Skip to main content

DBSecSys: a database of Burkholderia malleisecretion systems

Abstract

Background

Bacterial pathogenicity represents a major public health concern worldwide. Secretion systems are a key component of bacterial pathogenicity, as they provide the means for bacterial proteins to penetrate host-cell membranes and insert themselves directly into the host cells’ cytosol. Burkholderia mallei is a Gram-negative bacterium that uses multiple secretion systems during its host infection life cycle. To date, the identities of secretion system proteins for B. mallei are not well known, and their pathogenic mechanisms of action and host factors are largely uncharacterized.

Description

We present the Database of B urkholderia mallei Secretion Systems (DBSecSys), a compilation of manually curated and computationally predicted bacterial secretion system proteins and their host factors. Currently, DBSecSys contains comprehensive experimentally and computationally derived information about B. mallei strain ATCC 23344. The database includes 143 B. mallei proteins associated with five secretion systems, their 1,635 human and murine interacting targets, and the corresponding 2,400 host-B. mallei interactions. The database also includes information about 10 pathogenic mechanisms of action for B. mallei secretion system proteins inferred from the available literature. Additionally, DBSecSys provides details about 42 virulence attenuation experiments for 27 B. mallei secretion system proteins. Users interact with DBSecSys through a Web interface that allows for data browsing, querying, visualizing, and downloading.

Conclusions

DBSecSys provides a comprehensive, systematically organized resource of experimental and computational data associated with B. mallei secretion systems. It provides the unique ability to study secretion systems not only through characterization of their corresponding pathogen proteins, but also through characterization of their host-interacting partners.

The database is available at https://applications.bhsai.org/dbsecsys.

Background

Introduction

Pathogenic bacteria cause a variety of diseases and represent a major public health concern worldwide. Secretion systems are a key component of bacterial pathogenicity, as they facilitate pathogenic internalization, survival, and replication within host cells through controlled transport and translocation of pathogenic molecules, e.g., proteins or DNA, from the interior of the bacterial cell into the host cell’s cytosol [1, 2]. Bacterial species frequently contain more than one type of secretion system, and some secretion systems are often encoded in multiple gene clusters (also called pathogenicity islands) [37].

The Gram-negative bacterium Burkholderia mallei, the causative agent of glanders, uses a variety of specialized secretion systems to interfere with host defense mechanisms and promote pathogen colonization. Given its ability to infect hosts via aerosol exposure, considerable antibiotic resistance, and the lack of a vaccine, B. mallei represents an emerging public health threat [8]. While the research into B. mallei pathogenicity has recently expanded, the identities of all of its secretion systems’ proteins are not completely cataloged and their pathogenic mechanisms are largely unknown.

To date, several database systems provide information about Burkholderia species proteins. Some provide general B. mallei protein information [9, 10], such as sequence annotation, naming schemes, and functional and pathway associations, but such systems lack systematic organization of the pathogen proteins that are part of secretion systems. Other databases are more specific and encompass information about single [1113] or multiple [1416] bacterial secretion systems for many, but not all, Burkholderia species. While these databases provide a useful characterization of proteins based on the secretion system type, they lack information about pathogenic mechanisms of action associated with secretion systems and their host targets.

Our contribution

As none of the available secretion system databases includes B. mallei secretion systems data, the goal of the Database of B urkholderia mallei Secretion Systems (DBSecSys) is to provide information about B. mallei (strain ATCC 23344) proteins associated with secretion systems and their host factors. Currently, DBSecSys contains experimentally and computationally derived information about B. mallei proteins associated with secretion systems. The information includes their secretion system types, involvement in virulence, associated mechanisms of action, host targets (interacting host proteins), and general annotation (assigned names and IDs, protein sequence, and functional and pathway association). These features provide researchers with the unique ability to study B. mallei pathogenicity and secretion systems not only through characterization of the corresponding pathogen proteins, but also through characterization of their host interacting partners.

Construction and content

Database content

Table 1 summarizes the content of DBSecSys. Using the available literature on Burkholderia species, we manually compiled a list of B. mallei proteins and their associated secretion systems. The resulting list contains five secretion system types associated with 143 B. mallei proteins, where each protein is associated with only one secretion system type. Additionally, as represented in DBSecSys, the list contains the available information about gene clusters associated with secretion systems, protein descriptions, and secretion system protein characterizations, e.g., effector or secretion apparatus proteins. B. mallei secretion system proteins are also characterized based on their association with one of 10 inferred pathogenic mechanisms of action.

