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NeuroGeM, a knowledgebase of genetic modifiers in neurodegenerative diseases Na, Dokyun; Rouf, Mushfiqur; O’Kane, Cahir J; Rubinsztein, David C; Gsponer, Jörg Nov 14, 2013

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DATABASE Open AccessNeuroGeM, a knowledgebase of genetic modifiersin neurodegenerative diseasesDokyun Na1,5, Mushfiqur Rouf2, Cahir J O’Kane3, David C Rubinsztein4 and Jörg Gsponer1*AbstractBackground: Neurodegenerative diseases (NDs) are characterized by the progressive loss of neurons in the humanbrain. Although the majority of NDs are sporadic, evidence is accumulating that they have a strong geneticcomponent. Therefore, significant efforts have been made in recent years to not only identify disease-causing genesbut also genes that modify the severity of NDs, so-called genetic modifiers. To date there exists no compendiumthat lists and cross-links genetic modifiers of different NDs.Description: In order to address this need, we present NeuroGeM, the first comprehensive knowledgebase providingintegrated information on genetic modifiers of nine different NDs in the model organisms D. melanogaster, C. elegans,and S. cerevisiae. NeuroGeM cross-links curated genetic modifier information from the different NDs and provides detailson experimental conditions used for modifier identification, functional annotations, links to homologous proteins andcolor-coded protein-protein interaction networks to visualize modifier interactions. We demonstrate how this databasecan be used to generate new understanding through meta-analysis. For instance, we reveal that the Drosophila genesDnaJ-1, thread, Atx2, and mub are generic modifiers that affect multiple if not all NDs.Conclusion: As the first compendium of genetic modifiers, NeuroGeM will assist experimental and computationalscientists in their search for the pathophysiological mechanisms underlying NDs. http://chibi.ubc.ca/neurogem.Keywords: Neurodegenerative diseases, Genetic modifiers, Database, Knowledgebase, Alzheimer’s disease, Parkinson’sdisease, Huntington’s diseaseBackgroundIntracellular protein aggregation is a feature of manylate-onset neurodegenerative diseases (NDs), also calledproteinopathies. These include Alzheimer’s disease (AD),Parkinson’s disease (PD), and nine polyglutamine expan-sion diseases exemplified by Huntington’s disease (HD).The pathophysiology of NDs is very complex, which isone of the reasons why there are no effective strategiesthat slow or prevent neurodegeneration.In recent years, significant efforts have been made toidentify genes that modify the severity of NDs. Alteringthe activities of these genetic modifier genes on theirown may not result in obvious phenotypes in the ab-sence of the conditioning (neurodegeneration-causing)mutation. However, the identified genetic modifiersallow the characterization of biological pathways thatmodulate the disease and, in some cases, discovery oftractable therapeutic targets. The identification of geneticmodifiers has been facilitated due to the development ofin vivo models of different proteinopathies in organismssuch as D. melanogaster and C. elegans [1-3]. Moreover,genome-wide screens for genetic modifiers have becomepossible because of high-throughput technologies such asRNA interference [4] or public availability of varioustransgenic stocks covering most genes such as fly stockswith P-element insertion mutations [5].Nevertheless, due to the complex nature of the patho-logical processes underlying proteinopathies, there are largeinconsistences in the collected data. Even more import-antly, data alone without knowledge or integration intoexisting databases is bound to remain inaccessible and thuscannot be utilized by the broad scientific community. Anintegrated database of genetic modifiers of NDs would as-sist computational and experimental scientists alike in* Correspondence: gsponer@chibi.ubc.ca1Department of Biochemistry and Molecular Biology, Centre forHigh-throughput Biology, University of British Columbia, 2125 East MallVancouver, BC V6T 1Z4, CanadaFull list of author information is available at the end of the article© 2013 Na et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the CreativeCommons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, andreproduction in any medium, provided the original work is properly cited.Na et al. BMC Medical Genomics 2013, 6:52http://www.biomedcentral.com/1755-8794/6/52improving their approaches to discover what is common toand distinct for different proteinopathies.In order to address this need, we assembled the firstcomprehensive database of genetic modifiers in NDs.NeuroGeM (‘neurodegenerative disease genetic modifiersdatabase’) catalogues and cross-links genetic modifiers of9 different NDs in three different model organisms, andassociates them with information on protein function andother annotations. NeuroGeM contains detailed informa-tion on the experimental conditions in which the modifierswere identified, displays the protein-protein interactionsub-network around modifiers, and provides search anddisplay tools to deduce testable hypotheses. Furthermore,in order to demonstrate the broad applicability of the dataand tools provided by NeuroGeM, we present the results ofa first meta-analysis.Construction and contentData collectionNeuroGeM is a comprehensive collection of literaturedata on genetic modifiers of NDs and associated