synaptome.db

Oksana Sorokina, Anatoly Sorokin, J. Douglas Armstrong

08.10.2021

suppressMessages(library(synaptome.db))
suppressMessages(library(dplyr))
library(pander)

Title SynaptomeDB: database for Synaptic proteome

Manual for querying SynaptomeDB

Authors: Oksana Sorokina, Anatoly Sorokin, J. Douglas Armstrong

Introduction

58 published synaptic proteomic datasets (2000-2020 years) that describe over 8,000 proteins were integrated and combined with direct protein-protein interactions and functional metadata to build a network resource.

The set includes 29 post synaptic proteome (PSP) studies (2000 to 2019) contributing a total of 5,560 mouse and human unique gene identifiers; 18 presynaptic studies (2004 to 2020) describe 2,772 unique human and mouse gene IDs, and 11 studies that span the whole synaptosome and report 7,198 unique genes.

To reconstruct protein-protein interaction (PPI) networks for the pre- and post-synaptic proteomes we used human PPI data filtered for the highest confidence direct and physical interactions from BioGRID, Intact and DIP. The resulting postsynaptic proteome (PSP) network contains 4,817 nodes and 27,788 edges in the Largest Connected Component (LCC). The presynaptic network is significantly smaller and comprises 2,221 nodes and 8,678 edges in the LCC.

The database includes: proteomic and interactomic data with supporting information on compartment, specie and brain region, GO function information for three species: mouse, rat and human, disease annotation for human (based on Human Disease Ontology (HDO)) and GeneToModel table, which links certain synaptic proteins to existing computational models of synaptic plasticity and synaptic signal transduction

The original files are maintained at Eidnburgh Datashare https://doi.org/10.7488/ds/3017

The dataset was described in the Sorokina et al. (2021).

Overview of capabilities

1.Get information for a specific gene or gene set.

The dataset can be used to answer frequent questions such as “What is known about my favourite gene? Is it pre- or postsynaptic? Which brain region was it identified in?” “Which publication it was reported in?” Information could be obtained by submitting gene EntrezID or Gene name

t <- getGeneInfoByEntrez(1742) 
head(t)
#> # A tibble: 6 × 12
#>   GeneID Localisation MGI         HumanEntrez MouseEntrez HumanName MouseName
#>    <int> <chr>        <chr>             <int>       <int> <chr>     <chr>    
#> 1      1 Postsynaptic MGI:1277959        1742       13385 DLG4      Dlg4     
#> 2      1 Postsynaptic MGI:1277959        1742       13385 DLG4      Dlg4     
#> 3      1 Postsynaptic MGI:1277959        1742       13385 DLG4      Dlg4     
#> 4      1 Postsynaptic MGI:1277959        1742       13385 DLG4      Dlg4     
#> 5      1 Postsynaptic MGI:1277959        1742       13385 DLG4      Dlg4     
#> 6      1 Postsynaptic MGI:1277959        1742       13385 DLG4      Dlg4     
#> # … with 5 more variables: PaperPMID <int>, Paper <chr>, Year <int>,
#> #   SpeciesTaxID <int>, BrainRegion <chr>
t <- getGeneInfoByName("CASK")
head(t)
#> # A tibble: 6 × 12
#>   GeneID Localisation MGI         HumanEntrez MouseEntrez HumanName MouseName
#>    <int> <chr>        <chr>             <int>       <int> <chr>     <chr>    
#> 1    409 Postsynaptic MGI:1309489        8573       12361 CASK      Cask     
#> 2    409 Postsynaptic MGI:1309489        8573       12361 CASK      Cask     
#> 3    409 Postsynaptic MGI:1309489        8573       12361 CASK      Cask     
#> 4    409 Postsynaptic MGI:1309489        8573       12361 CASK      Cask     
#> 5    409 Postsynaptic MGI:1309489        8573       12361 CASK      Cask     
#> 6    409 Postsynaptic MGI:1309489        8573       12361 CASK      Cask     
#> # … with 5 more variables: PaperPMID <int>, Paper <chr>, Year <int>,
#> #   SpeciesTaxID <int>, BrainRegion <chr>
t <- getGeneInfoByName(c("CASK", "DLG2"))
head(t)                      
#> # A tibble: 6 × 12
#>   GeneID Localisation MGI         HumanEntrez MouseEntrez HumanName MouseName
#>    <int> <chr>        <chr>             <int>       <int> <chr>     <chr>    
#> 1      6 Postsynaptic MGI:1344351        1740       23859 DLG2      Dlg2     
#> 2      6 Postsynaptic MGI:1344351        1740       23859 DLG2      Dlg2     
#> 3      6 Postsynaptic MGI:1344351        1740       23859 DLG2      Dlg2     
#> 4      6 Postsynaptic MGI:1344351        1740       23859 DLG2      Dlg2     
#> 5      6 Postsynaptic MGI:1344351        1740       23859 DLG2      Dlg2     
#> 6      6 Postsynaptic MGI:1344351        1740       23859 DLG2      Dlg2     
#> # … with 5 more variables: PaperPMID <int>, Paper <chr>, Year <int>,
#> #   SpeciesTaxID <int>, BrainRegion <chr>