Table 1 Summary of the current content of DBSecSys

In addition to pathogen proteins, DBSecSys contains information on 1,635 host (797 human and 838 murine) proteins, as well as information on 2,400 host-pathogen interactions between B. mallei and host proteins. Of these, 1,357 interactions have been detected experimentally: 569 human-B. mallei and 788 murine-B. mallei interactions. The remaining interactions (608 human-B. mallei and 435 murine-B. mallei interactions), have been determined computationally based on orthology between human and murine proteins provided in the National Center for Biotechnology Information (NCBI) HomoloGene database [17, 18]. In addition, DBSecSys contains a set of 491 protein-protein interactions (PPIs) [19] among 357 human proteins that interact with B. mallei, and 36 PPIs [20, 21] among 47 murine proteins that interact with B. mallei.

DBSecSys also provides information about 42 virulence attenuation (by genetic ablation) experiments for 27 of the 143 listed B. mallei proteins (21 of which are virulence factors). This information includes seven infection delivery routes, eight animal models used, and four observed virulence attenuation levels (full, moderate, mild, or no attenuation).

DBSecSys uses a standard nomenclature to describe secretion systems (see Table 2) and commonly used literature terms to define pathogenic mechanisms of action (see Table 3). All data in the database can be tracked to their experimental or computational source.

Table 2 Description of secretion systems
Table 3 Description of pathogenic mechanisms of action included in DBSecSys

Data sources

The list of B. mallei proteins associated with each secretion system was compiled using the results of B. mallei experimental studies [2224], computational predictions based on orthology, and de-novo computational predictions. We used orthology to infer B. mallei proteins associated with secretion systems based on the results of B. pseudomallei experimental studies, as these two species share a large number of genes with high sequence similarity [2227]. We used only a subset of predicted B. mallei secretion system proteins that has been shown to interact with host proteins experimentally [28].

Twenty-four B. mallei proteins associated with secretion systems participate in PPIs with human and murine hosts [28]. Given that the PPI detection experiments did not exhaustively screen for all possible host-B. mallei interactions, we used human-murine orthology information [17, 18] to infer additional interactions. This procedure generated a list of experimentally and computationally identified human- and murine-B. mallei PPIs associated with secretion systems. Furthermore, to account for the relationship among host proteins that interact with B. mallei proteins, we used information from host PPI networks. For human PPIs, we used data from an experimentally detected PPI network compiled by Yu et al.[19], while for murine PPIs, we used data from an experimentally detected PPI network available in the BIOGRID database (Release 3.2.105) [20, 21].

We compiled the list of B. mallei pathogenic mechanisms of action based on the results of B. mallei experimental studies [22, 29] and the computational predictions using orthology and host-pathogen PPI data. We used information about orthology between B. mallei and B. pseudomallei proteins to infer mechanisms of action experimentally identified for B. pseudomallei[3032]. We inferred B. mallei mechanisms of action from host-pathogen PPIs as follows. First, we surveyed the available pathogenicity literature for associations between specific host protein functions and pathogenic mechanisms of action. Then, for each pathogenic mechanism of action, we compiled a set of its corresponding host proteins' functions. Next, for each B. mallei protein present in host-B. mallei PPI data [28], we counted the number of its host-interacting partners that were annotated with at least one function associated with each of the mechanisms. If a B. mallei protein interacted with ≥ 3 host proteins that corresponded to a single mechanism, we assigned the protein to that mechanism of action.

Finally, using literature information, we manually compiled a list of B. mallei secretion system proteins that have been experimentally evaluated for virulence attenuation (by genetic ablation) in an animal model. The list contains proteins identified in B. mallei studies [28] and proteins computationally inferred from B. pseudomallei studies using orthology information [30, 33, 34].

We used NCBI [17, 18] and Uniprot [35] to annotate pathogen and host proteins, e.g., protein names and sequence information. For functional annotation of host proteins, we used Gene Ontology (GO) terms [36] and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways [37, 38].

