InterMitoBase: An annotated database and analysis platform of protein-protein interactions for human mitochondria
- Zuguang Gu†1,
- Jie Li†1,
- Song Gao1,
- Ming Gong1,
- Junling Wang1,
- Hua Xu1,
- Chenyu Zhang1Email author and
- Jin Wang1Email author
© Gu et al; licensee BioMed Central Ltd. 2011
Received: 19 March 2011
Accepted: 30 June 2011
Published: 30 June 2011
The mitochondrion is an essential organelle which plays important roles in diverse biological processes, such as metabolism, apoptosis, signal transduction and cell cycle. Characterizing protein-protein interactions (PPIs) that execute mitochondrial functions is fundamental in understanding the mechanisms underlying biological functions and diseases associated with mitochondria. Investigations examining mitochondria are expanding to the system level because of the accumulation of mitochondrial proteomes and human interactome. Consequently, the development of a database that provides the entire protein interaction map of the human mitochondrion is urgently required.
InterMitoBase provides a comprehensive interactome of human mitochondria. It contains the PPIs in biological pathways mediated by mitochondrial proteins, the PPIs between mitochondrial proteins and non-mitochondrial proteins as well as the PPIs between mitochondrial proteins. The current version of InterMitoBase covers 5,883 non-redundant PPIs of 2,813 proteins integrated from a wide range of resources including PubMed, KEGG, BioGRID, HPRD, DIP and IntAct. Comprehensive curations have been made on the interactions derived from PubMed. All the interactions in InterMitoBase are annotated according to the information collected from their original sources, GenBank and GO. Additionally, InterMitoBase features a user-friendly graphic visualization platform to present functional and topological analysis of PPI networks identified. This should aid researchers in the study of underlying biological properties.
InterMitoBase is designed as an integrated PPI database which provides the most up-to-date PPI information for human mitochondria. It also works as a platform by integrating several on-line tools for the PPI analysis. As an analysis platform and as a PPI database, InterMitoBase will be an important database for the study of mitochondria biochemistry, and should be particularly helpful in comprehensive analyses of complex biological mechanisms underlying mitochondrial functions.
The mitochondrion is an essential organelle in eukaryotic cells that plays important roles in a variety of important processes such as apoptosis, signal transduction and cell cycle . Mitochondrial dysfunction is linked to many common diseases including heart disease, diabetes, Parkinson disease and dementia. To understand the mechanism underlying the biological functions and diseases associated with the mitochondria, it is important to determine protein-protein interactions (PPIs) that facilitate mitochondrial functions.
The extensive use of experimental approaches including 2D gel electrophoresis and mass spectrometry, has led to the construction of many databases for mitochondrial proteomics, such as MitoCarta , MitoProteome , MitoP2  and HMPDb . Increasing interest in mitochondrial proteomics is promoting studies on PPIs of mitochondria at a systems level. By unraveling the interplays between mitochondrial proteins and mitochondrial/non-mitochondrial proteins, the entire interaction map that contributes to mitochondrial functions will be revealed.
Although several PPI databases have been distributed, such as HPRD , BioGRID , IntAct  and DIP , there are very few PPI databases that are designed specifically for mitochondria. MitoInteractome  is a representative interaction database for mitochondria. However, this database only contains interactions between mitochondrial proteins which are predicted based on structural and homologous information. None of the interactions between mitochondrial proteins and non-mitochondrial proteins have been included. These types of interactions are very important for characterizing the mechanisms of mitochondrial function because they contain information about how the mitochondrion communicates with the intracellular environment. Therefore, it is necessary to construct a database covering the entire PPI map that characterizes the global mitochondrial functions.