2.Get internal GeneIDs for the list of genes.

Obtaining Internal database GeneIDs is a usefil intermediate step for more complex queries including those for building protein-protein interaction (PPI) networks for compartments and brain regions. Internal GeneID is specie-neutral and unique, which allows exact indentification of the object of interest in case of redundancy (e.g. one Human genes matchs on a few mouse ones, etc.)

t <- findGenesByEntrez(c(1742, 1741, 1739, 1740))
head(t)
#> # A tibble: 4 × 8
#>   GeneID MGI         HumanEntrez MouseEntrez RatEntrez HumanName MouseName RatName
#>    <int> <chr>             <int>       <int>     <int> <chr>     <chr>     <chr>  
#> 1      1 MGI:1277959        1742       13385     29495 DLG4      Dlg4      Dlg4   
#> 2      6 MGI:1344351        1740       23859     64053 DLG2      Dlg2      Dlg2   
#> 3     15 MGI:1888986        1741       53310     58948 DLG3      Dlg3      Dlg3   
#> 4     46 MGI:107231         1739       13383     25252 DLG1      Dlg1      Dlg1
t <- findGenesByName(c("SRC", "SRCIN1", "FYN"))
head(t)
#> # A tibble: 3 × 8
#>   GeneID MGI         HumanEntrez MouseEntrez RatEntrez HumanName MouseName RatName
#>    <int> <chr>             <int>       <int>     <int> <chr>     <chr>     <chr>  
#> 1     48 MGI:1933179       80725       56013     56029 SRCIN1    Srcin1    Srcin1 
#> 2    585 MGI:98397          6714       20779     83805 SRC       Src       Src    
#> 3    710 MGI:95602          2534       14360     25150 FYN       Fyn       Fyn

3.Get disease information for the gene set

Synaptic genes are annotated with disease information from Human Disease Ontology, where available. To get disease information one can submit the list of Human Entrez Is or Human genes names, it could be also the list of Internal GeneIDs if using getGeneDiseaseByIDs function

t <- getGeneDiseaseByName (c("CASK", "DLG2", "DLG1"))
head(t)
#> # A tibble: 6 × 4
#>   HumanEntrez HumanName HDOID    Description                   
#>         <int> <chr>     <chr>    <chr>                         
#> 1        1740 DLG2      DOID:936 brain_disease                 
#> 2        8573 CASK      DOID:936 brain_disease                 
#> 3        1739 DLG1      DOID:331 central_nervous_system_disease
#> 4        1740 DLG2      DOID:331 central_nervous_system_disease
#> 5        8573 CASK      DOID:331 central_nervous_system_disease
#> 6        1739 DLG1      DOID:863 nervous_system_disease
t <- getGeneDiseaseByEntres (c(8573, 1742, 1739))
head(t)
#> # A tibble: 6 × 4
#>   HumanEntrez HumanName HDOID    Description                   
#>         <int> <chr>     <chr>    <chr>                         
#> 1        8573 CASK      DOID:936 brain_disease                 
#> 2        1739 DLG1      DOID:331 central_nervous_system_disease
#> 3        1742 DLG4      DOID:331 central_nervous_system_disease
#> 4        8573 CASK      DOID:331 central_nervous_system_disease
#> 5        1739 DLG1      DOID:863 nervous_system_disease        
#> 6        1742 DLG4      DOID:863 nervous_system_disease

5.Get PPI interactions for my list of genes

Custom Protein-protein interactions based on bespoke subsets of molecules could be extracted in two general ways: “induced” and “limited.” In the first case, the command will return all possible interactors for the genes within the whole interactome. In the second case it will return only interactions between the genes of interest. PPIs could be obtained by submitting list of EntrezIDs or gene names, or Internal IDs - in all cases the interactions will be returned as a list of interacting pairs of Intenal GeneIDs.