Software architecture

We developed the DBSecSys database and Web interface using a three-tier architecture comprising a backend database, controller, and presentation tiers. The first tier consists of an Oracle database server that stores the data contained in the DBSecSys database using a relational schema. We developed the controller and presentation tiers using Java Platform, Enterprise Edition 7, JavaServer Faces 2, and ICEfaces 3 technologies. The presentation tier comprising a Web interface also uses JBrowse [3941], D3.js [42], NVD3.js [43], and Cytoscape.js [44] JavaScript libraries to create detailed interactive visualizations of genes on the reference sequence and of protein-protein interactions. All visualizations are based on modern Web standards and do not require any plugins. The DBSecSys Web application is hosted on an Apache Tomcat Web server at: https://applications.bhsai.org/dbsecsys.

Utility and discussion

At its core, DBSecSys contains information about B. mallei secretion system proteins, their inferred pathogenic mechanisms of action, host protein interacting partners, and virulence attenuation experiments. A Web-based graphical user interface presents the DBSecSys data with interactive search and visualization capabilities. It provides the ability to browse, query, and visualize these data in multiple ways, allowing users to explore secretion systems through pathogen proteins, as well as their host-interacting partners (Figures 1 and 2). All data within DBSecSys are cross-linked. This feature facilitates efficient navigation between different DBSecSys pages. It also allows users to apply retrieved results as queries for new searches, minimizing the need to navigate to a designated query page. DBSecSys query results can be filtered and downloaded in a tab-delimited format. Host-pathogen PPIs are available for download in the Proteomics Standards Initiative Molecular Interactions (PSI-MI) Tab 2.5 format [45]. DBSecSys also provides links to external resources for detailed information about individual proteins (NCBI [17, 18], Uniprot [35], GO [36], and KEGG [37, 38]). For all experimental data and observations contained in the database, DBSecSys provides links to their original sources and publications through PubMed.

Figure 1
figure1

DBSecSys home page. The Web interface of DBSecSys provides features for browsing, querying, visualizing, and downloading detailed information about Burkholderia mallei (strain ATCC 23344) proteins associated with secretion systems, their involvement in virulence, their host protein targets, and their mechanisms of action inferred from host-pathogen interaction data and literature review.

Figure 2
figure2

Secretion systems search results. (A) A search for B. mallei (strain ATCC 23344) type 3 secretion system proteins yields a list of pathogen proteins associated with that secretion system. The content of the list can be filtered by cluster type and mechanisms of action, e.g., by the animal-associated T3SS cluster and the actin cytoskeleton rearrangement mechanism. (B) The genome browser shows the location of the bacterial proteins associated with the secretion system on the bacterial reference sequence. (C) Users can visualize host-pathogen protein-protein interactions (PPIs) with the help of the PPI browser. Interactions are represented in the form of networks, where proteins correspond to network nodes (blue circles and red squares), and interactions between those proteins correspond to network edges (gray and green lines).

DBSecSys queries are anchored around five applications (Figure 1), which search for 1) pathogen protein or host protein annotation, 2) all pathogen proteins associated with a secretion system, 3) all pathogen proteins associated with the pathogen’s mechanism of action, 4) host-pathogen interactions, and 5) experimentally screened virulence factors.

Application 1: Study of individual proteins

Users can study individual (pathogen or host) proteins on the “Proteins” page of the Web interface. For example, users can search either for a B. mallei protein to determine its association with a secretion system, or for a host (human or murine) protein to determine whether it interacts with a pathogen protein associated with a secretion system. Protein searches can be performed using one of the following known protein/gene identifiers: Locus Tag, Name, Uniprot ID, Uniprot Name, Gene ID, GenInfo Identifier (GI), and KEGG ID.

When the query protein is represented in the database, DBSecSys returns its detailed annotation information, including protein/gene identifiers from different annotation databases (see above), chromosomal location, amino acid sequence information, and corresponding GO [36] and KEGG [37, 38] annotations. All annotation information is linked with its external source. If the host-B. mallei interactions data is available for the queried protein, DBSecSys provides a list of host-pathogen PPIs for that protein. Additionally, if the queried protein is a pathogen protein, the database provides information about its associated secretion system, inferred mechanisms of action, and, if it was tested for virulence attenuation, information about its effect on virulence. Conversely, if the queried protein is a host protein, DBSecSys provides information about pathogen proteins that target this host protein, e.g., the secretion systems with which they are associated and their inferred mechanisms of action.

