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. 2014 Nov 10;31(6):933–939. doi: 10.1093/bioinformatics/btu741

EVpedia: a community web portal for extracellular vesicles research

Dae-Kyum Kim 1,†,, Jaewook Lee 1,†,, Sae Rom Kim 1,, Dong-Sic Choi 1,, Yae Jin Yoon 2,, Ji Hyun Kim 1,, Gyeongyun Go 1,, Dinh Nhung 1,, Kahye Hong 1,, Su Chul Jang 1,, Si-Hyun Kim 1,, Kyong-Su Park 1,, Oh Youn Kim 1,, Hyun Taek Park 1,, Ji Hye Seo 1,, Elena Aikawa 3, Monika Baj-Krzyworzeka 4, Bas W M van Balkom 5, Mattias Belting 6, Lionel Blanc 7, Vincent Bond 8, Antonella Bongiovanni 9, Francesc E Borràs 10, Luc Buée 11, Edit I Buzás 12, Lesley Cheng 13, Aled Clayton 14, Emanuele Cocucci 15, Charles S Dela Cruz 16, Dominic M Desiderio 17, Dolores Di Vizio 18, Karin Ekström 19,20, Juan M Falcon-Perez 21, Chris Gardiner 22, Bernd Giebel 23, David W Greening 24, Julia Christina Gross 25, Dwijendra Gupta 26, An Hendrix 27, Andrew F Hill 13, Michelle M Hill 28, Esther Nolte-'t Hoen 29, Do Won Hwang 30, Jameel Inal 31, Medicharla V Jagannadham 32, Muthuvel Jayachandran 33, Young-Koo Jee 34, Malene Jørgensen 35, Kwang Pyo Kim 36, Yoon-Keun Kim 37, Thomas Kislinger 38, Cecilia Lässer 39, Dong Soo Lee 30, Hakmo Lee 40, Johannes van Leeuwen 41, Thomas Lener 42,43, Ming-Lin Liu 44,45, Jan Lötvall 39, Antonio Marcilla 46, Suresh Mathivanan 24, Andreas Möller 47, Jess Morhayim 41, François Mullier 48,49, Irina Nazarenko 50, Rienk Nieuwland 51, Diana N Nunes 52, Ken Pang 53,54, Jaesung Park 55, Tushar Patel 56, Gabriella Pocsfalvi 57, Hernando del Portillo 58, Ulrich Putz 59, Marcel I Ramirez 60, Marcio L Rodrigues 61,62, Tae-Young Roh 1,2, Felix Royo 21, Susmita Sahoo 63, Raymond Schiffelers 64, Shivani Sharma 65, Pia Siljander 66, Richard J Simpson 24, Carolina Soekmadji 67, Philip Stahl 68, Allan Stensballe 69, Ewa Stępień 70, Hidetoshi Tahara 71, Arne Trummer 72, Hadi Valadi 73, Laura J Vella 74, Sun Nyunt Wai 75, Kenneth Witwer 76, María Yáñez-Mó 77, Hyewon Youn 30, Reinhard Zeidler 78, Yong Song Gho 1,‡,*
PMCID: PMC4375401  PMID: 25388151

Abstract

Motivation: Extracellular vesicles (EVs) are spherical bilayered proteolipids, harboring various bioactive molecules. Due to the complexity of the vesicular nomenclatures and components, online searches for EV-related publications and vesicular components are currently challenging.

Results: We present an improved version of EVpedia, a public database for EVs research. This community web portal contains a database of publications and vesicular components, identification of orthologous vesicular components, bioinformatic tools and a personalized function. EVpedia includes 6879 publications, 172 080 vesicular components from 263 high-throughput datasets, and has been accessed more than 65 000 times from more than 750 cities. In addition, about 350 members from 73 international research groups have participated in developing EVpedia. This free web-based database might serve as a useful resource to stimulate the emerging field of EV research.

Availability and implementation: The web site was implemented in PHP, Java, MySQL and Apache, and is freely available at http://evpedia.info.

Contact: ysgho@postech.ac.kr

1 Introduction

Almost all living organisms on earth shed extracellular vesicles (EVs) into their microenvironment. EVs are spherical bilayered proteolipids with an average diameter of 20–1000 nm (Ellen et al., 2009; Lee et al., 2008; Théry et al., 2009). Cells release EVs either constitutively or in a regulated manner and these vesicles harbor a specific subset of proteins, mRNAs, miRNAs, lipids and metabolites reflecting their originating cell types and conditions (Bellingham et al., 2012; Choi et al., 2014; de Jong et al., 2012; Duperthuy et al., 2013; Mayr et al., 2009; Raimondo et al., 2011; Simpson et al., 2008; Subra et al., 2007; Wai et al., 2003). EVs are also found in various biological fluids, such as amniotic fluid, ascites, breast milk, plasma, saliva, semen, serum and urine (Asea et al., 2008; Caby et al., 2005; Cheng et al., 2014a, b; Dai et al., 2008; George et al., 1982; Lässer et al., 2011; Poliakov et al., 2009; Raj et al., 2012; Witwer et al., 2013). Recent advances in this fast growing field (Fig. 1) have facilitated several insights: (1) EVs play multifaceted functions in intercellular communication (Cocucci et al., 2009; Lee et al., 2008; Simons and Raposo, 2009; Théry et al., 2009); (2) EV-mediated intercellular communication is an evolutionarily conserved phenomenon (Deatherage and Cookson, 2012; Lee et al., 2008, 2009); (3) EVs are rich sources of biomarkers for non-invasive diagnosis and prognosis of various human diseases (Chaput et al., 2005; Choi et al., 2013a; D'Souza-Schorey and Clancy, 2012; Mullier et al., 2013; Sarlon-Bartoli et al., 2013; Shedden et al., 2003; Simpson et al., 2009); and (4) Diverse therapeutic approaches have been pursued to utilize EVs and their mimetics for vaccine, chemotherapeutic drug and siRNA delivery (Alvarez-Erviti et al., 2011; Chaput et al., 2005; Jang et al., 2013; Kordelas et al., 2014; Lai et al., 2010; Lee et al., 2012; Simpson et al., 2009; Sun et al., 2010).

