with ASD [72]. Consequently, using patient information provided by papers describing the association of variants in these genes with ASD, it was possible to apply several behavioral GO terms to these protein records, including ‘social behavior’, ‘vocal learning’ and ‘adult behavior’ (Table 2, see Additional files 5 and 8).

Table 2 Biological process behavioral GO terms associated with the ASD-relevant human and mouse proteins
Full size table

Model organisms have been used to investigate the impact of ASD-associated gene mutations on animal behavior. Simple behaviors in model organisms can provide information about the more complex behaviors seen in humans. For example, Hines et al. [68], measured social behavior in rats by recording how often a rat would choose to visit a room with another rat in it, compared to visiting a room without another rat in it. While a wild type rat would visit the occupied room more often than the empty room, a rat carrying an Nlgn2 mutation displayed no preference between either rooms [68]. These types of rat and mouse behavioral studies were annotated, using the GO term ‘social behavior’. This simple behavior is similar to that observed in ASD individuals, where there is no eye contact or interest in another person in the room, and this ASD phenotype is also captured using the GO term ‘social behavior’ [24, 39, 44, 72].

Vocalization is also impaired in mice and rats carrying defects in Shank [73], Nlgn4l [74] or Dlg4 [75] genes. For example, male Shank3 heterozygous mice make fewer ultrasonic vocalizations to female mice, compared to their male wild type littermates [70]. This experiment supports the annotation ‘vocalization behavior’. Similarly, there is often an impairment of communication in ASD individuals with variants in the NRXN1-α [38], NRXN1-β [26], CNTNAP2 [41], NLGN4X [24, 28] and SHANK1 [76] genes (Table 2). These ASD communication traits are captured either with the use of ‘vocalization behavior’ or more specifically ‘vocal learning’. Human proteins are only associated with the GO term ‘vocal learning’ when the authors provide detailed information about a lack of speech or very limited word usage [39, 43, 72] (see Additional files 3 and 5).

Behavior phenotypes described in mouse or rat cannot be mapped exactly to human behaviors. Furthermore, some of these behavioral traits are not relevant to human, such as ‘male courtship behavior’ and ‘olfactory behavior’. Consequently, very few behavioral annotations have been transferred from mouse or rat proteins to the human orthologs (Table 2, see Additional files 5 and 8). For example, behavioral studies support the association of the GO term ‘regulation of grooming behavior’ with the mouse Shank3, Dlg4 and Nrxn1-α proteins [23, 75, 77]. These annotations have been transferred, using the ISS evidence code, to the rat orthologs but not to the orthologous human proteins. Similarly the GO term ‘exploration behavior’ and its child term ‘locomotory exploration behavior’ are associated with the mouse Shank2 [54] and Shank3, Dlg4 and Nlgn2 [23, 75, 78] proteins (respectively), but not propagated to the human orthologs. In addition, we found no published evidence that variants in DLG4 and NLGN2 are associated with behavioral traits in human (Table 2, see Additional files 5 and 8).

Molecular function GO terms

The majority of molecular function GO terms associated with the members of the NRXN, NLGN, SHANK and DLG4 gene families capture information about the protein interactions they participate in (Table 3, see Additional file 6). To provide full annotation of the function of these proteins two new GO terms were created: ‘neuroligin family protein binding’ and ‘scaffold protein binding’ (Table 1). These new terms enable the interactions between the NRXN, NLGN, SHANK and DLG4 proteins to be described with the use of the GO terms ‘neurexin family protein binding’ [28, 35, 39, 71], ‘neuroli gin family protein binding’ [39, 79] and ‘scaffold protein binding’ [79–81] (Table 3, see Additional file 6). The molecular function annotations were also used to identify the specific classes of receptors bound by the SHANK and DLG4 proteins, such as ‘ionotropic glutamate receptor binding’ [53], ‘beta-1 adrenergic receptor binding’ [82] and ‘P2Y1 nucleotide receptor binding’ [83]. In addition, the NRXN and NLGN proteins are associated with ‘receptor activity’ [35, 39, 56, 68], whereas the SHANK proteins are associated with the GO term ‘GKAP/Homer scaffold activity’ [48, 52, 80] (Table 3, see Additional file 6).

Table 3 Selection of the molecular function GO terms associated with the ASD-relevant human proteins
Full size table

Impact of focused annotation approaches on data interpretation

Members of the GO Consortium have undertaken a variety of focused manual annotation approaches to annotate the human proteome. Two large manual annotation efforts include renal and cardiovascular focuses [7, 84], while other projects have focused on specific cellular components, e.g. the peroxisome [85], or specific individual genes, e.g. those annotated by the Reference Genome Project [86]. It is possible that these focused annotation approaches could lead to a bias in the human annotation data, which could impact on the analysis of high-throughput datasets. However, to date, there is no evidence of unexpected cardiovascular, renal, peroxisome or neurological terms being detected in term enrichment analyses [7, 84]. Furthermore, during the manual annotation of these 4 protein families, 68 additional genes were annotated based on the evidence presented in these 68 papers, reducing any potential arising bias.