geneticinformation from a variety of databases (Figure 1). TheND models include AD subclassified as ADAβ (amyloid-beta models) and ADTau (tau models), HD, PD, Spino-cerebellar ataxia type 1, 3 and 7 (SCA1, SCA3, SCA7),Amyotrophic lateral sclerosis (ALS) and generic polyQ-induced disease in D. melanogaster, C. elegans, andS. cerevisiae. At the time of data compilation, all knownhigh-throughput (HT) screens for modifiers carried out inthe model organisms and a handful of low-throughput(LT) experimental results were included. Overall, Neuro-GeM contains 87,864 experimental records (3,618 formodifiers and 84,246 for non-modifiers) from the 9 differ-ent disease models in the three different species (Table 1).Importantly, we continually update the knowledgebasewith newly published results. In addition, users can sub-mit their own data upon request of a login and theuploaded data will be made accessible to all users aftercuration (see Additional file 1).In order to provide comprehensive information ongenetic modifiers, we integrated relevant data from othersources into NeuroGeM. All entries in NeuroGeM con-tain information on gene function/annotations and areaccompanied by direct links to the relevant informationon FlyBase for D. melanogaster (ver Feb 2012) [6],WormBase for C. elegans (ver WS230) [7] and SGD forFigure 1 The contents of NeuroGeM. NeuroGeM is a comprehensive collection of genetic modifiers of ND models in D. melanogaster, C. elegans,and S. cerevisiae. In order to provide comprehensive information on genetic modifiers, NeuroGeM also integrates information from genome databases(FlyBase, WormBase, SGD, EBML, HGNC, and MGI), the protein interaction database STRING, GeneOntology, and homologous gene databases(HomoloGene and InParanoid). The statistics of the data currently available in NeuroGeM is shown in Table 1, and all terms used in the database arelisted in Table 2.Na et al. BMC Medical Genomics 2013, 6:52 Page 2 of 14http://www.biomedcentral.com/1755-8794/6/52S. cerevisiae (downloaded in Jan 2012) [8]. Each geneentered in NeuroGeM contains an ID that is identical tothe primary ID of the gene in its respective genomedatabase (FlyBase, WormBase, or SGD). These IDs willallow users to easily access other databases and avoidthe effort required for ID conversion. NeuroGeM re-cords have a link to the PubMed entry of the originalstudy from which the records stem.As protein-protein interaction networks allow identifi-cation of functionally associated proteins or importantfunctional clusters [9,10], NeuroGeM visualizes the pro-tein interaction sub-network around a queried gene;each protein node in the network is color-coded accord-ing to the available experimental results deposited inNeuroGeM. For this feature, NeuroGeM utilizes the pro-tein interaction data from STRING (ver 9.05) [11]. Inorder to facilitate the identification of genetic modifierswith the same function or involved in the same process,NeuroGeM provides an ontology-based search functional-ity that searches for genes by GeneOntology (GO) annota-tions and the hierarchical structure of GO terms [12].As users might be interested in finding homologs of thegenetic modifiers entered in NeuroGeM, we also inte-grated homologous gene data from NCBI HomoloGene(build 65) [13] and InParanoid (ver7) [14,15]. Orthologsare defined as genes in different species that have evolvedfrom a common ancestral gene, while paralogs are genesrelated by duplication within the same species that oftenhave different functions. Homologs are either paralogs ororthologs (for details see NCBI HomoloGene (build 65)[13] and InParanoid (ver7) [14,15]). The homology datacovers not only the three model organisms but alsoH. sapiens and M. musculus, though no modifiers fromthese organisms are deposited in NeuroGeM yet. Cross-linking genes via homology should facilitate the expan-sion of modifier studies in other organisms; for instance,confirmation of important modifiers in higher organ-isms. Gene information, GO annotations and proteininteraction data for human and mouse genes were alsointegrated to help users search for modifiers that arehomologous to human and mouse genes of interest.(source: EMBL Rel 68 [16], HGNC downloaded in Jan2013 [17]; EMBL Rel 68 [18]; and MGI downloaded inDec 2012 [19]). As detailed information on genes andproteins is frequently updated in their source databases,NeuroGeM also provides links to those databases whenavailable.Database implementationThe database was implemented with a web interfacecompatible with common web browsers to provide ac-cess to researchers. The data is stored in a relationaldatabase using a MySQL 5.0.59 server. Data processingand HTML generation for displaying information areTable 1 Statistics of genetic modifiers in NeuroGeMSpecies DiseasemodelsExperimental records Modifiers1 Genecoverage2 (%)Positive records Negative records Enhancers Suppressors Non-modifiersD. melanogaster ADTau 144 571 65 60 549 4.89ADAβ 61 6062 25 22 6059 44.43PD 1 - - 1 - 0.01HD 1260 8142 130 90 7732 57.82SCA1 66 24 16 21 21 0.42SCA3 623 52 55 520 49 4.54SCA7 14 19 8 5 18 0.23PolyQ 22 44 7 10 43 0.44C. elegans ADTau 75 15909 - 75 15899 97.86ADAβ 6 - 1 5 - 0.04HD 22 2 1 17 2 0.12PD 290 18121 - 268 15767 98.24ALS 168 15909 88 80 15817 97.92PolyQ 459 40 152 195 20 2.11S. cerevisiae ADAβ 106 5262 18 23 5248 78.83HD 82 9422 28 54 4670 70.83PD 216 4699 22 181 4577 71.26PolyQ 1 - - 1 - 0.011Certain genes have been identified