Application 2: Study of pathogen proteins associated with a secretion system

Users can study pathogen proteins associated with a specific secretion system on the “Secretion Systems” page of the Web interface. This page allows users to select one of the secretion systems associated with B. mallei and list all pathogen proteins associated with it. DBSecSys retrieves a list of B. mallei proteins associated with the queried secretion system and the information about their role, the pathogenicity island/cluster type (for the secretion systems that are encoded in two or more gene clusters), details about their associated mechanism(s) of action, and details about the corresponding virulence attenuation experiments. If host-B. mallei interaction data are available for the listed B. mallei proteins, DBSecSys also retrieves the corresponding host-pathogen PPIs. Users have the option to filter the retrieved list of proteins based on a secretion system cluster or a mechanism of action. DBSecSys also filters the corresponding list of host-pathogen PPIs, ensuring that it contains only proteins that satisfy user-specified criteria (filters).Figure 2A shows a list of proteins associated with the type 3 secretion system that are filtered based on the cluster type (animal-associated cluster) and the mechanism of action association (actin cytoskeleton rearrangement), while Figure 2B and C show visualization options for proteins associated with secretion system clusters and their associated host-pathogen PPIs, respectively.

Application 3: Study of pathogen proteins associated with an inferred pathogenic mechanism of action

Users can also study pathogen proteins associated with a specific pathogenic mechanism of action using the “Mechanisms of Action” page of the Web interface. The querying process for this application is similar to the one for Application 2, and the result of a query is a list of B. mallei proteins, their associated secretion system types, and detailed information about their association with the queried mechanism of action. The retrieved list can be filtered by secretion system type. If host-B. mallei interaction data are available for the listed B. mallei proteins, DBSecSys also retrieves their corresponding host-pathogen PPIs.

Application 4: Host-pathogen interactions associated with secretion systems

DBSecSys allows users to browse all available host-pathogen interactions in the database using the “Host-Pathogen Interactions” page. This page gives users an option to retrieve host-specific interactions or all host-B. mallei interactions. Additionally, this page allows users to select the type of interactions they want to retrieve – only experimentally detected PPIs (the default option) or experimentally detected and computationally predicted PPIs. The resulting list of host-B. mallei interactions can be filtered based on host and pathogen protein names. Subsets of host-B. mallei PPIs can also be accessed through multiple other pages on the DBSecSys Web interface, e.g., through searches by protein, secretion system, or mechanisms of action.

Application 5: Experimentally screened virulence factors associated with secretion systems

DBSecSys contains detailed information about B. mallei secretion system proteins tested for virulence attenuation (by genetic ablation) in animal model experiments, which can be queried using the “Virulence Attenuation” page on the Web interface. The resulting list of proteins contains information about the virulence attenuation level, animal model and infection route used, and the associated secretion system type. Users can filter the list of proteins based on the secretion system type, animal model, and infection route.

Data visualization

The DBSecSys Web interface provides two visualization tools: 1) an interactive genome browser for displaying pathogen genes on the reference sequence and 2) an interactive network visualization tool for displaying host-pathogen PPIs.

Figure 2B shows the genome browser that displays the B. mallei reference sequence and highlights the chromosomal location of secretion system genes and gene clusters, allowing users to locate the positions of genes associated with the secretion systems and interactively explore different parts of the sequence. It also enables users to study the individual secretion system genes in the context of the adjacent genes that are not associated with secretion systems, and to study multiple genes that are part of a specific secretion system or a secretion system cluster, or are associated with the same mechanism of action.Figure 2C shows the PPI browser that provides visualization of host-pathogen PPIs in the form of networks, where proteins are represented as network nodes and interactions between proteins are represented as network edges. Visualization of host-pathogen PPIs may help users identify connectivity patterns underlying these interactions. By default, the PPI browser displays the interactions using a circular breadth-first search layout. However, users can choose one of the provided layout options (breadth first linear, circle, and force-directed arbor layouts) or interactively move and rearrange all nodes and edges. The PPI browser also allows users to select additional visualization options, e.g., to display or hide computationally predicted PPIs and to visualize the interactions between host proteins by displaying host PPIs.