Since their creation, the 19 new GO terms have been associated with 58 distinct human proteins creating 158 annotations (see Additional file 9); of these, only 31 annotations are based on experimental data, the majority of the remainder have been created through the transfer of annotations dependent on orthology assertions. Fifty-two of these annotations capture protein binding interactions whereas the remaining 106 are associated with the new synaptogenesis related terms. In addition to the NRXN, NLGN, SHANK and DLG4 proteins, 27 other proteins, APOE, ATP2B4, CELF4, CEP112, CHRNA7, DRD4, GRID2, GRIN1, HOMER1, IKBKB, IL1RAPL1, LRP4, MAP3K7, MDM2, MTMR2, NLGN4X, NOS1, NPY2R, P2RX7, P2RY1, PANX1, PRKCZ, PTEN, PTK2B, PTPRD, RELN, S1PR2, SCN5A, are annotated to these new biological process GO terms. All of the new GO terms presented in this paper, apart from scaffold protein binding, are directly relevant to synaptogenesis in the context of ASD, demonstrating the impairment in the NGLN-NRXN-SHANK complex. Future annotation of proteins involved in synaptogenesis may provide a useful approach to explore and identify other ASD risk candidates. All of the new GO terms presented here were created in August 2011 or earlier, and yet the majority of 104 manual annotations to human proteins applying these GO terms (in March 2014) were created during this focused annotation project (91 annotations). For highly specific annotations to be created by manual GO curation the curators need to feel confident in the biological field they are annotating. Curators working within a specific annotation project improve their understanding of a biological area and improve their knowledge of the GO terms available to describe the experiments they are annotating. The high number of annotations using these new GO terms created by this focused project, compared to the number created by other groups, highlights the importance of focused annotation approaches to comprehensive annotation of the human genome. However, annotation projects that target the annotation of large number of proteins, such as the UniProt-GO annotation project [87], ensure the breadth of annotation is maintained and reduce the bias within the GO database.

Several groups have now used functional annotation data to identify candidate risk alleles associated with complex multigenic diseases [88, 89]. Continued annotation of neurological processes, as well as other ASD-relevant processes such as chromatin re-modeling, developmental processes, calcium homeostasis and mitochondrial function, and the application of pathway-based analysis statistical approaches may, therefore, help with the identification of additional ASD risk alleles within genome-wide association studies and next generation sequencing projects.

Conclusion

The annotation data and ontology terms within the GO database have been improved through this focused annotation project. Published experimental and patient data was used to capture the involvement of the NRXN, NLGN, SHANK and DLG4 proteins in synaptogenesis, neurogenesis and the behavioral traits seen in ASD. In order to create descriptive annotations the representation of synaptogenesis in GO was expanded, with the addition of 14 expressive terms within the synapse organization domain (Table 1, Fig. 4, 5). These new GO terms describe the more specific aspects of the synapse complex assembly, such as ‘N-methyl-D-aspartate receptor clustering’, ‘neurexin clustering involved in presynaptic membrane assembly’ and ‘presynaptic membrane assembly’, and enable a detailed description of the biological role of the NRXN, NLGN, DLG4 and SHANK proteins in synaptogenesis (see Additional files 5 and 8). Further work on the ontology is still needed to improve the description of synaptic processes using GO terms. Moreover, additional annotation projects would enable the comprehensive annotation of all ASD-relevant proteins, as well as, full annotation of neurological processes such as synaptic plasticity, synaptic organization and synaptic transmission.

GO is a dynamic database that is always expanding as new annotations are added and new GO terms are created in the ontology. The advantage of a focused annotation approach is that it ensures the immediate use of newly created GO terms for annotations (see Additional file 9). In contrast, GO terms created during the annotation of unrelated proteins may end up applied to only a few proteins, for a considerable time. The main challenge in annotating autism-relevant proteins was finding detailed experimental evidence for each protein. For example, despite extensive literature review, human NRXN1-β, NRXN2-α, NRXN2-β, NRXN3-α, and NRXN3-β have no experimental evidence code supported GO molecular function terms annotations. Furthermore, there are limited terms available in the behavioral domain of GO, and the Neurobehavior Ontology [90] may be better suited to provide a more comprehensive interpretation of complex behavioral traits than can be achieved with GO.

Variants in the NRXN, NLGN and SHANK gene families, and in the DLG4 gene, have the potential to result in impaired synaptic formation and impaired regulation of synaptic transmission; however, not all of these proteins have been associated with ASD [19]. The quality of gene annotations incorporated into statistical analysis tools has a direct impact on the effective interpretation of many genomic and proteomic datasets. Unfortunately, not all functional analysis tools include current annotation data; some tools use annotation datasets that are over a year old. Additional ontology development and the continued comprehensive annotation of the proteins involved in ASD-relevant processes, capturing more data as it emerges in the literature, would ensure the maximum utility of the GO data for interpretation of ASD-focused transcriptomic, proteomic, genome-wide association studies and next generation sequencing.

Abbreviations

ASD:

Autistic spectrum disorder

BLAST:

Basic Local Alignment Search Tool

GO:

Gene Ontology

HCOP:

HUGO Gene Nomenclature Committee ortholog prediction tool

IC:

Inferred by Curator

IDA:

Inferred from Direct Assay

IEA:

Inferred from Electronic Annotation

IGI:

Inferred from Genetic Interaction

IMP:

Inferred from Mutant Phenotype

ISS:

Inferred from Sequence Similarity

MGI:

Mouse Genome Informatics

NRXN:

Neurexin

NLGN:

Neuroligin

RGD:

Rat Genome Database

SNP:

Single Nucleotide Polymorphism

References

  1. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene Ontology: tool for the unification of biology. Nat Genet. 2000;25(1):25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Balakrishnan R, Harris MA, Huntley R, Van Auken K, Cherry JM. A guide to best practices for Gene Ontology (GO) manual annotation. Database (Oxford). 2013;2013:bat054. doi:10.1093/database/bat054.

    Article  CAS  Google Scholar 

  3. Pavlidis P, Qin J, Arango V, Mann JJ, Sibille E. Using the gene ontology for microarray data mining: a comparison of methods and application to age effects in human prefrontal cortex. Neurochem Res. 2004;29(6):1213–22.