Fig. 1.

Fig. 1.

Publication trend of EV studies. This graph shows the number of publications on EVs per year, indicating that the field of EVs is expanding rapidly

Extracellular Vesicles: Diverse Nomenclature.

  • Prokaryotes

  • Archaea

  •  Membrane Vesicles

  • Gram-negative Bacteria

  •  Extracellular Vesicles, Membrane Blebs, Outer Membrane Blebs, Outer Membrane Vesicles

  • Gram-positive Bacteria

  •  Extracellular Vesicles, Membrane Vesicles

  • Eukaryotes
    •  Argosomes, Blebbing Vesicles, Budding Vesicles, Dexosomes, Ectosomes, Exosome-like Vesicles, Exosomes, Exovesicles, Extracellular Membrane Vesicles, Extracellular Vesicles,
    •  Matrix Vesicles, Membrane Particles, Membrane Vesicles,
    •  Microparticles, Microvesicles, Nanovesicles, Oncosomes,
    •  Prominosomes, Prostasomes, Shedding Microvesicles,
    •  Shedding Vesicles, Tolerosomes

Publications on EVs have grown rapidly during the last several years, indicating that the field of EVs is expanding intensively (Fig. 1). The identification of vesicle-specific cargos could help us to unravel the molecular mechanisms underlying the cargo sorting and biogenesis of EVs. In addition, this will lead to better comprehension of the pathophysiological functions of EVs, and discovery of EV-based potential biomarkers of human diseases. Therefore, many investigators have focused on categorizing these complex vesicular components by various high-throughput technologies, such as mass spectrometry-based proteomics and lipidomics as well as microarray- and next-generation sequencing-based transcriptomics (Barry et al., 1997; Choi et al., 2013b; Koh et al., 2010; Utleg et al., 2003; Valadi et al., 2007). Together with conventional biological approaches, these multiomics-based analyses of EVs derived from various cell types and body fluids have identified several thousands of different vesicular components.

EV secretion and EV-mediated intercellular communication are evolutionarily conserved (Biller et al., 2014; Deatherage and Cookson, 2012; Lee et al., 2008, 2009). Researchers in this field have coined dozens of different names for EVs, especially for more complex eukaryotic cell-derived EVs as listed in Box (‘Extracellular Vesicles: Diverse Nomenclature’) (Choi et al., 2014; Gould and Raposo, 2013; Kim et al., 2013). Nevertheless, there is progress toward a single nomenclature, since the different names in use make it difficult to follow the progress in the field. In addition, most vesicular components identified by multiomics-based high throughput analyses are presented in the supplementary information of published articles. Taken together, online searches for EV-related publications and vesicular components are currently challenging, especially for start-up researchers in this field. Therefore, a comprehensive public repository of EV-related publications and vesicular components will help the community of EV research to understand various aspects of these complex extracellular organelles.

2 Databases that store EV data

The explosion of EV data has justified the need for databases that catalog proteins, nucleic acids and lipids associated with EVs. Currently, three databases exist for EV research including ExoCarta (Simpson et al., 2012), EVpedia (Kim et al., 2013) and Vesiclepedia (Kalra et al., 2012). These existing databases have made large-scale bioinformatics analyses feasible and provide an ideal platform for EV-based biomarker studies. EVpedia provides additional benefits compared with ExoCarta and Vesiclepedia. EVpedia is the only resource that contains data on both prokaryotes and eukaryotes. In addition, EVpedia allows for Gene Ontology enrichment analysis, network analysis of vesicular proteins and mRNAs, and set analysis of vesicular datasets by ortholog identification. Other databases do not have any such embedded analysis tools.

3 Launch of EVpedia

EVpedia (http://evpedia.info) was first launched in January 2012 (Kim et al., 2013). To construct this public web-based database, we first collected publications on prokaryotic and eukaryotic EVs through a combination of NCBI PubMed searches (http://www.ncbi.nlm.nih.gov/pubmed) for text-mining solutions and manual curation using all nomenclatures assigned to EVs described in Box (see also Kim et al., 2013). Based on these EV-related publications, we constructed the comprehensive and integrated database of proteins, mRNAs, miRNAs, lipids and metabolites for systematic analyses of prokaryotic and eukaryotic EVs.