independently as both an enhancer and suppressor under different experimental conditions.2With respect to the protein coding genes.Na et al. BMC Medical Genomics 2013, 6:52 Page 3 of 14http://www.biomedcentral.com/1755-8794/6/52carried out using PHP 5.3.3. Javascript and AJAX tech-nology are used to improve search functions. Cytoscape-Web [20] is employed to visualize protein-proteininteraction networks. The current database is runningon Redhat Linux 5.6 with an Apache server 2.2.3. All thedata in NeuroGeM can be downloaded as plain text files.Utility and discussionNeuroGeM allows users to access the integrated data inthree different ways: (i) a categorical search, (ii) a key-word search, and (iii) an ontology-based search. Allsearch methods take users first to a list of publicationsor a list of genes that fit the search criteria, from whichusers can select a gene of interest and consult its modifierinformation page. Figures 2 and 3 and Additional file 2:Figures S1 and Additional file 3: Figure S2 illustrate thethree ways to search genetic modifiers in NeuroGeM andshow detailed information on genetic modifiers providedby NeuroGeM.Categorical search for studies that identified modifiersOn the front page of the NeuroGeM web site, research re-sults are organized according to disease and model-organism categories: studies that identified modifiers werecategorized according to model organism (n = 3) and dis-eases (n = 9 including two AD subtypes) (Additional file 2:Figure S1). Clicking on an organism icon or disease-specific icon directs the user to a list of publications thatbelong to that category. Clicking on a pie chart at theintersection of a specific model organism and disease typedirects the user to a list of publications that searched formodifiers in the corresponding disease model and organ-ism. Clicking on a specific publication then directs theuser to a list of genes that have been tested in that study.For instance, clicking on the pie chart at the intersectionof D. melanogaster and HD (Additional file 2: Figure S1)lists 24 publications in which genetic modifiers of HDhave been identified in D. melanogaster. Clicking on thePubMed ID 17984172 will open a web page that lists the26 genes that have been examined in this specific studyand provides a summary of the experimental recordsdeposited in NeuroGeM for each of these 26 genes (seebelow for the detailed explanation of the record sum-mary). Clicking on the gene name Nup44A will then directthe user to the genetic modifier information page ofNup44A, which contains the details of the results of dif-ferent genetic modifier screens for this gene as well asadditional information that is discussed in further detail inthe “Genetic modifier information” section below.Keyword-based search for specific genesNeuroGeM also provides different search methods froma unified search box (Figure 2). As a user starts typingletters in the search box, NeuroGeM suggests matchingkeywords (gene name, synonyms, IDs, GO terms, etc.). Ifthe user selects one of the suggestions, NeuroGeM willopen this gene’s modifier information page (Figure 2, redarrow). If the user presses the ‘Search’ button instead,NeuroGeM will list all genes that contain the keywordin their names, synonyms, or IDs. Then, the user canopen a specific genetic information page by clicking onone of the listed genes (Figure 2, blue arrow). ForFigure 2 Search to access information on genetic modifiers in NeuroGeM. This figure illustrates a keyword-based search to get informationof a specific genetic modifier. Other search methods are available in Additional file 2: Figure S1 (categorical search) and Additional file 3: FigureS2 (ontology-based search). The genetic modifier information page includes (a) a report summary of experimental records entered in NeuroGeM,(b) general information on the gene with links to relevant databases, and (c) the protein-protein interaction sub-network around this gene.Details about experimental parameters and results are also shown (Figure 3). The homologous genes section (not shown in this figure) listsgenetic information and experimental details of homologous genes.Na et al. BMC Medical Genomics 2013, 6:52 Page 4 of 14http://www.biomedcentral.com/1755-8794/6/52example, when typing ‘nup4’ in the search box, severalgenes that contain the keyword ‘nup4’ are suggested.Selecting the D. melanogaster Nup44A gene from thesuggestions will open this gene’s page directly. On theother hand, entering ‘nup4’ and clicking the ‘Search’ but-ton will list all genes that contain the keyword and showrespective experimental record summaries. The user canopen the Nup44A gene page by clicking on the genename Nup44A of the listed genes.Ontology-based search for related genesThe keyword-based search directs the user to a specificgene, but this feature is not appropriate when searchingfor functionally related genes, e.g. searching for genesinvolved in the cell cycle. For such a relation-based search,we provide the ontology-based search. To use theontology-based search, the user has to type a GO term orGO ID in the search box. Just like the keyword search,suggestions will pop up and the user can select one of thesuggested GO terms. Then, NeuroGeM searches for notonly genes with the user-specified GO term but also geneswith a related (child) term. This ontology-based searchwill assist users in the identification of modifiers that areassociated with specific cellular functions