    Article  CAS  PubMed  Google Scholar 

  4. Holmans P, Green EK, Pahwa JS, Ferreira MA, Purcell SM, Sklar P, et al. Gene ontology analysis of GWA study data sets provides insights into the biology of bipolar disorder. Am J Hum Genet. 2009;85(1):13–24. doi:10.1016/j.ajhg.2009.05.011.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Kendler KS, Kalsi G, Holmans PA, Sanders AR, Aggen SH, Dick DM, et al. Genomewide association analysis of symptoms of alcohol dependence in the molecular genetics of schizophrenia (MGS2) control sample. Alcohol Clin Exp Res. 2011;35(5):963–75. doi:10.1111/j.1530-0277.2010.01427.x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Shi M, Jin J, Wang Y, Beyer RP, Kitsou E, Albin RL, et al. Mortalin: a protein associated with progression of Parkinson disease? J Neuropathol Exp Neurol. 2008;67(2):117–24. doi:10.1097/nen.0b013e318163354a.

    Article  CAS  PubMed  Google Scholar 

  7. Alam-Faruque Y, Huntley RP, Khodiyar VK, Camon EB, Dimmer EC, Sawford T, et al. The impact of focused Gene Ontology curation of specific mammalian systems. PLoS One. 2011;6(12):e27541. doi:10.1371/journal.pone.0027541.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Bailey A, Le Couteur A, Gottesman I, Bolton P, Simonoff E, Yuzda E, et al. Autism as a strongly genetic disorder: evidence from a British twin study. Psychol Med. 1995;25(1):63–77.

    Article  CAS  PubMed  Google Scholar 

  9. Muhle R, Trentacoste SV, Rapin I. The genetics of autism. Pediatrics. 2004;113(5):e472–86.

    Article  PubMed  Google Scholar 

  10. Kanner L. Autistic disturbances of affective contact. Acta Paedopsychiatrica. 1968;35(4):100.

    CAS  PubMed  Google Scholar 

  11. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 5th ed. Washington, DC: American Psychiatric Publishing; 2013.

  12. Volkmar FR, McPartland JC. From Kanner to DSM-5: autism as an evolving diagnostic concept. Annu Rev Clin Psychol. 2014;10:193–212. doi:10.1146/annurev-clinpsy-032813-153710.

    Article  PubMed  Google Scholar 

  13. Frith U. Autism: A very short introduction. USA: Oxford University Press; 2008.

    Book  Google Scholar 

  14. DiCicco-Bloom E, Lord C, Zwaigenbaum L, Courchesne E, Dager SR, Schmitz C, et al. The developmental neurobiology of autism spectrum disorder. J Neuroscience. 2006;26(26):6897.

    Article  CAS  PubMed  Google Scholar 

  15. Sebat J, Lakshmi B, Malhotra D, Troge J, Lese-Martin C, Walsh T, et al. Strong association of de novo copy number mutations with autism. Science. 2007;316(5823):445.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Devlin B, Scherer SW. Genetic architecture in autism spectrum disorder. Curr Opin Genet Dev. 2012;22(3):229–37. doi:10.1016/j.gde.2012.03.002.

    Article  CAS  PubMed  Google Scholar 

  17. Abrahams BS, Geschwind DH. Advances in autism genetics: on the threshold of a new neurobiology. Nat Rev Genet. 2008;9(5):341–55.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Persico AM, Napolioni V. Autism genetics. Behav Brain Res. 2013;251:95–112. doi:10.1016/j.bbr.2013.06.012.

    Article  PubMed  Google Scholar 

  19. Pardo CA, Eberhart CG. The neurobiology of autism. Brain Pathol. 2007;17(4):434–47. doi:10.1111/j.1750-3639.2007.00102.x.

    Article  CAS  PubMed  Google Scholar 

  20. Nguyen T, Sudhof TC. Binding properties of neuroligin 1 and neurexin 1beta reveal function as heterophilic cell adhesion molecules. J Biol Chem. 1997;272(41):26032–9.

    Article  CAS  PubMed  Google Scholar 

  21. Scheiffele P, Fan J, Choih J, Fetter R, Serafini T. Neuroligin expressed in nonneuronal cells triggers presynaptic development in contacting axons. Cell. 2000;101(6):657–69.

    Article  CAS  PubMed  Google Scholar 

  22. Waites CL, Craig AM, Garner CC. Mechanisms of vertebrate synaptogenesis. Annu Rev Neurosci. 2005;28:251–74.

    Article  CAS  PubMed  Google Scholar 

  23. Peca J, Feliciano C, Ting JT, Wang W, Wells MF, Venkatraman TN, et al. Shank3 mutant mice display autistic-like behaviours and striatal dysfunction. Nature. 2011;472(7344):437–42. doi:10.1038/nature09965.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Jamain S, Quach H, Betancur C, Rastam M, Colineaux C, Gillberg IC, et al. Mutations of the X-linked genes encoding neuroligins NLGN3 and NLGN4 are associated with autism. Nat Genet. 2003;34(1):27–9. doi:10.1038/ng1136.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Szatmari P, Paterson AD, Zwaigenbaum L, Roberts W, Brian J, Liu XQ, et al. Mapping autism risk loci using genetic linkage and chromosomal rearrangements. Nat Genet. 2007;39(3):319–28. doi:10.1038/ng1985.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Feng J, Schroer R, Yan J, Song W, Yang C, Bockholt A, et al. High frequency of neurexin 1beta signal peptide structural variants in patients with autism. Neurosci Lett. 2006;409(1):10–3. doi:10.1016/j.neulet.2006.08.017.

    Article  CAS  PubMed  Google Scholar 

  27. Varoqueaux F, Aramuni G, Rawson RL, Mohrmann R, Missler M, Gottmann K, et al. Neuroligins determine synapse maturation and function. Neuron. 2006;51(6):741–54. doi:10.1016/j.neuron.2006.09.003.