4 Overview of current EVpedia

Since the launch of EVpedia, we have improved this database by continually collecting additional EV-related publications and datasets, by adding more tools for systematic analyses of EVs, and by supplementing the menu bars for ‘Top 100+ EV markers’, ‘User forum’ as well as ‘My EVpedia’. Through closed and open beta tests, we built an ‘EVpedia Community’ (about 350 world-wide EV researchers) and updated EVpedia most recently in May 2014.

The updated EVpedia has five functional modules for systematic analyses of EVs derived from prokaryotic and eukaryotic cells (Fig. 2): (i) a database of publications and principal investigators, (ii) a database of vesicular proteins, mRNAs, miRNAs, lipids and metabolites, (iii) identification of orthologous vesicular components, (iv) an array of tools for bioinformatic analyses including sequence search, set analysis, Gene Ontology enrichment analysis and network analysis, as well as (v) ‘My EVpedia’, a personalized function of EVpedia. Using ‘My EVpedia’, users privately store their own datasets, analysis results and publications of interest by creating their own accounts. New functions of this updated EVpedia are indicated in red texts in Figure 2.

Fig. 2.

Fig. 2.

Overall structure of EVpedia. EVpedia provides a comprehensive database for (i) publications and principal investigators, (ii) vesicular proteins, mRNAs, miRNAs, lipids and metabolites and (iii) identification of orthologous vesicular components. For systematic analyses of vesicular components, there is an array of tools for sequence search, set analysis, Gene Ontology enrichment analysis and network analysis. ‘My EVpedia’ is a personalized function of EVpedia to deposit the user’s own datasets, analysis results and publications of interest. Note that red texts indicate newly included functions in the updated version of EVpedia

We invite the research community to submit EV-related multiomics data and publications to EVpedia.

As of May 2014, a total of 6879 EV-related publications with 3336 principal investigators have been cataloged in EVpedia. In addition, a total of 172 080 vesicular components from 263 high-throughput datasets are listed (Table 1). All of these vesicular components could be searched by their sequences and browsed. Furthermore, in the ‘Top 100+ EV markers’ menu, the current vesicular components are sorted in the descending order of their identification counts, which are the numbers of datasets identifying those vesicular components or their orthologs.

Table 1.

EVpedia statistics

All Eukaryotes Prokaryotes
Publications
 Articles 6879 6021 858
 Principal investigators 3336 2886 483
Proteomes
 Studies 117 97 20
 Datasets 176 148 28
 Proteins 78 971 74 696 4275
Transcriptomes
 mRNA
  Studies 17 17 0
  Datasets 28 28 0
  mRNAs 74 430 74 430 0
 miRNA
  Studies 11 11 0
  Datasets 29 29 0
  miRNAs 18 119 18 119 0
Lipidomes
 Studies 22 21 1
 Datasets 29 28 1
 Lipids 550 534 16
Metabolomes
 Studies 1 1 0
 Datasets 1 1 0
 Metabolites 10 10 0
Participating
 Laboratories (countries) 73 (20)
 Accesses (countries) 66 617 (73)

5 Community participation and annotation in EVpedia

After the initial launch in January 2012, EVpedia has been globally accessed more than 65 000 times from more than 750 cities (Table 1). For community annotation of EVpedia, we built ‘EVpedia Community’. About 350 members from 73 international EV research groups have joined this community, in which they can exchange EV-related information and submit their multiomics data via the ‘User forum’ and ‘Upload’ menu bars in EVpedia, respectively. In addition, non-members can easily join the ‘EVpedia Community’ by adding their information via clicking the ‘Sign In’ menu. Moreover, EVpedia has been cross-linked with the website of the ‘International Society of Extracellular Vesicles’ (http://www.isev.org).

6 Concluding remarks

EVpedia is a comprehensive database of EVs derived from prokaryotes and eukaryotes. Currently, a total of 6879 EV-related publications and 172 080 vesicular components (proteins, mRNAs, miRNAs, lipids and metabolites) are deposited in this public repository. For the systematic analysis of EVs, EVpedia also provides integrated systems biology research tools such as ‘Experiment’, ‘Browse’, ‘Analysis’, ‘Top 100+ EV markers’ and ‘My EVpedia’ menu bars. In the future, additional multiomics datasets and publications will be deposited, and we expect more researchers to join the ‘EVpedia Community’ and to share their research data. The community database is scheduled to be updated every 3 months. EVpedia, a community web portal for EV research, should serve as a useful resource to stimulate the emerging field of EV biology research and to help us to elucidate the fundamental roles of these complex extracellular organelles.

Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) [No. 2014-023004], the Ministry of Health and Welfare grant funded by the Korea government [No. A120273] and a grant from KRIBB Research Initiative Program. D.-K. K is supported by a National Junior Research Fellowship [No. 2014-048579] and Y. J. Y by BK21 PLUS program [10Z20130012243] funded by Ministry of Education, Science and Technology, Republic of Korea.

Conflict of Interest: none declared.

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