Here, we have developed a database termed InterMitoBase, which covers the biological pathways mediated by mitochondrial proteins and the PPIs between mitochondrial and mitochondrial/non-mitochondrial proteins. The interactions in InterMitoBase are integrated from a wide range of resources including PubMed, KEGG , HPRD, BioGRID, IntAct and DIP, all of which are well annotated according to the information collected from their original sources GenBank and GO. InterMitoBase features as a user-friendly graphic visualization tool and provides functional and topological analysis of PPI networks that should facilitate an understanding of the underlying biological properties. As an analysis platform and a PPI database for human mitochondria, InterMitoBase should significantly aid researchers aiming to develop a comprehensive and deep understanding of complex mitochondrial functions.
Construction and Content
InterMitoBase is designed as a web-based database providing graphic visualization of annotated PPI interactions that relate to human mitochondrial functions. It integrates the data from diverse sources such as MitoCarta, HPRD, the KEGG pathway database, PubMed and Gene Ontology . Several on-line tools are also embedded in InterMitobase for functional and topological analyses of protein-protein networks.
Protein-protein Interaction Data
Annotation of Protein-protein Interactions
PPIs in InterMitoBase are annotated as follows: 1) the basic information (e.g., name, transcript, genomic location and GO term) of the interacting proteins are annotated referring to GenBank and GO; 2) the information about subcellular location of every protein is provided according to MitoCarta, MitoProteome, MitoP2, HMPDb and UniProt; 3) the information about the direction and regulation type of each interaction is provided; 4) the full context that contains the interaction (i.e., the map of the KEGG pathway or the PubMed literature abstract) is supplied.
Network Visualization and analysis
InterMitoBase provides an intuitionistic visualization of a protein-protein network formed by a set of PPIs. It also integrates a tool to analyze the degree distribution of the network. These will be helpful to uncover the global structural features and key elements of the network.
Functional Enrichment Analysis
InterMitobase also embeds a tool that can evaluate the enriched functions of the proteins involved in a specific protein-protein network based on the Gene Ontology (GO) terms. The significance of the enriched functions is represented by the p-value that is evaluated following the Fisher's exact test. The false discovery rate (FDR) is controlled by the Benjamini-Hochberg process . The analysis of the functional enrichment provides the general feature of the network function.
System Architecture and Implementation
Utility and Discussion
Searching Proteins of Interest
Getting Protein-protein Interactions
The PPIs are navigated through the outcome page of proteins. Two retrieving processes are supported. One approach is to obtain all the interactions of a selected protein. The other approach is to get the interactions between all the outcome proteins. The retrieving returns a page where the outcome PPIs associated with related sources, directions and regulation types are also recorded (see Figure 5C).
The protein-protein network formed by the searched PPIs can be visualized graphically (see Figure 5D). The graph of over 300 proteins will not be illustrated since it is very time-consuming. However, its Graphviz file can be downloaded so that users can view the graph on a local machine.
Functional Enrichment Analysis
InterMitoBase supports the functional enrichment analysis of proteins in a selected network (see Figure 5D). The analysis system returns the enriched GO terms, together with the p-values, the false discovery rates (FDR) and the numbers of related proteins in the network. Specifically, the proteins related to a selected GO term could be highlighted in the network graph.
InterMitoBase also provides a general topological analysis on networks, i.e., the analysis of the degree distribution. Two transformations of the degree distribution (i.e., single log-plot and double log-plot) are given to judge whether the degree distribution is exponential or power-law (see Figure 5E). In addition, the degree and the degree frequency of each protein are listed.
InterMitoBase is designed for quick retrieving, visualizing and analyzing PPIs that contribute to human mitochondrial functions. It integrates the most up-to-date PPIs for human mitochondria from diverse resources. Several on-line tools are also embedded to uncover the underlying biological properties of PPIs. Besides performing as an analysis platform and a PPI database, InterMitoBase will aid researchers aiming to obtain a comprehensive understanding of complex biological mechanisms underlying mitochondrial functions.
Availability and Requirements
Kyoto Encyclopedia of Genes and Genomes
False Discovery Rate.
This work was supported by grants from the National Natural Science Foundation of China (30890044), the National Basic Research Program (2007CB814806), German-China joint project (Grant No. CHN08/031) and Jiangsu Province Innovation Fund for PhD Candidates (CX10B_014Z).