    Article  CAS  PubMed  Google Scholar 

  28. Zhang C, Milunsky JM, Newton S, Ko J, Zhao G, Maher TA, et al. A neuroligin-4 missense mutation associated with autism impairs neuroligin-4 folding and endoplasmic reticulum export. J Neurosci. 2009;29(35):10843–54. doi:10.1523/JNEUROSCI.1248-09.2009.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Sudhof TC. Neuroligins and neurexins link synaptic function to cognitive disease. Nature. 2008;455(7215):903–11. doi:10.1038/nature07456.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Ylisaukko-oja T, Rehnström K, Auranen M, Vanhala R, Alen R, Kempas E, et al. Analysis of four neuroligin genes as candidates for autism. Eur J Hum Genet. 2005;13(12):1285–92.

    Article  CAS  PubMed  Google Scholar 

  31. Laumonnier F, Bonnet-Brilhault F, Gomot M, Blanc R, David A, Moizard MP, et al. X-linked mental retardation and autism are associated with a mutation in the NLGN4 gene, a member of the neuroligin family. Am J Hum Genet. 2004;74(3):552–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Yan J, Feng J, Schroer R, Li W, Skinner C, Schwartz CE, et al. Analysis of the neuroligin 4Y gene in patients with autism. Psychiatr Genet. 2008;18(4):204–7. doi:10.1097/YPG.0b013e3282fb7fe6.

    Article  PubMed  Google Scholar 

  33. Ushkaryov YA, Hata Y, Ichtchenko K, Moomaw C, Afendis S, Slaughter CA, et al. Conserved domain structure of beta-neurexins. Unusual cleaved signal sequences in receptor-like neuronal cell-surface proteins. J Biol Chem. 1994;269(16):11987–92.

    CAS  PubMed  Google Scholar 

  34. Bellen HJ, Lu Y, Beckstead R, Bhat MA. Neurexin IV, caspr and paranodin–novel members of the neurexin family: encounters of axons and glia. Trends Neurosci. 1998;21(10):444–9.

    Article  CAS  PubMed  Google Scholar 

  35. Dean C, Scholl FG, Choih J, DeMaria S, Berger J, Isacoff E, et al. Neurexin mediates the assembly of presynaptic terminals. Nat Neurosci. 2003;6(7):708–16. doi:10.1038/nn1074.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Poliak S, Gollan L, Martinez R, Custer A, Einheber S, Salzer JL, et al. Caspr2, a new member of the neurexin superfamily, is localized at the juxtaparanodes of myelinated axons and associates with K+ channels. Neuron. 1999;24(4):1037–47.

    Article  CAS  PubMed  Google Scholar 

  37. Garcia-Fresco GP, Sousa AD, Pillai AM, Moy SS, Crawley JN, Tessarollo L, et al. Disruption of axo-glial junctions causes cytoskeletal disorganization and degeneration of Purkinje neuron axons. Proc Natl Acad Sci U S A. 2006;103(13):5137–42. doi:10.1073/pnas.0601082103.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Zahir FR, Baross A, Delaney AD, Eydoux P, Fernandes ND, Pugh T, et al. A patient with vertebral, cognitive and behavioural abnormalities and a de novo deletion of NRXN1alpha. J Med Genet. 2008;45(4):239–43. doi:10.1136/jmg.2007.054437.

    Article  CAS  PubMed  Google Scholar 

  39. Gauthier J, Siddiqui TJ, Huashan P, Yokomaku D, Hamdan FF, Champagne N, et al. Truncating mutations in NRXN2 and NRXN1 in autism spectrum disorders and schizophrenia. Hum Genet. 2011;130(4):563–73. doi:10.1007/s00439-011-0975-z.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Vaags AK, Lionel AC, Sato D, Goodenberger M, Stein QP, Curran S, et al. Rare deletions at the neurexin 3 locus in autism spectrum disorder. Am J Hum Genet. 2012;90(1):133–41. doi:10.1016/j.ajhg.2011.11.025.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Alarcon M, Abrahams BS, Stone JL, Duvall JA, Perederiy JV, Bomar JM, et al. Linkage, association, and gene-expression analyses identify CNTNAP2 as an autism-susceptibility gene. Am J Hum Genet. 2008;82(1):150–9. doi:10.1016/j.ajhg.2007.09.005.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Berkel S, Marshall CR, Weiss B, Howe J, Roeth R, Moog U, et al. Mutations in the SHANK2 synaptic scaffolding gene in autism spectrum disorder and mental retardation. Nat Genet. 2010;42(6):489–91. doi:10.1038/ng.589.

    Article  CAS  PubMed  Google Scholar 

  43. Durand CM, Betancur C, Boeckers TM, Bockmann J, Chaste P, Fauchereau F, et al. Mutations in the gene encoding the synaptic scaffolding protein SHANK3 are associated with autism spectrum disorders. Nat Genet. 2007;39(1):25–7. doi:10.1038/ng1933.

    Article  CAS  PubMed  Google Scholar 

  44. Sato D, Lionel AC, Leblond CS, Prasad A, Pinto D, Walker S, et al. SHANK1 Deletions in Males with Autism Spectrum Disorder. Am J Hum Genet. 2012;90(5):879–87. doi:10.1016/j.ajhg.2012.03.017.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Craig AM, Kang Y. Neurexin-neuroligin signaling in synapse development. Curr Opin Neurobiol. 2007;17(1):43–52. doi:10.1016/j.conb.2007.01.011.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Eyre TA, Wright MW, Lush MJ, Bruford EA. HCOP: a searchable database of human orthology predictions. Brief Bioinform. 2007;8(1):2–5. doi:10.1093/bib/bbl030.