t <- getPPIbyName(
    c("CASK", "DLG4", "GRIN2A", "GRIN2B","GRIN1"), 
    type = "limited")
head(t)
#> # A tibble: 6 × 2
#>       A     B
#>   <int> <int>
#> 1    38     1
#> 2     7     1
#> 3     1     7
#> 4     1    38
#> 5     1     9
#> 6     9     1
t <- getPPIbyEntrez(c(1739, 1740, 1742, 1741), type='induced')
head(t)
#> # A tibble: 6 × 2
#>       A     B
#>   <int> <int>
#> 1     1  2871
#> 2     6  2871
#> 3    15  2871
#> 4     1   617
#> 5     1    30
#> 6   156     1

6.Get the molecular structure of synaptic compartment

Three main synaptic compartments considered in the database are “presynaptic,” “postsynaptic” and “synaptosome.” Genes are classified to compartments based on respective publications, so that each gene can belong to one or two, or even three compartments. The full list of genes for specific compartment could be obtained wuth command getAllGenes4Compartment, which returns the table with main gene identifiers, like internal GeneIDs, MGI ID, Human Entrez ID, Human Gene Name, Mouse Entrez ID, Mouse Gene Name, Rat Entrez ID, Rat Gene Name.

If you need to check which genes of your list belong to specific compartment, you can use getGenes4Compartment command, which will select from your list only genes associated with specific compartment. To obtain the PPI network for compartment one has to submit the lis of Internal GeneIDs obtained with previous commands.

#getting the list of compartment
comp <- getCompartments()
pander(comp)
ID Name Description
1 Postsynaptic Postsynaptic
2 Presynaptic Presynaptic
3 Synaptosome Synaptosome 
#getting all genes for postsynaptic compartment
gns <- getAllGenes4Compartment(compartmentID = 1) 
head(gns)
#> # A tibble: 6 × 8
#>   GeneID MGI         HumanEntrez MouseEntrez RatEntrez HumanName MouseName RatName
#>    <int> <chr>             <int>       <int>     <int> <chr>     <chr>     <chr>  
#> 1      1 MGI:1277959        1742       13385     29495 DLG4      Dlg4      Dlg4   
#> 2      2 MGI:88256           815       12322     25400 CAMK2A    Camk2a    Camk2a 
#> 3      3 MGI:96568          9118      226180     24503 INA       Ina       Ina    
#> 4      4 MGI:98388          6711       20742    305614 SPTBN1    Sptbn1    Sptbn1 
#> 5      5 MGI:88257           816       12323     24245 CAMK2B    Camk2b    Camk2b 
#> 6      6 MGI:1344351        1740       23859     64053 DLG2      Dlg2      Dlg2
#getting full PPI network for postsynaptic compartment
ppi <- getPPIbyIDs4Compartment(gns$GeneID,compartmentID =1, type = "induced")
head(ppi)
#> # A tibble: 6 × 2
#>       A     B
#>   <int> <int>
#> 1   365   148
#> 2  1048   148
#> 3    52   365
#> 4    52  1048
#> 5   321  1048
#> 6   321   365

7.Get the molecular structure of the brain region.

Three are 12 brain regions considered in the database based on respective publications, so that each gene can belong to the single or to the several brain regions. Brain regions differ between species, and specie brain region information is not 100% covered in the database(e.g. we don’t have yet studies for Human Striatum, but do have for Mouse and Rat), that’s why when querying the database for brain region information you will need to specify the specie. The full list of genes for speciifiic region could be obtained wuth command getAllGenes4BrainRegion, which returns the table with main gene identifiers, like internal Gene IDs, MGI ID, Human Entrez ID, Human Gene Name, Mouse Entrez ID, Mouse Gene Name, Rat Entrez ID, Rat Gene Name.

If you need to check which genes of your list were identified in specific region, you can use getGenes4BrainRegion command, which will select only genes associated with specific region from your list.

To obtain the PPI network for brain region you need to submit the list of Internal GGeneIDs obtained with previous commands.