- McBride HM, Neuspiel M, Wasiak S: Mitochondria: more than just a powerhouse. Curr Biol. 2006, 16 (14): R551-R560. 10.1016/j.cub.2006.06.054.PubMedView ArticleGoogle Scholar
- Pagliarini DJ, Calvo SE, Chang B, Sheth SA, Vafai SB, Ong SE, Walford GA, Sugiana C, Boneh A, Chen WK, Hill DE, Vidal M, Evans JG, Thorburn DR, Carr SA, Mootha VK: A mitochondrial protein compendium elucidates complex I disease biology. Cell. 2008, 134 (1): 112-123. 10.1016/j.cell.2008.06.016.PubMed CentralPubMedView ArticleGoogle Scholar
- Cotter D, Guda P, Fahy E, Subramaniam S: MitoProteome: mitochondrial protein sequence database and annotation system. Nucleic Acids Res. 2004, 32 (Suppl 1): D463-D467.PubMed CentralPubMedView ArticleGoogle Scholar
- Prokisch H, Andreoli C, Ahting U, Heiss K, Ruepp A, Scharfe C, Meitinger T: MitoP2: the mitochondrial proteome database--now including mouse data. Nucleic Acids Res. 2006, 34 (Suppl 1): D705-D711.PubMed CentralPubMedView ArticleGoogle Scholar
- Human Mitochondrial Protein Database. [http://bioinfo.nist.gov/]
- Human Protein Reference Database. [http://www.hprd.org/]
- Stark C, Breitkreutz BJ, Reguly T, Boucher L, Breitkreutz A, Tyers M: BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 2006, 34 (Suppl 1): D535-D539.PubMed CentralPubMedView ArticleGoogle Scholar
- Aranda B, Achuthan P, Alam-Faruque Y, Armean I, Bridge A, Derow C, Feuermann M, Ghanbarian AT, Kerrien S, Khadake J, Kerssemakers J, Leroy C, Menden M, Michaut M, Montecchi-Palazzi L, Neuhauser SN, Orchard S, Perreau V, Roechert B, van Eijk K, Hermjakob H: The IntAct molecular interaction database in 2010. Nucleic Acids Res. 2010, 38 (Suppl 1): D525-D531.PubMed CentralPubMedView ArticleGoogle Scholar
- Salwinski L, Miller CS, Smith AJ, Pettit FK, Bowie JU, Eisenberg D: The Database of Interacting Proteins: 2004 update. Nucleic Acids Res. 2004, 32 (Suppl 1): D449-D451.PubMed CentralPubMedView ArticleGoogle Scholar
- Reja R, Venkatakrishnan AJ, Lee J, Kim BC, Ryu JW, Gong S, Bhak J, Park D: MitoInteractome: mitochondrial protein interactome database, and its application in "aging network" analysis. BMC genomics. 2009, 10 (Suppl 3): S20-10.1186/1471-2164-10-S3-S20.PubMed CentralPubMedView ArticleGoogle Scholar
- KEGG Pathway Database. [http://www.genome.jp/kegg/pathway.html]
- The Gene Ontology Consortium: Gene ontology: tool for the unification of biology. Nat Genet. 2000, 25 (1): 25-29. 10.1038/75556.PubMed CentralView ArticleGoogle Scholar
- Benjamini Y, Hochberg Y: Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society Series B (Methodological). 1995, 579 (1): 289-300.Google Scholar
- Wain HM, Bruford EA, Lovering RC, Lush MJ, Wright MW, Povey S: Guidelines for human gene nomenclature. Genomics. 2002, 79 (4): 464-470. 10.1006/geno.2002.6748.PubMedView ArticleGoogle Scholar
- Uniprot Consortium: The Universal Protein Resource (UniProt) in 2010. Nucleic Acids Res. 2010, 38 (Suppl 1): D142-D148.View ArticleGoogle Scholar