    Article  CAS  PubMed  Google Scholar 

  47. Day-Richter J, Harris MA, Haendel M, Lewis S. OBO-Edit–an ontology editor for biologists. Bioinformatics. 2007;23(16):2198–200. doi:10.1093/bioinformatics/btm112.

    Article  CAS  PubMed  Google Scholar 

  48. Graf ER, Zhang X, Jin SX, Linhoff MW, Craig AM. Neurexins induce differentiation of GABA and glutamate postsynaptic specializations via neuroligins. Cell. 2004;119(7):1013–26. doi:10.1016/j.cell.2004.11.035.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Ko J, Zhang C, Arac D, Boucard AA, Brunger AT, Sudhof TC. Neuroligin-1 performs neurexin-dependent and neurexin-independent functions in synapse validation. EMBO J. 2009;28(20):3244–55. doi:10.1038/emboj.2009.249.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Levinson JN, Li R, Kang R, Moukhles H, El-Husseini A, Bamji SX. Postsynaptic scaffolding molecules modulate the localization of neuroligins. Neuroscience. 2010;165(3):782–93. doi:10.1016/j.neuroscience.2009.11.016.

    Article  CAS  PubMed  Google Scholar 

  51. Chubykin AA, Atasoy D, Etherton MR, Brose N, Kavalali ET, Gibson JR, et al. Activity-dependent validation of excitatory versus inhibitory synapses by neuroligin-1 versus neuroligin-2. Neuron. 2007;54(6):919–31. doi:10.1016/j.neuron.2007.05.029.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Hung AY, Futai K, Sala C, Valtschanoff JG, Ryu J, Woodworth MA, et al. Smaller dendritic spines, weaker synaptic transmission, but enhanced spatial learning in mice lacking Shank1. J Neurosci. 2008;28(7):1697–708. doi:10.1523/JNEUROSCI.3032-07.2008.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Uemura T, Mori H, Mishina M. Direct interaction of GluRdelta2 with Shank scaffold proteins in cerebellar Purkinje cells. Mol Cell Neurosci. 2004;26(2):330–41. doi:10.1016/j.mcn.2004.02.007.

    Article  CAS  PubMed  Google Scholar 

  54. Cho KO, Hunt CA, Kennedy MB. The rat brain postsynaptic density fraction contains a homolog of the Drosophila discs-large tumor suppressor protein. Neuron. 1992;9(5):929–42.

    Article  CAS  PubMed  Google Scholar 

  55. Sun XY, Takagishi Y, Okabe E, Chishima Y, Kanou Y, Murase S, et al. A novel Caspr mutation causes the shambling mouse phenotype by disrupting axoglial interactions of myelinated nerves. J Neuropathol Exp Neurol. 2009;68(11):1207–18. doi:10.1097/NEN.0b013e3181be2e96.

    Article  PubMed  Google Scholar 

  56. Chubykin AA, Liu X, Comoletti D, Tsigelny I, Taylor P, Sudhof TC. Dissection of synapse induction by neuroligins: effect of a neuroligin mutation associated with autism. J Biol Chem. 2005;280(23):22365–74. doi:10.1074/jbc.M410723200.

    Article  CAS  PubMed  Google Scholar 

  57. Uemura T, Lee SJ, Yasumura M, Takeuchi T, Yoshida T, Ra M, et al. Trans-synaptic interaction of GluRdelta2 and Neurexin through Cbln1 mediates synapse formation in the cerebellum. Cell. 2010;141(6):1068–79. doi:10.1016/j.cell.2010.04.035.

    Article  CAS  PubMed  Google Scholar 

  58. Etherton MR, Tabuchi K, Sharma M, Ko J, Sudhof TC. An autism-associated point mutation in the neuroligin cytoplasmic tail selectively impairs AMPA receptor-mediated synaptic transmission in hippocampus. EMBO J. 2011;30(14):2908–19. doi:10.1038/emboj.2011.182.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Barrow SL, Constable JR, Clark E, El-Sabeawy F, McAllister AK, Washbourne P. Neuroligin1: a cell adhesion molecule that recruits PSD-95 and NMDA receptors by distinct mechanisms during synaptogenesis. Neural Dev. 2009;4:17. doi:10.1186/1749-8104-4-17.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Wittenmayer N, Korber C, Liu H, Kremer T, Varoqueaux F, Chapman ER, et al. Postsynaptic Neuroligin1 regulates presynaptic maturation. Proc Natl Acad Sci U S A. 2009;106(32):13564–9. doi:10.1073/pnas.0905819106.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Bakkaloglu B, O’Roak BJ, Louvi A, Gupta AR, Abelson JF, Morgan TM, et al. Molecular cytogenetic analysis and resequencing of contactin associated protein-like 2 in autism spectrum disorders. Am J Hum Genet. 2008;82(1):165–73. doi:10.1016/j.ajhg.2007.09.017.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Oiso S, Takeda Y, Futagawa T, Miura T, Kuchiiwa S, Nishida K, et al. Contactin-associated protein (Caspr) 2 interacts with carboxypeptidase E in the CNS. J Neurochem. 2009;109(1):158–67. doi:10.1111/j.1471-4159.2009.05928.x.