Both tools provide zoom in/out options and the ability to save the resulting view as an image. Additionally, visualization tools provide contextual pop-up displays that allow users to efficiently retrieve more information about proteins directly from the visualization tools, e.g., information provided by NCBI (genome browser) or information about the protein secretion system type and virulence attenuation (PPI browser).

Database updates

The database content will be updated periodically to include new experimental and computational evidence. Additionally, the database will be expanded to include additional pathogens, their corresponding secretion systems, mechanisms of action, and virulence attenuation information. Before each update, the current state of the database will be frozen and archived.

Conclusions

We developed a curated database of bacterial secretion system proteins and their host factors. Currently, the database contains comprehensive information about B. mallei proteins associated with secretion systems, their involvement in virulence, their host targets (proteins), and their mechanisms of action inferred from literature and host-pathogen interaction data. Systematical organization of experimental and computational data allows for efficient data retrieval, browsing, querying, and visualization, and enables users to study secretion systems not only through characterization of the corresponding pathogen proteins but also through characterization of host-interacting partners. Together, these features make DBSecSys a unique resource for B. mallei secretion system research.

Availability and requirements

The Web-enabled DBSecSys database is freely accessible at https://applications.bhsai.org/dbsecsys. The DBSecSys Web interface has been tested in the following Web browsers: Google Chrome (version 31), Microsoft Internet Explorer (version 10), and Mozilla Firefox (version 25). The “User Guide” page of the DBSecSys Web interface includes a step-by-step description of all DBSecSys features.

Abbreviations

DBSecSys:

Database of Burkholderia mallei Secretion Systems

GO:

Gene Ontology

KEGG:

Kyoto Encyclopedia of Genes and Genomes

NCBI:

National Center for Biotechnology Information

PPI:

Protein-protein interaction

PSI MI:

Proteomics Standards Initiative Molecular Interactions.

References

  1. 1.

    Desvaux M, Hebraud M, Talon R, Henderson IR: Secretion and subcellular localizations of bacterial proteins: A semantic awareness issue. Trends Microbiol. 2009, 17 (4): 139-145.

    Article  PubMed  CAS  Google Scholar 

  2. 2.

    Tseng TT, Tyler BM, Setubal JC: Protein secretion systems in bacterial-host associations, and their description in the Gene Ontology. BMC Microbiol. 2009, 9: S2-

    Article  PubMed Central  PubMed  Google Scholar 

  3. 3.

    Silverman JM, Brunet YR, Cascales E, Mougous JD: Structure and regulation of the type VI secretion system. Annu Rev Microbiol. 2012, 66: 453-472.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  4. 4.

    Galan JE, Collmer A: Type III secretion machines: Bacterial devices for protein delivery into host cells. Science. 1999, 284 (5418): 1322-1328.

    Article  PubMed  CAS  Google Scholar 

  5. 5.

    Hueck CJ: Type III protein secretion systems in bacterial pathogens of animals and plants. Microbiol Mol Biol Rev. 1998, 62 (2): 379-433.

    PubMed Central  PubMed  CAS  Google Scholar 

  6. 6.

    Coburn B, Sekirov I, Finlay BB: Type III secretion systems and disease. Clin Microbiol Rev. 2007, 20 (4): 535-549.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  7. 7.

    Pukatzki S, McAuley SB, Miyata ST: The type VI secretion system: Translocation of effectors and effector-domains. Curr Opin Microbiol. 2009, 12 (1): 11-17.

    Article  PubMed  CAS  Google Scholar 

  8. 8.

    Estes DM, Dow SW, Schweizer HP, Torres AG: Present and future therapeutic strategies for melioidosis and glanders. Expert Rev Anti Infect Ther. 2010, 8 (3): 325-338.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  9. 9.

    Brinkac LM, Davidsen T, Beck E, Ganapathy A, Caler E, Dodson RJ, Durkin AS, Harkins DM, Lorenzi H, Madupu R, Sebastian Y, Shrivastava S, Thiagarajan M, Orvis J, Sundaram JP, Crabtree J, Galens K, Zhao Y, Inman JM, Montgomery R, Schobel S, Galinsky K, Tanenbaum DM, Resnick A, Zafar N, White O, Sutton G: Pathema: A clade-specific bioinformatics resource center for pathogen research. Nucleic Acids Res. 2010, 38: D408-D414.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  10. 10.