#getting the full list of brain regions
reg <- getBrainRegions()
pander(reg)
ID Name Description InterlexID ParentID
1 Brain Whole brain ILX:0101431 1
2 Forebrain Whole forebrain ILX:0104355 1
3 Midbrain Midbrain ILX:0106935 1
4 Cerebellum Cerebellum ILX:0101963 1
5 Telencephalon Telencephalon ILX:0111558 2
6 Hypothalamus Hypothalamus ILX:0105177 2
7 Hippocampus Hippocampus ILX:0105021 5
8 Striatum Striatum ILX:0111098 5
9 Cerebral cortex Neocortex ILX:0101978 5
10 Frontal lobe Frontal lobe/frontal cortex ILX:0104451 9
11 Occipital lobe Occipital lobe ILX:0107883 9
12 Temporal lobe Temporal lobe ILX:0111590 9
13 Parietal lobe Parietal lobe ILX:0108534 9
14 Prefrontal cortex Prefrontal cortex ILX:0109209 10
15 Motor cortex Motor cortex ILX:0107119 10
16 Visual cortex Visual cortex ILX:0112513 11
17 Medial cortex Medial cortex ILX:0106634 9
18 Caudal cortex Caudal cortex NA 9 
#getting all genes for mouse Striatum
gns <- getAllGenes4BrainRegion(brainRegion = "Striatum",taxID = 10090)
head(gns)
#> # A tibble: 6 × 12
#>   GeneID Localisation MGI     HumanEntrez MouseEntrez HumanName MouseName   PMID
#>    <int> <chr>        <chr>         <int>       <int> <chr>     <chr>      <int>
#> 1      1 Postsynaptic MGI:12…        1742       13385 DLG4      Dlg4      3.01e7
#> 2      2 Postsynaptic MGI:88…         815       12322 CAMK2A    Camk2a    3.01e7
#> 3      3 Postsynaptic MGI:96…        9118      226180 INA       Ina       3.01e7
#> 4      4 Postsynaptic MGI:98…        6711       20742 SPTBN1    Sptbn1    3.01e7
#> 5      6 Postsynaptic MGI:13…        1740       23859 DLG2      Dlg2      3.01e7
#> 6      7 Postsynaptic MGI:95…        2904       14812 GRIN2B    Grin2b    3.01e7
#> # … with 4 more variables: Paper <chr>, Year <int>, SpeciesTaxID <int>,
#> #   BrainRegion <chr>
#getting full PPI network for postsynaptic compartment
ppi <- getPPIbyIDs4BrainRegion(
    gns$GeneID, brainRegion = "Striatum", 
    taxID = 10090, type = "limited")
head(ppi)
#> # A tibble: 6 × 2
#>       A     B
#>   <int> <int>
#> 1   365   148
#> 2    52   365
#> 3   321   365
#> 4   257   365
#> 5   846  1587
#> 6   407   348

8.Visualisatiion of PPI network with Igraph.

Combine information from PPI data.frame obtained with functions like getPPIbyName, getPPIbyEntrez, getPPIbyIDs4Compartment or getPPIbyIDs4BrainRegion with information about genes obtained from getGenesByID to make interpretable undirected PPI graph in igraph format. In this format network could be further analysed and visualized by algorithms from igraph package.

library(igraph)
#> 
#> Attaching package: 'igraph'
#> The following objects are masked from 'package:dplyr':
#> 
#>     as_data_frame, groups, union
#> The following objects are masked from 'package:BiocGenerics':
#> 
#>     normalize, path, union
#> The following objects are masked from 'package:stats':
#> 
#>     decompose, spectrum
#> The following object is masked from 'package:base':
#> 
#>     union
g<-getIGraphFromPPI(
    getPPIbyIDs(c(48, 129,  975,  4422, 5715, 5835), type='lim'))
plot(g,vertex.label=V(g)$RatName,vertex.size=25)

9.Export of PPI network as a table.

If Igraph is not an option, the PPI network could be exported as an interpretible table to be processed with other tools, e.g. Cytoscape,etc.

tbl<-getTableFromPPI(getPPIbyIDs(c(48, 585, 710), type='limited'))
tbl
#> # A tibble: 2 × 16
#>       A     B MGI.A     HumanEntrez.A MouseEntrez.A RatEntrez.A HumanName.A
#>   <int> <int> <chr>             <int>         <int>       <int> <chr>      
#> 1   710   710 MGI:95602          2534         14360       25150 FYN        
#> 2   585   585 MGI:98397          6714         20779       83805 SRC        
#> # … with 9 more variables: MouseName.A <chr>, RatName.A <chr>, MGI.B <chr>,
#> #   HumanEntrez.B <int>, MouseEntrez.B <int>, RatEntrez.B <int>,
#> #   HumanName.B <chr>, MouseName.B <chr>, RatName.B <chr>

References

Sorokina, Oksana, Colin Mclean, Mike D R Croning, Katharina F Heil, Emilia Wysocka, Xin He, David Sterratt, Seth G N Grant, T Ian Simpson, and J Douglas Armstrong. 2021. “A unified resource and configurable model of the synapse proteome and its role in disease.” Scientific Reports 11 (1): 9967–69. https://doi.org/10.1038/s41598-021-88945-7.