    Article  CAS  PubMed  Google Scholar 

  63. Poliak S, Salomon D, Elhanany H, Sabanay H, Kiernan B, Pevny L, et al. Juxtaparanodal clustering of Shaker-like K+ channels in myelinated axons depends on Caspr2 and TAG-1. J Cell Biol. 2003;162(6):1149–60. doi:10.1083/jcb.200305018.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Wang X, McCoy PA, Rodriguiz RM, Pan Y, Je HS, Roberts AC, et al. Synaptic dysfunction and abnormal behaviors in mice lacking major isoforms of Shank3. Hum Mol Genet. 2011;20(15):3093–108. doi:10.1093/hmg/ddr212.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Chen X, Nelson CD, Li X, Winters CA, Azzam R, Sousa AA, et al. PSD-95 is required to sustain the molecular organization of the postsynaptic density. J Neurosci. 2011;31(17):6329–38. doi:10.1523/JNEUROSCI.5968-10.2011.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Mondin M, Labrousse V, Hosy E, Heine M, Tessier B, Levet F, et al. Neurexin-neuroligin adhesions capture surface-diffusing AMPA receptors through PSD-95 scaffolds. J Neurosci. 2011;31(38):13500–15. doi:10.1523/JNEUROSCI.6439-10.2011.

    Article  CAS  PubMed  Google Scholar 

  67. Verpelli C, Dvoretskova E, Vicidomini C, Rossi F, Chiappalone M, Schoen M, et al. Importance of Shank3 protein in regulating metabotropic glutamate receptor 5 (mGluR5) expression and signaling at synapses. J Biol Chem. 2011;286(40):34839–50. doi:10.1074/jbc.M111.258384.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Hines RM, Wu L, Hines DJ, Steenland H, Mansour S, Dahlhaus R, et al. Synaptic imbalance, stereotypies, and impaired social interactions in mice with altered neuroligin 2 expression. J Neurosci. 2008;28(24):6055–67. doi:10.1523/JNEUROSCI.0032-08.2008.

    Article  CAS  PubMed  Google Scholar 

  69. Gutierrez RC, Hung J, Zhang Y, Kertesz AC, Espina FJ, Colicos MA. Altered synchrony and connectivity in neuronal networks expressing an autism-related mutation of neuroligin 3. Neuroscience. 2009;162(1):208–21. doi:10.1016/j.neuroscience.2009.04.062.

    Article  CAS  PubMed  Google Scholar 

  70. Bang ML, Owczarek S. A matter of balance: role of neurexin and neuroligin at the synapse. Neurochem Res. 2013;38(6):1174–89. doi:10.1007/s11064-013-1029-9.

    Article  CAS  PubMed  Google Scholar 

  71. Comoletti D, De Jaco A, Jennings LL, Flynn RE, Gaietta G, Tsigelny I, et al. The Arg451Cys-neuroligin-3 mutation associated with autism reveals a defect in protein processing. J Neurosci. 2004;24(20):4889–93. doi:10.1523/JNEUROSCI.0468-04.2004.

    Article  CAS  PubMed  Google Scholar 

  72. Zweier C, de Jong EK, Zweier M, Orrico A, Ousager LB, Collins AL, et al. CNTNAP2 and NRXN1 are mutated in autosomal-recessive Pitt-Hopkins-like mental retardation and determine the level of a common synaptic protein in Drosophila. Am J Hum Genet. 2009;85(5):655–66. doi:10.1016/j.ajhg.2009.10.004.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Bozdagi O, Sakurai T, Papapetrou D, Wang X, Dickstein DL, Takahashi N, et al. Haploinsufficiency of the autism-associated Shank3 gene leads to deficits in synaptic function, social interaction, and social communication. Mol Autism. 2010;1(1):15. doi:10.1186/2040-2392-1-15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Jamain S, Radyushkin K, Hammerschmidt K, Granon S, Boretius S, Varoqueaux F, et al. Reduced social interaction and ultrasonic communication in a mouse model of monogenic heritable autism. Proc Natl Acad Sci U S A. 2008;105(5):1710–5. doi:10.1073/pnas.0711555105.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Feyder M, Karlsson RM, Mathur P, Lyman M, Bock R, Momenan R, et al. Association of mouse Dlg4 (PSD-95) gene deletion and human DLG4 gene variation with phenotypes relevant to autism spectrum disorders and Williams’ syndrome. Am J Psychiatry. 2010;167(12):1508–17. doi:10.1176/appi.ajp.2010.10040484.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Wohr M, Roullet FI, Hung AY, Sheng M, Crawley JN. Communication impairments in mice lacking Shank1: reduced levels of ultrasonic vocalizations and scent marking behavior. PLoS One. 2011;6(6):e20631. doi:10.1371/journal.pone.0020631.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Etherton MR, Blaiss CA, Powell CM, Sudhof TC. Mouse neurexin-1alpha deletion causes correlated electrophysiological and behavioral changes consistent with cognitive impairments. Proc Natl Acad Sci U S A. 2009;106(42):17998–8003. doi:10.1073/pnas.0910297106.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Blundell J, Tabuchi K, Bolliger MF, Blaiss CA, Brose N, Liu X, et al. Increased anxiety-like behavior in mice lacking the inhibitory synapse cell adhesion molecule neuroligin 2. Genes Brain Behav. 2009;8(1):114–26. doi:10.1111/j.1601-183X.2008.00455.x.

    Article  CAS  PubMed  Google Scholar 

  79. Bolliger MF, Frei K, Winterhalter KH, Gloor SM. Identification of a novel neuroligin in humans which binds to PSD-95 and has a widespread expression. Biochem J. 2001;356(Pt 2):581–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Tu JC, Xiao B, Naisbitt S, Yuan JP, Petralia RS, Brakeman P, et al. Coupling of mGluR/Homer and PSD-95 complexes by the Shank family of postsynaptic density proteins. Neuron. 1999;23(3):583–92.

    Article  CAS  PubMed  Google Scholar 

  81. Uchino S, Wada H, Honda S, Nakamura Y, Ondo Y, Uchiyama T, et al. Direct interaction of post-synaptic density-95/Dlg/ZO-1 domain-containing synaptic molecule Shank3 with GluR1 alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptor. J Neurochem. 2006;97(4):1203–14. doi:10.1111/j.1471-4159.2006.03831.x.