    Gillespie JJ, Wattam AR, Cammer SA, Gabbard JL, Shukla MP, Dalay O, Driscoll T, Hix D, Mane SP, Mao C, Nordberg EK, Scott M, Schulman JR, Snyder EE, Sullivan DE, Wang C, Warren A, Williams KP, Xue T, Yoo HS, Zhang C, Zhang Y, Will R, Kenyon RW, Sobral BW: PATRIC: The comprehensive bacterial bioinformatics resource with a focus on human pathogenic species. Infect Immun. 2011, 79 (11): 4286-4298.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  11. 11.

    Bi D, Liu L, Tai C, Deng Z, Rajakumar K, Ou HY: SecReT4: A web-based bacterial type IV secretion system resource. Nucleic Acids Res. 2013, 41: D660-D665.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  12. 12.

    Souza RC, del Rosario Quispe Saji G, Costa MO, Netto DS, Lima NC, Klein CC, Vasconcelos AT, Nicolas MF: AtlasT4SS: A curated database for type IV secretion systems. BMC Microbiol. 2012, 12: 172-

    Article  PubMed Central  PubMed  Google Scholar 

  13. 13.

    Wang Y, Huang H, Sun M, Zhang Q, Guo D: T3DB: An integrated database for bacterial type III secretion system. BMC Bioinformatics. 2012, 13: 66-

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  14. 14.

    Bleves S, Dunger I, Walter MC, Frangoulidis D, Kastenmuller G, Voulhoux R, Ruepp A: HoPaCI-DB: Host-Pseudomonas and Coxiella interaction database. Nucleic Acids Res. 2014, 42: D671-D676.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  15. 15.

    Chen L, Xiong Z, Sun L, Yang J, Jin Q: VFDB 2012 update: Toward the genetic diversity and molecular evolution of bacterial virulence factors. Nucleic Acids Res. 2012, 40: D641-D645.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  16. 16.

    Niu C, Yu D, Wang Y, Ren H, Jin Y, Zhou W, Li B, Cheng Y, Yue J, Gao Z, Liang L: Common and pathogen-specific virulence factors are different in function and structure. Virulence. 2013, 4 (6): 473-482.

    Article  PubMed  Google Scholar 

  17. 17.

    Sayers EW, Barrett T, Benson DA, Bolton E, Bryant SH, Canese K, Chetvernin V, Church DM, Dicuccio M, Federhen S, Feolo M, Fingerman IM, Geer LY, Helmberg W, Kapustin Y, Krasnov S, Landsman D, Lipman DJ, Lu Z, Madden TL, Madej T, Maglott DR, Marchler-Bauer A, Miller V, Karsch-Mizrachi I, Ostell J, Panchenko A, Phan L, Pruitt KD, Schuler GD, et al: Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2012, 40: D13-D25.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  18. 18.

    Wheeler DL, Barrett T, Benson DA, Bryant SH, Canese K, Church DM, DiCuccio M, Edgar R, Federhen S, Helmberg W, Kenton DL, Khovayko O, Lipman DJ, Madden TL, Maglott DR, Ostell J, Pontius JU, Pruitt KD, Schuler GD, Schriml LM, Sequeira E, Sherry ST, Sirotkin K, Starchenko G, Suzek TO, Tatusov R, Tatusova TA, Wagner L, Yaschenko E: Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2005, 33: D39-D45.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  19. 19.

    Yu X, Wallqvist A, Reifman J: Inferring high-confidence human protein-protein interactions. BMC Bioinformatics. 2012, 13 (1): 79-

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  20. 20.

    Chatr-Aryamontri A, Breitkreutz BJ, Heinicke S, Boucher L, Winter A, Stark C, Nixon J, Ramage L, Kolas N, O’Donnell L, Reguly T, Breitkreutz A, Sellam A, Chen D, Chang C, Rust J, Livstone M, Oughtred R, Dolinski K, Tyers M: The BioGRID interaction database: 2013 update. Nucleic Acids Res. 2013, 41: D816-D823.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  21. 21.

    Stark C, Breitkreutz BJ, Reguly T, Boucher L, Breitkreutz A, Tyers M: BioGRID: A general repository for interaction datasets. Nucleic Acids Res. 2006, 34: D535-D539.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  22. 22.