Appendix

Versions

Session Info

version R version 4.1.1 Patched (2021-09-10 r80880)
os Ubuntu 18.04.5 LTS
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date 2021-10-08
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AnnotationHub Bioconductor
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BiocVersion Bioconductor
Biostrings Bioconductor
bit CRAN (R 4.1.0)
bit64 CRAN (R 4.1.0)
bitops CRAN (R 4.1.0)
blob CRAN (R 4.1.0)
bslib CRAN (R 4.1.1)
cachem CRAN (R 4.1.0)
callr CRAN (R 4.1.0)
cli CRAN (R 4.1.0)
crayon CRAN (R 4.1.0)
curl CRAN (R 4.1.0)
DBI CRAN (R 4.1.0)
dbplyr CRAN (R 4.1.0)
desc CRAN (R 4.1.0)
devtools CRAN (R 4.1.0)
digest CRAN (R 4.1.1)
dplyr CRAN (R 4.1.0)
ellipsis CRAN (R 4.1.0)
evaluate CRAN (R 4.1.0)
fansi CRAN (R 4.1.0)
fastmap CRAN (R 4.1.0)
filelock CRAN (R 4.1.0)
fs CRAN (R 4.1.0)
generics CRAN (R 4.1.0)
GenomeInfoDb Bioconductor
GenomeInfoDbData Bioconductor
glue CRAN (R 4.1.0)
highr CRAN (R 4.1.0)
htmltools CRAN (R 4.1.0)
httpuv CRAN (R 4.1.1)
httr CRAN (R 4.1.0)
igraph CRAN (R 4.1.0)
interactiveDisplayBase Bioconductor
IRanges Bioconductor
jquerylib CRAN (R 4.1.0)
jsonlite CRAN (R 4.1.0)
KEGGREST Bioconductor
knitr CRAN (R 4.1.1)
later CRAN (R 4.1.0)
lifecycle CRAN (R 4.1.0)
magrittr CRAN (R 4.1.0)
memoise CRAN (R 4.1.0)
mime CRAN (R 4.1.0)
pander CRAN (R 4.1.1)
pillar CRAN (R 4.1.0)
pkgbuild CRAN (R 4.1.0)
pkgconfig CRAN (R 4.1.0)
pkgload CRAN (R 4.1.1)
png CRAN (R 4.1.0)
prettyunits CRAN (R 4.1.0)
processx CRAN (R 4.1.0)
promises CRAN (R 4.1.0)
ps CRAN (R 4.1.0)
purrr CRAN (R 4.1.0)
R6 CRAN (R 4.1.0)
rappdirs CRAN (R 4.1.0)
rbibutils CRAN (R 4.1.1)
Rcpp CRAN (R 4.1.0)
RCurl CRAN (R 4.1.1)
Rdpack CRAN (R 4.1.1)
remotes CRAN (R 4.1.0)
rlang CRAN (R 4.1.0)
rmarkdown CRAN (R 4.1.1)
rprojroot CRAN (R 4.1.0)
RSQLite CRAN (R 4.1.0)
S4Vectors Bioconductor
sass CRAN (R 4.1.0)
sessioninfo CRAN (R 4.1.0)
shiny CRAN (R 4.1.1)
stringi CRAN (R 4.1.0)
stringr CRAN (R 4.1.0)
synaptome.data Bioconductor
synaptome.db Bioconductor
testthat CRAN (R 4.1.0)
tibble CRAN (R 4.1.0)
tidyselect CRAN (R 4.1.0)
usethis CRAN (R 4.1.0)
utf8 CRAN (R 4.1.0)
vctrs CRAN (R 4.1.0)
withr CRAN (R 4.1.0)
xfun CRAN (R 4.1.1)
xtable CRAN (R 4.1.0)
XVector Bioconductor
yaml CRAN (R 4.1.0)
zlibbioc Bioconductor