    Article  CAS  PubMed  Google Scholar 

  82. Xu J, Paquet M, Lau AG, Wood JD, Ross CA, Hall RA. beta 1-adrenergic receptor association with the synaptic scaffolding protein membrane-associated guanylate kinase inverted-2 (MAGI-2). Differential regulation of receptor internalization by MAGI-2 and PSD-95. J Biol Chem. 2001;276(44):41310–7. doi:10.1074/jbc.M107480200.

    Article  CAS  PubMed  Google Scholar 

  83. Siow NL, Choi RC, Xie HQ, Kong LW, Chu GK, Chan GK, et al. ATP induces synaptic gene expressions in cortical neurons: transduction and transcription control via P2Y1 receptors. Mol Pharmacol. 2010;78(6):1059–71. doi:10.1124/mol.110.066506.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Alam-Faruque Y, Hill DP, Dimmer EC, Harris MA, Foulger RE, Tweedie S, et al. Representing kidney development using the gene ontology. PLoS One. 2014;9(6):e99864. doi:10.1371/journal.pone.0099864.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Mutowo-Meullenet P, Huntley RP, Dimmer EC, Alam-Faruque Y, Sawford T, Jesus Martin M, O’Donovan C, Apweiler R. Use of Gene Ontology Annotation to understand the peroxisome proteome in humans. Database (Oxford). 2013;2013:bas062. doi:10.1093/database/bas062.

  86. The Gene Ontology’s Reference Genome Project: a unified framework for functional annotation across species. PLoS Comput Biol. 2009;5(7):e1000431. doi:10.1371/journal.pcbi.1000431.

  87. Activities at the Universal Protein Resource (UniProt). Nucleic Acids Res. 2014;42(Database issue):D191-8. doi:10.1093/nar/gkt1140.

  88. Holmans P, Moskvina V, Jones L, Sharma M, Vedernikov A, Buchel F, et al. A pathway-based analysis provides additional support for an immune-related genetic susceptibility to Parkinson’s disease. Hum Mol Genet. 2013;22(5):1039–49. doi:10.1093/hmg/dds492.

    Article  CAS  PubMed  Google Scholar 

  89. Duncan LE, Holmans PA, Lee PH, O’Dushlaine CT, Kirby AW, Smoller JW, et al. Pathway analyses implicate glial cells in schizophrenia. PLoS One. 2014;9(2):e89441. doi:10.1371/journal.pone.0089441.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Gkoutos GV, Schofield PN, Hoehndorf R. The neurobehavior ontology: an ontology for annotation and integration of behavior and behavioral phenotypes. Int Rev Neurobiol. 2012;103:69–87. doi:10.1016/B978-0-12-388408-4.00004-6.

    Article  PubMed  Google Scholar 

  91. Kim S, Burette A, Chung HS, Kwon SK, Woo J, Lee HW, et al. NGL family PSD-95-interacting adhesion molecules regulate excitatory synapse formation. Nat Neurosci. 2006;9(10):1294–301. doi:10.1038/nn1763.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

R.C.L received support as a British Heart Foundation (BHF) chair scholar and through funding by BHF grants (RG/13/5/30112 and SP/07/007/23671), a Parkinson’s UK grant (G-1307), and is supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre. P.R. is funded by the National Human Genome Research Institute (NHGRI) P41 grant 5P41HG002273-09 to the Gene Ontology Consortium. We are indebted to Tony Sawford and Dr Maria Martin for the development of the online EBI GO annotation tool, which was an essential component of this annotation project. Many thanks to Dr. Jane Lomax, Dr. David Osumi-Sutherland and other members of the Gene Ontology Editorial Office for useful discussions about the placement and definition of new GO terms, and to Dr Paul Denny and Dr Jo Knight for their critical reading of the manuscript. We are also grateful to all of the curators at UniProt-GOA, the Jackson Laboratory and the Rat Genome Database, and to Dr. Varsha Khodiyar, for their contributions to the annotation of these autism-relevant proteins.

Author information

Authors and Affiliations

  1. Institute of Medical Science, University of Toronto, Medical Sciences Building, 1 King’s College Circle, Toronto, M5S 1A8, Canada

    Sejal Patel

  2. Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, 250 College Street, Toronto, M5T 1R8, Canada

    Sejal Patel

  3. Centre for Cardiovascular Genetics, Institute of Cardiovascular Science, University College London, Rayne Building, 5 University Street, London, WC1E 6JF, UK

    Sejal Patel & Ruth C. Lovering

  4. European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK

    Paola Roncaglia

  5. The Gene Ontology Consortium http://geneontology.org/

    Paola Roncaglia

Authors
  1. Sejal Patel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paola Roncaglia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ruth C. Lovering
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ruth C. Lovering.

Additional information

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

SP collected and manually annotated primary articles of genes (NRXN, NLGN and SHANK gene families, and the DLG4 gene) associated with autism, with additional annotations by RCL. SP and RCL analyzed the annotation data to develop the figures and tables. PR created new GO terms in discussion with SP. SP and RCL drafted the manuscript. All authors revised and approved the final manuscript.

Additional files

Additional file 1:

References used to support the annotation of NLGN, NRXN, SHANK and PSD-95 proteins with GO terms, as part of the ASD focused annotation project.

Additional file 2: Table S1.

List of NRXN, NLGN, SHANK and DLG4 proteins annotated with GO terms, relevant to ASD. Includes hyperlinks to the QuickGO browser listing current annotations associated with these proteins.

Additional file 3: Table S2.