    Lazar Adler NR, Stevens JM, Stevens MP, Galyov EE: Autotransporters and their role in the virulence of Burkholderia pseudomallei and Burkholderia mallei. Front Microbiol. 2011, 2: 151-

    PubMed  CAS  Google Scholar 

  23. 23.

    Schell MA, Ulrich RL, Ribot WJ, Brueggemann EE, Hines HB, Chen D, Lipscomb L, Kim HS, Mrazek J, Nierman WC, Deshazer D: Type VI secretion is a major virulence determinant in Burkholderia mallei. Mol Microbiol. 2007, 64 (6): 1466-1485.

    Article  PubMed  CAS  Google Scholar 

  24. 24.

    Ulrich RL, DeShazer D: Type III secretion: A virulence factor delivery system essential for the pathogenicity of Burkholderia mallei. Infect Immun. 2004, 72 (2): 1150-1154.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  25. 25.

    Burtnick MN, Brett PJ, Harding SV, Ngugi SA, Ribot WJ, Chantratita N, Scorpio A, Milne TS, Dean RE, Fritz DL, Peacock SJ, Prior JL, Atkins TP, Deshazer D: The cluster 1 type VI secretion system is a major virulence determinant in Burkholderia pseudomallei. Infect Immun. 2011, 79 (4): 1512-1525.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  26. 26.

    Galyov EE, Brett PJ, DeShazer D: Molecular insights into Burkholderia pseudomallei and Burkholderia mallei pathogenesis. Annu Rev Microbiol. 2010, 64: 495-517.

    Article  PubMed  CAS  Google Scholar 

  27. 27.

    Godoy D, Randle G, Simpson AJ, Aanensen DM, Pitt TL, Kinoshita R, Spratt BG: Multilocus sequence typing and evolutionary relationships among the causative agents of melioidosis and glanders, Burkholderia pseudomallei and Burkholderia mallei. J Clin Microbiol. 2003, 41 (5): 2068-2079.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  28. 28.

    Memišević V, Zavaljevski N, Pieper R, Rajagopala SV, Kwon K, Townsend K, Yu C, Yu X, DeShazer D, Reifman J, Wallqvist A: Novel Burkholderia mallei virulence factors linked to specific host-pathogen protein interactions. Mol Cell Proteomics. 2013, 12 (11): 3036-3051.

    Article  PubMed Central  PubMed  Google Scholar 

  29. 29.

    Shanks J, Burtnick MN, Brett PJ, Waag DM, Spurgers KB, Ribot WJ, Schell MA, Panchal RG, Gherardini FC, Wilkinson KD, Deshazer D: Burkholderia mallei tssM encodes a putative deubiquitinase that is secreted and expressed inside infected RAW 264.7 murine macrophages. Infect Immun. 2009, 77 (4): 1636-1648.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  30. 30.

    Allwood EM, Devenish RJ, Prescott M, Adler B, Boyce JD: Strategies for intracellular survival of Burkholderia pseudomallei. Front Microbiol. 2011, 2: 170-

    Article  PubMed Central  PubMed  Google Scholar 

  31. 31.

    Essex-Lopresti AE, Boddey JA, Thomas R, Smith MP, Hartley MG, Atkins T, Brown NF, Tsang CH, Peak IR, Hill J, Beacham IR, Titball RW: A type IV pilin, PilA, contributes to adherence of Burkholderia pseudomallei and virulence in vivo. Infect Immun. 2005, 73 (2): 1260-1264.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  32. 32.

    Gong L, Cullinane M, Treerat P, Ramm G, Prescott M, Adler B, Boyce JD, Devenish RJ: The Burkholderia pseudomallei type III secretion system and BopA are required for evasion of LC3-associated phagocytosis. PLoS One. 2011, 6 (3): e17852-

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  33. 33.

    Stevens MP, Friebel A, Taylor LA, Wood MW, Brown PJ, Hardt WD, Galyov EE: A Burkholderia pseudomallei type III secreted protein, BopE, facilitates bacterial invasion of epithelial cells and exhibits guanine nucleotide exchange factor activity. J Bacteriol. 2003, 185 (16): 4992-4996.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  34. 34.