or processes.For instance, as shown in Additional file 3: Figure S2,the query of ‘cell cycle (GO:0007049)’ in D. melanogasterFigure 3 Details of the experiments that identified the genetic modifiers. This section of the genetic modifier information page includesthe detailed results of the modifier screens (whether the gene is a suppressor, enhancer, or non-modifier), experimental details (mutated genes,method for gene expression modulation, phenotype observation, etc.), and a link to the original article in which the experiment wasdescribed (PubMed).Na et al. BMC Medical Genomics 2013, 6:52 Page 5 of 14http://www.biomedcentral.com/1755-8794/6/52returns 656 genes that are involved in the cell cycle: 622out of 656 genes have been evaluated experimentally,and 256 out of 622 genes have been identified as modi-fiers. Drosophila’s Nup44A gene is also shown in thegene list since Nup44A has GO annotations for regula-tion of mitotic cell cycle (GO:0007346) and regulation ofmeiotic cell cycle (GO:0051445), which are child terms ofcell cycle. Clicking on the name of Nup44A will directthe user to Nup44A genetic modifier information page.Genetic modifier informationAt the end of each search, NeuroGeM directs users tothe genetic modifier information page of a specific gene.As an example, the genetic modifier information page ofthe Drosophila gene Nup44A (FBgn0033247) is shownin Figures 2 and 3. Terms and definitions used in geneticmodifier information pages are listed in Table 2.At the top of the genetic modifier information page(Figure 2a), the number of experimental records that areavailable in NeuroGeM for a specific gene and its homo-logs are reported (record summary) for each experimen-tal scale (“L” stands for LT, “H2” for secondary HT, and“H1” for primary HT). If experimental data that identi-fies a specific gene as a modifier has been entered inNeuroGeM, this fact is highlighted in red, light red if itis only one HT experiment, and dark red if it is at leasttwo HT experiments or at least one LT experiment.Similarly, records that report that a gene is a non-modifier are colored in light and dark blue. For example,there are currently two records in NeuroGeM forNup44A indicating that Nup44A (FBgn0033247) is anon-modifier in HD, one based on a LT and one on aprimary HT experiment. In addition, there are two re-cords of LT experiments indicating that Nup44A is amodifier in SCA1. By clicking on a specific record in the“record summary” at the top of the page (e.g. “2 0 0”under the column header “L H2 H1” for Nup44A andSCA1), the user is immediately guided to the details ofthat entry (Figure 3).Below the report summary, detailed information of thegene is displayed on the left side (Figure 2b), includingsynonyms, alternative gene names, and IDs used in otherdatabases as well as GO annotations. On the right side(Figure 2c), NeuroGeM displays the protein-proteininteraction sub-network around the gene, in which inter-acting proteins are colored by the type and result of ex-periments that tested them as modifiers. Specifically, theleft half of each node (protein) is colored according tothe evidence for it being a modifier. The right half ofeach node is colored according to the evidence for it be-ing a non-modifier. The same coloring scheme as for therecord summary is used. Importantly, the protein-protein interaction network and the coloring areorganism- and disease-specific (coloring by differentdiseases can be selected below the network). Users cannavigate to other genetic modifier information pages byclicking on the corresponding nodes. Figure 2c showsthe protein-protein interaction sub-network of Nup44Acolored according to the results of screens for modifiersof HD in Drosophila. A look at the network immediatelyreveals that Nup44A, Nup75, and Rae1 were identifiedas non-modifiers, while Nup107 was identified as modi-fier of HD in Drosophila. By contrast, SmB, SmD1,Cbp20, and Nup154 were identified as modifiers in someexperiments but not in others.In the next section of the genetic modifier informationpage, experimental details are displayed (Figure 3). Ex-periments are categorized by the disease model, and thenumber of experimental records for each disease modelis shown with respect to experimental scale by using thesame coloring scheme as for the report summary. Thereported experimental details include: (i) type of modifi-cation; indicates whether the experiment found the geneto be a suppressor, enhancer, or non-modifier, (ii) modeof action; reports whether the queried gene modified ag-gregation size/number or changed disease symptoms,(iii) disease induction; denotes which (mutant) gene wasused to cause disease symptoms, and shows the mutantgene and its expression cassette information, (iv) modu-lation method; denotes how the expression of the quer-ied gene was modulated (e.g. over-expressed, knockedout, repressed by RNAi), (v) experimental scale; denotesthe scale of performed experiments (LT, primary HT,secondary HT; secondary HT stands for experiments toconfirm the results obtained from primary HT experi-ments), (vi) measurement; denotes what was quantifiedto identify a modifier and (vii) cell type; denotes the celllines or stocks utilized in the experiment. For instance,Nup44A was tested as modifier in a Drosophila model ofSCA1 that was created by expressing Ataxin-1 with apolyQ expansion of 82 (Figure 3). The impact of theoverexpression of Nup44A on the disease model wasquantified