Summary of the number of GO annotations associated with the NRXN, NLGN, SHANK and DLG4 human, mouse and rat protein families. The summary tab lists the number of GO annotations associated with the NRXN, NLGN, SHANK and DLG4 human, mouse and rat protein families. The all_data tab includes all annotations associated with these proteins. Downloaded using the QuickGO browser, filtering on the 47 protein IDs (see Additional file 2), 28 March 2014. Pre-focus annotations are those generated before the start of this autism-focused annotation project; Focus annotations are those generated by this autism-focused annotation project; Other annotations are those generated by other databases not involved in this autism-focused annotation project (e.g. MGI, RGD, UniProt); Automatic annotations are those generated by a variety of automatic scripts based on orthology (Ensembl), protein domain structure (InterPro) or UniProt Keyword mapping files (UniProt).

Additional file 4: Table S3.

Cellular component GO annotations associated with the NRXN, NLGN, SHANK and DLG4 human, mouse and rat protein families. Downloaded using the QuickGO browser, filtering on the 47 protein IDs (see Additional file 2) and the GO ID GO:0005575, 28 March 2014. An additional column is provided entitled Pre-focus/Focus/Other/Automatic: Pre-focus annotations are those generated before the start of this autism-focused annotation project; Focus annotations are those generated by this autism-focused annotation project; Other annotations are those generated by other databases after the start of this autism-focused annotation project (e.g. MGI, RGD, UniProt); Automatic annotations are those generated by a variety of automatic scripts based on orthology (Ensembl), protein domain structure (InterPro) or UniProt Keyword mapping files (UniProt).

Additional file 5: Table S4.

Biological process GO annotations associated with the NRXN, NLGN, SHANK and DLG4 human, mouse and rat protein families. The ‘direct’ GO term annotations are the GO terms that have been associated with the protein record. These direct GO terms have been grouped into broader biological process ‘grouping’ parent GO terms, or categories. Due to the structure of the ontology there are some direct GO terms that are child terms of more than one of these broader GO grouping parent terms. In these instances the annotation is then represented more than once in the dataset. Downloaded using the QuickGO browser, filtering on the 47 protein IDs (see Additional file 2) and the GO ID GO:0008150, 28 March 2014. An additional column is provided entitled Pre-focus/Focus/Other/Automatic: Pre-focus annotations are those generated before the start of this autism-focused annotation project; Focus annotations are those generated by this autism-focused annotation project; Other annotations are those generated by other databases after the start of this autism-focused annotation project (e.g. MGI, RGD, UniProt); Automatic annotations are those generated by a variety of automatic scripts based on orthology (Ensembl), protein domain structure (InterPro) or UniProt Keyword mapping files (UniProt).

Additional file 6: Table S5.

Molecular function GO annotations associated with the NRXN, NLGN, SHANK and DLG4 human, mouse and rat protein families. Downloaded using the QuickGO browser, filtering on the 47 protein IDs (see Additional file 2) and the GO ID GO:0003674, 28 March 2014. An additional column is provided entitled Pre-focus/Focus/Other/Automatic: Pre-focus annotations are those generated before the start of this autism-focused annotation project; Focus annotations are those generated by this autism-focused annotation project; Other annotations are those generated by other databases after the start of this autism-focused annotation project (e.g. MGI, RGD, UniProt); Automatic annotations are those generated by a variety of automatic scripts based on orthology (Ensembl), protein domain structure (InterPro) or UniProt Keyword mapping files (UniProt).

Additional file 7: Table S6.

QuickGO statistics for the source of GO annotations associated with the NRXN, NLGN, SHANK and DLG4 human, mouse and rat protein families. Downloaded using the QuickGO browser, filtering on the 47 protein IDs (see Additional file 2), 28 March 2014.

Additional file 8: Table S7.

Summary table of the biological process GO annotations associated with human DLG4 and the human NRXN, NLGN and SHANK protein families. All annotations to the human protein records are included. The ‘direct’ GO term annotations are the GO terms that have been associated with the protein record (white rows). These direct GO terms have been grouped into broader biological process ‘grouping’ parent GO terms, or categories (green rows). Due to the structure of the ontology there are some direct GO terms that are child terms of more than one of these broader GO grouping parent terms. In these instances the annotation is then represented more than once in the dataset. Downloaded using the QuickGO browser, filtering on the 17 human protein IDs (see Additional file 2) and the GO ID GO:0008150, 28 March 2014.

Additional file 9: Table S8.

Number of annotations associated with new GO terms created to support detailed ASD-relevant annotations. Downloaded using the QuickGO browser, filtering on the new GO IDs (Table 1), 11 July 2014. Focus annotations are those generated by this autism-focused annotation project; Other annotations are those generated by other databases not involved in this autism-focused annotation project (e.g. MGI, RGD, UniProt); Automatic annotations are those generated by a variety of automatic scripts based on orthology (Ensembl), protein domain structure (InterPro) or UniProt Keyword mapping files (UniProt). For cases when multiple protein IDs are mapped to a single gene, the manual annotations are counted as a single annotation not multiple annotations, e.g. MGI has associated Lrcc4 with postsynaptic density protein 95 clustering [91], based on evidence presented in Kim S et al., 2006, this annotation is mapped to 4 protein IDs (E9PUX8, Q149E6, Q8BJ09, Q99PH1) in QuickGO; this is counted as a single annotation.

Rights and permissions

Open Access  This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/.

The Creative Commons Public Domain Dedication waiver (https://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Patel, S., Roncaglia, P. & Lovering, R.C. Using Gene Ontology to describe the role of the neurexin-neuroligin-SHANK complex in human, mouse and rat and its relevance to autism. BMC Bioinformatics 16, 186 (2015). https://doi.org/10.1186/s12859-015-0622-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12859-015-0622-0

Keywords

BMC Bioinformatics

ISSN: 1471-2105

Contact us