    Whitlock GC, Valbuena GA, Popov VL, Judy BM, Estes DM, Torres AG: Burkholderia mallei cellular interactions in a respiratory cell model. J Med Microbiol. 2009, 58 (Pt 5): 554-562.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  35. 35.

    Consortium U: Update on activities at the Universal Protein Resource (UniProt) in 2013. Nucleic Acids Res. 2013, 41 (Database issue): D43-D47.

    Article  Google Scholar 

  36. 36.

    Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G: Gene Ontology: Tool for the unification of biology. Nat Genet. 2000, 25 (1): 25-29.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  37. 37.

    Kanehisa M, Goto S: KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 2000, 28 (1): 27-30.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  38. 38.

    Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M: KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 2012, 40: D109-D114.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  39. 39.

    Skinner ME, Holmes IH: Setting up the JBrowse genome browser. Curr Protoc Bioinformatics. 2010, Chapter 9: Unit 9.13-

    PubMed  Google Scholar 

  40. 40.

    Skinner ME, Uzilov AV, Stein LD, Mungall CJ, Holmes IH: JBrowse: A next-generation genome browser. Genome Res. 2009, 19 (9): 1630-1638.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  41. 41.

    Westesson O, Skinner M, Holmes I: Visualizing next-generation sequencing data with JBrowse. Brief Bioinform. 2013, 14 (2): 172-177.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  42. 42.

    Bostock M, Ogievetsky V, Heer J: D(3): Data-Driven Documents. IEEE Trans Vis Comput Graph. 2011, 17 (12): 2301-2309.

    Article  PubMed  Google Scholar 

  43. 43.

    NVD3.js. http://nvd3.org,

  44. 44.

    Lopes CT, Franz M, Kazi F, Donaldson SL, Morris Q, Bader GD: Cytoscape Web: An interactive web-based network browser. Bioinformatics. 2010, 26 (18): 2347-2348.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  45. 45.

    Kerrien S, Orchard S, Montecchi-Palazzi L, Aranda B, Quinn AF, Vinod N, Bader GD, Xenarios I, Wojcik J, Sherman D, Tyers M, Salama JJ, Moore S, Ceol A, Chatr-Aryamontri A, Oesterheld M, Stumpflen V, Salwinski L, Nerothin J, Cerami E, Cusick ME, Vidal M, Gilson M, Armstrong J, Woollard P, Hogue C, Eisenberg D, Cesareni G, Apweiler R, Hermjakob H: Broadening the horizon - level 2.5 of the HUPO-PSI format for molecular interactions. BMC Biol. 2007, 5: 44-

    Article  PubMed Central  PubMed  Google Scholar 

Download references

Acknowledgments

This work was supported by the Defense Threat Reduction Agency (project CBS.MEDBIO.02.10.BH.021) and by the U.S. Army Medical Research and Materiel Command (Ft. Detrick, MD) as part of the U.S. Army’s Network Science Initiative.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Jaques Reifman.

Additional information

Competing interests

The authors declare that they have no competing interests. The opinions and assertions contained herein are the private views of the authors and are not to be construed as official or as reflecting the views of the U.S. Army or of the U.S. Department of Defense. This paper has been approved for public release with unlimited distribution. The manuscript, “DBSecSys: A Database of Burkholderia mallei Secretion Systems,” is cleared for all audiences WRT security (OPSEC).

Authors’ contributions

VM, NZ, DDS, and AW performed the data curation. VM, LC, and KK designed the database schema. VM, LC, KK, and JR designed the Web interface. LC and KK developed the front- and back-end infrastructure. VM, NZ, AW, and JR conceived the work. VM, KK, and JR wrote the manuscript. All authors read and approved the final manuscript.

Authors’ original submitted files for images

Below are the links to the authors’ original submitted files for images.

Authors’ original file for figure 1

Authors’ original file for figure 2

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

Verify currency and authenticity via CrossMark

Cite this article

Memišević, V., Kumar, K., Cheng, L. et al. DBSecSys: a database of Burkholderia malleisecretion systems. BMC Bioinformatics 15, 244 (2014). https://doi.org/10.1186/1471-2105-15-244

Download citation

Keywords

  • Bacterial secretion system
  • Virulence factors
  • Pathogenic mechanisms of action
  • Host-pathogen interactions
  • Burkholderia mallei