based on changes in the severity of an eyephenotype. Nup44A was categorized as a suppressor, in-dicating that the over-expression of the Nup44A gene al-leviated the severity of the eye phenotype.Below the experimental details of a specific gene, itshomologous genes are displayed with genetic informa-tion, protein interaction sub-network, and experimentaldetails if available.Search for orthologs of human and mouse genesThe current version of NeuroGeM does not contain anygenetic modifiers in human and mouse (to be includedat a later stage). Nevertheless, researchers studying gen-etic modifiers of NDs in the three model organisms arelikely to be interested in the homologous genes of themodifiers in higher organisms such as human andNa et al. BMC Medical Genomics 2013, 6:52 Page 6 of 14http://www.biomedcentral.com/1755-8794/6/52mouse. Thus, we also integrated human genome informa-tion from the EMBL and HGNC databases and mousegenome information from the EMBL and MGI databases.The user can search for human and mouse genes usingtheir gene names or EMBL IDs, and then obtain not onlyinformation on the queried genes but also information ontheir homologous genes in D. melanogaster, C. elegans,and S. cerevisiae. For example, Drosophila’s Nup44A geneand its homologous genes in other species are listed inFigure 2a and the information on those genes is displayedat the bottom of the Nup44A gene page (omitted inFigures 2 and 3). The user can also search for human andTable 2 Terms used in NeuroGeMTerms Values MeaningOrganism D. melanogaster Three model organismsC. elegansS. cerevisiaeGene ID FBgn———— Primary IDs used in the respective genome databases (FlyBase, WormBase,and SGD). These IDs are also used as primary IDs in NeuroGeM.W——————S———————Type of modification Suppressor Suppressors are those genes that alleviate disease pathology or slow diseaseprogression when over-expressed, and those that aggravate disease pathologyor accelerate disease progression when down-regulated or deleted. Enhancersare those genes that alleviate disease pathology when down-regulated, andthose that aggravate pathology when over-expressed. Non-modifiers have noeffect on disease progression.EnhancerNon-modifierMode of action Toxicity modification Toxicity modifiers are those genes that change disease pathology. Aggregationmodifiers are those genes that change the size or number of protein aggregates.Aggregation modificationDisease model Alzheimer’s disease (AD) ND models compiled in the current version of NeuroGeM. We divided AD intothe subtypes ADTau and ADAβ according to the gene used to induce the diseasephenotype (mutant Tau protein and Aβ42, respectively).Huntington’s disease (HD)Parkinson’s disease (PD)Spinocerebellar ataxia type 1 (SCA1)Spinocerebellar ataxia type 3 (SCA3)Spinocerebellar ataxia type 7 (SCA7)Amyotrophic lateral sclerosis (ALS)PolyQ disease (PolyQ)Disease induction Various This field contains expression cassette information described in the literatureincluding promoter and disease-causing gene (e.g. polyQ stretch length).Disease-causing mutant proteins compiled in NeuroGeM are Aβ and tau proteinfor AD, SOD1 for ALS, huntingtin for HD, α-synuclein for PD, Ataxin-1 for SCA1,Ataxin-3 (MJD) for SCA3, Ataxin-7 for SCA7 and polyQ stretches for the PolyQdisease model.Modulation method Overexpression This field describes whether the expression level of the target gene increased(overexpression or gain-of-function) or decreased (knockdown, knockout, orloss-of-function). We adopted the same terms used in the original articles.Gain-of-functionKnockdownKnockoutLoss-of-functionExperimental scale Primary high-throughput This field describes the scale of the experiments. Experiments that were not high-throughput (HT) were assigned as low-throughput (LT). Experiments performed inprimary screens were assigned as Primary high-throughput. Experiments to confirmthe results obtained from the Primary high-throughput screens were assigned asSecondary high-throughput.Secondary high-throughputLow-throughputMeasurement Various This field describes how the change of pathology was evaluated. For example,change in the eye phenotype is a common readout in D. melanogaster, and cellgrowth rate is a common readout in S. cerevisiae.Cell type Various This field briefly describes what cell lines and organs were utilized to carry out theexperiment.Na et al. BMC Medical Genomics 2013, 6:52 Page 7 of 14http://www.biomedcentral.com/1755-8794/6/52mouse genes homologous to Nup44A by their gene names(Seh1l and SEH1L) or EMBL IDs (ENSMUSG00000079614and ENSG00000085415) in the unified search box.Applications of data in NeuroGeMIn order to demonstrate the broad applicability of Neuro-GeM and how it can provide new understanding, we per-formed a variety of meta-analyses using the geneticmodifiers data from NeuroGeM. In this section, theresults of the meta-analyses are discussed briefly. The de-tailed methods, results and discussion for the meta-analyses are available in the Additional file 1. Due to theabundance of both HT and LT experimental data fromD. melanogaster, mainly results obtained from the meta-analysis of genetic modifiers of D. melanogaster are pre-sented here. Results from the analysis of genetic modifiersin other model organism as well as the comparison ofmodifiers in all three model organisms are available inAdditional file 1.i) NeuroGeM can be used to identify biologicalprocesses that are enriched within genetic modifiersin a specific disease or in groups of diseases. Geneswith annotations for these processes can beprioritized for testing in other model organisms orfor drug screenings. A meta-analysis of the datadeposited in NeuroGeM revealed that modifiersacross species are often involved in protein folding(Figure 4a). However, they account for only 3% of allgenetic modifiers. The analysis revealed that modifiersare equally often involved in cell cycle and splicing,accounting for 7% and 3% of all genetic modifiers,respectively. This analysis result suggests thatresearchers expecting to discover more geneticmodifiers should focus their efforts also on genesinvolved in cell cycle and splicing, biological processesthat are also often enriched in modifiers. As shown inFigure 4b, a correlation analysis of modifiers betweendiseases revealed that polyQ diseases (HD, genericPolyQ, SCA1, SCA3, and SCA7 in Figure 4b) sharemany genetic modifiers and non-modifiers whichare not seen in AD models, which is consistent witha previous report [21]. Specifically, a stronganti-correlation is observed when comparing themodifiers and non-modifiers of ADAβ and SCA3.Many SCA3-specific genetic modifiers are involved inprotein folding and splicing [22,23], while ADAβ-specificmodifiers are involved in protein synthesis [24].Figure 4 Compilation of meta-analysis results. (a) Functional classification of genetic modifiers and their enrichments represented by p-values.(b) (Left) Correlation analysis of modifiers and non-modifiers between diseases. Pairwise correlations (Matthew’s correlation coefficient) are representedby a color matrix ranging from −1 (inverse correlation, red), via 0 (no correlation, yellow), to 1 (high correlation, green). (Right) Modifiers inSCA3 and ADTau were further analyzed to see which functional categories are enriched among these genetic modifiers. (c) List of genesidentified as modifiers of several diseases in D. melanogaster. Red and grey denote the number of diseases in which a gene is identified as amodifier and non-modifier, respectively (see Additional file 1: Figure S7 for full list). (d) List of disease-specific genetic modifiers. The same colorannotation as in (c) is used here (see Additional file 1: Figure S8 for full list). (e) Enrichment analysis results of HD modifiers in D. melanogasterand PD modifiers in C. elegans with respect to the mode of action of the modifiers. Tox, toxicity modifiers; Agg, aggregation modifiers; Both,aggregation and toxicity modifiers. (f) Identification of modifiers and non-modifiers depending on the length of the polyQ stretch in HDmodels in D. melanogaster. Each line refers to one gene and each green dot refers to one experiment with a specific length of the polyQstretch. If dots are in the purple and green region, it means the gene has been identified as a non-modifier and modifier, respectively. SeeAdditional file 1 for details of meta-analysis.Na et al. BMC Medical Genomics 2013, 6:52 Page 8 of 14http://www.biomedcentral.com/1755-8794/6/52Untested genes involved in modifier-enrichedprocesses can be prioritized in future screens toconfirm these trends and elucidate their mechanisms.See Additional file 1.ii) NeuroGeM allows easy identification (by noncomputational experts) of genes that modify theneurodegenerative toxicity in several ND models.Hence, cross-disease comparisons can identifypotential generic modifiers that then can be testedexperimentally in other disease models in otherorganisms (rodents) or compared to human geneticdata. A first search for generic modifiers revealedthat the genes DnaJ-1, thread, Atx2, and mub aremodifiers in 5 out of 7 ND models in D. melanogaster(Figure 4c). Interestingly, DNAJB4 and BIRC3, themammalian orthologs of DnaJ-1 and thread, haverecently been shown to reduce neuronal cell deathwhen up-regulated in multiple mammalian NDmodels (see Additional file 1). Moreover, Atx2 hasrecently also been associated with an increased riskfor ALS [25]. Further experiments are necessary toconfirm this hypothesis that DnaJ-1, thread, Atx2,and mub are generic modifiers.iii) Equally, NeuroGeM facilitates the identification ofgenes that only affect the phenotype of specific NDs.Though all NDs are caused by aggregates, theydefinitely show different pathophysiology. In thisregard, genes capable of modulating diseasephenotype only in a specific ND give us hints tounderstand the difference in disease progression. Forinstance, modifiers currently confined to ADTau inD. melanogaster include sgg and par-1 (Figure 4d).This finding is consistent with the specific importanceof hyper-phosphorylation of the tau protein in AD,which is a process that may be accelerated by par-1and sgg [26,27]. In S. cerevisiae, modifiers are enrichedTable 3 Genes that are toxicity and aggregation modifiers in D. melanogaster and their orthologsin H. sapiens and M. musculus1D. melanogaster genes D. melanogasterND models2Ref H. sapiens orthologs M. musculus orthologs Ref3DnaJ-1 ADTau, HD, PolyQ,SCA1, SCA3[30-34] DNAJB1, DNAJB4, DNAJB5,DNAJB13Dnajb1, Dnajb4, Dnajb5,Dnajb13[35-40]thread ADTau, HD, SCA1,SCA3, SCA7[21,32,41,42] BIRC2, BIRC3 Birc2, Birc3 [43-45]Atx2 ADTau, HD, SCA1,SCA3, SCA7[21,41,42,46] Atxn2, Atxn2L Atxn2, Atxn2L [47-52]Hsc70-3 HD, SCA1, SCA7 [32,42] HSPA5 Hspa5 [53]Hsc70Cb ADTau, HD, SCA3 [5,54,55] HSPH1(HSP110), HSPA4,HSPA4LHsph1 (Hsp110), Hspa4,Hsp4l[56,57]Rpd3 HD, SCA1, SCA7 [34,42,58] HDAC1, HDAC2 Hdac1, Hdac2 [59-65]14-3-3epsilon HD, SCA1 [66,67] YWHAZ, YWHAB, YWHAE Ywhaz, Ywhab, Ywhae [67-73]CG5537 HD [74] UPRT Uprt N/AHsf HD, SCA3 [75] Hsf2, Hsf4, Hsfx1, Hsfx2,Hsfy1, Hsfy2, Hsf5Hsf2, Hsf3, Hsf4, Hsfy2,Hsf5[76-78]Nipped-A HD, SCA3, SCA7 [42,54,55] TRRAP Trrap [79,80]Sec61alpha HD, SCA3 [81] Sec61A1, Sec61A2 Sec61a1, Sec61a2 [82,83]Nup160 HD, SCA3 [55,74] Nup160 NUP160 [84-86]CG1109 HD [74] WDR33(WDC146) Wdr33 (Wdc146) N/ASnap HD [66] NAPA, NAPB Napa, Napb N/Asmt3 HD [74] SUMO1, SUMO2, SUMO3,SUMO4Sumo1, Sumo2, Sumo3 [87-91]Mef2 HD [66] MEF2A, MEF2B, MEF2BNB,MEF2C, MEF2DMef2a, Mef2b, Mef2c,Mef2d[92-97]chic HD [98] PFN4 Pfn4 [99]Rpt1 HD [74] PSMC2 Psmc2 [100-102]Sin3A HD, SCA1, SCA3 [5,32,34] Sin3A, Sin3B Sin3a, Sin3b [103]Rheb ADTau, HD [74,104] Rheb, RhebL1 Rheb, Rhebl1 [105,106]1Orthologs were obtained from NeuroGeM in which protein homolog groups of NCBI HomoloGene and InParanoid were integrated.2Disease models in which the genes were identified as modifiers.3Studies in which the mammalian orthologs were identified as modifiers.Na et al. BMC Medical Genomics 2013, 6:52 Page 9 of 14http://www.biomedcentral.com/1755-8794/6/52in protein synthesis in AD (RTG3, TEC1, SPT21,PPR1, MBP1, SRO9, SLF1, and SLS1), protein foldingin HD (HSP26, HSP42 and APJ1), and transport in PD(FUN26, YCK3, and GOS1), which is consistent withrecent results (Additional file 1).iv) The database can help get new insights into themechanism of disease modulation. In NeuroGeM,each modifier is classified as toxicity modifier and/oran aggregation modifier: toxicity modifiers changedisease phenotype (eye development, motility, etc.),and aggregation modifiers primarily affect aggregatesize or number. As shown in Figure 4e, toxicitymodifiers are enriched in cell cycle, cytoskeleton,signaling, and protein folding categories; thesemodifiers are involved in cellular pathways thatmodulate the level of tolerance to the stress causedby the aggregates and ultimately lead to phenotypicchanges. On the other hand, aggregation modifiersare enriched in splicing, proteolysis, and proteinfolding, which are the processes directly or indirectlyassociated with aggregate formation and elimination.Interestingly, modifiers belonging to both groups areonly enriched in protein folding. This functionalcategory includes protein quality control, which is anetwork of cellular processes that in an orchestratedmanner works against protein misfolding andaggregation. Therefore, proteins involved in proteinfolding would be prime therapeutic targets, sincethey are able to resolve the problem of aggregateformation, and are involved in cellular processes thatcan increase the tolerance to the cellular stresscaused by protein aggregation [28,29]. Moreover,these proteins may play a key role in thepathophysiology of many NDs due to their dualeffect. In order to test this hypothesis, we identifiedDrosophila modifiers that are both aggregation andtoxicity modifiers. We found that many of them areindeed able to modulate neurodegeneration inseveral different disease models (see Table 3 andAdditional file 1). Interestingly, the list includesthree of the previously identified generic modifiers;DnaJ-1, thread and Atx2. Next, we tested whetherthis is true across species, i.e. also for higherorganisms. We identified human and mouseorthologs of the Drosophila aggregation and toxicitymodifiers by using the feature of NeuroGeM. Acareful literature search confirmed that for most ofthe mammalian orthologs of these modifiers thereexists experimental evidence that they modify thephenotype of several ND models in mammalian cells(see Table 3 and Additional file 1 for details about theorthologs).v) NeuroGeM allows assessing the effect ofexperimental conditions on the consistency andreliability of the identified modifiers. The results ofdifferent screens for genetic modifiers are ofteninconsistent because of the use of differentexperimental set-ups. NeuroGeM enables the user toinfer the best experimental conditions for consistentidentification of modifiers. As an example, weinvestigated the effect of polyQ stretch length onmodifier identification in HDmodels ofD.melanogaster.In Figure 4f, each line refers to one gene identifiedas a modifier or non-modifier in secondary HT orLT experiments in HD models with different polyQlengths, and each green dot on the line refers to theidentification result at a specific polyQ length.Figure 4f suggests some genes were not identified asmodifiers in HD models with a polyQ length of 40(which is above the canonical threshold of 35), butwere then identified as modifier in models with apolyQ length of 60. Hence, this analysis suggestsFigure 5 Ontology-based search results of “anti-apoptosis”. (a) Genes retrieved from NeuroGeM using the Ontology-based search for“anti-apoptosis” (GO:0006916). Genes annotated with anti-apoptosis or its child terms are listed. (b) Protein interaction clusters of the retrievedgenes. Proteins that are directly interacting with the discovered modifiers are shown: red nodes, known modifiers; white nodes, untested genes;green nodes, genes with literature evidence (not yet entered into NeuroGeM) for being modifiers.Na et al. BMC Medical Genomics 2013, 6:52 Page 10 of 14http://www.biomedcentral.com/1755-8794/6/52that HD models with polyQ > 60 may provide moresensitivity (see Additional file 1 for details).vi) Most importantly, NeuroGeM facilitates theidentification of new, so far untested modifiers.Mapping of genes on the protein interactionnetworks allows identification of new, untestedgenetic modifiers based on guilt-by-association. Weillustrate this idea on genes involved in anti-apoptosis(GO:0006916). We first obtained 24 anti-apoptoticproteins in D. melanogaster (Figure 5a), and amongthese genes debcl, Buffy, and thread are interconnectedwith each other in the protein network (Figure 5b). Inorder to investigate whether genes interacting withthese anti-apoptotic modifiers could also be modifiers,we extended the sub-network by adding proteins thatinteract with the three proteins. This extension can beeasily done, as NeuroGeM allows the user tonavigate from one gene to another by clicking on anode in a network. The newly added genes arehighly interconnected with each other. Detailedliterature surveys of the genes connected to debcl,Buffy, and thread revealed that 5 out of 15 interactors(marked in green in Figure 5b) are modifiers or at leasthighly related to disease progression. For example, oneof the interactors is Ark; inactivation of Ark(FBgn0263864), a key regulator of apoptosis, is knownto suppress formation and ubiquitination of polyQaggregates [107] (Please see Additional file 1 for thedetails about the other four interactors and potentialmodifiers). Hence, the search tools of NeuroGeM willfacilitate the identification of new modifiers that areinvolved in specific pathways or cellular processes.ConclusionHere we report, to the best of our knowledge, the firstdatabase (NeuroGeM) of genetic modifiers of NDs. Neuro-GeM provides a platform for searching modifiers, retrievingexperimental conditions used for modifier identifica-tion, interpreting the roles of a queried modifier in thecontext of the protein interaction network, and expand-ing knowledge in one organism to other organismsthrough homologous genes. Therefore, NeuroGeM al-lows users to evaluate their hypotheses and develop newresearch directions. Furthermore, NeuroGeM providesall information, including gene information, protein in-teractions, experimental set-ups, etc. in down-loadablefiles, which will facilitate other computational analysesof modifiers similar to the meta-analysis presented inthis work. Consequently, NeuroGeM will assist scien-tists immensely in their search for the pathophysio-logical mechanisms underlying NDs by providing thefirst compendium that catalogues and cross-links theirgenetic modifiers.Availability and requirementsNeuroGeM can be accessed from a web browser and isavailable at http://chibi.ubc.ca/neurogem.Additional filesAdditional file 1: A text with figures addressing the meta-analysisresults in detail mentioned in the main text.Additional file 2: Figure S1. A figure to illustrate a categorical search.Additional file 3: Figure S2. A figure to illustrate an ontology-basedsearch.AbbreviationsND: Neurodegenerative diseases; AD: Alzheimer’s disease; ADTau: Alzheimer’sdisease caused by tau; ADAβ: Alzheimer’s disease caused by Aβ;ALS: Amyotrophic lateral sclerosis; PD: Parkinson’s disease; PolyQ: PolyQdisease; SCA1: Spinocerebellar ataxia type 1; SCA3: Spinocerebellar ataxiatype 3; SCA7: Spinocerebellar ataxia type 7; HT: High-throughput;LT: Low-throughput; GO: GeneOntology.Competing interestsThe authors declare that they have no competing interests.Authors’ contributionDN built the database of genetic modifiers with integration of availabledatabases and performed meta-analysis of genetic modifiers. DN, JG, CO,and DR designed the database and web site. MR implemented the web siteto access the database of genetic modifiers. JG supervised the project. DNand JG wrote the manuscript. All authors read and approved the finalmanuscript.AcknowledgementJörg Gsponer, David C. Rubinsztein and Cahir J. O’Kane were supported bythe Centres of Excellence in Neurodegeneration Research (CoEN). JörgGsponer was supported by the Canadian Institutes of Health Research (CIHR),and David C. Rubinsztein was supported by the Medical Research Council(MRC) and a Wellcome Trust Principal Fellowship.Author details1Department of Biochemistry and Molecular Biology, Centre forHigh-throughput Biology, University of British Columbia, 2125 East MallVancouver, BC V6T 1Z4, Canada. 2Department of Computer Science,University of British Columbia, 2366 East Mall, Vancouver, BC V6T 1Z4,Canada. 3Department of Genetics, University of Cambridge, Downing Street,Cambridge CB2 3EH, UK. 4Department of Medical Genetics, University ofCambridge, Cambridge Institute for Medical Research, Addenbrooke’sHospital, Hills Road, Cambridge, CB2 0XY, UK. 5Present address: School ofIntegrative Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu,Seoul 156-756, Republic of Korea.Received: 21 August 2013 Accepted: 8 November 2013Published: 14 November 2013References1. Marsh JL, Thompson LM: Drosophila in the study of neurodegenerativedisease. Neuron 2006, 52:169–178.2. 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BMC Medical Genomics2013 6:52.Submit your next manuscript to BioMed Centraland take full advantage of: • Convenient online submission• Thorough peer review• No space constraints or color figure charges• Immediate publication on acceptance• Inclusion in PubMed, CAS, Scopus and Google Scholar• Research which is freely available for redistributionSubmit your manuscript at www.biomedcentral.com/submitNa et al. BMC Medical Genomics 2013, 6:52 Page 14 of 14http://www.biomedcentral.com/1755-8794/6/52

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